None:\\n self._found_devices = {} # type: Dict[IPv4Address, conf.BaseService]\",\n \"def setUp(self):\\n self.manager, self.proxy = tests.utils.setup_xmlrpc()\\n self.proxy.provider.register(\\n PROVIDER_ID, USERNAME, PASSWORD, URL, TENANT, PROVIDER_TYPE\\n )\",\n \"def register(self):\\n\\n def nsr_id_from_keyspec(ks):\\n nsr_path_entry = NsrYang.YangData_Nsr_NsInstanceConfig_Nsr.schema().keyspec_to_entry(ks)\\n nsr_id = nsr_path_entry.key00.id\\n return nsr_id\\n\\n def group_name_from_keyspec(ks):\\n group_path_entry = NsrYang.YangData_Nsr_NsInstanceConfig_Nsr_ScalingGroup.schema().keyspec_to_entry(ks)\\n group_name = group_path_entry.key00.scaling_group_name_ref\\n return group_name\\n\\n def is_instance_in_reg_elements(nsr_id, group_name, instance_id):\\n \\\"\\\"\\\" Return boolean indicating if scaling group instance was already commited previously.\\n\\n By looking at the existing elements in this registration handle (elements not part\\n of this current xact), we can tell if the instance was configured previously without\\n keeping any application state.\\n \\\"\\\"\\\"\\n for instance_cfg, keyspec in self._nsr_regh.get_xact_elements(include_keyspec=True):\\n elem_nsr_id = nsr_id_from_keyspec(keyspec)\\n elem_group_name = group_name_from_keyspec(keyspec)\\n\\n if elem_nsr_id != nsr_id or group_name != elem_group_name:\\n continue\\n\\n if instance_cfg.id == instance_id:\\n return True\\n\\n return False\\n\\n def get_scale_group_instance_delta(nsr_id, group_name, xact):\\n\\n #1. Find all elements in the transaction add to the \\\"added\\\"\\n #2. Find matching elements in current elements, remove from \\\"added\\\".\\n #3. Find elements only in current, add to \\\"deleted\\\"\\n\\n xact_ids = set()\\n for instance_cfg, keyspec in self._scale_regh.get_xact_elements(xact, include_keyspec=True):\\n elem_nsr_id = nsr_id_from_keyspec(keyspec)\\n if elem_nsr_id != nsr_id:\\n continue\\n\\n elem_group_name = group_name_from_keyspec(keyspec)\\n if elem_group_name != group_name:\\n continue\\n\\n xact_ids.add(instance_cfg.id)\\n\\n current_ids = set()\\n for instance_cfg, keyspec in self._scale_regh.get_xact_elements(include_keyspec=True):\\n elem_nsr_id = nsr_id_from_keyspec(keyspec)\\n if elem_nsr_id != nsr_id:\\n continue\\n\\n elem_group_name = group_name_from_keyspec(keyspec)\\n if elem_group_name != group_name:\\n continue\\n\\n current_ids.add(instance_cfg.id)\\n\\n delta = {\\n \\\"added\\\": xact_ids - current_ids,\\n \\\"deleted\\\": current_ids - xact_ids\\n }\\n return delta\\n\\n def get_add_delete_update_cfgs(dts_member_reg, xact, key_name):\\n # Unforunately, it is currently difficult to figure out what has exactly\\n # changed in this xact without Pbdelta support (RIFT-4916)\\n # As a workaround, we can fetch the pre and post xact elements and\\n # perform a comparison to figure out adds/deletes/updates\\n xact_cfgs = list(dts_member_reg.get_xact_elements(xact))\\n curr_cfgs = list(dts_member_reg.elements)\\n\\n xact_key_map = {getattr(cfg, key_name): cfg for cfg in xact_cfgs}\\n curr_key_map = {getattr(cfg, key_name): cfg for cfg in curr_cfgs}\\n\\n # Find Adds\\n added_keys = set(xact_key_map) - set(curr_key_map)\\n added_cfgs = [xact_key_map[key] for key in added_keys]\\n\\n # Find Deletes\\n deleted_keys = set(curr_key_map) - set(xact_key_map)\\n deleted_cfgs = [curr_key_map[key] for key in deleted_keys]\\n\\n # Find Updates\\n updated_keys = set(curr_key_map) & set(xact_key_map)\\n updated_cfgs = [xact_key_map[key] for key in updated_keys if xact_key_map[key] != curr_key_map[key]]\\n\\n return added_cfgs, deleted_cfgs, updated_cfgs\\n\\n def on_apply(dts, acg, xact, action, scratch):\\n \\\"\\\"\\\"Apply the configuration\\\"\\\"\\\"\\n def handle_create_nsr(msg):\\n # Handle create nsr requests \\\"\\\"\\\"\\n # Do some validations\\n if not msg.has_field(\\\"nsd_ref\\\"):\\n err = \\\"NSD reference not provided\\\"\\n self._log.error(err)\\n raise NetworkServiceRecordError(err)\\n\\n self._log.info(\\\"Creating NetworkServiceRecord %s from nsd_id %s\\\",\\n msg.id, msg.nsd_ref)\\n\\n #nsr = self.nsm.create_nsr(msg)\\n return nsr\\n\\n def handle_delete_nsr(msg):\\n @asyncio.coroutine\\n def delete_instantiation(ns_id):\\n \\\"\\\"\\\" Delete instantiation \\\"\\\"\\\"\\n pass\\n #with self._dts.transaction() as xact:\\n #yield from self._nsm.terminate_ns(ns_id, xact)\\n\\n # Handle delete NSR requests\\n self._log.info(\\\"Delete req for NSR Id: %s received\\\", msg.id)\\n # Terminate the NSR instance\\n #nsr = self._nsm.get_ns_by_nsr_id(msg.id)\\n\\n #nsr.set_state(NetworkServiceRecordState.TERMINATE_RCVD)\\n #event_descr = \\\"Terminate rcvd for NS Id:%s\\\" % msg.id\\n #nsr.record_event(\\\"terminate-rcvd\\\", event_descr)\\n\\n #self._loop.create_task(delete_instantiation(msg.id))\\n\\n @asyncio.coroutine\\n def begin_instantiation(nsr):\\n # Begin instantiation\\n pass\\n #self._log.info(\\\"Beginning NS instantiation: %s\\\", nsr.id)\\n #yield from self._nsm.instantiate_ns(nsr.id, xact)\\n\\n self._log.debug(\\\"Got nsr apply (xact: %s) (action: %s)(scr: %s)\\\",\\n xact, action, scratch)\\n\\n if action == rwdts.AppconfAction.INSTALL and xact.id is None:\\n self._log.debug(\\\"No xact handle. Skipping apply config\\\")\\n xact = None\\n\\n (added_msgs, deleted_msgs, updated_msgs) = get_add_delete_update_cfgs(self._nsr_regh, xact, \\\"id\\\")\\n\\n for msg in added_msgs:\\n self._log.info(\\\"Create NSR received in on_apply to instantiate NS:%s\\\", msg.id)\\n #if msg.id not in self._nsm.nsrs:\\n # self._log.info(\\\"Create NSR received in on_apply to instantiate NS:%s\\\", msg.id)\\n # nsr = handle_create_nsr(msg)\\n # self._loop.create_task(begin_instantiation(nsr))\\n\\n for msg in deleted_msgs:\\n self._log.info(\\\"Delete NSR received in on_apply to terminate NS:%s\\\", msg.id)\\n try:\\n handle_delete_nsr(msg)\\n except Exception:\\n self._log.exception(\\\"Failed to terminate NS:%s\\\", msg.id)\\n\\n for msg in updated_msgs:\\n self._log.info(\\\"Update NSR received in on_apply to change scaling groups in NS:%s\\\", msg.id)\\n\\n for group in msg.scaling_group:\\n instance_delta = get_scale_group_instance_delta(msg.id, group.scaling_group_name_ref, xact)\\n self._log.debug(\\\"Got NSR:%s scale group instance delta: %s\\\", msg.id, instance_delta)\\n\\n #for instance_id in instance_delta[\\\"added\\\"]:\\n # self._nsm.scale_nsr_out(msg.id, group.scaling_group_name_ref, instance_id, xact)\\n\\n #for instance_id in instance_delta[\\\"deleted\\\"]:\\n # self._nsm.scale_nsr_in(msg.id, group.scaling_group_name_ref, instance_id)\\n\\n\\n return RwTypes.RwStatus.SUCCESS\\n\\n @asyncio.coroutine\\n def on_prepare(dts, acg, xact, xact_info, ks_path, msg, scratch):\\n \\\"\\\"\\\" Prepare calllback from DTS for NSR \\\"\\\"\\\"\\n\\n xpath = ks_path.to_xpath(NsrYang.get_schema())\\n action = xact_info.query_action\\n self._log.debug(\\n \\\"Got Nsr prepare callback (xact: %s) (action: %s) (info: %s), %s:%s)\\\",\\n xact, action, xact_info, xpath, msg\\n )\\n\\n fref = ProtobufC.FieldReference.alloc()\\n fref.goto_whole_message(msg.to_pbcm())\\n\\n if action in [rwdts.QueryAction.CREATE, rwdts.QueryAction.UPDATE]:\\n pass\\n # Ensure the Cloud account has been specified if this is an NSR create\\n #if msg.id not in self._nsm.nsrs:\\n # if not msg.has_field(\\\"cloud_account\\\"):\\n # raise NsrInstantiationFailed(\\\"Cloud account not specified in NSR\\\")\\n\\n # We do not allow scaling actions to occur if the NS is not in running state\\n #elif msg.has_field(\\\"scaling_group\\\"):\\n # nsr = self._nsm.nsrs[msg.id]\\n # if nsr.state != NetworkServiceRecordState.RUNNING:\\n # raise ScalingOperationError(\\\"Unable to perform scaling action when NS is not in running state\\\")\\n\\n # if len(msg.scaling_group) > 1:\\n # raise ScalingOperationError(\\\"Only a single scaling group can be configured at a time\\\")\\n\\n # for group_msg in msg.scaling_group:\\n # num_new_group_instances = len(group_msg.instance)\\n # if num_new_group_instances > 1:\\n # raise ScalingOperationError(\\\"Only a single scaling instance can be created at a time\\\")\\n\\n # elif num_new_group_instances == 1:\\n # scale_group = nsr.scaling_groups[group_msg.scaling_group_name_ref]\\n # if len(scale_group.instances) == scale_group.max_instance_count:\\n # raise ScalingOperationError(\\\"Max instances for %s reached\\\" % scale_group)\\n\\n\\n acg.handle.prepare_complete_ok(xact_info.handle)\\n\\n\\n self._log.debug(\\\"Registering for NSR config using xpath: %s\\\",\\n NsrDtsHandler.NSR_XPATH)\\n\\n acg_hdl = rift.tasklets.AppConfGroup.Handler(on_apply=on_apply)\\n with self._dts.appconf_group_create(handler=acg_hdl) as acg:\\n self._nsr_regh = acg.register(xpath=NsrDtsHandler.NSR_XPATH,\\n flags=rwdts.Flag.SUBSCRIBER | rwdts.Flag.DELTA_READY | rwdts.Flag.CACHE,\\n on_prepare=on_prepare)\\n\\n self._scale_regh = acg.register(\\n xpath=NsrDtsHandler.SCALE_INSTANCE_XPATH,\\n flags=rwdts.Flag.SUBSCRIBER | rwdts.Flag.DELTA_READY | rwdts.Flag.CACHE,\\n )\",\n \"def save(self):\\n try:\\n root = ET.Element(\\\"registry\\\", attrib={\\\"save\\\":str(time.time())})\\n root.append(ET.Comment(__WARNING_TEXT__))\\n \\n # Make sure we have the daemon's ID for next time we load!\\n ET.SubElement(root, \\\"daemon\\\", attrib={\\\"id\\\":self._did})\\n \\n #Add attachments to the registry!\\n attachments = ET.Element(\\\"attachments\\\")\\n for attach in self._attachments.values():\\n if attach.type in [PLUG, ALARM]:\\n ET.SubElement(attachments, attach.type, attrib=attach.getAttrib())\\n root.append(attachments)\\n \\n #Add all the events\\n events = ET.Element(\\\"events\\\")\\n for event in self._events.values():\\n ET.SubElement(events, \\\"event\\\", attrib=event.getAttrib())\\n root.append(events)\\n \\n #Add all registered alerts\\n alerts = ET.Element(\\\"alerts\\\")\\n for alert in self._alerts.values():\\n ET.SubElement(alerts, \\\"alert\\\", attrib=alert.getAttrib())\\n root.append(alerts)\\n \\n #Add all subscriptions\\n subscriptions = ET.Element(\\\"subscriptions\\\")\\n for subscription in self._subscriptions.values():\\n ET.SubElement(subscriptions, \\\"sub\\\", attrib=subscription.getAttrib())\\n root.append(subscriptions)\\n \\n # Save everything to the file that we were given on startup.\\n logging.debug(\\\"Saving registry to %s\\\"%self._file)\\n if self.__try_setup_path(self._file):\\n self.__makeBackup()\\n \\n tree = ET.ElementTree(root)\\n try: \\n # First try to save via a byte stream, if that doesn't work \\n # utilize the basic string save. which might not work.\\n with open(self._file, \\\"wb\\\") as savefile:\\n tree.write(savefile)\\n except:\\n with open(self._file, \\\"w\\\") as savefile:\\n tree.write(savefile)\\n # if the second attempt fails it is caught by the function\\n # try-catch. Which will restore backups and log the errors.\\n logging.debug(\\\"Registry Saved!\\\")\\n self.__removeBackup()\\n return True\\n else: return False\\n except Exception as e:\\n logging.exception(e)\\n self.__restoreBackup()\\n return False\",\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 __init__(self, resources={}):\\n super().__init__()\\n self._lock = threading.Lock()\\n self._resources = {}\\n for key, resource in resources.items():\\n self.register(key, resource)\",\n \"def __init__(self):\\n self.redis = RedisClient()\\n self.crawlers = [crawler_cls() for crawler_cls in crawlers_cls]\",\n \"def register(cls, L):\\r\\n ...\",\n \"def register_instance(self, instance):\\n self.instance = instance\",\n \"def setup(self):\\n # Instrument names\\n instruments = list(self.features_df[\\\"instrument\\\"].unique())\\n\\n # Get Muxes for each instrument.\\n inst_muxes = [self._instrument_mux(i) for i in instruments]\\n\\n # Construct the streams for each mux.\\n mux_streams = [pescador.Streamer(x) for x in inst_muxes\\n if x is not None]\\n\\n # Construct the master mux\\n master_mux = pescador.mux(mux_streams, **self.master_mux_params)\\n # We have to wrap the mux in a stream so that the buffer\\n # knows what to do with it.\\n self.master_stream = pescador.Streamer(master_mux)\\n\\n # Now construct the final streamer\\n if self.use_zmq:\\n self.buffered_streamer = zmq_buffered_stream(\\n self.master_stream, self.batch_size)\\n else:\\n self.buffered_streamer = buffer_stream(\\n self.master_stream, self.batch_size)\",\n \"def registration_started(self):\\n pass\",\n \"def test_register(self):\\n self._configure_testshib_provider()\\n self._test_register()\",\n \"def _register(service, notifier=None):\\n\\n full_name = service.iden\\n slot = service_store[full_name]\\n try:\\n slot['msg'] = 'Async image creation started'\\n slot['stage'] = 2\\n service_store[full_name] = slot\\n\\n service.make_image()\\n\\n slot['msg'] = 'Image for service created'\\n slot['stage'] = 3\\n service_store[full_name] = slot\\n\\n service.start_workers()\\n\\n slot['msg'] = 'Workers started'\\n slot['stage'] = 4\\n service_store[full_name] = slot\\n\\n service.check_health()\\n\\n slot['msg'] = 'Service ready'\\n slot['stage'] = 5\\n slot['slot'] = 'ready'\\n slot['service'] = service\\n service_store[full_name] = slot\\n\\n result = ok\\n data = service\\n except Exception as exc:\\n slot['msg'] = 'Error: {}'.format(exc)\\n slot['slot'] = 'error'\\n service_store[full_name] = slot\\n\\n result = error\\n data = str(exc)\\n\\n if service.notify and notifier is not None:\\n notifier(service.notify, result, data)\",\n \"def __init__(self):\\n self.uris = AsyncSet('uris')\\n self.sockets = AsyncSet('sockets')\",\n \"def register_artifacts(self, which_pass):\\n\\n artifact_manager.register_temp_file(\\n config.MIRROR_NODES_INDEX_TABLE, which_pass\\n )\\n artifact_manager.register_temp_file(\\n config.MIRROR_NODES_STORE, which_pass\\n )\",\n \"def setup(self):\\n self.config = pau.IConfig\\n self.session = pau.ISession\\n pau.resolve(self)\\n\\n self.session.assets = Assets()\\n self.config.db = self.db_name\\n\\n self.db = pau.IDb\\n pau.resolve(self)\\n\\n # Instance\\n i = Setup()\\n pau.resolve(i)\\n return i\",\n \"def __init__(self):\\n self.__instance = None\\n self.__configured_visitors = {}\",\n \"def register_instance(self, obj):\\n self.__instances.append(obj)\\n self._proxy_class_methods(obj)\",\n \"def _register(self, comm, handler):\",\n \"def insertInstrument(self, instrument):\\n self.instruments.append(instrument)\\n if instrument.bench and instrument.bench not in self.benches:\\n logger.warning(\\\"Insterting *new* bench %s\\\", instrument.bench.name)\\n self.benches.append(instrument.bench)\\n if instrument.host and instrument.host not in self.hosts:\\n logger.warning(\\\"Inserting *new* host %s\\\", instrument.host.name)\\n self.hosts.append(instrument.host)\",\n \"def __init__(self, instrument):\\n self._unsub_dispatcher = None\\n self._instrument = instrument\\n self._state = instrument.state\",\n \"def register(self, events=[]):\\n self.events = events\\n if not self in manager.handler:\\n manager.handler.append(self)\",\n \"def register_instances(self, instances):\\r\\n if isinstance(instances, str) or isinstance(instances, unicode):\\r\\n instances = [instances]\\r\\n new_instances = self.connection.register_instances(self.name, instances)\\r\\n self.instances = new_instances\",\n \"def __init__(self):\\n description = \\\"Manage Yarely sensors\\\"\\n\\n # The parent constructor provides config and logging and gets a\\n # starting set of handlers using this classes _init_handlers() method.\\n super().__init__(ZMQ_SENSORMANAGER_REP_PORT, description)\\n self.registered = False\\n\\n # Setup for ZMQ Scheduler Messaging\\n sensor_term_id = \\\"sensormanager_term_{id}\\\"\\n self.zmq_sensormanager_term_identifier = sensor_term_id.format(\\n id=id(self)\\n )\\n self.zmq_scheduler_req_addr = ZMQ_ADDRESS_LOCALHOST.format(\\n port=ZMQ_SENSORMANAGER_REQ_PORT\\n )\\n self.zmq_scheduler_request_queue = queue.Queue() # Q of infinite size\",\n \"def load_epicsLive(self, instrument=None, **kwargs):\\n# if 'instrument' in kwargs:\\n# instrument = kwargs.get('instrument')\\n# self.instrument = instrument\\n if not instrument:\\n instrument = self.instrument\\n\\n self.ioc = psioc.IOC(instrument=self.instrument)\\n if self.instrument in ['cxi', 'mfx', 'xcs', 'mec']:\\n self.ioc.load_cfg('xrt')\\n \\n print 'Loading EpicsSets for', instrument\\n self.epicsLive = lcls_devices.EpicsSets(instrument=instrument, **kwargs)\\n self.update_epicsLive()\",\n \"def __init__(self):\\n self._pio = pio\\n self._RUN_PATH = system_files.GEOPM_SERVICE_RUN_PATH\\n self._SAVE_DIR = 'SAVE_FILES'\\n self._WATCH_INTERVAL_SEC = 1\\n self._active_sessions = system_files.ActiveSessions()\\n self._access_lists = system_files.AccessLists()\\n for client_pid in self._active_sessions.get_clients():\\n is_active = self.check_client(client_pid)\\n if is_active:\\n watch_id = self._watch_client(client_pid)\\n self._active_sessions.set_watch_id(client_pid, watch_id)\\n with system_files.WriteLock(self._RUN_PATH) as lock:\\n write_pid = lock.try_lock()\\n if write_pid is not None and not self._active_sessions.is_client_active(write_pid):\\n self._close_session_write(lock, write_pid)\",\n \"def register(self):\\n mDNSServices = {}\\n for srv in ServiceType:\\n mDNSServices.update({srv.name: {}})\\n self.service = srv.name\\n self.regtype, self.port = ServiceType[self.service].value\\n if not self._is_running():\\n continue\\n\\n txtrecord, interfaceIndex = self._generate_txtRecord()\\n if txtrecord is None:\\n continue\\n\\n port = self._get_port()\\n\\n self.logger.trace(\\n 'Registering mDNS service host: %s, regtype: %s, port: %s, interface: %s, TXTRecord: %s',\\n self.hostname, self.regtype, port, interfaceIndex, txtrecord\\n )\\n for i in interfaceIndex:\\n mDNSServices[srv.name].update({i: {\\n 'sdRef': None,\\n 'interfaceIndex': i,\\n 'regtype': self.regtype,\\n 'port': port,\\n 'txtrecord': txtrecord,\\n 'name': self.hostname\\n }})\\n\\n mDNSServices[srv.name][i]['sdRef'] = pybonjour.DNSServiceRegister(\\n name=self.hostname,\\n regtype=self.regtype,\\n interfaceIndex=i,\\n port=port,\\n txtRecord=txtrecord,\\n callBack=None\\n )\\n\\n self.finished.wait()\\n for srv in mDNSServices.keys():\\n for i in mDNSServices[srv].keys():\\n self.logger.trace('Unregistering %s %s.',\\n mDNSServices[srv][i]['name'], mDNSServices[srv][i]['regtype'])\\n mDNSServices[srv][i]['sdRef'].close()\",\n \"def __init__(self):\\n thisType = type(self)\\n if not thisType._initialized:\\n thisType._initialized = True\\n self._embedded_device_registry = {}\\n self._root_device_registry = {}\\n self._service_registry = {}\\n self._scan_for_device_extensions_under_code_container(dynamic_extensions)\\n self._scan_for_device_extensions_under_code_container(standard_extensions)\\n self._scan_for_service_extensions_under_code_container(dynamic_extensions)\\n self._scan_for_service_extensions_under_code_container(standard_extensions)\\n return\",\n \"def registered(self):\\n log.info(\\\"Registered.\\\")\\n pass\",\n \"def register_captured_url(url_list, url): \\n if gs.local:\\n register_captured_url_local(url_list, url)\\n else:\\n register_captured_url_aws(url_list, url)\",\n \"def _generate_trading_instances(self, start_date, end_date, instruments, params):\\n configuration = self.configuration\\n configuration.start_date = start_date\\n configuration.end_date = end_date\\n configuration.instruments = instruments\\n\\n logger.info(\\\"Creating DataHandler, Strategy, Portfolio and ExecutionHandler\\\")\\n logger.info(\\\"Start date: %s\\\" % start_date)\\n logger.info(\\\"End date: %s\\\" % end_date)\\n logger.info(\\\"Instrument(s): %s...\\\" % instruments)\\n logger.info(\\\"Params: %s...\\\" % params)\\n\\n self.data_handler = self.data_handler_cls(self.events, configuration)\\n self.strategy = self.strategy_cls(self.data_handler, self.events, configuration, **params)\\n self.portfolio = self.portfolio_cls(self.data_handler, self.events, configuration)\\n self.execution_handler = self.execution_handler_cls(self.data_handler, self.events, configuration)\",\n \"def __init__(self, datadir, wsgi_mountpoint='/swab'):\\n self.datadir = datadir\\n\\n #: Mapping of {<experiment name>: <Experiment object>}\\n self.experiments = {}\\n\\n #: Mapping of {<goal name>: [<Experiment object>, ...]}\\n self.experiments_by_goal = {}\\n\\n self.wsgi_mountpoint = wsgi_mountpoint\\n makedir(self.datadir)\",\n \"def __init__(self):\\n\\n self.db = ImageDB()\\n self.vitess = VitessConn()\\n self.minio = MinioConn()\",\n \"def __init__(self, *args, **kwargs):\\n super().__init__(*args, **kwargs)\\n self.start_agents_once = False\\n self.start_servers_once = False\\n self.setup_start_agents = False\\n self.setup_start_servers = False\",\n \"def __init__(self, name, daemon, nameserver,vectorClock):\\n self._type = constants.ProcessConstants.TYPE_GATEWAY\\n self._name = name\\n self._ID = 1\\n\\n self._IDtoTypeMap = {}\\n self._databaseName = None\\n self._counter = 1\\n self.eligible = True\\n self.timeServer = False\\n self.timeStamp = time()\\n self.vectorClock = vectorClock\\n\\n self._registerOnServer(daemon, nameserver,self.vectorClock)\",\n \"def __init__(self):\\n self.policy_store = PolicyStore()\\n self.service_store = {}\\n\\n from ranger_performance_tool import perf_globals\\n enabled_services = perf_globals.CONFIG_READER.get_config_value(\\\"secondary\\\", \\\"enabled_services\\\")\\n service_type_mapping = perf_globals.CONFIG_READER.get_config_value(\\\"secondary\\\", \\\"service_type_mapping\\\")\\n for service_name in enabled_services:\\n if service_name not in service_type_mapping:\\n raise Exception(f\\\"Unknown service name:{service_name}. \\\"\\n f\\\"Add it to service_type_mapping in secondary config file\\\")\\n service_type = service_type_mapping[service_name]\\n random_type = perf_globals.CONFIG_READER.get_config_value(\\\"secondary\\\", \\\"services\\\",\\n service_type, \\\"random_type\\\")\\n self.service_store[service_type] = RangerAPIObjectStore.service_store_def[service_type](random_type=random_type)\",\n \"def __init__(self):\\n # BASE_DIR:///artifice/scraper/\\n self.BASE_DIR = os.path.dirname(loc)\\n\\n # prototypes\\n self._eth0 = '0.0.0.0'\\n self._exposed_port = 8080\\n self._db_name = 'site.db'\\n self._redis_pword = 'password'\\n self._redis_host = 'localhost'\\n self._redis_port = 6379\\n self._celery_broker_uname = 'michael'\\n self._celery_broker_pword = 'michael123'\\n self._celery_broker_host = 'localhost'\\n self._celery_broker_virtual_host = 'michael_vhost'\\n\\n # flask\\n self.TESTING = False\\n self.URL_PREFIX = ''\\n self.FLASK_PORT = self._exposed_port\\n self.FLASK_HOST = '0.0.0.0'\\n self.FLASK_DEBUG = False\\n self.FLASK_USE_RELOADER = False\\n self.FLASK_THREADED = True\\n\\n # logging\\n self.LOG_FILE = 'flask.log'\\n self.LOG_LEVEL = 'INFO'\\n self.CELERY_LOG_LEVEL = 'ERROR'\\n self.CELERY_LOG_FILE = 'celery.log'\\n self.STDOUT = True\\n\\n # database\\n self.DROP_TABLES = True\\n self.SQLALCHEMY_TRACK_MODIFICATIONS = False\\n self.SQLALCHEMY_DATABASE_URI = 'sqlite:///{}'.format(\\n os.path.join(self.BASE_DIR, self._db_name))\\n\\n # redis\\n self.REDIS_URL = 'redis://{}:@{}:{}/0'.format(\\n self._redis_pword,\\n self._redis_host,\\n self._redis_port)\\n self.REDIS_HIT_COUNTER = 'HIT_COUNTER'\\n\\n # defaults\\n self.ARGS_DEFAULT_LIMIT = 10\\n self.ARGS_DEFAULT_STATUS = ['READY', 'TASKED', 'DONE']\\n\\n self.SUPERVISOR_ENABLED = True\\n self.SUPERVISOR_DEBUG = False\\n self.SUPERVISOR_POLITE = 1\\n\\n # celery\\n self.CELERY_WORKERS = 8\\n self.CELERY_MODULE = 'background'\\n self.CELERY_BROKER = 'amqp://{}:{}@{}/{}'.format(\\n self._celery_broker_uname,\\n self._celery_broker_pword,\\n self._celery_broker_host,\\n self._celery_broker_virtual_host)\\n self.CELERY_BACKEND = 'rpc://'\\n self.CELERY_INCLUDE = ['artifice.scraper.background.tasks']\\n\\n # endpoints\\n self.URL_FOR_STATUS = 'http://{}:{}/status'.format(self._eth0, self._exposed_port)\\n self.URL_FOR_QUEUE = 'http://{}:{}/queue'.format(self._eth0, self._exposed_port)\\n self.URL_FOR_CONTENT = 'http://{}:{}/content'.format(self._eth0, self._exposed_port)\",\n \"def __init__(self):\\n\\t\\tself.instances = {}\\n\\t\\twith open(os.path.join(os.path.dirname(__file__), 'conf', 'parliaments.json'), 'r') as f:\\n\\t\\t\\tparliaments = json.load(f)\\n\\t\\tfor c, cp in parliaments.items():\\n\\t\\t\\tfor p in cp:\\n\\t\\t\\t\\tpfx = c + '/' + p['code']\\n\\t\\t\\t\\tself.instances[pfx] = create_app(c, p)\",\n \"def register_resources(self):\\n raise NotImplementedError\",\n \"def account_instruments(self, instruments: Instruments = sentinel):\\n pass\",\n \"def register(self):\\n if self.registered:\\n return\\n\\n config = current_app.config.get('TERMINAL_CONFIGS', {})\\n apps = config.get('apps', [])\\n\\n for app in apps:\\n cls, mod = app.rsplit('.', maxsplit=1)\\n imported = import_module(cls)\\n instance = getattr(imported, mod)()\\n\\n if getattr(instance, 'name', None) is None:\\n continue\\n\\n if getattr(instance, 'hidden', False):\\n self.hidden[getattr(instance, 'name')] = instance\\n else:\\n self.apps[getattr(instance, 'name')] = instance\\n\\n self.__set_apps_aliases(getattr(instance, 'name'), getattr(instance, 'aliases'))\\n\\n self.registered = True\",\n \"def __init__(self, epics_only=False, *args, **kwargs):\\n self._kwargs = {}\\n self._detectors = {}\\n self._det_list = [] \\n self._det_aliases = {}\\n self._psplots = {}\\n self._event_functions = {}\\n self._source_attrs = []\\n self._evt_time_last = (0,0)\\n self.ievent = 0\\n self._reloadOnLoadRun = False\\n self._reloadOnNextEvent = False\\n self.psana_cfg_dict = {}\\n self._default_module_path = ''\\n\\n# self._user_attrs = {}\\n# self._histograms = {}\\n \\n for key in kwargs:\\n self._kwargs[key] = kwargs[key] \\n if key in self._exp_defaults:\\n setattr(self,key,kwargs[key])\\n print 'setting ',key, kwargs[key]\\n\\n self._device_config = read_device_config(**kwargs)\\n self._device_sets = self._device_config['device_sets'] \\n self._device_types = self._device_config['device_types'] \\n\\n for det in self._device_sets:\\n if 'det' in self._device_sets[det]:\\n if ('detName' in self._device_sets[det]['det'] or\\n 'typeName' in self._device_sets[det]['det']):\\n self._det_list.append(det)\\n if 'det_key' in self._device_sets[det]['det']:\\n det_key = self._device_sets[det]['det']['det_key']\\n self._det_aliases[det_key] = det \\n else:\\n pass\\n \\n# if 'pvs' in self._device_sets[det]:\\n# for attr in self._device_sets[det]['pvs']:\\n# pvbase = self._device_sets[det]['pvs'][attr]['base']\\n# alias = '_'.join([det,attr])\\n# self.add_pv(pvbase, alias)\\n\\n self.set_exp_defaults(**kwargs)\\n if not self._kwargs.get('noload'):\\n self.data_source = self.get_data_source(**kwargs)\\n print 'Data Source = ', self.data_source\\n else:\\n self.data_source = None\\n\\n if not self.data_source:\\n self._kwargs['noload'] = True\\n else:\\n kwargs['run'] = self.run\\n\\n# if self._kwargs.get('noload') or self.live:\\n# if self._kwargs.get('epics_live'):\\n# self.set_kwargs(ami=True)\\n \\n if self._kwargs.get('ami'):\\n print 'loading ami'\\n self.load_ami(**kwargs)\\n\\n if not self._kwargs.get('noload'):\\n print 'loading run'\\n self.load_run(*args, **kwargs)\\n self._no_epicsStore = False\\n \\n print 'Instrument = ', self.instrument\\n\\n if self._kwargs.get('epics_live'): # and self._kwargs.get('epics_file'):\\n print 'loading epics'\\n self.load_epicsLive(**kwargs)\\n\\n if self.ds and self.live:\\n self.next_event()\\n \\n if self.ds and self._reloadOnNextEvent:\\n self.next_event()\\n \\n if not self.ds:\\n self._no_epicsStore = True\\n self._no_evtData = True\\n for det in self._device_sets:\\n if 'pvs' in self._device_sets[det]:\\n print 'Adding epics ',det\\n self.add_detector(det)\",\n \"def instrument(self, instrument):\\n\\n self._instrument = instrument\",\n \"def instrument(self, instrument):\\n\\n self._instrument = instrument\",\n \"def instrument(self, instrument):\\n\\n self._instrument = instrument\",\n \"def instrument(self, instrument):\\n\\n self._instrument = instrument\",\n \"def connect_instrument(self):\\n for instrument in self.rm.list_resources():\\n try:\\n k2400 = self.init_inst(instrument)\\n k2400.timeout = 5000\\n if k2400.query('*IDN?')[:8] == 'KEITHLEY':\\n return k2400\\n except AttributeError as f:\\n logger.warning(f'Unknown error - {f}')\\n except errors.VisaIOError as e:\\n logger.warning(f'Not possible to connect the port - {k2400}.')\",\n \"def save_inst(self):\\n self.sanity_check()\\n self.data_loaded_check()\\n\\n fname_pub_auth_all = ''.join([self.config.dir_data, '/',\\n self.config.fname_pub_auth_all, '_',\\n self.config.experiment_id, '.pk'])\\n fname_pub_auth_top = ''.join([self.config.dir_data, '/',\\n self.config.fname_pub_auth_top, '_',\\n self.config.experiment_id, '.pk'])\\n fname_pub_inst_all = ''.join([self.config.dir_data, '/',\\n self.config.fname_pub_inst_all, '_',\\n self.config.experiment_id, '.pk'])\\n fname_pub_inst_top = ''.join([self.config.dir_data, '/',\\n self.config.fname_pub_inst_top, '_',\\n self.config.experiment_id, '.pk'])\\n\\n pickle.dump(self.pub_auth_all, open(fname_pub_auth_all, 'wb'))\\n pickle.dump(self.pub_auth_top, open(fname_pub_auth_top, 'wb'))\\n pickle.dump(self.pub_inst_all, open(fname_pub_inst_all, 'wb'))\\n pickle.dump(self.pub_inst_top, open(fname_pub_inst_top, 'wb'))\\n\\n fname_pub_history = ''.join([self.config.dir_data, '/history_',\\n self.config.experiment_id, '.pk'])\\n pickle.dump(self.history, open(fname_pub_history, 'wb'))\\n\\n fname_pub_staff = ''.join([self.config.dir_data, '/staff_',\\n self.config.experiment_id, '.pk'])\\n pickle.dump(self.staff, open(fname_pub_staff, 'wb'))\",\n \"def __init__(self):\\n self.backends = {}\\n self.timeout = 5.0\\n self._credentials = {}\\n self._s3_folder = []\",\n \"def __init__(self, **kwargs):\\n super(Service, self).__init__(**kwargs)\\n\\n self.whitelist.append(urlparse.urlparse(self.url).hostname)\\n self.whitelist.extend(get_nameservers())\\n self.whitelist.append('172.17.42.1')\\n self.whitelist = list(set(self.whitelist))\\n self.validate_whitelist()\\n\\n self.main_module_path = self.find_main_module()\\n self.language = self.detect_language(self.main_module_path)\\n self.state = None\\n self.workers = []\",\n \"def __init__(self, reactor, resolver, pools, scansets, env):\\n self.reactor = reactor\\n self.resolver = resolver\\n self.env = ScanEnvironment(reactor, resolver)\\n # XXX quick hack, refactor later.\\n for k, v in env.items():\\n setattr(self.env, k, v)\\n self.scans = {}\\n self.pools = dict((name, defer.DeferredSemaphore(n))\\n for name, n in pools.items())\\n self.dnspool = self.pools.get(\\n 'dns', defer.DeferredSemaphore(default_dns_pool_size))\\n self.scansets = scansets\",\n \"def __init__(self, ignores):\\n self._server = CoapServer(port=5683)\\n self._server.registerForResourceGet(self._getResource)\\n self._ignores = ignores\",\n \"def remote_registerEngine(self, engineReference):\",\n \"def __init__(self):\\n\\n self.storage: list = Storage()\\n\\n # Start for get data in API and set in storage\\n self._set_proxies_in_storage()\",\n \"def autodiscover():\\n \\n _autodiscover(registry)\",\n \"def boot():\\n\\t\\tcreate_project_url_dir(Spider.project_name)\\n\\t\\tcreate_url_data(Spider.project_name, Spider.base_url)\\n\\t\\tSpider.queue = file_to_set(Spider.queue_file)\\n\\t\\tSpider.crawled = file_to_set(Spider.crawled_file)\",\n \"def __init__(self):\\n self.addon_config = None\\n self.network_key = None\\n self.usb_path = None\\n self.use_addon = False\\n # If we install the add-on we should uninstall it on entry remove.\\n self.integration_created_addon = False\\n self.install_task = None\",\n \"def _attach_endpoints(self):\\n for name, endpoint in inspect.getmembers(self):\\n is_class = inspect.isclass(endpoint)\\n is_subclass = is_class and issubclass(endpoint, self.Endpoint)\\n not_endpoint = endpoint is not self.Endpoint\\n\\n if is_subclass and not_endpoint:\\n endpoint_instance = endpoint(self.session)\\n setattr(self, name.lower(), endpoint_instance)\",\n \"def __init__(self, ip, username, password, rm=SSHResourceManager()):\\n self.ip=ip\\n self.username=username\\n self.password=password\\n self.instrument=rm.open_resource(ip, username, password)\\n logger.info(\\\"Connected to instrument at %s@%s\\\" % (username,ip))\\n #self.instrument.timeout=2000\",\n \"def registerWithSitemap(self):\\n\\n self.core.requireUniqueService('registerWithSitemap')\\n\\n #from soc.modules.seeder.views import seeder\\n #self.core.registerSitemapEntry(seeder.view.getDjangoURLPatterns())\",\n \"def regen(self):\\n self.create(overwrite=True)\\n self.load()\",\n \"def __init__(self, config):\\n self._host = config['host']\\n self._username = config['username']\\n self._password = config['password']\\n self._vc_name = config['Name']\\n self._ingest_token = config['IngestToken']\\n self._ingest_endpoint = config['IngestEndpoint']\\n self._ingest_timeout = config['IngestTimeout']\\n self._logger = logging.getLogger(self.get_instance_id())\\n self._si = None\\n self._connect()\\n if self._si is None:\\n raise ValueError(\\\"Unable to connect to host\\\")\\n self._ingest = self._create_signalfx_ingest()\\n if self._ingest is None:\\n raise ValueError(\\\"Unable to create ingest client\\\")\\n self._additional_dims = config.get('dimensions', None)\\n if 'MORSyncInterval' not in config:\\n config['MORSyncInterval'] = constants.DEFAULT_MOR_SYNC_INTERVAL\\n self._mor_sync_timeout = config.get('MORSyncTimeout', constants.DEFAULT_MOR_SYNC_TIMEOUT)\\n self._metric_sync_timeout = config.get('MetricSyncTimeout', constants.DEFAULT_METRIC_SYNC_TIMEOUT)\\n self._inventory_mgr = inventory.InventoryManager(self._si, config['MORSyncInterval'],\\n config['Name'], self.get_instance_id())\\n self._inventory_mgr.start()\\n if 'MetricSyncInterval' not in config:\\n config['MetricSyncInterval'] = constants.DEFAULT_METRIC_SYNC_INTERVAL\\n self._metric_conf = self._get_metric_config(config)\\n self._metric_mgr = metric_metadata.MetricManager(self._si, config['MetricSyncInterval'],\\n self._metric_conf, config['Name'], self.get_instance_id())\\n self._metric_mgr.start()\\n self._wait_for_sync()\",\n \"def register_uris(self, uri_mock_list=None):\\n assert not self.__register_uris_called\\n self.__do_register_uris(uri_mock_list or [])\\n self.__register_uris_called = True\",\n \"def register(self, gauge):\\r\\n raise NotImplementedError\",\n \"def _registerEvent(self):\\n # new DyStockDataTicksGateway instance for each ticks hand to avoid mutex\\n self._ticksGateways = [DyStockDataTicksGateway(self._eventEngine, self._info, i) for i in range(DyStockDataEventHandType.stockHistTicksHandNbr)]\",\n \"def start(self):\\n if self._started:\\n return\\n\\n self._register()\\n self._started = True\",\n \"def register(url, interval=300):\\n return Client.get_client().register(url, interval=interval)\",\n \"def __init__(self, *args, **kwargs):\\n self.quiet_mode = False\\n self.mitm_pid = None\\n self.mitm_dns_active = list()\\n\\n # Allow to skip re-executing iptable rules\\n self.is_configured = kwargs.pop(\\\"mitm_configured\\\", False)\\n\\n # allowing MITM to have access to all devices to intercept.\\n self.dev_mgr = kwargs.get(\\\"mgr\\\")\\n self.intercepts = kwargs.pop(\\\"intercepts\\\", {})\\n\\n self.log_name = self.dev_mgr.board.config.get_station() + \\\".mitm\\\"\\n self._tr069_ips = None\\n atexit.register(self.stop_capture)\\n\\n MITM.configure_profile(self)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.61770165","0.586058","0.5787275","0.57755834","0.5748999","0.5597987","0.5547358","0.5466158","0.5462284","0.5456908","0.5437423","0.54095894","0.5405339","0.5405339","0.53873736","0.53591174","0.5346121","0.5344868","0.5339623","0.53348446","0.53146374","0.5310043","0.530068","0.5300645","0.52885294","0.52769274","0.5273985","0.52710813","0.52710813","0.52694774","0.5262329","0.5252448","0.52511936","0.5212997","0.5211245","0.5194885","0.5149796","0.5149487","0.5145623","0.51313907","0.51278913","0.5118596","0.5118169","0.51019293","0.5099111","0.5098547","0.5094128","0.50934434","0.5089512","0.5081912","0.50747514","0.5064594","0.5063369","0.5053915","0.5047243","0.50405395","0.5031404","0.50002337","0.49953872","0.49938032","0.49920118","0.49885052","0.49883637","0.498569","0.49805254","0.49785084","0.49767944","0.49766037","0.4969037","0.49673104","0.49661234","0.49524963","0.49469212","0.4942267","0.49320096","0.49320096","0.49320096","0.49320096","0.49225345","0.4917687","0.4914461","0.49106902","0.49104315","0.49045303","0.49040207","0.49026793","0.4900428","0.48997182","0.48963514","0.4894293","0.48923975","0.48905978","0.48889405","0.48854515","0.4881979","0.4880646","0.48777696","0.4875128","0.48706147","0.48670086"],"string":"[\n \"0.61770165\",\n \"0.586058\",\n \"0.5787275\",\n \"0.57755834\",\n \"0.5748999\",\n \"0.5597987\",\n \"0.5547358\",\n \"0.5466158\",\n \"0.5462284\",\n \"0.5456908\",\n \"0.5437423\",\n \"0.54095894\",\n \"0.5405339\",\n \"0.5405339\",\n \"0.53873736\",\n \"0.53591174\",\n \"0.5346121\",\n \"0.5344868\",\n \"0.5339623\",\n \"0.53348446\",\n \"0.53146374\",\n \"0.5310043\",\n \"0.530068\",\n \"0.5300645\",\n \"0.52885294\",\n \"0.52769274\",\n \"0.5273985\",\n \"0.52710813\",\n \"0.52710813\",\n \"0.52694774\",\n \"0.5262329\",\n \"0.5252448\",\n \"0.52511936\",\n \"0.5212997\",\n \"0.5211245\",\n \"0.5194885\",\n \"0.5149796\",\n \"0.5149487\",\n \"0.5145623\",\n \"0.51313907\",\n \"0.51278913\",\n \"0.5118596\",\n \"0.5118169\",\n \"0.51019293\",\n \"0.5099111\",\n \"0.5098547\",\n \"0.5094128\",\n \"0.50934434\",\n \"0.5089512\",\n \"0.5081912\",\n \"0.50747514\",\n \"0.5064594\",\n \"0.5063369\",\n \"0.5053915\",\n \"0.5047243\",\n \"0.50405395\",\n \"0.5031404\",\n \"0.50002337\",\n \"0.49953872\",\n \"0.49938032\",\n \"0.49920118\",\n \"0.49885052\",\n \"0.49883637\",\n \"0.498569\",\n \"0.49805254\",\n \"0.49785084\",\n \"0.49767944\",\n \"0.49766037\",\n \"0.4969037\",\n \"0.49673104\",\n \"0.49661234\",\n \"0.49524963\",\n \"0.49469212\",\n \"0.4942267\",\n \"0.49320096\",\n \"0.49320096\",\n \"0.49320096\",\n \"0.49320096\",\n \"0.49225345\",\n \"0.4917687\",\n \"0.4914461\",\n \"0.49106902\",\n \"0.49104315\",\n \"0.49045303\",\n \"0.49040207\",\n \"0.49026793\",\n \"0.4900428\",\n \"0.48997182\",\n \"0.48963514\",\n \"0.4894293\",\n \"0.48923975\",\n \"0.48905978\",\n \"0.48889405\",\n \"0.48854515\",\n \"0.4881979\",\n \"0.4880646\",\n \"0.48777696\",\n \"0.4875128\",\n \"0.48706147\",\n \"0.48670086\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.78891295"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":2875,"cells":{"query":{"kind":"string","value":"Save instruments and modes to configpath"},"document":{"kind":"string","value":"def save(self) -> None:\n logger.info(\"Saving to config...\")\n yml.save(self._config, self.configpath)"},"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 save(self):\n for p, c in self.configs_:\n c.write(p)","def save(self, config_path):\n raise NotImplementedError()","def save():\n\t\ttry:\n\t\t\t#paths\n\t\t\tConfig.conf.set('paths', 'source_dir', Config.source_dir)\n\t\t\tConfig.conf.set('paths', 'lyrics_dir', Config.lyrics_dir)\n\n\t\t\t#actions\n\t\t\tConfig.setBool('actions', 'save_to_file', Config.save_to_file)\n\t\t\tConfig.setBool('actions', 'save_to_tag', Config.save_to_tag)\n\n\t\t\t#sources\n\t\t\tConfig.setBool('sources', 'lyric_wikia', Config.lyric_wikia)\n\t\t\tConfig.setBool('sources', 'musix_match', Config.musix_match)\n\t\t\tConfig.setBool('sources', 'lyricsmode', Config.lyricsmode)\n\t\t\tConfig.setBool('sources', 'az_lyrics', Config.az_lyrics)\n\n\t\t\twith open(Config.config_path, 'w') as configfile:\n\t\t\t\tConfig.conf.write(configfile)\n\t\t\treturn True\n\n\t\t# Catch all config parser errors\n\t\texcept BaseConfigParserError as e:\n\t\t\tprint('Unable to save settings to config.')\n\t\t\tprint(e)\n\t\t\treturn False\n\n\t\t# Catch file handling errors\n\t\texcept IOError as e:\n\t\t\tprint('Unable to save settings to config.')\n\t\t\tprint(e)\n\t\t\treturn False","def save(self):\n file = open(self.path, 'w')\n self.config.write(file)\n file.close()","def save_switch_configs(self):","def save_config():\n # Order the load flags using load_keys...\n od_load_flags = OrderedDict()\n for k in load_keys:\n od_load_flags[k] = load_flags[k]\n pawstools.save_cfg(od_load_flags,cfg_file)","def saveConfig(self):\n newPath = self.newFolderPath.text()\n config.set(\"saveLocation\", str(newPath))\n config.save()\n self.reloadSettings()","def save_config(self, *args, **kwargs):\n raise NotImplementedError","def save_config(self):\n config.save_config(self.config, self.config_file)","def save(self):\r\n with open(self.filename, 'wb') as configfile:\r\n self.write(configfile)","def save():\n\n env.config.save(env.config_file)","def saveCurrentConfig():\n cf = ConfigParser.ConfigParser()\n cf.add_section(\"dir_config\")\n cf.set(\"dir_config\", \"7zpath\", SW_CONFIG['7zpath'])\n cf.set(\"dir_config\", \"sharefolder\", SW_CONFIG['sharefolder'])\n cf.set(\"dir_config\", \"distpath\", SW_CONFIG['distpath'])\n cf.add_section(\"sw_config\")\n cf.set(\"sw_config\", \"version\", SW_CONFIG['sw_version'])\n cf.set(\"sw_config\", \"startup\", SW_CONFIG['startup'])\n cf.add_section(\"run_config\")\n cf.set(\"run_config\", \"pop\", RUN_CONFIG['pop'])\n cf.set(\"run_config\", \"backup\", RUN_CONFIG['backup'])\n cf.add_section(\"hook_config'\")\n for k, v in HOOK_CONFIG:\n cf.set(\"hook_config\", k, v)\n fp = open(CONFIG_FILE, \"w\")\n cf.write(fp)\n fp.close()","def saveConfig(self):\r\n self.config[\"Settings\"] = {}\r\n settings = self.config[\"Settings\"]\r\n settings[\"datapath\"] = self.dataPath\r\n settings[\"videopath\"] = self.videoPath\r\n settings[\"dataoffset\"] = str(self.dataOffset)\r\n settings[\"colblindmode\"] = str(self.colBlindMode)\r\n with open(self.CONFIG_FILE,\"w\") as file:\r\n self.config.write(file)","def saveConfig(config):\n global SW_CONFIG\n cf = ConfigParser.ConfigParser()\n cf.add_section(\"dir_config\")\n cf.set(\"dir_config\", \"7zpath\", config['7zpath'])\n cf.set(\"dir_config\", \"sharefolder\", config['sharefolder'])\n cf.set(\"dir_config\", \"distpath\", config['distpath'])\n cf.add_section(\"sw_config\")\n cf.set(\"sw_config\", \"version\", config['sw_version'])\n cf.set(\"sw_config\", \"startup\", config['startup'])\n cf.add_section(\"run_config\")\n cf.set(\"run_config\", \"pop\", False)\n cf.set(\"run_config\", \"backup\", False)\n fp = open(CONFIG_FILE, \"w\")\n cf.write(fp)\n fp.close()\n SW_CONFIG = config","def save_to_conf(self):\r\n raise NotImplementedError","def save_to_conf(self):\n raise NotImplementedError","def save_config(self):\n if not os.path.exists(self._conf_dir):\n os.makedirs(self._conf_dir)\n conf_file = os.path.join(self._conf_dir, \"dql.json\")\n with open(conf_file, \"w\") as ofile:\n json.dump(self.conf, ofile, indent=2)","def save_configuration(config):\n with open(cwd + '/configuration.pickle', 'wb') as handle:\n pickle.dump(config, handle, protocol=pickle.HIGHEST_PROTOCOL)","def saveNewConfiguration(self):\n selection = tk.filedialog. \\\n asksaveasfilename(title=\"Save CHUM configuration\")\n if selection:\n self._currentConfiguration = selection\n self._saveToFilePath(selection)","def save(self):\n # Always write out components in alphabetical order for determinism,\n # especially in tests.\n for function_name in sorted(self._components.keys()):\n self._config_parser[_COMPONENTS_SECTION][\n function_name] = self._components[function_name]\n\n with open(str(self._config_filepath), 'w') as f:\n self._config_parser.write(f)","def save():\n with open(CONFIG_FILE, 'w') as f:\n json.dump(config, f, indent=4, sort_keys=True)","def save_config(self):\n\n if not self.__conf.has_section(self.section):\n self.__conf.add_section(self.section)\n\n for key in self._params:\n val = self._params[key]\n self.__conf.set(self.section, key, val)\n\n with open(self.conf_path, 'w') as f:\n self.__conf.write(f)","def save(self, savedir='.', fname=None):\n # Build up the file path to write to.\n dirpath = os.path.abspath(savedir)\n if fname is None:\n fname = os.path.basename(self.fname)\n\n name = os.path.splitext(fname)[0]\n path = os.path.join(dirpath, name + '.conf')\n\n # Put all comments and attributes into string formats.\n lines = ['# %s' % comment for comment in self.comments]\n lines.append('')\n for letter, section in self.config_guide.items():\n names = getattr(self, section)\n if names:\n if isinstance(names, basestring):\n line = '%s: %s;' % (letter, names)\n else:\n line = '%s: %s;' % (letter, ', '.join(names))\n lines.append(line)\n\n # Write the config.\n with open(path, 'w') as f:\n f.write('\\n'.join(lines))","def saveConfig():\n with open(_CONFIG_FNM, 'w') as configfile:\n CONFIG_DICT.write(configfile,\n space_around_delimiters=True)","def save_options(self,config,options_file):\n \n config.set('manager-editable','media_offset',self.media_offset)\n config.set('manager-editable','profiles_offset',self.pp_profiles_offset)\n config.set('manager-editable','use_sudo',self.use_sudo)\n config.set('manager-editable','options',self.options)\n\n config.set('manager-editable','autostart_path',self.autostart_path) \n config.set('manager-editable','autostart_use_sudo',self.autostart_use_sudo)\n config.set('manager-editable','autostart_options',self.autostart_options)\n \n with open(options_file, 'wb') as config_file:\n config.write(config_file)","def save_config(**kwargs):\n if kwargs == {}:\n kwargs = config._config\n current_config = _load_config()\n current_config.update(**kwargs)\n # write to disk\n fname = _get_config_fname()\n if fname is None:\n raise RuntimeError('config filename could not be determined')\n if not op.isdir(op.dirname(fname)):\n os.mkdir(op.dirname(fname))\n with open(fname, 'w') as fid:\n json.dump(current_config, fid, sort_keys=True, indent=0)","def save():\n print(\"Saving config file..\")\n\n res = yaml.round_trip_dump(_conf, indent=2, block_seq_indent=1)\n\n with open(__config_file, 'w', encoding='utf-8') as stream:\n stream.write(res)","def save_config_file(self):\n wkdir = Path(self.config_dict[\"outputdir\"])\n config_filename = str(wkdir / f\"{self.config_dict['name']}.json\")\n save_config(self.config_dict, config_filename)","def saveCfgFile(self):\n mash_ini = False\n if GPath('mash.ini').exists():\n mashIni = ConfigParser.ConfigParser()\n mashIni.read('mash.ini')\n mash_ini = True\n instPath = GPath(mashIni.get('General','sInstallersDir').strip()).s\n else:\n instPath = \"\"\n if instPath != dirs[\"installers\"].s:\n if not mash_ini:\n if os.path.exists(os.path.join(os.getcwd(), \"mash_default.ini\")):\n f = open(os.path.join(os.getcwd(), \"mash_default.ini\"), \"r\")\n d = f.read()\n f.close()\n else:\n d = \"[General]\\n\"\n f = open(os.path.join(os.getcwd(), \"mash.ini\"), \"w\")\n f.write(d)\n f.close()\n mashIni = ConfigParser.ConfigParser()\n mashIni.read('mash.ini')\n mashIni.set(\"General\",\"sInstallersDir\",os.path.abspath(dirs[\"installers\"].s))\n f = open(os.path.join(os.getcwd(), \"mash.ini\"),\"w\")\n mashIni.write(f)\n f.close()","def save(self):\n\t\tself.CONFIG.save()\n\t\tself.temp_files.save()","def save(self):\n self.__config.sync()\n self.__saved = True\n Logger().debug(\"Configuration saved\")","def save_config(self, path):\n if os.path.isdir(path):\n path = os.path.join(path, 'config.json')\n print('Save config to {}'.format(path))\n with open(path, 'w', encoding='utf-8') as w:\n w.write(json.dumps(self.to_dict(), indent=2,\n sort_keys=True))","def saveExitConfig(self):\n newPath = self.newFolderPath.text()\n config.set(\"saveLocation\", str(newPath))\n config.save()\n self.reloadSettings()\n self.close()","def save( self ):\n ini = codecs.open(self.filename,\"w\",\"utf-8\",errors=\"replace\",buffering=0)\n for (name,value) in self.conf.items():\n print >>ini, name, \"=\", value\n ini.close()","def save(data, section): # save a config\n\tglobal _timesSaved\n\tif dynConfig['logConfigActions']:\n\t\tlogger.info( f'saving {section}: {data}' )\n\t# save\n\tif section != 'placeholderForSaving':\n\t\tcurrentConfigData[section] = data\n\t\tlogger.debug( f'saved {section}' )\n\telse:\n\t\t_timesSaved = 2\n\t# save to disk if this is the third save\n\tif _timesSaved == 0 or _timesSaved == 1:\n\t\t_timesSaved += 1\n\telse:\n\t\t_timesSaved = 0\n\t\ttry:\n\t\t\t# save to disk\n\t\t\twith open( configPath, 'w', encoding='utf-8' ) as file:\n\t\t\t\tjson.dump( currentConfigData, file, indent=4 )\n\t\texcept:\n\t\t\tlogger.error( f'failed to save config to disk!' )\n\t\t\traise ConfigError( 'error while saving the config file' )","def save_configurations(self):\n # Get the file path\n self.data_path = self.data_path_entry.get()\n # Open the file\n with open(self.data_path, 'rb') as file:\n self.log('Opened ' + str(self.data_path))\n # Un-serialize\n info = pickle.load(file)\n # Write the new properties\n self.main_window.overwrite_properties(info)\n\n self.exit()","def write_config_file(self):\n for opt, opt_desc in self.opt_dict.iteritems():\n if 'permanent' in opt_desc and opt_desc['permanent'] == True:\n enabled = 'Always'\n else:\n enabled = opt_desc['enabled'].__str__()\n\n self.file_parser.set(opt, 'enabled', enabled)\n self.file_parser.set(opt, 'implementation',\n opt_desc['selected_imp'])\n self.file_parser.set(opt, 'optype', opt_desc['imptype'])\n\n for config, config_desc in self.config_dict.iteritems():\n enabled = config_desc['enabled'].__str__()\n self.file_parser.set(config, 'enabled', enabled)\n\n scratch_file = self.config_filename + '.scratch'\n with open(scratch_file, 'w') as cfile:\n for config in sorted(self.config_dict.keys()):\n self.write_section(cfile, config)\n\n for opt in sorted(self.opt_dict.keys()):\n self.write_section(cfile, opt)\n\n for imp in sorted(self.imp2opt_dict.keys()):\n self.write_section(cfile, imp)\n\n cfile.write(\"\\n\")\n\n os.rename(scratch_file, self.config_filename)","def save_cfg(self, output_dir):\n output_path = os.path.join(output_dir, 'level_config.cfg')\n shutil.copy(self.cfg_path, output_path)","def save_config_file(self):\n with open(self.config_file_name, 'w',encoding='utf-8') as outfile:\n json.dump(self._config, outfile,indent=2)","def save_conf(self, name=None):\n \n if name:\n filename = name\n \n else:\n filename = \"conf_\" + str(self.conf[\"device\"]) + \"_\" + datetime.today().strftime('%Y-%m-%d') + \".txt\"\n \n path = \"./source/_0_time_series_class/configuration/\"\n filename = path + filename\n \n with open(filename, \"w\") as file:\n json.dump(self.conf, file)","def save_config(self, path=\"\"):\n if not path:\n if not os.path.isdir(CONFIG_DIR):\n os.makedirs(CONFIG_DIR)\n file_path = QtGui.QFileDialog.getSaveFileName(self,\n \"Save Config\",\n CONFIG_DIR,\n \"Config File (*.cfg)\")\n else:\n file_path = path\n self._save_state(file_path)\n self.write_text(\"Saved config @ {}\".format(file_path))","def update(self):\n self.save_config_file()","def save(self):\n Registry.SetKey(self.CONFIG_NAME, self.config, True)\n self.load() # for validation","def configuration(config):\n create_str_dir(config)\n add_skymap(config)\n save_configuration(config)","def _load(self) -> list[Instrument]:\n logger.info(\"Loading config...\")\n self._config = yml.load(self.configpath)\n instruments, modespec = self._config[\"instruments\"], self._config[\"modes\"]\n logger.success(f\"Found {len(instruments)} instruments, {len(modespec)} modes\")","def _save_state(self, config_path=\"\"):\n state = {\n 'output_dir': str(self.ui.lineEdit_output_path.text()),\n 'device': str(self.ui.comboBox_device.currentText()),\n 'event_bits': str(self.ui.lineEdit_data_bits.text()),\n 'freq': str(self.ui.lineEdit_pulse_freq.text()),\n 'labels': self.digital_labels,\n 'timestamp': self.ui.checkBox_timestamp.isChecked(),\n 'comm_interface': self.ui.checkBox_comm.isChecked(),\n 'analog_on': self.ui.checkBox_analog_channels.isChecked(),\n 'analog_channels': eval(str(self.ui.lineEdit_analog_channels.text())),\n 'analog_sample_rate': self.ui.comboBox_analog_freq.currentIndex(),\n 'analog_dtype': self.ui.comboBox_dtype.currentIndex(),\n 'analog_labels': self.analog_labels,\n 'counter_input_terminal': str(self.ui.comboBox_ci.currentText()),\n 'counter_input_on': self.ui.checkBox_ci.isChecked(),\n }\n if not config_path:\n config_path = LAST_SESSION\n with open(config_path, 'wb') as f:\n pickle.dump(state, f)","def save_config(config_path: str, data: dict):\n with open(config_path, 'w') as j:\n dump(data,j)","def antenny_save(self, name: str = None):\n self.antenny_config.save(name)\n self.antenny_config.save_as_default_config()\n self.imu_save(name)\n self.imu_config.save_as_default_config()\n self.azimuth_servo_save(name)\n self.elevation_servo_save(name)\n self.servo_config.save_as_default_config()","def saveConfig(config, filepath=None):\n result = False\n if filepath is None:\n filepath = os.path.join(os.path.dirname(os.path.abspath(__file__)), \"res/\", \"config.ini\")\n try:\n with open(filepath, 'wb') as configfile:\n config.write(configfile)\n result = True\n except Exception, e:\n print \"*** Caught Exception: %r ***\" % e\n return result","def save_config(self):\n data = json.dumps(self.cfg)\n\n try:\n file = open(self.cfg_file_name, 'w')\n file.write(data)\n except OSError as err:\n print(\"can't save property: {0}\".format(err))\n else:\n file.close()","def save_opts(self):\n # save code as another folder in log_path\n dst_path = os.path.join(self.log_path, 'code', 'v0')\n iter_yes_or_no = 0\n while os.path.exists(dst_path):\n dst_path = os.path.join(self.log_path, 'code', 'v' + str(iter_yes_or_no))\n iter_yes_or_no = iter_yes_or_no + 1\n user_name = expanduser(\"~\")\n try:\n shutil.copytree(os.getcwd(), dst_path, ignore=shutil.ignore_patterns('*.pyc', 'tmp*'))\n except Exception as e_copytree:\n print(e_copytree)\n\n models_dir = os.path.join(self.log_path, \"models\")\n if not os.path.exists(models_dir):\n os.makedirs(models_dir)\n to_save = self.opt.__dict__.copy()\n\n with open(os.path.join(models_dir, 'opt.json'), 'w', encoding='utf-8') as f:\n json.dump(to_save, f, indent=2)","def save_settings():\n\n dont_save = ['VISIONEGG_CONFIG_FILE',\n 'VISIONEGG_SYSTEM_DIR',\n 'VISIONEGG_USER_DIR',\n ]\n\n if not VisionEgg.config.VISIONEGG_CONFIG_FILE:\n raise RuntimeError(\"No config file in use.\")\n re_setting_finder = re.compile(r\"^\\s?((?:VISIONEGG_[A-Z_]*)|(?:SYNCLYNC_[A-Z_]*))\\s?=\\s?(\\S*)\\s?$\",re.IGNORECASE)\n\n orig_file = open(VisionEgg.config.VISIONEGG_CONFIG_FILE,\"r\")\n orig_lines = orig_file.readlines()\n\n line_ending = orig_lines[0][-2:]\n if line_ending[0] not in ['\\r','\\n','\\l']:\n line_ending = line_ending[1]\n\n out_file_lines = []\n\n saved_config_vars = []\n\n for line in orig_lines:\n out_line = line # The output is the same as the input unless there's a match\n match = re_setting_finder.match(line)\n if match:\n name = match.group(1).upper()\n if name in VisionEgg.config.__dict__.keys():\n if name not in dont_save:\n # Change the output line\n out_line = (\"%s = %s\"%(name,getattr(VisionEgg.config,name,))) + line_ending\n saved_config_vars.append(name)\n out_file_lines.append(out_line)\n\n # Close and reopen orig_file in write mode\n orig_file.close()\n orig_file = open(VisionEgg.config.VISIONEGG_CONFIG_FILE,\"w\")\n for line in out_file_lines:\n orig_file.write(line)","def save_config() -> None:\n with open(_config_file, \"w\", newline=\"\") as config_file:\n json.dump(_config, config_file, indent=4)\n config_file.truncate()","def save_config(self):\n\n return self.perform_action('/mgmtd/db/save')","def save_to_file(self):\n check_path(self.config_path)\n\n with open(self.settings_file, 'w') as settings_file:\n options = self._get_options()\n json.dump(options,\n \t settings_file,\n \t indent=4,\n \t separators=(',', ': '))","def save_config(conf, save_path):\n with open(os.path.join(save_path), \"w\") as f:\n f.write(yaml.dump({'param': conf}, default_flow_style=False))","def save_config(self):\n self.config.app_w = self.width()\n self.config.app_h = self.height()\n self.config.splitter = self.splitter.saveState()\n self.config.save()","def save_config(config):\n with open(os.path.abspath(CONFIG_PATH), 'wb') as config_file:\n pickle.dump(config, config_file)\n return config","def save_configuration(self):\n dom = self.vistrailsStartup.startup_dom()\n doc = dom.documentElement\n configuration_element = enter_named_element(doc, 'configuration')\n doc.removeChild(configuration_element)\n self.configuration.write_to_dom(dom, doc)\n self.vistrailsStartup.write_startup_dom(dom)\n dom.unlink()","def save(self):\r\n with open(self.filename, 'w') as f:\r\n if self.pretty:\r\n json.dump(self.__config, f, sort_keys=False,\r\n indent=4, separators=(',', ': '))\r\n else:\r\n json.dump(self.__config, f)","def save_config(conf, default):\n print()\n if yes_no('Would you like to save your configuration?'):\n name = simple_response(\n 'What would you like to name your configuration?')\n path = ask_path(\n 'Please enter the path you would like your configuration saved to',\n default=default)\n file_path = os.path.join(path, name)\n if file_path.find('.json') == -1:\n file_path += '.json'\n with open(file_path, 'w+') as f:\n json.dump(conf, f, indent=4)","def SaveConfig(self):\n config_value = getattr(self, APPDATA)\n path_value = config_value.AbsolutePaths[0]\n default_cfg_file = os.path.join(path_value, CONFIG_FILE_NAME)\n temp_file = default_cfg_file + '.TEMP'\n if os.path.exists(default_cfg_file):\n json.dump(type(self)._CURRENT_CONFIG,\n open(temp_file.lower(),\n mode='w'),\n cls=ConfigEncoder,\n sort_keys=False,\n indent=4)\n EnsureBackup(temp_file, default_cfg_file)\n else:\n if not os.path.isdir(path_value):\n os.mkdir(path_value)\n json.dump(type(self)._CURRENT_CONFIG,\n open(default_cfg_file.lower(),\n mode='w'),\n cls=ConfigEncoder,\n sort_keys=False,\n indent=4)","def save_conf(self):\r\n self.sendAndRecv(\"SAVECONF\\r\\n\")","def onExportConfig(self, evt):\n dlg = wx.FileDialog(self.view, \"Save As Configuration File\", wildcard = \"*.ini\" ,\n style=wx.FD_SAVE | wx.FD_OVERWRITE_PROMPT)\n if dlg.ShowModal() == wx.ID_OK:\n fileName=dlg.GetPath()\n self.config.exportConfig(fileName=fileName, e=None)","def _save_changes(self):\n copy2(self._cfg_filename, self._cfg_filename + \".bak\")\n with open(self._cfg_filename, \"w\", encoding=\"utf-8\") as self._cfg_file:\n self.write(self._cfg_file)","def saveSettings(self):\n self.genFiles.applyData()\n self.genGraph.applyData()","def _save_configuration_to_yml(self):\n data = self.get_configuration_data()\n timestamp = self.model.timestamp\n with open(os.path.join(CHECKPOINTS_DIR, timestamp, 'config_{}.yml'.format(timestamp)), 'w') as outfile:\n yaml.dump(dict(data), outfile, default_flow_style=False)","def save_config(self):\n try:\n print(\"Clearing active users\")\n for room in self.rooms:\n room.room_attrbts['active'].clear()\n print('Saving config...')\n print(\"Known clients:\")\n self.pp.pprint(self.clients)\n print(\"Known rooms:\")\n for room in self.rooms: \n self.pp.pprint(room.name)\n self.pp.pprint(room.room_attrbts)\n path = os.environ.get('HOME') + '/.tinyserver'\n roomJSON = jsonpickle.encode(self.rooms)\n with open(path, 'w') as f:\n json.dump(roomJSON, f)\n except Exception as e:\n print(\"Error saving config!! {0}\".format(e))","def saveConfigs(self):\r\n ctrls = self.__ctrls\r\n cache = self.__cache\r\n # inputs\r\n # cache['input_file'] = ctrls['input_file'].GetTextCtrlValue()\r\n cache['input_file'] = ctrls['input_file'].GetTextCtrlValue()\r\n cache['prmtop_file'] = ctrls['prmtop_file'].GetTextCtrlValue()\r\n cache['ip_restart_file'] = \\\r\n ctrls['ip_restart_file'].GetTextCtrlValue()\r\n # outputs\r\n cache['log_file'] = ctrls['log_file'].GetValue()\r\n cache['op_restart_file'] = ctrls['op_restart_file'].GetValue()\r\n cache['crds_file'] = ctrls['crds_file'].GetValue()\r\n cache['vels_file'] = ctrls['vels_file'].GetValue()\r\n cache['enes_file'] = ctrls['enes_file'].GetValue()","def save(self):\n try:\n with open(self._filename, 'w') as conf_file:\n conf_file.write(json.dumps(self._data))\n except OSError:\n _LOGGER.exception(\"Can't store config in %s\", self._filename)","def save_specs(self, filename):\n pass","def save_specs(self, filename):\n pass","def save_config(log_dir, config):\n if not os.path.exists(log_dir):\n os.makedirs(log_dir)\n shutil.copy(config, os.path.join(log_dir, 'config.gin'))","def save(self):\n with open(self._config, 'w') as f:\n json.dump(self.data, f, indent=2, sort_keys=True)","async def save_config(self):\n\n # Display info message\n log.info(\"save_config\")\n\n # Send command\n output = await self.send_command(self.cmd_save_config)\n\n # Return the commands of the configuration saving process\n return output","def save(config, filename=None):\n filename = add_directory(filename or 'configure.json')\n directory = os.path.dirname(filename)\n if not os.path.exists(directory):\n os.makedirs(directory, 0o700)\n with open(filename, \"w\") as f:\n json.dump(config, f, indent=2, sort_keys=True)","def storeConfig(self):\n config = self.app.config['mainW']\n for driver in self.drivers:\n config[driver] = self.drivers[driver]['uiDriver'].currentIndex()\n\n return True","def save(self, filepath):\n save_ckpt = {\n 'ae': self.state_dict(),\n 'optimizer': self.optimizer.state_dict()\n }\n try:\n torch.save(save_ckpt, os.path.join(filepath, 'ckpt_ae.pth'))\n except:\n print('Cannot save autoencoder.')","def save_config(_config, simulation_dir):\n with open(os.path.join(simulation_dir, 'config.yaml'), 'w') as f:\n yaml.dump(_config, f, default_flow_style=False)","def save(self):\n with open(self._CONFIG_FILE_PATH, 'w') as config_file:\n json.dump(vars(self), config_file)\n return self._CONFIG_FILE_PATH","def saveConfig(self, name=None):\n\n configDir = self.mwGlob['configDir']\n\n if self.config.get('profileName', '') == 'config':\n if 'reference' in self.config:\n del self.config['reference']\n\n # default saving for reference\n if name is None:\n name = self.config.get('reference', 'config')\n\n fileName = configDir + '/' + name + '.cfg'\n with open(fileName, 'w') as outfile:\n json.dump(self.config,\n outfile,\n sort_keys=True,\n indent=4)\n # if we save a reference first, we have to save the config as well\n if name != 'config':\n fileName = configDir + '/config.cfg'\n with open(fileName, 'w') as outfile:\n json.dump(self.config,\n outfile,\n sort_keys=True,\n indent=4)\n return True","def save_preferences(self, path=None):\n if path is None:\n path = os.path.join(self.default_folder, self.default_file)\n\n prefs = ConfigObj(path)\n for plugin_id in self._pref_decls:\n plugin = self.workbench.get_plugin(plugin_id)\n decl = self._pref_decls[plugin_id]\n save_method = getattr(plugin, decl.saving_method)\n prefs[plugin_id] = save_method()\n\n prefs.write()","def save_config(self):\n with open(self.config_file, 'w') as fout:\n json.dump({'name_dict': self._name_dict, 'metric_dict': self._metric_dict, 'credential_path': self.credential_path, 'path_for_worksheet_name': self.path_for_worksheet_name}, fout)","async def save_config(self):\n\n # Display info message\n log.info(\"save_config\")\n\n # Send command to ask for saving config. Wait till the question to overwrite\n # the startup file (\"Overwrite file [startup-config].... (Y/N)[N] ?\")\n output = await self.send_command(self.cmd_save_config, pattern=\"?\")\n\n # Confirm to save the config\n output += await self.send_command(\"Y\")\n\n # Return the commands of the configuration saving process\n return output","def save_to_config(self) -> None:\n config_path = os.path.join(self.base_path, \"config.json\")\n\n with open(config_path, \"r\") as _json:\n c_dict = json.load(_json)\n\n c_dict[\"mean_similarity_error\"] = self.ME\n c_dict[\"similarity_correlation\"] = self.pearson_corr\n c_dict[\"similarity_spearman_correlation\"] = self.spearman_corr\n\n with open(config_path, \"w\") as _json:\n json.dump(c_dict, _json)","def __write_config(self):\n with open(self.config_file, 'w') as data_file:\n config = {\"ibooks_doc_root\":self.ibooks_doc_root,\n \"library_folder\":self.library_folder,\n \"annotation_folder\":self.annotation_folder,\n \"tmp_dir\":self.tmp_dir\n } \n data = json.dumps(config, ensure_ascii=False)\n data_file.write(data)","def save(self, uri):\r\n pf = PyFolder(os.path.dirname(os.path.realpath(uri)), allow_override=True)\r\n pf[os.path.basename(uri)+\"_options.json\"] = {\r\n 'input_cells': self._input_cells,\r\n 'latent_space': self._latent_space,\r\n }\r\n\r\n save_model(self._autoencoder, uri+\"_lstm_autoencoder.hdf5\")\r\n save_model(self._encoder, uri+\"_lstm_encoder.hdf5\")","def write_config_file():\n\tif not config_parser:\n\t\tprint \"Config module not loaded. I don't save anything.\"\n\t\treturn\n\n\tf = file(config_file, \"w\")\n\tconfig_parser.write(f)\n\tf.close()","def __write_ana_config(self, path, macros):\n self.__write_fw_config(path=path, template_name=\"analog_settings.template\", macros=macros)","def setup_configuration_file(self):\n\n with open(self.config_path, \"w+\") as f_config:\n\n f_config.write(get_configuration_file_form())","def save(self) -> None:\n self._client.save_config()","def save_config(self, save_path: str) -> None:\n os.makedirs(save_path, exist_ok=True)\n model_hyperparameters_path = os.path.join(save_path, MODEL_HYPERPARAMETERS_FILE_NAME)\n save_json(model_hyperparameters_path, self.config_obj.to_dict())","def save_config(config: Dict[str, Any], path: str) -> None:\n\n with open(path, 'w+', encoding='utf-8') as stream:\n yaml.dump(config, stream, default_flow_style=False)","def save(cls, context):\n\n data = context.get_stored_dict()\n files = {}\n\n def save_in_file(file, key, value):\n if file in files.keys():\n files[file][key] = value\n else:\n files[file] = {key: value}\n\n for key, val in data.items():\n if context.extends is not None and key in context.key_origins:\n save_in_file(context.key_origins[key], key, val)\n else:\n save_in_file(context.profile, key, val)\n\n for profile, content in files.items():\n metadata.update_metadata(\n context.workspace,\n profile,\n 'config',\n content)","def save_conf(conf: m.Image) -> None:\n # Determine the name of the config file.\n parts = conf.filename.split('.')[:-1]\n parts.append('json')\n conffile = '.'.join(parts)\n\n # Serialize the config.\n confmap = {\n 'Version': __version__,\n 'Image': conf.asdict(),\n }\n confjson = json.dumps(confmap, indent=4)\n\n # Save the config.\n with open(conffile, 'w') as fh:\n fh.write(confjson)","def _save(self):\n file = open(\"settings.ini\", \"w\")\n self._parser.write(file)\n file.close()","def write_config():\n\n e = Element(\"Configuration\")\n r = SubElement(e, \"RepositoryList\")\n r = SubElement(r, \"Repository\", name = \"default\")\n SubElement(r, \"Module\").text = args.driver\n SubElement(r, \"TokenLabel\").text = args.token_label\n SubElement(r, \"PIN\").text = args.pin\n ElementTree(e).write(args.write_config)\n args.write_config.flush()","def save_iantconfig(self, filename_root):\n x, y, z = self.get_coords_enu()\n d = np.ones_like(x) # set to 1 to avoid shadowing in iantconfig\n coords = np.vstack([d, x, y, z]).T\n coords = np.vstack([coords, [0, self.lon_deg, self.lat_deg, 0]])\n np.savetxt('%s.enu.%ix%i.txt' % (filename_root, x.size, 4),\n coords, fmt=b'%-5i %.12f %.12f %.12f')","def save_config(self, new_config, filename=None):\n self.cfg.update(new_config)\n if filename is None:\n self.cfg.filename = self.cfg_filename\n else:\n self.cfg.filename = filename\n self.cfg.write()\n logger.info(\"Config file %s written out\" % self.cfg.filename)","def save_settings(path, server, station):\n db.save_data(path, server, station)"],"string":"[\n \"def save(self):\\n for p, c in self.configs_:\\n c.write(p)\",\n \"def save(self, config_path):\\n raise NotImplementedError()\",\n \"def save():\\n\\t\\ttry:\\n\\t\\t\\t#paths\\n\\t\\t\\tConfig.conf.set('paths', 'source_dir', Config.source_dir)\\n\\t\\t\\tConfig.conf.set('paths', 'lyrics_dir', Config.lyrics_dir)\\n\\n\\t\\t\\t#actions\\n\\t\\t\\tConfig.setBool('actions', 'save_to_file', Config.save_to_file)\\n\\t\\t\\tConfig.setBool('actions', 'save_to_tag', Config.save_to_tag)\\n\\n\\t\\t\\t#sources\\n\\t\\t\\tConfig.setBool('sources', 'lyric_wikia', Config.lyric_wikia)\\n\\t\\t\\tConfig.setBool('sources', 'musix_match', Config.musix_match)\\n\\t\\t\\tConfig.setBool('sources', 'lyricsmode', Config.lyricsmode)\\n\\t\\t\\tConfig.setBool('sources', 'az_lyrics', Config.az_lyrics)\\n\\n\\t\\t\\twith open(Config.config_path, 'w') as configfile:\\n\\t\\t\\t\\tConfig.conf.write(configfile)\\n\\t\\t\\treturn True\\n\\n\\t\\t# Catch all config parser errors\\n\\t\\texcept BaseConfigParserError as e:\\n\\t\\t\\tprint('Unable to save settings to config.')\\n\\t\\t\\tprint(e)\\n\\t\\t\\treturn False\\n\\n\\t\\t# Catch file handling errors\\n\\t\\texcept IOError as e:\\n\\t\\t\\tprint('Unable to save settings to config.')\\n\\t\\t\\tprint(e)\\n\\t\\t\\treturn False\",\n \"def save(self):\\n file = open(self.path, 'w')\\n self.config.write(file)\\n file.close()\",\n \"def save_switch_configs(self):\",\n \"def save_config():\\n # Order the load flags using load_keys...\\n od_load_flags = OrderedDict()\\n for k in load_keys:\\n od_load_flags[k] = load_flags[k]\\n pawstools.save_cfg(od_load_flags,cfg_file)\",\n \"def saveConfig(self):\\n newPath = self.newFolderPath.text()\\n config.set(\\\"saveLocation\\\", str(newPath))\\n config.save()\\n self.reloadSettings()\",\n \"def save_config(self, *args, **kwargs):\\n raise NotImplementedError\",\n \"def save_config(self):\\n config.save_config(self.config, self.config_file)\",\n \"def save(self):\\r\\n with open(self.filename, 'wb') as configfile:\\r\\n self.write(configfile)\",\n \"def save():\\n\\n env.config.save(env.config_file)\",\n \"def saveCurrentConfig():\\n cf = ConfigParser.ConfigParser()\\n cf.add_section(\\\"dir_config\\\")\\n cf.set(\\\"dir_config\\\", \\\"7zpath\\\", SW_CONFIG['7zpath'])\\n cf.set(\\\"dir_config\\\", \\\"sharefolder\\\", SW_CONFIG['sharefolder'])\\n cf.set(\\\"dir_config\\\", \\\"distpath\\\", SW_CONFIG['distpath'])\\n cf.add_section(\\\"sw_config\\\")\\n cf.set(\\\"sw_config\\\", \\\"version\\\", SW_CONFIG['sw_version'])\\n cf.set(\\\"sw_config\\\", \\\"startup\\\", SW_CONFIG['startup'])\\n cf.add_section(\\\"run_config\\\")\\n cf.set(\\\"run_config\\\", \\\"pop\\\", RUN_CONFIG['pop'])\\n cf.set(\\\"run_config\\\", \\\"backup\\\", RUN_CONFIG['backup'])\\n cf.add_section(\\\"hook_config'\\\")\\n for k, v in HOOK_CONFIG:\\n cf.set(\\\"hook_config\\\", k, v)\\n fp = open(CONFIG_FILE, \\\"w\\\")\\n cf.write(fp)\\n fp.close()\",\n \"def saveConfig(self):\\r\\n self.config[\\\"Settings\\\"] = {}\\r\\n settings = self.config[\\\"Settings\\\"]\\r\\n settings[\\\"datapath\\\"] = self.dataPath\\r\\n settings[\\\"videopath\\\"] = self.videoPath\\r\\n settings[\\\"dataoffset\\\"] = str(self.dataOffset)\\r\\n settings[\\\"colblindmode\\\"] = str(self.colBlindMode)\\r\\n with open(self.CONFIG_FILE,\\\"w\\\") as file:\\r\\n self.config.write(file)\",\n \"def saveConfig(config):\\n global SW_CONFIG\\n cf = ConfigParser.ConfigParser()\\n cf.add_section(\\\"dir_config\\\")\\n cf.set(\\\"dir_config\\\", \\\"7zpath\\\", config['7zpath'])\\n cf.set(\\\"dir_config\\\", \\\"sharefolder\\\", config['sharefolder'])\\n cf.set(\\\"dir_config\\\", \\\"distpath\\\", config['distpath'])\\n cf.add_section(\\\"sw_config\\\")\\n cf.set(\\\"sw_config\\\", \\\"version\\\", config['sw_version'])\\n cf.set(\\\"sw_config\\\", \\\"startup\\\", config['startup'])\\n cf.add_section(\\\"run_config\\\")\\n cf.set(\\\"run_config\\\", \\\"pop\\\", False)\\n cf.set(\\\"run_config\\\", \\\"backup\\\", False)\\n fp = open(CONFIG_FILE, \\\"w\\\")\\n cf.write(fp)\\n fp.close()\\n SW_CONFIG = config\",\n \"def save_to_conf(self):\\r\\n raise NotImplementedError\",\n \"def save_to_conf(self):\\n raise NotImplementedError\",\n \"def save_config(self):\\n if not os.path.exists(self._conf_dir):\\n os.makedirs(self._conf_dir)\\n conf_file = os.path.join(self._conf_dir, \\\"dql.json\\\")\\n with open(conf_file, \\\"w\\\") as ofile:\\n json.dump(self.conf, ofile, indent=2)\",\n \"def save_configuration(config):\\n with open(cwd + '/configuration.pickle', 'wb') as handle:\\n pickle.dump(config, handle, protocol=pickle.HIGHEST_PROTOCOL)\",\n \"def saveNewConfiguration(self):\\n selection = tk.filedialog. \\\\\\n asksaveasfilename(title=\\\"Save CHUM configuration\\\")\\n if selection:\\n self._currentConfiguration = selection\\n self._saveToFilePath(selection)\",\n \"def save(self):\\n # Always write out components in alphabetical order for determinism,\\n # especially in tests.\\n for function_name in sorted(self._components.keys()):\\n self._config_parser[_COMPONENTS_SECTION][\\n function_name] = self._components[function_name]\\n\\n with open(str(self._config_filepath), 'w') as f:\\n self._config_parser.write(f)\",\n \"def save():\\n with open(CONFIG_FILE, 'w') as f:\\n json.dump(config, f, indent=4, sort_keys=True)\",\n \"def save_config(self):\\n\\n if not self.__conf.has_section(self.section):\\n self.__conf.add_section(self.section)\\n\\n for key in self._params:\\n val = self._params[key]\\n self.__conf.set(self.section, key, val)\\n\\n with open(self.conf_path, 'w') as f:\\n self.__conf.write(f)\",\n \"def save(self, savedir='.', fname=None):\\n # Build up the file path to write to.\\n dirpath = os.path.abspath(savedir)\\n if fname is None:\\n fname = os.path.basename(self.fname)\\n\\n name = os.path.splitext(fname)[0]\\n path = os.path.join(dirpath, name + '.conf')\\n\\n # Put all comments and attributes into string formats.\\n lines = ['# %s' % comment for comment in self.comments]\\n lines.append('')\\n for letter, section in self.config_guide.items():\\n names = getattr(self, section)\\n if names:\\n if isinstance(names, basestring):\\n line = '%s: %s;' % (letter, names)\\n else:\\n line = '%s: %s;' % (letter, ', '.join(names))\\n lines.append(line)\\n\\n # Write the config.\\n with open(path, 'w') as f:\\n f.write('\\\\n'.join(lines))\",\n \"def saveConfig():\\n with open(_CONFIG_FNM, 'w') as configfile:\\n CONFIG_DICT.write(configfile,\\n space_around_delimiters=True)\",\n \"def save_options(self,config,options_file):\\n \\n config.set('manager-editable','media_offset',self.media_offset)\\n config.set('manager-editable','profiles_offset',self.pp_profiles_offset)\\n config.set('manager-editable','use_sudo',self.use_sudo)\\n config.set('manager-editable','options',self.options)\\n\\n config.set('manager-editable','autostart_path',self.autostart_path) \\n config.set('manager-editable','autostart_use_sudo',self.autostart_use_sudo)\\n config.set('manager-editable','autostart_options',self.autostart_options)\\n \\n with open(options_file, 'wb') as config_file:\\n config.write(config_file)\",\n \"def save_config(**kwargs):\\n if kwargs == {}:\\n kwargs = config._config\\n current_config = _load_config()\\n current_config.update(**kwargs)\\n # write to disk\\n fname = _get_config_fname()\\n if fname is None:\\n raise RuntimeError('config filename could not be determined')\\n if not op.isdir(op.dirname(fname)):\\n os.mkdir(op.dirname(fname))\\n with open(fname, 'w') as fid:\\n json.dump(current_config, fid, sort_keys=True, indent=0)\",\n \"def save():\\n print(\\\"Saving config file..\\\")\\n\\n res = yaml.round_trip_dump(_conf, indent=2, block_seq_indent=1)\\n\\n with open(__config_file, 'w', encoding='utf-8') as stream:\\n stream.write(res)\",\n \"def save_config_file(self):\\n wkdir = Path(self.config_dict[\\\"outputdir\\\"])\\n config_filename = str(wkdir / f\\\"{self.config_dict['name']}.json\\\")\\n save_config(self.config_dict, config_filename)\",\n \"def saveCfgFile(self):\\n mash_ini = False\\n if GPath('mash.ini').exists():\\n mashIni = ConfigParser.ConfigParser()\\n mashIni.read('mash.ini')\\n mash_ini = True\\n instPath = GPath(mashIni.get('General','sInstallersDir').strip()).s\\n else:\\n instPath = \\\"\\\"\\n if instPath != dirs[\\\"installers\\\"].s:\\n if not mash_ini:\\n if os.path.exists(os.path.join(os.getcwd(), \\\"mash_default.ini\\\")):\\n f = open(os.path.join(os.getcwd(), \\\"mash_default.ini\\\"), \\\"r\\\")\\n d = f.read()\\n f.close()\\n else:\\n d = \\\"[General]\\\\n\\\"\\n f = open(os.path.join(os.getcwd(), \\\"mash.ini\\\"), \\\"w\\\")\\n f.write(d)\\n f.close()\\n mashIni = ConfigParser.ConfigParser()\\n mashIni.read('mash.ini')\\n mashIni.set(\\\"General\\\",\\\"sInstallersDir\\\",os.path.abspath(dirs[\\\"installers\\\"].s))\\n f = open(os.path.join(os.getcwd(), \\\"mash.ini\\\"),\\\"w\\\")\\n mashIni.write(f)\\n f.close()\",\n \"def save(self):\\n\\t\\tself.CONFIG.save()\\n\\t\\tself.temp_files.save()\",\n \"def save(self):\\n self.__config.sync()\\n self.__saved = True\\n Logger().debug(\\\"Configuration saved\\\")\",\n \"def save_config(self, path):\\n if os.path.isdir(path):\\n path = os.path.join(path, 'config.json')\\n print('Save config to {}'.format(path))\\n with open(path, 'w', encoding='utf-8') as w:\\n w.write(json.dumps(self.to_dict(), indent=2,\\n sort_keys=True))\",\n \"def saveExitConfig(self):\\n newPath = self.newFolderPath.text()\\n config.set(\\\"saveLocation\\\", str(newPath))\\n config.save()\\n self.reloadSettings()\\n self.close()\",\n \"def save( self ):\\n ini = codecs.open(self.filename,\\\"w\\\",\\\"utf-8\\\",errors=\\\"replace\\\",buffering=0)\\n for (name,value) in self.conf.items():\\n print >>ini, name, \\\"=\\\", value\\n ini.close()\",\n \"def save(data, section): # save a config\\n\\tglobal _timesSaved\\n\\tif dynConfig['logConfigActions']:\\n\\t\\tlogger.info( f'saving {section}: {data}' )\\n\\t# save\\n\\tif section != 'placeholderForSaving':\\n\\t\\tcurrentConfigData[section] = data\\n\\t\\tlogger.debug( f'saved {section}' )\\n\\telse:\\n\\t\\t_timesSaved = 2\\n\\t# save to disk if this is the third save\\n\\tif _timesSaved == 0 or _timesSaved == 1:\\n\\t\\t_timesSaved += 1\\n\\telse:\\n\\t\\t_timesSaved = 0\\n\\t\\ttry:\\n\\t\\t\\t# save to disk\\n\\t\\t\\twith open( configPath, 'w', encoding='utf-8' ) as file:\\n\\t\\t\\t\\tjson.dump( currentConfigData, file, indent=4 )\\n\\t\\texcept:\\n\\t\\t\\tlogger.error( f'failed to save config to disk!' )\\n\\t\\t\\traise ConfigError( 'error while saving the config file' )\",\n \"def save_configurations(self):\\n # Get the file path\\n self.data_path = self.data_path_entry.get()\\n # Open the file\\n with open(self.data_path, 'rb') as file:\\n self.log('Opened ' + str(self.data_path))\\n # Un-serialize\\n info = pickle.load(file)\\n # Write the new properties\\n self.main_window.overwrite_properties(info)\\n\\n self.exit()\",\n \"def write_config_file(self):\\n for opt, opt_desc in self.opt_dict.iteritems():\\n if 'permanent' in opt_desc and opt_desc['permanent'] == True:\\n enabled = 'Always'\\n else:\\n enabled = opt_desc['enabled'].__str__()\\n\\n self.file_parser.set(opt, 'enabled', enabled)\\n self.file_parser.set(opt, 'implementation',\\n opt_desc['selected_imp'])\\n self.file_parser.set(opt, 'optype', opt_desc['imptype'])\\n\\n for config, config_desc in self.config_dict.iteritems():\\n enabled = config_desc['enabled'].__str__()\\n self.file_parser.set(config, 'enabled', enabled)\\n\\n scratch_file = self.config_filename + '.scratch'\\n with open(scratch_file, 'w') as cfile:\\n for config in sorted(self.config_dict.keys()):\\n self.write_section(cfile, config)\\n\\n for opt in sorted(self.opt_dict.keys()):\\n self.write_section(cfile, opt)\\n\\n for imp in sorted(self.imp2opt_dict.keys()):\\n self.write_section(cfile, imp)\\n\\n cfile.write(\\\"\\\\n\\\")\\n\\n os.rename(scratch_file, self.config_filename)\",\n \"def save_cfg(self, output_dir):\\n output_path = os.path.join(output_dir, 'level_config.cfg')\\n shutil.copy(self.cfg_path, output_path)\",\n \"def save_config_file(self):\\n with open(self.config_file_name, 'w',encoding='utf-8') as outfile:\\n json.dump(self._config, outfile,indent=2)\",\n \"def save_conf(self, name=None):\\n \\n if name:\\n filename = name\\n \\n else:\\n filename = \\\"conf_\\\" + str(self.conf[\\\"device\\\"]) + \\\"_\\\" + datetime.today().strftime('%Y-%m-%d') + \\\".txt\\\"\\n \\n path = \\\"./source/_0_time_series_class/configuration/\\\"\\n filename = path + filename\\n \\n with open(filename, \\\"w\\\") as file:\\n json.dump(self.conf, file)\",\n \"def save_config(self, path=\\\"\\\"):\\n if not path:\\n if not os.path.isdir(CONFIG_DIR):\\n os.makedirs(CONFIG_DIR)\\n file_path = QtGui.QFileDialog.getSaveFileName(self,\\n \\\"Save Config\\\",\\n CONFIG_DIR,\\n \\\"Config File (*.cfg)\\\")\\n else:\\n file_path = path\\n self._save_state(file_path)\\n self.write_text(\\\"Saved config @ {}\\\".format(file_path))\",\n \"def update(self):\\n self.save_config_file()\",\n \"def save(self):\\n Registry.SetKey(self.CONFIG_NAME, self.config, True)\\n self.load() # for validation\",\n \"def configuration(config):\\n create_str_dir(config)\\n add_skymap(config)\\n save_configuration(config)\",\n \"def _load(self) -> list[Instrument]:\\n logger.info(\\\"Loading config...\\\")\\n self._config = yml.load(self.configpath)\\n instruments, modespec = self._config[\\\"instruments\\\"], self._config[\\\"modes\\\"]\\n logger.success(f\\\"Found {len(instruments)} instruments, {len(modespec)} modes\\\")\",\n \"def _save_state(self, config_path=\\\"\\\"):\\n state = {\\n 'output_dir': str(self.ui.lineEdit_output_path.text()),\\n 'device': str(self.ui.comboBox_device.currentText()),\\n 'event_bits': str(self.ui.lineEdit_data_bits.text()),\\n 'freq': str(self.ui.lineEdit_pulse_freq.text()),\\n 'labels': self.digital_labels,\\n 'timestamp': self.ui.checkBox_timestamp.isChecked(),\\n 'comm_interface': self.ui.checkBox_comm.isChecked(),\\n 'analog_on': self.ui.checkBox_analog_channels.isChecked(),\\n 'analog_channels': eval(str(self.ui.lineEdit_analog_channels.text())),\\n 'analog_sample_rate': self.ui.comboBox_analog_freq.currentIndex(),\\n 'analog_dtype': self.ui.comboBox_dtype.currentIndex(),\\n 'analog_labels': self.analog_labels,\\n 'counter_input_terminal': str(self.ui.comboBox_ci.currentText()),\\n 'counter_input_on': self.ui.checkBox_ci.isChecked(),\\n }\\n if not config_path:\\n config_path = LAST_SESSION\\n with open(config_path, 'wb') as f:\\n pickle.dump(state, f)\",\n \"def save_config(config_path: str, data: dict):\\n with open(config_path, 'w') as j:\\n dump(data,j)\",\n \"def antenny_save(self, name: str = None):\\n self.antenny_config.save(name)\\n self.antenny_config.save_as_default_config()\\n self.imu_save(name)\\n self.imu_config.save_as_default_config()\\n self.azimuth_servo_save(name)\\n self.elevation_servo_save(name)\\n self.servo_config.save_as_default_config()\",\n \"def saveConfig(config, filepath=None):\\n result = False\\n if filepath is None:\\n filepath = os.path.join(os.path.dirname(os.path.abspath(__file__)), \\\"res/\\\", \\\"config.ini\\\")\\n try:\\n with open(filepath, 'wb') as configfile:\\n config.write(configfile)\\n result = True\\n except Exception, e:\\n print \\\"*** Caught Exception: %r ***\\\" % e\\n return result\",\n \"def save_config(self):\\n data = json.dumps(self.cfg)\\n\\n try:\\n file = open(self.cfg_file_name, 'w')\\n file.write(data)\\n except OSError as err:\\n print(\\\"can't save property: {0}\\\".format(err))\\n else:\\n file.close()\",\n \"def save_opts(self):\\n # save code as another folder in log_path\\n dst_path = os.path.join(self.log_path, 'code', 'v0')\\n iter_yes_or_no = 0\\n while os.path.exists(dst_path):\\n dst_path = os.path.join(self.log_path, 'code', 'v' + str(iter_yes_or_no))\\n iter_yes_or_no = iter_yes_or_no + 1\\n user_name = expanduser(\\\"~\\\")\\n try:\\n shutil.copytree(os.getcwd(), dst_path, ignore=shutil.ignore_patterns('*.pyc', 'tmp*'))\\n except Exception as e_copytree:\\n print(e_copytree)\\n\\n models_dir = os.path.join(self.log_path, \\\"models\\\")\\n if not os.path.exists(models_dir):\\n os.makedirs(models_dir)\\n to_save = self.opt.__dict__.copy()\\n\\n with open(os.path.join(models_dir, 'opt.json'), 'w', encoding='utf-8') as f:\\n json.dump(to_save, f, indent=2)\",\n \"def save_settings():\\n\\n dont_save = ['VISIONEGG_CONFIG_FILE',\\n 'VISIONEGG_SYSTEM_DIR',\\n 'VISIONEGG_USER_DIR',\\n ]\\n\\n if not VisionEgg.config.VISIONEGG_CONFIG_FILE:\\n raise RuntimeError(\\\"No config file in use.\\\")\\n re_setting_finder = re.compile(r\\\"^\\\\s?((?:VISIONEGG_[A-Z_]*)|(?:SYNCLYNC_[A-Z_]*))\\\\s?=\\\\s?(\\\\S*)\\\\s?$\\\",re.IGNORECASE)\\n\\n orig_file = open(VisionEgg.config.VISIONEGG_CONFIG_FILE,\\\"r\\\")\\n orig_lines = orig_file.readlines()\\n\\n line_ending = orig_lines[0][-2:]\\n if line_ending[0] not in ['\\\\r','\\\\n','\\\\l']:\\n line_ending = line_ending[1]\\n\\n out_file_lines = []\\n\\n saved_config_vars = []\\n\\n for line in orig_lines:\\n out_line = line # The output is the same as the input unless there's a match\\n match = re_setting_finder.match(line)\\n if match:\\n name = match.group(1).upper()\\n if name in VisionEgg.config.__dict__.keys():\\n if name not in dont_save:\\n # Change the output line\\n out_line = (\\\"%s = %s\\\"%(name,getattr(VisionEgg.config,name,))) + line_ending\\n saved_config_vars.append(name)\\n out_file_lines.append(out_line)\\n\\n # Close and reopen orig_file in write mode\\n orig_file.close()\\n orig_file = open(VisionEgg.config.VISIONEGG_CONFIG_FILE,\\\"w\\\")\\n for line in out_file_lines:\\n orig_file.write(line)\",\n \"def save_config() -> None:\\n with open(_config_file, \\\"w\\\", newline=\\\"\\\") as config_file:\\n json.dump(_config, config_file, indent=4)\\n config_file.truncate()\",\n \"def save_config(self):\\n\\n return self.perform_action('/mgmtd/db/save')\",\n \"def save_to_file(self):\\n check_path(self.config_path)\\n\\n with open(self.settings_file, 'w') as settings_file:\\n options = self._get_options()\\n json.dump(options,\\n \\t settings_file,\\n \\t indent=4,\\n \\t separators=(',', ': '))\",\n \"def save_config(conf, save_path):\\n with open(os.path.join(save_path), \\\"w\\\") as f:\\n f.write(yaml.dump({'param': conf}, default_flow_style=False))\",\n \"def save_config(self):\\n self.config.app_w = self.width()\\n self.config.app_h = self.height()\\n self.config.splitter = self.splitter.saveState()\\n self.config.save()\",\n \"def save_config(config):\\n with open(os.path.abspath(CONFIG_PATH), 'wb') as config_file:\\n pickle.dump(config, config_file)\\n return config\",\n \"def save_configuration(self):\\n dom = self.vistrailsStartup.startup_dom()\\n doc = dom.documentElement\\n configuration_element = enter_named_element(doc, 'configuration')\\n doc.removeChild(configuration_element)\\n self.configuration.write_to_dom(dom, doc)\\n self.vistrailsStartup.write_startup_dom(dom)\\n dom.unlink()\",\n \"def save(self):\\r\\n with open(self.filename, 'w') as f:\\r\\n if self.pretty:\\r\\n json.dump(self.__config, f, sort_keys=False,\\r\\n indent=4, separators=(',', ': '))\\r\\n else:\\r\\n json.dump(self.__config, f)\",\n \"def save_config(conf, default):\\n print()\\n if yes_no('Would you like to save your configuration?'):\\n name = simple_response(\\n 'What would you like to name your configuration?')\\n path = ask_path(\\n 'Please enter the path you would like your configuration saved to',\\n default=default)\\n file_path = os.path.join(path, name)\\n if file_path.find('.json') == -1:\\n file_path += '.json'\\n with open(file_path, 'w+') as f:\\n json.dump(conf, f, indent=4)\",\n \"def SaveConfig(self):\\n config_value = getattr(self, APPDATA)\\n path_value = config_value.AbsolutePaths[0]\\n default_cfg_file = os.path.join(path_value, CONFIG_FILE_NAME)\\n temp_file = default_cfg_file + '.TEMP'\\n if os.path.exists(default_cfg_file):\\n json.dump(type(self)._CURRENT_CONFIG,\\n open(temp_file.lower(),\\n mode='w'),\\n cls=ConfigEncoder,\\n sort_keys=False,\\n indent=4)\\n EnsureBackup(temp_file, default_cfg_file)\\n else:\\n if not os.path.isdir(path_value):\\n os.mkdir(path_value)\\n json.dump(type(self)._CURRENT_CONFIG,\\n open(default_cfg_file.lower(),\\n mode='w'),\\n cls=ConfigEncoder,\\n sort_keys=False,\\n indent=4)\",\n \"def save_conf(self):\\r\\n self.sendAndRecv(\\\"SAVECONF\\\\r\\\\n\\\")\",\n \"def onExportConfig(self, evt):\\n dlg = wx.FileDialog(self.view, \\\"Save As Configuration File\\\", wildcard = \\\"*.ini\\\" ,\\n style=wx.FD_SAVE | wx.FD_OVERWRITE_PROMPT)\\n if dlg.ShowModal() == wx.ID_OK:\\n fileName=dlg.GetPath()\\n self.config.exportConfig(fileName=fileName, e=None)\",\n \"def _save_changes(self):\\n copy2(self._cfg_filename, self._cfg_filename + \\\".bak\\\")\\n with open(self._cfg_filename, \\\"w\\\", encoding=\\\"utf-8\\\") as self._cfg_file:\\n self.write(self._cfg_file)\",\n \"def saveSettings(self):\\n self.genFiles.applyData()\\n self.genGraph.applyData()\",\n \"def _save_configuration_to_yml(self):\\n data = self.get_configuration_data()\\n timestamp = self.model.timestamp\\n with open(os.path.join(CHECKPOINTS_DIR, timestamp, 'config_{}.yml'.format(timestamp)), 'w') as outfile:\\n yaml.dump(dict(data), outfile, default_flow_style=False)\",\n \"def save_config(self):\\n try:\\n print(\\\"Clearing active users\\\")\\n for room in self.rooms:\\n room.room_attrbts['active'].clear()\\n print('Saving config...')\\n print(\\\"Known clients:\\\")\\n self.pp.pprint(self.clients)\\n print(\\\"Known rooms:\\\")\\n for room in self.rooms: \\n self.pp.pprint(room.name)\\n self.pp.pprint(room.room_attrbts)\\n path = os.environ.get('HOME') + '/.tinyserver'\\n roomJSON = jsonpickle.encode(self.rooms)\\n with open(path, 'w') as f:\\n json.dump(roomJSON, f)\\n except Exception as e:\\n print(\\\"Error saving config!! {0}\\\".format(e))\",\n \"def saveConfigs(self):\\r\\n ctrls = self.__ctrls\\r\\n cache = self.__cache\\r\\n # inputs\\r\\n # cache['input_file'] = ctrls['input_file'].GetTextCtrlValue()\\r\\n cache['input_file'] = ctrls['input_file'].GetTextCtrlValue()\\r\\n cache['prmtop_file'] = ctrls['prmtop_file'].GetTextCtrlValue()\\r\\n cache['ip_restart_file'] = \\\\\\r\\n ctrls['ip_restart_file'].GetTextCtrlValue()\\r\\n # outputs\\r\\n cache['log_file'] = ctrls['log_file'].GetValue()\\r\\n cache['op_restart_file'] = ctrls['op_restart_file'].GetValue()\\r\\n cache['crds_file'] = ctrls['crds_file'].GetValue()\\r\\n cache['vels_file'] = ctrls['vels_file'].GetValue()\\r\\n cache['enes_file'] = ctrls['enes_file'].GetValue()\",\n \"def save(self):\\n try:\\n with open(self._filename, 'w') as conf_file:\\n conf_file.write(json.dumps(self._data))\\n except OSError:\\n _LOGGER.exception(\\\"Can't store config in %s\\\", self._filename)\",\n \"def save_specs(self, filename):\\n pass\",\n \"def save_specs(self, filename):\\n pass\",\n \"def save_config(log_dir, config):\\n if not os.path.exists(log_dir):\\n os.makedirs(log_dir)\\n shutil.copy(config, os.path.join(log_dir, 'config.gin'))\",\n \"def save(self):\\n with open(self._config, 'w') as f:\\n json.dump(self.data, f, indent=2, sort_keys=True)\",\n \"async def save_config(self):\\n\\n # Display info message\\n log.info(\\\"save_config\\\")\\n\\n # Send command\\n output = await self.send_command(self.cmd_save_config)\\n\\n # Return the commands of the configuration saving process\\n return output\",\n \"def save(config, filename=None):\\n filename = add_directory(filename or 'configure.json')\\n directory = os.path.dirname(filename)\\n if not os.path.exists(directory):\\n os.makedirs(directory, 0o700)\\n with open(filename, \\\"w\\\") as f:\\n json.dump(config, f, indent=2, sort_keys=True)\",\n \"def storeConfig(self):\\n config = self.app.config['mainW']\\n for driver in self.drivers:\\n config[driver] = self.drivers[driver]['uiDriver'].currentIndex()\\n\\n return True\",\n \"def save(self, filepath):\\n save_ckpt = {\\n 'ae': self.state_dict(),\\n 'optimizer': self.optimizer.state_dict()\\n }\\n try:\\n torch.save(save_ckpt, os.path.join(filepath, 'ckpt_ae.pth'))\\n except:\\n print('Cannot save autoencoder.')\",\n \"def save_config(_config, simulation_dir):\\n with open(os.path.join(simulation_dir, 'config.yaml'), 'w') as f:\\n yaml.dump(_config, f, default_flow_style=False)\",\n \"def save(self):\\n with open(self._CONFIG_FILE_PATH, 'w') as config_file:\\n json.dump(vars(self), config_file)\\n return self._CONFIG_FILE_PATH\",\n \"def saveConfig(self, name=None):\\n\\n configDir = self.mwGlob['configDir']\\n\\n if self.config.get('profileName', '') == 'config':\\n if 'reference' in self.config:\\n del self.config['reference']\\n\\n # default saving for reference\\n if name is None:\\n name = self.config.get('reference', 'config')\\n\\n fileName = configDir + '/' + name + '.cfg'\\n with open(fileName, 'w') as outfile:\\n json.dump(self.config,\\n outfile,\\n sort_keys=True,\\n indent=4)\\n # if we save a reference first, we have to save the config as well\\n if name != 'config':\\n fileName = configDir + '/config.cfg'\\n with open(fileName, 'w') as outfile:\\n json.dump(self.config,\\n outfile,\\n sort_keys=True,\\n indent=4)\\n return True\",\n \"def save_preferences(self, path=None):\\n if path is None:\\n path = os.path.join(self.default_folder, self.default_file)\\n\\n prefs = ConfigObj(path)\\n for plugin_id in self._pref_decls:\\n plugin = self.workbench.get_plugin(plugin_id)\\n decl = self._pref_decls[plugin_id]\\n save_method = getattr(plugin, decl.saving_method)\\n prefs[plugin_id] = save_method()\\n\\n prefs.write()\",\n \"def save_config(self):\\n with open(self.config_file, 'w') as fout:\\n json.dump({'name_dict': self._name_dict, 'metric_dict': self._metric_dict, 'credential_path': self.credential_path, 'path_for_worksheet_name': self.path_for_worksheet_name}, fout)\",\n \"async def save_config(self):\\n\\n # Display info message\\n log.info(\\\"save_config\\\")\\n\\n # Send command to ask for saving config. Wait till the question to overwrite\\n # the startup file (\\\"Overwrite file [startup-config].... (Y/N)[N] ?\\\")\\n output = await self.send_command(self.cmd_save_config, pattern=\\\"?\\\")\\n\\n # Confirm to save the config\\n output += await self.send_command(\\\"Y\\\")\\n\\n # Return the commands of the configuration saving process\\n return output\",\n \"def save_to_config(self) -> None:\\n config_path = os.path.join(self.base_path, \\\"config.json\\\")\\n\\n with open(config_path, \\\"r\\\") as _json:\\n c_dict = json.load(_json)\\n\\n c_dict[\\\"mean_similarity_error\\\"] = self.ME\\n c_dict[\\\"similarity_correlation\\\"] = self.pearson_corr\\n c_dict[\\\"similarity_spearman_correlation\\\"] = self.spearman_corr\\n\\n with open(config_path, \\\"w\\\") as _json:\\n json.dump(c_dict, _json)\",\n \"def __write_config(self):\\n with open(self.config_file, 'w') as data_file:\\n config = {\\\"ibooks_doc_root\\\":self.ibooks_doc_root,\\n \\\"library_folder\\\":self.library_folder,\\n \\\"annotation_folder\\\":self.annotation_folder,\\n \\\"tmp_dir\\\":self.tmp_dir\\n } \\n data = json.dumps(config, ensure_ascii=False)\\n data_file.write(data)\",\n \"def save(self, uri):\\r\\n pf = PyFolder(os.path.dirname(os.path.realpath(uri)), allow_override=True)\\r\\n pf[os.path.basename(uri)+\\\"_options.json\\\"] = {\\r\\n 'input_cells': self._input_cells,\\r\\n 'latent_space': self._latent_space,\\r\\n }\\r\\n\\r\\n save_model(self._autoencoder, uri+\\\"_lstm_autoencoder.hdf5\\\")\\r\\n save_model(self._encoder, uri+\\\"_lstm_encoder.hdf5\\\")\",\n \"def write_config_file():\\n\\tif not config_parser:\\n\\t\\tprint \\\"Config module not loaded. I don't save anything.\\\"\\n\\t\\treturn\\n\\n\\tf = file(config_file, \\\"w\\\")\\n\\tconfig_parser.write(f)\\n\\tf.close()\",\n \"def __write_ana_config(self, path, macros):\\n self.__write_fw_config(path=path, template_name=\\\"analog_settings.template\\\", macros=macros)\",\n \"def setup_configuration_file(self):\\n\\n with open(self.config_path, \\\"w+\\\") as f_config:\\n\\n f_config.write(get_configuration_file_form())\",\n \"def save(self) -> None:\\n self._client.save_config()\",\n \"def save_config(self, save_path: str) -> None:\\n os.makedirs(save_path, exist_ok=True)\\n model_hyperparameters_path = os.path.join(save_path, MODEL_HYPERPARAMETERS_FILE_NAME)\\n save_json(model_hyperparameters_path, self.config_obj.to_dict())\",\n \"def save_config(config: Dict[str, Any], path: str) -> None:\\n\\n with open(path, 'w+', encoding='utf-8') as stream:\\n yaml.dump(config, stream, default_flow_style=False)\",\n \"def save(cls, context):\\n\\n data = context.get_stored_dict()\\n files = {}\\n\\n def save_in_file(file, key, value):\\n if file in files.keys():\\n files[file][key] = value\\n else:\\n files[file] = {key: value}\\n\\n for key, val in data.items():\\n if context.extends is not None and key in context.key_origins:\\n save_in_file(context.key_origins[key], key, val)\\n else:\\n save_in_file(context.profile, key, val)\\n\\n for profile, content in files.items():\\n metadata.update_metadata(\\n context.workspace,\\n profile,\\n 'config',\\n content)\",\n \"def save_conf(conf: m.Image) -> None:\\n # Determine the name of the config file.\\n parts = conf.filename.split('.')[:-1]\\n parts.append('json')\\n conffile = '.'.join(parts)\\n\\n # Serialize the config.\\n confmap = {\\n 'Version': __version__,\\n 'Image': conf.asdict(),\\n }\\n confjson = json.dumps(confmap, indent=4)\\n\\n # Save the config.\\n with open(conffile, 'w') as fh:\\n fh.write(confjson)\",\n \"def _save(self):\\n file = open(\\\"settings.ini\\\", \\\"w\\\")\\n self._parser.write(file)\\n file.close()\",\n \"def write_config():\\n\\n e = Element(\\\"Configuration\\\")\\n r = SubElement(e, \\\"RepositoryList\\\")\\n r = SubElement(r, \\\"Repository\\\", name = \\\"default\\\")\\n SubElement(r, \\\"Module\\\").text = args.driver\\n SubElement(r, \\\"TokenLabel\\\").text = args.token_label\\n SubElement(r, \\\"PIN\\\").text = args.pin\\n ElementTree(e).write(args.write_config)\\n args.write_config.flush()\",\n \"def save_iantconfig(self, filename_root):\\n x, y, z = self.get_coords_enu()\\n d = np.ones_like(x) # set to 1 to avoid shadowing in iantconfig\\n coords = np.vstack([d, x, y, z]).T\\n coords = np.vstack([coords, [0, self.lon_deg, self.lat_deg, 0]])\\n np.savetxt('%s.enu.%ix%i.txt' % (filename_root, x.size, 4),\\n coords, fmt=b'%-5i %.12f %.12f %.12f')\",\n \"def save_config(self, new_config, filename=None):\\n self.cfg.update(new_config)\\n if filename is None:\\n self.cfg.filename = self.cfg_filename\\n else:\\n self.cfg.filename = filename\\n self.cfg.write()\\n logger.info(\\\"Config file %s written out\\\" % self.cfg.filename)\",\n \"def save_settings(path, server, station):\\n db.save_data(path, server, station)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.69961077","0.6905757","0.67979085","0.6779115","0.663721","0.6624185","0.65824383","0.6569083","0.6564401","0.6522336","0.6489692","0.646409","0.6439854","0.6433161","0.637511","0.63696384","0.635844","0.63290244","0.6287945","0.62523466","0.62460214","0.6223467","0.6212512","0.62030524","0.61702484","0.61660534","0.6145498","0.61387694","0.61150604","0.6108188","0.6106222","0.60989314","0.6056947","0.6002333","0.600038","0.59870327","0.5966106","0.59608066","0.5954657","0.59528136","0.59497964","0.59484977","0.59305274","0.5918328","0.5915777","0.5913141","0.5878504","0.5855133","0.58507246","0.5833274","0.5819746","0.58178157","0.58130544","0.5793689","0.57824713","0.57771766","0.57747614","0.5773099","0.5772615","0.5755837","0.57522714","0.57424635","0.573362","0.57137305","0.5710153","0.57099193","0.5704594","0.5702872","0.57003886","0.5694628","0.5689682","0.5689682","0.56720215","0.56589407","0.5648019","0.5642906","0.56279194","0.5619065","0.5607385","0.5598558","0.55919886","0.5583519","0.5577158","0.5574596","0.556888","0.5557109","0.5551564","0.5551492","0.55501753","0.5549369","0.55296916","0.5527461","0.5515508","0.55141634","0.55088764","0.5507245","0.5492734","0.54877007","0.5480388","0.54614127"],"string":"[\n \"0.69961077\",\n \"0.6905757\",\n \"0.67979085\",\n \"0.6779115\",\n \"0.663721\",\n \"0.6624185\",\n \"0.65824383\",\n \"0.6569083\",\n \"0.6564401\",\n \"0.6522336\",\n \"0.6489692\",\n \"0.646409\",\n \"0.6439854\",\n \"0.6433161\",\n \"0.637511\",\n \"0.63696384\",\n \"0.635844\",\n \"0.63290244\",\n \"0.6287945\",\n \"0.62523466\",\n \"0.62460214\",\n \"0.6223467\",\n \"0.6212512\",\n \"0.62030524\",\n \"0.61702484\",\n \"0.61660534\",\n \"0.6145498\",\n \"0.61387694\",\n \"0.61150604\",\n \"0.6108188\",\n \"0.6106222\",\n \"0.60989314\",\n \"0.6056947\",\n \"0.6002333\",\n \"0.600038\",\n \"0.59870327\",\n \"0.5966106\",\n \"0.59608066\",\n \"0.5954657\",\n \"0.59528136\",\n \"0.59497964\",\n \"0.59484977\",\n \"0.59305274\",\n \"0.5918328\",\n \"0.5915777\",\n \"0.5913141\",\n \"0.5878504\",\n \"0.5855133\",\n \"0.58507246\",\n \"0.5833274\",\n \"0.5819746\",\n \"0.58178157\",\n \"0.58130544\",\n \"0.5793689\",\n \"0.57824713\",\n \"0.57771766\",\n \"0.57747614\",\n \"0.5773099\",\n \"0.5772615\",\n \"0.5755837\",\n \"0.57522714\",\n \"0.57424635\",\n \"0.573362\",\n \"0.57137305\",\n \"0.5710153\",\n \"0.57099193\",\n \"0.5704594\",\n \"0.5702872\",\n \"0.57003886\",\n \"0.5694628\",\n \"0.5689682\",\n \"0.5689682\",\n \"0.56720215\",\n \"0.56589407\",\n \"0.5648019\",\n \"0.5642906\",\n \"0.56279194\",\n \"0.5619065\",\n \"0.5607385\",\n \"0.5598558\",\n \"0.55919886\",\n \"0.5583519\",\n \"0.5577158\",\n \"0.5574596\",\n \"0.556888\",\n \"0.5557109\",\n \"0.5551564\",\n \"0.5551492\",\n \"0.55501753\",\n \"0.5549369\",\n \"0.55296916\",\n \"0.5527461\",\n \"0.5515508\",\n \"0.55141634\",\n \"0.55088764\",\n \"0.5507245\",\n \"0.5492734\",\n \"0.54877007\",\n \"0.5480388\",\n \"0.54614127\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5880172"},"document_rank":{"kind":"string","value":"46"}}},{"rowIdx":2876,"cells":{"query":{"kind":"string","value":"Disconnect instruments and shutdown daemon"},"document":{"kind":"string","value":"def shutdown(self) -> None:\n logger.info(\"Disconnecting instruments...\")\n for instrument in self._config[\"instruments\"]:\n instrument.disconnect()\n logger.info(f\"Shutting down {self}...\")\n self._daemon.shutdown()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def shutdown(self):\n if self.alive:\n libplasma.disconnect(self.conn)\n self.alive = False","def shutdown(self):","def stopAndDisconnectWalabot():\n wlbt.Stop()\n wlbt.Disconnect()\n print ('Termination successful')","def shutdown(self) -> None:","def shutdown(self) -> None:","def shutdown(self):\n ...","def test_disconnect(self):\n self.inverter.disconnect()\n sleep(0.02)","def initiate_shutdown(self) -> None:","def device_disconnect(self):\n pass","def close(self):\n logger.warning('Shutting down')\n self.display.off()\n self.mqtt.disconnect()","def shutdown(self):\t\r\n\t\tself.is_running = False\r\n\t\tfor connection in self.established_connection_list:\r\n\t\t\tconnection.send('The server has been shutdown adruptly by the server owner.\\n')\r\n\t\t\tconnection.socket_send()","def shutdown(self):\n try:\n self.driver.stop()\n except:\n logging.exception(\"Could not stop driver on shutdown\")\n\n self.arduino.stop()","def disconnectAllServers():\n _disconnectAllServers()","def disconnect(self):","def Disconnect_from_ePCSim_Server(ePCSim_conn):\r\n ePCSim_conn.Disconnect()","async def shutdown(self) -> int:","async def shutdown(self):","def shutdown(self):\n pass","def shutdown(self):\n pass","def shutdown(self):\n pass","def _shutdown(self):","def signal_handler(*args):\n if station:\n station.shutdown()","def shutdown(self):\n\n raise NotImplementedError","def disconnect(self):\n self.arduino.close()\n self.arduino = None","def shutdown_all(self, now=False):","def shutdown(self):\n\n pass","def disconnect(self) -> None:\n ...","def shutdown():\n\n cmd = dict()\n cmd[\"type_\"] = \"shutdown\"\n cmd[\"name_\"] = \"all\"\n\n ## In case of the shutdown there will be no returned message to\n ## check the success.\n s = comm.send_and_receive_socket(cmd)\n\n s.close()","def _stop(self):\n\n if self._daemon_id:\n pyro_proxy_name = 'PySwitchLib.' + self._daemon_id\n uri = None\n\n try:\n with Pyro4.locateNS(host='localhost', port=self._pyro_ns_port) as ns:\n try:\n uri = ns.lookup(pyro_proxy_name)\n except:\n pass\n\n if uri:\n ns.remove(pyro_proxy_name)\n except:\n pass\n finally:\n ns_daemon_dict = ConfigFileUtil().read(filename=pyswitchlib_ns_daemon_file)\n\n if self._daemon_id in ns_daemon_dict:\n uri = ns_daemon_dict[self._daemon_id]\n del ns_daemon_dict[self._daemon_id]\n\n if len(ns_daemon_dict):\n ConfigFileUtil().write(filename=pyswitchlib_ns_daemon_file, conf_dict=ns_daemon_dict, do_merge=False)\n else:\n try:\n os.unlink(pyswitchlib_ns_daemon_file)\n except:\n pass\n\n if uri:\n try:\n with Pyro4.Proxy(uri) as pyro_proxy:\n pyro_proxy.shutdown()\n pyro_proxy._pyroRelease()\n except:\n pass\n\n super(PySwitchLibApiDaemonRunner, self)._stop()","def disconnect(self):\n pass","def disconnect(self):\n pass","def disconnect(self):\n pass","def disconnect(self):\n pass","def disconnect(self):\n pass","async def on_disconnect(self) -> None:","def teardown_test(self):\n self.log.info('Tearing down the test case')\n self.iperf_server.stop()\n self.access_point.bridge.teardown(self.brconfigs)\n self.access_point.close()\n wputils.reset_host_interface(self.pkt_sender.interface)\n self.mon.usb('on')","def action_exit():\n modTimer.stop_all()\n mqtt_publish_lwt(iot.Status.OFFLINE)\n mqtt.disconnect()","def disconnect(self):\r\n # TODO: Should destroy all instances which have been started dynamically\r\n raise NotImplementedError","def perform_teardown():\n global credentials, connection, channel\n connection.close()","def stopEngines():\n pass","def test_disconnect_multiple(self):\n self.inverter.disconnect()\n self.inverter.disconnect() # Should not raise exception","def disconnect(self):\n self.stop()\n self._send_command('exit')\n self.sock.close()\n self.disconnected = True","def _disconnect(self):\n self.socket.send_json({\"worker_id\": self.socket_id, \"message\": \"disconnect\"})\n self.socket.close()\n self.context.term()\n exit()","async def callback_homeassistant_stop(self, event: \"Event\") -> NoReturn:\n _LOGGER.debug(\"Hekr system is shutting down\")\n for device_id, device in self.devices.items():\n connector = device.connector\n listener = connector.listener\n if listener is not None and listener.is_running:\n _LOGGER.debug('Shutting down listener for device ID \"%s\"' % device_id)\n listener.stop()\n\n if connector.is_connected:\n _LOGGER.debug('Shutting down connector for device ID \"%s\"' % device_id)\n await connector.close_connection()","def disconnect(self) -> None:\n try:\n self.s.close()\n except OSError as e:\n logging.exception(e)","def cleanup():\n broadcast_proc.terminate()\n subprocess.call('sudo hciconfig hci0 noleadv', shell=True)\n if CELL:\n ser_command('Cell off', cell_ser)\n cell_ser.close()\n grovepi.digitalWrite(LED, 0)","def shutdown(bot):\n setup(bot)","def shutdown(self):\n\n self.active = False\n\n try:\n self.listen_socket.shutdown(socket.SHUT_RDWR)\n except:\n self.logger.info(\"Ignoring listen soc shutdown error\")\n self.listen_socket = None\n\n with self.connect_cv:\n self.connect_cv.notifyAll()\n\n self.wakeup()\n self.dbg_state = \"down\"","async def disconnect(self):","def daemonControlStop (self):\n self.stop()","def plugin_shutdown():\n collectd.info('Shutting down collectd-mlab plugin.')","def terminateDaemon( self ):\n self._connection.terminate()\n print \"'+++ JPy/sessionended/\"\n sys.stdout = self.stdout\n sys.stdin = self.stdin\n print \"deamon ended\\n\"\n sys.exit()","def Shutdown(self):\n pass","def Shutdown(self):\n pass","def Stop_ePCSim(ePCSim_conn):\r\n ePCSim_conn.SendCmd(\"exit\")\r\n ePCSim_conn.prompt = \"#\"\r\n ePCSim_conn.SendCmd(\"pkill egate\")\r\n ePCSim_conn.SendCmd(\"pkill edaemon\")","def tear_down_all(self):\n self.dut.send_expect(\"quit\", \"# \")\n time.sleep(2)\n self.dut.kill_all()","def stop(self):\n shutdown_url = self._env[\"DATASTORE_HOST\"] + \"/shutdown\"\n req = urllib.request.Request(shutdown_url, method=\"POST\")\n urllib.request.urlopen(req)","async def shutdown_gracefully(self) -> None:","async def shutdown_gracefully(self) -> None:","def shutdown(self):\n self._send_command('shutdown')\n self.sock.close()\n self.disconnected = True","def shutdown_plugin(self):\n pass","def _disconnect(self):\n self._factory.stopTrying()\n self._connection.disconnect()","def stop(self) -> None:\n self.mqttc.disconnect()","def cleanup_and_exit():\n logger.warn(\"Terminating the program\")\n try:\n for key in connections:\n try:\n connections[key].disconnect()\n except AttributeError:\n pass\n for s in sensors:\n try:\n sensors[s].cleanup()\n except AttributeError:\n pass\n except:\n pass\n sys.exit(0)","def stop(self):\n self.disconnect()","def stop(self):\n self.disconnect()","def _plugin_stop(handle):\n GPIO.cleanup()\n _LOGGER.info('MAX31865 (async) Disconnected.')","def teardown(self):\n self.loop.run_until_complete(self.prometheus_con.disconnect())","def shutdown(self):\r\n self.socket.close()\r\n # self.socket_video.close()\r\n self.socket_state.close()","def stop(self,dummy=None):\n if DEBUG: print \"Closing Python Manager\"\n self.flush_all()\n for key in self.connection_manager.peer_servers.keys():\n self.connection_manager.peer_servers[key].protocol.sendClose()\n for key in self.connection_manager.script_servers.keys():\n self.connection_manager.script_servers[key].protocol.sendClose()\n for key in self.connection_manager.data_gui_servers.keys():\n self.connection_manager.data_gui_servers[key].protocol.sendClose()\n self.close_all()\n #self.connection_manager.laud.loseConnection()\n if DEBUG: print self.connection_manager.is_connections_closed()\n reactor.stop()\n if DEBUG: print \"Done\"","def shutdown(self):\n self.action('shutdown')","def shutdown(self) -> None:\n pass","def cleanupAtExit():\n \n global client\n \n client.stop()","def stop(self):\n self._listeners = None\n\n try:\n if self._started_daemon:\n logging.info('Stopping Transmission daemon')\n exec_cmd(['transmission-remote', '--exit'], wait_after=2)\n\n except subprocess.CalledProcessError:\n logging.error('Unable to stop daemon')\n logging.debug('Error details', stack_info=True, exc_info=True)\n\n self._done = True","def disconnect():\n logging.info('Client disconnected')","def __exit__(self, exc_type, exc_val, exc_tb):\n self.server.stop()\n self.driver.quit()","def stop():\n rdebug(\"storpool-common.stop invoked\")\n\n rdebug(\"letting storpool-config know\")\n run_config.stop()","def close(self):\n self.controller.DisableDevice()\n self.controller.StopPolling()\n self.controller.Disconnect(False)","def disconnect(self):\n _abstract()","def disconnect(self):\n _abstract()","def disconnect(self):\n\n\t\tself.send(\"QUIT\", True)\n\n\t\tif self.s is not None:\n\t\t\tself.s.close()","def Shutdown(self):\n self.conn.send(False)\n self.process.join()","def stop(self):\n self.stopped = True\n self.broadcast('host down')","def disconnect(self):\n # Nothing to do\n pass","def cleanup(self):\n if self.vehicle:\n print(f\"Disconnecting from vehicle on {self._connection_string}\")\n self.vehicle.close()\n if self._sitl:\n print(\"Shutting down SITL instance\")\n self._sitl.stop()","def __shut_down(self):\n\n for line, buses in self.__bus_dict.items():\n for bus in buses:\n bus.send_to_bus(\"Server Shut Down\")","def _disconnect(self) -> None:\n self._agent.stop()\n self._agent_thread.join()","def atexit(self):\n self.stop_listen()\n for driver in self.drivers.values():\n driver.stop()\n if hasattr(driver, \"atexit\"):\n driver.atexit()\n try:\n self.processor_thread.join()\n except AttributeError:\n pass","def shutdown(self):\n raise NotImplementedError","def terminate():\n leds.on()\n time.sleep(1)\n leds.off()\n\n GPIO.cleanup()","def _shutdown(self, signum=None, frame=None):\n if not self.shutdown_requested:\n self.logger.info(\"shutdown initiated\")\n\n self._shutdown_requested = True\n if self.connected:\n self.disconnect()\n self.stop_listening()","def tear_down(self):\n LOGGER.info('Tearing down plugin service...')\n self.reset()\n self.server.stop(grace=None)\n\n if self.device_proxy:\n self.device_proxy.tear_down()","async def shutdown() -> None:\n await database.disconnect()","def bcp_goodbye(self, **kwargs):\n if self.config['mediacontroller']['exit_on_disconnect']:\n self.socket_thread.sending_thread.stop()\n sys.exit()","def shutup():\n try:\n ttsEng.shutup()\n except Exception, e:\n logging.error(e)","def shutdown(self):\n\n if self.sessionState in (JT808SessionStates.OPEN,):\n self.log.warning(\"Shutdown requested...disconnecting\")\n self.disconnect()\n else:\n self.log.debug(\"Shutdown already in progress\")","def disconnect():\n return c.close()","def shutdown(self,):\n if hasattr(self, 'gpu_process_ids'):\n for ppid in self.gpu_process_ids:\n kill_proc_tree(ppid)\n for dispatcher in self.dispatchers:\n silence_function(1, dispatcher.shutdown)\n print('DistributedBackend shutdown.')","def shutdown(self):\n print \"Plugin is shutting down.\"","def __del__(self):\n self.shutdown()"],"string":"[\n \"def shutdown(self):\\n if self.alive:\\n libplasma.disconnect(self.conn)\\n self.alive = False\",\n \"def shutdown(self):\",\n \"def stopAndDisconnectWalabot():\\n wlbt.Stop()\\n wlbt.Disconnect()\\n print ('Termination successful')\",\n \"def shutdown(self) -> None:\",\n \"def shutdown(self) -> None:\",\n \"def shutdown(self):\\n ...\",\n \"def test_disconnect(self):\\n self.inverter.disconnect()\\n sleep(0.02)\",\n \"def initiate_shutdown(self) -> None:\",\n \"def device_disconnect(self):\\n pass\",\n \"def close(self):\\n logger.warning('Shutting down')\\n self.display.off()\\n self.mqtt.disconnect()\",\n \"def shutdown(self):\\t\\r\\n\\t\\tself.is_running = False\\r\\n\\t\\tfor connection in self.established_connection_list:\\r\\n\\t\\t\\tconnection.send('The server has been shutdown adruptly by the server owner.\\\\n')\\r\\n\\t\\t\\tconnection.socket_send()\",\n \"def shutdown(self):\\n try:\\n self.driver.stop()\\n except:\\n logging.exception(\\\"Could not stop driver on shutdown\\\")\\n\\n self.arduino.stop()\",\n \"def disconnectAllServers():\\n _disconnectAllServers()\",\n \"def disconnect(self):\",\n \"def Disconnect_from_ePCSim_Server(ePCSim_conn):\\r\\n ePCSim_conn.Disconnect()\",\n \"async def shutdown(self) -> int:\",\n \"async def shutdown(self):\",\n \"def shutdown(self):\\n pass\",\n \"def shutdown(self):\\n pass\",\n \"def shutdown(self):\\n pass\",\n \"def _shutdown(self):\",\n \"def signal_handler(*args):\\n if station:\\n station.shutdown()\",\n \"def shutdown(self):\\n\\n raise NotImplementedError\",\n \"def disconnect(self):\\n self.arduino.close()\\n self.arduino = None\",\n \"def shutdown_all(self, now=False):\",\n \"def shutdown(self):\\n\\n pass\",\n \"def disconnect(self) -> None:\\n ...\",\n \"def shutdown():\\n\\n cmd = dict()\\n cmd[\\\"type_\\\"] = \\\"shutdown\\\"\\n cmd[\\\"name_\\\"] = \\\"all\\\"\\n\\n ## In case of the shutdown there will be no returned message to\\n ## check the success.\\n s = comm.send_and_receive_socket(cmd)\\n\\n s.close()\",\n \"def _stop(self):\\n\\n if self._daemon_id:\\n pyro_proxy_name = 'PySwitchLib.' + self._daemon_id\\n uri = None\\n\\n try:\\n with Pyro4.locateNS(host='localhost', port=self._pyro_ns_port) as ns:\\n try:\\n uri = ns.lookup(pyro_proxy_name)\\n except:\\n pass\\n\\n if uri:\\n ns.remove(pyro_proxy_name)\\n except:\\n pass\\n finally:\\n ns_daemon_dict = ConfigFileUtil().read(filename=pyswitchlib_ns_daemon_file)\\n\\n if self._daemon_id in ns_daemon_dict:\\n uri = ns_daemon_dict[self._daemon_id]\\n del ns_daemon_dict[self._daemon_id]\\n\\n if len(ns_daemon_dict):\\n ConfigFileUtil().write(filename=pyswitchlib_ns_daemon_file, conf_dict=ns_daemon_dict, do_merge=False)\\n else:\\n try:\\n os.unlink(pyswitchlib_ns_daemon_file)\\n except:\\n pass\\n\\n if uri:\\n try:\\n with Pyro4.Proxy(uri) as pyro_proxy:\\n pyro_proxy.shutdown()\\n pyro_proxy._pyroRelease()\\n except:\\n pass\\n\\n super(PySwitchLibApiDaemonRunner, self)._stop()\",\n \"def disconnect(self):\\n pass\",\n \"def disconnect(self):\\n pass\",\n \"def disconnect(self):\\n pass\",\n \"def disconnect(self):\\n pass\",\n \"def disconnect(self):\\n pass\",\n \"async def on_disconnect(self) -> None:\",\n \"def teardown_test(self):\\n self.log.info('Tearing down the test case')\\n self.iperf_server.stop()\\n self.access_point.bridge.teardown(self.brconfigs)\\n self.access_point.close()\\n wputils.reset_host_interface(self.pkt_sender.interface)\\n self.mon.usb('on')\",\n \"def action_exit():\\n modTimer.stop_all()\\n mqtt_publish_lwt(iot.Status.OFFLINE)\\n mqtt.disconnect()\",\n \"def disconnect(self):\\r\\n # TODO: Should destroy all instances which have been started dynamically\\r\\n raise NotImplementedError\",\n \"def perform_teardown():\\n global credentials, connection, channel\\n connection.close()\",\n \"def stopEngines():\\n pass\",\n \"def test_disconnect_multiple(self):\\n self.inverter.disconnect()\\n self.inverter.disconnect() # Should not raise exception\",\n \"def disconnect(self):\\n self.stop()\\n self._send_command('exit')\\n self.sock.close()\\n self.disconnected = True\",\n \"def _disconnect(self):\\n self.socket.send_json({\\\"worker_id\\\": self.socket_id, \\\"message\\\": \\\"disconnect\\\"})\\n self.socket.close()\\n self.context.term()\\n exit()\",\n \"async def callback_homeassistant_stop(self, event: \\\"Event\\\") -> NoReturn:\\n _LOGGER.debug(\\\"Hekr system is shutting down\\\")\\n for device_id, device in self.devices.items():\\n connector = device.connector\\n listener = connector.listener\\n if listener is not None and listener.is_running:\\n _LOGGER.debug('Shutting down listener for device ID \\\"%s\\\"' % device_id)\\n listener.stop()\\n\\n if connector.is_connected:\\n _LOGGER.debug('Shutting down connector for device ID \\\"%s\\\"' % device_id)\\n await connector.close_connection()\",\n \"def disconnect(self) -> None:\\n try:\\n self.s.close()\\n except OSError as e:\\n logging.exception(e)\",\n \"def cleanup():\\n broadcast_proc.terminate()\\n subprocess.call('sudo hciconfig hci0 noleadv', shell=True)\\n if CELL:\\n ser_command('Cell off', cell_ser)\\n cell_ser.close()\\n grovepi.digitalWrite(LED, 0)\",\n \"def shutdown(bot):\\n setup(bot)\",\n \"def shutdown(self):\\n\\n self.active = False\\n\\n try:\\n self.listen_socket.shutdown(socket.SHUT_RDWR)\\n except:\\n self.logger.info(\\\"Ignoring listen soc shutdown error\\\")\\n self.listen_socket = None\\n\\n with self.connect_cv:\\n self.connect_cv.notifyAll()\\n\\n self.wakeup()\\n self.dbg_state = \\\"down\\\"\",\n \"async def disconnect(self):\",\n \"def daemonControlStop (self):\\n self.stop()\",\n \"def plugin_shutdown():\\n collectd.info('Shutting down collectd-mlab plugin.')\",\n \"def terminateDaemon( self ):\\n self._connection.terminate()\\n print \\\"'+++ JPy/sessionended/\\\"\\n sys.stdout = self.stdout\\n sys.stdin = self.stdin\\n print \\\"deamon ended\\\\n\\\"\\n sys.exit()\",\n \"def Shutdown(self):\\n pass\",\n \"def Shutdown(self):\\n pass\",\n \"def Stop_ePCSim(ePCSim_conn):\\r\\n ePCSim_conn.SendCmd(\\\"exit\\\")\\r\\n ePCSim_conn.prompt = \\\"#\\\"\\r\\n ePCSim_conn.SendCmd(\\\"pkill egate\\\")\\r\\n ePCSim_conn.SendCmd(\\\"pkill edaemon\\\")\",\n \"def tear_down_all(self):\\n self.dut.send_expect(\\\"quit\\\", \\\"# \\\")\\n time.sleep(2)\\n self.dut.kill_all()\",\n \"def stop(self):\\n shutdown_url = self._env[\\\"DATASTORE_HOST\\\"] + \\\"/shutdown\\\"\\n req = urllib.request.Request(shutdown_url, method=\\\"POST\\\")\\n urllib.request.urlopen(req)\",\n \"async def shutdown_gracefully(self) -> None:\",\n \"async def shutdown_gracefully(self) -> None:\",\n \"def shutdown(self):\\n self._send_command('shutdown')\\n self.sock.close()\\n self.disconnected = True\",\n \"def shutdown_plugin(self):\\n pass\",\n \"def _disconnect(self):\\n self._factory.stopTrying()\\n self._connection.disconnect()\",\n \"def stop(self) -> None:\\n self.mqttc.disconnect()\",\n \"def cleanup_and_exit():\\n logger.warn(\\\"Terminating the program\\\")\\n try:\\n for key in connections:\\n try:\\n connections[key].disconnect()\\n except AttributeError:\\n pass\\n for s in sensors:\\n try:\\n sensors[s].cleanup()\\n except AttributeError:\\n pass\\n except:\\n pass\\n sys.exit(0)\",\n \"def stop(self):\\n self.disconnect()\",\n \"def stop(self):\\n self.disconnect()\",\n \"def _plugin_stop(handle):\\n GPIO.cleanup()\\n _LOGGER.info('MAX31865 (async) Disconnected.')\",\n \"def teardown(self):\\n self.loop.run_until_complete(self.prometheus_con.disconnect())\",\n \"def shutdown(self):\\r\\n self.socket.close()\\r\\n # self.socket_video.close()\\r\\n self.socket_state.close()\",\n \"def stop(self,dummy=None):\\n if DEBUG: print \\\"Closing Python Manager\\\"\\n self.flush_all()\\n for key in self.connection_manager.peer_servers.keys():\\n self.connection_manager.peer_servers[key].protocol.sendClose()\\n for key in self.connection_manager.script_servers.keys():\\n self.connection_manager.script_servers[key].protocol.sendClose()\\n for key in self.connection_manager.data_gui_servers.keys():\\n self.connection_manager.data_gui_servers[key].protocol.sendClose()\\n self.close_all()\\n #self.connection_manager.laud.loseConnection()\\n if DEBUG: print self.connection_manager.is_connections_closed()\\n reactor.stop()\\n if DEBUG: print \\\"Done\\\"\",\n \"def shutdown(self):\\n self.action('shutdown')\",\n \"def shutdown(self) -> None:\\n pass\",\n \"def cleanupAtExit():\\n \\n global client\\n \\n client.stop()\",\n \"def stop(self):\\n self._listeners = None\\n\\n try:\\n if self._started_daemon:\\n logging.info('Stopping Transmission daemon')\\n exec_cmd(['transmission-remote', '--exit'], wait_after=2)\\n\\n except subprocess.CalledProcessError:\\n logging.error('Unable to stop daemon')\\n logging.debug('Error details', stack_info=True, exc_info=True)\\n\\n self._done = True\",\n \"def disconnect():\\n logging.info('Client disconnected')\",\n \"def __exit__(self, exc_type, exc_val, exc_tb):\\n self.server.stop()\\n self.driver.quit()\",\n \"def stop():\\n rdebug(\\\"storpool-common.stop invoked\\\")\\n\\n rdebug(\\\"letting storpool-config know\\\")\\n run_config.stop()\",\n \"def close(self):\\n self.controller.DisableDevice()\\n self.controller.StopPolling()\\n self.controller.Disconnect(False)\",\n \"def disconnect(self):\\n _abstract()\",\n \"def disconnect(self):\\n _abstract()\",\n \"def disconnect(self):\\n\\n\\t\\tself.send(\\\"QUIT\\\", True)\\n\\n\\t\\tif self.s is not None:\\n\\t\\t\\tself.s.close()\",\n \"def Shutdown(self):\\n self.conn.send(False)\\n self.process.join()\",\n \"def stop(self):\\n self.stopped = True\\n self.broadcast('host down')\",\n \"def disconnect(self):\\n # Nothing to do\\n pass\",\n \"def cleanup(self):\\n if self.vehicle:\\n print(f\\\"Disconnecting from vehicle on {self._connection_string}\\\")\\n self.vehicle.close()\\n if self._sitl:\\n print(\\\"Shutting down SITL instance\\\")\\n self._sitl.stop()\",\n \"def __shut_down(self):\\n\\n for line, buses in self.__bus_dict.items():\\n for bus in buses:\\n bus.send_to_bus(\\\"Server Shut Down\\\")\",\n \"def _disconnect(self) -> None:\\n self._agent.stop()\\n self._agent_thread.join()\",\n \"def atexit(self):\\n self.stop_listen()\\n for driver in self.drivers.values():\\n driver.stop()\\n if hasattr(driver, \\\"atexit\\\"):\\n driver.atexit()\\n try:\\n self.processor_thread.join()\\n except AttributeError:\\n pass\",\n \"def shutdown(self):\\n raise NotImplementedError\",\n \"def terminate():\\n leds.on()\\n time.sleep(1)\\n leds.off()\\n\\n GPIO.cleanup()\",\n \"def _shutdown(self, signum=None, frame=None):\\n if not self.shutdown_requested:\\n self.logger.info(\\\"shutdown initiated\\\")\\n\\n self._shutdown_requested = True\\n if self.connected:\\n self.disconnect()\\n self.stop_listening()\",\n \"def tear_down(self):\\n LOGGER.info('Tearing down plugin service...')\\n self.reset()\\n self.server.stop(grace=None)\\n\\n if self.device_proxy:\\n self.device_proxy.tear_down()\",\n \"async def shutdown() -> None:\\n await database.disconnect()\",\n \"def bcp_goodbye(self, **kwargs):\\n if self.config['mediacontroller']['exit_on_disconnect']:\\n self.socket_thread.sending_thread.stop()\\n sys.exit()\",\n \"def shutup():\\n try:\\n ttsEng.shutup()\\n except Exception, e:\\n logging.error(e)\",\n \"def shutdown(self):\\n\\n if self.sessionState in (JT808SessionStates.OPEN,):\\n self.log.warning(\\\"Shutdown requested...disconnecting\\\")\\n self.disconnect()\\n else:\\n self.log.debug(\\\"Shutdown already in progress\\\")\",\n \"def disconnect():\\n return c.close()\",\n \"def shutdown(self,):\\n if hasattr(self, 'gpu_process_ids'):\\n for ppid in self.gpu_process_ids:\\n kill_proc_tree(ppid)\\n for dispatcher in self.dispatchers:\\n silence_function(1, dispatcher.shutdown)\\n print('DistributedBackend shutdown.')\",\n \"def shutdown(self):\\n print \\\"Plugin is shutting down.\\\"\",\n \"def __del__(self):\\n self.shutdown()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.67708665","0.6769665","0.6741867","0.66888916","0.66888916","0.6571312","0.65665215","0.6500033","0.64636064","0.64133114","0.6387295","0.6361316","0.63584465","0.6352385","0.6346766","0.6346196","0.6339963","0.63314235","0.63314235","0.63314235","0.6310234","0.63076043","0.6282716","0.62790745","0.62790096","0.62705433","0.6265983","0.6265871","0.62589806","0.6241178","0.6241178","0.6241178","0.6241178","0.6241178","0.6203971","0.62030715","0.6199466","0.61959845","0.6192926","0.6191811","0.6189165","0.61840355","0.61686563","0.6161696","0.61610687","0.6160541","0.61557364","0.6148195","0.61478555","0.61372524","0.6137132","0.6128593","0.6125277","0.6125277","0.6124497","0.6121725","0.6119041","0.6117649","0.6117649","0.61163825","0.6105023","0.6097425","0.60950065","0.60940534","0.6092371","0.6092371","0.6084466","0.608349","0.60823256","0.60631365","0.60629165","0.6047397","0.60452247","0.6037044","0.603304","0.6021318","0.60146683","0.6012993","0.600724","0.600724","0.6001238","0.59986097","0.59953946","0.59909564","0.59805524","0.59770966","0.5976663","0.597399","0.59671664","0.59657985","0.5946471","0.5943132","0.5933771","0.5930338","0.5928991","0.59255075","0.5924221","0.59208184","0.5912063","0.59090465"],"string":"[\n \"0.67708665\",\n \"0.6769665\",\n \"0.6741867\",\n \"0.66888916\",\n \"0.66888916\",\n \"0.6571312\",\n \"0.65665215\",\n \"0.6500033\",\n \"0.64636064\",\n \"0.64133114\",\n \"0.6387295\",\n \"0.6361316\",\n \"0.63584465\",\n \"0.6352385\",\n \"0.6346766\",\n \"0.6346196\",\n \"0.6339963\",\n \"0.63314235\",\n \"0.63314235\",\n \"0.63314235\",\n \"0.6310234\",\n \"0.63076043\",\n \"0.6282716\",\n \"0.62790745\",\n \"0.62790096\",\n \"0.62705433\",\n \"0.6265983\",\n \"0.6265871\",\n \"0.62589806\",\n \"0.6241178\",\n \"0.6241178\",\n \"0.6241178\",\n \"0.6241178\",\n \"0.6241178\",\n \"0.6203971\",\n \"0.62030715\",\n \"0.6199466\",\n \"0.61959845\",\n \"0.6192926\",\n \"0.6191811\",\n \"0.6189165\",\n \"0.61840355\",\n \"0.61686563\",\n \"0.6161696\",\n \"0.61610687\",\n \"0.6160541\",\n \"0.61557364\",\n \"0.6148195\",\n \"0.61478555\",\n \"0.61372524\",\n \"0.6137132\",\n \"0.6128593\",\n \"0.6125277\",\n \"0.6125277\",\n \"0.6124497\",\n \"0.6121725\",\n \"0.6119041\",\n \"0.6117649\",\n \"0.6117649\",\n \"0.61163825\",\n \"0.6105023\",\n \"0.6097425\",\n \"0.60950065\",\n \"0.60940534\",\n \"0.6092371\",\n \"0.6092371\",\n \"0.6084466\",\n \"0.608349\",\n \"0.60823256\",\n \"0.60631365\",\n \"0.60629165\",\n \"0.6047397\",\n \"0.60452247\",\n \"0.6037044\",\n \"0.603304\",\n \"0.6021318\",\n \"0.60146683\",\n \"0.6012993\",\n \"0.600724\",\n \"0.600724\",\n \"0.6001238\",\n \"0.59986097\",\n \"0.59953946\",\n \"0.59909564\",\n \"0.59805524\",\n \"0.59770966\",\n \"0.5976663\",\n \"0.597399\",\n \"0.59671664\",\n \"0.59657985\",\n \"0.5946471\",\n \"0.5943132\",\n \"0.5933771\",\n \"0.5930338\",\n \"0.5928991\",\n \"0.59255075\",\n \"0.5924221\",\n \"0.59208184\",\n \"0.5912063\",\n \"0.59090465\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7881212"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":2877,"cells":{"query":{"kind":"string","value":"python ~/code/xdoctest/testing/test_linenos.py test_lineno_failcase_called_code python ~/code/xdoctest/testing/test_linenos.py"},"document":{"kind":"string","value":"def test_lineno_failcase_called_code():\n text = _run_case(utils.codeblock(\n r'''\n def func(a):\n \"\"\"\n Example:\n >>> func(0)\n >>> # this doesnt do anything\n >>> print('this passes')\n this passes\n >>> # call the failing code\n >>> func(3)\n \"\"\"\n if a > 0:\n nested_failure(a)\n return a\n\n def nested_failure(a):\n if a > 0:\n nested_failure(a - 1)\n else:\n raise Exception('fail case')\n '''))\n assert 'rel: 6, abs: 9,' in text\n assert text"},"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 test_error(doctest):","def test_expt(doctest):","def test_exp(doctest):","def testit(did_pass):\n\n # This function works correctly--it is verbatim from the text, chapter 6\n linenum = sys._getframe(1).f_lineno # Get the caller's line number.\n if did_pass:\n msg = \"Test at line {0} ok.\".format(linenum)\n else:\n msg = (\"Test at line {0} FAILED.\".format(linenum))\n print(msg)","def test_error_control(testdir):\n testdir.makepyfile(\"\"\"import sys\"\"\")\n result = testdir.runpytest('--pylint', '--pylint-error-types=EF')\n assert '1 passed' in result.stdout.str()","def testit(did_pass):\n\n linenum = sys._getframe(1).f_lineno # Get the caller's line number.\n if did_pass:\n msg = \"Test at line {0} ok.\".format(linenum)\n else:\n msg = (\"Test at line {0} FAILED.\".format(linenum))\n print(msg)","def _test():\n\n # perform doctest\n import sys\n import doctest\n\n doctest.testmod()\n\n sys.exit(0)","def test_edge_cases(doctest):","def test_expected_failures(modpath, expected_failure):\n code = os.path.dirname(expected_failure)\n retcode, out = flake8(join(modpath, expected_failure))\n assert retcode, \"expected failure (%s), got success\" % code\n needle = \": %s \" % code\n assert needle in out\n\n with open(os.path.join(modpath, expected_failure)) as f:\n doc = ast.get_docstring(\n ast.parse(f.read(), expected_failure),\n clean=True,\n )\n\n # keep \"literal\" lines, skip shell lines\n result_check = \"\".join(\n line + \"\\n\" for line in doc.splitlines() if line.startswith(\" RST\")\n )\n if result_check:\n modpath = os.path.join(modpath, \"\")\n assert out.replace(modpath, \" \") == result_check","def _test():\n import doctest","def test_basic(testdir):\n testdir.makepyfile(\"\"\"import sys\"\"\")\n result = testdir.runpytest('--pylint')\n assert 'Missing module docstring' in result.stdout.str()\n assert 'Unused import sys' in result.stdout.str()\n assert 'Final newline missing' in result.stdout.str()\n assert 'passed' not in result.stdout.str()","def test(did_pass):\r\n linenum = sys._getframe(1).f_lineno # Get the caller's line number\r\n if did_pass:\r\n msg = \"Test at line {0} is ok\".format(linenum)\r\n else:\r\n msg = \"Test at line {0} is FAILED\".format(linenum)\r\n print(msg)","def test(did_pass):\n linenum = sys._getframe(1).f_lineno\n if did_pass:\n msg = 'Test at line {0} ok.'.format(linenum)\n else:\n msg = 'Test at line {0} FAILED.'.format(linenum)\n print(msg)","def _test():\n import doctest\n doctest.testmod(verbose=1)","def test(did_pass):\n linenum = sys._getframe(1).f_lineno # Get the caller's line number.\n if did_pass:\n msg = \"Test at line {0} ok.\".format(linenum)\n else:\n msg = (\"Test at line {0} FAILIED.\".format(linenum))\n print(msg)","def test(did_pass):\n linenum = sys._getframe(1).f_lineno # Get the caller's line number.\n if did_pass:\n msg = \"Test at line {0} ok.\".format(linenum)\n else:\n msg = (\"Test at line {0} FAILIED.\".format(linenum))\n print(msg)","def test(cline):\n print(\"Running unit tests.\")\n cline.run(\"TF_CPP_MIN_LOG_LEVEL=3 python3 -m unittest\")","def test_simple_pytest_import_error_cli():\n module_text = utils.codeblock(\n '''\n # There are lines before the bad line\n import os\n import sys\n import does_not_exist\n\n def module_func1():\n \"\"\"\n This module has a doctest\n\n Example:\n >>> print('hello world')\n \"\"\"\n ''')\n temp_module = util_misc.TempModule(module_text, modname='imperr_test_mod')\n command = sys.executable + ' -m pytest -v -s --xdoctest-verbose=3 --xdoctest-supress-import-errors --xdoctest ' + temp_module.dpath\n print(command)\n print('--')\n info = cmd(command)\n print('--')\n # print('info = {}'.format(info))\n print(info['out'])\n # We patched doctest_example so it no longer outputs this in the traceback\n assert 'util_import' not in info['out']\n print(info['out'])\n # Note: flaky changes the return code from 1 to 3, so test non-zero\n assert info['ret'] != 0\n\n # Remove the supress import error flag and now we should get the traceback\n temp_module = util_misc.TempModule(module_text, modname='imperr_test_mod')\n command = sys.executable + ' -m pytest -v -s --xdoctest-verbose=3 --xdoctest ' + temp_module.dpath\n print(command)\n print('--')\n info = cmd(command)\n print('--')\n # print('info = {}'.format(info))\n print(info['out'])\n # We patched doctest_example so it no longer outputs this in the traceback\n assert 'util_import' in info['out']\n print(info['out'])\n # Note: flaky changes the return code from 1 to 3, so test non-zero\n assert info['ret'] != 0","def test(did_pass):\n linenum = sys._getframe(1).f_lineno # Get the caller's line number.\n if did_pass:\n msg = \"Test at line {0} ok.\".format(linenum)\n else:\n msg = (\"Test at line {0} FAILED.\".format(linenum))\n print(msg)","def test(did_pass):\n linenum = sys._getframe(1).f_lineno # Get the caller's line number.\n if did_pass:\n msg = \"Test at line {0} ok.\".format(linenum)\n else:\n msg = (\"Test at line {0} FAILED.\".format(linenum))\n print(msg)","def test_pylint_works(capsys: \"CaptureFixture\") -> None:\n # Pass one file with absolute path and the other one with relative path\n notebook1 = os.path.join(\"tests\", \"data\", \"notebook_for_testing.ipynb\")\n notebook2 = os.path.join(\"tests\", \"data\", \"notebook_with_indented_magics.ipynb\")\n\n main([\"pylint\", notebook1, notebook2, \"--disable=C0114\"])\n\n # check out and err\n out, _ = capsys.readouterr()\n\n expected_out = (\n \"************* Module tests.data.notebook_for_testing\\n\" # noqa: E501\n f\"{notebook1}:cell_2:19:8: C0303: Trailing whitespace (trailing-whitespace)\\n\" # noqa: E501\n f\"{notebook1}:cell_2:15:11: C0209: Formatting a regular string which could be a f-string (consider-using-f-string)\\n\" # noqa: E501\n f'{notebook1}:cell_4:1:0: C0413: Import \"from random import randint\" should be placed at the top of the module (wrong-import-position)\\n' # noqa: E501\n f'{notebook1}:cell_5:1:0: C0413: Import \"import pprint\" should be placed at the top of the module (wrong-import-position)\\n' # noqa: E501\n f'{notebook1}:cell_5:2:0: C0413: Import \"import sys\" should be placed at the top of the module (wrong-import-position)\\n' # noqa: E501\n f\"{notebook1}:cell_1:1:0: W0611: Unused import os (unused-import)\\n\" # noqa: E501\n f\"{notebook1}:cell_1:3:0: W0611: Unused import glob (unused-import)\\n\" # noqa: E501\n f\"{notebook1}:cell_1:5:0: W0611: Unused import nbqa (unused-import)\\n\" # noqa: E501\n f\"{notebook1}:cell_4:1:0: W0611: Unused randint imported from random (unused-import)\\n\" # noqa: E501\n f'{notebook1}:cell_4:1:0: C0411: standard import \"from random import randint\" should be placed before \"import nbqa\" (wrong-import-order)\\n' # noqa: E501\n f'{notebook1}:cell_5:1:0: C0411: standard import \"import pprint\" should be placed before \"import nbqa\" (wrong-import-order)\\n' # noqa: E501\n f'{notebook1}:cell_5:2:0: C0411: standard import \"import sys\" should be placed before \"import nbqa\" (wrong-import-order)\\n' # noqa: E501\n \"************* Module tests.data.notebook_with_indented_magics\\n\" # noqa: E501\n f\"{notebook2}:cell_1:1:0: W0611: Unused randint imported from random (unused-import)\\n\" # noqa: E501\n f\"{notebook2}:cell_1:2:0: W0611: Unused get_ipython imported from IPython (unused-import)\\n\" # noqa: E501\n f'{notebook2}:cell_3:3:0: C0411: standard import \"import operator\" should be placed before \"from IPython import get_ipython\" (wrong-import-order)\\n' # noqa: E501\n \"\\n\"\n \"-----------------------------------\\n\"\n \"Your code has been rated at 4.32/10\\n\"\n \"\\n\"\n )\n horizontal_bar = \"-----------------------------------\"\n assert out.split(horizontal_bar)[0] == expected_out.split(horizontal_bar)[0]","def unitdoctest():\r\n\r\n pass","def test(did_pass):\n linenum = sys._getframe(1).f_lineno # Get the caller's line number.\n if did_pass:\n msg = \"Test at line {0} ok.\".format(linenum)\n else:\n msg = (\"Test at line {0} FAILED.\".format(linenum))\n print(msg)","def test(did_pass):\n linenum = sys._getframe(1).f_lineno # Get the caller's line number.\n if did_pass:\n msg = \"Test at line {0} ok.\".format(linenum)\n else:\n msg = (\"Test at line {0} FAILED.\".format(linenum))\n print(msg)","def test(did_pass):\n linenum = sys._getframe(1).f_lineno # Get the caller's line number.\n if did_pass:\n msg = \"Test at line {0} ok.\".format(linenum)\n else:\n msg = (\"Test at line {0} FAILED.\".format(linenum))\n print(msg)","def test(did_pass):\n linenum = sys._getframe(1).f_lineno # Get the caller's line number.\n if did_pass:\n msg = \"Test at line {0} ok.\".format(linenum)\n else:\n msg = (\"Test at line {0} FAILED.\".format(linenum))\n print(msg)","def test(did_pass):\n linenum = sys._getframe(1).f_lineno # Get the caller's line number.\n if did_pass:\n msg = \"Test at line {0} ok.\".format(linenum)\n else:\n msg = (\"Test at line {0} FAILED.\".format(linenum))\n print(msg)","def test(did_pass):\n linenum = sys._getframe(1).f_lineno # Get the caller's line number.\n if did_pass:\n msg = \"Test at line {0} ok.\".format(linenum)\n else:\n msg = (\"Test at line {0} FAILED.\".format(linenum))\n print(msg)","def test(did_pass):\n linenum = sys._getframe(1).f_lineno # Get the caller's line number.\n if did_pass:\n msg = \"Test at line {0} ok.\".format(linenum)\n else:\n msg = (\"Test at line {0} FAILED.\".format(linenum))\n print(msg)","def test(did_pass):\n linenum = sys._getframe(1).f_lineno # Get the caller's line number.\n if did_pass:\n msg = \"Test at line {0} ok.\".format(linenum)\n else:\n msg = (\"Test at line {0} FAILED.\".format(linenum))\n print(msg)","def test(did_pass):\r\n linenum = sys._getframe(1).f_lineno # Get the caller's line number.\r\n if did_pass:\r\n msg = \"Test at line {0} ok.\".format(linenum)\r\n else:\r\n msg = (\"Test at line {0} FAILED.\".format(linenum))\r\n print(msg)","def test(did_pass):\r\n linenum = sys._getframe(1).f_lineno # Get the caller's line number.\r\n if did_pass:\r\n msg = \"Test at line {0} ok.\".format(linenum)\r\n else:\r\n msg = (\"Test at line {0} FAILED.\".format(linenum))\r\n print(msg)","def test(did_pass):\r\n linenum = sys._getframe(1).f_lineno # Get the caller's line number.\r\n if did_pass:\r\n msg = \"Test at line {0} ok.\".format(linenum)\r\n else:\r\n msg = (\"Test at line {0} FAILED.\".format(linenum))\r\n print(msg)","def test(did_pass):\r\n linenum = sys._getframe(1).f_lineno # Get the caller's line number.\r\n if did_pass:\r\n msg = \"Test at line {0} ok.\".format(linenum)\r\n else:\r\n msg = (\"Test at line {0} FAILED.\".format(linenum))\r\n print(msg)","def test(did_pass):\r\n linenum = sys._getframe(1).f_lineno # Get the caller's line number.\r\n if did_pass:\r\n msg = \"Test at line {0} ok.\".format(linenum)\r\n else:\r\n msg = (\"Test at line {0} FAILED.\".format(linenum))\r\n print(msg)","def _test(): # pragma: no cover\r\n print('Starting doctest')\r\n doctest.testmod()\r\n print('Completed doctest')","def test_noop_function_call(self) -> None:\n before = after = \"\"\"\n for line in xreadlines(r):\n print(line)\n \"\"\"\n self.assertCodemod(before, after)","def test(did_pass):\n linenum = sys._getframe(1).f_lineno # Get the caller's line number.\n if did_pass:\n msg = \"Test at line {0} ok.\".format(linenum)\n else:\n msg = (\"Test at line {0} FAILED.\".format(linenum))\n print(msg)","def test_comparison_doctest(doctest):","def def_textface_doctests():","def _test():\n import doctest\n return doctest.testmod(verbose=True)","def _test():\n import doctest\n return doctest.testmod(verbose=True)","def test_basic():\n line = \"test\"\n assert wrap_line(line) == \"test\"","def _test():\n import doctest\n doctest.testmod()","def _test():\n import doctest\n doctest.testmod()","def test_xfailed_but_passed():\n pass","def test_doctests_run(self):\n results = doctest.testmod(lab, optionflags=TESTDOC_FLAGS, report=False)\n self.assertEqual(results[0], 0)","def test_compiler_arguments_fist_line(patch, compiler, lines, tree):\n patch.init(StorySyntaxError)\n lines.last.return_value = None\n with raises(StorySyntaxError):\n compiler.arguments(tree, '0')\n error = 'arguments_noservice'\n StorySyntaxError.__init__.assert_called_with(error, tree=tree)","def test_zero(doctest):","def main():\n import argparse\n from xdoctest import utils\n description = utils.codeblock(\n '''\n discover and run doctests within a python package\n ''')\n\n parser = argparse.ArgumentParser(prog='python -m xdoctest', description=description)\n parser.add_argument('modname', help='what files to run')\n parser.add_argument('command', help='a doctest name or a command (list|all)', default='list')\n parser.add_argument(*('--style',), type=str, help='choose your style',\n choices=['auto', 'google', 'freeform'], default='auto')\n parser.add_argument(*('--options',), type=str,\n help='specify the default directive state',\n default=None)\n parser.add_argument(*('--offset',), dest='offset_linenos', action='store_true',\n help=('Doctest outputs will display line numbers '\n 'wrt to the source file.'))\n\n args, unknown = parser.parse_known_args()\n ns = args.__dict__.copy()\n\n # ... postprocess args\n modname = ns['modname']\n command = ns['command']\n style = ns['style']\n offset_linenos = ns['offset_linenos']\n\n if ns['options'] is None:\n from os.path import exists\n ns['options'] = ''\n if exists('pytest.ini'):\n from six.moves import configparser\n parser = configparser.ConfigParser()\n parser.read('pytest.ini')\n try:\n ns['options'] = parser.get('pytest', 'xdoctest_options')\n except configparser.NoOptionError:\n pass\n\n from xdoctest.directive import parse_directive_optstr\n default_runtime_state = {}\n for optpart in ns['options'].split(','):\n if optpart:\n directive = parse_directive_optstr(optpart)\n if directive is not None:\n default_runtime_state[directive.name] = directive.positive\n\n # Specify a default doctest_example.Config state\n config = {\n 'default_runtime_state': default_runtime_state,\n 'offset_linenos': offset_linenos,\n }\n\n import xdoctest\n xdoctest.doctest_module(modname, argv=[command], style=style,\n config=config)","def _expected_lines_and_line_numbers(path, check_prefix):\n with open(path) as f:\n for index, line in enumerate(f):\n if 'RUN:' in line:\n # Ignore lit directives, which may include a call to\n # xctest_checker that specifies a check prefix.\n continue\n\n # Note that line numbers are not zero-indexed; we must add one to\n # the loop index.\n line_number = index + 1\n\n components = line.split(check_prefix)\n if len(components) == 2:\n yield (replace_offsets(components[1].strip(), line_number),\n line_number)\n elif len(components) > 2:\n # Include a newline, then the file name and line number in the\n # exception in order to have it appear as an inline failure in\n # Xcode.\n raise XCTestCheckerError(\n path, line_number,\n 'Usage violation: prefix \"{}\" appears twice in the same '\n 'line.'.format(check_prefix))","def test_remainder(doctest):","def test():\n import doctest\n doctest.testmod(verbose=0)\n test_int()\n test_tuple()","def test_compiler_arguments_not_execute(patch, compiler, lines, tree):\n patch.init(StorySyntaxError)\n patch.object(Objects, 'arguments')\n lines.last.return_value = '1'\n lines.lines = {'1': {'method': 'whatever'}}\n with raises(StorySyntaxError):\n compiler.arguments(tree, '0')\n error = 'arguments_noservice'\n StorySyntaxError.__init__.assert_called_with(error, tree=tree)","def test_noop_wrong_name(self) -> None:\n before = after = \"\"\"\n for line in r.xreadlines():\n print(line)\n \"\"\"\n self.assertCodemod(before, after)","def scope_doctest():\n pass","def test_compiler_indented_chain_first_line(patch, compiler, lines, tree):\n patch.init(StorySyntaxError)\n lines.last.return_value = None\n with raises(StorySyntaxError):\n compiler.indented_chain(tree, '0')\n error = 'arguments_nomutation'\n StorySyntaxError.__init__.assert_called_with(error, tree=tree)","def test1(self):\n\n log.info('This is a test')\n self.assertTrue((random.randint(0,9) % 2) == 0)#! /usr/bin/env python","def test_doc_code_cells(fname, globalns=globals()):\n text = Path(fname).read_text()\n code_cells = re.findall(r\"```{code-cell}[^\\n]+\\n(.*?)`{3}\", text, re.S)\n for cell in code_cells:\n header = re.search(r\"-{3}(.+?)-{3}\", cell, re.S)\n if header:\n cell = cell.replace(header.group(), \"\")\n if \"warns\" in header.group():\n with pytest.warns(None):\n exec(cell, globalns)\n continue\n if \"raises-exception\" in header.group():\n with pytest.raises(Exception):\n exec(cell, globalns)\n continue\n exec(cell, globalns)","def test_expected_successes(modpath):\n retcode, out = flake8(join(modpath, \"test_cases\"))\n assert not retcode, out","def test_0_check_xc_docstring(self):\n self.banner(\"Checking the docstring on your extra credit.\") \n filename = self.find_file('project9_xc.py')\n self.check_docstring(filename)","def test_start_of_line_2(self):\n before_b = \"\"\"\\\n first line\n line 1\n line a\n line b\n line c\n last line\n \"\"\"\n after_b = \"\"\"\\\n first line\n line 1\n line a\n line b\n line c\n last line\n \"\"\"\n self.run_test(\n before_b=before_b,\n after_b=after_b,\n before_sel=(\"3.1\", \"3.1\"),\n after_sel=(\"3.4\", \"3.4\"),\n command_name=\"start-of-line\",\n )","def test_line_count(self):\n self.assertEqual(analyze_text(self.filename)[0], 11)","def test_main():\n # Setup\n # Exercise\n # Verify","def test_next_line(self):\n before_b = \"\"\"\\\n a\n\n b\n \"\"\"\n after_b = \"\"\"\\\n a\n\n b\n \"\"\"\n self.run_test(\n before_b=before_b,\n after_b=after_b,\n before_sel=(\"1.1\", \"1.1\"),\n after_sel=(\"2.0\", \"2.0\"),\n command_name=\"next-line\",\n )","def test_T01():","def test_4():","def test_line_count(self):\n\t\tself.assertEqual(analyse_text(self.filename)[0], 4)","def test_3():","def test_5():","def test_for_mistake(func, *args, **kw):\n\n global global_fail_counter\n global global_success_counter\n\n # print test number\n test_num = global_fail_counter + global_success_counter\n print('Test # %d: ' % test_num, end='')\n #print('Test # %d: ' % test_num)\n\n # Run function\n obj = func(*args, **kw)\n # Increment appropriate counter\n if obj.mistake:\n global_fail_counter += 1\n else:\n global_success_counter += 1","def test_start_of_line(self):\n before_b = \"\"\"\\\n first line\n line 1\n line a\n line b\n line c\n last line\n \"\"\"\n after_b = \"\"\"\\\n first line\n line 1\n line a\n line b\n line c\n last line\n \"\"\"\n self.run_test(\n before_b=before_b,\n after_b=after_b,\n before_sel=(\"3.10\", \"3.10\"),\n after_sel=(\"3.4\", \"3.4\"),\n command_name=\"start-of-line\",\n )","def theTests(path_to_code_to_check=\"../me\"):\n print(\"\\nWelcome to the exam!\")\n print(\"May the odds be ever in your favour.\\nEspecially today!\")\n\n if ex_runs(path_to_code_to_check, exerciseNumber=1, weekNumber=SET_NUMBER):\n exam = loadExerciseFile(\n path_to_code_to_check, weekNumber=SET_NUMBER, exerciseNumber=1\n )\n\n # testResults.append(test(test_flake8(ex1path), \"pass the linter\"))\n exam_test(\n True,\n [],\n exam.string_please,\n finishing_function=lambda x: type(x) is str,\n extra_message=\"Don't over think this! just return a string!\",\n )\n exam_test(\n True,\n [],\n exam.list_please,\n finishing_function=lambda x: type(x) is list,\n extra_message=\"Don't over think this! just return a list!\",\n )\n exam_test(\n True,\n [],\n exam.dictionary_please,\n finishing_function=lambda x: type(x) is dict,\n extra_message=\"Don't over think this! just return a dictionary!\",\n )\n exam_test(True, [5], exam.is_it_5)\n exam_test(False, [4], exam.is_it_5)\n exam_test(False, [\"cats\"], exam.is_it_5)\n exam_test(0, [5], exam.take_five)\n exam_test(5, [10], exam.take_five)\n exam_test(-5, [0], exam.take_five)\n\n exam_test(\"Hello the Queen\", [\"the Queen\"], exam.greet)\n exam_test(\"Hello Pr♂nc♀♂\", [\"Pr♂nc♀♂\"], exam.greet)\n\n exam_test(4, [[3, 3, 3, 3, 1]], exam.three_counter)\n exam_test(0, [[0, 1, 2, 5, -9]], exam.three_counter)\n\n exam_test(2, [7], exam.n_counter)\n exam_test(5, [0, [0, 0, 0, 0, 0, [0]]], exam.n_counter)\n\n # fmt: off\n fizza = [\n 1, 2, \"Fizz\", 4, \"Buzz\", \"Fizz\", 7, 8, \"Fizz\", \"Buzz\", 11, \"Fizz\", \n 13, 14, \"FizzBuzz\", 16, 17, \"Fizz\", 19, \"Buzz\", \"Fizz\", 22, 23, \n \"Fizz\", \"Buzz\", 26, \"Fizz\", 28, 29, \"FizzBuzz\", 31, 32, \"Fizz\", 34, \n \"Buzz\", \"Fizz\", 37, 38, \"Fizz\", \"Buzz\", 41, \"Fizz\", 43, 44, \n \"FizzBuzz\", 46, 47, \"Fizz\", 49, \"Buzz\", \"Fizz\", 52, 53, \"Fizz\", \n \"Buzz\", 56, \"Fizz\", 58, 59, \"FizzBuzz\", 61, 62, \"Fizz\", 64, \"Buzz\", \n \"Fizz\", 67, 68, \"Fizz\", \"Buzz\", 71, \"Fizz\", 73, 74, \"FizzBuzz\", 76, \n 77, \"Fizz\", 79, \"Buzz\", \"Fizz\", 82, 83, \"Fizz\", \"Buzz\", 86, \"Fizz\", \n 88, 89, \"FizzBuzz\", 91, 92, \"Fizz\", 94, \"Buzz\", \"Fizz\", 97, 98, \n \"Fizz\", \"Buzz\", ]\n # fmt: on\n exam_test(fizza, [], exam.fizz_buzz)\n\n exam_test(\n \"|a| |s|e|r|i|a|l| |k|i|l|l|e|r|\", [\"a serial killer\"], exam.put_behind_bars\n )\n exam_test(\"|a| |b|a|r|t|e|n|d|e|r|\", [\"a bartender\"], exam.put_behind_bars)\n\n exam_test([\"red fox\"], [\"x\"], exam.pet_filter)\n exam_test([], [\"q\"], exam.pet_filter)\n exam_test(\n [\"pig\", \"sheep\", \"guinea pig\", \"pigeon\", \"alpaca\", \"guppy\"],\n [\"p\"],\n exam.pet_filter,\n )\n\n exam_test(\"e\", [], exam.best_letter_for_pets)\n\n word_lengths = [[3, 3, 3], [4, 4, 4], [5, 5, 5], [6, 6, 6], [7, 7, 7]]\n exam_test(\n word_lengths,\n [],\n exam.make_filler_text_dictionary,\n lambda x: [[len(w) for w in x[k]] for k in x.keys()],\n )\n\n exam_test(\n True,\n [50],\n exam.random_filler_text,\n lambda x: len(x.split(\" \")) == 50 and len(x) > 3 * 50,\n )\n\n exam_test(\n True,\n [1000],\n exam.random_filler_text,\n lambda x: len(x.split(\" \")) == 1000 and len(x) > 3 * 1000,\n )\n\n clean_out_old_env()\n\n exam_test(\n True,\n [100],\n exam.fast_filler,\n lambda x: len(x.split(\" \")) == 100 and len(x) > 3 * 100,\n # chdir=True, # NFI what this does :(\n )\n\n # exam_test(True, [\"./week8/dict_racey.json\"], os.path.exists)\n\n exam_test(\n True,\n [10],\n exam.fast_filler,\n lambda x: x[0] in string.ascii_uppercase and x[1] in string.ascii_lowercase,\n \"Test if fast_filler is capitalised\",\n )\n exam_test(\n True,\n [10],\n exam.fast_filler,\n lambda x: x[-1] == \".\",\n \"Test if fast_filler finishes with a .\",\n )\n\n print(\n \"The point of saving the dictionary is that it's fast!\",\n \"The pattern of saving a value locally so that you don't\",\n \"need to go and get it is called caching.\",\n \"This test runs fast_filler 10 times, and if it manages it in less\",\n \"than a second, then you're good to go!\",\n sep=\"\\n\",\n )\n try:\n TIMEOUT_IN_SECONDS = 1\n func_timeout(\n TIMEOUT_IN_SECONDS,\n lambda: [exam.fast_filler(1000) for _ in range(10)],\n args=[],\n )\n testResults.append(test(True, \"subsequent fast_filler\"))\n except FunctionTimedOut as t:\n m = (\n \"Timed out trying to run fast filler 10 times in 1 second, \"\n \"subsequent fast_filler probably wasn't fast enough\"\n )\n print(m, str(t))\n testResults.append(test(False, m + str(t)))\n except Exception as e:\n testResults.append(test(False, \"subsequent fast_filler failed: \" + str(e)))\n\n message = (\n \"Cowabunga! You've got all the tests passing!\\n\"\n \"Well done, that's all the exercises for this term out of the way!\"\n )\n print(testResults)\n return finish_up(testResults, message, nyan_cat())","def test_previous_line(self):\n before_b = \"\"\"\\\n a\n\n b\n \"\"\"\n after_b = \"\"\"\\\n a\n\n b\n \"\"\"\n self.run_test(\n before_b=before_b,\n after_b=after_b,\n before_sel=(\"3.0\", \"3.0\"),\n after_sel=(\"2.0\", \"2.0\"),\n command_name=\"previous-line\",\n )","def test_passed():\n pass","def test():\n import doctest\n from . import locate\n return doctest.testmod(locate)","def test_sqrt(doctest):","def test_03_pass(self):\n if x==1:\n pass","def test_code010(self, *test_code_args, **test_code_kwargs):\n print(\"This is test_code010 from TestDefinition #\", self.ID, \", test case #\", self.test_case_ID, sep='')","def test_twentythree_no_args():\n sys.argv = ['test']\n with pytest.raises(SystemExit):\n TwentyThree()","def test_shift(doctest):","def test_move_lines_up_into_docstring(self):\n before_b = '''\\\n #@@language python\n def test():\n \"\"\" a\n b\n c\n \"\"\"\n print 1\n \n print 2\n '''\n after_b = '''\\\n #@@language python\n def test():\n \"\"\" a\n b\n c\n print 1\n \"\"\"\n \n print 2\n '''\n self.run_test(\n before_b=before_b,\n after_b=after_b,\n before_sel=(\"7.1\", \"7.1\"),\n after_sel=(\"6.1\", \"6.1\"),\n command_name=\"move-lines-up\",\n )","def test_abs(doctest):","def test_examples():\n argv = [\"py.test\", \"-examples\"]\n assert get_sargs(argv) is None","def test_compiler_return_statement(patch, compiler, lines, tree):\n tree.expression = None\n compiler.return_statement(tree, '1')\n line = tree.line()\n kwargs = {'args': None, 'parent': '1'}\n lines.append.assert_called_with('return', line, **kwargs)","def test_line_count(self):\n self.assertEqual(analyze_text(self.filename)[0], 4)","def test_line_count(self):\n self.assertEqual(analyze_text(self.filename)[0], 4)","def __integration_doctest():\n pass","def test_02_pass(self):\n if x==1:\n pass","def lloc():\n load_env_vars('dev')\n from tools.static_code_analysis import LogicalLinesOfCode\n radon_raw = LogicalLinesOfCode()\n score = radon_raw.run_test()\n radon_raw.create_badge(score)","def test_compiler_indented_chain_not_mutation(patch, compiler, lines, tree):\n patch.init(StorySyntaxError)\n patch.object(Compiler, 'chained_mutations')\n lines.last.return_value = '1'\n lines.lines = {'1': {'method': 'whatever'}}\n with raises(StorySyntaxError):\n compiler.indented_chain(tree, '0')\n error = 'arguments_nomutation'\n StorySyntaxError.__init__.assert_called_with(error, tree=tree)","def test_py_compile_basic(self):\n self._test_py_compile('basic')","def test_main_succeed_en(runner: CliRunner) -> None:\n result = runner.invoke(__main__.main, \"-c tests/clippings-en.txt\")\n assert result.exit_code == 0","def main(argv=None):\n if argv is None:\n argv = sys.argv\n\n import doctest\n verbose = (len(argv) >= 2 and argv[1] == \"--Test\")\n if verbose:\n print(\"Running doctests (verbose mode)\")\n else:\n print(\"Running doctests\")\n doctest.testmod(verbose=verbose)\n return 0","def test(self):\n # -- Test --\n\n # (1)\n\n # (2)\n\n # (3)\n\n # (4)\n # -- Test --","def test_kill_line_start_line(self):\n before_b = \"\"\"\\\n line 1\n line 2\n line 3\n line 4\n \"\"\"\n after_b = \"\"\"\\\n line 1\n line 2\n\n line 4\n \"\"\"\n self.run_test(\n before_b=before_b,\n after_b=after_b,\n before_sel=(\"3.0\", \"3.0\"),\n after_sel=(\"3.0\", \"3.0\"),\n command_name=\"kill-line\",\n )","def test_get_lines(self):\r\n \r\n self.assertEqual(get_lines(\"test_20191206_1140000.txt\", os.getcwd()),\r\n 299,\r\n \"Should be equal number of lines.\")","def test_pylintrc_file(testdir):\n rcfile = testdir.makefile('rc', \"\"\"\n[FORMAT]\n\nmax-line-length=3\n\"\"\")\n testdir.makepyfile(\"\"\"import sys\"\"\")\n result = testdir.runpytest(\n '--pylint', '--pylint-rcfile={0}'.format(rcfile.strpath)\n )\n assert 'Line too long (10/3)' in result.stdout.str()","def test(self):\n self.skipped_test('doctest module has no DocTestSuite class')","def test_beginning_of_line(self):\n before_b = \"\"\"\\\n first line\n line 1\n line a\n line b\n line c\n last line\n \"\"\"\n after_b = \"\"\"\\\n first line\n line 1\n line a\n line b\n line c\n last line\n \"\"\"\n self.run_test(\n before_b=before_b,\n after_b=after_b,\n before_sel=(\"3.10\", \"3.10\"),\n after_sel=(\"3.0\", \"3.0\"),\n command_name=\"beginning-of-line\",\n )"],"string":"[\n \"def test_error(doctest):\",\n \"def test_expt(doctest):\",\n \"def test_exp(doctest):\",\n \"def testit(did_pass):\\n\\n # This function works correctly--it is verbatim from the text, chapter 6\\n linenum = sys._getframe(1).f_lineno # Get the caller's line number.\\n if did_pass:\\n msg = \\\"Test at line {0} ok.\\\".format(linenum)\\n else:\\n msg = (\\\"Test at line {0} FAILED.\\\".format(linenum))\\n print(msg)\",\n \"def test_error_control(testdir):\\n testdir.makepyfile(\\\"\\\"\\\"import sys\\\"\\\"\\\")\\n result = testdir.runpytest('--pylint', '--pylint-error-types=EF')\\n assert '1 passed' in result.stdout.str()\",\n \"def testit(did_pass):\\n\\n linenum = sys._getframe(1).f_lineno # Get the caller's line number.\\n if did_pass:\\n msg = \\\"Test at line {0} ok.\\\".format(linenum)\\n else:\\n msg = (\\\"Test at line {0} FAILED.\\\".format(linenum))\\n print(msg)\",\n \"def _test():\\n\\n # perform doctest\\n import sys\\n import doctest\\n\\n doctest.testmod()\\n\\n sys.exit(0)\",\n \"def test_edge_cases(doctest):\",\n \"def test_expected_failures(modpath, expected_failure):\\n code = os.path.dirname(expected_failure)\\n retcode, out = flake8(join(modpath, expected_failure))\\n assert retcode, \\\"expected failure (%s), got success\\\" % code\\n needle = \\\": %s \\\" % code\\n assert needle in out\\n\\n with open(os.path.join(modpath, expected_failure)) as f:\\n doc = ast.get_docstring(\\n ast.parse(f.read(), expected_failure),\\n clean=True,\\n )\\n\\n # keep \\\"literal\\\" lines, skip shell lines\\n result_check = \\\"\\\".join(\\n line + \\\"\\\\n\\\" for line in doc.splitlines() if line.startswith(\\\" RST\\\")\\n )\\n if result_check:\\n modpath = os.path.join(modpath, \\\"\\\")\\n assert out.replace(modpath, \\\" \\\") == result_check\",\n \"def _test():\\n import doctest\",\n \"def test_basic(testdir):\\n testdir.makepyfile(\\\"\\\"\\\"import sys\\\"\\\"\\\")\\n result = testdir.runpytest('--pylint')\\n assert 'Missing module docstring' in result.stdout.str()\\n assert 'Unused import sys' in result.stdout.str()\\n assert 'Final newline missing' in result.stdout.str()\\n assert 'passed' not in result.stdout.str()\",\n \"def test(did_pass):\\r\\n linenum = sys._getframe(1).f_lineno # Get the caller's line number\\r\\n if did_pass:\\r\\n msg = \\\"Test at line {0} is ok\\\".format(linenum)\\r\\n else:\\r\\n msg = \\\"Test at line {0} is FAILED\\\".format(linenum)\\r\\n print(msg)\",\n \"def test(did_pass):\\n linenum = sys._getframe(1).f_lineno\\n if did_pass:\\n msg = 'Test at line {0} ok.'.format(linenum)\\n else:\\n msg = 'Test at line {0} FAILED.'.format(linenum)\\n print(msg)\",\n \"def _test():\\n import doctest\\n doctest.testmod(verbose=1)\",\n \"def test(did_pass):\\n linenum = sys._getframe(1).f_lineno # Get the caller's line number.\\n if did_pass:\\n msg = \\\"Test at line {0} ok.\\\".format(linenum)\\n else:\\n msg = (\\\"Test at line {0} FAILIED.\\\".format(linenum))\\n print(msg)\",\n \"def test(did_pass):\\n linenum = sys._getframe(1).f_lineno # Get the caller's line number.\\n if did_pass:\\n msg = \\\"Test at line {0} ok.\\\".format(linenum)\\n else:\\n msg = (\\\"Test at line {0} FAILIED.\\\".format(linenum))\\n print(msg)\",\n \"def test(cline):\\n print(\\\"Running unit tests.\\\")\\n cline.run(\\\"TF_CPP_MIN_LOG_LEVEL=3 python3 -m unittest\\\")\",\n \"def test_simple_pytest_import_error_cli():\\n module_text = utils.codeblock(\\n '''\\n # There are lines before the bad line\\n import os\\n import sys\\n import does_not_exist\\n\\n def module_func1():\\n \\\"\\\"\\\"\\n This module has a doctest\\n\\n Example:\\n >>> print('hello world')\\n \\\"\\\"\\\"\\n ''')\\n temp_module = util_misc.TempModule(module_text, modname='imperr_test_mod')\\n command = sys.executable + ' -m pytest -v -s --xdoctest-verbose=3 --xdoctest-supress-import-errors --xdoctest ' + temp_module.dpath\\n print(command)\\n print('--')\\n info = cmd(command)\\n print('--')\\n # print('info = {}'.format(info))\\n print(info['out'])\\n # We patched doctest_example so it no longer outputs this in the traceback\\n assert 'util_import' not in info['out']\\n print(info['out'])\\n # Note: flaky changes the return code from 1 to 3, so test non-zero\\n assert info['ret'] != 0\\n\\n # Remove the supress import error flag and now we should get the traceback\\n temp_module = util_misc.TempModule(module_text, modname='imperr_test_mod')\\n command = sys.executable + ' -m pytest -v -s --xdoctest-verbose=3 --xdoctest ' + temp_module.dpath\\n print(command)\\n print('--')\\n info = cmd(command)\\n print('--')\\n # print('info = {}'.format(info))\\n print(info['out'])\\n # We patched doctest_example so it no longer outputs this in the traceback\\n assert 'util_import' in info['out']\\n print(info['out'])\\n # Note: flaky changes the return code from 1 to 3, so test non-zero\\n assert info['ret'] != 0\",\n \"def test(did_pass):\\n linenum = sys._getframe(1).f_lineno # Get the caller's line number.\\n if did_pass:\\n msg = \\\"Test at line {0} ok.\\\".format(linenum)\\n else:\\n msg = (\\\"Test at line {0} FAILED.\\\".format(linenum))\\n print(msg)\",\n \"def test(did_pass):\\n linenum = sys._getframe(1).f_lineno # Get the caller's line number.\\n if did_pass:\\n msg = \\\"Test at line {0} ok.\\\".format(linenum)\\n else:\\n msg = (\\\"Test at line {0} FAILED.\\\".format(linenum))\\n print(msg)\",\n \"def test_pylint_works(capsys: \\\"CaptureFixture\\\") -> None:\\n # Pass one file with absolute path and the other one with relative path\\n notebook1 = os.path.join(\\\"tests\\\", \\\"data\\\", \\\"notebook_for_testing.ipynb\\\")\\n notebook2 = os.path.join(\\\"tests\\\", \\\"data\\\", \\\"notebook_with_indented_magics.ipynb\\\")\\n\\n main([\\\"pylint\\\", notebook1, notebook2, \\\"--disable=C0114\\\"])\\n\\n # check out and err\\n out, _ = capsys.readouterr()\\n\\n expected_out = (\\n \\\"************* Module tests.data.notebook_for_testing\\\\n\\\" # noqa: E501\\n f\\\"{notebook1}:cell_2:19:8: C0303: Trailing whitespace (trailing-whitespace)\\\\n\\\" # noqa: E501\\n f\\\"{notebook1}:cell_2:15:11: C0209: Formatting a regular string which could be a f-string (consider-using-f-string)\\\\n\\\" # noqa: E501\\n f'{notebook1}:cell_4:1:0: C0413: Import \\\"from random import randint\\\" should be placed at the top of the module (wrong-import-position)\\\\n' # noqa: E501\\n f'{notebook1}:cell_5:1:0: C0413: Import \\\"import pprint\\\" should be placed at the top of the module (wrong-import-position)\\\\n' # noqa: E501\\n f'{notebook1}:cell_5:2:0: C0413: Import \\\"import sys\\\" should be placed at the top of the module (wrong-import-position)\\\\n' # noqa: E501\\n f\\\"{notebook1}:cell_1:1:0: W0611: Unused import os (unused-import)\\\\n\\\" # noqa: E501\\n f\\\"{notebook1}:cell_1:3:0: W0611: Unused import glob (unused-import)\\\\n\\\" # noqa: E501\\n f\\\"{notebook1}:cell_1:5:0: W0611: Unused import nbqa (unused-import)\\\\n\\\" # noqa: E501\\n f\\\"{notebook1}:cell_4:1:0: W0611: Unused randint imported from random (unused-import)\\\\n\\\" # noqa: E501\\n f'{notebook1}:cell_4:1:0: C0411: standard import \\\"from random import randint\\\" should be placed before \\\"import nbqa\\\" (wrong-import-order)\\\\n' # noqa: E501\\n f'{notebook1}:cell_5:1:0: C0411: standard import \\\"import pprint\\\" should be placed before \\\"import nbqa\\\" (wrong-import-order)\\\\n' # noqa: E501\\n f'{notebook1}:cell_5:2:0: C0411: standard import \\\"import sys\\\" should be placed before \\\"import nbqa\\\" (wrong-import-order)\\\\n' # noqa: E501\\n \\\"************* Module tests.data.notebook_with_indented_magics\\\\n\\\" # noqa: E501\\n f\\\"{notebook2}:cell_1:1:0: W0611: Unused randint imported from random (unused-import)\\\\n\\\" # noqa: E501\\n f\\\"{notebook2}:cell_1:2:0: W0611: Unused get_ipython imported from IPython (unused-import)\\\\n\\\" # noqa: E501\\n f'{notebook2}:cell_3:3:0: C0411: standard import \\\"import operator\\\" should be placed before \\\"from IPython import get_ipython\\\" (wrong-import-order)\\\\n' # noqa: E501\\n \\\"\\\\n\\\"\\n \\\"-----------------------------------\\\\n\\\"\\n \\\"Your code has been rated at 4.32/10\\\\n\\\"\\n \\\"\\\\n\\\"\\n )\\n horizontal_bar = \\\"-----------------------------------\\\"\\n assert out.split(horizontal_bar)[0] == expected_out.split(horizontal_bar)[0]\",\n \"def unitdoctest():\\r\\n\\r\\n pass\",\n \"def test(did_pass):\\n linenum = sys._getframe(1).f_lineno # Get the caller's line number.\\n if did_pass:\\n msg = \\\"Test at line {0} ok.\\\".format(linenum)\\n else:\\n msg = (\\\"Test at line {0} FAILED.\\\".format(linenum))\\n print(msg)\",\n \"def test(did_pass):\\n linenum = sys._getframe(1).f_lineno # Get the caller's line number.\\n if did_pass:\\n msg = \\\"Test at line {0} ok.\\\".format(linenum)\\n else:\\n msg = (\\\"Test at line {0} FAILED.\\\".format(linenum))\\n print(msg)\",\n \"def test(did_pass):\\n linenum = sys._getframe(1).f_lineno # Get the caller's line number.\\n if did_pass:\\n msg = \\\"Test at line {0} ok.\\\".format(linenum)\\n else:\\n msg = (\\\"Test at line {0} FAILED.\\\".format(linenum))\\n print(msg)\",\n \"def test(did_pass):\\n linenum = sys._getframe(1).f_lineno # Get the caller's line number.\\n if did_pass:\\n msg = \\\"Test at line {0} ok.\\\".format(linenum)\\n else:\\n msg = (\\\"Test at line {0} FAILED.\\\".format(linenum))\\n print(msg)\",\n \"def test(did_pass):\\n linenum = sys._getframe(1).f_lineno # Get the caller's line number.\\n if did_pass:\\n msg = \\\"Test at line {0} ok.\\\".format(linenum)\\n else:\\n msg = (\\\"Test at line {0} FAILED.\\\".format(linenum))\\n print(msg)\",\n \"def test(did_pass):\\n linenum = sys._getframe(1).f_lineno # Get the caller's line number.\\n if did_pass:\\n msg = \\\"Test at line {0} ok.\\\".format(linenum)\\n else:\\n msg = (\\\"Test at line {0} FAILED.\\\".format(linenum))\\n print(msg)\",\n \"def test(did_pass):\\n linenum = sys._getframe(1).f_lineno # Get the caller's line number.\\n if did_pass:\\n msg = \\\"Test at line {0} ok.\\\".format(linenum)\\n else:\\n msg = (\\\"Test at line {0} FAILED.\\\".format(linenum))\\n print(msg)\",\n \"def test(did_pass):\\n linenum = sys._getframe(1).f_lineno # Get the caller's line number.\\n if did_pass:\\n msg = \\\"Test at line {0} ok.\\\".format(linenum)\\n else:\\n msg = (\\\"Test at line {0} FAILED.\\\".format(linenum))\\n print(msg)\",\n \"def test(did_pass):\\r\\n linenum = sys._getframe(1).f_lineno # Get the caller's line number.\\r\\n if did_pass:\\r\\n msg = \\\"Test at line {0} ok.\\\".format(linenum)\\r\\n else:\\r\\n msg = (\\\"Test at line {0} FAILED.\\\".format(linenum))\\r\\n print(msg)\",\n \"def test(did_pass):\\r\\n linenum = sys._getframe(1).f_lineno # Get the caller's line number.\\r\\n if did_pass:\\r\\n msg = \\\"Test at line {0} ok.\\\".format(linenum)\\r\\n else:\\r\\n msg = (\\\"Test at line {0} FAILED.\\\".format(linenum))\\r\\n print(msg)\",\n \"def test(did_pass):\\r\\n linenum = sys._getframe(1).f_lineno # Get the caller's line number.\\r\\n if did_pass:\\r\\n msg = \\\"Test at line {0} ok.\\\".format(linenum)\\r\\n else:\\r\\n msg = (\\\"Test at line {0} FAILED.\\\".format(linenum))\\r\\n print(msg)\",\n \"def test(did_pass):\\r\\n linenum = sys._getframe(1).f_lineno # Get the caller's line number.\\r\\n if did_pass:\\r\\n msg = \\\"Test at line {0} ok.\\\".format(linenum)\\r\\n else:\\r\\n msg = (\\\"Test at line {0} FAILED.\\\".format(linenum))\\r\\n print(msg)\",\n \"def test(did_pass):\\r\\n linenum = sys._getframe(1).f_lineno # Get the caller's line number.\\r\\n if did_pass:\\r\\n msg = \\\"Test at line {0} ok.\\\".format(linenum)\\r\\n else:\\r\\n msg = (\\\"Test at line {0} FAILED.\\\".format(linenum))\\r\\n print(msg)\",\n \"def _test(): # pragma: no cover\\r\\n print('Starting doctest')\\r\\n doctest.testmod()\\r\\n print('Completed doctest')\",\n \"def test_noop_function_call(self) -> None:\\n before = after = \\\"\\\"\\\"\\n for line in xreadlines(r):\\n print(line)\\n \\\"\\\"\\\"\\n self.assertCodemod(before, after)\",\n \"def test(did_pass):\\n linenum = sys._getframe(1).f_lineno # Get the caller's line number.\\n if did_pass:\\n msg = \\\"Test at line {0} ok.\\\".format(linenum)\\n else:\\n msg = (\\\"Test at line {0} FAILED.\\\".format(linenum))\\n print(msg)\",\n \"def test_comparison_doctest(doctest):\",\n \"def def_textface_doctests():\",\n \"def _test():\\n import doctest\\n return doctest.testmod(verbose=True)\",\n \"def _test():\\n import doctest\\n return doctest.testmod(verbose=True)\",\n \"def test_basic():\\n line = \\\"test\\\"\\n assert wrap_line(line) == \\\"test\\\"\",\n \"def _test():\\n import doctest\\n doctest.testmod()\",\n \"def _test():\\n import doctest\\n doctest.testmod()\",\n \"def test_xfailed_but_passed():\\n pass\",\n \"def test_doctests_run(self):\\n results = doctest.testmod(lab, optionflags=TESTDOC_FLAGS, report=False)\\n self.assertEqual(results[0], 0)\",\n \"def test_compiler_arguments_fist_line(patch, compiler, lines, tree):\\n patch.init(StorySyntaxError)\\n lines.last.return_value = None\\n with raises(StorySyntaxError):\\n compiler.arguments(tree, '0')\\n error = 'arguments_noservice'\\n StorySyntaxError.__init__.assert_called_with(error, tree=tree)\",\n \"def test_zero(doctest):\",\n \"def main():\\n import argparse\\n from xdoctest import utils\\n description = utils.codeblock(\\n '''\\n discover and run doctests within a python package\\n ''')\\n\\n parser = argparse.ArgumentParser(prog='python -m xdoctest', description=description)\\n parser.add_argument('modname', help='what files to run')\\n parser.add_argument('command', help='a doctest name or a command (list|all)', default='list')\\n parser.add_argument(*('--style',), type=str, help='choose your style',\\n choices=['auto', 'google', 'freeform'], default='auto')\\n parser.add_argument(*('--options',), type=str,\\n help='specify the default directive state',\\n default=None)\\n parser.add_argument(*('--offset',), dest='offset_linenos', action='store_true',\\n help=('Doctest outputs will display line numbers '\\n 'wrt to the source file.'))\\n\\n args, unknown = parser.parse_known_args()\\n ns = args.__dict__.copy()\\n\\n # ... postprocess args\\n modname = ns['modname']\\n command = ns['command']\\n style = ns['style']\\n offset_linenos = ns['offset_linenos']\\n\\n if ns['options'] is None:\\n from os.path import exists\\n ns['options'] = ''\\n if exists('pytest.ini'):\\n from six.moves import configparser\\n parser = configparser.ConfigParser()\\n parser.read('pytest.ini')\\n try:\\n ns['options'] = parser.get('pytest', 'xdoctest_options')\\n except configparser.NoOptionError:\\n pass\\n\\n from xdoctest.directive import parse_directive_optstr\\n default_runtime_state = {}\\n for optpart in ns['options'].split(','):\\n if optpart:\\n directive = parse_directive_optstr(optpart)\\n if directive is not None:\\n default_runtime_state[directive.name] = directive.positive\\n\\n # Specify a default doctest_example.Config state\\n config = {\\n 'default_runtime_state': default_runtime_state,\\n 'offset_linenos': offset_linenos,\\n }\\n\\n import xdoctest\\n xdoctest.doctest_module(modname, argv=[command], style=style,\\n config=config)\",\n \"def _expected_lines_and_line_numbers(path, check_prefix):\\n with open(path) as f:\\n for index, line in enumerate(f):\\n if 'RUN:' in line:\\n # Ignore lit directives, which may include a call to\\n # xctest_checker that specifies a check prefix.\\n continue\\n\\n # Note that line numbers are not zero-indexed; we must add one to\\n # the loop index.\\n line_number = index + 1\\n\\n components = line.split(check_prefix)\\n if len(components) == 2:\\n yield (replace_offsets(components[1].strip(), line_number),\\n line_number)\\n elif len(components) > 2:\\n # Include a newline, then the file name and line number in the\\n # exception in order to have it appear as an inline failure in\\n # Xcode.\\n raise XCTestCheckerError(\\n path, line_number,\\n 'Usage violation: prefix \\\"{}\\\" appears twice in the same '\\n 'line.'.format(check_prefix))\",\n \"def test_remainder(doctest):\",\n \"def test():\\n import doctest\\n doctest.testmod(verbose=0)\\n test_int()\\n test_tuple()\",\n \"def test_compiler_arguments_not_execute(patch, compiler, lines, tree):\\n patch.init(StorySyntaxError)\\n patch.object(Objects, 'arguments')\\n lines.last.return_value = '1'\\n lines.lines = {'1': {'method': 'whatever'}}\\n with raises(StorySyntaxError):\\n compiler.arguments(tree, '0')\\n error = 'arguments_noservice'\\n StorySyntaxError.__init__.assert_called_with(error, tree=tree)\",\n \"def test_noop_wrong_name(self) -> None:\\n before = after = \\\"\\\"\\\"\\n for line in r.xreadlines():\\n print(line)\\n \\\"\\\"\\\"\\n self.assertCodemod(before, after)\",\n \"def scope_doctest():\\n pass\",\n \"def test_compiler_indented_chain_first_line(patch, compiler, lines, tree):\\n patch.init(StorySyntaxError)\\n lines.last.return_value = None\\n with raises(StorySyntaxError):\\n compiler.indented_chain(tree, '0')\\n error = 'arguments_nomutation'\\n StorySyntaxError.__init__.assert_called_with(error, tree=tree)\",\n \"def test1(self):\\n\\n log.info('This is a test')\\n self.assertTrue((random.randint(0,9) % 2) == 0)#! /usr/bin/env python\",\n \"def test_doc_code_cells(fname, globalns=globals()):\\n text = Path(fname).read_text()\\n code_cells = re.findall(r\\\"```{code-cell}[^\\\\n]+\\\\n(.*?)`{3}\\\", text, re.S)\\n for cell in code_cells:\\n header = re.search(r\\\"-{3}(.+?)-{3}\\\", cell, re.S)\\n if header:\\n cell = cell.replace(header.group(), \\\"\\\")\\n if \\\"warns\\\" in header.group():\\n with pytest.warns(None):\\n exec(cell, globalns)\\n continue\\n if \\\"raises-exception\\\" in header.group():\\n with pytest.raises(Exception):\\n exec(cell, globalns)\\n continue\\n exec(cell, globalns)\",\n \"def test_expected_successes(modpath):\\n retcode, out = flake8(join(modpath, \\\"test_cases\\\"))\\n assert not retcode, out\",\n \"def test_0_check_xc_docstring(self):\\n self.banner(\\\"Checking the docstring on your extra credit.\\\") \\n filename = self.find_file('project9_xc.py')\\n self.check_docstring(filename)\",\n \"def test_start_of_line_2(self):\\n before_b = \\\"\\\"\\\"\\\\\\n first line\\n line 1\\n line a\\n line b\\n line c\\n last line\\n \\\"\\\"\\\"\\n after_b = \\\"\\\"\\\"\\\\\\n first line\\n line 1\\n line a\\n line b\\n line c\\n last line\\n \\\"\\\"\\\"\\n self.run_test(\\n before_b=before_b,\\n after_b=after_b,\\n before_sel=(\\\"3.1\\\", \\\"3.1\\\"),\\n after_sel=(\\\"3.4\\\", \\\"3.4\\\"),\\n command_name=\\\"start-of-line\\\",\\n )\",\n \"def test_line_count(self):\\n self.assertEqual(analyze_text(self.filename)[0], 11)\",\n \"def test_main():\\n # Setup\\n # Exercise\\n # Verify\",\n \"def test_next_line(self):\\n before_b = \\\"\\\"\\\"\\\\\\n a\\n\\n b\\n \\\"\\\"\\\"\\n after_b = \\\"\\\"\\\"\\\\\\n a\\n\\n b\\n \\\"\\\"\\\"\\n self.run_test(\\n before_b=before_b,\\n after_b=after_b,\\n before_sel=(\\\"1.1\\\", \\\"1.1\\\"),\\n after_sel=(\\\"2.0\\\", \\\"2.0\\\"),\\n command_name=\\\"next-line\\\",\\n )\",\n \"def test_T01():\",\n \"def test_4():\",\n \"def test_line_count(self):\\n\\t\\tself.assertEqual(analyse_text(self.filename)[0], 4)\",\n \"def test_3():\",\n \"def test_5():\",\n \"def test_for_mistake(func, *args, **kw):\\n\\n global global_fail_counter\\n global global_success_counter\\n\\n # print test number\\n test_num = global_fail_counter + global_success_counter\\n print('Test # %d: ' % test_num, end='')\\n #print('Test # %d: ' % test_num)\\n\\n # Run function\\n obj = func(*args, **kw)\\n # Increment appropriate counter\\n if obj.mistake:\\n global_fail_counter += 1\\n else:\\n global_success_counter += 1\",\n \"def test_start_of_line(self):\\n before_b = \\\"\\\"\\\"\\\\\\n first line\\n line 1\\n line a\\n line b\\n line c\\n last line\\n \\\"\\\"\\\"\\n after_b = \\\"\\\"\\\"\\\\\\n first line\\n line 1\\n line a\\n line b\\n line c\\n last line\\n \\\"\\\"\\\"\\n self.run_test(\\n before_b=before_b,\\n after_b=after_b,\\n before_sel=(\\\"3.10\\\", \\\"3.10\\\"),\\n after_sel=(\\\"3.4\\\", \\\"3.4\\\"),\\n command_name=\\\"start-of-line\\\",\\n )\",\n \"def theTests(path_to_code_to_check=\\\"../me\\\"):\\n print(\\\"\\\\nWelcome to the exam!\\\")\\n print(\\\"May the odds be ever in your favour.\\\\nEspecially today!\\\")\\n\\n if ex_runs(path_to_code_to_check, exerciseNumber=1, weekNumber=SET_NUMBER):\\n exam = loadExerciseFile(\\n path_to_code_to_check, weekNumber=SET_NUMBER, exerciseNumber=1\\n )\\n\\n # testResults.append(test(test_flake8(ex1path), \\\"pass the linter\\\"))\\n exam_test(\\n True,\\n [],\\n exam.string_please,\\n finishing_function=lambda x: type(x) is str,\\n extra_message=\\\"Don't over think this! just return a string!\\\",\\n )\\n exam_test(\\n True,\\n [],\\n exam.list_please,\\n finishing_function=lambda x: type(x) is list,\\n extra_message=\\\"Don't over think this! just return a list!\\\",\\n )\\n exam_test(\\n True,\\n [],\\n exam.dictionary_please,\\n finishing_function=lambda x: type(x) is dict,\\n extra_message=\\\"Don't over think this! just return a dictionary!\\\",\\n )\\n exam_test(True, [5], exam.is_it_5)\\n exam_test(False, [4], exam.is_it_5)\\n exam_test(False, [\\\"cats\\\"], exam.is_it_5)\\n exam_test(0, [5], exam.take_five)\\n exam_test(5, [10], exam.take_five)\\n exam_test(-5, [0], exam.take_five)\\n\\n exam_test(\\\"Hello the Queen\\\", [\\\"the Queen\\\"], exam.greet)\\n exam_test(\\\"Hello Pr♂nc♀♂\\\", [\\\"Pr♂nc♀♂\\\"], exam.greet)\\n\\n exam_test(4, [[3, 3, 3, 3, 1]], exam.three_counter)\\n exam_test(0, [[0, 1, 2, 5, -9]], exam.three_counter)\\n\\n exam_test(2, [7], exam.n_counter)\\n exam_test(5, [0, [0, 0, 0, 0, 0, [0]]], exam.n_counter)\\n\\n # fmt: off\\n fizza = [\\n 1, 2, \\\"Fizz\\\", 4, \\\"Buzz\\\", \\\"Fizz\\\", 7, 8, \\\"Fizz\\\", \\\"Buzz\\\", 11, \\\"Fizz\\\", \\n 13, 14, \\\"FizzBuzz\\\", 16, 17, \\\"Fizz\\\", 19, \\\"Buzz\\\", \\\"Fizz\\\", 22, 23, \\n \\\"Fizz\\\", \\\"Buzz\\\", 26, \\\"Fizz\\\", 28, 29, \\\"FizzBuzz\\\", 31, 32, \\\"Fizz\\\", 34, \\n \\\"Buzz\\\", \\\"Fizz\\\", 37, 38, \\\"Fizz\\\", \\\"Buzz\\\", 41, \\\"Fizz\\\", 43, 44, \\n \\\"FizzBuzz\\\", 46, 47, \\\"Fizz\\\", 49, \\\"Buzz\\\", \\\"Fizz\\\", 52, 53, \\\"Fizz\\\", \\n \\\"Buzz\\\", 56, \\\"Fizz\\\", 58, 59, \\\"FizzBuzz\\\", 61, 62, \\\"Fizz\\\", 64, \\\"Buzz\\\", \\n \\\"Fizz\\\", 67, 68, \\\"Fizz\\\", \\\"Buzz\\\", 71, \\\"Fizz\\\", 73, 74, \\\"FizzBuzz\\\", 76, \\n 77, \\\"Fizz\\\", 79, \\\"Buzz\\\", \\\"Fizz\\\", 82, 83, \\\"Fizz\\\", \\\"Buzz\\\", 86, \\\"Fizz\\\", \\n 88, 89, \\\"FizzBuzz\\\", 91, 92, \\\"Fizz\\\", 94, \\\"Buzz\\\", \\\"Fizz\\\", 97, 98, \\n \\\"Fizz\\\", \\\"Buzz\\\", ]\\n # fmt: on\\n exam_test(fizza, [], exam.fizz_buzz)\\n\\n exam_test(\\n \\\"|a| |s|e|r|i|a|l| |k|i|l|l|e|r|\\\", [\\\"a serial killer\\\"], exam.put_behind_bars\\n )\\n exam_test(\\\"|a| |b|a|r|t|e|n|d|e|r|\\\", [\\\"a bartender\\\"], exam.put_behind_bars)\\n\\n exam_test([\\\"red fox\\\"], [\\\"x\\\"], exam.pet_filter)\\n exam_test([], [\\\"q\\\"], exam.pet_filter)\\n exam_test(\\n [\\\"pig\\\", \\\"sheep\\\", \\\"guinea pig\\\", \\\"pigeon\\\", \\\"alpaca\\\", \\\"guppy\\\"],\\n [\\\"p\\\"],\\n exam.pet_filter,\\n )\\n\\n exam_test(\\\"e\\\", [], exam.best_letter_for_pets)\\n\\n word_lengths = [[3, 3, 3], [4, 4, 4], [5, 5, 5], [6, 6, 6], [7, 7, 7]]\\n exam_test(\\n word_lengths,\\n [],\\n exam.make_filler_text_dictionary,\\n lambda x: [[len(w) for w in x[k]] for k in x.keys()],\\n )\\n\\n exam_test(\\n True,\\n [50],\\n exam.random_filler_text,\\n lambda x: len(x.split(\\\" \\\")) == 50 and len(x) > 3 * 50,\\n )\\n\\n exam_test(\\n True,\\n [1000],\\n exam.random_filler_text,\\n lambda x: len(x.split(\\\" \\\")) == 1000 and len(x) > 3 * 1000,\\n )\\n\\n clean_out_old_env()\\n\\n exam_test(\\n True,\\n [100],\\n exam.fast_filler,\\n lambda x: len(x.split(\\\" \\\")) == 100 and len(x) > 3 * 100,\\n # chdir=True, # NFI what this does :(\\n )\\n\\n # exam_test(True, [\\\"./week8/dict_racey.json\\\"], os.path.exists)\\n\\n exam_test(\\n True,\\n [10],\\n exam.fast_filler,\\n lambda x: x[0] in string.ascii_uppercase and x[1] in string.ascii_lowercase,\\n \\\"Test if fast_filler is capitalised\\\",\\n )\\n exam_test(\\n True,\\n [10],\\n exam.fast_filler,\\n lambda x: x[-1] == \\\".\\\",\\n \\\"Test if fast_filler finishes with a .\\\",\\n )\\n\\n print(\\n \\\"The point of saving the dictionary is that it's fast!\\\",\\n \\\"The pattern of saving a value locally so that you don't\\\",\\n \\\"need to go and get it is called caching.\\\",\\n \\\"This test runs fast_filler 10 times, and if it manages it in less\\\",\\n \\\"than a second, then you're good to go!\\\",\\n sep=\\\"\\\\n\\\",\\n )\\n try:\\n TIMEOUT_IN_SECONDS = 1\\n func_timeout(\\n TIMEOUT_IN_SECONDS,\\n lambda: [exam.fast_filler(1000) for _ in range(10)],\\n args=[],\\n )\\n testResults.append(test(True, \\\"subsequent fast_filler\\\"))\\n except FunctionTimedOut as t:\\n m = (\\n \\\"Timed out trying to run fast filler 10 times in 1 second, \\\"\\n \\\"subsequent fast_filler probably wasn't fast enough\\\"\\n )\\n print(m, str(t))\\n testResults.append(test(False, m + str(t)))\\n except Exception as e:\\n testResults.append(test(False, \\\"subsequent fast_filler failed: \\\" + str(e)))\\n\\n message = (\\n \\\"Cowabunga! You've got all the tests passing!\\\\n\\\"\\n \\\"Well done, that's all the exercises for this term out of the way!\\\"\\n )\\n print(testResults)\\n return finish_up(testResults, message, nyan_cat())\",\n \"def test_previous_line(self):\\n before_b = \\\"\\\"\\\"\\\\\\n a\\n\\n b\\n \\\"\\\"\\\"\\n after_b = \\\"\\\"\\\"\\\\\\n a\\n\\n b\\n \\\"\\\"\\\"\\n self.run_test(\\n before_b=before_b,\\n after_b=after_b,\\n before_sel=(\\\"3.0\\\", \\\"3.0\\\"),\\n after_sel=(\\\"2.0\\\", \\\"2.0\\\"),\\n command_name=\\\"previous-line\\\",\\n )\",\n \"def test_passed():\\n pass\",\n \"def test():\\n import doctest\\n from . import locate\\n return doctest.testmod(locate)\",\n \"def test_sqrt(doctest):\",\n \"def test_03_pass(self):\\n if x==1:\\n pass\",\n \"def test_code010(self, *test_code_args, **test_code_kwargs):\\n print(\\\"This is test_code010 from TestDefinition #\\\", self.ID, \\\", test case #\\\", self.test_case_ID, sep='')\",\n \"def test_twentythree_no_args():\\n sys.argv = ['test']\\n with pytest.raises(SystemExit):\\n TwentyThree()\",\n \"def test_shift(doctest):\",\n \"def test_move_lines_up_into_docstring(self):\\n before_b = '''\\\\\\n #@@language python\\n def test():\\n \\\"\\\"\\\" a\\n b\\n c\\n \\\"\\\"\\\"\\n print 1\\n \\n print 2\\n '''\\n after_b = '''\\\\\\n #@@language python\\n def test():\\n \\\"\\\"\\\" a\\n b\\n c\\n print 1\\n \\\"\\\"\\\"\\n \\n print 2\\n '''\\n self.run_test(\\n before_b=before_b,\\n after_b=after_b,\\n before_sel=(\\\"7.1\\\", \\\"7.1\\\"),\\n after_sel=(\\\"6.1\\\", \\\"6.1\\\"),\\n command_name=\\\"move-lines-up\\\",\\n )\",\n \"def test_abs(doctest):\",\n \"def test_examples():\\n argv = [\\\"py.test\\\", \\\"-examples\\\"]\\n assert get_sargs(argv) is None\",\n \"def test_compiler_return_statement(patch, compiler, lines, tree):\\n tree.expression = None\\n compiler.return_statement(tree, '1')\\n line = tree.line()\\n kwargs = {'args': None, 'parent': '1'}\\n lines.append.assert_called_with('return', line, **kwargs)\",\n \"def test_line_count(self):\\n self.assertEqual(analyze_text(self.filename)[0], 4)\",\n \"def test_line_count(self):\\n self.assertEqual(analyze_text(self.filename)[0], 4)\",\n \"def __integration_doctest():\\n pass\",\n \"def test_02_pass(self):\\n if x==1:\\n pass\",\n \"def lloc():\\n load_env_vars('dev')\\n from tools.static_code_analysis import LogicalLinesOfCode\\n radon_raw = LogicalLinesOfCode()\\n score = radon_raw.run_test()\\n radon_raw.create_badge(score)\",\n \"def test_compiler_indented_chain_not_mutation(patch, compiler, lines, tree):\\n patch.init(StorySyntaxError)\\n patch.object(Compiler, 'chained_mutations')\\n lines.last.return_value = '1'\\n lines.lines = {'1': {'method': 'whatever'}}\\n with raises(StorySyntaxError):\\n compiler.indented_chain(tree, '0')\\n error = 'arguments_nomutation'\\n StorySyntaxError.__init__.assert_called_with(error, tree=tree)\",\n \"def test_py_compile_basic(self):\\n self._test_py_compile('basic')\",\n \"def test_main_succeed_en(runner: CliRunner) -> None:\\n result = runner.invoke(__main__.main, \\\"-c tests/clippings-en.txt\\\")\\n assert result.exit_code == 0\",\n \"def main(argv=None):\\n if argv is None:\\n argv = sys.argv\\n\\n import doctest\\n verbose = (len(argv) >= 2 and argv[1] == \\\"--Test\\\")\\n if verbose:\\n print(\\\"Running doctests (verbose mode)\\\")\\n else:\\n print(\\\"Running doctests\\\")\\n doctest.testmod(verbose=verbose)\\n return 0\",\n \"def test(self):\\n # -- Test --\\n\\n # (1)\\n\\n # (2)\\n\\n # (3)\\n\\n # (4)\\n # -- Test --\",\n \"def test_kill_line_start_line(self):\\n before_b = \\\"\\\"\\\"\\\\\\n line 1\\n line 2\\n line 3\\n line 4\\n \\\"\\\"\\\"\\n after_b = \\\"\\\"\\\"\\\\\\n line 1\\n line 2\\n\\n line 4\\n \\\"\\\"\\\"\\n self.run_test(\\n before_b=before_b,\\n after_b=after_b,\\n before_sel=(\\\"3.0\\\", \\\"3.0\\\"),\\n after_sel=(\\\"3.0\\\", \\\"3.0\\\"),\\n command_name=\\\"kill-line\\\",\\n )\",\n \"def test_get_lines(self):\\r\\n \\r\\n self.assertEqual(get_lines(\\\"test_20191206_1140000.txt\\\", os.getcwd()),\\r\\n 299,\\r\\n \\\"Should be equal number of lines.\\\")\",\n \"def test_pylintrc_file(testdir):\\n rcfile = testdir.makefile('rc', \\\"\\\"\\\"\\n[FORMAT]\\n\\nmax-line-length=3\\n\\\"\\\"\\\")\\n testdir.makepyfile(\\\"\\\"\\\"import sys\\\"\\\"\\\")\\n result = testdir.runpytest(\\n '--pylint', '--pylint-rcfile={0}'.format(rcfile.strpath)\\n )\\n assert 'Line too long (10/3)' in result.stdout.str()\",\n \"def test(self):\\n self.skipped_test('doctest module has no DocTestSuite class')\",\n \"def test_beginning_of_line(self):\\n before_b = \\\"\\\"\\\"\\\\\\n first line\\n line 1\\n line a\\n line b\\n line c\\n last line\\n \\\"\\\"\\\"\\n after_b = \\\"\\\"\\\"\\\\\\n first line\\n line 1\\n line a\\n line b\\n line c\\n last line\\n \\\"\\\"\\\"\\n self.run_test(\\n before_b=before_b,\\n after_b=after_b,\\n before_sel=(\\\"3.10\\\", \\\"3.10\\\"),\\n after_sel=(\\\"3.0\\\", \\\"3.0\\\"),\\n command_name=\\\"beginning-of-line\\\",\\n )\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.704092","0.70044655","0.6639948","0.6638953","0.65686834","0.65359443","0.6508109","0.6495619","0.643054","0.6405408","0.63980496","0.63929","0.6392692","0.63884795","0.63859123","0.63859123","0.6350053","0.6331821","0.6329094","0.6329094","0.63262904","0.63060284","0.6253524","0.6253524","0.6253524","0.6253524","0.6253524","0.6253524","0.6253524","0.62514967","0.6249631","0.6249631","0.6249631","0.6249631","0.6249631","0.6249001","0.62446743","0.6241719","0.62371904","0.6230489","0.6214128","0.6214128","0.61893773","0.6179329","0.6179329","0.61680174","0.61649674","0.6147933","0.6141428","0.6136835","0.61358947","0.6129527","0.6090829","0.6072843","0.6069914","0.60606474","0.60161585","0.6010203","0.59641236","0.5962467","0.5948293","0.5937274","0.5915456","0.589643","0.58834976","0.5881341","0.58642316","0.58549494","0.58529156","0.58365774","0.5833128","0.58308595","0.5805898","0.57914305","0.57837254","0.578372","0.57814515","0.576918","0.5768244","0.5765805","0.57635105","0.5763297","0.57630616","0.57611245","0.5760603","0.5729843","0.5729843","0.57277656","0.5723648","0.57130367","0.57070506","0.56994814","0.5695621","0.56912017","0.56866896","0.56841797","0.5675818","0.56715494","0.56694996","0.56670964"],"string":"[\n \"0.704092\",\n \"0.70044655\",\n \"0.6639948\",\n \"0.6638953\",\n \"0.65686834\",\n \"0.65359443\",\n \"0.6508109\",\n \"0.6495619\",\n \"0.643054\",\n \"0.6405408\",\n \"0.63980496\",\n \"0.63929\",\n \"0.6392692\",\n \"0.63884795\",\n \"0.63859123\",\n \"0.63859123\",\n \"0.6350053\",\n \"0.6331821\",\n \"0.6329094\",\n \"0.6329094\",\n \"0.63262904\",\n \"0.63060284\",\n \"0.6253524\",\n \"0.6253524\",\n \"0.6253524\",\n \"0.6253524\",\n \"0.6253524\",\n \"0.6253524\",\n \"0.6253524\",\n \"0.62514967\",\n \"0.6249631\",\n \"0.6249631\",\n \"0.6249631\",\n \"0.6249631\",\n \"0.6249631\",\n \"0.6249001\",\n \"0.62446743\",\n \"0.6241719\",\n \"0.62371904\",\n \"0.6230489\",\n \"0.6214128\",\n \"0.6214128\",\n \"0.61893773\",\n \"0.6179329\",\n \"0.6179329\",\n \"0.61680174\",\n \"0.61649674\",\n \"0.6147933\",\n \"0.6141428\",\n \"0.6136835\",\n \"0.61358947\",\n \"0.6129527\",\n \"0.6090829\",\n \"0.6072843\",\n \"0.6069914\",\n \"0.60606474\",\n \"0.60161585\",\n \"0.6010203\",\n \"0.59641236\",\n \"0.5962467\",\n \"0.5948293\",\n \"0.5937274\",\n \"0.5915456\",\n \"0.589643\",\n \"0.58834976\",\n \"0.5881341\",\n \"0.58642316\",\n \"0.58549494\",\n \"0.58529156\",\n \"0.58365774\",\n \"0.5833128\",\n \"0.58308595\",\n \"0.5805898\",\n \"0.57914305\",\n \"0.57837254\",\n \"0.578372\",\n \"0.57814515\",\n \"0.576918\",\n \"0.5768244\",\n \"0.5765805\",\n \"0.57635105\",\n \"0.5763297\",\n \"0.57630616\",\n \"0.57611245\",\n \"0.5760603\",\n \"0.5729843\",\n \"0.5729843\",\n \"0.57277656\",\n \"0.5723648\",\n \"0.57130367\",\n \"0.57070506\",\n \"0.56994814\",\n \"0.5695621\",\n \"0.56912017\",\n \"0.56866896\",\n \"0.56841797\",\n \"0.5675818\",\n \"0.56715494\",\n \"0.56694996\",\n \"0.56670964\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7327849"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":2878,"cells":{"query":{"kind":"string","value":"Add to the list of describing adjectives."},"document":{"kind":"string","value":"def add_adjectives(self, adjective):\n self.adjectives += [adjective]"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def add_adjectives(self, *sAdjs):\n self.adjectives += list(sAdjs)","def add(self):\n pass","def add_many_descriptors(self, descriptors):","def add(self, PlugLead):\n\n self.check_conflicts(PlugLead)\n self.plugleads.append(PlugLead)","def add_disease(self, disease):\n self.diseases.append(disease)","def addObjective(self, *args):\n return _libsbml.ListOfObjectives_addObjective(self, *args)","def add_experience(self, state, action, reward, next_state, done):\n experience = (state, action, reward, next_state, done)\n for i, k in enumerate(self.data_keys):\n getattr(self, k).append(experience[i])\n self.size += 1","def add_diameter(self, dia):\n self.diameters.append(dia)","def addEntry(self, listDictions):\n ## load xml\n improvDoc = loadIMProvFile(self.argsFile)\n entrname= 'Job'\n for dictions in listDictions:\n report = IMProvNode(entrname , None, **dictions)\n improvDoc.addNode(report)\n outfile = file( self.argsFile, 'w').write(str(improvDoc))\n return","def Add(self, *args):\n return _BRepAlgo.BRepAlgo_AsDes_Add(self, *args)","def add(self, *args):\n pass","def add(self, *args):\n pass","def AddConcept(self, concept):\n self.concepts.append(concept)","def add_descriptor(self, descriptor):","def do_add(self):\n\n\t\t#debug\n\t\t'''\n\t\tself.debug(('self.',self,['AddingVariablesList']))\n\t\t'''\n\n\t\t#Apply\t\n\t\tself.map('append',map(\n\t\t\t\t\t\t\t\t\tlambda __AddingVariable:\n\t\t\t\t\t\t\t\t\t{'LiargVariablesList':[__AddingVariable]},\n\t\t\t\t\t\t\t\t\tself.AddingVariablesList\n\t\t\t\t\t\t\t\t)\n\t\t\t\t)","def addDemographics(self):\n p = self.p\n demographics_data = {\n 'dob': p.dob,\n 'gender': p.gender,\n 'email': p.email,\n 'fname': p.fname,\n 'lname': p.lname,\n 'hphone': p.home,\n 'cphone': p.cell,\n 'country': p.country,\n 'city': p.city,\n 'pcode': p.pcode,\n 'region': p.region,\n 'street': p.street,\n }\n self.demographics_doc = DEMOGRAPHICS.sub(demographics_data).done()","def add(self, name, content):\n raise NotImplementedError","def add(self, el):\n raise Exception('TODO IMPLEMENT ME !')","def add_tag(self, tag):\n\n # directional relation: tag is the blank of everything in the list\n self.relations[tag] = {\n \"overlord\": [],\n \"hegemon\": [], # for tributary\n \"tributary\": [],\n \"vassal\": [],\n \"guaranteeing\": [],\n \"guarantor\": [],\n \"alliance\": [],\n \"senior\": [],\n \"junior\": [],\n \"marriage\": []\n }","def add_condiments(self):\n print(\"Adding Lemon\")","def add_food(self, _food):\n self.food.append(_food)","def add_objective(self, objective):\n self.objectives.append(objective)","def __add__(self, notes):\n self.add_notes(notes)\n return self","def add_emb(self, emb):\n self.embs.append(emb)","def add(self, *items):","def append(self, dpr):\r\n self.childlist.append(dpr)","def add(self, experience):\n self.buffer.append(experience)","def add_descriptions_to_confusion_matrix(self):\n topic_names = []\n for topic_num in self.topic_numbers:\n topic_names.append(self.topic_names[topic_num])\n for index, row in enumerate(self.confusion_matrix):\n row.insert(0,topic_names[index])\n topic_names_for_matrix = topic_names.copy()\n topic_names_for_matrix.insert(0,\"\")\n self.confusion_matrix.insert(0,topic_names_for_matrix)\n self.confusion_matrix_true.insert(0,topic_names_for_matrix)","def to_add(self):\n pass","def add(self, item):","def add_description(self, description):\n self.quest_node['description'] = description\n self.description = description\n graph.push(self.quest_node)","def add_animals(self, *args):\n if self.validate_requirements(args):\n [self.animals.append(arg) for arg in args]\n else:\n print(\"foobar\")","def EventContentMissionExcelAddDescription(builder, Description):\n return AddDescription(builder, Description)","def add_description(self, desc):\n self.description = desc","def add_advices_to_user(self, id_user):\n # get data\n advice_type_id = RobotAdviceType.objects.values_list(\"id\").get(type=\"default\")\n advices_id = RobotAdvices.objects.values_list(\"id\").filter(robot_advice_type=advice_type_id)\n\n # add new advices to user\n for advice_id in advices_id:\n advice = RobotAdvices.objects.get(id=advice_id[0])\n user = self.user.objects.get(id=id_user)\n AdvicesToUser.objects.create(user=user, advice=advice)","def add(lst):\n # TODO","def _add_descriptors(related):\n\n for r in related:\n r[\"descriptors\"] = []\n for edge in G.edges(data=True):\n sibling_idx = _get_connected(edge, r[\"tokenIndex\"])\n if sibling_idx and (A.lookup[int(sibling_idx)][\"pos\"] == \"JJ\" or edge[2][\"dep\"] in [\"amod\", \"compound\"]):\n r[\"descriptors\"].append(\n {\n \"tokenIndex\": sibling_idx,\n \"rawName\": A.lookup[int(sibling_idx)][\"word\"]\n }\n )\n\n if sibling_idx and \"NN\" in A.lookup[int(sibling_idx)][\"pos\"] and \"amod\" in edge[2][\"dep\"]:\n additional_related = _get_cousin(sibling_idx, [\"nmod\"])\n for add in set(additional_related):\n related = _add_related(add, \"nmod\", related, A.index_lookup[add],\n connector=G.nodes[sibling_idx]['word'])\n return related","def adjoint(self, add, model, data):\n self.checkDomainRange(model, data)\n self.ops[0].adjoint(add, model, data.vecs[0])\n for idx in range(1, self.n):\n self.ops[idx].adjoint(True, model, data.vecs[idx])","def add(self, element):\n pass","def add(self, Links__=None, AllLinks=None, Enabled=None, Name=None, Priority=None):\n # type: (List[str], bool, bool, str, int) -> Profile\n return self._create(self._map_locals(self._SDM_ATT_MAP, locals()))","def add(self):\n return self._add_xpath(self._map_locals(self._SDM_ATT_MAP, locals()))","def add(self, optim=None, more_data=[]):\r\n raise NotImplementedError()","def add(self, optim=None, more_data=[]):\r\n raise NotImplementedError()","def add(self, product):\n pass","def add(self, *args):\n return _libsbml.ListWrapperCVTerm_add(self, *args)","def add_food_to_bag(self):\n self.food_eaten.set(sum([species.food.get() for species in self.ecosystem]))","def create_alternatives(self, alternatives, question):\n\n logging.info(f\"Alternativas: {alternatives}\")\n\n for alternative in alternatives:\n result = Alternative.objects.create(\n **alternative,\n question=question\n )\n question.alternatives.add(result)","def add_exercise( self, exercise ):\n self.exercises.append( exercise )","def addPersonality(self):\n\t\tself.personality += 1\n\t\tif self.personality > 10:\n\t\t\tself.personality = 10","def __add__(self, element):\r\n self.elements += element","def addProblems(self):\n if self.pid in Problem.problems: \n for prob in Problem.problems[self.pid]:\n subs = {'end': {'end': '2010-09-13'}}\n self._set_default_attrs(prob, subs)\n prob_string = PROBLEM.sub({\n 'onset':prob.start,\n 'resolution':prob.end,\n 'snomed':prob.snomed, \n 'name':prob.name\n }).done()\n self.data.append(SDMX.sub({'models':prob_string}, escape=False).done())","def __add__(self, other: Union['_Imagine', '_ImagineMany']) -> '_ImagineMany':\n return _ImagineMany(self, other)","def addQuestion(self):\n self.questions.append(Question(self))","def add_deputy(self, deputy):\n self.deputies.append(deputy)","def add_detachment(self):\n print(\"Which detachment would you like to add?\")\n for index, keys in enumerate(init.detachments_dict.keys()):\n print(str(index + 1) + '. ' + keys)\n # get input to decide which detachments to add\n user_input = input(\">> \")\n\n # allows users to add multiple detachments at once\n user_input = re.findall(r'[0-9]+|[a-zA-Z]+', user_input)\n for i in user_input:\n if i.isdigit():\n i = list(init.detachments_dict.keys())[int(i) - 1]\n print(\"Adding {} to army\".format(i))\n detach = Detachment(i)\n\n # populate compulsory slots\n for keys, values in detach.units_dict.items():\n if keys == \"Dedicated Transports\":\n continue\n while len(values) < detach.foc[keys][0]:\n print(\"***Adding compulsory units from \" + keys + \"***\")\n unit = self._create_user_unit(keys)\n self._add_unit(detach, unit)\n\n self.army.add_detachment(detach)\n return","def add_architectural_elements(conn, cur, objects):\n \n print 'Adding architectural elements...',\n \n sift_detector = cv2.FeatureDetector_create('SIFT')\n sift_descriptor = cv2.DescriptorExtractor_create('SIFT')\n \n feature_id = 1\n \n for i, object in enumerate(objects):\n \n cur.execute('INSERT INTO views VALUES (\"{_id}\", \"{task_id}\", \"{name}\")'.format(\n _id = i+1,\n task_id = object['task_id'],\n name = object['view_name'],\n )\n )\n \n img = cv2.imread(VIEWS_PATH + object['view_name'])\n \n keypoints = sift_detector.detect(img)\n features = sift_descriptor.compute(img, keypoints)\n \n for keypoint, feature in zip(features[0], features[1]):\n \n cur.execute('INSERT INTO features VALUES (\"{_id}\", \"{view_id}\", \"{pt_x}\", \"{pt_y}\", \"{desc}\")'.format(\n _id = feature_id,\n view_id = i+1,\n pt_x = keypoint.pt[0],\n pt_y = keypoint.pt[1],\n desc = str(list(feature))[1:-1].replace('.0', '').replace(' ', '')\n )\n )\n \n feature_id += 1\n \n print 'done.'","def add(self, *drawables):\n self.drawables.extend(drawables)","def add(self, specification):\n self.specifications.append(specification)","def add(self, obj):\n raise NotImplementedError","def add_many(self, pair_list):\n\n for pair in pair_list:\n plug = PlugLead(pair)\n self.check_conflicts(plug)\n self.plugleads.append(plug)","def add_dut(self):\n pass","def add_words(self, words):\r\n for word in words:\r\n self.add(word)","def appendFrom(self, *args):\n return _libsbml.ListOfObjectives_appendFrom(self, *args)","def add():\n pass","def add_item(self, command, description):\n self.append((command, description))","def add_advice_to_user_created(cls, user, list_advice_id):\n for id_advice in list_advice_id:\n advice = RobotAdvices.objects.get(id=id_advice)\n AdvicesToUser.objects.create(user=user, advice=advice)","def addModifiers(self):\n return _libsbml.Model_addModifiers(self)","def add_paragraph_option():\n extra_content_lst = []\n paragraph = input('Do you want to add another paragraph to your website? [yes]')\n while paragraph == 'yes' or paragraph == '':\n title = input('Title of your paragraph?')\n content = input('Content of your paragraph (single line)')\n images = image_option()\n extra_content_lst.append((ContentInfo(title, content, images, None)))\n paragraph = input('Do you want to add another paragraph to your website? [yes]')\n\n return extra_content_lst","def apilar(self,dato):\r\n\t\tself.elementos.append(dato)\r\n\t\tself.len += 1","def append_collectable(self, newnotes: List):\n self.notes.extend(newnotes)","def add(self, game_obj):\r\n self.game_objects_for_adding.append(game_obj)","def add_AdDescription(self, url, description):\n layout = BoxLayout(size_hint_y = 4)\n layout.add_widget(AsyncImage(source = url))\n layout.add_widget(Label(text = description))\n self.layout.add_widget(layout)","def add(element):","def addKeyWord(self, kWord):\n #kWord.printKeyWord()\n self.sentence.append(kWord)","def add(self, iterable):\n raise NotImplementedError()","def add(self, elem):","def add(self, elem):","def add_descriptors(self, mapping):\n for key, desc in mapping.iteritems():\n self.descriptors[int(key, 16)] = desc","def add_descriptors(self, mapping):\n for key, desc in mapping.iteritems():\n self.descriptors[int(key, 16)] = desc","def add(self, name, value) -> None:\n ...","def __add__(self, other: Union[_Imagine, '_ImagineMany']) -> '_ImagineMany':\n return _ImagineMany(self, other)","def add(self, die_):\n self._dice.append(die_)","def add(self, sentence):\n self._sentences.add(sentence)","def add_D(self, D):\n self.Ds.append(D)","def append(self, forms, lemmas, cpostags, postags, feats, heads, deprels):\n self.__init__(\n self._forms + list(forms),\n self._lemmas + list(lemmas),\n self._cpostags + list(cpostags),\n self._postags + list(postags),\n self._feats + list(feats),\n self._heads + list(heads),\n self._deprels + list(deprels)\n )","def add(self, cards):\n\n super().add(cards)\n self._update_value()","def add_discount(self, discount):\n self.discounts.append(discount)","def add_questions(self, questions):\n for question in questions:\n self.questions.append(question)","def add_content(self, more_content, no_docstring=False):\r\n # MatAttributeDocumenter.add_content(self, more_content,\r\n # no_docstring=True)\r\n MatAttributeDocumenter.add_content(self, more_content, no_docstring)","def add(self, item: Any) -> None:\n pass","def descriptions(self, descriptions):\n\n self._descriptions = descriptions","def __init__(self, description):\n self.contents = HashMap()\n for gdl in description:\n if not self.contents.containsKey(key):\n self.contents.put(key, ArrayList())\n self.contents.get(key).add(rule)","def register_adhocs(self):\n aboutform = self.plugin['xep_0004'].makeForm('form', \"About SleekBot\")\n aboutform.addField('about', 'fixed', value= self.__doc__)\n self.plugin['xep_0050'].addCommand('about', 'About Sleekbot', aboutform)\n pluginform = self.plugin['xep_0004'].makeForm('form', 'Plugins')\n plugins = pluginform.addField('plugin', 'list-single', 'Plugins')\n for key in self.cmd_plugins:\n plugins.addOption(key, key)\n plugins = pluginform.addField('option', 'list-single', 'Commands')\n plugins.addOption('about', 'About')\n #plugins.addOption('config', 'Configure')\n self.plugin['xep_0050'].addCommand('plugins', 'Plugins', pluginform, self.form_plugin_command, True)","def add(self, experience: []):\n if len(self.buffer) + len(experience) >= self.buffer_size:\n self.buffer[0:1] = []\n self.buffer.append(experience)","def _populate(self):\n self.addDemographics()\n self.addLabs()\n self.addProblems()\n self.addMeds()\n self.addAllergies()\n self.addImmunizations()\n self.addVitals()\n self.populated_p = True","def add(self, states, actions, rewards, next_states, dones):\n assert len(states) == self.num_agents, 'ERROR> group states size mismatch'\n assert len(actions) == self.num_agents, 'ERROR> group actions size mismatch'\n assert len(rewards) == self.num_agents, 'ERROR> group rewards size mismatch'\n assert len(next_states) == self.num_agents, 'ERROR> group next states size mismatch'\n assert len(dones) == self.num_agents, 'ERROR> group dones size mismatch'\n\n experience = (states, actions, rewards, next_states, dones)\n self.memory.append(experience)","def _add_meta(self, *args, **kwargs) -> None:\n raise NotImplementedError","def get_adjectives(self):\n random.shuffle(self.adjectives)\n return self.adjectives","def __add__(self,_VariablesList):\n\n\t\t#Call the add method\n\t\tself.add(_VariablesList)\n\n\t\t#Return \n\t\treturn self","def add(self, d):\n new = self.copy()\n new.update(d)\n return new"],"string":"[\n \"def add_adjectives(self, *sAdjs):\\n self.adjectives += list(sAdjs)\",\n \"def add(self):\\n pass\",\n \"def add_many_descriptors(self, descriptors):\",\n \"def add(self, PlugLead):\\n\\n self.check_conflicts(PlugLead)\\n self.plugleads.append(PlugLead)\",\n \"def add_disease(self, disease):\\n self.diseases.append(disease)\",\n \"def addObjective(self, *args):\\n return _libsbml.ListOfObjectives_addObjective(self, *args)\",\n \"def add_experience(self, state, action, reward, next_state, done):\\n experience = (state, action, reward, next_state, done)\\n for i, k in enumerate(self.data_keys):\\n getattr(self, k).append(experience[i])\\n self.size += 1\",\n \"def add_diameter(self, dia):\\n self.diameters.append(dia)\",\n \"def addEntry(self, listDictions):\\n ## load xml\\n improvDoc = loadIMProvFile(self.argsFile)\\n entrname= 'Job'\\n for dictions in listDictions:\\n report = IMProvNode(entrname , None, **dictions)\\n improvDoc.addNode(report)\\n outfile = file( self.argsFile, 'w').write(str(improvDoc))\\n return\",\n \"def Add(self, *args):\\n return _BRepAlgo.BRepAlgo_AsDes_Add(self, *args)\",\n \"def add(self, *args):\\n pass\",\n \"def add(self, *args):\\n pass\",\n \"def AddConcept(self, concept):\\n self.concepts.append(concept)\",\n \"def add_descriptor(self, descriptor):\",\n \"def do_add(self):\\n\\n\\t\\t#debug\\n\\t\\t'''\\n\\t\\tself.debug(('self.',self,['AddingVariablesList']))\\n\\t\\t'''\\n\\n\\t\\t#Apply\\t\\n\\t\\tself.map('append',map(\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tlambda __AddingVariable:\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t{'LiargVariablesList':[__AddingVariable]},\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tself.AddingVariablesList\\n\\t\\t\\t\\t\\t\\t\\t\\t)\\n\\t\\t\\t\\t)\",\n \"def addDemographics(self):\\n p = self.p\\n demographics_data = {\\n 'dob': p.dob,\\n 'gender': p.gender,\\n 'email': p.email,\\n 'fname': p.fname,\\n 'lname': p.lname,\\n 'hphone': p.home,\\n 'cphone': p.cell,\\n 'country': p.country,\\n 'city': p.city,\\n 'pcode': p.pcode,\\n 'region': p.region,\\n 'street': p.street,\\n }\\n self.demographics_doc = DEMOGRAPHICS.sub(demographics_data).done()\",\n \"def add(self, name, content):\\n raise NotImplementedError\",\n \"def add(self, el):\\n raise Exception('TODO IMPLEMENT ME !')\",\n \"def add_tag(self, tag):\\n\\n # directional relation: tag is the blank of everything in the list\\n self.relations[tag] = {\\n \\\"overlord\\\": [],\\n \\\"hegemon\\\": [], # for tributary\\n \\\"tributary\\\": [],\\n \\\"vassal\\\": [],\\n \\\"guaranteeing\\\": [],\\n \\\"guarantor\\\": [],\\n \\\"alliance\\\": [],\\n \\\"senior\\\": [],\\n \\\"junior\\\": [],\\n \\\"marriage\\\": []\\n }\",\n \"def add_condiments(self):\\n print(\\\"Adding Lemon\\\")\",\n \"def add_food(self, _food):\\n self.food.append(_food)\",\n \"def add_objective(self, objective):\\n self.objectives.append(objective)\",\n \"def __add__(self, notes):\\n self.add_notes(notes)\\n return self\",\n \"def add_emb(self, emb):\\n self.embs.append(emb)\",\n \"def add(self, *items):\",\n \"def append(self, dpr):\\r\\n self.childlist.append(dpr)\",\n \"def add(self, experience):\\n self.buffer.append(experience)\",\n \"def add_descriptions_to_confusion_matrix(self):\\n topic_names = []\\n for topic_num in self.topic_numbers:\\n topic_names.append(self.topic_names[topic_num])\\n for index, row in enumerate(self.confusion_matrix):\\n row.insert(0,topic_names[index])\\n topic_names_for_matrix = topic_names.copy()\\n topic_names_for_matrix.insert(0,\\\"\\\")\\n self.confusion_matrix.insert(0,topic_names_for_matrix)\\n self.confusion_matrix_true.insert(0,topic_names_for_matrix)\",\n \"def to_add(self):\\n pass\",\n \"def add(self, item):\",\n \"def add_description(self, description):\\n self.quest_node['description'] = description\\n self.description = description\\n graph.push(self.quest_node)\",\n \"def add_animals(self, *args):\\n if self.validate_requirements(args):\\n [self.animals.append(arg) for arg in args]\\n else:\\n print(\\\"foobar\\\")\",\n \"def EventContentMissionExcelAddDescription(builder, Description):\\n return AddDescription(builder, Description)\",\n \"def add_description(self, desc):\\n self.description = desc\",\n \"def add_advices_to_user(self, id_user):\\n # get data\\n advice_type_id = RobotAdviceType.objects.values_list(\\\"id\\\").get(type=\\\"default\\\")\\n advices_id = RobotAdvices.objects.values_list(\\\"id\\\").filter(robot_advice_type=advice_type_id)\\n\\n # add new advices to user\\n for advice_id in advices_id:\\n advice = RobotAdvices.objects.get(id=advice_id[0])\\n user = self.user.objects.get(id=id_user)\\n AdvicesToUser.objects.create(user=user, advice=advice)\",\n \"def add(lst):\\n # TODO\",\n \"def _add_descriptors(related):\\n\\n for r in related:\\n r[\\\"descriptors\\\"] = []\\n for edge in G.edges(data=True):\\n sibling_idx = _get_connected(edge, r[\\\"tokenIndex\\\"])\\n if sibling_idx and (A.lookup[int(sibling_idx)][\\\"pos\\\"] == \\\"JJ\\\" or edge[2][\\\"dep\\\"] in [\\\"amod\\\", \\\"compound\\\"]):\\n r[\\\"descriptors\\\"].append(\\n {\\n \\\"tokenIndex\\\": sibling_idx,\\n \\\"rawName\\\": A.lookup[int(sibling_idx)][\\\"word\\\"]\\n }\\n )\\n\\n if sibling_idx and \\\"NN\\\" in A.lookup[int(sibling_idx)][\\\"pos\\\"] and \\\"amod\\\" in edge[2][\\\"dep\\\"]:\\n additional_related = _get_cousin(sibling_idx, [\\\"nmod\\\"])\\n for add in set(additional_related):\\n related = _add_related(add, \\\"nmod\\\", related, A.index_lookup[add],\\n connector=G.nodes[sibling_idx]['word'])\\n return related\",\n \"def adjoint(self, add, model, data):\\n self.checkDomainRange(model, data)\\n self.ops[0].adjoint(add, model, data.vecs[0])\\n for idx in range(1, self.n):\\n self.ops[idx].adjoint(True, model, data.vecs[idx])\",\n \"def add(self, element):\\n pass\",\n \"def add(self, Links__=None, AllLinks=None, Enabled=None, Name=None, Priority=None):\\n # type: (List[str], bool, bool, str, int) -> Profile\\n return self._create(self._map_locals(self._SDM_ATT_MAP, locals()))\",\n \"def add(self):\\n return self._add_xpath(self._map_locals(self._SDM_ATT_MAP, locals()))\",\n \"def add(self, optim=None, more_data=[]):\\r\\n raise NotImplementedError()\",\n \"def add(self, optim=None, more_data=[]):\\r\\n raise NotImplementedError()\",\n \"def add(self, product):\\n pass\",\n \"def add(self, *args):\\n return _libsbml.ListWrapperCVTerm_add(self, *args)\",\n \"def add_food_to_bag(self):\\n self.food_eaten.set(sum([species.food.get() for species in self.ecosystem]))\",\n \"def create_alternatives(self, alternatives, question):\\n\\n logging.info(f\\\"Alternativas: {alternatives}\\\")\\n\\n for alternative in alternatives:\\n result = Alternative.objects.create(\\n **alternative,\\n question=question\\n )\\n question.alternatives.add(result)\",\n \"def add_exercise( self, exercise ):\\n self.exercises.append( exercise )\",\n \"def addPersonality(self):\\n\\t\\tself.personality += 1\\n\\t\\tif self.personality > 10:\\n\\t\\t\\tself.personality = 10\",\n \"def __add__(self, element):\\r\\n self.elements += element\",\n \"def addProblems(self):\\n if self.pid in Problem.problems: \\n for prob in Problem.problems[self.pid]:\\n subs = {'end': {'end': '2010-09-13'}}\\n self._set_default_attrs(prob, subs)\\n prob_string = PROBLEM.sub({\\n 'onset':prob.start,\\n 'resolution':prob.end,\\n 'snomed':prob.snomed, \\n 'name':prob.name\\n }).done()\\n self.data.append(SDMX.sub({'models':prob_string}, escape=False).done())\",\n \"def __add__(self, other: Union['_Imagine', '_ImagineMany']) -> '_ImagineMany':\\n return _ImagineMany(self, other)\",\n \"def addQuestion(self):\\n self.questions.append(Question(self))\",\n \"def add_deputy(self, deputy):\\n self.deputies.append(deputy)\",\n \"def add_detachment(self):\\n print(\\\"Which detachment would you like to add?\\\")\\n for index, keys in enumerate(init.detachments_dict.keys()):\\n print(str(index + 1) + '. ' + keys)\\n # get input to decide which detachments to add\\n user_input = input(\\\">> \\\")\\n\\n # allows users to add multiple detachments at once\\n user_input = re.findall(r'[0-9]+|[a-zA-Z]+', user_input)\\n for i in user_input:\\n if i.isdigit():\\n i = list(init.detachments_dict.keys())[int(i) - 1]\\n print(\\\"Adding {} to army\\\".format(i))\\n detach = Detachment(i)\\n\\n # populate compulsory slots\\n for keys, values in detach.units_dict.items():\\n if keys == \\\"Dedicated Transports\\\":\\n continue\\n while len(values) < detach.foc[keys][0]:\\n print(\\\"***Adding compulsory units from \\\" + keys + \\\"***\\\")\\n unit = self._create_user_unit(keys)\\n self._add_unit(detach, unit)\\n\\n self.army.add_detachment(detach)\\n return\",\n \"def add_architectural_elements(conn, cur, objects):\\n \\n print 'Adding architectural elements...',\\n \\n sift_detector = cv2.FeatureDetector_create('SIFT')\\n sift_descriptor = cv2.DescriptorExtractor_create('SIFT')\\n \\n feature_id = 1\\n \\n for i, object in enumerate(objects):\\n \\n cur.execute('INSERT INTO views VALUES (\\\"{_id}\\\", \\\"{task_id}\\\", \\\"{name}\\\")'.format(\\n _id = i+1,\\n task_id = object['task_id'],\\n name = object['view_name'],\\n )\\n )\\n \\n img = cv2.imread(VIEWS_PATH + object['view_name'])\\n \\n keypoints = sift_detector.detect(img)\\n features = sift_descriptor.compute(img, keypoints)\\n \\n for keypoint, feature in zip(features[0], features[1]):\\n \\n cur.execute('INSERT INTO features VALUES (\\\"{_id}\\\", \\\"{view_id}\\\", \\\"{pt_x}\\\", \\\"{pt_y}\\\", \\\"{desc}\\\")'.format(\\n _id = feature_id,\\n view_id = i+1,\\n pt_x = keypoint.pt[0],\\n pt_y = keypoint.pt[1],\\n desc = str(list(feature))[1:-1].replace('.0', '').replace(' ', '')\\n )\\n )\\n \\n feature_id += 1\\n \\n print 'done.'\",\n \"def add(self, *drawables):\\n self.drawables.extend(drawables)\",\n \"def add(self, specification):\\n self.specifications.append(specification)\",\n \"def add(self, obj):\\n raise NotImplementedError\",\n \"def add_many(self, pair_list):\\n\\n for pair in pair_list:\\n plug = PlugLead(pair)\\n self.check_conflicts(plug)\\n self.plugleads.append(plug)\",\n \"def add_dut(self):\\n pass\",\n \"def add_words(self, words):\\r\\n for word in words:\\r\\n self.add(word)\",\n \"def appendFrom(self, *args):\\n return _libsbml.ListOfObjectives_appendFrom(self, *args)\",\n \"def add():\\n pass\",\n \"def add_item(self, command, description):\\n self.append((command, description))\",\n \"def add_advice_to_user_created(cls, user, list_advice_id):\\n for id_advice in list_advice_id:\\n advice = RobotAdvices.objects.get(id=id_advice)\\n AdvicesToUser.objects.create(user=user, advice=advice)\",\n \"def addModifiers(self):\\n return _libsbml.Model_addModifiers(self)\",\n \"def add_paragraph_option():\\n extra_content_lst = []\\n paragraph = input('Do you want to add another paragraph to your website? [yes]')\\n while paragraph == 'yes' or paragraph == '':\\n title = input('Title of your paragraph?')\\n content = input('Content of your paragraph (single line)')\\n images = image_option()\\n extra_content_lst.append((ContentInfo(title, content, images, None)))\\n paragraph = input('Do you want to add another paragraph to your website? [yes]')\\n\\n return extra_content_lst\",\n \"def apilar(self,dato):\\r\\n\\t\\tself.elementos.append(dato)\\r\\n\\t\\tself.len += 1\",\n \"def append_collectable(self, newnotes: List):\\n self.notes.extend(newnotes)\",\n \"def add(self, game_obj):\\r\\n self.game_objects_for_adding.append(game_obj)\",\n \"def add_AdDescription(self, url, description):\\n layout = BoxLayout(size_hint_y = 4)\\n layout.add_widget(AsyncImage(source = url))\\n layout.add_widget(Label(text = description))\\n self.layout.add_widget(layout)\",\n \"def add(element):\",\n \"def addKeyWord(self, kWord):\\n #kWord.printKeyWord()\\n self.sentence.append(kWord)\",\n \"def add(self, iterable):\\n raise NotImplementedError()\",\n \"def add(self, elem):\",\n \"def add(self, elem):\",\n \"def add_descriptors(self, mapping):\\n for key, desc in mapping.iteritems():\\n self.descriptors[int(key, 16)] = desc\",\n \"def add_descriptors(self, mapping):\\n for key, desc in mapping.iteritems():\\n self.descriptors[int(key, 16)] = desc\",\n \"def add(self, name, value) -> None:\\n ...\",\n \"def __add__(self, other: Union[_Imagine, '_ImagineMany']) -> '_ImagineMany':\\n return _ImagineMany(self, other)\",\n \"def add(self, die_):\\n self._dice.append(die_)\",\n \"def add(self, sentence):\\n self._sentences.add(sentence)\",\n \"def add_D(self, D):\\n self.Ds.append(D)\",\n \"def append(self, forms, lemmas, cpostags, postags, feats, heads, deprels):\\n self.__init__(\\n self._forms + list(forms),\\n self._lemmas + list(lemmas),\\n self._cpostags + list(cpostags),\\n self._postags + list(postags),\\n self._feats + list(feats),\\n self._heads + list(heads),\\n self._deprels + list(deprels)\\n )\",\n \"def add(self, cards):\\n\\n super().add(cards)\\n self._update_value()\",\n \"def add_discount(self, discount):\\n self.discounts.append(discount)\",\n \"def add_questions(self, questions):\\n for question in questions:\\n self.questions.append(question)\",\n \"def add_content(self, more_content, no_docstring=False):\\r\\n # MatAttributeDocumenter.add_content(self, more_content,\\r\\n # no_docstring=True)\\r\\n MatAttributeDocumenter.add_content(self, more_content, no_docstring)\",\n \"def add(self, item: Any) -> None:\\n pass\",\n \"def descriptions(self, descriptions):\\n\\n self._descriptions = descriptions\",\n \"def __init__(self, description):\\n self.contents = HashMap()\\n for gdl in description:\\n if not self.contents.containsKey(key):\\n self.contents.put(key, ArrayList())\\n self.contents.get(key).add(rule)\",\n \"def register_adhocs(self):\\n aboutform = self.plugin['xep_0004'].makeForm('form', \\\"About SleekBot\\\")\\n aboutform.addField('about', 'fixed', value= self.__doc__)\\n self.plugin['xep_0050'].addCommand('about', 'About Sleekbot', aboutform)\\n pluginform = self.plugin['xep_0004'].makeForm('form', 'Plugins')\\n plugins = pluginform.addField('plugin', 'list-single', 'Plugins')\\n for key in self.cmd_plugins:\\n plugins.addOption(key, key)\\n plugins = pluginform.addField('option', 'list-single', 'Commands')\\n plugins.addOption('about', 'About')\\n #plugins.addOption('config', 'Configure')\\n self.plugin['xep_0050'].addCommand('plugins', 'Plugins', pluginform, self.form_plugin_command, True)\",\n \"def add(self, experience: []):\\n if len(self.buffer) + len(experience) >= self.buffer_size:\\n self.buffer[0:1] = []\\n self.buffer.append(experience)\",\n \"def _populate(self):\\n self.addDemographics()\\n self.addLabs()\\n self.addProblems()\\n self.addMeds()\\n self.addAllergies()\\n self.addImmunizations()\\n self.addVitals()\\n self.populated_p = True\",\n \"def add(self, states, actions, rewards, next_states, dones):\\n assert len(states) == self.num_agents, 'ERROR> group states size mismatch'\\n assert len(actions) == self.num_agents, 'ERROR> group actions size mismatch'\\n assert len(rewards) == self.num_agents, 'ERROR> group rewards size mismatch'\\n assert len(next_states) == self.num_agents, 'ERROR> group next states size mismatch'\\n assert len(dones) == self.num_agents, 'ERROR> group dones size mismatch'\\n\\n experience = (states, actions, rewards, next_states, dones)\\n self.memory.append(experience)\",\n \"def _add_meta(self, *args, **kwargs) -> None:\\n raise NotImplementedError\",\n \"def get_adjectives(self):\\n random.shuffle(self.adjectives)\\n return self.adjectives\",\n \"def __add__(self,_VariablesList):\\n\\n\\t\\t#Call the add method\\n\\t\\tself.add(_VariablesList)\\n\\n\\t\\t#Return \\n\\t\\treturn self\",\n \"def add(self, d):\\n new = self.copy()\\n new.update(d)\\n return new\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7263275","0.62598264","0.5947216","0.57094675","0.5695133","0.5606961","0.56066847","0.5586834","0.5575344","0.5548141","0.5520845","0.5520845","0.55087703","0.5452977","0.5440875","0.53886217","0.5388016","0.5352896","0.5326072","0.5325428","0.5324635","0.5306004","0.5275961","0.5274832","0.52725625","0.52449703","0.522418","0.52171415","0.52053314","0.5205009","0.5202241","0.51998526","0.5199701","0.51987034","0.5193855","0.5191535","0.51829636","0.5177303","0.51657784","0.5130658","0.5126315","0.51199913","0.51199913","0.5113955","0.5113316","0.5103743","0.5102698","0.5098974","0.50988907","0.50975025","0.50972795","0.5086273","0.507103","0.5067208","0.50651103","0.5062401","0.5060921","0.50474197","0.50471115","0.5035069","0.5032484","0.503024","0.503004","0.5028697","0.50273687","0.50254565","0.5023989","0.50236714","0.50146836","0.49945998","0.49920094","0.49913198","0.49905953","0.49824607","0.49759683","0.49692625","0.49692625","0.4967445","0.4967445","0.49640262","0.4960409","0.49577758","0.49571782","0.4950486","0.49419475","0.494125","0.4939759","0.49387482","0.49319613","0.493164","0.49288365","0.49266714","0.49256387","0.49239784","0.49219733","0.49213344","0.49208766","0.49206114","0.49155834","0.4911131"],"string":"[\n \"0.7263275\",\n \"0.62598264\",\n \"0.5947216\",\n \"0.57094675\",\n \"0.5695133\",\n \"0.5606961\",\n \"0.56066847\",\n \"0.5586834\",\n \"0.5575344\",\n \"0.5548141\",\n \"0.5520845\",\n \"0.5520845\",\n \"0.55087703\",\n \"0.5452977\",\n \"0.5440875\",\n \"0.53886217\",\n \"0.5388016\",\n \"0.5352896\",\n \"0.5326072\",\n \"0.5325428\",\n \"0.5324635\",\n \"0.5306004\",\n \"0.5275961\",\n \"0.5274832\",\n \"0.52725625\",\n \"0.52449703\",\n \"0.522418\",\n \"0.52171415\",\n \"0.52053314\",\n \"0.5205009\",\n \"0.5202241\",\n \"0.51998526\",\n \"0.5199701\",\n \"0.51987034\",\n \"0.5193855\",\n \"0.5191535\",\n \"0.51829636\",\n \"0.5177303\",\n \"0.51657784\",\n \"0.5130658\",\n \"0.5126315\",\n \"0.51199913\",\n \"0.51199913\",\n \"0.5113955\",\n \"0.5113316\",\n \"0.5103743\",\n \"0.5102698\",\n \"0.5098974\",\n \"0.50988907\",\n \"0.50975025\",\n \"0.50972795\",\n \"0.5086273\",\n \"0.507103\",\n \"0.5067208\",\n \"0.50651103\",\n \"0.5062401\",\n \"0.5060921\",\n \"0.50474197\",\n \"0.50471115\",\n \"0.5035069\",\n \"0.5032484\",\n \"0.503024\",\n \"0.503004\",\n \"0.5028697\",\n \"0.50273687\",\n \"0.50254565\",\n \"0.5023989\",\n \"0.50236714\",\n \"0.50146836\",\n \"0.49945998\",\n \"0.49920094\",\n \"0.49913198\",\n \"0.49905953\",\n \"0.49824607\",\n \"0.49759683\",\n \"0.49692625\",\n \"0.49692625\",\n \"0.4967445\",\n \"0.4967445\",\n \"0.49640262\",\n \"0.4960409\",\n \"0.49577758\",\n \"0.49571782\",\n \"0.4950486\",\n \"0.49419475\",\n \"0.494125\",\n \"0.4939759\",\n \"0.49387482\",\n \"0.49319613\",\n \"0.493164\",\n \"0.49288365\",\n \"0.49266714\",\n \"0.49256387\",\n \"0.49239784\",\n \"0.49219733\",\n \"0.49213344\",\n \"0.49208766\",\n \"0.49206114\",\n \"0.49155834\",\n \"0.4911131\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7504388"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":2879,"cells":{"query":{"kind":"string","value":"Returns the list of describing adjectives. The list is shuffled first because generally this is used to get a random adjective."},"document":{"kind":"string","value":"def get_adjectives(self):\n random.shuffle(self.adjectives)\n return self.adjectives"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_adjectives(lyrics):\n doc = nlp(lyrics.lower())\n all_adjectives = [token.lemma_ for token in doc if token.pos_ == \"ADJ\"]\n return all_adjectives","def succ(self):\n return [ self.simple_reflection(i) for i in self.descents(positive=True) ]","def getAdjectives(self, word):\n\t\tadjectives = set()\n\t\tfor synset in wordnet.synsets(word):\n\t\t\tif synset.pos == ADJ:\n\t\t\t\tfor synonym in synset.lemma_names:\n\t\t\t\t\tadjectives.add(synonym)\n\t\treturn adjectives","def adjectives_sorted(lyrics):\n adjectives = get_adjectives(lyrics)\n sorted_adjectives = Counter(adjectives)\n return sorted_adjectives","def desc_with_default(self) -> List[str]:\n return self.desc[:]","def pred(self):\n return [ self.simple_reflection(i) for i in self.descents() ]","def getAllDescWords(itemList):\r\n itemList = list(set(itemList)) # make itemList unique\r\n descWords = []\r\n for item in itemList:\r\n descWords.extend(worldItems[item][DESCWORDS])\r\n return list(set(descWords))","def _read_advantages(root):\n output_list = []\n for _, value in enumerate(root[0][3]):\n output_list.append(Advantage(value))\n return output_list","def get_advice():\n json_response = random_adviceslip()\n advice = parse_advice(json_response=json_response)\n return advice","def get_objectives(self):\n return copy.deepcopy(self.objectives), self.gates_names","def get_advisories(self):\n\n advisories = []\n\n for i in range(len(self.__data['advisories'])):\n data = requests.get(self.__data['advisories'][i]['links']['self']['href'], headers=getHeaders()).json()\n this = {}\n this['id'] = data['id']\n this['name'] = data['name']\n advisories.append(this)\n\n return advisories","def get_ads():\n return coll_ad.distinct(KEY_AD_ID)","def disease_descriptors(civic_did8):\n return [civic_did8]","def _choose_babble_phrases(self) -> tuple:\n noun_choices = ('singular nouns', 'plural nouns')\n noun_choice = self.random_element(noun_choices)\n\n adjective_choices = (\n 'adjectives starting with consonant',\n 'adjectives starting with vowel')\n\n if noun_choice == 'singular nouns':\n article_choice = self.random_element(self.article_choices)\n else:\n article_choice = 'the'\n\n if article_choice == 'an':\n adjective_choice = 'adjectives starting with vowel'\n elif article_choice == 'a':\n adjective_choice = 'adjectives starting with consonant'\n else:\n adjective_choice = self.random_element(adjective_choices)\n\n return (\n self.technobabble['verbs'],\n article_choice,\n self.technobabble[adjective_choice],\n self.technobabble[noun_choice])","def load_verbs(self, verbs: List[str]) -> List[str]:\n\n neg_verbs = get_verbs(verbs)\n\n neg_verbs_preceding = neg_verbs.loc[\n ((neg_verbs[\"mode\"] == \"Indicatif\") & (neg_verbs[\"tense\"] == \"Présent\"))\n | (neg_verbs[\"tense\"] == \"Participe Présent\")\n | (neg_verbs[\"tense\"] == \"Participe Passé\")\n | (neg_verbs[\"tense\"] == \"Infinitif Présent\")\n ]\n neg_verbs_following = neg_verbs.loc[neg_verbs[\"tense\"] == \"Participe Passé\"]\n list_neg_verbs_preceding = list(neg_verbs_preceding[\"term\"].unique())\n list_neg_verbs_following = list(neg_verbs_following[\"term\"].unique())\n\n return (list_neg_verbs_preceding, list_neg_verbs_following)","def getEssentialList(self):\n return self.essentials","def card_fields_in_order(self) -> List[str]:\n card_in_anki_order = [self.word, self.pronunciation, self.sentence,\n self.definitions, self.book_title, self.author]\n return card_in_anki_order","def DumpDetails(self, sentences, label=\"N.A.\"):\n AdjR = 0.0\n adjAll = []\n for sentence in sentences:\n # if sentence[\"Text\"].startswith(\"Joanie is not helpful\"):\n # x = 1\n adjectives, dependencies = self.ExtractSentDetails(sentence)\n adjAll.extend(adjectives)\n allAdjectives = adjectives | Angel.GlobalAdjList\n AdjS = 0.0\n words = wordpunct_tokenize(sentence[\"Text\"])\n if len(words) <= 3:\n allAdjectives |= set([x.lower() for x in words])\n for i in range(len(words)):\n word = words[i].lower()\n if word in {\"but\", \"if\"}:\n AdjS = 0.0\n print words[i],\n elif word in allAdjectives and word in self.lexicon:\n multiplier = self.PredictMultiplier(word, dependencies[word], words, i)\n score = float(self.lexicon[word]) * multiplier\n if multiplier < 1:\n colortext = colored(words[i] + \" (\" + '{:.3}'.format(score) + \")\", 'red',None,['underline'])\n elif multiplier > 1:\n colortext = colored(words[i] + \" (\" + '{:.3}'.format(score) + \")\", 'red',None,['bold'])\n else:\n colortext = colored(words[i] + \" (\" + '{:.3}'.format(score) + \")\", 'red')\n AdjS += score\n print colortext,\n else:\n print words[i],\n print\n colortext = colored(\"Adjectives: \" + '{:.3}'.format(AdjS),'red')\n print colortext\n AdjR += AdjS\n print\n print \"Label:\", label\n base = self.PredictBase(adjAll)\n colortext = colored(\"Adjectives: \" + str(AdjR) + \"*\" + str(base) + \" = \" + str(AdjR*base),'red')\n print colortext","def add_adjectives(self, adjective):\n self.adjectives += [adjective]","def random_advice(message):\n advice = requests.get(\"https://api.adviceslip.com/advice\").json()['slip']['advice']\n\n return advice","def generate_products():\n # initialize list of noun and adj\n num_products = 30\n products = [0] * num_products\n prices = [0] * num_products\n weights = [0] * num_products\n flammabilities = [0] * num_products\n\n # initlize random word object\n random = RandomWords()\n\n adj = [random.get_random_word(includePartOfSpeech=\"adjective\")\n for product in products]\n noun = [random.get_random_word(includePartOfSpeech=\"noun\")\n for product in products]\n products = [noun + \" \" + adj for noun, adj in zip(adj, noun)]\n\n prices = [random.randint(5, 100) for price in prices]\n weights = [random.randint(5, 100) for weight in weights]\n flammabilities = [random.randint(0.0, 2.5)\n for flammability in flammabilities]\n\n return products, prices, weights, flammabilities","def setup_random_opinions_representatives():\r\n global CATEGORIES\r\n \r\n ideas_dic = {}\r\n \r\n for i in CATEGORIES:\r\n #idea = Idea(1,\"\",i, 1-random.expovariate(6))\r\n if i == 1:\r\n idea = Idea(1,\"\",i, random.uniform(-1,-0.5))\r\n elif i == 2:\r\n idea = Idea(1,\"\",i, random.uniform(-1,-0.5))\r\n elif i == 3:\r\n idea = Idea(1,\"\",i, random.uniform(0.5,1))\r\n \r\n# idea = Idea(1,\"\",i, random.uniform(0.5,1))\r\n ideas_dic[i] = idea\r\n \r\n return ideas_dic","def getAchievements(self) -> list:\n return self.state[ACHIEVEMENTS]","def darts(self):\r\n return self.alphas[0].keys()","def add_adjectives(self, *sAdjs):\n self.adjectives += list(sAdjs)","def skills():\n all_skills = [\"strength\", \"patience\", \"cleanliness\", \"leadership\", \"communication\",\n \"science\", \"math\", \"engineering\", \"intelligence\", \"driving\"]\n\n random.shuffle(all_skills)\n return all_skills[0:2]","def effect_list(self):\n moods = []\n for mood in self._moodlist:\n if \"name\" in mood:\n moods.append(mood['name'])\n return moods","def get_ideas(self):\n fields = ['content', 'clusterIDs', 'isGamechanger',\n 'userID', 'promptID']\n return self.get_data(\"ideas\", fields)","def get_list(self):\n categories = []\n for attribut in self.attributes:\n attr = getattr(self, attribut, False)\n if attr is True:\n categories.append(attribut)\n if getattr(self, 'education') is True:\n categories.append(_(u'education'))\n if getattr(self, 'training') is True:\n categories.append(_(u'training'))\n if getattr(self, 'tutoring') is True:\n categories.append(_(u'tutoring'))\n\n return categories","def p_banner():\n return random.choice([banner, banner_two, banner_three, banner_four, banner_five])","def give_lists(self):\r\n hard = []\r\n easy = []\r\n medium = []\r\n for question in self.get_all():\r\n if question.difficulty_level == \"easy\":\r\n easy.append(question)\r\n elif question.difficulty_level == \"medium\":\r\n medium.append(question)\r\n else:\r\n hard.append(question)\r\n return easy,medium,hard","def shuffled_answers(self):\n if self.answers is None:\n return []\n answers = list(self.answers)\n shuffle(answers)\n return answers","def _generateDescription(self, obj, **args):\n result = []\n if obj.description:\n label = self._script.utilities.displayedLabel(obj) or \"\"\n name = obj.name or \"\"\n desc = obj.description.lower()\n if not (desc in name.lower() or desc in label.lower()):\n result.append(obj.description)\n return result","def get_dialogue_acts(self):\n return self.DAs","def variation_descriptors(civic_vid33):\n return [civic_vid33]","def getArrettes(self) -> list:\n return self._arrettes","def get_describable_list(request):\n describables = []\n\n from django.apps import apps\n for entity in apps.get_app_config('descriptor').describable_entities:\n content_type = get_object_or_404(\n ContentType, app_label=entity._meta.app_label, model=entity._meta.model_name)\n\n describables.append({\n 'id': content_type.pk,\n 'value': \"%s.%s\" % (entity._meta.app_label, entity._meta.model_name),\n 'label': str(entity._meta.verbose_name.capitalize())\n })\n\n return HttpResponseRest(request, describables)","def get_description():\r\n return{\"I'll never yield!\":\"Grants a shield.\",\r\n \"Stay still!\":\"Affected unit cannot act in their turn.\"\r\n }","def single_introduction(end):\n return [random.randint(0,end)]","def description_ques(analysis):\n if analysis.sv[0].vrb_tense.startswith('present'):\n analysis.sv[0].vrb_tense = 'present progressive'\n if analysis.sv[0].vrb_tense.startswith('past'):\n analysis.sv[0].vrb_tense = 'present progressive'\n sentence = y_o_question(analysis)\n for i in sentence:\n if i == 'liking':\n sentence[sentence.index(i)] = 'like'\n return ['what'] + sentence","def get_paragraphs():\n soup = get_html()\n paragraphs = []\n for i in soup.findAll('div', {'class': 'faq-list1__hide'}):\n p = str(i.get_text().strip())\n paragraphs.append(p)\n return paragraphs","def _sample_episode(self):\n\n Knovel = random.sample(self.labelIds, self.nKnovel)\n #print(Knovel)\n #exit(0)\n nKnovel = len(Knovel)\n assert((self.nTestNovel % nKnovel) == 0)\n nEvalExamplesPerClass = int(self.nTestNovel / nKnovel)\n #print(nEvalExamplesPerClass)\n #exit(0)\n Tnovel = []\n Exemplars = []\n for Knovel_idx in range(len(Knovel)):\n ids = (nEvalExamplesPerClass + self.nExemplars)\n img_ids = random.sample(self.labels2inds[Knovel[Knovel_idx]], ids) \n\n imgs_tnovel = img_ids[:nEvalExamplesPerClass]\n imgs_emeplars = img_ids[nEvalExamplesPerClass:]\n #print(imgs_tnovel)\n #exit(0)\n Tnovel += [(img_id, Knovel_idx) for img_id in imgs_tnovel]\n Exemplars += [(img_id, Knovel_idx) for img_id in imgs_emeplars]\n assert(len(Tnovel) == self.nTestNovel)\n assert(len(Exemplars) == nKnovel * self.nExemplars)\n random.shuffle(Exemplars)\n random.shuffle(Tnovel)\n\n return Tnovel, Exemplars","def getAllFirstDescWords(itemList):\r\n itemList = list(set(itemList)) # make itemList unique\r\n descWords = []\r\n for item in itemList:\r\n descWords.append(worldItems[item][DESCWORDS][0])\r\n return list(set(descWords))","def get_techniques(db):\n\n techniques = []\n for element in db:\n for technique in element['techniques_used']:\n if technique not in techniques:\n techniques.append(technique)\n \n return sorted(techniques)","def animals(self):\n return self.herbivores + self.carnivores","def buscaPalavras(self):\n dataSet=self.stemmerAplay()\n todasPalavras =[]\n for (notice, clazz) in dataSet:\n todasPalavras.extend(notice)\n return todasPalavras","def get_feature_names(self):\n\t\treturn np.array(['nouns', 'adjectives', 'verbs', 'adverbs'])","def getListOfObjectives(self, *args):\n return _libsbml.FbcModelPlugin_getListOfObjectives(self, *args)","def get_verb_list(lang: Lang) -> VerbsList:\n with open(f'languages/{lang}/verbs.csv', newline='') as csv_file:\n return list(reversed([VerbInf(word[0]) for word in csv.reader(csv_file, delimiter=',')]))","def diamonds(self):\n return sorted(tuple([v for v in self if v.suit == 'diamonds']), reverse=True)","def get_hero_list(self):\n out_list = []\n for key, _ in self._heroes.items():\n out_list.append(key)\n out_list.sort()\n return out_list","def getIngredients():\n ingredients = ['Whiskey', 'Tequila', 'Vodka', 'Blue Curacao', 'Orange Juice',\n 'Pineapple Juice', 'Cranberry Juice', 'Sour Mix']\n return ingredients","def list(self, frame=0):\n text = []\n if not self.number_of_variations:\n return \"\"\n for group_number in range(1, len(self.varexercise_numbers)+1):\n text.extend(\n self.group_list(group_number))\n return text","def listPalettes(show=True, getdescriptions=True):\n\n # Print palette names to terminal if that's what the user wants\n if show:\n print \"Currently available palettes:\"\n \n for palette_name in sorted(palettes.keys()):\n print palette_name,\n if not getdescriptions:\n print\n else:\n if ((not (palette_name in descriptions)) \n or (descriptions[palette_name] == \"\")):\n print ((\": Palette %s does not have a description, \"\n \"please write one and add it to the descriptions dict \"\n \"in palettes.py.\") % palette_name)\n continue\n # end if\n print \": %s\" % descriptions[palette_name]\n # end if\n # end for\n # end if\n \n if not getdescriptions:\n return palettes.keys()\n else:\n return (palettes.keys(), descriptions)\n # end if","def get_to_review(self) -> VerbsList:\n to_review = [verb for verb, stats in self.practice_list.items() if pick(stats)]\n\n if len(to_review) < self.min_to_review:\n for _ in range(self.min_to_review - len(to_review)):\n to_review.append(self.to_learn_list.pop())\n\n return to_review","def Obtener_Lista(self):\n\t\treturn [self,self.Nombre+\" \"+self.Apellido,self.ID,self.Fecha, \n\t\tself.Edad,self.Test,self.Posicion,self.Prioridad,self.Progreso,self.Informe]","def random_data(self) -> (str, str):\n random_animal = random.choice(self.animals_list)\n synsets = wn.synsets(str(random_animal))\n definition = \"\"\n while True:\n if len(synsets) != 0:\n for synset in synsets:\n if synset.lexname() == 'noun.animal':\n definition = synset.definition()\n break\n else:\n random_animal = random.choice(self.animals_list)\n synsets = wn.synsets(str(random_animal))\n return random_animal, definition","def hp_descartes(self, hyper_params=None):\n if hyper_params is None: hyper_params = list(self.hyper_params.values())\n if len(hyper_params) == 0: yield OrderedDict()\n else:\n first_hp = hyper_params[0]\n assert isinstance(first_hp, CategoricalHP)\n for val in first_hp.choices:\n configs = OrderedDict()\n configs[first_hp.name] = val\n for cfg_dict in self.hp_descartes(hyper_params[1:]):\n configs.update(cfg_dict)\n yield configs","def get_samples(meta, classes_list):\n\n h = list(meta[meta['DiseaseState'].isin(classes_list[0])].index)\n dis = list(meta[meta['DiseaseState'].isin(classes_list[1])].index)\n\n return [h, dis]","def get_des(tree_before: Node, tree_after: Node) -> List[Description]:\n\n des_gen: Iterator[Description] = get_des_gen(tree_before, tree_after)\n\n return list(des_gen)","def apertures(self):\n return self._apertures","def get_details(disease):\n\treturn d_desc_map[disease]","def list(self):\n\t\treturn self.link_words","def get_introduction(length=128, words=None):","def getaslist(self):\n l = []\n l.append(self.title.strip().encode('utf-8'))\n l.append(self.desc.strip().encode('utf-8'));\n l.append(self.course_number.strip().encode('utf-8'))\n l.append(self.duration.strip().encode('utf-8'))\n l.append(self.difficulty.strip().encode('utf-8'))\n l.append(self.instructors.strip().encode('utf-8'))\n l.append(self.url.strip().encode('utf-8'))\n return l","def show_pairs(self):\n\n all_pairs = []\n for plug in self.plugleads:\n all_pairs.append(plug.pair)\n return all_pairs","def get_verbs(self) -> Set[str]:","def get_attributes(cls):\r\n return [\r\n Attribute(\"show_correctness\", \"always\"),\r\n Attribute(\"submitted_message\", \"Answer received.\"),\r\n Attribute(\"label\", \"\"),\r\n ]","def make_ad(state):\n return [adnumber(val) for val in state]","def list(self):\n a0 = map(self._alphabet.unrank, self._labels[0])\n a1 = map(self._alphabet.unrank, self._labels[1])\n return [a0, a1]","def sample_effects(self) -> List[List[str]]:\n return [\n [\n str(item.sample() if isinstance(item, RandomValue) else item)\n for item in effect\n ]\n for effect in self.effects\n ]","def desc (self):\n uni = {\n \"name\": self.name,\n \"G\": self.G,\n \"time\": self.time,\n \"bodies\": {bid: body.desc() for bid, body in self.bodies.iteritems()},\n \"description\": self.description,\n \"datatime\": self.datatime\n }\n\n return uni","def create_samples(descriptions, candidates_lists, labels):\n samples = []\n for description, candidate_list, label in tqdm(\n zip(descriptions, candidates_lists, labels)\n ):\n negative_examples = [\n candidate for candidate in candidate_list if candidate != label\n ]\n negative_examples = random.choices(negative_examples, k=4)\n if label in candidate_list:\n positive_example = [\n candidate for candidate in candidate_list if candidate == label\n ][0]\n else:\n positive_example = label\n samples.append(InputExample(texts=[description, positive_example], label=1))\n for neg_ex in negative_examples:\n samples.append(InputExample(texts=[description, neg_ex], label=0))\n return samples","def list(cls):\n\n forges = cls.forges()\n\n return {\"forges\": [{\"id\": id, \"description\": forges[id]} for id in sorted(forges.keys())]}","def get_alignments(self) -> list:","def return_augmented_sentences(self) -> list:\n return self.augmented_sentence_list","def describe(self):\n if not self.name:\n raise ValueError(\"Sorry! id_type must be 'name'\")\n r = requests.get(f\"https://api.fda.gov/drug/ndc.json?search=brand_name:{self.drug_id}\")\n response = r.json()\n data = response['results'][0]\n self.brand_name = data['brand_name']\n self.generic_name = data['generic_name']\n self.active_ingredients = [i['name'] for i in data['active_ingredients']]\n self.pharm_class = get_pharm_class(self.drug_id, as_df=False)\n self.route = data['route']\n self.ndc = data['product_ndc']\n self.product_type = data['product_type']\n\n print(f\"Generic name: {self.generic_name}\")\n print(f\"Brand name: {self.brand_name}\")\n print(f\"Active ingredients: {self.active_ingredients}\")\n print(f\"Routes of administration: {self.route}\")\n print(f\"Pharmacologic Classes: {self.pharm_class}\")\n print(f\"NDC: {self.ndc}\")\n print(f\"Product type: {self.product_type}\")","def get_vo_descriptions(vos):\n all_vos = vo_core.list_vos()\n vos_with_desc = []\n for vo in all_vos:\n if vo['vo'] in vos:\n vos_with_desc.append((vo['vo'], vo['description']))\n return vos_with_desc","def therapy_descriptors(civic_tid146):\n return [civic_tid146]","def get_antags(self):\n antags = []\n for obj in self.antagobjs.group_by(AntagObjective.mindkey):\n antag = {'key': obj.mindkey, 'name': obj.mindname, 'role': obj.special_role}\n antags.append(antag)\n return antags","def sample_apr(self):\n FLAGS.full_wiki = True\n FLAGS.apr_dir = 'Directory Name'\n apr = apr_lib.ApproximatePageRank()\n seeds = [\n 'Q7755', 'Q878070', 'Q428148', 'Q679847', 'Q2609670', 'Q174834',\n 'Q188628'\n ]\n unique_facts = apr.get_facts(\n seeds, topk=200, alpha=0.9, seed_weighting=True)\n facts = sorted(unique_facts, key=lambda tup: tup[1][1], reverse=True)\n nl_facts = ' . '.join([\n str(x[0][0][1]) + ' ' + str(x[1][0][1]) + ' ' + str(x[0][1][1])\n for x in facts\n ])\n tf.logging.info('Extracted facts: %s', nl_facts)","def get_pledge_list():\n with open('pledge.txt') as f:\n pledge_list = f.read().split()\n\n #Eliminate punctuation from the end of words\n pledge_list_noPunct = [word if word[-1] in ascii_letters else word[:-1] for word in pledge_list]\n\n return pledge_list_noPunct","def _process_examples(self):\n logger = getLogger(\"problog_lfi\")\n # value can be True / False / None\n # ( atom ), ( ( value, ... ), ... )\n\n # Simple implementation: don't add neutral evidence.\n\n # ad_groups is a list of lists where each list contains an AD\n ad_groups = list()\n for ad in self._adatoms:\n # if it's an AD group\n if len(ad[1]) > 1:\n ad_list = []\n for var in ad[1]:\n ad_list.append(Term(self.names[var].functor, *self.names[var].args))\n ad_groups.append(tuple(ad_list))\n logger.debug(\"AD Groups\\t\\t:\" + str(ad_groups))\n\n def all_false(d):\n \"\"\"\n This function recognizes inconsistent evidence s.t. all values are False in AD.\n :param d: dictionary of ADs in form {term: value}\n value can be True, False, None, \"Template\"\n :return: whether all values are False\n \"\"\"\n # false_count should be the same as the length of d\n false_count = sum(v is False for v in d.values())\n return false_count == len(d)\n\n def all_false_except_one(d):\n \"\"\"\n This function recognizes incomplete evidence s.t.\n the non-False value in AD needs to be set to True.\n :param d: dictionary of ADs in form {term: value}\n value can be True, False, None, \"Template\"\n :return: whether all values except one are False\n \"\"\"\n false_count = sum(v is False for v in d.values())\n the_left_is_none = bool(sum(v is None for v in d.values()))\n return (false_count == len(d) - 1) and the_left_is_none\n\n def getADtemplate(d, atom=None):\n \"\"\"\n This function gets atom's complement AD template.\n This should only be used when the AD contains non-ground terms.\n :param d: dictionary of ADs in form {term: value}\n value can be True, False, None, \"Template\"\n :param atom: an evidence\n :return: atom's complement AD template\n \"\"\"\n if atom is not None:\n temp_dict = {\n k: v\n for k, v in d.items()\n if v == \"Template\" and atom.signature != k.signature\n }\n return temp_dict\n else:\n temp_dict = {k: v for k, v in d.items() if v == \"Template\"}\n return temp_dict\n\n def add_to_ad_evidence(pair, l, ADtemplate):\n \"\"\"\n :param pair: a new pair of (atom, value)\n :param l: a list of dictionaries, all dictionaries need to have the same format\n :return:\n \"\"\"\n (k, v) = pair\n # if entry k exists, update the value with k\n for d in l:\n if k in d:\n d[k] = v\n return\n # if entry k does not exist, create a new dictionary from template\n # and instantiate it with k\n new_d = dict()\n for temp_k in ADtemplate.keys():\n new_key = Term(temp_k.functor, *k.args)\n new_d[new_key] = None\n # put v in there\n new_d[k] = v\n l.append(new_d)\n\n if self.infer_AD_values:\n result = ExampleSet()\n inconsistent = False\n # iterate over all examples given in .ev\n for index, example in enumerate(self.examples):\n ad_evidences = []\n non_ad_evidence = {}\n for ad_group in ad_groups:\n # create a dictionary to memorize what evidence is given in AD\n d = dict()\n # TODO: what if the AD contains both ground and non-ground????\n # e.g. t(_)::a; t(_)::b(X)\n for var in ad_group:\n if var.is_ground():\n d[var] = None # for ground unknown evidence\n else:\n d[var] = \"Template\" # for unground unknown evidence\n ad_evidences.append(d)\n\n # add all evidence in the example to ad_evidences\n for atom, value in example:\n # if atom has a tunable probability to learn\n if any([atom.signature == name.signature for name in self.names]):\n if len(atom.args) == 0: # Propositional evidence\n # insert evidence\n is_ad = False\n for d in ad_evidences:\n if atom in d:\n d[atom] = value\n is_ad = True\n if not is_ad:\n non_ad_evidence[\n atom\n ] = value\n else: # First Order evidence\n # find the right AD dictionary : AD_dict\n AD_dict = None\n for d in ad_evidences:\n if any([atom.signature == k.signature for k in d]):\n AD_dict = d\n # if the instantiation is new, add it as a key to the dictionary\n if AD_dict and AD_dict.get(atom) is None:\n AD_dict[atom] = value\n # also add other AD parts in the dictionary with value==None\n other_ADs = getADtemplate(AD_dict, atom)\n for otherAD in other_ADs.keys():\n new_key = Term(otherAD.functor, *atom.args)\n AD_dict[new_key] = AD_dict.get(new_key, None)\n else:\n non_ad_evidence[atom] = value\n else:\n non_ad_evidence[atom] = value\n\n # grounded_ad_evidences contains all usable evidence (gound, not template)\n grounded_ad_evidences = []\n for d in ad_evidences:\n # for first order evidence dictionaries\n if \"Template\" in d.values():\n # new_ad_evidence is a list of dictionaries\n # each dictionary is a group of the AD template instantiation\n new_ad_evidence = list()\n # get template AD evidence\n ADtemplate = getADtemplate(d)\n # group all pairs according to ADtemplate\n for k, v in d.items():\n if v != \"Template\":\n add_to_ad_evidence((k, v), new_ad_evidence, ADtemplate)\n grounded_ad_evidences += new_ad_evidence\n else:\n # simply us them\n grounded_ad_evidences.append(d)\n\n inconsistent_example = False\n for i, d in enumerate(grounded_ad_evidences):\n # inconsistent1 = multiple_true(d)\n inconsistent2 = all_false(d)\n add_compliment = all_false_except_one(d)\n\n if inconsistent2:\n inconsistent_example = True\n continue\n elif add_compliment:\n for key, value in d.items():\n if value is None:\n grounded_ad_evidences[i][key] = True\n\n if not inconsistent_example and len(grounded_ad_evidences) > 0:\n # There are (fully tunable) ADs in the program\n evidence_list = []\n for d in grounded_ad_evidences:\n for key, value in d.items():\n if value is not None:\n if (key, value) not in evidence_list:\n # TODO: Switch to orderedSet so we don't have to check whether it's in already\n evidence_list.append((key, value))\n\n for key, value in non_ad_evidence.items():\n evidence_list.append((key, value))\n\n atoms, values = zip(*evidence_list)\n result.add(index, atoms, values)\n\n else:\n # (No AD case) or (Inconsistent Evidence Case)\n atoms, values = zip(*example)\n result.add(index, atoms, values)\n return result\n else:\n # smarter: compile-once all examples with same atoms\n result = ExampleSet()\n for index, example in enumerate(self.examples):\n atoms, values = zip(*example)\n result.add(index, atoms, values)\n return result","def ad_meta(self, d):\n names = []\n if is_str(d):\n aname = d[:-2] + 'name'\n if aname in self.ad_cols:\n names = [aname]\n else:\n names = [n[:-2] + 'name' for n in d if n[:-2] + 'name' in self.ad_cols]\n return self.configs['meta_ad'] + names","def get_assay_solutions(self):\n vocabs = []\n assay = self.get_assay()\n if not assay:\n return vocabs\n for solution_type_name in assay.needed_solutions:\n type_batches = find(Type=solution_type_name,\n expires={'query': datetime.today().date(),\n 'range': 'min'},\n sort_on='expires')\n\n tmp = []\n for batch in type_batches:\n tmp.append([batch.id,\n batch.Title,\n batch.expires.strftime('%Y-%m-%d')])\n vocabs.append([solution_type_name, tmp])\n return vocabs","def elements_sequence(cls):\n return [\n \"id\",\n \"extension\",\n \"modifierExtension\",\n \"definition\",\n \"doseForm\",\n \"intendedRoute\",\n \"ingredient\",\n \"drugCharacteristic\",\n ]","def descents(self, index_set=None, positive=False):\n if index_set==None:\n index_set=self.parent().index_set()\n return [ i for i in index_set if self.has_descent(i, positive) ]","def getDescriptors(self, dsc = \"\"):\r\n return \"\"","def get_descuentos(self):\n return float(\n self.input.get_text(liquidaciones_historicas_catalog.DESCUENTOS).replace(\".\", \"\").replace(\",\", \".\"))","def desc(self) -> List[str]:\n format_data: dict = {\n key: val\n for key, val in self.__dict__.items()\n if (\n not key.startswith(\"_\") and key != \"desc\" and not callable(val)\n )\n }\n format_data.update(self._kwargs)\n ret: List[str] = []\n for descr in self._desc:\n try:\n descr = descr % format_data\n except Exception as kerr:\n raise PyParamException(\n f\"Description of {self.namestr()!r} is formatting \"\n \"using kwargs from contructor. \\n\"\n f\"- desc: {descr}\\n\"\n f\"- key : {{... {str(kerr)[1:-1]} ...}}\"\n ) from None\n else:\n ret.append(descr)\n return ret","def prepare_text_data(descriptions):\n text_data = []\n for line in descriptions:\n tokens = prepare_text_for_lda(line)\n text_data.append(tokens)\n return text_data","def collection(self):\n questions = []\n choice_list = []\n answers = []\n\n if self.form=='The correct German word':\n for i in range(self.num_ques):\n question, options, answer = self.generate_eng2ger()\n questions.append(question)\n choice_list.append(options)\n answers.append(answer)\n else:\n for i in range(self.num_ques):\n question, options, answer = self.generate_ger2eng()\n questions.append(question)\n choice_list.append(options)\n answers.append(answer)\n\n return questions, choice_list, answers","def apertures(self):\n return tuple(a for a in self._apertures if a is not None)","def _getChoices(self, acronym):\n # get matches from acronymDB\n matches = []\n if(acronym in self.acronymDB):\n matches += self.acronymDB[acronym]\n if(acronym[-1] == \"s\" and acronym[:-1] in self.acronymDB):\n matches += self.acronymDB[acronym]\n\n # create training data\n X_train, y_train = [], []\n for definition, articleID, ignored_var in matches:\n text = self.articleDB[articleID]\n X_train.append(\n ExpansionChoice(article_id=articleID, article_text=text))\n y_train.append(definition)\n\n # create y labels to group similar acronyms\n y_labels, labelToExpansion = self._processChoices(y_train)\n\n return X_train, y_labels, labelToExpansion","def PredictBase(self, adjectives):\n # Get the list of Adjectives which have sentiment polarity greater than 0.1\n if self.smallReviews:\n return 1.0\n PolarAdjList = [l for l in adjectives if l in self.lexicon and math.fabs(float(self.lexicon[l])) > 0.1]\n if len(PolarAdjList) > 0:\n return 12.0/len(PolarAdjList)\n # elif len(list2) < 8:\n # return 2.0\n else:\n return 1.0","def get_description(self):","def ExtractSentDetails(self, sentence):\n if \"Adjectives\" in sentence:\n adjList = [w.lower() for w in sentence[\"Adjectives\"] if w.lower() not in Angel.stopWords and w.lower() not in Angel.engNames]\n adjectives = set(adjList)\n else:\n adjectives = set()\n dependencies = defaultdict(dict)\n if \"Dependencies\" in sentence:\n if not isinstance(sentence[\"Dependencies\"],list):\n sentence[\"Dependencies\"] = [sentence[\"Dependencies\"]]\n for dep in sentence[\"Dependencies\"]:\n line = dep.split(',')\n if len(line) != 3:\n continue\n relation, adj, other = line\n adj, other = adj.lower(), other.lower()\n if relation in {'amod', 'acomp', 'ccomp', 'pobj', 'dep'}:\n adj, other = other, adj\n if relation not in dependencies[adj]:\n dependencies[adj][relation] = set()\n dependencies[adj][relation].add(other)\n if relation == 'conj':\n adjectives.add(other)\n dictconj, other = defaultdict(dict), None\n for adj in dependencies:\n if 'conj' in dependencies[adj]:\n for other in dependencies[adj]['conj']:\n dictconj[other] = copy.deepcopy(dependencies[adj])\n for adj in dictconj:\n for relation in dictconj[adj]:\n if relation not in dependencies[adj]:\n dependencies[adj][relation] = set()\n dependencies[adj][relation] |= dictconj[adj][relation]\n return adjectives, dependencies","def _get_appdesc_embeddings(self, features, training):\n # Using GloVe, embed each token then aggregate them.\n appdesc_embeddings = self._word_embedding_layer(\n features['appdesc_token_id'])\n appdesc_embeddings = self._aggregate_text_embedding(\n features['appdesc_token_id'], appdesc_embeddings)\n\n if training:\n appdesc_embeddings = tf.nn.dropout(\n appdesc_embeddings, rate=self._hparams['dropout'])\n\n return appdesc_embeddings","def load_description():\n with open('description.txt') as description:\n return [line.strip() for line in description]","def to_adverb(self):\n return self"],"string":"[\n \"def get_adjectives(lyrics):\\n doc = nlp(lyrics.lower())\\n all_adjectives = [token.lemma_ for token in doc if token.pos_ == \\\"ADJ\\\"]\\n return all_adjectives\",\n \"def succ(self):\\n return [ self.simple_reflection(i) for i in self.descents(positive=True) ]\",\n \"def getAdjectives(self, word):\\n\\t\\tadjectives = set()\\n\\t\\tfor synset in wordnet.synsets(word):\\n\\t\\t\\tif synset.pos == ADJ:\\n\\t\\t\\t\\tfor synonym in synset.lemma_names:\\n\\t\\t\\t\\t\\tadjectives.add(synonym)\\n\\t\\treturn adjectives\",\n \"def adjectives_sorted(lyrics):\\n adjectives = get_adjectives(lyrics)\\n sorted_adjectives = Counter(adjectives)\\n return sorted_adjectives\",\n \"def desc_with_default(self) -> List[str]:\\n return self.desc[:]\",\n \"def pred(self):\\n return [ self.simple_reflection(i) for i in self.descents() ]\",\n \"def getAllDescWords(itemList):\\r\\n itemList = list(set(itemList)) # make itemList unique\\r\\n descWords = []\\r\\n for item in itemList:\\r\\n descWords.extend(worldItems[item][DESCWORDS])\\r\\n return list(set(descWords))\",\n \"def _read_advantages(root):\\n output_list = []\\n for _, value in enumerate(root[0][3]):\\n output_list.append(Advantage(value))\\n return output_list\",\n \"def get_advice():\\n json_response = random_adviceslip()\\n advice = parse_advice(json_response=json_response)\\n return advice\",\n \"def get_objectives(self):\\n return copy.deepcopy(self.objectives), self.gates_names\",\n \"def get_advisories(self):\\n\\n advisories = []\\n\\n for i in range(len(self.__data['advisories'])):\\n data = requests.get(self.__data['advisories'][i]['links']['self']['href'], headers=getHeaders()).json()\\n this = {}\\n this['id'] = data['id']\\n this['name'] = data['name']\\n advisories.append(this)\\n\\n return advisories\",\n \"def get_ads():\\n return coll_ad.distinct(KEY_AD_ID)\",\n \"def disease_descriptors(civic_did8):\\n return [civic_did8]\",\n \"def _choose_babble_phrases(self) -> tuple:\\n noun_choices = ('singular nouns', 'plural nouns')\\n noun_choice = self.random_element(noun_choices)\\n\\n adjective_choices = (\\n 'adjectives starting with consonant',\\n 'adjectives starting with vowel')\\n\\n if noun_choice == 'singular nouns':\\n article_choice = self.random_element(self.article_choices)\\n else:\\n article_choice = 'the'\\n\\n if article_choice == 'an':\\n adjective_choice = 'adjectives starting with vowel'\\n elif article_choice == 'a':\\n adjective_choice = 'adjectives starting with consonant'\\n else:\\n adjective_choice = self.random_element(adjective_choices)\\n\\n return (\\n self.technobabble['verbs'],\\n article_choice,\\n self.technobabble[adjective_choice],\\n self.technobabble[noun_choice])\",\n \"def load_verbs(self, verbs: List[str]) -> List[str]:\\n\\n neg_verbs = get_verbs(verbs)\\n\\n neg_verbs_preceding = neg_verbs.loc[\\n ((neg_verbs[\\\"mode\\\"] == \\\"Indicatif\\\") & (neg_verbs[\\\"tense\\\"] == \\\"Présent\\\"))\\n | (neg_verbs[\\\"tense\\\"] == \\\"Participe Présent\\\")\\n | (neg_verbs[\\\"tense\\\"] == \\\"Participe Passé\\\")\\n | (neg_verbs[\\\"tense\\\"] == \\\"Infinitif Présent\\\")\\n ]\\n neg_verbs_following = neg_verbs.loc[neg_verbs[\\\"tense\\\"] == \\\"Participe Passé\\\"]\\n list_neg_verbs_preceding = list(neg_verbs_preceding[\\\"term\\\"].unique())\\n list_neg_verbs_following = list(neg_verbs_following[\\\"term\\\"].unique())\\n\\n return (list_neg_verbs_preceding, list_neg_verbs_following)\",\n \"def getEssentialList(self):\\n return self.essentials\",\n \"def card_fields_in_order(self) -> List[str]:\\n card_in_anki_order = [self.word, self.pronunciation, self.sentence,\\n self.definitions, self.book_title, self.author]\\n return card_in_anki_order\",\n \"def DumpDetails(self, sentences, label=\\\"N.A.\\\"):\\n AdjR = 0.0\\n adjAll = []\\n for sentence in sentences:\\n # if sentence[\\\"Text\\\"].startswith(\\\"Joanie is not helpful\\\"):\\n # x = 1\\n adjectives, dependencies = self.ExtractSentDetails(sentence)\\n adjAll.extend(adjectives)\\n allAdjectives = adjectives | Angel.GlobalAdjList\\n AdjS = 0.0\\n words = wordpunct_tokenize(sentence[\\\"Text\\\"])\\n if len(words) <= 3:\\n allAdjectives |= set([x.lower() for x in words])\\n for i in range(len(words)):\\n word = words[i].lower()\\n if word in {\\\"but\\\", \\\"if\\\"}:\\n AdjS = 0.0\\n print words[i],\\n elif word in allAdjectives and word in self.lexicon:\\n multiplier = self.PredictMultiplier(word, dependencies[word], words, i)\\n score = float(self.lexicon[word]) * multiplier\\n if multiplier < 1:\\n colortext = colored(words[i] + \\\" (\\\" + '{:.3}'.format(score) + \\\")\\\", 'red',None,['underline'])\\n elif multiplier > 1:\\n colortext = colored(words[i] + \\\" (\\\" + '{:.3}'.format(score) + \\\")\\\", 'red',None,['bold'])\\n else:\\n colortext = colored(words[i] + \\\" (\\\" + '{:.3}'.format(score) + \\\")\\\", 'red')\\n AdjS += score\\n print colortext,\\n else:\\n print words[i],\\n print\\n colortext = colored(\\\"Adjectives: \\\" + '{:.3}'.format(AdjS),'red')\\n print colortext\\n AdjR += AdjS\\n print\\n print \\\"Label:\\\", label\\n base = self.PredictBase(adjAll)\\n colortext = colored(\\\"Adjectives: \\\" + str(AdjR) + \\\"*\\\" + str(base) + \\\" = \\\" + str(AdjR*base),'red')\\n print colortext\",\n \"def add_adjectives(self, adjective):\\n self.adjectives += [adjective]\",\n \"def random_advice(message):\\n advice = requests.get(\\\"https://api.adviceslip.com/advice\\\").json()['slip']['advice']\\n\\n return advice\",\n \"def generate_products():\\n # initialize list of noun and adj\\n num_products = 30\\n products = [0] * num_products\\n prices = [0] * num_products\\n weights = [0] * num_products\\n flammabilities = [0] * num_products\\n\\n # initlize random word object\\n random = RandomWords()\\n\\n adj = [random.get_random_word(includePartOfSpeech=\\\"adjective\\\")\\n for product in products]\\n noun = [random.get_random_word(includePartOfSpeech=\\\"noun\\\")\\n for product in products]\\n products = [noun + \\\" \\\" + adj for noun, adj in zip(adj, noun)]\\n\\n prices = [random.randint(5, 100) for price in prices]\\n weights = [random.randint(5, 100) for weight in weights]\\n flammabilities = [random.randint(0.0, 2.5)\\n for flammability in flammabilities]\\n\\n return products, prices, weights, flammabilities\",\n \"def setup_random_opinions_representatives():\\r\\n global CATEGORIES\\r\\n \\r\\n ideas_dic = {}\\r\\n \\r\\n for i in CATEGORIES:\\r\\n #idea = Idea(1,\\\"\\\",i, 1-random.expovariate(6))\\r\\n if i == 1:\\r\\n idea = Idea(1,\\\"\\\",i, random.uniform(-1,-0.5))\\r\\n elif i == 2:\\r\\n idea = Idea(1,\\\"\\\",i, random.uniform(-1,-0.5))\\r\\n elif i == 3:\\r\\n idea = Idea(1,\\\"\\\",i, random.uniform(0.5,1))\\r\\n \\r\\n# idea = Idea(1,\\\"\\\",i, random.uniform(0.5,1))\\r\\n ideas_dic[i] = idea\\r\\n \\r\\n return ideas_dic\",\n \"def getAchievements(self) -> list:\\n return self.state[ACHIEVEMENTS]\",\n \"def darts(self):\\r\\n return self.alphas[0].keys()\",\n \"def add_adjectives(self, *sAdjs):\\n self.adjectives += list(sAdjs)\",\n \"def skills():\\n all_skills = [\\\"strength\\\", \\\"patience\\\", \\\"cleanliness\\\", \\\"leadership\\\", \\\"communication\\\",\\n \\\"science\\\", \\\"math\\\", \\\"engineering\\\", \\\"intelligence\\\", \\\"driving\\\"]\\n\\n random.shuffle(all_skills)\\n return all_skills[0:2]\",\n \"def effect_list(self):\\n moods = []\\n for mood in self._moodlist:\\n if \\\"name\\\" in mood:\\n moods.append(mood['name'])\\n return moods\",\n \"def get_ideas(self):\\n fields = ['content', 'clusterIDs', 'isGamechanger',\\n 'userID', 'promptID']\\n return self.get_data(\\\"ideas\\\", fields)\",\n \"def get_list(self):\\n categories = []\\n for attribut in self.attributes:\\n attr = getattr(self, attribut, False)\\n if attr is True:\\n categories.append(attribut)\\n if getattr(self, 'education') is True:\\n categories.append(_(u'education'))\\n if getattr(self, 'training') is True:\\n categories.append(_(u'training'))\\n if getattr(self, 'tutoring') is True:\\n categories.append(_(u'tutoring'))\\n\\n return categories\",\n \"def p_banner():\\n return random.choice([banner, banner_two, banner_three, banner_four, banner_five])\",\n \"def give_lists(self):\\r\\n hard = []\\r\\n easy = []\\r\\n medium = []\\r\\n for question in self.get_all():\\r\\n if question.difficulty_level == \\\"easy\\\":\\r\\n easy.append(question)\\r\\n elif question.difficulty_level == \\\"medium\\\":\\r\\n medium.append(question)\\r\\n else:\\r\\n hard.append(question)\\r\\n return easy,medium,hard\",\n \"def shuffled_answers(self):\\n if self.answers is None:\\n return []\\n answers = list(self.answers)\\n shuffle(answers)\\n return answers\",\n \"def _generateDescription(self, obj, **args):\\n result = []\\n if obj.description:\\n label = self._script.utilities.displayedLabel(obj) or \\\"\\\"\\n name = obj.name or \\\"\\\"\\n desc = obj.description.lower()\\n if not (desc in name.lower() or desc in label.lower()):\\n result.append(obj.description)\\n return result\",\n \"def get_dialogue_acts(self):\\n return self.DAs\",\n \"def variation_descriptors(civic_vid33):\\n return [civic_vid33]\",\n \"def getArrettes(self) -> list:\\n return self._arrettes\",\n \"def get_describable_list(request):\\n describables = []\\n\\n from django.apps import apps\\n for entity in apps.get_app_config('descriptor').describable_entities:\\n content_type = get_object_or_404(\\n ContentType, app_label=entity._meta.app_label, model=entity._meta.model_name)\\n\\n describables.append({\\n 'id': content_type.pk,\\n 'value': \\\"%s.%s\\\" % (entity._meta.app_label, entity._meta.model_name),\\n 'label': str(entity._meta.verbose_name.capitalize())\\n })\\n\\n return HttpResponseRest(request, describables)\",\n \"def get_description():\\r\\n return{\\\"I'll never yield!\\\":\\\"Grants a shield.\\\",\\r\\n \\\"Stay still!\\\":\\\"Affected unit cannot act in their turn.\\\"\\r\\n }\",\n \"def single_introduction(end):\\n return [random.randint(0,end)]\",\n \"def description_ques(analysis):\\n if analysis.sv[0].vrb_tense.startswith('present'):\\n analysis.sv[0].vrb_tense = 'present progressive'\\n if analysis.sv[0].vrb_tense.startswith('past'):\\n analysis.sv[0].vrb_tense = 'present progressive'\\n sentence = y_o_question(analysis)\\n for i in sentence:\\n if i == 'liking':\\n sentence[sentence.index(i)] = 'like'\\n return ['what'] + sentence\",\n \"def get_paragraphs():\\n soup = get_html()\\n paragraphs = []\\n for i in soup.findAll('div', {'class': 'faq-list1__hide'}):\\n p = str(i.get_text().strip())\\n paragraphs.append(p)\\n return paragraphs\",\n \"def _sample_episode(self):\\n\\n Knovel = random.sample(self.labelIds, self.nKnovel)\\n #print(Knovel)\\n #exit(0)\\n nKnovel = len(Knovel)\\n assert((self.nTestNovel % nKnovel) == 0)\\n nEvalExamplesPerClass = int(self.nTestNovel / nKnovel)\\n #print(nEvalExamplesPerClass)\\n #exit(0)\\n Tnovel = []\\n Exemplars = []\\n for Knovel_idx in range(len(Knovel)):\\n ids = (nEvalExamplesPerClass + self.nExemplars)\\n img_ids = random.sample(self.labels2inds[Knovel[Knovel_idx]], ids) \\n\\n imgs_tnovel = img_ids[:nEvalExamplesPerClass]\\n imgs_emeplars = img_ids[nEvalExamplesPerClass:]\\n #print(imgs_tnovel)\\n #exit(0)\\n Tnovel += [(img_id, Knovel_idx) for img_id in imgs_tnovel]\\n Exemplars += [(img_id, Knovel_idx) for img_id in imgs_emeplars]\\n assert(len(Tnovel) == self.nTestNovel)\\n assert(len(Exemplars) == nKnovel * self.nExemplars)\\n random.shuffle(Exemplars)\\n random.shuffle(Tnovel)\\n\\n return Tnovel, Exemplars\",\n \"def getAllFirstDescWords(itemList):\\r\\n itemList = list(set(itemList)) # make itemList unique\\r\\n descWords = []\\r\\n for item in itemList:\\r\\n descWords.append(worldItems[item][DESCWORDS][0])\\r\\n return list(set(descWords))\",\n \"def get_techniques(db):\\n\\n techniques = []\\n for element in db:\\n for technique in element['techniques_used']:\\n if technique not in techniques:\\n techniques.append(technique)\\n \\n return sorted(techniques)\",\n \"def animals(self):\\n return self.herbivores + self.carnivores\",\n \"def buscaPalavras(self):\\n dataSet=self.stemmerAplay()\\n todasPalavras =[]\\n for (notice, clazz) in dataSet:\\n todasPalavras.extend(notice)\\n return todasPalavras\",\n \"def get_feature_names(self):\\n\\t\\treturn np.array(['nouns', 'adjectives', 'verbs', 'adverbs'])\",\n \"def getListOfObjectives(self, *args):\\n return _libsbml.FbcModelPlugin_getListOfObjectives(self, *args)\",\n \"def get_verb_list(lang: Lang) -> VerbsList:\\n with open(f'languages/{lang}/verbs.csv', newline='') as csv_file:\\n return list(reversed([VerbInf(word[0]) for word in csv.reader(csv_file, delimiter=',')]))\",\n \"def diamonds(self):\\n return sorted(tuple([v for v in self if v.suit == 'diamonds']), reverse=True)\",\n \"def get_hero_list(self):\\n out_list = []\\n for key, _ in self._heroes.items():\\n out_list.append(key)\\n out_list.sort()\\n return out_list\",\n \"def getIngredients():\\n ingredients = ['Whiskey', 'Tequila', 'Vodka', 'Blue Curacao', 'Orange Juice',\\n 'Pineapple Juice', 'Cranberry Juice', 'Sour Mix']\\n return ingredients\",\n \"def list(self, frame=0):\\n text = []\\n if not self.number_of_variations:\\n return \\\"\\\"\\n for group_number in range(1, len(self.varexercise_numbers)+1):\\n text.extend(\\n self.group_list(group_number))\\n return text\",\n \"def listPalettes(show=True, getdescriptions=True):\\n\\n # Print palette names to terminal if that's what the user wants\\n if show:\\n print \\\"Currently available palettes:\\\"\\n \\n for palette_name in sorted(palettes.keys()):\\n print palette_name,\\n if not getdescriptions:\\n print\\n else:\\n if ((not (palette_name in descriptions)) \\n or (descriptions[palette_name] == \\\"\\\")):\\n print ((\\\": Palette %s does not have a description, \\\"\\n \\\"please write one and add it to the descriptions dict \\\"\\n \\\"in palettes.py.\\\") % palette_name)\\n continue\\n # end if\\n print \\\": %s\\\" % descriptions[palette_name]\\n # end if\\n # end for\\n # end if\\n \\n if not getdescriptions:\\n return palettes.keys()\\n else:\\n return (palettes.keys(), descriptions)\\n # end if\",\n \"def get_to_review(self) -> VerbsList:\\n to_review = [verb for verb, stats in self.practice_list.items() if pick(stats)]\\n\\n if len(to_review) < self.min_to_review:\\n for _ in range(self.min_to_review - len(to_review)):\\n to_review.append(self.to_learn_list.pop())\\n\\n return to_review\",\n \"def Obtener_Lista(self):\\n\\t\\treturn [self,self.Nombre+\\\" \\\"+self.Apellido,self.ID,self.Fecha, \\n\\t\\tself.Edad,self.Test,self.Posicion,self.Prioridad,self.Progreso,self.Informe]\",\n \"def random_data(self) -> (str, str):\\n random_animal = random.choice(self.animals_list)\\n synsets = wn.synsets(str(random_animal))\\n definition = \\\"\\\"\\n while True:\\n if len(synsets) != 0:\\n for synset in synsets:\\n if synset.lexname() == 'noun.animal':\\n definition = synset.definition()\\n break\\n else:\\n random_animal = random.choice(self.animals_list)\\n synsets = wn.synsets(str(random_animal))\\n return random_animal, definition\",\n \"def hp_descartes(self, hyper_params=None):\\n if hyper_params is None: hyper_params = list(self.hyper_params.values())\\n if len(hyper_params) == 0: yield OrderedDict()\\n else:\\n first_hp = hyper_params[0]\\n assert isinstance(first_hp, CategoricalHP)\\n for val in first_hp.choices:\\n configs = OrderedDict()\\n configs[first_hp.name] = val\\n for cfg_dict in self.hp_descartes(hyper_params[1:]):\\n configs.update(cfg_dict)\\n yield configs\",\n \"def get_samples(meta, classes_list):\\n\\n h = list(meta[meta['DiseaseState'].isin(classes_list[0])].index)\\n dis = list(meta[meta['DiseaseState'].isin(classes_list[1])].index)\\n\\n return [h, dis]\",\n \"def get_des(tree_before: Node, tree_after: Node) -> List[Description]:\\n\\n des_gen: Iterator[Description] = get_des_gen(tree_before, tree_after)\\n\\n return list(des_gen)\",\n \"def apertures(self):\\n return self._apertures\",\n \"def get_details(disease):\\n\\treturn d_desc_map[disease]\",\n \"def list(self):\\n\\t\\treturn self.link_words\",\n \"def get_introduction(length=128, words=None):\",\n \"def getaslist(self):\\n l = []\\n l.append(self.title.strip().encode('utf-8'))\\n l.append(self.desc.strip().encode('utf-8'));\\n l.append(self.course_number.strip().encode('utf-8'))\\n l.append(self.duration.strip().encode('utf-8'))\\n l.append(self.difficulty.strip().encode('utf-8'))\\n l.append(self.instructors.strip().encode('utf-8'))\\n l.append(self.url.strip().encode('utf-8'))\\n return l\",\n \"def show_pairs(self):\\n\\n all_pairs = []\\n for plug in self.plugleads:\\n all_pairs.append(plug.pair)\\n return all_pairs\",\n \"def get_verbs(self) -> Set[str]:\",\n \"def get_attributes(cls):\\r\\n return [\\r\\n Attribute(\\\"show_correctness\\\", \\\"always\\\"),\\r\\n Attribute(\\\"submitted_message\\\", \\\"Answer received.\\\"),\\r\\n Attribute(\\\"label\\\", \\\"\\\"),\\r\\n ]\",\n \"def make_ad(state):\\n return [adnumber(val) for val in state]\",\n \"def list(self):\\n a0 = map(self._alphabet.unrank, self._labels[0])\\n a1 = map(self._alphabet.unrank, self._labels[1])\\n return [a0, a1]\",\n \"def sample_effects(self) -> List[List[str]]:\\n return [\\n [\\n str(item.sample() if isinstance(item, RandomValue) else item)\\n for item in effect\\n ]\\n for effect in self.effects\\n ]\",\n \"def desc (self):\\n uni = {\\n \\\"name\\\": self.name,\\n \\\"G\\\": self.G,\\n \\\"time\\\": self.time,\\n \\\"bodies\\\": {bid: body.desc() for bid, body in self.bodies.iteritems()},\\n \\\"description\\\": self.description,\\n \\\"datatime\\\": self.datatime\\n }\\n\\n return uni\",\n \"def create_samples(descriptions, candidates_lists, labels):\\n samples = []\\n for description, candidate_list, label in tqdm(\\n zip(descriptions, candidates_lists, labels)\\n ):\\n negative_examples = [\\n candidate for candidate in candidate_list if candidate != label\\n ]\\n negative_examples = random.choices(negative_examples, k=4)\\n if label in candidate_list:\\n positive_example = [\\n candidate for candidate in candidate_list if candidate == label\\n ][0]\\n else:\\n positive_example = label\\n samples.append(InputExample(texts=[description, positive_example], label=1))\\n for neg_ex in negative_examples:\\n samples.append(InputExample(texts=[description, neg_ex], label=0))\\n return samples\",\n \"def list(cls):\\n\\n forges = cls.forges()\\n\\n return {\\\"forges\\\": [{\\\"id\\\": id, \\\"description\\\": forges[id]} for id in sorted(forges.keys())]}\",\n \"def get_alignments(self) -> list:\",\n \"def return_augmented_sentences(self) -> list:\\n return self.augmented_sentence_list\",\n \"def describe(self):\\n if not self.name:\\n raise ValueError(\\\"Sorry! id_type must be 'name'\\\")\\n r = requests.get(f\\\"https://api.fda.gov/drug/ndc.json?search=brand_name:{self.drug_id}\\\")\\n response = r.json()\\n data = response['results'][0]\\n self.brand_name = data['brand_name']\\n self.generic_name = data['generic_name']\\n self.active_ingredients = [i['name'] for i in data['active_ingredients']]\\n self.pharm_class = get_pharm_class(self.drug_id, as_df=False)\\n self.route = data['route']\\n self.ndc = data['product_ndc']\\n self.product_type = data['product_type']\\n\\n print(f\\\"Generic name: {self.generic_name}\\\")\\n print(f\\\"Brand name: {self.brand_name}\\\")\\n print(f\\\"Active ingredients: {self.active_ingredients}\\\")\\n print(f\\\"Routes of administration: {self.route}\\\")\\n print(f\\\"Pharmacologic Classes: {self.pharm_class}\\\")\\n print(f\\\"NDC: {self.ndc}\\\")\\n print(f\\\"Product type: {self.product_type}\\\")\",\n \"def get_vo_descriptions(vos):\\n all_vos = vo_core.list_vos()\\n vos_with_desc = []\\n for vo in all_vos:\\n if vo['vo'] in vos:\\n vos_with_desc.append((vo['vo'], vo['description']))\\n return vos_with_desc\",\n \"def therapy_descriptors(civic_tid146):\\n return [civic_tid146]\",\n \"def get_antags(self):\\n antags = []\\n for obj in self.antagobjs.group_by(AntagObjective.mindkey):\\n antag = {'key': obj.mindkey, 'name': obj.mindname, 'role': obj.special_role}\\n antags.append(antag)\\n return antags\",\n \"def sample_apr(self):\\n FLAGS.full_wiki = True\\n FLAGS.apr_dir = 'Directory Name'\\n apr = apr_lib.ApproximatePageRank()\\n seeds = [\\n 'Q7755', 'Q878070', 'Q428148', 'Q679847', 'Q2609670', 'Q174834',\\n 'Q188628'\\n ]\\n unique_facts = apr.get_facts(\\n seeds, topk=200, alpha=0.9, seed_weighting=True)\\n facts = sorted(unique_facts, key=lambda tup: tup[1][1], reverse=True)\\n nl_facts = ' . '.join([\\n str(x[0][0][1]) + ' ' + str(x[1][0][1]) + ' ' + str(x[0][1][1])\\n for x in facts\\n ])\\n tf.logging.info('Extracted facts: %s', nl_facts)\",\n \"def get_pledge_list():\\n with open('pledge.txt') as f:\\n pledge_list = f.read().split()\\n\\n #Eliminate punctuation from the end of words\\n pledge_list_noPunct = [word if word[-1] in ascii_letters else word[:-1] for word in pledge_list]\\n\\n return pledge_list_noPunct\",\n \"def _process_examples(self):\\n logger = getLogger(\\\"problog_lfi\\\")\\n # value can be True / False / None\\n # ( atom ), ( ( value, ... ), ... )\\n\\n # Simple implementation: don't add neutral evidence.\\n\\n # ad_groups is a list of lists where each list contains an AD\\n ad_groups = list()\\n for ad in self._adatoms:\\n # if it's an AD group\\n if len(ad[1]) > 1:\\n ad_list = []\\n for var in ad[1]:\\n ad_list.append(Term(self.names[var].functor, *self.names[var].args))\\n ad_groups.append(tuple(ad_list))\\n logger.debug(\\\"AD Groups\\\\t\\\\t:\\\" + str(ad_groups))\\n\\n def all_false(d):\\n \\\"\\\"\\\"\\n This function recognizes inconsistent evidence s.t. all values are False in AD.\\n :param d: dictionary of ADs in form {term: value}\\n value can be True, False, None, \\\"Template\\\"\\n :return: whether all values are False\\n \\\"\\\"\\\"\\n # false_count should be the same as the length of d\\n false_count = sum(v is False for v in d.values())\\n return false_count == len(d)\\n\\n def all_false_except_one(d):\\n \\\"\\\"\\\"\\n This function recognizes incomplete evidence s.t.\\n the non-False value in AD needs to be set to True.\\n :param d: dictionary of ADs in form {term: value}\\n value can be True, False, None, \\\"Template\\\"\\n :return: whether all values except one are False\\n \\\"\\\"\\\"\\n false_count = sum(v is False for v in d.values())\\n the_left_is_none = bool(sum(v is None for v in d.values()))\\n return (false_count == len(d) - 1) and the_left_is_none\\n\\n def getADtemplate(d, atom=None):\\n \\\"\\\"\\\"\\n This function gets atom's complement AD template.\\n This should only be used when the AD contains non-ground terms.\\n :param d: dictionary of ADs in form {term: value}\\n value can be True, False, None, \\\"Template\\\"\\n :param atom: an evidence\\n :return: atom's complement AD template\\n \\\"\\\"\\\"\\n if atom is not None:\\n temp_dict = {\\n k: v\\n for k, v in d.items()\\n if v == \\\"Template\\\" and atom.signature != k.signature\\n }\\n return temp_dict\\n else:\\n temp_dict = {k: v for k, v in d.items() if v == \\\"Template\\\"}\\n return temp_dict\\n\\n def add_to_ad_evidence(pair, l, ADtemplate):\\n \\\"\\\"\\\"\\n :param pair: a new pair of (atom, value)\\n :param l: a list of dictionaries, all dictionaries need to have the same format\\n :return:\\n \\\"\\\"\\\"\\n (k, v) = pair\\n # if entry k exists, update the value with k\\n for d in l:\\n if k in d:\\n d[k] = v\\n return\\n # if entry k does not exist, create a new dictionary from template\\n # and instantiate it with k\\n new_d = dict()\\n for temp_k in ADtemplate.keys():\\n new_key = Term(temp_k.functor, *k.args)\\n new_d[new_key] = None\\n # put v in there\\n new_d[k] = v\\n l.append(new_d)\\n\\n if self.infer_AD_values:\\n result = ExampleSet()\\n inconsistent = False\\n # iterate over all examples given in .ev\\n for index, example in enumerate(self.examples):\\n ad_evidences = []\\n non_ad_evidence = {}\\n for ad_group in ad_groups:\\n # create a dictionary to memorize what evidence is given in AD\\n d = dict()\\n # TODO: what if the AD contains both ground and non-ground????\\n # e.g. t(_)::a; t(_)::b(X)\\n for var in ad_group:\\n if var.is_ground():\\n d[var] = None # for ground unknown evidence\\n else:\\n d[var] = \\\"Template\\\" # for unground unknown evidence\\n ad_evidences.append(d)\\n\\n # add all evidence in the example to ad_evidences\\n for atom, value in example:\\n # if atom has a tunable probability to learn\\n if any([atom.signature == name.signature for name in self.names]):\\n if len(atom.args) == 0: # Propositional evidence\\n # insert evidence\\n is_ad = False\\n for d in ad_evidences:\\n if atom in d:\\n d[atom] = value\\n is_ad = True\\n if not is_ad:\\n non_ad_evidence[\\n atom\\n ] = value\\n else: # First Order evidence\\n # find the right AD dictionary : AD_dict\\n AD_dict = None\\n for d in ad_evidences:\\n if any([atom.signature == k.signature for k in d]):\\n AD_dict = d\\n # if the instantiation is new, add it as a key to the dictionary\\n if AD_dict and AD_dict.get(atom) is None:\\n AD_dict[atom] = value\\n # also add other AD parts in the dictionary with value==None\\n other_ADs = getADtemplate(AD_dict, atom)\\n for otherAD in other_ADs.keys():\\n new_key = Term(otherAD.functor, *atom.args)\\n AD_dict[new_key] = AD_dict.get(new_key, None)\\n else:\\n non_ad_evidence[atom] = value\\n else:\\n non_ad_evidence[atom] = value\\n\\n # grounded_ad_evidences contains all usable evidence (gound, not template)\\n grounded_ad_evidences = []\\n for d in ad_evidences:\\n # for first order evidence dictionaries\\n if \\\"Template\\\" in d.values():\\n # new_ad_evidence is a list of dictionaries\\n # each dictionary is a group of the AD template instantiation\\n new_ad_evidence = list()\\n # get template AD evidence\\n ADtemplate = getADtemplate(d)\\n # group all pairs according to ADtemplate\\n for k, v in d.items():\\n if v != \\\"Template\\\":\\n add_to_ad_evidence((k, v), new_ad_evidence, ADtemplate)\\n grounded_ad_evidences += new_ad_evidence\\n else:\\n # simply us them\\n grounded_ad_evidences.append(d)\\n\\n inconsistent_example = False\\n for i, d in enumerate(grounded_ad_evidences):\\n # inconsistent1 = multiple_true(d)\\n inconsistent2 = all_false(d)\\n add_compliment = all_false_except_one(d)\\n\\n if inconsistent2:\\n inconsistent_example = True\\n continue\\n elif add_compliment:\\n for key, value in d.items():\\n if value is None:\\n grounded_ad_evidences[i][key] = True\\n\\n if not inconsistent_example and len(grounded_ad_evidences) > 0:\\n # There are (fully tunable) ADs in the program\\n evidence_list = []\\n for d in grounded_ad_evidences:\\n for key, value in d.items():\\n if value is not None:\\n if (key, value) not in evidence_list:\\n # TODO: Switch to orderedSet so we don't have to check whether it's in already\\n evidence_list.append((key, value))\\n\\n for key, value in non_ad_evidence.items():\\n evidence_list.append((key, value))\\n\\n atoms, values = zip(*evidence_list)\\n result.add(index, atoms, values)\\n\\n else:\\n # (No AD case) or (Inconsistent Evidence Case)\\n atoms, values = zip(*example)\\n result.add(index, atoms, values)\\n return result\\n else:\\n # smarter: compile-once all examples with same atoms\\n result = ExampleSet()\\n for index, example in enumerate(self.examples):\\n atoms, values = zip(*example)\\n result.add(index, atoms, values)\\n return result\",\n \"def ad_meta(self, d):\\n names = []\\n if is_str(d):\\n aname = d[:-2] + 'name'\\n if aname in self.ad_cols:\\n names = [aname]\\n else:\\n names = [n[:-2] + 'name' for n in d if n[:-2] + 'name' in self.ad_cols]\\n return self.configs['meta_ad'] + names\",\n \"def get_assay_solutions(self):\\n vocabs = []\\n assay = self.get_assay()\\n if not assay:\\n return vocabs\\n for solution_type_name in assay.needed_solutions:\\n type_batches = find(Type=solution_type_name,\\n expires={'query': datetime.today().date(),\\n 'range': 'min'},\\n sort_on='expires')\\n\\n tmp = []\\n for batch in type_batches:\\n tmp.append([batch.id,\\n batch.Title,\\n batch.expires.strftime('%Y-%m-%d')])\\n vocabs.append([solution_type_name, tmp])\\n return vocabs\",\n \"def elements_sequence(cls):\\n return [\\n \\\"id\\\",\\n \\\"extension\\\",\\n \\\"modifierExtension\\\",\\n \\\"definition\\\",\\n \\\"doseForm\\\",\\n \\\"intendedRoute\\\",\\n \\\"ingredient\\\",\\n \\\"drugCharacteristic\\\",\\n ]\",\n \"def descents(self, index_set=None, positive=False):\\n if index_set==None:\\n index_set=self.parent().index_set()\\n return [ i for i in index_set if self.has_descent(i, positive) ]\",\n \"def getDescriptors(self, dsc = \\\"\\\"):\\r\\n return \\\"\\\"\",\n \"def get_descuentos(self):\\n return float(\\n self.input.get_text(liquidaciones_historicas_catalog.DESCUENTOS).replace(\\\".\\\", \\\"\\\").replace(\\\",\\\", \\\".\\\"))\",\n \"def desc(self) -> List[str]:\\n format_data: dict = {\\n key: val\\n for key, val in self.__dict__.items()\\n if (\\n not key.startswith(\\\"_\\\") and key != \\\"desc\\\" and not callable(val)\\n )\\n }\\n format_data.update(self._kwargs)\\n ret: List[str] = []\\n for descr in self._desc:\\n try:\\n descr = descr % format_data\\n except Exception as kerr:\\n raise PyParamException(\\n f\\\"Description of {self.namestr()!r} is formatting \\\"\\n \\\"using kwargs from contructor. \\\\n\\\"\\n f\\\"- desc: {descr}\\\\n\\\"\\n f\\\"- key : {{... {str(kerr)[1:-1]} ...}}\\\"\\n ) from None\\n else:\\n ret.append(descr)\\n return ret\",\n \"def prepare_text_data(descriptions):\\n text_data = []\\n for line in descriptions:\\n tokens = prepare_text_for_lda(line)\\n text_data.append(tokens)\\n return text_data\",\n \"def collection(self):\\n questions = []\\n choice_list = []\\n answers = []\\n\\n if self.form=='The correct German word':\\n for i in range(self.num_ques):\\n question, options, answer = self.generate_eng2ger()\\n questions.append(question)\\n choice_list.append(options)\\n answers.append(answer)\\n else:\\n for i in range(self.num_ques):\\n question, options, answer = self.generate_ger2eng()\\n questions.append(question)\\n choice_list.append(options)\\n answers.append(answer)\\n\\n return questions, choice_list, answers\",\n \"def apertures(self):\\n return tuple(a for a in self._apertures if a is not None)\",\n \"def _getChoices(self, acronym):\\n # get matches from acronymDB\\n matches = []\\n if(acronym in self.acronymDB):\\n matches += self.acronymDB[acronym]\\n if(acronym[-1] == \\\"s\\\" and acronym[:-1] in self.acronymDB):\\n matches += self.acronymDB[acronym]\\n\\n # create training data\\n X_train, y_train = [], []\\n for definition, articleID, ignored_var in matches:\\n text = self.articleDB[articleID]\\n X_train.append(\\n ExpansionChoice(article_id=articleID, article_text=text))\\n y_train.append(definition)\\n\\n # create y labels to group similar acronyms\\n y_labels, labelToExpansion = self._processChoices(y_train)\\n\\n return X_train, y_labels, labelToExpansion\",\n \"def PredictBase(self, adjectives):\\n # Get the list of Adjectives which have sentiment polarity greater than 0.1\\n if self.smallReviews:\\n return 1.0\\n PolarAdjList = [l for l in adjectives if l in self.lexicon and math.fabs(float(self.lexicon[l])) > 0.1]\\n if len(PolarAdjList) > 0:\\n return 12.0/len(PolarAdjList)\\n # elif len(list2) < 8:\\n # return 2.0\\n else:\\n return 1.0\",\n \"def get_description(self):\",\n \"def ExtractSentDetails(self, sentence):\\n if \\\"Adjectives\\\" in sentence:\\n adjList = [w.lower() for w in sentence[\\\"Adjectives\\\"] if w.lower() not in Angel.stopWords and w.lower() not in Angel.engNames]\\n adjectives = set(adjList)\\n else:\\n adjectives = set()\\n dependencies = defaultdict(dict)\\n if \\\"Dependencies\\\" in sentence:\\n if not isinstance(sentence[\\\"Dependencies\\\"],list):\\n sentence[\\\"Dependencies\\\"] = [sentence[\\\"Dependencies\\\"]]\\n for dep in sentence[\\\"Dependencies\\\"]:\\n line = dep.split(',')\\n if len(line) != 3:\\n continue\\n relation, adj, other = line\\n adj, other = adj.lower(), other.lower()\\n if relation in {'amod', 'acomp', 'ccomp', 'pobj', 'dep'}:\\n adj, other = other, adj\\n if relation not in dependencies[adj]:\\n dependencies[adj][relation] = set()\\n dependencies[adj][relation].add(other)\\n if relation == 'conj':\\n adjectives.add(other)\\n dictconj, other = defaultdict(dict), None\\n for adj in dependencies:\\n if 'conj' in dependencies[adj]:\\n for other in dependencies[adj]['conj']:\\n dictconj[other] = copy.deepcopy(dependencies[adj])\\n for adj in dictconj:\\n for relation in dictconj[adj]:\\n if relation not in dependencies[adj]:\\n dependencies[adj][relation] = set()\\n dependencies[adj][relation] |= dictconj[adj][relation]\\n return adjectives, dependencies\",\n \"def _get_appdesc_embeddings(self, features, training):\\n # Using GloVe, embed each token then aggregate them.\\n appdesc_embeddings = self._word_embedding_layer(\\n features['appdesc_token_id'])\\n appdesc_embeddings = self._aggregate_text_embedding(\\n features['appdesc_token_id'], appdesc_embeddings)\\n\\n if training:\\n appdesc_embeddings = tf.nn.dropout(\\n appdesc_embeddings, rate=self._hparams['dropout'])\\n\\n return appdesc_embeddings\",\n \"def load_description():\\n with open('description.txt') as description:\\n return [line.strip() for line in description]\",\n \"def to_adverb(self):\\n return self\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.605831","0.5822134","0.57419574","0.573242","0.55850464","0.5502515","0.5492401","0.5489824","0.5483387","0.5447286","0.54260534","0.5316058","0.52798134","0.5279397","0.5267892","0.5265623","0.525828","0.52477735","0.5244685","0.5200043","0.51937425","0.51871073","0.51700115","0.5165577","0.51619357","0.5148933","0.5103721","0.5102739","0.5100347","0.5086335","0.50788647","0.50779635","0.50728434","0.50336117","0.50302374","0.5019083","0.5006002","0.5004376","0.4975105","0.49738634","0.49696755","0.4968562","0.496571","0.49637082","0.49592713","0.492924","0.4923535","0.49155068","0.4911112","0.4907792","0.4878104","0.48605114","0.4850821","0.4838524","0.4831847","0.48165113","0.48138934","0.48116457","0.47989595","0.47848463","0.47799423","0.47780952","0.4775788","0.47711614","0.4767932","0.47675723","0.4766291","0.47629923","0.4759205","0.4755667","0.47549325","0.47523794","0.47502387","0.474666","0.47450402","0.4741852","0.47403827","0.47392106","0.4737233","0.47322586","0.4723873","0.47166508","0.4715262","0.4712803","0.4711702","0.47082305","0.4706386","0.47063842","0.47056648","0.47053358","0.470437","0.47019207","0.47002774","0.46991658","0.4698273","0.46982226","0.469817","0.46952024","0.46864656","0.4686065"],"string":"[\n \"0.605831\",\n \"0.5822134\",\n \"0.57419574\",\n \"0.573242\",\n \"0.55850464\",\n \"0.5502515\",\n \"0.5492401\",\n \"0.5489824\",\n \"0.5483387\",\n \"0.5447286\",\n \"0.54260534\",\n \"0.5316058\",\n \"0.52798134\",\n \"0.5279397\",\n \"0.5267892\",\n \"0.5265623\",\n \"0.525828\",\n \"0.52477735\",\n \"0.5244685\",\n \"0.5200043\",\n \"0.51937425\",\n \"0.51871073\",\n \"0.51700115\",\n \"0.5165577\",\n \"0.51619357\",\n \"0.5148933\",\n \"0.5103721\",\n \"0.5102739\",\n \"0.5100347\",\n \"0.5086335\",\n \"0.50788647\",\n \"0.50779635\",\n \"0.50728434\",\n \"0.50336117\",\n \"0.50302374\",\n \"0.5019083\",\n \"0.5006002\",\n \"0.5004376\",\n \"0.4975105\",\n \"0.49738634\",\n \"0.49696755\",\n \"0.4968562\",\n \"0.496571\",\n \"0.49637082\",\n \"0.49592713\",\n \"0.492924\",\n \"0.4923535\",\n \"0.49155068\",\n \"0.4911112\",\n \"0.4907792\",\n \"0.4878104\",\n \"0.48605114\",\n \"0.4850821\",\n \"0.4838524\",\n \"0.4831847\",\n \"0.48165113\",\n \"0.48138934\",\n \"0.48116457\",\n \"0.47989595\",\n \"0.47848463\",\n \"0.47799423\",\n \"0.47780952\",\n \"0.4775788\",\n \"0.47711614\",\n \"0.4767932\",\n \"0.47675723\",\n \"0.4766291\",\n \"0.47629923\",\n \"0.4759205\",\n \"0.4755667\",\n \"0.47549325\",\n \"0.47523794\",\n \"0.47502387\",\n \"0.474666\",\n \"0.47450402\",\n \"0.4741852\",\n \"0.47403827\",\n \"0.47392106\",\n \"0.4737233\",\n \"0.47322586\",\n \"0.4723873\",\n \"0.47166508\",\n \"0.4715262\",\n \"0.4712803\",\n \"0.4711702\",\n \"0.47082305\",\n \"0.4706386\",\n \"0.47063842\",\n \"0.47056648\",\n \"0.47053358\",\n \"0.470437\",\n \"0.47019207\",\n \"0.47002774\",\n \"0.46991658\",\n \"0.4698273\",\n \"0.46982226\",\n \"0.469817\",\n \"0.46952024\",\n \"0.46864656\",\n \"0.4686065\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.81158966"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":2880,"cells":{"query":{"kind":"string","value":"Returns the noun, including all its describing adjectives, as a string."},"document":{"kind":"string","value":"def full_string(self):\n return \"{}: {}\".format(str(self.word), \" \".join([str(adj) for adj in self.adjectives]))"},"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 en_noun(t, label):\r\n s = label\r\n p = []\r\n is_uncountable = False\r\n \r\n # http://en.wiktionary.org/wiki/Template:en-noun\r\n head = t.arg(\"head\", label)\r\n p1 = t.arg(0)\r\n p2 = t.arg(1)\r\n \r\n if p1 == \"-\":\r\n # uncountable\r\n is_uncountable = True\r\n \r\n if p2 == \"s\":\r\n # ends by s\r\n p.append(head + \"s\")\r\n \r\n elif p2 is not None:\r\n # word\r\n p.append(p2)\r\n \r\n elif p1 == \"es\":\r\n # add es\r\n p.append(head + \"es\")\r\n \r\n elif p1 is not None:\r\n # use term\r\n p.append(p1)\r\n \r\n elif p1 is None and p2 is None:\r\n p.append(head+\"s\")\r\n\r\n for k,a in t.args.items():\r\n if not a.is_named():\r\n if k == 0 or k == 1:\r\n continue\r\n \r\n p.append(a.as_string())\r\n \r\n return (s, p, is_uncountable)","def get_nouns(self):\n\t\tblob = TextBlob(self.raw_string)\n\n\t\tfor word, tag in blob.tags:\n\t\t\tif tag in ['NNP', 'NN']:\n\t\t\t\tself.nouns.append(word.lemmatize())","def to_adverb(self):\n\n if 'AdverbPhrase' in self.variants:\n return self.variants['AdverbPhrase']\n\n return self.copy_with(typ=\"AdverbPhrase\",\n text=\"with \" + self.text)","def noun_string(data_org):\n chains = []\n tokens = word_tokenize(data_org)\n #tokenize to prepare for tagging\n w_tag = dict(nltk.pos_tag(tokens))\n chain = []\n for w, tag in w_tag.items():\n #find all nouns based on treebank format\n if tag.startswith('N'):\n chain.append(w)\n else:\n if len(chain) >= 3:\n chains.append(\" \".join(chain))\n chain = []\n\n #move information to dataframe for printing to excel\n df_noun_string = pd.DataFrame({'Noun Strings (3+ Nouns in a row)': chains}, columns = ['Noun Strings (3+ Nouns in a row)'])\n return df_noun_string","def __str__(self):\n out = self.san\n if self.comment != \"\":\n out += \" {\" + self.comment.replace('\\n', ' ') + \"}\"\n if len(self.nags) > 0:\n for n in self.nags:\n out += \" \" + n\n for v in self.variations:\n out += \" (\" + str(v).strip(' ') + \")\"\n return out","def get_compound_noun( ngram ):\n try:\n pattern = re.compile( '((?: ?\\\\b[^\\\\s]+(?:/NN.?/[a-z]+/[\\\\d]+)){2,})' )\n match = re.search( pattern, ngram )\n if match:\n compound = ''\n contains_root = False\n tokens = match.group().strip().split(' ')\n for t in tokens:\n # embed()\n items = t.split('/')\n compound += ( items[0] + ' ' )\n if items[3] == 0:\n contains_root = True\n if contains_root:\n return compound\n else:\n return None\n else:\n return None\n \n except ValueError:\n return None","def get_article_str(article_sents):\n article_str = \"\"\n for nlp_sent in article_sents:\n article_str += (' ' + nlp_sent.text + ' ')\n return article_str","def get_noun_phrases(blob):\n return blob.noun_phrases","def to_adverb(self):\n\n if 'AdverbPhrase' in self.variants:\n return self.variants['AdverbPhrase']\n\n return self.copy_with(typ=\"AdverbPhrase\",\n text=self.text + \" to\")","def indefinite(self):\n return \"an\" if self.short_desc[0] in 'aeiou' else \"a\"","def noun_lemma(word):\n if word.endswith(\"s\"):\n if word.endswith(\"ss\"):\n return word.lower()\n elif word.endswith(\"ies\"):\n return word[:-3].lower() + (\"y\")\n else:\n return word[:-1].lower()\n if word.endswith(\"men\"):\n return word[:-2].lower() + (\"an\")\n else:\n return word.lower()","def to_adverb(self):\n\n text = self.text\n ending = text[-1]\n if ending == \"e\":\n text = text[0:-1]+\"ly\"\n else:\n text = text+\"ly\"\n\n return self.copy_with(typ=\"AdverbPhrase\",\n text=text)\n\n # return AdverbPhrase(**self.locals(skip=[\"text\", \"typ\", \"variants\"]),\n # text=text,\n # **self.variants)","def a(noun):\n if p.singular_noun(noun) is not False:\n return noun\n else:\n return p.a(noun)","def get_abbreviated_description(self):\n word_array = str(self.description).split()[:25]\n abbreviated_description = \" \".join(word_array)\n return abbreviated_description","def __str__(self):\n return self.underscoreSentence.get().__str__()","def get_sentence(self):","def short_description(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"short_description\")","def greco_latin_plural_noun(base_token=None):\n\n output_string = \"\"\n if base_token is not None:\n if base_token.endswith(\"us\"):\n output_string = base_token[:-2] + \"i\"\n elif base_token.endswith(\"ma\"):\n output_string = base_token + \"ta\"\n elif base_token.endswith(\"a\"):\n output_string = base_token[:-1] + \"ae\"\n elif base_token.endswith((\"on\", \"um\")):\n output_string = base_token[:-2] + \"a\"\n elif base_token.endswith(\"sis\"):\n output_string = base_token[:-3] + \"ses\"\n elif base_token.endswith(\"is\"):\n output_string = base_token[:-2] + \"ides\"\n elif base_token.endswith(\"men\"):\n output_string = base_token[:-3] + \"mina\"\n elif base_token.endswith(\"ex\"):\n output_string = base_token[:-2] + \"ices\"\n elif base_token.endswith(\"x\"):\n output_string = base_token[:-1] + \"ces\"\n\n return output_string","def to_string(self):\n return \" \".join(self._words)","def __repr__(self):\r\n s = 'Words:\\n' + str(self.words) + '\\n\\n'\r\n s += 'Word lengths:\\n' + str(self.wordlengths) + '\\n\\n'\r\n s += 'Stems:\\n' + str(self.stems) + '\\n\\n'\r\n s += 'Sentence lengths:\\n' + str(self.sentencelengths) + '\\n\\n'\r\n s += 'Gerunds:\\n' + str(self.gerund)\r\n return s","def _get_sentence(sentence_data):\n return \" \".join([word for word, ne_tag in sentence_data])","def dish_str(n:Dish):\r\n return (n.name + \" $\" + str(n.price) + \" \" + str(n.calories) + \" cal\")","def get_description(self):\n return \"-\".join(\n map(str, (self.release, self.chromosome, self.start, self.reference, self.alternative))\n )","def find_noun(sent):\n noun = None\n\n if not noun:\n for w, p in sent.pos_tags:\n if p == 'NN': # This is a noun\n noun = w\n break\n if noun:\n #logger.info(\"Found noun: %s\", noun)\n pprint(\"FOUND NOUN\")\n pprint(noun)\n\n return noun","def NoDeltaGExplanation(self):\n for compound in self.reactants:\n if compound.compound.no_dg_explanation:\n name = compound.compound.common_names.all()[0].name\n return '%s %s' % (name,\n compound.compound.no_dg_explanation.lower())\n return None","def base_verb_string(self): \n if self.is_derived: \n _base_verb_str= getattr(self, '_base_verb_string', None)\n if is_empty_str(_base_verb_str):\n if self.is_phrase:\n # a phrase means the base verb is the actual verb being conjugated.\n self._base_verb_string = self.inf_verb_string\n elif self.reflexive == Reflexive.base_reflexive:\n self._base_verb_string = self.core_characters + self.inf_ending +'se'\n else: \n self._base_verb_string = self.core_characters + self.inf_ending\n return self._base_verb_string\n else:\n return None","def construct_response(pronoun, noun, verb):\n resp = []\n\n if pronoun:\n resp.append(pronoun)\n\n # We always respond in the present tense, and the pronoun will always either be a passthrough\n # from the user, or 'you' or 'I', in which case we might need to change the tense for some\n # irregular verbs.\n if verb:\n verb_word = verb[0]\n if verb_word in ('be', 'am', 'is', \"'m\"): # This would be an excellent place to use lemmas!\n if pronoun.lower() == 'you':\n # The bot will always tell the person they aren't whatever they said they were\n resp.append(\"aren't really\")\n else:\n resp.append(verb_word)\n if noun:\n pronoun = \"an\" if starts_with_vowel(noun) else \"a\"\n resp.append(pronoun + \" \" + noun)\n\n resp.append(random.choice((\"bro\", \"lol\", \"bruh\", \"nigga\", \"ha ha ha xD\", \"zzz.. oh i fell asleep :P\")))\n\n return \" \".join(resp)","def is_noun(tag):\r\n return tag in ['NN', 'NNS', 'NNP', 'NNPS']","def _create_formatted_string(self):\n string = NALSyntax.StatementSyntax.Start.value + \\\n self.get_subject_term().get_formatted_string()\n\n string += \" \" + self.get_copula_string() + \" \"\n\n string += self.get_predicate_term().get_formatted_string() + \\\n NALSyntax.StatementSyntax.End.value\n\n return string","def get_sentence(self):\n words = []\n for i in sorted([ind.index for ind in self.subtree_dict.keys()]):\n if isinstance(self.subtree_dict[i].label, (str, unicode)):\n words.append(self.subtree_dict[i].label)\n return ' '.join(words)","def morph_noun(word, number, a_an, feature):\n word = morph_number(word, number)\n if not (number in ['first', 'second']) and word[-1] == 's':\n return mark_noun_as_plural(word)\n else:\n return word\n\n if a_an == 'an':\n return mark_noun_as_an(word)\n else:\n return word","def _get_audio_feature_name(self, adjective):\n return self.SONG_ADJECTIVES.get(adjective, {}).get(\"name\")","def __get_relevant_words(sentence):\n nouns = None\n try:\n if sentence:\n tokens = nltk.word_tokenize(sentence)\n pos = nltk.pos_tag(tokens)\n nouns = [x[0] for x in pos if x[1].startswith('N') or x[1].startswith('F')]\n except Exception as e:\n nouns = None\n return ' '.join(nouns) if nouns else None","def get_extras(self, text=None):\n if text is None:\n text = self.nltk_text(self.text)\n # Tag parts of speech\n tagged = nltk.pos_tag(text)\n # Try for composed NNP / NNPS\n is_proper_noun = False\n text = []\n proper_noun = \"\"\n for (word, tag) in tagged:\n if not is_proper_noun and (tag == 'NNP' or tag == 'NNPS'):\n # Start building a proper noun\n proper_noun = word\n # Set it true\n is_proper_noun = True\n # Add it to annotations anyway\n text.append(word)\n elif tag == 'NNP' or tag == 'NNPS':\n # Previous was proper noun. So it may be combined\n proper_noun += \" \" + word\n # Add the single word to annotations anyway\n text.append(word)\n elif is_proper_noun and tag == 'IN':\n # Add what we have by now to the text\n text.append(proper_noun)\n # Previous was proper noun. So it may be composed\n proper_noun += \" \" + word\n elif is_proper_noun:\n # Add what we have by now to the text\n text.append(proper_noun)\n # Finished with proper noun, so set it false\n is_proper_noun = False\n # Remove duplicates\n seen = {}\n result = []\n for w in text:\n if w in seen:\n continue\n seen[w] = 1\n result.append(w)\n # Eliminate common\n result = [w for w in result if w.lower() not in self.common_words and\n w.lower() not in stopwords.words('english')]\n return result","def _get_full_entity(entity: spacy.tokens.Token) -> str:\n entity_string = SpacyEventExtractor._get_chunk(entity)\n\n word = entity\n while True:\n prep, word = SpacyEventExtractor._get_prep_with_word(word)\n if word is None:\n break\n entity_string += \" \" + prep\n return entity_string","def tag_to_wordnet(tag):\n if (tag == 'ADJ'): return('a')\n elif (tag == 'ADV'): return('r')\n elif (tag == 'NOUN'): return('n')\n elif (tag == 'VERB'): return('v')\n else: return None","def dc_title(self):\n return u\"{0} ({1}): {2} {3}\".format(\n self.label, self.in_assessment[0].timepoint,\n self.subjects[0].code_in_study,\n \"...\" if len(self.subjects) > 1 else \"\")","def get_descriptive_name(self):\r\n long_name=str(self.year)+' '+self.make+' '+self.model\r\n return long_name.title()","def get_description(self):\n text = \"is a student's t distribution; characterised by its degrees of freedom, which here is\"+str(self.dofs)+\".\"\n return text","def find_pronoun(sent):\n pronoun = None\n\n for word, part_of_speech in sent.pos_tags:\n # Disambiguate pronouns\n if part_of_speech == 'PRP' and word.lower() == 'you':\n # pronoun = 'I' +++++++++ORIGINAL++++++++++++\n pronoun = 'I'\n elif part_of_speech == 'PRP' and word == 'I':\n # If the user mentioned themselves, then they will definitely be the pronoun\n # pronoun = 'You' +++++++++ORIGINAL++++++++++++\n pronoun = 'You'\n return pronoun","def get_descriptive_name(self):\n long_name = f\"{self.make} {self.model} {self.year}\"\n \n return long_name.title()","def description(self):\n retstr = \"GNP with \" + str(self.n) + \" nodes and p = \" + str(self.p)\n return retstr","def conjugate_present_ere_verb(verb, pronoun, tense):\n avere = {\"io\": \"ho\", \"tu\": \"hai\", \"lui\": \"ha\", \"lei\": \"ha\", \"noi\": \"abbiamo\", \"voi\": \"avete\", \"loro\": \"hanno\"}\n\n irregulars = [\"avere\"]\n if verb in irregulars:\n if verb == \"avere\":\n return avere[pronoun]\n\n ere_endings = {\"io\": \"o\", \"tu\": \"i\", \"lui\": \"e\", \"lei\": \"e\", \"noi\": \"iamo\", \"voi\": \"ete\", \"loro\": \"ono\"}\n compiere_endings = {\"io\": \"o\", \"tu\": \"\", \"lei\": \"e\", \"lui\": \"e\", \"noi\": \"amo\", \"voi\": \"ete\", \"loro\": \"ono\"}\n\n if verb == \"compiere\":\n if tense == \"presente\":\n stripped_verb = strip_off_ending(verb, tense)\n new_verb = stripped_verb + compiere_endings[pronoun]\n return new_verb\n else:\n stripped_verb = strip_off_ending(verb, tense)\n new_verb = stripped_verb + ere_endings[pronoun]\n return new_verb\n else:\n stripped_verb = strip_off_ending(verb, tense)\n new_verb = stripped_verb + ere_endings[pronoun]\n return new_verb","def get_nouns(lemmas_tags):\r\n nouns = []\r\n for lemma in lemmas_tags:\r\n \"\"\"si la etiqueta es None porque no tiene lemma o es un sustantivo\"\"\"\r\n if lemma[1] == None or lemma[1][0] == 'n':\r\n \"\"\"se agrega solamente el lemma\"\"\"\r\n nouns.append(lemma[0])\r\n return nouns","def get_article_as_string(article,\n preprocess_type=PreprocessWordType.LEMMATIZE):\n article_string = ''\n for word in article.words:\n preprocessed_word = query_utils.preprocess_word(word, preprocess_type)\n if article_string == '':\n article_string = preprocessed_word\n else:\n article_string += (' ' + preprocessed_word)\n return article_string","def get_string(self, n):\n pad = self.get_pad(n)\n string = pad + self.word\n string += \"\\n\" + self.children[0].get_string(n + 1)\n string += \"\\n\" + self.children[1].get_string(n + 1)\n return string","def __str__(self):\n st=\"\"\n for g in self:\n st+=g.fasta()\n st+=\"\\n\"\n return st","def get_descriptive_name(self):\r\n long_name = str(self.year)+' '+self.make + ' '+self.model\r\n return long_name.title()","def get_descriptive_name(self): # 定义描述完整信息的方法\n long_name = str(self.year) + \" \" + self.make + \" \" + self.model # 拼接变量字符串并赋值变量\n return long_name.title() # 返回字符串并首字母大写","def verb_lemma(word):\n if word.endswith(\"ed\"):\n if word[:-2].endswith(\"v\"):\n return word[:-2].lower() + \"e\"\n elif word[:-2].endswith(\"at\"):\n return word[:-2].lower() + \"e\"\n elif word[:-2].endswith(\"it\"):\n return word[:-2].lower() + \"e\"\n elif word[:-2].endswith(\"et\"):\n return word[:-2].lower() + \"e\"\n elif word[:-2].endswith(\"ut\"):\n return word[:-2].lower() + \"e\"\n elif word[:-2].endswith(\"ac\"):\n return word[:-2].lower() + \"e\"\n elif word[:-2].endswith(\"i\"):\n return word[:-3].lower() + \"y\"\n elif word[:-2].endswith(\"ir\"):\n return word[:-2].lower() + \"e\"\n elif word[:-2].endswith(\"ag\"):\n return word[:-2].lower() + \"e\"\n elif word[:-2].endswith(\"nc\"):\n return word[:-2].lower() + \"e\"\n elif word[:-2].endswith(\"nu\"):\n return word[:-2].lower() + \"e\"\n else:\n return word[:-2].lower() \n elif word.endswith(\"ing\"):\n if word[:-3].endswith(\"v\"):\n return word[:-3].lower() + \"e\"\n elif word[:-3].endswith(\"at\"):\n return word[:-3].lower() + \"e\"\n elif word[:-3].endswith(\"it\"):\n return word[:-3].lower() + \"e\"\n elif word[:-3].endswith(\"et\"):\n return word[:-3].lower() + \"e\"\n elif word[:-3].endswith(\"ut\"):\n return word[:-3].lower() + \"e\"\n elif word[:-3].endswith(\"ac\"):\n return word[:-3].lower() + \"e\"\n elif word[:-3].endswith(\"i\"):\n return word[:-4].lower() + \"y\"\n elif word[:-3].endswith(\"ir\"):\n return word[:-3].lower() + \"e\"\n elif word[:-3].endswith(\"ag\"):\n return word[:-3].lower() + \"e\"\n elif word[:-3].endswith(\"nc\"):\n return word[:-3].lower() + \"e\"\n elif word[:-3].endswith(\"nu\"):\n return word[:-3].lower() + \"e\"\n else:\n return word[:-3].lower()\n elif re.match(r\"(does|did|done)\", word):\n return (\"do\")\n elif re.match(r\"(is|are|am|was|will|were|been)\", word):\n return (\"be\")\n elif word == (\"'s\"):\n return (\"be\")\n elif re.match(r\"(had|has|'ve)\", word):\n return (\"have\")\n else:\n return word.lower()","def lemma(self) -> str:","def __str__(self):\n return \"{}\".format(self.word)","def createSentence(self, n=0, v=0, o=0, p=0,prep=True):\n sentence = ''\n if not n:\n n = np.random.randint(1, 5)\n if not v:\n v = np.random.randint(1, 5)\n if not o:\n o = np.random.randint(1, 5)\n sentence += self.createPhrase(nwords=n) + ' '\n if sentence[:-1] not in ('mi', 'sina'):\n sentence += 'li '\n sentence += self.createPhrase(nwords=v) + ' e '\n sentence += self.createPhrase(nwords=o)\n if prep:\n if not p:\n p = np.random.randint(1, 5)\n sentence += ' ' + np.random.choice(self.prepositions) + ' ' + self.createPhrase(nwords=p)\n return sentence","def get_descriptive_name(self):\r\n long_name = str(self.year) + ' ' + self.make + ' ' + self.model\r\n return long_name.title()","def descString(self):\n return \"\".join ([self.Name, \" (AR \", str(self.AR), \", Max DEX \"\\\n , str(self.MaxDEXMod), \") - \", str(self.Value), \" gp\"])","def get_descriptive_name(self):\n long_name = str(self.year) + ' ' + self.make + ' ' + self.model\n return long_name.title()","def get_descriptive_name(self):\n long_name = str(self.year) + ' ' + self.make + ' ' + self.model\n return long_name.title()","def get_descriptive_name(self):\n long_name = str(self.year) + ' ' + self.make + ' ' + self.model\n return long_name.title()","def get_descriptive_name(self):\n long_name = str(self.year) + ' ' + self.make + ' ' + self.model\n return long_name.title()","def get_descriptive_name(self):\n long_name = str(self.year) + ' ' + self.make + ' ' + self.model\n return long_name.title()","def get_descriptive_name(self):\n long_name = str(self.year) + ' ' + self.make + ' ' + self.model\n return long_name.title()","def get_descriptive_name(self):\n long_name = str(self.year) + ' ' + self.make + ' ' + self.model\n return long_name.title()","def get_descriptive_name(self):\n long_name = str(self.year) + ' ' + self.make + ' ' + self.model\n return long_name.title()","def provn_representation(self):\n return \"'%s'\" % self._str","def get_human_readable(self):\n\n def yesno(key):\n if getattr(self, key) and getattr(self, key) > 0:\n return \"Y\"\n else:\n return \"N\"\n\n keys = (\n \"pvs1\",\n \"ps1\",\n \"ps2\",\n \"ps3\",\n \"ps4\",\n \"pm1\",\n \"pm2\",\n \"pm3\",\n \"pm4\",\n \"pm5\",\n \"pm6\",\n \"pp1\",\n \"pp2\",\n \"pp3\",\n \"pp4\",\n \"pp5\",\n \"ba1\",\n \"bs1\",\n \"bs2\",\n \"bs3\",\n \"bs4\",\n \"bp1\",\n \"bp2\",\n \"bp3\",\n \"bp4\",\n \"bp5\",\n \"bp6\",\n \"bp7\",\n )\n result = \", \".join([\"%s: %s\" % (key.upper(), yesno(key)) for key in keys])\n result += \", ACMG classification: %s\" % self.class_auto\n if self.class_override:\n result += \", ACMG class. override: %s\" % self.class_override\n return result","def __str__(self):\n # TODO also show relative abundance\n s = \"{} ion species\\n\".format(len(self.ions))\n for ion in self.ions:\n s += \" {:2s} (Z = {:3d}) {:.3e} particles\\n\".format(ion.getName(), ion.getCharge(), ion.getParticleNumber())\n \n return s","def summary_string(self) -> str:\n return f\"dixonoid: {self.plain_rules}\"","def sentence(self) -> str:\n id_word = {nodeattr['position']-1: nodeattr['form']\n for nodeid, nodeattr in self.syntax_nodes.items()}\n return ' '.join([id_word[i] for i in range(max(list(id_word.keys()))+1)])","def get_descriptive_name(self):\n long_name = f\"{self.year} {self.make} {self.model}\"\n return long_name.title()","def get_nouns(self):\n word_punct_token = WordPunctTokenizer().tokenize(self.sentence_string)\n\n clean_tokens = []\n for token in word_punct_token:\n token = token.lower()\n\n # remove any value that are not alphabetical\n new_token = re.sub(r\"[^a-zA-Z]+\", \"\", token)\n\n # remove empty value and single character value\n if new_token != \"\" and len(new_token) >= 2:\n vowels = len([v for v in new_token if v in \"aeiou\"])\n if vowels != 0: # remove line that only contains consonants\n clean_tokens.append(new_token)\n\n noun_types = [\"NN\", \"NNS\", \"NNP\", \"NNPS\", \"N\"]\n is_noun = lambda pos: pos in noun_types\n nouns = [word for (word, pos) in nltk.pos_tag(clean_tokens) if is_noun(pos)]\n\n if nouns:\n return nouns\n else:\n raise InvalidSentenceError(self.sentence_string)","def print_sentence_voice(self, final_subj, final_obj, verb, v_aux, v_tense, subj_tag, subj_word, final_mod2=None, final_root=None):\n new_verb = ''\n s_sentence1 = s_sentence2 = ''\n\n new_verb = gl.verb_conjugate(verb, v_aux, v_tense) + \" \"\n\n if new_verb.strip() == \"\":\n new_verb = gl.verb_conjugate(verb, v_aux, \"VMIS3S0\") + \" \"\n\n\n for k in sorted(final_subj.keys()):\n s_sentence1 += final_subj[k] + \" \"\n\n for k in sorted(final_obj.keys()):\n s_sentence2 += final_obj[k] + \" \"\n\n if final_mod2 != None:\n for k in sorted(final_mod2.keys()):\n s_sentence2 += final_mod2[k] + \" \"\n if final_root != None:\n for k in sorted(final_root.keys()):\n s_sentence2 += final_root[k] + \" \"\n\n\n #removing errors in punctuation\n s_sentence1 = s_sentence1.replace(\", .\", \".\").replace(\"; .\", \".\").replace(\": .\", \".\").replace(\", ?\", \"?\").replace(\"; ?\", \"?\").replace(\": ?\", \"?\").replace(\", !\", \"!\").replace(\"; !\", \"!\").replace(\": !\", \"!\").replace(\". .\", \".\")\n s_sentence2 = s_sentence2.replace(\", .\", \".\").replace(\"; .\", \".\").replace(\": .\", \".\").replace(\", ?\", \"?\").replace(\"; ?\", \"?\").replace(\": ?\", \"?\").replace(\", !\", \"!\").replace(\"; !\", \"!\").replace(\": !\", \"!\").replace(\". .\", \".\")\n\n\n return self.runTrueCaser(s_sentence1 + new_verb + s_sentence2)","def getSynopsis(self):\r\n\r\n if self.parent is None:\r\n command = self.__class__.__name__\r\n offset = command.find('Options')\r\n default = \"Usage: {0}{1}\".format(command[:offset],\r\n (self.longOpt and \" [options]\") or '')\r\n else:\r\n default = (self.longOpt and \" [options]\") or ''\r\n\r\n synopsis = getattr(self, \"synopsis\", default).rstrip()\r\n\r\n if self.parent is not None:\r\n synopsis = ' '.join((self.parent.getSynopsis(),\r\n self.parent.subCommand, synopsis))\r\n\r\n return synopsis","def getDisambiguatedByNextNoun(self, word):\n\t\treturn disambig_const.DISAMBIGUATATION_TABLE.get(word, {}).get('noun', {}).get('vocalized', word);","def get_root_verb(text):\n doc = nlp(text)\n for token in doc:\n if token.dep_ == \"ROOT\" and token.head.pos_ == \"VERB\":\n return str(token)\n else:\n return \"\"","def parse(text):\n parts = text.split(' ')\n noun = Noun(parts[0], int(parts[1]))\n\n parts = parts[2:]\n while len(parts) > 0:\n noun.add_adjectives(Word(parts[0], int(parts[1])))\n parts = parts[2:]\n return noun","def name(self):\n return self.tr('NATCAPES')","def convert(tag):\r\n if is_noun(tag):\r\n return wn.NOUN\r\n if is_adjective(tag):\r\n return wn.ADJ","def __str__(self):\n segments = []\n if self.comment:\n segments.append('\"%s\"' % self.comment)\n if self.sg:\n if self.dbow_words:\n segments.append('dbow+w') # also training words\n else:\n segments.append('dbow') # PV-DBOW (skip-gram-style)\n\n else: # PV-DM...\n if self.dm_concat:\n segments.append('dm/c') # ...with concatenative context layer\n else:\n if self.cbow_mean:\n segments.append('dm/m')\n else:\n segments.append('dm/s')\n segments.append('d%d' % self.docvecs.vector_size) # dimensions\n if self.negative:\n segments.append('n%d' % self.negative) # negative samples\n if self.hs:\n segments.append('hs')\n if not self.sg or (self.sg and self.dbow_words):\n segments.append('w%d' % self.window) # window size, when relevant\n if self.vocabulary.min_count > 1:\n segments.append('mc%d' % self.vocabulary.min_count)\n if self.vocabulary.sample > 0:\n segments.append('s%g' % self.vocabulary.sample)\n if self.workers > 1:\n segments.append('t%d' % self.workers)\n return '%s(%s)' % (self.__class__.__name__, ','.join(segments))","def _get_sentence(ne_tagged_line):\n return \" \".join([word for word, tag in ne_tagged_line])","def lemma(self):\n if self.metadata.get('COMLEX'):\n return self.metadata['COMLEX'][0].features['ORTH'][0][1:-1]\n elif self.label in ['NNP', 'NNPS']:\n return self.text\n else:\n return self.text.lower()","def description(self, depth=1, indent=0, itemize=\"\", output=None, **kwargs):\n if depth is None or depth < 0:\n return \"\"\n output_strio = StringIO()\n if self.label is None:\n label = \"<Unnamed Taxon>\"\n else:\n label = \"'%s'\" % self.label\n output_strio.write('%s%s Taxon object at %s (%s): %s' % (indent*' ', itemize, hex(id(self)), self.oid, label))\n s = output_strio.getvalue()\n if output is not None:\n output.write(s)\n return s","def __repr__(self):\n s= 'text model name: ' + self.name + '\\n'\n s+= 'number of words: ' + str(len(self.words)) + '\\n'\n s+='number of word lengths: ' + str(len(self.word_lengths))+'\\n'\n s+='number of word stems: ' + str(len(self.stems)) + '\\n'\n s+='number of sentence lengths: ' + str(len(self.sentence_lengths)) +'\\n'\n s+='number of word suffixes: '+ str(len(self.endings))\n \n return s","def full_text(self) -> str:\n return self._full_text","def __repr__(self):\n return '{} <{}:{} \"{}\" ({}; Pop: {}; Disc Pops: {}; Rep Pops: {})>'.\\\n format(self.id, self.author, self.journal, self.title,\n self.phenotype.phenotype, self.population.population,\n self.disc_pops.to_simple_str(),\n self.rep_pops.to_simple_str())","def get_descriptive_name(self):\n return f\"{self.year} {self.make} {self.model}\".title()","def get_nouns(root):\n nouns = []\n for child in root.findall(\"./xdrs/taggedtokens/tagtoken/tags\"):\n noun = False\n for grandchildren in child.findall(\"./tag[@type='pos']\"):\n if grandchildren.text == 'NN' or grandchildren.text == 'NNS':\n noun = True\n if noun == True:\n for grandchildren in child.findall(\"./tag[@type='lemma']\"):\n nouns.append(grandchildren.text)\n return nouns","def get_descriptive_name(self):\r\n long_name = str(self.year) + ' ' + self.make + ' ' + self.model\r\n #Mostrar_Grande = long_name.upper()\r\n #return long_name.upper()\r\n #return Mostrar_Grande #Funciona Com Return TAMBÉM, mas olhe na linha 39 como seria necessário usar.\r\n print(long_name.upper())","def __str__(self):\n s = \"\"\n s += self.synset.name + \"\\t\"\n s += \"PosScore: %s\\t\" % self.pos_score\n s += \"NegScore: %s\" % self.neg_score\n return s","def _get_nouns(self, review):\n review_features = []\n for sent in review:\n doc = self.nlp(sent)\n # noun_phrase = [np.text for np in doc.noun_chunks]\n nouns = [unicode(lemma(str(word).lower())) for word in doc if word.pos == NOUN]\n review_features.append(nouns)\n return review_features","def to_adverb(self):\n return self","def asngen(pool):\n pool = AssociationPool.read(pool)\n rules = AssociationRegistry()\n (asns, orphaned) = generate(pool, rules)\n result = []\n result.append('There where {:d} associations found.'.format(len(asns)))\n result.append('There where {:d} orphaned exposures.'.format(len(orphaned)))\n for assocs in asns:\n result.append(assocs.__str__())\n\n return '\\n'.join(result)","def to_string(self):\n return self.dungeon_string","def get_short_name(self):\n split = self.name.split(' - ')\n # author, year, and first couple of words of paper title\n return \"{} ({}), {}\".format(split[0], split[1], \" \".join(split[2].split(' ')[:3]))","def obj_pretty(objective):\n if objective.objective_type == u'/datum/objective/assassinate':\n return 'Asassinate {} the {}.'.format(objective.target_name, objective.target_role)\n else:\n return objective.objective_desc","def calc_conservation_string(aln):\n\n percids = calc_conservation(aln)\n\n # find identity positions\n identity = \"\"\n for pid in percids:\n if pid == 1:\n identity += \"*\"\n elif pid > .5:\n identity += \".\"\n else:\n identity += \" \"\n\n return identity","def describe(self) -> str:\n return (\n \"{name} {surname} è nata/o a {birth_municipality} ({birth_province_code}) il {birthdate}.\"\n \" Ora vive a {municipality} ({province_code}) in {address} {house_number}.\"\n ).format(**self._data)","def get_transcript(self):\n\n rna = \"\"\n for i in range(len(self.__sequentie)):\n if self.__sequentie[i] == \"A\":\n rna += \"U\"\n if self.__sequentie[i] == \"T\":\n rna += \"A\"\n if self.__sequentie[i] == \"C\":\n rna += \"G\"\n if self.__sequentie[i] == \"G\":\n rna += \"C\"\n if self.__sequentie[i] == \"N\":\n rna += \"N\"\n return rna","def is_noun(tag_string):\n result = True if tag_string in POS.POS_tags.noun_tags else False\n return result","def __repr__(self):\n ct = shortest_string_in_list(self.cause_texts)\n et = shortest_string_in_list(self.effect_texts)\n ev = ','.join(self.evidence_texts)\n return '%s -> %s [%s, %s, %s]' % (ct, et, ev,\n repr(self.cause_polarity),\n repr(self.effect_polarity))","def de_or_het(noun):\n\tnoun = noun.lower()\n\n\t# we have an excel files saved as csv, that contain words that are definately het-words\n\twith open('het_woorden', 'r') as f:\n\t\thet_woorden = f.read().split('\\n')\n\n\thet_woorden = [w.lower() for w in het_woorden]\t\n\n\t\n\t# and we have an excel file that contains dubious words\n\twith open('ambiguous_nouns', 'r') as f:\n\t\tambiguous_nouns = f.read().split('\\n')\n\t\n\tambiguous_nouns = [w.lower() for w in ambiguous_nouns]\t\n\tmessage = \"\"\"\\nYour noun may be either a 'de' or a 'het' word. Chosing one or the\nother may change the meaning of '{0}'. When in doubt, advice a dictionary.\n\t\"\"\"\n\n\t# next we want to see whether our noun is a 'de' or a 'het' noun:\n\tif noun in ambiguous_nouns:\n\t\traise Exception(message.format(noun))\n\telif noun in het_woorden or noun.endswith('je'):\n\t\tarticle = 'het'\n\telse:\n\t\tarticle = 'de'\t\n\t\n\t# last, we want the article and the noun returned as one string.\t\n\treturn article + \" \" + noun"],"string":"[\n \"def en_noun(t, label):\\r\\n s = label\\r\\n p = []\\r\\n is_uncountable = False\\r\\n \\r\\n # http://en.wiktionary.org/wiki/Template:en-noun\\r\\n head = t.arg(\\\"head\\\", label)\\r\\n p1 = t.arg(0)\\r\\n p2 = t.arg(1)\\r\\n \\r\\n if p1 == \\\"-\\\":\\r\\n # uncountable\\r\\n is_uncountable = True\\r\\n \\r\\n if p2 == \\\"s\\\":\\r\\n # ends by s\\r\\n p.append(head + \\\"s\\\")\\r\\n \\r\\n elif p2 is not None:\\r\\n # word\\r\\n p.append(p2)\\r\\n \\r\\n elif p1 == \\\"es\\\":\\r\\n # add es\\r\\n p.append(head + \\\"es\\\")\\r\\n \\r\\n elif p1 is not None:\\r\\n # use term\\r\\n p.append(p1)\\r\\n \\r\\n elif p1 is None and p2 is None:\\r\\n p.append(head+\\\"s\\\")\\r\\n\\r\\n for k,a in t.args.items():\\r\\n if not a.is_named():\\r\\n if k == 0 or k == 1:\\r\\n continue\\r\\n \\r\\n p.append(a.as_string())\\r\\n \\r\\n return (s, p, is_uncountable)\",\n \"def get_nouns(self):\\n\\t\\tblob = TextBlob(self.raw_string)\\n\\n\\t\\tfor word, tag in blob.tags:\\n\\t\\t\\tif tag in ['NNP', 'NN']:\\n\\t\\t\\t\\tself.nouns.append(word.lemmatize())\",\n \"def to_adverb(self):\\n\\n if 'AdverbPhrase' in self.variants:\\n return self.variants['AdverbPhrase']\\n\\n return self.copy_with(typ=\\\"AdverbPhrase\\\",\\n text=\\\"with \\\" + self.text)\",\n \"def noun_string(data_org):\\n chains = []\\n tokens = word_tokenize(data_org)\\n #tokenize to prepare for tagging\\n w_tag = dict(nltk.pos_tag(tokens))\\n chain = []\\n for w, tag in w_tag.items():\\n #find all nouns based on treebank format\\n if tag.startswith('N'):\\n chain.append(w)\\n else:\\n if len(chain) >= 3:\\n chains.append(\\\" \\\".join(chain))\\n chain = []\\n\\n #move information to dataframe for printing to excel\\n df_noun_string = pd.DataFrame({'Noun Strings (3+ Nouns in a row)': chains}, columns = ['Noun Strings (3+ Nouns in a row)'])\\n return df_noun_string\",\n \"def __str__(self):\\n out = self.san\\n if self.comment != \\\"\\\":\\n out += \\\" {\\\" + self.comment.replace('\\\\n', ' ') + \\\"}\\\"\\n if len(self.nags) > 0:\\n for n in self.nags:\\n out += \\\" \\\" + n\\n for v in self.variations:\\n out += \\\" (\\\" + str(v).strip(' ') + \\\")\\\"\\n return out\",\n \"def get_compound_noun( ngram ):\\n try:\\n pattern = re.compile( '((?: ?\\\\\\\\b[^\\\\\\\\s]+(?:/NN.?/[a-z]+/[\\\\\\\\d]+)){2,})' )\\n match = re.search( pattern, ngram )\\n if match:\\n compound = ''\\n contains_root = False\\n tokens = match.group().strip().split(' ')\\n for t in tokens:\\n # embed()\\n items = t.split('/')\\n compound += ( items[0] + ' ' )\\n if items[3] == 0:\\n contains_root = True\\n if contains_root:\\n return compound\\n else:\\n return None\\n else:\\n return None\\n \\n except ValueError:\\n return None\",\n \"def get_article_str(article_sents):\\n article_str = \\\"\\\"\\n for nlp_sent in article_sents:\\n article_str += (' ' + nlp_sent.text + ' ')\\n return article_str\",\n \"def get_noun_phrases(blob):\\n return blob.noun_phrases\",\n \"def to_adverb(self):\\n\\n if 'AdverbPhrase' in self.variants:\\n return self.variants['AdverbPhrase']\\n\\n return self.copy_with(typ=\\\"AdverbPhrase\\\",\\n text=self.text + \\\" to\\\")\",\n \"def indefinite(self):\\n return \\\"an\\\" if self.short_desc[0] in 'aeiou' else \\\"a\\\"\",\n \"def noun_lemma(word):\\n if word.endswith(\\\"s\\\"):\\n if word.endswith(\\\"ss\\\"):\\n return word.lower()\\n elif word.endswith(\\\"ies\\\"):\\n return word[:-3].lower() + (\\\"y\\\")\\n else:\\n return word[:-1].lower()\\n if word.endswith(\\\"men\\\"):\\n return word[:-2].lower() + (\\\"an\\\")\\n else:\\n return word.lower()\",\n \"def to_adverb(self):\\n\\n text = self.text\\n ending = text[-1]\\n if ending == \\\"e\\\":\\n text = text[0:-1]+\\\"ly\\\"\\n else:\\n text = text+\\\"ly\\\"\\n\\n return self.copy_with(typ=\\\"AdverbPhrase\\\",\\n text=text)\\n\\n # return AdverbPhrase(**self.locals(skip=[\\\"text\\\", \\\"typ\\\", \\\"variants\\\"]),\\n # text=text,\\n # **self.variants)\",\n \"def a(noun):\\n if p.singular_noun(noun) is not False:\\n return noun\\n else:\\n return p.a(noun)\",\n \"def get_abbreviated_description(self):\\n word_array = str(self.description).split()[:25]\\n abbreviated_description = \\\" \\\".join(word_array)\\n return abbreviated_description\",\n \"def __str__(self):\\n return self.underscoreSentence.get().__str__()\",\n \"def get_sentence(self):\",\n \"def short_description(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"short_description\\\")\",\n \"def greco_latin_plural_noun(base_token=None):\\n\\n output_string = \\\"\\\"\\n if base_token is not None:\\n if base_token.endswith(\\\"us\\\"):\\n output_string = base_token[:-2] + \\\"i\\\"\\n elif base_token.endswith(\\\"ma\\\"):\\n output_string = base_token + \\\"ta\\\"\\n elif base_token.endswith(\\\"a\\\"):\\n output_string = base_token[:-1] + \\\"ae\\\"\\n elif base_token.endswith((\\\"on\\\", \\\"um\\\")):\\n output_string = base_token[:-2] + \\\"a\\\"\\n elif base_token.endswith(\\\"sis\\\"):\\n output_string = base_token[:-3] + \\\"ses\\\"\\n elif base_token.endswith(\\\"is\\\"):\\n output_string = base_token[:-2] + \\\"ides\\\"\\n elif base_token.endswith(\\\"men\\\"):\\n output_string = base_token[:-3] + \\\"mina\\\"\\n elif base_token.endswith(\\\"ex\\\"):\\n output_string = base_token[:-2] + \\\"ices\\\"\\n elif base_token.endswith(\\\"x\\\"):\\n output_string = base_token[:-1] + \\\"ces\\\"\\n\\n return output_string\",\n \"def to_string(self):\\n return \\\" \\\".join(self._words)\",\n \"def __repr__(self):\\r\\n s = 'Words:\\\\n' + str(self.words) + '\\\\n\\\\n'\\r\\n s += 'Word lengths:\\\\n' + str(self.wordlengths) + '\\\\n\\\\n'\\r\\n s += 'Stems:\\\\n' + str(self.stems) + '\\\\n\\\\n'\\r\\n s += 'Sentence lengths:\\\\n' + str(self.sentencelengths) + '\\\\n\\\\n'\\r\\n s += 'Gerunds:\\\\n' + str(self.gerund)\\r\\n return s\",\n \"def _get_sentence(sentence_data):\\n return \\\" \\\".join([word for word, ne_tag in sentence_data])\",\n \"def dish_str(n:Dish):\\r\\n return (n.name + \\\" $\\\" + str(n.price) + \\\" \\\" + str(n.calories) + \\\" cal\\\")\",\n \"def get_description(self):\\n return \\\"-\\\".join(\\n map(str, (self.release, self.chromosome, self.start, self.reference, self.alternative))\\n )\",\n \"def find_noun(sent):\\n noun = None\\n\\n if not noun:\\n for w, p in sent.pos_tags:\\n if p == 'NN': # This is a noun\\n noun = w\\n break\\n if noun:\\n #logger.info(\\\"Found noun: %s\\\", noun)\\n pprint(\\\"FOUND NOUN\\\")\\n pprint(noun)\\n\\n return noun\",\n \"def NoDeltaGExplanation(self):\\n for compound in self.reactants:\\n if compound.compound.no_dg_explanation:\\n name = compound.compound.common_names.all()[0].name\\n return '%s %s' % (name,\\n compound.compound.no_dg_explanation.lower())\\n return None\",\n \"def base_verb_string(self): \\n if self.is_derived: \\n _base_verb_str= getattr(self, '_base_verb_string', None)\\n if is_empty_str(_base_verb_str):\\n if self.is_phrase:\\n # a phrase means the base verb is the actual verb being conjugated.\\n self._base_verb_string = self.inf_verb_string\\n elif self.reflexive == Reflexive.base_reflexive:\\n self._base_verb_string = self.core_characters + self.inf_ending +'se'\\n else: \\n self._base_verb_string = self.core_characters + self.inf_ending\\n return self._base_verb_string\\n else:\\n return None\",\n \"def construct_response(pronoun, noun, verb):\\n resp = []\\n\\n if pronoun:\\n resp.append(pronoun)\\n\\n # We always respond in the present tense, and the pronoun will always either be a passthrough\\n # from the user, or 'you' or 'I', in which case we might need to change the tense for some\\n # irregular verbs.\\n if verb:\\n verb_word = verb[0]\\n if verb_word in ('be', 'am', 'is', \\\"'m\\\"): # This would be an excellent place to use lemmas!\\n if pronoun.lower() == 'you':\\n # The bot will always tell the person they aren't whatever they said they were\\n resp.append(\\\"aren't really\\\")\\n else:\\n resp.append(verb_word)\\n if noun:\\n pronoun = \\\"an\\\" if starts_with_vowel(noun) else \\\"a\\\"\\n resp.append(pronoun + \\\" \\\" + noun)\\n\\n resp.append(random.choice((\\\"bro\\\", \\\"lol\\\", \\\"bruh\\\", \\\"nigga\\\", \\\"ha ha ha xD\\\", \\\"zzz.. oh i fell asleep :P\\\")))\\n\\n return \\\" \\\".join(resp)\",\n \"def is_noun(tag):\\r\\n return tag in ['NN', 'NNS', 'NNP', 'NNPS']\",\n \"def _create_formatted_string(self):\\n string = NALSyntax.StatementSyntax.Start.value + \\\\\\n self.get_subject_term().get_formatted_string()\\n\\n string += \\\" \\\" + self.get_copula_string() + \\\" \\\"\\n\\n string += self.get_predicate_term().get_formatted_string() + \\\\\\n NALSyntax.StatementSyntax.End.value\\n\\n return string\",\n \"def get_sentence(self):\\n words = []\\n for i in sorted([ind.index for ind in self.subtree_dict.keys()]):\\n if isinstance(self.subtree_dict[i].label, (str, unicode)):\\n words.append(self.subtree_dict[i].label)\\n return ' '.join(words)\",\n \"def morph_noun(word, number, a_an, feature):\\n word = morph_number(word, number)\\n if not (number in ['first', 'second']) and word[-1] == 's':\\n return mark_noun_as_plural(word)\\n else:\\n return word\\n\\n if a_an == 'an':\\n return mark_noun_as_an(word)\\n else:\\n return word\",\n \"def _get_audio_feature_name(self, adjective):\\n return self.SONG_ADJECTIVES.get(adjective, {}).get(\\\"name\\\")\",\n \"def __get_relevant_words(sentence):\\n nouns = None\\n try:\\n if sentence:\\n tokens = nltk.word_tokenize(sentence)\\n pos = nltk.pos_tag(tokens)\\n nouns = [x[0] for x in pos if x[1].startswith('N') or x[1].startswith('F')]\\n except Exception as e:\\n nouns = None\\n return ' '.join(nouns) if nouns else None\",\n \"def get_extras(self, text=None):\\n if text is None:\\n text = self.nltk_text(self.text)\\n # Tag parts of speech\\n tagged = nltk.pos_tag(text)\\n # Try for composed NNP / NNPS\\n is_proper_noun = False\\n text = []\\n proper_noun = \\\"\\\"\\n for (word, tag) in tagged:\\n if not is_proper_noun and (tag == 'NNP' or tag == 'NNPS'):\\n # Start building a proper noun\\n proper_noun = word\\n # Set it true\\n is_proper_noun = True\\n # Add it to annotations anyway\\n text.append(word)\\n elif tag == 'NNP' or tag == 'NNPS':\\n # Previous was proper noun. So it may be combined\\n proper_noun += \\\" \\\" + word\\n # Add the single word to annotations anyway\\n text.append(word)\\n elif is_proper_noun and tag == 'IN':\\n # Add what we have by now to the text\\n text.append(proper_noun)\\n # Previous was proper noun. So it may be composed\\n proper_noun += \\\" \\\" + word\\n elif is_proper_noun:\\n # Add what we have by now to the text\\n text.append(proper_noun)\\n # Finished with proper noun, so set it false\\n is_proper_noun = False\\n # Remove duplicates\\n seen = {}\\n result = []\\n for w in text:\\n if w in seen:\\n continue\\n seen[w] = 1\\n result.append(w)\\n # Eliminate common\\n result = [w for w in result if w.lower() not in self.common_words and\\n w.lower() not in stopwords.words('english')]\\n return result\",\n \"def _get_full_entity(entity: spacy.tokens.Token) -> str:\\n entity_string = SpacyEventExtractor._get_chunk(entity)\\n\\n word = entity\\n while True:\\n prep, word = SpacyEventExtractor._get_prep_with_word(word)\\n if word is None:\\n break\\n entity_string += \\\" \\\" + prep\\n return entity_string\",\n \"def tag_to_wordnet(tag):\\n if (tag == 'ADJ'): return('a')\\n elif (tag == 'ADV'): return('r')\\n elif (tag == 'NOUN'): return('n')\\n elif (tag == 'VERB'): return('v')\\n else: return None\",\n \"def dc_title(self):\\n return u\\\"{0} ({1}): {2} {3}\\\".format(\\n self.label, self.in_assessment[0].timepoint,\\n self.subjects[0].code_in_study,\\n \\\"...\\\" if len(self.subjects) > 1 else \\\"\\\")\",\n \"def get_descriptive_name(self):\\r\\n long_name=str(self.year)+' '+self.make+' '+self.model\\r\\n return long_name.title()\",\n \"def get_description(self):\\n text = \\\"is a student's t distribution; characterised by its degrees of freedom, which here is\\\"+str(self.dofs)+\\\".\\\"\\n return text\",\n \"def find_pronoun(sent):\\n pronoun = None\\n\\n for word, part_of_speech in sent.pos_tags:\\n # Disambiguate pronouns\\n if part_of_speech == 'PRP' and word.lower() == 'you':\\n # pronoun = 'I' +++++++++ORIGINAL++++++++++++\\n pronoun = 'I'\\n elif part_of_speech == 'PRP' and word == 'I':\\n # If the user mentioned themselves, then they will definitely be the pronoun\\n # pronoun = 'You' +++++++++ORIGINAL++++++++++++\\n pronoun = 'You'\\n return pronoun\",\n \"def get_descriptive_name(self):\\n long_name = f\\\"{self.make} {self.model} {self.year}\\\"\\n \\n return long_name.title()\",\n \"def description(self):\\n retstr = \\\"GNP with \\\" + str(self.n) + \\\" nodes and p = \\\" + str(self.p)\\n return retstr\",\n \"def conjugate_present_ere_verb(verb, pronoun, tense):\\n avere = {\\\"io\\\": \\\"ho\\\", \\\"tu\\\": \\\"hai\\\", \\\"lui\\\": \\\"ha\\\", \\\"lei\\\": \\\"ha\\\", \\\"noi\\\": \\\"abbiamo\\\", \\\"voi\\\": \\\"avete\\\", \\\"loro\\\": \\\"hanno\\\"}\\n\\n irregulars = [\\\"avere\\\"]\\n if verb in irregulars:\\n if verb == \\\"avere\\\":\\n return avere[pronoun]\\n\\n ere_endings = {\\\"io\\\": \\\"o\\\", \\\"tu\\\": \\\"i\\\", \\\"lui\\\": \\\"e\\\", \\\"lei\\\": \\\"e\\\", \\\"noi\\\": \\\"iamo\\\", \\\"voi\\\": \\\"ete\\\", \\\"loro\\\": \\\"ono\\\"}\\n compiere_endings = {\\\"io\\\": \\\"o\\\", \\\"tu\\\": \\\"\\\", \\\"lei\\\": \\\"e\\\", \\\"lui\\\": \\\"e\\\", \\\"noi\\\": \\\"amo\\\", \\\"voi\\\": \\\"ete\\\", \\\"loro\\\": \\\"ono\\\"}\\n\\n if verb == \\\"compiere\\\":\\n if tense == \\\"presente\\\":\\n stripped_verb = strip_off_ending(verb, tense)\\n new_verb = stripped_verb + compiere_endings[pronoun]\\n return new_verb\\n else:\\n stripped_verb = strip_off_ending(verb, tense)\\n new_verb = stripped_verb + ere_endings[pronoun]\\n return new_verb\\n else:\\n stripped_verb = strip_off_ending(verb, tense)\\n new_verb = stripped_verb + ere_endings[pronoun]\\n return new_verb\",\n \"def get_nouns(lemmas_tags):\\r\\n nouns = []\\r\\n for lemma in lemmas_tags:\\r\\n \\\"\\\"\\\"si la etiqueta es None porque no tiene lemma o es un sustantivo\\\"\\\"\\\"\\r\\n if lemma[1] == None or lemma[1][0] == 'n':\\r\\n \\\"\\\"\\\"se agrega solamente el lemma\\\"\\\"\\\"\\r\\n nouns.append(lemma[0])\\r\\n return nouns\",\n \"def get_article_as_string(article,\\n preprocess_type=PreprocessWordType.LEMMATIZE):\\n article_string = ''\\n for word in article.words:\\n preprocessed_word = query_utils.preprocess_word(word, preprocess_type)\\n if article_string == '':\\n article_string = preprocessed_word\\n else:\\n article_string += (' ' + preprocessed_word)\\n return article_string\",\n \"def get_string(self, n):\\n pad = self.get_pad(n)\\n string = pad + self.word\\n string += \\\"\\\\n\\\" + self.children[0].get_string(n + 1)\\n string += \\\"\\\\n\\\" + self.children[1].get_string(n + 1)\\n return string\",\n \"def __str__(self):\\n st=\\\"\\\"\\n for g in self:\\n st+=g.fasta()\\n st+=\\\"\\\\n\\\"\\n return st\",\n \"def get_descriptive_name(self):\\r\\n long_name = str(self.year)+' '+self.make + ' '+self.model\\r\\n return long_name.title()\",\n \"def get_descriptive_name(self): # 定义描述完整信息的方法\\n long_name = str(self.year) + \\\" \\\" + self.make + \\\" \\\" + self.model # 拼接变量字符串并赋值变量\\n return long_name.title() # 返回字符串并首字母大写\",\n \"def verb_lemma(word):\\n if word.endswith(\\\"ed\\\"):\\n if word[:-2].endswith(\\\"v\\\"):\\n return word[:-2].lower() + \\\"e\\\"\\n elif word[:-2].endswith(\\\"at\\\"):\\n return word[:-2].lower() + \\\"e\\\"\\n elif word[:-2].endswith(\\\"it\\\"):\\n return word[:-2].lower() + \\\"e\\\"\\n elif word[:-2].endswith(\\\"et\\\"):\\n return word[:-2].lower() + \\\"e\\\"\\n elif word[:-2].endswith(\\\"ut\\\"):\\n return word[:-2].lower() + \\\"e\\\"\\n elif word[:-2].endswith(\\\"ac\\\"):\\n return word[:-2].lower() + \\\"e\\\"\\n elif word[:-2].endswith(\\\"i\\\"):\\n return word[:-3].lower() + \\\"y\\\"\\n elif word[:-2].endswith(\\\"ir\\\"):\\n return word[:-2].lower() + \\\"e\\\"\\n elif word[:-2].endswith(\\\"ag\\\"):\\n return word[:-2].lower() + \\\"e\\\"\\n elif word[:-2].endswith(\\\"nc\\\"):\\n return word[:-2].lower() + \\\"e\\\"\\n elif word[:-2].endswith(\\\"nu\\\"):\\n return word[:-2].lower() + \\\"e\\\"\\n else:\\n return word[:-2].lower() \\n elif word.endswith(\\\"ing\\\"):\\n if word[:-3].endswith(\\\"v\\\"):\\n return word[:-3].lower() + \\\"e\\\"\\n elif word[:-3].endswith(\\\"at\\\"):\\n return word[:-3].lower() + \\\"e\\\"\\n elif word[:-3].endswith(\\\"it\\\"):\\n return word[:-3].lower() + \\\"e\\\"\\n elif word[:-3].endswith(\\\"et\\\"):\\n return word[:-3].lower() + \\\"e\\\"\\n elif word[:-3].endswith(\\\"ut\\\"):\\n return word[:-3].lower() + \\\"e\\\"\\n elif word[:-3].endswith(\\\"ac\\\"):\\n return word[:-3].lower() + \\\"e\\\"\\n elif word[:-3].endswith(\\\"i\\\"):\\n return word[:-4].lower() + \\\"y\\\"\\n elif word[:-3].endswith(\\\"ir\\\"):\\n return word[:-3].lower() + \\\"e\\\"\\n elif word[:-3].endswith(\\\"ag\\\"):\\n return word[:-3].lower() + \\\"e\\\"\\n elif word[:-3].endswith(\\\"nc\\\"):\\n return word[:-3].lower() + \\\"e\\\"\\n elif word[:-3].endswith(\\\"nu\\\"):\\n return word[:-3].lower() + \\\"e\\\"\\n else:\\n return word[:-3].lower()\\n elif re.match(r\\\"(does|did|done)\\\", word):\\n return (\\\"do\\\")\\n elif re.match(r\\\"(is|are|am|was|will|were|been)\\\", word):\\n return (\\\"be\\\")\\n elif word == (\\\"'s\\\"):\\n return (\\\"be\\\")\\n elif re.match(r\\\"(had|has|'ve)\\\", word):\\n return (\\\"have\\\")\\n else:\\n return word.lower()\",\n \"def lemma(self) -> str:\",\n \"def __str__(self):\\n return \\\"{}\\\".format(self.word)\",\n \"def createSentence(self, n=0, v=0, o=0, p=0,prep=True):\\n sentence = ''\\n if not n:\\n n = np.random.randint(1, 5)\\n if not v:\\n v = np.random.randint(1, 5)\\n if not o:\\n o = np.random.randint(1, 5)\\n sentence += self.createPhrase(nwords=n) + ' '\\n if sentence[:-1] not in ('mi', 'sina'):\\n sentence += 'li '\\n sentence += self.createPhrase(nwords=v) + ' e '\\n sentence += self.createPhrase(nwords=o)\\n if prep:\\n if not p:\\n p = np.random.randint(1, 5)\\n sentence += ' ' + np.random.choice(self.prepositions) + ' ' + self.createPhrase(nwords=p)\\n return sentence\",\n \"def get_descriptive_name(self):\\r\\n long_name = str(self.year) + ' ' + self.make + ' ' + self.model\\r\\n return long_name.title()\",\n \"def descString(self):\\n return \\\"\\\".join ([self.Name, \\\" (AR \\\", str(self.AR), \\\", Max DEX \\\"\\\\\\n , str(self.MaxDEXMod), \\\") - \\\", str(self.Value), \\\" gp\\\"])\",\n \"def get_descriptive_name(self):\\n long_name = str(self.year) + ' ' + self.make + ' ' + self.model\\n return long_name.title()\",\n \"def get_descriptive_name(self):\\n long_name = str(self.year) + ' ' + self.make + ' ' + self.model\\n return long_name.title()\",\n \"def get_descriptive_name(self):\\n long_name = str(self.year) + ' ' + self.make + ' ' + self.model\\n return long_name.title()\",\n \"def get_descriptive_name(self):\\n long_name = str(self.year) + ' ' + self.make + ' ' + self.model\\n return long_name.title()\",\n \"def get_descriptive_name(self):\\n long_name = str(self.year) + ' ' + self.make + ' ' + self.model\\n return long_name.title()\",\n \"def get_descriptive_name(self):\\n long_name = str(self.year) + ' ' + self.make + ' ' + self.model\\n return long_name.title()\",\n \"def get_descriptive_name(self):\\n long_name = str(self.year) + ' ' + self.make + ' ' + self.model\\n return long_name.title()\",\n \"def get_descriptive_name(self):\\n long_name = str(self.year) + ' ' + self.make + ' ' + self.model\\n return long_name.title()\",\n \"def provn_representation(self):\\n return \\\"'%s'\\\" % self._str\",\n \"def get_human_readable(self):\\n\\n def yesno(key):\\n if getattr(self, key) and getattr(self, key) > 0:\\n return \\\"Y\\\"\\n else:\\n return \\\"N\\\"\\n\\n keys = (\\n \\\"pvs1\\\",\\n \\\"ps1\\\",\\n \\\"ps2\\\",\\n \\\"ps3\\\",\\n \\\"ps4\\\",\\n \\\"pm1\\\",\\n \\\"pm2\\\",\\n \\\"pm3\\\",\\n \\\"pm4\\\",\\n \\\"pm5\\\",\\n \\\"pm6\\\",\\n \\\"pp1\\\",\\n \\\"pp2\\\",\\n \\\"pp3\\\",\\n \\\"pp4\\\",\\n \\\"pp5\\\",\\n \\\"ba1\\\",\\n \\\"bs1\\\",\\n \\\"bs2\\\",\\n \\\"bs3\\\",\\n \\\"bs4\\\",\\n \\\"bp1\\\",\\n \\\"bp2\\\",\\n \\\"bp3\\\",\\n \\\"bp4\\\",\\n \\\"bp5\\\",\\n \\\"bp6\\\",\\n \\\"bp7\\\",\\n )\\n result = \\\", \\\".join([\\\"%s: %s\\\" % (key.upper(), yesno(key)) for key in keys])\\n result += \\\", ACMG classification: %s\\\" % self.class_auto\\n if self.class_override:\\n result += \\\", ACMG class. override: %s\\\" % self.class_override\\n return result\",\n \"def __str__(self):\\n # TODO also show relative abundance\\n s = \\\"{} ion species\\\\n\\\".format(len(self.ions))\\n for ion in self.ions:\\n s += \\\" {:2s} (Z = {:3d}) {:.3e} particles\\\\n\\\".format(ion.getName(), ion.getCharge(), ion.getParticleNumber())\\n \\n return s\",\n \"def summary_string(self) -> str:\\n return f\\\"dixonoid: {self.plain_rules}\\\"\",\n \"def sentence(self) -> str:\\n id_word = {nodeattr['position']-1: nodeattr['form']\\n for nodeid, nodeattr in self.syntax_nodes.items()}\\n return ' '.join([id_word[i] for i in range(max(list(id_word.keys()))+1)])\",\n \"def get_descriptive_name(self):\\n long_name = f\\\"{self.year} {self.make} {self.model}\\\"\\n return long_name.title()\",\n \"def get_nouns(self):\\n word_punct_token = WordPunctTokenizer().tokenize(self.sentence_string)\\n\\n clean_tokens = []\\n for token in word_punct_token:\\n token = token.lower()\\n\\n # remove any value that are not alphabetical\\n new_token = re.sub(r\\\"[^a-zA-Z]+\\\", \\\"\\\", token)\\n\\n # remove empty value and single character value\\n if new_token != \\\"\\\" and len(new_token) >= 2:\\n vowels = len([v for v in new_token if v in \\\"aeiou\\\"])\\n if vowels != 0: # remove line that only contains consonants\\n clean_tokens.append(new_token)\\n\\n noun_types = [\\\"NN\\\", \\\"NNS\\\", \\\"NNP\\\", \\\"NNPS\\\", \\\"N\\\"]\\n is_noun = lambda pos: pos in noun_types\\n nouns = [word for (word, pos) in nltk.pos_tag(clean_tokens) if is_noun(pos)]\\n\\n if nouns:\\n return nouns\\n else:\\n raise InvalidSentenceError(self.sentence_string)\",\n \"def print_sentence_voice(self, final_subj, final_obj, verb, v_aux, v_tense, subj_tag, subj_word, final_mod2=None, final_root=None):\\n new_verb = ''\\n s_sentence1 = s_sentence2 = ''\\n\\n new_verb = gl.verb_conjugate(verb, v_aux, v_tense) + \\\" \\\"\\n\\n if new_verb.strip() == \\\"\\\":\\n new_verb = gl.verb_conjugate(verb, v_aux, \\\"VMIS3S0\\\") + \\\" \\\"\\n\\n\\n for k in sorted(final_subj.keys()):\\n s_sentence1 += final_subj[k] + \\\" \\\"\\n\\n for k in sorted(final_obj.keys()):\\n s_sentence2 += final_obj[k] + \\\" \\\"\\n\\n if final_mod2 != None:\\n for k in sorted(final_mod2.keys()):\\n s_sentence2 += final_mod2[k] + \\\" \\\"\\n if final_root != None:\\n for k in sorted(final_root.keys()):\\n s_sentence2 += final_root[k] + \\\" \\\"\\n\\n\\n #removing errors in punctuation\\n s_sentence1 = s_sentence1.replace(\\\", .\\\", \\\".\\\").replace(\\\"; .\\\", \\\".\\\").replace(\\\": .\\\", \\\".\\\").replace(\\\", ?\\\", \\\"?\\\").replace(\\\"; ?\\\", \\\"?\\\").replace(\\\": ?\\\", \\\"?\\\").replace(\\\", !\\\", \\\"!\\\").replace(\\\"; !\\\", \\\"!\\\").replace(\\\": !\\\", \\\"!\\\").replace(\\\". .\\\", \\\".\\\")\\n s_sentence2 = s_sentence2.replace(\\\", .\\\", \\\".\\\").replace(\\\"; .\\\", \\\".\\\").replace(\\\": .\\\", \\\".\\\").replace(\\\", ?\\\", \\\"?\\\").replace(\\\"; ?\\\", \\\"?\\\").replace(\\\": ?\\\", \\\"?\\\").replace(\\\", !\\\", \\\"!\\\").replace(\\\"; !\\\", \\\"!\\\").replace(\\\": !\\\", \\\"!\\\").replace(\\\". .\\\", \\\".\\\")\\n\\n\\n return self.runTrueCaser(s_sentence1 + new_verb + s_sentence2)\",\n \"def getSynopsis(self):\\r\\n\\r\\n if self.parent is None:\\r\\n command = self.__class__.__name__\\r\\n offset = command.find('Options')\\r\\n default = \\\"Usage: {0}{1}\\\".format(command[:offset],\\r\\n (self.longOpt and \\\" [options]\\\") or '')\\r\\n else:\\r\\n default = (self.longOpt and \\\" [options]\\\") or ''\\r\\n\\r\\n synopsis = getattr(self, \\\"synopsis\\\", default).rstrip()\\r\\n\\r\\n if self.parent is not None:\\r\\n synopsis = ' '.join((self.parent.getSynopsis(),\\r\\n self.parent.subCommand, synopsis))\\r\\n\\r\\n return synopsis\",\n \"def getDisambiguatedByNextNoun(self, word):\\n\\t\\treturn disambig_const.DISAMBIGUATATION_TABLE.get(word, {}).get('noun', {}).get('vocalized', word);\",\n \"def get_root_verb(text):\\n doc = nlp(text)\\n for token in doc:\\n if token.dep_ == \\\"ROOT\\\" and token.head.pos_ == \\\"VERB\\\":\\n return str(token)\\n else:\\n return \\\"\\\"\",\n \"def parse(text):\\n parts = text.split(' ')\\n noun = Noun(parts[0], int(parts[1]))\\n\\n parts = parts[2:]\\n while len(parts) > 0:\\n noun.add_adjectives(Word(parts[0], int(parts[1])))\\n parts = parts[2:]\\n return noun\",\n \"def name(self):\\n return self.tr('NATCAPES')\",\n \"def convert(tag):\\r\\n if is_noun(tag):\\r\\n return wn.NOUN\\r\\n if is_adjective(tag):\\r\\n return wn.ADJ\",\n \"def __str__(self):\\n segments = []\\n if self.comment:\\n segments.append('\\\"%s\\\"' % self.comment)\\n if self.sg:\\n if self.dbow_words:\\n segments.append('dbow+w') # also training words\\n else:\\n segments.append('dbow') # PV-DBOW (skip-gram-style)\\n\\n else: # PV-DM...\\n if self.dm_concat:\\n segments.append('dm/c') # ...with concatenative context layer\\n else:\\n if self.cbow_mean:\\n segments.append('dm/m')\\n else:\\n segments.append('dm/s')\\n segments.append('d%d' % self.docvecs.vector_size) # dimensions\\n if self.negative:\\n segments.append('n%d' % self.negative) # negative samples\\n if self.hs:\\n segments.append('hs')\\n if not self.sg or (self.sg and self.dbow_words):\\n segments.append('w%d' % self.window) # window size, when relevant\\n if self.vocabulary.min_count > 1:\\n segments.append('mc%d' % self.vocabulary.min_count)\\n if self.vocabulary.sample > 0:\\n segments.append('s%g' % self.vocabulary.sample)\\n if self.workers > 1:\\n segments.append('t%d' % self.workers)\\n return '%s(%s)' % (self.__class__.__name__, ','.join(segments))\",\n \"def _get_sentence(ne_tagged_line):\\n return \\\" \\\".join([word for word, tag in ne_tagged_line])\",\n \"def lemma(self):\\n if self.metadata.get('COMLEX'):\\n return self.metadata['COMLEX'][0].features['ORTH'][0][1:-1]\\n elif self.label in ['NNP', 'NNPS']:\\n return self.text\\n else:\\n return self.text.lower()\",\n \"def description(self, depth=1, indent=0, itemize=\\\"\\\", output=None, **kwargs):\\n if depth is None or depth < 0:\\n return \\\"\\\"\\n output_strio = StringIO()\\n if self.label is None:\\n label = \\\"<Unnamed Taxon>\\\"\\n else:\\n label = \\\"'%s'\\\" % self.label\\n output_strio.write('%s%s Taxon object at %s (%s): %s' % (indent*' ', itemize, hex(id(self)), self.oid, label))\\n s = output_strio.getvalue()\\n if output is not None:\\n output.write(s)\\n return s\",\n \"def __repr__(self):\\n s= 'text model name: ' + self.name + '\\\\n'\\n s+= 'number of words: ' + str(len(self.words)) + '\\\\n'\\n s+='number of word lengths: ' + str(len(self.word_lengths))+'\\\\n'\\n s+='number of word stems: ' + str(len(self.stems)) + '\\\\n'\\n s+='number of sentence lengths: ' + str(len(self.sentence_lengths)) +'\\\\n'\\n s+='number of word suffixes: '+ str(len(self.endings))\\n \\n return s\",\n \"def full_text(self) -> str:\\n return self._full_text\",\n \"def __repr__(self):\\n return '{} <{}:{} \\\"{}\\\" ({}; Pop: {}; Disc Pops: {}; Rep Pops: {})>'.\\\\\\n format(self.id, self.author, self.journal, self.title,\\n self.phenotype.phenotype, self.population.population,\\n self.disc_pops.to_simple_str(),\\n self.rep_pops.to_simple_str())\",\n \"def get_descriptive_name(self):\\n return f\\\"{self.year} {self.make} {self.model}\\\".title()\",\n \"def get_nouns(root):\\n nouns = []\\n for child in root.findall(\\\"./xdrs/taggedtokens/tagtoken/tags\\\"):\\n noun = False\\n for grandchildren in child.findall(\\\"./tag[@type='pos']\\\"):\\n if grandchildren.text == 'NN' or grandchildren.text == 'NNS':\\n noun = True\\n if noun == True:\\n for grandchildren in child.findall(\\\"./tag[@type='lemma']\\\"):\\n nouns.append(grandchildren.text)\\n return nouns\",\n \"def get_descriptive_name(self):\\r\\n long_name = str(self.year) + ' ' + self.make + ' ' + self.model\\r\\n #Mostrar_Grande = long_name.upper()\\r\\n #return long_name.upper()\\r\\n #return Mostrar_Grande #Funciona Com Return TAMBÉM, mas olhe na linha 39 como seria necessário usar.\\r\\n print(long_name.upper())\",\n \"def __str__(self):\\n s = \\\"\\\"\\n s += self.synset.name + \\\"\\\\t\\\"\\n s += \\\"PosScore: %s\\\\t\\\" % self.pos_score\\n s += \\\"NegScore: %s\\\" % self.neg_score\\n return s\",\n \"def _get_nouns(self, review):\\n review_features = []\\n for sent in review:\\n doc = self.nlp(sent)\\n # noun_phrase = [np.text for np in doc.noun_chunks]\\n nouns = [unicode(lemma(str(word).lower())) for word in doc if word.pos == NOUN]\\n review_features.append(nouns)\\n return review_features\",\n \"def to_adverb(self):\\n return self\",\n \"def asngen(pool):\\n pool = AssociationPool.read(pool)\\n rules = AssociationRegistry()\\n (asns, orphaned) = generate(pool, rules)\\n result = []\\n result.append('There where {:d} associations found.'.format(len(asns)))\\n result.append('There where {:d} orphaned exposures.'.format(len(orphaned)))\\n for assocs in asns:\\n result.append(assocs.__str__())\\n\\n return '\\\\n'.join(result)\",\n \"def to_string(self):\\n return self.dungeon_string\",\n \"def get_short_name(self):\\n split = self.name.split(' - ')\\n # author, year, and first couple of words of paper title\\n return \\\"{} ({}), {}\\\".format(split[0], split[1], \\\" \\\".join(split[2].split(' ')[:3]))\",\n \"def obj_pretty(objective):\\n if objective.objective_type == u'/datum/objective/assassinate':\\n return 'Asassinate {} the {}.'.format(objective.target_name, objective.target_role)\\n else:\\n return objective.objective_desc\",\n \"def calc_conservation_string(aln):\\n\\n percids = calc_conservation(aln)\\n\\n # find identity positions\\n identity = \\\"\\\"\\n for pid in percids:\\n if pid == 1:\\n identity += \\\"*\\\"\\n elif pid > .5:\\n identity += \\\".\\\"\\n else:\\n identity += \\\" \\\"\\n\\n return identity\",\n \"def describe(self) -> str:\\n return (\\n \\\"{name} {surname} è nata/o a {birth_municipality} ({birth_province_code}) il {birthdate}.\\\"\\n \\\" Ora vive a {municipality} ({province_code}) in {address} {house_number}.\\\"\\n ).format(**self._data)\",\n \"def get_transcript(self):\\n\\n rna = \\\"\\\"\\n for i in range(len(self.__sequentie)):\\n if self.__sequentie[i] == \\\"A\\\":\\n rna += \\\"U\\\"\\n if self.__sequentie[i] == \\\"T\\\":\\n rna += \\\"A\\\"\\n if self.__sequentie[i] == \\\"C\\\":\\n rna += \\\"G\\\"\\n if self.__sequentie[i] == \\\"G\\\":\\n rna += \\\"C\\\"\\n if self.__sequentie[i] == \\\"N\\\":\\n rna += \\\"N\\\"\\n return rna\",\n \"def is_noun(tag_string):\\n result = True if tag_string in POS.POS_tags.noun_tags else False\\n return result\",\n \"def __repr__(self):\\n ct = shortest_string_in_list(self.cause_texts)\\n et = shortest_string_in_list(self.effect_texts)\\n ev = ','.join(self.evidence_texts)\\n return '%s -> %s [%s, %s, %s]' % (ct, et, ev,\\n repr(self.cause_polarity),\\n repr(self.effect_polarity))\",\n \"def de_or_het(noun):\\n\\tnoun = noun.lower()\\n\\n\\t# we have an excel files saved as csv, that contain words that are definately het-words\\n\\twith open('het_woorden', 'r') as f:\\n\\t\\thet_woorden = f.read().split('\\\\n')\\n\\n\\thet_woorden = [w.lower() for w in het_woorden]\\t\\n\\n\\t\\n\\t# and we have an excel file that contains dubious words\\n\\twith open('ambiguous_nouns', 'r') as f:\\n\\t\\tambiguous_nouns = f.read().split('\\\\n')\\n\\t\\n\\tambiguous_nouns = [w.lower() for w in ambiguous_nouns]\\t\\n\\tmessage = \\\"\\\"\\\"\\\\nYour noun may be either a 'de' or a 'het' word. Chosing one or the\\nother may change the meaning of '{0}'. When in doubt, advice a dictionary.\\n\\t\\\"\\\"\\\"\\n\\n\\t# next we want to see whether our noun is a 'de' or a 'het' noun:\\n\\tif noun in ambiguous_nouns:\\n\\t\\traise Exception(message.format(noun))\\n\\telif noun in het_woorden or noun.endswith('je'):\\n\\t\\tarticle = 'het'\\n\\telse:\\n\\t\\tarticle = 'de'\\t\\n\\t\\n\\t# last, we want the article and the noun returned as one string.\\t\\n\\treturn article + \\\" \\\" + noun\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6249946","0.6021164","0.600624","0.5979195","0.59327227","0.58711636","0.57597136","0.5741161","0.57387596","0.57079136","0.56950766","0.5683717","0.56733876","0.5652428","0.5563646","0.55163616","0.5513116","0.5472722","0.5459045","0.5425532","0.5423237","0.5419519","0.541946","0.5412078","0.5409855","0.5401926","0.5398518","0.5398444","0.53919876","0.5374007","0.5357182","0.53556603","0.53436214","0.53412294","0.5340815","0.53275573","0.5325488","0.53232026","0.5322284","0.5319297","0.5308966","0.53024125","0.53006464","0.53005457","0.52948767","0.52943075","0.5293416","0.5290383","0.5288274","0.52802414","0.52763605","0.527504","0.5272397","0.52679","0.52650994","0.5264727","0.5264727","0.5264727","0.5264727","0.5264727","0.5264727","0.5264727","0.5264727","0.52604556","0.5249635","0.52434605","0.5237351","0.5231963","0.5230937","0.52306044","0.5226371","0.52149886","0.5214102","0.5203698","0.52020395","0.5201215","0.5201148","0.5188947","0.5186473","0.51836175","0.51794434","0.5169279","0.5169076","0.5164772","0.5161175","0.515577","0.5152566","0.5148206","0.5139596","0.51323354","0.51312345","0.5130883","0.5129728","0.5128561","0.51131666","0.5110764","0.5109203","0.51040524","0.5102234","0.50952137"],"string":"[\n \"0.6249946\",\n \"0.6021164\",\n \"0.600624\",\n \"0.5979195\",\n \"0.59327227\",\n \"0.58711636\",\n \"0.57597136\",\n \"0.5741161\",\n \"0.57387596\",\n \"0.57079136\",\n \"0.56950766\",\n \"0.5683717\",\n \"0.56733876\",\n \"0.5652428\",\n \"0.5563646\",\n \"0.55163616\",\n \"0.5513116\",\n \"0.5472722\",\n \"0.5459045\",\n \"0.5425532\",\n \"0.5423237\",\n \"0.5419519\",\n \"0.541946\",\n \"0.5412078\",\n \"0.5409855\",\n \"0.5401926\",\n \"0.5398518\",\n \"0.5398444\",\n \"0.53919876\",\n \"0.5374007\",\n \"0.5357182\",\n \"0.53556603\",\n \"0.53436214\",\n \"0.53412294\",\n \"0.5340815\",\n \"0.53275573\",\n \"0.5325488\",\n \"0.53232026\",\n \"0.5322284\",\n \"0.5319297\",\n \"0.5308966\",\n \"0.53024125\",\n \"0.53006464\",\n \"0.53005457\",\n \"0.52948767\",\n \"0.52943075\",\n \"0.5293416\",\n \"0.5290383\",\n \"0.5288274\",\n \"0.52802414\",\n \"0.52763605\",\n \"0.527504\",\n \"0.5272397\",\n \"0.52679\",\n \"0.52650994\",\n \"0.5264727\",\n \"0.5264727\",\n \"0.5264727\",\n \"0.5264727\",\n \"0.5264727\",\n \"0.5264727\",\n \"0.5264727\",\n \"0.5264727\",\n \"0.52604556\",\n \"0.5249635\",\n \"0.52434605\",\n \"0.5237351\",\n \"0.5231963\",\n \"0.5230937\",\n \"0.52306044\",\n \"0.5226371\",\n \"0.52149886\",\n \"0.5214102\",\n \"0.5203698\",\n \"0.52020395\",\n \"0.5201215\",\n \"0.5201148\",\n \"0.5188947\",\n \"0.5186473\",\n \"0.51836175\",\n \"0.51794434\",\n \"0.5169279\",\n \"0.5169076\",\n \"0.5164772\",\n \"0.5161175\",\n \"0.515577\",\n \"0.5152566\",\n \"0.5148206\",\n \"0.5139596\",\n \"0.51323354\",\n \"0.51312345\",\n \"0.5130883\",\n \"0.5129728\",\n \"0.5128561\",\n \"0.51131666\",\n \"0.5110764\",\n \"0.5109203\",\n \"0.51040524\",\n \"0.5102234\",\n \"0.50952137\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.69946307"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":2881,"cells":{"query":{"kind":"string","value":"Parse a noun object from a data file containing nouns and their describing adjectives."},"document":{"kind":"string","value":"def parse(text):\n parts = text.split(' ')\n noun = Noun(parts[0], int(parts[1]))\n\n parts = parts[2:]\n while len(parts) > 0:\n noun.add_adjectives(Word(parts[0], int(parts[1])))\n parts = parts[2:]\n return noun"},"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 __read_data__(self):\n with open(self.file, 'r') as data:\n sentence = []\n tags = []\n for line in data:\n terms = line.rstrip().split(WHITESPACE)\n for term in terms:\n word_tag = tuple(term.split(TAGCHAR))\n word = word_tag[0]\n tag = word_tag[1]\n self.word_tag_dict[word_tag] += 1\n self.tag_dict[tag] += 1\n self.__add_to_word_dict__(word, tag)\n if self.isNumberWord(word):\n self.numbers += 1\n if word[0].isupper() and len(sentence) > 0:\n self.cap_no_start += 1\n sentence.append(word)\n tags.append(tag)\n if tag == ENDOFSENTENCE:\n self.sentences.append(tuple(sentence))\n self.tags.append(tuple(tags))\n sentence = []\n tags = []","def process_data_from_input_file(triplet):\n\n sentence = triplet.subject + ' ' + triplet.predicate + ' ' + triplet.object\n doc = nlp(unicode(sentence))\n root = doc[0]\n for t in doc:\n if t.pos_ == 'VERB' and t.head == t:\n root = t\n # elif t.pos_ == 'NOUN'\n\n # also, if only one sentence\n # root = doc[:].root\n\n\n \"\"\"\n CURRENT ASSUMPTIONS:\n - People's names are unique (i.e. there only exists one person with a certain name).\n - Pet's names are unique\n - The only pets are dogs and cats\n - Only one person can own a specific pet\n - A person can own only one pet\n \"\"\"\n\n\n # Process (PERSON, likes, PERSON) relations\n if root.lemma_ == 'like':\n if triplet.subject in [e.text for e in doc.ents if e.label_ == 'PERSON' or e.label_ == 'ORG'] and triplet.object in [e.text for e in doc.ents if e.label_ == 'PERSON' or e.label_ == 'ORG'] and \"n't\" not in triplet.predicate:\n s = add_person(triplet.subject)\n o = add_person(triplet.object)\n s.likes.append(o)\n\n if root.lemma_ == 'be' and triplet.object.startswith('friends with'):\n fw_doc = nlp(unicode(triplet.object))\n with_token = [t for t in fw_doc if t.text == 'with'][0]\n # get text after with\n after_with = fw_doc.text.split(with_token.text+ ' ')[1]\n people = []\n for p in after_with.split(' '):\n if nlp(p)[0].tag_ == 'NNP':\n people.append(nlp(p)[0].text)\n # fw_who = [t for t in with_token.children if t.dep_ == 'pobj'][0].text\n # fw_who = [e for e in fw_doc.ents if e.label_ == 'PERSON'][0].text\n for p in people:\n if triplet.subject in [e.text for e in doc.ents if e.label_ == 'PERSON']:\n s = add_person(triplet.subject)\n o = add_person(p)\n s.likes.append(o)\n o.likes.append(s)\n if root.lemma_ == 'be' and triplet.object == 'friends':\n fw_doc = nlp(unicode(triplet.subject))\n and_token = [t for t in fw_doc if t.text == 'and']\n if and_token:\n and_token = and_token[0].text\n if and_token == 'and' and fw_doc[0].text in [e.text for e in doc.ents if e.label_ == 'PERSON'] and fw_doc[2].text in [e.text for e in doc.ents if e.label_ == 'PERSON']:\n s = add_person(fw_doc[0].text)\n o = add_person(fw_doc[2].text)\n s.likes.append(o)\n o.likes.append(s)\n\n # Process (PET, has, NAME) Mary's dog's name is Rover\n if triplet.subject.endswith('name') and ('dog' in triplet.subject or 'cat' in triplet.subject):\n obj_span = doc.char_span(sentence.find(triplet.object), len(sentence))\n\n # handle single names, but what about compound names? Noun chunks might help.\n if (len(obj_span) == 1 or len(obj_span) == 2) and obj_span[-1].pos_ == 'PROPN':\n name = triplet.object\n subj_start = sentence.find(triplet.subject)\n subj_doc = doc.char_span(subj_start, subj_start + len(triplet.subject))\n\n s_people = [token.text for token in subj_doc if token.ent_type_ == 'PERSON']\n assert len(s_people) == 1\n s_person = select_person(s_people[0])\n\n pet = get_persons_pet(s_person.name)\n\n pet.name = name\n s_person.has.append(pet)\n\n # Process (Who has dog)\n if root.lemma_ == 'have'and ('dog' in triplet.object or 'cat' in triplet.object):\n # find pets name and instantiate name empty str\n obj_span = doc.char_span(sentence.find(triplet.object), len(sentence))\n name = ''\n\n if obj_span[-1].pos_ == 'PROPN':\n name = obj_span[-1].text\n s = add_person(triplet.subject)\n s_pet_type = 'dog' if 'dog' in triplet.object else 'cat'\n pet = add_pet(s_pet_type, name)\n s.has.append(pet)\n\n date = [e.text for e in doc.ents if e.label_ == 'DATE']\n gpe = [e.text for e in doc.ents if e.label_ == 'GPE']\n person = [e.text for e in doc.ents if e.label_ == 'PERSON' or e.label_ == 'ORG']\n # if person and GPE exists, we add it into trip(departs_on, departs_to)\n if person and (gpe or date):\n s = add_person(triplet.subject)\n o = add_trip(date, gpe)\n s.travels.append(o)","def loadDataFile(self, filename):\n self.datafile = vocloadlib.readTabFile(filename,\n [ 'term', 'accID', 'status', 'abbreviation',\n 'note', 'comment', 'synonyms', 'synonymTypes',\n 'otherIDs', 'start', 'end', 'parent' \n ])","def noun_phrase_chunking(part_of_speech_data):\n\n grammar = r\"\"\"\n NP: {<DT|JJ|NN.*>+}\n PP: {<IN><NP>}\n VP: {<VB.*><NP|PP|CLAUSE>+$}\n CLAUSE: {<NP><VP>}\n \"\"\"\n\n grammar2 = r\"\"\"\n NP: {<DT|NN>+} # Chunk sequences of NN and DT\n {<DT><JJ><NN>} # Chunk det+adj+noun\n \"\"\"\n\n return RegexpParser(grammar).parse(part_of_speech_data).draw()","def parse(self, word):\n word = self.son.segs(word)\n son_map = self._sonority_map(word)\n son_map = self._mark_offglides(son_map)\n son_map = self._adjust_anom_fric_cod(son_map)\n son_map = self._adjust_anom_fric_ons(son_map)\n ons_son = self._initial_onset(son_map)\n cod_son = self._final_coda(son_map)\n ons = self.from_map(ons_son, word)\n cod = self.from_reverse_map(cod_son, word)\n return (ons, cod)","def get_nouns(self):\n\t\tblob = TextBlob(self.raw_string)\n\n\t\tfor word, tag in blob.tags:\n\t\t\tif tag in ['NNP', 'NN']:\n\t\t\t\tself.nouns.append(word.lemmatize())","def parse_data(filename):\r\n labels = []\r\n documents = []\r\n with open(filename, 'r') as f:\r\n for line in f:\r\n values = line.split()\r\n label = values[0]\r\n document = []\r\n for wordCount in values[1:]:\r\n parsed = wordCount.split(':')\r\n word = parsed[0]\r\n count = int(parsed[1])\r\n document.append((word, count))\r\n labels.append(label)\r\n documents.append(document)\r\n return (labels, documents)","def parse(self, infile):\r\n raise NotImplementedError()","def loadDataFile(self, filename):\n \n self.datafile = vocloadlib.readTabFile(filename,\n [ 'term', 'accID', 'status', 'abbreviation',\n 'note', 'comment', 'synonyms', 'synonymTypes',\n 'otherIDs', 'emapa', 'ts', 'parent']\n )","def read_tagged_word_list(filename):\n # TODO: write and test this method\n print 'reading tagged file'","def parse_label(self):\n # TODO: make this work with attached labels as well as\n # stand alone labels.\n # Save the RAW full text of the label to self._raw\n input_stream = FileStream(self.infile)\n lexer = ODLv21Lexer(input_stream)\n tokens = CommonTokenStream(lexer)\n\n parser = ODLv21Parser(tokens)\n parse_tree = parser.label()\n self._parse_tree = parse_tree\n visitor = Pds3LabelVisitor()\n visitor.visit(parse_tree)\n return visitor.root_dict","def parse(las_file):\n io_stream = io.TextIOWrapper(las_file)\n \n entry_date = datetime.now().strftime('%Y-%m-%d-%H-%M-%S')\n entry_filename = 'las_file-' + entry_date + '.las'\n\n entry = SectionInfo()\n entry.filename = entry_filename\n section = ''\n\n for line in io_stream.readlines():\n\n line = line.rstrip()\n\n if not line:\n continue\n\n # Lines beginning with '~' denote the next section header.\n if line[0] == '~':\n section = line\n continue\n # Skip comment lines.\n elif line[0] == '#':\n continue\n\n # LAS standard option 'OTHER' section\n if section[1] == 'O': \n entry.value = line\n entry.section = section\n # The rest of the standard metadata sections\n elif section[1] in ['V', 'W', 'C', 'P']:\n entry = parse_formatted_section_line(section, line, entry)\n # the data section and non-standard sections\n else:\n # print(\"Non-Metadata-Section: [{}]: [{}]\".format(section[0:2], line))\n continue\n\n # Write entry to db\n entry.save()\n\n # Initialize next entry\n entry = SectionInfo()\n entry.filename = entry_filename\n\n return entry_filename","def read_data(filename, prefix=None):\n p_data = {}\n with open(filename) as f:\n # This first line is the header for the entire file.\n line = f.next()\n line = line.strip()\n # prev_line = line\n top_header = line.split(',')\n if not top_header:\n # Don't parse this for now.\n pass\n # Now read in per-participant data.\n while True:\n word_list = []\n all_words_data = {}\n # The first line for the participant is a header.\n try:\n line = f.next()\n except StopIteration:\n # We had previously read everything, so we're done.\n break\n line = line.strip()\n p_header = line.split(',')\n\n # The participant's ID # comes first.\n p_id = p_header[0]\n if not p_id:\n # This happens when the previous participant didn't answer.\n \"\"\"\n print 'previous line:', prev_line\n print 'current line:', line\n print 'p header:', p_header\n print\n \"\"\"\n continue\n if prefix:\n p_id = prefix + p_id\n # print 'SN #', p_id\n # The number of N/A's this p is at 28.\n try:\n p_nas = int(p_header[28])\n except ValueError:\n # This happens when an RA messes up the file.\n \"\"\"\n print 'nas: previous line:', prev_line\n print 'nas: current line:', line\n print 'nas: p header:', p_header\n print\n \"\"\"\n raise\n # print \"NA's: #\", p_nas\n # Check if this participant left everything blank.\n # XXX: Have to hard-code this.\n if p_nas == 20:\n \"\"\"Don't record anything.\n p_data[p_id] = {'words': None,\n 'word_data': None,\n 'nas': None,\n 'overall': None}\n \"\"\"\n continue\n # The next line after the header has both the data\n # for the first word and overall statistics.\n # prev_line = line\n try:\n line = f.next()\n except StopIteration:\n # We had previously read everything, so we're done.\n break\n line = line.strip()\n word, word_data, overall_data = parse_first_line(line.split(','))\n word_list.append(word)\n all_words_data[word] = word_data\n # Now read data for the rest of the words.\n for line in f:\n line = line.strip()\n word, word_data = parse_data_lines(line.split(','))\n if word == '':\n \"\"\"\n print \"loop's previous line:\", prev_line\n print \"loop's current line:\", line\n print\n \"\"\"\n # prev_line = line\n break\n word_list.append(word)\n all_words_data[word] = word_data\n # prev_line = line\n # Compute per-word averages\n all_total_avg, future_total_avg, past_total_avg = \\\n datacomputer.compute_all_future_past(all_words_data)\n overall_data['all'] = all_total_avg\n overall_data['future'] = future_total_avg\n overall_data['past'] = past_total_avg\n p_data[p_id] = {'words': word_list,\n 'word_data': all_words_data,\n 'nas': p_nas,\n 'overall': overall_data}\n # print 'p_data'\n # print p_data[p_id]\n # print\n print \"Processed {} participants' data\".format(len(p_data))\n return p_data","def _parse(self, infile):\n raise NotImplementedError()","def parse_voc(filename):\n tree = ET.parse(filename)\n objects = []\n for obj in tree.findall('object'):\n obj_struct = {}\n obj_struct['name'] = obj.find('name').text\n obj_struct['difficult'] = int(obj.find('difficult').text)\n bbox = obj.find('bndbox')\n obj_struct['bbox'] = [int(bbox.find('xmin').text),\n int(bbox.find('ymin').text),\n int(bbox.find('xmax').text),\n int(bbox.find('ymax').text)]\n objects.append(obj_struct)\n\n return objects","def get_noun_phrases(blob):\n return blob.noun_phrases","def import_data(in_file):\n\n print '\\n\\tImport data'\n sentence = []\n concept = []\n sentences = []\n concepts = []\n for line in open(in_file, 'r'):\n if line != '\\n':\n sentence += [ line.split()[0] ]\n concept += [ line.split()[1] ]\n else:\n sentences += [ sentence ]\n concepts += [ concept ]\n sentence = [ ]\n concept = [ ]\n pos = []\n lemma = []\n poss = []\n lemmas = []\n for line in open(in_file.replace('.data', '.feats.txt'), 'r'):\n if line != '\\n':\n pos += [ line.split()[ 1 ] ]\n lemma += [ line.split()[ 2 ] ]\n else:\n poss += [ pos ]\n lemmas += [ lemma ]\n pos = [ ]\n lemma = [ ]\n print '\\t--done'\n return sentences, poss, lemmas, concepts","def read_pronunciation(pronunciation_file):\n # file = open('dictionary.txt', 'r')\n #\n # for line in file:\n # print line\n\n ################# https://m.reddit.com/r/CompSciPortfolio/comments/303fyo/assignment_3_poetry_reader/\n\n pronunciation_dictionary = {}\n line = pronunciation_file.readline()\n while line.startswith(';;;'):\n line = pronunciation_file.readline()\n while line != '':\n stripped_line = line.strip()\n separation = stripped_line.find(' ')\n pronunciation_dictionary[stripped_line[:separation]] = stripped_line[(separation + 2):].split()\n line = pronunciation_file.readline()\n return pronunciation_dictionary\n\n\n\n # my_list = {}\n # for line in pronunciation_file.readlines():\n # line = line.strip()\n # if line and \";;;\" not in line:\n # r = line.split()\n # word = r[0]\n # phonemes = r[1:]\n # my_list[word] = phonemes\n # return my_list","def main ():\n\n\tfio = fileIo('input.txt')\n text = fio.getInput()\n\n\tp = re.compile(r'#?\\d[\\s\\.]?[\\s]?')\n\tout = filter(None, p.split(text))\n\ti = 0\n\tlistOfLists = []\n\t\n\n\tfor s in out:\n\t\ti += 1\n\t\ttext = nltk.word_tokenize(s)\n\t\tpos = nltk.pos_tag(text)\n\t\tpattern = \"NP: {<DT>?<JJ>*<NN>}\"\n\t\tNPChunker = nltk.RegexpParser(pattern)\n\t\tresult = NPChunker.parse(pos)\n\t\tlistOfLists.append( result )\n\n\tprint \"Noun Count:\\n\" + str(countNouns( listOfLists ))\n\tprint \"Verb Count:\\n\" + str(countVerbs( listOfLists ))\n\tprint \"Adjective Count:\\n\" + str(countAdjectives( listOfLists ))","def load_file(filename):\n\tlabels = []\n\tdocs = []\n\n\twith open(filename) as f:\n\t\tfor line in f:\n\t\t\tcontent = line.split('\\t')\n\n\t\t\tif len(content) > 2:\n\t\t\t\tprint('incorrect read')\n\t\t\t\texit()\n\n\t\t\tif len(content[1]) == 0: continue\n\n\t\t\tdocs.append(str(content[1]).strip('\\r').strip('\\n').strip('\\r\\n'))\n\t\t\tlabels.append(content[0])\n\n\treturn docs, labels","def parseFile(self, filename):\n self.__filename = filename\n\n if os.path.isfile(filename) == False:\n self.LogError(\"Unable to open input file \" + str(filename))\n raise IOError\n\n self.__file = open(filename, 'r')\n\n while True:\n string = self.__file.readline()\n if string == \"\":\n break\n\n if string.upper().find(\"[SYSTEM]\") != -1:\n #print string.upper()\n self.__parseSystem()\n\n if string.upper().find(\"[GRASS]\") != -1:\n #print string.upper()\n self.__parseGrass()\n\n if string.upper().find(\"[COMPLEXDATA]\") != -1:\n #print string.upper()\n self.complexDataList.append(ComplexData(self.__file))\n\n if string.upper().find(\"[COMPLEXOUTPUT]\") != -1:\n #print string.upper()\n self.complexOutputList.append(ComplexOutput(self.__file))\n\n if string.upper().find(\"[LITERALDATA]\") != -1:\n #print string.upper()\n LD = LiteralData(self.__file)\n if LD.identifier == 'multi_output':\n self.LogWarning(\"multi_output: \" + LD.value.upper())\n if LD.value.upper() == 'TRUE':\n self.multiOutput = True\n else:\n self.literalDataList.append(LD)","def parseFileName(filename):\n entry = DataEntry(\"\",0,{},{},0,0)\n wordArray = filename.split(\".\")\n entry.publication_name = wordArray[1]\n entry.year = wordArray[0]\n return entry","def load(path: str) -> \"DataDescriptor\":\n\n\t\twith open(path, \"r\") as f:\n\t\t\tinfo_dict = json.load(f)\n\n\t\treturn DataDescriptor(\n\t\t\tn_gram_size=int(info_dict[\"n_gram_size\"]),\n\t\t\tcaseless=bool(info_dict[\"caseless\"]),\n\t\t\tignore_punctuation=bool(info_dict[\"ignore_punctuation\"]),\n\t\t\tadd_pos_tags=bool(info_dict[\"add_pos_tags\"]),\n\t\t\tuses_lemma=bool(info_dict[\"uses_lemma\"]),\n\t\t\tuses_sentences=bool(info_dict[\"uses_sentences\"])\n\t\t)","def parse_object(self, word_list):\n\n self.skip(word_list, 'stop')\n\n next_word = self.peek(word_list)\n\n if next_word == 'noun':\n return self.match(word_list, 'noun')\n\n elif next_word == 'direction':\n return self.match(word_list, 'direction')\n\n else:\n raise ParserError(\n 'Expected a noun or direction. Got a %s.' % next_word\n )","def parse_data(fp):\n pass","def _parse(self):\n with open(_join(self.man_dir, self.man_fn)) as fp:\n lines = fp.readlines()\n \n desc_indxs = []\n for i, L in enumerate(lines):\n if \"#landuse\" in L or \" # landuse\" in L:\n desc_indxs.append(i-1)\n desc_indxs.append(i-2)\n desc_indxs.append(i-3)\n \n lines = [L[:L.find('#')].strip() for L in lines]\n lines = [L for i, L in enumerate(lines) if len(L) > 0 or i in desc_indxs]\n\n del desc_indxs\n \n self.datver = lines.pop(0)\n self.nofe = int(lines.pop(0))\n self.sim_years = int(lines.pop(0))\n \n # Read Plant Growth Section\n self.plants = PlantLoops(lines, self)\n\n # Read Operation Section\n self.ops = OpLoops(lines, self)\n \n # Read Initial Condition Section\n self.inis = IniLoops(lines, self)\n \n # Read Surface Effects Section\n self.surfs = SurfLoops(lines, self)\n \n # Read Contour Section\n self.contours = ContourLoops(lines, self)\n \n # Read Drainage Section\n self.drains = DrainLoops(lines, self)\n \n # Read Yearly Section\n self.years = YearLoops(lines, self)\n \n # Read Management Section \n self.man = ManagementLoop(lines, self)","def parse_file(self, infile, chardict, labeldict):\n examples = []\n fin = io.open(infile, 'r')\n # idx is for the index of the row in the \n # original file before shuffling and randomization\n idx = 0\n for line in fin: \n entity, label = map(clean, line.rstrip().split('\\t')[:2])\n # print entity\n ent = map(lambda c:chardict[c], list(entity))\n lab = map(lambda l:labeldict[l] if l in labeldict else 0, label.split(','))\n examples.append((idx, ent, lab))\n idx += 1\n fin.close()\n print \"num_rows:\", len(examples), \" index\", idx\n return examples","def __init__(self, variable, pnoun, nucleus):\n super(ProperNounExpression, self).__init__(variable, EmptyExpression(), nucleus)\n assert(pnoun in proper_nouns)\n self.pnoun = pnoun","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 data_parser(data):\n\n with open(data, 'r') as inp:\n\n # take every sample\n # the last line in the text file is empty, so reading until -1\n samples = inp.read().split('\\n')[:-1]\n\n vec = []\n labels = []\n for sample in samples:\n # file is tab delimited\n split_samples = sample.split('\\t')\n # last column contains the label\n labels.append(int(split_samples[-1]))\n\n features = []\n for feature in split_samples[:-1]:\n features.append(float(feature))\n vec.append(features)\n\n # make the features and labels as a numpy array\n vec = np.array(vec)\n labels = np.array(labels)\n return vec, labels","def parse_fasta(fasta_f, contig_data):\n\n basen = os.path.basename(fasta_f)\n [soil, ecotype, media] = basen.split(\"_\")[:3]\n\n with open(fasta_f, 'rU') as IN:\n for record in SeqIO.parse(IN, \"fasta\"):\n contig_data[record.description] = {'length': len(record.seq), 'soil': soil, 'ecotype': ecotype, 'media': media}","def load_morphoit(self, path=None):\n path = path or self.path\n # f = open(path,'r')\n f = codecs.open(path, 'r', 'latin-1')\n lines = f.readlines()\n f.close()\n self.lemma_dict.clear()\n self.tag_dict.clear()\n self.suffix_dict.clear()\n self.word_tag_dict.clear()\n n_entries = 0\n for line in lines:\n line = line.strip()\n if line:\n # entry = line.split()\n entry = line.split(u'\\u0009') # entry has TAB delimited items\n n = len(entry)\n word = entry[0]\n if n > 1:\n lemma = entry[1]\n else:\n lemma = 'lemma?'\n if n > 2:\n tags = entry[2]\n if tags:\n splitted_tags = tags.split(u':')\n tag = splitted_tags[0]\n if tag == u'SMI': # smile ?\n continue\n else:\n tag = 'tag?'\n n_entries += 1\n if self.use_defaultdict:\n self.lemma_dict[lemma].append(word)\n self.tag_dict[tag] += 1\n self.word_tag_dict[word][tag] += 1\n else:\n words = self.lemma_dict.get(lemma, []); words.append(word); self.lemma_dict[lemma] = words\n self.tag_dict[tag] = self.tag_dict.get(tag, 0) + 1\n dict = self.word_tag_dict.get(word, {})\n \"\"\" only occurrence in corpus will increment the counter !\n dict[tag] = dict.get(tag, 0) + 1\n \"\"\"\n if dict.get(tag, None) is None:\n dict[tag] = 0\n self.word_tag_dict[word] = dict\n length = len(word)\n reversed = util.reverse(word)\n max = min(length, MAX_SUFFIX)\n for i in range(MIN_SUFFIX, max):\n suffix = reversed[:i]\n if self.use_defaultdict:\n self.suffix_dict[suffix][tag] += 1\n else: # defaultdicts cannot be pickled !\n dict = self.suffix_dict.get(suffix, {})\n dict[tag] = dict.get(tag, 0) + 1\n self.suffix_dict[suffix] = dict\n return len(lines), n_entries","def get_nouns(txt):\n query = 'https://api.textgain.com/1/tag?q='\n query += urllib.parse.quote(txt, safe='')\n query += '&lang=fr&key=***'\n resp = requests.get(query)\n\n body = json.loads(resp.text)['text'][0]\n\n nouns = {}\n for iterable_elem in body:\n for elem in iterable_elem:\n if elem['tag'] == 'NOUN':\n word = elem['word']\n if word in nouns.keys():\n nouns[word] += 1\n else:\n nouns[word] = 1\n print(nouns)\n return nouns","def read_file(f, prefix=None):\n p_data = {}\n # This first line is the header for the entire file.\n line = f.next()\n line = line.strip()\n # prev_line = line\n top_header = line.split(',')\n if not top_header:\n # Don't parse this for now.\n pass\n # Now read in per-participant data.\n while True:\n word_list = []\n all_words_data = {}\n # The first line for the participant is a header.\n try:\n line = f.next()\n except StopIteration:\n # We had previously read everything, so we're done.\n break\n line = line.strip()\n p_header = line.split(',')\n\n # The participant's ID # comes first.\n p_id = p_header[0]\n if not p_id:\n # This happens when the previous participant didn't answer.\n \"\"\"\n print 'previous line:', prev_line\n print 'current line:', line\n print 'p header:', p_header\n print\n \"\"\"\n continue\n if prefix:\n p_id = prefix + p_id\n # print 'SN #', p_id\n # The number of N/A's this p is at 28.\n try:\n p_nas = int(p_header[28])\n except ValueError:\n # This happens when an RA messes up the file.\n \"\"\"\n print 'nas: previous line:', prev_line\n print 'nas: current line:', line\n print 'nas: p header:', p_header\n print\n \"\"\"\n raise\n # print \"NA's: #\", p_nas\n # Check if this participant left everything blank.\n # XXX: Have to hard-code this.\n if p_nas == 20:\n p_data[p_id] = {'words': None,\n 'word_data': None,\n 'nas': None,\n 'overall': None}\n continue\n # The next line after the header has both the data\n # for the first word and overall statistics.\n # prev_line = line\n try:\n line = f.next()\n except StopIteration:\n # We had previously read everything, so we're done.\n break\n line = line.strip()\n word, word_data, overall_data = parse_first_line(line.split(','))\n word_list.append(word)\n all_words_data[word] = word_data\n # Now read data for the rest of the words.\n for line in f:\n line = line.strip()\n word, word_data = parse_data_lines(line.split(','))\n if word == '':\n \"\"\"\n print \"loop's previous line:\", prev_line\n print \"loop's current line:\", line\n print\n \"\"\"\n # prev_line = line\n break\n word_list.append(word)\n all_words_data[word] = word_data\n # prev_line = line\n # Compute per-word averages\n all_total_avg, future_total_avg, past_total_avg = \\\n datacomputer.compute_all_future_past(all_words_data)\n overall_data['all'] = all_total_avg\n overall_data['future'] = future_total_avg\n overall_data['past'] = past_total_avg\n p_data[p_id] = {'words': word_list,\n 'word_data': all_words_data,\n 'nas': p_nas,\n 'overall': overall_data}\n # print 'p_data'\n # print p_data[p_id]\n # print\n print \"Processed {} participants' data\".format(len(p_data))\n return p_data","def load_conll_notags(unfile, max_slen, vocab=[], oovs={}, pads={}, lower=False, mwe=True, unk_case=True):\n # special characters used for splitting words\n split_chars = set([',', '.', ':', '-', '~', \"'\", '\"'])\n\n # punctuation that denotes when a sentence finishes\n sent_split_words = set(['.', '?', '!', ';', '—'])\n\n input_sents = []\n input_words = []\n windex = -1\n\n # number of words from which to split sentences\n LIMIT_SENT_LEN = max_slen\n\n sents = []\n if 'begin' in pads:\n next_words = [pads['begin']]\n next_syms = ['']\n next_indexs = [windex]\n sent_base_length = 1\n else:\n next_words = []\n next_syms = []\n next_indexs = []\n sent_base_length = 0\n\n # select files to use\n input_files = [unfile]\n\n # counters\n num_raw_sents = 0\n num_sents = 0\n num_words = 0\n num_oovs = 0\n\n # iterate over lines in the input files\n for ifile in input_files:\n for line in codecs.open(ifile, mode = 'r', errors = 'ignore', encoding = 'utf-8'):\n # discard newline character\n line = line[:-1]\n\n # keep adding words while in the middle of a sentence\n if line:\n word = line.split('\\t')[0]\n sym = word\n # add new original word\n windex += 1\n input_words.append(word)\n num_words += 1\n # lowercase when indicated\n if lower:\n word = word.lower()\n # use an heuristic and try to map oov words\n if vocab and word not in vocab:\n if word not in split_chars:\n if re.match('^[0-9\\.\\,-]+$', word):\n word = oovs['number']\n elif _match_word_vocab(word, vocab) != word:\n word = _match_word_vocab(word, vocab)\n elif ' ' in word or '~' in word or '-' in word and mwe:\n # attempt to split multi-word expressions\n constituents_text = re.split('[\\s~ | \\s-]+', word)\n constituents = [_match_word_vocab(w, vocab) for w in constituents_text]\n if all([True if c in vocab else False for c in constituents]):\n next_words += constituents[:-1]\n next_syms += constituents[:-1]\n next_indexs += [windex] * len(constituents[:-1])\n word = constituents[-1]\n sym = constituents[-1]\n else:\n if unk_case and word[0].isupper():\n word = oovs['UNKNOWN']\n else:\n word = oovs['unknown']\n num_oovs += 1\n else:\n if unk_case and word[0].isupper():\n word = oovs['UNKNOWN']\n else:\n word = oovs['unknown']\n num_oovs += 1\n else:\n word = oovs['unknown']\n num_oovs += 1\n\n next_words.append(word)\n next_syms.append(sym)\n next_indexs.append(windex)\n\n # stack the current sentence upon seeing an empty line or a sentence end mark\n if not line or (len(next_words) > 3 and next_words[-4] in sent_split_words) or (len(next_words) >= LIMIT_SENT_LEN and len(sent_split_words.intersection(next_words)) < 1):\n if len(next_words) > sent_base_length:\n # split when an empty line marks a sentence end\n if not line:\n if 'end' in pads:\n next_words.append(pads['end'])\n next_syms.append('')\n next_indexs.append(-1)\n sents.append(list(zip(next_words, next_indexs, next_syms)))\n input_sents.append(input_words)\n input_words = []\n windex = -1\n next_words = []\n next_syms = []\n next_indexs = []\n num_raw_sents += 1\n num_sents += 1\n # split when punctuation marks a sentence end\n elif len(next_words) > 3 and next_words[-4] in sent_split_words:\n split_words = next_words[:-3]\n split_syms = next_syms[:-3]\n split_indexs = next_indexs[:-3]\n if 'end' in pads:\n split_words.append(pads['end'])\n split_syms.append('')\n split_indexs.append(-1)\n sents.append(list(zip(split_words, split_indexs, split_syms)))\n next_words = next_words[-3:]\n next_syms = next_syms[-3:]\n next_indexs = next_indexs[-3:]\n num_sents += 1\n # split when the maximum sentence length is reached\n # a bad guess is better than not guessing when predicting tags\n else:\n sents.append(list(zip(next_words, next_indexs, next_syms)))\n next_words = []\n next_syms = []\n next_indexs = []\n num_sents += 1\n\n if 'begin' in pads:\n next_words = [pads['begin']] + next_words\n next_syms = [''] + next_syms\n next_indexs = [-1] + next_indexs\n\n else:\n input_sents.append(input_words)\n input_words = []\n windex = -1\n num_raw_sents += 1\n\n # double check the last sentence\n if len(next_words) > sent_base_length:\n if 'end' in pads:\n next_words.append(pads['end'])\n next_syms.append('')\n next_indexs.append(-1)\n sents.append(list(zip(next_words, next_indexs, next_syms)))\n input_sents.append(input_words)\n input_words = []\n windex = -1\n num_raw_sents += 1\n num_sents += 1\n\n # find the allowed sentence length\n print('[INFO] Number of unlabelled OOV words: ' + str(num_oovs) + ' / ' + str(num_words))\n print('[INFO] Original number of unlabelled sentences: ' + str(num_raw_sents))\n print('[INFO] Number of extracted unlabelled sentences ' + str(num_sents))\n return input_sents, sents","def load_annos(self):\n data = None\n with open(self.anno_path, 'r') as file:\n if self.ext == '.json':\n data = json.load(file)\n\n # Label start at index 0\n if data is not None:\n for anno in data['annotations']:\n anno['category_id'] -= 1\n\n for anno in data['categories']:\n anno['id'] -= 1\n\n return data","def _load_dat(self):\n modelfile = self.filename\n with open(modelfile) as f:\n content = f.readlines()\n\n self.comment = content.pop(0) # Comment line\n content = [x for x in content if not x.startswith('#')]\n\n for line in content:\n if('atoms' in line): self.natoms = int(line.split()[0])\n if('xlo' in line and 'xhi' in line):\n self.xsize = abs(float(line.split()[0])) + abs(float(line.split()[1]))\n if('ylo' in line and 'yhi' in line):\n self.ysize = abs(float(line.split()[0])) + abs(float(line.split()[1]))\n if('zlo' in line and 'zhi' in line):\n self.zsize = abs(float(line.split()[0])) + abs(float(line.split()[1]))\n if('atom types' in line): nelems = int(line.split()[0])\n if('Masses' in line): mflag = content.index(line) + 1\n if('Atoms' in line): aflag = content.index(line) + 1\n try:\n mflag\n except NameError:\n raise Exception(\"ERROR! You need to define the masses in the .dat file.\")\n atomtypes = {}\n while(nelems > 0):\n if(len(content[mflag].split()) == 2):\n atomtypes[int(content[mflag].split()[0])] = masses.get_znum(float(content[mflag].split()[1]))\n nelems -= 1\n mflag += 1\n self.atoms = []\n natoms = self.natoms\n while(natoms > 0):\n sline = content[aflag].split()\n if(len(sline) >= 5):\n # We found an atom\n id = int(sline[0])\n type = int(sline[1])\n x = float(sline[2])\n y = float(sline[3])\n z = float(sline[4])\n znum = atomtypes[type]\n # Add it to the model\n self.atoms.append(Atom(id,znum,x,y,z))\n natoms -= 1\n aflag += 1","def load_ud_english(fpath):\n import os\n import re\n from collections import defaultdict\n n = 1\n\n fname = os.path.split(fpath)[1]\n\n parses = defaultdict(list)\n sent_ids = []\n newdoc_ids = []\n \n for l in open(fpath):\n ident = fname+' '+str(n)\n \n if re.match(r'\\# newdoc id', l):\n newdoc_ids.append(n)\n #newdoc_ids.append(l.split(\"=\")[-1].strip())\n \n if re.match(r'^\\d', l):\n l_split = l.strip().split()\n parses[ident].append(l_split)\n \n elif parses[ident]:\n sent_ids.append(ident)\n n += 1\n\n return newdoc_ids, len(sent_ids)","def en_noun(t, label):\r\n s = label\r\n p = []\r\n is_uncountable = False\r\n \r\n # http://en.wiktionary.org/wiki/Template:en-noun\r\n head = t.arg(\"head\", label)\r\n p1 = t.arg(0)\r\n p2 = t.arg(1)\r\n \r\n if p1 == \"-\":\r\n # uncountable\r\n is_uncountable = True\r\n \r\n if p2 == \"s\":\r\n # ends by s\r\n p.append(head + \"s\")\r\n \r\n elif p2 is not None:\r\n # word\r\n p.append(p2)\r\n \r\n elif p1 == \"es\":\r\n # add es\r\n p.append(head + \"es\")\r\n \r\n elif p1 is not None:\r\n # use term\r\n p.append(p1)\r\n \r\n elif p1 is None and p2 is None:\r\n p.append(head+\"s\")\r\n\r\n for k,a in t.args.items():\r\n if not a.is_named():\r\n if k == 0 or k == 1:\r\n continue\r\n \r\n p.append(a.as_string())\r\n \r\n return (s, p, is_uncountable)","def parse_rosalind(filename):\n print \"parse_rosalind should be called parse_fasta\"\n return parse_fasta(filename)","def load_data(filename):\n with open(filename, 'r') as f:\n d = json.load(f)\n\n return d['nicknames'], d['relations'], d['directed']","def parse (cls, raw_data):\n # Parse text\n model = NFFGModel.parse(raw_data)\n # Create new NFFG\n nffg = NFFG(id=model.id, name=model.name, service_id=model.service_id,\n version=model.version, mode=model.mode, metadata=model.metadata)\n # Load Infras\n for infra in model.node_infras:\n nffg.add_node(infra)\n # Load SAPs\n for sap in model.node_saps:\n nffg.add_node(sap)\n # Load NFs\n for nf in model.node_nfs:\n nffg.add_node(nf)\n # Load Links\n for link in model.edge_links:\n if link.src.node.type == NFFG.TYPE_NF or \\\n link.dst.node.type == NFFG.TYPE_NF:\n link.type = str(NFFG.TYPE_LINK_DYNAMIC)\n nffg.add_edge(link.src.node, link.dst.node, link)\n # Load SG next hops\n for hop in model.edge_sg_nexthops:\n nffg.add_edge(hop.src.node, hop.dst.node, hop)\n # Load Requirements\n for req in model.edge_reqs:\n nffg.add_edge(req.src.node, req.dst.node, req)\n return nffg","def _parse_ml(self, line):\n # Parse the line\n fields = line.split('\\\\')\n if self.lang == ENGLISH:\n # pylint: disable=C0301\n # English sample:\n # 14\\abandonment\\94\\C\\\\1\\N\\N\\N\\N\\Y\\abandon+ment\\2x\\SA\\N\\N\\N\\#\\N\\N\\SA\\((abandon)[V],(ment)[N|V.])[N]\\N\\N\\N\n # From the README:\n # The eml.cd file contains the following fields:\n # 1. IdNum\n # 2. Head\n # 3. Cob\n # 4. MorphStatus\n # 5. Lang\n # 6. MorphCnt\n # 7. NVAffComp\n # 8. Der\n # 9. Comp\n # 10. DerComp\n # 11. Def\n # 12. Imm\n # 13. ImmSubCat\n # 14. ImmSA\n # 15. ImmAllo\n # 16. ImmSubst\n # 17. ImmOpac\n # 18. TransDer\n # 19. ImmInfix\n # 20. ImmRevers\n # 21 FlatSA\n # 22. StrucLab\n # 23. StrucAllo\n # 24. StrucSubst\n # 25. StrucOpac\n lemma = fields[0]\n word = fields[1]\n derivation = fields[21]\n elif self.lang == DUTCH:\n # pylint: disable=C0301\n # Dutch sample:\n # 19\\aalbessengelei\\7\\C\\1\\Y\\Y\\Y\\aalbes+en+gelei\\NxN\\N\\N\\(((aal)[N],(bes)[N])[N],(en)[N|N.N],(gelei)[N])[N]\\N\\N\\N\n # The dml.cd file contains the following fields:\n # 1. IdNum\n # 2. Head\n # 3. Inl\n # 4. MorphStatus\n # 5. MorphCnt\n # 6. DerComp\n # 7. Comp\n # 8. Def\n # 9. Imm\n # 10. ImmSubCat\n # 11. ImmAllo\n # 12. ImmSubst\n # 13. StrucLab\n # 14. StruAcAllo\n # 15. StrucSubst\n # 16. Sepa\n lemma = fields[0]\n word = fields[1]\n derivation = fields[12]\n\n # Skip multi-word entries for roots\n roots = self._get_root(derivation) if \" \" not in word else None\n return (lemma, word, roots)","def parse_from_file (path):\n with open(path) as f:\n return NFFG.parse(f.read())","def check_nouns(elem_dictionary: dict, key: str, alet_dict: dict, last_nouns: list,\n last_events: list, turtle: list, ext_sources: bool) -> list:\n nouns = []\n for elem in elem_dictionary[key]: # The subject or object nouns\n elem_key = key[0:-1] # Create dictionary key = 'subject' or 'object'\n elem_type = elem[f'{elem_key}_type']\n elem_text = elem[f'{elem_key}_text']\n # Get rid of titles (such as Ms, Miss, Mr, ...)\n if 'FEMALE' in elem_type:\n elem_text = _remove_title_from_name(female_titles, elem_text)\n elif 'MALE' in elem_type:\n elem_text = _remove_title_from_name(male_titles, elem_text)\n head_lemma, head_text = get_head_word(elem_text)\n # poss_dict = Dictionary of nouns (keys) with their possessive modifiers (values)\n # Revised elem_text = noun text with possessives removed\n poss_dict, elem_text = _separate_possessives(elem_text)\n new_tuple = tuple()\n possible_name = empty_string # For a proper name, may contain shortened form = given + surname (any order)\n if elem_type == 'CARDINAL': # For example, 'one' in 'he has one' or in 'one of the band'\n if 'preps' in elem:\n new_tuple = _account_for_cardinal_noun(elem, elem_text, head_lemma,\n alet_dict, last_nouns, last_events, turtle, ext_sources)\n else:\n iri = re.sub(r'[^:a-zA-Z0-9_]', '_', f':{elem_text}_{str(uuid.uuid4())[:13]}').replace('__', '_')\n new_tuple = (elem_text, 'CARDINAL', [owl_thing2], iri)\n turtle.extend([f'{iri} a owl:Thing .',\n f'{iri} rdfs:label \"{elem_text}\" .'])\n elif elem_text.lower() in personal_pronouns:\n # Array of tuples of matched text, type, mappings and IRIs\n new_tuples = _check_personal_pronouns(elem_text, last_nouns)\n nouns.extend(new_tuples)\n last_nouns.extend(new_tuples)\n continue # More than 1 new tuple, so handled specifically in this code block; No need to 'drop through'\n # Not a pronoun; Check for a match in instances of the ontology\n elif ('PERSON' in elem_type or elem_type.endswith('GPE') or\n elem_type.endswith('ORG') or elem_type.endswith('NORP')):\n if space in head_lemma:\n # Get last two words in the name (for given+surname or surname+given name, Eastern or Western ordering)\n names = head_lemma.split(space)\n possible_name = f'{names[-2]} {names[-1]}'\n match_iri, match_type = check_specific_match(head_lemma, elem_type)\n if not match_iri and possible_name:\n match_iri, match_type = check_specific_match(possible_name, elem_type)\n if match_iri:\n new_tuple = (elem_text, elem_type, match_type, match_iri)\n else:\n # Check for family role and match to a name\n new_tuple = _process_family_role(head_text, elem_text, elem_type, alet_dict)\n if not new_tuple:\n # No match - Try to match text and type in last_nouns\n match_noun_tuples = _check_last_nouns(elem_text, elem_type, last_nouns)\n if match_noun_tuples:\n new_tuple = (elem_text, elem_type, match_noun_tuples[0][0], match_noun_tuples[0][1])\n elif possible_name:\n # Also check given + surname\n match_noun_tuples = _check_last_nouns(possible_name, elem_type, last_nouns)\n if match_noun_tuples:\n new_tuple = (possible_name, elem_type, match_noun_tuples[0][0], match_noun_tuples[0][1])\n if not new_tuple:\n # No match - Try to match text and type in alet_dict\n match_maps, match_iri = _check_alet_dict(elem_text, elem_type, alet_dict, last_nouns) # Updates last nouns\n if match_iri:\n new_tuple = (elem_text, elem_type, match_maps, match_iri)\n elif possible_name:\n # Also check given + surname\n match_maps, match_iri = _check_alet_dict(possible_name, elem_type, alet_dict, last_nouns)\n if match_iri:\n new_tuple = (possible_name, elem_type, match_maps, match_iri)\n if not new_tuple:\n # No match - Check if the noun is aligned with an event that has already been described\n event_classes, event_iri = check_event(elem_text, last_events)\n if event_iri:\n new_tuple = (elem_text, elem_type, event_classes, event_iri)\n if not new_tuple:\n # No match - Create new entity\n iri = re.sub(r'[^:a-zA-Z0-9_]', underscore, f':{elem_text.lower()}_{str(uuid.uuid4())[:13]}').\\\n replace('__', '_')\n noun_mappings, noun_turtle = create_noun_ttl(iri, elem_text, elem_type, alet_dict, ext_sources)\n new_tuple = (elem_text, elem_type, noun_mappings, iri)\n turtle.extend(noun_turtle)\n nouns.append(new_tuple)\n last_nouns.append(new_tuple)\n return nouns","def parse_rec(filename):\n tree = et.parse(filename)\n objects = []\n for obj in tree.findall('object'):\n obj_struct = {}\n obj_struct['name'] = obj.find('name').text\n obj_struct['pose'] = obj.find('pose').text\n obj_struct['truncated'] = int(obj.find('truncated').text)\n obj_struct['difficult'] = int(obj.find('difficult').text)\n bbox = obj.find('bndbox')\n obj_struct['bbox'] = [int(bbox.find('xmin').text) - 1,\n int(bbox.find('ymin').text) - 1,\n int(bbox.find('xmax').text) - 1,\n int(bbox.find('ymax').text) - 1]\n objects.append(obj_struct)\n\n return objects","def _parse_tsv_vocab_file(self, vocab_file: str):\n with open(vocab_file, \"r\", encoding=\"utf-8\") as f:\n for (index, line) in enumerate(f):\n title, count = line.rstrip().split(\"\\t\")\n entity = Entity(title, None)\n self.vocab[entity] = index\n self.counter[entity] = int(count)\n self.inv_vocab[index] = [entity]","def parse(string):\n doc = nlp(string)\n return [str(n) for n in doc.noun_chunks]","def ReadMorphit(self):\r\n self.words = {}\r\n with codecs.open(self.MorphItFileName, 'r', 'utf-8') as f:\r\n for line in f.readlines():\r\n line = line.split()\r\n try:\r\n# print (line)\r\n self.words[line[0]] = line[2][:3]\r\n# if line[2][:3] in self.verbTags:\r\n# line[2]=line[2].split(u'+')\r\n# line[2][0]=line[2][0][line[2][0].find(u':')+1:]\r\n except:\r\n pass\r\n return self.words","def __init__(self, file):\n with open(file, 'r') as f:\n self.vocab = json.loads(f.read())","def from_file(file_handle, delimiter=\",\"):\r\n # Reading in the data\r\n line = file_handle.readline()\r\n line = line.rstrip(\"\\n\")\r\n line = line.split(delimiter)\r\n\r\n # Assigning the data\r\n label = line[0]\r\n mass = float(line[1])\r\n position = np.array(line[2:5], float)\r\n velocity = np.array(line[5:], float)\r\n\r\n return Particle3D(label, mass, position, velocity)","def load_data(self):\n with open(self.file_name) as f:\n lines = f.readlines()\n\n labels = list()\n all_dat = list()\n for i, l in enumerate(lines):\n\n labels.append(int(l[0]))\n\n l = gensim.utils.any2unicode(l)\n all_dat.append(LabeledSentence(l.split(\"\\t\")[-1], [i]))\n\n return all_dat, np.asarray(labels)","def parse(self, word):\n son_map = self._sonority_map(word)\n ons_son = self._initial_onset(son_map)\n cod_son = self._final_coda(son_map)\n ons = self.from_map(ons_son, word)\n cod = self.from_reverse_map(cod_son, word)\n return (ons, cod)","def process_raw_phrases(file_path):","def parse_taxonomy(infile):\r\n\r\n res = {}\r\n for line in infile:\r\n if not line or line.startswith('#'):\r\n continue\r\n line = line.rstrip(\"\\n\")\r\n fields = line.split('\\t')\r\n otu = fields[0].split(' ')[0]\r\n res[otu] = taxa_split(fields[1])\r\n\r\n return res","def __init__(self, filename=None, label=None, tokens=None):\n if label: # specify from label/tokens, for testing.\n self.label = label\n self.tokens = tokens\n self.postID = -1\n self.likes = -1\n else: # specify from file.\n self.filename = filename\n parsedNames = filename.split(\"#\")\n if 'pop' in parsedNames[0]:\n self.label = 'pop'\n else:\n self.label = 'sod'\n self.postID = parsedNames[1]\n self.likes = parsedNames[2]\n self.tokenize()","def locateObjNumberNoun(data, questionDict, questionIdict):\n how = questionDict['how']\n many = questionDict['many']\n for t in range(data.shape[0] - 2):\n if data[t, 0] == how and \\\n data[t + 1, 0] == many:\n for u in range(t + 2, data.shape[0]):\n word = questionIdict[data[u, 0] - 1]\n lexname = lookupLexname(word)\n if (lexname is not None and \\\n lexname.startswith('noun')) or \\\n (lexname is None):\n return data[u, 0]\n print 'not found'\n return data[-1, 0]","def parse_syn(syntenic):\n syn_ages = {}\n with open(syntenic, 'r') as f:\n for index, line in enumerate(f):\n if index == 0:\n continue\n else:\n tmp = line.strip().split('\\t')\n # keep only the tandems\n b_genes = []\n p_genes = []\n if ',' in tmp[2]:\n b_genes += tmp[2].split(',')\n if ',' in tmp[3]:\n b_genes += tmp[3].split(',')\n if ',' in tmp[4]:\n p_genes += tmp[4].split(',')\n if ',' in tmp[5]:\n p_genes += tmp[5].split(',')\n b_ages = []\n p_ages = []\n if tmp[6] != 'NA':\n b_ages += tmp[6].split(',')\n if tmp[7] != 'NA':\n b_ages += tmp[7].split(',')\n if tmp[8] != 'NA':\n p_ages += tmp[8].split(',')\n if tmp[9] !='NA':\n p_ages += tmp[9].split(',')\n # If there are B73 tandem duplicates, then we iterate through\n # and build the dictionary\n if b_ages:\n ct = 1\n for age in b_ages:\n b1 = b_genes[ct-1]\n b2 = b_genes[ct]\n syn_ages[(b1, b2)] = age\n ct += 1\n if p_ages:\n ct = 1\n for age in p_ages:\n p1 = p_genes[ct-1]\n p2 = p_genes[ct]\n syn_ages[(p1, p2)] = age\n ct += 1\n return syn_ages","def toLingDataToken(token):\n\n t = Token()\n\n t.set(\n id=token.i,\n word=token.orth_,\n lemma=token.lemma_,\n POS=token.tag_,\n SPOS=token.pos_,\n depID=token.dep,\n depStr=token.dep_,\n NE=token.ent_type_,\n foreign=token.is_oov\n )\n\n # setting features\n '''\n t.features = {}\n #print(t.POS)\n featureStr = translate(t.POS)\n # save string form of feature translation\n t.features['str'] = featureStr\n\n featureArr = featureStr.split(\"+\")\n #print(featureArr)\n # find the first feature\n i = 0\n while len(featureArr[i]) < 1:\n i += 1\n\n t.features['type'] = featureArr[i]\n if t.features['type'] in [\"N\"]:\n # look for number\n i += 1\n while i < len(featureArr):\n # this means it's probably a number declaration\n if len(featureArr[i]) < 4:\n t.features['number'] = featureArr[i]\n # and next feature could be type of noun\n if i + 1 < len(featureArr):\n t.features['isProper'] = featureArr[i + 1]\n break\n i += 1\n\n if t.features['type'] in [\"V\"]:\n # look for person and number\n i += 1\n while i < len(featureArr):\n # this means it's probably a person declaration\n if len(featureArr[i]) < 4:\n t.features['person'] = featureArr[i]\n # and next feature could be number\n if i + 1 < len(featureArr):\n t.features['number'] = featureArr[i + 1]\n break\n else:\n # probably a tense\n t.features['tense'] = featureArr[i]\n t.features['isParticiple'] = (\"Part\" in featureArr[i])\n\n i += 1\n #print(t.features)\n '''\n\n # setting wordType\n if token.tag_ == \"BES\": # copula\n t.set(wordType=4)\n elif token.pos_ == \"VERB\":\n t.set(wordType=1)\n elif token.pos_ == \"NOUN\" or token.pos_ == \"PROPN\":\n t.set(wordType=2)\n elif token.pos_ == \"PRON\":\n t.set(wordType=3)\n else:\n t.set(wordType=5)\n\n # spaCy does not have coreferencing...\n\n return t","def read_data(self, filepath):\n with open(filepath, \"r\", encoding=\"utf8\") as input_data:\n ids, premises, hypotheses, premises_length, hypotheses_length, premise_polarities, hypothesis_polarities, labels = [], [], [], [], [], [], [], []\n\n for idx, line in enumerate(input_data):\n print(f\"Reading instance {idx}\", end='\\r')\n line = eval(line)\n hypothesis = eval(line['hypothesis'])\n premise = eval(line['premise'])\n label = line['label']\n\n # Align the nltk premise and the umls premise\n premise, premise_umls, premise_polarity = self.aligner(premise)\n hyp, hyp_umls, hypothesis_polarity = self.aligner(hypothesis)\n\n ids.append(idx)\n premises.append((premise, premise_umls))\n hypotheses.append((hyp, hyp_umls))\n labels.append(self.labeldict[label])\n premises_length.append(len(premise))\n hypotheses_length.append(len(hyp))\n premise_polarities.append(premise_polarity)\n hypothesis_polarities.append(hypothesis_polarity)\n\n return {\"ids\": ids,\n \"premises\": premises,\n \"hypotheses\": hypotheses,\n \"labels\": labels,\n \"premises_lengths\": premises_length,\n \"hypotheses_lengths\": hypotheses_length,\n \"premise_polarities\": premise_polarities,\n \"hypothesis_polarities\": hypothesis_polarities,\n \"max_premise_length\": max(premises_length),\n \"max_hypothesis_length\": max(hypotheses_length)}","def parse(self, filename):\n infile = file(filename)\n for line in infile:\n self.parseLine(line)","def readObject(f):\n name = f.readline().rstrip()\n if name == \"\":\n name = f.readline().rstrip()\n if name == \"\":\n return None\n description = f.readline().rstrip()\n location = f.readline().rstrip()\n return AdvObject(name, description, location )","def get_nouns(self):\n word_punct_token = WordPunctTokenizer().tokenize(self.sentence_string)\n\n clean_tokens = []\n for token in word_punct_token:\n token = token.lower()\n\n # remove any value that are not alphabetical\n new_token = re.sub(r\"[^a-zA-Z]+\", \"\", token)\n\n # remove empty value and single character value\n if new_token != \"\" and len(new_token) >= 2:\n vowels = len([v for v in new_token if v in \"aeiou\"])\n if vowels != 0: # remove line that only contains consonants\n clean_tokens.append(new_token)\n\n noun_types = [\"NN\", \"NNS\", \"NNP\", \"NNPS\", \"N\"]\n is_noun = lambda pos: pos in noun_types\n nouns = [word for (word, pos) in nltk.pos_tag(clean_tokens) if is_noun(pos)]\n\n if nouns:\n return nouns\n else:\n raise InvalidSentenceError(self.sentence_string)","def load_cmudict():\n with open(\"text/en/cmudict-0.7b.txt\", encoding=\"ISO-8859-1\") as file_reader:\n cmudict = (line.strip().split(\" \") for line in islice(file_reader, 126, 133905))\n\n cmudict = {format_alt_entry(word): pronunciation for word, pronunciation in cmudict}\n\n return cmudict","def _process_data_file(self):\n \n with open(self.data_file, 'r') as f:\n self.description = f.readline().strip()\n data = np.loadtxt(self.data_file, skiprows=1)\n\n return data","def read(self, filename):\n with RavenFileReader(filename) as f:\n line = f.nexttag()\n while line:\n # Begin data type checks\n if self.cleantag(line) == 'Gauge':\n self.read_metgauge(line, f)\n elif self.cleantag(line) == 'ObservationData':\n self.read_obsgauge(line, f)\n # Next line\n line = f.nexttag()","def load_raw_annot(filename):\n with open(filename, 'r') as fp:\n data = json.loads(fp.read())\n\n mapping = _create_mapping()\n\n for k in data.keys():\n for i in xrange(len(data[k])):\n data[k][i] = eval_utils.revise_sentiment(data[k][i], mapping)\n return data","def read(self):\n try:\n f = open(self.datfile, 'r')\n except:\n print('ERROR: data file not found!')\n exit()\n\n # Get rid of the header\n for _ in range(self.header_length):\n f.readline()\n\n # Read nuclide mass data\n for line in f:\n ls = line.strip()\n n, z, ebind = ls.split()\n nuclide = BindingNuclide(n, z, ebind)\n self.nuclides.append(nuclide)\n\n f.close()","def read_file(self, filename, per_atom=True):\n if isinstance(filename, str):\n fileobj = open(filename)\n elif isinstance(filename, file):\n fileobj = filename\n elif isinstance(filename, type(StringIO())):\n fileobj = filename\n fileobj.name = None\n thermodata = fileobj.readlines()\n headers = [h.lower() for h in thermodata.pop(0).strip().split()]\n if \"composition\" not in headers:\n raise PhaseDataError(\n \"Found columns: %s. Must provide composition in\\\n a column labelled composition.\"\n % (\", \".join(headers))\n )\n if \"energy\" not in headers and \"delta_e\" not in headers:\n raise PhaseDataError(\n \"Found columns: %s. Must provide energies in\\\n a column labelled delta_e or energy.\"\n % (\", \".join(headers))\n )\n\n keywords = {\n \"energy\": \"energy\",\n \"composition\": \"composition\",\n \"delta_e\": \"energy\",\n \"delta_h\": \"energy\",\n \"delta_g\": \"energy\",\n \"comp\": \"composition\",\n \"name\": \"composition\",\n \"desc\": \"description\",\n \"description\": \"description\",\n }\n\n headers = [keywords[h] for h in headers if h in keywords]\n\n name = filename.split(\"/\")[-1]\n\n for i, line in enumerate(thermodata):\n line = line.strip().split()\n if not line:\n continue\n ddict = dict(list(zip(headers, line)))\n phase = Phase(\n composition=ddict[\"composition\"],\n energy=float(ddict[\"energy\"]),\n description=ddict.get(\n \"description\", \"{file}:{line}\".format(file=name, line=i)\n ),\n per_atom=per_atom,\n )\n self.add_phase(phase)","def parser(filename):\n\n regex = re.compile(\n # prolog\n r\"run(?P<run>\\w+)\"\n ##r\"\\-(?P<code_name>((mfdn)|(obscalc-ob))[^\\-]*)\"\n r\"\\-(?P<descriptor>\"\n # descriptor contents\n r\"Z(?P<Z>\\d+)\\-N(?P<N>\\d+)\"\n r\"\\-(?P<interaction>.+)\\-(?P<coulomb>\\d)\"\n r\"\\-(?P<truncation_descriptor>.+)\"\n ## r\"\\-Nmax(?P<Nmax>\\d+)\"\n # epilog\n r\").res\"\n )\n\n conversions = {\n \"Z\" : int,\n \"N\" : int,\n \"interaction\" : str,\n \"coulomb\" : int,\n }\n\n match = regex.match(filename)\n if (match == None):\n raise ValueError(\"bad form for spncci results filename: \" + filename)\n info = match.groupdict()\n\n # convert fields\n for key in conversions:\n conversion = conversions[key]\n info[key] = conversion(info[key]) if (info[key] is not None) else None\n\n return info","def NOAD_to_wordnet(data):\r\n NOAD_to_wordnet = {}\r\n with open(algorithmic_map, 'r') as f:\r\n lines = f.readlines()\r\n for line in lines:\r\n noad, wordnet = line.split()\r\n NOAD_to_wordnet[noad] = wordnet\r\n with open(manual_map, 'r') as f:\r\n lines = f.readlines()\r\n for line in lines:\r\n noad, wordnet = line.split()\r\n NOAD_to_wordnet[noad] = wordnet\r\n \r\n count = 0\r\n for elem in data: \r\n if elem[\"is_target\"]:\r\n if elem[\"sense\"] not in NOAD_to_wordnet:\r\n count += 1\r\n continue\r\n noad_sense = elem[\"sense\"]\r\n elem[\"sense\"] = NOAD_to_wordnet[noad_sense]\r\n print(\"NOAD sense not in mapping text: %d\" %count)\r\n return data","def read_po(self, inputfile):\n is_index = False\n lines = inputfile.readlines()\n index = ''\n value = ''\n for line in lines:\n if line.startswith('#'):\n continue\n elif line.startswith('msgid'):\n is_index = True\n self.translations[index] = value\n index = ''\n value = ''\n elif line.startswith('msgstr'):\n is_index = False\n\n v = re.match('.*\"(.*)\".*', line)\n if v:\n if is_index:\n index += ''.join(v.groups())\n else:\n value += ''.join(v.groups())","def make_from_file(filehandle):\n lines = filehandle.readlines()\n label = str(lines[0].rstrip('\\n'))\n mass = float(lines[1].rstrip('\\n'))\n position = list(lines[2].rstrip('\\n').split(','))\n velocity = list(lines[3].rstrip('\\n').split(','))\n particle = Particle3D(label=label, mass=mass, position=position, velocity=velocity)\n filehandle.close()\n return particle","def get_compound_noun( ngram ):\n try:\n pattern = re.compile( '((?: ?\\\\b[^\\\\s]+(?:/NN.?/[a-z]+/[\\\\d]+)){2,})' )\n match = re.search( pattern, ngram )\n if match:\n compound = ''\n contains_root = False\n tokens = match.group().strip().split(' ')\n for t in tokens:\n # embed()\n items = t.split('/')\n compound += ( items[0] + ' ' )\n if items[3] == 0:\n contains_root = True\n if contains_root:\n return compound\n else:\n return None\n else:\n return None\n \n except ValueError:\n return None","def is_noun(tag):\r\n return tag in ['NN', 'NNS', 'NNP', 'NNPS']","def __init__(self, infile):\n txt = infile.read()\n for block in self.splitter.split(txt):\n block = block.strip()\n if block:\n term = block.splitlines()[0].strip().decode('utf8')\n defn = \"\\n\".join(line.strip() for line in block.splitlines()[1:])\n self[term] = defn.decode('utf8')","def read_data(self, filePath):\n with open(filePath, 'r', encoding='iso-8859-1') as f:\n for sentence in f.readlines():\n sentence = sentence.replace('\\n', '')\\\n .replace('\"', '')\\\n .replace('\\'', '')\\\n .replace('.', '')\\\n .replace(',', '')\\\n .replace('[', '')\\\n .replace(']', '')\\\n .replace('(', '')\\\n .replace(')', '')\\\n .replace(':', '')\\\n .replace('--', '')\\\n .replace('-', '')\\\n .replace('\\\\', '')\\\n .replace('0', '')\\\n .replace('1', '')\\\n .replace('2', '')\\\n .replace('3', '')\\\n .replace('4', '')\\\n .replace('5', '')\\\n .replace('6', '')\\\n .replace('7', '')\\\n .replace('8', '')\\\n .replace('9', '')\\\n .replace('`', '')\\\n .replace('=', '')\\\n .replace('$', '')\\\n .replace('/', '')\\\n .replace('*', '')\\\n .replace(';', '')\\\n .replace('<b>', '')\\\n .replace('%', '')\n sentence = sentence.split(' ')\n sentence = list(filter(lambda x: x, sentence))\n if sentence:\n self.word_num += len(sentence)\n self.maxlen = self.maxlen if self.maxlen >= len(\n sentence) else len(sentence)\n self.minlen = self.minlen if self.minlen <= len(\n sentence) else len(sentence)\n if 'pos' in filePath:\n self.Pos.append([sentence, self.feelMap['pos']])\n else:\n self.Neg.append([sentence, self.feelMap['neg']])","def load(filename):\n\n print \"Loading dictionary...\"\n dictionary = Dictionary()\n print \" Loading file...\"\n whole_file = file(filename).read().upper()\n print \" Splitting file...\"\n words = whole_file.split()\n print \" Removing unsuitable words...\"\n words = dictionary.remove_unsuitable_words(words)\n print \" Building data structures...\"\n dictionary.set_words(words)\n\n print \" Loaded %d words\" % len(dictionary.words)\n print \" Unique letter size:\"\n print \" No blanks: %d\" % len(dictionary.letters_map)\n print \" One blank: %d\" % len(dictionary.letters_map_one_blank)\n print \" Two blanks: %d\" % len(dictionary.letters_map_two_blanks)\n\n return dictionary","def load_subj(loc='./data/'):\n pos, neg = [], []\n with open(os.path.join(loc, 'SUBJ', 'subj.objective'), 'rb') as f:\n for line in f:\n pos.append(line.decode('latin-1').strip())\n with open(os.path.join(loc, 'SUBJ', 'subj.subjective'), 'rb') as f:\n for line in f:\n neg.append(line.decode('latin-1').strip())\n return pos, neg","def __init__(self, file_name: str):\n self._file_name = file_name\n self._afinn = {}\n self._afinn_phrase = []\n self._reg_affin_phrase_str = \"\\\\s|[!,.\\'\\\"]\"\n # read the file AFFIN and map words to score\n with open(self._file_name, \"r\") as f:\n for str in f.readlines():\n entry = str.split()\n if (len(entry) > 2):\n length = len(entry)\n words = entry[0]\n for i in range(1, length - 1):\n words = words + ' ' + entry[i]\n self._reg_affin_phrase_str += \"|\"+words\n self._afinn_phrase.append(words)\n self._afinn[words] = int(entry[length - 1])\n else:\n self._afinn[entry[0]] = int(entry[1])","def read_data(filename, eos='#'):\n ### Exercise 6.1\n\n with open(filename) as f:\n utterances = []\n labels = []\n\n for line in f:\n # Get utterance output and length\n utter = line\n utter = utter.replace(\" \", \"\").replace(\"\\n\", \"\") + \"#\"\n utterances.append(utter)\n # Make empty sequence\n sequence = np.zeros(len(utter), dtype=int)\n sequence[0], sequence[len(utter) - 1] = 1, 1\n # Find indexes of beginning of words\n prev_char = \"\"\n count = 0\n new_word_indexs = []\n for char in line:\n if char == \" \":\n prev_char = char\n continue\n if prev_char == \" \":\n prev_char = char\n new_word_indexs.append(count)\n count += 1\n else:\n prev_char = char\n count += 1\n for index in new_word_indexs:\n sequence[index] = 1\n labels.append(sequence)\n\n return (utterances, labels)","def load_onto(filename=onto_filename):\n onto_dict = parse_yaml(filename)\n onto = CrosslinksOnto()\n for id, bond_dict in onto_dict.items():\n bond = Bond()\n onto[id] = bond\n\n bond.id = id\n bond.name = bond_dict.get('name', None)\n bond.synonyms = bond_dict.get('synonyms', []) \n\n l_alph = get_alphabet(bond_dict['l_monomer_alphabet'])\n r_alph = get_alphabet(bond_dict['r_monomer_alphabet'])\n bond.l_monomer = l_alph.monomers.get(bond_dict['l_monomer'])\n bond.r_monomer = r_alph.monomers.get(bond_dict['r_monomer'])\n\n for atom_type in ['l_bond_atoms', 'r_bond_atoms', 'l_displaced_atoms', 'r_displaced_atoms']:\n for atom in bond_dict[atom_type]:\n element, position, charge = parse_atom(atom)\n getattr(bond, atom_type).append(Atom(Monomer, element, position=position, charge=charge))\n\n bond.order = BondOrder[bond_dict.get('order', 'single')]\n stereo = bond_dict.get('stereo', None)\n if stereo is None:\n bond.stereo = None\n else:\n bond.stereo = BondStereo[stereo]\n\n bond.comments = bond_dict.get('comments', None)\n\n crosslink_to_id = {xlink: id for id, xlink in onto.items()}\n\n return onto, crosslink_to_id","def parse_domain(self, domainfile):\n\n with open(domainfile) as dfile:\n dfile_array = self._get_file_as_array(dfile)\n #Deal with front/end define, problem, :domain\n if dfile_array[0:4] != ['(', 'define', '(', 'domain']:\n print('PARSING ERROR: Expected (define (domain ... at start of domain file')\n sys.exit()\n self.domain = dfile_array[4]\n\n dfile_array = dfile_array[6:-1]\n opencounter = 0\n keyword = ''\n obj_list = []\n is_obj_list = True\n for word in dfile_array:\n if word == '(':\n opencounter += 1\n elif word == ')':\n opencounter -= 1\n elif word.startswith(':'):\n if word[1:] not in DFILE_KEYWORDS:\n pass\n elif keyword != 'requirements':\n keyword = word[1:]\n if opencounter == 0:\n if keyword == 'action':\n self.actions.append(obj_list)\n obj_list = []\n if keyword == 'types':\n for element in obj_list:\n self.types.setdefault('object', []).append(element)\n self.type_list.add('object')\n self.type_list.add(element)\n obj_list = []\n keyword = ''\n\n if keyword == 'requirements': #Requirements list\n if word != ':requirements':\n if not word.startswith(':'):\n print('PARSING ERROR: Expected requirement to start with :')\n sys.exit()\n elif word[1:] not in DFILE_REQ_KEYWORDS:\n print('WARNING: Unknown Rquierement ' + word[1:])\n #print 'Requirements must only be: ' + str(DFILE_REQ_KEYWORDS)\n #sys.exit()\n else:\n self.requirements.add(word[1:])\n elif keyword == 'action':\n obj_list.append(word)\n elif not word.startswith(':'):\n if keyword == 'types': #Typed list of objects\n if is_obj_list:\n if word == '-':\n is_obj_list = False\n else:\n obj_list.append(word)\n else:\n #word is type\n for element in obj_list:\n if not word in self.type_list:\n self.types.setdefault('object', []).append(word)\n self.type_list.add(word)\n self.types.setdefault(word, []).append(element)\n self.type_list.add(element)\n self.type_list.add(word)\n is_obj_list = True\n obj_list = []\n elif keyword == 'constants': #Typed list of objects\n if is_obj_list:\n if word == '-':\n is_obj_list = False\n else:\n obj_list.append(word)\n else:\n #word is type\n for element in obj_list:\n if word in self.type_list:\n self.constants.setdefault(word, []).append(element)\n #self.object_list.add(element)\n else:\n print(self.type_list)\n print(\"ERROR unknown type \" + word)\n sys.exit()\n is_obj_list = True\n obj_list = []\n elif keyword == 'predicates' or keyword == 'private': #Internally typed predicates\n if word == ')':\n if keyword == 'private':\n #print \"...skip agent: \" + str(obj_list[:3])\n obj_list = obj_list[3:]\n keyword = 'predicates'\n if len(obj_list) == 0:\n #print \"...skip )\"\n continue\n p_name = obj_list[0]\n #print \"parse predicate: \" + p_name + \" \" + str(obj_list)\n pred_list = self._parse_name_type_pairs(obj_list[1:],self.type_list)\n self.predicates.append(Predicate(p_name, pred_list, True, False))\n obj_list = []\n elif word != '(':\n obj_list.append(word)\n elif keyword == 'functions': #functions\n if word == ')':\n p_name = obj_list[0]\n if obj_list[0] == '-':\n obj_list = obj_list[2:]\n #print \"function: \" + word + \" - \" + str(obj_list)\n self.functions.append(Function(obj_list))\n obj_list = []\n elif word != '(':\n obj_list.append(word)\n\n #Work on the actions\n new_actions = []\n for action in self.actions:\n if action[0] == '-':\n action = action[2:]\n act_name = action[1]\n act = {}\n action = action[2:]\n keyword = ''\n for word in action:\n if word.startswith(':'):\n keyword = word[1:]\n else:\n act.setdefault(keyword, []).append(word)\n self.agent_types.add(act.get('agent')[2])\n agent = self._parse_name_type_pairs(act.get('agent'),self.type_list)\n param_list = agent + self._parse_name_type_pairs(act.get('parameters')[1:-1],self.type_list)\n up_params = Predicate('', param_list, True, False)\n pre_list = self._parse_unground_propositions(act.get('precondition'))\n eff_list = self._parse_unground_propositions(act.get('effect'))\n new_act = Action(act_name, up_params, pre_list, eff_list)\n\n new_actions.append(new_act)\n self.actions = new_actions","def extract_nouns(tagged_abstracts_list, def_tags_per_abs = 0.3):\n\n noun_counter = []\n per_abstract_counts = []\n per_abstract_counts_list = []\n normalized_all_counts = {}\n per_abstract_tag_counts = []\n\n for tags in tagged_abstracts_list:\n\n for tag in tags:\n\n if tag[1] == \"NN\" or tag[1] == \"NNS\" or tag[1] == \"NNP\" or tag[1] == \"NNPS\":\n\n per_abstract_tag_counts.append(str(tag[0].encode('ascii', 'ignore')))\n\n noun_counter.append(str(tag[0].encode('ascii', 'ignore')))\n\n per_abstract_dict = dict(Counter(per_abstract_tag_counts))\n per_abstract_counts_list.append(per_abstract_dict)\n\n all_counts = dict(Counter(noun_counter))\n\n num_abstracts = float(len(tagged_abstracts_list))\n\n for key in all_counts.keys():\n\n if key in all_counts:\n\n total_occurrences = float(all_counts[key])\n else:\n\n total_occurrences = 0\n\n for abstract_ in per_abstract_counts_list:\n #print abstract_\n\n if key in abstract_ and key in all_counts:\n\n single_abstract_count = float(abstract_[key])\n\n if (single_abstract_count/total_occurrences) < def_tags_per_abs:\n normalized_all_counts[key] = float(all_counts[key])/num_abstracts\n\n return (normalized_all_counts)","def process_file(self, data, filename):\n\n for relation in data:\n if \"start_date\" in relation[1]:\n start = convert_to_date(relation[1][\"start_date\"])\n end = convert_to_date(relation[1][\"end_date\"])\n\n if start <= START_DATE and end >= START_DATE:\n first = relation[1][\"first\"]\n second = relation[1][\"second\"]\n if first not in self.relations:\n self.add_tag(first)\n if second not in self.relations:\n self.add_tag(second)\n\n if relation[0] == \"vassal\": \n self.relations[first][\"vassal\"].append(second)\n self.relations[second][\"overlord\"].append(first)\n elif relation[0] == \"alliance\":\n self.relations[first][\"alliance\"].append(second)\n self.relations[second][\"alliance\"].append(first)\n elif relation[0] == \"dependency\":\n self.relations[first][\"tributary\"].append(second)\n self.relations[second][\"hegemon\"].append(first)\n elif relation[0] == \"guarantee\":\n self.relations[first][\"guaranteeing\"].append(second)\n self.relations[second][\"guarantor\"].append(first)\n elif relation[0] == \"union\":\n self.relations[first][\"junior\"].append(second)\n self.relations[second][\"senior\"].append(first)\n elif relation[0] == \"royal_marriage\":\n self.relations[first][\"marriage\"].append(second)\n self.relations[second][\"marriage\"].append(first)","def load_data_and_labels(data_file):\n # Load data from files\n obj = open(data_file, \"r\")\n y, x_text, query= [],[],[]\n for ele in obj:\n ele = ele.strip().split(\"\\t\")\n if len(ele) !=5 or ele[0].strip() not in [\"1\", \"-1\"]:\n #print ele\n continue\n if (ele[0].strip() == \"1\"):\n y.append([0])\n else:\n y.append([1])\n\n query_text = ele[1].strip().decode(\"utf8\")\n doc_text = ele[2].strip().decode(\"utf8\")\n x_text.append( \" \".join( jieba.cut(doc_text) ) )\n query.append( \" \".join( jieba.cut(query_text) ) )\n return [x_text, np.array(y), np.array(query)]","def __init__(self, txt_path, in_vocab_path, out_vocab_path):\n self.txt_seqs = open(txt_path, encoding='utf8', errors='ignore').readlines()\n self.word2id = utils.load_vocab(in_vocab_path,\n extra_word_list=[\"<UNK>\", \"<END>\"])\n self.punc2id = utils.load_vocab(out_vocab_path,\n extra_word_list=[\" \"])\n self.class2punc = { k : v for (v, k) in self.punc2id.items()}","def read(self, filename):\n with RavenFileReader(filename) as f:\n line = f.nexttag()\n while line:\n # Begin data type checks\n if self.cleantag(line) == 'SubBasins':\n self.read_subbasins(f)\n elif self.cleantag(line) == 'HRUs':\n self.read_HRUs(f)\n # Next line\n line = f.nexttag()","def _read_lexicon(a_dname):\n if not a_dname:\n return\n elif a_dname[-1] == '/':\n a_dname = os.path.dirname(a_dname)\n basename = os.path.basename(a_dname)\n if basename == HSAN:\n return _read_hsan(a_dname)\n elif basename == S140:\n return _read_s140(a_dname)\n elif basename == SUBJCL:\n return _read_subjcl(a_dname)\n elif basename == NRC_HSHTAG:\n return _read_nrc_hshtag(a_dname)\n else:\n raise Exception(\"Unknown dictionary format: '{:s}'\".format(basename))","def read_bnf_file(self, file_name):\n # <.+?> Non greedy match of anything between brackets\n non_terminal_pattern = \"(<.+?>)\"\n rule_separator = \"::=\"\n production_separator = \"|\"\n\n # Read the grammar file\n for line in open(file_name, 'r'):\n if not line.startswith(\"#\") and line.strip() != \"\":\n # Split rules. Everything must be on one line\n if line.find(rule_separator):\n lhs, productions = line.split(rule_separator)\n lhs = lhs.strip()\n if not re.search(non_terminal_pattern, lhs):\n raise ValueError(\"lhs is not a NT:\", lhs)\n self.non_terminals.add(lhs)\n if self.start_rule == None:\n self.start_rule = (lhs, self.NT)\n # Find terminals\n tmp_productions = []\n for production in [production.strip()\n for production in\n productions.split(production_separator)]:\n tmp_production = []\n if not re.search(non_terminal_pattern, production):\n self.terminals.add(production)\n tmp_production.append((production, self.T))\n else:\n # Match non terminal or terminal pattern\n # TODO does this handle quoted NT symbols?\n for value in re.findall(\"<.+?>|[^<>]*\", production):\n if value != '':\n if not re.search(non_terminal_pattern,\n value):\n symbol = (value, self.T)\n self.terminals.add(value)\n else:\n symbol = (value, self.NT)\n tmp_production.append(symbol)\n tmp_productions.append(tmp_production)\n # Create a rule\n if not lhs in self.rules:\n self.rules[lhs] = tmp_productions\n else:\n raise ValueError(\"lhs should be unique\", lhs)\n else:\n raise ValueError(\"Each rule must be on one line\")","def read_processed_data_from_file(file, encoding='latin1'):\n\n with open(file, encoding=encoding) as f:\n raw = f.read()\n\n lines = raw.split('\\n')\n labeled_texts = []\n n = len(lines) - 1\n for i, line in enumerate(lines):\n print(f'\\rLoading review {i} of {n}', end='')\n if line == '':\n continue\n tagged_words = re.findall(r'(.+?\\\\.+?) ', line)\n label = re.findall(r'#(\\d+.\\d)#', line)[0]\n labeled_texts.append((tagged_words, label))\n print()\n return labeled_texts","def parse_text(filehandle: TextIO) -> Iterator[Fasta]:\n\n # Check that the file looks like UniProt text format\n first_line = next(filehandle)\n if not first_line.startswith(\"ID\"):\n raise TextParserError(\n \"Unexpected file format: first line of UniProt text file should start with 'ID'\"\n )\n filehandle.seek(0)\n\n fasta = Fasta(sequence=\"\")\n for line in filehandle:\n key = line[:2] # This is more efficient than using line.startswith\n if key == \"ID\":\n tokens = line.split()\n fasta.entry_name = tokens[1]\n fasta.reviewed = True if tokens[2] == \"Reviewed;\" else False\n elif key == \"AC\":\n if fasta.accession is None:\n accessions = line[5:].rstrip(\";\\n\").split(\"; \")\n fasta.accession = accessions[0]\n elif key == \"DT\":\n if \"sequence version\" in line:\n tokens = line[5:].strip(\".\\n\").split()\n fasta.version = int(tokens[3])\n elif key == \"DE\":\n if \"RecName\" in line:\n fasta.name = _extract_name(line)\n # Get the first SubName if no RecName found\n elif fasta.name is None and line[5:12] == \"SubName\":\n fasta.name = _extract_name(line)\n elif line[5:10] == \"Flags\" and \"Fragment\" in line:\n fasta.fragment = True\n elif key == \"GN\":\n if line[5:10] == \"Name=\":\n tokens = line[10:].split(\";\")\n # Remove evidence tags, if present\n gene_tokens = tokens[0].split(\" {\")\n fasta.gene = gene_tokens[0]\n elif key == \"OS\":\n # TODO: check for multiline species name (excluding brackets)\n if fasta.species is None:\n species_line = line[5:].strip().split(\" (\")\n fasta.species = species_line[0].strip(\".\")\n elif key == \"OX\":\n if \"NCBI_TaxID\" in line:\n tokens = line[5:].strip(\";\\n\").split(\"; \")\n # Remove evidence tag if present\n taxid_tokens = tokens[0][11:].split(\" {\")\n fasta.taxid = taxid_tokens[0]\n elif key == \"PE\":\n fasta.evidence = int(line[5])\n elif key == \" \":\n sequence_line = line.strip().replace(\" \", \"\")\n fasta.sequence += sequence_line\n elif key == \"//\":\n yield fasta\n fasta = Fasta(sequence=\"\")","def read_dictionary_from_file(self, stem_flag):\n file_name = \"/dictionary.txt\" if not stem_flag else \"/dictionaryWithStemming.txt\"\n with open(self.posting_and_dictionary_path + file_name, \"r\") as f:\n txt = f.readlines()\n for line in txt:\n l = line.split(\":\")\n pos = l[1].split(\",\")\n e = DictionaryElement(pos[0])\n e.pointer = int(pos[1])\n e.corpus_tf = int(pos[2])\n if not stem_flag:\n self.term_dictionary[l[0]] = e\n else:\n self.term_dictionary_with_stemming[l[0]] = e\n f.close()","def parseLabels(filename):\n r = parse(filename)\n res = {}\n for qid in r:\n lst = []\n for y in r[qid].split(\";\"):\n doc, score = y.split(\",\")\n lst.append((int(doc), float(score)))\n res[qid] = lst\n return res","def read(self, filename, species=None, poscar=None, sort=True):\n self.tags = dict()\n try:\n file = open(filename,'r')\n except:\n raise IncarError(\"Could not open file: '\" + filename + \"'\")\n\n # parse INCAR into self.tags\n for line in file:\n line = re.split('=',re.split('#',line)[0])\n if len(line) == 2:\n self.tags[line[0].strip()] = line[1].strip()\n self._verify_tags()\n self._make_natural_type()\n\n if species != None:\n self.update(species, poscar, sort)\n\n file.close()","def parse_game_data(file_name : str) -> List[Topic]:\n # Opens file.\n data = open(file_name, 'r')\n\n # Sets up variables.\n current_topic = None\n parsed_data = []\n\n # Loops over each line in the file.\n for line in data:\n \n # Creates new topic if there is none.\n if current_topic is None:\n current_topic = Topic(line[:-1])\n\n # Saves topic if encountered empty line.\n elif line == '\\n':\n parsed_data.append(current_topic)\n current_topic = None\n\n # Parses question line and adds to topic.\n else:\n question, answer, points = parse_line(line)\n new_question = Question(question, answer, points)\n current_topic.questions.append(new_question)\n\n return parsed_data","def parse_nonsyn(nonsyn):\n nonsyn_ages = {}\n with open(nonsyn, 'r') as f:\n for index, line in enumerate(f):\n if index == 0:\n continue\n else:\n tmp = line.strip().split('\\t')\n b_genes = []\n p_genes = []\n if ',' in tmp[1]:\n b_genes += tmp[1].split(',')\n if ',' in tmp[2]:\n p_genes += tmp[2].split(',')\n b_ages = []\n p_ages = []\n if tmp[3] != 'NA':\n b_ages += tmp[3].split(',')\n if tmp[4] != 'NA':\n p_ages += tmp[4].split(',')\n if b_ages:\n ct = 1\n for age in b_ages:\n b1 = b_genes[ct-1]\n b2 = b_genes[ct]\n nonsyn_ages[(b1, b2)] = age\n ct += 1\n if p_ages:\n ct = 1\n for age in p_ages:\n p1 = p_genes[ct-1]\n p2 = p_genes[ct]\n nonsyn_ages[(p1, p2)] = age\n ct += 1\n return nonsyn_ages","def parse_file(\n self, filename: Path, encoding: Optional[str] = None, debug: bool = False\n ) -> NL:\n with open(filename, encoding=encoding) as stream:\n return self.parse_stream(stream, debug)","def from_psana_file(cls, filename):\n ret = translate.load_psana(cls, filename)\n ret._sort_tree()\n return ret","def extract_data(file_ner,file_pos,separator=\" \"):\n\n # read NER and POS from the two files\n words_tags=read_conll_file(file_ner)\n words_pos=read_conll_file(file_pos)\n \n ## some checks, e.g., that both files have same length, same tokens\n assert(len(words_tags)==len(words_pos))\n \n for (words,tags),(_,pos) in zip(words_tags,words_pos):\n for word,pos,tag in zip(words,pos,tags):\n # first letter is capitalized\n cap=\"+\" if word[0].isupper() else \"-\"\n hyphen = '+' if '-' in word else '-'\n l = str(len(word))\n #vowels = \"\".join(sorted([w for w in word.lower() if w in ['a','e','i','o','u','y']]))\n #################################\n ###### YOUR FEATURES HERE ####### \n #################################\n # 0=separator\n \n ## todo: output the cap feature and more \n ## make sure the format you output here is what the nerfeats.py script expects as fields!\n print separator.join([word.lower(),pos,cap, l, hyphen, tag])\n # sentence separator\n print \"\""],"string":"[\n \"def __read_data__(self):\\n with open(self.file, 'r') as data:\\n sentence = []\\n tags = []\\n for line in data:\\n terms = line.rstrip().split(WHITESPACE)\\n for term in terms:\\n word_tag = tuple(term.split(TAGCHAR))\\n word = word_tag[0]\\n tag = word_tag[1]\\n self.word_tag_dict[word_tag] += 1\\n self.tag_dict[tag] += 1\\n self.__add_to_word_dict__(word, tag)\\n if self.isNumberWord(word):\\n self.numbers += 1\\n if word[0].isupper() and len(sentence) > 0:\\n self.cap_no_start += 1\\n sentence.append(word)\\n tags.append(tag)\\n if tag == ENDOFSENTENCE:\\n self.sentences.append(tuple(sentence))\\n self.tags.append(tuple(tags))\\n sentence = []\\n tags = []\",\n \"def process_data_from_input_file(triplet):\\n\\n sentence = triplet.subject + ' ' + triplet.predicate + ' ' + triplet.object\\n doc = nlp(unicode(sentence))\\n root = doc[0]\\n for t in doc:\\n if t.pos_ == 'VERB' and t.head == t:\\n root = t\\n # elif t.pos_ == 'NOUN'\\n\\n # also, if only one sentence\\n # root = doc[:].root\\n\\n\\n \\\"\\\"\\\"\\n CURRENT ASSUMPTIONS:\\n - People's names are unique (i.e. there only exists one person with a certain name).\\n - Pet's names are unique\\n - The only pets are dogs and cats\\n - Only one person can own a specific pet\\n - A person can own only one pet\\n \\\"\\\"\\\"\\n\\n\\n # Process (PERSON, likes, PERSON) relations\\n if root.lemma_ == 'like':\\n if triplet.subject in [e.text for e in doc.ents if e.label_ == 'PERSON' or e.label_ == 'ORG'] and triplet.object in [e.text for e in doc.ents if e.label_ == 'PERSON' or e.label_ == 'ORG'] and \\\"n't\\\" not in triplet.predicate:\\n s = add_person(triplet.subject)\\n o = add_person(triplet.object)\\n s.likes.append(o)\\n\\n if root.lemma_ == 'be' and triplet.object.startswith('friends with'):\\n fw_doc = nlp(unicode(triplet.object))\\n with_token = [t for t in fw_doc if t.text == 'with'][0]\\n # get text after with\\n after_with = fw_doc.text.split(with_token.text+ ' ')[1]\\n people = []\\n for p in after_with.split(' '):\\n if nlp(p)[0].tag_ == 'NNP':\\n people.append(nlp(p)[0].text)\\n # fw_who = [t for t in with_token.children if t.dep_ == 'pobj'][0].text\\n # fw_who = [e for e in fw_doc.ents if e.label_ == 'PERSON'][0].text\\n for p in people:\\n if triplet.subject in [e.text for e in doc.ents if e.label_ == 'PERSON']:\\n s = add_person(triplet.subject)\\n o = add_person(p)\\n s.likes.append(o)\\n o.likes.append(s)\\n if root.lemma_ == 'be' and triplet.object == 'friends':\\n fw_doc = nlp(unicode(triplet.subject))\\n and_token = [t for t in fw_doc if t.text == 'and']\\n if and_token:\\n and_token = and_token[0].text\\n if and_token == 'and' and fw_doc[0].text in [e.text for e in doc.ents if e.label_ == 'PERSON'] and fw_doc[2].text in [e.text for e in doc.ents if e.label_ == 'PERSON']:\\n s = add_person(fw_doc[0].text)\\n o = add_person(fw_doc[2].text)\\n s.likes.append(o)\\n o.likes.append(s)\\n\\n # Process (PET, has, NAME) Mary's dog's name is Rover\\n if triplet.subject.endswith('name') and ('dog' in triplet.subject or 'cat' in triplet.subject):\\n obj_span = doc.char_span(sentence.find(triplet.object), len(sentence))\\n\\n # handle single names, but what about compound names? Noun chunks might help.\\n if (len(obj_span) == 1 or len(obj_span) == 2) and obj_span[-1].pos_ == 'PROPN':\\n name = triplet.object\\n subj_start = sentence.find(triplet.subject)\\n subj_doc = doc.char_span(subj_start, subj_start + len(triplet.subject))\\n\\n s_people = [token.text for token in subj_doc if token.ent_type_ == 'PERSON']\\n assert len(s_people) == 1\\n s_person = select_person(s_people[0])\\n\\n pet = get_persons_pet(s_person.name)\\n\\n pet.name = name\\n s_person.has.append(pet)\\n\\n # Process (Who has dog)\\n if root.lemma_ == 'have'and ('dog' in triplet.object or 'cat' in triplet.object):\\n # find pets name and instantiate name empty str\\n obj_span = doc.char_span(sentence.find(triplet.object), len(sentence))\\n name = ''\\n\\n if obj_span[-1].pos_ == 'PROPN':\\n name = obj_span[-1].text\\n s = add_person(triplet.subject)\\n s_pet_type = 'dog' if 'dog' in triplet.object else 'cat'\\n pet = add_pet(s_pet_type, name)\\n s.has.append(pet)\\n\\n date = [e.text for e in doc.ents if e.label_ == 'DATE']\\n gpe = [e.text for e in doc.ents if e.label_ == 'GPE']\\n person = [e.text for e in doc.ents if e.label_ == 'PERSON' or e.label_ == 'ORG']\\n # if person and GPE exists, we add it into trip(departs_on, departs_to)\\n if person and (gpe or date):\\n s = add_person(triplet.subject)\\n o = add_trip(date, gpe)\\n s.travels.append(o)\",\n \"def loadDataFile(self, filename):\\n self.datafile = vocloadlib.readTabFile(filename,\\n [ 'term', 'accID', 'status', 'abbreviation',\\n 'note', 'comment', 'synonyms', 'synonymTypes',\\n 'otherIDs', 'start', 'end', 'parent' \\n ])\",\n \"def noun_phrase_chunking(part_of_speech_data):\\n\\n grammar = r\\\"\\\"\\\"\\n NP: {<DT|JJ|NN.*>+}\\n PP: {<IN><NP>}\\n VP: {<VB.*><NP|PP|CLAUSE>+$}\\n CLAUSE: {<NP><VP>}\\n \\\"\\\"\\\"\\n\\n grammar2 = r\\\"\\\"\\\"\\n NP: {<DT|NN>+} # Chunk sequences of NN and DT\\n {<DT><JJ><NN>} # Chunk det+adj+noun\\n \\\"\\\"\\\"\\n\\n return RegexpParser(grammar).parse(part_of_speech_data).draw()\",\n \"def parse(self, word):\\n word = self.son.segs(word)\\n son_map = self._sonority_map(word)\\n son_map = self._mark_offglides(son_map)\\n son_map = self._adjust_anom_fric_cod(son_map)\\n son_map = self._adjust_anom_fric_ons(son_map)\\n ons_son = self._initial_onset(son_map)\\n cod_son = self._final_coda(son_map)\\n ons = self.from_map(ons_son, word)\\n cod = self.from_reverse_map(cod_son, word)\\n return (ons, cod)\",\n \"def get_nouns(self):\\n\\t\\tblob = TextBlob(self.raw_string)\\n\\n\\t\\tfor word, tag in blob.tags:\\n\\t\\t\\tif tag in ['NNP', 'NN']:\\n\\t\\t\\t\\tself.nouns.append(word.lemmatize())\",\n \"def parse_data(filename):\\r\\n labels = []\\r\\n documents = []\\r\\n with open(filename, 'r') as f:\\r\\n for line in f:\\r\\n values = line.split()\\r\\n label = values[0]\\r\\n document = []\\r\\n for wordCount in values[1:]:\\r\\n parsed = wordCount.split(':')\\r\\n word = parsed[0]\\r\\n count = int(parsed[1])\\r\\n document.append((word, count))\\r\\n labels.append(label)\\r\\n documents.append(document)\\r\\n return (labels, documents)\",\n \"def parse(self, infile):\\r\\n raise NotImplementedError()\",\n \"def loadDataFile(self, filename):\\n \\n self.datafile = vocloadlib.readTabFile(filename,\\n [ 'term', 'accID', 'status', 'abbreviation',\\n 'note', 'comment', 'synonyms', 'synonymTypes',\\n 'otherIDs', 'emapa', 'ts', 'parent']\\n )\",\n \"def read_tagged_word_list(filename):\\n # TODO: write and test this method\\n print 'reading tagged file'\",\n \"def parse_label(self):\\n # TODO: make this work with attached labels as well as\\n # stand alone labels.\\n # Save the RAW full text of the label to self._raw\\n input_stream = FileStream(self.infile)\\n lexer = ODLv21Lexer(input_stream)\\n tokens = CommonTokenStream(lexer)\\n\\n parser = ODLv21Parser(tokens)\\n parse_tree = parser.label()\\n self._parse_tree = parse_tree\\n visitor = Pds3LabelVisitor()\\n visitor.visit(parse_tree)\\n return visitor.root_dict\",\n \"def parse(las_file):\\n io_stream = io.TextIOWrapper(las_file)\\n \\n entry_date = datetime.now().strftime('%Y-%m-%d-%H-%M-%S')\\n entry_filename = 'las_file-' + entry_date + '.las'\\n\\n entry = SectionInfo()\\n entry.filename = entry_filename\\n section = ''\\n\\n for line in io_stream.readlines():\\n\\n line = line.rstrip()\\n\\n if not line:\\n continue\\n\\n # Lines beginning with '~' denote the next section header.\\n if line[0] == '~':\\n section = line\\n continue\\n # Skip comment lines.\\n elif line[0] == '#':\\n continue\\n\\n # LAS standard option 'OTHER' section\\n if section[1] == 'O': \\n entry.value = line\\n entry.section = section\\n # The rest of the standard metadata sections\\n elif section[1] in ['V', 'W', 'C', 'P']:\\n entry = parse_formatted_section_line(section, line, entry)\\n # the data section and non-standard sections\\n else:\\n # print(\\\"Non-Metadata-Section: [{}]: [{}]\\\".format(section[0:2], line))\\n continue\\n\\n # Write entry to db\\n entry.save()\\n\\n # Initialize next entry\\n entry = SectionInfo()\\n entry.filename = entry_filename\\n\\n return entry_filename\",\n \"def read_data(filename, prefix=None):\\n p_data = {}\\n with open(filename) as f:\\n # This first line is the header for the entire file.\\n line = f.next()\\n line = line.strip()\\n # prev_line = line\\n top_header = line.split(',')\\n if not top_header:\\n # Don't parse this for now.\\n pass\\n # Now read in per-participant data.\\n while True:\\n word_list = []\\n all_words_data = {}\\n # The first line for the participant is a header.\\n try:\\n line = f.next()\\n except StopIteration:\\n # We had previously read everything, so we're done.\\n break\\n line = line.strip()\\n p_header = line.split(',')\\n\\n # The participant's ID # comes first.\\n p_id = p_header[0]\\n if not p_id:\\n # This happens when the previous participant didn't answer.\\n \\\"\\\"\\\"\\n print 'previous line:', prev_line\\n print 'current line:', line\\n print 'p header:', p_header\\n print\\n \\\"\\\"\\\"\\n continue\\n if prefix:\\n p_id = prefix + p_id\\n # print 'SN #', p_id\\n # The number of N/A's this p is at 28.\\n try:\\n p_nas = int(p_header[28])\\n except ValueError:\\n # This happens when an RA messes up the file.\\n \\\"\\\"\\\"\\n print 'nas: previous line:', prev_line\\n print 'nas: current line:', line\\n print 'nas: p header:', p_header\\n print\\n \\\"\\\"\\\"\\n raise\\n # print \\\"NA's: #\\\", p_nas\\n # Check if this participant left everything blank.\\n # XXX: Have to hard-code this.\\n if p_nas == 20:\\n \\\"\\\"\\\"Don't record anything.\\n p_data[p_id] = {'words': None,\\n 'word_data': None,\\n 'nas': None,\\n 'overall': None}\\n \\\"\\\"\\\"\\n continue\\n # The next line after the header has both the data\\n # for the first word and overall statistics.\\n # prev_line = line\\n try:\\n line = f.next()\\n except StopIteration:\\n # We had previously read everything, so we're done.\\n break\\n line = line.strip()\\n word, word_data, overall_data = parse_first_line(line.split(','))\\n word_list.append(word)\\n all_words_data[word] = word_data\\n # Now read data for the rest of the words.\\n for line in f:\\n line = line.strip()\\n word, word_data = parse_data_lines(line.split(','))\\n if word == '':\\n \\\"\\\"\\\"\\n print \\\"loop's previous line:\\\", prev_line\\n print \\\"loop's current line:\\\", line\\n print\\n \\\"\\\"\\\"\\n # prev_line = line\\n break\\n word_list.append(word)\\n all_words_data[word] = word_data\\n # prev_line = line\\n # Compute per-word averages\\n all_total_avg, future_total_avg, past_total_avg = \\\\\\n datacomputer.compute_all_future_past(all_words_data)\\n overall_data['all'] = all_total_avg\\n overall_data['future'] = future_total_avg\\n overall_data['past'] = past_total_avg\\n p_data[p_id] = {'words': word_list,\\n 'word_data': all_words_data,\\n 'nas': p_nas,\\n 'overall': overall_data}\\n # print 'p_data'\\n # print p_data[p_id]\\n # print\\n print \\\"Processed {} participants' data\\\".format(len(p_data))\\n return p_data\",\n \"def _parse(self, infile):\\n raise NotImplementedError()\",\n \"def parse_voc(filename):\\n tree = ET.parse(filename)\\n objects = []\\n for obj in tree.findall('object'):\\n obj_struct = {}\\n obj_struct['name'] = obj.find('name').text\\n obj_struct['difficult'] = int(obj.find('difficult').text)\\n bbox = obj.find('bndbox')\\n obj_struct['bbox'] = [int(bbox.find('xmin').text),\\n int(bbox.find('ymin').text),\\n int(bbox.find('xmax').text),\\n int(bbox.find('ymax').text)]\\n objects.append(obj_struct)\\n\\n return objects\",\n \"def get_noun_phrases(blob):\\n return blob.noun_phrases\",\n \"def import_data(in_file):\\n\\n print '\\\\n\\\\tImport data'\\n sentence = []\\n concept = []\\n sentences = []\\n concepts = []\\n for line in open(in_file, 'r'):\\n if line != '\\\\n':\\n sentence += [ line.split()[0] ]\\n concept += [ line.split()[1] ]\\n else:\\n sentences += [ sentence ]\\n concepts += [ concept ]\\n sentence = [ ]\\n concept = [ ]\\n pos = []\\n lemma = []\\n poss = []\\n lemmas = []\\n for line in open(in_file.replace('.data', '.feats.txt'), 'r'):\\n if line != '\\\\n':\\n pos += [ line.split()[ 1 ] ]\\n lemma += [ line.split()[ 2 ] ]\\n else:\\n poss += [ pos ]\\n lemmas += [ lemma ]\\n pos = [ ]\\n lemma = [ ]\\n print '\\\\t--done'\\n return sentences, poss, lemmas, concepts\",\n \"def read_pronunciation(pronunciation_file):\\n # file = open('dictionary.txt', 'r')\\n #\\n # for line in file:\\n # print line\\n\\n ################# https://m.reddit.com/r/CompSciPortfolio/comments/303fyo/assignment_3_poetry_reader/\\n\\n pronunciation_dictionary = {}\\n line = pronunciation_file.readline()\\n while line.startswith(';;;'):\\n line = pronunciation_file.readline()\\n while line != '':\\n stripped_line = line.strip()\\n separation = stripped_line.find(' ')\\n pronunciation_dictionary[stripped_line[:separation]] = stripped_line[(separation + 2):].split()\\n line = pronunciation_file.readline()\\n return pronunciation_dictionary\\n\\n\\n\\n # my_list = {}\\n # for line in pronunciation_file.readlines():\\n # line = line.strip()\\n # if line and \\\";;;\\\" not in line:\\n # r = line.split()\\n # word = r[0]\\n # phonemes = r[1:]\\n # my_list[word] = phonemes\\n # return my_list\",\n \"def main ():\\n\\n\\tfio = fileIo('input.txt')\\n text = fio.getInput()\\n\\n\\tp = re.compile(r'#?\\\\d[\\\\s\\\\.]?[\\\\s]?')\\n\\tout = filter(None, p.split(text))\\n\\ti = 0\\n\\tlistOfLists = []\\n\\t\\n\\n\\tfor s in out:\\n\\t\\ti += 1\\n\\t\\ttext = nltk.word_tokenize(s)\\n\\t\\tpos = nltk.pos_tag(text)\\n\\t\\tpattern = \\\"NP: {<DT>?<JJ>*<NN>}\\\"\\n\\t\\tNPChunker = nltk.RegexpParser(pattern)\\n\\t\\tresult = NPChunker.parse(pos)\\n\\t\\tlistOfLists.append( result )\\n\\n\\tprint \\\"Noun Count:\\\\n\\\" + str(countNouns( listOfLists ))\\n\\tprint \\\"Verb Count:\\\\n\\\" + str(countVerbs( listOfLists ))\\n\\tprint \\\"Adjective Count:\\\\n\\\" + str(countAdjectives( listOfLists ))\",\n \"def load_file(filename):\\n\\tlabels = []\\n\\tdocs = []\\n\\n\\twith open(filename) as f:\\n\\t\\tfor line in f:\\n\\t\\t\\tcontent = line.split('\\\\t')\\n\\n\\t\\t\\tif len(content) > 2:\\n\\t\\t\\t\\tprint('incorrect read')\\n\\t\\t\\t\\texit()\\n\\n\\t\\t\\tif len(content[1]) == 0: continue\\n\\n\\t\\t\\tdocs.append(str(content[1]).strip('\\\\r').strip('\\\\n').strip('\\\\r\\\\n'))\\n\\t\\t\\tlabels.append(content[0])\\n\\n\\treturn docs, labels\",\n \"def parseFile(self, filename):\\n self.__filename = filename\\n\\n if os.path.isfile(filename) == False:\\n self.LogError(\\\"Unable to open input file \\\" + str(filename))\\n raise IOError\\n\\n self.__file = open(filename, 'r')\\n\\n while True:\\n string = self.__file.readline()\\n if string == \\\"\\\":\\n break\\n\\n if string.upper().find(\\\"[SYSTEM]\\\") != -1:\\n #print string.upper()\\n self.__parseSystem()\\n\\n if string.upper().find(\\\"[GRASS]\\\") != -1:\\n #print string.upper()\\n self.__parseGrass()\\n\\n if string.upper().find(\\\"[COMPLEXDATA]\\\") != -1:\\n #print string.upper()\\n self.complexDataList.append(ComplexData(self.__file))\\n\\n if string.upper().find(\\\"[COMPLEXOUTPUT]\\\") != -1:\\n #print string.upper()\\n self.complexOutputList.append(ComplexOutput(self.__file))\\n\\n if string.upper().find(\\\"[LITERALDATA]\\\") != -1:\\n #print string.upper()\\n LD = LiteralData(self.__file)\\n if LD.identifier == 'multi_output':\\n self.LogWarning(\\\"multi_output: \\\" + LD.value.upper())\\n if LD.value.upper() == 'TRUE':\\n self.multiOutput = True\\n else:\\n self.literalDataList.append(LD)\",\n \"def parseFileName(filename):\\n entry = DataEntry(\\\"\\\",0,{},{},0,0)\\n wordArray = filename.split(\\\".\\\")\\n entry.publication_name = wordArray[1]\\n entry.year = wordArray[0]\\n return entry\",\n \"def load(path: str) -> \\\"DataDescriptor\\\":\\n\\n\\t\\twith open(path, \\\"r\\\") as f:\\n\\t\\t\\tinfo_dict = json.load(f)\\n\\n\\t\\treturn DataDescriptor(\\n\\t\\t\\tn_gram_size=int(info_dict[\\\"n_gram_size\\\"]),\\n\\t\\t\\tcaseless=bool(info_dict[\\\"caseless\\\"]),\\n\\t\\t\\tignore_punctuation=bool(info_dict[\\\"ignore_punctuation\\\"]),\\n\\t\\t\\tadd_pos_tags=bool(info_dict[\\\"add_pos_tags\\\"]),\\n\\t\\t\\tuses_lemma=bool(info_dict[\\\"uses_lemma\\\"]),\\n\\t\\t\\tuses_sentences=bool(info_dict[\\\"uses_sentences\\\"])\\n\\t\\t)\",\n \"def parse_object(self, word_list):\\n\\n self.skip(word_list, 'stop')\\n\\n next_word = self.peek(word_list)\\n\\n if next_word == 'noun':\\n return self.match(word_list, 'noun')\\n\\n elif next_word == 'direction':\\n return self.match(word_list, 'direction')\\n\\n else:\\n raise ParserError(\\n 'Expected a noun or direction. Got a %s.' % next_word\\n )\",\n \"def parse_data(fp):\\n pass\",\n \"def _parse(self):\\n with open(_join(self.man_dir, self.man_fn)) as fp:\\n lines = fp.readlines()\\n \\n desc_indxs = []\\n for i, L in enumerate(lines):\\n if \\\"#landuse\\\" in L or \\\" # landuse\\\" in L:\\n desc_indxs.append(i-1)\\n desc_indxs.append(i-2)\\n desc_indxs.append(i-3)\\n \\n lines = [L[:L.find('#')].strip() for L in lines]\\n lines = [L for i, L in enumerate(lines) if len(L) > 0 or i in desc_indxs]\\n\\n del desc_indxs\\n \\n self.datver = lines.pop(0)\\n self.nofe = int(lines.pop(0))\\n self.sim_years = int(lines.pop(0))\\n \\n # Read Plant Growth Section\\n self.plants = PlantLoops(lines, self)\\n\\n # Read Operation Section\\n self.ops = OpLoops(lines, self)\\n \\n # Read Initial Condition Section\\n self.inis = IniLoops(lines, self)\\n \\n # Read Surface Effects Section\\n self.surfs = SurfLoops(lines, self)\\n \\n # Read Contour Section\\n self.contours = ContourLoops(lines, self)\\n \\n # Read Drainage Section\\n self.drains = DrainLoops(lines, self)\\n \\n # Read Yearly Section\\n self.years = YearLoops(lines, self)\\n \\n # Read Management Section \\n self.man = ManagementLoop(lines, self)\",\n \"def parse_file(self, infile, chardict, labeldict):\\n examples = []\\n fin = io.open(infile, 'r')\\n # idx is for the index of the row in the \\n # original file before shuffling and randomization\\n idx = 0\\n for line in fin: \\n entity, label = map(clean, line.rstrip().split('\\\\t')[:2])\\n # print entity\\n ent = map(lambda c:chardict[c], list(entity))\\n lab = map(lambda l:labeldict[l] if l in labeldict else 0, label.split(','))\\n examples.append((idx, ent, lab))\\n idx += 1\\n fin.close()\\n print \\\"num_rows:\\\", len(examples), \\\" index\\\", idx\\n return examples\",\n \"def __init__(self, variable, pnoun, nucleus):\\n super(ProperNounExpression, self).__init__(variable, EmptyExpression(), nucleus)\\n assert(pnoun in proper_nouns)\\n self.pnoun = pnoun\",\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 data_parser(data):\\n\\n with open(data, 'r') as inp:\\n\\n # take every sample\\n # the last line in the text file is empty, so reading until -1\\n samples = inp.read().split('\\\\n')[:-1]\\n\\n vec = []\\n labels = []\\n for sample in samples:\\n # file is tab delimited\\n split_samples = sample.split('\\\\t')\\n # last column contains the label\\n labels.append(int(split_samples[-1]))\\n\\n features = []\\n for feature in split_samples[:-1]:\\n features.append(float(feature))\\n vec.append(features)\\n\\n # make the features and labels as a numpy array\\n vec = np.array(vec)\\n labels = np.array(labels)\\n return vec, labels\",\n \"def parse_fasta(fasta_f, contig_data):\\n\\n basen = os.path.basename(fasta_f)\\n [soil, ecotype, media] = basen.split(\\\"_\\\")[:3]\\n\\n with open(fasta_f, 'rU') as IN:\\n for record in SeqIO.parse(IN, \\\"fasta\\\"):\\n contig_data[record.description] = {'length': len(record.seq), 'soil': soil, 'ecotype': ecotype, 'media': media}\",\n \"def load_morphoit(self, path=None):\\n path = path or self.path\\n # f = open(path,'r')\\n f = codecs.open(path, 'r', 'latin-1')\\n lines = f.readlines()\\n f.close()\\n self.lemma_dict.clear()\\n self.tag_dict.clear()\\n self.suffix_dict.clear()\\n self.word_tag_dict.clear()\\n n_entries = 0\\n for line in lines:\\n line = line.strip()\\n if line:\\n # entry = line.split()\\n entry = line.split(u'\\\\u0009') # entry has TAB delimited items\\n n = len(entry)\\n word = entry[0]\\n if n > 1:\\n lemma = entry[1]\\n else:\\n lemma = 'lemma?'\\n if n > 2:\\n tags = entry[2]\\n if tags:\\n splitted_tags = tags.split(u':')\\n tag = splitted_tags[0]\\n if tag == u'SMI': # smile ?\\n continue\\n else:\\n tag = 'tag?'\\n n_entries += 1\\n if self.use_defaultdict:\\n self.lemma_dict[lemma].append(word)\\n self.tag_dict[tag] += 1\\n self.word_tag_dict[word][tag] += 1\\n else:\\n words = self.lemma_dict.get(lemma, []); words.append(word); self.lemma_dict[lemma] = words\\n self.tag_dict[tag] = self.tag_dict.get(tag, 0) + 1\\n dict = self.word_tag_dict.get(word, {})\\n \\\"\\\"\\\" only occurrence in corpus will increment the counter !\\n dict[tag] = dict.get(tag, 0) + 1\\n \\\"\\\"\\\"\\n if dict.get(tag, None) is None:\\n dict[tag] = 0\\n self.word_tag_dict[word] = dict\\n length = len(word)\\n reversed = util.reverse(word)\\n max = min(length, MAX_SUFFIX)\\n for i in range(MIN_SUFFIX, max):\\n suffix = reversed[:i]\\n if self.use_defaultdict:\\n self.suffix_dict[suffix][tag] += 1\\n else: # defaultdicts cannot be pickled !\\n dict = self.suffix_dict.get(suffix, {})\\n dict[tag] = dict.get(tag, 0) + 1\\n self.suffix_dict[suffix] = dict\\n return len(lines), n_entries\",\n \"def get_nouns(txt):\\n query = 'https://api.textgain.com/1/tag?q='\\n query += urllib.parse.quote(txt, safe='')\\n query += '&lang=fr&key=***'\\n resp = requests.get(query)\\n\\n body = json.loads(resp.text)['text'][0]\\n\\n nouns = {}\\n for iterable_elem in body:\\n for elem in iterable_elem:\\n if elem['tag'] == 'NOUN':\\n word = elem['word']\\n if word in nouns.keys():\\n nouns[word] += 1\\n else:\\n nouns[word] = 1\\n print(nouns)\\n return nouns\",\n \"def read_file(f, prefix=None):\\n p_data = {}\\n # This first line is the header for the entire file.\\n line = f.next()\\n line = line.strip()\\n # prev_line = line\\n top_header = line.split(',')\\n if not top_header:\\n # Don't parse this for now.\\n pass\\n # Now read in per-participant data.\\n while True:\\n word_list = []\\n all_words_data = {}\\n # The first line for the participant is a header.\\n try:\\n line = f.next()\\n except StopIteration:\\n # We had previously read everything, so we're done.\\n break\\n line = line.strip()\\n p_header = line.split(',')\\n\\n # The participant's ID # comes first.\\n p_id = p_header[0]\\n if not p_id:\\n # This happens when the previous participant didn't answer.\\n \\\"\\\"\\\"\\n print 'previous line:', prev_line\\n print 'current line:', line\\n print 'p header:', p_header\\n print\\n \\\"\\\"\\\"\\n continue\\n if prefix:\\n p_id = prefix + p_id\\n # print 'SN #', p_id\\n # The number of N/A's this p is at 28.\\n try:\\n p_nas = int(p_header[28])\\n except ValueError:\\n # This happens when an RA messes up the file.\\n \\\"\\\"\\\"\\n print 'nas: previous line:', prev_line\\n print 'nas: current line:', line\\n print 'nas: p header:', p_header\\n print\\n \\\"\\\"\\\"\\n raise\\n # print \\\"NA's: #\\\", p_nas\\n # Check if this participant left everything blank.\\n # XXX: Have to hard-code this.\\n if p_nas == 20:\\n p_data[p_id] = {'words': None,\\n 'word_data': None,\\n 'nas': None,\\n 'overall': None}\\n continue\\n # The next line after the header has both the data\\n # for the first word and overall statistics.\\n # prev_line = line\\n try:\\n line = f.next()\\n except StopIteration:\\n # We had previously read everything, so we're done.\\n break\\n line = line.strip()\\n word, word_data, overall_data = parse_first_line(line.split(','))\\n word_list.append(word)\\n all_words_data[word] = word_data\\n # Now read data for the rest of the words.\\n for line in f:\\n line = line.strip()\\n word, word_data = parse_data_lines(line.split(','))\\n if word == '':\\n \\\"\\\"\\\"\\n print \\\"loop's previous line:\\\", prev_line\\n print \\\"loop's current line:\\\", line\\n print\\n \\\"\\\"\\\"\\n # prev_line = line\\n break\\n word_list.append(word)\\n all_words_data[word] = word_data\\n # prev_line = line\\n # Compute per-word averages\\n all_total_avg, future_total_avg, past_total_avg = \\\\\\n datacomputer.compute_all_future_past(all_words_data)\\n overall_data['all'] = all_total_avg\\n overall_data['future'] = future_total_avg\\n overall_data['past'] = past_total_avg\\n p_data[p_id] = {'words': word_list,\\n 'word_data': all_words_data,\\n 'nas': p_nas,\\n 'overall': overall_data}\\n # print 'p_data'\\n # print p_data[p_id]\\n # print\\n print \\\"Processed {} participants' data\\\".format(len(p_data))\\n return p_data\",\n \"def load_conll_notags(unfile, max_slen, vocab=[], oovs={}, pads={}, lower=False, mwe=True, unk_case=True):\\n # special characters used for splitting words\\n split_chars = set([',', '.', ':', '-', '~', \\\"'\\\", '\\\"'])\\n\\n # punctuation that denotes when a sentence finishes\\n sent_split_words = set(['.', '?', '!', ';', '—'])\\n\\n input_sents = []\\n input_words = []\\n windex = -1\\n\\n # number of words from which to split sentences\\n LIMIT_SENT_LEN = max_slen\\n\\n sents = []\\n if 'begin' in pads:\\n next_words = [pads['begin']]\\n next_syms = ['']\\n next_indexs = [windex]\\n sent_base_length = 1\\n else:\\n next_words = []\\n next_syms = []\\n next_indexs = []\\n sent_base_length = 0\\n\\n # select files to use\\n input_files = [unfile]\\n\\n # counters\\n num_raw_sents = 0\\n num_sents = 0\\n num_words = 0\\n num_oovs = 0\\n\\n # iterate over lines in the input files\\n for ifile in input_files:\\n for line in codecs.open(ifile, mode = 'r', errors = 'ignore', encoding = 'utf-8'):\\n # discard newline character\\n line = line[:-1]\\n\\n # keep adding words while in the middle of a sentence\\n if line:\\n word = line.split('\\\\t')[0]\\n sym = word\\n # add new original word\\n windex += 1\\n input_words.append(word)\\n num_words += 1\\n # lowercase when indicated\\n if lower:\\n word = word.lower()\\n # use an heuristic and try to map oov words\\n if vocab and word not in vocab:\\n if word not in split_chars:\\n if re.match('^[0-9\\\\.\\\\,-]+$', word):\\n word = oovs['number']\\n elif _match_word_vocab(word, vocab) != word:\\n word = _match_word_vocab(word, vocab)\\n elif ' ' in word or '~' in word or '-' in word and mwe:\\n # attempt to split multi-word expressions\\n constituents_text = re.split('[\\\\s~ | \\\\s-]+', word)\\n constituents = [_match_word_vocab(w, vocab) for w in constituents_text]\\n if all([True if c in vocab else False for c in constituents]):\\n next_words += constituents[:-1]\\n next_syms += constituents[:-1]\\n next_indexs += [windex] * len(constituents[:-1])\\n word = constituents[-1]\\n sym = constituents[-1]\\n else:\\n if unk_case and word[0].isupper():\\n word = oovs['UNKNOWN']\\n else:\\n word = oovs['unknown']\\n num_oovs += 1\\n else:\\n if unk_case and word[0].isupper():\\n word = oovs['UNKNOWN']\\n else:\\n word = oovs['unknown']\\n num_oovs += 1\\n else:\\n word = oovs['unknown']\\n num_oovs += 1\\n\\n next_words.append(word)\\n next_syms.append(sym)\\n next_indexs.append(windex)\\n\\n # stack the current sentence upon seeing an empty line or a sentence end mark\\n if not line or (len(next_words) > 3 and next_words[-4] in sent_split_words) or (len(next_words) >= LIMIT_SENT_LEN and len(sent_split_words.intersection(next_words)) < 1):\\n if len(next_words) > sent_base_length:\\n # split when an empty line marks a sentence end\\n if not line:\\n if 'end' in pads:\\n next_words.append(pads['end'])\\n next_syms.append('')\\n next_indexs.append(-1)\\n sents.append(list(zip(next_words, next_indexs, next_syms)))\\n input_sents.append(input_words)\\n input_words = []\\n windex = -1\\n next_words = []\\n next_syms = []\\n next_indexs = []\\n num_raw_sents += 1\\n num_sents += 1\\n # split when punctuation marks a sentence end\\n elif len(next_words) > 3 and next_words[-4] in sent_split_words:\\n split_words = next_words[:-3]\\n split_syms = next_syms[:-3]\\n split_indexs = next_indexs[:-3]\\n if 'end' in pads:\\n split_words.append(pads['end'])\\n split_syms.append('')\\n split_indexs.append(-1)\\n sents.append(list(zip(split_words, split_indexs, split_syms)))\\n next_words = next_words[-3:]\\n next_syms = next_syms[-3:]\\n next_indexs = next_indexs[-3:]\\n num_sents += 1\\n # split when the maximum sentence length is reached\\n # a bad guess is better than not guessing when predicting tags\\n else:\\n sents.append(list(zip(next_words, next_indexs, next_syms)))\\n next_words = []\\n next_syms = []\\n next_indexs = []\\n num_sents += 1\\n\\n if 'begin' in pads:\\n next_words = [pads['begin']] + next_words\\n next_syms = [''] + next_syms\\n next_indexs = [-1] + next_indexs\\n\\n else:\\n input_sents.append(input_words)\\n input_words = []\\n windex = -1\\n num_raw_sents += 1\\n\\n # double check the last sentence\\n if len(next_words) > sent_base_length:\\n if 'end' in pads:\\n next_words.append(pads['end'])\\n next_syms.append('')\\n next_indexs.append(-1)\\n sents.append(list(zip(next_words, next_indexs, next_syms)))\\n input_sents.append(input_words)\\n input_words = []\\n windex = -1\\n num_raw_sents += 1\\n num_sents += 1\\n\\n # find the allowed sentence length\\n print('[INFO] Number of unlabelled OOV words: ' + str(num_oovs) + ' / ' + str(num_words))\\n print('[INFO] Original number of unlabelled sentences: ' + str(num_raw_sents))\\n print('[INFO] Number of extracted unlabelled sentences ' + str(num_sents))\\n return input_sents, sents\",\n \"def load_annos(self):\\n data = None\\n with open(self.anno_path, 'r') as file:\\n if self.ext == '.json':\\n data = json.load(file)\\n\\n # Label start at index 0\\n if data is not None:\\n for anno in data['annotations']:\\n anno['category_id'] -= 1\\n\\n for anno in data['categories']:\\n anno['id'] -= 1\\n\\n return data\",\n \"def _load_dat(self):\\n modelfile = self.filename\\n with open(modelfile) as f:\\n content = f.readlines()\\n\\n self.comment = content.pop(0) # Comment line\\n content = [x for x in content if not x.startswith('#')]\\n\\n for line in content:\\n if('atoms' in line): self.natoms = int(line.split()[0])\\n if('xlo' in line and 'xhi' in line):\\n self.xsize = abs(float(line.split()[0])) + abs(float(line.split()[1]))\\n if('ylo' in line and 'yhi' in line):\\n self.ysize = abs(float(line.split()[0])) + abs(float(line.split()[1]))\\n if('zlo' in line and 'zhi' in line):\\n self.zsize = abs(float(line.split()[0])) + abs(float(line.split()[1]))\\n if('atom types' in line): nelems = int(line.split()[0])\\n if('Masses' in line): mflag = content.index(line) + 1\\n if('Atoms' in line): aflag = content.index(line) + 1\\n try:\\n mflag\\n except NameError:\\n raise Exception(\\\"ERROR! You need to define the masses in the .dat file.\\\")\\n atomtypes = {}\\n while(nelems > 0):\\n if(len(content[mflag].split()) == 2):\\n atomtypes[int(content[mflag].split()[0])] = masses.get_znum(float(content[mflag].split()[1]))\\n nelems -= 1\\n mflag += 1\\n self.atoms = []\\n natoms = self.natoms\\n while(natoms > 0):\\n sline = content[aflag].split()\\n if(len(sline) >= 5):\\n # We found an atom\\n id = int(sline[0])\\n type = int(sline[1])\\n x = float(sline[2])\\n y = float(sline[3])\\n z = float(sline[4])\\n znum = atomtypes[type]\\n # Add it to the model\\n self.atoms.append(Atom(id,znum,x,y,z))\\n natoms -= 1\\n aflag += 1\",\n \"def load_ud_english(fpath):\\n import os\\n import re\\n from collections import defaultdict\\n n = 1\\n\\n fname = os.path.split(fpath)[1]\\n\\n parses = defaultdict(list)\\n sent_ids = []\\n newdoc_ids = []\\n \\n for l in open(fpath):\\n ident = fname+' '+str(n)\\n \\n if re.match(r'\\\\# newdoc id', l):\\n newdoc_ids.append(n)\\n #newdoc_ids.append(l.split(\\\"=\\\")[-1].strip())\\n \\n if re.match(r'^\\\\d', l):\\n l_split = l.strip().split()\\n parses[ident].append(l_split)\\n \\n elif parses[ident]:\\n sent_ids.append(ident)\\n n += 1\\n\\n return newdoc_ids, len(sent_ids)\",\n \"def en_noun(t, label):\\r\\n s = label\\r\\n p = []\\r\\n is_uncountable = False\\r\\n \\r\\n # http://en.wiktionary.org/wiki/Template:en-noun\\r\\n head = t.arg(\\\"head\\\", label)\\r\\n p1 = t.arg(0)\\r\\n p2 = t.arg(1)\\r\\n \\r\\n if p1 == \\\"-\\\":\\r\\n # uncountable\\r\\n is_uncountable = True\\r\\n \\r\\n if p2 == \\\"s\\\":\\r\\n # ends by s\\r\\n p.append(head + \\\"s\\\")\\r\\n \\r\\n elif p2 is not None:\\r\\n # word\\r\\n p.append(p2)\\r\\n \\r\\n elif p1 == \\\"es\\\":\\r\\n # add es\\r\\n p.append(head + \\\"es\\\")\\r\\n \\r\\n elif p1 is not None:\\r\\n # use term\\r\\n p.append(p1)\\r\\n \\r\\n elif p1 is None and p2 is None:\\r\\n p.append(head+\\\"s\\\")\\r\\n\\r\\n for k,a in t.args.items():\\r\\n if not a.is_named():\\r\\n if k == 0 or k == 1:\\r\\n continue\\r\\n \\r\\n p.append(a.as_string())\\r\\n \\r\\n return (s, p, is_uncountable)\",\n \"def parse_rosalind(filename):\\n print \\\"parse_rosalind should be called parse_fasta\\\"\\n return parse_fasta(filename)\",\n \"def load_data(filename):\\n with open(filename, 'r') as f:\\n d = json.load(f)\\n\\n return d['nicknames'], d['relations'], d['directed']\",\n \"def parse (cls, raw_data):\\n # Parse text\\n model = NFFGModel.parse(raw_data)\\n # Create new NFFG\\n nffg = NFFG(id=model.id, name=model.name, service_id=model.service_id,\\n version=model.version, mode=model.mode, metadata=model.metadata)\\n # Load Infras\\n for infra in model.node_infras:\\n nffg.add_node(infra)\\n # Load SAPs\\n for sap in model.node_saps:\\n nffg.add_node(sap)\\n # Load NFs\\n for nf in model.node_nfs:\\n nffg.add_node(nf)\\n # Load Links\\n for link in model.edge_links:\\n if link.src.node.type == NFFG.TYPE_NF or \\\\\\n link.dst.node.type == NFFG.TYPE_NF:\\n link.type = str(NFFG.TYPE_LINK_DYNAMIC)\\n nffg.add_edge(link.src.node, link.dst.node, link)\\n # Load SG next hops\\n for hop in model.edge_sg_nexthops:\\n nffg.add_edge(hop.src.node, hop.dst.node, hop)\\n # Load Requirements\\n for req in model.edge_reqs:\\n nffg.add_edge(req.src.node, req.dst.node, req)\\n return nffg\",\n \"def _parse_ml(self, line):\\n # Parse the line\\n fields = line.split('\\\\\\\\')\\n if self.lang == ENGLISH:\\n # pylint: disable=C0301\\n # English sample:\\n # 14\\\\abandonment\\\\94\\\\C\\\\\\\\1\\\\N\\\\N\\\\N\\\\N\\\\Y\\\\abandon+ment\\\\2x\\\\SA\\\\N\\\\N\\\\N\\\\#\\\\N\\\\N\\\\SA\\\\((abandon)[V],(ment)[N|V.])[N]\\\\N\\\\N\\\\N\\n # From the README:\\n # The eml.cd file contains the following fields:\\n # 1. IdNum\\n # 2. Head\\n # 3. Cob\\n # 4. MorphStatus\\n # 5. Lang\\n # 6. MorphCnt\\n # 7. NVAffComp\\n # 8. Der\\n # 9. Comp\\n # 10. DerComp\\n # 11. Def\\n # 12. Imm\\n # 13. ImmSubCat\\n # 14. ImmSA\\n # 15. ImmAllo\\n # 16. ImmSubst\\n # 17. ImmOpac\\n # 18. TransDer\\n # 19. ImmInfix\\n # 20. ImmRevers\\n # 21 FlatSA\\n # 22. StrucLab\\n # 23. StrucAllo\\n # 24. StrucSubst\\n # 25. StrucOpac\\n lemma = fields[0]\\n word = fields[1]\\n derivation = fields[21]\\n elif self.lang == DUTCH:\\n # pylint: disable=C0301\\n # Dutch sample:\\n # 19\\\\aalbessengelei\\\\7\\\\C\\\\1\\\\Y\\\\Y\\\\Y\\\\aalbes+en+gelei\\\\NxN\\\\N\\\\N\\\\(((aal)[N],(bes)[N])[N],(en)[N|N.N],(gelei)[N])[N]\\\\N\\\\N\\\\N\\n # The dml.cd file contains the following fields:\\n # 1. IdNum\\n # 2. Head\\n # 3. Inl\\n # 4. MorphStatus\\n # 5. MorphCnt\\n # 6. DerComp\\n # 7. Comp\\n # 8. Def\\n # 9. Imm\\n # 10. ImmSubCat\\n # 11. ImmAllo\\n # 12. ImmSubst\\n # 13. StrucLab\\n # 14. StruAcAllo\\n # 15. StrucSubst\\n # 16. Sepa\\n lemma = fields[0]\\n word = fields[1]\\n derivation = fields[12]\\n\\n # Skip multi-word entries for roots\\n roots = self._get_root(derivation) if \\\" \\\" not in word else None\\n return (lemma, word, roots)\",\n \"def parse_from_file (path):\\n with open(path) as f:\\n return NFFG.parse(f.read())\",\n \"def check_nouns(elem_dictionary: dict, key: str, alet_dict: dict, last_nouns: list,\\n last_events: list, turtle: list, ext_sources: bool) -> list:\\n nouns = []\\n for elem in elem_dictionary[key]: # The subject or object nouns\\n elem_key = key[0:-1] # Create dictionary key = 'subject' or 'object'\\n elem_type = elem[f'{elem_key}_type']\\n elem_text = elem[f'{elem_key}_text']\\n # Get rid of titles (such as Ms, Miss, Mr, ...)\\n if 'FEMALE' in elem_type:\\n elem_text = _remove_title_from_name(female_titles, elem_text)\\n elif 'MALE' in elem_type:\\n elem_text = _remove_title_from_name(male_titles, elem_text)\\n head_lemma, head_text = get_head_word(elem_text)\\n # poss_dict = Dictionary of nouns (keys) with their possessive modifiers (values)\\n # Revised elem_text = noun text with possessives removed\\n poss_dict, elem_text = _separate_possessives(elem_text)\\n new_tuple = tuple()\\n possible_name = empty_string # For a proper name, may contain shortened form = given + surname (any order)\\n if elem_type == 'CARDINAL': # For example, 'one' in 'he has one' or in 'one of the band'\\n if 'preps' in elem:\\n new_tuple = _account_for_cardinal_noun(elem, elem_text, head_lemma,\\n alet_dict, last_nouns, last_events, turtle, ext_sources)\\n else:\\n iri = re.sub(r'[^:a-zA-Z0-9_]', '_', f':{elem_text}_{str(uuid.uuid4())[:13]}').replace('__', '_')\\n new_tuple = (elem_text, 'CARDINAL', [owl_thing2], iri)\\n turtle.extend([f'{iri} a owl:Thing .',\\n f'{iri} rdfs:label \\\"{elem_text}\\\" .'])\\n elif elem_text.lower() in personal_pronouns:\\n # Array of tuples of matched text, type, mappings and IRIs\\n new_tuples = _check_personal_pronouns(elem_text, last_nouns)\\n nouns.extend(new_tuples)\\n last_nouns.extend(new_tuples)\\n continue # More than 1 new tuple, so handled specifically in this code block; No need to 'drop through'\\n # Not a pronoun; Check for a match in instances of the ontology\\n elif ('PERSON' in elem_type or elem_type.endswith('GPE') or\\n elem_type.endswith('ORG') or elem_type.endswith('NORP')):\\n if space in head_lemma:\\n # Get last two words in the name (for given+surname or surname+given name, Eastern or Western ordering)\\n names = head_lemma.split(space)\\n possible_name = f'{names[-2]} {names[-1]}'\\n match_iri, match_type = check_specific_match(head_lemma, elem_type)\\n if not match_iri and possible_name:\\n match_iri, match_type = check_specific_match(possible_name, elem_type)\\n if match_iri:\\n new_tuple = (elem_text, elem_type, match_type, match_iri)\\n else:\\n # Check for family role and match to a name\\n new_tuple = _process_family_role(head_text, elem_text, elem_type, alet_dict)\\n if not new_tuple:\\n # No match - Try to match text and type in last_nouns\\n match_noun_tuples = _check_last_nouns(elem_text, elem_type, last_nouns)\\n if match_noun_tuples:\\n new_tuple = (elem_text, elem_type, match_noun_tuples[0][0], match_noun_tuples[0][1])\\n elif possible_name:\\n # Also check given + surname\\n match_noun_tuples = _check_last_nouns(possible_name, elem_type, last_nouns)\\n if match_noun_tuples:\\n new_tuple = (possible_name, elem_type, match_noun_tuples[0][0], match_noun_tuples[0][1])\\n if not new_tuple:\\n # No match - Try to match text and type in alet_dict\\n match_maps, match_iri = _check_alet_dict(elem_text, elem_type, alet_dict, last_nouns) # Updates last nouns\\n if match_iri:\\n new_tuple = (elem_text, elem_type, match_maps, match_iri)\\n elif possible_name:\\n # Also check given + surname\\n match_maps, match_iri = _check_alet_dict(possible_name, elem_type, alet_dict, last_nouns)\\n if match_iri:\\n new_tuple = (possible_name, elem_type, match_maps, match_iri)\\n if not new_tuple:\\n # No match - Check if the noun is aligned with an event that has already been described\\n event_classes, event_iri = check_event(elem_text, last_events)\\n if event_iri:\\n new_tuple = (elem_text, elem_type, event_classes, event_iri)\\n if not new_tuple:\\n # No match - Create new entity\\n iri = re.sub(r'[^:a-zA-Z0-9_]', underscore, f':{elem_text.lower()}_{str(uuid.uuid4())[:13]}').\\\\\\n replace('__', '_')\\n noun_mappings, noun_turtle = create_noun_ttl(iri, elem_text, elem_type, alet_dict, ext_sources)\\n new_tuple = (elem_text, elem_type, noun_mappings, iri)\\n turtle.extend(noun_turtle)\\n nouns.append(new_tuple)\\n last_nouns.append(new_tuple)\\n return nouns\",\n \"def parse_rec(filename):\\n tree = et.parse(filename)\\n objects = []\\n for obj in tree.findall('object'):\\n obj_struct = {}\\n obj_struct['name'] = obj.find('name').text\\n obj_struct['pose'] = obj.find('pose').text\\n obj_struct['truncated'] = int(obj.find('truncated').text)\\n obj_struct['difficult'] = int(obj.find('difficult').text)\\n bbox = obj.find('bndbox')\\n obj_struct['bbox'] = [int(bbox.find('xmin').text) - 1,\\n int(bbox.find('ymin').text) - 1,\\n int(bbox.find('xmax').text) - 1,\\n int(bbox.find('ymax').text) - 1]\\n objects.append(obj_struct)\\n\\n return objects\",\n \"def _parse_tsv_vocab_file(self, vocab_file: str):\\n with open(vocab_file, \\\"r\\\", encoding=\\\"utf-8\\\") as f:\\n for (index, line) in enumerate(f):\\n title, count = line.rstrip().split(\\\"\\\\t\\\")\\n entity = Entity(title, None)\\n self.vocab[entity] = index\\n self.counter[entity] = int(count)\\n self.inv_vocab[index] = [entity]\",\n \"def parse(string):\\n doc = nlp(string)\\n return [str(n) for n in doc.noun_chunks]\",\n \"def ReadMorphit(self):\\r\\n self.words = {}\\r\\n with codecs.open(self.MorphItFileName, 'r', 'utf-8') as f:\\r\\n for line in f.readlines():\\r\\n line = line.split()\\r\\n try:\\r\\n# print (line)\\r\\n self.words[line[0]] = line[2][:3]\\r\\n# if line[2][:3] in self.verbTags:\\r\\n# line[2]=line[2].split(u'+')\\r\\n# line[2][0]=line[2][0][line[2][0].find(u':')+1:]\\r\\n except:\\r\\n pass\\r\\n return self.words\",\n \"def __init__(self, file):\\n with open(file, 'r') as f:\\n self.vocab = json.loads(f.read())\",\n \"def from_file(file_handle, delimiter=\\\",\\\"):\\r\\n # Reading in the data\\r\\n line = file_handle.readline()\\r\\n line = line.rstrip(\\\"\\\\n\\\")\\r\\n line = line.split(delimiter)\\r\\n\\r\\n # Assigning the data\\r\\n label = line[0]\\r\\n mass = float(line[1])\\r\\n position = np.array(line[2:5], float)\\r\\n velocity = np.array(line[5:], float)\\r\\n\\r\\n return Particle3D(label, mass, position, velocity)\",\n \"def load_data(self):\\n with open(self.file_name) as f:\\n lines = f.readlines()\\n\\n labels = list()\\n all_dat = list()\\n for i, l in enumerate(lines):\\n\\n labels.append(int(l[0]))\\n\\n l = gensim.utils.any2unicode(l)\\n all_dat.append(LabeledSentence(l.split(\\\"\\\\t\\\")[-1], [i]))\\n\\n return all_dat, np.asarray(labels)\",\n \"def parse(self, word):\\n son_map = self._sonority_map(word)\\n ons_son = self._initial_onset(son_map)\\n cod_son = self._final_coda(son_map)\\n ons = self.from_map(ons_son, word)\\n cod = self.from_reverse_map(cod_son, word)\\n return (ons, cod)\",\n \"def process_raw_phrases(file_path):\",\n \"def parse_taxonomy(infile):\\r\\n\\r\\n res = {}\\r\\n for line in infile:\\r\\n if not line or line.startswith('#'):\\r\\n continue\\r\\n line = line.rstrip(\\\"\\\\n\\\")\\r\\n fields = line.split('\\\\t')\\r\\n otu = fields[0].split(' ')[0]\\r\\n res[otu] = taxa_split(fields[1])\\r\\n\\r\\n return res\",\n \"def __init__(self, filename=None, label=None, tokens=None):\\n if label: # specify from label/tokens, for testing.\\n self.label = label\\n self.tokens = tokens\\n self.postID = -1\\n self.likes = -1\\n else: # specify from file.\\n self.filename = filename\\n parsedNames = filename.split(\\\"#\\\")\\n if 'pop' in parsedNames[0]:\\n self.label = 'pop'\\n else:\\n self.label = 'sod'\\n self.postID = parsedNames[1]\\n self.likes = parsedNames[2]\\n self.tokenize()\",\n \"def locateObjNumberNoun(data, questionDict, questionIdict):\\n how = questionDict['how']\\n many = questionDict['many']\\n for t in range(data.shape[0] - 2):\\n if data[t, 0] == how and \\\\\\n data[t + 1, 0] == many:\\n for u in range(t + 2, data.shape[0]):\\n word = questionIdict[data[u, 0] - 1]\\n lexname = lookupLexname(word)\\n if (lexname is not None and \\\\\\n lexname.startswith('noun')) or \\\\\\n (lexname is None):\\n return data[u, 0]\\n print 'not found'\\n return data[-1, 0]\",\n \"def parse_syn(syntenic):\\n syn_ages = {}\\n with open(syntenic, 'r') as f:\\n for index, line in enumerate(f):\\n if index == 0:\\n continue\\n else:\\n tmp = line.strip().split('\\\\t')\\n # keep only the tandems\\n b_genes = []\\n p_genes = []\\n if ',' in tmp[2]:\\n b_genes += tmp[2].split(',')\\n if ',' in tmp[3]:\\n b_genes += tmp[3].split(',')\\n if ',' in tmp[4]:\\n p_genes += tmp[4].split(',')\\n if ',' in tmp[5]:\\n p_genes += tmp[5].split(',')\\n b_ages = []\\n p_ages = []\\n if tmp[6] != 'NA':\\n b_ages += tmp[6].split(',')\\n if tmp[7] != 'NA':\\n b_ages += tmp[7].split(',')\\n if tmp[8] != 'NA':\\n p_ages += tmp[8].split(',')\\n if tmp[9] !='NA':\\n p_ages += tmp[9].split(',')\\n # If there are B73 tandem duplicates, then we iterate through\\n # and build the dictionary\\n if b_ages:\\n ct = 1\\n for age in b_ages:\\n b1 = b_genes[ct-1]\\n b2 = b_genes[ct]\\n syn_ages[(b1, b2)] = age\\n ct += 1\\n if p_ages:\\n ct = 1\\n for age in p_ages:\\n p1 = p_genes[ct-1]\\n p2 = p_genes[ct]\\n syn_ages[(p1, p2)] = age\\n ct += 1\\n return syn_ages\",\n \"def toLingDataToken(token):\\n\\n t = Token()\\n\\n t.set(\\n id=token.i,\\n word=token.orth_,\\n lemma=token.lemma_,\\n POS=token.tag_,\\n SPOS=token.pos_,\\n depID=token.dep,\\n depStr=token.dep_,\\n NE=token.ent_type_,\\n foreign=token.is_oov\\n )\\n\\n # setting features\\n '''\\n t.features = {}\\n #print(t.POS)\\n featureStr = translate(t.POS)\\n # save string form of feature translation\\n t.features['str'] = featureStr\\n\\n featureArr = featureStr.split(\\\"+\\\")\\n #print(featureArr)\\n # find the first feature\\n i = 0\\n while len(featureArr[i]) < 1:\\n i += 1\\n\\n t.features['type'] = featureArr[i]\\n if t.features['type'] in [\\\"N\\\"]:\\n # look for number\\n i += 1\\n while i < len(featureArr):\\n # this means it's probably a number declaration\\n if len(featureArr[i]) < 4:\\n t.features['number'] = featureArr[i]\\n # and next feature could be type of noun\\n if i + 1 < len(featureArr):\\n t.features['isProper'] = featureArr[i + 1]\\n break\\n i += 1\\n\\n if t.features['type'] in [\\\"V\\\"]:\\n # look for person and number\\n i += 1\\n while i < len(featureArr):\\n # this means it's probably a person declaration\\n if len(featureArr[i]) < 4:\\n t.features['person'] = featureArr[i]\\n # and next feature could be number\\n if i + 1 < len(featureArr):\\n t.features['number'] = featureArr[i + 1]\\n break\\n else:\\n # probably a tense\\n t.features['tense'] = featureArr[i]\\n t.features['isParticiple'] = (\\\"Part\\\" in featureArr[i])\\n\\n i += 1\\n #print(t.features)\\n '''\\n\\n # setting wordType\\n if token.tag_ == \\\"BES\\\": # copula\\n t.set(wordType=4)\\n elif token.pos_ == \\\"VERB\\\":\\n t.set(wordType=1)\\n elif token.pos_ == \\\"NOUN\\\" or token.pos_ == \\\"PROPN\\\":\\n t.set(wordType=2)\\n elif token.pos_ == \\\"PRON\\\":\\n t.set(wordType=3)\\n else:\\n t.set(wordType=5)\\n\\n # spaCy does not have coreferencing...\\n\\n return t\",\n \"def read_data(self, filepath):\\n with open(filepath, \\\"r\\\", encoding=\\\"utf8\\\") as input_data:\\n ids, premises, hypotheses, premises_length, hypotheses_length, premise_polarities, hypothesis_polarities, labels = [], [], [], [], [], [], [], []\\n\\n for idx, line in enumerate(input_data):\\n print(f\\\"Reading instance {idx}\\\", end='\\\\r')\\n line = eval(line)\\n hypothesis = eval(line['hypothesis'])\\n premise = eval(line['premise'])\\n label = line['label']\\n\\n # Align the nltk premise and the umls premise\\n premise, premise_umls, premise_polarity = self.aligner(premise)\\n hyp, hyp_umls, hypothesis_polarity = self.aligner(hypothesis)\\n\\n ids.append(idx)\\n premises.append((premise, premise_umls))\\n hypotheses.append((hyp, hyp_umls))\\n labels.append(self.labeldict[label])\\n premises_length.append(len(premise))\\n hypotheses_length.append(len(hyp))\\n premise_polarities.append(premise_polarity)\\n hypothesis_polarities.append(hypothesis_polarity)\\n\\n return {\\\"ids\\\": ids,\\n \\\"premises\\\": premises,\\n \\\"hypotheses\\\": hypotheses,\\n \\\"labels\\\": labels,\\n \\\"premises_lengths\\\": premises_length,\\n \\\"hypotheses_lengths\\\": hypotheses_length,\\n \\\"premise_polarities\\\": premise_polarities,\\n \\\"hypothesis_polarities\\\": hypothesis_polarities,\\n \\\"max_premise_length\\\": max(premises_length),\\n \\\"max_hypothesis_length\\\": max(hypotheses_length)}\",\n \"def parse(self, filename):\\n infile = file(filename)\\n for line in infile:\\n self.parseLine(line)\",\n \"def readObject(f):\\n name = f.readline().rstrip()\\n if name == \\\"\\\":\\n name = f.readline().rstrip()\\n if name == \\\"\\\":\\n return None\\n description = f.readline().rstrip()\\n location = f.readline().rstrip()\\n return AdvObject(name, description, location )\",\n \"def get_nouns(self):\\n word_punct_token = WordPunctTokenizer().tokenize(self.sentence_string)\\n\\n clean_tokens = []\\n for token in word_punct_token:\\n token = token.lower()\\n\\n # remove any value that are not alphabetical\\n new_token = re.sub(r\\\"[^a-zA-Z]+\\\", \\\"\\\", token)\\n\\n # remove empty value and single character value\\n if new_token != \\\"\\\" and len(new_token) >= 2:\\n vowels = len([v for v in new_token if v in \\\"aeiou\\\"])\\n if vowels != 0: # remove line that only contains consonants\\n clean_tokens.append(new_token)\\n\\n noun_types = [\\\"NN\\\", \\\"NNS\\\", \\\"NNP\\\", \\\"NNPS\\\", \\\"N\\\"]\\n is_noun = lambda pos: pos in noun_types\\n nouns = [word for (word, pos) in nltk.pos_tag(clean_tokens) if is_noun(pos)]\\n\\n if nouns:\\n return nouns\\n else:\\n raise InvalidSentenceError(self.sentence_string)\",\n \"def load_cmudict():\\n with open(\\\"text/en/cmudict-0.7b.txt\\\", encoding=\\\"ISO-8859-1\\\") as file_reader:\\n cmudict = (line.strip().split(\\\" \\\") for line in islice(file_reader, 126, 133905))\\n\\n cmudict = {format_alt_entry(word): pronunciation for word, pronunciation in cmudict}\\n\\n return cmudict\",\n \"def _process_data_file(self):\\n \\n with open(self.data_file, 'r') as f:\\n self.description = f.readline().strip()\\n data = np.loadtxt(self.data_file, skiprows=1)\\n\\n return data\",\n \"def read(self, filename):\\n with RavenFileReader(filename) as f:\\n line = f.nexttag()\\n while line:\\n # Begin data type checks\\n if self.cleantag(line) == 'Gauge':\\n self.read_metgauge(line, f)\\n elif self.cleantag(line) == 'ObservationData':\\n self.read_obsgauge(line, f)\\n # Next line\\n line = f.nexttag()\",\n \"def load_raw_annot(filename):\\n with open(filename, 'r') as fp:\\n data = json.loads(fp.read())\\n\\n mapping = _create_mapping()\\n\\n for k in data.keys():\\n for i in xrange(len(data[k])):\\n data[k][i] = eval_utils.revise_sentiment(data[k][i], mapping)\\n return data\",\n \"def read(self):\\n try:\\n f = open(self.datfile, 'r')\\n except:\\n print('ERROR: data file not found!')\\n exit()\\n\\n # Get rid of the header\\n for _ in range(self.header_length):\\n f.readline()\\n\\n # Read nuclide mass data\\n for line in f:\\n ls = line.strip()\\n n, z, ebind = ls.split()\\n nuclide = BindingNuclide(n, z, ebind)\\n self.nuclides.append(nuclide)\\n\\n f.close()\",\n \"def read_file(self, filename, per_atom=True):\\n if isinstance(filename, str):\\n fileobj = open(filename)\\n elif isinstance(filename, file):\\n fileobj = filename\\n elif isinstance(filename, type(StringIO())):\\n fileobj = filename\\n fileobj.name = None\\n thermodata = fileobj.readlines()\\n headers = [h.lower() for h in thermodata.pop(0).strip().split()]\\n if \\\"composition\\\" not in headers:\\n raise PhaseDataError(\\n \\\"Found columns: %s. Must provide composition in\\\\\\n a column labelled composition.\\\"\\n % (\\\", \\\".join(headers))\\n )\\n if \\\"energy\\\" not in headers and \\\"delta_e\\\" not in headers:\\n raise PhaseDataError(\\n \\\"Found columns: %s. Must provide energies in\\\\\\n a column labelled delta_e or energy.\\\"\\n % (\\\", \\\".join(headers))\\n )\\n\\n keywords = {\\n \\\"energy\\\": \\\"energy\\\",\\n \\\"composition\\\": \\\"composition\\\",\\n \\\"delta_e\\\": \\\"energy\\\",\\n \\\"delta_h\\\": \\\"energy\\\",\\n \\\"delta_g\\\": \\\"energy\\\",\\n \\\"comp\\\": \\\"composition\\\",\\n \\\"name\\\": \\\"composition\\\",\\n \\\"desc\\\": \\\"description\\\",\\n \\\"description\\\": \\\"description\\\",\\n }\\n\\n headers = [keywords[h] for h in headers if h in keywords]\\n\\n name = filename.split(\\\"/\\\")[-1]\\n\\n for i, line in enumerate(thermodata):\\n line = line.strip().split()\\n if not line:\\n continue\\n ddict = dict(list(zip(headers, line)))\\n phase = Phase(\\n composition=ddict[\\\"composition\\\"],\\n energy=float(ddict[\\\"energy\\\"]),\\n description=ddict.get(\\n \\\"description\\\", \\\"{file}:{line}\\\".format(file=name, line=i)\\n ),\\n per_atom=per_atom,\\n )\\n self.add_phase(phase)\",\n \"def parser(filename):\\n\\n regex = re.compile(\\n # prolog\\n r\\\"run(?P<run>\\\\w+)\\\"\\n ##r\\\"\\\\-(?P<code_name>((mfdn)|(obscalc-ob))[^\\\\-]*)\\\"\\n r\\\"\\\\-(?P<descriptor>\\\"\\n # descriptor contents\\n r\\\"Z(?P<Z>\\\\d+)\\\\-N(?P<N>\\\\d+)\\\"\\n r\\\"\\\\-(?P<interaction>.+)\\\\-(?P<coulomb>\\\\d)\\\"\\n r\\\"\\\\-(?P<truncation_descriptor>.+)\\\"\\n ## r\\\"\\\\-Nmax(?P<Nmax>\\\\d+)\\\"\\n # epilog\\n r\\\").res\\\"\\n )\\n\\n conversions = {\\n \\\"Z\\\" : int,\\n \\\"N\\\" : int,\\n \\\"interaction\\\" : str,\\n \\\"coulomb\\\" : int,\\n }\\n\\n match = regex.match(filename)\\n if (match == None):\\n raise ValueError(\\\"bad form for spncci results filename: \\\" + filename)\\n info = match.groupdict()\\n\\n # convert fields\\n for key in conversions:\\n conversion = conversions[key]\\n info[key] = conversion(info[key]) if (info[key] is not None) else None\\n\\n return info\",\n \"def NOAD_to_wordnet(data):\\r\\n NOAD_to_wordnet = {}\\r\\n with open(algorithmic_map, 'r') as f:\\r\\n lines = f.readlines()\\r\\n for line in lines:\\r\\n noad, wordnet = line.split()\\r\\n NOAD_to_wordnet[noad] = wordnet\\r\\n with open(manual_map, 'r') as f:\\r\\n lines = f.readlines()\\r\\n for line in lines:\\r\\n noad, wordnet = line.split()\\r\\n NOAD_to_wordnet[noad] = wordnet\\r\\n \\r\\n count = 0\\r\\n for elem in data: \\r\\n if elem[\\\"is_target\\\"]:\\r\\n if elem[\\\"sense\\\"] not in NOAD_to_wordnet:\\r\\n count += 1\\r\\n continue\\r\\n noad_sense = elem[\\\"sense\\\"]\\r\\n elem[\\\"sense\\\"] = NOAD_to_wordnet[noad_sense]\\r\\n print(\\\"NOAD sense not in mapping text: %d\\\" %count)\\r\\n return data\",\n \"def read_po(self, inputfile):\\n is_index = False\\n lines = inputfile.readlines()\\n index = ''\\n value = ''\\n for line in lines:\\n if line.startswith('#'):\\n continue\\n elif line.startswith('msgid'):\\n is_index = True\\n self.translations[index] = value\\n index = ''\\n value = ''\\n elif line.startswith('msgstr'):\\n is_index = False\\n\\n v = re.match('.*\\\"(.*)\\\".*', line)\\n if v:\\n if is_index:\\n index += ''.join(v.groups())\\n else:\\n value += ''.join(v.groups())\",\n \"def make_from_file(filehandle):\\n lines = filehandle.readlines()\\n label = str(lines[0].rstrip('\\\\n'))\\n mass = float(lines[1].rstrip('\\\\n'))\\n position = list(lines[2].rstrip('\\\\n').split(','))\\n velocity = list(lines[3].rstrip('\\\\n').split(','))\\n particle = Particle3D(label=label, mass=mass, position=position, velocity=velocity)\\n filehandle.close()\\n return particle\",\n \"def get_compound_noun( ngram ):\\n try:\\n pattern = re.compile( '((?: ?\\\\\\\\b[^\\\\\\\\s]+(?:/NN.?/[a-z]+/[\\\\\\\\d]+)){2,})' )\\n match = re.search( pattern, ngram )\\n if match:\\n compound = ''\\n contains_root = False\\n tokens = match.group().strip().split(' ')\\n for t in tokens:\\n # embed()\\n items = t.split('/')\\n compound += ( items[0] + ' ' )\\n if items[3] == 0:\\n contains_root = True\\n if contains_root:\\n return compound\\n else:\\n return None\\n else:\\n return None\\n \\n except ValueError:\\n return None\",\n \"def is_noun(tag):\\r\\n return tag in ['NN', 'NNS', 'NNP', 'NNPS']\",\n \"def __init__(self, infile):\\n txt = infile.read()\\n for block in self.splitter.split(txt):\\n block = block.strip()\\n if block:\\n term = block.splitlines()[0].strip().decode('utf8')\\n defn = \\\"\\\\n\\\".join(line.strip() for line in block.splitlines()[1:])\\n self[term] = defn.decode('utf8')\",\n \"def read_data(self, filePath):\\n with open(filePath, 'r', encoding='iso-8859-1') as f:\\n for sentence in f.readlines():\\n sentence = sentence.replace('\\\\n', '')\\\\\\n .replace('\\\"', '')\\\\\\n .replace('\\\\'', '')\\\\\\n .replace('.', '')\\\\\\n .replace(',', '')\\\\\\n .replace('[', '')\\\\\\n .replace(']', '')\\\\\\n .replace('(', '')\\\\\\n .replace(')', '')\\\\\\n .replace(':', '')\\\\\\n .replace('--', '')\\\\\\n .replace('-', '')\\\\\\n .replace('\\\\\\\\', '')\\\\\\n .replace('0', '')\\\\\\n .replace('1', '')\\\\\\n .replace('2', '')\\\\\\n .replace('3', '')\\\\\\n .replace('4', '')\\\\\\n .replace('5', '')\\\\\\n .replace('6', '')\\\\\\n .replace('7', '')\\\\\\n .replace('8', '')\\\\\\n .replace('9', '')\\\\\\n .replace('`', '')\\\\\\n .replace('=', '')\\\\\\n .replace('$', '')\\\\\\n .replace('/', '')\\\\\\n .replace('*', '')\\\\\\n .replace(';', '')\\\\\\n .replace('<b>', '')\\\\\\n .replace('%', '')\\n sentence = sentence.split(' ')\\n sentence = list(filter(lambda x: x, sentence))\\n if sentence:\\n self.word_num += len(sentence)\\n self.maxlen = self.maxlen if self.maxlen >= len(\\n sentence) else len(sentence)\\n self.minlen = self.minlen if self.minlen <= len(\\n sentence) else len(sentence)\\n if 'pos' in filePath:\\n self.Pos.append([sentence, self.feelMap['pos']])\\n else:\\n self.Neg.append([sentence, self.feelMap['neg']])\",\n \"def load(filename):\\n\\n print \\\"Loading dictionary...\\\"\\n dictionary = Dictionary()\\n print \\\" Loading file...\\\"\\n whole_file = file(filename).read().upper()\\n print \\\" Splitting file...\\\"\\n words = whole_file.split()\\n print \\\" Removing unsuitable words...\\\"\\n words = dictionary.remove_unsuitable_words(words)\\n print \\\" Building data structures...\\\"\\n dictionary.set_words(words)\\n\\n print \\\" Loaded %d words\\\" % len(dictionary.words)\\n print \\\" Unique letter size:\\\"\\n print \\\" No blanks: %d\\\" % len(dictionary.letters_map)\\n print \\\" One blank: %d\\\" % len(dictionary.letters_map_one_blank)\\n print \\\" Two blanks: %d\\\" % len(dictionary.letters_map_two_blanks)\\n\\n return dictionary\",\n \"def load_subj(loc='./data/'):\\n pos, neg = [], []\\n with open(os.path.join(loc, 'SUBJ', 'subj.objective'), 'rb') as f:\\n for line in f:\\n pos.append(line.decode('latin-1').strip())\\n with open(os.path.join(loc, 'SUBJ', 'subj.subjective'), 'rb') as f:\\n for line in f:\\n neg.append(line.decode('latin-1').strip())\\n return pos, neg\",\n \"def __init__(self, file_name: str):\\n self._file_name = file_name\\n self._afinn = {}\\n self._afinn_phrase = []\\n self._reg_affin_phrase_str = \\\"\\\\\\\\s|[!,.\\\\'\\\\\\\"]\\\"\\n # read the file AFFIN and map words to score\\n with open(self._file_name, \\\"r\\\") as f:\\n for str in f.readlines():\\n entry = str.split()\\n if (len(entry) > 2):\\n length = len(entry)\\n words = entry[0]\\n for i in range(1, length - 1):\\n words = words + ' ' + entry[i]\\n self._reg_affin_phrase_str += \\\"|\\\"+words\\n self._afinn_phrase.append(words)\\n self._afinn[words] = int(entry[length - 1])\\n else:\\n self._afinn[entry[0]] = int(entry[1])\",\n \"def read_data(filename, eos='#'):\\n ### Exercise 6.1\\n\\n with open(filename) as f:\\n utterances = []\\n labels = []\\n\\n for line in f:\\n # Get utterance output and length\\n utter = line\\n utter = utter.replace(\\\" \\\", \\\"\\\").replace(\\\"\\\\n\\\", \\\"\\\") + \\\"#\\\"\\n utterances.append(utter)\\n # Make empty sequence\\n sequence = np.zeros(len(utter), dtype=int)\\n sequence[0], sequence[len(utter) - 1] = 1, 1\\n # Find indexes of beginning of words\\n prev_char = \\\"\\\"\\n count = 0\\n new_word_indexs = []\\n for char in line:\\n if char == \\\" \\\":\\n prev_char = char\\n continue\\n if prev_char == \\\" \\\":\\n prev_char = char\\n new_word_indexs.append(count)\\n count += 1\\n else:\\n prev_char = char\\n count += 1\\n for index in new_word_indexs:\\n sequence[index] = 1\\n labels.append(sequence)\\n\\n return (utterances, labels)\",\n \"def load_onto(filename=onto_filename):\\n onto_dict = parse_yaml(filename)\\n onto = CrosslinksOnto()\\n for id, bond_dict in onto_dict.items():\\n bond = Bond()\\n onto[id] = bond\\n\\n bond.id = id\\n bond.name = bond_dict.get('name', None)\\n bond.synonyms = bond_dict.get('synonyms', []) \\n\\n l_alph = get_alphabet(bond_dict['l_monomer_alphabet'])\\n r_alph = get_alphabet(bond_dict['r_monomer_alphabet'])\\n bond.l_monomer = l_alph.monomers.get(bond_dict['l_monomer'])\\n bond.r_monomer = r_alph.monomers.get(bond_dict['r_monomer'])\\n\\n for atom_type in ['l_bond_atoms', 'r_bond_atoms', 'l_displaced_atoms', 'r_displaced_atoms']:\\n for atom in bond_dict[atom_type]:\\n element, position, charge = parse_atom(atom)\\n getattr(bond, atom_type).append(Atom(Monomer, element, position=position, charge=charge))\\n\\n bond.order = BondOrder[bond_dict.get('order', 'single')]\\n stereo = bond_dict.get('stereo', None)\\n if stereo is None:\\n bond.stereo = None\\n else:\\n bond.stereo = BondStereo[stereo]\\n\\n bond.comments = bond_dict.get('comments', None)\\n\\n crosslink_to_id = {xlink: id for id, xlink in onto.items()}\\n\\n return onto, crosslink_to_id\",\n \"def parse_domain(self, domainfile):\\n\\n with open(domainfile) as dfile:\\n dfile_array = self._get_file_as_array(dfile)\\n #Deal with front/end define, problem, :domain\\n if dfile_array[0:4] != ['(', 'define', '(', 'domain']:\\n print('PARSING ERROR: Expected (define (domain ... at start of domain file')\\n sys.exit()\\n self.domain = dfile_array[4]\\n\\n dfile_array = dfile_array[6:-1]\\n opencounter = 0\\n keyword = ''\\n obj_list = []\\n is_obj_list = True\\n for word in dfile_array:\\n if word == '(':\\n opencounter += 1\\n elif word == ')':\\n opencounter -= 1\\n elif word.startswith(':'):\\n if word[1:] not in DFILE_KEYWORDS:\\n pass\\n elif keyword != 'requirements':\\n keyword = word[1:]\\n if opencounter == 0:\\n if keyword == 'action':\\n self.actions.append(obj_list)\\n obj_list = []\\n if keyword == 'types':\\n for element in obj_list:\\n self.types.setdefault('object', []).append(element)\\n self.type_list.add('object')\\n self.type_list.add(element)\\n obj_list = []\\n keyword = ''\\n\\n if keyword == 'requirements': #Requirements list\\n if word != ':requirements':\\n if not word.startswith(':'):\\n print('PARSING ERROR: Expected requirement to start with :')\\n sys.exit()\\n elif word[1:] not in DFILE_REQ_KEYWORDS:\\n print('WARNING: Unknown Rquierement ' + word[1:])\\n #print 'Requirements must only be: ' + str(DFILE_REQ_KEYWORDS)\\n #sys.exit()\\n else:\\n self.requirements.add(word[1:])\\n elif keyword == 'action':\\n obj_list.append(word)\\n elif not word.startswith(':'):\\n if keyword == 'types': #Typed list of objects\\n if is_obj_list:\\n if word == '-':\\n is_obj_list = False\\n else:\\n obj_list.append(word)\\n else:\\n #word is type\\n for element in obj_list:\\n if not word in self.type_list:\\n self.types.setdefault('object', []).append(word)\\n self.type_list.add(word)\\n self.types.setdefault(word, []).append(element)\\n self.type_list.add(element)\\n self.type_list.add(word)\\n is_obj_list = True\\n obj_list = []\\n elif keyword == 'constants': #Typed list of objects\\n if is_obj_list:\\n if word == '-':\\n is_obj_list = False\\n else:\\n obj_list.append(word)\\n else:\\n #word is type\\n for element in obj_list:\\n if word in self.type_list:\\n self.constants.setdefault(word, []).append(element)\\n #self.object_list.add(element)\\n else:\\n print(self.type_list)\\n print(\\\"ERROR unknown type \\\" + word)\\n sys.exit()\\n is_obj_list = True\\n obj_list = []\\n elif keyword == 'predicates' or keyword == 'private': #Internally typed predicates\\n if word == ')':\\n if keyword == 'private':\\n #print \\\"...skip agent: \\\" + str(obj_list[:3])\\n obj_list = obj_list[3:]\\n keyword = 'predicates'\\n if len(obj_list) == 0:\\n #print \\\"...skip )\\\"\\n continue\\n p_name = obj_list[0]\\n #print \\\"parse predicate: \\\" + p_name + \\\" \\\" + str(obj_list)\\n pred_list = self._parse_name_type_pairs(obj_list[1:],self.type_list)\\n self.predicates.append(Predicate(p_name, pred_list, True, False))\\n obj_list = []\\n elif word != '(':\\n obj_list.append(word)\\n elif keyword == 'functions': #functions\\n if word == ')':\\n p_name = obj_list[0]\\n if obj_list[0] == '-':\\n obj_list = obj_list[2:]\\n #print \\\"function: \\\" + word + \\\" - \\\" + str(obj_list)\\n self.functions.append(Function(obj_list))\\n obj_list = []\\n elif word != '(':\\n obj_list.append(word)\\n\\n #Work on the actions\\n new_actions = []\\n for action in self.actions:\\n if action[0] == '-':\\n action = action[2:]\\n act_name = action[1]\\n act = {}\\n action = action[2:]\\n keyword = ''\\n for word in action:\\n if word.startswith(':'):\\n keyword = word[1:]\\n else:\\n act.setdefault(keyword, []).append(word)\\n self.agent_types.add(act.get('agent')[2])\\n agent = self._parse_name_type_pairs(act.get('agent'),self.type_list)\\n param_list = agent + self._parse_name_type_pairs(act.get('parameters')[1:-1],self.type_list)\\n up_params = Predicate('', param_list, True, False)\\n pre_list = self._parse_unground_propositions(act.get('precondition'))\\n eff_list = self._parse_unground_propositions(act.get('effect'))\\n new_act = Action(act_name, up_params, pre_list, eff_list)\\n\\n new_actions.append(new_act)\\n self.actions = new_actions\",\n \"def extract_nouns(tagged_abstracts_list, def_tags_per_abs = 0.3):\\n\\n noun_counter = []\\n per_abstract_counts = []\\n per_abstract_counts_list = []\\n normalized_all_counts = {}\\n per_abstract_tag_counts = []\\n\\n for tags in tagged_abstracts_list:\\n\\n for tag in tags:\\n\\n if tag[1] == \\\"NN\\\" or tag[1] == \\\"NNS\\\" or tag[1] == \\\"NNP\\\" or tag[1] == \\\"NNPS\\\":\\n\\n per_abstract_tag_counts.append(str(tag[0].encode('ascii', 'ignore')))\\n\\n noun_counter.append(str(tag[0].encode('ascii', 'ignore')))\\n\\n per_abstract_dict = dict(Counter(per_abstract_tag_counts))\\n per_abstract_counts_list.append(per_abstract_dict)\\n\\n all_counts = dict(Counter(noun_counter))\\n\\n num_abstracts = float(len(tagged_abstracts_list))\\n\\n for key in all_counts.keys():\\n\\n if key in all_counts:\\n\\n total_occurrences = float(all_counts[key])\\n else:\\n\\n total_occurrences = 0\\n\\n for abstract_ in per_abstract_counts_list:\\n #print abstract_\\n\\n if key in abstract_ and key in all_counts:\\n\\n single_abstract_count = float(abstract_[key])\\n\\n if (single_abstract_count/total_occurrences) < def_tags_per_abs:\\n normalized_all_counts[key] = float(all_counts[key])/num_abstracts\\n\\n return (normalized_all_counts)\",\n \"def process_file(self, data, filename):\\n\\n for relation in data:\\n if \\\"start_date\\\" in relation[1]:\\n start = convert_to_date(relation[1][\\\"start_date\\\"])\\n end = convert_to_date(relation[1][\\\"end_date\\\"])\\n\\n if start <= START_DATE and end >= START_DATE:\\n first = relation[1][\\\"first\\\"]\\n second = relation[1][\\\"second\\\"]\\n if first not in self.relations:\\n self.add_tag(first)\\n if second not in self.relations:\\n self.add_tag(second)\\n\\n if relation[0] == \\\"vassal\\\": \\n self.relations[first][\\\"vassal\\\"].append(second)\\n self.relations[second][\\\"overlord\\\"].append(first)\\n elif relation[0] == \\\"alliance\\\":\\n self.relations[first][\\\"alliance\\\"].append(second)\\n self.relations[second][\\\"alliance\\\"].append(first)\\n elif relation[0] == \\\"dependency\\\":\\n self.relations[first][\\\"tributary\\\"].append(second)\\n self.relations[second][\\\"hegemon\\\"].append(first)\\n elif relation[0] == \\\"guarantee\\\":\\n self.relations[first][\\\"guaranteeing\\\"].append(second)\\n self.relations[second][\\\"guarantor\\\"].append(first)\\n elif relation[0] == \\\"union\\\":\\n self.relations[first][\\\"junior\\\"].append(second)\\n self.relations[second][\\\"senior\\\"].append(first)\\n elif relation[0] == \\\"royal_marriage\\\":\\n self.relations[first][\\\"marriage\\\"].append(second)\\n self.relations[second][\\\"marriage\\\"].append(first)\",\n \"def load_data_and_labels(data_file):\\n # Load data from files\\n obj = open(data_file, \\\"r\\\")\\n y, x_text, query= [],[],[]\\n for ele in obj:\\n ele = ele.strip().split(\\\"\\\\t\\\")\\n if len(ele) !=5 or ele[0].strip() not in [\\\"1\\\", \\\"-1\\\"]:\\n #print ele\\n continue\\n if (ele[0].strip() == \\\"1\\\"):\\n y.append([0])\\n else:\\n y.append([1])\\n\\n query_text = ele[1].strip().decode(\\\"utf8\\\")\\n doc_text = ele[2].strip().decode(\\\"utf8\\\")\\n x_text.append( \\\" \\\".join( jieba.cut(doc_text) ) )\\n query.append( \\\" \\\".join( jieba.cut(query_text) ) )\\n return [x_text, np.array(y), np.array(query)]\",\n \"def __init__(self, txt_path, in_vocab_path, out_vocab_path):\\n self.txt_seqs = open(txt_path, encoding='utf8', errors='ignore').readlines()\\n self.word2id = utils.load_vocab(in_vocab_path,\\n extra_word_list=[\\\"<UNK>\\\", \\\"<END>\\\"])\\n self.punc2id = utils.load_vocab(out_vocab_path,\\n extra_word_list=[\\\" \\\"])\\n self.class2punc = { k : v for (v, k) in self.punc2id.items()}\",\n \"def read(self, filename):\\n with RavenFileReader(filename) as f:\\n line = f.nexttag()\\n while line:\\n # Begin data type checks\\n if self.cleantag(line) == 'SubBasins':\\n self.read_subbasins(f)\\n elif self.cleantag(line) == 'HRUs':\\n self.read_HRUs(f)\\n # Next line\\n line = f.nexttag()\",\n \"def _read_lexicon(a_dname):\\n if not a_dname:\\n return\\n elif a_dname[-1] == '/':\\n a_dname = os.path.dirname(a_dname)\\n basename = os.path.basename(a_dname)\\n if basename == HSAN:\\n return _read_hsan(a_dname)\\n elif basename == S140:\\n return _read_s140(a_dname)\\n elif basename == SUBJCL:\\n return _read_subjcl(a_dname)\\n elif basename == NRC_HSHTAG:\\n return _read_nrc_hshtag(a_dname)\\n else:\\n raise Exception(\\\"Unknown dictionary format: '{:s}'\\\".format(basename))\",\n \"def read_bnf_file(self, file_name):\\n # <.+?> Non greedy match of anything between brackets\\n non_terminal_pattern = \\\"(<.+?>)\\\"\\n rule_separator = \\\"::=\\\"\\n production_separator = \\\"|\\\"\\n\\n # Read the grammar file\\n for line in open(file_name, 'r'):\\n if not line.startswith(\\\"#\\\") and line.strip() != \\\"\\\":\\n # Split rules. Everything must be on one line\\n if line.find(rule_separator):\\n lhs, productions = line.split(rule_separator)\\n lhs = lhs.strip()\\n if not re.search(non_terminal_pattern, lhs):\\n raise ValueError(\\\"lhs is not a NT:\\\", lhs)\\n self.non_terminals.add(lhs)\\n if self.start_rule == None:\\n self.start_rule = (lhs, self.NT)\\n # Find terminals\\n tmp_productions = []\\n for production in [production.strip()\\n for production in\\n productions.split(production_separator)]:\\n tmp_production = []\\n if not re.search(non_terminal_pattern, production):\\n self.terminals.add(production)\\n tmp_production.append((production, self.T))\\n else:\\n # Match non terminal or terminal pattern\\n # TODO does this handle quoted NT symbols?\\n for value in re.findall(\\\"<.+?>|[^<>]*\\\", production):\\n if value != '':\\n if not re.search(non_terminal_pattern,\\n value):\\n symbol = (value, self.T)\\n self.terminals.add(value)\\n else:\\n symbol = (value, self.NT)\\n tmp_production.append(symbol)\\n tmp_productions.append(tmp_production)\\n # Create a rule\\n if not lhs in self.rules:\\n self.rules[lhs] = tmp_productions\\n else:\\n raise ValueError(\\\"lhs should be unique\\\", lhs)\\n else:\\n raise ValueError(\\\"Each rule must be on one line\\\")\",\n \"def read_processed_data_from_file(file, encoding='latin1'):\\n\\n with open(file, encoding=encoding) as f:\\n raw = f.read()\\n\\n lines = raw.split('\\\\n')\\n labeled_texts = []\\n n = len(lines) - 1\\n for i, line in enumerate(lines):\\n print(f'\\\\rLoading review {i} of {n}', end='')\\n if line == '':\\n continue\\n tagged_words = re.findall(r'(.+?\\\\\\\\.+?) ', line)\\n label = re.findall(r'#(\\\\d+.\\\\d)#', line)[0]\\n labeled_texts.append((tagged_words, label))\\n print()\\n return labeled_texts\",\n \"def parse_text(filehandle: TextIO) -> Iterator[Fasta]:\\n\\n # Check that the file looks like UniProt text format\\n first_line = next(filehandle)\\n if not first_line.startswith(\\\"ID\\\"):\\n raise TextParserError(\\n \\\"Unexpected file format: first line of UniProt text file should start with 'ID'\\\"\\n )\\n filehandle.seek(0)\\n\\n fasta = Fasta(sequence=\\\"\\\")\\n for line in filehandle:\\n key = line[:2] # This is more efficient than using line.startswith\\n if key == \\\"ID\\\":\\n tokens = line.split()\\n fasta.entry_name = tokens[1]\\n fasta.reviewed = True if tokens[2] == \\\"Reviewed;\\\" else False\\n elif key == \\\"AC\\\":\\n if fasta.accession is None:\\n accessions = line[5:].rstrip(\\\";\\\\n\\\").split(\\\"; \\\")\\n fasta.accession = accessions[0]\\n elif key == \\\"DT\\\":\\n if \\\"sequence version\\\" in line:\\n tokens = line[5:].strip(\\\".\\\\n\\\").split()\\n fasta.version = int(tokens[3])\\n elif key == \\\"DE\\\":\\n if \\\"RecName\\\" in line:\\n fasta.name = _extract_name(line)\\n # Get the first SubName if no RecName found\\n elif fasta.name is None and line[5:12] == \\\"SubName\\\":\\n fasta.name = _extract_name(line)\\n elif line[5:10] == \\\"Flags\\\" and \\\"Fragment\\\" in line:\\n fasta.fragment = True\\n elif key == \\\"GN\\\":\\n if line[5:10] == \\\"Name=\\\":\\n tokens = line[10:].split(\\\";\\\")\\n # Remove evidence tags, if present\\n gene_tokens = tokens[0].split(\\\" {\\\")\\n fasta.gene = gene_tokens[0]\\n elif key == \\\"OS\\\":\\n # TODO: check for multiline species name (excluding brackets)\\n if fasta.species is None:\\n species_line = line[5:].strip().split(\\\" (\\\")\\n fasta.species = species_line[0].strip(\\\".\\\")\\n elif key == \\\"OX\\\":\\n if \\\"NCBI_TaxID\\\" in line:\\n tokens = line[5:].strip(\\\";\\\\n\\\").split(\\\"; \\\")\\n # Remove evidence tag if present\\n taxid_tokens = tokens[0][11:].split(\\\" {\\\")\\n fasta.taxid = taxid_tokens[0]\\n elif key == \\\"PE\\\":\\n fasta.evidence = int(line[5])\\n elif key == \\\" \\\":\\n sequence_line = line.strip().replace(\\\" \\\", \\\"\\\")\\n fasta.sequence += sequence_line\\n elif key == \\\"//\\\":\\n yield fasta\\n fasta = Fasta(sequence=\\\"\\\")\",\n \"def read_dictionary_from_file(self, stem_flag):\\n file_name = \\\"/dictionary.txt\\\" if not stem_flag else \\\"/dictionaryWithStemming.txt\\\"\\n with open(self.posting_and_dictionary_path + file_name, \\\"r\\\") as f:\\n txt = f.readlines()\\n for line in txt:\\n l = line.split(\\\":\\\")\\n pos = l[1].split(\\\",\\\")\\n e = DictionaryElement(pos[0])\\n e.pointer = int(pos[1])\\n e.corpus_tf = int(pos[2])\\n if not stem_flag:\\n self.term_dictionary[l[0]] = e\\n else:\\n self.term_dictionary_with_stemming[l[0]] = e\\n f.close()\",\n \"def parseLabels(filename):\\n r = parse(filename)\\n res = {}\\n for qid in r:\\n lst = []\\n for y in r[qid].split(\\\";\\\"):\\n doc, score = y.split(\\\",\\\")\\n lst.append((int(doc), float(score)))\\n res[qid] = lst\\n return res\",\n \"def read(self, filename, species=None, poscar=None, sort=True):\\n self.tags = dict()\\n try:\\n file = open(filename,'r')\\n except:\\n raise IncarError(\\\"Could not open file: '\\\" + filename + \\\"'\\\")\\n\\n # parse INCAR into self.tags\\n for line in file:\\n line = re.split('=',re.split('#',line)[0])\\n if len(line) == 2:\\n self.tags[line[0].strip()] = line[1].strip()\\n self._verify_tags()\\n self._make_natural_type()\\n\\n if species != None:\\n self.update(species, poscar, sort)\\n\\n file.close()\",\n \"def parse_game_data(file_name : str) -> List[Topic]:\\n # Opens file.\\n data = open(file_name, 'r')\\n\\n # Sets up variables.\\n current_topic = None\\n parsed_data = []\\n\\n # Loops over each line in the file.\\n for line in data:\\n \\n # Creates new topic if there is none.\\n if current_topic is None:\\n current_topic = Topic(line[:-1])\\n\\n # Saves topic if encountered empty line.\\n elif line == '\\\\n':\\n parsed_data.append(current_topic)\\n current_topic = None\\n\\n # Parses question line and adds to topic.\\n else:\\n question, answer, points = parse_line(line)\\n new_question = Question(question, answer, points)\\n current_topic.questions.append(new_question)\\n\\n return parsed_data\",\n \"def parse_nonsyn(nonsyn):\\n nonsyn_ages = {}\\n with open(nonsyn, 'r') as f:\\n for index, line in enumerate(f):\\n if index == 0:\\n continue\\n else:\\n tmp = line.strip().split('\\\\t')\\n b_genes = []\\n p_genes = []\\n if ',' in tmp[1]:\\n b_genes += tmp[1].split(',')\\n if ',' in tmp[2]:\\n p_genes += tmp[2].split(',')\\n b_ages = []\\n p_ages = []\\n if tmp[3] != 'NA':\\n b_ages += tmp[3].split(',')\\n if tmp[4] != 'NA':\\n p_ages += tmp[4].split(',')\\n if b_ages:\\n ct = 1\\n for age in b_ages:\\n b1 = b_genes[ct-1]\\n b2 = b_genes[ct]\\n nonsyn_ages[(b1, b2)] = age\\n ct += 1\\n if p_ages:\\n ct = 1\\n for age in p_ages:\\n p1 = p_genes[ct-1]\\n p2 = p_genes[ct]\\n nonsyn_ages[(p1, p2)] = age\\n ct += 1\\n return nonsyn_ages\",\n \"def parse_file(\\n self, filename: Path, encoding: Optional[str] = None, debug: bool = False\\n ) -> NL:\\n with open(filename, encoding=encoding) as stream:\\n return self.parse_stream(stream, debug)\",\n \"def from_psana_file(cls, filename):\\n ret = translate.load_psana(cls, filename)\\n ret._sort_tree()\\n return ret\",\n \"def extract_data(file_ner,file_pos,separator=\\\" \\\"):\\n\\n # read NER and POS from the two files\\n words_tags=read_conll_file(file_ner)\\n words_pos=read_conll_file(file_pos)\\n \\n ## some checks, e.g., that both files have same length, same tokens\\n assert(len(words_tags)==len(words_pos))\\n \\n for (words,tags),(_,pos) in zip(words_tags,words_pos):\\n for word,pos,tag in zip(words,pos,tags):\\n # first letter is capitalized\\n cap=\\\"+\\\" if word[0].isupper() else \\\"-\\\"\\n hyphen = '+' if '-' in word else '-'\\n l = str(len(word))\\n #vowels = \\\"\\\".join(sorted([w for w in word.lower() if w in ['a','e','i','o','u','y']]))\\n #################################\\n ###### YOUR FEATURES HERE ####### \\n #################################\\n # 0=separator\\n \\n ## todo: output the cap feature and more \\n ## make sure the format you output here is what the nerfeats.py script expects as fields!\\n print separator.join([word.lower(),pos,cap, l, hyphen, tag])\\n # sentence separator\\n print \\\"\\\"\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5926569","0.56615496","0.5575726","0.5492108","0.54724497","0.54638004","0.5449956","0.5432941","0.5421955","0.53711075","0.53692234","0.535761","0.53158194","0.5268704","0.52373093","0.5237189","0.5234773","0.5220252","0.52158135","0.5205877","0.52025634","0.5196424","0.51920056","0.51561964","0.51454395","0.5133324","0.5114785","0.511348","0.5100287","0.5081667","0.5076924","0.50706595","0.50689846","0.5049998","0.5047136","0.50470495","0.5041752","0.5037247","0.50327754","0.5028755","0.50259846","0.50230914","0.50159854","0.49958083","0.49870625","0.49765825","0.49618733","0.49589851","0.49481818","0.4941247","0.49389294","0.49353433","0.4934022","0.49314773","0.4927532","0.49209815","0.49182317","0.49180028","0.49105716","0.49091825","0.4905054","0.49043468","0.49008638","0.48914766","0.48857662","0.48852444","0.4883498","0.4878825","0.48639783","0.48600167","0.48586133","0.48578298","0.48566645","0.48505566","0.48480323","0.48450273","0.48387247","0.48349926","0.4833491","0.4827303","0.4821407","0.48071674","0.48009306","0.4800658","0.48004252","0.48000848","0.47996783","0.47940934","0.47876686","0.47848424","0.4780564","0.47760433","0.47759178","0.47758463","0.47720712","0.4759228","0.47574964","0.475458","0.47463918","0.47450367"],"string":"[\n \"0.5926569\",\n \"0.56615496\",\n \"0.5575726\",\n \"0.5492108\",\n \"0.54724497\",\n \"0.54638004\",\n \"0.5449956\",\n \"0.5432941\",\n \"0.5421955\",\n \"0.53711075\",\n \"0.53692234\",\n \"0.535761\",\n \"0.53158194\",\n \"0.5268704\",\n \"0.52373093\",\n \"0.5237189\",\n \"0.5234773\",\n \"0.5220252\",\n \"0.52158135\",\n \"0.5205877\",\n \"0.52025634\",\n \"0.5196424\",\n \"0.51920056\",\n \"0.51561964\",\n \"0.51454395\",\n \"0.5133324\",\n \"0.5114785\",\n \"0.511348\",\n \"0.5100287\",\n \"0.5081667\",\n \"0.5076924\",\n \"0.50706595\",\n \"0.50689846\",\n \"0.5049998\",\n \"0.5047136\",\n \"0.50470495\",\n \"0.5041752\",\n \"0.5037247\",\n \"0.50327754\",\n \"0.5028755\",\n \"0.50259846\",\n \"0.50230914\",\n \"0.50159854\",\n \"0.49958083\",\n \"0.49870625\",\n \"0.49765825\",\n \"0.49618733\",\n \"0.49589851\",\n \"0.49481818\",\n \"0.4941247\",\n \"0.49389294\",\n \"0.49353433\",\n \"0.4934022\",\n \"0.49314773\",\n \"0.4927532\",\n \"0.49209815\",\n \"0.49182317\",\n \"0.49180028\",\n \"0.49105716\",\n \"0.49091825\",\n \"0.4905054\",\n \"0.49043468\",\n \"0.49008638\",\n \"0.48914766\",\n \"0.48857662\",\n \"0.48852444\",\n \"0.4883498\",\n \"0.4878825\",\n \"0.48639783\",\n \"0.48600167\",\n \"0.48586133\",\n \"0.48578298\",\n \"0.48566645\",\n \"0.48505566\",\n \"0.48480323\",\n \"0.48450273\",\n \"0.48387247\",\n \"0.48349926\",\n \"0.4833491\",\n \"0.4827303\",\n \"0.4821407\",\n \"0.48071674\",\n \"0.48009306\",\n \"0.4800658\",\n \"0.48004252\",\n \"0.48000848\",\n \"0.47996783\",\n \"0.47940934\",\n \"0.47876686\",\n \"0.47848424\",\n \"0.4780564\",\n \"0.47760433\",\n \"0.47759178\",\n \"0.47758463\",\n \"0.47720712\",\n \"0.4759228\",\n \"0.47574964\",\n \"0.475458\",\n \"0.47463918\",\n \"0.47450367\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.69166636"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":2882,"cells":{"query":{"kind":"string","value":"Returns the self.guessed_by and self.metaphors_used data as a readable string."},"document":{"kind":"string","value":"def get_str_metadata(self):\n return \"\\n\".join([\"Guessed by {}\".format(self.guessed_by), \"{} metaphors used\".format(self.metaphors_used)])"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_strings(self):\n return self._guessed_strings","def __str__(self):\n d = {}\n d[\"tuner_number\"] = self.tuner_number\n d[\"output_format\"] = self.output_format\n d[\"output_source\"] = self.output_source\n return str(d)","def get_human_readable(self):\n\n def yesno(key):\n if getattr(self, key) and getattr(self, key) > 0:\n return \"Y\"\n else:\n return \"N\"\n\n keys = (\n \"pvs1\",\n \"ps1\",\n \"ps2\",\n \"ps3\",\n \"ps4\",\n \"pm1\",\n \"pm2\",\n \"pm3\",\n \"pm4\",\n \"pm5\",\n \"pm6\",\n \"pp1\",\n \"pp2\",\n \"pp3\",\n \"pp4\",\n \"pp5\",\n \"ba1\",\n \"bs1\",\n \"bs2\",\n \"bs3\",\n \"bs4\",\n \"bp1\",\n \"bp2\",\n \"bp3\",\n \"bp4\",\n \"bp5\",\n \"bp6\",\n \"bp7\",\n )\n result = \", \".join([\"%s: %s\" % (key.upper(), yesno(key)) for key in keys])\n result += \", ACMG classification: %s\" % self.class_auto\n if self.class_override:\n result += \", ACMG class. override: %s\" % self.class_override\n return result","def to_formatted(self) -> str:\n return str(self.google_confidence) + \\\n \"\\t\" + str(self.normalized_sentence_score) + \\\n \"\\t\" + str(self.gaps_transcript) + \\\n \"\\t\" + str(self.gaps_google)","def __str__(self):\n result = \", \".join(map(str, self.hand))\n result += \"\\n \" + str(self.get_score()) + \" points\"\n return result","def details(self) -> str:\n return f\"- **language**: [{self.language}]\\n\" \\\n f\"- **opengame**: [{self.opengame}]\\n\" \\\n f\"- **system**: [{self.system}]\\n\" \\\n f\"- **mode**: [{self.mode}]\\n\" \\\n f\"- **attributes**: [{self.attributes}]\\n \" \\\n f\"- **score_threshold**: [{self.score_threshold}]\\n \" \\\n f\"- **monsters**: [{self.monsters}]\\n\"","def __repr__(self):\n return \"{} hp:{:.1f} stunned: {} potions: {}\".format(self.name, self.hitpoints,\n self.stunned, self.potions)","def __str__(self):\r\n to_string = \"ID: \" + str(self.dat_id) + \" --- CLASSIFICATION: \" + str(self.dat_party) + \" --- VOTED: \" + str(self.dat_votes)\r\n return to_string","def __str__(self):\n s = 'hit '+str(self.hit)+'\\n'\n s+= 'states '+str(self.states)+'\\n'\n s+= 'chi2 '+str(self.chi2)\n return s","def __str__(self):\n header = [\n ' GnoweeHeuristics:']\n header += [('Population = {}').format(self.population)]\n header += [('Sampling Method = {}').format(self.initSampling)]\n header += [('Discovery Fraction = {}').format(self.fracMutation)]\n header += [('Elitism Fraction = {}').format(self.fracElite)]\n header += [('Levy Fraction = {}').format(self.fracLevy)]\n header += [('Levy Alpha = {}').format(self.alpha)]\n header += [('Levy Gamma = {}').format(self.gamma)]\n header += [('Levy Independent Samples = {}').format(self.n)]\n header += [('Levy Scaling Parameter = {}').format(self.scalingFactor)]\n header += [('Constraint Violaition Penalty = {}').format(self.penalty)]\n header += [('Max # of Generations = {}').format(self.maxGens)]\n header += [('Max # of Function Evaluations = {}').format(self.maxFevals)]\n header += [('Convergence Tolerance = {}').format(self.convTol)]\n header += [('Stall Limit = {}').format(self.stallLimit)]\n header += [('Optimal Convergence Tolerance = {}').format(self.optConvTol)]\n header += [' Attributes Inhereted from ProblemParameters:']\n header += [('{}').format(ProblemParameters.__str__(self))]\n return ('\\n').join(header) + '\\n'","def __str__(self):\n out = \"!!!!!!! REPORTED STATISTICS !!!!!!!\\n\"\n for k in self.order:\n if k in self.keys():\n if k in self.explainer.keys():\n out += self.explainer[k].replace('XXXX', str(\n self[k])) + \"\\n\"\n else:\n out += self[k] + \"\\n\"\n for k in self.keys():\n if k not in self.order:\n out += str(self[k])\n return out","def __str__(self):\n return (str(self.chromosome_id) + '. Chromosome: Genes: ' + str(\n self.genes) + '; Fitness: ' + str(self.fitness_value))","def human_readable_info(self) -> str:\n next_session = unix_str(self._stat.next_session)\n last_session = unix_str(self._stat.last_session)\n return \"\"\"\n Next Session: {}\n Last Session: {}\n Repetitions: {}\n Health: {}\n ------------------------\n Past Quality (last 20):\n ------------------------\n {}\n \"\"\".format(\n next_session,\n last_session,\n self._stat.actual_repetitions,\n self._health(),\n self._past_quality_graph(),\n )","def info(self):\n return (f\"Match id: {self._id}\\n\"\n f\"dire_score: {self.dire_score}\\n\"\n f\"dire_team: {self.dire_team}\\n\"\n f\"duration: {self.duration}\\n\"\n f\"game_mode: {self.game_mode}\\n\"\n f\"patch: {self.patch}\\n\"\n f\"radiant_score: {self.radiant_score}\\n\"\n f\"radiant_team: {self.radiant_team}\\n\"\n f\"radiant_win: {self.radiant_win}\\n\"\n f\"skill: {self.skill}\\n\"\n f\"start_time: {self.start_time}\\n\")","def _to_str(self):\n\t\tprint(\"predictors: {}, types: {} \\n method: {}, preprocessing: {}\\\n\t\t\t \\n partition_rate: {}, metric: {}, file name: {}\".format(\n\t\t\t self.predictors, self.predictors_types, self.method_name,\n\t\t\t self.preprocessing_methods, self.data_split, self.metric,\n\t\t\t self.plotting_file_name))","def __str__(self):\n return \"{}\".format(self._matches.keys())","def __str__(self):\n prob = str(round(self.probability, 5))\n dprob = str(round(self.postdProbability, 5))\n output = \"dprob: \" + dprob + \" \\tprob: \" + prob + \"\\t: \"\n for key in self.attackDict.keys():\n output += key + \" \"\n return output","def __str__(self):\n sorted_table = InferenceUtils.get_n_best(self._table, max(len(self._table), 1))\n\n result = []\n for key, value in sorted_table.items():\n result.append('P(%s):=%f\\n' % (str(key), value))\n\n return ''.join(result)[:-1] if len(result) > 0 else ''","def getOpponentFormat(self):\r\n return self.opponent + \"\\t\"","def str(self):\n out = \"{0}:\".format(self.gtype) if self.gtype else \"\"\n out += \"{0}\".format(repr(self.coords))\n out += \"[{0}]\".format(str(self.goalPtr)) if self.goalPtr else \"\"\n return out","def to_string(self):\n\n return '[[%s], [%s]], [%d, %d], [%s], %s, %s, [%s]' % \\\n (', '.join(INT2STRING_CARD[h] for h in self.hand[0]),\n ', '.join(INT2STRING_CARD[h] for h in self.hand[1]),\n self.pot[0], self.pot[1],\n ', '.join(INT2STRING_CARD[p] for p in self.pub),\n INT2STRING_PHASE[self.phase],\n INT2STRING_PLAYER[self.player],\n ', '.join(INT2STRING_STATUS[s] for s in self.status))","def __repr__(self):\n return \"{} hp:{:.1f} stunned: {}\".format(self.name, self.hitpoints,\n self.stunned)","def __str__(self):\n return \"{} : {}\".format(self._conference, self.win_ratio_avg())","def human_readable(self):\n if self.no_flags_set():\n return \"no flags set\"\n else:\n flag_desc = []\n for name in (\"bookmarked\", \"for_validation\", \"candidate\", \"final causative\"):\n if getattr(self, \"flag_%s\" % name.replace(\" \", \"_\")):\n flag_desc.append(name)\n for name in (\"visual\", \"validation\", \"molecular\", \"phenotype_match\", \"summary\"):\n field = getattr(self, \"flag_%s\" % name)\n if field and field != \"empty\":\n flag_desc.append(\"%s rating is %s\" % (name.split(\"_\")[0], field))\n return \", \".join(flag_desc)","def get_info(self):\n out = ''\n for k in sorted(self.components.keys()):\n out += '{:s}: {:s}'.format(k, self.info[k]) + '\\n'\n return(out)","def _player_info(self):\n return \"%r %s seat:%s m:%r c:%s b:%s \" % (self.name, self.serial, self.seat, self.money, self._chips, self._bet)","def to_string(self):\n return \"User: {} Description: {} Ratings: {}\".format(self.id_user, self.description, self.ratings)","def __str__(self):\n return ', '.join([self.yftf_data, self.info_hash, str(self.num_pieces), str(self.peers)])","def __str__(self):\n return str((self.code, self.fitness,))","def _get_problem_report_results_str(self):\n return 'curr_rew: %0.3f, best_rew: %0.3f'%(self.curr_reward, self.curr_best_reward)","def __str__(self):\n out = self.san\n if self.comment != \"\":\n out += \" {\" + self.comment.replace('\\n', ' ') + \"}\"\n if len(self.nags) > 0:\n for n in self.nags:\n out += \" \" + n\n for v in self.variations:\n out += \" (\" + str(v).strip(' ') + \")\"\n return out","def __str__(self):\n value = str(self.puzzle) + str(\" \") + str(self.g) + str(\" \") + str(self.h)\n return value","def __str__(self):\n return \"{} : {}\".format(self._team_name, self._win_ratio)","def __str__(self):\n return f\"A {self.color} {self.__class__.__name__} worth {self.points} points \" \\\n f\"with one hot encoding {self.oneHotEncoding}\"","def get_summary(self):\n \n text = \"word: {}, total_score: {} \\n\".format(self.clue, self.total_score)\n for card, score in self.sorted_card_score_pairs:\n card_text = \"\\t card.name:{} (team:{}), similarity: {} \\n\".format(card.name, card.color, score)\n text += card_text\n return text","def stats_get_str(self):\n return self.stats.get_all_str()","def stats_get_str(self):\n return self.stats.get_all_str()","def __str__(self):\n \n s = \"(R: \" + str(self.r) + \", G: \" + str(self.g) + \", B: \" + str(self.b) + \")\"\n return s","def __str__(self):\n return \"{\" + (\", \".join(\"%s: %s\"%(ngram, value) for (ngram, value) in self.items())) + \"}\"","def __str__(self):\n result = ('---> Population - Generation: ' + str(self.generation)\n + '<--- \\n')\n result += 'Fittest Chromosome: \\n' + str(self.fittest_chromosome)\n\n for chromosome in self.chromosomes:\n result += str(chromosome) + '\\n'\n\n return result","def __str__(self):\n r = []\n for item in sorted(self._data.keys()):\n correct, incorrect = self._data[item][True], self._data[item][False]\n acc = correct / (correct + incorrect)\n s = f\"{item:4} | Accuracy: {acc:.2f}% (diff {'+' if acc-item >=0 else ''}{acc-item:.2f}%) | correct: {correct:2}, incorrect: {incorrect:2}\" \n r.append(s)\n\n return \"\\n\".join(r)","def __str__(self):\n return Hand.__str__(self) + '\\nHand Rank: ' + self.get_full_label()","def __str__(self):\n return \"Name: \" + self._name + \"\\nScores: \" + \\\n \" \".join(map(str, self._scores))","def __str__(self):\n result = \", \".join(map(str, self.cards))\n result += \"\\n \" + str(self.getPoints()) + \" points\"\n return result","def __str__(self):\n output = 'Pathogens:\\n'\n for x in self.extant_p:\n output += ' n %s h %f d %f host %s extant\\n' % (x.name, x.height, x.dist, x.host.name)\n for x in self.not_extant_p:\n output += ' n %s h %f d %f host %s not extant\\n' % (x.name, x.height, x.dist, x.host.name)\n for x in self.not_yet_sampled_p:\n output += ' n %s h %f d %f host %s not yet sampled\\n' % (x.name, x.height, x.dist, x.host.name)\n\n output += 'Hosts:\\n'\n for x in self.extant_h:\n output += ' %s %f %f extant\\n' % (x.name, x.height, x.dist)\n for x in self.not_extant_h:\n output += ' %s %f %f not extant\\n' % (x.name, x.height, x.dist)\n for x in self.not_yet_sampled_h:\n output += ' %s %f %f not yet sampled\\n' % (x.name, x.height, x.dist)\n\n return output","def to_string(self):\n return self.dungeon_string","def to_string(self):\n if self.is_power_onoff():\n return 'Power On/Off'\n else:\n gain = str(hex(int(self['gain_speed'])))\n out = self['target'].ljust(20) + ' ' + self['filters'].ljust(11) + ' ' + self['x_bin'] + 'x' + self['y_bin'] + ' ' + gain[2:].upper()\n \n \n if self.number_windows() > 0:\n out += ' ' + self['x1_size'].rjust(4) + 'x' + self['y1_size'].ljust(4) + ' ' + self['x1_start'].ljust(3) + ' ' + self['y1_start'].ljust(4)\n if self.number_windows() > 1:\n out += ' ' + self['x2_size'].rjust(4) + 'x' + self['y2_size'].ljust(4) + ' ' + self['x2_start'].ljust(3) + ' ' + self['y2_start'].ljust(4)\n \n if 'Comment' in self:\n out += ' ' + self['Comment']\n return out","def summary(self):\n name = 'name : ' + self.get_name()\n damage = 'damage : ' + str(self.get_damage())\n ammos = 'ammo : ' + str(self.get_ammos())\n owner = 'owner : ' + str(self.get_owner())\n return '\\n'.join([name, damage, ammos, owner])","def __str__(self):\n summary = '{} object in the {} language, consisting of {} tokens.'\n return summary.format(\n type(self).__name__, self.language,\n len(self.hand_tagged)\n )","def to_str(self):\n return pprint.pformat(self.to_dict())","def to_str(self):\n return pprint.pformat(self.to_dict())","def to_str(self):\n return pprint.pformat(self.to_dict())","def to_str(self):\n return pprint.pformat(self.to_dict())","def to_str(self):\n return pprint.pformat(self.to_dict())","def to_str(self):\n return pprint.pformat(self.to_dict())","def to_str(self):\n return pprint.pformat(self.to_dict())","def to_str(self):\n return pprint.pformat(self.to_dict())","def to_str(self):\n return pprint.pformat(self.to_dict())","def to_str(self):\n return pprint.pformat(self.to_dict())","def to_str(self):\n return pprint.pformat(self.to_dict())","def to_str(self):\n return pprint.pformat(self.to_dict())","def to_str(self):\n return pprint.pformat(self.to_dict())","def to_str(self):\n return pprint.pformat(self.to_dict())","def to_str(self):\n return pprint.pformat(self.to_dict())","def to_str(self):\n return pprint.pformat(self.to_dict())","def to_str(self):\n return pprint.pformat(self.to_dict())","def to_str(self):\n return pprint.pformat(self.to_dict())","def to_str(self):\n return pprint.pformat(self.to_dict())","def to_str(self):\n return pprint.pformat(self.to_dict())","def to_str(self):\n return pprint.pformat(self.to_dict())","def to_str(self):\n return pprint.pformat(self.to_dict())","def to_str(self):\n return pprint.pformat(self.to_dict())","def to_str(self):\n return pprint.pformat(self.to_dict())","def to_str(self):\n return pprint.pformat(self.to_dict())","def to_str(self):\n return pprint.pformat(self.to_dict())","def to_str(self):\n return pprint.pformat(self.to_dict())","def to_str(self):\n return pprint.pformat(self.to_dict())","def to_str(self):\n return pprint.pformat(self.to_dict())","def to_str(self):\n return pprint.pformat(self.to_dict())","def to_str(self):\n return pprint.pformat(self.to_dict())","def to_str(self):\n return pprint.pformat(self.to_dict())","def to_str(self):\n return pprint.pformat(self.to_dict())","def to_str(self):\n return pprint.pformat(self.to_dict())","def to_str(self):\n return pprint.pformat(self.to_dict())","def to_str(self):\n return pprint.pformat(self.to_dict())","def to_str(self):\n return pprint.pformat(self.to_dict())","def to_str(self):\n return pprint.pformat(self.to_dict())","def to_str(self):\n return pprint.pformat(self.to_dict())","def to_str(self):\n return pprint.pformat(self.to_dict())","def to_str(self):\n return pprint.pformat(self.to_dict())","def to_str(self):\n return pprint.pformat(self.to_dict())","def to_str(self):\n return pprint.pformat(self.to_dict())","def to_str(self):\n return pprint.pformat(self.to_dict())","def to_str(self):\n return pprint.pformat(self.to_dict())","def to_str(self):\n return pprint.pformat(self.to_dict())","def to_str(self):\n return pprint.pformat(self.to_dict())","def to_str(self):\n return pprint.pformat(self.to_dict())","def to_str(self):\n return pprint.pformat(self.to_dict())","def to_str(self):\n return pprint.pformat(self.to_dict())","def to_str(self):\n return pprint.pformat(self.to_dict())"],"string":"[\n \"def get_strings(self):\\n return self._guessed_strings\",\n \"def __str__(self):\\n d = {}\\n d[\\\"tuner_number\\\"] = self.tuner_number\\n d[\\\"output_format\\\"] = self.output_format\\n d[\\\"output_source\\\"] = self.output_source\\n return str(d)\",\n \"def get_human_readable(self):\\n\\n def yesno(key):\\n if getattr(self, key) and getattr(self, key) > 0:\\n return \\\"Y\\\"\\n else:\\n return \\\"N\\\"\\n\\n keys = (\\n \\\"pvs1\\\",\\n \\\"ps1\\\",\\n \\\"ps2\\\",\\n \\\"ps3\\\",\\n \\\"ps4\\\",\\n \\\"pm1\\\",\\n \\\"pm2\\\",\\n \\\"pm3\\\",\\n \\\"pm4\\\",\\n \\\"pm5\\\",\\n \\\"pm6\\\",\\n \\\"pp1\\\",\\n \\\"pp2\\\",\\n \\\"pp3\\\",\\n \\\"pp4\\\",\\n \\\"pp5\\\",\\n \\\"ba1\\\",\\n \\\"bs1\\\",\\n \\\"bs2\\\",\\n \\\"bs3\\\",\\n \\\"bs4\\\",\\n \\\"bp1\\\",\\n \\\"bp2\\\",\\n \\\"bp3\\\",\\n \\\"bp4\\\",\\n \\\"bp5\\\",\\n \\\"bp6\\\",\\n \\\"bp7\\\",\\n )\\n result = \\\", \\\".join([\\\"%s: %s\\\" % (key.upper(), yesno(key)) for key in keys])\\n result += \\\", ACMG classification: %s\\\" % self.class_auto\\n if self.class_override:\\n result += \\\", ACMG class. override: %s\\\" % self.class_override\\n return result\",\n \"def to_formatted(self) -> str:\\n return str(self.google_confidence) + \\\\\\n \\\"\\\\t\\\" + str(self.normalized_sentence_score) + \\\\\\n \\\"\\\\t\\\" + str(self.gaps_transcript) + \\\\\\n \\\"\\\\t\\\" + str(self.gaps_google)\",\n \"def __str__(self):\\n result = \\\", \\\".join(map(str, self.hand))\\n result += \\\"\\\\n \\\" + str(self.get_score()) + \\\" points\\\"\\n return result\",\n \"def details(self) -> str:\\n return f\\\"- **language**: [{self.language}]\\\\n\\\" \\\\\\n f\\\"- **opengame**: [{self.opengame}]\\\\n\\\" \\\\\\n f\\\"- **system**: [{self.system}]\\\\n\\\" \\\\\\n f\\\"- **mode**: [{self.mode}]\\\\n\\\" \\\\\\n f\\\"- **attributes**: [{self.attributes}]\\\\n \\\" \\\\\\n f\\\"- **score_threshold**: [{self.score_threshold}]\\\\n \\\" \\\\\\n f\\\"- **monsters**: [{self.monsters}]\\\\n\\\"\",\n \"def __repr__(self):\\n return \\\"{} hp:{:.1f} stunned: {} potions: {}\\\".format(self.name, self.hitpoints,\\n self.stunned, self.potions)\",\n \"def __str__(self):\\r\\n to_string = \\\"ID: \\\" + str(self.dat_id) + \\\" --- CLASSIFICATION: \\\" + str(self.dat_party) + \\\" --- VOTED: \\\" + str(self.dat_votes)\\r\\n return to_string\",\n \"def __str__(self):\\n s = 'hit '+str(self.hit)+'\\\\n'\\n s+= 'states '+str(self.states)+'\\\\n'\\n s+= 'chi2 '+str(self.chi2)\\n return s\",\n \"def __str__(self):\\n header = [\\n ' GnoweeHeuristics:']\\n header += [('Population = {}').format(self.population)]\\n header += [('Sampling Method = {}').format(self.initSampling)]\\n header += [('Discovery Fraction = {}').format(self.fracMutation)]\\n header += [('Elitism Fraction = {}').format(self.fracElite)]\\n header += [('Levy Fraction = {}').format(self.fracLevy)]\\n header += [('Levy Alpha = {}').format(self.alpha)]\\n header += [('Levy Gamma = {}').format(self.gamma)]\\n header += [('Levy Independent Samples = {}').format(self.n)]\\n header += [('Levy Scaling Parameter = {}').format(self.scalingFactor)]\\n header += [('Constraint Violaition Penalty = {}').format(self.penalty)]\\n header += [('Max # of Generations = {}').format(self.maxGens)]\\n header += [('Max # of Function Evaluations = {}').format(self.maxFevals)]\\n header += [('Convergence Tolerance = {}').format(self.convTol)]\\n header += [('Stall Limit = {}').format(self.stallLimit)]\\n header += [('Optimal Convergence Tolerance = {}').format(self.optConvTol)]\\n header += [' Attributes Inhereted from ProblemParameters:']\\n header += [('{}').format(ProblemParameters.__str__(self))]\\n return ('\\\\n').join(header) + '\\\\n'\",\n \"def __str__(self):\\n out = \\\"!!!!!!! REPORTED STATISTICS !!!!!!!\\\\n\\\"\\n for k in self.order:\\n if k in self.keys():\\n if k in self.explainer.keys():\\n out += self.explainer[k].replace('XXXX', str(\\n self[k])) + \\\"\\\\n\\\"\\n else:\\n out += self[k] + \\\"\\\\n\\\"\\n for k in self.keys():\\n if k not in self.order:\\n out += str(self[k])\\n return out\",\n \"def __str__(self):\\n return (str(self.chromosome_id) + '. Chromosome: Genes: ' + str(\\n self.genes) + '; Fitness: ' + str(self.fitness_value))\",\n \"def human_readable_info(self) -> str:\\n next_session = unix_str(self._stat.next_session)\\n last_session = unix_str(self._stat.last_session)\\n return \\\"\\\"\\\"\\n Next Session: {}\\n Last Session: {}\\n Repetitions: {}\\n Health: {}\\n ------------------------\\n Past Quality (last 20):\\n ------------------------\\n {}\\n \\\"\\\"\\\".format(\\n next_session,\\n last_session,\\n self._stat.actual_repetitions,\\n self._health(),\\n self._past_quality_graph(),\\n )\",\n \"def info(self):\\n return (f\\\"Match id: {self._id}\\\\n\\\"\\n f\\\"dire_score: {self.dire_score}\\\\n\\\"\\n f\\\"dire_team: {self.dire_team}\\\\n\\\"\\n f\\\"duration: {self.duration}\\\\n\\\"\\n f\\\"game_mode: {self.game_mode}\\\\n\\\"\\n f\\\"patch: {self.patch}\\\\n\\\"\\n f\\\"radiant_score: {self.radiant_score}\\\\n\\\"\\n f\\\"radiant_team: {self.radiant_team}\\\\n\\\"\\n f\\\"radiant_win: {self.radiant_win}\\\\n\\\"\\n f\\\"skill: {self.skill}\\\\n\\\"\\n f\\\"start_time: {self.start_time}\\\\n\\\")\",\n \"def _to_str(self):\\n\\t\\tprint(\\\"predictors: {}, types: {} \\\\n method: {}, preprocessing: {}\\\\\\n\\t\\t\\t \\\\n partition_rate: {}, metric: {}, file name: {}\\\".format(\\n\\t\\t\\t self.predictors, self.predictors_types, self.method_name,\\n\\t\\t\\t self.preprocessing_methods, self.data_split, self.metric,\\n\\t\\t\\t self.plotting_file_name))\",\n \"def __str__(self):\\n return \\\"{}\\\".format(self._matches.keys())\",\n \"def __str__(self):\\n prob = str(round(self.probability, 5))\\n dprob = str(round(self.postdProbability, 5))\\n output = \\\"dprob: \\\" + dprob + \\\" \\\\tprob: \\\" + prob + \\\"\\\\t: \\\"\\n for key in self.attackDict.keys():\\n output += key + \\\" \\\"\\n return output\",\n \"def __str__(self):\\n sorted_table = InferenceUtils.get_n_best(self._table, max(len(self._table), 1))\\n\\n result = []\\n for key, value in sorted_table.items():\\n result.append('P(%s):=%f\\\\n' % (str(key), value))\\n\\n return ''.join(result)[:-1] if len(result) > 0 else ''\",\n \"def getOpponentFormat(self):\\r\\n return self.opponent + \\\"\\\\t\\\"\",\n \"def str(self):\\n out = \\\"{0}:\\\".format(self.gtype) if self.gtype else \\\"\\\"\\n out += \\\"{0}\\\".format(repr(self.coords))\\n out += \\\"[{0}]\\\".format(str(self.goalPtr)) if self.goalPtr else \\\"\\\"\\n return out\",\n \"def to_string(self):\\n\\n return '[[%s], [%s]], [%d, %d], [%s], %s, %s, [%s]' % \\\\\\n (', '.join(INT2STRING_CARD[h] for h in self.hand[0]),\\n ', '.join(INT2STRING_CARD[h] for h in self.hand[1]),\\n self.pot[0], self.pot[1],\\n ', '.join(INT2STRING_CARD[p] for p in self.pub),\\n INT2STRING_PHASE[self.phase],\\n INT2STRING_PLAYER[self.player],\\n ', '.join(INT2STRING_STATUS[s] for s in self.status))\",\n \"def __repr__(self):\\n return \\\"{} hp:{:.1f} stunned: {}\\\".format(self.name, self.hitpoints,\\n self.stunned)\",\n \"def __str__(self):\\n return \\\"{} : {}\\\".format(self._conference, self.win_ratio_avg())\",\n \"def human_readable(self):\\n if self.no_flags_set():\\n return \\\"no flags set\\\"\\n else:\\n flag_desc = []\\n for name in (\\\"bookmarked\\\", \\\"for_validation\\\", \\\"candidate\\\", \\\"final causative\\\"):\\n if getattr(self, \\\"flag_%s\\\" % name.replace(\\\" \\\", \\\"_\\\")):\\n flag_desc.append(name)\\n for name in (\\\"visual\\\", \\\"validation\\\", \\\"molecular\\\", \\\"phenotype_match\\\", \\\"summary\\\"):\\n field = getattr(self, \\\"flag_%s\\\" % name)\\n if field and field != \\\"empty\\\":\\n flag_desc.append(\\\"%s rating is %s\\\" % (name.split(\\\"_\\\")[0], field))\\n return \\\", \\\".join(flag_desc)\",\n \"def get_info(self):\\n out = ''\\n for k in sorted(self.components.keys()):\\n out += '{:s}: {:s}'.format(k, self.info[k]) + '\\\\n'\\n return(out)\",\n \"def _player_info(self):\\n return \\\"%r %s seat:%s m:%r c:%s b:%s \\\" % (self.name, self.serial, self.seat, self.money, self._chips, self._bet)\",\n \"def to_string(self):\\n return \\\"User: {} Description: {} Ratings: {}\\\".format(self.id_user, self.description, self.ratings)\",\n \"def __str__(self):\\n return ', '.join([self.yftf_data, self.info_hash, str(self.num_pieces), str(self.peers)])\",\n \"def __str__(self):\\n return str((self.code, self.fitness,))\",\n \"def _get_problem_report_results_str(self):\\n return 'curr_rew: %0.3f, best_rew: %0.3f'%(self.curr_reward, self.curr_best_reward)\",\n \"def __str__(self):\\n out = self.san\\n if self.comment != \\\"\\\":\\n out += \\\" {\\\" + self.comment.replace('\\\\n', ' ') + \\\"}\\\"\\n if len(self.nags) > 0:\\n for n in self.nags:\\n out += \\\" \\\" + n\\n for v in self.variations:\\n out += \\\" (\\\" + str(v).strip(' ') + \\\")\\\"\\n return out\",\n \"def __str__(self):\\n value = str(self.puzzle) + str(\\\" \\\") + str(self.g) + str(\\\" \\\") + str(self.h)\\n return value\",\n \"def __str__(self):\\n return \\\"{} : {}\\\".format(self._team_name, self._win_ratio)\",\n \"def __str__(self):\\n return f\\\"A {self.color} {self.__class__.__name__} worth {self.points} points \\\" \\\\\\n f\\\"with one hot encoding {self.oneHotEncoding}\\\"\",\n \"def get_summary(self):\\n \\n text = \\\"word: {}, total_score: {} \\\\n\\\".format(self.clue, self.total_score)\\n for card, score in self.sorted_card_score_pairs:\\n card_text = \\\"\\\\t card.name:{} (team:{}), similarity: {} \\\\n\\\".format(card.name, card.color, score)\\n text += card_text\\n return text\",\n \"def stats_get_str(self):\\n return self.stats.get_all_str()\",\n \"def stats_get_str(self):\\n return self.stats.get_all_str()\",\n \"def __str__(self):\\n \\n s = \\\"(R: \\\" + str(self.r) + \\\", G: \\\" + str(self.g) + \\\", B: \\\" + str(self.b) + \\\")\\\"\\n return s\",\n \"def __str__(self):\\n return \\\"{\\\" + (\\\", \\\".join(\\\"%s: %s\\\"%(ngram, value) for (ngram, value) in self.items())) + \\\"}\\\"\",\n \"def __str__(self):\\n result = ('---> Population - Generation: ' + str(self.generation)\\n + '<--- \\\\n')\\n result += 'Fittest Chromosome: \\\\n' + str(self.fittest_chromosome)\\n\\n for chromosome in self.chromosomes:\\n result += str(chromosome) + '\\\\n'\\n\\n return result\",\n \"def __str__(self):\\n r = []\\n for item in sorted(self._data.keys()):\\n correct, incorrect = self._data[item][True], self._data[item][False]\\n acc = correct / (correct + incorrect)\\n s = f\\\"{item:4} | Accuracy: {acc:.2f}% (diff {'+' if acc-item >=0 else ''}{acc-item:.2f}%) | correct: {correct:2}, incorrect: {incorrect:2}\\\" \\n r.append(s)\\n\\n return \\\"\\\\n\\\".join(r)\",\n \"def __str__(self):\\n return Hand.__str__(self) + '\\\\nHand Rank: ' + self.get_full_label()\",\n \"def __str__(self):\\n return \\\"Name: \\\" + self._name + \\\"\\\\nScores: \\\" + \\\\\\n \\\" \\\".join(map(str, self._scores))\",\n \"def __str__(self):\\n result = \\\", \\\".join(map(str, self.cards))\\n result += \\\"\\\\n \\\" + str(self.getPoints()) + \\\" points\\\"\\n return result\",\n \"def __str__(self):\\n output = 'Pathogens:\\\\n'\\n for x in self.extant_p:\\n output += ' n %s h %f d %f host %s extant\\\\n' % (x.name, x.height, x.dist, x.host.name)\\n for x in self.not_extant_p:\\n output += ' n %s h %f d %f host %s not extant\\\\n' % (x.name, x.height, x.dist, x.host.name)\\n for x in self.not_yet_sampled_p:\\n output += ' n %s h %f d %f host %s not yet sampled\\\\n' % (x.name, x.height, x.dist, x.host.name)\\n\\n output += 'Hosts:\\\\n'\\n for x in self.extant_h:\\n output += ' %s %f %f extant\\\\n' % (x.name, x.height, x.dist)\\n for x in self.not_extant_h:\\n output += ' %s %f %f not extant\\\\n' % (x.name, x.height, x.dist)\\n for x in self.not_yet_sampled_h:\\n output += ' %s %f %f not yet sampled\\\\n' % (x.name, x.height, x.dist)\\n\\n return output\",\n \"def to_string(self):\\n return self.dungeon_string\",\n \"def to_string(self):\\n if self.is_power_onoff():\\n return 'Power On/Off'\\n else:\\n gain = str(hex(int(self['gain_speed'])))\\n out = self['target'].ljust(20) + ' ' + self['filters'].ljust(11) + ' ' + self['x_bin'] + 'x' + self['y_bin'] + ' ' + gain[2:].upper()\\n \\n \\n if self.number_windows() > 0:\\n out += ' ' + self['x1_size'].rjust(4) + 'x' + self['y1_size'].ljust(4) + ' ' + self['x1_start'].ljust(3) + ' ' + self['y1_start'].ljust(4)\\n if self.number_windows() > 1:\\n out += ' ' + self['x2_size'].rjust(4) + 'x' + self['y2_size'].ljust(4) + ' ' + self['x2_start'].ljust(3) + ' ' + self['y2_start'].ljust(4)\\n \\n if 'Comment' in self:\\n out += ' ' + self['Comment']\\n return out\",\n \"def summary(self):\\n name = 'name : ' + self.get_name()\\n damage = 'damage : ' + str(self.get_damage())\\n ammos = 'ammo : ' + str(self.get_ammos())\\n owner = 'owner : ' + str(self.get_owner())\\n return '\\\\n'.join([name, damage, ammos, owner])\",\n \"def __str__(self):\\n summary = '{} object in the {} language, consisting of {} tokens.'\\n return summary.format(\\n type(self).__name__, self.language,\\n len(self.hand_tagged)\\n )\",\n \"def to_str(self):\\n return pprint.pformat(self.to_dict())\",\n \"def to_str(self):\\n return pprint.pformat(self.to_dict())\",\n \"def to_str(self):\\n return pprint.pformat(self.to_dict())\",\n \"def to_str(self):\\n return pprint.pformat(self.to_dict())\",\n \"def to_str(self):\\n return pprint.pformat(self.to_dict())\",\n \"def to_str(self):\\n return pprint.pformat(self.to_dict())\",\n \"def to_str(self):\\n return pprint.pformat(self.to_dict())\",\n \"def to_str(self):\\n return pprint.pformat(self.to_dict())\",\n \"def to_str(self):\\n return pprint.pformat(self.to_dict())\",\n \"def to_str(self):\\n return pprint.pformat(self.to_dict())\",\n \"def to_str(self):\\n return pprint.pformat(self.to_dict())\",\n \"def to_str(self):\\n return pprint.pformat(self.to_dict())\",\n \"def to_str(self):\\n return pprint.pformat(self.to_dict())\",\n \"def to_str(self):\\n return pprint.pformat(self.to_dict())\",\n \"def to_str(self):\\n return pprint.pformat(self.to_dict())\",\n \"def to_str(self):\\n return pprint.pformat(self.to_dict())\",\n \"def to_str(self):\\n return pprint.pformat(self.to_dict())\",\n \"def to_str(self):\\n return pprint.pformat(self.to_dict())\",\n \"def to_str(self):\\n return pprint.pformat(self.to_dict())\",\n \"def to_str(self):\\n return pprint.pformat(self.to_dict())\",\n \"def to_str(self):\\n return pprint.pformat(self.to_dict())\",\n \"def to_str(self):\\n return pprint.pformat(self.to_dict())\",\n \"def to_str(self):\\n return pprint.pformat(self.to_dict())\",\n \"def to_str(self):\\n return pprint.pformat(self.to_dict())\",\n \"def to_str(self):\\n return pprint.pformat(self.to_dict())\",\n \"def to_str(self):\\n return pprint.pformat(self.to_dict())\",\n \"def to_str(self):\\n return pprint.pformat(self.to_dict())\",\n \"def to_str(self):\\n return pprint.pformat(self.to_dict())\",\n \"def to_str(self):\\n return pprint.pformat(self.to_dict())\",\n \"def to_str(self):\\n return pprint.pformat(self.to_dict())\",\n \"def to_str(self):\\n return pprint.pformat(self.to_dict())\",\n \"def to_str(self):\\n return pprint.pformat(self.to_dict())\",\n \"def to_str(self):\\n return pprint.pformat(self.to_dict())\",\n \"def to_str(self):\\n return pprint.pformat(self.to_dict())\",\n \"def to_str(self):\\n return pprint.pformat(self.to_dict())\",\n \"def to_str(self):\\n return pprint.pformat(self.to_dict())\",\n \"def to_str(self):\\n return pprint.pformat(self.to_dict())\",\n \"def to_str(self):\\n return pprint.pformat(self.to_dict())\",\n \"def to_str(self):\\n return pprint.pformat(self.to_dict())\",\n \"def to_str(self):\\n return pprint.pformat(self.to_dict())\",\n \"def to_str(self):\\n return pprint.pformat(self.to_dict())\",\n \"def to_str(self):\\n return pprint.pformat(self.to_dict())\",\n \"def to_str(self):\\n return pprint.pformat(self.to_dict())\",\n \"def to_str(self):\\n return pprint.pformat(self.to_dict())\",\n \"def to_str(self):\\n return pprint.pformat(self.to_dict())\",\n \"def to_str(self):\\n return pprint.pformat(self.to_dict())\",\n \"def to_str(self):\\n return pprint.pformat(self.to_dict())\",\n \"def to_str(self):\\n return pprint.pformat(self.to_dict())\",\n \"def to_str(self):\\n return pprint.pformat(self.to_dict())\",\n \"def to_str(self):\\n return pprint.pformat(self.to_dict())\",\n \"def to_str(self):\\n return pprint.pformat(self.to_dict())\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.66056585","0.6369337","0.63330597","0.63225114","0.62778735","0.62415534","0.6170479","0.6140569","0.60968494","0.60799277","0.60438675","0.60398","0.60284495","0.6009198","0.59779775","0.59726894","0.597072","0.59602815","0.594238","0.5917474","0.5892665","0.5871034","0.5856106","0.5852676","0.58474237","0.5828681","0.5828217","0.5819358","0.58059597","0.5805112","0.5796561","0.579408","0.5786001","0.57851934","0.5763738","0.57520485","0.57520485","0.5740636","0.5740179","0.5727682","0.5727199","0.5713208","0.5701119","0.5699988","0.5696428","0.56847936","0.5673011","0.56681913","0.5662719","0.5656391","0.5656391","0.5656391","0.5656391","0.5656391","0.5656391","0.5656391","0.5656391","0.5656391","0.5656391","0.5656391","0.5656391","0.5656391","0.5656391","0.5656391","0.5656391","0.5656391","0.5656391","0.5656391","0.5656391","0.5656391","0.5656391","0.5656391","0.5656391","0.5656391","0.5656391","0.5656391","0.5656391","0.5656391","0.5656391","0.5656391","0.5656391","0.5656391","0.5656391","0.5656391","0.5656391","0.5656391","0.5656391","0.5656391","0.5656391","0.5656391","0.5656391","0.5656391","0.5656391","0.5656391","0.5656391","0.5656391","0.5656391","0.5656391","0.5656391","0.5656391"],"string":"[\n \"0.66056585\",\n \"0.6369337\",\n \"0.63330597\",\n \"0.63225114\",\n \"0.62778735\",\n \"0.62415534\",\n \"0.6170479\",\n \"0.6140569\",\n \"0.60968494\",\n \"0.60799277\",\n \"0.60438675\",\n \"0.60398\",\n \"0.60284495\",\n \"0.6009198\",\n \"0.59779775\",\n \"0.59726894\",\n \"0.597072\",\n \"0.59602815\",\n \"0.594238\",\n \"0.5917474\",\n \"0.5892665\",\n \"0.5871034\",\n \"0.5856106\",\n \"0.5852676\",\n \"0.58474237\",\n \"0.5828681\",\n \"0.5828217\",\n \"0.5819358\",\n \"0.58059597\",\n \"0.5805112\",\n \"0.5796561\",\n \"0.579408\",\n \"0.5786001\",\n \"0.57851934\",\n \"0.5763738\",\n \"0.57520485\",\n \"0.57520485\",\n \"0.5740636\",\n \"0.5740179\",\n \"0.5727682\",\n \"0.5727199\",\n \"0.5713208\",\n \"0.5701119\",\n \"0.5699988\",\n \"0.5696428\",\n \"0.56847936\",\n \"0.5673011\",\n \"0.56681913\",\n \"0.5662719\",\n \"0.5656391\",\n \"0.5656391\",\n \"0.5656391\",\n \"0.5656391\",\n \"0.5656391\",\n \"0.5656391\",\n \"0.5656391\",\n \"0.5656391\",\n \"0.5656391\",\n \"0.5656391\",\n \"0.5656391\",\n \"0.5656391\",\n \"0.5656391\",\n \"0.5656391\",\n \"0.5656391\",\n \"0.5656391\",\n \"0.5656391\",\n \"0.5656391\",\n \"0.5656391\",\n \"0.5656391\",\n \"0.5656391\",\n \"0.5656391\",\n \"0.5656391\",\n \"0.5656391\",\n \"0.5656391\",\n \"0.5656391\",\n \"0.5656391\",\n \"0.5656391\",\n \"0.5656391\",\n \"0.5656391\",\n \"0.5656391\",\n \"0.5656391\",\n \"0.5656391\",\n \"0.5656391\",\n \"0.5656391\",\n \"0.5656391\",\n \"0.5656391\",\n \"0.5656391\",\n \"0.5656391\",\n \"0.5656391\",\n \"0.5656391\",\n \"0.5656391\",\n \"0.5656391\",\n \"0.5656391\",\n \"0.5656391\",\n \"0.5656391\",\n \"0.5656391\",\n \"0.5656391\",\n \"0.5656391\",\n \"0.5656391\",\n \"0.5656391\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.80824745"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":2883,"cells":{"query":{"kind":"string","value":"Takes `x_axis` and returns an uniformly sampled array of values from its minimum to its maximum with few extra points"},"document":{"kind":"string","value":"def _extended_discrete_xaxis(x_axis, n_points=100, eps=0.10):\n min_value = np.min(x_axis)\n max_value = np.max(x_axis)\n distance = max_value - min_value\n return np.linspace(min_value - eps * distance, max_value + eps * distance,\n num=n_points)"},"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 sample(self):\n # For each row: round(random .* (max - min) + min, 0)\n random_array = prng.np_random.rand(self.num_discrete_space)\n return [int(x) for x in np.floor(np.multiply((self.high - self.low + 1.), random_array) + self.low)]","def scale(x_range=1, y_range=1):\r\n x = rand_val(x_range)\r\n y = rand_val(y_range)\r\n return np.array(((x, 0, 0),\r\n (0, y, 0),\r\n (0, 0, 1)), dtype=np.float)","def simulate_x_values(self, minimum = -10, maximum = 10, length = 100):\n return np.sort(np.random.uniform(minimum, maximum, length) )","def _default_sampling_xrange(self):\n from scipy.stats import rv_continuous\n dataset = self.rvdist.rvs(1000) if rv_continuous in self.rvdist.__class__.__mro__ \\\n else self.rvdist.dataset\n scale = np.nanmax(dataset) - np.nanmin(dataset)\n return [np.nanmin(dataset) - scale*0.05, np.nanmax(dataset) + scale*0.05]","def uniform_sample(x):\n return np.random.choice(x)","def _random_x(self):\n return np.random.uniform(-self._extent, self._extent, self._batchsize)","def sample_from_zero_axis(x):\n idx = np.random.choice(x.shape[0], 1, replace=True)[0]\n return x[idx], idx","def sampler(xaxis, yaxis, vals, x, y):\n i = 0\n while xaxis[i] < x:\n i += 1\n j = 0\n while yaxis[j] < y:\n j += 1\n return vals[i, j]","def rand_custom(x: np.ndarray, y: np.ndarray, \n shape: tuple=(1,), interp_type: str='linear') -> np.ndarray :\n \n assert (y >= 0).any(), \"y shouldn't contain negative numbers\"\n \n size = 1\n for n in shape:\n size *= n\n \n y_norm = y/y.max()\n func = interp1d(x, y_norm, kind=interp_type)\n integr_ratio = (x.max() - x.min())/trapz(y_norm, x)\n \n full = np.array([])\n \n while full.shape[0] < size:\n size_all = int(np.round(integr_ratio*(size - full.shape[0])))\n \n a = np.random.uniform(size=size_all)\n b = np.random.uniform(x.min(), x.max(), size=size_all)\n \n full = np.hstack([full, b[np.where(a < func(b))[0]]])\n \n return full[:size].reshape(shape).copy()","def initial_sampling(y):\n samples = list(np.random.randint(0, len(y), 2))\n while len(np.unique(y[samples] > 0.5)) != 2:\n samples = list(np.random.randint(0, len(y), 2))\n return samples","def _ScatterXUniformlyExtendedRange(self, num_points, lattice_sizes,\n input_dims):\n x = []\n for _ in range(num_points):\n point = [\n np.random.random() * (lattice_sizes + 1.0) - 1.0\n for _ in range(input_dims)\n ]\n x.append(np.asarray(point))\n if input_dims == 1:\n x.sort()\n return x","def bernoulliSample(x):\n \n g = tf.get_default_graph()\n \n with ops.name_scope(\"BernoulliSample\") as name:\n with g.gradient_override_map({\"Ceil\": \"Identity\", \"Sub\": \"BernoulliSample_ST\"}):\n return tf.ceil(x - tf.random_uniform(tf.shape(x)),name=name)","def x_uniform1(xlim, n):\n return linspace(xlim[0], xlim[1], n)","def rand(self, x):\r\n return np.random.random(1)[0]","def scale_1d(x):\n return (min(x), max(x), len(x))","def generate_random_data(min_, max_, len_):\n return np.random.uniform(min_, max_, len_)","def sample(self):\n high = self.high.type(torch.float64) if self.dtype.is_floating_point else self.high.type(torch.int64) + 1\n sample = torch.empty(self.shape, dtype=torch.float64)\n\n # Masking arrays which classify the coordinates according to interval\n # type\n unbounded = ~self.bounded_below & ~self.bounded_above\n upp_bounded = ~self.bounded_below & self.bounded_above\n low_bounded = self.bounded_below & ~self.bounded_above\n bounded = self.bounded_below & self.bounded_above\n\n # Vectorized sampling by interval type\n sample[unbounded] = torch.randn(unbounded[unbounded].shape, dtype=torch.float64)\n\n sample[low_bounded] = (-torch.rand(low_bounded[low_bounded].shape, dtype=torch.float64)).exponential_() + \\\n self.low[low_bounded]\n\n sample[upp_bounded] = self.high[upp_bounded] - (\n -torch.rand(upp_bounded[upp_bounded].shape, dtype=torch.float64)).exponential_()\n\n sample[bounded] = (self.low[bounded] - high[bounded]) * torch.rand(bounded[bounded].shape,\n dtype=torch.float64) + high[bounded]\n\n if not self.dtype.is_floating_point: # integer\n sample = torch.floor(sample)\n\n return sample.type(self.dtype)","def _get_random_sample(self):\n p=np.zeros(len(self.dim_ranges))\n for i in range(len(self.dim_ranges)):\n temp=np.linspace(self.dim_ranges[i][0],self.dim_ranges[i][1],1000)\n p[i]=np.random.choice(temp,1,True,None)\n\n return p","def _sample_schechter(x0, alpha, x_min, size=100, max_iter=1000):\n out = []\n n = 0\n num_iter = 0\n while (n<size) & (num_iter<max_iter):\n x = np.random.gamma(scale=x0, shape=alpha+2, size=size)\n x = x[x>x_min]\n u = np.random.uniform(size=x.size)\n x = x[u<x_min/x]\n out.append(x)\n n+=x.size\n num_iter += 1\n\n if num_iter >= max_iter:\n msg = (\"The maximum number of iterations reached.\",\n \"Random variates may not be representitive.\",\n \"Try increasing `max_iter`.\")\n print(msg)\n\n return np.concatenate(out)[:size]","def minmax_scale(X, feature_range=..., *, axis=..., copy=...):\n ...","def make_uniform_x(self, x_resolution, min_x = None, max_x = None, bin_above = 2.0, **kwargs):\n \n if min_x is None or max_x is None:\n a, b = self.get_min_max_x(**kwargs)\n if min_x is None:\n min_x = a\n if max_x is None:\n max_x = b\n \n new_x = numpy.arange(min_x, max_x + x_resolution / 2, x_resolution)\n \n for m in range(len(self.mess)):\n if m not in exclude and self.mess[m][\"class\"] not in exclude:\n resolution = (numpy.amax(m.x) - numpy.amin(m.x)) / len(m.x)","def uniform(feature, bins):\n t = (feature.max()-feature.min())/bins\n return [t*i for i in range(1, bins)]","def get_uniform_axis_sample(a, b, N, dtype):\n assert a < b, \"condition a < b violated!\"\n assert isinstance(N, int), \"condition N of type int violated!\"\n if dtype is int:\n return list(np.linspace(a, b, N).astype(int))\n elif dtype is float:\n return list(np.linspace(a, b, N))\n else:\n raise AssertionError(\"dtype {} not supported for uniform sampling!\".format(dtype))","def _ScatterXUniformly(self, num_points, lattice_sizes, input_dims):\n x = []\n for _ in range(num_points):\n point = [\n np.random.random() * (lattice_sizes - 1.0) for _ in range(input_dims)\n ]\n x.append(np.asarray(point))\n if input_dims == 1:\n x.sort()\n return x","def upsample(x):\n return F.interpolate(x, scale_factor=2, mode=\"nearest\")","def myTakeStep2(x):\n s = 0.5\n x += np.random.uniform(-s, s, np.shape(x))\n return x","def random_transform(self, x, seed=None):\n # x is a single audio, so it doesn't have image number at index 0\n img_row_axis = self.row_axis - 1\n img_channel_axis = self.channel_axis - 1\n\n if seed is not None:\n np.random.seed(seed)\n\n if not (self.zoom_range[0] == 1 and self.zoom_range[1] == 1):\n zx = np.random.uniform(self.zoom_range[0], self.zoom_range[1])\n input_length = x.shape[img_row_axis]\n x = resample(x, num=int(zx * x.shape[img_row_axis]), axis=img_row_axis)\n if x.shape[img_row_axis] >= input_length:\n x = x[:input_length]\n else:\n x = np.pad(x, ((0, input_length - x.shape[img_row_axis]), (0, 0)),\n 'constant', constant_values=(0, np.mean(x)))\n\n if shift:\n hx = np.random.uniform(-self.shift, self.shift)\n x = shift(x, (int(hx * x.shape[img_row_axis]), 0), mode=self.fill_mode, cval=self.cval)\n\n if self.roll_range:\n tx = np.random.uniform(-self.roll_range, self.roll_range)\n if self.roll_range < 1:\n tx *= x.shape[img_row_axis]\n x = np.roll(x, int(tx), axis=(img_row_axis))\n\n if self.horizontal_flip:\n if np.random.random() < 0.5:\n x = np.flip(x, axis=img_row_axis)\n\n if (self.noise):\n if np.random.random() < 0.5:\n if self.noise[-1] == 'Uniform':\n x = x + np.random.uniform(self.noise[0], self.noise[1], size=x.shape)\n elif self.noise[-1] == 'Normal':\n x = x + np.random.normal(self.noise[0], self.noise[1], size=x.shape)\n\n if self.brightness_range is not None:\n x = random_brightness(x, self.brightness_range)\n\n return x","def random_shift(x, fraction):\n min_x, max_x = np.min(x), np.max(x)\n m = np.random.uniform(-fraction, fraction, size=x.shape) + 1\n return np.clip(x * m, min_x, max_x)","def gen_data(min_coord, max_coord, size):\r\n data = np.random.randint(min_coord, max_coord, size)\r\n return data","def randrange(n, vmin, vmax):\n return (vmax - vmin) * np.random.rand(n) + vmin","def get_gaussian_axis_sample(a, b, N, dtype):\n assert a < b, \"condition a < b violated!\"\n assert isinstance(N, int), \"condition N of type int violated!\"\n\n data = []\n for n in range(N):\n x = a + get_norm_cdf(N)[n]*(b-a)\n if dtype is int:\n data.append(int(x))\n elif dtype is float:\n data.append(x)\n else:\n raise AssertionError(\"dtype {} not supported for uniform sampling!\".format(dtype))\n return data","def sample_points(x, n_samples, axis=0, replace=True):\n n_original = x.shape[axis]\n indices = np.random.choice(n_original, n_samples, replace=replace)\n return x.take(indices, axis=axis)","def scale(x):\n min_x, max_x = numpy.min(x), numpy.max(x)\n if min_x != max_x:\n x = (x-min_x)/(max_x-min_x)\n else:\n # all the numbers are the same in x\n x = numpy.asarray([1/len(x) for i in range(len(x)) ])\n return x.tolist()","def sample(self):\n ndim = len(self.lower_bounds)\n pts = numpy.zeros(ndim)\n for j in range(ndim):\n lb = self.lower_bounds[j]\n ub = self.upper_bounds[j]\n pts[j] = numpy.random.uniform(lb, ub)\n return pts","def _generate_signal(self):\n x = np.arange(self.n, dtype='float')\n resample = np.random.rand(self.n) >= self.proba\n resample[0] = True # randomly initialize first sample\n x[resample] = np.random.randn(np.sum(resample))\n for i in x[~resample]:\n x[int(i)] = x[int(i)-1]\n return x","def log_uniform_sample(sample_range):\n log_min = np.log10(sample_range[0])\n log_max = np.log10(sample_range[1])\n u = np.random.rand()*(log_max-log_min) + log_min\n return np.power(10.0,u)","def xavier_init(dims, uniform=True):\n n_inputs,n_outputs = dims\n if uniform:\n # 6 was used in the paper.\n init_range = np.sqrt(6.0 / (n_inputs + n_outputs))\n return tf.random_uniform(shape=dims,minval=-init_range, maxval=init_range)\n else:\n # 3 gives us approximately the same limits as above since this repicks\n # values greater than 2 standard deviations from the mean.\n stddev = np.sqrt(3.0 / (n_inputs + n_outputs))\n return tf.truncated_normal(shape=dims,stddev=stddev)","def min_max_normalization(x, min_x = None, max_x = None):\n if min_x is None:\n min_x = np.min(x, axis=0)\n if max_x is None:\n max_x = np.max(x, axis=0)\n return (x - (min_x)) / (max_x - min_x), min_x, max_x","def get_dummy_sample():\n return np.array([softmax(np.random.rand(5)), softmax(np.random.rand(5))])","def normalize_axis(x, axis, dtype=float):\n x = x.astype(dtype)\n ind_list = [slice(None) for i in range(x.ndim)]\n try:\n for i in range(x.shape[axis]):\n ind_list[axis] = i\n ind = tuple(ind_list)\n minn = x[ind].min()\n maxx = x[ind].max()\n x[ind] = (x[ind]-minn) / (maxx-minn)\n except IndexError:\n raise np.AxisError(\n \"axis {} is out of bounds for array of dimension {}\".format(\n axis, x.ndim\n )\n )\n return x","def sample_from_prior(self, n_samples):\n\n p0 = self.min + self.rng.rand(n_samples) * (self.max - self.min)\n return p0[:, np.newaxis]","def _uniform(val_range):\r\n return np.random.uniform(val_range[0], val_range[1])","def random_sample(grid_size):\r\n g = grid_size\r\n x_range = g[1] - g[0]\r\n\r\n y_range = g[3] - g[2]\r\n\r\n x_off = g[0]\r\n y_off = g[2]\r\n (x,y) = (x_range*np.random.ranf()+x_off,y_range*np.random.ranf()+y_off) \r\n return (x,y)","def getScaleValues(a, x):\n raise NotImplementedError('getScaleValues not implemented')","def scale_data(x):\n mu = x.mean(axis=0)\n sigma = x.std(axis=0)\n x = (x - mu) / sigma\n return (x, mu, sigma)","def _random2min_max(points):\n x_max = max([x for x, y in points])\n x_min = min([x for x, y in points])\n y_max = max([y for x, y in points])\n y_min = min([y for x, y in points])\n return np.array([x_min, y_min, x_max, y_max])","def perturb_point(self, x, scale):\n x_samp = x + (scale / 2.0) * (np.random.rand(3) - 0.5)\n return x_samp","def data_range(x):\n return max(x)-min(x)","def sample_input_domain(num_samples):\n s1 = np.random.random(num_samples) * 10\n s2 = np.random.random(num_samples) * 2 - 5\n s3 = np.random.random(num_samples)\n s4 = np.random.random(num_samples) * 30 + 20\n return s1, s2, s3, s4","def bernoulliSample(x):\r\n g = tf.get_default_graph()\r\n\r\n with ops.name_scope(\"BernoulliSample\") as name:\r\n with g.gradient_override_map({\"Ceil\": \"Identity\", \"Sub\": \"BernoulliSample_ST\"}):\r\n\r\n if args[\"deterministic_train\"]:\r\n train_fn = lambda: tf.minimum(tf.ones(tf.shape(x)), tf.ones(tf.shape(x)) * 0.5)\r\n else:\r\n train_fn = lambda: tf.minimum(tf.ones(tf.shape(x)), tf.random_uniform(tf.shape(x)))\r\n\r\n if args[\"deterministic_eval\"]:\r\n eval_fn = lambda: tf.minimum(tf.ones(tf.shape(x)), tf.ones(tf.shape(x)) * 0.5)\r\n else:\r\n eval_fn = lambda: tf.minimum(tf.ones(tf.shape(x)), tf.random_uniform(tf.shape(x)))\r\n\r\n mus = tf.cond(is_training, train_fn, eval_fn)\r\n\r\n return tf.ceil(x - mus, name=name)","def generate_real_samples(dataset, n_samples):\r\n ix = np.random.randint(0, dataset.shape[0], n_samples)\r\n X = dataset[ix]\r\n y = np.ones((n_samples, 1))\r\n return X, y","def bootstrap_resample(X, n=None):\r\n if n == None:\r\n n = len(X)\r\n \r\n resample_i = np.floor(np.random.rand(n)*len(X)).astype(int)\r\n X_resample = X[resample_i]\r\n return X_resample","def yield_spectral_range(self) -> Tuple[float, float, float]:\n return [min(self.x), max(self.x), len(self.x)]","def discrete_uniform_sampler(upper_value):\n return int(np.random.random() * upper_value)","def initial_x():\n\n # RANDOMLY GENERATES the INITIAL VALUES of the independent variables:\n temp = [uniform(1, cfg.n) for i in range(cfg.n)]\n\n return np.array(temp, dtype=np.float_)","def unif_sample(x_tensor, sample_size, dist):\n feature_len = x_tensor.size()[1]\n samples_tensor = torch.zeros((sample_size, feature_len),\n dtype=torch.float64, device=config.DEVICE).uniform_(-1.0 * dist, dist)\n samples_tensor += x_tensor\n return samples_tensor","def _scale(x, axis=None):\n x = _remove_baseline(x, axis=axis)\n x /= np.std(x, ddof=1, axis=axis, keepdims=True)\n return x","def get_random_position(limits):\n x = (limits[0][1]-limits[0][0])*np.random.random_sample() + limits[0][0]\n y = (limits[1][1]-limits[1][0])*np.random.random_sample() + limits[1][0]\n return np.array([x, y])","def random_five(min_x, max_x):\n return random.sample(xrange(min_x, max_x), 5)","def normalize(x):\n # TODO: Implement Function\n data_max = np.max(x)\n data_min = np.min(x)\n x = (x - data_min) / (data_max - data_min)\n return x","def naive_act_norm_initialize(x, axis):\n x = np.asarray(x)\n axis = list(sorted(set([a + len(x.shape) if a < 0 else a for a in axis])))\n min_axis = np.min(axis)\n reduce_axis = tuple(a for a in range(len(x.shape)) if a not in axis)\n var_shape = [x.shape[a] for a in axis]\n var_shape_aligned = [x.shape[a] if a in axis else 1\n for a in range(min_axis, len(x.shape))]\n mean = np.reshape(np.mean(x, axis=reduce_axis), var_shape)\n bias = -mean\n scale = 1. / np.reshape(\n np.sqrt(np.mean((x - np.reshape(mean, var_shape_aligned)) ** 2,\n axis=reduce_axis)),\n var_shape\n )\n return scale, bias, var_shape_aligned","def sampleUnif(x, n: int = 10000, seed: Optional[int] = None):\n rng = np.random.default_rng(seed)\n\n xMin, xMax = np.nanmin(x, axis=0), np.nanmax(x, axis=0)\n refSamples = rng.uniform(low=xMin.tolist(), high=xMax.tolist(), size=(n, xMin.shape[0]))\n\n assert refSamples.shape[1] == x.shape[1]\n return refSamples","def noise_data(self, x):\n return x + np.random.normal(size=x.shape)","def translate(x_range=0, y_range=0):\r\n x = rand_val(x_range)\r\n y = rand_val(y_range)\r\n return np.array(((1, 0, x),\r\n (0, 1, y),\r\n (0, 0, 1)), dtype=np.float)","def bootstrap_resample(X, n=None):\r\n if n == None:\r\n n = len(X)\r\n\r\n resample_i = N.floor(N.random.rand(n)*len(X)).astype(int)\r\n X_resample = X[resample_i]\r\n return X_resample","def slice_sample_bounded_max(N, burn, logdist, xx, widths, step_out, max_attempts, bounds):\n xx = copy.deepcopy(xx)\n D = len(xx)\n samples = []\n if (not isinstance(widths, list)) or len(widths) == 1:\n widths = np.ones(D) * widths\n\n log_Px = logdist(xx)\n\n for ii in range(N + burn):\n log_uprime = np.log(random.random()) + log_Px\n for dd in random.sample(range(D), D):\n x_l = copy.deepcopy(xx)\n x_r = copy.deepcopy(xx)\n xprime = copy.deepcopy(xx)\n\n # Create a horizontal interval (x_l, x_r) enclosing xx\n rr = random.random()\n x_l[dd] = max(xx[dd] - rr*widths[dd], bounds[dd][0])\n x_r[dd] = min(xx[dd] + (1-rr)*widths[dd], bounds[dd][1])\n\n if step_out:\n while logdist(x_l) > log_uprime and x_l[dd] > bounds[dd][0]:\n\n x_l[dd] = max(x_l[dd] - widths[dd], bounds[dd][0])\n while logdist(x_r) > log_uprime and x_r[dd] < bounds[dd][1]:\n x_r[dd] = min(x_r[dd] + widths[dd], bounds[dd][1])\n\n # Propose xprimes and shrink interval until good one found\n zz = 0\n num_attempts = 0\n while True:\n zz += 1\n # print(x_l)\n xprime[dd] = random.random()*(x_r[dd] - x_l[dd]) + x_l[dd]\n \n log_Px = logdist(xx)\n if log_Px > log_uprime:\n xx[dd] = xprime[dd]\n break\n else:\n # Shrink in\n num_attempts += 1\n if num_attempts >= max_attempts:\n # print('Failed to find something')\n break\n elif xprime[dd] > xx[dd]:\n x_r[dd] = xprime[dd]\n elif xprime[dd] < xx[dd]:\n x_l[dd] = xprime[dd]\n else:\n raise Exception('Slice sampling failed to find an acceptable point')\n # Record samples\n if ii >= burn:\n samples.append(copy.deepcopy(xx))\n return samples","def generate_random_data(size, x_min=X_MIN, x_max=X_MAX, y_min=Y_MIN, y_max=Y_MAX):\n result = []\n for _i in range(size):\n result.append((randint(x_min, x_max), randint(y_min, y_max)))\n\n return result","def random_zoom(x, zoom_range, row_axis=0, col_axis=1, channel_axis=2,\n fill_mode='nearest', cval=0., interpolation_order=1):\n if len(zoom_range) != 2:\n raise ValueError('`zoom_range` should be a tuple or list of two'\n ' floats. Received: %s' % (zoom_range,))\n\n if zoom_range[0] == 1 and zoom_range[1] == 1:\n zx, zy = 1, 1\n else:\n zx, zy = np.random.uniform(zoom_range[0], zoom_range[1], 2)\n x = apply_affine_transform(x, zx=zx, zy=zy, channel_axis=channel_axis,\n fill_mode=fill_mode, cval=cval,\n order=interpolation_order)\n return x","def scale(inp: np.ndarray, new_min: float = 0., new_max: float = 1.,\n axis: int = -1) -> np.ndarray:\n xmax = inp.max(axis=axis, keepdims=True)\n xmin = inp.min(axis=axis, keepdims=True)\n a = (inp-xmin) / (xmax - xmin)\n y = a * (new_max - new_min) + new_min\n return y","def scale(x, feature_range=(-1,1)):\r\n x = x * 2 - 1\r\n return x","def scale_data(x_data):\n\n # Scale based on maximum\n x_max = np.amax(x_data)\n scaled_data = x_data / x_max\n return scaled_data","def create_random_sample(idx_bins,count_array):\n idxs=[]\n for i,x in enumerate(count_array):\n if x > 0:\n idxs.extend(np.random.choice(idx_bins[i],size=x,replace=False))\n return idxs","def rangeSample(val, minLim, maxLim):\n\tif val < minLim or val > maxLim:\n\t\tval = randint(minLim, maxLim)\n\treturn val","def get_ranx0(rng):\n xr = range(-100, 101)\n x1 = rng.choice(xr)\n x0 = (x1,)\n return x0","def sample(self) -> np.ndarray:\n q_target = np.empty(self.d)\n for i in range(self.d):\n q_target[i] = self.lim_lo[i] + np.random.rand() * (self.lim_up[i] - self.lim_lo[i])\n return q_target","def uniform_sample(n, m):\n interval = m / n\n indices = [0]\n index = 0.0\n while True:\n index += interval\n if index >= m - 1:\n indices.append(int(m - 1))\n break\n else:\n indices.append(int(index))\n\n return np.array(indices)","def abnormal_detection(x_axis):\n bool_normal = (x_axis.mean() - 3 * x_axis.std() <= x_axis) & \\\n (x_axis <= x_axis.mean() + 3 * x_axis.std())\n result = x_axis[bool_normal]\n return result","def _sample_loguniform_real_point(\n self, dimension: Real, below_points: numpy.ndarray, above_points: numpy.ndarray\n ) -> numpy.ndarray:\n return self._sample_real_point(\n dimension, below_points, above_points, is_log=True\n )","def __call__(self, *args):\n r = np.random.rand(*args)\n if type(r) is float:\n samples = self.values[(r < self.p).nonzero()[0][0]]\n elif type(r) is np.ndarray:\n samples = np.array(\n [self.values[np.nonzero(x < self.p)[0][0]] \n for x in r.flat]).reshape(r.shape)\n return samples","def sample_matrix(dim, bound):\n return np.random.uniform(low=-bound, high=bound, size=(dim, dim))","def create_random_sample_alt(idx_bins,count_array):\n idxs=[]\n for i,x in enumerate(count_array):\n if x > 0:\n idxs.extend([ idx_bins[i][ind] for ind in unique_sample_of_int(len(idx_bins[i])-1,x) ] )\n return idxs","def random_zoom(x, zoom_range, row_axis=1, col_axis=2, channel_axis=0,\n fill_mode='nearest', cval=0.):\n if len(zoom_range) != 2:\n raise ValueError('`zoom_range` should be a tuple or list of two floats. '\n 'Received arg: ', zoom_range)\n\n if zoom_range[0] == 1 and zoom_range[1] == 1:\n zx, zy = 1, 1\n else:\n zx, zy = np.random.uniform(zoom_range[0], zoom_range[1], 2)\n zoom_matrix = np.array([[zx, 0, 0],\n [0, zy, 0],\n [0, 0, 1]])\n\n h, w = x.shape[row_axis], x.shape[col_axis]\n transform_matrix = transform_matrix_offset_center(zoom_matrix, h, w)\n x = apply_transform(x, transform_matrix, channel_axis, fill_mode, cval)\n return x","def sample_softmax(x : Union[List[float], np.ndarray], t : float = 1):\n x = np.array(x)\n x = x - np.max(x)\n x = np.exp(x / t)\n x = x / np.sum(x)\n return np.random.choice(range(len(x)), p = x)","def get_logarithmic_axis_sample(a, b, N, dtype):\n assert a < b, \"condition a < b violated!\"\n assert a > 0, \"condition a > 0 violated!\"\n assert isinstance(N, int), \"condition N of type int violated!\"\n\n # convert input range into exponent range\n lexp = np.log(a)\n rexp = np.log(b)\n exp_range = np.linspace(lexp, rexp, N)\n\n data = []\n for n in range(exp_range.shape[0]):\n x = np.exp(exp_range[n])\n if dtype is int:\n data.append(int(x))\n elif dtype is float:\n data.append(x)\n else:\n raise AssertionError(\"dtype {} not supported for uniform sampling!\".format(dtype))\n return data","def x0_rand(mu3,xb,num_min):\n Px_i = np.random.rand() * (xb[0,1] - xb[0,0] - 2*num_min) + xb[0,0] + \\\n num_min\n pk1_i, pk2_i = 1.0, 1.0\n while (pk1_i+pk2_i >= 1.0):\n pk1_i = np.random.rand() * (xb[1,1] - xb[1,0] - 2*num_min) + \\\n xb[1,0] + num_min\n pk2_i = np.random.rand() * (min(xb[2,1],1-pk1_i) - xb[2,0] - \\\n 2*num_min) + xb[2,0] + num_min\n mu1_i = np.random.rand() * (xb[3,1] - max(xb[3,0],2*mu3) - 2*num_min) + \\\n max(xb[3,0],2*mu3) + num_min\n mu2_i = np.random.rand() * (min(xb[4,1],mu1_i) - max(xb[4,0],mu3) - \\\n 2*num_min) + max(xb[4,0],mu3) + num_min\n return np.array([Px_i,pk1_i,pk2_i,mu1_i,mu2_i])","def scale_range(data, minTo, maxTo):\n minFrom = np.min(data)\n maxFrom = np.max(data)\n \n scaled_data = []\n \n for point in data:\n new_point = minTo + (maxTo - minTo) * ((point - minFrom)/(maxFrom - minFrom))\n scaled_data.append(new_point)\n \n return scaled_data","def initial_vector(self):\n\n return asarray([np.random.uniform(l, u) for l, u in self.bounds])","def sample (self, n):\n y = self.bins\n x = np.r_[0, self.values.cumsum ()] / self.sum\n # interpolate inverse CDF\n out = np.interp (np.random.random (n), x, y)\n if n == 1:\n return out[0]\n else:\n return out.reshape ((n,))","def subsample(y, lims):\n buckets = len(lims) - 1\n y_subs = np.zeros(buckets)\n for i in range(buckets):\n y_subs[i] = np.min(y[lims[i]:lims[i+1]])\n\n return y_subs","def sample(x, p=None):\n s = np.random.random_sample()\n if p is None:\n return x[int(s*len(x))]\n else:\n p = np.cumsum(p)\n p = p / float(p[-1])\n return x[sum(s >= p)]","def noise(x: np.ndarray) -> np.ndarray:\n\n return np.random.normal(loc=MEAN, scale=1e-2, size=1)","def sample(self, n_samples=1):\n\n\t\tsamples = np.random.random((n_samples, self.__ndims))*self.__range + self.__low\n\t\treturn samples","def var(x, axis=None):\r\n x = asarray(x)\r\n # figure out sample size along the axis\r\n if axis is None:\r\n n = x.size\r\n else:\r\n n = x.shape[axis]\r\n # compute the sum of squares from the mean(s)\r\n sample_SS = sum(x ** 2, axis) - sum(x, axis) ** 2 / n\r\n return sample_SS / (n - 1)","def random(self=None, sample=100, min=0, max=100):\r\n\t\treturn DataStatistics([randint(min, max) for i in range(sample)])","def generate_random_scatter(x_range, w, b, k):\n\tx_1 = []\n\ty_1 = []\n\tx_2 = []\n\ty_2 = []\n\tfor i in range(k):\n\t\txx = random.random() * (x_range[1] - x_range[0]) + x_range[0]\n\t\tx_1.append(xx)\n\t\tamplitude = random.randint(4, 15)\n\t\tyy = w * xx + b + amplitude\n\t\ty_1.append(yy)\n\n\t\txx = random.random() * (x_range[1] - x_range[0]) + x_range[0]\n\t\tx_2.append(xx)\n\t\tamplitude = random.randint(4, 15)\n\t\tyy = w * xx + b - amplitude\n\t\ty_2.append(yy)\n\treturn x_1, y_1, x_2, y_2","def custom_argmin(arr):\n return np.random.choice(np.flatnonzero(arr == arr.min()))","def lhsample(N, bounds):\n \n D = len(bounds)\n sample = vstack(arange(a,b,(b-a)/N) for (a,b) in bounds).T + rand(N,D) / N \n for d in xrange(D): \n shuffle(sample[:,d])\n return sample","def get_xrange(self) -> np.array:\n # todo: ensure this functions work as well for y_values\n lower, upper = self.get_xrange_indices()\n return self.x[lower, upper + 1]","def random_brightness(x, brightness_range):\n if len(brightness_range) != 2:\n raise ValueError('`brightness_range should be tuple or list of two floats. '\n 'Received arg: ', brightness_range)\n\n u = np.random.uniform(brightness_range[0], brightness_range[1])\n x = u * x\n\n return x","def thinning_sampler(rng, lamb, xmin=0, lamb_min=1e-10):\n while lamb(xmin) > lamb_min:\n dx = -np.log(rng.rand()) / lamb(xmin)\n x = xmin + dx\n accept_rate = lamb(x) / lamb(xmin)\n\n if rng.rand() < accept_rate:\n return x\n xmin = x\n raise ValueError(\n f\"require lamb({xmin})>{lamb_min} to guarantee cdf(infty)=1\"\n )"],"string":"[\n \"def sample(self):\\n # For each row: round(random .* (max - min) + min, 0)\\n random_array = prng.np_random.rand(self.num_discrete_space)\\n return [int(x) for x in np.floor(np.multiply((self.high - self.low + 1.), random_array) + self.low)]\",\n \"def scale(x_range=1, y_range=1):\\r\\n x = rand_val(x_range)\\r\\n y = rand_val(y_range)\\r\\n return np.array(((x, 0, 0),\\r\\n (0, y, 0),\\r\\n (0, 0, 1)), dtype=np.float)\",\n \"def simulate_x_values(self, minimum = -10, maximum = 10, length = 100):\\n return np.sort(np.random.uniform(minimum, maximum, length) )\",\n \"def _default_sampling_xrange(self):\\n from scipy.stats import rv_continuous\\n dataset = self.rvdist.rvs(1000) if rv_continuous in self.rvdist.__class__.__mro__ \\\\\\n else self.rvdist.dataset\\n scale = np.nanmax(dataset) - np.nanmin(dataset)\\n return [np.nanmin(dataset) - scale*0.05, np.nanmax(dataset) + scale*0.05]\",\n \"def uniform_sample(x):\\n return np.random.choice(x)\",\n \"def _random_x(self):\\n return np.random.uniform(-self._extent, self._extent, self._batchsize)\",\n \"def sample_from_zero_axis(x):\\n idx = np.random.choice(x.shape[0], 1, replace=True)[0]\\n return x[idx], idx\",\n \"def sampler(xaxis, yaxis, vals, x, y):\\n i = 0\\n while xaxis[i] < x:\\n i += 1\\n j = 0\\n while yaxis[j] < y:\\n j += 1\\n return vals[i, j]\",\n \"def rand_custom(x: np.ndarray, y: np.ndarray, \\n shape: tuple=(1,), interp_type: str='linear') -> np.ndarray :\\n \\n assert (y >= 0).any(), \\\"y shouldn't contain negative numbers\\\"\\n \\n size = 1\\n for n in shape:\\n size *= n\\n \\n y_norm = y/y.max()\\n func = interp1d(x, y_norm, kind=interp_type)\\n integr_ratio = (x.max() - x.min())/trapz(y_norm, x)\\n \\n full = np.array([])\\n \\n while full.shape[0] < size:\\n size_all = int(np.round(integr_ratio*(size - full.shape[0])))\\n \\n a = np.random.uniform(size=size_all)\\n b = np.random.uniform(x.min(), x.max(), size=size_all)\\n \\n full = np.hstack([full, b[np.where(a < func(b))[0]]])\\n \\n return full[:size].reshape(shape).copy()\",\n \"def initial_sampling(y):\\n samples = list(np.random.randint(0, len(y), 2))\\n while len(np.unique(y[samples] > 0.5)) != 2:\\n samples = list(np.random.randint(0, len(y), 2))\\n return samples\",\n \"def _ScatterXUniformlyExtendedRange(self, num_points, lattice_sizes,\\n input_dims):\\n x = []\\n for _ in range(num_points):\\n point = [\\n np.random.random() * (lattice_sizes + 1.0) - 1.0\\n for _ in range(input_dims)\\n ]\\n x.append(np.asarray(point))\\n if input_dims == 1:\\n x.sort()\\n return x\",\n \"def bernoulliSample(x):\\n \\n g = tf.get_default_graph()\\n \\n with ops.name_scope(\\\"BernoulliSample\\\") as name:\\n with g.gradient_override_map({\\\"Ceil\\\": \\\"Identity\\\", \\\"Sub\\\": \\\"BernoulliSample_ST\\\"}):\\n return tf.ceil(x - tf.random_uniform(tf.shape(x)),name=name)\",\n \"def x_uniform1(xlim, n):\\n return linspace(xlim[0], xlim[1], n)\",\n \"def rand(self, x):\\r\\n return np.random.random(1)[0]\",\n \"def scale_1d(x):\\n return (min(x), max(x), len(x))\",\n \"def generate_random_data(min_, max_, len_):\\n return np.random.uniform(min_, max_, len_)\",\n \"def sample(self):\\n high = self.high.type(torch.float64) if self.dtype.is_floating_point else self.high.type(torch.int64) + 1\\n sample = torch.empty(self.shape, dtype=torch.float64)\\n\\n # Masking arrays which classify the coordinates according to interval\\n # type\\n unbounded = ~self.bounded_below & ~self.bounded_above\\n upp_bounded = ~self.bounded_below & self.bounded_above\\n low_bounded = self.bounded_below & ~self.bounded_above\\n bounded = self.bounded_below & self.bounded_above\\n\\n # Vectorized sampling by interval type\\n sample[unbounded] = torch.randn(unbounded[unbounded].shape, dtype=torch.float64)\\n\\n sample[low_bounded] = (-torch.rand(low_bounded[low_bounded].shape, dtype=torch.float64)).exponential_() + \\\\\\n self.low[low_bounded]\\n\\n sample[upp_bounded] = self.high[upp_bounded] - (\\n -torch.rand(upp_bounded[upp_bounded].shape, dtype=torch.float64)).exponential_()\\n\\n sample[bounded] = (self.low[bounded] - high[bounded]) * torch.rand(bounded[bounded].shape,\\n dtype=torch.float64) + high[bounded]\\n\\n if not self.dtype.is_floating_point: # integer\\n sample = torch.floor(sample)\\n\\n return sample.type(self.dtype)\",\n \"def _get_random_sample(self):\\n p=np.zeros(len(self.dim_ranges))\\n for i in range(len(self.dim_ranges)):\\n temp=np.linspace(self.dim_ranges[i][0],self.dim_ranges[i][1],1000)\\n p[i]=np.random.choice(temp,1,True,None)\\n\\n return p\",\n \"def _sample_schechter(x0, alpha, x_min, size=100, max_iter=1000):\\n out = []\\n n = 0\\n num_iter = 0\\n while (n<size) & (num_iter<max_iter):\\n x = np.random.gamma(scale=x0, shape=alpha+2, size=size)\\n x = x[x>x_min]\\n u = np.random.uniform(size=x.size)\\n x = x[u<x_min/x]\\n out.append(x)\\n n+=x.size\\n num_iter += 1\\n\\n if num_iter >= max_iter:\\n msg = (\\\"The maximum number of iterations reached.\\\",\\n \\\"Random variates may not be representitive.\\\",\\n \\\"Try increasing `max_iter`.\\\")\\n print(msg)\\n\\n return np.concatenate(out)[:size]\",\n \"def minmax_scale(X, feature_range=..., *, axis=..., copy=...):\\n ...\",\n \"def make_uniform_x(self, x_resolution, min_x = None, max_x = None, bin_above = 2.0, **kwargs):\\n \\n if min_x is None or max_x is None:\\n a, b = self.get_min_max_x(**kwargs)\\n if min_x is None:\\n min_x = a\\n if max_x is None:\\n max_x = b\\n \\n new_x = numpy.arange(min_x, max_x + x_resolution / 2, x_resolution)\\n \\n for m in range(len(self.mess)):\\n if m not in exclude and self.mess[m][\\\"class\\\"] not in exclude:\\n resolution = (numpy.amax(m.x) - numpy.amin(m.x)) / len(m.x)\",\n \"def uniform(feature, bins):\\n t = (feature.max()-feature.min())/bins\\n return [t*i for i in range(1, bins)]\",\n \"def get_uniform_axis_sample(a, b, N, dtype):\\n assert a < b, \\\"condition a < b violated!\\\"\\n assert isinstance(N, int), \\\"condition N of type int violated!\\\"\\n if dtype is int:\\n return list(np.linspace(a, b, N).astype(int))\\n elif dtype is float:\\n return list(np.linspace(a, b, N))\\n else:\\n raise AssertionError(\\\"dtype {} not supported for uniform sampling!\\\".format(dtype))\",\n \"def _ScatterXUniformly(self, num_points, lattice_sizes, input_dims):\\n x = []\\n for _ in range(num_points):\\n point = [\\n np.random.random() * (lattice_sizes - 1.0) for _ in range(input_dims)\\n ]\\n x.append(np.asarray(point))\\n if input_dims == 1:\\n x.sort()\\n return x\",\n \"def upsample(x):\\n return F.interpolate(x, scale_factor=2, mode=\\\"nearest\\\")\",\n \"def myTakeStep2(x):\\n s = 0.5\\n x += np.random.uniform(-s, s, np.shape(x))\\n return x\",\n \"def random_transform(self, x, seed=None):\\n # x is a single audio, so it doesn't have image number at index 0\\n img_row_axis = self.row_axis - 1\\n img_channel_axis = self.channel_axis - 1\\n\\n if seed is not None:\\n np.random.seed(seed)\\n\\n if not (self.zoom_range[0] == 1 and self.zoom_range[1] == 1):\\n zx = np.random.uniform(self.zoom_range[0], self.zoom_range[1])\\n input_length = x.shape[img_row_axis]\\n x = resample(x, num=int(zx * x.shape[img_row_axis]), axis=img_row_axis)\\n if x.shape[img_row_axis] >= input_length:\\n x = x[:input_length]\\n else:\\n x = np.pad(x, ((0, input_length - x.shape[img_row_axis]), (0, 0)),\\n 'constant', constant_values=(0, np.mean(x)))\\n\\n if shift:\\n hx = np.random.uniform(-self.shift, self.shift)\\n x = shift(x, (int(hx * x.shape[img_row_axis]), 0), mode=self.fill_mode, cval=self.cval)\\n\\n if self.roll_range:\\n tx = np.random.uniform(-self.roll_range, self.roll_range)\\n if self.roll_range < 1:\\n tx *= x.shape[img_row_axis]\\n x = np.roll(x, int(tx), axis=(img_row_axis))\\n\\n if self.horizontal_flip:\\n if np.random.random() < 0.5:\\n x = np.flip(x, axis=img_row_axis)\\n\\n if (self.noise):\\n if np.random.random() < 0.5:\\n if self.noise[-1] == 'Uniform':\\n x = x + np.random.uniform(self.noise[0], self.noise[1], size=x.shape)\\n elif self.noise[-1] == 'Normal':\\n x = x + np.random.normal(self.noise[0], self.noise[1], size=x.shape)\\n\\n if self.brightness_range is not None:\\n x = random_brightness(x, self.brightness_range)\\n\\n return x\",\n \"def random_shift(x, fraction):\\n min_x, max_x = np.min(x), np.max(x)\\n m = np.random.uniform(-fraction, fraction, size=x.shape) + 1\\n return np.clip(x * m, min_x, max_x)\",\n \"def gen_data(min_coord, max_coord, size):\\r\\n data = np.random.randint(min_coord, max_coord, size)\\r\\n return data\",\n \"def randrange(n, vmin, vmax):\\n return (vmax - vmin) * np.random.rand(n) + vmin\",\n \"def get_gaussian_axis_sample(a, b, N, dtype):\\n assert a < b, \\\"condition a < b violated!\\\"\\n assert isinstance(N, int), \\\"condition N of type int violated!\\\"\\n\\n data = []\\n for n in range(N):\\n x = a + get_norm_cdf(N)[n]*(b-a)\\n if dtype is int:\\n data.append(int(x))\\n elif dtype is float:\\n data.append(x)\\n else:\\n raise AssertionError(\\\"dtype {} not supported for uniform sampling!\\\".format(dtype))\\n return data\",\n \"def sample_points(x, n_samples, axis=0, replace=True):\\n n_original = x.shape[axis]\\n indices = np.random.choice(n_original, n_samples, replace=replace)\\n return x.take(indices, axis=axis)\",\n \"def scale(x):\\n min_x, max_x = numpy.min(x), numpy.max(x)\\n if min_x != max_x:\\n x = (x-min_x)/(max_x-min_x)\\n else:\\n # all the numbers are the same in x\\n x = numpy.asarray([1/len(x) for i in range(len(x)) ])\\n return x.tolist()\",\n \"def sample(self):\\n ndim = len(self.lower_bounds)\\n pts = numpy.zeros(ndim)\\n for j in range(ndim):\\n lb = self.lower_bounds[j]\\n ub = self.upper_bounds[j]\\n pts[j] = numpy.random.uniform(lb, ub)\\n return pts\",\n \"def _generate_signal(self):\\n x = np.arange(self.n, dtype='float')\\n resample = np.random.rand(self.n) >= self.proba\\n resample[0] = True # randomly initialize first sample\\n x[resample] = np.random.randn(np.sum(resample))\\n for i in x[~resample]:\\n x[int(i)] = x[int(i)-1]\\n return x\",\n \"def log_uniform_sample(sample_range):\\n log_min = np.log10(sample_range[0])\\n log_max = np.log10(sample_range[1])\\n u = np.random.rand()*(log_max-log_min) + log_min\\n return np.power(10.0,u)\",\n \"def xavier_init(dims, uniform=True):\\n n_inputs,n_outputs = dims\\n if uniform:\\n # 6 was used in the paper.\\n init_range = np.sqrt(6.0 / (n_inputs + n_outputs))\\n return tf.random_uniform(shape=dims,minval=-init_range, maxval=init_range)\\n else:\\n # 3 gives us approximately the same limits as above since this repicks\\n # values greater than 2 standard deviations from the mean.\\n stddev = np.sqrt(3.0 / (n_inputs + n_outputs))\\n return tf.truncated_normal(shape=dims,stddev=stddev)\",\n \"def min_max_normalization(x, min_x = None, max_x = None):\\n if min_x is None:\\n min_x = np.min(x, axis=0)\\n if max_x is None:\\n max_x = np.max(x, axis=0)\\n return (x - (min_x)) / (max_x - min_x), min_x, max_x\",\n \"def get_dummy_sample():\\n return np.array([softmax(np.random.rand(5)), softmax(np.random.rand(5))])\",\n \"def normalize_axis(x, axis, dtype=float):\\n x = x.astype(dtype)\\n ind_list = [slice(None) for i in range(x.ndim)]\\n try:\\n for i in range(x.shape[axis]):\\n ind_list[axis] = i\\n ind = tuple(ind_list)\\n minn = x[ind].min()\\n maxx = x[ind].max()\\n x[ind] = (x[ind]-minn) / (maxx-minn)\\n except IndexError:\\n raise np.AxisError(\\n \\\"axis {} is out of bounds for array of dimension {}\\\".format(\\n axis, x.ndim\\n )\\n )\\n return x\",\n \"def sample_from_prior(self, n_samples):\\n\\n p0 = self.min + self.rng.rand(n_samples) * (self.max - self.min)\\n return p0[:, np.newaxis]\",\n \"def _uniform(val_range):\\r\\n return np.random.uniform(val_range[0], val_range[1])\",\n \"def random_sample(grid_size):\\r\\n g = grid_size\\r\\n x_range = g[1] - g[0]\\r\\n\\r\\n y_range = g[3] - g[2]\\r\\n\\r\\n x_off = g[0]\\r\\n y_off = g[2]\\r\\n (x,y) = (x_range*np.random.ranf()+x_off,y_range*np.random.ranf()+y_off) \\r\\n return (x,y)\",\n \"def getScaleValues(a, x):\\n raise NotImplementedError('getScaleValues not implemented')\",\n \"def scale_data(x):\\n mu = x.mean(axis=0)\\n sigma = x.std(axis=0)\\n x = (x - mu) / sigma\\n return (x, mu, sigma)\",\n \"def _random2min_max(points):\\n x_max = max([x for x, y in points])\\n x_min = min([x for x, y in points])\\n y_max = max([y for x, y in points])\\n y_min = min([y for x, y in points])\\n return np.array([x_min, y_min, x_max, y_max])\",\n \"def perturb_point(self, x, scale):\\n x_samp = x + (scale / 2.0) * (np.random.rand(3) - 0.5)\\n return x_samp\",\n \"def data_range(x):\\n return max(x)-min(x)\",\n \"def sample_input_domain(num_samples):\\n s1 = np.random.random(num_samples) * 10\\n s2 = np.random.random(num_samples) * 2 - 5\\n s3 = np.random.random(num_samples)\\n s4 = np.random.random(num_samples) * 30 + 20\\n return s1, s2, s3, s4\",\n \"def bernoulliSample(x):\\r\\n g = tf.get_default_graph()\\r\\n\\r\\n with ops.name_scope(\\\"BernoulliSample\\\") as name:\\r\\n with g.gradient_override_map({\\\"Ceil\\\": \\\"Identity\\\", \\\"Sub\\\": \\\"BernoulliSample_ST\\\"}):\\r\\n\\r\\n if args[\\\"deterministic_train\\\"]:\\r\\n train_fn = lambda: tf.minimum(tf.ones(tf.shape(x)), tf.ones(tf.shape(x)) * 0.5)\\r\\n else:\\r\\n train_fn = lambda: tf.minimum(tf.ones(tf.shape(x)), tf.random_uniform(tf.shape(x)))\\r\\n\\r\\n if args[\\\"deterministic_eval\\\"]:\\r\\n eval_fn = lambda: tf.minimum(tf.ones(tf.shape(x)), tf.ones(tf.shape(x)) * 0.5)\\r\\n else:\\r\\n eval_fn = lambda: tf.minimum(tf.ones(tf.shape(x)), tf.random_uniform(tf.shape(x)))\\r\\n\\r\\n mus = tf.cond(is_training, train_fn, eval_fn)\\r\\n\\r\\n return tf.ceil(x - mus, name=name)\",\n \"def generate_real_samples(dataset, n_samples):\\r\\n ix = np.random.randint(0, dataset.shape[0], n_samples)\\r\\n X = dataset[ix]\\r\\n y = np.ones((n_samples, 1))\\r\\n return X, y\",\n \"def bootstrap_resample(X, n=None):\\r\\n if n == None:\\r\\n n = len(X)\\r\\n \\r\\n resample_i = np.floor(np.random.rand(n)*len(X)).astype(int)\\r\\n X_resample = X[resample_i]\\r\\n return X_resample\",\n \"def yield_spectral_range(self) -> Tuple[float, float, float]:\\n return [min(self.x), max(self.x), len(self.x)]\",\n \"def discrete_uniform_sampler(upper_value):\\n return int(np.random.random() * upper_value)\",\n \"def initial_x():\\n\\n # RANDOMLY GENERATES the INITIAL VALUES of the independent variables:\\n temp = [uniform(1, cfg.n) for i in range(cfg.n)]\\n\\n return np.array(temp, dtype=np.float_)\",\n \"def unif_sample(x_tensor, sample_size, dist):\\n feature_len = x_tensor.size()[1]\\n samples_tensor = torch.zeros((sample_size, feature_len),\\n dtype=torch.float64, device=config.DEVICE).uniform_(-1.0 * dist, dist)\\n samples_tensor += x_tensor\\n return samples_tensor\",\n \"def _scale(x, axis=None):\\n x = _remove_baseline(x, axis=axis)\\n x /= np.std(x, ddof=1, axis=axis, keepdims=True)\\n return x\",\n \"def get_random_position(limits):\\n x = (limits[0][1]-limits[0][0])*np.random.random_sample() + limits[0][0]\\n y = (limits[1][1]-limits[1][0])*np.random.random_sample() + limits[1][0]\\n return np.array([x, y])\",\n \"def random_five(min_x, max_x):\\n return random.sample(xrange(min_x, max_x), 5)\",\n \"def normalize(x):\\n # TODO: Implement Function\\n data_max = np.max(x)\\n data_min = np.min(x)\\n x = (x - data_min) / (data_max - data_min)\\n return x\",\n \"def naive_act_norm_initialize(x, axis):\\n x = np.asarray(x)\\n axis = list(sorted(set([a + len(x.shape) if a < 0 else a for a in axis])))\\n min_axis = np.min(axis)\\n reduce_axis = tuple(a for a in range(len(x.shape)) if a not in axis)\\n var_shape = [x.shape[a] for a in axis]\\n var_shape_aligned = [x.shape[a] if a in axis else 1\\n for a in range(min_axis, len(x.shape))]\\n mean = np.reshape(np.mean(x, axis=reduce_axis), var_shape)\\n bias = -mean\\n scale = 1. / np.reshape(\\n np.sqrt(np.mean((x - np.reshape(mean, var_shape_aligned)) ** 2,\\n axis=reduce_axis)),\\n var_shape\\n )\\n return scale, bias, var_shape_aligned\",\n \"def sampleUnif(x, n: int = 10000, seed: Optional[int] = None):\\n rng = np.random.default_rng(seed)\\n\\n xMin, xMax = np.nanmin(x, axis=0), np.nanmax(x, axis=0)\\n refSamples = rng.uniform(low=xMin.tolist(), high=xMax.tolist(), size=(n, xMin.shape[0]))\\n\\n assert refSamples.shape[1] == x.shape[1]\\n return refSamples\",\n \"def noise_data(self, x):\\n return x + np.random.normal(size=x.shape)\",\n \"def translate(x_range=0, y_range=0):\\r\\n x = rand_val(x_range)\\r\\n y = rand_val(y_range)\\r\\n return np.array(((1, 0, x),\\r\\n (0, 1, y),\\r\\n (0, 0, 1)), dtype=np.float)\",\n \"def bootstrap_resample(X, n=None):\\r\\n if n == None:\\r\\n n = len(X)\\r\\n\\r\\n resample_i = N.floor(N.random.rand(n)*len(X)).astype(int)\\r\\n X_resample = X[resample_i]\\r\\n return X_resample\",\n \"def slice_sample_bounded_max(N, burn, logdist, xx, widths, step_out, max_attempts, bounds):\\n xx = copy.deepcopy(xx)\\n D = len(xx)\\n samples = []\\n if (not isinstance(widths, list)) or len(widths) == 1:\\n widths = np.ones(D) * widths\\n\\n log_Px = logdist(xx)\\n\\n for ii in range(N + burn):\\n log_uprime = np.log(random.random()) + log_Px\\n for dd in random.sample(range(D), D):\\n x_l = copy.deepcopy(xx)\\n x_r = copy.deepcopy(xx)\\n xprime = copy.deepcopy(xx)\\n\\n # Create a horizontal interval (x_l, x_r) enclosing xx\\n rr = random.random()\\n x_l[dd] = max(xx[dd] - rr*widths[dd], bounds[dd][0])\\n x_r[dd] = min(xx[dd] + (1-rr)*widths[dd], bounds[dd][1])\\n\\n if step_out:\\n while logdist(x_l) > log_uprime and x_l[dd] > bounds[dd][0]:\\n\\n x_l[dd] = max(x_l[dd] - widths[dd], bounds[dd][0])\\n while logdist(x_r) > log_uprime and x_r[dd] < bounds[dd][1]:\\n x_r[dd] = min(x_r[dd] + widths[dd], bounds[dd][1])\\n\\n # Propose xprimes and shrink interval until good one found\\n zz = 0\\n num_attempts = 0\\n while True:\\n zz += 1\\n # print(x_l)\\n xprime[dd] = random.random()*(x_r[dd] - x_l[dd]) + x_l[dd]\\n \\n log_Px = logdist(xx)\\n if log_Px > log_uprime:\\n xx[dd] = xprime[dd]\\n break\\n else:\\n # Shrink in\\n num_attempts += 1\\n if num_attempts >= max_attempts:\\n # print('Failed to find something')\\n break\\n elif xprime[dd] > xx[dd]:\\n x_r[dd] = xprime[dd]\\n elif xprime[dd] < xx[dd]:\\n x_l[dd] = xprime[dd]\\n else:\\n raise Exception('Slice sampling failed to find an acceptable point')\\n # Record samples\\n if ii >= burn:\\n samples.append(copy.deepcopy(xx))\\n return samples\",\n \"def generate_random_data(size, x_min=X_MIN, x_max=X_MAX, y_min=Y_MIN, y_max=Y_MAX):\\n result = []\\n for _i in range(size):\\n result.append((randint(x_min, x_max), randint(y_min, y_max)))\\n\\n return result\",\n \"def random_zoom(x, zoom_range, row_axis=0, col_axis=1, channel_axis=2,\\n fill_mode='nearest', cval=0., interpolation_order=1):\\n if len(zoom_range) != 2:\\n raise ValueError('`zoom_range` should be a tuple or list of two'\\n ' floats. Received: %s' % (zoom_range,))\\n\\n if zoom_range[0] == 1 and zoom_range[1] == 1:\\n zx, zy = 1, 1\\n else:\\n zx, zy = np.random.uniform(zoom_range[0], zoom_range[1], 2)\\n x = apply_affine_transform(x, zx=zx, zy=zy, channel_axis=channel_axis,\\n fill_mode=fill_mode, cval=cval,\\n order=interpolation_order)\\n return x\",\n \"def scale(inp: np.ndarray, new_min: float = 0., new_max: float = 1.,\\n axis: int = -1) -> np.ndarray:\\n xmax = inp.max(axis=axis, keepdims=True)\\n xmin = inp.min(axis=axis, keepdims=True)\\n a = (inp-xmin) / (xmax - xmin)\\n y = a * (new_max - new_min) + new_min\\n return y\",\n \"def scale(x, feature_range=(-1,1)):\\r\\n x = x * 2 - 1\\r\\n return x\",\n \"def scale_data(x_data):\\n\\n # Scale based on maximum\\n x_max = np.amax(x_data)\\n scaled_data = x_data / x_max\\n return scaled_data\",\n \"def create_random_sample(idx_bins,count_array):\\n idxs=[]\\n for i,x in enumerate(count_array):\\n if x > 0:\\n idxs.extend(np.random.choice(idx_bins[i],size=x,replace=False))\\n return idxs\",\n \"def rangeSample(val, minLim, maxLim):\\n\\tif val < minLim or val > maxLim:\\n\\t\\tval = randint(minLim, maxLim)\\n\\treturn val\",\n \"def get_ranx0(rng):\\n xr = range(-100, 101)\\n x1 = rng.choice(xr)\\n x0 = (x1,)\\n return x0\",\n \"def sample(self) -> np.ndarray:\\n q_target = np.empty(self.d)\\n for i in range(self.d):\\n q_target[i] = self.lim_lo[i] + np.random.rand() * (self.lim_up[i] - self.lim_lo[i])\\n return q_target\",\n \"def uniform_sample(n, m):\\n interval = m / n\\n indices = [0]\\n index = 0.0\\n while True:\\n index += interval\\n if index >= m - 1:\\n indices.append(int(m - 1))\\n break\\n else:\\n indices.append(int(index))\\n\\n return np.array(indices)\",\n \"def abnormal_detection(x_axis):\\n bool_normal = (x_axis.mean() - 3 * x_axis.std() <= x_axis) & \\\\\\n (x_axis <= x_axis.mean() + 3 * x_axis.std())\\n result = x_axis[bool_normal]\\n return result\",\n \"def _sample_loguniform_real_point(\\n self, dimension: Real, below_points: numpy.ndarray, above_points: numpy.ndarray\\n ) -> numpy.ndarray:\\n return self._sample_real_point(\\n dimension, below_points, above_points, is_log=True\\n )\",\n \"def __call__(self, *args):\\n r = np.random.rand(*args)\\n if type(r) is float:\\n samples = self.values[(r < self.p).nonzero()[0][0]]\\n elif type(r) is np.ndarray:\\n samples = np.array(\\n [self.values[np.nonzero(x < self.p)[0][0]] \\n for x in r.flat]).reshape(r.shape)\\n return samples\",\n \"def sample_matrix(dim, bound):\\n return np.random.uniform(low=-bound, high=bound, size=(dim, dim))\",\n \"def create_random_sample_alt(idx_bins,count_array):\\n idxs=[]\\n for i,x in enumerate(count_array):\\n if x > 0:\\n idxs.extend([ idx_bins[i][ind] for ind in unique_sample_of_int(len(idx_bins[i])-1,x) ] )\\n return idxs\",\n \"def random_zoom(x, zoom_range, row_axis=1, col_axis=2, channel_axis=0,\\n fill_mode='nearest', cval=0.):\\n if len(zoom_range) != 2:\\n raise ValueError('`zoom_range` should be a tuple or list of two floats. '\\n 'Received arg: ', zoom_range)\\n\\n if zoom_range[0] == 1 and zoom_range[1] == 1:\\n zx, zy = 1, 1\\n else:\\n zx, zy = np.random.uniform(zoom_range[0], zoom_range[1], 2)\\n zoom_matrix = np.array([[zx, 0, 0],\\n [0, zy, 0],\\n [0, 0, 1]])\\n\\n h, w = x.shape[row_axis], x.shape[col_axis]\\n transform_matrix = transform_matrix_offset_center(zoom_matrix, h, w)\\n x = apply_transform(x, transform_matrix, channel_axis, fill_mode, cval)\\n return x\",\n \"def sample_softmax(x : Union[List[float], np.ndarray], t : float = 1):\\n x = np.array(x)\\n x = x - np.max(x)\\n x = np.exp(x / t)\\n x = x / np.sum(x)\\n return np.random.choice(range(len(x)), p = x)\",\n \"def get_logarithmic_axis_sample(a, b, N, dtype):\\n assert a < b, \\\"condition a < b violated!\\\"\\n assert a > 0, \\\"condition a > 0 violated!\\\"\\n assert isinstance(N, int), \\\"condition N of type int violated!\\\"\\n\\n # convert input range into exponent range\\n lexp = np.log(a)\\n rexp = np.log(b)\\n exp_range = np.linspace(lexp, rexp, N)\\n\\n data = []\\n for n in range(exp_range.shape[0]):\\n x = np.exp(exp_range[n])\\n if dtype is int:\\n data.append(int(x))\\n elif dtype is float:\\n data.append(x)\\n else:\\n raise AssertionError(\\\"dtype {} not supported for uniform sampling!\\\".format(dtype))\\n return data\",\n \"def x0_rand(mu3,xb,num_min):\\n Px_i = np.random.rand() * (xb[0,1] - xb[0,0] - 2*num_min) + xb[0,0] + \\\\\\n num_min\\n pk1_i, pk2_i = 1.0, 1.0\\n while (pk1_i+pk2_i >= 1.0):\\n pk1_i = np.random.rand() * (xb[1,1] - xb[1,0] - 2*num_min) + \\\\\\n xb[1,0] + num_min\\n pk2_i = np.random.rand() * (min(xb[2,1],1-pk1_i) - xb[2,0] - \\\\\\n 2*num_min) + xb[2,0] + num_min\\n mu1_i = np.random.rand() * (xb[3,1] - max(xb[3,0],2*mu3) - 2*num_min) + \\\\\\n max(xb[3,0],2*mu3) + num_min\\n mu2_i = np.random.rand() * (min(xb[4,1],mu1_i) - max(xb[4,0],mu3) - \\\\\\n 2*num_min) + max(xb[4,0],mu3) + num_min\\n return np.array([Px_i,pk1_i,pk2_i,mu1_i,mu2_i])\",\n \"def scale_range(data, minTo, maxTo):\\n minFrom = np.min(data)\\n maxFrom = np.max(data)\\n \\n scaled_data = []\\n \\n for point in data:\\n new_point = minTo + (maxTo - minTo) * ((point - minFrom)/(maxFrom - minFrom))\\n scaled_data.append(new_point)\\n \\n return scaled_data\",\n \"def initial_vector(self):\\n\\n return asarray([np.random.uniform(l, u) for l, u in self.bounds])\",\n \"def sample (self, n):\\n y = self.bins\\n x = np.r_[0, self.values.cumsum ()] / self.sum\\n # interpolate inverse CDF\\n out = np.interp (np.random.random (n), x, y)\\n if n == 1:\\n return out[0]\\n else:\\n return out.reshape ((n,))\",\n \"def subsample(y, lims):\\n buckets = len(lims) - 1\\n y_subs = np.zeros(buckets)\\n for i in range(buckets):\\n y_subs[i] = np.min(y[lims[i]:lims[i+1]])\\n\\n return y_subs\",\n \"def sample(x, p=None):\\n s = np.random.random_sample()\\n if p is None:\\n return x[int(s*len(x))]\\n else:\\n p = np.cumsum(p)\\n p = p / float(p[-1])\\n return x[sum(s >= p)]\",\n \"def noise(x: np.ndarray) -> np.ndarray:\\n\\n return np.random.normal(loc=MEAN, scale=1e-2, size=1)\",\n \"def sample(self, n_samples=1):\\n\\n\\t\\tsamples = np.random.random((n_samples, self.__ndims))*self.__range + self.__low\\n\\t\\treturn samples\",\n \"def var(x, axis=None):\\r\\n x = asarray(x)\\r\\n # figure out sample size along the axis\\r\\n if axis is None:\\r\\n n = x.size\\r\\n else:\\r\\n n = x.shape[axis]\\r\\n # compute the sum of squares from the mean(s)\\r\\n sample_SS = sum(x ** 2, axis) - sum(x, axis) ** 2 / n\\r\\n return sample_SS / (n - 1)\",\n \"def random(self=None, sample=100, min=0, max=100):\\r\\n\\t\\treturn DataStatistics([randint(min, max) for i in range(sample)])\",\n \"def generate_random_scatter(x_range, w, b, k):\\n\\tx_1 = []\\n\\ty_1 = []\\n\\tx_2 = []\\n\\ty_2 = []\\n\\tfor i in range(k):\\n\\t\\txx = random.random() * (x_range[1] - x_range[0]) + x_range[0]\\n\\t\\tx_1.append(xx)\\n\\t\\tamplitude = random.randint(4, 15)\\n\\t\\tyy = w * xx + b + amplitude\\n\\t\\ty_1.append(yy)\\n\\n\\t\\txx = random.random() * (x_range[1] - x_range[0]) + x_range[0]\\n\\t\\tx_2.append(xx)\\n\\t\\tamplitude = random.randint(4, 15)\\n\\t\\tyy = w * xx + b - amplitude\\n\\t\\ty_2.append(yy)\\n\\treturn x_1, y_1, x_2, y_2\",\n \"def custom_argmin(arr):\\n return np.random.choice(np.flatnonzero(arr == arr.min()))\",\n \"def lhsample(N, bounds):\\n \\n D = len(bounds)\\n sample = vstack(arange(a,b,(b-a)/N) for (a,b) in bounds).T + rand(N,D) / N \\n for d in xrange(D): \\n shuffle(sample[:,d])\\n return sample\",\n \"def get_xrange(self) -> np.array:\\n # todo: ensure this functions work as well for y_values\\n lower, upper = self.get_xrange_indices()\\n return self.x[lower, upper + 1]\",\n \"def random_brightness(x, brightness_range):\\n if len(brightness_range) != 2:\\n raise ValueError('`brightness_range should be tuple or list of two floats. '\\n 'Received arg: ', brightness_range)\\n\\n u = np.random.uniform(brightness_range[0], brightness_range[1])\\n x = u * x\\n\\n return x\",\n \"def thinning_sampler(rng, lamb, xmin=0, lamb_min=1e-10):\\n while lamb(xmin) > lamb_min:\\n dx = -np.log(rng.rand()) / lamb(xmin)\\n x = xmin + dx\\n accept_rate = lamb(x) / lamb(xmin)\\n\\n if rng.rand() < accept_rate:\\n return x\\n xmin = x\\n raise ValueError(\\n f\\\"require lamb({xmin})>{lamb_min} to guarantee cdf(infty)=1\\\"\\n )\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6698139","0.6558226","0.65457267","0.64248043","0.6340408","0.6254296","0.61594844","0.6148418","0.60947704","0.60901093","0.5998522","0.5891753","0.5868367","0.58548534","0.5790514","0.5744083","0.5735349","0.57298034","0.57228523","0.5720773","0.5694697","0.56932783","0.56902844","0.5680441","0.5627093","0.56042904","0.5602855","0.55887944","0.5578321","0.5570794","0.5563616","0.55588645","0.5547898","0.55314845","0.55296904","0.55205816","0.5517501","0.55137676","0.5511837","0.5509596","0.5507557","0.5505392","0.5498944","0.54835546","0.5483243","0.547789","0.5477683","0.54715914","0.54690224","0.54523987","0.5438893","0.5436108","0.542254","0.5415916","0.54146683","0.5405445","0.53802294","0.5370432","0.536054","0.5352187","0.5350395","0.53443956","0.5342975","0.53250027","0.5319233","0.5302199","0.530089","0.53007656","0.5294612","0.5290604","0.5287535","0.52799785","0.5270537","0.5263525","0.5260026","0.52589905","0.5254856","0.5254445","0.5254314","0.52484614","0.52314925","0.52312064","0.5229239","0.5227659","0.5226755","0.521929","0.5213589","0.52044785","0.51941913","0.51929206","0.5191202","0.5188624","0.51875436","0.51838356","0.5181683","0.51756066","0.51645476","0.5164329","0.51613444","0.5159732"],"string":"[\n \"0.6698139\",\n \"0.6558226\",\n \"0.65457267\",\n \"0.64248043\",\n \"0.6340408\",\n \"0.6254296\",\n \"0.61594844\",\n \"0.6148418\",\n \"0.60947704\",\n \"0.60901093\",\n \"0.5998522\",\n \"0.5891753\",\n \"0.5868367\",\n \"0.58548534\",\n \"0.5790514\",\n \"0.5744083\",\n \"0.5735349\",\n \"0.57298034\",\n \"0.57228523\",\n \"0.5720773\",\n \"0.5694697\",\n \"0.56932783\",\n \"0.56902844\",\n \"0.5680441\",\n \"0.5627093\",\n \"0.56042904\",\n \"0.5602855\",\n \"0.55887944\",\n \"0.5578321\",\n \"0.5570794\",\n \"0.5563616\",\n \"0.55588645\",\n \"0.5547898\",\n \"0.55314845\",\n \"0.55296904\",\n \"0.55205816\",\n \"0.5517501\",\n \"0.55137676\",\n \"0.5511837\",\n \"0.5509596\",\n \"0.5507557\",\n \"0.5505392\",\n \"0.5498944\",\n \"0.54835546\",\n \"0.5483243\",\n \"0.547789\",\n \"0.5477683\",\n \"0.54715914\",\n \"0.54690224\",\n \"0.54523987\",\n \"0.5438893\",\n \"0.5436108\",\n \"0.542254\",\n \"0.5415916\",\n \"0.54146683\",\n \"0.5405445\",\n \"0.53802294\",\n \"0.5370432\",\n \"0.536054\",\n \"0.5352187\",\n \"0.5350395\",\n \"0.53443956\",\n \"0.5342975\",\n \"0.53250027\",\n \"0.5319233\",\n \"0.5302199\",\n \"0.530089\",\n \"0.53007656\",\n \"0.5294612\",\n \"0.5290604\",\n \"0.5287535\",\n \"0.52799785\",\n \"0.5270537\",\n \"0.5263525\",\n \"0.5260026\",\n \"0.52589905\",\n \"0.5254856\",\n \"0.5254445\",\n \"0.5254314\",\n \"0.52484614\",\n \"0.52314925\",\n \"0.52312064\",\n \"0.5229239\",\n \"0.5227659\",\n \"0.5226755\",\n \"0.521929\",\n \"0.5213589\",\n \"0.52044785\",\n \"0.51941913\",\n \"0.51929206\",\n \"0.5191202\",\n \"0.5188624\",\n \"0.51875436\",\n \"0.51838356\",\n \"0.5181683\",\n \"0.51756066\",\n \"0.51645476\",\n \"0.5164329\",\n \"0.51613444\",\n \"0.5159732\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.59077674"},"document_rank":{"kind":"string","value":"11"}}},{"rowIdx":2884,"cells":{"query":{"kind":"string","value":"Quick plot of a `tick.base.TimeFunction`"},"document":{"kind":"string","value":"def plot_timefunction(time_function, labels=None, n_points=300, show=True,\n ax=None):\n if ax is None:\n fig, ax = plt.subplots(1, 1, figsize=(4, 4))\n else:\n show = False\n\n if time_function.is_constant:\n if labels is None:\n labels = ['value = %.3g' % time_function.border_value]\n\n t_values = np.arange(10).astype('float')\n ax.plot(t_values, time_function.value(t_values), label=labels[0])\n\n else:\n if labels is None:\n interpolation_to_legend = {\n TimeFunction.InterLinear: 'Linear',\n TimeFunction.InterConstLeft: 'Constant on left',\n TimeFunction.InterConstRight: 'Constant on right'\n }\n\n border_to_legend = {\n TimeFunction.Border0:\n 'border zero',\n TimeFunction.BorderConstant:\n 'border constant at %.3g' % time_function.border_value,\n TimeFunction.BorderContinue:\n 'border continue',\n TimeFunction.Cyclic:\n 'cyclic'\n }\n\n labels = [\n 'original points',\n '%s and %s' %\n (interpolation_to_legend[time_function.inter_mode],\n border_to_legend[time_function.border_type])\n ]\n\n original_t = time_function.original_t\n if time_function.border_type == TimeFunction.Cyclic:\n cycle_length = original_t[-1]\n original_t = np.hstack((original_t, original_t + cycle_length,\n original_t + 2 * cycle_length))\n\n t_values = _extended_discrete_xaxis(original_t, n_points=n_points)\n\n ax.plot(time_function.original_t, time_function.original_y, ls='',\n marker='o', label=labels[0])\n ax.plot(t_values, time_function.value(t_values), label=labels[1])\n\n ax.legend()\n if show is True:\n plt.show()\n\n return ax.figure"},"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 plotTime(self):\n plt.figure()\n t = [i for i in range(len(self.nodes_infected))]\n print(t)\n plt.title('Nodos infectados vs Tiempo')\n plt.xlabel('Instantes de tiempo')\n plt.ylabel('# de nodos infectados')\n plt.plot(t, self.nodes_infected)\n plt.grid(True)\n plt.show()","def plot_time_series(self, *args, **kwargs):\n return SimulationStaticVisualizer(self, *args, **kwargs)","def plot_times(self, train_time, title=None, xmin=None, xmax=None,\n ymin=None, ymax=None, ax=None, show=True, color=None,\n xlabel=True, ylabel=True, legend=True, chance=True,\n label='Classif. score'):\n if not np.array(train_time).dtype is np.dtype('float'):\n raise ValueError('train_time must be float | list or array of '\n 'floats. Got %s.' % type(train_time))\n\n return plot_gat_times(self, train_time=train_time, title=title,\n xmin=xmin, xmax=xmax,\n ymin=ymin, ymax=ymax, ax=ax, show=show,\n color=color, xlabel=xlabel, ylabel=ylabel,\n legend=legend, chance=chance, label=label)","def plot(axes, axis, values, c='chartreuse'):\n a = axes[axis]\n a.set_xlabel('time (s)')\n x = np.array(range(len(values))) / 1000\n dim = 'x' if axis == 0 else 'y' if axis == 1 else 'z'\n a.set_title('-'.join([dim, 'acceleration']))\n a.plot(x, values / 1000, c=c)","def time_function(t):\n\n omega = np.pi\n return np.sin(omega * t) + np.sin(10 * omega * t) + np.sin(20 * omega * t)","def plot():\n pass","def visualize(x, y, xlabel=None, ylabel=None, title=None, ylim=None):\n total_seconds = (x[-1] - x[0]).total_seconds()\n if total_seconds <= 86400 * 1 * 3:\n return plot_one_day(x, y, xlabel, ylabel, title, ylim)\n \n elif total_seconds <= 86400 * 7 * 2:\n return plot_one_week(x, y, xlabel, ylabel, title, ylim)\n \n elif total_seconds <= 86400 * 30 * 1.5:\n return plot_one_month(x, y, xlabel, ylabel, title, ylim)\n \n elif total_seconds <= 86400 * 90 * 1.5:\n return plot_one_quarter(x, y, xlabel, ylabel, title, ylim)\n \n elif total_seconds <= 86400 * 365 * 1.5:\n return plot_one_year(x, y, xlabel, ylabel, title, ylim)","def matshow_tseries(time_series, fig=None, axis=0, xtick_n=5, time_unit=None,\r\n xlabel=None, ylabel=None):\r\n\r\n if fig is None:\r\n fig = plt.figure()\r\n\r\n if not fig.get_axes():\r\n ax = fig.add_subplot(1, 1, 1)\r\n else:\r\n ax = fig.get_axes()[axis]\r\n\r\n #Make sure that time displays on the x axis with the units you want:\r\n #If you want to change the time-unit on the visualization from that used to\r\n #represent the time-series:\r\n if time_unit is not None:\r\n tu = time_unit\r\n conv_fac = ts.time_unit_conversion[time_unit]\r\n #Otherwise, get the information from your input:\r\n else:\r\n tu = time_series.time_unit\r\n conv_fac = time_series.time._conversion_factor\r\n\r\n this_time = time_series.time / float(conv_fac)\r\n ax.matshow(time_series.data)\r\n\r\n ax.set_xticks(list(range(len(this_time)))[::len(this_time) / xtick_n])\r\n ax.set_xticklabels(this_time[::len(this_time) / xtick_n])\r\n\r\n if xlabel is None:\r\n ax.set_xlabel('Time (%s)' % tu)\r\n else:\r\n ax.set_xlabel(xlabel)\r\n\r\n if ylabel is not None:\r\n ax.set_ylabel(ylabel)\r\n\r\n return fig","def plot_time(self, X, x0, t):\n\n Pressure = [Solution(self, (x-x0)/t).pressure for x in X]\n Velocity = [Solution(self, (x-x0)/t).velocity for x in X]\n Density = [Solution(self, (x-x0)/t).rho for x in X]\n\n fig, axs = plt.subplots(3, sharex=True)\n fig.suptitle(\"Solution of the Riemann problem\\nat t = {}s\".format(t))\n axs[0].plot(X, Density)\n axs[1].plot(X, Velocity)\n axs[2].plot(X, Pressure)\n\n axs[0].grid()\n axs[0].set(ylabel = \"Density\")\n axs[1].grid()\n axs[1].set(ylabel = \"Velocity\")\n axs[2].grid()\n axs[2].set(ylabel = \"Pressure\")\n\n plt.xlabel(\"Location x\")","def plottf(tfarray,tlst,flst,fignum=1,starttime=0,timeinc='hrs',\r\n dt=1.0,title=None,vmm=None,cmap=None,aspect=None,interpolation=None,\r\n cbori=None,cbshrink=None,cbaspect=None,cbpad=None,scale='log',\r\n normalize='n',):\r\n \r\n #time increment\r\n if timeinc=='hrs':\r\n tinc=3600/dt\r\n elif timeinc=='min':\r\n tinc=60/dt\r\n elif timeinc=='sec':\r\n tinc=1/dt\r\n else:\r\n raise ValueError(timeinc+'is not defined')\r\n #colormap\r\n if cmap==None:\r\n cmap='jet'\r\n else:\r\n cmap=cmap\r\n #aspect ratio\r\n if aspect==None:\r\n aspect='auto'\r\n else:\r\n aspect=aspect\r\n #interpolation\r\n if interpolation==None:\r\n interpolation='gaussian'\r\n else:\r\n interpolation=interpolation\r\n #colorbar orientation\r\n if cbori==None:\r\n cbori='vertical'\r\n else:\r\n cbori=cbori\r\n #colorbar shinkage\r\n if cbshrink==None:\r\n cbshrink=.99\r\n else:\r\n cbshrink=cbshrink\r\n #colorbar aspect\r\n if cbaspect==None:\r\n cbaspect=20\r\n else:\r\n cbaspect=cbaspect\r\n #colorbar pad\r\n if cbpad==None:\r\n cbpad=.1\r\n else:\r\n cbpad=cbpad\r\n #scale\r\n if scale=='log':\r\n zerofind=np.where(abs(tfarray)==0)\r\n tfarray[zerofind]=1.0\r\n if normalize=='y':\r\n plottfarray=10*np.log10(abs(tfarray/np.max(abs(tfarray))))\r\n else:\r\n plottfarray=10*np.log10(abs(tfarray))\r\n elif scale=='linear':\r\n if normalize=='y':\r\n plottfarray=abs(tfarray/np.max(abs(tfarray)))\r\n else:\r\n plottfarray=abs(tfarray)\r\n \r\n plt.rcParams['font.size']=10\r\n plt.rcParams['figure.subplot.left']=.08\r\n plt.rcParams['figure.subplot.right']=.99\r\n plt.rcParams['figure.subplot.bottom']=.07\r\n plt.rcParams['figure.subplot.top']=.96\r\n plt.rcParams['figure.subplot.wspace']=.25\r\n plt.rcParams['figure.subplot.hspace']=.20\r\n \r\n \r\n plt.figure(fignum)\r\n if vmm!=None:\r\n vmin=vmm[0]\r\n vmax=vmm[1]\r\n plt.imshow(plottfarray,extent=(tlst[0]/tinc+starttime,\r\n tlst[-1]/tinc+starttime,flst[0],flst[-1]),aspect=aspect,\r\n vmin=vmin,vmax=vmax,cmap=cmap,interpolation=interpolation)\r\n else:\r\n plt.imshow(plottfarray,extent=(tlst[0]/tinc+starttime,\r\n tlst[-1]/tinc+starttime,flst[0],flst[-1]),aspect=aspect,\r\n cmap=cmap,interpolation=interpolation)\r\n plt.xlabel('Time('+timeinc+')',fontsize=12,fontweight='bold')\r\n plt.ylabel('Frequency (Hz)',fontsize=12,fontweight='bold')\r\n if title!=None:\r\n plt.title(title,fontsize=14,fontweight='bold')\r\n plt.colorbar(orientation=cbori,shrink=cbshrink,pad=cbpad,aspect=cbaspect)\r\n plt.show()","def running_time(func, counter, plot_type, *args, **kwargs):\n plots = []\n counter = 0\n #include counter\n output = (func(counter, *args, **kwargs)) #returns [value, counter]\n value, counter = output\n if plot_type.upper() == \"STANDARD\":\n plots.append(output)\n else:\n plots.append((log(float(value)), log(float(counter))))\n #print plots\n return plt.plot(plots)","def plot_time_frames(self):\n\n fig = plt.figure()\n plt.grid(True)\n\n plt.ylim([-1.5,1.5])\n plt.xlim([0,1])\n\n for key in self.timeframes.keys():\n if key == 0:\n plt.plot(self.x, self.timeframes[key], label=\"time: \" + str(round(key*self.dt, 3)), linewidth=5)\n else:\n plt.plot(self.x, self.timeframes[key], label=\"time: \" + str(round(key*self.dt, 3)))\n\n plt.title(\"Wave at different times\")\n plt.legend(loc=\"upper right\")\n plt.show()\n\n # fig.savefig('results/pics_wave/vibrating_string_'+ self.type + '.png', dpi=150)","def plotAll(fx,tfarray,tlst,flst,fignum=1,starttime=0,timeinc='hrs',\r\n dt=1.0,title=None,vmm=None,cmap=None,aspect=None,interpolation=None,\r\n cbori=None,cbshrink=None,cbaspect=None,cbpad=None,normalize='n',\r\n scale='log'):\r\n \r\n #time increment\r\n if timeinc=='hrs':\r\n tinc=3600/dt\r\n elif timeinc=='min':\r\n tinc=60/dt\r\n elif timeinc=='sec':\r\n tinc=1/dt\r\n else:\r\n raise ValueError(timeinc+'is not defined')\r\n #colormap\r\n if cmap==None:\r\n cmap='jet'\r\n else:\r\n cmap=cmap\r\n #aspect ratio\r\n if aspect==None:\r\n aspect='auto'\r\n else:\r\n aspect=aspect\r\n #interpolation\r\n if interpolation==None:\r\n interpolation='gaussian'\r\n else:\r\n interpolation=interpolation\r\n #colorbar orientation\r\n if cbori==None:\r\n cbori='vertical'\r\n else:\r\n cbori=cbori\r\n #colorbar shinkage\r\n if cbshrink==None:\r\n cbshrink=.99\r\n else:\r\n cbshrink=cbshrink\r\n #colorbar aspect\r\n if cbaspect==None:\r\n cbaspect=20\r\n else:\r\n cbaspect=cbaspect\r\n #colorbar pad\r\n if cbpad==None:\r\n cbpad=.1\r\n else:\r\n cbpad=cbpad\r\n \r\n #scale\r\n if scale=='log':\r\n zerofind=np.where(abs(tfarray)==0)\r\n tfarray[zerofind]=1.0\r\n if normalize=='y':\r\n plottfarray=20*np.log10(abs(tfarray/np.max(abs(tfarray))))\r\n else:\r\n plottfarray=20*np.log10(abs(tfarray))\r\n elif scale=='linear':\r\n if normalize=='y':\r\n plottfarray=abs(plottfarray/np.max(abs(plottfarray)))**2\r\n else:\r\n plottfarray=abs(tfarray)**2\r\n \r\n t=np.arange(len(fx))*dt+starttime*dt\r\n FX=np.fft.fft(padzeros(fx))\r\n FXfreq=np.fft.fftfreq(len(FX),dt)\r\n \r\n #set some plot parameters\r\n plt.rcParams['font.size']=10\r\n plt.rcParams['figure.subplot.left']=.13\r\n plt.rcParams['figure.subplot.right']=.98\r\n plt.rcParams['figure.subplot.bottom']=.07\r\n plt.rcParams['figure.subplot.top']=.96\r\n plt.rcParams['figure.subplot.wspace']=.25\r\n plt.rcParams['figure.subplot.hspace']=.20\r\n #plt.rcParams['font.family']='helvetica'\r\n \r\n fig=plt.figure(fignum)\r\n \r\n #plot FFT of fx\r\n fax=fig.add_axes([.05,.25,.1,.7])\r\n plt.plot(abs(FX[0:len(FX)/2]/max(abs(FX)))**2,FXfreq[0:len(FX)/2],'-k')\r\n plt.xlim(0,1)\r\n plt.ylim(0,FXfreq[len(FX)/2-1])\r\n fax.xaxis.set_major_locator(MultipleLocator(.5))\r\n \r\n #plot TFD\r\n pax=fig.add_axes([.25,.25,.75,.7])\r\n if vmm!=None:\r\n vmin=vmm[0]\r\n vmax=vmm[1]\r\n plt.imshow(plottfarray,extent=(tlst[0]/tinc,tlst[-1]/tinc,\r\n flst[0],flst[-1]),aspect=aspect,vmin=vmin,vmax=vmax,cmap=cmap,\r\n interpolation=interpolation)\r\n else:\r\n plt.imshow(plottfarray,extent=(tlst[0]/tinc,tlst[-1]/tinc,\r\n flst[0],flst[-1]),aspect=aspect,cmap=cmap,\r\n interpolation=interpolation)\r\n plt.xlabel('Time('+timeinc+')',fontsize=12,fontweight='bold')\r\n plt.ylabel('Frequency (Hz)',fontsize=12,fontweight='bold')\r\n if title!=None:\r\n plt.title(title,fontsize=14,fontweight='bold')\r\n plt.colorbar(orientation=cbori,shrink=cbshrink,pad=cbpad,aspect=cbaspect)\r\n \r\n #plot timeseries\r\n tax=fig.add_axes([.25,.05,.60,.1])\r\n plt.plot(t,fx,'-k')\r\n plt.axis('tight')\r\n plt.show()","def example3():\n arrive_time=example2() # Get packets arrive time using example1\n time_series.plot_time_series(arrive_time) # Plot time series using packets arrive time","def drawAxes(t):\r\n t.speed(0)\r\n t.pd()\r\n t.forward(500)\r\n t.back(500)","def plot_dt_signal(x, title=None):\n pylab.figure()\n pylab.stem(range(len(x)), x)\n pylab.title(title)\n pylab.xlabel(\"samples\")","def plot_time_slices(self):\n U = self.r.u[:, 15:-15, :]\n T = range(U.shape[2])\n kwarglist = [dict(t=t,\n index=self.index,\n U=U,\n levels=self.levels,\n fname=self.time_slice_path(t))\n for t in T]\n util.parallel_process(plot_time_slice, kwarglist=kwarglist)","def plot_time(signal,\n fs=None,\n ax=None,\n scale='linear',\n sides='onesided',\n title=None,\n label=None,\n **kwargs):\n if ax is None:\n ax = plt.gca()\n if fs is None:\n fs = 1\n ax.set_xlabel(\"Samples\")\n else:\n ax.set_xlabel(\"t / s\")\n t = _time_vector_onesided(signal, fs)\n if scale == 'linear':\n ax.set_ylabel('Amplitude (linear)')\n elif scale == 'db':\n signal = _db_calculation(signal)\n ax.set_ylabel('Amplitude / dB')\n else:\n raise NameError(\"Invalid scale\")\n if sides == 'onesided':\n ax.plot(t, signal, label=label, linewidth=2.0)\n elif sides == 'twosided':\n ax.plot(\n _time_vector_twosided(signal,\n fs),\n np.fft.fftshift(signal),\n label=label, linewidth=1.0)\n else:\n raise NameError(\"Invalid sides\")\n if title is not None:\n ax.set_title(title)\n ax.grid(True)\n ax.ticklabel_format(useOffset=False)\n return ax","def plot_x(t, x):\n plt.figure()\n plt.plot(t, x)\n plt.title(\"Vertical position of the skydiver as a function of time\")\n plt.xlabel(\"Time t [s]\")\n plt.ylabel(\"Height [m]\")\n plt.savefig('Parachute_position.png')","def plot_tseries(time_series, fig=None, axis=0,\r\n xticks=None, xunits=None, yticks=None, yunits=None,\r\n xlabel=None, ylabel=None, yerror=None, error_alpha=0.1,\r\n time_unit=None, **kwargs):\r\n\r\n if fig is None:\r\n fig = plt.figure()\r\n\r\n if not fig.get_axes():\r\n ax = fig.add_subplot(1, 1, 1)\r\n else:\r\n ax = fig.get_axes()[axis]\r\n\r\n #Make sure that time displays on the x axis with the units you want:\r\n #If you want to change the time-unit on the visualization from that used to\r\n #represent the time-series:\r\n if time_unit is not None:\r\n tu = time_unit\r\n conv_fac = ts.time_unit_conversion[time_unit]\r\n #Otherwise, get the information from your input:\r\n else:\r\n tu = time_series.time_unit\r\n conv_fac = time_series.time._conversion_factor\r\n\r\n this_time = time_series.time / float(conv_fac)\r\n ax.plot(this_time, time_series.data.T, **kwargs)\r\n\r\n if xlabel is None:\r\n ax.set_xlabel('Time (%s)' % tu)\r\n else:\r\n ax.set_xlabel(xlabel)\r\n\r\n if ylabel is not None:\r\n ax.set_ylabel(ylabel)\r\n\r\n if yerror is not None:\r\n if len(yerror.data.shape) == 1:\r\n this_e = yerror.data[np.newaxis, :]\r\n else:\r\n this_e = yerror.data\r\n delta = this_e\r\n e_u = time_series.data + delta\r\n e_d = time_series.data - delta\r\n for i in range(e_u.shape[0]):\r\n ax.fill_between(this_time, e_d[i], e_u[i], alpha=error_alpha)\r\n\r\n return fig","def coordinate_vs_time_plotter(array, xyz_axis=0, bird=0, axis_of_time_steps=2, start=0., end=1.):\r\n y_values = array[bird, xyz_axis, :]\r\n x_values = get_time_array(array, axis_of_time_steps, start, end)\r\n\r\n fig = plt.figure()\r\n ax = fig.add_subplot()\r\n\r\n if xyz_axis == 0:\r\n ax.set_ylabel('X (m)')\r\n elif xyz_axis == 1:\r\n ax.set_ylabel('Y (m)')\r\n elif xyz_axis == 2:\r\n ax.set_ylabel('Z (m)')\r\n else:\r\n print(\"That is not a valid axis choice. Please choose one of: 0, 1, 2\")\r\n ax.set_xlabel('Time (s)')\r\n ax.scatter(x_values, y_values)\r\n return fig.show()","def plot_global(type):\n click.echo(click.style(\n \"Generating Plot....\", fg='cyan', bold='true'))\n plot_time_series.TimeSeriesPloTs.plot_global(type)\n click.echo(click.style(\n \"Done....\", fg='green', bold='true'))","def demo(self, tmin=0, tmax=27.4, cadence=30.0 / 60.0 / 24.0, offset=0, raw=False, ax=None):\n t = np.arange(tmin, tmax, cadence)\n if ax is None:\n plt.figure('demo', figsize=(8, 3))\n else:\n plt.sca(ax)\n y = self.model(t)\n if raw:\n plt.plot(t, y + offset, alpha=0.25, linewidth=1, color='royalblue')\n plt.plot(t, self.integrated(t) + offset, alpha=0.5, linewidth=1, color='darkorange')\n plt.xlim(tmin, tmax)\n # plt.ylim(np.max(y)+0.01, np.min(y)-0.01)\n plt.xlabel('Time (days)')\n plt.ylabel('Flux (mag.)')","def plot_timefrequency(z, time, f, signal=None, method=\"stft\"):\n\n if method == \"stft\":\n figure_title = \"Short-time Fourier Transform Magnitude\"\n fig, ax = plt.subplots()\n for i in range(len(time)):\n ax.plot(f, z[:, i], label=\"Segment\" + str(np.arange(len(time))[i] + 1))\n ax.legend()\n ax.set_title(\"Signal Spectrogram\")\n ax.set_ylabel(\"STFT Magnitude\")\n ax.set_xlabel(\"Frequency (Hz)\")\n\n elif method == \"cwt\":\n figure_title = \"Continuous Wavelet Transform Magnitude\"\n elif method == \"wvd\":\n figure_title = \"Wigner Ville Distrubution Spectrogram\"\n fig = plt.figure()\n plt.plot(time, signal)\n plt.xlabel(\"Time (sec)\")\n plt.ylabel(\"Signal\")\n\n elif method == \"pwvd\":\n figure_title = \"Pseudo Wigner Ville Distribution Spectrogram\"\n\n fig, ax = plt.subplots()\n spec = ax.pcolormesh(time, f, z, cmap=plt.get_cmap(\"magma\"), shading=\"auto\")\n plt.colorbar(spec)\n ax.set_title(figure_title)\n ax.set_ylabel(\"Frequency (Hz)\")\n ax.set_xlabel(\"Time (sec)\")\n return fig","def __plot_T__(self, refresh=False, *args):\n # If plot is not requested, return:\n if not self.plotTeVar.get() or not self.plotTiVar.get():\n return\n\n # Check for a closed window:\n if 'T' in self.plots.keys() and not matplotlib.pyplot.fignum_exists(self.plots['T'].number):\n del self.plots['T']\n refresh = False\n # Update the existing plot, if it exists\n refresh = refresh or 'T' in self.plots.keys()\n if refresh:\n if 'T' in self.plots.keys():\n fig = self.plots['T']\n fig = matplotlib.pyplot.figure(fig.number)\n fig.clear()\n else:\n return\n # Make a Tew window:\n else:\n fig = matplotlib.pyplot.figure(figsize=(4,3))\n fig.canvas.set_window_title('T, time = ' + '{:.3f}'.format(1e9*self.imp.t(self.it)))\n ax = fig.add_subplot(111)\n\n # Plot:\n if self.plotTeVar.get():\n ax.plot(1e4*self.imp.r((self.it), self.ir)[0], self.imp.Te((self.it), self.ir)[0], 'r-', label='e')\n if self.plotTiVar.get():\n ax.plot(1e4*self.imp.r((self.it), self.ir)[0], self.imp.Ti((self.it), self.ir)[0], 'b-', label='i')\n\n ax.set_xlabel('r (um)', fontsize=12)\n ax.set_ylabel('T (keV)', fontsize=12)\n ax.legend()\n\n if self.logxVar.get():\n ax.set_xscale('log')\n if self.logyVar.get():\n ax.set_yscale('log')\n\n matplotlib.pyplot.tight_layout()\n\n if not refresh:\n fig.show()\n fig.canvas.draw()\n if self.wm is not None:\n self.wm.addWindow(matplotlib.pyplot.get_current_fig_manager().window)\n self.plots['T'] = fig","def plot_basic(time, data, lgnd=None):\n pylab.figure()\n pylab.plot(time, data)\n pylab.xlabel('time, s')\n pylab.ylabel('data')\n pylab.title('Basic Plotter')\n if lgnd != None:\n pylab.legend(lgnd)\n pylab.grid(True)\n pylab.show()","def comp_time_plot(p1=database['K+'], p2=database['pi+'], pmax=80, plot=True):\r\n dt = []\r\n p_range = np.linspace(10, pmax, 1000)\r\n m1 = p1.mass\r\n m2 = p2.mass\r\n for p in p_range:\r\n t1_per_m = 76.273/(beta(p, m1)*gamma(p, m1)*c)\r\n t2_per_m = 76.273/(beta(p, m2)*gamma(p, m2)*c)\r\n dt.append(abs(t1_per_m - t2_per_m)*1e12)\r\n dt_12_5 = dt[np.argmin(abs(p_range-12.5))]\r\n dt_75 = dt[np.argmin(abs(p_range-75))]\r\n ratio = dt_12_5/dt_75\r\n if plot==True:\r\n fig = plt.figure(figsize=[10, 5])\r\n ax = fig.add_subplot(1, 1, 1)\r\n ax.plot(p_range, dt, 'b', label=r'$\\Delta t$')\r\n ax.axvline(12.5, color='r', label='p=12.5 GeV')\r\n ax.axvline(75, color='g', label='p=75 GeV')\r\n ax.set_xlim(10, pmax)\r\n ax.set_ylim(0)\r\n ax.set_xlabel('p / GeV', fontsize=20)\r\n# ax.set_yscale('log')\r\n ax.set_ylabel(r'$\\Delta t$ / ps', fontsize=20)\r\n title = f'{p1.name} to {p2.name} '\r\n title += r'$\\Delta t$ dependancy on particle momenta'\r\n ax.set_title(title, fontsize=20)\r\n ax.legend(fontsize=20)\r\n text = 'dt(12.5) = {0:.2f} ps, '.format(dt_12_5)\r\n text += 'dt(75) = {0:.2f} ps, '.format(dt_75)\r\n text += 'ratio = {0:.3f}'.format(ratio)\r\n plt.show()\r\n print(text)\r\n return [dt_12_5, dt_75, ratio]","def plot_time(time_to_complete, plot_num):\n average = []\n for i, point in enumerate(time_to_complete):\n average.append(sum(time_to_complete[:i+1])/ (i+1))\n plt.plot(time_to_complete, color= 'blue', label=\"Epoch Time\")\n plt.plot(average, color = 'red', label= \"Average Time\", zorder = 3)\n plt.legend()\n plt.title(\"Time to complete FetchReach\")\n plt.ylabel(\"Time (seconds)\")\n plt.xlabel(\"Number iterations\")\n plt.savefig(\"./plots/time/time_to_complete_{}.png\".format(plot_num))\n plt.clf()","def tick(self):","def test_plot_timeseries_univariate(tmpdir, random):\n x = np.linspace(0, 10, 20)\n y = np.sin(x)\n segments = get_test_segments(data=np.expand_dims(y, 0))\n\n output_path = Path(tmpdir) / 'temp_visualization_test_univariate.png'\n\n plot_timeseries(x=x,\n y=y,\n segments=segments,\n show_plot=False,\n output_filename=output_path)\n\n assert output_path.exists()","def callback_time_cut(val):\n global plot_mode\n global idx_time\n last_plot_mode = plot_mode\n plot_mode = 'time_cut'\n idx_time = int(val)\n update_num_shadow(int(sld['neighbors'].val))\n # plot 121\n lcuttime.set_xdata( [val, val] )\n lcuttime.set_alpha( alpha_hm )\n lcutfreq.set_alpha( 0.0 )\n # plot 122\n if plot_mode == last_plot_mode:\n replot_flags = get_replot_flag( idx_time ) # [True/False, True/False]\n replot_shadow( replot_flags )\n update_shadow( ~replot_flags )\n update_light()\n else:\n replot_shadow( [True, True ] )\n replot_light()\n reform_axis()\n\n fig.canvas.draw_idle()","def test_plot_ts(kwargs):\n nchains = 4\n ndraws = 500\n obs_data = {\n \"y\": 2 * np.arange(1, 9) + 3,\n \"z\": 2 * np.arange(8, 12) + 3,\n }\n\n posterior_predictive = {\n \"y\": np.random.normal(\n (obs_data[\"y\"] * 1.2) - 3, size=(nchains, ndraws, len(obs_data[\"y\"]))\n ),\n \"z\": np.random.normal(\n (obs_data[\"z\"] * 1.2) - 3, size=(nchains, ndraws, len(obs_data[\"z\"]))\n ),\n }\n\n const_data = {\"x\": np.arange(1, 9), \"x_pred\": np.arange(8, 12)}\n\n idata = from_dict(\n observed_data=obs_data,\n posterior_predictive=posterior_predictive,\n constant_data=const_data,\n coords={\"obs_dim\": np.arange(1, 9), \"pred_dim\": np.arange(8, 12)},\n dims={\"y\": [\"obs_dim\"], \"z\": [\"pred_dim\"]},\n )\n\n ax = plot_ts(idata=idata, y=\"y\", show=True, **kwargs)\n assert np.all(ax)","def draw_spike_times(spike_times):\n for line in spike_times:\n plt.axvline(x=line, color='y')","def ProfilePlot(t,y,z,scale=86400, axis=0,color=[0.5,0.5,0.5]):\r\n from matplotlib import collections\r\n from matplotlib.ticker import Formatter\r\n\r\n class MyFormatter(Formatter):\r\n def __init__(self, dates, fmt='%b %d %Y'):\r\n self.fmt = fmt\r\n self.dates = dates\r\n\r\n def __call__(self, x, pos=0):\r\n 'Return the label for time x s'\r\n return datetime.strftime(datetime(1990,1,1)+timedelta(seconds=x),self.fmt)\r\n\r\n tsec = othertime.SecondsSince(t)\r\n formatter = MyFormatter(tsec)\r\n \r\n y = np.swapaxes(y,0,axis)\r\n \r\n lines=[]\r\n line2 =[]\r\n for ii, tt in enumerate(tsec):\r\n #xplot = set_scale(y[:,ii],tt)\r\n xplot = tt + y[:,ii]*scale\r\n lines.append(np.array((xplot,z)).T)\r\n line2.append(np.array([[tt,tt],[z[0],z[-1]]]).T)\r\n \r\n \r\n LC1 = collections.LineCollection(lines,colors=color,linewidths=1.5)\r\n LC2 = collections.LineCollection(line2,colors='k',linestyles='dashed') # Zero axis\r\n \r\n ax=plt.gca()\r\n ax.add_collection(LC1)\r\n ax.add_collection(LC2)\r\n ax.set_ylim((z.min(),z.max()))\r\n ax.xaxis.set_major_formatter(formatter)\r\n ax.set_xlim((tsec[0],tsec[-1]))\r\n plt.xticks(rotation=17) \r\n \r\n return ax","def PlotTimes(metadata, data):\n\n gp = Gnuplot.Gnuplot(persist=1)\n gp('set data style impulses')\n gp('set xtics 1')\n gp.clear()\n gp.xlabel('seconds')\n gp.ylabel('duration in second')\n gp.title(metadata.AsTitle())\n styles = {}\n line_style = 1\n\n for dataset in data:\n x = numpy.array(dataset.time, dtype='float_')\n if not dataset.name in styles:\n styles[dataset.name] = line_style\n line_style += 1\n d = Gnuplot.Data(x, dataset.data,\n title=dataset.name,\n with_='impulses ls %d' % styles[dataset.name])\n else: # no need to repeat a title that exists already.\n d = Gnuplot.Data(x, dataset.data,\n with_='impulses ls %d' % styles[dataset.name])\n\n gp.replot(d)\n gp.hardcopy('/tmp/%s-%s-%f.png' %\n (metadata.name, metadata.kernel, metadata.duration),\n terminal='png')","def plot_series(self, t1=0, t2=100, t1p=None, t2p=None):\n \n plot_discretized(self.ts, self.ts_dis, t1=t1, t2=t2, t1p=t1p, t2p=t2p)","def make_time_axis(axis=None,time_unit='s'):\n if axis is None:\n axis = plt.gca().xaxis\n class TimeFormatter(matplotlib.ticker.FuncFormatter):\n def __init__(self,time_unit):\n self.time_unit = time_unit\n conv = nitime.time_unit_conversion\n self.func = lambda x,y: (1.0*x) / conv[self.time_unit]\n def format_data_short(self,value):\n 'return a short string version'\n return \"%-12g\"%self.format_data(value)\n f = TimeFormatter(time_unit)\n axis.set_major_formatter(f)","def plot_tcv(self):\n self.plot_profiles(0, title='Shot #{:d} @ t={:.2f} s'.format(self.shot, self.t))","def test_plot_timeseries_multivariate(tmpdir, random):\n x = np.linspace(start=0, stop=10, num=20)\n ys = np.stack((np.sin(x), np.cos(x), np.tan(0.4 * x)))\n segments = get_test_segments(data=ys)\n output_path = Path(tmpdir) / 'temp_visualization_test_multivariate.png'\n\n plot_timeseries(x=x,\n y=ys.T,\n segments=segments,\n show_plot=False,\n output_filename=output_path)\n\n assert output_path.exists()","def plot_time_series(df, plt):\n # Remove the plot frame lines\n delta = (df[\"timestamp\"].max() - df[\"timestamp\"].min()) / 10\n plt.xticks(\n np.arange(\n df[\"timestamp\"].min(),\n df[\"timestamp\"].max(),\n step=np.around(delta, decimals=1),\n )\n )\n plt.grid()","def liveplot(x, y, xlim, ylim, title):\n plt.plot(x,y,'b.')\n plt.xlim(xlim)\n plt.ylim(ylim)\n plt.xlabel('North-South Axis')\n plt.ylabel('East-West Axis')\n plt.title(title)\n plt.show()","def update_plot(axes):\n axes.clear()\n\n i = C.i\n C.i += di # globale Zählvariable erhöhen\n if C.i >= len(tt):\n time.sleep(2)\n C.i = 0\n\n t = tt[i]\n q1 = qq1[i]\n q2 = qq2[i]\n q3 = qq3[i]\n CCframe(q1, q2, q3)\n\n # Ausgabe der aktuellen Zeit\n pl.text(0.06, 0.05, \"t = %3.2fs\" % t, transform = axes.transAxes)\n pl.axis([-3, 3, -3, 3])\n axes.figure.canvas.draw()","def plotTimeDelta(data, type_plot, device):\n mean = data.mean()\n std = data.std()\n max_data = data.max()\n min_data = data.min()\n max_indx = np.argmax(data) # max value index\n min_indx = np.argmin(data) # min value index\n x = np.arange(min_data, max_data, 0.1)\n y = normfun(x, mean, std)\n res_quantile = quantileValues(data, device)\n if type_plot == 0:\n plt.plot(x, y, color='blue')\n annot_max_min(x, y)\n # plt.hist(data.dropna(), bins=500, rwidth=0.9, normed=True)\n plt.title('Time Delta distribution')\n plt.xlabel('Time Delta')\n plt.ylabel('Probability')\n sns.distplot(tmp.deltaSeconds.dropna(),\n kde=True, rug=True, rug_kws={\"color\": \"k\"},\n kde_kws={\"color\": \"red\", \"lw\": 3, \"label\": \"KDE\"},\n hist_kws={\"histtype\": \"step\", \"lw\": 3, \"alpha\": 1,\n \"color\": \"g\"},\n bins=500)\n # ax.set(xlabel='Vibration Intensity', ylabel='Probability')\n elif type_plot == 1: # plot the max and min point\n plt.plot(data)\n plt.plot(max_indx, data[max_indx], 'ks')\n show_max = '['+str(max_indx)+' '+str(data[max_indx])+']'\n plt.annotate(show_max,\n xytext=(max_indx, data[max_indx]),\n xy=(max_indx, data[max_indx]))\n plt.plot(min_indx, data[min_indx], 'gs')\n show_min = '['+str(min_indx)+' '+str(data[min_indx])+']'\n plt.annotate(show_min,\n xytext=(min_indx, data[min_indx]),\n xy=(min_indx, data[min_indx]))\n plt.title('Time Delta')\n plt.xlabel('Index')\n plt.ylabel('Vibration Intensity Value')\n elif type_plot == 2: # boxplot\n boxplot(data.dropna())\n return res_quantile","def pf_plot(pf, t):\n xx = pf.XS[t, :, 0]\n yy = pf.XS[t, :, 1]\n ww = pf.WS[t, :]\n plt.scatter(xx, yy, s=ww * 5000)","def plot_curve(self, label='', axis=None):\n controller = deepcopy(self._controller)\n data = np.zeros(len(self._ts))\n for i in range(len(self._ts)):\n controller.step(self._dt)\n data[i] = controller.sim.get_output()\n \n if axis is None:\n plt.plot(self._ts, data, label=label)\n else:\n axis.plot(self._ts, data, label=label)","def plot(self):\n\t\t\n\t\ttf=tfData(self.shotno,tStart=None,tStop=None)\n\t\t\n\t\t_plt.figure()\n\t\tax1 = _plt.subplot2grid((3,2), (0,1), rowspan=3) #tf\n\t\tax2 = _plt.subplot2grid((3,2), (0,0)) #vf\n\t\tax3 = _plt.subplot2grid((3,2), (1,0),sharex=ax2) #oh\n\t\tax4 = _plt.subplot2grid((3,2), (2, 0),sharex=ax2) #sh\n\t\tfig=_plt.gcf()\n\t\tfig.set_size_inches(10,5)\n\t\t\t\t\n\t\ttStart=-2\n\t\ttStop=20\n\t\t\n\t\tax1.plot(tf.time*1e3,tf.tfBankField)\n\t\tax1.axvspan(tStart,tStop,color='r',alpha=0.3)\n\t\t_plot.finalizeSubplot(ax1,xlabel='Time (s)',xlim=[-150,450],ylabel='TF Field (T)')#,title=self.title\n\t\t\n\t\tax2.plot(self.vfTime*1e3,self.vfBankCurrent*1e-3)\n\t\t_plot.finalizeSubplot(ax2,ylabel='VF Current\\n(kA)')\n\t\t\n\t\tax3.plot(self.ohTime*1e3,self.ohBankCurrent*1e-3)\n\t\t_plot.finalizeSubplot(ax3,ylim=[-20,30],ylabel='OH Current\\n(kA)')\n\t\t\n\t\tax4.plot(self.shTime*1e3,self.shBankCurrent*1e-3)\n\t\t_plot.finalizeSubplot(ax4,ylim=[tStart,tStop],xlabel='Time (s)',ylabel='SH Current\\n(kA)')\n\t\t\n\t\t_plot.finalizeFigure(fig,title=self.title)\n#\t\tfig.set_tight_layout(True)\n\t\t\n\t\treturn fig","def plot_fitter(self):\n\n total_time=self.interval*self.maxspectra\n times = np.linspace(self.interval,total_time + 1,self.interval)\n spectra_fitter.main(self.rt_plot.sum_data, times)","def lastTick():","def SFplot(profile, time, Xlim=[], ax=None, **kwargs):\n X = profile.X\n sf = SF(profile, time, Xlim)\n clw = {'marker': '.', 'ls': 'none'}\n if 'label' not in kwargs:\n clw['label'] = profile.name+'_%.1fh_SF' % (time/3600)\n args = {**clw, **kwargs}\n if ax is None:\n fig = plt.figure()\n ax = fig.add_subplot(111)\n ax.semilogy(X, sf, **args)\n ax.set_xlabel('Mole fraction', fontsize=label_fontsize)\n ax.set_ylabel('Diffusion Coefficients '+'$(m^2/s)$', fontsize=label_fontsize)\n ax.set_xlim(plot_lim(X.min(), X.max()))\n ax.tick_params(labelsize=tick_fontsize)\n leg = ax.legend(numpoints=1, fontsize=leg_fontsize)\n leg.get_frame().set_linewidth(0.0)\n leg.set_draggable(True)\n plt.tight_layout()\n\n return ax","def __plot(name, x, y):\n import matplotlib.pyplot as plt\n\n plt.plot(x, y)\n plt.xlabel('elements')\n plt.ylabel('time (seconds)')\n plt.savefig(\"{}\".format(name))","def v_from_p_function(self):\r\n\r\n track_c = [] # p classical function,\r\n for i in range(len(self.dt.momentum_t)):\r\n track_c.append(self.dt.momentum_t[i] / self.dt.mass)\r\n\r\n (fig, ax) = plt.subplots()\r\n\r\n ax.plot(\r\n self.dt.momentum_t,\r\n track_c,\r\n linestyle=':',\r\n linewidth=1,\r\n color='b',\r\n label='classic',\r\n )\r\n\r\n # marker=\"+\", markersize = 13,\r\n # ax.plot(self.dt.momentum_t, self.observer.velT, linestyle=\" \",\r\n # color=\"k\",marker=\"+\", markersize = 13, label=\"measurement\")\r\n\r\n ax.plot(\r\n self.dt.momentum_t,\r\n self.dt.vel_t,\r\n linestyle=' ',\r\n color='k',\r\n marker='o',\r\n label='result of measurements',\r\n )\r\n ax.plot(\r\n self.dt.momentum_t,\r\n self.dt.vel_anl,\r\n linestyle='-',\r\n color='red',\r\n linewidth=1,\r\n label='continuum',\r\n )\r\n\r\n # Euler's method == analitical function. We not plot it.\r\n\r\n ax.plot(\r\n self.dt.momentum_t,\r\n self.dt.vn,\r\n linestyle='--',\r\n color='blue',\r\n marker='x',\r\n linewidth=1,\r\n label=\"Euler's method\",\r\n )\r\n\r\n # error\r\n\r\n ax.errorbar(self.dt.momentum_t, self.dt.vel_t, fmt='k ',\r\n yerr=self.dt.vel_t_err)\r\n\r\n xm = -1.0\r\n for i in range(len(self.dt.momentum_t)):\r\n if self.dt.momentum_t[i] > xm:\r\n xm = self.dt.momentum_t[i]\r\n stepx = round(xm / float(len(self.dt.momentum_t)), 1)\r\n xm = round(xm + stepx, 1)\r\n ax.set_xlim([0, xm]) # xm = 0.85\r\n\r\n # signature on the horizontal x-axis\r\n\r\n ax.set_xlabel('p')\r\n\r\n # Create an instance of the class that will be responsible for the location of the labels (base is step on x)\r\n\r\n locatorx = matplotlib.ticker.MultipleLocator(base=stepx) # step on x is base=0.1\r\n\r\n # Set the locator for the main labels\r\n\r\n ax.xaxis.set_major_locator(locatorx)\r\n\r\n # line draw\r\n\r\n line = matplotlib.lines.Line2D([0.0, 9.0], [1.0, 1.0], color='b'\r\n )\r\n ax.add_line(line)\r\n plt.text(0.7, 1.01, u'light speed', horizontalalignment='center'\r\n )\r\n ax.set_ylim([0, 1.1])\r\n\r\n # signature on vertical y axis\r\n\r\n ax.set_ylabel('v')\r\n\r\n # Create an instance of the class that will be responsible for the location of the labels (base is step on y)\r\n\r\n locatory = matplotlib.ticker.MultipleLocator(base=0.1) # step on y is base=0.1\r\n\r\n # Set the locator for the main labels\r\n\r\n ax.yaxis.set_major_locator(locatory)\r\n\r\n ax.grid()\r\n\r\n # show legend\r\n\r\n ax.legend(loc='upper left')\r\n\r\n # show drawing\r\n # pylab.show()\r\n\r\n plt.show()","def plot_tseries(*args, **kwargs) :\n data = kwargs.pop('data')\n return data.dropna().plot(x=args[0], y=args[1], **kwargs)","def plot_f(self, *args, **kwargs):\r\n kwargs['plot_raw'] = True\r\n self.plot(*args, **kwargs)","def footprint_demo(**kw):\n # Note: needs fixed slits in single_point()\n count = 1500000\n data = []\n for theta in np.linspace(0.15, 5, 30):\n n = single_point(theta=theta, count=count, trace=False, **kw)\n data.append((theta, np.sum(n.active)))\n print(data[-1])\n x, y = zip(*data)\n pylab.plot(x, np.array(y)/count)\n pylab.show()","def on_tick(self, time):\n pass","def plotTrace(trace):\n for t in trace:\n plt.plot(range(len(t)),t,alpha=0.5)\n plt.ylabel(\"Trace\")\n plt.xlabel(\"Step\")\n\n return","def visualize(self, time, pred, true):\n plt.plot(time, true, label='Actual')\n plt.plot(time, pred, label='Predicted')\n plt.xlabel('Time')\n plt.ylabel('Price ($)')\n plt.legend(bbox_to_anchor=(0.1, 1), loc=2, borderaxespad=0.,\n prop={'size': 14})\n plt.show()","def plot_timeseries(self, x, *a, **kw):\n v, t = self.timeseries(x, **kw)\n utcoffset = kw.pop('utcoffset', None)\n if utcoffset is not None: # temporary hack because plot_date seems to be ignoring tz kwarg...\n t += utcoffset/24.\n for k in ('convert', 'timeslice', 'rmnans'): \n trash = kw.pop(k, None)\n if not a: a = '-' # plot a line by default\n if 'label' not in kw: \n kw.update({'label': x.replace('platform ','').replace('_',' ')})\n if 'axes' in kw: # deal with possible bug in plot_date?\n ax = kw.pop('axes')\n ax.plot_date(t, v, *a, **kw)\n ax.set_xlim(ax.xaxis.get_data_interval()) # update time limits\n else: # just make a new axis\n plt.plot_date(t, v, *a, **kw)\n ax = plt.gca()\n plt.gcf().autofmt_xdate()\n return ax","def plot_running_time(num_clusters):\n slow_running = []\n fast_running = []\n for dummy_i in range(2, num_clusters):\n cluster_list = gen_random_clusters(dummy_i)\n start = timer()\n fast_closest_pair(cluster_list)\n end = timer()\n fast_running.append((end - start))\n \n start = timer()\n slow_closest_pair(cluster_list)\n end = timer()\n slow_running.append((end - start))\n #\n plt.plot(range(2, num_clusters), fast_running)\n plt.plot(range(2, num_clusters), slow_running)\n plt.xlabel(\"num clusters\")\n plt.ylabel(\"running time in seconds\")\n plt.title(\"Running time slow closest pair vs fast closest pair.\")\n plt.legend([\"fast closest pair\", \"slow closest pair\"])\n plt.show()","def diagrama_ts(S: [np.ndarray] or [list], T: [np.ndarray] or [list], title: str, hide_values: bool) -> (Figure, Axes):\n return diagram(S, T, title, \"TS\", hide_values)","def time_support(*, scale=None, format=None, simplify=True):\n import matplotlib.units as units\n from matplotlib.ticker import MaxNLocator, ScalarFormatter\n\n from astropy.visualization.wcsaxes.utils import select_step_hour, select_step_scalar\n\n class AstropyTimeLocator(MaxNLocator):\n # Note: we default to AutoLocator since many time formats\n # can just use this.\n\n def __init__(self, converter, *args, **kwargs):\n kwargs[\"nbins\"] = 4\n super().__init__(*args, **kwargs)\n self._converter = converter\n\n def tick_values(self, vmin, vmax):\n # Where we put the ticks depends on the format we are using\n if self._converter.format in YMDHMS_FORMATS:\n # If we are here, we need to check what the range of values\n # is and decide how to find tick locations accordingly\n\n vrange = vmax - vmin\n\n if (\n self._converter.format != \"yday\" and vrange > 31\n ) or vrange > 366: # greater than a month\n # We need to be careful here since not all years and months have\n # the same length\n\n # Start off by converting the values from the range to\n # datetime objects, so that we can easily extract the year and\n # month.\n\n tmin = Time(\n vmin, scale=self._converter.scale, format=\"mjd\"\n ).datetime\n tmax = Time(\n vmax, scale=self._converter.scale, format=\"mjd\"\n ).datetime\n\n # Find the range of years\n ymin = tmin.year\n ymax = tmax.year\n\n if ymax > ymin + 1: # greater than a year\n # Find the step we want to use\n ystep = int(select_step_scalar(max(1, (ymax - ymin) / 3)))\n\n ymin = ystep * (ymin // ystep)\n\n # Generate the years for these steps\n times = []\n for year in range(ymin, ymax + 1, ystep):\n times.append(datetime(year=year, month=1, day=1))\n\n else: # greater than a month but less than a year\n mmin = tmin.month\n mmax = tmax.month + 12 * (ymax - ymin)\n\n mstep = int(select_step_scalar(max(1, (mmax - mmin) / 3)))\n\n mmin = mstep * max(1, mmin // mstep)\n\n # Generate the months for these steps\n times = []\n for month in range(mmin, mmax + 1, mstep):\n times.append(\n datetime(\n year=ymin + (month - 1) // 12,\n month=(month - 1) % 12 + 1,\n day=1,\n )\n )\n\n # Convert back to MJD\n values = Time(times, scale=self._converter.scale).mjd\n\n elif vrange > 1: # greater than a day\n self.set_params(steps=[1, 2, 5, 10])\n values = super().tick_values(vmin, vmax)\n\n else:\n # Determine ideal step\n dv = (vmax - vmin) / 3 * 24 << u.hourangle\n\n # And round to nearest sensible value\n dv = select_step_hour(dv).to_value(u.hourangle) / 24\n\n # Determine tick locations\n imin = np.ceil(vmin / dv)\n imax = np.floor(vmax / dv)\n values = np.arange(imin, imax + 1, dtype=np.int64) * dv\n\n else:\n values = super().tick_values(vmin, vmax)\n\n # Get rid of values outside of the input interval\n values = values[(values >= vmin) & (values <= vmax)]\n\n return values\n\n def __call__(self):\n vmin, vmax = self.axis.get_view_interval()\n return self.tick_values(vmin, vmax)\n\n class AstropyTimeFormatter(ScalarFormatter):\n def __init__(self, converter, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self._converter = converter\n self.set_useOffset(False)\n self.set_scientific(False)\n\n def format_ticks(self, values):\n if len(values) == 0:\n return []\n if self._converter.format in YMDHMS_FORMATS:\n times = Time(values, format=\"mjd\", scale=self._converter.scale)\n formatted = getattr(times, self._converter.format)\n if self._converter.simplify:\n if self._converter.format in (\"fits\", \"iso\", \"isot\"):\n if all(x.endswith(\"00:00:00.000\") for x in formatted):\n split = \" \" if self._converter.format == \"iso\" else \"T\"\n formatted = [x.split(split)[0] for x in formatted]\n elif self._converter.format == \"yday\":\n if all(x.endswith(\":001:00:00:00.000\") for x in formatted):\n formatted = [x.split(\":\", 1)[0] for x in formatted]\n return formatted\n elif self._converter.format == \"byear_str\":\n return Time(\n values, format=\"byear\", scale=self._converter.scale\n ).byear_str\n elif self._converter.format == \"jyear_str\":\n return Time(\n values, format=\"jyear\", scale=self._converter.scale\n ).jyear_str\n else:\n return super().format_ticks(values)\n\n class MplTimeConverter(units.ConversionInterface):\n def __init__(self, scale=None, format=None, simplify=None):\n super().__init__()\n\n self.format = format\n self.scale = scale\n self.simplify = simplify\n\n # Keep track of original converter in case the context manager is\n # used in a nested way.\n self._original_converter = units.registry.get(Time)\n\n units.registry[Time] = self\n\n @property\n def format(self):\n return self._format\n\n @format.setter\n def format(self, value):\n if value in UNSUPPORTED_FORMATS:\n raise ValueError(f\"time_support does not support format={value}\")\n self._format = value\n\n def __enter__(self):\n return self\n\n def __exit__(self, type, value, tb):\n if self._original_converter is None:\n del units.registry[Time]\n else:\n units.registry[Time] = self._original_converter\n\n def default_units(self, x, axis):\n if isinstance(x, tuple):\n x = x[0]\n if self.format is None:\n self.format = x.format\n if self.scale is None:\n self.scale = x.scale\n return \"astropy_time\"\n\n def convert(self, value, unit, axis):\n \"\"\"\n Convert a Time value to a scalar or array.\n \"\"\"\n scaled = getattr(value, self.scale)\n if self.format in YMDHMS_FORMATS:\n return scaled.mjd\n elif self.format == \"byear_str\":\n return scaled.byear\n elif self.format == \"jyear_str\":\n return scaled.jyear\n else:\n return getattr(scaled, self.format)\n\n def axisinfo(self, unit, axis):\n \"\"\"\n Return major and minor tick locators and formatters.\n \"\"\"\n majloc = AstropyTimeLocator(self)\n majfmt = AstropyTimeFormatter(self)\n return units.AxisInfo(\n majfmt=majfmt, majloc=majloc, label=f\"Time ({self.scale})\"\n )\n\n return MplTimeConverter(scale=scale, format=format, simplify=simplify)","def time_series(t, f=0.02):\n T = t.size\n # Seasonal component and time-varying trend component\n ys = np.sin(2 * np.pi * f * t) * 0.6 + np.sin(1 / 5 * 2 * np.pi * f * t) * 0.2\n # Amplitude modulation component\n amp_mod = 0.5 * np.sin(1 / 6 * 2 * np.pi * f * t) + 0.8\n ys *= amp_mod\n ys = np.reshape(ys, (T,1))\n return ys","def make_plot(x,y):","def plot_running_time():\n global counter\n counter += 1\n running_time_targeted = []\n running_time_fast_targeted = []\n \n for node_number in range(10, 1000, 10):\n synthetic_undirected_graph = make_synthetic_undirected_graph(node_number, 5)\n\n start_time = time.time()\n attack_order = targeted_order(synthetic_undirected_graph)\n stop_time = time.time()\n running_time_targeted.append(stop_time - start_time)\n \n start_time = time.time()\n attack_order = fast_targeted_order(synthetic_undirected_graph)\n stop_time = time.time()\n running_time_fast_targeted.append(stop_time - start_time)\n \n plt.plot(range(10, 1000, 10), running_time_targeted, '-b', label = 'targeted_order')\n plt.plot(range(10, 1000, 10), running_time_fast_targeted, '-r', label = 'fast_targeted_order')\n \n plt.legend(loc='upper right')\n\n\n plt.title(\" plot of running time of desktop Python\")\n plt.xlabel(\"the number of nodes\")\n plt.ylabel(\"running times\")\n plt.savefig(\"running_time_\"+str(counter)+\".png\", dpi = 72)\n plt.gcf().clear() # hose-keeping","def plot(self):\n\t\t\n\t\tfig,p1=_plt.subplots()\n\t\tp1.plot(self.time*1e3,self.qStar,label=r'q$^*$')\n\t\tp1.plot(self.time*1e3,self.qStarCorrected,label=r'q$^* * 1.15$')\n\t\t_plot.finalizeSubplot(p1,xlabel='Time (ms)',ylabel=r'q$^*$',ylim=[1,5])\n\t\t_plot.finalizeFigure(fig,title=self.title)","def pyts_time_series(x):\n p = circle(x, r=0.2, a=0, b=0, x_lim=(-0.2, 0.2))\n y = - circle(x, r=0.2, a=0.4, b=0, x_lim=(0.2, 0.6))\n t = 0\n s = circle(x, r=0.125, a=0.9, b=-0.025, x_lim=(0.775, 0.9))\n s += 0.1 * (x >= 0.9) * (x <= 1.2)\n return p + y + t + s","def plotPerTimeStamp(options):\n name = options['name'] + '_' + options['scan'] + '_perTime'\n if options['extra']:\n name += '_' + options['extra']\n f = openRootFileR(options['name']+'_perTime')\n histname = plotName(name, timestamp=False)\n filename = plotName(name, timestamp=True)\n filepath = plotPath(name, timestamp=True)\n print '<<< Save plot:', filepath\n hist = f.Get(histname)\n hist.SetErrorOption(options['error'])\n if options['big']:\n canvas = TCanvas('c', '', 8000, 1200)\n else:\n canvas = TCanvas('c', '', 1400, 500)\n canvas.SetLogy(options['logy'])\n gStyle.SetOptStat(options['optstat'])\n hist.Draw()\n gPad.Update()\n hist.GetXaxis().SetTimeDisplay(1)\n hist.GetXaxis().SetTimeFormat('#splitline{%d.%m.%y}{%H:%M:%S}%F1969-12-31' \\\n +' 22:00:00')\n hist.GetXaxis().SetLabelOffset(0.03)\n hist.GetXaxis().SetTitle('')\n if 'xmin' in options and 'xmax' in options:\n hist.GetXaxis().SetRangeUser(options['xmin'], options['xmax'])\n hist.GetYaxis().SetTitle(options['ytitle'])\n hist.GetYaxis().SetTitleOffset(1.2)\n for axis in [hist.GetXaxis(), hist.GetYaxis()]:\n axis.SetTitleFont(133)\n axis.SetTitleSize(16)\n axis.SetLabelFont(133)\n axis.SetLabelSize(12)\n axis.CenterTitle()\n if options['big']:\n axis.SetTickLength(0.01)\n if options['big']:\n hist.GetYaxis().SetTitleOffset(0.25)\n drawSignature(filename)\n gPad.Modified()\n gPad.Update()\n if options['retrn']:\n return [canvas, hist, f]\n else:\n canvas.Print(filepath)\n canvas.Close()\n closeRootFile(f, options['name']+'_perTime')","def plot_v(t, v):\n p1 = plt.plot(t,v)\n plt.xlabel('Time [s]')\n plt.ylabel('Velocity [m/s]')\n plt.title('Velocity for the skydiver as a function of time')\n plt.show()\n plt.savefig('Parachute_velocity.png')","def draw_plot(yscale='linear'):\n plt.yscale(yscale)\n plt.xticks(list(range(0, 101, 5)))\n plt.xlabel('percentile [%]')\n plt.grid(True)\n plt.ylabel('operation time [ns]')\n plt.legend()\n plt.show()","def tick(self, dt):\n pass","def plot(self, set_xticks_from_index=True,\n xlabel=None,\n ylabel=None,\n colormap='jet',\n logx=False,\n logy=None,\n grid=True,\n linewidth=2,\n rot=None,\n dpi=None,\n fontsize=14,\n specs_fontsize=None,\n tick_fontsize=None,\n label_fontsize=None,\n legend_fontsize=None,\n figsize=None,\n specs_position='left',\n debug_plotfs=False,\n pause_timefs=0.1,\n modules=None,\n use_frame=False,\n sp_argID=0,\n sp_ncols=-1,\n sp_sharey=None,\n sp_title_position='center',\n sp_title_fontsize=None,\n sp_show_specs=True,\n save=None,\n **kwargs):\n\n # Get dataframe and dtype\n dtype = self.dtype\n df = self.__df_timings\n multiindex = self.multiindex\n\n style.use('bmh' if use_frame else 'fivethirtyeight')\n\n # Setup styles\n available_linestyles = ['-.', '--', '-']\n extls = np.resize(available_linestyles, df.shape[1]).tolist()\n\n # Get plot params - logy, ylabel\n if ylabel is None:\n ylabel_map = {'timings': 'Runtime [s]', 'speedups': 'Speedup [x]', 'scaled_timings': 'Scaled Runtime [x]'}\n ylabel = ylabel_map[dtype]\n\n if logy is None:\n logy_map = {'timings': True, 'speedups': False, 'scaled_timings': False}\n logy = logy_map[dtype]\n\n groupings_done = False\n if multiindex:\n # Get groupings\n groupings_done, ncols, out = _get_groupings(df, sp_argID)\n if not groupings_done:\n warnings.warn('Groupings are not possible. Hence, resorting to normal plot.', stacklevel=2)\n\n if not groupings_done: # normal plot\n is_xticks_number, xticks = _getxticks(df, set_xticks_from_index=set_xticks_from_index)\n\n if not is_xticks_number and logx:\n xticks = None\n warnings.warn('Some xticks might be not be seen.', stacklevel=2)\n\n tick_fontsize = _assign_mplibrcparams(fontsize, specs_fontsize, tick_fontsize,\n label_fontsize, legend_fontsize, specs_position, dpi)\n\n # Plot using dataframe data and its attributes\n ax = df.plot(style=extls,\n colormap=_truncate_cmap(colormap),\n title=specs(modules=modules),\n rot=rot,\n fontsize=tick_fontsize,\n linewidth=linewidth,\n logx=logx,\n logy=logy,\n figsize=figsize,\n xticks=xticks,\n **kwargs)\n\n if grid:\n ax.grid(True, which=\"both\", ls=\"-\")\n if xlabel is not None:\n ax.set_xlabel(xlabel)\n ax.set_ylabel(ylabel)\n\n if set_xticks_from_index and is_xticks_number:\n ax.set_xticklabels(df.index)\n if not is_xticks_number: # earlier set_xticks_from_index or not is_xticks_number\n ax.set_xticks([], minor=True)\n\n # Show in fullscreen\n fullscreenfig(ax, pause_timefs, print_info=debug_plotfs)\n ax_fig = ax.figure\n else: # subplot\n _SUBPLOT_FIGSIZE = (6.4, 3.6)\n\n if figsize is None:\n figsize = _SUBPLOT_FIGSIZE\n\n if sp_ncols == -1:\n sp_ncols = ncols\n\n tick_fontsize, specs_fontsize = _assign_mplibrcparams_for_subplot(fontsize, label_fontsize, sp_title_fontsize,\n legend_fontsize, tick_fontsize, sp_title_position,\n specs_fontsize, dpi)\n\n len1 = len(out)\n nrows = int(np.ceil(len1 / float(sp_ncols)))\n r, c = np.unravel_index(np.arange(len1), (nrows, sp_ncols))\n\n map_dict = {None: False, 'row': 'row', 'r': 'row', 'global': True, 'g': True}\n sharey_val = _mapvals(sp_sharey, map_dict, name='subplot_sharey')\n\n # Setup title str for each subplot\n title_str = _subplot_title_str(df, sp_argID)\n\n df0 = out[list(out.keys())[0]]\n is_xticks_number, xticks = _getxticks(df0, set_xticks_from_index=set_xticks_from_index)\n\n if not is_xticks_number and logx:\n xticks = None\n warnings.warn('Some xticks might be not be seen.', stacklevel=2)\n\n figS = (sp_ncols * figsize[0], nrows * figsize[1])\n fig, axs = plt.subplots(nrows, sp_ncols, sharex='col', constrained_layout=True, sharey=sharey_val, figsize=figS)\n axs = axs.reshape(-1, sp_ncols)\n\n for i, (k, df0) in enumerate(out.items()):\n ri, ci = r[i], c[i]\n ax = axs[ri, ci]\n is_first_subplot = (ri == 0) and (ci == 0)\n\n subplot_title = str(k)\n if is_first_subplot:\n subplot_title = title_str + subplot_title\n\n df0.plot(ax=ax,\n style=extls,\n linewidth=linewidth,\n colormap=_truncate_cmap(colormap),\n xticks=xticks,\n logx=logx,\n logy=logy,\n rot=rot,\n title=subplot_title,\n fontsize=tick_fontsize,\n legend=i == 0,\n **kwargs)\n\n if grid:\n ax.grid(True, which=\"both\", ls=\"-\")\n\n # Next two IFs are needed for logx=True plots\n if set_xticks_from_index and is_xticks_number:\n ax.set_xticklabels(df0.index)\n if not is_xticks_number: # earlier set_xticks_from_index or not is_xticks_number\n ax.set_xticks([], minor=True)\n\n # Set ylabel on middle row ID\n axs[nrows // 2, 0].set_ylabel(ylabel)\n\n if sp_show_specs:\n fig.suptitle(specs_short(), fontsize=specs_fontsize)\n\n # remove unused axes\n xlabel = df.index.names[sp_argID]\n r, c = np.unravel_index(np.arange(len1, axs.size), (nrows, sp_ncols))\n for (i, j) in zip(r, c):\n ax = axs[i, j]\n ax.set_xlabel(xlabel)\n ax.tick_params(labelrotation=rot)\n ax.yaxis.set_visible(False)\n ax.patch.set_visible(False)\n plt.setp(ax.spines.values(), visible=False)\n ax_fig = fig\n\n # Save figure\n if save is not None:\n ax_fig.savefig(save, bbox_inches='tight')\n return","def show_events(ticks):\n for tick in i_prune_ticks(ticks):\n print(tick)","def stft_plot(x: np.ndarray, *args, **kwargs) -> go.Figure:\n\n # calculate stft\n f, t, x_stft = signal.stft(x, *args, **kwargs)\n\n # compensate for scaling factor used in scipy's stft\n if \"window\" in kwargs.keys():\n if isinstance(kwargs[\"window\"], str):\n window = signal.get_window(kwargs[\"window\"], kwargs[\"nperseg\"])\n else:\n window = kwargs[\"window\"]\n factor = window.sum()\n else:\n factor = kwargs[\"nperseg\"]\n x_stft *= factor\n\n # plot\n power = 20 * np.log10(np.abs(x_stft))\n fig = px.imshow(power, x=t, y=f, aspect=\"auto\", origin=\"lower\")\n fig.update_yaxes(title=\"Frequency\")\n fig.update_xaxes(title=\"Time\")\n fig.update_layout(coloraxis_colorbar=dict(title=\"[dB]\"))\n fig.show()\n\n return fig","def plotting(x,y,name,variable,unit,label_name=\"Simulation\",title=None,mins=False):\n ax = plt.figure(str(name))\n # ax.legend(\"best\")\n\n if mins:\n x/=60 #change time to mins from secs\n plt.xlabel(\"t [min]\")\n else:\n plt.xlabel(\"t [s]\")\n\n if title!= None:\n plt.title(str(title))\n\n plt.plot(x-x[0],y,label=label_name)\n\n\n lab = str(str(variable)+\" \"+\"[\"+unit+\"]\")\n plt.legend(loc='best')\n plt.ylabel(lab)\n plt.grid(True)\n # plt.savefig(title)\n plt.show()\n\n return ax","def visualize_time_series(fig_ax, data, inp_color, missing_data, lag_color, first_date,\n x_label=\"Number of Days\", y_label=\"Log of Aluminium Price\", title=\"Prices over time\"):\n fig, ax = fig_ax\n ((x_train_raw, y_train_raw), y_pred_list) = data\n\n missing_x, missing_y = missing_data\n is_missing = len(missing_x) != 0\n\n first_date = datetime.strptime(first_date, '%Y-%m-%d')\n\n convert_date = lambda x: [\n np.datetime64((first_date + timedelta(days=d)).strftime('%Y-%m-%d'))\n for d in x\n ]\n convert_price = lambda x: x[\"Output\"].to_list()\n\n x_train = convert_date(x_train_raw[\"Date\"].to_list())\n y_train = convert_price(y_train_raw)\n \n cut_point = x_train[-1]\n ax.plot(x_train, y_train, color=color[inp_color])\n\n for i, y_pred in enumerate(y_pred_list):\n data, plot_name, color_code, is_bridge = y_pred\n mean_pred, x_test_raw = data[\"mean\"], data[\"x\"]\n x_test = convert_date(x_test_raw)\n\n if i == 0 and is_missing:\n missing_x = convert_date(missing_x)\n ax.axvline(x_test[0], color=color[lag_color], linestyle='--', linewidth=0.5, dashes=(5, 0), alpha=0.2)\n ax.plot([missing_x[-1], x_test[0]], [missing_y[-1], mean_pred[0]], color[lag_color], linestyle=\"dashed\")\n ax.axvspan(cut_point, x_test[0], color=color[lag_color], alpha=0.1)\n\n plot_bound(ax, data, x_test, color[color_code], plot_name)\n\n if is_bridge and (not is_missing): \n ax.plot([x_train[-1], x_test[0]], [y_train[-1], mean_pred[0]], color[color_code], linewidth=1.5)\n\n if is_missing:\n ax.plot(missing_x, missing_y, color=color[lag_color], linestyle=\"dashed\")\n ax.plot([x_train[-1], missing_x[0]], [y_train[-1], missing_y[0]], color[lag_color], linestyle=\"dashed\")\n ax.axvline(cut_point, color=color[lag_color], linestyle='--', linewidth=0.5, dashes=(5, 0), alpha=0.2)\n else:\n ax.axvline(cut_point, color=color[\"k\"], linestyle='--')\n\n ax.xaxis.set_minor_locator(AutoMinorLocator())\n ax.legend()\n\n # ax.set_xlabel(x_label)\n ax.set_ylabel(y_label)\n ax.set_title(title)\n\n # ax.set_xlim(left=cut_point-np.timedelta64(1, 'm'))\n plot_axis_date(ax, x_train + missing_x + x_test)\n ax.grid()\n return fig, ax","def plot2dTimeSeries(values, title='series', xLabel='time', yLabel='values', savePath='.'):\n plt.plot(values)\n plt.ylabel(yLabel)\n plt.xlabel(xLabel)\n plt.xticks(np.linspace(0, len(values), 11))\n plt.title(title)\n plt.savefig(f'{savePath}/{title}.png')\n plt.show(block=False)\n plt.pause(2)\n plt.close()","def paper_plot_tstats_times(self, tstat, indexes, time=None, freq=None, params = None, er = None, domain = None, yrange = None, label= None, lande=None,\n xi_range=None, s_range=None, pred=None, compare=None, pos =None):\n\n if pos is None:\n pos = 'both'\n\n multiply_se_by = 1.96\n\n if label is None:\n label = tstat\n\n no_undertext = False\n no_legend = False\n if tstat == 'd2ax_per_mut_input':\n no_legend = True\n\n possible_compare_stats = ['d2ax_scaled_per_mut_input']\n\n if time is None:\n time = False\n\n if compare is None:\n compare = False\n if pos != 'both':\n compare = False\n\n if er is None:\n er = True\n if freq is None:\n freq = False\n if lande is None:\n lande = False\n\n if freq:\n loglinear = False\n else:\n loglinear = True\n\n if s_range is None:\n sr = False\n else:\n sr = True\n if xi_range is None:\n xir = False\n else:\n xir = True\n if pred is None:\n pred = False\n legend_with_dclass_param = False\n if params is not None:\n legend_with_dclass_param = True\n\n data_classes = [self.data_classes[indi] for indi in indexes]\n\n if lande is None:\n if data_classes[0]._THERE_ARE_FINAL_TRAJ_NON_LANDE:\n lande = False\n else:\n lande = True\n\n only_lande = False\n for dc in data_classes:\n if 'U' not in dc.param_dict:\n only_lande = True\n lande = True\n #if there are no nonlande trajectories then we must use the Lande ones\n if not data_classes[0]._THERE_ARE_FINAL_TRAJ_NON_LANDE:\n only_lande = True\n lande = True\n\n if only_lande:\n undertext_params = [['N', 'Vs'], ['shift_s0', 'sigma_0_del']]\n else:\n undertext_params = [['N', 'U'], ['shift_s0', 'sigma_0_del'], ['E2Ns', 'V2Ns']]\n\n if tstat not in data_classes[0]._tstats:\n print(str(tstat) + ' not in dataclass ')\n return\n\n if freq or tstat not in possible_compare_stats:# or len(data_classes) >1:\n compare = False\n\n\n plot_dict = dict()\n plotspecs = dict()\n plotspecs['legend_anchor'] = 'upper left'\n plotspecs['legend_loc'] = (1.02, 1.03)\n plot_dict['savedir'] = self.base_dir\n plotspecs['fsize'] = (28, 16)\n plotspecs['dpi'] = 200\n plotspecs['linewidth'] = 1.5\n plotspecs['ticksize'] = 30\n plotspecs['legend_font'] = {'size': '54'}\n plotspecs['axis_font'] = {'fontname': 'Arial', 'size': '28'}\n plot_dict['linestyles'] = ['-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-']\n plotspecs['marker_size'] = 15\n plotspecs['cap_size'] = 20\n #plotspecs['nxticks'] = 2\n plotspecs['undertext_font'] = {'color': 'black', 'weight': 'roman', 'size': '16'}\n extra_text = ''\n\n #print groupings\n\n # plotspecs['fsize'] = (17, 11)\n # plotspecs['legend_anchor'] = 'upper right'\n # plotspecs['legend_loc'] = (0.98, 0.98)\n # plotspecs['legend_anchor'] = 'upper right'\n # if tstat == 'mean_freq':\n # plotspecs['legend_loc'] = (0.98,0.98)\n # elif tstat == 'mean_adx':\n # plotspecs['legend_loc'] = (0.98, 0.45)\n #\n # plotspecs['ticksize'] = 35\n # plotspecs['linewidth'] = 5\n # plotspecs['marker_size'] = 20\n # #plotspecs['nyticks'] = 2\n # plotspecs['axis_font'] = {'fontname': 'Arial', 'size': '44'}\n # plotspecs['legend_font'] = {'size': '38'}\n\n\n if not no_undertext and len(data_classes)==1:\n undertext = []\n #str(int(data_class.number_population_simulations()))\n if not lande:\n number_runs_string = \"Obtained from \" + str(int(data_classes[0].number_allele_pairs(False,final=False))) + \\\n \" allele traj's using NonLande D(t) averaged over \" \\\n +str(int(data_classes[0].number_population_simulations()))\\\n + \" population sims with parameters:\"\n elif lande and only_lande:\n number_runs_string = \"Obtained from \" + str(int(data_classes[0].number_allele_pairs(True, final=False))) + \\\n \" allele traj's for each effect size, with Lande D(t) and parameters:\"\n elif lande and not only_lande:\n number_runs_string1st = \"NonLande obtained from \" + str(int(data_classes[0].number_allele_pairs(False,final=False))) + \\\n \" allele traj's with NonLande D(t) averaged over \" \\\n +str(int(data_classes[0].number_population_simulations()))+ \" population sims\"\n undertext.append(number_runs_string1st)\n number_runs_string = \"Lande obtained from \" + str(int(data_classes[0].number_allele_pairs(True, final=False))) + \\\n \" allele traj's for each effect size, with corresponding Lande D(t)\"\n undertext.append(number_runs_string)\n for listi in undertext_params:\n text_list = self._plot_text(index_list=[indexes[0]],params_list=listi)\n if text_list:\n text_string = ', '.join(text_list)\n undertext.append(text_string )\n plot_dict['undertext'] = undertext#data_class.plot_text()\n\n if not no_legend:\n if len(data_classes) ==1:\n if compare:\n if tstat == 'd2ax_scaled_per_mut_input':\n anal_name = r\"$(1-D_{L}($\"+str(int(time))+ r\"$)/\\Lambda)\\cdot v(a)$\"\n else:\n anal_name ='Analytic'\n plot_dict['ynames'] = ['Simulations', anal_name]\n if lande and not only_lande:\n plot_dict['ynames'] = ['NonLande', 'lande']\n if compare:\n plot_dict['ynames'] = ['NonLande', 'lande','Analytic']\n else:\n if lande and not only_lande:\n plot_dict['groupings_labels_within'] = ['NonLande', 'lande']\n if compare:\n plot_dict['groupings_labels_within'] = ['NonLande', 'lande','Analytic']\n if compare:\n plot_dict['groupings_labels_within'] = ['Simulations', 'Analytic']\n\n if freq:\n if pred:\n plot_dict['xlabel'] = r'$x_i $ percentile'\n else:\n plot_dict['xlabel'] = r'$x_i$'\n else:\n plot_dict['xlabel'] = \"Effect size squared (\" + r\"$S=a^2$\" + ')'\n if pred:\n if not legend_with_dclass_param:\n plot_dict['legend_title'] = 'Percentile of \\n initial frequency_pos'\n\n\n plot_dict['savedir'] = os.path.join(plot_dict['savedir'],'tstats_times')\n if pred:\n plot_dict['savedir'] = os.path.join(plot_dict['savedir'],'pred')\n if freq:\n plot_dict['savedir'] = os.path.join(plot_dict['savedir'],'S_sorted')\n else:\n plot_dict['savedir'] = os.path.join(plot_dict['savedir'], 'XI_sorted')\n\n savedir = os.path.join(self.base_dir, 'tstats_times')\n\n if pred:\n savedir = os.path.join(savedir, 'pred')\n if freq:\n savedir = os.path.join(savedir, 'S_sorted')\n else:\n savedir = os.path.join(savedir, 'XI_sorted')\n\n if pos == 'both':\n label = 'dpn_' + label\n savedir = os.path.join(savedir, 'combined')\n elif pos == 'pos':\n label ='p_' + label\n savedir = os.path.join(savedir,'positives')\n else:\n label = 'n_' + label\n savedir = os.path.join(savedir, 'negatives')\n\n\n if domain is None:\n if not freq:\n epsl = 0.05\n epsh =2\n domain = [0.1-epsl,100+epsh ]\n\n plot_dict['domain']= domain\n plot_dict['yrange'] = yrange\n\n at_time_text = ' at generation '\n\n if time:\n at_time_text += str(time)\n else:\n time = data_classes[0].get_phase_two_time()\n\n plot_dict['ylabel'] = self.name_class.yname(tstat)\n\n if tstat == 'd2ax_per_mut_input':\n if pos == 'both':\n plot_dict['ylabel'] = 'Contribution to change in mean\\n phenotype '\n if data_classes[0].units_s0:\n plot_dict['ylabel'] += r' (units trait SD)'\n else:\n plot_dict['ylabel'] += r' (units $\\omega/\\sqrt{2N}$)'\n plot_dict['ylabel']+= ' per\\n unit mutational input'\n elif tstat == 'x_per_seg_var' or tstat == 'x_per_seg_var':\n #plot_dict['ylabel'] = 'Average increased frequency of\\n aligned alleles'\n if pos == 'both':\n plot_dict['ylabel'] = 'Increased frequency of aligned\\n alleles per seg variant'\n else:\n plot_dict['ylabel'] = 'Average allele frequency'\n\n\n plot_dict['marker'] = True\n if compare and (not lande or only_lande):\n if len(data_classes) == 1:\n plot_dict['colors'] = ['deepskyblue','black']\n\n\n ts_string = '_' +tstat\n _mylabel = ts_string\n\n\n less = False\n\n x = []\n y = []\n yer = []\n ynames = []\n\n #maybe\n x_other = []\n y_other = []\n yer_other = []\n ynames_other = []\n\n\n\n tstati = tstat\n\n\n for data_class in data_classes:\n\n\n if 'var_0' in data_class.param_dict:\n var_0 = data_class.param_dict['var_0']\n else:\n var_0 = data_class.param_dict['sigma_0'] ** 2\n N = data_class.param_dict['N']\n Vs = data_class.param_dict['Vs']\n var_0_delta_square = var_0 * 2 * N / float(Vs)\n sig_0_del = np.sqrt(var_0_delta_square)\n\n if 'shift_s0' in data_class.param_dict:\n D_sig_0 = data_class.param_dict['shift_s0']\n D_del = sig_0_del * D_sig_0\n\n x_1 = defaultdict(list)\n y_1 = defaultdict(list)\n yer_1 = defaultdict(list)\n ynames_1 = dict()\n\n x_theory_1 = []\n y_theory_1 = []\n\n if lande and not only_lande:\n x_1_other = defaultdict(list)\n y_1_other = defaultdict(list)\n yer_1_other = defaultdict(list)\n ynames_1_other = dict()\n\n\n triples = deepcopy(data_class.tuples)\n if xir:\n remov = []\n xilow = xi_range[0]\n xihigh = xi_range[1]\n for trip in triples:\n if pred:\n XPI = data_class.xpercentile_dict[trip]\n else:\n XPI = trip[1]\n if XPI < xilow or XPI > xihigh:\n remov.append(trip)\n for trip in remov:\n triples.remove(trip)\n if sr:\n #print 's_range', s_range\n remov = []\n slow = s_range[0]\n shigh = s_range[1]\n for trip in triples:\n s = trip[0]\n if s < slow or s > shigh:\n remov.append(trip)\n for trip in remov:\n triples.remove(trip)\n\n name = ''\n lib = ''\n if params is None:\n # if len(data_classes) > 1:\n # name += str(data_class.index)\n lib += str(data_class.index)\n else:\n for param in params:\n try:\n name += param + ' = ' + '{0:.2f}'.format(data_class.param_dict[param]) + ' '\n lib += param + '{0:.0f}'.format(data_class.param_dict[param]) + '_'\n except KeyError:\n print('KeyError: ' + param)\n\n\n stat_dict = data_class.read_tstats(tuples=triples, requiredstats=[tstati],lande=only_lande)\n\n if lande and not only_lande:\n stat_dict_other = data_class.read_tstats(tuples=triples, requiredstats=[tstati], lande=True)\n print('lande stat dict')\n\n\n for triple in triples:\n\n name_1 = ''\n\n times = sorted(stat_dict[triple][tstati].keys())\n if time:\n time_real = find_nearest(times,time)\n\n y_val_2 = stat_dict[triple][tstati][time_real][pos]['mean']\n y_val_er_2 = stat_dict[triple][tstati][time_real][pos]['se']\n if y_val_2 == 0.0:\n print('yval is zero for S ', triple[0])\n if lande and not only_lande:\n y_val_2_other = stat_dict_other[triple][tstati][time_real][pos]['mean']\n y_val_er_2_other = stat_dict_other[triple][tstati][time_real][pos]['se']\n XI = triple[1]\n if XI < 0:\n less = True\n XI = -XI\n else:\n less = False\n S = triple[0]\n if freq:\n lenformat = 1\n if pred and not less:\n val = data_class.xpercentile_dict[triple]\n else:\n val = XI\n # if root_s:\n # name_1 = r'$\\sqrt{S} =$ '\n # else:\n # name_1 = r'$S =$ '\n if legend_with_dclass_param:\n key = data_class.param_dict[params[0]]\n else:\n key = S\n else:\n #key =XI\n val = S\n lenformat = 3\n if xir:\n if xi_range[1] < 1:\n lenformat = 2\n if pred:\n key = data_class.xpercentile_dict[triple]\n if key < 0.91:\n key = round(key,1)\n elif key <0.991:\n key = round(key,2)\n else:\n key = round(key,3)\n if key < 0:\n if legend_with_dclass_param:\n key = data_class.param_dict[params[0]]\n else:\n key = -key\n # name_1 = ''\n else:\n if legend_with_dclass_param:\n key = data_class.param_dict[params[0]]\n else:\n key = XI\n # name_1 = ''\n if less:\n lenformat =0\n x_1[key].append(val)\n y_1[key].append(y_val_2)\n\n if lande and not only_lande:\n x_1_other[key].append(val)\n y_1_other[key].append(y_val_2_other)\n\n if er:\n yer_1[key].append(y_val_er_2*multiply_se_by)\n if lande and not only_lande:\n yer_1_other[key].append(y_val_er_2_other*multiply_se_by)\n if legend_with_dclass_param:\n name_1 += self.name_class.param_text(value=key, digits=2)\n else:\n name_1 += self.name_class.param_text(value=key, digits=lenformat) + ' ' + name\n\n\n name_2_lande = 'lande: '+ name_1\n\n if name_1 not in ynames_1:\n ynames_1[key] = name_1\n if lande and not only_lande:\n if name_2_lande not in ynames_1_other:\n ynames_1_other[key] = name_2_lande\n\n x.append(x_1)\n y.append(y_1)\n yer.append(yer_1)\n ynames.append(ynames_1)\n if lande and not only_lande:\n x_other.append(x_1_other)\n y_other.append(y_1_other)\n yer_other.append(yer_1_other)\n ynames_other.append(ynames_1_other)\n\n time_string = \"generation_\" + str(time_real)\n savedir = os.path.join(savedir, time_string)\n plot_dict['savedir'] = savedir\n\n if compare:\n kkey = list(x_1.keys())[0]\n smin = min(x_1[kkey])\n smax = max(x_1[kkey])\n xtheorylowers = [xx for xx in np.linspace(smin, smin+1, 50)]\n xtheoryhighers = [xx for xx in np.linspace(smin+1, smax, 100)]\n x_theory = xtheorylowers+xtheoryhighers[1:]\n s_theory = [ssi for ssi in x_theory]\n y_theory = [0 for _ in x_theory]\n if tstat == 'd2ax_scaled_per_mut_input':\n frac_integral_lande_dt = 1.0 - data_class._get_lande_dist_over_shift_at_time(time)\n if pos == 'both':\n y_theory = [frac_integral_lande_dt*myv(np.sqrt(ss)) for ss\n in s_theory]\n\n x_theory_1.append(x_theory)\n y_theory_1.append(y_theory)\n\n _mylabel = _mylabel + lib\n\n if len(data_classes)>1:\n if less:\n if not freq:\n plot_dict['legend_title'] = r'Shift (units $\\sigma_0$)'\n if legend_with_dclass_param:\n plot_dict['legend_title'] = self.name_class.param_text(name=params[0])\n\n\n x_2 =[]\n y_2 =[]\n yer_2 = []\n ynames_2 = []\n jj = 0\n groupings = []\n keys_list = []\n # for k in xrange(len(x)):\n # keys_list+=x[k].keys()\n # keys_list = list(set(keys_list))\n\n for key in sorted(x[0]):\n seti = set()\n for k in range(len(x)):\n if er:\n zipi = list(zip(*sorted(zip(x[k][key], y[k][key], yer[k][key]))))\n x_temp = [xi for xi in zipi[0]]\n y_temp = [yi for yi in zipi[1]]\n yer_temp = zipi[2]\n yer_2.append(yer_temp)\n else:\n zipi = list(zip(*sorted(zip(x[k][key], y[k][key]))))\n x_temp = [xi for xi in zipi[0]]\n y_temp = [yi for yi in zipi[1]]\n x_2.append(x_temp)\n y_2.append(y_temp)\n ynames_2.append(ynames[k][key])\n seti.add(jj)\n jj += 1\n\n if lande and not only_lande:\n if er:\n zipi = list(zip(*sorted(zip(x_other[k][key], y_other[k][key], yer_other[k][key]))))\n x_temp = [xi for xi in zipi[0]]\n y_temp = [yi for yi in zipi[1]]\n yer_temp = zipi[2]\n yer_2.append(yer_temp)\n else:\n zipi = list(zip(*sorted(zip(x_other[k][key], y_other[k][key]))))\n x_temp = [xi for xi in zipi[0]]\n y_temp = [yi for yi in zipi[1]]\n x_2.append(x_temp)\n y_2.append(y_temp)\n ynames_2.append(ynames_other[k][key])\n seti.add(jj)\n jj += 1\n\n if compare:\n if x_theory_1:\n x_2.append(x_theory_1[k])\n y_2.append(y_theory_1[k])\n seti.add(jj)\n jj += 1\n if er:\n yer_2.append([0 for _ in x_theory_1[k]])\n\n groupings.append(seti)\n\n\n plot_dict['x'] = x_2\n plot_dict['y'] = y_2\n if er:\n plot_dict['yer'] = yer_2\n\n #experimenting\n # if len(data_classes) >1:\n # plot_dict['groupings'] = groupings\n plot_dict['groupings'] = groupings\n\n\n linestyles =['','-']\n markerstyles = ['o','']\n if lande and not only_lande:\n markerstyles = ['o', '*','-']\n linestyles = ['','','']\n if compare:\n linestyles = ['','-','']\n markerstyles =['o','','*']\n if lande and not only_lande:\n linestyles = ['', '-','--']\n markerstyles = ['o', '','']\n\n plot_dict['linestyles'] = linestyles #+linestyles+linestyles+linestyles\n plot_dict['markerstyles'] = markerstyles #+markerstyles+markerstyles+markerstyles\n\n size_group = len(groupings[0])\n if label is None:\n if len(_mylabel) <30:\n plot_dict['label'] = _mylabel +'_many_cl'\n else:\n plot_dict['label'] = tstat\n else:\n plot_dict['label'] = label\n\n if freq:\n plot_dict['label']+= '_xi_x'\n\n\n if pred:\n plot_dict['label'] += '_pred'\n\n if compare:\n plot_dict['label'] += '_comp'\n\n\n\n if time:\n plot_dict['label'] += '_t_' + str(time_real)\n\n\n #List with [text_top, text_bottom] containing relevant parameters\n # if len(data_classes) == 1:\n # if extra_text:\n # undertext.append(extra_text)\n #\n # if not no_undertext:\n # plot_dict['undertext'] = undertext\n\n if len(data_classes)>1:\n plot_dict['ynames'] = ynames_2\n\n if loglinear:\n plotspecs['xlog'] = True\n\n\n plot_dict['plotspecs'] = plotspecs\n\n plot_many_y(**plot_dict)","def get_timescale_lines(ax, td):\n\t\n\tneur_N = [1e6, 1e4, 1e2, 1e0, 1e-2]\n\tneur_clabel = [r'$\\tau_{\\nu}=10^6$',r'$10^4$',r'$10^2$',r'$10^0$',r'$10^{-2}$']\n\n\tfor i in range(len(neur_N)):\n\t\tcs = ax.contour(td.rho_p, td.T_p, td.data_p[\"tau_neu\"], [neur_N[i]], colors='b', linestyles='dotted')\n\t\tax.clabel(cs, fontsize=14, inline=1, fmt=neur_clabel[i])\t#inline_spacing = -50\n\n\tnucr_N = [1e6, 1e4, 1e2, 1e0, 1e-2,1e-4,1e-6]\n\tnucr_clabel = [r'$\\tau_\\mathrm{cc}=10^6$',r'$10^4$',r'$10^2$',r'$10^0$',r'$10^{-2}$',r'$10^{-4}$',r'$10^{-6}$']\n\n\tfor i in range(len(nucr_N)):\n\t\tcs = ax.contour(td.rho_p, td.T_p, td.data_p[\"tau_nuc\"], [nucr_N[i]], colors='r', linestyles=':')\n\t\tax.clabel(cs, fontsize=14, inline=1, fmt=nucr_clabel[i])\n\n\tS_N = [1e8, 10**8.1, 10**8.2]\n\tS_clabel = [r'$s = 10^8$',r'$10^{8.1}$',r'$10^{8.2}$']\n\n\tfor i in range(len(S_N)):\n\t\tcs = ax.contour(td.rho_p, td.T_p, td.data_p[\"S\"], [S_N[i]], colors='g', linestyles='dotted', linewidth=4)\n\t\tax.clabel(cs, fontsize=14, inline=1, fmt=S_clabel[i])\n\n\tcs = ax.contour(td.rho_p, td.T_p, td.data_p[\"tau_eq_neunuc\"], [0], colors='m', linestyles='-', linewidth=6)\n\tax.clabel(cs, fontsize=14, inline=1, fmt=r'$\\tau_\\mathrm{cc}=\\tau_{\\nu}$')\n\n\tcs = ax.contour(td.rho_p, td.T_p, td.data_p[\"Prat\"], [2.0], colors='k', linestyles='--', linewidth=6)\n\tax.clabel(cs, fontsize=14, inline=1, fmt=r'$P = 2P(T=0)$')\n\n\tcs = ax.contour(td.rho_p, td.T_p, td.data_p[\"tau_eq_cdnuc\"], [0], colors='r', linestyles=':', linewidth=4)\n\tax.clabel(cs, fontsize=14, inline=1, fmt=r'$\\tau_\\mathrm{cc}=(G\\rho_c)^{-1/2}$')\n\n\tcs = ax.contour(td.rho_p, td.T_p, td.data_p[\"tau_eq_dynnuc\"], [0], colors=\"r\", linestyles='-', linewidths=2)\n\tax.clabel(cs, fontsize=14, inline=1, fmt=r\"$\\tau_\\mathrm{cc} = \\tau_\\mathrm{dyn}$\")","def coffee_plot(times, Temps, axes, legends=[None, None], split=2):\n plot_model(times[split:], Temps[split:], axes[0], legends[0])\n plot_samples(times[:split], Temps[:split], axes[1], legends[1])","def plot(self, title=None, xmin=None, xmax=None, ymin=None, ymax=None,\n ax=None, show=True, color=None, xlabel=True, ylabel=True,\n legend=True, chance=True, label='Classif. score'):\n # XXX JRK: need cleanup in viz\n self._prep_times()\n fig = plot_gat_times(self, train_time='diagonal', title=title,\n xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, ax=ax,\n show=show, color=color, xlabel=xlabel,\n ylabel=ylabel, legend=legend, chance=chance,\n label=label)\n self._clean_times()\n return fig","def plot_frequency(self):\n canvas = xboa.common.make_root_canvas(\"frequency vs time\")\n canvas.Draw()\n freq_list = [freq for freq in self.freq_list]\n hist, graph = xboa.common.make_root_graph(\"frequency vs time\",\n self.time_list, \"time [ns]\",\n freq_list, \"f [GHz]\")\n hist.Draw()\n graph.Draw(\"sameL\")\n fit = ROOT.TF1(\"fit\", \"pol4\", 0, 20*1e6)\n fit.FixParameter(0, freq_list[0])\n graph.Fit(fit)\n canvas.Update()","def showCl(ell,temps,title='CAMB ISWout power spectrum'):\n plt.plot(ell,temps*ell*(ell+1)/(2*np.pi) *1e12) #1e12 to convert to microK**2\n plt.xlabel('multipole moment l')\n plt.ylabel('l(l+1)C_l/(2pi) [microK**2]')\n plt.title(title)\n plt.show()","def plot_ins_state(time, state):\n pylab.ion()\n\n plot_trajectory(state[:,0], state[:,1], state[:,2])\n\n\n # Plot position vs. time\n\n\n pylab.figure()\n pylab.subplot(311)\n pylab.plot(time, state[:,0],'r')\n pylab.xlabel('time (s)')\n pylab.ylabel('$\\\\phi$, rad')\n pylab.title('Latitude')\n pylab.grid(True)\n\n pylab.subplot(312)\n pylab.plot(time, state[:,1],'g')\n pylab.xlabel('time (s)')\n pylab.ylabel('$\\\\lambda$, rad')\n pylab.title('Longitude')\n pylab.grid(True)\n\n pylab.subplot(313)\n pylab.plot(time, state[:,2],'b')\n pylab.xlabel('time, s')\n pylab.ylabel('$h$, m')\n pylab.title('Altitude')\n pylab.grid(True)\n pylab.show()\n\n\n # Plot velocity vs. time\n pylab.figure()\n pylab.plot(time, state[:,3:6])\n pylab.xlabel('time, s')\n pylab.ylabel('Vn, Ve, Vd')\n pylab.title('Velocity vs. time')\n\n pylab.grid(True)\n pylab.show()\n\n # Plot acceleration vs. time\n pylab.figure()\n pylab.plot(time, state[:,6:9])\n pylab.xlabel('time, s')\n pylab.ylabel('an, ae, ad')\n pylab.title('Acceleration vs. time')\n\n pylab.grid(True)\n pylab.show()\n pylab.ioff()\n\n # Plot quaternions vs. time\n pylab.figure()\n pylab.plot(time, state[:,9:])\n pylab.xlabel('time, s')\n pylab.ylabel('q0, q1, q2, q3')\n pylab.title('Quaternion vs. time')\n\n pylab.grid(True)\n pylab.show()\n pylab.ioff()","def plot(t): \n assert isinstance(t, int), \"'t' argument should be an integer.\"\n assert t > 0, \"'t' argument should be a positive integer.\" \n # Initialize arrays with zeros to store mean cumulative rewards upto t \n # rounds for each of the three implemented bandit algorithms\n EpsGreedy_rewards = np.zeros(t)\n UCB_rewards = np.zeros(t)\n LinUCB_rewards = np.zeros(t)\n # For each round, store the mean cumulative rewards upto that round\n for i in range(1,t):\n EpsGreedy_rewards[i] = np.sum(results_EpsGreedy[0:i]) / t\n UCB_rewards[i] = np.sum(results_UCB[0:i]) / t\n LinUCB_rewards[i] = np.sum(results_LinUCB[0:i]) / t\n # Plot running per round cumulative reward\n plt.plot(range(0,t), EpsGreedy_rewards, color='b', label='e-Greedy')\n plt.plot(range(0,t), UCB_rewards, color='g', label='UCB')\n plt.plot(range(0,t), LinUCB_rewards, color='orange', label='LinUCB')\n plt.xlabel('Round')\n plt.ylabel('Mean Cumulative Reward')\n plt.title('Running Per Round Cumulative Reward')\n plt.legend()\n plt.show()","def plot(self, *args, **kwargs):\n pass","def plot_credible_intervals_for_time(\n histories: Union[List[History], History],\n labels: Union[List[str], str] = None,\n ms: Union[List[int], int] = None,\n ts: Union[List[int], int] = None,\n par_names: List[str] = None,\n levels: List[float] = None,\n show_mean: bool = False,\n show_kde_max: bool = False,\n show_kde_max_1d: bool = False,\n size: tuple = None,\n rotation: int = 0,\n refvals: Union[List[dict], dict] = None,\n kde: Transition = None,\n kde_1d: Transition = None,\n):\n histories = to_lists(histories)\n labels = get_labels(labels, len(histories))\n n_run = len(histories)\n if ms is None:\n ms = [0] * n_run\n elif not isinstance(ms, list) or len(ms) == 1:\n ms = [ms] * n_run\n if levels is None:\n levels = [0.95]\n levels = sorted(levels)\n if par_names is None:\n # extract all parameter names\n df, _ = histories[0].get_distribution(m=ms[0])\n par_names = list(df.columns.values)\n n_par = len(par_names)\n n_confidence = len(levels)\n if ts is None:\n ts = [history.max_t for history in histories]\n if refvals is not None and not isinstance(refvals, list):\n refvals = [refvals] * n_run\n\n # prepare axes\n fig, arr_ax = plt.subplots(\n nrows=n_par, ncols=1, sharex=False, sharey=False\n )\n if n_par == 1:\n arr_ax = [arr_ax]\n\n # prepare matrices\n cis = np.empty((n_par, n_run, 2 * n_confidence))\n median = np.empty((n_par, n_run))\n if show_mean:\n mean = np.empty((n_par, n_run))\n if show_kde_max:\n kde_max = np.empty((n_par, n_run))\n if show_kde_max_1d:\n kde_max_1d = np.empty((n_par, n_run))\n if kde is None and show_kde_max:\n kde = MultivariateNormalTransition()\n if kde_1d is None and show_kde_max_1d:\n kde_1d = MultivariateNormalTransition()\n\n # fill matrices\n # iterate over populations\n for i_run, (h, t, m) in enumerate(zip(histories, ts, ms)):\n df, w = h.get_distribution(m=m, t=t)\n # normalize weights to be sure\n w /= w.sum()\n # fit kde\n if show_kde_max:\n _kde_max_pnt = compute_kde_max(kde, df, w)\n # iterate over parameters\n for i_par, par in enumerate(par_names):\n # as numpy array\n vals = np.array(df[par])\n # median\n median[i_par, i_run] = compute_quantile(vals, w, 0.5)\n # mean\n if show_mean:\n mean[i_par, i_run] = np.sum(w * vals)\n # kde max\n if show_kde_max:\n kde_max[i_par, i_run] = _kde_max_pnt[par]\n if show_kde_max_1d:\n _kde_max_1d_pnt = compute_kde_max(kde_1d, df[[par]], w)\n kde_max_1d[i_par, i_run] = _kde_max_1d_pnt[par]\n # levels\n for i_c, confidence in enumerate(levels):\n lb, ub = compute_credible_interval(vals, w, confidence)\n cis[i_par, i_run, i_c] = lb\n cis[i_par, i_run, -1 - i_c] = ub\n\n # plot\n for i_par, (par, ax) in enumerate(zip(par_names, arr_ax)):\n for i_run in range(len(histories)):\n for i_c in reversed(range(len(levels))):\n y_err = np.array(\n [\n median[i_par, i_run] - cis[i_par, i_run, i_c],\n cis[i_par, i_run, -1 - i_c] - median[i_par, i_run],\n ]\n )\n y_err = y_err.reshape((2, 1))\n ax.errorbar(\n x=[i_run],\n y=median[i_par, i_run],\n yerr=y_err,\n capsize=(10.0 / n_confidence) * (i_c + 1),\n color=f'C{i_c}',\n )\n # reference value\n if refvals[i_run] is not None:\n ax.plot([i_run], [refvals[i_run][par]], 'x', color='black')\n ax.set_title(f\"Parameter {par}\")\n # mean\n if show_mean:\n ax.plot(range(n_run), mean[i_par], 'x', color=f'C{n_confidence}')\n # kde max\n if show_kde_max:\n ax.plot(\n range(n_run), kde_max[i_par], 'x', color=f'C{n_confidence + 1}'\n )\n if show_kde_max_1d:\n ax.plot(\n range(n_run),\n kde_max_1d[i_par],\n 'x',\n color=f'C{n_confidence + 2}',\n )\n ax.set_xticks(range(n_run))\n ax.set_xticklabels(labels, rotation=rotation)\n leg_colors = [f'C{i_c}' for i_c in reversed(range(n_confidence))]\n leg_labels = ['{:.2f}'.format(c) for c in reversed(levels)]\n if show_mean:\n leg_colors.append(f'C{n_confidence}')\n leg_labels.append(\"Mean\")\n if show_kde_max:\n leg_colors.append(f'C{n_confidence + 1}')\n leg_labels.append(\"Max KDE\")\n if show_kde_max_1d:\n leg_colors.append(f'C{n_confidence + 2}')\n leg_labels.append(\"Max KDE 1d\")\n if refvals is not None:\n leg_colors.append('black')\n leg_labels.append(\"Reference value\")\n handles = [\n Line2D([0], [0], color=c, label=l)\n for c, l in zip(leg_colors, leg_labels)\n ]\n ax.legend(handles=handles, bbox_to_anchor=(1.04, 1), loc=\"upper left\")\n\n # format\n arr_ax[-1].set_xlabel(\"Population t\")\n if size is not None:\n fig.set_size_inches(size)\n fig.tight_layout()\n\n return arr_ax","def plotOfHeatingCurrent(self):\n\t\tfig,p1=_plt.subplots()\n\t\tp1.plot(self.time*1e3,self.heatingCurrent)\n\t\t_plot.finalizeSubplot(p1,xlabel='Time (ms)',ylabel='Current (A)')\n\t\t_plot.finalizeFigure(fig,title=self.title)\n\t\t\n\t\treturn p1","def time_axis(self):\n if self.axes_wcs.wcs.ctype[0] not in ['TIME', 'UTC']:\n raise cu.CubeError(1, 'No time axis present')\n delta = self.axes_wcs.wcs.cdelt[0]\n crpix = self.axes_wcs.wcs.crpix[0]\n crval = self.axes_wcs.wcs.crval[0]\n start = crval - crpix * delta\n stop = start + len(self.data) * delta\n cunit = u.Unit(self.axes_wcs.wcs.cunit[0])\n return np.linspace(start, stop, num=self.data.shape[-1]) * cunit","def plotTimeDepth(d,v):\n\n dpth,t = getTimeDepth(d,v)\n plt.figure(num=0, figsize = (6, 4))\n plt.plot(dpth,t,linewidth=2);\n plt.title('Depth-Time');\n plt.grid()\n plt.gca().set_xlabel('Depth (m)',fontsize=9)\n plt.gca().set_ylabel('Two Way Time (s)',fontsize=9)\n\n plt.tight_layout()\n plt.show()","def plot(self,angle=17,**kwargs):\r\n \r\n h1=plt.plot(self.t,self.y,**kwargs)\r\n plt.xticks(rotation=angle)\r\n \r\n return h1","def generate_time_series(num_points, num_dims):\n phase = np.random.randn()\n period = np.random.uniform()\n times = np.linspace(0, 10, num_dims)\n scale = np.random.exponential(size=num_points)\n return np.outer(scale, np.sin(times / period + phase))","def tick(self, tick):\n pass","def tick(self, tick):\n pass","def plot_spectrum(wf, t=0, f_start=None, f_stop=None, logged=False, if_id=0, c=None, **kwargs):\n if wf.header['nbits'] <= 2:\n logged = False\n t = 'all'\n ax = plt.gca()\n\n plot_f, plot_data = wf.grab_data(f_start, f_stop, if_id)\n\n # Using accending frequency for all plots.\n if wf.header['foff'] < 0:\n plot_data = plot_data[..., ::-1] # Reverse data\n plot_f = plot_f[::-1]\n\n if isinstance(t, int):\n print(\"extracting integration %i...\" % t)\n plot_data = plot_data[t]\n elif t == 'all':\n print(\"averaging along time axis...\")\n # Since the data has been squeezed, the axis for time goes away if only one bin, causing a bug with axis=1\n if len(plot_data.shape) > 1:\n plot_data = plot_data.mean(axis=0)\n else:\n plot_data = plot_data.mean()\n else:\n raise RuntimeError(\"Unknown integration %s\" % t)\n\n # Rebin to max number of points\n dec_fac_x = 1\n if plot_data.shape[0] > MAX_PLT_POINTS:\n dec_fac_x = int(plot_data.shape[0] / MAX_PLT_POINTS)\n\n plot_data = rebin(plot_data, dec_fac_x, 1)\n plot_f = rebin(plot_f, dec_fac_x, 1)\n\n if not c:\n kwargs['c'] = '#333333'\n\n if logged:\n plt.plot(plot_f, db(plot_data), label='Stokes I', **kwargs)\n plt.ylabel(\"Power [dB]\")\n else:\n\n plt.plot(plot_f, plot_data, label='Stokes I', **kwargs)\n plt.ylabel(\"Power [counts]\")\n plt.xlabel(\"Frequency [MHz]\")\n plt.legend()\n\n try:\n plt.title(wf.header['source_name'])\n except KeyError:\n plt.title(wf.filename)\n\n plt.xlim(plot_f[0], plot_f[-1])","def tgfs_plot():\n plt.rc('font', size=10) # control the default font size\n plt.rc('axes', titlesize=11) # fontsize of the axes title\n plt.rc('axes', labelsize=12) # fontsize of the axes x and y labels\n plt.rc('xtick', labelsize=10) # fontsize of the tick label\n plt.rc('ytick', labelsize=10) # fontsize of the tick label\n plt.rc('legend', fontsize=11) # legend fontsize\n\n # generate data\n x = np.arange(-10, 10.1, 0.1)\n y = x**3\n\n # plot figure\n plt.figure()\n plt.plot(x, y, label='$x^3$')\n plt.xlabel('$x$')\n plt.ylabel('$y$')\n plt.title('$y = x^3 for title, grid, & font size control \"plt.rc()\"$')\n plt.legend(loc='upper left')\n plt.grid()\n plt.show()\n\n return None","def plot_vanHove_dt(comp,conn,start,step_size,steps):\n \n (fin,) = conn.execute(\"select fout from comps where comp_key = ?\",comp).fetchone()\n (max_step,) = conn.execute(\"select max_step from vanHove_prams where comp_key = ?\",comp).fetchone()\n Fin = h5py.File(fin,'r')\n g = Fin[fd('vanHove',comp[0])]\n\n temp = g.attrs['temperature']\n dtime = g.attrs['dtime']\n\n\n # istatus = plots.non_i_plot_start()\n \n fig = mplt.figure()\n fig.suptitle(r'van Hove dist temp: %.2f dtime: %d'% (temp,dtime))\n dims = figure_out_grid(steps)\n \n plt_count = 1\n outs = []\n tmps = []\n for j in range(start,start+step_size*steps, step_size):\n (edges,count,x_lim) = _extract_vanHove(g,j+1,1,5)\n if len(count) < 50:\n plt_count += 1\n continue\n #count = count/np.sum(count)\n \n sp_arg = dims +(plt_count,)\n ax = fig.add_subplot(*sp_arg)\n ax.grid(True)\n\n \n alpha = _alpha2(edges,count)\n \n ax.set_ylabel(r'$\\log{P(N)}$')\n ax.step(edges,np.log((count/np.sum(count))),lw=2)\n ax.set_title(r'$\\alpha_2 = %.2f$'%alpha + ' j:%d '%j )\n ax.set_xlim(x_lim)\n plt_count += 1\n\n mplt.draw()\n\n # plots.non_i_plot_start(istatus)\n\n del g\n Fin.close()\n del Fin","def tick(self):\r\n pass","def plot(self, add_labels=False,\n _type=\"scatter\", color=None,\n layer=1, edgecolor='black'):\n _type = _type.lower()\n assert _type in (\"scatter\", \"line\")\n if _type == \"scatter\":\n scatter(\n self.times,\n self.temperatures,\n alpha=.8,\n zorder=layer,\n edgecolors=edgecolor\n )\n elif _type == \"line\":\n plt.plot(self.times,\n self.temperatures,\n \"-\", color=color,\n zorder=layer)\n if add_labels:\n self.set_plot_labels()","def plot_u(times, u, t_deltas, is_own_event, figsize=(16, 6)):\n\n t_deltas = np.asarray(t_deltas)\n is_own_event = np.asarray(is_own_event)\n\n seq_len = np.nonzero(t_deltas == 0)[0][0] # First index where t_delta = 0\n abs_t = np.cumsum(t_deltas[:seq_len])\n abs_own = is_own_event[:seq_len]\n\n our_events = [t for (t, o) in zip(abs_t, abs_own) if o]\n other_events = [t for (t, o) in zip(abs_t, abs_own) if not o]\n\n u_max = np.max(u)\n\n plt.figure(figsize=(16, 6))\n\n c1, c2, c3 = sns.color_palette(n_colors=3)\n\n plt.plot(times, u, label='$u(t)$', color=c1)\n plt.vlines(our_events, 0, 0.75 * u_max, label='Us', alpha=0.5, color=c2)\n plt.vlines(other_events, 0, 0.75 * u_max, label='Others', alpha=0.5, color=c3)\n plt.xlabel('Time')\n plt.ylabel('$u(t)$')\n plt.legend()","def get_timing_signal(length,\n min_timescale=1,\n max_timescale=1e4,\n num_timescales=16):\n positions = tf.to_float(tf.range(length))\n log_timescale_increment = (\n math.log(max_timescale / min_timescale) / (num_timescales - 1))\n inv_timescales = min_timescale * tf.exp(\n tf.to_float(tf.range(num_timescales)) * -log_timescale_increment)\n scaled_time = tf.expand_dims(positions, 1) * tf.expand_dims(inv_timescales, 0)\n return tf.concat([tf.sin(scaled_time), tf.cos(scaled_time)], axis=1)"],"string":"[\n \"def plotTime(self):\\n plt.figure()\\n t = [i for i in range(len(self.nodes_infected))]\\n print(t)\\n plt.title('Nodos infectados vs Tiempo')\\n plt.xlabel('Instantes de tiempo')\\n plt.ylabel('# de nodos infectados')\\n plt.plot(t, self.nodes_infected)\\n plt.grid(True)\\n plt.show()\",\n \"def plot_time_series(self, *args, **kwargs):\\n return SimulationStaticVisualizer(self, *args, **kwargs)\",\n \"def plot_times(self, train_time, title=None, xmin=None, xmax=None,\\n ymin=None, ymax=None, ax=None, show=True, color=None,\\n xlabel=True, ylabel=True, legend=True, chance=True,\\n label='Classif. score'):\\n if not np.array(train_time).dtype is np.dtype('float'):\\n raise ValueError('train_time must be float | list or array of '\\n 'floats. Got %s.' % type(train_time))\\n\\n return plot_gat_times(self, train_time=train_time, title=title,\\n xmin=xmin, xmax=xmax,\\n ymin=ymin, ymax=ymax, ax=ax, show=show,\\n color=color, xlabel=xlabel, ylabel=ylabel,\\n legend=legend, chance=chance, label=label)\",\n \"def plot(axes, axis, values, c='chartreuse'):\\n a = axes[axis]\\n a.set_xlabel('time (s)')\\n x = np.array(range(len(values))) / 1000\\n dim = 'x' if axis == 0 else 'y' if axis == 1 else 'z'\\n a.set_title('-'.join([dim, 'acceleration']))\\n a.plot(x, values / 1000, c=c)\",\n \"def time_function(t):\\n\\n omega = np.pi\\n return np.sin(omega * t) + np.sin(10 * omega * t) + np.sin(20 * omega * t)\",\n \"def plot():\\n pass\",\n \"def visualize(x, y, xlabel=None, ylabel=None, title=None, ylim=None):\\n total_seconds = (x[-1] - x[0]).total_seconds()\\n if total_seconds <= 86400 * 1 * 3:\\n return plot_one_day(x, y, xlabel, ylabel, title, ylim)\\n \\n elif total_seconds <= 86400 * 7 * 2:\\n return plot_one_week(x, y, xlabel, ylabel, title, ylim)\\n \\n elif total_seconds <= 86400 * 30 * 1.5:\\n return plot_one_month(x, y, xlabel, ylabel, title, ylim)\\n \\n elif total_seconds <= 86400 * 90 * 1.5:\\n return plot_one_quarter(x, y, xlabel, ylabel, title, ylim)\\n \\n elif total_seconds <= 86400 * 365 * 1.5:\\n return plot_one_year(x, y, xlabel, ylabel, title, ylim)\",\n \"def matshow_tseries(time_series, fig=None, axis=0, xtick_n=5, time_unit=None,\\r\\n xlabel=None, ylabel=None):\\r\\n\\r\\n if fig is None:\\r\\n fig = plt.figure()\\r\\n\\r\\n if not fig.get_axes():\\r\\n ax = fig.add_subplot(1, 1, 1)\\r\\n else:\\r\\n ax = fig.get_axes()[axis]\\r\\n\\r\\n #Make sure that time displays on the x axis with the units you want:\\r\\n #If you want to change the time-unit on the visualization from that used to\\r\\n #represent the time-series:\\r\\n if time_unit is not None:\\r\\n tu = time_unit\\r\\n conv_fac = ts.time_unit_conversion[time_unit]\\r\\n #Otherwise, get the information from your input:\\r\\n else:\\r\\n tu = time_series.time_unit\\r\\n conv_fac = time_series.time._conversion_factor\\r\\n\\r\\n this_time = time_series.time / float(conv_fac)\\r\\n ax.matshow(time_series.data)\\r\\n\\r\\n ax.set_xticks(list(range(len(this_time)))[::len(this_time) / xtick_n])\\r\\n ax.set_xticklabels(this_time[::len(this_time) / xtick_n])\\r\\n\\r\\n if xlabel is None:\\r\\n ax.set_xlabel('Time (%s)' % tu)\\r\\n else:\\r\\n ax.set_xlabel(xlabel)\\r\\n\\r\\n if ylabel is not None:\\r\\n ax.set_ylabel(ylabel)\\r\\n\\r\\n return fig\",\n \"def plot_time(self, X, x0, t):\\n\\n Pressure = [Solution(self, (x-x0)/t).pressure for x in X]\\n Velocity = [Solution(self, (x-x0)/t).velocity for x in X]\\n Density = [Solution(self, (x-x0)/t).rho for x in X]\\n\\n fig, axs = plt.subplots(3, sharex=True)\\n fig.suptitle(\\\"Solution of the Riemann problem\\\\nat t = {}s\\\".format(t))\\n axs[0].plot(X, Density)\\n axs[1].plot(X, Velocity)\\n axs[2].plot(X, Pressure)\\n\\n axs[0].grid()\\n axs[0].set(ylabel = \\\"Density\\\")\\n axs[1].grid()\\n axs[1].set(ylabel = \\\"Velocity\\\")\\n axs[2].grid()\\n axs[2].set(ylabel = \\\"Pressure\\\")\\n\\n plt.xlabel(\\\"Location x\\\")\",\n \"def plottf(tfarray,tlst,flst,fignum=1,starttime=0,timeinc='hrs',\\r\\n dt=1.0,title=None,vmm=None,cmap=None,aspect=None,interpolation=None,\\r\\n cbori=None,cbshrink=None,cbaspect=None,cbpad=None,scale='log',\\r\\n normalize='n',):\\r\\n \\r\\n #time increment\\r\\n if timeinc=='hrs':\\r\\n tinc=3600/dt\\r\\n elif timeinc=='min':\\r\\n tinc=60/dt\\r\\n elif timeinc=='sec':\\r\\n tinc=1/dt\\r\\n else:\\r\\n raise ValueError(timeinc+'is not defined')\\r\\n #colormap\\r\\n if cmap==None:\\r\\n cmap='jet'\\r\\n else:\\r\\n cmap=cmap\\r\\n #aspect ratio\\r\\n if aspect==None:\\r\\n aspect='auto'\\r\\n else:\\r\\n aspect=aspect\\r\\n #interpolation\\r\\n if interpolation==None:\\r\\n interpolation='gaussian'\\r\\n else:\\r\\n interpolation=interpolation\\r\\n #colorbar orientation\\r\\n if cbori==None:\\r\\n cbori='vertical'\\r\\n else:\\r\\n cbori=cbori\\r\\n #colorbar shinkage\\r\\n if cbshrink==None:\\r\\n cbshrink=.99\\r\\n else:\\r\\n cbshrink=cbshrink\\r\\n #colorbar aspect\\r\\n if cbaspect==None:\\r\\n cbaspect=20\\r\\n else:\\r\\n cbaspect=cbaspect\\r\\n #colorbar pad\\r\\n if cbpad==None:\\r\\n cbpad=.1\\r\\n else:\\r\\n cbpad=cbpad\\r\\n #scale\\r\\n if scale=='log':\\r\\n zerofind=np.where(abs(tfarray)==0)\\r\\n tfarray[zerofind]=1.0\\r\\n if normalize=='y':\\r\\n plottfarray=10*np.log10(abs(tfarray/np.max(abs(tfarray))))\\r\\n else:\\r\\n plottfarray=10*np.log10(abs(tfarray))\\r\\n elif scale=='linear':\\r\\n if normalize=='y':\\r\\n plottfarray=abs(tfarray/np.max(abs(tfarray)))\\r\\n else:\\r\\n plottfarray=abs(tfarray)\\r\\n \\r\\n plt.rcParams['font.size']=10\\r\\n plt.rcParams['figure.subplot.left']=.08\\r\\n plt.rcParams['figure.subplot.right']=.99\\r\\n plt.rcParams['figure.subplot.bottom']=.07\\r\\n plt.rcParams['figure.subplot.top']=.96\\r\\n plt.rcParams['figure.subplot.wspace']=.25\\r\\n plt.rcParams['figure.subplot.hspace']=.20\\r\\n \\r\\n \\r\\n plt.figure(fignum)\\r\\n if vmm!=None:\\r\\n vmin=vmm[0]\\r\\n vmax=vmm[1]\\r\\n plt.imshow(plottfarray,extent=(tlst[0]/tinc+starttime,\\r\\n tlst[-1]/tinc+starttime,flst[0],flst[-1]),aspect=aspect,\\r\\n vmin=vmin,vmax=vmax,cmap=cmap,interpolation=interpolation)\\r\\n else:\\r\\n plt.imshow(plottfarray,extent=(tlst[0]/tinc+starttime,\\r\\n tlst[-1]/tinc+starttime,flst[0],flst[-1]),aspect=aspect,\\r\\n cmap=cmap,interpolation=interpolation)\\r\\n plt.xlabel('Time('+timeinc+')',fontsize=12,fontweight='bold')\\r\\n plt.ylabel('Frequency (Hz)',fontsize=12,fontweight='bold')\\r\\n if title!=None:\\r\\n plt.title(title,fontsize=14,fontweight='bold')\\r\\n plt.colorbar(orientation=cbori,shrink=cbshrink,pad=cbpad,aspect=cbaspect)\\r\\n plt.show()\",\n \"def running_time(func, counter, plot_type, *args, **kwargs):\\n plots = []\\n counter = 0\\n #include counter\\n output = (func(counter, *args, **kwargs)) #returns [value, counter]\\n value, counter = output\\n if plot_type.upper() == \\\"STANDARD\\\":\\n plots.append(output)\\n else:\\n plots.append((log(float(value)), log(float(counter))))\\n #print plots\\n return plt.plot(plots)\",\n \"def plot_time_frames(self):\\n\\n fig = plt.figure()\\n plt.grid(True)\\n\\n plt.ylim([-1.5,1.5])\\n plt.xlim([0,1])\\n\\n for key in self.timeframes.keys():\\n if key == 0:\\n plt.plot(self.x, self.timeframes[key], label=\\\"time: \\\" + str(round(key*self.dt, 3)), linewidth=5)\\n else:\\n plt.plot(self.x, self.timeframes[key], label=\\\"time: \\\" + str(round(key*self.dt, 3)))\\n\\n plt.title(\\\"Wave at different times\\\")\\n plt.legend(loc=\\\"upper right\\\")\\n plt.show()\\n\\n # fig.savefig('results/pics_wave/vibrating_string_'+ self.type + '.png', dpi=150)\",\n \"def plotAll(fx,tfarray,tlst,flst,fignum=1,starttime=0,timeinc='hrs',\\r\\n dt=1.0,title=None,vmm=None,cmap=None,aspect=None,interpolation=None,\\r\\n cbori=None,cbshrink=None,cbaspect=None,cbpad=None,normalize='n',\\r\\n scale='log'):\\r\\n \\r\\n #time increment\\r\\n if timeinc=='hrs':\\r\\n tinc=3600/dt\\r\\n elif timeinc=='min':\\r\\n tinc=60/dt\\r\\n elif timeinc=='sec':\\r\\n tinc=1/dt\\r\\n else:\\r\\n raise ValueError(timeinc+'is not defined')\\r\\n #colormap\\r\\n if cmap==None:\\r\\n cmap='jet'\\r\\n else:\\r\\n cmap=cmap\\r\\n #aspect ratio\\r\\n if aspect==None:\\r\\n aspect='auto'\\r\\n else:\\r\\n aspect=aspect\\r\\n #interpolation\\r\\n if interpolation==None:\\r\\n interpolation='gaussian'\\r\\n else:\\r\\n interpolation=interpolation\\r\\n #colorbar orientation\\r\\n if cbori==None:\\r\\n cbori='vertical'\\r\\n else:\\r\\n cbori=cbori\\r\\n #colorbar shinkage\\r\\n if cbshrink==None:\\r\\n cbshrink=.99\\r\\n else:\\r\\n cbshrink=cbshrink\\r\\n #colorbar aspect\\r\\n if cbaspect==None:\\r\\n cbaspect=20\\r\\n else:\\r\\n cbaspect=cbaspect\\r\\n #colorbar pad\\r\\n if cbpad==None:\\r\\n cbpad=.1\\r\\n else:\\r\\n cbpad=cbpad\\r\\n \\r\\n #scale\\r\\n if scale=='log':\\r\\n zerofind=np.where(abs(tfarray)==0)\\r\\n tfarray[zerofind]=1.0\\r\\n if normalize=='y':\\r\\n plottfarray=20*np.log10(abs(tfarray/np.max(abs(tfarray))))\\r\\n else:\\r\\n plottfarray=20*np.log10(abs(tfarray))\\r\\n elif scale=='linear':\\r\\n if normalize=='y':\\r\\n plottfarray=abs(plottfarray/np.max(abs(plottfarray)))**2\\r\\n else:\\r\\n plottfarray=abs(tfarray)**2\\r\\n \\r\\n t=np.arange(len(fx))*dt+starttime*dt\\r\\n FX=np.fft.fft(padzeros(fx))\\r\\n FXfreq=np.fft.fftfreq(len(FX),dt)\\r\\n \\r\\n #set some plot parameters\\r\\n plt.rcParams['font.size']=10\\r\\n plt.rcParams['figure.subplot.left']=.13\\r\\n plt.rcParams['figure.subplot.right']=.98\\r\\n plt.rcParams['figure.subplot.bottom']=.07\\r\\n plt.rcParams['figure.subplot.top']=.96\\r\\n plt.rcParams['figure.subplot.wspace']=.25\\r\\n plt.rcParams['figure.subplot.hspace']=.20\\r\\n #plt.rcParams['font.family']='helvetica'\\r\\n \\r\\n fig=plt.figure(fignum)\\r\\n \\r\\n #plot FFT of fx\\r\\n fax=fig.add_axes([.05,.25,.1,.7])\\r\\n plt.plot(abs(FX[0:len(FX)/2]/max(abs(FX)))**2,FXfreq[0:len(FX)/2],'-k')\\r\\n plt.xlim(0,1)\\r\\n plt.ylim(0,FXfreq[len(FX)/2-1])\\r\\n fax.xaxis.set_major_locator(MultipleLocator(.5))\\r\\n \\r\\n #plot TFD\\r\\n pax=fig.add_axes([.25,.25,.75,.7])\\r\\n if vmm!=None:\\r\\n vmin=vmm[0]\\r\\n vmax=vmm[1]\\r\\n plt.imshow(plottfarray,extent=(tlst[0]/tinc,tlst[-1]/tinc,\\r\\n flst[0],flst[-1]),aspect=aspect,vmin=vmin,vmax=vmax,cmap=cmap,\\r\\n interpolation=interpolation)\\r\\n else:\\r\\n plt.imshow(plottfarray,extent=(tlst[0]/tinc,tlst[-1]/tinc,\\r\\n flst[0],flst[-1]),aspect=aspect,cmap=cmap,\\r\\n interpolation=interpolation)\\r\\n plt.xlabel('Time('+timeinc+')',fontsize=12,fontweight='bold')\\r\\n plt.ylabel('Frequency (Hz)',fontsize=12,fontweight='bold')\\r\\n if title!=None:\\r\\n plt.title(title,fontsize=14,fontweight='bold')\\r\\n plt.colorbar(orientation=cbori,shrink=cbshrink,pad=cbpad,aspect=cbaspect)\\r\\n \\r\\n #plot timeseries\\r\\n tax=fig.add_axes([.25,.05,.60,.1])\\r\\n plt.plot(t,fx,'-k')\\r\\n plt.axis('tight')\\r\\n plt.show()\",\n \"def example3():\\n arrive_time=example2() # Get packets arrive time using example1\\n time_series.plot_time_series(arrive_time) # Plot time series using packets arrive time\",\n \"def drawAxes(t):\\r\\n t.speed(0)\\r\\n t.pd()\\r\\n t.forward(500)\\r\\n t.back(500)\",\n \"def plot_dt_signal(x, title=None):\\n pylab.figure()\\n pylab.stem(range(len(x)), x)\\n pylab.title(title)\\n pylab.xlabel(\\\"samples\\\")\",\n \"def plot_time_slices(self):\\n U = self.r.u[:, 15:-15, :]\\n T = range(U.shape[2])\\n kwarglist = [dict(t=t,\\n index=self.index,\\n U=U,\\n levels=self.levels,\\n fname=self.time_slice_path(t))\\n for t in T]\\n util.parallel_process(plot_time_slice, kwarglist=kwarglist)\",\n \"def plot_time(signal,\\n fs=None,\\n ax=None,\\n scale='linear',\\n sides='onesided',\\n title=None,\\n label=None,\\n **kwargs):\\n if ax is None:\\n ax = plt.gca()\\n if fs is None:\\n fs = 1\\n ax.set_xlabel(\\\"Samples\\\")\\n else:\\n ax.set_xlabel(\\\"t / s\\\")\\n t = _time_vector_onesided(signal, fs)\\n if scale == 'linear':\\n ax.set_ylabel('Amplitude (linear)')\\n elif scale == 'db':\\n signal = _db_calculation(signal)\\n ax.set_ylabel('Amplitude / dB')\\n else:\\n raise NameError(\\\"Invalid scale\\\")\\n if sides == 'onesided':\\n ax.plot(t, signal, label=label, linewidth=2.0)\\n elif sides == 'twosided':\\n ax.plot(\\n _time_vector_twosided(signal,\\n fs),\\n np.fft.fftshift(signal),\\n label=label, linewidth=1.0)\\n else:\\n raise NameError(\\\"Invalid sides\\\")\\n if title is not None:\\n ax.set_title(title)\\n ax.grid(True)\\n ax.ticklabel_format(useOffset=False)\\n return ax\",\n \"def plot_x(t, x):\\n plt.figure()\\n plt.plot(t, x)\\n plt.title(\\\"Vertical position of the skydiver as a function of time\\\")\\n plt.xlabel(\\\"Time t [s]\\\")\\n plt.ylabel(\\\"Height [m]\\\")\\n plt.savefig('Parachute_position.png')\",\n \"def plot_tseries(time_series, fig=None, axis=0,\\r\\n xticks=None, xunits=None, yticks=None, yunits=None,\\r\\n xlabel=None, ylabel=None, yerror=None, error_alpha=0.1,\\r\\n time_unit=None, **kwargs):\\r\\n\\r\\n if fig is None:\\r\\n fig = plt.figure()\\r\\n\\r\\n if not fig.get_axes():\\r\\n ax = fig.add_subplot(1, 1, 1)\\r\\n else:\\r\\n ax = fig.get_axes()[axis]\\r\\n\\r\\n #Make sure that time displays on the x axis with the units you want:\\r\\n #If you want to change the time-unit on the visualization from that used to\\r\\n #represent the time-series:\\r\\n if time_unit is not None:\\r\\n tu = time_unit\\r\\n conv_fac = ts.time_unit_conversion[time_unit]\\r\\n #Otherwise, get the information from your input:\\r\\n else:\\r\\n tu = time_series.time_unit\\r\\n conv_fac = time_series.time._conversion_factor\\r\\n\\r\\n this_time = time_series.time / float(conv_fac)\\r\\n ax.plot(this_time, time_series.data.T, **kwargs)\\r\\n\\r\\n if xlabel is None:\\r\\n ax.set_xlabel('Time (%s)' % tu)\\r\\n else:\\r\\n ax.set_xlabel(xlabel)\\r\\n\\r\\n if ylabel is not None:\\r\\n ax.set_ylabel(ylabel)\\r\\n\\r\\n if yerror is not None:\\r\\n if len(yerror.data.shape) == 1:\\r\\n this_e = yerror.data[np.newaxis, :]\\r\\n else:\\r\\n this_e = yerror.data\\r\\n delta = this_e\\r\\n e_u = time_series.data + delta\\r\\n e_d = time_series.data - delta\\r\\n for i in range(e_u.shape[0]):\\r\\n ax.fill_between(this_time, e_d[i], e_u[i], alpha=error_alpha)\\r\\n\\r\\n return fig\",\n \"def coordinate_vs_time_plotter(array, xyz_axis=0, bird=0, axis_of_time_steps=2, start=0., end=1.):\\r\\n y_values = array[bird, xyz_axis, :]\\r\\n x_values = get_time_array(array, axis_of_time_steps, start, end)\\r\\n\\r\\n fig = plt.figure()\\r\\n ax = fig.add_subplot()\\r\\n\\r\\n if xyz_axis == 0:\\r\\n ax.set_ylabel('X (m)')\\r\\n elif xyz_axis == 1:\\r\\n ax.set_ylabel('Y (m)')\\r\\n elif xyz_axis == 2:\\r\\n ax.set_ylabel('Z (m)')\\r\\n else:\\r\\n print(\\\"That is not a valid axis choice. Please choose one of: 0, 1, 2\\\")\\r\\n ax.set_xlabel('Time (s)')\\r\\n ax.scatter(x_values, y_values)\\r\\n return fig.show()\",\n \"def plot_global(type):\\n click.echo(click.style(\\n \\\"Generating Plot....\\\", fg='cyan', bold='true'))\\n plot_time_series.TimeSeriesPloTs.plot_global(type)\\n click.echo(click.style(\\n \\\"Done....\\\", fg='green', bold='true'))\",\n \"def demo(self, tmin=0, tmax=27.4, cadence=30.0 / 60.0 / 24.0, offset=0, raw=False, ax=None):\\n t = np.arange(tmin, tmax, cadence)\\n if ax is None:\\n plt.figure('demo', figsize=(8, 3))\\n else:\\n plt.sca(ax)\\n y = self.model(t)\\n if raw:\\n plt.plot(t, y + offset, alpha=0.25, linewidth=1, color='royalblue')\\n plt.plot(t, self.integrated(t) + offset, alpha=0.5, linewidth=1, color='darkorange')\\n plt.xlim(tmin, tmax)\\n # plt.ylim(np.max(y)+0.01, np.min(y)-0.01)\\n plt.xlabel('Time (days)')\\n plt.ylabel('Flux (mag.)')\",\n \"def plot_timefrequency(z, time, f, signal=None, method=\\\"stft\\\"):\\n\\n if method == \\\"stft\\\":\\n figure_title = \\\"Short-time Fourier Transform Magnitude\\\"\\n fig, ax = plt.subplots()\\n for i in range(len(time)):\\n ax.plot(f, z[:, i], label=\\\"Segment\\\" + str(np.arange(len(time))[i] + 1))\\n ax.legend()\\n ax.set_title(\\\"Signal Spectrogram\\\")\\n ax.set_ylabel(\\\"STFT Magnitude\\\")\\n ax.set_xlabel(\\\"Frequency (Hz)\\\")\\n\\n elif method == \\\"cwt\\\":\\n figure_title = \\\"Continuous Wavelet Transform Magnitude\\\"\\n elif method == \\\"wvd\\\":\\n figure_title = \\\"Wigner Ville Distrubution Spectrogram\\\"\\n fig = plt.figure()\\n plt.plot(time, signal)\\n plt.xlabel(\\\"Time (sec)\\\")\\n plt.ylabel(\\\"Signal\\\")\\n\\n elif method == \\\"pwvd\\\":\\n figure_title = \\\"Pseudo Wigner Ville Distribution Spectrogram\\\"\\n\\n fig, ax = plt.subplots()\\n spec = ax.pcolormesh(time, f, z, cmap=plt.get_cmap(\\\"magma\\\"), shading=\\\"auto\\\")\\n plt.colorbar(spec)\\n ax.set_title(figure_title)\\n ax.set_ylabel(\\\"Frequency (Hz)\\\")\\n ax.set_xlabel(\\\"Time (sec)\\\")\\n return fig\",\n \"def __plot_T__(self, refresh=False, *args):\\n # If plot is not requested, return:\\n if not self.plotTeVar.get() or not self.plotTiVar.get():\\n return\\n\\n # Check for a closed window:\\n if 'T' in self.plots.keys() and not matplotlib.pyplot.fignum_exists(self.plots['T'].number):\\n del self.plots['T']\\n refresh = False\\n # Update the existing plot, if it exists\\n refresh = refresh or 'T' in self.plots.keys()\\n if refresh:\\n if 'T' in self.plots.keys():\\n fig = self.plots['T']\\n fig = matplotlib.pyplot.figure(fig.number)\\n fig.clear()\\n else:\\n return\\n # Make a Tew window:\\n else:\\n fig = matplotlib.pyplot.figure(figsize=(4,3))\\n fig.canvas.set_window_title('T, time = ' + '{:.3f}'.format(1e9*self.imp.t(self.it)))\\n ax = fig.add_subplot(111)\\n\\n # Plot:\\n if self.plotTeVar.get():\\n ax.plot(1e4*self.imp.r((self.it), self.ir)[0], self.imp.Te((self.it), self.ir)[0], 'r-', label='e')\\n if self.plotTiVar.get():\\n ax.plot(1e4*self.imp.r((self.it), self.ir)[0], self.imp.Ti((self.it), self.ir)[0], 'b-', label='i')\\n\\n ax.set_xlabel('r (um)', fontsize=12)\\n ax.set_ylabel('T (keV)', fontsize=12)\\n ax.legend()\\n\\n if self.logxVar.get():\\n ax.set_xscale('log')\\n if self.logyVar.get():\\n ax.set_yscale('log')\\n\\n matplotlib.pyplot.tight_layout()\\n\\n if not refresh:\\n fig.show()\\n fig.canvas.draw()\\n if self.wm is not None:\\n self.wm.addWindow(matplotlib.pyplot.get_current_fig_manager().window)\\n self.plots['T'] = fig\",\n \"def plot_basic(time, data, lgnd=None):\\n pylab.figure()\\n pylab.plot(time, data)\\n pylab.xlabel('time, s')\\n pylab.ylabel('data')\\n pylab.title('Basic Plotter')\\n if lgnd != None:\\n pylab.legend(lgnd)\\n pylab.grid(True)\\n pylab.show()\",\n \"def comp_time_plot(p1=database['K+'], p2=database['pi+'], pmax=80, plot=True):\\r\\n dt = []\\r\\n p_range = np.linspace(10, pmax, 1000)\\r\\n m1 = p1.mass\\r\\n m2 = p2.mass\\r\\n for p in p_range:\\r\\n t1_per_m = 76.273/(beta(p, m1)*gamma(p, m1)*c)\\r\\n t2_per_m = 76.273/(beta(p, m2)*gamma(p, m2)*c)\\r\\n dt.append(abs(t1_per_m - t2_per_m)*1e12)\\r\\n dt_12_5 = dt[np.argmin(abs(p_range-12.5))]\\r\\n dt_75 = dt[np.argmin(abs(p_range-75))]\\r\\n ratio = dt_12_5/dt_75\\r\\n if plot==True:\\r\\n fig = plt.figure(figsize=[10, 5])\\r\\n ax = fig.add_subplot(1, 1, 1)\\r\\n ax.plot(p_range, dt, 'b', label=r'$\\\\Delta t$')\\r\\n ax.axvline(12.5, color='r', label='p=12.5 GeV')\\r\\n ax.axvline(75, color='g', label='p=75 GeV')\\r\\n ax.set_xlim(10, pmax)\\r\\n ax.set_ylim(0)\\r\\n ax.set_xlabel('p / GeV', fontsize=20)\\r\\n# ax.set_yscale('log')\\r\\n ax.set_ylabel(r'$\\\\Delta t$ / ps', fontsize=20)\\r\\n title = f'{p1.name} to {p2.name} '\\r\\n title += r'$\\\\Delta t$ dependancy on particle momenta'\\r\\n ax.set_title(title, fontsize=20)\\r\\n ax.legend(fontsize=20)\\r\\n text = 'dt(12.5) = {0:.2f} ps, '.format(dt_12_5)\\r\\n text += 'dt(75) = {0:.2f} ps, '.format(dt_75)\\r\\n text += 'ratio = {0:.3f}'.format(ratio)\\r\\n plt.show()\\r\\n print(text)\\r\\n return [dt_12_5, dt_75, ratio]\",\n \"def plot_time(time_to_complete, plot_num):\\n average = []\\n for i, point in enumerate(time_to_complete):\\n average.append(sum(time_to_complete[:i+1])/ (i+1))\\n plt.plot(time_to_complete, color= 'blue', label=\\\"Epoch Time\\\")\\n plt.plot(average, color = 'red', label= \\\"Average Time\\\", zorder = 3)\\n plt.legend()\\n plt.title(\\\"Time to complete FetchReach\\\")\\n plt.ylabel(\\\"Time (seconds)\\\")\\n plt.xlabel(\\\"Number iterations\\\")\\n plt.savefig(\\\"./plots/time/time_to_complete_{}.png\\\".format(plot_num))\\n plt.clf()\",\n \"def tick(self):\",\n \"def test_plot_timeseries_univariate(tmpdir, random):\\n x = np.linspace(0, 10, 20)\\n y = np.sin(x)\\n segments = get_test_segments(data=np.expand_dims(y, 0))\\n\\n output_path = Path(tmpdir) / 'temp_visualization_test_univariate.png'\\n\\n plot_timeseries(x=x,\\n y=y,\\n segments=segments,\\n show_plot=False,\\n output_filename=output_path)\\n\\n assert output_path.exists()\",\n \"def callback_time_cut(val):\\n global plot_mode\\n global idx_time\\n last_plot_mode = plot_mode\\n plot_mode = 'time_cut'\\n idx_time = int(val)\\n update_num_shadow(int(sld['neighbors'].val))\\n # plot 121\\n lcuttime.set_xdata( [val, val] )\\n lcuttime.set_alpha( alpha_hm )\\n lcutfreq.set_alpha( 0.0 )\\n # plot 122\\n if plot_mode == last_plot_mode:\\n replot_flags = get_replot_flag( idx_time ) # [True/False, True/False]\\n replot_shadow( replot_flags )\\n update_shadow( ~replot_flags )\\n update_light()\\n else:\\n replot_shadow( [True, True ] )\\n replot_light()\\n reform_axis()\\n\\n fig.canvas.draw_idle()\",\n \"def test_plot_ts(kwargs):\\n nchains = 4\\n ndraws = 500\\n obs_data = {\\n \\\"y\\\": 2 * np.arange(1, 9) + 3,\\n \\\"z\\\": 2 * np.arange(8, 12) + 3,\\n }\\n\\n posterior_predictive = {\\n \\\"y\\\": np.random.normal(\\n (obs_data[\\\"y\\\"] * 1.2) - 3, size=(nchains, ndraws, len(obs_data[\\\"y\\\"]))\\n ),\\n \\\"z\\\": np.random.normal(\\n (obs_data[\\\"z\\\"] * 1.2) - 3, size=(nchains, ndraws, len(obs_data[\\\"z\\\"]))\\n ),\\n }\\n\\n const_data = {\\\"x\\\": np.arange(1, 9), \\\"x_pred\\\": np.arange(8, 12)}\\n\\n idata = from_dict(\\n observed_data=obs_data,\\n posterior_predictive=posterior_predictive,\\n constant_data=const_data,\\n coords={\\\"obs_dim\\\": np.arange(1, 9), \\\"pred_dim\\\": np.arange(8, 12)},\\n dims={\\\"y\\\": [\\\"obs_dim\\\"], \\\"z\\\": [\\\"pred_dim\\\"]},\\n )\\n\\n ax = plot_ts(idata=idata, y=\\\"y\\\", show=True, **kwargs)\\n assert np.all(ax)\",\n \"def draw_spike_times(spike_times):\\n for line in spike_times:\\n plt.axvline(x=line, color='y')\",\n \"def ProfilePlot(t,y,z,scale=86400, axis=0,color=[0.5,0.5,0.5]):\\r\\n from matplotlib import collections\\r\\n from matplotlib.ticker import Formatter\\r\\n\\r\\n class MyFormatter(Formatter):\\r\\n def __init__(self, dates, fmt='%b %d %Y'):\\r\\n self.fmt = fmt\\r\\n self.dates = dates\\r\\n\\r\\n def __call__(self, x, pos=0):\\r\\n 'Return the label for time x s'\\r\\n return datetime.strftime(datetime(1990,1,1)+timedelta(seconds=x),self.fmt)\\r\\n\\r\\n tsec = othertime.SecondsSince(t)\\r\\n formatter = MyFormatter(tsec)\\r\\n \\r\\n y = np.swapaxes(y,0,axis)\\r\\n \\r\\n lines=[]\\r\\n line2 =[]\\r\\n for ii, tt in enumerate(tsec):\\r\\n #xplot = set_scale(y[:,ii],tt)\\r\\n xplot = tt + y[:,ii]*scale\\r\\n lines.append(np.array((xplot,z)).T)\\r\\n line2.append(np.array([[tt,tt],[z[0],z[-1]]]).T)\\r\\n \\r\\n \\r\\n LC1 = collections.LineCollection(lines,colors=color,linewidths=1.5)\\r\\n LC2 = collections.LineCollection(line2,colors='k',linestyles='dashed') # Zero axis\\r\\n \\r\\n ax=plt.gca()\\r\\n ax.add_collection(LC1)\\r\\n ax.add_collection(LC2)\\r\\n ax.set_ylim((z.min(),z.max()))\\r\\n ax.xaxis.set_major_formatter(formatter)\\r\\n ax.set_xlim((tsec[0],tsec[-1]))\\r\\n plt.xticks(rotation=17) \\r\\n \\r\\n return ax\",\n \"def PlotTimes(metadata, data):\\n\\n gp = Gnuplot.Gnuplot(persist=1)\\n gp('set data style impulses')\\n gp('set xtics 1')\\n gp.clear()\\n gp.xlabel('seconds')\\n gp.ylabel('duration in second')\\n gp.title(metadata.AsTitle())\\n styles = {}\\n line_style = 1\\n\\n for dataset in data:\\n x = numpy.array(dataset.time, dtype='float_')\\n if not dataset.name in styles:\\n styles[dataset.name] = line_style\\n line_style += 1\\n d = Gnuplot.Data(x, dataset.data,\\n title=dataset.name,\\n with_='impulses ls %d' % styles[dataset.name])\\n else: # no need to repeat a title that exists already.\\n d = Gnuplot.Data(x, dataset.data,\\n with_='impulses ls %d' % styles[dataset.name])\\n\\n gp.replot(d)\\n gp.hardcopy('/tmp/%s-%s-%f.png' %\\n (metadata.name, metadata.kernel, metadata.duration),\\n terminal='png')\",\n \"def plot_series(self, t1=0, t2=100, t1p=None, t2p=None):\\n \\n plot_discretized(self.ts, self.ts_dis, t1=t1, t2=t2, t1p=t1p, t2p=t2p)\",\n \"def make_time_axis(axis=None,time_unit='s'):\\n if axis is None:\\n axis = plt.gca().xaxis\\n class TimeFormatter(matplotlib.ticker.FuncFormatter):\\n def __init__(self,time_unit):\\n self.time_unit = time_unit\\n conv = nitime.time_unit_conversion\\n self.func = lambda x,y: (1.0*x) / conv[self.time_unit]\\n def format_data_short(self,value):\\n 'return a short string version'\\n return \\\"%-12g\\\"%self.format_data(value)\\n f = TimeFormatter(time_unit)\\n axis.set_major_formatter(f)\",\n \"def plot_tcv(self):\\n self.plot_profiles(0, title='Shot #{:d} @ t={:.2f} s'.format(self.shot, self.t))\",\n \"def test_plot_timeseries_multivariate(tmpdir, random):\\n x = np.linspace(start=0, stop=10, num=20)\\n ys = np.stack((np.sin(x), np.cos(x), np.tan(0.4 * x)))\\n segments = get_test_segments(data=ys)\\n output_path = Path(tmpdir) / 'temp_visualization_test_multivariate.png'\\n\\n plot_timeseries(x=x,\\n y=ys.T,\\n segments=segments,\\n show_plot=False,\\n output_filename=output_path)\\n\\n assert output_path.exists()\",\n \"def plot_time_series(df, plt):\\n # Remove the plot frame lines\\n delta = (df[\\\"timestamp\\\"].max() - df[\\\"timestamp\\\"].min()) / 10\\n plt.xticks(\\n np.arange(\\n df[\\\"timestamp\\\"].min(),\\n df[\\\"timestamp\\\"].max(),\\n step=np.around(delta, decimals=1),\\n )\\n )\\n plt.grid()\",\n \"def liveplot(x, y, xlim, ylim, title):\\n plt.plot(x,y,'b.')\\n plt.xlim(xlim)\\n plt.ylim(ylim)\\n plt.xlabel('North-South Axis')\\n plt.ylabel('East-West Axis')\\n plt.title(title)\\n plt.show()\",\n \"def update_plot(axes):\\n axes.clear()\\n\\n i = C.i\\n C.i += di # globale Zählvariable erhöhen\\n if C.i >= len(tt):\\n time.sleep(2)\\n C.i = 0\\n\\n t = tt[i]\\n q1 = qq1[i]\\n q2 = qq2[i]\\n q3 = qq3[i]\\n CCframe(q1, q2, q3)\\n\\n # Ausgabe der aktuellen Zeit\\n pl.text(0.06, 0.05, \\\"t = %3.2fs\\\" % t, transform = axes.transAxes)\\n pl.axis([-3, 3, -3, 3])\\n axes.figure.canvas.draw()\",\n \"def plotTimeDelta(data, type_plot, device):\\n mean = data.mean()\\n std = data.std()\\n max_data = data.max()\\n min_data = data.min()\\n max_indx = np.argmax(data) # max value index\\n min_indx = np.argmin(data) # min value index\\n x = np.arange(min_data, max_data, 0.1)\\n y = normfun(x, mean, std)\\n res_quantile = quantileValues(data, device)\\n if type_plot == 0:\\n plt.plot(x, y, color='blue')\\n annot_max_min(x, y)\\n # plt.hist(data.dropna(), bins=500, rwidth=0.9, normed=True)\\n plt.title('Time Delta distribution')\\n plt.xlabel('Time Delta')\\n plt.ylabel('Probability')\\n sns.distplot(tmp.deltaSeconds.dropna(),\\n kde=True, rug=True, rug_kws={\\\"color\\\": \\\"k\\\"},\\n kde_kws={\\\"color\\\": \\\"red\\\", \\\"lw\\\": 3, \\\"label\\\": \\\"KDE\\\"},\\n hist_kws={\\\"histtype\\\": \\\"step\\\", \\\"lw\\\": 3, \\\"alpha\\\": 1,\\n \\\"color\\\": \\\"g\\\"},\\n bins=500)\\n # ax.set(xlabel='Vibration Intensity', ylabel='Probability')\\n elif type_plot == 1: # plot the max and min point\\n plt.plot(data)\\n plt.plot(max_indx, data[max_indx], 'ks')\\n show_max = '['+str(max_indx)+' '+str(data[max_indx])+']'\\n plt.annotate(show_max,\\n xytext=(max_indx, data[max_indx]),\\n xy=(max_indx, data[max_indx]))\\n plt.plot(min_indx, data[min_indx], 'gs')\\n show_min = '['+str(min_indx)+' '+str(data[min_indx])+']'\\n plt.annotate(show_min,\\n xytext=(min_indx, data[min_indx]),\\n xy=(min_indx, data[min_indx]))\\n plt.title('Time Delta')\\n plt.xlabel('Index')\\n plt.ylabel('Vibration Intensity Value')\\n elif type_plot == 2: # boxplot\\n boxplot(data.dropna())\\n return res_quantile\",\n \"def pf_plot(pf, t):\\n xx = pf.XS[t, :, 0]\\n yy = pf.XS[t, :, 1]\\n ww = pf.WS[t, :]\\n plt.scatter(xx, yy, s=ww * 5000)\",\n \"def plot_curve(self, label='', axis=None):\\n controller = deepcopy(self._controller)\\n data = np.zeros(len(self._ts))\\n for i in range(len(self._ts)):\\n controller.step(self._dt)\\n data[i] = controller.sim.get_output()\\n \\n if axis is None:\\n plt.plot(self._ts, data, label=label)\\n else:\\n axis.plot(self._ts, data, label=label)\",\n \"def plot(self):\\n\\t\\t\\n\\t\\ttf=tfData(self.shotno,tStart=None,tStop=None)\\n\\t\\t\\n\\t\\t_plt.figure()\\n\\t\\tax1 = _plt.subplot2grid((3,2), (0,1), rowspan=3) #tf\\n\\t\\tax2 = _plt.subplot2grid((3,2), (0,0)) #vf\\n\\t\\tax3 = _plt.subplot2grid((3,2), (1,0),sharex=ax2) #oh\\n\\t\\tax4 = _plt.subplot2grid((3,2), (2, 0),sharex=ax2) #sh\\n\\t\\tfig=_plt.gcf()\\n\\t\\tfig.set_size_inches(10,5)\\n\\t\\t\\t\\t\\n\\t\\ttStart=-2\\n\\t\\ttStop=20\\n\\t\\t\\n\\t\\tax1.plot(tf.time*1e3,tf.tfBankField)\\n\\t\\tax1.axvspan(tStart,tStop,color='r',alpha=0.3)\\n\\t\\t_plot.finalizeSubplot(ax1,xlabel='Time (s)',xlim=[-150,450],ylabel='TF Field (T)')#,title=self.title\\n\\t\\t\\n\\t\\tax2.plot(self.vfTime*1e3,self.vfBankCurrent*1e-3)\\n\\t\\t_plot.finalizeSubplot(ax2,ylabel='VF Current\\\\n(kA)')\\n\\t\\t\\n\\t\\tax3.plot(self.ohTime*1e3,self.ohBankCurrent*1e-3)\\n\\t\\t_plot.finalizeSubplot(ax3,ylim=[-20,30],ylabel='OH Current\\\\n(kA)')\\n\\t\\t\\n\\t\\tax4.plot(self.shTime*1e3,self.shBankCurrent*1e-3)\\n\\t\\t_plot.finalizeSubplot(ax4,ylim=[tStart,tStop],xlabel='Time (s)',ylabel='SH Current\\\\n(kA)')\\n\\t\\t\\n\\t\\t_plot.finalizeFigure(fig,title=self.title)\\n#\\t\\tfig.set_tight_layout(True)\\n\\t\\t\\n\\t\\treturn fig\",\n \"def plot_fitter(self):\\n\\n total_time=self.interval*self.maxspectra\\n times = np.linspace(self.interval,total_time + 1,self.interval)\\n spectra_fitter.main(self.rt_plot.sum_data, times)\",\n \"def lastTick():\",\n \"def SFplot(profile, time, Xlim=[], ax=None, **kwargs):\\n X = profile.X\\n sf = SF(profile, time, Xlim)\\n clw = {'marker': '.', 'ls': 'none'}\\n if 'label' not in kwargs:\\n clw['label'] = profile.name+'_%.1fh_SF' % (time/3600)\\n args = {**clw, **kwargs}\\n if ax is None:\\n fig = plt.figure()\\n ax = fig.add_subplot(111)\\n ax.semilogy(X, sf, **args)\\n ax.set_xlabel('Mole fraction', fontsize=label_fontsize)\\n ax.set_ylabel('Diffusion Coefficients '+'$(m^2/s)$', fontsize=label_fontsize)\\n ax.set_xlim(plot_lim(X.min(), X.max()))\\n ax.tick_params(labelsize=tick_fontsize)\\n leg = ax.legend(numpoints=1, fontsize=leg_fontsize)\\n leg.get_frame().set_linewidth(0.0)\\n leg.set_draggable(True)\\n plt.tight_layout()\\n\\n return ax\",\n \"def __plot(name, x, y):\\n import matplotlib.pyplot as plt\\n\\n plt.plot(x, y)\\n plt.xlabel('elements')\\n plt.ylabel('time (seconds)')\\n plt.savefig(\\\"{}\\\".format(name))\",\n \"def v_from_p_function(self):\\r\\n\\r\\n track_c = [] # p classical function,\\r\\n for i in range(len(self.dt.momentum_t)):\\r\\n track_c.append(self.dt.momentum_t[i] / self.dt.mass)\\r\\n\\r\\n (fig, ax) = plt.subplots()\\r\\n\\r\\n ax.plot(\\r\\n self.dt.momentum_t,\\r\\n track_c,\\r\\n linestyle=':',\\r\\n linewidth=1,\\r\\n color='b',\\r\\n label='classic',\\r\\n )\\r\\n\\r\\n # marker=\\\"+\\\", markersize = 13,\\r\\n # ax.plot(self.dt.momentum_t, self.observer.velT, linestyle=\\\" \\\",\\r\\n # color=\\\"k\\\",marker=\\\"+\\\", markersize = 13, label=\\\"measurement\\\")\\r\\n\\r\\n ax.plot(\\r\\n self.dt.momentum_t,\\r\\n self.dt.vel_t,\\r\\n linestyle=' ',\\r\\n color='k',\\r\\n marker='o',\\r\\n label='result of measurements',\\r\\n )\\r\\n ax.plot(\\r\\n self.dt.momentum_t,\\r\\n self.dt.vel_anl,\\r\\n linestyle='-',\\r\\n color='red',\\r\\n linewidth=1,\\r\\n label='continuum',\\r\\n )\\r\\n\\r\\n # Euler's method == analitical function. We not plot it.\\r\\n\\r\\n ax.plot(\\r\\n self.dt.momentum_t,\\r\\n self.dt.vn,\\r\\n linestyle='--',\\r\\n color='blue',\\r\\n marker='x',\\r\\n linewidth=1,\\r\\n label=\\\"Euler's method\\\",\\r\\n )\\r\\n\\r\\n # error\\r\\n\\r\\n ax.errorbar(self.dt.momentum_t, self.dt.vel_t, fmt='k ',\\r\\n yerr=self.dt.vel_t_err)\\r\\n\\r\\n xm = -1.0\\r\\n for i in range(len(self.dt.momentum_t)):\\r\\n if self.dt.momentum_t[i] > xm:\\r\\n xm = self.dt.momentum_t[i]\\r\\n stepx = round(xm / float(len(self.dt.momentum_t)), 1)\\r\\n xm = round(xm + stepx, 1)\\r\\n ax.set_xlim([0, xm]) # xm = 0.85\\r\\n\\r\\n # signature on the horizontal x-axis\\r\\n\\r\\n ax.set_xlabel('p')\\r\\n\\r\\n # Create an instance of the class that will be responsible for the location of the labels (base is step on x)\\r\\n\\r\\n locatorx = matplotlib.ticker.MultipleLocator(base=stepx) # step on x is base=0.1\\r\\n\\r\\n # Set the locator for the main labels\\r\\n\\r\\n ax.xaxis.set_major_locator(locatorx)\\r\\n\\r\\n # line draw\\r\\n\\r\\n line = matplotlib.lines.Line2D([0.0, 9.0], [1.0, 1.0], color='b'\\r\\n )\\r\\n ax.add_line(line)\\r\\n plt.text(0.7, 1.01, u'light speed', horizontalalignment='center'\\r\\n )\\r\\n ax.set_ylim([0, 1.1])\\r\\n\\r\\n # signature on vertical y axis\\r\\n\\r\\n ax.set_ylabel('v')\\r\\n\\r\\n # Create an instance of the class that will be responsible for the location of the labels (base is step on y)\\r\\n\\r\\n locatory = matplotlib.ticker.MultipleLocator(base=0.1) # step on y is base=0.1\\r\\n\\r\\n # Set the locator for the main labels\\r\\n\\r\\n ax.yaxis.set_major_locator(locatory)\\r\\n\\r\\n ax.grid()\\r\\n\\r\\n # show legend\\r\\n\\r\\n ax.legend(loc='upper left')\\r\\n\\r\\n # show drawing\\r\\n # pylab.show()\\r\\n\\r\\n plt.show()\",\n \"def plot_tseries(*args, **kwargs) :\\n data = kwargs.pop('data')\\n return data.dropna().plot(x=args[0], y=args[1], **kwargs)\",\n \"def plot_f(self, *args, **kwargs):\\r\\n kwargs['plot_raw'] = True\\r\\n self.plot(*args, **kwargs)\",\n \"def footprint_demo(**kw):\\n # Note: needs fixed slits in single_point()\\n count = 1500000\\n data = []\\n for theta in np.linspace(0.15, 5, 30):\\n n = single_point(theta=theta, count=count, trace=False, **kw)\\n data.append((theta, np.sum(n.active)))\\n print(data[-1])\\n x, y = zip(*data)\\n pylab.plot(x, np.array(y)/count)\\n pylab.show()\",\n \"def on_tick(self, time):\\n pass\",\n \"def plotTrace(trace):\\n for t in trace:\\n plt.plot(range(len(t)),t,alpha=0.5)\\n plt.ylabel(\\\"Trace\\\")\\n plt.xlabel(\\\"Step\\\")\\n\\n return\",\n \"def visualize(self, time, pred, true):\\n plt.plot(time, true, label='Actual')\\n plt.plot(time, pred, label='Predicted')\\n plt.xlabel('Time')\\n plt.ylabel('Price ($)')\\n plt.legend(bbox_to_anchor=(0.1, 1), loc=2, borderaxespad=0.,\\n prop={'size': 14})\\n plt.show()\",\n \"def plot_timeseries(self, x, *a, **kw):\\n v, t = self.timeseries(x, **kw)\\n utcoffset = kw.pop('utcoffset', None)\\n if utcoffset is not None: # temporary hack because plot_date seems to be ignoring tz kwarg...\\n t += utcoffset/24.\\n for k in ('convert', 'timeslice', 'rmnans'): \\n trash = kw.pop(k, None)\\n if not a: a = '-' # plot a line by default\\n if 'label' not in kw: \\n kw.update({'label': x.replace('platform ','').replace('_',' ')})\\n if 'axes' in kw: # deal with possible bug in plot_date?\\n ax = kw.pop('axes')\\n ax.plot_date(t, v, *a, **kw)\\n ax.set_xlim(ax.xaxis.get_data_interval()) # update time limits\\n else: # just make a new axis\\n plt.plot_date(t, v, *a, **kw)\\n ax = plt.gca()\\n plt.gcf().autofmt_xdate()\\n return ax\",\n \"def plot_running_time(num_clusters):\\n slow_running = []\\n fast_running = []\\n for dummy_i in range(2, num_clusters):\\n cluster_list = gen_random_clusters(dummy_i)\\n start = timer()\\n fast_closest_pair(cluster_list)\\n end = timer()\\n fast_running.append((end - start))\\n \\n start = timer()\\n slow_closest_pair(cluster_list)\\n end = timer()\\n slow_running.append((end - start))\\n #\\n plt.plot(range(2, num_clusters), fast_running)\\n plt.plot(range(2, num_clusters), slow_running)\\n plt.xlabel(\\\"num clusters\\\")\\n plt.ylabel(\\\"running time in seconds\\\")\\n plt.title(\\\"Running time slow closest pair vs fast closest pair.\\\")\\n plt.legend([\\\"fast closest pair\\\", \\\"slow closest pair\\\"])\\n plt.show()\",\n \"def diagrama_ts(S: [np.ndarray] or [list], T: [np.ndarray] or [list], title: str, hide_values: bool) -> (Figure, Axes):\\n return diagram(S, T, title, \\\"TS\\\", hide_values)\",\n \"def time_support(*, scale=None, format=None, simplify=True):\\n import matplotlib.units as units\\n from matplotlib.ticker import MaxNLocator, ScalarFormatter\\n\\n from astropy.visualization.wcsaxes.utils import select_step_hour, select_step_scalar\\n\\n class AstropyTimeLocator(MaxNLocator):\\n # Note: we default to AutoLocator since many time formats\\n # can just use this.\\n\\n def __init__(self, converter, *args, **kwargs):\\n kwargs[\\\"nbins\\\"] = 4\\n super().__init__(*args, **kwargs)\\n self._converter = converter\\n\\n def tick_values(self, vmin, vmax):\\n # Where we put the ticks depends on the format we are using\\n if self._converter.format in YMDHMS_FORMATS:\\n # If we are here, we need to check what the range of values\\n # is and decide how to find tick locations accordingly\\n\\n vrange = vmax - vmin\\n\\n if (\\n self._converter.format != \\\"yday\\\" and vrange > 31\\n ) or vrange > 366: # greater than a month\\n # We need to be careful here since not all years and months have\\n # the same length\\n\\n # Start off by converting the values from the range to\\n # datetime objects, so that we can easily extract the year and\\n # month.\\n\\n tmin = Time(\\n vmin, scale=self._converter.scale, format=\\\"mjd\\\"\\n ).datetime\\n tmax = Time(\\n vmax, scale=self._converter.scale, format=\\\"mjd\\\"\\n ).datetime\\n\\n # Find the range of years\\n ymin = tmin.year\\n ymax = tmax.year\\n\\n if ymax > ymin + 1: # greater than a year\\n # Find the step we want to use\\n ystep = int(select_step_scalar(max(1, (ymax - ymin) / 3)))\\n\\n ymin = ystep * (ymin // ystep)\\n\\n # Generate the years for these steps\\n times = []\\n for year in range(ymin, ymax + 1, ystep):\\n times.append(datetime(year=year, month=1, day=1))\\n\\n else: # greater than a month but less than a year\\n mmin = tmin.month\\n mmax = tmax.month + 12 * (ymax - ymin)\\n\\n mstep = int(select_step_scalar(max(1, (mmax - mmin) / 3)))\\n\\n mmin = mstep * max(1, mmin // mstep)\\n\\n # Generate the months for these steps\\n times = []\\n for month in range(mmin, mmax + 1, mstep):\\n times.append(\\n datetime(\\n year=ymin + (month - 1) // 12,\\n month=(month - 1) % 12 + 1,\\n day=1,\\n )\\n )\\n\\n # Convert back to MJD\\n values = Time(times, scale=self._converter.scale).mjd\\n\\n elif vrange > 1: # greater than a day\\n self.set_params(steps=[1, 2, 5, 10])\\n values = super().tick_values(vmin, vmax)\\n\\n else:\\n # Determine ideal step\\n dv = (vmax - vmin) / 3 * 24 << u.hourangle\\n\\n # And round to nearest sensible value\\n dv = select_step_hour(dv).to_value(u.hourangle) / 24\\n\\n # Determine tick locations\\n imin = np.ceil(vmin / dv)\\n imax = np.floor(vmax / dv)\\n values = np.arange(imin, imax + 1, dtype=np.int64) * dv\\n\\n else:\\n values = super().tick_values(vmin, vmax)\\n\\n # Get rid of values outside of the input interval\\n values = values[(values >= vmin) & (values <= vmax)]\\n\\n return values\\n\\n def __call__(self):\\n vmin, vmax = self.axis.get_view_interval()\\n return self.tick_values(vmin, vmax)\\n\\n class AstropyTimeFormatter(ScalarFormatter):\\n def __init__(self, converter, *args, **kwargs):\\n super().__init__(*args, **kwargs)\\n self._converter = converter\\n self.set_useOffset(False)\\n self.set_scientific(False)\\n\\n def format_ticks(self, values):\\n if len(values) == 0:\\n return []\\n if self._converter.format in YMDHMS_FORMATS:\\n times = Time(values, format=\\\"mjd\\\", scale=self._converter.scale)\\n formatted = getattr(times, self._converter.format)\\n if self._converter.simplify:\\n if self._converter.format in (\\\"fits\\\", \\\"iso\\\", \\\"isot\\\"):\\n if all(x.endswith(\\\"00:00:00.000\\\") for x in formatted):\\n split = \\\" \\\" if self._converter.format == \\\"iso\\\" else \\\"T\\\"\\n formatted = [x.split(split)[0] for x in formatted]\\n elif self._converter.format == \\\"yday\\\":\\n if all(x.endswith(\\\":001:00:00:00.000\\\") for x in formatted):\\n formatted = [x.split(\\\":\\\", 1)[0] for x in formatted]\\n return formatted\\n elif self._converter.format == \\\"byear_str\\\":\\n return Time(\\n values, format=\\\"byear\\\", scale=self._converter.scale\\n ).byear_str\\n elif self._converter.format == \\\"jyear_str\\\":\\n return Time(\\n values, format=\\\"jyear\\\", scale=self._converter.scale\\n ).jyear_str\\n else:\\n return super().format_ticks(values)\\n\\n class MplTimeConverter(units.ConversionInterface):\\n def __init__(self, scale=None, format=None, simplify=None):\\n super().__init__()\\n\\n self.format = format\\n self.scale = scale\\n self.simplify = simplify\\n\\n # Keep track of original converter in case the context manager is\\n # used in a nested way.\\n self._original_converter = units.registry.get(Time)\\n\\n units.registry[Time] = self\\n\\n @property\\n def format(self):\\n return self._format\\n\\n @format.setter\\n def format(self, value):\\n if value in UNSUPPORTED_FORMATS:\\n raise ValueError(f\\\"time_support does not support format={value}\\\")\\n self._format = value\\n\\n def __enter__(self):\\n return self\\n\\n def __exit__(self, type, value, tb):\\n if self._original_converter is None:\\n del units.registry[Time]\\n else:\\n units.registry[Time] = self._original_converter\\n\\n def default_units(self, x, axis):\\n if isinstance(x, tuple):\\n x = x[0]\\n if self.format is None:\\n self.format = x.format\\n if self.scale is None:\\n self.scale = x.scale\\n return \\\"astropy_time\\\"\\n\\n def convert(self, value, unit, axis):\\n \\\"\\\"\\\"\\n Convert a Time value to a scalar or array.\\n \\\"\\\"\\\"\\n scaled = getattr(value, self.scale)\\n if self.format in YMDHMS_FORMATS:\\n return scaled.mjd\\n elif self.format == \\\"byear_str\\\":\\n return scaled.byear\\n elif self.format == \\\"jyear_str\\\":\\n return scaled.jyear\\n else:\\n return getattr(scaled, self.format)\\n\\n def axisinfo(self, unit, axis):\\n \\\"\\\"\\\"\\n Return major and minor tick locators and formatters.\\n \\\"\\\"\\\"\\n majloc = AstropyTimeLocator(self)\\n majfmt = AstropyTimeFormatter(self)\\n return units.AxisInfo(\\n majfmt=majfmt, majloc=majloc, label=f\\\"Time ({self.scale})\\\"\\n )\\n\\n return MplTimeConverter(scale=scale, format=format, simplify=simplify)\",\n \"def time_series(t, f=0.02):\\n T = t.size\\n # Seasonal component and time-varying trend component\\n ys = np.sin(2 * np.pi * f * t) * 0.6 + np.sin(1 / 5 * 2 * np.pi * f * t) * 0.2\\n # Amplitude modulation component\\n amp_mod = 0.5 * np.sin(1 / 6 * 2 * np.pi * f * t) + 0.8\\n ys *= amp_mod\\n ys = np.reshape(ys, (T,1))\\n return ys\",\n \"def make_plot(x,y):\",\n \"def plot_running_time():\\n global counter\\n counter += 1\\n running_time_targeted = []\\n running_time_fast_targeted = []\\n \\n for node_number in range(10, 1000, 10):\\n synthetic_undirected_graph = make_synthetic_undirected_graph(node_number, 5)\\n\\n start_time = time.time()\\n attack_order = targeted_order(synthetic_undirected_graph)\\n stop_time = time.time()\\n running_time_targeted.append(stop_time - start_time)\\n \\n start_time = time.time()\\n attack_order = fast_targeted_order(synthetic_undirected_graph)\\n stop_time = time.time()\\n running_time_fast_targeted.append(stop_time - start_time)\\n \\n plt.plot(range(10, 1000, 10), running_time_targeted, '-b', label = 'targeted_order')\\n plt.plot(range(10, 1000, 10), running_time_fast_targeted, '-r', label = 'fast_targeted_order')\\n \\n plt.legend(loc='upper right')\\n\\n\\n plt.title(\\\" plot of running time of desktop Python\\\")\\n plt.xlabel(\\\"the number of nodes\\\")\\n plt.ylabel(\\\"running times\\\")\\n plt.savefig(\\\"running_time_\\\"+str(counter)+\\\".png\\\", dpi = 72)\\n plt.gcf().clear() # hose-keeping\",\n \"def plot(self):\\n\\t\\t\\n\\t\\tfig,p1=_plt.subplots()\\n\\t\\tp1.plot(self.time*1e3,self.qStar,label=r'q$^*$')\\n\\t\\tp1.plot(self.time*1e3,self.qStarCorrected,label=r'q$^* * 1.15$')\\n\\t\\t_plot.finalizeSubplot(p1,xlabel='Time (ms)',ylabel=r'q$^*$',ylim=[1,5])\\n\\t\\t_plot.finalizeFigure(fig,title=self.title)\",\n \"def pyts_time_series(x):\\n p = circle(x, r=0.2, a=0, b=0, x_lim=(-0.2, 0.2))\\n y = - circle(x, r=0.2, a=0.4, b=0, x_lim=(0.2, 0.6))\\n t = 0\\n s = circle(x, r=0.125, a=0.9, b=-0.025, x_lim=(0.775, 0.9))\\n s += 0.1 * (x >= 0.9) * (x <= 1.2)\\n return p + y + t + s\",\n \"def plotPerTimeStamp(options):\\n name = options['name'] + '_' + options['scan'] + '_perTime'\\n if options['extra']:\\n name += '_' + options['extra']\\n f = openRootFileR(options['name']+'_perTime')\\n histname = plotName(name, timestamp=False)\\n filename = plotName(name, timestamp=True)\\n filepath = plotPath(name, timestamp=True)\\n print '<<< Save plot:', filepath\\n hist = f.Get(histname)\\n hist.SetErrorOption(options['error'])\\n if options['big']:\\n canvas = TCanvas('c', '', 8000, 1200)\\n else:\\n canvas = TCanvas('c', '', 1400, 500)\\n canvas.SetLogy(options['logy'])\\n gStyle.SetOptStat(options['optstat'])\\n hist.Draw()\\n gPad.Update()\\n hist.GetXaxis().SetTimeDisplay(1)\\n hist.GetXaxis().SetTimeFormat('#splitline{%d.%m.%y}{%H:%M:%S}%F1969-12-31' \\\\\\n +' 22:00:00')\\n hist.GetXaxis().SetLabelOffset(0.03)\\n hist.GetXaxis().SetTitle('')\\n if 'xmin' in options and 'xmax' in options:\\n hist.GetXaxis().SetRangeUser(options['xmin'], options['xmax'])\\n hist.GetYaxis().SetTitle(options['ytitle'])\\n hist.GetYaxis().SetTitleOffset(1.2)\\n for axis in [hist.GetXaxis(), hist.GetYaxis()]:\\n axis.SetTitleFont(133)\\n axis.SetTitleSize(16)\\n axis.SetLabelFont(133)\\n axis.SetLabelSize(12)\\n axis.CenterTitle()\\n if options['big']:\\n axis.SetTickLength(0.01)\\n if options['big']:\\n hist.GetYaxis().SetTitleOffset(0.25)\\n drawSignature(filename)\\n gPad.Modified()\\n gPad.Update()\\n if options['retrn']:\\n return [canvas, hist, f]\\n else:\\n canvas.Print(filepath)\\n canvas.Close()\\n closeRootFile(f, options['name']+'_perTime')\",\n \"def plot_v(t, v):\\n p1 = plt.plot(t,v)\\n plt.xlabel('Time [s]')\\n plt.ylabel('Velocity [m/s]')\\n plt.title('Velocity for the skydiver as a function of time')\\n plt.show()\\n plt.savefig('Parachute_velocity.png')\",\n \"def draw_plot(yscale='linear'):\\n plt.yscale(yscale)\\n plt.xticks(list(range(0, 101, 5)))\\n plt.xlabel('percentile [%]')\\n plt.grid(True)\\n plt.ylabel('operation time [ns]')\\n plt.legend()\\n plt.show()\",\n \"def tick(self, dt):\\n pass\",\n \"def plot(self, set_xticks_from_index=True,\\n xlabel=None,\\n ylabel=None,\\n colormap='jet',\\n logx=False,\\n logy=None,\\n grid=True,\\n linewidth=2,\\n rot=None,\\n dpi=None,\\n fontsize=14,\\n specs_fontsize=None,\\n tick_fontsize=None,\\n label_fontsize=None,\\n legend_fontsize=None,\\n figsize=None,\\n specs_position='left',\\n debug_plotfs=False,\\n pause_timefs=0.1,\\n modules=None,\\n use_frame=False,\\n sp_argID=0,\\n sp_ncols=-1,\\n sp_sharey=None,\\n sp_title_position='center',\\n sp_title_fontsize=None,\\n sp_show_specs=True,\\n save=None,\\n **kwargs):\\n\\n # Get dataframe and dtype\\n dtype = self.dtype\\n df = self.__df_timings\\n multiindex = self.multiindex\\n\\n style.use('bmh' if use_frame else 'fivethirtyeight')\\n\\n # Setup styles\\n available_linestyles = ['-.', '--', '-']\\n extls = np.resize(available_linestyles, df.shape[1]).tolist()\\n\\n # Get plot params - logy, ylabel\\n if ylabel is None:\\n ylabel_map = {'timings': 'Runtime [s]', 'speedups': 'Speedup [x]', 'scaled_timings': 'Scaled Runtime [x]'}\\n ylabel = ylabel_map[dtype]\\n\\n if logy is None:\\n logy_map = {'timings': True, 'speedups': False, 'scaled_timings': False}\\n logy = logy_map[dtype]\\n\\n groupings_done = False\\n if multiindex:\\n # Get groupings\\n groupings_done, ncols, out = _get_groupings(df, sp_argID)\\n if not groupings_done:\\n warnings.warn('Groupings are not possible. Hence, resorting to normal plot.', stacklevel=2)\\n\\n if not groupings_done: # normal plot\\n is_xticks_number, xticks = _getxticks(df, set_xticks_from_index=set_xticks_from_index)\\n\\n if not is_xticks_number and logx:\\n xticks = None\\n warnings.warn('Some xticks might be not be seen.', stacklevel=2)\\n\\n tick_fontsize = _assign_mplibrcparams(fontsize, specs_fontsize, tick_fontsize,\\n label_fontsize, legend_fontsize, specs_position, dpi)\\n\\n # Plot using dataframe data and its attributes\\n ax = df.plot(style=extls,\\n colormap=_truncate_cmap(colormap),\\n title=specs(modules=modules),\\n rot=rot,\\n fontsize=tick_fontsize,\\n linewidth=linewidth,\\n logx=logx,\\n logy=logy,\\n figsize=figsize,\\n xticks=xticks,\\n **kwargs)\\n\\n if grid:\\n ax.grid(True, which=\\\"both\\\", ls=\\\"-\\\")\\n if xlabel is not None:\\n ax.set_xlabel(xlabel)\\n ax.set_ylabel(ylabel)\\n\\n if set_xticks_from_index and is_xticks_number:\\n ax.set_xticklabels(df.index)\\n if not is_xticks_number: # earlier set_xticks_from_index or not is_xticks_number\\n ax.set_xticks([], minor=True)\\n\\n # Show in fullscreen\\n fullscreenfig(ax, pause_timefs, print_info=debug_plotfs)\\n ax_fig = ax.figure\\n else: # subplot\\n _SUBPLOT_FIGSIZE = (6.4, 3.6)\\n\\n if figsize is None:\\n figsize = _SUBPLOT_FIGSIZE\\n\\n if sp_ncols == -1:\\n sp_ncols = ncols\\n\\n tick_fontsize, specs_fontsize = _assign_mplibrcparams_for_subplot(fontsize, label_fontsize, sp_title_fontsize,\\n legend_fontsize, tick_fontsize, sp_title_position,\\n specs_fontsize, dpi)\\n\\n len1 = len(out)\\n nrows = int(np.ceil(len1 / float(sp_ncols)))\\n r, c = np.unravel_index(np.arange(len1), (nrows, sp_ncols))\\n\\n map_dict = {None: False, 'row': 'row', 'r': 'row', 'global': True, 'g': True}\\n sharey_val = _mapvals(sp_sharey, map_dict, name='subplot_sharey')\\n\\n # Setup title str for each subplot\\n title_str = _subplot_title_str(df, sp_argID)\\n\\n df0 = out[list(out.keys())[0]]\\n is_xticks_number, xticks = _getxticks(df0, set_xticks_from_index=set_xticks_from_index)\\n\\n if not is_xticks_number and logx:\\n xticks = None\\n warnings.warn('Some xticks might be not be seen.', stacklevel=2)\\n\\n figS = (sp_ncols * figsize[0], nrows * figsize[1])\\n fig, axs = plt.subplots(nrows, sp_ncols, sharex='col', constrained_layout=True, sharey=sharey_val, figsize=figS)\\n axs = axs.reshape(-1, sp_ncols)\\n\\n for i, (k, df0) in enumerate(out.items()):\\n ri, ci = r[i], c[i]\\n ax = axs[ri, ci]\\n is_first_subplot = (ri == 0) and (ci == 0)\\n\\n subplot_title = str(k)\\n if is_first_subplot:\\n subplot_title = title_str + subplot_title\\n\\n df0.plot(ax=ax,\\n style=extls,\\n linewidth=linewidth,\\n colormap=_truncate_cmap(colormap),\\n xticks=xticks,\\n logx=logx,\\n logy=logy,\\n rot=rot,\\n title=subplot_title,\\n fontsize=tick_fontsize,\\n legend=i == 0,\\n **kwargs)\\n\\n if grid:\\n ax.grid(True, which=\\\"both\\\", ls=\\\"-\\\")\\n\\n # Next two IFs are needed for logx=True plots\\n if set_xticks_from_index and is_xticks_number:\\n ax.set_xticklabels(df0.index)\\n if not is_xticks_number: # earlier set_xticks_from_index or not is_xticks_number\\n ax.set_xticks([], minor=True)\\n\\n # Set ylabel on middle row ID\\n axs[nrows // 2, 0].set_ylabel(ylabel)\\n\\n if sp_show_specs:\\n fig.suptitle(specs_short(), fontsize=specs_fontsize)\\n\\n # remove unused axes\\n xlabel = df.index.names[sp_argID]\\n r, c = np.unravel_index(np.arange(len1, axs.size), (nrows, sp_ncols))\\n for (i, j) in zip(r, c):\\n ax = axs[i, j]\\n ax.set_xlabel(xlabel)\\n ax.tick_params(labelrotation=rot)\\n ax.yaxis.set_visible(False)\\n ax.patch.set_visible(False)\\n plt.setp(ax.spines.values(), visible=False)\\n ax_fig = fig\\n\\n # Save figure\\n if save is not None:\\n ax_fig.savefig(save, bbox_inches='tight')\\n return\",\n \"def show_events(ticks):\\n for tick in i_prune_ticks(ticks):\\n print(tick)\",\n \"def stft_plot(x: np.ndarray, *args, **kwargs) -> go.Figure:\\n\\n # calculate stft\\n f, t, x_stft = signal.stft(x, *args, **kwargs)\\n\\n # compensate for scaling factor used in scipy's stft\\n if \\\"window\\\" in kwargs.keys():\\n if isinstance(kwargs[\\\"window\\\"], str):\\n window = signal.get_window(kwargs[\\\"window\\\"], kwargs[\\\"nperseg\\\"])\\n else:\\n window = kwargs[\\\"window\\\"]\\n factor = window.sum()\\n else:\\n factor = kwargs[\\\"nperseg\\\"]\\n x_stft *= factor\\n\\n # plot\\n power = 20 * np.log10(np.abs(x_stft))\\n fig = px.imshow(power, x=t, y=f, aspect=\\\"auto\\\", origin=\\\"lower\\\")\\n fig.update_yaxes(title=\\\"Frequency\\\")\\n fig.update_xaxes(title=\\\"Time\\\")\\n fig.update_layout(coloraxis_colorbar=dict(title=\\\"[dB]\\\"))\\n fig.show()\\n\\n return fig\",\n \"def plotting(x,y,name,variable,unit,label_name=\\\"Simulation\\\",title=None,mins=False):\\n ax = plt.figure(str(name))\\n # ax.legend(\\\"best\\\")\\n\\n if mins:\\n x/=60 #change time to mins from secs\\n plt.xlabel(\\\"t [min]\\\")\\n else:\\n plt.xlabel(\\\"t [s]\\\")\\n\\n if title!= None:\\n plt.title(str(title))\\n\\n plt.plot(x-x[0],y,label=label_name)\\n\\n\\n lab = str(str(variable)+\\\" \\\"+\\\"[\\\"+unit+\\\"]\\\")\\n plt.legend(loc='best')\\n plt.ylabel(lab)\\n plt.grid(True)\\n # plt.savefig(title)\\n plt.show()\\n\\n return ax\",\n \"def visualize_time_series(fig_ax, data, inp_color, missing_data, lag_color, first_date,\\n x_label=\\\"Number of Days\\\", y_label=\\\"Log of Aluminium Price\\\", title=\\\"Prices over time\\\"):\\n fig, ax = fig_ax\\n ((x_train_raw, y_train_raw), y_pred_list) = data\\n\\n missing_x, missing_y = missing_data\\n is_missing = len(missing_x) != 0\\n\\n first_date = datetime.strptime(first_date, '%Y-%m-%d')\\n\\n convert_date = lambda x: [\\n np.datetime64((first_date + timedelta(days=d)).strftime('%Y-%m-%d'))\\n for d in x\\n ]\\n convert_price = lambda x: x[\\\"Output\\\"].to_list()\\n\\n x_train = convert_date(x_train_raw[\\\"Date\\\"].to_list())\\n y_train = convert_price(y_train_raw)\\n \\n cut_point = x_train[-1]\\n ax.plot(x_train, y_train, color=color[inp_color])\\n\\n for i, y_pred in enumerate(y_pred_list):\\n data, plot_name, color_code, is_bridge = y_pred\\n mean_pred, x_test_raw = data[\\\"mean\\\"], data[\\\"x\\\"]\\n x_test = convert_date(x_test_raw)\\n\\n if i == 0 and is_missing:\\n missing_x = convert_date(missing_x)\\n ax.axvline(x_test[0], color=color[lag_color], linestyle='--', linewidth=0.5, dashes=(5, 0), alpha=0.2)\\n ax.plot([missing_x[-1], x_test[0]], [missing_y[-1], mean_pred[0]], color[lag_color], linestyle=\\\"dashed\\\")\\n ax.axvspan(cut_point, x_test[0], color=color[lag_color], alpha=0.1)\\n\\n plot_bound(ax, data, x_test, color[color_code], plot_name)\\n\\n if is_bridge and (not is_missing): \\n ax.plot([x_train[-1], x_test[0]], [y_train[-1], mean_pred[0]], color[color_code], linewidth=1.5)\\n\\n if is_missing:\\n ax.plot(missing_x, missing_y, color=color[lag_color], linestyle=\\\"dashed\\\")\\n ax.plot([x_train[-1], missing_x[0]], [y_train[-1], missing_y[0]], color[lag_color], linestyle=\\\"dashed\\\")\\n ax.axvline(cut_point, color=color[lag_color], linestyle='--', linewidth=0.5, dashes=(5, 0), alpha=0.2)\\n else:\\n ax.axvline(cut_point, color=color[\\\"k\\\"], linestyle='--')\\n\\n ax.xaxis.set_minor_locator(AutoMinorLocator())\\n ax.legend()\\n\\n # ax.set_xlabel(x_label)\\n ax.set_ylabel(y_label)\\n ax.set_title(title)\\n\\n # ax.set_xlim(left=cut_point-np.timedelta64(1, 'm'))\\n plot_axis_date(ax, x_train + missing_x + x_test)\\n ax.grid()\\n return fig, ax\",\n \"def plot2dTimeSeries(values, title='series', xLabel='time', yLabel='values', savePath='.'):\\n plt.plot(values)\\n plt.ylabel(yLabel)\\n plt.xlabel(xLabel)\\n plt.xticks(np.linspace(0, len(values), 11))\\n plt.title(title)\\n plt.savefig(f'{savePath}/{title}.png')\\n plt.show(block=False)\\n plt.pause(2)\\n plt.close()\",\n \"def paper_plot_tstats_times(self, tstat, indexes, time=None, freq=None, params = None, er = None, domain = None, yrange = None, label= None, lande=None,\\n xi_range=None, s_range=None, pred=None, compare=None, pos =None):\\n\\n if pos is None:\\n pos = 'both'\\n\\n multiply_se_by = 1.96\\n\\n if label is None:\\n label = tstat\\n\\n no_undertext = False\\n no_legend = False\\n if tstat == 'd2ax_per_mut_input':\\n no_legend = True\\n\\n possible_compare_stats = ['d2ax_scaled_per_mut_input']\\n\\n if time is None:\\n time = False\\n\\n if compare is None:\\n compare = False\\n if pos != 'both':\\n compare = False\\n\\n if er is None:\\n er = True\\n if freq is None:\\n freq = False\\n if lande is None:\\n lande = False\\n\\n if freq:\\n loglinear = False\\n else:\\n loglinear = True\\n\\n if s_range is None:\\n sr = False\\n else:\\n sr = True\\n if xi_range is None:\\n xir = False\\n else:\\n xir = True\\n if pred is None:\\n pred = False\\n legend_with_dclass_param = False\\n if params is not None:\\n legend_with_dclass_param = True\\n\\n data_classes = [self.data_classes[indi] for indi in indexes]\\n\\n if lande is None:\\n if data_classes[0]._THERE_ARE_FINAL_TRAJ_NON_LANDE:\\n lande = False\\n else:\\n lande = True\\n\\n only_lande = False\\n for dc in data_classes:\\n if 'U' not in dc.param_dict:\\n only_lande = True\\n lande = True\\n #if there are no nonlande trajectories then we must use the Lande ones\\n if not data_classes[0]._THERE_ARE_FINAL_TRAJ_NON_LANDE:\\n only_lande = True\\n lande = True\\n\\n if only_lande:\\n undertext_params = [['N', 'Vs'], ['shift_s0', 'sigma_0_del']]\\n else:\\n undertext_params = [['N', 'U'], ['shift_s0', 'sigma_0_del'], ['E2Ns', 'V2Ns']]\\n\\n if tstat not in data_classes[0]._tstats:\\n print(str(tstat) + ' not in dataclass ')\\n return\\n\\n if freq or tstat not in possible_compare_stats:# or len(data_classes) >1:\\n compare = False\\n\\n\\n plot_dict = dict()\\n plotspecs = dict()\\n plotspecs['legend_anchor'] = 'upper left'\\n plotspecs['legend_loc'] = (1.02, 1.03)\\n plot_dict['savedir'] = self.base_dir\\n plotspecs['fsize'] = (28, 16)\\n plotspecs['dpi'] = 200\\n plotspecs['linewidth'] = 1.5\\n plotspecs['ticksize'] = 30\\n plotspecs['legend_font'] = {'size': '54'}\\n plotspecs['axis_font'] = {'fontname': 'Arial', 'size': '28'}\\n plot_dict['linestyles'] = ['-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-','-']\\n plotspecs['marker_size'] = 15\\n plotspecs['cap_size'] = 20\\n #plotspecs['nxticks'] = 2\\n plotspecs['undertext_font'] = {'color': 'black', 'weight': 'roman', 'size': '16'}\\n extra_text = ''\\n\\n #print groupings\\n\\n # plotspecs['fsize'] = (17, 11)\\n # plotspecs['legend_anchor'] = 'upper right'\\n # plotspecs['legend_loc'] = (0.98, 0.98)\\n # plotspecs['legend_anchor'] = 'upper right'\\n # if tstat == 'mean_freq':\\n # plotspecs['legend_loc'] = (0.98,0.98)\\n # elif tstat == 'mean_adx':\\n # plotspecs['legend_loc'] = (0.98, 0.45)\\n #\\n # plotspecs['ticksize'] = 35\\n # plotspecs['linewidth'] = 5\\n # plotspecs['marker_size'] = 20\\n # #plotspecs['nyticks'] = 2\\n # plotspecs['axis_font'] = {'fontname': 'Arial', 'size': '44'}\\n # plotspecs['legend_font'] = {'size': '38'}\\n\\n\\n if not no_undertext and len(data_classes)==1:\\n undertext = []\\n #str(int(data_class.number_population_simulations()))\\n if not lande:\\n number_runs_string = \\\"Obtained from \\\" + str(int(data_classes[0].number_allele_pairs(False,final=False))) + \\\\\\n \\\" allele traj's using NonLande D(t) averaged over \\\" \\\\\\n +str(int(data_classes[0].number_population_simulations()))\\\\\\n + \\\" population sims with parameters:\\\"\\n elif lande and only_lande:\\n number_runs_string = \\\"Obtained from \\\" + str(int(data_classes[0].number_allele_pairs(True, final=False))) + \\\\\\n \\\" allele traj's for each effect size, with Lande D(t) and parameters:\\\"\\n elif lande and not only_lande:\\n number_runs_string1st = \\\"NonLande obtained from \\\" + str(int(data_classes[0].number_allele_pairs(False,final=False))) + \\\\\\n \\\" allele traj's with NonLande D(t) averaged over \\\" \\\\\\n +str(int(data_classes[0].number_population_simulations()))+ \\\" population sims\\\"\\n undertext.append(number_runs_string1st)\\n number_runs_string = \\\"Lande obtained from \\\" + str(int(data_classes[0].number_allele_pairs(True, final=False))) + \\\\\\n \\\" allele traj's for each effect size, with corresponding Lande D(t)\\\"\\n undertext.append(number_runs_string)\\n for listi in undertext_params:\\n text_list = self._plot_text(index_list=[indexes[0]],params_list=listi)\\n if text_list:\\n text_string = ', '.join(text_list)\\n undertext.append(text_string )\\n plot_dict['undertext'] = undertext#data_class.plot_text()\\n\\n if not no_legend:\\n if len(data_classes) ==1:\\n if compare:\\n if tstat == 'd2ax_scaled_per_mut_input':\\n anal_name = r\\\"$(1-D_{L}($\\\"+str(int(time))+ r\\\"$)/\\\\Lambda)\\\\cdot v(a)$\\\"\\n else:\\n anal_name ='Analytic'\\n plot_dict['ynames'] = ['Simulations', anal_name]\\n if lande and not only_lande:\\n plot_dict['ynames'] = ['NonLande', 'lande']\\n if compare:\\n plot_dict['ynames'] = ['NonLande', 'lande','Analytic']\\n else:\\n if lande and not only_lande:\\n plot_dict['groupings_labels_within'] = ['NonLande', 'lande']\\n if compare:\\n plot_dict['groupings_labels_within'] = ['NonLande', 'lande','Analytic']\\n if compare:\\n plot_dict['groupings_labels_within'] = ['Simulations', 'Analytic']\\n\\n if freq:\\n if pred:\\n plot_dict['xlabel'] = r'$x_i $ percentile'\\n else:\\n plot_dict['xlabel'] = r'$x_i$'\\n else:\\n plot_dict['xlabel'] = \\\"Effect size squared (\\\" + r\\\"$S=a^2$\\\" + ')'\\n if pred:\\n if not legend_with_dclass_param:\\n plot_dict['legend_title'] = 'Percentile of \\\\n initial frequency_pos'\\n\\n\\n plot_dict['savedir'] = os.path.join(plot_dict['savedir'],'tstats_times')\\n if pred:\\n plot_dict['savedir'] = os.path.join(plot_dict['savedir'],'pred')\\n if freq:\\n plot_dict['savedir'] = os.path.join(plot_dict['savedir'],'S_sorted')\\n else:\\n plot_dict['savedir'] = os.path.join(plot_dict['savedir'], 'XI_sorted')\\n\\n savedir = os.path.join(self.base_dir, 'tstats_times')\\n\\n if pred:\\n savedir = os.path.join(savedir, 'pred')\\n if freq:\\n savedir = os.path.join(savedir, 'S_sorted')\\n else:\\n savedir = os.path.join(savedir, 'XI_sorted')\\n\\n if pos == 'both':\\n label = 'dpn_' + label\\n savedir = os.path.join(savedir, 'combined')\\n elif pos == 'pos':\\n label ='p_' + label\\n savedir = os.path.join(savedir,'positives')\\n else:\\n label = 'n_' + label\\n savedir = os.path.join(savedir, 'negatives')\\n\\n\\n if domain is None:\\n if not freq:\\n epsl = 0.05\\n epsh =2\\n domain = [0.1-epsl,100+epsh ]\\n\\n plot_dict['domain']= domain\\n plot_dict['yrange'] = yrange\\n\\n at_time_text = ' at generation '\\n\\n if time:\\n at_time_text += str(time)\\n else:\\n time = data_classes[0].get_phase_two_time()\\n\\n plot_dict['ylabel'] = self.name_class.yname(tstat)\\n\\n if tstat == 'd2ax_per_mut_input':\\n if pos == 'both':\\n plot_dict['ylabel'] = 'Contribution to change in mean\\\\n phenotype '\\n if data_classes[0].units_s0:\\n plot_dict['ylabel'] += r' (units trait SD)'\\n else:\\n plot_dict['ylabel'] += r' (units $\\\\omega/\\\\sqrt{2N}$)'\\n plot_dict['ylabel']+= ' per\\\\n unit mutational input'\\n elif tstat == 'x_per_seg_var' or tstat == 'x_per_seg_var':\\n #plot_dict['ylabel'] = 'Average increased frequency of\\\\n aligned alleles'\\n if pos == 'both':\\n plot_dict['ylabel'] = 'Increased frequency of aligned\\\\n alleles per seg variant'\\n else:\\n plot_dict['ylabel'] = 'Average allele frequency'\\n\\n\\n plot_dict['marker'] = True\\n if compare and (not lande or only_lande):\\n if len(data_classes) == 1:\\n plot_dict['colors'] = ['deepskyblue','black']\\n\\n\\n ts_string = '_' +tstat\\n _mylabel = ts_string\\n\\n\\n less = False\\n\\n x = []\\n y = []\\n yer = []\\n ynames = []\\n\\n #maybe\\n x_other = []\\n y_other = []\\n yer_other = []\\n ynames_other = []\\n\\n\\n\\n tstati = tstat\\n\\n\\n for data_class in data_classes:\\n\\n\\n if 'var_0' in data_class.param_dict:\\n var_0 = data_class.param_dict['var_0']\\n else:\\n var_0 = data_class.param_dict['sigma_0'] ** 2\\n N = data_class.param_dict['N']\\n Vs = data_class.param_dict['Vs']\\n var_0_delta_square = var_0 * 2 * N / float(Vs)\\n sig_0_del = np.sqrt(var_0_delta_square)\\n\\n if 'shift_s0' in data_class.param_dict:\\n D_sig_0 = data_class.param_dict['shift_s0']\\n D_del = sig_0_del * D_sig_0\\n\\n x_1 = defaultdict(list)\\n y_1 = defaultdict(list)\\n yer_1 = defaultdict(list)\\n ynames_1 = dict()\\n\\n x_theory_1 = []\\n y_theory_1 = []\\n\\n if lande and not only_lande:\\n x_1_other = defaultdict(list)\\n y_1_other = defaultdict(list)\\n yer_1_other = defaultdict(list)\\n ynames_1_other = dict()\\n\\n\\n triples = deepcopy(data_class.tuples)\\n if xir:\\n remov = []\\n xilow = xi_range[0]\\n xihigh = xi_range[1]\\n for trip in triples:\\n if pred:\\n XPI = data_class.xpercentile_dict[trip]\\n else:\\n XPI = trip[1]\\n if XPI < xilow or XPI > xihigh:\\n remov.append(trip)\\n for trip in remov:\\n triples.remove(trip)\\n if sr:\\n #print 's_range', s_range\\n remov = []\\n slow = s_range[0]\\n shigh = s_range[1]\\n for trip in triples:\\n s = trip[0]\\n if s < slow or s > shigh:\\n remov.append(trip)\\n for trip in remov:\\n triples.remove(trip)\\n\\n name = ''\\n lib = ''\\n if params is None:\\n # if len(data_classes) > 1:\\n # name += str(data_class.index)\\n lib += str(data_class.index)\\n else:\\n for param in params:\\n try:\\n name += param + ' = ' + '{0:.2f}'.format(data_class.param_dict[param]) + ' '\\n lib += param + '{0:.0f}'.format(data_class.param_dict[param]) + '_'\\n except KeyError:\\n print('KeyError: ' + param)\\n\\n\\n stat_dict = data_class.read_tstats(tuples=triples, requiredstats=[tstati],lande=only_lande)\\n\\n if lande and not only_lande:\\n stat_dict_other = data_class.read_tstats(tuples=triples, requiredstats=[tstati], lande=True)\\n print('lande stat dict')\\n\\n\\n for triple in triples:\\n\\n name_1 = ''\\n\\n times = sorted(stat_dict[triple][tstati].keys())\\n if time:\\n time_real = find_nearest(times,time)\\n\\n y_val_2 = stat_dict[triple][tstati][time_real][pos]['mean']\\n y_val_er_2 = stat_dict[triple][tstati][time_real][pos]['se']\\n if y_val_2 == 0.0:\\n print('yval is zero for S ', triple[0])\\n if lande and not only_lande:\\n y_val_2_other = stat_dict_other[triple][tstati][time_real][pos]['mean']\\n y_val_er_2_other = stat_dict_other[triple][tstati][time_real][pos]['se']\\n XI = triple[1]\\n if XI < 0:\\n less = True\\n XI = -XI\\n else:\\n less = False\\n S = triple[0]\\n if freq:\\n lenformat = 1\\n if pred and not less:\\n val = data_class.xpercentile_dict[triple]\\n else:\\n val = XI\\n # if root_s:\\n # name_1 = r'$\\\\sqrt{S} =$ '\\n # else:\\n # name_1 = r'$S =$ '\\n if legend_with_dclass_param:\\n key = data_class.param_dict[params[0]]\\n else:\\n key = S\\n else:\\n #key =XI\\n val = S\\n lenformat = 3\\n if xir:\\n if xi_range[1] < 1:\\n lenformat = 2\\n if pred:\\n key = data_class.xpercentile_dict[triple]\\n if key < 0.91:\\n key = round(key,1)\\n elif key <0.991:\\n key = round(key,2)\\n else:\\n key = round(key,3)\\n if key < 0:\\n if legend_with_dclass_param:\\n key = data_class.param_dict[params[0]]\\n else:\\n key = -key\\n # name_1 = ''\\n else:\\n if legend_with_dclass_param:\\n key = data_class.param_dict[params[0]]\\n else:\\n key = XI\\n # name_1 = ''\\n if less:\\n lenformat =0\\n x_1[key].append(val)\\n y_1[key].append(y_val_2)\\n\\n if lande and not only_lande:\\n x_1_other[key].append(val)\\n y_1_other[key].append(y_val_2_other)\\n\\n if er:\\n yer_1[key].append(y_val_er_2*multiply_se_by)\\n if lande and not only_lande:\\n yer_1_other[key].append(y_val_er_2_other*multiply_se_by)\\n if legend_with_dclass_param:\\n name_1 += self.name_class.param_text(value=key, digits=2)\\n else:\\n name_1 += self.name_class.param_text(value=key, digits=lenformat) + ' ' + name\\n\\n\\n name_2_lande = 'lande: '+ name_1\\n\\n if name_1 not in ynames_1:\\n ynames_1[key] = name_1\\n if lande and not only_lande:\\n if name_2_lande not in ynames_1_other:\\n ynames_1_other[key] = name_2_lande\\n\\n x.append(x_1)\\n y.append(y_1)\\n yer.append(yer_1)\\n ynames.append(ynames_1)\\n if lande and not only_lande:\\n x_other.append(x_1_other)\\n y_other.append(y_1_other)\\n yer_other.append(yer_1_other)\\n ynames_other.append(ynames_1_other)\\n\\n time_string = \\\"generation_\\\" + str(time_real)\\n savedir = os.path.join(savedir, time_string)\\n plot_dict['savedir'] = savedir\\n\\n if compare:\\n kkey = list(x_1.keys())[0]\\n smin = min(x_1[kkey])\\n smax = max(x_1[kkey])\\n xtheorylowers = [xx for xx in np.linspace(smin, smin+1, 50)]\\n xtheoryhighers = [xx for xx in np.linspace(smin+1, smax, 100)]\\n x_theory = xtheorylowers+xtheoryhighers[1:]\\n s_theory = [ssi for ssi in x_theory]\\n y_theory = [0 for _ in x_theory]\\n if tstat == 'd2ax_scaled_per_mut_input':\\n frac_integral_lande_dt = 1.0 - data_class._get_lande_dist_over_shift_at_time(time)\\n if pos == 'both':\\n y_theory = [frac_integral_lande_dt*myv(np.sqrt(ss)) for ss\\n in s_theory]\\n\\n x_theory_1.append(x_theory)\\n y_theory_1.append(y_theory)\\n\\n _mylabel = _mylabel + lib\\n\\n if len(data_classes)>1:\\n if less:\\n if not freq:\\n plot_dict['legend_title'] = r'Shift (units $\\\\sigma_0$)'\\n if legend_with_dclass_param:\\n plot_dict['legend_title'] = self.name_class.param_text(name=params[0])\\n\\n\\n x_2 =[]\\n y_2 =[]\\n yer_2 = []\\n ynames_2 = []\\n jj = 0\\n groupings = []\\n keys_list = []\\n # for k in xrange(len(x)):\\n # keys_list+=x[k].keys()\\n # keys_list = list(set(keys_list))\\n\\n for key in sorted(x[0]):\\n seti = set()\\n for k in range(len(x)):\\n if er:\\n zipi = list(zip(*sorted(zip(x[k][key], y[k][key], yer[k][key]))))\\n x_temp = [xi for xi in zipi[0]]\\n y_temp = [yi for yi in zipi[1]]\\n yer_temp = zipi[2]\\n yer_2.append(yer_temp)\\n else:\\n zipi = list(zip(*sorted(zip(x[k][key], y[k][key]))))\\n x_temp = [xi for xi in zipi[0]]\\n y_temp = [yi for yi in zipi[1]]\\n x_2.append(x_temp)\\n y_2.append(y_temp)\\n ynames_2.append(ynames[k][key])\\n seti.add(jj)\\n jj += 1\\n\\n if lande and not only_lande:\\n if er:\\n zipi = list(zip(*sorted(zip(x_other[k][key], y_other[k][key], yer_other[k][key]))))\\n x_temp = [xi for xi in zipi[0]]\\n y_temp = [yi for yi in zipi[1]]\\n yer_temp = zipi[2]\\n yer_2.append(yer_temp)\\n else:\\n zipi = list(zip(*sorted(zip(x_other[k][key], y_other[k][key]))))\\n x_temp = [xi for xi in zipi[0]]\\n y_temp = [yi for yi in zipi[1]]\\n x_2.append(x_temp)\\n y_2.append(y_temp)\\n ynames_2.append(ynames_other[k][key])\\n seti.add(jj)\\n jj += 1\\n\\n if compare:\\n if x_theory_1:\\n x_2.append(x_theory_1[k])\\n y_2.append(y_theory_1[k])\\n seti.add(jj)\\n jj += 1\\n if er:\\n yer_2.append([0 for _ in x_theory_1[k]])\\n\\n groupings.append(seti)\\n\\n\\n plot_dict['x'] = x_2\\n plot_dict['y'] = y_2\\n if er:\\n plot_dict['yer'] = yer_2\\n\\n #experimenting\\n # if len(data_classes) >1:\\n # plot_dict['groupings'] = groupings\\n plot_dict['groupings'] = groupings\\n\\n\\n linestyles =['','-']\\n markerstyles = ['o','']\\n if lande and not only_lande:\\n markerstyles = ['o', '*','-']\\n linestyles = ['','','']\\n if compare:\\n linestyles = ['','-','']\\n markerstyles =['o','','*']\\n if lande and not only_lande:\\n linestyles = ['', '-','--']\\n markerstyles = ['o', '','']\\n\\n plot_dict['linestyles'] = linestyles #+linestyles+linestyles+linestyles\\n plot_dict['markerstyles'] = markerstyles #+markerstyles+markerstyles+markerstyles\\n\\n size_group = len(groupings[0])\\n if label is None:\\n if len(_mylabel) <30:\\n plot_dict['label'] = _mylabel +'_many_cl'\\n else:\\n plot_dict['label'] = tstat\\n else:\\n plot_dict['label'] = label\\n\\n if freq:\\n plot_dict['label']+= '_xi_x'\\n\\n\\n if pred:\\n plot_dict['label'] += '_pred'\\n\\n if compare:\\n plot_dict['label'] += '_comp'\\n\\n\\n\\n if time:\\n plot_dict['label'] += '_t_' + str(time_real)\\n\\n\\n #List with [text_top, text_bottom] containing relevant parameters\\n # if len(data_classes) == 1:\\n # if extra_text:\\n # undertext.append(extra_text)\\n #\\n # if not no_undertext:\\n # plot_dict['undertext'] = undertext\\n\\n if len(data_classes)>1:\\n plot_dict['ynames'] = ynames_2\\n\\n if loglinear:\\n plotspecs['xlog'] = True\\n\\n\\n plot_dict['plotspecs'] = plotspecs\\n\\n plot_many_y(**plot_dict)\",\n \"def get_timescale_lines(ax, td):\\n\\t\\n\\tneur_N = [1e6, 1e4, 1e2, 1e0, 1e-2]\\n\\tneur_clabel = [r'$\\\\tau_{\\\\nu}=10^6$',r'$10^4$',r'$10^2$',r'$10^0$',r'$10^{-2}$']\\n\\n\\tfor i in range(len(neur_N)):\\n\\t\\tcs = ax.contour(td.rho_p, td.T_p, td.data_p[\\\"tau_neu\\\"], [neur_N[i]], colors='b', linestyles='dotted')\\n\\t\\tax.clabel(cs, fontsize=14, inline=1, fmt=neur_clabel[i])\\t#inline_spacing = -50\\n\\n\\tnucr_N = [1e6, 1e4, 1e2, 1e0, 1e-2,1e-4,1e-6]\\n\\tnucr_clabel = [r'$\\\\tau_\\\\mathrm{cc}=10^6$',r'$10^4$',r'$10^2$',r'$10^0$',r'$10^{-2}$',r'$10^{-4}$',r'$10^{-6}$']\\n\\n\\tfor i in range(len(nucr_N)):\\n\\t\\tcs = ax.contour(td.rho_p, td.T_p, td.data_p[\\\"tau_nuc\\\"], [nucr_N[i]], colors='r', linestyles=':')\\n\\t\\tax.clabel(cs, fontsize=14, inline=1, fmt=nucr_clabel[i])\\n\\n\\tS_N = [1e8, 10**8.1, 10**8.2]\\n\\tS_clabel = [r'$s = 10^8$',r'$10^{8.1}$',r'$10^{8.2}$']\\n\\n\\tfor i in range(len(S_N)):\\n\\t\\tcs = ax.contour(td.rho_p, td.T_p, td.data_p[\\\"S\\\"], [S_N[i]], colors='g', linestyles='dotted', linewidth=4)\\n\\t\\tax.clabel(cs, fontsize=14, inline=1, fmt=S_clabel[i])\\n\\n\\tcs = ax.contour(td.rho_p, td.T_p, td.data_p[\\\"tau_eq_neunuc\\\"], [0], colors='m', linestyles='-', linewidth=6)\\n\\tax.clabel(cs, fontsize=14, inline=1, fmt=r'$\\\\tau_\\\\mathrm{cc}=\\\\tau_{\\\\nu}$')\\n\\n\\tcs = ax.contour(td.rho_p, td.T_p, td.data_p[\\\"Prat\\\"], [2.0], colors='k', linestyles='--', linewidth=6)\\n\\tax.clabel(cs, fontsize=14, inline=1, fmt=r'$P = 2P(T=0)$')\\n\\n\\tcs = ax.contour(td.rho_p, td.T_p, td.data_p[\\\"tau_eq_cdnuc\\\"], [0], colors='r', linestyles=':', linewidth=4)\\n\\tax.clabel(cs, fontsize=14, inline=1, fmt=r'$\\\\tau_\\\\mathrm{cc}=(G\\\\rho_c)^{-1/2}$')\\n\\n\\tcs = ax.contour(td.rho_p, td.T_p, td.data_p[\\\"tau_eq_dynnuc\\\"], [0], colors=\\\"r\\\", linestyles='-', linewidths=2)\\n\\tax.clabel(cs, fontsize=14, inline=1, fmt=r\\\"$\\\\tau_\\\\mathrm{cc} = \\\\tau_\\\\mathrm{dyn}$\\\")\",\n \"def coffee_plot(times, Temps, axes, legends=[None, None], split=2):\\n plot_model(times[split:], Temps[split:], axes[0], legends[0])\\n plot_samples(times[:split], Temps[:split], axes[1], legends[1])\",\n \"def plot(self, title=None, xmin=None, xmax=None, ymin=None, ymax=None,\\n ax=None, show=True, color=None, xlabel=True, ylabel=True,\\n legend=True, chance=True, label='Classif. score'):\\n # XXX JRK: need cleanup in viz\\n self._prep_times()\\n fig = plot_gat_times(self, train_time='diagonal', title=title,\\n xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, ax=ax,\\n show=show, color=color, xlabel=xlabel,\\n ylabel=ylabel, legend=legend, chance=chance,\\n label=label)\\n self._clean_times()\\n return fig\",\n \"def plot_frequency(self):\\n canvas = xboa.common.make_root_canvas(\\\"frequency vs time\\\")\\n canvas.Draw()\\n freq_list = [freq for freq in self.freq_list]\\n hist, graph = xboa.common.make_root_graph(\\\"frequency vs time\\\",\\n self.time_list, \\\"time [ns]\\\",\\n freq_list, \\\"f [GHz]\\\")\\n hist.Draw()\\n graph.Draw(\\\"sameL\\\")\\n fit = ROOT.TF1(\\\"fit\\\", \\\"pol4\\\", 0, 20*1e6)\\n fit.FixParameter(0, freq_list[0])\\n graph.Fit(fit)\\n canvas.Update()\",\n \"def showCl(ell,temps,title='CAMB ISWout power spectrum'):\\n plt.plot(ell,temps*ell*(ell+1)/(2*np.pi) *1e12) #1e12 to convert to microK**2\\n plt.xlabel('multipole moment l')\\n plt.ylabel('l(l+1)C_l/(2pi) [microK**2]')\\n plt.title(title)\\n plt.show()\",\n \"def plot_ins_state(time, state):\\n pylab.ion()\\n\\n plot_trajectory(state[:,0], state[:,1], state[:,2])\\n\\n\\n # Plot position vs. time\\n\\n\\n pylab.figure()\\n pylab.subplot(311)\\n pylab.plot(time, state[:,0],'r')\\n pylab.xlabel('time (s)')\\n pylab.ylabel('$\\\\\\\\phi$, rad')\\n pylab.title('Latitude')\\n pylab.grid(True)\\n\\n pylab.subplot(312)\\n pylab.plot(time, state[:,1],'g')\\n pylab.xlabel('time (s)')\\n pylab.ylabel('$\\\\\\\\lambda$, rad')\\n pylab.title('Longitude')\\n pylab.grid(True)\\n\\n pylab.subplot(313)\\n pylab.plot(time, state[:,2],'b')\\n pylab.xlabel('time, s')\\n pylab.ylabel('$h$, m')\\n pylab.title('Altitude')\\n pylab.grid(True)\\n pylab.show()\\n\\n\\n # Plot velocity vs. time\\n pylab.figure()\\n pylab.plot(time, state[:,3:6])\\n pylab.xlabel('time, s')\\n pylab.ylabel('Vn, Ve, Vd')\\n pylab.title('Velocity vs. time')\\n\\n pylab.grid(True)\\n pylab.show()\\n\\n # Plot acceleration vs. time\\n pylab.figure()\\n pylab.plot(time, state[:,6:9])\\n pylab.xlabel('time, s')\\n pylab.ylabel('an, ae, ad')\\n pylab.title('Acceleration vs. time')\\n\\n pylab.grid(True)\\n pylab.show()\\n pylab.ioff()\\n\\n # Plot quaternions vs. time\\n pylab.figure()\\n pylab.plot(time, state[:,9:])\\n pylab.xlabel('time, s')\\n pylab.ylabel('q0, q1, q2, q3')\\n pylab.title('Quaternion vs. time')\\n\\n pylab.grid(True)\\n pylab.show()\\n pylab.ioff()\",\n \"def plot(t): \\n assert isinstance(t, int), \\\"'t' argument should be an integer.\\\"\\n assert t > 0, \\\"'t' argument should be a positive integer.\\\" \\n # Initialize arrays with zeros to store mean cumulative rewards upto t \\n # rounds for each of the three implemented bandit algorithms\\n EpsGreedy_rewards = np.zeros(t)\\n UCB_rewards = np.zeros(t)\\n LinUCB_rewards = np.zeros(t)\\n # For each round, store the mean cumulative rewards upto that round\\n for i in range(1,t):\\n EpsGreedy_rewards[i] = np.sum(results_EpsGreedy[0:i]) / t\\n UCB_rewards[i] = np.sum(results_UCB[0:i]) / t\\n LinUCB_rewards[i] = np.sum(results_LinUCB[0:i]) / t\\n # Plot running per round cumulative reward\\n plt.plot(range(0,t), EpsGreedy_rewards, color='b', label='e-Greedy')\\n plt.plot(range(0,t), UCB_rewards, color='g', label='UCB')\\n plt.plot(range(0,t), LinUCB_rewards, color='orange', label='LinUCB')\\n plt.xlabel('Round')\\n plt.ylabel('Mean Cumulative Reward')\\n plt.title('Running Per Round Cumulative Reward')\\n plt.legend()\\n plt.show()\",\n \"def plot(self, *args, **kwargs):\\n pass\",\n \"def plot_credible_intervals_for_time(\\n histories: Union[List[History], History],\\n labels: Union[List[str], str] = None,\\n ms: Union[List[int], int] = None,\\n ts: Union[List[int], int] = None,\\n par_names: List[str] = None,\\n levels: List[float] = None,\\n show_mean: bool = False,\\n show_kde_max: bool = False,\\n show_kde_max_1d: bool = False,\\n size: tuple = None,\\n rotation: int = 0,\\n refvals: Union[List[dict], dict] = None,\\n kde: Transition = None,\\n kde_1d: Transition = None,\\n):\\n histories = to_lists(histories)\\n labels = get_labels(labels, len(histories))\\n n_run = len(histories)\\n if ms is None:\\n ms = [0] * n_run\\n elif not isinstance(ms, list) or len(ms) == 1:\\n ms = [ms] * n_run\\n if levels is None:\\n levels = [0.95]\\n levels = sorted(levels)\\n if par_names is None:\\n # extract all parameter names\\n df, _ = histories[0].get_distribution(m=ms[0])\\n par_names = list(df.columns.values)\\n n_par = len(par_names)\\n n_confidence = len(levels)\\n if ts is None:\\n ts = [history.max_t for history in histories]\\n if refvals is not None and not isinstance(refvals, list):\\n refvals = [refvals] * n_run\\n\\n # prepare axes\\n fig, arr_ax = plt.subplots(\\n nrows=n_par, ncols=1, sharex=False, sharey=False\\n )\\n if n_par == 1:\\n arr_ax = [arr_ax]\\n\\n # prepare matrices\\n cis = np.empty((n_par, n_run, 2 * n_confidence))\\n median = np.empty((n_par, n_run))\\n if show_mean:\\n mean = np.empty((n_par, n_run))\\n if show_kde_max:\\n kde_max = np.empty((n_par, n_run))\\n if show_kde_max_1d:\\n kde_max_1d = np.empty((n_par, n_run))\\n if kde is None and show_kde_max:\\n kde = MultivariateNormalTransition()\\n if kde_1d is None and show_kde_max_1d:\\n kde_1d = MultivariateNormalTransition()\\n\\n # fill matrices\\n # iterate over populations\\n for i_run, (h, t, m) in enumerate(zip(histories, ts, ms)):\\n df, w = h.get_distribution(m=m, t=t)\\n # normalize weights to be sure\\n w /= w.sum()\\n # fit kde\\n if show_kde_max:\\n _kde_max_pnt = compute_kde_max(kde, df, w)\\n # iterate over parameters\\n for i_par, par in enumerate(par_names):\\n # as numpy array\\n vals = np.array(df[par])\\n # median\\n median[i_par, i_run] = compute_quantile(vals, w, 0.5)\\n # mean\\n if show_mean:\\n mean[i_par, i_run] = np.sum(w * vals)\\n # kde max\\n if show_kde_max:\\n kde_max[i_par, i_run] = _kde_max_pnt[par]\\n if show_kde_max_1d:\\n _kde_max_1d_pnt = compute_kde_max(kde_1d, df[[par]], w)\\n kde_max_1d[i_par, i_run] = _kde_max_1d_pnt[par]\\n # levels\\n for i_c, confidence in enumerate(levels):\\n lb, ub = compute_credible_interval(vals, w, confidence)\\n cis[i_par, i_run, i_c] = lb\\n cis[i_par, i_run, -1 - i_c] = ub\\n\\n # plot\\n for i_par, (par, ax) in enumerate(zip(par_names, arr_ax)):\\n for i_run in range(len(histories)):\\n for i_c in reversed(range(len(levels))):\\n y_err = np.array(\\n [\\n median[i_par, i_run] - cis[i_par, i_run, i_c],\\n cis[i_par, i_run, -1 - i_c] - median[i_par, i_run],\\n ]\\n )\\n y_err = y_err.reshape((2, 1))\\n ax.errorbar(\\n x=[i_run],\\n y=median[i_par, i_run],\\n yerr=y_err,\\n capsize=(10.0 / n_confidence) * (i_c + 1),\\n color=f'C{i_c}',\\n )\\n # reference value\\n if refvals[i_run] is not None:\\n ax.plot([i_run], [refvals[i_run][par]], 'x', color='black')\\n ax.set_title(f\\\"Parameter {par}\\\")\\n # mean\\n if show_mean:\\n ax.plot(range(n_run), mean[i_par], 'x', color=f'C{n_confidence}')\\n # kde max\\n if show_kde_max:\\n ax.plot(\\n range(n_run), kde_max[i_par], 'x', color=f'C{n_confidence + 1}'\\n )\\n if show_kde_max_1d:\\n ax.plot(\\n range(n_run),\\n kde_max_1d[i_par],\\n 'x',\\n color=f'C{n_confidence + 2}',\\n )\\n ax.set_xticks(range(n_run))\\n ax.set_xticklabels(labels, rotation=rotation)\\n leg_colors = [f'C{i_c}' for i_c in reversed(range(n_confidence))]\\n leg_labels = ['{:.2f}'.format(c) for c in reversed(levels)]\\n if show_mean:\\n leg_colors.append(f'C{n_confidence}')\\n leg_labels.append(\\\"Mean\\\")\\n if show_kde_max:\\n leg_colors.append(f'C{n_confidence + 1}')\\n leg_labels.append(\\\"Max KDE\\\")\\n if show_kde_max_1d:\\n leg_colors.append(f'C{n_confidence + 2}')\\n leg_labels.append(\\\"Max KDE 1d\\\")\\n if refvals is not None:\\n leg_colors.append('black')\\n leg_labels.append(\\\"Reference value\\\")\\n handles = [\\n Line2D([0], [0], color=c, label=l)\\n for c, l in zip(leg_colors, leg_labels)\\n ]\\n ax.legend(handles=handles, bbox_to_anchor=(1.04, 1), loc=\\\"upper left\\\")\\n\\n # format\\n arr_ax[-1].set_xlabel(\\\"Population t\\\")\\n if size is not None:\\n fig.set_size_inches(size)\\n fig.tight_layout()\\n\\n return arr_ax\",\n \"def plotOfHeatingCurrent(self):\\n\\t\\tfig,p1=_plt.subplots()\\n\\t\\tp1.plot(self.time*1e3,self.heatingCurrent)\\n\\t\\t_plot.finalizeSubplot(p1,xlabel='Time (ms)',ylabel='Current (A)')\\n\\t\\t_plot.finalizeFigure(fig,title=self.title)\\n\\t\\t\\n\\t\\treturn p1\",\n \"def time_axis(self):\\n if self.axes_wcs.wcs.ctype[0] not in ['TIME', 'UTC']:\\n raise cu.CubeError(1, 'No time axis present')\\n delta = self.axes_wcs.wcs.cdelt[0]\\n crpix = self.axes_wcs.wcs.crpix[0]\\n crval = self.axes_wcs.wcs.crval[0]\\n start = crval - crpix * delta\\n stop = start + len(self.data) * delta\\n cunit = u.Unit(self.axes_wcs.wcs.cunit[0])\\n return np.linspace(start, stop, num=self.data.shape[-1]) * cunit\",\n \"def plotTimeDepth(d,v):\\n\\n dpth,t = getTimeDepth(d,v)\\n plt.figure(num=0, figsize = (6, 4))\\n plt.plot(dpth,t,linewidth=2);\\n plt.title('Depth-Time');\\n plt.grid()\\n plt.gca().set_xlabel('Depth (m)',fontsize=9)\\n plt.gca().set_ylabel('Two Way Time (s)',fontsize=9)\\n\\n plt.tight_layout()\\n plt.show()\",\n \"def plot(self,angle=17,**kwargs):\\r\\n \\r\\n h1=plt.plot(self.t,self.y,**kwargs)\\r\\n plt.xticks(rotation=angle)\\r\\n \\r\\n return h1\",\n \"def generate_time_series(num_points, num_dims):\\n phase = np.random.randn()\\n period = np.random.uniform()\\n times = np.linspace(0, 10, num_dims)\\n scale = np.random.exponential(size=num_points)\\n return np.outer(scale, np.sin(times / period + phase))\",\n \"def tick(self, tick):\\n pass\",\n \"def tick(self, tick):\\n pass\",\n \"def plot_spectrum(wf, t=0, f_start=None, f_stop=None, logged=False, if_id=0, c=None, **kwargs):\\n if wf.header['nbits'] <= 2:\\n logged = False\\n t = 'all'\\n ax = plt.gca()\\n\\n plot_f, plot_data = wf.grab_data(f_start, f_stop, if_id)\\n\\n # Using accending frequency for all plots.\\n if wf.header['foff'] < 0:\\n plot_data = plot_data[..., ::-1] # Reverse data\\n plot_f = plot_f[::-1]\\n\\n if isinstance(t, int):\\n print(\\\"extracting integration %i...\\\" % t)\\n plot_data = plot_data[t]\\n elif t == 'all':\\n print(\\\"averaging along time axis...\\\")\\n # Since the data has been squeezed, the axis for time goes away if only one bin, causing a bug with axis=1\\n if len(plot_data.shape) > 1:\\n plot_data = plot_data.mean(axis=0)\\n else:\\n plot_data = plot_data.mean()\\n else:\\n raise RuntimeError(\\\"Unknown integration %s\\\" % t)\\n\\n # Rebin to max number of points\\n dec_fac_x = 1\\n if plot_data.shape[0] > MAX_PLT_POINTS:\\n dec_fac_x = int(plot_data.shape[0] / MAX_PLT_POINTS)\\n\\n plot_data = rebin(plot_data, dec_fac_x, 1)\\n plot_f = rebin(plot_f, dec_fac_x, 1)\\n\\n if not c:\\n kwargs['c'] = '#333333'\\n\\n if logged:\\n plt.plot(plot_f, db(plot_data), label='Stokes I', **kwargs)\\n plt.ylabel(\\\"Power [dB]\\\")\\n else:\\n\\n plt.plot(plot_f, plot_data, label='Stokes I', **kwargs)\\n plt.ylabel(\\\"Power [counts]\\\")\\n plt.xlabel(\\\"Frequency [MHz]\\\")\\n plt.legend()\\n\\n try:\\n plt.title(wf.header['source_name'])\\n except KeyError:\\n plt.title(wf.filename)\\n\\n plt.xlim(plot_f[0], plot_f[-1])\",\n \"def tgfs_plot():\\n plt.rc('font', size=10) # control the default font size\\n plt.rc('axes', titlesize=11) # fontsize of the axes title\\n plt.rc('axes', labelsize=12) # fontsize of the axes x and y labels\\n plt.rc('xtick', labelsize=10) # fontsize of the tick label\\n plt.rc('ytick', labelsize=10) # fontsize of the tick label\\n plt.rc('legend', fontsize=11) # legend fontsize\\n\\n # generate data\\n x = np.arange(-10, 10.1, 0.1)\\n y = x**3\\n\\n # plot figure\\n plt.figure()\\n plt.plot(x, y, label='$x^3$')\\n plt.xlabel('$x$')\\n plt.ylabel('$y$')\\n plt.title('$y = x^3 for title, grid, & font size control \\\"plt.rc()\\\"$')\\n plt.legend(loc='upper left')\\n plt.grid()\\n plt.show()\\n\\n return None\",\n \"def plot_vanHove_dt(comp,conn,start,step_size,steps):\\n \\n (fin,) = conn.execute(\\\"select fout from comps where comp_key = ?\\\",comp).fetchone()\\n (max_step,) = conn.execute(\\\"select max_step from vanHove_prams where comp_key = ?\\\",comp).fetchone()\\n Fin = h5py.File(fin,'r')\\n g = Fin[fd('vanHove',comp[0])]\\n\\n temp = g.attrs['temperature']\\n dtime = g.attrs['dtime']\\n\\n\\n # istatus = plots.non_i_plot_start()\\n \\n fig = mplt.figure()\\n fig.suptitle(r'van Hove dist temp: %.2f dtime: %d'% (temp,dtime))\\n dims = figure_out_grid(steps)\\n \\n plt_count = 1\\n outs = []\\n tmps = []\\n for j in range(start,start+step_size*steps, step_size):\\n (edges,count,x_lim) = _extract_vanHove(g,j+1,1,5)\\n if len(count) < 50:\\n plt_count += 1\\n continue\\n #count = count/np.sum(count)\\n \\n sp_arg = dims +(plt_count,)\\n ax = fig.add_subplot(*sp_arg)\\n ax.grid(True)\\n\\n \\n alpha = _alpha2(edges,count)\\n \\n ax.set_ylabel(r'$\\\\log{P(N)}$')\\n ax.step(edges,np.log((count/np.sum(count))),lw=2)\\n ax.set_title(r'$\\\\alpha_2 = %.2f$'%alpha + ' j:%d '%j )\\n ax.set_xlim(x_lim)\\n plt_count += 1\\n\\n mplt.draw()\\n\\n # plots.non_i_plot_start(istatus)\\n\\n del g\\n Fin.close()\\n del Fin\",\n \"def tick(self):\\r\\n pass\",\n \"def plot(self, add_labels=False,\\n _type=\\\"scatter\\\", color=None,\\n layer=1, edgecolor='black'):\\n _type = _type.lower()\\n assert _type in (\\\"scatter\\\", \\\"line\\\")\\n if _type == \\\"scatter\\\":\\n scatter(\\n self.times,\\n self.temperatures,\\n alpha=.8,\\n zorder=layer,\\n edgecolors=edgecolor\\n )\\n elif _type == \\\"line\\\":\\n plt.plot(self.times,\\n self.temperatures,\\n \\\"-\\\", color=color,\\n zorder=layer)\\n if add_labels:\\n self.set_plot_labels()\",\n \"def plot_u(times, u, t_deltas, is_own_event, figsize=(16, 6)):\\n\\n t_deltas = np.asarray(t_deltas)\\n is_own_event = np.asarray(is_own_event)\\n\\n seq_len = np.nonzero(t_deltas == 0)[0][0] # First index where t_delta = 0\\n abs_t = np.cumsum(t_deltas[:seq_len])\\n abs_own = is_own_event[:seq_len]\\n\\n our_events = [t for (t, o) in zip(abs_t, abs_own) if o]\\n other_events = [t for (t, o) in zip(abs_t, abs_own) if not o]\\n\\n u_max = np.max(u)\\n\\n plt.figure(figsize=(16, 6))\\n\\n c1, c2, c3 = sns.color_palette(n_colors=3)\\n\\n plt.plot(times, u, label='$u(t)$', color=c1)\\n plt.vlines(our_events, 0, 0.75 * u_max, label='Us', alpha=0.5, color=c2)\\n plt.vlines(other_events, 0, 0.75 * u_max, label='Others', alpha=0.5, color=c3)\\n plt.xlabel('Time')\\n plt.ylabel('$u(t)$')\\n plt.legend()\",\n \"def get_timing_signal(length,\\n min_timescale=1,\\n max_timescale=1e4,\\n num_timescales=16):\\n positions = tf.to_float(tf.range(length))\\n log_timescale_increment = (\\n math.log(max_timescale / min_timescale) / (num_timescales - 1))\\n inv_timescales = min_timescale * tf.exp(\\n tf.to_float(tf.range(num_timescales)) * -log_timescale_increment)\\n scaled_time = tf.expand_dims(positions, 1) * tf.expand_dims(inv_timescales, 0)\\n return tf.concat([tf.sin(scaled_time), tf.cos(scaled_time)], axis=1)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6426607","0.62802076","0.62569886","0.62506014","0.6236783","0.6092056","0.6068509","0.60091037","0.60037977","0.60024494","0.6001193","0.59628236","0.59590256","0.5951325","0.59470856","0.59453624","0.5932996","0.5908867","0.5891108","0.5865043","0.58623916","0.58553123","0.5847916","0.5842294","0.5838787","0.5835769","0.5827261","0.5806125","0.5788757","0.5769874","0.5757819","0.57442766","0.5732562","0.5716214","0.56921726","0.56603086","0.56530446","0.5647014","0.56410336","0.5638046","0.56163913","0.56142664","0.5597951","0.5592093","0.5589442","0.5573702","0.5527901","0.55177313","0.55170363","0.5504068","0.55018246","0.54961497","0.5489874","0.5487542","0.54855007","0.5484473","0.54785186","0.54728293","0.54647636","0.54558146","0.54287356","0.5424739","0.5416077","0.54079515","0.5405577","0.5404089","0.54000074","0.53952485","0.53602546","0.5358707","0.5353974","0.5350642","0.5349267","0.5344882","0.53390926","0.5335862","0.5333289","0.5330468","0.53300047","0.53239506","0.53235006","0.5321521","0.53205174","0.53179973","0.53117746","0.530653","0.5300981","0.529513","0.52894044","0.52817804","0.5276066","0.5274625","0.5274625","0.5273512","0.527326","0.5270635","0.5270284","0.5268577","0.52681667","0.52672297"],"string":"[\n \"0.6426607\",\n \"0.62802076\",\n \"0.62569886\",\n \"0.62506014\",\n \"0.6236783\",\n \"0.6092056\",\n \"0.6068509\",\n \"0.60091037\",\n \"0.60037977\",\n \"0.60024494\",\n \"0.6001193\",\n \"0.59628236\",\n \"0.59590256\",\n \"0.5951325\",\n \"0.59470856\",\n \"0.59453624\",\n \"0.5932996\",\n \"0.5908867\",\n \"0.5891108\",\n \"0.5865043\",\n \"0.58623916\",\n \"0.58553123\",\n \"0.5847916\",\n \"0.5842294\",\n \"0.5838787\",\n \"0.5835769\",\n \"0.5827261\",\n \"0.5806125\",\n \"0.5788757\",\n \"0.5769874\",\n \"0.5757819\",\n \"0.57442766\",\n \"0.5732562\",\n \"0.5716214\",\n \"0.56921726\",\n \"0.56603086\",\n \"0.56530446\",\n \"0.5647014\",\n \"0.56410336\",\n \"0.5638046\",\n \"0.56163913\",\n \"0.56142664\",\n \"0.5597951\",\n \"0.5592093\",\n \"0.5589442\",\n \"0.5573702\",\n \"0.5527901\",\n \"0.55177313\",\n \"0.55170363\",\n \"0.5504068\",\n \"0.55018246\",\n \"0.54961497\",\n \"0.5489874\",\n \"0.5487542\",\n \"0.54855007\",\n \"0.5484473\",\n \"0.54785186\",\n \"0.54728293\",\n \"0.54647636\",\n \"0.54558146\",\n \"0.54287356\",\n \"0.5424739\",\n \"0.5416077\",\n \"0.54079515\",\n \"0.5405577\",\n \"0.5404089\",\n \"0.54000074\",\n \"0.53952485\",\n \"0.53602546\",\n \"0.5358707\",\n \"0.5353974\",\n \"0.5350642\",\n \"0.5349267\",\n \"0.5344882\",\n \"0.53390926\",\n \"0.5335862\",\n \"0.5333289\",\n \"0.5330468\",\n \"0.53300047\",\n \"0.53239506\",\n \"0.53235006\",\n \"0.5321521\",\n \"0.53205174\",\n \"0.53179973\",\n \"0.53117746\",\n \"0.530653\",\n \"0.5300981\",\n \"0.529513\",\n \"0.52894044\",\n \"0.52817804\",\n \"0.5276066\",\n \"0.5274625\",\n \"0.5274625\",\n \"0.5273512\",\n \"0.527326\",\n \"0.5270635\",\n \"0.5270284\",\n \"0.5268577\",\n \"0.52681667\",\n \"0.52672297\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.68075925"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":2885,"cells":{"query":{"kind":"string","value":"Generates mapping from water measurements column names to indices of the given header."},"document":{"kind":"string","value":"def get_water_index_map(archive, header):\n column_re = {\n 'surface': {\n 'flow': 'pretok',\n 'level': 'vodostaj'\n },\n 'ground': {\n 'altitude': 'nivo',\n 'level': 'vodostaj'\n }\n }\n column_map = {key: -1 for key in column_re[archive].keys()}\n empty = True\n\n # Do regex search of every db column for every CSV file column heading.\n for i, column in enumerate(header):\n for column_name in column_re[archive].keys():\n if re.search(column_re[archive][column_name], column, re.IGNORECASE):\n if column_map[column_name] != -1:\n continue\n column_map[column_name] = i\n empty = False\n\n return None if empty else column_map"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def __get_column_mapping(self):\n\n s1 = list(Sample(\"FL\", \"M1.0@265_Primary_ar115_s2010-08-06T06_36_00_e2010-08-06T18_24_00.csv\").get_data().columns)[:25]\n column_mapping = {}\n for i in range(len(s1)):\n column_mapping[i] = s1[i]\n\n return column_mapping","def column_indexer(data):\n idCol = {label: index for index, label in enumerate(data.columns)}\n return idCol","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 getIndividual2ColIndex(cls, header, col_name2index, sampleStartingColumn=9):\n\t\tsys.stderr.write(\"Finding all individuals ...\")\n\t\tno_of_cols = len(header)\n\t\tindividual_name2col_index = {}\t#individual's column name -> an opened file handler to store genetic data\n\t\tcounter = 0\n\t\tfor i in xrange(sampleStartingColumn, no_of_cols):\n\t\t\tindividualName = header[i]\n\t\t\tcol_index = col_name2index.get(individualName)\n\t\t\tif not individualName:\t#ignore empty column\n\t\t\t\tcontinue\n\t\t\tif individualName[:-4]=='.bam':\n\t\t\t\tindividualCode = individualName[:-4]\t#get rid of .bam\n\t\t\telse:\n\t\t\t\tindividualCode = individualName\n\t\t\tindividual_name2col_index[individualCode] = col_index\n\t\t\tcounter += 1\n\t\tsys.stderr.write(\"%s individuals added. Done.\\n\"%(counter))\n\t\treturn individual_name2col_index","def find_indeces(self, header):\n indeces = {'T': None, 'WV': None, 'WK': None, 'BZ': None, 'SPR': None,\n 'WBER': None, 'ABG.': None, 'UNG.': None, 'SPOE': None,\n 'FPOE': None, 'OEVP': None, 'GRUE': None, 'NEOS': None,\n 'WWW': None, 'ANDAS': None, 'GFW': None, 'SLP': None,\n 'WIFF': None, 'M': None, 'FREIE': None}\n for index, item in enumerate(header):\n indeces[item] = index\n return indeces","def _get_columns_mapping_dict():\n\n columns_mapping_dict = {}\n for original_header in COLUMN_HEADERS_MAPPER:\n new_header = COLUMN_HEADERS_MAPPER[original_header]\n columns_mapping_dict[new_header] = [original_header]\n return columns_mapping_dict","def get_header_indices(filepath):\n\theaders = get_header_list(filepath, sort=False)\n\treturn {h: i for i, h in enumerate(headers)}","def _get_header_index(self, columnname):\n\n return self.headers.index(columnname)","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 create_matrix_mapping(train_mh, unk_vec_id):\n mh_index_map = {}\n matrix_idx = 0\n for vector_idx in train_mh:\n if vector_idx == unk_vec_id:\n unk_matrix_id = matrix_idx\n mh_index_map[vector_idx] = matrix_idx\n matrix_idx += 1\n return mh_index_map, unk_matrix_id","def column_info(colum_header):\n commas = colum_header.count(',')\n if commas == 0:\n return (column_name(clean_token(colum_header)))\n\n (key, units, location) = colum_header.split(',')\n key = column_name(clean_token(key))\n units = clean_token(units)\n location = clean_token(location)\n return (key, units, location)","def parseHeader(header):\n tokens = [t for t in header.split(' ') if t]\n result = {}\n for i in range(len(tokens)):\n result[tokens[i]] = i \n\n return result","def _get_table_columns(self):\n try:\n table_header = parse_table_head(self.table.value, version=self.version)\n merged_data = self.table.value[table_header.tdef_header_end:]\n if table_header.TDEF_header.next_page_ptr:\n merged_data = merged_data + self._merge_table_data(table_header.TDEF_header.next_page_ptr)\n\n parsed_data = parse_table_data(merged_data, table_header.real_index_count,\n table_header.column_count, version=self.version)\n\n # Merge Data back to table_header\n table_header['column'] = parsed_data['column']\n table_header['column_names'] = parsed_data['column_names']\n\n except ConstructError:\n logging.error(f\"Failed to parse table header {self.table.value}\")\n return\n col_names = table_header.column_names\n columns = table_header.column\n\n # Add names to columns metadata so we can use only columns for parsing\n for i, c in enumerate(columns):\n c.col_name_str = col_names[i].col_name_str\n\n # column_index is more accurate(id is always incremented so it is wrong when a column is deleted).\n # Some tables like the catalog don't have index, so if indexes are 0 use id.\n\n # create a dict of index to column to make it easier to access. offset is used to make this zero based\n offset = min(x.column_index for x in columns)\n column_dict = {x.column_index - offset: x for x in columns}\n # If column index is not unique try best effort\n if len(column_dict) != len(columns):\n # create a dict of id to column to make it easier to access\n column_dict = {x.column_id: x for x in columns}\n\n if len(column_dict) != table_header.column_count:\n logging.debug(f\"expected {table_header.column_count} columns got {len(column_dict)}\")\n return column_dict, table_header","def get_colnumber(self, header):\n for i in range(0, len(self.data)):\n if self.data[i][0] == header:\n return i\n return None","def header_population(headers):\n return [{'id': field, 'name': field, 'field': field, 'sortable': True} for field in headers]","def get_report_column_names(self):\r\n # Compose the list of report_column names required for\r\n # summary_report.dsw.DictWriter()\r\n sr = self.summary_report\r\n dict_leader = sr.dict_leader\r\n dict_out = sr.dict_out\r\n column_names = self.column_names\r\n report_column_names = []\r\n #if dict_leader is not None and dict_out is not None:\r\n if dict_leader is not None and dict_out is not None:\r\n for key,value in dict_leader.iteritems():\r\n #print \"Adding report_column_name(from dict_leader)=\",key\r\n report_column_names.append(key)\r\n dict_out[key] = value\r\n # We have to initialize the DictWriter with the report_column_names\r\n # below. \r\n # Also need matched coord_val and var names for calling node_report()\r\n # below, so we do this duplication of storage of names. \r\n coord_var_names = []\r\n coord_val_names = []\r\n for idx, column_name in enumerate(column_names):\r\n var_name = \"Var_%s\" % str(idx+1)\r\n report_column_names.append(var_name)\r\n coord_var_names.append(var_name)\r\n val_name = \"Val_%s\" % str(idx+1)\r\n report_column_names.append(val_name)\r\n coord_val_names.append(val_name)\r\n # Add the entry report_column_names\r\n report_column_names += self.EntryClass.report_column_names\r\n return report_column_names","def parse_header(f):\n columns = ['pokemon', 'species_id', 'height', 'weight', 'type_1', 'type_2',\n 'url_image', 'generation_id', 'evolves_from_species_id']\n sep = ','\n result = {}\n allData = []\n with open(const.DATA_FILENAME, newline=\"\") as myData:\n for line in myData:\n line = line.strip()\n line = line.split(sep)\n allData.append(line)\n for i in columns:\n j = 0\n while j < len(allData[0]):\n if allData[0][j] == i:\n result[i] = j\n j += 1\n return result","def set_column_headers(self, headers):\n if isinstance(self.columns.idx[0], int):\n self.data = [sorted(headers)] + self.data\n\n increment = [i + 1 for i in self.rows.idx]\n self.rows.idx = [0] + increment\n\n elif isinstance(self.columns.idx[0], str):\n datum = {}\n for i, key in enumerate(self.columns.idx):\n datum.update({key: headers[i]})\n self.data = [datum] + self.data\n\n increment = [i + 1 for i in self.rows.idx]\n self.rows.idx = [0] + increment","def get_interesting_mapping_fields(mapping_data, mapping_headers):\r\n result = []\r\n num_samples = len(mapping_data)\r\n num_cols = len(mapping_headers)\r\n transposed_data = array(mapping_data).T\r\n for header, datum in zip(mapping_headers, transposed_data):\r\n d = set(datum)\r\n len_d = len(d)\r\n if len_d > 1 and len_d < num_samples:\r\n result.append(header)\r\n return result","def header(self) -> list:\n cols = self.data.columns.tolist()\n header = [\"index\"]\n for col_int in cols:\n header.append(col_int)\n return header","def map_column_indexes(self, merge_specification, ingredients):\n last_column = len(ingredients) - 1\n accumulating = {}\n remove = set()\n # default behavior, no column merge\n for column_index in range(0, last_column + 1):\n self.column_index_to_columns[column_index] \\\n = [(column_index, 1.0)]\n \n for columns in merge_specification:\n accumulating_column = columns[0][0]\n if accumulating_column > last_column or accumulating_column < 0:\n raise MergeConfigError(\n \"Attempted to merge missing column %d\" % accumulating_column)\n # specifies which columns should be merged into this one\n accumulating[accumulating_column] = columns\n for column_index, _ in columns[1:]:\n column_index = column_index\n if column_index > last_column or column_index < 0:\n raise MergeConfigError(\n \"Attempted to merge missing column %d\" % column_index) \n # drop this column; it will be merged into another\n remove.add(column_index)\n \n # drop columns first so that any columns both specified as\n # accumulating *and* merged columns do not get dropped\n for column_index in remove:\n self.column_index_to_columns[column_index] = None\n \n for column_index, columns in accumulating.items():\n self.column_index_to_columns[column_index] = columns","def get_columns_dict(table, replace):\n # 0 is name, 1 is id\n if type(table.index) == pd.MultiIndex:\n colcount = 1 + len(table.index[0])\n else:\n colcount = 2\n cols = {}\n for c in table.columns:\n c_repres = \",\".join(c)\n if \"Filtergroups\" not in c:\n cols[colcount] = replace_in_str(str(c_repres), replace)\n colcount = colcount + 1\n return cols","def _transform_map_data(self):\n WARD_FMT = '%s-%s'\n self.map_data_trans = []\n lookup = {i.column: ''.join(filter(lambda x: x.isdigit(), i.value)) for i in self.sht[1]}\n\n #skip over header\n rs = iter(self.sht.rows)\n next(rs)\n next(rs)\n for r in rs:\n pka = r[0].value\n for c in r[1:]:\n if c.value is None:\n c.value = 0\n\n self.map_data_trans.append((WARD_FMT%(pka, lookup[c.column]), c.value))","def indices(self):\n if self._indices is None:\n i = []\n\n # TODO: this is not right for multi-column keys\n # TODO: new style indexes\n\n global_name = '^DD(%s,0,\"IX\",\"0\")' % self.fileid\n prefix = '^DD(%s,0,\"IX\",' % self.fileid\n while 1:\n global_name = M.mexec('set s0=$query(%s)' % global_name, M.INOUT(\"\"))[0]\n if not global_name or not global_name.startswith(prefix):\n break\n suffix = global_name[len(prefix):-1]\n parts = suffix.split(\",\")\n idx_name = parts[0][1:-1]\n idx_table = parts[1]\n idx_columns = parts[2:]\n index = Index(idx_name, idx_table, idx_columns)\n i.append(index)\n\n # A second list, gives indices for a field\n columns = {}\n for idx in i:\n for c in idx.columns:\n columns[c] = 1\n\n # Now trawl the listed columns in the data dictionary, and load their\n # cross references.\n cr_names = {}\n for c in columns.keys():\n idx_root = M.Globals[\"^DD\"][self.fileid][c][1]\n if not idx_root[0].exists():\n continue\n for cr_id, val in idx_root.keys_with_decendants():\n if float(cr_id) > 0:\n cr_header = idx_root[cr_id][0].value\n parts = cr_header.split(\"^\")\n if len(parts) == 2 and parts[1]: # if more than 2 parts, assume MUMPs trigger\n f = cr_names.get(parts[1], list())\n f.append(c)\n cr_names[parts[1]] = f\n\n # Now, just delete items from the index list if they are not in cr_names\n self._indices = []\n for index in i:\n cr = cr_names.get(index.name)\n if cr:\n # verify columns - lots of errors in real systems\n if len(cr) == len(index.columns):\n invalid = False\n for c in cr:\n if c not in index.columns:\n invalid = True\n continue\n if not invalid:\n self._indices.append(index)\n\n return self._indices","def get_sip_keywords(header):\n cd = np.matrix([[header.get('CD1_1', 0.0), header.get('CD1_2', 0.0)],\n [header.get('CD2_1', 0.0), header.get('CD2_2', 0.0)]], dtype=np.float64)\n a_order = int(header.get('A_ORDER', 0))\n b_order = int(header.get('B_ORDER', 0))\n ac = np.matrix(np.zeros((a_order+1, a_order+1), dtype=np.float64))\n bc = np.matrix(np.zeros((b_order+1, b_order+1), dtype=np.float64))\n for m in range(a_order+1):\n for n in range(0, a_order+1-m):\n ac[m, n] = header.get('A_%d_%d' % (m, n), 0.0)\n for m in range(b_order+1):\n for n in range(0, b_order+1-m):\n bc[m, n] = header.get('B_%d_%d' % (m, n), 0.0)\n return cd, ac, bc","def header(self, cols, parent_row):\n out = []\n for col in cols:\n if col == 'gau_id':\n out.append(self.name_for('Geographies', parent_row['geography_id']))\n elif col == 'oth_1_id':\n out.append(self.name_for('OtherIndexes', parent_row['other_index_1_id']))\n elif col == 'oth_2_id':\n out.append(self.name_for('OtherIndexes', parent_row['other_index_2_id']))\n else:\n out.append(col)\n return out","def get_tbl_headers(rows):\n tbl_header = rows.pop(0)\n tbl_headers = {}\n for index, header_name in enumerate(tbl_header.find_all('th')):\n if header_name.text in conf.TABLE_HEADER_COLS:\n tbl_headers[header_name.text] = index\n return tbl_headers","def getIndex(self,filt):\n indx = [i for i in xrange(len(self._header)) if filt == self._header[i]]\n return indx","def vector_indx_to_map_matrix_indx(index,senzory_map):\n xs = dict(zip(np.unique(senzory_map[:,0]), it.count()))\n ys = dict(zip(np.negative(np.unique(senzory_map[:,1])), it.count()))\n x, y = senzory_map[index]\n return ys[y],xs[x]","def _create_indices(cls):\r\n from thunderdome.connection import _hosts, _index_all_fields, create_key_index\r\n \r\n if not _hosts: return\r\n for column in cls._columns.values():\r\n if column.index or _index_all_fields:\r\n create_key_index(column.db_field_name)","def get_mention_id_mappings(input_file_name, out_file, field_threshold, column_indices):\n out = open(out_file, 'w')\n if input_file_name.endswith('.csv'):\n with open(input_file_name, 'r') as f:\n reader = csv.reader(f)\n for line in reader:\n if len(line) >= field_threshold:\n if not line[0].startswith(\"#\"):\n row = []\n for index in column_indices:\n row.append(line[index])\n out.write(\"|\".join(row) + \"\\n\")\n out.close()","def headers_processor(headers):\n def apply_headers(row_set, row):\n _row = []\n pairs = izip_longest(row, headers)\n for i, (cell, header) in enumerate(pairs):\n if cell is None:\n cell = Cell(None)\n cell.column = header\n if not cell.column:\n cell.column = \"column_%d\" % i\n cell.column_autogenerated = True\n _row.append(cell)\n return _row\n return apply_headers","def writeTotalIndex(map_data):\n ids = map_data['id']\n index = [0.0] * len(ids)\n\n colnames = ['cars', 'bikes', 'ages', 'parking', 'male_singles',\n 'female_singles', 'digging', 'freeparking']\n weights = [-0.5, 0.5, 0.1, -0.5, 1.0, 1.0, -1.0, 0.25]\n for colname, weight in zip(colnames, weights):\n values = map_data[colname]\n\n index += values * (weight / values.max())\n\n toJson('final', pd.DataFrame({'id': ids, 'counts': index}))","def get_column_dict(self) -> HeaderToWells:\n return self._grid.columns","def add_columns(self, npy_arr, tile):\n size = len(npy_arr)\n\n tile_name = Tile.ZONES[tile.name[0].lower()] + tile.name[1:]\n\n # create ids\n def get_id(order):\n order = str(order).rjust(10, \"0\")\n return int(tile_name + order)\n ids = np.fromiter(\n (get_id(idx + 1) for idx in range(size)), dtype=np.int64)\n\n # calculate the hjds\n mjd_h, mjd_j, mjd_k = tile.epochs\n\n gen_h = (pyasl.helio_jd(mjd_h, ra, dec)\n for ra, dec in zip(npy_arr[\"ra_h\"], npy_arr[\"dec_h\"]))\n hjd_h = np.fromiter(gen_h, dtype=float)\n\n gen_j = (pyasl.helio_jd(mjd_j, ra, dec)\n for ra, dec in zip(npy_arr[\"ra_j\"], npy_arr[\"dec_j\"]))\n hjd_j = np.fromiter(gen_j, dtype=float)\n\n gen_k = (pyasl.helio_jd(mjd_k, ra, dec)\n for ra, dec in zip(npy_arr[\"ra_k\"], npy_arr[\"dec_k\"]))\n hjd_k = np.fromiter(gen_k, dtype=float)\n\n columns = [\n (\"id\", ids),\n (\"hjd_h\", hjd_h),\n (\"hjd_j\", hjd_j),\n (\"hjd_k\", hjd_k)]\n return add_columns(npy_arr, columns)","def get_pv_keywords(header):\n cd = np.matrix([[header.get('CD1_1', 0.0), header.get('CD1_2', 0.0)],\n [header.get('CD2_1', 0.0), header.get('CD2_2', 0.0)]], dtype=np.float64)\n pv1 = np.zeros((40,), dtype=np.float64)\n pv2 = np.zeros((40,), dtype=np.float64)\n for k in range(40):\n pv1[k] = header.get('PV1_%d' % k, 0.0)\n pv2[k] = header.get('PV2_%d' % k, 0.0)\n return cd, pv1, pv2","def MakeDictMatrix(D, header, lev=72):\n Ms = np.zeros((1, lev, len(header)))\n Mp = np.ones((1, lev, len(header)))\n for var in D.dict.keys():\n i = header.index(var)\n Ms[0, :, i] = D[var].sub_vec(lev)\n Mp[0, :, i] = D[var].prod_vec(lev)\n return(Ms,Mp)","def map_column_to_index(self, col):\n if col in self.column_maps:\n return\n\n # First construct the map from original ids to new ones.\n ids = pd.concat((self.train[col], self.test[col])).unique()\n n = len(ids)\n idmap = dict(itertools.izip(ids, xrange(n)))\n\n # Next use the map to convert the ids in-place.\n self.train.loc[:, col] = self.train[col].apply(lambda _id: idmap[_id])\n self.test.loc[:, col] = self.test[col].apply(lambda _id: idmap[_id])\n\n # Now swap key for value in the idmap to provide a way to convert back.\n reverse_map = {val: key for key, val in idmap.iteritems()}\n self.column_maps[col] = reverse_map","def prep_hd(header,phi_c,lambda_c,nx,ny,dx,dy):\n header_out = {}\n\n # Keywords to get from original header\n keys_hd = ['TELESCOP', 'INSTRUME', 'WAVELNTH', 'CAMERA','DATE',\n 'DATE_S','DATE-OBS','T_OBS','T_REC','TRECEPOC',\n 'TRECSTEP','TRECUNIT','HARPNUM','DSUN_OBS','DSUN_REF',\n 'RSUN_REF','CRLN_OBS','CRLT_OBS','CAR_ROT','OBS_VR',\n 'OBS_VW','OBS_VN','RSUN_OBS','QUALITY','QUAL_S','QUALLEV1']\n\n for key in keys_hd:\n header_out[key] = header[key]\n\n # Add new keywords\n header_out['NAXIS'] = 2\n header_out['NAXIS1'] = nx\n header_out['NAXIS2'] = ny\n\n header_out['CUNIT1'] = 'degree'\n header_out['CUNIT2'] = 'degree'\n\n header_out['CRPIX1'] = (nx - 1) / 2 + 1\n header_out['CRPIX2'] = (ny - 1) / 2 + 1\n header_out['CRVAL1'] = phi_c\n header_out['CRVAL2'] = lambda_c\n header_out['CDELT1'] = dx\n header_out['CDELT2'] = dy\n header_out['CTYPE1'] = 'CRLN-CEA'\n header_out['CTYPE2'] = 'CRLT-CEA'\n header_out['CROTA2'] = 0.0\n\n header_out['WCSNAME'] = 'Carrington Heliographic'\n header_out['BUNIT'] = 'Mx/cm^2'\n\n return header_out","def create_index(log_df, column):\n temp_list = log_df[[column]].values.tolist()\n subsec_set = {(x[0]) for x in temp_list}\n subsec_set = sorted(list(subsec_set))\n alias = dict()\n for i, _ in enumerate(subsec_set):\n alias[subsec_set[i]] = i + 1\n return alias","def get_column_indices(indices, inputs, multiple):\n if multiple:\n res = OrderedDict()\n for p in indices:\n ov, onnx_i = get_column_index(p, inputs)\n if ov not in res:\n res[ov] = []\n res[ov].append(onnx_i)\n return res\n\n onnx_var = None\n onnx_is = []\n for p in indices:\n ov, onnx_i = get_column_index(p, inputs)\n onnx_is.append(onnx_i)\n if onnx_var is None:\n onnx_var = ov\n elif onnx_var != ov:\n cols = [onnx_var, ov]\n raise NotImplementedError( # pragma: no cover\n \"sklearn-onnx is not able to merge multiple columns from \"\n \"multiple variables ({0}). You should think about merging \"\n \"initial types.\".format(cols))\n return onnx_var, onnx_is","def _index_from_key(self, key):\n\t\t\n\t\treturn self.columns.index(str.upper(key[0])), self.rows.index(key[1])","def _create_img_id_to_idx(self):\n with h5py.File(self.image_features_path, 'r') as features_file:\n coco_ids = features_file['ids'][()]\n coco_id_to_index = {id: i for i, id in enumerate(coco_ids)}\n return coco_id_to_index","def determine_coordinates_and_cell_names(self):\n self.coordinates_and_cell_headers = [\n annot[0]\n for annot in self.file.columns\n if annot[0].lower() in (\"z\", \"y\", \"x\", \"name\")\n ]\n # annotation column names\n self.annot_column_headers = [\n annot\n for annot in self.file.columns\n if annot[0].lower() not in (\"z\", \"y\", \"x\", \"name\")\n ]","def build_multi_index_tuples(header: list, sub_header: list) -> list:\r\n\r\n tuples = []\r\n\r\n for head in header:\r\n for sub_head in sub_header:\r\n tuples.append((head, sub_head))\r\n\r\n return tuples","def create_deft_table_csv_mappings():\n mappings = list()\n mappings.append(CsvColumnMapping(columnName=\"rownumber\", cslDataType=\"int\", ordinal=0))\n mappings.append(CsvColumnMapping(columnName=\"rowguid\", cslDataType=\"string\", ordinal=1))\n mappings.append(CsvColumnMapping(columnName=\"xdouble\", cslDataType=\"real\", ordinal=2))\n mappings.append(CsvColumnMapping(columnName=\"xfloat\", cslDataType=\"real\", ordinal=3))\n mappings.append(CsvColumnMapping(columnName=\"xbool\", cslDataType=\"bool\", ordinal=4))\n mappings.append(CsvColumnMapping(columnName=\"xint16\", cslDataType=\"int\", ordinal=5))\n mappings.append(CsvColumnMapping(columnName=\"xint32\", cslDataType=\"int\", ordinal=6))\n mappings.append(CsvColumnMapping(columnName=\"xint64\", cslDataType=\"long\", ordinal=7))\n mappings.append(CsvColumnMapping(columnName=\"xuint8\", cslDataType=\"long\", ordinal=8))\n mappings.append(CsvColumnMapping(columnName=\"xuint16\", cslDataType=\"long\", ordinal=9))\n mappings.append(CsvColumnMapping(columnName=\"xuint32\", cslDataType=\"long\", ordinal=10))\n mappings.append(CsvColumnMapping(columnName=\"xuint64\", cslDataType=\"long\", ordinal=11))\n mappings.append(CsvColumnMapping(columnName=\"xdate\", cslDataType=\"datetime\", ordinal=12))\n mappings.append(CsvColumnMapping(columnName=\"xsmalltext\", cslDataType=\"string\", ordinal=13))\n mappings.append(CsvColumnMapping(columnName=\"xtext\", cslDataType=\"string\", ordinal=14))\n mappings.append(CsvColumnMapping(columnName=\"xnumberAsText\", cslDataType=\"string\", ordinal=15))\n mappings.append(CsvColumnMapping(columnName=\"xtime\", cslDataType=\"timespan\", ordinal=16))\n mappings.append(CsvColumnMapping(columnName=\"xtextWithNulls\", cslDataType=\"string\", ordinal=17))\n mappings.append(CsvColumnMapping(columnName=\"xdynamicWithNulls\", cslDataType=\"dynamic\", ordinal=18))\n return mappings","def create_columns(headers: List, annot_types: List):\n new_column_names: List[Tuple] = []\n for annotation, annot_type in zip(headers, annot_types):\n new_column_names.append((annotation, annot_type))\n return new_column_names","def column_name(name):\n # Only needs exceptions to standard token cleanup\n column_map = {\n \"line#\" : \"ignore\",\n \"date\" : \"timestamp\",\n \"rh\" : \"humidity\",\n \"par\" : \"par_ue\"\n }\n\n if name in column_map:\n return column_map[name]\n \n return name","def _tables_from_headers(headers):\n return {table for field, table in _DATA_TABLES_BY_UNIQUE_FIELD.items() if field in headers}","def convert_header_to_multi_index(df, header_names: List[Tuple]):\n index = pd.MultiIndex.from_tuples(header_names, names=[\"NAME\", \"TYPE\"])\n df.columns = pd.MultiIndex.from_tuples(index)\n return df","def minimize_header(self, header):\n header = self.locate.fits_to_parkeys(header) # reference vocab --> dataset vocab\n if isinstance(self, PipelineContext):\n instrument = self.get_instrument(header)\n mapping = self.get_imap(instrument)\n keys = mapping.get_required_parkeys() + [self.instrument_key]\n else:\n keys = self.get_required_parkeys()\n minimized = {}\n for key in keys:\n minimized[key] = header.get(key.lower(),\n header.get(key.upper(), \"UNDEFINED\"))\n return minimized","def get_column_reference(headers, name):\n return chr(ord('A') + headers.index(name))","def table_key(self, reindex_dict):\n reindexed_marks = []\n for m in self.component1.marks:\n new_m = reindex_dict.get(m)\n if new_m == None:\n if len(reindex_dict) == 0:\n new_m = 0\n else:\n new_m = max(reindex_dict.values())+1\n reindex_dict[m] = new_m\n reindexed_marks.append(new_m)\n return tuple( [self.component1.genus] + sorted(reindexed_marks) )","def _build_header_dictionary(self):\n start = 0\n #print self.raw_data\n for a in range(20):\n redatapuller = re.compile(\"\\r\\n\\r\\n\\r\\n(?P<word>.*?)\\t.*?\\n\", re.DOTALL)\n m = redatapuller.search(self.raw_data[start:])\n if not(m):\n break\n self.header_dictionary[m.group(\"word\")] = start + m.end()\n if a==0:\n self.header_dictionary[\"main\"] = start + m.end()\n start += m.end()","def generate_colnames(df, labelnum=0): # need to be adjusted for GC content\n colnames = []\n for field in range(len(df.columns) - labelnum):\n colnames.append(BEDCOLS[field])\n for label in range(labelnum):\n colnames.append(f\"label_{label+1}\")\n return colnames","def _bin_hits_to_unit_indices(rows, cols, target_breaks, source_breaks,\n su_start):\n # keep track of mapping between matrix row index and target unit index\n # in ``target_units``\n row2t_unit_ind = np.array([\n u_ind\n for u_ind in range(len(target_breaks) - 1)\n for _ in range(target_breaks[u_ind+1] - target_breaks[u_ind])])\n # keep track of mapping between matrix column index and source unit index\n # in ``source_units``\n col2s_unit_ind = np.array([\n u_ind\n for u_ind in range(len(source_breaks) - 1)\n for _ in range(source_breaks[u_ind+1] - source_breaks[u_ind])])\n tmp = {}\n hits2positions = {}\n t_inds = row2t_unit_ind[rows]\n s_inds = col2s_unit_ind[cols]\n t_poses = rows - target_breaks[t_inds]\n s_poses = cols - source_breaks[s_inds]\n # although s_inds needs to index the source_breaks by the ordering of this\n # batch of source_units, s_inds needs to account for source_unit indices as\n # referenced from outside of this batch\n s_inds += su_start\n for t_ind, s_ind, t_pos, s_pos in zip(t_inds, s_inds, t_poses, s_poses):\n key = (t_ind, s_ind)\n if key not in tmp:\n tmp[key] = (t_pos, s_pos)\n elif key not in hits2positions:\n hits2positions[key] = [tmp[key], (t_pos, s_pos)]\n else:\n hits2positions[key].append((t_pos, s_pos))\n hits2positions = {k: np.array(v) for k, v in hits2positions.items()}\n return hits2positions","def get_cols_dummy():","def _get_idx_maps(self, types, initial_mapping=None):\n initial_mapping = constants.INITIAL_MAPPING if initial_mapping is None else initial_mapping\n # generate type to index mappings\n self.type_to_idx['word'] = Preprocessor.type_to_idx(types['word'], initial_mapping['word'])\n self.type_to_idx['char'] = Preprocessor.type_to_idx(types['char'], initial_mapping['word'])\n self.type_to_idx['tag'] = Preprocessor.type_to_idx(types['tag'], initial_mapping['tag'])","def filter_mapping_file(map_data, map_header, good_sample_ids,\r\n include_repeat_cols=False, column_rename_ids=None):\r\n # keeping samples\r\n to_keep = []\r\n to_keep.extend([i for i in map_data if i[0] in good_sample_ids])\r\n\r\n # keeping columns\r\n headers = []\r\n to_keep = zip(*to_keep)\r\n headers.append(map_header[0])\r\n result = [to_keep[0]]\r\n\r\n if column_rename_ids:\r\n # reduce in 1 as we are not using the first colum (SampleID)\r\n column_rename_ids = column_rename_ids - 1\r\n for i, l in enumerate(to_keep[1:-1]):\r\n if i == column_rename_ids:\r\n if len(set(l)) != len(result[0]):\r\n raise ValueError(\r\n \"The column to rename the samples is not unique.\")\r\n result.append(result[0])\r\n result[0] = l\r\n headers.append('SampleID_was_' + map_header[i + 1])\r\n elif include_repeat_cols or len(set(l)) > 1:\r\n headers.append(map_header[i + 1])\r\n result.append(l)\r\n else:\r\n for i, l in enumerate(to_keep[1:-1]):\r\n if include_repeat_cols or len(set(l)) > 1:\r\n headers.append(map_header[i + 1])\r\n result.append(l)\r\n headers.append(map_header[-1])\r\n result.append(to_keep[-1])\r\n\r\n result = map(list, zip(*result))\r\n\r\n return headers, result","def convert_field_name_to_indexes(field_name):\n rows = '87654321'\n columns = 'abcdefgh'\n row_index = column_index = None\n\n row_name = field_name[0]\n for i in range(8):\n if rows[i] == row_name:\n row_index = i\n\n column_name = field_name[1]\n for i in range(8):\n if columns[i] == column_name:\n column_index = i\n\n return row_index, column_index","def index(self):\n path = self.path.format('index')\n \n with open(path, 'r', newline='') as file:\n l = list(csv.reader(file))\n \n index = [v for _ in l for v in _]\n index = dict((v, i) for (i, v) in enumerate(index))\n \n return index","def build_index(text: Iterable) -> Dict[str, List[Tuple[int, int]]]:\n index = defaultdict(list)\n for line_no, line in enumerate(text, 1):\n for match in WORD_RE.finditer(line):\n word = match.group()\n column_no = match.start() + 1\n location = (line_no, column_no)\n index[word].append(location)\n return index","def __generate_features_index__(self, feature_names, dictionaries):\n keys = []\n for name, dictionary in zip(feature_names, dictionaries):\n features = []\n for feature in dictionary.keys():\n if dictionary.get(feature) > self._cutoff:\n features.append((name, feature))\n self.feature_freq[name] += 1\n keys.extend(features)\n for i in range(len(keys)):\n self._features_index[keys[i]] = i\n self.features_list = tuple(keys)\n self._features_vector_length = len(keys)","def index_by(self, column_or_label):\n column = self._get_column(column_or_label)\n index = {}\n for key, row in zip(column, self.rows):\n index.setdefault(key, []).append(row)\n return index","def wind_profile_indices_map(self):\n if self._hybrid_meta is None:\n return np.array([]), np.array([])\n\n idxs = self._hybrid_meta[self.__wind_rpi_n].astype(int)\n idxs = idxs[idxs >= 0]\n\n return idxs.index.values, idxs.values","def column_index(input_file, name):\n col, com = find_columns(input_file)\n col_name = name\n contents = open(input_file, 'r').readlines()\n for line in contents:\n if com[col.index(col_name)] in line:\n line_index = contents.index(line)+1\n return line_index","def generate_bitmap_to_linear_index_map(bladeTupList, firstIdx):\n bitmap_map = np.zeros(len(bladeTupList), dtype=int)\n for ind, blade in enumerate(bladeTupList):\n bitmap_map[compute_bitmap_representation(blade, firstIdx)] = ind\n return bitmap_map","def map_column_to_index(self, col):\n if col in self.column_maps:\n return\n\n # First construct the map from original ids to new ones.\n ids = self.dataset[col].unique()\n n = len(ids)\n idmap = dict(itertools.izip(ids, xrange(n)))\n\n # Next use the map to conver the ids in-place.\n self.dataset[col] = self.dataset[col].apply(lambda _id: idmap[_id])\n\n # Now swap key for value in the idmap to provide a way to convert back.\n reverse_map = {val: key for key, val in idmap.iteritems()}\n self.column_maps[col] = reverse_map","def index_value(self):\r\n\t\tfor index, column_header in enumerate(self.header_row):\r\n\t\t\tprint(index, column_header)","def label_columns(mapping):\n columns = []\n for name, column in mapping.items():\n columns.append(column.label(name))\n return columns","def pivot_rows_to_keys(header_list, row_list, key_names=[], whitelist_headers=None):\n header_map = {v: header_list.index(v) for v in\n [x for x in header_list if not whitelist_headers or x in whitelist_headers or x in key_names]}\n\n key_idxs = []\n for key_name in key_names:\n key_idxs.append(header_map[key_name])\n\n #key_idx = header_map[key_name]\n #return {\"{}{}\".format(x[key_idx],x[keya_idx]): {k: x[v] for k, v in list(header_map.items())} for x in row_list}\n\n return {\":\".join(itemgetter(*key_idxs)(x)): {k: x[v] for k, v in list(header_map.items())} for x in row_list}","def writeheader(writer):\n writer.writerow(dict((fn, fn) for fn in writer.fieldnames))","def __valuesToIndices(self, mappings, values):\n indices = np.empty(0, dtype=np.int_)\n\n for key, _ in mappings.items():\n # Lookup the index of the value of the values in the map.\n index = mappings[key](values[key])\n\n indices = np.hstack((indices, index))\n\n return indices","def nomenclatura():\n df = pd.read_csv(\"Data/nomenclatura_1.csv\", encoding = \"latin1\")\n #dict_axis = df.set_index('id').T.to_dict('list')\n dict_axis = dict( [ (i, [a,b]) for i, a,b in zip(df.id, df.latitude, df.longitude) ] )\n\n return dict_axis","def parse_header(header):\n if header[0] != '@':\n return None\n \n instrument, run_number, flowcell_id, lane, tile, x_pos, y_pos_read, is_filtered, control_number, index = header[1:].split(\":\")\n y_pos, read = y_pos_read.split()\n return {'instrument': str(instrument),\n 'run_number': int(run_number),\n 'flowcell_id': str(flowcell_id),\n 'lane': int(lane),\n 'tile': int(tile),\n 'x_pos': int(x_pos),\n 'y_pos': int(y_pos),\n 'read': int(read),\n 'is_filtered': (is_filtered == 'Y'),\n 'control_number': int(control_number),\n 'index': str(index)} # Note that MiSeq Reporter outputs a SampleSheet index rather than the index sequence","def make_headers(worksheet):\n headers = {}\n cell_idx = 0\n while cell_idx < worksheet.ncols:\n cell_type = worksheet.cell_type(0, cell_idx)\n if cell_type == 1:\n header = slughifi(worksheet.cell_value(0, cell_idx))\n if not header.startswith(\"_\"):\n headers[cell_idx] = header\n cell_idx += 1\n return headers","def build_data_dict(stats_arr, prefix):\n # Get no. of points in each dimension.\n N_sam, N_thres, N_z, N_kbins = stats_arr.shape\n\n # Create dictionary with column names that can be used by ColumnDataSource\n data_dict = {}\n for n in range(N_thres):\n for j in range(N_z):\n for m in range(N_kbins):\n key = \"tot_%s_h%d_k%d_z%d\" % (prefix, n+1, m+1, j+1)\n data_dict[key] = stats_arr[:,n, j, m]\n return data_dict","def _infer_ipa_col(self, header):\n\t\tif self.ipa_col and isinstance(self.ipa_col, str):\n\t\t\tif self.ipa_col in header:\n\t\t\t\treturn header.index(self.ipa_col)\n\n\t\t\ttry:\n\t\t\t\tipa_col = int(self.ipa_col)\n\t\t\texcept ValueError: pass\n\t\t\telse:\n\t\t\t\treturn ipa_col\n\n\t\t\traise ValueError('Could not find column: {}'.format(self.ipa_col))\n\n\t\tpot = []\n\n\t\tfor index, col_name in enumerate(header):\n\t\t\tcol_name = col_name.lower()\n\t\t\tfor name in IPA_COL_NAMES:\n\t\t\t\tif col_name.startswith(name):\n\t\t\t\t\tpot.append(index)\n\n\t\tif len(pot) == 0:\n\t\t\traise ValueError('Could not find an IPA column')\n\t\telif len(pot) > 1:\n\t\t\traise ValueError('Could not decide which is the IPA column')\n\n\t\treturn pot[0]","def reference_keys_to_dataset_keys(rmapping, header):\n header = dict(header)\n try:\n translations = rmapping.reference_to_dataset\n except AttributeError:\n pass\n else:\n # Add replacements for translations *if* the existing untranslated value\n # is poor and the translated value is better defined. This is to do\n # translations w/o replacing valid/concrete DM values with something \n # like guessed values of \"UNDEFINED\" or \"N/A\".\n for rkey in translations:\n if rkey in header:\n dkey = translations[rkey]\n dval = header.get(translations[rkey], None)\n rval = header[rkey]\n if dval in [None, \"N/A\", \"UNDEFINED\"] and rval not in [None, \"UNDEFINED\"]:\n header[dkey] = rval\n if \"USEAFTER\" in header: # and \"DATE-OBS\" not in header:\n reformatted = timestamp.reformat_useafter(rmapping, header).split()\n header[\"DATE-OBS\"] = header[\"META.OBSERVATION.DATE\"] = reformatted[0]\n header[\"TIME-OBS\"] = header[\"META.OBSERVATION.TIME\"] = reformatted[1]\n return header","def get_sample_idx(sample, header):\n\n for item in header:\n if sample in item:\n return header.index(item)\n\n print(sample + \" not found in header, check input files.\")\n sys.exit()","def header_data_columns(head_line, data_cols, header):\n\n colnames = head_line.split(\",\")\n\n # Remove triling blancks and end of lines\n colnames = [x.strip() for x in colnames]\n\n # Difference between columns in the header and in the data\n diff = len(data_cols) - len(colnames)\n\n if diff > 0:\n # Add dum headers\n dums = \"\"\n for idiff in range(diff):\n dums = dums + \",dum\" + str(idiff)\n\n new_head = str(head_line.rstrip()) + dums + \" \\n\"\n header.append(new_head)\n\n elif diff < 0:\n sys.exit(\n \"STOP novonix_clean.header_data_columns \\n\"\n + \"REASON less data columns than header names \\n\"\n )\n else:\n header.append(head_line)\n\n return","def format_header(line):\n cols = []\n for n, item in enumerate(line.replace('#', '').strip().split('\\t')):\n if item == 'id/name':\n cols.append('name')\n elif item == 'brass_score':\n cols.append('score')\n elif item.startswith('strand') and n > 9:\n if item == 'strand1':\n cols.append('transcript1_strand')\n elif item == 'strand2':\n cols.append('transcript2_strand')\n else:\n raise ValueError(\"Unknown strand value column {0} - {1}\".format(n, item))\n else:\n cols.append(item.lower().replace(' ', '_').replace('/', '_').replace('-', '_'))\n return cols","def writeheader(fh,colnames):\n for i in range(len(colnames)):\n fh.write('# %d %s\\n'%(i+1,colnames[i]))","def fits_to_parkeys(fits_header):\n if MODEL is None:\n raise MissingDependencyError(\"JWST data models are not installed. Cannot fits_to_parkeys().\")\n parkeys = {}\n for key, value in fits_header.items():\n key, value = str(key), str(value)\n if not key.lower().startswith(\"meta.\"):\n pk = cached_dm_find_fits_keyword(key)\n if not pk:\n pk = key\n else:\n assert len(pk) == 1, \"CRDS JWST Data Model ambiguity on \" + \\\n repr(key) + \" = \" + repr(pk)\n pk = pk[0]\n else:\n pk = key\n pk = pk.upper()\n parkeys[pk] = value\n return parkeys","def header(self):\n\n data = {}\n data['latitude'] = self.latitude()\n data['latitude_unc'] = self.latitude_unc()\n data['longitude'] = self.longitude()\n data['longitude_unc'] = self.longitude_unc()\n data['uid'] = self.uid()\n data['n_levels'] = self.n_levels()\n data['year'] = self.year()\n data['month'] = self.month()\n data['day'] = self.day()\n data['time'] = self.time()\n data['cruise'] = self.cruise()\n data['probe_type'] = self.probe_type()\n \n header = pd.Series(data)\n\n return header","def __get_column_names(self, path_to_xml):\n span_table = xmlet.parse(path_to_xml / Path(\"SSTDataFormatTimeSpanTable.xml\")).getroot()\n filetype = MAP_RBD_TYPE_TO_FILE_TYPE[self.__rbd_type]\n\n for item in span_table:\n if item[0].text == filetype and item[1].text <= self.date <= item[2].text:\n data_description_file_name = item[3].text\n\n xml = xmlet.parse(path_to_xml / Path(data_description_file_name)).getroot()\n\n header = dict()\n for child in xml:\n var_name = child[0].text\n var_dim = int(child[1].text)\n var_type = child[2].text\n var_unit = child[3].text\n\n np_type = XML_TYPE_TO_NUMPY_TYPE[var_type]\n\n header.update({var_name: [var_dim, np_type, var_unit]})\n\n return header","def _column_sorting_key(self, c):\n first_index = 0\n if c.startswith('hybrid'):\n first_index = 1\n elif c.startswith('solar'):\n first_index = 2\n elif c.startswith('wind'):\n first_index = 3\n elif c == MERGE_COLUMN:\n first_index = -1\n return first_index, self._hybrid_meta.columns.get_loc(c)","def index_dict(self):\n msk = self.load_mask()\n mski = enumerate(msk)\n ifiltered = (i for (i, m) in mski if m == 1)\n return {i: j for (j, i) in enumerate(ifiltered)}","def index_dict(self):\n msk = self.load_mask()\n mski = enumerate(msk)\n ifiltered = (i for (i, m) in mski if m == 1)\n return {i: j for (j, i) in enumerate(ifiltered)}","def make_dof_value_map(robot):\n names = [j.GetName() for j in robot.GetJoints()]\n indices = [j.GetDOFIndex() for j in robot.GetJoints()]\n\n def get_dofs():\n pose={}\n values=robot.GetDOFValues()\n for (i,n) in zip(indices,names):\n pose.setdefault(n,values[i])\n return pose\n\n return get_dofs","def get_label_ix_mapping(labels):\n return {label: i for i, label in enumerate(labels)}","def data_population(in_data, headers=None):\n headers = headers or in_data[0]\n\n return [\n dict([(header, row[cindex])\n for cindex, header in enumerate(headers)])\n for row in in_data\n ]","def get_horiz_structure(col_names, num_rows):\n struct = [{name: random.randint(0, max_cell_value) for name in col_names} for _ in range(num_rows)]\n return struct","def index_prob_id(blocks):\n return blocks.set_index(\n pd.Index(\n blocks.index.str.extract(r\"HOAF_(\\d\\d)\\.vhdr\").astype(int)[0],\n name=\"prob_id\",\n )\n )","def get_indices(self):\n\n def query(rel): \n return \"\"\"SELECT pg_class.relname, pg_index.indkey\n FROM pg_class, pg_index\n WHERE (pg_index.indexrelid = pg_class.oid)\n AND (pg_index.indrelid = (SELECT pg_class.oid FROM pg_class WHERE pg_class.relname = \\'{}\\'));\n \"\"\".format(rel)\n\n rels = tpch.schema.keys()\n idxs = dict.fromkeys(rels)\n\n with self.tpch_cxn.cursor() as curs:\n for rel in rels:\n curs.execute(query(rel))\n idxs_ = curs.fetchall()\n idxs_ = dict(idxs_) # index -> index keys \n \n # TODO this can be done cleanly in query\n # pg_index.indkey is a SQL array of attributes indices in their respective tables\n split=lambda attrs: attrs.split() \n cast=lambda attrs: list(map(lambda attr: int(attr)-1, attrs))\n invertindex=lambda attrs: list(np.array(schema[rel])[attrs])\n\n attrs = idxs_.values() \n attrs = list(map(split, attrs))\n attrs = list(map(cast, attrs))\n attrs = list(map(invertindex, attrs))\n\n idxs_ = {key : attrs[i] for i, key in enumerate(idxs_.keys())}\n idxs[rel] = idxs_\n return idxs","def setup_normalyzer_header(design_matrix: DF, annot_cols: List[str], normalyzer_vals:DF) -> DF:\n\n # Get numbers set up as list of stringified numbers ('-1', '0', '0', '1', '1')\n nbr_annot_cols = len(annot_cols)\n sample_head = [-1] + [0] * (nbr_annot_cols - 1) + list(design_matrix['biorepgroup'])\n sample_head_str = [str(e) for e in sample_head]\n\n # Get text-information about each column\n label_row = list(normalyzer_vals.columns)[:nbr_annot_cols] + list(design_matrix['name'])\n\n headers = pd.DataFrame([sample_head_str, label_row])\n headers.columns = normalyzer_vals.columns\n\n return headers","def _index_column_set(column_set: List[ColumnSchema]) -> List[Tuple[str, int]]:\n out = defaultdict(int)\n for column_schema in column_set:\n key = column_schema_to_keys(column_schema)\n out[key] += 1\n return list(out.items())","def indices_get_mapping(es):\n index = 'customer'\n\n print(es.indices.get_mapping(index=index))","def create_class_indices(self) -> None:\n\n categories = list(np.loadtxt(self.path_manager.categories_file(), delimiter=\",\", dtype=str))\n\n if self.include_noise_samples and not self.multi_label_classification:\n categories.append(\"noise\")\n\n self.class_to_idx = {}\n\n for idx, class_name in enumerate(sorted(categories)):\n self.class_to_idx[class_name] = idx","def mask_header(data_mask, header_mask):\n\n mask = {}\n text = {'bad': 'BP', 'edge': 'EP', 'saturated': 'SP',\n 'saturated-connected': 'SCP', 'satellite trail': 'STP',\n 'cosmic ray': 'CRP'}\n\n for mask_type in text.keys():\n value = get_par(set_zogy.mask_value[mask_type],tel)\n mask[mask_type] = (data_mask & value == value)\n header_mask['M-{}'.format(text[mask_type])] = (\n True, '{} pixels included in mask?'.format(mask_type))\n header_mask['M-{}VAL'.format(text[mask_type])] = (\n value, 'value added to mask for {} pixels'.format(mask_type))\n header_mask['M-{}NUM'.format(text[mask_type])] = (\n np.sum(mask[mask_type]), 'number of {} pixels'.format(mask_type))\n\n return"],"string":"[\n \"def __get_column_mapping(self):\\n\\n s1 = list(Sample(\\\"FL\\\", \\\"M1.0@265_Primary_ar115_s2010-08-06T06_36_00_e2010-08-06T18_24_00.csv\\\").get_data().columns)[:25]\\n column_mapping = {}\\n for i in range(len(s1)):\\n column_mapping[i] = s1[i]\\n\\n return column_mapping\",\n \"def column_indexer(data):\\n idCol = {label: index for index, label in enumerate(data.columns)}\\n return idCol\",\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 getIndividual2ColIndex(cls, header, col_name2index, sampleStartingColumn=9):\\n\\t\\tsys.stderr.write(\\\"Finding all individuals ...\\\")\\n\\t\\tno_of_cols = len(header)\\n\\t\\tindividual_name2col_index = {}\\t#individual's column name -> an opened file handler to store genetic data\\n\\t\\tcounter = 0\\n\\t\\tfor i in xrange(sampleStartingColumn, no_of_cols):\\n\\t\\t\\tindividualName = header[i]\\n\\t\\t\\tcol_index = col_name2index.get(individualName)\\n\\t\\t\\tif not individualName:\\t#ignore empty column\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\tif individualName[:-4]=='.bam':\\n\\t\\t\\t\\tindividualCode = individualName[:-4]\\t#get rid of .bam\\n\\t\\t\\telse:\\n\\t\\t\\t\\tindividualCode = individualName\\n\\t\\t\\tindividual_name2col_index[individualCode] = col_index\\n\\t\\t\\tcounter += 1\\n\\t\\tsys.stderr.write(\\\"%s individuals added. Done.\\\\n\\\"%(counter))\\n\\t\\treturn individual_name2col_index\",\n \"def find_indeces(self, header):\\n indeces = {'T': None, 'WV': None, 'WK': None, 'BZ': None, 'SPR': None,\\n 'WBER': None, 'ABG.': None, 'UNG.': None, 'SPOE': None,\\n 'FPOE': None, 'OEVP': None, 'GRUE': None, 'NEOS': None,\\n 'WWW': None, 'ANDAS': None, 'GFW': None, 'SLP': None,\\n 'WIFF': None, 'M': None, 'FREIE': None}\\n for index, item in enumerate(header):\\n indeces[item] = index\\n return indeces\",\n \"def _get_columns_mapping_dict():\\n\\n columns_mapping_dict = {}\\n for original_header in COLUMN_HEADERS_MAPPER:\\n new_header = COLUMN_HEADERS_MAPPER[original_header]\\n columns_mapping_dict[new_header] = [original_header]\\n return columns_mapping_dict\",\n \"def get_header_indices(filepath):\\n\\theaders = get_header_list(filepath, sort=False)\\n\\treturn {h: i for i, h in enumerate(headers)}\",\n \"def _get_header_index(self, columnname):\\n\\n return self.headers.index(columnname)\",\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 create_matrix_mapping(train_mh, unk_vec_id):\\n mh_index_map = {}\\n matrix_idx = 0\\n for vector_idx in train_mh:\\n if vector_idx == unk_vec_id:\\n unk_matrix_id = matrix_idx\\n mh_index_map[vector_idx] = matrix_idx\\n matrix_idx += 1\\n return mh_index_map, unk_matrix_id\",\n \"def column_info(colum_header):\\n commas = colum_header.count(',')\\n if commas == 0:\\n return (column_name(clean_token(colum_header)))\\n\\n (key, units, location) = colum_header.split(',')\\n key = column_name(clean_token(key))\\n units = clean_token(units)\\n location = clean_token(location)\\n return (key, units, location)\",\n \"def parseHeader(header):\\n tokens = [t for t in header.split(' ') if t]\\n result = {}\\n for i in range(len(tokens)):\\n result[tokens[i]] = i \\n\\n return result\",\n \"def _get_table_columns(self):\\n try:\\n table_header = parse_table_head(self.table.value, version=self.version)\\n merged_data = self.table.value[table_header.tdef_header_end:]\\n if table_header.TDEF_header.next_page_ptr:\\n merged_data = merged_data + self._merge_table_data(table_header.TDEF_header.next_page_ptr)\\n\\n parsed_data = parse_table_data(merged_data, table_header.real_index_count,\\n table_header.column_count, version=self.version)\\n\\n # Merge Data back to table_header\\n table_header['column'] = parsed_data['column']\\n table_header['column_names'] = parsed_data['column_names']\\n\\n except ConstructError:\\n logging.error(f\\\"Failed to parse table header {self.table.value}\\\")\\n return\\n col_names = table_header.column_names\\n columns = table_header.column\\n\\n # Add names to columns metadata so we can use only columns for parsing\\n for i, c in enumerate(columns):\\n c.col_name_str = col_names[i].col_name_str\\n\\n # column_index is more accurate(id is always incremented so it is wrong when a column is deleted).\\n # Some tables like the catalog don't have index, so if indexes are 0 use id.\\n\\n # create a dict of index to column to make it easier to access. offset is used to make this zero based\\n offset = min(x.column_index for x in columns)\\n column_dict = {x.column_index - offset: x for x in columns}\\n # If column index is not unique try best effort\\n if len(column_dict) != len(columns):\\n # create a dict of id to column to make it easier to access\\n column_dict = {x.column_id: x for x in columns}\\n\\n if len(column_dict) != table_header.column_count:\\n logging.debug(f\\\"expected {table_header.column_count} columns got {len(column_dict)}\\\")\\n return column_dict, table_header\",\n \"def get_colnumber(self, header):\\n for i in range(0, len(self.data)):\\n if self.data[i][0] == header:\\n return i\\n return None\",\n \"def header_population(headers):\\n return [{'id': field, 'name': field, 'field': field, 'sortable': True} for field in headers]\",\n \"def get_report_column_names(self):\\r\\n # Compose the list of report_column names required for\\r\\n # summary_report.dsw.DictWriter()\\r\\n sr = self.summary_report\\r\\n dict_leader = sr.dict_leader\\r\\n dict_out = sr.dict_out\\r\\n column_names = self.column_names\\r\\n report_column_names = []\\r\\n #if dict_leader is not None and dict_out is not None:\\r\\n if dict_leader is not None and dict_out is not None:\\r\\n for key,value in dict_leader.iteritems():\\r\\n #print \\\"Adding report_column_name(from dict_leader)=\\\",key\\r\\n report_column_names.append(key)\\r\\n dict_out[key] = value\\r\\n # We have to initialize the DictWriter with the report_column_names\\r\\n # below. \\r\\n # Also need matched coord_val and var names for calling node_report()\\r\\n # below, so we do this duplication of storage of names. \\r\\n coord_var_names = []\\r\\n coord_val_names = []\\r\\n for idx, column_name in enumerate(column_names):\\r\\n var_name = \\\"Var_%s\\\" % str(idx+1)\\r\\n report_column_names.append(var_name)\\r\\n coord_var_names.append(var_name)\\r\\n val_name = \\\"Val_%s\\\" % str(idx+1)\\r\\n report_column_names.append(val_name)\\r\\n coord_val_names.append(val_name)\\r\\n # Add the entry report_column_names\\r\\n report_column_names += self.EntryClass.report_column_names\\r\\n return report_column_names\",\n \"def parse_header(f):\\n columns = ['pokemon', 'species_id', 'height', 'weight', 'type_1', 'type_2',\\n 'url_image', 'generation_id', 'evolves_from_species_id']\\n sep = ','\\n result = {}\\n allData = []\\n with open(const.DATA_FILENAME, newline=\\\"\\\") as myData:\\n for line in myData:\\n line = line.strip()\\n line = line.split(sep)\\n allData.append(line)\\n for i in columns:\\n j = 0\\n while j < len(allData[0]):\\n if allData[0][j] == i:\\n result[i] = j\\n j += 1\\n return result\",\n \"def set_column_headers(self, headers):\\n if isinstance(self.columns.idx[0], int):\\n self.data = [sorted(headers)] + self.data\\n\\n increment = [i + 1 for i in self.rows.idx]\\n self.rows.idx = [0] + increment\\n\\n elif isinstance(self.columns.idx[0], str):\\n datum = {}\\n for i, key in enumerate(self.columns.idx):\\n datum.update({key: headers[i]})\\n self.data = [datum] + self.data\\n\\n increment = [i + 1 for i in self.rows.idx]\\n self.rows.idx = [0] + increment\",\n \"def get_interesting_mapping_fields(mapping_data, mapping_headers):\\r\\n result = []\\r\\n num_samples = len(mapping_data)\\r\\n num_cols = len(mapping_headers)\\r\\n transposed_data = array(mapping_data).T\\r\\n for header, datum in zip(mapping_headers, transposed_data):\\r\\n d = set(datum)\\r\\n len_d = len(d)\\r\\n if len_d > 1 and len_d < num_samples:\\r\\n result.append(header)\\r\\n return result\",\n \"def header(self) -> list:\\n cols = self.data.columns.tolist()\\n header = [\\\"index\\\"]\\n for col_int in cols:\\n header.append(col_int)\\n return header\",\n \"def map_column_indexes(self, merge_specification, ingredients):\\n last_column = len(ingredients) - 1\\n accumulating = {}\\n remove = set()\\n # default behavior, no column merge\\n for column_index in range(0, last_column + 1):\\n self.column_index_to_columns[column_index] \\\\\\n = [(column_index, 1.0)]\\n \\n for columns in merge_specification:\\n accumulating_column = columns[0][0]\\n if accumulating_column > last_column or accumulating_column < 0:\\n raise MergeConfigError(\\n \\\"Attempted to merge missing column %d\\\" % accumulating_column)\\n # specifies which columns should be merged into this one\\n accumulating[accumulating_column] = columns\\n for column_index, _ in columns[1:]:\\n column_index = column_index\\n if column_index > last_column or column_index < 0:\\n raise MergeConfigError(\\n \\\"Attempted to merge missing column %d\\\" % column_index) \\n # drop this column; it will be merged into another\\n remove.add(column_index)\\n \\n # drop columns first so that any columns both specified as\\n # accumulating *and* merged columns do not get dropped\\n for column_index in remove:\\n self.column_index_to_columns[column_index] = None\\n \\n for column_index, columns in accumulating.items():\\n self.column_index_to_columns[column_index] = columns\",\n \"def get_columns_dict(table, replace):\\n # 0 is name, 1 is id\\n if type(table.index) == pd.MultiIndex:\\n colcount = 1 + len(table.index[0])\\n else:\\n colcount = 2\\n cols = {}\\n for c in table.columns:\\n c_repres = \\\",\\\".join(c)\\n if \\\"Filtergroups\\\" not in c:\\n cols[colcount] = replace_in_str(str(c_repres), replace)\\n colcount = colcount + 1\\n return cols\",\n \"def _transform_map_data(self):\\n WARD_FMT = '%s-%s'\\n self.map_data_trans = []\\n lookup = {i.column: ''.join(filter(lambda x: x.isdigit(), i.value)) for i in self.sht[1]}\\n\\n #skip over header\\n rs = iter(self.sht.rows)\\n next(rs)\\n next(rs)\\n for r in rs:\\n pka = r[0].value\\n for c in r[1:]:\\n if c.value is None:\\n c.value = 0\\n\\n self.map_data_trans.append((WARD_FMT%(pka, lookup[c.column]), c.value))\",\n \"def indices(self):\\n if self._indices is None:\\n i = []\\n\\n # TODO: this is not right for multi-column keys\\n # TODO: new style indexes\\n\\n global_name = '^DD(%s,0,\\\"IX\\\",\\\"0\\\")' % self.fileid\\n prefix = '^DD(%s,0,\\\"IX\\\",' % self.fileid\\n while 1:\\n global_name = M.mexec('set s0=$query(%s)' % global_name, M.INOUT(\\\"\\\"))[0]\\n if not global_name or not global_name.startswith(prefix):\\n break\\n suffix = global_name[len(prefix):-1]\\n parts = suffix.split(\\\",\\\")\\n idx_name = parts[0][1:-1]\\n idx_table = parts[1]\\n idx_columns = parts[2:]\\n index = Index(idx_name, idx_table, idx_columns)\\n i.append(index)\\n\\n # A second list, gives indices for a field\\n columns = {}\\n for idx in i:\\n for c in idx.columns:\\n columns[c] = 1\\n\\n # Now trawl the listed columns in the data dictionary, and load their\\n # cross references.\\n cr_names = {}\\n for c in columns.keys():\\n idx_root = M.Globals[\\\"^DD\\\"][self.fileid][c][1]\\n if not idx_root[0].exists():\\n continue\\n for cr_id, val in idx_root.keys_with_decendants():\\n if float(cr_id) > 0:\\n cr_header = idx_root[cr_id][0].value\\n parts = cr_header.split(\\\"^\\\")\\n if len(parts) == 2 and parts[1]: # if more than 2 parts, assume MUMPs trigger\\n f = cr_names.get(parts[1], list())\\n f.append(c)\\n cr_names[parts[1]] = f\\n\\n # Now, just delete items from the index list if they are not in cr_names\\n self._indices = []\\n for index in i:\\n cr = cr_names.get(index.name)\\n if cr:\\n # verify columns - lots of errors in real systems\\n if len(cr) == len(index.columns):\\n invalid = False\\n for c in cr:\\n if c not in index.columns:\\n invalid = True\\n continue\\n if not invalid:\\n self._indices.append(index)\\n\\n return self._indices\",\n \"def get_sip_keywords(header):\\n cd = np.matrix([[header.get('CD1_1', 0.0), header.get('CD1_2', 0.0)],\\n [header.get('CD2_1', 0.0), header.get('CD2_2', 0.0)]], dtype=np.float64)\\n a_order = int(header.get('A_ORDER', 0))\\n b_order = int(header.get('B_ORDER', 0))\\n ac = np.matrix(np.zeros((a_order+1, a_order+1), dtype=np.float64))\\n bc = np.matrix(np.zeros((b_order+1, b_order+1), dtype=np.float64))\\n for m in range(a_order+1):\\n for n in range(0, a_order+1-m):\\n ac[m, n] = header.get('A_%d_%d' % (m, n), 0.0)\\n for m in range(b_order+1):\\n for n in range(0, b_order+1-m):\\n bc[m, n] = header.get('B_%d_%d' % (m, n), 0.0)\\n return cd, ac, bc\",\n \"def header(self, cols, parent_row):\\n out = []\\n for col in cols:\\n if col == 'gau_id':\\n out.append(self.name_for('Geographies', parent_row['geography_id']))\\n elif col == 'oth_1_id':\\n out.append(self.name_for('OtherIndexes', parent_row['other_index_1_id']))\\n elif col == 'oth_2_id':\\n out.append(self.name_for('OtherIndexes', parent_row['other_index_2_id']))\\n else:\\n out.append(col)\\n return out\",\n \"def get_tbl_headers(rows):\\n tbl_header = rows.pop(0)\\n tbl_headers = {}\\n for index, header_name in enumerate(tbl_header.find_all('th')):\\n if header_name.text in conf.TABLE_HEADER_COLS:\\n tbl_headers[header_name.text] = index\\n return tbl_headers\",\n \"def getIndex(self,filt):\\n indx = [i for i in xrange(len(self._header)) if filt == self._header[i]]\\n return indx\",\n \"def vector_indx_to_map_matrix_indx(index,senzory_map):\\n xs = dict(zip(np.unique(senzory_map[:,0]), it.count()))\\n ys = dict(zip(np.negative(np.unique(senzory_map[:,1])), it.count()))\\n x, y = senzory_map[index]\\n return ys[y],xs[x]\",\n \"def _create_indices(cls):\\r\\n from thunderdome.connection import _hosts, _index_all_fields, create_key_index\\r\\n \\r\\n if not _hosts: return\\r\\n for column in cls._columns.values():\\r\\n if column.index or _index_all_fields:\\r\\n create_key_index(column.db_field_name)\",\n \"def get_mention_id_mappings(input_file_name, out_file, field_threshold, column_indices):\\n out = open(out_file, 'w')\\n if input_file_name.endswith('.csv'):\\n with open(input_file_name, 'r') as f:\\n reader = csv.reader(f)\\n for line in reader:\\n if len(line) >= field_threshold:\\n if not line[0].startswith(\\\"#\\\"):\\n row = []\\n for index in column_indices:\\n row.append(line[index])\\n out.write(\\\"|\\\".join(row) + \\\"\\\\n\\\")\\n out.close()\",\n \"def headers_processor(headers):\\n def apply_headers(row_set, row):\\n _row = []\\n pairs = izip_longest(row, headers)\\n for i, (cell, header) in enumerate(pairs):\\n if cell is None:\\n cell = Cell(None)\\n cell.column = header\\n if not cell.column:\\n cell.column = \\\"column_%d\\\" % i\\n cell.column_autogenerated = True\\n _row.append(cell)\\n return _row\\n return apply_headers\",\n \"def writeTotalIndex(map_data):\\n ids = map_data['id']\\n index = [0.0] * len(ids)\\n\\n colnames = ['cars', 'bikes', 'ages', 'parking', 'male_singles',\\n 'female_singles', 'digging', 'freeparking']\\n weights = [-0.5, 0.5, 0.1, -0.5, 1.0, 1.0, -1.0, 0.25]\\n for colname, weight in zip(colnames, weights):\\n values = map_data[colname]\\n\\n index += values * (weight / values.max())\\n\\n toJson('final', pd.DataFrame({'id': ids, 'counts': index}))\",\n \"def get_column_dict(self) -> HeaderToWells:\\n return self._grid.columns\",\n \"def add_columns(self, npy_arr, tile):\\n size = len(npy_arr)\\n\\n tile_name = Tile.ZONES[tile.name[0].lower()] + tile.name[1:]\\n\\n # create ids\\n def get_id(order):\\n order = str(order).rjust(10, \\\"0\\\")\\n return int(tile_name + order)\\n ids = np.fromiter(\\n (get_id(idx + 1) for idx in range(size)), dtype=np.int64)\\n\\n # calculate the hjds\\n mjd_h, mjd_j, mjd_k = tile.epochs\\n\\n gen_h = (pyasl.helio_jd(mjd_h, ra, dec)\\n for ra, dec in zip(npy_arr[\\\"ra_h\\\"], npy_arr[\\\"dec_h\\\"]))\\n hjd_h = np.fromiter(gen_h, dtype=float)\\n\\n gen_j = (pyasl.helio_jd(mjd_j, ra, dec)\\n for ra, dec in zip(npy_arr[\\\"ra_j\\\"], npy_arr[\\\"dec_j\\\"]))\\n hjd_j = np.fromiter(gen_j, dtype=float)\\n\\n gen_k = (pyasl.helio_jd(mjd_k, ra, dec)\\n for ra, dec in zip(npy_arr[\\\"ra_k\\\"], npy_arr[\\\"dec_k\\\"]))\\n hjd_k = np.fromiter(gen_k, dtype=float)\\n\\n columns = [\\n (\\\"id\\\", ids),\\n (\\\"hjd_h\\\", hjd_h),\\n (\\\"hjd_j\\\", hjd_j),\\n (\\\"hjd_k\\\", hjd_k)]\\n return add_columns(npy_arr, columns)\",\n \"def get_pv_keywords(header):\\n cd = np.matrix([[header.get('CD1_1', 0.0), header.get('CD1_2', 0.0)],\\n [header.get('CD2_1', 0.0), header.get('CD2_2', 0.0)]], dtype=np.float64)\\n pv1 = np.zeros((40,), dtype=np.float64)\\n pv2 = np.zeros((40,), dtype=np.float64)\\n for k in range(40):\\n pv1[k] = header.get('PV1_%d' % k, 0.0)\\n pv2[k] = header.get('PV2_%d' % k, 0.0)\\n return cd, pv1, pv2\",\n \"def MakeDictMatrix(D, header, lev=72):\\n Ms = np.zeros((1, lev, len(header)))\\n Mp = np.ones((1, lev, len(header)))\\n for var in D.dict.keys():\\n i = header.index(var)\\n Ms[0, :, i] = D[var].sub_vec(lev)\\n Mp[0, :, i] = D[var].prod_vec(lev)\\n return(Ms,Mp)\",\n \"def map_column_to_index(self, col):\\n if col in self.column_maps:\\n return\\n\\n # First construct the map from original ids to new ones.\\n ids = pd.concat((self.train[col], self.test[col])).unique()\\n n = len(ids)\\n idmap = dict(itertools.izip(ids, xrange(n)))\\n\\n # Next use the map to convert the ids in-place.\\n self.train.loc[:, col] = self.train[col].apply(lambda _id: idmap[_id])\\n self.test.loc[:, col] = self.test[col].apply(lambda _id: idmap[_id])\\n\\n # Now swap key for value in the idmap to provide a way to convert back.\\n reverse_map = {val: key for key, val in idmap.iteritems()}\\n self.column_maps[col] = reverse_map\",\n \"def prep_hd(header,phi_c,lambda_c,nx,ny,dx,dy):\\n header_out = {}\\n\\n # Keywords to get from original header\\n keys_hd = ['TELESCOP', 'INSTRUME', 'WAVELNTH', 'CAMERA','DATE',\\n 'DATE_S','DATE-OBS','T_OBS','T_REC','TRECEPOC',\\n 'TRECSTEP','TRECUNIT','HARPNUM','DSUN_OBS','DSUN_REF',\\n 'RSUN_REF','CRLN_OBS','CRLT_OBS','CAR_ROT','OBS_VR',\\n 'OBS_VW','OBS_VN','RSUN_OBS','QUALITY','QUAL_S','QUALLEV1']\\n\\n for key in keys_hd:\\n header_out[key] = header[key]\\n\\n # Add new keywords\\n header_out['NAXIS'] = 2\\n header_out['NAXIS1'] = nx\\n header_out['NAXIS2'] = ny\\n\\n header_out['CUNIT1'] = 'degree'\\n header_out['CUNIT2'] = 'degree'\\n\\n header_out['CRPIX1'] = (nx - 1) / 2 + 1\\n header_out['CRPIX2'] = (ny - 1) / 2 + 1\\n header_out['CRVAL1'] = phi_c\\n header_out['CRVAL2'] = lambda_c\\n header_out['CDELT1'] = dx\\n header_out['CDELT2'] = dy\\n header_out['CTYPE1'] = 'CRLN-CEA'\\n header_out['CTYPE2'] = 'CRLT-CEA'\\n header_out['CROTA2'] = 0.0\\n\\n header_out['WCSNAME'] = 'Carrington Heliographic'\\n header_out['BUNIT'] = 'Mx/cm^2'\\n\\n return header_out\",\n \"def create_index(log_df, column):\\n temp_list = log_df[[column]].values.tolist()\\n subsec_set = {(x[0]) for x in temp_list}\\n subsec_set = sorted(list(subsec_set))\\n alias = dict()\\n for i, _ in enumerate(subsec_set):\\n alias[subsec_set[i]] = i + 1\\n return alias\",\n \"def get_column_indices(indices, inputs, multiple):\\n if multiple:\\n res = OrderedDict()\\n for p in indices:\\n ov, onnx_i = get_column_index(p, inputs)\\n if ov not in res:\\n res[ov] = []\\n res[ov].append(onnx_i)\\n return res\\n\\n onnx_var = None\\n onnx_is = []\\n for p in indices:\\n ov, onnx_i = get_column_index(p, inputs)\\n onnx_is.append(onnx_i)\\n if onnx_var is None:\\n onnx_var = ov\\n elif onnx_var != ov:\\n cols = [onnx_var, ov]\\n raise NotImplementedError( # pragma: no cover\\n \\\"sklearn-onnx is not able to merge multiple columns from \\\"\\n \\\"multiple variables ({0}). You should think about merging \\\"\\n \\\"initial types.\\\".format(cols))\\n return onnx_var, onnx_is\",\n \"def _index_from_key(self, key):\\n\\t\\t\\n\\t\\treturn self.columns.index(str.upper(key[0])), self.rows.index(key[1])\",\n \"def _create_img_id_to_idx(self):\\n with h5py.File(self.image_features_path, 'r') as features_file:\\n coco_ids = features_file['ids'][()]\\n coco_id_to_index = {id: i for i, id in enumerate(coco_ids)}\\n return coco_id_to_index\",\n \"def determine_coordinates_and_cell_names(self):\\n self.coordinates_and_cell_headers = [\\n annot[0]\\n for annot in self.file.columns\\n if annot[0].lower() in (\\\"z\\\", \\\"y\\\", \\\"x\\\", \\\"name\\\")\\n ]\\n # annotation column names\\n self.annot_column_headers = [\\n annot\\n for annot in self.file.columns\\n if annot[0].lower() not in (\\\"z\\\", \\\"y\\\", \\\"x\\\", \\\"name\\\")\\n ]\",\n \"def build_multi_index_tuples(header: list, sub_header: list) -> list:\\r\\n\\r\\n tuples = []\\r\\n\\r\\n for head in header:\\r\\n for sub_head in sub_header:\\r\\n tuples.append((head, sub_head))\\r\\n\\r\\n return tuples\",\n \"def create_deft_table_csv_mappings():\\n mappings = list()\\n mappings.append(CsvColumnMapping(columnName=\\\"rownumber\\\", cslDataType=\\\"int\\\", ordinal=0))\\n mappings.append(CsvColumnMapping(columnName=\\\"rowguid\\\", cslDataType=\\\"string\\\", ordinal=1))\\n mappings.append(CsvColumnMapping(columnName=\\\"xdouble\\\", cslDataType=\\\"real\\\", ordinal=2))\\n mappings.append(CsvColumnMapping(columnName=\\\"xfloat\\\", cslDataType=\\\"real\\\", ordinal=3))\\n mappings.append(CsvColumnMapping(columnName=\\\"xbool\\\", cslDataType=\\\"bool\\\", ordinal=4))\\n mappings.append(CsvColumnMapping(columnName=\\\"xint16\\\", cslDataType=\\\"int\\\", ordinal=5))\\n mappings.append(CsvColumnMapping(columnName=\\\"xint32\\\", cslDataType=\\\"int\\\", ordinal=6))\\n mappings.append(CsvColumnMapping(columnName=\\\"xint64\\\", cslDataType=\\\"long\\\", ordinal=7))\\n mappings.append(CsvColumnMapping(columnName=\\\"xuint8\\\", cslDataType=\\\"long\\\", ordinal=8))\\n mappings.append(CsvColumnMapping(columnName=\\\"xuint16\\\", cslDataType=\\\"long\\\", ordinal=9))\\n mappings.append(CsvColumnMapping(columnName=\\\"xuint32\\\", cslDataType=\\\"long\\\", ordinal=10))\\n mappings.append(CsvColumnMapping(columnName=\\\"xuint64\\\", cslDataType=\\\"long\\\", ordinal=11))\\n mappings.append(CsvColumnMapping(columnName=\\\"xdate\\\", cslDataType=\\\"datetime\\\", ordinal=12))\\n mappings.append(CsvColumnMapping(columnName=\\\"xsmalltext\\\", cslDataType=\\\"string\\\", ordinal=13))\\n mappings.append(CsvColumnMapping(columnName=\\\"xtext\\\", cslDataType=\\\"string\\\", ordinal=14))\\n mappings.append(CsvColumnMapping(columnName=\\\"xnumberAsText\\\", cslDataType=\\\"string\\\", ordinal=15))\\n mappings.append(CsvColumnMapping(columnName=\\\"xtime\\\", cslDataType=\\\"timespan\\\", ordinal=16))\\n mappings.append(CsvColumnMapping(columnName=\\\"xtextWithNulls\\\", cslDataType=\\\"string\\\", ordinal=17))\\n mappings.append(CsvColumnMapping(columnName=\\\"xdynamicWithNulls\\\", cslDataType=\\\"dynamic\\\", ordinal=18))\\n return mappings\",\n \"def create_columns(headers: List, annot_types: List):\\n new_column_names: List[Tuple] = []\\n for annotation, annot_type in zip(headers, annot_types):\\n new_column_names.append((annotation, annot_type))\\n return new_column_names\",\n \"def column_name(name):\\n # Only needs exceptions to standard token cleanup\\n column_map = {\\n \\\"line#\\\" : \\\"ignore\\\",\\n \\\"date\\\" : \\\"timestamp\\\",\\n \\\"rh\\\" : \\\"humidity\\\",\\n \\\"par\\\" : \\\"par_ue\\\"\\n }\\n\\n if name in column_map:\\n return column_map[name]\\n \\n return name\",\n \"def _tables_from_headers(headers):\\n return {table for field, table in _DATA_TABLES_BY_UNIQUE_FIELD.items() if field in headers}\",\n \"def convert_header_to_multi_index(df, header_names: List[Tuple]):\\n index = pd.MultiIndex.from_tuples(header_names, names=[\\\"NAME\\\", \\\"TYPE\\\"])\\n df.columns = pd.MultiIndex.from_tuples(index)\\n return df\",\n \"def minimize_header(self, header):\\n header = self.locate.fits_to_parkeys(header) # reference vocab --> dataset vocab\\n if isinstance(self, PipelineContext):\\n instrument = self.get_instrument(header)\\n mapping = self.get_imap(instrument)\\n keys = mapping.get_required_parkeys() + [self.instrument_key]\\n else:\\n keys = self.get_required_parkeys()\\n minimized = {}\\n for key in keys:\\n minimized[key] = header.get(key.lower(),\\n header.get(key.upper(), \\\"UNDEFINED\\\"))\\n return minimized\",\n \"def get_column_reference(headers, name):\\n return chr(ord('A') + headers.index(name))\",\n \"def table_key(self, reindex_dict):\\n reindexed_marks = []\\n for m in self.component1.marks:\\n new_m = reindex_dict.get(m)\\n if new_m == None:\\n if len(reindex_dict) == 0:\\n new_m = 0\\n else:\\n new_m = max(reindex_dict.values())+1\\n reindex_dict[m] = new_m\\n reindexed_marks.append(new_m)\\n return tuple( [self.component1.genus] + sorted(reindexed_marks) )\",\n \"def _build_header_dictionary(self):\\n start = 0\\n #print self.raw_data\\n for a in range(20):\\n redatapuller = re.compile(\\\"\\\\r\\\\n\\\\r\\\\n\\\\r\\\\n(?P<word>.*?)\\\\t.*?\\\\n\\\", re.DOTALL)\\n m = redatapuller.search(self.raw_data[start:])\\n if not(m):\\n break\\n self.header_dictionary[m.group(\\\"word\\\")] = start + m.end()\\n if a==0:\\n self.header_dictionary[\\\"main\\\"] = start + m.end()\\n start += m.end()\",\n \"def generate_colnames(df, labelnum=0): # need to be adjusted for GC content\\n colnames = []\\n for field in range(len(df.columns) - labelnum):\\n colnames.append(BEDCOLS[field])\\n for label in range(labelnum):\\n colnames.append(f\\\"label_{label+1}\\\")\\n return colnames\",\n \"def _bin_hits_to_unit_indices(rows, cols, target_breaks, source_breaks,\\n su_start):\\n # keep track of mapping between matrix row index and target unit index\\n # in ``target_units``\\n row2t_unit_ind = np.array([\\n u_ind\\n for u_ind in range(len(target_breaks) - 1)\\n for _ in range(target_breaks[u_ind+1] - target_breaks[u_ind])])\\n # keep track of mapping between matrix column index and source unit index\\n # in ``source_units``\\n col2s_unit_ind = np.array([\\n u_ind\\n for u_ind in range(len(source_breaks) - 1)\\n for _ in range(source_breaks[u_ind+1] - source_breaks[u_ind])])\\n tmp = {}\\n hits2positions = {}\\n t_inds = row2t_unit_ind[rows]\\n s_inds = col2s_unit_ind[cols]\\n t_poses = rows - target_breaks[t_inds]\\n s_poses = cols - source_breaks[s_inds]\\n # although s_inds needs to index the source_breaks by the ordering of this\\n # batch of source_units, s_inds needs to account for source_unit indices as\\n # referenced from outside of this batch\\n s_inds += su_start\\n for t_ind, s_ind, t_pos, s_pos in zip(t_inds, s_inds, t_poses, s_poses):\\n key = (t_ind, s_ind)\\n if key not in tmp:\\n tmp[key] = (t_pos, s_pos)\\n elif key not in hits2positions:\\n hits2positions[key] = [tmp[key], (t_pos, s_pos)]\\n else:\\n hits2positions[key].append((t_pos, s_pos))\\n hits2positions = {k: np.array(v) for k, v in hits2positions.items()}\\n return hits2positions\",\n \"def get_cols_dummy():\",\n \"def _get_idx_maps(self, types, initial_mapping=None):\\n initial_mapping = constants.INITIAL_MAPPING if initial_mapping is None else initial_mapping\\n # generate type to index mappings\\n self.type_to_idx['word'] = Preprocessor.type_to_idx(types['word'], initial_mapping['word'])\\n self.type_to_idx['char'] = Preprocessor.type_to_idx(types['char'], initial_mapping['word'])\\n self.type_to_idx['tag'] = Preprocessor.type_to_idx(types['tag'], initial_mapping['tag'])\",\n \"def filter_mapping_file(map_data, map_header, good_sample_ids,\\r\\n include_repeat_cols=False, column_rename_ids=None):\\r\\n # keeping samples\\r\\n to_keep = []\\r\\n to_keep.extend([i for i in map_data if i[0] in good_sample_ids])\\r\\n\\r\\n # keeping columns\\r\\n headers = []\\r\\n to_keep = zip(*to_keep)\\r\\n headers.append(map_header[0])\\r\\n result = [to_keep[0]]\\r\\n\\r\\n if column_rename_ids:\\r\\n # reduce in 1 as we are not using the first colum (SampleID)\\r\\n column_rename_ids = column_rename_ids - 1\\r\\n for i, l in enumerate(to_keep[1:-1]):\\r\\n if i == column_rename_ids:\\r\\n if len(set(l)) != len(result[0]):\\r\\n raise ValueError(\\r\\n \\\"The column to rename the samples is not unique.\\\")\\r\\n result.append(result[0])\\r\\n result[0] = l\\r\\n headers.append('SampleID_was_' + map_header[i + 1])\\r\\n elif include_repeat_cols or len(set(l)) > 1:\\r\\n headers.append(map_header[i + 1])\\r\\n result.append(l)\\r\\n else:\\r\\n for i, l in enumerate(to_keep[1:-1]):\\r\\n if include_repeat_cols or len(set(l)) > 1:\\r\\n headers.append(map_header[i + 1])\\r\\n result.append(l)\\r\\n headers.append(map_header[-1])\\r\\n result.append(to_keep[-1])\\r\\n\\r\\n result = map(list, zip(*result))\\r\\n\\r\\n return headers, result\",\n \"def convert_field_name_to_indexes(field_name):\\n rows = '87654321'\\n columns = 'abcdefgh'\\n row_index = column_index = None\\n\\n row_name = field_name[0]\\n for i in range(8):\\n if rows[i] == row_name:\\n row_index = i\\n\\n column_name = field_name[1]\\n for i in range(8):\\n if columns[i] == column_name:\\n column_index = i\\n\\n return row_index, column_index\",\n \"def index(self):\\n path = self.path.format('index')\\n \\n with open(path, 'r', newline='') as file:\\n l = list(csv.reader(file))\\n \\n index = [v for _ in l for v in _]\\n index = dict((v, i) for (i, v) in enumerate(index))\\n \\n return index\",\n \"def build_index(text: Iterable) -> Dict[str, List[Tuple[int, int]]]:\\n index = defaultdict(list)\\n for line_no, line in enumerate(text, 1):\\n for match in WORD_RE.finditer(line):\\n word = match.group()\\n column_no = match.start() + 1\\n location = (line_no, column_no)\\n index[word].append(location)\\n return index\",\n \"def __generate_features_index__(self, feature_names, dictionaries):\\n keys = []\\n for name, dictionary in zip(feature_names, dictionaries):\\n features = []\\n for feature in dictionary.keys():\\n if dictionary.get(feature) > self._cutoff:\\n features.append((name, feature))\\n self.feature_freq[name] += 1\\n keys.extend(features)\\n for i in range(len(keys)):\\n self._features_index[keys[i]] = i\\n self.features_list = tuple(keys)\\n self._features_vector_length = len(keys)\",\n \"def index_by(self, column_or_label):\\n column = self._get_column(column_or_label)\\n index = {}\\n for key, row in zip(column, self.rows):\\n index.setdefault(key, []).append(row)\\n return index\",\n \"def wind_profile_indices_map(self):\\n if self._hybrid_meta is None:\\n return np.array([]), np.array([])\\n\\n idxs = self._hybrid_meta[self.__wind_rpi_n].astype(int)\\n idxs = idxs[idxs >= 0]\\n\\n return idxs.index.values, idxs.values\",\n \"def column_index(input_file, name):\\n col, com = find_columns(input_file)\\n col_name = name\\n contents = open(input_file, 'r').readlines()\\n for line in contents:\\n if com[col.index(col_name)] in line:\\n line_index = contents.index(line)+1\\n return line_index\",\n \"def generate_bitmap_to_linear_index_map(bladeTupList, firstIdx):\\n bitmap_map = np.zeros(len(bladeTupList), dtype=int)\\n for ind, blade in enumerate(bladeTupList):\\n bitmap_map[compute_bitmap_representation(blade, firstIdx)] = ind\\n return bitmap_map\",\n \"def map_column_to_index(self, col):\\n if col in self.column_maps:\\n return\\n\\n # First construct the map from original ids to new ones.\\n ids = self.dataset[col].unique()\\n n = len(ids)\\n idmap = dict(itertools.izip(ids, xrange(n)))\\n\\n # Next use the map to conver the ids in-place.\\n self.dataset[col] = self.dataset[col].apply(lambda _id: idmap[_id])\\n\\n # Now swap key for value in the idmap to provide a way to convert back.\\n reverse_map = {val: key for key, val in idmap.iteritems()}\\n self.column_maps[col] = reverse_map\",\n \"def index_value(self):\\r\\n\\t\\tfor index, column_header in enumerate(self.header_row):\\r\\n\\t\\t\\tprint(index, column_header)\",\n \"def label_columns(mapping):\\n columns = []\\n for name, column in mapping.items():\\n columns.append(column.label(name))\\n return columns\",\n \"def pivot_rows_to_keys(header_list, row_list, key_names=[], whitelist_headers=None):\\n header_map = {v: header_list.index(v) for v in\\n [x for x in header_list if not whitelist_headers or x in whitelist_headers or x in key_names]}\\n\\n key_idxs = []\\n for key_name in key_names:\\n key_idxs.append(header_map[key_name])\\n\\n #key_idx = header_map[key_name]\\n #return {\\\"{}{}\\\".format(x[key_idx],x[keya_idx]): {k: x[v] for k, v in list(header_map.items())} for x in row_list}\\n\\n return {\\\":\\\".join(itemgetter(*key_idxs)(x)): {k: x[v] for k, v in list(header_map.items())} for x in row_list}\",\n \"def writeheader(writer):\\n writer.writerow(dict((fn, fn) for fn in writer.fieldnames))\",\n \"def __valuesToIndices(self, mappings, values):\\n indices = np.empty(0, dtype=np.int_)\\n\\n for key, _ in mappings.items():\\n # Lookup the index of the value of the values in the map.\\n index = mappings[key](values[key])\\n\\n indices = np.hstack((indices, index))\\n\\n return indices\",\n \"def nomenclatura():\\n df = pd.read_csv(\\\"Data/nomenclatura_1.csv\\\", encoding = \\\"latin1\\\")\\n #dict_axis = df.set_index('id').T.to_dict('list')\\n dict_axis = dict( [ (i, [a,b]) for i, a,b in zip(df.id, df.latitude, df.longitude) ] )\\n\\n return dict_axis\",\n \"def parse_header(header):\\n if header[0] != '@':\\n return None\\n \\n instrument, run_number, flowcell_id, lane, tile, x_pos, y_pos_read, is_filtered, control_number, index = header[1:].split(\\\":\\\")\\n y_pos, read = y_pos_read.split()\\n return {'instrument': str(instrument),\\n 'run_number': int(run_number),\\n 'flowcell_id': str(flowcell_id),\\n 'lane': int(lane),\\n 'tile': int(tile),\\n 'x_pos': int(x_pos),\\n 'y_pos': int(y_pos),\\n 'read': int(read),\\n 'is_filtered': (is_filtered == 'Y'),\\n 'control_number': int(control_number),\\n 'index': str(index)} # Note that MiSeq Reporter outputs a SampleSheet index rather than the index sequence\",\n \"def make_headers(worksheet):\\n headers = {}\\n cell_idx = 0\\n while cell_idx < worksheet.ncols:\\n cell_type = worksheet.cell_type(0, cell_idx)\\n if cell_type == 1:\\n header = slughifi(worksheet.cell_value(0, cell_idx))\\n if not header.startswith(\\\"_\\\"):\\n headers[cell_idx] = header\\n cell_idx += 1\\n return headers\",\n \"def build_data_dict(stats_arr, prefix):\\n # Get no. of points in each dimension.\\n N_sam, N_thres, N_z, N_kbins = stats_arr.shape\\n\\n # Create dictionary with column names that can be used by ColumnDataSource\\n data_dict = {}\\n for n in range(N_thres):\\n for j in range(N_z):\\n for m in range(N_kbins):\\n key = \\\"tot_%s_h%d_k%d_z%d\\\" % (prefix, n+1, m+1, j+1)\\n data_dict[key] = stats_arr[:,n, j, m]\\n return data_dict\",\n \"def _infer_ipa_col(self, header):\\n\\t\\tif self.ipa_col and isinstance(self.ipa_col, str):\\n\\t\\t\\tif self.ipa_col in header:\\n\\t\\t\\t\\treturn header.index(self.ipa_col)\\n\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tipa_col = int(self.ipa_col)\\n\\t\\t\\texcept ValueError: pass\\n\\t\\t\\telse:\\n\\t\\t\\t\\treturn ipa_col\\n\\n\\t\\t\\traise ValueError('Could not find column: {}'.format(self.ipa_col))\\n\\n\\t\\tpot = []\\n\\n\\t\\tfor index, col_name in enumerate(header):\\n\\t\\t\\tcol_name = col_name.lower()\\n\\t\\t\\tfor name in IPA_COL_NAMES:\\n\\t\\t\\t\\tif col_name.startswith(name):\\n\\t\\t\\t\\t\\tpot.append(index)\\n\\n\\t\\tif len(pot) == 0:\\n\\t\\t\\traise ValueError('Could not find an IPA column')\\n\\t\\telif len(pot) > 1:\\n\\t\\t\\traise ValueError('Could not decide which is the IPA column')\\n\\n\\t\\treturn pot[0]\",\n \"def reference_keys_to_dataset_keys(rmapping, header):\\n header = dict(header)\\n try:\\n translations = rmapping.reference_to_dataset\\n except AttributeError:\\n pass\\n else:\\n # Add replacements for translations *if* the existing untranslated value\\n # is poor and the translated value is better defined. This is to do\\n # translations w/o replacing valid/concrete DM values with something \\n # like guessed values of \\\"UNDEFINED\\\" or \\\"N/A\\\".\\n for rkey in translations:\\n if rkey in header:\\n dkey = translations[rkey]\\n dval = header.get(translations[rkey], None)\\n rval = header[rkey]\\n if dval in [None, \\\"N/A\\\", \\\"UNDEFINED\\\"] and rval not in [None, \\\"UNDEFINED\\\"]:\\n header[dkey] = rval\\n if \\\"USEAFTER\\\" in header: # and \\\"DATE-OBS\\\" not in header:\\n reformatted = timestamp.reformat_useafter(rmapping, header).split()\\n header[\\\"DATE-OBS\\\"] = header[\\\"META.OBSERVATION.DATE\\\"] = reformatted[0]\\n header[\\\"TIME-OBS\\\"] = header[\\\"META.OBSERVATION.TIME\\\"] = reformatted[1]\\n return header\",\n \"def get_sample_idx(sample, header):\\n\\n for item in header:\\n if sample in item:\\n return header.index(item)\\n\\n print(sample + \\\" not found in header, check input files.\\\")\\n sys.exit()\",\n \"def header_data_columns(head_line, data_cols, header):\\n\\n colnames = head_line.split(\\\",\\\")\\n\\n # Remove triling blancks and end of lines\\n colnames = [x.strip() for x in colnames]\\n\\n # Difference between columns in the header and in the data\\n diff = len(data_cols) - len(colnames)\\n\\n if diff > 0:\\n # Add dum headers\\n dums = \\\"\\\"\\n for idiff in range(diff):\\n dums = dums + \\\",dum\\\" + str(idiff)\\n\\n new_head = str(head_line.rstrip()) + dums + \\\" \\\\n\\\"\\n header.append(new_head)\\n\\n elif diff < 0:\\n sys.exit(\\n \\\"STOP novonix_clean.header_data_columns \\\\n\\\"\\n + \\\"REASON less data columns than header names \\\\n\\\"\\n )\\n else:\\n header.append(head_line)\\n\\n return\",\n \"def format_header(line):\\n cols = []\\n for n, item in enumerate(line.replace('#', '').strip().split('\\\\t')):\\n if item == 'id/name':\\n cols.append('name')\\n elif item == 'brass_score':\\n cols.append('score')\\n elif item.startswith('strand') and n > 9:\\n if item == 'strand1':\\n cols.append('transcript1_strand')\\n elif item == 'strand2':\\n cols.append('transcript2_strand')\\n else:\\n raise ValueError(\\\"Unknown strand value column {0} - {1}\\\".format(n, item))\\n else:\\n cols.append(item.lower().replace(' ', '_').replace('/', '_').replace('-', '_'))\\n return cols\",\n \"def writeheader(fh,colnames):\\n for i in range(len(colnames)):\\n fh.write('# %d %s\\\\n'%(i+1,colnames[i]))\",\n \"def fits_to_parkeys(fits_header):\\n if MODEL is None:\\n raise MissingDependencyError(\\\"JWST data models are not installed. Cannot fits_to_parkeys().\\\")\\n parkeys = {}\\n for key, value in fits_header.items():\\n key, value = str(key), str(value)\\n if not key.lower().startswith(\\\"meta.\\\"):\\n pk = cached_dm_find_fits_keyword(key)\\n if not pk:\\n pk = key\\n else:\\n assert len(pk) == 1, \\\"CRDS JWST Data Model ambiguity on \\\" + \\\\\\n repr(key) + \\\" = \\\" + repr(pk)\\n pk = pk[0]\\n else:\\n pk = key\\n pk = pk.upper()\\n parkeys[pk] = value\\n return parkeys\",\n \"def header(self):\\n\\n data = {}\\n data['latitude'] = self.latitude()\\n data['latitude_unc'] = self.latitude_unc()\\n data['longitude'] = self.longitude()\\n data['longitude_unc'] = self.longitude_unc()\\n data['uid'] = self.uid()\\n data['n_levels'] = self.n_levels()\\n data['year'] = self.year()\\n data['month'] = self.month()\\n data['day'] = self.day()\\n data['time'] = self.time()\\n data['cruise'] = self.cruise()\\n data['probe_type'] = self.probe_type()\\n \\n header = pd.Series(data)\\n\\n return header\",\n \"def __get_column_names(self, path_to_xml):\\n span_table = xmlet.parse(path_to_xml / Path(\\\"SSTDataFormatTimeSpanTable.xml\\\")).getroot()\\n filetype = MAP_RBD_TYPE_TO_FILE_TYPE[self.__rbd_type]\\n\\n for item in span_table:\\n if item[0].text == filetype and item[1].text <= self.date <= item[2].text:\\n data_description_file_name = item[3].text\\n\\n xml = xmlet.parse(path_to_xml / Path(data_description_file_name)).getroot()\\n\\n header = dict()\\n for child in xml:\\n var_name = child[0].text\\n var_dim = int(child[1].text)\\n var_type = child[2].text\\n var_unit = child[3].text\\n\\n np_type = XML_TYPE_TO_NUMPY_TYPE[var_type]\\n\\n header.update({var_name: [var_dim, np_type, var_unit]})\\n\\n return header\",\n \"def _column_sorting_key(self, c):\\n first_index = 0\\n if c.startswith('hybrid'):\\n first_index = 1\\n elif c.startswith('solar'):\\n first_index = 2\\n elif c.startswith('wind'):\\n first_index = 3\\n elif c == MERGE_COLUMN:\\n first_index = -1\\n return first_index, self._hybrid_meta.columns.get_loc(c)\",\n \"def index_dict(self):\\n msk = self.load_mask()\\n mski = enumerate(msk)\\n ifiltered = (i for (i, m) in mski if m == 1)\\n return {i: j for (j, i) in enumerate(ifiltered)}\",\n \"def index_dict(self):\\n msk = self.load_mask()\\n mski = enumerate(msk)\\n ifiltered = (i for (i, m) in mski if m == 1)\\n return {i: j for (j, i) in enumerate(ifiltered)}\",\n \"def make_dof_value_map(robot):\\n names = [j.GetName() for j in robot.GetJoints()]\\n indices = [j.GetDOFIndex() for j in robot.GetJoints()]\\n\\n def get_dofs():\\n pose={}\\n values=robot.GetDOFValues()\\n for (i,n) in zip(indices,names):\\n pose.setdefault(n,values[i])\\n return pose\\n\\n return get_dofs\",\n \"def get_label_ix_mapping(labels):\\n return {label: i for i, label in enumerate(labels)}\",\n \"def data_population(in_data, headers=None):\\n headers = headers or in_data[0]\\n\\n return [\\n dict([(header, row[cindex])\\n for cindex, header in enumerate(headers)])\\n for row in in_data\\n ]\",\n \"def get_horiz_structure(col_names, num_rows):\\n struct = [{name: random.randint(0, max_cell_value) for name in col_names} for _ in range(num_rows)]\\n return struct\",\n \"def index_prob_id(blocks):\\n return blocks.set_index(\\n pd.Index(\\n blocks.index.str.extract(r\\\"HOAF_(\\\\d\\\\d)\\\\.vhdr\\\").astype(int)[0],\\n name=\\\"prob_id\\\",\\n )\\n )\",\n \"def get_indices(self):\\n\\n def query(rel): \\n return \\\"\\\"\\\"SELECT pg_class.relname, pg_index.indkey\\n FROM pg_class, pg_index\\n WHERE (pg_index.indexrelid = pg_class.oid)\\n AND (pg_index.indrelid = (SELECT pg_class.oid FROM pg_class WHERE pg_class.relname = \\\\'{}\\\\'));\\n \\\"\\\"\\\".format(rel)\\n\\n rels = tpch.schema.keys()\\n idxs = dict.fromkeys(rels)\\n\\n with self.tpch_cxn.cursor() as curs:\\n for rel in rels:\\n curs.execute(query(rel))\\n idxs_ = curs.fetchall()\\n idxs_ = dict(idxs_) # index -> index keys \\n \\n # TODO this can be done cleanly in query\\n # pg_index.indkey is a SQL array of attributes indices in their respective tables\\n split=lambda attrs: attrs.split() \\n cast=lambda attrs: list(map(lambda attr: int(attr)-1, attrs))\\n invertindex=lambda attrs: list(np.array(schema[rel])[attrs])\\n\\n attrs = idxs_.values() \\n attrs = list(map(split, attrs))\\n attrs = list(map(cast, attrs))\\n attrs = list(map(invertindex, attrs))\\n\\n idxs_ = {key : attrs[i] for i, key in enumerate(idxs_.keys())}\\n idxs[rel] = idxs_\\n return idxs\",\n \"def setup_normalyzer_header(design_matrix: DF, annot_cols: List[str], normalyzer_vals:DF) -> DF:\\n\\n # Get numbers set up as list of stringified numbers ('-1', '0', '0', '1', '1')\\n nbr_annot_cols = len(annot_cols)\\n sample_head = [-1] + [0] * (nbr_annot_cols - 1) + list(design_matrix['biorepgroup'])\\n sample_head_str = [str(e) for e in sample_head]\\n\\n # Get text-information about each column\\n label_row = list(normalyzer_vals.columns)[:nbr_annot_cols] + list(design_matrix['name'])\\n\\n headers = pd.DataFrame([sample_head_str, label_row])\\n headers.columns = normalyzer_vals.columns\\n\\n return headers\",\n \"def _index_column_set(column_set: List[ColumnSchema]) -> List[Tuple[str, int]]:\\n out = defaultdict(int)\\n for column_schema in column_set:\\n key = column_schema_to_keys(column_schema)\\n out[key] += 1\\n return list(out.items())\",\n \"def indices_get_mapping(es):\\n index = 'customer'\\n\\n print(es.indices.get_mapping(index=index))\",\n \"def create_class_indices(self) -> None:\\n\\n categories = list(np.loadtxt(self.path_manager.categories_file(), delimiter=\\\",\\\", dtype=str))\\n\\n if self.include_noise_samples and not self.multi_label_classification:\\n categories.append(\\\"noise\\\")\\n\\n self.class_to_idx = {}\\n\\n for idx, class_name in enumerate(sorted(categories)):\\n self.class_to_idx[class_name] = idx\",\n \"def mask_header(data_mask, header_mask):\\n\\n mask = {}\\n text = {'bad': 'BP', 'edge': 'EP', 'saturated': 'SP',\\n 'saturated-connected': 'SCP', 'satellite trail': 'STP',\\n 'cosmic ray': 'CRP'}\\n\\n for mask_type in text.keys():\\n value = get_par(set_zogy.mask_value[mask_type],tel)\\n mask[mask_type] = (data_mask & value == value)\\n header_mask['M-{}'.format(text[mask_type])] = (\\n True, '{} pixels included in mask?'.format(mask_type))\\n header_mask['M-{}VAL'.format(text[mask_type])] = (\\n value, 'value added to mask for {} pixels'.format(mask_type))\\n header_mask['M-{}NUM'.format(text[mask_type])] = (\\n np.sum(mask[mask_type]), 'number of {} pixels'.format(mask_type))\\n\\n return\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.60388774","0.5985021","0.5983487","0.5812272","0.573689","0.57180965","0.5631869","0.55975854","0.55681896","0.5565681","0.554784","0.5505632","0.5441756","0.5394642","0.53900725","0.53899986","0.5387639","0.537637","0.5363915","0.53568316","0.53414994","0.53264475","0.53180736","0.52968967","0.5296696","0.52717334","0.523432","0.52338445","0.5221073","0.5197063","0.51961637","0.51677465","0.51670617","0.5143451","0.5141884","0.51299953","0.5112296","0.50799704","0.5070182","0.50370145","0.50309104","0.50275165","0.5022587","0.5021352","0.5011724","0.501067","0.5006809","0.49966705","0.49958965","0.4993834","0.49890873","0.4977726","0.49753785","0.49732548","0.49728718","0.49683043","0.4963201","0.49614576","0.4956038","0.4955202","0.49545345","0.49531713","0.49473414","0.49463058","0.49354705","0.49321753","0.49301812","0.49239618","0.4923402","0.49156016","0.49089992","0.4907962","0.49009293","0.4899426","0.489764","0.48914996","0.48797262","0.4871322","0.48672453","0.48628187","0.4862035","0.4850852","0.4849413","0.48449454","0.4843381","0.484153","0.48386338","0.48382095","0.48382095","0.48362377","0.48265663","0.48213154","0.48168445","0.4811637","0.48025075","0.47980416","0.47882167","0.47843325","0.47802198","0.4780186"],"string":"[\n \"0.60388774\",\n \"0.5985021\",\n \"0.5983487\",\n \"0.5812272\",\n \"0.573689\",\n \"0.57180965\",\n \"0.5631869\",\n \"0.55975854\",\n \"0.55681896\",\n \"0.5565681\",\n \"0.554784\",\n \"0.5505632\",\n \"0.5441756\",\n \"0.5394642\",\n \"0.53900725\",\n \"0.53899986\",\n \"0.5387639\",\n \"0.537637\",\n \"0.5363915\",\n \"0.53568316\",\n \"0.53414994\",\n \"0.53264475\",\n \"0.53180736\",\n \"0.52968967\",\n \"0.5296696\",\n \"0.52717334\",\n \"0.523432\",\n \"0.52338445\",\n \"0.5221073\",\n \"0.5197063\",\n \"0.51961637\",\n \"0.51677465\",\n \"0.51670617\",\n \"0.5143451\",\n \"0.5141884\",\n \"0.51299953\",\n \"0.5112296\",\n \"0.50799704\",\n \"0.5070182\",\n \"0.50370145\",\n \"0.50309104\",\n \"0.50275165\",\n \"0.5022587\",\n \"0.5021352\",\n \"0.5011724\",\n \"0.501067\",\n \"0.5006809\",\n \"0.49966705\",\n \"0.49958965\",\n \"0.4993834\",\n \"0.49890873\",\n \"0.4977726\",\n \"0.49753785\",\n \"0.49732548\",\n \"0.49728718\",\n \"0.49683043\",\n \"0.4963201\",\n \"0.49614576\",\n \"0.4956038\",\n \"0.4955202\",\n \"0.49545345\",\n \"0.49531713\",\n \"0.49473414\",\n \"0.49463058\",\n \"0.49354705\",\n \"0.49321753\",\n \"0.49301812\",\n \"0.49239618\",\n \"0.4923402\",\n \"0.49156016\",\n \"0.49089992\",\n \"0.4907962\",\n \"0.49009293\",\n \"0.4899426\",\n \"0.489764\",\n \"0.48914996\",\n \"0.48797262\",\n \"0.4871322\",\n \"0.48672453\",\n \"0.48628187\",\n \"0.4862035\",\n \"0.4850852\",\n \"0.4849413\",\n \"0.48449454\",\n \"0.4843381\",\n \"0.484153\",\n \"0.48386338\",\n \"0.48382095\",\n \"0.48382095\",\n \"0.48362377\",\n \"0.48265663\",\n \"0.48213154\",\n \"0.48168445\",\n \"0.4811637\",\n \"0.48025075\",\n \"0.47980416\",\n \"0.47882167\",\n \"0.47843325\",\n \"0.47802198\",\n \"0.4780186\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6957255"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":2886,"cells":{"query":{"kind":"string","value":"Generates mapping from water measurements column names to values of the given CSV row."},"document":{"kind":"string","value":"def get_water_value_map(row, column_names_map):\n column_values_map = column_names_map.copy()\n row_length = len(row)\n empty = True\n\n for key, index in column_names_map.items():\n # Check if non-empty value exist for given index.\n if -1 < index < row_length:\n value = row[index].strip()\n if value:\n column_values_map[key] = value\n empty = False\n continue\n # Else NULL is inserted in db.\n column_values_map[key] = 'NULL'\n\n return None if empty else column_values_map"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def __get_column_mapping(self):\n\n s1 = list(Sample(\"FL\", \"M1.0@265_Primary_ar115_s2010-08-06T06_36_00_e2010-08-06T18_24_00.csv\").get_data().columns)[:25]\n column_mapping = {}\n for i in range(len(s1)):\n column_mapping[i] = s1[i]\n\n return column_mapping","def create_deft_table_csv_mappings():\n mappings = list()\n mappings.append(CsvColumnMapping(columnName=\"rownumber\", cslDataType=\"int\", ordinal=0))\n mappings.append(CsvColumnMapping(columnName=\"rowguid\", cslDataType=\"string\", ordinal=1))\n mappings.append(CsvColumnMapping(columnName=\"xdouble\", cslDataType=\"real\", ordinal=2))\n mappings.append(CsvColumnMapping(columnName=\"xfloat\", cslDataType=\"real\", ordinal=3))\n mappings.append(CsvColumnMapping(columnName=\"xbool\", cslDataType=\"bool\", ordinal=4))\n mappings.append(CsvColumnMapping(columnName=\"xint16\", cslDataType=\"int\", ordinal=5))\n mappings.append(CsvColumnMapping(columnName=\"xint32\", cslDataType=\"int\", ordinal=6))\n mappings.append(CsvColumnMapping(columnName=\"xint64\", cslDataType=\"long\", ordinal=7))\n mappings.append(CsvColumnMapping(columnName=\"xuint8\", cslDataType=\"long\", ordinal=8))\n mappings.append(CsvColumnMapping(columnName=\"xuint16\", cslDataType=\"long\", ordinal=9))\n mappings.append(CsvColumnMapping(columnName=\"xuint32\", cslDataType=\"long\", ordinal=10))\n mappings.append(CsvColumnMapping(columnName=\"xuint64\", cslDataType=\"long\", ordinal=11))\n mappings.append(CsvColumnMapping(columnName=\"xdate\", cslDataType=\"datetime\", ordinal=12))\n mappings.append(CsvColumnMapping(columnName=\"xsmalltext\", cslDataType=\"string\", ordinal=13))\n mappings.append(CsvColumnMapping(columnName=\"xtext\", cslDataType=\"string\", ordinal=14))\n mappings.append(CsvColumnMapping(columnName=\"xnumberAsText\", cslDataType=\"string\", ordinal=15))\n mappings.append(CsvColumnMapping(columnName=\"xtime\", cslDataType=\"timespan\", ordinal=16))\n mappings.append(CsvColumnMapping(columnName=\"xtextWithNulls\", cslDataType=\"string\", ordinal=17))\n mappings.append(CsvColumnMapping(columnName=\"xdynamicWithNulls\", cslDataType=\"dynamic\", ordinal=18))\n return mappings","def tsvRowToDict(row):\n return {col: getattr(row, col) for col in row._columns_}","def _properties_from_csv_row(row, header, ignored_columns):\n props = {}\n for h, prop in enumerate(header):\n # Handle a strange edge case where the length of the row is longer than the length of the header.\n # We do this to prevent an out of range error.\n x = h\n if x > len(row) - 1:\n x = len(row) - 1\n if row[x] == '' or prop in ignored_columns:\n continue\n else:\n try:\n # We use literal_eval() here to de-stringify numbers, lists and objects in the CSV data\n p = literal_eval(row[x])\n props[prop] = p\n except (SyntaxError, ValueError) as e:\n props[prop] = row[x]\n return props","def make_dict(row):\n return dict((key[0], value) for key, value in zip(colnames, row))","def _transform_map_data(self):\n WARD_FMT = '%s-%s'\n self.map_data_trans = []\n lookup = {i.column: ''.join(filter(lambda x: x.isdigit(), i.value)) for i in self.sht[1]}\n\n #skip over header\n rs = iter(self.sht.rows)\n next(rs)\n next(rs)\n for r in rs:\n pka = r[0].value\n for c in r[1:]:\n if c.value is None:\n c.value = 0\n\n self.map_data_trans.append((WARD_FMT%(pka, lookup[c.column]), c.value))","def read_name_map( name_map_path) :\n with open( name_map_path, newline=\"\") as csvfile:\n table = { }\n reader = csv.reader(csvfile)\n for row in reader:\n if len(row) < 2:\n continue\n if row[key_col] == key_header:\n continue\n key = row[key_col]\n val = row[val_col]\n table[key] = val\n return table","def process_csv():\n csv_rows = []\n fieldnames = ['site',\n 'latitude',\n 'longitude',\n 'city',\n 'region_code',\n 'country_code',\n 'continent_code',\n 'min_ip_hex',\n 'max_ip_hex',\n 'transit_provider',\n 'min_ip',\n 'max_ip',\n 'ip_prefix',\n 'min_ipv6_hex',\n 'max_ipv6_hex',\n 'min_ipv6',\n 'max_ipv6',\n 'ipv6_prefix']\n\n location_map = build_location_map()\n\n # Read in the CSV file and augment the columns\n with open(INPUT_FILE, 'rb') as csvfile:\n reader = csv.DictReader(csvfile)\n\n for row in reader:\n csv_rows.append(process_row(row, location_map))\n\n # Write the new CSV file with new columns\n with open(OUTPUT_FILE, 'w') as csvfile:\n writer = csv.DictWriter(csvfile, fieldnames=fieldnames)\n writer.writeheader()\n for row in csv_rows:\n writer.writerow(row)\n\n print(\"MLab Sites CSV generated at {0}\".format(OUTPUT_FILE))","def create_waves_dict(csv_file):\n with open(csv_file) as file:\n reader = csv.DictReader(file)\n waves_dict = {row[\"Date\"]: row[\"Wave Height\"] for row in reader}\n return waves_dict","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 get_m_to_me_metabolite_mapping():\n f = pandas.read_csv(fixpath(\"m_to_me_mets.csv\"), index_col=0)[\"me_name\"]\n return f.dropna().to_dict()","def map_csv_fields(self):\n etod_csv_fields = {\n 'ctry_id': None,\n 'obst_identifier': None,\n 'obst_name': None,\n 'lon_src': None,\n 'lat_src': None,\n 'agl': None,\n 'amsl': None,\n 'vert_uom': None,\n 'hor_acc': None,\n 'hor_acc_uom': None,\n 'vert_acc': None,\n 'vert_acc_uom': None,\n 'obst_type': None,\n 'lighting': None,\n 'marking': None,\n 'is_group': None,\n }\n\n for field in etod_csv_fields:\n try:\n etod_csv_fields[field] = etod_map[self.ctry_short_name]['fields'][field]\n except KeyError:\n etod_csv_fields[field] = None\n\n self.field_map = etod_csv_fields","def map_line(reader, headers):\n\n readings = {}\n\n line = reader.readline()\n\n if len(line) == 0:\n raise EOFError('That\\'s all the data!')\n\n line = line.rstrip()\n\n value_strings = line.split(' ')\n for i, string in enumerate(value_strings):\n if string != 'NaN':\n value = float(string)\n\n if i < len(headers):\n if headers[i]['is_point']:\n value = get_decimal_degrees(value)\n key = headers[i]['name'] + \"-\" + headers[i]['units']\n readings[key] = value\n\n # Provide generic timestamp regardless of type for iterator\n # convenience\n # Keep originals for those interested\n if 'm_present_time-timestamp' in readings:\n readings['timestamp'] = readings['m_present_time-timestamp']\n elif 'sci_m_present_time-timestamp' in readings:\n readings['timestamp'] = readings['sci_m_present_time-timestamp']\n\n return readings","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 conform_input_data(rowdict):\n # rowdict['Value'] = float(rowdict['Value'])\n rowdict['TimeStamp'] = TS_to_date(rowdict['TimeStamp'][:19])\n for floatcolumn in ['LowPx','OpenPx','ClosePx','QuoteCount','HighPx','TradeCount']:\n if floatcolumn in rowdict:\n rowdict[floatcolumn] = float(rowdict[floatcolumn])\n return rowdict","def table_row_to_dict(row, make_quantity=True):\n data = {}\n for name, col in row.columns.items():\n val = row[name]\n\n if make_quantity and col.unit:\n val = Quantity(val, unit=col.unit)\n data[name] = val\n return data","def map_cols_to_attr(self):\n ## this is from the base class:\n ## for attr, label in zip(self.attr_names, self.labels):\n ## col_ind = self.col_inds[label]\n ## if len(self.data) > 0:\n ## setattr(self, attr, self.data[:,col_ind])\n #\n # hard coding based on what I know about saleae files:\n self.t = self.data[:,0]#.astype(float)\n nr, nc = self.data.shape\n self.num_cols = nc-1\n \n for i in range(0,self.num_cols):\n attr = 'ch_%i' % i\n j = i+1\n setattr(self, attr, self.data[:,j])#.astype(float))","def make_row_map(file_path, key_field, field_map=None, transforms=None, \\\n file_encoding=None):\n\n with open(file_path, encoding=file_encoding) as file:\n # preprocess transforms\n if transforms:\n _transforms = {}\n for tf_field, tf in transforms.items():\n _type = type(tf).__name__\n if _type not in ['str', 'function']:\n raise ValueError('Invalid transform')\n _transforms[tf_field] = {\n 'transform': tf,\n 'type': _type\n }\n\n # get fields from csv\n fields_reader = csv.reader(file)\n fields = next(fields_reader)\n\n # make sure we aren't missing any field names\n first_row = next(fields_reader)\n if len(fields) != len(first_row):\n raise ValueError('Header has a different number of columns than data')\n\n # apply field map\n if field_map:\n # TODO use a case insensitive dictionary for field map\n fields = [field_map.get(field.lower()) or field for field in fields]\n key_field = field_map.get(key_field) or key_field\n\n # lowercase\n fields = [field.lower() for field in fields]\n\n # handle spaces\n fields = [field.replace(' ', '_') for field in fields]\n\n # use namedtuple for rows\n fields_joined = ' '.join(fields)\n Row = namedtuple('Row', fields_joined)\n\n # make map\n row_map = {}\n reader = csv.DictReader(file, fieldnames=fields)\n\n for i, row in enumerate(reader):\n key = row[key_field]\n\n # apply transforms\n if transforms:\n for tf_field, tf_map in _transforms.items():\n tf = tf_map['transform']\n tf_type = tf_map['type']\n source_val = row[tf_field]\n if tf_type == 'str':\n val = getattr(source_val, tf)()\n else:\n val = tf(source_val)\n row[tf_field] = val\n\n # row_map[key] = row\n # str_row = {key: str(val) for key, val in row.items()}\n row_map[key] = Row(**row)\n # from pprint import pprint\n # pprint(str_row)\n # row_map[key] = Row(**str_row)\n\n return row_map","def list_water_temps(csv_file):\n with open(csv_file) as file:\n reader = csv.DictReader(file)\n temp_list = [temp[\"Water Temp\"] for temp in reader]\n return temp_list","def create_dict_from_file(filename, delimeters, first_char, column_names):\n\n # This opens the\n measurement_output = open('measurement_output.txt', \"w\", encoding=\"utf8\")\n # This creates and initializes a list to serve as a dictionary container outside of the for-loop.\n measurements_file_container = {}\n\n # This opens the file and then splits it (preserving the commas because of the landfall count requirement).\n if not filename.endswith('.txt'):\n print('Input File Must Be a .txt File')\n return None\n elif delimeters != '{}=|{}=|{}='.format(column_names[0], column_names[1], column_names[2]):\n print('Please Check Syntax for Delimeters and colunm_names.')\n return None\n else:\n with open(filename, 'r') as infile:\n for line in infile:\n line = line.strip()\n # This checks to see if line begins with a numeric character; if so, it is a header for a new measurement.\n if line[0].isnumeric():\n measurement_current_line = line.split()\n # This initializes a new measurement dictionary with the 3 items in column_names\n key = measurement_current_line[0]\n new_measurement_dictionary = {\n column_names[0]: '0',\n column_names[1]: '0',\n column_names[2]: '0',\n }\n #print(measurement_current_line)\n # this determines if a line starts with 'X', splits it at the X =,Y =,Z = indicators\n # to spit out a list containing only the 3 values and then updates the corresponding\n # value in the dictionary\n if line[0] == first_char:\n measurement_current_line = re.split(delimeters, line.strip(' '))\n if len(measurement_current_line) == 4:\n new_measurement_dictionary[column_names[0]] = float(measurement_current_line[1].strip())\n new_measurement_dictionary[column_names[1]] = float(measurement_current_line[2].strip())\n new_measurement_dictionary[column_names[2]] = float(measurement_current_line[3].strip())\n measurements_file_container[key] = new_measurement_dictionary\n # this stops the processing when the end of data key '$$EOE' is reached.\n elif line == '$$EOE':\n break\n\n\n return(measurements_file_container)","def mapper(self, line_no, line):\n cell = csv_readline(line)\n if cell[0] == 'V':\n yield cell[4],1","def gen_dict():\n lines = [line for line in csv.reader(open(__ppath__ + \"/data/occupations.csv\"))] # uses a csv.reader to parse the file, converts the generic iterable to a list\n lines = [(line[0],float(line[1])) for line in lines[1:-2]]# removes the column names and \"Total\" row, re-expresses as a list of tuples to enable dictionary conversion\n lines.append((\"Unemployed\",0.2)) # accounts for missing 0.2% of jobs\n return dict(lines) # converts to dictionary","def columnar(row_table: list[dict[str, str]]) -> dict[str, list[str]]:\n result: dict[str, list[str]] = {}\n \n first_row: dict[str, str] = row_table[0]\n for column in first_row:\n result[column] = column_values(row_table, column)\n \n return result","def write_row(row: dict):\n row = {k: format_float(v) for k, v in row.items()}\n writer.writerow(row)\n csvfile.flush()","def loadData(self, aircraftCSV='aircraft.csv'):\n aircraftDict = {}\n \n with open(aircraftCSV, 'r') as f:\n reader = csv.reader(f, delimiter=',')\n for line in reader:\n #if imperial convert to metric\n if line[2] == 'imperial':\n range = float(line[4]) * 8 / 5\n else:\n range = float(line[4])\n aircraftDict[line[0]] = [line[1], line[3], range]\n self.aircraftDict = aircraftDict","def process_columns(tup: tuple):\n column_name, data, source_name, data_type, quantiles = tup\n column = Column(column_name, data, source_name, data_type, quantiles)\n print(\"Processing column: \", column.get_long_name())\n column.quantile_histogram = QuantileHistogram(column.get_long_name(), column.ranks, column.size, quantiles)\n with open('cache/' + column.get_long_name() + '.pkl', 'wb') as output:\n pickle.dump(column, output, pickle.HIGHEST_PROTOCOL)","def _deduct_types(cls, row: Row) -> ColumnTypes:\n return {\n key: get_value_type(cls.whole_number_to_int(value))\n for key, value in row.items()\n }","def map_csv_dimensions(length: str, width: str, height: str):\n return {\n \"length\": length,\n \"width\": width,\n \"height\": height\n }","def getHourlyWeatherFromCSV(self,town,scale,key):\n\n\t\t# Variables\n\t\tfile = \"data/weather/\"+town+\"_\"+scale+\".csv\"\n\t\tcsv_data = []\n\t\tweather_data = []\n\t\tweather = {}\n\n\t\t# Reading csv file and storing data in file\n\t\twith open(file) as csvfile:\n\t\t\treader = csv.DictReader(csvfile)\n\t\t\tfor row in reader:\n\t\t\t\tcsv_data.append(row) \n\t\t# Getting data that is needed for visualization\n\n\t\tprint csv_data\n\n\t\tfor data in csv_data:\n\t\t\t# Parsing date\n\t\t\thour = int(data[\"date\"].split(\" \")[4].split(\":\")[0])\n\t\t\tpm_or_am = data[\"date\"].split(\" \")[5]\n\t\t\tday = data[\"date\"].split(\",\")[0]\n\t\t\tif hour == 12 and pm_or_am == \"AM\":\n\t\t\t\tdata[\"date\"] = \"\".join(data[\"date\"].split(\" \")[:-2]) + \" 00:00\"\n\t\t\telif hour < 10 and pm_or_am == \"AM\":\n\t\t\t\tdata[\"date\"] = \"\".join(data[\"date\"].split(\" \")[:-2]) + \" 0\" + str(hour) + \":00\"\n\t\t\telif hour >= 10 and pm_or_am == \"AM\":\n\t\t\t\tdata[\"date\"] = \"\".join(data[\"date\"].split(\" \")[:-2]) + \" \" + str(hour) + \":00\"\n\t\t\tif pm_or_am == \"PM\":\n\t\t\t\tif hour == 12: \n\t\t\t\t\tdata[\"date\"] = \"\".join(data[\"date\"].split(\" \")[:-2]) + \" \" + str(hour) + \":00\"\n\t\t\t\telse:\n\t\t\t\t\thour +=12\n\t\t\t\t\tdata[\"date\"] = \"\".join(data[\"date\"].split(\" \")[:-2]) + \" \" + str(hour) + \":00\"\n\t\t\tweather[\"date\"] = data[\"date\"]\n\n\t\t\t# Appending weather data\n\t\t\tweather[key] = data[key]\n\t\t\tweather_data.append(weather)\n\t\t\tweather = {}\n\t\treturn weather_data","def parse_trick_ascii(csv_file):\n data_file = csv.DictReader(open(csv_file))\n single_run_data_dict = {'altitude' : [0.0],\n 'latitude' : [0.0],\n 'longitude' : [0.0]}\n # Your code here\n # ...\n # return the dict\n return single_run_data_dict","def _process_csv_data(csv_file, user_data_map):\n with open(csv_file, 'r') as csvfile:\n rows = csv.reader(csvfile)\n for row in rows:\n if len(row) < 2:\n print('The CSV file is not in expected format.')\n raise Exception\n user_data_map[row[1].lower()] = row[0]","def mapper(self, _, doc):\n ret = doc.split('\\t')\n key = ret[2]\n values = {}\n try:\n values[\"ts_ini\"] = datetime.utcfromtimestamp(float(ret[0]))\n except:\n values[\"ts_ini\"] = None\n try:\n values[\"ts_end\"] = datetime.utcfromtimestamp(float(ret[1]))\n except:\n values[\"ts_end\"] = None\n try:\n values[\"value\"] = ret[3]\n except:\n values[\"value\"] = None\n try:\n values[\"energytype\"] = ret[4]\n except:\n values[\"energytype\"] = None\n try:\n values[\"source\"] = ret[5]\n except:\n values[\"source\"] = None\n\n yield key, values","def _get_cell_map(row: TRowResult) -> Dict[bytes, TCell]:\n if row.sortedColumns is not None:\n return {c.columnName: c.cell for c in row.sortedColumns}\n elif row.columns is not None:\n return row.columns\n else: # pragma: no cover\n raise RuntimeError(\"Neither columns nor sortedColumns is available!\")","def SAM_CSV_to_solar_data(filename):\n if not os.path.isfile(filename):\n raise FileNotFoundError(filename + \" does not exist.\")\n wfd = defaultdict(list)\n with open(filename) as file_in:\n info = []\n for i in range(2):\n info.append(file_in.readline().rstrip())\n info[i] = info[i].split(\",\")\n if \"Time Zone\" not in info[0]:\n raise ValueError(\"`Time Zone` field not found in solar resource file.\")\n latitude = info[1][info[0].index(\"Latitude\")]\n longitude = info[1][info[0].index(\"Longitude\")]\n tz = info[1][info[0].index(\"Time Zone\")]\n elev = info[1][info[0].index(\"Elevation\")]\n source = info[1][info[0].index(\"Source\")]\n reader = csv.DictReader(file_in)\n for row in reader:\n for col, dat in row.items():\n if len(col) > 0:\n wfd[col].append(float(dat))\n\n weather = dict()\n weather['tz'] = float(tz)\n weather['elev'] = float(elev)\n weather['lat'] = float(latitude)\n weather['lon'] = float(longitude)\n\n # Create dict with keys = keys passed to SAM and values = list of possible key versions found in resource files (NREL / NASA POWER)\n acceptable_keys = {\n 'year' : ['year', 'Year', 'yr'],\n 'month' : ['month', 'Month', 'mo'],\n 'day' : ['day', 'Day'],\n 'hour' : ['hour', 'Hour', 'hr'],\n 'minute' : ['minute', 'Minute', 'min'],\n 'dn' : ['dn', 'DNI','dni', 'beam', 'direct normal', 'direct normal irradiance'],\n 'df' : ['df', 'DHI', 'dhi', 'diffuse', 'diffuse horizontal', 'diffuse horizontal irradiance'],\n 'gh' : ['gh', 'GHI','ghi', 'global', 'global horizontal', 'global horizontal irradiance'],\n 'wspd' : ['wspd', 'Wind Speed', 'wind speed'],\n 'tdry' : ['tdry', 'Temperature', 'dry bulb', 'dry bulb temp', 'temperature', 'ambient', 'ambient temp'],\n 'wdir' : ['wdir', 'Wind Direction', 'wind direction'],\n 'pres' : ['pres', 'Pressure', 'pressure'],\n 'tdew' : ['tdew', 'Dew Point', 'Tdew', 'dew point', 'dew point temperature'],\n 'rhum' : ['rhum', 'Relative Humidity', 'rh', 'RH', 'relative humidity', 'humidity'],\n 'alb' : ['alb', 'Surface Albedo', 'albedo', 'surface albedo'],\n 'snow' : ['snow', 'Snow Depth', 'snow depth', 'snow cover']\n }\n \n # enumerates acceptable_keys, inserts key and values into weather dictionary if found in the resource file\n for key, list_of_keys in acceptable_keys.items():\n for good_key in list_of_keys:\n if good_key in wfd.keys():\n weather[key] = wfd.pop(good_key)\n break\n\n # handles averaged hourly data with no minute column provided by NASA POWER and removes 2/29 data for leap years\n # this is a workaround so PySAM/SAM processes as instantaneous data (not setup to handle no minute column)\n if source == 'NASA/POWER':\n weather['minute'] = [30] * len(weather['hour'])\n if len(weather['hour']) == 8784:\n for key in weather.keys():\n if key not in ['tz','elev','lat','lon']:\n del weather[key][1416:1440]\n\n\n return weather","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 cell_map_from_csv(self, source_file: str) -> None:\n if source_file[-4:] == '.csv':\n try:\n self._import_source_data(source_file)\n except Exception:\n print(\"Problem with that CSV file. File extension?\")","def map_column_values(self, table: Table, column: Column, name: str, *args):\n self._requires_table(table)\n\n values = []\n for index in table.index:\n cell = table.get_cell(index, column)\n output = BuiltIn().run_keyword(name, cell, *args)\n values.append(output)\n\n table.set_column(column, values)","def mapRow(this_row, header_dict, precursors_mapping, sequences_mapping, protein_mapping):\n\n if \"FullPeptideName\" in header_dict:\n\n peptide_name = this_row[header_dict[\"FullPeptideName\"]]\n\n transitions = []\n pr_transitions = []\n if \"aggr_Fragment_Annotation\" in header_dict:\n transitions = this_row[ header_dict[\"aggr_Fragment_Annotation\"] ].split(\";\")\n if \"aggr_prec_Fragment_Annotation\" in header_dict:\n pr_transitions = this_row[ header_dict[\"aggr_prec_Fragment_Annotation\"] ].split(\";\")\n\n # Skip row if there are no transitions\n if len(transitions) == 0:\n return\n\n if len(transitions[-1]) == 0:\n transitions = transitions[:-1]\n if len(pr_transitions) > 0 and len(pr_transitions[-1]) == 0:\n pr_transitions = pr_transitions[:-1]\n\n # Get charge state (may be absent)\n charge_state = \"0\"\n if \"Charge\" in header_dict:\n charge_state = this_row[header_dict[\"Charge\"]]\n\n if charge_state == \"NA\" or charge_state == \"\":\n charge_state = \"0\"\n\n key = peptide_name + \"/\" + charge_state\n prkey = peptide_name + \"/\" + charge_state + \"_pr\"\n precursors_mapping [ key ] = transitions\n precursors_mapping [ prkey ] = pr_transitions\n mapped_precursors = sequences_mapping.get( peptide_name, [] )\n mapped_precursors.extend([key, prkey])\n sequences_mapping[peptide_name] = mapped_precursors # = [ key, prkey ]\n\n if \"ProteinName\" in header_dict:\n protein_name = this_row[header_dict[\"ProteinName\"]]\n\n tmp = protein_mapping.get(protein_name, [])\n if peptide_name not in tmp:\n tmp.append(peptide_name)\n protein_mapping[protein_name] = tmp","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 load_csv(file):\n import csv\n reader = csv.reader(open(file, 'r'))\n columns = reader.next()\n c2i = dict((columns[i], i) for i in range(len(columns)))\n data = {}\n excluded = set([REP_CSV_HED_TIM, REP_CSV_HED_HER])\n for row in reader:\n \n # get relevant info from the line\n time = float(row[c2i[REP_CSV_HED_TIM]])\n hero = row[c2i[REP_CSV_HED_HER]]\n other = dict((c, REP_CSV_HANDLERS.get(c, REP_CSV_DEFHANDLER)(row[c2i[c]])) for c in columns if c not in excluded)\n \n # add to the data dictionary\n if hero not in data: data[hero] = []\n data[hero].append([time] + [other])\n \n return data","def read_features_dict(path):\n # type_dict specifies the type conversion to be applied. Each key denotes\n # a column name and the value is the conversion. Columns not included are\n # converted to floats.\n type_dict = {'source': str, 'target': str, 'status': int}\n with open(path) as feature_file:\n reader = csv.DictReader(feature_file, delimiter='\\t')\n for row in reader:\n yield {key: type_dict.get(key, float)(value) for key, value in row.items()}","def map_field_value(\n row: DLCSRecord, field_name: str, config: typing.Dict\n) -> typing.Any:\n mapping: mapper.MappigDictValue = mapper.FIELD_MAPPING[field_name]\n\n if mapping is None:\n return None\n\n if callable(mapping):\n return mapping(row)\n\n if isinstance(mapping, str):\n mapping = [mapping]\n\n if not isinstance(mapping, typing.Collection):\n raise TypeError(\n f\"FIELD_MAPPING[field_name] must be iterable, unless it is None, Callable, or a string.\"\n )\n\n output: typing.List[str] = []\n for csv_field in mapping:\n input_value = row.get(csv_field)\n if input_value:\n if isinstance(input_value, str):\n output.extend(input_value.split(\"|~|\"))\n else:\n output.append(input_value)\n\n bare_field_name = get_bare_field_name(field_name)\n if bare_field_name in config.get(\"controlled_fields\", {}):\n terms = config[\"controlled_fields\"][bare_field_name][\"terms\"]\n output = [terms.get(value, value) for value in output]\n\n return [value for value in output if value] # remove untruthy values like ''","def process_row(row, location_map):\n\n # Add in IP columns\n ips = get_ip_extent(row[IP_PREFIX_COLUMN])\n ipv6_ips = get_ip_extent(row[IPV6_PREFIX_COLUMN])\n\n continent_code = CONTINTENT_CODES[row[CONTINENT_COLUMN]] if row[CONTINENT_COLUMN] in CONTINTENT_CODES else None\n site_id = row[SITE_COLUMN]\n\n location = location_map[site_id.lower()]\n\n # simplify\n row = {\n 'site': site_id.lower(),\n 'latitude': location['latitude'],\n 'longitude': location['longitude'],\n 'city': row[CITY_COLUMN],\n 'region_code': row[REGION_CODE_COLUMN],\n 'country_code': row[COUNTRY_CODE_COLUMN],\n 'continent_code': continent_code,\n 'min_ip_hex': hex_encode_ip(ips[0]) if ips else None,\n 'max_ip_hex': hex_encode_ip(ips[-1]) if ips else None,\n 'transit_provider': row[TRANSIT_PROVIDER],\n 'min_ip': str(ips[0]) if ips else None,\n 'max_ip': str(ips[-1]) if ips else None,\n 'ip_prefix': normalize_ip(row[IP_PREFIX_COLUMN]),\n 'min_ipv6_hex': hex_encode_ip(ipv6_ips[0]) if ipv6_ips else None,\n 'max_ipv6_hex': hex_encode_ip(ipv6_ips[-1]) if ipv6_ips else None,\n 'min_ipv6': str(ipv6_ips[0]) if ipv6_ips else None,\n 'max_ipv6': str(ipv6_ips[-1]) if ipv6_ips else None,\n 'ipv6_prefix': normalize_ip(row[IPV6_PREFIX_COLUMN]),\n }\n\n return row","def create_table(f, geoinfo):\n bounds_cols = xb_points + yb_points\n df = pd.read_csv(f, delimiter=\";\", index_col=\"INDEX_RC\")\n df[duration_name] = parse_duration_level(f)\n df = df.join(geoinfo[[\"X_CENT_GEO\", \"Y_CENT_GEO\", \"Col\", \"Row\"]])\n df = df.rename(columns={\"Col\": x, \"Row\": y, \"X_CENT_GEO\": lon, \"Y_CENT_GEO\": lat})\n return df","def read_file(file):\n \n dictionary = {}\n csv_fp = csv.reader(file)\n #L[46] = manufacturer, L[63] = year\n #L[4]= city mileage, L[34]=highway mileage\n for line in csv_fp:\n #Skip the headings and the year 2017\n if (not (line[46] == 'make')) and (not (line[63] == '2017')):\n if line[46] in dictionary:\n #Add the city and highway mileage if the year has been made\n if line[63] in dictionary[line[46]]:\n dictionary[line[46]][line[63]][0] += [int(line[4])]\n dictionary[line[46]][line[63]][1] += [int(line[34])]\n #Add the year and data if it was not made previously\n else:\n dictionary[line[46]][line[63]] = [[int(line[4])],\\\n [int(line[34])]]\n #Adds a new manufacturer\n else:\n dictionary[line[46]] = {line[63]:[[int(line[4])],\\\n [int(line[34])]]}\n return dictionary","def read_delsys_csv(filename: str) -> Dict[str, Dict[str, TimeSeries]]:\n # Check the number of rows to skip\n n_rows = 0\n with open(filename, 'r') as fid:\n while True:\n s = fid.readline()\n if s.startswith('X[s]'):\n break\n else:\n n_rows += 1\n\n # Open the CSV\n df = pd.read_csv(filename, skiprows=n_rows)\n\n # Create a TimeSeries for each signal since they all have different time\n # vectors\n n_signals = int(len(df.columns) / 2)\n\n emg = {}\n acc = {}\n gyro = {}\n mag = {}\n\n for i_signal in range(n_signals):\n time = df.iloc[:, i_signal * 2].to_numpy()\n name = df.columns[i_signal * 2 + 1]\n data = df.iloc[:, i_signal * 2 + 1].to_numpy()\n\n if ': Acc' in name:\n short_name = name\n ts = TimeSeries(time=time, data={short_name: data})\n acc[short_name] = ts\n elif ': Mag' in name:\n short_name = name\n ts = TimeSeries(time=time, data={short_name: data})\n mag[short_name] = ts\n elif ': Gyro' in name:\n short_name = name\n ts = TimeSeries(time=time, data={short_name: data})\n gyro[short_name] = ts\n elif ': EMG' in name:\n short_name = name.split(':')[0]\n ts = TimeSeries(time=time, data={short_name: data})\n emg[short_name] = ts\n\n return {'emg': emg, 'acc': acc, 'gyro': gyro, 'mag': mag}","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 csv2dicts(csvfile, names=None):\n data = []\n for row_index, row in enumerate(csvfile):\n if row_index == 0:\n if names:\n keys = names\n else:\n keys = row\n print(keys)\n continue\n data.append({key: value for key, value in zip(keys, row)})\n return data","def extract_sensors_data(dataframe, ms_column='ms_ticker',\n time_column = 'Tstamp',\n ppg_columns=['led_1', 'led_2'],\n acc_columns=['acc_x', 'acc_y', 'acc_z']):\n\n sensors_dict = {}\n sensors = dataframe.loc[1:, 1:]\n sensors_columns = dataframe.head(1).values[0]\n sensors_columns = [i.replace(\" \", \"\") for i in sensors_columns if i.find('Index') == -1]\n sensors.columns = sensors_columns\n check_columns_exist(ppg_columns, sensors_columns)\n check_columns_exist(acc_columns, sensors_columns)\n check_columns_exist(ms_column, sensors_columns)\n check_columns_exist(time_column, sensors_columns)\n ppg = np.array(sensors[ppg_columns].values[1:, :], dtype=int)\n ms = np.array(sensors[ms_column].values[1:, ])\n ms_ints = np.array([int(str(i)[-3:]) for i in ms], dtype=float)\n ms_delta = [datetime.timedelta(milliseconds=i) for i in ms_ints]\n\n time = dataframe.loc[:,1].values[1:]\n time = np.array([pd.to_datetime(i) for i in time])\n time_with_ms = np.array(ms_delta) + time\n\n sensors_dict['PPG'] = ppg\n sensors_dict['time_sensors'] = time_with_ms.astype('datetime64[us]')\n sensors_dict['ms_ticker_sensors'] = ms\n acc = np.array(sensors[acc_columns].values[1:, :], dtype=float)\n sensors_dict['ACC'] = acc\n\n return sensors_dict","def csv2dict(filename):\n dis_dict = {}\n with open(filename) as csvfile:\n reader = csv.DictReader(csvfile)\n for row in reader:\n el_a = row[\"Element Name\"]\n dis_dict[el_a] = {}\n for entry in row:\n if entry != \"Element Name\":\n dis_dict[el_a][entry] = float(row[entry])\n csvfile.close()\n return dis_dict","def generic_mapper(fname, path, delimiter='\\t'):\n\tout = {'filename':fname}\n\t_, meta_data = read_radiant_txt(path+fname, delimiter=delimiter, return_meta_data=True)\n\tkeys = ['Type', 'SampleName', 'SampleArea(cm2)', 'SampleThickness(um)', 'Volts', 'Field', 'PulseWidth(ms)', 'PulseDelay(ms)']\n\tfor key in keys:\n\t\ttry:\n\t\t\tout.update({key:float(meta_data[key])})\n\t\texcept ValueError:\n\t\t\tout.update({key:meta_data[key]})\n\t\texcept KeyError:\n\t\t\tout.update({key:np.nan})\n\t\t\t\n\t# period is a weird one\n\ttry:\n\t\tout.update({'Period(ms)':float(meta_data['{}Period(ms)'.format(meta_data['Type'].capitalize())])})\n\texcept:\n\t\tpass\n\treturn out","def format_row(rowvals, dtypes, colnames):\n mapped_vals = []\n for colnum, val in enumerate(rowvals):\n pgtype = dtype_to_pgtype(dtypes[colnum], colnames[colnum])\n mapped_vals.append(numpy_val_to_pg_val(val, pgtype))\n\n return ','.join(mapped_vals)","def agline(line):\n\n vals = {}\n x = ['date', 'movie', 'offset', 'well', 'gen', 'flarem', 'flares', \n 'chargem', 'charges', 'charget', 'escdm', 'escds', 'escddur', 'escmm', 'escms', \n 'escmdur']\n y = line.strip('\\n').split(',')[0:16]\n z = zip(x, y)\n\n for item in z:\n vals[item[0]] = item[1]\n\n return(vals)","def _row_to_dict(row, fields):\n dict_row = {}\n for i, value in enumerate(row):\n key = fields[i]\n if value and str(value).lower() == 'nan':\n value = None\n dict_row[key] = value\n return dict_row","def read_polyidus_csv(filename):\n\twith open(filename, newline = '') as filehandle:\n\t\t\n\t\t# create DictReader objects for inputs and read into memory\n\t\treader = csv.DictReader(filehandle, delimiter = '\\t')\n\t\tdata = []\n\t\tread_ids = []\n\t\t\n\t\tfor row in reader:\n\t\t\trow_data = {}\n\t\t\trow_data['Chr'] = row['Chrom']\n\t\t\trow_data['VirusRef'] = row['ChromVirus']\n\t\t\trow_data['OverlapType'] = 'none'\n\t\t\trow_data['Type'] = 'chimeric'\n\t\t\t\n\t\t\thPosition = int(row['IntegrationSite'])\n\t\t\tvPosition = int(row['ViralIntegrationSite'])\n\t\t\t#hOris = row['StrandHost'].split(', ')\n\t\t\treadIDs = row['FragmentName'].split(', ')\n\t\t\t\n\t\t\t# make one row per read, if we haven't already used this read\n\t\t\tfor i, read in enumerate(readIDs):\n\t\t\t\tif read not in read_ids:\n\t\t\t\t\tread_ids.append(read)\n\t\t\t\t\t\n\t\t\t\t\t# need to make copy of dict\n\t\t\t\t\trow_data = dict(row_data)\n\t\t\t\t\t\n\t\t\t\t\t# add info about this read to dict\n\t\t\t\t\trow_data['IntStart'] = hPosition\n\t\t\t\t\trow_data['IntStop'] = hPosition\n\t\t\t\t\trow_data['VirusStart'] = vPosition\n\t\t\t\t\trow_data['VirusStop'] = vPosition\n\t\t\t\t\t#row_data['Orientation'] = 'hv' if hOris[i] == \"Positive\" else 'vh'\n\t\t\t\t\trow_data['type'] = 'chimeric'\n\t\t\t\t\trow_data['ReadID'] = read\n\t\t\t\t\t\n\t\t\t\t\tdata.append(row_data)\n\n\treturn data","def _getValues(self):\n res = {}\n for colname, column in self._iterNameColumn():\n res[colname] = column.toSql(self._values[colname])\n return res","def create_assumption_map(columns, df):\n assumption_map = pd.DataFrame(columns=columns)\n\n for fuel in fuel_types:\n for value in values:\n if fuel == \"coal\" and value == \"delta_capex\":\n retrofit = True\n else:\n retrofit = False\n array = values_array(df, fuel, value, retrofit=retrofit)\n mean, trim_mean_v, std, top_p, bot_p = value_stats(array)\n\n if value == \"delta_capex\":\n units = \"2019€_KW\"\n elif value == \"delta_om\":\n units = \"2019€_KWh\"\n elif value == \"delta_heatrate\":\n units = \"KW_KWh\"\n assumption_map = assumption_map.append({\"fuel_type\": fuel, \"value\": value, \"range_low\": bot_p,\n \"range_high\": top_p, \"reference_value\": mean, \"units\": units},\n ignore_index=True)\n return assumption_map","def parse_column_units(\n filepath: Path, sheet_name: str = None\n) -> Dict[str, ureg.Quantity]:\n method = map_input_method(filepath)\n unit_header = method(str(filepath), nrows=1, header=0)\n given_unit = lambda s: unit_header[s].values[0]\n parsed_units = {}\n required_columns = [\n \"Supply Temperature\",\n \"Target Temperature\",\n \"Heat Capacity Flowrate\",\n \"Enthalpy\",\n ]\n\n for column in required_columns:\n units = ureg.parse_units(given_unit(column))\n parsed_units[column] = units\n\n return parsed_units","def input_row():\n return {\n 'foo': 1,\n 'bar': 2,\n 'spam': 3,\n 'eggs': 4\n }","def process_csv(self):\n with open(self.filepath, mode=\"r\") as csv_file:\n csv_reader = csv.reader(csv_file, delimiter=\",\")\n header = next(csv_reader)\n\n date_idx = self._get_header_position(header, \"Label\")\n power_idx = self._get_header_position(header, \"kW\")\n\n if self.inverter_id not in header[power_idx]:\n raise Exception(\"Inverter data returned for the incorrect meter.\")\n\n for row in csv_reader:\n date_obj = self.csv_str_to_date(row[date_idx])\n power = float(row[power_idx] or 0)\n\n current_date = self.date_to_final_str(date_obj)\n rounded_time = self.round_up_to_quarter_hour(date_obj)\n\n if current_date not in self.intermediate_readings:\n self.intermediate_readings[\n current_date\n ] = self.build_intermediate_dict()\n\n current_reading = self.intermediate_readings[current_date][rounded_time]\n # Here's where we sum power readings together - in to fifteen min intervals\n self.intermediate_readings[current_date][rounded_time] = (\n current_reading + power\n )\n\n actual_time = self.date_to_intermediate_time_str(date_obj)\n if rounded_time == actual_time:\n # Here's where we average power readings together, in fifteen minute intervals\n self.intermediate_readings[current_date][rounded_time] = round(\n float(\n self.intermediate_readings[current_date][rounded_time] / 3\n ),\n 2,\n )\n\n return self.finalize_readings()","def getRiverIDs(lookupCsv):\n\n d = {}\n with open(lookupCsv, \"rb\") as f:\n reader = csv.reader(f)\n\n # Discard header row\n reader.next()\n\n for row in reader:\n d[row[0]] = row[1]\n\n return d","def _row_to_labels(row):\n labels = {}\n label_keys = ['name', 'qty', 'range_end', 'unit', 'comment']\n for key in label_keys:\n labels[key] = row[key]\n return labels","def load_columns(self, csv_data):\n column_date = []\n column_time = []\n column_hold = []\n column_outcome = []\n for row in dataframe_to_rows(csv_data, index=False):\n cell_date = row[18]\n cell_date = cell_date.split(': ')[1]\n cell_time = row[23]\n cell_hold = row[24]\n cell_outcome = row[25]\n column_date.append(cell_date)\n column_time.append(cell_time)\n column_hold.append(cell_hold)\n column_outcome.append(cell_outcome)\n return column_date, column_time, column_hold, column_outcome","def _convert_genres(self, df, genre_map):\n genre_transfrm = pd.read_csv(genre_map)\n genre = {}\n for row_label, row_array_Series in genre_transfrm.iterrows():\n genre[row_array_Series['Old']] = row_array_Series['New']\n\n df['MappedGenres'] = df['Genres'].map(genre)\n try:\n assert df['MappedGenres'].notna().all()\n except:\n print(\"WARNING: The Genre CSV needs to be updated. The following Genres need to be mapped: \")\n print([*df['Genres'][df['MappedGenres'].isna()].values])","def meta_properties(self, date_col=\"Date\", type_col=\"Primary Type\", lat_col=\"Latitude\",\\\n lon_col=\"Longitude\", loc_col=\"Location\", out_fname=\"data_formated.csv\"):\n # implement keywords\n # would we have to deal w/ file w/o headers?\n data = pd.read_csv(self._path, usecols=[date_col, type_col, lat_col, lon_col, loc_col],\\\n parse_dates=[date_col], infer_datetime_format=True)\n data.sort_values(date_col, inplace=True)\n min_date = data.iloc[0][date_col]\n max_date = data.iloc[(data.shape[0]-1)][date_col]\n\n lat = []\n lon = []\n\n nulls = []\n for row in data.itertuples(index=True, name='Pandas'):\n index = (row.Index)\n # if lat, lon = nan, drop the row\n # update: confirmed that issue is with code, not with data; for some reason\n # csv is actually correctly grabbing location, there just legitimately are\n # entries w/o location data\n if pd.isnull(getattr(row, loc_col)):\n # print(\"row: {} got a {} for the 'Location' column with date: {}\".format(index, \\\n # getattr(row, loc_col), getattr(row, date_col)))\n if not pd.isnull(getattr(row, lat_col)) and not pd.isnull(getattr(row, lon_col)):\n lat.append(str(getattr(row, lat_col)))\n lon.append(str(getattr(row, lon_col)))\n if \",\" in data.loc[index, type_col]:\n data.loc[index, type_col] = data.loc[index, type_col].replace(\",\", \" \")\n print(\\\n \"Successfully extracted lat, lon from lat_col, lon_col for row: {}\".format(index))\n else:\n nulls.append((index, getattr(row, date_col)))\n data.drop(index, inplace=True)\n # print(\"No location data available for row: {} with date: {}\".format(index,\\\n # getattr(row, date_col)))\n else:\n loc = literal_eval(getattr(row, loc_col))\n lat.append(loc[0])\n lon.append(loc[1])\n if \",\" in data.loc[index, type_col]:\n data.loc[index, type_col] = data.loc[index, type_col].replace(\",\", \" \")\n\n data[\"Latitude\"] = lat\n data[\"Longitude\"] = lon\n data.drop(loc_col, axis=1, inplace=True)\n\n data.sort_values(\"Latitude\", inplace=True)\n min_lat = float(data.iloc[0][\"Latitude\"])\n max_lat = float(data.iloc[(data.shape[0]-1)][\"Latitude\"])\n\n data.sort_values(\"Longitude\", inplace=True)\n min_lon = float(data.iloc[0][\"Longitude\"])\n max_lon = float(data.iloc[(data.shape[0]-1)][\"Longitude\"])\n\n data.to_csv(self.__file_dir+'/'+out_fname, na_rep=\"\", header=False, index=False)\n\n attrs = {'min_date': min_date, 'max_date': max_date, \"min_lat\":min_lat,\\\n \"max_lat\":max_lat, \"min_lon\":min_lon, \"max_lon\":max_lon, \\\n \"dates\":pd.date_range(min_date, max_date), \"num_attributes\": data.shape[1],\\\n \"num_entries\":data.shape[0]}\n self._meta_dict = attrs\n self._meta_dict['df'] = data\n pickle.dump(data, open(CWD + \"/meta_dict.p\", \"wb\"))\n print(\"Num entries w/o location data: {}\".format(len(nulls)))\n pickle.dump(nulls, open(CWD + \"/nulls.p\", \"wb\"))\n\n # not include the formatted dataset?\n return attrs","def counters(line):\n reader = csv.reader(StringIO(line), delimiter=',')\n row = reader.next()\n\n airport = row[4]\n\n try:\n delay = float(row[8])\n except ValueError:\n delay = 0.0\n\n return (airport, (1, delay, delay ** 2, delay, delay))","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 _gen_data(fhs, columns, sep):\n for fh in fhs:\n for line in fh:\n if line[0] == \"#\": continue\n toks = line.split(sep)\n yield toks[columns[0]], int(toks[columns[1]]), float(toks[columns[2]])","def load_csv(input_filename_state):\n dataset = {}\n with open(input_filename_state) as f:\n reader = csv.reader(f)\n header = next(reader, None)\n\n location_col = -1\n week_ahead_col = -1\n quantile_col = -1\n value_col = -1 \n\n\n for i in range(len(header)):\n if header[i] == \"place\":\n location_col = i\n elif header[i] == \"week_ahead\":\n week_ahead_col = i\n elif header[i] == \"quantile\":\n quantile_col = i \n elif header[i] == \"value\":\n value_col = i\n \n for row in reader:\n state = row[location_col]\n\n # Skip the state if it is not listed in reichlab's state list.\n if state not in STATE_ID_MAPPING:\n continue\n state_id = STATE_ID_MAPPING[state]\n week_ahead = int(row[week_ahead_col])\n quantile = row[quantile_col]\n val = max(float(row[value_col]), 0)\n if week_ahead not in dataset:\n dataset[week_ahead] = {}\n if state_id not in dataset[week_ahead]:\n dataset[week_ahead][state_id] = {}\n dataset[week_ahead][state_id][quantile] = val\n return dataset","def _compute_from_table_values(\n name=\"plant\", color=(0/255, 128/255, 0/255),\n germination_time=(3, 1), \n r_max=(1.0,1.0),\n maturation_time=(10, 1),\n stopping_color=(0, 0, 1),\n color_step=(10/255, 0, 0),\n c1=0.1,\n r_0=0.04\n ):\n\n c2 = 1\n k1, k2 = 0.3, 0.7\n unoccluded_c1 = c1 / k2\n h_0 = 0.1\n r_max = max(1, np.random.normal(MAX_RADIUS[name][0], MAX_RADIUS[name][1]))\n # r_max = MAX_RADIUS[name][0] + MAX_RADIUS[name][1]\n growth_time = generate_growth_time(germination_time, maturation_time, r_max, r_0, k2, c2)\n\n return {\n \"germination_time\": germination_time,\n \"maturation_time\": maturation_time,\n \"k1\": k1,\n \"k2\": k2,\n \"c1\": unoccluded_c1,\n \"c2\": c2,\n \"start_radius\": r_0,\n \"start_height\": h_0,\n \"r_max\": r_max,\n \"growth_time\": growth_time,\n \"plant_type\": name,\n \"color\": color,\n \"stopping_color\": stopping_color,\n \"color_step\": color_step\n }","def daily_speed_sum_map(data):\n\t(byte_offset, line_value) = data\n\tcolumns = split_into_columns(line_value)\n\tif columns[3] != 'speed':\n\t\tyield (\"%s_%s\" % (columns[0], columns[1][:10]), columns[3])","def column_name(name):\n # Only needs exceptions to standard token cleanup\n column_map = {\n \"line#\" : \"ignore\",\n \"date\" : \"timestamp\",\n \"rh\" : \"humidity\",\n \"par\" : \"par_ue\"\n }\n\n if name in column_map:\n return column_map[name]\n \n return name","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 to_python(self):\r\n mapping = {}\r\n for row in self.rows:\r\n mapping[row[0]] = _format_python_value(row[1])\r\n return mapping","def load(filename):\n with open(filename,'r') as fd:\n csv_in = csv.reader(fd, delimiter=',', quotechar='\"')\n keys = csv_in.next()\n data = {k:[] for k in keys}\n for row in csv_in:\n for k,v in zip(keys,row):\n data[k].append(float(v))\n return data","def row_to_colKey(row):\n return [(i[0], (row[0], i[1])) for i in row[1]]","def func_value(file):\n \n with open( file ) as f:\n csv_reader = csv.DictReader(f)\n \n for row in csv_reader:\n Cl , Cd = row[' \"CL\" '] , row[' \"CD\" ']\n \n return float(Cl) , float(Cd)","def dict_factory(self, cursor, row):\n results = {}\n for index, col_name in enumerate(cursor.description):\n results[col_name[0]] = row[index]\n\n return results","def mapper(self, _, line):\n linea = line.split()\n causa, fallecidos = linea[0], linea[1]\n fallecidos_f = float(fallecidos)\n \n yield causa, (1, round(fallecidos_f))","def map_row(row, mapping, model_class, extra_data_fields=[], cleaner=None, **kwargs):\n initial_data = kwargs.get('initial_data', None)\n model = model_class()\n\n # _log.debug(\"map_row's mappings {}\".format(mapping))\n\n # If there are any initial states we need to set prior to mapping.\n if initial_data:\n model = apply_initial_data(model, initial_data)\n\n # concat is not used as of 2016-09-14\n # concat = _set_default_concat_config(concat)\n\n for raw_field, value in row.items():\n is_extra_data = True if raw_field in extra_data_fields else False\n\n # Save the value if is is not None, keep empty fields.\n if value is not None:\n model = apply_column_value(raw_field, value, model, mapping, is_extra_data, cleaner)\n\n return model","def get_data(file_name):\n csv_file = open(file_name, 'rb')\n train_content = csv.reader(csv_file)\n\n # ignore header\n train_content.next()\n\n # preprocessing functions for each column index\n # Several preprocessing can be defined for each column.\n # A new variable is associated to EACH preprocessing function\n preproc_funcs = {0: ['get_hour']}\n\n # Read data from file, store it as an integer\n data = []\n for row in train_content:\n data_row = []\n for n, col in enumerate(row):\n # if the current column requires preprocessing functions, apply them\n if preproc_funcs.has_key(n):\n # Each preprocessing give a new column\n for preproc_func in preproc_funcs[n]:\n func = globals().get(preproc_func)\n data_row.append(int(float(func(col))))\n # If no preprocessing, do nothing\n else:\n data_row.append(int(float(col)))\n\n data.append(data_row)\n\n csv_file.close()\n\n return data","def get_mapping_info(in_path):\n if not utils.exists_file(in_path):\n utils.exit(f\"Mapping file does not exist:\\n{in_path}\")\n df = pd.read_csv(in_path, sep=\";\", header=None)\n mapping = {}\n case_video_count = {}\n for index, row in df.iterrows():\n # parts = row[0].split('_')\n # video_id = \"-1\"\n # if len(parts) == 1 or \"video\" not in parts[0]:\n # video_id = row[0]\n # else:\n # video_id = parts[1]\n # video_id = video_id.replace('.mp4', '')\n video_id = row[0]\n case_id = int(row[2])\n mapping[f\"{video_id}\"] = case_id\n utils.increment_dict_key(case_video_count, f\"{case_id}\")\n return mapping, case_video_count","def read_csv(file_name):\n data = {}\n with open(file_name) as f:\n f = MyIter(f)\n try:\n for line in f:\n if not line.strip():\n continue\n if line == 'Points\\n':\n break\n key, val = read_key_value(line, separator=',')\n key = key.lower().replace(' ', '_')\n data[key] = val\n\n x_units, y_units = next(f).split(',')\n data['x_units'], data['y_units'] = x_units.strip(), y_units.strip()\n\n xs, ys = [], []\n for line in f:\n x, y = line.split(',')\n xs.append(float(x.strip()))\n ys.append(float(y.strip()))\n except Exception as e:\n print(f'Error on line {f._index}')\n print(f._line)\n raise e\n\n elong = Elongation(\n np.array(xs), np.array(ys),\n float(data['gauge_length']),\n float(data['sample_width']),\n float(data['sample_thickness'])\n )\n return [elong]","def getColumnDictionary(self):\n try:\n column_dictionary = []\n con = self.getMetadataDatabaseConnection()\n column_values = con.cursor()\n con.cursor().callproc('qiime_assets.get_column_dictionary', [column_values])\n for row in column_values:\n # Skip if no column name is found\n if row[0] is None:\n continue\n\n # Some variables to allow for re-assignment should any of them be None\n column_name = row[0].lower()\n expected_values = row[1]\n description = row[2]\n data_type = row[3]\n max_length = row[4]\n min_length = row[5]\n active = row[6]\n \n if row[1] == None:\n expected_values == ''\n elif row[2] == None:\n description == ''\n elif row[3] == None:\n data_type = ''\n elif row[4] == None:\n max_length = ''\n elif row[5] == None:\n min_length = ''\n elif row[6] == None:\n min_length = ''\n \n list_item = (column_name, expected_values, description, data_type, max_length, min_length, active)\n column_dictionary.append(list_item)\n return column_dictionary\n except Exception, e:\n print 'Exception caught: %s.\\nThe error is: %s' % (type(e), e)\n return False","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 build_sample_map(flowcell):\n result = {}\n rows = [(lane, lib[\"name\"]) for lib in flowcell[\"libraries\"] for lane in lib[\"lanes\"]]\n i = 1\n for _, name in sorted(set(rows)):\n if name not in result:\n result[name] = \"S{}\".format(i)\n i += 1\n return result","def get_header(header_row):\n header = {}\n header['station'], c1, c2, c3, date, time, tz = header_row.split()\n header['short_model'] = c1\n header['model'] = f'{c1} {c2} {c3}' \n header['runtime'] = dateutil.parser.parse(f'{date} {time} {tz}')\n return header","def test_csvfile_get_columns(fs: FakeFilesystem) -> None:\n fs.create_file(\"test.csv\", contents=CONTENTS)\n\n adapter = CSVFile(\"test.csv\")\n\n assert adapter.get_columns() == {\n \"index\": Float(\n filters=[Range, Equal, NotEqual, IsNull, IsNotNull],\n order=Order.ASCENDING,\n exact=True,\n ),\n \"temperature\": Float(\n filters=[Range, Equal, NotEqual, IsNull, IsNotNull],\n order=Order.NONE,\n exact=True,\n ),\n \"site\": String(\n filters=[Range, Equal, NotEqual, IsNull, IsNotNull],\n order=Order.NONE,\n exact=True,\n ),\n }","def creating_dict_from_csv(self) -> dict:\n dictionary = {}\n for row in self.__read_csv():\n if dictionary.get(row[0]):\n dictionary[row[0]].append((row[1], row[2]))\n else:\n dictionary[row[0]] = [(row[1], row[2])]\n\n for key, value in dictionary.items():\n dictionary[key] = sorted(value, key=lambda x: x[1], reverse=True)\n\n return dictionary","def dictionary_formation():\r\n sales_data = {}\r\n with open('beer_data.csv', \"r\") as data_file:\r\n file_contents = csv.reader(data_file, delimiter=',')\r\n #Value of lines_read used as key value for each dictionary\r\n #in sales_data\r\n lines_read = 1\r\n for line in file_contents:\r\n if lines_read == 1:\r\n lines_read = lines_read + 1\r\n else:\r\n #Stores each column in row as key value in dictionary\r\n sales_data[str(lines_read)] = {\r\n \"invoice_number\": line[0],\r\n \"customer\": line[1],\r\n \"date_required\": line[2],\r\n \"recipe\": line[3],\r\n \"gyle_number\": line[4],\r\n \"quantity_ordered\": int(line[5])\r\n }\r\n lines_read = lines_read + 1\r\n data_file.close()\r\n return sales_data","def distance(row):\n return row['distance']","def data_parser(df):\n\n chunks = []\n for row in df.itertuples():\n piece = {}\n piece['datetime'] = row[1]\n piece[row[3]] = row[4]\n chunks.append(piece)\n\n # Join dicts on shared 'datetime' keys.\n combine = defaultdict(dict)\n for elem in chunks:\n combine[elem['datetime']].update(elem)\n\n ordered = sorted(combine.values(), key=itemgetter(\"datetime\"))\n\n mapped_generation = []\n for item in ordered:\n mapped_types = [(mapping.get(k, k), v) for k, v in item.items()]\n\n # Need to avoid multiple 'unknown' keys overwriting.\n complete_production = defaultdict(lambda: 0.0)\n for key, val in mapped_types:\n try:\n complete_production[key] += val\n except TypeError:\n # Datetime is a string at this point!\n complete_production[key] = val\n\n dt = complete_production.pop('datetime')\n final = (dt, dict(complete_production))\n mapped_generation.append(final)\n\n return mapped_generation","def readcsv(csvreader,item:int,count:int):\n\n headerstring = next(csvreader)\n data = {}\n if item >= len(headerstring) or item < 0 or count >= len(headerstring) or count < 0:\n \n sys.stderr.write(\"your index values are out of bounds\")\n\n else:\n header = headerstring[item] + ',' + headerstring[count]\n for i in csvreader:\n key = i[item]\n try:\n if key in data:\n data[key] = float(data[key]) + float(i[count])\n else:\n data[key] = float(i[count])\n except ValueError:\n if key in data:\n data[key]+=0\n else:\n data[key] = 0\n return data,header","def get_positions(directory): \n positions = {}\n names = {}\n pos_dict = {'1': \"GK\", '2': \"DEF\", '3': \"MID\", '4': \"FWD\"}\n fin = open(directory + \"/players_raw.csv\", 'rU',encoding=\"utf-8\")\n reader = csv.DictReader(fin)\n for row in reader:\n positions[int(row['id'])] = pos_dict[row['element_type']] \n names[int(row['id'])] = row['first_name'] + ' ' + row['second_name']\n return names, positions","def cuttoff_column_generator(A: dict, columns: list, threshold: float):\n for column_name in columns:\n with open('cache/' + column_name + '.pkl', 'rb') as pkl_file:\n column = pickle.load(pkl_file)\n yield A, column, threshold","def parse_csv(data):\n\n # scan for CSRs first, so it's easier to resolve CSR-related constants\n # in the second pass\n for _type, _name, _address, _, __ in data:\n if _type == 'csr_base':\n peripherals[_name] = {'name': _name,\n 'address': _address,\n 'constants': {}}\n\n for _type, _name, _val, _val2, _ in data:\n if _type == 'csr_base':\n # CSRs have already been parsed\n pass\n elif _type == 'csr_register':\n # we are currently not interested in this\n pass\n elif _type == 'constant':\n found = False\n for _csr_name in peripherals:\n if _name.startswith(_csr_name):\n local_name = _name[len(_csr_name)+1:]\n peripherals[_csr_name]['constants'][local_name] = _val\n found = True\n break\n if not found:\n # if it's not a CSR-related constant, it must be a global one\n constants[_name] = {'name': _name, 'value': _val}\n elif _type == 'memory_region':\n mem_regions[_name] = {'name': _name,\n 'address': _val,\n 'size': _val2}\n else:\n print('Skipping unexpected CSV entry: {} {}'.format(_type, _name))","def columndict_callback(c):\r\n\r\n def subQ(txt):\r\n return re.sub(r\"'([^']*)'\", r\"`\\1'\", (txt or '')).replace(r'|', r'\\|')\r\n\r\n return {\r\n 'name' : c.name,\r\n 'type' : c.type,\r\n 'nullable' : c.nullable,\r\n 'value' : subQ(c.value),\r\n 'default' : subQ(c.default),\r\n 'defaultf' : (\"\\n\\n*Default: %s*\" % subQ(c.default))\r\n if c.default else '',\r\n 'notnull' : '' if c.nullable else ' not null',\r\n 'desc': c.desc,\r\n 'descf': preformat_coldesc(c.desc), \r\n }","def readExperimentParameters(filename): \n \n csvFile = csv.reader(open(filename))\n pythonSucks = next(csvFile)\n \n data = []\n for row in csvFile:\n list = []\n for i in range(len(row)):\n if(row[i+1] == \"\"):\n list.append(int(row[i]))\n break\n elif(row[i+1] == \"h\"):\n list.append(int(row[i]) * 60)\n break\n else:\n list.append(int(row[i]))\n \n data.append(tuple(list))\n return data","def nomenclatura():\n df = pd.read_csv(\"Data/nomenclatura_1.csv\", encoding = \"latin1\")\n #dict_axis = df.set_index('id').T.to_dict('list')\n dict_axis = dict( [ (i, [a,b]) for i, a,b in zip(df.id, df.latitude, df.longitude) ] )\n\n return dict_axis","def read_names_into_dict():\n d = dict()\n with open(\"SP_500_firms.csv\") as csvfile:\n input_file = csv.DictReader(csvfile)\n for row in input_file:\n #print(row)\n d[row['Symbol']] = [row['Name'],row['Sector']]\n return d"],"string":"[\n \"def __get_column_mapping(self):\\n\\n s1 = list(Sample(\\\"FL\\\", \\\"M1.0@265_Primary_ar115_s2010-08-06T06_36_00_e2010-08-06T18_24_00.csv\\\").get_data().columns)[:25]\\n column_mapping = {}\\n for i in range(len(s1)):\\n column_mapping[i] = s1[i]\\n\\n return column_mapping\",\n \"def create_deft_table_csv_mappings():\\n mappings = list()\\n mappings.append(CsvColumnMapping(columnName=\\\"rownumber\\\", cslDataType=\\\"int\\\", ordinal=0))\\n mappings.append(CsvColumnMapping(columnName=\\\"rowguid\\\", cslDataType=\\\"string\\\", ordinal=1))\\n mappings.append(CsvColumnMapping(columnName=\\\"xdouble\\\", cslDataType=\\\"real\\\", ordinal=2))\\n mappings.append(CsvColumnMapping(columnName=\\\"xfloat\\\", cslDataType=\\\"real\\\", ordinal=3))\\n mappings.append(CsvColumnMapping(columnName=\\\"xbool\\\", cslDataType=\\\"bool\\\", ordinal=4))\\n mappings.append(CsvColumnMapping(columnName=\\\"xint16\\\", cslDataType=\\\"int\\\", ordinal=5))\\n mappings.append(CsvColumnMapping(columnName=\\\"xint32\\\", cslDataType=\\\"int\\\", ordinal=6))\\n mappings.append(CsvColumnMapping(columnName=\\\"xint64\\\", cslDataType=\\\"long\\\", ordinal=7))\\n mappings.append(CsvColumnMapping(columnName=\\\"xuint8\\\", cslDataType=\\\"long\\\", ordinal=8))\\n mappings.append(CsvColumnMapping(columnName=\\\"xuint16\\\", cslDataType=\\\"long\\\", ordinal=9))\\n mappings.append(CsvColumnMapping(columnName=\\\"xuint32\\\", cslDataType=\\\"long\\\", ordinal=10))\\n mappings.append(CsvColumnMapping(columnName=\\\"xuint64\\\", cslDataType=\\\"long\\\", ordinal=11))\\n mappings.append(CsvColumnMapping(columnName=\\\"xdate\\\", cslDataType=\\\"datetime\\\", ordinal=12))\\n mappings.append(CsvColumnMapping(columnName=\\\"xsmalltext\\\", cslDataType=\\\"string\\\", ordinal=13))\\n mappings.append(CsvColumnMapping(columnName=\\\"xtext\\\", cslDataType=\\\"string\\\", ordinal=14))\\n mappings.append(CsvColumnMapping(columnName=\\\"xnumberAsText\\\", cslDataType=\\\"string\\\", ordinal=15))\\n mappings.append(CsvColumnMapping(columnName=\\\"xtime\\\", cslDataType=\\\"timespan\\\", ordinal=16))\\n mappings.append(CsvColumnMapping(columnName=\\\"xtextWithNulls\\\", cslDataType=\\\"string\\\", ordinal=17))\\n mappings.append(CsvColumnMapping(columnName=\\\"xdynamicWithNulls\\\", cslDataType=\\\"dynamic\\\", ordinal=18))\\n return mappings\",\n \"def tsvRowToDict(row):\\n return {col: getattr(row, col) for col in row._columns_}\",\n \"def _properties_from_csv_row(row, header, ignored_columns):\\n props = {}\\n for h, prop in enumerate(header):\\n # Handle a strange edge case where the length of the row is longer than the length of the header.\\n # We do this to prevent an out of range error.\\n x = h\\n if x > len(row) - 1:\\n x = len(row) - 1\\n if row[x] == '' or prop in ignored_columns:\\n continue\\n else:\\n try:\\n # We use literal_eval() here to de-stringify numbers, lists and objects in the CSV data\\n p = literal_eval(row[x])\\n props[prop] = p\\n except (SyntaxError, ValueError) as e:\\n props[prop] = row[x]\\n return props\",\n \"def make_dict(row):\\n return dict((key[0], value) for key, value in zip(colnames, row))\",\n \"def _transform_map_data(self):\\n WARD_FMT = '%s-%s'\\n self.map_data_trans = []\\n lookup = {i.column: ''.join(filter(lambda x: x.isdigit(), i.value)) for i in self.sht[1]}\\n\\n #skip over header\\n rs = iter(self.sht.rows)\\n next(rs)\\n next(rs)\\n for r in rs:\\n pka = r[0].value\\n for c in r[1:]:\\n if c.value is None:\\n c.value = 0\\n\\n self.map_data_trans.append((WARD_FMT%(pka, lookup[c.column]), c.value))\",\n \"def read_name_map( name_map_path) :\\n with open( name_map_path, newline=\\\"\\\") as csvfile:\\n table = { }\\n reader = csv.reader(csvfile)\\n for row in reader:\\n if len(row) < 2:\\n continue\\n if row[key_col] == key_header:\\n continue\\n key = row[key_col]\\n val = row[val_col]\\n table[key] = val\\n return table\",\n \"def process_csv():\\n csv_rows = []\\n fieldnames = ['site',\\n 'latitude',\\n 'longitude',\\n 'city',\\n 'region_code',\\n 'country_code',\\n 'continent_code',\\n 'min_ip_hex',\\n 'max_ip_hex',\\n 'transit_provider',\\n 'min_ip',\\n 'max_ip',\\n 'ip_prefix',\\n 'min_ipv6_hex',\\n 'max_ipv6_hex',\\n 'min_ipv6',\\n 'max_ipv6',\\n 'ipv6_prefix']\\n\\n location_map = build_location_map()\\n\\n # Read in the CSV file and augment the columns\\n with open(INPUT_FILE, 'rb') as csvfile:\\n reader = csv.DictReader(csvfile)\\n\\n for row in reader:\\n csv_rows.append(process_row(row, location_map))\\n\\n # Write the new CSV file with new columns\\n with open(OUTPUT_FILE, 'w') as csvfile:\\n writer = csv.DictWriter(csvfile, fieldnames=fieldnames)\\n writer.writeheader()\\n for row in csv_rows:\\n writer.writerow(row)\\n\\n print(\\\"MLab Sites CSV generated at {0}\\\".format(OUTPUT_FILE))\",\n \"def create_waves_dict(csv_file):\\n with open(csv_file) as file:\\n reader = csv.DictReader(file)\\n waves_dict = {row[\\\"Date\\\"]: row[\\\"Wave Height\\\"] for row in reader}\\n return waves_dict\",\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 get_m_to_me_metabolite_mapping():\\n f = pandas.read_csv(fixpath(\\\"m_to_me_mets.csv\\\"), index_col=0)[\\\"me_name\\\"]\\n return f.dropna().to_dict()\",\n \"def map_csv_fields(self):\\n etod_csv_fields = {\\n 'ctry_id': None,\\n 'obst_identifier': None,\\n 'obst_name': None,\\n 'lon_src': None,\\n 'lat_src': None,\\n 'agl': None,\\n 'amsl': None,\\n 'vert_uom': None,\\n 'hor_acc': None,\\n 'hor_acc_uom': None,\\n 'vert_acc': None,\\n 'vert_acc_uom': None,\\n 'obst_type': None,\\n 'lighting': None,\\n 'marking': None,\\n 'is_group': None,\\n }\\n\\n for field in etod_csv_fields:\\n try:\\n etod_csv_fields[field] = etod_map[self.ctry_short_name]['fields'][field]\\n except KeyError:\\n etod_csv_fields[field] = None\\n\\n self.field_map = etod_csv_fields\",\n \"def map_line(reader, headers):\\n\\n readings = {}\\n\\n line = reader.readline()\\n\\n if len(line) == 0:\\n raise EOFError('That\\\\'s all the data!')\\n\\n line = line.rstrip()\\n\\n value_strings = line.split(' ')\\n for i, string in enumerate(value_strings):\\n if string != 'NaN':\\n value = float(string)\\n\\n if i < len(headers):\\n if headers[i]['is_point']:\\n value = get_decimal_degrees(value)\\n key = headers[i]['name'] + \\\"-\\\" + headers[i]['units']\\n readings[key] = value\\n\\n # Provide generic timestamp regardless of type for iterator\\n # convenience\\n # Keep originals for those interested\\n if 'm_present_time-timestamp' in readings:\\n readings['timestamp'] = readings['m_present_time-timestamp']\\n elif 'sci_m_present_time-timestamp' in readings:\\n readings['timestamp'] = readings['sci_m_present_time-timestamp']\\n\\n return readings\",\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 conform_input_data(rowdict):\\n # rowdict['Value'] = float(rowdict['Value'])\\n rowdict['TimeStamp'] = TS_to_date(rowdict['TimeStamp'][:19])\\n for floatcolumn in ['LowPx','OpenPx','ClosePx','QuoteCount','HighPx','TradeCount']:\\n if floatcolumn in rowdict:\\n rowdict[floatcolumn] = float(rowdict[floatcolumn])\\n return rowdict\",\n \"def table_row_to_dict(row, make_quantity=True):\\n data = {}\\n for name, col in row.columns.items():\\n val = row[name]\\n\\n if make_quantity and col.unit:\\n val = Quantity(val, unit=col.unit)\\n data[name] = val\\n return data\",\n \"def map_cols_to_attr(self):\\n ## this is from the base class:\\n ## for attr, label in zip(self.attr_names, self.labels):\\n ## col_ind = self.col_inds[label]\\n ## if len(self.data) > 0:\\n ## setattr(self, attr, self.data[:,col_ind])\\n #\\n # hard coding based on what I know about saleae files:\\n self.t = self.data[:,0]#.astype(float)\\n nr, nc = self.data.shape\\n self.num_cols = nc-1\\n \\n for i in range(0,self.num_cols):\\n attr = 'ch_%i' % i\\n j = i+1\\n setattr(self, attr, self.data[:,j])#.astype(float))\",\n \"def make_row_map(file_path, key_field, field_map=None, transforms=None, \\\\\\n file_encoding=None):\\n\\n with open(file_path, encoding=file_encoding) as file:\\n # preprocess transforms\\n if transforms:\\n _transforms = {}\\n for tf_field, tf in transforms.items():\\n _type = type(tf).__name__\\n if _type not in ['str', 'function']:\\n raise ValueError('Invalid transform')\\n _transforms[tf_field] = {\\n 'transform': tf,\\n 'type': _type\\n }\\n\\n # get fields from csv\\n fields_reader = csv.reader(file)\\n fields = next(fields_reader)\\n\\n # make sure we aren't missing any field names\\n first_row = next(fields_reader)\\n if len(fields) != len(first_row):\\n raise ValueError('Header has a different number of columns than data')\\n\\n # apply field map\\n if field_map:\\n # TODO use a case insensitive dictionary for field map\\n fields = [field_map.get(field.lower()) or field for field in fields]\\n key_field = field_map.get(key_field) or key_field\\n\\n # lowercase\\n fields = [field.lower() for field in fields]\\n\\n # handle spaces\\n fields = [field.replace(' ', '_') for field in fields]\\n\\n # use namedtuple for rows\\n fields_joined = ' '.join(fields)\\n Row = namedtuple('Row', fields_joined)\\n\\n # make map\\n row_map = {}\\n reader = csv.DictReader(file, fieldnames=fields)\\n\\n for i, row in enumerate(reader):\\n key = row[key_field]\\n\\n # apply transforms\\n if transforms:\\n for tf_field, tf_map in _transforms.items():\\n tf = tf_map['transform']\\n tf_type = tf_map['type']\\n source_val = row[tf_field]\\n if tf_type == 'str':\\n val = getattr(source_val, tf)()\\n else:\\n val = tf(source_val)\\n row[tf_field] = val\\n\\n # row_map[key] = row\\n # str_row = {key: str(val) for key, val in row.items()}\\n row_map[key] = Row(**row)\\n # from pprint import pprint\\n # pprint(str_row)\\n # row_map[key] = Row(**str_row)\\n\\n return row_map\",\n \"def list_water_temps(csv_file):\\n with open(csv_file) as file:\\n reader = csv.DictReader(file)\\n temp_list = [temp[\\\"Water Temp\\\"] for temp in reader]\\n return temp_list\",\n \"def create_dict_from_file(filename, delimeters, first_char, column_names):\\n\\n # This opens the\\n measurement_output = open('measurement_output.txt', \\\"w\\\", encoding=\\\"utf8\\\")\\n # This creates and initializes a list to serve as a dictionary container outside of the for-loop.\\n measurements_file_container = {}\\n\\n # This opens the file and then splits it (preserving the commas because of the landfall count requirement).\\n if not filename.endswith('.txt'):\\n print('Input File Must Be a .txt File')\\n return None\\n elif delimeters != '{}=|{}=|{}='.format(column_names[0], column_names[1], column_names[2]):\\n print('Please Check Syntax for Delimeters and colunm_names.')\\n return None\\n else:\\n with open(filename, 'r') as infile:\\n for line in infile:\\n line = line.strip()\\n # This checks to see if line begins with a numeric character; if so, it is a header for a new measurement.\\n if line[0].isnumeric():\\n measurement_current_line = line.split()\\n # This initializes a new measurement dictionary with the 3 items in column_names\\n key = measurement_current_line[0]\\n new_measurement_dictionary = {\\n column_names[0]: '0',\\n column_names[1]: '0',\\n column_names[2]: '0',\\n }\\n #print(measurement_current_line)\\n # this determines if a line starts with 'X', splits it at the X =,Y =,Z = indicators\\n # to spit out a list containing only the 3 values and then updates the corresponding\\n # value in the dictionary\\n if line[0] == first_char:\\n measurement_current_line = re.split(delimeters, line.strip(' '))\\n if len(measurement_current_line) == 4:\\n new_measurement_dictionary[column_names[0]] = float(measurement_current_line[1].strip())\\n new_measurement_dictionary[column_names[1]] = float(measurement_current_line[2].strip())\\n new_measurement_dictionary[column_names[2]] = float(measurement_current_line[3].strip())\\n measurements_file_container[key] = new_measurement_dictionary\\n # this stops the processing when the end of data key '$$EOE' is reached.\\n elif line == '$$EOE':\\n break\\n\\n\\n return(measurements_file_container)\",\n \"def mapper(self, line_no, line):\\n cell = csv_readline(line)\\n if cell[0] == 'V':\\n yield cell[4],1\",\n \"def gen_dict():\\n lines = [line for line in csv.reader(open(__ppath__ + \\\"/data/occupations.csv\\\"))] # uses a csv.reader to parse the file, converts the generic iterable to a list\\n lines = [(line[0],float(line[1])) for line in lines[1:-2]]# removes the column names and \\\"Total\\\" row, re-expresses as a list of tuples to enable dictionary conversion\\n lines.append((\\\"Unemployed\\\",0.2)) # accounts for missing 0.2% of jobs\\n return dict(lines) # converts to dictionary\",\n \"def columnar(row_table: list[dict[str, str]]) -> dict[str, list[str]]:\\n result: dict[str, list[str]] = {}\\n \\n first_row: dict[str, str] = row_table[0]\\n for column in first_row:\\n result[column] = column_values(row_table, column)\\n \\n return result\",\n \"def write_row(row: dict):\\n row = {k: format_float(v) for k, v in row.items()}\\n writer.writerow(row)\\n csvfile.flush()\",\n \"def loadData(self, aircraftCSV='aircraft.csv'):\\n aircraftDict = {}\\n \\n with open(aircraftCSV, 'r') as f:\\n reader = csv.reader(f, delimiter=',')\\n for line in reader:\\n #if imperial convert to metric\\n if line[2] == 'imperial':\\n range = float(line[4]) * 8 / 5\\n else:\\n range = float(line[4])\\n aircraftDict[line[0]] = [line[1], line[3], range]\\n self.aircraftDict = aircraftDict\",\n \"def process_columns(tup: tuple):\\n column_name, data, source_name, data_type, quantiles = tup\\n column = Column(column_name, data, source_name, data_type, quantiles)\\n print(\\\"Processing column: \\\", column.get_long_name())\\n column.quantile_histogram = QuantileHistogram(column.get_long_name(), column.ranks, column.size, quantiles)\\n with open('cache/' + column.get_long_name() + '.pkl', 'wb') as output:\\n pickle.dump(column, output, pickle.HIGHEST_PROTOCOL)\",\n \"def _deduct_types(cls, row: Row) -> ColumnTypes:\\n return {\\n key: get_value_type(cls.whole_number_to_int(value))\\n for key, value in row.items()\\n }\",\n \"def map_csv_dimensions(length: str, width: str, height: str):\\n return {\\n \\\"length\\\": length,\\n \\\"width\\\": width,\\n \\\"height\\\": height\\n }\",\n \"def getHourlyWeatherFromCSV(self,town,scale,key):\\n\\n\\t\\t# Variables\\n\\t\\tfile = \\\"data/weather/\\\"+town+\\\"_\\\"+scale+\\\".csv\\\"\\n\\t\\tcsv_data = []\\n\\t\\tweather_data = []\\n\\t\\tweather = {}\\n\\n\\t\\t# Reading csv file and storing data in file\\n\\t\\twith open(file) as csvfile:\\n\\t\\t\\treader = csv.DictReader(csvfile)\\n\\t\\t\\tfor row in reader:\\n\\t\\t\\t\\tcsv_data.append(row) \\n\\t\\t# Getting data that is needed for visualization\\n\\n\\t\\tprint csv_data\\n\\n\\t\\tfor data in csv_data:\\n\\t\\t\\t# Parsing date\\n\\t\\t\\thour = int(data[\\\"date\\\"].split(\\\" \\\")[4].split(\\\":\\\")[0])\\n\\t\\t\\tpm_or_am = data[\\\"date\\\"].split(\\\" \\\")[5]\\n\\t\\t\\tday = data[\\\"date\\\"].split(\\\",\\\")[0]\\n\\t\\t\\tif hour == 12 and pm_or_am == \\\"AM\\\":\\n\\t\\t\\t\\tdata[\\\"date\\\"] = \\\"\\\".join(data[\\\"date\\\"].split(\\\" \\\")[:-2]) + \\\" 00:00\\\"\\n\\t\\t\\telif hour < 10 and pm_or_am == \\\"AM\\\":\\n\\t\\t\\t\\tdata[\\\"date\\\"] = \\\"\\\".join(data[\\\"date\\\"].split(\\\" \\\")[:-2]) + \\\" 0\\\" + str(hour) + \\\":00\\\"\\n\\t\\t\\telif hour >= 10 and pm_or_am == \\\"AM\\\":\\n\\t\\t\\t\\tdata[\\\"date\\\"] = \\\"\\\".join(data[\\\"date\\\"].split(\\\" \\\")[:-2]) + \\\" \\\" + str(hour) + \\\":00\\\"\\n\\t\\t\\tif pm_or_am == \\\"PM\\\":\\n\\t\\t\\t\\tif hour == 12: \\n\\t\\t\\t\\t\\tdata[\\\"date\\\"] = \\\"\\\".join(data[\\\"date\\\"].split(\\\" \\\")[:-2]) + \\\" \\\" + str(hour) + \\\":00\\\"\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\thour +=12\\n\\t\\t\\t\\t\\tdata[\\\"date\\\"] = \\\"\\\".join(data[\\\"date\\\"].split(\\\" \\\")[:-2]) + \\\" \\\" + str(hour) + \\\":00\\\"\\n\\t\\t\\tweather[\\\"date\\\"] = data[\\\"date\\\"]\\n\\n\\t\\t\\t# Appending weather data\\n\\t\\t\\tweather[key] = data[key]\\n\\t\\t\\tweather_data.append(weather)\\n\\t\\t\\tweather = {}\\n\\t\\treturn weather_data\",\n \"def parse_trick_ascii(csv_file):\\n data_file = csv.DictReader(open(csv_file))\\n single_run_data_dict = {'altitude' : [0.0],\\n 'latitude' : [0.0],\\n 'longitude' : [0.0]}\\n # Your code here\\n # ...\\n # return the dict\\n return single_run_data_dict\",\n \"def _process_csv_data(csv_file, user_data_map):\\n with open(csv_file, 'r') as csvfile:\\n rows = csv.reader(csvfile)\\n for row in rows:\\n if len(row) < 2:\\n print('The CSV file is not in expected format.')\\n raise Exception\\n user_data_map[row[1].lower()] = row[0]\",\n \"def mapper(self, _, doc):\\n ret = doc.split('\\\\t')\\n key = ret[2]\\n values = {}\\n try:\\n values[\\\"ts_ini\\\"] = datetime.utcfromtimestamp(float(ret[0]))\\n except:\\n values[\\\"ts_ini\\\"] = None\\n try:\\n values[\\\"ts_end\\\"] = datetime.utcfromtimestamp(float(ret[1]))\\n except:\\n values[\\\"ts_end\\\"] = None\\n try:\\n values[\\\"value\\\"] = ret[3]\\n except:\\n values[\\\"value\\\"] = None\\n try:\\n values[\\\"energytype\\\"] = ret[4]\\n except:\\n values[\\\"energytype\\\"] = None\\n try:\\n values[\\\"source\\\"] = ret[5]\\n except:\\n values[\\\"source\\\"] = None\\n\\n yield key, values\",\n \"def _get_cell_map(row: TRowResult) -> Dict[bytes, TCell]:\\n if row.sortedColumns is not None:\\n return {c.columnName: c.cell for c in row.sortedColumns}\\n elif row.columns is not None:\\n return row.columns\\n else: # pragma: no cover\\n raise RuntimeError(\\\"Neither columns nor sortedColumns is available!\\\")\",\n \"def SAM_CSV_to_solar_data(filename):\\n if not os.path.isfile(filename):\\n raise FileNotFoundError(filename + \\\" does not exist.\\\")\\n wfd = defaultdict(list)\\n with open(filename) as file_in:\\n info = []\\n for i in range(2):\\n info.append(file_in.readline().rstrip())\\n info[i] = info[i].split(\\\",\\\")\\n if \\\"Time Zone\\\" not in info[0]:\\n raise ValueError(\\\"`Time Zone` field not found in solar resource file.\\\")\\n latitude = info[1][info[0].index(\\\"Latitude\\\")]\\n longitude = info[1][info[0].index(\\\"Longitude\\\")]\\n tz = info[1][info[0].index(\\\"Time Zone\\\")]\\n elev = info[1][info[0].index(\\\"Elevation\\\")]\\n source = info[1][info[0].index(\\\"Source\\\")]\\n reader = csv.DictReader(file_in)\\n for row in reader:\\n for col, dat in row.items():\\n if len(col) > 0:\\n wfd[col].append(float(dat))\\n\\n weather = dict()\\n weather['tz'] = float(tz)\\n weather['elev'] = float(elev)\\n weather['lat'] = float(latitude)\\n weather['lon'] = float(longitude)\\n\\n # Create dict with keys = keys passed to SAM and values = list of possible key versions found in resource files (NREL / NASA POWER)\\n acceptable_keys = {\\n 'year' : ['year', 'Year', 'yr'],\\n 'month' : ['month', 'Month', 'mo'],\\n 'day' : ['day', 'Day'],\\n 'hour' : ['hour', 'Hour', 'hr'],\\n 'minute' : ['minute', 'Minute', 'min'],\\n 'dn' : ['dn', 'DNI','dni', 'beam', 'direct normal', 'direct normal irradiance'],\\n 'df' : ['df', 'DHI', 'dhi', 'diffuse', 'diffuse horizontal', 'diffuse horizontal irradiance'],\\n 'gh' : ['gh', 'GHI','ghi', 'global', 'global horizontal', 'global horizontal irradiance'],\\n 'wspd' : ['wspd', 'Wind Speed', 'wind speed'],\\n 'tdry' : ['tdry', 'Temperature', 'dry bulb', 'dry bulb temp', 'temperature', 'ambient', 'ambient temp'],\\n 'wdir' : ['wdir', 'Wind Direction', 'wind direction'],\\n 'pres' : ['pres', 'Pressure', 'pressure'],\\n 'tdew' : ['tdew', 'Dew Point', 'Tdew', 'dew point', 'dew point temperature'],\\n 'rhum' : ['rhum', 'Relative Humidity', 'rh', 'RH', 'relative humidity', 'humidity'],\\n 'alb' : ['alb', 'Surface Albedo', 'albedo', 'surface albedo'],\\n 'snow' : ['snow', 'Snow Depth', 'snow depth', 'snow cover']\\n }\\n \\n # enumerates acceptable_keys, inserts key and values into weather dictionary if found in the resource file\\n for key, list_of_keys in acceptable_keys.items():\\n for good_key in list_of_keys:\\n if good_key in wfd.keys():\\n weather[key] = wfd.pop(good_key)\\n break\\n\\n # handles averaged hourly data with no minute column provided by NASA POWER and removes 2/29 data for leap years\\n # this is a workaround so PySAM/SAM processes as instantaneous data (not setup to handle no minute column)\\n if source == 'NASA/POWER':\\n weather['minute'] = [30] * len(weather['hour'])\\n if len(weather['hour']) == 8784:\\n for key in weather.keys():\\n if key not in ['tz','elev','lat','lon']:\\n del weather[key][1416:1440]\\n\\n\\n return weather\",\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 cell_map_from_csv(self, source_file: str) -> None:\\n if source_file[-4:] == '.csv':\\n try:\\n self._import_source_data(source_file)\\n except Exception:\\n print(\\\"Problem with that CSV file. File extension?\\\")\",\n \"def map_column_values(self, table: Table, column: Column, name: str, *args):\\n self._requires_table(table)\\n\\n values = []\\n for index in table.index:\\n cell = table.get_cell(index, column)\\n output = BuiltIn().run_keyword(name, cell, *args)\\n values.append(output)\\n\\n table.set_column(column, values)\",\n \"def mapRow(this_row, header_dict, precursors_mapping, sequences_mapping, protein_mapping):\\n\\n if \\\"FullPeptideName\\\" in header_dict:\\n\\n peptide_name = this_row[header_dict[\\\"FullPeptideName\\\"]]\\n\\n transitions = []\\n pr_transitions = []\\n if \\\"aggr_Fragment_Annotation\\\" in header_dict:\\n transitions = this_row[ header_dict[\\\"aggr_Fragment_Annotation\\\"] ].split(\\\";\\\")\\n if \\\"aggr_prec_Fragment_Annotation\\\" in header_dict:\\n pr_transitions = this_row[ header_dict[\\\"aggr_prec_Fragment_Annotation\\\"] ].split(\\\";\\\")\\n\\n # Skip row if there are no transitions\\n if len(transitions) == 0:\\n return\\n\\n if len(transitions[-1]) == 0:\\n transitions = transitions[:-1]\\n if len(pr_transitions) > 0 and len(pr_transitions[-1]) == 0:\\n pr_transitions = pr_transitions[:-1]\\n\\n # Get charge state (may be absent)\\n charge_state = \\\"0\\\"\\n if \\\"Charge\\\" in header_dict:\\n charge_state = this_row[header_dict[\\\"Charge\\\"]]\\n\\n if charge_state == \\\"NA\\\" or charge_state == \\\"\\\":\\n charge_state = \\\"0\\\"\\n\\n key = peptide_name + \\\"/\\\" + charge_state\\n prkey = peptide_name + \\\"/\\\" + charge_state + \\\"_pr\\\"\\n precursors_mapping [ key ] = transitions\\n precursors_mapping [ prkey ] = pr_transitions\\n mapped_precursors = sequences_mapping.get( peptide_name, [] )\\n mapped_precursors.extend([key, prkey])\\n sequences_mapping[peptide_name] = mapped_precursors # = [ key, prkey ]\\n\\n if \\\"ProteinName\\\" in header_dict:\\n protein_name = this_row[header_dict[\\\"ProteinName\\\"]]\\n\\n tmp = protein_mapping.get(protein_name, [])\\n if peptide_name not in tmp:\\n tmp.append(peptide_name)\\n protein_mapping[protein_name] = tmp\",\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 load_csv(file):\\n import csv\\n reader = csv.reader(open(file, 'r'))\\n columns = reader.next()\\n c2i = dict((columns[i], i) for i in range(len(columns)))\\n data = {}\\n excluded = set([REP_CSV_HED_TIM, REP_CSV_HED_HER])\\n for row in reader:\\n \\n # get relevant info from the line\\n time = float(row[c2i[REP_CSV_HED_TIM]])\\n hero = row[c2i[REP_CSV_HED_HER]]\\n other = dict((c, REP_CSV_HANDLERS.get(c, REP_CSV_DEFHANDLER)(row[c2i[c]])) for c in columns if c not in excluded)\\n \\n # add to the data dictionary\\n if hero not in data: data[hero] = []\\n data[hero].append([time] + [other])\\n \\n return data\",\n \"def read_features_dict(path):\\n # type_dict specifies the type conversion to be applied. Each key denotes\\n # a column name and the value is the conversion. Columns not included are\\n # converted to floats.\\n type_dict = {'source': str, 'target': str, 'status': int}\\n with open(path) as feature_file:\\n reader = csv.DictReader(feature_file, delimiter='\\\\t')\\n for row in reader:\\n yield {key: type_dict.get(key, float)(value) for key, value in row.items()}\",\n \"def map_field_value(\\n row: DLCSRecord, field_name: str, config: typing.Dict\\n) -> typing.Any:\\n mapping: mapper.MappigDictValue = mapper.FIELD_MAPPING[field_name]\\n\\n if mapping is None:\\n return None\\n\\n if callable(mapping):\\n return mapping(row)\\n\\n if isinstance(mapping, str):\\n mapping = [mapping]\\n\\n if not isinstance(mapping, typing.Collection):\\n raise TypeError(\\n f\\\"FIELD_MAPPING[field_name] must be iterable, unless it is None, Callable, or a string.\\\"\\n )\\n\\n output: typing.List[str] = []\\n for csv_field in mapping:\\n input_value = row.get(csv_field)\\n if input_value:\\n if isinstance(input_value, str):\\n output.extend(input_value.split(\\\"|~|\\\"))\\n else:\\n output.append(input_value)\\n\\n bare_field_name = get_bare_field_name(field_name)\\n if bare_field_name in config.get(\\\"controlled_fields\\\", {}):\\n terms = config[\\\"controlled_fields\\\"][bare_field_name][\\\"terms\\\"]\\n output = [terms.get(value, value) for value in output]\\n\\n return [value for value in output if value] # remove untruthy values like ''\",\n \"def process_row(row, location_map):\\n\\n # Add in IP columns\\n ips = get_ip_extent(row[IP_PREFIX_COLUMN])\\n ipv6_ips = get_ip_extent(row[IPV6_PREFIX_COLUMN])\\n\\n continent_code = CONTINTENT_CODES[row[CONTINENT_COLUMN]] if row[CONTINENT_COLUMN] in CONTINTENT_CODES else None\\n site_id = row[SITE_COLUMN]\\n\\n location = location_map[site_id.lower()]\\n\\n # simplify\\n row = {\\n 'site': site_id.lower(),\\n 'latitude': location['latitude'],\\n 'longitude': location['longitude'],\\n 'city': row[CITY_COLUMN],\\n 'region_code': row[REGION_CODE_COLUMN],\\n 'country_code': row[COUNTRY_CODE_COLUMN],\\n 'continent_code': continent_code,\\n 'min_ip_hex': hex_encode_ip(ips[0]) if ips else None,\\n 'max_ip_hex': hex_encode_ip(ips[-1]) if ips else None,\\n 'transit_provider': row[TRANSIT_PROVIDER],\\n 'min_ip': str(ips[0]) if ips else None,\\n 'max_ip': str(ips[-1]) if ips else None,\\n 'ip_prefix': normalize_ip(row[IP_PREFIX_COLUMN]),\\n 'min_ipv6_hex': hex_encode_ip(ipv6_ips[0]) if ipv6_ips else None,\\n 'max_ipv6_hex': hex_encode_ip(ipv6_ips[-1]) if ipv6_ips else None,\\n 'min_ipv6': str(ipv6_ips[0]) if ipv6_ips else None,\\n 'max_ipv6': str(ipv6_ips[-1]) if ipv6_ips else None,\\n 'ipv6_prefix': normalize_ip(row[IPV6_PREFIX_COLUMN]),\\n }\\n\\n return row\",\n \"def create_table(f, geoinfo):\\n bounds_cols = xb_points + yb_points\\n df = pd.read_csv(f, delimiter=\\\";\\\", index_col=\\\"INDEX_RC\\\")\\n df[duration_name] = parse_duration_level(f)\\n df = df.join(geoinfo[[\\\"X_CENT_GEO\\\", \\\"Y_CENT_GEO\\\", \\\"Col\\\", \\\"Row\\\"]])\\n df = df.rename(columns={\\\"Col\\\": x, \\\"Row\\\": y, \\\"X_CENT_GEO\\\": lon, \\\"Y_CENT_GEO\\\": lat})\\n return df\",\n \"def read_file(file):\\n \\n dictionary = {}\\n csv_fp = csv.reader(file)\\n #L[46] = manufacturer, L[63] = year\\n #L[4]= city mileage, L[34]=highway mileage\\n for line in csv_fp:\\n #Skip the headings and the year 2017\\n if (not (line[46] == 'make')) and (not (line[63] == '2017')):\\n if line[46] in dictionary:\\n #Add the city and highway mileage if the year has been made\\n if line[63] in dictionary[line[46]]:\\n dictionary[line[46]][line[63]][0] += [int(line[4])]\\n dictionary[line[46]][line[63]][1] += [int(line[34])]\\n #Add the year and data if it was not made previously\\n else:\\n dictionary[line[46]][line[63]] = [[int(line[4])],\\\\\\n [int(line[34])]]\\n #Adds a new manufacturer\\n else:\\n dictionary[line[46]] = {line[63]:[[int(line[4])],\\\\\\n [int(line[34])]]}\\n return dictionary\",\n \"def read_delsys_csv(filename: str) -> Dict[str, Dict[str, TimeSeries]]:\\n # Check the number of rows to skip\\n n_rows = 0\\n with open(filename, 'r') as fid:\\n while True:\\n s = fid.readline()\\n if s.startswith('X[s]'):\\n break\\n else:\\n n_rows += 1\\n\\n # Open the CSV\\n df = pd.read_csv(filename, skiprows=n_rows)\\n\\n # Create a TimeSeries for each signal since they all have different time\\n # vectors\\n n_signals = int(len(df.columns) / 2)\\n\\n emg = {}\\n acc = {}\\n gyro = {}\\n mag = {}\\n\\n for i_signal in range(n_signals):\\n time = df.iloc[:, i_signal * 2].to_numpy()\\n name = df.columns[i_signal * 2 + 1]\\n data = df.iloc[:, i_signal * 2 + 1].to_numpy()\\n\\n if ': Acc' in name:\\n short_name = name\\n ts = TimeSeries(time=time, data={short_name: data})\\n acc[short_name] = ts\\n elif ': Mag' in name:\\n short_name = name\\n ts = TimeSeries(time=time, data={short_name: data})\\n mag[short_name] = ts\\n elif ': Gyro' in name:\\n short_name = name\\n ts = TimeSeries(time=time, data={short_name: data})\\n gyro[short_name] = ts\\n elif ': EMG' in name:\\n short_name = name.split(':')[0]\\n ts = TimeSeries(time=time, data={short_name: data})\\n emg[short_name] = ts\\n\\n return {'emg': emg, 'acc': acc, 'gyro': gyro, 'mag': mag}\",\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 csv2dicts(csvfile, names=None):\\n data = []\\n for row_index, row in enumerate(csvfile):\\n if row_index == 0:\\n if names:\\n keys = names\\n else:\\n keys = row\\n print(keys)\\n continue\\n data.append({key: value for key, value in zip(keys, row)})\\n return data\",\n \"def extract_sensors_data(dataframe, ms_column='ms_ticker',\\n time_column = 'Tstamp',\\n ppg_columns=['led_1', 'led_2'],\\n acc_columns=['acc_x', 'acc_y', 'acc_z']):\\n\\n sensors_dict = {}\\n sensors = dataframe.loc[1:, 1:]\\n sensors_columns = dataframe.head(1).values[0]\\n sensors_columns = [i.replace(\\\" \\\", \\\"\\\") for i in sensors_columns if i.find('Index') == -1]\\n sensors.columns = sensors_columns\\n check_columns_exist(ppg_columns, sensors_columns)\\n check_columns_exist(acc_columns, sensors_columns)\\n check_columns_exist(ms_column, sensors_columns)\\n check_columns_exist(time_column, sensors_columns)\\n ppg = np.array(sensors[ppg_columns].values[1:, :], dtype=int)\\n ms = np.array(sensors[ms_column].values[1:, ])\\n ms_ints = np.array([int(str(i)[-3:]) for i in ms], dtype=float)\\n ms_delta = [datetime.timedelta(milliseconds=i) for i in ms_ints]\\n\\n time = dataframe.loc[:,1].values[1:]\\n time = np.array([pd.to_datetime(i) for i in time])\\n time_with_ms = np.array(ms_delta) + time\\n\\n sensors_dict['PPG'] = ppg\\n sensors_dict['time_sensors'] = time_with_ms.astype('datetime64[us]')\\n sensors_dict['ms_ticker_sensors'] = ms\\n acc = np.array(sensors[acc_columns].values[1:, :], dtype=float)\\n sensors_dict['ACC'] = acc\\n\\n return sensors_dict\",\n \"def csv2dict(filename):\\n dis_dict = {}\\n with open(filename) as csvfile:\\n reader = csv.DictReader(csvfile)\\n for row in reader:\\n el_a = row[\\\"Element Name\\\"]\\n dis_dict[el_a] = {}\\n for entry in row:\\n if entry != \\\"Element Name\\\":\\n dis_dict[el_a][entry] = float(row[entry])\\n csvfile.close()\\n return dis_dict\",\n \"def generic_mapper(fname, path, delimiter='\\\\t'):\\n\\tout = {'filename':fname}\\n\\t_, meta_data = read_radiant_txt(path+fname, delimiter=delimiter, return_meta_data=True)\\n\\tkeys = ['Type', 'SampleName', 'SampleArea(cm2)', 'SampleThickness(um)', 'Volts', 'Field', 'PulseWidth(ms)', 'PulseDelay(ms)']\\n\\tfor key in keys:\\n\\t\\ttry:\\n\\t\\t\\tout.update({key:float(meta_data[key])})\\n\\t\\texcept ValueError:\\n\\t\\t\\tout.update({key:meta_data[key]})\\n\\t\\texcept KeyError:\\n\\t\\t\\tout.update({key:np.nan})\\n\\t\\t\\t\\n\\t# period is a weird one\\n\\ttry:\\n\\t\\tout.update({'Period(ms)':float(meta_data['{}Period(ms)'.format(meta_data['Type'].capitalize())])})\\n\\texcept:\\n\\t\\tpass\\n\\treturn out\",\n \"def format_row(rowvals, dtypes, colnames):\\n mapped_vals = []\\n for colnum, val in enumerate(rowvals):\\n pgtype = dtype_to_pgtype(dtypes[colnum], colnames[colnum])\\n mapped_vals.append(numpy_val_to_pg_val(val, pgtype))\\n\\n return ','.join(mapped_vals)\",\n \"def agline(line):\\n\\n vals = {}\\n x = ['date', 'movie', 'offset', 'well', 'gen', 'flarem', 'flares', \\n 'chargem', 'charges', 'charget', 'escdm', 'escds', 'escddur', 'escmm', 'escms', \\n 'escmdur']\\n y = line.strip('\\\\n').split(',')[0:16]\\n z = zip(x, y)\\n\\n for item in z:\\n vals[item[0]] = item[1]\\n\\n return(vals)\",\n \"def _row_to_dict(row, fields):\\n dict_row = {}\\n for i, value in enumerate(row):\\n key = fields[i]\\n if value and str(value).lower() == 'nan':\\n value = None\\n dict_row[key] = value\\n return dict_row\",\n \"def read_polyidus_csv(filename):\\n\\twith open(filename, newline = '') as filehandle:\\n\\t\\t\\n\\t\\t# create DictReader objects for inputs and read into memory\\n\\t\\treader = csv.DictReader(filehandle, delimiter = '\\\\t')\\n\\t\\tdata = []\\n\\t\\tread_ids = []\\n\\t\\t\\n\\t\\tfor row in reader:\\n\\t\\t\\trow_data = {}\\n\\t\\t\\trow_data['Chr'] = row['Chrom']\\n\\t\\t\\trow_data['VirusRef'] = row['ChromVirus']\\n\\t\\t\\trow_data['OverlapType'] = 'none'\\n\\t\\t\\trow_data['Type'] = 'chimeric'\\n\\t\\t\\t\\n\\t\\t\\thPosition = int(row['IntegrationSite'])\\n\\t\\t\\tvPosition = int(row['ViralIntegrationSite'])\\n\\t\\t\\t#hOris = row['StrandHost'].split(', ')\\n\\t\\t\\treadIDs = row['FragmentName'].split(', ')\\n\\t\\t\\t\\n\\t\\t\\t# make one row per read, if we haven't already used this read\\n\\t\\t\\tfor i, read in enumerate(readIDs):\\n\\t\\t\\t\\tif read not in read_ids:\\n\\t\\t\\t\\t\\tread_ids.append(read)\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t# need to make copy of dict\\n\\t\\t\\t\\t\\trow_data = dict(row_data)\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t# add info about this read to dict\\n\\t\\t\\t\\t\\trow_data['IntStart'] = hPosition\\n\\t\\t\\t\\t\\trow_data['IntStop'] = hPosition\\n\\t\\t\\t\\t\\trow_data['VirusStart'] = vPosition\\n\\t\\t\\t\\t\\trow_data['VirusStop'] = vPosition\\n\\t\\t\\t\\t\\t#row_data['Orientation'] = 'hv' if hOris[i] == \\\"Positive\\\" else 'vh'\\n\\t\\t\\t\\t\\trow_data['type'] = 'chimeric'\\n\\t\\t\\t\\t\\trow_data['ReadID'] = read\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\tdata.append(row_data)\\n\\n\\treturn data\",\n \"def _getValues(self):\\n res = {}\\n for colname, column in self._iterNameColumn():\\n res[colname] = column.toSql(self._values[colname])\\n return res\",\n \"def create_assumption_map(columns, df):\\n assumption_map = pd.DataFrame(columns=columns)\\n\\n for fuel in fuel_types:\\n for value in values:\\n if fuel == \\\"coal\\\" and value == \\\"delta_capex\\\":\\n retrofit = True\\n else:\\n retrofit = False\\n array = values_array(df, fuel, value, retrofit=retrofit)\\n mean, trim_mean_v, std, top_p, bot_p = value_stats(array)\\n\\n if value == \\\"delta_capex\\\":\\n units = \\\"2019€_KW\\\"\\n elif value == \\\"delta_om\\\":\\n units = \\\"2019€_KWh\\\"\\n elif value == \\\"delta_heatrate\\\":\\n units = \\\"KW_KWh\\\"\\n assumption_map = assumption_map.append({\\\"fuel_type\\\": fuel, \\\"value\\\": value, \\\"range_low\\\": bot_p,\\n \\\"range_high\\\": top_p, \\\"reference_value\\\": mean, \\\"units\\\": units},\\n ignore_index=True)\\n return assumption_map\",\n \"def parse_column_units(\\n filepath: Path, sheet_name: str = None\\n) -> Dict[str, ureg.Quantity]:\\n method = map_input_method(filepath)\\n unit_header = method(str(filepath), nrows=1, header=0)\\n given_unit = lambda s: unit_header[s].values[0]\\n parsed_units = {}\\n required_columns = [\\n \\\"Supply Temperature\\\",\\n \\\"Target Temperature\\\",\\n \\\"Heat Capacity Flowrate\\\",\\n \\\"Enthalpy\\\",\\n ]\\n\\n for column in required_columns:\\n units = ureg.parse_units(given_unit(column))\\n parsed_units[column] = units\\n\\n return parsed_units\",\n \"def input_row():\\n return {\\n 'foo': 1,\\n 'bar': 2,\\n 'spam': 3,\\n 'eggs': 4\\n }\",\n \"def process_csv(self):\\n with open(self.filepath, mode=\\\"r\\\") as csv_file:\\n csv_reader = csv.reader(csv_file, delimiter=\\\",\\\")\\n header = next(csv_reader)\\n\\n date_idx = self._get_header_position(header, \\\"Label\\\")\\n power_idx = self._get_header_position(header, \\\"kW\\\")\\n\\n if self.inverter_id not in header[power_idx]:\\n raise Exception(\\\"Inverter data returned for the incorrect meter.\\\")\\n\\n for row in csv_reader:\\n date_obj = self.csv_str_to_date(row[date_idx])\\n power = float(row[power_idx] or 0)\\n\\n current_date = self.date_to_final_str(date_obj)\\n rounded_time = self.round_up_to_quarter_hour(date_obj)\\n\\n if current_date not in self.intermediate_readings:\\n self.intermediate_readings[\\n current_date\\n ] = self.build_intermediate_dict()\\n\\n current_reading = self.intermediate_readings[current_date][rounded_time]\\n # Here's where we sum power readings together - in to fifteen min intervals\\n self.intermediate_readings[current_date][rounded_time] = (\\n current_reading + power\\n )\\n\\n actual_time = self.date_to_intermediate_time_str(date_obj)\\n if rounded_time == actual_time:\\n # Here's where we average power readings together, in fifteen minute intervals\\n self.intermediate_readings[current_date][rounded_time] = round(\\n float(\\n self.intermediate_readings[current_date][rounded_time] / 3\\n ),\\n 2,\\n )\\n\\n return self.finalize_readings()\",\n \"def getRiverIDs(lookupCsv):\\n\\n d = {}\\n with open(lookupCsv, \\\"rb\\\") as f:\\n reader = csv.reader(f)\\n\\n # Discard header row\\n reader.next()\\n\\n for row in reader:\\n d[row[0]] = row[1]\\n\\n return d\",\n \"def _row_to_labels(row):\\n labels = {}\\n label_keys = ['name', 'qty', 'range_end', 'unit', 'comment']\\n for key in label_keys:\\n labels[key] = row[key]\\n return labels\",\n \"def load_columns(self, csv_data):\\n column_date = []\\n column_time = []\\n column_hold = []\\n column_outcome = []\\n for row in dataframe_to_rows(csv_data, index=False):\\n cell_date = row[18]\\n cell_date = cell_date.split(': ')[1]\\n cell_time = row[23]\\n cell_hold = row[24]\\n cell_outcome = row[25]\\n column_date.append(cell_date)\\n column_time.append(cell_time)\\n column_hold.append(cell_hold)\\n column_outcome.append(cell_outcome)\\n return column_date, column_time, column_hold, column_outcome\",\n \"def _convert_genres(self, df, genre_map):\\n genre_transfrm = pd.read_csv(genre_map)\\n genre = {}\\n for row_label, row_array_Series in genre_transfrm.iterrows():\\n genre[row_array_Series['Old']] = row_array_Series['New']\\n\\n df['MappedGenres'] = df['Genres'].map(genre)\\n try:\\n assert df['MappedGenres'].notna().all()\\n except:\\n print(\\\"WARNING: The Genre CSV needs to be updated. The following Genres need to be mapped: \\\")\\n print([*df['Genres'][df['MappedGenres'].isna()].values])\",\n \"def meta_properties(self, date_col=\\\"Date\\\", type_col=\\\"Primary Type\\\", lat_col=\\\"Latitude\\\",\\\\\\n lon_col=\\\"Longitude\\\", loc_col=\\\"Location\\\", out_fname=\\\"data_formated.csv\\\"):\\n # implement keywords\\n # would we have to deal w/ file w/o headers?\\n data = pd.read_csv(self._path, usecols=[date_col, type_col, lat_col, lon_col, loc_col],\\\\\\n parse_dates=[date_col], infer_datetime_format=True)\\n data.sort_values(date_col, inplace=True)\\n min_date = data.iloc[0][date_col]\\n max_date = data.iloc[(data.shape[0]-1)][date_col]\\n\\n lat = []\\n lon = []\\n\\n nulls = []\\n for row in data.itertuples(index=True, name='Pandas'):\\n index = (row.Index)\\n # if lat, lon = nan, drop the row\\n # update: confirmed that issue is with code, not with data; for some reason\\n # csv is actually correctly grabbing location, there just legitimately are\\n # entries w/o location data\\n if pd.isnull(getattr(row, loc_col)):\\n # print(\\\"row: {} got a {} for the 'Location' column with date: {}\\\".format(index, \\\\\\n # getattr(row, loc_col), getattr(row, date_col)))\\n if not pd.isnull(getattr(row, lat_col)) and not pd.isnull(getattr(row, lon_col)):\\n lat.append(str(getattr(row, lat_col)))\\n lon.append(str(getattr(row, lon_col)))\\n if \\\",\\\" in data.loc[index, type_col]:\\n data.loc[index, type_col] = data.loc[index, type_col].replace(\\\",\\\", \\\" \\\")\\n print(\\\\\\n \\\"Successfully extracted lat, lon from lat_col, lon_col for row: {}\\\".format(index))\\n else:\\n nulls.append((index, getattr(row, date_col)))\\n data.drop(index, inplace=True)\\n # print(\\\"No location data available for row: {} with date: {}\\\".format(index,\\\\\\n # getattr(row, date_col)))\\n else:\\n loc = literal_eval(getattr(row, loc_col))\\n lat.append(loc[0])\\n lon.append(loc[1])\\n if \\\",\\\" in data.loc[index, type_col]:\\n data.loc[index, type_col] = data.loc[index, type_col].replace(\\\",\\\", \\\" \\\")\\n\\n data[\\\"Latitude\\\"] = lat\\n data[\\\"Longitude\\\"] = lon\\n data.drop(loc_col, axis=1, inplace=True)\\n\\n data.sort_values(\\\"Latitude\\\", inplace=True)\\n min_lat = float(data.iloc[0][\\\"Latitude\\\"])\\n max_lat = float(data.iloc[(data.shape[0]-1)][\\\"Latitude\\\"])\\n\\n data.sort_values(\\\"Longitude\\\", inplace=True)\\n min_lon = float(data.iloc[0][\\\"Longitude\\\"])\\n max_lon = float(data.iloc[(data.shape[0]-1)][\\\"Longitude\\\"])\\n\\n data.to_csv(self.__file_dir+'/'+out_fname, na_rep=\\\"\\\", header=False, index=False)\\n\\n attrs = {'min_date': min_date, 'max_date': max_date, \\\"min_lat\\\":min_lat,\\\\\\n \\\"max_lat\\\":max_lat, \\\"min_lon\\\":min_lon, \\\"max_lon\\\":max_lon, \\\\\\n \\\"dates\\\":pd.date_range(min_date, max_date), \\\"num_attributes\\\": data.shape[1],\\\\\\n \\\"num_entries\\\":data.shape[0]}\\n self._meta_dict = attrs\\n self._meta_dict['df'] = data\\n pickle.dump(data, open(CWD + \\\"/meta_dict.p\\\", \\\"wb\\\"))\\n print(\\\"Num entries w/o location data: {}\\\".format(len(nulls)))\\n pickle.dump(nulls, open(CWD + \\\"/nulls.p\\\", \\\"wb\\\"))\\n\\n # not include the formatted dataset?\\n return attrs\",\n \"def counters(line):\\n reader = csv.reader(StringIO(line), delimiter=',')\\n row = reader.next()\\n\\n airport = row[4]\\n\\n try:\\n delay = float(row[8])\\n except ValueError:\\n delay = 0.0\\n\\n return (airport, (1, delay, delay ** 2, delay, delay))\",\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 _gen_data(fhs, columns, sep):\\n for fh in fhs:\\n for line in fh:\\n if line[0] == \\\"#\\\": continue\\n toks = line.split(sep)\\n yield toks[columns[0]], int(toks[columns[1]]), float(toks[columns[2]])\",\n \"def load_csv(input_filename_state):\\n dataset = {}\\n with open(input_filename_state) as f:\\n reader = csv.reader(f)\\n header = next(reader, None)\\n\\n location_col = -1\\n week_ahead_col = -1\\n quantile_col = -1\\n value_col = -1 \\n\\n\\n for i in range(len(header)):\\n if header[i] == \\\"place\\\":\\n location_col = i\\n elif header[i] == \\\"week_ahead\\\":\\n week_ahead_col = i\\n elif header[i] == \\\"quantile\\\":\\n quantile_col = i \\n elif header[i] == \\\"value\\\":\\n value_col = i\\n \\n for row in reader:\\n state = row[location_col]\\n\\n # Skip the state if it is not listed in reichlab's state list.\\n if state not in STATE_ID_MAPPING:\\n continue\\n state_id = STATE_ID_MAPPING[state]\\n week_ahead = int(row[week_ahead_col])\\n quantile = row[quantile_col]\\n val = max(float(row[value_col]), 0)\\n if week_ahead not in dataset:\\n dataset[week_ahead] = {}\\n if state_id not in dataset[week_ahead]:\\n dataset[week_ahead][state_id] = {}\\n dataset[week_ahead][state_id][quantile] = val\\n return dataset\",\n \"def _compute_from_table_values(\\n name=\\\"plant\\\", color=(0/255, 128/255, 0/255),\\n germination_time=(3, 1), \\n r_max=(1.0,1.0),\\n maturation_time=(10, 1),\\n stopping_color=(0, 0, 1),\\n color_step=(10/255, 0, 0),\\n c1=0.1,\\n r_0=0.04\\n ):\\n\\n c2 = 1\\n k1, k2 = 0.3, 0.7\\n unoccluded_c1 = c1 / k2\\n h_0 = 0.1\\n r_max = max(1, np.random.normal(MAX_RADIUS[name][0], MAX_RADIUS[name][1]))\\n # r_max = MAX_RADIUS[name][0] + MAX_RADIUS[name][1]\\n growth_time = generate_growth_time(germination_time, maturation_time, r_max, r_0, k2, c2)\\n\\n return {\\n \\\"germination_time\\\": germination_time,\\n \\\"maturation_time\\\": maturation_time,\\n \\\"k1\\\": k1,\\n \\\"k2\\\": k2,\\n \\\"c1\\\": unoccluded_c1,\\n \\\"c2\\\": c2,\\n \\\"start_radius\\\": r_0,\\n \\\"start_height\\\": h_0,\\n \\\"r_max\\\": r_max,\\n \\\"growth_time\\\": growth_time,\\n \\\"plant_type\\\": name,\\n \\\"color\\\": color,\\n \\\"stopping_color\\\": stopping_color,\\n \\\"color_step\\\": color_step\\n }\",\n \"def daily_speed_sum_map(data):\\n\\t(byte_offset, line_value) = data\\n\\tcolumns = split_into_columns(line_value)\\n\\tif columns[3] != 'speed':\\n\\t\\tyield (\\\"%s_%s\\\" % (columns[0], columns[1][:10]), columns[3])\",\n \"def column_name(name):\\n # Only needs exceptions to standard token cleanup\\n column_map = {\\n \\\"line#\\\" : \\\"ignore\\\",\\n \\\"date\\\" : \\\"timestamp\\\",\\n \\\"rh\\\" : \\\"humidity\\\",\\n \\\"par\\\" : \\\"par_ue\\\"\\n }\\n\\n if name in column_map:\\n return column_map[name]\\n \\n return name\",\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 to_python(self):\\r\\n mapping = {}\\r\\n for row in self.rows:\\r\\n mapping[row[0]] = _format_python_value(row[1])\\r\\n return mapping\",\n \"def load(filename):\\n with open(filename,'r') as fd:\\n csv_in = csv.reader(fd, delimiter=',', quotechar='\\\"')\\n keys = csv_in.next()\\n data = {k:[] for k in keys}\\n for row in csv_in:\\n for k,v in zip(keys,row):\\n data[k].append(float(v))\\n return data\",\n \"def row_to_colKey(row):\\n return [(i[0], (row[0], i[1])) for i in row[1]]\",\n \"def func_value(file):\\n \\n with open( file ) as f:\\n csv_reader = csv.DictReader(f)\\n \\n for row in csv_reader:\\n Cl , Cd = row[' \\\"CL\\\" '] , row[' \\\"CD\\\" ']\\n \\n return float(Cl) , float(Cd)\",\n \"def dict_factory(self, cursor, row):\\n results = {}\\n for index, col_name in enumerate(cursor.description):\\n results[col_name[0]] = row[index]\\n\\n return results\",\n \"def mapper(self, _, line):\\n linea = line.split()\\n causa, fallecidos = linea[0], linea[1]\\n fallecidos_f = float(fallecidos)\\n \\n yield causa, (1, round(fallecidos_f))\",\n \"def map_row(row, mapping, model_class, extra_data_fields=[], cleaner=None, **kwargs):\\n initial_data = kwargs.get('initial_data', None)\\n model = model_class()\\n\\n # _log.debug(\\\"map_row's mappings {}\\\".format(mapping))\\n\\n # If there are any initial states we need to set prior to mapping.\\n if initial_data:\\n model = apply_initial_data(model, initial_data)\\n\\n # concat is not used as of 2016-09-14\\n # concat = _set_default_concat_config(concat)\\n\\n for raw_field, value in row.items():\\n is_extra_data = True if raw_field in extra_data_fields else False\\n\\n # Save the value if is is not None, keep empty fields.\\n if value is not None:\\n model = apply_column_value(raw_field, value, model, mapping, is_extra_data, cleaner)\\n\\n return model\",\n \"def get_data(file_name):\\n csv_file = open(file_name, 'rb')\\n train_content = csv.reader(csv_file)\\n\\n # ignore header\\n train_content.next()\\n\\n # preprocessing functions for each column index\\n # Several preprocessing can be defined for each column.\\n # A new variable is associated to EACH preprocessing function\\n preproc_funcs = {0: ['get_hour']}\\n\\n # Read data from file, store it as an integer\\n data = []\\n for row in train_content:\\n data_row = []\\n for n, col in enumerate(row):\\n # if the current column requires preprocessing functions, apply them\\n if preproc_funcs.has_key(n):\\n # Each preprocessing give a new column\\n for preproc_func in preproc_funcs[n]:\\n func = globals().get(preproc_func)\\n data_row.append(int(float(func(col))))\\n # If no preprocessing, do nothing\\n else:\\n data_row.append(int(float(col)))\\n\\n data.append(data_row)\\n\\n csv_file.close()\\n\\n return data\",\n \"def get_mapping_info(in_path):\\n if not utils.exists_file(in_path):\\n utils.exit(f\\\"Mapping file does not exist:\\\\n{in_path}\\\")\\n df = pd.read_csv(in_path, sep=\\\";\\\", header=None)\\n mapping = {}\\n case_video_count = {}\\n for index, row in df.iterrows():\\n # parts = row[0].split('_')\\n # video_id = \\\"-1\\\"\\n # if len(parts) == 1 or \\\"video\\\" not in parts[0]:\\n # video_id = row[0]\\n # else:\\n # video_id = parts[1]\\n # video_id = video_id.replace('.mp4', '')\\n video_id = row[0]\\n case_id = int(row[2])\\n mapping[f\\\"{video_id}\\\"] = case_id\\n utils.increment_dict_key(case_video_count, f\\\"{case_id}\\\")\\n return mapping, case_video_count\",\n \"def read_csv(file_name):\\n data = {}\\n with open(file_name) as f:\\n f = MyIter(f)\\n try:\\n for line in f:\\n if not line.strip():\\n continue\\n if line == 'Points\\\\n':\\n break\\n key, val = read_key_value(line, separator=',')\\n key = key.lower().replace(' ', '_')\\n data[key] = val\\n\\n x_units, y_units = next(f).split(',')\\n data['x_units'], data['y_units'] = x_units.strip(), y_units.strip()\\n\\n xs, ys = [], []\\n for line in f:\\n x, y = line.split(',')\\n xs.append(float(x.strip()))\\n ys.append(float(y.strip()))\\n except Exception as e:\\n print(f'Error on line {f._index}')\\n print(f._line)\\n raise e\\n\\n elong = Elongation(\\n np.array(xs), np.array(ys),\\n float(data['gauge_length']),\\n float(data['sample_width']),\\n float(data['sample_thickness'])\\n )\\n return [elong]\",\n \"def getColumnDictionary(self):\\n try:\\n column_dictionary = []\\n con = self.getMetadataDatabaseConnection()\\n column_values = con.cursor()\\n con.cursor().callproc('qiime_assets.get_column_dictionary', [column_values])\\n for row in column_values:\\n # Skip if no column name is found\\n if row[0] is None:\\n continue\\n\\n # Some variables to allow for re-assignment should any of them be None\\n column_name = row[0].lower()\\n expected_values = row[1]\\n description = row[2]\\n data_type = row[3]\\n max_length = row[4]\\n min_length = row[5]\\n active = row[6]\\n \\n if row[1] == None:\\n expected_values == ''\\n elif row[2] == None:\\n description == ''\\n elif row[3] == None:\\n data_type = ''\\n elif row[4] == None:\\n max_length = ''\\n elif row[5] == None:\\n min_length = ''\\n elif row[6] == None:\\n min_length = ''\\n \\n list_item = (column_name, expected_values, description, data_type, max_length, min_length, active)\\n column_dictionary.append(list_item)\\n return column_dictionary\\n except Exception, e:\\n print 'Exception caught: %s.\\\\nThe error is: %s' % (type(e), e)\\n return False\",\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 build_sample_map(flowcell):\\n result = {}\\n rows = [(lane, lib[\\\"name\\\"]) for lib in flowcell[\\\"libraries\\\"] for lane in lib[\\\"lanes\\\"]]\\n i = 1\\n for _, name in sorted(set(rows)):\\n if name not in result:\\n result[name] = \\\"S{}\\\".format(i)\\n i += 1\\n return result\",\n \"def get_header(header_row):\\n header = {}\\n header['station'], c1, c2, c3, date, time, tz = header_row.split()\\n header['short_model'] = c1\\n header['model'] = f'{c1} {c2} {c3}' \\n header['runtime'] = dateutil.parser.parse(f'{date} {time} {tz}')\\n return header\",\n \"def test_csvfile_get_columns(fs: FakeFilesystem) -> None:\\n fs.create_file(\\\"test.csv\\\", contents=CONTENTS)\\n\\n adapter = CSVFile(\\\"test.csv\\\")\\n\\n assert adapter.get_columns() == {\\n \\\"index\\\": Float(\\n filters=[Range, Equal, NotEqual, IsNull, IsNotNull],\\n order=Order.ASCENDING,\\n exact=True,\\n ),\\n \\\"temperature\\\": Float(\\n filters=[Range, Equal, NotEqual, IsNull, IsNotNull],\\n order=Order.NONE,\\n exact=True,\\n ),\\n \\\"site\\\": String(\\n filters=[Range, Equal, NotEqual, IsNull, IsNotNull],\\n order=Order.NONE,\\n exact=True,\\n ),\\n }\",\n \"def creating_dict_from_csv(self) -> dict:\\n dictionary = {}\\n for row in self.__read_csv():\\n if dictionary.get(row[0]):\\n dictionary[row[0]].append((row[1], row[2]))\\n else:\\n dictionary[row[0]] = [(row[1], row[2])]\\n\\n for key, value in dictionary.items():\\n dictionary[key] = sorted(value, key=lambda x: x[1], reverse=True)\\n\\n return dictionary\",\n \"def dictionary_formation():\\r\\n sales_data = {}\\r\\n with open('beer_data.csv', \\\"r\\\") as data_file:\\r\\n file_contents = csv.reader(data_file, delimiter=',')\\r\\n #Value of lines_read used as key value for each dictionary\\r\\n #in sales_data\\r\\n lines_read = 1\\r\\n for line in file_contents:\\r\\n if lines_read == 1:\\r\\n lines_read = lines_read + 1\\r\\n else:\\r\\n #Stores each column in row as key value in dictionary\\r\\n sales_data[str(lines_read)] = {\\r\\n \\\"invoice_number\\\": line[0],\\r\\n \\\"customer\\\": line[1],\\r\\n \\\"date_required\\\": line[2],\\r\\n \\\"recipe\\\": line[3],\\r\\n \\\"gyle_number\\\": line[4],\\r\\n \\\"quantity_ordered\\\": int(line[5])\\r\\n }\\r\\n lines_read = lines_read + 1\\r\\n data_file.close()\\r\\n return sales_data\",\n \"def distance(row):\\n return row['distance']\",\n \"def data_parser(df):\\n\\n chunks = []\\n for row in df.itertuples():\\n piece = {}\\n piece['datetime'] = row[1]\\n piece[row[3]] = row[4]\\n chunks.append(piece)\\n\\n # Join dicts on shared 'datetime' keys.\\n combine = defaultdict(dict)\\n for elem in chunks:\\n combine[elem['datetime']].update(elem)\\n\\n ordered = sorted(combine.values(), key=itemgetter(\\\"datetime\\\"))\\n\\n mapped_generation = []\\n for item in ordered:\\n mapped_types = [(mapping.get(k, k), v) for k, v in item.items()]\\n\\n # Need to avoid multiple 'unknown' keys overwriting.\\n complete_production = defaultdict(lambda: 0.0)\\n for key, val in mapped_types:\\n try:\\n complete_production[key] += val\\n except TypeError:\\n # Datetime is a string at this point!\\n complete_production[key] = val\\n\\n dt = complete_production.pop('datetime')\\n final = (dt, dict(complete_production))\\n mapped_generation.append(final)\\n\\n return mapped_generation\",\n \"def readcsv(csvreader,item:int,count:int):\\n\\n headerstring = next(csvreader)\\n data = {}\\n if item >= len(headerstring) or item < 0 or count >= len(headerstring) or count < 0:\\n \\n sys.stderr.write(\\\"your index values are out of bounds\\\")\\n\\n else:\\n header = headerstring[item] + ',' + headerstring[count]\\n for i in csvreader:\\n key = i[item]\\n try:\\n if key in data:\\n data[key] = float(data[key]) + float(i[count])\\n else:\\n data[key] = float(i[count])\\n except ValueError:\\n if key in data:\\n data[key]+=0\\n else:\\n data[key] = 0\\n return data,header\",\n \"def get_positions(directory): \\n positions = {}\\n names = {}\\n pos_dict = {'1': \\\"GK\\\", '2': \\\"DEF\\\", '3': \\\"MID\\\", '4': \\\"FWD\\\"}\\n fin = open(directory + \\\"/players_raw.csv\\\", 'rU',encoding=\\\"utf-8\\\")\\n reader = csv.DictReader(fin)\\n for row in reader:\\n positions[int(row['id'])] = pos_dict[row['element_type']] \\n names[int(row['id'])] = row['first_name'] + ' ' + row['second_name']\\n return names, positions\",\n \"def cuttoff_column_generator(A: dict, columns: list, threshold: float):\\n for column_name in columns:\\n with open('cache/' + column_name + '.pkl', 'rb') as pkl_file:\\n column = pickle.load(pkl_file)\\n yield A, column, threshold\",\n \"def parse_csv(data):\\n\\n # scan for CSRs first, so it's easier to resolve CSR-related constants\\n # in the second pass\\n for _type, _name, _address, _, __ in data:\\n if _type == 'csr_base':\\n peripherals[_name] = {'name': _name,\\n 'address': _address,\\n 'constants': {}}\\n\\n for _type, _name, _val, _val2, _ in data:\\n if _type == 'csr_base':\\n # CSRs have already been parsed\\n pass\\n elif _type == 'csr_register':\\n # we are currently not interested in this\\n pass\\n elif _type == 'constant':\\n found = False\\n for _csr_name in peripherals:\\n if _name.startswith(_csr_name):\\n local_name = _name[len(_csr_name)+1:]\\n peripherals[_csr_name]['constants'][local_name] = _val\\n found = True\\n break\\n if not found:\\n # if it's not a CSR-related constant, it must be a global one\\n constants[_name] = {'name': _name, 'value': _val}\\n elif _type == 'memory_region':\\n mem_regions[_name] = {'name': _name,\\n 'address': _val,\\n 'size': _val2}\\n else:\\n print('Skipping unexpected CSV entry: {} {}'.format(_type, _name))\",\n \"def columndict_callback(c):\\r\\n\\r\\n def subQ(txt):\\r\\n return re.sub(r\\\"'([^']*)'\\\", r\\\"`\\\\1'\\\", (txt or '')).replace(r'|', r'\\\\|')\\r\\n\\r\\n return {\\r\\n 'name' : c.name,\\r\\n 'type' : c.type,\\r\\n 'nullable' : c.nullable,\\r\\n 'value' : subQ(c.value),\\r\\n 'default' : subQ(c.default),\\r\\n 'defaultf' : (\\\"\\\\n\\\\n*Default: %s*\\\" % subQ(c.default))\\r\\n if c.default else '',\\r\\n 'notnull' : '' if c.nullable else ' not null',\\r\\n 'desc': c.desc,\\r\\n 'descf': preformat_coldesc(c.desc), \\r\\n }\",\n \"def readExperimentParameters(filename): \\n \\n csvFile = csv.reader(open(filename))\\n pythonSucks = next(csvFile)\\n \\n data = []\\n for row in csvFile:\\n list = []\\n for i in range(len(row)):\\n if(row[i+1] == \\\"\\\"):\\n list.append(int(row[i]))\\n break\\n elif(row[i+1] == \\\"h\\\"):\\n list.append(int(row[i]) * 60)\\n break\\n else:\\n list.append(int(row[i]))\\n \\n data.append(tuple(list))\\n return data\",\n \"def nomenclatura():\\n df = pd.read_csv(\\\"Data/nomenclatura_1.csv\\\", encoding = \\\"latin1\\\")\\n #dict_axis = df.set_index('id').T.to_dict('list')\\n dict_axis = dict( [ (i, [a,b]) for i, a,b in zip(df.id, df.latitude, df.longitude) ] )\\n\\n return dict_axis\",\n \"def read_names_into_dict():\\n d = dict()\\n with open(\\\"SP_500_firms.csv\\\") as csvfile:\\n input_file = csv.DictReader(csvfile)\\n for row in input_file:\\n #print(row)\\n d[row['Symbol']] = [row['Name'],row['Sector']]\\n return d\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.62421864","0.57918304","0.5663072","0.5657489","0.56149256","0.5612935","0.5567542","0.5544542","0.5500382","0.5480754","0.545259","0.544672","0.5385878","0.53811884","0.5380167","0.5332743","0.53060913","0.5292364","0.527309","0.52203315","0.5217865","0.5204893","0.51956916","0.51823163","0.5171073","0.51400554","0.5128875","0.5127345","0.5125955","0.5108462","0.50986177","0.50906026","0.50851643","0.507701","0.5058715","0.5047698","0.50358063","0.5025449","0.50119126","0.5000118","0.49874973","0.49859902","0.49794003","0.49692628","0.49686694","0.49564287","0.49562824","0.4944726","0.49357253","0.4931802","0.49308315","0.4929742","0.49268916","0.49139735","0.49004272","0.48895863","0.4879501","0.48757288","0.4849314","0.48488393","0.48460367","0.48438165","0.48395935","0.48366308","0.4826754","0.48108453","0.480941","0.48063457","0.47943133","0.47894722","0.47881287","0.4784646","0.47833073","0.47790784","0.47728813","0.47690222","0.4764609","0.47631037","0.4759319","0.47532785","0.4744826","0.47333178","0.47310844","0.47217765","0.47126067","0.47113746","0.47110203","0.4708233","0.4706068","0.47044417","0.47009397","0.47003907","0.46994057","0.46980676","0.46959034","0.46934825","0.468143","0.467427","0.46720108","0.46703273"],"string":"[\n \"0.62421864\",\n \"0.57918304\",\n \"0.5663072\",\n \"0.5657489\",\n \"0.56149256\",\n \"0.5612935\",\n \"0.5567542\",\n \"0.5544542\",\n \"0.5500382\",\n \"0.5480754\",\n \"0.545259\",\n \"0.544672\",\n \"0.5385878\",\n \"0.53811884\",\n \"0.5380167\",\n \"0.5332743\",\n \"0.53060913\",\n \"0.5292364\",\n \"0.527309\",\n \"0.52203315\",\n \"0.5217865\",\n \"0.5204893\",\n \"0.51956916\",\n \"0.51823163\",\n \"0.5171073\",\n \"0.51400554\",\n \"0.5128875\",\n \"0.5127345\",\n \"0.5125955\",\n \"0.5108462\",\n \"0.50986177\",\n \"0.50906026\",\n \"0.50851643\",\n \"0.507701\",\n \"0.5058715\",\n \"0.5047698\",\n \"0.50358063\",\n \"0.5025449\",\n \"0.50119126\",\n \"0.5000118\",\n \"0.49874973\",\n \"0.49859902\",\n \"0.49794003\",\n \"0.49692628\",\n \"0.49686694\",\n \"0.49564287\",\n \"0.49562824\",\n \"0.4944726\",\n \"0.49357253\",\n \"0.4931802\",\n \"0.49308315\",\n \"0.4929742\",\n \"0.49268916\",\n \"0.49139735\",\n \"0.49004272\",\n \"0.48895863\",\n \"0.4879501\",\n \"0.48757288\",\n \"0.4849314\",\n \"0.48488393\",\n \"0.48460367\",\n \"0.48438165\",\n \"0.48395935\",\n \"0.48366308\",\n \"0.4826754\",\n \"0.48108453\",\n \"0.480941\",\n \"0.48063457\",\n \"0.47943133\",\n \"0.47894722\",\n \"0.47881287\",\n \"0.4784646\",\n \"0.47833073\",\n \"0.47790784\",\n \"0.47728813\",\n \"0.47690222\",\n \"0.4764609\",\n \"0.47631037\",\n \"0.4759319\",\n \"0.47532785\",\n \"0.4744826\",\n \"0.47333178\",\n \"0.47310844\",\n \"0.47217765\",\n \"0.47126067\",\n \"0.47113746\",\n \"0.47110203\",\n \"0.4708233\",\n \"0.4706068\",\n \"0.47044417\",\n \"0.47009397\",\n \"0.47003907\",\n \"0.46994057\",\n \"0.46980676\",\n \"0.46959034\",\n \"0.46934825\",\n \"0.468143\",\n \"0.467427\",\n \"0.46720108\",\n \"0.46703273\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.62722737"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":2887,"cells":{"query":{"kind":"string","value":"Populate water measurements table for selected `archive`, `directory` and `stations`."},"document":{"kind":"string","value":"def populate_water_measurements(cursor, archive, directory, station):\n csv_path = get_data_path(\n 'water',\n 'raw',\n archive,\n directory,\n f'{station}.csv'\n )\n\n with open(csv_path, 'r', encoding='utf-8') as file:\n reader = csv.reader(file, delimiter=';')\n header = next(reader)\n column_names_map = get_water_index_map(archive, header)\n\n if not column_names_map:\n return False\n\n water_body = get_water_definitions(archive)['body']\n\n for row in reader:\n column_values_map = get_water_value_map(row, column_names_map)\n if column_values_map:\n date = datetime.strptime(row[0], '%d.%m.%Y').date()\n data_columns = ', '.join(column_values_map.keys())\n data_values = ', '.join(column_values_map.values())\n cursor.execute(f'''INSERT INTO {water_body}_measurements (station_id, date, {data_columns})\n VALUES ({station}, '{str(date)}', {data_values})''')\n\n return True"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def populate_water_tables(connection):\n metadata = load_metadata('water')\n cursor = connection.cursor()\n\n # Check if tables are already populated.\n cursor.execute('SELECT count(*) FROM watercourses')\n watercourse_count = cursor.fetchone()[0]\n cursor.execute('SELECT count(*) FROM aquifers')\n aquifer_count = cursor.fetchone()[0]\n\n if watercourse_count and aquifer_count:\n print('Water tables already populated!')\n return\n\n station_data = get_station_data()\n\n for archive in metadata.keys():\n print(f'{archive}-water:'.upper())\n water_body = get_water_definitions(archive)['body']\n\n # 1. Populate watercourses/aquifers:\n stations = {}\n for water_body_name in metadata[archive].keys():\n print(f'\\tPopulating {water_body}: \"{water_body_name}\"')\n cursor.execute(f'''INSERT INTO {water_body}s(location_id, name)\n VALUES (0, '{water_body_name}')''')\n water_body_id = cursor.lastrowid\n\n # 2. Populate watercourse_stations/aquifer_stations:\n for station_id in metadata[archive][water_body_name]['stations']:\n station_name = clean_name(metadata[archive][water_body_name]['stations'][station_id]['name'])\n\n if station_id in stations:\n # Prefer watercourses/aquifer with more stations\n current_len = len(metadata[archive][water_body_name]['stations'])\n previous_len = len(metadata[archive][stations[station_id]]['stations'])\n\n if current_len < previous_len:\n print(f'\\t\\tStation already exists: {station_id} - \"{station_name}\" (\"{water_body_name}\")')\n continue\n else:\n cursor.execute(f'''DELETE \n FROM {water_body}_stations\n WHERE id = {station_id}''')\n print(f'\\t\\tRemoved station: {station_id} - \"{station_name}\" from \"{stations[station_id]}\")')\n\n stations[station_id] = water_body_name\n print(f'\\t\\tPopulating station: {station_id} - \"{station_name}\"')\n\n # Insert station location if station data exists.\n location_id = 0\n station_row = station_data.query(f'ŠIFRA == \"{station_id}\"')\n if not station_row.empty:\n index = station_row.index[0]\n lat = station_row.at[index, 'LAT']\n lng = station_row.at[index, 'LON']\n if not np.isnan(lat) and not np.isnan(lng):\n name = f\"{station_row.at[index, 'VODOMERNA POSTAJA']} ({station_row.at[index, 'VODOTOK']})\"\n cursor.execute(f'''INSERT INTO locations(name, lat, lng)\n VALUES ('{name}', {lat}, {lng})''')\n location_id = cursor.lastrowid\n\n # Insert station.\n cursor.execute(f'''INSERT INTO {water_body}_stations(id, {water_body}_id, location_id, name)\n VALUES ({station_id}, {water_body_id}, {location_id}, '{station_name}')''')\n\n # 3. Populate watercourse_measurements/aquifer_measurements:\n if not populate_water_measurements(cursor, archive, metadata[archive][water_body_name]['dir'],\n station_id):\n cursor.execute(f'''DELETE \n FROM {water_body}_stations\n WHERE id = {station_id}''')\n print(f'\\t\\tRemoved station with useless data: {station_id} - \"{station_name}\"')\n\n # Remove empty watercourses/aquifers.\n cursor.execute(f'''SELECT w.id, w.name\n FROM {water_body}s w\n WHERE NOT EXISTS (\n SELECT s.id \n FROM {water_body}_stations s \n WHERE w.id = s.{water_body}_id\n )''')\n\n for row in cursor.fetchall():\n cursor.execute(f'''DELETE \n FROM {water_body}s\n WHERE id = {row[0]}''')\n print(f'\\tRemoved empty {water_body}: \"{row[1]}\"')","def populate_weather(connection):\n metadata = load_metadata('weather')\n cursor = connection.cursor()\n water_defs = get_water_definitions()\n\n # Check if tables are already populated.\n cursor.execute('SELECT count(*) FROM weather')\n weather_count = cursor.fetchone()[0]\n\n if weather_count:\n print('Weather tables already populated!')\n return\n\n print('WEATHER:')\n\n # Darksky data\n for dir_name, location in metadata.items():\n print(f'\\tPopulating weather: \"{location[\"name\"]}\".')\n\n # Insert location.\n cursor.execute(f'''INSERT INTO locations(name, lat, lng)\n VALUES ('{location['name']}', {location['lat']}, {location['lng']})''')\n location_id = cursor.lastrowid\n\n # Set weather locations for watercourses/aquifers.\n for water_body in [d['body'] for d in water_defs.values()]:\n if water_body in location:\n cursor.execute(f'''UPDATE {water_body}s\n SET location_id = {location_id}\n WHERE name IN ('{\"','\".join(location[water_body])}')''')\n break\n\n dir_path = get_data_path('weather', 'raw', dir_name)\n for json_file_name in os.listdir(dir_path):\n json_path = os.path.join(dir_path, json_file_name)\n with open(json_path, 'r', encoding='utf-8') as json_file:\n print(f'\\t\\tPopulating year: {json_file_name[0:-5]}')\n year_forecasts = json.load(json_file)\n for date, date_forecast in year_forecasts.items():\n hourly_forecasts = date_forecast['hourly']\n\n if not hourly_forecasts:\n print(f'\\t\\tNo hourly forecasts for {date}!')\n continue\n\n daily_forecast = {\n 'location_id': location_id,\n 'time': date_forecast['time'],\n 'day_time': date_forecast['sunset_time'] - date_forecast['sunrise_time'],\n 'precipitation': 0,\n 'snow_accumulation': 0\n }\n # List of value names with `avg`, `min` and `max` values\n value_names = {\n 'temperature': 'temperature',\n 'cloud_cover': 'cloudCover',\n 'dew_point': 'dewPoint',\n 'humidity': 'humidity',\n 'pressure': 'pressure',\n 'uv_index': 'uvIndex',\n 'precipitation_probability': 'precipProbability',\n 'precipitation_intensity': 'precipIntensity'\n }\n # Value name counters, which indicate how many times (out of 24)\n # certain value appears in hourly data.\n value_counts = {k: 0 for k in value_names.keys()}\n\n for value_name in value_names.keys():\n daily_forecast[f'{value_name}_avg'] = 0.0\n daily_forecast[f'{value_name}_min'] = float('inf')\n daily_forecast[f'{value_name}_max'] = float('-inf')\n\n # Calculate daily forecast values from hourly forecasts.\n for hourly_forecast in hourly_forecasts:\n for value_name in value_names.keys():\n orig_value_name = value_names[value_name]\n if is_forecast_number(orig_value_name, hourly_forecast):\n daily_forecast[f'{value_name}_avg'] += hourly_forecast[orig_value_name]\n daily_forecast[f'{value_name}_min'] = min(\n hourly_forecast[orig_value_name],\n daily_forecast[f'{value_name}_min']\n )\n daily_forecast[f'{value_name}_max'] = max(\n hourly_forecast[orig_value_name],\n daily_forecast[f'{value_name}_max']\n )\n value_counts[value_name] += 1\n\n if is_forecast_number('precipAccumulation', hourly_forecast) \\\n and hourly_forecast['precipType'] == 'snow':\n daily_forecast['snow_accumulation'] += hourly_forecast['precipAccumulation']\n elif is_forecast_number('precipIntensity', hourly_forecast) \\\n and is_forecast_number('precipProbability', hourly_forecast):\n daily_forecast['precipitation'] += \\\n hourly_forecast['precipIntensity'] * hourly_forecast['precipProbability']\n\n for value_name, value_count in value_counts.items():\n if value_count:\n # Calculate average.\n daily_forecast[f'{value_name}_avg'] = daily_forecast[f'{value_name}_avg'] / value_count\n else:\n # If value never appeared\n daily_forecast[f'{value_name}_avg'] = 'NULL'\n daily_forecast[f'{value_name}_min'] = 'NULL'\n daily_forecast[f'{value_name}_max'] = 'NULL'\n\n cursor.execute(f'''INSERT INTO weather({', '.join(daily_forecast.keys())})\n VALUES ({', '.join([str(v) for v in daily_forecast.values()])})''')\n\n # IOT data:\n for location in SETTINGS['weather_locations_iot']:\n print(f'\\tPopulating weather: \"{location[\"name\"]}\".')\n\n # Insert location.\n cursor.execute(f'''INSERT INTO locations(name, lat, lng)\n VALUES ('{location['name']}', {location['lat']}, {location['lng']})''')\n location_id = cursor.lastrowid\n\n # Set weather locations for watercourses/aquifers.\n for water_body in [d['body'] for d in water_defs.values()]:\n if water_body in location:\n cursor.execute(f'''UPDATE {water_body}s\n SET location_id = {location_id}\n WHERE name IN ('{\"', '\".join(location[water_body])}')''')\n\n # Set locations for all stations on given water body to match its location.\n cursor.execute(f'''SELECT id\n FROM {water_body}s\n WHERE location_id = {location_id}''')\n ids = [row[0] for row in cursor.fetchall()]\n if len(ids):\n cursor.execute(f'''UPDATE {water_body}_stations\n SET location_id = {location_id}\n WHERE {water_body}_id IN ({', '.join([str(v) for v in ids])})''')\n\n break \n \n file_name = f'''{location['lat']}-{location['lng']}.json'''\n json_path = get_data_path('weather', 'raw', file_name)\n\n # If data file doesn't exist, download it first.\n if not os.path.isfile(json_path):\n with open(json_path, 'wb', encoding=\"utf-8\") as file:\n file.write(read_from_url(location['url'], decode=False))\n \n with open(json_path, 'r', encoding='utf-8') as json_file:\n row_names = {\n \"Sun_duration\": \"sun_duration\",\n \"CloudCover\": \"cloud_cover_avg\",\n \"Percipitation\": \"precipitation\",\n \"New_snow_blanket\": \"snow_accumulation\",\n \"Snow_blanket\": \"snow_depth\",\n \"TemperatureAvg\": \"temperature_avg\",\n \"TemperatureMin\": \"temperature_min\",\n \"TemperatureMax\": \"temperature_max\"\n }\n forecasts = json.load(json_file)\n for forecast in forecasts:\n f = {row_names[k]: forecast[k] for k in row_names.keys()}\n f['location_id'] = location_id\n f['time'] = round(forecast['LastUpdatedEpoch'] / 1000)\n cursor.execute(f'''INSERT INTO weather({', '.join(f.keys())})\n VALUES ({', '.join([str(v) for v in f.values()])})''')","def main(daymet_dir,pickles,start_date='1980-10-01',end_date='2020-09-30',huc_col = 'huc8', **kwargs):\r\n\tprint(f'The huc col being processed is: {huc_col}')\r\n\t################################################################\r\n\t#first do the daymet data \r\n\t#read in all the files in this dir and combine them into one df\r\n\tearly=FormatData(glob.glob(daymet_dir+f'*_12_{huc_col}.csv'),drop_cols=['system:index','.geo','dayl','vp']).read_in_csvs()\r\n\tmid=FormatData(glob.glob(daymet_dir+f'*_2_{huc_col}.csv'),drop_cols=['system:index','.geo','dayl','vp']).read_in_csvs()\r\n\tlate=FormatData(glob.glob(daymet_dir+f'*_4_{huc_col}.csv'),drop_cols=['system:index','.geo','dayl','vp']).read_in_csvs()\r\n\t################################################################\r\n\t#next do the snotel data \r\n\toutput=[]\r\n\r\n\t#read in some pickled objects, these look like a list of dfs with each being a station for the full time period \r\n\tfor item in ['PREC','TAVG','WTEQ']:\r\n\t\t#get the pickled objects for each parameter \r\n\t\tfiles = glob.glob(pickles+f'*{item}_{start_date}_{end_date}_snotel_data_list') #hardcoded currently\r\n\t\tdf=FormatData(files,drop_cols=['year','month','day']).read_in_pickles()\r\n\t\toutput.append(df) #the df here is 365 days x ~30 yrs x 237 stations so these are pretty big dfs\r\n\t\r\n\t#join the three enviro params \r\n\toutput_df = reduce(lambda left,right: pd.merge(left,right,how='inner',on=['date','id']), output)\r\n\t\r\n\t\r\n\t#convert the temp column from F to C \r\n\toutput_df['TAVG'] = (output_df['TAVG']-32)*(5/9) \r\n\t#there are a couple of erroneous temp values, remove those \r\n\toutput_df = output_df.loc[output_df['TAVG'] <= 50]\r\n\r\n\t#convert prec and swe cols from inches to cm \r\n\toutput_df['PREC'] = output_df['PREC']*2.54\r\n\toutput_df['WTEQ'] = output_df['WTEQ']*2.54\r\n\t\r\n\t#remove rows that have one of the data types missing- this might need to be amended because \r\n\t#it means that there are different numbers of records in some of the periods. \r\n\toutput_df=output_df.dropna()\r\n\t\r\n\t#cast the snotel id col to int to add the hucs \r\n\toutput_df['id'] = output_df['id'].astype('int')\r\n\r\n\t#add the as yet nonexistant hucs data to the outputs \r\n\thucs = kwargs.get('hucs')\r\n\toutput_df[huc_col] = output_df['id'].map(hucs)\r\n\r\n\t#there are multiple snotel stations in some of the basins, \r\n\t#combine those so there is just one number per basin like the \r\n\t#daymet and RS data. \r\n\r\n\toutput_df=output_df.groupby([huc_col,'date'])[['PREC','WTEQ','TAVG']].mean().reset_index()\r\n\r\n\tperiod_list = []\r\n\tfor p1,p2 in zip(['early','mid','late'],[early,mid,late]): \r\n\t\t\t#get snotel first\r\n\t\t#make a temporal chunk of data \r\n\t\tsnotel_chunk=FormatData(None,time_period=p1).split_yearly_data(output_df)\r\n\r\n\t\t##########working below here\r\n\t\t############################\r\n\t\t#calculate the snow droughts for that chunk \r\n\t\tif (p1 == 'mid') | (p1 == 'late'): \r\n\t\t\tsnotel_drought=CalcSnowDroughts(snotel_chunk,swe_c='WTEQ',precip='PREC',temp='TAVG',start_year=1991,sort_col=huc_col).prepare_df_cols()\r\n\t\t\t#print('snotel')\r\n\t\t\t#print(snotel_drought)\r\n\t\telse: \r\n\t\t\tsnotel_drought=CalcSnowDroughts(snotel_chunk,swe_c='WTEQ',precip='PREC',temp='TAVG',sort_col=huc_col).prepare_df_cols()\r\n\r\n\t\t#get cols of interest \r\n\t\t#snotel_drought=snotel_drought[['huc8','year','dry','warm','warm_dry']]\r\n\t\t#rename cols so they don't get confused when data are merged \r\n\t\t#snotel_drought.columns=['huc8','year']+['s_'+column for column in snotel_drought.columns if not (column =='huc8') | (column=='year')]\r\n\t\t\r\n\t\t#then do the same for daymet \r\n\t\tif (p1 == 'mid') | (p1 == 'late'): \r\n\t\t\tdaymet_drought=CalcSnowDroughts(p2,start_year=1991,sort_col=huc_col).prepare_df_cols()\r\n\t\telse: \r\n\t\t\tdaymet_drought=CalcSnowDroughts(p2,sort_col=huc_col).prepare_df_cols()\r\n\t\t#print('daymet',daymet_drought)\r\n\t\t#daymet_drought=daymet_drought[['huc8','year','dry','warm','warm_dry']]\r\n\t\t\r\n\t\t#daymet_drought.columns=['huc8','year']+['d_'+column for column in daymet_drought.columns if not (column =='huc8') | (column=='year')]\r\n\r\n\t##########################################\r\n\t\r\n\t\t#run the kmeans with drought types as intiilization conditions (centroids) for the clusters\r\n\t\t\r\n\t\t#these are all of the huc 4 basins in the study area \r\n\t\thuc4s = ['1708','1801','1710','1711','1709','1701','1702','1705','1703','1601','1707','1706','1712','1704']\r\n\t\ts_output = []\r\n\t\td_output = []\r\n\t\tfor huc4 in huc4s: \r\n\t\t\thuc4_s = sd.prep_clusters(snotel_drought,huc4,huc_col=huc_col) #get the subset of the snow drought data for a given huc4\r\n\t\t\thuc4_d = sd.prep_clusters(daymet_drought,huc4,huc_col=huc_col)\r\n\t\t\t#make the centroids that serve as the intialization for the kmeans clusters- these are like endmembers (ish)\r\n\t\t\ts_centroids = DefineClusterCenters(huc4_s,'WTEQ','PREC','TAVG').combine_centroids() #makes a numpy array with four centroids\r\n\t\t\td_centroids = DefineClusterCenters(huc4_d,'swe','prcp','tavg').combine_centroids() #makes a numpy array with four centroids\r\n\r\n\t\t\t#clusters should be like: {0:dry, 1:warm, 2:warm_dry, 3:no_drought} 6/8/2021 DOUBLE CHECK\r\n\t\t\t#run kmeans for the snotel data\r\n\t\t\ts_clusters = sd.run_kmeans(huc4_s[['WTEQ','PREC','TAVG']].to_numpy(),huc4_s['label'],s_centroids)\r\n\t\t\ts_clusters = sd.add_drought_cols_to_kmeans_output(s_clusters, huc_col=huc_col) #add a few cols needed for plotting \r\n\t\t\t#run kmeans for the daymet data \r\n\t\t\td_clusters = sd.run_kmeans(huc4_d[['swe','prcp','tavg']].to_numpy(),huc4_d['label'],d_centroids)\r\n\t\t\td_clusters = sd.add_drought_cols_to_kmeans_output(d_clusters, huc_col=huc_col) #add a few cols needed for plotting \r\n\r\n\t\t\ts_output.append(s_clusters)\r\n\t\t\td_output.append(d_clusters)\r\n\t\ts_plot = pd.concat(s_output)\r\n\r\n\t\t#select the cols of interest and rename so there's no confusion when dfs are merged \r\n\t\ts_plot=s_plot[[huc_col,'year','dry','warm','warm_dry']]\r\n\t\ts_plot.columns=[huc_col,'year']+['s_'+column for column in s_plot.columns if not (column == huc_col) | (column=='year')]\r\n\r\n\t\td_plot = pd.concat(d_output)\r\n\t\td_plot=d_plot[[huc_col,'year','dry','warm','warm_dry']]\r\n\t\td_plot.columns=[huc_col,'year']+['d_'+column for column in d_plot.columns if not (column == huc_col) | (column=='year')]\r\n\t\r\n\t\t#merge the two datasets into one df \r\n\t\tdfs = s_plot.merge(d_plot,on=[huc_col,'year'],how='inner')\r\n\t\t\r\n\t\t#deal with the scenario that there are basins with less than 30 years of data, remove those here\r\n\t\tdfs = sd.remove_short_dataset_stations(dfs,huc_col)\r\n\t\tperiod_list.append(dfs)\r\n\r\n\tplot_counts(period_list,kwargs.get('stats_dir'),huc_col=huc_col,**kwargs)","def get_outdoor_data(temp_dir,site):\n if site == 'berk':\n files_od = glob(join(temp_dir,'outdoor','20*.xlsx'))\n elif site == 'bus':\n files_od = glob(join(temp_dir,'outdoor','Busara*.csv'))\n else:\n raise NameError(site)\n\n dfs = []\n for f in files_od:\n if site == 'berk':\n this_df = pd.read_excel(f,sheet_name=0,usecols='B:D',index_col=0,parse_dates=True, header=1)\n elif site == 'bus':\n this_df = pd.read_csv(f,usecols=[0,1,2],index_col=0,parse_dates=True,header=2)\n \n # drop missing values that prevented conversion to float type\n if this_df.iloc[:,0].dtype != np.float64:\n this_df = this_df[this_df.iloc[:,0] != ' ']\n this_df = this_df.astype(np.float64)\n\n # correct for weird timezones in berkeley datalogger\n this_df = correct_tz(this_df,site)\n \n this_df.columns = ['T','RH']\n this_df.index.name = 'time'\n\n # convert to celsius\n this_df['T'] = (this_df['T'] - 32) * 5/9\n dfs.append(this_df)\n \n df_od = pd.concat(dfs)\n\n # drop duplicated measurements\n df_od = df_od[~df_od.index.duplicated(keep='last')].sort_index()\n \n # separate out into daily min,mean,max\n groups = df_od.groupby(df_od.index.date)\n dfs_od = {'all':df_od,\n 'min': groups.min(),\n 'mean': groups.mean(),\n 'max': groups.max()}\n \n for i in ['min','mean','max']:\n # remove first and last day to ignore days where we did not get full recording\n dfs_od[i] = dfs_od[i].iloc[1:-1,:]\n \n # name index so that we can merge onto multiIndex'd dataframe\n dfs_od[i].index.name = 'date'\n \n return dfs_od","def extract_archive_data():\n extract_from_db_info = [\n {\n 'source_db': 'ecommerce_db',\n 'dest_db': 'ecommerce_db',\n 'source_table': 'raw_customer',\n 'dest_table': 'raw_customer_archive',\n 'sql_select': None,\n 'sql_insert': '../sql/insert/insert_raw_customer_archive.sql'\n },\n {\n 'source_db': 'ecommerce_db',\n 'dest_db': 'ecommerce_db',\n 'source_table': 'raw_product',\n 'dest_table': 'raw_product_archive',\n 'sql_select': None,\n 'sql_insert': '../sql/insert/insert_raw_product_archive.sql'\n },\n {\n 'source_db': 'ecommerce_db',\n 'dest_db': 'ecommerce_db',\n 'source_table': 'raw_sales',\n 'dest_table': 'raw_sales_archive',\n 'sql_select': None,\n 'sql_insert': '../sql/insert/insert_raw_sales_archive.sql'\n }\n ]\n\n for extract_info in extract_from_db_info:\n try:\n extract_data_from_db(extract_info['source_db'], extract_info['dest_db'], extract_info['dest_table'], extract_info['sql_select'], extract_info['sql_insert'])\n except Exception as e:\n print(\"An error occurred: \", e)\n else:\n print(\"Successfully inserted records in {} table of {} database from {} table of {} database.\".format(extract_info['dest_table'], extract_info['dest_db'], extract_info['source_table'], extract_info['source_db']))","def import_stations(time_res='hourly',time_format='%Y%m%d%H',\r\n campaign_time=[datetime(2018,12,9), datetime(2018,12,12)],\r\n data_category='air_temperature', station_ids=['00044','00091'],\r\n dbase_dir='dbase', table_dir='tables',Output=True,\r\n memory_save=True):\r\n timeranges=['recent','historical']\r\n #%%load the datasets available at each timestep\r\n dwd_datasets_meta=dwd_datasets_meta=json.load(open(table_dir+\"\\\\dwd_station_meta.txt\"))\r\n #try to get a variable from the category, otherwise use interpolation of higher frequency data\r\n resample_frequency=None\r\n time_res_dbase=time_res\r\n try:\r\n dwd_datasets_meta[time_res][data_category]\r\n except Exception:\r\n if time_res=='daily':\r\n try:\r\n dwd_datasets_meta['hourly'][data_category]\r\n print(data_category,' is not provided at the required resolution, daily_mean of hourly data used instead')\r\n resample_frequency='D'\r\n time_res_dbase='hourly'\r\n except Exception:\r\n try: \r\n dwd_datasets_meta['10_minutes'][data_category]\r\n print(data_category,' is not provided at the required resolution, daily_mean of 10_minutes data used instead')\r\n resample_frequency='D'\r\n time_res_dbase='10_minutes'\r\n except Exception:\r\n print(data_category, 'not available')\r\n sys.exit(1)\r\n if time_res=='hourly':\r\n try: \r\n dwd_datasets_meta['10_minutes'][data_category]\r\n print(data_category,' is not provided at the required resolution, hourly_mean of 10_minutes data used instead')\r\n resample_frequency='H'\r\n time_res_dbase='10_minutes'\r\n except Exception:\r\n print(data_category, 'not available')\r\n sys.exit(1)\r\n \r\n \r\n #%% download from dwd if necessary\r\n #connect to server\r\n server='opendata.dwd.de'\r\n ftp=connect_ftp(server = server,connected = False)\r\n #get the mean time of the campaign\r\n date_mean=campaign_time[0]+(campaign_time[1]-campaign_time[0])/2 \r\n # load the inititial ds\r\n dbase_path=dbase_dir+'\\\\db_stations_'+time_res+'_'+data_category+'.nc'\r\n if os.path.exists(dbase_path):\r\n with xr.open_dataset(dbase_path) as dwd_dbase:\r\n dwd_dbase.load()\r\n print('Existing database imported')\r\n #get the non_nans stations\r\n current_stations=np.array(dwd_dbase[list(dwd_dbase.keys())[0]].sel(time=date_mean,method='nearest').dropna('STATIONS_ID').coords['STATIONS_ID'])\r\n else:\r\n print(dbase_path, 'does not exist, we create a new netcdf_file')\r\n dwd_dbase=xr.Dataset()\r\n current_stations=np.array((-9999)).reshape(1)\r\n #change directory on server\r\n for timerange in timeranges:\r\n archive_url='/climate_environment/CDC/observations_germany/climate/'+time_res_dbase+'/'+data_category+'/'+timerange \r\n ftp.cwd(archive_url)\r\n #get the archive\r\n for station_id in station_ids:\r\n #we check whether the station is in the database with this parameter already\r\n if int(station_id) in current_stations:\r\n print('Station', station_id, 'with category', data_category,'in ',timerange,'dbase already')\r\n continue\r\n try:\r\n archive_name=[s for s in ftp.nlst() if station_id in s][0]\r\n except:\r\n print('No ',timerange,'data for station',station_id)\r\n continue\r\n print('Retrieving {}...'.format(archive_name))\r\n retrieved = False\r\n archive = io.BytesIO()\r\n # try to retrieve file\r\n while not retrieved:\r\n try:\r\n ftp.retrbinary(\"RETR \" + archive_name, archive.write)\r\n retrieved = True\r\n except:\r\n ftp=connect_ftp(server = server,connected = False)\r\n ftp.cwd(archive_url)\r\n archive.seek(0)\r\n with ZipFile(archive) as myzip:\r\n for f in myzip.infolist():\r\n # This is the data file\r\n #print('zip content:', f.filename)\r\n if f.filename.startswith('produkt_'):\r\n product = io.StringIO(str(myzip.read(f.filename),'utf-8'))\r\n #get dataframe from product \r\n dwd_product=pd.read_csv(product,sep=';',skipinitialspace=True)\r\n #get datetime\r\n dwd_product['time']=pd.to_datetime(dwd_product['MESS_DATUM'],format=time_format) \r\n dwd_product=dwd_product.rename(columns=dwd_datasets_meta[time_res_dbase][data_category])\r\n dwd_product=dwd_product.reset_index()\r\n dwd_product=dwd_product.set_index(['time','STATIONS_ID'])\r\n dwd_product=dwd_product.drop(columns=['MESS_DATUM','quality_level_of_next_columns','end_of_record','index'])\r\n #append to database\r\n dwd_xr=dwd_product.to_xarray()\r\n #replace all values equal to -999 to nan\r\n for data_var in dwd_xr.data_vars:\r\n dwd_xr[data_var]=dwd_xr[data_var].where(dwd_xr[data_var]>-999)\r\n if station_id=='05009':\r\n print('ok') \r\n #only add relevant dates if available memoryis rather small\r\n \r\n if memory_save and timerange=='historical':\r\n dwd_xr=dwd_xr.sel(time=slice(campaign_time[0]-timedelta(days=1),campaign_time[1]+timedelta(days=1)))\r\n #dwd_xr=dwd_xr.squeeze()\r\n \r\n try:\r\n dwd_dbase=xr.merge([dwd_dbase,dwd_xr])\r\n except Exception as e:\r\n print(e)\r\n print('try merging with compat=override')\r\n dwd_dbase=xr.merge([dwd_dbase,dwd_xr],compat='override')\r\n print(archive_name,' added to database')\r\n #upscale to required temporal resolution\r\n if resample_frequency is not None:\r\n dwd_dbase=dwd_dbase.resample(time=resample_frequency).mean(skipna=True)\r\n print('DWD data upscaled to',time_res,'averages')\r\n if Output==True:\r\n dwd_dbase.to_netcdf(dbase_path)\r\n print('Updated database' ,dbase_path)\r\n return dwd_dbase","def compute_aggregate_weather_data():\n\n # get a list of all the csv files names in the 'weather_data' directory\n files = get_all_csv_files_in_directory('weather_data')\n\n # Todo: if the number of csv files doesn't match the expected value, unzip remaining using the 'os' module\n\n if len(files) == 0:\n\n # Unzip all files in current directory and subdirectories\n print \"unzipping weather files...\"\n os.system(\"unzip 'weather_data/*.zip' -d weather_data\")\n\n\n # Try again to get files\n files = get_all_csv_files_in_directory('weather_data')\n\n # Throw exception if still missing csv files\n if len(files) == 0:\n raise ValueError(\"Missing weather data in csv format in the 'weather_data' directory\")\n\n # convert the list of csv file names to a list of corresponding DataFrames\n dallas_files = filter(lambda file_name : \"KDAL\" in file_name, files)\n houston_files = filter(lambda file_name : \"KHOU\" in file_name, files)\n san_antonio_files = filter(lambda file_name : \"KSAT\" in file_name, files)\n\n print \"Retrieved weather data files...\"\n print \"\\t# of Dallas weather files found: \", len(dallas_files)\n print \"\\t# of Houston weather files found: \", len(houston_files)\n print \"\\t# of San Antonio weather files found: \", len(san_antonio_files)\n\n dallas_dfs = map(lambda file_name: read_weather_data_from_csv(\"./weather_data/\" + file_name), dallas_files)\n houston_dfs = map(lambda file_name: read_weather_data_from_csv(\"./weather_data/\" + file_name), houston_files)\n san_antonio_dfs = map(lambda file_name: read_weather_data_from_csv(\"./weather_data/\" + file_name), san_antonio_files)\n\n dallas_df = pd.concat(dallas_dfs)\n houston_df = pd.concat(houston_dfs)\n san_antonio_df = pd.concat(san_antonio_dfs)\n\n print \"Aggregating all of the weather data...\"\n # fold the list of data frames into a single data frame\n aggregate_df = reduce(lambda df1, df2: pd.merge(df1, df2, on=\"Date\", how=\"outer\"), [dallas_df, houston_df, san_antonio_df]).sort_values(\"Date\")\n\n return aggregate_df","def collect_stations(self):\n # First, iterate provinces and build url's\n site = urllib.request.urlopen(self.base_url)\n\n # Check that the site is still valid or operating by collecting a list of provinces\n print(\"Collecting provinces\")\n provinces = [s[9:11] for s in re.findall('<a href=\"../\">../</a>', site.read())]\n\n # Iterate provinces and collect list of available times\n print(\"Collecting time periods and station ID's\")\n self.stations = defaultdict(dict)\n for prov in provinces:\n site = urllib.request.urlopen(self.build_url(prov))\n expression = '<a href=\"[hd][a-zA-Z]*/\">[hd][a-zA-Z]*/</a>'\n times = [s.split('>')[1].split('<')[0].replace('/', '') for s in re.findall(expression, site.read())]\n\n # Iterate times and collect the station ID's\n for time in times:\n site = urllib.request.urlopen(self.build_url(prov, time))\n expression = '<a href=\"{0}_[a-zA-Z0-9]*_{1}_hydrometric.csv\">{0}_[a-zA-Z0-9]*_{1}_hydrometric.csv</a>'\n expression = expression.format(prov.upper(), time.lower())\n stations = [s.split('_')[1] for s in re.findall(expression, site.read())]\n self.stations[prov][time] = stations","def gatherStationData():\n flist = list_files()\n station_dics = {}\n print(\"Reading in csv data...\")\n for f_in in flist:\n start,end = find_timespan(f_in)\n station = station_name(f=f_in)\n print(\"File: {0} Station: {1} {2}--{3}\".format(f_in, \n station, start, end))\n station_dics[station] = read_precip(fname=f_in, \n label=station, start_year=start, end_year=end)\n data_list = []\n for s in station_dics:\n data_list.append(station_dics[s]) \n return pd.concat(data_list,axis=1)","def run():\n #Initialise variables\n data = build_station_list()\n update_water_levels(data)\n ls = []\n ID = []\n \n #Number of days in past taken data from\n dt = 7\n #How many graphs per window\n limit = 4\n #How many stations\n number = 6\n \n #Create list of measuring_id's sorted by water level\n for station in data:\n if station.typical_range_consistent() == True and station.relative_water_level() != None:\n ls.append((station, station.relative_water_level()))\n\n ls = sorted_by_key(ls, 1)\n \n for station in ls:\n ID.append(station[0])\n \n s = count_inconsistent_sets(ID[:number], dt)\n \n ID = ID[:number+s]\n\n plot_water_levels(ID, dt, limit, s)","def import_temp_data(counties):\n for index, row in counties.iterrows():\n station = row[2]\n url = f'https://wrcc.dri.edu/WRCCWrappers.py?sodxtrmts+0{station}+por+por+maxt+none+mave+5+01+F'\n result = requests.get(url)\n soup = BeautifulSoup(result.text, 'html.parser')\n table = soup.find('table')\n data = pd.read_html(str(table))\n df = data[0]\n df.columns = df.iloc[0]\n df = df.drop([0])\n df = df.iloc[-65:-8, :]\n df = df.rename(columns={'YEAR(S)': 'Year'})\n df['Year'] = pd.to_datetime(df['Year'], format='%Y')\n df = df.set_index('Year')\n df = df.dropna(axis=1)\n df = df.replace(to_replace='-----', value=np.nan)\n df = df.astype('float64')\n df = df.fillna(df.mean().round(2))\n df = df.add_suffix('_t')\n name = row[0]\n df['County'] = name\n df.to_csv(f'{name}_avgmaxtemp.csv')\n print(f'Avg. max. temp. data from {name} saved')\n time.sleep(3.14)\n print('Done')","def _get_data(self, gas, loc, voltage, speed, trial):\n cols = []\n for g in gas:\n for l in loc:\n try:\n (sub, files) = self._get_sensor_col_files(g, l)\n except OSError as e:\n print('{}\\n Keeping calm and carrying on.'.format(e))\n continue\n for v in voltage:\n for s in speed:\n end = \"_board_setPoint_%s_fan_setPoint_%s_mfc_setPoint_%sppm_p%s\" % (\n self.SensorVoltages[v],\n self.FanSpeeds[s],\n self.GasNames[g],\n self.AltLocs[l])\n filtered = [f.split('/')[-1] for f in files if f.endswith(end)]\n if not filtered:\n if self._args['verbose']:\n print('No valid files found for \"%s\", skipping!' % sub)\n continue\n timeStamp = [filt.split('_', 1)[0] for filt in filtered]\n date = [time.strptime(ts, '%Y%m%d%H%M') for ts in timeStamp]\n date = [time.strftime('%Y-%m-%d %H:%M', d) for d in date]\n filtered = [os.path.join(sub, f) for f in filtered]\n for i, filt in enumerate(filtered):\n j = i + 1\n if j in trial:\n p = os.path.sep.join([self.dataloc_prefix,\n self.data_location,\n filt])\n\n cols.append(SensorColumn(data_location=p,\n gas=self.GasNames[g],\n loc=self.Locs[l],\n voltage=self.SensorVoltages[v],\n speed=self.AltFanSpeeds[s],\n trial=j,\n _args=self._args))\n\n if self._args['verbose']:\n print('\\nSelected %i single trial SensorColumns!' % len(cols))\n return cols","def populate_database(telescope_name, instrument_name):\n telescope = Telescope.objects.create(\n name=telescope_name, latitude=25.0, longitude=45.0)\n instrument = Instrument.objects.create(\n name=instrument_name, telescope=telescope)\n for year_int in (2012, 2013):\n for month_int in range(1, 13):\n for night_int in (1, monthrange(year_int, month_int)[1]):\n ut_date = date(year_int, month_int, night_int)\n night = Night.objects.create(\n ut_date=ut_date, instrument=instrument, observers='Smith')\n Exposure.objects.create(\n night=night, run_number=1, ut_start=time(10, 0, 0),\n exposed=20.0, ra=60.0, dec=30.0, object_exp=True)\n Exposure.objects.create(\n night=night, run_number=2, ut_start=time(11, 0, 0),\n exposed=30.0, ra=90.0, dec=0.0, object_exp=True)\n Exposure.objects.create(\n night=night, run_number=3, ut_start=time(12, 0, 0),\n exposed=40.0, ra=120.0, dec=-30.0, object_exp=False)","def ingest():\n\n base_path = '/home/mnichol3/Coding/wx-scripts/wtlma'\n\n flash_files = ['flash-out-05232019-2050.txt',\n 'flash-out-05232019-2100.txt',\n 'flash-out-05232019-2110.txt',\n 'flash-out-05232019-2120.txt',\n 'flash-out-05232019-2130.txt',\n 'flash-out-05232019-2140.txt',\n 'flash-out-05232019-2150.txt']\n\n df_cols = ['start', 'end', 'duration', 'area', 'ctr_alt', 'ctr_lat', 'ctr_lon',\n 'tot_energy']\n\n flash_df = pd.read_csv(join(base_path, flash_files[0]), sep=',', names=df_cols)\n\n for f in flash_files[1:]:\n curr_path = join(base_path, f)\n curr_df = pd.read_csv(curr_path, sep=',', names=df_cols)\n flash_df = pd.concat([flash_df, curr_df], ignore_index=True)\n\n return flash_df","def _setData(self):\n\n if not self.stationId:\n return\n \"\"\" \n # get the ressource url and adjust lat and lon from data portal\n query = sparqls.stationResource(self.stationId)\n key, val = RunSparql(query, 'array').run()\n if val: \n self.url = val[0][0]\n self.lat = float(val[0][2])\n self.lon = float(val[0][3])\n \"\"\"\n\n # it is possible, that a station id has multiple URI\n # ask for all URI\n query = sparqls.stationData(self.uri, 'all')\n data = RunSparql(query, 'pandas').run()\n\n if not data.empty:\n self._data = data\n else:\n self._data = 'no data available'\n\n # check if data is available and extract the 'unique' data products\n if isinstance(self._data, pd.DataFrame):\n p = self._data['specLabel'].unique()\n self._products = pd.DataFrame(p)\n\n # replace samplingheight=None with empty string\n self._data.samplingheight.replace(to_replace=[None], value=\"\", inplace=True)\n else:\n self._products = 'no data available'","def get_data():\n \n data = {\n 'loadAvg1Min': 0, #load average 1 min\n 'loadAvg5Min': 0, #load average 5 min\n 'loadAvg15Min': 0, #load average 15 min\n 'cpuUsage': [], #usage distribution for each cpu\n 'memUsage': {}, #memory usage \n 'networkReads': [], #network reads per second for each interface\n 'networkWrites': [], #network writes per second for each interface\n 'diskReads': [], #disk reads per second for each disk\n 'diskWrites': [] #disk writes per second for each disk\n }\n \n #metrics that doesnt need sampling\n data['loadAvg1Min'], data['loadAvg5Min'], data['loadAvg15Min'] = get_load_avg() #get load avg\n data['memUsage'].update(get_mem_usage()) #memory usage\n \n #metrics that needs sampling\n #they are written as a generator so that we can sleep before collection again\n sampling_duration = 1\n cpu_usage_gen = get_cpu_usage(sampling_duration) #generator for cpu usage\n net_rw_gen = get_net_rw(sampling_duration) #generator for network read write\n disk_rw_gen = get_disk_rw(sampling_duration) #generator for disk read write\n \n while 1: #now start sampling, whenever we have walid data, we can exit the loop\n cpu_usage = next(cpu_usage_gen)\n net_rw = next(net_rw_gen)\n disk_rw = next(disk_rw_gen)\n \n if cpu_usage or net_rw or disk_rw: #we have valid data\n break\n \n time.sleep(sampling_duration)\n \n #append cpu usage for each cpu core\n for cpu, usage in cpu_usage.items():\n data['cpuUsage'].append({'name': cpu, 'value': usage})\n \n #append network read and write for each interface\n for interface, rw in net_rw.items():\n data['networkReads'].append({'name': interface, 'value': rw['reads']})\n data['networkWrites'].append({'name': interface, 'value': rw['writes']}) \n \n #append disk read and write for each logical disk\n for device, rw in disk_rw.items():\n data['diskReads'].append({'name': device, 'value': rw['reads']})\n data['diskWrites'].append({'name': device, 'value': rw['writes']})\n \n return data","def test_seed_station_information(self):\n\t\tget_info.seed_station_information()\n\n\t\tMacDougal_Prince = db.session.query(Station).filter(Station.id == 128).one()\n\t\tself.assertTrue(MacDougal_Prince, 'Station at MacDogual/Pride did not get sucessfully added.')\n\n\t\tself.assertEqual(MacDougal_Prince.num_bikes_available, 0, 'Bike counts were not initialized properly')\n\t\tself.assertEqual(MacDougal_Prince.num_docks_available, 0, 'Dock counts were not initialized properly')","def get_prepared_data(cls, ext_stations=None):\n ext_stations = ext_stations or StationDAO.get_all_with_prices()\n features = (cls.get_station_features(row) for row in ext_stations)\n classes = (cls.get_category(row) for row in ext_stations)\n return features, classes","def getAllDataFromDirectory(prediction_directory, actual_directory, write_directory, cities_file, utc_offset = False):\n city_dictionary = getCities(cities_file)\n actualGetter = getActualWeather(actual_directory, city_dictionary, get_API_keys())\n #For each day and for each city, get all the data and put it into a spreadsheet.","def initialize_data(self , station = '', datasets = {} ): \n self.datasets = datasets\n self.datasets_keys = datasets.keys()\n self.station = station\n self.out_name = self.out_dir + '/' + self.station + '_CEUAS_premerged_v0.nc'\n\n self.observations_table_vars = ['date_time', 'z_coordinate' , 'z_coordinate_type', 'observed_variable' , 'observation_value' , 'report_id' , 'observation_id' , 'latitude' , 'longitude', 'units', 'source_id']\n\n \"\"\" Loading the econding of the tables created from the harvester script and to be applied again \"\"\"\n self.encodings = np.load('groups_encodings.npy' , allow_pickle = True ).item()\n self.encodings['era5fb'] = np.load('era5fb_encodings_all.npy' , allow_pickle = True ).item() \n self.dic_type_attributes = np.load('dic_type_attributes.npy',allow_pickle= True).item()\n \n self.era5fb_columns = self.dic_type_attributes['era5fb'].keys()\n\n self.obstab_nans_filled = False \n\n data['cdm_tables'] = {} \n \n \"\"\" Loop over all the datasets \n k: name of the dataset \n v: list of file paths, eg 'era5_1':[filepath_1, filepath_2 ] \"\"\" \n for k,v in self.datasets.items() :\n data[k] = {}\n for F in v:\n \n logging.info(' Dataset ::: *** %s %s ' , k , F ) \n \n data[k][F] = {}\n\n h5py_file = h5py.File(F, 'r')\n data[k][F]['h5py_file'] = h5py_file \n \n a = h5py_file['recordtimestamp']\n \n data[k][F]['recordtimestamp'] = a\n data[k][F]['recordindex'] = h5py_file['recordindex']\n data[k][F]['dateindex'] = h5py_file['dateindex']\n a = h5py_file['recordtimestamp']\n data[k][F]['max_date'] = max(a)\n data[k][F]['min_date'] = min(a)\n \n data[k][F]['counter'] = 0\n\n #######\n # HEADER TABLE\n #######\n head_tab = h5py_file['header_table']\n logging.info('*** header_table')\n data[k][F]['header_table'] = {}\n for var in head_tab.keys():\n if ('string' in var or 'hdrlen' in var): continue\n try: \n data[k][F]['header_table'][var] = (np.array(head_tab[var][:])).astype(self.dic_type_attributes['header_table'][var]['type'] )\n except:\n print('failed convertion type header' , k , ' ' , F , ' ' , var )\n \n ####### \n # STATION CONFIGURATION\n ####### \n d = xr.open_dataset(F , engine = 'h5netcdf' , group = 'station_configuration' , decode_times = False )\n data[k][F]['station_configuration'] = d.to_dataframe()\n logging.debug('Done with %s station_configuration' , str(k) )\n d.close()\n\n ####### \n # SOURCE CONFIGURATION \n ####### \n d = xr.open_dataset(F , engine = 'h5netcdf' , group = 'source_configuration' , decode_times = False )\n data[k][F]['source_configuration'] = d\n logging.debug('Done with %s source_configuration' , str(k) )\n d.close()\n\n\n data['cdm_tables'] = {}\n \"\"\" Reading the CDM tables that do not depend on specific stations or observations (fixed values), for the first file only \"\"\"\n for t in self.standard_cdm: # [ 'crs' , 'observed_variable', 'units' , 'z_coordinate_type' , 'station_type', 'station_configuration_codes'] \n if t not in data['cdm_tables'].keys():\n #data['cdm_tables'][t] = ''\n cdm = xr.open_dataset(F , engine = 'h5netcdf' , group = t )\n data['cdm_tables'][t] = cdm \n\n print(blue + 'Memory used after reading data: ', process.memory_info().rss/1000000000 , cend)\n\n self.data = data\n\n \"\"\" Making all date_times \"\"\"\n self.make_all_datetime()","def Find_nearest_dwd_stations(inpt_data,\r\n date_start='20051201',\r\n date_end='20201231',\r\n dwd_time_format='%Y%m%d%H',\r\n data_category='air_temperature',\r\n temp_resolution='hourly',\r\n no_of_nearest_stations=4,\r\n memory_save=True,\r\n Output='True'):\r\n if isinstance(data_category,list):\r\n if len(list(data_category)) > 1:\r\n print(\r\n 'Currently only one dwd category allowed, please run function multiple times for each category'\r\n )\r\n return None\r\n \r\n #convert time to datetime\r\n dt_start=datetime.strptime(date_start,'%Y%m%d')\r\n dt_end=datetime.strptime(date_end,'%Y%m%d')\r\n print('Start quering data from DWD')\r\n #define the database folder\r\n pypath = os.path.dirname(os.path.abspath(__file__))\r\n table_dir = pypath + '\\\\' + 'tables'\r\n dbase_dir = pypath + '\\\\' + 'dbase' \r\n #%% we check all available stations and create a valid list\r\n filename_stations=update_stationlist(time_res='hourly',dbase_dir=table_dir)\r\n stations_all=pd.read_csv(filename_stations, dtype={'STATIONS_ID': object})\r\n # delete all stations which do not cover the category\r\n dwd_stations=stations_all[stations_all[data_category]==True].copy()\r\n #correct to datetime\r\n dwd_stations['date_end']=pd.to_datetime(stations_all.date_end,format='%Y%m%d')\r\n dwd_stations['date_start']=pd.to_datetime(stations_all.date_start,format='%Y%m%d')\r\n # clean to stations which cover the campaign time #dt_low <= dt <= dt_high:\r\n dwd_stations=dwd_stations[(dwd_stations.date_start<=dt_start) & (dwd_stations.date_end>=dt_end)]\r\n #make a geodataframe out of it\r\n dwd_stations=gpd.GeoDataFrame(dwd_stations,geometry=gpd.points_from_xy(dwd_stations.geo_lon, dwd_stations.geo_lat))\r\n \r\n #loop through all rows to get the n closest points\r\n distances=pd.DataFrame()\r\n for _, station in dwd_stations.iterrows():\r\n distances[station.STATIONS_ID]=inpt_data.distance(station.geometry)\r\n \r\n #%% get the n stations with smallest distance and update database\r\n id_nearest_stations=distances.apply(lambda s: s.nsmallest(no_of_nearest_stations).index.tolist(), axis=1).values.tolist() #station ids\r\n #get them as unique values by sum a list of lists https://bit.ly/353iZQB\r\n id_dwd_stations=list(set(sum(id_nearest_stations,[])))\r\n \r\n #update the database\r\n db_dwd_stations=import_stations(time_res=temp_resolution,time_format=dwd_time_format,campaign_time=[dt_start,dt_end],data_category=data_category,station_ids=id_dwd_stations,dbase_dir=dbase_dir,Output=Output,table_dir=table_dir,memory_save=memory_save)\r\n \r\n #distance of nearest stattions\r\n dist_nearest_stations=pd.DataFrame(np.sort(distances.values)[:,:no_of_nearest_stations]).values.tolist() #distances themself\r\n #create new columns in the input data\r\n station_col_nm=list()\r\n for i in range(0,no_of_nearest_stations):\r\n station_col_nm.append(data_category+'_station_'+str(i))\r\n for i in range(0,no_of_nearest_stations):\r\n station_col_nm.append(data_category+'_distance_'+str(i))\r\n #create new dataframe\r\n distance_data=pd.concat([pd.DataFrame(id_nearest_stations).astype(int),pd.DataFrame(dist_nearest_stations)],axis=1)\r\n distance_data.columns=station_col_nm\r\n #add to main dataset\r\n inpt_data=pd.concat([inpt_data, distance_data],axis=1) \r\n \r\n return inpt_data,db_dwd_stations","def generate_training_testing_dataset(store_id, transactions, meteo_day, max_days=2500,\n single_barcode=0):\n\n # Get the minimum and maximum of date in the transactions\n min_date = transactions[(transactions['STO_EAN'] == store_id)].min()['TRX_DATETIME'].date()\n max_date = transactions[(transactions['STO_EAN'] == store_id)].max()['TRX_DATETIME'].date()\n\n # Get the number of days between the two date\n num_days = (max_date - min_date).days\n\n # Get the list of unique products barcode in the transactions\n products_barcode = transactions['BARCODE'].unique()\n\n # Only do one single barcode if activated\n if single_barcode is not None:\n products_barcode = [products_barcode[single_barcode]]\n\n\n # Array to contain all training data\n all_data_first_level = []\n\n # For each day and for each product\n for day in xrange(num_days):\n\n print(day)\n\n # If we have already considered more days than allowed, stop\n if day > max_days:\n break\n\n\n # Get the date corresponding to this day\n date = min_date + pd.DateOffset(day)\n # Get the weather of the date\n weather = get_weather_on_date(date, meteo_day, store_id).head(n=1)\n\n # If the weather is empty we skip this day\n if weather.empty:\n continue\n\n # For each product to include\n for product_barcode in products_barcode:\n\n # Get the volume and inventory data\n volume = get_volume_product_on_date(product_barcode, date, store_id, transactions)\n\n # If no volume could be found skip this date,product pair\n if volume is None:\n continue\n\n # Get the type of the current date\n day_type = generate_day_type(date)\n\n\n # Generating complex features based on the simpler one\n\n # This contains respectively yesterday, the day before yesterday and the same day as current one in\n # previous week\n yesterday = date - pd.DateOffset(1)\n two_days_ago = date - pd.DateOffset(2)\n one_week_ago = date - pd.DateOffset(7)\n\n # Get the day type of yesterday and 2 days ago\n day_type_yesterday = generate_day_type(yesterday)\n day_type_2days_ago = generate_day_type(two_days_ago)\n\n # Get the volume of yesterday, 2days ago and 1 week ago\n volume_yesterday = get_volume_product_on_date(product_barcode, yesterday, store_id, transactions)\n volume_2days_ago = get_volume_product_on_date(product_barcode, two_days_ago, store_id, transactions)\n volume_one_week_ago = get_volume_product_on_date(product_barcode, one_week_ago, store_id, transactions)\n\n\n # Get the total sales and the total weight of product done yesterday, 2 days ago and 1 week ago\n volume_price_yesterday = 0\n volume_weight_yesterday = 0\n if volume_yesterday is not None:\n volume_price_yesterday = volume_yesterday[\"price\"]\n volume_weight_yesterday = volume_yesterday[\"weight\"]\n\n volume_price_2days_ago = 0\n volume_weight_2days_ago = 0\n if volume_2days_ago is not None:\n volume_price_2days_ago = volume_2days_ago[\"price\"]\n volume_weight_2days_ago = volume_2days_ago[\"weight\"]\n\n volume_price_one_week_ago = 0\n volume_weight_one_week_ago = 0\n if volume_one_week_ago is not None:\n volume_price_one_week_ago = volume_one_week_ago[\"price\"]\n volume_weight_one_week_ago = volume_one_week_ago[\"weight\"]\n\n\n\n # Using historical weather data\n weather_yesterday = get_weather_on_date(yesterday, meteo_day, store_id).head(n=1)\n temperature_min_yesterday = 0\n temperature_max_yesterday = 0\n if not weather_yesterday.empty:\n temperature_min_yesterday = weather_yesterday['TEMPERATURE_VALUE_MIN'].values[0]\n temperature_max_yesterday = weather_yesterday['TEMPERATURE_VALUE_MIN'].values[0]\n\n\n #tmp = [weather['TEMPERATURE_VALUE_MIN'].values[0], weather['TEMPERATURE_VALUE_MAX'].values[0],\n # weather['PRECIPITATION_VALUE'].values[0], weather['SUNSHINE_DURATION'].values[0],\n # weather['SNOW_DEPTH'].values[0], day_type, volume[\"price\"], volume[\"weight\"]]\n\n\n # Saving Features\n tmp = [weather['TEMPERATURE_VALUE_MIN'].values[0], weather['TEMPERATURE_VALUE_MAX'].values[0],\n day_type, volume[\"price\"], volume_price_yesterday,volume_weight_yesterday,\n volume_price_2days_ago, volume_weight_2days_ago,\n volume_price_one_week_ago, volume_weight_one_week_ago, temperature_min_yesterday,\n temperature_max_yesterday,day_type_yesterday, day_type_2days_ago,\n volume[\"weight\"]]\n\n all_data_first_level.append(tmp)\n\n return all_data_first_level","def _populate_output(self):\n self._store_atomic_queries_table()\n self._store_composite_queries_table()","def run(self):\n\t\tdf_iter = self.file_to_df(50000)\n\t\tdf_airport = self.airport_file_to_df()\n\t\tfor df in df_iter: # type: pd.DataFrame\n\t\t\tdf.drop_duplicates(inplace=True)\n\t\t\tdf = self.transform(df, df_airport)\n\n\t\t\tdf_result = self.get_only_new_records(\n\t\t\t\tdf=df,\n\t\t\t\tdf_columns=self.join_columns,\n\t\t\t\ttable_columns=self.join_columns\n\t\t\t)\n\n\t\t\tif len(df_result) > 0:\n\t\t\t\t# df_result.drop(self.table_columns, axis=1)\n\n\t\t\t\tself.save(\n\t\t\t\t\tdf=df_result,\n\t\t\t\t\ttable_name=\"travel_dimension\",\n\t\t\t\t\tdf_columns=self.table_columns,\n\t\t\t\t\ttable_colums=self.table_columns\n\t\t\t\t)","def padova_interpolated_isomake(directories, bands_dict, output_filename,\n bands_ordered=None):\n\n if isinstance(directories, basestring):\n directories = [directories]\n\n if bands_ordered is None:\n bands_ordered = bands_dict.values()\n\n output_obj = open(output_filename, \"w\")\n\n header_string = \"#\\t[M/H]\\tMi\\tlogAge\\tlogTe\\tlogg\\tJacobian\"\n for band in bands_ordered:\n header_string += \"\\t{}\".format(band)\n header_string += \"\\tinner_count\\touter_count\\n\"\n output_obj.write(header_string)\n\n iso_metal_dict = {}\n bands_metal_dicts = {}\n for band in bands_dict.keys():\n bands_metal_dicts[band] = {}\n\n # instead do this on band-by-band basis? *******************\n\n for direc in directories:\n iso_files_gz = gb.glob(\"{}/*.dat.gz\".format(direc.rstrip(\"/\")))\n iso_files = gb.glob(\"{}/*.dat\".format(direc.rstrip(\"/\")))\n\n # check for metallicity of each file\n # and check which bands it has\n\n for iso_file1 in iso_files_gz:\n metal = None\n iso_data = gz.open(\"{0}\".format(iso_file1))\n for line in iso_data:\n split_line = line.split()\n if \"[M/H]\" in split_line:\n metal = float(split_line[split_line.index(\"[M/H]\")+2])\n if \"M_ini\" in split_line:\n for band in bands_metal_dicts.keys():\n if band in split_line:\n bands_metal_dicts[band][metal] = iso_file1\n\n for iso_file1 in iso_files:\n metal = None\n iso_data = open(\"{0}\".format(iso_file1), \"r\")\n for line in iso_data:\n split_line = line.split()\n if \"[M/H]\" in split_line:\n metal = float(split_line[split_line.index(\"[M/H]\")+2])\n if \"M_ini\" in split_line:\n for band in bands_metal_dicts.keys():\n if band in split_line:\n bands_metal_dicts[band][metal] = iso_file1\n\n for metal in bands_metal_dicts[bands_metal_dicts.keys()[0]]:\n filenames = []\n for band in bands_metal_dicts:\n if metal in bands_metal_dicts[band]:\n if bands_metal_dicts[band][metal] not in filenames:\n filenames.append(bands_metal_dicts[band][metal])\n else:\n break\n else:\n iso_metal_dict[metal] = filenames\n\n print(iso_metal_dict)\n keys = iso_metal_dict.keys()\n keys.sort()\n\n if len(keys) > 2:\n # iso_metal_weights=dict(zip(keys, np.gradient(np.array(keys)) ) )\n # in numpy 1.9.0 gradient has changed to use second order behaviour\n # at boundaries which gives wrong results in this context\n iso_metal_weights = dict(zip(keys,\n replacement_gradient(np.array(keys))))\n else:\n iso_metal_weights = dict(zip(keys, np.ones(len(keys))))\n print(\"metals and weights: \", iso_metal_weights)\n\n# interp in metallicity order\n\n for key in keys:\n iso_interp(iso_metal_dict[key], key, iso_metal_weights[key],\n output_obj, bands_dict, bands_ordered)\n\n output_obj.close()","def ddf_parser():\n num_available, total = 0, 0\n indicator_twn_tuples = list() # format of a single tuple: (indicator_name, #twn rows, earliest available year)\n concept_metadata = dict() # {top_tag: second_layer_tag:\n\n # parse all ddf files provided by GapMinder and find how many of them with Taiwan statistics\n for f_path in glob.glob(os.path.join('statistics', '*datapoints*.csv')):\n total += 1\n df = pd.read_csv(f_path)\n if 'twn' in df.geo.unique():\n num_available += 1\n indicator = f_path.replace('statistics/ddf--datapoints--', '').replace('--by--geo--time.csv', '')\n # print('[Indicator]', indicator)\n print(f\"\\t{len(df[df.geo == 'twn'])} indicators including Taiwan statistics.\")\n\n # stat_name = df.columns[-1]\n # df_p = df.pivot(index='geo', columns='time')[stat_name]\n # df_p.insert(loc=0, column='indicator', value=stat_name)\n # df_p.to_csv(f'statistics_transformed/{stat_name}.csv', sep=';')\n\n indicators.append(indicator)\n\n\n # print(\"{:.1f}% datapoints have Taiwan statistics\".format(num_available / float(total) * 100))\n\n\n\n df_c = pd.read_csv(CONCEPT_CSV_PATH)\n df_t = pd.read_csv(TAG_CSV_PATH)\n df = pd.merge(df_c, df_t, how='left', left_on='tags', right_on='tag')\n for idr, num_rows, earliest_year in indicator_twn_tuples:\n ancestors = list()\n\n row_values = df[df['concept'] == idr].values[0]\n name_catalog, parent, ancestor = (row_values[i] for i in [9, 17, 18])\n if type(parent) is str:\n ancestors.append(parent)\n\n # get ancestors recursively\n while type(ancestor) is str:\n tag_row_values = df_t[df_t['tag'] == ancestor].values[0]\n ancestors.append(tag_row_values[1])\n ancestor = tag_row_values[2]\n\n # build concept structure\n ancestors.insert(0, name_catalog)\n print('/'.join(ancestors[::-1]))","def create_station_dics(data_directories):\n \n files_all = {} \n for k,v in data_directories.items() :\n files = os.listdir(v)\n \n for f in files:\n station = f.split('_')[0] \n if station not in files_all.keys():\n files_all[station] = {}\n \n if k == 'ncar': # separating ncar temperature and wind files \n if 'trhc' in f:\n k = 'ncar_t'\n elif 'windc' in f:\n k = 'ncar_w'\n files_all[station][k] = ''\n files_all[station][k] = v + '/' + f # compelte path to the netCDF file \n\n #print('check') \n \n \n return files_all","def generate_weather_data(self):\n months = pd.to_datetime(self.output['Local Time']).dt.month\n self.output['Month'] = months # set month values for later joins\n\n # merge output data frame with historical data to get ranges\n keys = ['Location', 'Month']\n m = pd.merge(self.output, self.histdata, how='left',\n left_on=keys, right_on=keys)\n\n # uniformly select random pressure, temperature\n # and humidity values between the historical max and min ranges\n r = np.random.rand(m.shape[0])\n m['Temperature'] = ((m['Tmean_high'] - m['Tmean_low']\n ) * r + m['Tmean_low']).round(1)\n m['Pressure'] = ((m['Pmax'] - m['Pmin']) * r + m['Pmin']).round(1)\n m['Humidity'] = ((m['Hmax'] - m['Hmin']) * r + m['Hmin']).astype(int)\n\n # drop redundant columns and assign to output\n dcols = ['Month', 'Timezone', 'Pmax', 'Pmin',\n 'Hmax', 'Hmin', 'Tmean_high', 'Tmean_low']\n m.drop(columns=dcols, inplace=True)\n self.output = m","def write_stations_to_metro_stations_table(config, tables_cache):\r\n database = deepcopy(config[\"database\"])\r\n metro_stations_table = deepcopy(config[\"tables\"][\"metro_stations_table\"])\r\n in_tests.test_write_to_database_from_dict(\r\n database, metro_stations_table, tables_cache)\r\n\r\n station_id = tables_cache[\"address_metro_stations_station_id\"]\r\n station_name = tables_cache[\"address_metro_stations_station_name\"]\r\n line_name = tables_cache[\"address_metro_stations_line_name\"]\r\n station_lat = tables_cache[\"address_metro_stations_lat\"]\r\n station_lng = tables_cache[\"address_metro_stations_lng\"]\r\n\r\n if station_id:\r\n write_to_database(database, metro_stations_table, {\r\n \"station_id\": station_id,\r\n \"station_name\": station_name,\r\n \"line_name\": line_name,\r\n \"station_lat\": station_lat,\r\n \"station_lng\": station_lng\r\n })\r\n return ()","def load_data(city, month, week_day):\n# name for day variable changed from \"day_name\" into week_day to take into account new pandas method \".day_name()\"\n# read in file form selected city\n df = pd.read_csv(CITY_DATA[city])\n# create additional columns for months, days, start/ end times, hours and station combinations\n df['Start Time'] = pd.to_datetime(df['Start Time'])\n df['End Time'] = pd.to_datetime(df['End Time'])\n df['month_start'] = df['Start Time'].dt.month\n df['month_end'] = df['End Time'].dt.month\n df['day_start'] = df['Start Time'].dt.day_name()\n df['day_end'] = df['End Time'].dt.day_name()\n df['hour'] = df['Start Time'].dt.hour\n df['station_comb'] = df['Start Station'] + ' &AND& ' + df['End Station']\n# filter data file by month: capture start and end months\n if month != 7:\n df1 = df[df['month_start'] == month]\n df2 = df1.append(df[df['month_end'] == month])\n df = df2.drop_duplicates()\n# filter data file by day: capture start and end days\n if week_day != 'All':\n df3 = df[df['day_start'] == week_day]\n df4 = df3.append(df[df['day_end'] == week_day])\n df = df4.drop_duplicates()\n# reset index to facilitate looping in station_stats function\n df = df.reset_index()\n# check if user wants to check first data lines\n req_check_df = input('\\nIf you want to check the selected data please enter y.')\n if req_check_df[0:1].lower() == 'y':\n print('check df = \\n', df.head())\n wait = input('Press Enter to continue. ')\n\n return df","def load_vals_berkeley(s):\n \n download_dates = s.berk.download_dates\n download_dates_oth = s.berk.download_dates_oth\n sensor_swap_date = s.berk.sensor_swap_date\n \n dfs = {'indoor':{}}\n \n # outdoor temp\n dfs['outdoor'] = get_outdoor_data(s.berk.temps_dir,'berk')\n \n # base directory for indoor temp measurements\n indoor_temp_dir = join(s.berk.temps_dir,'indoor')\n\n print(\"Downloading temps: {}...\".format(download_dates[0]))\n \n # grab room temp data from both control and treatment rooms\n for gi,g in enumerate(['control','treatment']):\n this_df = dfs['indoor'][g] = {}\n \n ## download data from early in experiment when we were using different sensors\n this_df['partition'] = pd.read_excel(join(indoor_temp_dir,download_dates[0].strftime('%Y%m%d'),'{}_p.xls'.format(g)),\n sheet_name='Records',parse_dates=True,index_col=0).loc[:pd.to_datetime(sensor_swap_date),:]\n \n this_df['RA'] = pd.read_excel(join(indoor_temp_dir,download_dates[0].strftime('%Y%m%d'),'{}_RA.xls'.format(g)),\n sheet_name='Data Table',parse_dates=True,index_col=1,header=21).iloc[:,1].loc[:pd.to_datetime(sensor_swap_date)]\n this_df['RA'].name = 'T'\n this_df['RA'] = pd.DataFrame(this_df['RA'])\n this_df['RA']['RH'] = np.nan\n \n for loc in ['partition','RA']:\n this_df[loc].columns = ['T','RH']\n this_df[loc].index.name='time'\n \n ## now download data from sensors we switched to\n for d in download_dates[1:]:\n csv_dir = join(indoor_temp_dir,d.strftime('%Y%m%d'),'csvs')\n print(\"Downloading temps: {}...\".format(d))\n for loc in [('partition','p'),('RA','RA')]:\n fpath = join(csv_dir,'{}_{}.csv'.format(g,loc[1]))\n this_df = add_file_to_dfs(fpath, this_df, [1,2,3], ['T','RH'], loc[0], sensor_swap = sensor_swap_date)\n \n ## add individual temp/RH \n for s_ix in range(1,7):\n if isfile(join(csv_dir,'{}_{}.csv'.format(g,s_ix))):\n fpath = join(csv_dir,'{}_{}.csv'.format(g,s_ix))\n this_df = add_file_to_dfs(fpath, this_df, [1,2,3], ['T','RH'], str(s_ix))\n \n ## add operative temp\n if isfile(join(csv_dir,'{}_ot.csv'.format(g))):\n fpath = join(csv_dir, '{}_ot.csv'.format(g))\n this_df = add_file_to_dfs(fpath, this_df, [1,4], ['Top'], 'Top')\n \n ## add CO2\n for d in download_dates_oth:\n print(\"Downloading co2: {}...\".format(d))\n csv_dir = join(s.berk.other_dir,d.strftime('%Y%m%d'))\n \n # pass when the file doesn't exist (aka when\n # the CO2 sensor's batteries died\n fpath = join(csv_dir,'{}_co2.csv'.format(g))\n if not isfile(fpath):\n continue\n \n # otherwise, parse\n this_df = add_file_to_dfs(fpath, this_df, [1,4], ['co2'], 'co2')\n \n this_df = drop_duplicates_and_flags(this_df)\n \n return dfs","def fill_dataset(self):\n rm, rstd = self.get_rolling_stats()\n\n self.add_rolling_mean(rm)\n self.add_bollinger_bands(rstd)\n self.add_spy_info()\n self.add_beta_and_sharpe()\n self.add_stlouis_data()","def load_vals_bus(s):\n \n download_dates = s.bus.download_dates\n \n dfs = {'indoor':{}}\n \n dfs['outdoor'] = get_outdoor_data(s.bus.temps_dir,'bus')\n\n # grab room temp data from both control and treatment rooms\n for gi,gx in enumerate([('Cool','control'),('Warm','treatment')]):\n ga = gx[0]\n g = gx[1]\n tx_lab = ga[0].upper()\n \n \n print('Downloading {} room data...'.format(g))\n\n this_df = dfs['indoor'][g] = {}\n \n ## now download data from sensors we switched to\n for dx,d in enumerate(download_dates):\n \n d_str = d.strftime('%Y%m%d')\n dirname = join(s.bus.temps_dir,'indoor','{}_{}'.format(d_str,ga))\n print('Downloading {}...'.format(d_str))\n \n for loc in [('far','F'),('near','N')]:\n print('Downloading {} sensor...'.format(loc[0]))\n \n # need to use glob because some files have two \".\"s and some have one\n fname = '{}_Temp_{}{}.*csv'.format(d_str,tx_lab,loc[1])\n fpath = join(dirname, fname)\n files = glob(fpath)\n fpath = files[0]\n \n # check for missing files\n if len(files) != 1:\n with warnings.catch_warnings():\n warnings.simplefilter('always')\n warnings.warn('Missing file: {}'.format(fname))\n continue\n this_df = add_file_to_dfs(fpath, this_df, [1,2,3], ['T','RH'], loc[0])\n \n ## add individual data\n for s_ix in range(1,7):\n fname = '{}_Temp_{}{}.csv'.format(d_str,tx_lab,s_ix)\n fpath = join(dirname, fname)\n this_df = add_file_to_dfs(fpath, this_df, [1,2,3], ['T','RH'], str(s_ix))\n \n ## add T_operative\n fname = '{}_PingPong_{}.csv'.format(d_str,ga)\n fpath = join(dirname, fname)\n this_df = add_file_to_dfs(fpath, this_df, [1,4], ['Top'], 'Top')\n \n ## add CO2\n fname = '{}_{}_co2.csv'.format(d_str,g)\n fpath = join(dirname, fname)\n this_df = add_file_to_dfs(fpath, this_df, [1,4], ['co2'], 'co2')\n\n this_df = drop_duplicates_and_flags(this_df)\n \n return dfs","def get_all_station_feature(city):\n poi_frequency = np.load(exp_data_path + os.sep + 'poi_frequency' + os.sep + 'poi_frequency_{}.npy'.format(city),\n allow_pickle=True) # .tolist()\n poi_num = np.load(exp_data_path + os.sep + 'poi' + os.sep + 'poi_{}.npy'.format(city), allow_pickle=True)\n poi_entropy = np.load(exp_data_path + os.sep + 'poi_entropy' + os.sep + 'poi_entropy_{}.npy'.format(city),\n allow_pickle=True)\n road = np.load(exp_data_path + os.sep + 'roadnet' + os.sep + 'roadnet_{}.npy'.format(city), allow_pickle=True)\n transportation = np.load(exp_data_path + os.sep + 'transportation' + os.sep + 'transportation_{}.npy'.format(city),\n allow_pickle=True)\n commerce = np.load(exp_data_path + os.sep + 'commerce' + os.sep + 'commerce_{}.npy'.format(city), allow_pickle=True)\n\n file_name = exp_data_path + os.sep + 'station' + os.sep + 'all_demand_{}.npy'.format(city)\n demand_data = np.load(file_name, allow_pickle=True)\n num = demand_data[:, 0, -2, np.newaxis] # todo check meaning here, get quick and slow feature\n\n raw_data = np.concatenate((num, poi_frequency, poi_num, poi_entropy, road, transportation, commerce), axis=1)\n csv_data = pd.DataFrame(raw_data, columns=GENERAL_HEADER)\n\n file_path = exp_data_path + os.sep + 'static' + os.sep + 'static_feature_{}.csv'.format(city)\n if os.path.exists(file_path):\n os.remove(file_path)\n csv_data.to_csv(file_path)\n pass","def loadDBstation(dbfile,stationID,varname,timeinfo=None,filttype=None,cutoff=3600.0,output_meta=False):\r\n from netcdfio import queryNC\r\n \r\n outvar = ['NetCDF_Filename','NetCDF_GroupID','StationName']\r\n tablename = 'observations'\r\n #condition = 'Variable_Name = \"%s\" and StationID = \"%s\"' % (varname,stationID)\r\n condition = 'Variable_Name = \"%s\" and StationID LIKE \"%%%s\"' % (varname,stationID)\r\n \r\n print 'Querying database...'\r\n print condition\r\n data, query = queryNC(dbfile,outvar,tablename,condition) \r\n \r\n if len(data)==0:\r\n print '!!! Warning - Did not find any stations matching query. Returning -1 !!!'\r\n return -1\r\n else:\r\n ts = timeseries(data[0]['time'],data[0][varname].squeeze())\r\n \r\n \r\n if not timeinfo==None:\r\n print 'Interpolating station data between %s and %s\\n'%(timeinfo[0],timeinfo[1])\r\n tnew,ynew = ts.interp((timeinfo[0],timeinfo[1],timeinfo[2]))\r\n ts = timeseries(tnew,ynew)\r\n ts.dt = timeinfo[2] # This needs updating\r\n \r\n if not filttype==None:\r\n print '%s-pass filtering output data. Cutoff period = %f [s].'%(filttype,cutoff)\r\n yfilt = ts.filt(cutoff,btype=filttype,axis=-1)\r\n ts.y = yfilt.copy()\r\n \r\n if output_meta:\r\n if data[0].has_key('elevation'):\r\n ele = data[0]['elevation']\r\n else:\r\n ele = 0.0\r\n meta = {'longitude':data[0]['longitude'],'latitude':data[0]['latitude'],'elevation':ele,'StationName':query['StationName'][0]}\r\n return ts, meta \r\n else:\r\n return ts","def dataLoader(stationDict, startDate, endDate):\n\n # Generate a URL\n url = ('https://waterservices.usgs.gov/nwis/dv/?format=json' +\n # Specify the sites to download\n '&sites=' + stationDict['DatasetExternalID'] +\n # Specify the start date\n '&startDT=' + datetime.strftime( startDate, '%Y-%m-%d' ) +\n #Specify the end data\n '&endDT=' + datetime.strftime( endDate, '%Y-%m-%d' ) +\n # Specify that we want streamflow\n '¶meterCd=00060' +\n # Specify that we want daily means\n '&statCd=00003' +\n # Allow all sites\n '&siteStatus=all' )\n \n # Get the data\n response = requests.get(url)\n\n # Check the status code\n if response.status_code != 200:\n return \n else:\n response = response.json()\n \n # Create a dataframe from the data\n df = pd.DataFrame(response['value']['timeSeries'][0]['values'][0]['value'])\n\n # Set the index to the dateTime index\n df.set_index(pd.DatetimeIndex(pd.to_datetime(df['dateTime'])), inplace = True)\n del df['dateTime'] # Delete the redundant column\n\n # Replace missing data with NaN's\n df['value'].replace(to_replace = '-999999', value = np.nan, inplace = True)\n\n # Convert to numeric\n df['value'] = pd.to_numeric(df['value'])\n \n # Remove any duplicate data in the dataset\n df = df[~df.index.duplicated(keep='last')] # Remove duplicates from the dataset\n df = df[~df.index.isnull()]\n\n # Rename the columns\n df.columns = ['USGS | ' + stationDict['DatasetExternalID'] + ' | Flag', 'USGS | ' + stationDict['DatasetExternalID'] + ' | Streamflow | CFS']\n del df['USGS | ' + stationDict['DatasetExternalID'] + ' | Flag']\n\n # Return the data frame\n return df","def populate_stops(self):\n stops = self.load_csv('stops.txt')\n stops = self.process_stops(stops)\n\n connection = db.connect()\n for stop in stops:\n try:\n connection.execute(schema.stops.insert(), stop)\n except DataError:\n print \"Missing data for stop: %s\" % (stop)","def get_weather_data():\n keys = ['1364038.csv',\n '1364041.csv',\n '1364042.csv',\n '1364043.csv',\n '1364044.csv',\n '1364046.csv',\n '1364047.csv',\n '1364048.csv',\n '1364051.csv',\n '1364052.csv',\n '1364053.csv',\n '1364054.csv',\n '1364055.csv',\n '1364058.csv',\n '1364059.csv',\n '1364060.csv',\n '1364061.csv',\n '1364062.csv',\n '1364063.csv',\n '1364064.csv',\n '1364066.csv']\n df_weather = import_weather(keys)\n df_weather_dist = df_weather[[\n 'LATITUDE', 'LONGITUDE', 'name']].drop_duplicates().reset_index()\n return df_weather, df_weather_dist","def to_WTH_converter(self, weather_data, dest_dir):\n ds_all = weather_data.get_global_dataset()\n if self.country is None:\n print(\"Country given is erroneous:\")\n return\n elif self.country == \"globe\":\n lon_num_start = 0\n lon_num_stop = weather_data.get_num_of_attribute('longitude')\n lat_num_start = 0\n lat_num_stop = weather_data.get_num_of_attribute('latitude')\n else:\n lon_num_start, lon_num_stop, lat_num_start, lat_num_stop = weather_data.get_country_boundary(self.country)\n\n\n # top bottom, left to right\n lon_num_start = 397\n lat_num_start = 184\n for lon_i in range(lon_num_start, lon_num_stop + 1):\n # for lon_i in range(lon_num_start, lon_num_stop+1):\n lon = ds_all.longitude.isel(longitude=lon_i).values.tolist()\n\n for lat_i in range(lat_num_start, lat_num_stop+1):\n # for lat_i in range(lat_num_start, lat_num_stop + 1):\n lat = ds_all.latitude.isel(latitude=lat_i).values.tolist()\n\n # create a dynamic header with updated LON, LAT info and move it into the folder given\n wth_header_u = ut.format_header(lat_i + 1, lon_i + 1, lat, lon)\n wth_header = dest_dir + \"/\" + wth_header_u\n shutil.move(wth_header_u, wth_header)\n\n # open in appending mode\n fwth = open(wth_header, \"a+\")\n\n # loop through daily weather data\n for t, date in enumerate(self.years):\n daily_data_vars = ut.get_daily_data_vars(ds_all, lat_i, lon_i, t)\n # disregard all NAN values\n if daily_data_vars is None:\n fwth.close()\n os.remove(wth_header)\n break\n\n if t == 0:\n ut.update_table(wth_header_u, lat, lon)\n\n entry = ut.format_data_vars_entry(daily_data_vars, date)\n\n # append this entry into the file\n fwth.write(entry)\n print(\"Added entry:\", entry)\n\n # close file after writing\n fwth.close()\n print(\"Output WTH:\", wth_header)","def load_data(self, identifier):\n propertyType = self.soup.find('h2', class_='listing-details-h1').text\n propertyType = propertyType.replace(' for sale', '')\n beds = re.findall(r'\\d{1} bed', propertyType)\n if beds == []:\n # get first word\n beds = '1 bed'\n else:\n beds = beds[0]\n # get general info of property\n propertyType = propertyType.replace(str(beds) + ' ', '')\n address = self.soup.find('h2', {'itemprop':'streetAddress'}).text \n postCode = address.rsplit(None, 1)[-1]\n address = address.replace(postCode, '')\n latLong = self.soup.find('meta', {'itemprop':'latitude'}).attrs\n latitude = latLong['content']\n latLong = self.soup.find('meta', {'itemprop':'longitude'}).attrs\n longitude = latLong['content']\n # original price and changes to price are in sidebar\n for sidebar in self.soup.find_all('div', {\"class\":sCLASS_SECTION}):\n count = 0\n # price info in sidebar called 'Listing history' \n if sidebar.find(text=sSB_NAME):\n try:\n originalPriceAndDate = sidebar.find(text=sFIRST_LISTED).next_element.next_element\n originalPrice = originalPriceAndDate[:originalPriceAndDate.find(' on')]\n originalPrice = self.find_number(originalPrice)\n originalDate = originalPriceAndDate[originalPriceAndDate.find(' on') + 3:]\n originalDate = self.get_date(originalDate)\n # store original listing - in same order as headers!\n result = [identifier + \"_\" + str(count), originalDate, np.NaN, originalPrice, np.NaN, \n beds, propertyType, postCode, address, latitude, longitude, self.url]\n series = pd.Series(result, name=identifier + \"_\" + str(count), index=headers)\n# self.df = self.df.append(pd.Series(result, index=headers), ignore_index=True) \n# self.df = self.df.append(series) \n# self.df = pd.concat([self.df, series])\n self.df = self.df.append(series, ignore_index=False)\n except (AttributeError, UnboundLocalError):\n print('Error viewing this property')\n pass\n try:\n # store any changes to original listing\n for changes in sidebar.find_all('ul', {'class':sCHANGES}):\n for change in changes.find_all('li'):\n count += 1\n # get date and new price\n date = change.find('span').text\n date = date.replace('Reduced on:', '')\n date = date.replace('\\n', '')\n date = self.get_date(date)\n newPrice = self.find_number(change.next_element)\n # store result in order of headers\n result = [identifier + \"_\" + str(count), originalDate, date, originalPrice, newPrice, \n beds, propertyType, postCode, address, latitude, longitude, self.url]\n series = pd.Series(result, name=identifier + \"_\" + str(count), index=headers) \n# self.df = self.df.append(pd.Series(result, index=headers), ignore_index=True)\n self.df = self.df.append(series, ignore_index=False)\n# self.df = pd.concat([self.df, series])\n except (AttributeError, UnboundLocalError):\n print('No changes')\n pass","def collect(self, start_date=None, end_date=None):\n if start_date is None:\n start_date = self.default_start\n if end_date is None:\n end_date = self.default_end\n\n cur = self.conn.cursor()\n\n # Maximum return is 1000 entries\n num_days = 1000 // len(self.stations)\n # Maximum date-range is 1 year\n if num_days > 365:\n num_days = 365\n\n for interval in netzero.util.time_intervals(\n start_date, end_date, days=num_days\n ):\n netzero.util.print_status(\n \"Weather\",\n \"Collecting: {} to {}\".format(\n interval[0].strftime(\"%Y-%m-%d\"), interval[1].strftime(\"%Y-%m-%d\")\n ),\n )\n\n # TODO -- REMOVE ASSUMPTION THAT LEN(DATA) < LIMIT\n raw_data = self.query_api(interval[0], interval[1])\n\n if raw_data is None:\n print(\"ERROR QUERYING API\") # TODO exception here?\n continue\n\n for entry in raw_data.get(\"results\", []):\n # Insert the weather data to the table, to be averaged later\n date = datetime.datetime.strptime(\n entry[\"date\"], \"%Y-%m-%dT%H:%M:%S\"\n ).date()\n value = entry[\"value\"]\n station = entry[\"station\"]\n\n cur.execute(\n \"INSERT OR IGNORE INTO weather VALUES (?, ?, ?)\", (date, value, station)\n )\n\n self.conn.commit()\n\n cur.close()\n\n netzero.util.print_status(\"Weather\", \"Complete\", newline=True)","def _add_data(self, model_stations: Iterable[model.Station],\n validate_prefix: str = \"\") -> int:\n valid_station_count = 0\n jreast_merged_codes: dict[model.StationID, str] = load_csv_as_mapping(\n DIR_CURATED / \"jreast_merged_codes.csv\",\n itemgetter(\"sta_id\"),\n itemgetter(\"code\")\n )\n\n # Add data from model stations\n for model_sta in model_stations:\n is_invalid = False\n should_validate = model_sta.id.startswith(validate_prefix)\n\n # Find a matching geo_sta\n geo_sta = self.by_id.get(model_sta.id)\n if not geo_sta:\n if should_validate:\n self.logger.critical(f\"{Color.RED}geo.osm is missing station \"\n f\"{Color.MAGENTA}{model_sta.id}{Color.RESET}\")\n self.valid = False\n continue\n\n # Find a name\n name_id = last_part(geo_sta.id)\n geo_sta.name = self.names.get(name_id)\n if geo_sta.name is None and should_validate:\n self.logger.critical(f\"{Color.RED}sta_names.csv is missing name for \"\n f\"{Color.MAGENTA}{name_id}{Color.RESET}\")\n is_invalid = True\n\n # Copy stop_code\n geo_sta.code = model_sta.code\n\n # Check if station was valid\n if is_invalid:\n self.valid = False\n elif should_validate:\n valid_station_count += 1\n\n # Generate codes and names for mother stations\n for sta in self.by_id.values():\n if not sta.children:\n continue\n\n name_id = last_part(sta.id)\n sta.name = self.names.get(name_id)\n if not sta.name:\n self.logger.critical(f\"{Color.RED}sta_names.csv is missing name for \"\n f\"{Color.MAGENTA}{name_id}{Color.RESET}\")\n is_invalid = True\n\n # Get children codes\n children_codes = []\n jreast_merged_code = jreast_merged_codes.get(sta.id)\n if jreast_merged_code:\n children_codes.append(jreast_merged_code)\n\n for child in sta.children:\n # Ignore JR-East child codes if there's a JR-East merged code\n if child.id.startswith(\"JR-East\") and jreast_merged_code:\n continue\n elif child.code:\n children_codes.append(child.code)\n\n sta.code = \"/\".join(children_codes)\n\n return valid_station_count","def init_parameters(self, buildings, target_meters):\n if self.df is None:\n self.read_data_from_csv()\n\n if buildings is None:\n buildings = self.find_all_houses().tolist()\n\n if target_meters is None:\n target_meters = self.meter_name.keys()\n \n return buildings, target_meters","def archive_mds_data(self, lmtdb):\n\n dataset_names = [\n 'mdservers/cpuload',\n ]\n\n self.init_datasets(dataset_names, lmtdb.mds_names)\n\n # Now query the MDS_DATA table to get byte counts over the query time range\n results, columns = lmtdb.get_mds_data(self.query_start, self.query_end_plusplus)\n\n\n # Index the columns to speed up insertion of data\n col_map = {}\n try:\n for db_col in ['TIMESTAMP', 'MDS_ID', 'PCT_CPU']:\n col_map[db_col] = columns.index(db_col)\n except ValueError:\n raise ValueError(\"LMT database schema does not match expectation\")\n\n # Loop through all the results of the timeseries query\n for row in results:\n if isstr(row[col_map['TIMESTAMP']]):\n # SQLite stores timestamps as a unicode string\n timestamp = datetime.datetime.strptime(row[col_map['TIMESTAMP']],\n \"%Y-%m-%d %H:%M:%S\")\n else:\n # MySQL timestamps are automatically converted to datetime.datetime\n timestamp = row[col_map['TIMESTAMP']]\n target_name = lmtdb.mds_id_map[row[col_map['MDS_ID']]]\n for dataset_name in dataset_names:\n target_dbcol = self.config[dataset_name].get('column')\n # target_dbcol=PCT_CPU, target_name=snx11025n022\n if target_dbcol is not None:\n self[dataset_name].insert_element(\n timestamp,\n target_name,\n row[col_map[target_dbcol]])\n else:\n errmsg = \"%s in self.config but missing 'column' setting\" % dataset_name\n raise KeyError(errmsg)","def update_static_data(source: list):\r\n connect = sqlite3.connect(\"REDB_v2.sqlite\")\r\n cursor = connect.cursor()\r\n districts = []\r\n streets = []\r\n series = []\r\n amenities = []\r\n building_types = []\r\n for realestate in source:\r\n if realestate.street not in streets:\r\n streets.append(realestate.street)\r\n if realestate.series not in series:\r\n series.append(realestate.series)\r\n if realestate.building not in building_types:\r\n building_types.append(realestate.building)\r\n if realestate.district not in districts:\r\n districts.append(realestate.district)\r\n if realestate.amenities not in amenities:\r\n amenities.append(realestate.amenities)\r\n for item in streets:\r\n try:\r\n cursor.execute(\"INSERT INTO Streets (Name) VALUES ('\" + item + \"')\")\r\n except sqlite3.IntegrityError:\r\n pass\r\n for item in districts:\r\n try:\r\n cursor.execute(\"INSERT INTO Districts (Name) VALUES ('\" + item + \"')\")\r\n except sqlite3.IntegrityError:\r\n pass\r\n for item in series:\r\n try:\r\n cursor.execute(\"INSERT INTO Series (Name) VALUES ('\" + item + \"')\")\r\n except sqlite3.IntegrityError:\r\n pass\r\n for item in amenities:\r\n try:\r\n cursor.execute(\"INSERT INTO Amenities (Name) VALUES ('\" + item + \"')\")\r\n except sqlite3.IntegrityError:\r\n pass\r\n for item in building_types:\r\n try:\r\n cursor.execute(\"INSERT INTO Buildings (Name) VALUES ('\" + item + \"')\")\r\n except sqlite3.IntegrityError:\r\n pass\r\n connect.commit()\r\n connect.close()","def set_up_file(ds, total_sites, footprint_nbins,\n dimy, dimx, wrf_out, time_unit, site_names):\n if site_names.dtype.str[1] not in \"SU\":\n site_names = site_names.astype(\"S\")\n if site_names.dtype.str[2:] != \"1\":\n site_names = netCDF4.stringtochar(site_names)\n name_length = site_names.shape[-1]\n name_str_dim = \"string{len:d}\".format(len=name_length)\n\n ds.createDimension(\"observation_time\", 0)\n ds.createDimension(\"site\", total_sites)\n ds.createDimension(\"time_before_observation\", footprint_nbins)\n ds.createDimension(\"dim_y\", dimy)\n ds.createDimension(\"dim_x\", dimx)\n ds.createDimension(\"bnds2\", 2)\n ds.createDimension(name_str_dim, name_length)\n\n obs_time_var = ds.createVariable(\"observation_time\", \"f4\",\n (\"observation_time\",))\n obs_time_bounds_var = ds.createVariable(\"observation_time_bnds\", \"f4\",\n (\"observation_time\", \"bnds2\"))\n time_back_var = ds.createVariable(\"time_before_observation\", \"i2\",\n (\"time_before_observation\",))\n time_back_bounds_var = ds.createVariable(\n \"time_before_observation_bnds\", \"i2\",\n (\"time_before_observation\", \"bnds2\"))\n ds.createVariable(\"dim_y\", \"f4\", (\"dim_y\",))\n ds.createVariable(\"dim_y_bnds\", \"f4\", (\"dim_y\", \"bnds2\"))\n ds.createVariable(\"dim_x\", \"f4\", (\"dim_x\",))\n ds.createVariable(\"dim_x_bnds\", \"f4\", (\"dim_x\", \"bnds2\"))\n\n grid_mapping = create_grid_mapping(ds, wrf_out)\n\n site_name_var = ds.createVariable(\"site_names\", \"S1\",\n (\"site\", name_str_dim))\n # these are roughly 1MB for a three-week lag\n flux_time_var = ds.createVariable(\n \"flux_time\", \"f4\", (\"observation_time\", \"time_before_observation\"),\n fill_value=-255,\n zlib=True)\n flux_time_bounds_var = ds.createVariable(\n \"flux_time_bnds\", \"f4\",\n (\"observation_time\", \"time_before_observation\", \"bnds2\"),\n fill_value=-255,\n zlib=True)\n\n height_var = ds.createVariable(\"height\", \"f4\", ())\n height_bounds_var = ds.createVariable(\"height_bnds\", \"f4\", (\"bnds2\",))\n\n infl_fun_var = ds.createVariable(\n # Empirically, the most particles seen in a grid cell is\n # around 5e3. 35*180 particles/obs_time, 9 files/flux_time on 81km grid\n # We have a factor of six wiggle room with i2\n # u2 may be necessary for 3 hourly 243 km fluxes\n # or more particles/obs_time0\n \"H\", \"i2\",\n (\"observation_time\", \"site\", \"time_before_observation\",\n \"dim_y\", \"dim_x\"),\n zlib=True,\n # This will be written and read by flux time, usually,\n # so that chunksize should be 1\n # not sure if chunk should be total_sites or 1 for site dimension\n # total_size gives a chunk as around 5.3 MiB\n # setting this to 1 may help with file size\n # if some towers were not run all the time\n # NUG has default chunk size of 4 MiB\n # (roughly a disk read on a high-end system)\n chunksizes=(1, total_sites, 1, dimy, dimx),\n # This requires that every cell be written to.\n # This is my intent, and this (as opposed to fill_value=0)\n # will not have troubles with masking most of the domain.\n # Make sure this isn't what's inflating the size\n fill_value=-1,\n )\n\n lpdm_opts = ds.createVariable(\"lpdm_configuration\", \"i1\", ())\n lpdm_opts.setncatts({key: netcdf_compatible_array(config[key]).copy()\n for key in config.dtype.fields.keys()\n if key.islower()})\n\n wrf_opts = ds.createVariable(\"wrf_configuration\", \"i1\", ())\n with contextlib.closing(netCDF4.Dataset(\n config[\"wrf_file\"][0].decode(\"ascii\"))) as wrf_ds:\n wrf_opts.setncatts({att: wrf_ds.getncattr(att)\n for att in wrf_ds.ncattrs()})\n\n ########################################################\n\n obs_time_var.setncatts(dict(long_name=\"observation_time\",\n # not entirely sure this applies...\n standard_name=\"forecast_reference_time\",\n bounds=\"observation_time_bnds\",\n units=time_unit,\n calendar=CALENDAR,\n coverage_content_type=\"coordinate\",\n # might be a misapplication of CF 9.5\n cf_role=\"timeseries_id\"))\n obs_time_bounds_var.setncatts(dict(long_name=\"observation_time_bounds\",\n units=time_unit,\n calendar=CALENDAR))\n\n time_back_var.setncatts(dict(long_name=\"time_before_observation\",\n standard_name=\"forecast_period\",\n units=\"hours\",\n bounds=\"time_before_observation_bnds\",\n coverage_content_type=\"coordinate\",\n ))\n time_back_bounds_var.setncatts(dict(\n description=\"bounds of time_before_observation\",\n units=\"hours\"))\n\n flux_time_var.setncatts(dict(\n long_name=\"flux_time\",\n standard_name=\"time\",\n bounds=\"flux_time_bnds\",\n units=time_unit,\n calendar=CALENDAR,\n coverage_content_type=\"coordinate\",\n ))\n flux_time_bounds_var.setncatts(dict(\n long_name=\"flux_time\",\n units=time_unit,\n calendar=CALENDAR,\n ))\n\n infl_fun_var.setncatts(dict(\n long_name=\"influence_function\",\n description=(\"linearisation of the observation operator \"\n \"for carbon dioxide mixing ratios at the \"\n \"towers in terms of carbon dioxide mass fluxes\"),\n units=\"ppmv/(mol.m^-2.s^-1)\",\n long_units=\"ppmv/(mol_CO2.m^-2.s^-1)\",\n coordinates=(\"flux_time height latitude longitude \"\n \"site_names site_heights site_lats site_lons\"),\n # I don't think we can justify more than six or so digits\n # of precision. The transport is too uncertain.\n # The underlying int type doesn't support more than five.\n # The increased locality should also speed up use.\n scale_factor=np.array(CONVERSION_FACTOR, dtype=np.float32),\n grid_mapping=grid_mapping,\n valid_min=np.array(0, dtype=infl_fun_var.dtype),\n # description of coordinate relationships\n cell_methods=(\n # not entirely sure if space and obs time should be in\n # same sum. The two times are another possible\n # combination.\n \"height: dim_y: dim_x: sum \"\n \"observation_time: sum \"\n \"(interval: {lpdm_timestep:f} seconds) \"\n \"site: point \"\n # this sum is done later than the others\n \"flux_time: sum \"\n \"(interval: {minutes_per_file:d} minutes)\"\n \"\").format(minutes_per_file=(MINUTES_PER_HOUR //\n int(config[\"num_file_per_h\"])),\n lpdm_timestep=float(config[\"lpdm_timestep\"])),\n # What type of thing this is:\n coverage_content_type=\"modelResult\",\n ))\n # I want to store the counts directly\n infl_fun_var.set_auto_maskandscale(False)\n\n site_lats_var = ds.createVariable(\"site_lats\", \"f4\", (\"site\",))\n site_lons_var = ds.createVariable(\"site_lons\", \"f4\", (\"site\",))\n site_heights_var = ds.createVariable(\"site_heights\", \"f4\", (\"site\",))\n site_lats_var.setncatts(dict(\n units=\"degrees_north\", standard_name=\"latitude\",\n long_name=\"site_latitude\",\n coverage_content_type=\"coordinate\",\n description=\"latitude of the observation tower site\",\n origin=\"Set in LPD run script\"))\n site_lons_var.setncatts(dict(\n units=\"degrees_east\", standard_name=\"longitude\",\n long_name=\"site_longitude\",\n coverage_content_type=\"coordinate\",\n description=\"longitude of the observation tower site\",\n origin=\"Set in LPD run script\"))\n site_name_var.setncatts(dict(\n long_name=\"name_of_observation_site\",\n # most likely an abuse of CF section 9.5\n # cf_role=\"trajectory_id\"\n coverage_content_type=\"referenceInformation\",\n ))\n site_heights_var.setncatts(dict(\n standard_name=\"height\",\n long_name=\"site_heights\",\n description=\"height of the observation tower intake\",\n origin=\"Set in LPD run script\",\n coverage_content_type=\"coordinate\",\n positive=\"up\",\n units=\"m\"))\n\n height_var.setncatts(dict(\n standard_name=\"height\",\n long_name=\"flux_influence_height\",\n description=(\"How low the particles have to be \"\n \"to be \\\"influenced\\\" by the ground\"),\n origin=\"Constant CLOSE_TO_GROUND in carsurf_loop.py\",\n coverage_content_type=\"referenceInformation\",\n positive=\"up\",\n units=\"km\", bounds=\"height_bnds\"))\n height_bounds_var.setncatts(dict(\n long_name=\"height_bounds\",\n units=\"km\"))\n\n # pretty sure this fails somewhat badly at encapsulization\n set_coord_values(ds, wrf_out, footprint_nbins)\n site_name_var[:] = site_names\n\n return infl_fun_var","def make_df_an_table(an_string, site_name='DSW', min_moon_dist=MIN_MOON_DISTANCE,\n min_hours=MIN_HOURS_OBSERVABLE):\n an_string = str(an_string) # (precaution in case int passed in)\n an_object = Astronight(an_string, site_name)\n # dark_start, dark_end = an_object.ts_dark.start, an_object.ts_dark.end\n mid_dark = an_object.local_middark_utc\n # dark_no_moon_start, dark_no_moon_end = an_object.ts_dark_no_moon.start, an_object.ts_dark_no_moon.end\n mpfile_dict = make_mpfile_dict()\n\n an_dict_list = [] # results to be deposited here, to make a dataframe later.\n for mp in mpfile_dict.keys():\n mpfile = mpfile_dict[mp]\n # an_dict doesn't need to include defaults for case before or after mpfile ephemeris,\n # because making the dataframe should put in NANs for missing keys anyway (check this later):\n an_dict = {'MPnumber': mpfile.number, 'MPname': mpfile.name, 'Motive': mpfile.motive,\n 'Priority': mpfile.priority, 'Period': mpfile.period}\n # Interpolate within ephemeris (because MP is moving in sky); 2 iterations s/be enough:\n data, status, ts_observable, mp_radec = None, None, None, None # keep stupid IDE happy.\n best_utc = mid_dark # best_utc will = mid-observable time at converged RA,Dec.\n\n # Converge on best RA, Dec, observable timespan (they interact, as MP is moving):\n hours_observable = 0.0 # default to keep IDE happy.\n for i in range(2):\n data = mpfile.eph_from_utc(best_utc)\n if data is None:\n if mpfile.eph_range[1] < an_object.ts_dark.start:\n status = 'too late'\n else:\n status = 'too early'\n break\n status = 'ok'\n mp_radec = RaDec(data['RA'], data['Dec'])\n ts_observable = an_object.ts_observable(mp_radec,\n min_alt=MIN_MP_ALTITUDE,\n min_moon_dist=min_moon_dist) # Timespan object\n hours_observable = ts_observable.seconds / 3600.0\n mid_observable = ts_observable.midpoint # for loop exit\n best_utc = mid_observable # update for loop continuation.\n\n # Mark valid MPs that are observable too briefly:\n if status.lower() == 'ok':\n if hours_observable < min_hours:\n status = 'too brief'\n\n # For MPs observable this night, add one line to table:\n # print(mpfile.name, status)\n an_dict['Status'] = status\n if status.lower() == 'ok':\n an_dict['RA'] = data['RA']\n an_dict['Dec'] = data['Dec']\n an_dict['StartUTC'] = ts_observable.start\n an_dict['EndUTC'] = ts_observable.end\n an_dict['TransitUTC'] = an_object.transit(mp_radec)\n an_dict['MoonDist'] = mp_radec.degrees_from(an_object.moon_radec)\n an_dict['PhaseAngle'] = data['Phase']\n an_dict['V_mag'] = data['V_mag']\n an_dict['ExpTime'] = float(round(float(calc_exp_time(an_dict['V_mag'],\n EXP_TIME_TABLE_PHOTOMETRY))))\n if an_dict['Period'] is not None:\n # Duty cycle is % of time spent observing this MP if one exposure per 1/60 of period.\n an_dict['DutyCyclePct'] = 100.0 * ((an_dict['ExpTime'] + EXP_OVERHEAD) / 60.0) / \\\n an_dict['Period']\n else:\n an_dict['DutyCyclePct'] = None\n if status.lower() == 'ok':\n an_dict['PhotrixPlanning'] = 'IMAGE MP_' + mpfile.number + \\\n ' Clear=' + str(an_dict['ExpTime']) + 'sec(***) ' + \\\n ra_as_hours(an_dict['RA'], seconds_decimal_places=1) + ' ' + \\\n degrees_as_hex(an_dict['Dec'], arcseconds_decimal_places=0)\n if an_dict['Period'] is not None:\n an_dict['Coverage'] = make_df_coverage(an_dict['Period'],\n mpfile.obs_jd_ranges,\n (jd_from_datetime_utc(an_dict['StartUTC']),\n jd_from_datetime_utc(an_dict['EndUTC'])))\n an_dict['PhaseCoverage'] = make_df_phase_coverage(an_dict['Period'],\n mpfile.obs_jd_ranges)\n else:\n an_dict['Coverage'] = None\n an_dict_list.append(an_dict)\n if len(an_dict_list) == 0:\n return None\n df_an_table = pd.DataFrame(data=an_dict_list)\n df_an_table.index = df_an_table['MPnumber'].values\n df_an_table = df_an_table.sort_values(by='TransitUTC')\n return df_an_table","def update_stationlist(time_res='hourly',dbase_dir='dbase'):\r\n\r\n \r\n dwd_abbr = {'air_temperature': 'TU',\r\n 'cloud_type': 'CS', \r\n 'cloudiness': 'N',\r\n 'dew_point' : 'TD',\r\n 'extreme_temperature': 'TX',\r\n 'extreme_wind': 'FX',\r\n 'precipitation': 'RR',\r\n 'pressure': 'P0',\r\n 'soil_temperature': 'EB',\r\n 'solar': 'ST',\r\n 'sun': 'SD',\r\n 'visibility': 'VV',\r\n 'wind': 'FF',\r\n 'wind_synop': 'F'\r\n }\r\n \r\n # lets start\r\n print('Updating station list')\r\n \r\n # create output directory if not existing\r\n \r\n if not os.path.exists(dbase_dir):\r\n os.makedirs(dbase_dir)\r\n \r\n #check whether we have an up-to-date-station-list-already\r\n try:\r\n stations_network_old=[s for s in os.listdir(dbase_dir) if 'dwd_station_network' in s][0]\r\n datetime_network=datetime.date(datetime.strptime(re.findall('\\d+',stations_network_old)[0],'%Y%m%d'))\r\n #update if more than 24hours\r\n dt_today=datetime.date(datetime.now())\r\n if (dt_today-datetime_network)<timedelta(days=1):\r\n print('DWD network list is up-to-date, no update needed')\r\n filename_stations=dbase_dir+'\\\\'+stations_network_old\r\n return filename_stations\r\n else:\r\n print('DWD network list neeeds to be updated')\r\n os.remove(dbase_dir+'\\\\'+stations_network_old)\r\n except:\r\n print('DWD network list neeeds to be updated')\r\n pass\r\n \r\n \r\n # header\r\n stations_network=pd.DataFrame()\r\n \r\n # connect to ftp server and go to the folder\r\n \r\n # Connect to the Server\r\n server='opendata.dwd.de'\r\n ftp=connect_ftp(server = server,connected = False)\r\n #change to subfolder\r\n ftp.cwd('/climate_environment/CDC/observations_germany/climate/' + time_res +'/')\r\n #get dwd categories\r\n dwd_categories=ftp.nlst()\r\n #loop through the subfolders to get the station lists\r\n for category in dwd_categories:\r\n print('retrieve stationlist for', category)\r\n #try to get historical data\r\n try:\r\n dir_path='/climate_environment/CDC/observations_germany/climate/' + time_res +'/'+category+'/historical/'\r\n ftp.cwd(dir_path)\r\n except Exception as e:\r\n print(e, 'try to download category', category, 'from other folder')\r\n try:\r\n dir_path='/climate_environment/CDC/observations_germany/climate/' + time_res +'/'+category+'/'\r\n ftp.cwd(dir_path)\r\n except:\r\n print('Category', category, 'could not have been downloaded')\r\n pass\r\n #retrieve the stationlist\r\n stationlist = []\r\n # try to retrieve file\r\n retrieved=False\r\n filename=dwd_abbr[category]+'_Stundenwerte_Beschreibung_Stationen.txt'\r\n while not retrieved:\r\n try:\r\n ftp.retrlines(\"RETR \" + filename, stationlist.append)\r\n #ftp.retrbinary(\"RETR \" + filestr, stationlist.write)\r\n retrieved = True\r\n except:\r\n ftp=connect_ftp(server = server,connected = False)\r\n ftp.cwd(dir_path)\r\n #remove first two lines\r\n stationlist=stationlist[2:]\r\n #delete uncessary blanks\r\n stationlist=[re.sub(' +', ' ', station.rstrip()) for station in stationlist]\r\n #split the list\r\n stationlist=[station.split(\" \")[:7] for station in stationlist]\r\n #read as dataframe\r\n dfstations=pd.DataFrame(stationlist,columns=['STATIONS_ID','date_start','date_end','height','geo_lat','geo_lon','name'])\r\n #add true information to category\r\n dfstations[category]=True\r\n \r\n stations_network=stations_network.append(dfstations,sort=False,ignore_index=True)\r\n #A=[sub.split(\" \") for sub in stationlist] \r\n \r\n #replace all Na by False\r\n stations_network[stations_network.isna()]=0 \r\n #aggregate\r\n stations_network=stations_network.groupby(['STATIONS_ID'],as_index=False).agg('max')\r\n #replace zero by False in order to have pure boolean data\r\n stations_network.replace(0,False,inplace=True)\r\n #fix the error with station 14138 and 05614 and 07325, which does not have pressure cord\r\n stations_network.loc[stations_network.STATIONS_ID=='14138','pressure']=False\r\n stations_network.loc[stations_network.STATIONS_ID=='05614','pressure']=False\r\n stations_network.loc[stations_network.STATIONS_ID=='07325','pressure']=False\r\n stations_network.loc[stations_network.STATIONS_ID=='01572','pressure']=False\r\n #for temperature the same\r\n stations_network.loc[stations_network.STATIONS_ID=='14138','air_temperature']=False\r\n #save to database writing the time as well\r\n filename_stations=dbase_dir+'\\\\dwd_station_network_'+datetime.now().strftime('%Y%m%d')+'.csv'\r\n stations_network.to_csv(filename_stations,index=False)\r\n \r\n print('Updating station list...finished')\r\n \r\n return filename_stations","def synthesize_employment_data(self, config):\r\n jobs_by_zone_by_sector_table_name = config['jobs_by_zone_by_sector']\r\n gridcells_table_name = config['gridcells']\r\n jobs_table_name = config['jobs']\r\n gridcells_output_table_name = config['gridcells_output']\r\n jobs_output_table_name = config['jobs_output']\r\n \r\n input_db_name = config['db_config'].database_name\r\n output_db_name = config['output_database_name']\r\n \r\n sectors = config['sector_names_and_ids']\r\n building_types_and_ids_and_home_based = config[\r\n 'building_type_column_names_and_ids_and_home_based']\r\n \r\n building_types = []\r\n building_ids = []\r\n home_based = [] \r\n for type, id, home in building_types_and_ids_and_home_based:\r\n building_types += [type]\r\n building_ids += [id]\r\n home_based += [home]\r\n \r\n \r\n from_database_configuration = ScenarioDatabaseConfiguration(\r\n database_name = input_db_name,\r\n host_name = config['db_config'].host_name,\r\n user_name = config['db_config'].user_name,\r\n password = config['db_config'].password \r\n )\r\n to_database_configuration = ScenarioDatabaseConfiguration(\r\n database_name = output_db_name,\r\n host_name = config['db_config'].host_name,\r\n user_name = config['db_config'].user_name,\r\n password = config['db_config'].password \r\n )\r\n\r\n FlattenScenarioDatabaseChain().copy_scenario_database(\r\n from_database_configuration = from_database_configuration, \r\n to_database_configuration = to_database_configuration,\r\n tables_to_copy = [gridcells_table_name, jobs_table_name])\r\n \r\n db_server = DatabaseServer(to_database_configuration) \r\n output_database = db_server.get_database(output_db_name)\r\n \r\n sector_name = 0; sector_id = 1\r\n \r\n sector = {}\r\n for entry in sectors:\r\n name = entry[sector_name]\r\n id = entry[sector_id]\r\n sector[id] = self._get_jobs_per_building_type_in_sector_by_zone(\r\n output_database, jobs_by_zone_by_sector_table_name, \r\n jobs_table_name, name, id)\r\n\r\n results = self._get_building_type_proportion_by_zone(output_database, \r\n gridcells_table_name)\r\n \r\n grid_id = 0; zone_id = 1\r\n dist = {}\r\n \r\n type_index = {}\r\n \r\n for name in building_types:\r\n for i in range(len(results[0])):\r\n column_name = results[0][i]\r\n if name == column_name:\r\n type_index[name] = i\r\n break;\r\n else:\r\n raise KeyError, ('No column by the name of \\'%s\\' found in '\r\n 'the database.' % name) \r\n\r\n for name in building_types:\r\n dist[name] = {}\r\n \r\n for row in results[1:]:\r\n for name in building_types:\r\n dist[name][row[zone_id]] = []\r\n \r\n for row in results[1:]:\r\n for name in building_types:\r\n dist[name][row[zone_id]] += [(row[grid_id], \r\n row[type_index[name]])]\r\n \r\n jobs_table_data = self._create_jobs_table_data(dist, sector,\r\n building_types_and_ids_and_home_based)\r\n \r\n output_database.execute('USE %(out_db)s' % {'out_db':output_db_name})\r\n \r\n output_database.execute(\"\"\"\r\n CREATE TABLE %(jobs_out)s (\r\n JOB_ID INT AUTO_INCREMENT, PRIMARY KEY(JOB_ID),\r\n GRID_ID INT, HOME_BASED INT, SECTOR_ID INT, BUILDING_TYPE INT);\r\n \"\"\" % {'jobs_out':jobs_output_table_name})\r\n \r\n if len(jobs_table_data) > 0:\r\n output_prefix = (\r\n \"\"\"INSERT INTO %(jobs_out)s \r\n (GRID_ID, HOME_BASED, SECTOR_ID, BUILDING_TYPE) VALUES\r\n \"\"\" % {'jobs_out':jobs_output_table_name})\r\n output_postfix = ';'\r\n \r\n step = 1000\r\n length = len(jobs_table_data)\r\n iterations = int(length/step) + 1\r\n \r\n for i in range(iterations):\r\n low = i*step\r\n high = (i+1)*step\r\n \r\n if high > length: high = length\r\n \r\n output_body = \"\"\r\n \r\n for j in range(low, high):\r\n output_body += (\r\n '(%(grid)s, %(home)s, %(sector)s, %(building)s),\\n' \r\n % jobs_table_data[j])\r\n \r\n output_query = \"%s%s%s\" % (output_prefix, \r\n output_body[:-2], \r\n output_postfix)\r\n\r\n output_database.execute(output_query)\r\n \r\n \r\n ### TODO: \r","def insert_humans_staging(self):\n for year in range(1880, CURRENT_YEAR):\n self.load_wikidata(\"humans\", HUMANS_BY_YEAR_SPARQL_QUERY, INSERT_HUMAN_SQL_QUERY,\n INSERT_HUMAN_MAP_COLUMNS, year=year)","def disaggregate(self, mains, output_datastore):\n \n building_path = '/building{}'.format(mains.building())\n # only writes one appliance and meter per building\n meter_instance = 2\n mains_data_location = '{}/elec/meter1'.format(building_path)\n \n #dis_main = pd.DataFrame()\n chunk_number = 0\n timeframes = []\n\n for chunk in mains.power_series():\n \n # Record metadata\n timeframes.append(chunk.timeframe)\n measurement = chunk.name\n cols = pd.MultiIndex.from_tuples([chunk.name])\n \n dis_chunk = self.disaggregate_chunk(\n pd.DataFrame(chunk.resample(self.sample_period, how=self.sampling_method)))\n #dis_main = pd.concat([dis_main, dis_chunk])\n chunk_number += 1\n print(str(chunk_number) + \" chunks disaggregated\")\n \n # Write appliance data to disag output\n key = '{}/elec/meter{}'.format(building_path, meter_instance)\n df = pd.DataFrame(\n dis_chunk.values, index=dis_chunk.index,\n columns=cols)\n output_datastore.append(key, df)\n\n # Copy mains data to disag output\n output_datastore.append(key=mains_data_location,\n value=pd.DataFrame(chunk, columns=cols))\n\n # Saving output datastore:\n #output_datastore.append(key=mains.key, value=dis_main)\n \n ##################################\n # Add metadata to output_datastore\n\n # TODO: `preprocessing_applied` for all meters\n # TODO: split this metadata code into a separate function\n # TODO: submeter measurement should probably be the mains\n # measurement we used to train on, not the mains measurement.\n \n date_now = datetime.now().isoformat().split('.')[0]\n output_name = 'NILMTK_MLE_' + date_now\n resample_seconds = 10\n mains_data_location = '{}/elec/meter1'.format(building_path)\n\n # DataSet and MeterDevice metadata:\n meter_devices = {\n 'MLE': {\n 'model': 'MLE',\n 'sample_period': resample_seconds,\n 'max_sample_period': resample_seconds,\n 'measurements': [{\n 'physical_quantity': measurement[0],\n 'type': measurement[1]\n }]\n },\n 'mains': {\n 'model': 'mains',\n 'sample_period': resample_seconds,\n 'max_sample_period': resample_seconds,\n 'measurements': [{\n 'physical_quantity': measurement[0],\n 'type': measurement[1]\n }]\n }\n }\n\n merged_timeframes = merge_timeframes(timeframes, gap=resample_seconds)\n total_timeframe = TimeFrame(merged_timeframes[0].start,\n merged_timeframes[-1].end)\n\n dataset_metadata = {'name': output_name, 'date': date_now,\n 'meter_devices': meter_devices,\n 'timeframe': total_timeframe.to_dict()}\n output_datastore.save_metadata('/', dataset_metadata)\n\n # Building metadata\n\n # Mains meter:\n elec_meters = {\n 1: {\n 'device_model': 'mains',\n 'site_meter': True,\n 'data_location': mains_data_location,\n 'preprocessing_applied': {}, # TODO\n 'statistics': {\n 'timeframe': total_timeframe.to_dict()\n }\n }\n }\n\n # Appliances and submeters:\n appliances = []\n appliance = {\n 'meters': [meter_instance],\n 'type': 'kettle',\n 'instance': 1\n # TODO this `instance` will only be correct when the\n # model is trained on the same house as it is tested on.\n # https://github.com/nilmtk/nilmtk/issues/194\n }\n appliances.append(appliance)\n\n elec_meters.update({\n meter_instance: {\n 'device_model': 'MLE',\n 'submeter_of': 1,\n 'data_location': ('{}/elec/meter{}'\n .format(building_path, meter_instance)),\n 'preprocessing_applied': {}, # TODO\n 'statistics': {\n 'timeframe': total_timeframe.to_dict()\n }\n }\n })\n elec_meters[meter_instance]['name'] = 'kettle'\n\n building_metadata = {\n 'instance': mains.building(),\n 'elec_meters': elec_meters,\n 'appliances': appliances\n }\n\n output_datastore.save_metadata(building_path, building_metadata)","def _populate_zone_facts_table(self):\n census_fields = [\n 'poverty_rate', 'fraction_black', 'income_per_capita',\n 'labor_participation', 'fraction_foreign',\n 'fraction_single_mothers', 'acs_lower_rent_quartile',\n 'acs_median_rent', 'acs_upper_rent_quartile'\n ]\n\n zone_types = ['ward', 'neighborhood_cluster', 'census_tract']\n\n query_results = list()\n\n # populate columns accordingly for each zone_specific type\n for zone_type in zone_types:\n field_values = dict()\n\n # get field value for each zone_specific type\n for field in census_fields:\n result = self._census_with_weighting(data_id=field,\n grouping=zone_type)\n field_values[field] = result['items']\n\n zone_specifics = self._get_zone_specifics_for_zone_type(zone_type)\n\n # TODO: add aggregate for each zone_type into table\n for zone in zone_specifics:\n # get not None values so we can added to db\n columns = list()\n values = list()\n for field in census_fields:\n zone_value = field_values[field][zone]\n\n if zone_value is not None:\n columns.append(field)\n values.append(\"'\" + str(zone_value) + \"'\")\n\n # derive column and values strings needed for sql query\n columns = ', '.join(columns)\n columns = 'zone, ' + columns\n\n values = ', '.join(values)\n values = \"'\" + zone + \"', \" + values\n\n q = \"INSERT INTO zone_facts ({cols}) VALUES ({vals})\".format(\n cols=columns, vals=values)\n\n with self.engine.connect() as conn:\n result = conn.execute(q)\n query_results.append(result)\n\n return query_results","def office_station_statistics(parser, args, params): \n parser.add_argument(\n '--station_list', type=str, help='Station file from IRIS',\n required=True, metavar='x')\n \n local_args = parser.parse_known_args(args)\n \n station_list = local_args[0].station_list\n control.station_statistics(params, station_list)","def create_extract():\n with open(SCHEMA_FILE, \"r\") as f:\n SCHEMA = yaml.safe_load(f)\n\n with open(TOKEN_FILE, \"r\") as f:\n TOKEN = yaml.safe_load(f)\n\n hc = HyperCreator(SCHEMA, HYPER_FILE)\n ts = Tableau(TOKEN[\"server\"], TOKEN[\"site\"], TOKEN[\"name\"], TOKEN[\"value\"])\n\n for table in SCHEMA[\"tables\"]:\n with open(f\"{CONTENT_MANAGEMENT}/{table['query']}\", \"r\") as f:\n query = f.read()\n\n data = ts.query_metadata(query)\n data_map = getattr(GraphQL, table[\"name\"])(data)\n\n hc.populate_extract(table[\"name\"], data_map)","def __init__(self, locator, weather_data, building_names=None):\n\n if building_names is None:\n building_names = locator.get_zone_building_names()\n\n self.building_names = building_names\n print(\"read input files\")\n prop_geometry = Gdf.from_file(locator.get_zone_geometry())\n prop_geometry['footprint'] = prop_geometry.area\n prop_geometry['perimeter'] = prop_geometry.length\n prop_geometry['Blength'], prop_geometry['Bwidth'] = self.calc_bounding_box_geom(locator.get_zone_geometry())\n prop_geometry = prop_geometry.drop('geometry', axis=1).set_index('Name')\n prop_hvac = dbf_to_dataframe(locator.get_building_air_conditioning())\n\n prop_typology = dbf_to_dataframe(locator.get_building_typology()).set_index('Name')\n # Drop 'REFERENCE' column if it exists\n if 'REFERENCE' in prop_typology:\n prop_typology.drop('REFERENCE', axis=1, inplace=True)\n prop_architectures = dbf_to_dataframe(locator.get_building_architecture())\n prop_comfort = dbf_to_dataframe(locator.get_building_comfort()).set_index('Name')\n prop_internal_loads = dbf_to_dataframe(locator.get_building_internal()).set_index('Name')\n prop_supply_systems_building = dbf_to_dataframe(locator.get_building_supply())\n\n # GET SYSTEMS EFFICIENCIES\n prop_supply_systems = get_properties_supply_sytems(locator, prop_supply_systems_building).set_index('Name')\n\n # get temperatures of operation\n prop_HVAC_result = get_properties_technical_systems(locator, prop_hvac).set_index('Name')\n\n # get envelope properties\n prop_envelope = get_envelope_properties(locator, prop_architectures).set_index('Name')\n\n # get properties of rc demand model\n prop_rc_model = self.calc_prop_rc_model(locator, prop_typology, prop_envelope,\n prop_geometry, prop_HVAC_result)\n\n # get solar properties\n solar = get_prop_solar(locator, building_names, prop_rc_model, prop_envelope, weather_data).set_index('Name')\n\n # df_windows = geometry_reader.create_windows(surface_properties, prop_envelope)\n # TODO: to check if the Win_op and height of window is necessary.\n # TODO: maybe mergin branch i9 with CItyGML could help with this\n print(\"done\")\n\n # save resulting data\n self._prop_supply_systems = prop_supply_systems\n self._prop_geometry = prop_geometry\n self._prop_envelope = prop_envelope\n self._prop_typology = prop_typology\n self._prop_HVAC_result = prop_HVAC_result\n self._prop_comfort = prop_comfort\n self._prop_internal_loads = prop_internal_loads\n self._prop_age = prop_typology[['YEAR']]\n self._solar = solar\n self._prop_RC_model = prop_rc_model","def createStations (config):\n trace (\"createStations()\")\n for section in config.sections():\n if section.capitalize().startswith(\"Station\"):\n myPressureProbes = myHumidityProbes = []\n myTemperatureProbes= []\n name = section\n for option in config.options (section):\n value = config.get (section, option)\n opt = option.capitalize()\n if opt == \"Name\":\n name = value\n elif opt == \"Temperature\":\n myTemperatureProbes = getProbeList (value,\n temperatureProbes)\n elif opt == \"Pressure\":\n myPressureProbes = getProbeList (value,\n pressureProbes)\n elif opt == \"Humidity\":\n myHumidityProbes = getProbeList (value,\n humidityProbes)\n stations [name] = Station.Station(myTemperatureProbes,\n myPressureProbes, myHumidityProbes, name)","def read_data_files(self):\n\n for name, snap in zip(self.names, self.snaps):\n # build the very important dictionary:\n key = f'{name}_{snap:03}' # e.g 'MW_000'\n self.galaxies[key] = Galaxy(name, snap, self.path, \n self.usesql, self.ptype, self.stride)\n self.time = self.galaxies[key].time\n\n # bits of minor housekeeping:\n # self.path = self.galaxies[key].filepath # may speed up next search\n self.filenames.append(key)","def _obtain_data(self):\n (self.data_df, self.column_df, self.station_name, self.log_file, self.station_lat, self.station_lon,\n self.station_elev, self.ws_anemometer_height, self.missing_fill_value, self.script_mode,\n self.auto_mode, self.fill_mode, self.metadata_mode, self.generate_bokeh, self.metadata_df,\n metadata_series) = input_functions.obtain_data(self.config_path, self.metadata_path)\n\n if self.script_mode == 1: # correcting data\n self.mc_iterations = 1000 # Number of iters for MC simulation of thornton running solar radiation gen\n else:\n self.mc_iterations = 50 # if we're not correcting data then only do a few iterations to save time\n\n print(\"\\nSystem: Raw data successfully extracted from station file.\")\n\n # Extract individual variables from data frame back into to numpy arrays.\n self.data_year = np.array(self.data_df.year)\n self.data_month = np.array(self.data_df.month)\n self.data_day = np.array(self.data_df.day)\n self.data_tavg = np.array(self.data_df.tavg)\n self.data_tmax = np.array(self.data_df.tmax)\n self.data_tmin = np.array(self.data_df.tmin)\n self.data_tdew = np.array(self.data_df.tdew)\n self.data_ea = np.array(self.data_df.ea)\n self.data_rhavg = np.array(self.data_df.rhavg)\n self.data_rhmax = np.array(self.data_df.rhmax)\n self.data_rhmin = np.array(self.data_df.rhmin)\n self.data_rs = np.array(self.data_df.rs)\n self.data_ws = np.array(self.data_df.ws)\n self.data_precip = np.array(self.data_df.precip)\n\n self.output_file_path = \"correction_files/\" + self.station_name + \"_output\" + \".xlsx\"","def populate_locations(connection):\n print('Populating locations...')\n cursor = connection.cursor()\n with open(get_data_path('locations', 'locations.json'), 'r', encoding='utf-8') as json_file:\n locations = json.load(json_file)\n\n for station_id, location in locations.items():\n cursor.execute(f'''SELECT id \n FROM watercourse_stations \n WHERE id = {station_id}''')\n\n if len(cursor.fetchall()):\n cursor.execute(f'''INSERT INTO locations(name, lat, lng)\n VALUES ('{location['name']}', {location['lat']}, {location['lng']})''')\n cursor.execute(f'''UPDATE watercourse_stations\n SET location_id = {cursor.lastrowid}\n WHERE id = {station_id}''')","def run():\n # build the station list and update the current levels\n station_list = build_station_list()\n update_water_levels(station_list, use_cache=True)\n\n num_stations = 10\n highest_level_stations = stations_highest_rel_level(station_list, num_stations)\n\n print(\"{} stations with the highest relative water levels, in descending order:\".format(num_stations))\n for station in highest_level_stations:\n print(station.name, station.relative_water_level())","def populate_agdds(start_date, end_date, source, source_id, stations):\r\n # possibly grab ACIS station data (for entire date range)\r\n if source == 'ACIS':\r\n station_ids = []\r\n for station in stations:\r\n station_ids.append(station['char_network_id'])\r\n acis_data = get_acis_climate_data(\",\".join(station_ids), 'mint,maxt,gdd32,gdd50', start_date, end_date)\r\n\r\n for station in stations:\r\n print(station['char_network_id'])\r\n # grab previous days tmin, tmax, and agdd for both bases from mysql agdds table and start over at year breaks\r\n day_before_start_date = start_date - timedelta(days=1)\r\n if day_before_start_date.year == start_date.year:\r\n prev_tmin = get_element_from_qc_table(station['station_id'], source_id, day_before_start_date, 32, 'tmin')\r\n prev_tmax = get_element_from_qc_table(station['station_id'], source_id, day_before_start_date, 32, 'tmax')\r\n agdd32 = get_element_from_qc_table(station['station_id'], source_id, day_before_start_date, 32, 'agdd')\r\n agdd50 = get_element_from_qc_table(station['station_id'], source_id, day_before_start_date, 50, 'agdd')\r\n else:\r\n prev_tmin = None\r\n prev_tmax = None\r\n agdd32 = None\r\n agdd50 = None\r\n\r\n if prev_tmin is None or prev_tmin == 'M':\r\n prev_tmin = 0\r\n if prev_tmax is None or prev_tmax == 'M':\r\n prev_tmax = 0\r\n if agdd32 is None or agdd32 == 'M':\r\n agdd32 = 0\r\n if agdd50 is None or agdd50 == 'M':\r\n agdd50 = 0\r\n\r\n # possibly find station of interest from ACIS retrieved data\r\n acis_station = None\r\n if source == 'ACIS':\r\n station_found = False\r\n for a_station in acis_data['data']:\r\n if station_found:\r\n break\r\n for sid in a_station['meta']['sids']:\r\n # print(sid)\r\n # print(station['char_network_id'])\r\n if station['char_network_id'] in sid:\r\n station_found = True\r\n acis_station = a_station\r\n break\r\n if not station_found:\r\n print(\"Could not find station \" + station['char_network_id'])\r\n\r\n previous_year = start_date.year\r\n delta = end_date - start_date\r\n for i in range(delta.days + 1):\r\n day = start_date + timedelta(days=i)\r\n doy = day.timetuple().tm_yday\r\n\r\n # reset the agdd to 0 if we go into a new year\r\n if previous_year != day.year:\r\n agdd32 = 0\r\n agdd50 = 0\r\n previous_year = day.year\r\n\r\n missing_data = False\r\n print(day.strftime(\"%Y-%m-%d\"))\r\n\r\n # see if we already have tmin and tmax from local db\r\n # tmin = None\r\n # tmax = None\r\n tmin = get_element_from_qc_table(station['station_id'], source_id, day, 32, 'tmin')\r\n tmax = get_element_from_qc_table(station['station_id'], source_id, day, 32, 'tmax')\r\n\r\n already_retrieved = False\r\n if tmin is not None and tmin != 'M' and tmax is not None and tmax != 'M' and source != 'PRISM':\r\n already_retrieved = True\r\n\r\n # don't already have tmin and tmax locally so grab from URMA postgis db or ACIS data\r\n if not already_retrieved:\r\n if source == 'URMA':\r\n if station['char_value'] == 'AK':\r\n tmin = get_urma_climate_data(station['longitude'], station['latitude'], day, 'tmin', 'alaska')\r\n tmax = get_urma_climate_data(station['longitude'], station['latitude'], day, 'tmax', 'alaska')\r\n else:\r\n tmin = get_urma_climate_data(station['longitude'], station['latitude'], day, 'tmin', 'conus')\r\n tmax = get_urma_climate_data(station['longitude'], station['latitude'], day, 'tmax', 'conus')\r\n # URMA and PRISM are in celsius in our postgis db everything else is Fer so convert here\r\n if tmin is not None:\r\n tmin = tmin * 1.8 + 32\r\n if tmax is not None:\r\n tmax = tmax * 1.8 + 32\r\n elif source == 'PRISM':\r\n tmin = get_prism_climate_data(station['longitude'], station['latitude'], day, 'tmin')\r\n tmax = get_prism_climate_data(station['longitude'], station['latitude'], day, 'tmax')\r\n if tmin is not None:\r\n tmin = tmin * 1.8 + 32\r\n if tmax is not None:\r\n tmax = tmax * 1.8 + 32\r\n elif acis_station is not None:\r\n tmin = acis_station['data'][i][0]\r\n tmax = acis_station['data'][i][1]\r\n\r\n # if tmin or tmax is missing, set to previous day's and mark as missing\r\n if tmin is not None and tmin != 'M':\r\n tmin = float(tmin)\r\n prev_tmin = tmin\r\n else:\r\n missing_data = True\r\n tmin = prev_tmin\r\n if tmax is not None and tmax != 'M':\r\n tmax = float(tmax)\r\n prev_tmax = tmax\r\n else:\r\n missing_data = True\r\n tmax = prev_tmax\r\n\r\n # compute gdd and agdd for both bases\r\n gdd32 = compute_gdd(tmin, tmax, 32)\r\n gdd50 = compute_gdd(tmin, tmax, 50)\r\n\r\n agdd32 += gdd32\r\n agdd50 += gdd50\r\n\r\n if not already_retrieved:\r\n # do an insert or update\r\n add_agdd_row(station['station_id'], source_id, gdd32, agdd32, day.year, doy, day, 32, missing_data, tmin, tmax)\r\n add_agdd_row(station['station_id'], source_id, gdd50, agdd50, day.year, doy, day, 50, missing_data, tmin, tmax)","def datamaker(band, skypos, outfile, maglimit=20., margin=0.005,\n searchradius=0.1, radius=gt.aper2deg(4), annulus=[0.0083, 0.025],\n verbose=0):\n\n extant_objids = file_setup(outfile)\n\n if extant_objids == False:\n print('NOT RUNNING!!*!')\n return False\n\n uniques = dt.find_unique_sources(band, skypos[0], skypos[1], searchradius,\n maglimit=maglimit)\n\n if uniques is None:\n print('No sources at this position.')\n return\n\n for pos in uniques:\n mcat = dt.get_mcat_data(pos, margin)\n if not mcat:\n print('Nothing at {pos}.'.format(pos=pos))\n continue\n extant_objids = file_setup(outfile)\n for i, objid in enumerate(mcat['objid']):\n if mcat[band]['ra'][i] == -99. and mcat[band]['dec'][i] == -99.:\n print('No {b} source'.format(b=band))\n continue\n if objid in extant_objids:\n print('Already processed.')\n continue\n #exp = dt.exp_from_objid(objid)\n if mcat[band]['t0'][i] < 0:\n print('No MCAT exposure: skipping')\n continue\n print([mcat[band]['ra'][i], mcat[band]['dec'][i]])\n print([mcat[band]['t0'][i], mcat[band]['t1'][i]])\n data = gAperture(band, [mcat[band]['ra'][i], mcat[band]['dec'][i]],\n radius, annulus=annulus, verbose=verbose,\n coadd=True, trange=[mcat[band]['t0'][i],\n mcat[band]['t1'][i]],\n detsize=1.25)\n try:\n csv_construct = construct_row(i, band, objid, mcat, data)\n print(csv_construct)\n with open(outfile, 'ab') as csvfile:\n spreadsheet = csv.writer(csvfile, delimiter=',',\n quotechar='|',\n quoting=csv.QUOTE_MINIMAL)\n spreadsheet.writerow(csv_construct)\n except TypeError:\n continue\n\n return","def load_meters_from_buliding(self, target_building, meters_name=[], sample_rate = '1min'):\n if self.df is None:\n self.read_data_from_csv()\n \n if len(meters_name) < 1 :\n meters_name = self.meter_name.keys()\n\n if 'main' in meters_name:\n meters_name.remove('main')\n \n building_meters = self.df.groupby('buildingid').get_group(target_building)\n building_meters.index = pd.to_datetime(building_meters['reporttime'], format='%Y-%m-%d %H:%M:%S')\n building_meters = building_meters.groupby('channelid')\n building_channels = building_meters.groups.keys()\n \n if self.meter_name['main'][0] not in building_channels: return\n buliding_df = building_meters.get_group(self.meter_name['main'][0]).rename(columns={\"w\": \"main\"})\n buliding_df = buliding_df.resample(sample_rate, how='mean')\n target_meters = ['main']\n\n for meter, channel_ids in self.meter_name.iteritems():\n if meter in meters_name and channel_ids[0] in building_channels:\n appliance_meter = building_meters.get_group(channel_ids[0]).rename(columns={\"w\": meter})\n \n for channel_id in channel_ids[1:]:\n if channel_id not in building_channels: continue\n another_channel = building_meters.get_group(channel_id).rename(columns={\"w\": meter})\n appliance_meter.append(another_channel)\n\n appliance_meter = appliance_meter.resample(sample_rate, how='mean')\n buliding_df = pd.merge(buliding_df, appliance_meter, right_index=True, left_index=True, how='left')\n target_meters.append(meter)\n \n buliding_df = buliding_df[target_meters]\n buliding_df = buliding_df[~buliding_df.index.duplicated()]\n if buliding_df is not None:\n self.buliding_df.setdefault(target_building, buliding_df)\n \n return buliding_df","def main(self):\n\n assault_mech_df = self.get_mech_df(url=self.assault_url)\n heavy_mech_df = self.get_mech_df(url=self.heavy_url)\n medium_mech_df = self.get_mech_df(url=self.medium_url)\n light_mech_df = self.get_mech_df(url=self.light_url)\n all_weights_df = pd.concat([assault_mech_df, heavy_mech_df, medium_mech_df, \n light_mech_df])\n\n self.save_data(assault_mech_df, \"assault\")\n self.save_data(heavy_mech_df, \"heavy\")\n self.save_data(medium_mech_df, \"medium\")\n self.save_data(light_mech_df, \"light\")\n self.save_data(all_weights_df, \"all_weights\")\n #get maximum new columns needed for splitting variants\n max_cols = all_weights_df.variants.apply(lambda x: len(x)).max()\n melt_cols = []\n\n for i in range(max_cols):\n all_weights_df[\"var_\"+str(i)] = \"\"\n melt_cols.append(\"var_\"+str(i))\n\n variant_weights_df = pd.DataFrame()\n for index, row in all_weights_df.iterrows():\n for i in range(len(row[\"variants\"])):\n #add each variant to variant weights as a row with mech, tonnage, variant\n new_row_dict = {\n \"mech_name\":row[\"mechs\"],\n \"tonnage\":row[\"tonnage\"],\n \"variant\":row[\"variants\"][i].upper()\n }\n new_row_df = pd.DataFrame(new_row_dict, index=[0])\n variant_weights_df = pd.concat([variant_weights_df, new_row_df])\n\n for i in range(len(row[\"hero_chassis\"])):\n new_row_dict = {\n \"mech_name\":row[\"hero_names\"],\n \"tonnage\":row[\"tonnage\"],\n \"variant\":row[\"hero_chassis\"][i].upper()\n }\n new_row_df = pd.DataFrame(new_row_dict, index=[0])\n variant_weights_df = pd.concat([variant_weights_df, new_row_df])\n\n\n for i in range(len(row[\"special_variants\"])):\n new_row_dict = {\n \"mech_name\":row[\"mechs\"],\n \"tonnage\":row[\"tonnage\"],\n \"variant\":row[\"special_variants\"][i].upper()\n }\n new_row_df = pd.DataFrame(new_row_dict, index=[0])\n variant_weights_df = pd.concat([variant_weights_df, new_row_df]) \n\n #add champion variants by matching on \n for i in range(len(row[\"champion_variants\"])):\n new_row_dict = {\n \"mech_name\":row[\"mechs\"],\n \"tonnage\":row[\"tonnage\"],\n \"variant\":row[\"champion_variants\"][i].upper()\n }\n new_row_df = pd.DataFrame(new_row_dict, index=[0])\n variant_weights_df = pd.concat([variant_weights_df, new_row_df])\n #remove duplicate rows \n variant_weights_df = variant_weights_df[variant_weights_df.duplicated(keep=\"first\")==False]\n self.save_data(variant_weights_df, \"variant_weights\")","def dataframe():\n\t#allows function to access station, gmt, and miss_station functions\n global stations\n\tglobal gmt\n\tglobal miss_station\n\t\n\t#read predictor file\n\tcontrol = cfg.read_yaml('../registry/graphs.yaml')\n\tpred_ctrl = cfg.read_yaml(cfg.get_config_path(control.pred_file))\n\tpredd_ctrl = cfg.read_yaml(cfg.get_config_path(control.predd_file))\n\n\t#get file paths and update database\n\tpredictor_file_path = control.predictor_file_path\n\tpredictand_file_path = control.predictand_file_path\n\tpred_file_id = update(predictor_file_path)\n\tpredd_file_id = update(predictand_file_path)\n\t\n\t#store lead time and date range\n\tlead_time = control.lead_time\n\tdate_range = control.date_range\n\n\t#get info for fetch many dates\n\tstart,end,stride = read_pred.parse_range(date_range)\n\tfcst_ref_time = control.date_range[0].split('-')[0][-2:]\n\t\n\t#initialize list of predictors\n\tpred_list = pred_ctrl.predictors\n\tpredictor = []\n\n\t#loops through predictors to build camps data objects\n\tfor entry_dict in pred_list:\n\t\t#formats metadata\n\t\tpred = create.preprocess_entries(entry_dict, fcst_ref_time)\n\t\t\n\t\t#adds info to metadata that's not currently being stored\n\t\tpred.search_metadata['reserved2'] = lead_time*3600\n pred.search_metadata['file_id'] = pred_file_id\n\t\tpred.search_metadata['reserved1'] = 'vector'\n\n\t\t#build camps data objects for each day\n\t\tvariable = fetch_many_dates(predictor_file_path,start,end,stride,pred.search_metadata)\n\t\t\n\t\t#appends all data to single camps object\n\t\tif variable[0] is not None:\n\t\t\tvar = variable[0]\n\t\t\tarrs = []\n\t\t\tfor i in range(len(variable)):\n\t\t\t\tarrs.append(variable[i].data)\n\t\t\tvar.data = np.stack(arrs)\n\t\t\tpredictor.append(var)\n\n\t#initializes list of predictands\n\tpredd_list = predd_ctrl.predictands\n predictand = []\n\t\n\t#loops through predictands to build camps data objects\n for entry_dict in predd_list:\n\t\t#formats metadata\n \tvertical_coordinate = entry_dict.pop('Vertical_Coordinate')\n\t\tentry_dict['file_id'] = predd_file_id\n\n\t\t#build camps objects for each day\n variable = fetch_many_dates(predictand_file_path,start, end, stride, entry_dict)\n\n\t\t#append all data to single camps object\n var = variable[0]\n arrs = []\n for i in range(len(variable)):\n arrs.append(variable[i].data)\n try:\n\t\t\tvar.data = np.stack(arrs)\n\t\t\tpredictand.append(var)\n\t\texcept:\n\t\t\tprint(\"Can't read \" + variable.name)\n\n\t#getting predictor station and time data\n\tpredr = Dataset(predictor_file_path[0])\n\tpredr_stat = predr.variables['station'][:]\n\tif lead_time == 3:\n\t\tpredr_time = predr.variables['OM__phenomenonTimeInstant'][:]\n\telif lead_time == 6:\n\t\tpredr_time = predr.variables['OM__phenomenonTimeInstant1'][:]\n\telif lead_time == 12:\n\t\tpredr_time = predr.variables['OM__phenomenonTimeInstant2'][:]\n\tpredr.close()\n\n\t#reformatting predictor station and time data\n\tpredr_stations = stations(predr_stat)\n\tpredr_gmt = gmt(predr_time)\n\t\n\t#getting predictand station and time data\n\tpredd = Dataset(predictand_file_path[0])\n\tpredd_stat = predd.variables['station'][:]\n\tpredd_time = predd.variables['OM__resultTime'][:]\n\tpredd.close()\n\t\n\t#reformatting predictand station and time data\n\tpredd_stations = stations(predd_stat)\n\tpredd_gmt = gmt(predd_time)\n\n\t#choosing predictand observations that line up with predictor time\n\thour = (predictor[0].metadata['FcstTime_hour']/3600) + lead_time\n\tdays = len(predd_gmt)/24\n\tpredd_hours = [0]*days\n k=0\n for i in range(len(predd_gmt)):\n if i%24 == hour:\n\t\t\tpredd_hours[k]=predd_gmt[i]\n\t\t\tk+=1\n\t\n\t#catches when GFS data doesn't cover the last day of the month\n\tif len(predr_gmt) < len(predd_hours):\n\t\tpredd_hours = predd_hours[:-1]\t\n\t\n\t#find missing stations\n\tmiss_stations = miss_station(predr_stations,predd_stations)\n\tstations = predd_stations\n\t\n\t#station and time array\n\tinfo = [['',''] for k in range(len(predr_gmt)*len(stations))]\n\tfor i in range(len(predr_gmt)):\n\t\tfor j in range(len(stations)):\n\t\t\tk = i*len(stations)+j\n\t\t\tinfo[k][0]=predr_gmt[i]\n\t\t\tinfo[k][1]=stations[j]\n\n\t#create column names\n\tnames = ['']*(len(predictor)+len(predictand)+2)\n\tnames[0]='Time'\n\tnames[1]='Station'\n\n\t#creating array\n\tarr = np.zeros((len(stations)*len(predr_gmt),len(predictor)+len(predictand)))\n\t\n\t#adding predictor data\n\tfor i in range(len(predictor)):\n\t\t#remove lead time and forecast reference time from variable name\n\t\t#and add variable name to column list of final dataframe\n\t\tif lead_time == 12:\n\t\t\tnames[i+2]='GFS_'+predictor[i].get_variable_name()[:-11]\n\t\telse:\n\t\t\t names[i+2]='GFS_'+predictor[i].get_variable_name()[:-10]\n\n\t\t#create pandas dataframe of data and sort alphabetically by station name\n\t\tpredictor[i].data = np.squeeze(predictor[i].data,axis=2)\n\t\tpredictor[i].data = pd.DataFrame(predictor[i].data,columns=predr_stations,index=predr_gmt)\n\t\tpredictor[i].data = predictor[i].data.reindex(sorted(predictor[i].data.columns),axis=1)\n\t\t\n\t\t#remove stations with no predictand data\n\t\tk=0\n\t\ta=miss_stations[:]\n\t\tfor j in predictor[i].data.columns:\n\t\t\tif not a:\n\t\t\t\tbreak\n\t\t\tif j==a[k]:\n\t\t\t\tpredictor[i].data=predictor[i].data.drop(j,axis=1)\n\t\t\t\tdel a[k]\n\t\t\n\t\t#add data to final dataframe\n\t\tfor b in range(len(predr_gmt)):\n\t\t\tfor c in range(len(stations)):\n\t\t\t\tk = b*len(stations)+c\n\t\t\t\tarr[k][i] = predictor[i].data.iloc[b][c]\n\n\t#add predictand data\n\tfor i in range(len(predictand)):\n\t\t#removing extra underscore, adding variable name to column names\n\t\tnames[len(predictor)+2+i]='METAR_'+predictand[i].get_variable_name()[:-1]\n\t\n\t\t#resize array and create pandas dataframe\n\t\tpredictand[i].data = np.squeeze(predictand[i].data,axis=2)\n\t\tpredictand[i].data = pd.DataFrame(predictand[i].data,columns=predd_stations,index=predd_hours)\n\t\tpredictand[i].data = predictand[i].data.reindex(sorted(predictand[i].data.columns),axis=1)\n\t\t\n\t\t#remove extra days of predictand data\n\t\tpredictand[i].data = predictand[i].data.iloc[0:len(predr_time),:]\n\t\t\t\n\t\t#add predictand data to array\n\t\tfor b in range(len(predr_gmt)):\n\t\t\tfor c in range(len(stations)):\n\t\t\t\tk = b*len(stations)+c\n\t\t\t\tval = predictand[i].data.iloc[b][c]\n\t\t\t\t\n\t\t\t\t#catch metar fill data\n\t\t\t\tif val == 9999: \n\t\t\t\t\tval = np.nan\n\t\t\t\tarr[k][len(predictor)+i]=val\n\t\n\t#add station and time data to array and save as csv\n\tdata = np.concatenate([info,arr],axis = 1)\n\tto_save = pd.DataFrame(data,columns=names)\n\tto_save.to_csv(str(start)+'_'+str(end)+'_'+str(lead_time)+'hrs.csv')","def substation_main(locator, total_demand, building_names, gv, Flag):\n\n t0 = time.clock()\n # generate empty vectors\n Ths = np.zeros(8760)\n Tww = np.zeros(8760)\n Tcs = np.zeros(8760) + 1E6\n\n # determine grid target temperatures at costumer side.\n iteration = 0\n buildings = []\n for name in building_names:\n buildings.append(pd.read_csv(locator.get_demand_results_folder() + '//' + name + \".csv\",\n usecols=['Name', 'Thsf_sup_C', 'Thsf_re_C', 'Tcsf_sup_C', 'Tcsf_re_C',\n 'Twwf_sup_C', 'Twwf_re_C', 'Qhsf_kWh', 'Qcsf_kWh', 'Qwwf_kWh',\n 'mcphsf_kWperC', 'mcpwwf_kWperC', 'mcpcsf_kWperC',\n 'Ef_kWh']))\n Ths = np.vectorize(calc_DH_supply)(Ths.copy(), buildings[iteration].Thsf_sup_C.values)\n Tww = np.vectorize(calc_DH_supply)(Tww.copy(), buildings[iteration].Twwf_sup_C.values)\n Tcs = np.vectorize(calc_DC_supply)(Tcs.copy(), buildings[iteration].Tcsf_sup_C.values)\n iteration += 1\n T_DHS = np.vectorize(calc_DH_supply)(Ths, Tww)\n T_DHS_supply = np.where(T_DHS > 0, T_DHS + gv.dT_heat, T_DHS)\n T_DCS_supply = np.where(Tcs != 1E6, Tcs - gv.dT_cool, 0)\n\n # Calculate disconnected buildings files and substation operation.\n if Flag:\n index = 0\n combi = [0] * len(building_names)\n for name in building_names:\n print name\n dfRes = total_demand[(total_demand.Name == name)]\n combi[index] = 1\n key = \"\".join(str(e) for e in combi)\n dfRes.to_csv(locator.get_optimization_substations_total_file(key), sep=',', float_format='%.3f')\n combi[index] = 0\n # calculate substation parameters per building\n substation_model(locator, gv, buildings[index], T_DHS, T_DHS_supply, T_DCS_supply, Ths, Tww)\n index += 1\n else:\n index =0\n # calculate substation parameters per building\n for name in building_names:\n substation_model(locator, gv, buildings[index], T_DHS, T_DHS_supply, T_DCS_supply, Ths, Tww)\n index += 1\n print time.clock() - t0, \"seconds process time for the Substation Routine \\n\"","def measurements():\n measurements_for_displaying = db.session.query(Measurement).all()\n return render_template('measurement/measurements.html', measurements=measurements_for_displaying)","def initialize_data(self , station = '', datasets = {} ):\n \n self.datasets = datasets\n self.datasets_keys = datasets.keys()\n self.station = station\n \n data = {} # container for the data of each dataset\n source_configuration = {} # container for the source_configuration of each dataset\n \n\n \n \"\"\" Looping over the datasets \"\"\"\n logging.info('*** Reading and Initializing the data from the netCDF files ')\n \n \n for k,v in datasets.items() :\n logging.info(' Initialising the dataset: *** %s ' , k )\n data[k] = {} \n data['cdm_tables'] = {} \n \n ### alternative with xarray \n #ds = xr.load_dataset(v) \n #observations_table = xr.open_dataset(v , engine = 'h5netcdf' , group = 'observations_table') \n \n ### alternative with netCDF4\n #ds = nc.Dataset(v) \n #data[k]['dateindex'] = ds.variables['dateindex'][0,:] # storing the dateindex \n \n ###for h5py but cant extract date time units !!!\n ds = h5py.File(v , driver=\"core\" ) \n data[k]['df'] = ds # storing the entire file \n try: \n data[k]['source_file'] = ds['source_configuration']['source_file'][0]\n except:\n data[k]['source_file'] = str(v) # temp fix \n \n #data[k]['product_code'] = ds['source_configuration']['product_code'][0] \n #data[k]['recordtimestamp'] = ds['recordtimestamp'].value\n #data[k]['recordindex'] = ds['recordindex'].value \n #ds.close() \n logging.debug('Reading the file with h5py ')\n \n \n # add here appending datasets for the case of ncar_w and ncar_t \n \n \n self.data = data\n self.make_dataframe()\n ds.close()\n \n \"\"\" Reading the header_table, station_configuration, source_configuration \"\"\"\n for k,v in datasets.items() : \n \n #d = xr.open_dataset(v , engine = 'h5netcdf' ) \n #data[k]['recordtimestamp'] = d['recordtimestamp'].values\n #data[k]['recordindex'] = d['recordindex'].values \n \n \n d = xr.open_dataset(v , engine = 'h5netcdf' , group = 'station_configuration') \n data[k]['station_configuration'] = d.to_dataframe() \n #data[k]['station_configuration'] = d ### USELESS ? \n logging.debug('Done with %s station_configuration' , str(k) )\n \n \n d = xr.open_dataset(v , engine = 'h5netcdf' , group = 'header_table') \n logging.debug('Loading the header_table') \n if 'header_table' not in list( self.attributes.keys() ): # saving the attributes to be re-applied at the end\n self.attributes['header_table'] = {}\n for var in d.variables:\n self.attributes['header_table'][var] = {}\n self.attributes['header_table'][var]['description'] = d[var].description\n self.attributes['header_table'][var]['external_table'] = d[var].external_table \n data[k]['header_table'] = d.to_dataframe() \n logging.debug('Done with %s ' , k )\n \n logging.info(\"*** Loading the observations_table (might take time) %s\" , k ) \n d = xr.open_dataset(v , engine = 'h5netcdf' , group = 'observations_table') \n \n if 'observations_table' not in list( self.attributes.keys() ): # saving the attributes to be re-applied at the end\n self.attributes['observations_table'] = {}\n for var in d.variables:\n self.attributes['observations_table'][var] = {}\n self.attributes['observations_table'][var]['description'] = d[var].description\n self.attributes['observations_table'][var]['external_table'] = d[var].external_table\n \n \n logging.info(\"*** Loading the source configuration %s\" , k ) \n try: \n d = xr.open_dataset(v , engine = 'h5netcdf' , group = 'source_configuration')\n d = d.isel(hdrlen=[0])\n data[k]['source_configuration'] = d.to_dataframe() ### USELESS ? \n logging.debug('Done with %s source_configuration' , k )\n except: \n data[k]['source_configuration']= pd.DataFrame(np.array( [ [ self.data[k]['source_file'] ] ] ) , columns=['source_file'] ) \n \n if k == 'era5_1': # reading the whole era5_1 feedback (including reanalysis)\n d = xr.open_dataset(v , engine = 'h5netcdf' , group = 'era5fb') \n data[k]['era5fb'] = d.to_dataframe() \n logging.debug('Done with %s era5 feedback ', k )\n \n \"\"\" Reading the CDM tables that do not depend on specific stations or observations (fixed values), for the first file only \"\"\" \n if list(datasets.keys()).index(k) == 0 :\n for t in [ 'crs' , 'observed_variable', 'units' , 'z_coordinate_type' , 'station_type']: \n \n d = xr.open_dataset(v , engine = 'h5netcdf' , group = t) \n #data['cdm_tables'][t] = d.to_dataframe() ### USELESS ?\n data['cdm_tables'][t] = d \n \n d.close() \n ds.close()\n\n \"\"\" Reading the name of the original source file \"\"\"\n source_configuration[k] = {} \n source_configuration[k]['source_file'] = [ c for c in v.split('/') if '.nc' in c][0]\n\n \n \"\"\" Storing the station configurations \"\"\" \n self.source_configuration = source_configuration \n \n \"\"\" Making all date_times \"\"\" \n self.make_all_datetime()\n \n \n \"\"\" feedback columns \"\"\"\n if 'era5_1' in list (self.data.keys() ):\n self.fb_columns = list(self.data['era5_1']['era5fb'].columns ) \n else:\n self.fb_columns = ['empty']","def load_station_table(txn):\n LOG.info(\"load_station_table called() ...\")\n txn.execute(\n \"SELECT id, ST_x(geom) as lon, ST_y(geom) as lat from stations \"\n \"where network in ('NEXRAD','TWDR')\"\n )\n for row in txn.fetchall():\n ST[row[\"id\"]] = {\"lat\": row[\"lat\"], \"lon\": row[\"lon\"]}\n LOG.info(\"Station Table size %s\", len(ST.keys()))","def run(self):\n\n for table in self.TABLES:\n self.dictionary_cursor.execute(f\"TRUNCATE TABLE {table}_Work\")\n self.dictionary_conn.commit()\n self.logger.info(\"work tables cleared\")\n for id in self.ids:\n drug = self.Drug(self, id)\n if drug.wanted:\n drug.load()\n self.logger.info(\"work tables populated\")\n for table in self.TABLES:\n insert = f\"INSERT INTO {table} SELECT * FROM {table}_Work\"\n self.dictionary_cursor.execute(f\"TRUNCATE TABLE {table}\")\n self.dictionary_cursor.execute(insert)\n self.dictionary_conn.commit()\n self.logger.info(\"live tables ready\")","def read_table_stations(self):\n if not os.path.exists(self.station_table_filename):\n LOGGER.warning('could not find station.table file \"%s\"', self.station_table_filename)\n return self.known_stations\n count = 0\n with open(self.station_table_filename, 'r') as textfile:\n lines = textfile.read().split(LF)\n for line in lines:\n station_id, data = read_table_station_from_line(line)\n if station_id is not None:\n self.known_stations[station_id] = data\n count += 1\n self.station_file_age = os.path.getmtime(self.station_table_filename)\n LOGGER.info(' Loaded %i station records from \"%s\"', count, self.station_table_filename)\n return self.known_stations","def _extract_all(sel,\n rootpath=r'D:\\WorkStation_2018\\WorkStation_dynamicFC\\Workstation_dynamic_fc_baobaoComputer\\Data\\Dynamic',\n whichstate='state1'):\n\n # hc\n group_name = 'HC'\n data_hc = sel._extract_one(rootpath, whichstate, group_name)\n\n # mdd\n group_name = 'MDD'\n data_mdd = sel._extract_one(rootpath, whichstate, group_name)\n\n # bd\n group_name = 'BD'\n data_bd = sel._extract_one(rootpath, whichstate, group_name)\n\n # sz\n group_name = 'SZ'\n data_sz = sel._extract_one(rootpath, whichstate, group_name)\n\n # concat\n data_all = pd.concat([data_hc, data_mdd, data_bd, data_sz], axis=0)\n data_all.index = np.arange(0, np.shape(data_all)[0])\n\n return data_all","def station_stats(df):","def collect_data(default_path, elects_d, widths, lmbdas):\n cols = [\"elect\", \"d\", \"lmbda\", \\\n \"Dx\", \"Dy\", \"Dz\", \"Dxy\", \"Dyz\", \"Dxz\", \"D3d\"]\n index = np.arange(len(lmbdas) * len(widths) * len(elects_d))\n df = DataFrame(index=index, columns=cols)\n\n cnt = 0\n for el in sorted(elects_d.keys()):\n for d in widths:\n for l in lmbdas:\n data = [elects_d[el], d, l]\n fname = \"diffusivity_%s_d%i_l%i.log\" % (el, d, l)\n fpath = default_path + fname\n try:\n f = open(fpath, \"r\").readlines()\n for line in f:\n if \"1d in SI\" in line:\n data.extend(np.array(line.split()[3:]).astype(float))\n if \"2d in SI\" in line:\n data.extend(np.array(line.split()[3:]).astype(float))\n if \"3d in SI\" in line:\n data.extend(np.array(line.split()[3:]).astype(float))\n except FileNotFoundError:\n print(\"File not found: %s.\" % fpath)\n\n if len(data) == 10:\n df.loc[cnt] = data\n cnt += 1\n return df","def write_to_vacancies_metro_stations_table(config, tables_cache):\r\n database = deepcopy(config[\"database\"])\r\n vacancies_metro_stations_table = \\\r\n deepcopy(config[\"tables\"][\"vacancies_metro_stations_table\"])\r\n in_tests.test_write_to_database_from_dict(\r\n database, vacancies_metro_stations_table, tables_cache)\r\n\r\n vacancy_id = tables_cache[\"id\"]\r\n station_id = tables_cache[\"address_metro_stations_station_id\"]\r\n\r\n if station_id:\r\n write_to_database(database, vacancies_metro_stations_table, {\r\n \"vacancy_id\": vacancy_id,\r\n \"metro_station_id\": station_id\r\n })\r\n return ()","def populate_database(self):\n self.insert_products()\n self.insert_categories()\n self.insert_products_categories()\n self.insert_stores()\n self.insert_products_stores()","def fill_obs(self, observation_table, data_store):\n for obs in observation_table:\n events = data_store.obs(obs_id=obs['OBS_ID']).events\n\n # TODO: filter out (mask) possible sources in the data\n # for now, the observation table should not contain any\n # run at or near an existing source\n\n self.counts_cube.fill_events([events])\n self.livetime_cube.data += events.observation_live_time_duration","def _setupWeather(self, w, config):\n wnames = ('cloud', 'seeing')\n if w not in wnames:\n raise Exception('w should be one of %s' %(wnames))\n filename = config['%s_datafile' %(w)]\n file = open(filename, 'r')\n # Also assume flat file contains only date / value in a space or tab separated file. \n self.dates[w] = []\n self.weather[w] = []\n # Read the data file.\n print '# Reading weather data file %s' %(filename)\n for line in file:\n if line.startswith('#') | line.startswith('!'):\n continue\n self.dates[w].append(line.split()[0])\n self.weather[w].append(line.split()[1])\n file.close()\n self.dates[w] = numpy.array(self.dates[w], float)\n self.weather[w] = numpy.array(self.weather[w], float)\n # Check the total amount of data (mostly for user awareness):\n print '# Read %d weather values from %s file. ' %(len(self.weather[w]), filename)\n # Check that weather data is monotonically increasing in time. \n if not(numpy.all(numpy.diff(self.dates[w]))):\n order = self.dates[w].argsort()\n self.weather[w] = self.weather[w][order]\n self.dates[w] = self.dates[w][order]\n # Get the total length of time included in this (seeing/cloud) file,\n # so that we can determine a wrap-around date if we need that.\n self.maxtime[w] = self.dates[w].max()\n return","def collect_stats(self):\n\n df_avg, self.transport_df, self.customer_df, self.manager_df, self.station_df = self.get_stats_dataframes()\n\n columns = []\n if self.config.simulation_name:\n df_avg[\"Simulation Name\"] = self.config.simulation_name\n columns = [\"Simulation Name\"]\n columns += [\"Avg Waiting Time\", \"Avg Total Time\", \"Simulation Time\"]\n if self.config.max_time:\n df_avg[\"Max Time\"] = self.config.max_time\n columns += [\"Max Time\"]\n columns += [\"Simulation Finished\"]\n self.df_avg = df_avg[columns]","def get_basic_data(self):\n\n db = DataBase().clear_table()\n\n data = self.scraper.scrape_top_250()\n for d in data:\n title = d.find(\"td\", class_=\"titleColumn\")\n title = title.find(\"a\")\n title = re.sub(\"<.*?>\", \"\", str(title))\n\n film_id = d.find(\"td\", class_=\"watchlistColumn\")\n film_id = film_id.find(\"div\")\n film_id = film_id[\"data-tconst\"]\n\n year = d.find(\"span\", class_=\"secondaryInfo\")\n year = re.sub(\"<.*?>\", \"\", str(year)).replace(\"(\", \"\").replace(\")\", \"\")\n\n director = d.find(\"td\", class_=\"titleColumn\")\n director = director.find(\"a\")\n director = director[\"title\"]\n director, *cast = director.split(\", \")\n director = director.replace(\" (dir.)\", \"\")\n\n rating = d.find(\"td\", class_=\"ratingColumn imdbRating\")\n rating = rating.find(\"strong\")\n rating = re.sub(\"<.*?>\", \"\", str(rating))\n\n poster = d.find(\"td\", class_=\"posterColumn\")\n poster = poster.find(\"img\")[\"src\"]\n poster = re.sub(\"@.+\", \"@._V1_FMjpg_UY474_.jpg\", poster)\n\n DataBase().populate_table(\n (title, film_id, year, director, \", \".join(cast), rating, poster)\n )","def update(self):\n if self.last_update and (\n self.last_update + timedelta(hours=1)\n > datetime.utcnow().replace(tzinfo=dt_util.UTC)\n ):\n return # Not time to update yet; data is only hourly\n\n for row in self.current_observations():\n if row.get(\"Station\") == self._station_id:\n api_fields = {\n col_heading: (standard_name, dtype)\n for standard_name, (\n _,\n _,\n _,\n col_heading,\n dtype,\n ) in SENSOR_TYPES.items()\n }\n self.data = {\n api_fields.get(col_heading)[0]: api_fields.get(col_heading)[1](\n v.replace(\",\", \".\")\n )\n for col_heading, v in row.items()\n if col_heading in api_fields and v\n }\n break\n else:\n raise ValueError(f\"No weather data for station {self._station_id}\")","def asdf_create(asdf_name, wav_dirs, sta_dir):\n\n with pyasdf.ASDFDataSet(asdf_name) as ds:\n wav_files = []\n for wav_dir in wav_dirs:\n wav_files.extend([os.path.join(root, a_file)\n for root, dirs, files in os.walk(wav_dir)\n for a_file in files])\n for _i, filename in enumerate(wav_files):\n print(\"Adding mseed file %i of %i...\" % (_i+1, len(wav_files)))\n st = read(filename)\n #Add waveforms\n ds.add_waveforms(st, tag=\"raw_recording\")\n sta_files = glob('%s/*' % sta_dir)\n for filename in sta_files:\n ds.add_stationxml(filename)\n return","def download():\r\n reader = GSODDataReader()\r\n year_list = range(2001, 2012)\r\n austin = reader.collect_data(year_list, exact_station=True,\r\n station_name='AUSTIN CAMP MABRY', state='TX', country='US')\r\n houston = reader.collect_data(year_list, exact_station=True,\r\n station_name='HOUSTON/D.W. HOOKS', state='TX', country='US')\r\n new_york = reader.collect_data(year_list, exact_station=True,\r\n station_name='NEW YORK/LA GUARDIA', state='NY', country='US')\r\n newark = reader.collect_data(year_list, exact_station=True,\r\n station_name='NEWARK INTL AIRPORT', state='NJ', country='US')\r\n punta_arenas = reader.collect_data(year_list, exact_station=True,\r\n station_name='PUNTA ARENAS', country='CH')\r\n wellington = reader.collect_data(year_list, exact_station=True,\r\n station_name='WELLINGTON AIRPORT', country='NZ')\r\n store = HDFStore('weather.h5')\r\n store['austin'] = austin\r\n store['houston'] = houston\r\n store['nyc'] = new_york\r\n store['newark'] = newark\r\n store['punta_arenas'] = punta_arenas\r\n store['wellington'] = wellington\r\n store.close()","def populated_archivist_dataset(archivist_dataset, tmp_path_factory):\n wpath = tmp_path_factory.mktemp(\"archivistds\")\n\n ads = archivist_dataset\n\n dscontent = (\n ('azip/file1.txt', 'zipfile1'),\n ('azip/file2.csv', 'zipfile2_muchcontent'),\n ('atar/file1.txt', 'tarfile1'),\n ('atar/file2.csv', 'tarfile2_muchcontent'),\n )\n srcds = Dataset(wpath / 'srcds').create(**nonoise)\n for fpath, fcontent in dscontent:\n fpath = srcds.pathobj / (PurePosixPath(fpath))\n fpath.parent.mkdir(parents=True, exist_ok=True)\n fpath.write_text(fcontent)\n srcds.save(**nonoise)\n\n archive_root = wpath / 'myarchive'\n #archivetype = 'zip'\n\n akeys = {}\n\n # no ZIP just yet\n # for archivetype, ext in (('zip', ''), ('tar', '.gz')):\n for archivetype, ext in (('tar', '.gz'), ):\n archive_path = Path(f\"{archive_root}.{archivetype}{ext}\")\n\n archive_path_inds = ads.pathobj / '.archives' / archive_path.name\n # create an archive, the easy way, by simply exporting the\n # entire dataset worktree\n srcds.export_archive(archive_root, archivetype=archivetype,\n **nonoise)\n assert archive_path.exists()\n\n # add the archive (in a hidden dir) to be able to reference\n # it via a key\n aurl = archive_path.as_uri()\n ads.repo.call_annex([\n 'addurl', '--file', str(archive_path_inds), aurl])\n ads.save(**nonoise)\n # get the key of the archive\n akeys[archivetype] = ads.status(\n archive_path_inds, annex='basic', return_type='item-or-list',\n **nonoise)['key']\n return ads, akeys, archive_root, dscontent","def analyze_all(datadir, TPQI_starts, dataruns, save = 1, lower = 38.4):\n dirs = os.listdir(datadir)\n idx = 0\n right_dirs = list()\n\n\n for l in dataruns:\n for k in arange(len(dirs)):\n mark_right = '_interference_'+num2str(l,0) in dirs[k]\n \n if mark_right and (len(dirs[k]) > len('_interference_'+num2str(l,0))+6):\n mark_right = False\n\n if mark_right:\n right_dirs.append(dirs[k])\n idx += 1\n continue\n\n \n if len(right_dirs) == 0:\n print 'Did not find any files'\n\n if len(dataruns) == len(right_dirs):\n print 'Found all files...'\n else:\n print 'Beware, not all files are taken into account, file(s) missing.'\n \n tail_over_time = zeros(len(right_dirs))\n tpqi_starts = TPQI_starts[dataruns]\n statistics_info = zeros([len(right_dirs),4])\n \n for k in arange(len(right_dirs)):\n tail_over_time[k] = tail_cts_per_shot(datapath = datadir+'\\\\'+right_dirs[k], lower = lower, TPQI_starts = tpqi_starts[k], save = save)\n statistics_info[k,:] = analyze_thresholds(datapath = datadir+'\\\\'+right_dirs[k], threshold_lt1 = 0, threshold_lt2 = 9, normalize = True, save = save)\n\n\n os.chdir(datadir)\n percentage_finished = float(k+1)/len(right_dirs)*100\n print 'finished: '+num2str(percentage_finished,0)+'%'\n\n\n if save:\n times_passed_overall_lt1 = statistics_info[:,0]\n times_passed_after_seq_lt1 = statistics_info[:,1]\n times_passed_overall_lt2 = statistics_info[:,2]\n times_passed_after_seq_lt2 = statistics_info[:,3]\n filename = 'statistics_run_'+num2str(dataruns.min(),0)+'_to_'+num2str(dataruns.max(),0)+'.npz' \n savez(filename, tpqi_starts = tpqi_starts, tail_over_time = tail_over_time,\n times_passed_overall_lt1 = times_passed_overall_lt1, \n times_passed_after_seq_lt1 = times_passed_after_seq_lt1, \n times_passed_overall_lt2 = times_passed_overall_lt2,\n times_passed_after_seq_lt2 = times_passed_after_seq_lt2)\n\n \n\n figure3 = plt.figure(figsize=(12.0, 16.0))\n plt.subplot(211)\n plt.plot(dataruns,tail_over_time*1E4, '-k')\n plt.xlabel('TPQI run number')\n plt.ylabel('Tail counts per shot (x 1E-4)')\n plt.grid()\n plt.ylim([0,1.1*max(tail_over_time*1E4)])\n\n plt.subplot(212)\n plt.plot(dataruns,TPQI_starts[0:len(right_dirs)], '-k')\n plt.xlabel('TPQI run number')\n plt.ylabel('TPQI starts per run')\n plt.grid()\n plt.ylim([0, 1.1*TPQI_starts[0:len(right_dirs)].max()])\n if save:\n figure3.savefig('tpqi_starts_and_tail_over_time.png')","def write_all_data_tables( phasename, eos_prop_d, output_d ):\n\n dataio.write_data_table( 'temperature_' + phasename + '.dat',\n (eos_prop_d[key] for key in\n ('Pmesh_a', 'Smesh_a', 'Tmesh_a')),\n ('GPa', 'eV', 1), output_d )\n\n dataio.write_data_table( 'density_' + phasename + '.dat',\n (eos_prop_d[key] for key in\n ('Pmesh_a', 'Smesh_a', 'rhomesh_a')),\n ('GPa', 'eV','g_cc'), output_d )\n dataio.write_data_table( 'heat_capacity_' + phasename + '.dat',\n (eos_prop_d[key] for key in\n ('Pmesh_a', 'Smesh_a', 'Cpmesh_a')),\n ('GPa','eV','eV'), output_d )\n\n dataio.write_data_table( 'thermal_exp_' + phasename + '.dat',\n (eos_prop_d[key] for key in\n ('Pmesh_a', 'Smesh_a', 'alphamesh_a')),\n ('GPa','eV',1), output_d )\n\n dataio.write_data_table( 'adiabat_temp_grad_' + phasename + '.dat',\n (eos_prop_d[key] for key in\n ('Pmesh_a', 'Smesh_a', 'dTdP_Smesh_a')),\n ('GPa','eV','GPa-1'), output_d )\n pass","def prepare_input(self, only_center = True):\n \n if only_center:\n nx = [0]\n ny = [0]\n else:\n nx = [0,1,-1]\n ny = [0,1,-1]\n gauge = dd.read_csv(str(Path(self.db_location, 'gauge', '*.csv.gz')), \n compression='gzip', \n assume_missing=True,\n dtype = {'TIMESTAMP':int, 'STATION': str})\n \n gauge = gauge.compute().drop_duplicates()\n gauge = gauge.replace(-9999,np.nan)\n for x in nx:\n for y in ny:\n logging.info('Processing neighbour {:d}{:d}'.format(x, y))\n radar = dd.read_parquet(str(Path(self.db_location, 'radar',\n '*.parquet')))\n refer = dd.read_parquet(str(Path(self.db_location, 'reference', \n '*.parquet')))\n \n # Select only required pixel\n radar = radar.loc[np.logical_and(radar['NX'] == x, \n radar['NY'] == y)]\n refer = refer.loc[np.logical_and(refer['NX'] == x, \n refer['NY'] == y)]\n \n # Convert to pandas and remove duplicates \n radar = radar.compute().drop_duplicates(subset = ['TIMESTAMP',\n 'STATION',\n 'RADAR',\n 'NX','NY',\n 'SWEEP'])\n \n refer = refer.compute().drop_duplicates(subset = ['TIMESTAMP',\n 'STATION'])\n \n radar = radar.sort_values(by = ['TIMESTAMP','STATION','SWEEP'])\n refer = refer.sort_values(by = ['TIMESTAMP','STATION'])\n gauge = gauge.sort_values(by = ['TIMESTAMP','STATION'])\n # Get only valid precip data\n gauge = gauge[np.isfinite(gauge['RRE150Z0'])]\n \n # Create individual 10 min - station stamps\n gauge['s-tstamp'] = np.array(gauge['STATION'] + \n gauge['TIMESTAMP'].astype(str)).astype(str)\n radar['s-tstamp'] = np.array(radar['STATION'] + \n radar['TIMESTAMP'].astype(str)).astype(str)\n refer['s-tstamp'] = np.array(refer['STATION'] + \n refer['TIMESTAMP'].astype(str)).astype(str)\n \n # Get gauge and reference only when radar data available\n \n # Find timestamps that are in the three datasets\n ststamp_common = np.array(pd.Series(list(set(gauge['s-tstamp'])\n .intersection(set(refer['s-tstamp'])))))\n ststamp_common = np.array(pd.Series(list(set(radar['s-tstamp'])\n .intersection(set(ststamp_common)))))\n radar = radar.loc[radar['s-tstamp'].isin(ststamp_common)]\n gauge = gauge.loc[gauge['s-tstamp'].isin(ststamp_common)]\n refer = refer.loc[refer['s-tstamp'].isin(ststamp_common)]\n \n \n # Filter incomplete hours\n stahour = np.array(gauge['STATION'] + \n ((gauge['TIMESTAMP'] - 600 ) - \n (gauge['TIMESTAMP'] - 600 ) % 3600).astype(str)).astype(str)\n \n full_hours = np.array(gauge.groupby(stahour)['STATION']\n .transform('count') == 6)\n \n refer = refer.reindex[full_hours]\n gauge = gauge.reindex[full_hours] \n radar = radar.reindex[radar['s-tstamp'].\n isin(np.array(gauge['s-tstamp']))]\n \n stahour = stahour[full_hours]\n \n # Creating vertical grouping index\n \n _, idx, grp_vertical = np.unique(radar['s-tstamp'],\n return_inverse = True,\n return_index = True)\n # Get original order\n sta_tstamp_unique = radar['s-tstamp'][np.sort(idx)]\n # Preserves order and avoids sorting radar_statstamp\n grp_vertical = idx[grp_vertical]\n # However one issue is that the indexes are not starting from zero with increment\n # of one, though they are sorted, they are like 0,7,7,7,15,15,23,23\n # We want them starting from zero with step of one\n grp_vertical = rankdata(grp_vertical,method='dense') - 1\n \n # Repeat operation with gauge hours\n sta_hourly_unique, idx, grp_hourly = np.unique(stahour, \n return_inverse = True,\n return_index = True)\n grp_hourly = idx[grp_hourly]\n \n # Add derived variables height iso0 (HISO) and height above ground (HAG)\n # Radar\n stations = constants.METSTATIONS\n cols = list(stations.columns)\n cols[1] = 'STATION'\n stations.columns = cols\n radar = pd.merge(radar,stations, how = 'left', on = 'STATION',\n sort = False)\n \n radar['HISO'] = -radar['T'] / constants.LAPSE_RATE * 100\n radar['HAG'] = radar['HEIGHT'] - radar['Z']\n radar['HAG'][radar['HAG'] < 0] = 0\n \n # Gauge\n gauge['minutes'] = (gauge['TIMESTAMP'] % 3600)/60\n \n # Save all to file\n refer.to_parquet(str(Path(self.input_location, \n 'reference_x{:d}y{:d}.parquet'.format(x,y))),\n compression = 'gzip', index = False)\n \n radar.to_parquet(str(Path(self.input_location, \n 'radar_x{:d}y{:d}.parquet'.format(x,y))),\n compression = 'gzip', index = False)\n \n grp_idx = {}\n grp_idx['grp_vertical'] = grp_vertical\n grp_idx['grp_hourly'] = grp_hourly\n grp_idx['tstamp_unique'] = sta_tstamp_unique\n \n pickle.dump(grp_idx, \n open(str(Path(self.input_location, \n 'grouping_idx_x{:d}y{:d}.p'.format(x,y))),'wb'))\n \n if x == 0 and y == 0:\n # Save only gauge for center pixel since it's available only there\n gauge.to_parquet(str(Path(self.input_location, 'gauge.parquet')),\n compression = 'gzip', index = False)","def importAllDatasets(directory):\n head_index = findIndex(temp_list, \"Gaze\")\n point_index = findIndex(temp_list, \"Point\")\n grab_index = findIndex(temp_list, \"Grab\")\n pos_index = findIndex(temp_list, \"Position\")\n\n head_data = pd.read_csv(temp_list[head_index]) if head_index != None else None\n point_data = pd.read_csv(temp_list[point_index]) if point_index != None else None\n grab_data = pd.read_csv(temp_list[grab_index]) if grab_index != None else None\n pos_data = pd.read_csv(temp_list[pos_index]) if pos_index != None else None\n\n\n return head_data, point_data, grab_data, pos_data","def stations():\n # Query all station names from dataset\n station_list = session.query(Measurement.station).distinct().all()\n all_stations = list(np.ravel(station_list))\n\n return jsonify(all_stations)","def prepare_for_influxdb(df):\n df = df.drop(columns=\"landkreis\", errors=\"ignore\") # prevent name collision in get_ags()\n df = get_ags(df)\n df[\"time\"] = df.apply(lambda x: 1000000000*int(datetime.timestamp((pd.to_datetime(x[\"timestamp\"])))), 1)\n df[\"measurement\"] = \"hystreet\"\n df[\"origin\"] = \"https://hystreet.com\"\n df = df.rename(columns={\n 'station_id': '_id',\n 'pedestrians_count': 'pedestrian_count',\n 'state': 'bundesland'\n })\n df['ags'] = pd.to_numeric(df['ags'])\n # import pdb; pdb.set_trace()\n return df","def assemble(self, dt_range=None):\n if dt_range is not None:\n self.dt_list = trace_source.time_list(dt_range[0],\n dt_range[1],\n self.config['time']['step'])\n\n # only for the testcase\n traj_dir = self.config['partposit_dir']\n days_avail = os.listdir(traj_dir)\n # filter only for the trajectory files with tdump extension\n days_avail = [f for f in days_avail if len(f) == 11]\n print(days_avail)\n folders = [f for f in days_avail if datetime.datetime.strptime(f, \"%Y%m%d_%H\") in self.dt_list]\n\n assert len(folders) > 0, 'no folders with flexpart partposit data'\n\n # the defaultdict is used here to sort the files by datetime within a dictionary\n # filtered_files = defaultdict(list)\n # for f in files:\n # # regex the yyyymmdd-hh timestamp in the filename\n # dt = datetime.datetime.strptime(re.search('([0-9]{8})-([0-9]){2}', f).group(0), '%Y%m%d-%H')\n # height = float(re.search('([0-9]{3,6})(?=_0[0-9-]{1,4}.tdump)', f).group(0))\n # #print(f, dt, height)\n # if dt >= self.dt_list[0] and dt <= self.dt_list[-1]:\n # filtered_files[dt].append((f,height))\n\n # here an empty dict is generated with a zero containing array\n self.stat2d_dict = defaultdict(lambda: np.zeros((len(self.dt_list), len(self.height_list))))\n\n self.statls_dict = defaultdict(lambda: np.zeros((len(self.dt_list), len(self.height_list), 7)))\n\n self.raw_dict = defaultdict(lambda: np.zeros((len(self.dt_list), len(self.height_list),\n abs(self.config['time']['tr_duration'])+1)))\n\n # TODO make more than 7 geo names possible\n ng = trace_source.land_sfc.named_geography(self.config['geonames'])\n self.geo_names = ng.geo_names\n no_geo_names = len(list(self.geo_names.keys()))\n self.statgn_dict = defaultdict(lambda: np.zeros((len(self.dt_list),\n len(self.height_list),\n no_geo_names)))\n\n\n self.lat_names = {0: '<-60', 1: '-60..-30', 2:'-30..0', 3: '0..30', 4: '30..60', 5: '>60'}\n self.statlat_dict = defaultdict(lambda: np.zeros((len(self.dt_list),\n len(self.height_list),\n len(list(self.lat_names.keys())))))\n\n\n ls = trace_source.land_sfc.land_sfc()\n self.ls_categories = ls.categories\n\n\n for it, dt in enumerate(self.dt_list[:]):\n print('trajectories eding at ', dt)\n files_for_time = os.listdir(traj_dir + dt.strftime(\"%Y%m%d_%H\"))\n files_for_time = sorted([f for f in files_for_time if \"partposit_\" in f])\n folder = traj_dir + dt.strftime(\"%Y%m%d_%H\") + \"/\"\n print('files_for_time ', files_for_time)\n\n print('heights ', len(self.height_list), self.height_list)\n\n flex_stat = [flex_statistics(self.config, ls=ls, ng=ng) for h in self.height_list]\n traj_meta = read_flexpart_traj_meta(folder + \"trajectories.txt\")\n\n self.no_part.append(traj_meta['releases_meta'][1]['no_particles'])\n self.time_res.append(10*24/len(files_for_time))\n\n # different structure than hysplit\n # 1. loop through the ending times of the current day\n # 2. load partposit for a specified time\n # 3. loop through heights\n\n for f in files_for_time:\n print('files_for_time ', f)\n part_pos = read_partpositions(folder + f, 1, ctable=True)\n part_pos = np.array(part_pos)\n\n for ih, h in enumerate(self.height_list):\n #print(\"at \", ih, h)\n this_population = np.where(part_pos[:,0] == ih+1)[0]\n #release_sel = np.array([list(p) for p in part_pos if p[0]==ih+1])\n release_sel = part_pos[this_population, :]\n #assert np.all(release_sel == other_release)\n meta = traj_meta['releases_meta'][ih+1]\n #print(meta)\n assert np.mean(meta['heights']) == h, f\"{meta['heights']} {h} do not fit\"\n flex_stat[ih].add_partposits_gn(release_sel)\n\n flex_stat[ih].add_partposits_ls(release_sel)\n flex_stat[ih].add_partposits_thres(release_sel)\n\n # now assemble the statistics for all heights\n for ih, h in enumerate(self.height_list): \n flex_stat[ih].calc_gn_stat()\n for k in list(flex_stat[ih].stat_gn.keys()):\n self.stat2d_dict[k+'_no_below'][it, ih] = flex_stat[ih].stat_gn[k].no_below\n print('stat gn ', h, k, flex_stat[ih].stat_gn[k])\n self.statgn_dict[k][it, ih] = list(flex_stat[ih].stat_gn[k].counter.values())\n\n flex_stat[ih].calc_ls_stat()\n for k in list(flex_stat[ih].stat_ls.keys()):\n self.stat2d_dict[k+'_no_below'][it, ih] = flex_stat[ih].stat_ls[k].no_below\n print('stat ls ', h, k, flex_stat[ih].stat_ls[k])\n self.statls_dict[k][it, ih] = list(flex_stat[ih].stat_ls[k].counter.values())\n\n flex_stat[ih].calc_thres_stat()\n for k in list(flex_stat[ih].stat_lat.keys()):\n self.stat2d_dict[k+'_no_below'][it, ih] = flex_stat[ih].stat_lat[k].no_below\n print('stat_lat ', h, k, flex_stat[ih].stat_lat[k])\n self.statlat_dict[k][it, ih] = list(flex_stat[ih].stat_lat[k].counter.values())\n\n\n # #assert len(f_list) > 1\n # for ih, f in enumerate(f_list):\n # print(it, ih, f[1], dt)\n # traj = trajectory(self.config)\n # traj.load_file(traj_dir+f[0], silent=True)\n # savepath = '{}/{}'.format(self.config['plot_dir'], dt.strftime('%Y%m%d'))\n\n\n # if \"timeinterval\" in self.config['plotmap']:\n # timeinterval = self.config['plotmap']['timeinterval']\n # else:\n # timeinterval = 12\n # if \"heights\" in self.config['plotmap']:\n # heightlist = self.config['plotmap']['heights']\n # else:\n # heightlist = [1500.0, 3000.0, 4500.0]\n # #if f[1] == 3000.0 and dt.hour % 12 == 0:\n # if f[1] in heightlist and dt.hour % timeinterval == 0:\n # print(\"plotting \", f[1], dt.hour)\n # plot_trajectories_ens(traj, savepath, ls=ls, config=self.config)\n # #continue\n\n # traj.evaluate(silent=True)\n # traj.add_land_sfc(ls, silent=True)\n # traj.add_ensemble_land_sfc(ls)\n # traj.add_ensemble_geo_names(ng)\n # #traj.add_area_land_sfc('md', ls, silent=True)\n # #traj.add_area_land_sfc(2000, ls, silent=True)\n\n # #print(\"at step\", it, dt, ih, f)\n # #print('keys ', traj.statistics.keys())\n # # now the empty dict is filled with the keys (and values) of the statistics dict from traj\n # for k in list(traj.statistics.keys()):\n # self.stat2d_dict[k][it, ih] = traj.statistics[k]\n # # subset of trajectory data to collect\n # param_collect = ['latitude', 'longitude', 'height', \"PRESSURE\", \"AIR_TEMP\",\n # \"RAINFALL\", \"RELHUMID\", \"TERR_MSL\", 'age']\n # if 'land_sfc_category' in list(traj.data.keys()):\n # param_collect.append('land_sfc_category')\n # for k in param_collect:\n # #self.raw_dict[k][it, ih, :traj.data[1][k].shape[0]] = traj.data[1][k]\n # self.raw_dict[k][it, ih, :] = traj.data[1][k]\n # #self.raw_dict[k][it, ih, traj.data[1][k].shape[0]:] = -999.\n\n # for k in list(traj.stat_ls.keys()):\n # self.stat2d_dict[k+'_no_below'][it, ih] = traj.stat_ls[k].no_below\n # print('stat ls ', k, traj.stat_ls[k])\n # self.statls_dict[k][it, ih] = list(traj.stat_ls[k].counter.values())\n\n # for k in list(traj.stat_gn.keys()):\n # self.stat2d_dict[k+'_no_below'][it, ih] = traj.stat_gn[k].no_below\n # print('stat gn ', k, traj.stat_gn[k])\n # self.statgn_dict[k][it, ih] = list(traj.stat_gn[k].counter.values())\n\n # trying to free memory\n del ls\n del ng","def summarise_data(trip_in, station_data, trip_out):\n # generate dictionary of station - city mapping\n station_map = create_station_mapping(station_data)\n \n with open(trip_out, 'w') as f_out:\n # set up csv writer object \n out_colnames = ['duration', 'start_date', 'start_year',\n 'start_month', 'start_hour', 'weekday',\n 'start_city', 'end_city', 'subscription_type'] \n trip_writer = csv.DictWriter(f_out, fieldnames = out_colnames)\n trip_writer.writeheader()\n \n for data_file in trip_in:\n with open(data_file, 'r') as f_in:\n # set up csv reader object\n trip_reader = csv.DictReader(f_in)\n\n # collect data from and process each row\n for row in trip_reader:\n new_point = {}\n \n # convert duration units from seconds to minutes\n ### Question 3a: Add a mathematical operation below ###\n ### to convert durations from seconds to minutes. ###\n new_point['duration'] = float(row['Duration'])/60\n \n # reformat datestrings into multiple columns\n ### Question 3b: Fill in the blanks below to generate ###\n ### the expected time values. ###\n trip_date = datetime.strptime(row['Start Date'], '%m/%d/%Y %H:%M')\n new_point['start_date'] = trip_date.strftime('%Y-%m-%d')\n new_point['start_year'] = trip_date.strftime('%Y') # or : trip_date.year\n new_point['start_month'] = trip_date.strftime('%m') # or : trip_date.month\n new_point['start_hour'] = trip_date.strftime('%H') # or : trip_date.hour\n new_point['weekday'] = trip_date.strftime('%a') # or : trip_date.weekday() OR trip_date.isoweekday()\n\n \n # remap start and end terminal with start and end city\n new_point['start_city'] = station_map[row['Start Terminal']]\n new_point['end_city'] = station_map[row['End Terminal']]\n # two different column names for subscribers depending on file\n if 'Subscription Type' in row:\n new_point['subscription_type'] = row['Subscription Type']\n else:\n new_point['subscription_type'] = row['Subscriber Type']\n\n # write the processed information to the output file.\n trip_writer.writerow(new_point)","def stations():\n \n session = Session(engine)\n # Query to bring all stations\n results = pd.DataFrame(session.query(S.id.label('ID'),S.station.label('Station'),S.name.label('Name'),\\\n S.latitude.label('Latitude'),S.longitude.label('Longitude'), \\\n S.elevation.label('Elevation')).all())\n \n session.close()\n \n # Create a dictionary from the row data of the dataframe and return it as a JSON\n return jsonify(results.to_dict(orient = 'records'))","def run():\r\n\r\n # Build list of stations\r\n stations = build_station_list()\r\n\r\n # Find 5 stations at which the current level is the highest\r\n stations_highest_rel_level_list = []\r\n N = 5\r\n for i in range(len(stations_highest_rel_level(stations, N))):\r\n stations_highest_rel_level_list.append(stations_highest_rel_level(stations, N)[i][0])\r\n \r\n\r\n # Plot the water level for each of these stations over the past 10 days\r\n \r\n # First fetch the time history for a station\r\n for station in stations:\r\n if station.name in stations_highest_rel_level_list:\r\n \r\n dt = 2\r\n dates, levels = fetch_measure_levels(station.measure_id, dt=datetime.timedelta(days=dt))\r\n # This gives list of dates and levels to be passed into a plot\r\n plot_water_level_with_fit(station, dates, levels, 4)\r\n else:\r\n pass","def create_databases():\n db_connection = connect_to_db()\n\n # Create database tables.\n create_tables(db_connection)\n\n # Populate water tables.\n populate_water_tables(db_connection)\n\n # station_data = get_station_data()\n # station = station_data.query('ŠIFRA == 30301')\n # print(station)\n # index = station.index[0]\n # lat = station.at[index, 'LAT']\n # lng = station.at[index, 'LON']\n # name = f\"{station.at[index, 'VODOMERNA POSTAJA']} ({station.at[index, 'VODOTOK']})\"\n # print(index, lat, lng, name)\n\n # Populate location tables\n # populate_locations(db_connection)\n\n # Populate weather tables\n populate_weather(db_connection)\n\n db_connection.commit()\n db_connection.close()","def save_data(self, data_dict: dict, table_name: str):\n location = \"Winnipeg, MB\"\n insert_sql = f\"\"\"insert or ignore into {table_name}\n (sample_date, location, min_temp, max_temp, avg_temp)\n values\n (?,?,?,?,?)\"\"\"\n\n with DBOperations(self.name) as dbcm:\n for date, temps in data_dict.items():\n data_tuple = (date, location, temps['Min'] ,temps['Max'], temps['Mean'])\n dbcm.execute(insert_sql, data_tuple)","def water_supply(osm_path): \n i = retrieve(osm_path,'multipolygons',['man_made'],**{'man_made':[\"='water_tower' or \",\"='water_well' or \",\"='reservoir_covered' or \",\"='water_works'\"]})\n j = retrieve(osm_path,'multipolygons',['landuse'],**{'landuse':[\"='reservoir'\"]})\n \n i = i.rename(columns={'man_made': 'asset'})\n j = j.rename(columns={'landuse': 'asset'})\n \n combined_df = pandas.concat([i,j], ignore_index=True, sort=False) #append objects while ignoring that they may have overlapping index\n if combined_df.empty == True:\n return combined_df\n else:\n return combined_df[[\"osm_id\",\"asset\",\"geometry\"]]","def produce_all_database(is_debug):\n\tproduce_database([\"apnea-ecg\", \"train\"], is_debug)\n\tproduce_database([\"apnea-ecg\", \"test\"], is_debug)","def _interpolate_meteorological_data(dset, data, rundate):\n rundate = datetime(rundate.year, rundate.month, rundate.day)\n for field, station in [(f, f[4:]) for f in data.keys() if f.startswith(\"met_\")]:\n log.debug(f\"Meteorological data available for station {station}\")\n\n met_time = data[field].pop(\"met_time\")\n flat_list = [item for sublist in met_time for item in sublist]\n met_time_float = np.array([(flat_list[i] - rundate).total_seconds() for i in range(0, len(flat_list))])\n met_time_unique, met_index = np.unique(met_time_float, return_index=True)\n\n diff = len(met_time_float) - len(met_time_unique)\n if diff > 0:\n log.dev(f\"Removed duplicate met data for station {station}\")\n log.dev(\"Do this for the actual obs data also!\")\n if len(met_time_unique) == 1:\n for met_type in data[field].keys():\n data[field][met_type] = np.repeat(data[field][met_type][0], dset.num_obs)\n continue\n\n # Extrapolation one month before/after\n # (this is overkill, most of these values will be removed later when taking the diagonal)\n min_time = min(met_time_unique) - 31 * 86400\n max_time = max(met_time_unique) + 31 * 86400\n met_time_unique = np.hstack((np.array(min_time), met_time_unique, np.array(max_time)))\n\n for met_type in data[field].keys():\n met_data_array = data[field][met_type]\n flat_list = [item for sublist in met_data_array for item in sublist]\n met_data_array = np.array([flat_list[i] for i in met_index])\n met_data_array = np.hstack((met_data_array[0], met_data_array, met_data_array[-1]))\n data[field][met_type] = interpolation.interpolate(\n met_time_unique, met_data_array, dset.obs_time, kind=\"cubic\"\n )\n\n return data","def fillstation(self, stanames, all=None, plot=None, summary=None, From=None, To=None, by=None,\n how='mean', variables=None, distance=None, sort_cor=True, constant=True, cor_lim=None):\n\n if all == True:\n stations = self.network.getsta([], all=True).values()\n else:\n stations = self.network.getsta(stanames)\n\n for station in stations:\n staname = station.getpara('stanames')\n\n if variables == None:\n newdataframe = station.getData(reindex=True, From=From, To=To, by=by,\n how=how) # Dataframe which stock the new data of the stations\n newdataframe['U m/s'] = station.getData('U m/s', reindex=True, From=From, To=To, by=by, how=how)\n newdataframe['V m/s'] = station.getData('V m/s', reindex=True, From=From, To=To, by=by, how=how)\n newdataframe['Ua g/kg'] = station.getData('Ua g/kg', reindex=True, From=From, To=To, by=by, how=how)\n newdataframe['Theta C'] = station.getData('Theta C', reindex=True, From=From, To=To, by=by, how=how)\n variables_name = newdataframe.columns\n else:\n newdataframe = station.getData(var=variables, reindex=True, From=From, To=To, by=by,\n how=how) # Dataframe which stock the new data of the stations\n variables_name = variables\n # select and sort nearest stations\n selections, selectionsnames = self.__getpredictors_distance(staname, distance)\n\n for var in variables_name:\n print(\"I\" * 30)\n print(\"variable -> \" + var)\n\n try:\n selections, params = self.__sort_predictors_by_corr(station, selections, var, From, To, by, how,\n constant=constant,\n selectionsnames=selectionsnames,\n sort_cor=sort_cor, cor_lim=cor_lim)\n\n selections_iter = iter(selections)\n params_iter = iter(params)\n # print newdataframe\n idxmissing = newdataframe[var][\n newdataframe[var].isnull() == True].index # slect where their is missing data\n\n while len(idxmissing) > 0:\n print(\"Their is [\" + str(len(idxmissing)) + \"] events missing\")\n\n try: # Try if their is still other stations to fill with\n selection = selections_iter.next()\n param = params_iter.next()\n except StopIteration:\n print(\"NO MORE SELECTED STATIONS\")\n break\n\n try:\n Y = station.getData(var, From=From, To=To, by=by, how=how) # variable to be filled\n X1 = selection[0].getData(var, From=From, To=To, by=by,\n how=how) # stations variable used to fill\n X2 = selection[1].getData(var, From=From, To=To, by=by,\n how=how) # stations variable used to fill\n\n select = pd.concat([X1, X2], keys=['X1', 'X2'], axis=1, join='inner').dropna()\n\n if constant:\n newdata = param[0] + param[1] * select['X1'] + param[2] * select[\n 'X2'] # reconstruct the data\n else:\n newdata = param[0] * select['X1'] + param[1] * select['X2'] # reconstruct the data\n\n newdataframe.loc[idxmissing, var] = newdata.loc[idxmissing, var]\n idxmissing = newdataframe[var][\n newdataframe[var].isnull() == True].index # slect where their is missing data\n\n\n except KeyError:\n print(\"&\" * 60)\n print('Selected stations did not fill any events')\n except ValueError:\n print('The variable ' + var + \"Does not exist or no data to do the multilinear regression \")\n\n if plot == True:\n df = pd.concat([Y, X1, X2, newdata, newdataframe[var]],\n keys=['Y', 'X1', 'X2', 'estimated data', 'Estimated replaced'], axis=1,\n join='outer')\n self.plotcomparison(df)\n\n print(\"Their is [\" + str(len(idxmissing)) + \"] FINALLY events missing\")\n # Recalculate the wind direction and speed from the U an V components\n\n try:\n speed, dir = cart2pol(newdataframe['U m/s'], newdataframe['V m/s'])\n newdataframe['Dm G'] = dir\n newdataframe['Sm m/s'] = speed\n except ValueError:\n print\n 'No wind found in the dataframe'\n except KeyError:\n print('No wind found in the dataframe')\n\n self.newdataframes[staname] = newdataframe"],"string":"[\n \"def populate_water_tables(connection):\\n metadata = load_metadata('water')\\n cursor = connection.cursor()\\n\\n # Check if tables are already populated.\\n cursor.execute('SELECT count(*) FROM watercourses')\\n watercourse_count = cursor.fetchone()[0]\\n cursor.execute('SELECT count(*) FROM aquifers')\\n aquifer_count = cursor.fetchone()[0]\\n\\n if watercourse_count and aquifer_count:\\n print('Water tables already populated!')\\n return\\n\\n station_data = get_station_data()\\n\\n for archive in metadata.keys():\\n print(f'{archive}-water:'.upper())\\n water_body = get_water_definitions(archive)['body']\\n\\n # 1. Populate watercourses/aquifers:\\n stations = {}\\n for water_body_name in metadata[archive].keys():\\n print(f'\\\\tPopulating {water_body}: \\\"{water_body_name}\\\"')\\n cursor.execute(f'''INSERT INTO {water_body}s(location_id, name)\\n VALUES (0, '{water_body_name}')''')\\n water_body_id = cursor.lastrowid\\n\\n # 2. Populate watercourse_stations/aquifer_stations:\\n for station_id in metadata[archive][water_body_name]['stations']:\\n station_name = clean_name(metadata[archive][water_body_name]['stations'][station_id]['name'])\\n\\n if station_id in stations:\\n # Prefer watercourses/aquifer with more stations\\n current_len = len(metadata[archive][water_body_name]['stations'])\\n previous_len = len(metadata[archive][stations[station_id]]['stations'])\\n\\n if current_len < previous_len:\\n print(f'\\\\t\\\\tStation already exists: {station_id} - \\\"{station_name}\\\" (\\\"{water_body_name}\\\")')\\n continue\\n else:\\n cursor.execute(f'''DELETE \\n FROM {water_body}_stations\\n WHERE id = {station_id}''')\\n print(f'\\\\t\\\\tRemoved station: {station_id} - \\\"{station_name}\\\" from \\\"{stations[station_id]}\\\")')\\n\\n stations[station_id] = water_body_name\\n print(f'\\\\t\\\\tPopulating station: {station_id} - \\\"{station_name}\\\"')\\n\\n # Insert station location if station data exists.\\n location_id = 0\\n station_row = station_data.query(f'ŠIFRA == \\\"{station_id}\\\"')\\n if not station_row.empty:\\n index = station_row.index[0]\\n lat = station_row.at[index, 'LAT']\\n lng = station_row.at[index, 'LON']\\n if not np.isnan(lat) and not np.isnan(lng):\\n name = f\\\"{station_row.at[index, 'VODOMERNA POSTAJA']} ({station_row.at[index, 'VODOTOK']})\\\"\\n cursor.execute(f'''INSERT INTO locations(name, lat, lng)\\n VALUES ('{name}', {lat}, {lng})''')\\n location_id = cursor.lastrowid\\n\\n # Insert station.\\n cursor.execute(f'''INSERT INTO {water_body}_stations(id, {water_body}_id, location_id, name)\\n VALUES ({station_id}, {water_body_id}, {location_id}, '{station_name}')''')\\n\\n # 3. Populate watercourse_measurements/aquifer_measurements:\\n if not populate_water_measurements(cursor, archive, metadata[archive][water_body_name]['dir'],\\n station_id):\\n cursor.execute(f'''DELETE \\n FROM {water_body}_stations\\n WHERE id = {station_id}''')\\n print(f'\\\\t\\\\tRemoved station with useless data: {station_id} - \\\"{station_name}\\\"')\\n\\n # Remove empty watercourses/aquifers.\\n cursor.execute(f'''SELECT w.id, w.name\\n FROM {water_body}s w\\n WHERE NOT EXISTS (\\n SELECT s.id \\n FROM {water_body}_stations s \\n WHERE w.id = s.{water_body}_id\\n )''')\\n\\n for row in cursor.fetchall():\\n cursor.execute(f'''DELETE \\n FROM {water_body}s\\n WHERE id = {row[0]}''')\\n print(f'\\\\tRemoved empty {water_body}: \\\"{row[1]}\\\"')\",\n \"def populate_weather(connection):\\n metadata = load_metadata('weather')\\n cursor = connection.cursor()\\n water_defs = get_water_definitions()\\n\\n # Check if tables are already populated.\\n cursor.execute('SELECT count(*) FROM weather')\\n weather_count = cursor.fetchone()[0]\\n\\n if weather_count:\\n print('Weather tables already populated!')\\n return\\n\\n print('WEATHER:')\\n\\n # Darksky data\\n for dir_name, location in metadata.items():\\n print(f'\\\\tPopulating weather: \\\"{location[\\\"name\\\"]}\\\".')\\n\\n # Insert location.\\n cursor.execute(f'''INSERT INTO locations(name, lat, lng)\\n VALUES ('{location['name']}', {location['lat']}, {location['lng']})''')\\n location_id = cursor.lastrowid\\n\\n # Set weather locations for watercourses/aquifers.\\n for water_body in [d['body'] for d in water_defs.values()]:\\n if water_body in location:\\n cursor.execute(f'''UPDATE {water_body}s\\n SET location_id = {location_id}\\n WHERE name IN ('{\\\"','\\\".join(location[water_body])}')''')\\n break\\n\\n dir_path = get_data_path('weather', 'raw', dir_name)\\n for json_file_name in os.listdir(dir_path):\\n json_path = os.path.join(dir_path, json_file_name)\\n with open(json_path, 'r', encoding='utf-8') as json_file:\\n print(f'\\\\t\\\\tPopulating year: {json_file_name[0:-5]}')\\n year_forecasts = json.load(json_file)\\n for date, date_forecast in year_forecasts.items():\\n hourly_forecasts = date_forecast['hourly']\\n\\n if not hourly_forecasts:\\n print(f'\\\\t\\\\tNo hourly forecasts for {date}!')\\n continue\\n\\n daily_forecast = {\\n 'location_id': location_id,\\n 'time': date_forecast['time'],\\n 'day_time': date_forecast['sunset_time'] - date_forecast['sunrise_time'],\\n 'precipitation': 0,\\n 'snow_accumulation': 0\\n }\\n # List of value names with `avg`, `min` and `max` values\\n value_names = {\\n 'temperature': 'temperature',\\n 'cloud_cover': 'cloudCover',\\n 'dew_point': 'dewPoint',\\n 'humidity': 'humidity',\\n 'pressure': 'pressure',\\n 'uv_index': 'uvIndex',\\n 'precipitation_probability': 'precipProbability',\\n 'precipitation_intensity': 'precipIntensity'\\n }\\n # Value name counters, which indicate how many times (out of 24)\\n # certain value appears in hourly data.\\n value_counts = {k: 0 for k in value_names.keys()}\\n\\n for value_name in value_names.keys():\\n daily_forecast[f'{value_name}_avg'] = 0.0\\n daily_forecast[f'{value_name}_min'] = float('inf')\\n daily_forecast[f'{value_name}_max'] = float('-inf')\\n\\n # Calculate daily forecast values from hourly forecasts.\\n for hourly_forecast in hourly_forecasts:\\n for value_name in value_names.keys():\\n orig_value_name = value_names[value_name]\\n if is_forecast_number(orig_value_name, hourly_forecast):\\n daily_forecast[f'{value_name}_avg'] += hourly_forecast[orig_value_name]\\n daily_forecast[f'{value_name}_min'] = min(\\n hourly_forecast[orig_value_name],\\n daily_forecast[f'{value_name}_min']\\n )\\n daily_forecast[f'{value_name}_max'] = max(\\n hourly_forecast[orig_value_name],\\n daily_forecast[f'{value_name}_max']\\n )\\n value_counts[value_name] += 1\\n\\n if is_forecast_number('precipAccumulation', hourly_forecast) \\\\\\n and hourly_forecast['precipType'] == 'snow':\\n daily_forecast['snow_accumulation'] += hourly_forecast['precipAccumulation']\\n elif is_forecast_number('precipIntensity', hourly_forecast) \\\\\\n and is_forecast_number('precipProbability', hourly_forecast):\\n daily_forecast['precipitation'] += \\\\\\n hourly_forecast['precipIntensity'] * hourly_forecast['precipProbability']\\n\\n for value_name, value_count in value_counts.items():\\n if value_count:\\n # Calculate average.\\n daily_forecast[f'{value_name}_avg'] = daily_forecast[f'{value_name}_avg'] / value_count\\n else:\\n # If value never appeared\\n daily_forecast[f'{value_name}_avg'] = 'NULL'\\n daily_forecast[f'{value_name}_min'] = 'NULL'\\n daily_forecast[f'{value_name}_max'] = 'NULL'\\n\\n cursor.execute(f'''INSERT INTO weather({', '.join(daily_forecast.keys())})\\n VALUES ({', '.join([str(v) for v in daily_forecast.values()])})''')\\n\\n # IOT data:\\n for location in SETTINGS['weather_locations_iot']:\\n print(f'\\\\tPopulating weather: \\\"{location[\\\"name\\\"]}\\\".')\\n\\n # Insert location.\\n cursor.execute(f'''INSERT INTO locations(name, lat, lng)\\n VALUES ('{location['name']}', {location['lat']}, {location['lng']})''')\\n location_id = cursor.lastrowid\\n\\n # Set weather locations for watercourses/aquifers.\\n for water_body in [d['body'] for d in water_defs.values()]:\\n if water_body in location:\\n cursor.execute(f'''UPDATE {water_body}s\\n SET location_id = {location_id}\\n WHERE name IN ('{\\\"', '\\\".join(location[water_body])}')''')\\n\\n # Set locations for all stations on given water body to match its location.\\n cursor.execute(f'''SELECT id\\n FROM {water_body}s\\n WHERE location_id = {location_id}''')\\n ids = [row[0] for row in cursor.fetchall()]\\n if len(ids):\\n cursor.execute(f'''UPDATE {water_body}_stations\\n SET location_id = {location_id}\\n WHERE {water_body}_id IN ({', '.join([str(v) for v in ids])})''')\\n\\n break \\n \\n file_name = f'''{location['lat']}-{location['lng']}.json'''\\n json_path = get_data_path('weather', 'raw', file_name)\\n\\n # If data file doesn't exist, download it first.\\n if not os.path.isfile(json_path):\\n with open(json_path, 'wb', encoding=\\\"utf-8\\\") as file:\\n file.write(read_from_url(location['url'], decode=False))\\n \\n with open(json_path, 'r', encoding='utf-8') as json_file:\\n row_names = {\\n \\\"Sun_duration\\\": \\\"sun_duration\\\",\\n \\\"CloudCover\\\": \\\"cloud_cover_avg\\\",\\n \\\"Percipitation\\\": \\\"precipitation\\\",\\n \\\"New_snow_blanket\\\": \\\"snow_accumulation\\\",\\n \\\"Snow_blanket\\\": \\\"snow_depth\\\",\\n \\\"TemperatureAvg\\\": \\\"temperature_avg\\\",\\n \\\"TemperatureMin\\\": \\\"temperature_min\\\",\\n \\\"TemperatureMax\\\": \\\"temperature_max\\\"\\n }\\n forecasts = json.load(json_file)\\n for forecast in forecasts:\\n f = {row_names[k]: forecast[k] for k in row_names.keys()}\\n f['location_id'] = location_id\\n f['time'] = round(forecast['LastUpdatedEpoch'] / 1000)\\n cursor.execute(f'''INSERT INTO weather({', '.join(f.keys())})\\n VALUES ({', '.join([str(v) for v in f.values()])})''')\",\n \"def main(daymet_dir,pickles,start_date='1980-10-01',end_date='2020-09-30',huc_col = 'huc8', **kwargs):\\r\\n\\tprint(f'The huc col being processed is: {huc_col}')\\r\\n\\t################################################################\\r\\n\\t#first do the daymet data \\r\\n\\t#read in all the files in this dir and combine them into one df\\r\\n\\tearly=FormatData(glob.glob(daymet_dir+f'*_12_{huc_col}.csv'),drop_cols=['system:index','.geo','dayl','vp']).read_in_csvs()\\r\\n\\tmid=FormatData(glob.glob(daymet_dir+f'*_2_{huc_col}.csv'),drop_cols=['system:index','.geo','dayl','vp']).read_in_csvs()\\r\\n\\tlate=FormatData(glob.glob(daymet_dir+f'*_4_{huc_col}.csv'),drop_cols=['system:index','.geo','dayl','vp']).read_in_csvs()\\r\\n\\t################################################################\\r\\n\\t#next do the snotel data \\r\\n\\toutput=[]\\r\\n\\r\\n\\t#read in some pickled objects, these look like a list of dfs with each being a station for the full time period \\r\\n\\tfor item in ['PREC','TAVG','WTEQ']:\\r\\n\\t\\t#get the pickled objects for each parameter \\r\\n\\t\\tfiles = glob.glob(pickles+f'*{item}_{start_date}_{end_date}_snotel_data_list') #hardcoded currently\\r\\n\\t\\tdf=FormatData(files,drop_cols=['year','month','day']).read_in_pickles()\\r\\n\\t\\toutput.append(df) #the df here is 365 days x ~30 yrs x 237 stations so these are pretty big dfs\\r\\n\\t\\r\\n\\t#join the three enviro params \\r\\n\\toutput_df = reduce(lambda left,right: pd.merge(left,right,how='inner',on=['date','id']), output)\\r\\n\\t\\r\\n\\t\\r\\n\\t#convert the temp column from F to C \\r\\n\\toutput_df['TAVG'] = (output_df['TAVG']-32)*(5/9) \\r\\n\\t#there are a couple of erroneous temp values, remove those \\r\\n\\toutput_df = output_df.loc[output_df['TAVG'] <= 50]\\r\\n\\r\\n\\t#convert prec and swe cols from inches to cm \\r\\n\\toutput_df['PREC'] = output_df['PREC']*2.54\\r\\n\\toutput_df['WTEQ'] = output_df['WTEQ']*2.54\\r\\n\\t\\r\\n\\t#remove rows that have one of the data types missing- this might need to be amended because \\r\\n\\t#it means that there are different numbers of records in some of the periods. \\r\\n\\toutput_df=output_df.dropna()\\r\\n\\t\\r\\n\\t#cast the snotel id col to int to add the hucs \\r\\n\\toutput_df['id'] = output_df['id'].astype('int')\\r\\n\\r\\n\\t#add the as yet nonexistant hucs data to the outputs \\r\\n\\thucs = kwargs.get('hucs')\\r\\n\\toutput_df[huc_col] = output_df['id'].map(hucs)\\r\\n\\r\\n\\t#there are multiple snotel stations in some of the basins, \\r\\n\\t#combine those so there is just one number per basin like the \\r\\n\\t#daymet and RS data. \\r\\n\\r\\n\\toutput_df=output_df.groupby([huc_col,'date'])[['PREC','WTEQ','TAVG']].mean().reset_index()\\r\\n\\r\\n\\tperiod_list = []\\r\\n\\tfor p1,p2 in zip(['early','mid','late'],[early,mid,late]): \\r\\n\\t\\t\\t#get snotel first\\r\\n\\t\\t#make a temporal chunk of data \\r\\n\\t\\tsnotel_chunk=FormatData(None,time_period=p1).split_yearly_data(output_df)\\r\\n\\r\\n\\t\\t##########working below here\\r\\n\\t\\t############################\\r\\n\\t\\t#calculate the snow droughts for that chunk \\r\\n\\t\\tif (p1 == 'mid') | (p1 == 'late'): \\r\\n\\t\\t\\tsnotel_drought=CalcSnowDroughts(snotel_chunk,swe_c='WTEQ',precip='PREC',temp='TAVG',start_year=1991,sort_col=huc_col).prepare_df_cols()\\r\\n\\t\\t\\t#print('snotel')\\r\\n\\t\\t\\t#print(snotel_drought)\\r\\n\\t\\telse: \\r\\n\\t\\t\\tsnotel_drought=CalcSnowDroughts(snotel_chunk,swe_c='WTEQ',precip='PREC',temp='TAVG',sort_col=huc_col).prepare_df_cols()\\r\\n\\r\\n\\t\\t#get cols of interest \\r\\n\\t\\t#snotel_drought=snotel_drought[['huc8','year','dry','warm','warm_dry']]\\r\\n\\t\\t#rename cols so they don't get confused when data are merged \\r\\n\\t\\t#snotel_drought.columns=['huc8','year']+['s_'+column for column in snotel_drought.columns if not (column =='huc8') | (column=='year')]\\r\\n\\t\\t\\r\\n\\t\\t#then do the same for daymet \\r\\n\\t\\tif (p1 == 'mid') | (p1 == 'late'): \\r\\n\\t\\t\\tdaymet_drought=CalcSnowDroughts(p2,start_year=1991,sort_col=huc_col).prepare_df_cols()\\r\\n\\t\\telse: \\r\\n\\t\\t\\tdaymet_drought=CalcSnowDroughts(p2,sort_col=huc_col).prepare_df_cols()\\r\\n\\t\\t#print('daymet',daymet_drought)\\r\\n\\t\\t#daymet_drought=daymet_drought[['huc8','year','dry','warm','warm_dry']]\\r\\n\\t\\t\\r\\n\\t\\t#daymet_drought.columns=['huc8','year']+['d_'+column for column in daymet_drought.columns if not (column =='huc8') | (column=='year')]\\r\\n\\r\\n\\t##########################################\\r\\n\\t\\r\\n\\t\\t#run the kmeans with drought types as intiilization conditions (centroids) for the clusters\\r\\n\\t\\t\\r\\n\\t\\t#these are all of the huc 4 basins in the study area \\r\\n\\t\\thuc4s = ['1708','1801','1710','1711','1709','1701','1702','1705','1703','1601','1707','1706','1712','1704']\\r\\n\\t\\ts_output = []\\r\\n\\t\\td_output = []\\r\\n\\t\\tfor huc4 in huc4s: \\r\\n\\t\\t\\thuc4_s = sd.prep_clusters(snotel_drought,huc4,huc_col=huc_col) #get the subset of the snow drought data for a given huc4\\r\\n\\t\\t\\thuc4_d = sd.prep_clusters(daymet_drought,huc4,huc_col=huc_col)\\r\\n\\t\\t\\t#make the centroids that serve as the intialization for the kmeans clusters- these are like endmembers (ish)\\r\\n\\t\\t\\ts_centroids = DefineClusterCenters(huc4_s,'WTEQ','PREC','TAVG').combine_centroids() #makes a numpy array with four centroids\\r\\n\\t\\t\\td_centroids = DefineClusterCenters(huc4_d,'swe','prcp','tavg').combine_centroids() #makes a numpy array with four centroids\\r\\n\\r\\n\\t\\t\\t#clusters should be like: {0:dry, 1:warm, 2:warm_dry, 3:no_drought} 6/8/2021 DOUBLE CHECK\\r\\n\\t\\t\\t#run kmeans for the snotel data\\r\\n\\t\\t\\ts_clusters = sd.run_kmeans(huc4_s[['WTEQ','PREC','TAVG']].to_numpy(),huc4_s['label'],s_centroids)\\r\\n\\t\\t\\ts_clusters = sd.add_drought_cols_to_kmeans_output(s_clusters, huc_col=huc_col) #add a few cols needed for plotting \\r\\n\\t\\t\\t#run kmeans for the daymet data \\r\\n\\t\\t\\td_clusters = sd.run_kmeans(huc4_d[['swe','prcp','tavg']].to_numpy(),huc4_d['label'],d_centroids)\\r\\n\\t\\t\\td_clusters = sd.add_drought_cols_to_kmeans_output(d_clusters, huc_col=huc_col) #add a few cols needed for plotting \\r\\n\\r\\n\\t\\t\\ts_output.append(s_clusters)\\r\\n\\t\\t\\td_output.append(d_clusters)\\r\\n\\t\\ts_plot = pd.concat(s_output)\\r\\n\\r\\n\\t\\t#select the cols of interest and rename so there's no confusion when dfs are merged \\r\\n\\t\\ts_plot=s_plot[[huc_col,'year','dry','warm','warm_dry']]\\r\\n\\t\\ts_plot.columns=[huc_col,'year']+['s_'+column for column in s_plot.columns if not (column == huc_col) | (column=='year')]\\r\\n\\r\\n\\t\\td_plot = pd.concat(d_output)\\r\\n\\t\\td_plot=d_plot[[huc_col,'year','dry','warm','warm_dry']]\\r\\n\\t\\td_plot.columns=[huc_col,'year']+['d_'+column for column in d_plot.columns if not (column == huc_col) | (column=='year')]\\r\\n\\t\\r\\n\\t\\t#merge the two datasets into one df \\r\\n\\t\\tdfs = s_plot.merge(d_plot,on=[huc_col,'year'],how='inner')\\r\\n\\t\\t\\r\\n\\t\\t#deal with the scenario that there are basins with less than 30 years of data, remove those here\\r\\n\\t\\tdfs = sd.remove_short_dataset_stations(dfs,huc_col)\\r\\n\\t\\tperiod_list.append(dfs)\\r\\n\\r\\n\\tplot_counts(period_list,kwargs.get('stats_dir'),huc_col=huc_col,**kwargs)\",\n \"def get_outdoor_data(temp_dir,site):\\n if site == 'berk':\\n files_od = glob(join(temp_dir,'outdoor','20*.xlsx'))\\n elif site == 'bus':\\n files_od = glob(join(temp_dir,'outdoor','Busara*.csv'))\\n else:\\n raise NameError(site)\\n\\n dfs = []\\n for f in files_od:\\n if site == 'berk':\\n this_df = pd.read_excel(f,sheet_name=0,usecols='B:D',index_col=0,parse_dates=True, header=1)\\n elif site == 'bus':\\n this_df = pd.read_csv(f,usecols=[0,1,2],index_col=0,parse_dates=True,header=2)\\n \\n # drop missing values that prevented conversion to float type\\n if this_df.iloc[:,0].dtype != np.float64:\\n this_df = this_df[this_df.iloc[:,0] != ' ']\\n this_df = this_df.astype(np.float64)\\n\\n # correct for weird timezones in berkeley datalogger\\n this_df = correct_tz(this_df,site)\\n \\n this_df.columns = ['T','RH']\\n this_df.index.name = 'time'\\n\\n # convert to celsius\\n this_df['T'] = (this_df['T'] - 32) * 5/9\\n dfs.append(this_df)\\n \\n df_od = pd.concat(dfs)\\n\\n # drop duplicated measurements\\n df_od = df_od[~df_od.index.duplicated(keep='last')].sort_index()\\n \\n # separate out into daily min,mean,max\\n groups = df_od.groupby(df_od.index.date)\\n dfs_od = {'all':df_od,\\n 'min': groups.min(),\\n 'mean': groups.mean(),\\n 'max': groups.max()}\\n \\n for i in ['min','mean','max']:\\n # remove first and last day to ignore days where we did not get full recording\\n dfs_od[i] = dfs_od[i].iloc[1:-1,:]\\n \\n # name index so that we can merge onto multiIndex'd dataframe\\n dfs_od[i].index.name = 'date'\\n \\n return dfs_od\",\n \"def extract_archive_data():\\n extract_from_db_info = [\\n {\\n 'source_db': 'ecommerce_db',\\n 'dest_db': 'ecommerce_db',\\n 'source_table': 'raw_customer',\\n 'dest_table': 'raw_customer_archive',\\n 'sql_select': None,\\n 'sql_insert': '../sql/insert/insert_raw_customer_archive.sql'\\n },\\n {\\n 'source_db': 'ecommerce_db',\\n 'dest_db': 'ecommerce_db',\\n 'source_table': 'raw_product',\\n 'dest_table': 'raw_product_archive',\\n 'sql_select': None,\\n 'sql_insert': '../sql/insert/insert_raw_product_archive.sql'\\n },\\n {\\n 'source_db': 'ecommerce_db',\\n 'dest_db': 'ecommerce_db',\\n 'source_table': 'raw_sales',\\n 'dest_table': 'raw_sales_archive',\\n 'sql_select': None,\\n 'sql_insert': '../sql/insert/insert_raw_sales_archive.sql'\\n }\\n ]\\n\\n for extract_info in extract_from_db_info:\\n try:\\n extract_data_from_db(extract_info['source_db'], extract_info['dest_db'], extract_info['dest_table'], extract_info['sql_select'], extract_info['sql_insert'])\\n except Exception as e:\\n print(\\\"An error occurred: \\\", e)\\n else:\\n print(\\\"Successfully inserted records in {} table of {} database from {} table of {} database.\\\".format(extract_info['dest_table'], extract_info['dest_db'], extract_info['source_table'], extract_info['source_db']))\",\n \"def import_stations(time_res='hourly',time_format='%Y%m%d%H',\\r\\n campaign_time=[datetime(2018,12,9), datetime(2018,12,12)],\\r\\n data_category='air_temperature', station_ids=['00044','00091'],\\r\\n dbase_dir='dbase', table_dir='tables',Output=True,\\r\\n memory_save=True):\\r\\n timeranges=['recent','historical']\\r\\n #%%load the datasets available at each timestep\\r\\n dwd_datasets_meta=dwd_datasets_meta=json.load(open(table_dir+\\\"\\\\\\\\dwd_station_meta.txt\\\"))\\r\\n #try to get a variable from the category, otherwise use interpolation of higher frequency data\\r\\n resample_frequency=None\\r\\n time_res_dbase=time_res\\r\\n try:\\r\\n dwd_datasets_meta[time_res][data_category]\\r\\n except Exception:\\r\\n if time_res=='daily':\\r\\n try:\\r\\n dwd_datasets_meta['hourly'][data_category]\\r\\n print(data_category,' is not provided at the required resolution, daily_mean of hourly data used instead')\\r\\n resample_frequency='D'\\r\\n time_res_dbase='hourly'\\r\\n except Exception:\\r\\n try: \\r\\n dwd_datasets_meta['10_minutes'][data_category]\\r\\n print(data_category,' is not provided at the required resolution, daily_mean of 10_minutes data used instead')\\r\\n resample_frequency='D'\\r\\n time_res_dbase='10_minutes'\\r\\n except Exception:\\r\\n print(data_category, 'not available')\\r\\n sys.exit(1)\\r\\n if time_res=='hourly':\\r\\n try: \\r\\n dwd_datasets_meta['10_minutes'][data_category]\\r\\n print(data_category,' is not provided at the required resolution, hourly_mean of 10_minutes data used instead')\\r\\n resample_frequency='H'\\r\\n time_res_dbase='10_minutes'\\r\\n except Exception:\\r\\n print(data_category, 'not available')\\r\\n sys.exit(1)\\r\\n \\r\\n \\r\\n #%% download from dwd if necessary\\r\\n #connect to server\\r\\n server='opendata.dwd.de'\\r\\n ftp=connect_ftp(server = server,connected = False)\\r\\n #get the mean time of the campaign\\r\\n date_mean=campaign_time[0]+(campaign_time[1]-campaign_time[0])/2 \\r\\n # load the inititial ds\\r\\n dbase_path=dbase_dir+'\\\\\\\\db_stations_'+time_res+'_'+data_category+'.nc'\\r\\n if os.path.exists(dbase_path):\\r\\n with xr.open_dataset(dbase_path) as dwd_dbase:\\r\\n dwd_dbase.load()\\r\\n print('Existing database imported')\\r\\n #get the non_nans stations\\r\\n current_stations=np.array(dwd_dbase[list(dwd_dbase.keys())[0]].sel(time=date_mean,method='nearest').dropna('STATIONS_ID').coords['STATIONS_ID'])\\r\\n else:\\r\\n print(dbase_path, 'does not exist, we create a new netcdf_file')\\r\\n dwd_dbase=xr.Dataset()\\r\\n current_stations=np.array((-9999)).reshape(1)\\r\\n #change directory on server\\r\\n for timerange in timeranges:\\r\\n archive_url='/climate_environment/CDC/observations_germany/climate/'+time_res_dbase+'/'+data_category+'/'+timerange \\r\\n ftp.cwd(archive_url)\\r\\n #get the archive\\r\\n for station_id in station_ids:\\r\\n #we check whether the station is in the database with this parameter already\\r\\n if int(station_id) in current_stations:\\r\\n print('Station', station_id, 'with category', data_category,'in ',timerange,'dbase already')\\r\\n continue\\r\\n try:\\r\\n archive_name=[s for s in ftp.nlst() if station_id in s][0]\\r\\n except:\\r\\n print('No ',timerange,'data for station',station_id)\\r\\n continue\\r\\n print('Retrieving {}...'.format(archive_name))\\r\\n retrieved = False\\r\\n archive = io.BytesIO()\\r\\n # try to retrieve file\\r\\n while not retrieved:\\r\\n try:\\r\\n ftp.retrbinary(\\\"RETR \\\" + archive_name, archive.write)\\r\\n retrieved = True\\r\\n except:\\r\\n ftp=connect_ftp(server = server,connected = False)\\r\\n ftp.cwd(archive_url)\\r\\n archive.seek(0)\\r\\n with ZipFile(archive) as myzip:\\r\\n for f in myzip.infolist():\\r\\n # This is the data file\\r\\n #print('zip content:', f.filename)\\r\\n if f.filename.startswith('produkt_'):\\r\\n product = io.StringIO(str(myzip.read(f.filename),'utf-8'))\\r\\n #get dataframe from product \\r\\n dwd_product=pd.read_csv(product,sep=';',skipinitialspace=True)\\r\\n #get datetime\\r\\n dwd_product['time']=pd.to_datetime(dwd_product['MESS_DATUM'],format=time_format) \\r\\n dwd_product=dwd_product.rename(columns=dwd_datasets_meta[time_res_dbase][data_category])\\r\\n dwd_product=dwd_product.reset_index()\\r\\n dwd_product=dwd_product.set_index(['time','STATIONS_ID'])\\r\\n dwd_product=dwd_product.drop(columns=['MESS_DATUM','quality_level_of_next_columns','end_of_record','index'])\\r\\n #append to database\\r\\n dwd_xr=dwd_product.to_xarray()\\r\\n #replace all values equal to -999 to nan\\r\\n for data_var in dwd_xr.data_vars:\\r\\n dwd_xr[data_var]=dwd_xr[data_var].where(dwd_xr[data_var]>-999)\\r\\n if station_id=='05009':\\r\\n print('ok') \\r\\n #only add relevant dates if available memoryis rather small\\r\\n \\r\\n if memory_save and timerange=='historical':\\r\\n dwd_xr=dwd_xr.sel(time=slice(campaign_time[0]-timedelta(days=1),campaign_time[1]+timedelta(days=1)))\\r\\n #dwd_xr=dwd_xr.squeeze()\\r\\n \\r\\n try:\\r\\n dwd_dbase=xr.merge([dwd_dbase,dwd_xr])\\r\\n except Exception as e:\\r\\n print(e)\\r\\n print('try merging with compat=override')\\r\\n dwd_dbase=xr.merge([dwd_dbase,dwd_xr],compat='override')\\r\\n print(archive_name,' added to database')\\r\\n #upscale to required temporal resolution\\r\\n if resample_frequency is not None:\\r\\n dwd_dbase=dwd_dbase.resample(time=resample_frequency).mean(skipna=True)\\r\\n print('DWD data upscaled to',time_res,'averages')\\r\\n if Output==True:\\r\\n dwd_dbase.to_netcdf(dbase_path)\\r\\n print('Updated database' ,dbase_path)\\r\\n return dwd_dbase\",\n \"def compute_aggregate_weather_data():\\n\\n # get a list of all the csv files names in the 'weather_data' directory\\n files = get_all_csv_files_in_directory('weather_data')\\n\\n # Todo: if the number of csv files doesn't match the expected value, unzip remaining using the 'os' module\\n\\n if len(files) == 0:\\n\\n # Unzip all files in current directory and subdirectories\\n print \\\"unzipping weather files...\\\"\\n os.system(\\\"unzip 'weather_data/*.zip' -d weather_data\\\")\\n\\n\\n # Try again to get files\\n files = get_all_csv_files_in_directory('weather_data')\\n\\n # Throw exception if still missing csv files\\n if len(files) == 0:\\n raise ValueError(\\\"Missing weather data in csv format in the 'weather_data' directory\\\")\\n\\n # convert the list of csv file names to a list of corresponding DataFrames\\n dallas_files = filter(lambda file_name : \\\"KDAL\\\" in file_name, files)\\n houston_files = filter(lambda file_name : \\\"KHOU\\\" in file_name, files)\\n san_antonio_files = filter(lambda file_name : \\\"KSAT\\\" in file_name, files)\\n\\n print \\\"Retrieved weather data files...\\\"\\n print \\\"\\\\t# of Dallas weather files found: \\\", len(dallas_files)\\n print \\\"\\\\t# of Houston weather files found: \\\", len(houston_files)\\n print \\\"\\\\t# of San Antonio weather files found: \\\", len(san_antonio_files)\\n\\n dallas_dfs = map(lambda file_name: read_weather_data_from_csv(\\\"./weather_data/\\\" + file_name), dallas_files)\\n houston_dfs = map(lambda file_name: read_weather_data_from_csv(\\\"./weather_data/\\\" + file_name), houston_files)\\n san_antonio_dfs = map(lambda file_name: read_weather_data_from_csv(\\\"./weather_data/\\\" + file_name), san_antonio_files)\\n\\n dallas_df = pd.concat(dallas_dfs)\\n houston_df = pd.concat(houston_dfs)\\n san_antonio_df = pd.concat(san_antonio_dfs)\\n\\n print \\\"Aggregating all of the weather data...\\\"\\n # fold the list of data frames into a single data frame\\n aggregate_df = reduce(lambda df1, df2: pd.merge(df1, df2, on=\\\"Date\\\", how=\\\"outer\\\"), [dallas_df, houston_df, san_antonio_df]).sort_values(\\\"Date\\\")\\n\\n return aggregate_df\",\n \"def collect_stations(self):\\n # First, iterate provinces and build url's\\n site = urllib.request.urlopen(self.base_url)\\n\\n # Check that the site is still valid or operating by collecting a list of provinces\\n print(\\\"Collecting provinces\\\")\\n provinces = [s[9:11] for s in re.findall('<a href=\\\"../\\\">../</a>', site.read())]\\n\\n # Iterate provinces and collect list of available times\\n print(\\\"Collecting time periods and station ID's\\\")\\n self.stations = defaultdict(dict)\\n for prov in provinces:\\n site = urllib.request.urlopen(self.build_url(prov))\\n expression = '<a href=\\\"[hd][a-zA-Z]*/\\\">[hd][a-zA-Z]*/</a>'\\n times = [s.split('>')[1].split('<')[0].replace('/', '') for s in re.findall(expression, site.read())]\\n\\n # Iterate times and collect the station ID's\\n for time in times:\\n site = urllib.request.urlopen(self.build_url(prov, time))\\n expression = '<a href=\\\"{0}_[a-zA-Z0-9]*_{1}_hydrometric.csv\\\">{0}_[a-zA-Z0-9]*_{1}_hydrometric.csv</a>'\\n expression = expression.format(prov.upper(), time.lower())\\n stations = [s.split('_')[1] for s in re.findall(expression, site.read())]\\n self.stations[prov][time] = stations\",\n \"def gatherStationData():\\n flist = list_files()\\n station_dics = {}\\n print(\\\"Reading in csv data...\\\")\\n for f_in in flist:\\n start,end = find_timespan(f_in)\\n station = station_name(f=f_in)\\n print(\\\"File: {0} Station: {1} {2}--{3}\\\".format(f_in, \\n station, start, end))\\n station_dics[station] = read_precip(fname=f_in, \\n label=station, start_year=start, end_year=end)\\n data_list = []\\n for s in station_dics:\\n data_list.append(station_dics[s]) \\n return pd.concat(data_list,axis=1)\",\n \"def run():\\n #Initialise variables\\n data = build_station_list()\\n update_water_levels(data)\\n ls = []\\n ID = []\\n \\n #Number of days in past taken data from\\n dt = 7\\n #How many graphs per window\\n limit = 4\\n #How many stations\\n number = 6\\n \\n #Create list of measuring_id's sorted by water level\\n for station in data:\\n if station.typical_range_consistent() == True and station.relative_water_level() != None:\\n ls.append((station, station.relative_water_level()))\\n\\n ls = sorted_by_key(ls, 1)\\n \\n for station in ls:\\n ID.append(station[0])\\n \\n s = count_inconsistent_sets(ID[:number], dt)\\n \\n ID = ID[:number+s]\\n\\n plot_water_levels(ID, dt, limit, s)\",\n \"def import_temp_data(counties):\\n for index, row in counties.iterrows():\\n station = row[2]\\n url = f'https://wrcc.dri.edu/WRCCWrappers.py?sodxtrmts+0{station}+por+por+maxt+none+mave+5+01+F'\\n result = requests.get(url)\\n soup = BeautifulSoup(result.text, 'html.parser')\\n table = soup.find('table')\\n data = pd.read_html(str(table))\\n df = data[0]\\n df.columns = df.iloc[0]\\n df = df.drop([0])\\n df = df.iloc[-65:-8, :]\\n df = df.rename(columns={'YEAR(S)': 'Year'})\\n df['Year'] = pd.to_datetime(df['Year'], format='%Y')\\n df = df.set_index('Year')\\n df = df.dropna(axis=1)\\n df = df.replace(to_replace='-----', value=np.nan)\\n df = df.astype('float64')\\n df = df.fillna(df.mean().round(2))\\n df = df.add_suffix('_t')\\n name = row[0]\\n df['County'] = name\\n df.to_csv(f'{name}_avgmaxtemp.csv')\\n print(f'Avg. max. temp. data from {name} saved')\\n time.sleep(3.14)\\n print('Done')\",\n \"def _get_data(self, gas, loc, voltage, speed, trial):\\n cols = []\\n for g in gas:\\n for l in loc:\\n try:\\n (sub, files) = self._get_sensor_col_files(g, l)\\n except OSError as e:\\n print('{}\\\\n Keeping calm and carrying on.'.format(e))\\n continue\\n for v in voltage:\\n for s in speed:\\n end = \\\"_board_setPoint_%s_fan_setPoint_%s_mfc_setPoint_%sppm_p%s\\\" % (\\n self.SensorVoltages[v],\\n self.FanSpeeds[s],\\n self.GasNames[g],\\n self.AltLocs[l])\\n filtered = [f.split('/')[-1] for f in files if f.endswith(end)]\\n if not filtered:\\n if self._args['verbose']:\\n print('No valid files found for \\\"%s\\\", skipping!' % sub)\\n continue\\n timeStamp = [filt.split('_', 1)[0] for filt in filtered]\\n date = [time.strptime(ts, '%Y%m%d%H%M') for ts in timeStamp]\\n date = [time.strftime('%Y-%m-%d %H:%M', d) for d in date]\\n filtered = [os.path.join(sub, f) for f in filtered]\\n for i, filt in enumerate(filtered):\\n j = i + 1\\n if j in trial:\\n p = os.path.sep.join([self.dataloc_prefix,\\n self.data_location,\\n filt])\\n\\n cols.append(SensorColumn(data_location=p,\\n gas=self.GasNames[g],\\n loc=self.Locs[l],\\n voltage=self.SensorVoltages[v],\\n speed=self.AltFanSpeeds[s],\\n trial=j,\\n _args=self._args))\\n\\n if self._args['verbose']:\\n print('\\\\nSelected %i single trial SensorColumns!' % len(cols))\\n return cols\",\n \"def populate_database(telescope_name, instrument_name):\\n telescope = Telescope.objects.create(\\n name=telescope_name, latitude=25.0, longitude=45.0)\\n instrument = Instrument.objects.create(\\n name=instrument_name, telescope=telescope)\\n for year_int in (2012, 2013):\\n for month_int in range(1, 13):\\n for night_int in (1, monthrange(year_int, month_int)[1]):\\n ut_date = date(year_int, month_int, night_int)\\n night = Night.objects.create(\\n ut_date=ut_date, instrument=instrument, observers='Smith')\\n Exposure.objects.create(\\n night=night, run_number=1, ut_start=time(10, 0, 0),\\n exposed=20.0, ra=60.0, dec=30.0, object_exp=True)\\n Exposure.objects.create(\\n night=night, run_number=2, ut_start=time(11, 0, 0),\\n exposed=30.0, ra=90.0, dec=0.0, object_exp=True)\\n Exposure.objects.create(\\n night=night, run_number=3, ut_start=time(12, 0, 0),\\n exposed=40.0, ra=120.0, dec=-30.0, object_exp=False)\",\n \"def ingest():\\n\\n base_path = '/home/mnichol3/Coding/wx-scripts/wtlma'\\n\\n flash_files = ['flash-out-05232019-2050.txt',\\n 'flash-out-05232019-2100.txt',\\n 'flash-out-05232019-2110.txt',\\n 'flash-out-05232019-2120.txt',\\n 'flash-out-05232019-2130.txt',\\n 'flash-out-05232019-2140.txt',\\n 'flash-out-05232019-2150.txt']\\n\\n df_cols = ['start', 'end', 'duration', 'area', 'ctr_alt', 'ctr_lat', 'ctr_lon',\\n 'tot_energy']\\n\\n flash_df = pd.read_csv(join(base_path, flash_files[0]), sep=',', names=df_cols)\\n\\n for f in flash_files[1:]:\\n curr_path = join(base_path, f)\\n curr_df = pd.read_csv(curr_path, sep=',', names=df_cols)\\n flash_df = pd.concat([flash_df, curr_df], ignore_index=True)\\n\\n return flash_df\",\n \"def _setData(self):\\n\\n if not self.stationId:\\n return\\n \\\"\\\"\\\" \\n # get the ressource url and adjust lat and lon from data portal\\n query = sparqls.stationResource(self.stationId)\\n key, val = RunSparql(query, 'array').run()\\n if val: \\n self.url = val[0][0]\\n self.lat = float(val[0][2])\\n self.lon = float(val[0][3])\\n \\\"\\\"\\\"\\n\\n # it is possible, that a station id has multiple URI\\n # ask for all URI\\n query = sparqls.stationData(self.uri, 'all')\\n data = RunSparql(query, 'pandas').run()\\n\\n if not data.empty:\\n self._data = data\\n else:\\n self._data = 'no data available'\\n\\n # check if data is available and extract the 'unique' data products\\n if isinstance(self._data, pd.DataFrame):\\n p = self._data['specLabel'].unique()\\n self._products = pd.DataFrame(p)\\n\\n # replace samplingheight=None with empty string\\n self._data.samplingheight.replace(to_replace=[None], value=\\\"\\\", inplace=True)\\n else:\\n self._products = 'no data available'\",\n \"def get_data():\\n \\n data = {\\n 'loadAvg1Min': 0, #load average 1 min\\n 'loadAvg5Min': 0, #load average 5 min\\n 'loadAvg15Min': 0, #load average 15 min\\n 'cpuUsage': [], #usage distribution for each cpu\\n 'memUsage': {}, #memory usage \\n 'networkReads': [], #network reads per second for each interface\\n 'networkWrites': [], #network writes per second for each interface\\n 'diskReads': [], #disk reads per second for each disk\\n 'diskWrites': [] #disk writes per second for each disk\\n }\\n \\n #metrics that doesnt need sampling\\n data['loadAvg1Min'], data['loadAvg5Min'], data['loadAvg15Min'] = get_load_avg() #get load avg\\n data['memUsage'].update(get_mem_usage()) #memory usage\\n \\n #metrics that needs sampling\\n #they are written as a generator so that we can sleep before collection again\\n sampling_duration = 1\\n cpu_usage_gen = get_cpu_usage(sampling_duration) #generator for cpu usage\\n net_rw_gen = get_net_rw(sampling_duration) #generator for network read write\\n disk_rw_gen = get_disk_rw(sampling_duration) #generator for disk read write\\n \\n while 1: #now start sampling, whenever we have walid data, we can exit the loop\\n cpu_usage = next(cpu_usage_gen)\\n net_rw = next(net_rw_gen)\\n disk_rw = next(disk_rw_gen)\\n \\n if cpu_usage or net_rw or disk_rw: #we have valid data\\n break\\n \\n time.sleep(sampling_duration)\\n \\n #append cpu usage for each cpu core\\n for cpu, usage in cpu_usage.items():\\n data['cpuUsage'].append({'name': cpu, 'value': usage})\\n \\n #append network read and write for each interface\\n for interface, rw in net_rw.items():\\n data['networkReads'].append({'name': interface, 'value': rw['reads']})\\n data['networkWrites'].append({'name': interface, 'value': rw['writes']}) \\n \\n #append disk read and write for each logical disk\\n for device, rw in disk_rw.items():\\n data['diskReads'].append({'name': device, 'value': rw['reads']})\\n data['diskWrites'].append({'name': device, 'value': rw['writes']})\\n \\n return data\",\n \"def test_seed_station_information(self):\\n\\t\\tget_info.seed_station_information()\\n\\n\\t\\tMacDougal_Prince = db.session.query(Station).filter(Station.id == 128).one()\\n\\t\\tself.assertTrue(MacDougal_Prince, 'Station at MacDogual/Pride did not get sucessfully added.')\\n\\n\\t\\tself.assertEqual(MacDougal_Prince.num_bikes_available, 0, 'Bike counts were not initialized properly')\\n\\t\\tself.assertEqual(MacDougal_Prince.num_docks_available, 0, 'Dock counts were not initialized properly')\",\n \"def get_prepared_data(cls, ext_stations=None):\\n ext_stations = ext_stations or StationDAO.get_all_with_prices()\\n features = (cls.get_station_features(row) for row in ext_stations)\\n classes = (cls.get_category(row) for row in ext_stations)\\n return features, classes\",\n \"def getAllDataFromDirectory(prediction_directory, actual_directory, write_directory, cities_file, utc_offset = False):\\n city_dictionary = getCities(cities_file)\\n actualGetter = getActualWeather(actual_directory, city_dictionary, get_API_keys())\\n #For each day and for each city, get all the data and put it into a spreadsheet.\",\n \"def initialize_data(self , station = '', datasets = {} ): \\n self.datasets = datasets\\n self.datasets_keys = datasets.keys()\\n self.station = station\\n self.out_name = self.out_dir + '/' + self.station + '_CEUAS_premerged_v0.nc'\\n\\n self.observations_table_vars = ['date_time', 'z_coordinate' , 'z_coordinate_type', 'observed_variable' , 'observation_value' , 'report_id' , 'observation_id' , 'latitude' , 'longitude', 'units', 'source_id']\\n\\n \\\"\\\"\\\" Loading the econding of the tables created from the harvester script and to be applied again \\\"\\\"\\\"\\n self.encodings = np.load('groups_encodings.npy' , allow_pickle = True ).item()\\n self.encodings['era5fb'] = np.load('era5fb_encodings_all.npy' , allow_pickle = True ).item() \\n self.dic_type_attributes = np.load('dic_type_attributes.npy',allow_pickle= True).item()\\n \\n self.era5fb_columns = self.dic_type_attributes['era5fb'].keys()\\n\\n self.obstab_nans_filled = False \\n\\n data['cdm_tables'] = {} \\n \\n \\\"\\\"\\\" Loop over all the datasets \\n k: name of the dataset \\n v: list of file paths, eg 'era5_1':[filepath_1, filepath_2 ] \\\"\\\"\\\" \\n for k,v in self.datasets.items() :\\n data[k] = {}\\n for F in v:\\n \\n logging.info(' Dataset ::: *** %s %s ' , k , F ) \\n \\n data[k][F] = {}\\n\\n h5py_file = h5py.File(F, 'r')\\n data[k][F]['h5py_file'] = h5py_file \\n \\n a = h5py_file['recordtimestamp']\\n \\n data[k][F]['recordtimestamp'] = a\\n data[k][F]['recordindex'] = h5py_file['recordindex']\\n data[k][F]['dateindex'] = h5py_file['dateindex']\\n a = h5py_file['recordtimestamp']\\n data[k][F]['max_date'] = max(a)\\n data[k][F]['min_date'] = min(a)\\n \\n data[k][F]['counter'] = 0\\n\\n #######\\n # HEADER TABLE\\n #######\\n head_tab = h5py_file['header_table']\\n logging.info('*** header_table')\\n data[k][F]['header_table'] = {}\\n for var in head_tab.keys():\\n if ('string' in var or 'hdrlen' in var): continue\\n try: \\n data[k][F]['header_table'][var] = (np.array(head_tab[var][:])).astype(self.dic_type_attributes['header_table'][var]['type'] )\\n except:\\n print('failed convertion type header' , k , ' ' , F , ' ' , var )\\n \\n ####### \\n # STATION CONFIGURATION\\n ####### \\n d = xr.open_dataset(F , engine = 'h5netcdf' , group = 'station_configuration' , decode_times = False )\\n data[k][F]['station_configuration'] = d.to_dataframe()\\n logging.debug('Done with %s station_configuration' , str(k) )\\n d.close()\\n\\n ####### \\n # SOURCE CONFIGURATION \\n ####### \\n d = xr.open_dataset(F , engine = 'h5netcdf' , group = 'source_configuration' , decode_times = False )\\n data[k][F]['source_configuration'] = d\\n logging.debug('Done with %s source_configuration' , str(k) )\\n d.close()\\n\\n\\n data['cdm_tables'] = {}\\n \\\"\\\"\\\" Reading the CDM tables that do not depend on specific stations or observations (fixed values), for the first file only \\\"\\\"\\\"\\n for t in self.standard_cdm: # [ 'crs' , 'observed_variable', 'units' , 'z_coordinate_type' , 'station_type', 'station_configuration_codes'] \\n if t not in data['cdm_tables'].keys():\\n #data['cdm_tables'][t] = ''\\n cdm = xr.open_dataset(F , engine = 'h5netcdf' , group = t )\\n data['cdm_tables'][t] = cdm \\n\\n print(blue + 'Memory used after reading data: ', process.memory_info().rss/1000000000 , cend)\\n\\n self.data = data\\n\\n \\\"\\\"\\\" Making all date_times \\\"\\\"\\\"\\n self.make_all_datetime()\",\n \"def Find_nearest_dwd_stations(inpt_data,\\r\\n date_start='20051201',\\r\\n date_end='20201231',\\r\\n dwd_time_format='%Y%m%d%H',\\r\\n data_category='air_temperature',\\r\\n temp_resolution='hourly',\\r\\n no_of_nearest_stations=4,\\r\\n memory_save=True,\\r\\n Output='True'):\\r\\n if isinstance(data_category,list):\\r\\n if len(list(data_category)) > 1:\\r\\n print(\\r\\n 'Currently only one dwd category allowed, please run function multiple times for each category'\\r\\n )\\r\\n return None\\r\\n \\r\\n #convert time to datetime\\r\\n dt_start=datetime.strptime(date_start,'%Y%m%d')\\r\\n dt_end=datetime.strptime(date_end,'%Y%m%d')\\r\\n print('Start quering data from DWD')\\r\\n #define the database folder\\r\\n pypath = os.path.dirname(os.path.abspath(__file__))\\r\\n table_dir = pypath + '\\\\\\\\' + 'tables'\\r\\n dbase_dir = pypath + '\\\\\\\\' + 'dbase' \\r\\n #%% we check all available stations and create a valid list\\r\\n filename_stations=update_stationlist(time_res='hourly',dbase_dir=table_dir)\\r\\n stations_all=pd.read_csv(filename_stations, dtype={'STATIONS_ID': object})\\r\\n # delete all stations which do not cover the category\\r\\n dwd_stations=stations_all[stations_all[data_category]==True].copy()\\r\\n #correct to datetime\\r\\n dwd_stations['date_end']=pd.to_datetime(stations_all.date_end,format='%Y%m%d')\\r\\n dwd_stations['date_start']=pd.to_datetime(stations_all.date_start,format='%Y%m%d')\\r\\n # clean to stations which cover the campaign time #dt_low <= dt <= dt_high:\\r\\n dwd_stations=dwd_stations[(dwd_stations.date_start<=dt_start) & (dwd_stations.date_end>=dt_end)]\\r\\n #make a geodataframe out of it\\r\\n dwd_stations=gpd.GeoDataFrame(dwd_stations,geometry=gpd.points_from_xy(dwd_stations.geo_lon, dwd_stations.geo_lat))\\r\\n \\r\\n #loop through all rows to get the n closest points\\r\\n distances=pd.DataFrame()\\r\\n for _, station in dwd_stations.iterrows():\\r\\n distances[station.STATIONS_ID]=inpt_data.distance(station.geometry)\\r\\n \\r\\n #%% get the n stations with smallest distance and update database\\r\\n id_nearest_stations=distances.apply(lambda s: s.nsmallest(no_of_nearest_stations).index.tolist(), axis=1).values.tolist() #station ids\\r\\n #get them as unique values by sum a list of lists https://bit.ly/353iZQB\\r\\n id_dwd_stations=list(set(sum(id_nearest_stations,[])))\\r\\n \\r\\n #update the database\\r\\n db_dwd_stations=import_stations(time_res=temp_resolution,time_format=dwd_time_format,campaign_time=[dt_start,dt_end],data_category=data_category,station_ids=id_dwd_stations,dbase_dir=dbase_dir,Output=Output,table_dir=table_dir,memory_save=memory_save)\\r\\n \\r\\n #distance of nearest stattions\\r\\n dist_nearest_stations=pd.DataFrame(np.sort(distances.values)[:,:no_of_nearest_stations]).values.tolist() #distances themself\\r\\n #create new columns in the input data\\r\\n station_col_nm=list()\\r\\n for i in range(0,no_of_nearest_stations):\\r\\n station_col_nm.append(data_category+'_station_'+str(i))\\r\\n for i in range(0,no_of_nearest_stations):\\r\\n station_col_nm.append(data_category+'_distance_'+str(i))\\r\\n #create new dataframe\\r\\n distance_data=pd.concat([pd.DataFrame(id_nearest_stations).astype(int),pd.DataFrame(dist_nearest_stations)],axis=1)\\r\\n distance_data.columns=station_col_nm\\r\\n #add to main dataset\\r\\n inpt_data=pd.concat([inpt_data, distance_data],axis=1) \\r\\n \\r\\n return inpt_data,db_dwd_stations\",\n \"def generate_training_testing_dataset(store_id, transactions, meteo_day, max_days=2500,\\n single_barcode=0):\\n\\n # Get the minimum and maximum of date in the transactions\\n min_date = transactions[(transactions['STO_EAN'] == store_id)].min()['TRX_DATETIME'].date()\\n max_date = transactions[(transactions['STO_EAN'] == store_id)].max()['TRX_DATETIME'].date()\\n\\n # Get the number of days between the two date\\n num_days = (max_date - min_date).days\\n\\n # Get the list of unique products barcode in the transactions\\n products_barcode = transactions['BARCODE'].unique()\\n\\n # Only do one single barcode if activated\\n if single_barcode is not None:\\n products_barcode = [products_barcode[single_barcode]]\\n\\n\\n # Array to contain all training data\\n all_data_first_level = []\\n\\n # For each day and for each product\\n for day in xrange(num_days):\\n\\n print(day)\\n\\n # If we have already considered more days than allowed, stop\\n if day > max_days:\\n break\\n\\n\\n # Get the date corresponding to this day\\n date = min_date + pd.DateOffset(day)\\n # Get the weather of the date\\n weather = get_weather_on_date(date, meteo_day, store_id).head(n=1)\\n\\n # If the weather is empty we skip this day\\n if weather.empty:\\n continue\\n\\n # For each product to include\\n for product_barcode in products_barcode:\\n\\n # Get the volume and inventory data\\n volume = get_volume_product_on_date(product_barcode, date, store_id, transactions)\\n\\n # If no volume could be found skip this date,product pair\\n if volume is None:\\n continue\\n\\n # Get the type of the current date\\n day_type = generate_day_type(date)\\n\\n\\n # Generating complex features based on the simpler one\\n\\n # This contains respectively yesterday, the day before yesterday and the same day as current one in\\n # previous week\\n yesterday = date - pd.DateOffset(1)\\n two_days_ago = date - pd.DateOffset(2)\\n one_week_ago = date - pd.DateOffset(7)\\n\\n # Get the day type of yesterday and 2 days ago\\n day_type_yesterday = generate_day_type(yesterday)\\n day_type_2days_ago = generate_day_type(two_days_ago)\\n\\n # Get the volume of yesterday, 2days ago and 1 week ago\\n volume_yesterday = get_volume_product_on_date(product_barcode, yesterday, store_id, transactions)\\n volume_2days_ago = get_volume_product_on_date(product_barcode, two_days_ago, store_id, transactions)\\n volume_one_week_ago = get_volume_product_on_date(product_barcode, one_week_ago, store_id, transactions)\\n\\n\\n # Get the total sales and the total weight of product done yesterday, 2 days ago and 1 week ago\\n volume_price_yesterday = 0\\n volume_weight_yesterday = 0\\n if volume_yesterday is not None:\\n volume_price_yesterday = volume_yesterday[\\\"price\\\"]\\n volume_weight_yesterday = volume_yesterday[\\\"weight\\\"]\\n\\n volume_price_2days_ago = 0\\n volume_weight_2days_ago = 0\\n if volume_2days_ago is not None:\\n volume_price_2days_ago = volume_2days_ago[\\\"price\\\"]\\n volume_weight_2days_ago = volume_2days_ago[\\\"weight\\\"]\\n\\n volume_price_one_week_ago = 0\\n volume_weight_one_week_ago = 0\\n if volume_one_week_ago is not None:\\n volume_price_one_week_ago = volume_one_week_ago[\\\"price\\\"]\\n volume_weight_one_week_ago = volume_one_week_ago[\\\"weight\\\"]\\n\\n\\n\\n # Using historical weather data\\n weather_yesterday = get_weather_on_date(yesterday, meteo_day, store_id).head(n=1)\\n temperature_min_yesterday = 0\\n temperature_max_yesterday = 0\\n if not weather_yesterday.empty:\\n temperature_min_yesterday = weather_yesterday['TEMPERATURE_VALUE_MIN'].values[0]\\n temperature_max_yesterday = weather_yesterday['TEMPERATURE_VALUE_MIN'].values[0]\\n\\n\\n #tmp = [weather['TEMPERATURE_VALUE_MIN'].values[0], weather['TEMPERATURE_VALUE_MAX'].values[0],\\n # weather['PRECIPITATION_VALUE'].values[0], weather['SUNSHINE_DURATION'].values[0],\\n # weather['SNOW_DEPTH'].values[0], day_type, volume[\\\"price\\\"], volume[\\\"weight\\\"]]\\n\\n\\n # Saving Features\\n tmp = [weather['TEMPERATURE_VALUE_MIN'].values[0], weather['TEMPERATURE_VALUE_MAX'].values[0],\\n day_type, volume[\\\"price\\\"], volume_price_yesterday,volume_weight_yesterday,\\n volume_price_2days_ago, volume_weight_2days_ago,\\n volume_price_one_week_ago, volume_weight_one_week_ago, temperature_min_yesterday,\\n temperature_max_yesterday,day_type_yesterday, day_type_2days_ago,\\n volume[\\\"weight\\\"]]\\n\\n all_data_first_level.append(tmp)\\n\\n return all_data_first_level\",\n \"def _populate_output(self):\\n self._store_atomic_queries_table()\\n self._store_composite_queries_table()\",\n \"def run(self):\\n\\t\\tdf_iter = self.file_to_df(50000)\\n\\t\\tdf_airport = self.airport_file_to_df()\\n\\t\\tfor df in df_iter: # type: pd.DataFrame\\n\\t\\t\\tdf.drop_duplicates(inplace=True)\\n\\t\\t\\tdf = self.transform(df, df_airport)\\n\\n\\t\\t\\tdf_result = self.get_only_new_records(\\n\\t\\t\\t\\tdf=df,\\n\\t\\t\\t\\tdf_columns=self.join_columns,\\n\\t\\t\\t\\ttable_columns=self.join_columns\\n\\t\\t\\t)\\n\\n\\t\\t\\tif len(df_result) > 0:\\n\\t\\t\\t\\t# df_result.drop(self.table_columns, axis=1)\\n\\n\\t\\t\\t\\tself.save(\\n\\t\\t\\t\\t\\tdf=df_result,\\n\\t\\t\\t\\t\\ttable_name=\\\"travel_dimension\\\",\\n\\t\\t\\t\\t\\tdf_columns=self.table_columns,\\n\\t\\t\\t\\t\\ttable_colums=self.table_columns\\n\\t\\t\\t\\t)\",\n \"def padova_interpolated_isomake(directories, bands_dict, output_filename,\\n bands_ordered=None):\\n\\n if isinstance(directories, basestring):\\n directories = [directories]\\n\\n if bands_ordered is None:\\n bands_ordered = bands_dict.values()\\n\\n output_obj = open(output_filename, \\\"w\\\")\\n\\n header_string = \\\"#\\\\t[M/H]\\\\tMi\\\\tlogAge\\\\tlogTe\\\\tlogg\\\\tJacobian\\\"\\n for band in bands_ordered:\\n header_string += \\\"\\\\t{}\\\".format(band)\\n header_string += \\\"\\\\tinner_count\\\\touter_count\\\\n\\\"\\n output_obj.write(header_string)\\n\\n iso_metal_dict = {}\\n bands_metal_dicts = {}\\n for band in bands_dict.keys():\\n bands_metal_dicts[band] = {}\\n\\n # instead do this on band-by-band basis? *******************\\n\\n for direc in directories:\\n iso_files_gz = gb.glob(\\\"{}/*.dat.gz\\\".format(direc.rstrip(\\\"/\\\")))\\n iso_files = gb.glob(\\\"{}/*.dat\\\".format(direc.rstrip(\\\"/\\\")))\\n\\n # check for metallicity of each file\\n # and check which bands it has\\n\\n for iso_file1 in iso_files_gz:\\n metal = None\\n iso_data = gz.open(\\\"{0}\\\".format(iso_file1))\\n for line in iso_data:\\n split_line = line.split()\\n if \\\"[M/H]\\\" in split_line:\\n metal = float(split_line[split_line.index(\\\"[M/H]\\\")+2])\\n if \\\"M_ini\\\" in split_line:\\n for band in bands_metal_dicts.keys():\\n if band in split_line:\\n bands_metal_dicts[band][metal] = iso_file1\\n\\n for iso_file1 in iso_files:\\n metal = None\\n iso_data = open(\\\"{0}\\\".format(iso_file1), \\\"r\\\")\\n for line in iso_data:\\n split_line = line.split()\\n if \\\"[M/H]\\\" in split_line:\\n metal = float(split_line[split_line.index(\\\"[M/H]\\\")+2])\\n if \\\"M_ini\\\" in split_line:\\n for band in bands_metal_dicts.keys():\\n if band in split_line:\\n bands_metal_dicts[band][metal] = iso_file1\\n\\n for metal in bands_metal_dicts[bands_metal_dicts.keys()[0]]:\\n filenames = []\\n for band in bands_metal_dicts:\\n if metal in bands_metal_dicts[band]:\\n if bands_metal_dicts[band][metal] not in filenames:\\n filenames.append(bands_metal_dicts[band][metal])\\n else:\\n break\\n else:\\n iso_metal_dict[metal] = filenames\\n\\n print(iso_metal_dict)\\n keys = iso_metal_dict.keys()\\n keys.sort()\\n\\n if len(keys) > 2:\\n # iso_metal_weights=dict(zip(keys, np.gradient(np.array(keys)) ) )\\n # in numpy 1.9.0 gradient has changed to use second order behaviour\\n # at boundaries which gives wrong results in this context\\n iso_metal_weights = dict(zip(keys,\\n replacement_gradient(np.array(keys))))\\n else:\\n iso_metal_weights = dict(zip(keys, np.ones(len(keys))))\\n print(\\\"metals and weights: \\\", iso_metal_weights)\\n\\n# interp in metallicity order\\n\\n for key in keys:\\n iso_interp(iso_metal_dict[key], key, iso_metal_weights[key],\\n output_obj, bands_dict, bands_ordered)\\n\\n output_obj.close()\",\n \"def ddf_parser():\\n num_available, total = 0, 0\\n indicator_twn_tuples = list() # format of a single tuple: (indicator_name, #twn rows, earliest available year)\\n concept_metadata = dict() # {top_tag: second_layer_tag:\\n\\n # parse all ddf files provided by GapMinder and find how many of them with Taiwan statistics\\n for f_path in glob.glob(os.path.join('statistics', '*datapoints*.csv')):\\n total += 1\\n df = pd.read_csv(f_path)\\n if 'twn' in df.geo.unique():\\n num_available += 1\\n indicator = f_path.replace('statistics/ddf--datapoints--', '').replace('--by--geo--time.csv', '')\\n # print('[Indicator]', indicator)\\n print(f\\\"\\\\t{len(df[df.geo == 'twn'])} indicators including Taiwan statistics.\\\")\\n\\n # stat_name = df.columns[-1]\\n # df_p = df.pivot(index='geo', columns='time')[stat_name]\\n # df_p.insert(loc=0, column='indicator', value=stat_name)\\n # df_p.to_csv(f'statistics_transformed/{stat_name}.csv', sep=';')\\n\\n indicators.append(indicator)\\n\\n\\n # print(\\\"{:.1f}% datapoints have Taiwan statistics\\\".format(num_available / float(total) * 100))\\n\\n\\n\\n df_c = pd.read_csv(CONCEPT_CSV_PATH)\\n df_t = pd.read_csv(TAG_CSV_PATH)\\n df = pd.merge(df_c, df_t, how='left', left_on='tags', right_on='tag')\\n for idr, num_rows, earliest_year in indicator_twn_tuples:\\n ancestors = list()\\n\\n row_values = df[df['concept'] == idr].values[0]\\n name_catalog, parent, ancestor = (row_values[i] for i in [9, 17, 18])\\n if type(parent) is str:\\n ancestors.append(parent)\\n\\n # get ancestors recursively\\n while type(ancestor) is str:\\n tag_row_values = df_t[df_t['tag'] == ancestor].values[0]\\n ancestors.append(tag_row_values[1])\\n ancestor = tag_row_values[2]\\n\\n # build concept structure\\n ancestors.insert(0, name_catalog)\\n print('/'.join(ancestors[::-1]))\",\n \"def create_station_dics(data_directories):\\n \\n files_all = {} \\n for k,v in data_directories.items() :\\n files = os.listdir(v)\\n \\n for f in files:\\n station = f.split('_')[0] \\n if station not in files_all.keys():\\n files_all[station] = {}\\n \\n if k == 'ncar': # separating ncar temperature and wind files \\n if 'trhc' in f:\\n k = 'ncar_t'\\n elif 'windc' in f:\\n k = 'ncar_w'\\n files_all[station][k] = ''\\n files_all[station][k] = v + '/' + f # compelte path to the netCDF file \\n\\n #print('check') \\n \\n \\n return files_all\",\n \"def generate_weather_data(self):\\n months = pd.to_datetime(self.output['Local Time']).dt.month\\n self.output['Month'] = months # set month values for later joins\\n\\n # merge output data frame with historical data to get ranges\\n keys = ['Location', 'Month']\\n m = pd.merge(self.output, self.histdata, how='left',\\n left_on=keys, right_on=keys)\\n\\n # uniformly select random pressure, temperature\\n # and humidity values between the historical max and min ranges\\n r = np.random.rand(m.shape[0])\\n m['Temperature'] = ((m['Tmean_high'] - m['Tmean_low']\\n ) * r + m['Tmean_low']).round(1)\\n m['Pressure'] = ((m['Pmax'] - m['Pmin']) * r + m['Pmin']).round(1)\\n m['Humidity'] = ((m['Hmax'] - m['Hmin']) * r + m['Hmin']).astype(int)\\n\\n # drop redundant columns and assign to output\\n dcols = ['Month', 'Timezone', 'Pmax', 'Pmin',\\n 'Hmax', 'Hmin', 'Tmean_high', 'Tmean_low']\\n m.drop(columns=dcols, inplace=True)\\n self.output = m\",\n \"def write_stations_to_metro_stations_table(config, tables_cache):\\r\\n database = deepcopy(config[\\\"database\\\"])\\r\\n metro_stations_table = deepcopy(config[\\\"tables\\\"][\\\"metro_stations_table\\\"])\\r\\n in_tests.test_write_to_database_from_dict(\\r\\n database, metro_stations_table, tables_cache)\\r\\n\\r\\n station_id = tables_cache[\\\"address_metro_stations_station_id\\\"]\\r\\n station_name = tables_cache[\\\"address_metro_stations_station_name\\\"]\\r\\n line_name = tables_cache[\\\"address_metro_stations_line_name\\\"]\\r\\n station_lat = tables_cache[\\\"address_metro_stations_lat\\\"]\\r\\n station_lng = tables_cache[\\\"address_metro_stations_lng\\\"]\\r\\n\\r\\n if station_id:\\r\\n write_to_database(database, metro_stations_table, {\\r\\n \\\"station_id\\\": station_id,\\r\\n \\\"station_name\\\": station_name,\\r\\n \\\"line_name\\\": line_name,\\r\\n \\\"station_lat\\\": station_lat,\\r\\n \\\"station_lng\\\": station_lng\\r\\n })\\r\\n return ()\",\n \"def load_data(city, month, week_day):\\n# name for day variable changed from \\\"day_name\\\" into week_day to take into account new pandas method \\\".day_name()\\\"\\n# read in file form selected city\\n df = pd.read_csv(CITY_DATA[city])\\n# create additional columns for months, days, start/ end times, hours and station combinations\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n df['End Time'] = pd.to_datetime(df['End Time'])\\n df['month_start'] = df['Start Time'].dt.month\\n df['month_end'] = df['End Time'].dt.month\\n df['day_start'] = df['Start Time'].dt.day_name()\\n df['day_end'] = df['End Time'].dt.day_name()\\n df['hour'] = df['Start Time'].dt.hour\\n df['station_comb'] = df['Start Station'] + ' &AND& ' + df['End Station']\\n# filter data file by month: capture start and end months\\n if month != 7:\\n df1 = df[df['month_start'] == month]\\n df2 = df1.append(df[df['month_end'] == month])\\n df = df2.drop_duplicates()\\n# filter data file by day: capture start and end days\\n if week_day != 'All':\\n df3 = df[df['day_start'] == week_day]\\n df4 = df3.append(df[df['day_end'] == week_day])\\n df = df4.drop_duplicates()\\n# reset index to facilitate looping in station_stats function\\n df = df.reset_index()\\n# check if user wants to check first data lines\\n req_check_df = input('\\\\nIf you want to check the selected data please enter y.')\\n if req_check_df[0:1].lower() == 'y':\\n print('check df = \\\\n', df.head())\\n wait = input('Press Enter to continue. ')\\n\\n return df\",\n \"def load_vals_berkeley(s):\\n \\n download_dates = s.berk.download_dates\\n download_dates_oth = s.berk.download_dates_oth\\n sensor_swap_date = s.berk.sensor_swap_date\\n \\n dfs = {'indoor':{}}\\n \\n # outdoor temp\\n dfs['outdoor'] = get_outdoor_data(s.berk.temps_dir,'berk')\\n \\n # base directory for indoor temp measurements\\n indoor_temp_dir = join(s.berk.temps_dir,'indoor')\\n\\n print(\\\"Downloading temps: {}...\\\".format(download_dates[0]))\\n \\n # grab room temp data from both control and treatment rooms\\n for gi,g in enumerate(['control','treatment']):\\n this_df = dfs['indoor'][g] = {}\\n \\n ## download data from early in experiment when we were using different sensors\\n this_df['partition'] = pd.read_excel(join(indoor_temp_dir,download_dates[0].strftime('%Y%m%d'),'{}_p.xls'.format(g)),\\n sheet_name='Records',parse_dates=True,index_col=0).loc[:pd.to_datetime(sensor_swap_date),:]\\n \\n this_df['RA'] = pd.read_excel(join(indoor_temp_dir,download_dates[0].strftime('%Y%m%d'),'{}_RA.xls'.format(g)),\\n sheet_name='Data Table',parse_dates=True,index_col=1,header=21).iloc[:,1].loc[:pd.to_datetime(sensor_swap_date)]\\n this_df['RA'].name = 'T'\\n this_df['RA'] = pd.DataFrame(this_df['RA'])\\n this_df['RA']['RH'] = np.nan\\n \\n for loc in ['partition','RA']:\\n this_df[loc].columns = ['T','RH']\\n this_df[loc].index.name='time'\\n \\n ## now download data from sensors we switched to\\n for d in download_dates[1:]:\\n csv_dir = join(indoor_temp_dir,d.strftime('%Y%m%d'),'csvs')\\n print(\\\"Downloading temps: {}...\\\".format(d))\\n for loc in [('partition','p'),('RA','RA')]:\\n fpath = join(csv_dir,'{}_{}.csv'.format(g,loc[1]))\\n this_df = add_file_to_dfs(fpath, this_df, [1,2,3], ['T','RH'], loc[0], sensor_swap = sensor_swap_date)\\n \\n ## add individual temp/RH \\n for s_ix in range(1,7):\\n if isfile(join(csv_dir,'{}_{}.csv'.format(g,s_ix))):\\n fpath = join(csv_dir,'{}_{}.csv'.format(g,s_ix))\\n this_df = add_file_to_dfs(fpath, this_df, [1,2,3], ['T','RH'], str(s_ix))\\n \\n ## add operative temp\\n if isfile(join(csv_dir,'{}_ot.csv'.format(g))):\\n fpath = join(csv_dir, '{}_ot.csv'.format(g))\\n this_df = add_file_to_dfs(fpath, this_df, [1,4], ['Top'], 'Top')\\n \\n ## add CO2\\n for d in download_dates_oth:\\n print(\\\"Downloading co2: {}...\\\".format(d))\\n csv_dir = join(s.berk.other_dir,d.strftime('%Y%m%d'))\\n \\n # pass when the file doesn't exist (aka when\\n # the CO2 sensor's batteries died\\n fpath = join(csv_dir,'{}_co2.csv'.format(g))\\n if not isfile(fpath):\\n continue\\n \\n # otherwise, parse\\n this_df = add_file_to_dfs(fpath, this_df, [1,4], ['co2'], 'co2')\\n \\n this_df = drop_duplicates_and_flags(this_df)\\n \\n return dfs\",\n \"def fill_dataset(self):\\n rm, rstd = self.get_rolling_stats()\\n\\n self.add_rolling_mean(rm)\\n self.add_bollinger_bands(rstd)\\n self.add_spy_info()\\n self.add_beta_and_sharpe()\\n self.add_stlouis_data()\",\n \"def load_vals_bus(s):\\n \\n download_dates = s.bus.download_dates\\n \\n dfs = {'indoor':{}}\\n \\n dfs['outdoor'] = get_outdoor_data(s.bus.temps_dir,'bus')\\n\\n # grab room temp data from both control and treatment rooms\\n for gi,gx in enumerate([('Cool','control'),('Warm','treatment')]):\\n ga = gx[0]\\n g = gx[1]\\n tx_lab = ga[0].upper()\\n \\n \\n print('Downloading {} room data...'.format(g))\\n\\n this_df = dfs['indoor'][g] = {}\\n \\n ## now download data from sensors we switched to\\n for dx,d in enumerate(download_dates):\\n \\n d_str = d.strftime('%Y%m%d')\\n dirname = join(s.bus.temps_dir,'indoor','{}_{}'.format(d_str,ga))\\n print('Downloading {}...'.format(d_str))\\n \\n for loc in [('far','F'),('near','N')]:\\n print('Downloading {} sensor...'.format(loc[0]))\\n \\n # need to use glob because some files have two \\\".\\\"s and some have one\\n fname = '{}_Temp_{}{}.*csv'.format(d_str,tx_lab,loc[1])\\n fpath = join(dirname, fname)\\n files = glob(fpath)\\n fpath = files[0]\\n \\n # check for missing files\\n if len(files) != 1:\\n with warnings.catch_warnings():\\n warnings.simplefilter('always')\\n warnings.warn('Missing file: {}'.format(fname))\\n continue\\n this_df = add_file_to_dfs(fpath, this_df, [1,2,3], ['T','RH'], loc[0])\\n \\n ## add individual data\\n for s_ix in range(1,7):\\n fname = '{}_Temp_{}{}.csv'.format(d_str,tx_lab,s_ix)\\n fpath = join(dirname, fname)\\n this_df = add_file_to_dfs(fpath, this_df, [1,2,3], ['T','RH'], str(s_ix))\\n \\n ## add T_operative\\n fname = '{}_PingPong_{}.csv'.format(d_str,ga)\\n fpath = join(dirname, fname)\\n this_df = add_file_to_dfs(fpath, this_df, [1,4], ['Top'], 'Top')\\n \\n ## add CO2\\n fname = '{}_{}_co2.csv'.format(d_str,g)\\n fpath = join(dirname, fname)\\n this_df = add_file_to_dfs(fpath, this_df, [1,4], ['co2'], 'co2')\\n\\n this_df = drop_duplicates_and_flags(this_df)\\n \\n return dfs\",\n \"def get_all_station_feature(city):\\n poi_frequency = np.load(exp_data_path + os.sep + 'poi_frequency' + os.sep + 'poi_frequency_{}.npy'.format(city),\\n allow_pickle=True) # .tolist()\\n poi_num = np.load(exp_data_path + os.sep + 'poi' + os.sep + 'poi_{}.npy'.format(city), allow_pickle=True)\\n poi_entropy = np.load(exp_data_path + os.sep + 'poi_entropy' + os.sep + 'poi_entropy_{}.npy'.format(city),\\n allow_pickle=True)\\n road = np.load(exp_data_path + os.sep + 'roadnet' + os.sep + 'roadnet_{}.npy'.format(city), allow_pickle=True)\\n transportation = np.load(exp_data_path + os.sep + 'transportation' + os.sep + 'transportation_{}.npy'.format(city),\\n allow_pickle=True)\\n commerce = np.load(exp_data_path + os.sep + 'commerce' + os.sep + 'commerce_{}.npy'.format(city), allow_pickle=True)\\n\\n file_name = exp_data_path + os.sep + 'station' + os.sep + 'all_demand_{}.npy'.format(city)\\n demand_data = np.load(file_name, allow_pickle=True)\\n num = demand_data[:, 0, -2, np.newaxis] # todo check meaning here, get quick and slow feature\\n\\n raw_data = np.concatenate((num, poi_frequency, poi_num, poi_entropy, road, transportation, commerce), axis=1)\\n csv_data = pd.DataFrame(raw_data, columns=GENERAL_HEADER)\\n\\n file_path = exp_data_path + os.sep + 'static' + os.sep + 'static_feature_{}.csv'.format(city)\\n if os.path.exists(file_path):\\n os.remove(file_path)\\n csv_data.to_csv(file_path)\\n pass\",\n \"def loadDBstation(dbfile,stationID,varname,timeinfo=None,filttype=None,cutoff=3600.0,output_meta=False):\\r\\n from netcdfio import queryNC\\r\\n \\r\\n outvar = ['NetCDF_Filename','NetCDF_GroupID','StationName']\\r\\n tablename = 'observations'\\r\\n #condition = 'Variable_Name = \\\"%s\\\" and StationID = \\\"%s\\\"' % (varname,stationID)\\r\\n condition = 'Variable_Name = \\\"%s\\\" and StationID LIKE \\\"%%%s\\\"' % (varname,stationID)\\r\\n \\r\\n print 'Querying database...'\\r\\n print condition\\r\\n data, query = queryNC(dbfile,outvar,tablename,condition) \\r\\n \\r\\n if len(data)==0:\\r\\n print '!!! Warning - Did not find any stations matching query. Returning -1 !!!'\\r\\n return -1\\r\\n else:\\r\\n ts = timeseries(data[0]['time'],data[0][varname].squeeze())\\r\\n \\r\\n \\r\\n if not timeinfo==None:\\r\\n print 'Interpolating station data between %s and %s\\\\n'%(timeinfo[0],timeinfo[1])\\r\\n tnew,ynew = ts.interp((timeinfo[0],timeinfo[1],timeinfo[2]))\\r\\n ts = timeseries(tnew,ynew)\\r\\n ts.dt = timeinfo[2] # This needs updating\\r\\n \\r\\n if not filttype==None:\\r\\n print '%s-pass filtering output data. Cutoff period = %f [s].'%(filttype,cutoff)\\r\\n yfilt = ts.filt(cutoff,btype=filttype,axis=-1)\\r\\n ts.y = yfilt.copy()\\r\\n \\r\\n if output_meta:\\r\\n if data[0].has_key('elevation'):\\r\\n ele = data[0]['elevation']\\r\\n else:\\r\\n ele = 0.0\\r\\n meta = {'longitude':data[0]['longitude'],'latitude':data[0]['latitude'],'elevation':ele,'StationName':query['StationName'][0]}\\r\\n return ts, meta \\r\\n else:\\r\\n return ts\",\n \"def dataLoader(stationDict, startDate, endDate):\\n\\n # Generate a URL\\n url = ('https://waterservices.usgs.gov/nwis/dv/?format=json' +\\n # Specify the sites to download\\n '&sites=' + stationDict['DatasetExternalID'] +\\n # Specify the start date\\n '&startDT=' + datetime.strftime( startDate, '%Y-%m-%d' ) +\\n #Specify the end data\\n '&endDT=' + datetime.strftime( endDate, '%Y-%m-%d' ) +\\n # Specify that we want streamflow\\n '¶meterCd=00060' +\\n # Specify that we want daily means\\n '&statCd=00003' +\\n # Allow all sites\\n '&siteStatus=all' )\\n \\n # Get the data\\n response = requests.get(url)\\n\\n # Check the status code\\n if response.status_code != 200:\\n return \\n else:\\n response = response.json()\\n \\n # Create a dataframe from the data\\n df = pd.DataFrame(response['value']['timeSeries'][0]['values'][0]['value'])\\n\\n # Set the index to the dateTime index\\n df.set_index(pd.DatetimeIndex(pd.to_datetime(df['dateTime'])), inplace = True)\\n del df['dateTime'] # Delete the redundant column\\n\\n # Replace missing data with NaN's\\n df['value'].replace(to_replace = '-999999', value = np.nan, inplace = True)\\n\\n # Convert to numeric\\n df['value'] = pd.to_numeric(df['value'])\\n \\n # Remove any duplicate data in the dataset\\n df = df[~df.index.duplicated(keep='last')] # Remove duplicates from the dataset\\n df = df[~df.index.isnull()]\\n\\n # Rename the columns\\n df.columns = ['USGS | ' + stationDict['DatasetExternalID'] + ' | Flag', 'USGS | ' + stationDict['DatasetExternalID'] + ' | Streamflow | CFS']\\n del df['USGS | ' + stationDict['DatasetExternalID'] + ' | Flag']\\n\\n # Return the data frame\\n return df\",\n \"def populate_stops(self):\\n stops = self.load_csv('stops.txt')\\n stops = self.process_stops(stops)\\n\\n connection = db.connect()\\n for stop in stops:\\n try:\\n connection.execute(schema.stops.insert(), stop)\\n except DataError:\\n print \\\"Missing data for stop: %s\\\" % (stop)\",\n \"def get_weather_data():\\n keys = ['1364038.csv',\\n '1364041.csv',\\n '1364042.csv',\\n '1364043.csv',\\n '1364044.csv',\\n '1364046.csv',\\n '1364047.csv',\\n '1364048.csv',\\n '1364051.csv',\\n '1364052.csv',\\n '1364053.csv',\\n '1364054.csv',\\n '1364055.csv',\\n '1364058.csv',\\n '1364059.csv',\\n '1364060.csv',\\n '1364061.csv',\\n '1364062.csv',\\n '1364063.csv',\\n '1364064.csv',\\n '1364066.csv']\\n df_weather = import_weather(keys)\\n df_weather_dist = df_weather[[\\n 'LATITUDE', 'LONGITUDE', 'name']].drop_duplicates().reset_index()\\n return df_weather, df_weather_dist\",\n \"def to_WTH_converter(self, weather_data, dest_dir):\\n ds_all = weather_data.get_global_dataset()\\n if self.country is None:\\n print(\\\"Country given is erroneous:\\\")\\n return\\n elif self.country == \\\"globe\\\":\\n lon_num_start = 0\\n lon_num_stop = weather_data.get_num_of_attribute('longitude')\\n lat_num_start = 0\\n lat_num_stop = weather_data.get_num_of_attribute('latitude')\\n else:\\n lon_num_start, lon_num_stop, lat_num_start, lat_num_stop = weather_data.get_country_boundary(self.country)\\n\\n\\n # top bottom, left to right\\n lon_num_start = 397\\n lat_num_start = 184\\n for lon_i in range(lon_num_start, lon_num_stop + 1):\\n # for lon_i in range(lon_num_start, lon_num_stop+1):\\n lon = ds_all.longitude.isel(longitude=lon_i).values.tolist()\\n\\n for lat_i in range(lat_num_start, lat_num_stop+1):\\n # for lat_i in range(lat_num_start, lat_num_stop + 1):\\n lat = ds_all.latitude.isel(latitude=lat_i).values.tolist()\\n\\n # create a dynamic header with updated LON, LAT info and move it into the folder given\\n wth_header_u = ut.format_header(lat_i + 1, lon_i + 1, lat, lon)\\n wth_header = dest_dir + \\\"/\\\" + wth_header_u\\n shutil.move(wth_header_u, wth_header)\\n\\n # open in appending mode\\n fwth = open(wth_header, \\\"a+\\\")\\n\\n # loop through daily weather data\\n for t, date in enumerate(self.years):\\n daily_data_vars = ut.get_daily_data_vars(ds_all, lat_i, lon_i, t)\\n # disregard all NAN values\\n if daily_data_vars is None:\\n fwth.close()\\n os.remove(wth_header)\\n break\\n\\n if t == 0:\\n ut.update_table(wth_header_u, lat, lon)\\n\\n entry = ut.format_data_vars_entry(daily_data_vars, date)\\n\\n # append this entry into the file\\n fwth.write(entry)\\n print(\\\"Added entry:\\\", entry)\\n\\n # close file after writing\\n fwth.close()\\n print(\\\"Output WTH:\\\", wth_header)\",\n \"def load_data(self, identifier):\\n propertyType = self.soup.find('h2', class_='listing-details-h1').text\\n propertyType = propertyType.replace(' for sale', '')\\n beds = re.findall(r'\\\\d{1} bed', propertyType)\\n if beds == []:\\n # get first word\\n beds = '1 bed'\\n else:\\n beds = beds[0]\\n # get general info of property\\n propertyType = propertyType.replace(str(beds) + ' ', '')\\n address = self.soup.find('h2', {'itemprop':'streetAddress'}).text \\n postCode = address.rsplit(None, 1)[-1]\\n address = address.replace(postCode, '')\\n latLong = self.soup.find('meta', {'itemprop':'latitude'}).attrs\\n latitude = latLong['content']\\n latLong = self.soup.find('meta', {'itemprop':'longitude'}).attrs\\n longitude = latLong['content']\\n # original price and changes to price are in sidebar\\n for sidebar in self.soup.find_all('div', {\\\"class\\\":sCLASS_SECTION}):\\n count = 0\\n # price info in sidebar called 'Listing history' \\n if sidebar.find(text=sSB_NAME):\\n try:\\n originalPriceAndDate = sidebar.find(text=sFIRST_LISTED).next_element.next_element\\n originalPrice = originalPriceAndDate[:originalPriceAndDate.find(' on')]\\n originalPrice = self.find_number(originalPrice)\\n originalDate = originalPriceAndDate[originalPriceAndDate.find(' on') + 3:]\\n originalDate = self.get_date(originalDate)\\n # store original listing - in same order as headers!\\n result = [identifier + \\\"_\\\" + str(count), originalDate, np.NaN, originalPrice, np.NaN, \\n beds, propertyType, postCode, address, latitude, longitude, self.url]\\n series = pd.Series(result, name=identifier + \\\"_\\\" + str(count), index=headers)\\n# self.df = self.df.append(pd.Series(result, index=headers), ignore_index=True) \\n# self.df = self.df.append(series) \\n# self.df = pd.concat([self.df, series])\\n self.df = self.df.append(series, ignore_index=False)\\n except (AttributeError, UnboundLocalError):\\n print('Error viewing this property')\\n pass\\n try:\\n # store any changes to original listing\\n for changes in sidebar.find_all('ul', {'class':sCHANGES}):\\n for change in changes.find_all('li'):\\n count += 1\\n # get date and new price\\n date = change.find('span').text\\n date = date.replace('Reduced on:', '')\\n date = date.replace('\\\\n', '')\\n date = self.get_date(date)\\n newPrice = self.find_number(change.next_element)\\n # store result in order of headers\\n result = [identifier + \\\"_\\\" + str(count), originalDate, date, originalPrice, newPrice, \\n beds, propertyType, postCode, address, latitude, longitude, self.url]\\n series = pd.Series(result, name=identifier + \\\"_\\\" + str(count), index=headers) \\n# self.df = self.df.append(pd.Series(result, index=headers), ignore_index=True)\\n self.df = self.df.append(series, ignore_index=False)\\n# self.df = pd.concat([self.df, series])\\n except (AttributeError, UnboundLocalError):\\n print('No changes')\\n pass\",\n \"def collect(self, start_date=None, end_date=None):\\n if start_date is None:\\n start_date = self.default_start\\n if end_date is None:\\n end_date = self.default_end\\n\\n cur = self.conn.cursor()\\n\\n # Maximum return is 1000 entries\\n num_days = 1000 // len(self.stations)\\n # Maximum date-range is 1 year\\n if num_days > 365:\\n num_days = 365\\n\\n for interval in netzero.util.time_intervals(\\n start_date, end_date, days=num_days\\n ):\\n netzero.util.print_status(\\n \\\"Weather\\\",\\n \\\"Collecting: {} to {}\\\".format(\\n interval[0].strftime(\\\"%Y-%m-%d\\\"), interval[1].strftime(\\\"%Y-%m-%d\\\")\\n ),\\n )\\n\\n # TODO -- REMOVE ASSUMPTION THAT LEN(DATA) < LIMIT\\n raw_data = self.query_api(interval[0], interval[1])\\n\\n if raw_data is None:\\n print(\\\"ERROR QUERYING API\\\") # TODO exception here?\\n continue\\n\\n for entry in raw_data.get(\\\"results\\\", []):\\n # Insert the weather data to the table, to be averaged later\\n date = datetime.datetime.strptime(\\n entry[\\\"date\\\"], \\\"%Y-%m-%dT%H:%M:%S\\\"\\n ).date()\\n value = entry[\\\"value\\\"]\\n station = entry[\\\"station\\\"]\\n\\n cur.execute(\\n \\\"INSERT OR IGNORE INTO weather VALUES (?, ?, ?)\\\", (date, value, station)\\n )\\n\\n self.conn.commit()\\n\\n cur.close()\\n\\n netzero.util.print_status(\\\"Weather\\\", \\\"Complete\\\", newline=True)\",\n \"def _add_data(self, model_stations: Iterable[model.Station],\\n validate_prefix: str = \\\"\\\") -> int:\\n valid_station_count = 0\\n jreast_merged_codes: dict[model.StationID, str] = load_csv_as_mapping(\\n DIR_CURATED / \\\"jreast_merged_codes.csv\\\",\\n itemgetter(\\\"sta_id\\\"),\\n itemgetter(\\\"code\\\")\\n )\\n\\n # Add data from model stations\\n for model_sta in model_stations:\\n is_invalid = False\\n should_validate = model_sta.id.startswith(validate_prefix)\\n\\n # Find a matching geo_sta\\n geo_sta = self.by_id.get(model_sta.id)\\n if not geo_sta:\\n if should_validate:\\n self.logger.critical(f\\\"{Color.RED}geo.osm is missing station \\\"\\n f\\\"{Color.MAGENTA}{model_sta.id}{Color.RESET}\\\")\\n self.valid = False\\n continue\\n\\n # Find a name\\n name_id = last_part(geo_sta.id)\\n geo_sta.name = self.names.get(name_id)\\n if geo_sta.name is None and should_validate:\\n self.logger.critical(f\\\"{Color.RED}sta_names.csv is missing name for \\\"\\n f\\\"{Color.MAGENTA}{name_id}{Color.RESET}\\\")\\n is_invalid = True\\n\\n # Copy stop_code\\n geo_sta.code = model_sta.code\\n\\n # Check if station was valid\\n if is_invalid:\\n self.valid = False\\n elif should_validate:\\n valid_station_count += 1\\n\\n # Generate codes and names for mother stations\\n for sta in self.by_id.values():\\n if not sta.children:\\n continue\\n\\n name_id = last_part(sta.id)\\n sta.name = self.names.get(name_id)\\n if not sta.name:\\n self.logger.critical(f\\\"{Color.RED}sta_names.csv is missing name for \\\"\\n f\\\"{Color.MAGENTA}{name_id}{Color.RESET}\\\")\\n is_invalid = True\\n\\n # Get children codes\\n children_codes = []\\n jreast_merged_code = jreast_merged_codes.get(sta.id)\\n if jreast_merged_code:\\n children_codes.append(jreast_merged_code)\\n\\n for child in sta.children:\\n # Ignore JR-East child codes if there's a JR-East merged code\\n if child.id.startswith(\\\"JR-East\\\") and jreast_merged_code:\\n continue\\n elif child.code:\\n children_codes.append(child.code)\\n\\n sta.code = \\\"/\\\".join(children_codes)\\n\\n return valid_station_count\",\n \"def init_parameters(self, buildings, target_meters):\\n if self.df is None:\\n self.read_data_from_csv()\\n\\n if buildings is None:\\n buildings = self.find_all_houses().tolist()\\n\\n if target_meters is None:\\n target_meters = self.meter_name.keys()\\n \\n return buildings, target_meters\",\n \"def archive_mds_data(self, lmtdb):\\n\\n dataset_names = [\\n 'mdservers/cpuload',\\n ]\\n\\n self.init_datasets(dataset_names, lmtdb.mds_names)\\n\\n # Now query the MDS_DATA table to get byte counts over the query time range\\n results, columns = lmtdb.get_mds_data(self.query_start, self.query_end_plusplus)\\n\\n\\n # Index the columns to speed up insertion of data\\n col_map = {}\\n try:\\n for db_col in ['TIMESTAMP', 'MDS_ID', 'PCT_CPU']:\\n col_map[db_col] = columns.index(db_col)\\n except ValueError:\\n raise ValueError(\\\"LMT database schema does not match expectation\\\")\\n\\n # Loop through all the results of the timeseries query\\n for row in results:\\n if isstr(row[col_map['TIMESTAMP']]):\\n # SQLite stores timestamps as a unicode string\\n timestamp = datetime.datetime.strptime(row[col_map['TIMESTAMP']],\\n \\\"%Y-%m-%d %H:%M:%S\\\")\\n else:\\n # MySQL timestamps are automatically converted to datetime.datetime\\n timestamp = row[col_map['TIMESTAMP']]\\n target_name = lmtdb.mds_id_map[row[col_map['MDS_ID']]]\\n for dataset_name in dataset_names:\\n target_dbcol = self.config[dataset_name].get('column')\\n # target_dbcol=PCT_CPU, target_name=snx11025n022\\n if target_dbcol is not None:\\n self[dataset_name].insert_element(\\n timestamp,\\n target_name,\\n row[col_map[target_dbcol]])\\n else:\\n errmsg = \\\"%s in self.config but missing 'column' setting\\\" % dataset_name\\n raise KeyError(errmsg)\",\n \"def update_static_data(source: list):\\r\\n connect = sqlite3.connect(\\\"REDB_v2.sqlite\\\")\\r\\n cursor = connect.cursor()\\r\\n districts = []\\r\\n streets = []\\r\\n series = []\\r\\n amenities = []\\r\\n building_types = []\\r\\n for realestate in source:\\r\\n if realestate.street not in streets:\\r\\n streets.append(realestate.street)\\r\\n if realestate.series not in series:\\r\\n series.append(realestate.series)\\r\\n if realestate.building not in building_types:\\r\\n building_types.append(realestate.building)\\r\\n if realestate.district not in districts:\\r\\n districts.append(realestate.district)\\r\\n if realestate.amenities not in amenities:\\r\\n amenities.append(realestate.amenities)\\r\\n for item in streets:\\r\\n try:\\r\\n cursor.execute(\\\"INSERT INTO Streets (Name) VALUES ('\\\" + item + \\\"')\\\")\\r\\n except sqlite3.IntegrityError:\\r\\n pass\\r\\n for item in districts:\\r\\n try:\\r\\n cursor.execute(\\\"INSERT INTO Districts (Name) VALUES ('\\\" + item + \\\"')\\\")\\r\\n except sqlite3.IntegrityError:\\r\\n pass\\r\\n for item in series:\\r\\n try:\\r\\n cursor.execute(\\\"INSERT INTO Series (Name) VALUES ('\\\" + item + \\\"')\\\")\\r\\n except sqlite3.IntegrityError:\\r\\n pass\\r\\n for item in amenities:\\r\\n try:\\r\\n cursor.execute(\\\"INSERT INTO Amenities (Name) VALUES ('\\\" + item + \\\"')\\\")\\r\\n except sqlite3.IntegrityError:\\r\\n pass\\r\\n for item in building_types:\\r\\n try:\\r\\n cursor.execute(\\\"INSERT INTO Buildings (Name) VALUES ('\\\" + item + \\\"')\\\")\\r\\n except sqlite3.IntegrityError:\\r\\n pass\\r\\n connect.commit()\\r\\n connect.close()\",\n \"def set_up_file(ds, total_sites, footprint_nbins,\\n dimy, dimx, wrf_out, time_unit, site_names):\\n if site_names.dtype.str[1] not in \\\"SU\\\":\\n site_names = site_names.astype(\\\"S\\\")\\n if site_names.dtype.str[2:] != \\\"1\\\":\\n site_names = netCDF4.stringtochar(site_names)\\n name_length = site_names.shape[-1]\\n name_str_dim = \\\"string{len:d}\\\".format(len=name_length)\\n\\n ds.createDimension(\\\"observation_time\\\", 0)\\n ds.createDimension(\\\"site\\\", total_sites)\\n ds.createDimension(\\\"time_before_observation\\\", footprint_nbins)\\n ds.createDimension(\\\"dim_y\\\", dimy)\\n ds.createDimension(\\\"dim_x\\\", dimx)\\n ds.createDimension(\\\"bnds2\\\", 2)\\n ds.createDimension(name_str_dim, name_length)\\n\\n obs_time_var = ds.createVariable(\\\"observation_time\\\", \\\"f4\\\",\\n (\\\"observation_time\\\",))\\n obs_time_bounds_var = ds.createVariable(\\\"observation_time_bnds\\\", \\\"f4\\\",\\n (\\\"observation_time\\\", \\\"bnds2\\\"))\\n time_back_var = ds.createVariable(\\\"time_before_observation\\\", \\\"i2\\\",\\n (\\\"time_before_observation\\\",))\\n time_back_bounds_var = ds.createVariable(\\n \\\"time_before_observation_bnds\\\", \\\"i2\\\",\\n (\\\"time_before_observation\\\", \\\"bnds2\\\"))\\n ds.createVariable(\\\"dim_y\\\", \\\"f4\\\", (\\\"dim_y\\\",))\\n ds.createVariable(\\\"dim_y_bnds\\\", \\\"f4\\\", (\\\"dim_y\\\", \\\"bnds2\\\"))\\n ds.createVariable(\\\"dim_x\\\", \\\"f4\\\", (\\\"dim_x\\\",))\\n ds.createVariable(\\\"dim_x_bnds\\\", \\\"f4\\\", (\\\"dim_x\\\", \\\"bnds2\\\"))\\n\\n grid_mapping = create_grid_mapping(ds, wrf_out)\\n\\n site_name_var = ds.createVariable(\\\"site_names\\\", \\\"S1\\\",\\n (\\\"site\\\", name_str_dim))\\n # these are roughly 1MB for a three-week lag\\n flux_time_var = ds.createVariable(\\n \\\"flux_time\\\", \\\"f4\\\", (\\\"observation_time\\\", \\\"time_before_observation\\\"),\\n fill_value=-255,\\n zlib=True)\\n flux_time_bounds_var = ds.createVariable(\\n \\\"flux_time_bnds\\\", \\\"f4\\\",\\n (\\\"observation_time\\\", \\\"time_before_observation\\\", \\\"bnds2\\\"),\\n fill_value=-255,\\n zlib=True)\\n\\n height_var = ds.createVariable(\\\"height\\\", \\\"f4\\\", ())\\n height_bounds_var = ds.createVariable(\\\"height_bnds\\\", \\\"f4\\\", (\\\"bnds2\\\",))\\n\\n infl_fun_var = ds.createVariable(\\n # Empirically, the most particles seen in a grid cell is\\n # around 5e3. 35*180 particles/obs_time, 9 files/flux_time on 81km grid\\n # We have a factor of six wiggle room with i2\\n # u2 may be necessary for 3 hourly 243 km fluxes\\n # or more particles/obs_time0\\n \\\"H\\\", \\\"i2\\\",\\n (\\\"observation_time\\\", \\\"site\\\", \\\"time_before_observation\\\",\\n \\\"dim_y\\\", \\\"dim_x\\\"),\\n zlib=True,\\n # This will be written and read by flux time, usually,\\n # so that chunksize should be 1\\n # not sure if chunk should be total_sites or 1 for site dimension\\n # total_size gives a chunk as around 5.3 MiB\\n # setting this to 1 may help with file size\\n # if some towers were not run all the time\\n # NUG has default chunk size of 4 MiB\\n # (roughly a disk read on a high-end system)\\n chunksizes=(1, total_sites, 1, dimy, dimx),\\n # This requires that every cell be written to.\\n # This is my intent, and this (as opposed to fill_value=0)\\n # will not have troubles with masking most of the domain.\\n # Make sure this isn't what's inflating the size\\n fill_value=-1,\\n )\\n\\n lpdm_opts = ds.createVariable(\\\"lpdm_configuration\\\", \\\"i1\\\", ())\\n lpdm_opts.setncatts({key: netcdf_compatible_array(config[key]).copy()\\n for key in config.dtype.fields.keys()\\n if key.islower()})\\n\\n wrf_opts = ds.createVariable(\\\"wrf_configuration\\\", \\\"i1\\\", ())\\n with contextlib.closing(netCDF4.Dataset(\\n config[\\\"wrf_file\\\"][0].decode(\\\"ascii\\\"))) as wrf_ds:\\n wrf_opts.setncatts({att: wrf_ds.getncattr(att)\\n for att in wrf_ds.ncattrs()})\\n\\n ########################################################\\n\\n obs_time_var.setncatts(dict(long_name=\\\"observation_time\\\",\\n # not entirely sure this applies...\\n standard_name=\\\"forecast_reference_time\\\",\\n bounds=\\\"observation_time_bnds\\\",\\n units=time_unit,\\n calendar=CALENDAR,\\n coverage_content_type=\\\"coordinate\\\",\\n # might be a misapplication of CF 9.5\\n cf_role=\\\"timeseries_id\\\"))\\n obs_time_bounds_var.setncatts(dict(long_name=\\\"observation_time_bounds\\\",\\n units=time_unit,\\n calendar=CALENDAR))\\n\\n time_back_var.setncatts(dict(long_name=\\\"time_before_observation\\\",\\n standard_name=\\\"forecast_period\\\",\\n units=\\\"hours\\\",\\n bounds=\\\"time_before_observation_bnds\\\",\\n coverage_content_type=\\\"coordinate\\\",\\n ))\\n time_back_bounds_var.setncatts(dict(\\n description=\\\"bounds of time_before_observation\\\",\\n units=\\\"hours\\\"))\\n\\n flux_time_var.setncatts(dict(\\n long_name=\\\"flux_time\\\",\\n standard_name=\\\"time\\\",\\n bounds=\\\"flux_time_bnds\\\",\\n units=time_unit,\\n calendar=CALENDAR,\\n coverage_content_type=\\\"coordinate\\\",\\n ))\\n flux_time_bounds_var.setncatts(dict(\\n long_name=\\\"flux_time\\\",\\n units=time_unit,\\n calendar=CALENDAR,\\n ))\\n\\n infl_fun_var.setncatts(dict(\\n long_name=\\\"influence_function\\\",\\n description=(\\\"linearisation of the observation operator \\\"\\n \\\"for carbon dioxide mixing ratios at the \\\"\\n \\\"towers in terms of carbon dioxide mass fluxes\\\"),\\n units=\\\"ppmv/(mol.m^-2.s^-1)\\\",\\n long_units=\\\"ppmv/(mol_CO2.m^-2.s^-1)\\\",\\n coordinates=(\\\"flux_time height latitude longitude \\\"\\n \\\"site_names site_heights site_lats site_lons\\\"),\\n # I don't think we can justify more than six or so digits\\n # of precision. The transport is too uncertain.\\n # The underlying int type doesn't support more than five.\\n # The increased locality should also speed up use.\\n scale_factor=np.array(CONVERSION_FACTOR, dtype=np.float32),\\n grid_mapping=grid_mapping,\\n valid_min=np.array(0, dtype=infl_fun_var.dtype),\\n # description of coordinate relationships\\n cell_methods=(\\n # not entirely sure if space and obs time should be in\\n # same sum. The two times are another possible\\n # combination.\\n \\\"height: dim_y: dim_x: sum \\\"\\n \\\"observation_time: sum \\\"\\n \\\"(interval: {lpdm_timestep:f} seconds) \\\"\\n \\\"site: point \\\"\\n # this sum is done later than the others\\n \\\"flux_time: sum \\\"\\n \\\"(interval: {minutes_per_file:d} minutes)\\\"\\n \\\"\\\").format(minutes_per_file=(MINUTES_PER_HOUR //\\n int(config[\\\"num_file_per_h\\\"])),\\n lpdm_timestep=float(config[\\\"lpdm_timestep\\\"])),\\n # What type of thing this is:\\n coverage_content_type=\\\"modelResult\\\",\\n ))\\n # I want to store the counts directly\\n infl_fun_var.set_auto_maskandscale(False)\\n\\n site_lats_var = ds.createVariable(\\\"site_lats\\\", \\\"f4\\\", (\\\"site\\\",))\\n site_lons_var = ds.createVariable(\\\"site_lons\\\", \\\"f4\\\", (\\\"site\\\",))\\n site_heights_var = ds.createVariable(\\\"site_heights\\\", \\\"f4\\\", (\\\"site\\\",))\\n site_lats_var.setncatts(dict(\\n units=\\\"degrees_north\\\", standard_name=\\\"latitude\\\",\\n long_name=\\\"site_latitude\\\",\\n coverage_content_type=\\\"coordinate\\\",\\n description=\\\"latitude of the observation tower site\\\",\\n origin=\\\"Set in LPD run script\\\"))\\n site_lons_var.setncatts(dict(\\n units=\\\"degrees_east\\\", standard_name=\\\"longitude\\\",\\n long_name=\\\"site_longitude\\\",\\n coverage_content_type=\\\"coordinate\\\",\\n description=\\\"longitude of the observation tower site\\\",\\n origin=\\\"Set in LPD run script\\\"))\\n site_name_var.setncatts(dict(\\n long_name=\\\"name_of_observation_site\\\",\\n # most likely an abuse of CF section 9.5\\n # cf_role=\\\"trajectory_id\\\"\\n coverage_content_type=\\\"referenceInformation\\\",\\n ))\\n site_heights_var.setncatts(dict(\\n standard_name=\\\"height\\\",\\n long_name=\\\"site_heights\\\",\\n description=\\\"height of the observation tower intake\\\",\\n origin=\\\"Set in LPD run script\\\",\\n coverage_content_type=\\\"coordinate\\\",\\n positive=\\\"up\\\",\\n units=\\\"m\\\"))\\n\\n height_var.setncatts(dict(\\n standard_name=\\\"height\\\",\\n long_name=\\\"flux_influence_height\\\",\\n description=(\\\"How low the particles have to be \\\"\\n \\\"to be \\\\\\\"influenced\\\\\\\" by the ground\\\"),\\n origin=\\\"Constant CLOSE_TO_GROUND in carsurf_loop.py\\\",\\n coverage_content_type=\\\"referenceInformation\\\",\\n positive=\\\"up\\\",\\n units=\\\"km\\\", bounds=\\\"height_bnds\\\"))\\n height_bounds_var.setncatts(dict(\\n long_name=\\\"height_bounds\\\",\\n units=\\\"km\\\"))\\n\\n # pretty sure this fails somewhat badly at encapsulization\\n set_coord_values(ds, wrf_out, footprint_nbins)\\n site_name_var[:] = site_names\\n\\n return infl_fun_var\",\n \"def make_df_an_table(an_string, site_name='DSW', min_moon_dist=MIN_MOON_DISTANCE,\\n min_hours=MIN_HOURS_OBSERVABLE):\\n an_string = str(an_string) # (precaution in case int passed in)\\n an_object = Astronight(an_string, site_name)\\n # dark_start, dark_end = an_object.ts_dark.start, an_object.ts_dark.end\\n mid_dark = an_object.local_middark_utc\\n # dark_no_moon_start, dark_no_moon_end = an_object.ts_dark_no_moon.start, an_object.ts_dark_no_moon.end\\n mpfile_dict = make_mpfile_dict()\\n\\n an_dict_list = [] # results to be deposited here, to make a dataframe later.\\n for mp in mpfile_dict.keys():\\n mpfile = mpfile_dict[mp]\\n # an_dict doesn't need to include defaults for case before or after mpfile ephemeris,\\n # because making the dataframe should put in NANs for missing keys anyway (check this later):\\n an_dict = {'MPnumber': mpfile.number, 'MPname': mpfile.name, 'Motive': mpfile.motive,\\n 'Priority': mpfile.priority, 'Period': mpfile.period}\\n # Interpolate within ephemeris (because MP is moving in sky); 2 iterations s/be enough:\\n data, status, ts_observable, mp_radec = None, None, None, None # keep stupid IDE happy.\\n best_utc = mid_dark # best_utc will = mid-observable time at converged RA,Dec.\\n\\n # Converge on best RA, Dec, observable timespan (they interact, as MP is moving):\\n hours_observable = 0.0 # default to keep IDE happy.\\n for i in range(2):\\n data = mpfile.eph_from_utc(best_utc)\\n if data is None:\\n if mpfile.eph_range[1] < an_object.ts_dark.start:\\n status = 'too late'\\n else:\\n status = 'too early'\\n break\\n status = 'ok'\\n mp_radec = RaDec(data['RA'], data['Dec'])\\n ts_observable = an_object.ts_observable(mp_radec,\\n min_alt=MIN_MP_ALTITUDE,\\n min_moon_dist=min_moon_dist) # Timespan object\\n hours_observable = ts_observable.seconds / 3600.0\\n mid_observable = ts_observable.midpoint # for loop exit\\n best_utc = mid_observable # update for loop continuation.\\n\\n # Mark valid MPs that are observable too briefly:\\n if status.lower() == 'ok':\\n if hours_observable < min_hours:\\n status = 'too brief'\\n\\n # For MPs observable this night, add one line to table:\\n # print(mpfile.name, status)\\n an_dict['Status'] = status\\n if status.lower() == 'ok':\\n an_dict['RA'] = data['RA']\\n an_dict['Dec'] = data['Dec']\\n an_dict['StartUTC'] = ts_observable.start\\n an_dict['EndUTC'] = ts_observable.end\\n an_dict['TransitUTC'] = an_object.transit(mp_radec)\\n an_dict['MoonDist'] = mp_radec.degrees_from(an_object.moon_radec)\\n an_dict['PhaseAngle'] = data['Phase']\\n an_dict['V_mag'] = data['V_mag']\\n an_dict['ExpTime'] = float(round(float(calc_exp_time(an_dict['V_mag'],\\n EXP_TIME_TABLE_PHOTOMETRY))))\\n if an_dict['Period'] is not None:\\n # Duty cycle is % of time spent observing this MP if one exposure per 1/60 of period.\\n an_dict['DutyCyclePct'] = 100.0 * ((an_dict['ExpTime'] + EXP_OVERHEAD) / 60.0) / \\\\\\n an_dict['Period']\\n else:\\n an_dict['DutyCyclePct'] = None\\n if status.lower() == 'ok':\\n an_dict['PhotrixPlanning'] = 'IMAGE MP_' + mpfile.number + \\\\\\n ' Clear=' + str(an_dict['ExpTime']) + 'sec(***) ' + \\\\\\n ra_as_hours(an_dict['RA'], seconds_decimal_places=1) + ' ' + \\\\\\n degrees_as_hex(an_dict['Dec'], arcseconds_decimal_places=0)\\n if an_dict['Period'] is not None:\\n an_dict['Coverage'] = make_df_coverage(an_dict['Period'],\\n mpfile.obs_jd_ranges,\\n (jd_from_datetime_utc(an_dict['StartUTC']),\\n jd_from_datetime_utc(an_dict['EndUTC'])))\\n an_dict['PhaseCoverage'] = make_df_phase_coverage(an_dict['Period'],\\n mpfile.obs_jd_ranges)\\n else:\\n an_dict['Coverage'] = None\\n an_dict_list.append(an_dict)\\n if len(an_dict_list) == 0:\\n return None\\n df_an_table = pd.DataFrame(data=an_dict_list)\\n df_an_table.index = df_an_table['MPnumber'].values\\n df_an_table = df_an_table.sort_values(by='TransitUTC')\\n return df_an_table\",\n \"def update_stationlist(time_res='hourly',dbase_dir='dbase'):\\r\\n\\r\\n \\r\\n dwd_abbr = {'air_temperature': 'TU',\\r\\n 'cloud_type': 'CS', \\r\\n 'cloudiness': 'N',\\r\\n 'dew_point' : 'TD',\\r\\n 'extreme_temperature': 'TX',\\r\\n 'extreme_wind': 'FX',\\r\\n 'precipitation': 'RR',\\r\\n 'pressure': 'P0',\\r\\n 'soil_temperature': 'EB',\\r\\n 'solar': 'ST',\\r\\n 'sun': 'SD',\\r\\n 'visibility': 'VV',\\r\\n 'wind': 'FF',\\r\\n 'wind_synop': 'F'\\r\\n }\\r\\n \\r\\n # lets start\\r\\n print('Updating station list')\\r\\n \\r\\n # create output directory if not existing\\r\\n \\r\\n if not os.path.exists(dbase_dir):\\r\\n os.makedirs(dbase_dir)\\r\\n \\r\\n #check whether we have an up-to-date-station-list-already\\r\\n try:\\r\\n stations_network_old=[s for s in os.listdir(dbase_dir) if 'dwd_station_network' in s][0]\\r\\n datetime_network=datetime.date(datetime.strptime(re.findall('\\\\d+',stations_network_old)[0],'%Y%m%d'))\\r\\n #update if more than 24hours\\r\\n dt_today=datetime.date(datetime.now())\\r\\n if (dt_today-datetime_network)<timedelta(days=1):\\r\\n print('DWD network list is up-to-date, no update needed')\\r\\n filename_stations=dbase_dir+'\\\\\\\\'+stations_network_old\\r\\n return filename_stations\\r\\n else:\\r\\n print('DWD network list neeeds to be updated')\\r\\n os.remove(dbase_dir+'\\\\\\\\'+stations_network_old)\\r\\n except:\\r\\n print('DWD network list neeeds to be updated')\\r\\n pass\\r\\n \\r\\n \\r\\n # header\\r\\n stations_network=pd.DataFrame()\\r\\n \\r\\n # connect to ftp server and go to the folder\\r\\n \\r\\n # Connect to the Server\\r\\n server='opendata.dwd.de'\\r\\n ftp=connect_ftp(server = server,connected = False)\\r\\n #change to subfolder\\r\\n ftp.cwd('/climate_environment/CDC/observations_germany/climate/' + time_res +'/')\\r\\n #get dwd categories\\r\\n dwd_categories=ftp.nlst()\\r\\n #loop through the subfolders to get the station lists\\r\\n for category in dwd_categories:\\r\\n print('retrieve stationlist for', category)\\r\\n #try to get historical data\\r\\n try:\\r\\n dir_path='/climate_environment/CDC/observations_germany/climate/' + time_res +'/'+category+'/historical/'\\r\\n ftp.cwd(dir_path)\\r\\n except Exception as e:\\r\\n print(e, 'try to download category', category, 'from other folder')\\r\\n try:\\r\\n dir_path='/climate_environment/CDC/observations_germany/climate/' + time_res +'/'+category+'/'\\r\\n ftp.cwd(dir_path)\\r\\n except:\\r\\n print('Category', category, 'could not have been downloaded')\\r\\n pass\\r\\n #retrieve the stationlist\\r\\n stationlist = []\\r\\n # try to retrieve file\\r\\n retrieved=False\\r\\n filename=dwd_abbr[category]+'_Stundenwerte_Beschreibung_Stationen.txt'\\r\\n while not retrieved:\\r\\n try:\\r\\n ftp.retrlines(\\\"RETR \\\" + filename, stationlist.append)\\r\\n #ftp.retrbinary(\\\"RETR \\\" + filestr, stationlist.write)\\r\\n retrieved = True\\r\\n except:\\r\\n ftp=connect_ftp(server = server,connected = False)\\r\\n ftp.cwd(dir_path)\\r\\n #remove first two lines\\r\\n stationlist=stationlist[2:]\\r\\n #delete uncessary blanks\\r\\n stationlist=[re.sub(' +', ' ', station.rstrip()) for station in stationlist]\\r\\n #split the list\\r\\n stationlist=[station.split(\\\" \\\")[:7] for station in stationlist]\\r\\n #read as dataframe\\r\\n dfstations=pd.DataFrame(stationlist,columns=['STATIONS_ID','date_start','date_end','height','geo_lat','geo_lon','name'])\\r\\n #add true information to category\\r\\n dfstations[category]=True\\r\\n \\r\\n stations_network=stations_network.append(dfstations,sort=False,ignore_index=True)\\r\\n #A=[sub.split(\\\" \\\") for sub in stationlist] \\r\\n \\r\\n #replace all Na by False\\r\\n stations_network[stations_network.isna()]=0 \\r\\n #aggregate\\r\\n stations_network=stations_network.groupby(['STATIONS_ID'],as_index=False).agg('max')\\r\\n #replace zero by False in order to have pure boolean data\\r\\n stations_network.replace(0,False,inplace=True)\\r\\n #fix the error with station 14138 and 05614 and 07325, which does not have pressure cord\\r\\n stations_network.loc[stations_network.STATIONS_ID=='14138','pressure']=False\\r\\n stations_network.loc[stations_network.STATIONS_ID=='05614','pressure']=False\\r\\n stations_network.loc[stations_network.STATIONS_ID=='07325','pressure']=False\\r\\n stations_network.loc[stations_network.STATIONS_ID=='01572','pressure']=False\\r\\n #for temperature the same\\r\\n stations_network.loc[stations_network.STATIONS_ID=='14138','air_temperature']=False\\r\\n #save to database writing the time as well\\r\\n filename_stations=dbase_dir+'\\\\\\\\dwd_station_network_'+datetime.now().strftime('%Y%m%d')+'.csv'\\r\\n stations_network.to_csv(filename_stations,index=False)\\r\\n \\r\\n print('Updating station list...finished')\\r\\n \\r\\n return filename_stations\",\n \"def synthesize_employment_data(self, config):\\r\\n jobs_by_zone_by_sector_table_name = config['jobs_by_zone_by_sector']\\r\\n gridcells_table_name = config['gridcells']\\r\\n jobs_table_name = config['jobs']\\r\\n gridcells_output_table_name = config['gridcells_output']\\r\\n jobs_output_table_name = config['jobs_output']\\r\\n \\r\\n input_db_name = config['db_config'].database_name\\r\\n output_db_name = config['output_database_name']\\r\\n \\r\\n sectors = config['sector_names_and_ids']\\r\\n building_types_and_ids_and_home_based = config[\\r\\n 'building_type_column_names_and_ids_and_home_based']\\r\\n \\r\\n building_types = []\\r\\n building_ids = []\\r\\n home_based = [] \\r\\n for type, id, home in building_types_and_ids_and_home_based:\\r\\n building_types += [type]\\r\\n building_ids += [id]\\r\\n home_based += [home]\\r\\n \\r\\n \\r\\n from_database_configuration = ScenarioDatabaseConfiguration(\\r\\n database_name = input_db_name,\\r\\n host_name = config['db_config'].host_name,\\r\\n user_name = config['db_config'].user_name,\\r\\n password = config['db_config'].password \\r\\n )\\r\\n to_database_configuration = ScenarioDatabaseConfiguration(\\r\\n database_name = output_db_name,\\r\\n host_name = config['db_config'].host_name,\\r\\n user_name = config['db_config'].user_name,\\r\\n password = config['db_config'].password \\r\\n )\\r\\n\\r\\n FlattenScenarioDatabaseChain().copy_scenario_database(\\r\\n from_database_configuration = from_database_configuration, \\r\\n to_database_configuration = to_database_configuration,\\r\\n tables_to_copy = [gridcells_table_name, jobs_table_name])\\r\\n \\r\\n db_server = DatabaseServer(to_database_configuration) \\r\\n output_database = db_server.get_database(output_db_name)\\r\\n \\r\\n sector_name = 0; sector_id = 1\\r\\n \\r\\n sector = {}\\r\\n for entry in sectors:\\r\\n name = entry[sector_name]\\r\\n id = entry[sector_id]\\r\\n sector[id] = self._get_jobs_per_building_type_in_sector_by_zone(\\r\\n output_database, jobs_by_zone_by_sector_table_name, \\r\\n jobs_table_name, name, id)\\r\\n\\r\\n results = self._get_building_type_proportion_by_zone(output_database, \\r\\n gridcells_table_name)\\r\\n \\r\\n grid_id = 0; zone_id = 1\\r\\n dist = {}\\r\\n \\r\\n type_index = {}\\r\\n \\r\\n for name in building_types:\\r\\n for i in range(len(results[0])):\\r\\n column_name = results[0][i]\\r\\n if name == column_name:\\r\\n type_index[name] = i\\r\\n break;\\r\\n else:\\r\\n raise KeyError, ('No column by the name of \\\\'%s\\\\' found in '\\r\\n 'the database.' % name) \\r\\n\\r\\n for name in building_types:\\r\\n dist[name] = {}\\r\\n \\r\\n for row in results[1:]:\\r\\n for name in building_types:\\r\\n dist[name][row[zone_id]] = []\\r\\n \\r\\n for row in results[1:]:\\r\\n for name in building_types:\\r\\n dist[name][row[zone_id]] += [(row[grid_id], \\r\\n row[type_index[name]])]\\r\\n \\r\\n jobs_table_data = self._create_jobs_table_data(dist, sector,\\r\\n building_types_and_ids_and_home_based)\\r\\n \\r\\n output_database.execute('USE %(out_db)s' % {'out_db':output_db_name})\\r\\n \\r\\n output_database.execute(\\\"\\\"\\\"\\r\\n CREATE TABLE %(jobs_out)s (\\r\\n JOB_ID INT AUTO_INCREMENT, PRIMARY KEY(JOB_ID),\\r\\n GRID_ID INT, HOME_BASED INT, SECTOR_ID INT, BUILDING_TYPE INT);\\r\\n \\\"\\\"\\\" % {'jobs_out':jobs_output_table_name})\\r\\n \\r\\n if len(jobs_table_data) > 0:\\r\\n output_prefix = (\\r\\n \\\"\\\"\\\"INSERT INTO %(jobs_out)s \\r\\n (GRID_ID, HOME_BASED, SECTOR_ID, BUILDING_TYPE) VALUES\\r\\n \\\"\\\"\\\" % {'jobs_out':jobs_output_table_name})\\r\\n output_postfix = ';'\\r\\n \\r\\n step = 1000\\r\\n length = len(jobs_table_data)\\r\\n iterations = int(length/step) + 1\\r\\n \\r\\n for i in range(iterations):\\r\\n low = i*step\\r\\n high = (i+1)*step\\r\\n \\r\\n if high > length: high = length\\r\\n \\r\\n output_body = \\\"\\\"\\r\\n \\r\\n for j in range(low, high):\\r\\n output_body += (\\r\\n '(%(grid)s, %(home)s, %(sector)s, %(building)s),\\\\n' \\r\\n % jobs_table_data[j])\\r\\n \\r\\n output_query = \\\"%s%s%s\\\" % (output_prefix, \\r\\n output_body[:-2], \\r\\n output_postfix)\\r\\n\\r\\n output_database.execute(output_query)\\r\\n \\r\\n \\r\\n ### TODO: \\r\",\n \"def insert_humans_staging(self):\\n for year in range(1880, CURRENT_YEAR):\\n self.load_wikidata(\\\"humans\\\", HUMANS_BY_YEAR_SPARQL_QUERY, INSERT_HUMAN_SQL_QUERY,\\n INSERT_HUMAN_MAP_COLUMNS, year=year)\",\n \"def disaggregate(self, mains, output_datastore):\\n \\n building_path = '/building{}'.format(mains.building())\\n # only writes one appliance and meter per building\\n meter_instance = 2\\n mains_data_location = '{}/elec/meter1'.format(building_path)\\n \\n #dis_main = pd.DataFrame()\\n chunk_number = 0\\n timeframes = []\\n\\n for chunk in mains.power_series():\\n \\n # Record metadata\\n timeframes.append(chunk.timeframe)\\n measurement = chunk.name\\n cols = pd.MultiIndex.from_tuples([chunk.name])\\n \\n dis_chunk = self.disaggregate_chunk(\\n pd.DataFrame(chunk.resample(self.sample_period, how=self.sampling_method)))\\n #dis_main = pd.concat([dis_main, dis_chunk])\\n chunk_number += 1\\n print(str(chunk_number) + \\\" chunks disaggregated\\\")\\n \\n # Write appliance data to disag output\\n key = '{}/elec/meter{}'.format(building_path, meter_instance)\\n df = pd.DataFrame(\\n dis_chunk.values, index=dis_chunk.index,\\n columns=cols)\\n output_datastore.append(key, df)\\n\\n # Copy mains data to disag output\\n output_datastore.append(key=mains_data_location,\\n value=pd.DataFrame(chunk, columns=cols))\\n\\n # Saving output datastore:\\n #output_datastore.append(key=mains.key, value=dis_main)\\n \\n ##################################\\n # Add metadata to output_datastore\\n\\n # TODO: `preprocessing_applied` for all meters\\n # TODO: split this metadata code into a separate function\\n # TODO: submeter measurement should probably be the mains\\n # measurement we used to train on, not the mains measurement.\\n \\n date_now = datetime.now().isoformat().split('.')[0]\\n output_name = 'NILMTK_MLE_' + date_now\\n resample_seconds = 10\\n mains_data_location = '{}/elec/meter1'.format(building_path)\\n\\n # DataSet and MeterDevice metadata:\\n meter_devices = {\\n 'MLE': {\\n 'model': 'MLE',\\n 'sample_period': resample_seconds,\\n 'max_sample_period': resample_seconds,\\n 'measurements': [{\\n 'physical_quantity': measurement[0],\\n 'type': measurement[1]\\n }]\\n },\\n 'mains': {\\n 'model': 'mains',\\n 'sample_period': resample_seconds,\\n 'max_sample_period': resample_seconds,\\n 'measurements': [{\\n 'physical_quantity': measurement[0],\\n 'type': measurement[1]\\n }]\\n }\\n }\\n\\n merged_timeframes = merge_timeframes(timeframes, gap=resample_seconds)\\n total_timeframe = TimeFrame(merged_timeframes[0].start,\\n merged_timeframes[-1].end)\\n\\n dataset_metadata = {'name': output_name, 'date': date_now,\\n 'meter_devices': meter_devices,\\n 'timeframe': total_timeframe.to_dict()}\\n output_datastore.save_metadata('/', dataset_metadata)\\n\\n # Building metadata\\n\\n # Mains meter:\\n elec_meters = {\\n 1: {\\n 'device_model': 'mains',\\n 'site_meter': True,\\n 'data_location': mains_data_location,\\n 'preprocessing_applied': {}, # TODO\\n 'statistics': {\\n 'timeframe': total_timeframe.to_dict()\\n }\\n }\\n }\\n\\n # Appliances and submeters:\\n appliances = []\\n appliance = {\\n 'meters': [meter_instance],\\n 'type': 'kettle',\\n 'instance': 1\\n # TODO this `instance` will only be correct when the\\n # model is trained on the same house as it is tested on.\\n # https://github.com/nilmtk/nilmtk/issues/194\\n }\\n appliances.append(appliance)\\n\\n elec_meters.update({\\n meter_instance: {\\n 'device_model': 'MLE',\\n 'submeter_of': 1,\\n 'data_location': ('{}/elec/meter{}'\\n .format(building_path, meter_instance)),\\n 'preprocessing_applied': {}, # TODO\\n 'statistics': {\\n 'timeframe': total_timeframe.to_dict()\\n }\\n }\\n })\\n elec_meters[meter_instance]['name'] = 'kettle'\\n\\n building_metadata = {\\n 'instance': mains.building(),\\n 'elec_meters': elec_meters,\\n 'appliances': appliances\\n }\\n\\n output_datastore.save_metadata(building_path, building_metadata)\",\n \"def _populate_zone_facts_table(self):\\n census_fields = [\\n 'poverty_rate', 'fraction_black', 'income_per_capita',\\n 'labor_participation', 'fraction_foreign',\\n 'fraction_single_mothers', 'acs_lower_rent_quartile',\\n 'acs_median_rent', 'acs_upper_rent_quartile'\\n ]\\n\\n zone_types = ['ward', 'neighborhood_cluster', 'census_tract']\\n\\n query_results = list()\\n\\n # populate columns accordingly for each zone_specific type\\n for zone_type in zone_types:\\n field_values = dict()\\n\\n # get field value for each zone_specific type\\n for field in census_fields:\\n result = self._census_with_weighting(data_id=field,\\n grouping=zone_type)\\n field_values[field] = result['items']\\n\\n zone_specifics = self._get_zone_specifics_for_zone_type(zone_type)\\n\\n # TODO: add aggregate for each zone_type into table\\n for zone in zone_specifics:\\n # get not None values so we can added to db\\n columns = list()\\n values = list()\\n for field in census_fields:\\n zone_value = field_values[field][zone]\\n\\n if zone_value is not None:\\n columns.append(field)\\n values.append(\\\"'\\\" + str(zone_value) + \\\"'\\\")\\n\\n # derive column and values strings needed for sql query\\n columns = ', '.join(columns)\\n columns = 'zone, ' + columns\\n\\n values = ', '.join(values)\\n values = \\\"'\\\" + zone + \\\"', \\\" + values\\n\\n q = \\\"INSERT INTO zone_facts ({cols}) VALUES ({vals})\\\".format(\\n cols=columns, vals=values)\\n\\n with self.engine.connect() as conn:\\n result = conn.execute(q)\\n query_results.append(result)\\n\\n return query_results\",\n \"def office_station_statistics(parser, args, params): \\n parser.add_argument(\\n '--station_list', type=str, help='Station file from IRIS',\\n required=True, metavar='x')\\n \\n local_args = parser.parse_known_args(args)\\n \\n station_list = local_args[0].station_list\\n control.station_statistics(params, station_list)\",\n \"def create_extract():\\n with open(SCHEMA_FILE, \\\"r\\\") as f:\\n SCHEMA = yaml.safe_load(f)\\n\\n with open(TOKEN_FILE, \\\"r\\\") as f:\\n TOKEN = yaml.safe_load(f)\\n\\n hc = HyperCreator(SCHEMA, HYPER_FILE)\\n ts = Tableau(TOKEN[\\\"server\\\"], TOKEN[\\\"site\\\"], TOKEN[\\\"name\\\"], TOKEN[\\\"value\\\"])\\n\\n for table in SCHEMA[\\\"tables\\\"]:\\n with open(f\\\"{CONTENT_MANAGEMENT}/{table['query']}\\\", \\\"r\\\") as f:\\n query = f.read()\\n\\n data = ts.query_metadata(query)\\n data_map = getattr(GraphQL, table[\\\"name\\\"])(data)\\n\\n hc.populate_extract(table[\\\"name\\\"], data_map)\",\n \"def __init__(self, locator, weather_data, building_names=None):\\n\\n if building_names is None:\\n building_names = locator.get_zone_building_names()\\n\\n self.building_names = building_names\\n print(\\\"read input files\\\")\\n prop_geometry = Gdf.from_file(locator.get_zone_geometry())\\n prop_geometry['footprint'] = prop_geometry.area\\n prop_geometry['perimeter'] = prop_geometry.length\\n prop_geometry['Blength'], prop_geometry['Bwidth'] = self.calc_bounding_box_geom(locator.get_zone_geometry())\\n prop_geometry = prop_geometry.drop('geometry', axis=1).set_index('Name')\\n prop_hvac = dbf_to_dataframe(locator.get_building_air_conditioning())\\n\\n prop_typology = dbf_to_dataframe(locator.get_building_typology()).set_index('Name')\\n # Drop 'REFERENCE' column if it exists\\n if 'REFERENCE' in prop_typology:\\n prop_typology.drop('REFERENCE', axis=1, inplace=True)\\n prop_architectures = dbf_to_dataframe(locator.get_building_architecture())\\n prop_comfort = dbf_to_dataframe(locator.get_building_comfort()).set_index('Name')\\n prop_internal_loads = dbf_to_dataframe(locator.get_building_internal()).set_index('Name')\\n prop_supply_systems_building = dbf_to_dataframe(locator.get_building_supply())\\n\\n # GET SYSTEMS EFFICIENCIES\\n prop_supply_systems = get_properties_supply_sytems(locator, prop_supply_systems_building).set_index('Name')\\n\\n # get temperatures of operation\\n prop_HVAC_result = get_properties_technical_systems(locator, prop_hvac).set_index('Name')\\n\\n # get envelope properties\\n prop_envelope = get_envelope_properties(locator, prop_architectures).set_index('Name')\\n\\n # get properties of rc demand model\\n prop_rc_model = self.calc_prop_rc_model(locator, prop_typology, prop_envelope,\\n prop_geometry, prop_HVAC_result)\\n\\n # get solar properties\\n solar = get_prop_solar(locator, building_names, prop_rc_model, prop_envelope, weather_data).set_index('Name')\\n\\n # df_windows = geometry_reader.create_windows(surface_properties, prop_envelope)\\n # TODO: to check if the Win_op and height of window is necessary.\\n # TODO: maybe mergin branch i9 with CItyGML could help with this\\n print(\\\"done\\\")\\n\\n # save resulting data\\n self._prop_supply_systems = prop_supply_systems\\n self._prop_geometry = prop_geometry\\n self._prop_envelope = prop_envelope\\n self._prop_typology = prop_typology\\n self._prop_HVAC_result = prop_HVAC_result\\n self._prop_comfort = prop_comfort\\n self._prop_internal_loads = prop_internal_loads\\n self._prop_age = prop_typology[['YEAR']]\\n self._solar = solar\\n self._prop_RC_model = prop_rc_model\",\n \"def createStations (config):\\n trace (\\\"createStations()\\\")\\n for section in config.sections():\\n if section.capitalize().startswith(\\\"Station\\\"):\\n myPressureProbes = myHumidityProbes = []\\n myTemperatureProbes= []\\n name = section\\n for option in config.options (section):\\n value = config.get (section, option)\\n opt = option.capitalize()\\n if opt == \\\"Name\\\":\\n name = value\\n elif opt == \\\"Temperature\\\":\\n myTemperatureProbes = getProbeList (value,\\n temperatureProbes)\\n elif opt == \\\"Pressure\\\":\\n myPressureProbes = getProbeList (value,\\n pressureProbes)\\n elif opt == \\\"Humidity\\\":\\n myHumidityProbes = getProbeList (value,\\n humidityProbes)\\n stations [name] = Station.Station(myTemperatureProbes,\\n myPressureProbes, myHumidityProbes, name)\",\n \"def read_data_files(self):\\n\\n for name, snap in zip(self.names, self.snaps):\\n # build the very important dictionary:\\n key = f'{name}_{snap:03}' # e.g 'MW_000'\\n self.galaxies[key] = Galaxy(name, snap, self.path, \\n self.usesql, self.ptype, self.stride)\\n self.time = self.galaxies[key].time\\n\\n # bits of minor housekeeping:\\n # self.path = self.galaxies[key].filepath # may speed up next search\\n self.filenames.append(key)\",\n \"def _obtain_data(self):\\n (self.data_df, self.column_df, self.station_name, self.log_file, self.station_lat, self.station_lon,\\n self.station_elev, self.ws_anemometer_height, self.missing_fill_value, self.script_mode,\\n self.auto_mode, self.fill_mode, self.metadata_mode, self.generate_bokeh, self.metadata_df,\\n metadata_series) = input_functions.obtain_data(self.config_path, self.metadata_path)\\n\\n if self.script_mode == 1: # correcting data\\n self.mc_iterations = 1000 # Number of iters for MC simulation of thornton running solar radiation gen\\n else:\\n self.mc_iterations = 50 # if we're not correcting data then only do a few iterations to save time\\n\\n print(\\\"\\\\nSystem: Raw data successfully extracted from station file.\\\")\\n\\n # Extract individual variables from data frame back into to numpy arrays.\\n self.data_year = np.array(self.data_df.year)\\n self.data_month = np.array(self.data_df.month)\\n self.data_day = np.array(self.data_df.day)\\n self.data_tavg = np.array(self.data_df.tavg)\\n self.data_tmax = np.array(self.data_df.tmax)\\n self.data_tmin = np.array(self.data_df.tmin)\\n self.data_tdew = np.array(self.data_df.tdew)\\n self.data_ea = np.array(self.data_df.ea)\\n self.data_rhavg = np.array(self.data_df.rhavg)\\n self.data_rhmax = np.array(self.data_df.rhmax)\\n self.data_rhmin = np.array(self.data_df.rhmin)\\n self.data_rs = np.array(self.data_df.rs)\\n self.data_ws = np.array(self.data_df.ws)\\n self.data_precip = np.array(self.data_df.precip)\\n\\n self.output_file_path = \\\"correction_files/\\\" + self.station_name + \\\"_output\\\" + \\\".xlsx\\\"\",\n \"def populate_locations(connection):\\n print('Populating locations...')\\n cursor = connection.cursor()\\n with open(get_data_path('locations', 'locations.json'), 'r', encoding='utf-8') as json_file:\\n locations = json.load(json_file)\\n\\n for station_id, location in locations.items():\\n cursor.execute(f'''SELECT id \\n FROM watercourse_stations \\n WHERE id = {station_id}''')\\n\\n if len(cursor.fetchall()):\\n cursor.execute(f'''INSERT INTO locations(name, lat, lng)\\n VALUES ('{location['name']}', {location['lat']}, {location['lng']})''')\\n cursor.execute(f'''UPDATE watercourse_stations\\n SET location_id = {cursor.lastrowid}\\n WHERE id = {station_id}''')\",\n \"def run():\\n # build the station list and update the current levels\\n station_list = build_station_list()\\n update_water_levels(station_list, use_cache=True)\\n\\n num_stations = 10\\n highest_level_stations = stations_highest_rel_level(station_list, num_stations)\\n\\n print(\\\"{} stations with the highest relative water levels, in descending order:\\\".format(num_stations))\\n for station in highest_level_stations:\\n print(station.name, station.relative_water_level())\",\n \"def populate_agdds(start_date, end_date, source, source_id, stations):\\r\\n # possibly grab ACIS station data (for entire date range)\\r\\n if source == 'ACIS':\\r\\n station_ids = []\\r\\n for station in stations:\\r\\n station_ids.append(station['char_network_id'])\\r\\n acis_data = get_acis_climate_data(\\\",\\\".join(station_ids), 'mint,maxt,gdd32,gdd50', start_date, end_date)\\r\\n\\r\\n for station in stations:\\r\\n print(station['char_network_id'])\\r\\n # grab previous days tmin, tmax, and agdd for both bases from mysql agdds table and start over at year breaks\\r\\n day_before_start_date = start_date - timedelta(days=1)\\r\\n if day_before_start_date.year == start_date.year:\\r\\n prev_tmin = get_element_from_qc_table(station['station_id'], source_id, day_before_start_date, 32, 'tmin')\\r\\n prev_tmax = get_element_from_qc_table(station['station_id'], source_id, day_before_start_date, 32, 'tmax')\\r\\n agdd32 = get_element_from_qc_table(station['station_id'], source_id, day_before_start_date, 32, 'agdd')\\r\\n agdd50 = get_element_from_qc_table(station['station_id'], source_id, day_before_start_date, 50, 'agdd')\\r\\n else:\\r\\n prev_tmin = None\\r\\n prev_tmax = None\\r\\n agdd32 = None\\r\\n agdd50 = None\\r\\n\\r\\n if prev_tmin is None or prev_tmin == 'M':\\r\\n prev_tmin = 0\\r\\n if prev_tmax is None or prev_tmax == 'M':\\r\\n prev_tmax = 0\\r\\n if agdd32 is None or agdd32 == 'M':\\r\\n agdd32 = 0\\r\\n if agdd50 is None or agdd50 == 'M':\\r\\n agdd50 = 0\\r\\n\\r\\n # possibly find station of interest from ACIS retrieved data\\r\\n acis_station = None\\r\\n if source == 'ACIS':\\r\\n station_found = False\\r\\n for a_station in acis_data['data']:\\r\\n if station_found:\\r\\n break\\r\\n for sid in a_station['meta']['sids']:\\r\\n # print(sid)\\r\\n # print(station['char_network_id'])\\r\\n if station['char_network_id'] in sid:\\r\\n station_found = True\\r\\n acis_station = a_station\\r\\n break\\r\\n if not station_found:\\r\\n print(\\\"Could not find station \\\" + station['char_network_id'])\\r\\n\\r\\n previous_year = start_date.year\\r\\n delta = end_date - start_date\\r\\n for i in range(delta.days + 1):\\r\\n day = start_date + timedelta(days=i)\\r\\n doy = day.timetuple().tm_yday\\r\\n\\r\\n # reset the agdd to 0 if we go into a new year\\r\\n if previous_year != day.year:\\r\\n agdd32 = 0\\r\\n agdd50 = 0\\r\\n previous_year = day.year\\r\\n\\r\\n missing_data = False\\r\\n print(day.strftime(\\\"%Y-%m-%d\\\"))\\r\\n\\r\\n # see if we already have tmin and tmax from local db\\r\\n # tmin = None\\r\\n # tmax = None\\r\\n tmin = get_element_from_qc_table(station['station_id'], source_id, day, 32, 'tmin')\\r\\n tmax = get_element_from_qc_table(station['station_id'], source_id, day, 32, 'tmax')\\r\\n\\r\\n already_retrieved = False\\r\\n if tmin is not None and tmin != 'M' and tmax is not None and tmax != 'M' and source != 'PRISM':\\r\\n already_retrieved = True\\r\\n\\r\\n # don't already have tmin and tmax locally so grab from URMA postgis db or ACIS data\\r\\n if not already_retrieved:\\r\\n if source == 'URMA':\\r\\n if station['char_value'] == 'AK':\\r\\n tmin = get_urma_climate_data(station['longitude'], station['latitude'], day, 'tmin', 'alaska')\\r\\n tmax = get_urma_climate_data(station['longitude'], station['latitude'], day, 'tmax', 'alaska')\\r\\n else:\\r\\n tmin = get_urma_climate_data(station['longitude'], station['latitude'], day, 'tmin', 'conus')\\r\\n tmax = get_urma_climate_data(station['longitude'], station['latitude'], day, 'tmax', 'conus')\\r\\n # URMA and PRISM are in celsius in our postgis db everything else is Fer so convert here\\r\\n if tmin is not None:\\r\\n tmin = tmin * 1.8 + 32\\r\\n if tmax is not None:\\r\\n tmax = tmax * 1.8 + 32\\r\\n elif source == 'PRISM':\\r\\n tmin = get_prism_climate_data(station['longitude'], station['latitude'], day, 'tmin')\\r\\n tmax = get_prism_climate_data(station['longitude'], station['latitude'], day, 'tmax')\\r\\n if tmin is not None:\\r\\n tmin = tmin * 1.8 + 32\\r\\n if tmax is not None:\\r\\n tmax = tmax * 1.8 + 32\\r\\n elif acis_station is not None:\\r\\n tmin = acis_station['data'][i][0]\\r\\n tmax = acis_station['data'][i][1]\\r\\n\\r\\n # if tmin or tmax is missing, set to previous day's and mark as missing\\r\\n if tmin is not None and tmin != 'M':\\r\\n tmin = float(tmin)\\r\\n prev_tmin = tmin\\r\\n else:\\r\\n missing_data = True\\r\\n tmin = prev_tmin\\r\\n if tmax is not None and tmax != 'M':\\r\\n tmax = float(tmax)\\r\\n prev_tmax = tmax\\r\\n else:\\r\\n missing_data = True\\r\\n tmax = prev_tmax\\r\\n\\r\\n # compute gdd and agdd for both bases\\r\\n gdd32 = compute_gdd(tmin, tmax, 32)\\r\\n gdd50 = compute_gdd(tmin, tmax, 50)\\r\\n\\r\\n agdd32 += gdd32\\r\\n agdd50 += gdd50\\r\\n\\r\\n if not already_retrieved:\\r\\n # do an insert or update\\r\\n add_agdd_row(station['station_id'], source_id, gdd32, agdd32, day.year, doy, day, 32, missing_data, tmin, tmax)\\r\\n add_agdd_row(station['station_id'], source_id, gdd50, agdd50, day.year, doy, day, 50, missing_data, tmin, tmax)\",\n \"def datamaker(band, skypos, outfile, maglimit=20., margin=0.005,\\n searchradius=0.1, radius=gt.aper2deg(4), annulus=[0.0083, 0.025],\\n verbose=0):\\n\\n extant_objids = file_setup(outfile)\\n\\n if extant_objids == False:\\n print('NOT RUNNING!!*!')\\n return False\\n\\n uniques = dt.find_unique_sources(band, skypos[0], skypos[1], searchradius,\\n maglimit=maglimit)\\n\\n if uniques is None:\\n print('No sources at this position.')\\n return\\n\\n for pos in uniques:\\n mcat = dt.get_mcat_data(pos, margin)\\n if not mcat:\\n print('Nothing at {pos}.'.format(pos=pos))\\n continue\\n extant_objids = file_setup(outfile)\\n for i, objid in enumerate(mcat['objid']):\\n if mcat[band]['ra'][i] == -99. and mcat[band]['dec'][i] == -99.:\\n print('No {b} source'.format(b=band))\\n continue\\n if objid in extant_objids:\\n print('Already processed.')\\n continue\\n #exp = dt.exp_from_objid(objid)\\n if mcat[band]['t0'][i] < 0:\\n print('No MCAT exposure: skipping')\\n continue\\n print([mcat[band]['ra'][i], mcat[band]['dec'][i]])\\n print([mcat[band]['t0'][i], mcat[band]['t1'][i]])\\n data = gAperture(band, [mcat[band]['ra'][i], mcat[band]['dec'][i]],\\n radius, annulus=annulus, verbose=verbose,\\n coadd=True, trange=[mcat[band]['t0'][i],\\n mcat[band]['t1'][i]],\\n detsize=1.25)\\n try:\\n csv_construct = construct_row(i, band, objid, mcat, data)\\n print(csv_construct)\\n with open(outfile, 'ab') as csvfile:\\n spreadsheet = csv.writer(csvfile, delimiter=',',\\n quotechar='|',\\n quoting=csv.QUOTE_MINIMAL)\\n spreadsheet.writerow(csv_construct)\\n except TypeError:\\n continue\\n\\n return\",\n \"def load_meters_from_buliding(self, target_building, meters_name=[], sample_rate = '1min'):\\n if self.df is None:\\n self.read_data_from_csv()\\n \\n if len(meters_name) < 1 :\\n meters_name = self.meter_name.keys()\\n\\n if 'main' in meters_name:\\n meters_name.remove('main')\\n \\n building_meters = self.df.groupby('buildingid').get_group(target_building)\\n building_meters.index = pd.to_datetime(building_meters['reporttime'], format='%Y-%m-%d %H:%M:%S')\\n building_meters = building_meters.groupby('channelid')\\n building_channels = building_meters.groups.keys()\\n \\n if self.meter_name['main'][0] not in building_channels: return\\n buliding_df = building_meters.get_group(self.meter_name['main'][0]).rename(columns={\\\"w\\\": \\\"main\\\"})\\n buliding_df = buliding_df.resample(sample_rate, how='mean')\\n target_meters = ['main']\\n\\n for meter, channel_ids in self.meter_name.iteritems():\\n if meter in meters_name and channel_ids[0] in building_channels:\\n appliance_meter = building_meters.get_group(channel_ids[0]).rename(columns={\\\"w\\\": meter})\\n \\n for channel_id in channel_ids[1:]:\\n if channel_id not in building_channels: continue\\n another_channel = building_meters.get_group(channel_id).rename(columns={\\\"w\\\": meter})\\n appliance_meter.append(another_channel)\\n\\n appliance_meter = appliance_meter.resample(sample_rate, how='mean')\\n buliding_df = pd.merge(buliding_df, appliance_meter, right_index=True, left_index=True, how='left')\\n target_meters.append(meter)\\n \\n buliding_df = buliding_df[target_meters]\\n buliding_df = buliding_df[~buliding_df.index.duplicated()]\\n if buliding_df is not None:\\n self.buliding_df.setdefault(target_building, buliding_df)\\n \\n return buliding_df\",\n \"def main(self):\\n\\n assault_mech_df = self.get_mech_df(url=self.assault_url)\\n heavy_mech_df = self.get_mech_df(url=self.heavy_url)\\n medium_mech_df = self.get_mech_df(url=self.medium_url)\\n light_mech_df = self.get_mech_df(url=self.light_url)\\n all_weights_df = pd.concat([assault_mech_df, heavy_mech_df, medium_mech_df, \\n light_mech_df])\\n\\n self.save_data(assault_mech_df, \\\"assault\\\")\\n self.save_data(heavy_mech_df, \\\"heavy\\\")\\n self.save_data(medium_mech_df, \\\"medium\\\")\\n self.save_data(light_mech_df, \\\"light\\\")\\n self.save_data(all_weights_df, \\\"all_weights\\\")\\n #get maximum new columns needed for splitting variants\\n max_cols = all_weights_df.variants.apply(lambda x: len(x)).max()\\n melt_cols = []\\n\\n for i in range(max_cols):\\n all_weights_df[\\\"var_\\\"+str(i)] = \\\"\\\"\\n melt_cols.append(\\\"var_\\\"+str(i))\\n\\n variant_weights_df = pd.DataFrame()\\n for index, row in all_weights_df.iterrows():\\n for i in range(len(row[\\\"variants\\\"])):\\n #add each variant to variant weights as a row with mech, tonnage, variant\\n new_row_dict = {\\n \\\"mech_name\\\":row[\\\"mechs\\\"],\\n \\\"tonnage\\\":row[\\\"tonnage\\\"],\\n \\\"variant\\\":row[\\\"variants\\\"][i].upper()\\n }\\n new_row_df = pd.DataFrame(new_row_dict, index=[0])\\n variant_weights_df = pd.concat([variant_weights_df, new_row_df])\\n\\n for i in range(len(row[\\\"hero_chassis\\\"])):\\n new_row_dict = {\\n \\\"mech_name\\\":row[\\\"hero_names\\\"],\\n \\\"tonnage\\\":row[\\\"tonnage\\\"],\\n \\\"variant\\\":row[\\\"hero_chassis\\\"][i].upper()\\n }\\n new_row_df = pd.DataFrame(new_row_dict, index=[0])\\n variant_weights_df = pd.concat([variant_weights_df, new_row_df])\\n\\n\\n for i in range(len(row[\\\"special_variants\\\"])):\\n new_row_dict = {\\n \\\"mech_name\\\":row[\\\"mechs\\\"],\\n \\\"tonnage\\\":row[\\\"tonnage\\\"],\\n \\\"variant\\\":row[\\\"special_variants\\\"][i].upper()\\n }\\n new_row_df = pd.DataFrame(new_row_dict, index=[0])\\n variant_weights_df = pd.concat([variant_weights_df, new_row_df]) \\n\\n #add champion variants by matching on \\n for i in range(len(row[\\\"champion_variants\\\"])):\\n new_row_dict = {\\n \\\"mech_name\\\":row[\\\"mechs\\\"],\\n \\\"tonnage\\\":row[\\\"tonnage\\\"],\\n \\\"variant\\\":row[\\\"champion_variants\\\"][i].upper()\\n }\\n new_row_df = pd.DataFrame(new_row_dict, index=[0])\\n variant_weights_df = pd.concat([variant_weights_df, new_row_df])\\n #remove duplicate rows \\n variant_weights_df = variant_weights_df[variant_weights_df.duplicated(keep=\\\"first\\\")==False]\\n self.save_data(variant_weights_df, \\\"variant_weights\\\")\",\n \"def dataframe():\\n\\t#allows function to access station, gmt, and miss_station functions\\n global stations\\n\\tglobal gmt\\n\\tglobal miss_station\\n\\t\\n\\t#read predictor file\\n\\tcontrol = cfg.read_yaml('../registry/graphs.yaml')\\n\\tpred_ctrl = cfg.read_yaml(cfg.get_config_path(control.pred_file))\\n\\tpredd_ctrl = cfg.read_yaml(cfg.get_config_path(control.predd_file))\\n\\n\\t#get file paths and update database\\n\\tpredictor_file_path = control.predictor_file_path\\n\\tpredictand_file_path = control.predictand_file_path\\n\\tpred_file_id = update(predictor_file_path)\\n\\tpredd_file_id = update(predictand_file_path)\\n\\t\\n\\t#store lead time and date range\\n\\tlead_time = control.lead_time\\n\\tdate_range = control.date_range\\n\\n\\t#get info for fetch many dates\\n\\tstart,end,stride = read_pred.parse_range(date_range)\\n\\tfcst_ref_time = control.date_range[0].split('-')[0][-2:]\\n\\t\\n\\t#initialize list of predictors\\n\\tpred_list = pred_ctrl.predictors\\n\\tpredictor = []\\n\\n\\t#loops through predictors to build camps data objects\\n\\tfor entry_dict in pred_list:\\n\\t\\t#formats metadata\\n\\t\\tpred = create.preprocess_entries(entry_dict, fcst_ref_time)\\n\\t\\t\\n\\t\\t#adds info to metadata that's not currently being stored\\n\\t\\tpred.search_metadata['reserved2'] = lead_time*3600\\n pred.search_metadata['file_id'] = pred_file_id\\n\\t\\tpred.search_metadata['reserved1'] = 'vector'\\n\\n\\t\\t#build camps data objects for each day\\n\\t\\tvariable = fetch_many_dates(predictor_file_path,start,end,stride,pred.search_metadata)\\n\\t\\t\\n\\t\\t#appends all data to single camps object\\n\\t\\tif variable[0] is not None:\\n\\t\\t\\tvar = variable[0]\\n\\t\\t\\tarrs = []\\n\\t\\t\\tfor i in range(len(variable)):\\n\\t\\t\\t\\tarrs.append(variable[i].data)\\n\\t\\t\\tvar.data = np.stack(arrs)\\n\\t\\t\\tpredictor.append(var)\\n\\n\\t#initializes list of predictands\\n\\tpredd_list = predd_ctrl.predictands\\n predictand = []\\n\\t\\n\\t#loops through predictands to build camps data objects\\n for entry_dict in predd_list:\\n\\t\\t#formats metadata\\n \\tvertical_coordinate = entry_dict.pop('Vertical_Coordinate')\\n\\t\\tentry_dict['file_id'] = predd_file_id\\n\\n\\t\\t#build camps objects for each day\\n variable = fetch_many_dates(predictand_file_path,start, end, stride, entry_dict)\\n\\n\\t\\t#append all data to single camps object\\n var = variable[0]\\n arrs = []\\n for i in range(len(variable)):\\n arrs.append(variable[i].data)\\n try:\\n\\t\\t\\tvar.data = np.stack(arrs)\\n\\t\\t\\tpredictand.append(var)\\n\\t\\texcept:\\n\\t\\t\\tprint(\\\"Can't read \\\" + variable.name)\\n\\n\\t#getting predictor station and time data\\n\\tpredr = Dataset(predictor_file_path[0])\\n\\tpredr_stat = predr.variables['station'][:]\\n\\tif lead_time == 3:\\n\\t\\tpredr_time = predr.variables['OM__phenomenonTimeInstant'][:]\\n\\telif lead_time == 6:\\n\\t\\tpredr_time = predr.variables['OM__phenomenonTimeInstant1'][:]\\n\\telif lead_time == 12:\\n\\t\\tpredr_time = predr.variables['OM__phenomenonTimeInstant2'][:]\\n\\tpredr.close()\\n\\n\\t#reformatting predictor station and time data\\n\\tpredr_stations = stations(predr_stat)\\n\\tpredr_gmt = gmt(predr_time)\\n\\t\\n\\t#getting predictand station and time data\\n\\tpredd = Dataset(predictand_file_path[0])\\n\\tpredd_stat = predd.variables['station'][:]\\n\\tpredd_time = predd.variables['OM__resultTime'][:]\\n\\tpredd.close()\\n\\t\\n\\t#reformatting predictand station and time data\\n\\tpredd_stations = stations(predd_stat)\\n\\tpredd_gmt = gmt(predd_time)\\n\\n\\t#choosing predictand observations that line up with predictor time\\n\\thour = (predictor[0].metadata['FcstTime_hour']/3600) + lead_time\\n\\tdays = len(predd_gmt)/24\\n\\tpredd_hours = [0]*days\\n k=0\\n for i in range(len(predd_gmt)):\\n if i%24 == hour:\\n\\t\\t\\tpredd_hours[k]=predd_gmt[i]\\n\\t\\t\\tk+=1\\n\\t\\n\\t#catches when GFS data doesn't cover the last day of the month\\n\\tif len(predr_gmt) < len(predd_hours):\\n\\t\\tpredd_hours = predd_hours[:-1]\\t\\n\\t\\n\\t#find missing stations\\n\\tmiss_stations = miss_station(predr_stations,predd_stations)\\n\\tstations = predd_stations\\n\\t\\n\\t#station and time array\\n\\tinfo = [['',''] for k in range(len(predr_gmt)*len(stations))]\\n\\tfor i in range(len(predr_gmt)):\\n\\t\\tfor j in range(len(stations)):\\n\\t\\t\\tk = i*len(stations)+j\\n\\t\\t\\tinfo[k][0]=predr_gmt[i]\\n\\t\\t\\tinfo[k][1]=stations[j]\\n\\n\\t#create column names\\n\\tnames = ['']*(len(predictor)+len(predictand)+2)\\n\\tnames[0]='Time'\\n\\tnames[1]='Station'\\n\\n\\t#creating array\\n\\tarr = np.zeros((len(stations)*len(predr_gmt),len(predictor)+len(predictand)))\\n\\t\\n\\t#adding predictor data\\n\\tfor i in range(len(predictor)):\\n\\t\\t#remove lead time and forecast reference time from variable name\\n\\t\\t#and add variable name to column list of final dataframe\\n\\t\\tif lead_time == 12:\\n\\t\\t\\tnames[i+2]='GFS_'+predictor[i].get_variable_name()[:-11]\\n\\t\\telse:\\n\\t\\t\\t names[i+2]='GFS_'+predictor[i].get_variable_name()[:-10]\\n\\n\\t\\t#create pandas dataframe of data and sort alphabetically by station name\\n\\t\\tpredictor[i].data = np.squeeze(predictor[i].data,axis=2)\\n\\t\\tpredictor[i].data = pd.DataFrame(predictor[i].data,columns=predr_stations,index=predr_gmt)\\n\\t\\tpredictor[i].data = predictor[i].data.reindex(sorted(predictor[i].data.columns),axis=1)\\n\\t\\t\\n\\t\\t#remove stations with no predictand data\\n\\t\\tk=0\\n\\t\\ta=miss_stations[:]\\n\\t\\tfor j in predictor[i].data.columns:\\n\\t\\t\\tif not a:\\n\\t\\t\\t\\tbreak\\n\\t\\t\\tif j==a[k]:\\n\\t\\t\\t\\tpredictor[i].data=predictor[i].data.drop(j,axis=1)\\n\\t\\t\\t\\tdel a[k]\\n\\t\\t\\n\\t\\t#add data to final dataframe\\n\\t\\tfor b in range(len(predr_gmt)):\\n\\t\\t\\tfor c in range(len(stations)):\\n\\t\\t\\t\\tk = b*len(stations)+c\\n\\t\\t\\t\\tarr[k][i] = predictor[i].data.iloc[b][c]\\n\\n\\t#add predictand data\\n\\tfor i in range(len(predictand)):\\n\\t\\t#removing extra underscore, adding variable name to column names\\n\\t\\tnames[len(predictor)+2+i]='METAR_'+predictand[i].get_variable_name()[:-1]\\n\\t\\n\\t\\t#resize array and create pandas dataframe\\n\\t\\tpredictand[i].data = np.squeeze(predictand[i].data,axis=2)\\n\\t\\tpredictand[i].data = pd.DataFrame(predictand[i].data,columns=predd_stations,index=predd_hours)\\n\\t\\tpredictand[i].data = predictand[i].data.reindex(sorted(predictand[i].data.columns),axis=1)\\n\\t\\t\\n\\t\\t#remove extra days of predictand data\\n\\t\\tpredictand[i].data = predictand[i].data.iloc[0:len(predr_time),:]\\n\\t\\t\\t\\n\\t\\t#add predictand data to array\\n\\t\\tfor b in range(len(predr_gmt)):\\n\\t\\t\\tfor c in range(len(stations)):\\n\\t\\t\\t\\tk = b*len(stations)+c\\n\\t\\t\\t\\tval = predictand[i].data.iloc[b][c]\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t#catch metar fill data\\n\\t\\t\\t\\tif val == 9999: \\n\\t\\t\\t\\t\\tval = np.nan\\n\\t\\t\\t\\tarr[k][len(predictor)+i]=val\\n\\t\\n\\t#add station and time data to array and save as csv\\n\\tdata = np.concatenate([info,arr],axis = 1)\\n\\tto_save = pd.DataFrame(data,columns=names)\\n\\tto_save.to_csv(str(start)+'_'+str(end)+'_'+str(lead_time)+'hrs.csv')\",\n \"def substation_main(locator, total_demand, building_names, gv, Flag):\\n\\n t0 = time.clock()\\n # generate empty vectors\\n Ths = np.zeros(8760)\\n Tww = np.zeros(8760)\\n Tcs = np.zeros(8760) + 1E6\\n\\n # determine grid target temperatures at costumer side.\\n iteration = 0\\n buildings = []\\n for name in building_names:\\n buildings.append(pd.read_csv(locator.get_demand_results_folder() + '//' + name + \\\".csv\\\",\\n usecols=['Name', 'Thsf_sup_C', 'Thsf_re_C', 'Tcsf_sup_C', 'Tcsf_re_C',\\n 'Twwf_sup_C', 'Twwf_re_C', 'Qhsf_kWh', 'Qcsf_kWh', 'Qwwf_kWh',\\n 'mcphsf_kWperC', 'mcpwwf_kWperC', 'mcpcsf_kWperC',\\n 'Ef_kWh']))\\n Ths = np.vectorize(calc_DH_supply)(Ths.copy(), buildings[iteration].Thsf_sup_C.values)\\n Tww = np.vectorize(calc_DH_supply)(Tww.copy(), buildings[iteration].Twwf_sup_C.values)\\n Tcs = np.vectorize(calc_DC_supply)(Tcs.copy(), buildings[iteration].Tcsf_sup_C.values)\\n iteration += 1\\n T_DHS = np.vectorize(calc_DH_supply)(Ths, Tww)\\n T_DHS_supply = np.where(T_DHS > 0, T_DHS + gv.dT_heat, T_DHS)\\n T_DCS_supply = np.where(Tcs != 1E6, Tcs - gv.dT_cool, 0)\\n\\n # Calculate disconnected buildings files and substation operation.\\n if Flag:\\n index = 0\\n combi = [0] * len(building_names)\\n for name in building_names:\\n print name\\n dfRes = total_demand[(total_demand.Name == name)]\\n combi[index] = 1\\n key = \\\"\\\".join(str(e) for e in combi)\\n dfRes.to_csv(locator.get_optimization_substations_total_file(key), sep=',', float_format='%.3f')\\n combi[index] = 0\\n # calculate substation parameters per building\\n substation_model(locator, gv, buildings[index], T_DHS, T_DHS_supply, T_DCS_supply, Ths, Tww)\\n index += 1\\n else:\\n index =0\\n # calculate substation parameters per building\\n for name in building_names:\\n substation_model(locator, gv, buildings[index], T_DHS, T_DHS_supply, T_DCS_supply, Ths, Tww)\\n index += 1\\n print time.clock() - t0, \\\"seconds process time for the Substation Routine \\\\n\\\"\",\n \"def measurements():\\n measurements_for_displaying = db.session.query(Measurement).all()\\n return render_template('measurement/measurements.html', measurements=measurements_for_displaying)\",\n \"def initialize_data(self , station = '', datasets = {} ):\\n \\n self.datasets = datasets\\n self.datasets_keys = datasets.keys()\\n self.station = station\\n \\n data = {} # container for the data of each dataset\\n source_configuration = {} # container for the source_configuration of each dataset\\n \\n\\n \\n \\\"\\\"\\\" Looping over the datasets \\\"\\\"\\\"\\n logging.info('*** Reading and Initializing the data from the netCDF files ')\\n \\n \\n for k,v in datasets.items() :\\n logging.info(' Initialising the dataset: *** %s ' , k )\\n data[k] = {} \\n data['cdm_tables'] = {} \\n \\n ### alternative with xarray \\n #ds = xr.load_dataset(v) \\n #observations_table = xr.open_dataset(v , engine = 'h5netcdf' , group = 'observations_table') \\n \\n ### alternative with netCDF4\\n #ds = nc.Dataset(v) \\n #data[k]['dateindex'] = ds.variables['dateindex'][0,:] # storing the dateindex \\n \\n ###for h5py but cant extract date time units !!!\\n ds = h5py.File(v , driver=\\\"core\\\" ) \\n data[k]['df'] = ds # storing the entire file \\n try: \\n data[k]['source_file'] = ds['source_configuration']['source_file'][0]\\n except:\\n data[k]['source_file'] = str(v) # temp fix \\n \\n #data[k]['product_code'] = ds['source_configuration']['product_code'][0] \\n #data[k]['recordtimestamp'] = ds['recordtimestamp'].value\\n #data[k]['recordindex'] = ds['recordindex'].value \\n #ds.close() \\n logging.debug('Reading the file with h5py ')\\n \\n \\n # add here appending datasets for the case of ncar_w and ncar_t \\n \\n \\n self.data = data\\n self.make_dataframe()\\n ds.close()\\n \\n \\\"\\\"\\\" Reading the header_table, station_configuration, source_configuration \\\"\\\"\\\"\\n for k,v in datasets.items() : \\n \\n #d = xr.open_dataset(v , engine = 'h5netcdf' ) \\n #data[k]['recordtimestamp'] = d['recordtimestamp'].values\\n #data[k]['recordindex'] = d['recordindex'].values \\n \\n \\n d = xr.open_dataset(v , engine = 'h5netcdf' , group = 'station_configuration') \\n data[k]['station_configuration'] = d.to_dataframe() \\n #data[k]['station_configuration'] = d ### USELESS ? \\n logging.debug('Done with %s station_configuration' , str(k) )\\n \\n \\n d = xr.open_dataset(v , engine = 'h5netcdf' , group = 'header_table') \\n logging.debug('Loading the header_table') \\n if 'header_table' not in list( self.attributes.keys() ): # saving the attributes to be re-applied at the end\\n self.attributes['header_table'] = {}\\n for var in d.variables:\\n self.attributes['header_table'][var] = {}\\n self.attributes['header_table'][var]['description'] = d[var].description\\n self.attributes['header_table'][var]['external_table'] = d[var].external_table \\n data[k]['header_table'] = d.to_dataframe() \\n logging.debug('Done with %s ' , k )\\n \\n logging.info(\\\"*** Loading the observations_table (might take time) %s\\\" , k ) \\n d = xr.open_dataset(v , engine = 'h5netcdf' , group = 'observations_table') \\n \\n if 'observations_table' not in list( self.attributes.keys() ): # saving the attributes to be re-applied at the end\\n self.attributes['observations_table'] = {}\\n for var in d.variables:\\n self.attributes['observations_table'][var] = {}\\n self.attributes['observations_table'][var]['description'] = d[var].description\\n self.attributes['observations_table'][var]['external_table'] = d[var].external_table\\n \\n \\n logging.info(\\\"*** Loading the source configuration %s\\\" , k ) \\n try: \\n d = xr.open_dataset(v , engine = 'h5netcdf' , group = 'source_configuration')\\n d = d.isel(hdrlen=[0])\\n data[k]['source_configuration'] = d.to_dataframe() ### USELESS ? \\n logging.debug('Done with %s source_configuration' , k )\\n except: \\n data[k]['source_configuration']= pd.DataFrame(np.array( [ [ self.data[k]['source_file'] ] ] ) , columns=['source_file'] ) \\n \\n if k == 'era5_1': # reading the whole era5_1 feedback (including reanalysis)\\n d = xr.open_dataset(v , engine = 'h5netcdf' , group = 'era5fb') \\n data[k]['era5fb'] = d.to_dataframe() \\n logging.debug('Done with %s era5 feedback ', k )\\n \\n \\\"\\\"\\\" Reading the CDM tables that do not depend on specific stations or observations (fixed values), for the first file only \\\"\\\"\\\" \\n if list(datasets.keys()).index(k) == 0 :\\n for t in [ 'crs' , 'observed_variable', 'units' , 'z_coordinate_type' , 'station_type']: \\n \\n d = xr.open_dataset(v , engine = 'h5netcdf' , group = t) \\n #data['cdm_tables'][t] = d.to_dataframe() ### USELESS ?\\n data['cdm_tables'][t] = d \\n \\n d.close() \\n ds.close()\\n\\n \\\"\\\"\\\" Reading the name of the original source file \\\"\\\"\\\"\\n source_configuration[k] = {} \\n source_configuration[k]['source_file'] = [ c for c in v.split('/') if '.nc' in c][0]\\n\\n \\n \\\"\\\"\\\" Storing the station configurations \\\"\\\"\\\" \\n self.source_configuration = source_configuration \\n \\n \\\"\\\"\\\" Making all date_times \\\"\\\"\\\" \\n self.make_all_datetime()\\n \\n \\n \\\"\\\"\\\" feedback columns \\\"\\\"\\\"\\n if 'era5_1' in list (self.data.keys() ):\\n self.fb_columns = list(self.data['era5_1']['era5fb'].columns ) \\n else:\\n self.fb_columns = ['empty']\",\n \"def load_station_table(txn):\\n LOG.info(\\\"load_station_table called() ...\\\")\\n txn.execute(\\n \\\"SELECT id, ST_x(geom) as lon, ST_y(geom) as lat from stations \\\"\\n \\\"where network in ('NEXRAD','TWDR')\\\"\\n )\\n for row in txn.fetchall():\\n ST[row[\\\"id\\\"]] = {\\\"lat\\\": row[\\\"lat\\\"], \\\"lon\\\": row[\\\"lon\\\"]}\\n LOG.info(\\\"Station Table size %s\\\", len(ST.keys()))\",\n \"def run(self):\\n\\n for table in self.TABLES:\\n self.dictionary_cursor.execute(f\\\"TRUNCATE TABLE {table}_Work\\\")\\n self.dictionary_conn.commit()\\n self.logger.info(\\\"work tables cleared\\\")\\n for id in self.ids:\\n drug = self.Drug(self, id)\\n if drug.wanted:\\n drug.load()\\n self.logger.info(\\\"work tables populated\\\")\\n for table in self.TABLES:\\n insert = f\\\"INSERT INTO {table} SELECT * FROM {table}_Work\\\"\\n self.dictionary_cursor.execute(f\\\"TRUNCATE TABLE {table}\\\")\\n self.dictionary_cursor.execute(insert)\\n self.dictionary_conn.commit()\\n self.logger.info(\\\"live tables ready\\\")\",\n \"def read_table_stations(self):\\n if not os.path.exists(self.station_table_filename):\\n LOGGER.warning('could not find station.table file \\\"%s\\\"', self.station_table_filename)\\n return self.known_stations\\n count = 0\\n with open(self.station_table_filename, 'r') as textfile:\\n lines = textfile.read().split(LF)\\n for line in lines:\\n station_id, data = read_table_station_from_line(line)\\n if station_id is not None:\\n self.known_stations[station_id] = data\\n count += 1\\n self.station_file_age = os.path.getmtime(self.station_table_filename)\\n LOGGER.info(' Loaded %i station records from \\\"%s\\\"', count, self.station_table_filename)\\n return self.known_stations\",\n \"def _extract_all(sel,\\n rootpath=r'D:\\\\WorkStation_2018\\\\WorkStation_dynamicFC\\\\Workstation_dynamic_fc_baobaoComputer\\\\Data\\\\Dynamic',\\n whichstate='state1'):\\n\\n # hc\\n group_name = 'HC'\\n data_hc = sel._extract_one(rootpath, whichstate, group_name)\\n\\n # mdd\\n group_name = 'MDD'\\n data_mdd = sel._extract_one(rootpath, whichstate, group_name)\\n\\n # bd\\n group_name = 'BD'\\n data_bd = sel._extract_one(rootpath, whichstate, group_name)\\n\\n # sz\\n group_name = 'SZ'\\n data_sz = sel._extract_one(rootpath, whichstate, group_name)\\n\\n # concat\\n data_all = pd.concat([data_hc, data_mdd, data_bd, data_sz], axis=0)\\n data_all.index = np.arange(0, np.shape(data_all)[0])\\n\\n return data_all\",\n \"def station_stats(df):\",\n \"def collect_data(default_path, elects_d, widths, lmbdas):\\n cols = [\\\"elect\\\", \\\"d\\\", \\\"lmbda\\\", \\\\\\n \\\"Dx\\\", \\\"Dy\\\", \\\"Dz\\\", \\\"Dxy\\\", \\\"Dyz\\\", \\\"Dxz\\\", \\\"D3d\\\"]\\n index = np.arange(len(lmbdas) * len(widths) * len(elects_d))\\n df = DataFrame(index=index, columns=cols)\\n\\n cnt = 0\\n for el in sorted(elects_d.keys()):\\n for d in widths:\\n for l in lmbdas:\\n data = [elects_d[el], d, l]\\n fname = \\\"diffusivity_%s_d%i_l%i.log\\\" % (el, d, l)\\n fpath = default_path + fname\\n try:\\n f = open(fpath, \\\"r\\\").readlines()\\n for line in f:\\n if \\\"1d in SI\\\" in line:\\n data.extend(np.array(line.split()[3:]).astype(float))\\n if \\\"2d in SI\\\" in line:\\n data.extend(np.array(line.split()[3:]).astype(float))\\n if \\\"3d in SI\\\" in line:\\n data.extend(np.array(line.split()[3:]).astype(float))\\n except FileNotFoundError:\\n print(\\\"File not found: %s.\\\" % fpath)\\n\\n if len(data) == 10:\\n df.loc[cnt] = data\\n cnt += 1\\n return df\",\n \"def write_to_vacancies_metro_stations_table(config, tables_cache):\\r\\n database = deepcopy(config[\\\"database\\\"])\\r\\n vacancies_metro_stations_table = \\\\\\r\\n deepcopy(config[\\\"tables\\\"][\\\"vacancies_metro_stations_table\\\"])\\r\\n in_tests.test_write_to_database_from_dict(\\r\\n database, vacancies_metro_stations_table, tables_cache)\\r\\n\\r\\n vacancy_id = tables_cache[\\\"id\\\"]\\r\\n station_id = tables_cache[\\\"address_metro_stations_station_id\\\"]\\r\\n\\r\\n if station_id:\\r\\n write_to_database(database, vacancies_metro_stations_table, {\\r\\n \\\"vacancy_id\\\": vacancy_id,\\r\\n \\\"metro_station_id\\\": station_id\\r\\n })\\r\\n return ()\",\n \"def populate_database(self):\\n self.insert_products()\\n self.insert_categories()\\n self.insert_products_categories()\\n self.insert_stores()\\n self.insert_products_stores()\",\n \"def fill_obs(self, observation_table, data_store):\\n for obs in observation_table:\\n events = data_store.obs(obs_id=obs['OBS_ID']).events\\n\\n # TODO: filter out (mask) possible sources in the data\\n # for now, the observation table should not contain any\\n # run at or near an existing source\\n\\n self.counts_cube.fill_events([events])\\n self.livetime_cube.data += events.observation_live_time_duration\",\n \"def _setupWeather(self, w, config):\\n wnames = ('cloud', 'seeing')\\n if w not in wnames:\\n raise Exception('w should be one of %s' %(wnames))\\n filename = config['%s_datafile' %(w)]\\n file = open(filename, 'r')\\n # Also assume flat file contains only date / value in a space or tab separated file. \\n self.dates[w] = []\\n self.weather[w] = []\\n # Read the data file.\\n print '# Reading weather data file %s' %(filename)\\n for line in file:\\n if line.startswith('#') | line.startswith('!'):\\n continue\\n self.dates[w].append(line.split()[0])\\n self.weather[w].append(line.split()[1])\\n file.close()\\n self.dates[w] = numpy.array(self.dates[w], float)\\n self.weather[w] = numpy.array(self.weather[w], float)\\n # Check the total amount of data (mostly for user awareness):\\n print '# Read %d weather values from %s file. ' %(len(self.weather[w]), filename)\\n # Check that weather data is monotonically increasing in time. \\n if not(numpy.all(numpy.diff(self.dates[w]))):\\n order = self.dates[w].argsort()\\n self.weather[w] = self.weather[w][order]\\n self.dates[w] = self.dates[w][order]\\n # Get the total length of time included in this (seeing/cloud) file,\\n # so that we can determine a wrap-around date if we need that.\\n self.maxtime[w] = self.dates[w].max()\\n return\",\n \"def collect_stats(self):\\n\\n df_avg, self.transport_df, self.customer_df, self.manager_df, self.station_df = self.get_stats_dataframes()\\n\\n columns = []\\n if self.config.simulation_name:\\n df_avg[\\\"Simulation Name\\\"] = self.config.simulation_name\\n columns = [\\\"Simulation Name\\\"]\\n columns += [\\\"Avg Waiting Time\\\", \\\"Avg Total Time\\\", \\\"Simulation Time\\\"]\\n if self.config.max_time:\\n df_avg[\\\"Max Time\\\"] = self.config.max_time\\n columns += [\\\"Max Time\\\"]\\n columns += [\\\"Simulation Finished\\\"]\\n self.df_avg = df_avg[columns]\",\n \"def get_basic_data(self):\\n\\n db = DataBase().clear_table()\\n\\n data = self.scraper.scrape_top_250()\\n for d in data:\\n title = d.find(\\\"td\\\", class_=\\\"titleColumn\\\")\\n title = title.find(\\\"a\\\")\\n title = re.sub(\\\"<.*?>\\\", \\\"\\\", str(title))\\n\\n film_id = d.find(\\\"td\\\", class_=\\\"watchlistColumn\\\")\\n film_id = film_id.find(\\\"div\\\")\\n film_id = film_id[\\\"data-tconst\\\"]\\n\\n year = d.find(\\\"span\\\", class_=\\\"secondaryInfo\\\")\\n year = re.sub(\\\"<.*?>\\\", \\\"\\\", str(year)).replace(\\\"(\\\", \\\"\\\").replace(\\\")\\\", \\\"\\\")\\n\\n director = d.find(\\\"td\\\", class_=\\\"titleColumn\\\")\\n director = director.find(\\\"a\\\")\\n director = director[\\\"title\\\"]\\n director, *cast = director.split(\\\", \\\")\\n director = director.replace(\\\" (dir.)\\\", \\\"\\\")\\n\\n rating = d.find(\\\"td\\\", class_=\\\"ratingColumn imdbRating\\\")\\n rating = rating.find(\\\"strong\\\")\\n rating = re.sub(\\\"<.*?>\\\", \\\"\\\", str(rating))\\n\\n poster = d.find(\\\"td\\\", class_=\\\"posterColumn\\\")\\n poster = poster.find(\\\"img\\\")[\\\"src\\\"]\\n poster = re.sub(\\\"@.+\\\", \\\"@._V1_FMjpg_UY474_.jpg\\\", poster)\\n\\n DataBase().populate_table(\\n (title, film_id, year, director, \\\", \\\".join(cast), rating, poster)\\n )\",\n \"def update(self):\\n if self.last_update and (\\n self.last_update + timedelta(hours=1)\\n > datetime.utcnow().replace(tzinfo=dt_util.UTC)\\n ):\\n return # Not time to update yet; data is only hourly\\n\\n for row in self.current_observations():\\n if row.get(\\\"Station\\\") == self._station_id:\\n api_fields = {\\n col_heading: (standard_name, dtype)\\n for standard_name, (\\n _,\\n _,\\n _,\\n col_heading,\\n dtype,\\n ) in SENSOR_TYPES.items()\\n }\\n self.data = {\\n api_fields.get(col_heading)[0]: api_fields.get(col_heading)[1](\\n v.replace(\\\",\\\", \\\".\\\")\\n )\\n for col_heading, v in row.items()\\n if col_heading in api_fields and v\\n }\\n break\\n else:\\n raise ValueError(f\\\"No weather data for station {self._station_id}\\\")\",\n \"def asdf_create(asdf_name, wav_dirs, sta_dir):\\n\\n with pyasdf.ASDFDataSet(asdf_name) as ds:\\n wav_files = []\\n for wav_dir in wav_dirs:\\n wav_files.extend([os.path.join(root, a_file)\\n for root, dirs, files in os.walk(wav_dir)\\n for a_file in files])\\n for _i, filename in enumerate(wav_files):\\n print(\\\"Adding mseed file %i of %i...\\\" % (_i+1, len(wav_files)))\\n st = read(filename)\\n #Add waveforms\\n ds.add_waveforms(st, tag=\\\"raw_recording\\\")\\n sta_files = glob('%s/*' % sta_dir)\\n for filename in sta_files:\\n ds.add_stationxml(filename)\\n return\",\n \"def download():\\r\\n reader = GSODDataReader()\\r\\n year_list = range(2001, 2012)\\r\\n austin = reader.collect_data(year_list, exact_station=True,\\r\\n station_name='AUSTIN CAMP MABRY', state='TX', country='US')\\r\\n houston = reader.collect_data(year_list, exact_station=True,\\r\\n station_name='HOUSTON/D.W. HOOKS', state='TX', country='US')\\r\\n new_york = reader.collect_data(year_list, exact_station=True,\\r\\n station_name='NEW YORK/LA GUARDIA', state='NY', country='US')\\r\\n newark = reader.collect_data(year_list, exact_station=True,\\r\\n station_name='NEWARK INTL AIRPORT', state='NJ', country='US')\\r\\n punta_arenas = reader.collect_data(year_list, exact_station=True,\\r\\n station_name='PUNTA ARENAS', country='CH')\\r\\n wellington = reader.collect_data(year_list, exact_station=True,\\r\\n station_name='WELLINGTON AIRPORT', country='NZ')\\r\\n store = HDFStore('weather.h5')\\r\\n store['austin'] = austin\\r\\n store['houston'] = houston\\r\\n store['nyc'] = new_york\\r\\n store['newark'] = newark\\r\\n store['punta_arenas'] = punta_arenas\\r\\n store['wellington'] = wellington\\r\\n store.close()\",\n \"def populated_archivist_dataset(archivist_dataset, tmp_path_factory):\\n wpath = tmp_path_factory.mktemp(\\\"archivistds\\\")\\n\\n ads = archivist_dataset\\n\\n dscontent = (\\n ('azip/file1.txt', 'zipfile1'),\\n ('azip/file2.csv', 'zipfile2_muchcontent'),\\n ('atar/file1.txt', 'tarfile1'),\\n ('atar/file2.csv', 'tarfile2_muchcontent'),\\n )\\n srcds = Dataset(wpath / 'srcds').create(**nonoise)\\n for fpath, fcontent in dscontent:\\n fpath = srcds.pathobj / (PurePosixPath(fpath))\\n fpath.parent.mkdir(parents=True, exist_ok=True)\\n fpath.write_text(fcontent)\\n srcds.save(**nonoise)\\n\\n archive_root = wpath / 'myarchive'\\n #archivetype = 'zip'\\n\\n akeys = {}\\n\\n # no ZIP just yet\\n # for archivetype, ext in (('zip', ''), ('tar', '.gz')):\\n for archivetype, ext in (('tar', '.gz'), ):\\n archive_path = Path(f\\\"{archive_root}.{archivetype}{ext}\\\")\\n\\n archive_path_inds = ads.pathobj / '.archives' / archive_path.name\\n # create an archive, the easy way, by simply exporting the\\n # entire dataset worktree\\n srcds.export_archive(archive_root, archivetype=archivetype,\\n **nonoise)\\n assert archive_path.exists()\\n\\n # add the archive (in a hidden dir) to be able to reference\\n # it via a key\\n aurl = archive_path.as_uri()\\n ads.repo.call_annex([\\n 'addurl', '--file', str(archive_path_inds), aurl])\\n ads.save(**nonoise)\\n # get the key of the archive\\n akeys[archivetype] = ads.status(\\n archive_path_inds, annex='basic', return_type='item-or-list',\\n **nonoise)['key']\\n return ads, akeys, archive_root, dscontent\",\n \"def analyze_all(datadir, TPQI_starts, dataruns, save = 1, lower = 38.4):\\n dirs = os.listdir(datadir)\\n idx = 0\\n right_dirs = list()\\n\\n\\n for l in dataruns:\\n for k in arange(len(dirs)):\\n mark_right = '_interference_'+num2str(l,0) in dirs[k]\\n \\n if mark_right and (len(dirs[k]) > len('_interference_'+num2str(l,0))+6):\\n mark_right = False\\n\\n if mark_right:\\n right_dirs.append(dirs[k])\\n idx += 1\\n continue\\n\\n \\n if len(right_dirs) == 0:\\n print 'Did not find any files'\\n\\n if len(dataruns) == len(right_dirs):\\n print 'Found all files...'\\n else:\\n print 'Beware, not all files are taken into account, file(s) missing.'\\n \\n tail_over_time = zeros(len(right_dirs))\\n tpqi_starts = TPQI_starts[dataruns]\\n statistics_info = zeros([len(right_dirs),4])\\n \\n for k in arange(len(right_dirs)):\\n tail_over_time[k] = tail_cts_per_shot(datapath = datadir+'\\\\\\\\'+right_dirs[k], lower = lower, TPQI_starts = tpqi_starts[k], save = save)\\n statistics_info[k,:] = analyze_thresholds(datapath = datadir+'\\\\\\\\'+right_dirs[k], threshold_lt1 = 0, threshold_lt2 = 9, normalize = True, save = save)\\n\\n\\n os.chdir(datadir)\\n percentage_finished = float(k+1)/len(right_dirs)*100\\n print 'finished: '+num2str(percentage_finished,0)+'%'\\n\\n\\n if save:\\n times_passed_overall_lt1 = statistics_info[:,0]\\n times_passed_after_seq_lt1 = statistics_info[:,1]\\n times_passed_overall_lt2 = statistics_info[:,2]\\n times_passed_after_seq_lt2 = statistics_info[:,3]\\n filename = 'statistics_run_'+num2str(dataruns.min(),0)+'_to_'+num2str(dataruns.max(),0)+'.npz' \\n savez(filename, tpqi_starts = tpqi_starts, tail_over_time = tail_over_time,\\n times_passed_overall_lt1 = times_passed_overall_lt1, \\n times_passed_after_seq_lt1 = times_passed_after_seq_lt1, \\n times_passed_overall_lt2 = times_passed_overall_lt2,\\n times_passed_after_seq_lt2 = times_passed_after_seq_lt2)\\n\\n \\n\\n figure3 = plt.figure(figsize=(12.0, 16.0))\\n plt.subplot(211)\\n plt.plot(dataruns,tail_over_time*1E4, '-k')\\n plt.xlabel('TPQI run number')\\n plt.ylabel('Tail counts per shot (x 1E-4)')\\n plt.grid()\\n plt.ylim([0,1.1*max(tail_over_time*1E4)])\\n\\n plt.subplot(212)\\n plt.plot(dataruns,TPQI_starts[0:len(right_dirs)], '-k')\\n plt.xlabel('TPQI run number')\\n plt.ylabel('TPQI starts per run')\\n plt.grid()\\n plt.ylim([0, 1.1*TPQI_starts[0:len(right_dirs)].max()])\\n if save:\\n figure3.savefig('tpqi_starts_and_tail_over_time.png')\",\n \"def write_all_data_tables( phasename, eos_prop_d, output_d ):\\n\\n dataio.write_data_table( 'temperature_' + phasename + '.dat',\\n (eos_prop_d[key] for key in\\n ('Pmesh_a', 'Smesh_a', 'Tmesh_a')),\\n ('GPa', 'eV', 1), output_d )\\n\\n dataio.write_data_table( 'density_' + phasename + '.dat',\\n (eos_prop_d[key] for key in\\n ('Pmesh_a', 'Smesh_a', 'rhomesh_a')),\\n ('GPa', 'eV','g_cc'), output_d )\\n dataio.write_data_table( 'heat_capacity_' + phasename + '.dat',\\n (eos_prop_d[key] for key in\\n ('Pmesh_a', 'Smesh_a', 'Cpmesh_a')),\\n ('GPa','eV','eV'), output_d )\\n\\n dataio.write_data_table( 'thermal_exp_' + phasename + '.dat',\\n (eos_prop_d[key] for key in\\n ('Pmesh_a', 'Smesh_a', 'alphamesh_a')),\\n ('GPa','eV',1), output_d )\\n\\n dataio.write_data_table( 'adiabat_temp_grad_' + phasename + '.dat',\\n (eos_prop_d[key] for key in\\n ('Pmesh_a', 'Smesh_a', 'dTdP_Smesh_a')),\\n ('GPa','eV','GPa-1'), output_d )\\n pass\",\n \"def prepare_input(self, only_center = True):\\n \\n if only_center:\\n nx = [0]\\n ny = [0]\\n else:\\n nx = [0,1,-1]\\n ny = [0,1,-1]\\n gauge = dd.read_csv(str(Path(self.db_location, 'gauge', '*.csv.gz')), \\n compression='gzip', \\n assume_missing=True,\\n dtype = {'TIMESTAMP':int, 'STATION': str})\\n \\n gauge = gauge.compute().drop_duplicates()\\n gauge = gauge.replace(-9999,np.nan)\\n for x in nx:\\n for y in ny:\\n logging.info('Processing neighbour {:d}{:d}'.format(x, y))\\n radar = dd.read_parquet(str(Path(self.db_location, 'radar',\\n '*.parquet')))\\n refer = dd.read_parquet(str(Path(self.db_location, 'reference', \\n '*.parquet')))\\n \\n # Select only required pixel\\n radar = radar.loc[np.logical_and(radar['NX'] == x, \\n radar['NY'] == y)]\\n refer = refer.loc[np.logical_and(refer['NX'] == x, \\n refer['NY'] == y)]\\n \\n # Convert to pandas and remove duplicates \\n radar = radar.compute().drop_duplicates(subset = ['TIMESTAMP',\\n 'STATION',\\n 'RADAR',\\n 'NX','NY',\\n 'SWEEP'])\\n \\n refer = refer.compute().drop_duplicates(subset = ['TIMESTAMP',\\n 'STATION'])\\n \\n radar = radar.sort_values(by = ['TIMESTAMP','STATION','SWEEP'])\\n refer = refer.sort_values(by = ['TIMESTAMP','STATION'])\\n gauge = gauge.sort_values(by = ['TIMESTAMP','STATION'])\\n # Get only valid precip data\\n gauge = gauge[np.isfinite(gauge['RRE150Z0'])]\\n \\n # Create individual 10 min - station stamps\\n gauge['s-tstamp'] = np.array(gauge['STATION'] + \\n gauge['TIMESTAMP'].astype(str)).astype(str)\\n radar['s-tstamp'] = np.array(radar['STATION'] + \\n radar['TIMESTAMP'].astype(str)).astype(str)\\n refer['s-tstamp'] = np.array(refer['STATION'] + \\n refer['TIMESTAMP'].astype(str)).astype(str)\\n \\n # Get gauge and reference only when radar data available\\n \\n # Find timestamps that are in the three datasets\\n ststamp_common = np.array(pd.Series(list(set(gauge['s-tstamp'])\\n .intersection(set(refer['s-tstamp'])))))\\n ststamp_common = np.array(pd.Series(list(set(radar['s-tstamp'])\\n .intersection(set(ststamp_common)))))\\n radar = radar.loc[radar['s-tstamp'].isin(ststamp_common)]\\n gauge = gauge.loc[gauge['s-tstamp'].isin(ststamp_common)]\\n refer = refer.loc[refer['s-tstamp'].isin(ststamp_common)]\\n \\n \\n # Filter incomplete hours\\n stahour = np.array(gauge['STATION'] + \\n ((gauge['TIMESTAMP'] - 600 ) - \\n (gauge['TIMESTAMP'] - 600 ) % 3600).astype(str)).astype(str)\\n \\n full_hours = np.array(gauge.groupby(stahour)['STATION']\\n .transform('count') == 6)\\n \\n refer = refer.reindex[full_hours]\\n gauge = gauge.reindex[full_hours] \\n radar = radar.reindex[radar['s-tstamp'].\\n isin(np.array(gauge['s-tstamp']))]\\n \\n stahour = stahour[full_hours]\\n \\n # Creating vertical grouping index\\n \\n _, idx, grp_vertical = np.unique(radar['s-tstamp'],\\n return_inverse = True,\\n return_index = True)\\n # Get original order\\n sta_tstamp_unique = radar['s-tstamp'][np.sort(idx)]\\n # Preserves order and avoids sorting radar_statstamp\\n grp_vertical = idx[grp_vertical]\\n # However one issue is that the indexes are not starting from zero with increment\\n # of one, though they are sorted, they are like 0,7,7,7,15,15,23,23\\n # We want them starting from zero with step of one\\n grp_vertical = rankdata(grp_vertical,method='dense') - 1\\n \\n # Repeat operation with gauge hours\\n sta_hourly_unique, idx, grp_hourly = np.unique(stahour, \\n return_inverse = True,\\n return_index = True)\\n grp_hourly = idx[grp_hourly]\\n \\n # Add derived variables height iso0 (HISO) and height above ground (HAG)\\n # Radar\\n stations = constants.METSTATIONS\\n cols = list(stations.columns)\\n cols[1] = 'STATION'\\n stations.columns = cols\\n radar = pd.merge(radar,stations, how = 'left', on = 'STATION',\\n sort = False)\\n \\n radar['HISO'] = -radar['T'] / constants.LAPSE_RATE * 100\\n radar['HAG'] = radar['HEIGHT'] - radar['Z']\\n radar['HAG'][radar['HAG'] < 0] = 0\\n \\n # Gauge\\n gauge['minutes'] = (gauge['TIMESTAMP'] % 3600)/60\\n \\n # Save all to file\\n refer.to_parquet(str(Path(self.input_location, \\n 'reference_x{:d}y{:d}.parquet'.format(x,y))),\\n compression = 'gzip', index = False)\\n \\n radar.to_parquet(str(Path(self.input_location, \\n 'radar_x{:d}y{:d}.parquet'.format(x,y))),\\n compression = 'gzip', index = False)\\n \\n grp_idx = {}\\n grp_idx['grp_vertical'] = grp_vertical\\n grp_idx['grp_hourly'] = grp_hourly\\n grp_idx['tstamp_unique'] = sta_tstamp_unique\\n \\n pickle.dump(grp_idx, \\n open(str(Path(self.input_location, \\n 'grouping_idx_x{:d}y{:d}.p'.format(x,y))),'wb'))\\n \\n if x == 0 and y == 0:\\n # Save only gauge for center pixel since it's available only there\\n gauge.to_parquet(str(Path(self.input_location, 'gauge.parquet')),\\n compression = 'gzip', index = False)\",\n \"def importAllDatasets(directory):\\n head_index = findIndex(temp_list, \\\"Gaze\\\")\\n point_index = findIndex(temp_list, \\\"Point\\\")\\n grab_index = findIndex(temp_list, \\\"Grab\\\")\\n pos_index = findIndex(temp_list, \\\"Position\\\")\\n\\n head_data = pd.read_csv(temp_list[head_index]) if head_index != None else None\\n point_data = pd.read_csv(temp_list[point_index]) if point_index != None else None\\n grab_data = pd.read_csv(temp_list[grab_index]) if grab_index != None else None\\n pos_data = pd.read_csv(temp_list[pos_index]) if pos_index != None else None\\n\\n\\n return head_data, point_data, grab_data, pos_data\",\n \"def stations():\\n # Query all station names from dataset\\n station_list = session.query(Measurement.station).distinct().all()\\n all_stations = list(np.ravel(station_list))\\n\\n return jsonify(all_stations)\",\n \"def prepare_for_influxdb(df):\\n df = df.drop(columns=\\\"landkreis\\\", errors=\\\"ignore\\\") # prevent name collision in get_ags()\\n df = get_ags(df)\\n df[\\\"time\\\"] = df.apply(lambda x: 1000000000*int(datetime.timestamp((pd.to_datetime(x[\\\"timestamp\\\"])))), 1)\\n df[\\\"measurement\\\"] = \\\"hystreet\\\"\\n df[\\\"origin\\\"] = \\\"https://hystreet.com\\\"\\n df = df.rename(columns={\\n 'station_id': '_id',\\n 'pedestrians_count': 'pedestrian_count',\\n 'state': 'bundesland'\\n })\\n df['ags'] = pd.to_numeric(df['ags'])\\n # import pdb; pdb.set_trace()\\n return df\",\n \"def assemble(self, dt_range=None):\\n if dt_range is not None:\\n self.dt_list = trace_source.time_list(dt_range[0],\\n dt_range[1],\\n self.config['time']['step'])\\n\\n # only for the testcase\\n traj_dir = self.config['partposit_dir']\\n days_avail = os.listdir(traj_dir)\\n # filter only for the trajectory files with tdump extension\\n days_avail = [f for f in days_avail if len(f) == 11]\\n print(days_avail)\\n folders = [f for f in days_avail if datetime.datetime.strptime(f, \\\"%Y%m%d_%H\\\") in self.dt_list]\\n\\n assert len(folders) > 0, 'no folders with flexpart partposit data'\\n\\n # the defaultdict is used here to sort the files by datetime within a dictionary\\n # filtered_files = defaultdict(list)\\n # for f in files:\\n # # regex the yyyymmdd-hh timestamp in the filename\\n # dt = datetime.datetime.strptime(re.search('([0-9]{8})-([0-9]){2}', f).group(0), '%Y%m%d-%H')\\n # height = float(re.search('([0-9]{3,6})(?=_0[0-9-]{1,4}.tdump)', f).group(0))\\n # #print(f, dt, height)\\n # if dt >= self.dt_list[0] and dt <= self.dt_list[-1]:\\n # filtered_files[dt].append((f,height))\\n\\n # here an empty dict is generated with a zero containing array\\n self.stat2d_dict = defaultdict(lambda: np.zeros((len(self.dt_list), len(self.height_list))))\\n\\n self.statls_dict = defaultdict(lambda: np.zeros((len(self.dt_list), len(self.height_list), 7)))\\n\\n self.raw_dict = defaultdict(lambda: np.zeros((len(self.dt_list), len(self.height_list),\\n abs(self.config['time']['tr_duration'])+1)))\\n\\n # TODO make more than 7 geo names possible\\n ng = trace_source.land_sfc.named_geography(self.config['geonames'])\\n self.geo_names = ng.geo_names\\n no_geo_names = len(list(self.geo_names.keys()))\\n self.statgn_dict = defaultdict(lambda: np.zeros((len(self.dt_list),\\n len(self.height_list),\\n no_geo_names)))\\n\\n\\n self.lat_names = {0: '<-60', 1: '-60..-30', 2:'-30..0', 3: '0..30', 4: '30..60', 5: '>60'}\\n self.statlat_dict = defaultdict(lambda: np.zeros((len(self.dt_list),\\n len(self.height_list),\\n len(list(self.lat_names.keys())))))\\n\\n\\n ls = trace_source.land_sfc.land_sfc()\\n self.ls_categories = ls.categories\\n\\n\\n for it, dt in enumerate(self.dt_list[:]):\\n print('trajectories eding at ', dt)\\n files_for_time = os.listdir(traj_dir + dt.strftime(\\\"%Y%m%d_%H\\\"))\\n files_for_time = sorted([f for f in files_for_time if \\\"partposit_\\\" in f])\\n folder = traj_dir + dt.strftime(\\\"%Y%m%d_%H\\\") + \\\"/\\\"\\n print('files_for_time ', files_for_time)\\n\\n print('heights ', len(self.height_list), self.height_list)\\n\\n flex_stat = [flex_statistics(self.config, ls=ls, ng=ng) for h in self.height_list]\\n traj_meta = read_flexpart_traj_meta(folder + \\\"trajectories.txt\\\")\\n\\n self.no_part.append(traj_meta['releases_meta'][1]['no_particles'])\\n self.time_res.append(10*24/len(files_for_time))\\n\\n # different structure than hysplit\\n # 1. loop through the ending times of the current day\\n # 2. load partposit for a specified time\\n # 3. loop through heights\\n\\n for f in files_for_time:\\n print('files_for_time ', f)\\n part_pos = read_partpositions(folder + f, 1, ctable=True)\\n part_pos = np.array(part_pos)\\n\\n for ih, h in enumerate(self.height_list):\\n #print(\\\"at \\\", ih, h)\\n this_population = np.where(part_pos[:,0] == ih+1)[0]\\n #release_sel = np.array([list(p) for p in part_pos if p[0]==ih+1])\\n release_sel = part_pos[this_population, :]\\n #assert np.all(release_sel == other_release)\\n meta = traj_meta['releases_meta'][ih+1]\\n #print(meta)\\n assert np.mean(meta['heights']) == h, f\\\"{meta['heights']} {h} do not fit\\\"\\n flex_stat[ih].add_partposits_gn(release_sel)\\n\\n flex_stat[ih].add_partposits_ls(release_sel)\\n flex_stat[ih].add_partposits_thres(release_sel)\\n\\n # now assemble the statistics for all heights\\n for ih, h in enumerate(self.height_list): \\n flex_stat[ih].calc_gn_stat()\\n for k in list(flex_stat[ih].stat_gn.keys()):\\n self.stat2d_dict[k+'_no_below'][it, ih] = flex_stat[ih].stat_gn[k].no_below\\n print('stat gn ', h, k, flex_stat[ih].stat_gn[k])\\n self.statgn_dict[k][it, ih] = list(flex_stat[ih].stat_gn[k].counter.values())\\n\\n flex_stat[ih].calc_ls_stat()\\n for k in list(flex_stat[ih].stat_ls.keys()):\\n self.stat2d_dict[k+'_no_below'][it, ih] = flex_stat[ih].stat_ls[k].no_below\\n print('stat ls ', h, k, flex_stat[ih].stat_ls[k])\\n self.statls_dict[k][it, ih] = list(flex_stat[ih].stat_ls[k].counter.values())\\n\\n flex_stat[ih].calc_thres_stat()\\n for k in list(flex_stat[ih].stat_lat.keys()):\\n self.stat2d_dict[k+'_no_below'][it, ih] = flex_stat[ih].stat_lat[k].no_below\\n print('stat_lat ', h, k, flex_stat[ih].stat_lat[k])\\n self.statlat_dict[k][it, ih] = list(flex_stat[ih].stat_lat[k].counter.values())\\n\\n\\n # #assert len(f_list) > 1\\n # for ih, f in enumerate(f_list):\\n # print(it, ih, f[1], dt)\\n # traj = trajectory(self.config)\\n # traj.load_file(traj_dir+f[0], silent=True)\\n # savepath = '{}/{}'.format(self.config['plot_dir'], dt.strftime('%Y%m%d'))\\n\\n\\n # if \\\"timeinterval\\\" in self.config['plotmap']:\\n # timeinterval = self.config['plotmap']['timeinterval']\\n # else:\\n # timeinterval = 12\\n # if \\\"heights\\\" in self.config['plotmap']:\\n # heightlist = self.config['plotmap']['heights']\\n # else:\\n # heightlist = [1500.0, 3000.0, 4500.0]\\n # #if f[1] == 3000.0 and dt.hour % 12 == 0:\\n # if f[1] in heightlist and dt.hour % timeinterval == 0:\\n # print(\\\"plotting \\\", f[1], dt.hour)\\n # plot_trajectories_ens(traj, savepath, ls=ls, config=self.config)\\n # #continue\\n\\n # traj.evaluate(silent=True)\\n # traj.add_land_sfc(ls, silent=True)\\n # traj.add_ensemble_land_sfc(ls)\\n # traj.add_ensemble_geo_names(ng)\\n # #traj.add_area_land_sfc('md', ls, silent=True)\\n # #traj.add_area_land_sfc(2000, ls, silent=True)\\n\\n # #print(\\\"at step\\\", it, dt, ih, f)\\n # #print('keys ', traj.statistics.keys())\\n # # now the empty dict is filled with the keys (and values) of the statistics dict from traj\\n # for k in list(traj.statistics.keys()):\\n # self.stat2d_dict[k][it, ih] = traj.statistics[k]\\n # # subset of trajectory data to collect\\n # param_collect = ['latitude', 'longitude', 'height', \\\"PRESSURE\\\", \\\"AIR_TEMP\\\",\\n # \\\"RAINFALL\\\", \\\"RELHUMID\\\", \\\"TERR_MSL\\\", 'age']\\n # if 'land_sfc_category' in list(traj.data.keys()):\\n # param_collect.append('land_sfc_category')\\n # for k in param_collect:\\n # #self.raw_dict[k][it, ih, :traj.data[1][k].shape[0]] = traj.data[1][k]\\n # self.raw_dict[k][it, ih, :] = traj.data[1][k]\\n # #self.raw_dict[k][it, ih, traj.data[1][k].shape[0]:] = -999.\\n\\n # for k in list(traj.stat_ls.keys()):\\n # self.stat2d_dict[k+'_no_below'][it, ih] = traj.stat_ls[k].no_below\\n # print('stat ls ', k, traj.stat_ls[k])\\n # self.statls_dict[k][it, ih] = list(traj.stat_ls[k].counter.values())\\n\\n # for k in list(traj.stat_gn.keys()):\\n # self.stat2d_dict[k+'_no_below'][it, ih] = traj.stat_gn[k].no_below\\n # print('stat gn ', k, traj.stat_gn[k])\\n # self.statgn_dict[k][it, ih] = list(traj.stat_gn[k].counter.values())\\n\\n # trying to free memory\\n del ls\\n del ng\",\n \"def summarise_data(trip_in, station_data, trip_out):\\n # generate dictionary of station - city mapping\\n station_map = create_station_mapping(station_data)\\n \\n with open(trip_out, 'w') as f_out:\\n # set up csv writer object \\n out_colnames = ['duration', 'start_date', 'start_year',\\n 'start_month', 'start_hour', 'weekday',\\n 'start_city', 'end_city', 'subscription_type'] \\n trip_writer = csv.DictWriter(f_out, fieldnames = out_colnames)\\n trip_writer.writeheader()\\n \\n for data_file in trip_in:\\n with open(data_file, 'r') as f_in:\\n # set up csv reader object\\n trip_reader = csv.DictReader(f_in)\\n\\n # collect data from and process each row\\n for row in trip_reader:\\n new_point = {}\\n \\n # convert duration units from seconds to minutes\\n ### Question 3a: Add a mathematical operation below ###\\n ### to convert durations from seconds to minutes. ###\\n new_point['duration'] = float(row['Duration'])/60\\n \\n # reformat datestrings into multiple columns\\n ### Question 3b: Fill in the blanks below to generate ###\\n ### the expected time values. ###\\n trip_date = datetime.strptime(row['Start Date'], '%m/%d/%Y %H:%M')\\n new_point['start_date'] = trip_date.strftime('%Y-%m-%d')\\n new_point['start_year'] = trip_date.strftime('%Y') # or : trip_date.year\\n new_point['start_month'] = trip_date.strftime('%m') # or : trip_date.month\\n new_point['start_hour'] = trip_date.strftime('%H') # or : trip_date.hour\\n new_point['weekday'] = trip_date.strftime('%a') # or : trip_date.weekday() OR trip_date.isoweekday()\\n\\n \\n # remap start and end terminal with start and end city\\n new_point['start_city'] = station_map[row['Start Terminal']]\\n new_point['end_city'] = station_map[row['End Terminal']]\\n # two different column names for subscribers depending on file\\n if 'Subscription Type' in row:\\n new_point['subscription_type'] = row['Subscription Type']\\n else:\\n new_point['subscription_type'] = row['Subscriber Type']\\n\\n # write the processed information to the output file.\\n trip_writer.writerow(new_point)\",\n \"def stations():\\n \\n session = Session(engine)\\n # Query to bring all stations\\n results = pd.DataFrame(session.query(S.id.label('ID'),S.station.label('Station'),S.name.label('Name'),\\\\\\n S.latitude.label('Latitude'),S.longitude.label('Longitude'), \\\\\\n S.elevation.label('Elevation')).all())\\n \\n session.close()\\n \\n # Create a dictionary from the row data of the dataframe and return it as a JSON\\n return jsonify(results.to_dict(orient = 'records'))\",\n \"def run():\\r\\n\\r\\n # Build list of stations\\r\\n stations = build_station_list()\\r\\n\\r\\n # Find 5 stations at which the current level is the highest\\r\\n stations_highest_rel_level_list = []\\r\\n N = 5\\r\\n for i in range(len(stations_highest_rel_level(stations, N))):\\r\\n stations_highest_rel_level_list.append(stations_highest_rel_level(stations, N)[i][0])\\r\\n \\r\\n\\r\\n # Plot the water level for each of these stations over the past 10 days\\r\\n \\r\\n # First fetch the time history for a station\\r\\n for station in stations:\\r\\n if station.name in stations_highest_rel_level_list:\\r\\n \\r\\n dt = 2\\r\\n dates, levels = fetch_measure_levels(station.measure_id, dt=datetime.timedelta(days=dt))\\r\\n # This gives list of dates and levels to be passed into a plot\\r\\n plot_water_level_with_fit(station, dates, levels, 4)\\r\\n else:\\r\\n pass\",\n \"def create_databases():\\n db_connection = connect_to_db()\\n\\n # Create database tables.\\n create_tables(db_connection)\\n\\n # Populate water tables.\\n populate_water_tables(db_connection)\\n\\n # station_data = get_station_data()\\n # station = station_data.query('ŠIFRA == 30301')\\n # print(station)\\n # index = station.index[0]\\n # lat = station.at[index, 'LAT']\\n # lng = station.at[index, 'LON']\\n # name = f\\\"{station.at[index, 'VODOMERNA POSTAJA']} ({station.at[index, 'VODOTOK']})\\\"\\n # print(index, lat, lng, name)\\n\\n # Populate location tables\\n # populate_locations(db_connection)\\n\\n # Populate weather tables\\n populate_weather(db_connection)\\n\\n db_connection.commit()\\n db_connection.close()\",\n \"def save_data(self, data_dict: dict, table_name: str):\\n location = \\\"Winnipeg, MB\\\"\\n insert_sql = f\\\"\\\"\\\"insert or ignore into {table_name}\\n (sample_date, location, min_temp, max_temp, avg_temp)\\n values\\n (?,?,?,?,?)\\\"\\\"\\\"\\n\\n with DBOperations(self.name) as dbcm:\\n for date, temps in data_dict.items():\\n data_tuple = (date, location, temps['Min'] ,temps['Max'], temps['Mean'])\\n dbcm.execute(insert_sql, data_tuple)\",\n \"def water_supply(osm_path): \\n i = retrieve(osm_path,'multipolygons',['man_made'],**{'man_made':[\\\"='water_tower' or \\\",\\\"='water_well' or \\\",\\\"='reservoir_covered' or \\\",\\\"='water_works'\\\"]})\\n j = retrieve(osm_path,'multipolygons',['landuse'],**{'landuse':[\\\"='reservoir'\\\"]})\\n \\n i = i.rename(columns={'man_made': 'asset'})\\n j = j.rename(columns={'landuse': 'asset'})\\n \\n combined_df = pandas.concat([i,j], ignore_index=True, sort=False) #append objects while ignoring that they may have overlapping index\\n if combined_df.empty == True:\\n return combined_df\\n else:\\n return combined_df[[\\\"osm_id\\\",\\\"asset\\\",\\\"geometry\\\"]]\",\n \"def produce_all_database(is_debug):\\n\\tproduce_database([\\\"apnea-ecg\\\", \\\"train\\\"], is_debug)\\n\\tproduce_database([\\\"apnea-ecg\\\", \\\"test\\\"], is_debug)\",\n \"def _interpolate_meteorological_data(dset, data, rundate):\\n rundate = datetime(rundate.year, rundate.month, rundate.day)\\n for field, station in [(f, f[4:]) for f in data.keys() if f.startswith(\\\"met_\\\")]:\\n log.debug(f\\\"Meteorological data available for station {station}\\\")\\n\\n met_time = data[field].pop(\\\"met_time\\\")\\n flat_list = [item for sublist in met_time for item in sublist]\\n met_time_float = np.array([(flat_list[i] - rundate).total_seconds() for i in range(0, len(flat_list))])\\n met_time_unique, met_index = np.unique(met_time_float, return_index=True)\\n\\n diff = len(met_time_float) - len(met_time_unique)\\n if diff > 0:\\n log.dev(f\\\"Removed duplicate met data for station {station}\\\")\\n log.dev(\\\"Do this for the actual obs data also!\\\")\\n if len(met_time_unique) == 1:\\n for met_type in data[field].keys():\\n data[field][met_type] = np.repeat(data[field][met_type][0], dset.num_obs)\\n continue\\n\\n # Extrapolation one month before/after\\n # (this is overkill, most of these values will be removed later when taking the diagonal)\\n min_time = min(met_time_unique) - 31 * 86400\\n max_time = max(met_time_unique) + 31 * 86400\\n met_time_unique = np.hstack((np.array(min_time), met_time_unique, np.array(max_time)))\\n\\n for met_type in data[field].keys():\\n met_data_array = data[field][met_type]\\n flat_list = [item for sublist in met_data_array for item in sublist]\\n met_data_array = np.array([flat_list[i] for i in met_index])\\n met_data_array = np.hstack((met_data_array[0], met_data_array, met_data_array[-1]))\\n data[field][met_type] = interpolation.interpolate(\\n met_time_unique, met_data_array, dset.obs_time, kind=\\\"cubic\\\"\\n )\\n\\n return data\",\n \"def fillstation(self, stanames, all=None, plot=None, summary=None, From=None, To=None, by=None,\\n how='mean', variables=None, distance=None, sort_cor=True, constant=True, cor_lim=None):\\n\\n if all == True:\\n stations = self.network.getsta([], all=True).values()\\n else:\\n stations = self.network.getsta(stanames)\\n\\n for station in stations:\\n staname = station.getpara('stanames')\\n\\n if variables == None:\\n newdataframe = station.getData(reindex=True, From=From, To=To, by=by,\\n how=how) # Dataframe which stock the new data of the stations\\n newdataframe['U m/s'] = station.getData('U m/s', reindex=True, From=From, To=To, by=by, how=how)\\n newdataframe['V m/s'] = station.getData('V m/s', reindex=True, From=From, To=To, by=by, how=how)\\n newdataframe['Ua g/kg'] = station.getData('Ua g/kg', reindex=True, From=From, To=To, by=by, how=how)\\n newdataframe['Theta C'] = station.getData('Theta C', reindex=True, From=From, To=To, by=by, how=how)\\n variables_name = newdataframe.columns\\n else:\\n newdataframe = station.getData(var=variables, reindex=True, From=From, To=To, by=by,\\n how=how) # Dataframe which stock the new data of the stations\\n variables_name = variables\\n # select and sort nearest stations\\n selections, selectionsnames = self.__getpredictors_distance(staname, distance)\\n\\n for var in variables_name:\\n print(\\\"I\\\" * 30)\\n print(\\\"variable -> \\\" + var)\\n\\n try:\\n selections, params = self.__sort_predictors_by_corr(station, selections, var, From, To, by, how,\\n constant=constant,\\n selectionsnames=selectionsnames,\\n sort_cor=sort_cor, cor_lim=cor_lim)\\n\\n selections_iter = iter(selections)\\n params_iter = iter(params)\\n # print newdataframe\\n idxmissing = newdataframe[var][\\n newdataframe[var].isnull() == True].index # slect where their is missing data\\n\\n while len(idxmissing) > 0:\\n print(\\\"Their is [\\\" + str(len(idxmissing)) + \\\"] events missing\\\")\\n\\n try: # Try if their is still other stations to fill with\\n selection = selections_iter.next()\\n param = params_iter.next()\\n except StopIteration:\\n print(\\\"NO MORE SELECTED STATIONS\\\")\\n break\\n\\n try:\\n Y = station.getData(var, From=From, To=To, by=by, how=how) # variable to be filled\\n X1 = selection[0].getData(var, From=From, To=To, by=by,\\n how=how) # stations variable used to fill\\n X2 = selection[1].getData(var, From=From, To=To, by=by,\\n how=how) # stations variable used to fill\\n\\n select = pd.concat([X1, X2], keys=['X1', 'X2'], axis=1, join='inner').dropna()\\n\\n if constant:\\n newdata = param[0] + param[1] * select['X1'] + param[2] * select[\\n 'X2'] # reconstruct the data\\n else:\\n newdata = param[0] * select['X1'] + param[1] * select['X2'] # reconstruct the data\\n\\n newdataframe.loc[idxmissing, var] = newdata.loc[idxmissing, var]\\n idxmissing = newdataframe[var][\\n newdataframe[var].isnull() == True].index # slect where their is missing data\\n\\n\\n except KeyError:\\n print(\\\"&\\\" * 60)\\n print('Selected stations did not fill any events')\\n except ValueError:\\n print('The variable ' + var + \\\"Does not exist or no data to do the multilinear regression \\\")\\n\\n if plot == True:\\n df = pd.concat([Y, X1, X2, newdata, newdataframe[var]],\\n keys=['Y', 'X1', 'X2', 'estimated data', 'Estimated replaced'], axis=1,\\n join='outer')\\n self.plotcomparison(df)\\n\\n print(\\\"Their is [\\\" + str(len(idxmissing)) + \\\"] FINALLY events missing\\\")\\n # Recalculate the wind direction and speed from the U an V components\\n\\n try:\\n speed, dir = cart2pol(newdataframe['U m/s'], newdataframe['V m/s'])\\n newdataframe['Dm G'] = dir\\n newdataframe['Sm m/s'] = speed\\n except ValueError:\\n print\\n 'No wind found in the dataframe'\\n except KeyError:\\n print('No wind found in the dataframe')\\n\\n self.newdataframes[staname] = newdataframe\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.69976664","0.58139586","0.5748849","0.5552527","0.5550863","0.5530793","0.55279726","0.5518004","0.5421447","0.54047465","0.5358192","0.53122264","0.52683693","0.5263279","0.52280056","0.5153076","0.51038533","0.51007193","0.507282","0.50530833","0.50488514","0.50456476","0.50298846","0.50112516","0.50000465","0.49984008","0.49943525","0.4993162","0.49832112","0.49795166","0.49727067","0.4969099","0.49649596","0.4964497","0.4952834","0.49483946","0.49351195","0.4934195","0.4921215","0.49154627","0.4912562","0.49108472","0.49077004","0.49049252","0.48978424","0.48970366","0.48966184","0.48919708","0.4889385","0.48891345","0.48885944","0.48873556","0.48856896","0.48652816","0.485487","0.48464245","0.4844927","0.48428643","0.48416328","0.48298717","0.4827711","0.48242176","0.48128083","0.47982547","0.47971055","0.47878516","0.47831237","0.47785854","0.4775928","0.47717708","0.47715655","0.47695407","0.47690982","0.47598824","0.47513562","0.47510457","0.47391307","0.47313213","0.47283602","0.4726484","0.47223568","0.472172","0.4719994","0.47194037","0.4719325","0.47151005","0.470958","0.47080472","0.47074664","0.47060654","0.47041047","0.4702029","0.46960562","0.4694235","0.46813357","0.46789417","0.4678458","0.46775183","0.46750462","0.46748582"],"string":"[\n \"0.69976664\",\n \"0.58139586\",\n \"0.5748849\",\n \"0.5552527\",\n \"0.5550863\",\n \"0.5530793\",\n \"0.55279726\",\n \"0.5518004\",\n \"0.5421447\",\n \"0.54047465\",\n \"0.5358192\",\n \"0.53122264\",\n \"0.52683693\",\n \"0.5263279\",\n \"0.52280056\",\n \"0.5153076\",\n \"0.51038533\",\n \"0.51007193\",\n \"0.507282\",\n \"0.50530833\",\n \"0.50488514\",\n \"0.50456476\",\n \"0.50298846\",\n \"0.50112516\",\n \"0.50000465\",\n \"0.49984008\",\n \"0.49943525\",\n \"0.4993162\",\n \"0.49832112\",\n \"0.49795166\",\n \"0.49727067\",\n \"0.4969099\",\n \"0.49649596\",\n \"0.4964497\",\n \"0.4952834\",\n \"0.49483946\",\n \"0.49351195\",\n \"0.4934195\",\n \"0.4921215\",\n \"0.49154627\",\n \"0.4912562\",\n \"0.49108472\",\n \"0.49077004\",\n \"0.49049252\",\n \"0.48978424\",\n \"0.48970366\",\n \"0.48966184\",\n \"0.48919708\",\n \"0.4889385\",\n \"0.48891345\",\n \"0.48885944\",\n \"0.48873556\",\n \"0.48856896\",\n \"0.48652816\",\n \"0.485487\",\n \"0.48464245\",\n \"0.4844927\",\n \"0.48428643\",\n \"0.48416328\",\n \"0.48298717\",\n \"0.4827711\",\n \"0.48242176\",\n \"0.48128083\",\n \"0.47982547\",\n \"0.47971055\",\n \"0.47878516\",\n \"0.47831237\",\n \"0.47785854\",\n \"0.4775928\",\n \"0.47717708\",\n \"0.47715655\",\n \"0.47695407\",\n \"0.47690982\",\n \"0.47598824\",\n \"0.47513562\",\n \"0.47510457\",\n \"0.47391307\",\n \"0.47313213\",\n \"0.47283602\",\n \"0.4726484\",\n \"0.47223568\",\n \"0.472172\",\n \"0.4719994\",\n \"0.47194037\",\n \"0.4719325\",\n \"0.47151005\",\n \"0.470958\",\n \"0.47080472\",\n \"0.47074664\",\n \"0.47060654\",\n \"0.47041047\",\n \"0.4702029\",\n \"0.46960562\",\n \"0.4694235\",\n \"0.46813357\",\n \"0.46789417\",\n \"0.4678458\",\n \"0.46775183\",\n \"0.46750462\",\n \"0.46748582\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7368926"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":2888,"cells":{"query":{"kind":"string","value":"Populate watercourse and aquifer related data tables."},"document":{"kind":"string","value":"def populate_water_tables(connection):\n metadata = load_metadata('water')\n cursor = connection.cursor()\n\n # Check if tables are already populated.\n cursor.execute('SELECT count(*) FROM watercourses')\n watercourse_count = cursor.fetchone()[0]\n cursor.execute('SELECT count(*) FROM aquifers')\n aquifer_count = cursor.fetchone()[0]\n\n if watercourse_count and aquifer_count:\n print('Water tables already populated!')\n return\n\n station_data = get_station_data()\n\n for archive in metadata.keys():\n print(f'{archive}-water:'.upper())\n water_body = get_water_definitions(archive)['body']\n\n # 1. Populate watercourses/aquifers:\n stations = {}\n for water_body_name in metadata[archive].keys():\n print(f'\\tPopulating {water_body}: \"{water_body_name}\"')\n cursor.execute(f'''INSERT INTO {water_body}s(location_id, name)\n VALUES (0, '{water_body_name}')''')\n water_body_id = cursor.lastrowid\n\n # 2. Populate watercourse_stations/aquifer_stations:\n for station_id in metadata[archive][water_body_name]['stations']:\n station_name = clean_name(metadata[archive][water_body_name]['stations'][station_id]['name'])\n\n if station_id in stations:\n # Prefer watercourses/aquifer with more stations\n current_len = len(metadata[archive][water_body_name]['stations'])\n previous_len = len(metadata[archive][stations[station_id]]['stations'])\n\n if current_len < previous_len:\n print(f'\\t\\tStation already exists: {station_id} - \"{station_name}\" (\"{water_body_name}\")')\n continue\n else:\n cursor.execute(f'''DELETE \n FROM {water_body}_stations\n WHERE id = {station_id}''')\n print(f'\\t\\tRemoved station: {station_id} - \"{station_name}\" from \"{stations[station_id]}\")')\n\n stations[station_id] = water_body_name\n print(f'\\t\\tPopulating station: {station_id} - \"{station_name}\"')\n\n # Insert station location if station data exists.\n location_id = 0\n station_row = station_data.query(f'ŠIFRA == \"{station_id}\"')\n if not station_row.empty:\n index = station_row.index[0]\n lat = station_row.at[index, 'LAT']\n lng = station_row.at[index, 'LON']\n if not np.isnan(lat) and not np.isnan(lng):\n name = f\"{station_row.at[index, 'VODOMERNA POSTAJA']} ({station_row.at[index, 'VODOTOK']})\"\n cursor.execute(f'''INSERT INTO locations(name, lat, lng)\n VALUES ('{name}', {lat}, {lng})''')\n location_id = cursor.lastrowid\n\n # Insert station.\n cursor.execute(f'''INSERT INTO {water_body}_stations(id, {water_body}_id, location_id, name)\n VALUES ({station_id}, {water_body_id}, {location_id}, '{station_name}')''')\n\n # 3. Populate watercourse_measurements/aquifer_measurements:\n if not populate_water_measurements(cursor, archive, metadata[archive][water_body_name]['dir'],\n station_id):\n cursor.execute(f'''DELETE \n FROM {water_body}_stations\n WHERE id = {station_id}''')\n print(f'\\t\\tRemoved station with useless data: {station_id} - \"{station_name}\"')\n\n # Remove empty watercourses/aquifers.\n cursor.execute(f'''SELECT w.id, w.name\n FROM {water_body}s w\n WHERE NOT EXISTS (\n SELECT s.id \n FROM {water_body}_stations s \n WHERE w.id = s.{water_body}_id\n )''')\n\n for row in cursor.fetchall():\n cursor.execute(f'''DELETE \n FROM {water_body}s\n WHERE id = {row[0]}''')\n print(f'\\tRemoved empty {water_body}: \"{row[1]}\"')"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def populate_db():\n\n populate_table(db, models.Department, departments_data)\n populate_table(db, models.Employee, employees_data)","def populate_tables(connection: sqlite3.Connection) -> None:\n fake = Faker()\n Faker.seed(0)\n\n c = conn.cursor()\n\n number_of_courses = fake.pyint(min_value=5, max_value=20)\n\n for _ in range(number_of_courses):\n course_name = fake.word()\n\n insert_statement = f'insert into courses (name) values (\"{course_name}\");'\n c.execute(insert_statement)\n\n connection.commit()\n\n number_of_users = fake.pyint(min_value=1, max_value=23)\n\n Faker.seed()\n\n for _ in range(number_of_users):\n\n if fake.pybool():\n user_name = f'{fake.first_name_female()} {fake.last_name_female()}'\n else:\n user_name = f'{fake.first_name()} {fake.last_name()}'\n\n insert_statement = f'insert into users (name) values (\"{user_name}\");'\n c.execute(insert_statement)\n\n connection.commit()\n\n for _ in range(50000):\n Faker.seed()\n\n random_user_id = fake.pyint(1, number_of_users)\n random_course_id = fake.pyint(1, number_of_courses)\n Faker.seed()\n random_lesson_no = fake.pyint(3, 12)\n Faker.seed()\n random_exercise_no = fake.pyint(1, 50)\n random_data = fake.sentence()\n\n insert_statement = f\"\"\"insert into saves (user_id, course_id, lesson_no, exercise_no,data) \n values ({random_user_id}, {random_course_id}, {random_lesson_no}, \n {random_exercise_no}, '{random_data}');\"\"\"\n c.execute(insert_statement)\n\n connection.commit()","def populate_database(self):\n self.insert_products()\n self.insert_categories()\n self.insert_products_categories()\n self.insert_stores()\n self.insert_products_stores()","def insert_relations_staging(self):\n\n for year in range(START_YEAR_CREATIVE_WORKS, CURRENT_YEAR, YEARS_RANGE):\n self.load_wikidata(\"movie_roles\", MOVIE_ROLES_BY_YEAR_SPARQL_QUERY, INSERT_MOVIE_ROLE_SQL_QUERY, INSERT_MOVIE_ROLE_MAP_COLUMNS, year, YEARS_RANGE)\n\n self.load_wikidata(\"song_roles\", SONG_ROLES_BY_YEAR_SPARQL_QUERY, INSERT_SONG_ROLE_SQL_QUERY, INSERT_SONG_ROLE_MAP_COLUMNS, year, YEARS_RANGE, True)\n self.load_wikidata(\"tvshow_roles\", TVSHOW_ROLES_SPARQL_QUERY, INSERT_TVSHOW_ROLE_SQL_QUERY,\n INSERT_TVSHOW_ROLE_MAP_COLUMNS)\n self.load_wikidata(\"animatedmovie_roles\", ANIMATEDMOVIE_ROLES_SPARQL_QUERY, INSERT_ANIMATEDMOVIE_ROLE_SQL_QUERY,\n INSERT_ANIMATEDMOVIE_ROLE_MAP_COLUMNS)\n self.load_wikidata(\"videogame_roles\", VIDEOGAME_ROLES_SPARQL_QUERY, INSERT_VIDEOGAME_ROLE_SQL_QUERY, INSERT_VIDEOGAME_ROLE_MAP_COLUMNS)\n self.load_wikidata(\"book_roles\", BOOK_ROLES_SPARQL_QUERY, INSERT_BOOK_ROLE_SQL_QUERY, INSERT_BOOk_ROLE_SQL_QUERY)","def populate_db():\n stdout.write('Emptying the tables...\\n')\n empty_tables()\n stdout.write('Populating Language records...\\n')\n populate_language()\n stdout.write('Populating Lemma, Wordform, and Definition records...\\n')\n populate_lexical()\n stdout.write('Populating ProperName records...\\n')\n populate_proper_names()","def load_dwh_tables(self):\n print(\"Loading the creative works table\")\n self.cur.execute(dwh_queries.INSERT_CREATIVE_WORKS_SQL_QUERY)\n self.conn.commit()\n\n print(\"Loading the participations table\")\n\n self.cur.execute(dwh_queries.INSERT_PARTICIPATIONS_SQL_QUERY)\n self.conn.commit()","def setUp(self):\n resume.objects.create(\n first_name='Nicholas',\n last_name='Bielinski',\n )\n experience.objects.create(\n title='Helpdesk Technician',\n location='L3 Technologies',\n start_date='6/26/2017',\n end_date='present',\n description='blah blah blah'\n )\n education.objects.create(\n institution_name='UNH Manchester',\n location='Manchester',\n degree='Bachelor',\n major='CIS',\n gpa = '3.5'\n )","def synthesize_employment_data(self, config):\r\n jobs_by_zone_by_sector_table_name = config['jobs_by_zone_by_sector']\r\n gridcells_table_name = config['gridcells']\r\n jobs_table_name = config['jobs']\r\n gridcells_output_table_name = config['gridcells_output']\r\n jobs_output_table_name = config['jobs_output']\r\n \r\n input_db_name = config['db_config'].database_name\r\n output_db_name = config['output_database_name']\r\n \r\n sectors = config['sector_names_and_ids']\r\n building_types_and_ids_and_home_based = config[\r\n 'building_type_column_names_and_ids_and_home_based']\r\n \r\n building_types = []\r\n building_ids = []\r\n home_based = [] \r\n for type, id, home in building_types_and_ids_and_home_based:\r\n building_types += [type]\r\n building_ids += [id]\r\n home_based += [home]\r\n \r\n \r\n from_database_configuration = ScenarioDatabaseConfiguration(\r\n database_name = input_db_name,\r\n host_name = config['db_config'].host_name,\r\n user_name = config['db_config'].user_name,\r\n password = config['db_config'].password \r\n )\r\n to_database_configuration = ScenarioDatabaseConfiguration(\r\n database_name = output_db_name,\r\n host_name = config['db_config'].host_name,\r\n user_name = config['db_config'].user_name,\r\n password = config['db_config'].password \r\n )\r\n\r\n FlattenScenarioDatabaseChain().copy_scenario_database(\r\n from_database_configuration = from_database_configuration, \r\n to_database_configuration = to_database_configuration,\r\n tables_to_copy = [gridcells_table_name, jobs_table_name])\r\n \r\n db_server = DatabaseServer(to_database_configuration) \r\n output_database = db_server.get_database(output_db_name)\r\n \r\n sector_name = 0; sector_id = 1\r\n \r\n sector = {}\r\n for entry in sectors:\r\n name = entry[sector_name]\r\n id = entry[sector_id]\r\n sector[id] = self._get_jobs_per_building_type_in_sector_by_zone(\r\n output_database, jobs_by_zone_by_sector_table_name, \r\n jobs_table_name, name, id)\r\n\r\n results = self._get_building_type_proportion_by_zone(output_database, \r\n gridcells_table_name)\r\n \r\n grid_id = 0; zone_id = 1\r\n dist = {}\r\n \r\n type_index = {}\r\n \r\n for name in building_types:\r\n for i in range(len(results[0])):\r\n column_name = results[0][i]\r\n if name == column_name:\r\n type_index[name] = i\r\n break;\r\n else:\r\n raise KeyError, ('No column by the name of \\'%s\\' found in '\r\n 'the database.' % name) \r\n\r\n for name in building_types:\r\n dist[name] = {}\r\n \r\n for row in results[1:]:\r\n for name in building_types:\r\n dist[name][row[zone_id]] = []\r\n \r\n for row in results[1:]:\r\n for name in building_types:\r\n dist[name][row[zone_id]] += [(row[grid_id], \r\n row[type_index[name]])]\r\n \r\n jobs_table_data = self._create_jobs_table_data(dist, sector,\r\n building_types_and_ids_and_home_based)\r\n \r\n output_database.execute('USE %(out_db)s' % {'out_db':output_db_name})\r\n \r\n output_database.execute(\"\"\"\r\n CREATE TABLE %(jobs_out)s (\r\n JOB_ID INT AUTO_INCREMENT, PRIMARY KEY(JOB_ID),\r\n GRID_ID INT, HOME_BASED INT, SECTOR_ID INT, BUILDING_TYPE INT);\r\n \"\"\" % {'jobs_out':jobs_output_table_name})\r\n \r\n if len(jobs_table_data) > 0:\r\n output_prefix = (\r\n \"\"\"INSERT INTO %(jobs_out)s \r\n (GRID_ID, HOME_BASED, SECTOR_ID, BUILDING_TYPE) VALUES\r\n \"\"\" % {'jobs_out':jobs_output_table_name})\r\n output_postfix = ';'\r\n \r\n step = 1000\r\n length = len(jobs_table_data)\r\n iterations = int(length/step) + 1\r\n \r\n for i in range(iterations):\r\n low = i*step\r\n high = (i+1)*step\r\n \r\n if high > length: high = length\r\n \r\n output_body = \"\"\r\n \r\n for j in range(low, high):\r\n output_body += (\r\n '(%(grid)s, %(home)s, %(sector)s, %(building)s),\\n' \r\n % jobs_table_data[j])\r\n \r\n output_query = \"%s%s%s\" % (output_prefix, \r\n output_body[:-2], \r\n output_postfix)\r\n\r\n output_database.execute(output_query)\r\n \r\n \r\n ### TODO: \r","def populate_database(telescope_name, instrument_name):\n telescope = Telescope.objects.create(\n name=telescope_name, latitude=25.0, longitude=45.0)\n instrument = Instrument.objects.create(\n name=instrument_name, telescope=telescope)\n for year_int in (2012, 2013):\n for month_int in range(1, 13):\n for night_int in (1, monthrange(year_int, month_int)[1]):\n ut_date = date(year_int, month_int, night_int)\n night = Night.objects.create(\n ut_date=ut_date, instrument=instrument, observers='Smith')\n Exposure.objects.create(\n night=night, run_number=1, ut_start=time(10, 0, 0),\n exposed=20.0, ra=60.0, dec=30.0, object_exp=True)\n Exposure.objects.create(\n night=night, run_number=2, ut_start=time(11, 0, 0),\n exposed=30.0, ra=90.0, dec=0.0, object_exp=True)\n Exposure.objects.create(\n night=night, run_number=3, ut_start=time(12, 0, 0),\n exposed=40.0, ra=120.0, dec=-30.0, object_exp=False)","def populate_database(num_patients, min_checkins, max_checkins):\n departments = [\n Department(department_name=\"Cardiology\"),\n Department(department_name=\"Emergency\"),\n Department(department_name=\"Gynecology\"),\n Department(department_name=\"Pediatrics\"),\n Department(department_name=\"Obstetrics\"),\n Department(department_name=\"Oncology\"),\n Department(department_name=\"Orthopedics\"),\n Department(department_name=\"Neurology\")\n ]\n\n for i in xrange(num_patients):\n patient = Patient(**generate_patient())\n patient.departments.append(choice(departments))\n db.add(patient)\n\n for j in xrange(randrange(min_checkins, max_checkins)):\n checkin = CheckIn(**generate_checkin())\n checkin.patient_nhi = patient.nhi\n\n lci = patient.latest_checkin_time\n vid = checkin.checkin_time\n\n lci = vid if lci is None or vid > lci else lci\n patient.latest_checkin_time = lci\n\n db.add(checkin)\n\n for k in xrange(randrange(0, 3)):\n appointment = Appointment(**generate_appointment())\n appointment.patient_nhi = patient.nhi\n\n db.add(appointment)\n\n db.commit()","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 _setup_all_awardees():\n hpo_data = _prep_awardee_csv_data('tests/test-data/fixtures/awardees.csv')\n org_data = _prep_awardee_csv_data('tests/test-data/fixtures/organizations.csv')\n site_data = _prep_awardee_csv_data('tests/test-data/fixtures/sites.csv')\n dao = HPODao()\n #\n # Import HPO records\n #\n for column in range(0, len(hpo_data[0]) - 1):\n data = _convert_csv_column_to_dict(hpo_data, column)\n dao.insert(HPO(hpoId=column+1, displayName=data['Name'], name=data['Awardee ID'],\n organizationType=OrganizationType(data['Type']), isObsolete=ObsoleteStatus.ACTIVE))\n #\n # Import Organization records\n #\n with dao.session() as session:\n for column in range(0, len(org_data[0]) - 1):\n data = _convert_csv_column_to_dict(org_data, column)\n result = session.query(HPO.hpoId).filter(HPO.name == data['Awardee ID']).first()\n dao.insert(Organization(externalId=data['Organization ID'], displayName=data['Name'], hpoId=result.hpoId))\n #\n # Import Site records\n #\n with dao.session() as session:\n for column in range(0, len(site_data[0]) - 1):\n data = _convert_csv_column_to_dict(site_data, column)\n result = session.query(Organization.hpoId, Organization.organizationId).\\\n filter(Organization.externalId == data['Organization ID']).first()\n try:\n mayo_link_id = data['MayoLINK Client #']\n except KeyError:\n mayo_link_id = str(random.randint(7040000, 7999999))\n dao.insert(Site(siteName=data['Site'], googleGroup=data['Site ID / Google Group'].lower(),\n mayolinkClientNumber=mayo_link_id, hpoId=result.hpoId,\n organizationId=result.organizationId))","def setup_vars(self):\n # Add Full time positions\n self.manager_id = self._add_person(\"Manager\", \"ARRAY['Database', 'OS', 'AI']\", 30)\n self.admin_id = self._add_person(\"Admin\", salary=40)\n self.full_instructor_id = self._add_person(\n \"Instructor\", \"ARRAY['Database']\", 20\n )\n\n # Add Part time instructor\n self.part_instructor_id = self._add_part_time_instr(\"ARRAY['OS']\", 10)\n self.part_instructor_id = self._add_part_time_instr(\"ARRAY['AI']\", 10)\n\n # Add courses\n self.course_id1 = self._add_course(\"Database\", 1, \"Database\")\n self.course_id2 = self._add_course(\"OS\", 1, \"OS\")\n self.course_id3 = self._add_course(\"AI\", 1, \"AI\")\n\n # Add room\n self.room_id = self._add_room(1, 'Test room', 20)\n self.room_id2 = self._add_room(2, 'Test room 2', 20)\n\n # Add course offerings\n self.course_offering1 = self._add_course_offering('2021-01-21', 10, [('2021-06-21', 9, self.room_id), ('2021-06-21', 11, self.room_id)], '2021-05-31', 20, self.course_id1, self.admin_id)\n self.course_offering2 = self._add_course_offering('2021-01-21', 10, [('2021-06-22', 9, self.room_id), ('2021-06-22', 11, self.room_id)], '2021-05-31', 20, self.course_id2, self.admin_id)\n self.course_offering3 = self._add_course_offering('2021-01-21', 10, [('2021-06-22', 9, self.room_id2), ('2021-06-22', 11, self.room_id2)], '2021-05-31', 20, self.course_id3, self.admin_id)\n\n # Add customers\n self.customer_id1 = self._add_customer('Test1', \"test\", 987654321, 'test@test.com', '1234123412341234', '123', '2025-05-31')\n self.customer_id2 = self._add_customer('Test2', \"test\", 987654321, 'test@test.com', '1234123412341235', '123', '2025-05-31')\n self.customer_id3 = self._add_customer('Test3', \"test\", 987654321, 'test@test.com', '1234123412341236', '123', '2025-05-31')\n\n # Register sessions\n self._register_credit_card('2021-01-21', self.course_id1, 1, self.customer_id1)\n self._register_credit_card('2021-01-21', self.course_id1, 1, self.customer_id2)\n self._register_credit_card('2021-01-21', self.course_id1, 1, self.customer_id3)\n\n # Add course packages\n self.package1 = self._add_course_package(\"Best Package\", 2, '2021-03-01', '2021-08-02', 50)\n self.package2 = self._add_course_package(\"Medium Package\", 2, '2021-03-01', '2021-08-02', 100)\n self.package3 = self._add_course_package(\"Worst Package\", 2, '2021-03-01', '2021-08-02', 150)\n\n # Buy course packages\n self._buy_package(self.customer_id1, self.package1)\n self._buy_package(self.customer_id2, self.package2)\n self._buy_package(self.customer_id3, self.package3)\n\n # Redeem sessions\n self._register_redeems('2021-01-21', self.course_id2, 1, self.customer_id1)\n self._register_redeems('2021-01-21', self.course_id2, 1, self.customer_id2)\n self._register_redeems('2021-01-21', self.course_id2, 1, self.customer_id3)\n\n # Cancel registrations\n self._cancel_registration(self.customer_id1, self.course_id1)\n self._cancel_registration(self.customer_id2, self.course_id2)","def insert_entities_staging(self):\n\n for year in range(1900, CURRENT_YEAR, YEARS_RANGE):\n self.load_wikidata(\"movies\", MOVIES_BY_YEAR_SPARQL_QUERY, INSERT_MOVIE_SQL_QUERY, INSERT_MOVIE_MAP_COLUMNS, year, YEARS_RANGE)\n\n self.load_wikidata(\"songs\", SONGS_BY_YEAR_SPARQL_QUERY, INSERT_SONG_SQL_QUERY, INSERT_SONG_MAP_COLUMNS, year, YEARS_RANGE, True)\n self.load_wikidata(\"tvshows\", TVSHOWS_SPARQL_QUERY, INSERT_TVSHOW_SQL_QUERY, INSERT_TVSHOW_MAP_COLUMNS)\n self.load_wikidata(\"animatedmovies\", ANIMATEDMOVIES_SPARQL_QUERY, INSERT_ANIMATEDMOVIE_SQL_QUERY,\n INSERT_ANIMATEDMOVIE_MAP_COLUMNS)\n self.load_wikidata(\"videogames\", VIDEOGAMES_SPARQL_QUERY, INSERT_VIDEOGAME_SQL_QUERY, INSERT_VIDEOGAME_MAP_COLUMNS)\n self.load_wikidata(\"books\", BOOKS_SPARQL_QUERY, INSERT_BOOK_SQL_QUERY, INSERT_BOOK_MAP_COLUMNS)","def run(self):\n\n for table in self.TABLES:\n self.dictionary_cursor.execute(f\"TRUNCATE TABLE {table}_Work\")\n self.dictionary_conn.commit()\n self.logger.info(\"work tables cleared\")\n for id in self.ids:\n drug = self.Drug(self, id)\n if drug.wanted:\n drug.load()\n self.logger.info(\"work tables populated\")\n for table in self.TABLES:\n insert = f\"INSERT INTO {table} SELECT * FROM {table}_Work\"\n self.dictionary_cursor.execute(f\"TRUNCATE TABLE {table}\")\n self.dictionary_cursor.execute(insert)\n self.dictionary_conn.commit()\n self.logger.info(\"live tables ready\")","def setup_table(self):\n\n self.setup.create_basic_table_in_dev()\n self.setup.insert_random_records_into_dev()","def init_tables(self) -> None:\n with self.table_access_condition:\n conn = self._get_connection()\n conn.execute(\"PRAGMA foreign_keys = 1\")\n c = conn.cursor()\n c.execute(tables.CREATE_STUDIES_TABLE)\n c.execute(tables.CREATE_SUBMISSIONS_TABLE)\n c.execute(tables.CREATE_REQUESTERS_TABLE)\n c.execute(tables.CREATE_UNITS_TABLE)\n c.execute(tables.CREATE_WORKERS_TABLE)\n c.execute(tables.CREATE_RUNS_TABLE)\n c.execute(tables.CREATE_RUN_MAP_TABLE)\n c.execute(tables.CREATE_PARTICIPANT_GROUPS_TABLE)\n c.execute(tables.CREATE_PARTICIPANT_GROUP_QUALIFICATIONS_MAPPING_TABLE)\n conn.commit()","def _populate(self):\n self.addDemographics()\n self.addLabs()\n self.addProblems()\n self.addMeds()\n self.addAllergies()\n self.addImmunizations()\n self.addVitals()\n self.populated_p = True","def populate_weather(connection):\n metadata = load_metadata('weather')\n cursor = connection.cursor()\n water_defs = get_water_definitions()\n\n # Check if tables are already populated.\n cursor.execute('SELECT count(*) FROM weather')\n weather_count = cursor.fetchone()[0]\n\n if weather_count:\n print('Weather tables already populated!')\n return\n\n print('WEATHER:')\n\n # Darksky data\n for dir_name, location in metadata.items():\n print(f'\\tPopulating weather: \"{location[\"name\"]}\".')\n\n # Insert location.\n cursor.execute(f'''INSERT INTO locations(name, lat, lng)\n VALUES ('{location['name']}', {location['lat']}, {location['lng']})''')\n location_id = cursor.lastrowid\n\n # Set weather locations for watercourses/aquifers.\n for water_body in [d['body'] for d in water_defs.values()]:\n if water_body in location:\n cursor.execute(f'''UPDATE {water_body}s\n SET location_id = {location_id}\n WHERE name IN ('{\"','\".join(location[water_body])}')''')\n break\n\n dir_path = get_data_path('weather', 'raw', dir_name)\n for json_file_name in os.listdir(dir_path):\n json_path = os.path.join(dir_path, json_file_name)\n with open(json_path, 'r', encoding='utf-8') as json_file:\n print(f'\\t\\tPopulating year: {json_file_name[0:-5]}')\n year_forecasts = json.load(json_file)\n for date, date_forecast in year_forecasts.items():\n hourly_forecasts = date_forecast['hourly']\n\n if not hourly_forecasts:\n print(f'\\t\\tNo hourly forecasts for {date}!')\n continue\n\n daily_forecast = {\n 'location_id': location_id,\n 'time': date_forecast['time'],\n 'day_time': date_forecast['sunset_time'] - date_forecast['sunrise_time'],\n 'precipitation': 0,\n 'snow_accumulation': 0\n }\n # List of value names with `avg`, `min` and `max` values\n value_names = {\n 'temperature': 'temperature',\n 'cloud_cover': 'cloudCover',\n 'dew_point': 'dewPoint',\n 'humidity': 'humidity',\n 'pressure': 'pressure',\n 'uv_index': 'uvIndex',\n 'precipitation_probability': 'precipProbability',\n 'precipitation_intensity': 'precipIntensity'\n }\n # Value name counters, which indicate how many times (out of 24)\n # certain value appears in hourly data.\n value_counts = {k: 0 for k in value_names.keys()}\n\n for value_name in value_names.keys():\n daily_forecast[f'{value_name}_avg'] = 0.0\n daily_forecast[f'{value_name}_min'] = float('inf')\n daily_forecast[f'{value_name}_max'] = float('-inf')\n\n # Calculate daily forecast values from hourly forecasts.\n for hourly_forecast in hourly_forecasts:\n for value_name in value_names.keys():\n orig_value_name = value_names[value_name]\n if is_forecast_number(orig_value_name, hourly_forecast):\n daily_forecast[f'{value_name}_avg'] += hourly_forecast[orig_value_name]\n daily_forecast[f'{value_name}_min'] = min(\n hourly_forecast[orig_value_name],\n daily_forecast[f'{value_name}_min']\n )\n daily_forecast[f'{value_name}_max'] = max(\n hourly_forecast[orig_value_name],\n daily_forecast[f'{value_name}_max']\n )\n value_counts[value_name] += 1\n\n if is_forecast_number('precipAccumulation', hourly_forecast) \\\n and hourly_forecast['precipType'] == 'snow':\n daily_forecast['snow_accumulation'] += hourly_forecast['precipAccumulation']\n elif is_forecast_number('precipIntensity', hourly_forecast) \\\n and is_forecast_number('precipProbability', hourly_forecast):\n daily_forecast['precipitation'] += \\\n hourly_forecast['precipIntensity'] * hourly_forecast['precipProbability']\n\n for value_name, value_count in value_counts.items():\n if value_count:\n # Calculate average.\n daily_forecast[f'{value_name}_avg'] = daily_forecast[f'{value_name}_avg'] / value_count\n else:\n # If value never appeared\n daily_forecast[f'{value_name}_avg'] = 'NULL'\n daily_forecast[f'{value_name}_min'] = 'NULL'\n daily_forecast[f'{value_name}_max'] = 'NULL'\n\n cursor.execute(f'''INSERT INTO weather({', '.join(daily_forecast.keys())})\n VALUES ({', '.join([str(v) for v in daily_forecast.values()])})''')\n\n # IOT data:\n for location in SETTINGS['weather_locations_iot']:\n print(f'\\tPopulating weather: \"{location[\"name\"]}\".')\n\n # Insert location.\n cursor.execute(f'''INSERT INTO locations(name, lat, lng)\n VALUES ('{location['name']}', {location['lat']}, {location['lng']})''')\n location_id = cursor.lastrowid\n\n # Set weather locations for watercourses/aquifers.\n for water_body in [d['body'] for d in water_defs.values()]:\n if water_body in location:\n cursor.execute(f'''UPDATE {water_body}s\n SET location_id = {location_id}\n WHERE name IN ('{\"', '\".join(location[water_body])}')''')\n\n # Set locations for all stations on given water body to match its location.\n cursor.execute(f'''SELECT id\n FROM {water_body}s\n WHERE location_id = {location_id}''')\n ids = [row[0] for row in cursor.fetchall()]\n if len(ids):\n cursor.execute(f'''UPDATE {water_body}_stations\n SET location_id = {location_id}\n WHERE {water_body}_id IN ({', '.join([str(v) for v in ids])})''')\n\n break \n \n file_name = f'''{location['lat']}-{location['lng']}.json'''\n json_path = get_data_path('weather', 'raw', file_name)\n\n # If data file doesn't exist, download it first.\n if not os.path.isfile(json_path):\n with open(json_path, 'wb', encoding=\"utf-8\") as file:\n file.write(read_from_url(location['url'], decode=False))\n \n with open(json_path, 'r', encoding='utf-8') as json_file:\n row_names = {\n \"Sun_duration\": \"sun_duration\",\n \"CloudCover\": \"cloud_cover_avg\",\n \"Percipitation\": \"precipitation\",\n \"New_snow_blanket\": \"snow_accumulation\",\n \"Snow_blanket\": \"snow_depth\",\n \"TemperatureAvg\": \"temperature_avg\",\n \"TemperatureMin\": \"temperature_min\",\n \"TemperatureMax\": \"temperature_max\"\n }\n forecasts = json.load(json_file)\n for forecast in forecasts:\n f = {row_names[k]: forecast[k] for k in row_names.keys()}\n f['location_id'] = location_id\n f['time'] = round(forecast['LastUpdatedEpoch'] / 1000)\n cursor.execute(f'''INSERT INTO weather({', '.join(f.keys())})\n VALUES ({', '.join([str(v) for v in f.values()])})''')","def _populate():\n models.Base.metadata.create_all(engine)\n logger.info(\"Initalized database\")\n db = Session()\n\n hermann = models.Account(id=\"test\",\n name=\"Hermann Dörkschneider\",\n email=\"fakeaddress@lumeh.org\")\n db.add(hermann)\n\n journey = models.Journey(id=str(uuid.uuid4()),\n account_id=\"test\",\n visibility=\"PUBLIC\",\n start_time_utc=datetime.datetime.now(),\n stop_time_utc=datetime.datetime.now())\n db.add(journey)\n\n waypoint1 = models.Waypoint(journey=journey,\n time_utc=datetime.datetime.now(),\n accuracy_m=2.71,\n latitude=3.1416,\n longitude=1.618,\n height_m=10)\n db.add(waypoint1)\n\n waypoint2 = models.Waypoint(journey=journey,\n time_utc=datetime.datetime.now(),\n accuracy_m=5.1,\n latitude=3.1410,\n longitude=1.620,\n height_m=5)\n db.add(waypoint2)\n\n db.commit()\n logger.info(\"Created test account {}\".format(hermann))\n logger.info(\"Created test journey {}\".format(journey))","def database_setup():\n Base.metadata.create_all(bind=engine)\n db = LocalSession()\n try:\n populate_from_json(db, Vector, str(VECTORS))\n populate_from_json(db, Gender, str(GENDERS))\n populate_from_json(db, Tag, str(TAGS))\n finally:\n db.close()","def importData():\n #importChallengeDataToDB()\n importTrendingDataToDB()","def set_up_tables():\n table_users = \"\"\"\n CREATE TABLE IF NOT EXISTS users (\n id SERIAL PRIMARY KEY,\n username VARCHAR (24) NOT NULL UNIQUE,\n firstname VARCHAR (24) NOT NULL,\n lastname VARCHAR (24) NOT NULL,\n othername VARCHAR (24),\n phone VARCHAR (24) NOT NULL,\n email VARCHAR (30) NOT NULL UNIQUE,\n password VARCHAR (128) NOT NULL,\n passportUrl VARCHAR (200),\n isPolitician BOOLEAN,\n isAdmin BOOLEAN\n )\"\"\"\n\n parties_table = \"\"\" \n CREATE TABLE IF NOT EXISTS parties (\n id SERIAL PRIMARY KEY,\n name VARCHAR (35) NOT NULL UNIQUE,\n hqAddress VARCHAR (30),\n logoUrl VARCHAR\n )\"\"\"\n\n offices_table = \"\"\"\n CREATE TABLE IF NOT EXISTS offices (\n id SERIAL PRIMARY KEY,\n name VARCHAR (35) NOT NULL UNIQUE,\n type VARCHAR (35)\n )\"\"\"\n\n canditates_table = \"\"\"\n CREATE TABLE IF NOT EXISTS candidates (\n id SERIAL,\n candidate INTEGER,\n office INTEGER,\n PRIMARY KEY (office, candidate),\n FOREIGN KEY (candidate) REFERENCES users(id) ON DELETE CASCADE,\n FOREIGN KEY (office) REFERENCES offices(id) ON DELETE CASCADE\n )\"\"\"\n\n voters_table = \"\"\"\n CREATE TABLE IF NOT EXISTS votes (\n id SERIAL,\n office INTEGER,\n candidate INTEGER,\n voter INTEGER,\n PRIMARY KEY (office, voter),\n FOREIGN KEY (office) REFERENCES offices(id) ON DELETE CASCADE,\n FOREIGN KEY (candidate) REFERENCES users(id) ON DELETE CASCADE,\n FOREIGN KEY (voter) REFERENCES users(id) ON DELETE CASCADE\n )\"\"\"\n\n return [table_users, parties_table,\n offices_table, canditates_table, voters_table]","def create_tables(): \n \n pk_contraint = \"CONSTRAINT {}_pk PRIMARY KEY ({})\"\n uq_contraint = \"CONSTRAINT {}_uq UNIQUE ({})\"\n fk_query = \"\"\"CONSTRAINT {}_fk_{} \n FOREIGN KEY ({}) \n REFERENCES {}({}) \n ON UPDATE CASCADE \n ON DELETE RESTRICT\n \"\"\"\n \n create_dict = {}\n index = 1\n\n\n ############################## public SCHEMA ##############################\n \n schema = 'public'\n create_schema(schema)\n\n #################### site ####################\n table_name = 'site'\n pk_id = 'site_id'\n uq_list = ['site_code']\n fk_dict = {}\n query = \"\"\"CREATE TABLE IF NOT EXISTS {}.{} (\n {} SERIAL, \n site_code CHAR(3),\n purok VARCHAR,\n sitio VARCHAR,\n barangay VARCHAR,\n municipality VARCHAR,\n province VARCHAR,\n region VARCHAR,\n psgc INTEGER,\n active BOOLEAN NOT NULL DEFAULT TRUE,\n season SMALLINT,\n {}, {} {}\n );\n \"\"\" \n create_dict[index] = {'schema': schema,\n 'table_name': table_name,\n 'query': query,\n 'pk_id': pk_id,\n 'uq_list': uq_list,\n 'fk_dict': fk_dict}\n index += 1\n\n\n ############################## spatial SCHEMA ##############################\n \n schema = 'spatial'\n create_schema(schema)\n \n #################### exposure ####################\n table_name = 'exposure'\n pk_id = 'exp_id'\n uq_list = ['exp_name']\n fk_dict = {}\n query = \"\"\"CREATE TABLE IF NOT EXISTS {}.{} (\n {} SERIAL, \n exp_name VARCHAR,\n {}, {} {}\n );\n \"\"\" \n create_dict[index] = {'schema': schema,\n 'table_name': table_name,\n 'query': query,\n 'pk_id': pk_id,\n 'uq_list': uq_list,\n 'fk_dict': fk_dict}\n index += 1\n \n #################### site_exposure ####################\n table_name = 'site_exposure'\n pk_id = 'se_id'\n uq_list = ['site_id', 'exp_id', 'geom']\n fk_dict = {'site_id': {'ref_schema': 'public', 'ref_table': 'site'},\n 'exp_id': {'ref_schema': 'spatial', 'ref_table': 'exposure'}}\n query = \"\"\"CREATE TABLE IF NOT EXISTS {}.{} (\n {} SERIAL, \n site_id INTEGER,\n exp_id INTEGER,\n label_name VARCHAR,\n geom GEOMETRY,\n activated DATE NOT NULL DEFAULT CURRENT_DATE,\n deactivated DATE,\n {}, {} {}\n );\n \"\"\" \n create_dict[index] = {'schema': schema,\n 'table_name': table_name,\n 'query': query,\n 'pk_id': pk_id,\n 'uq_list': uq_list,\n 'fk_dict': fk_dict}\n index += 1\n \n #################### feature ####################\n table_name = 'feature'\n pk_id = 'feat_id'\n uq_list = ['feat_name']\n fk_dict = {}\n query = \"\"\"CREATE TABLE IF NOT EXISTS {}.{} (\n {} SERIAL, \n feat_name VARCHAR,\n {}, {} {}\n );\n \"\"\" \n create_dict[index] = {'schema': schema,\n 'table_name': table_name,\n 'query': query,\n 'pk_id': pk_id,\n 'uq_list': uq_list,\n 'fk_dict': fk_dict}\n index += 1\n\n #################### site_feature ####################\n table_name = 'site_feature'\n pk_id = 'sf_id'\n uq_list = ['site_id', 'feat_id', 'geom']\n fk_dict = {'site_id': {'ref_schema': 'public', 'ref_table': 'site'},\n 'feat_id': {'ref_schema': 'spatial', 'ref_table': 'feature'}}\n query = \"\"\"CREATE TABLE IF NOT EXISTS {}.{} (\n {} SERIAL, \n site_id INTEGER,\n feat_id INTEGER,\n geom GEOMETRY,\n activated DATE NOT NULL DEFAULT CURRENT_DATE,\n deactivated DATE,\n {}, {} {}\n );\n \"\"\" \n create_dict[index] = {'schema': schema,\n 'table_name': table_name,\n 'query': query,\n 'pk_id': pk_id,\n 'uq_list': uq_list,\n 'fk_dict': fk_dict}\n index += 1\n\n #################### hazard_zone ####################\n table_name = 'hazard_zone'\n pk_id = 'hz_id'\n uq_list = ['site_id, geom']\n fk_dict = {'site_id': {'ref_schema': 'public', 'ref_table': 'site'}}\n query = \"\"\"CREATE TABLE IF NOT EXISTS {}.{} (\n {} SERIAL, \n site_id INTEGER,\n geom GEOMETRY,\n activated DATE NOT NULL DEFAULT CURRENT_DATE,\n deactivated DATE,\n {}, {} {}\n );\n \"\"\"\n create_dict[index] = {'schema': schema,\n 'table_name': table_name,\n 'query': query,\n 'pk_id': pk_id,\n 'uq_list': uq_list,\n 'fk_dict': fk_dict}\n index += 1\n\n #################### monitoring ####################\n table_name = 'monitoring'\n pk_id = 'mon_id'\n uq_list = ['mon_name']\n fk_dict = {}\n query = \"\"\"CREATE TABLE IF NOT EXISTS {}.{} (\n {} SERIAL, \n mon_name VARCHAR,\n {}, {} {}\n );\n \"\"\" \n create_dict[index] = {'schema': schema,\n 'table_name': table_name,\n 'query': query,\n 'pk_id': pk_id,\n 'uq_list': uq_list,\n 'fk_dict': fk_dict}\n index += 1\n\n #################### site_monitoring ####################\n table_name = 'site_monitoring'\n pk_id = 'sm_id'\n uq_list = ['site_id', 'mon_id', 'geom']\n fk_dict = {'site_id': {'ref_schema': 'public', 'ref_table': 'site'},\n 'mon_id': {'ref_schema': 'spatial', 'ref_table': 'monitoring'}}\n query = \"\"\"CREATE TABLE IF NOT EXISTS {}.{} (\n {} SERIAL, \n site_id INTEGER,\n mon_id INTEGER,\n label_name VARCHAR,\n geom GEOMETRY,\n activated DATE NOT NULL DEFAULT CURRENT_DATE,\n deactivated DATE,\n {}, {} {}\n );\n \"\"\" \n create_dict[index] = {'schema': schema,\n 'table_name': table_name,\n 'query': query,\n 'pk_id': pk_id,\n 'uq_list': uq_list,\n 'fk_dict': fk_dict}\n index += 1\n\n\n ############################### comm SCHEMA ###############################\n \n schema = 'comm'\n create_schema(schema)\n\n #################### gsm_server ####################\n table_name = 'gsm_server'\n pk_id = 'server_id'\n uq_list = ['server_name']\n fk_dict = {}\n query = \"\"\"CREATE TABLE IF NOT EXISTS {}.{} (\n {} SERIAL, \n server_name VARCHAR,\n platform_type VARCHAR,\n version SMALLINT,\n {}, {} {}\n );\n \"\"\" \n create_dict[index] = {'schema': schema,\n 'table_name': table_name,\n 'query': query,\n 'pk_id': pk_id,\n 'uq_list': uq_list,\n 'fk_dict': fk_dict}\n index += 1\n\n #################### server_port ####################\n table_name = 'server_port'\n pk_id = 'port_id'\n uq_list = ['server_id', 'port']\n fk_dict = {'server_id': {'ref_schema': 'comm', 'ref_table': 'gsm_server'}}\n query = \"\"\"CREATE TABLE IF NOT EXISTS {}.{} (\n {} SERIAL, \n server_id INTEGER,\n port BOOLEAN,\n ser_port VARCHAR,\n pwr_on_pin SMALLINT,\n ring_pin SMALLINT,\n module_type SMALLINT,\n {}, {} {}\n );\n \"\"\"\n query += \"\"\" COMMENT ON TABLE {}.{} IS \n '0- left\n 1- right'\n ;\"\"\".format(schema, table_name)\n create_dict[index] = {'schema': schema,\n 'table_name': table_name,\n 'query': query,\n 'pk_id': pk_id,\n 'uq_list': uq_list,\n 'fk_dict': fk_dict}\n index += 1\n\n #################### network_type ####################\n table_name = 'network_type'\n pk_id = 'prefix'\n uq_list = ['prefix']\n fk_dict = {}\n query = \"\"\"CREATE TABLE IF NOT EXISTS {}.{} (\n {} VARCHAR(3), \n carrier SMALLINT,\n {}, {} {}\n );\n \"\"\"\n query += \"\"\" COMMENT ON TABLE {}.{} IS \n '1- globe\n 2- smart\n 3- landline'\n ;\"\"\".format(schema, table_name)\n create_dict[index] = {'schema': schema,\n 'table_name': table_name,\n 'query': query,\n 'pk_id': pk_id,\n 'uq_list': uq_list,\n 'fk_dict': fk_dict}\n index += 1\n\n #################### gsm_module ####################\n table_name = 'gsm_module'\n pk_id = 'gsm_id'\n uq_list = ['prefix', 'num', 'activated']\n fk_dict = {'prefix': {'ref_schema': 'comm', 'ref_table': 'network_type'},\n 'port_id': {'ref_schema': 'comm', 'ref_table': 'server_port'}}\n query = \"\"\"CREATE TABLE IF NOT EXISTS {}.{} (\n {} SERIAL, \n prefix VARCHAR(3),\n num CHAR(7),\n activated DATE NOT NULL DEFAULT CURRENT_DATE,\n port_id INTEGER,\n {}, {} {}\n );\n \"\"\"\n create_dict[index] = {'schema': schema,\n 'table_name': table_name,\n 'query': query,\n 'pk_id': pk_id,\n 'uq_list': uq_list,\n 'fk_dict': fk_dict}\n index += 1\n\n\n ############################# temporal SCHEMA #############################\n \n schema = 'temporal'\n create_schema(schema)\n\n #################### marker_observation ####################\n table_name = 'marker_observation'\n pk_id = 'mo_id'\n uq_list = ['site_id', 'ts']\n fk_dict = {'site_id': {'ref_schema': 'public', 'ref_table': 'site'}}\n query = \"\"\"CREATE TABLE IF NOT EXISTS {}.{} (\n {} SERIAL, \n site_id INTEGER,\n ts TIMESTAMP,\n meas_type VARCHAR(7),\n weather VARCHAR,\n observer_name VARCHAR,\n {}, {} {}\n );\n \"\"\" \n create_dict[index] = {'schema': schema,\n 'table_name': table_name,\n 'query': query,\n 'pk_id': pk_id,\n 'uq_list': uq_list,\n 'fk_dict': fk_dict}\n index += 1\n\n #################### marker_history ####################\n table_name = 'marker_history'\n pk_id = 'hist_id'\n uq_list = ['sm_id', 'ts', 'event']\n fk_dict = {'sm_id': {'ref_schema': 'spatial', 'ref_table': 'site_monitoring'}}\n query = \"\"\"CREATE TABLE IF NOT EXISTS {}.{} (\n {} SERIAL,\n sm_id BIGINT,\n ts TIMESTAMP,\n event BOOLEAN,\n label_name VARCHAR,\n {}, {} {}\n );\n \"\"\"\n query += \"\"\" COMMENT ON TABLE {}.{} IS \n '0- rename\n 1- reposition'\n ;\"\"\".format(schema, table_name)\n create_dict[index] = {'schema': schema,\n 'table_name': table_name,\n 'query': query,\n 'pk_id': pk_id,\n 'uq_list': uq_list,\n 'fk_dict': fk_dict}\n index += 1\n\n #################### marker_data ####################\n table_name = 'marker_data'\n pk_id = 'data_id'\n uq_list = ['sm_id', 'mo_id']\n fk_dict = {'sm_id': {'ref_schema': 'spatial', 'ref_table': 'site_monitoring'},\n 'mo_id': {'ref_schema': 'temporal', 'ref_table': 'marker_observation'}}\n query = \"\"\"CREATE TABLE IF NOT EXISTS {}.{} (\n {} SERIAL,\n mo_id BIGINT,\n sm_id BIGINT,\n measurement NUMERIC(5,1),\n {}, {} {}\n );\n \"\"\" \n create_dict[index] = {'schema': schema,\n 'table_name': table_name,\n 'query': query,\n 'pk_id': pk_id,\n 'uq_list': uq_list,\n 'fk_dict': fk_dict}\n index += 1\n\n #################### marker_alert ####################\n table_name = 'marker_alert'\n pk_id = 'alert_id'\n uq_list = ['data_id']\n fk_dict = {'data_id': {'ref_schema': 'temporal', 'ref_table': 'marker_data'}}\n query = \"\"\"CREATE TABLE IF NOT EXISTS {}.{} (\n {} SERIAL,\n data_id BIGINT,\n displacement NUMERIC(4,1),\n time_delta FLOAT,\n alert_level SMALLINT,\n {}, {} {}\n );\n \"\"\" \n create_dict[index] = {'schema': schema,\n 'table_name': table_name,\n 'query': query,\n 'pk_id': pk_id,\n 'uq_list': uq_list,\n 'fk_dict': fk_dict}\n index += 1\n\n #################### logger_model ####################\n table_name = 'logger_model'\n pk_id = 'model_id'\n uq_list = ['has_tilt', 'has_rain', 'has_piezo', 'has_soms', 'logger_type']\n fk_dict = {}\n query = \"\"\"CREATE TABLE IF NOT EXISTS {}.{} (\n {} SERIAL, \n has_tilt BOOLEAN,\n has_rain BOOLEAN,\n has_piezo BOOLEAN,\n has_soms BOOLEAN,\n logger_type SMALLINT,\n {}, {} {}\n );\n \"\"\" \n create_dict[index] = {'schema': schema,\n 'table_name': table_name,\n 'query': query,\n 'pk_id': pk_id,\n 'uq_list': uq_list,\n 'fk_dict': fk_dict}\n index += 1\n\n #################### logger ####################\n table_name = 'logger'\n pk_id = 'logger_id'\n uq_list = ['sm_id']\n fk_dict = {'sm_id': {'ref_schema': 'spatial', 'ref_table': 'site_monitoring'},\n 'model_id': {'ref_schema': 'temporal', 'ref_table': 'logger_model'}}\n query = \"\"\"CREATE TABLE IF NOT EXISTS {}.{} (\n {} SERIAL, \n sm_id BIGINT,\n model_id INTEGER,\n {}, {} {}\n );\n \"\"\" \n create_dict[index] = {'schema': schema,\n 'table_name': table_name,\n 'query': query,\n 'pk_id': pk_id,\n 'uq_list': uq_list,\n 'fk_dict': fk_dict}\n index += 1\n \n #################### logger_mobile ####################\n table_name = 'logger_mobile'\n pk_id = 'mobile_id'\n uq_list = ['logger_id', 'activated']\n fk_dict = {'logger_id': {'ref_schema': 'temporal', 'ref_table': 'logger'},\n 'gsm_id': {'ref_schema': 'comm', 'ref_table': 'gsm_module'}}\n query = \"\"\"CREATE TABLE IF NOT EXISTS {}.{} (\n {} SERIAL,\n logger_id INTEGER,\n activated DATE NOT NULL DEFAULT CURRENT_DATE,\n sim_num VARCHAR(12),\n gsm_id INTEGER,\n {}, {} {}\n );\n \"\"\" \n create_dict[index] = {'schema': schema,\n 'table_name': table_name,\n 'query': query,\n 'pk_id': pk_id,\n 'uq_list': uq_list,\n 'fk_dict': fk_dict}\n index += 1\n\n\n #################### EXECUTE QUERY TO CREATE TABLES ####################\n for index in create_dict.keys():\n dct = create_dict[index]\n schema = dct['schema']\n table_name = dct['table_name']\n query = dct['query']\n pk_id = dct['pk_id']\n uq_list = dct['uq_list']\n fk_dict = dct['fk_dict']\n if len(fk_dict.keys()) == 0:\n fk_constraint = ''\n else:\n fk_constraint_list = ['']\n for fk_id in fk_dict.keys():\n ref_schema = fk_dict.get(fk_id)['ref_schema']\n ref_table = fk_dict.get(fk_id)['ref_table']\n fk_part = fk_query.format(table_name, ref_table, fk_id,\n \"{}.{}\".format(ref_schema, ref_table),\n fk_id)\n fk_constraint_list.append(fk_part)\n fk_constraint = ', '.join(fk_constraint_list)\n \n query = query.format(schema, table_name, pk_id, \n pk_contraint.format(table_name, pk_id),\n uq_contraint.format(table_name, ', '.join(uq_list)),\n \"{}\".format(fk_constraint))\n qdb.execute(query)","def init_db():\n db.drop_all()\n db.create_all()\n seed_companies()\n seed_emission_reports()\n seed_reduction_targets()\n seed_milestones()","def example_data():\n\n # In case this is run more than once, empty out existing data\n EmployeeProject.query.delete()\n Employee.query.delete()\n Department.query.delete()\n Project.query.delete()\n\n # Add sample employees and departments\n df = Department(dept_code='fin', dept_name='Finance', phone='555-1000')\n dl = Department(dept_code='legal', dept_name='Legal', phone='555-2222')\n dm = Department(dept_code='mktg', dept_name='Marketing', phone='555-9999')\n\n leonard = Employee(name='Leonard', dept=dl)\n liz = Employee(name='Liz', dept=dl)\n maggie = Employee(name='Maggie', state='DC', dept=dm)\n nadine = Employee(name='Nadine')\n\n db.session.add_all([df, dl, dm, leonard, liz, maggie, nadine])\n db.session.commit()\n\n pc = Project(proj_code='car', proj_name='Design Car',\n assignments=[EmployeeProject(emp_id=liz.id, role='Chair'),\n EmployeeProject(emp_id=maggie.id)])\n ps = Project(proj_code='server', proj_name='Deploy Server',\n assignments=[EmployeeProject(emp_id=liz.id),\n EmployeeProject(emp_id=leonard.id, role='Auditor')])\n\n db.session.add_all([ps, pc])\n db.session.commit()","def prepare_database(self, waterscenario=None, trafficscenario=None):\n\n # Validate input\n if waterscenario:\n waterscenario = Path(waterscenario)\n assert waterscenario.exists(), 'Waterscenario file not found'\n\n BIVAS = pyBIVAS(self.BIVAS_database)\n df_trafficscenarios = BIVAS.trafficscenario_numberoftrips()\n\n\n # Do changes to database:\n con = sqlite3.connect(self.BIVAS_database)\n c = con.cursor()\n\n # Update waterscenario with given file\n if waterscenario:\n # Delete current water_scenario_values\n sql = \"DELETE FROM water_scenario_values WHERE 1\"\n c.execute(sql)\n\n sql = \"DELETE FROM water_scenarios WHERE 1\"\n c.execute(sql)\n\n # Write waterdata to database\n\n # Read waterscenario file\n df = pd.read_csv(waterscenario, header=0, index_col=None)\n df = df[['ArcID', 'SeasonID', 'WaterLevel__m', 'RateOfFlow__m3_s', 'WaterSpeed__m_s', 'WaterDepth__m']]\n df['WaterScenarioID'] = 1\n\n # Add new water_scenario\n df.to_sql('water_scenario_values', con,\n if_exists='append', index=False)\n\n # Rename water_scenario\n # waterscenario_name = waterscenario.stem\n # sql = \"\"\"UPDATE water_scenarios SET Description = \"{}\" WHERE ID = {}\"\"\".format(\n # waterscenario_name, waterscenario)\n # c.execute(sql)\n\n\n waterscenario_id = 1\n waterscenario_name = 'TEST waterscenario'\n waterscenario_type = 1\n sql = \"\"\"INSERT into water_scenarios VALUES ({}, '{}', {})\"\"\".format(\n waterscenario_id,\n waterscenario_name,\n waterscenario_type\n )\n c.execute(sql)\n\n # Remove water scenario. I'm simply updating all scenarios\n # Otherwise I should check the BranchSet structure\n sql = \"\"\"UPDATE parameters SET WaterScenarioID = 1 WHERE 1\"\"\"\n c.execute(sql)\n\n else:\n # Remove water scenario. I'm simply updating all scenarios\n # Otherwise I should check the BranchSet structure\n sql = \"\"\"UPDATE parameters SET WaterScenarioID = NULL WHERE 1\"\"\"\n c.execute(sql)\n\n # Set scenario name and description\n date_string = datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n self.description = f'Date: {date_string}, Waterscenario: {waterscenario}, TrafficScenario: {trafficscenario},'\n\n sql = \"\"\"\n UPDATE scenarios\n SET Name = \"{}\",\n Description = \"{}\"\n WHERE ID = {}\n \"\"\".format(\n self.scenarioName, self.description, self.scenarioID)\n c.execute(sql)\n\n # Update traffic Scenario. I'm simply updating all scenarios\n # Otherwise I should check the BranchSet structure\n if trafficscenario:\n if isinstance(trafficscenario, int):\n sql = \"\"\"UPDATE parameters SET TrafficScenarioID = \"{}\" WHERE 1\"\"\".format(trafficscenario)\n c.execute(sql)\n else:\n trafficScenarioID = df_trafficscenarios.index[df_trafficscenarios['Description'] == trafficscenario][0]\n sql = \"\"\"UPDATE parameters SET TrafficScenarioID = \"{}\" WHERE 1\"\"\".format(trafficScenarioID)\n c.execute(sql)\n\n con.commit()\n con.close()\n\n logger.info('BIVAS database copied and updated')","def update_data(self):\n staff = Staff.objects.all()\n orgs = Organization.objects.all()\n depts = Department.objects.all()\n\n existing = self.all()\n if existing.count():\n existing.delete()\n\n if staff.count():\n for s in staff:\n record = CombinedTeledata(\n id=s.id,\n alpha=s.alpha,\n name=s.name,\n first_name=s.first_name,\n last_name=s.last_name,\n sort_name=s.sort_name,\n email=s.email,\n phone=s.phone,\n postal=s.postal,\n job_position=s.job_position,\n department=s.dept.name,\n dept_id=s.dept.id,\n organization=s.dept.org.name,\n org_id=s.dept.org.id,\n building=s.bldg.name,\n bldg_id=s.bldg.import_id,\n room=s.room,\n from_table='staff'\n )\n\n try:\n record.save(doing_import=True)\n record.keywords_combined.set(s.keywords.all())\n except Exception as e:\n logger.error(str(e))\n\n if orgs.count():\n for o in orgs:\n record = CombinedTeledata(\n id=o.id,\n name=o.name,\n sort_name=o.name,\n phone=o.phone,\n fax=o.fax,\n building=o.bldg.name,\n bldg_id=o.bldg.import_id,\n room=o.room,\n from_table='organizations'\n )\n\n try:\n record.save(doing_import=True)\n record.keywords_combined.set(o.keywords.all())\n except Exception as e:\n logger.error(str(e))\n\n if depts.count():\n for d in depts:\n record = CombinedTeledata(\n id=d.id,\n name=d.name,\n sort_name=d.name,\n phone=d.phone,\n fax=d.fax,\n organization=d.org.name,\n org_id=d.org.id,\n building=d.bldg.name,\n bldg_id=d.bldg.import_id,\n room=d.room,\n from_table='departments'\n )\n\n try:\n record.save(doing_import=True)\n record.keywords_combined.set(d.keywords.all())\n except Exception as e:\n logger.error(str(e))","def create_final_table(conn, county):\r\n for county in county:\r\n query = f\"SELECT name FROM sqlite_master WHERE type ='table' AND name NOT LIKE 'sqlite_%' AND name = '{county}'\"\r\n result = execute_query(conn, query)\r\n try:\r\n if len(result) == 0:\r\n query = f\"create table {county} as select * from {county}_stg;\"\r\n execute_query(conn, query)\r\n\r\n load_final_table(conn, county)\r\n except Exception as e:\r\n print(f\"This query {query} failed with exception {e}\")","def example_data():\n\n seed.add_sfpl_branches()\n seed.add_formats()\n\n book_1 = Book(title=\"Alanna: The First Adventure\", author=\"Tamora Pierce\")\n book_2 = Book(title=\"The Hitchhiker's Guide to the Galaxy\", author=\"Douglas Adams\")\n book_3 = Book(title=\"The Hobbit\", author=\"J.R.R. Tolkien\")\n db.session.add(book_1)\n db.session.add(book_2)\n db.session.add(book_3)\n\n esqg = User(first_name=\"Elizabeth\", last_name=\"Goodman\", email=\"esqg@nowhere.com\", password=\"programmer\")\n db.session.add(esqg)\n db.session.commit()\n\n esqg_gr = GoodreadsUser(user_id=esqg.user_id, goodreads_id=ESQG)\n db.session.add(esqg_gr)\n\n my_mission = UserBranch(branch_code=\"miss\", user_id=esqg.user_id)\n db.session.add(my_mission)\n\n my_main = UserBranch(branch_code=\"main\", user_id=esqg.user_id)\n db.session.add(my_main)\n\n db.session.commit()","def initial_db_setup() -> None:\n db_filename = \"twdft.db\"\n db_path = os.path.join(TWDFT_DATA_DIR, db_filename)\n csv_filename = \"sites.csv\"\n csv_path = os.path.join(TWDFT_DATA_DIR, csv_filename)\n db_is_new = not os.path.exists(db_path)\n sites_csv = os.path.join(TWDFT_DATA_DIR, csv_filename)\n\n if db_is_new:\n with sqlite3.connect(db_path) as conn:\n c = conn.cursor()\n\n # first we create a site object\n c.execute(\n \"\"\"\n CREATE TABLE site(\n id INTEGER PRIMARY KEY,\n name TEXT,\n site_type TEXT,\n sub_category TEXT,\n address_1 TEXT,\n address_2 TEXT,\n town TEXT,\n county TEXT,\n country TEXT,\n postcode TEXT,\n site_category TEXT,\n freq_target TEXT,\n created TEXT,\n notes TEXT,\n last_inspection TEXT,\n next_inspection TEXT,\n pfsp_approval TEXT,\n pfsp_expiry TEXT,\n unlocode TEXT,\n pfso TEXT,\n pso TEXT,\n pfsa_approval TEXT,\n pfsa_expiry TEXT,\n team TEXT,\n created_by TEXT,\n last_updated TEXT,\n updated_by TEXT,\n afp_loc TEXT,\n rdf TEXT,\n classification TEXT,\n article24 TEXT,\n psa_approval TEXT,\n inspection_due TEXT\n )\n \"\"\"\n )\n conn.commit()\n\n # next we want an inspection table\n\n c.execute(\n \"\"\"\n CREATE TABLE inspection(\n id INTEGER PRIMARY KEY,\n site INTEGER,\n date TEXT,\n status TEXT,\n time TEXT,\n FOREIGN KEY(site) REFERENCES site(id)\n )\n \"\"\"\n )\n conn.commit()\n\n # next we want an inspector table\n c.execute(\n \"\"\"\n create table inspector(\n id integer primary key,\n first_name text,\n last_name text\n )\n \"\"\"\n )\n conn.commit()\n\n for i in INSPECTORS:\n first = i.split(\" \")[0]\n last = i.split(\" \")[1]\n c.execute(\n \"INSERT INTO inspector(first_name, last_name) VALUES (?,?)\",\n (first, last),\n )\n\n # a table that links inspectors with inspections\n c.execute(\n \"\"\"\n CREATE TABLE inspector_inspections(\n inspector INTEGER,\n inspection INTEGER,\n FOREIGN KEY (inspector) REFERENCES inspector(id),\n FOREIGN KEY (inspection) REFERENCES inspection(id)\n )\n \"\"\"\n )\n conn.commit()\n\n for site in map(Site._make, csv.reader(open(csv_path, \"r\"))):\n try:\n c.execute(\n f\"\"\"\n INSERT INTO site VALUES(?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)\"\"\",\n (\n int(site.id.replace(\",\", \"\")),\n site.name,\n site.site_type,\n site.sub_category,\n site.address_1,\n site.address_2,\n site.town,\n site.county,\n site.country,\n site.postcode,\n site.site_category,\n site.freq_target,\n site.created,\n site.notes,\n site.last_inspection,\n site.next_inspection,\n site.pfsp_approval,\n site.pfsp_expiry,\n site.unlocode,\n site.pfso,\n site.pso,\n site.pfsa_approval,\n site.pfsa_expiry,\n site.team,\n site.created_by,\n site.last_updated,\n site.updated_by,\n site.afp_loc,\n site.rdf,\n site.classification,\n site.article24,\n site.psa_approval,\n site.inspection_due,\n ),\n )\n except sqlite3.IntegrityError as e:\n print(\"That hasnae worked\", site.inspection_due)","def populate_db(self):\n # Get donors\n log.info(\"Populating donors.\")\n\n self.r.hmset('Thomas', {'donations': '500', 'email': 'thomas@thomas.com', 'city': 'Athens', 'state': 'GA', 'zip': 30606})\n\n self.r.hmset('Ted', {'donations': '1', 'email': 'ted@ted.com', 'city': 'Memphis', 'state': 'TN', 'zip': 38104})\n\n self.r.hmset(\"Bailey\", {'donations': '1000', 'email': 'bailey@bailey.com', 'city': 'Washington', 'state': 'DC', 'zip': 12345})","def setUp(self):\r\n super(TestAnswerDistributions, self).setUp()\r\n\r\n self.homework = self.add_graded_section_to_course('homework')\r\n self.add_dropdown_to_section(self.homework.location, 'p1', 1)\r\n self.add_dropdown_to_section(self.homework.location, 'p2', 1)\r\n self.add_dropdown_to_section(self.homework.location, 'p3', 1)\r\n self.refresh_course()","def prep(self):\n sq1 = 'create table TCVR ( ID, T, C, V, R , primary key ( ID ) ) ;'\n sq2 = 'create table IDX ( ID , A , primary key(A) ) ; '\n self.sq.SQX(sq1)\n self.sq.SQX(sq2)\n sq3 = \"insert into IDX VALUES ( 1 , 'A' ) ; \"\n self.sq.SQX(sq3)","def setup_data(es_with_collector):\n country_uk = constants.Country.united_kingdom.value.id\n country_us = constants.Country.united_states.value.id\n uk_region = constants.UKRegion.south_east.value.id\n CompanyFactory(\n name='abc defg ltd',\n trading_names=['helm', 'nop'],\n address_1='1 Fake Lane',\n address_town='Downtown',\n address_country_id=country_uk,\n uk_region_id=uk_region,\n )\n CompanyFactory(\n name='abc defg us ltd',\n trading_names=['helm', 'nop', 'qrs'],\n address_1='1 Fake Lane',\n address_town='Downtown',\n address_country_id=country_us,\n registered_address_country_id=country_us,\n )\n es_with_collector.flush_and_refresh()","def fill_target_table(new_data, curs, conn, overwrite=False):\n for i in new_data:\n connect_database.add_target_to_database(list(i), curs, conn, overwrite_exsiting = overwrite)\n conn.commit()","def seed():\n\n try:\n body_parts = app.config.get(\"BODY_PARTS\")\n body_parts_model = get_class_by_tablename(\"body_parts\")\n\n current_records = body_parts_model.query.all()\n\n if current_records:\n for key, value in body_parts.items():\n if not body_parts_model.find(key):\n\n body_parts_model.create(id=key, name=value)\n\n else:\n\n for key, value in body_parts.items():\n body_parts_model.create(id=key, name=value)\n\n try:\n body_parts_model.session.commit()\n except IntegrityError as err:\n print(\"Error seeding the database: \", err)\n\n except:\n body_parts_model.session.rollback()\n print('Body parts records already exist in database.')\n\n try:\n body_subparts = app.config.get(\"BODY_SUBPARTS\")\n body_subparts_model = get_class_by_tablename(\"subparts\")\n\n current_records = body_subparts_model.query.all()\n\n if current_records:\n for key, value in body_subparts.items():\n if not body_subparts_model.find(key):\n body_subparts_model.create(id=key,\n name=value['name'],\n coordinates=value['coordinates'],\n active=value['active'],\n body_part_id=value['body_part_id']\n )\n\n else:\n for key, value in body_subparts.items():\n\n body_subparts_model.create(id=key,\n name=value['name'],\n coordinates=value['coordinates'],\n active=value['active'],\n body_part_id=value['body_part_id']\n )\n try:\n body_subparts_model.session.commit()\n except IntegrityError as err:\n print(\"Error seeding the database: \", err)\n\n except Exception as e:\n body_subparts_model.session.rollback()\n print(e)\n print('Subparts records already exist in database.')\n\n\n try:\n symptoms = app.config.get(\"SYMPTOMS\")\n symptoms_model = get_class_by_tablename(\"symptoms\")\n\n current_records = symptoms_model.query.all()\n\n if current_records:\n for key, value in symptoms.items():\n if not symptoms_model.find(key):\n symptoms_model.create(id=key,\n name=value['name'],\n active=value['active']\n )\n\n else:\n for key, value in symptoms.items():\n symptoms_model.create(id=key,\n name=value['name'],\n active=value['active']\n )\n try:\n symptoms_model.session.commit()\n except IntegrityError as err:\n print(\"Error seeding the database: \", err)\n\n except Exception as e:\n symptoms_model.session.rollback()\n print(e)\n print('Symptom records already exist in database.')\n\n\n try:\n suggestions = app.config.get(\"SUGGESTIONS\")\n suggestions_model = get_class_by_tablename(\"suggestions\")\n\n current_records = suggestions_model.query.all()\n\n if current_records:\n for key, value in suggestions.items():\n if not suggestions_model.find(key):\n suggestions_model.create(id=key,\n name=value['name'],\n active=value['active'],\n description=value['description'],\n link=value['link'],\n video_start=value['video_start'],\n video_end=value['video_end']\n )\n\n else:\n for key, value in suggestions.items():\n suggestions_model.create(id=key,\n name=value['name'],\n active=value['active'],\n description=value['description'],\n link=value['link'],\n video_start=value['video_start'],\n video_end=value['video_end']\n )\n try:\n suggestions_model.session.commit()\n except IntegrityError as err:\n print(\"Error seeding the database: \", err)\n\n except Exception as e:\n suggestions_model.session.rollback()\n print(e)\n print('Suggestion records already exist in database.')\n\n try:\n conditions = app.config.get(\"CONDITIONS\")\n conditions_model = get_class_by_tablename(\"conditions\")\n symptoms_model = get_class_by_tablename(\"symptoms\")\n suggestions_model = get_class_by_tablename(\"symptoms\")\n symptom_relation = app.config.get(\"CONDITION_SYMPTOM_RELATION\")\n suggestion_relation = app.config.get(\"CONDITION_SUGGESTION_RELATION\")\n\n current_records = conditions_model.query.all()\n\n if current_records:\n for key, value in conditions.items():\n if not conditions_model.find(key):\n conditions_model.create(id=key,\n name=value['name'],\n active=value['active'],\n description=value['description']\n )\n\n else:\n for key, value in conditions.items():\n conditions_model.create(id=key,\n name=value['name'],\n active=value['active'],\n description=value['description']\n )\n\n for key, value in symptom_relation.items():\n condition = conditions_model.query.filter(conditions_model.id == value['condition_id']).first()\n symptom = symptoms_model.query.filter(symptoms_model.id == value['symptom_id']).first()\n condition.symptoms.append(symptom)\n\n # for key, value in suggestion_relation.items():\n # condition = conditions_model.query.filter(conditions_model.id == value['condition_id']).first()\n # suggestion = suggestions_model.query.filter(suggestions_model.id == value['suggestion_id']).first()\n # condition.suggestions.append(suggestion)\n\n try:\n conditions_model.session.commit()\n except IntegrityError as err:\n print(\"Error seeding the database: \", err)\n\n except Exception as e:\n conditions_model.session.rollback()\n print(e)\n print('Condition records already exist in database.')\n\n try:\n body_subparts_model = get_class_by_tablename(\"subparts\")\n relationship = app.config.get(\"SUBPARTS_CONDITION_RELATION\")\n conditions_model = get_class_by_tablename(\"conditions\")\n\n for key, value in relationship.items():\n subpart = body_subparts_model.query.filter(body_subparts_model.id == value['subpart_id']).first()\n condition = conditions_model.query.filter(conditions_model.id == value['condition_id']).first()\n subpart.conditions.append(condition)\n try:\n body_subparts_model.session.commit()\n except IntegrityError as err:\n print(\"Error seeding the database: \", err)\n\n except Exception as e:\n body_subparts_model.session.rollback()\n print(e)\n print('Subparts Relation records already exist in database.')","async def populate_test_data(self):\n async with (await self._get_connection_pool()).acquire() as conn:\n await conn.execute('delete from foglamp.tasks')\n await conn.execute('delete from foglamp.schedules')\n await conn.execute('delete from foglamp.scheduled_processes')\n await conn.execute(\n '''insert into foglamp.scheduled_processes(name, script)\n values('sleep1', '[\"python3\", \"../scripts/sleep.py\", \"1\"]')''')\n await conn.execute(\n '''insert into foglamp.scheduled_processes(name, script)\n values('sleep10', '[\"python3\", \"../scripts/sleep.py\", \"10\"]')''')\n await conn.execute(\n '''insert into foglamp.scheduled_processes(name, script)\n values('sleep30', '[\"python3\", \"../scripts/sleep.py\", \"30\"]')''')\n await conn.execute(\n '''insert into foglamp.scheduled_processes(name, script)\n values('sleep5', '[\"python3\", \"../scripts/sleep.py\", \"5\"]')''')","def initialize(self):\n\n cursor = self.conn.cursor()\n\n # This table can be used as a parent for a collection of runs\n cursor.execute(\n \"\"\"CREATE TABLE IF NOT EXISTS RunCollections (\n id INT AUTO_INCREMENT PRIMARY KEY,\n name VARCHAR(14) UNIQUE\n );\"\"\"\n )\n\n # This table holds in which run each appears.\n cursor.execute(\n \"\"\"CREATE TABLE IF NOT EXISTS Runs (\n id INT AUTO_INCREMENT PRIMARY KEY,\n name VARCHAR(14) UNIQUE,\n collection_id INT,\n FOREIGN KEY (collection_id) REFERENCES RunCollections (id) ON DELETE CASCADE);\"\"\"\n )\n\n # This table holds resources, which can be in multiple runs and have multiple varieties\n cursor.execute(\n \"\"\"CREATE TABLE IF NOT EXISTS Resources (\n id INT AUTO_INCREMENT PRIMARY KEY, \n extension VARCHAR(20), \n webpage VARCHAR(30),\n run_id INT NOT NULL,\n FOREIGN KEY (run_id) REFERENCES Runs (id) ON DELETE CASCADE);\"\"\"\n )\n\n cursor.execute(\n 'SELECT Table_name FROM information_schema.tables WHERE table_schema = \"vpntfg0\" AND Table_name LIKE \"%Varieties_%\" ORDER BY Table_name'\n )\n for row in cursor.fetchall():\n self.variety_tables.append(row[0])\n\n cursor.close()\n _logger.info(\"Variety tables are: %s\" % self.variety_tables)\n\n _logger.info(\"Database initialized\")","def create_tables():\n commands = (\n \"\"\"\n CREATE TABLE STUDENT(\n ADMISSION INT PRIMARY KEY NOT NULL,\n NAME TEXT NOT NULL,\n AGE INT NOT NULL,\n COURSE CHAR(50),\n DEPARTMENT CHAR(50)\n )\n \"\"\",\n \"\"\"\n CREATE TABLE vendors (\n vendor_id SERIAL PRIMARY KEY,\n vendor_name VARCHAR(255) NOT NULL\n )\n \"\"\",\n \"\"\" CREATE TABLE parts (\n part_id SERIAL PRIMARY KEY,\n part_name VARCHAR(255) NOT NULL\n )\n \"\"\",\n \"\"\"\n CREATE TABLE part_drawings (\n part_id INTEGER PRIMARY KEY,\n file_extension VARCHAR(5) NOT NULL,\n drawing_data BYTEA NOT NULL,\n FOREIGN KEY (part_id)\n REFERENCES parts (part_id)\n ON UPDATE CASCADE ON DELETE CASCADE\n )\n \"\"\",\n \"\"\"\n CREATE TABLE vendor_parts (\n vendor_id INTEGER NOT NULL,\n part_id INTEGER NOT NULL,\n PRIMARY KEY (vendor_id , part_id),\n FOREIGN KEY (vendor_id)\n REFERENCES vendors (vendor_id)\n ON UPDATE CASCADE ON DELETE CASCADE,\n FOREIGN KEY (part_id)\n REFERENCES parts (part_id)\n ON UPDATE CASCADE ON DELETE CASCADE\n )\n \"\"\")\n\n conn = None\n try:\n # read the connection parameters\n params = config()\n # connect to the PostgreSQL server\n conn = psycopg2.connect(**params)\n cur = conn.cursor()\n # create table one by one\n for command in commands:\n cur.execute(command)\n # close communication with the PostgreSQL database server\n cur.close()\n # commit the changes\n conn.commit()\n except (Exception, psycopg2.DatabaseError) as error:\n print(error)\n finally:\n if conn is not None:\n conn.close()","def load_products():\n\n for i, row in enumerate(open(\"seed_data/category.product\")):\n row = row.rstrip()\n name = row.split(\"|\")\n product_category = ProductCategoryModel(name=name)\n db.session.add(product_category)\n\n for i, row in enumerate(open(\"seed_data/product.product\")):\n row = row.rstrip()\n name, short_description, long_description, product_category_id, img_path_xs, img_path_sm, img_path_md, img_path_lg = row.split(\"|\")\n product = ProductModel(name=name,\n short_description=short_description,\n long_description=long_description,\n product_category_id=product_category_id,\n img_path_xs=img_path_xs,\n img_path_sm=img_path_sm,\n img_path_md=img_path_md,\n img_path_lg=img_path_lg)\n db.session.add(product)\n\n for i, row in enumerate(open(\"seed_data/location.product\")):\n row = row.rstrip()\n name, description, address1, address2, city, state, zip_code, country, latitude, longitude, direction_url = row.split(\"|\")\n location = LocationModel(name=name,\n description=description,\n address1=address1,\n address2=address2,\n city=city,\n state=state,\n zip_code=zip_code,\n country=country,\n latitude=latitude,\n longitude=longitude,\n direction_url=direction_url)\n db.session.add(location)\n\n for i, row in enumerate(open(\"seed_data/location_product.product\")):\n row = row.rstrip()\n location_id, product_id, price, num_available = row.split(\"|\")\n location_product = LocationProductModel(location_id=location_id,\n product_id=product_id,\n price=price,\n num_available=num_available)\n db.session.add(location_product)\n\n db.session.commit()","def insert_data():\n\tBase.metadata.drop_all(engine)\n\tBase.metadata.create_all(engine)\n\tu1 = insert_user(\"233@B.com\", \"/static/image/avatar.JPG\")\n\tu2 = insert_user(\"fy@B.com\", \"/static/image/avatar.JPG\")\n\tc = insert_catalog(u1.id, \"Sichuan Dish\")\n\tinsert_catalog(u1.id, \"Fujian Dish\")\n\tinsert_catalog(u1.id, \"Guangdong Dish\")\n\tinsert_catalog(u2.id, \"Zhejiang Dish\")\n\tinsert_catalog(u2.id, \"Beijing Dish\")\n\tinsert_item(u1.id, \"Iphone 6 plus\", c, 'Is a phone', None)\n\tinsert_item(u1.id, \"Hot pot\", c, \"Hot hot hot\", None)\n\tinsert_item(u2.id, \"Kong Bao Chicken\", c, \"Classic\", None)","def intialize_db(self):\n self.db.query(\"\"\"\n CREATE TABLE IF NOT EXISTS \"rooms\" (\n \"id\" integer PRIMARY KEY AUTOINCREMENT,\n \"name\" text UNIQUE,\n \"type\" text\n );\n \"\"\")\n self.db.query(\"\"\"\n CREATE TABLE IF NOT EXISTS \"fellows\" (\n \"id\" integer PRIMARY KEY AUTOINCREMENT,\n \"name\" text,\n \"accomodation\" text\n );\n \"\"\")\n self.db.query(\"\"\"\n CREATE TABLE IF NOT EXISTS \"staff\" (\n \"id\" integer PRIMARY KEY AUTOINCREMENT,\n \"name\" text\n );\n \"\"\")\n self.db.query(\"\"\"\n CREATE TABLE IF NOT EXISTS \"fellows_rooms\" (\n \"id\" integer PRIMARY KEY AUTOINCREMENT,\n \"fellow_id\" integer NOT NULL REFERENCES \"fellows\" (\"id\"),\n \"room_id\" integer NOT NULL REFERENCES \"rooms\" (\"id\")\n );\n \"\"\")\n self.db.query(\"\"\"\n CREATE TABLE IF NOT EXISTS \"staff_rooms\" (\n \"id\" integer PRIMARY KEY AUTOINCREMENT,\n \"staff_id\" integer NOT NULL REFERENCES \"staff\" (\"id\"),\n \"room_id\" integer NOT NULL REFERENCES \"rooms\" (\"id\")\n );\n \"\"\")","def main():\n\n #Courses\n years = [2016, 2017, 2018, 2019, 2020]\n with sqlite3.connect(\"determined.db\") as conn:\n c = conn.cursor()\n c.execute(\n \"\"\"\n CREATE TABLE \"course\" (\n \"course_number\"\tint NOT NULL,\n \"dept_id\"\tvarchar(4) NOT NULL,\n \"title\"\tvarchar(100) NOT NULL,\n \"instructor_fname\"\tvarchar(35) DEFAULT NULL,\n \"instructor_lname\"\tvarchar(35) DEFAULT NULL,\n \"student_work_products\"\tjson DEFAULT NULL,\n `term` varchar(7) NOT NULL,\n `year` int NOT NULL,\n PRIMARY KEY(\"course_number\", \"term\", \"year\")) \n \"\"\"\n )\n conn.commit()\n courses = [\n (1370, \"CPSC\", \"Computer Literacy\", names.get_first_name(), names.get_last_name(), json.dumps({\"swp\": [\"Midterm\", \"Final Exam\", \"Project 1\"]}), \"Fall\"),\n (1375, \"CPSC\", \"Programming I\", names.get_first_name(), names.get_last_name(), json.dumps({\"swp\": [\"Midterm\", \"Final Exam\", \"Project 1\"]}), \"Fall\"),\n (2376, \"CPSC\", \"Intro to Game Programming\", names.get_first_name(), names.get_last_name(), json.dumps({\"swp\": [\"Midterm\", \"Final Exam\", \"Project 1\"]}), \"Fall\"),\n (2380, \"CPSC\", \"Algorithms\", names.get_first_name(), names.get_last_name(), json.dumps({\"swp\": [\"Midterm\", \"Final Exam\", \"Project 1\"]}), \"Fall\"),\n (2482, \"CPSC\", \"Computer Organization\", names.get_first_name(), names.get_last_name(), json.dumps({\"swp\": [\"Midterm\", \"Final Exam\", \"Project 1\"]}), \"Spring\"),\n (3377, \"CPSC\", \"Advanced Game Programming\", names.get_first_name(), names.get_last_name(), json.dumps({\"swp\": [\"Midterm\", \"Final Exam\", \"Project 1\"]}), \"Spring\"),\n (3380, \"CPSC\", \"Operating Systems\", names.get_first_name(), names.get_last_name(), json.dumps({\"swp\": [\"Midterm\", \"Final Exam\", \"Project 1\"]}), \"Spring\"),\n (3383, \"CPSC\", \"Programming Languages\", names.get_first_name(), names.get_last_name(), json.dumps({\"swp\": [\"Midterm\", \"Final Exam\", \"Project 1\"]}), \"Spring\"),\n (3384, \"CPSC\", \"Computer Networks\", names.get_first_name(), names.get_last_name(), json.dumps({\"swp\": [\"Midterm\", \"Final Exam\", \"Project 1\"]}), \"Summer\"),\n (4360, \"CPSC\", \"Computer Security\", names.get_first_name(), names.get_last_name(), json.dumps({\"swp\": [\"Midterm\", \"Final Exam\", \"Project 1\"]}), \"Summer\")\n ]\n #Adding years\n upload_courses = []\n for year in years:\n upload_courses += [x + (year,) for x in courses]\n #Making a few instructors teach multiple course\n new_courses = [\n (4557, \"CPSC\", \"Natural Language Processing\", ),\n (2375, \"CPSC\", \"Programming II\",),\n (2776, \"CPSC\", \"Data Structures and Algorithms\",),\n (4862, \"CPSC\", \"Image Recognition\", ),\n ]\n for i in range(0,len(new_courses)):\n year = choice(years)\n for y in range(0,2): #Number of times new course is taught\n c = upload_courses[i]\n new_data = (c[3], c[4], c[5], choice([\"Fall\", \"Spring\", \"Summer\"]), year+y)\n data = new_courses[i] + new_data\n upload_courses.append(data)\n #Adding solo instructors and solo courses\n upload_courses += [\n (4672, \"CPSC\", \"Programming Memes\", names.get_first_name(), names.get_last_name(), json.dumps({\"swp\": [\"Midterm\", \"Final Exam\", \"Project 1\"]}), \"Spring\", choice(years)),\n (1872, \"CPSC\", \"Information Systems\", names.get_first_name(), names.get_last_name(), json.dumps({\"swp\": [\"Midterm\", \"Final Exam\", \"Project 1\"]}), \"Summer\", choice(years)),\n (1123, \"CPSC\", \"Microsoft Office\", names.get_first_name(), names.get_last_name(), json.dumps({\"swp\": [\"Midterm\", \"Final Exam\", \"Project 1\"]}), \"Fall\", choice(years))\n ]\n\n with sqlite3.connect(\"determined.db\") as conn:\n c = conn.cursor()\n c.executemany('''INSERT INTO course (course_number, dept_id, title, instructor_fname, instructor_lname, student_work_products, term, year)\n VALUES ( ?, ?, ?, ?, ?, ?, ?, ?)''', upload_courses)\n conn.commit()\n\n #SWP\n with sqlite3.connect(\"determined.db\") as conn:\n c = conn.cursor()\n c.execute(\n \"\"\"\n CREATE TABLE `student_work_product` (\n `id` INTEGER PRIMARY KEY,\n `product` varchar(250) NOT NULL,\n `course_id` int NOT NULL,\n `dept_id` int NOT NULL,\n `student_fname` varchar(35) NOT NULL,\n `student_lname` varchar(35) NOT NULL,\n `student_outcome` int DEFAULT NULL,\n `score` int DEFAULT NULL,\n `term` varchar(7) NOT NULL,\n `year` int NOT NULL,\n CONSTRAINT `course` FOREIGN KEY (`course_id`) REFERENCES `course` (`course_number`)\n CONSTRAINT `course` FOREIGN KEY (`dept_id`) REFERENCES `course` (`dept_id`)\n )\n \"\"\"\n )\n conn.commit()\n \n swps = []\n with sqlite3.connect(\"determined.db\") as conn:\n conn.row_factory = sqlite3.Row\n c = conn.cursor()\n c.execute (\"Select * from course\")\n records = [dict(x) for x in c.fetchall()]\n #Generating 20 student records for each swp in each course\n for i, course in enumerate(records):\n student_names = []\n for _ in range(20):\n student_names.append({'fname': names.get_first_name(),\n 'lname': names.get_last_name()})\n for product in json.loads(course['student_work_products'])['swp']:\n for student in student_names:\n if i%7 == 0:\n score = int(triangular(50, 85))\n else:\n score = int(triangular(50, 100))\n if score >= 90: outcome = 4\n elif score >= 80: outcome = 3\n elif score >= 70: outcome = 2\n elif score >= 60: outcome = 1\n else: outcome = 0 \n swps.append((\n product,\n course['course_number'],\n \"CPSC\",\n student['fname'],\n student['lname'],\n outcome,\n score, \n course['term'], \n course['year']\n ))\n \n c.executemany('''INSERT INTO student_work_product (product, course_id, dept_id, student_fname, student_lname, student_outcome, score, term, year)\n VALUES ( ?, ?, ?, ?, ?, ?, ?, ?, ?)''', swps)\n conn.commit()","def generate_courses():\r\n for category in CourseCategory.objects.all():\r\n Course.objects.create(name=category.name, category=category, is_active=True,\r\n is_featured=True)","def __create_tables(self):\n self.__cur.execute(\"\"\"\n CREATE TABLE IF NOT EXISTS federations(\n id INTEGER PRIMARY KEY UNIQUE,\n name TEXT,\n category TEXT\n )\n \"\"\")\n self.__cur.execute(\"\"\"\n CREATE TABLE IF NOT EXISTS clubs(\n id INTEGER PRIMARY KEY AUTOINCREMENT UNIQUE,\n code_fede INT,\n dpt TEXT,\n nb_clubs INT,\n year INT,\n FOREIGN KEY(code_fede) REFERENCES federations(i)\n )\n \"\"\")\n self.__cur.execute(\"\"\"\n CREATE TABLE IF NOT EXISTS by_age(\n id INTEGER PRIMARY KEY AUTOINCREMENT UNIQUE,\n code_fede INT,\n age TEXT,\n sex TEXT,\n nb INT,\n year INT,\n FOREIGN KEY(code_fede) REFERENCES federations(id)\n )\n \"\"\")\n self.__cur.execute(\"\"\"\n CREATE TABLE IF NOT EXISTS by_dpt(\n id INTEGER PRIMARY KEY AUTOINCREMENT UNIQUE,\n code_fede INT,\n dpt TEXT,\n nb INT,\n sex TEXT,\n year INT,\n FOREIGN KEY(code_fede) REFERENCES federations(id)\n )\n \"\"\")\n\n # Save all the changes\n self.save()","def update_static_data(source: list):\r\n connect = sqlite3.connect(\"REDB_v2.sqlite\")\r\n cursor = connect.cursor()\r\n districts = []\r\n streets = []\r\n series = []\r\n amenities = []\r\n building_types = []\r\n for realestate in source:\r\n if realestate.street not in streets:\r\n streets.append(realestate.street)\r\n if realestate.series not in series:\r\n series.append(realestate.series)\r\n if realestate.building not in building_types:\r\n building_types.append(realestate.building)\r\n if realestate.district not in districts:\r\n districts.append(realestate.district)\r\n if realestate.amenities not in amenities:\r\n amenities.append(realestate.amenities)\r\n for item in streets:\r\n try:\r\n cursor.execute(\"INSERT INTO Streets (Name) VALUES ('\" + item + \"')\")\r\n except sqlite3.IntegrityError:\r\n pass\r\n for item in districts:\r\n try:\r\n cursor.execute(\"INSERT INTO Districts (Name) VALUES ('\" + item + \"')\")\r\n except sqlite3.IntegrityError:\r\n pass\r\n for item in series:\r\n try:\r\n cursor.execute(\"INSERT INTO Series (Name) VALUES ('\" + item + \"')\")\r\n except sqlite3.IntegrityError:\r\n pass\r\n for item in amenities:\r\n try:\r\n cursor.execute(\"INSERT INTO Amenities (Name) VALUES ('\" + item + \"')\")\r\n except sqlite3.IntegrityError:\r\n pass\r\n for item in building_types:\r\n try:\r\n cursor.execute(\"INSERT INTO Buildings (Name) VALUES ('\" + item + \"')\")\r\n except sqlite3.IntegrityError:\r\n pass\r\n connect.commit()\r\n connect.close()","def init():\n database.create_tables([Tracker])\n database.commit()","def __init__(self):\n self._db = db\n # Connect to DB\n self._db.connect()\n # Create tables\n self._db.create_tables([Teachers, Parents, Tutors, Students, Homework, Groups, StudentsGroups, Courses])\n # Create filling entries\n self.__create_dummies()\n self._db.close()","def load_data(data):\n\n # Agencies\n name = data['name']\n slug = Agency.slug_for(name)\n\n a, created = Agency.objects.get_or_create(slug=slug, name=name)\n\n a.abbreviation = data['abbreviation']\n a.description = data.get('description')\n a.keywords = data.get('keywords')\n a.common_requests = data.get('common_requests', [])\n a.no_records_about = data.get('no_records_about', [])\n\n # Only has a single, main branch/office\n if len(data['departments']) == 1:\n dept_rec = data['departments'][0]\n contactable_fields(a, dept_rec)\n\n a.save()\n add_request_time_statistics(data, a)\n\n # Offices\n if len(data['departments']) > 1:\n for dept_rec in data['departments']:\n if dept_rec.get('top_level'):\n # This is actually an agency\n sub_agency_name = dept_rec['name']\n sub_agency_slug = Agency.slug_for(sub_agency_name)\n\n sub_agency, created = Agency.objects.get_or_create(\n slug=sub_agency_slug, name=sub_agency_name)\n sub_agency.parent = a\n\n abbreviation = build_abbreviation(sub_agency_name)\n sub_agency.abbreviation = abbreviation\n sub_agency.description = dept_rec.get('description')\n sub_agency.keywords = dept_rec.get('keywords')\n sub_agency.common_requests = dept_rec.get(\n 'common_requests', [])\n sub_agency.no_records_about = dept_rec.get(\n 'no_records_about', [])\n contactable_fields(sub_agency, dept_rec)\n sub_agency.save()\n add_request_time_statistics(dept_rec, sub_agency)\n else:\n # Just an office\n office_name = dept_rec['name']\n office_slug = Office.slug_for(office_name)\n full_slug = slug + '--' + office_slug\n\n o, created = Office.objects.get_or_create(\n agency=a, slug=full_slug)\n\n o.office_slug = office_slug\n o.name = office_name\n contactable_fields(o, dept_rec)\n o.save()\n add_request_time_statistics(dept_rec, a, o)","def create_tables():\n inf(\"Creating tables\")\n \n pinners = Table('pinners', metadata,\n Column('pinner_id', Integer, primary_key=True),\n Column('name', String(40)),\n Column('email', String(40))\n )\n pinners.create()\n \n contents = Table('contents', metadata,\n Column('content_id', Integer, primary_key=True),\n Column('url', String(80)),\n Column('display_status', String(20)), # good, objectionable, copyright\n Column('pinner_id', Integer, ForeignKey('pinners.pinner_id'))\n )\n contents.create()\n\n reviewers = Table('reviewers', metadata,\n Column('reviewer_id', Integer, primary_key=True),\n Column('name', String(40)),\n Column('email', String(40))\n )\n reviewers.create()\n\n complaints = Table('complaints', metadata,\n Column('complaint_id', Integer, primary_key=True),\n Column('complaint_timestamp', DateTime), # when the complaint was filed\n Column('complaint_type', String(80)), # objectionable, copyright\n Column('process_status', String(20)), # complaint, review, done\n Column('display_status', String(20)), # good, objectionable, copyright\n Column('review_timestamp', DateTime), # when the compliant was resolved\n Column('pinner_id', Integer, ForeignKey('pinners.pinner_id')),\n Column('reviewer_id', Integer, ForeignKey('reviewers.reviewer_id')),\n Column('content_id', Integer, ForeignKey('contents.content_id'))\n )\n complaints.create()\n \n # could create a table of \"near by\" images and/or near by features and \n # include these in the review","def create_tables():\n # Depending on your local settings, you may need to specify a user and password, e.g.\n # conn = psycopg2.connect(dbname=DBNAME, user=\"postgres\", password=\"password\")\n conn = psycopg2.connect(dbname=DBNAME)\n\n create_train_table(conn)\n create_questions_table(conn)\n create_lectures_table(conn)\n create_example_test_table(conn)\n create_example_test_table(conn)\n\n conn.close()","def initialize_db(self) -> None:\n if not self.check_schema_initialized():\n self._create_genes_table()\n self._create_meta_data_table()","def _create_tables():\n from Model.DataAccessor.DbAccessor.DbOrmAccessor import db\n db.create_tables([SubjectType, SubjectRegion, Subject])","def _initialize(self):\n for s in Subsidiary.all():\n self.__create_stock(s)\n self.get_stock()","def _create_intermediate_new_tables(self):\n with futures.ThreadPoolExecutor(max_workers=self._nworkers) as executor:\n # Create all the tables and populate initial tables. Need to commit so that threadpool jobs can\n # see the results\n with self._conn:\n self._run_intermediate_table_job(self._conn, self._create_intermediate_new_tables_structure)\n # Create utility tables\n self._run_intermediate_table_job(self._conn, self._populate_blocking_conditions_table,\n description='blocking conditions')\n self._run_intermediate_table_job(self._conn, self._populate_mcc_mnc_table,\n description='MCC-MNC operator mappings')\n # Create new blacklist table\n self._run_intermediate_table_job(self._conn, self._populate_new_blacklist,\n description='IMEIs to blacklist')\n\n # Create required notifications and pairings tables in parallel before we can kick off the per-MNO\n # pairing and and notifications. These jobs have the responsibilities of kicking off those per-MNO jobs\n futures_to_cb = {}\n self._queue_intermediate_table_job(executor,\n futures_to_cb,\n self._populate_new_notifications_lists,\n 'per-MNO notifications for all operators')\n self._queue_intermediate_table_job(executor,\n futures_to_cb,\n self._populate_new_exceptions_lists,\n 'per-MNO exceptions for all operators')\n self._wait_for_futures(futures_to_cb)","def insert_into_db(self, database):\n\n # insert person\n keys = \"\"\n values = \"\"\n for key, value in self.person.items():\n # location\n if key == \"location\":\n # ensure location is in table\n database.select(f\"\"\"DO $do$ BEGIN IF NOT EXISTS (SELECT * FROM p21_cdm.location WHERE city='{value['city']}' \n AND zip='{value['zip']}') THEN INSERT INTO p21_cdm.location (city, zip) \n VALUES ('{value['city']}', '{value['zip']}'); END IF; END; $do$\"\"\")\n continue\n\n keys += f\"{key},\"\n values += f\"'{value}',\"\n\n database.select(f\"\"\"INSERT INTO p21_cdm.person (location_id, {keys[:-1]}) \n VALUES((SELECT location_id \n FROM p21_cdm.location\n WHERE city='{self.person['location']['city']}' \n and zip='{self.person['location']['zip']}'), \n {values[:-1]})\"\"\")\n\n # insert visits\n for visit in self.visits:\n keys = \"person_id,\"\n values = f\"'{self.person['person_id']}',\"\n for key, value in visit.items():\n if key == \"care_site_name\":\n # ensure care site is in table\n database.select(f\"\"\"DO $do$ BEGIN IF NOT EXISTS (SELECT * \n FROM p21_cdm.care_site \n WHERE care_site_name='{value}') \n THEN INSERT INTO p21_cdm.care_site (care_site_name) \n VALUES ('{value}'); END IF; END; $do$\"\"\")\n continue\n\n keys += f\"{key},\"\n values += f\"'{value}',\"\n\n database.select(f\"\"\"INSERT INTO p21_cdm.visit_occurrence (care_site_id, {keys[:-1]}) \n VALUES((SELECT care_site_id\n FROM p21_cdm.care_site\n WHERE care_site_name='{visit['care_site_name']}'),\n {values[:-1]}) \n RETURNING visit_occurrence_id\"\"\")\n\n # insert measurements, observations, conditions & procedures\n for data, tablename in [(self.measurements, \"measurement\"),\n (self.observations, \"observation\"),\n (self.conditions, \"condition_occurrence\"),\n (self.procedures, \"procedure_occurrence\")]:\n for entry in data:\n keys = \"person_id,\"\n values = f\"'{self.person['person_id']}',\"\n\n for key, value in entry.items():\n keys += f\"{key},\"\n values += f\"'{value}',\"\n\n entry[\"sql_id\"] = database.select(f\"\"\"INSERT INTO p21_cdm.{tablename}({keys[:-1]})\n VALUES({values[:-1]}) RETURNING {tablename}_id\"\"\")[0][0]\n\n # insert fact_relationships in both directions\n for table1, entry1, table2, entry2 in self.fact_relations:\n # 44818890 = Finding associated with (SNOMED)\n database.select(f\"\"\"INSERT INTO p21_cdm.fact_relationship(domain_concept_id_1, fact_id_1, \n domain_concept_id_2, fact_id_2, \n relationship_concept_id)\n VALUES('{table1}','{entry1['sql_id']}','{table2}','{entry2['sql_id']}','44818890')\"\"\")\n # 44818792 = Associated with finding (SNOMED)\n database.select(f\"\"\"INSERT INTO p21_cdm.fact_relationship(domain_concept_id_1, fact_id_1, \n domain_concept_id_2, fact_id_2, \n relationship_concept_id)\n VALUES('{table2}','{entry2['sql_id']}','{table1}','{entry1['sql_id']}','44818792')\"\"\")\n\n # make transactions persistent\n database.commit()","def populate_db():\n\n logging.basicConfig(level=logging.INFO)\n logger = logging.getLogger(__name__)\n\n database = SqliteDatabase('../data/personjob.db') # navigate relative path to the db\n\n logger.info('Working with Department class')\n logger.info('Creating department records')\n\n department_number = 0\n department_name = 1\n department_manager = 2\n\n departments = [\n ('A111', 'Asset Management', 'Dave Sanders'),\n ('B222', 'Human Resources', 'Tammy Murray'),\n ('C333', 'Payroll', 'Daddy Warbucks'),\n ]\n\n try:\n database.connect()\n database.execute_sql('PRAGMA foreign_keys = ON;')\n for department in departments:\n with database.transaction():\n new_department = Department.create(\n department_number = department[department_number],\n department_name = department[department_name],\n deptartment_manager = department[department_manager]\n )\n new_department.save()\n logger.info('Department has been added to the database')\n\n logger.info('Reading and print all department data...')\n for saved_department in Department:\n logger.info(f'{saved_department.department_name} ' + \\\n f'Manager: {saved_department.department_manager}. ' + \\\n f'Department number: {saved_department.department_number}')\n\n except Exception as e:\n logger.info(f'Error creating = {department[department_number]}')\n logger.info(e)\n\n finally:\n logger.info('database closes')\n database.close()","def fill_dataset(self):\n rm, rstd = self.get_rolling_stats()\n\n self.add_rolling_mean(rm)\n self.add_bollinger_bands(rstd)\n self.add_spy_info()\n self.add_beta_and_sharpe()\n self.add_stlouis_data()","def insert_db():\n populate_tables()","def example_data():\n\n db.create_all()\n\n\n #Empty out data from previous runs\n User.query.delete()\n Book.query.delete()\n Rating.query.delete()\n\n #Add sample users, books, and ratings\n\n #sample users\n user1 = User(user_id=1, email='123@test.com', password='password')\n user2 = User(user_id=2, email='456@test.com', password='password')\n user3 = User(user_id=3, email='789@test.com', password='password')\n user4 = User(user_id=4, email='987@test.com', password='password')\n user5 = User(user_id=5, email='654@test.com', password='password')\n\n #sample books\n book1 = Book(book_id=7627, work_id=16683183, isbn='0007331789', title='Death of Kings (The Saxon Stories, #6)', author='Bernard Cornwell')\n book2 = Book(book_id=7695, work_id=16947613, isbn='0007350430', title='The Time of My Life', author='Cecelia Ahern')\n book3 = Book(book_id=69, work_id=15524542, isbn='0007442912', title='Insurgent (Divergent #2)', author='Veronica Roth')\n book4 = Book(book_id=3327, work_id=23906880, isbn='0007491433', title='The Shock of the Fall', author='Nathan Filer')\n book5 = Book(book_id=8387, work_id=67116, isbn='0099464691', title='The White Lioness (Kurt Wallander, #3)', author='Henning Mankell')\n\n\n #sample ratings\n rating1 = Rating(rating_id=1, book_id=7627, user_id=1, score=5)\n rating2 = Rating(rating_id=2, book_id=7627, user_id=2, score=5)\n rating3 = Rating(rating_id=3, book_id=7627, user_id=3, score=3)\n rating4 = Rating(rating_id=4, book_id=7627, user_id=4, score=3)\n rating5 = Rating(rating_id=5, book_id=7627, user_id=5, score=1)\n rating6 = Rating(rating_id=6, book_id=8387, user_id=1, score=5)\n rating7 = Rating(rating_id=7, book_id=8387, user_id=2, score=5)\n rating8 = Rating(rating_id=8, book_id=8387, user_id=3, score=3)\n rating9 = Rating(rating_id=9, book_id=8387, user_id=4, score=3)\n rating10 = Rating(rating_id=10, book_id=8387, user_id=5, score=1)\n rating11 = Rating(rating_id=11, book_id=69, user_id=5, score=5)\n rating12 = Rating(rating_id=12, book_id=3327, user_id=5, score=5)\n rating13 = Rating(rating_id=13, book_id=3327, user_id=2, score=5)\n\n #Add all to session and commit\n db.session.add_all([user1, user2, user3, user4, user5, book1, book2, book3, \n book4, book5, rating1, rating2, rating3, rating4, \n rating5, rating6, rating7, rating8, rating9, rating10, rating11,\n rating12, rating13])\n db.session.commit()","def build(self):\n \n # create db with empty tables\n dbpath, config = self.setup()\n \n # avoid work if setup decided db exists and build can be skipped\n if dbpath is None:\n return \n \n # check prerequisite files \n obopath = check_file(config.obo, dbpath, \"obo\")\n refpath = check_file(config.reference_phenotypes, dbpath, \n \"reference_phenotypes\")\n freqpath = check_file(config.phenotype_frequencies, dbpath,\n \"phenotype_frequencies\")\n\n self.logger.msg1(\"Loading ontology\") \n obo = MinimalObo(obopath, True)\n \n self.logger.msg1(\"Preparing phenotype frequencies\")\n fill_phenotype_frequency_table(dbpath, freqpath)\n \n # fill database with data\n self.logger.msg1(\"Preparing references\")\n fill_concise_reference_table(dbpath, refpath) \n fill_complete_reference_table(dbpath, obo, config) \n \n self._end()","def insert_humans_staging(self):\n for year in range(1880, CURRENT_YEAR):\n self.load_wikidata(\"humans\", HUMANS_BY_YEAR_SPARQL_QUERY, INSERT_HUMAN_SQL_QUERY,\n INSERT_HUMAN_MAP_COLUMNS, year=year)","def createSchoolEnrollmentDataSet(baseyear_tazdata_df, tazdata_df):\n HIGH_SCHOOL_ENROLLMENT_MODEL_SHARE_OF_HS_AGE_KIDS_ENROLLED_IN_HS = 0.966\n\n ageShares = pandas.read_csv(SCHOOLAGE_DISTRIBUTION)\n collegeStudentShares = pandas.read_csv(COLLEGEAGE_DISTRIBUTION)\n\n logging.debug(\"Age shares:\\n{}\".format(ageShares.head()))\n logging.debug(\"College Student shares:\\n{}\".format(collegeStudentShares.head()))\n\n # join to tazdata, baseyear_tazdata_df\n tazdata_df = pandas.merge(left= tazdata_df, right=ageShares, how=\"left\")\n tazdata_df = pandas.merge(left= tazdata_df, right=collegeStudentShares, how=\"left\")\n baseyear_tazdata_df = pandas.merge(left=baseyear_tazdata_df, right=ageShares, how=\"left\")\n baseyear_tazdata_df = pandas.merge(left=baseyear_tazdata_df, right=collegeStudentShares, how=\"left\")\n\n # compute the number of high school students -- assume X percent of kids this age attend high school, as calculated by Chuck\n tazdata_df[\"highSchoolStudents\"] = tazdata_df[\"AGE0519\"]* tazdata_df[\"14to17In05to19\"]*HIGH_SCHOOL_ENROLLMENT_MODEL_SHARE_OF_HS_AGE_KIDS_ENROLLED_IN_HS\n baseyear_tazdata_df[\"highSchoolStudents\"] = baseyear_tazdata_df[\"AGE0519\"]*baseyear_tazdata_df[\"14to17In05to19\"]*HIGH_SCHOOL_ENROLLMENT_MODEL_SHARE_OF_HS_AGE_KIDS_ENROLLED_IN_HS\n\n # compute the number of college students\n tazdata_df[\"collegeStudents\"] = ( tazdata_df[\"AGE0519\"]* tazdata_df[\"18to19In05to19\"]* tazdata_df[\"StudentsAge18to19\"]) + \\\n ( tazdata_df[\"AGE2044\"]* tazdata_df[\"20to24In20to44\"]* tazdata_df[\"StudentsAge20to24\"]) + \\\n ( tazdata_df[\"AGE2044\"]* tazdata_df[\"25to44In20to44\"]* tazdata_df[\"StudentsAge25to44\"])\n baseyear_tazdata_df[\"collegeStudents\"] = (baseyear_tazdata_df[\"AGE0519\"]*baseyear_tazdata_df[\"18to19In05to19\"]*baseyear_tazdata_df[\"StudentsAge18to19\"]) + \\\n (baseyear_tazdata_df[\"AGE2044\"]*baseyear_tazdata_df[\"20to24In20to44\"]*baseyear_tazdata_df[\"StudentsAge20to24\"]) + \\\n (baseyear_tazdata_df[\"AGE2044\"]*baseyear_tazdata_df[\"25to44In20to44\"]*baseyear_tazdata_df[\"StudentsAge25to44\"])\n\n # sum high school and college students by county\n baseyear_students_df = baseyear_tazdata_df[[\"COUNTY\",\"highSchoolStudents\",\"collegeStudents\"]].groupby(\"COUNTY\").agg(\"sum\")\n forecast_students_df = tazdata_df[[\"COUNTY\",\"highSchoolStudents\",\"collegeStudents\"]].groupby(\"COUNTY\").agg(\"sum\")\n\n # rename and join baseyear and forecast county tables\n baseyear_students_df.rename(columns={\"highSchoolStudents\":\"highSchoolStudents_baseyear\", \"collegeStudents\":\"collegeStudents_baseyear\"}, inplace=True)\n forecast_students_df.rename(columns={\"highSchoolStudents\":\"highSchoolStudents_forecast\", \"collegeStudents\":\"collegeStudents_forecast\"}, inplace=True)\n students_df = pandas.merge(left=baseyear_students_df, right=forecast_students_df, left_index=True, right_index=True)\n\n students_df[\"highSchoolGrowthRatio\"] = students_df[\"highSchoolStudents_forecast\"]/students_df[\"highSchoolStudents_baseyear\"]\n students_df[\"collegeGrowthRatio\" ] = students_df[\"collegeStudents_forecast\"] /students_df[\"collegeStudents_baseyear\"]\n logging.debug(\"students_df:\\n{}\".format(students_df))\n\n # join growth ratios to baseyear_tazdata_df and apply\n baseyear_tazdata_df = pandas.merge(left=baseyear_tazdata_df, right=students_df[[\"highSchoolGrowthRatio\",\"collegeGrowthRatio\"]], how=\"left\", left_on=\"COUNTY\", right_index=True)\n baseyear_tazdata_df[\"HSENROLL_forecast\"] = baseyear_tazdata_df[\"HSENROLL\"]*baseyear_tazdata_df[\"highSchoolGrowthRatio\"]\n baseyear_tazdata_df[\"COLLFTE_forecast\"] = baseyear_tazdata_df[\"COLLFTE\"] *baseyear_tazdata_df[\"collegeGrowthRatio\"]\n baseyear_tazdata_df[\"COLLPTE_forecast\"] = baseyear_tazdata_df[\"COLLPTE\"] *baseyear_tazdata_df[\"collegeGrowthRatio\"]\n\n # join result to tazdata_df\n tazdata_df = pandas.merge(left=tazdata_df, right=baseyear_tazdata_df[[\"ZONE\",\"HSENROLL_forecast\",\"COLLFTE_forecast\",\"COLLPTE_forecast\"]], how=\"left\", on=\"ZONE\")\n tazdata_df.rename(columns={\"HSENROLL_forecast\":\"HSENROLL\",\n \"COLLFTE_forecast\" :\"COLLFTE\",\n \"COLLPTE_forecast\" :\"COLLPTE\"}, inplace=True)\n\n # remove intermediate variables\n tazdata_df.drop(columns=[\"05to13In05to19\",\"14to17In05to19\",\"18to19In05to19\",\"25to44In20to44\",\"20to24In20to44\",\n \"StudentsAge18to19\",\"StudentsAge20to24\",\"StudentsAge25to44\",\n \"highSchoolStudents\",\"collegeStudents\"], inplace=True)\n return tazdata_df","def init_tables(self) -> None:\n # TODO(#93) maybe raise flag when the schema of existing tables isn't what we expect\n # it to be?\n # \"How to know that schema changes?\"\n # logger.warning(\"some message\")\n with self.table_access_condition:\n conn = self._get_connection()\n conn.execute(\"PRAGMA foreign_keys = 1\")\n with conn:\n c = conn.cursor()\n c.execute(CREATE_PROJECTS_TABLE)\n c.execute(CREATE_TASKS_TABLE)\n c.execute(CREATE_REQUESTERS_TABLE)\n c.execute(CREATE_TASK_RUNS_TABLE)\n c.execute(CREATE_ASSIGNMENTS_TABLE)\n c.execute(CREATE_UNITS_TABLE)\n c.execute(CREATE_WORKERS_TABLE)\n c.execute(CREATE_AGENTS_TABLE)\n c.execute(CREATE_QUALIFICATIONS_TABLE)\n c.execute(CREATE_GRANTED_QUALIFICATIONS_TABLE)\n c.execute(CREATE_ONBOARDING_AGENTS_TABLE)","def initialize():\n db.connect()\n db.create_tables([Expense], safe=True)","def __create_dummies(self):\n zero_teacher = Teachers.get_or_create(teacher_key='0', UID='0')\n zero_parent = Parents.get_or_create(parent_key='0', UID='0')\n zero_tutor = Tutors.get_or_create(tutor_key='0', UID='0')\n zero_student = Students.get_or_create(student_key='0', UID='0', parent=zero_parent[0])\n zero_course = Courses.get_or_create(course_key='0')\n zero_group = Groups.get_or_create(group_key='0', teacher=zero_teacher[0], tutor=zero_tutor[0],\n course=zero_course[0])","def set_data(self):\n # take care of samples\n patients = self.samples.iloc[:,1].tolist()\n samples = self.samples.iloc[:,0].tolist()\n self.samples = pd.DataFrame(patients,index = samples,columns = ['patient']) # indexed by sample\n #\n # take care of expression data\n cols = self.expression.SYMBOL.tolist() # set new column names to transposed expression_data \n \n new_exp = self.expression.T.ix[1:,:] # transpose\n new_exp.columns = cols\n self.expression = new_exp # add columns\n self.data = pd.merge(self.expression,self.samples,left_index = True,right_index=True) # merged data sets\n #pd.merge(df1,df2,how = 'left',left_index=True,right_index=True) # do a left join","def load_disasters():\n\n print \"Disasters\"\n\n #deletes any data within the table before seeding\n Disaster.query.delete()\n\n\n result_count = 1000\n iteration = 0\n records_returned = 1000\n\n # makes payload requests from FEMA API\n while records_returned == 1000:\n payload = {'$top': result_count,\n '$skip': result_count * iteration,\n '$inlinecount': 'allpages',\n '$filter': 'declarationDate ge \\'1990-01-01T04:00:00.000z\\'',\n '$select': 'disasterNumber,declarationDate,state,incidentType,title,incidentBeginDate,incidentEndDate,placeCode,declaredCountyArea'}\n r = requests.get(\n \"http://www.fema.gov/api/open/v1/DisasterDeclarationsSummaries\",\n params=payload)\n\n # iteration counter, starts at zero, for every iteration add 1\n iteration += 1\n\n disaster_info = r.json()\n metadata = disaster_info['metadata']\n record_count = metadata['count']\n records_returned = len(disaster_info['DisasterDeclarationsSummaries'])\n\n # parsing through the information returned from API\n for incident_dict in disaster_info['DisasterDeclarationsSummaries']:\n disasterNumber = incident_dict.get('disasterNumber')\n declarationDate = incident_dict.get('declarationDate')\n state = incident_dict.get('state')\n incidentType = incident_dict.get('incidentType')\n title = incident_dict.get('title')\n incidentBeginDate = incident_dict.get('incidentBeginDate')\n incidentEndDate = incident_dict.get('incidentEndDate')\n placeCode = incident_dict.get('placeCode')\n declaredCountyArea = incident_dict.get('declaredCountyArea')\n\n \"\"\"Try/Except does two things: the try is doing a check to see if the county is already in the counties tables and if it is, then setting the Disaster.countyArea_id in the disasters table. The except is occuring only when the NoResultFound occurs and is creating the county and adding it to the counties table.\"\"\"\n try:\n #variable county set to \"answer\" of query\n county_check = County.query.filter(County.county_name==declaredCountyArea, County.state_code==state).one()\n\n countyArea_id = county_check.county_id\n\n # creating a county when NoResultFound error occurs\n except NoResultFound:\n\n county = County(state_code=state,\n county_name=declaredCountyArea)\n\n db.session.add(county)\n #!!!!!!!!! ask bonnie about this again !!!!!!!!!!#\n db.session.flush()\n\n countyArea_id = county.county_id\n\n county = County.query.filter(County.county_name==declaredCountyArea, County.state_code==state).one()\n countyArea_id = county.county_id\n\n disaster = Disaster(disasterNumber=disasterNumber,\n state=state,\n declarationDate=declarationDate,\n incidentType=incidentType,\n title=title,\n incidentBeginDate=incidentBeginDate,\n incidentEndDate=incidentEndDate,\n placeCode=placeCode,\n declaredCountyArea=declaredCountyArea,\n countyArea_id=countyArea_id)\n\n db.session.add(disaster)\n\n db.session.commit()\n print \"Disasters and Counties seeded\"","def create_tables(cur, country_json, xml_state, body_json):\n print(\"Creating the 3 first tables...\")\n cur.execute('CREATE TABLE IF NOT EXISTS country_purchases(idx INTEGER PRIMARY KEY, state TEXT, amount INTEGER)')\n with open(\"country_purchases.csv\", 'r') as f:\n for idx, line in enumerate(f.read().split('\\n')):\n line_splt = line.split(',')\n try:\n cur.execute('INSERT INTO country_purchases VALUES(\"%s\", \"%s\", \"%s\")' % (idx, line_splt[0], line_splt[1]))\n except IndexError:\n pass\n cur.execute('CREATE TABLE IF NOT EXISTS country_total_purchases(idx INTEGER PRIMARY KEY, state TEXT, amount INTEGER)')\n with open(\"country_total_purchases.csv\", 'r') as f:\n for idx, line in enumerate(f.read().split('\\n')):\n line_splt = line.split(',')\n try:\n cur.execute('INSERT INTO country_total_purchases VALUES(\"%s\", \"%s\", \"%s\")' % (idx, line_splt[0], line_splt[1]))\n except IndexError:\n pass\n cur.execute('CREATE TABLE IF NOT EXISTS country_albums(ID INTEGER PRIMARY KEY, state TEXT, year INTEGER, genre TEXT, album TEXT, amount INTEGER)')\n for idx, album in enumerate(country_json[body_json['state']]):\n cur.execute('INSERT INTO country_albums VALUES (\"%s\", \"%s\", \"%s\", \"%s\", \"%s\", \"%s\")' %\n (idx, body_json['state'], body_json['year'], body_json['genre'], album,\n xml_state[0][0][idx].text))","def populate_db(dbase):\n # In this order: Iron, Blood, Shadow, Fel, Storm\n wowhead_ids = []\n wowhead_ids.extend(item.get_ids_from_wowhead_by_type(-8))\n wowhead_ids.extend(item.get_ids_from_wowhead_by_type(-9))\n wowhead_ids.extend(item.get_ids_from_wowhead_by_type(-10))\n wowhead_ids.extend(item.get_ids_from_wowhead_by_type(-11))\n wowhead_ids.extend(item.get_ids_from_wowhead_by_type(-17))\n item_ids = set(wowhead_ids)\n print(item_ids)\n\n pos = 0\n for item_id in item_ids:\n if pos % 10 == 0:\n print(\"Relic %d of %d\" % (pos, len(item_ids)))\n pos += 1\n import_relic(dbase, item_id)","def populate_db(dbase):\n\n enchants = [\n # Cloak\n {\n 'spell_id': 5432,\n 'stats': {'agility': 150},\n 'icon': 'inv_enchant_formulagood_01',\n 'item_name': 'Enchant Cloak - Word of Agility',\n 'equip_location': 16,\n 'tooltip_spell': 128546\n },\n {\n 'spell_id': 5435,\n 'stats': {'agility': 200},\n 'icon': 'inv_enchant_formulasuperior_01',\n 'item_name': 'Enchant Cloak - Binding of Agility',\n 'equip_location': 16,\n 'tooltip_spell': 128549\n },\n\n # Ring\n {\n 'spell_id': 5423,\n 'stats': {'crit': 150},\n 'icon': 'inv_enchant_formulagood_01',\n 'item_name': 'Enchant Ring - Word of Critical Strike',\n 'equip_location': 11,\n 'tooltip_spell': 128537\n },\n {\n 'spell_id': 5424,\n 'stats': {'haste': 150},\n 'icon': 'inv_enchant_formulagood_01',\n 'item_name': 'Enchant Ring - Word of Haste',\n 'equip_location': 11,\n 'tooltip_spell': 128538,\n },\n {\n 'spell_id': 5425,\n 'stats': {'mastery': 150},\n 'icon': 'inv_enchant_formulagood_01',\n 'item_name': 'Enchant Ring - Word of Mastery',\n 'equip_location': 11,\n 'tooltip_spell': 128539,\n },\n {\n 'spell_id': 5426,\n 'stats': {'versatility': 150},\n 'icon': 'inv_enchant_formulagood_01',\n 'item_name': 'Enchant Ring - Word of Versatility',\n 'equip_location': 11,\n 'tooltip_spell': 128540,\n },\n {\n 'spell_id': 5427,\n 'stats': {'crit': 200},\n 'icon': 'inv_enchant_formulasuperior_01',\n 'item_name': 'Enchant Ring - Binding of Critical Strike',\n 'equip_location': 11,\n 'tooltip_spell': 128541,\n },\n {\n 'spell_id': 5428,\n 'stats': {'haste': 200},\n 'icon': 'inv_enchant_formulasuperior_01',\n 'item_name': 'Enchant Ring - Binding of Haste',\n 'equip_location': 11,\n 'tooltip_spell': 128542,\n },\n {\n 'spell_id': 5429,\n 'stats': {'mastery': 200},\n 'icon': 'inv_enchant_formulasuperior_01',\n 'item_name': 'Enchant Ring - Binding of Mastery',\n 'equip_location': 11,\n 'tooltip_spell': 128543,\n },\n {\n 'spell_id': 5430,\n 'stats': {'versatility': 200},\n 'icon': 'inv_enchant_formulasuperior_01',\n 'item_name': 'Enchant Ring - Binding of Versatility',\n 'equip_location': 128544,\n },\n\n # Neck\n {\n 'spell_id': 5437,\n 'stats': {},\n 'icon': 'inv_enchant_formulasuperior_01',\n 'item_name': 'Enchant Neck - Mark of the Claw',\n 'equip_location': 2,\n 'tooltip_spell': 128551,\n 'is_proc': True\n },\n {\n 'spell_id': 5438,\n 'stats': {},\n 'icon': 'inv_enchant_formulasuperior_01',\n 'item_name': 'Enchant Neck - Mark of the Distant Army',\n 'equip_location': 2,\n 'tooltip_spell': 128552,\n 'is_proc': True\n },\n {\n 'spell_id': 5439,\n 'stats': {},\n 'icon': 'inv_enchant_formulasuperior_01',\n 'item_name': 'Enchant Neck - Mark of the Hidden Satyr',\n 'equip_location': 2,\n 'tooltip_spell': 128553,\n 'is_proc': True\n },\n {\n 'spell_id': 5890,\n 'stats': {'mastery': 300},\n 'icon': 'inv_enchant_formulasuperior_01',\n 'item_name': 'Enchant Neck - Mark of the Trained Soldier',\n 'equip_location': 2,\n 'tooltip_spell': 141909,\n 'is_proc': False\n }\n ]\n\n for enchant in enchants:\n dbase.enchants.replace_one({'spell_id': enchant['spell_id']},\n enchant, upsert=True)","def populate_employees():\n employees = get_employees()\n\n db.session.bulk_save_objects(employees)\n db.session.commit()","def setUp(self):\n # Create domain tables required for the test\n super().setUp()\n\n # Load the test data\n condition_occurrence_data_template = self.jinja_env.from_string(\"\"\"\n CREATE OR REPLACE TABLE `{{project_id}}.{{dataset_id}}.condition_occurrence`\n (\n condition_occurrence_id int64, \n person_id int64, \n condition_concept_id int64, \n stop_reason STRING,\n condition_source_value STRING,\n condition_status_source_value STRING)\n AS (\n WITH w AS (\n SELECT ARRAY<STRUCT<\n condition_occurrence_id int64, \n person_id int64, \n condition_concept_id int64, \n stop_reason STRING,\n condition_source_value STRING,\n condition_status_source_value STRING\n >>\n [(1, 1, 0, 'stop reason', 'source value', 'status'),\n (2, 1, 0, 'stop reason', 'source value', 'status'),\n (3, 1, 0, 'stop reason', 'source value', 'status'),\n (4, 1, 0, 'stop reason', 'source value', 'status')] col\n )\n SELECT \n condition_occurrence_id, \n person_id, \n condition_concept_id, \n stop_reason,\n condition_source_value,\n condition_status_source_value \n FROM w, UNNEST(w.col))\n \"\"\")\n\n # Load the test data\n observation_data_template = self.jinja_env.from_string(\"\"\"\n CREATE OR REPLACE TABLE `{{project_id}}.{{dataset_id}}.observation`\n (\n observation_id int64,\n person_id int64,\n observation_concept_id int64,\n observation_source_concept_id int64,\n value_as_string STRING,\n observation_source_value STRING,\n unit_source_value STRING,\n qualifier_source_value STRING,\n value_source_value STRING\n )\n AS (\n -- 1585250 corresponds to the zipcode concept that is not subject to string suppression, value_as_string for this record should be kept --\n WITH w AS (\n SELECT ARRAY<STRUCT<\n observation_id int64, \n person_id int64, \n observation_concept_id int64,\n observation_source_concept_id int64,\n value_as_string STRING, \n observation_source_value STRING,\n unit_source_value STRING,\n qualifier_source_value STRING,\n value_source_value STRING\n >>\n [(1, 1, 0, 1585250, '111111', 'observation_source_value', 'unit_source_value', 'qualifier_source_value', 'value_source_value'),\n (2, 1, 0, 0, 'value_as_string', 'observation_source_value', 'unit_source_value', 'qualifier_source_value', 'value_source_value'),\n (3, 1, 0, 0, 'value_as_string', 'observation_source_value', 'unit_source_value', 'qualifier_source_value', 'value_source_value'),\n (4, 1, 0, 0, 'value_as_string', 'observation_source_value', 'unit_source_value', 'qualifier_source_value', 'value_source_value'),\n (5, 1, 0, 0, 'value_as_string', 'observation_source_value', 'unit_source_value', 'qualifier_source_value', 'value_source_value'),\n (6, 1, 0, 715711, 'foo_date', 'observation_source_value', 'unit_source_value', 'qualifier_source_value', 'value_source_value')] col\n )\n SELECT \n observation_id,\n person_id,\n observation_concept_id,\n observation_source_concept_id,\n value_as_string, \n observation_source_value,\n unit_source_value,\n qualifier_source_value,\n value_source_value \n FROM w, UNNEST(w.col))\n \"\"\")\n\n insert_condition_query = condition_occurrence_data_template.render(\n project_id=self.project_id, dataset_id=self.dataset_id)\n\n insert_observation_query = observation_data_template.render(\n project_id=self.project_id, dataset_id=self.dataset_id)\n\n # Load test data\n self.load_test_data([\n f'''{insert_condition_query};\n {insert_observation_query};'''\n ])","def init_beeswax_db(cls):\n global _INITIALIZED\n if _INITIALIZED:\n return\n\n make_query(cls.client, 'CREATE DATABASE IF NOT EXISTS %(db)s' % {'db': cls.db_name}, wait=True)\n make_query(cls.client, 'CREATE DATABASE IF NOT EXISTS %(db)s_other' % {'db': cls.db_name}, wait=True)\n\n if cls.load_data:\n\n data_file = cls.cluster.fs_prefix + u'/beeswax/sample_data_échantillon_%d.tsv'\n\n # Create a \"test_partitions\" table.\n CREATE_PARTITIONED_TABLE = \"\"\"\n CREATE TABLE `%(db)s`.`test_partitions` (foo INT, bar STRING)\n PARTITIONED BY (baz STRING, boom INT)\n ROW FORMAT DELIMITED\n FIELDS TERMINATED BY '\\t'\n LINES TERMINATED BY '\\n'\n \"\"\" % {'db': cls.db_name}\n make_query(cls.client, CREATE_PARTITIONED_TABLE, wait=True)\n cls._make_data_file(data_file % 1)\n\n LOAD_DATA = \"\"\"\n LOAD DATA INPATH '%(data_file)s'\n OVERWRITE INTO TABLE `%(db)s`.`test_partitions`\n PARTITION (baz='baz_one', boom=12345)\n \"\"\" % {'db': cls.db_name, 'data_file': data_file % 1}\n make_query(cls.client, LOAD_DATA, wait=True, local=False)\n\n # Insert additional partition data into \"test_partitions\" table\n ADD_PARTITION = \"\"\"\n ALTER TABLE `%(db)s`.`test_partitions` ADD PARTITION(baz='baz_foo', boom=67890) LOCATION '%(fs_prefix)s/baz_foo/boom_bar'\n \"\"\" % {'db': cls.db_name, 'fs_prefix': cls.cluster.fs_prefix}\n make_query(cls.client, ADD_PARTITION, wait=True, local=False)\n\n # Create a bunch of other tables\n CREATE_TABLE = \"\"\"\n CREATE TABLE `%(db)s`.`%(name)s` (foo INT, bar STRING)\n COMMENT \"%(comment)s\"\n ROW FORMAT DELIMITED\n FIELDS TERMINATED BY '\\t'\n LINES TERMINATED BY '\\n'\n \"\"\"\n\n # Create a \"test\" table.\n table_info = {'db': cls.db_name, 'name': 'test', 'comment': 'Test table'}\n cls._make_data_file(data_file % 2)\n cls._make_table(table_info['name'], CREATE_TABLE % table_info, data_file % 2)\n\n if is_live_cluster():\n LOG.warning('HUE-2884: We cannot create Hive UTF8 tables when live cluster testing at the moment')\n else:\n # Create a \"test_utf8\" table.\n table_info = {'db': cls.db_name, 'name': 'test_utf8', 'comment': cls.get_i18n_table_comment()}\n cls._make_i18n_data_file(data_file % 3, 'utf-8')\n cls._make_table(table_info['name'], CREATE_TABLE % table_info, data_file % 3)\n\n # Create a \"test_latin1\" table.\n table_info = {'db': cls.db_name, 'name': 'test_latin1', 'comment': cls.get_i18n_table_comment()}\n cls._make_i18n_data_file(data_file % 4, 'latin1')\n cls._make_table(table_info['name'], CREATE_TABLE % table_info, data_file % 4)\n\n # Create a \"myview\" view.\n make_query(cls.client, \"CREATE VIEW `%(db)s`.`myview` (foo, bar) as SELECT * FROM `%(db)s`.`test`\" % {'db': cls.db_name}, wait=True)\n\n _INITIALIZED = True","def set_training_data(self):\n # Optional training data period\n # TODO: add training data period feature to training data query\n if not self.training_period == None:\n training_period_date = (datetime.datetime.utcnow() - timedelta(minutes=self.training_period)).strftime(\"%Y-%m-%d\")\n print(f\"Training data start date: {training_period_date}\")\n # Extract queried data from Athena\n #athena = athena_connect.Athena()\n #features_df = athena.pandas_read_athena(self.training_data_sql)\n with open('feature_sql.txt', 'w') as f:\n print(self.training_data_sql, file=f) \n features_df = pd.read_sql(self.training_data_sql, self.logic_db_engine())\n features_df.fillna(0, inplace=True)\n print(features_df.shape)\n features_df = features_df[max(self.feature_minutes_list):]\n print(features_df.shape)\n # Remove infinity string\n features_df.replace({'Infinity': 0}, inplace=True)\n # Convert all object fields to numeric except date fields\n object_col_list = features_df.columns[features_df.dtypes.eq('object')]\n object_col_list = [col for col in object_col_list if 'trade_date' not in col]\n features_df[object_col_list] = features_df[object_col_list].apply(pd.to_numeric, errors='coerce')\n self.training_df = features_df","def seed_all():\n seed_client()\n seed_staff()\n seed_request()\n seed_comment()","def initialize_database(study_file, grasp_file, commit_every=250000,\n progress=False):\n rows = 0\n count = commit_every\n pphenos = {}\n phenos = {}\n platforms = {}\n populations = {}\n\n\n # Create tables\n _, engine = get_session()\n print('Dropping and creating database tables, this may take a while if',\n 'the old database is large.')\n if _config['DEFAULT']['DatabaseType'] == 'sqlite':\n cfile = _os.path.isfile(_config['sqlite']['DatabaseFile'])\n if _os.path.isfile(cfile):\n _os.remove(cfile)\n _Base.metadata.drop_all(engine)\n _Base.metadata.create_all(engine)\n print('Tables created.')\n conn = engine.connect()\n\n # Get tables\n study_table = _Study.__table__\n snp_table = _SNP.__table__\n pheno_table = _Phenotype.__table__\n pcat_table = _PhenoCats.__table__\n plat_table = _Platform.__table__\n pop_table = _Population.__table__\n\n # Create insert statements\n study_ins = study_table.insert()\n snp_ins = snp_table.insert()\n pheno_ins = pheno_table.insert()\n pcat_ins = pcat_table.insert()\n plat_ins = plat_table.insert()\n pop_ins = pop_table.insert()\n phsnp_ins = _snp_pheno_assoc.insert()\n phstudy_ins = _study_pheno_assoc.insert()\n plstudy_ins = _study_plat_assoc.insert()\n\n # Unique ID counters\n spare_id = 1\n pheno_id = 1\n pcat_id = 1\n plat_id = 1\n pop_id = 1\n\n # Lists to hold records\n pheno_records = []\n pcat_records = []\n plat_records = []\n pop_records = []\n study_records = []\n snp_records = []\n phsnp_records = []\n phstudy_records = []\n plstudy_records = []\n\n # Platform parsing regex\n plat_parser = _recompile(r'^([^[]*)\\[([^]]+)\\]?(.*)')\n\n # Build study information from study file\n print('Parsing study information.')\n with _open_zipped(study_file) as fin:\n # Drop header\n fin.readline()\n\n if progress:\n pbar = _tqdm(total=2083, unit='studies')\n for line in fin:\n f = line.rstrip().split('\\t')\n\n # Get primary phenotype\n ppheno = _cleanstr(f[7].strip())\n if ppheno not in pphenos:\n pheno_records.append({'phenotype': ppheno,\n 'id': pheno_id})\n pphenos[ppheno] = pheno_id\n pheno_id += 1\n\n # Get phenotype categories\n pheno_cats = f[8].strip().split(';')\n our_phenos = []\n for pcat in pheno_cats:\n pcat = pcat.strip()\n if not pcat:\n continue\n if pcat not in phenos:\n pcat_records.append({\n 'id': pcat_id,\n 'category': pcat,\n 'alias': pheno_synonyms[pcat],\n })\n phenos[pcat] = pcat_id\n pcat_id += 1\n our_phenos.append(phenos[pcat])\n\n # Get platform info\n our_platforms = []\n try:\n plat, snp_count, impt = [\n i.strip() for i in plat_parser.findall(f[18].strip())[0]\n ]\n imputed = True if impt == '(imputed)' else False\n plats = _split_mesy_list(plat)\n for plat in plats:\n plat = plat.strip()\n if plat not in platforms:\n plat_records.append({'id': plat_id,\n 'platform': plat})\n platforms[plat] = plat_id\n plat_id += 1\n our_platforms.append(platforms[plat])\n except IndexError:\n plat, snp_count, impt = None, None, None\n imputed = None\n\n # Get population description\n try:\n pop = f[19].strip()\n try:\n pop = pop_correction[pop]\n except KeyError:\n pass\n if pop not in populations:\n pop_records.append({'id': pop_id,\n 'population': pop})\n populations[pop] = pop_id\n pop_id += 1\n population = populations[pop]\n except IndexError:\n population = None\n\n # Set populaion flags\n pflag = _PopFlag\n disc_pop = pflag(0)\n rep_pop = pflag(0)\n l = len(f)\n if l > 22 and f[22]:\n disc_pop |= pflag.eur\n if l > 23 and f[23]:\n disc_pop |= pflag.afr\n if l > 24 and f[24]:\n disc_pop |= pflag.east_asian\n if l > 25 and f[25]:\n disc_pop |= pflag.south_asian\n if l > 26 and f[26]:\n disc_pop |= pflag.his\n if l > 27 and f[27]:\n disc_pop |= pflag.native\n if l > 28 and f[28]:\n disc_pop |= pflag.micro\n if l > 29 and f[29]:\n disc_pop |= pflag.arab\n if l > 30 and f[30]:\n disc_pop |= pflag.mix\n if l > 31 and f[31]:\n disc_pop |= pflag.uns\n if l > 32 and f[32]:\n disc_pop |= pflag.filipino\n if l > 33 and f[33]:\n disc_pop |= pflag.indonesian\n if l > 35 and f[35]:\n rep_pop |= pflag.eur\n if l > 36 and f[36]:\n rep_pop |= pflag.afr\n if l > 37 and f[37]:\n rep_pop |= pflag.east_asian\n if l > 38 and f[38]:\n rep_pop |= pflag.south_asian\n if l > 39 and f[39]:\n rep_pop |= pflag.his\n if l > 40 and f[40]:\n rep_pop |= pflag.native\n if l > 41 and f[41]:\n rep_pop |= pflag.micro\n if l > 42 and f[42]:\n rep_pop |= pflag.arab\n if l > 43 and f[43]:\n rep_pop |= pflag.mix\n if l > 44 and f[44]:\n rep_pop |= pflag.uns\n if l > 45 and f[45]:\n rep_pop |= pflag.filipino\n if l > 46 and f[46]:\n rep_pop |= pflag.indonesian\n\n # Set the global population flag\n pop_flag = disc_pop | rep_pop\n\n # Create study\n study_records.append({\n 'id': int(f[0]),\n 'author': _cleanstr(f[1]),\n 'pmid': _cleanstr(f[2]),\n 'grasp_ver': 1 if '1.0' in f[3] else 2,\n 'noresults': True if f[4] else False,\n 'results': int(f[5]),\n 'qtl': True if f[6] == '1' else False,\n 'phenotype_id': pphenos[ppheno],\n 'phenotype_desc': ppheno,\n 'phenotype': pphenos[ppheno],\n 'phenotype_cats': our_phenos,\n 'datepub': _get_date(f[9]),\n 'in_nhgri': _get_bool(f[10]),\n 'journal': _cleanstr(f[11]),\n 'title': _cleanstr(f[12]),\n 'locations': _cleanstr(f[13]),\n 'mf': _get_bool(f[14]),\n 'mf_only': _get_bool(f[15]),\n 'sample_size': _cleanstr(f[16]),\n 'replication_size': _cleanstr(f[17]),\n 'platforms': platforms,\n 'snp_count': snp_count,\n 'imputed': imputed,\n 'population_id': population,\n 'population': population,\n 'total': int(f[20]),\n 'total_disc': int(f[21]),\n 'pop_flag': int(pop_flag),\n 'disc_pop_flag': int(disc_pop),\n 'european': int(f[22]) if l > 22 and f[22] else None,\n 'african': int(f[23]) if l > 23 and f[23] else None,\n 'east_asian': int(f[24]) if l > 24 and f[24] else None,\n 'south_asian': int(f[25]) if l > 25 and f[25] else None,\n 'hispanic': int(f[26]) if l > 26 and f[26] else None,\n 'native': int(f[27]) if l > 27 and f[27] else None,\n 'micronesian': int(f[28]) if l > 28 and f[28] else None,\n 'arab': int(f[29]) if l > 29 and f[29] else None,\n 'mixed': int(f[30]) if l > 30 and f[30] else None,\n 'unspecified': int(f[31]) if l > 31 and f[31] else None,\n 'filipino': int(f[32]) if l > 32 and f[32] else None,\n 'indonesian': int(f[33]) if l > 33 and f[33] else None,\n 'total_rep': int(f[34]) if l > 34 and f[34] else None,\n 'rep_pop_flag': int(rep_pop),\n 'rep_european': int(f[35]) if l > 35 and f[35] else None,\n 'rep_african': int(f[36]) if l > 36 and f[36] else None,\n 'rep_east_asian': int(f[37]) if l > 37 and f[37] else None,\n 'rep_south_asian': int(f[38]) if l > 38 and f[38] else None,\n 'rep_hispanic': int(f[39]) if l > 39 and f[39] else None,\n 'rep_native': int(f[40]) if l > 40 and f[40] else None,\n 'rep_micronesian': int(f[41]) if l > 41 and f[41] else None,\n 'rep_arab': int(f[42]) if l > 42 and f[42] else None,\n 'rep_mixed': int(f[43]) if l > 43 and f[43] else None,\n 'rep_unspecified': int(f[44]) if l > 44 and f[44] else None,\n 'rep_filipino': int(f[45]) if l > 45 and f[45] else None,\n 'rep_indonesian': int(f[46]) if l > 46 and f[46] else None,\n })\n\n # Create association records\n for i in our_phenos:\n phstudy_records.append({'study_id': int(f[0]),\n 'pheno_id': i})\n for i in our_platforms:\n plstudy_records.append({'study_id': int(f[0]),\n 'platform_id': i})\n\n pbar.update()\n\n pbar.close()\n print('Writing study information...')\n conn.execute(pheno_ins, pheno_records)\n conn.execute(pcat_ins, pcat_records)\n conn.execute(plat_ins, plat_records)\n conn.execute(pop_ins, pop_records)\n conn.execute(study_ins, study_records)\n conn.execute(phstudy_ins, phstudy_records)\n conn.execute(plstudy_ins, plstudy_records)\n print('Done')\n\n # Reinitialize lists for main GRASP parser\n pheno_records = []\n pcat_records = []\n plat_records = []\n pop_records = []\n\n # Get full study info from database for use in SNPs\n sinfo = conn.execute(_select([study_table.c.id, study_table.c.pmid])).fetchall()\n studies = {}\n for i, p in sinfo:\n studies[p] = i\n no_pmid = {\n 'Dissertation (https://openaccess.leidenuniv.nl/handle/1887/17746)': 1,\n 'KARE Genomewide Association Study of Blood Pressure Using Imputed SNPs': 2,\n 'Genome-wide Association Study Identification of a New Genetic Locus with Susceptibility to Osteoporotic Fracture in the Korean Population.': 3,\n 'Genome-wide Association Study Identified TIMP2 Genetic Variant with Susceptibility to Osteoarthritis': 4,\n 'Application of Structural Equation Models to Genome-wide Association Analysis ': 5,\n 'Comparison of Erythrocyte Traits Among European, Japanese and Korean': 6,\n 'Genomewide Association Study Identification of a New Genetic Locus with Susceptibility to Osteoporotic Fracture in the Korean Population': 7,\n 'Joint identification of multiple genetic variants of obesity in A Korean Genome-wide association study': 8,\n 'Genome-Wide Association Analyses on Blood Pressure Using Three Different Phenotype Definitions': 9,\n 'Association of intronic sequence variant in the gene encoding spleen tyrosine kinase with susceptibility to vascular dementia': 10,\n }\n\n print('Parsing SNP information...')\n with _open_zipped(grasp_file, encoding='latin1') as fin:\n # Drop header\n fin.readline()\n\n if progress:\n pbar = _tqdm(total=8864717, unit='snps')\n\n for line in fin:\n f = line.rstrip().split('\\t')\n\n # Get primary phenotype\n ppheno = _cleanstr(f[11])\n # These are poorly curated, so there is no need to use a\n # separate table for them.\n # if ppheno not in pphenos:\n # conn.execute(pheno_ins.values(\n # phenotype=ppheno\n # ))\n # pphenos[ppheno] = conn.execute(\n # select([pheno_table.c.id]).where(\n # pheno_table.c.phenotype == ppheno\n # )\n # ).first()[0]\n\n # Get phenotype categories\n pheno_cats = f[13].strip().split(';')\n our_phenos = []\n for pcat in pheno_cats:\n pcat = pcat.strip()\n if not pcat:\n continue\n if pcat not in phenos:\n pcat_records.append({\n 'id': pcat_id,\n 'category': pcat,\n 'alias': pheno_synonyms[pcat],\n })\n phenos[pcat] = pcat_id\n pcat_id += 1\n our_phenos.append(phenos[pcat])\n\n # Get population description\n try:\n pop = f[23].strip()\n try:\n pop = pop_correction[pop]\n except KeyError:\n pass\n if pop not in populations:\n pop_records.append({'id': pop_id,\n 'population': pop})\n populations[pop] = pop_id\n pop_id += 1\n population = populations[pop]\n except IndexError:\n population = None\n\n # Create record for SNP\n try:\n sid = int(f[0])\n except ValueError:\n sid = spare_id\n spare_id += 1\n l = len(f)\n try:\n study = studies[f[7].strip()]\n except KeyError:\n study = no_pmid[f[17].strip()]\n record = {\n 'id': sid,\n 'NHLBIkey': f[0],\n 'HUPfield': f[1],\n 'LastCurationDate': _get_date(f[2]),\n 'CreationDate': _get_date(f[3]),\n 'snpid': f[4],\n 'chrom': f[5],\n 'pos': int(f[6]),\n 'population_id': population,\n 'population': population,\n 'study_id': study,\n 'study': study,\n 'study_snpid': f[8],\n 'paper_loc': f[9],\n 'pval': float(f[10]) if f[10] else None,\n 'phenotype_desc': ppheno,\n 'phenotype_cats': our_phenos,\n }\n record['InGene'] = f[51] if l > 52 else None\n record['NearestGene'] = f[52] if l > 53 else None\n record['InLincRNA'] = f[53] if l > 54 else None\n record['InMiRNA'] = f[54] if l > 55 else None\n record['InMiRNABS'] = f[55] if l > 56 else None\n record['dbSNPfxn'] = f[56] if l > 57 else None\n record['dbSNPMAF'] = f[57] if l > 58 else None\n record['dbSNPinfo'] = f[58] if l > 59 else None\n record['dbSNPvalidation'] = f[59] if l > 60 else None\n record['dbSNPClinStatus'] = f[60] if l > 61 else None\n record['ORegAnno'] = f[61] if l > 62 else None\n record['ConservPredTFBS'] = f[62] if l > 63 else None\n record['HumanEnhancer'] = f[63] if l > 64 else None\n record['RNAedit'] = f[64] if l > 65 else None\n record['PolyPhen2'] = f[65] if l > 66 else None\n record['SIFT'] = f[66] if l > 67 else None\n record['LSSNP'] = f[67] if l > 68 else None\n record['UniProt'] = f[68] if l > 69 else None\n record['EqtlMethMetabStudy'] = f[69] if l > 70 else None\n snp_records.append(record)\n\n # Create association records\n for i in our_phenos:\n phsnp_records.append({'snp_id' : sid, 'pheno_id' : i})\n\n # Decide when to execute\n if count:\n count -= 1\n else:\n if progress:\n pbar.write('Writing rows...')\n else:\n print('Writing rows...')\n if pcat_records:\n conn.execute(pcat_ins, pcat_records)\n if plat_records:\n conn.execute(plat_ins, plat_records)\n if pop_records:\n conn.execute(pop_ins, pop_records)\n conn.execute(snp_ins, snp_records)\n conn.execute(phsnp_ins, phsnp_records)\n if progress:\n pbar.write('{} rows written'.format(rows))\n else:\n print('{} rows written'.format(rows))\n count = commit_every-1\n pcat_records = []\n plat_records = []\n pop_records = []\n snp_records = []\n phsnp_records = []\n rows += 1\n if progress:\n pbar.update()\n\n # Final insert\n pbar.close()\n print('Writing final rows...')\n conn.execute(snp_ins, snp_records)\n conn.execute(phsnp_ins, phsnp_records)\n print('{} rows written'.format(rows))\n print('Done!')","def setUp(self):\n try:\n # Get default data from medical_forum_data_dump.sql, populate tables and connect to DB\n ENGINE.populate_tables()\n self.connection = ENGINE.connect()\n\n # In case of error/exception in populating tables, clear all tables data\n except Exception as exception:\n print(exception)\n ENGINE.clear()","def populate_t_database():\n with open('minerals.json') as file:\n file = json.loads(file.read())\n\n for mineral in file[:22]:\n mineral_entry = Mineral.objects.get_or_create(**mineral)","def create_all_tables(self):\n pass","def full_initialization_process():\n\n db1 = Database('TOBACCO_RAW;')\n con1, cur1 = db1.connect()\n cur1.execute('create index idl_doc_field_id_idx on idl_doc_field(id);')\n cur1.execute('create index idl_doc_id_idx on idl_doc(id);')\n add_timestamp_to_idl_doc()\n\n create_utf_text_files()\n\n initialize_tables()\n fill_tables()","def ingest():\n db.delete_dataset_records(DATASET_ID)\n\n db.insert_dataset({\n 'dataset_id': DATASET_ID,\n 'title': 'North American Breeding Bird Survey (BBS)',\n 'version': '2016.0',\n 'url': 'https://www.pwrc.usgs.gov/bbs/'})\n\n to_taxon_id = insert_taxa()\n to_place_id = insert_places()\n to_event_id = insert_events(to_place_id)\n insert_counts(to_event_id, to_taxon_id)","def load_fhwa_records():\n print('--- Importing FHWA DFLTD v.2 records ---')\n for i in tqdm(range(len(tbl_project))):\n prj_id = tbl_project['lng_KeyProject'][i]\n\n expl_in_project = tbl_exploration[\n tbl_exploration.lng_KeyProject == prj_id].index\n for i_exp in expl_in_project:\n expl_id = tbl_exploration['txt_KeyExplorationName'][i_exp]\n\n piles_in_project = tbl_deepfoundation[\n tbl_deepfoundation.lng_KeyProject == prj_id].index\n for i_pile in piles_in_project:\n pile_id = tbl_deepfoundation['lng_KeyDeepFoundation'][i_pile]\n\n tests_for_pile = tbl_loadtest[\n (tbl_loadtest.lng_KeyProject == prj_id) &\n (tbl_loadtest.lng_KeyDeepFoundation == pile_id)\n ].index\n for i_lt in tests_for_pile:\n test_id = tbl_loadtest['lng_KeyLoadTest'][i_lt]\n\n # -- Adding Project Data -------------------------------- #\n if len(piles_in_project) > 1 and len(expl_in_project) < 2:\n wrn = 'Expanded from a project with multiple piles '\\\n 'and/or retests'\n prj = add_loc_proj(i, wrn)\n elif len(piles_in_project) < 2 and len(expl_in_project) > 1:\n wrn = 'Expanded from a project with multiple '\\\n 'explorations'\n prj = add_loc_proj(i, wrn)\n elif len(piles_in_project) > 1 and len(expl_in_project) > 1:\n wrn = 'Expanded from a project with multiple '\\\n 'explorations and multiple piles/retests'\n prj = add_loc_proj(i, wrn)\n else:\n prj = add_loc_proj(i)\n db.session.add(prj)\n\n # -- Adding Exploration Data ---------------------------- #\n exploration = add_expl_data(i_exp, expl_id, prj)\n db.session.add(exploration)\n\n # -- Adding Layer Data ---------------------------------- #\n add_layer_data(prj_id, expl_id, exploration)\n\n # -- Adding Pile Data ----------------------------------- #\n pile = add_pile_data(i_pile, prj_id, pile_id, prj)\n db.session.add(pile)\n\n # -- Adding Load Test Data ------------------------------ #\n load_test = add_load_test_data(i_lt, pile)\n db.session.add(load_test)\n\n # -- Adding Static Test Data ---------------------------- #\n add_static_test_data(prj_id, pile_id, test_id, load_test)\n\n # -- Adding Interpreted Data ---------------------------- #\n add_interp_data(prj_id, pile_id, test_id, load_test)\n\n db.session.commit()","def fill_in(self,category):\r\n api_json = JsonFromApi(category)\r\n extracted_data = ExtractFromJson(api_json.get_json())\r\n self.fill_in_db = DatabaseUpdator(\r\n extracted_data.extract_json(), self.mydb)\r\n self.fill_in_db.table_product_update()\r\n self.fill_in_db.table_category_update()","def setUp(self):\n self.listMembre = []\n self.listMembre.append(\"bras gauche\")\n self.listMembre.append(\"bras droit\")\n self.exercice = data.Exercice(\"Mouvement des bras\",self.listMembre,\"Pour rééducation\", \"ex/path\")\n self.exercice_a = data.Exercice(\"Mouvement des jambes\",self.listMembre,\"Pour rééducation\", \"ex/path\")\n self.exercice_b = data.Exercice(\"Mouvement des hanches\",self.listMembre,\"Pour rééducation\", \"ex/path\")\n self.exercice_1 = data.Exercice(\"Mouvement des\",self.listMembre,\"Pour rééducation\", \"ex/path\")\n self.exercice_2 = data.Exercice(\"Mouvement hanches\",self.listMembre,\"Pour rééducation\", \"ex/path\")\n\n self.patient = data.Patient(\"Claude\" , \"123\")\n self.patient_b = data.Patient(\"Gaetan\" , \"789\")\n\n self.patient_1 = data.Patient(\"JP\" , \"456\")\n self.patient_2 = data.Patient(\"JN\" , \"654\")\n\n self.listExo = [self.exercice_a,self.exercice_b]\n self.listExo_b = [self.exercice_1,self.exercice_2]\n self.program_a = data.Program(self.listExo,self.patient_b)\n self.program_b = data.Program(self.listExo_b,self.patient_b)\n\n self.bib_a = [self.exercice,self.exercice_a,self.exercice_b]\n self.bib_b = [self.exercice_1,self.exercice_2]\n\n self.listP_a = [self.patient,self.patient_b]\n self.listP_b = [self.patient_1,self.patient_2]\n\n self.allData = data.AppData(self.bib_a,self.listP_a,[self.program_a,self.program_b])","def populate_jobs():\n\n logging.basicConfig(level=logging.INFO)\n logger = logging.getLogger(__name__)\n\n database = SqliteDatabase('personjob.db')\n\n logger.info('Working with Job class')\n logger.info('Creating Job records: just like Person. We use the foreign key')\n\n JOB_NAME = 0\n START_DATE = 1\n END_DATE = 2\n SALARY = 3\n PERSON_EMPLOYED = 4\n DEPARTMENT = 5\n\n jobs = [\n ('Analyst', '2001-09-22', '2003-01-30',65500, 'Andrew', 'ASYS'),\n ('Senior analyst', '2003-02-01', '2006-10-22', 70000, 'Andrew', 'ASYS'),\n ('Senior business analyst', '2006-10-23', '2016-12-24', 80000, 'Andrew', 'BUSI'),\n ('Admin supervisor', '2012-10-01', '2014-11-10', 45900, 'Peter', 'ADMN'),\n ('Admin manager', '2014-11-14', '2018-01-05', 45900, 'Peter', 'ADMN'),\n ('Sr Project Manager', '2014-11-14', '2018-01-05', 100000, 'Ryan', 'ASYS'),\n ('Manager', '2014-11-14', '2018-01-05', 100000, 'Pamela', 'BUSI'),\n ('Director', '2014-11-14', '2018-01-05', 120000, 'Monica', 'MGMT'),\n ]\n\n try:\n database.connect()\n database.execute_sql('PRAGMA foreign_keys = ON;')\n for job in jobs:\n with database.transaction():\n new_job = Job.create(\n job_name = job[JOB_NAME],\n start_date = job[START_DATE],\n end_date = job[END_DATE],\n duration = dates_diff(job[END_DATE], job[START_DATE]),\n salary = job[SALARY],\n person_employed = job[PERSON_EMPLOYED],\n job_department = job[DEPARTMENT])\n new_job.save()\n\n logger.info('Reading and print all Job rows (note the value of person)...')\n for job in Job:\n logger.info(f'{job.job_name} : {job.start_date} to {job.end_date} for {job.person_employed} in {job.job_department}')\n\n except Exception as e:\n logger.info(f'Error creating = {job[JOB_NAME]}')\n logger.info(e)\n\n finally:\n logger.info('database closes')\n database.close()","def _load_db(self):\n for type_ in self._types:\n try:\n type_.table(self._metadata)\n except InvalidRequestError:\n pass\n # Reflect metadata so auto-mapping works\n self._metadata.reflect(self._engine)\n # Make sure the tables exist\n self._metadata.create_all()","def setup(self):\n # TODO check if need both dataset together\n self.train_dataset = ABSADataset(data_path=self.train_path, mode=self.in_mode, task=self.task, \n tokenizer=self.tokenizer, vocab=\"bert\")\n self.vocabulary = self.train_dataset.vocabulary\n\n self.eval_dataset = ABSADataset(data_path=self.dev_path, mode=self.in_mode, task=self.task,\n tokenizer=self.tokenizer, vocab=self.vocabulary)\n #self.train_restaurant = ABSADataset(data_path=RESTAURANT_TRAIN)\n #self.eval_restaurant = ABSADataset(data_path=RESTAURANT_DEV)","def set_up_table(db):\n db.bulk_query(\"\"\"\n SET @@SQL_MODE = REPLACE(@@SQL_MODE, 'STRICT_TRANS_TABLES', '');\n \"\"\")\n db.bulk_query(\"\"\"\n CREATE TABLE cats\n (\n id INT unsigned NOT NULL AUTO_INCREMENT, # Unique ID for the record\n name VARCHAR(150) NOT NULL DEFAULT '', # Name of the cat\n owner VARCHAR(150) NOT NULL DEFAULT '', # Owner of the cat\n birth DATE NOT NULL, # Birthday of the cat\n PRIMARY KEY (id) # Make the id the primary key\n );\n \"\"\")\n db.bulk_query(\"\"\"\n INSERT INTO cats ( name, owner, birth) VALUES\n ( 'Sandy', 'Lennon', '2015-01-03' ),\n ( 'Cookie', 'Casey', '2013-11-13' ),\n ( 'Charlie', 'River', '2016-05-21' );\n \"\"\")","def prepare_data(self):\r\n annual_df = self.annual_df\r\n coef_df = self.coef_df\r\n quarter_df = self.quarter_df\r\n # historical_df = self.historical_df\r\n Event_Buffer = self.Event_Buffer\r\n\r\n Tot_Prod = coef_df[\"Product\"].nunique()\r\n # Tot_Week = coef_df[\"wk\"].nunique()\r\n Tot_Week = 52\r\n\r\n EDLP_Events = list(annual_df[\"RP_Events\"])\r\n Min_EDLP_Events = [\r\n i - Event_Buffer if i - Event_Buffer >= 0 else 0 for i in EDLP_Events\r\n ]\r\n Max_EDLP_Events = [\r\n i + Event_Buffer if i + Event_Buffer < Tot_Week + 1 else Tot_Week\r\n for i in EDLP_Events\r\n ]\r\n\r\n TPR_Events = list(annual_df[\"TPR_Events\"])\r\n Min_TPR_Events = [\r\n i - Event_Buffer if i - Event_Buffer >= 0 else 0 for i in TPR_Events\r\n ]\r\n Max_TPR_Events = [\r\n i + Event_Buffer if i + Event_Buffer < Tot_Week + 1 else Tot_Week\r\n for i in TPR_Events\r\n ]\r\n\r\n Target_EDLP_Spend = [i for i in annual_df[\"PPG_RP_Spend\"]]\r\n Target_TPR_Spend = [i for i in annual_df[\"PPG_TPR_Spend\"]]\r\n Target_Trade_Spend = [i for i in annual_df[\"PPG_Total_Spend\"]]\r\n\r\n Mapping = {}\r\n Prod_Ind = coef_df[\"Product\"][0:Tot_Prod]\r\n for i, j in zip(Prod_Ind.index, Prod_Ind.values):\r\n Mapping[j] = i\r\n Mapping_reverse = {i: j for j, i in Mapping.items()}\r\n\r\n constants = [i for i in coef_df[\"constant\"]]\r\n\r\n Cat_Coef = coef_df[\"Catalogue\"][0:Tot_Prod]\r\n\r\n Disp_Coef = coef_df[\"Display\"][0:Tot_Prod]\r\n\r\n Base_Price_stg1 = [i for i in quarter_df[\"Final_baseprice\"]]\r\n Intercepts_stg1 = []\r\n for pr in range(Tot_Prod):\r\n Intercepts_stg1.append(\r\n np.mean([constants[j * Tot_Prod + pr] for j in range(0, Tot_Week)])\r\n )\r\n\r\n Base_Price_stg2 = [[i] * Tot_Week for i in quarter_df[\"Final_baseprice\"]]\r\n Intercepts_stg2 = [\r\n constants[j : j + Tot_Prod] for j in range(0, len(constants), Tot_Prod)\r\n ] # noqa\r\n\r\n EDLP_Coef = np.array(\r\n coef_df[[i for i in coef_df.columns if i.count(\"Retailer_Regular\") == 1]]\r\n )\r\n TPR_Coef = np.array(\r\n coef_df[[i for i in coef_df.columns if i.count(\"Retailer_Promoted\") == 1]]\r\n )\r\n\r\n # ################################ Available EDLP Interactions pairs ##############################\r\n\r\n EDLP = [\r\n re.findall(r\"[0-9]+\", i)\r\n for i in coef_df.columns\r\n if i.count(\"Retailer_Regular\") > 1\r\n ]\r\n EDLP_Interactions = []\r\n for i in EDLP:\r\n temp = []\r\n for j in i:\r\n temp.append(int(j))\r\n EDLP_Interactions.append(temp)\r\n\r\n # ###################################### Available TPR Interactions pairs #########################\r\n\r\n TPR = [\r\n re.findall(r\"[0-9]+\", i)\r\n for i in coef_df.columns\r\n if i.count(\"Retailer_Promoted\") > 1\r\n ]\r\n TPR_Interactions = []\r\n for i in TPR:\r\n temp = []\r\n for j in i:\r\n temp.append(int(j))\r\n TPR_Interactions.append(temp)\r\n\r\n # ###################################### EDLP_Interaction_Coef_Values ############################\r\n\r\n EDLP_Int_Coef_Values = {}\r\n for col in coef_df.columns:\r\n if col.count(\"Retailer_Regular\") > 1:\r\n Pair_name = \"_\".join([str(int(i)) for i in re.findall(r\"[0-9]+\", col)])\r\n EDLP_Int_Coef_Values[Pair_name] = list(coef_df[col])\r\n\r\n # ###################################### TPR_Interaction_Coef_Values #############################\r\n\r\n TPR_Int_Coef_Values = {}\r\n for col in coef_df.columns:\r\n if col.count(\"Retailer_Promoted\") > 1:\r\n Pair_name = \"_\".join([str(int(i)) for i in re.findall(r\"[0-9]+\", col)])\r\n TPR_Int_Coef_Values[Pair_name] = list(coef_df[col])\r\n\r\n # ##################################### Loading Pantry Loading Coefficients #######################\r\n\r\n Pantry_1 = list(coef_df[\"Pantry_Loading_1\"])\r\n Pantry_1 = [\r\n Pantry_1[j : j + Tot_Prod] for j in range(0, len(Pantry_1), Tot_Prod)\r\n ]\r\n Pantry_2 = list(coef_df[\"Pantry_Loading_2\"])\r\n Pantry_2 = [\r\n Pantry_2[j : j + Tot_Prod] for j in range(0, len(Pantry_2), Tot_Prod)\r\n ]\r\n\r\n # TE_Coeff = np.array(Promo_df[[\"TE_Promo\",\"TE_NoPromo\"]])\r\n self.Tot_Prod = Tot_Prod\r\n self.Tot_Week = Tot_Week\r\n self.EDLP_Events = EDLP_Events\r\n self.Min_EDLP_Events = Min_EDLP_Events\r\n self.Max_EDLP_Events = Max_EDLP_Events\r\n self.TPR_Events = TPR_Events\r\n self.Min_TPR_Events = Min_TPR_Events\r\n self.Max_TPR_Events = Max_TPR_Events\r\n\r\n self.Target_EDLP_Spend = Target_EDLP_Spend\r\n self.Target_TPR_Spend = Target_TPR_Spend\r\n self.Target_Trade_Spend = Target_Trade_Spend\r\n self.Mapping = Mapping\r\n self.Mapping_reverse = Mapping_reverse\r\n self.constants = constants\r\n self.EDLP_Coef = EDLP_Coef\r\n self.TPR_Coef = TPR_Coef\r\n\r\n self.EDLP_Interactions = EDLP_Interactions\r\n self.TPR_Interactions = TPR_Interactions\r\n self.EDLP_Int_Coef_Values = EDLP_Int_Coef_Values\r\n self.TPR_Int_Coef_Values = TPR_Int_Coef_Values\r\n self.Pantry_1 = Pantry_1\r\n self.Pantry_2 = Pantry_2\r\n\r\n self.Base_Price_stg1 = Base_Price_stg1\r\n self.Intercepts_stg1 = Intercepts_stg1\r\n self.Base_Price_stg2 = Base_Price_stg2\r\n self.Intercepts_stg2 = Intercepts_stg2\r\n\r\n self.Cat_Coef = Cat_Coef\r\n self.Disp_Coef = Disp_Coef","def setup(self): \n self.suburbs_dict = dict()\n self.raw_proIds_dict = dict()\n self.propertyIds_dict = dict()\n self.valuations = dict()","def init_db(self):\n\n cursor = self._db_connection.cursor()\n\n cursor.execute('''\n CREATE TABLE IF NOT EXISTS users (\n uid INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL,\n username VARCHAR(255) UNIQUE NOT NULL,\n full_name VARCHAR(255),\n password VARCHAR(255) NOT NULL\n );\n \n CREATE TABLE IF NOT EXISTS access_control (\n access_id INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL,\n uid INTEGER NOT NULL,\n privilege VARCHAR(255) NOT NULL,\n UNIQUE (uid, privilege),\n CONSTRAINT fk_associated_user FOREIGN KEY (uid) REFERENCES users (uid)\n );\n \n CREATE TABLE IF NOT EXISTS tokens (\n token_id INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL,\n token_content VARCHAR(255) NOT NULL,\n expiration_datetime VARCHAR(255),\n uid INTEGER NOT NULL,\n CONSTRAINT fk_associated_user FOREIGN KEY (uid) REFERENCES users (uid)\n );\n \n CREATE TABLE IF NOT EXISTS courses (\n course_id INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL,\n course_abbreviation VARCHAR(255) NOT NULL,\n course_name VARCHAR(255) NOT NULL,\n instructor_id INTEGER NOT NULL,\n time VARCHAR(255) NOT NULL,\n seats INTEGER NOT NULL,\n CONSTRAINT fk_instructors FOREIGN KEY (instructor_id) REFERENCES users (uid)\n );\n \n CREATE TABLE IF NOT EXISTS enrollment_records (\n enrollment_id INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL,\n uid INTEGER NOT NULL,\n course_id INTEGER NOT NULL,\n grade NUMERIC NOT NULL DEFAULT 100.0,\n UNIQUE (uid, course_id),\n CONSTRAINT fk_associated_user FOREIGN KEY (uid) REFERENCES users (uid),\n CONSTRAINT fk_associated_course FOREIGN KEY (course_id) references courses (course_id)\n );\n ''')","def populate(self):\n\n NUM_COUNTRIES = 2 # random.randint(1, 4)\n\n # find a suitable hex\n with Timer(\"Creating initial data\", debug=self.debug):\n\n for i in range(NUM_COUNTRIES):\n country, provinces, pops = create_country(self, self.map)\n country.determine_tax_policy()\n self.countries.append(country)","def example_data():\n\n # In case this is run more than once, empty out existing data\n User.query.delete()\n Trip.query.delete()\n Entry.query.delete()\n Category.query.delete()\n Share.query.delete()\n\n # Add sample data\n user1 = User(email='user1@gmail.com', password=bcrypt.hashpw('user1'.encode('utf8'), bcrypt.gensalt(9)), name='One')\n user2 = User(email='user2@gmail.com', password=bcrypt.hashpw('user2'.encode('utf8'), bcrypt.gensalt(9)), name='Two')\n trip1 = Trip(location='Spain', date='08/09/2017', name='Abroad Trip', user_id=1)\n entry1 = Entry(trip_id=1, name='Tibidabo', address='08035 Barcelona, Spain', notes='Fun day trip!',\n type_id=1)\n category1 = Category(name='Attraction')\n share1 = Share(viewer_id=2, trip_id=1)\n\n db.session.add_all([user1, user2, trip1, entry1, category1, share1])\n db.session.commit()","def setup(self):\n #print \"Creating test database...\"\n files = glob.glob(os.path.join(self.home_dir, 'sqlFiles', '*.sql'))\n for fls in files:\n loc = fls.rfind('/')\n #print(\" \" + fls.replace('.sql', '')[loc + 1:])\n flh = open(fls, 'r')\n curs = self.cursor()\n curs.executescript(flh.read())\n self.commit()\n curs.close()\n flh.close()\n for fls in ['INSERTS', 'TRIGGERS']:\n #print(fls)\n flh = open(os.path.join(self.home_dir, 'sqlFiles', fls), 'r')\n curs = self.cursor()\n curs.executescript(flh.read())\n self.commit()\n curs.close()\n flh.close()","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\")","def import_counties():\n\n query = 'INSERT INTO texas_counties(county, region) VALUES(%s,%s)'\n with persistence() as db:\n # create new cursor instance\n cursor = db.cursor(cursor_factory=psycopg2.extras.RealDictCursor)\n\n for council, counties in COUNCIL_DATA.items():\n for county in counties:\n cursor.execute(query, (county, council))\n db.commit()","def setUpTestData(cls):\n countries = [\"MX\", \"CHL\", \"USA\", \"PER\", \"COL\"]\n slack_user_ids = [\"UP0918MAV\", \"UP0918MAV\", \"UP0918MAV\", None, None]\n cls.menu = Menu.objects.create(available_on=date.today())\n for count in range(5):\n user = User.objects.create(username=f\"johny.doe {count}\")\n Employee.objects.create(\n user=user, country=countries[count], slack_user_id=slack_user_ids[count]\n )","def create_tables(self):\n\n # Uses methods from class DbAuth\n cursor = self.connect.create_cursor()\n cursor.execute(\"USE `dbPurBeurre`\")\n\n cursor.execute(\n \"CREATE TABLE IF NOT EXISTS `dbPurBeurre`.`Categories` (\"\n \" `num` INT UNSIGNED AUTO_INCREMENT,\"\n \" `id` VARCHAR(80) NOT NULL UNIQUE,\"\n \" `name` VARCHAR(80) NOT NULL,\"\n \" `url` VARCHAR(255) NOT NULL,\"\n \" `products` INT NULL,\"\n \" PRIMARY KEY (`num`))\"\n \" ENGINE = InnoDB\"\n )\n\n cursor.execute(\n \"CREATE TABLE IF NOT EXISTS `dbPurBeurre`.`Produits` (\"\n \" `num` INT UNSIGNED AUTO_INCREMENT,\"\n \" `id` VARCHAR(80) NOT NULL UNIQUE,\"\n \" `product_name` VARCHAR(80) NOT NULL,\"\n \" `nutrition_grade_fr` CHAR(1) NOT NULL,\"\n \" `brands` VARCHAR(80) NULL,\"\n \" `stores` VARCHAR(80) NOT NULL,\"\n \" `url` VARCHAR(255) NOT NULL,\"\n \" `watchlist` DATE NULL,\"\n \" PRIMARY KEY (`num`, `id`))\"\n \" ENGINE = InnoDB\"\n )\n\n cursor.execute(\n \"CREATE TABLE IF NOT EXISTS `dbPurBeurre`.`Asso_Prod_Cat` (\"\n \" `id_categories` VARCHAR(80) NOT NULL,\"\n \" `id_produits` VARCHAR(80) NOT NULL,\"\n \" PRIMARY KEY (`id_categories`, `id_produits`),\"\n \" CONSTRAINT `fk_id_categories`\"\n \" FOREIGN KEY (`id_categories`)\"\n \" REFERENCES `Categories` (`id`),\"\n \" CONSTRAINT `fk_id_produits`\"\n \" FOREIGN KEY (`id_produits`)\"\n \" REFERENCES `Produits` (`id`))\"\n \" ENGINE = InnoDB\"\n )"],"string":"[\n \"def populate_db():\\n\\n populate_table(db, models.Department, departments_data)\\n populate_table(db, models.Employee, employees_data)\",\n \"def populate_tables(connection: sqlite3.Connection) -> None:\\n fake = Faker()\\n Faker.seed(0)\\n\\n c = conn.cursor()\\n\\n number_of_courses = fake.pyint(min_value=5, max_value=20)\\n\\n for _ in range(number_of_courses):\\n course_name = fake.word()\\n\\n insert_statement = f'insert into courses (name) values (\\\"{course_name}\\\");'\\n c.execute(insert_statement)\\n\\n connection.commit()\\n\\n number_of_users = fake.pyint(min_value=1, max_value=23)\\n\\n Faker.seed()\\n\\n for _ in range(number_of_users):\\n\\n if fake.pybool():\\n user_name = f'{fake.first_name_female()} {fake.last_name_female()}'\\n else:\\n user_name = f'{fake.first_name()} {fake.last_name()}'\\n\\n insert_statement = f'insert into users (name) values (\\\"{user_name}\\\");'\\n c.execute(insert_statement)\\n\\n connection.commit()\\n\\n for _ in range(50000):\\n Faker.seed()\\n\\n random_user_id = fake.pyint(1, number_of_users)\\n random_course_id = fake.pyint(1, number_of_courses)\\n Faker.seed()\\n random_lesson_no = fake.pyint(3, 12)\\n Faker.seed()\\n random_exercise_no = fake.pyint(1, 50)\\n random_data = fake.sentence()\\n\\n insert_statement = f\\\"\\\"\\\"insert into saves (user_id, course_id, lesson_no, exercise_no,data) \\n values ({random_user_id}, {random_course_id}, {random_lesson_no}, \\n {random_exercise_no}, '{random_data}');\\\"\\\"\\\"\\n c.execute(insert_statement)\\n\\n connection.commit()\",\n \"def populate_database(self):\\n self.insert_products()\\n self.insert_categories()\\n self.insert_products_categories()\\n self.insert_stores()\\n self.insert_products_stores()\",\n \"def insert_relations_staging(self):\\n\\n for year in range(START_YEAR_CREATIVE_WORKS, CURRENT_YEAR, YEARS_RANGE):\\n self.load_wikidata(\\\"movie_roles\\\", MOVIE_ROLES_BY_YEAR_SPARQL_QUERY, INSERT_MOVIE_ROLE_SQL_QUERY, INSERT_MOVIE_ROLE_MAP_COLUMNS, year, YEARS_RANGE)\\n\\n self.load_wikidata(\\\"song_roles\\\", SONG_ROLES_BY_YEAR_SPARQL_QUERY, INSERT_SONG_ROLE_SQL_QUERY, INSERT_SONG_ROLE_MAP_COLUMNS, year, YEARS_RANGE, True)\\n self.load_wikidata(\\\"tvshow_roles\\\", TVSHOW_ROLES_SPARQL_QUERY, INSERT_TVSHOW_ROLE_SQL_QUERY,\\n INSERT_TVSHOW_ROLE_MAP_COLUMNS)\\n self.load_wikidata(\\\"animatedmovie_roles\\\", ANIMATEDMOVIE_ROLES_SPARQL_QUERY, INSERT_ANIMATEDMOVIE_ROLE_SQL_QUERY,\\n INSERT_ANIMATEDMOVIE_ROLE_MAP_COLUMNS)\\n self.load_wikidata(\\\"videogame_roles\\\", VIDEOGAME_ROLES_SPARQL_QUERY, INSERT_VIDEOGAME_ROLE_SQL_QUERY, INSERT_VIDEOGAME_ROLE_MAP_COLUMNS)\\n self.load_wikidata(\\\"book_roles\\\", BOOK_ROLES_SPARQL_QUERY, INSERT_BOOK_ROLE_SQL_QUERY, INSERT_BOOk_ROLE_SQL_QUERY)\",\n \"def populate_db():\\n stdout.write('Emptying the tables...\\\\n')\\n empty_tables()\\n stdout.write('Populating Language records...\\\\n')\\n populate_language()\\n stdout.write('Populating Lemma, Wordform, and Definition records...\\\\n')\\n populate_lexical()\\n stdout.write('Populating ProperName records...\\\\n')\\n populate_proper_names()\",\n \"def load_dwh_tables(self):\\n print(\\\"Loading the creative works table\\\")\\n self.cur.execute(dwh_queries.INSERT_CREATIVE_WORKS_SQL_QUERY)\\n self.conn.commit()\\n\\n print(\\\"Loading the participations table\\\")\\n\\n self.cur.execute(dwh_queries.INSERT_PARTICIPATIONS_SQL_QUERY)\\n self.conn.commit()\",\n \"def setUp(self):\\n resume.objects.create(\\n first_name='Nicholas',\\n last_name='Bielinski',\\n )\\n experience.objects.create(\\n title='Helpdesk Technician',\\n location='L3 Technologies',\\n start_date='6/26/2017',\\n end_date='present',\\n description='blah blah blah'\\n )\\n education.objects.create(\\n institution_name='UNH Manchester',\\n location='Manchester',\\n degree='Bachelor',\\n major='CIS',\\n gpa = '3.5'\\n )\",\n \"def synthesize_employment_data(self, config):\\r\\n jobs_by_zone_by_sector_table_name = config['jobs_by_zone_by_sector']\\r\\n gridcells_table_name = config['gridcells']\\r\\n jobs_table_name = config['jobs']\\r\\n gridcells_output_table_name = config['gridcells_output']\\r\\n jobs_output_table_name = config['jobs_output']\\r\\n \\r\\n input_db_name = config['db_config'].database_name\\r\\n output_db_name = config['output_database_name']\\r\\n \\r\\n sectors = config['sector_names_and_ids']\\r\\n building_types_and_ids_and_home_based = config[\\r\\n 'building_type_column_names_and_ids_and_home_based']\\r\\n \\r\\n building_types = []\\r\\n building_ids = []\\r\\n home_based = [] \\r\\n for type, id, home in building_types_and_ids_and_home_based:\\r\\n building_types += [type]\\r\\n building_ids += [id]\\r\\n home_based += [home]\\r\\n \\r\\n \\r\\n from_database_configuration = ScenarioDatabaseConfiguration(\\r\\n database_name = input_db_name,\\r\\n host_name = config['db_config'].host_name,\\r\\n user_name = config['db_config'].user_name,\\r\\n password = config['db_config'].password \\r\\n )\\r\\n to_database_configuration = ScenarioDatabaseConfiguration(\\r\\n database_name = output_db_name,\\r\\n host_name = config['db_config'].host_name,\\r\\n user_name = config['db_config'].user_name,\\r\\n password = config['db_config'].password \\r\\n )\\r\\n\\r\\n FlattenScenarioDatabaseChain().copy_scenario_database(\\r\\n from_database_configuration = from_database_configuration, \\r\\n to_database_configuration = to_database_configuration,\\r\\n tables_to_copy = [gridcells_table_name, jobs_table_name])\\r\\n \\r\\n db_server = DatabaseServer(to_database_configuration) \\r\\n output_database = db_server.get_database(output_db_name)\\r\\n \\r\\n sector_name = 0; sector_id = 1\\r\\n \\r\\n sector = {}\\r\\n for entry in sectors:\\r\\n name = entry[sector_name]\\r\\n id = entry[sector_id]\\r\\n sector[id] = self._get_jobs_per_building_type_in_sector_by_zone(\\r\\n output_database, jobs_by_zone_by_sector_table_name, \\r\\n jobs_table_name, name, id)\\r\\n\\r\\n results = self._get_building_type_proportion_by_zone(output_database, \\r\\n gridcells_table_name)\\r\\n \\r\\n grid_id = 0; zone_id = 1\\r\\n dist = {}\\r\\n \\r\\n type_index = {}\\r\\n \\r\\n for name in building_types:\\r\\n for i in range(len(results[0])):\\r\\n column_name = results[0][i]\\r\\n if name == column_name:\\r\\n type_index[name] = i\\r\\n break;\\r\\n else:\\r\\n raise KeyError, ('No column by the name of \\\\'%s\\\\' found in '\\r\\n 'the database.' % name) \\r\\n\\r\\n for name in building_types:\\r\\n dist[name] = {}\\r\\n \\r\\n for row in results[1:]:\\r\\n for name in building_types:\\r\\n dist[name][row[zone_id]] = []\\r\\n \\r\\n for row in results[1:]:\\r\\n for name in building_types:\\r\\n dist[name][row[zone_id]] += [(row[grid_id], \\r\\n row[type_index[name]])]\\r\\n \\r\\n jobs_table_data = self._create_jobs_table_data(dist, sector,\\r\\n building_types_and_ids_and_home_based)\\r\\n \\r\\n output_database.execute('USE %(out_db)s' % {'out_db':output_db_name})\\r\\n \\r\\n output_database.execute(\\\"\\\"\\\"\\r\\n CREATE TABLE %(jobs_out)s (\\r\\n JOB_ID INT AUTO_INCREMENT, PRIMARY KEY(JOB_ID),\\r\\n GRID_ID INT, HOME_BASED INT, SECTOR_ID INT, BUILDING_TYPE INT);\\r\\n \\\"\\\"\\\" % {'jobs_out':jobs_output_table_name})\\r\\n \\r\\n if len(jobs_table_data) > 0:\\r\\n output_prefix = (\\r\\n \\\"\\\"\\\"INSERT INTO %(jobs_out)s \\r\\n (GRID_ID, HOME_BASED, SECTOR_ID, BUILDING_TYPE) VALUES\\r\\n \\\"\\\"\\\" % {'jobs_out':jobs_output_table_name})\\r\\n output_postfix = ';'\\r\\n \\r\\n step = 1000\\r\\n length = len(jobs_table_data)\\r\\n iterations = int(length/step) + 1\\r\\n \\r\\n for i in range(iterations):\\r\\n low = i*step\\r\\n high = (i+1)*step\\r\\n \\r\\n if high > length: high = length\\r\\n \\r\\n output_body = \\\"\\\"\\r\\n \\r\\n for j in range(low, high):\\r\\n output_body += (\\r\\n '(%(grid)s, %(home)s, %(sector)s, %(building)s),\\\\n' \\r\\n % jobs_table_data[j])\\r\\n \\r\\n output_query = \\\"%s%s%s\\\" % (output_prefix, \\r\\n output_body[:-2], \\r\\n output_postfix)\\r\\n\\r\\n output_database.execute(output_query)\\r\\n \\r\\n \\r\\n ### TODO: \\r\",\n \"def populate_database(telescope_name, instrument_name):\\n telescope = Telescope.objects.create(\\n name=telescope_name, latitude=25.0, longitude=45.0)\\n instrument = Instrument.objects.create(\\n name=instrument_name, telescope=telescope)\\n for year_int in (2012, 2013):\\n for month_int in range(1, 13):\\n for night_int in (1, monthrange(year_int, month_int)[1]):\\n ut_date = date(year_int, month_int, night_int)\\n night = Night.objects.create(\\n ut_date=ut_date, instrument=instrument, observers='Smith')\\n Exposure.objects.create(\\n night=night, run_number=1, ut_start=time(10, 0, 0),\\n exposed=20.0, ra=60.0, dec=30.0, object_exp=True)\\n Exposure.objects.create(\\n night=night, run_number=2, ut_start=time(11, 0, 0),\\n exposed=30.0, ra=90.0, dec=0.0, object_exp=True)\\n Exposure.objects.create(\\n night=night, run_number=3, ut_start=time(12, 0, 0),\\n exposed=40.0, ra=120.0, dec=-30.0, object_exp=False)\",\n \"def populate_database(num_patients, min_checkins, max_checkins):\\n departments = [\\n Department(department_name=\\\"Cardiology\\\"),\\n Department(department_name=\\\"Emergency\\\"),\\n Department(department_name=\\\"Gynecology\\\"),\\n Department(department_name=\\\"Pediatrics\\\"),\\n Department(department_name=\\\"Obstetrics\\\"),\\n Department(department_name=\\\"Oncology\\\"),\\n Department(department_name=\\\"Orthopedics\\\"),\\n Department(department_name=\\\"Neurology\\\")\\n ]\\n\\n for i in xrange(num_patients):\\n patient = Patient(**generate_patient())\\n patient.departments.append(choice(departments))\\n db.add(patient)\\n\\n for j in xrange(randrange(min_checkins, max_checkins)):\\n checkin = CheckIn(**generate_checkin())\\n checkin.patient_nhi = patient.nhi\\n\\n lci = patient.latest_checkin_time\\n vid = checkin.checkin_time\\n\\n lci = vid if lci is None or vid > lci else lci\\n patient.latest_checkin_time = lci\\n\\n db.add(checkin)\\n\\n for k in xrange(randrange(0, 3)):\\n appointment = Appointment(**generate_appointment())\\n appointment.patient_nhi = patient.nhi\\n\\n db.add(appointment)\\n\\n db.commit()\",\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 _setup_all_awardees():\\n hpo_data = _prep_awardee_csv_data('tests/test-data/fixtures/awardees.csv')\\n org_data = _prep_awardee_csv_data('tests/test-data/fixtures/organizations.csv')\\n site_data = _prep_awardee_csv_data('tests/test-data/fixtures/sites.csv')\\n dao = HPODao()\\n #\\n # Import HPO records\\n #\\n for column in range(0, len(hpo_data[0]) - 1):\\n data = _convert_csv_column_to_dict(hpo_data, column)\\n dao.insert(HPO(hpoId=column+1, displayName=data['Name'], name=data['Awardee ID'],\\n organizationType=OrganizationType(data['Type']), isObsolete=ObsoleteStatus.ACTIVE))\\n #\\n # Import Organization records\\n #\\n with dao.session() as session:\\n for column in range(0, len(org_data[0]) - 1):\\n data = _convert_csv_column_to_dict(org_data, column)\\n result = session.query(HPO.hpoId).filter(HPO.name == data['Awardee ID']).first()\\n dao.insert(Organization(externalId=data['Organization ID'], displayName=data['Name'], hpoId=result.hpoId))\\n #\\n # Import Site records\\n #\\n with dao.session() as session:\\n for column in range(0, len(site_data[0]) - 1):\\n data = _convert_csv_column_to_dict(site_data, column)\\n result = session.query(Organization.hpoId, Organization.organizationId).\\\\\\n filter(Organization.externalId == data['Organization ID']).first()\\n try:\\n mayo_link_id = data['MayoLINK Client #']\\n except KeyError:\\n mayo_link_id = str(random.randint(7040000, 7999999))\\n dao.insert(Site(siteName=data['Site'], googleGroup=data['Site ID / Google Group'].lower(),\\n mayolinkClientNumber=mayo_link_id, hpoId=result.hpoId,\\n organizationId=result.organizationId))\",\n \"def setup_vars(self):\\n # Add Full time positions\\n self.manager_id = self._add_person(\\\"Manager\\\", \\\"ARRAY['Database', 'OS', 'AI']\\\", 30)\\n self.admin_id = self._add_person(\\\"Admin\\\", salary=40)\\n self.full_instructor_id = self._add_person(\\n \\\"Instructor\\\", \\\"ARRAY['Database']\\\", 20\\n )\\n\\n # Add Part time instructor\\n self.part_instructor_id = self._add_part_time_instr(\\\"ARRAY['OS']\\\", 10)\\n self.part_instructor_id = self._add_part_time_instr(\\\"ARRAY['AI']\\\", 10)\\n\\n # Add courses\\n self.course_id1 = self._add_course(\\\"Database\\\", 1, \\\"Database\\\")\\n self.course_id2 = self._add_course(\\\"OS\\\", 1, \\\"OS\\\")\\n self.course_id3 = self._add_course(\\\"AI\\\", 1, \\\"AI\\\")\\n\\n # Add room\\n self.room_id = self._add_room(1, 'Test room', 20)\\n self.room_id2 = self._add_room(2, 'Test room 2', 20)\\n\\n # Add course offerings\\n self.course_offering1 = self._add_course_offering('2021-01-21', 10, [('2021-06-21', 9, self.room_id), ('2021-06-21', 11, self.room_id)], '2021-05-31', 20, self.course_id1, self.admin_id)\\n self.course_offering2 = self._add_course_offering('2021-01-21', 10, [('2021-06-22', 9, self.room_id), ('2021-06-22', 11, self.room_id)], '2021-05-31', 20, self.course_id2, self.admin_id)\\n self.course_offering3 = self._add_course_offering('2021-01-21', 10, [('2021-06-22', 9, self.room_id2), ('2021-06-22', 11, self.room_id2)], '2021-05-31', 20, self.course_id3, self.admin_id)\\n\\n # Add customers\\n self.customer_id1 = self._add_customer('Test1', \\\"test\\\", 987654321, 'test@test.com', '1234123412341234', '123', '2025-05-31')\\n self.customer_id2 = self._add_customer('Test2', \\\"test\\\", 987654321, 'test@test.com', '1234123412341235', '123', '2025-05-31')\\n self.customer_id3 = self._add_customer('Test3', \\\"test\\\", 987654321, 'test@test.com', '1234123412341236', '123', '2025-05-31')\\n\\n # Register sessions\\n self._register_credit_card('2021-01-21', self.course_id1, 1, self.customer_id1)\\n self._register_credit_card('2021-01-21', self.course_id1, 1, self.customer_id2)\\n self._register_credit_card('2021-01-21', self.course_id1, 1, self.customer_id3)\\n\\n # Add course packages\\n self.package1 = self._add_course_package(\\\"Best Package\\\", 2, '2021-03-01', '2021-08-02', 50)\\n self.package2 = self._add_course_package(\\\"Medium Package\\\", 2, '2021-03-01', '2021-08-02', 100)\\n self.package3 = self._add_course_package(\\\"Worst Package\\\", 2, '2021-03-01', '2021-08-02', 150)\\n\\n # Buy course packages\\n self._buy_package(self.customer_id1, self.package1)\\n self._buy_package(self.customer_id2, self.package2)\\n self._buy_package(self.customer_id3, self.package3)\\n\\n # Redeem sessions\\n self._register_redeems('2021-01-21', self.course_id2, 1, self.customer_id1)\\n self._register_redeems('2021-01-21', self.course_id2, 1, self.customer_id2)\\n self._register_redeems('2021-01-21', self.course_id2, 1, self.customer_id3)\\n\\n # Cancel registrations\\n self._cancel_registration(self.customer_id1, self.course_id1)\\n self._cancel_registration(self.customer_id2, self.course_id2)\",\n \"def insert_entities_staging(self):\\n\\n for year in range(1900, CURRENT_YEAR, YEARS_RANGE):\\n self.load_wikidata(\\\"movies\\\", MOVIES_BY_YEAR_SPARQL_QUERY, INSERT_MOVIE_SQL_QUERY, INSERT_MOVIE_MAP_COLUMNS, year, YEARS_RANGE)\\n\\n self.load_wikidata(\\\"songs\\\", SONGS_BY_YEAR_SPARQL_QUERY, INSERT_SONG_SQL_QUERY, INSERT_SONG_MAP_COLUMNS, year, YEARS_RANGE, True)\\n self.load_wikidata(\\\"tvshows\\\", TVSHOWS_SPARQL_QUERY, INSERT_TVSHOW_SQL_QUERY, INSERT_TVSHOW_MAP_COLUMNS)\\n self.load_wikidata(\\\"animatedmovies\\\", ANIMATEDMOVIES_SPARQL_QUERY, INSERT_ANIMATEDMOVIE_SQL_QUERY,\\n INSERT_ANIMATEDMOVIE_MAP_COLUMNS)\\n self.load_wikidata(\\\"videogames\\\", VIDEOGAMES_SPARQL_QUERY, INSERT_VIDEOGAME_SQL_QUERY, INSERT_VIDEOGAME_MAP_COLUMNS)\\n self.load_wikidata(\\\"books\\\", BOOKS_SPARQL_QUERY, INSERT_BOOK_SQL_QUERY, INSERT_BOOK_MAP_COLUMNS)\",\n \"def run(self):\\n\\n for table in self.TABLES:\\n self.dictionary_cursor.execute(f\\\"TRUNCATE TABLE {table}_Work\\\")\\n self.dictionary_conn.commit()\\n self.logger.info(\\\"work tables cleared\\\")\\n for id in self.ids:\\n drug = self.Drug(self, id)\\n if drug.wanted:\\n drug.load()\\n self.logger.info(\\\"work tables populated\\\")\\n for table in self.TABLES:\\n insert = f\\\"INSERT INTO {table} SELECT * FROM {table}_Work\\\"\\n self.dictionary_cursor.execute(f\\\"TRUNCATE TABLE {table}\\\")\\n self.dictionary_cursor.execute(insert)\\n self.dictionary_conn.commit()\\n self.logger.info(\\\"live tables ready\\\")\",\n \"def setup_table(self):\\n\\n self.setup.create_basic_table_in_dev()\\n self.setup.insert_random_records_into_dev()\",\n \"def init_tables(self) -> None:\\n with self.table_access_condition:\\n conn = self._get_connection()\\n conn.execute(\\\"PRAGMA foreign_keys = 1\\\")\\n c = conn.cursor()\\n c.execute(tables.CREATE_STUDIES_TABLE)\\n c.execute(tables.CREATE_SUBMISSIONS_TABLE)\\n c.execute(tables.CREATE_REQUESTERS_TABLE)\\n c.execute(tables.CREATE_UNITS_TABLE)\\n c.execute(tables.CREATE_WORKERS_TABLE)\\n c.execute(tables.CREATE_RUNS_TABLE)\\n c.execute(tables.CREATE_RUN_MAP_TABLE)\\n c.execute(tables.CREATE_PARTICIPANT_GROUPS_TABLE)\\n c.execute(tables.CREATE_PARTICIPANT_GROUP_QUALIFICATIONS_MAPPING_TABLE)\\n conn.commit()\",\n \"def _populate(self):\\n self.addDemographics()\\n self.addLabs()\\n self.addProblems()\\n self.addMeds()\\n self.addAllergies()\\n self.addImmunizations()\\n self.addVitals()\\n self.populated_p = True\",\n \"def populate_weather(connection):\\n metadata = load_metadata('weather')\\n cursor = connection.cursor()\\n water_defs = get_water_definitions()\\n\\n # Check if tables are already populated.\\n cursor.execute('SELECT count(*) FROM weather')\\n weather_count = cursor.fetchone()[0]\\n\\n if weather_count:\\n print('Weather tables already populated!')\\n return\\n\\n print('WEATHER:')\\n\\n # Darksky data\\n for dir_name, location in metadata.items():\\n print(f'\\\\tPopulating weather: \\\"{location[\\\"name\\\"]}\\\".')\\n\\n # Insert location.\\n cursor.execute(f'''INSERT INTO locations(name, lat, lng)\\n VALUES ('{location['name']}', {location['lat']}, {location['lng']})''')\\n location_id = cursor.lastrowid\\n\\n # Set weather locations for watercourses/aquifers.\\n for water_body in [d['body'] for d in water_defs.values()]:\\n if water_body in location:\\n cursor.execute(f'''UPDATE {water_body}s\\n SET location_id = {location_id}\\n WHERE name IN ('{\\\"','\\\".join(location[water_body])}')''')\\n break\\n\\n dir_path = get_data_path('weather', 'raw', dir_name)\\n for json_file_name in os.listdir(dir_path):\\n json_path = os.path.join(dir_path, json_file_name)\\n with open(json_path, 'r', encoding='utf-8') as json_file:\\n print(f'\\\\t\\\\tPopulating year: {json_file_name[0:-5]}')\\n year_forecasts = json.load(json_file)\\n for date, date_forecast in year_forecasts.items():\\n hourly_forecasts = date_forecast['hourly']\\n\\n if not hourly_forecasts:\\n print(f'\\\\t\\\\tNo hourly forecasts for {date}!')\\n continue\\n\\n daily_forecast = {\\n 'location_id': location_id,\\n 'time': date_forecast['time'],\\n 'day_time': date_forecast['sunset_time'] - date_forecast['sunrise_time'],\\n 'precipitation': 0,\\n 'snow_accumulation': 0\\n }\\n # List of value names with `avg`, `min` and `max` values\\n value_names = {\\n 'temperature': 'temperature',\\n 'cloud_cover': 'cloudCover',\\n 'dew_point': 'dewPoint',\\n 'humidity': 'humidity',\\n 'pressure': 'pressure',\\n 'uv_index': 'uvIndex',\\n 'precipitation_probability': 'precipProbability',\\n 'precipitation_intensity': 'precipIntensity'\\n }\\n # Value name counters, which indicate how many times (out of 24)\\n # certain value appears in hourly data.\\n value_counts = {k: 0 for k in value_names.keys()}\\n\\n for value_name in value_names.keys():\\n daily_forecast[f'{value_name}_avg'] = 0.0\\n daily_forecast[f'{value_name}_min'] = float('inf')\\n daily_forecast[f'{value_name}_max'] = float('-inf')\\n\\n # Calculate daily forecast values from hourly forecasts.\\n for hourly_forecast in hourly_forecasts:\\n for value_name in value_names.keys():\\n orig_value_name = value_names[value_name]\\n if is_forecast_number(orig_value_name, hourly_forecast):\\n daily_forecast[f'{value_name}_avg'] += hourly_forecast[orig_value_name]\\n daily_forecast[f'{value_name}_min'] = min(\\n hourly_forecast[orig_value_name],\\n daily_forecast[f'{value_name}_min']\\n )\\n daily_forecast[f'{value_name}_max'] = max(\\n hourly_forecast[orig_value_name],\\n daily_forecast[f'{value_name}_max']\\n )\\n value_counts[value_name] += 1\\n\\n if is_forecast_number('precipAccumulation', hourly_forecast) \\\\\\n and hourly_forecast['precipType'] == 'snow':\\n daily_forecast['snow_accumulation'] += hourly_forecast['precipAccumulation']\\n elif is_forecast_number('precipIntensity', hourly_forecast) \\\\\\n and is_forecast_number('precipProbability', hourly_forecast):\\n daily_forecast['precipitation'] += \\\\\\n hourly_forecast['precipIntensity'] * hourly_forecast['precipProbability']\\n\\n for value_name, value_count in value_counts.items():\\n if value_count:\\n # Calculate average.\\n daily_forecast[f'{value_name}_avg'] = daily_forecast[f'{value_name}_avg'] / value_count\\n else:\\n # If value never appeared\\n daily_forecast[f'{value_name}_avg'] = 'NULL'\\n daily_forecast[f'{value_name}_min'] = 'NULL'\\n daily_forecast[f'{value_name}_max'] = 'NULL'\\n\\n cursor.execute(f'''INSERT INTO weather({', '.join(daily_forecast.keys())})\\n VALUES ({', '.join([str(v) for v in daily_forecast.values()])})''')\\n\\n # IOT data:\\n for location in SETTINGS['weather_locations_iot']:\\n print(f'\\\\tPopulating weather: \\\"{location[\\\"name\\\"]}\\\".')\\n\\n # Insert location.\\n cursor.execute(f'''INSERT INTO locations(name, lat, lng)\\n VALUES ('{location['name']}', {location['lat']}, {location['lng']})''')\\n location_id = cursor.lastrowid\\n\\n # Set weather locations for watercourses/aquifers.\\n for water_body in [d['body'] for d in water_defs.values()]:\\n if water_body in location:\\n cursor.execute(f'''UPDATE {water_body}s\\n SET location_id = {location_id}\\n WHERE name IN ('{\\\"', '\\\".join(location[water_body])}')''')\\n\\n # Set locations for all stations on given water body to match its location.\\n cursor.execute(f'''SELECT id\\n FROM {water_body}s\\n WHERE location_id = {location_id}''')\\n ids = [row[0] for row in cursor.fetchall()]\\n if len(ids):\\n cursor.execute(f'''UPDATE {water_body}_stations\\n SET location_id = {location_id}\\n WHERE {water_body}_id IN ({', '.join([str(v) for v in ids])})''')\\n\\n break \\n \\n file_name = f'''{location['lat']}-{location['lng']}.json'''\\n json_path = get_data_path('weather', 'raw', file_name)\\n\\n # If data file doesn't exist, download it first.\\n if not os.path.isfile(json_path):\\n with open(json_path, 'wb', encoding=\\\"utf-8\\\") as file:\\n file.write(read_from_url(location['url'], decode=False))\\n \\n with open(json_path, 'r', encoding='utf-8') as json_file:\\n row_names = {\\n \\\"Sun_duration\\\": \\\"sun_duration\\\",\\n \\\"CloudCover\\\": \\\"cloud_cover_avg\\\",\\n \\\"Percipitation\\\": \\\"precipitation\\\",\\n \\\"New_snow_blanket\\\": \\\"snow_accumulation\\\",\\n \\\"Snow_blanket\\\": \\\"snow_depth\\\",\\n \\\"TemperatureAvg\\\": \\\"temperature_avg\\\",\\n \\\"TemperatureMin\\\": \\\"temperature_min\\\",\\n \\\"TemperatureMax\\\": \\\"temperature_max\\\"\\n }\\n forecasts = json.load(json_file)\\n for forecast in forecasts:\\n f = {row_names[k]: forecast[k] for k in row_names.keys()}\\n f['location_id'] = location_id\\n f['time'] = round(forecast['LastUpdatedEpoch'] / 1000)\\n cursor.execute(f'''INSERT INTO weather({', '.join(f.keys())})\\n VALUES ({', '.join([str(v) for v in f.values()])})''')\",\n \"def _populate():\\n models.Base.metadata.create_all(engine)\\n logger.info(\\\"Initalized database\\\")\\n db = Session()\\n\\n hermann = models.Account(id=\\\"test\\\",\\n name=\\\"Hermann Dörkschneider\\\",\\n email=\\\"fakeaddress@lumeh.org\\\")\\n db.add(hermann)\\n\\n journey = models.Journey(id=str(uuid.uuid4()),\\n account_id=\\\"test\\\",\\n visibility=\\\"PUBLIC\\\",\\n start_time_utc=datetime.datetime.now(),\\n stop_time_utc=datetime.datetime.now())\\n db.add(journey)\\n\\n waypoint1 = models.Waypoint(journey=journey,\\n time_utc=datetime.datetime.now(),\\n accuracy_m=2.71,\\n latitude=3.1416,\\n longitude=1.618,\\n height_m=10)\\n db.add(waypoint1)\\n\\n waypoint2 = models.Waypoint(journey=journey,\\n time_utc=datetime.datetime.now(),\\n accuracy_m=5.1,\\n latitude=3.1410,\\n longitude=1.620,\\n height_m=5)\\n db.add(waypoint2)\\n\\n db.commit()\\n logger.info(\\\"Created test account {}\\\".format(hermann))\\n logger.info(\\\"Created test journey {}\\\".format(journey))\",\n \"def database_setup():\\n Base.metadata.create_all(bind=engine)\\n db = LocalSession()\\n try:\\n populate_from_json(db, Vector, str(VECTORS))\\n populate_from_json(db, Gender, str(GENDERS))\\n populate_from_json(db, Tag, str(TAGS))\\n finally:\\n db.close()\",\n \"def importData():\\n #importChallengeDataToDB()\\n importTrendingDataToDB()\",\n \"def set_up_tables():\\n table_users = \\\"\\\"\\\"\\n CREATE TABLE IF NOT EXISTS users (\\n id SERIAL PRIMARY KEY,\\n username VARCHAR (24) NOT NULL UNIQUE,\\n firstname VARCHAR (24) NOT NULL,\\n lastname VARCHAR (24) NOT NULL,\\n othername VARCHAR (24),\\n phone VARCHAR (24) NOT NULL,\\n email VARCHAR (30) NOT NULL UNIQUE,\\n password VARCHAR (128) NOT NULL,\\n passportUrl VARCHAR (200),\\n isPolitician BOOLEAN,\\n isAdmin BOOLEAN\\n )\\\"\\\"\\\"\\n\\n parties_table = \\\"\\\"\\\" \\n CREATE TABLE IF NOT EXISTS parties (\\n id SERIAL PRIMARY KEY,\\n name VARCHAR (35) NOT NULL UNIQUE,\\n hqAddress VARCHAR (30),\\n logoUrl VARCHAR\\n )\\\"\\\"\\\"\\n\\n offices_table = \\\"\\\"\\\"\\n CREATE TABLE IF NOT EXISTS offices (\\n id SERIAL PRIMARY KEY,\\n name VARCHAR (35) NOT NULL UNIQUE,\\n type VARCHAR (35)\\n )\\\"\\\"\\\"\\n\\n canditates_table = \\\"\\\"\\\"\\n CREATE TABLE IF NOT EXISTS candidates (\\n id SERIAL,\\n candidate INTEGER,\\n office INTEGER,\\n PRIMARY KEY (office, candidate),\\n FOREIGN KEY (candidate) REFERENCES users(id) ON DELETE CASCADE,\\n FOREIGN KEY (office) REFERENCES offices(id) ON DELETE CASCADE\\n )\\\"\\\"\\\"\\n\\n voters_table = \\\"\\\"\\\"\\n CREATE TABLE IF NOT EXISTS votes (\\n id SERIAL,\\n office INTEGER,\\n candidate INTEGER,\\n voter INTEGER,\\n PRIMARY KEY (office, voter),\\n FOREIGN KEY (office) REFERENCES offices(id) ON DELETE CASCADE,\\n FOREIGN KEY (candidate) REFERENCES users(id) ON DELETE CASCADE,\\n FOREIGN KEY (voter) REFERENCES users(id) ON DELETE CASCADE\\n )\\\"\\\"\\\"\\n\\n return [table_users, parties_table,\\n offices_table, canditates_table, voters_table]\",\n \"def create_tables(): \\n \\n pk_contraint = \\\"CONSTRAINT {}_pk PRIMARY KEY ({})\\\"\\n uq_contraint = \\\"CONSTRAINT {}_uq UNIQUE ({})\\\"\\n fk_query = \\\"\\\"\\\"CONSTRAINT {}_fk_{} \\n FOREIGN KEY ({}) \\n REFERENCES {}({}) \\n ON UPDATE CASCADE \\n ON DELETE RESTRICT\\n \\\"\\\"\\\"\\n \\n create_dict = {}\\n index = 1\\n\\n\\n ############################## public SCHEMA ##############################\\n \\n schema = 'public'\\n create_schema(schema)\\n\\n #################### site ####################\\n table_name = 'site'\\n pk_id = 'site_id'\\n uq_list = ['site_code']\\n fk_dict = {}\\n query = \\\"\\\"\\\"CREATE TABLE IF NOT EXISTS {}.{} (\\n {} SERIAL, \\n site_code CHAR(3),\\n purok VARCHAR,\\n sitio VARCHAR,\\n barangay VARCHAR,\\n municipality VARCHAR,\\n province VARCHAR,\\n region VARCHAR,\\n psgc INTEGER,\\n active BOOLEAN NOT NULL DEFAULT TRUE,\\n season SMALLINT,\\n {}, {} {}\\n );\\n \\\"\\\"\\\" \\n create_dict[index] = {'schema': schema,\\n 'table_name': table_name,\\n 'query': query,\\n 'pk_id': pk_id,\\n 'uq_list': uq_list,\\n 'fk_dict': fk_dict}\\n index += 1\\n\\n\\n ############################## spatial SCHEMA ##############################\\n \\n schema = 'spatial'\\n create_schema(schema)\\n \\n #################### exposure ####################\\n table_name = 'exposure'\\n pk_id = 'exp_id'\\n uq_list = ['exp_name']\\n fk_dict = {}\\n query = \\\"\\\"\\\"CREATE TABLE IF NOT EXISTS {}.{} (\\n {} SERIAL, \\n exp_name VARCHAR,\\n {}, {} {}\\n );\\n \\\"\\\"\\\" \\n create_dict[index] = {'schema': schema,\\n 'table_name': table_name,\\n 'query': query,\\n 'pk_id': pk_id,\\n 'uq_list': uq_list,\\n 'fk_dict': fk_dict}\\n index += 1\\n \\n #################### site_exposure ####################\\n table_name = 'site_exposure'\\n pk_id = 'se_id'\\n uq_list = ['site_id', 'exp_id', 'geom']\\n fk_dict = {'site_id': {'ref_schema': 'public', 'ref_table': 'site'},\\n 'exp_id': {'ref_schema': 'spatial', 'ref_table': 'exposure'}}\\n query = \\\"\\\"\\\"CREATE TABLE IF NOT EXISTS {}.{} (\\n {} SERIAL, \\n site_id INTEGER,\\n exp_id INTEGER,\\n label_name VARCHAR,\\n geom GEOMETRY,\\n activated DATE NOT NULL DEFAULT CURRENT_DATE,\\n deactivated DATE,\\n {}, {} {}\\n );\\n \\\"\\\"\\\" \\n create_dict[index] = {'schema': schema,\\n 'table_name': table_name,\\n 'query': query,\\n 'pk_id': pk_id,\\n 'uq_list': uq_list,\\n 'fk_dict': fk_dict}\\n index += 1\\n \\n #################### feature ####################\\n table_name = 'feature'\\n pk_id = 'feat_id'\\n uq_list = ['feat_name']\\n fk_dict = {}\\n query = \\\"\\\"\\\"CREATE TABLE IF NOT EXISTS {}.{} (\\n {} SERIAL, \\n feat_name VARCHAR,\\n {}, {} {}\\n );\\n \\\"\\\"\\\" \\n create_dict[index] = {'schema': schema,\\n 'table_name': table_name,\\n 'query': query,\\n 'pk_id': pk_id,\\n 'uq_list': uq_list,\\n 'fk_dict': fk_dict}\\n index += 1\\n\\n #################### site_feature ####################\\n table_name = 'site_feature'\\n pk_id = 'sf_id'\\n uq_list = ['site_id', 'feat_id', 'geom']\\n fk_dict = {'site_id': {'ref_schema': 'public', 'ref_table': 'site'},\\n 'feat_id': {'ref_schema': 'spatial', 'ref_table': 'feature'}}\\n query = \\\"\\\"\\\"CREATE TABLE IF NOT EXISTS {}.{} (\\n {} SERIAL, \\n site_id INTEGER,\\n feat_id INTEGER,\\n geom GEOMETRY,\\n activated DATE NOT NULL DEFAULT CURRENT_DATE,\\n deactivated DATE,\\n {}, {} {}\\n );\\n \\\"\\\"\\\" \\n create_dict[index] = {'schema': schema,\\n 'table_name': table_name,\\n 'query': query,\\n 'pk_id': pk_id,\\n 'uq_list': uq_list,\\n 'fk_dict': fk_dict}\\n index += 1\\n\\n #################### hazard_zone ####################\\n table_name = 'hazard_zone'\\n pk_id = 'hz_id'\\n uq_list = ['site_id, geom']\\n fk_dict = {'site_id': {'ref_schema': 'public', 'ref_table': 'site'}}\\n query = \\\"\\\"\\\"CREATE TABLE IF NOT EXISTS {}.{} (\\n {} SERIAL, \\n site_id INTEGER,\\n geom GEOMETRY,\\n activated DATE NOT NULL DEFAULT CURRENT_DATE,\\n deactivated DATE,\\n {}, {} {}\\n );\\n \\\"\\\"\\\"\\n create_dict[index] = {'schema': schema,\\n 'table_name': table_name,\\n 'query': query,\\n 'pk_id': pk_id,\\n 'uq_list': uq_list,\\n 'fk_dict': fk_dict}\\n index += 1\\n\\n #################### monitoring ####################\\n table_name = 'monitoring'\\n pk_id = 'mon_id'\\n uq_list = ['mon_name']\\n fk_dict = {}\\n query = \\\"\\\"\\\"CREATE TABLE IF NOT EXISTS {}.{} (\\n {} SERIAL, \\n mon_name VARCHAR,\\n {}, {} {}\\n );\\n \\\"\\\"\\\" \\n create_dict[index] = {'schema': schema,\\n 'table_name': table_name,\\n 'query': query,\\n 'pk_id': pk_id,\\n 'uq_list': uq_list,\\n 'fk_dict': fk_dict}\\n index += 1\\n\\n #################### site_monitoring ####################\\n table_name = 'site_monitoring'\\n pk_id = 'sm_id'\\n uq_list = ['site_id', 'mon_id', 'geom']\\n fk_dict = {'site_id': {'ref_schema': 'public', 'ref_table': 'site'},\\n 'mon_id': {'ref_schema': 'spatial', 'ref_table': 'monitoring'}}\\n query = \\\"\\\"\\\"CREATE TABLE IF NOT EXISTS {}.{} (\\n {} SERIAL, \\n site_id INTEGER,\\n mon_id INTEGER,\\n label_name VARCHAR,\\n geom GEOMETRY,\\n activated DATE NOT NULL DEFAULT CURRENT_DATE,\\n deactivated DATE,\\n {}, {} {}\\n );\\n \\\"\\\"\\\" \\n create_dict[index] = {'schema': schema,\\n 'table_name': table_name,\\n 'query': query,\\n 'pk_id': pk_id,\\n 'uq_list': uq_list,\\n 'fk_dict': fk_dict}\\n index += 1\\n\\n\\n ############################### comm SCHEMA ###############################\\n \\n schema = 'comm'\\n create_schema(schema)\\n\\n #################### gsm_server ####################\\n table_name = 'gsm_server'\\n pk_id = 'server_id'\\n uq_list = ['server_name']\\n fk_dict = {}\\n query = \\\"\\\"\\\"CREATE TABLE IF NOT EXISTS {}.{} (\\n {} SERIAL, \\n server_name VARCHAR,\\n platform_type VARCHAR,\\n version SMALLINT,\\n {}, {} {}\\n );\\n \\\"\\\"\\\" \\n create_dict[index] = {'schema': schema,\\n 'table_name': table_name,\\n 'query': query,\\n 'pk_id': pk_id,\\n 'uq_list': uq_list,\\n 'fk_dict': fk_dict}\\n index += 1\\n\\n #################### server_port ####################\\n table_name = 'server_port'\\n pk_id = 'port_id'\\n uq_list = ['server_id', 'port']\\n fk_dict = {'server_id': {'ref_schema': 'comm', 'ref_table': 'gsm_server'}}\\n query = \\\"\\\"\\\"CREATE TABLE IF NOT EXISTS {}.{} (\\n {} SERIAL, \\n server_id INTEGER,\\n port BOOLEAN,\\n ser_port VARCHAR,\\n pwr_on_pin SMALLINT,\\n ring_pin SMALLINT,\\n module_type SMALLINT,\\n {}, {} {}\\n );\\n \\\"\\\"\\\"\\n query += \\\"\\\"\\\" COMMENT ON TABLE {}.{} IS \\n '0- left\\n 1- right'\\n ;\\\"\\\"\\\".format(schema, table_name)\\n create_dict[index] = {'schema': schema,\\n 'table_name': table_name,\\n 'query': query,\\n 'pk_id': pk_id,\\n 'uq_list': uq_list,\\n 'fk_dict': fk_dict}\\n index += 1\\n\\n #################### network_type ####################\\n table_name = 'network_type'\\n pk_id = 'prefix'\\n uq_list = ['prefix']\\n fk_dict = {}\\n query = \\\"\\\"\\\"CREATE TABLE IF NOT EXISTS {}.{} (\\n {} VARCHAR(3), \\n carrier SMALLINT,\\n {}, {} {}\\n );\\n \\\"\\\"\\\"\\n query += \\\"\\\"\\\" COMMENT ON TABLE {}.{} IS \\n '1- globe\\n 2- smart\\n 3- landline'\\n ;\\\"\\\"\\\".format(schema, table_name)\\n create_dict[index] = {'schema': schema,\\n 'table_name': table_name,\\n 'query': query,\\n 'pk_id': pk_id,\\n 'uq_list': uq_list,\\n 'fk_dict': fk_dict}\\n index += 1\\n\\n #################### gsm_module ####################\\n table_name = 'gsm_module'\\n pk_id = 'gsm_id'\\n uq_list = ['prefix', 'num', 'activated']\\n fk_dict = {'prefix': {'ref_schema': 'comm', 'ref_table': 'network_type'},\\n 'port_id': {'ref_schema': 'comm', 'ref_table': 'server_port'}}\\n query = \\\"\\\"\\\"CREATE TABLE IF NOT EXISTS {}.{} (\\n {} SERIAL, \\n prefix VARCHAR(3),\\n num CHAR(7),\\n activated DATE NOT NULL DEFAULT CURRENT_DATE,\\n port_id INTEGER,\\n {}, {} {}\\n );\\n \\\"\\\"\\\"\\n create_dict[index] = {'schema': schema,\\n 'table_name': table_name,\\n 'query': query,\\n 'pk_id': pk_id,\\n 'uq_list': uq_list,\\n 'fk_dict': fk_dict}\\n index += 1\\n\\n\\n ############################# temporal SCHEMA #############################\\n \\n schema = 'temporal'\\n create_schema(schema)\\n\\n #################### marker_observation ####################\\n table_name = 'marker_observation'\\n pk_id = 'mo_id'\\n uq_list = ['site_id', 'ts']\\n fk_dict = {'site_id': {'ref_schema': 'public', 'ref_table': 'site'}}\\n query = \\\"\\\"\\\"CREATE TABLE IF NOT EXISTS {}.{} (\\n {} SERIAL, \\n site_id INTEGER,\\n ts TIMESTAMP,\\n meas_type VARCHAR(7),\\n weather VARCHAR,\\n observer_name VARCHAR,\\n {}, {} {}\\n );\\n \\\"\\\"\\\" \\n create_dict[index] = {'schema': schema,\\n 'table_name': table_name,\\n 'query': query,\\n 'pk_id': pk_id,\\n 'uq_list': uq_list,\\n 'fk_dict': fk_dict}\\n index += 1\\n\\n #################### marker_history ####################\\n table_name = 'marker_history'\\n pk_id = 'hist_id'\\n uq_list = ['sm_id', 'ts', 'event']\\n fk_dict = {'sm_id': {'ref_schema': 'spatial', 'ref_table': 'site_monitoring'}}\\n query = \\\"\\\"\\\"CREATE TABLE IF NOT EXISTS {}.{} (\\n {} SERIAL,\\n sm_id BIGINT,\\n ts TIMESTAMP,\\n event BOOLEAN,\\n label_name VARCHAR,\\n {}, {} {}\\n );\\n \\\"\\\"\\\"\\n query += \\\"\\\"\\\" COMMENT ON TABLE {}.{} IS \\n '0- rename\\n 1- reposition'\\n ;\\\"\\\"\\\".format(schema, table_name)\\n create_dict[index] = {'schema': schema,\\n 'table_name': table_name,\\n 'query': query,\\n 'pk_id': pk_id,\\n 'uq_list': uq_list,\\n 'fk_dict': fk_dict}\\n index += 1\\n\\n #################### marker_data ####################\\n table_name = 'marker_data'\\n pk_id = 'data_id'\\n uq_list = ['sm_id', 'mo_id']\\n fk_dict = {'sm_id': {'ref_schema': 'spatial', 'ref_table': 'site_monitoring'},\\n 'mo_id': {'ref_schema': 'temporal', 'ref_table': 'marker_observation'}}\\n query = \\\"\\\"\\\"CREATE TABLE IF NOT EXISTS {}.{} (\\n {} SERIAL,\\n mo_id BIGINT,\\n sm_id BIGINT,\\n measurement NUMERIC(5,1),\\n {}, {} {}\\n );\\n \\\"\\\"\\\" \\n create_dict[index] = {'schema': schema,\\n 'table_name': table_name,\\n 'query': query,\\n 'pk_id': pk_id,\\n 'uq_list': uq_list,\\n 'fk_dict': fk_dict}\\n index += 1\\n\\n #################### marker_alert ####################\\n table_name = 'marker_alert'\\n pk_id = 'alert_id'\\n uq_list = ['data_id']\\n fk_dict = {'data_id': {'ref_schema': 'temporal', 'ref_table': 'marker_data'}}\\n query = \\\"\\\"\\\"CREATE TABLE IF NOT EXISTS {}.{} (\\n {} SERIAL,\\n data_id BIGINT,\\n displacement NUMERIC(4,1),\\n time_delta FLOAT,\\n alert_level SMALLINT,\\n {}, {} {}\\n );\\n \\\"\\\"\\\" \\n create_dict[index] = {'schema': schema,\\n 'table_name': table_name,\\n 'query': query,\\n 'pk_id': pk_id,\\n 'uq_list': uq_list,\\n 'fk_dict': fk_dict}\\n index += 1\\n\\n #################### logger_model ####################\\n table_name = 'logger_model'\\n pk_id = 'model_id'\\n uq_list = ['has_tilt', 'has_rain', 'has_piezo', 'has_soms', 'logger_type']\\n fk_dict = {}\\n query = \\\"\\\"\\\"CREATE TABLE IF NOT EXISTS {}.{} (\\n {} SERIAL, \\n has_tilt BOOLEAN,\\n has_rain BOOLEAN,\\n has_piezo BOOLEAN,\\n has_soms BOOLEAN,\\n logger_type SMALLINT,\\n {}, {} {}\\n );\\n \\\"\\\"\\\" \\n create_dict[index] = {'schema': schema,\\n 'table_name': table_name,\\n 'query': query,\\n 'pk_id': pk_id,\\n 'uq_list': uq_list,\\n 'fk_dict': fk_dict}\\n index += 1\\n\\n #################### logger ####################\\n table_name = 'logger'\\n pk_id = 'logger_id'\\n uq_list = ['sm_id']\\n fk_dict = {'sm_id': {'ref_schema': 'spatial', 'ref_table': 'site_monitoring'},\\n 'model_id': {'ref_schema': 'temporal', 'ref_table': 'logger_model'}}\\n query = \\\"\\\"\\\"CREATE TABLE IF NOT EXISTS {}.{} (\\n {} SERIAL, \\n sm_id BIGINT,\\n model_id INTEGER,\\n {}, {} {}\\n );\\n \\\"\\\"\\\" \\n create_dict[index] = {'schema': schema,\\n 'table_name': table_name,\\n 'query': query,\\n 'pk_id': pk_id,\\n 'uq_list': uq_list,\\n 'fk_dict': fk_dict}\\n index += 1\\n \\n #################### logger_mobile ####################\\n table_name = 'logger_mobile'\\n pk_id = 'mobile_id'\\n uq_list = ['logger_id', 'activated']\\n fk_dict = {'logger_id': {'ref_schema': 'temporal', 'ref_table': 'logger'},\\n 'gsm_id': {'ref_schema': 'comm', 'ref_table': 'gsm_module'}}\\n query = \\\"\\\"\\\"CREATE TABLE IF NOT EXISTS {}.{} (\\n {} SERIAL,\\n logger_id INTEGER,\\n activated DATE NOT NULL DEFAULT CURRENT_DATE,\\n sim_num VARCHAR(12),\\n gsm_id INTEGER,\\n {}, {} {}\\n );\\n \\\"\\\"\\\" \\n create_dict[index] = {'schema': schema,\\n 'table_name': table_name,\\n 'query': query,\\n 'pk_id': pk_id,\\n 'uq_list': uq_list,\\n 'fk_dict': fk_dict}\\n index += 1\\n\\n\\n #################### EXECUTE QUERY TO CREATE TABLES ####################\\n for index in create_dict.keys():\\n dct = create_dict[index]\\n schema = dct['schema']\\n table_name = dct['table_name']\\n query = dct['query']\\n pk_id = dct['pk_id']\\n uq_list = dct['uq_list']\\n fk_dict = dct['fk_dict']\\n if len(fk_dict.keys()) == 0:\\n fk_constraint = ''\\n else:\\n fk_constraint_list = ['']\\n for fk_id in fk_dict.keys():\\n ref_schema = fk_dict.get(fk_id)['ref_schema']\\n ref_table = fk_dict.get(fk_id)['ref_table']\\n fk_part = fk_query.format(table_name, ref_table, fk_id,\\n \\\"{}.{}\\\".format(ref_schema, ref_table),\\n fk_id)\\n fk_constraint_list.append(fk_part)\\n fk_constraint = ', '.join(fk_constraint_list)\\n \\n query = query.format(schema, table_name, pk_id, \\n pk_contraint.format(table_name, pk_id),\\n uq_contraint.format(table_name, ', '.join(uq_list)),\\n \\\"{}\\\".format(fk_constraint))\\n qdb.execute(query)\",\n \"def init_db():\\n db.drop_all()\\n db.create_all()\\n seed_companies()\\n seed_emission_reports()\\n seed_reduction_targets()\\n seed_milestones()\",\n \"def example_data():\\n\\n # In case this is run more than once, empty out existing data\\n EmployeeProject.query.delete()\\n Employee.query.delete()\\n Department.query.delete()\\n Project.query.delete()\\n\\n # Add sample employees and departments\\n df = Department(dept_code='fin', dept_name='Finance', phone='555-1000')\\n dl = Department(dept_code='legal', dept_name='Legal', phone='555-2222')\\n dm = Department(dept_code='mktg', dept_name='Marketing', phone='555-9999')\\n\\n leonard = Employee(name='Leonard', dept=dl)\\n liz = Employee(name='Liz', dept=dl)\\n maggie = Employee(name='Maggie', state='DC', dept=dm)\\n nadine = Employee(name='Nadine')\\n\\n db.session.add_all([df, dl, dm, leonard, liz, maggie, nadine])\\n db.session.commit()\\n\\n pc = Project(proj_code='car', proj_name='Design Car',\\n assignments=[EmployeeProject(emp_id=liz.id, role='Chair'),\\n EmployeeProject(emp_id=maggie.id)])\\n ps = Project(proj_code='server', proj_name='Deploy Server',\\n assignments=[EmployeeProject(emp_id=liz.id),\\n EmployeeProject(emp_id=leonard.id, role='Auditor')])\\n\\n db.session.add_all([ps, pc])\\n db.session.commit()\",\n \"def prepare_database(self, waterscenario=None, trafficscenario=None):\\n\\n # Validate input\\n if waterscenario:\\n waterscenario = Path(waterscenario)\\n assert waterscenario.exists(), 'Waterscenario file not found'\\n\\n BIVAS = pyBIVAS(self.BIVAS_database)\\n df_trafficscenarios = BIVAS.trafficscenario_numberoftrips()\\n\\n\\n # Do changes to database:\\n con = sqlite3.connect(self.BIVAS_database)\\n c = con.cursor()\\n\\n # Update waterscenario with given file\\n if waterscenario:\\n # Delete current water_scenario_values\\n sql = \\\"DELETE FROM water_scenario_values WHERE 1\\\"\\n c.execute(sql)\\n\\n sql = \\\"DELETE FROM water_scenarios WHERE 1\\\"\\n c.execute(sql)\\n\\n # Write waterdata to database\\n\\n # Read waterscenario file\\n df = pd.read_csv(waterscenario, header=0, index_col=None)\\n df = df[['ArcID', 'SeasonID', 'WaterLevel__m', 'RateOfFlow__m3_s', 'WaterSpeed__m_s', 'WaterDepth__m']]\\n df['WaterScenarioID'] = 1\\n\\n # Add new water_scenario\\n df.to_sql('water_scenario_values', con,\\n if_exists='append', index=False)\\n\\n # Rename water_scenario\\n # waterscenario_name = waterscenario.stem\\n # sql = \\\"\\\"\\\"UPDATE water_scenarios SET Description = \\\"{}\\\" WHERE ID = {}\\\"\\\"\\\".format(\\n # waterscenario_name, waterscenario)\\n # c.execute(sql)\\n\\n\\n waterscenario_id = 1\\n waterscenario_name = 'TEST waterscenario'\\n waterscenario_type = 1\\n sql = \\\"\\\"\\\"INSERT into water_scenarios VALUES ({}, '{}', {})\\\"\\\"\\\".format(\\n waterscenario_id,\\n waterscenario_name,\\n waterscenario_type\\n )\\n c.execute(sql)\\n\\n # Remove water scenario. I'm simply updating all scenarios\\n # Otherwise I should check the BranchSet structure\\n sql = \\\"\\\"\\\"UPDATE parameters SET WaterScenarioID = 1 WHERE 1\\\"\\\"\\\"\\n c.execute(sql)\\n\\n else:\\n # Remove water scenario. I'm simply updating all scenarios\\n # Otherwise I should check the BranchSet structure\\n sql = \\\"\\\"\\\"UPDATE parameters SET WaterScenarioID = NULL WHERE 1\\\"\\\"\\\"\\n c.execute(sql)\\n\\n # Set scenario name and description\\n date_string = datetime.now().strftime(\\\"%Y-%m-%d %H:%M:%S\\\")\\n self.description = f'Date: {date_string}, Waterscenario: {waterscenario}, TrafficScenario: {trafficscenario},'\\n\\n sql = \\\"\\\"\\\"\\n UPDATE scenarios\\n SET Name = \\\"{}\\\",\\n Description = \\\"{}\\\"\\n WHERE ID = {}\\n \\\"\\\"\\\".format(\\n self.scenarioName, self.description, self.scenarioID)\\n c.execute(sql)\\n\\n # Update traffic Scenario. I'm simply updating all scenarios\\n # Otherwise I should check the BranchSet structure\\n if trafficscenario:\\n if isinstance(trafficscenario, int):\\n sql = \\\"\\\"\\\"UPDATE parameters SET TrafficScenarioID = \\\"{}\\\" WHERE 1\\\"\\\"\\\".format(trafficscenario)\\n c.execute(sql)\\n else:\\n trafficScenarioID = df_trafficscenarios.index[df_trafficscenarios['Description'] == trafficscenario][0]\\n sql = \\\"\\\"\\\"UPDATE parameters SET TrafficScenarioID = \\\"{}\\\" WHERE 1\\\"\\\"\\\".format(trafficScenarioID)\\n c.execute(sql)\\n\\n con.commit()\\n con.close()\\n\\n logger.info('BIVAS database copied and updated')\",\n \"def update_data(self):\\n staff = Staff.objects.all()\\n orgs = Organization.objects.all()\\n depts = Department.objects.all()\\n\\n existing = self.all()\\n if existing.count():\\n existing.delete()\\n\\n if staff.count():\\n for s in staff:\\n record = CombinedTeledata(\\n id=s.id,\\n alpha=s.alpha,\\n name=s.name,\\n first_name=s.first_name,\\n last_name=s.last_name,\\n sort_name=s.sort_name,\\n email=s.email,\\n phone=s.phone,\\n postal=s.postal,\\n job_position=s.job_position,\\n department=s.dept.name,\\n dept_id=s.dept.id,\\n organization=s.dept.org.name,\\n org_id=s.dept.org.id,\\n building=s.bldg.name,\\n bldg_id=s.bldg.import_id,\\n room=s.room,\\n from_table='staff'\\n )\\n\\n try:\\n record.save(doing_import=True)\\n record.keywords_combined.set(s.keywords.all())\\n except Exception as e:\\n logger.error(str(e))\\n\\n if orgs.count():\\n for o in orgs:\\n record = CombinedTeledata(\\n id=o.id,\\n name=o.name,\\n sort_name=o.name,\\n phone=o.phone,\\n fax=o.fax,\\n building=o.bldg.name,\\n bldg_id=o.bldg.import_id,\\n room=o.room,\\n from_table='organizations'\\n )\\n\\n try:\\n record.save(doing_import=True)\\n record.keywords_combined.set(o.keywords.all())\\n except Exception as e:\\n logger.error(str(e))\\n\\n if depts.count():\\n for d in depts:\\n record = CombinedTeledata(\\n id=d.id,\\n name=d.name,\\n sort_name=d.name,\\n phone=d.phone,\\n fax=d.fax,\\n organization=d.org.name,\\n org_id=d.org.id,\\n building=d.bldg.name,\\n bldg_id=d.bldg.import_id,\\n room=d.room,\\n from_table='departments'\\n )\\n\\n try:\\n record.save(doing_import=True)\\n record.keywords_combined.set(d.keywords.all())\\n except Exception as e:\\n logger.error(str(e))\",\n \"def create_final_table(conn, county):\\r\\n for county in county:\\r\\n query = f\\\"SELECT name FROM sqlite_master WHERE type ='table' AND name NOT LIKE 'sqlite_%' AND name = '{county}'\\\"\\r\\n result = execute_query(conn, query)\\r\\n try:\\r\\n if len(result) == 0:\\r\\n query = f\\\"create table {county} as select * from {county}_stg;\\\"\\r\\n execute_query(conn, query)\\r\\n\\r\\n load_final_table(conn, county)\\r\\n except Exception as e:\\r\\n print(f\\\"This query {query} failed with exception {e}\\\")\",\n \"def example_data():\\n\\n seed.add_sfpl_branches()\\n seed.add_formats()\\n\\n book_1 = Book(title=\\\"Alanna: The First Adventure\\\", author=\\\"Tamora Pierce\\\")\\n book_2 = Book(title=\\\"The Hitchhiker's Guide to the Galaxy\\\", author=\\\"Douglas Adams\\\")\\n book_3 = Book(title=\\\"The Hobbit\\\", author=\\\"J.R.R. Tolkien\\\")\\n db.session.add(book_1)\\n db.session.add(book_2)\\n db.session.add(book_3)\\n\\n esqg = User(first_name=\\\"Elizabeth\\\", last_name=\\\"Goodman\\\", email=\\\"esqg@nowhere.com\\\", password=\\\"programmer\\\")\\n db.session.add(esqg)\\n db.session.commit()\\n\\n esqg_gr = GoodreadsUser(user_id=esqg.user_id, goodreads_id=ESQG)\\n db.session.add(esqg_gr)\\n\\n my_mission = UserBranch(branch_code=\\\"miss\\\", user_id=esqg.user_id)\\n db.session.add(my_mission)\\n\\n my_main = UserBranch(branch_code=\\\"main\\\", user_id=esqg.user_id)\\n db.session.add(my_main)\\n\\n db.session.commit()\",\n \"def initial_db_setup() -> None:\\n db_filename = \\\"twdft.db\\\"\\n db_path = os.path.join(TWDFT_DATA_DIR, db_filename)\\n csv_filename = \\\"sites.csv\\\"\\n csv_path = os.path.join(TWDFT_DATA_DIR, csv_filename)\\n db_is_new = not os.path.exists(db_path)\\n sites_csv = os.path.join(TWDFT_DATA_DIR, csv_filename)\\n\\n if db_is_new:\\n with sqlite3.connect(db_path) as conn:\\n c = conn.cursor()\\n\\n # first we create a site object\\n c.execute(\\n \\\"\\\"\\\"\\n CREATE TABLE site(\\n id INTEGER PRIMARY KEY,\\n name TEXT,\\n site_type TEXT,\\n sub_category TEXT,\\n address_1 TEXT,\\n address_2 TEXT,\\n town TEXT,\\n county TEXT,\\n country TEXT,\\n postcode TEXT,\\n site_category TEXT,\\n freq_target TEXT,\\n created TEXT,\\n notes TEXT,\\n last_inspection TEXT,\\n next_inspection TEXT,\\n pfsp_approval TEXT,\\n pfsp_expiry TEXT,\\n unlocode TEXT,\\n pfso TEXT,\\n pso TEXT,\\n pfsa_approval TEXT,\\n pfsa_expiry TEXT,\\n team TEXT,\\n created_by TEXT,\\n last_updated TEXT,\\n updated_by TEXT,\\n afp_loc TEXT,\\n rdf TEXT,\\n classification TEXT,\\n article24 TEXT,\\n psa_approval TEXT,\\n inspection_due TEXT\\n )\\n \\\"\\\"\\\"\\n )\\n conn.commit()\\n\\n # next we want an inspection table\\n\\n c.execute(\\n \\\"\\\"\\\"\\n CREATE TABLE inspection(\\n id INTEGER PRIMARY KEY,\\n site INTEGER,\\n date TEXT,\\n status TEXT,\\n time TEXT,\\n FOREIGN KEY(site) REFERENCES site(id)\\n )\\n \\\"\\\"\\\"\\n )\\n conn.commit()\\n\\n # next we want an inspector table\\n c.execute(\\n \\\"\\\"\\\"\\n create table inspector(\\n id integer primary key,\\n first_name text,\\n last_name text\\n )\\n \\\"\\\"\\\"\\n )\\n conn.commit()\\n\\n for i in INSPECTORS:\\n first = i.split(\\\" \\\")[0]\\n last = i.split(\\\" \\\")[1]\\n c.execute(\\n \\\"INSERT INTO inspector(first_name, last_name) VALUES (?,?)\\\",\\n (first, last),\\n )\\n\\n # a table that links inspectors with inspections\\n c.execute(\\n \\\"\\\"\\\"\\n CREATE TABLE inspector_inspections(\\n inspector INTEGER,\\n inspection INTEGER,\\n FOREIGN KEY (inspector) REFERENCES inspector(id),\\n FOREIGN KEY (inspection) REFERENCES inspection(id)\\n )\\n \\\"\\\"\\\"\\n )\\n conn.commit()\\n\\n for site in map(Site._make, csv.reader(open(csv_path, \\\"r\\\"))):\\n try:\\n c.execute(\\n f\\\"\\\"\\\"\\n INSERT INTO site VALUES(?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)\\\"\\\"\\\",\\n (\\n int(site.id.replace(\\\",\\\", \\\"\\\")),\\n site.name,\\n site.site_type,\\n site.sub_category,\\n site.address_1,\\n site.address_2,\\n site.town,\\n site.county,\\n site.country,\\n site.postcode,\\n site.site_category,\\n site.freq_target,\\n site.created,\\n site.notes,\\n site.last_inspection,\\n site.next_inspection,\\n site.pfsp_approval,\\n site.pfsp_expiry,\\n site.unlocode,\\n site.pfso,\\n site.pso,\\n site.pfsa_approval,\\n site.pfsa_expiry,\\n site.team,\\n site.created_by,\\n site.last_updated,\\n site.updated_by,\\n site.afp_loc,\\n site.rdf,\\n site.classification,\\n site.article24,\\n site.psa_approval,\\n site.inspection_due,\\n ),\\n )\\n except sqlite3.IntegrityError as e:\\n print(\\\"That hasnae worked\\\", site.inspection_due)\",\n \"def populate_db(self):\\n # Get donors\\n log.info(\\\"Populating donors.\\\")\\n\\n self.r.hmset('Thomas', {'donations': '500', 'email': 'thomas@thomas.com', 'city': 'Athens', 'state': 'GA', 'zip': 30606})\\n\\n self.r.hmset('Ted', {'donations': '1', 'email': 'ted@ted.com', 'city': 'Memphis', 'state': 'TN', 'zip': 38104})\\n\\n self.r.hmset(\\\"Bailey\\\", {'donations': '1000', 'email': 'bailey@bailey.com', 'city': 'Washington', 'state': 'DC', 'zip': 12345})\",\n \"def setUp(self):\\r\\n super(TestAnswerDistributions, self).setUp()\\r\\n\\r\\n self.homework = self.add_graded_section_to_course('homework')\\r\\n self.add_dropdown_to_section(self.homework.location, 'p1', 1)\\r\\n self.add_dropdown_to_section(self.homework.location, 'p2', 1)\\r\\n self.add_dropdown_to_section(self.homework.location, 'p3', 1)\\r\\n self.refresh_course()\",\n \"def prep(self):\\n sq1 = 'create table TCVR ( ID, T, C, V, R , primary key ( ID ) ) ;'\\n sq2 = 'create table IDX ( ID , A , primary key(A) ) ; '\\n self.sq.SQX(sq1)\\n self.sq.SQX(sq2)\\n sq3 = \\\"insert into IDX VALUES ( 1 , 'A' ) ; \\\"\\n self.sq.SQX(sq3)\",\n \"def setup_data(es_with_collector):\\n country_uk = constants.Country.united_kingdom.value.id\\n country_us = constants.Country.united_states.value.id\\n uk_region = constants.UKRegion.south_east.value.id\\n CompanyFactory(\\n name='abc defg ltd',\\n trading_names=['helm', 'nop'],\\n address_1='1 Fake Lane',\\n address_town='Downtown',\\n address_country_id=country_uk,\\n uk_region_id=uk_region,\\n )\\n CompanyFactory(\\n name='abc defg us ltd',\\n trading_names=['helm', 'nop', 'qrs'],\\n address_1='1 Fake Lane',\\n address_town='Downtown',\\n address_country_id=country_us,\\n registered_address_country_id=country_us,\\n )\\n es_with_collector.flush_and_refresh()\",\n \"def fill_target_table(new_data, curs, conn, overwrite=False):\\n for i in new_data:\\n connect_database.add_target_to_database(list(i), curs, conn, overwrite_exsiting = overwrite)\\n conn.commit()\",\n \"def seed():\\n\\n try:\\n body_parts = app.config.get(\\\"BODY_PARTS\\\")\\n body_parts_model = get_class_by_tablename(\\\"body_parts\\\")\\n\\n current_records = body_parts_model.query.all()\\n\\n if current_records:\\n for key, value in body_parts.items():\\n if not body_parts_model.find(key):\\n\\n body_parts_model.create(id=key, name=value)\\n\\n else:\\n\\n for key, value in body_parts.items():\\n body_parts_model.create(id=key, name=value)\\n\\n try:\\n body_parts_model.session.commit()\\n except IntegrityError as err:\\n print(\\\"Error seeding the database: \\\", err)\\n\\n except:\\n body_parts_model.session.rollback()\\n print('Body parts records already exist in database.')\\n\\n try:\\n body_subparts = app.config.get(\\\"BODY_SUBPARTS\\\")\\n body_subparts_model = get_class_by_tablename(\\\"subparts\\\")\\n\\n current_records = body_subparts_model.query.all()\\n\\n if current_records:\\n for key, value in body_subparts.items():\\n if not body_subparts_model.find(key):\\n body_subparts_model.create(id=key,\\n name=value['name'],\\n coordinates=value['coordinates'],\\n active=value['active'],\\n body_part_id=value['body_part_id']\\n )\\n\\n else:\\n for key, value in body_subparts.items():\\n\\n body_subparts_model.create(id=key,\\n name=value['name'],\\n coordinates=value['coordinates'],\\n active=value['active'],\\n body_part_id=value['body_part_id']\\n )\\n try:\\n body_subparts_model.session.commit()\\n except IntegrityError as err:\\n print(\\\"Error seeding the database: \\\", err)\\n\\n except Exception as e:\\n body_subparts_model.session.rollback()\\n print(e)\\n print('Subparts records already exist in database.')\\n\\n\\n try:\\n symptoms = app.config.get(\\\"SYMPTOMS\\\")\\n symptoms_model = get_class_by_tablename(\\\"symptoms\\\")\\n\\n current_records = symptoms_model.query.all()\\n\\n if current_records:\\n for key, value in symptoms.items():\\n if not symptoms_model.find(key):\\n symptoms_model.create(id=key,\\n name=value['name'],\\n active=value['active']\\n )\\n\\n else:\\n for key, value in symptoms.items():\\n symptoms_model.create(id=key,\\n name=value['name'],\\n active=value['active']\\n )\\n try:\\n symptoms_model.session.commit()\\n except IntegrityError as err:\\n print(\\\"Error seeding the database: \\\", err)\\n\\n except Exception as e:\\n symptoms_model.session.rollback()\\n print(e)\\n print('Symptom records already exist in database.')\\n\\n\\n try:\\n suggestions = app.config.get(\\\"SUGGESTIONS\\\")\\n suggestions_model = get_class_by_tablename(\\\"suggestions\\\")\\n\\n current_records = suggestions_model.query.all()\\n\\n if current_records:\\n for key, value in suggestions.items():\\n if not suggestions_model.find(key):\\n suggestions_model.create(id=key,\\n name=value['name'],\\n active=value['active'],\\n description=value['description'],\\n link=value['link'],\\n video_start=value['video_start'],\\n video_end=value['video_end']\\n )\\n\\n else:\\n for key, value in suggestions.items():\\n suggestions_model.create(id=key,\\n name=value['name'],\\n active=value['active'],\\n description=value['description'],\\n link=value['link'],\\n video_start=value['video_start'],\\n video_end=value['video_end']\\n )\\n try:\\n suggestions_model.session.commit()\\n except IntegrityError as err:\\n print(\\\"Error seeding the database: \\\", err)\\n\\n except Exception as e:\\n suggestions_model.session.rollback()\\n print(e)\\n print('Suggestion records already exist in database.')\\n\\n try:\\n conditions = app.config.get(\\\"CONDITIONS\\\")\\n conditions_model = get_class_by_tablename(\\\"conditions\\\")\\n symptoms_model = get_class_by_tablename(\\\"symptoms\\\")\\n suggestions_model = get_class_by_tablename(\\\"symptoms\\\")\\n symptom_relation = app.config.get(\\\"CONDITION_SYMPTOM_RELATION\\\")\\n suggestion_relation = app.config.get(\\\"CONDITION_SUGGESTION_RELATION\\\")\\n\\n current_records = conditions_model.query.all()\\n\\n if current_records:\\n for key, value in conditions.items():\\n if not conditions_model.find(key):\\n conditions_model.create(id=key,\\n name=value['name'],\\n active=value['active'],\\n description=value['description']\\n )\\n\\n else:\\n for key, value in conditions.items():\\n conditions_model.create(id=key,\\n name=value['name'],\\n active=value['active'],\\n description=value['description']\\n )\\n\\n for key, value in symptom_relation.items():\\n condition = conditions_model.query.filter(conditions_model.id == value['condition_id']).first()\\n symptom = symptoms_model.query.filter(symptoms_model.id == value['symptom_id']).first()\\n condition.symptoms.append(symptom)\\n\\n # for key, value in suggestion_relation.items():\\n # condition = conditions_model.query.filter(conditions_model.id == value['condition_id']).first()\\n # suggestion = suggestions_model.query.filter(suggestions_model.id == value['suggestion_id']).first()\\n # condition.suggestions.append(suggestion)\\n\\n try:\\n conditions_model.session.commit()\\n except IntegrityError as err:\\n print(\\\"Error seeding the database: \\\", err)\\n\\n except Exception as e:\\n conditions_model.session.rollback()\\n print(e)\\n print('Condition records already exist in database.')\\n\\n try:\\n body_subparts_model = get_class_by_tablename(\\\"subparts\\\")\\n relationship = app.config.get(\\\"SUBPARTS_CONDITION_RELATION\\\")\\n conditions_model = get_class_by_tablename(\\\"conditions\\\")\\n\\n for key, value in relationship.items():\\n subpart = body_subparts_model.query.filter(body_subparts_model.id == value['subpart_id']).first()\\n condition = conditions_model.query.filter(conditions_model.id == value['condition_id']).first()\\n subpart.conditions.append(condition)\\n try:\\n body_subparts_model.session.commit()\\n except IntegrityError as err:\\n print(\\\"Error seeding the database: \\\", err)\\n\\n except Exception as e:\\n body_subparts_model.session.rollback()\\n print(e)\\n print('Subparts Relation records already exist in database.')\",\n \"async def populate_test_data(self):\\n async with (await self._get_connection_pool()).acquire() as conn:\\n await conn.execute('delete from foglamp.tasks')\\n await conn.execute('delete from foglamp.schedules')\\n await conn.execute('delete from foglamp.scheduled_processes')\\n await conn.execute(\\n '''insert into foglamp.scheduled_processes(name, script)\\n values('sleep1', '[\\\"python3\\\", \\\"../scripts/sleep.py\\\", \\\"1\\\"]')''')\\n await conn.execute(\\n '''insert into foglamp.scheduled_processes(name, script)\\n values('sleep10', '[\\\"python3\\\", \\\"../scripts/sleep.py\\\", \\\"10\\\"]')''')\\n await conn.execute(\\n '''insert into foglamp.scheduled_processes(name, script)\\n values('sleep30', '[\\\"python3\\\", \\\"../scripts/sleep.py\\\", \\\"30\\\"]')''')\\n await conn.execute(\\n '''insert into foglamp.scheduled_processes(name, script)\\n values('sleep5', '[\\\"python3\\\", \\\"../scripts/sleep.py\\\", \\\"5\\\"]')''')\",\n \"def initialize(self):\\n\\n cursor = self.conn.cursor()\\n\\n # This table can be used as a parent for a collection of runs\\n cursor.execute(\\n \\\"\\\"\\\"CREATE TABLE IF NOT EXISTS RunCollections (\\n id INT AUTO_INCREMENT PRIMARY KEY,\\n name VARCHAR(14) UNIQUE\\n );\\\"\\\"\\\"\\n )\\n\\n # This table holds in which run each appears.\\n cursor.execute(\\n \\\"\\\"\\\"CREATE TABLE IF NOT EXISTS Runs (\\n id INT AUTO_INCREMENT PRIMARY KEY,\\n name VARCHAR(14) UNIQUE,\\n collection_id INT,\\n FOREIGN KEY (collection_id) REFERENCES RunCollections (id) ON DELETE CASCADE);\\\"\\\"\\\"\\n )\\n\\n # This table holds resources, which can be in multiple runs and have multiple varieties\\n cursor.execute(\\n \\\"\\\"\\\"CREATE TABLE IF NOT EXISTS Resources (\\n id INT AUTO_INCREMENT PRIMARY KEY, \\n extension VARCHAR(20), \\n webpage VARCHAR(30),\\n run_id INT NOT NULL,\\n FOREIGN KEY (run_id) REFERENCES Runs (id) ON DELETE CASCADE);\\\"\\\"\\\"\\n )\\n\\n cursor.execute(\\n 'SELECT Table_name FROM information_schema.tables WHERE table_schema = \\\"vpntfg0\\\" AND Table_name LIKE \\\"%Varieties_%\\\" ORDER BY Table_name'\\n )\\n for row in cursor.fetchall():\\n self.variety_tables.append(row[0])\\n\\n cursor.close()\\n _logger.info(\\\"Variety tables are: %s\\\" % self.variety_tables)\\n\\n _logger.info(\\\"Database initialized\\\")\",\n \"def create_tables():\\n commands = (\\n \\\"\\\"\\\"\\n CREATE TABLE STUDENT(\\n ADMISSION INT PRIMARY KEY NOT NULL,\\n NAME TEXT NOT NULL,\\n AGE INT NOT NULL,\\n COURSE CHAR(50),\\n DEPARTMENT CHAR(50)\\n )\\n \\\"\\\"\\\",\\n \\\"\\\"\\\"\\n CREATE TABLE vendors (\\n vendor_id SERIAL PRIMARY KEY,\\n vendor_name VARCHAR(255) NOT NULL\\n )\\n \\\"\\\"\\\",\\n \\\"\\\"\\\" CREATE TABLE parts (\\n part_id SERIAL PRIMARY KEY,\\n part_name VARCHAR(255) NOT NULL\\n )\\n \\\"\\\"\\\",\\n \\\"\\\"\\\"\\n CREATE TABLE part_drawings (\\n part_id INTEGER PRIMARY KEY,\\n file_extension VARCHAR(5) NOT NULL,\\n drawing_data BYTEA NOT NULL,\\n FOREIGN KEY (part_id)\\n REFERENCES parts (part_id)\\n ON UPDATE CASCADE ON DELETE CASCADE\\n )\\n \\\"\\\"\\\",\\n \\\"\\\"\\\"\\n CREATE TABLE vendor_parts (\\n vendor_id INTEGER NOT NULL,\\n part_id INTEGER NOT NULL,\\n PRIMARY KEY (vendor_id , part_id),\\n FOREIGN KEY (vendor_id)\\n REFERENCES vendors (vendor_id)\\n ON UPDATE CASCADE ON DELETE CASCADE,\\n FOREIGN KEY (part_id)\\n REFERENCES parts (part_id)\\n ON UPDATE CASCADE ON DELETE CASCADE\\n )\\n \\\"\\\"\\\")\\n\\n conn = None\\n try:\\n # read the connection parameters\\n params = config()\\n # connect to the PostgreSQL server\\n conn = psycopg2.connect(**params)\\n cur = conn.cursor()\\n # create table one by one\\n for command in commands:\\n cur.execute(command)\\n # close communication with the PostgreSQL database server\\n cur.close()\\n # commit the changes\\n conn.commit()\\n except (Exception, psycopg2.DatabaseError) as error:\\n print(error)\\n finally:\\n if conn is not None:\\n conn.close()\",\n \"def load_products():\\n\\n for i, row in enumerate(open(\\\"seed_data/category.product\\\")):\\n row = row.rstrip()\\n name = row.split(\\\"|\\\")\\n product_category = ProductCategoryModel(name=name)\\n db.session.add(product_category)\\n\\n for i, row in enumerate(open(\\\"seed_data/product.product\\\")):\\n row = row.rstrip()\\n name, short_description, long_description, product_category_id, img_path_xs, img_path_sm, img_path_md, img_path_lg = row.split(\\\"|\\\")\\n product = ProductModel(name=name,\\n short_description=short_description,\\n long_description=long_description,\\n product_category_id=product_category_id,\\n img_path_xs=img_path_xs,\\n img_path_sm=img_path_sm,\\n img_path_md=img_path_md,\\n img_path_lg=img_path_lg)\\n db.session.add(product)\\n\\n for i, row in enumerate(open(\\\"seed_data/location.product\\\")):\\n row = row.rstrip()\\n name, description, address1, address2, city, state, zip_code, country, latitude, longitude, direction_url = row.split(\\\"|\\\")\\n location = LocationModel(name=name,\\n description=description,\\n address1=address1,\\n address2=address2,\\n city=city,\\n state=state,\\n zip_code=zip_code,\\n country=country,\\n latitude=latitude,\\n longitude=longitude,\\n direction_url=direction_url)\\n db.session.add(location)\\n\\n for i, row in enumerate(open(\\\"seed_data/location_product.product\\\")):\\n row = row.rstrip()\\n location_id, product_id, price, num_available = row.split(\\\"|\\\")\\n location_product = LocationProductModel(location_id=location_id,\\n product_id=product_id,\\n price=price,\\n num_available=num_available)\\n db.session.add(location_product)\\n\\n db.session.commit()\",\n \"def insert_data():\\n\\tBase.metadata.drop_all(engine)\\n\\tBase.metadata.create_all(engine)\\n\\tu1 = insert_user(\\\"233@B.com\\\", \\\"/static/image/avatar.JPG\\\")\\n\\tu2 = insert_user(\\\"fy@B.com\\\", \\\"/static/image/avatar.JPG\\\")\\n\\tc = insert_catalog(u1.id, \\\"Sichuan Dish\\\")\\n\\tinsert_catalog(u1.id, \\\"Fujian Dish\\\")\\n\\tinsert_catalog(u1.id, \\\"Guangdong Dish\\\")\\n\\tinsert_catalog(u2.id, \\\"Zhejiang Dish\\\")\\n\\tinsert_catalog(u2.id, \\\"Beijing Dish\\\")\\n\\tinsert_item(u1.id, \\\"Iphone 6 plus\\\", c, 'Is a phone', None)\\n\\tinsert_item(u1.id, \\\"Hot pot\\\", c, \\\"Hot hot hot\\\", None)\\n\\tinsert_item(u2.id, \\\"Kong Bao Chicken\\\", c, \\\"Classic\\\", None)\",\n \"def intialize_db(self):\\n self.db.query(\\\"\\\"\\\"\\n CREATE TABLE IF NOT EXISTS \\\"rooms\\\" (\\n \\\"id\\\" integer PRIMARY KEY AUTOINCREMENT,\\n \\\"name\\\" text UNIQUE,\\n \\\"type\\\" text\\n );\\n \\\"\\\"\\\")\\n self.db.query(\\\"\\\"\\\"\\n CREATE TABLE IF NOT EXISTS \\\"fellows\\\" (\\n \\\"id\\\" integer PRIMARY KEY AUTOINCREMENT,\\n \\\"name\\\" text,\\n \\\"accomodation\\\" text\\n );\\n \\\"\\\"\\\")\\n self.db.query(\\\"\\\"\\\"\\n CREATE TABLE IF NOT EXISTS \\\"staff\\\" (\\n \\\"id\\\" integer PRIMARY KEY AUTOINCREMENT,\\n \\\"name\\\" text\\n );\\n \\\"\\\"\\\")\\n self.db.query(\\\"\\\"\\\"\\n CREATE TABLE IF NOT EXISTS \\\"fellows_rooms\\\" (\\n \\\"id\\\" integer PRIMARY KEY AUTOINCREMENT,\\n \\\"fellow_id\\\" integer NOT NULL REFERENCES \\\"fellows\\\" (\\\"id\\\"),\\n \\\"room_id\\\" integer NOT NULL REFERENCES \\\"rooms\\\" (\\\"id\\\")\\n );\\n \\\"\\\"\\\")\\n self.db.query(\\\"\\\"\\\"\\n CREATE TABLE IF NOT EXISTS \\\"staff_rooms\\\" (\\n \\\"id\\\" integer PRIMARY KEY AUTOINCREMENT,\\n \\\"staff_id\\\" integer NOT NULL REFERENCES \\\"staff\\\" (\\\"id\\\"),\\n \\\"room_id\\\" integer NOT NULL REFERENCES \\\"rooms\\\" (\\\"id\\\")\\n );\\n \\\"\\\"\\\")\",\n \"def main():\\n\\n #Courses\\n years = [2016, 2017, 2018, 2019, 2020]\\n with sqlite3.connect(\\\"determined.db\\\") as conn:\\n c = conn.cursor()\\n c.execute(\\n \\\"\\\"\\\"\\n CREATE TABLE \\\"course\\\" (\\n \\\"course_number\\\"\\tint NOT NULL,\\n \\\"dept_id\\\"\\tvarchar(4) NOT NULL,\\n \\\"title\\\"\\tvarchar(100) NOT NULL,\\n \\\"instructor_fname\\\"\\tvarchar(35) DEFAULT NULL,\\n \\\"instructor_lname\\\"\\tvarchar(35) DEFAULT NULL,\\n \\\"student_work_products\\\"\\tjson DEFAULT NULL,\\n `term` varchar(7) NOT NULL,\\n `year` int NOT NULL,\\n PRIMARY KEY(\\\"course_number\\\", \\\"term\\\", \\\"year\\\")) \\n \\\"\\\"\\\"\\n )\\n conn.commit()\\n courses = [\\n (1370, \\\"CPSC\\\", \\\"Computer Literacy\\\", names.get_first_name(), names.get_last_name(), json.dumps({\\\"swp\\\": [\\\"Midterm\\\", \\\"Final Exam\\\", \\\"Project 1\\\"]}), \\\"Fall\\\"),\\n (1375, \\\"CPSC\\\", \\\"Programming I\\\", names.get_first_name(), names.get_last_name(), json.dumps({\\\"swp\\\": [\\\"Midterm\\\", \\\"Final Exam\\\", \\\"Project 1\\\"]}), \\\"Fall\\\"),\\n (2376, \\\"CPSC\\\", \\\"Intro to Game Programming\\\", names.get_first_name(), names.get_last_name(), json.dumps({\\\"swp\\\": [\\\"Midterm\\\", \\\"Final Exam\\\", \\\"Project 1\\\"]}), \\\"Fall\\\"),\\n (2380, \\\"CPSC\\\", \\\"Algorithms\\\", names.get_first_name(), names.get_last_name(), json.dumps({\\\"swp\\\": [\\\"Midterm\\\", \\\"Final Exam\\\", \\\"Project 1\\\"]}), \\\"Fall\\\"),\\n (2482, \\\"CPSC\\\", \\\"Computer Organization\\\", names.get_first_name(), names.get_last_name(), json.dumps({\\\"swp\\\": [\\\"Midterm\\\", \\\"Final Exam\\\", \\\"Project 1\\\"]}), \\\"Spring\\\"),\\n (3377, \\\"CPSC\\\", \\\"Advanced Game Programming\\\", names.get_first_name(), names.get_last_name(), json.dumps({\\\"swp\\\": [\\\"Midterm\\\", \\\"Final Exam\\\", \\\"Project 1\\\"]}), \\\"Spring\\\"),\\n (3380, \\\"CPSC\\\", \\\"Operating Systems\\\", names.get_first_name(), names.get_last_name(), json.dumps({\\\"swp\\\": [\\\"Midterm\\\", \\\"Final Exam\\\", \\\"Project 1\\\"]}), \\\"Spring\\\"),\\n (3383, \\\"CPSC\\\", \\\"Programming Languages\\\", names.get_first_name(), names.get_last_name(), json.dumps({\\\"swp\\\": [\\\"Midterm\\\", \\\"Final Exam\\\", \\\"Project 1\\\"]}), \\\"Spring\\\"),\\n (3384, \\\"CPSC\\\", \\\"Computer Networks\\\", names.get_first_name(), names.get_last_name(), json.dumps({\\\"swp\\\": [\\\"Midterm\\\", \\\"Final Exam\\\", \\\"Project 1\\\"]}), \\\"Summer\\\"),\\n (4360, \\\"CPSC\\\", \\\"Computer Security\\\", names.get_first_name(), names.get_last_name(), json.dumps({\\\"swp\\\": [\\\"Midterm\\\", \\\"Final Exam\\\", \\\"Project 1\\\"]}), \\\"Summer\\\")\\n ]\\n #Adding years\\n upload_courses = []\\n for year in years:\\n upload_courses += [x + (year,) for x in courses]\\n #Making a few instructors teach multiple course\\n new_courses = [\\n (4557, \\\"CPSC\\\", \\\"Natural Language Processing\\\", ),\\n (2375, \\\"CPSC\\\", \\\"Programming II\\\",),\\n (2776, \\\"CPSC\\\", \\\"Data Structures and Algorithms\\\",),\\n (4862, \\\"CPSC\\\", \\\"Image Recognition\\\", ),\\n ]\\n for i in range(0,len(new_courses)):\\n year = choice(years)\\n for y in range(0,2): #Number of times new course is taught\\n c = upload_courses[i]\\n new_data = (c[3], c[4], c[5], choice([\\\"Fall\\\", \\\"Spring\\\", \\\"Summer\\\"]), year+y)\\n data = new_courses[i] + new_data\\n upload_courses.append(data)\\n #Adding solo instructors and solo courses\\n upload_courses += [\\n (4672, \\\"CPSC\\\", \\\"Programming Memes\\\", names.get_first_name(), names.get_last_name(), json.dumps({\\\"swp\\\": [\\\"Midterm\\\", \\\"Final Exam\\\", \\\"Project 1\\\"]}), \\\"Spring\\\", choice(years)),\\n (1872, \\\"CPSC\\\", \\\"Information Systems\\\", names.get_first_name(), names.get_last_name(), json.dumps({\\\"swp\\\": [\\\"Midterm\\\", \\\"Final Exam\\\", \\\"Project 1\\\"]}), \\\"Summer\\\", choice(years)),\\n (1123, \\\"CPSC\\\", \\\"Microsoft Office\\\", names.get_first_name(), names.get_last_name(), json.dumps({\\\"swp\\\": [\\\"Midterm\\\", \\\"Final Exam\\\", \\\"Project 1\\\"]}), \\\"Fall\\\", choice(years))\\n ]\\n\\n with sqlite3.connect(\\\"determined.db\\\") as conn:\\n c = conn.cursor()\\n c.executemany('''INSERT INTO course (course_number, dept_id, title, instructor_fname, instructor_lname, student_work_products, term, year)\\n VALUES ( ?, ?, ?, ?, ?, ?, ?, ?)''', upload_courses)\\n conn.commit()\\n\\n #SWP\\n with sqlite3.connect(\\\"determined.db\\\") as conn:\\n c = conn.cursor()\\n c.execute(\\n \\\"\\\"\\\"\\n CREATE TABLE `student_work_product` (\\n `id` INTEGER PRIMARY KEY,\\n `product` varchar(250) NOT NULL,\\n `course_id` int NOT NULL,\\n `dept_id` int NOT NULL,\\n `student_fname` varchar(35) NOT NULL,\\n `student_lname` varchar(35) NOT NULL,\\n `student_outcome` int DEFAULT NULL,\\n `score` int DEFAULT NULL,\\n `term` varchar(7) NOT NULL,\\n `year` int NOT NULL,\\n CONSTRAINT `course` FOREIGN KEY (`course_id`) REFERENCES `course` (`course_number`)\\n CONSTRAINT `course` FOREIGN KEY (`dept_id`) REFERENCES `course` (`dept_id`)\\n )\\n \\\"\\\"\\\"\\n )\\n conn.commit()\\n \\n swps = []\\n with sqlite3.connect(\\\"determined.db\\\") as conn:\\n conn.row_factory = sqlite3.Row\\n c = conn.cursor()\\n c.execute (\\\"Select * from course\\\")\\n records = [dict(x) for x in c.fetchall()]\\n #Generating 20 student records for each swp in each course\\n for i, course in enumerate(records):\\n student_names = []\\n for _ in range(20):\\n student_names.append({'fname': names.get_first_name(),\\n 'lname': names.get_last_name()})\\n for product in json.loads(course['student_work_products'])['swp']:\\n for student in student_names:\\n if i%7 == 0:\\n score = int(triangular(50, 85))\\n else:\\n score = int(triangular(50, 100))\\n if score >= 90: outcome = 4\\n elif score >= 80: outcome = 3\\n elif score >= 70: outcome = 2\\n elif score >= 60: outcome = 1\\n else: outcome = 0 \\n swps.append((\\n product,\\n course['course_number'],\\n \\\"CPSC\\\",\\n student['fname'],\\n student['lname'],\\n outcome,\\n score, \\n course['term'], \\n course['year']\\n ))\\n \\n c.executemany('''INSERT INTO student_work_product (product, course_id, dept_id, student_fname, student_lname, student_outcome, score, term, year)\\n VALUES ( ?, ?, ?, ?, ?, ?, ?, ?, ?)''', swps)\\n conn.commit()\",\n \"def generate_courses():\\r\\n for category in CourseCategory.objects.all():\\r\\n Course.objects.create(name=category.name, category=category, is_active=True,\\r\\n is_featured=True)\",\n \"def __create_tables(self):\\n self.__cur.execute(\\\"\\\"\\\"\\n CREATE TABLE IF NOT EXISTS federations(\\n id INTEGER PRIMARY KEY UNIQUE,\\n name TEXT,\\n category TEXT\\n )\\n \\\"\\\"\\\")\\n self.__cur.execute(\\\"\\\"\\\"\\n CREATE TABLE IF NOT EXISTS clubs(\\n id INTEGER PRIMARY KEY AUTOINCREMENT UNIQUE,\\n code_fede INT,\\n dpt TEXT,\\n nb_clubs INT,\\n year INT,\\n FOREIGN KEY(code_fede) REFERENCES federations(i)\\n )\\n \\\"\\\"\\\")\\n self.__cur.execute(\\\"\\\"\\\"\\n CREATE TABLE IF NOT EXISTS by_age(\\n id INTEGER PRIMARY KEY AUTOINCREMENT UNIQUE,\\n code_fede INT,\\n age TEXT,\\n sex TEXT,\\n nb INT,\\n year INT,\\n FOREIGN KEY(code_fede) REFERENCES federations(id)\\n )\\n \\\"\\\"\\\")\\n self.__cur.execute(\\\"\\\"\\\"\\n CREATE TABLE IF NOT EXISTS by_dpt(\\n id INTEGER PRIMARY KEY AUTOINCREMENT UNIQUE,\\n code_fede INT,\\n dpt TEXT,\\n nb INT,\\n sex TEXT,\\n year INT,\\n FOREIGN KEY(code_fede) REFERENCES federations(id)\\n )\\n \\\"\\\"\\\")\\n\\n # Save all the changes\\n self.save()\",\n \"def update_static_data(source: list):\\r\\n connect = sqlite3.connect(\\\"REDB_v2.sqlite\\\")\\r\\n cursor = connect.cursor()\\r\\n districts = []\\r\\n streets = []\\r\\n series = []\\r\\n amenities = []\\r\\n building_types = []\\r\\n for realestate in source:\\r\\n if realestate.street not in streets:\\r\\n streets.append(realestate.street)\\r\\n if realestate.series not in series:\\r\\n series.append(realestate.series)\\r\\n if realestate.building not in building_types:\\r\\n building_types.append(realestate.building)\\r\\n if realestate.district not in districts:\\r\\n districts.append(realestate.district)\\r\\n if realestate.amenities not in amenities:\\r\\n amenities.append(realestate.amenities)\\r\\n for item in streets:\\r\\n try:\\r\\n cursor.execute(\\\"INSERT INTO Streets (Name) VALUES ('\\\" + item + \\\"')\\\")\\r\\n except sqlite3.IntegrityError:\\r\\n pass\\r\\n for item in districts:\\r\\n try:\\r\\n cursor.execute(\\\"INSERT INTO Districts (Name) VALUES ('\\\" + item + \\\"')\\\")\\r\\n except sqlite3.IntegrityError:\\r\\n pass\\r\\n for item in series:\\r\\n try:\\r\\n cursor.execute(\\\"INSERT INTO Series (Name) VALUES ('\\\" + item + \\\"')\\\")\\r\\n except sqlite3.IntegrityError:\\r\\n pass\\r\\n for item in amenities:\\r\\n try:\\r\\n cursor.execute(\\\"INSERT INTO Amenities (Name) VALUES ('\\\" + item + \\\"')\\\")\\r\\n except sqlite3.IntegrityError:\\r\\n pass\\r\\n for item in building_types:\\r\\n try:\\r\\n cursor.execute(\\\"INSERT INTO Buildings (Name) VALUES ('\\\" + item + \\\"')\\\")\\r\\n except sqlite3.IntegrityError:\\r\\n pass\\r\\n connect.commit()\\r\\n connect.close()\",\n \"def init():\\n database.create_tables([Tracker])\\n database.commit()\",\n \"def __init__(self):\\n self._db = db\\n # Connect to DB\\n self._db.connect()\\n # Create tables\\n self._db.create_tables([Teachers, Parents, Tutors, Students, Homework, Groups, StudentsGroups, Courses])\\n # Create filling entries\\n self.__create_dummies()\\n self._db.close()\",\n \"def load_data(data):\\n\\n # Agencies\\n name = data['name']\\n slug = Agency.slug_for(name)\\n\\n a, created = Agency.objects.get_or_create(slug=slug, name=name)\\n\\n a.abbreviation = data['abbreviation']\\n a.description = data.get('description')\\n a.keywords = data.get('keywords')\\n a.common_requests = data.get('common_requests', [])\\n a.no_records_about = data.get('no_records_about', [])\\n\\n # Only has a single, main branch/office\\n if len(data['departments']) == 1:\\n dept_rec = data['departments'][0]\\n contactable_fields(a, dept_rec)\\n\\n a.save()\\n add_request_time_statistics(data, a)\\n\\n # Offices\\n if len(data['departments']) > 1:\\n for dept_rec in data['departments']:\\n if dept_rec.get('top_level'):\\n # This is actually an agency\\n sub_agency_name = dept_rec['name']\\n sub_agency_slug = Agency.slug_for(sub_agency_name)\\n\\n sub_agency, created = Agency.objects.get_or_create(\\n slug=sub_agency_slug, name=sub_agency_name)\\n sub_agency.parent = a\\n\\n abbreviation = build_abbreviation(sub_agency_name)\\n sub_agency.abbreviation = abbreviation\\n sub_agency.description = dept_rec.get('description')\\n sub_agency.keywords = dept_rec.get('keywords')\\n sub_agency.common_requests = dept_rec.get(\\n 'common_requests', [])\\n sub_agency.no_records_about = dept_rec.get(\\n 'no_records_about', [])\\n contactable_fields(sub_agency, dept_rec)\\n sub_agency.save()\\n add_request_time_statistics(dept_rec, sub_agency)\\n else:\\n # Just an office\\n office_name = dept_rec['name']\\n office_slug = Office.slug_for(office_name)\\n full_slug = slug + '--' + office_slug\\n\\n o, created = Office.objects.get_or_create(\\n agency=a, slug=full_slug)\\n\\n o.office_slug = office_slug\\n o.name = office_name\\n contactable_fields(o, dept_rec)\\n o.save()\\n add_request_time_statistics(dept_rec, a, o)\",\n \"def create_tables():\\n inf(\\\"Creating tables\\\")\\n \\n pinners = Table('pinners', metadata,\\n Column('pinner_id', Integer, primary_key=True),\\n Column('name', String(40)),\\n Column('email', String(40))\\n )\\n pinners.create()\\n \\n contents = Table('contents', metadata,\\n Column('content_id', Integer, primary_key=True),\\n Column('url', String(80)),\\n Column('display_status', String(20)), # good, objectionable, copyright\\n Column('pinner_id', Integer, ForeignKey('pinners.pinner_id'))\\n )\\n contents.create()\\n\\n reviewers = Table('reviewers', metadata,\\n Column('reviewer_id', Integer, primary_key=True),\\n Column('name', String(40)),\\n Column('email', String(40))\\n )\\n reviewers.create()\\n\\n complaints = Table('complaints', metadata,\\n Column('complaint_id', Integer, primary_key=True),\\n Column('complaint_timestamp', DateTime), # when the complaint was filed\\n Column('complaint_type', String(80)), # objectionable, copyright\\n Column('process_status', String(20)), # complaint, review, done\\n Column('display_status', String(20)), # good, objectionable, copyright\\n Column('review_timestamp', DateTime), # when the compliant was resolved\\n Column('pinner_id', Integer, ForeignKey('pinners.pinner_id')),\\n Column('reviewer_id', Integer, ForeignKey('reviewers.reviewer_id')),\\n Column('content_id', Integer, ForeignKey('contents.content_id'))\\n )\\n complaints.create()\\n \\n # could create a table of \\\"near by\\\" images and/or near by features and \\n # include these in the review\",\n \"def create_tables():\\n # Depending on your local settings, you may need to specify a user and password, e.g.\\n # conn = psycopg2.connect(dbname=DBNAME, user=\\\"postgres\\\", password=\\\"password\\\")\\n conn = psycopg2.connect(dbname=DBNAME)\\n\\n create_train_table(conn)\\n create_questions_table(conn)\\n create_lectures_table(conn)\\n create_example_test_table(conn)\\n create_example_test_table(conn)\\n\\n conn.close()\",\n \"def initialize_db(self) -> None:\\n if not self.check_schema_initialized():\\n self._create_genes_table()\\n self._create_meta_data_table()\",\n \"def _create_tables():\\n from Model.DataAccessor.DbAccessor.DbOrmAccessor import db\\n db.create_tables([SubjectType, SubjectRegion, Subject])\",\n \"def _initialize(self):\\n for s in Subsidiary.all():\\n self.__create_stock(s)\\n self.get_stock()\",\n \"def _create_intermediate_new_tables(self):\\n with futures.ThreadPoolExecutor(max_workers=self._nworkers) as executor:\\n # Create all the tables and populate initial tables. Need to commit so that threadpool jobs can\\n # see the results\\n with self._conn:\\n self._run_intermediate_table_job(self._conn, self._create_intermediate_new_tables_structure)\\n # Create utility tables\\n self._run_intermediate_table_job(self._conn, self._populate_blocking_conditions_table,\\n description='blocking conditions')\\n self._run_intermediate_table_job(self._conn, self._populate_mcc_mnc_table,\\n description='MCC-MNC operator mappings')\\n # Create new blacklist table\\n self._run_intermediate_table_job(self._conn, self._populate_new_blacklist,\\n description='IMEIs to blacklist')\\n\\n # Create required notifications and pairings tables in parallel before we can kick off the per-MNO\\n # pairing and and notifications. These jobs have the responsibilities of kicking off those per-MNO jobs\\n futures_to_cb = {}\\n self._queue_intermediate_table_job(executor,\\n futures_to_cb,\\n self._populate_new_notifications_lists,\\n 'per-MNO notifications for all operators')\\n self._queue_intermediate_table_job(executor,\\n futures_to_cb,\\n self._populate_new_exceptions_lists,\\n 'per-MNO exceptions for all operators')\\n self._wait_for_futures(futures_to_cb)\",\n \"def insert_into_db(self, database):\\n\\n # insert person\\n keys = \\\"\\\"\\n values = \\\"\\\"\\n for key, value in self.person.items():\\n # location\\n if key == \\\"location\\\":\\n # ensure location is in table\\n database.select(f\\\"\\\"\\\"DO $do$ BEGIN IF NOT EXISTS (SELECT * FROM p21_cdm.location WHERE city='{value['city']}' \\n AND zip='{value['zip']}') THEN INSERT INTO p21_cdm.location (city, zip) \\n VALUES ('{value['city']}', '{value['zip']}'); END IF; END; $do$\\\"\\\"\\\")\\n continue\\n\\n keys += f\\\"{key},\\\"\\n values += f\\\"'{value}',\\\"\\n\\n database.select(f\\\"\\\"\\\"INSERT INTO p21_cdm.person (location_id, {keys[:-1]}) \\n VALUES((SELECT location_id \\n FROM p21_cdm.location\\n WHERE city='{self.person['location']['city']}' \\n and zip='{self.person['location']['zip']}'), \\n {values[:-1]})\\\"\\\"\\\")\\n\\n # insert visits\\n for visit in self.visits:\\n keys = \\\"person_id,\\\"\\n values = f\\\"'{self.person['person_id']}',\\\"\\n for key, value in visit.items():\\n if key == \\\"care_site_name\\\":\\n # ensure care site is in table\\n database.select(f\\\"\\\"\\\"DO $do$ BEGIN IF NOT EXISTS (SELECT * \\n FROM p21_cdm.care_site \\n WHERE care_site_name='{value}') \\n THEN INSERT INTO p21_cdm.care_site (care_site_name) \\n VALUES ('{value}'); END IF; END; $do$\\\"\\\"\\\")\\n continue\\n\\n keys += f\\\"{key},\\\"\\n values += f\\\"'{value}',\\\"\\n\\n database.select(f\\\"\\\"\\\"INSERT INTO p21_cdm.visit_occurrence (care_site_id, {keys[:-1]}) \\n VALUES((SELECT care_site_id\\n FROM p21_cdm.care_site\\n WHERE care_site_name='{visit['care_site_name']}'),\\n {values[:-1]}) \\n RETURNING visit_occurrence_id\\\"\\\"\\\")\\n\\n # insert measurements, observations, conditions & procedures\\n for data, tablename in [(self.measurements, \\\"measurement\\\"),\\n (self.observations, \\\"observation\\\"),\\n (self.conditions, \\\"condition_occurrence\\\"),\\n (self.procedures, \\\"procedure_occurrence\\\")]:\\n for entry in data:\\n keys = \\\"person_id,\\\"\\n values = f\\\"'{self.person['person_id']}',\\\"\\n\\n for key, value in entry.items():\\n keys += f\\\"{key},\\\"\\n values += f\\\"'{value}',\\\"\\n\\n entry[\\\"sql_id\\\"] = database.select(f\\\"\\\"\\\"INSERT INTO p21_cdm.{tablename}({keys[:-1]})\\n VALUES({values[:-1]}) RETURNING {tablename}_id\\\"\\\"\\\")[0][0]\\n\\n # insert fact_relationships in both directions\\n for table1, entry1, table2, entry2 in self.fact_relations:\\n # 44818890 = Finding associated with (SNOMED)\\n database.select(f\\\"\\\"\\\"INSERT INTO p21_cdm.fact_relationship(domain_concept_id_1, fact_id_1, \\n domain_concept_id_2, fact_id_2, \\n relationship_concept_id)\\n VALUES('{table1}','{entry1['sql_id']}','{table2}','{entry2['sql_id']}','44818890')\\\"\\\"\\\")\\n # 44818792 = Associated with finding (SNOMED)\\n database.select(f\\\"\\\"\\\"INSERT INTO p21_cdm.fact_relationship(domain_concept_id_1, fact_id_1, \\n domain_concept_id_2, fact_id_2, \\n relationship_concept_id)\\n VALUES('{table2}','{entry2['sql_id']}','{table1}','{entry1['sql_id']}','44818792')\\\"\\\"\\\")\\n\\n # make transactions persistent\\n database.commit()\",\n \"def populate_db():\\n\\n logging.basicConfig(level=logging.INFO)\\n logger = logging.getLogger(__name__)\\n\\n database = SqliteDatabase('../data/personjob.db') # navigate relative path to the db\\n\\n logger.info('Working with Department class')\\n logger.info('Creating department records')\\n\\n department_number = 0\\n department_name = 1\\n department_manager = 2\\n\\n departments = [\\n ('A111', 'Asset Management', 'Dave Sanders'),\\n ('B222', 'Human Resources', 'Tammy Murray'),\\n ('C333', 'Payroll', 'Daddy Warbucks'),\\n ]\\n\\n try:\\n database.connect()\\n database.execute_sql('PRAGMA foreign_keys = ON;')\\n for department in departments:\\n with database.transaction():\\n new_department = Department.create(\\n department_number = department[department_number],\\n department_name = department[department_name],\\n deptartment_manager = department[department_manager]\\n )\\n new_department.save()\\n logger.info('Department has been added to the database')\\n\\n logger.info('Reading and print all department data...')\\n for saved_department in Department:\\n logger.info(f'{saved_department.department_name} ' + \\\\\\n f'Manager: {saved_department.department_manager}. ' + \\\\\\n f'Department number: {saved_department.department_number}')\\n\\n except Exception as e:\\n logger.info(f'Error creating = {department[department_number]}')\\n logger.info(e)\\n\\n finally:\\n logger.info('database closes')\\n database.close()\",\n \"def fill_dataset(self):\\n rm, rstd = self.get_rolling_stats()\\n\\n self.add_rolling_mean(rm)\\n self.add_bollinger_bands(rstd)\\n self.add_spy_info()\\n self.add_beta_and_sharpe()\\n self.add_stlouis_data()\",\n \"def insert_db():\\n populate_tables()\",\n \"def example_data():\\n\\n db.create_all()\\n\\n\\n #Empty out data from previous runs\\n User.query.delete()\\n Book.query.delete()\\n Rating.query.delete()\\n\\n #Add sample users, books, and ratings\\n\\n #sample users\\n user1 = User(user_id=1, email='123@test.com', password='password')\\n user2 = User(user_id=2, email='456@test.com', password='password')\\n user3 = User(user_id=3, email='789@test.com', password='password')\\n user4 = User(user_id=4, email='987@test.com', password='password')\\n user5 = User(user_id=5, email='654@test.com', password='password')\\n\\n #sample books\\n book1 = Book(book_id=7627, work_id=16683183, isbn='0007331789', title='Death of Kings (The Saxon Stories, #6)', author='Bernard Cornwell')\\n book2 = Book(book_id=7695, work_id=16947613, isbn='0007350430', title='The Time of My Life', author='Cecelia Ahern')\\n book3 = Book(book_id=69, work_id=15524542, isbn='0007442912', title='Insurgent (Divergent #2)', author='Veronica Roth')\\n book4 = Book(book_id=3327, work_id=23906880, isbn='0007491433', title='The Shock of the Fall', author='Nathan Filer')\\n book5 = Book(book_id=8387, work_id=67116, isbn='0099464691', title='The White Lioness (Kurt Wallander, #3)', author='Henning Mankell')\\n\\n\\n #sample ratings\\n rating1 = Rating(rating_id=1, book_id=7627, user_id=1, score=5)\\n rating2 = Rating(rating_id=2, book_id=7627, user_id=2, score=5)\\n rating3 = Rating(rating_id=3, book_id=7627, user_id=3, score=3)\\n rating4 = Rating(rating_id=4, book_id=7627, user_id=4, score=3)\\n rating5 = Rating(rating_id=5, book_id=7627, user_id=5, score=1)\\n rating6 = Rating(rating_id=6, book_id=8387, user_id=1, score=5)\\n rating7 = Rating(rating_id=7, book_id=8387, user_id=2, score=5)\\n rating8 = Rating(rating_id=8, book_id=8387, user_id=3, score=3)\\n rating9 = Rating(rating_id=9, book_id=8387, user_id=4, score=3)\\n rating10 = Rating(rating_id=10, book_id=8387, user_id=5, score=1)\\n rating11 = Rating(rating_id=11, book_id=69, user_id=5, score=5)\\n rating12 = Rating(rating_id=12, book_id=3327, user_id=5, score=5)\\n rating13 = Rating(rating_id=13, book_id=3327, user_id=2, score=5)\\n\\n #Add all to session and commit\\n db.session.add_all([user1, user2, user3, user4, user5, book1, book2, book3, \\n book4, book5, rating1, rating2, rating3, rating4, \\n rating5, rating6, rating7, rating8, rating9, rating10, rating11,\\n rating12, rating13])\\n db.session.commit()\",\n \"def build(self):\\n \\n # create db with empty tables\\n dbpath, config = self.setup()\\n \\n # avoid work if setup decided db exists and build can be skipped\\n if dbpath is None:\\n return \\n \\n # check prerequisite files \\n obopath = check_file(config.obo, dbpath, \\\"obo\\\")\\n refpath = check_file(config.reference_phenotypes, dbpath, \\n \\\"reference_phenotypes\\\")\\n freqpath = check_file(config.phenotype_frequencies, dbpath,\\n \\\"phenotype_frequencies\\\")\\n\\n self.logger.msg1(\\\"Loading ontology\\\") \\n obo = MinimalObo(obopath, True)\\n \\n self.logger.msg1(\\\"Preparing phenotype frequencies\\\")\\n fill_phenotype_frequency_table(dbpath, freqpath)\\n \\n # fill database with data\\n self.logger.msg1(\\\"Preparing references\\\")\\n fill_concise_reference_table(dbpath, refpath) \\n fill_complete_reference_table(dbpath, obo, config) \\n \\n self._end()\",\n \"def insert_humans_staging(self):\\n for year in range(1880, CURRENT_YEAR):\\n self.load_wikidata(\\\"humans\\\", HUMANS_BY_YEAR_SPARQL_QUERY, INSERT_HUMAN_SQL_QUERY,\\n INSERT_HUMAN_MAP_COLUMNS, year=year)\",\n \"def createSchoolEnrollmentDataSet(baseyear_tazdata_df, tazdata_df):\\n HIGH_SCHOOL_ENROLLMENT_MODEL_SHARE_OF_HS_AGE_KIDS_ENROLLED_IN_HS = 0.966\\n\\n ageShares = pandas.read_csv(SCHOOLAGE_DISTRIBUTION)\\n collegeStudentShares = pandas.read_csv(COLLEGEAGE_DISTRIBUTION)\\n\\n logging.debug(\\\"Age shares:\\\\n{}\\\".format(ageShares.head()))\\n logging.debug(\\\"College Student shares:\\\\n{}\\\".format(collegeStudentShares.head()))\\n\\n # join to tazdata, baseyear_tazdata_df\\n tazdata_df = pandas.merge(left= tazdata_df, right=ageShares, how=\\\"left\\\")\\n tazdata_df = pandas.merge(left= tazdata_df, right=collegeStudentShares, how=\\\"left\\\")\\n baseyear_tazdata_df = pandas.merge(left=baseyear_tazdata_df, right=ageShares, how=\\\"left\\\")\\n baseyear_tazdata_df = pandas.merge(left=baseyear_tazdata_df, right=collegeStudentShares, how=\\\"left\\\")\\n\\n # compute the number of high school students -- assume X percent of kids this age attend high school, as calculated by Chuck\\n tazdata_df[\\\"highSchoolStudents\\\"] = tazdata_df[\\\"AGE0519\\\"]* tazdata_df[\\\"14to17In05to19\\\"]*HIGH_SCHOOL_ENROLLMENT_MODEL_SHARE_OF_HS_AGE_KIDS_ENROLLED_IN_HS\\n baseyear_tazdata_df[\\\"highSchoolStudents\\\"] = baseyear_tazdata_df[\\\"AGE0519\\\"]*baseyear_tazdata_df[\\\"14to17In05to19\\\"]*HIGH_SCHOOL_ENROLLMENT_MODEL_SHARE_OF_HS_AGE_KIDS_ENROLLED_IN_HS\\n\\n # compute the number of college students\\n tazdata_df[\\\"collegeStudents\\\"] = ( tazdata_df[\\\"AGE0519\\\"]* tazdata_df[\\\"18to19In05to19\\\"]* tazdata_df[\\\"StudentsAge18to19\\\"]) + \\\\\\n ( tazdata_df[\\\"AGE2044\\\"]* tazdata_df[\\\"20to24In20to44\\\"]* tazdata_df[\\\"StudentsAge20to24\\\"]) + \\\\\\n ( tazdata_df[\\\"AGE2044\\\"]* tazdata_df[\\\"25to44In20to44\\\"]* tazdata_df[\\\"StudentsAge25to44\\\"])\\n baseyear_tazdata_df[\\\"collegeStudents\\\"] = (baseyear_tazdata_df[\\\"AGE0519\\\"]*baseyear_tazdata_df[\\\"18to19In05to19\\\"]*baseyear_tazdata_df[\\\"StudentsAge18to19\\\"]) + \\\\\\n (baseyear_tazdata_df[\\\"AGE2044\\\"]*baseyear_tazdata_df[\\\"20to24In20to44\\\"]*baseyear_tazdata_df[\\\"StudentsAge20to24\\\"]) + \\\\\\n (baseyear_tazdata_df[\\\"AGE2044\\\"]*baseyear_tazdata_df[\\\"25to44In20to44\\\"]*baseyear_tazdata_df[\\\"StudentsAge25to44\\\"])\\n\\n # sum high school and college students by county\\n baseyear_students_df = baseyear_tazdata_df[[\\\"COUNTY\\\",\\\"highSchoolStudents\\\",\\\"collegeStudents\\\"]].groupby(\\\"COUNTY\\\").agg(\\\"sum\\\")\\n forecast_students_df = tazdata_df[[\\\"COUNTY\\\",\\\"highSchoolStudents\\\",\\\"collegeStudents\\\"]].groupby(\\\"COUNTY\\\").agg(\\\"sum\\\")\\n\\n # rename and join baseyear and forecast county tables\\n baseyear_students_df.rename(columns={\\\"highSchoolStudents\\\":\\\"highSchoolStudents_baseyear\\\", \\\"collegeStudents\\\":\\\"collegeStudents_baseyear\\\"}, inplace=True)\\n forecast_students_df.rename(columns={\\\"highSchoolStudents\\\":\\\"highSchoolStudents_forecast\\\", \\\"collegeStudents\\\":\\\"collegeStudents_forecast\\\"}, inplace=True)\\n students_df = pandas.merge(left=baseyear_students_df, right=forecast_students_df, left_index=True, right_index=True)\\n\\n students_df[\\\"highSchoolGrowthRatio\\\"] = students_df[\\\"highSchoolStudents_forecast\\\"]/students_df[\\\"highSchoolStudents_baseyear\\\"]\\n students_df[\\\"collegeGrowthRatio\\\" ] = students_df[\\\"collegeStudents_forecast\\\"] /students_df[\\\"collegeStudents_baseyear\\\"]\\n logging.debug(\\\"students_df:\\\\n{}\\\".format(students_df))\\n\\n # join growth ratios to baseyear_tazdata_df and apply\\n baseyear_tazdata_df = pandas.merge(left=baseyear_tazdata_df, right=students_df[[\\\"highSchoolGrowthRatio\\\",\\\"collegeGrowthRatio\\\"]], how=\\\"left\\\", left_on=\\\"COUNTY\\\", right_index=True)\\n baseyear_tazdata_df[\\\"HSENROLL_forecast\\\"] = baseyear_tazdata_df[\\\"HSENROLL\\\"]*baseyear_tazdata_df[\\\"highSchoolGrowthRatio\\\"]\\n baseyear_tazdata_df[\\\"COLLFTE_forecast\\\"] = baseyear_tazdata_df[\\\"COLLFTE\\\"] *baseyear_tazdata_df[\\\"collegeGrowthRatio\\\"]\\n baseyear_tazdata_df[\\\"COLLPTE_forecast\\\"] = baseyear_tazdata_df[\\\"COLLPTE\\\"] *baseyear_tazdata_df[\\\"collegeGrowthRatio\\\"]\\n\\n # join result to tazdata_df\\n tazdata_df = pandas.merge(left=tazdata_df, right=baseyear_tazdata_df[[\\\"ZONE\\\",\\\"HSENROLL_forecast\\\",\\\"COLLFTE_forecast\\\",\\\"COLLPTE_forecast\\\"]], how=\\\"left\\\", on=\\\"ZONE\\\")\\n tazdata_df.rename(columns={\\\"HSENROLL_forecast\\\":\\\"HSENROLL\\\",\\n \\\"COLLFTE_forecast\\\" :\\\"COLLFTE\\\",\\n \\\"COLLPTE_forecast\\\" :\\\"COLLPTE\\\"}, inplace=True)\\n\\n # remove intermediate variables\\n tazdata_df.drop(columns=[\\\"05to13In05to19\\\",\\\"14to17In05to19\\\",\\\"18to19In05to19\\\",\\\"25to44In20to44\\\",\\\"20to24In20to44\\\",\\n \\\"StudentsAge18to19\\\",\\\"StudentsAge20to24\\\",\\\"StudentsAge25to44\\\",\\n \\\"highSchoolStudents\\\",\\\"collegeStudents\\\"], inplace=True)\\n return tazdata_df\",\n \"def init_tables(self) -> None:\\n # TODO(#93) maybe raise flag when the schema of existing tables isn't what we expect\\n # it to be?\\n # \\\"How to know that schema changes?\\\"\\n # logger.warning(\\\"some message\\\")\\n with self.table_access_condition:\\n conn = self._get_connection()\\n conn.execute(\\\"PRAGMA foreign_keys = 1\\\")\\n with conn:\\n c = conn.cursor()\\n c.execute(CREATE_PROJECTS_TABLE)\\n c.execute(CREATE_TASKS_TABLE)\\n c.execute(CREATE_REQUESTERS_TABLE)\\n c.execute(CREATE_TASK_RUNS_TABLE)\\n c.execute(CREATE_ASSIGNMENTS_TABLE)\\n c.execute(CREATE_UNITS_TABLE)\\n c.execute(CREATE_WORKERS_TABLE)\\n c.execute(CREATE_AGENTS_TABLE)\\n c.execute(CREATE_QUALIFICATIONS_TABLE)\\n c.execute(CREATE_GRANTED_QUALIFICATIONS_TABLE)\\n c.execute(CREATE_ONBOARDING_AGENTS_TABLE)\",\n \"def initialize():\\n db.connect()\\n db.create_tables([Expense], safe=True)\",\n \"def __create_dummies(self):\\n zero_teacher = Teachers.get_or_create(teacher_key='0', UID='0')\\n zero_parent = Parents.get_or_create(parent_key='0', UID='0')\\n zero_tutor = Tutors.get_or_create(tutor_key='0', UID='0')\\n zero_student = Students.get_or_create(student_key='0', UID='0', parent=zero_parent[0])\\n zero_course = Courses.get_or_create(course_key='0')\\n zero_group = Groups.get_or_create(group_key='0', teacher=zero_teacher[0], tutor=zero_tutor[0],\\n course=zero_course[0])\",\n \"def set_data(self):\\n # take care of samples\\n patients = self.samples.iloc[:,1].tolist()\\n samples = self.samples.iloc[:,0].tolist()\\n self.samples = pd.DataFrame(patients,index = samples,columns = ['patient']) # indexed by sample\\n #\\n # take care of expression data\\n cols = self.expression.SYMBOL.tolist() # set new column names to transposed expression_data \\n \\n new_exp = self.expression.T.ix[1:,:] # transpose\\n new_exp.columns = cols\\n self.expression = new_exp # add columns\\n self.data = pd.merge(self.expression,self.samples,left_index = True,right_index=True) # merged data sets\\n #pd.merge(df1,df2,how = 'left',left_index=True,right_index=True) # do a left join\",\n \"def load_disasters():\\n\\n print \\\"Disasters\\\"\\n\\n #deletes any data within the table before seeding\\n Disaster.query.delete()\\n\\n\\n result_count = 1000\\n iteration = 0\\n records_returned = 1000\\n\\n # makes payload requests from FEMA API\\n while records_returned == 1000:\\n payload = {'$top': result_count,\\n '$skip': result_count * iteration,\\n '$inlinecount': 'allpages',\\n '$filter': 'declarationDate ge \\\\'1990-01-01T04:00:00.000z\\\\'',\\n '$select': 'disasterNumber,declarationDate,state,incidentType,title,incidentBeginDate,incidentEndDate,placeCode,declaredCountyArea'}\\n r = requests.get(\\n \\\"http://www.fema.gov/api/open/v1/DisasterDeclarationsSummaries\\\",\\n params=payload)\\n\\n # iteration counter, starts at zero, for every iteration add 1\\n iteration += 1\\n\\n disaster_info = r.json()\\n metadata = disaster_info['metadata']\\n record_count = metadata['count']\\n records_returned = len(disaster_info['DisasterDeclarationsSummaries'])\\n\\n # parsing through the information returned from API\\n for incident_dict in disaster_info['DisasterDeclarationsSummaries']:\\n disasterNumber = incident_dict.get('disasterNumber')\\n declarationDate = incident_dict.get('declarationDate')\\n state = incident_dict.get('state')\\n incidentType = incident_dict.get('incidentType')\\n title = incident_dict.get('title')\\n incidentBeginDate = incident_dict.get('incidentBeginDate')\\n incidentEndDate = incident_dict.get('incidentEndDate')\\n placeCode = incident_dict.get('placeCode')\\n declaredCountyArea = incident_dict.get('declaredCountyArea')\\n\\n \\\"\\\"\\\"Try/Except does two things: the try is doing a check to see if the county is already in the counties tables and if it is, then setting the Disaster.countyArea_id in the disasters table. The except is occuring only when the NoResultFound occurs and is creating the county and adding it to the counties table.\\\"\\\"\\\"\\n try:\\n #variable county set to \\\"answer\\\" of query\\n county_check = County.query.filter(County.county_name==declaredCountyArea, County.state_code==state).one()\\n\\n countyArea_id = county_check.county_id\\n\\n # creating a county when NoResultFound error occurs\\n except NoResultFound:\\n\\n county = County(state_code=state,\\n county_name=declaredCountyArea)\\n\\n db.session.add(county)\\n #!!!!!!!!! ask bonnie about this again !!!!!!!!!!#\\n db.session.flush()\\n\\n countyArea_id = county.county_id\\n\\n county = County.query.filter(County.county_name==declaredCountyArea, County.state_code==state).one()\\n countyArea_id = county.county_id\\n\\n disaster = Disaster(disasterNumber=disasterNumber,\\n state=state,\\n declarationDate=declarationDate,\\n incidentType=incidentType,\\n title=title,\\n incidentBeginDate=incidentBeginDate,\\n incidentEndDate=incidentEndDate,\\n placeCode=placeCode,\\n declaredCountyArea=declaredCountyArea,\\n countyArea_id=countyArea_id)\\n\\n db.session.add(disaster)\\n\\n db.session.commit()\\n print \\\"Disasters and Counties seeded\\\"\",\n \"def create_tables(cur, country_json, xml_state, body_json):\\n print(\\\"Creating the 3 first tables...\\\")\\n cur.execute('CREATE TABLE IF NOT EXISTS country_purchases(idx INTEGER PRIMARY KEY, state TEXT, amount INTEGER)')\\n with open(\\\"country_purchases.csv\\\", 'r') as f:\\n for idx, line in enumerate(f.read().split('\\\\n')):\\n line_splt = line.split(',')\\n try:\\n cur.execute('INSERT INTO country_purchases VALUES(\\\"%s\\\", \\\"%s\\\", \\\"%s\\\")' % (idx, line_splt[0], line_splt[1]))\\n except IndexError:\\n pass\\n cur.execute('CREATE TABLE IF NOT EXISTS country_total_purchases(idx INTEGER PRIMARY KEY, state TEXT, amount INTEGER)')\\n with open(\\\"country_total_purchases.csv\\\", 'r') as f:\\n for idx, line in enumerate(f.read().split('\\\\n')):\\n line_splt = line.split(',')\\n try:\\n cur.execute('INSERT INTO country_total_purchases VALUES(\\\"%s\\\", \\\"%s\\\", \\\"%s\\\")' % (idx, line_splt[0], line_splt[1]))\\n except IndexError:\\n pass\\n cur.execute('CREATE TABLE IF NOT EXISTS country_albums(ID INTEGER PRIMARY KEY, state TEXT, year INTEGER, genre TEXT, album TEXT, amount INTEGER)')\\n for idx, album in enumerate(country_json[body_json['state']]):\\n cur.execute('INSERT INTO country_albums VALUES (\\\"%s\\\", \\\"%s\\\", \\\"%s\\\", \\\"%s\\\", \\\"%s\\\", \\\"%s\\\")' %\\n (idx, body_json['state'], body_json['year'], body_json['genre'], album,\\n xml_state[0][0][idx].text))\",\n \"def populate_db(dbase):\\n # In this order: Iron, Blood, Shadow, Fel, Storm\\n wowhead_ids = []\\n wowhead_ids.extend(item.get_ids_from_wowhead_by_type(-8))\\n wowhead_ids.extend(item.get_ids_from_wowhead_by_type(-9))\\n wowhead_ids.extend(item.get_ids_from_wowhead_by_type(-10))\\n wowhead_ids.extend(item.get_ids_from_wowhead_by_type(-11))\\n wowhead_ids.extend(item.get_ids_from_wowhead_by_type(-17))\\n item_ids = set(wowhead_ids)\\n print(item_ids)\\n\\n pos = 0\\n for item_id in item_ids:\\n if pos % 10 == 0:\\n print(\\\"Relic %d of %d\\\" % (pos, len(item_ids)))\\n pos += 1\\n import_relic(dbase, item_id)\",\n \"def populate_db(dbase):\\n\\n enchants = [\\n # Cloak\\n {\\n 'spell_id': 5432,\\n 'stats': {'agility': 150},\\n 'icon': 'inv_enchant_formulagood_01',\\n 'item_name': 'Enchant Cloak - Word of Agility',\\n 'equip_location': 16,\\n 'tooltip_spell': 128546\\n },\\n {\\n 'spell_id': 5435,\\n 'stats': {'agility': 200},\\n 'icon': 'inv_enchant_formulasuperior_01',\\n 'item_name': 'Enchant Cloak - Binding of Agility',\\n 'equip_location': 16,\\n 'tooltip_spell': 128549\\n },\\n\\n # Ring\\n {\\n 'spell_id': 5423,\\n 'stats': {'crit': 150},\\n 'icon': 'inv_enchant_formulagood_01',\\n 'item_name': 'Enchant Ring - Word of Critical Strike',\\n 'equip_location': 11,\\n 'tooltip_spell': 128537\\n },\\n {\\n 'spell_id': 5424,\\n 'stats': {'haste': 150},\\n 'icon': 'inv_enchant_formulagood_01',\\n 'item_name': 'Enchant Ring - Word of Haste',\\n 'equip_location': 11,\\n 'tooltip_spell': 128538,\\n },\\n {\\n 'spell_id': 5425,\\n 'stats': {'mastery': 150},\\n 'icon': 'inv_enchant_formulagood_01',\\n 'item_name': 'Enchant Ring - Word of Mastery',\\n 'equip_location': 11,\\n 'tooltip_spell': 128539,\\n },\\n {\\n 'spell_id': 5426,\\n 'stats': {'versatility': 150},\\n 'icon': 'inv_enchant_formulagood_01',\\n 'item_name': 'Enchant Ring - Word of Versatility',\\n 'equip_location': 11,\\n 'tooltip_spell': 128540,\\n },\\n {\\n 'spell_id': 5427,\\n 'stats': {'crit': 200},\\n 'icon': 'inv_enchant_formulasuperior_01',\\n 'item_name': 'Enchant Ring - Binding of Critical Strike',\\n 'equip_location': 11,\\n 'tooltip_spell': 128541,\\n },\\n {\\n 'spell_id': 5428,\\n 'stats': {'haste': 200},\\n 'icon': 'inv_enchant_formulasuperior_01',\\n 'item_name': 'Enchant Ring - Binding of Haste',\\n 'equip_location': 11,\\n 'tooltip_spell': 128542,\\n },\\n {\\n 'spell_id': 5429,\\n 'stats': {'mastery': 200},\\n 'icon': 'inv_enchant_formulasuperior_01',\\n 'item_name': 'Enchant Ring - Binding of Mastery',\\n 'equip_location': 11,\\n 'tooltip_spell': 128543,\\n },\\n {\\n 'spell_id': 5430,\\n 'stats': {'versatility': 200},\\n 'icon': 'inv_enchant_formulasuperior_01',\\n 'item_name': 'Enchant Ring - Binding of Versatility',\\n 'equip_location': 128544,\\n },\\n\\n # Neck\\n {\\n 'spell_id': 5437,\\n 'stats': {},\\n 'icon': 'inv_enchant_formulasuperior_01',\\n 'item_name': 'Enchant Neck - Mark of the Claw',\\n 'equip_location': 2,\\n 'tooltip_spell': 128551,\\n 'is_proc': True\\n },\\n {\\n 'spell_id': 5438,\\n 'stats': {},\\n 'icon': 'inv_enchant_formulasuperior_01',\\n 'item_name': 'Enchant Neck - Mark of the Distant Army',\\n 'equip_location': 2,\\n 'tooltip_spell': 128552,\\n 'is_proc': True\\n },\\n {\\n 'spell_id': 5439,\\n 'stats': {},\\n 'icon': 'inv_enchant_formulasuperior_01',\\n 'item_name': 'Enchant Neck - Mark of the Hidden Satyr',\\n 'equip_location': 2,\\n 'tooltip_spell': 128553,\\n 'is_proc': True\\n },\\n {\\n 'spell_id': 5890,\\n 'stats': {'mastery': 300},\\n 'icon': 'inv_enchant_formulasuperior_01',\\n 'item_name': 'Enchant Neck - Mark of the Trained Soldier',\\n 'equip_location': 2,\\n 'tooltip_spell': 141909,\\n 'is_proc': False\\n }\\n ]\\n\\n for enchant in enchants:\\n dbase.enchants.replace_one({'spell_id': enchant['spell_id']},\\n enchant, upsert=True)\",\n \"def populate_employees():\\n employees = get_employees()\\n\\n db.session.bulk_save_objects(employees)\\n db.session.commit()\",\n \"def setUp(self):\\n # Create domain tables required for the test\\n super().setUp()\\n\\n # Load the test data\\n condition_occurrence_data_template = self.jinja_env.from_string(\\\"\\\"\\\"\\n CREATE OR REPLACE TABLE `{{project_id}}.{{dataset_id}}.condition_occurrence`\\n (\\n condition_occurrence_id int64, \\n person_id int64, \\n condition_concept_id int64, \\n stop_reason STRING,\\n condition_source_value STRING,\\n condition_status_source_value STRING)\\n AS (\\n WITH w AS (\\n SELECT ARRAY<STRUCT<\\n condition_occurrence_id int64, \\n person_id int64, \\n condition_concept_id int64, \\n stop_reason STRING,\\n condition_source_value STRING,\\n condition_status_source_value STRING\\n >>\\n [(1, 1, 0, 'stop reason', 'source value', 'status'),\\n (2, 1, 0, 'stop reason', 'source value', 'status'),\\n (3, 1, 0, 'stop reason', 'source value', 'status'),\\n (4, 1, 0, 'stop reason', 'source value', 'status')] col\\n )\\n SELECT \\n condition_occurrence_id, \\n person_id, \\n condition_concept_id, \\n stop_reason,\\n condition_source_value,\\n condition_status_source_value \\n FROM w, UNNEST(w.col))\\n \\\"\\\"\\\")\\n\\n # Load the test data\\n observation_data_template = self.jinja_env.from_string(\\\"\\\"\\\"\\n CREATE OR REPLACE TABLE `{{project_id}}.{{dataset_id}}.observation`\\n (\\n observation_id int64,\\n person_id int64,\\n observation_concept_id int64,\\n observation_source_concept_id int64,\\n value_as_string STRING,\\n observation_source_value STRING,\\n unit_source_value STRING,\\n qualifier_source_value STRING,\\n value_source_value STRING\\n )\\n AS (\\n -- 1585250 corresponds to the zipcode concept that is not subject to string suppression, value_as_string for this record should be kept --\\n WITH w AS (\\n SELECT ARRAY<STRUCT<\\n observation_id int64, \\n person_id int64, \\n observation_concept_id int64,\\n observation_source_concept_id int64,\\n value_as_string STRING, \\n observation_source_value STRING,\\n unit_source_value STRING,\\n qualifier_source_value STRING,\\n value_source_value STRING\\n >>\\n [(1, 1, 0, 1585250, '111111', 'observation_source_value', 'unit_source_value', 'qualifier_source_value', 'value_source_value'),\\n (2, 1, 0, 0, 'value_as_string', 'observation_source_value', 'unit_source_value', 'qualifier_source_value', 'value_source_value'),\\n (3, 1, 0, 0, 'value_as_string', 'observation_source_value', 'unit_source_value', 'qualifier_source_value', 'value_source_value'),\\n (4, 1, 0, 0, 'value_as_string', 'observation_source_value', 'unit_source_value', 'qualifier_source_value', 'value_source_value'),\\n (5, 1, 0, 0, 'value_as_string', 'observation_source_value', 'unit_source_value', 'qualifier_source_value', 'value_source_value'),\\n (6, 1, 0, 715711, 'foo_date', 'observation_source_value', 'unit_source_value', 'qualifier_source_value', 'value_source_value')] col\\n )\\n SELECT \\n observation_id,\\n person_id,\\n observation_concept_id,\\n observation_source_concept_id,\\n value_as_string, \\n observation_source_value,\\n unit_source_value,\\n qualifier_source_value,\\n value_source_value \\n FROM w, UNNEST(w.col))\\n \\\"\\\"\\\")\\n\\n insert_condition_query = condition_occurrence_data_template.render(\\n project_id=self.project_id, dataset_id=self.dataset_id)\\n\\n insert_observation_query = observation_data_template.render(\\n project_id=self.project_id, dataset_id=self.dataset_id)\\n\\n # Load test data\\n self.load_test_data([\\n f'''{insert_condition_query};\\n {insert_observation_query};'''\\n ])\",\n \"def init_beeswax_db(cls):\\n global _INITIALIZED\\n if _INITIALIZED:\\n return\\n\\n make_query(cls.client, 'CREATE DATABASE IF NOT EXISTS %(db)s' % {'db': cls.db_name}, wait=True)\\n make_query(cls.client, 'CREATE DATABASE IF NOT EXISTS %(db)s_other' % {'db': cls.db_name}, wait=True)\\n\\n if cls.load_data:\\n\\n data_file = cls.cluster.fs_prefix + u'/beeswax/sample_data_échantillon_%d.tsv'\\n\\n # Create a \\\"test_partitions\\\" table.\\n CREATE_PARTITIONED_TABLE = \\\"\\\"\\\"\\n CREATE TABLE `%(db)s`.`test_partitions` (foo INT, bar STRING)\\n PARTITIONED BY (baz STRING, boom INT)\\n ROW FORMAT DELIMITED\\n FIELDS TERMINATED BY '\\\\t'\\n LINES TERMINATED BY '\\\\n'\\n \\\"\\\"\\\" % {'db': cls.db_name}\\n make_query(cls.client, CREATE_PARTITIONED_TABLE, wait=True)\\n cls._make_data_file(data_file % 1)\\n\\n LOAD_DATA = \\\"\\\"\\\"\\n LOAD DATA INPATH '%(data_file)s'\\n OVERWRITE INTO TABLE `%(db)s`.`test_partitions`\\n PARTITION (baz='baz_one', boom=12345)\\n \\\"\\\"\\\" % {'db': cls.db_name, 'data_file': data_file % 1}\\n make_query(cls.client, LOAD_DATA, wait=True, local=False)\\n\\n # Insert additional partition data into \\\"test_partitions\\\" table\\n ADD_PARTITION = \\\"\\\"\\\"\\n ALTER TABLE `%(db)s`.`test_partitions` ADD PARTITION(baz='baz_foo', boom=67890) LOCATION '%(fs_prefix)s/baz_foo/boom_bar'\\n \\\"\\\"\\\" % {'db': cls.db_name, 'fs_prefix': cls.cluster.fs_prefix}\\n make_query(cls.client, ADD_PARTITION, wait=True, local=False)\\n\\n # Create a bunch of other tables\\n CREATE_TABLE = \\\"\\\"\\\"\\n CREATE TABLE `%(db)s`.`%(name)s` (foo INT, bar STRING)\\n COMMENT \\\"%(comment)s\\\"\\n ROW FORMAT DELIMITED\\n FIELDS TERMINATED BY '\\\\t'\\n LINES TERMINATED BY '\\\\n'\\n \\\"\\\"\\\"\\n\\n # Create a \\\"test\\\" table.\\n table_info = {'db': cls.db_name, 'name': 'test', 'comment': 'Test table'}\\n cls._make_data_file(data_file % 2)\\n cls._make_table(table_info['name'], CREATE_TABLE % table_info, data_file % 2)\\n\\n if is_live_cluster():\\n LOG.warning('HUE-2884: We cannot create Hive UTF8 tables when live cluster testing at the moment')\\n else:\\n # Create a \\\"test_utf8\\\" table.\\n table_info = {'db': cls.db_name, 'name': 'test_utf8', 'comment': cls.get_i18n_table_comment()}\\n cls._make_i18n_data_file(data_file % 3, 'utf-8')\\n cls._make_table(table_info['name'], CREATE_TABLE % table_info, data_file % 3)\\n\\n # Create a \\\"test_latin1\\\" table.\\n table_info = {'db': cls.db_name, 'name': 'test_latin1', 'comment': cls.get_i18n_table_comment()}\\n cls._make_i18n_data_file(data_file % 4, 'latin1')\\n cls._make_table(table_info['name'], CREATE_TABLE % table_info, data_file % 4)\\n\\n # Create a \\\"myview\\\" view.\\n make_query(cls.client, \\\"CREATE VIEW `%(db)s`.`myview` (foo, bar) as SELECT * FROM `%(db)s`.`test`\\\" % {'db': cls.db_name}, wait=True)\\n\\n _INITIALIZED = True\",\n \"def set_training_data(self):\\n # Optional training data period\\n # TODO: add training data period feature to training data query\\n if not self.training_period == None:\\n training_period_date = (datetime.datetime.utcnow() - timedelta(minutes=self.training_period)).strftime(\\\"%Y-%m-%d\\\")\\n print(f\\\"Training data start date: {training_period_date}\\\")\\n # Extract queried data from Athena\\n #athena = athena_connect.Athena()\\n #features_df = athena.pandas_read_athena(self.training_data_sql)\\n with open('feature_sql.txt', 'w') as f:\\n print(self.training_data_sql, file=f) \\n features_df = pd.read_sql(self.training_data_sql, self.logic_db_engine())\\n features_df.fillna(0, inplace=True)\\n print(features_df.shape)\\n features_df = features_df[max(self.feature_minutes_list):]\\n print(features_df.shape)\\n # Remove infinity string\\n features_df.replace({'Infinity': 0}, inplace=True)\\n # Convert all object fields to numeric except date fields\\n object_col_list = features_df.columns[features_df.dtypes.eq('object')]\\n object_col_list = [col for col in object_col_list if 'trade_date' not in col]\\n features_df[object_col_list] = features_df[object_col_list].apply(pd.to_numeric, errors='coerce')\\n self.training_df = features_df\",\n \"def seed_all():\\n seed_client()\\n seed_staff()\\n seed_request()\\n seed_comment()\",\n \"def initialize_database(study_file, grasp_file, commit_every=250000,\\n progress=False):\\n rows = 0\\n count = commit_every\\n pphenos = {}\\n phenos = {}\\n platforms = {}\\n populations = {}\\n\\n\\n # Create tables\\n _, engine = get_session()\\n print('Dropping and creating database tables, this may take a while if',\\n 'the old database is large.')\\n if _config['DEFAULT']['DatabaseType'] == 'sqlite':\\n cfile = _os.path.isfile(_config['sqlite']['DatabaseFile'])\\n if _os.path.isfile(cfile):\\n _os.remove(cfile)\\n _Base.metadata.drop_all(engine)\\n _Base.metadata.create_all(engine)\\n print('Tables created.')\\n conn = engine.connect()\\n\\n # Get tables\\n study_table = _Study.__table__\\n snp_table = _SNP.__table__\\n pheno_table = _Phenotype.__table__\\n pcat_table = _PhenoCats.__table__\\n plat_table = _Platform.__table__\\n pop_table = _Population.__table__\\n\\n # Create insert statements\\n study_ins = study_table.insert()\\n snp_ins = snp_table.insert()\\n pheno_ins = pheno_table.insert()\\n pcat_ins = pcat_table.insert()\\n plat_ins = plat_table.insert()\\n pop_ins = pop_table.insert()\\n phsnp_ins = _snp_pheno_assoc.insert()\\n phstudy_ins = _study_pheno_assoc.insert()\\n plstudy_ins = _study_plat_assoc.insert()\\n\\n # Unique ID counters\\n spare_id = 1\\n pheno_id = 1\\n pcat_id = 1\\n plat_id = 1\\n pop_id = 1\\n\\n # Lists to hold records\\n pheno_records = []\\n pcat_records = []\\n plat_records = []\\n pop_records = []\\n study_records = []\\n snp_records = []\\n phsnp_records = []\\n phstudy_records = []\\n plstudy_records = []\\n\\n # Platform parsing regex\\n plat_parser = _recompile(r'^([^[]*)\\\\[([^]]+)\\\\]?(.*)')\\n\\n # Build study information from study file\\n print('Parsing study information.')\\n with _open_zipped(study_file) as fin:\\n # Drop header\\n fin.readline()\\n\\n if progress:\\n pbar = _tqdm(total=2083, unit='studies')\\n for line in fin:\\n f = line.rstrip().split('\\\\t')\\n\\n # Get primary phenotype\\n ppheno = _cleanstr(f[7].strip())\\n if ppheno not in pphenos:\\n pheno_records.append({'phenotype': ppheno,\\n 'id': pheno_id})\\n pphenos[ppheno] = pheno_id\\n pheno_id += 1\\n\\n # Get phenotype categories\\n pheno_cats = f[8].strip().split(';')\\n our_phenos = []\\n for pcat in pheno_cats:\\n pcat = pcat.strip()\\n if not pcat:\\n continue\\n if pcat not in phenos:\\n pcat_records.append({\\n 'id': pcat_id,\\n 'category': pcat,\\n 'alias': pheno_synonyms[pcat],\\n })\\n phenos[pcat] = pcat_id\\n pcat_id += 1\\n our_phenos.append(phenos[pcat])\\n\\n # Get platform info\\n our_platforms = []\\n try:\\n plat, snp_count, impt = [\\n i.strip() for i in plat_parser.findall(f[18].strip())[0]\\n ]\\n imputed = True if impt == '(imputed)' else False\\n plats = _split_mesy_list(plat)\\n for plat in plats:\\n plat = plat.strip()\\n if plat not in platforms:\\n plat_records.append({'id': plat_id,\\n 'platform': plat})\\n platforms[plat] = plat_id\\n plat_id += 1\\n our_platforms.append(platforms[plat])\\n except IndexError:\\n plat, snp_count, impt = None, None, None\\n imputed = None\\n\\n # Get population description\\n try:\\n pop = f[19].strip()\\n try:\\n pop = pop_correction[pop]\\n except KeyError:\\n pass\\n if pop not in populations:\\n pop_records.append({'id': pop_id,\\n 'population': pop})\\n populations[pop] = pop_id\\n pop_id += 1\\n population = populations[pop]\\n except IndexError:\\n population = None\\n\\n # Set populaion flags\\n pflag = _PopFlag\\n disc_pop = pflag(0)\\n rep_pop = pflag(0)\\n l = len(f)\\n if l > 22 and f[22]:\\n disc_pop |= pflag.eur\\n if l > 23 and f[23]:\\n disc_pop |= pflag.afr\\n if l > 24 and f[24]:\\n disc_pop |= pflag.east_asian\\n if l > 25 and f[25]:\\n disc_pop |= pflag.south_asian\\n if l > 26 and f[26]:\\n disc_pop |= pflag.his\\n if l > 27 and f[27]:\\n disc_pop |= pflag.native\\n if l > 28 and f[28]:\\n disc_pop |= pflag.micro\\n if l > 29 and f[29]:\\n disc_pop |= pflag.arab\\n if l > 30 and f[30]:\\n disc_pop |= pflag.mix\\n if l > 31 and f[31]:\\n disc_pop |= pflag.uns\\n if l > 32 and f[32]:\\n disc_pop |= pflag.filipino\\n if l > 33 and f[33]:\\n disc_pop |= pflag.indonesian\\n if l > 35 and f[35]:\\n rep_pop |= pflag.eur\\n if l > 36 and f[36]:\\n rep_pop |= pflag.afr\\n if l > 37 and f[37]:\\n rep_pop |= pflag.east_asian\\n if l > 38 and f[38]:\\n rep_pop |= pflag.south_asian\\n if l > 39 and f[39]:\\n rep_pop |= pflag.his\\n if l > 40 and f[40]:\\n rep_pop |= pflag.native\\n if l > 41 and f[41]:\\n rep_pop |= pflag.micro\\n if l > 42 and f[42]:\\n rep_pop |= pflag.arab\\n if l > 43 and f[43]:\\n rep_pop |= pflag.mix\\n if l > 44 and f[44]:\\n rep_pop |= pflag.uns\\n if l > 45 and f[45]:\\n rep_pop |= pflag.filipino\\n if l > 46 and f[46]:\\n rep_pop |= pflag.indonesian\\n\\n # Set the global population flag\\n pop_flag = disc_pop | rep_pop\\n\\n # Create study\\n study_records.append({\\n 'id': int(f[0]),\\n 'author': _cleanstr(f[1]),\\n 'pmid': _cleanstr(f[2]),\\n 'grasp_ver': 1 if '1.0' in f[3] else 2,\\n 'noresults': True if f[4] else False,\\n 'results': int(f[5]),\\n 'qtl': True if f[6] == '1' else False,\\n 'phenotype_id': pphenos[ppheno],\\n 'phenotype_desc': ppheno,\\n 'phenotype': pphenos[ppheno],\\n 'phenotype_cats': our_phenos,\\n 'datepub': _get_date(f[9]),\\n 'in_nhgri': _get_bool(f[10]),\\n 'journal': _cleanstr(f[11]),\\n 'title': _cleanstr(f[12]),\\n 'locations': _cleanstr(f[13]),\\n 'mf': _get_bool(f[14]),\\n 'mf_only': _get_bool(f[15]),\\n 'sample_size': _cleanstr(f[16]),\\n 'replication_size': _cleanstr(f[17]),\\n 'platforms': platforms,\\n 'snp_count': snp_count,\\n 'imputed': imputed,\\n 'population_id': population,\\n 'population': population,\\n 'total': int(f[20]),\\n 'total_disc': int(f[21]),\\n 'pop_flag': int(pop_flag),\\n 'disc_pop_flag': int(disc_pop),\\n 'european': int(f[22]) if l > 22 and f[22] else None,\\n 'african': int(f[23]) if l > 23 and f[23] else None,\\n 'east_asian': int(f[24]) if l > 24 and f[24] else None,\\n 'south_asian': int(f[25]) if l > 25 and f[25] else None,\\n 'hispanic': int(f[26]) if l > 26 and f[26] else None,\\n 'native': int(f[27]) if l > 27 and f[27] else None,\\n 'micronesian': int(f[28]) if l > 28 and f[28] else None,\\n 'arab': int(f[29]) if l > 29 and f[29] else None,\\n 'mixed': int(f[30]) if l > 30 and f[30] else None,\\n 'unspecified': int(f[31]) if l > 31 and f[31] else None,\\n 'filipino': int(f[32]) if l > 32 and f[32] else None,\\n 'indonesian': int(f[33]) if l > 33 and f[33] else None,\\n 'total_rep': int(f[34]) if l > 34 and f[34] else None,\\n 'rep_pop_flag': int(rep_pop),\\n 'rep_european': int(f[35]) if l > 35 and f[35] else None,\\n 'rep_african': int(f[36]) if l > 36 and f[36] else None,\\n 'rep_east_asian': int(f[37]) if l > 37 and f[37] else None,\\n 'rep_south_asian': int(f[38]) if l > 38 and f[38] else None,\\n 'rep_hispanic': int(f[39]) if l > 39 and f[39] else None,\\n 'rep_native': int(f[40]) if l > 40 and f[40] else None,\\n 'rep_micronesian': int(f[41]) if l > 41 and f[41] else None,\\n 'rep_arab': int(f[42]) if l > 42 and f[42] else None,\\n 'rep_mixed': int(f[43]) if l > 43 and f[43] else None,\\n 'rep_unspecified': int(f[44]) if l > 44 and f[44] else None,\\n 'rep_filipino': int(f[45]) if l > 45 and f[45] else None,\\n 'rep_indonesian': int(f[46]) if l > 46 and f[46] else None,\\n })\\n\\n # Create association records\\n for i in our_phenos:\\n phstudy_records.append({'study_id': int(f[0]),\\n 'pheno_id': i})\\n for i in our_platforms:\\n plstudy_records.append({'study_id': int(f[0]),\\n 'platform_id': i})\\n\\n pbar.update()\\n\\n pbar.close()\\n print('Writing study information...')\\n conn.execute(pheno_ins, pheno_records)\\n conn.execute(pcat_ins, pcat_records)\\n conn.execute(plat_ins, plat_records)\\n conn.execute(pop_ins, pop_records)\\n conn.execute(study_ins, study_records)\\n conn.execute(phstudy_ins, phstudy_records)\\n conn.execute(plstudy_ins, plstudy_records)\\n print('Done')\\n\\n # Reinitialize lists for main GRASP parser\\n pheno_records = []\\n pcat_records = []\\n plat_records = []\\n pop_records = []\\n\\n # Get full study info from database for use in SNPs\\n sinfo = conn.execute(_select([study_table.c.id, study_table.c.pmid])).fetchall()\\n studies = {}\\n for i, p in sinfo:\\n studies[p] = i\\n no_pmid = {\\n 'Dissertation (https://openaccess.leidenuniv.nl/handle/1887/17746)': 1,\\n 'KARE Genomewide Association Study of Blood Pressure Using Imputed SNPs': 2,\\n 'Genome-wide Association Study Identification of a New Genetic Locus with Susceptibility to Osteoporotic Fracture in the Korean Population.': 3,\\n 'Genome-wide Association Study Identified TIMP2 Genetic Variant with Susceptibility to Osteoarthritis': 4,\\n 'Application of Structural Equation Models to Genome-wide Association Analysis ': 5,\\n 'Comparison of Erythrocyte Traits Among European, Japanese and Korean': 6,\\n 'Genomewide Association Study Identification of a New Genetic Locus with Susceptibility to Osteoporotic Fracture in the Korean Population': 7,\\n 'Joint identification of multiple genetic variants of obesity in A Korean Genome-wide association study': 8,\\n 'Genome-Wide Association Analyses on Blood Pressure Using Three Different Phenotype Definitions': 9,\\n 'Association of intronic sequence variant in the gene encoding spleen tyrosine kinase with susceptibility to vascular dementia': 10,\\n }\\n\\n print('Parsing SNP information...')\\n with _open_zipped(grasp_file, encoding='latin1') as fin:\\n # Drop header\\n fin.readline()\\n\\n if progress:\\n pbar = _tqdm(total=8864717, unit='snps')\\n\\n for line in fin:\\n f = line.rstrip().split('\\\\t')\\n\\n # Get primary phenotype\\n ppheno = _cleanstr(f[11])\\n # These are poorly curated, so there is no need to use a\\n # separate table for them.\\n # if ppheno not in pphenos:\\n # conn.execute(pheno_ins.values(\\n # phenotype=ppheno\\n # ))\\n # pphenos[ppheno] = conn.execute(\\n # select([pheno_table.c.id]).where(\\n # pheno_table.c.phenotype == ppheno\\n # )\\n # ).first()[0]\\n\\n # Get phenotype categories\\n pheno_cats = f[13].strip().split(';')\\n our_phenos = []\\n for pcat in pheno_cats:\\n pcat = pcat.strip()\\n if not pcat:\\n continue\\n if pcat not in phenos:\\n pcat_records.append({\\n 'id': pcat_id,\\n 'category': pcat,\\n 'alias': pheno_synonyms[pcat],\\n })\\n phenos[pcat] = pcat_id\\n pcat_id += 1\\n our_phenos.append(phenos[pcat])\\n\\n # Get population description\\n try:\\n pop = f[23].strip()\\n try:\\n pop = pop_correction[pop]\\n except KeyError:\\n pass\\n if pop not in populations:\\n pop_records.append({'id': pop_id,\\n 'population': pop})\\n populations[pop] = pop_id\\n pop_id += 1\\n population = populations[pop]\\n except IndexError:\\n population = None\\n\\n # Create record for SNP\\n try:\\n sid = int(f[0])\\n except ValueError:\\n sid = spare_id\\n spare_id += 1\\n l = len(f)\\n try:\\n study = studies[f[7].strip()]\\n except KeyError:\\n study = no_pmid[f[17].strip()]\\n record = {\\n 'id': sid,\\n 'NHLBIkey': f[0],\\n 'HUPfield': f[1],\\n 'LastCurationDate': _get_date(f[2]),\\n 'CreationDate': _get_date(f[3]),\\n 'snpid': f[4],\\n 'chrom': f[5],\\n 'pos': int(f[6]),\\n 'population_id': population,\\n 'population': population,\\n 'study_id': study,\\n 'study': study,\\n 'study_snpid': f[8],\\n 'paper_loc': f[9],\\n 'pval': float(f[10]) if f[10] else None,\\n 'phenotype_desc': ppheno,\\n 'phenotype_cats': our_phenos,\\n }\\n record['InGene'] = f[51] if l > 52 else None\\n record['NearestGene'] = f[52] if l > 53 else None\\n record['InLincRNA'] = f[53] if l > 54 else None\\n record['InMiRNA'] = f[54] if l > 55 else None\\n record['InMiRNABS'] = f[55] if l > 56 else None\\n record['dbSNPfxn'] = f[56] if l > 57 else None\\n record['dbSNPMAF'] = f[57] if l > 58 else None\\n record['dbSNPinfo'] = f[58] if l > 59 else None\\n record['dbSNPvalidation'] = f[59] if l > 60 else None\\n record['dbSNPClinStatus'] = f[60] if l > 61 else None\\n record['ORegAnno'] = f[61] if l > 62 else None\\n record['ConservPredTFBS'] = f[62] if l > 63 else None\\n record['HumanEnhancer'] = f[63] if l > 64 else None\\n record['RNAedit'] = f[64] if l > 65 else None\\n record['PolyPhen2'] = f[65] if l > 66 else None\\n record['SIFT'] = f[66] if l > 67 else None\\n record['LSSNP'] = f[67] if l > 68 else None\\n record['UniProt'] = f[68] if l > 69 else None\\n record['EqtlMethMetabStudy'] = f[69] if l > 70 else None\\n snp_records.append(record)\\n\\n # Create association records\\n for i in our_phenos:\\n phsnp_records.append({'snp_id' : sid, 'pheno_id' : i})\\n\\n # Decide when to execute\\n if count:\\n count -= 1\\n else:\\n if progress:\\n pbar.write('Writing rows...')\\n else:\\n print('Writing rows...')\\n if pcat_records:\\n conn.execute(pcat_ins, pcat_records)\\n if plat_records:\\n conn.execute(plat_ins, plat_records)\\n if pop_records:\\n conn.execute(pop_ins, pop_records)\\n conn.execute(snp_ins, snp_records)\\n conn.execute(phsnp_ins, phsnp_records)\\n if progress:\\n pbar.write('{} rows written'.format(rows))\\n else:\\n print('{} rows written'.format(rows))\\n count = commit_every-1\\n pcat_records = []\\n plat_records = []\\n pop_records = []\\n snp_records = []\\n phsnp_records = []\\n rows += 1\\n if progress:\\n pbar.update()\\n\\n # Final insert\\n pbar.close()\\n print('Writing final rows...')\\n conn.execute(snp_ins, snp_records)\\n conn.execute(phsnp_ins, phsnp_records)\\n print('{} rows written'.format(rows))\\n print('Done!')\",\n \"def setUp(self):\\n try:\\n # Get default data from medical_forum_data_dump.sql, populate tables and connect to DB\\n ENGINE.populate_tables()\\n self.connection = ENGINE.connect()\\n\\n # In case of error/exception in populating tables, clear all tables data\\n except Exception as exception:\\n print(exception)\\n ENGINE.clear()\",\n \"def populate_t_database():\\n with open('minerals.json') as file:\\n file = json.loads(file.read())\\n\\n for mineral in file[:22]:\\n mineral_entry = Mineral.objects.get_or_create(**mineral)\",\n \"def create_all_tables(self):\\n pass\",\n \"def full_initialization_process():\\n\\n db1 = Database('TOBACCO_RAW;')\\n con1, cur1 = db1.connect()\\n cur1.execute('create index idl_doc_field_id_idx on idl_doc_field(id);')\\n cur1.execute('create index idl_doc_id_idx on idl_doc(id);')\\n add_timestamp_to_idl_doc()\\n\\n create_utf_text_files()\\n\\n initialize_tables()\\n fill_tables()\",\n \"def ingest():\\n db.delete_dataset_records(DATASET_ID)\\n\\n db.insert_dataset({\\n 'dataset_id': DATASET_ID,\\n 'title': 'North American Breeding Bird Survey (BBS)',\\n 'version': '2016.0',\\n 'url': 'https://www.pwrc.usgs.gov/bbs/'})\\n\\n to_taxon_id = insert_taxa()\\n to_place_id = insert_places()\\n to_event_id = insert_events(to_place_id)\\n insert_counts(to_event_id, to_taxon_id)\",\n \"def load_fhwa_records():\\n print('--- Importing FHWA DFLTD v.2 records ---')\\n for i in tqdm(range(len(tbl_project))):\\n prj_id = tbl_project['lng_KeyProject'][i]\\n\\n expl_in_project = tbl_exploration[\\n tbl_exploration.lng_KeyProject == prj_id].index\\n for i_exp in expl_in_project:\\n expl_id = tbl_exploration['txt_KeyExplorationName'][i_exp]\\n\\n piles_in_project = tbl_deepfoundation[\\n tbl_deepfoundation.lng_KeyProject == prj_id].index\\n for i_pile in piles_in_project:\\n pile_id = tbl_deepfoundation['lng_KeyDeepFoundation'][i_pile]\\n\\n tests_for_pile = tbl_loadtest[\\n (tbl_loadtest.lng_KeyProject == prj_id) &\\n (tbl_loadtest.lng_KeyDeepFoundation == pile_id)\\n ].index\\n for i_lt in tests_for_pile:\\n test_id = tbl_loadtest['lng_KeyLoadTest'][i_lt]\\n\\n # -- Adding Project Data -------------------------------- #\\n if len(piles_in_project) > 1 and len(expl_in_project) < 2:\\n wrn = 'Expanded from a project with multiple piles '\\\\\\n 'and/or retests'\\n prj = add_loc_proj(i, wrn)\\n elif len(piles_in_project) < 2 and len(expl_in_project) > 1:\\n wrn = 'Expanded from a project with multiple '\\\\\\n 'explorations'\\n prj = add_loc_proj(i, wrn)\\n elif len(piles_in_project) > 1 and len(expl_in_project) > 1:\\n wrn = 'Expanded from a project with multiple '\\\\\\n 'explorations and multiple piles/retests'\\n prj = add_loc_proj(i, wrn)\\n else:\\n prj = add_loc_proj(i)\\n db.session.add(prj)\\n\\n # -- Adding Exploration Data ---------------------------- #\\n exploration = add_expl_data(i_exp, expl_id, prj)\\n db.session.add(exploration)\\n\\n # -- Adding Layer Data ---------------------------------- #\\n add_layer_data(prj_id, expl_id, exploration)\\n\\n # -- Adding Pile Data ----------------------------------- #\\n pile = add_pile_data(i_pile, prj_id, pile_id, prj)\\n db.session.add(pile)\\n\\n # -- Adding Load Test Data ------------------------------ #\\n load_test = add_load_test_data(i_lt, pile)\\n db.session.add(load_test)\\n\\n # -- Adding Static Test Data ---------------------------- #\\n add_static_test_data(prj_id, pile_id, test_id, load_test)\\n\\n # -- Adding Interpreted Data ---------------------------- #\\n add_interp_data(prj_id, pile_id, test_id, load_test)\\n\\n db.session.commit()\",\n \"def fill_in(self,category):\\r\\n api_json = JsonFromApi(category)\\r\\n extracted_data = ExtractFromJson(api_json.get_json())\\r\\n self.fill_in_db = DatabaseUpdator(\\r\\n extracted_data.extract_json(), self.mydb)\\r\\n self.fill_in_db.table_product_update()\\r\\n self.fill_in_db.table_category_update()\",\n \"def setUp(self):\\n self.listMembre = []\\n self.listMembre.append(\\\"bras gauche\\\")\\n self.listMembre.append(\\\"bras droit\\\")\\n self.exercice = data.Exercice(\\\"Mouvement des bras\\\",self.listMembre,\\\"Pour rééducation\\\", \\\"ex/path\\\")\\n self.exercice_a = data.Exercice(\\\"Mouvement des jambes\\\",self.listMembre,\\\"Pour rééducation\\\", \\\"ex/path\\\")\\n self.exercice_b = data.Exercice(\\\"Mouvement des hanches\\\",self.listMembre,\\\"Pour rééducation\\\", \\\"ex/path\\\")\\n self.exercice_1 = data.Exercice(\\\"Mouvement des\\\",self.listMembre,\\\"Pour rééducation\\\", \\\"ex/path\\\")\\n self.exercice_2 = data.Exercice(\\\"Mouvement hanches\\\",self.listMembre,\\\"Pour rééducation\\\", \\\"ex/path\\\")\\n\\n self.patient = data.Patient(\\\"Claude\\\" , \\\"123\\\")\\n self.patient_b = data.Patient(\\\"Gaetan\\\" , \\\"789\\\")\\n\\n self.patient_1 = data.Patient(\\\"JP\\\" , \\\"456\\\")\\n self.patient_2 = data.Patient(\\\"JN\\\" , \\\"654\\\")\\n\\n self.listExo = [self.exercice_a,self.exercice_b]\\n self.listExo_b = [self.exercice_1,self.exercice_2]\\n self.program_a = data.Program(self.listExo,self.patient_b)\\n self.program_b = data.Program(self.listExo_b,self.patient_b)\\n\\n self.bib_a = [self.exercice,self.exercice_a,self.exercice_b]\\n self.bib_b = [self.exercice_1,self.exercice_2]\\n\\n self.listP_a = [self.patient,self.patient_b]\\n self.listP_b = [self.patient_1,self.patient_2]\\n\\n self.allData = data.AppData(self.bib_a,self.listP_a,[self.program_a,self.program_b])\",\n \"def populate_jobs():\\n\\n logging.basicConfig(level=logging.INFO)\\n logger = logging.getLogger(__name__)\\n\\n database = SqliteDatabase('personjob.db')\\n\\n logger.info('Working with Job class')\\n logger.info('Creating Job records: just like Person. We use the foreign key')\\n\\n JOB_NAME = 0\\n START_DATE = 1\\n END_DATE = 2\\n SALARY = 3\\n PERSON_EMPLOYED = 4\\n DEPARTMENT = 5\\n\\n jobs = [\\n ('Analyst', '2001-09-22', '2003-01-30',65500, 'Andrew', 'ASYS'),\\n ('Senior analyst', '2003-02-01', '2006-10-22', 70000, 'Andrew', 'ASYS'),\\n ('Senior business analyst', '2006-10-23', '2016-12-24', 80000, 'Andrew', 'BUSI'),\\n ('Admin supervisor', '2012-10-01', '2014-11-10', 45900, 'Peter', 'ADMN'),\\n ('Admin manager', '2014-11-14', '2018-01-05', 45900, 'Peter', 'ADMN'),\\n ('Sr Project Manager', '2014-11-14', '2018-01-05', 100000, 'Ryan', 'ASYS'),\\n ('Manager', '2014-11-14', '2018-01-05', 100000, 'Pamela', 'BUSI'),\\n ('Director', '2014-11-14', '2018-01-05', 120000, 'Monica', 'MGMT'),\\n ]\\n\\n try:\\n database.connect()\\n database.execute_sql('PRAGMA foreign_keys = ON;')\\n for job in jobs:\\n with database.transaction():\\n new_job = Job.create(\\n job_name = job[JOB_NAME],\\n start_date = job[START_DATE],\\n end_date = job[END_DATE],\\n duration = dates_diff(job[END_DATE], job[START_DATE]),\\n salary = job[SALARY],\\n person_employed = job[PERSON_EMPLOYED],\\n job_department = job[DEPARTMENT])\\n new_job.save()\\n\\n logger.info('Reading and print all Job rows (note the value of person)...')\\n for job in Job:\\n logger.info(f'{job.job_name} : {job.start_date} to {job.end_date} for {job.person_employed} in {job.job_department}')\\n\\n except Exception as e:\\n logger.info(f'Error creating = {job[JOB_NAME]}')\\n logger.info(e)\\n\\n finally:\\n logger.info('database closes')\\n database.close()\",\n \"def _load_db(self):\\n for type_ in self._types:\\n try:\\n type_.table(self._metadata)\\n except InvalidRequestError:\\n pass\\n # Reflect metadata so auto-mapping works\\n self._metadata.reflect(self._engine)\\n # Make sure the tables exist\\n self._metadata.create_all()\",\n \"def setup(self):\\n # TODO check if need both dataset together\\n self.train_dataset = ABSADataset(data_path=self.train_path, mode=self.in_mode, task=self.task, \\n tokenizer=self.tokenizer, vocab=\\\"bert\\\")\\n self.vocabulary = self.train_dataset.vocabulary\\n\\n self.eval_dataset = ABSADataset(data_path=self.dev_path, mode=self.in_mode, task=self.task,\\n tokenizer=self.tokenizer, vocab=self.vocabulary)\\n #self.train_restaurant = ABSADataset(data_path=RESTAURANT_TRAIN)\\n #self.eval_restaurant = ABSADataset(data_path=RESTAURANT_DEV)\",\n \"def set_up_table(db):\\n db.bulk_query(\\\"\\\"\\\"\\n SET @@SQL_MODE = REPLACE(@@SQL_MODE, 'STRICT_TRANS_TABLES', '');\\n \\\"\\\"\\\")\\n db.bulk_query(\\\"\\\"\\\"\\n CREATE TABLE cats\\n (\\n id INT unsigned NOT NULL AUTO_INCREMENT, # Unique ID for the record\\n name VARCHAR(150) NOT NULL DEFAULT '', # Name of the cat\\n owner VARCHAR(150) NOT NULL DEFAULT '', # Owner of the cat\\n birth DATE NOT NULL, # Birthday of the cat\\n PRIMARY KEY (id) # Make the id the primary key\\n );\\n \\\"\\\"\\\")\\n db.bulk_query(\\\"\\\"\\\"\\n INSERT INTO cats ( name, owner, birth) VALUES\\n ( 'Sandy', 'Lennon', '2015-01-03' ),\\n ( 'Cookie', 'Casey', '2013-11-13' ),\\n ( 'Charlie', 'River', '2016-05-21' );\\n \\\"\\\"\\\")\",\n \"def prepare_data(self):\\r\\n annual_df = self.annual_df\\r\\n coef_df = self.coef_df\\r\\n quarter_df = self.quarter_df\\r\\n # historical_df = self.historical_df\\r\\n Event_Buffer = self.Event_Buffer\\r\\n\\r\\n Tot_Prod = coef_df[\\\"Product\\\"].nunique()\\r\\n # Tot_Week = coef_df[\\\"wk\\\"].nunique()\\r\\n Tot_Week = 52\\r\\n\\r\\n EDLP_Events = list(annual_df[\\\"RP_Events\\\"])\\r\\n Min_EDLP_Events = [\\r\\n i - Event_Buffer if i - Event_Buffer >= 0 else 0 for i in EDLP_Events\\r\\n ]\\r\\n Max_EDLP_Events = [\\r\\n i + Event_Buffer if i + Event_Buffer < Tot_Week + 1 else Tot_Week\\r\\n for i in EDLP_Events\\r\\n ]\\r\\n\\r\\n TPR_Events = list(annual_df[\\\"TPR_Events\\\"])\\r\\n Min_TPR_Events = [\\r\\n i - Event_Buffer if i - Event_Buffer >= 0 else 0 for i in TPR_Events\\r\\n ]\\r\\n Max_TPR_Events = [\\r\\n i + Event_Buffer if i + Event_Buffer < Tot_Week + 1 else Tot_Week\\r\\n for i in TPR_Events\\r\\n ]\\r\\n\\r\\n Target_EDLP_Spend = [i for i in annual_df[\\\"PPG_RP_Spend\\\"]]\\r\\n Target_TPR_Spend = [i for i in annual_df[\\\"PPG_TPR_Spend\\\"]]\\r\\n Target_Trade_Spend = [i for i in annual_df[\\\"PPG_Total_Spend\\\"]]\\r\\n\\r\\n Mapping = {}\\r\\n Prod_Ind = coef_df[\\\"Product\\\"][0:Tot_Prod]\\r\\n for i, j in zip(Prod_Ind.index, Prod_Ind.values):\\r\\n Mapping[j] = i\\r\\n Mapping_reverse = {i: j for j, i in Mapping.items()}\\r\\n\\r\\n constants = [i for i in coef_df[\\\"constant\\\"]]\\r\\n\\r\\n Cat_Coef = coef_df[\\\"Catalogue\\\"][0:Tot_Prod]\\r\\n\\r\\n Disp_Coef = coef_df[\\\"Display\\\"][0:Tot_Prod]\\r\\n\\r\\n Base_Price_stg1 = [i for i in quarter_df[\\\"Final_baseprice\\\"]]\\r\\n Intercepts_stg1 = []\\r\\n for pr in range(Tot_Prod):\\r\\n Intercepts_stg1.append(\\r\\n np.mean([constants[j * Tot_Prod + pr] for j in range(0, Tot_Week)])\\r\\n )\\r\\n\\r\\n Base_Price_stg2 = [[i] * Tot_Week for i in quarter_df[\\\"Final_baseprice\\\"]]\\r\\n Intercepts_stg2 = [\\r\\n constants[j : j + Tot_Prod] for j in range(0, len(constants), Tot_Prod)\\r\\n ] # noqa\\r\\n\\r\\n EDLP_Coef = np.array(\\r\\n coef_df[[i for i in coef_df.columns if i.count(\\\"Retailer_Regular\\\") == 1]]\\r\\n )\\r\\n TPR_Coef = np.array(\\r\\n coef_df[[i for i in coef_df.columns if i.count(\\\"Retailer_Promoted\\\") == 1]]\\r\\n )\\r\\n\\r\\n # ################################ Available EDLP Interactions pairs ##############################\\r\\n\\r\\n EDLP = [\\r\\n re.findall(r\\\"[0-9]+\\\", i)\\r\\n for i in coef_df.columns\\r\\n if i.count(\\\"Retailer_Regular\\\") > 1\\r\\n ]\\r\\n EDLP_Interactions = []\\r\\n for i in EDLP:\\r\\n temp = []\\r\\n for j in i:\\r\\n temp.append(int(j))\\r\\n EDLP_Interactions.append(temp)\\r\\n\\r\\n # ###################################### Available TPR Interactions pairs #########################\\r\\n\\r\\n TPR = [\\r\\n re.findall(r\\\"[0-9]+\\\", i)\\r\\n for i in coef_df.columns\\r\\n if i.count(\\\"Retailer_Promoted\\\") > 1\\r\\n ]\\r\\n TPR_Interactions = []\\r\\n for i in TPR:\\r\\n temp = []\\r\\n for j in i:\\r\\n temp.append(int(j))\\r\\n TPR_Interactions.append(temp)\\r\\n\\r\\n # ###################################### EDLP_Interaction_Coef_Values ############################\\r\\n\\r\\n EDLP_Int_Coef_Values = {}\\r\\n for col in coef_df.columns:\\r\\n if col.count(\\\"Retailer_Regular\\\") > 1:\\r\\n Pair_name = \\\"_\\\".join([str(int(i)) for i in re.findall(r\\\"[0-9]+\\\", col)])\\r\\n EDLP_Int_Coef_Values[Pair_name] = list(coef_df[col])\\r\\n\\r\\n # ###################################### TPR_Interaction_Coef_Values #############################\\r\\n\\r\\n TPR_Int_Coef_Values = {}\\r\\n for col in coef_df.columns:\\r\\n if col.count(\\\"Retailer_Promoted\\\") > 1:\\r\\n Pair_name = \\\"_\\\".join([str(int(i)) for i in re.findall(r\\\"[0-9]+\\\", col)])\\r\\n TPR_Int_Coef_Values[Pair_name] = list(coef_df[col])\\r\\n\\r\\n # ##################################### Loading Pantry Loading Coefficients #######################\\r\\n\\r\\n Pantry_1 = list(coef_df[\\\"Pantry_Loading_1\\\"])\\r\\n Pantry_1 = [\\r\\n Pantry_1[j : j + Tot_Prod] for j in range(0, len(Pantry_1), Tot_Prod)\\r\\n ]\\r\\n Pantry_2 = list(coef_df[\\\"Pantry_Loading_2\\\"])\\r\\n Pantry_2 = [\\r\\n Pantry_2[j : j + Tot_Prod] for j in range(0, len(Pantry_2), Tot_Prod)\\r\\n ]\\r\\n\\r\\n # TE_Coeff = np.array(Promo_df[[\\\"TE_Promo\\\",\\\"TE_NoPromo\\\"]])\\r\\n self.Tot_Prod = Tot_Prod\\r\\n self.Tot_Week = Tot_Week\\r\\n self.EDLP_Events = EDLP_Events\\r\\n self.Min_EDLP_Events = Min_EDLP_Events\\r\\n self.Max_EDLP_Events = Max_EDLP_Events\\r\\n self.TPR_Events = TPR_Events\\r\\n self.Min_TPR_Events = Min_TPR_Events\\r\\n self.Max_TPR_Events = Max_TPR_Events\\r\\n\\r\\n self.Target_EDLP_Spend = Target_EDLP_Spend\\r\\n self.Target_TPR_Spend = Target_TPR_Spend\\r\\n self.Target_Trade_Spend = Target_Trade_Spend\\r\\n self.Mapping = Mapping\\r\\n self.Mapping_reverse = Mapping_reverse\\r\\n self.constants = constants\\r\\n self.EDLP_Coef = EDLP_Coef\\r\\n self.TPR_Coef = TPR_Coef\\r\\n\\r\\n self.EDLP_Interactions = EDLP_Interactions\\r\\n self.TPR_Interactions = TPR_Interactions\\r\\n self.EDLP_Int_Coef_Values = EDLP_Int_Coef_Values\\r\\n self.TPR_Int_Coef_Values = TPR_Int_Coef_Values\\r\\n self.Pantry_1 = Pantry_1\\r\\n self.Pantry_2 = Pantry_2\\r\\n\\r\\n self.Base_Price_stg1 = Base_Price_stg1\\r\\n self.Intercepts_stg1 = Intercepts_stg1\\r\\n self.Base_Price_stg2 = Base_Price_stg2\\r\\n self.Intercepts_stg2 = Intercepts_stg2\\r\\n\\r\\n self.Cat_Coef = Cat_Coef\\r\\n self.Disp_Coef = Disp_Coef\",\n \"def setup(self): \\n self.suburbs_dict = dict()\\n self.raw_proIds_dict = dict()\\n self.propertyIds_dict = dict()\\n self.valuations = dict()\",\n \"def init_db(self):\\n\\n cursor = self._db_connection.cursor()\\n\\n cursor.execute('''\\n CREATE TABLE IF NOT EXISTS users (\\n uid INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL,\\n username VARCHAR(255) UNIQUE NOT NULL,\\n full_name VARCHAR(255),\\n password VARCHAR(255) NOT NULL\\n );\\n \\n CREATE TABLE IF NOT EXISTS access_control (\\n access_id INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL,\\n uid INTEGER NOT NULL,\\n privilege VARCHAR(255) NOT NULL,\\n UNIQUE (uid, privilege),\\n CONSTRAINT fk_associated_user FOREIGN KEY (uid) REFERENCES users (uid)\\n );\\n \\n CREATE TABLE IF NOT EXISTS tokens (\\n token_id INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL,\\n token_content VARCHAR(255) NOT NULL,\\n expiration_datetime VARCHAR(255),\\n uid INTEGER NOT NULL,\\n CONSTRAINT fk_associated_user FOREIGN KEY (uid) REFERENCES users (uid)\\n );\\n \\n CREATE TABLE IF NOT EXISTS courses (\\n course_id INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL,\\n course_abbreviation VARCHAR(255) NOT NULL,\\n course_name VARCHAR(255) NOT NULL,\\n instructor_id INTEGER NOT NULL,\\n time VARCHAR(255) NOT NULL,\\n seats INTEGER NOT NULL,\\n CONSTRAINT fk_instructors FOREIGN KEY (instructor_id) REFERENCES users (uid)\\n );\\n \\n CREATE TABLE IF NOT EXISTS enrollment_records (\\n enrollment_id INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL,\\n uid INTEGER NOT NULL,\\n course_id INTEGER NOT NULL,\\n grade NUMERIC NOT NULL DEFAULT 100.0,\\n UNIQUE (uid, course_id),\\n CONSTRAINT fk_associated_user FOREIGN KEY (uid) REFERENCES users (uid),\\n CONSTRAINT fk_associated_course FOREIGN KEY (course_id) references courses (course_id)\\n );\\n ''')\",\n \"def populate(self):\\n\\n NUM_COUNTRIES = 2 # random.randint(1, 4)\\n\\n # find a suitable hex\\n with Timer(\\\"Creating initial data\\\", debug=self.debug):\\n\\n for i in range(NUM_COUNTRIES):\\n country, provinces, pops = create_country(self, self.map)\\n country.determine_tax_policy()\\n self.countries.append(country)\",\n \"def example_data():\\n\\n # In case this is run more than once, empty out existing data\\n User.query.delete()\\n Trip.query.delete()\\n Entry.query.delete()\\n Category.query.delete()\\n Share.query.delete()\\n\\n # Add sample data\\n user1 = User(email='user1@gmail.com', password=bcrypt.hashpw('user1'.encode('utf8'), bcrypt.gensalt(9)), name='One')\\n user2 = User(email='user2@gmail.com', password=bcrypt.hashpw('user2'.encode('utf8'), bcrypt.gensalt(9)), name='Two')\\n trip1 = Trip(location='Spain', date='08/09/2017', name='Abroad Trip', user_id=1)\\n entry1 = Entry(trip_id=1, name='Tibidabo', address='08035 Barcelona, Spain', notes='Fun day trip!',\\n type_id=1)\\n category1 = Category(name='Attraction')\\n share1 = Share(viewer_id=2, trip_id=1)\\n\\n db.session.add_all([user1, user2, trip1, entry1, category1, share1])\\n db.session.commit()\",\n \"def setup(self):\\n #print \\\"Creating test database...\\\"\\n files = glob.glob(os.path.join(self.home_dir, 'sqlFiles', '*.sql'))\\n for fls in files:\\n loc = fls.rfind('/')\\n #print(\\\" \\\" + fls.replace('.sql', '')[loc + 1:])\\n flh = open(fls, 'r')\\n curs = self.cursor()\\n curs.executescript(flh.read())\\n self.commit()\\n curs.close()\\n flh.close()\\n for fls in ['INSERTS', 'TRIGGERS']:\\n #print(fls)\\n flh = open(os.path.join(self.home_dir, 'sqlFiles', fls), 'r')\\n curs = self.cursor()\\n curs.executescript(flh.read())\\n self.commit()\\n curs.close()\\n flh.close()\",\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 import_counties():\\n\\n query = 'INSERT INTO texas_counties(county, region) VALUES(%s,%s)'\\n with persistence() as db:\\n # create new cursor instance\\n cursor = db.cursor(cursor_factory=psycopg2.extras.RealDictCursor)\\n\\n for council, counties in COUNCIL_DATA.items():\\n for county in counties:\\n cursor.execute(query, (county, council))\\n db.commit()\",\n \"def setUpTestData(cls):\\n countries = [\\\"MX\\\", \\\"CHL\\\", \\\"USA\\\", \\\"PER\\\", \\\"COL\\\"]\\n slack_user_ids = [\\\"UP0918MAV\\\", \\\"UP0918MAV\\\", \\\"UP0918MAV\\\", None, None]\\n cls.menu = Menu.objects.create(available_on=date.today())\\n for count in range(5):\\n user = User.objects.create(username=f\\\"johny.doe {count}\\\")\\n Employee.objects.create(\\n user=user, country=countries[count], slack_user_id=slack_user_ids[count]\\n )\",\n \"def create_tables(self):\\n\\n # Uses methods from class DbAuth\\n cursor = self.connect.create_cursor()\\n cursor.execute(\\\"USE `dbPurBeurre`\\\")\\n\\n cursor.execute(\\n \\\"CREATE TABLE IF NOT EXISTS `dbPurBeurre`.`Categories` (\\\"\\n \\\" `num` INT UNSIGNED AUTO_INCREMENT,\\\"\\n \\\" `id` VARCHAR(80) NOT NULL UNIQUE,\\\"\\n \\\" `name` VARCHAR(80) NOT NULL,\\\"\\n \\\" `url` VARCHAR(255) NOT NULL,\\\"\\n \\\" `products` INT NULL,\\\"\\n \\\" PRIMARY KEY (`num`))\\\"\\n \\\" ENGINE = InnoDB\\\"\\n )\\n\\n cursor.execute(\\n \\\"CREATE TABLE IF NOT EXISTS `dbPurBeurre`.`Produits` (\\\"\\n \\\" `num` INT UNSIGNED AUTO_INCREMENT,\\\"\\n \\\" `id` VARCHAR(80) NOT NULL UNIQUE,\\\"\\n \\\" `product_name` VARCHAR(80) NOT NULL,\\\"\\n \\\" `nutrition_grade_fr` CHAR(1) NOT NULL,\\\"\\n \\\" `brands` VARCHAR(80) NULL,\\\"\\n \\\" `stores` VARCHAR(80) NOT NULL,\\\"\\n \\\" `url` VARCHAR(255) NOT NULL,\\\"\\n \\\" `watchlist` DATE NULL,\\\"\\n \\\" PRIMARY KEY (`num`, `id`))\\\"\\n \\\" ENGINE = InnoDB\\\"\\n )\\n\\n cursor.execute(\\n \\\"CREATE TABLE IF NOT EXISTS `dbPurBeurre`.`Asso_Prod_Cat` (\\\"\\n \\\" `id_categories` VARCHAR(80) NOT NULL,\\\"\\n \\\" `id_produits` VARCHAR(80) NOT NULL,\\\"\\n \\\" PRIMARY KEY (`id_categories`, `id_produits`),\\\"\\n \\\" CONSTRAINT `fk_id_categories`\\\"\\n \\\" FOREIGN KEY (`id_categories`)\\\"\\n \\\" REFERENCES `Categories` (`id`),\\\"\\n \\\" CONSTRAINT `fk_id_produits`\\\"\\n \\\" FOREIGN KEY (`id_produits`)\\\"\\n \\\" REFERENCES `Produits` (`id`))\\\"\\n \\\" ENGINE = InnoDB\\\"\\n )\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.69008756","0.66442764","0.6618634","0.65720314","0.6367745","0.63610566","0.61599475","0.61476094","0.60915166","0.60889375","0.60289264","0.59944206","0.59331673","0.5864972","0.582192","0.5799883","0.5796334","0.5792097","0.5777224","0.5760808","0.5741017","0.5714303","0.5696844","0.5693156","0.5693014","0.5668316","0.5635157","0.56118464","0.55973214","0.55897576","0.5568381","0.5547426","0.5530872","0.55300045","0.55061775","0.55056775","0.55005866","0.5480517","0.54755205","0.54691803","0.5467102","0.545828","0.5447578","0.54278415","0.54269683","0.5426604","0.541382","0.540727","0.54033107","0.540009","0.5394304","0.5392521","0.5385077","0.53843945","0.536677","0.5363409","0.53619033","0.53585196","0.5355416","0.5349354","0.5348342","0.53446865","0.5343504","0.53412694","0.5336077","0.5329909","0.53268015","0.53242385","0.53196704","0.5317065","0.5316943","0.53139275","0.5312529","0.5310118","0.53096795","0.530875","0.5300851","0.5295947","0.5293937","0.5289882","0.5281536","0.52807945","0.52653956","0.5263308","0.52626914","0.5255497","0.52540356","0.5251065","0.5247085","0.5246556","0.52445936","0.52440107","0.52363193","0.52333736","0.52211064","0.52153844","0.5212769","0.5208228","0.5203642","0.5203103"],"string":"[\n \"0.69008756\",\n \"0.66442764\",\n \"0.6618634\",\n \"0.65720314\",\n \"0.6367745\",\n \"0.63610566\",\n \"0.61599475\",\n \"0.61476094\",\n \"0.60915166\",\n \"0.60889375\",\n \"0.60289264\",\n \"0.59944206\",\n \"0.59331673\",\n \"0.5864972\",\n \"0.582192\",\n \"0.5799883\",\n \"0.5796334\",\n \"0.5792097\",\n \"0.5777224\",\n \"0.5760808\",\n \"0.5741017\",\n \"0.5714303\",\n \"0.5696844\",\n \"0.5693156\",\n \"0.5693014\",\n \"0.5668316\",\n \"0.5635157\",\n \"0.56118464\",\n \"0.55973214\",\n \"0.55897576\",\n \"0.5568381\",\n \"0.5547426\",\n \"0.5530872\",\n \"0.55300045\",\n \"0.55061775\",\n \"0.55056775\",\n \"0.55005866\",\n \"0.5480517\",\n \"0.54755205\",\n \"0.54691803\",\n \"0.5467102\",\n \"0.545828\",\n \"0.5447578\",\n \"0.54278415\",\n \"0.54269683\",\n \"0.5426604\",\n \"0.541382\",\n \"0.540727\",\n \"0.54033107\",\n \"0.540009\",\n \"0.5394304\",\n \"0.5392521\",\n \"0.5385077\",\n \"0.53843945\",\n \"0.536677\",\n \"0.5363409\",\n \"0.53619033\",\n \"0.53585196\",\n \"0.5355416\",\n \"0.5349354\",\n \"0.5348342\",\n \"0.53446865\",\n \"0.5343504\",\n \"0.53412694\",\n \"0.5336077\",\n \"0.5329909\",\n \"0.53268015\",\n \"0.53242385\",\n \"0.53196704\",\n \"0.5317065\",\n \"0.5316943\",\n \"0.53139275\",\n \"0.5312529\",\n \"0.5310118\",\n \"0.53096795\",\n \"0.530875\",\n \"0.5300851\",\n \"0.5295947\",\n \"0.5293937\",\n \"0.5289882\",\n \"0.5281536\",\n \"0.52807945\",\n \"0.52653956\",\n \"0.5263308\",\n \"0.52626914\",\n \"0.5255497\",\n \"0.52540356\",\n \"0.5251065\",\n \"0.5247085\",\n \"0.5246556\",\n \"0.52445936\",\n \"0.52440107\",\n \"0.52363193\",\n \"0.52333736\",\n \"0.52211064\",\n \"0.52153844\",\n \"0.5212769\",\n \"0.5208228\",\n \"0.5203642\",\n \"0.5203103\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.71547"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":2889,"cells":{"query":{"kind":"string","value":"Populate locations data table."},"document":{"kind":"string","value":"def populate_locations(connection):\n print('Populating locations...')\n cursor = connection.cursor()\n with open(get_data_path('locations', 'locations.json'), 'r', encoding='utf-8') as json_file:\n locations = json.load(json_file)\n\n for station_id, location in locations.items():\n cursor.execute(f'''SELECT id \n FROM watercourse_stations \n WHERE id = {station_id}''')\n\n if len(cursor.fetchall()):\n cursor.execute(f'''INSERT INTO locations(name, lat, lng)\n VALUES ('{location['name']}', {location['lat']}, {location['lng']})''')\n cursor.execute(f'''UPDATE watercourse_stations\n SET location_id = {cursor.lastrowid}\n WHERE id = {station_id}''')"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def generate_position_data(self):\n # populate 'Location' field randomly\n self.output['Location'] = np.random.choice(self.locations, self.obs)\n\n # clean up geodata data frame and create 'Position' attribute\n nc = self.geodata[['Lat', 'Lng', 'Elevation']].round(2)\n nc['Elevation'] = nc['Elevation'].astype(int)\n self.geodata['Position'] = nc.astype(\n str).apply(lambda x: ','.join(x), axis=1)\n self.geodata.drop(columns=['Lat', 'Lng', 'Elevation'], inplace=True)\n\n # update 'Position' column in output data frame\n left = self.output.set_index('Location') # set left index\n right = self.geodata.set_index('Location') # set right index\n self.output = left.loc[:, left.columns.union(right.columns)] # union\n self.output.update(right) # update self.output \"Position\" column\n self.output.reset_index(inplace=True)","def add_locations(self):\n for _ in range(0, self.num_locations):\n detector_id = self.generate_id()\n detector_direction = self.generate_direction()\n detector_point = self.generate_point()\n self.dataset[detector_id] = (detector_direction, detector_point)\n assert len(self.dataset) == self.num_locations","def get_all_locations(self):","def load_data():\n if _LOCATIONS_BY_ID:\n return _LOCATIONS_BY_NAME, _LOCATIONS_BY_ID\n\n # We need to read the locations in order of country -> admin level 1 -> admin level 2 -> city.\n # This is so that the higher resolution locations can look up the lower resolution locations\n # that they belong to, and compute the necessary fields.\n countries_by_code = _load_country_data(_DATA_FILES['country'])\n admin1_by_code = _load_admin1_data(_DATA_FILES['admin_1'], countries_by_code)\n admin2_by_code = _load_admin2_data(_DATA_FILES['admin_2'], countries_by_code, admin1_by_code)\n _load_city_data(_DATA_FILES['city'], countries_by_code, admin1_by_code, admin2_by_code)\n _add_alternate_names(_DATA_FILES['alt_wiki_names'])\n _add_estimated_importances(_DATA_FILES['estimated_importance'])\n\n return _LOCATIONS_BY_NAME, _LOCATIONS_BY_ID","def generate_test_locations(self):\n def generate_locations_for_organization(\n location_names, organization_name):\n item_dict = {}\n for name in location_names:\n item_dict['{}_{}'.format(name, organization_name)] = {\n 'name': name,\n 'organization': organization_name\n }\n return item_dict\n\n self.ls_o1_dict = \\\n generate_locations_for_organization(\n ['l1', 'l2', 'l3', 'l4', 'l5'], 'o1')\n\n self.ls_sub1_o1_dict = \\\n generate_locations_for_organization(\n ['l1', 'l2', 'l3', 'l4'], 'sub1_o1')\n\n self.ls_o2_dict = \\\n generate_locations_for_organization(['l1', 'l2', 'l3', 'l4'], 'o2')\n\n self.ls_sub1_o2_dict = \\\n generate_locations_for_organization(['l1', 'l2'], 'sub1_o2')\n\n # generate locations of org_3\n self.ls_o3_dict = \\\n generate_locations_for_organization(['l1', 'l2'], 'o3')\n\n # generate locations dictionary\n self.ls_dict = {\n **self.ls_o1_dict,\n **self.ls_sub1_o1_dict,\n **self.ls_o2_dict,\n **self.ls_sub1_o2_dict,\n **self.ls_o3_dict,\n }\n\n # generate locations in database\n self.locations = self.create_locations_from_data(\n self.ls_dict, self.orgs)","def populate_stops(self):\n stops = self.load_csv('stops.txt')\n stops = self.process_stops(stops)\n\n connection = db.connect()\n for stop in stops:\n try:\n connection.execute(schema.stops.insert(), stop)\n except DataError:\n print \"Missing data for stop: %s\" % (stop)","def create_locations(self, data):\n total_objects = len(data)\n parsed_objects = 0\n\n for object in data:\n # Get location title. 'name' val is available to all objects, but Building 'title'\n # and RegionalCampus 'description' are more descriptive. Use them if available.\n if hasattr(object, 'title'):\n title = object['title']\n elif hasattr(object, 'description'):\n title = object['description']\n else:\n title = object['name']\n\n # Get other data.\n mapurl = object['profile_link']\n import_id = object['id']\n\n if title:\n # Check to see if the location name, map url are too long\n if len(title) > 256:\n title = title[0:256]\n if len(mapurl) > 400:\n mapurl = mapurl[0:400]\n if len(import_id) > 256:\n import_id = import_id[0:256]\n\n # See if an existing location exists with the current object ID.\n # Update the existing location if it exists; else, save the new location\n try:\n old_location = Location.objects.get(import_id=import_id)\n except Exception as e:\n logging.debug('No existing location found for %s: %s. Creating new location...' % (title, e))\n # No existing matches found, or the matches were duplicate\n new_location = Location(title=title, url=mapurl, room='', import_id=import_id, reviewed=True)\n try:\n new_location.save()\n except Exception as e:\n logging.error('Unable to save new location %s: %s' % (title, str(e)))\n else:\n parsed_objects += 1\n logging.info('New location %s created.' % title)\n else:\n logging.debug('Existing location %s found with Import ID %s. Updating existing location...' % (title, import_id))\n old_location.title = title\n old_location.url = mapurl\n old_location.room = ''\n old_location.reviewed = True\n try:\n old_location.save()\n except Exception as e:\n logging.error('Unable to save existing location %s: %s' % (title, str(e)))\n else:\n parsed_objects += 1\n logging.info('Existing location %s with Import ID %s updated.' % (title, import_id))\n\n logging.info('Done. %s of %s available objects successfully imported.' % (parsed_objects, total_objects))","def add_locations(updatemedf):\n # List of unique identifiers to search in text\n uniquedf = pd.read_csv('uniquelist.csv', encoding=\"latin-1\")\n\n # Use lookup list to find text and look for institution matches, \n # once found append unique list of matching institution lookups\n df = add_univ_city(updatemedf, uniquedf)\n # Output this to a file for checking and adding more values as needed\n return df","def __init__(self, num_locations):\n self.dataset = {}\n self.num_locations = num_locations\n self.add_locations()","def test_bulk_locations(self):\n # do twice to check if it really updates\n lengths = []\n for i in range(2):\n file_path = os.path.join(os.path.dirname(__file__),\n self.testdata_folder,\n self.filename_locations)\n data = {\n 'bulk_upload' : open(file_path, 'rb'),\n }\n\n res = self.client.post(self.location_url, data)\n assert res.status_code == status.HTTP_201_CREATED, (\n responses.get(res.status_code, res.status_code), res.content)\n lengths.append(len(AdministrativeLocation.objects.all()))\n\n assert lengths[0] == lengths[1]\n\n file_path = os.path.join(os.path.dirname(__file__),\n self.testdata_folder,\n self.filename_locations_duplicates)\n data = {\n 'bulk_upload' : open(file_path, 'rb'),\n }\n\n res = self.client.post(self.location_url, data)\n assert res.status_code == status.HTTP_400_BAD_REQUEST","def get_locations():\n\n dtype = {'id': str,\\\n 'lat': float,\n 'lon': float,\n 'address': str,\n }\n\n try: \n locations = pd.read_csv('Data/kulkijat-mittauspisteet.csv', sep=',', dtype=dtype)\n except FileNotFoundError:\n print('\\nMittauspisteet sisältävää tiedostoa kulkijat-mittauspisteet.csv ei löytynyt.\\n')\n locations = pd.DataFrame()\n\n return locations","def _update_locations(self):\n raw_data = self._read_data()\n processed_data = self._process_data(raw_data) if self._process_data is not None else raw_data\n if processed_data:\n for k, v in processed_data.items():\n if k in self._locations.keys():\n self.predictors[k].update(*v)\n self._locations[k] = v\n for k, v in self._locations.items():\n if k not in processed_data:\n self._locations[k] = self.predictors[k].predict()\n else:\n self._locations = {k: self.predictors[k].predict() for k in self._locations.keys()}\n self._logger.debug(\"Locator updated locations\")","def set_locations():\n STATUS['locations']['monster'][0] = generate_random_coord(STATUS['grid_size'])\n STATUS['locations']['monster'][1] = generate_random_coord(STATUS['grid_size'])\n STATUS['locations']['weapon'][0] = generate_random_coord(STATUS['grid_size'])\n STATUS['locations']['weapon'][1] = generate_random_coord(STATUS['grid_size'])","def get_locations(self):\n try:\n output_json = {}\n total_locations = list(self.mongo_db_object.find_all(AppConfigurations.MONGO_DATABASE,\n AppConstants.LOCATION.MONGO_LOCATION_COLLECTION_NAME))\n output_json = total_locations\n return AppConstants.result_success_template(output_json)\n\n except Exception as e:\n print(\"Error while fetching the Location Data.\", str(e))","def load(self, locations):\n try:\n assert isinstance(locations, list)\n super(Arrivals, self).load({'locIDs': locations})\n except AssertionError:\n print(\"Locations must be a list\")","def _gen_locs_dbscan(sp, distance_metric, db):\n p = np.array([sp.geometry.x, sp.geometry.y]).transpose()\n if distance_metric == \"haversine\":\n p = np.deg2rad(p) # haversine distance metric assumes input is in rad\n labels = db.fit_predict(p)\n sp[\"location_id\"] = labels\n return sp","def load(self):\n return list(self.obj.locations_set.all())","def populate_contents(self):\n\n data_table = self.data_table\n world = self.world\n\n self.add_text_row('World Name', data_table.world_name_label.text())\n self.add_text_row('Coordinates', data_table.world_coords_label.text())\n self.add_text_row('World Type', data_table.world_type_label.text())\n if data_table.world_extra_label.text() != '':\n self.add_text_row('Extra Info', data_table.world_extra_label.text())\n self.add_text_row('Filename', world.base_filename)\n self.add_text_row('Size', '{}x{}'.format(*world.info.size))\n\n if len(world.info.dungeons) > 0:\n dungeons = self.add_text_row('Dungeons', '<br/>'.join(sorted(world.info.dungeons)))\n else:\n self.add_text_row('Dungeons', '-')\n\n if len(world.info.biomes) > 0:\n biomes = self.add_text_row('Biomes', '<br/>'.join(sorted(world.info.biomes)))\n else:\n self.add_text_row('Biomes', '-')","def populate_cities():\n if City.query.filter_by(name=CITIES[0]).first():\n return\n\n for city in CITIES:\n _add_city(city)","def create_data():\n # Locations\n data = {}\n num_vehicles = 20\n depot = 0\n locations = loc1\n demands = popn\n\n num_locations = len(locations)\n dist_matrix = {}\n\n for from_node in range(0,num_locations):\n dist_matrix[from_node] = {}\n\n for to_node in range(0,num_locations):\n dist_matrix[from_node][to_node] = (\n haversine(\n locations[from_node],[to_node])\n #locations[to_node],[from_node])\n \"\"\"\n data[\"distances\"] =dist_matrix\n data[\"num_locations\"] = len(dist_matrix)\n data[\"num_vehicles\"] = 6\n data[\"depot\"] = 0\n data[\"demands\"] = demands\n #data[\"vehicle_capacities\"] = capacities\n data[\"time_per_demand_unit\"] = 0.05\n return data\n \"\"\"\n return [ num_vehicles, depot, locations, dist_matrix]","def create_locations_from_data(self, data, orgs):\n item_dict = {}\n for (item_name, data) in data.items():\n item_dict[item_name] = \\\n Location(\n name=data['name'],\n organization=orgs.get(data['organization']))\n item_dict[item_name].save()\n return item_dict","def getAllLocation(table):\n\tlocs = []\n\n\tnum = len(table)\n\n\tfor i in range(num):\n\t\t# first field is the name\n\t\tloc = getLatAndLong(table[i][1])\n\n\t\tlocs.append(loc)\n\n\treturn locs","def get_all_locations():\n rs = run_query('''select * from zlrz_office_location''')\n return [] if rs is None else list(map(lambda t: Location(t[1], t[2], t[3], t[4], t[5], t[0]), rs))","def locations_fixture(location):\n return [location, _create_location()]","def build_polling_location_txt(self):\n self.base_df['address_line'] = self.base_df.apply(\n lambda row: self.get_address_line(row['index'], row['address1'], row['address2'], row['city'],\n row['state'], row['zip_code']), axis=1)\n\n self.base_df['directions'] = self.base_df.apply(\n lambda row: self.get_directions(), axis=1)\n #\n self.base_df['hours'] = self.base_df.apply(\n lambda row: self.get_hours(row['index'],row['start_time'], row['end_time']), axis=1)\n\n self.base_df['photo_uri'] = self.base_df.apply(\n lambda row: self.get_photo_uri(), axis=1)\n\n self.base_df['hours_open_id'] = self.base_df.apply(\n lambda row: self.create_hours_open_id(row['index'], row['address1'], row['address2'], row['city'],\n row['state'], row['zip_code']), axis=1)\n\n self.base_df['is_drop_box'] = self.base_df.apply(\n lambda row: self.is_drop_box(), axis=1)\n\n self.base_df['is_early_voting'] = self.base_df.apply(\n lambda row: self.is_early_voting(), axis=1)\n\n self.base_df['latitude'] = self.base_df.apply(\n lambda row: self.get_latitude(), axis=1)\n\n self.base_df['longitude'] = self.base_df.apply(\n lambda row: self.get_longitude(), axis=1)\n\n self.base_df['latlng_source'] = self.base_df.apply(\n lambda row: self.get_latlng_source(), axis=1)\n\n self.base_df['id'] = self.base_df.apply(\n lambda row: self.create_id(row['index'], row['ocd_division'],row['address1'], row['address2'],\n row['city'], row['state'], row['zip_code']), axis=1)\n\n return self.base_df","def process_location(pid, data_source, out_loc, start_date, end_date, debug=False):\n\n #query = client.query_data_points(page_size=PAGE_SIZE, source=pid)\n query = try_query(pid)\n\n location_query = try_filter(query, pid, 'pdk-location', start_date, end_date)\n \"\"\" location_query = query.filter(source=pid, \n generator_identifier='pdk-location',\n created__gte=start_date,\n created__lte=end_date).order_by('created')\n \"\"\"\n tot_count = location_query.count()\n count = 0\n frac = int(tot_count / 100)\n\n loc_df = pd.DataFrame()\n for point in location_query:\n point_df = pd.DataFrame.from_dict(point).iloc[0].to_frame().transpose()\n metadata_df = pd.Series(point['passive-data-metadata']).to_frame().transpose()\n # TODO check if ignoring errors is safe\n metadata_df = metadata_df.drop(['latitude', 'longitude'], axis='columns', errors=\"ignore\")\n point_df.reset_index(inplace=True, drop=True)\n point_df = pd.concat([metadata_df, point_df], axis=1, sort=True)\n \n point_df.drop('passive-data-metadata', axis='columns', inplace=True)\n missing_cols = [col for col in loc_df.columns.values if col not in point_df.columns.values]\n \n if len(missing_cols) > 0 and loc_df.shape[0] > 0:\n for col in missing_cols:\n point_df[col] = np.nan\n point_df = point_df[loc_df.columns]\n loc_df = loc_df.append(point_df)\n count += 1\n if debug and (count % frac == 0):\n print(\"{0:.2f}% complete\".format(float(count)/float(tot_count)*100))\n\n loc_df['pid'] = pid \n loc_df['data_source'] = data_source\n print(loc_df.shape)\n \n pickle.dump(loc_df, open(\"{}/pdk-location/{}.df\".format(out_loc, pid), 'wb'), -1)","def populate_stat(self, table):\n myrow = table.row\n # HDF5 doesn't handle unicode strings, so we need to convert to \n # *byte* strings, which we can put in the HDF5 file \n addy = numpy.zeros(len(self.address), \n dtype=(numpy.str, glob.nchar_address))\n for i in range(len(addy)):\n addy[i] = (self.address[i]).encode('utf8')\n\n myrow[\"address\"] = addy\n myrow[\"bike_stands\"] = self.bike_stands\n myrow[\"number\"] = self.number\n myrow[\"position\"] = self.position\n myrow.append()\n table.flush()","def __init__(self,\n locations: List['LocationOutput']) -> None:\n self.locations = locations","def test_locations(self):\n url = reverse(\"locations\", args=[00000])\n response = self.client.get(url)\n\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n self.assertTrue(isinstance(response.data, list))\n self.assertTrue(response.data) # list not empty\n\n location_data = response.data[0]\n data_keys = [\n \"title\",\n \"address\",\n \"address2\",\n \"city\",\n \"state\",\n \"postalCode\",\n \"distance\",\n \"hours\",\n \"phone\",\n \"geocode\",\n ]\n self.assertEqual(list(location_data.keys()), data_keys)","def populate(self):\n\n NUM_COUNTRIES = 2 # random.randint(1, 4)\n\n # find a suitable hex\n with Timer(\"Creating initial data\", debug=self.debug):\n\n for i in range(NUM_COUNTRIES):\n country, provinces, pops = create_country(self, self.map)\n country.determine_tax_policy()\n self.countries.append(country)","def populate(self):\n pass","def _populate_output(self):\n self._store_atomic_queries_table()\n self._store_composite_queries_table()","def __init__(self, obs, start_date, end_date, histdata, geodata):\n self.start_date = start_date\n self.end_date = end_date\n self.obs = obs\n self.geodata = pd.read_csv(get_datafile(geodata))\n self.histdata = aggregate_data(histdata)\n self.locations = self.histdata['Location'].unique().tolist()\n self.output = None\n self.cols = ['Location', 'Position', 'Local Time',\n 'Conditions', 'Temperature', 'Pressure', 'Humidity']","def populateTable(self):\n\n output_list = self.output_ports.split(', ')\n\n for i in output_list:\n values = i.split('-')\n nextHopPort = values[0]\n linkCost = values[1]\n destId = values[2]\n learnedFrom = 0 # As it was learned from ConfigFile\n row = routing_row.RoutingRow(nextHopPort, destId, linkCost, destId, learnedFrom)\n self.addToRoutingTable(row)","def extract_locations(self):\n default_pos_columns = common_cfg.coord_col_names\n if set(default_pos_columns).issubset(set(self._raw_data.columns)):\n print('Location data found')\n # check and drop units outside provided city boundary\n geometry = [shapely.geometry.Point(xy) for xy in zip(\n self._raw_data[default_pos_columns[0]], # Long\n self._raw_data[default_pos_columns[1]])] # Lat\n b_within_boundary = np.array(list(map(\n lambda p: p.within(self.model_city.convhull), geometry)))\n\n if not all(b_within_boundary):\n print('%s -- dropping %i units outside city.' %\n (self.servicetype,\n sum(np.bitwise_not(b_within_boundary))))\n self._raw_data = self._raw_data.iloc[\n b_within_boundary, :].reset_index()\n\n # store geolocations as geopy Point\n locations = [geopy.Point(yx) for yx in zip(\n self._raw_data[default_pos_columns[1]], # Lat\n self._raw_data[default_pos_columns[0]])] # Long\n\n propert_data = self._raw_data.drop(default_pos_columns, axis=1)\n\n else:\n raise NotImplementedError('Locations not found - not implemented!')\n\n return propert_data, locations","def get_all_locations():\n with mysql.db_session(read_only=True) as session:\n locations = session.query(Location)\n\n if not locations:\n return response.create_not_found_response(message='No data found.')\n locations_list = [location.to_dict() for location in locations.all()]\n\n return response.Response(message=locations_list)","def get_location_hash_table():\n return locationHashTable","def locations(self):\n return self.data.get(\"locations\", [])","def example_data():\n\n # In case this is run more than once, empty out existing data\n User.query.delete()\n Location.query.delete()\n\n # Add sample users and locations\n\n trinity = User(fname='Trinity', email='trin@unplugged.com',\n username='questionit', password='l0lagent')\n neo = User(fname='Neo', email='theone@unplugged.com',\n username='neo', password='l0lagent')\n\n tacorea = Location(yelp_id='tacorea-san-francisco', name='Tacorea',\n latitude='37.7749', longitude='122.3392',\n address='809 Bush St, San Francisco, CA 94108',\n yelp_url='tacorea@yelp.com', pic='pic')\n\n db.session.add_all([trinity, neo, tacorea])\n db.session.commit()","def _setData(self):\n self._data = [ item for ministry in self.query.all() \n for item in self._getItems(ministry) ]","def _get_initial_location(self):\n for i in self.modelunctab.tableview.selectedIndexes():\n self.locid = i.row()\n break\n else:\n self.locid = 0\n\n self.locnaam = self.result_locations[self.locid]","def _set_location(self):\n if self._report_key == ReportTypes.MHR_REGISTRATION:\n location = self._report_data.get('location')\n if location.get('lot') or location.get('parcel') or location.get('block') or location.get('districtLot') or\\\n location.get('partOf') or location.get('section') or location.get('township') or \\\n location.get('range') or location.get('meridian') or location.get('landDistrict') or \\\n location.get('plan'):\n location['hasLTSAInfo'] = True\n else:\n location['hasLTSAInfo'] = False\n if location.get('pidNumber'):\n pid = location.get('pidNumber')\n location['pidNumber'] = pid[0:3] + '-' + pid[3:6] + '-' + pid[6:]\n elif self._report_key in (ReportTypes.SEARCH_DETAIL_REPORT, ReportTypes.SEARCH_BODY_REPORT):\n for detail in self._report_data['details']:\n location = detail.get('location')\n if location.get('lot') or location.get('parcel') or location.get('block') or \\\n location.get('districtLot') or location.get('partOf') or location.get('section') or \\\n location.get('township') or location.get('range') or location.get('meridian') or \\\n location.get('landDistrict') or location.get('plan'):\n location['hasLTSAInfo'] = True\n else:\n location['hasLTSAInfo'] = False\n if location.get('pidNumber'):\n pid = location.get('pidNumber')\n location['pidNumber'] = pid[0:3] + '-' + pid[3:6] + '-' + pid[6:]","def _populate_zone_facts_table(self):\n census_fields = [\n 'poverty_rate', 'fraction_black', 'income_per_capita',\n 'labor_participation', 'fraction_foreign',\n 'fraction_single_mothers', 'acs_lower_rent_quartile',\n 'acs_median_rent', 'acs_upper_rent_quartile'\n ]\n\n zone_types = ['ward', 'neighborhood_cluster', 'census_tract']\n\n query_results = list()\n\n # populate columns accordingly for each zone_specific type\n for zone_type in zone_types:\n field_values = dict()\n\n # get field value for each zone_specific type\n for field in census_fields:\n result = self._census_with_weighting(data_id=field,\n grouping=zone_type)\n field_values[field] = result['items']\n\n zone_specifics = self._get_zone_specifics_for_zone_type(zone_type)\n\n # TODO: add aggregate for each zone_type into table\n for zone in zone_specifics:\n # get not None values so we can added to db\n columns = list()\n values = list()\n for field in census_fields:\n zone_value = field_values[field][zone]\n\n if zone_value is not None:\n columns.append(field)\n values.append(\"'\" + str(zone_value) + \"'\")\n\n # derive column and values strings needed for sql query\n columns = ', '.join(columns)\n columns = 'zone, ' + columns\n\n values = ', '.join(values)\n values = \"'\" + zone + \"', \" + values\n\n q = \"INSERT INTO zone_facts ({cols}) VALUES ({vals})\".format(\n cols=columns, vals=values)\n\n with self.engine.connect() as conn:\n result = conn.execute(q)\n query_results.append(result)\n\n return query_results","def locate(self):\n \n #CONNECT TO API\n api = GoogleV3(api_key = self.google_key)\n\n #INITALIZE ARRAY\n array = []\n\n #START GEOCODING ADDRESSES\n for i in tqdm(range(len(self.df)), desc='Geocoding Addresses'):\n\n \n row = self.df.iloc[i]\n\n #GET ADDRESS VARIABLES\n st_name = row['street_name']\n st_number = row['house_number']\n city = row['city']\n state = row['state/province']\n listing_number = row['listing_number']\n zip = row['postal_code']\n\n\n #FORMAT ADDRESS FOR API\n full_address = str(\"{} {},{},{},{}\".format(st_number, st_name, city, state, zip))\n\n #TRY TO LOCATE WITH GOOGLE\n try:\n \n location = api.geocode(full_address, timeout=10)\n\n lat = location.latitude\n lon = location.longitude\n \n\n info = [lat,lon, listing_number]\n\n array.append(info)\n\n next \n\n #Go to next if you cant locate\n except:\n\n info = [0,0, listing_number]\n\n array.append(info)\n\n next\n\n #CONVERT SERIES TO DATAFRAME\n geo_data = pd.DataFrame(data = array, columns = ['lat', 'lon', 'listing_number'])\n \n #INNER JOIN DATA TO DATAFRAME\n self.df = pd.merge(self.df, geo_data, on= 'listing_number', how = 'inner')","def test_list_zr_locations(self):\n pass","def get_location_list(self) -> DBRecList:\n raise NotImplementedError('not implemented')","def locations():\n sql = \"\"\"SELECT DISTINCT sample_location\n FROM barcodes.sample\n ORDER BY sample_location\"\"\"\n with pm.sql.TRN:\n pm.sql.TRN.add(sql)\n return pm.sql.TRN.execute_fetchflatten()","def places_process_rows(self):\n\n for index in range(len(self.table)):\n row_rdf = self.places_map_row_to_rdf(self.table.iloc[index])\n if row_rdf is not None:\n self.data += row_rdf","def table_allocations(\n self, id_value, id_type, start_date=None, end_date=None, freq=None\n ):\n\n start_date, end_date, freq = self.get_time_parameters(\n start_date, end_date, freq\n )\n\n try:\n df = self.get_allocations(id_value, id_type, start_date, end_date, freq)\n\n if id_type == \"project\" and \"ALL\" not in str(id_value):\n # add the project's missing people allocation\n if freq == \"D\":\n df[\"UNALLOCATED\"] = self.wim.project_peoplereq[id_value]\n else:\n df[\"UNALLOCATED\"] = (\n self.wim.project_peoplereq[id_value].resample(freq).mean()\n )\n\n elif id_type == \"person\" and \"ALL\" not in str(id_value):\n # add the person's total project assignment to the data frame\n if freq == \"D\":\n df[\"TOTAL\"] = self.wim.people_totals[id_value]\n else:\n df[\"TOTAL\"] = self.wim.people_totals[id_value].resample(freq).mean()\n\n df = self.format_date_index(df, freq)\n\n return self.highlight_allocations(df)\n\n except ValueError as e:\n print(e)","def PopulatePointToPointDistancesResultsTable(self, markups, table):\n headerList = [markups.GetNthFiducialLabel(i) for i in range(markups.GetNumberOfFiducials())]\n table.setRowCount(len(headerList))\n table.setColumnCount(len(headerList))\n table.setHorizontalHeaderLabels(headerList)\n table.setVerticalHeaderLabels(headerList)\n\n for point1 in range(markups.GetNumberOfFiducials()):\n for point2 in range(point1 + 1, markups.GetNumberOfFiducials()):\n point1_RAS = [0, 0, 0]\n point2_RAS = [0, 0, 0]\n markups.GetNthFiducialPosition(point1, point1_RAS)\n markups.GetNthFiducialPosition(point2, point2_RAS)\n distance = round(self.GetPointToPointDistance(point1_RAS, point2_RAS), 1)\n table.setItem(point1, point2, qt.QTableWidgetItem(str(distance)))\n\n table.show()\n table.resizeColumnsToContents()\n table.resizeRowsToContents()","def location_fixture(self, db, scope=\"class\"):\n name = Name.objects.create(name=\"Event\", name_type=Name.SOFTWARE)\n loc1 = Location.objects.create(\n status=0,\n latitude=33.210241,\n longitude=-97.148857,\n belong_to_name=name)\n\n loc2 = Location.objects.create(\n status=0,\n latitude=33.210241,\n longitude=-97.148857,\n belong_to_name=name)\n\n loc3 = Location.objects.create(\n status=0,\n latitude=33.210241,\n longitude=-97.148857,\n belong_to_name=name)\n\n # Only return the ids, because the tests will\n # need to instantiate a new object to get the correct\n # statuses.\n return loc1.id, loc2.id, loc3.id","def findAllLocations(cls):\r\n return cls.query.all()","def meta_properties(self, date_col=\"Date\", type_col=\"Primary Type\", lat_col=\"Latitude\",\\\n lon_col=\"Longitude\", loc_col=\"Location\", out_fname=\"data_formated.csv\"):\n # implement keywords\n # would we have to deal w/ file w/o headers?\n data = pd.read_csv(self._path, usecols=[date_col, type_col, lat_col, lon_col, loc_col],\\\n parse_dates=[date_col], infer_datetime_format=True)\n data.sort_values(date_col, inplace=True)\n min_date = data.iloc[0][date_col]\n max_date = data.iloc[(data.shape[0]-1)][date_col]\n\n lat = []\n lon = []\n\n nulls = []\n for row in data.itertuples(index=True, name='Pandas'):\n index = (row.Index)\n # if lat, lon = nan, drop the row\n # update: confirmed that issue is with code, not with data; for some reason\n # csv is actually correctly grabbing location, there just legitimately are\n # entries w/o location data\n if pd.isnull(getattr(row, loc_col)):\n # print(\"row: {} got a {} for the 'Location' column with date: {}\".format(index, \\\n # getattr(row, loc_col), getattr(row, date_col)))\n if not pd.isnull(getattr(row, lat_col)) and not pd.isnull(getattr(row, lon_col)):\n lat.append(str(getattr(row, lat_col)))\n lon.append(str(getattr(row, lon_col)))\n if \",\" in data.loc[index, type_col]:\n data.loc[index, type_col] = data.loc[index, type_col].replace(\",\", \" \")\n print(\\\n \"Successfully extracted lat, lon from lat_col, lon_col for row: {}\".format(index))\n else:\n nulls.append((index, getattr(row, date_col)))\n data.drop(index, inplace=True)\n # print(\"No location data available for row: {} with date: {}\".format(index,\\\n # getattr(row, date_col)))\n else:\n loc = literal_eval(getattr(row, loc_col))\n lat.append(loc[0])\n lon.append(loc[1])\n if \",\" in data.loc[index, type_col]:\n data.loc[index, type_col] = data.loc[index, type_col].replace(\",\", \" \")\n\n data[\"Latitude\"] = lat\n data[\"Longitude\"] = lon\n data.drop(loc_col, axis=1, inplace=True)\n\n data.sort_values(\"Latitude\", inplace=True)\n min_lat = float(data.iloc[0][\"Latitude\"])\n max_lat = float(data.iloc[(data.shape[0]-1)][\"Latitude\"])\n\n data.sort_values(\"Longitude\", inplace=True)\n min_lon = float(data.iloc[0][\"Longitude\"])\n max_lon = float(data.iloc[(data.shape[0]-1)][\"Longitude\"])\n\n data.to_csv(self.__file_dir+'/'+out_fname, na_rep=\"\", header=False, index=False)\n\n attrs = {'min_date': min_date, 'max_date': max_date, \"min_lat\":min_lat,\\\n \"max_lat\":max_lat, \"min_lon\":min_lon, \"max_lon\":max_lon, \\\n \"dates\":pd.date_range(min_date, max_date), \"num_attributes\": data.shape[1],\\\n \"num_entries\":data.shape[0]}\n self._meta_dict = attrs\n self._meta_dict['df'] = data\n pickle.dump(data, open(CWD + \"/meta_dict.p\", \"wb\"))\n print(\"Num entries w/o location data: {}\".format(len(nulls)))\n pickle.dump(nulls, open(CWD + \"/nulls.p\", \"wb\"))\n\n # not include the formatted dataset?\n return attrs","def upsert_location(self, location):","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 populate_dyn(self, table):\n myrow = table.row\n myrow[\"sample_time\"] = int(time.time() - glob.base_time)\n myrow[\"available_bike_stands\"] = self.available_bike_stands\n myrow[\"available_bikes\"] = self.available_bikes\n myrow[\"last_update\"] = self.last_update\n myrow[\"status\"] = self.status\n myrow.append()\n table.flush()","def create_table(f, geoinfo):\n bounds_cols = xb_points + yb_points\n df = pd.read_csv(f, delimiter=\";\", index_col=\"INDEX_RC\")\n df[duration_name] = parse_duration_level(f)\n df = df.join(geoinfo[[\"X_CENT_GEO\", \"Y_CENT_GEO\", \"Col\", \"Row\"]])\n df = df.rename(columns={\"Col\": x, \"Row\": y, \"X_CENT_GEO\": lon, \"Y_CENT_GEO\": lat})\n return df","def set_location(self, location_set):","def generate_weather_data(self):\n months = pd.to_datetime(self.output['Local Time']).dt.month\n self.output['Month'] = months # set month values for later joins\n\n # merge output data frame with historical data to get ranges\n keys = ['Location', 'Month']\n m = pd.merge(self.output, self.histdata, how='left',\n left_on=keys, right_on=keys)\n\n # uniformly select random pressure, temperature\n # and humidity values between the historical max and min ranges\n r = np.random.rand(m.shape[0])\n m['Temperature'] = ((m['Tmean_high'] - m['Tmean_low']\n ) * r + m['Tmean_low']).round(1)\n m['Pressure'] = ((m['Pmax'] - m['Pmin']) * r + m['Pmin']).round(1)\n m['Humidity'] = ((m['Hmax'] - m['Hmin']) * r + m['Hmin']).astype(int)\n\n # drop redundant columns and assign to output\n dcols = ['Month', 'Timezone', 'Pmax', 'Pmin',\n 'Hmax', 'Hmin', 'Tmean_high', 'Tmean_low']\n m.drop(columns=dcols, inplace=True)\n self.output = m","def _setData(self):\n\n if not self.stationId:\n return\n \"\"\" \n # get the ressource url and adjust lat and lon from data portal\n query = sparqls.stationResource(self.stationId)\n key, val = RunSparql(query, 'array').run()\n if val: \n self.url = val[0][0]\n self.lat = float(val[0][2])\n self.lon = float(val[0][3])\n \"\"\"\n\n # it is possible, that a station id has multiple URI\n # ask for all URI\n query = sparqls.stationData(self.uri, 'all')\n data = RunSparql(query, 'pandas').run()\n\n if not data.empty:\n self._data = data\n else:\n self._data = 'no data available'\n\n # check if data is available and extract the 'unique' data products\n if isinstance(self._data, pd.DataFrame):\n p = self._data['specLabel'].unique()\n self._products = pd.DataFrame(p)\n\n # replace samplingheight=None with empty string\n self._data.samplingheight.replace(to_replace=[None], value=\"\", inplace=True)\n else:\n self._products = 'no data available'","def locations(self):\r\n return Locations(self)","def build(self):\n states = WOFRegion.query.filter(WOFRegion.country_iso=='US')\n\n logger.info('Indexing US states.')\n\n for row in tqdm(states):\n\n # Key -> id(s)\n for key in map(keyify, state_key_iter(row)):\n self.add_key(key, row.wof_id)\n\n # ID -> state\n self.add_location(row.wof_id, StateMatch(row))","def _populate_output(self):\n self._store_query_percentiles_table()","def construct_locations(path_to_shapes, path_to_land_eligibility_km2, path_to_hydro_capacities_mw,\n path_to_biofuel_potential_mwh, flat_roof_share, maximum_installable_power_density,\n scaling_factors, biofuel_efficiency, path_to_output_yaml, path_to_output_csv):\n locations = gpd.GeoDataFrame(\n gpd.read_file(path_to_shapes).set_index(\"id\")\n )\n locations = (\n locations\n .assign(centroid=locations.centroid.rename(\"centroid\"))\n .loc[:, [\"name\", \"centroid\"]]\n )\n capacities = _from_area_to_installed_capacity(\n land_eligibiligy_km2=pd.read_csv(path_to_land_eligibility_km2, index_col=0),\n flat_roof_share=flat_roof_share,\n maximum_installable_power_density=maximum_installable_power_density\n )\n hydro_capacities = pd.read_csv(path_to_hydro_capacities_mw, index_col=0)\n biofuel = pd.read_csv(path_to_biofuel_potential_mwh, index_col=0) * biofuel_efficiency\n locations = locations.merge(\n pd.concat([capacities, hydro_capacities, biofuel], axis=\"columns\", sort=True),\n how=\"left\",\n left_index=True,\n right_index=True,\n validate=\"one_to_one\"\n )\n locations = locations.assign(id=locations.index.str.replace(\".\", \"-\")).set_index(\"id\")\n\n env = jinja2.Environment()\n env.filters[\"unit\"] = filters.unit\n rendered = env.from_string(TEMPLATE).render(\n locations=locations,\n scaling_factors=scaling_factors\n )\n with open(path_to_output_yaml, \"w\") as result_file:\n result_file.write(rendered)\n locations.name.to_csv(path_to_output_csv, index=True, header=True)","def get_geo_data(self):\n # Get all countries and create a dictionary by name\n countries_shp = shpreader.natural_earth(\n resolution='10m',\n category='cultural',\n name='admin_0_countries',\n )\n self.countries = list(shpreader.Reader(countries_shp).records())\n self.countries_by_name = {}\n self.countries_by_iso_a2 = {}\n for country in shpreader.Reader(countries_shp).records():\n self.countries_by_name[country.attributes['NAME_LONG']] = country\n self.countries_by_iso_a2[country.attributes['ISO_A2']] = country\n\n # Get all states and create a dictionary by name\n states_provinces_shp = shpreader.natural_earth(\n resolution='50m',\n category='cultural',\n name='admin_1_states_provinces',\n )\n# full_list = list(shpreader.Reader(states_provinces_shp).records())\n# self.states = [x for x in full_list if x.attributes['type_en'] == 'State']\n self.states = list(shpreader.Reader(states_provinces_shp).records())\n self.states_by_name = {}\n for state in self.states:\n self.states_by_name[state.attributes['name']] = state\n\n # Get all timezones and create a dictionary by name\n timezones_shp = shpreader.natural_earth(\n resolution='10m',\n category='cultural',\n name='time_zones',\n )\n self.timezones = list(shpreader.Reader(timezones_shp).records())\n self.timezones_by_name = {}\n for timezone in shpreader.Reader(timezones_shp).records():\n # Try to get the actual name. Something like `Europe/Berlin`\n timezone_name = timezone.attributes['tz_name1st']\n # If there is no name, we default to the utc offset name `-5` `+4.5`\n if timezone_name == '':\n timezone_name = timezone.attributes['name']\n\n if timezone_name not in self.timezones_by_name.keys():\n self.timezones_by_name[timezone_name] = timezone","def load_geolocation_data():\n client = MongoClient(f'mongodb://{os.getenv(\"USR_\")}:{os.getenv(\"PWD_\")}@{os.getenv(\"REMOTE_HOST\")}:{os.getenv(\"REMOTE_PORT\")}/{os.getenv(\"AUTH_DB\")}')\n db = client[os.getenv(\"MAIN_DB\")]\n lk_collection = db[\"lkdata\"]\n data = pd.DataFrame(list(lk_collection.find()))\n data = data[[\"fields\"]]\n data = pd.concat([pd.DataFrame(data), pd.DataFrame(list(data[\"fields\"]))], axis=1).drop(\"fields\", 1)\n data[\"cca_2\"] = pd.to_numeric(data[\"cca_2\"])\n return data","def populate(self):\n raise NotImplementedError","def build(self):\n allow_bare = AllowBareCityName(blocklist=self.bare_name_blocklist)\n\n iter_keys = CityKeyIter(allow_bare)\n\n # Deduped cities.\n cities = WOFLocality.clean_us_cities()\n\n logger.info('Indexing US cities.')\n\n for row in tqdm(cities):\n\n # Key -> id(s)\n for key in map(keyify, iter_keys(row)):\n self.add_key(key, row.wof_id)\n\n # ID -> city\n self.add_location(row.wof_id, CityMatch(row))","def create_locations_in_numbers_section(self):\n section = self.sections['Locations in numbers:']\n column, row = Counter(), Counter()\n # first row:\n label = Label(section, text='Locations total:')\n label.grid(column=column.next(), row=row.next())\n label.bind('<Button-1>', partial(self.change_locations_filter, None))\n self.locations_counter = Entry(\n section, textvariable=self.locations_count, width=4,\n disabledbackground='white',\n disabledforeground='black', state=DISABLED, justify=CENTER\n ).grid(column=column.next(), row=row())\n for location, value in self.locations_by_type.items():\n if column() == 18:\n column.restart()\n row.next()\n text = plural(location.value, self.language)\n label = Label(section, text=f'{text.title()}:')\n label.grid(column=column.next(), row=row())\n label.bind('<Button-1>',\n partial(self.change_locations_filter, location))\n Entry(section, textvariable=value, width=4,\n disabledbackground='white',\n disabledforeground='black', state=DISABLED, justify=CENTER\n ).grid(column=column.next(), row=row())","def get_data(self):\n data = load.loader.get_data_for_hotel(self.hotel_name, self.filter)\n\n self.data_items = []\n\n if data:\n for row in data:\n for col in row:\n self.data_items.append(col)\n self.row_number = str(self.get_row_number())\n\n self.hotel_full_data = self.hotel_name + ' ' + load.loader.get_hotel_address(self.hotel_name)","def get_locations_by_ids(self, id_list):","def location_duplicates():\n\n if not auth.s3_has_role(1):\n redirect(URL(r=request, c=\"default\", f=\"index\"))\n \n # @ToDo: Set this via the UI & pass in as a var\n dupe_distance = 50 # km\n\n # Shortcut\n locations = db.gis_location\n\n table_header = THEAD(TR(TH(T(\"Location 1\")),\n TH(T(\"Location 2\")),\n TH(T(\"Distance(Kms)\")),\n TH(T(\"Resolve\"))))\n\n # Calculate max possible combinations of records\n # To handle the AJAX requests by the dataTables jQuery plugin.\n totalLocations = db(locations.id > 0).count()\n\n item_list = []\n if request.vars.iDisplayStart:\n end = int(request.vars.iDisplayLength) + int(request.vars.iDisplayStart)\n locations = db((locations.id > 0) & \\\n (locations.deleted == False) & \\\n (locations.lat != None) & \\\n (locations.lon != None)).select(locations.id,\n locations.name,\n locations.level,\n locations.lat,\n locations.lon)\n # Calculate the Great Circle distance\n count = 0\n for oneLocation in locations[:len(locations) / 2]:\n for anotherLocation in locations[len(locations) / 2:]:\n if count > end and request.vars.max != \"undefined\":\n count = int(request.vars.max)\n break\n if oneLocation.id == anotherLocation.id:\n continue\n else:\n dist = gis.greatCircleDistance(oneLocation.lat,\n oneLocation.lon,\n anotherLocation.lat,\n anotherLocation.lon)\n if dist < dupe_distance:\n count = count + 1\n item_list.append([oneLocation.name,\n anotherLocation.name,\n dist,\n \"<a href=\\\"../gis/location_resolve?locID1=%i&locID2=%i\\\", class=\\\"action-btn\\\">Resolve</a>\" % (oneLocation.id, anotherLocation.id)\n ])\n else:\n continue\n\n item_list = item_list[int(request.vars.iDisplayStart):end]\n # Convert data to JSON\n result = []\n result.append({\n \"sEcho\" : request.vars.sEcho,\n \"iTotalRecords\" : count,\n \"iTotalDisplayRecords\" : count,\n \"aaData\" : item_list\n })\n output = json.dumps(result)\n # Remove unwanted brackets\n output = output[1:]\n output = output[:-1]\n return output\n else:\n # Don't load records except via dataTables (saves duplicate loading & less confusing for user)\n items = DIV((TABLE(table_header,\n TBODY(),\n _id=\"list\",\n _class=\"dataTable display\")))\n return(dict(items=items))","def load_pneumonia_locations():\n pneumonia_locations = {}\n # load table\n\n with open(os.path.join(LABEL_PATHS), mode='r') as infile:\n # open reader\n reader = csv.reader(infile)\n # skip header\n next(reader, None)\n # loop through rows\n for rows in reader:\n # retrieve information\n filename = rows[0]\n location = rows[1:5]\n pneumonia = rows[5]\n # if row contains pneumonia add label to dictionary\n # which contains a list of pneumonia locations per filename\n if pneumonia == '1':\n # convert string to float to int\n location = [int(float(i)) for i in location]\n # save pneumonia location in dictionary\n if filename in pneumonia_locations:\n pneumonia_locations[filename].append(location)\n else:\n pneumonia_locations[filename] = [location]\n\n return pneumonia_locations","def populate_employees():\n employees = get_employees()\n\n db.session.bulk_save_objects(employees)\n db.session.commit()","def _populate_df(self, df, objs,):\n for obj in objs:\n for prop in df.columns:\n df.loc[obj.name, prop] = getattr(obj, prop)","async def synchronise_meta_data_table(\n location_id: LocationID, dry_run: bool = False, fire_and_forget: bool = False\n):","def add_amenities(self): \n amenity_count = [self.search(lat, lon) for lat, lon in\n zip(self.df_ads['latitude'], self.df_ads['longitude'])]\n \n self.df_ads_mapdata = pd.concat(\n [self.df_ads.reset_index(drop=True), pd.DataFrame(amenity_count)], axis=1)\n\n assert len(self.df_ads_mapdata) == len(self.df_ads)","def locations(request):\n locations = Location.objects.all()\n context = {'locations': locations}\n return render(request, 'std/Locations.html', context)","def load_data(self):\n @Logger.runtime\n def process_coords():\n \"\"\"\n The placement of locations on our minimap is crucial. Panda3D objects however have a coordinate range from\n -1 to 1 on all axis, meaning that if we read a coordinate of a location from some image processing software\n by hand, we have to transform those coordinates into coordinates Panda would understand. This function does\n just that.\n :return: Normalized coordinates of location coordinates.\n \"\"\"\n split_coords = row[\"map_coord\"].split(',')\n map_x, map_y = [int(i) for i in split_coords]\n map_x_normed = ((map_x*2) / self.MINIMAP_DIM) - 1\n map_y_normed = -(((map_y*2) / self.MINIMAP_DIM) - 1)\n return map_x_normed, map_y_normed\n\n @Logger.runtime\n def process_texture():\n texture_path = Path(\"resource/textures/{}\".format(row[\"texture\"]))\n texture = self.loader.loadTexture(texture_path)\n return texture\n\n # the cylinder is loaded here but it does not yet show up, until it's specifically asked to\n self.scene_3d_model = self.loader.loadModel(self.PATHS[\"3D_SCENE_MODEL\"])\n\n try:\n with open(self.PATHS[\"LOCATIONS_DB\"], \"r\") as l_file:\n data = csv.DictReader(l_file, delimiter=\"|\")\n for row in data:\n id = int(row[\"id\"])\n x, y = process_coords()\n neighbors = [int(neighbor_id) for neighbor_id in row[\"neighbors\"].split(',')]\n texture = process_texture()\n location = Location(id, x, y, neighbors, texture)\n location.reparentTo(self.render2d)\n self.locations.append(location)\n Logger.log_info('The locations_db has been loaded')\n except:\n Logger.error('{} file not found!'.format(self.PATHS[\"LOCATIONS_DB\"]))\n\n self.active_location = self.locations[0]","def _init_list(self, data: List[Any]):\n # Assume data is homogenous in regard to row type\n obj = data[0]\n column_names = self._column_name_getter(obj)\n column_values = self._column_value_getter(obj)\n\n # Map of source to destination column\n column_map = {}\n\n # Do not update columns if predefined\n add_columns = not bool(self._columns)\n\n for obj in data:\n row = [None] * len(self._columns)\n\n for column_src in column_names(obj):\n # Check if column has been added with different name\n column_dst = column_map.get(column_src, column_src)\n\n # Dictionaries and namedtuples can\n # contain unknown columns\n if column_dst not in self._columns:\n if not add_columns:\n continue\n\n col = self._add_column(column_dst)\n\n # Store map of source column name to created name\n column_dst = self._columns[col]\n column_map[column_src] = column_dst\n\n while len(row) < len(self._columns):\n row.append(None)\n\n col = self.column_location(column_dst)\n row[col] = column_values(obj, column_src)\n\n self._data.append(row)","def populate_countries(self):\n # For each country in population.\n for name, pop in self.population.iterrows():\n p = pop['Population']\n # Get all relevant time series based on country name.\n c = self.raw_confirmed.loc[self.raw_confirmed['Country/Region'] == name].sum(numeric_only=True)\n d = self.raw_deceased.loc[self.raw_deceased['Country/Region'] == name].sum(numeric_only=True)\n r = self.raw_recovered.loc[self.raw_recovered['Country/Region'] == name].sum(numeric_only=True)\n # Create new country object.\n self.countries.append(country.Country(name, p, c, d, r))","def describe_locations():\n pass","def location():\n\n tablename = \"%s_%s\" % (module, resourcename)\n table = db[tablename]\n\n # Allow prep to pass vars back to the controller\n vars = {}\n\n # @ToDo: Clean up what needs to be done only for interactive views,\n # vs. what needs to be done generally. E.g. some tooltips are defined\n # for non-interactive.\n # Pre-processor\n def prep(r, vars):\n\n def get_location_info():\n query = (db.gis_location.id == r.id)\n return db(query).select(db.gis_location.lat,\n db.gis_location.lon,\n db.gis_location.level,\n limitby=(0, 1)).first()\n\n # Restrict access to Polygons to just MapAdmins\n if deployment_settings.get_security_map() and not s3_has_role(\"MapAdmin\"):\n table.code.writable = False\n if r.method == \"create\":\n table.code.readable = False\n table.gis_feature_type.writable = table.gis_feature_type.readable = False\n table.wkt.writable = table.wkt.readable = False\n elif r.interactive:\n table.code.comment = DIV(_class=\"tooltip\",\n _title=\"%s|%s\" % (T(\"Code\"),\n T(\"For a country this would be the ISO2 code, for a Town, it would be the Airport Locode.\")))\n table.wkt.comment = DIV(_class=\"stickytip\",\n _title=\"WKT|%s %s%s %s%s\" % (T(\"The\"),\n \"<a href='http://en.wikipedia.org/wiki/Well-known_text' target=_blank>\",\n T(\"Well-Known Text\"),\n \"</a>\",\n T(\"representation of the Polygon/Line.\")))\n\n if r.method == \"update\" and r.id:\n # We don't allow converting a location group to non-group and\n # vice versa. We also don't allow taking away all the members of\n # a group -- setting \"notnull\" gets the \"required\" * displayed.\n # Groups don't have parents. (This is all checked in onvalidation.)\n # NB r.id is None for update.url\n location = get_location_info()\n if location.level == \"GR\":\n table.level.writable = False\n table.parent.readable = table.parent.writable = False\n table.members.notnull = True\n # Record that this is a group location. Since we're setting\n # level to not writable, it won't be in either form.vars or\n # request.vars. Saving it while we have it avoids another\n # db access.\n response.s3.location_is_group = True\n else:\n table.members.writable = table.members.readable = False\n response.s3.location_is_group = False\n\n if r.interactive:\n if not \"group\" in r.request.vars:\n # Hide the Members List (a big download when many records are entered)\n table.members.writable = table.members.readable = False\n # Don't show street address, postcode for hierarchy on read or update.\n if r.method != \"create\" and r.id:\n try:\n location\n except:\n location = get_location_info()\n if location.level:\n table.addr_street.writable = table.addr_street.readable = False\n table.addr_postcode.writable = table.addr_postcode.readable = False\n\n # Options which are only required in interactive HTML views\n table.level.comment = DIV(_class=\"tooltip\",\n _title=\"%s|%s\" % (T(\"Level\"),\n T(\"If the location is a geographic area, then state at what level here.\")))\n parent_comment = DIV(_class=\"tooltip\",\n _title=\"%s|%s\" % (T(\"Parent\"),\n T(\"The Area which this Site is located within.\")))\n if r.representation == \"popup\":\n table.parent.comment = parent_comment\n else:\n # Include 'Add Location' button\n table.parent.comment = DIV(A(ADD_LOCATION,\n _class=\"colorbox\",\n _href=URL(r=request, c=\"gis\", f=\"location\",\n args=\"create\",\n vars=dict(format=\"popup\",\n child=\"parent\")),\n _target=\"top\",\n _title=ADD_LOCATION),\n parent_comment),\n table.osm_id.comment = DIV(_class=\"stickytip\",\n _title=\"OpenStreetMap ID|%s%s%s\" % (T(\"The\"),\n \" <a href='http://openstreetmap.org' target=_blank>OpenStreetMap</a> ID. \",\n T(\"If you know what the OSM ID of this location is then you can enter it here.\")))\n table.geonames_id.comment = DIV(_class=\"stickytip\",\n _title=\"Geonames ID|%s%s%s\" % (T(\"The\"),\n \" <a href='http://geonames.org' target=_blank>Geonames</a> ID. \",\n T(\"If you know what the Geonames ID of this location is then you can enter it here.\")))\n table.comments.comment = DIV(_class=\"tooltip\",\n _title=\"%s|%s\" % (T(\"Comments\"),\n T(\"Please use this field to record any additional information, such as Ushahidi instance IDs. Include a history of the record if it is updated.\")))\n\n if r.representation == \"iframe\":\n # De-duplicator needs to be able to access UUID fields\n table.uuid.readable = table.uuid.writable = True\n table.uuid.label = \"UUID\"\n table.uuid.comment = DIV(_class=\"stickytip\",\n _title=\"UUID|%s%s%s\" % (T(\"The\"),\n \" <a href='http://eden.sahanafoundation.org/wiki/UUID#Mapping' target=_blank>Universally Unique ID</a>. \",\n T(\"Suggest not changing this field unless you know what you are doing.\")))\n\n if r.method in (None, \"list\") and r.record is None:\n # List\n pass\n elif r.method in (\"delete\", \"search\"):\n pass\n else:\n # Add Map to allow locations to be found this way\n config = gis.get_config()\n lat = config.lat\n lon = config.lon\n zoom = config.zoom\n feature_queries = []\n\n if r.method == \"create\":\n add_feature = True\n add_feature_active = True\n else:\n if r.method == \"update\":\n add_feature = True\n add_feature_active = False\n else:\n # Read\n add_feature = False\n add_feature_active = False\n\n try:\n location\n except:\n location = get_location_info()\n if location and location.lat is not None and location.lon is not None:\n lat = location.lat\n lon = location.lon\n # Same as a single zoom on a cluster\n zoom = zoom + 2\n\n # @ToDo: Does map make sense if the user is updating a group?\n # If not, maybe leave it out. OTOH, might be nice to select\n # admin regions to include in the group by clicking on them in\n # the map. Would involve boundaries...\n _map = gis.show_map(lat = lat,\n lon = lon,\n zoom = zoom,\n feature_queries = feature_queries,\n add_feature = add_feature,\n add_feature_active = add_feature_active,\n toolbar = True,\n collapsed = True)\n\n # Pass the map back to the main controller\n vars.update(_map=_map)\n return True\n response.s3.prep = lambda r, vars=vars: prep(r, vars)\n\n # Options\n _vars = request.vars\n filters = []\n\n parent = _vars.get(\"parent_\", None)\n # Don't use 'parent' as the var name as otherwise it conflicts with the form's var of the same name & hence this will be triggered during form submission\n if parent:\n # We want to do case-insensitive searches\n # (default anyway on MySQL/SQLite, but not PostgreSQL)\n _parent = parent.lower()\n\n # Can't do this using a JOIN in DAL syntax\n # .belongs() not GAE-compatible!\n query = (db.gis_location.name.lower().like(_parent))\n filters.append((db.gis_location.parent.belongs(db(query).select(db.gis_location.id))))\n # ToDo: Make this recursive - want descendants not just direct children!\n # Use new gis.get_children() function\n\n # ToDo\n # bbox = _vars.get(\"bbox\", None):\n\n if filters:\n from operator import __and__\n response.s3.filter = reduce(__and__, filters)\n\n caller = _vars.get(\"caller\", None)\n if caller:\n # We've been called as a Popup\n if \"gis_location_parent\" in caller:\n # Hide unnecessary rows\n table.addr_street.readable = table.addr_street.writable = False\n else:\n parent = _vars.get(\"parent_\", None)\n # Don't use 'parent' as the var name as otherwise it conflicts with the form's var of the same name & hence this will be triggered during form submission\n if parent:\n table.parent.default = parent\n\n # Hide unnecessary rows\n table.level.readable = table.level.writable = False\n table.geonames_id.readable = table.geonames_id.writable = False\n table.osm_id.readable = table.osm_id.writable = False\n table.source.readable = table.source.writable = False\n table.url.readable = table.url.writable = False\n\n level = _vars.get(\"level\", None)\n if level:\n # We've been called from the Location Selector widget\n table.addr_street.readable = table.addr_street.writable = False\n\n output = s3_rest_controller(module, resourcename)\n\n _map = vars.get(\"_map\", None)\n if _map and isinstance(output, dict):\n output.update(_map=_map)\n\n return output","def populate_db():\n\n populate_table(db, models.Department, departments_data)\n populate_table(db, models.Employee, employees_data)","def stats_data_by_location():\n by_city = db_session.query(Locations.city, func.count(MediaFiles.id)) \\\n .outerjoin(MediaFiles, MediaFiles.location_id == Locations.id) \\\n .group_by(Locations.city) \\\n .all()\n by_country = db_session.query(Locations.country, func.count(MediaFiles.id)) \\\n .outerjoin(MediaFiles, MediaFiles.location_id == Locations.id) \\\n .group_by(Locations.country) \\\n .all()\n data_by_city = [{'name': item[0].title(), 'y': item[1]} for item in by_city]\n data_by_country = [{'name': item[0].title(), 'y': item[1]} for item in by_country]\n return data_by_city, data_by_country","def initialize(self) -> None:\n all_queries: List[Union[DataQueryInfo, MeasureQueryInfo]] = []\n entity_cells: List[DataCell] = []\n current_row_group = None\n\n # Loop over rows, columns\n for row_index, row in enumerate(self.rows):\n if isinstance(row, RowSeparator):\n current_row_group = row.name\n continue\n entity: Entity = row.entity\n if isinstance(entity, Entity):\n self.entity_map[entity.get_marquee_id()] = entity\n else:\n self.entity_map[''] = entity\n cells: List[DataCell] = []\n row_overrides = row.overrides\n\n for column_index, column in enumerate(self.columns):\n column_name = column.name\n column_processor = column.processor\n\n # Get all the data coordinate overrides and apply the processor override if it exists\n data_overrides, value_override, processor_override = _get_overrides(row_overrides, column_name)\n\n # Create the cell\n cell: DataCell = DataCell(column_name,\n column_processor,\n entity,\n data_overrides,\n column_index,\n row_index,\n current_row_group)\n\n if processor_override:\n # Check if there is a processor override and apply if so\n cell.processor = processor_override\n if value_override:\n cell.value = ProcessorResult(True, value_override.value)\n cell.updated_time = get_utc_now()\n elif isinstance(column_processor, EntityProcessor):\n # store these cells to fetch entity data during poll\n entity_cells.append(cell)\n elif isinstance(column_processor, CoordinateProcessor):\n # store these cells to fetch entity data during poll\n if len(data_overrides):\n # Get the last in the list if more than 1 override is given\n cell.processor.children['a'].set_dimensions(data_overrides[-1].dimensions)\n\n self._coord_processor_cells.append(cell)\n elif column_processor.measure_processor:\n all_queries.append(MeasureQueryInfo(attr='', entity=entity, processor=column_processor))\n else:\n # append the required queries to the map\n cell.build_cell_graph(all_queries, self.rdate_entity_map)\n\n cells.append(cell)\n\n self._cells.extend(cells)\n self.results.append(cells)\n\n self._data_queries = all_queries\n self._entity_cells = entity_cells\n self.is_initialized = True","def append_locations(self, newlocs: List):\n self.locations.extend(newlocs)","def build_localisations(csv_locations):\n # Open each of the files\n # Build the dictionaries by column\n localisations = []\n for csv_location in csv_locations:\n with open(csv_location) as csvfile:\n reader = csv.DictReader(csvfile)\n locs = {}\n for key in reader.fieldnames:\n locs[key] = []\n for row in reader:\n for key in reader.fieldnames:\n locs[key].append(row[key])\n\n localisations.append(locs)\n return localisations","def load_store_location_data():\n try:\n store_location_data = []\n with open('store-locations.csv') as csvfile:\n reader = csv.DictReader(csvfile, dialect=csv.excel)\n for row in reader:\n store_location_data.append(dict(OrderedDict(row)))\n\n return store_location_data\n except IOError as error:\n print(\"I/O error({0}): {1}\".format(error.errno, error.strerror))\n return None","def _populate_table(self):\n self._table.setSortingEnabled(False)\n self._table.setRowCount(len(self._module_names))\n for i, module_name in enumerate(self._module_names, 0):\n self._table.setItem(i, 0, QtWidgets.QTableWidgetItem(module_name))\n self._table.resizeRowsToContents()\n self._table.setSortingEnabled(True)","async def test_get_location_data(self):\n for city_name in ['dublin', 'London', 'Copenhagen']:\n response = await self.http_client.fetch(request=HTTPRequest(\n url=self.get_url(path=\"/location-data/{}\".format(city_name)),\n method='GET'\n ))\n self.assertEqual(response.code, HTTPStatus.OK)\n self.check_city_response(response, city_name.lower())","def location_ids(self, location_ids):\n\n self._location_ids = location_ids","def insert_values():\n pass","def run(self, locations):\n return locations","def test_storage_locations_data(\n storage_locations,\n storage_service_id,\n storage_service_name,\n start_date,\n end_date,\n locations_count,\n location_1_aips_count,\n location_1_total_size,\n location_2_aips_count,\n location_2_total_size,\n):\n report = report_data.storage_locations(\n storage_service_id=storage_service_id, start_date=start_date, end_date=end_date\n )\n\n assert report[fields.FIELD_STORAGE_NAME] == storage_service_name\n\n locations = report[fields.FIELD_LOCATIONS]\n assert len(locations) == locations_count\n\n if locations_count < 1:\n return\n\n first_location = locations[0]\n # Account for sorting changes made possible by date filtering.\n if location_1_aips_count > location_2_aips_count:\n assert first_location[fields.FIELD_UUID] == STORAGE_LOCATION_1_UUID\n assert (\n first_location[fields.FIELD_STORAGE_LOCATION]\n == STORAGE_LOCATION_1_DESCRIPTION\n )\n assert first_location[fields.FIELD_AIPS] == location_1_aips_count\n assert first_location[fields.FIELD_SIZE] == location_1_total_size\n else:\n assert first_location[fields.FIELD_UUID] == STORAGE_LOCATION_2_UUID\n assert (\n first_location[fields.FIELD_STORAGE_LOCATION]\n == STORAGE_LOCATION_2_DESCRIPTION\n )\n assert first_location[fields.FIELD_AIPS] == location_2_aips_count\n assert first_location[fields.FIELD_SIZE] == location_2_total_size\n\n second_location = locations[1]\n # Account for sorting changes made possible by date filtering.\n if location_1_aips_count > location_2_aips_count:\n assert second_location[fields.FIELD_UUID] == STORAGE_LOCATION_2_UUID\n assert (\n second_location[fields.FIELD_STORAGE_LOCATION]\n == STORAGE_LOCATION_2_DESCRIPTION\n )\n assert second_location[fields.FIELD_AIPS] == location_2_aips_count\n assert second_location[fields.FIELD_SIZE] == location_2_total_size\n else:\n assert second_location[fields.FIELD_UUID] == STORAGE_LOCATION_1_UUID\n assert (\n second_location[fields.FIELD_STORAGE_LOCATION]\n == STORAGE_LOCATION_1_DESCRIPTION\n )\n assert second_location[fields.FIELD_AIPS] == location_1_aips_count\n assert second_location[fields.FIELD_SIZE] == location_1_total_size","def __init__(self):\n self._distance_data = []\n self._location_data = []\n self._package_data = []","def populate_database(self):\n self.insert_products()\n self.insert_categories()\n self.insert_products_categories()\n self.insert_stores()\n self.insert_products_stores()","def fill_obs(self, observation_table, data_store):\n for obs in observation_table:\n events = data_store.obs(obs_id=obs['OBS_ID']).events\n\n # TODO: filter out (mask) possible sources in the data\n # for now, the observation table should not contain any\n # run at or near an existing source\n\n self.counts_cube.fill_events([events])\n self.livetime_cube.data += events.observation_live_time_duration","def getData(locations, soup):\n chromeIndex = locations.index('On chromosome')\n positionIndex = locations.index('Chromosome position')\n if 'Summary' in locations:\n traitIndex = locations.index('Summary')\n else:\n traitIndex = locations.index('Trait')\n\n tvalues = soup('table')[1].find_all('td')\n chromosome = tvalues[chromeIndex].find_all('span')[0].get_text().split()[0]\n position = ''.join(tvalues[positionIndex].find_all('span')[0].get_text().split()[0].split(','))\n trait = tvalues[traitIndex].find_all('span')[0].get_text().split(u\"\\u00A0\")[0]\n result = {\n 'chromosome': chromosome,\n 'position': position,\n 'trait': trait\n }\n return result","def location_links():\n\n try:\n record_id = request.args[0]\n except:\n item = s3xrc.xml.json_message(False, 400, \"Need to specify a record ID!\")\n raise HTTP(400, body=item)\n\n try:\n # Shortcut\n locations = db.gis_location\n\n deleted = (locations.deleted == False)\n query = (locations.id == record_id)\n query = deleted & query\n record = db(query).select(locations.id, limitby=(0, 1)).first().id\n except:\n item = s3xrc.xml.json_message(False, 404, \"Record not found!\")\n raise HTTP(404, body=item)\n\n # Find all tables which link to the Locations table\n # @ToDo: Replace with db.gis_location._referenced_by\n tables = shn_table_links(\"gis_location\")\n\n results = []\n for table in tables:\n for count in range(len(tables[table])):\n field = tables[str(db[table])][count]\n query = db[table][field] == record_id\n _results = db(query).select()\n module, resourcename = table.split(\"_\", 1)\n for result in _results:\n id = result.id\n # We currently have no easy way to get the default represent for a table!\n try:\n # Locations & Persons\n represent = eval(\"shn_%s_represent(id)\" % table)\n except:\n try:\n # Organisations\n represent = eval(\"shn_%s_represent(id)\" % resourcename)\n except:\n try:\n # Many tables have a Name field\n represent = (id and [db[table][id].name] or [\"None\"])[0]\n except:\n # Fallback\n represent = id\n results.append({\n \"module\" : module,\n \"resource\" : resourcename,\n \"id\" : id,\n \"represent\" : represent\n })\n\n output = json.dumps(results)\n return output"],"string":"[\n \"def generate_position_data(self):\\n # populate 'Location' field randomly\\n self.output['Location'] = np.random.choice(self.locations, self.obs)\\n\\n # clean up geodata data frame and create 'Position' attribute\\n nc = self.geodata[['Lat', 'Lng', 'Elevation']].round(2)\\n nc['Elevation'] = nc['Elevation'].astype(int)\\n self.geodata['Position'] = nc.astype(\\n str).apply(lambda x: ','.join(x), axis=1)\\n self.geodata.drop(columns=['Lat', 'Lng', 'Elevation'], inplace=True)\\n\\n # update 'Position' column in output data frame\\n left = self.output.set_index('Location') # set left index\\n right = self.geodata.set_index('Location') # set right index\\n self.output = left.loc[:, left.columns.union(right.columns)] # union\\n self.output.update(right) # update self.output \\\"Position\\\" column\\n self.output.reset_index(inplace=True)\",\n \"def add_locations(self):\\n for _ in range(0, self.num_locations):\\n detector_id = self.generate_id()\\n detector_direction = self.generate_direction()\\n detector_point = self.generate_point()\\n self.dataset[detector_id] = (detector_direction, detector_point)\\n assert len(self.dataset) == self.num_locations\",\n \"def get_all_locations(self):\",\n \"def load_data():\\n if _LOCATIONS_BY_ID:\\n return _LOCATIONS_BY_NAME, _LOCATIONS_BY_ID\\n\\n # We need to read the locations in order of country -> admin level 1 -> admin level 2 -> city.\\n # This is so that the higher resolution locations can look up the lower resolution locations\\n # that they belong to, and compute the necessary fields.\\n countries_by_code = _load_country_data(_DATA_FILES['country'])\\n admin1_by_code = _load_admin1_data(_DATA_FILES['admin_1'], countries_by_code)\\n admin2_by_code = _load_admin2_data(_DATA_FILES['admin_2'], countries_by_code, admin1_by_code)\\n _load_city_data(_DATA_FILES['city'], countries_by_code, admin1_by_code, admin2_by_code)\\n _add_alternate_names(_DATA_FILES['alt_wiki_names'])\\n _add_estimated_importances(_DATA_FILES['estimated_importance'])\\n\\n return _LOCATIONS_BY_NAME, _LOCATIONS_BY_ID\",\n \"def generate_test_locations(self):\\n def generate_locations_for_organization(\\n location_names, organization_name):\\n item_dict = {}\\n for name in location_names:\\n item_dict['{}_{}'.format(name, organization_name)] = {\\n 'name': name,\\n 'organization': organization_name\\n }\\n return item_dict\\n\\n self.ls_o1_dict = \\\\\\n generate_locations_for_organization(\\n ['l1', 'l2', 'l3', 'l4', 'l5'], 'o1')\\n\\n self.ls_sub1_o1_dict = \\\\\\n generate_locations_for_organization(\\n ['l1', 'l2', 'l3', 'l4'], 'sub1_o1')\\n\\n self.ls_o2_dict = \\\\\\n generate_locations_for_organization(['l1', 'l2', 'l3', 'l4'], 'o2')\\n\\n self.ls_sub1_o2_dict = \\\\\\n generate_locations_for_organization(['l1', 'l2'], 'sub1_o2')\\n\\n # generate locations of org_3\\n self.ls_o3_dict = \\\\\\n generate_locations_for_organization(['l1', 'l2'], 'o3')\\n\\n # generate locations dictionary\\n self.ls_dict = {\\n **self.ls_o1_dict,\\n **self.ls_sub1_o1_dict,\\n **self.ls_o2_dict,\\n **self.ls_sub1_o2_dict,\\n **self.ls_o3_dict,\\n }\\n\\n # generate locations in database\\n self.locations = self.create_locations_from_data(\\n self.ls_dict, self.orgs)\",\n \"def populate_stops(self):\\n stops = self.load_csv('stops.txt')\\n stops = self.process_stops(stops)\\n\\n connection = db.connect()\\n for stop in stops:\\n try:\\n connection.execute(schema.stops.insert(), stop)\\n except DataError:\\n print \\\"Missing data for stop: %s\\\" % (stop)\",\n \"def create_locations(self, data):\\n total_objects = len(data)\\n parsed_objects = 0\\n\\n for object in data:\\n # Get location title. 'name' val is available to all objects, but Building 'title'\\n # and RegionalCampus 'description' are more descriptive. Use them if available.\\n if hasattr(object, 'title'):\\n title = object['title']\\n elif hasattr(object, 'description'):\\n title = object['description']\\n else:\\n title = object['name']\\n\\n # Get other data.\\n mapurl = object['profile_link']\\n import_id = object['id']\\n\\n if title:\\n # Check to see if the location name, map url are too long\\n if len(title) > 256:\\n title = title[0:256]\\n if len(mapurl) > 400:\\n mapurl = mapurl[0:400]\\n if len(import_id) > 256:\\n import_id = import_id[0:256]\\n\\n # See if an existing location exists with the current object ID.\\n # Update the existing location if it exists; else, save the new location\\n try:\\n old_location = Location.objects.get(import_id=import_id)\\n except Exception as e:\\n logging.debug('No existing location found for %s: %s. Creating new location...' % (title, e))\\n # No existing matches found, or the matches were duplicate\\n new_location = Location(title=title, url=mapurl, room='', import_id=import_id, reviewed=True)\\n try:\\n new_location.save()\\n except Exception as e:\\n logging.error('Unable to save new location %s: %s' % (title, str(e)))\\n else:\\n parsed_objects += 1\\n logging.info('New location %s created.' % title)\\n else:\\n logging.debug('Existing location %s found with Import ID %s. Updating existing location...' % (title, import_id))\\n old_location.title = title\\n old_location.url = mapurl\\n old_location.room = ''\\n old_location.reviewed = True\\n try:\\n old_location.save()\\n except Exception as e:\\n logging.error('Unable to save existing location %s: %s' % (title, str(e)))\\n else:\\n parsed_objects += 1\\n logging.info('Existing location %s with Import ID %s updated.' % (title, import_id))\\n\\n logging.info('Done. %s of %s available objects successfully imported.' % (parsed_objects, total_objects))\",\n \"def add_locations(updatemedf):\\n # List of unique identifiers to search in text\\n uniquedf = pd.read_csv('uniquelist.csv', encoding=\\\"latin-1\\\")\\n\\n # Use lookup list to find text and look for institution matches, \\n # once found append unique list of matching institution lookups\\n df = add_univ_city(updatemedf, uniquedf)\\n # Output this to a file for checking and adding more values as needed\\n return df\",\n \"def __init__(self, num_locations):\\n self.dataset = {}\\n self.num_locations = num_locations\\n self.add_locations()\",\n \"def test_bulk_locations(self):\\n # do twice to check if it really updates\\n lengths = []\\n for i in range(2):\\n file_path = os.path.join(os.path.dirname(__file__),\\n self.testdata_folder,\\n self.filename_locations)\\n data = {\\n 'bulk_upload' : open(file_path, 'rb'),\\n }\\n\\n res = self.client.post(self.location_url, data)\\n assert res.status_code == status.HTTP_201_CREATED, (\\n responses.get(res.status_code, res.status_code), res.content)\\n lengths.append(len(AdministrativeLocation.objects.all()))\\n\\n assert lengths[0] == lengths[1]\\n\\n file_path = os.path.join(os.path.dirname(__file__),\\n self.testdata_folder,\\n self.filename_locations_duplicates)\\n data = {\\n 'bulk_upload' : open(file_path, 'rb'),\\n }\\n\\n res = self.client.post(self.location_url, data)\\n assert res.status_code == status.HTTP_400_BAD_REQUEST\",\n \"def get_locations():\\n\\n dtype = {'id': str,\\\\\\n 'lat': float,\\n 'lon': float,\\n 'address': str,\\n }\\n\\n try: \\n locations = pd.read_csv('Data/kulkijat-mittauspisteet.csv', sep=',', dtype=dtype)\\n except FileNotFoundError:\\n print('\\\\nMittauspisteet sisältävää tiedostoa kulkijat-mittauspisteet.csv ei löytynyt.\\\\n')\\n locations = pd.DataFrame()\\n\\n return locations\",\n \"def _update_locations(self):\\n raw_data = self._read_data()\\n processed_data = self._process_data(raw_data) if self._process_data is not None else raw_data\\n if processed_data:\\n for k, v in processed_data.items():\\n if k in self._locations.keys():\\n self.predictors[k].update(*v)\\n self._locations[k] = v\\n for k, v in self._locations.items():\\n if k not in processed_data:\\n self._locations[k] = self.predictors[k].predict()\\n else:\\n self._locations = {k: self.predictors[k].predict() for k in self._locations.keys()}\\n self._logger.debug(\\\"Locator updated locations\\\")\",\n \"def set_locations():\\n STATUS['locations']['monster'][0] = generate_random_coord(STATUS['grid_size'])\\n STATUS['locations']['monster'][1] = generate_random_coord(STATUS['grid_size'])\\n STATUS['locations']['weapon'][0] = generate_random_coord(STATUS['grid_size'])\\n STATUS['locations']['weapon'][1] = generate_random_coord(STATUS['grid_size'])\",\n \"def get_locations(self):\\n try:\\n output_json = {}\\n total_locations = list(self.mongo_db_object.find_all(AppConfigurations.MONGO_DATABASE,\\n AppConstants.LOCATION.MONGO_LOCATION_COLLECTION_NAME))\\n output_json = total_locations\\n return AppConstants.result_success_template(output_json)\\n\\n except Exception as e:\\n print(\\\"Error while fetching the Location Data.\\\", str(e))\",\n \"def load(self, locations):\\n try:\\n assert isinstance(locations, list)\\n super(Arrivals, self).load({'locIDs': locations})\\n except AssertionError:\\n print(\\\"Locations must be a list\\\")\",\n \"def _gen_locs_dbscan(sp, distance_metric, db):\\n p = np.array([sp.geometry.x, sp.geometry.y]).transpose()\\n if distance_metric == \\\"haversine\\\":\\n p = np.deg2rad(p) # haversine distance metric assumes input is in rad\\n labels = db.fit_predict(p)\\n sp[\\\"location_id\\\"] = labels\\n return sp\",\n \"def load(self):\\n return list(self.obj.locations_set.all())\",\n \"def populate_contents(self):\\n\\n data_table = self.data_table\\n world = self.world\\n\\n self.add_text_row('World Name', data_table.world_name_label.text())\\n self.add_text_row('Coordinates', data_table.world_coords_label.text())\\n self.add_text_row('World Type', data_table.world_type_label.text())\\n if data_table.world_extra_label.text() != '':\\n self.add_text_row('Extra Info', data_table.world_extra_label.text())\\n self.add_text_row('Filename', world.base_filename)\\n self.add_text_row('Size', '{}x{}'.format(*world.info.size))\\n\\n if len(world.info.dungeons) > 0:\\n dungeons = self.add_text_row('Dungeons', '<br/>'.join(sorted(world.info.dungeons)))\\n else:\\n self.add_text_row('Dungeons', '-')\\n\\n if len(world.info.biomes) > 0:\\n biomes = self.add_text_row('Biomes', '<br/>'.join(sorted(world.info.biomes)))\\n else:\\n self.add_text_row('Biomes', '-')\",\n \"def populate_cities():\\n if City.query.filter_by(name=CITIES[0]).first():\\n return\\n\\n for city in CITIES:\\n _add_city(city)\",\n \"def create_data():\\n # Locations\\n data = {}\\n num_vehicles = 20\\n depot = 0\\n locations = loc1\\n demands = popn\\n\\n num_locations = len(locations)\\n dist_matrix = {}\\n\\n for from_node in range(0,num_locations):\\n dist_matrix[from_node] = {}\\n\\n for to_node in range(0,num_locations):\\n dist_matrix[from_node][to_node] = (\\n haversine(\\n locations[from_node],[to_node])\\n #locations[to_node],[from_node])\\n \\\"\\\"\\\"\\n data[\\\"distances\\\"] =dist_matrix\\n data[\\\"num_locations\\\"] = len(dist_matrix)\\n data[\\\"num_vehicles\\\"] = 6\\n data[\\\"depot\\\"] = 0\\n data[\\\"demands\\\"] = demands\\n #data[\\\"vehicle_capacities\\\"] = capacities\\n data[\\\"time_per_demand_unit\\\"] = 0.05\\n return data\\n \\\"\\\"\\\"\\n return [ num_vehicles, depot, locations, dist_matrix]\",\n \"def create_locations_from_data(self, data, orgs):\\n item_dict = {}\\n for (item_name, data) in data.items():\\n item_dict[item_name] = \\\\\\n Location(\\n name=data['name'],\\n organization=orgs.get(data['organization']))\\n item_dict[item_name].save()\\n return item_dict\",\n \"def getAllLocation(table):\\n\\tlocs = []\\n\\n\\tnum = len(table)\\n\\n\\tfor i in range(num):\\n\\t\\t# first field is the name\\n\\t\\tloc = getLatAndLong(table[i][1])\\n\\n\\t\\tlocs.append(loc)\\n\\n\\treturn locs\",\n \"def get_all_locations():\\n rs = run_query('''select * from zlrz_office_location''')\\n return [] if rs is None else list(map(lambda t: Location(t[1], t[2], t[3], t[4], t[5], t[0]), rs))\",\n \"def locations_fixture(location):\\n return [location, _create_location()]\",\n \"def build_polling_location_txt(self):\\n self.base_df['address_line'] = self.base_df.apply(\\n lambda row: self.get_address_line(row['index'], row['address1'], row['address2'], row['city'],\\n row['state'], row['zip_code']), axis=1)\\n\\n self.base_df['directions'] = self.base_df.apply(\\n lambda row: self.get_directions(), axis=1)\\n #\\n self.base_df['hours'] = self.base_df.apply(\\n lambda row: self.get_hours(row['index'],row['start_time'], row['end_time']), axis=1)\\n\\n self.base_df['photo_uri'] = self.base_df.apply(\\n lambda row: self.get_photo_uri(), axis=1)\\n\\n self.base_df['hours_open_id'] = self.base_df.apply(\\n lambda row: self.create_hours_open_id(row['index'], row['address1'], row['address2'], row['city'],\\n row['state'], row['zip_code']), axis=1)\\n\\n self.base_df['is_drop_box'] = self.base_df.apply(\\n lambda row: self.is_drop_box(), axis=1)\\n\\n self.base_df['is_early_voting'] = self.base_df.apply(\\n lambda row: self.is_early_voting(), axis=1)\\n\\n self.base_df['latitude'] = self.base_df.apply(\\n lambda row: self.get_latitude(), axis=1)\\n\\n self.base_df['longitude'] = self.base_df.apply(\\n lambda row: self.get_longitude(), axis=1)\\n\\n self.base_df['latlng_source'] = self.base_df.apply(\\n lambda row: self.get_latlng_source(), axis=1)\\n\\n self.base_df['id'] = self.base_df.apply(\\n lambda row: self.create_id(row['index'], row['ocd_division'],row['address1'], row['address2'],\\n row['city'], row['state'], row['zip_code']), axis=1)\\n\\n return self.base_df\",\n \"def process_location(pid, data_source, out_loc, start_date, end_date, debug=False):\\n\\n #query = client.query_data_points(page_size=PAGE_SIZE, source=pid)\\n query = try_query(pid)\\n\\n location_query = try_filter(query, pid, 'pdk-location', start_date, end_date)\\n \\\"\\\"\\\" location_query = query.filter(source=pid, \\n generator_identifier='pdk-location',\\n created__gte=start_date,\\n created__lte=end_date).order_by('created')\\n \\\"\\\"\\\"\\n tot_count = location_query.count()\\n count = 0\\n frac = int(tot_count / 100)\\n\\n loc_df = pd.DataFrame()\\n for point in location_query:\\n point_df = pd.DataFrame.from_dict(point).iloc[0].to_frame().transpose()\\n metadata_df = pd.Series(point['passive-data-metadata']).to_frame().transpose()\\n # TODO check if ignoring errors is safe\\n metadata_df = metadata_df.drop(['latitude', 'longitude'], axis='columns', errors=\\\"ignore\\\")\\n point_df.reset_index(inplace=True, drop=True)\\n point_df = pd.concat([metadata_df, point_df], axis=1, sort=True)\\n \\n point_df.drop('passive-data-metadata', axis='columns', inplace=True)\\n missing_cols = [col for col in loc_df.columns.values if col not in point_df.columns.values]\\n \\n if len(missing_cols) > 0 and loc_df.shape[0] > 0:\\n for col in missing_cols:\\n point_df[col] = np.nan\\n point_df = point_df[loc_df.columns]\\n loc_df = loc_df.append(point_df)\\n count += 1\\n if debug and (count % frac == 0):\\n print(\\\"{0:.2f}% complete\\\".format(float(count)/float(tot_count)*100))\\n\\n loc_df['pid'] = pid \\n loc_df['data_source'] = data_source\\n print(loc_df.shape)\\n \\n pickle.dump(loc_df, open(\\\"{}/pdk-location/{}.df\\\".format(out_loc, pid), 'wb'), -1)\",\n \"def populate_stat(self, table):\\n myrow = table.row\\n # HDF5 doesn't handle unicode strings, so we need to convert to \\n # *byte* strings, which we can put in the HDF5 file \\n addy = numpy.zeros(len(self.address), \\n dtype=(numpy.str, glob.nchar_address))\\n for i in range(len(addy)):\\n addy[i] = (self.address[i]).encode('utf8')\\n\\n myrow[\\\"address\\\"] = addy\\n myrow[\\\"bike_stands\\\"] = self.bike_stands\\n myrow[\\\"number\\\"] = self.number\\n myrow[\\\"position\\\"] = self.position\\n myrow.append()\\n table.flush()\",\n \"def __init__(self,\\n locations: List['LocationOutput']) -> None:\\n self.locations = locations\",\n \"def test_locations(self):\\n url = reverse(\\\"locations\\\", args=[00000])\\n response = self.client.get(url)\\n\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\\n self.assertTrue(isinstance(response.data, list))\\n self.assertTrue(response.data) # list not empty\\n\\n location_data = response.data[0]\\n data_keys = [\\n \\\"title\\\",\\n \\\"address\\\",\\n \\\"address2\\\",\\n \\\"city\\\",\\n \\\"state\\\",\\n \\\"postalCode\\\",\\n \\\"distance\\\",\\n \\\"hours\\\",\\n \\\"phone\\\",\\n \\\"geocode\\\",\\n ]\\n self.assertEqual(list(location_data.keys()), data_keys)\",\n \"def populate(self):\\n\\n NUM_COUNTRIES = 2 # random.randint(1, 4)\\n\\n # find a suitable hex\\n with Timer(\\\"Creating initial data\\\", debug=self.debug):\\n\\n for i in range(NUM_COUNTRIES):\\n country, provinces, pops = create_country(self, self.map)\\n country.determine_tax_policy()\\n self.countries.append(country)\",\n \"def populate(self):\\n pass\",\n \"def _populate_output(self):\\n self._store_atomic_queries_table()\\n self._store_composite_queries_table()\",\n \"def __init__(self, obs, start_date, end_date, histdata, geodata):\\n self.start_date = start_date\\n self.end_date = end_date\\n self.obs = obs\\n self.geodata = pd.read_csv(get_datafile(geodata))\\n self.histdata = aggregate_data(histdata)\\n self.locations = self.histdata['Location'].unique().tolist()\\n self.output = None\\n self.cols = ['Location', 'Position', 'Local Time',\\n 'Conditions', 'Temperature', 'Pressure', 'Humidity']\",\n \"def populateTable(self):\\n\\n output_list = self.output_ports.split(', ')\\n\\n for i in output_list:\\n values = i.split('-')\\n nextHopPort = values[0]\\n linkCost = values[1]\\n destId = values[2]\\n learnedFrom = 0 # As it was learned from ConfigFile\\n row = routing_row.RoutingRow(nextHopPort, destId, linkCost, destId, learnedFrom)\\n self.addToRoutingTable(row)\",\n \"def extract_locations(self):\\n default_pos_columns = common_cfg.coord_col_names\\n if set(default_pos_columns).issubset(set(self._raw_data.columns)):\\n print('Location data found')\\n # check and drop units outside provided city boundary\\n geometry = [shapely.geometry.Point(xy) for xy in zip(\\n self._raw_data[default_pos_columns[0]], # Long\\n self._raw_data[default_pos_columns[1]])] # Lat\\n b_within_boundary = np.array(list(map(\\n lambda p: p.within(self.model_city.convhull), geometry)))\\n\\n if not all(b_within_boundary):\\n print('%s -- dropping %i units outside city.' %\\n (self.servicetype,\\n sum(np.bitwise_not(b_within_boundary))))\\n self._raw_data = self._raw_data.iloc[\\n b_within_boundary, :].reset_index()\\n\\n # store geolocations as geopy Point\\n locations = [geopy.Point(yx) for yx in zip(\\n self._raw_data[default_pos_columns[1]], # Lat\\n self._raw_data[default_pos_columns[0]])] # Long\\n\\n propert_data = self._raw_data.drop(default_pos_columns, axis=1)\\n\\n else:\\n raise NotImplementedError('Locations not found - not implemented!')\\n\\n return propert_data, locations\",\n \"def get_all_locations():\\n with mysql.db_session(read_only=True) as session:\\n locations = session.query(Location)\\n\\n if not locations:\\n return response.create_not_found_response(message='No data found.')\\n locations_list = [location.to_dict() for location in locations.all()]\\n\\n return response.Response(message=locations_list)\",\n \"def get_location_hash_table():\\n return locationHashTable\",\n \"def locations(self):\\n return self.data.get(\\\"locations\\\", [])\",\n \"def example_data():\\n\\n # In case this is run more than once, empty out existing data\\n User.query.delete()\\n Location.query.delete()\\n\\n # Add sample users and locations\\n\\n trinity = User(fname='Trinity', email='trin@unplugged.com',\\n username='questionit', password='l0lagent')\\n neo = User(fname='Neo', email='theone@unplugged.com',\\n username='neo', password='l0lagent')\\n\\n tacorea = Location(yelp_id='tacorea-san-francisco', name='Tacorea',\\n latitude='37.7749', longitude='122.3392',\\n address='809 Bush St, San Francisco, CA 94108',\\n yelp_url='tacorea@yelp.com', pic='pic')\\n\\n db.session.add_all([trinity, neo, tacorea])\\n db.session.commit()\",\n \"def _setData(self):\\n self._data = [ item for ministry in self.query.all() \\n for item in self._getItems(ministry) ]\",\n \"def _get_initial_location(self):\\n for i in self.modelunctab.tableview.selectedIndexes():\\n self.locid = i.row()\\n break\\n else:\\n self.locid = 0\\n\\n self.locnaam = self.result_locations[self.locid]\",\n \"def _set_location(self):\\n if self._report_key == ReportTypes.MHR_REGISTRATION:\\n location = self._report_data.get('location')\\n if location.get('lot') or location.get('parcel') or location.get('block') or location.get('districtLot') or\\\\\\n location.get('partOf') or location.get('section') or location.get('township') or \\\\\\n location.get('range') or location.get('meridian') or location.get('landDistrict') or \\\\\\n location.get('plan'):\\n location['hasLTSAInfo'] = True\\n else:\\n location['hasLTSAInfo'] = False\\n if location.get('pidNumber'):\\n pid = location.get('pidNumber')\\n location['pidNumber'] = pid[0:3] + '-' + pid[3:6] + '-' + pid[6:]\\n elif self._report_key in (ReportTypes.SEARCH_DETAIL_REPORT, ReportTypes.SEARCH_BODY_REPORT):\\n for detail in self._report_data['details']:\\n location = detail.get('location')\\n if location.get('lot') or location.get('parcel') or location.get('block') or \\\\\\n location.get('districtLot') or location.get('partOf') or location.get('section') or \\\\\\n location.get('township') or location.get('range') or location.get('meridian') or \\\\\\n location.get('landDistrict') or location.get('plan'):\\n location['hasLTSAInfo'] = True\\n else:\\n location['hasLTSAInfo'] = False\\n if location.get('pidNumber'):\\n pid = location.get('pidNumber')\\n location['pidNumber'] = pid[0:3] + '-' + pid[3:6] + '-' + pid[6:]\",\n \"def _populate_zone_facts_table(self):\\n census_fields = [\\n 'poverty_rate', 'fraction_black', 'income_per_capita',\\n 'labor_participation', 'fraction_foreign',\\n 'fraction_single_mothers', 'acs_lower_rent_quartile',\\n 'acs_median_rent', 'acs_upper_rent_quartile'\\n ]\\n\\n zone_types = ['ward', 'neighborhood_cluster', 'census_tract']\\n\\n query_results = list()\\n\\n # populate columns accordingly for each zone_specific type\\n for zone_type in zone_types:\\n field_values = dict()\\n\\n # get field value for each zone_specific type\\n for field in census_fields:\\n result = self._census_with_weighting(data_id=field,\\n grouping=zone_type)\\n field_values[field] = result['items']\\n\\n zone_specifics = self._get_zone_specifics_for_zone_type(zone_type)\\n\\n # TODO: add aggregate for each zone_type into table\\n for zone in zone_specifics:\\n # get not None values so we can added to db\\n columns = list()\\n values = list()\\n for field in census_fields:\\n zone_value = field_values[field][zone]\\n\\n if zone_value is not None:\\n columns.append(field)\\n values.append(\\\"'\\\" + str(zone_value) + \\\"'\\\")\\n\\n # derive column and values strings needed for sql query\\n columns = ', '.join(columns)\\n columns = 'zone, ' + columns\\n\\n values = ', '.join(values)\\n values = \\\"'\\\" + zone + \\\"', \\\" + values\\n\\n q = \\\"INSERT INTO zone_facts ({cols}) VALUES ({vals})\\\".format(\\n cols=columns, vals=values)\\n\\n with self.engine.connect() as conn:\\n result = conn.execute(q)\\n query_results.append(result)\\n\\n return query_results\",\n \"def locate(self):\\n \\n #CONNECT TO API\\n api = GoogleV3(api_key = self.google_key)\\n\\n #INITALIZE ARRAY\\n array = []\\n\\n #START GEOCODING ADDRESSES\\n for i in tqdm(range(len(self.df)), desc='Geocoding Addresses'):\\n\\n \\n row = self.df.iloc[i]\\n\\n #GET ADDRESS VARIABLES\\n st_name = row['street_name']\\n st_number = row['house_number']\\n city = row['city']\\n state = row['state/province']\\n listing_number = row['listing_number']\\n zip = row['postal_code']\\n\\n\\n #FORMAT ADDRESS FOR API\\n full_address = str(\\\"{} {},{},{},{}\\\".format(st_number, st_name, city, state, zip))\\n\\n #TRY TO LOCATE WITH GOOGLE\\n try:\\n \\n location = api.geocode(full_address, timeout=10)\\n\\n lat = location.latitude\\n lon = location.longitude\\n \\n\\n info = [lat,lon, listing_number]\\n\\n array.append(info)\\n\\n next \\n\\n #Go to next if you cant locate\\n except:\\n\\n info = [0,0, listing_number]\\n\\n array.append(info)\\n\\n next\\n\\n #CONVERT SERIES TO DATAFRAME\\n geo_data = pd.DataFrame(data = array, columns = ['lat', 'lon', 'listing_number'])\\n \\n #INNER JOIN DATA TO DATAFRAME\\n self.df = pd.merge(self.df, geo_data, on= 'listing_number', how = 'inner')\",\n \"def test_list_zr_locations(self):\\n pass\",\n \"def get_location_list(self) -> DBRecList:\\n raise NotImplementedError('not implemented')\",\n \"def locations():\\n sql = \\\"\\\"\\\"SELECT DISTINCT sample_location\\n FROM barcodes.sample\\n ORDER BY sample_location\\\"\\\"\\\"\\n with pm.sql.TRN:\\n pm.sql.TRN.add(sql)\\n return pm.sql.TRN.execute_fetchflatten()\",\n \"def places_process_rows(self):\\n\\n for index in range(len(self.table)):\\n row_rdf = self.places_map_row_to_rdf(self.table.iloc[index])\\n if row_rdf is not None:\\n self.data += row_rdf\",\n \"def table_allocations(\\n self, id_value, id_type, start_date=None, end_date=None, freq=None\\n ):\\n\\n start_date, end_date, freq = self.get_time_parameters(\\n start_date, end_date, freq\\n )\\n\\n try:\\n df = self.get_allocations(id_value, id_type, start_date, end_date, freq)\\n\\n if id_type == \\\"project\\\" and \\\"ALL\\\" not in str(id_value):\\n # add the project's missing people allocation\\n if freq == \\\"D\\\":\\n df[\\\"UNALLOCATED\\\"] = self.wim.project_peoplereq[id_value]\\n else:\\n df[\\\"UNALLOCATED\\\"] = (\\n self.wim.project_peoplereq[id_value].resample(freq).mean()\\n )\\n\\n elif id_type == \\\"person\\\" and \\\"ALL\\\" not in str(id_value):\\n # add the person's total project assignment to the data frame\\n if freq == \\\"D\\\":\\n df[\\\"TOTAL\\\"] = self.wim.people_totals[id_value]\\n else:\\n df[\\\"TOTAL\\\"] = self.wim.people_totals[id_value].resample(freq).mean()\\n\\n df = self.format_date_index(df, freq)\\n\\n return self.highlight_allocations(df)\\n\\n except ValueError as e:\\n print(e)\",\n \"def PopulatePointToPointDistancesResultsTable(self, markups, table):\\n headerList = [markups.GetNthFiducialLabel(i) for i in range(markups.GetNumberOfFiducials())]\\n table.setRowCount(len(headerList))\\n table.setColumnCount(len(headerList))\\n table.setHorizontalHeaderLabels(headerList)\\n table.setVerticalHeaderLabels(headerList)\\n\\n for point1 in range(markups.GetNumberOfFiducials()):\\n for point2 in range(point1 + 1, markups.GetNumberOfFiducials()):\\n point1_RAS = [0, 0, 0]\\n point2_RAS = [0, 0, 0]\\n markups.GetNthFiducialPosition(point1, point1_RAS)\\n markups.GetNthFiducialPosition(point2, point2_RAS)\\n distance = round(self.GetPointToPointDistance(point1_RAS, point2_RAS), 1)\\n table.setItem(point1, point2, qt.QTableWidgetItem(str(distance)))\\n\\n table.show()\\n table.resizeColumnsToContents()\\n table.resizeRowsToContents()\",\n \"def location_fixture(self, db, scope=\\\"class\\\"):\\n name = Name.objects.create(name=\\\"Event\\\", name_type=Name.SOFTWARE)\\n loc1 = Location.objects.create(\\n status=0,\\n latitude=33.210241,\\n longitude=-97.148857,\\n belong_to_name=name)\\n\\n loc2 = Location.objects.create(\\n status=0,\\n latitude=33.210241,\\n longitude=-97.148857,\\n belong_to_name=name)\\n\\n loc3 = Location.objects.create(\\n status=0,\\n latitude=33.210241,\\n longitude=-97.148857,\\n belong_to_name=name)\\n\\n # Only return the ids, because the tests will\\n # need to instantiate a new object to get the correct\\n # statuses.\\n return loc1.id, loc2.id, loc3.id\",\n \"def findAllLocations(cls):\\r\\n return cls.query.all()\",\n \"def meta_properties(self, date_col=\\\"Date\\\", type_col=\\\"Primary Type\\\", lat_col=\\\"Latitude\\\",\\\\\\n lon_col=\\\"Longitude\\\", loc_col=\\\"Location\\\", out_fname=\\\"data_formated.csv\\\"):\\n # implement keywords\\n # would we have to deal w/ file w/o headers?\\n data = pd.read_csv(self._path, usecols=[date_col, type_col, lat_col, lon_col, loc_col],\\\\\\n parse_dates=[date_col], infer_datetime_format=True)\\n data.sort_values(date_col, inplace=True)\\n min_date = data.iloc[0][date_col]\\n max_date = data.iloc[(data.shape[0]-1)][date_col]\\n\\n lat = []\\n lon = []\\n\\n nulls = []\\n for row in data.itertuples(index=True, name='Pandas'):\\n index = (row.Index)\\n # if lat, lon = nan, drop the row\\n # update: confirmed that issue is with code, not with data; for some reason\\n # csv is actually correctly grabbing location, there just legitimately are\\n # entries w/o location data\\n if pd.isnull(getattr(row, loc_col)):\\n # print(\\\"row: {} got a {} for the 'Location' column with date: {}\\\".format(index, \\\\\\n # getattr(row, loc_col), getattr(row, date_col)))\\n if not pd.isnull(getattr(row, lat_col)) and not pd.isnull(getattr(row, lon_col)):\\n lat.append(str(getattr(row, lat_col)))\\n lon.append(str(getattr(row, lon_col)))\\n if \\\",\\\" in data.loc[index, type_col]:\\n data.loc[index, type_col] = data.loc[index, type_col].replace(\\\",\\\", \\\" \\\")\\n print(\\\\\\n \\\"Successfully extracted lat, lon from lat_col, lon_col for row: {}\\\".format(index))\\n else:\\n nulls.append((index, getattr(row, date_col)))\\n data.drop(index, inplace=True)\\n # print(\\\"No location data available for row: {} with date: {}\\\".format(index,\\\\\\n # getattr(row, date_col)))\\n else:\\n loc = literal_eval(getattr(row, loc_col))\\n lat.append(loc[0])\\n lon.append(loc[1])\\n if \\\",\\\" in data.loc[index, type_col]:\\n data.loc[index, type_col] = data.loc[index, type_col].replace(\\\",\\\", \\\" \\\")\\n\\n data[\\\"Latitude\\\"] = lat\\n data[\\\"Longitude\\\"] = lon\\n data.drop(loc_col, axis=1, inplace=True)\\n\\n data.sort_values(\\\"Latitude\\\", inplace=True)\\n min_lat = float(data.iloc[0][\\\"Latitude\\\"])\\n max_lat = float(data.iloc[(data.shape[0]-1)][\\\"Latitude\\\"])\\n\\n data.sort_values(\\\"Longitude\\\", inplace=True)\\n min_lon = float(data.iloc[0][\\\"Longitude\\\"])\\n max_lon = float(data.iloc[(data.shape[0]-1)][\\\"Longitude\\\"])\\n\\n data.to_csv(self.__file_dir+'/'+out_fname, na_rep=\\\"\\\", header=False, index=False)\\n\\n attrs = {'min_date': min_date, 'max_date': max_date, \\\"min_lat\\\":min_lat,\\\\\\n \\\"max_lat\\\":max_lat, \\\"min_lon\\\":min_lon, \\\"max_lon\\\":max_lon, \\\\\\n \\\"dates\\\":pd.date_range(min_date, max_date), \\\"num_attributes\\\": data.shape[1],\\\\\\n \\\"num_entries\\\":data.shape[0]}\\n self._meta_dict = attrs\\n self._meta_dict['df'] = data\\n pickle.dump(data, open(CWD + \\\"/meta_dict.p\\\", \\\"wb\\\"))\\n print(\\\"Num entries w/o location data: {}\\\".format(len(nulls)))\\n pickle.dump(nulls, open(CWD + \\\"/nulls.p\\\", \\\"wb\\\"))\\n\\n # not include the formatted dataset?\\n return attrs\",\n \"def upsert_location(self, location):\",\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 populate_dyn(self, table):\\n myrow = table.row\\n myrow[\\\"sample_time\\\"] = int(time.time() - glob.base_time)\\n myrow[\\\"available_bike_stands\\\"] = self.available_bike_stands\\n myrow[\\\"available_bikes\\\"] = self.available_bikes\\n myrow[\\\"last_update\\\"] = self.last_update\\n myrow[\\\"status\\\"] = self.status\\n myrow.append()\\n table.flush()\",\n \"def create_table(f, geoinfo):\\n bounds_cols = xb_points + yb_points\\n df = pd.read_csv(f, delimiter=\\\";\\\", index_col=\\\"INDEX_RC\\\")\\n df[duration_name] = parse_duration_level(f)\\n df = df.join(geoinfo[[\\\"X_CENT_GEO\\\", \\\"Y_CENT_GEO\\\", \\\"Col\\\", \\\"Row\\\"]])\\n df = df.rename(columns={\\\"Col\\\": x, \\\"Row\\\": y, \\\"X_CENT_GEO\\\": lon, \\\"Y_CENT_GEO\\\": lat})\\n return df\",\n \"def set_location(self, location_set):\",\n \"def generate_weather_data(self):\\n months = pd.to_datetime(self.output['Local Time']).dt.month\\n self.output['Month'] = months # set month values for later joins\\n\\n # merge output data frame with historical data to get ranges\\n keys = ['Location', 'Month']\\n m = pd.merge(self.output, self.histdata, how='left',\\n left_on=keys, right_on=keys)\\n\\n # uniformly select random pressure, temperature\\n # and humidity values between the historical max and min ranges\\n r = np.random.rand(m.shape[0])\\n m['Temperature'] = ((m['Tmean_high'] - m['Tmean_low']\\n ) * r + m['Tmean_low']).round(1)\\n m['Pressure'] = ((m['Pmax'] - m['Pmin']) * r + m['Pmin']).round(1)\\n m['Humidity'] = ((m['Hmax'] - m['Hmin']) * r + m['Hmin']).astype(int)\\n\\n # drop redundant columns and assign to output\\n dcols = ['Month', 'Timezone', 'Pmax', 'Pmin',\\n 'Hmax', 'Hmin', 'Tmean_high', 'Tmean_low']\\n m.drop(columns=dcols, inplace=True)\\n self.output = m\",\n \"def _setData(self):\\n\\n if not self.stationId:\\n return\\n \\\"\\\"\\\" \\n # get the ressource url and adjust lat and lon from data portal\\n query = sparqls.stationResource(self.stationId)\\n key, val = RunSparql(query, 'array').run()\\n if val: \\n self.url = val[0][0]\\n self.lat = float(val[0][2])\\n self.lon = float(val[0][3])\\n \\\"\\\"\\\"\\n\\n # it is possible, that a station id has multiple URI\\n # ask for all URI\\n query = sparqls.stationData(self.uri, 'all')\\n data = RunSparql(query, 'pandas').run()\\n\\n if not data.empty:\\n self._data = data\\n else:\\n self._data = 'no data available'\\n\\n # check if data is available and extract the 'unique' data products\\n if isinstance(self._data, pd.DataFrame):\\n p = self._data['specLabel'].unique()\\n self._products = pd.DataFrame(p)\\n\\n # replace samplingheight=None with empty string\\n self._data.samplingheight.replace(to_replace=[None], value=\\\"\\\", inplace=True)\\n else:\\n self._products = 'no data available'\",\n \"def locations(self):\\r\\n return Locations(self)\",\n \"def build(self):\\n states = WOFRegion.query.filter(WOFRegion.country_iso=='US')\\n\\n logger.info('Indexing US states.')\\n\\n for row in tqdm(states):\\n\\n # Key -> id(s)\\n for key in map(keyify, state_key_iter(row)):\\n self.add_key(key, row.wof_id)\\n\\n # ID -> state\\n self.add_location(row.wof_id, StateMatch(row))\",\n \"def _populate_output(self):\\n self._store_query_percentiles_table()\",\n \"def construct_locations(path_to_shapes, path_to_land_eligibility_km2, path_to_hydro_capacities_mw,\\n path_to_biofuel_potential_mwh, flat_roof_share, maximum_installable_power_density,\\n scaling_factors, biofuel_efficiency, path_to_output_yaml, path_to_output_csv):\\n locations = gpd.GeoDataFrame(\\n gpd.read_file(path_to_shapes).set_index(\\\"id\\\")\\n )\\n locations = (\\n locations\\n .assign(centroid=locations.centroid.rename(\\\"centroid\\\"))\\n .loc[:, [\\\"name\\\", \\\"centroid\\\"]]\\n )\\n capacities = _from_area_to_installed_capacity(\\n land_eligibiligy_km2=pd.read_csv(path_to_land_eligibility_km2, index_col=0),\\n flat_roof_share=flat_roof_share,\\n maximum_installable_power_density=maximum_installable_power_density\\n )\\n hydro_capacities = pd.read_csv(path_to_hydro_capacities_mw, index_col=0)\\n biofuel = pd.read_csv(path_to_biofuel_potential_mwh, index_col=0) * biofuel_efficiency\\n locations = locations.merge(\\n pd.concat([capacities, hydro_capacities, biofuel], axis=\\\"columns\\\", sort=True),\\n how=\\\"left\\\",\\n left_index=True,\\n right_index=True,\\n validate=\\\"one_to_one\\\"\\n )\\n locations = locations.assign(id=locations.index.str.replace(\\\".\\\", \\\"-\\\")).set_index(\\\"id\\\")\\n\\n env = jinja2.Environment()\\n env.filters[\\\"unit\\\"] = filters.unit\\n rendered = env.from_string(TEMPLATE).render(\\n locations=locations,\\n scaling_factors=scaling_factors\\n )\\n with open(path_to_output_yaml, \\\"w\\\") as result_file:\\n result_file.write(rendered)\\n locations.name.to_csv(path_to_output_csv, index=True, header=True)\",\n \"def get_geo_data(self):\\n # Get all countries and create a dictionary by name\\n countries_shp = shpreader.natural_earth(\\n resolution='10m',\\n category='cultural',\\n name='admin_0_countries',\\n )\\n self.countries = list(shpreader.Reader(countries_shp).records())\\n self.countries_by_name = {}\\n self.countries_by_iso_a2 = {}\\n for country in shpreader.Reader(countries_shp).records():\\n self.countries_by_name[country.attributes['NAME_LONG']] = country\\n self.countries_by_iso_a2[country.attributes['ISO_A2']] = country\\n\\n # Get all states and create a dictionary by name\\n states_provinces_shp = shpreader.natural_earth(\\n resolution='50m',\\n category='cultural',\\n name='admin_1_states_provinces',\\n )\\n# full_list = list(shpreader.Reader(states_provinces_shp).records())\\n# self.states = [x for x in full_list if x.attributes['type_en'] == 'State']\\n self.states = list(shpreader.Reader(states_provinces_shp).records())\\n self.states_by_name = {}\\n for state in self.states:\\n self.states_by_name[state.attributes['name']] = state\\n\\n # Get all timezones and create a dictionary by name\\n timezones_shp = shpreader.natural_earth(\\n resolution='10m',\\n category='cultural',\\n name='time_zones',\\n )\\n self.timezones = list(shpreader.Reader(timezones_shp).records())\\n self.timezones_by_name = {}\\n for timezone in shpreader.Reader(timezones_shp).records():\\n # Try to get the actual name. Something like `Europe/Berlin`\\n timezone_name = timezone.attributes['tz_name1st']\\n # If there is no name, we default to the utc offset name `-5` `+4.5`\\n if timezone_name == '':\\n timezone_name = timezone.attributes['name']\\n\\n if timezone_name not in self.timezones_by_name.keys():\\n self.timezones_by_name[timezone_name] = timezone\",\n \"def load_geolocation_data():\\n client = MongoClient(f'mongodb://{os.getenv(\\\"USR_\\\")}:{os.getenv(\\\"PWD_\\\")}@{os.getenv(\\\"REMOTE_HOST\\\")}:{os.getenv(\\\"REMOTE_PORT\\\")}/{os.getenv(\\\"AUTH_DB\\\")}')\\n db = client[os.getenv(\\\"MAIN_DB\\\")]\\n lk_collection = db[\\\"lkdata\\\"]\\n data = pd.DataFrame(list(lk_collection.find()))\\n data = data[[\\\"fields\\\"]]\\n data = pd.concat([pd.DataFrame(data), pd.DataFrame(list(data[\\\"fields\\\"]))], axis=1).drop(\\\"fields\\\", 1)\\n data[\\\"cca_2\\\"] = pd.to_numeric(data[\\\"cca_2\\\"])\\n return data\",\n \"def populate(self):\\n raise NotImplementedError\",\n \"def build(self):\\n allow_bare = AllowBareCityName(blocklist=self.bare_name_blocklist)\\n\\n iter_keys = CityKeyIter(allow_bare)\\n\\n # Deduped cities.\\n cities = WOFLocality.clean_us_cities()\\n\\n logger.info('Indexing US cities.')\\n\\n for row in tqdm(cities):\\n\\n # Key -> id(s)\\n for key in map(keyify, iter_keys(row)):\\n self.add_key(key, row.wof_id)\\n\\n # ID -> city\\n self.add_location(row.wof_id, CityMatch(row))\",\n \"def create_locations_in_numbers_section(self):\\n section = self.sections['Locations in numbers:']\\n column, row = Counter(), Counter()\\n # first row:\\n label = Label(section, text='Locations total:')\\n label.grid(column=column.next(), row=row.next())\\n label.bind('<Button-1>', partial(self.change_locations_filter, None))\\n self.locations_counter = Entry(\\n section, textvariable=self.locations_count, width=4,\\n disabledbackground='white',\\n disabledforeground='black', state=DISABLED, justify=CENTER\\n ).grid(column=column.next(), row=row())\\n for location, value in self.locations_by_type.items():\\n if column() == 18:\\n column.restart()\\n row.next()\\n text = plural(location.value, self.language)\\n label = Label(section, text=f'{text.title()}:')\\n label.grid(column=column.next(), row=row())\\n label.bind('<Button-1>',\\n partial(self.change_locations_filter, location))\\n Entry(section, textvariable=value, width=4,\\n disabledbackground='white',\\n disabledforeground='black', state=DISABLED, justify=CENTER\\n ).grid(column=column.next(), row=row())\",\n \"def get_data(self):\\n data = load.loader.get_data_for_hotel(self.hotel_name, self.filter)\\n\\n self.data_items = []\\n\\n if data:\\n for row in data:\\n for col in row:\\n self.data_items.append(col)\\n self.row_number = str(self.get_row_number())\\n\\n self.hotel_full_data = self.hotel_name + ' ' + load.loader.get_hotel_address(self.hotel_name)\",\n \"def get_locations_by_ids(self, id_list):\",\n \"def location_duplicates():\\n\\n if not auth.s3_has_role(1):\\n redirect(URL(r=request, c=\\\"default\\\", f=\\\"index\\\"))\\n \\n # @ToDo: Set this via the UI & pass in as a var\\n dupe_distance = 50 # km\\n\\n # Shortcut\\n locations = db.gis_location\\n\\n table_header = THEAD(TR(TH(T(\\\"Location 1\\\")),\\n TH(T(\\\"Location 2\\\")),\\n TH(T(\\\"Distance(Kms)\\\")),\\n TH(T(\\\"Resolve\\\"))))\\n\\n # Calculate max possible combinations of records\\n # To handle the AJAX requests by the dataTables jQuery plugin.\\n totalLocations = db(locations.id > 0).count()\\n\\n item_list = []\\n if request.vars.iDisplayStart:\\n end = int(request.vars.iDisplayLength) + int(request.vars.iDisplayStart)\\n locations = db((locations.id > 0) & \\\\\\n (locations.deleted == False) & \\\\\\n (locations.lat != None) & \\\\\\n (locations.lon != None)).select(locations.id,\\n locations.name,\\n locations.level,\\n locations.lat,\\n locations.lon)\\n # Calculate the Great Circle distance\\n count = 0\\n for oneLocation in locations[:len(locations) / 2]:\\n for anotherLocation in locations[len(locations) / 2:]:\\n if count > end and request.vars.max != \\\"undefined\\\":\\n count = int(request.vars.max)\\n break\\n if oneLocation.id == anotherLocation.id:\\n continue\\n else:\\n dist = gis.greatCircleDistance(oneLocation.lat,\\n oneLocation.lon,\\n anotherLocation.lat,\\n anotherLocation.lon)\\n if dist < dupe_distance:\\n count = count + 1\\n item_list.append([oneLocation.name,\\n anotherLocation.name,\\n dist,\\n \\\"<a href=\\\\\\\"../gis/location_resolve?locID1=%i&locID2=%i\\\\\\\", class=\\\\\\\"action-btn\\\\\\\">Resolve</a>\\\" % (oneLocation.id, anotherLocation.id)\\n ])\\n else:\\n continue\\n\\n item_list = item_list[int(request.vars.iDisplayStart):end]\\n # Convert data to JSON\\n result = []\\n result.append({\\n \\\"sEcho\\\" : request.vars.sEcho,\\n \\\"iTotalRecords\\\" : count,\\n \\\"iTotalDisplayRecords\\\" : count,\\n \\\"aaData\\\" : item_list\\n })\\n output = json.dumps(result)\\n # Remove unwanted brackets\\n output = output[1:]\\n output = output[:-1]\\n return output\\n else:\\n # Don't load records except via dataTables (saves duplicate loading & less confusing for user)\\n items = DIV((TABLE(table_header,\\n TBODY(),\\n _id=\\\"list\\\",\\n _class=\\\"dataTable display\\\")))\\n return(dict(items=items))\",\n \"def load_pneumonia_locations():\\n pneumonia_locations = {}\\n # load table\\n\\n with open(os.path.join(LABEL_PATHS), mode='r') as infile:\\n # open reader\\n reader = csv.reader(infile)\\n # skip header\\n next(reader, None)\\n # loop through rows\\n for rows in reader:\\n # retrieve information\\n filename = rows[0]\\n location = rows[1:5]\\n pneumonia = rows[5]\\n # if row contains pneumonia add label to dictionary\\n # which contains a list of pneumonia locations per filename\\n if pneumonia == '1':\\n # convert string to float to int\\n location = [int(float(i)) for i in location]\\n # save pneumonia location in dictionary\\n if filename in pneumonia_locations:\\n pneumonia_locations[filename].append(location)\\n else:\\n pneumonia_locations[filename] = [location]\\n\\n return pneumonia_locations\",\n \"def populate_employees():\\n employees = get_employees()\\n\\n db.session.bulk_save_objects(employees)\\n db.session.commit()\",\n \"def _populate_df(self, df, objs,):\\n for obj in objs:\\n for prop in df.columns:\\n df.loc[obj.name, prop] = getattr(obj, prop)\",\n \"async def synchronise_meta_data_table(\\n location_id: LocationID, dry_run: bool = False, fire_and_forget: bool = False\\n):\",\n \"def add_amenities(self): \\n amenity_count = [self.search(lat, lon) for lat, lon in\\n zip(self.df_ads['latitude'], self.df_ads['longitude'])]\\n \\n self.df_ads_mapdata = pd.concat(\\n [self.df_ads.reset_index(drop=True), pd.DataFrame(amenity_count)], axis=1)\\n\\n assert len(self.df_ads_mapdata) == len(self.df_ads)\",\n \"def locations(request):\\n locations = Location.objects.all()\\n context = {'locations': locations}\\n return render(request, 'std/Locations.html', context)\",\n \"def load_data(self):\\n @Logger.runtime\\n def process_coords():\\n \\\"\\\"\\\"\\n The placement of locations on our minimap is crucial. Panda3D objects however have a coordinate range from\\n -1 to 1 on all axis, meaning that if we read a coordinate of a location from some image processing software\\n by hand, we have to transform those coordinates into coordinates Panda would understand. This function does\\n just that.\\n :return: Normalized coordinates of location coordinates.\\n \\\"\\\"\\\"\\n split_coords = row[\\\"map_coord\\\"].split(',')\\n map_x, map_y = [int(i) for i in split_coords]\\n map_x_normed = ((map_x*2) / self.MINIMAP_DIM) - 1\\n map_y_normed = -(((map_y*2) / self.MINIMAP_DIM) - 1)\\n return map_x_normed, map_y_normed\\n\\n @Logger.runtime\\n def process_texture():\\n texture_path = Path(\\\"resource/textures/{}\\\".format(row[\\\"texture\\\"]))\\n texture = self.loader.loadTexture(texture_path)\\n return texture\\n\\n # the cylinder is loaded here but it does not yet show up, until it's specifically asked to\\n self.scene_3d_model = self.loader.loadModel(self.PATHS[\\\"3D_SCENE_MODEL\\\"])\\n\\n try:\\n with open(self.PATHS[\\\"LOCATIONS_DB\\\"], \\\"r\\\") as l_file:\\n data = csv.DictReader(l_file, delimiter=\\\"|\\\")\\n for row in data:\\n id = int(row[\\\"id\\\"])\\n x, y = process_coords()\\n neighbors = [int(neighbor_id) for neighbor_id in row[\\\"neighbors\\\"].split(',')]\\n texture = process_texture()\\n location = Location(id, x, y, neighbors, texture)\\n location.reparentTo(self.render2d)\\n self.locations.append(location)\\n Logger.log_info('The locations_db has been loaded')\\n except:\\n Logger.error('{} file not found!'.format(self.PATHS[\\\"LOCATIONS_DB\\\"]))\\n\\n self.active_location = self.locations[0]\",\n \"def _init_list(self, data: List[Any]):\\n # Assume data is homogenous in regard to row type\\n obj = data[0]\\n column_names = self._column_name_getter(obj)\\n column_values = self._column_value_getter(obj)\\n\\n # Map of source to destination column\\n column_map = {}\\n\\n # Do not update columns if predefined\\n add_columns = not bool(self._columns)\\n\\n for obj in data:\\n row = [None] * len(self._columns)\\n\\n for column_src in column_names(obj):\\n # Check if column has been added with different name\\n column_dst = column_map.get(column_src, column_src)\\n\\n # Dictionaries and namedtuples can\\n # contain unknown columns\\n if column_dst not in self._columns:\\n if not add_columns:\\n continue\\n\\n col = self._add_column(column_dst)\\n\\n # Store map of source column name to created name\\n column_dst = self._columns[col]\\n column_map[column_src] = column_dst\\n\\n while len(row) < len(self._columns):\\n row.append(None)\\n\\n col = self.column_location(column_dst)\\n row[col] = column_values(obj, column_src)\\n\\n self._data.append(row)\",\n \"def populate_countries(self):\\n # For each country in population.\\n for name, pop in self.population.iterrows():\\n p = pop['Population']\\n # Get all relevant time series based on country name.\\n c = self.raw_confirmed.loc[self.raw_confirmed['Country/Region'] == name].sum(numeric_only=True)\\n d = self.raw_deceased.loc[self.raw_deceased['Country/Region'] == name].sum(numeric_only=True)\\n r = self.raw_recovered.loc[self.raw_recovered['Country/Region'] == name].sum(numeric_only=True)\\n # Create new country object.\\n self.countries.append(country.Country(name, p, c, d, r))\",\n \"def describe_locations():\\n pass\",\n \"def location():\\n\\n tablename = \\\"%s_%s\\\" % (module, resourcename)\\n table = db[tablename]\\n\\n # Allow prep to pass vars back to the controller\\n vars = {}\\n\\n # @ToDo: Clean up what needs to be done only for interactive views,\\n # vs. what needs to be done generally. E.g. some tooltips are defined\\n # for non-interactive.\\n # Pre-processor\\n def prep(r, vars):\\n\\n def get_location_info():\\n query = (db.gis_location.id == r.id)\\n return db(query).select(db.gis_location.lat,\\n db.gis_location.lon,\\n db.gis_location.level,\\n limitby=(0, 1)).first()\\n\\n # Restrict access to Polygons to just MapAdmins\\n if deployment_settings.get_security_map() and not s3_has_role(\\\"MapAdmin\\\"):\\n table.code.writable = False\\n if r.method == \\\"create\\\":\\n table.code.readable = False\\n table.gis_feature_type.writable = table.gis_feature_type.readable = False\\n table.wkt.writable = table.wkt.readable = False\\n elif r.interactive:\\n table.code.comment = DIV(_class=\\\"tooltip\\\",\\n _title=\\\"%s|%s\\\" % (T(\\\"Code\\\"),\\n T(\\\"For a country this would be the ISO2 code, for a Town, it would be the Airport Locode.\\\")))\\n table.wkt.comment = DIV(_class=\\\"stickytip\\\",\\n _title=\\\"WKT|%s %s%s %s%s\\\" % (T(\\\"The\\\"),\\n \\\"<a href='http://en.wikipedia.org/wiki/Well-known_text' target=_blank>\\\",\\n T(\\\"Well-Known Text\\\"),\\n \\\"</a>\\\",\\n T(\\\"representation of the Polygon/Line.\\\")))\\n\\n if r.method == \\\"update\\\" and r.id:\\n # We don't allow converting a location group to non-group and\\n # vice versa. We also don't allow taking away all the members of\\n # a group -- setting \\\"notnull\\\" gets the \\\"required\\\" * displayed.\\n # Groups don't have parents. (This is all checked in onvalidation.)\\n # NB r.id is None for update.url\\n location = get_location_info()\\n if location.level == \\\"GR\\\":\\n table.level.writable = False\\n table.parent.readable = table.parent.writable = False\\n table.members.notnull = True\\n # Record that this is a group location. Since we're setting\\n # level to not writable, it won't be in either form.vars or\\n # request.vars. Saving it while we have it avoids another\\n # db access.\\n response.s3.location_is_group = True\\n else:\\n table.members.writable = table.members.readable = False\\n response.s3.location_is_group = False\\n\\n if r.interactive:\\n if not \\\"group\\\" in r.request.vars:\\n # Hide the Members List (a big download when many records are entered)\\n table.members.writable = table.members.readable = False\\n # Don't show street address, postcode for hierarchy on read or update.\\n if r.method != \\\"create\\\" and r.id:\\n try:\\n location\\n except:\\n location = get_location_info()\\n if location.level:\\n table.addr_street.writable = table.addr_street.readable = False\\n table.addr_postcode.writable = table.addr_postcode.readable = False\\n\\n # Options which are only required in interactive HTML views\\n table.level.comment = DIV(_class=\\\"tooltip\\\",\\n _title=\\\"%s|%s\\\" % (T(\\\"Level\\\"),\\n T(\\\"If the location is a geographic area, then state at what level here.\\\")))\\n parent_comment = DIV(_class=\\\"tooltip\\\",\\n _title=\\\"%s|%s\\\" % (T(\\\"Parent\\\"),\\n T(\\\"The Area which this Site is located within.\\\")))\\n if r.representation == \\\"popup\\\":\\n table.parent.comment = parent_comment\\n else:\\n # Include 'Add Location' button\\n table.parent.comment = DIV(A(ADD_LOCATION,\\n _class=\\\"colorbox\\\",\\n _href=URL(r=request, c=\\\"gis\\\", f=\\\"location\\\",\\n args=\\\"create\\\",\\n vars=dict(format=\\\"popup\\\",\\n child=\\\"parent\\\")),\\n _target=\\\"top\\\",\\n _title=ADD_LOCATION),\\n parent_comment),\\n table.osm_id.comment = DIV(_class=\\\"stickytip\\\",\\n _title=\\\"OpenStreetMap ID|%s%s%s\\\" % (T(\\\"The\\\"),\\n \\\" <a href='http://openstreetmap.org' target=_blank>OpenStreetMap</a> ID. \\\",\\n T(\\\"If you know what the OSM ID of this location is then you can enter it here.\\\")))\\n table.geonames_id.comment = DIV(_class=\\\"stickytip\\\",\\n _title=\\\"Geonames ID|%s%s%s\\\" % (T(\\\"The\\\"),\\n \\\" <a href='http://geonames.org' target=_blank>Geonames</a> ID. \\\",\\n T(\\\"If you know what the Geonames ID of this location is then you can enter it here.\\\")))\\n table.comments.comment = DIV(_class=\\\"tooltip\\\",\\n _title=\\\"%s|%s\\\" % (T(\\\"Comments\\\"),\\n T(\\\"Please use this field to record any additional information, such as Ushahidi instance IDs. Include a history of the record if it is updated.\\\")))\\n\\n if r.representation == \\\"iframe\\\":\\n # De-duplicator needs to be able to access UUID fields\\n table.uuid.readable = table.uuid.writable = True\\n table.uuid.label = \\\"UUID\\\"\\n table.uuid.comment = DIV(_class=\\\"stickytip\\\",\\n _title=\\\"UUID|%s%s%s\\\" % (T(\\\"The\\\"),\\n \\\" <a href='http://eden.sahanafoundation.org/wiki/UUID#Mapping' target=_blank>Universally Unique ID</a>. \\\",\\n T(\\\"Suggest not changing this field unless you know what you are doing.\\\")))\\n\\n if r.method in (None, \\\"list\\\") and r.record is None:\\n # List\\n pass\\n elif r.method in (\\\"delete\\\", \\\"search\\\"):\\n pass\\n else:\\n # Add Map to allow locations to be found this way\\n config = gis.get_config()\\n lat = config.lat\\n lon = config.lon\\n zoom = config.zoom\\n feature_queries = []\\n\\n if r.method == \\\"create\\\":\\n add_feature = True\\n add_feature_active = True\\n else:\\n if r.method == \\\"update\\\":\\n add_feature = True\\n add_feature_active = False\\n else:\\n # Read\\n add_feature = False\\n add_feature_active = False\\n\\n try:\\n location\\n except:\\n location = get_location_info()\\n if location and location.lat is not None and location.lon is not None:\\n lat = location.lat\\n lon = location.lon\\n # Same as a single zoom on a cluster\\n zoom = zoom + 2\\n\\n # @ToDo: Does map make sense if the user is updating a group?\\n # If not, maybe leave it out. OTOH, might be nice to select\\n # admin regions to include in the group by clicking on them in\\n # the map. Would involve boundaries...\\n _map = gis.show_map(lat = lat,\\n lon = lon,\\n zoom = zoom,\\n feature_queries = feature_queries,\\n add_feature = add_feature,\\n add_feature_active = add_feature_active,\\n toolbar = True,\\n collapsed = True)\\n\\n # Pass the map back to the main controller\\n vars.update(_map=_map)\\n return True\\n response.s3.prep = lambda r, vars=vars: prep(r, vars)\\n\\n # Options\\n _vars = request.vars\\n filters = []\\n\\n parent = _vars.get(\\\"parent_\\\", None)\\n # Don't use 'parent' as the var name as otherwise it conflicts with the form's var of the same name & hence this will be triggered during form submission\\n if parent:\\n # We want to do case-insensitive searches\\n # (default anyway on MySQL/SQLite, but not PostgreSQL)\\n _parent = parent.lower()\\n\\n # Can't do this using a JOIN in DAL syntax\\n # .belongs() not GAE-compatible!\\n query = (db.gis_location.name.lower().like(_parent))\\n filters.append((db.gis_location.parent.belongs(db(query).select(db.gis_location.id))))\\n # ToDo: Make this recursive - want descendants not just direct children!\\n # Use new gis.get_children() function\\n\\n # ToDo\\n # bbox = _vars.get(\\\"bbox\\\", None):\\n\\n if filters:\\n from operator import __and__\\n response.s3.filter = reduce(__and__, filters)\\n\\n caller = _vars.get(\\\"caller\\\", None)\\n if caller:\\n # We've been called as a Popup\\n if \\\"gis_location_parent\\\" in caller:\\n # Hide unnecessary rows\\n table.addr_street.readable = table.addr_street.writable = False\\n else:\\n parent = _vars.get(\\\"parent_\\\", None)\\n # Don't use 'parent' as the var name as otherwise it conflicts with the form's var of the same name & hence this will be triggered during form submission\\n if parent:\\n table.parent.default = parent\\n\\n # Hide unnecessary rows\\n table.level.readable = table.level.writable = False\\n table.geonames_id.readable = table.geonames_id.writable = False\\n table.osm_id.readable = table.osm_id.writable = False\\n table.source.readable = table.source.writable = False\\n table.url.readable = table.url.writable = False\\n\\n level = _vars.get(\\\"level\\\", None)\\n if level:\\n # We've been called from the Location Selector widget\\n table.addr_street.readable = table.addr_street.writable = False\\n\\n output = s3_rest_controller(module, resourcename)\\n\\n _map = vars.get(\\\"_map\\\", None)\\n if _map and isinstance(output, dict):\\n output.update(_map=_map)\\n\\n return output\",\n \"def populate_db():\\n\\n populate_table(db, models.Department, departments_data)\\n populate_table(db, models.Employee, employees_data)\",\n \"def stats_data_by_location():\\n by_city = db_session.query(Locations.city, func.count(MediaFiles.id)) \\\\\\n .outerjoin(MediaFiles, MediaFiles.location_id == Locations.id) \\\\\\n .group_by(Locations.city) \\\\\\n .all()\\n by_country = db_session.query(Locations.country, func.count(MediaFiles.id)) \\\\\\n .outerjoin(MediaFiles, MediaFiles.location_id == Locations.id) \\\\\\n .group_by(Locations.country) \\\\\\n .all()\\n data_by_city = [{'name': item[0].title(), 'y': item[1]} for item in by_city]\\n data_by_country = [{'name': item[0].title(), 'y': item[1]} for item in by_country]\\n return data_by_city, data_by_country\",\n \"def initialize(self) -> None:\\n all_queries: List[Union[DataQueryInfo, MeasureQueryInfo]] = []\\n entity_cells: List[DataCell] = []\\n current_row_group = None\\n\\n # Loop over rows, columns\\n for row_index, row in enumerate(self.rows):\\n if isinstance(row, RowSeparator):\\n current_row_group = row.name\\n continue\\n entity: Entity = row.entity\\n if isinstance(entity, Entity):\\n self.entity_map[entity.get_marquee_id()] = entity\\n else:\\n self.entity_map[''] = entity\\n cells: List[DataCell] = []\\n row_overrides = row.overrides\\n\\n for column_index, column in enumerate(self.columns):\\n column_name = column.name\\n column_processor = column.processor\\n\\n # Get all the data coordinate overrides and apply the processor override if it exists\\n data_overrides, value_override, processor_override = _get_overrides(row_overrides, column_name)\\n\\n # Create the cell\\n cell: DataCell = DataCell(column_name,\\n column_processor,\\n entity,\\n data_overrides,\\n column_index,\\n row_index,\\n current_row_group)\\n\\n if processor_override:\\n # Check if there is a processor override and apply if so\\n cell.processor = processor_override\\n if value_override:\\n cell.value = ProcessorResult(True, value_override.value)\\n cell.updated_time = get_utc_now()\\n elif isinstance(column_processor, EntityProcessor):\\n # store these cells to fetch entity data during poll\\n entity_cells.append(cell)\\n elif isinstance(column_processor, CoordinateProcessor):\\n # store these cells to fetch entity data during poll\\n if len(data_overrides):\\n # Get the last in the list if more than 1 override is given\\n cell.processor.children['a'].set_dimensions(data_overrides[-1].dimensions)\\n\\n self._coord_processor_cells.append(cell)\\n elif column_processor.measure_processor:\\n all_queries.append(MeasureQueryInfo(attr='', entity=entity, processor=column_processor))\\n else:\\n # append the required queries to the map\\n cell.build_cell_graph(all_queries, self.rdate_entity_map)\\n\\n cells.append(cell)\\n\\n self._cells.extend(cells)\\n self.results.append(cells)\\n\\n self._data_queries = all_queries\\n self._entity_cells = entity_cells\\n self.is_initialized = True\",\n \"def append_locations(self, newlocs: List):\\n self.locations.extend(newlocs)\",\n \"def build_localisations(csv_locations):\\n # Open each of the files\\n # Build the dictionaries by column\\n localisations = []\\n for csv_location in csv_locations:\\n with open(csv_location) as csvfile:\\n reader = csv.DictReader(csvfile)\\n locs = {}\\n for key in reader.fieldnames:\\n locs[key] = []\\n for row in reader:\\n for key in reader.fieldnames:\\n locs[key].append(row[key])\\n\\n localisations.append(locs)\\n return localisations\",\n \"def load_store_location_data():\\n try:\\n store_location_data = []\\n with open('store-locations.csv') as csvfile:\\n reader = csv.DictReader(csvfile, dialect=csv.excel)\\n for row in reader:\\n store_location_data.append(dict(OrderedDict(row)))\\n\\n return store_location_data\\n except IOError as error:\\n print(\\\"I/O error({0}): {1}\\\".format(error.errno, error.strerror))\\n return None\",\n \"def _populate_table(self):\\n self._table.setSortingEnabled(False)\\n self._table.setRowCount(len(self._module_names))\\n for i, module_name in enumerate(self._module_names, 0):\\n self._table.setItem(i, 0, QtWidgets.QTableWidgetItem(module_name))\\n self._table.resizeRowsToContents()\\n self._table.setSortingEnabled(True)\",\n \"async def test_get_location_data(self):\\n for city_name in ['dublin', 'London', 'Copenhagen']:\\n response = await self.http_client.fetch(request=HTTPRequest(\\n url=self.get_url(path=\\\"/location-data/{}\\\".format(city_name)),\\n method='GET'\\n ))\\n self.assertEqual(response.code, HTTPStatus.OK)\\n self.check_city_response(response, city_name.lower())\",\n \"def location_ids(self, location_ids):\\n\\n self._location_ids = location_ids\",\n \"def insert_values():\\n pass\",\n \"def run(self, locations):\\n return locations\",\n \"def test_storage_locations_data(\\n storage_locations,\\n storage_service_id,\\n storage_service_name,\\n start_date,\\n end_date,\\n locations_count,\\n location_1_aips_count,\\n location_1_total_size,\\n location_2_aips_count,\\n location_2_total_size,\\n):\\n report = report_data.storage_locations(\\n storage_service_id=storage_service_id, start_date=start_date, end_date=end_date\\n )\\n\\n assert report[fields.FIELD_STORAGE_NAME] == storage_service_name\\n\\n locations = report[fields.FIELD_LOCATIONS]\\n assert len(locations) == locations_count\\n\\n if locations_count < 1:\\n return\\n\\n first_location = locations[0]\\n # Account for sorting changes made possible by date filtering.\\n if location_1_aips_count > location_2_aips_count:\\n assert first_location[fields.FIELD_UUID] == STORAGE_LOCATION_1_UUID\\n assert (\\n first_location[fields.FIELD_STORAGE_LOCATION]\\n == STORAGE_LOCATION_1_DESCRIPTION\\n )\\n assert first_location[fields.FIELD_AIPS] == location_1_aips_count\\n assert first_location[fields.FIELD_SIZE] == location_1_total_size\\n else:\\n assert first_location[fields.FIELD_UUID] == STORAGE_LOCATION_2_UUID\\n assert (\\n first_location[fields.FIELD_STORAGE_LOCATION]\\n == STORAGE_LOCATION_2_DESCRIPTION\\n )\\n assert first_location[fields.FIELD_AIPS] == location_2_aips_count\\n assert first_location[fields.FIELD_SIZE] == location_2_total_size\\n\\n second_location = locations[1]\\n # Account for sorting changes made possible by date filtering.\\n if location_1_aips_count > location_2_aips_count:\\n assert second_location[fields.FIELD_UUID] == STORAGE_LOCATION_2_UUID\\n assert (\\n second_location[fields.FIELD_STORAGE_LOCATION]\\n == STORAGE_LOCATION_2_DESCRIPTION\\n )\\n assert second_location[fields.FIELD_AIPS] == location_2_aips_count\\n assert second_location[fields.FIELD_SIZE] == location_2_total_size\\n else:\\n assert second_location[fields.FIELD_UUID] == STORAGE_LOCATION_1_UUID\\n assert (\\n second_location[fields.FIELD_STORAGE_LOCATION]\\n == STORAGE_LOCATION_1_DESCRIPTION\\n )\\n assert second_location[fields.FIELD_AIPS] == location_1_aips_count\\n assert second_location[fields.FIELD_SIZE] == location_1_total_size\",\n \"def __init__(self):\\n self._distance_data = []\\n self._location_data = []\\n self._package_data = []\",\n \"def populate_database(self):\\n self.insert_products()\\n self.insert_categories()\\n self.insert_products_categories()\\n self.insert_stores()\\n self.insert_products_stores()\",\n \"def fill_obs(self, observation_table, data_store):\\n for obs in observation_table:\\n events = data_store.obs(obs_id=obs['OBS_ID']).events\\n\\n # TODO: filter out (mask) possible sources in the data\\n # for now, the observation table should not contain any\\n # run at or near an existing source\\n\\n self.counts_cube.fill_events([events])\\n self.livetime_cube.data += events.observation_live_time_duration\",\n \"def getData(locations, soup):\\n chromeIndex = locations.index('On chromosome')\\n positionIndex = locations.index('Chromosome position')\\n if 'Summary' in locations:\\n traitIndex = locations.index('Summary')\\n else:\\n traitIndex = locations.index('Trait')\\n\\n tvalues = soup('table')[1].find_all('td')\\n chromosome = tvalues[chromeIndex].find_all('span')[0].get_text().split()[0]\\n position = ''.join(tvalues[positionIndex].find_all('span')[0].get_text().split()[0].split(','))\\n trait = tvalues[traitIndex].find_all('span')[0].get_text().split(u\\\"\\\\u00A0\\\")[0]\\n result = {\\n 'chromosome': chromosome,\\n 'position': position,\\n 'trait': trait\\n }\\n return result\",\n \"def location_links():\\n\\n try:\\n record_id = request.args[0]\\n except:\\n item = s3xrc.xml.json_message(False, 400, \\\"Need to specify a record ID!\\\")\\n raise HTTP(400, body=item)\\n\\n try:\\n # Shortcut\\n locations = db.gis_location\\n\\n deleted = (locations.deleted == False)\\n query = (locations.id == record_id)\\n query = deleted & query\\n record = db(query).select(locations.id, limitby=(0, 1)).first().id\\n except:\\n item = s3xrc.xml.json_message(False, 404, \\\"Record not found!\\\")\\n raise HTTP(404, body=item)\\n\\n # Find all tables which link to the Locations table\\n # @ToDo: Replace with db.gis_location._referenced_by\\n tables = shn_table_links(\\\"gis_location\\\")\\n\\n results = []\\n for table in tables:\\n for count in range(len(tables[table])):\\n field = tables[str(db[table])][count]\\n query = db[table][field] == record_id\\n _results = db(query).select()\\n module, resourcename = table.split(\\\"_\\\", 1)\\n for result in _results:\\n id = result.id\\n # We currently have no easy way to get the default represent for a table!\\n try:\\n # Locations & Persons\\n represent = eval(\\\"shn_%s_represent(id)\\\" % table)\\n except:\\n try:\\n # Organisations\\n represent = eval(\\\"shn_%s_represent(id)\\\" % resourcename)\\n except:\\n try:\\n # Many tables have a Name field\\n represent = (id and [db[table][id].name] or [\\\"None\\\"])[0]\\n except:\\n # Fallback\\n represent = id\\n results.append({\\n \\\"module\\\" : module,\\n \\\"resource\\\" : resourcename,\\n \\\"id\\\" : id,\\n \\\"represent\\\" : represent\\n })\\n\\n output = json.dumps(results)\\n return output\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6048028","0.6013519","0.59421986","0.5901815","0.58811194","0.5729807","0.5710549","0.56738675","0.56535035","0.554302","0.5542974","0.55208075","0.5469795","0.54697716","0.5454492","0.54353935","0.5430382","0.542676","0.5424422","0.5422587","0.54031056","0.537971","0.5377886","0.5370541","0.53700954","0.53338283","0.5321584","0.5311861","0.5309858","0.52906114","0.527486","0.52735823","0.52685744","0.5267895","0.5265497","0.5261579","0.5250655","0.5245967","0.5245141","0.5244098","0.5243704","0.52265453","0.5211909","0.520407","0.5203368","0.52015173","0.5197916","0.51911277","0.51893866","0.5180723","0.5165066","0.5147126","0.5125742","0.51232415","0.51221913","0.51174223","0.5116288","0.5114895","0.511281","0.51093024","0.5104184","0.51007247","0.50942427","0.5093816","0.5088278","0.50851643","0.5075813","0.5073255","0.5067276","0.5056906","0.5052169","0.5050736","0.5048659","0.50483847","0.50458026","0.5039353","0.50260437","0.5025127","0.5020552","0.5017206","0.50163686","0.5012871","0.50111973","0.50108093","0.500284","0.499562","0.498823","0.49820465","0.49819988","0.49792093","0.49736127","0.49655977","0.4964482","0.49619696","0.4961521","0.49591538","0.49570963","0.4954403","0.4953951","0.49488878"],"string":"[\n \"0.6048028\",\n \"0.6013519\",\n \"0.59421986\",\n \"0.5901815\",\n \"0.58811194\",\n \"0.5729807\",\n \"0.5710549\",\n \"0.56738675\",\n \"0.56535035\",\n \"0.554302\",\n \"0.5542974\",\n \"0.55208075\",\n \"0.5469795\",\n \"0.54697716\",\n \"0.5454492\",\n \"0.54353935\",\n \"0.5430382\",\n \"0.542676\",\n \"0.5424422\",\n \"0.5422587\",\n \"0.54031056\",\n \"0.537971\",\n \"0.5377886\",\n \"0.5370541\",\n \"0.53700954\",\n \"0.53338283\",\n \"0.5321584\",\n \"0.5311861\",\n \"0.5309858\",\n \"0.52906114\",\n \"0.527486\",\n \"0.52735823\",\n \"0.52685744\",\n \"0.5267895\",\n \"0.5265497\",\n \"0.5261579\",\n \"0.5250655\",\n \"0.5245967\",\n \"0.5245141\",\n \"0.5244098\",\n \"0.5243704\",\n \"0.52265453\",\n \"0.5211909\",\n \"0.520407\",\n \"0.5203368\",\n \"0.52015173\",\n \"0.5197916\",\n \"0.51911277\",\n \"0.51893866\",\n \"0.5180723\",\n \"0.5165066\",\n \"0.5147126\",\n \"0.5125742\",\n \"0.51232415\",\n \"0.51221913\",\n \"0.51174223\",\n \"0.5116288\",\n \"0.5114895\",\n \"0.511281\",\n \"0.51093024\",\n \"0.5104184\",\n \"0.51007247\",\n \"0.50942427\",\n \"0.5093816\",\n \"0.5088278\",\n \"0.50851643\",\n \"0.5075813\",\n \"0.5073255\",\n \"0.5067276\",\n \"0.5056906\",\n \"0.5052169\",\n \"0.5050736\",\n \"0.5048659\",\n \"0.50483847\",\n \"0.50458026\",\n \"0.5039353\",\n \"0.50260437\",\n \"0.5025127\",\n \"0.5020552\",\n \"0.5017206\",\n \"0.50163686\",\n \"0.5012871\",\n \"0.50111973\",\n \"0.50108093\",\n \"0.500284\",\n \"0.499562\",\n \"0.498823\",\n \"0.49820465\",\n \"0.49819988\",\n \"0.49792093\",\n \"0.49736127\",\n \"0.49655977\",\n \"0.4964482\",\n \"0.49619696\",\n \"0.4961521\",\n \"0.49591538\",\n \"0.49570963\",\n \"0.4954403\",\n \"0.4953951\",\n \"0.49488878\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6565873"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":2890,"cells":{"query":{"kind":"string","value":"Check if given forecast dictionary contains a numeric value with provided key."},"document":{"kind":"string","value":"def is_forecast_number(key, forecast):\n return key in forecast and type(forecast[key]) in [float, int]"},"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 moreThanOne(dict, key):\n\treturn key in dict and dict[key] > 0","def _has_numeric_strict(self) -> bool:\n return bool({'i', 'f'} & self._data.keys())","def contains_200(dictnr):\n contains = False\n for i in dictnr:\n if dictnr[i] == 200:\n contains = True\n print(contains)","def is_key(number):\n res = False\n if is_integer(number):\n if int(number) > 0:\n res = True\n return res","def _cast_to_number(self, key):\n q = DBSession.query(cast(self.db_value.value, Float)). \\\n join(self.db_tag). \\\n join(self.db_key). \\\n filter(self.db_key.key == key)\n try:\n q.all()\n return True\n except:\n return False","def hasValue(self, key):\n return self.has_key('__' + key)","def _has_science_data(data_dict, particle_class):\n return_value = False\n\n # Modified to make this check more efficient\n if len(particle_class.science_parameters) < len(data_dict):\n for key in particle_class.science_parameters:\n value = data_dict.get(key, None)\n if value is not None and not(isnan(float(value))):\n return_value = True\n break\n if particle_class._data_particle_type == 'glider_eng_telemetered':\n log.info(\"GliderParser._has_science_data failed: key=[%s] value=[%s]\", key, value)\n else:\n for key, value in data_dict.iteritems():\n if not (isnan(float(value))) and key in particle_class.science_parameters:\n return_value = True\n break\n if particle_class._data_particle_type == 'glider_eng_telemetered':\n log.info(\"GliderParser._has_science_data failed: key=[%s] value=[%s]\", key, value)\n\n return return_value","def _has_numeric_or_bool(self) -> bool:\n dtypes: Set[str] = set(self._data.keys())\n return 'i' in dtypes or 'f' in dtypes or 'b' in dtypes","def contains(self, key: int) -> bool:\n if key in self.d:\n return True\n else:\n return False","def is_number(value):\n try:\n float(value)\n return True\n except ValueError:\n return False","def _is_number(value):\n try:\n float(value)\n return True\n except (TypeError, ValueError):\n return False","def contains(self, key):\n if key in self.nums:\n return True\n return False","def contains_double_count(key, value, similarity_dict):\n if value in similarity_dict.keys():\n if key in similarity_dict[value]:\n return True\n return False","def data_dict_points(data_dict, feature):\n return len(filter(lambda k: isinstance(data_dict[k][feature],\n (int, float)), data_dict))","def is_number(self,val):\n try:\n float(val)\n return True\n except ValueError:\n return False","def is_zero_dict( dict ):\n has_any_features = False\n for key in dict:\n has_any_features = has_any_features or dict[key]\n\n return not has_any_features","def exists(field):\n try:\n float(field)\n return True\n except:\n return False","def contains(self, key):\n\t\tfor i in self.getBitArrayIndices(key):\n\t\t\tif self.ba[i] <= 0:\n\t\t\t\treturn False\n\t\treturn True","def anyMoreThanOne(dict, keys):\n\tfor key in keys:\n\t\tif key in dict and dict[key] > 0:\n\t\t\treturn True\n\treturn False","def values_are_pandas_numbers(values: List[str]):\n for v in values:\n try:\n float(v)\n except ValueError:\n return False\n return True","def __contains__(self, k) :\n return k in self.precision()","def contains_value(kv_json, value):\n if isinstance(kv_json, str):\n kv_dict = loads(kv_json)\n for key in kv_dict:\n if kv_dict[key] == value: # Found value in dictionary\n return True\n return False\n else:\n print(\"Provide A JSON Key Value String\")","def is_float(possible_number):\r\n try:\r\n float(possible_number)\r\n return True\r\n except ValueError:\r\n return False","def checkNaN(data_dict):\n for k, v in data_dict.iteritems():\n mark = True\n for feature, value in v.iteritems():\n if (value != 'NaN') and (feature != 'poi'):\n mark = False\n break\n if mark:\n print k\n print v['poi']","def _check_feature_by_keys(service_data=None, service_keys=None, ns_data=None, ns_keys=None):\n\n if service_data and not isinstance(service_data, Exception) and service_keys:\n if _is_keyval_greater_than_value(service_data, service_keys):\n return True\n\n if ns_data and ns_keys:\n for ns, nsval in ns_data.iteritems():\n if not nsval or isinstance(nsval, Exception):\n continue\n if _is_keyval_greater_than_value(nsval, ns_keys):\n return True\n\n return False","def contains_key(kv_json, key):\n if isinstance(kv_json, str):\n kv_dict = loads(kv_json)\n try:\n res = kv_dict[key]\n return True\n except KeyError:\n return False\n else:\n print(\"Provide A JSON Key Value String\")","def validateNumber(key, value):\n if value is None or isinstance(value, (int, float)) and not isinstance(value, bool):\n return None\n else:\n return {'error': 'invalid value: %s (%s), valid values number/null' % (value, pythonTypeToJSONType(value))}","def isnumber(x):\n try:\n float(x)\n return True\n except ValueError:\n return False","def haskey(featureVals, fkey):\n try:\n featureVals[fkey]\n except KeyError:\n return False\n\n #warn(HASKEYMSG % (fkey))\n return True","def contains(self, key: int) -> bool:\n return self._find_key(key, find_empty=False) >= 0","def is_numeric(value):\n return any([\n type(value) is str and value.isnumeric(),\n hasattr(value, 'is_integer') and value.is_integer(),\n type(value) is int,\n ])","def has_value(cls, value):\n return bool(isinstance(value, numbers.Number) or isinstance(value, time) or \\\n isinstance(value, datetime) or value)","def checkifnumber(self, test_string):\r\n try:\r\n float(test_string)\r\n return(True)\r\n except ValueError:\r\n return(False)","def has_value(value):\n return IsDictContainingValue(wrap_matcher(value))","def is_number(num):\n try:\n float(num)\n return True\n except ValueError:\n return False","def is_number(number):\n try:\n float(number)\n return True\n except ValueError:\n return False","def isnumeric(number):\n try:\n float(number)\n return True\n except (TypeError, ValueError):\n return False","def eval_dict(self, value):\n\n okay = True\n if all(ast_class(k) == 'Str' for k in value.keys):\n count = 0\n for v in value.values:\n if not self.eval_value(v):\n okay = False\n break\n count += 1\n return okay","def contains(self, key: int) -> bool:\n y = key % 80\n return key in self.arr[y]","def operand_present(input_str): # HELPER\n try:\n float(input_str)\n return True\n except ValueError:\n return False","def is_number(value):\n try:\n int(value)\n return True\n except (ValueError, TypeError):\n return False","def func3(key):\n value = my_test_dict.get(key)\n if value is None:\n return False\n else:\n return True","def has_valid_values(self):\n for element, value in self.items():\n if not (0 <= value <= 1):\n return False\n return True","def is_number(n):\n\ttry:\n\t\tfloat(n)\n\t\treturn True\n\texcept ValueError:\n\t\treturn False","def __is_key_in_json(self, key=str, json_dict=json):\n if key in json_dict:\n # noinspection PyUnresolvedReferences\n return json_dict[key]\n else:\n return self.NO_KEY_VALUE_FOR_ENTRY","def check_for_float(check):","def isnum(value):\n\n try:\n return bool(isinstance(value, (float, int)))\n except RuntimeError:\n return False","def check_for_float_and_int(check):","def is_item_in_the_response(key, value, jsonResponse):\n flag = False\n for item in jsonResponse:\n if type(jsonResponse[item]) == int:\n if item == key and jsonResponse[item] == int(value):\n flag = True\n\n if type(jsonResponse[item]) == str:\n if item == key and jsonResponse[item] == str(value):\n flag = True\n\n if type(jsonResponse[item]) == bool:\n if item == key and jsonResponse[item] == bool(value):\n flag = True\n else:\n #log and error\n pass\n return flag","def is_numeric(rows, col):\n return rows.dtypes.values[col] in numerics","def CheckNumber(userInput):\n try:\n float(userInput)\n return True\n except(ValueError):\n return False","def func4(key):\n return key in list(my_test_dict.keys())","def __has_numbers(self, input_string):\n return bool(re.search(r'\\d', input_string))","def rsi_trend_positive(rsi_dict):\n\n rsi_iterable = list(rsi_dict.items())\n\n # Only looking at the last 10 days of data.\n test_batch = [float(x[1]['RSI']) for x in rsi_iterable[0:10]]\n\n if sum(test_batch) / len(test_batch) < test_batch[0]:\n return True\n\n return False","def valuecall(key, atom_dict):\n if key not in atom_dict:\n return 0\n else:\n return atom_dict[key]","def _is_number(s) -> bool:\n try:\n float(s)\n except ValueError:\n return False\n else:\n return True","def contains(self, val: float) -> bool:\n return self._check_lower(val) and self._check_upper(val)","def find(self, value):\n for key in self.dict:\n if (value-key==key and self.dict[key]>=2) or (value-key!=key and value-key in self.dict):\n return True\n \n return False","def is_numeric(value):\n return isinstance(value, int) or isinstance(value, float)","def _is_key_value(data):\n if data is None:\n return False\n return all(x in data for x in ['key', 'value'])","def is_float(word):\n try:\n float(word)\n return True\n except ValueError:\n return False","def is_number(s: Any) -> bool:\n try:\n int(s)\n return True\n except ValueError:\n pass\n\n try:\n float(s)\n return True\n except ValueError:\n pass\n\n return False","def contains(\n self, key: int | str | ir.IntegerValue | ir.StringValue\n ) -> ir.BooleanValue:\n return ops.MapContains(self, key).to_expr()","def is_number_tryexcept(s):\n try:\n float(s)\n return True\n except ValueError:\n return False","def has_number(any_string):\n return any(char.isdigit() for char in any_string)","def data_processing(data_dic: Dict[str, int]):\n\n sum_0 = 0\n for key, value in data_dic.items():\n if int(list(key)[0]) + int(list(key)[1]) == 0:\n sum_0 += value\n return sum_0 / shots","def has(self, key: str) -> Any:\n return key in self.variables","def is_number(text):\n return text.lower() in AVRO_NUMBERS","def is_number(s):\n try:\n float(s)\n return True\n except ValueError:\n return False","def is_number(s):\n try:\n float(s)\n return True\n except ValueError:\n return False","def is_number(s):\n try:\n float(s)\n return True\n except ValueError:\n return False","def is_number(s):\r\n try:\r\n float(s)\r\n return True\r\n except ValueError:\r\n return False","def isNumber(s):\n try:\n float(s)\n return True\n except ValueError:\n return False","def get_serendipity_val(dic, key):\n # The key was in the training set\n try:\n return dic[key]\n # The key wasn't in the training set, then the serendipity is 1\n except KeyError:\n return 1.0","def isNumericCategory(self, category):\r\n category_values = self.getCategoryValues(self.SampleIds, category)\r\n\r\n is_numeric = True\r\n for category_value in category_values:\r\n try:\r\n float(category_value)\r\n except ValueError:\r\n is_numeric = False\r\n return is_numeric","def could_be_number(val):\n if val == None:\n return False\n\n if isinstance(val, (float, int, long)):\n return True\n\n # allow coercion from str\n if isinstance(val, (str, unicode)):\n try:\n n = float(val)\n if not isinstance(n, float):\n raise ValueError\n else:\n return True\n except:\n return False\n\n #otherwise\n return False","def is_float(self, val):\n try:\n float(val)\n return True\n except ValueError:\n return False","def is_float(self, value):\n try:\n float(value)\n return True\n except ValueError:\n return False","def is_float(value):\n try:\n float(value)\n except ValueError:\n return False\n else:\n return True","def is_number(s):\n try:\n float(s)\n return True\n except ValueError:\n return False","def is_num(var):\n try:\n int(var)\n return True\n except ValueError:\n return False","def is_number(value):\n try:\n float(value.replace(',', ''))\n except ValueError:\n return False\n return True","def _is_positive_float(item):\n if not isinstance(item, (int, float)):\n return False\n return item > 0","def are_numeric(*values):\n\n for value in values:\n if not is_numeric(value):\n return False\n return True","def is_floatable(value):\n\n try:\n float(value)\n return True\n except:\n return False","def checkIndex(key):\n if not isinstance(key, (int, float)): raise TypeError\n if key<0: raise IndexError","def isfloat(value):\r\n try:\r\n float(value)\r\n return True\r\n except ValueError:\r\n return False","def is_float(val):\n try:\n float(val)\n return True\n except ValueError:\n return False","def isFloat(value): \n try:\n float(value)\n return True\n except ValueError:\n return False","def contains(self, key: int) -> bool:\n return key in self.data[key % self._MOD]","def test_if_keys_or_values_in_result_dict_are_int(self):\n for key, value in add_expressions(1, 2, 8)(2, 3).items():\n self.assertIsInstance(key, int)\n self.assertIsInstance(value, int)","def is_float(value):\n try:\n float(value)\n return True\n except ValueError:\n return False","def num_present(key, value, board):\n row, col = find_box(key) # checks for box\n for x in rows[row]:\n for y in cols[col]:\n if str(x + str(y)) != key and board[x + str(y)] == value:\n return True\n # checks for column\n for x in ROW:\n if str(x + str(key[1])) != key and board[x + str(key[1])] == value:\n return True\n # checks for row\n for i in COL:\n if str(key[0] + str(i)) != key and board[key[0] + str(i)] == value:\n return True\n return False","def isSetNumericValue(self):\n return _libsbml.PossibleSpeciesFeatureValue_isSetNumericValue(self)","def IsNumber(s):\n try:\n v = float(s)\n return True\n except ValueError:\n return False","def isfloat(value):\n try:\n float(value)\n return True\n except ValueError:\n return False","def value_is_float_not_int(value):\n # this is klugy and only needed to display deprecation warnings\n try:\n int(value)\n return False\n except ValueError:\n try:\n float(value)\n return True\n except ValueError:\n return False\n except TypeError:\n return False","def check_value(self, key: str, value: Any):\n # Check the value with a set of tests\n self._check_missing(key, value)\n self._check_allowed_values(key, value)\n self._check_data_type(key, value)\n self._check_value_range(key, value)","def is_float(self, input):\n try:\n float(input)\n return True\n except ValueError:\n return False","def numeric_check(param, name):\n\tif not isinstance(param, numbers.Number):\n\t\traise TypeError(\"Keyword arg '%s' must be a real number. Got: %s\" % (\n\t\t\tname, type(param)))\n\telse:\n\t\tpass"],"string":"[\n \"def moreThanOne(dict, key):\\n\\treturn key in dict and dict[key] > 0\",\n \"def _has_numeric_strict(self) -> bool:\\n return bool({'i', 'f'} & self._data.keys())\",\n \"def contains_200(dictnr):\\n contains = False\\n for i in dictnr:\\n if dictnr[i] == 200:\\n contains = True\\n print(contains)\",\n \"def is_key(number):\\n res = False\\n if is_integer(number):\\n if int(number) > 0:\\n res = True\\n return res\",\n \"def _cast_to_number(self, key):\\n q = DBSession.query(cast(self.db_value.value, Float)). \\\\\\n join(self.db_tag). \\\\\\n join(self.db_key). \\\\\\n filter(self.db_key.key == key)\\n try:\\n q.all()\\n return True\\n except:\\n return False\",\n \"def hasValue(self, key):\\n return self.has_key('__' + key)\",\n \"def _has_science_data(data_dict, particle_class):\\n return_value = False\\n\\n # Modified to make this check more efficient\\n if len(particle_class.science_parameters) < len(data_dict):\\n for key in particle_class.science_parameters:\\n value = data_dict.get(key, None)\\n if value is not None and not(isnan(float(value))):\\n return_value = True\\n break\\n if particle_class._data_particle_type == 'glider_eng_telemetered':\\n log.info(\\\"GliderParser._has_science_data failed: key=[%s] value=[%s]\\\", key, value)\\n else:\\n for key, value in data_dict.iteritems():\\n if not (isnan(float(value))) and key in particle_class.science_parameters:\\n return_value = True\\n break\\n if particle_class._data_particle_type == 'glider_eng_telemetered':\\n log.info(\\\"GliderParser._has_science_data failed: key=[%s] value=[%s]\\\", key, value)\\n\\n return return_value\",\n \"def _has_numeric_or_bool(self) -> bool:\\n dtypes: Set[str] = set(self._data.keys())\\n return 'i' in dtypes or 'f' in dtypes or 'b' in dtypes\",\n \"def contains(self, key: int) -> bool:\\n if key in self.d:\\n return True\\n else:\\n return False\",\n \"def is_number(value):\\n try:\\n float(value)\\n return True\\n except ValueError:\\n return False\",\n \"def _is_number(value):\\n try:\\n float(value)\\n return True\\n except (TypeError, ValueError):\\n return False\",\n \"def contains(self, key):\\n if key in self.nums:\\n return True\\n return False\",\n \"def contains_double_count(key, value, similarity_dict):\\n if value in similarity_dict.keys():\\n if key in similarity_dict[value]:\\n return True\\n return False\",\n \"def data_dict_points(data_dict, feature):\\n return len(filter(lambda k: isinstance(data_dict[k][feature],\\n (int, float)), data_dict))\",\n \"def is_number(self,val):\\n try:\\n float(val)\\n return True\\n except ValueError:\\n return False\",\n \"def is_zero_dict( dict ):\\n has_any_features = False\\n for key in dict:\\n has_any_features = has_any_features or dict[key]\\n\\n return not has_any_features\",\n \"def exists(field):\\n try:\\n float(field)\\n return True\\n except:\\n return False\",\n \"def contains(self, key):\\n\\t\\tfor i in self.getBitArrayIndices(key):\\n\\t\\t\\tif self.ba[i] <= 0:\\n\\t\\t\\t\\treturn False\\n\\t\\treturn True\",\n \"def anyMoreThanOne(dict, keys):\\n\\tfor key in keys:\\n\\t\\tif key in dict and dict[key] > 0:\\n\\t\\t\\treturn True\\n\\treturn False\",\n \"def values_are_pandas_numbers(values: List[str]):\\n for v in values:\\n try:\\n float(v)\\n except ValueError:\\n return False\\n return True\",\n \"def __contains__(self, k) :\\n return k in self.precision()\",\n \"def contains_value(kv_json, value):\\n if isinstance(kv_json, str):\\n kv_dict = loads(kv_json)\\n for key in kv_dict:\\n if kv_dict[key] == value: # Found value in dictionary\\n return True\\n return False\\n else:\\n print(\\\"Provide A JSON Key Value String\\\")\",\n \"def is_float(possible_number):\\r\\n try:\\r\\n float(possible_number)\\r\\n return True\\r\\n except ValueError:\\r\\n return False\",\n \"def checkNaN(data_dict):\\n for k, v in data_dict.iteritems():\\n mark = True\\n for feature, value in v.iteritems():\\n if (value != 'NaN') and (feature != 'poi'):\\n mark = False\\n break\\n if mark:\\n print k\\n print v['poi']\",\n \"def _check_feature_by_keys(service_data=None, service_keys=None, ns_data=None, ns_keys=None):\\n\\n if service_data and not isinstance(service_data, Exception) and service_keys:\\n if _is_keyval_greater_than_value(service_data, service_keys):\\n return True\\n\\n if ns_data and ns_keys:\\n for ns, nsval in ns_data.iteritems():\\n if not nsval or isinstance(nsval, Exception):\\n continue\\n if _is_keyval_greater_than_value(nsval, ns_keys):\\n return True\\n\\n return False\",\n \"def contains_key(kv_json, key):\\n if isinstance(kv_json, str):\\n kv_dict = loads(kv_json)\\n try:\\n res = kv_dict[key]\\n return True\\n except KeyError:\\n return False\\n else:\\n print(\\\"Provide A JSON Key Value String\\\")\",\n \"def validateNumber(key, value):\\n if value is None or isinstance(value, (int, float)) and not isinstance(value, bool):\\n return None\\n else:\\n return {'error': 'invalid value: %s (%s), valid values number/null' % (value, pythonTypeToJSONType(value))}\",\n \"def isnumber(x):\\n try:\\n float(x)\\n return True\\n except ValueError:\\n return False\",\n \"def haskey(featureVals, fkey):\\n try:\\n featureVals[fkey]\\n except KeyError:\\n return False\\n\\n #warn(HASKEYMSG % (fkey))\\n return True\",\n \"def contains(self, key: int) -> bool:\\n return self._find_key(key, find_empty=False) >= 0\",\n \"def is_numeric(value):\\n return any([\\n type(value) is str and value.isnumeric(),\\n hasattr(value, 'is_integer') and value.is_integer(),\\n type(value) is int,\\n ])\",\n \"def has_value(cls, value):\\n return bool(isinstance(value, numbers.Number) or isinstance(value, time) or \\\\\\n isinstance(value, datetime) or value)\",\n \"def checkifnumber(self, test_string):\\r\\n try:\\r\\n float(test_string)\\r\\n return(True)\\r\\n except ValueError:\\r\\n return(False)\",\n \"def has_value(value):\\n return IsDictContainingValue(wrap_matcher(value))\",\n \"def is_number(num):\\n try:\\n float(num)\\n return True\\n except ValueError:\\n return False\",\n \"def is_number(number):\\n try:\\n float(number)\\n return True\\n except ValueError:\\n return False\",\n \"def isnumeric(number):\\n try:\\n float(number)\\n return True\\n except (TypeError, ValueError):\\n return False\",\n \"def eval_dict(self, value):\\n\\n okay = True\\n if all(ast_class(k) == 'Str' for k in value.keys):\\n count = 0\\n for v in value.values:\\n if not self.eval_value(v):\\n okay = False\\n break\\n count += 1\\n return okay\",\n \"def contains(self, key: int) -> bool:\\n y = key % 80\\n return key in self.arr[y]\",\n \"def operand_present(input_str): # HELPER\\n try:\\n float(input_str)\\n return True\\n except ValueError:\\n return False\",\n \"def is_number(value):\\n try:\\n int(value)\\n return True\\n except (ValueError, TypeError):\\n return False\",\n \"def func3(key):\\n value = my_test_dict.get(key)\\n if value is None:\\n return False\\n else:\\n return True\",\n \"def has_valid_values(self):\\n for element, value in self.items():\\n if not (0 <= value <= 1):\\n return False\\n return True\",\n \"def is_number(n):\\n\\ttry:\\n\\t\\tfloat(n)\\n\\t\\treturn True\\n\\texcept ValueError:\\n\\t\\treturn False\",\n \"def __is_key_in_json(self, key=str, json_dict=json):\\n if key in json_dict:\\n # noinspection PyUnresolvedReferences\\n return json_dict[key]\\n else:\\n return self.NO_KEY_VALUE_FOR_ENTRY\",\n \"def check_for_float(check):\",\n \"def isnum(value):\\n\\n try:\\n return bool(isinstance(value, (float, int)))\\n except RuntimeError:\\n return False\",\n \"def check_for_float_and_int(check):\",\n \"def is_item_in_the_response(key, value, jsonResponse):\\n flag = False\\n for item in jsonResponse:\\n if type(jsonResponse[item]) == int:\\n if item == key and jsonResponse[item] == int(value):\\n flag = True\\n\\n if type(jsonResponse[item]) == str:\\n if item == key and jsonResponse[item] == str(value):\\n flag = True\\n\\n if type(jsonResponse[item]) == bool:\\n if item == key and jsonResponse[item] == bool(value):\\n flag = True\\n else:\\n #log and error\\n pass\\n return flag\",\n \"def is_numeric(rows, col):\\n return rows.dtypes.values[col] in numerics\",\n \"def CheckNumber(userInput):\\n try:\\n float(userInput)\\n return True\\n except(ValueError):\\n return False\",\n \"def func4(key):\\n return key in list(my_test_dict.keys())\",\n \"def __has_numbers(self, input_string):\\n return bool(re.search(r'\\\\d', input_string))\",\n \"def rsi_trend_positive(rsi_dict):\\n\\n rsi_iterable = list(rsi_dict.items())\\n\\n # Only looking at the last 10 days of data.\\n test_batch = [float(x[1]['RSI']) for x in rsi_iterable[0:10]]\\n\\n if sum(test_batch) / len(test_batch) < test_batch[0]:\\n return True\\n\\n return False\",\n \"def valuecall(key, atom_dict):\\n if key not in atom_dict:\\n return 0\\n else:\\n return atom_dict[key]\",\n \"def _is_number(s) -> bool:\\n try:\\n float(s)\\n except ValueError:\\n return False\\n else:\\n return True\",\n \"def contains(self, val: float) -> bool:\\n return self._check_lower(val) and self._check_upper(val)\",\n \"def find(self, value):\\n for key in self.dict:\\n if (value-key==key and self.dict[key]>=2) or (value-key!=key and value-key in self.dict):\\n return True\\n \\n return False\",\n \"def is_numeric(value):\\n return isinstance(value, int) or isinstance(value, float)\",\n \"def _is_key_value(data):\\n if data is None:\\n return False\\n return all(x in data for x in ['key', 'value'])\",\n \"def is_float(word):\\n try:\\n float(word)\\n return True\\n except ValueError:\\n return False\",\n \"def is_number(s: Any) -> bool:\\n try:\\n int(s)\\n return True\\n except ValueError:\\n pass\\n\\n try:\\n float(s)\\n return True\\n except ValueError:\\n pass\\n\\n return False\",\n \"def contains(\\n self, key: int | str | ir.IntegerValue | ir.StringValue\\n ) -> ir.BooleanValue:\\n return ops.MapContains(self, key).to_expr()\",\n \"def is_number_tryexcept(s):\\n try:\\n float(s)\\n return True\\n except ValueError:\\n return False\",\n \"def has_number(any_string):\\n return any(char.isdigit() for char in any_string)\",\n \"def data_processing(data_dic: Dict[str, int]):\\n\\n sum_0 = 0\\n for key, value in data_dic.items():\\n if int(list(key)[0]) + int(list(key)[1]) == 0:\\n sum_0 += value\\n return sum_0 / shots\",\n \"def has(self, key: str) -> Any:\\n return key in self.variables\",\n \"def is_number(text):\\n return text.lower() in AVRO_NUMBERS\",\n \"def is_number(s):\\n try:\\n float(s)\\n return True\\n except ValueError:\\n return False\",\n \"def is_number(s):\\n try:\\n float(s)\\n return True\\n except ValueError:\\n return False\",\n \"def is_number(s):\\n try:\\n float(s)\\n return True\\n except ValueError:\\n return False\",\n \"def is_number(s):\\r\\n try:\\r\\n float(s)\\r\\n return True\\r\\n except ValueError:\\r\\n return False\",\n \"def isNumber(s):\\n try:\\n float(s)\\n return True\\n except ValueError:\\n return False\",\n \"def get_serendipity_val(dic, key):\\n # The key was in the training set\\n try:\\n return dic[key]\\n # The key wasn't in the training set, then the serendipity is 1\\n except KeyError:\\n return 1.0\",\n \"def isNumericCategory(self, category):\\r\\n category_values = self.getCategoryValues(self.SampleIds, category)\\r\\n\\r\\n is_numeric = True\\r\\n for category_value in category_values:\\r\\n try:\\r\\n float(category_value)\\r\\n except ValueError:\\r\\n is_numeric = False\\r\\n return is_numeric\",\n \"def could_be_number(val):\\n if val == None:\\n return False\\n\\n if isinstance(val, (float, int, long)):\\n return True\\n\\n # allow coercion from str\\n if isinstance(val, (str, unicode)):\\n try:\\n n = float(val)\\n if not isinstance(n, float):\\n raise ValueError\\n else:\\n return True\\n except:\\n return False\\n\\n #otherwise\\n return False\",\n \"def is_float(self, val):\\n try:\\n float(val)\\n return True\\n except ValueError:\\n return False\",\n \"def is_float(self, value):\\n try:\\n float(value)\\n return True\\n except ValueError:\\n return False\",\n \"def is_float(value):\\n try:\\n float(value)\\n except ValueError:\\n return False\\n else:\\n return True\",\n \"def is_number(s):\\n try:\\n float(s)\\n return True\\n except ValueError:\\n return False\",\n \"def is_num(var):\\n try:\\n int(var)\\n return True\\n except ValueError:\\n return False\",\n \"def is_number(value):\\n try:\\n float(value.replace(',', ''))\\n except ValueError:\\n return False\\n return True\",\n \"def _is_positive_float(item):\\n if not isinstance(item, (int, float)):\\n return False\\n return item > 0\",\n \"def are_numeric(*values):\\n\\n for value in values:\\n if not is_numeric(value):\\n return False\\n return True\",\n \"def is_floatable(value):\\n\\n try:\\n float(value)\\n return True\\n except:\\n return False\",\n \"def checkIndex(key):\\n if not isinstance(key, (int, float)): raise TypeError\\n if key<0: raise IndexError\",\n \"def isfloat(value):\\r\\n try:\\r\\n float(value)\\r\\n return True\\r\\n except ValueError:\\r\\n return False\",\n \"def is_float(val):\\n try:\\n float(val)\\n return True\\n except ValueError:\\n return False\",\n \"def isFloat(value): \\n try:\\n float(value)\\n return True\\n except ValueError:\\n return False\",\n \"def contains(self, key: int) -> bool:\\n return key in self.data[key % self._MOD]\",\n \"def test_if_keys_or_values_in_result_dict_are_int(self):\\n for key, value in add_expressions(1, 2, 8)(2, 3).items():\\n self.assertIsInstance(key, int)\\n self.assertIsInstance(value, int)\",\n \"def is_float(value):\\n try:\\n float(value)\\n return True\\n except ValueError:\\n return False\",\n \"def num_present(key, value, board):\\n row, col = find_box(key) # checks for box\\n for x in rows[row]:\\n for y in cols[col]:\\n if str(x + str(y)) != key and board[x + str(y)] == value:\\n return True\\n # checks for column\\n for x in ROW:\\n if str(x + str(key[1])) != key and board[x + str(key[1])] == value:\\n return True\\n # checks for row\\n for i in COL:\\n if str(key[0] + str(i)) != key and board[key[0] + str(i)] == value:\\n return True\\n return False\",\n \"def isSetNumericValue(self):\\n return _libsbml.PossibleSpeciesFeatureValue_isSetNumericValue(self)\",\n \"def IsNumber(s):\\n try:\\n v = float(s)\\n return True\\n except ValueError:\\n return False\",\n \"def isfloat(value):\\n try:\\n float(value)\\n return True\\n except ValueError:\\n return False\",\n \"def value_is_float_not_int(value):\\n # this is klugy and only needed to display deprecation warnings\\n try:\\n int(value)\\n return False\\n except ValueError:\\n try:\\n float(value)\\n return True\\n except ValueError:\\n return False\\n except TypeError:\\n return False\",\n \"def check_value(self, key: str, value: Any):\\n # Check the value with a set of tests\\n self._check_missing(key, value)\\n self._check_allowed_values(key, value)\\n self._check_data_type(key, value)\\n self._check_value_range(key, value)\",\n \"def is_float(self, input):\\n try:\\n float(input)\\n return True\\n except ValueError:\\n return False\",\n \"def numeric_check(param, name):\\n\\tif not isinstance(param, numbers.Number):\\n\\t\\traise TypeError(\\\"Keyword arg '%s' must be a real number. Got: %s\\\" % (\\n\\t\\t\\tname, type(param)))\\n\\telse:\\n\\t\\tpass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6130827","0.6060219","0.5976841","0.5809231","0.5789039","0.5752602","0.56971747","0.5649147","0.56462014","0.5644481","0.56440645","0.5638638","0.56340736","0.560666","0.5580076","0.55792755","0.55783355","0.55679023","0.55529094","0.5530956","0.5520167","0.55132616","0.5469083","0.54569","0.54385626","0.54303396","0.54266495","0.5414168","0.5401341","0.54001594","0.5395861","0.5395672","0.5385847","0.537121","0.53685254","0.53639364","0.53529483","0.53525096","0.5344761","0.53408945","0.5338632","0.53309506","0.5324901","0.5310373","0.5310211","0.530742","0.5304139","0.53029907","0.5301828","0.5277144","0.5269609","0.5267187","0.52419263","0.52403307","0.5234311","0.5231246","0.5223664","0.52150464","0.5215039","0.5208167","0.51986223","0.5184296","0.51812357","0.5174362","0.5171757","0.5169111","0.5168917","0.51650286","0.5162473","0.5162473","0.5162473","0.5161335","0.51582676","0.51552325","0.5153366","0.51504785","0.5146285","0.5139923","0.513948","0.51351917","0.5134928","0.5134636","0.5126503","0.5119759","0.51196444","0.5116325","0.51154494","0.5115187","0.51132905","0.51070684","0.5106551","0.5105965","0.5093936","0.50907564","0.50854605","0.507771","0.5077662","0.50716114","0.5070471","0.5067336"],"string":"[\n \"0.6130827\",\n \"0.6060219\",\n \"0.5976841\",\n \"0.5809231\",\n \"0.5789039\",\n \"0.5752602\",\n \"0.56971747\",\n \"0.5649147\",\n \"0.56462014\",\n \"0.5644481\",\n \"0.56440645\",\n \"0.5638638\",\n \"0.56340736\",\n \"0.560666\",\n \"0.5580076\",\n \"0.55792755\",\n \"0.55783355\",\n \"0.55679023\",\n \"0.55529094\",\n \"0.5530956\",\n \"0.5520167\",\n \"0.55132616\",\n \"0.5469083\",\n \"0.54569\",\n \"0.54385626\",\n \"0.54303396\",\n \"0.54266495\",\n \"0.5414168\",\n \"0.5401341\",\n \"0.54001594\",\n \"0.5395861\",\n \"0.5395672\",\n \"0.5385847\",\n \"0.537121\",\n \"0.53685254\",\n \"0.53639364\",\n \"0.53529483\",\n \"0.53525096\",\n \"0.5344761\",\n \"0.53408945\",\n \"0.5338632\",\n \"0.53309506\",\n \"0.5324901\",\n \"0.5310373\",\n \"0.5310211\",\n \"0.530742\",\n \"0.5304139\",\n \"0.53029907\",\n \"0.5301828\",\n \"0.5277144\",\n \"0.5269609\",\n \"0.5267187\",\n \"0.52419263\",\n \"0.52403307\",\n \"0.5234311\",\n \"0.5231246\",\n \"0.5223664\",\n \"0.52150464\",\n \"0.5215039\",\n \"0.5208167\",\n \"0.51986223\",\n \"0.5184296\",\n \"0.51812357\",\n \"0.5174362\",\n \"0.5171757\",\n \"0.5169111\",\n \"0.5168917\",\n \"0.51650286\",\n \"0.5162473\",\n \"0.5162473\",\n \"0.5162473\",\n \"0.5161335\",\n \"0.51582676\",\n \"0.51552325\",\n \"0.5153366\",\n \"0.51504785\",\n \"0.5146285\",\n \"0.5139923\",\n \"0.513948\",\n \"0.51351917\",\n \"0.5134928\",\n \"0.5134636\",\n \"0.5126503\",\n \"0.5119759\",\n \"0.51196444\",\n \"0.5116325\",\n \"0.51154494\",\n \"0.5115187\",\n \"0.51132905\",\n \"0.51070684\",\n \"0.5106551\",\n \"0.5105965\",\n \"0.5093936\",\n \"0.50907564\",\n \"0.50854605\",\n \"0.507771\",\n \"0.5077662\",\n \"0.50716114\",\n \"0.5070471\",\n \"0.5067336\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.842359"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":2891,"cells":{"query":{"kind":"string","value":"Populate weather data tables."},"document":{"kind":"string","value":"def populate_weather(connection):\n metadata = load_metadata('weather')\n cursor = connection.cursor()\n water_defs = get_water_definitions()\n\n # Check if tables are already populated.\n cursor.execute('SELECT count(*) FROM weather')\n weather_count = cursor.fetchone()[0]\n\n if weather_count:\n print('Weather tables already populated!')\n return\n\n print('WEATHER:')\n\n # Darksky data\n for dir_name, location in metadata.items():\n print(f'\\tPopulating weather: \"{location[\"name\"]}\".')\n\n # Insert location.\n cursor.execute(f'''INSERT INTO locations(name, lat, lng)\n VALUES ('{location['name']}', {location['lat']}, {location['lng']})''')\n location_id = cursor.lastrowid\n\n # Set weather locations for watercourses/aquifers.\n for water_body in [d['body'] for d in water_defs.values()]:\n if water_body in location:\n cursor.execute(f'''UPDATE {water_body}s\n SET location_id = {location_id}\n WHERE name IN ('{\"','\".join(location[water_body])}')''')\n break\n\n dir_path = get_data_path('weather', 'raw', dir_name)\n for json_file_name in os.listdir(dir_path):\n json_path = os.path.join(dir_path, json_file_name)\n with open(json_path, 'r', encoding='utf-8') as json_file:\n print(f'\\t\\tPopulating year: {json_file_name[0:-5]}')\n year_forecasts = json.load(json_file)\n for date, date_forecast in year_forecasts.items():\n hourly_forecasts = date_forecast['hourly']\n\n if not hourly_forecasts:\n print(f'\\t\\tNo hourly forecasts for {date}!')\n continue\n\n daily_forecast = {\n 'location_id': location_id,\n 'time': date_forecast['time'],\n 'day_time': date_forecast['sunset_time'] - date_forecast['sunrise_time'],\n 'precipitation': 0,\n 'snow_accumulation': 0\n }\n # List of value names with `avg`, `min` and `max` values\n value_names = {\n 'temperature': 'temperature',\n 'cloud_cover': 'cloudCover',\n 'dew_point': 'dewPoint',\n 'humidity': 'humidity',\n 'pressure': 'pressure',\n 'uv_index': 'uvIndex',\n 'precipitation_probability': 'precipProbability',\n 'precipitation_intensity': 'precipIntensity'\n }\n # Value name counters, which indicate how many times (out of 24)\n # certain value appears in hourly data.\n value_counts = {k: 0 for k in value_names.keys()}\n\n for value_name in value_names.keys():\n daily_forecast[f'{value_name}_avg'] = 0.0\n daily_forecast[f'{value_name}_min'] = float('inf')\n daily_forecast[f'{value_name}_max'] = float('-inf')\n\n # Calculate daily forecast values from hourly forecasts.\n for hourly_forecast in hourly_forecasts:\n for value_name in value_names.keys():\n orig_value_name = value_names[value_name]\n if is_forecast_number(orig_value_name, hourly_forecast):\n daily_forecast[f'{value_name}_avg'] += hourly_forecast[orig_value_name]\n daily_forecast[f'{value_name}_min'] = min(\n hourly_forecast[orig_value_name],\n daily_forecast[f'{value_name}_min']\n )\n daily_forecast[f'{value_name}_max'] = max(\n hourly_forecast[orig_value_name],\n daily_forecast[f'{value_name}_max']\n )\n value_counts[value_name] += 1\n\n if is_forecast_number('precipAccumulation', hourly_forecast) \\\n and hourly_forecast['precipType'] == 'snow':\n daily_forecast['snow_accumulation'] += hourly_forecast['precipAccumulation']\n elif is_forecast_number('precipIntensity', hourly_forecast) \\\n and is_forecast_number('precipProbability', hourly_forecast):\n daily_forecast['precipitation'] += \\\n hourly_forecast['precipIntensity'] * hourly_forecast['precipProbability']\n\n for value_name, value_count in value_counts.items():\n if value_count:\n # Calculate average.\n daily_forecast[f'{value_name}_avg'] = daily_forecast[f'{value_name}_avg'] / value_count\n else:\n # If value never appeared\n daily_forecast[f'{value_name}_avg'] = 'NULL'\n daily_forecast[f'{value_name}_min'] = 'NULL'\n daily_forecast[f'{value_name}_max'] = 'NULL'\n\n cursor.execute(f'''INSERT INTO weather({', '.join(daily_forecast.keys())})\n VALUES ({', '.join([str(v) for v in daily_forecast.values()])})''')\n\n # IOT data:\n for location in SETTINGS['weather_locations_iot']:\n print(f'\\tPopulating weather: \"{location[\"name\"]}\".')\n\n # Insert location.\n cursor.execute(f'''INSERT INTO locations(name, lat, lng)\n VALUES ('{location['name']}', {location['lat']}, {location['lng']})''')\n location_id = cursor.lastrowid\n\n # Set weather locations for watercourses/aquifers.\n for water_body in [d['body'] for d in water_defs.values()]:\n if water_body in location:\n cursor.execute(f'''UPDATE {water_body}s\n SET location_id = {location_id}\n WHERE name IN ('{\"', '\".join(location[water_body])}')''')\n\n # Set locations for all stations on given water body to match its location.\n cursor.execute(f'''SELECT id\n FROM {water_body}s\n WHERE location_id = {location_id}''')\n ids = [row[0] for row in cursor.fetchall()]\n if len(ids):\n cursor.execute(f'''UPDATE {water_body}_stations\n SET location_id = {location_id}\n WHERE {water_body}_id IN ({', '.join([str(v) for v in ids])})''')\n\n break \n \n file_name = f'''{location['lat']}-{location['lng']}.json'''\n json_path = get_data_path('weather', 'raw', file_name)\n\n # If data file doesn't exist, download it first.\n if not os.path.isfile(json_path):\n with open(json_path, 'wb', encoding=\"utf-8\") as file:\n file.write(read_from_url(location['url'], decode=False))\n \n with open(json_path, 'r', encoding='utf-8') as json_file:\n row_names = {\n \"Sun_duration\": \"sun_duration\",\n \"CloudCover\": \"cloud_cover_avg\",\n \"Percipitation\": \"precipitation\",\n \"New_snow_blanket\": \"snow_accumulation\",\n \"Snow_blanket\": \"snow_depth\",\n \"TemperatureAvg\": \"temperature_avg\",\n \"TemperatureMin\": \"temperature_min\",\n \"TemperatureMax\": \"temperature_max\"\n }\n forecasts = json.load(json_file)\n for forecast in forecasts:\n f = {row_names[k]: forecast[k] for k in row_names.keys()}\n f['location_id'] = location_id\n f['time'] = round(forecast['LastUpdatedEpoch'] / 1000)\n cursor.execute(f'''INSERT INTO weather({', '.join(f.keys())})\n VALUES ({', '.join([str(v) for v in f.values()])})''')"},"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 populate_water_tables(connection):\n metadata = load_metadata('water')\n cursor = connection.cursor()\n\n # Check if tables are already populated.\n cursor.execute('SELECT count(*) FROM watercourses')\n watercourse_count = cursor.fetchone()[0]\n cursor.execute('SELECT count(*) FROM aquifers')\n aquifer_count = cursor.fetchone()[0]\n\n if watercourse_count and aquifer_count:\n print('Water tables already populated!')\n return\n\n station_data = get_station_data()\n\n for archive in metadata.keys():\n print(f'{archive}-water:'.upper())\n water_body = get_water_definitions(archive)['body']\n\n # 1. Populate watercourses/aquifers:\n stations = {}\n for water_body_name in metadata[archive].keys():\n print(f'\\tPopulating {water_body}: \"{water_body_name}\"')\n cursor.execute(f'''INSERT INTO {water_body}s(location_id, name)\n VALUES (0, '{water_body_name}')''')\n water_body_id = cursor.lastrowid\n\n # 2. Populate watercourse_stations/aquifer_stations:\n for station_id in metadata[archive][water_body_name]['stations']:\n station_name = clean_name(metadata[archive][water_body_name]['stations'][station_id]['name'])\n\n if station_id in stations:\n # Prefer watercourses/aquifer with more stations\n current_len = len(metadata[archive][water_body_name]['stations'])\n previous_len = len(metadata[archive][stations[station_id]]['stations'])\n\n if current_len < previous_len:\n print(f'\\t\\tStation already exists: {station_id} - \"{station_name}\" (\"{water_body_name}\")')\n continue\n else:\n cursor.execute(f'''DELETE \n FROM {water_body}_stations\n WHERE id = {station_id}''')\n print(f'\\t\\tRemoved station: {station_id} - \"{station_name}\" from \"{stations[station_id]}\")')\n\n stations[station_id] = water_body_name\n print(f'\\t\\tPopulating station: {station_id} - \"{station_name}\"')\n\n # Insert station location if station data exists.\n location_id = 0\n station_row = station_data.query(f'ŠIFRA == \"{station_id}\"')\n if not station_row.empty:\n index = station_row.index[0]\n lat = station_row.at[index, 'LAT']\n lng = station_row.at[index, 'LON']\n if not np.isnan(lat) and not np.isnan(lng):\n name = f\"{station_row.at[index, 'VODOMERNA POSTAJA']} ({station_row.at[index, 'VODOTOK']})\"\n cursor.execute(f'''INSERT INTO locations(name, lat, lng)\n VALUES ('{name}', {lat}, {lng})''')\n location_id = cursor.lastrowid\n\n # Insert station.\n cursor.execute(f'''INSERT INTO {water_body}_stations(id, {water_body}_id, location_id, name)\n VALUES ({station_id}, {water_body_id}, {location_id}, '{station_name}')''')\n\n # 3. Populate watercourse_measurements/aquifer_measurements:\n if not populate_water_measurements(cursor, archive, metadata[archive][water_body_name]['dir'],\n station_id):\n cursor.execute(f'''DELETE \n FROM {water_body}_stations\n WHERE id = {station_id}''')\n print(f'\\t\\tRemoved station with useless data: {station_id} - \"{station_name}\"')\n\n # Remove empty watercourses/aquifers.\n cursor.execute(f'''SELECT w.id, w.name\n FROM {water_body}s w\n WHERE NOT EXISTS (\n SELECT s.id \n FROM {water_body}_stations s \n WHERE w.id = s.{water_body}_id\n )''')\n\n for row in cursor.fetchall():\n cursor.execute(f'''DELETE \n FROM {water_body}s\n WHERE id = {row[0]}''')\n print(f'\\tRemoved empty {water_body}: \"{row[1]}\"')","def generate_weather_data(self):\n months = pd.to_datetime(self.output['Local Time']).dt.month\n self.output['Month'] = months # set month values for later joins\n\n # merge output data frame with historical data to get ranges\n keys = ['Location', 'Month']\n m = pd.merge(self.output, self.histdata, how='left',\n left_on=keys, right_on=keys)\n\n # uniformly select random pressure, temperature\n # and humidity values between the historical max and min ranges\n r = np.random.rand(m.shape[0])\n m['Temperature'] = ((m['Tmean_high'] - m['Tmean_low']\n ) * r + m['Tmean_low']).round(1)\n m['Pressure'] = ((m['Pmax'] - m['Pmin']) * r + m['Pmin']).round(1)\n m['Humidity'] = ((m['Hmax'] - m['Hmin']) * r + m['Hmin']).astype(int)\n\n # drop redundant columns and assign to output\n dcols = ['Month', 'Timezone', 'Pmax', 'Pmin',\n 'Hmax', 'Hmin', 'Tmean_high', 'Tmean_low']\n m.drop(columns=dcols, inplace=True)\n self.output = m","def init_and_update_observe_table(self):\n # print CHN_CITY_LIST_FILE\n location = ''\n id = ''\n f = open(CHN_CITY_LIST_FILE, 'r')\n for line in f.readlines():\n line_list = line.strip('\\n').split(':')\n location = line_list[0]\n id = line_list[1]\n pm = get_pm(location)\n # get current weather\n weather_dict = get_open_weather(id)\n if weather_dict not in ('', None, [], {}):\n if 'error' in pm or pm == False:\n weather_dict['aqi'] = '无数据'#'N/A'\n else:\n weather_dict['aqi'] = pm['quality'] + '(' + str(pm['aqi']) + ')'\n db_record = self.db.search_observe_record(str(id))\n # db_record = []\n now_date = get_local_format_time()\n if db_record != []:#update\n self.db.update_observe_data(weather_dict['ptime'],weather_dict['time'],now_date,weather_dict['WD'],weather_dict['WS'],weather_dict['SD'],weather_dict['weather'],weather_dict['img1'],weather_dict['img2'],weather_dict['temp'],weather_dict['temp1'],weather_dict['temp2'],weather_dict['aqi'],id)\n else:#insert\n self.db.insert_observe_data(id,weather_dict['city'],weather_dict['ptime'],weather_dict['time'],now_date,weather_dict['WD'],weather_dict['WS'],weather_dict['SD'],weather_dict['weather'],weather_dict['img1'],weather_dict['img2'],weather_dict['temp'],weather_dict['temp1'],weather_dict['temp2'],weather_dict['aqi'])\n f.close()\n return True","def insert_humans_staging(self):\n for year in range(1880, CURRENT_YEAR):\n self.load_wikidata(\"humans\", HUMANS_BY_YEAR_SPARQL_QUERY, INSERT_HUMAN_SQL_QUERY,\n INSERT_HUMAN_MAP_COLUMNS, year=year)","def populate_database(telescope_name, instrument_name):\n telescope = Telescope.objects.create(\n name=telescope_name, latitude=25.0, longitude=45.0)\n instrument = Instrument.objects.create(\n name=instrument_name, telescope=telescope)\n for year_int in (2012, 2013):\n for month_int in range(1, 13):\n for night_int in (1, monthrange(year_int, month_int)[1]):\n ut_date = date(year_int, month_int, night_int)\n night = Night.objects.create(\n ut_date=ut_date, instrument=instrument, observers='Smith')\n Exposure.objects.create(\n night=night, run_number=1, ut_start=time(10, 0, 0),\n exposed=20.0, ra=60.0, dec=30.0, object_exp=True)\n Exposure.objects.create(\n night=night, run_number=2, ut_start=time(11, 0, 0),\n exposed=30.0, ra=90.0, dec=0.0, object_exp=True)\n Exposure.objects.create(\n night=night, run_number=3, ut_start=time(12, 0, 0),\n exposed=40.0, ra=120.0, dec=-30.0, object_exp=False)","def get_data(table_name, end, num, start=None):\n if start == None:\n if table_name == \"days\": start = end - timedelta(days=num-1) \n if table_name == \"weeks\": start = end - timedelta(weeks=num-1) \n if table_name == \"months\": start = end - relativedelta(months=+num-1) \n if table_name == \"years\": start = end - relativedelta(years=+num-1) \n else: \n start = days.get_entry(table_name, start).date\n \n dates = []\n data = []\n weather = []\n density = []\n \n while start <= end:\n entry = days.get_entry(table_name, start)\n data.append(entry.sentiment)\n \n if table_name == \"days\": \n dates.append(entry.date.strftime(\"%B %d, %Y\"))\n start = start + timedelta(days=1)\n if table_name == \"weeks\": \n dates.append(entry.date.strftime(\"%B %d, %Y\"))\n start = start + timedelta(weeks=1) \n if table_name == \"months\": \n dates.append(entry.date.strftime(\"%B %Y\"))\n start = start + relativedelta(months=+1) \n if table_name == \"years\": \n dates.append(entry.date.strftime(\"%Y\"))\n start = start + relativedelta(years=+1) \n\n # 7/15/15 is the last entry in the current weather dictionary\n num_days = (min(start, date(2015,7,15)) - entry.date).days\n temp = {entry.date + timedelta(days=i): weather_dict[entry.date + timedelta(days=i)] for i in range(num_days)}\n weather.append(float(sum(temp.values()))/float(len(temp)))\n\n if density_dict != None:\n d = max(entry.date, date(2014,7,1))\n num_days = (min(start, date(2015,7,28)) - d).days\n rho = {d + timedelta(days=i): density_dict[d + timedelta(days=i)] for i in range(num_days)}\n density.append(float(sum(rho.values()))/float(len(rho)))\n\n return dates, data, weather, density","def setup_table(self):\n\n self.setup.create_basic_table_in_dev()\n self.setup.insert_random_records_into_dev()","def populate_db():\n\n populate_table(db, models.Department, departments_data)\n populate_table(db, models.Employee, employees_data)","def sql_functions(cities, weather):\n con = lite.connect(\"sql_database.db\")\n tables_tuple = (\"cities\", \"weather\")\n with con:\n #Generate tables in database:\n cur = con.cursor() #Get cursor object\n for table in tables_tuple:\n cur.execute(\"DROP TABLE IF EXISTS {0}\".format(table)) #Drop tables if they already exist.\n cur.execute(\"CREATE TABLE cities (name text, state text)\")\n cur.execute(\"CREATE TABLE weather (city text, year integer, warm_month text, cold_month text, average_high integer)\")\n #Populate tables in database:\n cur.executemany(\"INSERT INTO cities VALUES (?,?)\", cities)\n cur.executemany(\"INSERT INTO weather VALUES (?,?,?,?,?)\", weather)\n #Retrieve data from database:\n cur.execute(\"SELECT * FROM cities INNER JOIN weather ON city = name\")\n rows = cur.fetchall()\n cols = [desc[0] for desc in cur.description]\n output_dataframe = pd.DataFrame(rows, columns = cols)\n \n return output_dataframe","def create_tables(cxn):\n\tcursor = cxn.cursor()\n\tcursor.execute(\"DROP TABLE IF EXISTS WEATHER\")\n\tcursor.execute(\"\"\"CREATE TABLE IF NOT EXISTS WEATHER(\n\t\tstate varchar(3),\n\t\tcity varchar (15),\n\t\tobs_date varchar(12),\n\t\thour int,\n\t\tminute int,\n\t\tcurr_temp float,\n\t\tunique(state, city, obs_date, hour, minute)\n\t\t)\"\"\")\n\tcursor.close()","def create_db(temp: list, rain: list, humidity: list, wind: list) -> dict:\r\n weather = {}\r\n for i in range(len(temp)):\r\n weather[i+1] = [temp[i], rain[i], humidity[i], wind[i]]\r\n return weather","def _setupWeather(self, w, config):\n wnames = ('cloud', 'seeing')\n if w not in wnames:\n raise Exception('w should be one of %s' %(wnames))\n filename = config['%s_datafile' %(w)]\n file = open(filename, 'r')\n # Also assume flat file contains only date / value in a space or tab separated file. \n self.dates[w] = []\n self.weather[w] = []\n # Read the data file.\n print '# Reading weather data file %s' %(filename)\n for line in file:\n if line.startswith('#') | line.startswith('!'):\n continue\n self.dates[w].append(line.split()[0])\n self.weather[w].append(line.split()[1])\n file.close()\n self.dates[w] = numpy.array(self.dates[w], float)\n self.weather[w] = numpy.array(self.weather[w], float)\n # Check the total amount of data (mostly for user awareness):\n print '# Read %d weather values from %s file. ' %(len(self.weather[w]), filename)\n # Check that weather data is monotonically increasing in time. \n if not(numpy.all(numpy.diff(self.dates[w]))):\n order = self.dates[w].argsort()\n self.weather[w] = self.weather[w][order]\n self.dates[w] = self.dates[w][order]\n # Get the total length of time included in this (seeing/cloud) file,\n # so that we can determine a wrap-around date if we need that.\n self.maxtime[w] = self.dates[w].max()\n return","def set_tables(self):\n with sql.connect('./{}.db'.format(self.name)) as conn:\n conn.execute(\"\"\"CREATE TABLE IF NOT EXISTS tweets(\n id INTEGER PRIMARY KEY,\n tweet_id INTEGER,\n insert_date TEXT,\n created_at TEXT,\n hashtag TEXT)\n \"\"\")\n\n conn.execute(\"\"\"CREATE TABLE tweet_peaks(\n peak_datetime TEXT NOT NULL,\n hashtag TEXT NOT NULL,\n time_frame TEXT,\n mean REAL,\n std REAL,\n sensibility REAL,\n freq_limit REAL,\n qt_tweets INTEGER,\n id TEXT PRIMARY KEY,\n probability REAL);\n \"\"\")","def load_up_initial_db(self, date_dict):\n df_tot = []\n for chunk in pd.read_sql_table(self.table, self.disk_engine, chunksize=10000, parse_dates=date_dict):\n df_tot.append(chunk)\n self.df = pd.concat(df_tot)","def import_weather(keys):\n # imports weather and cleans\n df_all_weather = get_weather_as_df(keys)\n return clean_weather_df(df_all_weather)","def update_weather(location_request, db):\n with open(expanduser(\"~/bin/my_utilities/config/darksky-key\")) as f:\n ds_key = f.readline().strip()\n current = []\n current_day = 0\n with forecast(ds_key, *location_request, units=\"uk2\") as location:\n raw = location['hourly']['data'][0]\n current.append(datetime.datetime.now().hour)\n current.append(day_relative_to_absolute(current_day))\n current.append(raw[\"temperature\"])\n current.append(raw[\"apparentTemperature\"])\n current.append(raw[\"precipIntensity\"])\n current.append(raw[\"precipProbability\"] * 100)\n current.append(raw[\"humidity\"] * 100)\n current.append(raw[\"dewPoint\"])\n current.append(raw[\"windSpeed\"])\n current.append(raw[\"windBearing\"])\n current.append(raw[\"windGust\"])\n current.append(raw[\"pressure\"])\n current.append(raw[\"cloudCover\"] * 100)\n current.append(raw[\"uvIndex\"])\n current.append(raw[\"visibility\"])\n current = format_list_for_db(current)\n\n columns = [\"hour\", \"day\", \"temp\", \"apptemp\", \"precipint\", \"precipprob\",\n \"humidity\", \"dewpoint\", \"windspeed\", \"windbearing\",\n \"windgust\", \"pressure\", \"cloudcover\", \"uvindex\", \"visibility\"]\n columns = format_list_for_db(columns)\n statement = f\"INSERT INTO WEATHER {columns} VALUES {current}\"\n print(statement)\n cursor = db.cursor()\n cursor.execute(statement)\n cursor.close()","def compute_aggregate_weather_data():\n\n # get a list of all the csv files names in the 'weather_data' directory\n files = get_all_csv_files_in_directory('weather_data')\n\n # Todo: if the number of csv files doesn't match the expected value, unzip remaining using the 'os' module\n\n if len(files) == 0:\n\n # Unzip all files in current directory and subdirectories\n print \"unzipping weather files...\"\n os.system(\"unzip 'weather_data/*.zip' -d weather_data\")\n\n\n # Try again to get files\n files = get_all_csv_files_in_directory('weather_data')\n\n # Throw exception if still missing csv files\n if len(files) == 0:\n raise ValueError(\"Missing weather data in csv format in the 'weather_data' directory\")\n\n # convert the list of csv file names to a list of corresponding DataFrames\n dallas_files = filter(lambda file_name : \"KDAL\" in file_name, files)\n houston_files = filter(lambda file_name : \"KHOU\" in file_name, files)\n san_antonio_files = filter(lambda file_name : \"KSAT\" in file_name, files)\n\n print \"Retrieved weather data files...\"\n print \"\\t# of Dallas weather files found: \", len(dallas_files)\n print \"\\t# of Houston weather files found: \", len(houston_files)\n print \"\\t# of San Antonio weather files found: \", len(san_antonio_files)\n\n dallas_dfs = map(lambda file_name: read_weather_data_from_csv(\"./weather_data/\" + file_name), dallas_files)\n houston_dfs = map(lambda file_name: read_weather_data_from_csv(\"./weather_data/\" + file_name), houston_files)\n san_antonio_dfs = map(lambda file_name: read_weather_data_from_csv(\"./weather_data/\" + file_name), san_antonio_files)\n\n dallas_df = pd.concat(dallas_dfs)\n houston_df = pd.concat(houston_dfs)\n san_antonio_df = pd.concat(san_antonio_dfs)\n\n print \"Aggregating all of the weather data...\"\n # fold the list of data frames into a single data frame\n aggregate_df = reduce(lambda df1, df2: pd.merge(df1, df2, on=\"Date\", how=\"outer\"), [dallas_df, houston_df, san_antonio_df]).sort_values(\"Date\")\n\n return aggregate_df","def populate_db():\n stdout.write('Emptying the tables...\\n')\n empty_tables()\n stdout.write('Populating Language records...\\n')\n populate_language()\n stdout.write('Populating Lemma, Wordform, and Definition records...\\n')\n populate_lexical()\n stdout.write('Populating ProperName records...\\n')\n populate_proper_names()","def populate_stops(self):\n stops = self.load_csv('stops.txt')\n stops = self.process_stops(stops)\n\n connection = db.connect()\n for stop in stops:\n try:\n connection.execute(schema.stops.insert(), stop)\n except DataError:\n print \"Missing data for stop: %s\" % (stop)","def get_weather_data():\n keys = ['1364038.csv',\n '1364041.csv',\n '1364042.csv',\n '1364043.csv',\n '1364044.csv',\n '1364046.csv',\n '1364047.csv',\n '1364048.csv',\n '1364051.csv',\n '1364052.csv',\n '1364053.csv',\n '1364054.csv',\n '1364055.csv',\n '1364058.csv',\n '1364059.csv',\n '1364060.csv',\n '1364061.csv',\n '1364062.csv',\n '1364063.csv',\n '1364064.csv',\n '1364066.csv']\n df_weather = import_weather(keys)\n df_weather_dist = df_weather[[\n 'LATITUDE', 'LONGITUDE', 'name']].drop_duplicates().reset_index()\n return df_weather, df_weather_dist","def collect(self, start_date=None, end_date=None):\n if start_date is None:\n start_date = self.default_start\n if end_date is None:\n end_date = self.default_end\n\n cur = self.conn.cursor()\n\n # Maximum return is 1000 entries\n num_days = 1000 // len(self.stations)\n # Maximum date-range is 1 year\n if num_days > 365:\n num_days = 365\n\n for interval in netzero.util.time_intervals(\n start_date, end_date, days=num_days\n ):\n netzero.util.print_status(\n \"Weather\",\n \"Collecting: {} to {}\".format(\n interval[0].strftime(\"%Y-%m-%d\"), interval[1].strftime(\"%Y-%m-%d\")\n ),\n )\n\n # TODO -- REMOVE ASSUMPTION THAT LEN(DATA) < LIMIT\n raw_data = self.query_api(interval[0], interval[1])\n\n if raw_data is None:\n print(\"ERROR QUERYING API\") # TODO exception here?\n continue\n\n for entry in raw_data.get(\"results\", []):\n # Insert the weather data to the table, to be averaged later\n date = datetime.datetime.strptime(\n entry[\"date\"], \"%Y-%m-%dT%H:%M:%S\"\n ).date()\n value = entry[\"value\"]\n station = entry[\"station\"]\n\n cur.execute(\n \"INSERT OR IGNORE INTO weather VALUES (?, ?, ?)\", (date, value, station)\n )\n\n self.conn.commit()\n\n cur.close()\n\n netzero.util.print_status(\"Weather\", \"Complete\", newline=True)","def setUpClass(cls):\n dt_index = pd.date_range(start=datetime(2019, 1, 1, 0, 1), periods=15,\n freq='1Min')\n\n # Create a temperature array with an average of 2.\n temp = [1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3]\n\n # Create ghi array with an average of 3.\n ghi = [2, 3, 4, 2, 3, 4, 2, 3, 4, 2, 3, 4, 2, 3, 4]\n\n # Create DataFrame.\n cls.weather_data = pd.DataFrame({'temperature': temp, 'ghi': ghi},\n index=dt_index)\n\n # Create expected data.\n dt_index_2 = pd.date_range(start=datetime(2019, 1, 1, 0, 15), periods=1,\n freq='15Min')\n cls.expected_data = pd.DataFrame({'temperature': [2], 'ghi': [3]},\n index=dt_index_2)","def read_weather(self):\n print \"Reading weather data from file\",self.datafile\n tab = ascii.read(self.datafile)\n \n # Fix 'T' values in precipitation column, which represent tiny\n # amounts of rain (not measurable)\n TINY_VALUE = '.005' # 0.005 is half the smallest measurable value\n rain = tab['PrecipitationIn']\n wbad = (rain == 'T')\n rain[wbad] = TINY_VALUE\n rain = numpy.array(rain).astype(\"float\")\n\n # Replace string version of precip with float version\n tab['PrecipIn'] = rain\n tab.remove_column('PrecipitationIn')\n\n self.table = tab","def update(self):\n if self.last_update and (\n self.last_update + timedelta(hours=1)\n > datetime.utcnow().replace(tzinfo=dt_util.UTC)\n ):\n return # Not time to update yet; data is only hourly\n\n for row in self.current_observations():\n if row.get(\"Station\") == self._station_id:\n api_fields = {\n col_heading: (standard_name, dtype)\n for standard_name, (\n _,\n _,\n _,\n col_heading,\n dtype,\n ) in SENSOR_TYPES.items()\n }\n self.data = {\n api_fields.get(col_heading)[0]: api_fields.get(col_heading)[1](\n v.replace(\",\", \".\")\n )\n for col_heading, v in row.items()\n if col_heading in api_fields and v\n }\n break\n else:\n raise ValueError(f\"No weather data for station {self._station_id}\")","def create_table(self):\n # Connect to database\n conn = sqlite3.connect(self)\n # Create a cursor\n c = conn.cursor()\n\n # Create a Table\n c.execute(\"\"\"CREATE TABLE weather (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n sensor text,\n location text,\n temperature real,\n description text,\n time text\n )\"\"\")\n # Commit our command\n conn.commit()\n # Close our connection\n conn.close()","def init_datasets(self, dataset_names, columns):\n for dataset_name in dataset_names:\n hdf5_dataset_name = self.schema.get(dataset_name)\n if hdf5_dataset_name is None:\n warnings.warn(\"Skipping %s (not in schema)\" % dataset_name)\n else:\n self[dataset_name] = tokio.timeseries.TimeSeries(dataset_name=hdf5_dataset_name,\n start=self.query_start,\n end=self.query_end_plusplus,\n timestep=self.timestep,\n num_columns=len(columns),\n column_names=columns,\n sort_hex=self.sort_hex)","def populate(self):\n\n NUM_COUNTRIES = 2 # random.randint(1, 4)\n\n # find a suitable hex\n with Timer(\"Creating initial data\", debug=self.debug):\n\n for i in range(NUM_COUNTRIES):\n country, provinces, pops = create_country(self, self.map)\n country.determine_tax_policy()\n self.countries.append(country)","def testWeatherFetch(self):\n\n timeCol = 'timestamp'\n rows = []\n for row in self.aggregator.rawData(dataType = 'weather',\n orderBy = [timeCol],\n timestampCol = timeCol,\n startDate = self.testStart,\n endDate = self.testEnd):\n rows.append(row)\n self.assertIsNotNone(rows, 'Rows are present.')","def populate_database(self):\n self.insert_products()\n self.insert_categories()\n self.insert_products_categories()\n self.insert_stores()\n self.insert_products_stores()","def create_all_tables(self):\n pass","def populate(table_name, date):\n\tlog_msg3(\"Populando \" + table_name)\n\n\twsq_to_txt(table_name, date)\n\n\t# si es un nuevo año se crea una nueva tabla\n\tif(is_new_year(table_name) and not new_tables_created):\n\t\tcreate_tables()\n\n\ttxt_to_table(table_name)\n\n\tlog_msg_ok3()","def get_gis_historical_data():\n logging.info(\"Generating baseline reference and historical weather data.\")\n \n # Initialising function variables\n fake = Faker()\n geolocator = Nominatim()\n config_data = get_config()\n locations = config_data[\"location\"]\n \n # Check if there are no duplicate locations in the config.yaml file.\n if len(locations) != len(set(locations)):\n logging.error(\"Duplicate location found. Please check config.yaml file.\")\n raise ValueError\n \n # Initialise pandas dataframe column name for baseline reference\n # and historical data.\n df_ref = pd.DataFrame(columns=[\"Location\", \"Latitude\"\n ,\"Longitude\", \"Elevation\"\n ,\"Timezone\"])\n df_hist = pd.DataFrame(columns=[\"Location\", \"Date\"\n ,\"Month\", \"Temperature_Min\"\n ,\"Temperature_Max\", \"Humidity\"\n ,\"Pressure\"])\n \n # Generate weather data for each location.\n for idx, loc in enumerate(locations):\n \n logging.info(\"Retrieving geolocation data for {}.\".format(loc))\n \n # Retrieving geolocation data from geopy library.\n loc_data = geolocator.geocode(loc)\n \n logging.info(\"Check if the location {} is valid.\".format(loc))\n if loc_data is None:\n logging.error(\"Invalid location value supplied ({}). Please check config.yaml file.\".format(loc))\n raise ValueError\n logging.info(\"The location {} is valid.\".format(loc))\n \n city = get_city(loc)\n lat = loc_data.latitude\n lon = loc_data.longitude\n \n # Retrieving elevation data for the location.\n elev = get_elevation_data(lat, lon)\n \n for month in range(1, 13):\n \n logging.info(\"Retrieving {} weather data for month {}.\".format(loc, month))\n \n for sample in range(config_data[\"gis\"][\"sampling_number\"]):\n \n temp_min = None\n temp_max = None\n humidity = None\n pressure = None\n \n while temp_min is None or temp_max is None or humidity is None or pressure is None:\n \n year = random.randint(config_data[\"gis\"][\"year_start\"], config_data[\"gis\"][\"year_end\"])\n\n _, last_day = calendar.monthrange(year, month)\n\n datetime_start = datetime.datetime(year, month, 1)\n datetime_end = datetime.datetime(year, month, last_day)\n\n date_gen = fake.date_time_between_dates(datetime_start=datetime_start\n ,datetime_end=datetime_end)\n\n forecast = forecastio.load_forecast(config_data[\"forecastio_api_key\"]\n ,lat\n ,lon\n ,time=date_gen\n ,units=\"si\")\n\n historical_data = forecast.json[\"daily\"][\"data\"][0]\n \n timezone = forecast.json.get(\"timezone\", None)\n temp_min = historical_data.get(\"temperatureMin\", None)\n temp_max = historical_data.get(\"temperatureMax\", None)\n humidity = historical_data.get(\"humidity\", None) * 100\n pressure = historical_data.get(\"pressure\", None)\n \n df_temp_hist = pd.Series(dict(zip(df_hist.columns\n ,[city, date_gen\n ,date_gen.month, temp_min\n ,temp_max, humidity\n ,pressure])))\n \n df_hist = df_hist.append(df_temp_hist, ignore_index=True)\n \n df_temp_ref = pd.Series(dict(zip(df_ref.columns\n ,[city, lat\n ,lon, elev\n ,timezone])))\n df_ref = df_ref.append(df_temp_ref, ignore_index=True)\n \n logging.info(\"Generating position to consolidate latitude, longitude and elevation data\")\n df_pos = df_ref[[\"Latitude\", \"Longitude\", \"Elevation\"]].round(2)\n df_pos[\"Elevation\"] = df_pos[\"Elevation\"].astype(int) \n df_ref[\"Position\"] = df_pos.astype(str).apply(lambda x: \",\".join(x), axis=1)\n \n logging.info(\"Saving baseline reference data.\")\n df_ref.to_csv(get_file_path(folder_name=\"data\"\n ,subdirectory=config_data[\"gis\"][\"output_subdirectory\"]\n ,file_name=config_data[\"gis\"][\"output_base_reference_file_name\"])\n ,index=False)\n logging.info(\"Completed saving baseline reference data.\")\n\n logging.info(\"Saving baseline historical data.\")\n df_hist.to_csv(get_file_path(folder_name=\"data\"\n ,subdirectory=config_data[\"gis\"][\"output_subdirectory\"]\n ,file_name=config_data[\"gis\"][\"output_base_historical_file_name\"])\n ,index=False)\n logging.info(\"Completed saving baseline historical data.\")","def setupStockTable(self):\n # Get the date\n # NOTE: This is probably un\n date = datetime.date()\n dateStr = date.month() + \"/\" + date.day() + \"/\" + date.year()\n\n stocks = (\"INTC\", \"AAPL\", \"GOOG\", \"YHOO\", \"SYK\", \"VZ\")\n\n for stock in stocks:\n stockObj = self.securityFactory(stock)\n stockObj.queryAPI()\n\n self.stockDB.query(\"INSERT INTO basic_info (ticker, price, daily_change, company, year_high, year_low, \\\n daily_percent, date, streak) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)\", (stockObj.target, stockObj.curr, \\\n stockObj.daily_change, stockObj.company,\\\n stockObj.year_high, stockObj.year_low,\\\n stockObj.daily_percent, dateStr, 0))","def fill_target_table(new_data, curs, conn, overwrite=False):\n for i in new_data:\n connect_database.add_target_to_database(list(i), curs, conn, overwrite_exsiting = overwrite)\n conn.commit()","def create_forecast_dataset(self):\n pass","def fill_dataset(self):\n rm, rstd = self.get_rolling_stats()\n\n self.add_rolling_mean(rm)\n self.add_bollinger_bands(rstd)\n self.add_spy_info()\n self.add_beta_and_sharpe()\n self.add_stlouis_data()","def _update_on_refresh():\n cities = City.query.all()\n\n #Iterates over all cities in the database and updates their value\n for city in cities:\n metric_resp, imperial_resp = _get_open_weather_requests(city.name)\n\n metric_json = metric_resp.json()\n imperial_json = imperial_resp.json()\n\n city.temp_celsius = int(metric_json[MAIN][TEMPERATURE])\n city.temp_fahrenheit = int(imperial_json[MAIN][TEMPERATURE])\n db.session.commit()","def generate_polynesian_weather_data():\n weather_path = os.path.dirname(os.path.realpath(__file__))\n low_fp = weather_path + \"/polynesia_weather/low/1976/\"\n med_fp = weather_path + \"/polynesia_weather/med/1985/\"\n high_fp = weather_path + \"/polynesia_weather/high/1982/\"\n low_name = \"polynesia_1976\"\n med_name = \"polynesia_1985\"\n high_name = \"polynesia_1982\"\n generate_year_weather_data(low_fp, low_name)\n generate_year_weather_data(med_fp, med_name)\n generate_year_weather_data(high_fp, high_name)","def make_tables(self):\n for t in self.tables:\n self.add_table(groupname=t['groupname'],\n tablename=t['tablename'],\n description=t['description'],\n tabletitle=t['tabletitle'])","def run(self):\n\n for table in self.TABLES:\n self.dictionary_cursor.execute(f\"TRUNCATE TABLE {table}_Work\")\n self.dictionary_conn.commit()\n self.logger.info(\"work tables cleared\")\n for id in self.ids:\n drug = self.Drug(self, id)\n if drug.wanted:\n drug.load()\n self.logger.info(\"work tables populated\")\n for table in self.TABLES:\n insert = f\"INSERT INTO {table} SELECT * FROM {table}_Work\"\n self.dictionary_cursor.execute(f\"TRUNCATE TABLE {table}\")\n self.dictionary_cursor.execute(insert)\n self.dictionary_conn.commit()\n self.logger.info(\"live tables ready\")","def set_up_tables(self):\n tables = []\n tables.append({'groupname': 'metadata',\n 'tablename': 'sim_info',\n 'description': desc.SimInfoRow,\n 'tabletitle': 'Simulation Information'})\n tables.append({'groupname': 'metadata',\n 'tablename': 'sim_timeseries',\n 'description': desc.SimTimeseriesRow,\n 'tabletitle': 'Simulation Power Data'})\n tables.append({'groupname': 'th',\n 'tablename': 'th_params',\n 'description': desc.ThMetadataRow,\n 'tabletitle': 'TH Component Parameters'})\n tables.append({'groupname': 'th',\n 'tablename': 'th_timeseries',\n 'description': desc.ThTimeseriesRow,\n 'tabletitle': 'TH Timeseries'})\n tables.append({'groupname': 'neutronics',\n 'tablename': 'neutronics_timeseries',\n 'description': desc.NeutronicsTimeseriesRow,\n 'tabletitle': 'Neutronics Timeseries'})\n tables.append({'groupname': 'neutronics',\n 'tablename': 'neutronics_params',\n 'description': desc.NeutronicsParamsRow,\n 'tabletitle': 'Neutronics Metadata'})\n tables.append({'groupname': 'neutronics',\n 'tablename': 'zetas',\n 'description': desc.ZetasTimestepRow,\n 'tabletitle': 'Neutron Precursor Concentrations'})\n tables.append({'groupname': 'neutronics',\n 'tablename': 'omegas',\n 'description': desc.OmegasTimestepRow,\n 'tabletitle': 'Decay Heat Fractions'})\n return tables","def populate_db(self):\n # Get donors\n log.info(\"Populating donors.\")\n\n self.r.hmset('Thomas', {'donations': '500', 'email': 'thomas@thomas.com', 'city': 'Athens', 'state': 'GA', 'zip': 30606})\n\n self.r.hmset('Ted', {'donations': '1', 'email': 'ted@ted.com', 'city': 'Memphis', 'state': 'TN', 'zip': 38104})\n\n self.r.hmset(\"Bailey\", {'donations': '1000', 'email': 'bailey@bailey.com', 'city': 'Washington', 'state': 'DC', 'zip': 12345})","def weather_data(cities, openweathermap_api_key=openweathermap_api_key):\n L = []\n for c in cities:\n res = requests.get(f'http://api.openweathermap.org/data/2.5/weather?q={c}&appid={openweathermap_api_key}&units=imperial')\n L.append(res.json())\n\n df = pd.DataFrame(L)\n df['lon'] = df['coord'].map(op.itemgetter('lon'))\n df['lat'] = df['coord'].map(op.itemgetter('lat'))\n df['Temprature'] = df['main'].map(op.itemgetter('temp'))\n df['Humidity'] = df['main'].map(op.itemgetter('humidity'))\n df['Wind Speed'] = df['wind'].map(op.itemgetter('speed'))\n return df[['name','lon', 'lat','Temprature','Humidity','Wind Speed']]","def _load_data(self):\n if self._api_response.status_code == 200:\n self._dataset = self._api_response.json()\n self._fill_day_dicts()","def getAllDataFromDirectory(prediction_directory, actual_directory, write_directory, cities_file, utc_offset = False):\n city_dictionary = getCities(cities_file)\n actualGetter = getActualWeather(actual_directory, city_dictionary, get_API_keys())\n #For each day and for each city, get all the data and put it into a spreadsheet.","def setupTempTables(self):\n cur = self.cursor()\n cur.execute(\"PRAGMA temp_store = MEMORY\")\n for table, columns in self.temp_tables.items():\n cur.execute(\"create temporary table if not exists %s (\" % table + (',').join('\"' + key + '\" ' + val for key, val in columns.items()) + ')')","def process_table_init(self):\n logging.debug(\"Processing table initialization, %d entries\",\n len(self.table_initialization))\n\n for init_entry in self.table_initialization:\n for table_name, entry_desc in init_entry.items():\n self.air_table[table_name].add_entry(\n table_entry.description_to_entry(entry_desc))","def get_basic_data(self):\n\n db = DataBase().clear_table()\n\n data = self.scraper.scrape_top_250()\n for d in data:\n title = d.find(\"td\", class_=\"titleColumn\")\n title = title.find(\"a\")\n title = re.sub(\"<.*?>\", \"\", str(title))\n\n film_id = d.find(\"td\", class_=\"watchlistColumn\")\n film_id = film_id.find(\"div\")\n film_id = film_id[\"data-tconst\"]\n\n year = d.find(\"span\", class_=\"secondaryInfo\")\n year = re.sub(\"<.*?>\", \"\", str(year)).replace(\"(\", \"\").replace(\")\", \"\")\n\n director = d.find(\"td\", class_=\"titleColumn\")\n director = director.find(\"a\")\n director = director[\"title\"]\n director, *cast = director.split(\", \")\n director = director.replace(\" (dir.)\", \"\")\n\n rating = d.find(\"td\", class_=\"ratingColumn imdbRating\")\n rating = rating.find(\"strong\")\n rating = re.sub(\"<.*?>\", \"\", str(rating))\n\n poster = d.find(\"td\", class_=\"posterColumn\")\n poster = poster.find(\"img\")[\"src\"]\n poster = re.sub(\"@.+\", \"@._V1_FMjpg_UY474_.jpg\", poster)\n\n DataBase().populate_table(\n (title, film_id, year, director, \", \".join(cast), rating, poster)\n )","def update_data():\n etf_prices = get_prices(start=START_DATE, end=END_DATE)\n etf_returns = compute_returns(etf_prices)\n merged_etf_data = etf_prices.merge(etf_returns, right_index=True, left_index=True)\n indicators = compute_indicators(merged_etf_data) # this uses the \"ta\" lib, but it does not need\n # to be imported\n merged_etf_data = merged_etf_data.merge(indicators, right_index=True, left_index=True)\n vix_data = get_vix()\n data = merged_etf_data.merge(vix_data, right_index=True, left_index=True)\n data.to_csv('Data/database.csv')\n return","def process_weather(forecast_file):\n with open(forecast_file) as json_file:\n json_data = json.load(json_file)\n\n min_temp_store = {}\n max_temp_store = {}\n weather_results = str()\n header_results = str()\n\n for day_in_forecast in json_data['DailyForecasts']:\n day_date = day_in_forecast['Date']\n min_temp = day_in_forecast['Temperature']['Minimum'][\"Value\"]\n min_temp_c = convert_f_to_c(min_temp)\n min_temp_store[day_date] = min_temp_c\n max_temp = day_in_forecast['Temperature']['Maximum'][\"Value\"]\n max_temp_c = convert_f_to_c(max_temp)\n max_temp_store[day_date] = max_temp_c\n\n day_time_phrase = day_in_forecast['Day']['LongPhrase']\n rain_chance_day = day_in_forecast['Day']['RainProbability']\n night_time_phrase = day_in_forecast['Night']['LongPhrase']\n rain_chance_night = day_in_forecast['Night']['RainProbability']\n weather_results = weather_results + (f\"-------- {convert_date(day_date)} --------\\nMinimum Temperature: {format_temperature(round(min_temp_c,1))}\\nMaximum Temperature: {format_temperature(round(max_temp_c,1))}\\nDaytime: {day_time_phrase}\\n Chance of rain: {rain_chance_day}%\\nNighttime: {night_time_phrase}\\n Chance of rain: {rain_chance_night}%\\n\")+ \"\\n\"\n\n\n max_day = max(max_temp_store, key=max_temp_store.get)\n max_value = max_temp_store[max_day]\n min_day = min(min_temp_store, key=min_temp_store.get)\n min_value = min_temp_store[min_day]\n max_totals = (sum(max_temp_store.values()))\n min_totals = (sum(min_temp_store.values()))\n num_items = len(min_temp_store)\n mean_min = round(calculate_mean(min_totals,num_items),1)\n mean_max = round(calculate_mean(max_totals,num_items),1)\n\n save_header = (f\"{len(json_data['DailyForecasts'])} Day Overview\\n The lowest temperature will be {format_temperature(round((min_value),1))}, and will occur on {convert_date(min_day)}.\\n The highest temperature will be {format_temperature(round((max_value),1))}, and will occur on {convert_date(max_day)}.\\n The average low this week is {format_temperature(mean_min)}.\\n The average high this week is {format_temperature(mean_max)}.\\n\")\n\n header_results = save_header + \"\\n\"+ weather_results\n \n return(header_results)","def _populate_zone_facts_table(self):\n census_fields = [\n 'poverty_rate', 'fraction_black', 'income_per_capita',\n 'labor_participation', 'fraction_foreign',\n 'fraction_single_mothers', 'acs_lower_rent_quartile',\n 'acs_median_rent', 'acs_upper_rent_quartile'\n ]\n\n zone_types = ['ward', 'neighborhood_cluster', 'census_tract']\n\n query_results = list()\n\n # populate columns accordingly for each zone_specific type\n for zone_type in zone_types:\n field_values = dict()\n\n # get field value for each zone_specific type\n for field in census_fields:\n result = self._census_with_weighting(data_id=field,\n grouping=zone_type)\n field_values[field] = result['items']\n\n zone_specifics = self._get_zone_specifics_for_zone_type(zone_type)\n\n # TODO: add aggregate for each zone_type into table\n for zone in zone_specifics:\n # get not None values so we can added to db\n columns = list()\n values = list()\n for field in census_fields:\n zone_value = field_values[field][zone]\n\n if zone_value is not None:\n columns.append(field)\n values.append(\"'\" + str(zone_value) + \"'\")\n\n # derive column and values strings needed for sql query\n columns = ', '.join(columns)\n columns = 'zone, ' + columns\n\n values = ', '.join(values)\n values = \"'\" + zone + \"', \" + values\n\n q = \"INSERT INTO zone_facts ({cols}) VALUES ({vals})\".format(\n cols=columns, vals=values)\n\n with self.engine.connect() as conn:\n result = conn.execute(q)\n query_results.append(result)\n\n return query_results","def init_tables(self) -> None:\n with self.table_access_condition:\n conn = self._get_connection()\n conn.execute(\"PRAGMA foreign_keys = 1\")\n c = conn.cursor()\n c.execute(tables.CREATE_STUDIES_TABLE)\n c.execute(tables.CREATE_SUBMISSIONS_TABLE)\n c.execute(tables.CREATE_REQUESTERS_TABLE)\n c.execute(tables.CREATE_UNITS_TABLE)\n c.execute(tables.CREATE_WORKERS_TABLE)\n c.execute(tables.CREATE_RUNS_TABLE)\n c.execute(tables.CREATE_RUN_MAP_TABLE)\n c.execute(tables.CREATE_PARTICIPANT_GROUPS_TABLE)\n c.execute(tables.CREATE_PARTICIPANT_GROUP_QUALIFICATIONS_MAPPING_TABLE)\n conn.commit()","def populate_table(database, table, data):\n\n for row in data:\n database.session.add(table(row))\n database.session.commit()","def create_table(self):\n c = self.conn.cursor()\n c.execute(\"CREATE TABLE sensor_data (mac text, name text, temperature real, light integer, moisture real, conductivity real, battery real, ts_utc int, date_iso text, firmware text )\")","def populate_locations(connection):\n print('Populating locations...')\n cursor = connection.cursor()\n with open(get_data_path('locations', 'locations.json'), 'r', encoding='utf-8') as json_file:\n locations = json.load(json_file)\n\n for station_id, location in locations.items():\n cursor.execute(f'''SELECT id \n FROM watercourse_stations \n WHERE id = {station_id}''')\n\n if len(cursor.fetchall()):\n cursor.execute(f'''INSERT INTO locations(name, lat, lng)\n VALUES ('{location['name']}', {location['lat']}, {location['lng']})''')\n cursor.execute(f'''UPDATE watercourse_stations\n SET location_id = {cursor.lastrowid}\n WHERE id = {station_id}''')","def make_weather_features(self, timeline_dt_list):\n\n print \"Making weather features...\"\n\n N_FEATURES = 2\n n_examples = len(timeline_dt_list)\n XX = numpy.zeros((n_examples, N_FEATURES))\n indices = numpy.zeros(n_examples,dtype='int')\n ind_weatherday = 0\n\n # Loop over all times in the timeline\n for ii, time in enumerate(timeline_dt_list):\n # Find where this time in the timeline matches the date\n # of some weather data.\n jj = ind_weatherday\n while time.date() != self.datetimes[jj].date():\n # Make sure jj does not get too large to be an index to\n # the list.\n # Note this is probably a bad idea to do it this way.\n if jj == len(self.datetimes)-1:\n break\n jj += 1\n## print jj\n\n ind_weatherday = jj\n indices[ii] = ind_weatherday\n\n# XX[ii, 0] = self.table['PrecipIn'][ind_weatherday]\n# XX[ii, 1] = self.table['Mean TemperatureF'][ind_weatherday]\n## XX[ii, 2] = self.table['MeanDew PointF'][ind_weatherday]\n\n XX[:,0] = self.table['PrecipIn'][indices]\n XX[:,1] = self.table['Mean TemperatureF'][indices]\n self.weather_features = XX\n return XX","def insert_entities_staging(self):\n\n for year in range(1900, CURRENT_YEAR, YEARS_RANGE):\n self.load_wikidata(\"movies\", MOVIES_BY_YEAR_SPARQL_QUERY, INSERT_MOVIE_SQL_QUERY, INSERT_MOVIE_MAP_COLUMNS, year, YEARS_RANGE)\n\n self.load_wikidata(\"songs\", SONGS_BY_YEAR_SPARQL_QUERY, INSERT_SONG_SQL_QUERY, INSERT_SONG_MAP_COLUMNS, year, YEARS_RANGE, True)\n self.load_wikidata(\"tvshows\", TVSHOWS_SPARQL_QUERY, INSERT_TVSHOW_SQL_QUERY, INSERT_TVSHOW_MAP_COLUMNS)\n self.load_wikidata(\"animatedmovies\", ANIMATEDMOVIES_SPARQL_QUERY, INSERT_ANIMATEDMOVIE_SQL_QUERY,\n INSERT_ANIMATEDMOVIE_MAP_COLUMNS)\n self.load_wikidata(\"videogames\", VIDEOGAMES_SPARQL_QUERY, INSERT_VIDEOGAME_SQL_QUERY, INSERT_VIDEOGAME_MAP_COLUMNS)\n self.load_wikidata(\"books\", BOOKS_SPARQL_QUERY, INSERT_BOOK_SQL_QUERY, INSERT_BOOK_MAP_COLUMNS)","def populate_dyn(self, table):\n myrow = table.row\n myrow[\"sample_time\"] = int(time.time() - glob.base_time)\n myrow[\"available_bike_stands\"] = self.available_bike_stands\n myrow[\"available_bikes\"] = self.available_bikes\n myrow[\"last_update\"] = self.last_update\n myrow[\"status\"] = self.status\n myrow.append()\n table.flush()","def to_WTH_converter(self, weather_data, dest_dir):\n ds_all = weather_data.get_global_dataset()\n if self.country is None:\n print(\"Country given is erroneous:\")\n return\n elif self.country == \"globe\":\n lon_num_start = 0\n lon_num_stop = weather_data.get_num_of_attribute('longitude')\n lat_num_start = 0\n lat_num_stop = weather_data.get_num_of_attribute('latitude')\n else:\n lon_num_start, lon_num_stop, lat_num_start, lat_num_stop = weather_data.get_country_boundary(self.country)\n\n\n # top bottom, left to right\n lon_num_start = 397\n lat_num_start = 184\n for lon_i in range(lon_num_start, lon_num_stop + 1):\n # for lon_i in range(lon_num_start, lon_num_stop+1):\n lon = ds_all.longitude.isel(longitude=lon_i).values.tolist()\n\n for lat_i in range(lat_num_start, lat_num_stop+1):\n # for lat_i in range(lat_num_start, lat_num_stop + 1):\n lat = ds_all.latitude.isel(latitude=lat_i).values.tolist()\n\n # create a dynamic header with updated LON, LAT info and move it into the folder given\n wth_header_u = ut.format_header(lat_i + 1, lon_i + 1, lat, lon)\n wth_header = dest_dir + \"/\" + wth_header_u\n shutil.move(wth_header_u, wth_header)\n\n # open in appending mode\n fwth = open(wth_header, \"a+\")\n\n # loop through daily weather data\n for t, date in enumerate(self.years):\n daily_data_vars = ut.get_daily_data_vars(ds_all, lat_i, lon_i, t)\n # disregard all NAN values\n if daily_data_vars is None:\n fwth.close()\n os.remove(wth_header)\n break\n\n if t == 0:\n ut.update_table(wth_header_u, lat, lon)\n\n entry = ut.format_data_vars_entry(daily_data_vars, date)\n\n # append this entry into the file\n fwth.write(entry)\n print(\"Added entry:\", entry)\n\n # close file after writing\n fwth.close()\n print(\"Output WTH:\", wth_header)","def setUpClass(cls):\n cls.w = pd.read_csv(_df.WEATHER_TWO_WEEK, index_col='time',\n parse_dates=True)","def _perform_data_conversion(self):\n self.data = []\n items = 0\n for value in self.elements_to_convert:\n try:\n location = parse_int(value.get('location_id'), nullable=False)\n if not value.get('list', []):\n continue\n for obs in value['list']:\n items += 1\n # Setting timezone to pytz.UTC FIXES [BUG-039].\n timestamp = parse_date_utc(obs.get('dt') * 1000)\n date = timestamp.date()\n time = timestamp.time()\n temperature = parse_int(obs['main'].get('temp'))\n pressure = parse_float(obs['main'].get('pressure'))\n humidity = parse_int(obs['main'].get('humidity'))\n wind_speed = parse_int(obs.get('wind', {}).get('speed'))\n wind_degrees = parse_int(obs.get('wind', {}).get('deg'))\n wind_direction = compute_wind_direction(wind_degrees)\n weather = obs.get('weather', [{}])[0]\n if weather.get('icon') and weather.get('id'):\n weather = - parse_int(weather.get('id'), nullable=False) if 'n' in weather['icon'] else \\\n parse_int(weather.get('id'), nullable=False)\n self.data.append(WeatherForecastObservation(location_id=location, date=date, time=time,\n temperature=temperature, pressure=pressure, humidity=humidity, wind_speed=wind_speed,\n wind_degrees=wind_degrees, wind_direction=wind_direction, weather_id=weather))\n except (ValueError, AttributeError, KeyError, IndexError, TypeError):\n _id = value.get('_id', 'Unknown ID')\n self.logger.exception('An error occurred while parsing data. WeatherForecastObservation with ID \"%s\" '\n 'will not be converted.' % _id)\n self.state['elements_to_convert'] = items","def fill_table_for_ETF(conn):\n baseurl = f\"https://financialmodelingprep.com/api/v3/etf/list\"\n params = {\"apikey\": FMP_API_KEY}\n etfs = make_request(baseurl=baseurl, params=params)\n for etf in etfs:\n insert_etf(conn, etf[\"symbol\"], etf[\"name\"], etf[\"exchange\"])","def initial_db_setup() -> None:\n db_filename = \"twdft.db\"\n db_path = os.path.join(TWDFT_DATA_DIR, db_filename)\n csv_filename = \"sites.csv\"\n csv_path = os.path.join(TWDFT_DATA_DIR, csv_filename)\n db_is_new = not os.path.exists(db_path)\n sites_csv = os.path.join(TWDFT_DATA_DIR, csv_filename)\n\n if db_is_new:\n with sqlite3.connect(db_path) as conn:\n c = conn.cursor()\n\n # first we create a site object\n c.execute(\n \"\"\"\n CREATE TABLE site(\n id INTEGER PRIMARY KEY,\n name TEXT,\n site_type TEXT,\n sub_category TEXT,\n address_1 TEXT,\n address_2 TEXT,\n town TEXT,\n county TEXT,\n country TEXT,\n postcode TEXT,\n site_category TEXT,\n freq_target TEXT,\n created TEXT,\n notes TEXT,\n last_inspection TEXT,\n next_inspection TEXT,\n pfsp_approval TEXT,\n pfsp_expiry TEXT,\n unlocode TEXT,\n pfso TEXT,\n pso TEXT,\n pfsa_approval TEXT,\n pfsa_expiry TEXT,\n team TEXT,\n created_by TEXT,\n last_updated TEXT,\n updated_by TEXT,\n afp_loc TEXT,\n rdf TEXT,\n classification TEXT,\n article24 TEXT,\n psa_approval TEXT,\n inspection_due TEXT\n )\n \"\"\"\n )\n conn.commit()\n\n # next we want an inspection table\n\n c.execute(\n \"\"\"\n CREATE TABLE inspection(\n id INTEGER PRIMARY KEY,\n site INTEGER,\n date TEXT,\n status TEXT,\n time TEXT,\n FOREIGN KEY(site) REFERENCES site(id)\n )\n \"\"\"\n )\n conn.commit()\n\n # next we want an inspector table\n c.execute(\n \"\"\"\n create table inspector(\n id integer primary key,\n first_name text,\n last_name text\n )\n \"\"\"\n )\n conn.commit()\n\n for i in INSPECTORS:\n first = i.split(\" \")[0]\n last = i.split(\" \")[1]\n c.execute(\n \"INSERT INTO inspector(first_name, last_name) VALUES (?,?)\",\n (first, last),\n )\n\n # a table that links inspectors with inspections\n c.execute(\n \"\"\"\n CREATE TABLE inspector_inspections(\n inspector INTEGER,\n inspection INTEGER,\n FOREIGN KEY (inspector) REFERENCES inspector(id),\n FOREIGN KEY (inspection) REFERENCES inspection(id)\n )\n \"\"\"\n )\n conn.commit()\n\n for site in map(Site._make, csv.reader(open(csv_path, \"r\"))):\n try:\n c.execute(\n f\"\"\"\n INSERT INTO site VALUES(?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)\"\"\",\n (\n int(site.id.replace(\",\", \"\")),\n site.name,\n site.site_type,\n site.sub_category,\n site.address_1,\n site.address_2,\n site.town,\n site.county,\n site.country,\n site.postcode,\n site.site_category,\n site.freq_target,\n site.created,\n site.notes,\n site.last_inspection,\n site.next_inspection,\n site.pfsp_approval,\n site.pfsp_expiry,\n site.unlocode,\n site.pfso,\n site.pso,\n site.pfsa_approval,\n site.pfsa_expiry,\n site.team,\n site.created_by,\n site.last_updated,\n site.updated_by,\n site.afp_loc,\n site.rdf,\n site.classification,\n site.article24,\n site.psa_approval,\n site.inspection_due,\n ),\n )\n except sqlite3.IntegrityError as e:\n print(\"That hasnae worked\", site.inspection_due)","def prepare_train_data(self):\r\n ## Impute rlkpis\r\n print(\"Imputing rlKPI df\")\r\n self.rlkpi.add_target_labels(1)\r\n self.rlkpi.impute_rl_kpis()\r\n\r\n print(\"Add 'met-real-station_no' & met-forecast-station_no to rl_kpis_df\")\r\n self.add_met_real_forecast_station_col_to_rlkpis()\r\n print(\"Merge 'met-real-sampled df to rl kps \")\r\n self.merge_met_real_sampled_df_to_rlkpis()\r\n\r\n ## Imputations for met-forecast\r\n print(\"Impute met-forecast\")\r\n met_forecast_obj = self.metfcast\r\n met_forecast_obj.impute_met_forecast()\r\n\r\n #Merge met forecast data to earlier merged data\r\n print(\"Merge Train data with imputed forecast df\")\r\n self.train_data = pd.merge(self.train_data,\r\n met_forecast_obj.imputed_forecast_df,\r\n on=['datetime-station_no'], indicator=True, how='inner')\r\n print(\"Check any imputation needed\", self.train_data.isna().sum().sum())\r\n self.train_data.drop(['_merge'], axis=1, inplace=True)\r\n self.perform_data_under_sampling(self.train_data)","def process_weather(forecast_file):\n # Load json data file\n \n with open(forecast_file) as json_file:\n json_data = json.load(json_file)\n \n # Set Variables, Dictionaries and Lists\n days_list = []\n temp_dict = {}\n daily_dict = {}\n\n num_items = 0\n total_sum_min = 0\n total_sum_max = 0\n days = len(json_data['DailyForecasts'])\n days_list = days_in_data(days)\n\n t_temp_min = 100\n t_temp_max = 0\n\n # Pull through the data\n\n for day in days_list:\n num_items += 1\n date = convert_date(json_data['DailyForecasts'][day]['Date'])\n min_temp = convert_f_to_c(json_data['DailyForecasts'][day]['Temperature']['Minimum']['Value'])\n total_sum_min += min_temp\n max_temp = convert_f_to_c(json_data['DailyForecasts'][day]['Temperature']['Maximum']['Value'])\n total_sum_max += max_temp\n day_desc = json_data['DailyForecasts'][day]['Day']['LongPhrase']\n chance_rain_day = json_data['DailyForecasts'][day]['Day']['RainProbability']\n night_desc = json_data['DailyForecasts'][day]['Night']['LongPhrase']\n chance_rain_night = json_data['DailyForecasts'][day]['Night']['RainProbability']\n \n if min_temp < t_temp_min:\n t_temp_min = min_temp\n t_temp_mindate = date\n else:\n pass\n if max_temp > t_temp_max:\n t_temp_max = max_temp\n t_temp_maxdate = date\n else:\n pass\n \n daily_dict[day] = [date, min_temp, max_temp, day_desc, chance_rain_day, night_desc, chance_rain_night]\n # 0 1 2 3 4 5 6 \n \n # print(temp_dict)\n # print(daily_dict)\n\n # Calculate Minimum, Maximum and Mean temperatures\n\n mean_min = format_temperature(calculate_mean(total_sum_min, num_items))\n # print(mean_min)\n mean_max = format_temperature(calculate_mean(total_sum_max, num_items))\n # print(mean_max)\n\n # Format Minimum and Maximum temperatures\n min_temp_format = format_temperature(t_temp_min)\n max_temp_format = format_temperature(t_temp_max)\n\n ##############################################################################################\n\n # Combine string messages to return to user\n\n str_Output = \"\"\n Output_gen1 = (f\"{num_items} Day Overview\\n\")\n Output_gen2 = (f\" The lowest temperature will be {min_temp_format}, and will occur on {t_temp_mindate}.\\n\")\n Output_gen3 = (f\" The highest temperature will be {max_temp_format}, and will occur on {t_temp_maxdate}.\\n\")\n Output_gen4 = (f\" The average low this week is {mean_min}.\\n\")\n Output_gen5 = (f\" The average high this week is {mean_max}.\\n\")\n str_Output = Output_gen1 + Output_gen2 + Output_gen3 + Output_gen4 + Output_gen5\n for key, value in daily_dict.items():\n Output_daily0 = (\"\\n\")\n Output_daily1 = (f\"-------- {value[0]} --------\\n\")\n Output_daily2 = (f\"Minimum Temperature: {format_temperature(value[1])}\\n\")\n Output_daily3 = (f\"Maximum Temperature: {format_temperature(value[2])}\\n\")\n Output_daily4 = (f\"Daytime: {value[3]}\\n\")\n Output_daily5 = (f\" Chance of rain: {value[4]}%\\n\")\n Output_daily6 = (f\"Nighttime: {value[5]}\\n\")\n Output_daily7 = (f\" Chance of rain: {value[6]}%\\n\")\n str_Output = str_Output + Output_daily0 + Output_daily1 + Output_daily2 + Output_daily3 + Output_daily4 + Output_daily5 + Output_daily6 + Output_daily7\n str_Output = str_Output +\"\\n\"\n\n return str_Output","def generate_database_from_metadatas(metadata_dict, stimulus_dict):\n # Create empty database from template class\n indexes = sorted(metadata_dict.keys())\n database = pd.DataFrame(index=indexes, columns=['Number', 'Metadata', 'Tracking', 'Registration', 'Stimuli'])\n\n # Fill in metadata from the dictionary\n for sessname, metadata in sorted(metadata_dict.items()):\n database['Metadata'][sessname] = metadata\n database['Number'][sessname] = metadata['number']\n\n for sessname, stimulus in sorted(stimulus_dict.items()):\n database['Stimuli'][sessname] = stimulus\n\n print(colored('Database initialized.','yellow'))\n return database","def load_dwh_tables(self):\n print(\"Loading the creative works table\")\n self.cur.execute(dwh_queries.INSERT_CREATIVE_WORKS_SQL_QUERY)\n self.conn.commit()\n\n print(\"Loading the participations table\")\n\n self.cur.execute(dwh_queries.INSERT_PARTICIPATIONS_SQL_QUERY)\n self.conn.commit()","def populate_table(self, data):\n\n db = self.connection(database=\"imdb\")\n\n try:\n cur = db.cursor()\n sql = \"\"\"\n INSERT INTO film (title, film_id, year, director, cast, rating, poster_url) \n VALUES (%s, %s, %s, %s, %s, %s, %s);\n \"\"\"\n cur.execute(sql, data)\n db.commit()\n except:\n print(\"An error occurred when saving the data!\")\n\n db.close()","def testWeatherAggregation(self):\n\n rowCnt = 0\n agg = self.aggregator.aggregatedData(dataType = 'weather',\n aggregationType = 'agg_weather',\n timeColumnName = 'timestamp',\n subkeyColumnName = None,\n startDate = self.testStart,\n endDate = self.testEnd)\n for row in agg.data:\n print '%d: %s' % (rowCnt, row)\n rowCnt += 1\n self.assertEqual(rowCnt, 1, 'Row count not correct.')\n\n self.logger.log('agg cols: %d' % len(agg.columns))\n self.assertEqual(len(agg.columns), 3, 'Weather columns not equal to 3.')\n self.aggregator.insertAggregatedData(agg = agg)","def _get_information(self):\n weather_dict = {}\n table_body = self.climate_table\n\n rows = table_body.find_all('tr')\n months = [col.get_text() for col in rows[0].find_all('td')[1:]]\n\n for row in rows[1:]:\n cols = row.find_all('td')\n key = cols[0].get_text()\n value_getter = self._value_getters_by_key.get(key, self._get_remote_workers)\n\n weather_dict.update({key: [(months[i],) + value_getter(col) for i, col in enumerate(cols[1:])]})\n\n return weather_dict","def fill_obs(self, observation_table, data_store):\n for obs in observation_table:\n events = data_store.obs(obs_id=obs['OBS_ID']).events\n\n # TODO: filter out (mask) possible sources in the data\n # for now, the observation table should not contain any\n # run at or near an existing source\n\n self.counts_cube.fill_events([events])\n self.livetime_cube.data += events.observation_live_time_duration","def database_setup():\n Base.metadata.create_all(bind=engine)\n db = LocalSession()\n try:\n populate_from_json(db, Vector, str(VECTORS))\n populate_from_json(db, Gender, str(GENDERS))\n populate_from_json(db, Tag, str(TAGS))\n finally:\n db.close()","def _load_db(self):\n for type_ in self._types:\n try:\n type_.table(self._metadata)\n except InvalidRequestError:\n pass\n # Reflect metadata so auto-mapping works\n self._metadata.reflect(self._engine)\n # Make sure the tables exist\n self._metadata.create_all()","def _db_init_data_tables(self):\n\n #\n # TESTTYPE table\n #\n return self._db_execute(\n \"\"\"\n create table TESTTYPE (\n KEY text unique,\n VALUE text\n )\n \"\"\"\n )","def weather_script(\n init_data_path: str,\n output_path: str,\n workers: int,\n weatherAPI_rpm: int,\n geoAPI_rpm: int,\n) -> None:\n unzip(init_data_path, output_path)\n top_hotels_dataframe_without_addresses = primary_data_proc(output_path)\n geocoder = geocoder_setup(geoAPI_rpm)\n top_hotels_df_with_addresses = define_address(\n top_hotels_dataframe_without_addresses,\n workers,\n geocoder,\n )\n cities, countries, coordinates = city_center_coord(top_hotels_df_with_addresses)\n weather_df = pd.concat(\n [\n prev_weather(cities, countries, coordinates, workers, weatherAPI_rpm),\n forecast_weather(cities, countries, coordinates, workers, weatherAPI_rpm),\n ]\n )\n\n logger.info(\"Start to save results\")\n save_main_info(output_path, weather_df, top_hotels_df_with_addresses)\n logger.info(\"Finish\")","def get_usda_food_data (connection):\n\n tables = ['usda_food_access_feb2014', 'usda_food_assistance_feb2014',\n 'usda_food_health_feb2014', 'usda_food_insecurity_feb2014',\n 'usda_food_stores_feb2014']\n\n for table in tables:\n if table == tables[0]:\n sql_query = \"\"\"\nSELECT \"FIPS\",\n\"PCT_LACCESS_POP10\" AS \"low_access_food_pct10\",\n\"PCT_LACCESS_LOWI10\" AS \"low_access_food_low_inc_pct10\",\n\"PCT_LACCESS_SENIORS10\" AS \"low_access_food_snr_pct10\",\n\"PCT_LACCESS_HHNV10\" AS \"low_access_food_no_car_pct10\"\nFROM {0};\n \"\"\".format (table)\n # print (sql_query)\n elif table == tables[1]:\n sql_query = \"\"\"\nSELECT \"FIPS\",\n\"REDEMP_SNAPS12\" AS \"snap_redemp_per_store_2012\"\nFROM {0};\n \"\"\".format (table)\n # print (sql_query)\n elif table == tables[2]:\n sql_query = \"\"\"\nSELECT \"FIPS\",\n\"PCT_DIABETES_ADULTS10\" AS \"pct_diabetes_adults_2010\",\n\"PCT_OBESE_ADULTS13\" AS \"pct_obese_adults_2013\",\n\"RECFACPTH12\" AS \"rec_fac_2012\",\n\"NATAMEN\" AS \"ers_nat_amenity_index_1999\"\nFROM {0};\n \"\"\".format (table)\n # print (sql_query)\n elif table == tables[3]:\n sql_query = \"\"\"\nSELECT \"FIPS\",\n\"FOODINSEC_10_12\" AS \"food_insec_house_pct_10_12\",\n\"VLFOODSEC_10_12\" AS \"very_low_food_insec_house_pct_10_12\"\nFROM {0};\n \"\"\".format (table)\n # print (sql_query)\n elif table == tables[4]:\n sql_query = \"\"\"\nSELECT \"FIPS\",\n\"GROCPTH12\" AS \"grocery_pct10\"\nFROM {0};\n \"\"\".format (table)\n # print (sql_query)\n\n if table == tables[0]:\n data = pd.read_sql_query(sql_query, con)\n data.where ((pd.notnull (data)), other=np.nan, inplace=True)\n data = data.dropna (subset=['FIPS'])\n data['FIPS'] = data['FIPS'].apply (lambda x: str(x).zfill (5))\n else:\n data_tmp = pd.read_sql_query(sql_query, con)\n data_tmp.where ((pd.notnull (data_tmp)), other=np.nan, inplace=True)\n data_tmp = data_tmp.dropna (subset=['FIPS'])\n data_tmp['FIPS'] = data_tmp['FIPS'].apply (lambda x: str(x).zfill (5))\n data = pd.merge (data, data_tmp, on=\"FIPS\", how=\"left\")\n\n return (data)","def set_data(self):\n # take care of samples\n patients = self.samples.iloc[:,1].tolist()\n samples = self.samples.iloc[:,0].tolist()\n self.samples = pd.DataFrame(patients,index = samples,columns = ['patient']) # indexed by sample\n #\n # take care of expression data\n cols = self.expression.SYMBOL.tolist() # set new column names to transposed expression_data \n \n new_exp = self.expression.T.ix[1:,:] # transpose\n new_exp.columns = cols\n self.expression = new_exp # add columns\n self.data = pd.merge(self.expression,self.samples,left_index = True,right_index=True) # merged data sets\n #pd.merge(df1,df2,how = 'left',left_index=True,right_index=True) # do a left join","def set_training_data(self):\n # Optional training data period\n # TODO: add training data period feature to training data query\n if not self.training_period == None:\n training_period_date = (datetime.datetime.utcnow() - timedelta(minutes=self.training_period)).strftime(\"%Y-%m-%d\")\n print(f\"Training data start date: {training_period_date}\")\n # Extract queried data from Athena\n #athena = athena_connect.Athena()\n #features_df = athena.pandas_read_athena(self.training_data_sql)\n with open('feature_sql.txt', 'w') as f:\n print(self.training_data_sql, file=f) \n features_df = pd.read_sql(self.training_data_sql, self.logic_db_engine())\n features_df.fillna(0, inplace=True)\n print(features_df.shape)\n features_df = features_df[max(self.feature_minutes_list):]\n print(features_df.shape)\n # Remove infinity string\n features_df.replace({'Infinity': 0}, inplace=True)\n # Convert all object fields to numeric except date fields\n object_col_list = features_df.columns[features_df.dtypes.eq('object')]\n object_col_list = [col for col in object_col_list if 'trade_date' not in col]\n features_df[object_col_list] = features_df[object_col_list].apply(pd.to_numeric, errors='coerce')\n self.training_df = features_df","def fill_table(info):\n # extrac attributes from info struct\n data = info[\"data\"]\n table = info[\"table\"]\n header = info[\"header\"]\n row_num = info[\"row_num\"]\n\n currency_type_num = row_num - 1\n row_index = 0\n col_index = 0\n i = 0\n while i < len(data):\n if data[i].find(\"%\") > 0:\n # stat data\n while i < len(data) and row_index < currency_type_num:\n table[row_index+1].append(data[i])\n row_index += 1\n i += 1\n # Reset row_index\n row_index = 0\n else:\n if i < row_num - 1:\n # currency Type\n table[i+1].append(data[i])\n else:\n # time marker\n if data[i] != header:\n table[0].append(data[i])\n i += 1\n\n # End loop\n return None","def init():\n database.create_tables([Tracker])\n database.commit()","def filldf(tfinal):\n getbasics(tfinal)\n # getusermentions(tfinal)\n getretweets(tfinal)\n getinreply(tfinal)\n return tfinal","def forecast_weather(self):\n pass","def create_tables(cur, country_json, xml_state, body_json):\n print(\"Creating the 3 first tables...\")\n cur.execute('CREATE TABLE IF NOT EXISTS country_purchases(idx INTEGER PRIMARY KEY, state TEXT, amount INTEGER)')\n with open(\"country_purchases.csv\", 'r') as f:\n for idx, line in enumerate(f.read().split('\\n')):\n line_splt = line.split(',')\n try:\n cur.execute('INSERT INTO country_purchases VALUES(\"%s\", \"%s\", \"%s\")' % (idx, line_splt[0], line_splt[1]))\n except IndexError:\n pass\n cur.execute('CREATE TABLE IF NOT EXISTS country_total_purchases(idx INTEGER PRIMARY KEY, state TEXT, amount INTEGER)')\n with open(\"country_total_purchases.csv\", 'r') as f:\n for idx, line in enumerate(f.read().split('\\n')):\n line_splt = line.split(',')\n try:\n cur.execute('INSERT INTO country_total_purchases VALUES(\"%s\", \"%s\", \"%s\")' % (idx, line_splt[0], line_splt[1]))\n except IndexError:\n pass\n cur.execute('CREATE TABLE IF NOT EXISTS country_albums(ID INTEGER PRIMARY KEY, state TEXT, year INTEGER, genre TEXT, album TEXT, amount INTEGER)')\n for idx, album in enumerate(country_json[body_json['state']]):\n cur.execute('INSERT INTO country_albums VALUES (\"%s\", \"%s\", \"%s\", \"%s\", \"%s\", \"%s\")' %\n (idx, body_json['state'], body_json['year'], body_json['genre'], album,\n xml_state[0][0][idx].text))","def init_tables(self) -> None:\n # TODO(#93) maybe raise flag when the schema of existing tables isn't what we expect\n # it to be?\n # \"How to know that schema changes?\"\n # logger.warning(\"some message\")\n with self.table_access_condition:\n conn = self._get_connection()\n conn.execute(\"PRAGMA foreign_keys = 1\")\n with conn:\n c = conn.cursor()\n c.execute(CREATE_PROJECTS_TABLE)\n c.execute(CREATE_TASKS_TABLE)\n c.execute(CREATE_REQUESTERS_TABLE)\n c.execute(CREATE_TASK_RUNS_TABLE)\n c.execute(CREATE_ASSIGNMENTS_TABLE)\n c.execute(CREATE_UNITS_TABLE)\n c.execute(CREATE_WORKERS_TABLE)\n c.execute(CREATE_AGENTS_TABLE)\n c.execute(CREATE_QUALIFICATIONS_TABLE)\n c.execute(CREATE_GRANTED_QUALIFICATIONS_TABLE)\n c.execute(CREATE_ONBOARDING_AGENTS_TABLE)","def get_weather(start: str, end: str) -> Generator[Dict[str, str], None, None]:\n speeds = get_speeds(start, end)\n for data in get_temperatures(start, end):\n data.update(next(speeds))\n yield data","def create_final_table(conn, county):\r\n for county in county:\r\n query = f\"SELECT name FROM sqlite_master WHERE type ='table' AND name NOT LIKE 'sqlite_%' AND name = '{county}'\"\r\n result = execute_query(conn, query)\r\n try:\r\n if len(result) == 0:\r\n query = f\"create table {county} as select * from {county}_stg;\"\r\n execute_query(conn, query)\r\n\r\n load_final_table(conn, county)\r\n except Exception as e:\r\n print(f\"This query {query} failed with exception {e}\")","def load_data(city, month, day):\n\n if city == 'all':\n df = pd.read_csv(CITY_DATA['chicago'])\n df['city'] = 'chicago'\n ny = pd.read_csv(CITY_DATA['new york city'])\n ny['city'] = 'new york city'\n df = df.append(ny,sort = True)\n wa = pd.read_csv(CITY_DATA['washington'])\n wa['city'] = 'washington'\n df = df.append(wa,sort = True)\n else:\n df = pd.read_csv(CITY_DATA[city])\n df['city'] = CITY_DATA[city]\n\n df['Start Time'] = pd.to_datetime(df['Start Time']) #converts Start Time to datetime\n df['End Time'] = pd.to_datetime(df['End Time']) #converts End Time to datetime\n df['month'] = df['Start Time'].dt.month\n df['day_of_week'] = df['Start Time'].dt.weekday_name\n df['hour'] = df['Start Time'].dt.hour\n #print(df) #DataFrame\n\n #filter by month\n if month != 'all':\n month_name = ['january', 'february', 'march', 'april', 'may', 'june','july','august','september','october','november','december']\n month_num = month_name.index(month) + 1\n df = df[df['month'] == month_num]\n #filter by day\n if day != 'all':\n df = df[df['day_of_week'] == day.title()]\n\n recs = df['Start Time'].count()\n\n return df, recs","def setUp(self):\n table = self.get_local_dynamo_cli().Table(constants.get_configuration_table())\n response = table.scan()\n\n timeseries = [item['timeserie'] for item in response['Items']]\n\n for ts in timeseries:\n table.delete_item(Key={'timeserie': ts})","def temperature_only_data_prep(observations, predictors, for_prediction=False,\n verbose=True):\n predictors = predictors[['doy', 'site_id', 'year', 'temperature']].copy()\n doy_series = predictors.doy.dropna().unique()\n doy_series.sort()\n predictors = predictors.pivot_table(index=['site_id', 'year'], columns='doy', values='temperature').reset_index()\n\n # This first and last day of temperature data can causes NA issues because\n # of leap years.If thats the case try dropping them\n first_doy_has_na = predictors.iloc[:, 2].isna().any() # first day will always be col 2\n if first_doy_has_na:\n first_doy_column = predictors.columns[2]\n predictors.drop(first_doy_column, axis=1, inplace=True)\n doy_series = doy_series[1:]\n warn(\"\"\"Dropped temperature data for doy {d} due to missing data. Most likely from leap year mismatch\"\"\".format(d=first_doy_column))\n\n last_doy_index = predictors.shape[1] - 1\n last_doy_has_na = predictors.iloc[:, last_doy_index].isna().any()\n if last_doy_has_na:\n last_doy_column = predictors.columns[-1]\n predictors.drop(last_doy_column, axis=1, inplace=True)\n doy_series = doy_series[:-1]\n warn(\"\"\"Dropped temperature data for doy {d} due to missing data. Most likely from leap year mismatch\"\"\".format(d=last_doy_column))\n\n # Dont need the doy column if it's present and prediction is being done\n if for_prediction and 'doy' in observations.columns:\n observations = observations.drop('doy', axis=1)\n # Give each observation a temperature time series\n obs_with_temp = observations.merge(predictors, on=['site_id', 'year'], how='left')\n\n # Deal with any site/years that don't have temperature data\n original_sample_size = len(obs_with_temp)\n rows_with_missing_data = obs_with_temp.isnull().any(axis=1)\n missing_info = obs_with_temp[['site_id', 'year']][rows_with_missing_data].drop_duplicates()\n if len(missing_info) > 0:\n obs_with_temp.dropna(axis=0, inplace=True)\n n_dropped = original_sample_size - len(obs_with_temp)\n warn('Dropped {n0} of {n1} observations because of missing data'.format(n0=n_dropped, n1=original_sample_size) +\n '\\n Missing data from: \\n' + str(missing_info))\n\n temperature_array = obs_with_temp[doy_series].values.T\n\n if for_prediction:\n return temperature_array, doy_series\n else:\n observed_doy = obs_with_temp.doy.values\n return observed_doy, temperature_array, doy_series","def load_data(city, month, day):\n \n# Using pandas accessor to find month, day, hour from the Start Time column in the source data\n print(\"A moment please while I find the data....\\n\")\n df = pd.read_csv(CITY_DATA[city])\n df['Start Time'] = pd.to_datetime(df['Start Time'])\n df['day_of_week'] = df['Start Time'].dt.weekday_name\n df['month'] = df['Start Time'].dt.month\n df['hour'] = df['Start Time'].dt.hour\n\n return df","def load_data(city, month, day):\n while month != \"\":\n # load data file into a dataframe\n filename = CITY_DATA[city]\n df = pd.read_csv(filename)\n\n # convert the Start Time column to datetime\n df['Start Time'] = pd.to_datetime(df['Start Time'])\n\n # extract month and day of week from Start Time to create new columns\n df['month'] = df['Start Time'].dt.month\n # df['day_of_week'] = df['Start Time'].dt.day_name()\n\n\n try: df['day_of_week'] = df['Start Time'].dt.weekday_name\n except: df['day_of_week'] = df['Start Time'].dt.day_name()\n else: df['day_of_week'] = df['Start Time'].dt.weekday\n \n \n \n df['hour'] = df['Start Time'].dt.hour\n\n\n # filter by month if applicable\n if month != 'all':\n # use the index of the months list to get the corresponding int\n # months = ['january', 'february', 'march', 'april', 'may', 'june','july','august','september','october','november','december']\n month = int(months.index(month)) + 1\n \n # filter by month to create the new dataframe\n df = df.loc[df['month'] == month]\n\n # filter by day of week if applicable\n if day != 'all':\n # filter by day of week to create the new dataframe\n df = df.loc[df['day_of_week'] == day.title()]\n \n return df","def load_data(city_input, month_input, day_input):\n # Read csv for city_input using CITY_DATA dictionary to create df\n df = pd.read_csv(CITY_DATA[city_input])\n\n # Convert 'Start Time' and 'End Time' columns in df to datetime with pd.to_datetime function\n df['Start Time'] = pd.to_datetime(df['Start Time'])\n df['End Time'] = pd.to_datetime(df['End Time'])\n\n # Include month number in df using dt.month\n df['Start Month'] = df['Start Time'].dt.month\n\n # Include weekday in df using dt.weekday_name - note its format, e.g. Monday\n df['Start Day'] = df['Start Time'].dt.weekday_name\n\n # Include hour in df using dt.hour\n df['Start Hour'] = df['Start Time'].dt.hour\n\n ## Month\n if month_input != 'all':\n # Create a list of months based on months indices using .index(element)\n MONTHS = ['january', 'february', 'march', 'april', 'may', 'june']\n # Python uses 0 indexing so we need to increase the values by 1 to correspond with month numbers\n month = MONTHS.index(month_input) + 1\n # Filter by month to create the new dataframe\n df = df[df['Start Month'] == month] # where month is the indexed version of the user input\n\n ## Day\n # Reformat day_input to Friday, for example\n day = day_input.title()\n\n if day != 'All':\n # Create a list of days\n DAYS = ['Monday','Tuesday','Wednesday','Thursday','Friday','Saturday', 'Sunday', 'All']\n # Filter by day of week to create the new dataframe\n if day != 'All':\n df = df[df['Start Day'] == day]\n\n # Replace 'Trip Duration' with calculated version\n # This felt simpler than converting the number of seconds into days, hours, minutes, seconds ;)\n df['Trip Duration'] = df['End Time'] - df['Start Time']\n\n # print(df.head(20))\n return df","def load_metadata(self):\n self.meta[\"user_tables\"] = pd.read_sql(self.SQL[\"User Tables\"], self.engine)\n self.meta[\"all_tables\"] = pd.read_sql(self.SQL[\"All Tables\"], self.engine)\n self.meta[\"all_databases\"] = pd.read_sql(self.SQL[\"All Databases\"], self.engine)","def populate_tables(connection: sqlite3.Connection) -> None:\n fake = Faker()\n Faker.seed(0)\n\n c = conn.cursor()\n\n number_of_courses = fake.pyint(min_value=5, max_value=20)\n\n for _ in range(number_of_courses):\n course_name = fake.word()\n\n insert_statement = f'insert into courses (name) values (\"{course_name}\");'\n c.execute(insert_statement)\n\n connection.commit()\n\n number_of_users = fake.pyint(min_value=1, max_value=23)\n\n Faker.seed()\n\n for _ in range(number_of_users):\n\n if fake.pybool():\n user_name = f'{fake.first_name_female()} {fake.last_name_female()}'\n else:\n user_name = f'{fake.first_name()} {fake.last_name()}'\n\n insert_statement = f'insert into users (name) values (\"{user_name}\");'\n c.execute(insert_statement)\n\n connection.commit()\n\n for _ in range(50000):\n Faker.seed()\n\n random_user_id = fake.pyint(1, number_of_users)\n random_course_id = fake.pyint(1, number_of_courses)\n Faker.seed()\n random_lesson_no = fake.pyint(3, 12)\n Faker.seed()\n random_exercise_no = fake.pyint(1, 50)\n random_data = fake.sentence()\n\n insert_statement = f\"\"\"insert into saves (user_id, course_id, lesson_no, exercise_no,data) \n values ({random_user_id}, {random_course_id}, {random_lesson_no}, \n {random_exercise_no}, '{random_data}');\"\"\"\n c.execute(insert_statement)\n\n connection.commit()","def load_wdi() -> None:\n\n log.info(\"Started loading WDI.\")\n df = _load_and_stage_wdi()\n\n schema = \"wdi_202005\"\n db.drop_schema(schema)\n db.create_schema(schema)\n\n # Push completely raw but staged data\n log.debug(\"Done preparing raw WDI, pushing.\")\n fqtables = [f\"{schema}.cy_unimp_part_{p}\" for p in [1, 2]]\n db.df_to_split_db(df=df, fqtables=fqtables)\n\n _fill_and_push_wdi(df=df, schema=schema, n_imp=5)\n log.info(\"Finished loading WDI.\")","def read_weather_data():\n # Check if UTC to gmt+1 conversion is being handled correctly\n weather = pd.read_csv('//datc//opschaler//weather_data//knmi_10_min_raw_data//output//df_combined_uncleaned.csv',\n delimiter='\\t', comment='#',\n parse_dates=['datetime'])\n weather = weather.set_index(['datetime'])\n return weather","def set_data(self, year, month):\n data = _download_to_df(self.url, self.table_name, year, month)\n data = data.loc[:, self.table_columns]\n with self.con:\n data.to_sql(self.table_name, con=self.con, if_exists='replace', index=False)\n self.con.commit()","def generate_fake_data(context, db_connection):\n db_cursor = db_connection.cursor()\n\n for table in context.tables:\n faker = fake_data_gen.Faker(table, context)\n _fill_table(table, faker, db_cursor)\n db_connection.commit()\n\n db_cursor.close()","def load_data(city, month, day):\n\n print(\"\\nWe are loading the information for the selected filters.\")\n start_time = time.time()\n\n # filter the data according to the selected city\n if isinstance(city, list):\n df = pd.concat(map(lambda city: pd.read_csv(CITY_DATA[city]), city),\n sort=True)\n # reorganize DataFrame columns after a city concat\n try:\n df = df.reindex(columns=['Unnamed: 0', 'Start Time', 'End Time',\n 'Trip Duration', 'Start Station',\n 'End Station', 'User Type', 'Gender',\n 'Birth Year'])\n except:\n pass\n else:\n df = pd.read_csv(CITY_DATA[city])\n\n # create columns to see the statistics\n df['Start Time'] = pd.to_datetime(df['Start Time'])\n df['Month'] = df['Start Time'].dt.month\n df['day_of_week'] = df['Start Time'].dt.weekday_name\n df['Start Hour'] = df['Start Time'].dt.hour\n\n # filter month and weekday see the data in two new DataFrames\n if isinstance(month, list):\n df = pd.concat(map(lambda month: df[df['Month'] ==\n (months.index(month)+1)], month))\n else:\n df = df[df['Month'] == (months.index(month)+1)]\n\n if isinstance(day, list):\n df = pd.concat(map(lambda day: df[df['day_of_week'] ==\n (day.title())], day))\n else:\n df = df[df['day_of_week'] == day.title()]\n\n print(\"\\nThis took {} seconds.\".format((time.time() - start_time)))\n print('-'*40)\n\n return df","def init_tables(self):\n\n settings.Base.metadata.tables[\n 'session_master'].drop(bind=settings.engine)\n settings.Base.metadata.tables['uurl'].drop(bind=settings.engine)\n\n settings.Base.metadata.tables[\n 'session_master'].create(bind=settings.engine)\n settings.Base.metadata.tables['uurl'].create(bind=settings.engine)\n\n logging.info(\"Sessionization Tables created\")"],"string":"[\n \"def populate_water_tables(connection):\\n metadata = load_metadata('water')\\n cursor = connection.cursor()\\n\\n # Check if tables are already populated.\\n cursor.execute('SELECT count(*) FROM watercourses')\\n watercourse_count = cursor.fetchone()[0]\\n cursor.execute('SELECT count(*) FROM aquifers')\\n aquifer_count = cursor.fetchone()[0]\\n\\n if watercourse_count and aquifer_count:\\n print('Water tables already populated!')\\n return\\n\\n station_data = get_station_data()\\n\\n for archive in metadata.keys():\\n print(f'{archive}-water:'.upper())\\n water_body = get_water_definitions(archive)['body']\\n\\n # 1. Populate watercourses/aquifers:\\n stations = {}\\n for water_body_name in metadata[archive].keys():\\n print(f'\\\\tPopulating {water_body}: \\\"{water_body_name}\\\"')\\n cursor.execute(f'''INSERT INTO {water_body}s(location_id, name)\\n VALUES (0, '{water_body_name}')''')\\n water_body_id = cursor.lastrowid\\n\\n # 2. Populate watercourse_stations/aquifer_stations:\\n for station_id in metadata[archive][water_body_name]['stations']:\\n station_name = clean_name(metadata[archive][water_body_name]['stations'][station_id]['name'])\\n\\n if station_id in stations:\\n # Prefer watercourses/aquifer with more stations\\n current_len = len(metadata[archive][water_body_name]['stations'])\\n previous_len = len(metadata[archive][stations[station_id]]['stations'])\\n\\n if current_len < previous_len:\\n print(f'\\\\t\\\\tStation already exists: {station_id} - \\\"{station_name}\\\" (\\\"{water_body_name}\\\")')\\n continue\\n else:\\n cursor.execute(f'''DELETE \\n FROM {water_body}_stations\\n WHERE id = {station_id}''')\\n print(f'\\\\t\\\\tRemoved station: {station_id} - \\\"{station_name}\\\" from \\\"{stations[station_id]}\\\")')\\n\\n stations[station_id] = water_body_name\\n print(f'\\\\t\\\\tPopulating station: {station_id} - \\\"{station_name}\\\"')\\n\\n # Insert station location if station data exists.\\n location_id = 0\\n station_row = station_data.query(f'ŠIFRA == \\\"{station_id}\\\"')\\n if not station_row.empty:\\n index = station_row.index[0]\\n lat = station_row.at[index, 'LAT']\\n lng = station_row.at[index, 'LON']\\n if not np.isnan(lat) and not np.isnan(lng):\\n name = f\\\"{station_row.at[index, 'VODOMERNA POSTAJA']} ({station_row.at[index, 'VODOTOK']})\\\"\\n cursor.execute(f'''INSERT INTO locations(name, lat, lng)\\n VALUES ('{name}', {lat}, {lng})''')\\n location_id = cursor.lastrowid\\n\\n # Insert station.\\n cursor.execute(f'''INSERT INTO {water_body}_stations(id, {water_body}_id, location_id, name)\\n VALUES ({station_id}, {water_body_id}, {location_id}, '{station_name}')''')\\n\\n # 3. Populate watercourse_measurements/aquifer_measurements:\\n if not populate_water_measurements(cursor, archive, metadata[archive][water_body_name]['dir'],\\n station_id):\\n cursor.execute(f'''DELETE \\n FROM {water_body}_stations\\n WHERE id = {station_id}''')\\n print(f'\\\\t\\\\tRemoved station with useless data: {station_id} - \\\"{station_name}\\\"')\\n\\n # Remove empty watercourses/aquifers.\\n cursor.execute(f'''SELECT w.id, w.name\\n FROM {water_body}s w\\n WHERE NOT EXISTS (\\n SELECT s.id \\n FROM {water_body}_stations s \\n WHERE w.id = s.{water_body}_id\\n )''')\\n\\n for row in cursor.fetchall():\\n cursor.execute(f'''DELETE \\n FROM {water_body}s\\n WHERE id = {row[0]}''')\\n print(f'\\\\tRemoved empty {water_body}: \\\"{row[1]}\\\"')\",\n \"def generate_weather_data(self):\\n months = pd.to_datetime(self.output['Local Time']).dt.month\\n self.output['Month'] = months # set month values for later joins\\n\\n # merge output data frame with historical data to get ranges\\n keys = ['Location', 'Month']\\n m = pd.merge(self.output, self.histdata, how='left',\\n left_on=keys, right_on=keys)\\n\\n # uniformly select random pressure, temperature\\n # and humidity values between the historical max and min ranges\\n r = np.random.rand(m.shape[0])\\n m['Temperature'] = ((m['Tmean_high'] - m['Tmean_low']\\n ) * r + m['Tmean_low']).round(1)\\n m['Pressure'] = ((m['Pmax'] - m['Pmin']) * r + m['Pmin']).round(1)\\n m['Humidity'] = ((m['Hmax'] - m['Hmin']) * r + m['Hmin']).astype(int)\\n\\n # drop redundant columns and assign to output\\n dcols = ['Month', 'Timezone', 'Pmax', 'Pmin',\\n 'Hmax', 'Hmin', 'Tmean_high', 'Tmean_low']\\n m.drop(columns=dcols, inplace=True)\\n self.output = m\",\n \"def init_and_update_observe_table(self):\\n # print CHN_CITY_LIST_FILE\\n location = ''\\n id = ''\\n f = open(CHN_CITY_LIST_FILE, 'r')\\n for line in f.readlines():\\n line_list = line.strip('\\\\n').split(':')\\n location = line_list[0]\\n id = line_list[1]\\n pm = get_pm(location)\\n # get current weather\\n weather_dict = get_open_weather(id)\\n if weather_dict not in ('', None, [], {}):\\n if 'error' in pm or pm == False:\\n weather_dict['aqi'] = '无数据'#'N/A'\\n else:\\n weather_dict['aqi'] = pm['quality'] + '(' + str(pm['aqi']) + ')'\\n db_record = self.db.search_observe_record(str(id))\\n # db_record = []\\n now_date = get_local_format_time()\\n if db_record != []:#update\\n self.db.update_observe_data(weather_dict['ptime'],weather_dict['time'],now_date,weather_dict['WD'],weather_dict['WS'],weather_dict['SD'],weather_dict['weather'],weather_dict['img1'],weather_dict['img2'],weather_dict['temp'],weather_dict['temp1'],weather_dict['temp2'],weather_dict['aqi'],id)\\n else:#insert\\n self.db.insert_observe_data(id,weather_dict['city'],weather_dict['ptime'],weather_dict['time'],now_date,weather_dict['WD'],weather_dict['WS'],weather_dict['SD'],weather_dict['weather'],weather_dict['img1'],weather_dict['img2'],weather_dict['temp'],weather_dict['temp1'],weather_dict['temp2'],weather_dict['aqi'])\\n f.close()\\n return True\",\n \"def insert_humans_staging(self):\\n for year in range(1880, CURRENT_YEAR):\\n self.load_wikidata(\\\"humans\\\", HUMANS_BY_YEAR_SPARQL_QUERY, INSERT_HUMAN_SQL_QUERY,\\n INSERT_HUMAN_MAP_COLUMNS, year=year)\",\n \"def populate_database(telescope_name, instrument_name):\\n telescope = Telescope.objects.create(\\n name=telescope_name, latitude=25.0, longitude=45.0)\\n instrument = Instrument.objects.create(\\n name=instrument_name, telescope=telescope)\\n for year_int in (2012, 2013):\\n for month_int in range(1, 13):\\n for night_int in (1, monthrange(year_int, month_int)[1]):\\n ut_date = date(year_int, month_int, night_int)\\n night = Night.objects.create(\\n ut_date=ut_date, instrument=instrument, observers='Smith')\\n Exposure.objects.create(\\n night=night, run_number=1, ut_start=time(10, 0, 0),\\n exposed=20.0, ra=60.0, dec=30.0, object_exp=True)\\n Exposure.objects.create(\\n night=night, run_number=2, ut_start=time(11, 0, 0),\\n exposed=30.0, ra=90.0, dec=0.0, object_exp=True)\\n Exposure.objects.create(\\n night=night, run_number=3, ut_start=time(12, 0, 0),\\n exposed=40.0, ra=120.0, dec=-30.0, object_exp=False)\",\n \"def get_data(table_name, end, num, start=None):\\n if start == None:\\n if table_name == \\\"days\\\": start = end - timedelta(days=num-1) \\n if table_name == \\\"weeks\\\": start = end - timedelta(weeks=num-1) \\n if table_name == \\\"months\\\": start = end - relativedelta(months=+num-1) \\n if table_name == \\\"years\\\": start = end - relativedelta(years=+num-1) \\n else: \\n start = days.get_entry(table_name, start).date\\n \\n dates = []\\n data = []\\n weather = []\\n density = []\\n \\n while start <= end:\\n entry = days.get_entry(table_name, start)\\n data.append(entry.sentiment)\\n \\n if table_name == \\\"days\\\": \\n dates.append(entry.date.strftime(\\\"%B %d, %Y\\\"))\\n start = start + timedelta(days=1)\\n if table_name == \\\"weeks\\\": \\n dates.append(entry.date.strftime(\\\"%B %d, %Y\\\"))\\n start = start + timedelta(weeks=1) \\n if table_name == \\\"months\\\": \\n dates.append(entry.date.strftime(\\\"%B %Y\\\"))\\n start = start + relativedelta(months=+1) \\n if table_name == \\\"years\\\": \\n dates.append(entry.date.strftime(\\\"%Y\\\"))\\n start = start + relativedelta(years=+1) \\n\\n # 7/15/15 is the last entry in the current weather dictionary\\n num_days = (min(start, date(2015,7,15)) - entry.date).days\\n temp = {entry.date + timedelta(days=i): weather_dict[entry.date + timedelta(days=i)] for i in range(num_days)}\\n weather.append(float(sum(temp.values()))/float(len(temp)))\\n\\n if density_dict != None:\\n d = max(entry.date, date(2014,7,1))\\n num_days = (min(start, date(2015,7,28)) - d).days\\n rho = {d + timedelta(days=i): density_dict[d + timedelta(days=i)] for i in range(num_days)}\\n density.append(float(sum(rho.values()))/float(len(rho)))\\n\\n return dates, data, weather, density\",\n \"def setup_table(self):\\n\\n self.setup.create_basic_table_in_dev()\\n self.setup.insert_random_records_into_dev()\",\n \"def populate_db():\\n\\n populate_table(db, models.Department, departments_data)\\n populate_table(db, models.Employee, employees_data)\",\n \"def sql_functions(cities, weather):\\n con = lite.connect(\\\"sql_database.db\\\")\\n tables_tuple = (\\\"cities\\\", \\\"weather\\\")\\n with con:\\n #Generate tables in database:\\n cur = con.cursor() #Get cursor object\\n for table in tables_tuple:\\n cur.execute(\\\"DROP TABLE IF EXISTS {0}\\\".format(table)) #Drop tables if they already exist.\\n cur.execute(\\\"CREATE TABLE cities (name text, state text)\\\")\\n cur.execute(\\\"CREATE TABLE weather (city text, year integer, warm_month text, cold_month text, average_high integer)\\\")\\n #Populate tables in database:\\n cur.executemany(\\\"INSERT INTO cities VALUES (?,?)\\\", cities)\\n cur.executemany(\\\"INSERT INTO weather VALUES (?,?,?,?,?)\\\", weather)\\n #Retrieve data from database:\\n cur.execute(\\\"SELECT * FROM cities INNER JOIN weather ON city = name\\\")\\n rows = cur.fetchall()\\n cols = [desc[0] for desc in cur.description]\\n output_dataframe = pd.DataFrame(rows, columns = cols)\\n \\n return output_dataframe\",\n \"def create_tables(cxn):\\n\\tcursor = cxn.cursor()\\n\\tcursor.execute(\\\"DROP TABLE IF EXISTS WEATHER\\\")\\n\\tcursor.execute(\\\"\\\"\\\"CREATE TABLE IF NOT EXISTS WEATHER(\\n\\t\\tstate varchar(3),\\n\\t\\tcity varchar (15),\\n\\t\\tobs_date varchar(12),\\n\\t\\thour int,\\n\\t\\tminute int,\\n\\t\\tcurr_temp float,\\n\\t\\tunique(state, city, obs_date, hour, minute)\\n\\t\\t)\\\"\\\"\\\")\\n\\tcursor.close()\",\n \"def create_db(temp: list, rain: list, humidity: list, wind: list) -> dict:\\r\\n weather = {}\\r\\n for i in range(len(temp)):\\r\\n weather[i+1] = [temp[i], rain[i], humidity[i], wind[i]]\\r\\n return weather\",\n \"def _setupWeather(self, w, config):\\n wnames = ('cloud', 'seeing')\\n if w not in wnames:\\n raise Exception('w should be one of %s' %(wnames))\\n filename = config['%s_datafile' %(w)]\\n file = open(filename, 'r')\\n # Also assume flat file contains only date / value in a space or tab separated file. \\n self.dates[w] = []\\n self.weather[w] = []\\n # Read the data file.\\n print '# Reading weather data file %s' %(filename)\\n for line in file:\\n if line.startswith('#') | line.startswith('!'):\\n continue\\n self.dates[w].append(line.split()[0])\\n self.weather[w].append(line.split()[1])\\n file.close()\\n self.dates[w] = numpy.array(self.dates[w], float)\\n self.weather[w] = numpy.array(self.weather[w], float)\\n # Check the total amount of data (mostly for user awareness):\\n print '# Read %d weather values from %s file. ' %(len(self.weather[w]), filename)\\n # Check that weather data is monotonically increasing in time. \\n if not(numpy.all(numpy.diff(self.dates[w]))):\\n order = self.dates[w].argsort()\\n self.weather[w] = self.weather[w][order]\\n self.dates[w] = self.dates[w][order]\\n # Get the total length of time included in this (seeing/cloud) file,\\n # so that we can determine a wrap-around date if we need that.\\n self.maxtime[w] = self.dates[w].max()\\n return\",\n \"def set_tables(self):\\n with sql.connect('./{}.db'.format(self.name)) as conn:\\n conn.execute(\\\"\\\"\\\"CREATE TABLE IF NOT EXISTS tweets(\\n id INTEGER PRIMARY KEY,\\n tweet_id INTEGER,\\n insert_date TEXT,\\n created_at TEXT,\\n hashtag TEXT)\\n \\\"\\\"\\\")\\n\\n conn.execute(\\\"\\\"\\\"CREATE TABLE tweet_peaks(\\n peak_datetime TEXT NOT NULL,\\n hashtag TEXT NOT NULL,\\n time_frame TEXT,\\n mean REAL,\\n std REAL,\\n sensibility REAL,\\n freq_limit REAL,\\n qt_tweets INTEGER,\\n id TEXT PRIMARY KEY,\\n probability REAL);\\n \\\"\\\"\\\")\",\n \"def load_up_initial_db(self, date_dict):\\n df_tot = []\\n for chunk in pd.read_sql_table(self.table, self.disk_engine, chunksize=10000, parse_dates=date_dict):\\n df_tot.append(chunk)\\n self.df = pd.concat(df_tot)\",\n \"def import_weather(keys):\\n # imports weather and cleans\\n df_all_weather = get_weather_as_df(keys)\\n return clean_weather_df(df_all_weather)\",\n \"def update_weather(location_request, db):\\n with open(expanduser(\\\"~/bin/my_utilities/config/darksky-key\\\")) as f:\\n ds_key = f.readline().strip()\\n current = []\\n current_day = 0\\n with forecast(ds_key, *location_request, units=\\\"uk2\\\") as location:\\n raw = location['hourly']['data'][0]\\n current.append(datetime.datetime.now().hour)\\n current.append(day_relative_to_absolute(current_day))\\n current.append(raw[\\\"temperature\\\"])\\n current.append(raw[\\\"apparentTemperature\\\"])\\n current.append(raw[\\\"precipIntensity\\\"])\\n current.append(raw[\\\"precipProbability\\\"] * 100)\\n current.append(raw[\\\"humidity\\\"] * 100)\\n current.append(raw[\\\"dewPoint\\\"])\\n current.append(raw[\\\"windSpeed\\\"])\\n current.append(raw[\\\"windBearing\\\"])\\n current.append(raw[\\\"windGust\\\"])\\n current.append(raw[\\\"pressure\\\"])\\n current.append(raw[\\\"cloudCover\\\"] * 100)\\n current.append(raw[\\\"uvIndex\\\"])\\n current.append(raw[\\\"visibility\\\"])\\n current = format_list_for_db(current)\\n\\n columns = [\\\"hour\\\", \\\"day\\\", \\\"temp\\\", \\\"apptemp\\\", \\\"precipint\\\", \\\"precipprob\\\",\\n \\\"humidity\\\", \\\"dewpoint\\\", \\\"windspeed\\\", \\\"windbearing\\\",\\n \\\"windgust\\\", \\\"pressure\\\", \\\"cloudcover\\\", \\\"uvindex\\\", \\\"visibility\\\"]\\n columns = format_list_for_db(columns)\\n statement = f\\\"INSERT INTO WEATHER {columns} VALUES {current}\\\"\\n print(statement)\\n cursor = db.cursor()\\n cursor.execute(statement)\\n cursor.close()\",\n \"def compute_aggregate_weather_data():\\n\\n # get a list of all the csv files names in the 'weather_data' directory\\n files = get_all_csv_files_in_directory('weather_data')\\n\\n # Todo: if the number of csv files doesn't match the expected value, unzip remaining using the 'os' module\\n\\n if len(files) == 0:\\n\\n # Unzip all files in current directory and subdirectories\\n print \\\"unzipping weather files...\\\"\\n os.system(\\\"unzip 'weather_data/*.zip' -d weather_data\\\")\\n\\n\\n # Try again to get files\\n files = get_all_csv_files_in_directory('weather_data')\\n\\n # Throw exception if still missing csv files\\n if len(files) == 0:\\n raise ValueError(\\\"Missing weather data in csv format in the 'weather_data' directory\\\")\\n\\n # convert the list of csv file names to a list of corresponding DataFrames\\n dallas_files = filter(lambda file_name : \\\"KDAL\\\" in file_name, files)\\n houston_files = filter(lambda file_name : \\\"KHOU\\\" in file_name, files)\\n san_antonio_files = filter(lambda file_name : \\\"KSAT\\\" in file_name, files)\\n\\n print \\\"Retrieved weather data files...\\\"\\n print \\\"\\\\t# of Dallas weather files found: \\\", len(dallas_files)\\n print \\\"\\\\t# of Houston weather files found: \\\", len(houston_files)\\n print \\\"\\\\t# of San Antonio weather files found: \\\", len(san_antonio_files)\\n\\n dallas_dfs = map(lambda file_name: read_weather_data_from_csv(\\\"./weather_data/\\\" + file_name), dallas_files)\\n houston_dfs = map(lambda file_name: read_weather_data_from_csv(\\\"./weather_data/\\\" + file_name), houston_files)\\n san_antonio_dfs = map(lambda file_name: read_weather_data_from_csv(\\\"./weather_data/\\\" + file_name), san_antonio_files)\\n\\n dallas_df = pd.concat(dallas_dfs)\\n houston_df = pd.concat(houston_dfs)\\n san_antonio_df = pd.concat(san_antonio_dfs)\\n\\n print \\\"Aggregating all of the weather data...\\\"\\n # fold the list of data frames into a single data frame\\n aggregate_df = reduce(lambda df1, df2: pd.merge(df1, df2, on=\\\"Date\\\", how=\\\"outer\\\"), [dallas_df, houston_df, san_antonio_df]).sort_values(\\\"Date\\\")\\n\\n return aggregate_df\",\n \"def populate_db():\\n stdout.write('Emptying the tables...\\\\n')\\n empty_tables()\\n stdout.write('Populating Language records...\\\\n')\\n populate_language()\\n stdout.write('Populating Lemma, Wordform, and Definition records...\\\\n')\\n populate_lexical()\\n stdout.write('Populating ProperName records...\\\\n')\\n populate_proper_names()\",\n \"def populate_stops(self):\\n stops = self.load_csv('stops.txt')\\n stops = self.process_stops(stops)\\n\\n connection = db.connect()\\n for stop in stops:\\n try:\\n connection.execute(schema.stops.insert(), stop)\\n except DataError:\\n print \\\"Missing data for stop: %s\\\" % (stop)\",\n \"def get_weather_data():\\n keys = ['1364038.csv',\\n '1364041.csv',\\n '1364042.csv',\\n '1364043.csv',\\n '1364044.csv',\\n '1364046.csv',\\n '1364047.csv',\\n '1364048.csv',\\n '1364051.csv',\\n '1364052.csv',\\n '1364053.csv',\\n '1364054.csv',\\n '1364055.csv',\\n '1364058.csv',\\n '1364059.csv',\\n '1364060.csv',\\n '1364061.csv',\\n '1364062.csv',\\n '1364063.csv',\\n '1364064.csv',\\n '1364066.csv']\\n df_weather = import_weather(keys)\\n df_weather_dist = df_weather[[\\n 'LATITUDE', 'LONGITUDE', 'name']].drop_duplicates().reset_index()\\n return df_weather, df_weather_dist\",\n \"def collect(self, start_date=None, end_date=None):\\n if start_date is None:\\n start_date = self.default_start\\n if end_date is None:\\n end_date = self.default_end\\n\\n cur = self.conn.cursor()\\n\\n # Maximum return is 1000 entries\\n num_days = 1000 // len(self.stations)\\n # Maximum date-range is 1 year\\n if num_days > 365:\\n num_days = 365\\n\\n for interval in netzero.util.time_intervals(\\n start_date, end_date, days=num_days\\n ):\\n netzero.util.print_status(\\n \\\"Weather\\\",\\n \\\"Collecting: {} to {}\\\".format(\\n interval[0].strftime(\\\"%Y-%m-%d\\\"), interval[1].strftime(\\\"%Y-%m-%d\\\")\\n ),\\n )\\n\\n # TODO -- REMOVE ASSUMPTION THAT LEN(DATA) < LIMIT\\n raw_data = self.query_api(interval[0], interval[1])\\n\\n if raw_data is None:\\n print(\\\"ERROR QUERYING API\\\") # TODO exception here?\\n continue\\n\\n for entry in raw_data.get(\\\"results\\\", []):\\n # Insert the weather data to the table, to be averaged later\\n date = datetime.datetime.strptime(\\n entry[\\\"date\\\"], \\\"%Y-%m-%dT%H:%M:%S\\\"\\n ).date()\\n value = entry[\\\"value\\\"]\\n station = entry[\\\"station\\\"]\\n\\n cur.execute(\\n \\\"INSERT OR IGNORE INTO weather VALUES (?, ?, ?)\\\", (date, value, station)\\n )\\n\\n self.conn.commit()\\n\\n cur.close()\\n\\n netzero.util.print_status(\\\"Weather\\\", \\\"Complete\\\", newline=True)\",\n \"def setUpClass(cls):\\n dt_index = pd.date_range(start=datetime(2019, 1, 1, 0, 1), periods=15,\\n freq='1Min')\\n\\n # Create a temperature array with an average of 2.\\n temp = [1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3]\\n\\n # Create ghi array with an average of 3.\\n ghi = [2, 3, 4, 2, 3, 4, 2, 3, 4, 2, 3, 4, 2, 3, 4]\\n\\n # Create DataFrame.\\n cls.weather_data = pd.DataFrame({'temperature': temp, 'ghi': ghi},\\n index=dt_index)\\n\\n # Create expected data.\\n dt_index_2 = pd.date_range(start=datetime(2019, 1, 1, 0, 15), periods=1,\\n freq='15Min')\\n cls.expected_data = pd.DataFrame({'temperature': [2], 'ghi': [3]},\\n index=dt_index_2)\",\n \"def read_weather(self):\\n print \\\"Reading weather data from file\\\",self.datafile\\n tab = ascii.read(self.datafile)\\n \\n # Fix 'T' values in precipitation column, which represent tiny\\n # amounts of rain (not measurable)\\n TINY_VALUE = '.005' # 0.005 is half the smallest measurable value\\n rain = tab['PrecipitationIn']\\n wbad = (rain == 'T')\\n rain[wbad] = TINY_VALUE\\n rain = numpy.array(rain).astype(\\\"float\\\")\\n\\n # Replace string version of precip with float version\\n tab['PrecipIn'] = rain\\n tab.remove_column('PrecipitationIn')\\n\\n self.table = tab\",\n \"def update(self):\\n if self.last_update and (\\n self.last_update + timedelta(hours=1)\\n > datetime.utcnow().replace(tzinfo=dt_util.UTC)\\n ):\\n return # Not time to update yet; data is only hourly\\n\\n for row in self.current_observations():\\n if row.get(\\\"Station\\\") == self._station_id:\\n api_fields = {\\n col_heading: (standard_name, dtype)\\n for standard_name, (\\n _,\\n _,\\n _,\\n col_heading,\\n dtype,\\n ) in SENSOR_TYPES.items()\\n }\\n self.data = {\\n api_fields.get(col_heading)[0]: api_fields.get(col_heading)[1](\\n v.replace(\\\",\\\", \\\".\\\")\\n )\\n for col_heading, v in row.items()\\n if col_heading in api_fields and v\\n }\\n break\\n else:\\n raise ValueError(f\\\"No weather data for station {self._station_id}\\\")\",\n \"def create_table(self):\\n # Connect to database\\n conn = sqlite3.connect(self)\\n # Create a cursor\\n c = conn.cursor()\\n\\n # Create a Table\\n c.execute(\\\"\\\"\\\"CREATE TABLE weather (\\n id INTEGER PRIMARY KEY AUTOINCREMENT,\\n sensor text,\\n location text,\\n temperature real,\\n description text,\\n time text\\n )\\\"\\\"\\\")\\n # Commit our command\\n conn.commit()\\n # Close our connection\\n conn.close()\",\n \"def init_datasets(self, dataset_names, columns):\\n for dataset_name in dataset_names:\\n hdf5_dataset_name = self.schema.get(dataset_name)\\n if hdf5_dataset_name is None:\\n warnings.warn(\\\"Skipping %s (not in schema)\\\" % dataset_name)\\n else:\\n self[dataset_name] = tokio.timeseries.TimeSeries(dataset_name=hdf5_dataset_name,\\n start=self.query_start,\\n end=self.query_end_plusplus,\\n timestep=self.timestep,\\n num_columns=len(columns),\\n column_names=columns,\\n sort_hex=self.sort_hex)\",\n \"def populate(self):\\n\\n NUM_COUNTRIES = 2 # random.randint(1, 4)\\n\\n # find a suitable hex\\n with Timer(\\\"Creating initial data\\\", debug=self.debug):\\n\\n for i in range(NUM_COUNTRIES):\\n country, provinces, pops = create_country(self, self.map)\\n country.determine_tax_policy()\\n self.countries.append(country)\",\n \"def testWeatherFetch(self):\\n\\n timeCol = 'timestamp'\\n rows = []\\n for row in self.aggregator.rawData(dataType = 'weather',\\n orderBy = [timeCol],\\n timestampCol = timeCol,\\n startDate = self.testStart,\\n endDate = self.testEnd):\\n rows.append(row)\\n self.assertIsNotNone(rows, 'Rows are present.')\",\n \"def populate_database(self):\\n self.insert_products()\\n self.insert_categories()\\n self.insert_products_categories()\\n self.insert_stores()\\n self.insert_products_stores()\",\n \"def create_all_tables(self):\\n pass\",\n \"def populate(table_name, date):\\n\\tlog_msg3(\\\"Populando \\\" + table_name)\\n\\n\\twsq_to_txt(table_name, date)\\n\\n\\t# si es un nuevo año se crea una nueva tabla\\n\\tif(is_new_year(table_name) and not new_tables_created):\\n\\t\\tcreate_tables()\\n\\n\\ttxt_to_table(table_name)\\n\\n\\tlog_msg_ok3()\",\n \"def get_gis_historical_data():\\n logging.info(\\\"Generating baseline reference and historical weather data.\\\")\\n \\n # Initialising function variables\\n fake = Faker()\\n geolocator = Nominatim()\\n config_data = get_config()\\n locations = config_data[\\\"location\\\"]\\n \\n # Check if there are no duplicate locations in the config.yaml file.\\n if len(locations) != len(set(locations)):\\n logging.error(\\\"Duplicate location found. Please check config.yaml file.\\\")\\n raise ValueError\\n \\n # Initialise pandas dataframe column name for baseline reference\\n # and historical data.\\n df_ref = pd.DataFrame(columns=[\\\"Location\\\", \\\"Latitude\\\"\\n ,\\\"Longitude\\\", \\\"Elevation\\\"\\n ,\\\"Timezone\\\"])\\n df_hist = pd.DataFrame(columns=[\\\"Location\\\", \\\"Date\\\"\\n ,\\\"Month\\\", \\\"Temperature_Min\\\"\\n ,\\\"Temperature_Max\\\", \\\"Humidity\\\"\\n ,\\\"Pressure\\\"])\\n \\n # Generate weather data for each location.\\n for idx, loc in enumerate(locations):\\n \\n logging.info(\\\"Retrieving geolocation data for {}.\\\".format(loc))\\n \\n # Retrieving geolocation data from geopy library.\\n loc_data = geolocator.geocode(loc)\\n \\n logging.info(\\\"Check if the location {} is valid.\\\".format(loc))\\n if loc_data is None:\\n logging.error(\\\"Invalid location value supplied ({}). Please check config.yaml file.\\\".format(loc))\\n raise ValueError\\n logging.info(\\\"The location {} is valid.\\\".format(loc))\\n \\n city = get_city(loc)\\n lat = loc_data.latitude\\n lon = loc_data.longitude\\n \\n # Retrieving elevation data for the location.\\n elev = get_elevation_data(lat, lon)\\n \\n for month in range(1, 13):\\n \\n logging.info(\\\"Retrieving {} weather data for month {}.\\\".format(loc, month))\\n \\n for sample in range(config_data[\\\"gis\\\"][\\\"sampling_number\\\"]):\\n \\n temp_min = None\\n temp_max = None\\n humidity = None\\n pressure = None\\n \\n while temp_min is None or temp_max is None or humidity is None or pressure is None:\\n \\n year = random.randint(config_data[\\\"gis\\\"][\\\"year_start\\\"], config_data[\\\"gis\\\"][\\\"year_end\\\"])\\n\\n _, last_day = calendar.monthrange(year, month)\\n\\n datetime_start = datetime.datetime(year, month, 1)\\n datetime_end = datetime.datetime(year, month, last_day)\\n\\n date_gen = fake.date_time_between_dates(datetime_start=datetime_start\\n ,datetime_end=datetime_end)\\n\\n forecast = forecastio.load_forecast(config_data[\\\"forecastio_api_key\\\"]\\n ,lat\\n ,lon\\n ,time=date_gen\\n ,units=\\\"si\\\")\\n\\n historical_data = forecast.json[\\\"daily\\\"][\\\"data\\\"][0]\\n \\n timezone = forecast.json.get(\\\"timezone\\\", None)\\n temp_min = historical_data.get(\\\"temperatureMin\\\", None)\\n temp_max = historical_data.get(\\\"temperatureMax\\\", None)\\n humidity = historical_data.get(\\\"humidity\\\", None) * 100\\n pressure = historical_data.get(\\\"pressure\\\", None)\\n \\n df_temp_hist = pd.Series(dict(zip(df_hist.columns\\n ,[city, date_gen\\n ,date_gen.month, temp_min\\n ,temp_max, humidity\\n ,pressure])))\\n \\n df_hist = df_hist.append(df_temp_hist, ignore_index=True)\\n \\n df_temp_ref = pd.Series(dict(zip(df_ref.columns\\n ,[city, lat\\n ,lon, elev\\n ,timezone])))\\n df_ref = df_ref.append(df_temp_ref, ignore_index=True)\\n \\n logging.info(\\\"Generating position to consolidate latitude, longitude and elevation data\\\")\\n df_pos = df_ref[[\\\"Latitude\\\", \\\"Longitude\\\", \\\"Elevation\\\"]].round(2)\\n df_pos[\\\"Elevation\\\"] = df_pos[\\\"Elevation\\\"].astype(int) \\n df_ref[\\\"Position\\\"] = df_pos.astype(str).apply(lambda x: \\\",\\\".join(x), axis=1)\\n \\n logging.info(\\\"Saving baseline reference data.\\\")\\n df_ref.to_csv(get_file_path(folder_name=\\\"data\\\"\\n ,subdirectory=config_data[\\\"gis\\\"][\\\"output_subdirectory\\\"]\\n ,file_name=config_data[\\\"gis\\\"][\\\"output_base_reference_file_name\\\"])\\n ,index=False)\\n logging.info(\\\"Completed saving baseline reference data.\\\")\\n\\n logging.info(\\\"Saving baseline historical data.\\\")\\n df_hist.to_csv(get_file_path(folder_name=\\\"data\\\"\\n ,subdirectory=config_data[\\\"gis\\\"][\\\"output_subdirectory\\\"]\\n ,file_name=config_data[\\\"gis\\\"][\\\"output_base_historical_file_name\\\"])\\n ,index=False)\\n logging.info(\\\"Completed saving baseline historical data.\\\")\",\n \"def setupStockTable(self):\\n # Get the date\\n # NOTE: This is probably un\\n date = datetime.date()\\n dateStr = date.month() + \\\"/\\\" + date.day() + \\\"/\\\" + date.year()\\n\\n stocks = (\\\"INTC\\\", \\\"AAPL\\\", \\\"GOOG\\\", \\\"YHOO\\\", \\\"SYK\\\", \\\"VZ\\\")\\n\\n for stock in stocks:\\n stockObj = self.securityFactory(stock)\\n stockObj.queryAPI()\\n\\n self.stockDB.query(\\\"INSERT INTO basic_info (ticker, price, daily_change, company, year_high, year_low, \\\\\\n daily_percent, date, streak) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)\\\", (stockObj.target, stockObj.curr, \\\\\\n stockObj.daily_change, stockObj.company,\\\\\\n stockObj.year_high, stockObj.year_low,\\\\\\n stockObj.daily_percent, dateStr, 0))\",\n \"def fill_target_table(new_data, curs, conn, overwrite=False):\\n for i in new_data:\\n connect_database.add_target_to_database(list(i), curs, conn, overwrite_exsiting = overwrite)\\n conn.commit()\",\n \"def create_forecast_dataset(self):\\n pass\",\n \"def fill_dataset(self):\\n rm, rstd = self.get_rolling_stats()\\n\\n self.add_rolling_mean(rm)\\n self.add_bollinger_bands(rstd)\\n self.add_spy_info()\\n self.add_beta_and_sharpe()\\n self.add_stlouis_data()\",\n \"def _update_on_refresh():\\n cities = City.query.all()\\n\\n #Iterates over all cities in the database and updates their value\\n for city in cities:\\n metric_resp, imperial_resp = _get_open_weather_requests(city.name)\\n\\n metric_json = metric_resp.json()\\n imperial_json = imperial_resp.json()\\n\\n city.temp_celsius = int(metric_json[MAIN][TEMPERATURE])\\n city.temp_fahrenheit = int(imperial_json[MAIN][TEMPERATURE])\\n db.session.commit()\",\n \"def generate_polynesian_weather_data():\\n weather_path = os.path.dirname(os.path.realpath(__file__))\\n low_fp = weather_path + \\\"/polynesia_weather/low/1976/\\\"\\n med_fp = weather_path + \\\"/polynesia_weather/med/1985/\\\"\\n high_fp = weather_path + \\\"/polynesia_weather/high/1982/\\\"\\n low_name = \\\"polynesia_1976\\\"\\n med_name = \\\"polynesia_1985\\\"\\n high_name = \\\"polynesia_1982\\\"\\n generate_year_weather_data(low_fp, low_name)\\n generate_year_weather_data(med_fp, med_name)\\n generate_year_weather_data(high_fp, high_name)\",\n \"def make_tables(self):\\n for t in self.tables:\\n self.add_table(groupname=t['groupname'],\\n tablename=t['tablename'],\\n description=t['description'],\\n tabletitle=t['tabletitle'])\",\n \"def run(self):\\n\\n for table in self.TABLES:\\n self.dictionary_cursor.execute(f\\\"TRUNCATE TABLE {table}_Work\\\")\\n self.dictionary_conn.commit()\\n self.logger.info(\\\"work tables cleared\\\")\\n for id in self.ids:\\n drug = self.Drug(self, id)\\n if drug.wanted:\\n drug.load()\\n self.logger.info(\\\"work tables populated\\\")\\n for table in self.TABLES:\\n insert = f\\\"INSERT INTO {table} SELECT * FROM {table}_Work\\\"\\n self.dictionary_cursor.execute(f\\\"TRUNCATE TABLE {table}\\\")\\n self.dictionary_cursor.execute(insert)\\n self.dictionary_conn.commit()\\n self.logger.info(\\\"live tables ready\\\")\",\n \"def set_up_tables(self):\\n tables = []\\n tables.append({'groupname': 'metadata',\\n 'tablename': 'sim_info',\\n 'description': desc.SimInfoRow,\\n 'tabletitle': 'Simulation Information'})\\n tables.append({'groupname': 'metadata',\\n 'tablename': 'sim_timeseries',\\n 'description': desc.SimTimeseriesRow,\\n 'tabletitle': 'Simulation Power Data'})\\n tables.append({'groupname': 'th',\\n 'tablename': 'th_params',\\n 'description': desc.ThMetadataRow,\\n 'tabletitle': 'TH Component Parameters'})\\n tables.append({'groupname': 'th',\\n 'tablename': 'th_timeseries',\\n 'description': desc.ThTimeseriesRow,\\n 'tabletitle': 'TH Timeseries'})\\n tables.append({'groupname': 'neutronics',\\n 'tablename': 'neutronics_timeseries',\\n 'description': desc.NeutronicsTimeseriesRow,\\n 'tabletitle': 'Neutronics Timeseries'})\\n tables.append({'groupname': 'neutronics',\\n 'tablename': 'neutronics_params',\\n 'description': desc.NeutronicsParamsRow,\\n 'tabletitle': 'Neutronics Metadata'})\\n tables.append({'groupname': 'neutronics',\\n 'tablename': 'zetas',\\n 'description': desc.ZetasTimestepRow,\\n 'tabletitle': 'Neutron Precursor Concentrations'})\\n tables.append({'groupname': 'neutronics',\\n 'tablename': 'omegas',\\n 'description': desc.OmegasTimestepRow,\\n 'tabletitle': 'Decay Heat Fractions'})\\n return tables\",\n \"def populate_db(self):\\n # Get donors\\n log.info(\\\"Populating donors.\\\")\\n\\n self.r.hmset('Thomas', {'donations': '500', 'email': 'thomas@thomas.com', 'city': 'Athens', 'state': 'GA', 'zip': 30606})\\n\\n self.r.hmset('Ted', {'donations': '1', 'email': 'ted@ted.com', 'city': 'Memphis', 'state': 'TN', 'zip': 38104})\\n\\n self.r.hmset(\\\"Bailey\\\", {'donations': '1000', 'email': 'bailey@bailey.com', 'city': 'Washington', 'state': 'DC', 'zip': 12345})\",\n \"def weather_data(cities, openweathermap_api_key=openweathermap_api_key):\\n L = []\\n for c in cities:\\n res = requests.get(f'http://api.openweathermap.org/data/2.5/weather?q={c}&appid={openweathermap_api_key}&units=imperial')\\n L.append(res.json())\\n\\n df = pd.DataFrame(L)\\n df['lon'] = df['coord'].map(op.itemgetter('lon'))\\n df['lat'] = df['coord'].map(op.itemgetter('lat'))\\n df['Temprature'] = df['main'].map(op.itemgetter('temp'))\\n df['Humidity'] = df['main'].map(op.itemgetter('humidity'))\\n df['Wind Speed'] = df['wind'].map(op.itemgetter('speed'))\\n return df[['name','lon', 'lat','Temprature','Humidity','Wind Speed']]\",\n \"def _load_data(self):\\n if self._api_response.status_code == 200:\\n self._dataset = self._api_response.json()\\n self._fill_day_dicts()\",\n \"def getAllDataFromDirectory(prediction_directory, actual_directory, write_directory, cities_file, utc_offset = False):\\n city_dictionary = getCities(cities_file)\\n actualGetter = getActualWeather(actual_directory, city_dictionary, get_API_keys())\\n #For each day and for each city, get all the data and put it into a spreadsheet.\",\n \"def setupTempTables(self):\\n cur = self.cursor()\\n cur.execute(\\\"PRAGMA temp_store = MEMORY\\\")\\n for table, columns in self.temp_tables.items():\\n cur.execute(\\\"create temporary table if not exists %s (\\\" % table + (',').join('\\\"' + key + '\\\" ' + val for key, val in columns.items()) + ')')\",\n \"def process_table_init(self):\\n logging.debug(\\\"Processing table initialization, %d entries\\\",\\n len(self.table_initialization))\\n\\n for init_entry in self.table_initialization:\\n for table_name, entry_desc in init_entry.items():\\n self.air_table[table_name].add_entry(\\n table_entry.description_to_entry(entry_desc))\",\n \"def get_basic_data(self):\\n\\n db = DataBase().clear_table()\\n\\n data = self.scraper.scrape_top_250()\\n for d in data:\\n title = d.find(\\\"td\\\", class_=\\\"titleColumn\\\")\\n title = title.find(\\\"a\\\")\\n title = re.sub(\\\"<.*?>\\\", \\\"\\\", str(title))\\n\\n film_id = d.find(\\\"td\\\", class_=\\\"watchlistColumn\\\")\\n film_id = film_id.find(\\\"div\\\")\\n film_id = film_id[\\\"data-tconst\\\"]\\n\\n year = d.find(\\\"span\\\", class_=\\\"secondaryInfo\\\")\\n year = re.sub(\\\"<.*?>\\\", \\\"\\\", str(year)).replace(\\\"(\\\", \\\"\\\").replace(\\\")\\\", \\\"\\\")\\n\\n director = d.find(\\\"td\\\", class_=\\\"titleColumn\\\")\\n director = director.find(\\\"a\\\")\\n director = director[\\\"title\\\"]\\n director, *cast = director.split(\\\", \\\")\\n director = director.replace(\\\" (dir.)\\\", \\\"\\\")\\n\\n rating = d.find(\\\"td\\\", class_=\\\"ratingColumn imdbRating\\\")\\n rating = rating.find(\\\"strong\\\")\\n rating = re.sub(\\\"<.*?>\\\", \\\"\\\", str(rating))\\n\\n poster = d.find(\\\"td\\\", class_=\\\"posterColumn\\\")\\n poster = poster.find(\\\"img\\\")[\\\"src\\\"]\\n poster = re.sub(\\\"@.+\\\", \\\"@._V1_FMjpg_UY474_.jpg\\\", poster)\\n\\n DataBase().populate_table(\\n (title, film_id, year, director, \\\", \\\".join(cast), rating, poster)\\n )\",\n \"def update_data():\\n etf_prices = get_prices(start=START_DATE, end=END_DATE)\\n etf_returns = compute_returns(etf_prices)\\n merged_etf_data = etf_prices.merge(etf_returns, right_index=True, left_index=True)\\n indicators = compute_indicators(merged_etf_data) # this uses the \\\"ta\\\" lib, but it does not need\\n # to be imported\\n merged_etf_data = merged_etf_data.merge(indicators, right_index=True, left_index=True)\\n vix_data = get_vix()\\n data = merged_etf_data.merge(vix_data, right_index=True, left_index=True)\\n data.to_csv('Data/database.csv')\\n return\",\n \"def process_weather(forecast_file):\\n with open(forecast_file) as json_file:\\n json_data = json.load(json_file)\\n\\n min_temp_store = {}\\n max_temp_store = {}\\n weather_results = str()\\n header_results = str()\\n\\n for day_in_forecast in json_data['DailyForecasts']:\\n day_date = day_in_forecast['Date']\\n min_temp = day_in_forecast['Temperature']['Minimum'][\\\"Value\\\"]\\n min_temp_c = convert_f_to_c(min_temp)\\n min_temp_store[day_date] = min_temp_c\\n max_temp = day_in_forecast['Temperature']['Maximum'][\\\"Value\\\"]\\n max_temp_c = convert_f_to_c(max_temp)\\n max_temp_store[day_date] = max_temp_c\\n\\n day_time_phrase = day_in_forecast['Day']['LongPhrase']\\n rain_chance_day = day_in_forecast['Day']['RainProbability']\\n night_time_phrase = day_in_forecast['Night']['LongPhrase']\\n rain_chance_night = day_in_forecast['Night']['RainProbability']\\n weather_results = weather_results + (f\\\"-------- {convert_date(day_date)} --------\\\\nMinimum Temperature: {format_temperature(round(min_temp_c,1))}\\\\nMaximum Temperature: {format_temperature(round(max_temp_c,1))}\\\\nDaytime: {day_time_phrase}\\\\n Chance of rain: {rain_chance_day}%\\\\nNighttime: {night_time_phrase}\\\\n Chance of rain: {rain_chance_night}%\\\\n\\\")+ \\\"\\\\n\\\"\\n\\n\\n max_day = max(max_temp_store, key=max_temp_store.get)\\n max_value = max_temp_store[max_day]\\n min_day = min(min_temp_store, key=min_temp_store.get)\\n min_value = min_temp_store[min_day]\\n max_totals = (sum(max_temp_store.values()))\\n min_totals = (sum(min_temp_store.values()))\\n num_items = len(min_temp_store)\\n mean_min = round(calculate_mean(min_totals,num_items),1)\\n mean_max = round(calculate_mean(max_totals,num_items),1)\\n\\n save_header = (f\\\"{len(json_data['DailyForecasts'])} Day Overview\\\\n The lowest temperature will be {format_temperature(round((min_value),1))}, and will occur on {convert_date(min_day)}.\\\\n The highest temperature will be {format_temperature(round((max_value),1))}, and will occur on {convert_date(max_day)}.\\\\n The average low this week is {format_temperature(mean_min)}.\\\\n The average high this week is {format_temperature(mean_max)}.\\\\n\\\")\\n\\n header_results = save_header + \\\"\\\\n\\\"+ weather_results\\n \\n return(header_results)\",\n \"def _populate_zone_facts_table(self):\\n census_fields = [\\n 'poverty_rate', 'fraction_black', 'income_per_capita',\\n 'labor_participation', 'fraction_foreign',\\n 'fraction_single_mothers', 'acs_lower_rent_quartile',\\n 'acs_median_rent', 'acs_upper_rent_quartile'\\n ]\\n\\n zone_types = ['ward', 'neighborhood_cluster', 'census_tract']\\n\\n query_results = list()\\n\\n # populate columns accordingly for each zone_specific type\\n for zone_type in zone_types:\\n field_values = dict()\\n\\n # get field value for each zone_specific type\\n for field in census_fields:\\n result = self._census_with_weighting(data_id=field,\\n grouping=zone_type)\\n field_values[field] = result['items']\\n\\n zone_specifics = self._get_zone_specifics_for_zone_type(zone_type)\\n\\n # TODO: add aggregate for each zone_type into table\\n for zone in zone_specifics:\\n # get not None values so we can added to db\\n columns = list()\\n values = list()\\n for field in census_fields:\\n zone_value = field_values[field][zone]\\n\\n if zone_value is not None:\\n columns.append(field)\\n values.append(\\\"'\\\" + str(zone_value) + \\\"'\\\")\\n\\n # derive column and values strings needed for sql query\\n columns = ', '.join(columns)\\n columns = 'zone, ' + columns\\n\\n values = ', '.join(values)\\n values = \\\"'\\\" + zone + \\\"', \\\" + values\\n\\n q = \\\"INSERT INTO zone_facts ({cols}) VALUES ({vals})\\\".format(\\n cols=columns, vals=values)\\n\\n with self.engine.connect() as conn:\\n result = conn.execute(q)\\n query_results.append(result)\\n\\n return query_results\",\n \"def init_tables(self) -> None:\\n with self.table_access_condition:\\n conn = self._get_connection()\\n conn.execute(\\\"PRAGMA foreign_keys = 1\\\")\\n c = conn.cursor()\\n c.execute(tables.CREATE_STUDIES_TABLE)\\n c.execute(tables.CREATE_SUBMISSIONS_TABLE)\\n c.execute(tables.CREATE_REQUESTERS_TABLE)\\n c.execute(tables.CREATE_UNITS_TABLE)\\n c.execute(tables.CREATE_WORKERS_TABLE)\\n c.execute(tables.CREATE_RUNS_TABLE)\\n c.execute(tables.CREATE_RUN_MAP_TABLE)\\n c.execute(tables.CREATE_PARTICIPANT_GROUPS_TABLE)\\n c.execute(tables.CREATE_PARTICIPANT_GROUP_QUALIFICATIONS_MAPPING_TABLE)\\n conn.commit()\",\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 create_table(self):\\n c = self.conn.cursor()\\n c.execute(\\\"CREATE TABLE sensor_data (mac text, name text, temperature real, light integer, moisture real, conductivity real, battery real, ts_utc int, date_iso text, firmware text )\\\")\",\n \"def populate_locations(connection):\\n print('Populating locations...')\\n cursor = connection.cursor()\\n with open(get_data_path('locations', 'locations.json'), 'r', encoding='utf-8') as json_file:\\n locations = json.load(json_file)\\n\\n for station_id, location in locations.items():\\n cursor.execute(f'''SELECT id \\n FROM watercourse_stations \\n WHERE id = {station_id}''')\\n\\n if len(cursor.fetchall()):\\n cursor.execute(f'''INSERT INTO locations(name, lat, lng)\\n VALUES ('{location['name']}', {location['lat']}, {location['lng']})''')\\n cursor.execute(f'''UPDATE watercourse_stations\\n SET location_id = {cursor.lastrowid}\\n WHERE id = {station_id}''')\",\n \"def make_weather_features(self, timeline_dt_list):\\n\\n print \\\"Making weather features...\\\"\\n\\n N_FEATURES = 2\\n n_examples = len(timeline_dt_list)\\n XX = numpy.zeros((n_examples, N_FEATURES))\\n indices = numpy.zeros(n_examples,dtype='int')\\n ind_weatherday = 0\\n\\n # Loop over all times in the timeline\\n for ii, time in enumerate(timeline_dt_list):\\n # Find where this time in the timeline matches the date\\n # of some weather data.\\n jj = ind_weatherday\\n while time.date() != self.datetimes[jj].date():\\n # Make sure jj does not get too large to be an index to\\n # the list.\\n # Note this is probably a bad idea to do it this way.\\n if jj == len(self.datetimes)-1:\\n break\\n jj += 1\\n## print jj\\n\\n ind_weatherday = jj\\n indices[ii] = ind_weatherday\\n\\n# XX[ii, 0] = self.table['PrecipIn'][ind_weatherday]\\n# XX[ii, 1] = self.table['Mean TemperatureF'][ind_weatherday]\\n## XX[ii, 2] = self.table['MeanDew PointF'][ind_weatherday]\\n\\n XX[:,0] = self.table['PrecipIn'][indices]\\n XX[:,1] = self.table['Mean TemperatureF'][indices]\\n self.weather_features = XX\\n return XX\",\n \"def insert_entities_staging(self):\\n\\n for year in range(1900, CURRENT_YEAR, YEARS_RANGE):\\n self.load_wikidata(\\\"movies\\\", MOVIES_BY_YEAR_SPARQL_QUERY, INSERT_MOVIE_SQL_QUERY, INSERT_MOVIE_MAP_COLUMNS, year, YEARS_RANGE)\\n\\n self.load_wikidata(\\\"songs\\\", SONGS_BY_YEAR_SPARQL_QUERY, INSERT_SONG_SQL_QUERY, INSERT_SONG_MAP_COLUMNS, year, YEARS_RANGE, True)\\n self.load_wikidata(\\\"tvshows\\\", TVSHOWS_SPARQL_QUERY, INSERT_TVSHOW_SQL_QUERY, INSERT_TVSHOW_MAP_COLUMNS)\\n self.load_wikidata(\\\"animatedmovies\\\", ANIMATEDMOVIES_SPARQL_QUERY, INSERT_ANIMATEDMOVIE_SQL_QUERY,\\n INSERT_ANIMATEDMOVIE_MAP_COLUMNS)\\n self.load_wikidata(\\\"videogames\\\", VIDEOGAMES_SPARQL_QUERY, INSERT_VIDEOGAME_SQL_QUERY, INSERT_VIDEOGAME_MAP_COLUMNS)\\n self.load_wikidata(\\\"books\\\", BOOKS_SPARQL_QUERY, INSERT_BOOK_SQL_QUERY, INSERT_BOOK_MAP_COLUMNS)\",\n \"def populate_dyn(self, table):\\n myrow = table.row\\n myrow[\\\"sample_time\\\"] = int(time.time() - glob.base_time)\\n myrow[\\\"available_bike_stands\\\"] = self.available_bike_stands\\n myrow[\\\"available_bikes\\\"] = self.available_bikes\\n myrow[\\\"last_update\\\"] = self.last_update\\n myrow[\\\"status\\\"] = self.status\\n myrow.append()\\n table.flush()\",\n \"def to_WTH_converter(self, weather_data, dest_dir):\\n ds_all = weather_data.get_global_dataset()\\n if self.country is None:\\n print(\\\"Country given is erroneous:\\\")\\n return\\n elif self.country == \\\"globe\\\":\\n lon_num_start = 0\\n lon_num_stop = weather_data.get_num_of_attribute('longitude')\\n lat_num_start = 0\\n lat_num_stop = weather_data.get_num_of_attribute('latitude')\\n else:\\n lon_num_start, lon_num_stop, lat_num_start, lat_num_stop = weather_data.get_country_boundary(self.country)\\n\\n\\n # top bottom, left to right\\n lon_num_start = 397\\n lat_num_start = 184\\n for lon_i in range(lon_num_start, lon_num_stop + 1):\\n # for lon_i in range(lon_num_start, lon_num_stop+1):\\n lon = ds_all.longitude.isel(longitude=lon_i).values.tolist()\\n\\n for lat_i in range(lat_num_start, lat_num_stop+1):\\n # for lat_i in range(lat_num_start, lat_num_stop + 1):\\n lat = ds_all.latitude.isel(latitude=lat_i).values.tolist()\\n\\n # create a dynamic header with updated LON, LAT info and move it into the folder given\\n wth_header_u = ut.format_header(lat_i + 1, lon_i + 1, lat, lon)\\n wth_header = dest_dir + \\\"/\\\" + wth_header_u\\n shutil.move(wth_header_u, wth_header)\\n\\n # open in appending mode\\n fwth = open(wth_header, \\\"a+\\\")\\n\\n # loop through daily weather data\\n for t, date in enumerate(self.years):\\n daily_data_vars = ut.get_daily_data_vars(ds_all, lat_i, lon_i, t)\\n # disregard all NAN values\\n if daily_data_vars is None:\\n fwth.close()\\n os.remove(wth_header)\\n break\\n\\n if t == 0:\\n ut.update_table(wth_header_u, lat, lon)\\n\\n entry = ut.format_data_vars_entry(daily_data_vars, date)\\n\\n # append this entry into the file\\n fwth.write(entry)\\n print(\\\"Added entry:\\\", entry)\\n\\n # close file after writing\\n fwth.close()\\n print(\\\"Output WTH:\\\", wth_header)\",\n \"def setUpClass(cls):\\n cls.w = pd.read_csv(_df.WEATHER_TWO_WEEK, index_col='time',\\n parse_dates=True)\",\n \"def _perform_data_conversion(self):\\n self.data = []\\n items = 0\\n for value in self.elements_to_convert:\\n try:\\n location = parse_int(value.get('location_id'), nullable=False)\\n if not value.get('list', []):\\n continue\\n for obs in value['list']:\\n items += 1\\n # Setting timezone to pytz.UTC FIXES [BUG-039].\\n timestamp = parse_date_utc(obs.get('dt') * 1000)\\n date = timestamp.date()\\n time = timestamp.time()\\n temperature = parse_int(obs['main'].get('temp'))\\n pressure = parse_float(obs['main'].get('pressure'))\\n humidity = parse_int(obs['main'].get('humidity'))\\n wind_speed = parse_int(obs.get('wind', {}).get('speed'))\\n wind_degrees = parse_int(obs.get('wind', {}).get('deg'))\\n wind_direction = compute_wind_direction(wind_degrees)\\n weather = obs.get('weather', [{}])[0]\\n if weather.get('icon') and weather.get('id'):\\n weather = - parse_int(weather.get('id'), nullable=False) if 'n' in weather['icon'] else \\\\\\n parse_int(weather.get('id'), nullable=False)\\n self.data.append(WeatherForecastObservation(location_id=location, date=date, time=time,\\n temperature=temperature, pressure=pressure, humidity=humidity, wind_speed=wind_speed,\\n wind_degrees=wind_degrees, wind_direction=wind_direction, weather_id=weather))\\n except (ValueError, AttributeError, KeyError, IndexError, TypeError):\\n _id = value.get('_id', 'Unknown ID')\\n self.logger.exception('An error occurred while parsing data. WeatherForecastObservation with ID \\\"%s\\\" '\\n 'will not be converted.' % _id)\\n self.state['elements_to_convert'] = items\",\n \"def fill_table_for_ETF(conn):\\n baseurl = f\\\"https://financialmodelingprep.com/api/v3/etf/list\\\"\\n params = {\\\"apikey\\\": FMP_API_KEY}\\n etfs = make_request(baseurl=baseurl, params=params)\\n for etf in etfs:\\n insert_etf(conn, etf[\\\"symbol\\\"], etf[\\\"name\\\"], etf[\\\"exchange\\\"])\",\n \"def initial_db_setup() -> None:\\n db_filename = \\\"twdft.db\\\"\\n db_path = os.path.join(TWDFT_DATA_DIR, db_filename)\\n csv_filename = \\\"sites.csv\\\"\\n csv_path = os.path.join(TWDFT_DATA_DIR, csv_filename)\\n db_is_new = not os.path.exists(db_path)\\n sites_csv = os.path.join(TWDFT_DATA_DIR, csv_filename)\\n\\n if db_is_new:\\n with sqlite3.connect(db_path) as conn:\\n c = conn.cursor()\\n\\n # first we create a site object\\n c.execute(\\n \\\"\\\"\\\"\\n CREATE TABLE site(\\n id INTEGER PRIMARY KEY,\\n name TEXT,\\n site_type TEXT,\\n sub_category TEXT,\\n address_1 TEXT,\\n address_2 TEXT,\\n town TEXT,\\n county TEXT,\\n country TEXT,\\n postcode TEXT,\\n site_category TEXT,\\n freq_target TEXT,\\n created TEXT,\\n notes TEXT,\\n last_inspection TEXT,\\n next_inspection TEXT,\\n pfsp_approval TEXT,\\n pfsp_expiry TEXT,\\n unlocode TEXT,\\n pfso TEXT,\\n pso TEXT,\\n pfsa_approval TEXT,\\n pfsa_expiry TEXT,\\n team TEXT,\\n created_by TEXT,\\n last_updated TEXT,\\n updated_by TEXT,\\n afp_loc TEXT,\\n rdf TEXT,\\n classification TEXT,\\n article24 TEXT,\\n psa_approval TEXT,\\n inspection_due TEXT\\n )\\n \\\"\\\"\\\"\\n )\\n conn.commit()\\n\\n # next we want an inspection table\\n\\n c.execute(\\n \\\"\\\"\\\"\\n CREATE TABLE inspection(\\n id INTEGER PRIMARY KEY,\\n site INTEGER,\\n date TEXT,\\n status TEXT,\\n time TEXT,\\n FOREIGN KEY(site) REFERENCES site(id)\\n )\\n \\\"\\\"\\\"\\n )\\n conn.commit()\\n\\n # next we want an inspector table\\n c.execute(\\n \\\"\\\"\\\"\\n create table inspector(\\n id integer primary key,\\n first_name text,\\n last_name text\\n )\\n \\\"\\\"\\\"\\n )\\n conn.commit()\\n\\n for i in INSPECTORS:\\n first = i.split(\\\" \\\")[0]\\n last = i.split(\\\" \\\")[1]\\n c.execute(\\n \\\"INSERT INTO inspector(first_name, last_name) VALUES (?,?)\\\",\\n (first, last),\\n )\\n\\n # a table that links inspectors with inspections\\n c.execute(\\n \\\"\\\"\\\"\\n CREATE TABLE inspector_inspections(\\n inspector INTEGER,\\n inspection INTEGER,\\n FOREIGN KEY (inspector) REFERENCES inspector(id),\\n FOREIGN KEY (inspection) REFERENCES inspection(id)\\n )\\n \\\"\\\"\\\"\\n )\\n conn.commit()\\n\\n for site in map(Site._make, csv.reader(open(csv_path, \\\"r\\\"))):\\n try:\\n c.execute(\\n f\\\"\\\"\\\"\\n INSERT INTO site VALUES(?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)\\\"\\\"\\\",\\n (\\n int(site.id.replace(\\\",\\\", \\\"\\\")),\\n site.name,\\n site.site_type,\\n site.sub_category,\\n site.address_1,\\n site.address_2,\\n site.town,\\n site.county,\\n site.country,\\n site.postcode,\\n site.site_category,\\n site.freq_target,\\n site.created,\\n site.notes,\\n site.last_inspection,\\n site.next_inspection,\\n site.pfsp_approval,\\n site.pfsp_expiry,\\n site.unlocode,\\n site.pfso,\\n site.pso,\\n site.pfsa_approval,\\n site.pfsa_expiry,\\n site.team,\\n site.created_by,\\n site.last_updated,\\n site.updated_by,\\n site.afp_loc,\\n site.rdf,\\n site.classification,\\n site.article24,\\n site.psa_approval,\\n site.inspection_due,\\n ),\\n )\\n except sqlite3.IntegrityError as e:\\n print(\\\"That hasnae worked\\\", site.inspection_due)\",\n \"def prepare_train_data(self):\\r\\n ## Impute rlkpis\\r\\n print(\\\"Imputing rlKPI df\\\")\\r\\n self.rlkpi.add_target_labels(1)\\r\\n self.rlkpi.impute_rl_kpis()\\r\\n\\r\\n print(\\\"Add 'met-real-station_no' & met-forecast-station_no to rl_kpis_df\\\")\\r\\n self.add_met_real_forecast_station_col_to_rlkpis()\\r\\n print(\\\"Merge 'met-real-sampled df to rl kps \\\")\\r\\n self.merge_met_real_sampled_df_to_rlkpis()\\r\\n\\r\\n ## Imputations for met-forecast\\r\\n print(\\\"Impute met-forecast\\\")\\r\\n met_forecast_obj = self.metfcast\\r\\n met_forecast_obj.impute_met_forecast()\\r\\n\\r\\n #Merge met forecast data to earlier merged data\\r\\n print(\\\"Merge Train data with imputed forecast df\\\")\\r\\n self.train_data = pd.merge(self.train_data,\\r\\n met_forecast_obj.imputed_forecast_df,\\r\\n on=['datetime-station_no'], indicator=True, how='inner')\\r\\n print(\\\"Check any imputation needed\\\", self.train_data.isna().sum().sum())\\r\\n self.train_data.drop(['_merge'], axis=1, inplace=True)\\r\\n self.perform_data_under_sampling(self.train_data)\",\n \"def process_weather(forecast_file):\\n # Load json data file\\n \\n with open(forecast_file) as json_file:\\n json_data = json.load(json_file)\\n \\n # Set Variables, Dictionaries and Lists\\n days_list = []\\n temp_dict = {}\\n daily_dict = {}\\n\\n num_items = 0\\n total_sum_min = 0\\n total_sum_max = 0\\n days = len(json_data['DailyForecasts'])\\n days_list = days_in_data(days)\\n\\n t_temp_min = 100\\n t_temp_max = 0\\n\\n # Pull through the data\\n\\n for day in days_list:\\n num_items += 1\\n date = convert_date(json_data['DailyForecasts'][day]['Date'])\\n min_temp = convert_f_to_c(json_data['DailyForecasts'][day]['Temperature']['Minimum']['Value'])\\n total_sum_min += min_temp\\n max_temp = convert_f_to_c(json_data['DailyForecasts'][day]['Temperature']['Maximum']['Value'])\\n total_sum_max += max_temp\\n day_desc = json_data['DailyForecasts'][day]['Day']['LongPhrase']\\n chance_rain_day = json_data['DailyForecasts'][day]['Day']['RainProbability']\\n night_desc = json_data['DailyForecasts'][day]['Night']['LongPhrase']\\n chance_rain_night = json_data['DailyForecasts'][day]['Night']['RainProbability']\\n \\n if min_temp < t_temp_min:\\n t_temp_min = min_temp\\n t_temp_mindate = date\\n else:\\n pass\\n if max_temp > t_temp_max:\\n t_temp_max = max_temp\\n t_temp_maxdate = date\\n else:\\n pass\\n \\n daily_dict[day] = [date, min_temp, max_temp, day_desc, chance_rain_day, night_desc, chance_rain_night]\\n # 0 1 2 3 4 5 6 \\n \\n # print(temp_dict)\\n # print(daily_dict)\\n\\n # Calculate Minimum, Maximum and Mean temperatures\\n\\n mean_min = format_temperature(calculate_mean(total_sum_min, num_items))\\n # print(mean_min)\\n mean_max = format_temperature(calculate_mean(total_sum_max, num_items))\\n # print(mean_max)\\n\\n # Format Minimum and Maximum temperatures\\n min_temp_format = format_temperature(t_temp_min)\\n max_temp_format = format_temperature(t_temp_max)\\n\\n ##############################################################################################\\n\\n # Combine string messages to return to user\\n\\n str_Output = \\\"\\\"\\n Output_gen1 = (f\\\"{num_items} Day Overview\\\\n\\\")\\n Output_gen2 = (f\\\" The lowest temperature will be {min_temp_format}, and will occur on {t_temp_mindate}.\\\\n\\\")\\n Output_gen3 = (f\\\" The highest temperature will be {max_temp_format}, and will occur on {t_temp_maxdate}.\\\\n\\\")\\n Output_gen4 = (f\\\" The average low this week is {mean_min}.\\\\n\\\")\\n Output_gen5 = (f\\\" The average high this week is {mean_max}.\\\\n\\\")\\n str_Output = Output_gen1 + Output_gen2 + Output_gen3 + Output_gen4 + Output_gen5\\n for key, value in daily_dict.items():\\n Output_daily0 = (\\\"\\\\n\\\")\\n Output_daily1 = (f\\\"-------- {value[0]} --------\\\\n\\\")\\n Output_daily2 = (f\\\"Minimum Temperature: {format_temperature(value[1])}\\\\n\\\")\\n Output_daily3 = (f\\\"Maximum Temperature: {format_temperature(value[2])}\\\\n\\\")\\n Output_daily4 = (f\\\"Daytime: {value[3]}\\\\n\\\")\\n Output_daily5 = (f\\\" Chance of rain: {value[4]}%\\\\n\\\")\\n Output_daily6 = (f\\\"Nighttime: {value[5]}\\\\n\\\")\\n Output_daily7 = (f\\\" Chance of rain: {value[6]}%\\\\n\\\")\\n str_Output = str_Output + Output_daily0 + Output_daily1 + Output_daily2 + Output_daily3 + Output_daily4 + Output_daily5 + Output_daily6 + Output_daily7\\n str_Output = str_Output +\\\"\\\\n\\\"\\n\\n return str_Output\",\n \"def generate_database_from_metadatas(metadata_dict, stimulus_dict):\\n # Create empty database from template class\\n indexes = sorted(metadata_dict.keys())\\n database = pd.DataFrame(index=indexes, columns=['Number', 'Metadata', 'Tracking', 'Registration', 'Stimuli'])\\n\\n # Fill in metadata from the dictionary\\n for sessname, metadata in sorted(metadata_dict.items()):\\n database['Metadata'][sessname] = metadata\\n database['Number'][sessname] = metadata['number']\\n\\n for sessname, stimulus in sorted(stimulus_dict.items()):\\n database['Stimuli'][sessname] = stimulus\\n\\n print(colored('Database initialized.','yellow'))\\n return database\",\n \"def load_dwh_tables(self):\\n print(\\\"Loading the creative works table\\\")\\n self.cur.execute(dwh_queries.INSERT_CREATIVE_WORKS_SQL_QUERY)\\n self.conn.commit()\\n\\n print(\\\"Loading the participations table\\\")\\n\\n self.cur.execute(dwh_queries.INSERT_PARTICIPATIONS_SQL_QUERY)\\n self.conn.commit()\",\n \"def populate_table(self, data):\\n\\n db = self.connection(database=\\\"imdb\\\")\\n\\n try:\\n cur = db.cursor()\\n sql = \\\"\\\"\\\"\\n INSERT INTO film (title, film_id, year, director, cast, rating, poster_url) \\n VALUES (%s, %s, %s, %s, %s, %s, %s);\\n \\\"\\\"\\\"\\n cur.execute(sql, data)\\n db.commit()\\n except:\\n print(\\\"An error occurred when saving the data!\\\")\\n\\n db.close()\",\n \"def testWeatherAggregation(self):\\n\\n rowCnt = 0\\n agg = self.aggregator.aggregatedData(dataType = 'weather',\\n aggregationType = 'agg_weather',\\n timeColumnName = 'timestamp',\\n subkeyColumnName = None,\\n startDate = self.testStart,\\n endDate = self.testEnd)\\n for row in agg.data:\\n print '%d: %s' % (rowCnt, row)\\n rowCnt += 1\\n self.assertEqual(rowCnt, 1, 'Row count not correct.')\\n\\n self.logger.log('agg cols: %d' % len(agg.columns))\\n self.assertEqual(len(agg.columns), 3, 'Weather columns not equal to 3.')\\n self.aggregator.insertAggregatedData(agg = agg)\",\n \"def _get_information(self):\\n weather_dict = {}\\n table_body = self.climate_table\\n\\n rows = table_body.find_all('tr')\\n months = [col.get_text() for col in rows[0].find_all('td')[1:]]\\n\\n for row in rows[1:]:\\n cols = row.find_all('td')\\n key = cols[0].get_text()\\n value_getter = self._value_getters_by_key.get(key, self._get_remote_workers)\\n\\n weather_dict.update({key: [(months[i],) + value_getter(col) for i, col in enumerate(cols[1:])]})\\n\\n return weather_dict\",\n \"def fill_obs(self, observation_table, data_store):\\n for obs in observation_table:\\n events = data_store.obs(obs_id=obs['OBS_ID']).events\\n\\n # TODO: filter out (mask) possible sources in the data\\n # for now, the observation table should not contain any\\n # run at or near an existing source\\n\\n self.counts_cube.fill_events([events])\\n self.livetime_cube.data += events.observation_live_time_duration\",\n \"def database_setup():\\n Base.metadata.create_all(bind=engine)\\n db = LocalSession()\\n try:\\n populate_from_json(db, Vector, str(VECTORS))\\n populate_from_json(db, Gender, str(GENDERS))\\n populate_from_json(db, Tag, str(TAGS))\\n finally:\\n db.close()\",\n \"def _load_db(self):\\n for type_ in self._types:\\n try:\\n type_.table(self._metadata)\\n except InvalidRequestError:\\n pass\\n # Reflect metadata so auto-mapping works\\n self._metadata.reflect(self._engine)\\n # Make sure the tables exist\\n self._metadata.create_all()\",\n \"def _db_init_data_tables(self):\\n\\n #\\n # TESTTYPE table\\n #\\n return self._db_execute(\\n \\\"\\\"\\\"\\n create table TESTTYPE (\\n KEY text unique,\\n VALUE text\\n )\\n \\\"\\\"\\\"\\n )\",\n \"def weather_script(\\n init_data_path: str,\\n output_path: str,\\n workers: int,\\n weatherAPI_rpm: int,\\n geoAPI_rpm: int,\\n) -> None:\\n unzip(init_data_path, output_path)\\n top_hotels_dataframe_without_addresses = primary_data_proc(output_path)\\n geocoder = geocoder_setup(geoAPI_rpm)\\n top_hotels_df_with_addresses = define_address(\\n top_hotels_dataframe_without_addresses,\\n workers,\\n geocoder,\\n )\\n cities, countries, coordinates = city_center_coord(top_hotels_df_with_addresses)\\n weather_df = pd.concat(\\n [\\n prev_weather(cities, countries, coordinates, workers, weatherAPI_rpm),\\n forecast_weather(cities, countries, coordinates, workers, weatherAPI_rpm),\\n ]\\n )\\n\\n logger.info(\\\"Start to save results\\\")\\n save_main_info(output_path, weather_df, top_hotels_df_with_addresses)\\n logger.info(\\\"Finish\\\")\",\n \"def get_usda_food_data (connection):\\n\\n tables = ['usda_food_access_feb2014', 'usda_food_assistance_feb2014',\\n 'usda_food_health_feb2014', 'usda_food_insecurity_feb2014',\\n 'usda_food_stores_feb2014']\\n\\n for table in tables:\\n if table == tables[0]:\\n sql_query = \\\"\\\"\\\"\\nSELECT \\\"FIPS\\\",\\n\\\"PCT_LACCESS_POP10\\\" AS \\\"low_access_food_pct10\\\",\\n\\\"PCT_LACCESS_LOWI10\\\" AS \\\"low_access_food_low_inc_pct10\\\",\\n\\\"PCT_LACCESS_SENIORS10\\\" AS \\\"low_access_food_snr_pct10\\\",\\n\\\"PCT_LACCESS_HHNV10\\\" AS \\\"low_access_food_no_car_pct10\\\"\\nFROM {0};\\n \\\"\\\"\\\".format (table)\\n # print (sql_query)\\n elif table == tables[1]:\\n sql_query = \\\"\\\"\\\"\\nSELECT \\\"FIPS\\\",\\n\\\"REDEMP_SNAPS12\\\" AS \\\"snap_redemp_per_store_2012\\\"\\nFROM {0};\\n \\\"\\\"\\\".format (table)\\n # print (sql_query)\\n elif table == tables[2]:\\n sql_query = \\\"\\\"\\\"\\nSELECT \\\"FIPS\\\",\\n\\\"PCT_DIABETES_ADULTS10\\\" AS \\\"pct_diabetes_adults_2010\\\",\\n\\\"PCT_OBESE_ADULTS13\\\" AS \\\"pct_obese_adults_2013\\\",\\n\\\"RECFACPTH12\\\" AS \\\"rec_fac_2012\\\",\\n\\\"NATAMEN\\\" AS \\\"ers_nat_amenity_index_1999\\\"\\nFROM {0};\\n \\\"\\\"\\\".format (table)\\n # print (sql_query)\\n elif table == tables[3]:\\n sql_query = \\\"\\\"\\\"\\nSELECT \\\"FIPS\\\",\\n\\\"FOODINSEC_10_12\\\" AS \\\"food_insec_house_pct_10_12\\\",\\n\\\"VLFOODSEC_10_12\\\" AS \\\"very_low_food_insec_house_pct_10_12\\\"\\nFROM {0};\\n \\\"\\\"\\\".format (table)\\n # print (sql_query)\\n elif table == tables[4]:\\n sql_query = \\\"\\\"\\\"\\nSELECT \\\"FIPS\\\",\\n\\\"GROCPTH12\\\" AS \\\"grocery_pct10\\\"\\nFROM {0};\\n \\\"\\\"\\\".format (table)\\n # print (sql_query)\\n\\n if table == tables[0]:\\n data = pd.read_sql_query(sql_query, con)\\n data.where ((pd.notnull (data)), other=np.nan, inplace=True)\\n data = data.dropna (subset=['FIPS'])\\n data['FIPS'] = data['FIPS'].apply (lambda x: str(x).zfill (5))\\n else:\\n data_tmp = pd.read_sql_query(sql_query, con)\\n data_tmp.where ((pd.notnull (data_tmp)), other=np.nan, inplace=True)\\n data_tmp = data_tmp.dropna (subset=['FIPS'])\\n data_tmp['FIPS'] = data_tmp['FIPS'].apply (lambda x: str(x).zfill (5))\\n data = pd.merge (data, data_tmp, on=\\\"FIPS\\\", how=\\\"left\\\")\\n\\n return (data)\",\n \"def set_data(self):\\n # take care of samples\\n patients = self.samples.iloc[:,1].tolist()\\n samples = self.samples.iloc[:,0].tolist()\\n self.samples = pd.DataFrame(patients,index = samples,columns = ['patient']) # indexed by sample\\n #\\n # take care of expression data\\n cols = self.expression.SYMBOL.tolist() # set new column names to transposed expression_data \\n \\n new_exp = self.expression.T.ix[1:,:] # transpose\\n new_exp.columns = cols\\n self.expression = new_exp # add columns\\n self.data = pd.merge(self.expression,self.samples,left_index = True,right_index=True) # merged data sets\\n #pd.merge(df1,df2,how = 'left',left_index=True,right_index=True) # do a left join\",\n \"def set_training_data(self):\\n # Optional training data period\\n # TODO: add training data period feature to training data query\\n if not self.training_period == None:\\n training_period_date = (datetime.datetime.utcnow() - timedelta(minutes=self.training_period)).strftime(\\\"%Y-%m-%d\\\")\\n print(f\\\"Training data start date: {training_period_date}\\\")\\n # Extract queried data from Athena\\n #athena = athena_connect.Athena()\\n #features_df = athena.pandas_read_athena(self.training_data_sql)\\n with open('feature_sql.txt', 'w') as f:\\n print(self.training_data_sql, file=f) \\n features_df = pd.read_sql(self.training_data_sql, self.logic_db_engine())\\n features_df.fillna(0, inplace=True)\\n print(features_df.shape)\\n features_df = features_df[max(self.feature_minutes_list):]\\n print(features_df.shape)\\n # Remove infinity string\\n features_df.replace({'Infinity': 0}, inplace=True)\\n # Convert all object fields to numeric except date fields\\n object_col_list = features_df.columns[features_df.dtypes.eq('object')]\\n object_col_list = [col for col in object_col_list if 'trade_date' not in col]\\n features_df[object_col_list] = features_df[object_col_list].apply(pd.to_numeric, errors='coerce')\\n self.training_df = features_df\",\n \"def fill_table(info):\\n # extrac attributes from info struct\\n data = info[\\\"data\\\"]\\n table = info[\\\"table\\\"]\\n header = info[\\\"header\\\"]\\n row_num = info[\\\"row_num\\\"]\\n\\n currency_type_num = row_num - 1\\n row_index = 0\\n col_index = 0\\n i = 0\\n while i < len(data):\\n if data[i].find(\\\"%\\\") > 0:\\n # stat data\\n while i < len(data) and row_index < currency_type_num:\\n table[row_index+1].append(data[i])\\n row_index += 1\\n i += 1\\n # Reset row_index\\n row_index = 0\\n else:\\n if i < row_num - 1:\\n # currency Type\\n table[i+1].append(data[i])\\n else:\\n # time marker\\n if data[i] != header:\\n table[0].append(data[i])\\n i += 1\\n\\n # End loop\\n return None\",\n \"def init():\\n database.create_tables([Tracker])\\n database.commit()\",\n \"def filldf(tfinal):\\n getbasics(tfinal)\\n # getusermentions(tfinal)\\n getretweets(tfinal)\\n getinreply(tfinal)\\n return tfinal\",\n \"def forecast_weather(self):\\n pass\",\n \"def create_tables(cur, country_json, xml_state, body_json):\\n print(\\\"Creating the 3 first tables...\\\")\\n cur.execute('CREATE TABLE IF NOT EXISTS country_purchases(idx INTEGER PRIMARY KEY, state TEXT, amount INTEGER)')\\n with open(\\\"country_purchases.csv\\\", 'r') as f:\\n for idx, line in enumerate(f.read().split('\\\\n')):\\n line_splt = line.split(',')\\n try:\\n cur.execute('INSERT INTO country_purchases VALUES(\\\"%s\\\", \\\"%s\\\", \\\"%s\\\")' % (idx, line_splt[0], line_splt[1]))\\n except IndexError:\\n pass\\n cur.execute('CREATE TABLE IF NOT EXISTS country_total_purchases(idx INTEGER PRIMARY KEY, state TEXT, amount INTEGER)')\\n with open(\\\"country_total_purchases.csv\\\", 'r') as f:\\n for idx, line in enumerate(f.read().split('\\\\n')):\\n line_splt = line.split(',')\\n try:\\n cur.execute('INSERT INTO country_total_purchases VALUES(\\\"%s\\\", \\\"%s\\\", \\\"%s\\\")' % (idx, line_splt[0], line_splt[1]))\\n except IndexError:\\n pass\\n cur.execute('CREATE TABLE IF NOT EXISTS country_albums(ID INTEGER PRIMARY KEY, state TEXT, year INTEGER, genre TEXT, album TEXT, amount INTEGER)')\\n for idx, album in enumerate(country_json[body_json['state']]):\\n cur.execute('INSERT INTO country_albums VALUES (\\\"%s\\\", \\\"%s\\\", \\\"%s\\\", \\\"%s\\\", \\\"%s\\\", \\\"%s\\\")' %\\n (idx, body_json['state'], body_json['year'], body_json['genre'], album,\\n xml_state[0][0][idx].text))\",\n \"def init_tables(self) -> None:\\n # TODO(#93) maybe raise flag when the schema of existing tables isn't what we expect\\n # it to be?\\n # \\\"How to know that schema changes?\\\"\\n # logger.warning(\\\"some message\\\")\\n with self.table_access_condition:\\n conn = self._get_connection()\\n conn.execute(\\\"PRAGMA foreign_keys = 1\\\")\\n with conn:\\n c = conn.cursor()\\n c.execute(CREATE_PROJECTS_TABLE)\\n c.execute(CREATE_TASKS_TABLE)\\n c.execute(CREATE_REQUESTERS_TABLE)\\n c.execute(CREATE_TASK_RUNS_TABLE)\\n c.execute(CREATE_ASSIGNMENTS_TABLE)\\n c.execute(CREATE_UNITS_TABLE)\\n c.execute(CREATE_WORKERS_TABLE)\\n c.execute(CREATE_AGENTS_TABLE)\\n c.execute(CREATE_QUALIFICATIONS_TABLE)\\n c.execute(CREATE_GRANTED_QUALIFICATIONS_TABLE)\\n c.execute(CREATE_ONBOARDING_AGENTS_TABLE)\",\n \"def get_weather(start: str, end: str) -> Generator[Dict[str, str], None, None]:\\n speeds = get_speeds(start, end)\\n for data in get_temperatures(start, end):\\n data.update(next(speeds))\\n yield data\",\n \"def create_final_table(conn, county):\\r\\n for county in county:\\r\\n query = f\\\"SELECT name FROM sqlite_master WHERE type ='table' AND name NOT LIKE 'sqlite_%' AND name = '{county}'\\\"\\r\\n result = execute_query(conn, query)\\r\\n try:\\r\\n if len(result) == 0:\\r\\n query = f\\\"create table {county} as select * from {county}_stg;\\\"\\r\\n execute_query(conn, query)\\r\\n\\r\\n load_final_table(conn, county)\\r\\n except Exception as e:\\r\\n print(f\\\"This query {query} failed with exception {e}\\\")\",\n \"def load_data(city, month, day):\\n\\n if city == 'all':\\n df = pd.read_csv(CITY_DATA['chicago'])\\n df['city'] = 'chicago'\\n ny = pd.read_csv(CITY_DATA['new york city'])\\n ny['city'] = 'new york city'\\n df = df.append(ny,sort = True)\\n wa = pd.read_csv(CITY_DATA['washington'])\\n wa['city'] = 'washington'\\n df = df.append(wa,sort = True)\\n else:\\n df = pd.read_csv(CITY_DATA[city])\\n df['city'] = CITY_DATA[city]\\n\\n df['Start Time'] = pd.to_datetime(df['Start Time']) #converts Start Time to datetime\\n df['End Time'] = pd.to_datetime(df['End Time']) #converts End Time to datetime\\n df['month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.weekday_name\\n df['hour'] = df['Start Time'].dt.hour\\n #print(df) #DataFrame\\n\\n #filter by month\\n if month != 'all':\\n month_name = ['january', 'february', 'march', 'april', 'may', 'june','july','august','september','october','november','december']\\n month_num = month_name.index(month) + 1\\n df = df[df['month'] == month_num]\\n #filter by day\\n if day != 'all':\\n df = df[df['day_of_week'] == day.title()]\\n\\n recs = df['Start Time'].count()\\n\\n return df, recs\",\n \"def setUp(self):\\n table = self.get_local_dynamo_cli().Table(constants.get_configuration_table())\\n response = table.scan()\\n\\n timeseries = [item['timeserie'] for item in response['Items']]\\n\\n for ts in timeseries:\\n table.delete_item(Key={'timeserie': ts})\",\n \"def temperature_only_data_prep(observations, predictors, for_prediction=False,\\n verbose=True):\\n predictors = predictors[['doy', 'site_id', 'year', 'temperature']].copy()\\n doy_series = predictors.doy.dropna().unique()\\n doy_series.sort()\\n predictors = predictors.pivot_table(index=['site_id', 'year'], columns='doy', values='temperature').reset_index()\\n\\n # This first and last day of temperature data can causes NA issues because\\n # of leap years.If thats the case try dropping them\\n first_doy_has_na = predictors.iloc[:, 2].isna().any() # first day will always be col 2\\n if first_doy_has_na:\\n first_doy_column = predictors.columns[2]\\n predictors.drop(first_doy_column, axis=1, inplace=True)\\n doy_series = doy_series[1:]\\n warn(\\\"\\\"\\\"Dropped temperature data for doy {d} due to missing data. Most likely from leap year mismatch\\\"\\\"\\\".format(d=first_doy_column))\\n\\n last_doy_index = predictors.shape[1] - 1\\n last_doy_has_na = predictors.iloc[:, last_doy_index].isna().any()\\n if last_doy_has_na:\\n last_doy_column = predictors.columns[-1]\\n predictors.drop(last_doy_column, axis=1, inplace=True)\\n doy_series = doy_series[:-1]\\n warn(\\\"\\\"\\\"Dropped temperature data for doy {d} due to missing data. Most likely from leap year mismatch\\\"\\\"\\\".format(d=last_doy_column))\\n\\n # Dont need the doy column if it's present and prediction is being done\\n if for_prediction and 'doy' in observations.columns:\\n observations = observations.drop('doy', axis=1)\\n # Give each observation a temperature time series\\n obs_with_temp = observations.merge(predictors, on=['site_id', 'year'], how='left')\\n\\n # Deal with any site/years that don't have temperature data\\n original_sample_size = len(obs_with_temp)\\n rows_with_missing_data = obs_with_temp.isnull().any(axis=1)\\n missing_info = obs_with_temp[['site_id', 'year']][rows_with_missing_data].drop_duplicates()\\n if len(missing_info) > 0:\\n obs_with_temp.dropna(axis=0, inplace=True)\\n n_dropped = original_sample_size - len(obs_with_temp)\\n warn('Dropped {n0} of {n1} observations because of missing data'.format(n0=n_dropped, n1=original_sample_size) +\\n '\\\\n Missing data from: \\\\n' + str(missing_info))\\n\\n temperature_array = obs_with_temp[doy_series].values.T\\n\\n if for_prediction:\\n return temperature_array, doy_series\\n else:\\n observed_doy = obs_with_temp.doy.values\\n return observed_doy, temperature_array, doy_series\",\n \"def load_data(city, month, day):\\n \\n# Using pandas accessor to find month, day, hour from the Start Time column in the source data\\n print(\\\"A moment please while I find the data....\\\\n\\\")\\n df = pd.read_csv(CITY_DATA[city])\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n df['day_of_week'] = df['Start Time'].dt.weekday_name\\n df['month'] = df['Start Time'].dt.month\\n df['hour'] = df['Start Time'].dt.hour\\n\\n return df\",\n \"def load_data(city, month, day):\\n while month != \\\"\\\":\\n # load data file into a dataframe\\n filename = CITY_DATA[city]\\n df = pd.read_csv(filename)\\n\\n # convert the Start Time column to datetime\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n\\n # extract month and day of week from Start Time to create new columns\\n df['month'] = df['Start Time'].dt.month\\n # df['day_of_week'] = df['Start Time'].dt.day_name()\\n\\n\\n try: df['day_of_week'] = df['Start Time'].dt.weekday_name\\n except: df['day_of_week'] = df['Start Time'].dt.day_name()\\n else: df['day_of_week'] = df['Start Time'].dt.weekday\\n \\n \\n \\n df['hour'] = df['Start Time'].dt.hour\\n\\n\\n # filter by month if applicable\\n if month != 'all':\\n # use the index of the months list to get the corresponding int\\n # months = ['january', 'february', 'march', 'april', 'may', 'june','july','august','september','october','november','december']\\n month = int(months.index(month)) + 1\\n \\n # filter by month to create the new dataframe\\n df = df.loc[df['month'] == month]\\n\\n # filter by day of week if applicable\\n if day != 'all':\\n # filter by day of week to create the new dataframe\\n df = df.loc[df['day_of_week'] == day.title()]\\n \\n return df\",\n \"def load_data(city_input, month_input, day_input):\\n # Read csv for city_input using CITY_DATA dictionary to create df\\n df = pd.read_csv(CITY_DATA[city_input])\\n\\n # Convert 'Start Time' and 'End Time' columns in df to datetime with pd.to_datetime function\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n df['End Time'] = pd.to_datetime(df['End Time'])\\n\\n # Include month number in df using dt.month\\n df['Start Month'] = df['Start Time'].dt.month\\n\\n # Include weekday in df using dt.weekday_name - note its format, e.g. Monday\\n df['Start Day'] = df['Start Time'].dt.weekday_name\\n\\n # Include hour in df using dt.hour\\n df['Start Hour'] = df['Start Time'].dt.hour\\n\\n ## Month\\n if month_input != 'all':\\n # Create a list of months based on months indices using .index(element)\\n MONTHS = ['january', 'february', 'march', 'april', 'may', 'june']\\n # Python uses 0 indexing so we need to increase the values by 1 to correspond with month numbers\\n month = MONTHS.index(month_input) + 1\\n # Filter by month to create the new dataframe\\n df = df[df['Start Month'] == month] # where month is the indexed version of the user input\\n\\n ## Day\\n # Reformat day_input to Friday, for example\\n day = day_input.title()\\n\\n if day != 'All':\\n # Create a list of days\\n DAYS = ['Monday','Tuesday','Wednesday','Thursday','Friday','Saturday', 'Sunday', 'All']\\n # Filter by day of week to create the new dataframe\\n if day != 'All':\\n df = df[df['Start Day'] == day]\\n\\n # Replace 'Trip Duration' with calculated version\\n # This felt simpler than converting the number of seconds into days, hours, minutes, seconds ;)\\n df['Trip Duration'] = df['End Time'] - df['Start Time']\\n\\n # print(df.head(20))\\n return df\",\n \"def load_metadata(self):\\n self.meta[\\\"user_tables\\\"] = pd.read_sql(self.SQL[\\\"User Tables\\\"], self.engine)\\n self.meta[\\\"all_tables\\\"] = pd.read_sql(self.SQL[\\\"All Tables\\\"], self.engine)\\n self.meta[\\\"all_databases\\\"] = pd.read_sql(self.SQL[\\\"All Databases\\\"], self.engine)\",\n \"def populate_tables(connection: sqlite3.Connection) -> None:\\n fake = Faker()\\n Faker.seed(0)\\n\\n c = conn.cursor()\\n\\n number_of_courses = fake.pyint(min_value=5, max_value=20)\\n\\n for _ in range(number_of_courses):\\n course_name = fake.word()\\n\\n insert_statement = f'insert into courses (name) values (\\\"{course_name}\\\");'\\n c.execute(insert_statement)\\n\\n connection.commit()\\n\\n number_of_users = fake.pyint(min_value=1, max_value=23)\\n\\n Faker.seed()\\n\\n for _ in range(number_of_users):\\n\\n if fake.pybool():\\n user_name = f'{fake.first_name_female()} {fake.last_name_female()}'\\n else:\\n user_name = f'{fake.first_name()} {fake.last_name()}'\\n\\n insert_statement = f'insert into users (name) values (\\\"{user_name}\\\");'\\n c.execute(insert_statement)\\n\\n connection.commit()\\n\\n for _ in range(50000):\\n Faker.seed()\\n\\n random_user_id = fake.pyint(1, number_of_users)\\n random_course_id = fake.pyint(1, number_of_courses)\\n Faker.seed()\\n random_lesson_no = fake.pyint(3, 12)\\n Faker.seed()\\n random_exercise_no = fake.pyint(1, 50)\\n random_data = fake.sentence()\\n\\n insert_statement = f\\\"\\\"\\\"insert into saves (user_id, course_id, lesson_no, exercise_no,data) \\n values ({random_user_id}, {random_course_id}, {random_lesson_no}, \\n {random_exercise_no}, '{random_data}');\\\"\\\"\\\"\\n c.execute(insert_statement)\\n\\n connection.commit()\",\n \"def load_wdi() -> None:\\n\\n log.info(\\\"Started loading WDI.\\\")\\n df = _load_and_stage_wdi()\\n\\n schema = \\\"wdi_202005\\\"\\n db.drop_schema(schema)\\n db.create_schema(schema)\\n\\n # Push completely raw but staged data\\n log.debug(\\\"Done preparing raw WDI, pushing.\\\")\\n fqtables = [f\\\"{schema}.cy_unimp_part_{p}\\\" for p in [1, 2]]\\n db.df_to_split_db(df=df, fqtables=fqtables)\\n\\n _fill_and_push_wdi(df=df, schema=schema, n_imp=5)\\n log.info(\\\"Finished loading WDI.\\\")\",\n \"def read_weather_data():\\n # Check if UTC to gmt+1 conversion is being handled correctly\\n weather = pd.read_csv('//datc//opschaler//weather_data//knmi_10_min_raw_data//output//df_combined_uncleaned.csv',\\n delimiter='\\\\t', comment='#',\\n parse_dates=['datetime'])\\n weather = weather.set_index(['datetime'])\\n return weather\",\n \"def set_data(self, year, month):\\n data = _download_to_df(self.url, self.table_name, year, month)\\n data = data.loc[:, self.table_columns]\\n with self.con:\\n data.to_sql(self.table_name, con=self.con, if_exists='replace', index=False)\\n self.con.commit()\",\n \"def generate_fake_data(context, db_connection):\\n db_cursor = db_connection.cursor()\\n\\n for table in context.tables:\\n faker = fake_data_gen.Faker(table, context)\\n _fill_table(table, faker, db_cursor)\\n db_connection.commit()\\n\\n db_cursor.close()\",\n \"def load_data(city, month, day):\\n\\n print(\\\"\\\\nWe are loading the information for the selected filters.\\\")\\n start_time = time.time()\\n\\n # filter the data according to the selected city\\n if isinstance(city, list):\\n df = pd.concat(map(lambda city: pd.read_csv(CITY_DATA[city]), city),\\n sort=True)\\n # reorganize DataFrame columns after a city concat\\n try:\\n df = df.reindex(columns=['Unnamed: 0', 'Start Time', 'End Time',\\n 'Trip Duration', 'Start Station',\\n 'End Station', 'User Type', 'Gender',\\n 'Birth Year'])\\n except:\\n pass\\n else:\\n df = pd.read_csv(CITY_DATA[city])\\n\\n # create columns to see the statistics\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n df['Month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.weekday_name\\n df['Start Hour'] = df['Start Time'].dt.hour\\n\\n # filter month and weekday see the data in two new DataFrames\\n if isinstance(month, list):\\n df = pd.concat(map(lambda month: df[df['Month'] ==\\n (months.index(month)+1)], month))\\n else:\\n df = df[df['Month'] == (months.index(month)+1)]\\n\\n if isinstance(day, list):\\n df = pd.concat(map(lambda day: df[df['day_of_week'] ==\\n (day.title())], day))\\n else:\\n df = df[df['day_of_week'] == day.title()]\\n\\n print(\\\"\\\\nThis took {} seconds.\\\".format((time.time() - start_time)))\\n print('-'*40)\\n\\n return df\",\n \"def init_tables(self):\\n\\n settings.Base.metadata.tables[\\n 'session_master'].drop(bind=settings.engine)\\n settings.Base.metadata.tables['uurl'].drop(bind=settings.engine)\\n\\n settings.Base.metadata.tables[\\n 'session_master'].create(bind=settings.engine)\\n settings.Base.metadata.tables['uurl'].create(bind=settings.engine)\\n\\n logging.info(\\\"Sessionization Tables created\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6654514","0.66226166","0.6348557","0.632052","0.62991303","0.62082505","0.61876976","0.6142009","0.60627335","0.6002963","0.5983051","0.59750384","0.5964339","0.5926051","0.59136045","0.59097177","0.58774203","0.5861508","0.5857061","0.5843847","0.58242774","0.58109164","0.58074266","0.5801949","0.57935333","0.57928663","0.5791196","0.5760634","0.57488865","0.5742264","0.57342136","0.57302773","0.57123727","0.5710525","0.5705605","0.57034856","0.5700655","0.5695572","0.5690213","0.5680076","0.5660745","0.5651602","0.5647642","0.56444156","0.5639949","0.5622278","0.558798","0.5572142","0.55607635","0.5548546","0.5548171","0.55465627","0.55456936","0.5512822","0.5512715","0.5511217","0.550549","0.5504847","0.55042017","0.54941374","0.54896235","0.5459899","0.5448563","0.54432505","0.5442965","0.5441291","0.543846","0.54333615","0.54299766","0.5425626","0.54237247","0.54230624","0.54210615","0.5419529","0.5412184","0.5400605","0.5378381","0.53771275","0.53707474","0.53680986","0.5367555","0.5366793","0.53660566","0.53658164","0.53618175","0.53607786","0.53498614","0.5347209","0.5346267","0.53462064","0.5345792","0.5345397","0.53384537","0.53377444","0.5336784","0.5336686","0.53362316","0.53319883","0.5330822","0.53300315"],"string":"[\n \"0.6654514\",\n \"0.66226166\",\n \"0.6348557\",\n \"0.632052\",\n \"0.62991303\",\n \"0.62082505\",\n \"0.61876976\",\n \"0.6142009\",\n \"0.60627335\",\n \"0.6002963\",\n \"0.5983051\",\n \"0.59750384\",\n \"0.5964339\",\n \"0.5926051\",\n \"0.59136045\",\n \"0.59097177\",\n \"0.58774203\",\n \"0.5861508\",\n \"0.5857061\",\n \"0.5843847\",\n \"0.58242774\",\n \"0.58109164\",\n \"0.58074266\",\n \"0.5801949\",\n \"0.57935333\",\n \"0.57928663\",\n \"0.5791196\",\n \"0.5760634\",\n \"0.57488865\",\n \"0.5742264\",\n \"0.57342136\",\n \"0.57302773\",\n \"0.57123727\",\n \"0.5710525\",\n \"0.5705605\",\n \"0.57034856\",\n \"0.5700655\",\n \"0.5695572\",\n \"0.5690213\",\n \"0.5680076\",\n \"0.5660745\",\n \"0.5651602\",\n \"0.5647642\",\n \"0.56444156\",\n \"0.5639949\",\n \"0.5622278\",\n \"0.558798\",\n \"0.5572142\",\n \"0.55607635\",\n \"0.5548546\",\n \"0.5548171\",\n \"0.55465627\",\n \"0.55456936\",\n \"0.5512822\",\n \"0.5512715\",\n \"0.5511217\",\n \"0.550549\",\n \"0.5504847\",\n \"0.55042017\",\n \"0.54941374\",\n \"0.54896235\",\n \"0.5459899\",\n \"0.5448563\",\n \"0.54432505\",\n \"0.5442965\",\n \"0.5441291\",\n \"0.543846\",\n \"0.54333615\",\n \"0.54299766\",\n \"0.5425626\",\n \"0.54237247\",\n \"0.54230624\",\n \"0.54210615\",\n \"0.5419529\",\n \"0.5412184\",\n \"0.5400605\",\n \"0.5378381\",\n \"0.53771275\",\n \"0.53707474\",\n \"0.53680986\",\n \"0.5367555\",\n \"0.5366793\",\n \"0.53660566\",\n \"0.53658164\",\n \"0.53618175\",\n \"0.53607786\",\n \"0.53498614\",\n \"0.5347209\",\n \"0.5346267\",\n \"0.53462064\",\n \"0.5345792\",\n \"0.5345397\",\n \"0.53384537\",\n \"0.53377444\",\n \"0.5336784\",\n \"0.5336686\",\n \"0.53362316\",\n \"0.53319883\",\n \"0.5330822\",\n \"0.53300315\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.78376013"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":2892,"cells":{"query":{"kind":"string","value":"Creates and populates water and weather database. Returns None"},"document":{"kind":"string","value":"def create_databases():\n db_connection = connect_to_db()\n\n # Create database tables.\n create_tables(db_connection)\n\n # Populate water tables.\n populate_water_tables(db_connection)\n\n # station_data = get_station_data()\n # station = station_data.query('ŠIFRA == 30301')\n # print(station)\n # index = station.index[0]\n # lat = station.at[index, 'LAT']\n # lng = station.at[index, 'LON']\n # name = f\"{station.at[index, 'VODOMERNA POSTAJA']} ({station.at[index, 'VODOTOK']})\"\n # print(index, lat, lng, name)\n\n # Populate location tables\n # populate_locations(db_connection)\n\n # Populate weather tables\n populate_weather(db_connection)\n\n db_connection.commit()\n db_connection.close()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def populate_weather(connection):\n metadata = load_metadata('weather')\n cursor = connection.cursor()\n water_defs = get_water_definitions()\n\n # Check if tables are already populated.\n cursor.execute('SELECT count(*) FROM weather')\n weather_count = cursor.fetchone()[0]\n\n if weather_count:\n print('Weather tables already populated!')\n return\n\n print('WEATHER:')\n\n # Darksky data\n for dir_name, location in metadata.items():\n print(f'\\tPopulating weather: \"{location[\"name\"]}\".')\n\n # Insert location.\n cursor.execute(f'''INSERT INTO locations(name, lat, lng)\n VALUES ('{location['name']}', {location['lat']}, {location['lng']})''')\n location_id = cursor.lastrowid\n\n # Set weather locations for watercourses/aquifers.\n for water_body in [d['body'] for d in water_defs.values()]:\n if water_body in location:\n cursor.execute(f'''UPDATE {water_body}s\n SET location_id = {location_id}\n WHERE name IN ('{\"','\".join(location[water_body])}')''')\n break\n\n dir_path = get_data_path('weather', 'raw', dir_name)\n for json_file_name in os.listdir(dir_path):\n json_path = os.path.join(dir_path, json_file_name)\n with open(json_path, 'r', encoding='utf-8') as json_file:\n print(f'\\t\\tPopulating year: {json_file_name[0:-5]}')\n year_forecasts = json.load(json_file)\n for date, date_forecast in year_forecasts.items():\n hourly_forecasts = date_forecast['hourly']\n\n if not hourly_forecasts:\n print(f'\\t\\tNo hourly forecasts for {date}!')\n continue\n\n daily_forecast = {\n 'location_id': location_id,\n 'time': date_forecast['time'],\n 'day_time': date_forecast['sunset_time'] - date_forecast['sunrise_time'],\n 'precipitation': 0,\n 'snow_accumulation': 0\n }\n # List of value names with `avg`, `min` and `max` values\n value_names = {\n 'temperature': 'temperature',\n 'cloud_cover': 'cloudCover',\n 'dew_point': 'dewPoint',\n 'humidity': 'humidity',\n 'pressure': 'pressure',\n 'uv_index': 'uvIndex',\n 'precipitation_probability': 'precipProbability',\n 'precipitation_intensity': 'precipIntensity'\n }\n # Value name counters, which indicate how many times (out of 24)\n # certain value appears in hourly data.\n value_counts = {k: 0 for k in value_names.keys()}\n\n for value_name in value_names.keys():\n daily_forecast[f'{value_name}_avg'] = 0.0\n daily_forecast[f'{value_name}_min'] = float('inf')\n daily_forecast[f'{value_name}_max'] = float('-inf')\n\n # Calculate daily forecast values from hourly forecasts.\n for hourly_forecast in hourly_forecasts:\n for value_name in value_names.keys():\n orig_value_name = value_names[value_name]\n if is_forecast_number(orig_value_name, hourly_forecast):\n daily_forecast[f'{value_name}_avg'] += hourly_forecast[orig_value_name]\n daily_forecast[f'{value_name}_min'] = min(\n hourly_forecast[orig_value_name],\n daily_forecast[f'{value_name}_min']\n )\n daily_forecast[f'{value_name}_max'] = max(\n hourly_forecast[orig_value_name],\n daily_forecast[f'{value_name}_max']\n )\n value_counts[value_name] += 1\n\n if is_forecast_number('precipAccumulation', hourly_forecast) \\\n and hourly_forecast['precipType'] == 'snow':\n daily_forecast['snow_accumulation'] += hourly_forecast['precipAccumulation']\n elif is_forecast_number('precipIntensity', hourly_forecast) \\\n and is_forecast_number('precipProbability', hourly_forecast):\n daily_forecast['precipitation'] += \\\n hourly_forecast['precipIntensity'] * hourly_forecast['precipProbability']\n\n for value_name, value_count in value_counts.items():\n if value_count:\n # Calculate average.\n daily_forecast[f'{value_name}_avg'] = daily_forecast[f'{value_name}_avg'] / value_count\n else:\n # If value never appeared\n daily_forecast[f'{value_name}_avg'] = 'NULL'\n daily_forecast[f'{value_name}_min'] = 'NULL'\n daily_forecast[f'{value_name}_max'] = 'NULL'\n\n cursor.execute(f'''INSERT INTO weather({', '.join(daily_forecast.keys())})\n VALUES ({', '.join([str(v) for v in daily_forecast.values()])})''')\n\n # IOT data:\n for location in SETTINGS['weather_locations_iot']:\n print(f'\\tPopulating weather: \"{location[\"name\"]}\".')\n\n # Insert location.\n cursor.execute(f'''INSERT INTO locations(name, lat, lng)\n VALUES ('{location['name']}', {location['lat']}, {location['lng']})''')\n location_id = cursor.lastrowid\n\n # Set weather locations for watercourses/aquifers.\n for water_body in [d['body'] for d in water_defs.values()]:\n if water_body in location:\n cursor.execute(f'''UPDATE {water_body}s\n SET location_id = {location_id}\n WHERE name IN ('{\"', '\".join(location[water_body])}')''')\n\n # Set locations for all stations on given water body to match its location.\n cursor.execute(f'''SELECT id\n FROM {water_body}s\n WHERE location_id = {location_id}''')\n ids = [row[0] for row in cursor.fetchall()]\n if len(ids):\n cursor.execute(f'''UPDATE {water_body}_stations\n SET location_id = {location_id}\n WHERE {water_body}_id IN ({', '.join([str(v) for v in ids])})''')\n\n break \n \n file_name = f'''{location['lat']}-{location['lng']}.json'''\n json_path = get_data_path('weather', 'raw', file_name)\n\n # If data file doesn't exist, download it first.\n if not os.path.isfile(json_path):\n with open(json_path, 'wb', encoding=\"utf-8\") as file:\n file.write(read_from_url(location['url'], decode=False))\n \n with open(json_path, 'r', encoding='utf-8') as json_file:\n row_names = {\n \"Sun_duration\": \"sun_duration\",\n \"CloudCover\": \"cloud_cover_avg\",\n \"Percipitation\": \"precipitation\",\n \"New_snow_blanket\": \"snow_accumulation\",\n \"Snow_blanket\": \"snow_depth\",\n \"TemperatureAvg\": \"temperature_avg\",\n \"TemperatureMin\": \"temperature_min\",\n \"TemperatureMax\": \"temperature_max\"\n }\n forecasts = json.load(json_file)\n for forecast in forecasts:\n f = {row_names[k]: forecast[k] for k in row_names.keys()}\n f['location_id'] = location_id\n f['time'] = round(forecast['LastUpdatedEpoch'] / 1000)\n cursor.execute(f'''INSERT INTO weather({', '.join(f.keys())})\n VALUES ({', '.join([str(v) for v in f.values()])})''')","def create_db(temp: list, rain: list, humidity: list, wind: list) -> dict:\r\n weather = {}\r\n for i in range(len(temp)):\r\n weather[i+1] = [temp[i], rain[i], humidity[i], wind[i]]\r\n return weather","def populate_database(telescope_name, instrument_name):\n telescope = Telescope.objects.create(\n name=telescope_name, latitude=25.0, longitude=45.0)\n instrument = Instrument.objects.create(\n name=instrument_name, telescope=telescope)\n for year_int in (2012, 2013):\n for month_int in range(1, 13):\n for night_int in (1, monthrange(year_int, month_int)[1]):\n ut_date = date(year_int, month_int, night_int)\n night = Night.objects.create(\n ut_date=ut_date, instrument=instrument, observers='Smith')\n Exposure.objects.create(\n night=night, run_number=1, ut_start=time(10, 0, 0),\n exposed=20.0, ra=60.0, dec=30.0, object_exp=True)\n Exposure.objects.create(\n night=night, run_number=2, ut_start=time(11, 0, 0),\n exposed=30.0, ra=90.0, dec=0.0, object_exp=True)\n Exposure.objects.create(\n night=night, run_number=3, ut_start=time(12, 0, 0),\n exposed=40.0, ra=120.0, dec=-30.0, object_exp=False)","def populate_water_tables(connection):\n metadata = load_metadata('water')\n cursor = connection.cursor()\n\n # Check if tables are already populated.\n cursor.execute('SELECT count(*) FROM watercourses')\n watercourse_count = cursor.fetchone()[0]\n cursor.execute('SELECT count(*) FROM aquifers')\n aquifer_count = cursor.fetchone()[0]\n\n if watercourse_count and aquifer_count:\n print('Water tables already populated!')\n return\n\n station_data = get_station_data()\n\n for archive in metadata.keys():\n print(f'{archive}-water:'.upper())\n water_body = get_water_definitions(archive)['body']\n\n # 1. Populate watercourses/aquifers:\n stations = {}\n for water_body_name in metadata[archive].keys():\n print(f'\\tPopulating {water_body}: \"{water_body_name}\"')\n cursor.execute(f'''INSERT INTO {water_body}s(location_id, name)\n VALUES (0, '{water_body_name}')''')\n water_body_id = cursor.lastrowid\n\n # 2. Populate watercourse_stations/aquifer_stations:\n for station_id in metadata[archive][water_body_name]['stations']:\n station_name = clean_name(metadata[archive][water_body_name]['stations'][station_id]['name'])\n\n if station_id in stations:\n # Prefer watercourses/aquifer with more stations\n current_len = len(metadata[archive][water_body_name]['stations'])\n previous_len = len(metadata[archive][stations[station_id]]['stations'])\n\n if current_len < previous_len:\n print(f'\\t\\tStation already exists: {station_id} - \"{station_name}\" (\"{water_body_name}\")')\n continue\n else:\n cursor.execute(f'''DELETE \n FROM {water_body}_stations\n WHERE id = {station_id}''')\n print(f'\\t\\tRemoved station: {station_id} - \"{station_name}\" from \"{stations[station_id]}\")')\n\n stations[station_id] = water_body_name\n print(f'\\t\\tPopulating station: {station_id} - \"{station_name}\"')\n\n # Insert station location if station data exists.\n location_id = 0\n station_row = station_data.query(f'ŠIFRA == \"{station_id}\"')\n if not station_row.empty:\n index = station_row.index[0]\n lat = station_row.at[index, 'LAT']\n lng = station_row.at[index, 'LON']\n if not np.isnan(lat) and not np.isnan(lng):\n name = f\"{station_row.at[index, 'VODOMERNA POSTAJA']} ({station_row.at[index, 'VODOTOK']})\"\n cursor.execute(f'''INSERT INTO locations(name, lat, lng)\n VALUES ('{name}', {lat}, {lng})''')\n location_id = cursor.lastrowid\n\n # Insert station.\n cursor.execute(f'''INSERT INTO {water_body}_stations(id, {water_body}_id, location_id, name)\n VALUES ({station_id}, {water_body_id}, {location_id}, '{station_name}')''')\n\n # 3. Populate watercourse_measurements/aquifer_measurements:\n if not populate_water_measurements(cursor, archive, metadata[archive][water_body_name]['dir'],\n station_id):\n cursor.execute(f'''DELETE \n FROM {water_body}_stations\n WHERE id = {station_id}''')\n print(f'\\t\\tRemoved station with useless data: {station_id} - \"{station_name}\"')\n\n # Remove empty watercourses/aquifers.\n cursor.execute(f'''SELECT w.id, w.name\n FROM {water_body}s w\n WHERE NOT EXISTS (\n SELECT s.id \n FROM {water_body}_stations s \n WHERE w.id = s.{water_body}_id\n )''')\n\n for row in cursor.fetchall():\n cursor.execute(f'''DELETE \n FROM {water_body}s\n WHERE id = {row[0]}''')\n print(f'\\tRemoved empty {water_body}: \"{row[1]}\"')","def update_weather(location_request, db):\n with open(expanduser(\"~/bin/my_utilities/config/darksky-key\")) as f:\n ds_key = f.readline().strip()\n current = []\n current_day = 0\n with forecast(ds_key, *location_request, units=\"uk2\") as location:\n raw = location['hourly']['data'][0]\n current.append(datetime.datetime.now().hour)\n current.append(day_relative_to_absolute(current_day))\n current.append(raw[\"temperature\"])\n current.append(raw[\"apparentTemperature\"])\n current.append(raw[\"precipIntensity\"])\n current.append(raw[\"precipProbability\"] * 100)\n current.append(raw[\"humidity\"] * 100)\n current.append(raw[\"dewPoint\"])\n current.append(raw[\"windSpeed\"])\n current.append(raw[\"windBearing\"])\n current.append(raw[\"windGust\"])\n current.append(raw[\"pressure\"])\n current.append(raw[\"cloudCover\"] * 100)\n current.append(raw[\"uvIndex\"])\n current.append(raw[\"visibility\"])\n current = format_list_for_db(current)\n\n columns = [\"hour\", \"day\", \"temp\", \"apptemp\", \"precipint\", \"precipprob\",\n \"humidity\", \"dewpoint\", \"windspeed\", \"windbearing\",\n \"windgust\", \"pressure\", \"cloudcover\", \"uvindex\", \"visibility\"]\n columns = format_list_for_db(columns)\n statement = f\"INSERT INTO WEATHER {columns} VALUES {current}\"\n print(statement)\n cursor = db.cursor()\n cursor.execute(statement)\n cursor.close()","def prepare_database(self, waterscenario=None, trafficscenario=None):\n\n # Validate input\n if waterscenario:\n waterscenario = Path(waterscenario)\n assert waterscenario.exists(), 'Waterscenario file not found'\n\n BIVAS = pyBIVAS(self.BIVAS_database)\n df_trafficscenarios = BIVAS.trafficscenario_numberoftrips()\n\n\n # Do changes to database:\n con = sqlite3.connect(self.BIVAS_database)\n c = con.cursor()\n\n # Update waterscenario with given file\n if waterscenario:\n # Delete current water_scenario_values\n sql = \"DELETE FROM water_scenario_values WHERE 1\"\n c.execute(sql)\n\n sql = \"DELETE FROM water_scenarios WHERE 1\"\n c.execute(sql)\n\n # Write waterdata to database\n\n # Read waterscenario file\n df = pd.read_csv(waterscenario, header=0, index_col=None)\n df = df[['ArcID', 'SeasonID', 'WaterLevel__m', 'RateOfFlow__m3_s', 'WaterSpeed__m_s', 'WaterDepth__m']]\n df['WaterScenarioID'] = 1\n\n # Add new water_scenario\n df.to_sql('water_scenario_values', con,\n if_exists='append', index=False)\n\n # Rename water_scenario\n # waterscenario_name = waterscenario.stem\n # sql = \"\"\"UPDATE water_scenarios SET Description = \"{}\" WHERE ID = {}\"\"\".format(\n # waterscenario_name, waterscenario)\n # c.execute(sql)\n\n\n waterscenario_id = 1\n waterscenario_name = 'TEST waterscenario'\n waterscenario_type = 1\n sql = \"\"\"INSERT into water_scenarios VALUES ({}, '{}', {})\"\"\".format(\n waterscenario_id,\n waterscenario_name,\n waterscenario_type\n )\n c.execute(sql)\n\n # Remove water scenario. I'm simply updating all scenarios\n # Otherwise I should check the BranchSet structure\n sql = \"\"\"UPDATE parameters SET WaterScenarioID = 1 WHERE 1\"\"\"\n c.execute(sql)\n\n else:\n # Remove water scenario. I'm simply updating all scenarios\n # Otherwise I should check the BranchSet structure\n sql = \"\"\"UPDATE parameters SET WaterScenarioID = NULL WHERE 1\"\"\"\n c.execute(sql)\n\n # Set scenario name and description\n date_string = datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n self.description = f'Date: {date_string}, Waterscenario: {waterscenario}, TrafficScenario: {trafficscenario},'\n\n sql = \"\"\"\n UPDATE scenarios\n SET Name = \"{}\",\n Description = \"{}\"\n WHERE ID = {}\n \"\"\".format(\n self.scenarioName, self.description, self.scenarioID)\n c.execute(sql)\n\n # Update traffic Scenario. I'm simply updating all scenarios\n # Otherwise I should check the BranchSet structure\n if trafficscenario:\n if isinstance(trafficscenario, int):\n sql = \"\"\"UPDATE parameters SET TrafficScenarioID = \"{}\" WHERE 1\"\"\".format(trafficscenario)\n c.execute(sql)\n else:\n trafficScenarioID = df_trafficscenarios.index[df_trafficscenarios['Description'] == trafficscenario][0]\n sql = \"\"\"UPDATE parameters SET TrafficScenarioID = \"{}\" WHERE 1\"\"\".format(trafficScenarioID)\n c.execute(sql)\n\n con.commit()\n con.close()\n\n logger.info('BIVAS database copied and updated')","def store_weather(data: List[WeatherData]):\n insert_cmd = 'INSERT INTO weather VALUES (?, ?, ?)'\n with closing(sqlite3.connect('weather.db')) as conn:\n with closing(conn.cursor()) as cursor:\n cursor.executemany(insert_cmd, data)\n conn.commit()","def create_table(self):\n # Connect to database\n conn = sqlite3.connect(self)\n # Create a cursor\n c = conn.cursor()\n\n # Create a Table\n c.execute(\"\"\"CREATE TABLE weather (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n sensor text,\n location text,\n temperature real,\n description text,\n time text\n )\"\"\")\n # Commit our command\n conn.commit()\n # Close our connection\n conn.close()","def grasspi_add_db(table_name,row):\n\n if table_name == \"weatherdata\":\n\tconn = sqlite3.connect(grasspi_config.cfg.db_file)\n \tc = conn.cursor()\n\tc.execute('INSERT INTO ' + table_name + ' values (?,?,?,?,?,?,?,?,?,?,?,?,?)',[row[\"date\"],row[\"time\"],\n \trow[\"current_temp\"],row[\"current_rain\"],row[\"total_rain\"],row[\"current_wind_speed\"],\n \trow[\"current_wind_direction\"],row[\"current_humidity\"],row[\"current_air_pressure\"],\n \trow[\"current_shortwave_rad\"],row[\"current_atm_rad\"],row[\"day_length\"],row[\"elevation\"]])\n elif table_name == \"wateringschedule\":\n\tconn = sqlite3.connect(grasspi_config.cfg.db_file, detect_types=sqlite3.PARSE_DECLTYPES|sqlite3.PARSE_COLNAMES)\n \tc = conn.cursor()\n\tc.execute('INSERT INTO ' + table_name + ' values (?,?,?)',[row[\"zonenumber\"],\n\trow[\"starttime\"],row[\"duration\"]])\n # Save (commit) the changes\n conn.commit()\n # We can also close the cursor if we are done with it\n c.close()","def initial_db_setup() -> None:\n db_filename = \"twdft.db\"\n db_path = os.path.join(TWDFT_DATA_DIR, db_filename)\n csv_filename = \"sites.csv\"\n csv_path = os.path.join(TWDFT_DATA_DIR, csv_filename)\n db_is_new = not os.path.exists(db_path)\n sites_csv = os.path.join(TWDFT_DATA_DIR, csv_filename)\n\n if db_is_new:\n with sqlite3.connect(db_path) as conn:\n c = conn.cursor()\n\n # first we create a site object\n c.execute(\n \"\"\"\n CREATE TABLE site(\n id INTEGER PRIMARY KEY,\n name TEXT,\n site_type TEXT,\n sub_category TEXT,\n address_1 TEXT,\n address_2 TEXT,\n town TEXT,\n county TEXT,\n country TEXT,\n postcode TEXT,\n site_category TEXT,\n freq_target TEXT,\n created TEXT,\n notes TEXT,\n last_inspection TEXT,\n next_inspection TEXT,\n pfsp_approval TEXT,\n pfsp_expiry TEXT,\n unlocode TEXT,\n pfso TEXT,\n pso TEXT,\n pfsa_approval TEXT,\n pfsa_expiry TEXT,\n team TEXT,\n created_by TEXT,\n last_updated TEXT,\n updated_by TEXT,\n afp_loc TEXT,\n rdf TEXT,\n classification TEXT,\n article24 TEXT,\n psa_approval TEXT,\n inspection_due TEXT\n )\n \"\"\"\n )\n conn.commit()\n\n # next we want an inspection table\n\n c.execute(\n \"\"\"\n CREATE TABLE inspection(\n id INTEGER PRIMARY KEY,\n site INTEGER,\n date TEXT,\n status TEXT,\n time TEXT,\n FOREIGN KEY(site) REFERENCES site(id)\n )\n \"\"\"\n )\n conn.commit()\n\n # next we want an inspector table\n c.execute(\n \"\"\"\n create table inspector(\n id integer primary key,\n first_name text,\n last_name text\n )\n \"\"\"\n )\n conn.commit()\n\n for i in INSPECTORS:\n first = i.split(\" \")[0]\n last = i.split(\" \")[1]\n c.execute(\n \"INSERT INTO inspector(first_name, last_name) VALUES (?,?)\",\n (first, last),\n )\n\n # a table that links inspectors with inspections\n c.execute(\n \"\"\"\n CREATE TABLE inspector_inspections(\n inspector INTEGER,\n inspection INTEGER,\n FOREIGN KEY (inspector) REFERENCES inspector(id),\n FOREIGN KEY (inspection) REFERENCES inspection(id)\n )\n \"\"\"\n )\n conn.commit()\n\n for site in map(Site._make, csv.reader(open(csv_path, \"r\"))):\n try:\n c.execute(\n f\"\"\"\n INSERT INTO site VALUES(?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)\"\"\",\n (\n int(site.id.replace(\",\", \"\")),\n site.name,\n site.site_type,\n site.sub_category,\n site.address_1,\n site.address_2,\n site.town,\n site.county,\n site.country,\n site.postcode,\n site.site_category,\n site.freq_target,\n site.created,\n site.notes,\n site.last_inspection,\n site.next_inspection,\n site.pfsp_approval,\n site.pfsp_expiry,\n site.unlocode,\n site.pfso,\n site.pso,\n site.pfsa_approval,\n site.pfsa_expiry,\n site.team,\n site.created_by,\n site.last_updated,\n site.updated_by,\n site.afp_loc,\n site.rdf,\n site.classification,\n site.article24,\n site.psa_approval,\n site.inspection_due,\n ),\n )\n except sqlite3.IntegrityError as e:\n print(\"That hasnae worked\", site.inspection_due)","def load_wdi() -> None:\n\n log.info(\"Started loading WDI.\")\n df = _load_and_stage_wdi()\n\n schema = \"wdi_202005\"\n db.drop_schema(schema)\n db.create_schema(schema)\n\n # Push completely raw but staged data\n log.debug(\"Done preparing raw WDI, pushing.\")\n fqtables = [f\"{schema}.cy_unimp_part_{p}\" for p in [1, 2]]\n db.df_to_split_db(df=df, fqtables=fqtables)\n\n _fill_and_push_wdi(df=df, schema=schema, n_imp=5)\n log.info(\"Finished loading WDI.\")","def sql_functions(cities, weather):\n con = lite.connect(\"sql_database.db\")\n tables_tuple = (\"cities\", \"weather\")\n with con:\n #Generate tables in database:\n cur = con.cursor() #Get cursor object\n for table in tables_tuple:\n cur.execute(\"DROP TABLE IF EXISTS {0}\".format(table)) #Drop tables if they already exist.\n cur.execute(\"CREATE TABLE cities (name text, state text)\")\n cur.execute(\"CREATE TABLE weather (city text, year integer, warm_month text, cold_month text, average_high integer)\")\n #Populate tables in database:\n cur.executemany(\"INSERT INTO cities VALUES (?,?)\", cities)\n cur.executemany(\"INSERT INTO weather VALUES (?,?,?,?,?)\", weather)\n #Retrieve data from database:\n cur.execute(\"SELECT * FROM cities INNER JOIN weather ON city = name\")\n rows = cur.fetchall()\n cols = [desc[0] for desc in cur.description]\n output_dataframe = pd.DataFrame(rows, columns = cols)\n \n return output_dataframe","def prepare_db():\n conn = sqlite.connect(\"temp.db\")\n sql = conn.cursor()\n sql.execute(\"SELECT sql FROM sqlite_master WHERE name='points'\")\n rows = sql.fetchall()\n if len(rows) == 0:\n print \"Database does not exist. Creating Database...\"\n sql.execute('''CREATE TABLE points\n (date datetime, humidity real, temp_c real, temp_f real, index_c real, index_f)''')\n print \"Database created\"\n conn.close()","def init_db() -> None:\n conn = sqlite3.connect('../Utils/map_storage.db')\n cursor = conn.cursor()\n\n with conn:\n station_cmd = \"\"\"CREATE TABLE IF NOT EXISTS\n nodes(city TEXT, name TEXT, is_station TEXT, x INT, y INT, zone TEXT)\"\"\"\n\n cursor.execute(station_cmd)\n\n connection_cmd = \"\"\"CREATE TABLE IF NOT EXISTS\n connections(city TEXT, name_1 TEXT, name_2 TEXT, color TEXT)\"\"\"\n\n cursor.execute(connection_cmd)","def _setupWeather(self, w, config):\n wnames = ('cloud', 'seeing')\n if w not in wnames:\n raise Exception('w should be one of %s' %(wnames))\n filename = config['%s_datafile' %(w)]\n file = open(filename, 'r')\n # Also assume flat file contains only date / value in a space or tab separated file. \n self.dates[w] = []\n self.weather[w] = []\n # Read the data file.\n print '# Reading weather data file %s' %(filename)\n for line in file:\n if line.startswith('#') | line.startswith('!'):\n continue\n self.dates[w].append(line.split()[0])\n self.weather[w].append(line.split()[1])\n file.close()\n self.dates[w] = numpy.array(self.dates[w], float)\n self.weather[w] = numpy.array(self.weather[w], float)\n # Check the total amount of data (mostly for user awareness):\n print '# Read %d weather values from %s file. ' %(len(self.weather[w]), filename)\n # Check that weather data is monotonically increasing in time. \n if not(numpy.all(numpy.diff(self.dates[w]))):\n order = self.dates[w].argsort()\n self.weather[w] = self.weather[w][order]\n self.dates[w] = self.dates[w][order]\n # Get the total length of time included in this (seeing/cloud) file,\n # so that we can determine a wrap-around date if we need that.\n self.maxtime[w] = self.dates[w].max()\n return","def database_setup():\n Base.metadata.create_all(bind=engine)\n db = LocalSession()\n try:\n populate_from_json(db, Vector, str(VECTORS))\n populate_from_json(db, Gender, str(GENDERS))\n populate_from_json(db, Tag, str(TAGS))\n finally:\n db.close()","def populate_database(self):\n self.insert_products()\n self.insert_categories()\n self.insert_products_categories()\n self.insert_stores()\n self.insert_products_stores()","def save (self) :\n\n try:\n # This opens and closes the connections to the database.\n # Since writings occur only every 10 minutes it wouldn't be efficient to\n # let the connection open.\n db = MySQLdb.connect(\n host=config.db_host, db=config.db_name, user=config.db_user, passwd=config.db_password)\n cur = db.cursor()\n\n # get the reservoir height to be able to measure the water level\n sqlQuery = \"SELECT heigth FROM main_reservoir WHERE res_id = {}\".format(self.reservoir)\n cur.execute(sqlQuery)\n # self.waterGap is the height of the part of the reservoir\n # that's out of water. reservoir height minus that value will give the actual water level\n reservoirHeight = cur.fetchone()[0]\n waterLevel = reservoirHeight - self.waterGap\n\n sqlQuery = \"\"\"\n INSERT INTO main_measurement (packetNr, waterLevel, pH, conductivity, reservoir_id, dateTime, salinity, tds)\n VALUES ({}, {}, {}, {}, {}, now(), {}, {})\n \"\"\".format(\n self.packetNr,\n waterLevel,\n self.pH,\n self.conductivity,\n self.reservoir,\n self.salinity,\n self.tds)\n cur.execute(sqlQuery)\n\n db.commit()\n db.close()\n except Exception as e:\n print('[Measurement#save] failed to save instance: {}'.format(e))","def create_db(self):\n\t\tcur, conn = self.open_connection()\n\n\t\t# this creates the meta table\n\t\tcommand = \"\"\" DROP TABLE IF EXISTS main;\n\t\tCREATE TABLE meta (\n\t\t\tid INTEGER PRIMARY KEY AUTOINCREMENT,\n\t\t\tUT_date TEXT,\n\t\t\ttime_of_obs TEXT,\n\t\t\tobject_name TEXT,\n\t\t\tintegration_time FLOAT,\n\t\t\tgrating INTEGER,\n\t\t\tcentral_wavelength FLOAT,\n\t\t\tslit_width INTEGER,\n\t\t\tphase_angle FLOAT,\n\t\t\tcomments TEXT\n\t\t\t);\"\"\"\n\n\t\tcur.executescript(command)\n\n\t\t# this creates the spectra table\n\t\tcommand = \"\"\" DROP TABLE IF EXISTS spectrum;\n\t\tCREATE TABLE spectra (\n\t\t\tspec_id INTEGER PRIMARY KEY AUTOINCREMENT,\n\t\t\tid INTEGER,\n\t\t\twave array,\n\t\t\tspectrum array,\n\t\t\tFOREIGN KEY(id) REFERENCES meta(id)\n\t\t\t);\"\"\"\n\n\t\tcur.executescript(command)\n\n\t\tconn.commit()\n\t\tconn.close()","def _populate():\n models.Base.metadata.create_all(engine)\n logger.info(\"Initalized database\")\n db = Session()\n\n hermann = models.Account(id=\"test\",\n name=\"Hermann Dörkschneider\",\n email=\"fakeaddress@lumeh.org\")\n db.add(hermann)\n\n journey = models.Journey(id=str(uuid.uuid4()),\n account_id=\"test\",\n visibility=\"PUBLIC\",\n start_time_utc=datetime.datetime.now(),\n stop_time_utc=datetime.datetime.now())\n db.add(journey)\n\n waypoint1 = models.Waypoint(journey=journey,\n time_utc=datetime.datetime.now(),\n accuracy_m=2.71,\n latitude=3.1416,\n longitude=1.618,\n height_m=10)\n db.add(waypoint1)\n\n waypoint2 = models.Waypoint(journey=journey,\n time_utc=datetime.datetime.now(),\n accuracy_m=5.1,\n latitude=3.1410,\n longitude=1.620,\n height_m=5)\n db.add(waypoint2)\n\n db.commit()\n logger.info(\"Created test account {}\".format(hermann))\n logger.info(\"Created test journey {}\".format(journey))","def populate_t_database():\n with open('minerals.json') as file:\n file = json.loads(file.read())\n\n for mineral in file[:22]:\n mineral_entry = Mineral.objects.get_or_create(**mineral)","def init_sensor_db(self, drop_tables=True):\n logger.debug(\"Creating Database Engine.\")\n db_engine = sqlalchemy.create_engine(self.db_info_obj.dbConn)\n\n if drop_tables:\n logger.debug(\"Drop system table if within the existing database.\")\n Base.metadata.drop_all(db_engine)\n\n logger.debug(\"Creating Sentinel1ASF Database.\")\n Base.metadata.bind = db_engine\n Base.metadata.create_all()","def get_data(traffic, weather, db):\r\n traffic_data = traffic.get_traffic()\r\n weather_data = weather.get_weather()\r\n db.traffic.insert_many(traffic_data)\r\n db.weather.insert_many(weather_data)\r\n print('于 {} 完成数据爬取及存储'.format(datetime.now()))\r\n print('当前交通数据条数 {}'.format(db.traffic.count_documents({})))\r\n print('当前天气数据条数 {}'.format(db.weather.count_documents({})))","def setup_database() -> sqlite3.Cursor:\n conn = sqlite3.connect(':memory:')\n cursor = conn.cursor()\n\n insert_books(cursor)\n insert_lookups(cursor)\n\n return cursor","def populate_water_measurements(cursor, archive, directory, station):\n csv_path = get_data_path(\n 'water',\n 'raw',\n archive,\n directory,\n f'{station}.csv'\n )\n\n with open(csv_path, 'r', encoding='utf-8') as file:\n reader = csv.reader(file, delimiter=';')\n header = next(reader)\n column_names_map = get_water_index_map(archive, header)\n\n if not column_names_map:\n return False\n\n water_body = get_water_definitions(archive)['body']\n\n for row in reader:\n column_values_map = get_water_value_map(row, column_names_map)\n if column_values_map:\n date = datetime.strptime(row[0], '%d.%m.%Y').date()\n data_columns = ', '.join(column_values_map.keys())\n data_values = ', '.join(column_values_map.values())\n cursor.execute(f'''INSERT INTO {water_body}_measurements (station_id, date, {data_columns})\n VALUES ({station}, '{str(date)}', {data_values})''')\n\n return True","def weather_script(\n init_data_path: str,\n output_path: str,\n workers: int,\n weatherAPI_rpm: int,\n geoAPI_rpm: int,\n) -> None:\n unzip(init_data_path, output_path)\n top_hotels_dataframe_without_addresses = primary_data_proc(output_path)\n geocoder = geocoder_setup(geoAPI_rpm)\n top_hotels_df_with_addresses = define_address(\n top_hotels_dataframe_without_addresses,\n workers,\n geocoder,\n )\n cities, countries, coordinates = city_center_coord(top_hotels_df_with_addresses)\n weather_df = pd.concat(\n [\n prev_weather(cities, countries, coordinates, workers, weatherAPI_rpm),\n forecast_weather(cities, countries, coordinates, workers, weatherAPI_rpm),\n ]\n )\n\n logger.info(\"Start to save results\")\n save_main_info(output_path, weather_df, top_hotels_df_with_addresses)\n logger.info(\"Finish\")","def weather():\r\n def weather_api_call():\r\n with open('config.json', 'r') as conf:\r\n conf = json.load(conf)\r\n # Gets the API key from the config.json file\r\n weather_api_key = conf[\"weather_api_key\"]\r\n weather_city_name = conf['weather_city_name']\r\n response = requests.get(\r\n 'http://api.openweathermap.org/data/2.5/weather?'\r\n 'q=' + weather_city_name + '&units=metric&appid=' + weather_api_key)\r\n resp_json = response.json()\r\n with open('weather.json', 'w') as outfile:\r\n # Uses the data from the API to overwrite the weather data\r\n json.dump(resp_json, outfile)\r\n outfile.close()\r\n\r\n def weather_data_extractor():\r\n with open('weather.json', 'r') as weather_json:\r\n weather_json = json.load(weather_json)\r\n temp = weather_json[\"main\"]\r\n weather_item = weather_json[\"weather\"]\r\n desc = weather_item[0]\r\n current_temperature = \"The current temperature is: \" + \\\r\n str(int(temp[\"temp\"])) + \"C\"\r\n current_feels_like = \"Feels like: \" + \\\r\n str(int(temp[\"feels_like\"])) + \"C\"\r\n forecast = desc[\"main\"]\r\n return current_feels_like, current_temperature, forecast\r\n\r\n weather_api_call()\r\n return weather_data_extractor()","def init_and_update_observe_table(self):\n # print CHN_CITY_LIST_FILE\n location = ''\n id = ''\n f = open(CHN_CITY_LIST_FILE, 'r')\n for line in f.readlines():\n line_list = line.strip('\\n').split(':')\n location = line_list[0]\n id = line_list[1]\n pm = get_pm(location)\n # get current weather\n weather_dict = get_open_weather(id)\n if weather_dict not in ('', None, [], {}):\n if 'error' in pm or pm == False:\n weather_dict['aqi'] = '无数据'#'N/A'\n else:\n weather_dict['aqi'] = pm['quality'] + '(' + str(pm['aqi']) + ')'\n db_record = self.db.search_observe_record(str(id))\n # db_record = []\n now_date = get_local_format_time()\n if db_record != []:#update\n self.db.update_observe_data(weather_dict['ptime'],weather_dict['time'],now_date,weather_dict['WD'],weather_dict['WS'],weather_dict['SD'],weather_dict['weather'],weather_dict['img1'],weather_dict['img2'],weather_dict['temp'],weather_dict['temp1'],weather_dict['temp2'],weather_dict['aqi'],id)\n else:#insert\n self.db.insert_observe_data(id,weather_dict['city'],weather_dict['ptime'],weather_dict['time'],now_date,weather_dict['WD'],weather_dict['WS'],weather_dict['SD'],weather_dict['weather'],weather_dict['img1'],weather_dict['img2'],weather_dict['temp'],weather_dict['temp1'],weather_dict['temp2'],weather_dict['aqi'])\n f.close()\n return True","def get_gis_historical_data():\n logging.info(\"Generating baseline reference and historical weather data.\")\n \n # Initialising function variables\n fake = Faker()\n geolocator = Nominatim()\n config_data = get_config()\n locations = config_data[\"location\"]\n \n # Check if there are no duplicate locations in the config.yaml file.\n if len(locations) != len(set(locations)):\n logging.error(\"Duplicate location found. Please check config.yaml file.\")\n raise ValueError\n \n # Initialise pandas dataframe column name for baseline reference\n # and historical data.\n df_ref = pd.DataFrame(columns=[\"Location\", \"Latitude\"\n ,\"Longitude\", \"Elevation\"\n ,\"Timezone\"])\n df_hist = pd.DataFrame(columns=[\"Location\", \"Date\"\n ,\"Month\", \"Temperature_Min\"\n ,\"Temperature_Max\", \"Humidity\"\n ,\"Pressure\"])\n \n # Generate weather data for each location.\n for idx, loc in enumerate(locations):\n \n logging.info(\"Retrieving geolocation data for {}.\".format(loc))\n \n # Retrieving geolocation data from geopy library.\n loc_data = geolocator.geocode(loc)\n \n logging.info(\"Check if the location {} is valid.\".format(loc))\n if loc_data is None:\n logging.error(\"Invalid location value supplied ({}). Please check config.yaml file.\".format(loc))\n raise ValueError\n logging.info(\"The location {} is valid.\".format(loc))\n \n city = get_city(loc)\n lat = loc_data.latitude\n lon = loc_data.longitude\n \n # Retrieving elevation data for the location.\n elev = get_elevation_data(lat, lon)\n \n for month in range(1, 13):\n \n logging.info(\"Retrieving {} weather data for month {}.\".format(loc, month))\n \n for sample in range(config_data[\"gis\"][\"sampling_number\"]):\n \n temp_min = None\n temp_max = None\n humidity = None\n pressure = None\n \n while temp_min is None or temp_max is None or humidity is None or pressure is None:\n \n year = random.randint(config_data[\"gis\"][\"year_start\"], config_data[\"gis\"][\"year_end\"])\n\n _, last_day = calendar.monthrange(year, month)\n\n datetime_start = datetime.datetime(year, month, 1)\n datetime_end = datetime.datetime(year, month, last_day)\n\n date_gen = fake.date_time_between_dates(datetime_start=datetime_start\n ,datetime_end=datetime_end)\n\n forecast = forecastio.load_forecast(config_data[\"forecastio_api_key\"]\n ,lat\n ,lon\n ,time=date_gen\n ,units=\"si\")\n\n historical_data = forecast.json[\"daily\"][\"data\"][0]\n \n timezone = forecast.json.get(\"timezone\", None)\n temp_min = historical_data.get(\"temperatureMin\", None)\n temp_max = historical_data.get(\"temperatureMax\", None)\n humidity = historical_data.get(\"humidity\", None) * 100\n pressure = historical_data.get(\"pressure\", None)\n \n df_temp_hist = pd.Series(dict(zip(df_hist.columns\n ,[city, date_gen\n ,date_gen.month, temp_min\n ,temp_max, humidity\n ,pressure])))\n \n df_hist = df_hist.append(df_temp_hist, ignore_index=True)\n \n df_temp_ref = pd.Series(dict(zip(df_ref.columns\n ,[city, lat\n ,lon, elev\n ,timezone])))\n df_ref = df_ref.append(df_temp_ref, ignore_index=True)\n \n logging.info(\"Generating position to consolidate latitude, longitude and elevation data\")\n df_pos = df_ref[[\"Latitude\", \"Longitude\", \"Elevation\"]].round(2)\n df_pos[\"Elevation\"] = df_pos[\"Elevation\"].astype(int) \n df_ref[\"Position\"] = df_pos.astype(str).apply(lambda x: \",\".join(x), axis=1)\n \n logging.info(\"Saving baseline reference data.\")\n df_ref.to_csv(get_file_path(folder_name=\"data\"\n ,subdirectory=config_data[\"gis\"][\"output_subdirectory\"]\n ,file_name=config_data[\"gis\"][\"output_base_reference_file_name\"])\n ,index=False)\n logging.info(\"Completed saving baseline reference data.\")\n\n logging.info(\"Saving baseline historical data.\")\n df_hist.to_csv(get_file_path(folder_name=\"data\"\n ,subdirectory=config_data[\"gis\"][\"output_subdirectory\"]\n ,file_name=config_data[\"gis\"][\"output_base_historical_file_name\"])\n ,index=False)\n logging.info(\"Completed saving baseline historical data.\")","def init_db():\n db.drop_all()\n db.create_all()\n seed_companies()\n seed_emission_reports()\n seed_reduction_targets()\n seed_milestones()","def main():\n database = dbdir + \"pyflangun.db\"\n sql_create_airports_table = \"\"\"CREATE TABLE IF NOT EXISTS airports (\n id integer PRIMARY KEY,\n name text NOT NULL,\n icao text\n );\"\"\"\n sql_create_weather_table = \"\"\"CREATE TABLE IF NOT EXISTS weather (\n id integer PRIMARY KEY,\n icao text NOT NULL,\n temp_f integer NOT NULL,\n dew_pt_f integer NOT NULL,\n status integer NOT NULL,\n FOREIGN KEY (icao) REFERENCES airports (icao)\n );\"\"\"\n # create a database connection\n conn = create_connection(database)\n # create tables\n if conn is not None:\n create_table(conn, sql_create_airports_table)\n create_table(conn, sql_create_weather_table)\n else:\n print(\"Error! cannot create the database connection.\")","def main():\n # Create the flow\n with Flow('pickle flow') as flow:\n db_table = create_table()\n weather_data = get_weather(LAT_NYC, LON_NYC, 2018)\n parsed_data = parse_weather(weather_data)\n populated_table = store_weather(parsed_data)\n populated_table.set_upstream(db_table)\n\n # Run the flow\n flow.run()","def generate_weather_data(self):\n months = pd.to_datetime(self.output['Local Time']).dt.month\n self.output['Month'] = months # set month values for later joins\n\n # merge output data frame with historical data to get ranges\n keys = ['Location', 'Month']\n m = pd.merge(self.output, self.histdata, how='left',\n left_on=keys, right_on=keys)\n\n # uniformly select random pressure, temperature\n # and humidity values between the historical max and min ranges\n r = np.random.rand(m.shape[0])\n m['Temperature'] = ((m['Tmean_high'] - m['Tmean_low']\n ) * r + m['Tmean_low']).round(1)\n m['Pressure'] = ((m['Pmax'] - m['Pmin']) * r + m['Pmin']).round(1)\n m['Humidity'] = ((m['Hmax'] - m['Hmin']) * r + m['Hmin']).astype(int)\n\n # drop redundant columns and assign to output\n dcols = ['Month', 'Timezone', 'Pmax', 'Pmin',\n 'Hmax', 'Hmin', 'Tmean_high', 'Tmean_low']\n m.drop(columns=dcols, inplace=True)\n self.output = m","def store_data_in_db(sensors):\n connection = sqlite3.connect('sensordata.db')\n cursor = connection.cursor()\n cursor.execute(\"\"\"CREATE TABLE IF NOT EXISTS sensordata (\n ID int, NAME text, VALUE real, UNIT text, TIME text )\"\"\"\n )\n with connection:\n for sensor in sensors:\n cursor.execute(\"INSERT INTO sensordata VALUES (:id, :name, :value, :unit, :time)\",\n {'id': sensor['id'], 'name': sensor['name'],\n 'value': sensor['value'], 'unit': sensor['unit'], 'time': sensor['datetime']})","def collect(self, start_date=None, end_date=None):\n if start_date is None:\n start_date = self.default_start\n if end_date is None:\n end_date = self.default_end\n\n cur = self.conn.cursor()\n\n # Maximum return is 1000 entries\n num_days = 1000 // len(self.stations)\n # Maximum date-range is 1 year\n if num_days > 365:\n num_days = 365\n\n for interval in netzero.util.time_intervals(\n start_date, end_date, days=num_days\n ):\n netzero.util.print_status(\n \"Weather\",\n \"Collecting: {} to {}\".format(\n interval[0].strftime(\"%Y-%m-%d\"), interval[1].strftime(\"%Y-%m-%d\")\n ),\n )\n\n # TODO -- REMOVE ASSUMPTION THAT LEN(DATA) < LIMIT\n raw_data = self.query_api(interval[0], interval[1])\n\n if raw_data is None:\n print(\"ERROR QUERYING API\") # TODO exception here?\n continue\n\n for entry in raw_data.get(\"results\", []):\n # Insert the weather data to the table, to be averaged later\n date = datetime.datetime.strptime(\n entry[\"date\"], \"%Y-%m-%dT%H:%M:%S\"\n ).date()\n value = entry[\"value\"]\n station = entry[\"station\"]\n\n cur.execute(\n \"INSERT OR IGNORE INTO weather VALUES (?, ?, ?)\", (date, value, station)\n )\n\n self.conn.commit()\n\n cur.close()\n\n netzero.util.print_status(\"Weather\", \"Complete\", newline=True)","def create():\n\tcreate_db()","def generate_polynesian_weather_data():\n weather_path = os.path.dirname(os.path.realpath(__file__))\n low_fp = weather_path + \"/polynesia_weather/low/1976/\"\n med_fp = weather_path + \"/polynesia_weather/med/1985/\"\n high_fp = weather_path + \"/polynesia_weather/high/1982/\"\n low_name = \"polynesia_1976\"\n med_name = \"polynesia_1985\"\n high_name = \"polynesia_1982\"\n generate_year_weather_data(low_fp, low_name)\n generate_year_weather_data(med_fp, med_name)\n generate_year_weather_data(high_fp, high_name)","def create_database():\n with connection:\n connection.execute(CREATE_MOVIE_TABLE)\n connection.execute(CREATE_USER_TABLE)\n connection.execute(CREATE_WATCHED_TABLE)","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()","def _create_petition_(self):\n self.__weather = create(self.__latitude, self.__longitude)","def create_temporal_database(dbif):\n global tgis_backend\n global tgis_version\n global tgis_db_version\n global tgis_database_string\n\n template_path = get_sql_template_path()\n msgr = get_tgis_message_interface()\n\n # Read all SQL scripts and templates\n map_tables_template_sql = open(os.path.join(\n template_path, \"map_tables_template.sql\"), 'r').read()\n raster_metadata_sql = open(os.path.join(\n get_sql_template_path(), \"raster_metadata_table.sql\"), 'r').read()\n raster3d_metadata_sql = open(os.path.join(template_path,\n \"raster3d_metadata_table.sql\"),\n 'r').read()\n vector_metadata_sql = open(os.path.join(template_path,\n \"vector_metadata_table.sql\"),\n 'r').read()\n raster_views_sql = open(os.path.join(template_path, \"raster_views.sql\"),\n 'r').read()\n raster3d_views_sql = open(os.path.join(template_path,\n \"raster3d_views.sql\"), 'r').read()\n vector_views_sql = open(os.path.join(template_path, \"vector_views.sql\"),\n 'r').read()\n\n stds_tables_template_sql = open(os.path.join(template_path,\n \"stds_tables_template.sql\"),\n 'r').read()\n strds_metadata_sql = open(os.path.join(template_path,\n \"strds_metadata_table.sql\"),\n 'r').read()\n str3ds_metadata_sql = open(os.path.join(template_path,\n \"str3ds_metadata_table.sql\"),\n 'r').read()\n stvds_metadata_sql = open(os.path.join(template_path,\n \"stvds_metadata_table.sql\"),\n 'r').read()\n strds_views_sql = open(os.path.join(template_path, \"strds_views.sql\"),\n 'r').read()\n str3ds_views_sql = open(os.path.join(template_path, \"str3ds_views.sql\"),\n 'r').read()\n stvds_views_sql = open(os.path.join(template_path, \"stvds_views.sql\"),\n 'r').read()\n\n # Create the raster, raster3d and vector tables SQL statements\n raster_tables_sql = map_tables_template_sql.replace(\"GRASS_MAP\", \"raster\")\n vector_tables_sql = map_tables_template_sql.replace(\"GRASS_MAP\", \"vector\")\n raster3d_tables_sql = map_tables_template_sql.replace(\n \"GRASS_MAP\", \"raster3d\")\n\n # Create the space-time raster, raster3d and vector dataset tables\n # SQL statements\n strds_tables_sql = stds_tables_template_sql.replace(\"STDS\", \"strds\")\n stvds_tables_sql = stds_tables_template_sql.replace(\"STDS\", \"stvds\")\n str3ds_tables_sql = stds_tables_template_sql.replace(\"STDS\", \"str3ds\")\n\n msgr.message(_(\"Creating temporal database: %s\" % (str(tgis_database_string))))\n\n if tgis_backend == \"sqlite\":\n # We need to create the sqlite3 database path if it does not exist\n tgis_dir = os.path.dirname(tgis_database_string)\n if not os.path.exists(tgis_dir):\n try:\n os.makedirs(tgis_dir)\n except Exception as e:\n msgr.fatal(_(\"Unable to create SQLite temporal database\\n\"\n \"Exception: %s\\nPlease use t.connect to set a \"\n \"read- and writable temporal database path\" % (e)))\n\n # Set up the trigger that takes care of\n # the correct deletion of entries across the different tables\n delete_trigger_sql = open(os.path.join(template_path,\n \"sqlite3_delete_trigger.sql\"),\n 'r').read()\n indexes_sql = open(os.path.join(template_path, \"sqlite3_indexes.sql\"),\n 'r').read()\n else:\n # Set up the trigger that takes care of\n # the correct deletion of entries across the different tables\n delete_trigger_sql = open(os.path.join(template_path,\n \"postgresql_delete_trigger.sql\"),\n 'r').read()\n indexes_sql = open(os.path.join(template_path,\n \"postgresql_indexes.sql\"), 'r').read()\n\n # Connect now to the database\n if dbif.connected is not True:\n dbif.connect()\n\n # Execute the SQL statements for sqlite\n # Create the global tables for the native grass datatypes\n dbif.execute_transaction(raster_tables_sql)\n dbif.execute_transaction(raster_metadata_sql)\n dbif.execute_transaction(raster_views_sql)\n dbif.execute_transaction(vector_tables_sql)\n dbif.execute_transaction(vector_metadata_sql)\n dbif.execute_transaction(vector_views_sql)\n dbif.execute_transaction(raster3d_tables_sql)\n dbif.execute_transaction(raster3d_metadata_sql)\n dbif.execute_transaction(raster3d_views_sql)\n # Create the tables for the new space-time datatypes\n dbif.execute_transaction(strds_tables_sql)\n dbif.execute_transaction(strds_metadata_sql)\n dbif.execute_transaction(strds_views_sql)\n dbif.execute_transaction(stvds_tables_sql)\n dbif.execute_transaction(stvds_metadata_sql)\n dbif.execute_transaction(stvds_views_sql)\n dbif.execute_transaction(str3ds_tables_sql)\n dbif.execute_transaction(str3ds_metadata_sql)\n dbif.execute_transaction(str3ds_views_sql)\n\n # The delete trigger\n dbif.execute_transaction(delete_trigger_sql)\n # The indexes\n dbif.execute_transaction(indexes_sql)\n\n # Create the tgis metadata table to store the database\n # initial configuration\n # The metadata table content\n metadata = {}\n metadata[\"tgis_version\"] = tgis_version\n metadata[\"tgis_db_version\"] = tgis_db_version\n metadata[\"creation_time\"] = datetime.today()\n _create_tgis_metadata_table(metadata, dbif)\n\n dbif.close()","def init_beeswax_db(cls):\n global _INITIALIZED\n if _INITIALIZED:\n return\n\n make_query(cls.client, 'CREATE DATABASE IF NOT EXISTS %(db)s' % {'db': cls.db_name}, wait=True)\n make_query(cls.client, 'CREATE DATABASE IF NOT EXISTS %(db)s_other' % {'db': cls.db_name}, wait=True)\n\n if cls.load_data:\n\n data_file = cls.cluster.fs_prefix + u'/beeswax/sample_data_échantillon_%d.tsv'\n\n # Create a \"test_partitions\" table.\n CREATE_PARTITIONED_TABLE = \"\"\"\n CREATE TABLE `%(db)s`.`test_partitions` (foo INT, bar STRING)\n PARTITIONED BY (baz STRING, boom INT)\n ROW FORMAT DELIMITED\n FIELDS TERMINATED BY '\\t'\n LINES TERMINATED BY '\\n'\n \"\"\" % {'db': cls.db_name}\n make_query(cls.client, CREATE_PARTITIONED_TABLE, wait=True)\n cls._make_data_file(data_file % 1)\n\n LOAD_DATA = \"\"\"\n LOAD DATA INPATH '%(data_file)s'\n OVERWRITE INTO TABLE `%(db)s`.`test_partitions`\n PARTITION (baz='baz_one', boom=12345)\n \"\"\" % {'db': cls.db_name, 'data_file': data_file % 1}\n make_query(cls.client, LOAD_DATA, wait=True, local=False)\n\n # Insert additional partition data into \"test_partitions\" table\n ADD_PARTITION = \"\"\"\n ALTER TABLE `%(db)s`.`test_partitions` ADD PARTITION(baz='baz_foo', boom=67890) LOCATION '%(fs_prefix)s/baz_foo/boom_bar'\n \"\"\" % {'db': cls.db_name, 'fs_prefix': cls.cluster.fs_prefix}\n make_query(cls.client, ADD_PARTITION, wait=True, local=False)\n\n # Create a bunch of other tables\n CREATE_TABLE = \"\"\"\n CREATE TABLE `%(db)s`.`%(name)s` (foo INT, bar STRING)\n COMMENT \"%(comment)s\"\n ROW FORMAT DELIMITED\n FIELDS TERMINATED BY '\\t'\n LINES TERMINATED BY '\\n'\n \"\"\"\n\n # Create a \"test\" table.\n table_info = {'db': cls.db_name, 'name': 'test', 'comment': 'Test table'}\n cls._make_data_file(data_file % 2)\n cls._make_table(table_info['name'], CREATE_TABLE % table_info, data_file % 2)\n\n if is_live_cluster():\n LOG.warning('HUE-2884: We cannot create Hive UTF8 tables when live cluster testing at the moment')\n else:\n # Create a \"test_utf8\" table.\n table_info = {'db': cls.db_name, 'name': 'test_utf8', 'comment': cls.get_i18n_table_comment()}\n cls._make_i18n_data_file(data_file % 3, 'utf-8')\n cls._make_table(table_info['name'], CREATE_TABLE % table_info, data_file % 3)\n\n # Create a \"test_latin1\" table.\n table_info = {'db': cls.db_name, 'name': 'test_latin1', 'comment': cls.get_i18n_table_comment()}\n cls._make_i18n_data_file(data_file % 4, 'latin1')\n cls._make_table(table_info['name'], CREATE_TABLE % table_info, data_file % 4)\n\n # Create a \"myview\" view.\n make_query(cls.client, \"CREATE VIEW `%(db)s`.`myview` (foo, bar) as SELECT * FROM `%(db)s`.`test`\" % {'db': cls.db_name}, wait=True)\n\n _INITIALIZED = True","def dbUpdate():\n dbAddress = config.get('database', 'dbAddress')\n dbUser = config.get('database', 'dbUser')\n dbPassword = config.get('database', 'dbPassword')\n dbName = config.get('database', 'dbName')\n dbPort = config.getint('database', 'dbPort')\n con = MySQLdb.connect(host=dbAddress, port=dbPort, user=dbUser, passwd=dbPassword,\n db=dbName)\n c = con.cursor()\n\n date = datetime.datetime.now()\n c.execute(\"INSERT INTO sensor_data (date, dht_temp, dht_humidity, cpu_temp, \"\n \"solar_voltage, solar_current, battery_voltage, battery_current, \"\n \"load_voltage, load_current) VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s,\"\n \"%s)\",\n (date, dht_temp, dht_humidity, cpu_temp, sol_volt_v, sol_curr_ma,\n bat_volt_v, bat_curr_ma, load_volt_v, load_curr_ma))\n\n con.commit()\n con.close()","def create_db():\n database.db.create_all()\n get_ulm()\n for fixture_file in glob.glob(config.DevelopmentConfig.FIXTURES_DIRS + '/*.json'):\n fixtures = JSONLoader().load(fixture_file)\n load_fixtures(database.db, fixtures)\n MigrationManager().stamp_db()","def to_WTH_converter(self, weather_data, dest_dir):\n ds_all = weather_data.get_global_dataset()\n if self.country is None:\n print(\"Country given is erroneous:\")\n return\n elif self.country == \"globe\":\n lon_num_start = 0\n lon_num_stop = weather_data.get_num_of_attribute('longitude')\n lat_num_start = 0\n lat_num_stop = weather_data.get_num_of_attribute('latitude')\n else:\n lon_num_start, lon_num_stop, lat_num_start, lat_num_stop = weather_data.get_country_boundary(self.country)\n\n\n # top bottom, left to right\n lon_num_start = 397\n lat_num_start = 184\n for lon_i in range(lon_num_start, lon_num_stop + 1):\n # for lon_i in range(lon_num_start, lon_num_stop+1):\n lon = ds_all.longitude.isel(longitude=lon_i).values.tolist()\n\n for lat_i in range(lat_num_start, lat_num_stop+1):\n # for lat_i in range(lat_num_start, lat_num_stop + 1):\n lat = ds_all.latitude.isel(latitude=lat_i).values.tolist()\n\n # create a dynamic header with updated LON, LAT info and move it into the folder given\n wth_header_u = ut.format_header(lat_i + 1, lon_i + 1, lat, lon)\n wth_header = dest_dir + \"/\" + wth_header_u\n shutil.move(wth_header_u, wth_header)\n\n # open in appending mode\n fwth = open(wth_header, \"a+\")\n\n # loop through daily weather data\n for t, date in enumerate(self.years):\n daily_data_vars = ut.get_daily_data_vars(ds_all, lat_i, lon_i, t)\n # disregard all NAN values\n if daily_data_vars is None:\n fwth.close()\n os.remove(wth_header)\n break\n\n if t == 0:\n ut.update_table(wth_header_u, lat, lon)\n\n entry = ut.format_data_vars_entry(daily_data_vars, date)\n\n # append this entry into the file\n fwth.write(entry)\n print(\"Added entry:\", entry)\n\n # close file after writing\n fwth.close()\n print(\"Output WTH:\", wth_header)","def __upload_data(self):\n data_path = \"database\"\n os.makedirs(data_path, exist_ok=True)\n try:\n conn = sqlite3.connect('database/customers.db')\n query = '''CREATE TABLE IF NOT EXISTS all_customers_database (\n first_name TEXT, second_name TEXT,\n gender TEXT, account_type TEXT, account_number INTEGER PRIMARY KEY UNIQUE NOT NULL,\n account_password VARCHAR, account_balance REAL );'''\n #Create table\n cursor = conn.cursor()\n print(\"Connection sucessful\")\n cursor.execute(query)\n conn.commit()\n print(\"Table created\")\n #Insert a row to a database\n insert_query ='''INSERT INTO all_customers_database\n (first_name, second_name, gender, account_type, account_number, account_password, account_balance)\n VALUES \n (?, ?, ?, ?, ?, ?, ?);'''\n conn.execute(insert_query, (self.first_name, self.second_name, self.gender, self.account_type, self.account_number, self.account_password, self.account_balance))\n print(\"Your details saved successfully.\")\n except sqlite3.Error as err:\n # print(\"Error while creating a sqlite table \", err)\n print(\"Error creating database\")\n finally:\n if conn:\n conn.close()\n # print(\"Sqlite connection closed.\")","def checkFirstRun():\n my_cfg = Path.home() / \".bandaid\" / \"bandaid.cfg\"\n my_db = Path.home() / \".bandaid\" / \"bandaid.db\"\n if Path(f'{Path.home()}/.bandaid/').exists():\n return my_db\n print(\"First run, making the donuts...\")\n Path.mkdir(Path.home() / \".bandaid\", exist_ok=True)\n zipcode = inputZip()\n lat, lng = getLatLng(zipcode)\n with open(my_cfg, \"w+\") as f:\n f.write('DBPATH=~/.bandaid/bandaid.db\\n')\n f.write(f'ZIPCODE={zipcode}')\n user = (lambda: environ[\"USERNAME\"]\n if \"C:\" in getcwd() else environ[\"USER\"])()\n initDB(my_db, zipcode, user, lat, lng)\n print(f\"Database and config file created at {my_cfg}\")\n return my_db","def create_db(self):","def add_water():\n\n user_id = session['user_id']\n drink = request.form.get('drink')\n postal = request.form.get('postal')\n time_updated = datetime.now()\n new_drink = Water(ounces=drink, user_id=user_id, time_updated=time_updated, postal=postal)\n\n db.session.add(new_drink)\n db.session.commit()\n \n time_zone = session[\"user_timezone\"]\n \n current_time = datetime.now().astimezone(pytz.timezone(time_zone))\n\n current_date = current_time.date()\n\n total_water_today = db.session.query(func.sum(Water.ounces)).filter(Water.user_id==user_id, Water.time_updated >= current_date).scalar()\n\n if int(total_water_today) != None or int(total_water_today) != 0:\n total_cups_today = round((total_water_today/8),2)\n else:\n total_water_today = 0\n total_cups_today = 0\n\n print('user id', user_id)\n print('current date', current_date)\n return f\"Today's Water: {total_water_today} Oz ({total_cups_today} Cups)\"","def init():\n database.create_tables([Tracker])\n database.commit()","def initialize_sqlite_database(sel_wormbase_version,\n strain_only=False):\n start = arrow.utcnow()\n console.log(\"Initializing Database\")\n\n SQLITE_PATH = f\"base/cendr.{DATASET_RELEASE}.{sel_wormbase_version}.db\"\n SQLITE_BASENAME = os.path.basename(SQLITE_PATH)\n\n # Download wormbase files\n if strain_only is False:\n if os.path.exists(SQLITE_PATH):\n os.remove(SQLITE_PATH)\n\n if not os.path.exists(DOWNLOAD_PATH):\n os.makedirs(DOWNLOAD_PATH)\n\n # Parallel URL download\n console.log(\"Downloading Wormbase Data\")\n download([URLS.GENE_GFF_URL,\n URLS.GENE_GTF_URL,\n URLS.GENE_IDS_URL,\n URLS.HOMOLOGENE_URL,\n URLS.ORTHOLOG_URL,\n URLS.TAXON_ID_URL],\n DOWNLOAD_PATH)\n\n gff_fname = download_fname(DOWNLOAD_PATH, URLS.GENE_GFF_URL)\n gtf_fname = download_fname(DOWNLOAD_PATH, URLS.GENE_GTF_URL)\n gene_ids_fname = download_fname(DOWNLOAD_PATH, URLS.GENE_IDS_URL)\n homologene_fname = download_fname(DOWNLOAD_PATH, URLS.HOMOLOGENE_URL)\n ortholog_fname = download_fname(DOWNLOAD_PATH, URLS.ORTHOLOG_URL)\n\n from base.application import create_app\n app = create_app()\n app.config['SQLALCHEMY_DATABASE_URI'] = f\"sqlite:///{SQLITE_BASENAME}\"\n app.app_context().push()\n\n if strain_only is True:\n db.metadata.drop_all(bind=db.engine, checkfirst=True, tables=[Strain.__table__])\n db.metadata.create_all(bind=db.engine, tables=[Strain.__table__])\n else:\n db.create_all(app=app)\n db.session.commit()\n\n console.log(f\"Created {SQLITE_PATH}\")\n\n ################\n # Load Strains #\n ################\n console.log('Loading strains...')\n db.session.bulk_insert_mappings(Strain, fetch_andersen_strains())\n db.session.commit()\n console.log(f\"Inserted {Strain.query.count()} strains\")\n\n if strain_only is True:\n console.log('Finished loading strains')\n return\n\n ################\n # Set metadata #\n ################\n console.log('Inserting metadata')\n metadata = {}\n metadata.update(vars(constants))\n metadata.update({\"CENDR_VERSION\": CENDR_VERSION,\n \"APP_CONFIG\": APP_CONFIG,\n \"DATASET_RELEASE\": DATASET_RELEASE,\n \"WORMBASE_VERSION\": sel_wormbase_version,\n \"RELEASES\": RELEASES,\n \"DATE\": arrow.utcnow()})\n for k, v in metadata.items():\n if not k.startswith(\"_\"):\n # For nested constants:\n if type(v) == type:\n for name in [x for x in dir(v) if not x.startswith(\"_\")]:\n key_val = Metadata(key=\"{}/{}\".format(k, name),\n value=getattr(v, name))\n db.session.add(key_val)\n else:\n key_val = Metadata(key=k, value=str(v))\n db.session.add(key_val)\n\n db.session.commit()\n\n ##############\n # Load Genes #\n ##############\n console.log('Loading summary gene table')\n genes = fetch_gene_gff_summary(gff_fname)\n db.session.bulk_insert_mappings(WormbaseGeneSummary, genes)\n db.session.commit()\n\n console.log('Loading gene table')\n db.session.bulk_insert_mappings(WormbaseGene, fetch_gene_gtf(gtf_fname, gene_ids_fname))\n gene_summary = db.session.query(WormbaseGene.feature,\n db.func.count(WormbaseGene.feature)) \\\n .group_by(WormbaseGene.feature) \\\n .all()\n gene_summary = '\\n'.join([f\"{k}: {v}\" for k, v in gene_summary])\n console.log(f\"============\\nGene Summary\\n------------\\n{gene_summary}\\n============\")\n\n ###############################\n # Load homologs and orthologs #\n ###############################\n console.log('Loading homologs from homologene')\n db.session.bulk_insert_mappings(Homologs, fetch_homologene(homologene_fname))\n db.session.commit()\n\n console.log('Loading orthologs from WormBase')\n db.session.bulk_insert_mappings(Homologs, fetch_orthologs(ortholog_fname))\n db.session.commit()\n\n #############\n # Upload DB #\n #############\n\n # Upload the file using todays date for archiving purposes\n console.log(f\"Uploading Database ({SQLITE_BASENAME})\")\n upload_file(f\"db/{SQLITE_BASENAME}\", SQLITE_PATH)\n\n diff = int((arrow.utcnow() - start).total_seconds())\n console.log(f\"{diff} seconds\")\n\n # =========================== #\n # Generate gene id dict #\n # =========================== #\n # Create a gene dictionary to match wormbase IDs to either the locus name\n # or a sequence id\n gene_dict = {x.gene_id: x.locus or x.sequence_name for x in WormbaseGeneSummary.query.all()}\n pickle.dump(gene_dict, open(\"base/static/data/gene_dict.pkl\", 'wb'))","def initialization():\r\n\r\n logging.debug('initialization()')\r\n\r\n dir_path = os.path.join(os.environ['LOCALAPPDATA'],'WarietyWallpaperImages')\r\n os.makedirs(dir_path, exist_ok=True)\r\n db_file = os.path.join(dir_path,'wariety.db')\r\n conn = sqlite3.connect(db_file)\r\n c = conn.cursor()\r\n\r\n # Create tables\r\n c.execute(\"\"\"\r\n CREATE TABLE IF NOT EXISTS wallpapers (\r\n id integer primary key,\r\n iurl text unique,\r\n iname text,\r\n ipath text,\r\n isource text)\r\n \"\"\")\r\n\r\n conn.close()","def populate_db():\n stdout.write('Emptying the tables...\\n')\n empty_tables()\n stdout.write('Populating Language records...\\n')\n populate_language()\n stdout.write('Populating Lemma, Wordform, and Definition records...\\n')\n populate_lexical()\n stdout.write('Populating ProperName records...\\n')\n populate_proper_names()","def perform_watering_update(self, plant):\n try:\n with self.connection.cursor() as cursor:\n sql = \"INSERT INTO test.watering_data\\\n (balance_id, logdate, start_weight, end_weight, status, experiment_id)\\\n VALUES({0}, NOW(), {1}, {2}, {3}, '{4}')\".format(plant.get_balance(),\n plant.get_start_weight(),\n plant.get_end_weight(),\n plant.get_status(),\n plant.get_experiment_id())\n\n # Execute our statement\n if is_statement_safe(sql):\n cursor.execute(sql)\n self.connection.commit()\n else:\n print(\"DANGEROUS STATEMENT\")\n sys.exit()\n except (pymysql.err.DatabaseError,\n pymysql.err.IntegrityError,\n pymysql.err.MySQLError):\n sys.stderr.write(\n \"Most likely an IntegrityError error due to duplicate entry\")\n return 2","def CreateDB(self) :\r\n\t\ttry :\r\n\t\t\tself.DB_Cursor.execute(self.SQLCMDs['CreateClassTable'])\r\n\t\t\tfor ii,classname in enumerate(self.SQLCMDs['ClassesList']) :\r\n\t\t\t\tself.DB_Cursor.execute(self.SQLCMDs['InsertClass'],(ii,classname))\r\n\r\n\t\t\tself.DB_Cursor.execute(self.SQLCMDs['CreateSetTable'])\r\n\t\t\tfor ii,setname in enumerate(self.SQLCMDs['SetList']) :\r\n\t\t\t\tself.DB_Cursor.execute(self.SQLCMDs['InsertSet'],(ii,setname))\r\n\r\n\t\t\tself.DB_Cursor.execute(self.SQLCMDs['CreateSampleTable'])\r\n\t\t\tself.DB_Cursor.execute(self.SQLCMDs['CreateDictListTable'])\r\n\t\t\tself.DB_Cursor.execute(self.SQLCMDs['CreateDictBuildTable'])\r\n\t\t\tself.DB_Cursor.execute(self.SQLCMDs['CreateWordLists'])\r\n\t\t\tself.DB_Cursor.execute(self.SQLCMDs['CreateFeatureTable'])\r\n\t\t\tself.DB_Connect.commit()\r\n\t\texcept Exception as detail:\r\n\t\t\tlogging.error(\"Failed to create the database: %s\"%detail)\r\n\t\t\tself.DB_Connect.rollback()\r\n\t\treturn","def initialize():\n db.connect()\n db.create_tables([Expense], safe=True)","def main():\n\n database_file = Path(__file__).parent.parent / \"src\" / \"galois\" / \"_databases\" / \"irreducible_polys.db\"\n conn, cursor = create_database(database_file)\n\n _add_hpl_1998(conn, cursor)\n\n conn.close()","def create_db(self):\n return None","def init(self):\n self.db.connect()\n try:\n self.db.create_tables([JambiModel], safe=True)\n JambiModel.create(ref='0')\n self.logger.info('Database initialized')\n except IntegrityError:\n self.logger.info('Database was already initialized')\n self.db.close()","def getTodaysWeather(self, keyword, temp):\n\n\t\t# Variables\n\t\tweather = {} \n\t\tfio = self.helper.getFio(keyword, temp) # Getting fio object\n\t\t\n\t\t# Getting todays weather data and populating the dictionary\n\t\tif fio.has_daily() is True and fio.has_hourly() is True:\n\t\t daily = FIODaily.FIODaily(fio)\n\t\t hourly = FIOHourly.FIOHourly(fio)\n\t\t for day in xrange(0, 1):\n\t\t\t\tfor item in daily.get_day(day).keys():\n\t\t\t\t\tif item == \"temperatureMin\":\n\t\t\t\t\t\tweather[item] = str(daily.get_day(day)[item]).split(\".\")[0]\n\t\t\t\t\tif item == \"summary\":\n\t\t\t\t\t\tweather[item] = unicode(daily.get_day(day)[item])\n\t\t\t\t\tif item == \"temperatureMax\":\n\t\t\t\t\t\tweather[item] = str(daily.get_day(day)[item]).split(\".\")[0]\n\t\t\t\t\tif item == \"windSpeed\":\n\t\t\t\t\t\twindSpeed = unicode(daily.get_day(day)[item])\n\t\t\t\t\tif item == \"windBearing\":\n\t\t\t\t\t\twindBearing = unicode(daily.get_day(day)[item])\n\t\t\t\t\t\twindBearing = self.helper.convertWindBearing(windBearing)\n\t\t\t\t\tif item == \"sunsetTime\":\n\t\t\t\t\t\tweather[item] = self.helper.getDateForWeather(daily.get_day(day)[item])\n\t\t\t\t\tif item == \"sunriseTime\":\n\t\t\t\t\t\tweather[item] = self.helper.getDateForWeather(daily.get_day(day)[item])\n\t\t\t\t\tif item == \"precipProbability\":\n\t\t\t\t\t\tweather[item] = str(daily.get_day(day)[item] * 100).split(\".\")[0] + \"%\"\n\t\t\t\tweather[\"wind\"] = windBearing + \" \" + windSpeed + \" mph\"\n\t\t\t\tfor item in hourly.get_hour(day).keys():\n\t\t\t\t\tif item == \"summary\":\n\t\t\t\t\t\tweather[\"current\"] = unicode(hourly.get_hour(0)[item])\n\t\t\t\t\tif item == \"temperature\":\n\t\t\t\t\t\tweather[item] = str(hourly.get_hour(0)[item]).split(\".\")[0]\n\t\t\t\t\tif item == \"icon\":\n\t\t\t\t\t\tweather[item] = unicode(hourly.get_hour(0)[item])\n\t\t\t\t\tif item == \"cloudCover\":\n\t\t\t\t\t\tweather[item] = str(hourly.get_hour(0)[item] * 100).split(\".\")[0] + \"%\"\n\t\t\t\tweather[\"town\"] = self.helper.getCoords(keyword)[2]\n\t\telse:\n\t\t\treturn 'No Todays data'\n\n\t\treturn weather","def create_tables(cxn):\n\tcursor = cxn.cursor()\n\tcursor.execute(\"DROP TABLE IF EXISTS WEATHER\")\n\tcursor.execute(\"\"\"CREATE TABLE IF NOT EXISTS WEATHER(\n\t\tstate varchar(3),\n\t\tcity varchar (15),\n\t\tobs_date varchar(12),\n\t\thour int,\n\t\tminute int,\n\t\tcurr_temp float,\n\t\tunique(state, city, obs_date, hour, minute)\n\t\t)\"\"\")\n\tcursor.close()","def ready(self):\n try:\n from workouts.models import ExerciseType\n from workouts import EXERCISE_TYPE_ROUNDS, EXERCISE_TYPE_TIME\n exercise_type_time = ExerciseType.objects.get_or_create(type_name=EXERCISE_TYPE_TIME)\n exercise_type_rounds = ExerciseType.objects.get_or_create(type_name=EXERCISE_TYPE_ROUNDS)\n except Exception:\n pass","def setup_database(self, config_dict):\n\n # This will create the database if it doesn't exist, then return an\n # opened instance of the database manager. \n dbmanager = self.engine.db_binder.get_manager(self.data_binding, initialize=True)\n syslog.syslog(syslog.LOG_INFO, \"engine: Using binding '%s' to database '%s'\" % (self.data_binding, dbmanager.database_name))\n \n # Back fill the daily summaries.\n syslog.syslog(syslog.LOG_INFO, \"engine: Starting backfill of daily summaries\")\n t1 = time.time()\n nrecs, ndays = dbmanager.backfill_day_summary()\n tdiff = time.time() - t1\n if nrecs:\n syslog.syslog(syslog.LOG_INFO, \n \"engine: Processed %d records to backfill %d day summaries in %.2f seconds\" % (nrecs, ndays, tdiff))\n else:\n syslog.syslog(syslog.LOG_INFO,\n \"engine: Daily summaries up to date.\")","def initialize_database():\n # TODO: Refactor the funtime library\n this.db = Store(this.host).create_lib(this.store_name).get_store()","def set_tables(self):\n with sql.connect('./{}.db'.format(self.name)) as conn:\n conn.execute(\"\"\"CREATE TABLE IF NOT EXISTS tweets(\n id INTEGER PRIMARY KEY,\n tweet_id INTEGER,\n insert_date TEXT,\n created_at TEXT,\n hashtag TEXT)\n \"\"\")\n\n conn.execute(\"\"\"CREATE TABLE tweet_peaks(\n peak_datetime TEXT NOT NULL,\n hashtag TEXT NOT NULL,\n time_frame TEXT,\n mean REAL,\n std REAL,\n sensibility REAL,\n freq_limit REAL,\n qt_tweets INTEGER,\n id TEXT PRIMARY KEY,\n probability REAL);\n \"\"\")","def populate_stops(self):\n stops = self.load_csv('stops.txt')\n stops = self.process_stops(stops)\n\n connection = db.connect()\n for stop in stops:\n try:\n connection.execute(schema.stops.insert(), stop)\n except DataError:\n print \"Missing data for stop: %s\" % (stop)","def insert_db():\n populate_tables()","def insert_data():\n\tBase.metadata.drop_all(engine)\n\tBase.metadata.create_all(engine)\n\tu1 = insert_user(\"233@B.com\", \"/static/image/avatar.JPG\")\n\tu2 = insert_user(\"fy@B.com\", \"/static/image/avatar.JPG\")\n\tc = insert_catalog(u1.id, \"Sichuan Dish\")\n\tinsert_catalog(u1.id, \"Fujian Dish\")\n\tinsert_catalog(u1.id, \"Guangdong Dish\")\n\tinsert_catalog(u2.id, \"Zhejiang Dish\")\n\tinsert_catalog(u2.id, \"Beijing Dish\")\n\tinsert_item(u1.id, \"Iphone 6 plus\", c, 'Is a phone', None)\n\tinsert_item(u1.id, \"Hot pot\", c, \"Hot hot hot\", None)\n\tinsert_item(u2.id, \"Kong Bao Chicken\", c, \"Classic\", None)","def _create_pysam_wfile(self, resource, meta):\n # pylint: disable=attribute-defined-outside-init,consider-using-with\n self._temp_dir = TemporaryDirectory()\n fname = os.path.join(self._temp_dir.name, 'weather.csv')\n logger.debug('Creating PySAM weather data file: {}'.format(fname))\n\n # ------- Process metadata\n m = pd.DataFrame(meta).T\n timezone = m['timezone']\n m['Source'] = 'NSRDB'\n m['Location ID'] = meta.name\n m['City'] = '-'\n m['State'] = m['state'].apply(lambda x: '-' if x == 'None' else x)\n m['Country'] = m['country'].apply(lambda x: '-' if x == 'None' else x)\n m['Latitude'] = m['latitude']\n m['Longitude'] = m['longitude']\n m['Time Zone'] = timezone\n m['Elevation'] = m['elevation']\n m['Local Time Zone'] = timezone\n m['Dew Point Units'] = 'c'\n m['DHI Units'] = 'w/m2'\n m['DNI Units'] = 'w/m2'\n m['Temperature Units'] = 'c'\n m['Pressure Units'] = 'mbar'\n m['Wind Speed'] = 'm/s'\n keep_cols = [c for c in m.columns if c not in self.WF_META_DROP_COLS]\n m[keep_cols].to_csv(fname, index=False, mode='w')\n\n # --------- Process data\n var_map = {'dni': 'DNI',\n 'dhi': 'DHI',\n 'wind_speed': 'Wind Speed',\n 'air_temperature': 'Temperature',\n 'dew_point': 'Dew Point',\n 'surface_pressure': 'Pressure',\n }\n resource = resource.rename(mapper=var_map, axis='columns')\n\n time_index = resource.index\n # Adjust from UTC to local time\n local = np.roll(resource.values, int(timezone * self.time_interval),\n axis=0)\n resource = pd.DataFrame(local, columns=resource.columns,\n index=time_index)\n mask = (time_index.month == 2) & (time_index.day == 29)\n time_index = time_index[~mask]\n\n df = pd.DataFrame(index=time_index)\n df['Year'] = time_index.year\n df['Month'] = time_index.month\n df['Day'] = time_index.day\n df['Hour'] = time_index.hour\n df['Minute'] = time_index.minute\n df = df.join(resource.loc[~mask])\n\n df.to_csv(fname, index=False, mode='a')\n\n return fname","def init():\n\n # delete existing file\n if os.path.exists(DBFILE):\n os.remove(DBFILE)\n\n db = sqlite3.connect(DBFILE)\n # create tables\n create(db, PARAGRAPH, \"paragraph\")\n create(db, QUESTION, \"question\")\n create(db, ANSWER, \"answer\")\n\n return db","def main():\n langs = []\n\n with open(\"sql/07_populate.sql\", 'w', encoding='utf8') as sql:\n sql.write(\"--this file is generated from csv files in data folder\\n\\n\")\n\n langs = write_lang_city(sql)\n write_groups_diets(sql, langs)\n\n with open(\"sql/10_populate_test_data.sql\", 'w', encoding='utf8') as sql:\n sql.write(\"--this file is generated from csv files in moc_data folder\\n\\n\")\n write_test_data(sql)\n # This command sets postgis coordinates based on latitude and longitude\n sql.write(\"UPDATE restaurant SET geo_location = ST_POINT(latitude, longitude);\\n\")\n sql.close()","def write_to_db(batch_num,pH,cursor):\n\n\t#Batch Number\n\tbatch = 1\n\t\t\n\n\tcur_time = datetime.now()\n\theater_state = 1\n\ttemp = 30.1\n\tamb_hum, amb_temp = 25.1,30.2\n\n\tcursor.execute('''INSERT INTO Kombucha_Data('Time', 'Batch', 'Heater State', 'Temperature', 'Ambient Temperature', 'Ambient Humidity','pH')\n\t\t\t\t VALUES(?,?,?,?,?,?,NULL)''', (cur_time, batch_num, heater_state, temp, amb_temp, amb_hum))\n\tcursor.commit()\n\treturn","def create_sqlite_table(self):\n self.print_datetime_output('Connect to data base %s' % self.db_name)\n con = sqlite3.connect(self.db_name)\n cur = con.cursor()\n\n # check if table exists\n cur.execute(\"select count(*) from sqlite_master where type='table' and name='%s'\" % self.db_table)\n if cur.fetchall()[0][0] == 1:\n self.print_datetime_output('Previous table %s was dropped' % self.db_table)\n cur.execute(\"DROP TABLE %s;\" % self.db_table)\n\n self.print_datetime_output('Create table %s and import data from csv file %s' % (self.db_table,\n self.time_series_file_name))\n cur.execute(\"CREATE TABLE %s (timestamp, close_USD);\" % self.db_table)\n\n with open(self.file_name, 'r') as fin:\n dr = csv.DictReader(fin)\n to_db = [(i['timestamp'], i['close (USD)']) for i in dr]\n\n cur.executemany(\"INSERT INTO %s (timestamp, close_USD) VALUES (?, ?);\" % self.db_table, to_db)\n con.commit()\n return con","def build(self):\n \n # create db with empty tables\n dbpath, config = self.setup()\n \n # avoid work if setup decided db exists and build can be skipped\n if dbpath is None:\n return \n \n # check prerequisite files \n obopath = check_file(config.obo, dbpath, \"obo\")\n refpath = check_file(config.reference_phenotypes, dbpath, \n \"reference_phenotypes\")\n freqpath = check_file(config.phenotype_frequencies, dbpath,\n \"phenotype_frequencies\")\n\n self.logger.msg1(\"Loading ontology\") \n obo = MinimalObo(obopath, True)\n \n self.logger.msg1(\"Preparing phenotype frequencies\")\n fill_phenotype_frequency_table(dbpath, freqpath)\n \n # fill database with data\n self.logger.msg1(\"Preparing references\")\n fill_concise_reference_table(dbpath, refpath) \n fill_complete_reference_table(dbpath, obo, config) \n \n self._end()","def database_conn(self, table, hr, data):\n # origionally from https://www.w3schools.com/python/python_mysql_insert.asp\n mydb = mysql.connector.connect(\n host=\"localhost\",\n user=WEATHERUSER,\n password=DBPASS,\n database=\"weather\"\n )\n mycursor = mydb.cursor()\n sql = f\"UPDATE weather.{table} SET hr = {hr}, dt = %s, temp = %s, pressure = %s, humidity = %s, dewpoint = %s, rain = %s where hr = {hr}\"\n mycursor.execute(sql, data)\n mydb.commit()","def create(self):\n db.create_all()","def setupDatabases(self):\n param = self.getDefaultDatabaseConnectionParameter()\n db = DatabaseFactory.getDatabase(self.defaultDriver(), {})\n db.createDatabase(param)\n db.connect(param)\n if db.isConnected():\n self.settingsDb = db\n db.createObservations()\n db.createSensors()\n else:\n return False\n # replace by settings validation method later\n return self.check()","def init_database(self):\n engine = create_engine('sqlite:///todo.db?check_same_thread=False')\n self.Base.metadata.create_all(engine)\n self.session = sessionmaker(bind=engine)()","def initialize():\n\n db.connect() # Se conecta\n db.create_tables([Entry], safe=True) # Crea las tablas\n # safe=true evita crear modelos ya creados","def initialize():\n DATABASE.connect()\n DATABASE.drop_tables([Journal], safe=True)\n DATABASE.create_tables([Journal], safe=True)\n DATABASE.close()","def gatherData():\n\n # connect to database, set up the tweepy API object, and find the next date to search\n\n cnx = sqlite3.connect(DB_FILE)\n api = generateAPI(wait_on_rate_limit=True, wait_on_rate_limit_notify=True, **CREDENTIALS)\n\n nextdate = findNextDate(cnx, FIRSTDATE)\n year = nextdate[:4]\n\n # attempt to scrape box office data\n\n bodata = getTopMovies(BO_ENDPOINT, nextdate, CNT_MOVIES)\n\n if not bodata.empty:\n bodata.to_sql('boxoffice', ENGINE, if_exists='append', index=False)\n print(\"Box Office Data for [{0}] Written to Database\".format(nextdate))\n else:\n raise BOError(\"Error Scraping/Writing Box Office Data for [{0}]\".format(nextdate))\n\n # attempt to collect tweet data\n\n for movie in bodata.title:\n try:\n tweets = searchMovie(api, movie, nextdate, MAX_TWEETS)\n if not tweets.empty:\n tweets.to_sql('tweets', ENGINE, if_exists='append', index=False)\n print(\"Tweets for [{0}] Written to Database\".format(movie))\n else:\n raise TweetError(\"Error Fetching/Writing Tweets for [{0}]\".format(movie))\n except tweepy.error.TweepError:\n raise TweetError(\"Error Fetching/Writing Tweets for [{0}]\".format(movie))\n\n # attempt to collect movie metadata\n\n for movie in bodata.title:\n minfo = getMovieInfo(OMDB_ENDPOINT, processTitle(movie), year)\n if minfo:\n insertMovie(cnx, movie, nextdate, minfo)\n else:\n minfo = getMovieInfo(OMDB_ENDPOINT, processTitle(movie), str(int(year)-1))\n if minfo:\n insertMovie(cnx, movie, nextdate, minfo)\n else:\n print(\"Movie: [{0}] Could Not be Found via OMDB\".format(movie))\n\n # commit changes and close DB connection\n\n cnx.commit()\n cnx.close()\n\n print(\"\\nAll Data for {0} Successfully Added to the Database!\\n\".format(nextdate))\n return nextdate","def create():\n upgrade()\n populate()","def example_data():\n\n # In case this is run more than once, empty out existing data\n User.query.delete()\n Location.query.delete()\n\n # Add sample users and locations\n\n trinity = User(fname='Trinity', email='trin@unplugged.com',\n username='questionit', password='l0lagent')\n neo = User(fname='Neo', email='theone@unplugged.com',\n username='neo', password='l0lagent')\n\n tacorea = Location(yelp_id='tacorea-san-francisco', name='Tacorea',\n latitude='37.7749', longitude='122.3392',\n address='809 Bush St, San Francisco, CA 94108',\n yelp_url='tacorea@yelp.com', pic='pic')\n\n db.session.add_all([trinity, neo, tacorea])\n db.session.commit()","def get_forecast(location_list):\n #Might need to munge location to get a query out of it\n location, human_location = location_list\n date = datetime.datetime.today()\n query = location\n url = \"http://api.wunderground.com/auto/wui/geo/ForecastXML/index.xml?query=%s\" % query\n f = urllib2.urlopen(url)\n xml = f.read()\n root = ET.XML(xml)\n \n forecast = {'location': location, 'human_location': human_location}\n #Find forecast\n simple = root.find('simpleforecast')\n for day in simple.findall('forecastday'):\n forecast['forecast_date'] = parser.parse(day.find('date').find('pretty').text)\n forecast['high_temp'] = day.find('high').find('fahrenheit').text\n forecast['low_temp'] = day.find('low').find('fahrenheit').text\n forecast['conditions'] = day.find('conditions').text\n forecast['icon'] = day.find('icon').text\n forecast['skyicon'] = day.find('skyicon').text\n try:\n f, created = ForecastDay.objects.get_or_create(**forecast)\n if created:\n f.save()\n except:\n logging.info(\"Long Range Forecast Data missing or already created\")\n \n \n #Find Moon\n moon = root.find('moon_phase')\n illuminated = moon.find('percentIlluminated')\n age = moon.find('ageOfMoon')\n sun_rise = datetime.datetime(date.year, date.month, date.day, **_hour_minute(moon.find('sunrise')))\n sun_set = datetime.datetime(date.year, date.month, date.day, **_hour_minute(moon.find('sunset'))) \n #It doesn't error, so it appears to be doing what it should.\n f = ForecastDay.objects.get(forecast_date=date)\n f.sun_rise = sun_rise\n f.sun_set = sun_set\n f.moon_illuminated = illuminated.text\n f.moon_age = age.text\n try:\n f.save()\n except:\n logging.info(\"Moon Data missing or no new data available\")","def get_weather(days, hours, db):\n days = format_list_for_db(days)\n hours = format_list_for_db(hours)\n sql = f\"SELECT * FROM weather WHERE day in {days} AND HOUR in {hours}\"\n cursor = db.cursor()\n cursor.execute(sql)\n data = cursor.fetchall()\n cursor.close()\n\n weathers = []\n if len(data) > 0:\n for weather in data:\n weather = {\"hour\": weather[1],\n \"day\": day_absolute_to_relative(weather[2]),\n \"temperature\": weather[3],\n \"apparenttemperature\": weather[4],\n \"precipitationintensity\": weather[5],\n \"precipitationprobability\": weather[6],\n \"humidity\": weather[7],\n \"dewpoint\": weather[8],\n \"windspeed\": weather[9],\n \"windbearing\": weather[10],\n \"windgust\": weather[11],\n \"pressure\": weather[12],\n \"cloudcover\": weather[13],\n \"uvindex\": weather[14],\n \"visibility\": weather[15]}\n weathers.append(weather)\n return weathers","def create_table(self):\n c = self.conn.cursor()\n c.execute(\"CREATE TABLE sensor_data (mac text, name text, temperature real, light integer, moisture real, conductivity real, battery real, ts_utc int, date_iso text, firmware text )\")","def _create_db(self):\n self.db = easydms.dbcore.Database(\":memory:\")\n self.db.create_db()","def createDatabase(self):\n \n try:\n self.conn = MySQLdb.connect (host = settings.DATABASES['default']['HOST'], user = settings.DATABASES['default']['USER'], passwd = settings.DATABASES['default']['PASSWORD'])\n except MySQLdb.Error, e:\n raise Exception(\"Cannot connect to the MySQL server.\")\n\n try:\n cursor = self.conn.cursor()\n cursor.execute(\"CREATE DATABASE {};\".format(settings.DBNAME))\n except MySQLdb.Error, e:\n raise Exception(\"Cannot create the database {}. {}\".format(settings.DBNAME, e))\n finally:\n self.conn.close()\n\n try:\n self.conn = MySQLdb.connect (host = settings.DATABASES['default']['HOST'], user = settings.DATABASES['default']['USER'], passwd = settings.DATABASES['default']['PASSWORD'], db = settings.DBNAME)\n except MySQLdb.Error, e:\n raise Exception(\"Cannot connect to the database {}. {}\".format(settings.DBNAME, e))\n\n try:\n cursor = self.conn.cursor()\n # creating the datasets table\n cursor.execute(\"CREATE TABLE datasets (dataset VARCHAR(255) PRIMARY KEY, datasetid INT UNIQUE AUTO_INCREMENT, ximagesz INT, yimagesz INT, zimagesz INT, xoffset INT, yoffset INT, zoffset INT, xvoxelres DOUBLE, yvoxelres DOUBLE, zvoxelres DOUBLE, scalingoption INT, scalinglevels INT, starttime INT, endtime INT);\")\n cursor.execute(\"CREATE TABLE channels (channel_name VARCHAR(255) ,dataset VARCHAR(255) REFERENCES datasets(dataset), PRIMARY KEY (channel_name,dataset), channel_type VARCHAR(255), channel_datatype VARCHAR(255), startwindow INT, endwindow INT);\")\n self.conn.commit()\n except MySQLdb.Error, e:\n raise Exception(\"Cannot create the tables for the database {}, {}\".format(settings.DBNAME, e))\n finally:\n self.conn.close()\n \n def pipInstall(self):\n \"\"\"Installing all the pip packages\"\"\"\n\n print \"Does Nothing\"\n\n def restart(self):\n \"\"\"Restart all the services\"\"\"","def populate_db(self):\n # Get donors\n log.info(\"Populating donors.\")\n\n self.r.hmset('Thomas', {'donations': '500', 'email': 'thomas@thomas.com', 'city': 'Athens', 'state': 'GA', 'zip': 30606})\n\n self.r.hmset('Ted', {'donations': '1', 'email': 'ted@ted.com', 'city': 'Memphis', 'state': 'TN', 'zip': 38104})\n\n self.r.hmset(\"Bailey\", {'donations': '1000', 'email': 'bailey@bailey.com', 'city': 'Washington', 'state': 'DC', 'zip': 12345})","def main():\n\n conn = psycopg2.connect(**env.DATABASE)\n cursor = conn.cursor()\n\n for file, city in env.supported_cities().items():\n try:\n data = add_metadata(parse_html(city, get_html(city)))\n save_data_to_db(cursor, data, file.title())\n except Exception as e:\n print(\"Failed to scrape '%s': %s\" %(city, e))\n print(traceback.format_exc())\n\n conn.commit()\n conn.close()","def initialize():\n DATABASE.connect()\n DATABASE.create_tables([User, Entry], safe=True)\n DATABASE.close()","def _create_sql(self):\n\n pdbfile = self.pdbfile\n sqlfile = self.sqlfile\n\n if self.verbose:\n print('-- Create SQLite3 database')\n\n #name of the table\n #table = 'ATOM'\n\n # column names and types\n self.col = {'serial' : 'INT',\n 'name' : 'TEXT',\n 'altLoc' : 'TEXT',\n 'resName' : 'TEXT',\n 'chainID' : 'TEXT',\n 'resSeq' : 'INT',\n 'iCode' : 'TEXT',\n 'x' : 'REAL',\n 'y' : 'REAL',\n 'z' : 'REAL',\n 'occ' : 'REAL',\n 'temp' : 'REAL'}\n\n # delimtier of the column format\n # taken from http://www.wwpdb.org/documentation/file-format-content/format33/sect9.html#ATOM\n self.delimiter = {\n 'serial' : [6,11],\n 'name' : [12,16],\n 'altLoc' : [16,17],\n 'resName' :[17,20],\n 'chainID' :[21,22],\n 'resSeq' :[22,26],\n 'iCode' :[26,26],\n 'x' :[30,38],\n 'y' :[38,46],\n 'z' :[46,54],\n 'occ' :[54,60],\n 'temp' :[60,66]}\n\n if self.no_extra:\n del self.col['occ']\n del self.col['temp']\n\n # size of the things\n ncol = len(self.col)\n ndel = len(self.delimiter)\n\n\n # open the data base\n # if we do not specify a db name\n # the db is only in RAM\n # there might be little advantage to use memory\n # https://stackoverflow.com/questions/764710/sqlite-performance-benchmark-why-is-memory-so-slow-only-1-5x-as-fast-as-d\n if self.sqlfile is None:\n self.conn = sqlite3.connect(':memory:')\n \n # or we create a new db file\n else:\n if os.path.isfile(sqlfile):\n sp.call('rm %s' %sqlfile,shell=True)\n self.conn = sqlite3.connect(sqlfile)\n self.c = self.conn.cursor()\n\n # intialize the header/placeholder\n header,qm = '',''\n for ic,(colname,coltype) in enumerate(self.col.items()):\n header += '{cn} {ct}'.format(cn=colname,ct=coltype)\n qm += '?'\n if ic < ncol-1:\n header += ', '\n qm += ','\n\n # create the table\n query = 'CREATE TABLE ATOM ({hd})'.format(hd=header)\n self.c.execute(query)\n\n\n # read the pdb file\n # this is dangerous if there are ATOM written in the comment part\n # which happends often\n #data = sp.check_output(\"awk '/ATOM/' %s\" %pdbfile,shell=True).decode('utf8').split('\\n')\n\n # a safer version consist at matching against the first field\n # won't work on windows\n #data = sp.check_output(\"awk '$1 ~ /^ATOM/' %s\" %pdbfile,shell=True).decode('utf8').split('\\n')\n\n # a pure python way\n # RMK we go through the data twice here. Once to read the ATOM line and once to parse the data ...\n # we could do better than that. But the most time consuming step seems to be the CREATE TABLE query\n # if we path a file we read it\n if isinstance(pdbfile,str):\n if os.path.isfile(pdbfile):\n with open(pdbfile,'r') as fi:\n data = [line.split('\\n')[0] for line in fi if line.startswith('ATOM')]\n else:\n raise FileNotFoundError('File %s was not found',pdbfile)\n\n # if we pass a list as for h5py read/write\n # we directly use that\n elif isinstance(pdbfile,np.ndarray):\n data = [l.decode('utf-8') for l in pdbfile.tolist()]\n\n # if we cant read it\n else:\n print(pdbfile)\n raise ValueError('PDB data not recognized')\n\n # if there is no ATOM in the file\n if len(data)==1 and data[0]=='':\n print(\"-- Error : No ATOM in the pdb file.\")\n self.is_valid = False\n return\n\n # haddock chain ID fix\n del_copy = self.delimiter.copy()\n if data[0][del_copy['chainID'][0]] == ' ':\n del_copy['chainID'] = [72,73]\n\n # get all the data\n data_atom = []\n for iatom,atom in enumerate(data):\n\n # sometimes we still have an empty line somewhere\n if len(atom) == 0:\n continue\n\n # browse all attribute of each atom\n at = ()\n for ik,(colname,coltype) in enumerate(self.col.items()):\n\n # get the piece of data\n data = atom[del_copy[colname][0]:del_copy[colname][1]].strip()\n\n # convert it if necessary\n if coltype == 'INT':\n data = int(data)\n elif coltype == 'REAL':\n data = float(data)\n\n # append keep the comma !!\n # we need proper tuple\n at +=(data,)\n\n # append\n data_atom.append(at)\n\n\n # push in the database\n self.c.executemany('INSERT INTO ATOM VALUES ({qm})'.format(qm=qm),data_atom)","def create_database(self):\n\n try: \n connection = sqlite3.connect(self.filepath_render_database)\n pointer = connection.cursor()\n\n print(self.filepath_render_database)\n\n sql_instruction = \"\"\"\n CREATE TABLE IF NOT EXISTS render_information(\n \n object_type VARCHAR(255),\n name VARCHAR(255),\n radius REAL,\n polar_angle_min REAL,\n polar_anglel_max REAL,\n polar_angle_segments REAL,\n polar_angle_random_rad REAL,\n azimuth_angle_min REAL,\n azimuth_angle_max REAL,\n azimuth_angle_segments REAL,\n azimuth_angle_random_rad REAL,\n tracking_obj VARCHAR(255),\n segmentation VARCHAR(255)\n\n\n );\"\"\"\n pointer.execute(sql_instruction)\n connection.commit()\n connection.close()\n print(\"Creating render database file\")\n except:\n print(\"Was not able to create render database file\")\n \n try: \n connection = sqlite3.connect(self.filepath_object_database)\n pointer = connection.cursor()\n sql_instruction = \"\"\"\n CREATE TABLE IF NOT EXISTS object_information(\n obj_filepath VARCHAR(255),\n obj_name VARCHAR(255),\n obj_type VARCHAR(255),\n obj_scale_factor REAL,\n obj_location_x REAL,\n obj_location_y REAL,\n obj_location_z REAL,\n obj_rotation_x REAL,\n obj_rotation_y REAL,\n obj_rotation_z REAL,\n obj_amount_percent REAL,\n obj_material_path VARCHAR(255),\n obj_point_in_time VARCHAR(255),\n maximum_random_rotation_degree_z REAL,\n maximum_random_translation REAL,\n random_amount REAL\n );\"\"\"\n pointer.execute(sql_instruction)\n connection.commit()\n connection.close()\n print(\"Creating object database file\")\n except:\n print(\"Was not able to create object database file\")\n\n try: \n connection = sqlite3.connect(self.filepath_output_database)\n print(\"outputfilepath is:\", self.filepath_output_database)\n pointer = connection.cursor()\n sql_instruction = \"\"\"\n CREATE TABLE IF NOT EXISTS objects(\n image_id REAL,\n object_name VARCHAR(255),\n object_type VARCHAR(255),\n object_amount REAL,\n object_location_x REAL,\n object_location_y REAL,\n object_location_z REAL,\n object_rotation_x REAL,\n object_rotation_y REAL,\n object_rotation_z REAL,\n object_dimensions_x REAL,\n object_dimensions_y REAL,\n object_dimensions_z REAL\n );\"\"\"\n pointer.execute(sql_instruction)\n connection.commit()\n sql_instruction = \"\"\"\n CREATE TABLE IF NOT EXISTS camera_settings(\n image_id REAL,\n image_variation REAL,\n camera_name VARCHAR(255),\n camera_location_x REAL,\n camera_location_y REAL,\n camera_location_z REAL,\n camera_rotation_x REAL,\n camera_rotation_y REAL,\n camera_rotation_z REAL,\n camera_focal_length REAL,\n camera_polar_angle REAL,\n camera_azimuth_angle REAL\n );\"\"\"\n pointer.execute(sql_instruction)\n connection.commit()\n sql_instruction = \"\"\"\n CREATE TABLE IF NOT EXISTS light_settings(\n image_id REAL,\n image_variation REAL,\n light_name VARCHAR(255),\n light_location_x REAL,\n light_location_y REAL,\n light_location_z REAL,\n light_rotation_x REAL,\n light_rotation_y REAL,\n light_rotation_z REAL,\n light_intensity REAL,\n light_polar_angle REAL,\n light_azimuth_angle REAL\n );\"\"\"\n pointer.execute(sql_instruction)\n connection.commit()\n sql_instruction = \"\"\"\n CREATE TABLE IF NOT EXISTS general_settings(\n image_id REAL,\n render_type VARCHAR(255),\n render_frame REAL\n );\"\"\"\n pointer.execute(sql_instruction)\n connection.commit()\n sql_instruction = \"\"\"\n CREATE TABLE IF NOT EXISTS bounding_boxes(\n image_id REAL,\n image_variation REAL,\n object_name VARCHAR(255),\n object_type VARCHAR(255),\n min_x REAL,\n max_x REAL,\n min_y REAL,\n max_y REAL\n );\"\"\"\n pointer.execute(sql_instruction)\n connection.commit()\n connection.close()\n print(\"Creating output database file\")\n except:\n print(\"Was not able to create output database file\")","def initialize_database():\n db = Database(database_name)\n i, m, u, p = db.fetch_needed_data()\n\n return i, m, u, p","def init_db():\n\tdb.drop_all()\n\tdb.create_all()\n\n\tprint(\"Initialized Database.\")\n\treturn","def get_weather_data(weather_station):\n now = datetime.datetime.now()\n then = now - datetime.timedelta(days=7)\n\n query_date_start = (\"%d%02d%02d\" % (then.year, then.month, then.day))\n query_date_end = (\"%d%02d%02d\" % (now.year, now.month, now.day))\n\n api_key = '/api/%s' % WUNDERGROUND_KEY\n history_key = '/history_%s%s/lang:EN/units:english/bestfct:1/v:2.0' % (query_date_start, query_date_end)\n query = '/q/%s.json?showObs=0&ttl=120' % weather_station\n\n weather_url = (\"%s%s%s%s\" % (WUNDERGROUND_HOST, api_key, history_key, query))\n\n logger.info('Weather URL: %s', weather_url)\n response = requests.get(weather_url).text\n\n max_temp_avg = json.loads(response)['history']['summary']['max_temperature_avg']\n sum_precip = json.loads(response)['history']['summary']['precip_sum']\n\n return max_temp_avg, sum_precip","def __create_table(self):\n\n self.connection = self.db.connect()\n self.metadata = MetaData(self.connection)\n\n self.system = Table(self.table_name, self.metadata,\n Column('timestamp', DateTime(), primary_key=True, nullable=False),\n Column('vibration_sensor', Float()),\n Column('flow', Float()),\n Column('pressure', Float()),\n Column('power_consumption', Float()),\n Column('failure_times', Float()),\n Column('operational', Boolean())\n )\n\n self.metadata.create_all()","def create_db(name=_db_indicators,\n indi_file=os.path.join('Source', 'codes_need.csv'),\n country_file=os.path.join('Source', 'work_countries.txt')):\n\n def create_indi_country(pdfI, con, mess, db_name, freq):\n if pdfI.shape[0]==0:\n return\n print('+' * 50, '{} WORKS'.format(mess), '+' * 50)\n\n pdfI.to_sql(cmm.strINDI_db_name, con, if_exists='replace')\n print('CREATE IMF.INDICATORS table for {} indicators'.format(pdfI.shape[0]))\n pdfC = get_countryes(db_name=db_name, country_txt_file=country_file)\n pdfC.to_sql(cmm.strCOUNTRY_db_name, con=con, if_exists='replace')\n print('CREATE IMF.COUNTRIES for {0} countries.'.format(pdfC.shape[0]))\n\n update_db(db_name=db_name, start=1970, end=2000)\n update_db(db_name=db_name, start=1999)\n\n cmm.create_views(db_name, freq=freq)\n\n pdf = cmm.read_indicators_from_csv(indi_file)\n print(indi_file)\n\n pdfQ = pdf[pdf['Freq']=='Q']\n pdfA = pdf[pdf['Freq'] == 'Y']\n pdfM = pdf[pdf['Freq'] == 'M']\n\n #pdfC = cmm.read_countries(file_name=country_file)\n\n nameA=cmm.db_name2annu(name)\n nameM = cmm.db_name2annu(name, suff='_M')\n\n coni = sa.create_engine('sqlite+pysqlite:///{name}'.format(name=name))\n coniA = sa.create_engine('sqlite+pysqlite:///{name}'.format(name=nameA))\n coniM = sa.create_engine('sqlite+pysqlite:///{name}'.format(name=nameM))\n\n create_indi_country(pdfQ, coni, 'QUARTERLY', name, freq='Q')\n create_indi_country(pdfA, coniA, 'ANNUAL', nameA, freq='A')\n create_indi_country(pdfM, coniM, 'MONTHLY', nameM, freq='M')","def populate_db():\n\n populate_table(db, models.Department, departments_data)\n populate_table(db, models.Employee, employees_data)","def insert_data(self):\n # Make a connexion with a mock database\n self.generate_data_collection()"],"string":"[\n \"def populate_weather(connection):\\n metadata = load_metadata('weather')\\n cursor = connection.cursor()\\n water_defs = get_water_definitions()\\n\\n # Check if tables are already populated.\\n cursor.execute('SELECT count(*) FROM weather')\\n weather_count = cursor.fetchone()[0]\\n\\n if weather_count:\\n print('Weather tables already populated!')\\n return\\n\\n print('WEATHER:')\\n\\n # Darksky data\\n for dir_name, location in metadata.items():\\n print(f'\\\\tPopulating weather: \\\"{location[\\\"name\\\"]}\\\".')\\n\\n # Insert location.\\n cursor.execute(f'''INSERT INTO locations(name, lat, lng)\\n VALUES ('{location['name']}', {location['lat']}, {location['lng']})''')\\n location_id = cursor.lastrowid\\n\\n # Set weather locations for watercourses/aquifers.\\n for water_body in [d['body'] for d in water_defs.values()]:\\n if water_body in location:\\n cursor.execute(f'''UPDATE {water_body}s\\n SET location_id = {location_id}\\n WHERE name IN ('{\\\"','\\\".join(location[water_body])}')''')\\n break\\n\\n dir_path = get_data_path('weather', 'raw', dir_name)\\n for json_file_name in os.listdir(dir_path):\\n json_path = os.path.join(dir_path, json_file_name)\\n with open(json_path, 'r', encoding='utf-8') as json_file:\\n print(f'\\\\t\\\\tPopulating year: {json_file_name[0:-5]}')\\n year_forecasts = json.load(json_file)\\n for date, date_forecast in year_forecasts.items():\\n hourly_forecasts = date_forecast['hourly']\\n\\n if not hourly_forecasts:\\n print(f'\\\\t\\\\tNo hourly forecasts for {date}!')\\n continue\\n\\n daily_forecast = {\\n 'location_id': location_id,\\n 'time': date_forecast['time'],\\n 'day_time': date_forecast['sunset_time'] - date_forecast['sunrise_time'],\\n 'precipitation': 0,\\n 'snow_accumulation': 0\\n }\\n # List of value names with `avg`, `min` and `max` values\\n value_names = {\\n 'temperature': 'temperature',\\n 'cloud_cover': 'cloudCover',\\n 'dew_point': 'dewPoint',\\n 'humidity': 'humidity',\\n 'pressure': 'pressure',\\n 'uv_index': 'uvIndex',\\n 'precipitation_probability': 'precipProbability',\\n 'precipitation_intensity': 'precipIntensity'\\n }\\n # Value name counters, which indicate how many times (out of 24)\\n # certain value appears in hourly data.\\n value_counts = {k: 0 for k in value_names.keys()}\\n\\n for value_name in value_names.keys():\\n daily_forecast[f'{value_name}_avg'] = 0.0\\n daily_forecast[f'{value_name}_min'] = float('inf')\\n daily_forecast[f'{value_name}_max'] = float('-inf')\\n\\n # Calculate daily forecast values from hourly forecasts.\\n for hourly_forecast in hourly_forecasts:\\n for value_name in value_names.keys():\\n orig_value_name = value_names[value_name]\\n if is_forecast_number(orig_value_name, hourly_forecast):\\n daily_forecast[f'{value_name}_avg'] += hourly_forecast[orig_value_name]\\n daily_forecast[f'{value_name}_min'] = min(\\n hourly_forecast[orig_value_name],\\n daily_forecast[f'{value_name}_min']\\n )\\n daily_forecast[f'{value_name}_max'] = max(\\n hourly_forecast[orig_value_name],\\n daily_forecast[f'{value_name}_max']\\n )\\n value_counts[value_name] += 1\\n\\n if is_forecast_number('precipAccumulation', hourly_forecast) \\\\\\n and hourly_forecast['precipType'] == 'snow':\\n daily_forecast['snow_accumulation'] += hourly_forecast['precipAccumulation']\\n elif is_forecast_number('precipIntensity', hourly_forecast) \\\\\\n and is_forecast_number('precipProbability', hourly_forecast):\\n daily_forecast['precipitation'] += \\\\\\n hourly_forecast['precipIntensity'] * hourly_forecast['precipProbability']\\n\\n for value_name, value_count in value_counts.items():\\n if value_count:\\n # Calculate average.\\n daily_forecast[f'{value_name}_avg'] = daily_forecast[f'{value_name}_avg'] / value_count\\n else:\\n # If value never appeared\\n daily_forecast[f'{value_name}_avg'] = 'NULL'\\n daily_forecast[f'{value_name}_min'] = 'NULL'\\n daily_forecast[f'{value_name}_max'] = 'NULL'\\n\\n cursor.execute(f'''INSERT INTO weather({', '.join(daily_forecast.keys())})\\n VALUES ({', '.join([str(v) for v in daily_forecast.values()])})''')\\n\\n # IOT data:\\n for location in SETTINGS['weather_locations_iot']:\\n print(f'\\\\tPopulating weather: \\\"{location[\\\"name\\\"]}\\\".')\\n\\n # Insert location.\\n cursor.execute(f'''INSERT INTO locations(name, lat, lng)\\n VALUES ('{location['name']}', {location['lat']}, {location['lng']})''')\\n location_id = cursor.lastrowid\\n\\n # Set weather locations for watercourses/aquifers.\\n for water_body in [d['body'] for d in water_defs.values()]:\\n if water_body in location:\\n cursor.execute(f'''UPDATE {water_body}s\\n SET location_id = {location_id}\\n WHERE name IN ('{\\\"', '\\\".join(location[water_body])}')''')\\n\\n # Set locations for all stations on given water body to match its location.\\n cursor.execute(f'''SELECT id\\n FROM {water_body}s\\n WHERE location_id = {location_id}''')\\n ids = [row[0] for row in cursor.fetchall()]\\n if len(ids):\\n cursor.execute(f'''UPDATE {water_body}_stations\\n SET location_id = {location_id}\\n WHERE {water_body}_id IN ({', '.join([str(v) for v in ids])})''')\\n\\n break \\n \\n file_name = f'''{location['lat']}-{location['lng']}.json'''\\n json_path = get_data_path('weather', 'raw', file_name)\\n\\n # If data file doesn't exist, download it first.\\n if not os.path.isfile(json_path):\\n with open(json_path, 'wb', encoding=\\\"utf-8\\\") as file:\\n file.write(read_from_url(location['url'], decode=False))\\n \\n with open(json_path, 'r', encoding='utf-8') as json_file:\\n row_names = {\\n \\\"Sun_duration\\\": \\\"sun_duration\\\",\\n \\\"CloudCover\\\": \\\"cloud_cover_avg\\\",\\n \\\"Percipitation\\\": \\\"precipitation\\\",\\n \\\"New_snow_blanket\\\": \\\"snow_accumulation\\\",\\n \\\"Snow_blanket\\\": \\\"snow_depth\\\",\\n \\\"TemperatureAvg\\\": \\\"temperature_avg\\\",\\n \\\"TemperatureMin\\\": \\\"temperature_min\\\",\\n \\\"TemperatureMax\\\": \\\"temperature_max\\\"\\n }\\n forecasts = json.load(json_file)\\n for forecast in forecasts:\\n f = {row_names[k]: forecast[k] for k in row_names.keys()}\\n f['location_id'] = location_id\\n f['time'] = round(forecast['LastUpdatedEpoch'] / 1000)\\n cursor.execute(f'''INSERT INTO weather({', '.join(f.keys())})\\n VALUES ({', '.join([str(v) for v in f.values()])})''')\",\n \"def create_db(temp: list, rain: list, humidity: list, wind: list) -> dict:\\r\\n weather = {}\\r\\n for i in range(len(temp)):\\r\\n weather[i+1] = [temp[i], rain[i], humidity[i], wind[i]]\\r\\n return weather\",\n \"def populate_database(telescope_name, instrument_name):\\n telescope = Telescope.objects.create(\\n name=telescope_name, latitude=25.0, longitude=45.0)\\n instrument = Instrument.objects.create(\\n name=instrument_name, telescope=telescope)\\n for year_int in (2012, 2013):\\n for month_int in range(1, 13):\\n for night_int in (1, monthrange(year_int, month_int)[1]):\\n ut_date = date(year_int, month_int, night_int)\\n night = Night.objects.create(\\n ut_date=ut_date, instrument=instrument, observers='Smith')\\n Exposure.objects.create(\\n night=night, run_number=1, ut_start=time(10, 0, 0),\\n exposed=20.0, ra=60.0, dec=30.0, object_exp=True)\\n Exposure.objects.create(\\n night=night, run_number=2, ut_start=time(11, 0, 0),\\n exposed=30.0, ra=90.0, dec=0.0, object_exp=True)\\n Exposure.objects.create(\\n night=night, run_number=3, ut_start=time(12, 0, 0),\\n exposed=40.0, ra=120.0, dec=-30.0, object_exp=False)\",\n \"def populate_water_tables(connection):\\n metadata = load_metadata('water')\\n cursor = connection.cursor()\\n\\n # Check if tables are already populated.\\n cursor.execute('SELECT count(*) FROM watercourses')\\n watercourse_count = cursor.fetchone()[0]\\n cursor.execute('SELECT count(*) FROM aquifers')\\n aquifer_count = cursor.fetchone()[0]\\n\\n if watercourse_count and aquifer_count:\\n print('Water tables already populated!')\\n return\\n\\n station_data = get_station_data()\\n\\n for archive in metadata.keys():\\n print(f'{archive}-water:'.upper())\\n water_body = get_water_definitions(archive)['body']\\n\\n # 1. Populate watercourses/aquifers:\\n stations = {}\\n for water_body_name in metadata[archive].keys():\\n print(f'\\\\tPopulating {water_body}: \\\"{water_body_name}\\\"')\\n cursor.execute(f'''INSERT INTO {water_body}s(location_id, name)\\n VALUES (0, '{water_body_name}')''')\\n water_body_id = cursor.lastrowid\\n\\n # 2. Populate watercourse_stations/aquifer_stations:\\n for station_id in metadata[archive][water_body_name]['stations']:\\n station_name = clean_name(metadata[archive][water_body_name]['stations'][station_id]['name'])\\n\\n if station_id in stations:\\n # Prefer watercourses/aquifer with more stations\\n current_len = len(metadata[archive][water_body_name]['stations'])\\n previous_len = len(metadata[archive][stations[station_id]]['stations'])\\n\\n if current_len < previous_len:\\n print(f'\\\\t\\\\tStation already exists: {station_id} - \\\"{station_name}\\\" (\\\"{water_body_name}\\\")')\\n continue\\n else:\\n cursor.execute(f'''DELETE \\n FROM {water_body}_stations\\n WHERE id = {station_id}''')\\n print(f'\\\\t\\\\tRemoved station: {station_id} - \\\"{station_name}\\\" from \\\"{stations[station_id]}\\\")')\\n\\n stations[station_id] = water_body_name\\n print(f'\\\\t\\\\tPopulating station: {station_id} - \\\"{station_name}\\\"')\\n\\n # Insert station location if station data exists.\\n location_id = 0\\n station_row = station_data.query(f'ŠIFRA == \\\"{station_id}\\\"')\\n if not station_row.empty:\\n index = station_row.index[0]\\n lat = station_row.at[index, 'LAT']\\n lng = station_row.at[index, 'LON']\\n if not np.isnan(lat) and not np.isnan(lng):\\n name = f\\\"{station_row.at[index, 'VODOMERNA POSTAJA']} ({station_row.at[index, 'VODOTOK']})\\\"\\n cursor.execute(f'''INSERT INTO locations(name, lat, lng)\\n VALUES ('{name}', {lat}, {lng})''')\\n location_id = cursor.lastrowid\\n\\n # Insert station.\\n cursor.execute(f'''INSERT INTO {water_body}_stations(id, {water_body}_id, location_id, name)\\n VALUES ({station_id}, {water_body_id}, {location_id}, '{station_name}')''')\\n\\n # 3. Populate watercourse_measurements/aquifer_measurements:\\n if not populate_water_measurements(cursor, archive, metadata[archive][water_body_name]['dir'],\\n station_id):\\n cursor.execute(f'''DELETE \\n FROM {water_body}_stations\\n WHERE id = {station_id}''')\\n print(f'\\\\t\\\\tRemoved station with useless data: {station_id} - \\\"{station_name}\\\"')\\n\\n # Remove empty watercourses/aquifers.\\n cursor.execute(f'''SELECT w.id, w.name\\n FROM {water_body}s w\\n WHERE NOT EXISTS (\\n SELECT s.id \\n FROM {water_body}_stations s \\n WHERE w.id = s.{water_body}_id\\n )''')\\n\\n for row in cursor.fetchall():\\n cursor.execute(f'''DELETE \\n FROM {water_body}s\\n WHERE id = {row[0]}''')\\n print(f'\\\\tRemoved empty {water_body}: \\\"{row[1]}\\\"')\",\n \"def update_weather(location_request, db):\\n with open(expanduser(\\\"~/bin/my_utilities/config/darksky-key\\\")) as f:\\n ds_key = f.readline().strip()\\n current = []\\n current_day = 0\\n with forecast(ds_key, *location_request, units=\\\"uk2\\\") as location:\\n raw = location['hourly']['data'][0]\\n current.append(datetime.datetime.now().hour)\\n current.append(day_relative_to_absolute(current_day))\\n current.append(raw[\\\"temperature\\\"])\\n current.append(raw[\\\"apparentTemperature\\\"])\\n current.append(raw[\\\"precipIntensity\\\"])\\n current.append(raw[\\\"precipProbability\\\"] * 100)\\n current.append(raw[\\\"humidity\\\"] * 100)\\n current.append(raw[\\\"dewPoint\\\"])\\n current.append(raw[\\\"windSpeed\\\"])\\n current.append(raw[\\\"windBearing\\\"])\\n current.append(raw[\\\"windGust\\\"])\\n current.append(raw[\\\"pressure\\\"])\\n current.append(raw[\\\"cloudCover\\\"] * 100)\\n current.append(raw[\\\"uvIndex\\\"])\\n current.append(raw[\\\"visibility\\\"])\\n current = format_list_for_db(current)\\n\\n columns = [\\\"hour\\\", \\\"day\\\", \\\"temp\\\", \\\"apptemp\\\", \\\"precipint\\\", \\\"precipprob\\\",\\n \\\"humidity\\\", \\\"dewpoint\\\", \\\"windspeed\\\", \\\"windbearing\\\",\\n \\\"windgust\\\", \\\"pressure\\\", \\\"cloudcover\\\", \\\"uvindex\\\", \\\"visibility\\\"]\\n columns = format_list_for_db(columns)\\n statement = f\\\"INSERT INTO WEATHER {columns} VALUES {current}\\\"\\n print(statement)\\n cursor = db.cursor()\\n cursor.execute(statement)\\n cursor.close()\",\n \"def prepare_database(self, waterscenario=None, trafficscenario=None):\\n\\n # Validate input\\n if waterscenario:\\n waterscenario = Path(waterscenario)\\n assert waterscenario.exists(), 'Waterscenario file not found'\\n\\n BIVAS = pyBIVAS(self.BIVAS_database)\\n df_trafficscenarios = BIVAS.trafficscenario_numberoftrips()\\n\\n\\n # Do changes to database:\\n con = sqlite3.connect(self.BIVAS_database)\\n c = con.cursor()\\n\\n # Update waterscenario with given file\\n if waterscenario:\\n # Delete current water_scenario_values\\n sql = \\\"DELETE FROM water_scenario_values WHERE 1\\\"\\n c.execute(sql)\\n\\n sql = \\\"DELETE FROM water_scenarios WHERE 1\\\"\\n c.execute(sql)\\n\\n # Write waterdata to database\\n\\n # Read waterscenario file\\n df = pd.read_csv(waterscenario, header=0, index_col=None)\\n df = df[['ArcID', 'SeasonID', 'WaterLevel__m', 'RateOfFlow__m3_s', 'WaterSpeed__m_s', 'WaterDepth__m']]\\n df['WaterScenarioID'] = 1\\n\\n # Add new water_scenario\\n df.to_sql('water_scenario_values', con,\\n if_exists='append', index=False)\\n\\n # Rename water_scenario\\n # waterscenario_name = waterscenario.stem\\n # sql = \\\"\\\"\\\"UPDATE water_scenarios SET Description = \\\"{}\\\" WHERE ID = {}\\\"\\\"\\\".format(\\n # waterscenario_name, waterscenario)\\n # c.execute(sql)\\n\\n\\n waterscenario_id = 1\\n waterscenario_name = 'TEST waterscenario'\\n waterscenario_type = 1\\n sql = \\\"\\\"\\\"INSERT into water_scenarios VALUES ({}, '{}', {})\\\"\\\"\\\".format(\\n waterscenario_id,\\n waterscenario_name,\\n waterscenario_type\\n )\\n c.execute(sql)\\n\\n # Remove water scenario. I'm simply updating all scenarios\\n # Otherwise I should check the BranchSet structure\\n sql = \\\"\\\"\\\"UPDATE parameters SET WaterScenarioID = 1 WHERE 1\\\"\\\"\\\"\\n c.execute(sql)\\n\\n else:\\n # Remove water scenario. I'm simply updating all scenarios\\n # Otherwise I should check the BranchSet structure\\n sql = \\\"\\\"\\\"UPDATE parameters SET WaterScenarioID = NULL WHERE 1\\\"\\\"\\\"\\n c.execute(sql)\\n\\n # Set scenario name and description\\n date_string = datetime.now().strftime(\\\"%Y-%m-%d %H:%M:%S\\\")\\n self.description = f'Date: {date_string}, Waterscenario: {waterscenario}, TrafficScenario: {trafficscenario},'\\n\\n sql = \\\"\\\"\\\"\\n UPDATE scenarios\\n SET Name = \\\"{}\\\",\\n Description = \\\"{}\\\"\\n WHERE ID = {}\\n \\\"\\\"\\\".format(\\n self.scenarioName, self.description, self.scenarioID)\\n c.execute(sql)\\n\\n # Update traffic Scenario. I'm simply updating all scenarios\\n # Otherwise I should check the BranchSet structure\\n if trafficscenario:\\n if isinstance(trafficscenario, int):\\n sql = \\\"\\\"\\\"UPDATE parameters SET TrafficScenarioID = \\\"{}\\\" WHERE 1\\\"\\\"\\\".format(trafficscenario)\\n c.execute(sql)\\n else:\\n trafficScenarioID = df_trafficscenarios.index[df_trafficscenarios['Description'] == trafficscenario][0]\\n sql = \\\"\\\"\\\"UPDATE parameters SET TrafficScenarioID = \\\"{}\\\" WHERE 1\\\"\\\"\\\".format(trafficScenarioID)\\n c.execute(sql)\\n\\n con.commit()\\n con.close()\\n\\n logger.info('BIVAS database copied and updated')\",\n \"def store_weather(data: List[WeatherData]):\\n insert_cmd = 'INSERT INTO weather VALUES (?, ?, ?)'\\n with closing(sqlite3.connect('weather.db')) as conn:\\n with closing(conn.cursor()) as cursor:\\n cursor.executemany(insert_cmd, data)\\n conn.commit()\",\n \"def create_table(self):\\n # Connect to database\\n conn = sqlite3.connect(self)\\n # Create a cursor\\n c = conn.cursor()\\n\\n # Create a Table\\n c.execute(\\\"\\\"\\\"CREATE TABLE weather (\\n id INTEGER PRIMARY KEY AUTOINCREMENT,\\n sensor text,\\n location text,\\n temperature real,\\n description text,\\n time text\\n )\\\"\\\"\\\")\\n # Commit our command\\n conn.commit()\\n # Close our connection\\n conn.close()\",\n \"def grasspi_add_db(table_name,row):\\n\\n if table_name == \\\"weatherdata\\\":\\n\\tconn = sqlite3.connect(grasspi_config.cfg.db_file)\\n \\tc = conn.cursor()\\n\\tc.execute('INSERT INTO ' + table_name + ' values (?,?,?,?,?,?,?,?,?,?,?,?,?)',[row[\\\"date\\\"],row[\\\"time\\\"],\\n \\trow[\\\"current_temp\\\"],row[\\\"current_rain\\\"],row[\\\"total_rain\\\"],row[\\\"current_wind_speed\\\"],\\n \\trow[\\\"current_wind_direction\\\"],row[\\\"current_humidity\\\"],row[\\\"current_air_pressure\\\"],\\n \\trow[\\\"current_shortwave_rad\\\"],row[\\\"current_atm_rad\\\"],row[\\\"day_length\\\"],row[\\\"elevation\\\"]])\\n elif table_name == \\\"wateringschedule\\\":\\n\\tconn = sqlite3.connect(grasspi_config.cfg.db_file, detect_types=sqlite3.PARSE_DECLTYPES|sqlite3.PARSE_COLNAMES)\\n \\tc = conn.cursor()\\n\\tc.execute('INSERT INTO ' + table_name + ' values (?,?,?)',[row[\\\"zonenumber\\\"],\\n\\trow[\\\"starttime\\\"],row[\\\"duration\\\"]])\\n # Save (commit) the changes\\n conn.commit()\\n # We can also close the cursor if we are done with it\\n c.close()\",\n \"def initial_db_setup() -> None:\\n db_filename = \\\"twdft.db\\\"\\n db_path = os.path.join(TWDFT_DATA_DIR, db_filename)\\n csv_filename = \\\"sites.csv\\\"\\n csv_path = os.path.join(TWDFT_DATA_DIR, csv_filename)\\n db_is_new = not os.path.exists(db_path)\\n sites_csv = os.path.join(TWDFT_DATA_DIR, csv_filename)\\n\\n if db_is_new:\\n with sqlite3.connect(db_path) as conn:\\n c = conn.cursor()\\n\\n # first we create a site object\\n c.execute(\\n \\\"\\\"\\\"\\n CREATE TABLE site(\\n id INTEGER PRIMARY KEY,\\n name TEXT,\\n site_type TEXT,\\n sub_category TEXT,\\n address_1 TEXT,\\n address_2 TEXT,\\n town TEXT,\\n county TEXT,\\n country TEXT,\\n postcode TEXT,\\n site_category TEXT,\\n freq_target TEXT,\\n created TEXT,\\n notes TEXT,\\n last_inspection TEXT,\\n next_inspection TEXT,\\n pfsp_approval TEXT,\\n pfsp_expiry TEXT,\\n unlocode TEXT,\\n pfso TEXT,\\n pso TEXT,\\n pfsa_approval TEXT,\\n pfsa_expiry TEXT,\\n team TEXT,\\n created_by TEXT,\\n last_updated TEXT,\\n updated_by TEXT,\\n afp_loc TEXT,\\n rdf TEXT,\\n classification TEXT,\\n article24 TEXT,\\n psa_approval TEXT,\\n inspection_due TEXT\\n )\\n \\\"\\\"\\\"\\n )\\n conn.commit()\\n\\n # next we want an inspection table\\n\\n c.execute(\\n \\\"\\\"\\\"\\n CREATE TABLE inspection(\\n id INTEGER PRIMARY KEY,\\n site INTEGER,\\n date TEXT,\\n status TEXT,\\n time TEXT,\\n FOREIGN KEY(site) REFERENCES site(id)\\n )\\n \\\"\\\"\\\"\\n )\\n conn.commit()\\n\\n # next we want an inspector table\\n c.execute(\\n \\\"\\\"\\\"\\n create table inspector(\\n id integer primary key,\\n first_name text,\\n last_name text\\n )\\n \\\"\\\"\\\"\\n )\\n conn.commit()\\n\\n for i in INSPECTORS:\\n first = i.split(\\\" \\\")[0]\\n last = i.split(\\\" \\\")[1]\\n c.execute(\\n \\\"INSERT INTO inspector(first_name, last_name) VALUES (?,?)\\\",\\n (first, last),\\n )\\n\\n # a table that links inspectors with inspections\\n c.execute(\\n \\\"\\\"\\\"\\n CREATE TABLE inspector_inspections(\\n inspector INTEGER,\\n inspection INTEGER,\\n FOREIGN KEY (inspector) REFERENCES inspector(id),\\n FOREIGN KEY (inspection) REFERENCES inspection(id)\\n )\\n \\\"\\\"\\\"\\n )\\n conn.commit()\\n\\n for site in map(Site._make, csv.reader(open(csv_path, \\\"r\\\"))):\\n try:\\n c.execute(\\n f\\\"\\\"\\\"\\n INSERT INTO site VALUES(?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)\\\"\\\"\\\",\\n (\\n int(site.id.replace(\\\",\\\", \\\"\\\")),\\n site.name,\\n site.site_type,\\n site.sub_category,\\n site.address_1,\\n site.address_2,\\n site.town,\\n site.county,\\n site.country,\\n site.postcode,\\n site.site_category,\\n site.freq_target,\\n site.created,\\n site.notes,\\n site.last_inspection,\\n site.next_inspection,\\n site.pfsp_approval,\\n site.pfsp_expiry,\\n site.unlocode,\\n site.pfso,\\n site.pso,\\n site.pfsa_approval,\\n site.pfsa_expiry,\\n site.team,\\n site.created_by,\\n site.last_updated,\\n site.updated_by,\\n site.afp_loc,\\n site.rdf,\\n site.classification,\\n site.article24,\\n site.psa_approval,\\n site.inspection_due,\\n ),\\n )\\n except sqlite3.IntegrityError as e:\\n print(\\\"That hasnae worked\\\", site.inspection_due)\",\n \"def load_wdi() -> None:\\n\\n log.info(\\\"Started loading WDI.\\\")\\n df = _load_and_stage_wdi()\\n\\n schema = \\\"wdi_202005\\\"\\n db.drop_schema(schema)\\n db.create_schema(schema)\\n\\n # Push completely raw but staged data\\n log.debug(\\\"Done preparing raw WDI, pushing.\\\")\\n fqtables = [f\\\"{schema}.cy_unimp_part_{p}\\\" for p in [1, 2]]\\n db.df_to_split_db(df=df, fqtables=fqtables)\\n\\n _fill_and_push_wdi(df=df, schema=schema, n_imp=5)\\n log.info(\\\"Finished loading WDI.\\\")\",\n \"def sql_functions(cities, weather):\\n con = lite.connect(\\\"sql_database.db\\\")\\n tables_tuple = (\\\"cities\\\", \\\"weather\\\")\\n with con:\\n #Generate tables in database:\\n cur = con.cursor() #Get cursor object\\n for table in tables_tuple:\\n cur.execute(\\\"DROP TABLE IF EXISTS {0}\\\".format(table)) #Drop tables if they already exist.\\n cur.execute(\\\"CREATE TABLE cities (name text, state text)\\\")\\n cur.execute(\\\"CREATE TABLE weather (city text, year integer, warm_month text, cold_month text, average_high integer)\\\")\\n #Populate tables in database:\\n cur.executemany(\\\"INSERT INTO cities VALUES (?,?)\\\", cities)\\n cur.executemany(\\\"INSERT INTO weather VALUES (?,?,?,?,?)\\\", weather)\\n #Retrieve data from database:\\n cur.execute(\\\"SELECT * FROM cities INNER JOIN weather ON city = name\\\")\\n rows = cur.fetchall()\\n cols = [desc[0] for desc in cur.description]\\n output_dataframe = pd.DataFrame(rows, columns = cols)\\n \\n return output_dataframe\",\n \"def prepare_db():\\n conn = sqlite.connect(\\\"temp.db\\\")\\n sql = conn.cursor()\\n sql.execute(\\\"SELECT sql FROM sqlite_master WHERE name='points'\\\")\\n rows = sql.fetchall()\\n if len(rows) == 0:\\n print \\\"Database does not exist. Creating Database...\\\"\\n sql.execute('''CREATE TABLE points\\n (date datetime, humidity real, temp_c real, temp_f real, index_c real, index_f)''')\\n print \\\"Database created\\\"\\n conn.close()\",\n \"def init_db() -> None:\\n conn = sqlite3.connect('../Utils/map_storage.db')\\n cursor = conn.cursor()\\n\\n with conn:\\n station_cmd = \\\"\\\"\\\"CREATE TABLE IF NOT EXISTS\\n nodes(city TEXT, name TEXT, is_station TEXT, x INT, y INT, zone TEXT)\\\"\\\"\\\"\\n\\n cursor.execute(station_cmd)\\n\\n connection_cmd = \\\"\\\"\\\"CREATE TABLE IF NOT EXISTS\\n connections(city TEXT, name_1 TEXT, name_2 TEXT, color TEXT)\\\"\\\"\\\"\\n\\n cursor.execute(connection_cmd)\",\n \"def _setupWeather(self, w, config):\\n wnames = ('cloud', 'seeing')\\n if w not in wnames:\\n raise Exception('w should be one of %s' %(wnames))\\n filename = config['%s_datafile' %(w)]\\n file = open(filename, 'r')\\n # Also assume flat file contains only date / value in a space or tab separated file. \\n self.dates[w] = []\\n self.weather[w] = []\\n # Read the data file.\\n print '# Reading weather data file %s' %(filename)\\n for line in file:\\n if line.startswith('#') | line.startswith('!'):\\n continue\\n self.dates[w].append(line.split()[0])\\n self.weather[w].append(line.split()[1])\\n file.close()\\n self.dates[w] = numpy.array(self.dates[w], float)\\n self.weather[w] = numpy.array(self.weather[w], float)\\n # Check the total amount of data (mostly for user awareness):\\n print '# Read %d weather values from %s file. ' %(len(self.weather[w]), filename)\\n # Check that weather data is monotonically increasing in time. \\n if not(numpy.all(numpy.diff(self.dates[w]))):\\n order = self.dates[w].argsort()\\n self.weather[w] = self.weather[w][order]\\n self.dates[w] = self.dates[w][order]\\n # Get the total length of time included in this (seeing/cloud) file,\\n # so that we can determine a wrap-around date if we need that.\\n self.maxtime[w] = self.dates[w].max()\\n return\",\n \"def database_setup():\\n Base.metadata.create_all(bind=engine)\\n db = LocalSession()\\n try:\\n populate_from_json(db, Vector, str(VECTORS))\\n populate_from_json(db, Gender, str(GENDERS))\\n populate_from_json(db, Tag, str(TAGS))\\n finally:\\n db.close()\",\n \"def populate_database(self):\\n self.insert_products()\\n self.insert_categories()\\n self.insert_products_categories()\\n self.insert_stores()\\n self.insert_products_stores()\",\n \"def save (self) :\\n\\n try:\\n # This opens and closes the connections to the database.\\n # Since writings occur only every 10 minutes it wouldn't be efficient to\\n # let the connection open.\\n db = MySQLdb.connect(\\n host=config.db_host, db=config.db_name, user=config.db_user, passwd=config.db_password)\\n cur = db.cursor()\\n\\n # get the reservoir height to be able to measure the water level\\n sqlQuery = \\\"SELECT heigth FROM main_reservoir WHERE res_id = {}\\\".format(self.reservoir)\\n cur.execute(sqlQuery)\\n # self.waterGap is the height of the part of the reservoir\\n # that's out of water. reservoir height minus that value will give the actual water level\\n reservoirHeight = cur.fetchone()[0]\\n waterLevel = reservoirHeight - self.waterGap\\n\\n sqlQuery = \\\"\\\"\\\"\\n INSERT INTO main_measurement (packetNr, waterLevel, pH, conductivity, reservoir_id, dateTime, salinity, tds)\\n VALUES ({}, {}, {}, {}, {}, now(), {}, {})\\n \\\"\\\"\\\".format(\\n self.packetNr,\\n waterLevel,\\n self.pH,\\n self.conductivity,\\n self.reservoir,\\n self.salinity,\\n self.tds)\\n cur.execute(sqlQuery)\\n\\n db.commit()\\n db.close()\\n except Exception as e:\\n print('[Measurement#save] failed to save instance: {}'.format(e))\",\n \"def create_db(self):\\n\\t\\tcur, conn = self.open_connection()\\n\\n\\t\\t# this creates the meta table\\n\\t\\tcommand = \\\"\\\"\\\" DROP TABLE IF EXISTS main;\\n\\t\\tCREATE TABLE meta (\\n\\t\\t\\tid INTEGER PRIMARY KEY AUTOINCREMENT,\\n\\t\\t\\tUT_date TEXT,\\n\\t\\t\\ttime_of_obs TEXT,\\n\\t\\t\\tobject_name TEXT,\\n\\t\\t\\tintegration_time FLOAT,\\n\\t\\t\\tgrating INTEGER,\\n\\t\\t\\tcentral_wavelength FLOAT,\\n\\t\\t\\tslit_width INTEGER,\\n\\t\\t\\tphase_angle FLOAT,\\n\\t\\t\\tcomments TEXT\\n\\t\\t\\t);\\\"\\\"\\\"\\n\\n\\t\\tcur.executescript(command)\\n\\n\\t\\t# this creates the spectra table\\n\\t\\tcommand = \\\"\\\"\\\" DROP TABLE IF EXISTS spectrum;\\n\\t\\tCREATE TABLE spectra (\\n\\t\\t\\tspec_id INTEGER PRIMARY KEY AUTOINCREMENT,\\n\\t\\t\\tid INTEGER,\\n\\t\\t\\twave array,\\n\\t\\t\\tspectrum array,\\n\\t\\t\\tFOREIGN KEY(id) REFERENCES meta(id)\\n\\t\\t\\t);\\\"\\\"\\\"\\n\\n\\t\\tcur.executescript(command)\\n\\n\\t\\tconn.commit()\\n\\t\\tconn.close()\",\n \"def _populate():\\n models.Base.metadata.create_all(engine)\\n logger.info(\\\"Initalized database\\\")\\n db = Session()\\n\\n hermann = models.Account(id=\\\"test\\\",\\n name=\\\"Hermann Dörkschneider\\\",\\n email=\\\"fakeaddress@lumeh.org\\\")\\n db.add(hermann)\\n\\n journey = models.Journey(id=str(uuid.uuid4()),\\n account_id=\\\"test\\\",\\n visibility=\\\"PUBLIC\\\",\\n start_time_utc=datetime.datetime.now(),\\n stop_time_utc=datetime.datetime.now())\\n db.add(journey)\\n\\n waypoint1 = models.Waypoint(journey=journey,\\n time_utc=datetime.datetime.now(),\\n accuracy_m=2.71,\\n latitude=3.1416,\\n longitude=1.618,\\n height_m=10)\\n db.add(waypoint1)\\n\\n waypoint2 = models.Waypoint(journey=journey,\\n time_utc=datetime.datetime.now(),\\n accuracy_m=5.1,\\n latitude=3.1410,\\n longitude=1.620,\\n height_m=5)\\n db.add(waypoint2)\\n\\n db.commit()\\n logger.info(\\\"Created test account {}\\\".format(hermann))\\n logger.info(\\\"Created test journey {}\\\".format(journey))\",\n \"def populate_t_database():\\n with open('minerals.json') as file:\\n file = json.loads(file.read())\\n\\n for mineral in file[:22]:\\n mineral_entry = Mineral.objects.get_or_create(**mineral)\",\n \"def init_sensor_db(self, drop_tables=True):\\n logger.debug(\\\"Creating Database Engine.\\\")\\n db_engine = sqlalchemy.create_engine(self.db_info_obj.dbConn)\\n\\n if drop_tables:\\n logger.debug(\\\"Drop system table if within the existing database.\\\")\\n Base.metadata.drop_all(db_engine)\\n\\n logger.debug(\\\"Creating Sentinel1ASF Database.\\\")\\n Base.metadata.bind = db_engine\\n Base.metadata.create_all()\",\n \"def get_data(traffic, weather, db):\\r\\n traffic_data = traffic.get_traffic()\\r\\n weather_data = weather.get_weather()\\r\\n db.traffic.insert_many(traffic_data)\\r\\n db.weather.insert_many(weather_data)\\r\\n print('于 {} 完成数据爬取及存储'.format(datetime.now()))\\r\\n print('当前交通数据条数 {}'.format(db.traffic.count_documents({})))\\r\\n print('当前天气数据条数 {}'.format(db.weather.count_documents({})))\",\n \"def setup_database() -> sqlite3.Cursor:\\n conn = sqlite3.connect(':memory:')\\n cursor = conn.cursor()\\n\\n insert_books(cursor)\\n insert_lookups(cursor)\\n\\n return cursor\",\n \"def populate_water_measurements(cursor, archive, directory, station):\\n csv_path = get_data_path(\\n 'water',\\n 'raw',\\n archive,\\n directory,\\n f'{station}.csv'\\n )\\n\\n with open(csv_path, 'r', encoding='utf-8') as file:\\n reader = csv.reader(file, delimiter=';')\\n header = next(reader)\\n column_names_map = get_water_index_map(archive, header)\\n\\n if not column_names_map:\\n return False\\n\\n water_body = get_water_definitions(archive)['body']\\n\\n for row in reader:\\n column_values_map = get_water_value_map(row, column_names_map)\\n if column_values_map:\\n date = datetime.strptime(row[0], '%d.%m.%Y').date()\\n data_columns = ', '.join(column_values_map.keys())\\n data_values = ', '.join(column_values_map.values())\\n cursor.execute(f'''INSERT INTO {water_body}_measurements (station_id, date, {data_columns})\\n VALUES ({station}, '{str(date)}', {data_values})''')\\n\\n return True\",\n \"def weather_script(\\n init_data_path: str,\\n output_path: str,\\n workers: int,\\n weatherAPI_rpm: int,\\n geoAPI_rpm: int,\\n) -> None:\\n unzip(init_data_path, output_path)\\n top_hotels_dataframe_without_addresses = primary_data_proc(output_path)\\n geocoder = geocoder_setup(geoAPI_rpm)\\n top_hotels_df_with_addresses = define_address(\\n top_hotels_dataframe_without_addresses,\\n workers,\\n geocoder,\\n )\\n cities, countries, coordinates = city_center_coord(top_hotels_df_with_addresses)\\n weather_df = pd.concat(\\n [\\n prev_weather(cities, countries, coordinates, workers, weatherAPI_rpm),\\n forecast_weather(cities, countries, coordinates, workers, weatherAPI_rpm),\\n ]\\n )\\n\\n logger.info(\\\"Start to save results\\\")\\n save_main_info(output_path, weather_df, top_hotels_df_with_addresses)\\n logger.info(\\\"Finish\\\")\",\n \"def weather():\\r\\n def weather_api_call():\\r\\n with open('config.json', 'r') as conf:\\r\\n conf = json.load(conf)\\r\\n # Gets the API key from the config.json file\\r\\n weather_api_key = conf[\\\"weather_api_key\\\"]\\r\\n weather_city_name = conf['weather_city_name']\\r\\n response = requests.get(\\r\\n 'http://api.openweathermap.org/data/2.5/weather?'\\r\\n 'q=' + weather_city_name + '&units=metric&appid=' + weather_api_key)\\r\\n resp_json = response.json()\\r\\n with open('weather.json', 'w') as outfile:\\r\\n # Uses the data from the API to overwrite the weather data\\r\\n json.dump(resp_json, outfile)\\r\\n outfile.close()\\r\\n\\r\\n def weather_data_extractor():\\r\\n with open('weather.json', 'r') as weather_json:\\r\\n weather_json = json.load(weather_json)\\r\\n temp = weather_json[\\\"main\\\"]\\r\\n weather_item = weather_json[\\\"weather\\\"]\\r\\n desc = weather_item[0]\\r\\n current_temperature = \\\"The current temperature is: \\\" + \\\\\\r\\n str(int(temp[\\\"temp\\\"])) + \\\"C\\\"\\r\\n current_feels_like = \\\"Feels like: \\\" + \\\\\\r\\n str(int(temp[\\\"feels_like\\\"])) + \\\"C\\\"\\r\\n forecast = desc[\\\"main\\\"]\\r\\n return current_feels_like, current_temperature, forecast\\r\\n\\r\\n weather_api_call()\\r\\n return weather_data_extractor()\",\n \"def init_and_update_observe_table(self):\\n # print CHN_CITY_LIST_FILE\\n location = ''\\n id = ''\\n f = open(CHN_CITY_LIST_FILE, 'r')\\n for line in f.readlines():\\n line_list = line.strip('\\\\n').split(':')\\n location = line_list[0]\\n id = line_list[1]\\n pm = get_pm(location)\\n # get current weather\\n weather_dict = get_open_weather(id)\\n if weather_dict not in ('', None, [], {}):\\n if 'error' in pm or pm == False:\\n weather_dict['aqi'] = '无数据'#'N/A'\\n else:\\n weather_dict['aqi'] = pm['quality'] + '(' + str(pm['aqi']) + ')'\\n db_record = self.db.search_observe_record(str(id))\\n # db_record = []\\n now_date = get_local_format_time()\\n if db_record != []:#update\\n self.db.update_observe_data(weather_dict['ptime'],weather_dict['time'],now_date,weather_dict['WD'],weather_dict['WS'],weather_dict['SD'],weather_dict['weather'],weather_dict['img1'],weather_dict['img2'],weather_dict['temp'],weather_dict['temp1'],weather_dict['temp2'],weather_dict['aqi'],id)\\n else:#insert\\n self.db.insert_observe_data(id,weather_dict['city'],weather_dict['ptime'],weather_dict['time'],now_date,weather_dict['WD'],weather_dict['WS'],weather_dict['SD'],weather_dict['weather'],weather_dict['img1'],weather_dict['img2'],weather_dict['temp'],weather_dict['temp1'],weather_dict['temp2'],weather_dict['aqi'])\\n f.close()\\n return True\",\n \"def get_gis_historical_data():\\n logging.info(\\\"Generating baseline reference and historical weather data.\\\")\\n \\n # Initialising function variables\\n fake = Faker()\\n geolocator = Nominatim()\\n config_data = get_config()\\n locations = config_data[\\\"location\\\"]\\n \\n # Check if there are no duplicate locations in the config.yaml file.\\n if len(locations) != len(set(locations)):\\n logging.error(\\\"Duplicate location found. Please check config.yaml file.\\\")\\n raise ValueError\\n \\n # Initialise pandas dataframe column name for baseline reference\\n # and historical data.\\n df_ref = pd.DataFrame(columns=[\\\"Location\\\", \\\"Latitude\\\"\\n ,\\\"Longitude\\\", \\\"Elevation\\\"\\n ,\\\"Timezone\\\"])\\n df_hist = pd.DataFrame(columns=[\\\"Location\\\", \\\"Date\\\"\\n ,\\\"Month\\\", \\\"Temperature_Min\\\"\\n ,\\\"Temperature_Max\\\", \\\"Humidity\\\"\\n ,\\\"Pressure\\\"])\\n \\n # Generate weather data for each location.\\n for idx, loc in enumerate(locations):\\n \\n logging.info(\\\"Retrieving geolocation data for {}.\\\".format(loc))\\n \\n # Retrieving geolocation data from geopy library.\\n loc_data = geolocator.geocode(loc)\\n \\n logging.info(\\\"Check if the location {} is valid.\\\".format(loc))\\n if loc_data is None:\\n logging.error(\\\"Invalid location value supplied ({}). Please check config.yaml file.\\\".format(loc))\\n raise ValueError\\n logging.info(\\\"The location {} is valid.\\\".format(loc))\\n \\n city = get_city(loc)\\n lat = loc_data.latitude\\n lon = loc_data.longitude\\n \\n # Retrieving elevation data for the location.\\n elev = get_elevation_data(lat, lon)\\n \\n for month in range(1, 13):\\n \\n logging.info(\\\"Retrieving {} weather data for month {}.\\\".format(loc, month))\\n \\n for sample in range(config_data[\\\"gis\\\"][\\\"sampling_number\\\"]):\\n \\n temp_min = None\\n temp_max = None\\n humidity = None\\n pressure = None\\n \\n while temp_min is None or temp_max is None or humidity is None or pressure is None:\\n \\n year = random.randint(config_data[\\\"gis\\\"][\\\"year_start\\\"], config_data[\\\"gis\\\"][\\\"year_end\\\"])\\n\\n _, last_day = calendar.monthrange(year, month)\\n\\n datetime_start = datetime.datetime(year, month, 1)\\n datetime_end = datetime.datetime(year, month, last_day)\\n\\n date_gen = fake.date_time_between_dates(datetime_start=datetime_start\\n ,datetime_end=datetime_end)\\n\\n forecast = forecastio.load_forecast(config_data[\\\"forecastio_api_key\\\"]\\n ,lat\\n ,lon\\n ,time=date_gen\\n ,units=\\\"si\\\")\\n\\n historical_data = forecast.json[\\\"daily\\\"][\\\"data\\\"][0]\\n \\n timezone = forecast.json.get(\\\"timezone\\\", None)\\n temp_min = historical_data.get(\\\"temperatureMin\\\", None)\\n temp_max = historical_data.get(\\\"temperatureMax\\\", None)\\n humidity = historical_data.get(\\\"humidity\\\", None) * 100\\n pressure = historical_data.get(\\\"pressure\\\", None)\\n \\n df_temp_hist = pd.Series(dict(zip(df_hist.columns\\n ,[city, date_gen\\n ,date_gen.month, temp_min\\n ,temp_max, humidity\\n ,pressure])))\\n \\n df_hist = df_hist.append(df_temp_hist, ignore_index=True)\\n \\n df_temp_ref = pd.Series(dict(zip(df_ref.columns\\n ,[city, lat\\n ,lon, elev\\n ,timezone])))\\n df_ref = df_ref.append(df_temp_ref, ignore_index=True)\\n \\n logging.info(\\\"Generating position to consolidate latitude, longitude and elevation data\\\")\\n df_pos = df_ref[[\\\"Latitude\\\", \\\"Longitude\\\", \\\"Elevation\\\"]].round(2)\\n df_pos[\\\"Elevation\\\"] = df_pos[\\\"Elevation\\\"].astype(int) \\n df_ref[\\\"Position\\\"] = df_pos.astype(str).apply(lambda x: \\\",\\\".join(x), axis=1)\\n \\n logging.info(\\\"Saving baseline reference data.\\\")\\n df_ref.to_csv(get_file_path(folder_name=\\\"data\\\"\\n ,subdirectory=config_data[\\\"gis\\\"][\\\"output_subdirectory\\\"]\\n ,file_name=config_data[\\\"gis\\\"][\\\"output_base_reference_file_name\\\"])\\n ,index=False)\\n logging.info(\\\"Completed saving baseline reference data.\\\")\\n\\n logging.info(\\\"Saving baseline historical data.\\\")\\n df_hist.to_csv(get_file_path(folder_name=\\\"data\\\"\\n ,subdirectory=config_data[\\\"gis\\\"][\\\"output_subdirectory\\\"]\\n ,file_name=config_data[\\\"gis\\\"][\\\"output_base_historical_file_name\\\"])\\n ,index=False)\\n logging.info(\\\"Completed saving baseline historical data.\\\")\",\n \"def init_db():\\n db.drop_all()\\n db.create_all()\\n seed_companies()\\n seed_emission_reports()\\n seed_reduction_targets()\\n seed_milestones()\",\n \"def main():\\n database = dbdir + \\\"pyflangun.db\\\"\\n sql_create_airports_table = \\\"\\\"\\\"CREATE TABLE IF NOT EXISTS airports (\\n id integer PRIMARY KEY,\\n name text NOT NULL,\\n icao text\\n );\\\"\\\"\\\"\\n sql_create_weather_table = \\\"\\\"\\\"CREATE TABLE IF NOT EXISTS weather (\\n id integer PRIMARY KEY,\\n icao text NOT NULL,\\n temp_f integer NOT NULL,\\n dew_pt_f integer NOT NULL,\\n status integer NOT NULL,\\n FOREIGN KEY (icao) REFERENCES airports (icao)\\n );\\\"\\\"\\\"\\n # create a database connection\\n conn = create_connection(database)\\n # create tables\\n if conn is not None:\\n create_table(conn, sql_create_airports_table)\\n create_table(conn, sql_create_weather_table)\\n else:\\n print(\\\"Error! cannot create the database connection.\\\")\",\n \"def main():\\n # Create the flow\\n with Flow('pickle flow') as flow:\\n db_table = create_table()\\n weather_data = get_weather(LAT_NYC, LON_NYC, 2018)\\n parsed_data = parse_weather(weather_data)\\n populated_table = store_weather(parsed_data)\\n populated_table.set_upstream(db_table)\\n\\n # Run the flow\\n flow.run()\",\n \"def generate_weather_data(self):\\n months = pd.to_datetime(self.output['Local Time']).dt.month\\n self.output['Month'] = months # set month values for later joins\\n\\n # merge output data frame with historical data to get ranges\\n keys = ['Location', 'Month']\\n m = pd.merge(self.output, self.histdata, how='left',\\n left_on=keys, right_on=keys)\\n\\n # uniformly select random pressure, temperature\\n # and humidity values between the historical max and min ranges\\n r = np.random.rand(m.shape[0])\\n m['Temperature'] = ((m['Tmean_high'] - m['Tmean_low']\\n ) * r + m['Tmean_low']).round(1)\\n m['Pressure'] = ((m['Pmax'] - m['Pmin']) * r + m['Pmin']).round(1)\\n m['Humidity'] = ((m['Hmax'] - m['Hmin']) * r + m['Hmin']).astype(int)\\n\\n # drop redundant columns and assign to output\\n dcols = ['Month', 'Timezone', 'Pmax', 'Pmin',\\n 'Hmax', 'Hmin', 'Tmean_high', 'Tmean_low']\\n m.drop(columns=dcols, inplace=True)\\n self.output = m\",\n \"def store_data_in_db(sensors):\\n connection = sqlite3.connect('sensordata.db')\\n cursor = connection.cursor()\\n cursor.execute(\\\"\\\"\\\"CREATE TABLE IF NOT EXISTS sensordata (\\n ID int, NAME text, VALUE real, UNIT text, TIME text )\\\"\\\"\\\"\\n )\\n with connection:\\n for sensor in sensors:\\n cursor.execute(\\\"INSERT INTO sensordata VALUES (:id, :name, :value, :unit, :time)\\\",\\n {'id': sensor['id'], 'name': sensor['name'],\\n 'value': sensor['value'], 'unit': sensor['unit'], 'time': sensor['datetime']})\",\n \"def collect(self, start_date=None, end_date=None):\\n if start_date is None:\\n start_date = self.default_start\\n if end_date is None:\\n end_date = self.default_end\\n\\n cur = self.conn.cursor()\\n\\n # Maximum return is 1000 entries\\n num_days = 1000 // len(self.stations)\\n # Maximum date-range is 1 year\\n if num_days > 365:\\n num_days = 365\\n\\n for interval in netzero.util.time_intervals(\\n start_date, end_date, days=num_days\\n ):\\n netzero.util.print_status(\\n \\\"Weather\\\",\\n \\\"Collecting: {} to {}\\\".format(\\n interval[0].strftime(\\\"%Y-%m-%d\\\"), interval[1].strftime(\\\"%Y-%m-%d\\\")\\n ),\\n )\\n\\n # TODO -- REMOVE ASSUMPTION THAT LEN(DATA) < LIMIT\\n raw_data = self.query_api(interval[0], interval[1])\\n\\n if raw_data is None:\\n print(\\\"ERROR QUERYING API\\\") # TODO exception here?\\n continue\\n\\n for entry in raw_data.get(\\\"results\\\", []):\\n # Insert the weather data to the table, to be averaged later\\n date = datetime.datetime.strptime(\\n entry[\\\"date\\\"], \\\"%Y-%m-%dT%H:%M:%S\\\"\\n ).date()\\n value = entry[\\\"value\\\"]\\n station = entry[\\\"station\\\"]\\n\\n cur.execute(\\n \\\"INSERT OR IGNORE INTO weather VALUES (?, ?, ?)\\\", (date, value, station)\\n )\\n\\n self.conn.commit()\\n\\n cur.close()\\n\\n netzero.util.print_status(\\\"Weather\\\", \\\"Complete\\\", newline=True)\",\n \"def create():\\n\\tcreate_db()\",\n \"def generate_polynesian_weather_data():\\n weather_path = os.path.dirname(os.path.realpath(__file__))\\n low_fp = weather_path + \\\"/polynesia_weather/low/1976/\\\"\\n med_fp = weather_path + \\\"/polynesia_weather/med/1985/\\\"\\n high_fp = weather_path + \\\"/polynesia_weather/high/1982/\\\"\\n low_name = \\\"polynesia_1976\\\"\\n med_name = \\\"polynesia_1985\\\"\\n high_name = \\\"polynesia_1982\\\"\\n generate_year_weather_data(low_fp, low_name)\\n generate_year_weather_data(med_fp, med_name)\\n generate_year_weather_data(high_fp, high_name)\",\n \"def create_database():\\n with connection:\\n connection.execute(CREATE_MOVIE_TABLE)\\n connection.execute(CREATE_USER_TABLE)\\n connection.execute(CREATE_WATCHED_TABLE)\",\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 \"def _create_petition_(self):\\n self.__weather = create(self.__latitude, self.__longitude)\",\n \"def create_temporal_database(dbif):\\n global tgis_backend\\n global tgis_version\\n global tgis_db_version\\n global tgis_database_string\\n\\n template_path = get_sql_template_path()\\n msgr = get_tgis_message_interface()\\n\\n # Read all SQL scripts and templates\\n map_tables_template_sql = open(os.path.join(\\n template_path, \\\"map_tables_template.sql\\\"), 'r').read()\\n raster_metadata_sql = open(os.path.join(\\n get_sql_template_path(), \\\"raster_metadata_table.sql\\\"), 'r').read()\\n raster3d_metadata_sql = open(os.path.join(template_path,\\n \\\"raster3d_metadata_table.sql\\\"),\\n 'r').read()\\n vector_metadata_sql = open(os.path.join(template_path,\\n \\\"vector_metadata_table.sql\\\"),\\n 'r').read()\\n raster_views_sql = open(os.path.join(template_path, \\\"raster_views.sql\\\"),\\n 'r').read()\\n raster3d_views_sql = open(os.path.join(template_path,\\n \\\"raster3d_views.sql\\\"), 'r').read()\\n vector_views_sql = open(os.path.join(template_path, \\\"vector_views.sql\\\"),\\n 'r').read()\\n\\n stds_tables_template_sql = open(os.path.join(template_path,\\n \\\"stds_tables_template.sql\\\"),\\n 'r').read()\\n strds_metadata_sql = open(os.path.join(template_path,\\n \\\"strds_metadata_table.sql\\\"),\\n 'r').read()\\n str3ds_metadata_sql = open(os.path.join(template_path,\\n \\\"str3ds_metadata_table.sql\\\"),\\n 'r').read()\\n stvds_metadata_sql = open(os.path.join(template_path,\\n \\\"stvds_metadata_table.sql\\\"),\\n 'r').read()\\n strds_views_sql = open(os.path.join(template_path, \\\"strds_views.sql\\\"),\\n 'r').read()\\n str3ds_views_sql = open(os.path.join(template_path, \\\"str3ds_views.sql\\\"),\\n 'r').read()\\n stvds_views_sql = open(os.path.join(template_path, \\\"stvds_views.sql\\\"),\\n 'r').read()\\n\\n # Create the raster, raster3d and vector tables SQL statements\\n raster_tables_sql = map_tables_template_sql.replace(\\\"GRASS_MAP\\\", \\\"raster\\\")\\n vector_tables_sql = map_tables_template_sql.replace(\\\"GRASS_MAP\\\", \\\"vector\\\")\\n raster3d_tables_sql = map_tables_template_sql.replace(\\n \\\"GRASS_MAP\\\", \\\"raster3d\\\")\\n\\n # Create the space-time raster, raster3d and vector dataset tables\\n # SQL statements\\n strds_tables_sql = stds_tables_template_sql.replace(\\\"STDS\\\", \\\"strds\\\")\\n stvds_tables_sql = stds_tables_template_sql.replace(\\\"STDS\\\", \\\"stvds\\\")\\n str3ds_tables_sql = stds_tables_template_sql.replace(\\\"STDS\\\", \\\"str3ds\\\")\\n\\n msgr.message(_(\\\"Creating temporal database: %s\\\" % (str(tgis_database_string))))\\n\\n if tgis_backend == \\\"sqlite\\\":\\n # We need to create the sqlite3 database path if it does not exist\\n tgis_dir = os.path.dirname(tgis_database_string)\\n if not os.path.exists(tgis_dir):\\n try:\\n os.makedirs(tgis_dir)\\n except Exception as e:\\n msgr.fatal(_(\\\"Unable to create SQLite temporal database\\\\n\\\"\\n \\\"Exception: %s\\\\nPlease use t.connect to set a \\\"\\n \\\"read- and writable temporal database path\\\" % (e)))\\n\\n # Set up the trigger that takes care of\\n # the correct deletion of entries across the different tables\\n delete_trigger_sql = open(os.path.join(template_path,\\n \\\"sqlite3_delete_trigger.sql\\\"),\\n 'r').read()\\n indexes_sql = open(os.path.join(template_path, \\\"sqlite3_indexes.sql\\\"),\\n 'r').read()\\n else:\\n # Set up the trigger that takes care of\\n # the correct deletion of entries across the different tables\\n delete_trigger_sql = open(os.path.join(template_path,\\n \\\"postgresql_delete_trigger.sql\\\"),\\n 'r').read()\\n indexes_sql = open(os.path.join(template_path,\\n \\\"postgresql_indexes.sql\\\"), 'r').read()\\n\\n # Connect now to the database\\n if dbif.connected is not True:\\n dbif.connect()\\n\\n # Execute the SQL statements for sqlite\\n # Create the global tables for the native grass datatypes\\n dbif.execute_transaction(raster_tables_sql)\\n dbif.execute_transaction(raster_metadata_sql)\\n dbif.execute_transaction(raster_views_sql)\\n dbif.execute_transaction(vector_tables_sql)\\n dbif.execute_transaction(vector_metadata_sql)\\n dbif.execute_transaction(vector_views_sql)\\n dbif.execute_transaction(raster3d_tables_sql)\\n dbif.execute_transaction(raster3d_metadata_sql)\\n dbif.execute_transaction(raster3d_views_sql)\\n # Create the tables for the new space-time datatypes\\n dbif.execute_transaction(strds_tables_sql)\\n dbif.execute_transaction(strds_metadata_sql)\\n dbif.execute_transaction(strds_views_sql)\\n dbif.execute_transaction(stvds_tables_sql)\\n dbif.execute_transaction(stvds_metadata_sql)\\n dbif.execute_transaction(stvds_views_sql)\\n dbif.execute_transaction(str3ds_tables_sql)\\n dbif.execute_transaction(str3ds_metadata_sql)\\n dbif.execute_transaction(str3ds_views_sql)\\n\\n # The delete trigger\\n dbif.execute_transaction(delete_trigger_sql)\\n # The indexes\\n dbif.execute_transaction(indexes_sql)\\n\\n # Create the tgis metadata table to store the database\\n # initial configuration\\n # The metadata table content\\n metadata = {}\\n metadata[\\\"tgis_version\\\"] = tgis_version\\n metadata[\\\"tgis_db_version\\\"] = tgis_db_version\\n metadata[\\\"creation_time\\\"] = datetime.today()\\n _create_tgis_metadata_table(metadata, dbif)\\n\\n dbif.close()\",\n \"def init_beeswax_db(cls):\\n global _INITIALIZED\\n if _INITIALIZED:\\n return\\n\\n make_query(cls.client, 'CREATE DATABASE IF NOT EXISTS %(db)s' % {'db': cls.db_name}, wait=True)\\n make_query(cls.client, 'CREATE DATABASE IF NOT EXISTS %(db)s_other' % {'db': cls.db_name}, wait=True)\\n\\n if cls.load_data:\\n\\n data_file = cls.cluster.fs_prefix + u'/beeswax/sample_data_échantillon_%d.tsv'\\n\\n # Create a \\\"test_partitions\\\" table.\\n CREATE_PARTITIONED_TABLE = \\\"\\\"\\\"\\n CREATE TABLE `%(db)s`.`test_partitions` (foo INT, bar STRING)\\n PARTITIONED BY (baz STRING, boom INT)\\n ROW FORMAT DELIMITED\\n FIELDS TERMINATED BY '\\\\t'\\n LINES TERMINATED BY '\\\\n'\\n \\\"\\\"\\\" % {'db': cls.db_name}\\n make_query(cls.client, CREATE_PARTITIONED_TABLE, wait=True)\\n cls._make_data_file(data_file % 1)\\n\\n LOAD_DATA = \\\"\\\"\\\"\\n LOAD DATA INPATH '%(data_file)s'\\n OVERWRITE INTO TABLE `%(db)s`.`test_partitions`\\n PARTITION (baz='baz_one', boom=12345)\\n \\\"\\\"\\\" % {'db': cls.db_name, 'data_file': data_file % 1}\\n make_query(cls.client, LOAD_DATA, wait=True, local=False)\\n\\n # Insert additional partition data into \\\"test_partitions\\\" table\\n ADD_PARTITION = \\\"\\\"\\\"\\n ALTER TABLE `%(db)s`.`test_partitions` ADD PARTITION(baz='baz_foo', boom=67890) LOCATION '%(fs_prefix)s/baz_foo/boom_bar'\\n \\\"\\\"\\\" % {'db': cls.db_name, 'fs_prefix': cls.cluster.fs_prefix}\\n make_query(cls.client, ADD_PARTITION, wait=True, local=False)\\n\\n # Create a bunch of other tables\\n CREATE_TABLE = \\\"\\\"\\\"\\n CREATE TABLE `%(db)s`.`%(name)s` (foo INT, bar STRING)\\n COMMENT \\\"%(comment)s\\\"\\n ROW FORMAT DELIMITED\\n FIELDS TERMINATED BY '\\\\t'\\n LINES TERMINATED BY '\\\\n'\\n \\\"\\\"\\\"\\n\\n # Create a \\\"test\\\" table.\\n table_info = {'db': cls.db_name, 'name': 'test', 'comment': 'Test table'}\\n cls._make_data_file(data_file % 2)\\n cls._make_table(table_info['name'], CREATE_TABLE % table_info, data_file % 2)\\n\\n if is_live_cluster():\\n LOG.warning('HUE-2884: We cannot create Hive UTF8 tables when live cluster testing at the moment')\\n else:\\n # Create a \\\"test_utf8\\\" table.\\n table_info = {'db': cls.db_name, 'name': 'test_utf8', 'comment': cls.get_i18n_table_comment()}\\n cls._make_i18n_data_file(data_file % 3, 'utf-8')\\n cls._make_table(table_info['name'], CREATE_TABLE % table_info, data_file % 3)\\n\\n # Create a \\\"test_latin1\\\" table.\\n table_info = {'db': cls.db_name, 'name': 'test_latin1', 'comment': cls.get_i18n_table_comment()}\\n cls._make_i18n_data_file(data_file % 4, 'latin1')\\n cls._make_table(table_info['name'], CREATE_TABLE % table_info, data_file % 4)\\n\\n # Create a \\\"myview\\\" view.\\n make_query(cls.client, \\\"CREATE VIEW `%(db)s`.`myview` (foo, bar) as SELECT * FROM `%(db)s`.`test`\\\" % {'db': cls.db_name}, wait=True)\\n\\n _INITIALIZED = True\",\n \"def dbUpdate():\\n dbAddress = config.get('database', 'dbAddress')\\n dbUser = config.get('database', 'dbUser')\\n dbPassword = config.get('database', 'dbPassword')\\n dbName = config.get('database', 'dbName')\\n dbPort = config.getint('database', 'dbPort')\\n con = MySQLdb.connect(host=dbAddress, port=dbPort, user=dbUser, passwd=dbPassword,\\n db=dbName)\\n c = con.cursor()\\n\\n date = datetime.datetime.now()\\n c.execute(\\\"INSERT INTO sensor_data (date, dht_temp, dht_humidity, cpu_temp, \\\"\\n \\\"solar_voltage, solar_current, battery_voltage, battery_current, \\\"\\n \\\"load_voltage, load_current) VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s,\\\"\\n \\\"%s)\\\",\\n (date, dht_temp, dht_humidity, cpu_temp, sol_volt_v, sol_curr_ma,\\n bat_volt_v, bat_curr_ma, load_volt_v, load_curr_ma))\\n\\n con.commit()\\n con.close()\",\n \"def create_db():\\n database.db.create_all()\\n get_ulm()\\n for fixture_file in glob.glob(config.DevelopmentConfig.FIXTURES_DIRS + '/*.json'):\\n fixtures = JSONLoader().load(fixture_file)\\n load_fixtures(database.db, fixtures)\\n MigrationManager().stamp_db()\",\n \"def to_WTH_converter(self, weather_data, dest_dir):\\n ds_all = weather_data.get_global_dataset()\\n if self.country is None:\\n print(\\\"Country given is erroneous:\\\")\\n return\\n elif self.country == \\\"globe\\\":\\n lon_num_start = 0\\n lon_num_stop = weather_data.get_num_of_attribute('longitude')\\n lat_num_start = 0\\n lat_num_stop = weather_data.get_num_of_attribute('latitude')\\n else:\\n lon_num_start, lon_num_stop, lat_num_start, lat_num_stop = weather_data.get_country_boundary(self.country)\\n\\n\\n # top bottom, left to right\\n lon_num_start = 397\\n lat_num_start = 184\\n for lon_i in range(lon_num_start, lon_num_stop + 1):\\n # for lon_i in range(lon_num_start, lon_num_stop+1):\\n lon = ds_all.longitude.isel(longitude=lon_i).values.tolist()\\n\\n for lat_i in range(lat_num_start, lat_num_stop+1):\\n # for lat_i in range(lat_num_start, lat_num_stop + 1):\\n lat = ds_all.latitude.isel(latitude=lat_i).values.tolist()\\n\\n # create a dynamic header with updated LON, LAT info and move it into the folder given\\n wth_header_u = ut.format_header(lat_i + 1, lon_i + 1, lat, lon)\\n wth_header = dest_dir + \\\"/\\\" + wth_header_u\\n shutil.move(wth_header_u, wth_header)\\n\\n # open in appending mode\\n fwth = open(wth_header, \\\"a+\\\")\\n\\n # loop through daily weather data\\n for t, date in enumerate(self.years):\\n daily_data_vars = ut.get_daily_data_vars(ds_all, lat_i, lon_i, t)\\n # disregard all NAN values\\n if daily_data_vars is None:\\n fwth.close()\\n os.remove(wth_header)\\n break\\n\\n if t == 0:\\n ut.update_table(wth_header_u, lat, lon)\\n\\n entry = ut.format_data_vars_entry(daily_data_vars, date)\\n\\n # append this entry into the file\\n fwth.write(entry)\\n print(\\\"Added entry:\\\", entry)\\n\\n # close file after writing\\n fwth.close()\\n print(\\\"Output WTH:\\\", wth_header)\",\n \"def __upload_data(self):\\n data_path = \\\"database\\\"\\n os.makedirs(data_path, exist_ok=True)\\n try:\\n conn = sqlite3.connect('database/customers.db')\\n query = '''CREATE TABLE IF NOT EXISTS all_customers_database (\\n first_name TEXT, second_name TEXT,\\n gender TEXT, account_type TEXT, account_number INTEGER PRIMARY KEY UNIQUE NOT NULL,\\n account_password VARCHAR, account_balance REAL );'''\\n #Create table\\n cursor = conn.cursor()\\n print(\\\"Connection sucessful\\\")\\n cursor.execute(query)\\n conn.commit()\\n print(\\\"Table created\\\")\\n #Insert a row to a database\\n insert_query ='''INSERT INTO all_customers_database\\n (first_name, second_name, gender, account_type, account_number, account_password, account_balance)\\n VALUES \\n (?, ?, ?, ?, ?, ?, ?);'''\\n conn.execute(insert_query, (self.first_name, self.second_name, self.gender, self.account_type, self.account_number, self.account_password, self.account_balance))\\n print(\\\"Your details saved successfully.\\\")\\n except sqlite3.Error as err:\\n # print(\\\"Error while creating a sqlite table \\\", err)\\n print(\\\"Error creating database\\\")\\n finally:\\n if conn:\\n conn.close()\\n # print(\\\"Sqlite connection closed.\\\")\",\n \"def checkFirstRun():\\n my_cfg = Path.home() / \\\".bandaid\\\" / \\\"bandaid.cfg\\\"\\n my_db = Path.home() / \\\".bandaid\\\" / \\\"bandaid.db\\\"\\n if Path(f'{Path.home()}/.bandaid/').exists():\\n return my_db\\n print(\\\"First run, making the donuts...\\\")\\n Path.mkdir(Path.home() / \\\".bandaid\\\", exist_ok=True)\\n zipcode = inputZip()\\n lat, lng = getLatLng(zipcode)\\n with open(my_cfg, \\\"w+\\\") as f:\\n f.write('DBPATH=~/.bandaid/bandaid.db\\\\n')\\n f.write(f'ZIPCODE={zipcode}')\\n user = (lambda: environ[\\\"USERNAME\\\"]\\n if \\\"C:\\\" in getcwd() else environ[\\\"USER\\\"])()\\n initDB(my_db, zipcode, user, lat, lng)\\n print(f\\\"Database and config file created at {my_cfg}\\\")\\n return my_db\",\n \"def create_db(self):\",\n \"def add_water():\\n\\n user_id = session['user_id']\\n drink = request.form.get('drink')\\n postal = request.form.get('postal')\\n time_updated = datetime.now()\\n new_drink = Water(ounces=drink, user_id=user_id, time_updated=time_updated, postal=postal)\\n\\n db.session.add(new_drink)\\n db.session.commit()\\n \\n time_zone = session[\\\"user_timezone\\\"]\\n \\n current_time = datetime.now().astimezone(pytz.timezone(time_zone))\\n\\n current_date = current_time.date()\\n\\n total_water_today = db.session.query(func.sum(Water.ounces)).filter(Water.user_id==user_id, Water.time_updated >= current_date).scalar()\\n\\n if int(total_water_today) != None or int(total_water_today) != 0:\\n total_cups_today = round((total_water_today/8),2)\\n else:\\n total_water_today = 0\\n total_cups_today = 0\\n\\n print('user id', user_id)\\n print('current date', current_date)\\n return f\\\"Today's Water: {total_water_today} Oz ({total_cups_today} Cups)\\\"\",\n \"def init():\\n database.create_tables([Tracker])\\n database.commit()\",\n \"def initialize_sqlite_database(sel_wormbase_version,\\n strain_only=False):\\n start = arrow.utcnow()\\n console.log(\\\"Initializing Database\\\")\\n\\n SQLITE_PATH = f\\\"base/cendr.{DATASET_RELEASE}.{sel_wormbase_version}.db\\\"\\n SQLITE_BASENAME = os.path.basename(SQLITE_PATH)\\n\\n # Download wormbase files\\n if strain_only is False:\\n if os.path.exists(SQLITE_PATH):\\n os.remove(SQLITE_PATH)\\n\\n if not os.path.exists(DOWNLOAD_PATH):\\n os.makedirs(DOWNLOAD_PATH)\\n\\n # Parallel URL download\\n console.log(\\\"Downloading Wormbase Data\\\")\\n download([URLS.GENE_GFF_URL,\\n URLS.GENE_GTF_URL,\\n URLS.GENE_IDS_URL,\\n URLS.HOMOLOGENE_URL,\\n URLS.ORTHOLOG_URL,\\n URLS.TAXON_ID_URL],\\n DOWNLOAD_PATH)\\n\\n gff_fname = download_fname(DOWNLOAD_PATH, URLS.GENE_GFF_URL)\\n gtf_fname = download_fname(DOWNLOAD_PATH, URLS.GENE_GTF_URL)\\n gene_ids_fname = download_fname(DOWNLOAD_PATH, URLS.GENE_IDS_URL)\\n homologene_fname = download_fname(DOWNLOAD_PATH, URLS.HOMOLOGENE_URL)\\n ortholog_fname = download_fname(DOWNLOAD_PATH, URLS.ORTHOLOG_URL)\\n\\n from base.application import create_app\\n app = create_app()\\n app.config['SQLALCHEMY_DATABASE_URI'] = f\\\"sqlite:///{SQLITE_BASENAME}\\\"\\n app.app_context().push()\\n\\n if strain_only is True:\\n db.metadata.drop_all(bind=db.engine, checkfirst=True, tables=[Strain.__table__])\\n db.metadata.create_all(bind=db.engine, tables=[Strain.__table__])\\n else:\\n db.create_all(app=app)\\n db.session.commit()\\n\\n console.log(f\\\"Created {SQLITE_PATH}\\\")\\n\\n ################\\n # Load Strains #\\n ################\\n console.log('Loading strains...')\\n db.session.bulk_insert_mappings(Strain, fetch_andersen_strains())\\n db.session.commit()\\n console.log(f\\\"Inserted {Strain.query.count()} strains\\\")\\n\\n if strain_only is True:\\n console.log('Finished loading strains')\\n return\\n\\n ################\\n # Set metadata #\\n ################\\n console.log('Inserting metadata')\\n metadata = {}\\n metadata.update(vars(constants))\\n metadata.update({\\\"CENDR_VERSION\\\": CENDR_VERSION,\\n \\\"APP_CONFIG\\\": APP_CONFIG,\\n \\\"DATASET_RELEASE\\\": DATASET_RELEASE,\\n \\\"WORMBASE_VERSION\\\": sel_wormbase_version,\\n \\\"RELEASES\\\": RELEASES,\\n \\\"DATE\\\": arrow.utcnow()})\\n for k, v in metadata.items():\\n if not k.startswith(\\\"_\\\"):\\n # For nested constants:\\n if type(v) == type:\\n for name in [x for x in dir(v) if not x.startswith(\\\"_\\\")]:\\n key_val = Metadata(key=\\\"{}/{}\\\".format(k, name),\\n value=getattr(v, name))\\n db.session.add(key_val)\\n else:\\n key_val = Metadata(key=k, value=str(v))\\n db.session.add(key_val)\\n\\n db.session.commit()\\n\\n ##############\\n # Load Genes #\\n ##############\\n console.log('Loading summary gene table')\\n genes = fetch_gene_gff_summary(gff_fname)\\n db.session.bulk_insert_mappings(WormbaseGeneSummary, genes)\\n db.session.commit()\\n\\n console.log('Loading gene table')\\n db.session.bulk_insert_mappings(WormbaseGene, fetch_gene_gtf(gtf_fname, gene_ids_fname))\\n gene_summary = db.session.query(WormbaseGene.feature,\\n db.func.count(WormbaseGene.feature)) \\\\\\n .group_by(WormbaseGene.feature) \\\\\\n .all()\\n gene_summary = '\\\\n'.join([f\\\"{k}: {v}\\\" for k, v in gene_summary])\\n console.log(f\\\"============\\\\nGene Summary\\\\n------------\\\\n{gene_summary}\\\\n============\\\")\\n\\n ###############################\\n # Load homologs and orthologs #\\n ###############################\\n console.log('Loading homologs from homologene')\\n db.session.bulk_insert_mappings(Homologs, fetch_homologene(homologene_fname))\\n db.session.commit()\\n\\n console.log('Loading orthologs from WormBase')\\n db.session.bulk_insert_mappings(Homologs, fetch_orthologs(ortholog_fname))\\n db.session.commit()\\n\\n #############\\n # Upload DB #\\n #############\\n\\n # Upload the file using todays date for archiving purposes\\n console.log(f\\\"Uploading Database ({SQLITE_BASENAME})\\\")\\n upload_file(f\\\"db/{SQLITE_BASENAME}\\\", SQLITE_PATH)\\n\\n diff = int((arrow.utcnow() - start).total_seconds())\\n console.log(f\\\"{diff} seconds\\\")\\n\\n # =========================== #\\n # Generate gene id dict #\\n # =========================== #\\n # Create a gene dictionary to match wormbase IDs to either the locus name\\n # or a sequence id\\n gene_dict = {x.gene_id: x.locus or x.sequence_name for x in WormbaseGeneSummary.query.all()}\\n pickle.dump(gene_dict, open(\\\"base/static/data/gene_dict.pkl\\\", 'wb'))\",\n \"def initialization():\\r\\n\\r\\n logging.debug('initialization()')\\r\\n\\r\\n dir_path = os.path.join(os.environ['LOCALAPPDATA'],'WarietyWallpaperImages')\\r\\n os.makedirs(dir_path, exist_ok=True)\\r\\n db_file = os.path.join(dir_path,'wariety.db')\\r\\n conn = sqlite3.connect(db_file)\\r\\n c = conn.cursor()\\r\\n\\r\\n # Create tables\\r\\n c.execute(\\\"\\\"\\\"\\r\\n CREATE TABLE IF NOT EXISTS wallpapers (\\r\\n id integer primary key,\\r\\n iurl text unique,\\r\\n iname text,\\r\\n ipath text,\\r\\n isource text)\\r\\n \\\"\\\"\\\")\\r\\n\\r\\n conn.close()\",\n \"def populate_db():\\n stdout.write('Emptying the tables...\\\\n')\\n empty_tables()\\n stdout.write('Populating Language records...\\\\n')\\n populate_language()\\n stdout.write('Populating Lemma, Wordform, and Definition records...\\\\n')\\n populate_lexical()\\n stdout.write('Populating ProperName records...\\\\n')\\n populate_proper_names()\",\n \"def perform_watering_update(self, plant):\\n try:\\n with self.connection.cursor() as cursor:\\n sql = \\\"INSERT INTO test.watering_data\\\\\\n (balance_id, logdate, start_weight, end_weight, status, experiment_id)\\\\\\n VALUES({0}, NOW(), {1}, {2}, {3}, '{4}')\\\".format(plant.get_balance(),\\n plant.get_start_weight(),\\n plant.get_end_weight(),\\n plant.get_status(),\\n plant.get_experiment_id())\\n\\n # Execute our statement\\n if is_statement_safe(sql):\\n cursor.execute(sql)\\n self.connection.commit()\\n else:\\n print(\\\"DANGEROUS STATEMENT\\\")\\n sys.exit()\\n except (pymysql.err.DatabaseError,\\n pymysql.err.IntegrityError,\\n pymysql.err.MySQLError):\\n sys.stderr.write(\\n \\\"Most likely an IntegrityError error due to duplicate entry\\\")\\n return 2\",\n \"def CreateDB(self) :\\r\\n\\t\\ttry :\\r\\n\\t\\t\\tself.DB_Cursor.execute(self.SQLCMDs['CreateClassTable'])\\r\\n\\t\\t\\tfor ii,classname in enumerate(self.SQLCMDs['ClassesList']) :\\r\\n\\t\\t\\t\\tself.DB_Cursor.execute(self.SQLCMDs['InsertClass'],(ii,classname))\\r\\n\\r\\n\\t\\t\\tself.DB_Cursor.execute(self.SQLCMDs['CreateSetTable'])\\r\\n\\t\\t\\tfor ii,setname in enumerate(self.SQLCMDs['SetList']) :\\r\\n\\t\\t\\t\\tself.DB_Cursor.execute(self.SQLCMDs['InsertSet'],(ii,setname))\\r\\n\\r\\n\\t\\t\\tself.DB_Cursor.execute(self.SQLCMDs['CreateSampleTable'])\\r\\n\\t\\t\\tself.DB_Cursor.execute(self.SQLCMDs['CreateDictListTable'])\\r\\n\\t\\t\\tself.DB_Cursor.execute(self.SQLCMDs['CreateDictBuildTable'])\\r\\n\\t\\t\\tself.DB_Cursor.execute(self.SQLCMDs['CreateWordLists'])\\r\\n\\t\\t\\tself.DB_Cursor.execute(self.SQLCMDs['CreateFeatureTable'])\\r\\n\\t\\t\\tself.DB_Connect.commit()\\r\\n\\t\\texcept Exception as detail:\\r\\n\\t\\t\\tlogging.error(\\\"Failed to create the database: %s\\\"%detail)\\r\\n\\t\\t\\tself.DB_Connect.rollback()\\r\\n\\t\\treturn\",\n \"def initialize():\\n db.connect()\\n db.create_tables([Expense], safe=True)\",\n \"def main():\\n\\n database_file = Path(__file__).parent.parent / \\\"src\\\" / \\\"galois\\\" / \\\"_databases\\\" / \\\"irreducible_polys.db\\\"\\n conn, cursor = create_database(database_file)\\n\\n _add_hpl_1998(conn, cursor)\\n\\n conn.close()\",\n \"def create_db(self):\\n return None\",\n \"def init(self):\\n self.db.connect()\\n try:\\n self.db.create_tables([JambiModel], safe=True)\\n JambiModel.create(ref='0')\\n self.logger.info('Database initialized')\\n except IntegrityError:\\n self.logger.info('Database was already initialized')\\n self.db.close()\",\n \"def getTodaysWeather(self, keyword, temp):\\n\\n\\t\\t# Variables\\n\\t\\tweather = {} \\n\\t\\tfio = self.helper.getFio(keyword, temp) # Getting fio object\\n\\t\\t\\n\\t\\t# Getting todays weather data and populating the dictionary\\n\\t\\tif fio.has_daily() is True and fio.has_hourly() is True:\\n\\t\\t daily = FIODaily.FIODaily(fio)\\n\\t\\t hourly = FIOHourly.FIOHourly(fio)\\n\\t\\t for day in xrange(0, 1):\\n\\t\\t\\t\\tfor item in daily.get_day(day).keys():\\n\\t\\t\\t\\t\\tif item == \\\"temperatureMin\\\":\\n\\t\\t\\t\\t\\t\\tweather[item] = str(daily.get_day(day)[item]).split(\\\".\\\")[0]\\n\\t\\t\\t\\t\\tif item == \\\"summary\\\":\\n\\t\\t\\t\\t\\t\\tweather[item] = unicode(daily.get_day(day)[item])\\n\\t\\t\\t\\t\\tif item == \\\"temperatureMax\\\":\\n\\t\\t\\t\\t\\t\\tweather[item] = str(daily.get_day(day)[item]).split(\\\".\\\")[0]\\n\\t\\t\\t\\t\\tif item == \\\"windSpeed\\\":\\n\\t\\t\\t\\t\\t\\twindSpeed = unicode(daily.get_day(day)[item])\\n\\t\\t\\t\\t\\tif item == \\\"windBearing\\\":\\n\\t\\t\\t\\t\\t\\twindBearing = unicode(daily.get_day(day)[item])\\n\\t\\t\\t\\t\\t\\twindBearing = self.helper.convertWindBearing(windBearing)\\n\\t\\t\\t\\t\\tif item == \\\"sunsetTime\\\":\\n\\t\\t\\t\\t\\t\\tweather[item] = self.helper.getDateForWeather(daily.get_day(day)[item])\\n\\t\\t\\t\\t\\tif item == \\\"sunriseTime\\\":\\n\\t\\t\\t\\t\\t\\tweather[item] = self.helper.getDateForWeather(daily.get_day(day)[item])\\n\\t\\t\\t\\t\\tif item == \\\"precipProbability\\\":\\n\\t\\t\\t\\t\\t\\tweather[item] = str(daily.get_day(day)[item] * 100).split(\\\".\\\")[0] + \\\"%\\\"\\n\\t\\t\\t\\tweather[\\\"wind\\\"] = windBearing + \\\" \\\" + windSpeed + \\\" mph\\\"\\n\\t\\t\\t\\tfor item in hourly.get_hour(day).keys():\\n\\t\\t\\t\\t\\tif item == \\\"summary\\\":\\n\\t\\t\\t\\t\\t\\tweather[\\\"current\\\"] = unicode(hourly.get_hour(0)[item])\\n\\t\\t\\t\\t\\tif item == \\\"temperature\\\":\\n\\t\\t\\t\\t\\t\\tweather[item] = str(hourly.get_hour(0)[item]).split(\\\".\\\")[0]\\n\\t\\t\\t\\t\\tif item == \\\"icon\\\":\\n\\t\\t\\t\\t\\t\\tweather[item] = unicode(hourly.get_hour(0)[item])\\n\\t\\t\\t\\t\\tif item == \\\"cloudCover\\\":\\n\\t\\t\\t\\t\\t\\tweather[item] = str(hourly.get_hour(0)[item] * 100).split(\\\".\\\")[0] + \\\"%\\\"\\n\\t\\t\\t\\tweather[\\\"town\\\"] = self.helper.getCoords(keyword)[2]\\n\\t\\telse:\\n\\t\\t\\treturn 'No Todays data'\\n\\n\\t\\treturn weather\",\n \"def create_tables(cxn):\\n\\tcursor = cxn.cursor()\\n\\tcursor.execute(\\\"DROP TABLE IF EXISTS WEATHER\\\")\\n\\tcursor.execute(\\\"\\\"\\\"CREATE TABLE IF NOT EXISTS WEATHER(\\n\\t\\tstate varchar(3),\\n\\t\\tcity varchar (15),\\n\\t\\tobs_date varchar(12),\\n\\t\\thour int,\\n\\t\\tminute int,\\n\\t\\tcurr_temp float,\\n\\t\\tunique(state, city, obs_date, hour, minute)\\n\\t\\t)\\\"\\\"\\\")\\n\\tcursor.close()\",\n \"def ready(self):\\n try:\\n from workouts.models import ExerciseType\\n from workouts import EXERCISE_TYPE_ROUNDS, EXERCISE_TYPE_TIME\\n exercise_type_time = ExerciseType.objects.get_or_create(type_name=EXERCISE_TYPE_TIME)\\n exercise_type_rounds = ExerciseType.objects.get_or_create(type_name=EXERCISE_TYPE_ROUNDS)\\n except Exception:\\n pass\",\n \"def setup_database(self, config_dict):\\n\\n # This will create the database if it doesn't exist, then return an\\n # opened instance of the database manager. \\n dbmanager = self.engine.db_binder.get_manager(self.data_binding, initialize=True)\\n syslog.syslog(syslog.LOG_INFO, \\\"engine: Using binding '%s' to database '%s'\\\" % (self.data_binding, dbmanager.database_name))\\n \\n # Back fill the daily summaries.\\n syslog.syslog(syslog.LOG_INFO, \\\"engine: Starting backfill of daily summaries\\\")\\n t1 = time.time()\\n nrecs, ndays = dbmanager.backfill_day_summary()\\n tdiff = time.time() - t1\\n if nrecs:\\n syslog.syslog(syslog.LOG_INFO, \\n \\\"engine: Processed %d records to backfill %d day summaries in %.2f seconds\\\" % (nrecs, ndays, tdiff))\\n else:\\n syslog.syslog(syslog.LOG_INFO,\\n \\\"engine: Daily summaries up to date.\\\")\",\n \"def initialize_database():\\n # TODO: Refactor the funtime library\\n this.db = Store(this.host).create_lib(this.store_name).get_store()\",\n \"def set_tables(self):\\n with sql.connect('./{}.db'.format(self.name)) as conn:\\n conn.execute(\\\"\\\"\\\"CREATE TABLE IF NOT EXISTS tweets(\\n id INTEGER PRIMARY KEY,\\n tweet_id INTEGER,\\n insert_date TEXT,\\n created_at TEXT,\\n hashtag TEXT)\\n \\\"\\\"\\\")\\n\\n conn.execute(\\\"\\\"\\\"CREATE TABLE tweet_peaks(\\n peak_datetime TEXT NOT NULL,\\n hashtag TEXT NOT NULL,\\n time_frame TEXT,\\n mean REAL,\\n std REAL,\\n sensibility REAL,\\n freq_limit REAL,\\n qt_tweets INTEGER,\\n id TEXT PRIMARY KEY,\\n probability REAL);\\n \\\"\\\"\\\")\",\n \"def populate_stops(self):\\n stops = self.load_csv('stops.txt')\\n stops = self.process_stops(stops)\\n\\n connection = db.connect()\\n for stop in stops:\\n try:\\n connection.execute(schema.stops.insert(), stop)\\n except DataError:\\n print \\\"Missing data for stop: %s\\\" % (stop)\",\n \"def insert_db():\\n populate_tables()\",\n \"def insert_data():\\n\\tBase.metadata.drop_all(engine)\\n\\tBase.metadata.create_all(engine)\\n\\tu1 = insert_user(\\\"233@B.com\\\", \\\"/static/image/avatar.JPG\\\")\\n\\tu2 = insert_user(\\\"fy@B.com\\\", \\\"/static/image/avatar.JPG\\\")\\n\\tc = insert_catalog(u1.id, \\\"Sichuan Dish\\\")\\n\\tinsert_catalog(u1.id, \\\"Fujian Dish\\\")\\n\\tinsert_catalog(u1.id, \\\"Guangdong Dish\\\")\\n\\tinsert_catalog(u2.id, \\\"Zhejiang Dish\\\")\\n\\tinsert_catalog(u2.id, \\\"Beijing Dish\\\")\\n\\tinsert_item(u1.id, \\\"Iphone 6 plus\\\", c, 'Is a phone', None)\\n\\tinsert_item(u1.id, \\\"Hot pot\\\", c, \\\"Hot hot hot\\\", None)\\n\\tinsert_item(u2.id, \\\"Kong Bao Chicken\\\", c, \\\"Classic\\\", None)\",\n \"def _create_pysam_wfile(self, resource, meta):\\n # pylint: disable=attribute-defined-outside-init,consider-using-with\\n self._temp_dir = TemporaryDirectory()\\n fname = os.path.join(self._temp_dir.name, 'weather.csv')\\n logger.debug('Creating PySAM weather data file: {}'.format(fname))\\n\\n # ------- Process metadata\\n m = pd.DataFrame(meta).T\\n timezone = m['timezone']\\n m['Source'] = 'NSRDB'\\n m['Location ID'] = meta.name\\n m['City'] = '-'\\n m['State'] = m['state'].apply(lambda x: '-' if x == 'None' else x)\\n m['Country'] = m['country'].apply(lambda x: '-' if x == 'None' else x)\\n m['Latitude'] = m['latitude']\\n m['Longitude'] = m['longitude']\\n m['Time Zone'] = timezone\\n m['Elevation'] = m['elevation']\\n m['Local Time Zone'] = timezone\\n m['Dew Point Units'] = 'c'\\n m['DHI Units'] = 'w/m2'\\n m['DNI Units'] = 'w/m2'\\n m['Temperature Units'] = 'c'\\n m['Pressure Units'] = 'mbar'\\n m['Wind Speed'] = 'm/s'\\n keep_cols = [c for c in m.columns if c not in self.WF_META_DROP_COLS]\\n m[keep_cols].to_csv(fname, index=False, mode='w')\\n\\n # --------- Process data\\n var_map = {'dni': 'DNI',\\n 'dhi': 'DHI',\\n 'wind_speed': 'Wind Speed',\\n 'air_temperature': 'Temperature',\\n 'dew_point': 'Dew Point',\\n 'surface_pressure': 'Pressure',\\n }\\n resource = resource.rename(mapper=var_map, axis='columns')\\n\\n time_index = resource.index\\n # Adjust from UTC to local time\\n local = np.roll(resource.values, int(timezone * self.time_interval),\\n axis=0)\\n resource = pd.DataFrame(local, columns=resource.columns,\\n index=time_index)\\n mask = (time_index.month == 2) & (time_index.day == 29)\\n time_index = time_index[~mask]\\n\\n df = pd.DataFrame(index=time_index)\\n df['Year'] = time_index.year\\n df['Month'] = time_index.month\\n df['Day'] = time_index.day\\n df['Hour'] = time_index.hour\\n df['Minute'] = time_index.minute\\n df = df.join(resource.loc[~mask])\\n\\n df.to_csv(fname, index=False, mode='a')\\n\\n return fname\",\n \"def init():\\n\\n # delete existing file\\n if os.path.exists(DBFILE):\\n os.remove(DBFILE)\\n\\n db = sqlite3.connect(DBFILE)\\n # create tables\\n create(db, PARAGRAPH, \\\"paragraph\\\")\\n create(db, QUESTION, \\\"question\\\")\\n create(db, ANSWER, \\\"answer\\\")\\n\\n return db\",\n \"def main():\\n langs = []\\n\\n with open(\\\"sql/07_populate.sql\\\", 'w', encoding='utf8') as sql:\\n sql.write(\\\"--this file is generated from csv files in data folder\\\\n\\\\n\\\")\\n\\n langs = write_lang_city(sql)\\n write_groups_diets(sql, langs)\\n\\n with open(\\\"sql/10_populate_test_data.sql\\\", 'w', encoding='utf8') as sql:\\n sql.write(\\\"--this file is generated from csv files in moc_data folder\\\\n\\\\n\\\")\\n write_test_data(sql)\\n # This command sets postgis coordinates based on latitude and longitude\\n sql.write(\\\"UPDATE restaurant SET geo_location = ST_POINT(latitude, longitude);\\\\n\\\")\\n sql.close()\",\n \"def write_to_db(batch_num,pH,cursor):\\n\\n\\t#Batch Number\\n\\tbatch = 1\\n\\t\\t\\n\\n\\tcur_time = datetime.now()\\n\\theater_state = 1\\n\\ttemp = 30.1\\n\\tamb_hum, amb_temp = 25.1,30.2\\n\\n\\tcursor.execute('''INSERT INTO Kombucha_Data('Time', 'Batch', 'Heater State', 'Temperature', 'Ambient Temperature', 'Ambient Humidity','pH')\\n\\t\\t\\t\\t VALUES(?,?,?,?,?,?,NULL)''', (cur_time, batch_num, heater_state, temp, amb_temp, amb_hum))\\n\\tcursor.commit()\\n\\treturn\",\n \"def create_sqlite_table(self):\\n self.print_datetime_output('Connect to data base %s' % self.db_name)\\n con = sqlite3.connect(self.db_name)\\n cur = con.cursor()\\n\\n # check if table exists\\n cur.execute(\\\"select count(*) from sqlite_master where type='table' and name='%s'\\\" % self.db_table)\\n if cur.fetchall()[0][0] == 1:\\n self.print_datetime_output('Previous table %s was dropped' % self.db_table)\\n cur.execute(\\\"DROP TABLE %s;\\\" % self.db_table)\\n\\n self.print_datetime_output('Create table %s and import data from csv file %s' % (self.db_table,\\n self.time_series_file_name))\\n cur.execute(\\\"CREATE TABLE %s (timestamp, close_USD);\\\" % self.db_table)\\n\\n with open(self.file_name, 'r') as fin:\\n dr = csv.DictReader(fin)\\n to_db = [(i['timestamp'], i['close (USD)']) for i in dr]\\n\\n cur.executemany(\\\"INSERT INTO %s (timestamp, close_USD) VALUES (?, ?);\\\" % self.db_table, to_db)\\n con.commit()\\n return con\",\n \"def build(self):\\n \\n # create db with empty tables\\n dbpath, config = self.setup()\\n \\n # avoid work if setup decided db exists and build can be skipped\\n if dbpath is None:\\n return \\n \\n # check prerequisite files \\n obopath = check_file(config.obo, dbpath, \\\"obo\\\")\\n refpath = check_file(config.reference_phenotypes, dbpath, \\n \\\"reference_phenotypes\\\")\\n freqpath = check_file(config.phenotype_frequencies, dbpath,\\n \\\"phenotype_frequencies\\\")\\n\\n self.logger.msg1(\\\"Loading ontology\\\") \\n obo = MinimalObo(obopath, True)\\n \\n self.logger.msg1(\\\"Preparing phenotype frequencies\\\")\\n fill_phenotype_frequency_table(dbpath, freqpath)\\n \\n # fill database with data\\n self.logger.msg1(\\\"Preparing references\\\")\\n fill_concise_reference_table(dbpath, refpath) \\n fill_complete_reference_table(dbpath, obo, config) \\n \\n self._end()\",\n \"def database_conn(self, table, hr, data):\\n # origionally from https://www.w3schools.com/python/python_mysql_insert.asp\\n mydb = mysql.connector.connect(\\n host=\\\"localhost\\\",\\n user=WEATHERUSER,\\n password=DBPASS,\\n database=\\\"weather\\\"\\n )\\n mycursor = mydb.cursor()\\n sql = f\\\"UPDATE weather.{table} SET hr = {hr}, dt = %s, temp = %s, pressure = %s, humidity = %s, dewpoint = %s, rain = %s where hr = {hr}\\\"\\n mycursor.execute(sql, data)\\n mydb.commit()\",\n \"def create(self):\\n db.create_all()\",\n \"def setupDatabases(self):\\n param = self.getDefaultDatabaseConnectionParameter()\\n db = DatabaseFactory.getDatabase(self.defaultDriver(), {})\\n db.createDatabase(param)\\n db.connect(param)\\n if db.isConnected():\\n self.settingsDb = db\\n db.createObservations()\\n db.createSensors()\\n else:\\n return False\\n # replace by settings validation method later\\n return self.check()\",\n \"def init_database(self):\\n engine = create_engine('sqlite:///todo.db?check_same_thread=False')\\n self.Base.metadata.create_all(engine)\\n self.session = sessionmaker(bind=engine)()\",\n \"def initialize():\\n\\n db.connect() # Se conecta\\n db.create_tables([Entry], safe=True) # Crea las tablas\\n # safe=true evita crear modelos ya creados\",\n \"def initialize():\\n DATABASE.connect()\\n DATABASE.drop_tables([Journal], safe=True)\\n DATABASE.create_tables([Journal], safe=True)\\n DATABASE.close()\",\n \"def gatherData():\\n\\n # connect to database, set up the tweepy API object, and find the next date to search\\n\\n cnx = sqlite3.connect(DB_FILE)\\n api = generateAPI(wait_on_rate_limit=True, wait_on_rate_limit_notify=True, **CREDENTIALS)\\n\\n nextdate = findNextDate(cnx, FIRSTDATE)\\n year = nextdate[:4]\\n\\n # attempt to scrape box office data\\n\\n bodata = getTopMovies(BO_ENDPOINT, nextdate, CNT_MOVIES)\\n\\n if not bodata.empty:\\n bodata.to_sql('boxoffice', ENGINE, if_exists='append', index=False)\\n print(\\\"Box Office Data for [{0}] Written to Database\\\".format(nextdate))\\n else:\\n raise BOError(\\\"Error Scraping/Writing Box Office Data for [{0}]\\\".format(nextdate))\\n\\n # attempt to collect tweet data\\n\\n for movie in bodata.title:\\n try:\\n tweets = searchMovie(api, movie, nextdate, MAX_TWEETS)\\n if not tweets.empty:\\n tweets.to_sql('tweets', ENGINE, if_exists='append', index=False)\\n print(\\\"Tweets for [{0}] Written to Database\\\".format(movie))\\n else:\\n raise TweetError(\\\"Error Fetching/Writing Tweets for [{0}]\\\".format(movie))\\n except tweepy.error.TweepError:\\n raise TweetError(\\\"Error Fetching/Writing Tweets for [{0}]\\\".format(movie))\\n\\n # attempt to collect movie metadata\\n\\n for movie in bodata.title:\\n minfo = getMovieInfo(OMDB_ENDPOINT, processTitle(movie), year)\\n if minfo:\\n insertMovie(cnx, movie, nextdate, minfo)\\n else:\\n minfo = getMovieInfo(OMDB_ENDPOINT, processTitle(movie), str(int(year)-1))\\n if minfo:\\n insertMovie(cnx, movie, nextdate, minfo)\\n else:\\n print(\\\"Movie: [{0}] Could Not be Found via OMDB\\\".format(movie))\\n\\n # commit changes and close DB connection\\n\\n cnx.commit()\\n cnx.close()\\n\\n print(\\\"\\\\nAll Data for {0} Successfully Added to the Database!\\\\n\\\".format(nextdate))\\n return nextdate\",\n \"def create():\\n upgrade()\\n populate()\",\n \"def example_data():\\n\\n # In case this is run more than once, empty out existing data\\n User.query.delete()\\n Location.query.delete()\\n\\n # Add sample users and locations\\n\\n trinity = User(fname='Trinity', email='trin@unplugged.com',\\n username='questionit', password='l0lagent')\\n neo = User(fname='Neo', email='theone@unplugged.com',\\n username='neo', password='l0lagent')\\n\\n tacorea = Location(yelp_id='tacorea-san-francisco', name='Tacorea',\\n latitude='37.7749', longitude='122.3392',\\n address='809 Bush St, San Francisco, CA 94108',\\n yelp_url='tacorea@yelp.com', pic='pic')\\n\\n db.session.add_all([trinity, neo, tacorea])\\n db.session.commit()\",\n \"def get_forecast(location_list):\\n #Might need to munge location to get a query out of it\\n location, human_location = location_list\\n date = datetime.datetime.today()\\n query = location\\n url = \\\"http://api.wunderground.com/auto/wui/geo/ForecastXML/index.xml?query=%s\\\" % query\\n f = urllib2.urlopen(url)\\n xml = f.read()\\n root = ET.XML(xml)\\n \\n forecast = {'location': location, 'human_location': human_location}\\n #Find forecast\\n simple = root.find('simpleforecast')\\n for day in simple.findall('forecastday'):\\n forecast['forecast_date'] = parser.parse(day.find('date').find('pretty').text)\\n forecast['high_temp'] = day.find('high').find('fahrenheit').text\\n forecast['low_temp'] = day.find('low').find('fahrenheit').text\\n forecast['conditions'] = day.find('conditions').text\\n forecast['icon'] = day.find('icon').text\\n forecast['skyicon'] = day.find('skyicon').text\\n try:\\n f, created = ForecastDay.objects.get_or_create(**forecast)\\n if created:\\n f.save()\\n except:\\n logging.info(\\\"Long Range Forecast Data missing or already created\\\")\\n \\n \\n #Find Moon\\n moon = root.find('moon_phase')\\n illuminated = moon.find('percentIlluminated')\\n age = moon.find('ageOfMoon')\\n sun_rise = datetime.datetime(date.year, date.month, date.day, **_hour_minute(moon.find('sunrise')))\\n sun_set = datetime.datetime(date.year, date.month, date.day, **_hour_minute(moon.find('sunset'))) \\n #It doesn't error, so it appears to be doing what it should.\\n f = ForecastDay.objects.get(forecast_date=date)\\n f.sun_rise = sun_rise\\n f.sun_set = sun_set\\n f.moon_illuminated = illuminated.text\\n f.moon_age = age.text\\n try:\\n f.save()\\n except:\\n logging.info(\\\"Moon Data missing or no new data available\\\")\",\n \"def get_weather(days, hours, db):\\n days = format_list_for_db(days)\\n hours = format_list_for_db(hours)\\n sql = f\\\"SELECT * FROM weather WHERE day in {days} AND HOUR in {hours}\\\"\\n cursor = db.cursor()\\n cursor.execute(sql)\\n data = cursor.fetchall()\\n cursor.close()\\n\\n weathers = []\\n if len(data) > 0:\\n for weather in data:\\n weather = {\\\"hour\\\": weather[1],\\n \\\"day\\\": day_absolute_to_relative(weather[2]),\\n \\\"temperature\\\": weather[3],\\n \\\"apparenttemperature\\\": weather[4],\\n \\\"precipitationintensity\\\": weather[5],\\n \\\"precipitationprobability\\\": weather[6],\\n \\\"humidity\\\": weather[7],\\n \\\"dewpoint\\\": weather[8],\\n \\\"windspeed\\\": weather[9],\\n \\\"windbearing\\\": weather[10],\\n \\\"windgust\\\": weather[11],\\n \\\"pressure\\\": weather[12],\\n \\\"cloudcover\\\": weather[13],\\n \\\"uvindex\\\": weather[14],\\n \\\"visibility\\\": weather[15]}\\n weathers.append(weather)\\n return weathers\",\n \"def create_table(self):\\n c = self.conn.cursor()\\n c.execute(\\\"CREATE TABLE sensor_data (mac text, name text, temperature real, light integer, moisture real, conductivity real, battery real, ts_utc int, date_iso text, firmware text )\\\")\",\n \"def _create_db(self):\\n self.db = easydms.dbcore.Database(\\\":memory:\\\")\\n self.db.create_db()\",\n \"def createDatabase(self):\\n \\n try:\\n self.conn = MySQLdb.connect (host = settings.DATABASES['default']['HOST'], user = settings.DATABASES['default']['USER'], passwd = settings.DATABASES['default']['PASSWORD'])\\n except MySQLdb.Error, e:\\n raise Exception(\\\"Cannot connect to the MySQL server.\\\")\\n\\n try:\\n cursor = self.conn.cursor()\\n cursor.execute(\\\"CREATE DATABASE {};\\\".format(settings.DBNAME))\\n except MySQLdb.Error, e:\\n raise Exception(\\\"Cannot create the database {}. {}\\\".format(settings.DBNAME, e))\\n finally:\\n self.conn.close()\\n\\n try:\\n self.conn = MySQLdb.connect (host = settings.DATABASES['default']['HOST'], user = settings.DATABASES['default']['USER'], passwd = settings.DATABASES['default']['PASSWORD'], db = settings.DBNAME)\\n except MySQLdb.Error, e:\\n raise Exception(\\\"Cannot connect to the database {}. {}\\\".format(settings.DBNAME, e))\\n\\n try:\\n cursor = self.conn.cursor()\\n # creating the datasets table\\n cursor.execute(\\\"CREATE TABLE datasets (dataset VARCHAR(255) PRIMARY KEY, datasetid INT UNIQUE AUTO_INCREMENT, ximagesz INT, yimagesz INT, zimagesz INT, xoffset INT, yoffset INT, zoffset INT, xvoxelres DOUBLE, yvoxelres DOUBLE, zvoxelres DOUBLE, scalingoption INT, scalinglevels INT, starttime INT, endtime INT);\\\")\\n cursor.execute(\\\"CREATE TABLE channels (channel_name VARCHAR(255) ,dataset VARCHAR(255) REFERENCES datasets(dataset), PRIMARY KEY (channel_name,dataset), channel_type VARCHAR(255), channel_datatype VARCHAR(255), startwindow INT, endwindow INT);\\\")\\n self.conn.commit()\\n except MySQLdb.Error, e:\\n raise Exception(\\\"Cannot create the tables for the database {}, {}\\\".format(settings.DBNAME, e))\\n finally:\\n self.conn.close()\\n \\n def pipInstall(self):\\n \\\"\\\"\\\"Installing all the pip packages\\\"\\\"\\\"\\n\\n print \\\"Does Nothing\\\"\\n\\n def restart(self):\\n \\\"\\\"\\\"Restart all the services\\\"\\\"\\\"\",\n \"def populate_db(self):\\n # Get donors\\n log.info(\\\"Populating donors.\\\")\\n\\n self.r.hmset('Thomas', {'donations': '500', 'email': 'thomas@thomas.com', 'city': 'Athens', 'state': 'GA', 'zip': 30606})\\n\\n self.r.hmset('Ted', {'donations': '1', 'email': 'ted@ted.com', 'city': 'Memphis', 'state': 'TN', 'zip': 38104})\\n\\n self.r.hmset(\\\"Bailey\\\", {'donations': '1000', 'email': 'bailey@bailey.com', 'city': 'Washington', 'state': 'DC', 'zip': 12345})\",\n \"def main():\\n\\n conn = psycopg2.connect(**env.DATABASE)\\n cursor = conn.cursor()\\n\\n for file, city in env.supported_cities().items():\\n try:\\n data = add_metadata(parse_html(city, get_html(city)))\\n save_data_to_db(cursor, data, file.title())\\n except Exception as e:\\n print(\\\"Failed to scrape '%s': %s\\\" %(city, e))\\n print(traceback.format_exc())\\n\\n conn.commit()\\n conn.close()\",\n \"def initialize():\\n DATABASE.connect()\\n DATABASE.create_tables([User, Entry], safe=True)\\n DATABASE.close()\",\n \"def _create_sql(self):\\n\\n pdbfile = self.pdbfile\\n sqlfile = self.sqlfile\\n\\n if self.verbose:\\n print('-- Create SQLite3 database')\\n\\n #name of the table\\n #table = 'ATOM'\\n\\n # column names and types\\n self.col = {'serial' : 'INT',\\n 'name' : 'TEXT',\\n 'altLoc' : 'TEXT',\\n 'resName' : 'TEXT',\\n 'chainID' : 'TEXT',\\n 'resSeq' : 'INT',\\n 'iCode' : 'TEXT',\\n 'x' : 'REAL',\\n 'y' : 'REAL',\\n 'z' : 'REAL',\\n 'occ' : 'REAL',\\n 'temp' : 'REAL'}\\n\\n # delimtier of the column format\\n # taken from http://www.wwpdb.org/documentation/file-format-content/format33/sect9.html#ATOM\\n self.delimiter = {\\n 'serial' : [6,11],\\n 'name' : [12,16],\\n 'altLoc' : [16,17],\\n 'resName' :[17,20],\\n 'chainID' :[21,22],\\n 'resSeq' :[22,26],\\n 'iCode' :[26,26],\\n 'x' :[30,38],\\n 'y' :[38,46],\\n 'z' :[46,54],\\n 'occ' :[54,60],\\n 'temp' :[60,66]}\\n\\n if self.no_extra:\\n del self.col['occ']\\n del self.col['temp']\\n\\n # size of the things\\n ncol = len(self.col)\\n ndel = len(self.delimiter)\\n\\n\\n # open the data base\\n # if we do not specify a db name\\n # the db is only in RAM\\n # there might be little advantage to use memory\\n # https://stackoverflow.com/questions/764710/sqlite-performance-benchmark-why-is-memory-so-slow-only-1-5x-as-fast-as-d\\n if self.sqlfile is None:\\n self.conn = sqlite3.connect(':memory:')\\n \\n # or we create a new db file\\n else:\\n if os.path.isfile(sqlfile):\\n sp.call('rm %s' %sqlfile,shell=True)\\n self.conn = sqlite3.connect(sqlfile)\\n self.c = self.conn.cursor()\\n\\n # intialize the header/placeholder\\n header,qm = '',''\\n for ic,(colname,coltype) in enumerate(self.col.items()):\\n header += '{cn} {ct}'.format(cn=colname,ct=coltype)\\n qm += '?'\\n if ic < ncol-1:\\n header += ', '\\n qm += ','\\n\\n # create the table\\n query = 'CREATE TABLE ATOM ({hd})'.format(hd=header)\\n self.c.execute(query)\\n\\n\\n # read the pdb file\\n # this is dangerous if there are ATOM written in the comment part\\n # which happends often\\n #data = sp.check_output(\\\"awk '/ATOM/' %s\\\" %pdbfile,shell=True).decode('utf8').split('\\\\n')\\n\\n # a safer version consist at matching against the first field\\n # won't work on windows\\n #data = sp.check_output(\\\"awk '$1 ~ /^ATOM/' %s\\\" %pdbfile,shell=True).decode('utf8').split('\\\\n')\\n\\n # a pure python way\\n # RMK we go through the data twice here. Once to read the ATOM line and once to parse the data ...\\n # we could do better than that. But the most time consuming step seems to be the CREATE TABLE query\\n # if we path a file we read it\\n if isinstance(pdbfile,str):\\n if os.path.isfile(pdbfile):\\n with open(pdbfile,'r') as fi:\\n data = [line.split('\\\\n')[0] for line in fi if line.startswith('ATOM')]\\n else:\\n raise FileNotFoundError('File %s was not found',pdbfile)\\n\\n # if we pass a list as for h5py read/write\\n # we directly use that\\n elif isinstance(pdbfile,np.ndarray):\\n data = [l.decode('utf-8') for l in pdbfile.tolist()]\\n\\n # if we cant read it\\n else:\\n print(pdbfile)\\n raise ValueError('PDB data not recognized')\\n\\n # if there is no ATOM in the file\\n if len(data)==1 and data[0]=='':\\n print(\\\"-- Error : No ATOM in the pdb file.\\\")\\n self.is_valid = False\\n return\\n\\n # haddock chain ID fix\\n del_copy = self.delimiter.copy()\\n if data[0][del_copy['chainID'][0]] == ' ':\\n del_copy['chainID'] = [72,73]\\n\\n # get all the data\\n data_atom = []\\n for iatom,atom in enumerate(data):\\n\\n # sometimes we still have an empty line somewhere\\n if len(atom) == 0:\\n continue\\n\\n # browse all attribute of each atom\\n at = ()\\n for ik,(colname,coltype) in enumerate(self.col.items()):\\n\\n # get the piece of data\\n data = atom[del_copy[colname][0]:del_copy[colname][1]].strip()\\n\\n # convert it if necessary\\n if coltype == 'INT':\\n data = int(data)\\n elif coltype == 'REAL':\\n data = float(data)\\n\\n # append keep the comma !!\\n # we need proper tuple\\n at +=(data,)\\n\\n # append\\n data_atom.append(at)\\n\\n\\n # push in the database\\n self.c.executemany('INSERT INTO ATOM VALUES ({qm})'.format(qm=qm),data_atom)\",\n \"def create_database(self):\\n\\n try: \\n connection = sqlite3.connect(self.filepath_render_database)\\n pointer = connection.cursor()\\n\\n print(self.filepath_render_database)\\n\\n sql_instruction = \\\"\\\"\\\"\\n CREATE TABLE IF NOT EXISTS render_information(\\n \\n object_type VARCHAR(255),\\n name VARCHAR(255),\\n radius REAL,\\n polar_angle_min REAL,\\n polar_anglel_max REAL,\\n polar_angle_segments REAL,\\n polar_angle_random_rad REAL,\\n azimuth_angle_min REAL,\\n azimuth_angle_max REAL,\\n azimuth_angle_segments REAL,\\n azimuth_angle_random_rad REAL,\\n tracking_obj VARCHAR(255),\\n segmentation VARCHAR(255)\\n\\n\\n );\\\"\\\"\\\"\\n pointer.execute(sql_instruction)\\n connection.commit()\\n connection.close()\\n print(\\\"Creating render database file\\\")\\n except:\\n print(\\\"Was not able to create render database file\\\")\\n \\n try: \\n connection = sqlite3.connect(self.filepath_object_database)\\n pointer = connection.cursor()\\n sql_instruction = \\\"\\\"\\\"\\n CREATE TABLE IF NOT EXISTS object_information(\\n obj_filepath VARCHAR(255),\\n obj_name VARCHAR(255),\\n obj_type VARCHAR(255),\\n obj_scale_factor REAL,\\n obj_location_x REAL,\\n obj_location_y REAL,\\n obj_location_z REAL,\\n obj_rotation_x REAL,\\n obj_rotation_y REAL,\\n obj_rotation_z REAL,\\n obj_amount_percent REAL,\\n obj_material_path VARCHAR(255),\\n obj_point_in_time VARCHAR(255),\\n maximum_random_rotation_degree_z REAL,\\n maximum_random_translation REAL,\\n random_amount REAL\\n );\\\"\\\"\\\"\\n pointer.execute(sql_instruction)\\n connection.commit()\\n connection.close()\\n print(\\\"Creating object database file\\\")\\n except:\\n print(\\\"Was not able to create object database file\\\")\\n\\n try: \\n connection = sqlite3.connect(self.filepath_output_database)\\n print(\\\"outputfilepath is:\\\", self.filepath_output_database)\\n pointer = connection.cursor()\\n sql_instruction = \\\"\\\"\\\"\\n CREATE TABLE IF NOT EXISTS objects(\\n image_id REAL,\\n object_name VARCHAR(255),\\n object_type VARCHAR(255),\\n object_amount REAL,\\n object_location_x REAL,\\n object_location_y REAL,\\n object_location_z REAL,\\n object_rotation_x REAL,\\n object_rotation_y REAL,\\n object_rotation_z REAL,\\n object_dimensions_x REAL,\\n object_dimensions_y REAL,\\n object_dimensions_z REAL\\n );\\\"\\\"\\\"\\n pointer.execute(sql_instruction)\\n connection.commit()\\n sql_instruction = \\\"\\\"\\\"\\n CREATE TABLE IF NOT EXISTS camera_settings(\\n image_id REAL,\\n image_variation REAL,\\n camera_name VARCHAR(255),\\n camera_location_x REAL,\\n camera_location_y REAL,\\n camera_location_z REAL,\\n camera_rotation_x REAL,\\n camera_rotation_y REAL,\\n camera_rotation_z REAL,\\n camera_focal_length REAL,\\n camera_polar_angle REAL,\\n camera_azimuth_angle REAL\\n );\\\"\\\"\\\"\\n pointer.execute(sql_instruction)\\n connection.commit()\\n sql_instruction = \\\"\\\"\\\"\\n CREATE TABLE IF NOT EXISTS light_settings(\\n image_id REAL,\\n image_variation REAL,\\n light_name VARCHAR(255),\\n light_location_x REAL,\\n light_location_y REAL,\\n light_location_z REAL,\\n light_rotation_x REAL,\\n light_rotation_y REAL,\\n light_rotation_z REAL,\\n light_intensity REAL,\\n light_polar_angle REAL,\\n light_azimuth_angle REAL\\n );\\\"\\\"\\\"\\n pointer.execute(sql_instruction)\\n connection.commit()\\n sql_instruction = \\\"\\\"\\\"\\n CREATE TABLE IF NOT EXISTS general_settings(\\n image_id REAL,\\n render_type VARCHAR(255),\\n render_frame REAL\\n );\\\"\\\"\\\"\\n pointer.execute(sql_instruction)\\n connection.commit()\\n sql_instruction = \\\"\\\"\\\"\\n CREATE TABLE IF NOT EXISTS bounding_boxes(\\n image_id REAL,\\n image_variation REAL,\\n object_name VARCHAR(255),\\n object_type VARCHAR(255),\\n min_x REAL,\\n max_x REAL,\\n min_y REAL,\\n max_y REAL\\n );\\\"\\\"\\\"\\n pointer.execute(sql_instruction)\\n connection.commit()\\n connection.close()\\n print(\\\"Creating output database file\\\")\\n except:\\n print(\\\"Was not able to create output database file\\\")\",\n \"def initialize_database():\\n db = Database(database_name)\\n i, m, u, p = db.fetch_needed_data()\\n\\n return i, m, u, p\",\n \"def init_db():\\n\\tdb.drop_all()\\n\\tdb.create_all()\\n\\n\\tprint(\\\"Initialized Database.\\\")\\n\\treturn\",\n \"def get_weather_data(weather_station):\\n now = datetime.datetime.now()\\n then = now - datetime.timedelta(days=7)\\n\\n query_date_start = (\\\"%d%02d%02d\\\" % (then.year, then.month, then.day))\\n query_date_end = (\\\"%d%02d%02d\\\" % (now.year, now.month, now.day))\\n\\n api_key = '/api/%s' % WUNDERGROUND_KEY\\n history_key = '/history_%s%s/lang:EN/units:english/bestfct:1/v:2.0' % (query_date_start, query_date_end)\\n query = '/q/%s.json?showObs=0&ttl=120' % weather_station\\n\\n weather_url = (\\\"%s%s%s%s\\\" % (WUNDERGROUND_HOST, api_key, history_key, query))\\n\\n logger.info('Weather URL: %s', weather_url)\\n response = requests.get(weather_url).text\\n\\n max_temp_avg = json.loads(response)['history']['summary']['max_temperature_avg']\\n sum_precip = json.loads(response)['history']['summary']['precip_sum']\\n\\n return max_temp_avg, sum_precip\",\n \"def __create_table(self):\\n\\n self.connection = self.db.connect()\\n self.metadata = MetaData(self.connection)\\n\\n self.system = Table(self.table_name, self.metadata,\\n Column('timestamp', DateTime(), primary_key=True, nullable=False),\\n Column('vibration_sensor', Float()),\\n Column('flow', Float()),\\n Column('pressure', Float()),\\n Column('power_consumption', Float()),\\n Column('failure_times', Float()),\\n Column('operational', Boolean())\\n )\\n\\n self.metadata.create_all()\",\n \"def create_db(name=_db_indicators,\\n indi_file=os.path.join('Source', 'codes_need.csv'),\\n country_file=os.path.join('Source', 'work_countries.txt')):\\n\\n def create_indi_country(pdfI, con, mess, db_name, freq):\\n if pdfI.shape[0]==0:\\n return\\n print('+' * 50, '{} WORKS'.format(mess), '+' * 50)\\n\\n pdfI.to_sql(cmm.strINDI_db_name, con, if_exists='replace')\\n print('CREATE IMF.INDICATORS table for {} indicators'.format(pdfI.shape[0]))\\n pdfC = get_countryes(db_name=db_name, country_txt_file=country_file)\\n pdfC.to_sql(cmm.strCOUNTRY_db_name, con=con, if_exists='replace')\\n print('CREATE IMF.COUNTRIES for {0} countries.'.format(pdfC.shape[0]))\\n\\n update_db(db_name=db_name, start=1970, end=2000)\\n update_db(db_name=db_name, start=1999)\\n\\n cmm.create_views(db_name, freq=freq)\\n\\n pdf = cmm.read_indicators_from_csv(indi_file)\\n print(indi_file)\\n\\n pdfQ = pdf[pdf['Freq']=='Q']\\n pdfA = pdf[pdf['Freq'] == 'Y']\\n pdfM = pdf[pdf['Freq'] == 'M']\\n\\n #pdfC = cmm.read_countries(file_name=country_file)\\n\\n nameA=cmm.db_name2annu(name)\\n nameM = cmm.db_name2annu(name, suff='_M')\\n\\n coni = sa.create_engine('sqlite+pysqlite:///{name}'.format(name=name))\\n coniA = sa.create_engine('sqlite+pysqlite:///{name}'.format(name=nameA))\\n coniM = sa.create_engine('sqlite+pysqlite:///{name}'.format(name=nameM))\\n\\n create_indi_country(pdfQ, coni, 'QUARTERLY', name, freq='Q')\\n create_indi_country(pdfA, coniA, 'ANNUAL', nameA, freq='A')\\n create_indi_country(pdfM, coniM, 'MONTHLY', nameM, freq='M')\",\n \"def populate_db():\\n\\n populate_table(db, models.Department, departments_data)\\n populate_table(db, models.Employee, employees_data)\",\n \"def insert_data(self):\\n # Make a connexion with a mock database\\n self.generate_data_collection()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.753115","0.6986817","0.6771315","0.67573315","0.6443865","0.63336414","0.63230395","0.6307932","0.6303793","0.62672585","0.6251016","0.6219543","0.62010366","0.61712205","0.6160077","0.6145157","0.60383976","0.6035518","0.6035307","0.60166687","0.6011914","0.5976979","0.5968856","0.59316415","0.59218","0.5889934","0.5888027","0.5863287","0.585774","0.58497816","0.5834029","0.58237034","0.58095956","0.5793585","0.5788244","0.5784668","0.57826006","0.57734704","0.5720794","0.5690857","0.5688561","0.5683352","0.5666827","0.56634235","0.56456286","0.5634368","0.5624422","0.56020105","0.5584837","0.5582477","0.5580616","0.55791277","0.5564623","0.5553549","0.55505073","0.5544021","0.5542836","0.5519509","0.5510465","0.5508235","0.5495786","0.54933","0.5492006","0.5484443","0.54807585","0.5476533","0.54735786","0.54728097","0.54597944","0.5452989","0.54525787","0.5450085","0.5448643","0.54485667","0.5445965","0.54431033","0.543697","0.54342717","0.542369","0.54220575","0.54198116","0.5419669","0.5416143","0.54083467","0.5405662","0.5402131","0.5395727","0.539472","0.5394006","0.5393698","0.53933764","0.5391933","0.538455","0.5382807","0.5379051","0.53781664","0.5378032","0.53771746","0.53763866","0.5375355"],"string":"[\n \"0.753115\",\n \"0.6986817\",\n \"0.6771315\",\n \"0.67573315\",\n \"0.6443865\",\n \"0.63336414\",\n \"0.63230395\",\n \"0.6307932\",\n \"0.6303793\",\n \"0.62672585\",\n \"0.6251016\",\n \"0.6219543\",\n \"0.62010366\",\n \"0.61712205\",\n \"0.6160077\",\n \"0.6145157\",\n \"0.60383976\",\n \"0.6035518\",\n \"0.6035307\",\n \"0.60166687\",\n \"0.6011914\",\n \"0.5976979\",\n \"0.5968856\",\n \"0.59316415\",\n \"0.59218\",\n \"0.5889934\",\n \"0.5888027\",\n \"0.5863287\",\n \"0.585774\",\n \"0.58497816\",\n \"0.5834029\",\n \"0.58237034\",\n \"0.58095956\",\n \"0.5793585\",\n \"0.5788244\",\n \"0.5784668\",\n \"0.57826006\",\n \"0.57734704\",\n \"0.5720794\",\n \"0.5690857\",\n \"0.5688561\",\n \"0.5683352\",\n \"0.5666827\",\n \"0.56634235\",\n \"0.56456286\",\n \"0.5634368\",\n \"0.5624422\",\n \"0.56020105\",\n \"0.5584837\",\n \"0.5582477\",\n \"0.5580616\",\n \"0.55791277\",\n \"0.5564623\",\n \"0.5553549\",\n \"0.55505073\",\n \"0.5544021\",\n \"0.5542836\",\n \"0.5519509\",\n \"0.5510465\",\n \"0.5508235\",\n \"0.5495786\",\n \"0.54933\",\n \"0.5492006\",\n \"0.5484443\",\n \"0.54807585\",\n \"0.5476533\",\n \"0.54735786\",\n \"0.54728097\",\n \"0.54597944\",\n \"0.5452989\",\n \"0.54525787\",\n \"0.5450085\",\n \"0.5448643\",\n \"0.54485667\",\n \"0.5445965\",\n \"0.54431033\",\n \"0.543697\",\n \"0.54342717\",\n \"0.542369\",\n \"0.54220575\",\n \"0.54198116\",\n \"0.5419669\",\n \"0.5416143\",\n \"0.54083467\",\n \"0.5405662\",\n \"0.5402131\",\n \"0.5395727\",\n \"0.539472\",\n \"0.5394006\",\n \"0.5393698\",\n \"0.53933764\",\n \"0.5391933\",\n \"0.538455\",\n \"0.5382807\",\n \"0.5379051\",\n \"0.53781664\",\n \"0.5378032\",\n \"0.53771746\",\n \"0.53763866\",\n \"0.5375355\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.67318547"},"document_rank":{"kind":"string","value":"4"}}},{"rowIdx":2893,"cells":{"query":{"kind":"string","value":"Provide string of addresses and this class will extract \"street\" & \"house number\"."},"document":{"kind":"string","value":"def __init__(self, string):\n self.string = string"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _get_address(self, soup):\n street, city, state, zipcode = None, None, None, None\n try:\n # property detail tag\n street = soup.find('div', class_='main-address').get_text().strip()\n # find address tag\n address = soup.find('div', class_='c-address')\n \n # pattern for the address in this website\n locality = address.find_all('span', class_='locality')\n city = locality[0].get_text().strip()\n if len(locality) > 1:\n city = locality[1].get_text().strip()\n state = address.find('span', class_='region').get_text().strip()\n zipcode = address.find('span', class_='postal-code').get_text().strip()\n return street, city, state, zipcode\n except:\n return street, city, state, zipcode","def _get_address(self, soup):\n\n try:\n # from the content tag, extract the tag that contains all the address info\n address_tag = soup.find('div', class_='flex flex-col md:flex-row')\n # street tag\n street_tag = address_tag.find('h1', class_='h3')\n # street information\n street = street_tag.get_text()\\\n .strip()\\\n .replace(',', '')\n # region tag \n region_tag = address_tag.find('h5', class_='listing-card-location') \\\n .get_text() \\\n .strip() \\\n .split(' ')\n # city information\n city = region_tag[0].replace(',', '').title()\n # state information\n state = region_tag[1]\n # zipcode information\n zipcode = region_tag[2]\n\n return street, city, state, zipcode\n \n except:\n # return None if any of the above parts failed\n # if there's any part that's missing in the address part,\n # the whole address becomes useless\n return None, None, None, None","def parse_address_street(address_str, address_zip_us_re, address_zip_us_lax_re):\n address = {}\n errors = []\n parts = address_str.split('$')\n if DEBUG:\n address['debug_address_str'] = address_str\n address['debug_part_1'] = parts[0]\n address['debug_part_last'] = parts[-1]\n address['debug_length'] = len(parts)\n #if len(parts) == 1:\n #print('cannot split: {}: {}'.format(debug_type, address_str))\n match = re.search(address_zip_us_re, parts[-1])\n if match:\n if DEBUG:\n address['debug_parser'] = 'A'\n address['city'] = match.group(1)\n address['region'] = match.group(2).upper()\n address['postalCode'] = match.group(3)\n address['countryId'] = 'US'\n if len(parts) == 2:\n if DEBUG:\n address['debug_parser'] = 'B'\n address['addressLine1'] = parts[0]\n else:\n if len(parts) == 3:\n if DEBUG:\n address['debug_parser'] = 'C'\n address['addressLine1'] = parts[0]\n if parts[0] != parts[1]:\n if DEBUG:\n address['debug_parser'] = 'D'\n address['addressLine2'] = parts[1]\n else:\n match2 = re.search(address_zip_us_lax_re, address_str)\n if match2:\n if DEBUG:\n address['debug_parser'] = 'E'\n address['region'] = match2.group(2).upper()\n address['postalCode'] = match2.group(3)\n address['countryId'] = 'US'\n # FIXME: Cannot reliably parse the remainder for city and street address\n errors.append('Partial parse street address: {}'.format(address_str))\n address['addressLine1'] = match2.group(1)\n else:\n # This is the remainder that we could not parse.\n # So just put it all into \"addressLine1\" to be manually adjusted later.\n if DEBUG:\n address['debug_parser'] = 'F'\n errors.append('Cannot parse street address: {}'.format(address_str))\n address['addressLine1'] = address_str\n return (address, errors)","def get_address(self, list_item):\n Address = namedtuple('Address', ['addr', 'city', 'state', 'zip'])\n extract = [text for text in list_item.find('address').stripped_strings]\n\n # Sometimes a street address is not given\n if len(extract) == 1:\n addr, rest = None, extract[0]\n else:\n addr, rest = extract\n\n city, rest = rest.split(',')\n state, zip = rest.strip().split(' ')\n return Address(addr, city, state, zip)","def _get_address(self, address_tag, hdr):\n\n # try to find all the span tags in the address tag, the span tags\n # include all the address information we need \n try:\n elements = address_tag.find_all('span')\n\n # scrape the text out of the span tags and remove\n # all the whitespaces and punctuation marks\n address = elements[0].get_text()\\\n .replace(',','')\\\n .strip()\n city = elements[1].get_text().strip()\n state = elements[2].get_text().strip()\n zipcode = elements[3].get_text().strip()\n return address, city, state, zipcode\n # however, sometimes the address tag does not include the street\n # info, in this case, use the text in the header tag, which serves\n # as a replacement for the address \n except:\n address = hdr.get_text()\n elements = address_tag.find_all('span')\n city = elements[0].get_text()\\\n .replace(',','')\\\n .strip()\n state = elements[1].get_text().strip()\n zipcode = elements[2].get_text().strip()\n return address, city, state, zipcode","def get_str_address(address):\n return \\\n get_ob_value_primitive(address, 'AddrLine1', exception_return_value='') + ' ' + \\\n get_ob_value_primitive(address, 'AddrLine2', exception_return_value='') + ' ' + \\\n get_ob_value_primitive(address, 'AddrLine3', exception_return_value='') + ', ' + \\\n get_ob_value_primitive(address, 'City', exception_return_value='') + ' ' + \\\n get_ob_value_primitive(address, 'County', exception_return_value='') + ' ' + \\\n get_ob_value_primitive(address, 'StateProvince', exception_return_value='') + ' ' + \\\n get_ob_value_primitive(address, 'ZipPostalCode', exception_return_value='')","def street_addresses(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\n return pulumi.get(self, \"street_addresses\")","def street_addresses(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\n return pulumi.get(self, \"street_addresses\")","def street_addresses(self) -> pulumi.Output[Optional[Sequence[str]]]:\n return pulumi.get(self, \"street_addresses\")","def __getAddresses(parsed: BeautifulSoup) -> list:\n\n # Addresses container\n address_divs = parsed.find_all('div', class_='mailer')\n\n # Building RegEx for phone number\n # The following RegEx extracts phone numbers in the following formats:\n # 1. (###) ###-####\n # 2. ###-###-####\n # 3. ##########\n phone_number_regex = re.compile(\n r'(\\(\\d{3}\\) \\d{3}-\\d{4}|\\d{3}-\\d{3}-\\d{4}|\\d{10})')\n\n # List for final addresses\n addresses = list()\n\n for address in address_divs:\n # Create dict for address\n address_parsed = dict()\n # Split text by newline\n address_items = address.text.split('\\n')\n # Removing leading and trailing spaces\n address_items = [i.strip() for i in address_items]\n\n # Variable to store street address\n street_address = ''\n\n # Iterate through each line\n for idx, address_item in enumerate(address_items):\n # First line is address type\n if idx == 0:\n address_parsed['type'] = address_item\n continue\n\n # Check if line has phone number\n phone_matches = phone_number_regex.findall(address_item)\n if len(phone_matches) == 1:\n # Stripping non-digit characters from phone number\n phone_number = re.sub('[^0-9]', '', phone_matches[0])\n address_parsed['phone'] = phone_number\n continue\n \n # If no number, add to address line\n street_address += address_item.strip() + ' '\n \n # Adding street address to parsed address\n address_parsed['street_address'] = street_address.strip()\n\n # Adding parsed address to addresses master list\n addresses += [address_parsed]\n\n return addresses","def get_apartment_address(self, soup, apartment_dict):\n\n info_class = soup.find_all('div', {'class': 'info'})\n if info_class and len(info_class) > 0:\n info_class = info_class[0]\n address = info_class.find('h2').text.strip()\n\n from parse import parse\n address = parse(\"Location: {}\", address)[0]\n apartment_dict['address'] = address\n else:\n logging.warning(\"Failed to parse apartment address\")\n return","def get_address(self):\n return self.address.line[0]+\", \"+self.address.city+\", \"+self.address.state+\", \"+self.address.country","def addresses(self):\n if 'Ward Matters' in self.topics or 'City Matters' in self.topics:\n stname_pattern = \"(\\S*[a-z]\\S*\\s){1,4}?\"\n sttype_pattern = \"(ave|blvd|cres|ct|dr|hwy|ln|pkwy|pl|plz|rd|row|sq|st|ter|way)\"\n st_pattern = stname_pattern + sttype_pattern\n\n addr_pattern = \"(\\d(\\d|-)*\\s%s)\" %st_pattern\n intersec_pattern = exp = \"((?<=\\sat\\s)%s\\s?and\\s?%s)\" %(st_pattern, st_pattern)\n\n pattern = \"(%s|%s)\" %(addr_pattern, intersec_pattern)\n\n matches = re.findall(pattern, self.description, re.IGNORECASE)\n\n addresses = [m[0] for m in matches]\n return addresses\n\n return []","def _get_address(self, jdict):\n \n try:\n # access the location info dictionary\n loc_dict = jdict['props']['homeDetails']['location']\n state = loc_dict['stateCode']\n city = loc_dict['city']\n zipcode = loc_dict['zipCode']\n street = loc_dict['formattedLocation']\n return street, city, state, zipcode\n except:\n return None, None, None, None","def street_address(self):\n\t\tif self.address2:\n\t\t\treturn '{}, {}'.format(self.address, self.address2)\n\t\treturn self.address","def street_address1(self) -> str:\n return pulumi.get(self, \"street_address1\")","def address_regex(self) -> Any:","def get_address(address: str) -> Tuple[str, str, str]:\n\n # Try to geocode the address as given\n g = geocoder.osm(address)\n\n if g.json is not None:\n\n # TODO this is inefficient and hacky\n\n # First thing we attempt if the result isn't complete is just to\n # add the housenumber (often the issue).\n if not good_geocoder_result(g.json):\n g.json['housenumber'] = usaddress.tag(address)[0]['AddressNumber']\n\n # If the result is now good, return it\n if good_geocoder_result(g.json):\n\n # Geocoding was successful. Return the result\n return (\n # First part is a nicely formatted address\n f\"{g.json['housenumber']} {g.json['street']}, {g.json['city']}, {g.json['state']} {g.json['postal']}\",\n # Second is the latitude\n g.json['lat'],\n # And third is the longitude\n g.json['lng']\n )\n\n # Geocoding was unsuccessful.\n # Let's try to create a cleaner address by first parsing out the pieces we need, then try again.\n \n # Parsing the address components...\n parsed, addr_type = usaddress.tag(address)\n if addr_type != \"Street Address\":\n raise ValueError(f\"Address could not be properly parsed. Resulting type: {addr_type}. Result: \\n{parsed}\")\n \n # Trim off any whitespace from the parsed components.\n for part in parsed:\n parsed[part] = parsed[part].strip()\n\n reqd_address_parts = ['AddressNumber', 'StreetName', 'PlaceName']\n if any(address_part not in parsed for address_part in reqd_address_parts):\n raise ValueError(f\"The address must have at least a house number, street, and city.\")\n \n # Initialize the resulting address string with the address number (aka house/street number)\n new_address = parsed['AddressNumber']\n \n # If the streetname is just a number, make it ordinal\n if parsed['StreetName'].isnumeric():\n parsed['StreetName'] = ordinal(parsed['StreetName'])\n \n # Get the whole street name\n for k, v in [(k, v) for k, v in parsed.items() if k.startswith(\"StreetName\")]:\n new_address += f\" {v}\"\n \n # Add the city...\n new_address += f\", {parsed['PlaceName']}\"\n # Add the state, if it exists\n if 'StateName' in parsed:\n new_address += f\", {parsed['StateName']}\"\n # And the zip code, if it exists\n if 'ZipCode' in parsed:\n new_address += f\" {parsed['ZipCode']}\"\n \n # Now try to geocode this improved address\n g = geocoder.osm(new_address)\n\n if g.json is not None:\n\n # Geocoding was successful. Return the result\n return (\n # First part is a nicely formatted address\n f\"{g.json['housenumber']} {g.json['street']}, {g.json['city']}, {g.json['state']} {g.json['postal']}\",\n # Second is the latitude\n g.json['lat'],\n # And third is the longitude\n g.json['lng']\n )\n \n # Still can't geocode the address. Throw an error\n else:\n raise ValueError(f\"Could not geocode this address: {address}\")","def toAddr(self, addressString: unicode) -> ghidra.program.model.address.Address:\n ...","def getAddress(user):","def street_address():\r\n\r\n return _random.choice(\r\n [\r\n '%d-%d %s' % (\r\n _random.randrange(999),\r\n _random.randrange(999),\r\n street_name()\r\n ),\r\n '%d %s' % (\r\n _random.randrange(999),\r\n street_name()\r\n ),\r\n '%s %d, %s' % (\r\n 'P.O. Box',\r\n _random.randrange(999),\r\n street_name()\r\n )\r\n ]\r\n )","def address(self):\n return str(self.street) + str(self.city) + str(self.state) + str(self.zipcode)","def parse_address_campus(address_str, address_campus_re, address_campus_room_re):\n address = {}\n errors = []\n if '$' not in address_str:\n match = re.search(address_campus_room_re, address_str)\n if match:\n address['addressLine1'] = match.group(1)\n else:\n # This leftover is either an erroneous email address or a building name\n if '@' in address_str:\n errors.append('Campus address seems to be email: {}'.format(address_str))\n #FIXME: Should this be saved to addressLine1 anyway.\n else:\n # It seems to be a building address\n address['addressLine2'] = address_str\n else:\n match = re.search(address_campus_re, address_str)\n if match:\n address['addressLine2'] = match.group(1)\n address['addressLine1'] = match.group(2)\n #else:\n # FIXME: here just for debug\n #errors.append('Cannot parse campus address: {}'.format(address_str))\n return (address, errors)","def find_street(self, text):\n text = text.replace('\"', '')\n result = ''\n textarr = text.split()\n for i in range(0, len(textarr)):\n if textarr[i] in self.streets:\n # decide if the result is nearly empty or not to add the word with or without a space\n if len(result) < 2:\n result = result + textarr[i]\n else:\n result = result + ' ' + textarr[i]\n\n if result in self.streets:\n\n # ends the function if the street is found\n if (result + '\\n') in self.streets and result != 'straße' and result != 'in' and result != '' \\\n and result != 'ein' and result != 'der' and result != 'und' and result != ' ' \\\n and result != 'an':\n return result\n else:\n result_list = result.split()\n del result_list[0]\n result = ' '.join(w for w in result_list)\n result = result.lstrip(' ')\n # ends the function if the street is found\n if (result + '\\n') in self.streets and result != 'straße' and result != 'in' and result != '' \\\n and result != 'ein' and result != 'der' and result != 'und' and result != ' ' \\\n and result != 'ist' and result != 'an':\n return result\n elif i == len(textarr)-1:\n result_list = result.split()\n del result_list[0]\n result = ' '.join(w for w in result_list).lstrip(' ')\n if (result + '\\n') in self.streets and result != 'straße' and result != 'in' and result != '' \\\n and result != 'ein' and result != 'der' and result != 'und' and result != ' ' \\\n and result != 'ist' and result != 'an':\n return result\n print(result)\n return None","def _compute_adress(self):\r\n\t\tfor leads in self:\r\n\t\t\tleads.address = leads.street + \" \" + leads.street2","def address_str(self):\n return self._plrevgeoloc.addressString","def extract_addresses(addresses):\n \n # Since lists are iterated over in Python in an orderly fashion, \n # put 'Input Reg' before 'Input' such that the 'Reg' in an address \n # with an 'Input Reg' prefix doesn't get left behind\n address_prefixes= [\"Input Reg \", \"Holding Reg \", \"Input \", \"Coil \"]\n\n \n for idx,address in enumerate(addresses):\n # Replace prefixes with empty string\n for prefix in address_prefixes:\n addresses[idx]=addresses[idx].replace(prefix, \"\")\n # Extract numeral\n try:\n addresses[idx]= int(addresses[idx])\n except:\n logging.warning(\"Invalid modbus address suppied at index {}\".format(idx))\n\n # Return\n return addresses","def addresses(self) -> pulumi.Input[Sequence[pulumi.Input[str]]]:\n return pulumi.get(self, \"addresses\")","def parse_address(address: str) -> OrderedDict[str, str]:\n\n address = address.replace(\"\\n\", \", \")\n\n parsed_address, address_type = usaddress.tag(address)\n if address_type != \"Street Address\":\n logger.warning(\n f\"Couldn't parse address '{address}' of type {address_type}; best guess: {parsed_address}\"\n )\n\n # Fixup: At least one address has \"WI, USA\" in the \"StateName\" component.\n # Strip non-state components\n if parsed_address.get(\"StateName\"):\n parsed_address[\"StateName\"] = parsed_address[\"StateName\"].partition(\",\")[0]\n\n return parsed_address","def integrated_address_regex(self) -> Any:","def search_address(query: str) -> Tuple[int, str]:\n\n url = 'https://api.n1.ru/api/v1/geo/geocoder/with_cities/'\n params = _search_params.copy()\n params['q'] = query\n\n try:\n r = requests.get(url, params=params, headers=_headers)\n response = r.json()\n\n if not 'result' in response or not response['result']:\n raise NotFoundException('Result not found or empty.')\n \n address = None\n house_number = query.split(',')[-1].strip()\n for x in response['result']:\n if x['name_ru'].lower() == house_number:\n address = x\n break\n \n if address is None:\n raise NotFoundException(f'Not found house number {house_number} in result: {response[\"result\"]}')\n \n return address['street']['id'], address['name_ru']\n except requests.RequestException as e:\n raise ParserException(f'Fail make request. query: {query}') from e\n except NotFoundException as e:\n raise ParserException('Invalid result.') from e\n except (KeyError, IndexError) as e:\n raise ParserException(f'Fail get street id or house number. value: {response[\"result\"]}') from e","def street_parser(*street_data):\n\n # parsing tuples\n if len(street_data) == 2:\n if not isinstance(street_data[0], str) and not isinstance(street_data[1], str):\n raise WrongInput(\"Invalid format\")\n # street name as the tuple's first item\n strname, strnumber = street_data\n # street number as the tuple's first item\n if street_data[0][0] in digits:\n strname, strnumber = strnumber, strname\n\n # parsing strings\n else:\n if not isinstance(street_data[0], str):\n raise WrongInput(\"Invalid format\")\n if not street_data[0]:\n raise WrongInput(\"Input cannot be blank\")\n\n # string starting with street number\n if street_data[0][0] in digits:\n street_pattern = re.compile(r'''\n ^ # beginning of string\n (\\d+) # street number is any number of digits\n \\W+ # separator\n (\\w+\\W*\\w*\\W*) # street name is one or more words with optional separators\n $ # end of string\n ''', re.VERBOSE)\n street_obj = street_pattern.search(street_data[0])\n strnumber, strname = street_obj.groups()\n\n # string starting with street name\n else:\n street_pattern = re.compile(r'''\n ^ # beginning of string\n (\\w+\\W*\\w*\\s*) # street name is one or more words with optional separators\n \\W+ # separator\n (\\d+) # street number is any number of digits\n $ # end of string\n ''', re.VERBOSE)\n street_obj = street_pattern.search(street_data[0])\n (strname, strnumber) = street_obj.groups()\n\n # replace specific words in street name with their abbreviates\n strname = strname.lower()\n special = {r'\\baleje\\b': 'Al.', r'\\bavenue\\b': 'Av.', r'\\broad\\b': 'Rd.', r'\\bsquare\\b': 'Sq.',\n r'\\bstreet\\b': 'St.', r'\\bdrive\\b': 'Dr.'}\n for key in special:\n strname = re.sub(key, special[key], strname)\n return strname.title(), strnumber","def get_address(self):\n\n return \"{}\\n{}\\n{},\\n{},\\n{}\".format(\n self.address_line_1, self.city, self.state, self.postal_code, self.country\n )","def listToAddr(location):\n\n start_time = time.time()\n wk = [key for key in location.keys() if key in ('street', 'house_num', 'suburb', 'city', 'province', 'country', 'pos_code')]\n address = re.sub(',', '', ', '.join(value for value in dict(zip(wk, [location[k] for k in wk])).values() if value), 1)\n print('--- Tiempo de ejecucion listToAddr: {} segundos ---'.format((time.time() - start_time)))\n return address","def address():\n # We start with generating the street name. For this we choose\n # between the most common prefixes and our own prefixes\n prefix = dice.randint(1, 100)\n if prefix <= 10: # 10%\n prefix = \"Haupt\"\n elif prefix <= 18: # 8%\n prefix = \"Schul\"\n elif prefix <= 25: # 7%\n prefix = \"Garten\"\n elif prefix <= 32: # 7%\n prefix = \"Dorf\"\n elif prefix <= 39: # 7%\n prefix = \"Bahnhof\"\n elif prefix <= 46: # 7%\n prefix = \"Wiesen\"\n elif prefix <= 52: # 6%\n prefix = \"Berg\"\n elif prefix <= 56: # 4%\n prefix = \"Kirch\"\n elif prefix <= 60: # 4%\n prefix = \"Wald\"\n elif prefix <= 64: # 4%\n prefix = \"Ring\"\n else:\n prefix = dice.choice(names.prefix)\n\n # Now we can add the suffix\n suffix = dice.randint(1, 100)\n if suffix <= 78:\n suffix = \"straße\"\n elif suffix <= 96:\n suffix = \"weg\"\n elif suffix <= 98:\n suffix = \"allee\"\n elif suffix == 99:\n suffix = \"ring\"\n elif suffix == 100:\n suffix = \"platz\"\n\n # When we have a city name as prefix, we need to capitalize the\n # suffix since it will be two words\n if prefix[-1] == \" \":\n suffix = suffix.capitalize()\n\n # Now we can add them together\n street = prefix + suffix\n\n # We need a house number as well. In Germany most numbers have\n # between one and four digits, so we will use this as base. Lower\n # numbers are more common, so we'll give it a 10% probability of\n # using 3 digits and 1% of using 4 digits\n digits = dice.randint(1, 100)\n if digits == 100:\n house_number = str(dice.randint(1000, 9999))\n elif digits >= 90:\n house_number = str(dice.randint(100, 999))\n else:\n house_number = str(dice.randint(1, 99))\n address_full = street + \" \" + house_number\n return address_full","def street_address(full_address):\n full_address = normalise_address(full_address)\n capture_groups = ROAD_PATTERN.findall(full_address)\n if capture_groups:\n name, _type = process_capture_groups(capture_groups)\n return '{} {}'.format(name, _type)\n return ''","def build_address(record):\n pass","def get_postal_address(self):\n # print \"getting postal address\"\n address = []\n if self.name:\n address.append(self.name)\n if self.number:\n address.append(self.number + \" \" + self.street) # because building numbers and street names go on the same line\n elif self.street:\n address.append(self.street)\n if self.additional_street_address:\n address.append(self.additional_street_address)\n if self.site.post_town:\n address.append(self.site.post_town + \" \" + self.postcode)\n elif self.postcode:\n address.append(self.postcode)\n return address","def street_address(self):\n if \"streetAddress\" in self._prop_dict:\n return self._prop_dict[\"streetAddress\"]\n else:\n return None","def parse_address(self, address: str) -> Optional[Address]:\n raise NotImplemented","def parse_building_address(addr_string):\n addr_string = re.sub(_regexp, '', addr_string)\n addr_string = re.sub(r'(?P<key>[a-zA-Z]+)', _replace_dir, addr_string)\n addr_string = re.sub(r'(?P<key>[a-zA-Z]+)', _replace_suffix, addr_string)\n addr_string = re.sub(_regexp_extra_space, ' ', addr_string)\n return addr_string.strip().upper()","def find(self, text: unicode) -> ghidra.program.model.address.Address:\n ...","def find_places(query):\n parts = str(query).split(' ')\n for i, p in enumerate(parts):\n p = p.replace('-', ' ').strip()\n try:\n postal_code = int(p)\n if len(postal_code) == 4:\n print(postal_code, parts[i+1])\n # Check \n #response = get_osm_location(\"{postal_code} {name}\")\n #lon = response['lon']\n #lat = response['lat']\n #poly = \n except Exception as e:\n continue","def process_address(text):\n return sanitize(text[9:])","def get_address_string(self):\n output = ''\n if self.address_line_1:\n output += '{}'.format(self.address_line_1)\n if self.address_line_2:\n output += ', {}'.format(self.address_line_2)\n if self.city:\n output += ', {}'.format(self.city)\n if self.state:\n output += ', {}'.format(self.state)\n if self.zipcode:\n output += ' {}'.format(self.zipcode)\n return output","def test_get_address(self):\r\n note_data = self.tape.dataframe.iloc[0]\r\n note = self.tape._get_note_object(note_data)\r\n eq_(note.get_address(), '8 Brown St, Methuen, MA 01844')","def extract_city(full_address):\n full_address = full_address.strip()\n last_comma_index = full_address.rindex(\",\")\n mid_comma_index = full_address.rindex(\",\", 0, last_comma_index)\n city = full_address[mid_comma_index + 1 : last_comma_index]\n city = city.strip()\n return city","def fetch_address(cpr: str) -> str:\n\n return \"Åbogade 15, 8200 Aarhus N\"","def capture_address_element(regex_object, full_address):\n full_address = normalise_address(full_address)\n capture_groups = regex_object.search(full_address)\n if capture_groups:\n return capture_groups.group(0)\n return ''","def __str__(self):\n return format_address(**self._get_elements())","def street_address2(self) -> Optional[str]:\n return pulumi.get(self, \"street_address2\")","def test_address_other_parameters():\n address = lob.Address.create(name='Siddharth Saha', address_line1='104, Printing Boulevard',\n address_line2='Sunset Town', email='sidchilling@gmail.com',\n address_city='Boston', address_state='MA', address_country='US',\n address_zip='12345')\n print address.to_dict()","def address(self):\n return self.data.get('address')","def addresses(self) -> \"List[str]\":\n return self._attrs.get(\"addresses\")","def addresses(self) -> \"List[str]\":\n return self._attrs.get(\"addresses\")","def addresses(self) -> \"List[str]\":\n return self._attrs.get(\"addresses\")","def postcode(full_address):\n return capture_address_element(POSTCODE_PATTERN, full_address)","def address1(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"address1\")","def address1(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"address1\")","def extract_zipcode(full_address):\n full_address = full_address.strip()\n last_space_index = full_address.rindex(\" \")\n zipcode = full_address[last_space_index + 1 : ]\n return zipcode","def extract_state(full_address):\n full_address = full_address.strip()\n last_comma_index = full_address.rindex(\",\")\n last_space_index = full_address.rindex(\" \")\n state = full_address[last_comma_index + 1 : last_space_index]\n state = state.strip()\n return state","def street_address3(self) -> Optional[str]:\n return pulumi.get(self, \"street_address3\")","def get_address(post_content):\n post_address = post_content.find(\"div\", {\"class\": \"mapaddress\"})\n address_attr = {\"address\": \"\"}\n if post_address is not None:\n address_attr[\"address\"] = post_address.text\n return address_attr","def filter_string(self):\n logger.info(\"Information Gathering Finished!\")\n\n pattern_street = re.compile(r'[A-Za-z]+\\s?\\w+(?=\\s[Nn]o\\s\\d+$) |'\n r' [A-Za-z]+\\s?\\w+\\s?[A-Za-z]+\\s?[A-Za-z]+',\n re.X) # street pattern\n match_street = pattern_street.search(self.string)\n\n # If there are no house numbers provided in the input file,\n # print(not found) in the output JSON file\n numbers_instring = re.findall(r'\\d+', self.string) # digit counts in given string\n\n if len(numbers_instring) > 0:\n # In most cases we have: \"no\" followed by some digits\n pattern_housenumber = re.compile(r'(\\d+\\s?[A-Za-z]?$) |'\n r' (^\\d+) |'\n r' [Nn]o+[\\s?]+[0-9]+$',\n re.X) # house number pattern\n match_housenumber = pattern_housenumber.search(self.string)\n fin_housenumber = match_housenumber[0]\n else:\n match_housenumber = [\"not found\"]\n\n fin_housenumber = match_housenumber[0]\n fin_street = match_street[0]\n print(\"street: \", fin_street)\n print(\"housenumber: \", fin_housenumber)\n return {'street': fin_street, 'housenumber': fin_housenumber}","def address_details(self) -> 'outputs.AddressDetailsResponse':\n return pulumi.get(self, \"address_details\")","def address_line_1(self):\n return \"{} {} {}\".format(\n self.fake.randomize_nb_elements(1000),\n self.fake.last_name(),\n self.fake.random_element(elements=STREET_SUFFIX)\n )","def unpack_addresses(self, addresses_to_test):\n if len(addresses_to_test) == 0:\n raise ValueError(\n \"There were no arguments passed to the function. That is wrong. Closing\"\n )\n\n return_addresses = []\n for address in addresses_to_test:\n if \"/\" in address:\n try:\n six_or_four = ipaddress.ip_network(address)\n except ValueError:\n print(f\"{address} is not a valid subnet. Skipping.\")\n continue\n for address_host in six_or_four.hosts():\n return_addresses.append(str(address_host))\n else:\n try:\n ipaddress.ip_address(address)\n except ValueError:\n print(f\"{address} is not a valid address. Skipping.\")\n continue\n return_addresses.append(str(address))\n for address in return_addresses:\n try:\n ipaddress.ip_address(address)\n except ValueError:\n raise ValueError(f\"{address} is not an IPv4/v6 address. Shutting Down\")\n if len(return_addresses) > 0:\n return return_addresses\n else:\n raise ValueError(\"No usable addresses to scan\")","def address(self) -> tuple[str, int]:","def format_address(line1, line2, city, state, zipcode):\n\t\n\tstreetlines = line1\n\tcityline = city\n\t\n\tif len(streetlines) > 0 and len(line2) > 0:\n\t\tstreetlines += \"\\n\"\n\t\n\tif len(cityline) > 0 and len(state) > 0:\n\t\tcityline += \", \"\n\t\n\tstreetlines += line2\n\tcityline += state\n\t\n\treturn \"\\n\".join([streetlines, cityline, zipcode])","def street(self):\n if \"street\" in self._prop_dict:\n return self._prop_dict[\"street\"]\n else:\n return None","def _cleanupAddress(self, address):\n clean = []\n \n # This is sort of a desultory effort but I'm not convinced \n # that these cleanups will actually result in cleaner searches\n for word in address.split(None):\n lower = word.lower()\n \n # Some things we just nuke\n if lower == 'at': continue\n elif lower == 'btw': continue\n elif lower == 'btwn': continue\n elif word.isdigit(): continue\n \n # Or we make substitiutions\n elif lower == 'st' or lower == 'st.':\n word = 'Street'\n elif lower == 'ave' or lower == 'ave.':\n word = 'Avenue'\n elif lower == 'pl' or lower == 'pl.':\n word = 'Place'\n elif lower == 'n': word = 'North'\n elif lower == 'e': word = 'East'\n elif lower == 's': word = 'South'\n elif lower == 'w': word = 'West'\n \n clean.append(word)\n return ' '.join(clean)","def get_address(self):\n \n if \"'\" in self.data.get(\"AddressInfo\").get(\"AddressLine1\") :\n self.data.get(\"AddressInfo\").get(\"AddressLine1\").replace(\"'\",\"\")\n\n return self.data.get(\"AddressInfo\").get(\"AddressLine1\")","def getRestaurantAddresses(restaurants):\n addresslist = []\n for rest in restaurants:\n if 'address' in rest:\n addressstring = str(rest['address']) + ' ' + str(rest['city'])\n addresslist.append(addressstring)\n\n # pprint.pprint(addresslist)\n return addresslist","def normalize_address(patched_address: OrderedDict[str, str]) -> location.Address:\n\n address_kwargs = {\n # \"street1\",\n # \"city\",\n # \"state\",\n # \"zip\"\n }\n street_buffer: List[str] = []\n suite_buffer: List[str] = []\n while len(patched_address) > 0:\n component, value = patched_address.popitem(last=False)\n if component == \"PlaceName\":\n address_kwargs[\"city\"] = value\n elif component == \"StateName\":\n address_kwargs[\"state\"] = value\n elif component == \"ZipCode\":\n address_kwargs[\"zip\"] = value\n elif component == \"OccupancyType\":\n suite_buffer.append(value)\n elif component == \"OccupancyIdentifier\":\n suite_buffer.append(value)\n else:\n street_buffer.append(value)\n address_kwargs[\"street1\"] = \" \".join(street_buffer)\n if len(suite_buffer) > 0:\n address_kwargs[\"street2\"] = \" \".join(suite_buffer)\n\n return location.Address(**address_kwargs)","def test_address(residence):\n # We start by creating our result list\n result = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n # Then we split our given string so we only have the address left\n residence = residence.split(\", \")[1]\n\n # First we check for the prefix and increase the according value\n if \"Haupt\" in residence:\n result[0] = 1\n elif \"Schul\" in residence:\n result[1] = 1\n elif \"Garten\" in residence:\n result[2] = 1\n elif \"Dorf\" in residence:\n result[3] = 1\n elif \"Bahnhof\" in residence:\n result[4] = 1\n elif \"Wiesen\" in residence:\n result[5] = 1\n elif \"Berg\" in residence and residence[4] in (\"s\", \"w\", \"a\", \"r\", \"p\"):\n result[6] = 1\n elif \"Kirch\" in residence:\n result[7] = 1\n elif \"Wald\" in residence:\n result[8] = 1\n elif \"Ring\" == residence[0:4]:\n result[9] = 1\n else:\n result[10] = 1\n\n # Now we check the suffix\n if \"straße\" in residence or \"Straße\" in residence:\n result[11] = 1\n elif \"Weg\" in residence or \"weg\" in residence:\n result[12] = 1\n elif \"Allee\" in residence or \"allee\" in residence:\n result[13] = 1\n elif \"platz\" in residence or \"platz\" in residence:\n result[15] = 1\n else:\n result[14] = 1\n\n # And now we check the number\n number = int(residence.split()[-1])\n if number > 999:\n result[16] = 1\n elif number > 99:\n result[17] = 1\n else:\n result[18] = 1\n\n return result","def get_address(self):\n entity = self\n if entity.abstract_entity:\n entity = self.get_real_ancestor()\n if entity:\n address = entity.get_institutional_address()\n building = entity.get_building()\n if building:\n if entity.building_recapitulates_entity_name: \n address.extend(building.get_postal_address()[1:])\n else:\n address.extend(building.get_postal_address())\n return address","def test_address_other_parameters():\n address = lob.Address.create(name = 'Siddharth Saha', address_line1 = '104, Printing Boulevard',\n address_line2 = 'Sunset Town', email = 'sidchilling@gmail.com', \n address_city = 'Boston', address_state = 'MA', address_country = 'US',\n address_zip = '12345')\n print address.to_dict()","def clean_address(self, s):\n # The letter \"O\" instead of the numeral \"0\" is a common mistake.\n s = re.sub(\n r\"\\b[A-Z][O0-9][A-Z]\\s?[O0-9][A-Z][O0-9]\\b\", lambda x: x.group(0).replace(\"O\", \"0\"), clean_string(s)\n )\n for k, v in province_or_territory_abbreviations().items():\n # Replace a province/territory name with its abbreviation.\n s = re.sub(\n r\"[,\\n ]+\"\n r\"\\(?\" + k + r\"\\)?\"\n r\"(?=(?:[,\\n ]+Canada)?(?:[,\\n ]+[A-Z][0-9][A-Z]\\s?[0-9][A-Z][0-9])?\\Z)\",\n \" \" + v,\n s,\n )\n # Add spaces between province/territory abbreviation, FSA and LDU and remove \"Canada\".\n return re.sub(\n r\"[,\\n ]+\" r\"([A-Z]{2})\" r\"(?:[,\\n ]+Canada)?\" r\"[,\\n ]+([A-Z][0-9][A-Z])\\s?([0-9][A-Z][0-9])\" r\"\\Z\",\n r\" \\1 \\2 \\3\",\n s,\n )","def __str__(self):\n if self._street_name != self.DEFAULT_STREET_NAME and \\\n self._house_num != self.DEFAULT_HOUSE_NUM and \\\n self._apt_num != self.DEFAULT_APT_NUM:\n address = f\"\\n{self._house_num} {self._street_name} Street, \" \\\n f\"#{self._apt_num}\"\n return address\n else:\n return \"<None>\"","def get_address(self):\n if self.get_entity: # needs an entity to work\n if self.building:\n address = self.get_entity.get_institutional_address()\n address.extend(self.building.get_postal_address())\n return address\n else:\n return self.get_entity.get_address()","def addresses( data ) :\n return list( set(chain.from_iterable( [ re.sub(r'\\[.*?\\]\\s+','',x['C1']).split('; ') for x in data ] )))","def suggestions(self, input, borough_code=None):\n parsed = parser.address(input)\n if borough_code:\n parsed['BOROUGH_CODE'] = borough_code\n self.similiar_names = []\n self.results = []\n if parsed['PHN'] and parsed['STREET']:\n if not parsed['BOROUGH_CODE'] and not parsed['ZIP']:\n # iterate borocodes\n for x in range(1, 6):\n self._geocode(phn=parsed['PHN'], street=parsed['STREET'], borough_code=x)\n # try address with borough code if present\n elif parsed['BOROUGH_CODE']:\n self._geocode(phn=parsed['PHN'], street=parsed['STREET'], borough_code=parsed['BOROUGH_CODE'])\n # try address with zip code if present\n elif parsed['ZIP']:\n self._geocode(phn=parsed['PHN'], street=parsed['STREET'], zip=parsed['ZIP'])\n # validate and retrieve any addresses\n if len(self.similiar_names):\n for name in self.similiar_names:\n self._geocode(phn=parsed['PHN'], street=name['street'], borough_code=name['borough_code'])\n if None in self.results:\n self.results = list(filter(lambda v: v is not None, self.results))\n\n return self.results","def address1(self) -> pulumi.Output[Optional[str]]:\n return pulumi.get(self, \"address1\")","def address_str(self) -> str | None:\n pass","def _formatting_address_fields(self):\n return self._address_fields()","def format_address(**args):\n #Begin with the organisation and PO Box number, if applicable.\n address = ''.join([args[entry] + '\\n' \n for entry in ['organisation', 'PO box']\n if args.get(entry)])\n #Format building name/number components.\n address += format_building_components(*[args.get(x) for x in \n ['sub-building name', \n 'building name', \n 'building number',\n 'concatenation indicator']])\n #Add thoroughfare (if present), locality/town and postcode.\n address += ''.join([args[entry] + '\\n' \n for entry in ['dependent thoroughfare', \n 'thoroughfare',\n 'double dependent locality',\n 'dependent locality',\n 'town',\n 'postcode']\n if args.get(entry)])\n return address.strip()","def format_address(addresses: 'SequenceOrScalar[Address | str]') -> str:\n if isinstance(addresses, (Address, str)):\n return format_single_address(addresses)\n\n assert len(addresses) <= 50\n return ', '.join(format_single_address(a) for a in addresses)","def _check_address(self):\n for object_ in self.objects:\n if object_.object_name.endswith(' ЕС'):\n if object_.object_address[:6].isnumeric():\n object_.object_address = \\\n object_.object_address[:7] + \\\n object_.object_fed_subj + ', ' + \\\n object_.object_address[7:]","def get_address(query):\n address = \"Dis-moi, quel endroit tu cherches ?\"\n data = get_data(query)\n try:\n address_data = data[\"results\"][0][\"formatted_address\"]\n address = (\"Si je ne me trompe pas,\"\n \" l'adresse que tu cherche, c'est ... \" + address_data + \". Sinon\"\n \", dis-moi le nom de lieu exact\")\n except IndexError:\n address = \"Désolé, je n'ai pas compris quel endroit tu cherches ?\"\n finally:\n return address","def _get_address(self, address1, address2):\n return f'{address1}\\n{address2}' if address2 else address1","def readHouseAddresses():\n addressesRead = []\n with open(\"Files/HouseAddresses.txt\", 'r', encoding='utf8') as f:\n for line in f:\n if line == \"\\n\":\n continue\n details = line.split(\",\")\n address = []\n for detail in details:\n address.append(detail.rstrip('\\n').rstrip().lstrip())\n addressesRead.append(address)\n f.close()\n return addressesRead","def nomad_address():\n\n print(nomad.get_address())","def get_address(self, ):\n return self.get_parameter('address')","def street(self):\n return self._street","def maploc(loc):\n\n\n loc = REGEX['parens'].sub('', loc)\n loc = REGEX['and'].sub('', loc)\n loc = REGEX['num'].sub('', loc)\n\n \"\"\"\n 'parens' 'and' 'single' 'num' 'seeley' 'iab' 'brh'\n \"\"\"\n \"\"\"\n /* For non-street address, strip room numbers */\n if (!location.match(' Ave')) {\n location = location.replace(/LL[0-9]/g, '').replace(/[0-9]/g, '');\n }\n /* Some text substitutions */\n location = location.replace('Seeley W.', '').replace('International Affairs Building', '420 W 118th St').replace('Broadway Residence Hall', '2900 Broadway');\n\n \"\"\"\n return loc + ', New York, NY 10027'","def parse_address_from_geocoding_response(geocoded_data: dict) -> str:\n return geocoded_data[\n 'response'][\n 'GeoObjectCollection'][\n 'featureMember'][0][\n 'GeoObject'][\n 'metaDataProperty'][\n 'GeocoderMetaData'][\n 'text']","def test_get_order_address(self):\n pass","def load_addresses():\n with open('addresses.txt') as f:\n return [address.strip() for address in f.readlines()]","def do_getaddress(self,args):\n ppdict(bitstamp.get_depositaddress())","def _addressitem_from_line(line):\n sline = line.split(\"\\t\")\n if len(sline) < 2:\n raise IOError(\"Error parsing address from line. Malformed data.\")\n address = sline[0]\n name = sline[1]\n\n if len(sline) > 2:\n otherinfo = sline[2]\n else:\n otherinfo = \"\"\n if len(sline) > 3:\n extrainfo = sline[3]\n else:\n extrainfo = \"\"\n if len(sline) > 4:\n raw_misc = sline[4:]\n misc = _raw_misc_to_dict(raw_misc)\n else:\n misc = {}\n\n return AddressItem(\n _email_address=address,\n _name=name,\n _otherinfo=otherinfo,\n _extrainfo=extrainfo,\n **misc\n )","def _parse_location(self, response):\n loc_parts = [\n re.sub(r\"\\s+\", \" \", part).strip()\n for part in response.css(\n \"#contact-info .right-col-content .content *::text\"\n ).extract()\n if part.strip()\n ]\n return {\n \"name\": loc_parts[3],\n \"address\": \" \".join(loc_parts[4:]).replace(\" ,\", \",\").strip(),\n }"],"string":"[\n \"def _get_address(self, soup):\\n street, city, state, zipcode = None, None, None, None\\n try:\\n # property detail tag\\n street = soup.find('div', class_='main-address').get_text().strip()\\n # find address tag\\n address = soup.find('div', class_='c-address')\\n \\n # pattern for the address in this website\\n locality = address.find_all('span', class_='locality')\\n city = locality[0].get_text().strip()\\n if len(locality) > 1:\\n city = locality[1].get_text().strip()\\n state = address.find('span', class_='region').get_text().strip()\\n zipcode = address.find('span', class_='postal-code').get_text().strip()\\n return street, city, state, zipcode\\n except:\\n return street, city, state, zipcode\",\n \"def _get_address(self, soup):\\n\\n try:\\n # from the content tag, extract the tag that contains all the address info\\n address_tag = soup.find('div', class_='flex flex-col md:flex-row')\\n # street tag\\n street_tag = address_tag.find('h1', class_='h3')\\n # street information\\n street = street_tag.get_text()\\\\\\n .strip()\\\\\\n .replace(',', '')\\n # region tag \\n region_tag = address_tag.find('h5', class_='listing-card-location') \\\\\\n .get_text() \\\\\\n .strip() \\\\\\n .split(' ')\\n # city information\\n city = region_tag[0].replace(',', '').title()\\n # state information\\n state = region_tag[1]\\n # zipcode information\\n zipcode = region_tag[2]\\n\\n return street, city, state, zipcode\\n \\n except:\\n # return None if any of the above parts failed\\n # if there's any part that's missing in the address part,\\n # the whole address becomes useless\\n return None, None, None, None\",\n \"def parse_address_street(address_str, address_zip_us_re, address_zip_us_lax_re):\\n address = {}\\n errors = []\\n parts = address_str.split('$')\\n if DEBUG:\\n address['debug_address_str'] = address_str\\n address['debug_part_1'] = parts[0]\\n address['debug_part_last'] = parts[-1]\\n address['debug_length'] = len(parts)\\n #if len(parts) == 1:\\n #print('cannot split: {}: {}'.format(debug_type, address_str))\\n match = re.search(address_zip_us_re, parts[-1])\\n if match:\\n if DEBUG:\\n address['debug_parser'] = 'A'\\n address['city'] = match.group(1)\\n address['region'] = match.group(2).upper()\\n address['postalCode'] = match.group(3)\\n address['countryId'] = 'US'\\n if len(parts) == 2:\\n if DEBUG:\\n address['debug_parser'] = 'B'\\n address['addressLine1'] = parts[0]\\n else:\\n if len(parts) == 3:\\n if DEBUG:\\n address['debug_parser'] = 'C'\\n address['addressLine1'] = parts[0]\\n if parts[0] != parts[1]:\\n if DEBUG:\\n address['debug_parser'] = 'D'\\n address['addressLine2'] = parts[1]\\n else:\\n match2 = re.search(address_zip_us_lax_re, address_str)\\n if match2:\\n if DEBUG:\\n address['debug_parser'] = 'E'\\n address['region'] = match2.group(2).upper()\\n address['postalCode'] = match2.group(3)\\n address['countryId'] = 'US'\\n # FIXME: Cannot reliably parse the remainder for city and street address\\n errors.append('Partial parse street address: {}'.format(address_str))\\n address['addressLine1'] = match2.group(1)\\n else:\\n # This is the remainder that we could not parse.\\n # So just put it all into \\\"addressLine1\\\" to be manually adjusted later.\\n if DEBUG:\\n address['debug_parser'] = 'F'\\n errors.append('Cannot parse street address: {}'.format(address_str))\\n address['addressLine1'] = address_str\\n return (address, errors)\",\n \"def get_address(self, list_item):\\n Address = namedtuple('Address', ['addr', 'city', 'state', 'zip'])\\n extract = [text for text in list_item.find('address').stripped_strings]\\n\\n # Sometimes a street address is not given\\n if len(extract) == 1:\\n addr, rest = None, extract[0]\\n else:\\n addr, rest = extract\\n\\n city, rest = rest.split(',')\\n state, zip = rest.strip().split(' ')\\n return Address(addr, city, state, zip)\",\n \"def _get_address(self, address_tag, hdr):\\n\\n # try to find all the span tags in the address tag, the span tags\\n # include all the address information we need \\n try:\\n elements = address_tag.find_all('span')\\n\\n # scrape the text out of the span tags and remove\\n # all the whitespaces and punctuation marks\\n address = elements[0].get_text()\\\\\\n .replace(',','')\\\\\\n .strip()\\n city = elements[1].get_text().strip()\\n state = elements[2].get_text().strip()\\n zipcode = elements[3].get_text().strip()\\n return address, city, state, zipcode\\n # however, sometimes the address tag does not include the street\\n # info, in this case, use the text in the header tag, which serves\\n # as a replacement for the address \\n except:\\n address = hdr.get_text()\\n elements = address_tag.find_all('span')\\n city = elements[0].get_text()\\\\\\n .replace(',','')\\\\\\n .strip()\\n state = elements[1].get_text().strip()\\n zipcode = elements[2].get_text().strip()\\n return address, city, state, zipcode\",\n \"def get_str_address(address):\\n return \\\\\\n get_ob_value_primitive(address, 'AddrLine1', exception_return_value='') + ' ' + \\\\\\n get_ob_value_primitive(address, 'AddrLine2', exception_return_value='') + ' ' + \\\\\\n get_ob_value_primitive(address, 'AddrLine3', exception_return_value='') + ', ' + \\\\\\n get_ob_value_primitive(address, 'City', exception_return_value='') + ' ' + \\\\\\n get_ob_value_primitive(address, 'County', exception_return_value='') + ' ' + \\\\\\n get_ob_value_primitive(address, 'StateProvince', exception_return_value='') + ' ' + \\\\\\n get_ob_value_primitive(address, 'ZipPostalCode', exception_return_value='')\",\n \"def street_addresses(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\\n return pulumi.get(self, \\\"street_addresses\\\")\",\n \"def street_addresses(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\\n return pulumi.get(self, \\\"street_addresses\\\")\",\n \"def street_addresses(self) -> pulumi.Output[Optional[Sequence[str]]]:\\n return pulumi.get(self, \\\"street_addresses\\\")\",\n \"def __getAddresses(parsed: BeautifulSoup) -> list:\\n\\n # Addresses container\\n address_divs = parsed.find_all('div', class_='mailer')\\n\\n # Building RegEx for phone number\\n # The following RegEx extracts phone numbers in the following formats:\\n # 1. (###) ###-####\\n # 2. ###-###-####\\n # 3. ##########\\n phone_number_regex = re.compile(\\n r'(\\\\(\\\\d{3}\\\\) \\\\d{3}-\\\\d{4}|\\\\d{3}-\\\\d{3}-\\\\d{4}|\\\\d{10})')\\n\\n # List for final addresses\\n addresses = list()\\n\\n for address in address_divs:\\n # Create dict for address\\n address_parsed = dict()\\n # Split text by newline\\n address_items = address.text.split('\\\\n')\\n # Removing leading and trailing spaces\\n address_items = [i.strip() for i in address_items]\\n\\n # Variable to store street address\\n street_address = ''\\n\\n # Iterate through each line\\n for idx, address_item in enumerate(address_items):\\n # First line is address type\\n if idx == 0:\\n address_parsed['type'] = address_item\\n continue\\n\\n # Check if line has phone number\\n phone_matches = phone_number_regex.findall(address_item)\\n if len(phone_matches) == 1:\\n # Stripping non-digit characters from phone number\\n phone_number = re.sub('[^0-9]', '', phone_matches[0])\\n address_parsed['phone'] = phone_number\\n continue\\n \\n # If no number, add to address line\\n street_address += address_item.strip() + ' '\\n \\n # Adding street address to parsed address\\n address_parsed['street_address'] = street_address.strip()\\n\\n # Adding parsed address to addresses master list\\n addresses += [address_parsed]\\n\\n return addresses\",\n \"def get_apartment_address(self, soup, apartment_dict):\\n\\n info_class = soup.find_all('div', {'class': 'info'})\\n if info_class and len(info_class) > 0:\\n info_class = info_class[0]\\n address = info_class.find('h2').text.strip()\\n\\n from parse import parse\\n address = parse(\\\"Location: {}\\\", address)[0]\\n apartment_dict['address'] = address\\n else:\\n logging.warning(\\\"Failed to parse apartment address\\\")\\n return\",\n \"def get_address(self):\\n return self.address.line[0]+\\\", \\\"+self.address.city+\\\", \\\"+self.address.state+\\\", \\\"+self.address.country\",\n \"def addresses(self):\\n if 'Ward Matters' in self.topics or 'City Matters' in self.topics:\\n stname_pattern = \\\"(\\\\S*[a-z]\\\\S*\\\\s){1,4}?\\\"\\n sttype_pattern = \\\"(ave|blvd|cres|ct|dr|hwy|ln|pkwy|pl|plz|rd|row|sq|st|ter|way)\\\"\\n st_pattern = stname_pattern + sttype_pattern\\n\\n addr_pattern = \\\"(\\\\d(\\\\d|-)*\\\\s%s)\\\" %st_pattern\\n intersec_pattern = exp = \\\"((?<=\\\\sat\\\\s)%s\\\\s?and\\\\s?%s)\\\" %(st_pattern, st_pattern)\\n\\n pattern = \\\"(%s|%s)\\\" %(addr_pattern, intersec_pattern)\\n\\n matches = re.findall(pattern, self.description, re.IGNORECASE)\\n\\n addresses = [m[0] for m in matches]\\n return addresses\\n\\n return []\",\n \"def _get_address(self, jdict):\\n \\n try:\\n # access the location info dictionary\\n loc_dict = jdict['props']['homeDetails']['location']\\n state = loc_dict['stateCode']\\n city = loc_dict['city']\\n zipcode = loc_dict['zipCode']\\n street = loc_dict['formattedLocation']\\n return street, city, state, zipcode\\n except:\\n return None, None, None, None\",\n \"def street_address(self):\\n\\t\\tif self.address2:\\n\\t\\t\\treturn '{}, {}'.format(self.address, self.address2)\\n\\t\\treturn self.address\",\n \"def street_address1(self) -> str:\\n return pulumi.get(self, \\\"street_address1\\\")\",\n \"def address_regex(self) -> Any:\",\n \"def get_address(address: str) -> Tuple[str, str, str]:\\n\\n # Try to geocode the address as given\\n g = geocoder.osm(address)\\n\\n if g.json is not None:\\n\\n # TODO this is inefficient and hacky\\n\\n # First thing we attempt if the result isn't complete is just to\\n # add the housenumber (often the issue).\\n if not good_geocoder_result(g.json):\\n g.json['housenumber'] = usaddress.tag(address)[0]['AddressNumber']\\n\\n # If the result is now good, return it\\n if good_geocoder_result(g.json):\\n\\n # Geocoding was successful. Return the result\\n return (\\n # First part is a nicely formatted address\\n f\\\"{g.json['housenumber']} {g.json['street']}, {g.json['city']}, {g.json['state']} {g.json['postal']}\\\",\\n # Second is the latitude\\n g.json['lat'],\\n # And third is the longitude\\n g.json['lng']\\n )\\n\\n # Geocoding was unsuccessful.\\n # Let's try to create a cleaner address by first parsing out the pieces we need, then try again.\\n \\n # Parsing the address components...\\n parsed, addr_type = usaddress.tag(address)\\n if addr_type != \\\"Street Address\\\":\\n raise ValueError(f\\\"Address could not be properly parsed. Resulting type: {addr_type}. Result: \\\\n{parsed}\\\")\\n \\n # Trim off any whitespace from the parsed components.\\n for part in parsed:\\n parsed[part] = parsed[part].strip()\\n\\n reqd_address_parts = ['AddressNumber', 'StreetName', 'PlaceName']\\n if any(address_part not in parsed for address_part in reqd_address_parts):\\n raise ValueError(f\\\"The address must have at least a house number, street, and city.\\\")\\n \\n # Initialize the resulting address string with the address number (aka house/street number)\\n new_address = parsed['AddressNumber']\\n \\n # If the streetname is just a number, make it ordinal\\n if parsed['StreetName'].isnumeric():\\n parsed['StreetName'] = ordinal(parsed['StreetName'])\\n \\n # Get the whole street name\\n for k, v in [(k, v) for k, v in parsed.items() if k.startswith(\\\"StreetName\\\")]:\\n new_address += f\\\" {v}\\\"\\n \\n # Add the city...\\n new_address += f\\\", {parsed['PlaceName']}\\\"\\n # Add the state, if it exists\\n if 'StateName' in parsed:\\n new_address += f\\\", {parsed['StateName']}\\\"\\n # And the zip code, if it exists\\n if 'ZipCode' in parsed:\\n new_address += f\\\" {parsed['ZipCode']}\\\"\\n \\n # Now try to geocode this improved address\\n g = geocoder.osm(new_address)\\n\\n if g.json is not None:\\n\\n # Geocoding was successful. Return the result\\n return (\\n # First part is a nicely formatted address\\n f\\\"{g.json['housenumber']} {g.json['street']}, {g.json['city']}, {g.json['state']} {g.json['postal']}\\\",\\n # Second is the latitude\\n g.json['lat'],\\n # And third is the longitude\\n g.json['lng']\\n )\\n \\n # Still can't geocode the address. Throw an error\\n else:\\n raise ValueError(f\\\"Could not geocode this address: {address}\\\")\",\n \"def toAddr(self, addressString: unicode) -> ghidra.program.model.address.Address:\\n ...\",\n \"def getAddress(user):\",\n \"def street_address():\\r\\n\\r\\n return _random.choice(\\r\\n [\\r\\n '%d-%d %s' % (\\r\\n _random.randrange(999),\\r\\n _random.randrange(999),\\r\\n street_name()\\r\\n ),\\r\\n '%d %s' % (\\r\\n _random.randrange(999),\\r\\n street_name()\\r\\n ),\\r\\n '%s %d, %s' % (\\r\\n 'P.O. Box',\\r\\n _random.randrange(999),\\r\\n street_name()\\r\\n )\\r\\n ]\\r\\n )\",\n \"def address(self):\\n return str(self.street) + str(self.city) + str(self.state) + str(self.zipcode)\",\n \"def parse_address_campus(address_str, address_campus_re, address_campus_room_re):\\n address = {}\\n errors = []\\n if '$' not in address_str:\\n match = re.search(address_campus_room_re, address_str)\\n if match:\\n address['addressLine1'] = match.group(1)\\n else:\\n # This leftover is either an erroneous email address or a building name\\n if '@' in address_str:\\n errors.append('Campus address seems to be email: {}'.format(address_str))\\n #FIXME: Should this be saved to addressLine1 anyway.\\n else:\\n # It seems to be a building address\\n address['addressLine2'] = address_str\\n else:\\n match = re.search(address_campus_re, address_str)\\n if match:\\n address['addressLine2'] = match.group(1)\\n address['addressLine1'] = match.group(2)\\n #else:\\n # FIXME: here just for debug\\n #errors.append('Cannot parse campus address: {}'.format(address_str))\\n return (address, errors)\",\n \"def find_street(self, text):\\n text = text.replace('\\\"', '')\\n result = ''\\n textarr = text.split()\\n for i in range(0, len(textarr)):\\n if textarr[i] in self.streets:\\n # decide if the result is nearly empty or not to add the word with or without a space\\n if len(result) < 2:\\n result = result + textarr[i]\\n else:\\n result = result + ' ' + textarr[i]\\n\\n if result in self.streets:\\n\\n # ends the function if the street is found\\n if (result + '\\\\n') in self.streets and result != 'straße' and result != 'in' and result != '' \\\\\\n and result != 'ein' and result != 'der' and result != 'und' and result != ' ' \\\\\\n and result != 'an':\\n return result\\n else:\\n result_list = result.split()\\n del result_list[0]\\n result = ' '.join(w for w in result_list)\\n result = result.lstrip(' ')\\n # ends the function if the street is found\\n if (result + '\\\\n') in self.streets and result != 'straße' and result != 'in' and result != '' \\\\\\n and result != 'ein' and result != 'der' and result != 'und' and result != ' ' \\\\\\n and result != 'ist' and result != 'an':\\n return result\\n elif i == len(textarr)-1:\\n result_list = result.split()\\n del result_list[0]\\n result = ' '.join(w for w in result_list).lstrip(' ')\\n if (result + '\\\\n') in self.streets and result != 'straße' and result != 'in' and result != '' \\\\\\n and result != 'ein' and result != 'der' and result != 'und' and result != ' ' \\\\\\n and result != 'ist' and result != 'an':\\n return result\\n print(result)\\n return None\",\n \"def _compute_adress(self):\\r\\n\\t\\tfor leads in self:\\r\\n\\t\\t\\tleads.address = leads.street + \\\" \\\" + leads.street2\",\n \"def address_str(self):\\n return self._plrevgeoloc.addressString\",\n \"def extract_addresses(addresses):\\n \\n # Since lists are iterated over in Python in an orderly fashion, \\n # put 'Input Reg' before 'Input' such that the 'Reg' in an address \\n # with an 'Input Reg' prefix doesn't get left behind\\n address_prefixes= [\\\"Input Reg \\\", \\\"Holding Reg \\\", \\\"Input \\\", \\\"Coil \\\"]\\n\\n \\n for idx,address in enumerate(addresses):\\n # Replace prefixes with empty string\\n for prefix in address_prefixes:\\n addresses[idx]=addresses[idx].replace(prefix, \\\"\\\")\\n # Extract numeral\\n try:\\n addresses[idx]= int(addresses[idx])\\n except:\\n logging.warning(\\\"Invalid modbus address suppied at index {}\\\".format(idx))\\n\\n # Return\\n return addresses\",\n \"def addresses(self) -> pulumi.Input[Sequence[pulumi.Input[str]]]:\\n return pulumi.get(self, \\\"addresses\\\")\",\n \"def parse_address(address: str) -> OrderedDict[str, str]:\\n\\n address = address.replace(\\\"\\\\n\\\", \\\", \\\")\\n\\n parsed_address, address_type = usaddress.tag(address)\\n if address_type != \\\"Street Address\\\":\\n logger.warning(\\n f\\\"Couldn't parse address '{address}' of type {address_type}; best guess: {parsed_address}\\\"\\n )\\n\\n # Fixup: At least one address has \\\"WI, USA\\\" in the \\\"StateName\\\" component.\\n # Strip non-state components\\n if parsed_address.get(\\\"StateName\\\"):\\n parsed_address[\\\"StateName\\\"] = parsed_address[\\\"StateName\\\"].partition(\\\",\\\")[0]\\n\\n return parsed_address\",\n \"def integrated_address_regex(self) -> Any:\",\n \"def search_address(query: str) -> Tuple[int, str]:\\n\\n url = 'https://api.n1.ru/api/v1/geo/geocoder/with_cities/'\\n params = _search_params.copy()\\n params['q'] = query\\n\\n try:\\n r = requests.get(url, params=params, headers=_headers)\\n response = r.json()\\n\\n if not 'result' in response or not response['result']:\\n raise NotFoundException('Result not found or empty.')\\n \\n address = None\\n house_number = query.split(',')[-1].strip()\\n for x in response['result']:\\n if x['name_ru'].lower() == house_number:\\n address = x\\n break\\n \\n if address is None:\\n raise NotFoundException(f'Not found house number {house_number} in result: {response[\\\"result\\\"]}')\\n \\n return address['street']['id'], address['name_ru']\\n except requests.RequestException as e:\\n raise ParserException(f'Fail make request. query: {query}') from e\\n except NotFoundException as e:\\n raise ParserException('Invalid result.') from e\\n except (KeyError, IndexError) as e:\\n raise ParserException(f'Fail get street id or house number. value: {response[\\\"result\\\"]}') from e\",\n \"def street_parser(*street_data):\\n\\n # parsing tuples\\n if len(street_data) == 2:\\n if not isinstance(street_data[0], str) and not isinstance(street_data[1], str):\\n raise WrongInput(\\\"Invalid format\\\")\\n # street name as the tuple's first item\\n strname, strnumber = street_data\\n # street number as the tuple's first item\\n if street_data[0][0] in digits:\\n strname, strnumber = strnumber, strname\\n\\n # parsing strings\\n else:\\n if not isinstance(street_data[0], str):\\n raise WrongInput(\\\"Invalid format\\\")\\n if not street_data[0]:\\n raise WrongInput(\\\"Input cannot be blank\\\")\\n\\n # string starting with street number\\n if street_data[0][0] in digits:\\n street_pattern = re.compile(r'''\\n ^ # beginning of string\\n (\\\\d+) # street number is any number of digits\\n \\\\W+ # separator\\n (\\\\w+\\\\W*\\\\w*\\\\W*) # street name is one or more words with optional separators\\n $ # end of string\\n ''', re.VERBOSE)\\n street_obj = street_pattern.search(street_data[0])\\n strnumber, strname = street_obj.groups()\\n\\n # string starting with street name\\n else:\\n street_pattern = re.compile(r'''\\n ^ # beginning of string\\n (\\\\w+\\\\W*\\\\w*\\\\s*) # street name is one or more words with optional separators\\n \\\\W+ # separator\\n (\\\\d+) # street number is any number of digits\\n $ # end of string\\n ''', re.VERBOSE)\\n street_obj = street_pattern.search(street_data[0])\\n (strname, strnumber) = street_obj.groups()\\n\\n # replace specific words in street name with their abbreviates\\n strname = strname.lower()\\n special = {r'\\\\baleje\\\\b': 'Al.', r'\\\\bavenue\\\\b': 'Av.', r'\\\\broad\\\\b': 'Rd.', r'\\\\bsquare\\\\b': 'Sq.',\\n r'\\\\bstreet\\\\b': 'St.', r'\\\\bdrive\\\\b': 'Dr.'}\\n for key in special:\\n strname = re.sub(key, special[key], strname)\\n return strname.title(), strnumber\",\n \"def get_address(self):\\n\\n return \\\"{}\\\\n{}\\\\n{},\\\\n{},\\\\n{}\\\".format(\\n self.address_line_1, self.city, self.state, self.postal_code, self.country\\n )\",\n \"def listToAddr(location):\\n\\n start_time = time.time()\\n wk = [key for key in location.keys() if key in ('street', 'house_num', 'suburb', 'city', 'province', 'country', 'pos_code')]\\n address = re.sub(',', '', ', '.join(value for value in dict(zip(wk, [location[k] for k in wk])).values() if value), 1)\\n print('--- Tiempo de ejecucion listToAddr: {} segundos ---'.format((time.time() - start_time)))\\n return address\",\n \"def address():\\n # We start with generating the street name. For this we choose\\n # between the most common prefixes and our own prefixes\\n prefix = dice.randint(1, 100)\\n if prefix <= 10: # 10%\\n prefix = \\\"Haupt\\\"\\n elif prefix <= 18: # 8%\\n prefix = \\\"Schul\\\"\\n elif prefix <= 25: # 7%\\n prefix = \\\"Garten\\\"\\n elif prefix <= 32: # 7%\\n prefix = \\\"Dorf\\\"\\n elif prefix <= 39: # 7%\\n prefix = \\\"Bahnhof\\\"\\n elif prefix <= 46: # 7%\\n prefix = \\\"Wiesen\\\"\\n elif prefix <= 52: # 6%\\n prefix = \\\"Berg\\\"\\n elif prefix <= 56: # 4%\\n prefix = \\\"Kirch\\\"\\n elif prefix <= 60: # 4%\\n prefix = \\\"Wald\\\"\\n elif prefix <= 64: # 4%\\n prefix = \\\"Ring\\\"\\n else:\\n prefix = dice.choice(names.prefix)\\n\\n # Now we can add the suffix\\n suffix = dice.randint(1, 100)\\n if suffix <= 78:\\n suffix = \\\"straße\\\"\\n elif suffix <= 96:\\n suffix = \\\"weg\\\"\\n elif suffix <= 98:\\n suffix = \\\"allee\\\"\\n elif suffix == 99:\\n suffix = \\\"ring\\\"\\n elif suffix == 100:\\n suffix = \\\"platz\\\"\\n\\n # When we have a city name as prefix, we need to capitalize the\\n # suffix since it will be two words\\n if prefix[-1] == \\\" \\\":\\n suffix = suffix.capitalize()\\n\\n # Now we can add them together\\n street = prefix + suffix\\n\\n # We need a house number as well. In Germany most numbers have\\n # between one and four digits, so we will use this as base. Lower\\n # numbers are more common, so we'll give it a 10% probability of\\n # using 3 digits and 1% of using 4 digits\\n digits = dice.randint(1, 100)\\n if digits == 100:\\n house_number = str(dice.randint(1000, 9999))\\n elif digits >= 90:\\n house_number = str(dice.randint(100, 999))\\n else:\\n house_number = str(dice.randint(1, 99))\\n address_full = street + \\\" \\\" + house_number\\n return address_full\",\n \"def street_address(full_address):\\n full_address = normalise_address(full_address)\\n capture_groups = ROAD_PATTERN.findall(full_address)\\n if capture_groups:\\n name, _type = process_capture_groups(capture_groups)\\n return '{} {}'.format(name, _type)\\n return ''\",\n \"def build_address(record):\\n pass\",\n \"def get_postal_address(self):\\n # print \\\"getting postal address\\\"\\n address = []\\n if self.name:\\n address.append(self.name)\\n if self.number:\\n address.append(self.number + \\\" \\\" + self.street) # because building numbers and street names go on the same line\\n elif self.street:\\n address.append(self.street)\\n if self.additional_street_address:\\n address.append(self.additional_street_address)\\n if self.site.post_town:\\n address.append(self.site.post_town + \\\" \\\" + self.postcode)\\n elif self.postcode:\\n address.append(self.postcode)\\n return address\",\n \"def street_address(self):\\n if \\\"streetAddress\\\" in self._prop_dict:\\n return self._prop_dict[\\\"streetAddress\\\"]\\n else:\\n return None\",\n \"def parse_address(self, address: str) -> Optional[Address]:\\n raise NotImplemented\",\n \"def parse_building_address(addr_string):\\n addr_string = re.sub(_regexp, '', addr_string)\\n addr_string = re.sub(r'(?P<key>[a-zA-Z]+)', _replace_dir, addr_string)\\n addr_string = re.sub(r'(?P<key>[a-zA-Z]+)', _replace_suffix, addr_string)\\n addr_string = re.sub(_regexp_extra_space, ' ', addr_string)\\n return addr_string.strip().upper()\",\n \"def find(self, text: unicode) -> ghidra.program.model.address.Address:\\n ...\",\n \"def find_places(query):\\n parts = str(query).split(' ')\\n for i, p in enumerate(parts):\\n p = p.replace('-', ' ').strip()\\n try:\\n postal_code = int(p)\\n if len(postal_code) == 4:\\n print(postal_code, parts[i+1])\\n # Check \\n #response = get_osm_location(\\\"{postal_code} {name}\\\")\\n #lon = response['lon']\\n #lat = response['lat']\\n #poly = \\n except Exception as e:\\n continue\",\n \"def process_address(text):\\n return sanitize(text[9:])\",\n \"def get_address_string(self):\\n output = ''\\n if self.address_line_1:\\n output += '{}'.format(self.address_line_1)\\n if self.address_line_2:\\n output += ', {}'.format(self.address_line_2)\\n if self.city:\\n output += ', {}'.format(self.city)\\n if self.state:\\n output += ', {}'.format(self.state)\\n if self.zipcode:\\n output += ' {}'.format(self.zipcode)\\n return output\",\n \"def test_get_address(self):\\r\\n note_data = self.tape.dataframe.iloc[0]\\r\\n note = self.tape._get_note_object(note_data)\\r\\n eq_(note.get_address(), '8 Brown St, Methuen, MA 01844')\",\n \"def extract_city(full_address):\\n full_address = full_address.strip()\\n last_comma_index = full_address.rindex(\\\",\\\")\\n mid_comma_index = full_address.rindex(\\\",\\\", 0, last_comma_index)\\n city = full_address[mid_comma_index + 1 : last_comma_index]\\n city = city.strip()\\n return city\",\n \"def fetch_address(cpr: str) -> str:\\n\\n return \\\"Åbogade 15, 8200 Aarhus N\\\"\",\n \"def capture_address_element(regex_object, full_address):\\n full_address = normalise_address(full_address)\\n capture_groups = regex_object.search(full_address)\\n if capture_groups:\\n return capture_groups.group(0)\\n return ''\",\n \"def __str__(self):\\n return format_address(**self._get_elements())\",\n \"def street_address2(self) -> Optional[str]:\\n return pulumi.get(self, \\\"street_address2\\\")\",\n \"def test_address_other_parameters():\\n address = lob.Address.create(name='Siddharth Saha', address_line1='104, Printing Boulevard',\\n address_line2='Sunset Town', email='sidchilling@gmail.com',\\n address_city='Boston', address_state='MA', address_country='US',\\n address_zip='12345')\\n print address.to_dict()\",\n \"def address(self):\\n return self.data.get('address')\",\n \"def addresses(self) -> \\\"List[str]\\\":\\n return self._attrs.get(\\\"addresses\\\")\",\n \"def addresses(self) -> \\\"List[str]\\\":\\n return self._attrs.get(\\\"addresses\\\")\",\n \"def addresses(self) -> \\\"List[str]\\\":\\n return self._attrs.get(\\\"addresses\\\")\",\n \"def postcode(full_address):\\n return capture_address_element(POSTCODE_PATTERN, full_address)\",\n \"def address1(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"address1\\\")\",\n \"def address1(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"address1\\\")\",\n \"def extract_zipcode(full_address):\\n full_address = full_address.strip()\\n last_space_index = full_address.rindex(\\\" \\\")\\n zipcode = full_address[last_space_index + 1 : ]\\n return zipcode\",\n \"def extract_state(full_address):\\n full_address = full_address.strip()\\n last_comma_index = full_address.rindex(\\\",\\\")\\n last_space_index = full_address.rindex(\\\" \\\")\\n state = full_address[last_comma_index + 1 : last_space_index]\\n state = state.strip()\\n return state\",\n \"def street_address3(self) -> Optional[str]:\\n return pulumi.get(self, \\\"street_address3\\\")\",\n \"def get_address(post_content):\\n post_address = post_content.find(\\\"div\\\", {\\\"class\\\": \\\"mapaddress\\\"})\\n address_attr = {\\\"address\\\": \\\"\\\"}\\n if post_address is not None:\\n address_attr[\\\"address\\\"] = post_address.text\\n return address_attr\",\n \"def filter_string(self):\\n logger.info(\\\"Information Gathering Finished!\\\")\\n\\n pattern_street = re.compile(r'[A-Za-z]+\\\\s?\\\\w+(?=\\\\s[Nn]o\\\\s\\\\d+$) |'\\n r' [A-Za-z]+\\\\s?\\\\w+\\\\s?[A-Za-z]+\\\\s?[A-Za-z]+',\\n re.X) # street pattern\\n match_street = pattern_street.search(self.string)\\n\\n # If there are no house numbers provided in the input file,\\n # print(not found) in the output JSON file\\n numbers_instring = re.findall(r'\\\\d+', self.string) # digit counts in given string\\n\\n if len(numbers_instring) > 0:\\n # In most cases we have: \\\"no\\\" followed by some digits\\n pattern_housenumber = re.compile(r'(\\\\d+\\\\s?[A-Za-z]?$) |'\\n r' (^\\\\d+) |'\\n r' [Nn]o+[\\\\s?]+[0-9]+$',\\n re.X) # house number pattern\\n match_housenumber = pattern_housenumber.search(self.string)\\n fin_housenumber = match_housenumber[0]\\n else:\\n match_housenumber = [\\\"not found\\\"]\\n\\n fin_housenumber = match_housenumber[0]\\n fin_street = match_street[0]\\n print(\\\"street: \\\", fin_street)\\n print(\\\"housenumber: \\\", fin_housenumber)\\n return {'street': fin_street, 'housenumber': fin_housenumber}\",\n \"def address_details(self) -> 'outputs.AddressDetailsResponse':\\n return pulumi.get(self, \\\"address_details\\\")\",\n \"def address_line_1(self):\\n return \\\"{} {} {}\\\".format(\\n self.fake.randomize_nb_elements(1000),\\n self.fake.last_name(),\\n self.fake.random_element(elements=STREET_SUFFIX)\\n )\",\n \"def unpack_addresses(self, addresses_to_test):\\n if len(addresses_to_test) == 0:\\n raise ValueError(\\n \\\"There were no arguments passed to the function. That is wrong. Closing\\\"\\n )\\n\\n return_addresses = []\\n for address in addresses_to_test:\\n if \\\"/\\\" in address:\\n try:\\n six_or_four = ipaddress.ip_network(address)\\n except ValueError:\\n print(f\\\"{address} is not a valid subnet. Skipping.\\\")\\n continue\\n for address_host in six_or_four.hosts():\\n return_addresses.append(str(address_host))\\n else:\\n try:\\n ipaddress.ip_address(address)\\n except ValueError:\\n print(f\\\"{address} is not a valid address. Skipping.\\\")\\n continue\\n return_addresses.append(str(address))\\n for address in return_addresses:\\n try:\\n ipaddress.ip_address(address)\\n except ValueError:\\n raise ValueError(f\\\"{address} is not an IPv4/v6 address. Shutting Down\\\")\\n if len(return_addresses) > 0:\\n return return_addresses\\n else:\\n raise ValueError(\\\"No usable addresses to scan\\\")\",\n \"def address(self) -> tuple[str, int]:\",\n \"def format_address(line1, line2, city, state, zipcode):\\n\\t\\n\\tstreetlines = line1\\n\\tcityline = city\\n\\t\\n\\tif len(streetlines) > 0 and len(line2) > 0:\\n\\t\\tstreetlines += \\\"\\\\n\\\"\\n\\t\\n\\tif len(cityline) > 0 and len(state) > 0:\\n\\t\\tcityline += \\\", \\\"\\n\\t\\n\\tstreetlines += line2\\n\\tcityline += state\\n\\t\\n\\treturn \\\"\\\\n\\\".join([streetlines, cityline, zipcode])\",\n \"def street(self):\\n if \\\"street\\\" in self._prop_dict:\\n return self._prop_dict[\\\"street\\\"]\\n else:\\n return None\",\n \"def _cleanupAddress(self, address):\\n clean = []\\n \\n # This is sort of a desultory effort but I'm not convinced \\n # that these cleanups will actually result in cleaner searches\\n for word in address.split(None):\\n lower = word.lower()\\n \\n # Some things we just nuke\\n if lower == 'at': continue\\n elif lower == 'btw': continue\\n elif lower == 'btwn': continue\\n elif word.isdigit(): continue\\n \\n # Or we make substitiutions\\n elif lower == 'st' or lower == 'st.':\\n word = 'Street'\\n elif lower == 'ave' or lower == 'ave.':\\n word = 'Avenue'\\n elif lower == 'pl' or lower == 'pl.':\\n word = 'Place'\\n elif lower == 'n': word = 'North'\\n elif lower == 'e': word = 'East'\\n elif lower == 's': word = 'South'\\n elif lower == 'w': word = 'West'\\n \\n clean.append(word)\\n return ' '.join(clean)\",\n \"def get_address(self):\\n \\n if \\\"'\\\" in self.data.get(\\\"AddressInfo\\\").get(\\\"AddressLine1\\\") :\\n self.data.get(\\\"AddressInfo\\\").get(\\\"AddressLine1\\\").replace(\\\"'\\\",\\\"\\\")\\n\\n return self.data.get(\\\"AddressInfo\\\").get(\\\"AddressLine1\\\")\",\n \"def getRestaurantAddresses(restaurants):\\n addresslist = []\\n for rest in restaurants:\\n if 'address' in rest:\\n addressstring = str(rest['address']) + ' ' + str(rest['city'])\\n addresslist.append(addressstring)\\n\\n # pprint.pprint(addresslist)\\n return addresslist\",\n \"def normalize_address(patched_address: OrderedDict[str, str]) -> location.Address:\\n\\n address_kwargs = {\\n # \\\"street1\\\",\\n # \\\"city\\\",\\n # \\\"state\\\",\\n # \\\"zip\\\"\\n }\\n street_buffer: List[str] = []\\n suite_buffer: List[str] = []\\n while len(patched_address) > 0:\\n component, value = patched_address.popitem(last=False)\\n if component == \\\"PlaceName\\\":\\n address_kwargs[\\\"city\\\"] = value\\n elif component == \\\"StateName\\\":\\n address_kwargs[\\\"state\\\"] = value\\n elif component == \\\"ZipCode\\\":\\n address_kwargs[\\\"zip\\\"] = value\\n elif component == \\\"OccupancyType\\\":\\n suite_buffer.append(value)\\n elif component == \\\"OccupancyIdentifier\\\":\\n suite_buffer.append(value)\\n else:\\n street_buffer.append(value)\\n address_kwargs[\\\"street1\\\"] = \\\" \\\".join(street_buffer)\\n if len(suite_buffer) > 0:\\n address_kwargs[\\\"street2\\\"] = \\\" \\\".join(suite_buffer)\\n\\n return location.Address(**address_kwargs)\",\n \"def test_address(residence):\\n # We start by creating our result list\\n result = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\\n # Then we split our given string so we only have the address left\\n residence = residence.split(\\\", \\\")[1]\\n\\n # First we check for the prefix and increase the according value\\n if \\\"Haupt\\\" in residence:\\n result[0] = 1\\n elif \\\"Schul\\\" in residence:\\n result[1] = 1\\n elif \\\"Garten\\\" in residence:\\n result[2] = 1\\n elif \\\"Dorf\\\" in residence:\\n result[3] = 1\\n elif \\\"Bahnhof\\\" in residence:\\n result[4] = 1\\n elif \\\"Wiesen\\\" in residence:\\n result[5] = 1\\n elif \\\"Berg\\\" in residence and residence[4] in (\\\"s\\\", \\\"w\\\", \\\"a\\\", \\\"r\\\", \\\"p\\\"):\\n result[6] = 1\\n elif \\\"Kirch\\\" in residence:\\n result[7] = 1\\n elif \\\"Wald\\\" in residence:\\n result[8] = 1\\n elif \\\"Ring\\\" == residence[0:4]:\\n result[9] = 1\\n else:\\n result[10] = 1\\n\\n # Now we check the suffix\\n if \\\"straße\\\" in residence or \\\"Straße\\\" in residence:\\n result[11] = 1\\n elif \\\"Weg\\\" in residence or \\\"weg\\\" in residence:\\n result[12] = 1\\n elif \\\"Allee\\\" in residence or \\\"allee\\\" in residence:\\n result[13] = 1\\n elif \\\"platz\\\" in residence or \\\"platz\\\" in residence:\\n result[15] = 1\\n else:\\n result[14] = 1\\n\\n # And now we check the number\\n number = int(residence.split()[-1])\\n if number > 999:\\n result[16] = 1\\n elif number > 99:\\n result[17] = 1\\n else:\\n result[18] = 1\\n\\n return result\",\n \"def get_address(self):\\n entity = self\\n if entity.abstract_entity:\\n entity = self.get_real_ancestor()\\n if entity:\\n address = entity.get_institutional_address()\\n building = entity.get_building()\\n if building:\\n if entity.building_recapitulates_entity_name: \\n address.extend(building.get_postal_address()[1:])\\n else:\\n address.extend(building.get_postal_address())\\n return address\",\n \"def test_address_other_parameters():\\n address = lob.Address.create(name = 'Siddharth Saha', address_line1 = '104, Printing Boulevard',\\n address_line2 = 'Sunset Town', email = 'sidchilling@gmail.com', \\n address_city = 'Boston', address_state = 'MA', address_country = 'US',\\n address_zip = '12345')\\n print address.to_dict()\",\n \"def clean_address(self, s):\\n # The letter \\\"O\\\" instead of the numeral \\\"0\\\" is a common mistake.\\n s = re.sub(\\n r\\\"\\\\b[A-Z][O0-9][A-Z]\\\\s?[O0-9][A-Z][O0-9]\\\\b\\\", lambda x: x.group(0).replace(\\\"O\\\", \\\"0\\\"), clean_string(s)\\n )\\n for k, v in province_or_territory_abbreviations().items():\\n # Replace a province/territory name with its abbreviation.\\n s = re.sub(\\n r\\\"[,\\\\n ]+\\\"\\n r\\\"\\\\(?\\\" + k + r\\\"\\\\)?\\\"\\n r\\\"(?=(?:[,\\\\n ]+Canada)?(?:[,\\\\n ]+[A-Z][0-9][A-Z]\\\\s?[0-9][A-Z][0-9])?\\\\Z)\\\",\\n \\\" \\\" + v,\\n s,\\n )\\n # Add spaces between province/territory abbreviation, FSA and LDU and remove \\\"Canada\\\".\\n return re.sub(\\n r\\\"[,\\\\n ]+\\\" r\\\"([A-Z]{2})\\\" r\\\"(?:[,\\\\n ]+Canada)?\\\" r\\\"[,\\\\n ]+([A-Z][0-9][A-Z])\\\\s?([0-9][A-Z][0-9])\\\" r\\\"\\\\Z\\\",\\n r\\\" \\\\1 \\\\2 \\\\3\\\",\\n s,\\n )\",\n \"def __str__(self):\\n if self._street_name != self.DEFAULT_STREET_NAME and \\\\\\n self._house_num != self.DEFAULT_HOUSE_NUM and \\\\\\n self._apt_num != self.DEFAULT_APT_NUM:\\n address = f\\\"\\\\n{self._house_num} {self._street_name} Street, \\\" \\\\\\n f\\\"#{self._apt_num}\\\"\\n return address\\n else:\\n return \\\"<None>\\\"\",\n \"def get_address(self):\\n if self.get_entity: # needs an entity to work\\n if self.building:\\n address = self.get_entity.get_institutional_address()\\n address.extend(self.building.get_postal_address())\\n return address\\n else:\\n return self.get_entity.get_address()\",\n \"def addresses( data ) :\\n return list( set(chain.from_iterable( [ re.sub(r'\\\\[.*?\\\\]\\\\s+','',x['C1']).split('; ') for x in data ] )))\",\n \"def suggestions(self, input, borough_code=None):\\n parsed = parser.address(input)\\n if borough_code:\\n parsed['BOROUGH_CODE'] = borough_code\\n self.similiar_names = []\\n self.results = []\\n if parsed['PHN'] and parsed['STREET']:\\n if not parsed['BOROUGH_CODE'] and not parsed['ZIP']:\\n # iterate borocodes\\n for x in range(1, 6):\\n self._geocode(phn=parsed['PHN'], street=parsed['STREET'], borough_code=x)\\n # try address with borough code if present\\n elif parsed['BOROUGH_CODE']:\\n self._geocode(phn=parsed['PHN'], street=parsed['STREET'], borough_code=parsed['BOROUGH_CODE'])\\n # try address with zip code if present\\n elif parsed['ZIP']:\\n self._geocode(phn=parsed['PHN'], street=parsed['STREET'], zip=parsed['ZIP'])\\n # validate and retrieve any addresses\\n if len(self.similiar_names):\\n for name in self.similiar_names:\\n self._geocode(phn=parsed['PHN'], street=name['street'], borough_code=name['borough_code'])\\n if None in self.results:\\n self.results = list(filter(lambda v: v is not None, self.results))\\n\\n return self.results\",\n \"def address1(self) -> pulumi.Output[Optional[str]]:\\n return pulumi.get(self, \\\"address1\\\")\",\n \"def address_str(self) -> str | None:\\n pass\",\n \"def _formatting_address_fields(self):\\n return self._address_fields()\",\n \"def format_address(**args):\\n #Begin with the organisation and PO Box number, if applicable.\\n address = ''.join([args[entry] + '\\\\n' \\n for entry in ['organisation', 'PO box']\\n if args.get(entry)])\\n #Format building name/number components.\\n address += format_building_components(*[args.get(x) for x in \\n ['sub-building name', \\n 'building name', \\n 'building number',\\n 'concatenation indicator']])\\n #Add thoroughfare (if present), locality/town and postcode.\\n address += ''.join([args[entry] + '\\\\n' \\n for entry in ['dependent thoroughfare', \\n 'thoroughfare',\\n 'double dependent locality',\\n 'dependent locality',\\n 'town',\\n 'postcode']\\n if args.get(entry)])\\n return address.strip()\",\n \"def format_address(addresses: 'SequenceOrScalar[Address | str]') -> str:\\n if isinstance(addresses, (Address, str)):\\n return format_single_address(addresses)\\n\\n assert len(addresses) <= 50\\n return ', '.join(format_single_address(a) for a in addresses)\",\n \"def _check_address(self):\\n for object_ in self.objects:\\n if object_.object_name.endswith(' ЕС'):\\n if object_.object_address[:6].isnumeric():\\n object_.object_address = \\\\\\n object_.object_address[:7] + \\\\\\n object_.object_fed_subj + ', ' + \\\\\\n object_.object_address[7:]\",\n \"def get_address(query):\\n address = \\\"Dis-moi, quel endroit tu cherches ?\\\"\\n data = get_data(query)\\n try:\\n address_data = data[\\\"results\\\"][0][\\\"formatted_address\\\"]\\n address = (\\\"Si je ne me trompe pas,\\\"\\n \\\" l'adresse que tu cherche, c'est ... \\\" + address_data + \\\". Sinon\\\"\\n \\\", dis-moi le nom de lieu exact\\\")\\n except IndexError:\\n address = \\\"Désolé, je n'ai pas compris quel endroit tu cherches ?\\\"\\n finally:\\n return address\",\n \"def _get_address(self, address1, address2):\\n return f'{address1}\\\\n{address2}' if address2 else address1\",\n \"def readHouseAddresses():\\n addressesRead = []\\n with open(\\\"Files/HouseAddresses.txt\\\", 'r', encoding='utf8') as f:\\n for line in f:\\n if line == \\\"\\\\n\\\":\\n continue\\n details = line.split(\\\",\\\")\\n address = []\\n for detail in details:\\n address.append(detail.rstrip('\\\\n').rstrip().lstrip())\\n addressesRead.append(address)\\n f.close()\\n return addressesRead\",\n \"def nomad_address():\\n\\n print(nomad.get_address())\",\n \"def get_address(self, ):\\n return self.get_parameter('address')\",\n \"def street(self):\\n return self._street\",\n \"def maploc(loc):\\n\\n\\n loc = REGEX['parens'].sub('', loc)\\n loc = REGEX['and'].sub('', loc)\\n loc = REGEX['num'].sub('', loc)\\n\\n \\\"\\\"\\\"\\n 'parens' 'and' 'single' 'num' 'seeley' 'iab' 'brh'\\n \\\"\\\"\\\"\\n \\\"\\\"\\\"\\n /* For non-street address, strip room numbers */\\n if (!location.match(' Ave')) {\\n location = location.replace(/LL[0-9]/g, '').replace(/[0-9]/g, '');\\n }\\n /* Some text substitutions */\\n location = location.replace('Seeley W.', '').replace('International Affairs Building', '420 W 118th St').replace('Broadway Residence Hall', '2900 Broadway');\\n\\n \\\"\\\"\\\"\\n return loc + ', New York, NY 10027'\",\n \"def parse_address_from_geocoding_response(geocoded_data: dict) -> str:\\n return geocoded_data[\\n 'response'][\\n 'GeoObjectCollection'][\\n 'featureMember'][0][\\n 'GeoObject'][\\n 'metaDataProperty'][\\n 'GeocoderMetaData'][\\n 'text']\",\n \"def test_get_order_address(self):\\n pass\",\n \"def load_addresses():\\n with open('addresses.txt') as f:\\n return [address.strip() for address in f.readlines()]\",\n \"def do_getaddress(self,args):\\n ppdict(bitstamp.get_depositaddress())\",\n \"def _addressitem_from_line(line):\\n sline = line.split(\\\"\\\\t\\\")\\n if len(sline) < 2:\\n raise IOError(\\\"Error parsing address from line. Malformed data.\\\")\\n address = sline[0]\\n name = sline[1]\\n\\n if len(sline) > 2:\\n otherinfo = sline[2]\\n else:\\n otherinfo = \\\"\\\"\\n if len(sline) > 3:\\n extrainfo = sline[3]\\n else:\\n extrainfo = \\\"\\\"\\n if len(sline) > 4:\\n raw_misc = sline[4:]\\n misc = _raw_misc_to_dict(raw_misc)\\n else:\\n misc = {}\\n\\n return AddressItem(\\n _email_address=address,\\n _name=name,\\n _otherinfo=otherinfo,\\n _extrainfo=extrainfo,\\n **misc\\n )\",\n \"def _parse_location(self, response):\\n loc_parts = [\\n re.sub(r\\\"\\\\s+\\\", \\\" \\\", part).strip()\\n for part in response.css(\\n \\\"#contact-info .right-col-content .content *::text\\\"\\n ).extract()\\n if part.strip()\\n ]\\n return {\\n \\\"name\\\": loc_parts[3],\\n \\\"address\\\": \\\" \\\".join(loc_parts[4:]).replace(\\\" ,\\\", \\\",\\\").strip(),\\n }\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7285767","0.7073066","0.68373525","0.6803627","0.6702961","0.66992515","0.6645722","0.6645722","0.66008115","0.6555697","0.65275055","0.64732426","0.643964","0.64220065","0.6419511","0.63993114","0.63021076","0.62145597","0.6205807","0.62011707","0.61928886","0.61835444","0.61770445","0.61426693","0.61411965","0.6136066","0.6118628","0.6103913","0.60953367","0.60859036","0.60694325","0.6049867","0.6037654","0.6006664","0.5993662","0.599296","0.5984353","0.59528565","0.59359413","0.59303993","0.5898929","0.58695227","0.5835085","0.5827997","0.5818895","0.58097136","0.5797164","0.57949907","0.5775367","0.5765253","0.57513815","0.57500416","0.57481587","0.5725516","0.5725516","0.5725516","0.572122","0.5713988","0.5713988","0.57049704","0.5700431","0.56987596","0.56768906","0.567477","0.5666773","0.5662513","0.56544566","0.563921","0.56373787","0.5634221","0.5623397","0.5621658","0.5620116","0.55916774","0.5589411","0.5588374","0.5587982","0.5580453","0.55790925","0.55727684","0.55562145","0.5553777","0.5553763","0.5546229","0.5545192","0.5538874","0.55388194","0.55354","0.5528123","0.5509381","0.55091566","0.55088073","0.5508467","0.54950005","0.5482753","0.5481104","0.5467985","0.5465495","0.5461417","0.5461046","0.54587245"],"string":"[\n \"0.7285767\",\n \"0.7073066\",\n \"0.68373525\",\n \"0.6803627\",\n \"0.6702961\",\n \"0.66992515\",\n \"0.6645722\",\n \"0.6645722\",\n \"0.66008115\",\n \"0.6555697\",\n \"0.65275055\",\n \"0.64732426\",\n \"0.643964\",\n \"0.64220065\",\n \"0.6419511\",\n \"0.63993114\",\n \"0.63021076\",\n \"0.62145597\",\n \"0.6205807\",\n \"0.62011707\",\n \"0.61928886\",\n \"0.61835444\",\n \"0.61770445\",\n \"0.61426693\",\n \"0.61411965\",\n \"0.6136066\",\n \"0.6118628\",\n \"0.6103913\",\n \"0.60953367\",\n \"0.60859036\",\n \"0.60694325\",\n \"0.6049867\",\n \"0.6037654\",\n \"0.6006664\",\n \"0.5993662\",\n \"0.599296\",\n \"0.5984353\",\n \"0.59528565\",\n \"0.59359413\",\n \"0.59303993\",\n \"0.5898929\",\n \"0.58695227\",\n \"0.5835085\",\n \"0.5827997\",\n \"0.5818895\",\n \"0.58097136\",\n \"0.5797164\",\n \"0.57949907\",\n \"0.5775367\",\n \"0.5765253\",\n \"0.57513815\",\n \"0.57500416\",\n \"0.57481587\",\n \"0.5725516\",\n \"0.5725516\",\n \"0.5725516\",\n \"0.572122\",\n \"0.5713988\",\n \"0.5713988\",\n \"0.57049704\",\n \"0.5700431\",\n \"0.56987596\",\n \"0.56768906\",\n \"0.567477\",\n \"0.5666773\",\n \"0.5662513\",\n \"0.56544566\",\n \"0.563921\",\n \"0.56373787\",\n \"0.5634221\",\n \"0.5623397\",\n \"0.5621658\",\n \"0.5620116\",\n \"0.55916774\",\n \"0.5589411\",\n \"0.5588374\",\n \"0.5587982\",\n \"0.5580453\",\n \"0.55790925\",\n \"0.55727684\",\n \"0.55562145\",\n \"0.5553777\",\n \"0.5553763\",\n \"0.5546229\",\n \"0.5545192\",\n \"0.5538874\",\n \"0.55388194\",\n \"0.55354\",\n \"0.5528123\",\n \"0.5509381\",\n \"0.55091566\",\n \"0.55088073\",\n \"0.5508467\",\n \"0.54950005\",\n \"0.5482753\",\n \"0.5481104\",\n \"0.5467985\",\n \"0.5465495\",\n \"0.5461417\",\n \"0.5461046\",\n \"0.54587245\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":2894,"cells":{"query":{"kind":"string","value":"Given the input string, tries to separate them into \"street\" & \"house number\" variables"},"document":{"kind":"string","value":"def filter_string(self):\n logger.info(\"Information Gathering Finished!\")\n\n pattern_street = re.compile(r'[A-Za-z]+\\s?\\w+(?=\\s[Nn]o\\s\\d+$) |'\n r' [A-Za-z]+\\s?\\w+\\s?[A-Za-z]+\\s?[A-Za-z]+',\n re.X) # street pattern\n match_street = pattern_street.search(self.string)\n\n # If there are no house numbers provided in the input file,\n # print(not found) in the output JSON file\n numbers_instring = re.findall(r'\\d+', self.string) # digit counts in given string\n\n if len(numbers_instring) > 0:\n # In most cases we have: \"no\" followed by some digits\n pattern_housenumber = re.compile(r'(\\d+\\s?[A-Za-z]?$) |'\n r' (^\\d+) |'\n r' [Nn]o+[\\s?]+[0-9]+$',\n re.X) # house number pattern\n match_housenumber = pattern_housenumber.search(self.string)\n fin_housenumber = match_housenumber[0]\n else:\n match_housenumber = [\"not found\"]\n\n fin_housenumber = match_housenumber[0]\n fin_street = match_street[0]\n print(\"street: \", fin_street)\n print(\"housenumber: \", fin_housenumber)\n return {'street': fin_street, 'housenumber': fin_housenumber}"},"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 street_parser(*street_data):\n\n # parsing tuples\n if len(street_data) == 2:\n if not isinstance(street_data[0], str) and not isinstance(street_data[1], str):\n raise WrongInput(\"Invalid format\")\n # street name as the tuple's first item\n strname, strnumber = street_data\n # street number as the tuple's first item\n if street_data[0][0] in digits:\n strname, strnumber = strnumber, strname\n\n # parsing strings\n else:\n if not isinstance(street_data[0], str):\n raise WrongInput(\"Invalid format\")\n if not street_data[0]:\n raise WrongInput(\"Input cannot be blank\")\n\n # string starting with street number\n if street_data[0][0] in digits:\n street_pattern = re.compile(r'''\n ^ # beginning of string\n (\\d+) # street number is any number of digits\n \\W+ # separator\n (\\w+\\W*\\w*\\W*) # street name is one or more words with optional separators\n $ # end of string\n ''', re.VERBOSE)\n street_obj = street_pattern.search(street_data[0])\n strnumber, strname = street_obj.groups()\n\n # string starting with street name\n else:\n street_pattern = re.compile(r'''\n ^ # beginning of string\n (\\w+\\W*\\w*\\s*) # street name is one or more words with optional separators\n \\W+ # separator\n (\\d+) # street number is any number of digits\n $ # end of string\n ''', re.VERBOSE)\n street_obj = street_pattern.search(street_data[0])\n (strname, strnumber) = street_obj.groups()\n\n # replace specific words in street name with their abbreviates\n strname = strname.lower()\n special = {r'\\baleje\\b': 'Al.', r'\\bavenue\\b': 'Av.', r'\\broad\\b': 'Rd.', r'\\bsquare\\b': 'Sq.',\n r'\\bstreet\\b': 'St.', r'\\bdrive\\b': 'Dr.'}\n for key in special:\n strname = re.sub(key, special[key], strname)\n return strname.title(), strnumber","def parse_address_street(address_str, address_zip_us_re, address_zip_us_lax_re):\n address = {}\n errors = []\n parts = address_str.split('$')\n if DEBUG:\n address['debug_address_str'] = address_str\n address['debug_part_1'] = parts[0]\n address['debug_part_last'] = parts[-1]\n address['debug_length'] = len(parts)\n #if len(parts) == 1:\n #print('cannot split: {}: {}'.format(debug_type, address_str))\n match = re.search(address_zip_us_re, parts[-1])\n if match:\n if DEBUG:\n address['debug_parser'] = 'A'\n address['city'] = match.group(1)\n address['region'] = match.group(2).upper()\n address['postalCode'] = match.group(3)\n address['countryId'] = 'US'\n if len(parts) == 2:\n if DEBUG:\n address['debug_parser'] = 'B'\n address['addressLine1'] = parts[0]\n else:\n if len(parts) == 3:\n if DEBUG:\n address['debug_parser'] = 'C'\n address['addressLine1'] = parts[0]\n if parts[0] != parts[1]:\n if DEBUG:\n address['debug_parser'] = 'D'\n address['addressLine2'] = parts[1]\n else:\n match2 = re.search(address_zip_us_lax_re, address_str)\n if match2:\n if DEBUG:\n address['debug_parser'] = 'E'\n address['region'] = match2.group(2).upper()\n address['postalCode'] = match2.group(3)\n address['countryId'] = 'US'\n # FIXME: Cannot reliably parse the remainder for city and street address\n errors.append('Partial parse street address: {}'.format(address_str))\n address['addressLine1'] = match2.group(1)\n else:\n # This is the remainder that we could not parse.\n # So just put it all into \"addressLine1\" to be manually adjusted later.\n if DEBUG:\n address['debug_parser'] = 'F'\n errors.append('Cannot parse street address: {}'.format(address_str))\n address['addressLine1'] = address_str\n return (address, errors)","def get_address(address: str) -> Tuple[str, str, str]:\n\n # Try to geocode the address as given\n g = geocoder.osm(address)\n\n if g.json is not None:\n\n # TODO this is inefficient and hacky\n\n # First thing we attempt if the result isn't complete is just to\n # add the housenumber (often the issue).\n if not good_geocoder_result(g.json):\n g.json['housenumber'] = usaddress.tag(address)[0]['AddressNumber']\n\n # If the result is now good, return it\n if good_geocoder_result(g.json):\n\n # Geocoding was successful. Return the result\n return (\n # First part is a nicely formatted address\n f\"{g.json['housenumber']} {g.json['street']}, {g.json['city']}, {g.json['state']} {g.json['postal']}\",\n # Second is the latitude\n g.json['lat'],\n # And third is the longitude\n g.json['lng']\n )\n\n # Geocoding was unsuccessful.\n # Let's try to create a cleaner address by first parsing out the pieces we need, then try again.\n \n # Parsing the address components...\n parsed, addr_type = usaddress.tag(address)\n if addr_type != \"Street Address\":\n raise ValueError(f\"Address could not be properly parsed. Resulting type: {addr_type}. Result: \\n{parsed}\")\n \n # Trim off any whitespace from the parsed components.\n for part in parsed:\n parsed[part] = parsed[part].strip()\n\n reqd_address_parts = ['AddressNumber', 'StreetName', 'PlaceName']\n if any(address_part not in parsed for address_part in reqd_address_parts):\n raise ValueError(f\"The address must have at least a house number, street, and city.\")\n \n # Initialize the resulting address string with the address number (aka house/street number)\n new_address = parsed['AddressNumber']\n \n # If the streetname is just a number, make it ordinal\n if parsed['StreetName'].isnumeric():\n parsed['StreetName'] = ordinal(parsed['StreetName'])\n \n # Get the whole street name\n for k, v in [(k, v) for k, v in parsed.items() if k.startswith(\"StreetName\")]:\n new_address += f\" {v}\"\n \n # Add the city...\n new_address += f\", {parsed['PlaceName']}\"\n # Add the state, if it exists\n if 'StateName' in parsed:\n new_address += f\", {parsed['StateName']}\"\n # And the zip code, if it exists\n if 'ZipCode' in parsed:\n new_address += f\" {parsed['ZipCode']}\"\n \n # Now try to geocode this improved address\n g = geocoder.osm(new_address)\n\n if g.json is not None:\n\n # Geocoding was successful. Return the result\n return (\n # First part is a nicely formatted address\n f\"{g.json['housenumber']} {g.json['street']}, {g.json['city']}, {g.json['state']} {g.json['postal']}\",\n # Second is the latitude\n g.json['lat'],\n # And third is the longitude\n g.json['lng']\n )\n \n # Still can't geocode the address. Throw an error\n else:\n raise ValueError(f\"Could not geocode this address: {address}\")","def extract_unit_num_street(self, address):\n\n unit = street_num = street = None\n\n address_parts = self.clean(address).split()\n\n try:\n street_num = int(address_parts[1])\n street = address_parts[2]\n unit = int(address_parts[0])\n\n except IndexError:\n raise InvalidAddress(address)\n\n except ValueError:\n\n try:\n street_num = int(address_parts[0])\n except ValueError:\n # Not even a number\n raise InvalidAddress(address)\n\n street = address_parts[1]\n\n return (unit, street_num, street)","def process_capture_groups(group):\n group = group[0][0].replace(',', '')\n group = group.split(' ')\n street_type = group[-1]\n street_name = ' '.join(group[:-1])\n if street_type in STREET_ABBREVIATION_TO_NAME.keys():\n street_type = STREET_ABBREVIATION_TO_NAME[street_type]\n return street_name, street_type","def parse_address_campus(address_str, address_campus_re, address_campus_room_re):\n address = {}\n errors = []\n if '$' not in address_str:\n match = re.search(address_campus_room_re, address_str)\n if match:\n address['addressLine1'] = match.group(1)\n else:\n # This leftover is either an erroneous email address or a building name\n if '@' in address_str:\n errors.append('Campus address seems to be email: {}'.format(address_str))\n #FIXME: Should this be saved to addressLine1 anyway.\n else:\n # It seems to be a building address\n address['addressLine2'] = address_str\n else:\n match = re.search(address_campus_re, address_str)\n if match:\n address['addressLine2'] = match.group(1)\n address['addressLine1'] = match.group(2)\n #else:\n # FIXME: here just for debug\n #errors.append('Cannot parse campus address: {}'.format(address_str))\n return (address, errors)","def parse_building_address(addr_string):\n addr_string = re.sub(_regexp, '', addr_string)\n addr_string = re.sub(r'(?P<key>[a-zA-Z]+)', _replace_dir, addr_string)\n addr_string = re.sub(r'(?P<key>[a-zA-Z]+)', _replace_suffix, addr_string)\n addr_string = re.sub(_regexp_extra_space, ' ', addr_string)\n return addr_string.strip().upper()","def parse_streetname(self):\n index = self.index\n \n name = \"\"\n for i in range(4):\n if index + i == self.length:\n break\n if self.words[index+i]['word'] == ',':\n break\n # Hack\n if self.words[index+i]['word'] == 'doctor':\n self.words[index+i]['word'] = 'drive'\n break\n try:\n word = sttype[self.words[index+i]['word']]\n break\n except:\n try:\n word = vocab[self.words[index+i]['word']]\n if Vocabulary.STREET_TYPE in word['tag']:\n break\n if name != '':\n name += ' ' + word['lemma'][0]\n else:\n name = word['lemma'][0]\n except: \n if self.words[index+i]['word'][-2:] in [ 'th', 'st', 'nd', 'rd' ]:\n name = self.words[index+i]['word'][:-2]\n else:\n self.index += i\n _dir, _n = self.parse_streetdir()\n self.index -= i\n if _dir:\n break\n if name != '':\n name += ' ' + self.words[index+i]['word']\n else:\n name = self.words[index+i]['word']\n \n if i == 0 or i == 4:\n return None, 0\n else:\n return name, i","def address():\n # We start with generating the street name. For this we choose\n # between the most common prefixes and our own prefixes\n prefix = dice.randint(1, 100)\n if prefix <= 10: # 10%\n prefix = \"Haupt\"\n elif prefix <= 18: # 8%\n prefix = \"Schul\"\n elif prefix <= 25: # 7%\n prefix = \"Garten\"\n elif prefix <= 32: # 7%\n prefix = \"Dorf\"\n elif prefix <= 39: # 7%\n prefix = \"Bahnhof\"\n elif prefix <= 46: # 7%\n prefix = \"Wiesen\"\n elif prefix <= 52: # 6%\n prefix = \"Berg\"\n elif prefix <= 56: # 4%\n prefix = \"Kirch\"\n elif prefix <= 60: # 4%\n prefix = \"Wald\"\n elif prefix <= 64: # 4%\n prefix = \"Ring\"\n else:\n prefix = dice.choice(names.prefix)\n\n # Now we can add the suffix\n suffix = dice.randint(1, 100)\n if suffix <= 78:\n suffix = \"straße\"\n elif suffix <= 96:\n suffix = \"weg\"\n elif suffix <= 98:\n suffix = \"allee\"\n elif suffix == 99:\n suffix = \"ring\"\n elif suffix == 100:\n suffix = \"platz\"\n\n # When we have a city name as prefix, we need to capitalize the\n # suffix since it will be two words\n if prefix[-1] == \" \":\n suffix = suffix.capitalize()\n\n # Now we can add them together\n street = prefix + suffix\n\n # We need a house number as well. In Germany most numbers have\n # between one and four digits, so we will use this as base. Lower\n # numbers are more common, so we'll give it a 10% probability of\n # using 3 digits and 1% of using 4 digits\n digits = dice.randint(1, 100)\n if digits == 100:\n house_number = str(dice.randint(1000, 9999))\n elif digits >= 90:\n house_number = str(dice.randint(100, 999))\n else:\n house_number = str(dice.randint(1, 99))\n address_full = street + \" \" + house_number\n return address_full","def street_address(full_address):\n full_address = normalise_address(full_address)\n capture_groups = ROAD_PATTERN.findall(full_address)\n if capture_groups:\n name, _type = process_capture_groups(capture_groups)\n return '{} {}'.format(name, _type)\n return ''","def extract_state(full_address):\n full_address = full_address.strip()\n last_comma_index = full_address.rindex(\",\")\n last_space_index = full_address.rindex(\" \")\n state = full_address[last_comma_index + 1 : last_space_index]\n state = state.strip()\n return state","def find_places(query):\n parts = str(query).split(' ')\n for i, p in enumerate(parts):\n p = p.replace('-', ' ').strip()\n try:\n postal_code = int(p)\n if len(postal_code) == 4:\n print(postal_code, parts[i+1])\n # Check \n #response = get_osm_location(\"{postal_code} {name}\")\n #lon = response['lon']\n #lat = response['lat']\n #poly = \n except Exception as e:\n continue","def maploc(loc):\n\n\n loc = REGEX['parens'].sub('', loc)\n loc = REGEX['and'].sub('', loc)\n loc = REGEX['num'].sub('', loc)\n\n \"\"\"\n 'parens' 'and' 'single' 'num' 'seeley' 'iab' 'brh'\n \"\"\"\n \"\"\"\n /* For non-street address, strip room numbers */\n if (!location.match(' Ave')) {\n location = location.replace(/LL[0-9]/g, '').replace(/[0-9]/g, '');\n }\n /* Some text substitutions */\n location = location.replace('Seeley W.', '').replace('International Affairs Building', '420 W 118th St').replace('Broadway Residence Hall', '2900 Broadway');\n\n \"\"\"\n return loc + ', New York, NY 10027'","def parse_address(address: str) -> OrderedDict[str, str]:\n\n address = address.replace(\"\\n\", \", \")\n\n parsed_address, address_type = usaddress.tag(address)\n if address_type != \"Street Address\":\n logger.warning(\n f\"Couldn't parse address '{address}' of type {address_type}; best guess: {parsed_address}\"\n )\n\n # Fixup: At least one address has \"WI, USA\" in the \"StateName\" component.\n # Strip non-state components\n if parsed_address.get(\"StateName\"):\n parsed_address[\"StateName\"] = parsed_address[\"StateName\"].partition(\",\")[0]\n\n return parsed_address","def parse_phone(s):\n pattern = '''\n ^\\s* # Leading spaces\n (?P<areacode>\n \\d{3}-? # \"xxx\" or \"xxx-\"\n | \\(\\d{3}\\)\\s* # OR \"(xxx) \"\n )\n (?P<prefix>\\d{3}) # xxx\n -? # Dash (optional)\n (?P<suffix>\\d{4}) # xxxx\n \\s*$ # Trailing spaces\n '''\n matcher = re.compile(pattern, re.VERBOSE)\n matches = matcher.match(s)\n if matches is None:\n print(s)\n return s\n else:\n areacode = re.search('\\d{3}', matches.group ('areacode')).group()\n prefix = matches.group ('prefix')\n suffix = matches.group ('suffix')\n return areacode+'-'+prefix+'-'+suffix","def find_street(self, text):\n text = text.replace('\"', '')\n result = ''\n textarr = text.split()\n for i in range(0, len(textarr)):\n if textarr[i] in self.streets:\n # decide if the result is nearly empty or not to add the word with or without a space\n if len(result) < 2:\n result = result + textarr[i]\n else:\n result = result + ' ' + textarr[i]\n\n if result in self.streets:\n\n # ends the function if the street is found\n if (result + '\\n') in self.streets and result != 'straße' and result != 'in' and result != '' \\\n and result != 'ein' and result != 'der' and result != 'und' and result != ' ' \\\n and result != 'an':\n return result\n else:\n result_list = result.split()\n del result_list[0]\n result = ' '.join(w for w in result_list)\n result = result.lstrip(' ')\n # ends the function if the street is found\n if (result + '\\n') in self.streets and result != 'straße' and result != 'in' and result != '' \\\n and result != 'ein' and result != 'der' and result != 'und' and result != ' ' \\\n and result != 'ist' and result != 'an':\n return result\n elif i == len(textarr)-1:\n result_list = result.split()\n del result_list[0]\n result = ' '.join(w for w in result_list).lstrip(' ')\n if (result + '\\n') in self.streets and result != 'straße' and result != 'in' and result != '' \\\n and result != 'ein' and result != 'der' and result != 'und' and result != ' ' \\\n and result != 'ist' and result != 'an':\n return result\n print(result)\n return None","def extract_city(full_address):\n full_address = full_address.strip()\n last_comma_index = full_address.rindex(\",\")\n mid_comma_index = full_address.rindex(\",\", 0, last_comma_index)\n city = full_address[mid_comma_index + 1 : last_comma_index]\n city = city.strip()\n return city","def split_address(address):\n if '://' in address:\n protocol, address = address.split('://')\n else:\n protocol = 'http'\n\n if ':' in address:\n address, port = address.split(':')\n else:\n port = 443 if protocol == 'https' else 8000\n\n return protocol, address, int(port)","def parse_place_notation(input_string: str) -> Tuple[int, str]:\n\n # Looking for a string that matches <stage>:<place notation> where the\n # place notation is a series of bell numbers and 'x' characters\n parts = input_string.split(\":\")\n if len(parts) == 2:\n stage_part = parts[0]\n if len(stage_part) == 0 or not stage_part.isnumeric():\n raise PlaceNotationError(input_string, \"Stage must be a number\")\n stage = int(stage_part)\n place_notation = parts[1]\n if not valid_pn(place_notation):\n raise PlaceNotationError(input_string, \"Place notation is invalid\")\n else:\n raise PlaceNotationError(input_string, \"<stage>:<place notation> required\")\n\n return stage, place_notation","def extract_postcode(s):\n pc_regex = r'([Gg][Ii][Rr] 0[Aa]{2})|((([A-Za-z][0-9]{1,2})|(([A-Za-z][A-Ha-hJ-Yj-y]'\n pc_regex += r'[0-9]{1,2})|(([A-Za-z][0-9][A-Za-z])|([A-Za-z][A-Ha-hJ-Yj-y][0-9]?[A-Za-z]'\n pc_regex += r'))))\\s?[0-9][A-Za-z]{2})'\n\n re_search = re.search(pc_regex, s)\n if re_search:\n p = re_search.group(0)\n else:\n p = ''\n return p","def search_address(query: str) -> Tuple[int, str]:\n\n url = 'https://api.n1.ru/api/v1/geo/geocoder/with_cities/'\n params = _search_params.copy()\n params['q'] = query\n\n try:\n r = requests.get(url, params=params, headers=_headers)\n response = r.json()\n\n if not 'result' in response or not response['result']:\n raise NotFoundException('Result not found or empty.')\n \n address = None\n house_number = query.split(',')[-1].strip()\n for x in response['result']:\n if x['name_ru'].lower() == house_number:\n address = x\n break\n \n if address is None:\n raise NotFoundException(f'Not found house number {house_number} in result: {response[\"result\"]}')\n \n return address['street']['id'], address['name_ru']\n except requests.RequestException as e:\n raise ParserException(f'Fail make request. query: {query}') from e\n except NotFoundException as e:\n raise ParserException('Invalid result.') from e\n except (KeyError, IndexError) as e:\n raise ParserException(f'Fail get street id or house number. value: {response[\"result\"]}') from e","def parse(self, src, line):\n r = line.split('\\t')\n p = {}\n if src == 'sf':\n p['businessID'] = r[0]\n p['name'] = r[1]\n p['address'] = r[2]\n p['city'] = r[3]\n p['state'] = r[4]\n p['zip'] = r[5]\n p['latitude'] = r[6]\n p['longitude'] = r[7]\n p['phone'] = r[8]\n elif src == 'nyc':\n p['businessID'] = r[0]\n p['name'] = r[1]\n # nyc separates the building number from the street name\n p['address'] = ' '.join([r[3].strip(), r[4].strip()])\n p['city'] = 'NYC'\n p['state'] = 'NY'\n p['zip'] = r[5]\n p['latitude'] = None\n p['longitude'] = None\n p['phone'] = r[6]\n return p","def _get_address(self, soup):\n street, city, state, zipcode = None, None, None, None\n try:\n # property detail tag\n street = soup.find('div', class_='main-address').get_text().strip()\n # find address tag\n address = soup.find('div', class_='c-address')\n \n # pattern for the address in this website\n locality = address.find_all('span', class_='locality')\n city = locality[0].get_text().strip()\n if len(locality) > 1:\n city = locality[1].get_text().strip()\n state = address.find('span', class_='region').get_text().strip()\n zipcode = address.find('span', class_='postal-code').get_text().strip()\n return street, city, state, zipcode\n except:\n return street, city, state, zipcode","def extract_zipcode(full_address):\n full_address = full_address.strip()\n last_space_index = full_address.rindex(\" \")\n zipcode = full_address[last_space_index + 1 : ]\n return zipcode","def func2():\n\n\tnums = '''\n\t\t800-555-1212\n\t\t800 555 1212\n\t\t800.555.1212\n\t\t(800) 555-1212\n\t\t1-800-555-1212\n\t\t800-555-1212-1234\n\t\t800-555-1212x1234\n\t\t800-555-1212 ext. 1234\n\t\twork 1-(800) 555.1212 #1234\n\t'''\n\n\tpattern = r\"(\\d{3})\\D*(\\d{3})\\D*(\\d{4})\\D*(\\d*)$\"\n\tregex = re.compile(pattern)\n\n\tprint regex.search(\"800-555-1212\").groups()\n\tprint regex.search(\"800 555 1212\").groups()\n\tprint regex.search(\"800.555.1212\").groups()\n\n\tprint regex.search(\"(800) 555-1212\").groups()\n\tprint regex.search(\"1-800-555-1212\").groups()\n\n\tprint regex.search(\"800-555-1212-1234\").groups()\n\tprint regex.search(\"800-555-1212x1234\").groups()\n\n\tprint regex.search(\"800-555-1212 ext. 1234\").groups()\n\tprint regex.search(\"work 1-(800) 555.1212 #1234\").groups()","def split_well_name (well_name):\n\n letters = well_name.rstrip('0123456789')\n\n nums = well_name.lstrip(letters)\n\n\n #Do some checks to make sure it's a well name in for the format letter-letter-#-#\n if len(nums) == 0:\n raise ValueError('Something is wrong with your input, I cannot find a row number')\n\n\n for i in '0123456789':\n if i in letters:\n raise ValueError('Something is wrong with your input, I think there is a number in your column letter.')\n\n for j in nums:\n if j not in '0123456789':\n raise ValueError('Something is wrong with your input, I think there is a letter in your row number.')\n\n return letters, nums","def street_address():\r\n\r\n return _random.choice(\r\n [\r\n '%d-%d %s' % (\r\n _random.randrange(999),\r\n _random.randrange(999),\r\n street_name()\r\n ),\r\n '%d %s' % (\r\n _random.randrange(999),\r\n street_name()\r\n ),\r\n '%s %d, %s' % (\r\n 'P.O. Box',\r\n _random.randrange(999),\r\n street_name()\r\n )\r\n ]\r\n )","def _get_address(self, soup):\n\n try:\n # from the content tag, extract the tag that contains all the address info\n address_tag = soup.find('div', class_='flex flex-col md:flex-row')\n # street tag\n street_tag = address_tag.find('h1', class_='h3')\n # street information\n street = street_tag.get_text()\\\n .strip()\\\n .replace(',', '')\n # region tag \n region_tag = address_tag.find('h5', class_='listing-card-location') \\\n .get_text() \\\n .strip() \\\n .split(' ')\n # city information\n city = region_tag[0].replace(',', '').title()\n # state information\n state = region_tag[1]\n # zipcode information\n zipcode = region_tag[2]\n\n return street, city, state, zipcode\n \n except:\n # return None if any of the above parts failed\n # if there's any part that's missing in the address part,\n # the whole address becomes useless\n return None, None, None, None","def test_address(residence):\n # We start by creating our result list\n result = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n # Then we split our given string so we only have the address left\n residence = residence.split(\", \")[1]\n\n # First we check for the prefix and increase the according value\n if \"Haupt\" in residence:\n result[0] = 1\n elif \"Schul\" in residence:\n result[1] = 1\n elif \"Garten\" in residence:\n result[2] = 1\n elif \"Dorf\" in residence:\n result[3] = 1\n elif \"Bahnhof\" in residence:\n result[4] = 1\n elif \"Wiesen\" in residence:\n result[5] = 1\n elif \"Berg\" in residence and residence[4] in (\"s\", \"w\", \"a\", \"r\", \"p\"):\n result[6] = 1\n elif \"Kirch\" in residence:\n result[7] = 1\n elif \"Wald\" in residence:\n result[8] = 1\n elif \"Ring\" == residence[0:4]:\n result[9] = 1\n else:\n result[10] = 1\n\n # Now we check the suffix\n if \"straße\" in residence or \"Straße\" in residence:\n result[11] = 1\n elif \"Weg\" in residence or \"weg\" in residence:\n result[12] = 1\n elif \"Allee\" in residence or \"allee\" in residence:\n result[13] = 1\n elif \"platz\" in residence or \"platz\" in residence:\n result[15] = 1\n else:\n result[14] = 1\n\n # And now we check the number\n number = int(residence.split()[-1])\n if number > 999:\n result[16] = 1\n elif number > 99:\n result[17] = 1\n else:\n result[18] = 1\n\n return result","def extract_alleles_from_snp_string(snp_string):\n (allele1, allele2) = snp_string[1:4].split(\"/\")\n assert allele1 in \"ATGC\"\n assert allele2 in \"ATGC\"\n return allele1, allele2","def parse_address(address, sanity=True):\n address = address.split(':')\n address, port = ':'.join(address[:-1]), address[-1]\n\n guessed_type = 4\n if address.startswith('['):\n address = address[1:]\n guessed_type = 6\n if address.endswith(']') or (sanity and guessed_type == 6):\n if sanity:\n assert address.endswith(']')\n address = address[:-1]\n guessed_type = 6\n if address.count(':') > 3:\n if sanity:\n assert guessed_type == 6\n guessed_type = 6\n\n return address, int(port), guessed_type","def parse_location(location):\n city, state = location.strip().split(',')\n return f\"{city.strip().replace(' ', '-')}-{state.strip().replace(' ', '-')}\"","def _split_proxy_info(data: str) -> list:\n \n country = data[:2]\n anonymity = data[3:4]\n type_ = data[4:].strip('-+ ') # Remove splitting (- and space) and google_passed flag (+)\n google_passed = data[-1]\n\n return [country, anonymity, type_, google_passed]","def _parse_user_input(self):\n user_input = self.user_input.strip()\n if user_input:\n if user_input.find(',') > -1:\n # Location is either city/state or latitude/longitude.\n if user_input[0].isalpha():\n # City, state (lat/long handled elsewhere)\n city, state = [x.strip() for x in user_input.split(',')]\n self.city = city\n self.state = state\n elif (len(user_input) <= 10 and\n user_input[1].isdigit()): # 2nd char in US/Can. postal codes\n # Postal code\n self.postal_code = user_input.strip()","def normalise_address(address):\n return re.sub('\\s+', ' ', str(address).upper()).replace(' ,', ',')","def return_street(streetname):\r\n if streetname == None:\r\n return streetname\r\n if streetname.split(\" \")[-1] in valid_suffix:\r\n return \" \".join(str(streetname).split(\" \")[:-1])\r\n\r\n return streetname","def parse_streetdir(self):\n \n first = self.words[self.index]['word']\n if self.index + 1 < self.length:\n second = self.words[self.index+1]['word']\n else:\n second = None\n \n if first in ['northwest', 'northeast', 'southwest', 'southeast']:\n return first, 1 \n elif first == 'nw':\n return \"northwest\", 1\n elif first == 'ne':\n return \"northeast\", 1\n elif first == 'sw':\n return \"southwest\", 1\n elif first == 'se':\n return \"southeast\", 1\n \n if first in ['n', 'north']:\n if second in ['w', 'west']:\n return \"northwest\", 2\n elif second in ['e', 'east']:\n return \"northeast\", 2\n else:\n return \"north\", 1\n elif first in ['s', 'south']:\n if second in ['w', 'west']:\n return \"southwest\", 2\n elif second in ['e', 'east']:\n return \"southeast\", 2\n else:\n return \"south\", 1\n elif first in ['e', 'east']:\n return \"east\", 1\n elif first in ['w', 'west']:\n return \"west\", 1\n \n return None,0","def geo_coder(house_number, boro_code, street_name, zip_code): \r\n wa1 = '1B{}{}{}{}{}C{}{}'.format(rightpad(house_number, 16), rightpad('', 38), boro_code, rightpad('', 10), rightpad(street_name, 32), rightpad('', 113), rightpad(zip_code, 5))\r\n wa1 = rightpad(wa1, 1200)\r\n wa2 = rightpad('', 4300)\r\n NYCGeo.NYCgeo(wa1, wa2)\r\n return wa1, wa2","def parse_proasis(input_string):\n return (\n input_string[:3].strip(),\n int(input_string[5:].strip()),\n input_string[3:5].strip(),\n )","def parse_input(input: str) -> Tuple[int, int, int, int]:\n head, tail = input.split(\"@ \")\n points, measures = tail.split(\": \")\n x, y = points.split(\",\")\n w, h = measures.split(\"x\")\n return int(x), int(y), int(w), int(h)","def parse_station_name (station_name):\n try:\n _,chinese_name,code,full_pinyin,short_pinyin = station_name.split('|')\n except ValueError:\n # print(station_name)\n _,chinese_name,code,full_pinyin,short_pinyin,_ = station_name.split('|')\n return {chinese_name:code,full_pinyin:code,short_pinyin:code}","def _get_address(self, address_tag, hdr):\n\n # try to find all the span tags in the address tag, the span tags\n # include all the address information we need \n try:\n elements = address_tag.find_all('span')\n\n # scrape the text out of the span tags and remove\n # all the whitespaces and punctuation marks\n address = elements[0].get_text()\\\n .replace(',','')\\\n .strip()\n city = elements[1].get_text().strip()\n state = elements[2].get_text().strip()\n zipcode = elements[3].get_text().strip()\n return address, city, state, zipcode\n # however, sometimes the address tag does not include the street\n # info, in this case, use the text in the header tag, which serves\n # as a replacement for the address \n except:\n address = hdr.get_text()\n elements = address_tag.find_all('span')\n city = elements[0].get_text()\\\n .replace(',','')\\\n .strip()\n state = elements[1].get_text().strip()\n zipcode = elements[2].get_text().strip()\n return address, city, state, zipcode","def __init__(self, zip_code, house_number, house_addition=\"\"):\n self.zip_code = zip_code.replace(' ', '')\n self.house_number = house_number.strip()\n self.house_addition = house_addition.strip()","def get_string_info(string):\n line_count = 1\n column_count = 1\n for char in string:\n if char == '\\n':\n column_count = 1\n line_count += 1\n else:\n column_count += 1\n return Coords(line_count, column_count, len(string))","def _split_region(region: str) -> Tuple[bool, str, Optional[str]]:\n parts = region.lower().split(\"-\")\n\n if parts[0] == \"vip\":\n del parts[0]\n vip = True\n else:\n vip = False\n\n country = parts.pop(0)\n\n try:\n country_part = parts.pop(0)\n except IndexError:\n country_part = None\n\n return vip, country, country_part","def normalize_address(address):\n # Fix 'Place/Place' -> 'Place & Place'\n if re.findall(r'[a-zA-Z0-9]/[a-zA-Z0-9]', address):\n address = address.replace('/', ' & ')\n # Fix 'Place:Place' -> 'Place & Place'\n if re.findall(r'[a-zA-Z0-9]:[a-zA-Z0-9]', address):\n address = address.replace(':', ' & ')\n # Fix 'RD' -> 'Rd' & 'PK' -> 'Pk'\n if re.findall(r'[PRSA][KDTV]', address):\n address = re.sub(r'([PRSA][KDTV])', \\\n lambda x: x.group(0).title(), address)\n # Fix 'Bl' -> 'Blvd'\n if re.findall(r'(Bl)[\\ ]', address):\n address = address.replace('Bl', 'Blvd')\n # Fix 'w 156th' -> 'W 156th'\n if re.findall(r'[^a-zA-Z][wnse][/ ]', address):\n address = re.sub(r'[^a-zA-Z]([wnse])[/ ]', \\\n lambda x: x.group(0).upper(), address)\n # Fix '151 St' -> '151st St'\n if re.findall(r'[0-9][\\ ][SA][tv]', address):\n address = re.sub(r'[0-9]+', \\\n ordinal_conversion, address)\n return address","def net_xy(street):\r\n\r\n # api-endpoint\r\n URL = \"https://ags.govmap.gov.il/Search/FreeSearch\"\r\n # headers\r\n headers = {\"Content-Type\": \"application/json\", \"charset\": \"utf-8\"}\r\n # location given here\r\n try:\r\n p = \"{\\\"keyword\\\": \\\"\" + street + \"\\\",\\\"LstResult\\\": null}\"\r\n PARAMS = p.encode(\"utf-8\")\r\n\r\n # sending get request and saving the response as response object\r\n r = requests.post(url=URL, data=PARAMS, headers=headers)\r\n\r\n # extracting data in json format\r\n data = r.json()\r\n\r\n # extracting latitude, longitude and formatted address\r\n # of the first matching location\r\n\r\n X = data['data']['Result'][0]['X']\r\n Y = data['data']['Result'][0]['Y']\r\n except Exception as e:\r\n print(e)\r\n # print('exception ddamammnnnnn')\r\n print(street)\r\n return 0,0\r\n return X,Y","def extract_address(input_string):\n if input_string:\n addr_search = re.search(\n r'\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}(?:/\\d{1,2}|)',\n input_string)\n\n if bool(addr_search):\n return addr_search.group()\n\n return None","def split_address(address: str, prefix=\"address_\", max_lines=5) -> dict[str, str]:\n\n # TODO ICMSLST-1692: ILB to fix addresses which are more than 5 lines\n return {\n f\"{prefix}{i}\": address_line.strip()\n for i, address_line in enumerate(address.split(\"\\n\"), start=1)\n if address_line and address_line.strip() and i <= max_lines\n }","def seperate_City_State_Data(data, us_state_abbrev):\n assert data is not None\n dictionary = dict(data)\n keys = dictionary.keys()\n tmp = list(keys)\n v = list(dictionary.values())\n values = []\n res = []\n for i in range(len(keys)):\n state = tmp[i][1].strip()\n city = tmp[i][0].strip()\n# print(city)\n if state in us_state_abbrev:\n res.append((state, city))\n values.append(v[i])\n return res, list(values)","def parse_feature_value(s,next_index=0):\n next_index = jump_over_space(s,next_index)\n start_index = next_index\n while True:\n if not s[next_index].isspace():\n next_index += 1\n else:\n break\n feature_value = s[start_index:next_index]\n if feature_value == '':\n feature_value = None\n feature_value = feature_value.split('/')\n return (feature_value,next_index)","def process_address(text):\n return sanitize(text[9:])","def canon_station_name(s, line):\n s = s.strip()\n s = re.sub('^Heathrow$', 'Heathrow Terminals 1, 2, 3', s)\n s = re.sub('^Olympia$', 'Kensington (Olympia)', s)\n s = re.sub('^Warwick Ave$', 'Warwick Avenue', s)\n s = re.sub('^Camden$', 'Camden Town', s)\n s = re.sub('^Central$', 'Finchley Central', s) # They say \"Between Central and East Finchley\"\n s = re.sub('\\s*Platform \\d$', '', s)\n s = s + ' Station'\n s = s.replace('(Bakerloo)', 'Bakerloo').replace('Earls', 'Earl\\'s') \\\n .replace(' fast ', ' ') \\\n .replace('\\xe2\\x80\\x99', \"'\") \\\n .replace('St ', 'St. ') \\\n .replace('Elephant and Castle', 'Elephant & Castle') \\\n .replace('Lambeth Station', 'Lambeth North Station') \\\n .replace('Chalfont Station', 'Chalfont & Latimer Station') \\\n .replace('West Brompon', 'West Brompton') \\\n .replace('Picadilly Circus', 'Piccadilly Circus') \\\n .replace('High Barent', 'High Barnet') \\\n .replace('Bartnet', 'Barnet') \\\n .replace('Faringdon', 'Farringdon') \\\n .replace('Turnham Greens', 'Turnham Green') \\\n .replace('Ruilsip', 'Ruislip') \\\n .replace('Dagemham', 'Dagenham') \\\n .replace('Edgware Road (H & C)', 'Edgware Road Circle') \\\n .replace('Hammersmith (Circle and H&C)', 'Hammersmith') \\\n .replace('Shepherds Bush (Central Line)', \"Shepherd's Bush\") \\\n .replace('Terminals 123', 'Terminals 1, 2, 3').replace('Terminal 1,2,3', 'Terminals 1, 2, 3') \\\n .replace('Woodford Junction', 'Woodford') \\\n .replace(\"King's Cross Station\", \"King's Cross St. Pancras Station\") \\\n .replace(\"Kings Cross Station\", \"King's Cross St. Pancras Station\") \\\n .replace('Central Finchley', 'Finchley Central').replace('District and Picc', 'D & P') \\\n .replace('South Fields', 'Southfields') \\\n .replace('Regents Park', \"Regent's Park\") \\\n .replace('Bromley-by-Bow', \"Bromley-By-Bow\") \\\n .replace('Brent Oak', 'Burnt Oak') \\\n .replace('St. Johns Wood', \"St. John's Wood\") \\\n .replace('Totteridge and Whetstone', 'Totteridge & Whetstone') \\\n .replace('Newbury Park Loop', 'Newbury Park') \\\n .replace('Harrow-on-the-Hill', 'Harrow on the Hill')\n if s == 'Edgware Road Station' and line == 'B':\n s = 'Edgware Road Bakerloo Station'\n if s == 'Edgware Road Station' and line != 'B':\n s = 'Edgware Road Circle Station'\n return s","def get_by_altitute():\n try:\n lat, long = input(\"Enter Lat and Long of a place: \").split()\n return lat, long\n except ValueError:\n print(\"Error while getting values.\")\n get_by_altitute()","def extract_addresses(addresses):\n \n # Since lists are iterated over in Python in an orderly fashion, \n # put 'Input Reg' before 'Input' such that the 'Reg' in an address \n # with an 'Input Reg' prefix doesn't get left behind\n address_prefixes= [\"Input Reg \", \"Holding Reg \", \"Input \", \"Coil \"]\n\n \n for idx,address in enumerate(addresses):\n # Replace prefixes with empty string\n for prefix in address_prefixes:\n addresses[idx]=addresses[idx].replace(prefix, \"\")\n # Extract numeral\n try:\n addresses[idx]= int(addresses[idx])\n except:\n logging.warning(\"Invalid modbus address suppied at index {}\".format(idx))\n\n # Return\n return addresses","def clean_address(self, s):\n # The letter \"O\" instead of the numeral \"0\" is a common mistake.\n s = re.sub(\n r\"\\b[A-Z][O0-9][A-Z]\\s?[O0-9][A-Z][O0-9]\\b\", lambda x: x.group(0).replace(\"O\", \"0\"), clean_string(s)\n )\n for k, v in province_or_territory_abbreviations().items():\n # Replace a province/territory name with its abbreviation.\n s = re.sub(\n r\"[,\\n ]+\"\n r\"\\(?\" + k + r\"\\)?\"\n r\"(?=(?:[,\\n ]+Canada)?(?:[,\\n ]+[A-Z][0-9][A-Z]\\s?[0-9][A-Z][0-9])?\\Z)\",\n \" \" + v,\n s,\n )\n # Add spaces between province/territory abbreviation, FSA and LDU and remove \"Canada\".\n return re.sub(\n r\"[,\\n ]+\" r\"([A-Z]{2})\" r\"(?:[,\\n ]+Canada)?\" r\"[,\\n ]+([A-Z][0-9][A-Z])\\s?([0-9][A-Z][0-9])\" r\"\\Z\",\n r\" \\1 \\2 \\3\",\n s,\n )","def get_str_address(address):\n return \\\n get_ob_value_primitive(address, 'AddrLine1', exception_return_value='') + ' ' + \\\n get_ob_value_primitive(address, 'AddrLine2', exception_return_value='') + ' ' + \\\n get_ob_value_primitive(address, 'AddrLine3', exception_return_value='') + ', ' + \\\n get_ob_value_primitive(address, 'City', exception_return_value='') + ' ' + \\\n get_ob_value_primitive(address, 'County', exception_return_value='') + ' ' + \\\n get_ob_value_primitive(address, 'StateProvince', exception_return_value='') + ' ' + \\\n get_ob_value_primitive(address, 'ZipPostalCode', exception_return_value='')","def _parse_info(form) :\n w = 0\n n = 0\n nh = 0\n for part in re.findall(\"[A-Z]+[0-9]+\",form):\n m = re.match(\"([A-Z]+)([0-9]+)\",part)\n element = m.group(1)\n number = int(m.group(2))\n w += mass[element.capitalize()]*number\n n += number\n if element != \"H\" : nh += number\n return w,n,nh","def _parse_info(form) :\n w = 0\n n = 0\n nh = 0\n for part in re.findall(\"[A-Z]+[0-9]+\",form):\n m = re.match(\"([A-Z]+)([0-9]+)\",part)\n element = m.group(1)\n number = int(m.group(2))\n w += mass[element.capitalize()]*number\n n += number\n if element != \"H\" : nh += number\n return w,n,nh","def _cleanupAddress(self, address):\n clean = []\n \n # This is sort of a desultory effort but I'm not convinced \n # that these cleanups will actually result in cleaner searches\n for word in address.split(None):\n lower = word.lower()\n \n # Some things we just nuke\n if lower == 'at': continue\n elif lower == 'btw': continue\n elif lower == 'btwn': continue\n elif word.isdigit(): continue\n \n # Or we make substitiutions\n elif lower == 'st' or lower == 'st.':\n word = 'Street'\n elif lower == 'ave' or lower == 'ave.':\n word = 'Avenue'\n elif lower == 'pl' or lower == 'pl.':\n word = 'Place'\n elif lower == 'n': word = 'North'\n elif lower == 'e': word = 'East'\n elif lower == 's': word = 'South'\n elif lower == 'w': word = 'West'\n \n clean.append(word)\n return ' '.join(clean)","def part(string):\n characters = [] \n count = 0\n st = \"\"\n for char in string:\n if char == \" \":\n characters.append(\"space\")\n elif char ==\"?\" or char == \",\" or char == \";\" or char == \":\":\n characters.append(char)\n count = 0\n else:\n # st += char\n # count += 1\n characters.append(char)\n \n if count == 3:\n characters.append(st)\n st = \"\"\n count = 0\n return characters","def check_born_place(input_string: str) -> tuple:\n c = input_string[0]\n if c == 'A':\n return 'Taipei City', 10\n elif c == 'B':\n return 'Taichung City', 11\n elif c == 'C':\n return 'Keelung City', 12\n elif c == 'D':\n return 'Tainan City', 13\n elif c == 'E':\n return 'Kaohsiung City', 14\n elif c == 'F':\n return 'New Taipei City', 15\n elif c == 'G':\n return 'Yilan County', 16\n elif c == 'H':\n return 'Taoyuan City', 17\n elif c == 'I':\n return 'Chiayi City', 34\n elif c == 'J':\n return 'Hsinchu County', 18\n elif c == 'K':\n return 'Miaoli County', 19\n elif c == 'L':\n return 'Taichung County', 20\n elif c == 'M':\n return 'Nantou County', 21\n elif c == 'N':\n return 'Changhua County', 22\n elif c == 'O':\n return 'Hsinchu City', 35\n elif c == 'P':\n return 'Yunlin County', 23\n elif c == 'Q':\n return 'Chiayi County', 24\n elif c == 'R':\n return 'Tainan County', 25\n elif c == 'S':\n return 'Kaohsiung County', 26\n elif c == 'T':\n return 'Pingtung County', 27\n elif c == 'U':\n return 'Hualien County', 28\n elif c == 'V':\n return 'Taitung County', 29\n elif c == 'W':\n return 'Kinmen County', 32\n elif c == 'X':\n return 'Penghu County', 30\n elif c == 'Y':\n return 'Yangmingshan Management Bureau', 31\n elif c == 'Z':\n return 'Lienchiang County', 33\n else:\n # Should not happen\n return None, None # The return value is a tuple containing two values","def handle_special_cases(substring):\r\n if substring==['', 'EB/NB', '2'] or substring==['EB/NB', '2', ''] or substring==['', 'EB/NB', '2', '#1'] or substring==['', 'EB/NB', '2', '#2']:\r\n dir='E'\r\n road=2\r\n elif substring== ['', 'WB/SB', '2'] or substring==['WB/SB', '2', '']:\r\n dir='W'\r\n road=2\r\n elif substring==['WB210', '']:\r\n dir='W'\r\n road=210\r\n elif substring==['S', '605/W', '10', '']:\r\n dir='W'\r\n road=10 \r\n elif substring==['', 'NB5', 'TRK', 'RTE']:\r\n dir='N'\r\n road=5\r\n elif substring==['', 'S605/E10']:\r\n dir='E'\r\n road=10\r\n else:\r\n dir=None\r\n road=0\r\n return dir, road","def seperate_City_Data(data, us_state_abbrev):\n assert data is not None\n dictionary = dict(data)\n keys = dictionary.keys()\n tmp = list(keys)\n values = dictionary.values()\n res = []\n for elem in keys:\n state = elem[1].strip()\n city = elem[0].strip()\n# print(city)\n if state in us_state_abbrev:\n res.append(city)\n return res, list(values)","def phone_parser(phone, mode='PL'):\n\n if not phone:\n raise WrongInput(\"Input cannot be blank\")\n if not isinstance(phone, str):\n raise WrongInput(\"Invalid phone format\")\n\n if mode == 'PL':\n gsm_prefixes = ['50', '51', '53', '57', '60', '66', '69', '72', '73', '78', '79', '88']\n if phone[:2] in gsm_prefixes:\n phone_pattern = re.compile(r'''\n # don't match beginning of string\n (\\d{0,2}) # area code of 2 digits (e.g. '42')\n \\D* # optional separator\n (\\d{3}\\D*\\d{3}\\D*\\d{3}) # rest of number - divide into 3 3-digit sequences with optional separators\n # (e.g. '605-789-567')\n $ # end of string\n ''', re.VERBOSE)\n else:\n phone_pattern = re.compile(r'''\n # don't match beginning of string\n (\\d{0,2}) # area code of 2 digits (e.g. '42')\n \\D* # optional separator\n (\\d{3}\\D*\\d{2}\\D*\\d{2}) # rest of number - divide into 3 2-digit sequences with optional separators\n # (e.g. '605-78-56')\n $ # end of string\n ''', re.VERBOSE)\n else:\n phone_pattern = re.compile(r'''\n # don't match the beginning of the string\n (\\d{3}) # area code of 3 digits (e.g. '800')\n \\D* # optional separator\n (\\d{3}\\D*\\d{4}\\D*\\d+) # rest of number - divide into 3 sequences with optional separators: two obligatory\n # with 3 and 4 digits, one optional with any number of digits\n $ # end of string\n ''', re.VERBOSE)\n if not re.search(phone_pattern, phone):\n raise WrongInput(\"Invalid phone format.\")\n\n phone_obj = phone_pattern.search(phone)\n phone_area, phone_num = phone_obj.groups()\n phone = re.sub(r'\\D', '', phone_num)\n return phone, phone_area, phone_num","def __process_address(self, address: Tuple[int, int, int, int, int]) -> Dict[str, int]:\n return {\n 'interface': address[0],\n 'protocol': address[1],\n 'type': address[2],\n 'hardware_type': address[3],\n 'address': address[4],\n }","def parse_name(first_name, last_name):\n\n return first_name + \" \" + last_name","def get_address():\r\n address = input(\"What is the customer's address?: \")\r\n\r\n return address","def replace_street(street):\r\n if isinstance(street, str):\r\n for rep in replacements:\r\n street = re.sub(rep, \"\", street)\r\n\r\n streetint = re.findall(r'\\d+', str(street))\r\n if len(streetint) > 0 and int(streetint[0]) < 100:\r\n street = int(streetint[0])\r\n\r\n if street < 10:\r\n street = '0' + str(street) + str(streetnums[str(street)])\r\n elif street < 14:\r\n street = str(street) + 'TH'\r\n else:\r\n street = str(street) + str(streetnums[str(street)[-1]])\r\n\r\n\r\n return street","def number(full_address):\n warning_message = \"\"\"\\n\n This parser should be used with the knowledge that this\n function is open to four significant vulnerabilities:\n 1) `number()` will parse the first numeric characters it\n an address string contains (read from left to right).\n If the address string has:\n a) no building number\n b) numeric characters unrelated to addressable\n information at the start of the address string\n 2) Address numbers separated by `&` or `,` will not be parsed\n 3) Building names that include numeric characters are\n incorrectly parsed as building numbers\\n\n \"\"\"\n warnings.warn(warning_message)\n return capture_address_element(NUMBER_PATTERN, full_address)","def clean_street(self):\n street = self.cleaned_data['street'].strip().title()\n street = re.sub(r'\\bRoad\\b', 'Rd', street)\n street = re.sub(r'\\bStreet\\b', 'Str', street)\n street = re.sub(r'\\bAvenue\\b', 'Ave', street)\n street = re.sub(r'\\bParkway\\b', 'Pkwy', street)\n street = re.sub(r'\\bSuite\\b', 'Ste', street)\n street = re.sub(r'\\bApartment\\b', 'Apt', street)\n street = re.sub(r'\\s+', ' ', street) # Remove runs of spaces\n return street","def split_str(str):\n \n logger = logging.getLogger(__name__)\n \n logger.debug('{0}'.format(str))\n \n match = re.match(r\"([0-9]+.?\\d{0,32}?)(d|m|s)\", str)\n \n if match:\n items = match.groups()\n \n return items[0], items[1]","def parse_puzzle(puzzle):\n puzzle = re.sub(\"\\sGrid \\d{2}\",\"\", sample)\n puzzle = puzzle.strip().split(\"\\n\") \n return puzzle","def return_intersections(streetname):\r\n if streetname != None and isinstance(streetname, str) and ' AND ' in streetname:\r\n streetnames = streetname.split(' AND ')\r\n df = streetintersections[(streetintersections.Street == streetnames[0]) \\\r\n & (streetintersections.Isection == streetnames[1])]\r\n if df.shape[0] > 0:\r\n return str(int(df['Number'].iloc[0])) + ' ' + df['Street'].iloc[0] + ' ' + df['Suffix'].iloc[0]\r\n return None","def mysplit(string):\n result = []\n last_split = 0\n for i in range(len(string)-3):\n if( string[i] == \"a\" and\n string[i+1] == \"n\" and\n string[i+2] == \"d\"):\n partial = string[last_split:i]\n last_split = i+3\n result.append(partial)\n rest = string[last_split:]\n result.append(rest)\n return result","def extract(self, str):\n\n ips = re.match( r'^[0-9]+(?:\\.[0-9]+){3}', str)\n\n if ips:\n return ips.group(0)","def capture_address_element(regex_object, full_address):\n full_address = normalise_address(full_address)\n capture_groups = regex_object.search(full_address)\n if capture_groups:\n return capture_groups.group(0)\n return ''","def parse_info(s:str) -> dict:\n d = {}\n d[\"SVTYPE\"] = re.search(r'(?<=SVTYPE=)\\w+',s).group(0)\n d[\"SUPPORT\"] = re.search(r'(?<=SUPPORT=)\\d+',s).group(0)\n if d[\"SVTYPE\"] in [\"BND\"]:\n return d\n d[\"END\"] = re.search(r'(?<=END=)\\d+',s).group(0)\n if d[\"SVTYPE\"] in [\"INV\"]:\n return d\n d[\"SVLEN\"] = re.search(r'(?<=SVLEN=)(.*?)(?=;)',s).group(0)\n d[\"READS\"] = re.search(r'(?<=READS=)(.*?)(?=$)',s).group(0).split(\",\")\n if d[\"SVTYPE\"] == \"INS\":\n d[\"SEQS\"] = re.search(r'(?<=SEQS=)(.*?)(?=;)',s).group(0).split(\",\")\n return d","def test_parse():\n first = parse_formula(\"PO4H2(CH2)12CH3\")\n assert first == {\"P\":1, \"O\":4, \"H\":29, \"C\":13}\n\n second = parse_formula(\"H2O\")\n assert second == {\"H\":2, \"O\":1}","def parse_streettype(self):\n \n\n try:\n word = sttype[self.words[self.index]['word']]\n if Vocabulary.STREET_TYPE in word['tag']:\n itag = word['tag'].index(Vocabulary.STREET_TYPE)\n lemma = word['lemma'][itag]\n return lemma, 1\n return None, 0\n except: return None, 0","def parse(self, string):\n parse = re.match(\"^((?:[0-9]{1,3}\\.){3}[0-9]{1,3})\\s\\(((?:\\d)*\\.(?:\\d)*|(?:\\d)*)\\sms\\)$\", string)\n parse_result = parse.groups()\n return parse_result[0], parse_result[1]","def _parse_boardline(self, start, street):\n # Exceptions caught in _parse_street.\n board_line = self._splitted[start]\n\n match = self._STREET_RE.search(board_line)\n cards = match.group(1)\n cards = tuple(cards.split()) if street == 'flop' else cards\n setattr(self, street, cards)\n\n pot = match.group(2)\n setattr(self, \"%s_pot\" % street, Decimal(pot))\n\n num_players = int(match.group(3))\n setattr(self, \"%s_num_players\" % street, num_players)","def parse_drs_identifier(drs_candidate: str) -> Tuple[str, str, str]:\n # determine if hostname or compact identifier or unknown\n drs_regex = r\"drs://([A-Za-z0-9\\.\\-\\~]+)/([A-Za-z0-9\\.\\-\\_\\~\\/]+)\"\n # either a drs prefix:\n\n matches = re.findall(drs_regex, drs_candidate, re.UNICODE)\n\n if len(matches) == 1: # this could be a hostname DRS id\n hostname_regex = (\n r\"^(([a-zA-Z0-9]|[a-zA-Z0-9][a-zA-Z0-9\\-]*[a-zA-Z0-9])\\.)*\"\n r\"([A-Za-z0-9]|[A-Za-z0-9][A-Za-z0-9\\-]*[A-Za-z0-9])$\"\n )\n hostname_matches = re.findall(hostname_regex, matches[0][0], re.UNICODE)\n if len(hostname_matches) == 1:\n return matches[0][0], matches[0][1], \"hostname\"\n # possible compact rep\n compact_regex = r\"([A-Za-z0-9\\.\\-\\~]+)/([A-Za-z0-9\\.\\-\\_\\~\\/]+)\"\n matches = re.findall(compact_regex, drs_candidate, re.UNICODE)\n if len(matches) == 1 and len(matches[0]) == 2:\n return matches[0][0], matches[0][1], \"compact\"\n\n # can't figure out a this identifier\n return \"\", \"\", \"unknown\"","def split_addr(self, a):\n a = a.replace('http://', '')\n a = a.replace('https://', '')\n\n addr = tlde.extract(a)\n is_ip = tlde.tldextract.looks_like_ip(addr.domain)\n if is_ip:\n ip = addr.domain\n path_and_params = a[a.index(ip)+len(ip):].split('?')\n path = path_and_params[0]\n if len(path_and_params) > 1:\n params = path_and_params[1:]\n else:\n params = ''\n return {'ip': ip, 't3': None, 't2': None, 'path': path, 'params': params, 'url/ip': 'ip'}\n else:\n t3 = addr.subdomain\n t2 = addr.registered_domain\n path_and_params = a[a.index(addr.fqdn)+len(addr.fqdn):].split('?')\n path = path_and_params[0]\n if len(path_and_params) > 1:\n params = path_and_params[1:]\n else:\n params = ''\n return {'t3': t3, 't2': t2, 'ip': None, 'path': path, 'params': params, 'url/ip': 'url'}","def get_address_parts(ip, mask):\n\n ip_bin_str = address_to_bin(ip).replace('.', '')\n mask_bin_str = address_to_bin(mask).replace('.', '')\n\n net_size = mask_bin_str.rfind('1') + 1\n host_size = ADDR_LEN - net_size\n\n net = _address_from_bin_list(_split_bin_str_on_ocsets(ip_bin_str[:net_size] + '0' * host_size))\n host = _address_from_bin_list(_split_bin_str_on_ocsets('0' * net_size + ip_bin_str[-host_size:]))\n net_count = 2 ** host_size - 2\n count_string = '2^{0}-2'.format(host_size)\n return net, host, net_count, count_string","def input_parser(input_string: str) -> str: \n if is_int(input_string):\n return input_string\n #he is int, give back plz.\n else:\n try:\n modified_input: str = input_string.strip()\n\n evaluatable_pairs: str = regex_splitter(modified_input)\n\n while not (is_int(evaluatable_pairs)):\n evaluatable_pairs = regex_splitter(evaluatable_pairs)\n\n return (evaluatable_pairs)\n\n except:\n raise Exception(\"Invalid Input\")","def street_address1(self) -> str:\n return pulumi.get(self, \"street_address1\")","def get_city(string):\n city = \"\"\n previous_ch = None;\n\n #For each character in string\n for ch in string:\n #break if it is a comma, the city has been completed\n if ch == \",\":\n break\n #if the character is a letter, add it to the \"city\" string\n elif ch.isalpha():\n city += ch\n #if the character is a space, and the previous character is a letter, add the space to the \"city\" string. (This prevents duplicate spaces)\n elif ch.isspace() & previous_ch.isalpha():\n city += ch\n\n #update previous character\n previous_ch = ch\n\n return city","def format_address(line1, line2, city, state, zipcode):\n\t\n\tstreetlines = line1\n\tcityline = city\n\t\n\tif len(streetlines) > 0 and len(line2) > 0:\n\t\tstreetlines += \"\\n\"\n\t\n\tif len(cityline) > 0 and len(state) > 0:\n\t\tcityline += \", \"\n\t\n\tstreetlines += line2\n\tcityline += state\n\t\n\treturn \"\\n\".join([streetlines, cityline, zipcode])","def seperate_Loc_Data(data, us_state_abbrev):\n assert data is not None\n dictionary = dict(data)\n keys = dictionary.keys()\n tmp = list(keys)\n values = dictionary.values()\n res = []\n for elem in keys:\n state = elem[1].strip()\n if state in us_state_abbrev:\n res.append(us_state_abbrev[state])\n return res, list(values)","def get_first_and_last_name(full_name):\n try:\n result = full_name.split(' ', 1)\n except AttributeError:\n return ['', '']\n else:\n if len(result) == 2:\n return result\n return [full_name, full_name]","def parse_input(giant_string):\n X_train_part, Y_train_part, X_test_part = giant_string.split(\"XXX\")\n\n X_train_row_strings = X_train_part.split(\"S\")\n X_train_rows = [[float(x) for x in row.split(\",\")] for row in X_train_row_strings]\n X_train = np.array(X_train_rows)\n\n Y_train = concatenated_string_to_array(Y_train_part)\n\n X_test_row_strings = X_test_part.split(\"S\")\n X_test_rows = [[float(x) for x in row.split(\",\")] for row in X_test_row_strings]\n X_test = np.array(X_test_rows)\n\n return X_train, Y_train, X_test","def Parser(wa1, wa2):\r\n #Note that in the documentation, they start counting at position 1\r\n output = { \r\n 'First Borough Name': wa1[360 :369].strip(),\r\n 'House Number Display Format': wa1[369: 385].strip(),\r\n 'House Number Sort Format': wa1[385: 396].strip(),\r\n 'B10SC First Borough and Street Code': wa1[396: 407].strip(),\r\n 'Second Street Name Normalized': wa1[407:439].strip(),\r\n 'Community District': wa2[149:152].strip(),\r\n 'Zip Code': wa2[152:157].strip(),\r\n 'Election District': wa2[157:160].strip(),\r\n 'Assembly District': wa2[160:162].strip(),\r\n 'Congressional District': wa2[163:165].strip(),\r\n 'State Senatorial District': wa2[165:167].strip(),\r\n 'City Council District': wa2[169:171].strip(),\r\n 'Police Precinct': wa2[191:194].strip(),\r\n 'Community School District': wa2[203:205].strip(),\r\n 'Atomic Polygon': wa2[205: 208].strip(),\r\n '2010 Census Tract': wa2[223: 229].strip(),\r\n '2010 Census Block': wa2[229:233].strip(),\r\n '2010 Census Block Suffix': wa2[233].strip(),\r\n 'Neighborhood Tabulation Area (NTA)': wa2[245:249].strip(),\r\n 'DSNY Snow Priority Code': wa2[249].strip(),\r\n 'Hurricane Evacuation Zone (HEZ)': wa2[260:262].strip(),\r\n 'Spatial Coordinates of Segment': {'X Coordinate, Low Address End': wa2[313:320].strip(),\r\n 'Y Coordinate, Low Address End': wa2[320:327].strip(),\r\n 'Z Coordinate, Low Address End': wa2[327:334].strip(),\r\n 'X Coordinate, High Address End': wa2[334:341].strip(),\r\n 'Y Coordinate, High Address End': wa2[341:348].strip(),\r\n 'Z Coordinate, High Address End': wa2[348:355].strip(),\r\n },\r\n 'Roadway Type': wa2[444:446].strip(),\r\n 'Bike Lane': wa2[486].strip(),\r\n 'NTA Name': wa2[553: 628].strip(),\r\n 'USPS Preferred City Name': wa2[628:653].strip(),\r\n 'Latitude': wa2[653:662].strip(),\r\n 'Longitude': wa2[662: 673].strip(),\r\n 'Borough Block Lot (BBL)': {'Borough code': wa2[1533].strip(),\r\n 'Tax Block': wa2[1534:1539].strip(),\r\n 'Tax Lot': wa2[1539:1543].strip(),\r\n },\r\n 'Building Identification Number (BIN) of Input Address or NAP': wa2[1581:1588].strip(),\r\n 'X-Y Coordinates of Lot Centroid': wa2[1699:1713].strip(),\r\n 'Spatial X': wa2[125:132].strip(),\r\n 'Spatial Y': wa2[132:139].strip(),\r\n 'Message': wa1[579:659].strip(),\r\n }\r\n return output","def return_list_from_string(inputx):\n x = inputx.split(\";\")\n for wrd in x:\n if \"WORD1\" in wrd or \"NAME1\" in wrd:\n wrd2 = (\n wrd.replace(\"WORD1:\", \"\")\n .replace(\"{{WORD1}}:\", \"\")\n .replace(\"NAME1:\", \"\")\n .replace(\"{{NAME1}}:\", \"\")\n )\n wrd3 = wrd2.strip()\n wrds = wrd3.replace(\"[\", \"\").replace(\"]\", \"\")\n wrds1 = wrds.split(\",\")\n wrds1 = [w.strip() for w in wrds1]\n if \"WORD2\" in wrd or \"NAME2\" in wrd:\n wrd2 = (\n wrd.replace(\"WORD2:\", \"\")\n .replace(\"{{WORD2}}:\", \"\")\n .replace(\"NAME2:\", \"\")\n .replace(\"{{NAME2}}:\", \"\")\n )\n wrd3 = wrd2.strip()\n wrds = wrd3.replace(\"[\", \"\").replace(\"]\", \"\")\n wrds2 = wrds.split(\",\")\n wrds2 = [w.strip() for w in wrds2]\n else:\n wrds2 = \"\"\n return wrds1, wrds2","def _parse_location(location_string):\n location_regex = r\"(\\d+)-(\\d+)(\\(+\\)|\\(-\\)|)\"\n match = re.match(location_regex, location_string.strip())\n start, end, strand = match.groups()\n return int(start), int(end), -1 if strand == \"(-)\" else 1","def get_apartment_address(self, soup, apartment_dict):\n\n info_class = soup.find_all('div', {'class': 'info'})\n if info_class and len(info_class) > 0:\n info_class = info_class[0]\n address = info_class.find('h2').text.strip()\n\n from parse import parse\n address = parse(\"Location: {}\", address)[0]\n apartment_dict['address'] = address\n else:\n logging.warning(\"Failed to parse apartment address\")\n return","def ExtractNumbers(s):\n\n t = s.strip('[]\\n')\n comma_space = r', '\n re_comma_space = re.compile(comma_space)\n z = re_comma_space.split(t)\n #print z\n return z","def parse(self, item: str) -> Tuple[str, str]:\n try:\n city, *_, country = item.split(',')\n except ValueError:\n return '', item\n return city.strip(), country.strip()","def parse_input(giant_string):\r\n X_train_part, Y_train_part, X_test_part = giant_string.split(\"XXX\")\r\n\r\n X_train_row_strings = X_train_part.split(\"S\")\r\n X_train_rows = [[float(x) for x in row.split(\",\")] for row in X_train_row_strings]\r\n X_train = np.array(X_train_rows)\r\n\r\n Y_train = concatenated_string_to_array(Y_train_part)\r\n\r\n X_test_row_strings = X_test_part.split(\"S\")\r\n X_test_rows = [[float(x) for x in row.split(\",\")] for row in X_test_row_strings]\r\n X_test = np.array(X_test_rows)\r\n\r\n return X_train, Y_train, X_test","def city_parser(city: str = None):\n return city.text.strip().split(',')[1]"],"string":"[\n \"def street_parser(*street_data):\\n\\n # parsing tuples\\n if len(street_data) == 2:\\n if not isinstance(street_data[0], str) and not isinstance(street_data[1], str):\\n raise WrongInput(\\\"Invalid format\\\")\\n # street name as the tuple's first item\\n strname, strnumber = street_data\\n # street number as the tuple's first item\\n if street_data[0][0] in digits:\\n strname, strnumber = strnumber, strname\\n\\n # parsing strings\\n else:\\n if not isinstance(street_data[0], str):\\n raise WrongInput(\\\"Invalid format\\\")\\n if not street_data[0]:\\n raise WrongInput(\\\"Input cannot be blank\\\")\\n\\n # string starting with street number\\n if street_data[0][0] in digits:\\n street_pattern = re.compile(r'''\\n ^ # beginning of string\\n (\\\\d+) # street number is any number of digits\\n \\\\W+ # separator\\n (\\\\w+\\\\W*\\\\w*\\\\W*) # street name is one or more words with optional separators\\n $ # end of string\\n ''', re.VERBOSE)\\n street_obj = street_pattern.search(street_data[0])\\n strnumber, strname = street_obj.groups()\\n\\n # string starting with street name\\n else:\\n street_pattern = re.compile(r'''\\n ^ # beginning of string\\n (\\\\w+\\\\W*\\\\w*\\\\s*) # street name is one or more words with optional separators\\n \\\\W+ # separator\\n (\\\\d+) # street number is any number of digits\\n $ # end of string\\n ''', re.VERBOSE)\\n street_obj = street_pattern.search(street_data[0])\\n (strname, strnumber) = street_obj.groups()\\n\\n # replace specific words in street name with their abbreviates\\n strname = strname.lower()\\n special = {r'\\\\baleje\\\\b': 'Al.', r'\\\\bavenue\\\\b': 'Av.', r'\\\\broad\\\\b': 'Rd.', r'\\\\bsquare\\\\b': 'Sq.',\\n r'\\\\bstreet\\\\b': 'St.', r'\\\\bdrive\\\\b': 'Dr.'}\\n for key in special:\\n strname = re.sub(key, special[key], strname)\\n return strname.title(), strnumber\",\n \"def parse_address_street(address_str, address_zip_us_re, address_zip_us_lax_re):\\n address = {}\\n errors = []\\n parts = address_str.split('$')\\n if DEBUG:\\n address['debug_address_str'] = address_str\\n address['debug_part_1'] = parts[0]\\n address['debug_part_last'] = parts[-1]\\n address['debug_length'] = len(parts)\\n #if len(parts) == 1:\\n #print('cannot split: {}: {}'.format(debug_type, address_str))\\n match = re.search(address_zip_us_re, parts[-1])\\n if match:\\n if DEBUG:\\n address['debug_parser'] = 'A'\\n address['city'] = match.group(1)\\n address['region'] = match.group(2).upper()\\n address['postalCode'] = match.group(3)\\n address['countryId'] = 'US'\\n if len(parts) == 2:\\n if DEBUG:\\n address['debug_parser'] = 'B'\\n address['addressLine1'] = parts[0]\\n else:\\n if len(parts) == 3:\\n if DEBUG:\\n address['debug_parser'] = 'C'\\n address['addressLine1'] = parts[0]\\n if parts[0] != parts[1]:\\n if DEBUG:\\n address['debug_parser'] = 'D'\\n address['addressLine2'] = parts[1]\\n else:\\n match2 = re.search(address_zip_us_lax_re, address_str)\\n if match2:\\n if DEBUG:\\n address['debug_parser'] = 'E'\\n address['region'] = match2.group(2).upper()\\n address['postalCode'] = match2.group(3)\\n address['countryId'] = 'US'\\n # FIXME: Cannot reliably parse the remainder for city and street address\\n errors.append('Partial parse street address: {}'.format(address_str))\\n address['addressLine1'] = match2.group(1)\\n else:\\n # This is the remainder that we could not parse.\\n # So just put it all into \\\"addressLine1\\\" to be manually adjusted later.\\n if DEBUG:\\n address['debug_parser'] = 'F'\\n errors.append('Cannot parse street address: {}'.format(address_str))\\n address['addressLine1'] = address_str\\n return (address, errors)\",\n \"def get_address(address: str) -> Tuple[str, str, str]:\\n\\n # Try to geocode the address as given\\n g = geocoder.osm(address)\\n\\n if g.json is not None:\\n\\n # TODO this is inefficient and hacky\\n\\n # First thing we attempt if the result isn't complete is just to\\n # add the housenumber (often the issue).\\n if not good_geocoder_result(g.json):\\n g.json['housenumber'] = usaddress.tag(address)[0]['AddressNumber']\\n\\n # If the result is now good, return it\\n if good_geocoder_result(g.json):\\n\\n # Geocoding was successful. Return the result\\n return (\\n # First part is a nicely formatted address\\n f\\\"{g.json['housenumber']} {g.json['street']}, {g.json['city']}, {g.json['state']} {g.json['postal']}\\\",\\n # Second is the latitude\\n g.json['lat'],\\n # And third is the longitude\\n g.json['lng']\\n )\\n\\n # Geocoding was unsuccessful.\\n # Let's try to create a cleaner address by first parsing out the pieces we need, then try again.\\n \\n # Parsing the address components...\\n parsed, addr_type = usaddress.tag(address)\\n if addr_type != \\\"Street Address\\\":\\n raise ValueError(f\\\"Address could not be properly parsed. Resulting type: {addr_type}. Result: \\\\n{parsed}\\\")\\n \\n # Trim off any whitespace from the parsed components.\\n for part in parsed:\\n parsed[part] = parsed[part].strip()\\n\\n reqd_address_parts = ['AddressNumber', 'StreetName', 'PlaceName']\\n if any(address_part not in parsed for address_part in reqd_address_parts):\\n raise ValueError(f\\\"The address must have at least a house number, street, and city.\\\")\\n \\n # Initialize the resulting address string with the address number (aka house/street number)\\n new_address = parsed['AddressNumber']\\n \\n # If the streetname is just a number, make it ordinal\\n if parsed['StreetName'].isnumeric():\\n parsed['StreetName'] = ordinal(parsed['StreetName'])\\n \\n # Get the whole street name\\n for k, v in [(k, v) for k, v in parsed.items() if k.startswith(\\\"StreetName\\\")]:\\n new_address += f\\\" {v}\\\"\\n \\n # Add the city...\\n new_address += f\\\", {parsed['PlaceName']}\\\"\\n # Add the state, if it exists\\n if 'StateName' in parsed:\\n new_address += f\\\", {parsed['StateName']}\\\"\\n # And the zip code, if it exists\\n if 'ZipCode' in parsed:\\n new_address += f\\\" {parsed['ZipCode']}\\\"\\n \\n # Now try to geocode this improved address\\n g = geocoder.osm(new_address)\\n\\n if g.json is not None:\\n\\n # Geocoding was successful. Return the result\\n return (\\n # First part is a nicely formatted address\\n f\\\"{g.json['housenumber']} {g.json['street']}, {g.json['city']}, {g.json['state']} {g.json['postal']}\\\",\\n # Second is the latitude\\n g.json['lat'],\\n # And third is the longitude\\n g.json['lng']\\n )\\n \\n # Still can't geocode the address. Throw an error\\n else:\\n raise ValueError(f\\\"Could not geocode this address: {address}\\\")\",\n \"def extract_unit_num_street(self, address):\\n\\n unit = street_num = street = None\\n\\n address_parts = self.clean(address).split()\\n\\n try:\\n street_num = int(address_parts[1])\\n street = address_parts[2]\\n unit = int(address_parts[0])\\n\\n except IndexError:\\n raise InvalidAddress(address)\\n\\n except ValueError:\\n\\n try:\\n street_num = int(address_parts[0])\\n except ValueError:\\n # Not even a number\\n raise InvalidAddress(address)\\n\\n street = address_parts[1]\\n\\n return (unit, street_num, street)\",\n \"def process_capture_groups(group):\\n group = group[0][0].replace(',', '')\\n group = group.split(' ')\\n street_type = group[-1]\\n street_name = ' '.join(group[:-1])\\n if street_type in STREET_ABBREVIATION_TO_NAME.keys():\\n street_type = STREET_ABBREVIATION_TO_NAME[street_type]\\n return street_name, street_type\",\n \"def parse_address_campus(address_str, address_campus_re, address_campus_room_re):\\n address = {}\\n errors = []\\n if '$' not in address_str:\\n match = re.search(address_campus_room_re, address_str)\\n if match:\\n address['addressLine1'] = match.group(1)\\n else:\\n # This leftover is either an erroneous email address or a building name\\n if '@' in address_str:\\n errors.append('Campus address seems to be email: {}'.format(address_str))\\n #FIXME: Should this be saved to addressLine1 anyway.\\n else:\\n # It seems to be a building address\\n address['addressLine2'] = address_str\\n else:\\n match = re.search(address_campus_re, address_str)\\n if match:\\n address['addressLine2'] = match.group(1)\\n address['addressLine1'] = match.group(2)\\n #else:\\n # FIXME: here just for debug\\n #errors.append('Cannot parse campus address: {}'.format(address_str))\\n return (address, errors)\",\n \"def parse_building_address(addr_string):\\n addr_string = re.sub(_regexp, '', addr_string)\\n addr_string = re.sub(r'(?P<key>[a-zA-Z]+)', _replace_dir, addr_string)\\n addr_string = re.sub(r'(?P<key>[a-zA-Z]+)', _replace_suffix, addr_string)\\n addr_string = re.sub(_regexp_extra_space, ' ', addr_string)\\n return addr_string.strip().upper()\",\n \"def parse_streetname(self):\\n index = self.index\\n \\n name = \\\"\\\"\\n for i in range(4):\\n if index + i == self.length:\\n break\\n if self.words[index+i]['word'] == ',':\\n break\\n # Hack\\n if self.words[index+i]['word'] == 'doctor':\\n self.words[index+i]['word'] = 'drive'\\n break\\n try:\\n word = sttype[self.words[index+i]['word']]\\n break\\n except:\\n try:\\n word = vocab[self.words[index+i]['word']]\\n if Vocabulary.STREET_TYPE in word['tag']:\\n break\\n if name != '':\\n name += ' ' + word['lemma'][0]\\n else:\\n name = word['lemma'][0]\\n except: \\n if self.words[index+i]['word'][-2:] in [ 'th', 'st', 'nd', 'rd' ]:\\n name = self.words[index+i]['word'][:-2]\\n else:\\n self.index += i\\n _dir, _n = self.parse_streetdir()\\n self.index -= i\\n if _dir:\\n break\\n if name != '':\\n name += ' ' + self.words[index+i]['word']\\n else:\\n name = self.words[index+i]['word']\\n \\n if i == 0 or i == 4:\\n return None, 0\\n else:\\n return name, i\",\n \"def address():\\n # We start with generating the street name. For this we choose\\n # between the most common prefixes and our own prefixes\\n prefix = dice.randint(1, 100)\\n if prefix <= 10: # 10%\\n prefix = \\\"Haupt\\\"\\n elif prefix <= 18: # 8%\\n prefix = \\\"Schul\\\"\\n elif prefix <= 25: # 7%\\n prefix = \\\"Garten\\\"\\n elif prefix <= 32: # 7%\\n prefix = \\\"Dorf\\\"\\n elif prefix <= 39: # 7%\\n prefix = \\\"Bahnhof\\\"\\n elif prefix <= 46: # 7%\\n prefix = \\\"Wiesen\\\"\\n elif prefix <= 52: # 6%\\n prefix = \\\"Berg\\\"\\n elif prefix <= 56: # 4%\\n prefix = \\\"Kirch\\\"\\n elif prefix <= 60: # 4%\\n prefix = \\\"Wald\\\"\\n elif prefix <= 64: # 4%\\n prefix = \\\"Ring\\\"\\n else:\\n prefix = dice.choice(names.prefix)\\n\\n # Now we can add the suffix\\n suffix = dice.randint(1, 100)\\n if suffix <= 78:\\n suffix = \\\"straße\\\"\\n elif suffix <= 96:\\n suffix = \\\"weg\\\"\\n elif suffix <= 98:\\n suffix = \\\"allee\\\"\\n elif suffix == 99:\\n suffix = \\\"ring\\\"\\n elif suffix == 100:\\n suffix = \\\"platz\\\"\\n\\n # When we have a city name as prefix, we need to capitalize the\\n # suffix since it will be two words\\n if prefix[-1] == \\\" \\\":\\n suffix = suffix.capitalize()\\n\\n # Now we can add them together\\n street = prefix + suffix\\n\\n # We need a house number as well. In Germany most numbers have\\n # between one and four digits, so we will use this as base. Lower\\n # numbers are more common, so we'll give it a 10% probability of\\n # using 3 digits and 1% of using 4 digits\\n digits = dice.randint(1, 100)\\n if digits == 100:\\n house_number = str(dice.randint(1000, 9999))\\n elif digits >= 90:\\n house_number = str(dice.randint(100, 999))\\n else:\\n house_number = str(dice.randint(1, 99))\\n address_full = street + \\\" \\\" + house_number\\n return address_full\",\n \"def street_address(full_address):\\n full_address = normalise_address(full_address)\\n capture_groups = ROAD_PATTERN.findall(full_address)\\n if capture_groups:\\n name, _type = process_capture_groups(capture_groups)\\n return '{} {}'.format(name, _type)\\n return ''\",\n \"def extract_state(full_address):\\n full_address = full_address.strip()\\n last_comma_index = full_address.rindex(\\\",\\\")\\n last_space_index = full_address.rindex(\\\" \\\")\\n state = full_address[last_comma_index + 1 : last_space_index]\\n state = state.strip()\\n return state\",\n \"def find_places(query):\\n parts = str(query).split(' ')\\n for i, p in enumerate(parts):\\n p = p.replace('-', ' ').strip()\\n try:\\n postal_code = int(p)\\n if len(postal_code) == 4:\\n print(postal_code, parts[i+1])\\n # Check \\n #response = get_osm_location(\\\"{postal_code} {name}\\\")\\n #lon = response['lon']\\n #lat = response['lat']\\n #poly = \\n except Exception as e:\\n continue\",\n \"def maploc(loc):\\n\\n\\n loc = REGEX['parens'].sub('', loc)\\n loc = REGEX['and'].sub('', loc)\\n loc = REGEX['num'].sub('', loc)\\n\\n \\\"\\\"\\\"\\n 'parens' 'and' 'single' 'num' 'seeley' 'iab' 'brh'\\n \\\"\\\"\\\"\\n \\\"\\\"\\\"\\n /* For non-street address, strip room numbers */\\n if (!location.match(' Ave')) {\\n location = location.replace(/LL[0-9]/g, '').replace(/[0-9]/g, '');\\n }\\n /* Some text substitutions */\\n location = location.replace('Seeley W.', '').replace('International Affairs Building', '420 W 118th St').replace('Broadway Residence Hall', '2900 Broadway');\\n\\n \\\"\\\"\\\"\\n return loc + ', New York, NY 10027'\",\n \"def parse_address(address: str) -> OrderedDict[str, str]:\\n\\n address = address.replace(\\\"\\\\n\\\", \\\", \\\")\\n\\n parsed_address, address_type = usaddress.tag(address)\\n if address_type != \\\"Street Address\\\":\\n logger.warning(\\n f\\\"Couldn't parse address '{address}' of type {address_type}; best guess: {parsed_address}\\\"\\n )\\n\\n # Fixup: At least one address has \\\"WI, USA\\\" in the \\\"StateName\\\" component.\\n # Strip non-state components\\n if parsed_address.get(\\\"StateName\\\"):\\n parsed_address[\\\"StateName\\\"] = parsed_address[\\\"StateName\\\"].partition(\\\",\\\")[0]\\n\\n return parsed_address\",\n \"def parse_phone(s):\\n pattern = '''\\n ^\\\\s* # Leading spaces\\n (?P<areacode>\\n \\\\d{3}-? # \\\"xxx\\\" or \\\"xxx-\\\"\\n | \\\\(\\\\d{3}\\\\)\\\\s* # OR \\\"(xxx) \\\"\\n )\\n (?P<prefix>\\\\d{3}) # xxx\\n -? # Dash (optional)\\n (?P<suffix>\\\\d{4}) # xxxx\\n \\\\s*$ # Trailing spaces\\n '''\\n matcher = re.compile(pattern, re.VERBOSE)\\n matches = matcher.match(s)\\n if matches is None:\\n print(s)\\n return s\\n else:\\n areacode = re.search('\\\\d{3}', matches.group ('areacode')).group()\\n prefix = matches.group ('prefix')\\n suffix = matches.group ('suffix')\\n return areacode+'-'+prefix+'-'+suffix\",\n \"def find_street(self, text):\\n text = text.replace('\\\"', '')\\n result = ''\\n textarr = text.split()\\n for i in range(0, len(textarr)):\\n if textarr[i] in self.streets:\\n # decide if the result is nearly empty or not to add the word with or without a space\\n if len(result) < 2:\\n result = result + textarr[i]\\n else:\\n result = result + ' ' + textarr[i]\\n\\n if result in self.streets:\\n\\n # ends the function if the street is found\\n if (result + '\\\\n') in self.streets and result != 'straße' and result != 'in' and result != '' \\\\\\n and result != 'ein' and result != 'der' and result != 'und' and result != ' ' \\\\\\n and result != 'an':\\n return result\\n else:\\n result_list = result.split()\\n del result_list[0]\\n result = ' '.join(w for w in result_list)\\n result = result.lstrip(' ')\\n # ends the function if the street is found\\n if (result + '\\\\n') in self.streets and result != 'straße' and result != 'in' and result != '' \\\\\\n and result != 'ein' and result != 'der' and result != 'und' and result != ' ' \\\\\\n and result != 'ist' and result != 'an':\\n return result\\n elif i == len(textarr)-1:\\n result_list = result.split()\\n del result_list[0]\\n result = ' '.join(w for w in result_list).lstrip(' ')\\n if (result + '\\\\n') in self.streets and result != 'straße' and result != 'in' and result != '' \\\\\\n and result != 'ein' and result != 'der' and result != 'und' and result != ' ' \\\\\\n and result != 'ist' and result != 'an':\\n return result\\n print(result)\\n return None\",\n \"def extract_city(full_address):\\n full_address = full_address.strip()\\n last_comma_index = full_address.rindex(\\\",\\\")\\n mid_comma_index = full_address.rindex(\\\",\\\", 0, last_comma_index)\\n city = full_address[mid_comma_index + 1 : last_comma_index]\\n city = city.strip()\\n return city\",\n \"def split_address(address):\\n if '://' in address:\\n protocol, address = address.split('://')\\n else:\\n protocol = 'http'\\n\\n if ':' in address:\\n address, port = address.split(':')\\n else:\\n port = 443 if protocol == 'https' else 8000\\n\\n return protocol, address, int(port)\",\n \"def parse_place_notation(input_string: str) -> Tuple[int, str]:\\n\\n # Looking for a string that matches <stage>:<place notation> where the\\n # place notation is a series of bell numbers and 'x' characters\\n parts = input_string.split(\\\":\\\")\\n if len(parts) == 2:\\n stage_part = parts[0]\\n if len(stage_part) == 0 or not stage_part.isnumeric():\\n raise PlaceNotationError(input_string, \\\"Stage must be a number\\\")\\n stage = int(stage_part)\\n place_notation = parts[1]\\n if not valid_pn(place_notation):\\n raise PlaceNotationError(input_string, \\\"Place notation is invalid\\\")\\n else:\\n raise PlaceNotationError(input_string, \\\"<stage>:<place notation> required\\\")\\n\\n return stage, place_notation\",\n \"def extract_postcode(s):\\n pc_regex = r'([Gg][Ii][Rr] 0[Aa]{2})|((([A-Za-z][0-9]{1,2})|(([A-Za-z][A-Ha-hJ-Yj-y]'\\n pc_regex += r'[0-9]{1,2})|(([A-Za-z][0-9][A-Za-z])|([A-Za-z][A-Ha-hJ-Yj-y][0-9]?[A-Za-z]'\\n pc_regex += r'))))\\\\s?[0-9][A-Za-z]{2})'\\n\\n re_search = re.search(pc_regex, s)\\n if re_search:\\n p = re_search.group(0)\\n else:\\n p = ''\\n return p\",\n \"def search_address(query: str) -> Tuple[int, str]:\\n\\n url = 'https://api.n1.ru/api/v1/geo/geocoder/with_cities/'\\n params = _search_params.copy()\\n params['q'] = query\\n\\n try:\\n r = requests.get(url, params=params, headers=_headers)\\n response = r.json()\\n\\n if not 'result' in response or not response['result']:\\n raise NotFoundException('Result not found or empty.')\\n \\n address = None\\n house_number = query.split(',')[-1].strip()\\n for x in response['result']:\\n if x['name_ru'].lower() == house_number:\\n address = x\\n break\\n \\n if address is None:\\n raise NotFoundException(f'Not found house number {house_number} in result: {response[\\\"result\\\"]}')\\n \\n return address['street']['id'], address['name_ru']\\n except requests.RequestException as e:\\n raise ParserException(f'Fail make request. query: {query}') from e\\n except NotFoundException as e:\\n raise ParserException('Invalid result.') from e\\n except (KeyError, IndexError) as e:\\n raise ParserException(f'Fail get street id or house number. value: {response[\\\"result\\\"]}') from e\",\n \"def parse(self, src, line):\\n r = line.split('\\\\t')\\n p = {}\\n if src == 'sf':\\n p['businessID'] = r[0]\\n p['name'] = r[1]\\n p['address'] = r[2]\\n p['city'] = r[3]\\n p['state'] = r[4]\\n p['zip'] = r[5]\\n p['latitude'] = r[6]\\n p['longitude'] = r[7]\\n p['phone'] = r[8]\\n elif src == 'nyc':\\n p['businessID'] = r[0]\\n p['name'] = r[1]\\n # nyc separates the building number from the street name\\n p['address'] = ' '.join([r[3].strip(), r[4].strip()])\\n p['city'] = 'NYC'\\n p['state'] = 'NY'\\n p['zip'] = r[5]\\n p['latitude'] = None\\n p['longitude'] = None\\n p['phone'] = r[6]\\n return p\",\n \"def _get_address(self, soup):\\n street, city, state, zipcode = None, None, None, None\\n try:\\n # property detail tag\\n street = soup.find('div', class_='main-address').get_text().strip()\\n # find address tag\\n address = soup.find('div', class_='c-address')\\n \\n # pattern for the address in this website\\n locality = address.find_all('span', class_='locality')\\n city = locality[0].get_text().strip()\\n if len(locality) > 1:\\n city = locality[1].get_text().strip()\\n state = address.find('span', class_='region').get_text().strip()\\n zipcode = address.find('span', class_='postal-code').get_text().strip()\\n return street, city, state, zipcode\\n except:\\n return street, city, state, zipcode\",\n \"def extract_zipcode(full_address):\\n full_address = full_address.strip()\\n last_space_index = full_address.rindex(\\\" \\\")\\n zipcode = full_address[last_space_index + 1 : ]\\n return zipcode\",\n \"def func2():\\n\\n\\tnums = '''\\n\\t\\t800-555-1212\\n\\t\\t800 555 1212\\n\\t\\t800.555.1212\\n\\t\\t(800) 555-1212\\n\\t\\t1-800-555-1212\\n\\t\\t800-555-1212-1234\\n\\t\\t800-555-1212x1234\\n\\t\\t800-555-1212 ext. 1234\\n\\t\\twork 1-(800) 555.1212 #1234\\n\\t'''\\n\\n\\tpattern = r\\\"(\\\\d{3})\\\\D*(\\\\d{3})\\\\D*(\\\\d{4})\\\\D*(\\\\d*)$\\\"\\n\\tregex = re.compile(pattern)\\n\\n\\tprint regex.search(\\\"800-555-1212\\\").groups()\\n\\tprint regex.search(\\\"800 555 1212\\\").groups()\\n\\tprint regex.search(\\\"800.555.1212\\\").groups()\\n\\n\\tprint regex.search(\\\"(800) 555-1212\\\").groups()\\n\\tprint regex.search(\\\"1-800-555-1212\\\").groups()\\n\\n\\tprint regex.search(\\\"800-555-1212-1234\\\").groups()\\n\\tprint regex.search(\\\"800-555-1212x1234\\\").groups()\\n\\n\\tprint regex.search(\\\"800-555-1212 ext. 1234\\\").groups()\\n\\tprint regex.search(\\\"work 1-(800) 555.1212 #1234\\\").groups()\",\n \"def split_well_name (well_name):\\n\\n letters = well_name.rstrip('0123456789')\\n\\n nums = well_name.lstrip(letters)\\n\\n\\n #Do some checks to make sure it's a well name in for the format letter-letter-#-#\\n if len(nums) == 0:\\n raise ValueError('Something is wrong with your input, I cannot find a row number')\\n\\n\\n for i in '0123456789':\\n if i in letters:\\n raise ValueError('Something is wrong with your input, I think there is a number in your column letter.')\\n\\n for j in nums:\\n if j not in '0123456789':\\n raise ValueError('Something is wrong with your input, I think there is a letter in your row number.')\\n\\n return letters, nums\",\n \"def street_address():\\r\\n\\r\\n return _random.choice(\\r\\n [\\r\\n '%d-%d %s' % (\\r\\n _random.randrange(999),\\r\\n _random.randrange(999),\\r\\n street_name()\\r\\n ),\\r\\n '%d %s' % (\\r\\n _random.randrange(999),\\r\\n street_name()\\r\\n ),\\r\\n '%s %d, %s' % (\\r\\n 'P.O. Box',\\r\\n _random.randrange(999),\\r\\n street_name()\\r\\n )\\r\\n ]\\r\\n )\",\n \"def _get_address(self, soup):\\n\\n try:\\n # from the content tag, extract the tag that contains all the address info\\n address_tag = soup.find('div', class_='flex flex-col md:flex-row')\\n # street tag\\n street_tag = address_tag.find('h1', class_='h3')\\n # street information\\n street = street_tag.get_text()\\\\\\n .strip()\\\\\\n .replace(',', '')\\n # region tag \\n region_tag = address_tag.find('h5', class_='listing-card-location') \\\\\\n .get_text() \\\\\\n .strip() \\\\\\n .split(' ')\\n # city information\\n city = region_tag[0].replace(',', '').title()\\n # state information\\n state = region_tag[1]\\n # zipcode information\\n zipcode = region_tag[2]\\n\\n return street, city, state, zipcode\\n \\n except:\\n # return None if any of the above parts failed\\n # if there's any part that's missing in the address part,\\n # the whole address becomes useless\\n return None, None, None, None\",\n \"def test_address(residence):\\n # We start by creating our result list\\n result = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\\n # Then we split our given string so we only have the address left\\n residence = residence.split(\\\", \\\")[1]\\n\\n # First we check for the prefix and increase the according value\\n if \\\"Haupt\\\" in residence:\\n result[0] = 1\\n elif \\\"Schul\\\" in residence:\\n result[1] = 1\\n elif \\\"Garten\\\" in residence:\\n result[2] = 1\\n elif \\\"Dorf\\\" in residence:\\n result[3] = 1\\n elif \\\"Bahnhof\\\" in residence:\\n result[4] = 1\\n elif \\\"Wiesen\\\" in residence:\\n result[5] = 1\\n elif \\\"Berg\\\" in residence and residence[4] in (\\\"s\\\", \\\"w\\\", \\\"a\\\", \\\"r\\\", \\\"p\\\"):\\n result[6] = 1\\n elif \\\"Kirch\\\" in residence:\\n result[7] = 1\\n elif \\\"Wald\\\" in residence:\\n result[8] = 1\\n elif \\\"Ring\\\" == residence[0:4]:\\n result[9] = 1\\n else:\\n result[10] = 1\\n\\n # Now we check the suffix\\n if \\\"straße\\\" in residence or \\\"Straße\\\" in residence:\\n result[11] = 1\\n elif \\\"Weg\\\" in residence or \\\"weg\\\" in residence:\\n result[12] = 1\\n elif \\\"Allee\\\" in residence or \\\"allee\\\" in residence:\\n result[13] = 1\\n elif \\\"platz\\\" in residence or \\\"platz\\\" in residence:\\n result[15] = 1\\n else:\\n result[14] = 1\\n\\n # And now we check the number\\n number = int(residence.split()[-1])\\n if number > 999:\\n result[16] = 1\\n elif number > 99:\\n result[17] = 1\\n else:\\n result[18] = 1\\n\\n return result\",\n \"def extract_alleles_from_snp_string(snp_string):\\n (allele1, allele2) = snp_string[1:4].split(\\\"/\\\")\\n assert allele1 in \\\"ATGC\\\"\\n assert allele2 in \\\"ATGC\\\"\\n return allele1, allele2\",\n \"def parse_address(address, sanity=True):\\n address = address.split(':')\\n address, port = ':'.join(address[:-1]), address[-1]\\n\\n guessed_type = 4\\n if address.startswith('['):\\n address = address[1:]\\n guessed_type = 6\\n if address.endswith(']') or (sanity and guessed_type == 6):\\n if sanity:\\n assert address.endswith(']')\\n address = address[:-1]\\n guessed_type = 6\\n if address.count(':') > 3:\\n if sanity:\\n assert guessed_type == 6\\n guessed_type = 6\\n\\n return address, int(port), guessed_type\",\n \"def parse_location(location):\\n city, state = location.strip().split(',')\\n return f\\\"{city.strip().replace(' ', '-')}-{state.strip().replace(' ', '-')}\\\"\",\n \"def _split_proxy_info(data: str) -> list:\\n \\n country = data[:2]\\n anonymity = data[3:4]\\n type_ = data[4:].strip('-+ ') # Remove splitting (- and space) and google_passed flag (+)\\n google_passed = data[-1]\\n\\n return [country, anonymity, type_, google_passed]\",\n \"def _parse_user_input(self):\\n user_input = self.user_input.strip()\\n if user_input:\\n if user_input.find(',') > -1:\\n # Location is either city/state or latitude/longitude.\\n if user_input[0].isalpha():\\n # City, state (lat/long handled elsewhere)\\n city, state = [x.strip() for x in user_input.split(',')]\\n self.city = city\\n self.state = state\\n elif (len(user_input) <= 10 and\\n user_input[1].isdigit()): # 2nd char in US/Can. postal codes\\n # Postal code\\n self.postal_code = user_input.strip()\",\n \"def normalise_address(address):\\n return re.sub('\\\\s+', ' ', str(address).upper()).replace(' ,', ',')\",\n \"def return_street(streetname):\\r\\n if streetname == None:\\r\\n return streetname\\r\\n if streetname.split(\\\" \\\")[-1] in valid_suffix:\\r\\n return \\\" \\\".join(str(streetname).split(\\\" \\\")[:-1])\\r\\n\\r\\n return streetname\",\n \"def parse_streetdir(self):\\n \\n first = self.words[self.index]['word']\\n if self.index + 1 < self.length:\\n second = self.words[self.index+1]['word']\\n else:\\n second = None\\n \\n if first in ['northwest', 'northeast', 'southwest', 'southeast']:\\n return first, 1 \\n elif first == 'nw':\\n return \\\"northwest\\\", 1\\n elif first == 'ne':\\n return \\\"northeast\\\", 1\\n elif first == 'sw':\\n return \\\"southwest\\\", 1\\n elif first == 'se':\\n return \\\"southeast\\\", 1\\n \\n if first in ['n', 'north']:\\n if second in ['w', 'west']:\\n return \\\"northwest\\\", 2\\n elif second in ['e', 'east']:\\n return \\\"northeast\\\", 2\\n else:\\n return \\\"north\\\", 1\\n elif first in ['s', 'south']:\\n if second in ['w', 'west']:\\n return \\\"southwest\\\", 2\\n elif second in ['e', 'east']:\\n return \\\"southeast\\\", 2\\n else:\\n return \\\"south\\\", 1\\n elif first in ['e', 'east']:\\n return \\\"east\\\", 1\\n elif first in ['w', 'west']:\\n return \\\"west\\\", 1\\n \\n return None,0\",\n \"def geo_coder(house_number, boro_code, street_name, zip_code): \\r\\n wa1 = '1B{}{}{}{}{}C{}{}'.format(rightpad(house_number, 16), rightpad('', 38), boro_code, rightpad('', 10), rightpad(street_name, 32), rightpad('', 113), rightpad(zip_code, 5))\\r\\n wa1 = rightpad(wa1, 1200)\\r\\n wa2 = rightpad('', 4300)\\r\\n NYCGeo.NYCgeo(wa1, wa2)\\r\\n return wa1, wa2\",\n \"def parse_proasis(input_string):\\n return (\\n input_string[:3].strip(),\\n int(input_string[5:].strip()),\\n input_string[3:5].strip(),\\n )\",\n \"def parse_input(input: str) -> Tuple[int, int, int, int]:\\n head, tail = input.split(\\\"@ \\\")\\n points, measures = tail.split(\\\": \\\")\\n x, y = points.split(\\\",\\\")\\n w, h = measures.split(\\\"x\\\")\\n return int(x), int(y), int(w), int(h)\",\n \"def parse_station_name (station_name):\\n try:\\n _,chinese_name,code,full_pinyin,short_pinyin = station_name.split('|')\\n except ValueError:\\n # print(station_name)\\n _,chinese_name,code,full_pinyin,short_pinyin,_ = station_name.split('|')\\n return {chinese_name:code,full_pinyin:code,short_pinyin:code}\",\n \"def _get_address(self, address_tag, hdr):\\n\\n # try to find all the span tags in the address tag, the span tags\\n # include all the address information we need \\n try:\\n elements = address_tag.find_all('span')\\n\\n # scrape the text out of the span tags and remove\\n # all the whitespaces and punctuation marks\\n address = elements[0].get_text()\\\\\\n .replace(',','')\\\\\\n .strip()\\n city = elements[1].get_text().strip()\\n state = elements[2].get_text().strip()\\n zipcode = elements[3].get_text().strip()\\n return address, city, state, zipcode\\n # however, sometimes the address tag does not include the street\\n # info, in this case, use the text in the header tag, which serves\\n # as a replacement for the address \\n except:\\n address = hdr.get_text()\\n elements = address_tag.find_all('span')\\n city = elements[0].get_text()\\\\\\n .replace(',','')\\\\\\n .strip()\\n state = elements[1].get_text().strip()\\n zipcode = elements[2].get_text().strip()\\n return address, city, state, zipcode\",\n \"def __init__(self, zip_code, house_number, house_addition=\\\"\\\"):\\n self.zip_code = zip_code.replace(' ', '')\\n self.house_number = house_number.strip()\\n self.house_addition = house_addition.strip()\",\n \"def get_string_info(string):\\n line_count = 1\\n column_count = 1\\n for char in string:\\n if char == '\\\\n':\\n column_count = 1\\n line_count += 1\\n else:\\n column_count += 1\\n return Coords(line_count, column_count, len(string))\",\n \"def _split_region(region: str) -> Tuple[bool, str, Optional[str]]:\\n parts = region.lower().split(\\\"-\\\")\\n\\n if parts[0] == \\\"vip\\\":\\n del parts[0]\\n vip = True\\n else:\\n vip = False\\n\\n country = parts.pop(0)\\n\\n try:\\n country_part = parts.pop(0)\\n except IndexError:\\n country_part = None\\n\\n return vip, country, country_part\",\n \"def normalize_address(address):\\n # Fix 'Place/Place' -> 'Place & Place'\\n if re.findall(r'[a-zA-Z0-9]/[a-zA-Z0-9]', address):\\n address = address.replace('/', ' & ')\\n # Fix 'Place:Place' -> 'Place & Place'\\n if re.findall(r'[a-zA-Z0-9]:[a-zA-Z0-9]', address):\\n address = address.replace(':', ' & ')\\n # Fix 'RD' -> 'Rd' & 'PK' -> 'Pk'\\n if re.findall(r'[PRSA][KDTV]', address):\\n address = re.sub(r'([PRSA][KDTV])', \\\\\\n lambda x: x.group(0).title(), address)\\n # Fix 'Bl' -> 'Blvd'\\n if re.findall(r'(Bl)[\\\\ ]', address):\\n address = address.replace('Bl', 'Blvd')\\n # Fix 'w 156th' -> 'W 156th'\\n if re.findall(r'[^a-zA-Z][wnse][/ ]', address):\\n address = re.sub(r'[^a-zA-Z]([wnse])[/ ]', \\\\\\n lambda x: x.group(0).upper(), address)\\n # Fix '151 St' -> '151st St'\\n if re.findall(r'[0-9][\\\\ ][SA][tv]', address):\\n address = re.sub(r'[0-9]+', \\\\\\n ordinal_conversion, address)\\n return address\",\n \"def net_xy(street):\\r\\n\\r\\n # api-endpoint\\r\\n URL = \\\"https://ags.govmap.gov.il/Search/FreeSearch\\\"\\r\\n # headers\\r\\n headers = {\\\"Content-Type\\\": \\\"application/json\\\", \\\"charset\\\": \\\"utf-8\\\"}\\r\\n # location given here\\r\\n try:\\r\\n p = \\\"{\\\\\\\"keyword\\\\\\\": \\\\\\\"\\\" + street + \\\"\\\\\\\",\\\\\\\"LstResult\\\\\\\": null}\\\"\\r\\n PARAMS = p.encode(\\\"utf-8\\\")\\r\\n\\r\\n # sending get request and saving the response as response object\\r\\n r = requests.post(url=URL, data=PARAMS, headers=headers)\\r\\n\\r\\n # extracting data in json format\\r\\n data = r.json()\\r\\n\\r\\n # extracting latitude, longitude and formatted address\\r\\n # of the first matching location\\r\\n\\r\\n X = data['data']['Result'][0]['X']\\r\\n Y = data['data']['Result'][0]['Y']\\r\\n except Exception as e:\\r\\n print(e)\\r\\n # print('exception ddamammnnnnn')\\r\\n print(street)\\r\\n return 0,0\\r\\n return X,Y\",\n \"def extract_address(input_string):\\n if input_string:\\n addr_search = re.search(\\n r'\\\\d{1,3}\\\\.\\\\d{1,3}\\\\.\\\\d{1,3}\\\\.\\\\d{1,3}(?:/\\\\d{1,2}|)',\\n input_string)\\n\\n if bool(addr_search):\\n return addr_search.group()\\n\\n return None\",\n \"def split_address(address: str, prefix=\\\"address_\\\", max_lines=5) -> dict[str, str]:\\n\\n # TODO ICMSLST-1692: ILB to fix addresses which are more than 5 lines\\n return {\\n f\\\"{prefix}{i}\\\": address_line.strip()\\n for i, address_line in enumerate(address.split(\\\"\\\\n\\\"), start=1)\\n if address_line and address_line.strip() and i <= max_lines\\n }\",\n \"def seperate_City_State_Data(data, us_state_abbrev):\\n assert data is not None\\n dictionary = dict(data)\\n keys = dictionary.keys()\\n tmp = list(keys)\\n v = list(dictionary.values())\\n values = []\\n res = []\\n for i in range(len(keys)):\\n state = tmp[i][1].strip()\\n city = tmp[i][0].strip()\\n# print(city)\\n if state in us_state_abbrev:\\n res.append((state, city))\\n values.append(v[i])\\n return res, list(values)\",\n \"def parse_feature_value(s,next_index=0):\\n next_index = jump_over_space(s,next_index)\\n start_index = next_index\\n while True:\\n if not s[next_index].isspace():\\n next_index += 1\\n else:\\n break\\n feature_value = s[start_index:next_index]\\n if feature_value == '':\\n feature_value = None\\n feature_value = feature_value.split('/')\\n return (feature_value,next_index)\",\n \"def process_address(text):\\n return sanitize(text[9:])\",\n \"def canon_station_name(s, line):\\n s = s.strip()\\n s = re.sub('^Heathrow$', 'Heathrow Terminals 1, 2, 3', s)\\n s = re.sub('^Olympia$', 'Kensington (Olympia)', s)\\n s = re.sub('^Warwick Ave$', 'Warwick Avenue', s)\\n s = re.sub('^Camden$', 'Camden Town', s)\\n s = re.sub('^Central$', 'Finchley Central', s) # They say \\\"Between Central and East Finchley\\\"\\n s = re.sub('\\\\s*Platform \\\\d$', '', s)\\n s = s + ' Station'\\n s = s.replace('(Bakerloo)', 'Bakerloo').replace('Earls', 'Earl\\\\'s') \\\\\\n .replace(' fast ', ' ') \\\\\\n .replace('\\\\xe2\\\\x80\\\\x99', \\\"'\\\") \\\\\\n .replace('St ', 'St. ') \\\\\\n .replace('Elephant and Castle', 'Elephant & Castle') \\\\\\n .replace('Lambeth Station', 'Lambeth North Station') \\\\\\n .replace('Chalfont Station', 'Chalfont & Latimer Station') \\\\\\n .replace('West Brompon', 'West Brompton') \\\\\\n .replace('Picadilly Circus', 'Piccadilly Circus') \\\\\\n .replace('High Barent', 'High Barnet') \\\\\\n .replace('Bartnet', 'Barnet') \\\\\\n .replace('Faringdon', 'Farringdon') \\\\\\n .replace('Turnham Greens', 'Turnham Green') \\\\\\n .replace('Ruilsip', 'Ruislip') \\\\\\n .replace('Dagemham', 'Dagenham') \\\\\\n .replace('Edgware Road (H & C)', 'Edgware Road Circle') \\\\\\n .replace('Hammersmith (Circle and H&C)', 'Hammersmith') \\\\\\n .replace('Shepherds Bush (Central Line)', \\\"Shepherd's Bush\\\") \\\\\\n .replace('Terminals 123', 'Terminals 1, 2, 3').replace('Terminal 1,2,3', 'Terminals 1, 2, 3') \\\\\\n .replace('Woodford Junction', 'Woodford') \\\\\\n .replace(\\\"King's Cross Station\\\", \\\"King's Cross St. Pancras Station\\\") \\\\\\n .replace(\\\"Kings Cross Station\\\", \\\"King's Cross St. Pancras Station\\\") \\\\\\n .replace('Central Finchley', 'Finchley Central').replace('District and Picc', 'D & P') \\\\\\n .replace('South Fields', 'Southfields') \\\\\\n .replace('Regents Park', \\\"Regent's Park\\\") \\\\\\n .replace('Bromley-by-Bow', \\\"Bromley-By-Bow\\\") \\\\\\n .replace('Brent Oak', 'Burnt Oak') \\\\\\n .replace('St. Johns Wood', \\\"St. John's Wood\\\") \\\\\\n .replace('Totteridge and Whetstone', 'Totteridge & Whetstone') \\\\\\n .replace('Newbury Park Loop', 'Newbury Park') \\\\\\n .replace('Harrow-on-the-Hill', 'Harrow on the Hill')\\n if s == 'Edgware Road Station' and line == 'B':\\n s = 'Edgware Road Bakerloo Station'\\n if s == 'Edgware Road Station' and line != 'B':\\n s = 'Edgware Road Circle Station'\\n return s\",\n \"def get_by_altitute():\\n try:\\n lat, long = input(\\\"Enter Lat and Long of a place: \\\").split()\\n return lat, long\\n except ValueError:\\n print(\\\"Error while getting values.\\\")\\n get_by_altitute()\",\n \"def extract_addresses(addresses):\\n \\n # Since lists are iterated over in Python in an orderly fashion, \\n # put 'Input Reg' before 'Input' such that the 'Reg' in an address \\n # with an 'Input Reg' prefix doesn't get left behind\\n address_prefixes= [\\\"Input Reg \\\", \\\"Holding Reg \\\", \\\"Input \\\", \\\"Coil \\\"]\\n\\n \\n for idx,address in enumerate(addresses):\\n # Replace prefixes with empty string\\n for prefix in address_prefixes:\\n addresses[idx]=addresses[idx].replace(prefix, \\\"\\\")\\n # Extract numeral\\n try:\\n addresses[idx]= int(addresses[idx])\\n except:\\n logging.warning(\\\"Invalid modbus address suppied at index {}\\\".format(idx))\\n\\n # Return\\n return addresses\",\n \"def clean_address(self, s):\\n # The letter \\\"O\\\" instead of the numeral \\\"0\\\" is a common mistake.\\n s = re.sub(\\n r\\\"\\\\b[A-Z][O0-9][A-Z]\\\\s?[O0-9][A-Z][O0-9]\\\\b\\\", lambda x: x.group(0).replace(\\\"O\\\", \\\"0\\\"), clean_string(s)\\n )\\n for k, v in province_or_territory_abbreviations().items():\\n # Replace a province/territory name with its abbreviation.\\n s = re.sub(\\n r\\\"[,\\\\n ]+\\\"\\n r\\\"\\\\(?\\\" + k + r\\\"\\\\)?\\\"\\n r\\\"(?=(?:[,\\\\n ]+Canada)?(?:[,\\\\n ]+[A-Z][0-9][A-Z]\\\\s?[0-9][A-Z][0-9])?\\\\Z)\\\",\\n \\\" \\\" + v,\\n s,\\n )\\n # Add spaces between province/territory abbreviation, FSA and LDU and remove \\\"Canada\\\".\\n return re.sub(\\n r\\\"[,\\\\n ]+\\\" r\\\"([A-Z]{2})\\\" r\\\"(?:[,\\\\n ]+Canada)?\\\" r\\\"[,\\\\n ]+([A-Z][0-9][A-Z])\\\\s?([0-9][A-Z][0-9])\\\" r\\\"\\\\Z\\\",\\n r\\\" \\\\1 \\\\2 \\\\3\\\",\\n s,\\n )\",\n \"def get_str_address(address):\\n return \\\\\\n get_ob_value_primitive(address, 'AddrLine1', exception_return_value='') + ' ' + \\\\\\n get_ob_value_primitive(address, 'AddrLine2', exception_return_value='') + ' ' + \\\\\\n get_ob_value_primitive(address, 'AddrLine3', exception_return_value='') + ', ' + \\\\\\n get_ob_value_primitive(address, 'City', exception_return_value='') + ' ' + \\\\\\n get_ob_value_primitive(address, 'County', exception_return_value='') + ' ' + \\\\\\n get_ob_value_primitive(address, 'StateProvince', exception_return_value='') + ' ' + \\\\\\n get_ob_value_primitive(address, 'ZipPostalCode', exception_return_value='')\",\n \"def _parse_info(form) :\\n w = 0\\n n = 0\\n nh = 0\\n for part in re.findall(\\\"[A-Z]+[0-9]+\\\",form):\\n m = re.match(\\\"([A-Z]+)([0-9]+)\\\",part)\\n element = m.group(1)\\n number = int(m.group(2))\\n w += mass[element.capitalize()]*number\\n n += number\\n if element != \\\"H\\\" : nh += number\\n return w,n,nh\",\n \"def _parse_info(form) :\\n w = 0\\n n = 0\\n nh = 0\\n for part in re.findall(\\\"[A-Z]+[0-9]+\\\",form):\\n m = re.match(\\\"([A-Z]+)([0-9]+)\\\",part)\\n element = m.group(1)\\n number = int(m.group(2))\\n w += mass[element.capitalize()]*number\\n n += number\\n if element != \\\"H\\\" : nh += number\\n return w,n,nh\",\n \"def _cleanupAddress(self, address):\\n clean = []\\n \\n # This is sort of a desultory effort but I'm not convinced \\n # that these cleanups will actually result in cleaner searches\\n for word in address.split(None):\\n lower = word.lower()\\n \\n # Some things we just nuke\\n if lower == 'at': continue\\n elif lower == 'btw': continue\\n elif lower == 'btwn': continue\\n elif word.isdigit(): continue\\n \\n # Or we make substitiutions\\n elif lower == 'st' or lower == 'st.':\\n word = 'Street'\\n elif lower == 'ave' or lower == 'ave.':\\n word = 'Avenue'\\n elif lower == 'pl' or lower == 'pl.':\\n word = 'Place'\\n elif lower == 'n': word = 'North'\\n elif lower == 'e': word = 'East'\\n elif lower == 's': word = 'South'\\n elif lower == 'w': word = 'West'\\n \\n clean.append(word)\\n return ' '.join(clean)\",\n \"def part(string):\\n characters = [] \\n count = 0\\n st = \\\"\\\"\\n for char in string:\\n if char == \\\" \\\":\\n characters.append(\\\"space\\\")\\n elif char ==\\\"?\\\" or char == \\\",\\\" or char == \\\";\\\" or char == \\\":\\\":\\n characters.append(char)\\n count = 0\\n else:\\n # st += char\\n # count += 1\\n characters.append(char)\\n \\n if count == 3:\\n characters.append(st)\\n st = \\\"\\\"\\n count = 0\\n return characters\",\n \"def check_born_place(input_string: str) -> tuple:\\n c = input_string[0]\\n if c == 'A':\\n return 'Taipei City', 10\\n elif c == 'B':\\n return 'Taichung City', 11\\n elif c == 'C':\\n return 'Keelung City', 12\\n elif c == 'D':\\n return 'Tainan City', 13\\n elif c == 'E':\\n return 'Kaohsiung City', 14\\n elif c == 'F':\\n return 'New Taipei City', 15\\n elif c == 'G':\\n return 'Yilan County', 16\\n elif c == 'H':\\n return 'Taoyuan City', 17\\n elif c == 'I':\\n return 'Chiayi City', 34\\n elif c == 'J':\\n return 'Hsinchu County', 18\\n elif c == 'K':\\n return 'Miaoli County', 19\\n elif c == 'L':\\n return 'Taichung County', 20\\n elif c == 'M':\\n return 'Nantou County', 21\\n elif c == 'N':\\n return 'Changhua County', 22\\n elif c == 'O':\\n return 'Hsinchu City', 35\\n elif c == 'P':\\n return 'Yunlin County', 23\\n elif c == 'Q':\\n return 'Chiayi County', 24\\n elif c == 'R':\\n return 'Tainan County', 25\\n elif c == 'S':\\n return 'Kaohsiung County', 26\\n elif c == 'T':\\n return 'Pingtung County', 27\\n elif c == 'U':\\n return 'Hualien County', 28\\n elif c == 'V':\\n return 'Taitung County', 29\\n elif c == 'W':\\n return 'Kinmen County', 32\\n elif c == 'X':\\n return 'Penghu County', 30\\n elif c == 'Y':\\n return 'Yangmingshan Management Bureau', 31\\n elif c == 'Z':\\n return 'Lienchiang County', 33\\n else:\\n # Should not happen\\n return None, None # The return value is a tuple containing two values\",\n \"def handle_special_cases(substring):\\r\\n if substring==['', 'EB/NB', '2'] or substring==['EB/NB', '2', ''] or substring==['', 'EB/NB', '2', '#1'] or substring==['', 'EB/NB', '2', '#2']:\\r\\n dir='E'\\r\\n road=2\\r\\n elif substring== ['', 'WB/SB', '2'] or substring==['WB/SB', '2', '']:\\r\\n dir='W'\\r\\n road=2\\r\\n elif substring==['WB210', '']:\\r\\n dir='W'\\r\\n road=210\\r\\n elif substring==['S', '605/W', '10', '']:\\r\\n dir='W'\\r\\n road=10 \\r\\n elif substring==['', 'NB5', 'TRK', 'RTE']:\\r\\n dir='N'\\r\\n road=5\\r\\n elif substring==['', 'S605/E10']:\\r\\n dir='E'\\r\\n road=10\\r\\n else:\\r\\n dir=None\\r\\n road=0\\r\\n return dir, road\",\n \"def seperate_City_Data(data, us_state_abbrev):\\n assert data is not None\\n dictionary = dict(data)\\n keys = dictionary.keys()\\n tmp = list(keys)\\n values = dictionary.values()\\n res = []\\n for elem in keys:\\n state = elem[1].strip()\\n city = elem[0].strip()\\n# print(city)\\n if state in us_state_abbrev:\\n res.append(city)\\n return res, list(values)\",\n \"def phone_parser(phone, mode='PL'):\\n\\n if not phone:\\n raise WrongInput(\\\"Input cannot be blank\\\")\\n if not isinstance(phone, str):\\n raise WrongInput(\\\"Invalid phone format\\\")\\n\\n if mode == 'PL':\\n gsm_prefixes = ['50', '51', '53', '57', '60', '66', '69', '72', '73', '78', '79', '88']\\n if phone[:2] in gsm_prefixes:\\n phone_pattern = re.compile(r'''\\n # don't match beginning of string\\n (\\\\d{0,2}) # area code of 2 digits (e.g. '42')\\n \\\\D* # optional separator\\n (\\\\d{3}\\\\D*\\\\d{3}\\\\D*\\\\d{3}) # rest of number - divide into 3 3-digit sequences with optional separators\\n # (e.g. '605-789-567')\\n $ # end of string\\n ''', re.VERBOSE)\\n else:\\n phone_pattern = re.compile(r'''\\n # don't match beginning of string\\n (\\\\d{0,2}) # area code of 2 digits (e.g. '42')\\n \\\\D* # optional separator\\n (\\\\d{3}\\\\D*\\\\d{2}\\\\D*\\\\d{2}) # rest of number - divide into 3 2-digit sequences with optional separators\\n # (e.g. '605-78-56')\\n $ # end of string\\n ''', re.VERBOSE)\\n else:\\n phone_pattern = re.compile(r'''\\n # don't match the beginning of the string\\n (\\\\d{3}) # area code of 3 digits (e.g. '800')\\n \\\\D* # optional separator\\n (\\\\d{3}\\\\D*\\\\d{4}\\\\D*\\\\d+) # rest of number - divide into 3 sequences with optional separators: two obligatory\\n # with 3 and 4 digits, one optional with any number of digits\\n $ # end of string\\n ''', re.VERBOSE)\\n if not re.search(phone_pattern, phone):\\n raise WrongInput(\\\"Invalid phone format.\\\")\\n\\n phone_obj = phone_pattern.search(phone)\\n phone_area, phone_num = phone_obj.groups()\\n phone = re.sub(r'\\\\D', '', phone_num)\\n return phone, phone_area, phone_num\",\n \"def __process_address(self, address: Tuple[int, int, int, int, int]) -> Dict[str, int]:\\n return {\\n 'interface': address[0],\\n 'protocol': address[1],\\n 'type': address[2],\\n 'hardware_type': address[3],\\n 'address': address[4],\\n }\",\n \"def parse_name(first_name, last_name):\\n\\n return first_name + \\\" \\\" + last_name\",\n \"def get_address():\\r\\n address = input(\\\"What is the customer's address?: \\\")\\r\\n\\r\\n return address\",\n \"def replace_street(street):\\r\\n if isinstance(street, str):\\r\\n for rep in replacements:\\r\\n street = re.sub(rep, \\\"\\\", street)\\r\\n\\r\\n streetint = re.findall(r'\\\\d+', str(street))\\r\\n if len(streetint) > 0 and int(streetint[0]) < 100:\\r\\n street = int(streetint[0])\\r\\n\\r\\n if street < 10:\\r\\n street = '0' + str(street) + str(streetnums[str(street)])\\r\\n elif street < 14:\\r\\n street = str(street) + 'TH'\\r\\n else:\\r\\n street = str(street) + str(streetnums[str(street)[-1]])\\r\\n\\r\\n\\r\\n return street\",\n \"def number(full_address):\\n warning_message = \\\"\\\"\\\"\\\\n\\n This parser should be used with the knowledge that this\\n function is open to four significant vulnerabilities:\\n 1) `number()` will parse the first numeric characters it\\n an address string contains (read from left to right).\\n If the address string has:\\n a) no building number\\n b) numeric characters unrelated to addressable\\n information at the start of the address string\\n 2) Address numbers separated by `&` or `,` will not be parsed\\n 3) Building names that include numeric characters are\\n incorrectly parsed as building numbers\\\\n\\n \\\"\\\"\\\"\\n warnings.warn(warning_message)\\n return capture_address_element(NUMBER_PATTERN, full_address)\",\n \"def clean_street(self):\\n street = self.cleaned_data['street'].strip().title()\\n street = re.sub(r'\\\\bRoad\\\\b', 'Rd', street)\\n street = re.sub(r'\\\\bStreet\\\\b', 'Str', street)\\n street = re.sub(r'\\\\bAvenue\\\\b', 'Ave', street)\\n street = re.sub(r'\\\\bParkway\\\\b', 'Pkwy', street)\\n street = re.sub(r'\\\\bSuite\\\\b', 'Ste', street)\\n street = re.sub(r'\\\\bApartment\\\\b', 'Apt', street)\\n street = re.sub(r'\\\\s+', ' ', street) # Remove runs of spaces\\n return street\",\n \"def split_str(str):\\n \\n logger = logging.getLogger(__name__)\\n \\n logger.debug('{0}'.format(str))\\n \\n match = re.match(r\\\"([0-9]+.?\\\\d{0,32}?)(d|m|s)\\\", str)\\n \\n if match:\\n items = match.groups()\\n \\n return items[0], items[1]\",\n \"def parse_puzzle(puzzle):\\n puzzle = re.sub(\\\"\\\\sGrid \\\\d{2}\\\",\\\"\\\", sample)\\n puzzle = puzzle.strip().split(\\\"\\\\n\\\") \\n return puzzle\",\n \"def return_intersections(streetname):\\r\\n if streetname != None and isinstance(streetname, str) and ' AND ' in streetname:\\r\\n streetnames = streetname.split(' AND ')\\r\\n df = streetintersections[(streetintersections.Street == streetnames[0]) \\\\\\r\\n & (streetintersections.Isection == streetnames[1])]\\r\\n if df.shape[0] > 0:\\r\\n return str(int(df['Number'].iloc[0])) + ' ' + df['Street'].iloc[0] + ' ' + df['Suffix'].iloc[0]\\r\\n return None\",\n \"def mysplit(string):\\n result = []\\n last_split = 0\\n for i in range(len(string)-3):\\n if( string[i] == \\\"a\\\" and\\n string[i+1] == \\\"n\\\" and\\n string[i+2] == \\\"d\\\"):\\n partial = string[last_split:i]\\n last_split = i+3\\n result.append(partial)\\n rest = string[last_split:]\\n result.append(rest)\\n return result\",\n \"def extract(self, str):\\n\\n ips = re.match( r'^[0-9]+(?:\\\\.[0-9]+){3}', str)\\n\\n if ips:\\n return ips.group(0)\",\n \"def capture_address_element(regex_object, full_address):\\n full_address = normalise_address(full_address)\\n capture_groups = regex_object.search(full_address)\\n if capture_groups:\\n return capture_groups.group(0)\\n return ''\",\n \"def parse_info(s:str) -> dict:\\n d = {}\\n d[\\\"SVTYPE\\\"] = re.search(r'(?<=SVTYPE=)\\\\w+',s).group(0)\\n d[\\\"SUPPORT\\\"] = re.search(r'(?<=SUPPORT=)\\\\d+',s).group(0)\\n if d[\\\"SVTYPE\\\"] in [\\\"BND\\\"]:\\n return d\\n d[\\\"END\\\"] = re.search(r'(?<=END=)\\\\d+',s).group(0)\\n if d[\\\"SVTYPE\\\"] in [\\\"INV\\\"]:\\n return d\\n d[\\\"SVLEN\\\"] = re.search(r'(?<=SVLEN=)(.*?)(?=;)',s).group(0)\\n d[\\\"READS\\\"] = re.search(r'(?<=READS=)(.*?)(?=$)',s).group(0).split(\\\",\\\")\\n if d[\\\"SVTYPE\\\"] == \\\"INS\\\":\\n d[\\\"SEQS\\\"] = re.search(r'(?<=SEQS=)(.*?)(?=;)',s).group(0).split(\\\",\\\")\\n return d\",\n \"def test_parse():\\n first = parse_formula(\\\"PO4H2(CH2)12CH3\\\")\\n assert first == {\\\"P\\\":1, \\\"O\\\":4, \\\"H\\\":29, \\\"C\\\":13}\\n\\n second = parse_formula(\\\"H2O\\\")\\n assert second == {\\\"H\\\":2, \\\"O\\\":1}\",\n \"def parse_streettype(self):\\n \\n\\n try:\\n word = sttype[self.words[self.index]['word']]\\n if Vocabulary.STREET_TYPE in word['tag']:\\n itag = word['tag'].index(Vocabulary.STREET_TYPE)\\n lemma = word['lemma'][itag]\\n return lemma, 1\\n return None, 0\\n except: return None, 0\",\n \"def parse(self, string):\\n parse = re.match(\\\"^((?:[0-9]{1,3}\\\\.){3}[0-9]{1,3})\\\\s\\\\(((?:\\\\d)*\\\\.(?:\\\\d)*|(?:\\\\d)*)\\\\sms\\\\)$\\\", string)\\n parse_result = parse.groups()\\n return parse_result[0], parse_result[1]\",\n \"def _parse_boardline(self, start, street):\\n # Exceptions caught in _parse_street.\\n board_line = self._splitted[start]\\n\\n match = self._STREET_RE.search(board_line)\\n cards = match.group(1)\\n cards = tuple(cards.split()) if street == 'flop' else cards\\n setattr(self, street, cards)\\n\\n pot = match.group(2)\\n setattr(self, \\\"%s_pot\\\" % street, Decimal(pot))\\n\\n num_players = int(match.group(3))\\n setattr(self, \\\"%s_num_players\\\" % street, num_players)\",\n \"def parse_drs_identifier(drs_candidate: str) -> Tuple[str, str, str]:\\n # determine if hostname or compact identifier or unknown\\n drs_regex = r\\\"drs://([A-Za-z0-9\\\\.\\\\-\\\\~]+)/([A-Za-z0-9\\\\.\\\\-\\\\_\\\\~\\\\/]+)\\\"\\n # either a drs prefix:\\n\\n matches = re.findall(drs_regex, drs_candidate, re.UNICODE)\\n\\n if len(matches) == 1: # this could be a hostname DRS id\\n hostname_regex = (\\n r\\\"^(([a-zA-Z0-9]|[a-zA-Z0-9][a-zA-Z0-9\\\\-]*[a-zA-Z0-9])\\\\.)*\\\"\\n r\\\"([A-Za-z0-9]|[A-Za-z0-9][A-Za-z0-9\\\\-]*[A-Za-z0-9])$\\\"\\n )\\n hostname_matches = re.findall(hostname_regex, matches[0][0], re.UNICODE)\\n if len(hostname_matches) == 1:\\n return matches[0][0], matches[0][1], \\\"hostname\\\"\\n # possible compact rep\\n compact_regex = r\\\"([A-Za-z0-9\\\\.\\\\-\\\\~]+)/([A-Za-z0-9\\\\.\\\\-\\\\_\\\\~\\\\/]+)\\\"\\n matches = re.findall(compact_regex, drs_candidate, re.UNICODE)\\n if len(matches) == 1 and len(matches[0]) == 2:\\n return matches[0][0], matches[0][1], \\\"compact\\\"\\n\\n # can't figure out a this identifier\\n return \\\"\\\", \\\"\\\", \\\"unknown\\\"\",\n \"def split_addr(self, a):\\n a = a.replace('http://', '')\\n a = a.replace('https://', '')\\n\\n addr = tlde.extract(a)\\n is_ip = tlde.tldextract.looks_like_ip(addr.domain)\\n if is_ip:\\n ip = addr.domain\\n path_and_params = a[a.index(ip)+len(ip):].split('?')\\n path = path_and_params[0]\\n if len(path_and_params) > 1:\\n params = path_and_params[1:]\\n else:\\n params = ''\\n return {'ip': ip, 't3': None, 't2': None, 'path': path, 'params': params, 'url/ip': 'ip'}\\n else:\\n t3 = addr.subdomain\\n t2 = addr.registered_domain\\n path_and_params = a[a.index(addr.fqdn)+len(addr.fqdn):].split('?')\\n path = path_and_params[0]\\n if len(path_and_params) > 1:\\n params = path_and_params[1:]\\n else:\\n params = ''\\n return {'t3': t3, 't2': t2, 'ip': None, 'path': path, 'params': params, 'url/ip': 'url'}\",\n \"def get_address_parts(ip, mask):\\n\\n ip_bin_str = address_to_bin(ip).replace('.', '')\\n mask_bin_str = address_to_bin(mask).replace('.', '')\\n\\n net_size = mask_bin_str.rfind('1') + 1\\n host_size = ADDR_LEN - net_size\\n\\n net = _address_from_bin_list(_split_bin_str_on_ocsets(ip_bin_str[:net_size] + '0' * host_size))\\n host = _address_from_bin_list(_split_bin_str_on_ocsets('0' * net_size + ip_bin_str[-host_size:]))\\n net_count = 2 ** host_size - 2\\n count_string = '2^{0}-2'.format(host_size)\\n return net, host, net_count, count_string\",\n \"def input_parser(input_string: str) -> str: \\n if is_int(input_string):\\n return input_string\\n #he is int, give back plz.\\n else:\\n try:\\n modified_input: str = input_string.strip()\\n\\n evaluatable_pairs: str = regex_splitter(modified_input)\\n\\n while not (is_int(evaluatable_pairs)):\\n evaluatable_pairs = regex_splitter(evaluatable_pairs)\\n\\n return (evaluatable_pairs)\\n\\n except:\\n raise Exception(\\\"Invalid Input\\\")\",\n \"def street_address1(self) -> str:\\n return pulumi.get(self, \\\"street_address1\\\")\",\n \"def get_city(string):\\n city = \\\"\\\"\\n previous_ch = None;\\n\\n #For each character in string\\n for ch in string:\\n #break if it is a comma, the city has been completed\\n if ch == \\\",\\\":\\n break\\n #if the character is a letter, add it to the \\\"city\\\" string\\n elif ch.isalpha():\\n city += ch\\n #if the character is a space, and the previous character is a letter, add the space to the \\\"city\\\" string. (This prevents duplicate spaces)\\n elif ch.isspace() & previous_ch.isalpha():\\n city += ch\\n\\n #update previous character\\n previous_ch = ch\\n\\n return city\",\n \"def format_address(line1, line2, city, state, zipcode):\\n\\t\\n\\tstreetlines = line1\\n\\tcityline = city\\n\\t\\n\\tif len(streetlines) > 0 and len(line2) > 0:\\n\\t\\tstreetlines += \\\"\\\\n\\\"\\n\\t\\n\\tif len(cityline) > 0 and len(state) > 0:\\n\\t\\tcityline += \\\", \\\"\\n\\t\\n\\tstreetlines += line2\\n\\tcityline += state\\n\\t\\n\\treturn \\\"\\\\n\\\".join([streetlines, cityline, zipcode])\",\n \"def seperate_Loc_Data(data, us_state_abbrev):\\n assert data is not None\\n dictionary = dict(data)\\n keys = dictionary.keys()\\n tmp = list(keys)\\n values = dictionary.values()\\n res = []\\n for elem in keys:\\n state = elem[1].strip()\\n if state in us_state_abbrev:\\n res.append(us_state_abbrev[state])\\n return res, list(values)\",\n \"def get_first_and_last_name(full_name):\\n try:\\n result = full_name.split(' ', 1)\\n except AttributeError:\\n return ['', '']\\n else:\\n if len(result) == 2:\\n return result\\n return [full_name, full_name]\",\n \"def parse_input(giant_string):\\n X_train_part, Y_train_part, X_test_part = giant_string.split(\\\"XXX\\\")\\n\\n X_train_row_strings = X_train_part.split(\\\"S\\\")\\n X_train_rows = [[float(x) for x in row.split(\\\",\\\")] for row in X_train_row_strings]\\n X_train = np.array(X_train_rows)\\n\\n Y_train = concatenated_string_to_array(Y_train_part)\\n\\n X_test_row_strings = X_test_part.split(\\\"S\\\")\\n X_test_rows = [[float(x) for x in row.split(\\\",\\\")] for row in X_test_row_strings]\\n X_test = np.array(X_test_rows)\\n\\n return X_train, Y_train, X_test\",\n \"def Parser(wa1, wa2):\\r\\n #Note that in the documentation, they start counting at position 1\\r\\n output = { \\r\\n 'First Borough Name': wa1[360 :369].strip(),\\r\\n 'House Number Display Format': wa1[369: 385].strip(),\\r\\n 'House Number Sort Format': wa1[385: 396].strip(),\\r\\n 'B10SC First Borough and Street Code': wa1[396: 407].strip(),\\r\\n 'Second Street Name Normalized': wa1[407:439].strip(),\\r\\n 'Community District': wa2[149:152].strip(),\\r\\n 'Zip Code': wa2[152:157].strip(),\\r\\n 'Election District': wa2[157:160].strip(),\\r\\n 'Assembly District': wa2[160:162].strip(),\\r\\n 'Congressional District': wa2[163:165].strip(),\\r\\n 'State Senatorial District': wa2[165:167].strip(),\\r\\n 'City Council District': wa2[169:171].strip(),\\r\\n 'Police Precinct': wa2[191:194].strip(),\\r\\n 'Community School District': wa2[203:205].strip(),\\r\\n 'Atomic Polygon': wa2[205: 208].strip(),\\r\\n '2010 Census Tract': wa2[223: 229].strip(),\\r\\n '2010 Census Block': wa2[229:233].strip(),\\r\\n '2010 Census Block Suffix': wa2[233].strip(),\\r\\n 'Neighborhood Tabulation Area (NTA)': wa2[245:249].strip(),\\r\\n 'DSNY Snow Priority Code': wa2[249].strip(),\\r\\n 'Hurricane Evacuation Zone (HEZ)': wa2[260:262].strip(),\\r\\n 'Spatial Coordinates of Segment': {'X Coordinate, Low Address End': wa2[313:320].strip(),\\r\\n 'Y Coordinate, Low Address End': wa2[320:327].strip(),\\r\\n 'Z Coordinate, Low Address End': wa2[327:334].strip(),\\r\\n 'X Coordinate, High Address End': wa2[334:341].strip(),\\r\\n 'Y Coordinate, High Address End': wa2[341:348].strip(),\\r\\n 'Z Coordinate, High Address End': wa2[348:355].strip(),\\r\\n },\\r\\n 'Roadway Type': wa2[444:446].strip(),\\r\\n 'Bike Lane': wa2[486].strip(),\\r\\n 'NTA Name': wa2[553: 628].strip(),\\r\\n 'USPS Preferred City Name': wa2[628:653].strip(),\\r\\n 'Latitude': wa2[653:662].strip(),\\r\\n 'Longitude': wa2[662: 673].strip(),\\r\\n 'Borough Block Lot (BBL)': {'Borough code': wa2[1533].strip(),\\r\\n 'Tax Block': wa2[1534:1539].strip(),\\r\\n 'Tax Lot': wa2[1539:1543].strip(),\\r\\n },\\r\\n 'Building Identification Number (BIN) of Input Address or NAP': wa2[1581:1588].strip(),\\r\\n 'X-Y Coordinates of Lot Centroid': wa2[1699:1713].strip(),\\r\\n 'Spatial X': wa2[125:132].strip(),\\r\\n 'Spatial Y': wa2[132:139].strip(),\\r\\n 'Message': wa1[579:659].strip(),\\r\\n }\\r\\n return output\",\n \"def return_list_from_string(inputx):\\n x = inputx.split(\\\";\\\")\\n for wrd in x:\\n if \\\"WORD1\\\" in wrd or \\\"NAME1\\\" in wrd:\\n wrd2 = (\\n wrd.replace(\\\"WORD1:\\\", \\\"\\\")\\n .replace(\\\"{{WORD1}}:\\\", \\\"\\\")\\n .replace(\\\"NAME1:\\\", \\\"\\\")\\n .replace(\\\"{{NAME1}}:\\\", \\\"\\\")\\n )\\n wrd3 = wrd2.strip()\\n wrds = wrd3.replace(\\\"[\\\", \\\"\\\").replace(\\\"]\\\", \\\"\\\")\\n wrds1 = wrds.split(\\\",\\\")\\n wrds1 = [w.strip() for w in wrds1]\\n if \\\"WORD2\\\" in wrd or \\\"NAME2\\\" in wrd:\\n wrd2 = (\\n wrd.replace(\\\"WORD2:\\\", \\\"\\\")\\n .replace(\\\"{{WORD2}}:\\\", \\\"\\\")\\n .replace(\\\"NAME2:\\\", \\\"\\\")\\n .replace(\\\"{{NAME2}}:\\\", \\\"\\\")\\n )\\n wrd3 = wrd2.strip()\\n wrds = wrd3.replace(\\\"[\\\", \\\"\\\").replace(\\\"]\\\", \\\"\\\")\\n wrds2 = wrds.split(\\\",\\\")\\n wrds2 = [w.strip() for w in wrds2]\\n else:\\n wrds2 = \\\"\\\"\\n return wrds1, wrds2\",\n \"def _parse_location(location_string):\\n location_regex = r\\\"(\\\\d+)-(\\\\d+)(\\\\(+\\\\)|\\\\(-\\\\)|)\\\"\\n match = re.match(location_regex, location_string.strip())\\n start, end, strand = match.groups()\\n return int(start), int(end), -1 if strand == \\\"(-)\\\" else 1\",\n \"def get_apartment_address(self, soup, apartment_dict):\\n\\n info_class = soup.find_all('div', {'class': 'info'})\\n if info_class and len(info_class) > 0:\\n info_class = info_class[0]\\n address = info_class.find('h2').text.strip()\\n\\n from parse import parse\\n address = parse(\\\"Location: {}\\\", address)[0]\\n apartment_dict['address'] = address\\n else:\\n logging.warning(\\\"Failed to parse apartment address\\\")\\n return\",\n \"def ExtractNumbers(s):\\n\\n t = s.strip('[]\\\\n')\\n comma_space = r', '\\n re_comma_space = re.compile(comma_space)\\n z = re_comma_space.split(t)\\n #print z\\n return z\",\n \"def parse(self, item: str) -> Tuple[str, str]:\\n try:\\n city, *_, country = item.split(',')\\n except ValueError:\\n return '', item\\n return city.strip(), country.strip()\",\n \"def parse_input(giant_string):\\r\\n X_train_part, Y_train_part, X_test_part = giant_string.split(\\\"XXX\\\")\\r\\n\\r\\n X_train_row_strings = X_train_part.split(\\\"S\\\")\\r\\n X_train_rows = [[float(x) for x in row.split(\\\",\\\")] for row in X_train_row_strings]\\r\\n X_train = np.array(X_train_rows)\\r\\n\\r\\n Y_train = concatenated_string_to_array(Y_train_part)\\r\\n\\r\\n X_test_row_strings = X_test_part.split(\\\"S\\\")\\r\\n X_test_rows = [[float(x) for x in row.split(\\\",\\\")] for row in X_test_row_strings]\\r\\n X_test = np.array(X_test_rows)\\r\\n\\r\\n return X_train, Y_train, X_test\",\n \"def city_parser(city: str = None):\\n return city.text.strip().split(',')[1]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.70592016","0.66491073","0.61709803","0.6159874","0.614569","0.6053972","0.6050844","0.6037463","0.5887759","0.58091587","0.5799028","0.57310367","0.56918335","0.56567967","0.5650815","0.5538323","0.5499395","0.54938877","0.5493846","0.54662937","0.54369164","0.54308015","0.542838","0.5418099","0.54002714","0.5393423","0.5390481","0.5348042","0.531965","0.5287658","0.52861094","0.52745956","0.52560294","0.52428436","0.52399087","0.5206124","0.51955557","0.5187575","0.51825696","0.5165678","0.5143241","0.511956","0.5112951","0.51083136","0.5108105","0.5080235","0.5076768","0.50642395","0.5055932","0.5018596","0.5015713","0.50139517","0.500463","0.5000048","0.4993118","0.49846083","0.49795932","0.49770942","0.49770942","0.49735677","0.496703","0.49651313","0.49561158","0.49473786","0.49287015","0.49200198","0.49168384","0.4908089","0.4907499","0.49034625","0.48872048","0.48841047","0.48804402","0.4873048","0.4872637","0.48660403","0.48651472","0.48565593","0.48414782","0.4835003","0.48338005","0.48298162","0.48274857","0.4827307","0.4822161","0.4820303","0.48161325","0.47975326","0.47942722","0.4782617","0.47810772","0.47752944","0.47751775","0.477391","0.4767785","0.47657555","0.475858","0.4751523","0.47486657","0.47446442"],"string":"[\n \"0.70592016\",\n \"0.66491073\",\n \"0.61709803\",\n \"0.6159874\",\n \"0.614569\",\n \"0.6053972\",\n \"0.6050844\",\n \"0.6037463\",\n \"0.5887759\",\n \"0.58091587\",\n \"0.5799028\",\n \"0.57310367\",\n \"0.56918335\",\n \"0.56567967\",\n \"0.5650815\",\n \"0.5538323\",\n \"0.5499395\",\n \"0.54938877\",\n \"0.5493846\",\n \"0.54662937\",\n \"0.54369164\",\n \"0.54308015\",\n \"0.542838\",\n \"0.5418099\",\n \"0.54002714\",\n \"0.5393423\",\n \"0.5390481\",\n \"0.5348042\",\n \"0.531965\",\n \"0.5287658\",\n \"0.52861094\",\n \"0.52745956\",\n \"0.52560294\",\n \"0.52428436\",\n \"0.52399087\",\n \"0.5206124\",\n \"0.51955557\",\n \"0.5187575\",\n \"0.51825696\",\n \"0.5165678\",\n \"0.5143241\",\n \"0.511956\",\n \"0.5112951\",\n \"0.51083136\",\n \"0.5108105\",\n \"0.5080235\",\n \"0.5076768\",\n \"0.50642395\",\n \"0.5055932\",\n \"0.5018596\",\n \"0.5015713\",\n \"0.50139517\",\n \"0.500463\",\n \"0.5000048\",\n \"0.4993118\",\n \"0.49846083\",\n \"0.49795932\",\n \"0.49770942\",\n \"0.49770942\",\n \"0.49735677\",\n \"0.496703\",\n \"0.49651313\",\n \"0.49561158\",\n \"0.49473786\",\n \"0.49287015\",\n \"0.49200198\",\n \"0.49168384\",\n \"0.4908089\",\n \"0.4907499\",\n \"0.49034625\",\n \"0.48872048\",\n \"0.48841047\",\n \"0.48804402\",\n \"0.4873048\",\n \"0.4872637\",\n \"0.48660403\",\n \"0.48651472\",\n \"0.48565593\",\n \"0.48414782\",\n \"0.4835003\",\n \"0.48338005\",\n \"0.48298162\",\n \"0.48274857\",\n \"0.4827307\",\n \"0.4822161\",\n \"0.4820303\",\n \"0.48161325\",\n \"0.47975326\",\n \"0.47942722\",\n \"0.4782617\",\n \"0.47810772\",\n \"0.47752944\",\n \"0.47751775\",\n \"0.477391\",\n \"0.4767785\",\n \"0.47657555\",\n \"0.475858\",\n \"0.4751523\",\n \"0.47486657\",\n \"0.47446442\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.55393785"},"document_rank":{"kind":"string","value":"15"}}},{"rowIdx":2895,"cells":{"query":{"kind":"string","value":"Helper function to construct multidimensional dictionaries e.g myhash = _makehash() myhash[1][2] = 4 myhash[2][5][8] = 17"},"document":{"kind":"string","value":"def _makehash():\n return defaultdict(_makehash)"},"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 hashMap(self,arr):\r\n n = len(arr)\r\n dict1 = {}\r\n i = 1\r\n for i in range(n): \r\n if(i > 0): \r\n key=arr[i]\r\n value=arr[0]\r\n dict1[key] = value\r\n return dict1","def __init__(self):\n self.hashmap = [[[],[]] for _ in range(self.N)]","def boardtohashmap(board_2d: List[List[str]]) -> Dict[Tuple[int, int], Gridspace]:\n\n nrows, ncols = len(board_2d), len(board_2d[0])\n return {\n (r, c): Gridspace(r, c, board_2d[r][c], nrows, len(board_2d[r]))\n for r in range(nrows) for c in range(len(board_2d[r]))\n }","def generate_dict(length):\r\n primeDict = {}\r\n index = 2\r\n \r\n while (index < length):\r\n primeDict[index]=True\r\n index = index+1\r\n \r\n return primeDict","def _build_hash_table(arr: [str]):\n ht = {}\n for cur_str in arr:\n\n anagram = cur_str[::-1]\n if cur_str in ht.keys():\n # This string is an anagram of some previous\n # Increase anagram count for hash table item\n (original, orig_cnt, anag_cnt) = ht[cur_str]\n ht[cur_str] = (original, orig_cnt, anag_cnt + 1)\n elif anagram in ht.keys():\n # This string equals to some prevoius\n # Increase original count for hash table item\n (original, orig_cnt, anag_cnt) = ht[anagram]\n ht[anagram] = (original, orig_cnt+1, anag_cnt)\n else:\n # This string is new\n ht[anagram] = (cur_str, 1, 0)\n return ht","def _make_hashable(items):\n\n def convert(x):\n # Perform any conversions here to make a variable hashable\n if isinstance(x, np.ndarray):\n # Create an sha1 of the data, and throw in a string\n # and the shape.\n return ('__type_np.ndarray', x.shape,\n xxhash.xxh3_128_hexdigest(x))\n elif isinstance(x, (list, tuple)):\n return _make_hashable(x)\n elif isinstance(x, dict):\n return _make_hashable(sorted(x.items()))\n return x\n\n return tuple(map(convert, items))","def createdict(Matrix,List):\r\n n = len(List)\r\n #to get all possible combinations\r\n input_combns = list(itertools.combinations(range(0,n),2))\r\n d = defaultdict(dict)\r\n for x in input_combns:\r\n i,j = x\r\n p,q = List[i],List[j]\r\n d[p][q] = Matrix[i][j]\r\n return d","def generate_grid_dict(height, width):\n board = {}\n for i in range(height):\n for j in range(width):\n position = (i, j)\n board[position] = 0\n return board","def _hash(self) -> None:\r\n # for a unit cube there are 8 possible hashes\r\n # returns the tuple of with all 8 hashes\r\n\r\n self.hashes[\"aaa\"] = P[P[P[self.xi] + self.yi] + self.zi]\r\n self.hashes[\"aab\"] = P[P[P[self.xi] + self.yi] + self._inc(self.zi)]\r\n self.hashes[\"aba\"] = P[P[P[self.xi] + self._inc(self.yi)] + self.zi]\r\n self.hashes[\"abb\"] = P[P[P[self.xi] + self._inc(self.yi)] + self._inc(self.zi)]\r\n self.hashes[\"baa\"] = P[P[P[self._inc(self.xi)] + self.yi] + self.zi]\r\n self.hashes[\"bab\"] = P[P[P[self._inc(self.xi)] + self.yi] + self._inc(self.zi)]\r\n self.hashes[\"bba\"] = P[P[P[self._inc(self.xi)] + self._inc(self.yi)] + self.zi]\r\n self.hashes[\"bbb\"] = P[P[P[self._inc(self.xi)] + self._inc(self.yi)] + self._inc(self.zi)]","def initialize_d(d, square_sides, offset=0):\n return {key:[] for key in range(offset, square_sides ** 2 + offset)}","def fresh_hash(self):\n _h = defaultdict(lambda: 0)\n very_small = 0.000000000001\n for g in self.groups: _h[g] = { \"total\": very_small, \"var_all\": 0 }\n return _h","def make_dict(unused_s, unused_l, toks):\n result = {}\n key_value_pairs = chunks(toks, 2)\n for key_value_pair in key_value_pairs:\n result[key_value_pair[0]] = key_value_pair[1]\n return result","def create_dictionary():\n d = {}\n for y in range(HEIGHT):\n if (y % 2) != 0:\n pos = (10*y)+10\n else:\n pos =((10*y)-9)+10 \n for x in range(WIDTH):\n xy_tuple = (x,y)\n d[pos] = xy_tuple\n if (y % 2) != 0:\n pos = pos - 1\n else:\n pos = pos + 1\n \n return d","def create_dict(*args):\n output = {}\n idx = 0\n while idx < len(args):\n output[args[idx + 1]] = args[idx]\n idx += 2\n\n return output","def __init__(self,n):\n\t\tself._dictOut={}\n\t\tself._dictIn = {}\n\t\tfor i in range(n):\n\t\t\tself._dictOut[i]=[]\n\t\t\tself._dictIn[i] = []","def build_anagram_dict(word_gen, starting_dict={}, hash_fn=lambda x: tuple(sorted(x))):\n\n dict = starting_dict\n\n for word in word_gen:\n key = hash_fn(word)\n # Using dictionary as hashtable to eliminate duplicates (when reading from literature etc)\n word_list = dict.get(key, {})\n word_list[word] = None\n dict[key] = word_list\n\n return dict","def createMap(*values):\n\tresult = dict()\n\tfor i in range(0, len(values), 2):\n\t\tresult[values[i]] = values[i+1]\n\treturn result","def Dictionary_create_from(nMarkers, markerSize, baseDictionary):\n pass","def get_hash_map(init_addr):\n addr = init_addr\n hash_map = []\n for i in range(0, len(WIN_HASH), 2):\n pair = WIN_HASH[i:i+2]\n hash_map.append((addr, pair[1]))\n hash_map.append((addr+1, pair[0]))\n addr += 8\n\n return hash_map","def DictFunction2():\r\n print \"Create Second Dictionary\"\r\n NumberDict = dict(zip((i for i in range(16)), (hex(i) for i in range(16))))\r\n print NumberDict","def __init__(self, n):\n self._dictOut = {}\n self._dictIn = {}\n for i in range(n):\n self._dictOut[i] = []\n self._dictIn[i] = []","def Dictionary_create(nMarkers, markerSize):\n pass","def make_hash(o):\n\n if isinstance(o, (set, tuple, list)):\n\n return hash( tuple([make_hash(e) for e in o]) )\n\n elif not isinstance(o, dict):\n\n return hash(o)\n\n new_o = copy.deepcopy(o)\n for k, v in new_o.items():\n new_o[k] = make_hash(v)\n\n return hash(tuple(frozenset(sorted(new_o.items()))))","def create_pristine_board(size=100):\n board = defaultdict(dict)\n\n for i in xrange(1, size + 1):\n board[i] = {j: (j - i) for j in xrange(min(i + 1, size + 1), min(i + 7, size + 1))}\n\n return board","def construct(self):\n\n newSet = {}\n current_index = 0\n\n for key_1, value_1 in self._sets[self._currentSet].items():\n current_index += 1\n for key_2,value_2 in list(self._sets[self._currentSet].items())[current_index:]:\n # join the 2 tuples\n join = key_1 + key_2\n # remove duplicates\n join = tuple(set(join))\n # get combinations\n combined = tuple(combinations(join, self._currentSet+1))\n # sort combination\n combined = tuple(sorted(combined[0]))\n\n # append new combination to dict\n if len(combined) != 0 :\n newSet[combined] = 0\n\n self._currentSet += 1\n # append the new itemset in the sets dict \n self._sets[self._currentSet] = newSet","def __create_level_entries_dict__(self,\n tree_level_labels,\n tree_level_values,\n ):\n # | - create_level_entries_dict\n level_entries_dict = {}\n for index, variable in enumerate(tree_level_labels):\n level_entries_dict[variable] = tree_level_values[index]\n\n return(level_entries_dict)\n # __|","def test_hash_numpy():\r\n rnd = np.random.RandomState(0)\r\n arr1 = rnd.random_sample((10, 10))\r\n arr2 = arr1.copy()\r\n arr3 = arr2.copy()\r\n arr3[0] += 1\r\n obj_list = (arr1, arr2, arr3)\r\n for obj1 in obj_list:\r\n for obj2 in obj_list:\r\n yield nose.tools.assert_equal, hash(obj1) == hash(obj2), \\\r\n np.all(obj1 == obj2)\r\n\r\n d1 = {1: arr1, 2: arr1}\r\n d2 = {1: arr2, 2: arr2}\r\n yield nose.tools.assert_equal, hash(d1), hash(d2)\r\n\r\n d3 = {1: arr2, 2: arr3}\r\n yield nose.tools.assert_not_equal, hash(d1), hash(d3)\r\n\r\n yield nose.tools.assert_not_equal, hash(arr1), hash(arr1.T)","def _build_board(y_size, x_size, game_board):\n\n for y_coordinate in range(1, y_size + 1):\n for x_coordinate in range(1, x_size + 1):\n game_board[(x_coordinate, y_coordinate)] = {0: {}, 1: {}, 2: {}}","def _make_limb_dict():\n\n return {'left_arm_y': 10, 'right_arm_y': 13,\n 'left_arm_z': 11, 'right_arm_z': 14,\n 'left_leg_y': 4, 'right_leg_y': 7,\n 'left_leg_z': 5, 'right_leg_z': 8,\n 'hip_y': 2, 'hip_x': 1}","def make_dict(keys, values):\n\n return dict(zip(keys, values))","def hash_double(d):\n # initialize table\n table = [\"-\"] * 19\n # consider each integer k in the input\n for k in d:\n # if k is already in the table this is a duplicate so move to next integer in the input\n # note this check for a duplicate is using the functionality of python rather than checking using a linear probe\n if k in table:\n continue\n # apply the hash function\n i = (6 * k + 3) % 19\n t = i\n # initialize count that checks whether linear probe has considered each bucket and is now full\n count = 0\n j = 0\n # while bucket is already filled\n s = 11 - (k % 11)\n while table[i] != '-':\n j += 1\n # move to next bucket\n i = (t + j*s) % 19\n # increment count\n count += 1\n\n # if table is full\n if count >= 18:\n # can return table as nothing further can be added\n break\n\n # Ensure table[i] is empty so k can be added here\n if table[i] == '-':\n table[i] = k\n\n # now each part of the input has been considered return the table\n return table","def creating_dict(i, states):\n # base case\n if i == 5:\n # no more edges - recursion ends here\n return {'barcode': []}\n\n # iterative case\n else:\n # this is a tree structure where the node contains timepoint information and barcode information\n # and three edges link to other nodes that represent lineages in three differnet states\n updated_dict = {'t{}'.format(i): {state: creating_dict(i + 1, states) for state in states}}\n updated_dict['t{}'.format(i)].update({'barcode': []})\n return updated_dict","def create_dictionaries(chars):\n return dict((c, i) for i, c in enumerate(chars)), dict((i, c) for i, c in enumerate(chars))","def HashValue(self) -> _n_0_t_3[_n_0_t_9]:","def makedict(real, n):\n m = n/2\n d = nltk.defaultdict(list)\n for word in real:\n for num, syll in enumerate(word):\n if num != 0:\n ls = str(len(syll))\n nsylls_lsylls = [str(len(word)), str(num), ls]\n d[\"_\".join(nsylls_lsylls + [syll[:m], word[num - 1][-m:]])] += [syll]\n return(d)","def __init__(self,n):\n\t\tself._dict={}\n\t\tfor i in range(n):\n\t\t\tself._dict[i]=[]","def deep_hash(obj):\n pass","def _create_dictionaries(self, chars):\n dictionary = dict()\n for char in chars:\n dictionary[char] = len(dictionary)\n reverse_dictionary = dict(zip(dictionary.values(), dictionary.keys()))\n return dictionary, reverse_dictionary","def setup2():\n ht2 = HashTable()\n ht2.set('Apple', 'Dash')\n ht2.set('Flutter', 'Sparkle')\n ht2.set('Pinky', 'Pony')\n return ht2","def initialize_dicts(m, square_sides):\n rows_missing = defaultdict(list)\n rows_missing = initialize_d(rows_missing, square_sides)\n cols_missing = defaultdict(list)\n cols_missing = initialize_d(cols_missing, square_sides)\n squares_missing = defaultdict(list)\n squares_missing = initialize_d(cols_missing, square_sides, 1)\n return rows_missing, cols_missing, squares_missing","def build_unq_dict_lst(self, lst1, lst2, key1 = \"start_index\", key2 = \"random_seed\"):\n dict_lst = []\n for i in range(len(lst1)):\n for j in range(len(lst2)):\n dictt = {}\n dictt[key1] = lst1[i]\n dictt[key2] = lst2[j]\n dict_lst.append(dictt)\n return dict_lst","def ht_ant_two():\n ht = HashTable(10)\n ht.set('fond', 'averse')\n ht.set('diligent', 'idle')\n ht.set('guide', 'jam')\n return ht","def __init__(self):\n self.hash = [[] for _ in range(20011)]","def hashdict(self):\n return {\n 'pix': super(rmap, self).hashdict(),\n 'map': hashlib.sha1(self.map.view(np.uint8)).hexdigest()\n }","def test_deep_set_create(self):\n mdict = copy.deepcopy(self.dict1)\n res = dictupdate.set_dict_key_value(mdict, \"K:L:M\", \"Q\")\n self.assertEqual(\n {\n \"A\": \"B\",\n \"C\": {\"D\": \"E\", \"F\": {\"G\": \"H\", \"I\": \"J\"}},\n \"K\": {\"L\": {\"M\": \"Q\"}},\n },\n res,\n )","def create_dictionary(self,*key_value_pairs,**items):\r\n if len(key_value_pairs)%2 !=0:\r\n raise ValueError(\"create dictionary failed. there should be\"\r\n \"an even number of key-value-pairs\")\r\n return self.set_to_dictionary({},*key_value_pairs,**items)","def build_dict(arg):\n # helper function to the Evaluator.to_property_di_graph() method that\n # packages the dictionaries returned by the \"associate_\" family of\n # functions and then supplies the master dict (one_dict) to the Vertex\n # obj as **kwargs\n one_dict = {}\n for ar in arg:\n one_dict.update(ar)\n return one_dict","def make_hash(o):\n if isinstance(o, (set, tuple, list)):\n return hash(tuple([make_hash(e) for e in o]))\n elif not isinstance(o, dict) and o.__class__.__module__ == 'builtins':\n return hash(o)\n elif not isinstance(o, dict):\n return make_hash(o.__dict__)\n\n new_o = copy.deepcopy(o)\n for k, v in new_o.items():\n new_o[k] = make_hash(v)\n return hash(tuple(frozenset(sorted(new_o.items()))))","def multidict(ordered_pairs):\n # read all values into lists\n d = defaultdict(list)\n for k, v in ordered_pairs:\n d[k].append(v)\n # unpack lists that have only 1 item\n dict_copy = deepcopy(d)\n for k, v in iteritems(dict_copy):\n if len(v) == 1:\n d[k] = v[0]\n return dict(d)","def internal_hash(self): \n return hash(tuple(sorted(self.hashtriples())))","def nested_dict():\n return defaultdict(nested_dict)","def build_dict(infile):\n\n coords = {}\n sizes = {}\n\n for line in infile:\n fields = line.split()\n ref_st, ref_end, qry_st, qry_end = map(int, fields[0:4])\n qry_chr, qry_size = fields[14], int(fields[8])\n if qry_chr not in coords:\n coords[qry_chr] = {0:[], 1:[]} # 0=ref; 1=qry\n sizes[qry_chr] = qry_size\n coords[qry_chr][0].append([ref_st, ref_end])\n coords[qry_chr][1].append(sorted([qry_st, qry_end]))\n \n return coords, sizes","def new(num_buckets=256):#用空列表初始化字典\n\taMap=[]\n\tfor i in range(num_buckets):\n\t\taMap.append([])\n\treturn aMap","def make_employee_dict(names, ID_numbers, salaries, email_addresses):\r\n d = dict()\r\n for i in range(len(names)):\r\n d[ID_numbers[i]] = Employee(names[i], ID_numbers[i], salaries[i], email_addresses[i])\r\n return d","def hash_values_demo(r):\n record = {\n \"name\": \"Hackers and Slackers\",\n \"description\": \"Mediocre tutorials\",\n \"website\": \"https://hackersandslackers.com/\",\n \"github\": \"https://github.com/hackersandslackers\"\n }\n r.hmset('business', record)\n logger.info(f\"business: {r.hgetall('business')}\")","def buildDict(self, dict):\n for item in dict:\n length = len(item)\n if length not in self.dic:\n self.dic[length] = [item]\n else:\n self.dic[length].append(item)","def database(words):\n\n d={}\n if len(words) < 3:\n return\n \n for i,word in enumerate(words):\n try:\n first,second,third = (words[i], words[i+1], words[i+2])\n except IndexError:\n break\n key = (first,second)\n if key not in d:\n d[key] = []\n d[key].append(third)\n \n return d","def cons(map) -> HashMap:\r\n table = HashMap()\r\n from_dict(table, to_dict(map))\r\n return table","def hash_quadratic(d):\n # initialize table\n table = [\"-\"] * 19\n # consider each integer k in the input\n for k in d:\n # if k is already in the table this is a duplicate so move to next integer in the input\n # note this check for a duplicate is using the functionality of python rather than checking using a linear probe\n if k in table:\n continue\n # apply the hash function\n i = (6 * k + 3) % 19\n t = i\n # initialize count that checks whether linear probe has considered each bucket and is now full\n count = 0\n j = 0\n # while bucket is already filled\n\n while table[i] != '-':\n j += 1\n # move to next bucket\n i = (t + j ** 2) % 19\n # increment count\n count += 1\n\n # if table is full\n if count >= 18:\n # can return table as nothing further can be added\n break\n\n # Ensure table[i] is empty so k can be added here\n if table[i] == '-':\n table[i] = k\n\n # now each part of the input has been considered return the table\n return table","def make_hashable(value):\n if isinstance(value, dict):\n return tuple([\n (key, make_hashable(nested_value))\n for key, nested_value in sorted(value.items())\n ])\n # Try hash to avoid converting a hashable iterable (e.g. string, frozenset)\n # to a tuple.\n try:\n hash(value)\n except TypeError:\n if is_iterable(value):\n return tuple(map(make_hashable, value))\n # Non-hashable, non-iterable.\n raise\n return value","def _reference(self):\r\n return {1:2, \"key1\":\"value1\", \"key2\":(1,2,3)}","def h_python(key, N):\n return hash(key) % N","def create_triad_counts():\n triads = [str(i) + str(j) + str(k) for i in range(2) for j in range(2) for k in range(2)]\n triad_counts = {}\n\n for triad in triads:\n triad_counts[triad] = [0, 0]\n\n return triad_counts","def buildDict(self, words):\n for word in words:\n length = len(word)\n key = \"{}/{}\".format(length, word[0])\n ls = self.origin.get(key, [])\n ls.append(word)\n self.origin[key] = ls","def __init__(self, max_len, max_num):\n self.a = {}\n self.a[0] = [[]]\n self.a[1] = [[1]]\n\n self.max_len = max_len\n self.max_num = max_num","def gen_dict(keys, vals):\n retVal = {}\n for i in range(len(keys)):\n if i > len(vals):\n retVal[keys[i]] = \"\"\n continue\n retVal[keys[i]] = vals[i]\n return retVal","def hash_from_dict(d):\r\n items = d.items()\r\n items.sort()\r\n first_part = [k for k, v in items]\r\n second_part = []\r\n for k, v in items:\r\n if isinstance(v, (tuple, list)):\r\n second_part += [tuple(v)]\r\n else:\r\n second_part += [v]\r\n tuple_items = tuple(first_part + second_part)\r\n return hash(tuple_items)","def init_hash_uuid_lut(session, hashes):\n # Note: unhexlify is necessary since the database stores\n # binary representations of the hashes\n bin_hashes = [binascii.unhexlify(ahash.encode('utf-8'))\n for ahash in hashes]\n # print(\"==> Query hashes: {}\".format(bin_hashes))\n links = session.query(LinkageEntity).filter(\n LinkageEntity.linkage_hash.in_(bin_hashes)).all()\n\n # lut = defaultdict(lambda: [])\n lut = {}\n\n for ahash in hashes:\n # instantiate every bucket even if the hash has no record in the db\n lut[ahash] = []\n\n for link in links:\n # collect every link in the corresponding bucket\n lut[link.friendly_hash()].append(link)\n\n return lut","def new(num_buckets=256):\n\taMap = [] #creating empty list aMap\n\tfor i in range(0, num_buckets):\n\t\taMap.append([]) #append num_buckets into aMap\n\treturn aMap","def construct_kv_dict(self):\r\n key1 = self.key_factory('existing_field')\r\n key2 = self.key_factory('other_existing_field')\r\n new_value = 'new value'\r\n newer_value = 'newer value'\r\n return {key1: new_value, key2: newer_value}","def resize(self):\n\n # Stores the new size of the hash map\n new_size = len(self.table) * 2 - 1\n\n # Creates new hash map table (empty)\n new_table = [[]] * new_size\n\n # Creates new key hash table (empty)\n new_ref = [[]] * new_size\n\n # For key value pair in the current hash map\n for key, value in self:\n\n # Get new hashed key for new hash map\n i = hash(key) % len(new_table)\n\n # Put key in new key hash table for index reference\n new_ref[i].append(key)\n\n # Put value in hash map\n new_table[i].append(value)\n\n # Overwrite old hash map with new hash map\n self.table = new_table.copy()\n\n # Overwrite old key hash table with new key hash table\n self.keys_ref = new_ref.copy()","def createDictionary(dataset):\r\n for columnNumber in range(2, dataset.shape[1]):\r\n print(\"manipulating \", dataset.at[0, columnNumber])\r\n manipulateData(columnNumber, dataset)\r\n return Dictionary","def new(num_buckets=256):\n aMap=[]","def buildDict(self, words: List[str]) -> None:\n for word in words:\n self.buckets[len(word)].append(word)","def __hash__(self):\n return hash(('genes', tuple(self.genes), self.environment))","def put(hash, key: int, value: V) -> HashMap:\r\n if hash == None:\r\n hash = HashMap()\r\n hash_key = key % hash.size\r\n if hash.data[hash_key].key == None:\r\n hash.data[hash_key].value = value\r\n hash.data[hash_key].key = key\r\n hash.keyset.append(key)\r\n else:\r\n temp = Node(key, value)\r\n hash.keyset.append(key)\r\n p = hash.data[hash_key]\r\n while p.next != None:\r\n p = p.next\r\n p.next = temp\r\n\r\n return hash","def setup1():\n ht1 = HashTable()\n ht1.set('Apple', 'Jack')\n ht1.set('Pinky', 'Pie')\n ht1.set('Flutter', 'Shy')\n return ht1","def nested_set(data, keys, value):\n for key in keys[:-1]:\n data = data.setdefault(key, {})\n data[keys[-1]] = value","def mkPerfHash(keys, Hash):\n f1, f2, G = generate_hash(keys, Hash)\n return lambda k: (G[f1(k)] + G[f2(k)]) % len(G)","def hash_with_depth_int(func: HashFuncT) -> HashFuncT:\n\n @wraps(func)\n def hashing_func(key, depth=1):\n \"\"\"wrapper function\"\"\"\n res = []\n tmp = func(key, 0)\n res.append(tmp)\n for idx in range(1, depth):\n tmp = func(f\"{tmp:x}\", idx)\n res.append(tmp)\n return res\n\n return hashing_func","def hash_tuples(\n vals: MultiIndex | Iterable[tuple[Hashable, ...]],\n encoding: str = \"utf8\",\n hash_key: str = _default_hash_key,\n) -> npt.NDArray[np.uint64]:\n if not is_list_like(vals):\n raise TypeError(\"must be convertible to a list-of-tuples\")\n\n from pandas import (\n Categorical,\n MultiIndex,\n )\n\n if not isinstance(vals, ABCMultiIndex):\n mi = MultiIndex.from_tuples(vals)\n else:\n mi = vals\n\n # create a list-of-Categoricals\n cat_vals = [\n Categorical._simple_new(\n mi.codes[level],\n CategoricalDtype(categories=mi.levels[level], ordered=False),\n )\n for level in range(mi.nlevels)\n ]\n\n # hash the list-of-ndarrays\n hashes = (\n cat._hash_pandas_object(encoding=encoding, hash_key=hash_key, categorize=False)\n for cat in cat_vals\n )\n h = combine_hash_arrays(hashes, len(cat_vals))\n\n return h","def buildDict(self, words):\n self.dict = collections.defaultdict(set)\n for word in words:\n for i in xrange(len(word)):\n self.dict[word[:i] + '*' + word[i+1:]].add(word[i])","def new_dict(key, value, n_keys=0):\n # With JIT disabled, ignore all arguments and return a Python dict.\n return dict()","def construct(HashClass, empty_structure, memoization_key, store_key, play, m, stores, edge_nout_hash):\n pmts(edge_nout_hash, HashClass)\n\n memoization = getattr(m, memoization_key)\n store = getattr(stores, store_key)\n\n structure = empty_structure\n\n todo = []\n for tup in store.all_nhtups_for_nout_hash(edge_nout_hash):\n if tup.nout_hash in memoization:\n structure = memoization[tup.nout_hash]\n break\n todo.append(tup)\n\n for tup in reversed(todo):\n edge_nout = tup.nout\n edge_nout_hash = tup.nout_hash\n\n note = edge_nout.note\n\n structure = play(m, stores, structure, note, YourOwnHash(edge_nout_hash))\n memoization[edge_nout_hash] = structure\n\n return structure","def nested_dict():\n try:\n num_list = [1, 2, 3, 4]\n new_dict = current = {}\n for name in num_list:\n current[name] = {}\n current = current[name]\n print(new_dict)\n except ValueError as e:\n logger.error(\"Not find the dictnary\"+str(e))","def __init__(self):\n self.hashset = [[] for _ in range(self.N)]","def make_dict(cls, *args: Any, **kwargs: Any) -> Dict[str, Any]:\n return _DictMaker(struct_class=cls, positional_args=args, keyword_args=kwargs).make_dict()","def __init__(self):\n self.size = 10000\n self.hashmap = [None] * self.size","def __init__(self):\n self.size = 1000\n self.hash_table = [None] * self.size","def test_get_hash(get_all_structures):\n comp_matrix = np.zeros((len(get_all_structures), len(get_all_structures)))\n for i, structure_a in enumerate(get_all_structures):\n for j, structure_b in enumerate(get_all_structures):\n if i < j:\n hash_a = get_hash(structure_a)\n hash_b = get_hash(structure_b)\n if hash_a == hash_b:\n comp_matrix[i][j] = 1\n else:\n comp_matrix[i][j] = 0\n assert sum(comp_matrix) == sum(np.diag(comp_matrix))","def grid_vals(grid):\n\tletters = list(grid)\n\t#print \"---------------------------------\\n-------------------\"\n\t#print letters\n\t#print \"----------------------------------\\n-------------------\"\n\tassert len(letters) == 81\n\ttempdict = zip(squares, letters)\n\treturn dict(tempdict)","def create_label_map():\n\n cnt = 1\n tmp_array = np.array([10, 15, 25, 30, 40, 47, 57, 63, 69, 74, 81])\n dictionary = dict()\n dictionary[1] = 1\n for idx, val in enumerate(tmp_array):\n for j in range(cnt + 1, val):\n dictionary[j] = int(idx + 2)\n cnt = j\n return dictionary","def generate_pairs(self, _list_d):\n\n length = len(_list_d)\n result_list = {}\n\n for i in range(length):\n for j in xrange(i+1,length):\n l = len(result_list)\n result_list[l] = ((i, _list_d[i]),(j, _list_d[j]))\n\n return result_list","def triples_as_dict(tuples: Iterable[Tuple[Any, Any, Any]]) -> Dict[Any, List[Tuple[Any, Any]]]:\n d = defaultdict(list)\n for t in tuples:\n d[t[0]].append((t[1], t[2]))\n return d","def create_dict(info):\n \"\"\"\n dict = {ip: {counter:*}, {weekdays: []}, {hours: []}}\n \"\"\"\n dict_info = dict()\n for i in info:\n ip = i[0]\n hours = i[1]\n weekdays = i[2]\n if ip not in dict_info:\n dict_info[ip] = {}\n dict_info[ip]['counter'] = 0\n dict_info[ip]['hours'] = []\n dict_info[ip]['weekdays'] = []\n dict_info[ip]['counter'] += 1\n dict_info[ip]['hours'].append(hours)\n dict_info[ip]['weekdays'].append(weekdays)\n return dict_info","def tree_hash(hashes):\n while len(hashes) > 1:\n hashes = [hashlib.sha256(\"\".join(h[i:i+1])).digest() for i in range(i,2)]\n return hashes[0]","def add_hash(self, lst):\n self.__data = []\n num = len(lst) + 1\n self.add_data((\"\", 4, 1))\n self.add_data((\"\", 4, num))\n self.add_data((\"\", 4, num - 1))\n self.add_data((\"\", 4, 0))\n if 1 < num:\n for ii in range(num - 1):\n self.add_data((\"\", 4, ii))","def makeMap(pairs):\n\n return ConstMap.fromPairs(pairs)","def sur_mat2dict(mat,ndim):\n kwork = np.vstack((np.zeros((1,1),dtype=np.int_),ndim))\n dicts = {}\n ki = 0\n for r in range(1,len(kwork)):\n ki = ki + kwork[r-1][0] \n ke = ki + kwork[r][0]\n dicts[r-1] = mat[ki:ke,:]\n return(dicts)","def build_hap_dict(self, obs_tab, leg_tab, hap_tab, number_of_haplotypes):\n\n hap_dict = dict()\n mismatches = 0\n combined = {pos: (ref,alt,hap) for (chr_id,pos,ref,alt),hap in zip(leg_tab, hap_tab)}\n missing = 3*(None,)\n\n b = (1 << number_of_haplotypes) - 1 #### equivalent to int('1'*number_of_haplotypes,2)\n\n for (pos, read_id, base) in obs_tab:\n ref, alt, hap = combined.get(pos, missing)\n if base==alt:\n hap_dict[(pos,base)] = hap\n elif base==ref:\n hap_dict[(pos,base)] = hap ^ b ### ^b flips all bits of the binary number, hap_tab[ind] using bitwise xor operator.\n else:\n mismatches += 1\n\n fraction_of_matches = 1-mismatches/len(obs_tab)\n\n return hap_dict, fraction_of_matches"],"string":"[\n \"def hashMap(self,arr):\\r\\n n = len(arr)\\r\\n dict1 = {}\\r\\n i = 1\\r\\n for i in range(n): \\r\\n if(i > 0): \\r\\n key=arr[i]\\r\\n value=arr[0]\\r\\n dict1[key] = value\\r\\n return dict1\",\n \"def __init__(self):\\n self.hashmap = [[[],[]] for _ in range(self.N)]\",\n \"def boardtohashmap(board_2d: List[List[str]]) -> Dict[Tuple[int, int], Gridspace]:\\n\\n nrows, ncols = len(board_2d), len(board_2d[0])\\n return {\\n (r, c): Gridspace(r, c, board_2d[r][c], nrows, len(board_2d[r]))\\n for r in range(nrows) for c in range(len(board_2d[r]))\\n }\",\n \"def generate_dict(length):\\r\\n primeDict = {}\\r\\n index = 2\\r\\n \\r\\n while (index < length):\\r\\n primeDict[index]=True\\r\\n index = index+1\\r\\n \\r\\n return primeDict\",\n \"def _build_hash_table(arr: [str]):\\n ht = {}\\n for cur_str in arr:\\n\\n anagram = cur_str[::-1]\\n if cur_str in ht.keys():\\n # This string is an anagram of some previous\\n # Increase anagram count for hash table item\\n (original, orig_cnt, anag_cnt) = ht[cur_str]\\n ht[cur_str] = (original, orig_cnt, anag_cnt + 1)\\n elif anagram in ht.keys():\\n # This string equals to some prevoius\\n # Increase original count for hash table item\\n (original, orig_cnt, anag_cnt) = ht[anagram]\\n ht[anagram] = (original, orig_cnt+1, anag_cnt)\\n else:\\n # This string is new\\n ht[anagram] = (cur_str, 1, 0)\\n return ht\",\n \"def _make_hashable(items):\\n\\n def convert(x):\\n # Perform any conversions here to make a variable hashable\\n if isinstance(x, np.ndarray):\\n # Create an sha1 of the data, and throw in a string\\n # and the shape.\\n return ('__type_np.ndarray', x.shape,\\n xxhash.xxh3_128_hexdigest(x))\\n elif isinstance(x, (list, tuple)):\\n return _make_hashable(x)\\n elif isinstance(x, dict):\\n return _make_hashable(sorted(x.items()))\\n return x\\n\\n return tuple(map(convert, items))\",\n \"def createdict(Matrix,List):\\r\\n n = len(List)\\r\\n #to get all possible combinations\\r\\n input_combns = list(itertools.combinations(range(0,n),2))\\r\\n d = defaultdict(dict)\\r\\n for x in input_combns:\\r\\n i,j = x\\r\\n p,q = List[i],List[j]\\r\\n d[p][q] = Matrix[i][j]\\r\\n return d\",\n \"def generate_grid_dict(height, width):\\n board = {}\\n for i in range(height):\\n for j in range(width):\\n position = (i, j)\\n board[position] = 0\\n return board\",\n \"def _hash(self) -> None:\\r\\n # for a unit cube there are 8 possible hashes\\r\\n # returns the tuple of with all 8 hashes\\r\\n\\r\\n self.hashes[\\\"aaa\\\"] = P[P[P[self.xi] + self.yi] + self.zi]\\r\\n self.hashes[\\\"aab\\\"] = P[P[P[self.xi] + self.yi] + self._inc(self.zi)]\\r\\n self.hashes[\\\"aba\\\"] = P[P[P[self.xi] + self._inc(self.yi)] + self.zi]\\r\\n self.hashes[\\\"abb\\\"] = P[P[P[self.xi] + self._inc(self.yi)] + self._inc(self.zi)]\\r\\n self.hashes[\\\"baa\\\"] = P[P[P[self._inc(self.xi)] + self.yi] + self.zi]\\r\\n self.hashes[\\\"bab\\\"] = P[P[P[self._inc(self.xi)] + self.yi] + self._inc(self.zi)]\\r\\n self.hashes[\\\"bba\\\"] = P[P[P[self._inc(self.xi)] + self._inc(self.yi)] + self.zi]\\r\\n self.hashes[\\\"bbb\\\"] = P[P[P[self._inc(self.xi)] + self._inc(self.yi)] + self._inc(self.zi)]\",\n \"def initialize_d(d, square_sides, offset=0):\\n return {key:[] for key in range(offset, square_sides ** 2 + offset)}\",\n \"def fresh_hash(self):\\n _h = defaultdict(lambda: 0)\\n very_small = 0.000000000001\\n for g in self.groups: _h[g] = { \\\"total\\\": very_small, \\\"var_all\\\": 0 }\\n return _h\",\n \"def make_dict(unused_s, unused_l, toks):\\n result = {}\\n key_value_pairs = chunks(toks, 2)\\n for key_value_pair in key_value_pairs:\\n result[key_value_pair[0]] = key_value_pair[1]\\n return result\",\n \"def create_dictionary():\\n d = {}\\n for y in range(HEIGHT):\\n if (y % 2) != 0:\\n pos = (10*y)+10\\n else:\\n pos =((10*y)-9)+10 \\n for x in range(WIDTH):\\n xy_tuple = (x,y)\\n d[pos] = xy_tuple\\n if (y % 2) != 0:\\n pos = pos - 1\\n else:\\n pos = pos + 1\\n \\n return d\",\n \"def create_dict(*args):\\n output = {}\\n idx = 0\\n while idx < len(args):\\n output[args[idx + 1]] = args[idx]\\n idx += 2\\n\\n return output\",\n \"def __init__(self,n):\\n\\t\\tself._dictOut={}\\n\\t\\tself._dictIn = {}\\n\\t\\tfor i in range(n):\\n\\t\\t\\tself._dictOut[i]=[]\\n\\t\\t\\tself._dictIn[i] = []\",\n \"def build_anagram_dict(word_gen, starting_dict={}, hash_fn=lambda x: tuple(sorted(x))):\\n\\n dict = starting_dict\\n\\n for word in word_gen:\\n key = hash_fn(word)\\n # Using dictionary as hashtable to eliminate duplicates (when reading from literature etc)\\n word_list = dict.get(key, {})\\n word_list[word] = None\\n dict[key] = word_list\\n\\n return dict\",\n \"def createMap(*values):\\n\\tresult = dict()\\n\\tfor i in range(0, len(values), 2):\\n\\t\\tresult[values[i]] = values[i+1]\\n\\treturn result\",\n \"def Dictionary_create_from(nMarkers, markerSize, baseDictionary):\\n pass\",\n \"def get_hash_map(init_addr):\\n addr = init_addr\\n hash_map = []\\n for i in range(0, len(WIN_HASH), 2):\\n pair = WIN_HASH[i:i+2]\\n hash_map.append((addr, pair[1]))\\n hash_map.append((addr+1, pair[0]))\\n addr += 8\\n\\n return hash_map\",\n \"def DictFunction2():\\r\\n print \\\"Create Second Dictionary\\\"\\r\\n NumberDict = dict(zip((i for i in range(16)), (hex(i) for i in range(16))))\\r\\n print NumberDict\",\n \"def __init__(self, n):\\n self._dictOut = {}\\n self._dictIn = {}\\n for i in range(n):\\n self._dictOut[i] = []\\n self._dictIn[i] = []\",\n \"def Dictionary_create(nMarkers, markerSize):\\n pass\",\n \"def make_hash(o):\\n\\n if isinstance(o, (set, tuple, list)):\\n\\n return hash( tuple([make_hash(e) for e in o]) )\\n\\n elif not isinstance(o, dict):\\n\\n return hash(o)\\n\\n new_o = copy.deepcopy(o)\\n for k, v in new_o.items():\\n new_o[k] = make_hash(v)\\n\\n return hash(tuple(frozenset(sorted(new_o.items()))))\",\n \"def create_pristine_board(size=100):\\n board = defaultdict(dict)\\n\\n for i in xrange(1, size + 1):\\n board[i] = {j: (j - i) for j in xrange(min(i + 1, size + 1), min(i + 7, size + 1))}\\n\\n return board\",\n \"def construct(self):\\n\\n newSet = {}\\n current_index = 0\\n\\n for key_1, value_1 in self._sets[self._currentSet].items():\\n current_index += 1\\n for key_2,value_2 in list(self._sets[self._currentSet].items())[current_index:]:\\n # join the 2 tuples\\n join = key_1 + key_2\\n # remove duplicates\\n join = tuple(set(join))\\n # get combinations\\n combined = tuple(combinations(join, self._currentSet+1))\\n # sort combination\\n combined = tuple(sorted(combined[0]))\\n\\n # append new combination to dict\\n if len(combined) != 0 :\\n newSet[combined] = 0\\n\\n self._currentSet += 1\\n # append the new itemset in the sets dict \\n self._sets[self._currentSet] = newSet\",\n \"def __create_level_entries_dict__(self,\\n tree_level_labels,\\n tree_level_values,\\n ):\\n # | - create_level_entries_dict\\n level_entries_dict = {}\\n for index, variable in enumerate(tree_level_labels):\\n level_entries_dict[variable] = tree_level_values[index]\\n\\n return(level_entries_dict)\\n # __|\",\n \"def test_hash_numpy():\\r\\n rnd = np.random.RandomState(0)\\r\\n arr1 = rnd.random_sample((10, 10))\\r\\n arr2 = arr1.copy()\\r\\n arr3 = arr2.copy()\\r\\n arr3[0] += 1\\r\\n obj_list = (arr1, arr2, arr3)\\r\\n for obj1 in obj_list:\\r\\n for obj2 in obj_list:\\r\\n yield nose.tools.assert_equal, hash(obj1) == hash(obj2), \\\\\\r\\n np.all(obj1 == obj2)\\r\\n\\r\\n d1 = {1: arr1, 2: arr1}\\r\\n d2 = {1: arr2, 2: arr2}\\r\\n yield nose.tools.assert_equal, hash(d1), hash(d2)\\r\\n\\r\\n d3 = {1: arr2, 2: arr3}\\r\\n yield nose.tools.assert_not_equal, hash(d1), hash(d3)\\r\\n\\r\\n yield nose.tools.assert_not_equal, hash(arr1), hash(arr1.T)\",\n \"def _build_board(y_size, x_size, game_board):\\n\\n for y_coordinate in range(1, y_size + 1):\\n for x_coordinate in range(1, x_size + 1):\\n game_board[(x_coordinate, y_coordinate)] = {0: {}, 1: {}, 2: {}}\",\n \"def _make_limb_dict():\\n\\n return {'left_arm_y': 10, 'right_arm_y': 13,\\n 'left_arm_z': 11, 'right_arm_z': 14,\\n 'left_leg_y': 4, 'right_leg_y': 7,\\n 'left_leg_z': 5, 'right_leg_z': 8,\\n 'hip_y': 2, 'hip_x': 1}\",\n \"def make_dict(keys, values):\\n\\n return dict(zip(keys, values))\",\n \"def hash_double(d):\\n # initialize table\\n table = [\\\"-\\\"] * 19\\n # consider each integer k in the input\\n for k in d:\\n # if k is already in the table this is a duplicate so move to next integer in the input\\n # note this check for a duplicate is using the functionality of python rather than checking using a linear probe\\n if k in table:\\n continue\\n # apply the hash function\\n i = (6 * k + 3) % 19\\n t = i\\n # initialize count that checks whether linear probe has considered each bucket and is now full\\n count = 0\\n j = 0\\n # while bucket is already filled\\n s = 11 - (k % 11)\\n while table[i] != '-':\\n j += 1\\n # move to next bucket\\n i = (t + j*s) % 19\\n # increment count\\n count += 1\\n\\n # if table is full\\n if count >= 18:\\n # can return table as nothing further can be added\\n break\\n\\n # Ensure table[i] is empty so k can be added here\\n if table[i] == '-':\\n table[i] = k\\n\\n # now each part of the input has been considered return the table\\n return table\",\n \"def creating_dict(i, states):\\n # base case\\n if i == 5:\\n # no more edges - recursion ends here\\n return {'barcode': []}\\n\\n # iterative case\\n else:\\n # this is a tree structure where the node contains timepoint information and barcode information\\n # and three edges link to other nodes that represent lineages in three differnet states\\n updated_dict = {'t{}'.format(i): {state: creating_dict(i + 1, states) for state in states}}\\n updated_dict['t{}'.format(i)].update({'barcode': []})\\n return updated_dict\",\n \"def create_dictionaries(chars):\\n return dict((c, i) for i, c in enumerate(chars)), dict((i, c) for i, c in enumerate(chars))\",\n \"def HashValue(self) -> _n_0_t_3[_n_0_t_9]:\",\n \"def makedict(real, n):\\n m = n/2\\n d = nltk.defaultdict(list)\\n for word in real:\\n for num, syll in enumerate(word):\\n if num != 0:\\n ls = str(len(syll))\\n nsylls_lsylls = [str(len(word)), str(num), ls]\\n d[\\\"_\\\".join(nsylls_lsylls + [syll[:m], word[num - 1][-m:]])] += [syll]\\n return(d)\",\n \"def __init__(self,n):\\n\\t\\tself._dict={}\\n\\t\\tfor i in range(n):\\n\\t\\t\\tself._dict[i]=[]\",\n \"def deep_hash(obj):\\n pass\",\n \"def _create_dictionaries(self, chars):\\n dictionary = dict()\\n for char in chars:\\n dictionary[char] = len(dictionary)\\n reverse_dictionary = dict(zip(dictionary.values(), dictionary.keys()))\\n return dictionary, reverse_dictionary\",\n \"def setup2():\\n ht2 = HashTable()\\n ht2.set('Apple', 'Dash')\\n ht2.set('Flutter', 'Sparkle')\\n ht2.set('Pinky', 'Pony')\\n return ht2\",\n \"def initialize_dicts(m, square_sides):\\n rows_missing = defaultdict(list)\\n rows_missing = initialize_d(rows_missing, square_sides)\\n cols_missing = defaultdict(list)\\n cols_missing = initialize_d(cols_missing, square_sides)\\n squares_missing = defaultdict(list)\\n squares_missing = initialize_d(cols_missing, square_sides, 1)\\n return rows_missing, cols_missing, squares_missing\",\n \"def build_unq_dict_lst(self, lst1, lst2, key1 = \\\"start_index\\\", key2 = \\\"random_seed\\\"):\\n dict_lst = []\\n for i in range(len(lst1)):\\n for j in range(len(lst2)):\\n dictt = {}\\n dictt[key1] = lst1[i]\\n dictt[key2] = lst2[j]\\n dict_lst.append(dictt)\\n return dict_lst\",\n \"def ht_ant_two():\\n ht = HashTable(10)\\n ht.set('fond', 'averse')\\n ht.set('diligent', 'idle')\\n ht.set('guide', 'jam')\\n return ht\",\n \"def __init__(self):\\n self.hash = [[] for _ in range(20011)]\",\n \"def hashdict(self):\\n return {\\n 'pix': super(rmap, self).hashdict(),\\n 'map': hashlib.sha1(self.map.view(np.uint8)).hexdigest()\\n }\",\n \"def test_deep_set_create(self):\\n mdict = copy.deepcopy(self.dict1)\\n res = dictupdate.set_dict_key_value(mdict, \\\"K:L:M\\\", \\\"Q\\\")\\n self.assertEqual(\\n {\\n \\\"A\\\": \\\"B\\\",\\n \\\"C\\\": {\\\"D\\\": \\\"E\\\", \\\"F\\\": {\\\"G\\\": \\\"H\\\", \\\"I\\\": \\\"J\\\"}},\\n \\\"K\\\": {\\\"L\\\": {\\\"M\\\": \\\"Q\\\"}},\\n },\\n res,\\n )\",\n \"def create_dictionary(self,*key_value_pairs,**items):\\r\\n if len(key_value_pairs)%2 !=0:\\r\\n raise ValueError(\\\"create dictionary failed. there should be\\\"\\r\\n \\\"an even number of key-value-pairs\\\")\\r\\n return self.set_to_dictionary({},*key_value_pairs,**items)\",\n \"def build_dict(arg):\\n # helper function to the Evaluator.to_property_di_graph() method that\\n # packages the dictionaries returned by the \\\"associate_\\\" family of\\n # functions and then supplies the master dict (one_dict) to the Vertex\\n # obj as **kwargs\\n one_dict = {}\\n for ar in arg:\\n one_dict.update(ar)\\n return one_dict\",\n \"def make_hash(o):\\n if isinstance(o, (set, tuple, list)):\\n return hash(tuple([make_hash(e) for e in o]))\\n elif not isinstance(o, dict) and o.__class__.__module__ == 'builtins':\\n return hash(o)\\n elif not isinstance(o, dict):\\n return make_hash(o.__dict__)\\n\\n new_o = copy.deepcopy(o)\\n for k, v in new_o.items():\\n new_o[k] = make_hash(v)\\n return hash(tuple(frozenset(sorted(new_o.items()))))\",\n \"def multidict(ordered_pairs):\\n # read all values into lists\\n d = defaultdict(list)\\n for k, v in ordered_pairs:\\n d[k].append(v)\\n # unpack lists that have only 1 item\\n dict_copy = deepcopy(d)\\n for k, v in iteritems(dict_copy):\\n if len(v) == 1:\\n d[k] = v[0]\\n return dict(d)\",\n \"def internal_hash(self): \\n return hash(tuple(sorted(self.hashtriples())))\",\n \"def nested_dict():\\n return defaultdict(nested_dict)\",\n \"def build_dict(infile):\\n\\n coords = {}\\n sizes = {}\\n\\n for line in infile:\\n fields = line.split()\\n ref_st, ref_end, qry_st, qry_end = map(int, fields[0:4])\\n qry_chr, qry_size = fields[14], int(fields[8])\\n if qry_chr not in coords:\\n coords[qry_chr] = {0:[], 1:[]} # 0=ref; 1=qry\\n sizes[qry_chr] = qry_size\\n coords[qry_chr][0].append([ref_st, ref_end])\\n coords[qry_chr][1].append(sorted([qry_st, qry_end]))\\n \\n return coords, sizes\",\n \"def new(num_buckets=256):#用空列表初始化字典\\n\\taMap=[]\\n\\tfor i in range(num_buckets):\\n\\t\\taMap.append([])\\n\\treturn aMap\",\n \"def make_employee_dict(names, ID_numbers, salaries, email_addresses):\\r\\n d = dict()\\r\\n for i in range(len(names)):\\r\\n d[ID_numbers[i]] = Employee(names[i], ID_numbers[i], salaries[i], email_addresses[i])\\r\\n return d\",\n \"def hash_values_demo(r):\\n record = {\\n \\\"name\\\": \\\"Hackers and Slackers\\\",\\n \\\"description\\\": \\\"Mediocre tutorials\\\",\\n \\\"website\\\": \\\"https://hackersandslackers.com/\\\",\\n \\\"github\\\": \\\"https://github.com/hackersandslackers\\\"\\n }\\n r.hmset('business', record)\\n logger.info(f\\\"business: {r.hgetall('business')}\\\")\",\n \"def buildDict(self, dict):\\n for item in dict:\\n length = len(item)\\n if length not in self.dic:\\n self.dic[length] = [item]\\n else:\\n self.dic[length].append(item)\",\n \"def database(words):\\n\\n d={}\\n if len(words) < 3:\\n return\\n \\n for i,word in enumerate(words):\\n try:\\n first,second,third = (words[i], words[i+1], words[i+2])\\n except IndexError:\\n break\\n key = (first,second)\\n if key not in d:\\n d[key] = []\\n d[key].append(third)\\n \\n return d\",\n \"def cons(map) -> HashMap:\\r\\n table = HashMap()\\r\\n from_dict(table, to_dict(map))\\r\\n return table\",\n \"def hash_quadratic(d):\\n # initialize table\\n table = [\\\"-\\\"] * 19\\n # consider each integer k in the input\\n for k in d:\\n # if k is already in the table this is a duplicate so move to next integer in the input\\n # note this check for a duplicate is using the functionality of python rather than checking using a linear probe\\n if k in table:\\n continue\\n # apply the hash function\\n i = (6 * k + 3) % 19\\n t = i\\n # initialize count that checks whether linear probe has considered each bucket and is now full\\n count = 0\\n j = 0\\n # while bucket is already filled\\n\\n while table[i] != '-':\\n j += 1\\n # move to next bucket\\n i = (t + j ** 2) % 19\\n # increment count\\n count += 1\\n\\n # if table is full\\n if count >= 18:\\n # can return table as nothing further can be added\\n break\\n\\n # Ensure table[i] is empty so k can be added here\\n if table[i] == '-':\\n table[i] = k\\n\\n # now each part of the input has been considered return the table\\n return table\",\n \"def make_hashable(value):\\n if isinstance(value, dict):\\n return tuple([\\n (key, make_hashable(nested_value))\\n for key, nested_value in sorted(value.items())\\n ])\\n # Try hash to avoid converting a hashable iterable (e.g. string, frozenset)\\n # to a tuple.\\n try:\\n hash(value)\\n except TypeError:\\n if is_iterable(value):\\n return tuple(map(make_hashable, value))\\n # Non-hashable, non-iterable.\\n raise\\n return value\",\n \"def _reference(self):\\r\\n return {1:2, \\\"key1\\\":\\\"value1\\\", \\\"key2\\\":(1,2,3)}\",\n \"def h_python(key, N):\\n return hash(key) % N\",\n \"def create_triad_counts():\\n triads = [str(i) + str(j) + str(k) for i in range(2) for j in range(2) for k in range(2)]\\n triad_counts = {}\\n\\n for triad in triads:\\n triad_counts[triad] = [0, 0]\\n\\n return triad_counts\",\n \"def buildDict(self, words):\\n for word in words:\\n length = len(word)\\n key = \\\"{}/{}\\\".format(length, word[0])\\n ls = self.origin.get(key, [])\\n ls.append(word)\\n self.origin[key] = ls\",\n \"def __init__(self, max_len, max_num):\\n self.a = {}\\n self.a[0] = [[]]\\n self.a[1] = [[1]]\\n\\n self.max_len = max_len\\n self.max_num = max_num\",\n \"def gen_dict(keys, vals):\\n retVal = {}\\n for i in range(len(keys)):\\n if i > len(vals):\\n retVal[keys[i]] = \\\"\\\"\\n continue\\n retVal[keys[i]] = vals[i]\\n return retVal\",\n \"def hash_from_dict(d):\\r\\n items = d.items()\\r\\n items.sort()\\r\\n first_part = [k for k, v in items]\\r\\n second_part = []\\r\\n for k, v in items:\\r\\n if isinstance(v, (tuple, list)):\\r\\n second_part += [tuple(v)]\\r\\n else:\\r\\n second_part += [v]\\r\\n tuple_items = tuple(first_part + second_part)\\r\\n return hash(tuple_items)\",\n \"def init_hash_uuid_lut(session, hashes):\\n # Note: unhexlify is necessary since the database stores\\n # binary representations of the hashes\\n bin_hashes = [binascii.unhexlify(ahash.encode('utf-8'))\\n for ahash in hashes]\\n # print(\\\"==> Query hashes: {}\\\".format(bin_hashes))\\n links = session.query(LinkageEntity).filter(\\n LinkageEntity.linkage_hash.in_(bin_hashes)).all()\\n\\n # lut = defaultdict(lambda: [])\\n lut = {}\\n\\n for ahash in hashes:\\n # instantiate every bucket even if the hash has no record in the db\\n lut[ahash] = []\\n\\n for link in links:\\n # collect every link in the corresponding bucket\\n lut[link.friendly_hash()].append(link)\\n\\n return lut\",\n \"def new(num_buckets=256):\\n\\taMap = [] #creating empty list aMap\\n\\tfor i in range(0, num_buckets):\\n\\t\\taMap.append([]) #append num_buckets into aMap\\n\\treturn aMap\",\n \"def construct_kv_dict(self):\\r\\n key1 = self.key_factory('existing_field')\\r\\n key2 = self.key_factory('other_existing_field')\\r\\n new_value = 'new value'\\r\\n newer_value = 'newer value'\\r\\n return {key1: new_value, key2: newer_value}\",\n \"def resize(self):\\n\\n # Stores the new size of the hash map\\n new_size = len(self.table) * 2 - 1\\n\\n # Creates new hash map table (empty)\\n new_table = [[]] * new_size\\n\\n # Creates new key hash table (empty)\\n new_ref = [[]] * new_size\\n\\n # For key value pair in the current hash map\\n for key, value in self:\\n\\n # Get new hashed key for new hash map\\n i = hash(key) % len(new_table)\\n\\n # Put key in new key hash table for index reference\\n new_ref[i].append(key)\\n\\n # Put value in hash map\\n new_table[i].append(value)\\n\\n # Overwrite old hash map with new hash map\\n self.table = new_table.copy()\\n\\n # Overwrite old key hash table with new key hash table\\n self.keys_ref = new_ref.copy()\",\n \"def createDictionary(dataset):\\r\\n for columnNumber in range(2, dataset.shape[1]):\\r\\n print(\\\"manipulating \\\", dataset.at[0, columnNumber])\\r\\n manipulateData(columnNumber, dataset)\\r\\n return Dictionary\",\n \"def new(num_buckets=256):\\n aMap=[]\",\n \"def buildDict(self, words: List[str]) -> None:\\n for word in words:\\n self.buckets[len(word)].append(word)\",\n \"def __hash__(self):\\n return hash(('genes', tuple(self.genes), self.environment))\",\n \"def put(hash, key: int, value: V) -> HashMap:\\r\\n if hash == None:\\r\\n hash = HashMap()\\r\\n hash_key = key % hash.size\\r\\n if hash.data[hash_key].key == None:\\r\\n hash.data[hash_key].value = value\\r\\n hash.data[hash_key].key = key\\r\\n hash.keyset.append(key)\\r\\n else:\\r\\n temp = Node(key, value)\\r\\n hash.keyset.append(key)\\r\\n p = hash.data[hash_key]\\r\\n while p.next != None:\\r\\n p = p.next\\r\\n p.next = temp\\r\\n\\r\\n return hash\",\n \"def setup1():\\n ht1 = HashTable()\\n ht1.set('Apple', 'Jack')\\n ht1.set('Pinky', 'Pie')\\n ht1.set('Flutter', 'Shy')\\n return ht1\",\n \"def nested_set(data, keys, value):\\n for key in keys[:-1]:\\n data = data.setdefault(key, {})\\n data[keys[-1]] = value\",\n \"def mkPerfHash(keys, Hash):\\n f1, f2, G = generate_hash(keys, Hash)\\n return lambda k: (G[f1(k)] + G[f2(k)]) % len(G)\",\n \"def hash_with_depth_int(func: HashFuncT) -> HashFuncT:\\n\\n @wraps(func)\\n def hashing_func(key, depth=1):\\n \\\"\\\"\\\"wrapper function\\\"\\\"\\\"\\n res = []\\n tmp = func(key, 0)\\n res.append(tmp)\\n for idx in range(1, depth):\\n tmp = func(f\\\"{tmp:x}\\\", idx)\\n res.append(tmp)\\n return res\\n\\n return hashing_func\",\n \"def hash_tuples(\\n vals: MultiIndex | Iterable[tuple[Hashable, ...]],\\n encoding: str = \\\"utf8\\\",\\n hash_key: str = _default_hash_key,\\n) -> npt.NDArray[np.uint64]:\\n if not is_list_like(vals):\\n raise TypeError(\\\"must be convertible to a list-of-tuples\\\")\\n\\n from pandas import (\\n Categorical,\\n MultiIndex,\\n )\\n\\n if not isinstance(vals, ABCMultiIndex):\\n mi = MultiIndex.from_tuples(vals)\\n else:\\n mi = vals\\n\\n # create a list-of-Categoricals\\n cat_vals = [\\n Categorical._simple_new(\\n mi.codes[level],\\n CategoricalDtype(categories=mi.levels[level], ordered=False),\\n )\\n for level in range(mi.nlevels)\\n ]\\n\\n # hash the list-of-ndarrays\\n hashes = (\\n cat._hash_pandas_object(encoding=encoding, hash_key=hash_key, categorize=False)\\n for cat in cat_vals\\n )\\n h = combine_hash_arrays(hashes, len(cat_vals))\\n\\n return h\",\n \"def buildDict(self, words):\\n self.dict = collections.defaultdict(set)\\n for word in words:\\n for i in xrange(len(word)):\\n self.dict[word[:i] + '*' + word[i+1:]].add(word[i])\",\n \"def new_dict(key, value, n_keys=0):\\n # With JIT disabled, ignore all arguments and return a Python dict.\\n return dict()\",\n \"def construct(HashClass, empty_structure, memoization_key, store_key, play, m, stores, edge_nout_hash):\\n pmts(edge_nout_hash, HashClass)\\n\\n memoization = getattr(m, memoization_key)\\n store = getattr(stores, store_key)\\n\\n structure = empty_structure\\n\\n todo = []\\n for tup in store.all_nhtups_for_nout_hash(edge_nout_hash):\\n if tup.nout_hash in memoization:\\n structure = memoization[tup.nout_hash]\\n break\\n todo.append(tup)\\n\\n for tup in reversed(todo):\\n edge_nout = tup.nout\\n edge_nout_hash = tup.nout_hash\\n\\n note = edge_nout.note\\n\\n structure = play(m, stores, structure, note, YourOwnHash(edge_nout_hash))\\n memoization[edge_nout_hash] = structure\\n\\n return structure\",\n \"def nested_dict():\\n try:\\n num_list = [1, 2, 3, 4]\\n new_dict = current = {}\\n for name in num_list:\\n current[name] = {}\\n current = current[name]\\n print(new_dict)\\n except ValueError as e:\\n logger.error(\\\"Not find the dictnary\\\"+str(e))\",\n \"def __init__(self):\\n self.hashset = [[] for _ in range(self.N)]\",\n \"def make_dict(cls, *args: Any, **kwargs: Any) -> Dict[str, Any]:\\n return _DictMaker(struct_class=cls, positional_args=args, keyword_args=kwargs).make_dict()\",\n \"def __init__(self):\\n self.size = 10000\\n self.hashmap = [None] * self.size\",\n \"def __init__(self):\\n self.size = 1000\\n self.hash_table = [None] * self.size\",\n \"def test_get_hash(get_all_structures):\\n comp_matrix = np.zeros((len(get_all_structures), len(get_all_structures)))\\n for i, structure_a in enumerate(get_all_structures):\\n for j, structure_b in enumerate(get_all_structures):\\n if i < j:\\n hash_a = get_hash(structure_a)\\n hash_b = get_hash(structure_b)\\n if hash_a == hash_b:\\n comp_matrix[i][j] = 1\\n else:\\n comp_matrix[i][j] = 0\\n assert sum(comp_matrix) == sum(np.diag(comp_matrix))\",\n \"def grid_vals(grid):\\n\\tletters = list(grid)\\n\\t#print \\\"---------------------------------\\\\n-------------------\\\"\\n\\t#print letters\\n\\t#print \\\"----------------------------------\\\\n-------------------\\\"\\n\\tassert len(letters) == 81\\n\\ttempdict = zip(squares, letters)\\n\\treturn dict(tempdict)\",\n \"def create_label_map():\\n\\n cnt = 1\\n tmp_array = np.array([10, 15, 25, 30, 40, 47, 57, 63, 69, 74, 81])\\n dictionary = dict()\\n dictionary[1] = 1\\n for idx, val in enumerate(tmp_array):\\n for j in range(cnt + 1, val):\\n dictionary[j] = int(idx + 2)\\n cnt = j\\n return dictionary\",\n \"def generate_pairs(self, _list_d):\\n\\n length = len(_list_d)\\n result_list = {}\\n\\n for i in range(length):\\n for j in xrange(i+1,length):\\n l = len(result_list)\\n result_list[l] = ((i, _list_d[i]),(j, _list_d[j]))\\n\\n return result_list\",\n \"def triples_as_dict(tuples: Iterable[Tuple[Any, Any, Any]]) -> Dict[Any, List[Tuple[Any, Any]]]:\\n d = defaultdict(list)\\n for t in tuples:\\n d[t[0]].append((t[1], t[2]))\\n return d\",\n \"def create_dict(info):\\n \\\"\\\"\\\"\\n dict = {ip: {counter:*}, {weekdays: []}, {hours: []}}\\n \\\"\\\"\\\"\\n dict_info = dict()\\n for i in info:\\n ip = i[0]\\n hours = i[1]\\n weekdays = i[2]\\n if ip not in dict_info:\\n dict_info[ip] = {}\\n dict_info[ip]['counter'] = 0\\n dict_info[ip]['hours'] = []\\n dict_info[ip]['weekdays'] = []\\n dict_info[ip]['counter'] += 1\\n dict_info[ip]['hours'].append(hours)\\n dict_info[ip]['weekdays'].append(weekdays)\\n return dict_info\",\n \"def tree_hash(hashes):\\n while len(hashes) > 1:\\n hashes = [hashlib.sha256(\\\"\\\".join(h[i:i+1])).digest() for i in range(i,2)]\\n return hashes[0]\",\n \"def add_hash(self, lst):\\n self.__data = []\\n num = len(lst) + 1\\n self.add_data((\\\"\\\", 4, 1))\\n self.add_data((\\\"\\\", 4, num))\\n self.add_data((\\\"\\\", 4, num - 1))\\n self.add_data((\\\"\\\", 4, 0))\\n if 1 < num:\\n for ii in range(num - 1):\\n self.add_data((\\\"\\\", 4, ii))\",\n \"def makeMap(pairs):\\n\\n return ConstMap.fromPairs(pairs)\",\n \"def sur_mat2dict(mat,ndim):\\n kwork = np.vstack((np.zeros((1,1),dtype=np.int_),ndim))\\n dicts = {}\\n ki = 0\\n for r in range(1,len(kwork)):\\n ki = ki + kwork[r-1][0] \\n ke = ki + kwork[r][0]\\n dicts[r-1] = mat[ki:ke,:]\\n return(dicts)\",\n \"def build_hap_dict(self, obs_tab, leg_tab, hap_tab, number_of_haplotypes):\\n\\n hap_dict = dict()\\n mismatches = 0\\n combined = {pos: (ref,alt,hap) for (chr_id,pos,ref,alt),hap in zip(leg_tab, hap_tab)}\\n missing = 3*(None,)\\n\\n b = (1 << number_of_haplotypes) - 1 #### equivalent to int('1'*number_of_haplotypes,2)\\n\\n for (pos, read_id, base) in obs_tab:\\n ref, alt, hap = combined.get(pos, missing)\\n if base==alt:\\n hap_dict[(pos,base)] = hap\\n elif base==ref:\\n hap_dict[(pos,base)] = hap ^ b ### ^b flips all bits of the binary number, hap_tab[ind] using bitwise xor operator.\\n else:\\n mismatches += 1\\n\\n fraction_of_matches = 1-mismatches/len(obs_tab)\\n\\n return hap_dict, fraction_of_matches\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.67796296","0.5938041","0.5885213","0.5752976","0.5734703","0.5709291","0.5704826","0.5652041","0.5644233","0.56247675","0.56185186","0.5611323","0.56072676","0.5539448","0.5488976","0.5445115","0.54388916","0.54211164","0.54074925","0.53994673","0.537734","0.5345715","0.53311265","0.53041047","0.5297536","0.52854407","0.52676785","0.5266973","0.5258118","0.5250899","0.52496153","0.5241375","0.5231931","0.5218357","0.5207242","0.5207184","0.51917005","0.51881474","0.5186212","0.5170368","0.5154522","0.5148854","0.514845","0.5140317","0.51398796","0.51284385","0.51237446","0.51127976","0.51089096","0.51042277","0.51008743","0.50988376","0.5083378","0.507889","0.50674933","0.5054963","0.5053744","0.5046487","0.50411546","0.50395423","0.50364566","0.50306463","0.5029837","0.5019204","0.5018503","0.50161046","0.50142056","0.50092286","0.5005394","0.50008935","0.49992308","0.4986751","0.49827823","0.49817964","0.4977092","0.49751556","0.49742776","0.4972668","0.4971876","0.4970676","0.49635354","0.49612945","0.49601033","0.4959278","0.49579743","0.49539164","0.4944816","0.49408767","0.49396935","0.49371246","0.49094182","0.49085245","0.48967183","0.48946962","0.48938245","0.48915505","0.48869178","0.48836964","0.4882849","0.4877624"],"string":"[\n \"0.67796296\",\n \"0.5938041\",\n \"0.5885213\",\n \"0.5752976\",\n \"0.5734703\",\n \"0.5709291\",\n \"0.5704826\",\n \"0.5652041\",\n \"0.5644233\",\n \"0.56247675\",\n \"0.56185186\",\n \"0.5611323\",\n \"0.56072676\",\n \"0.5539448\",\n \"0.5488976\",\n \"0.5445115\",\n \"0.54388916\",\n \"0.54211164\",\n \"0.54074925\",\n \"0.53994673\",\n \"0.537734\",\n \"0.5345715\",\n \"0.53311265\",\n \"0.53041047\",\n \"0.5297536\",\n \"0.52854407\",\n \"0.52676785\",\n \"0.5266973\",\n \"0.5258118\",\n \"0.5250899\",\n \"0.52496153\",\n \"0.5241375\",\n \"0.5231931\",\n \"0.5218357\",\n \"0.5207242\",\n \"0.5207184\",\n \"0.51917005\",\n \"0.51881474\",\n \"0.5186212\",\n \"0.5170368\",\n \"0.5154522\",\n \"0.5148854\",\n \"0.514845\",\n \"0.5140317\",\n \"0.51398796\",\n \"0.51284385\",\n \"0.51237446\",\n \"0.51127976\",\n \"0.51089096\",\n \"0.51042277\",\n \"0.51008743\",\n \"0.50988376\",\n \"0.5083378\",\n \"0.507889\",\n \"0.50674933\",\n \"0.5054963\",\n \"0.5053744\",\n \"0.5046487\",\n \"0.50411546\",\n \"0.50395423\",\n \"0.50364566\",\n \"0.50306463\",\n \"0.5029837\",\n \"0.5019204\",\n \"0.5018503\",\n \"0.50161046\",\n \"0.50142056\",\n \"0.50092286\",\n \"0.5005394\",\n \"0.50008935\",\n \"0.49992308\",\n \"0.4986751\",\n \"0.49827823\",\n \"0.49817964\",\n \"0.4977092\",\n \"0.49751556\",\n \"0.49742776\",\n \"0.4972668\",\n \"0.4971876\",\n \"0.4970676\",\n \"0.49635354\",\n \"0.49612945\",\n \"0.49601033\",\n \"0.4959278\",\n \"0.49579743\",\n \"0.49539164\",\n \"0.4944816\",\n \"0.49408767\",\n \"0.49396935\",\n \"0.49371246\",\n \"0.49094182\",\n \"0.49085245\",\n \"0.48967183\",\n \"0.48946962\",\n \"0.48938245\",\n \"0.48915505\",\n \"0.48869178\",\n \"0.48836964\",\n \"0.4882849\",\n \"0.4877624\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6773854"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":2896,"cells":{"query":{"kind":"string","value":"Convert headers of fetched tickers to same format for convenient data storage in Database. This method assumes that parser's headers are configured properly(headers_dict), if one of the headers is missing in config file exception raised"},"document":{"kind":"string","value":"def convert_headers(self, tickers):\n\n result = _makehash()\n for pair_name, fetched_values_dict in list(tickers.items()):\n for header, value in list(fetched_values_dict.items()):\n result[pair_name][self.config['headers'][header]] = value\n return 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 _parse_headers(headers):\n\n headers_new = []\n # reformat column headers if needed\n for j, hd in enumerate(headers):\n # rename so always have T1/2 (s)\n if hd == \"T1/2 (num)\" or hd == \"T1/2 (seconds)\":\n hd = \"T1/2 (s)\"\n # for uncertainties, add previous column header to it\n if j > 0 and \"Unc\" in hd:\n hd = headers[j - 1] + \" \" + hd\n if \"Unc\" in hd and \"Unc.\" not in hd:\n hd = hd.replace(\"Unc\", \"Unc.\")\n # expand abbreviated headers\n if \"Energy\" in hd and \"Energy Level\" not in hd:\n hd = hd.replace(\"Energy\", \"Energy Level\")\n if \"Par. Elevel\" in hd:\n hd = hd.replace(\"Par. Elevel\", \"Parent Energy Level\")\n if \"Abund.\" in hd:\n hd = hd.replace(\"Abund.\", \"Abundance (%)\")\n if \"Ene.\" in hd:\n hd = hd.replace(\"Ene.\", \"Energy\")\n if \"Int.\" in hd:\n hd = hd.replace(\"Int.\", \"Intensity (%)\")\n if \"Dec\" in hd and \"Decay\" not in hd:\n hd = hd.replace(\"Dec\", \"Decay\")\n if \"Rad\" in hd and \"Radiation\" not in hd:\n hd = hd.replace(\"Rad\", \"Radiation\")\n if \"EP\" in hd:\n hd = hd.replace(\"EP\", \"Endpoint\")\n if \"Mass Exc\" in hd and \"Mass Excess\" not in hd:\n hd = hd.replace(\"Mass Exc\", \"Mass Excess\")\n headers_new.append(hd)\n if len(set(headers_new)) != len(headers_new):\n raise NNDCRequestError(\n \"Duplicate headers after parsing\\n\"\n + f' Original headers: \"{headers}\"\\n'\n + f' Parsed headers: \"{headers_new}\"'\n )\n return headers_new","def _unpack_headers(self, headers):\n return dict((k,v[0]) for (k,v) in headers.getAllRawHeaders())","def fill_headers(self, headers):\n self.headers = {h[0]: h[1] for h in headers}","def _parse_headers(raw_headers: List[str]) -> Dict[str, str]:\n headers: Dict[str, str] = {}\n for header in raw_headers:\n name = header[: header.find(\":\")].strip()\n value = header[header.find(\":\") + 1 :].strip()\n headers[name.lower()] = value\n\n return headers","def _parse_headers(headers):\n try:\n return dict(header.split(\":\") for header in headers)\n except:\n raise ValueError(\"Invalid headers %s\" % headers)","def _headers(self, headers_dict):\n return Headers(dict((k,[v]) for (k,v) in headers_dict.items()))","def manage_headers(dem_header_file, header_paths):\n dem_header = parse_dem_header(dem_header_file)\n # find param files containing filename dates\n if len(header_paths) == 2:\n headers = [parse_epoch_header(hp) for hp in header_paths]\n combined_header = combine_headers(headers[0], headers[1], dem_header)\n else:\n # probably have DEM or incidence file\n combined_header = dem_header\n combined_header[ifc.DATA_TYPE] = ifc.DEM\n\n return combined_header","def __parseHeaders(headers):\n global __all_headers\n if headers and len(headers) > 0:\n for header in headers:\n name = header.getElementsByTagName(\"name\")[0].childNodes[0].data\n value = header.getElementsByTagName(\"value\")[0].childNodes[0].data\n __addHeader(name, value)\n #print(__all_headers)","def _ToTuples(headers):\n all_headers = []\n for line in headers:\n if line[0] in '\\t ':\n if not all_headers:\n logging.warning(\n 'Unexpected response header continuation line [%s]', line)\n continue\n name, value = all_headers.pop()\n value += '\\n ' + line.strip()\n else:\n name_value = RealHttpFetch._GetHeaderNameValue(line)\n if not name_value:\n logging.warning(\n 'Response header in wrong format [%s]', line)\n continue\n name, value = name_value # pylint: disable=unpacking-non-sequence\n all_headers.append((name, value))\n return all_headers","def updateheader(self, headerlist=[], http_s_obj=None):\n header = {}\n for headerparam in headerlist:\n key_value = headerparam.split(\":\", 1)\n if len(key_value) == 2:\n try:\n key = key_value[0]\n value = key_value[1].strip()\n header.update({key: value})\n if http_s_obj:\n if http_s_obj.header.get(key):\n http_s_obj.header.update({key: value})\n except Exception:\n continue\n return header","def _build_headers(self):\n headers = {}\n headers.update(self.data_sources)\n headers.update(self.seasons)\n headers.update(self.region)\n headers.update(self.subregions)\n return headers","def test_normalize_headers():\n headers = [\n 'AllocationTransferAgencyIdentifier', 'BeginningPeriodOfAvailability', 'flex_mycol', 'FLEX_ANOTHER'\n ]\n mapping = {'allocationtransferagencyidentifier': 'ata', 'beginningperiodofavailability': 'boa'}\n\n result = csvReader.normalize_headers(headers, False, mapping)\n assert list(result) == [\n 'allocationtransferagencyidentifier', 'beginningperiodofavailability', 'flex_mycol', 'flex_another'\n ]\n result = csvReader.normalize_headers(headers, True, mapping)\n assert list(result) == ['ata', 'boa', 'flex_mycol', 'flex_another']","def _make_headers_df(headers_response):\n\n headers_df = util.make_dataframe(headers_response)\n headers_df = headers_df[\n [\"text\", \"column_index_begin\", \"column_index_end\", \"row_index_begin\", \"row_index_end\", \"cell_id\",\n \"text_normalized\"]]\n return headers_df","def _headers(self) -> Mapping[str, str]:\n return {}","def _normalize_headers(self):\n self.ncookies=dict((k.lower(), v) for k, v in self.request.cookies.iteritems())\n self.nheaders=dict((k.lower(), v) for k, v in self.request.headers.iteritems())","def scrub_headers(headers):\n if isinstance(headers, dict):\n headers = headers.items()\n headers = [\n (parse_header_string(key), parse_header_string(val))\n for (key, val) in headers\n ]\n if not logger_settings.get('redact_sensitive_headers', True):\n return dict(headers)\n if logger_settings.get('reveal_sensitive_prefix', 16) < 0:\n logger_settings['reveal_sensitive_prefix'] = 16\n return {key: safe_value(key, val) for (key, val) in headers}","def headers_raw_to_dict(headers_raw):\n\n if headers_raw is None:\n return None\n headers = headers_raw.splitlines()\n headers_tuples = [header.split(':', 1) for header in headers]\n\n result_dict = {}\n for header_item in headers_tuples:\n if not len(header_item) == 2:\n continue\n\n item_key = header_item[0].strip()\n item_value = header_item[1].strip()\n result_dict[item_key] = item_value\n\n return result_dict","def my_normalize(self, headers):\n ret = normalize(self, headers)\n if 'authorization' in ret:\n ret['Authorization'] = ret.pop('authorization')\n return ret","def parse_header(self):","def set_headers(self, headers):\n self.headers = headers\n process_headers(self)\n self.character_encoding = self.parsed_headers.get(\n 'content-type', (None, {})\n )[1].get('charset', 'utf-8') # default isn't UTF-8, but oh well","def decode_header(header):\n new_header = {}\n\n for item in header:\n split = item.split('\\t')\n new_header[split[0].replace(':', '')] = split[1].replace(\"\\r\\n\", \"\")\n\n return new_header","def from_headers(self, headers):\n try:\n # First IP address is the one of the client\n ip = headers['X_FORWARDED_FOR'].split(',')[0].strip()\n except KeyError:\n ip = headers.get('REMOTE_ADDR')\n\n if ip:\n # Double-check if the address has a valid format\n if re.match(r'^[\\d+]{1,3}\\.[\\d+]{1,3}\\.[\\d+]{1,3}\\.[\\d+]{1,3}$',\n ip, re.I):\n ip = None\n\n # Exclude private IP address ranges\n if re.match(r'^(?:127\\.0\\.0\\.1|10\\.|192\\.168\\.|172\\.(?:1[6-9]|2[0-9]|3[0-1])\\.)', ip):\n ip = None\n\n self.ip_address = ip\n\n self.user_agent = headers.get('HTTP_USER_AGENT')\n\n if 'HTTP_ACCEPT_LANGUAGE' in headers:\n parsed_locales = []\n res = re.findall(\n r'(^|\\s*,\\s*)([a-zA-Z]{1,8}(-[a-zA-Z]{1,8})*)\\s*(;\\s*q\\s*=\\s*(1(\\.0{0,3})?|0(\\.[0-9]{0,3})))?', \n headers['HTTP_ACCEPT_LANGUAGE'], re.I)\n for r in res:\n name = r[1].replace('-', '_')\n value = 1 if not r[4] else r[4]\n parsed_locales += [(name, value)]\n\n self.locale = sorted(parsed_locales, key=lambda x: x[1],\n reverse=True)[0][0]\n\n return self","def filter_headers(self, header):\n if header == \"Ticker symbol\":\n return \"symbol\"\n elif header == \"GICS Sector\":\n return \"sector\"\n elif header == \"Security\":\n return \"name\"\n elif header == \"GICS Sub Industry\":\n return \"industry\"\n else:\n return header","def _parse_rate_limit_headers(headers):\n limit = int(headers[\"X-RateLimit-Limit\"])\n remaining = int(headers[\"X-RateLimit-Remaining\"])\n reset_at_utc = int(headers[\"X-RateLimit-Reset\"])\n return {\n \"limit\": limit,\n \"used\": limit - remaining,\n \"remaining\": remaining,\n \"reset_at_utc\": reset_at_utc,\n \"reset_in_sec\": reset_at_utc - round(time.time()),\n \"last_update\": round(time.time())\n }","def generate_header_dic(self, header_strings):\n headers = dict()\n\n for header_values in header_strings:\n header_list = header_values.split(':')\n headers[header_list[0]] = header_list[1]\n return headers","def headers(self) -> dict:\n raise NotImplementedError # pragma: no cover","def _headercorrected(hdr):\n # COM*** -> COMMENT\n i = 1\n while 'COM%03d' % i in hdr:\n value = hdr['COM%03d' % i]\n comment = hdr.cards['COM%03d' % i].comment\n hdr['COMMENT'] = '[%s] %s' % (comment, value)\n del hdr['COM%03d' % i]\n i += 1\n # HIST*** -> HISTORY\n i = 1\n while 'HIST%03d' % i in hdr:\n value = hdr['HIST%03d' % i]\n comment = hdr.cards['HIST%03d' % i].comment\n hdr['HISTORY'] = '%s (%s)' % (value, comment)\n del hdr['HIST%03d' % i]\n i += 1\n # ORIGIN -> FROM\n if 'ORIGIN' in hdr.keys():\n hdr.rename_keyword('ORIGIN', 'FROM')\n if 'ORIGIN_V' in hdr.keys():\n hdr.rename_keyword('ORIGIN_V', 'FROM_V')\n # SOURCE_V -> FORMAT\n if 'SOURCE_V' in hdr.keys():\n hdr.rename_keyword('SOURCE_V', 'FORMAT')\n # SRC_VERS -> SRC_V\n if 'SRC_VERS' in hdr.keys():\n hdr.rename_keyword('SRC_VERS', 'SRC_V')","def missing_header_fields():\n auth_token = get_auth_token()\n\n headers = '{\"Host\": \"$host\",\"Date\": \"DATE\",'\n headers += '\"Accept-Encoding\": \"gzip\",'\n headers += '\"X-Auth-Token\": \"$token\"}'\n headers = string.Template(headers)\n\n return headers.substitute(host=CFG.host, token=auth_token)","def get_header(header_row):\n header = {}\n header['station'], c1, c2, c3, date, time, tz = header_row.split()\n header['short_model'] = c1\n header['model'] = f'{c1} {c2} {c3}' \n header['runtime'] = dateutil.parser.parse(f'{date} {time} {tz}')\n return header","def _split_headers(headers):\n amz_headers = {}\n reg_headers = {}\n for cur in headers:\n if cur.lower().startswith('x-amz-'):\n amz_headers[cur] = headers[cur]\n else:\n reg_headers[cur] = headers[cur]\n return (amz_headers, reg_headers)","def headers(self, v):\n raise NotImplementedError","def encode_meta_headers(headers):\n ret = {}\n for header, value in headers.items():\n value = encode_utf8(value)\n header = header.lower()\n\n if (isinstance(header, str) and\n header.startswith(USER_METADATA_TYPE)):\n header = encode_utf8(header)\n\n ret[header] = value\n return ret","def check_headers(self, headers):\n h = headers.values()[0]\n\n if 'DT' in PAR:\n if h.dt != PAR.DT:\n h.dt = PAR.DT\n\n if 'NT' in PAR:\n if h.nt != PAR.NT:\n print 'Warning: h.nt != PAR.NT'\n\n if 'NREC' in PAR:\n if h.nr != PAR.NREC:\n print 'Warning: h.nr != PAR.NREC'\n\n return h","def set_column_headers(self, headers):\n if isinstance(self.columns.idx[0], int):\n self.data = [sorted(headers)] + self.data\n\n increment = [i + 1 for i in self.rows.idx]\n self.rows.idx = [0] + increment\n\n elif isinstance(self.columns.idx[0], str):\n datum = {}\n for i, key in enumerate(self.columns.idx):\n datum.update({key: headers[i]})\n self.data = [datum] + self.data\n\n increment = [i + 1 for i in self.rows.idx]\n self.rows.idx = [0] + increment","def set_headers(self, headers):\n\n if isinstance(headers, dict):\n headers = headers.items()\n\n # NOTE(kgriffs): We can't use dict.update because we have to\n # normalize the header names.\n _headers = self._headers\n for name, value in headers:\n _headers[name.lower()] = value","def scrub_headers(self, header_dict):\n return self.__headers_scrubber(header_dict)","def setup_request_headers(self, uri: str = None) -> dict:\n\n uri = uri if uri is not None else self.host\n headers = {\n \"Accept\": \"application/atom+json,application/json\",\n \"Accept-Encoding\": \"gzip, deflate, br\",\n \"Accept-Language\": \"en_US\",\n \"Connection\": \"keep-alive\",\n \"User-Agent\": self.user_agent,\n \"Referer\": uri + \"/suite/tempo/\",\n \"X-Appian-Cached-Datatypes\": self.datatype_cache.get(),\n \"Cookie\": \"JSESSIONID={}; __appianCsrfToken={}; __appianMultipartCsrfToken={}\".format(\n self.client.cookies.get(\"JSESSIONID\", \"\"),\n self.client.cookies.get(\"__appianCsrfToken\", \"\"),\n self.client.cookies.get(\"__appianMultipartCsrfToken\", \"\"),\n ),\n \"DNT\": \"1\",\n \"X-APPIAN-CSRF-TOKEN\": self.client.cookies.get(\"__appianCsrfToken\", \"\"),\n \"X-APPIAN-MP-CSRF-TOKEN\": self.client.cookies.get(\"__appianMultipartCsrfToken\", \"\"),\n \"X-Appian-Ui-State\": \"stateful\",\n \"X-Appian-Features\": self.client.feature_flag,\n \"X-Appian-Features-Extended\": self.client.feature_flag_extended,\n \"x-libraries-suppress-www-authenticate\": \"true\",\n # this should probably go...\n \"X-Atom-Content-Type\": \"application/html\"\n }\n return headers","def build_headers(self):\n\n # User-agent is always sent\n headers = {'user-agent': self.useragent}\n for hdr in self.config.client_standard_headers:\n val = getattr(self.config, 'client_' + hdr.lower().replace('-','_'))\n headers[hdr] = val\n\n return headers","def test_header_parser_vanilla(self):\n lines = [\"Content-Type: application/json\", \"Accept: application/json\"]\n h = {\"Content-Type\": \"application/json\", \"Accept\": \"application/json\"}\n headers = parser._parse_headers(lines)\n self.assertEqual(h, headers)","def _parse_raw_header_entries(header_entries):\n\n def __check_key(key):\n return not(\"_\" in key or \" \" in key or \":\" in key or not len(key))\n\n result = {}\n if (len(header_entries) < 1):\n return result\n\n # Remove leading '--'\n header_entries = header_entries[1:]\n if (not len(header_entries) % 2 == 0):\n raise ValueError(\"last key does not have a value\")\n\n while (len(header_entries)):\n # Retrieve raw key\n logging.debug(\"current header content \" + str(header_entries))\n word = header_entries[0]\n header_entries = header_entries[1:]\n\n # Try to trim equal\n if (word[-1] == ':'):\n word = word[:-1]\n\n if(not __check_key(word)):\n raise ValueError(\"invalid key '{}' in key value list\".format(word))\n\n result[word] = header_entries[0]\n header_entries = header_entries[1:]\n\n return result","def _extract_metadata(self, header, cleaner):\n metadata = []\n for k, v in header.items():\n key = str(cleaner(k)) # clean key and ensure it is a string\n val = str(cleaner(v)) # clean value and ensure it is a string\n if (key and val):\n metadata.append(Metadatum(key, val))\n return metadata","def trim_headers(all_headers, relevant_headers=[\"From\", \"To\", \"Subject\", \"Date\"]):\n data = {}\n for header in all_headers:\n if header['name'] in relevant_headers:\n data[header['name']] = header['value']\n\n return data","def get_headers(headers: HTTPHeaders) -> Mapping[str, List[str]]:\r\n return {header.lower(): headers.get_list(header) for header in headers.keys()}","def _make_headers() -> CaseInsensitiveDict:\n headers = CaseInsensitiveDict()\n headers['Authorization'] = f'Token {os.environ[\"TOKEN\"]}'\n headers['Content-type'] = 'application/json'\n return headers","def __get_headers(self, passed_headers: Dict) -> Dict:\n\n # User-Agent for HTTP request\n library_details = [\n f\"requests {requests.__version__}\",\n f\"python {platform.python_version()}\",\n f\"connector {self.__class__.__name__}\",\n ]\n library_details = \"; \".join(library_details)\n user_agent = f\"Infermedica-API-Python {__version__} ({library_details})\"\n\n headers = {\n \"Accept\": \"application/json\",\n \"User-Agent\": user_agent,\n \"App-Id\": self.app_id,\n \"App-Key\": self.app_key,\n }\n headers.update(self.default_headers)\n headers.update(passed_headers) # Make sure passed headers take precedence\n return headers","def parse_header(header_lines):\n info = {}\n for line in header_lines:\n if line.startswith('Citation'):\n info['Citation'] = line.split()[-1].strip()\n elif ':' in line:\n try:\n field, value = map(strip,line.split(':',1))\n info[field] = value\n except ValueError:\n #no interesting header line\n continue\n else:\n continue\n return Info(info)","def parse_header_dict(self, header_dict=None):\n if header_dict is not None:\n self.header_dict = header_dict\n\n assert isinstance(self.header_dict, dict)\n\n for key, value in self.header_dict.items():\n if \"wire\" in key:\n if key.find(\"n\") == 0:\n self.ex_length = float(value.split()[0])\n self.ex_azimuth = float(value.split()[1])\n elif key.find(\"e\") == 0:\n self.ey_length = float(value.split()[0])\n self.ey_azimuth = float(value.split()[1])\n elif \"system\" in key:\n self.box_id = value.split(\";\")[0].strip()\n self.mag_id = value.split(\";\")[1].strip()\n elif \"gps\" in key:\n gps_list = value.split()\n self.header_gps_stamp = MTime(\n dateutil.parser.parse(\n \" \".join(gps_list[0:2]), dayfirst=True\n )\n )\n self.header_gps_latitude = self._get_latitude(\n gps_list[2], gps_list[3]\n )\n self.header_gps_longitude = self._get_longitude(\n gps_list[4], gps_list[5]\n )\n self.header_gps_elevation = float(gps_list[6])\n elif \"run\" in key:\n self.run_id = value.replace('\"', \"\")\n else:\n setattr(self, key.replace(\" \", \"_\").replace(\"/\", \"_\"), value)","def headersFromRawFile(self, rawFile: str, headers: Dict) -> None:\n dFile = open(os.path.join(self.dataPath, rawFile), \"r\", encoding=\"ISO-8859-1\")\n generalHeaderString = dFile.read(1000) # this should be long enough\n generalSplit = generalHeaderString.split()\n # read GENERAL HEADER\n generalHeader = {}\n generalHeader[\"recLength\"] = int(generalSplit[0])\n generalHeader[\"fileType\"] = generalSplit[1]\n generalHeader[\"wordLength\"] = int(generalSplit[2])\n generalHeader[\"version\"] = generalSplit[3]\n generalHeader[\"procId\"] = generalSplit[4]\n generalHeader[\"numCh\"] = int(generalSplit[5])\n generalHeader[\"totalRec\"] = int(generalSplit[6])\n generalHeader[\"firstEvent\"] = int(generalSplit[7])\n generalHeader[\"numEvent\"] = int(generalSplit[8])\n generalHeader[\"extend\"] = int(generalSplit[9])\n\n # read EVENT HEADER - there can be multiple of these, but normally only the one\n # Multiple events are largely deprecated. Only a single event is used\n eventHeaders = []\n fileSize = os.path.getsize(os.path.join(self.dataPath, rawFile))\n record = generalHeader[\"firstEvent\"]\n for ir in range(0, generalHeader[\"numEvent\"]):\n seekPt = (record - 1) * generalHeader[\"recLength\"]\n if not seekPt > fileSize:\n # seek from beginning of file\n dFile.seek(seekPt, 0)\n # read extra to make sure\n eventString = dFile.read(1000)\n eventSplit = eventString.split()\n eH = {}\n eH[\"start\"] = int(eventSplit[0])\n eH[\"startms\"] = int(eventSplit[1])\n eH[\"stop\"] = int(eventSplit[2])\n eH[\"stopms\"] = int(eventSplit[3])\n eH[\"cvalue1\"] = float(eventSplit[4])\n eH[\"cvalue2\"] = float(eventSplit[5])\n eH[\"cvalue3\"] = float(eventSplit[6])\n eH[\"EHInfile\"] = int(eventSplit[7])\n eH[\"nextEH\"] = int(eventSplit[8])\n eH[\"previousEH\"] = int(eventSplit[9])\n eH[\"numData\"] = int(eventSplit[10])\n eH[\"startData\"] = int(eventSplit[11])\n eH[\"extended\"] = int(eventSplit[12])\n eventHeaders.append(eH)\n if eH[\"nextEH\"] < generalHeader[\"totalRec\"]:\n record = eH[\"nextEH\"] # set to go to next eH\n else:\n break # otherwise break out of for loops\n # close the data file\n dFile.close()\n # now compare number of samples with that calculated previously\n if eventHeaders[0][\"numData\"] != headers[\"num_samples\"]:\n self.printWarning(\"Data file: {}\".format(dFile))\n self.printWarning(\n \"Number of samples in raw file header {} does not equal that calculated from data {}\".format(\n eventHeaders[0][\"numData\"], headers[\"num_samples\"]\n )\n )\n self.printWarning(\"Number of samples calculated from data will be used\")\n # set the byte offset for the file\n self.dataByteOffset[rawFile] = (\n eventHeaders[0][\"startData\"] - 1\n ) * generalHeader[\"recLength\"]\n self.recChannels[rawFile] = generalHeader[\"numCh\"]","def _api_headers(self, previous_headers=None):\n if self._headers is None:\n auth_headers = {}\n token = self._token\n if token:\n auth_headers[\"Authorization\"] = f\"token {token}\"\n self._headers = auth_headers\n\n if previous_headers is not None:\n headers = self._headers.copy()\n for condition, key in (\n (\"If-Modified-Since\", \"Last-Modified\"),\n (\"If-None-Match\", \"ETag\"),\n ):\n try:\n headers[condition] = previous_headers[key]\n except KeyError:\n continue\n return headers\n\n return self._headers","def _getHeaders(self, callName):\n return {\n 'X-EBAY-API-CALL-NAME': callName,\n 'X-EBAY-API-APP-NAME': settings.EBAY_API_APP_NAME,\n 'X-EBAY-API-CERT-NAME': settings.EBAY_API_CERT_NAME,\n 'X-EBAY-API-DEV-NAME': settings.EBAY_API_DEV_NAME,\n 'X-EBAY-API-SITEID': settings.EBAY_API_SITEID,\n 'X-EBAY-API-COMPATIBILITY-LEVEL': settings.EBAY_API_COMPATIBILITY_LEVEL\n }","def unpack_header(header):\n header_values = {}\n for line in header.split('\\n'):\n tokens = line.split('=')\n if len(tokens) > 1:\n header_values[tokens[0].strip()] = tokens[1].split(';')[0].strip()\n return header_values","def getAllHeaders():","def fitsio_header_to_dict(hdr):\n d = {}\n for key in hdr.keys():\n if key != 'HISTORY' or key != \"COMMENT\":\n d[key.lower()] = hdr.get(key)\n return d","def get_headers():\n if not headers:\n headers[\"Content-Type\"] = \"application/json\"\n headers[\"Accept\"] = \"application/json\"\n headers[\"User-Agent\"] = constants.USER_AGENT\n headers[\"Authorization\"] = get_token(constants.AUTH_URL, cfg[\"key\"])\n\n return headers\n\n return headers","def parse_header(self): # -> list[Unknown]:\n ...","def to_header(self) -> Dict[str, str]:\n tracer_from_context = self.get_current_tracer()\n temp_headers: Dict[str, str] = {}\n if tracer_from_context is not None:\n ctx = tracer_from_context.span_context\n try:\n temp_headers = tracer_from_context.propagator.to_headers(ctx)\n except AttributeError:\n pass\n return temp_headers","def _parse_header(self, line):\n if self._regex_helper.search_compiled(W._re_header, line):\n if not self.headers:\n for value in re.findall(W._re_header, line):\n self.headers.append(value[0])\n raise ParsingDone\n else:\n # Dictionary which is going to be appended to the returned list\n ret = dict()\n # List of entries\n _entries = list()\n # List of values in WHAT entry\n _what_entry = list()\n for value in re.findall(W._re_header, line):\n _entries.append(value[0])\n for what_index in range(len(self.headers) - 1, len(_entries)):\n _what_entry.append(_entries[what_index])\n _what_entry_string = ' '.join(_what_entry)\n for index in range(len(self.headers)):\n if index < len(self.headers) - 1:\n ret.update({self.headers[index]: _entries[index]})\n else:\n ret.update({self.headers[index]: _what_entry_string})\n self.current_ret['RESULT'].append(ret)\n raise ParsingDone","def extract_object_metadata_from_headers(headers):\n meta_headers = {}\n for header, value in headers.items():\n header = header.title()\n\n if (header.startswith(\"X-Object-Meta-\") or\n header.startswith(\"X-Object-Sysmeta-\") or\n header in SPECIAL_OBJECT_METADATA_HEADERS):\n\n # do not let a client pass in ORIGINAL_MD5_HEADER\n if header not in (ORIGINAL_MD5_HEADER,\n ORIGINAL_MD5_HEADER.title()):\n meta_headers[header] = value\n\n return meta_headers","def _parse_header(lines):\n # The dict into which we will store header fields.\n header = {}\n # Loop over lines in the header.\n for line in lines:\n # Find the first colon.\n index = line.index(COLON)\n # Up to the colon is the field name.\n name = line[: index]\n # After the colon is the field value.\n value = line[index + 1 :]\n # The field value may begin or end with extra space, which is not \n # significant. Remove it.\n value = value.strip()\n # Store the field.\n header[name] = value\n # All done.\n return header","def _headers(self, **kwargs):\n headers = BASE_HEADERS.copy()\n if self.headers:\n headers.update(self.headers)\n headers.update(kwargs)\n return headers","def _get_cleaned_headers(headers):\r\n cleaned_headers = []\r\n for header in headers:\r\n # Google strips special characters, whitespace, and underscores first,\r\n # and then strips any *leading* digits. This order is extremely\r\n # important!\r\n sanitized = sub(r'^\\d+', '', sub(r'[\\W_]', '', header.lower()))\r\n if len(sanitized) > 0:\r\n cleaned_headers.append(sanitized)\r\n else:\r\n raise GoogleSpreadsheetError(\"Encountered a header '%s' that was \"\r\n \"either blank or consisted only of special characters. \"\r\n \"Could not map the header to the internal representation \"\r\n \"used by the Google Spreadsheet. Please change the header \"\r\n \"to consist of at least one alphanumeric character.\"\r\n % header)\r\n\r\n # When the same sanitized header appears multiple times in the first row\r\n # of a spreadsheet, _n is appended to the name to make it unique.\r\n header_count = defaultdict(int)\r\n results = []\r\n\r\n for header, cleaned_header in zip(headers, cleaned_headers):\r\n new_header = cleaned_header\r\n\r\n if header_count[cleaned_header] > 0:\r\n # Google's numbering starts from _2, hence the +1.\r\n new_header = '%s_%d' % (cleaned_header,\r\n header_count[cleaned_header] + 1)\r\n\r\n header_count[cleaned_header] += 1\r\n results.append(new_header)\r\n\r\n return results","def test_headerFormatter(self):\n cases = [\n ({'Header1': 'Value1', 'Header2': 'Value2'},\n b'Header2: Value2\\r\\nHeader1: Value1\\r\\n'),\n ]\n\n for (input, expected) in cases:\n output = imap4._formatHeaders(input)\n self.assertEqual(sorted(output.splitlines(True)),\n sorted(expected.splitlines(True)))","def get_headers(self):\r\n raise NotImplementedError","def headers(self) -> Mapping[str, str]:\n return pulumi.get(self, \"headers\")","def headers_processor(headers):\n def apply_headers(row_set, row):\n _row = []\n pairs = izip_longest(row, headers)\n for i, (cell, header) in enumerate(pairs):\n if cell is None:\n cell = Cell(None)\n cell.column = header\n if not cell.column:\n cell.column = \"column_%d\" % i\n cell.column_autogenerated = True\n _row.append(cell)\n return _row\n return apply_headers","def _headers(self) -> Mapping[str, str]:\n return self.auth.headers() if self.auth else {}","def process_header_request(self, request, http_s_obj):\n response_dict = {}\n data = request.split(\"\\r\\n\\r\\n\")\n header_info = data[0].split(\"\\r\\n\")\n headers = self.updateheader(header_info, http_s_obj)\n response_dict.update({'type': header_info[0].split()[0]})\n response_dict.update({'headers': headers})\n body = data[1]\n response_dict.update({'data': body})\n path = header_info[0].split()[1]\n if path.find('?') != -1:\n split_sym = '?'\n if path.find('&') != -1:\n split_sym = '&'\n try:\n req = path.split(split_sym)\n path = req[0]\n query = req[1]\n except Exception as e:\n query = ''\n response_dict.update({'path': path})\n response_dict.update({'query': query})\n\n return response_dict","def _update_headers(self):\n if not self._header_updated:\n headers = self.head_obj(self._client, self._spec)\n self._headers.update(headers)\n self._header_updated = True","def header(self, header, default=None):\n result = []\n header_value = self.email.get(header, default)\n if header_value:\n for part in decode_header(header_value):\n if part[1]:\n encoded = part[0].decode(part[1])\n elif isinstance(part[0], bytes):\n encoded = part[0].decode('utf-8')\n else:\n encoded = part[0]\n result.append(encoded.strip())\n header_value = ' '.join(result)\n\n return header_value","def process_client_headers(self, *, scope: Scope, headers: Headers) -> Headerlike:\n return headers","def set_column_headers(self,param_headers):\n self.cur_quotes_parm_headers = param_headers","def _headers(self):\n\n auth_token = SendbeeAuth(self.client.api_secret).get_auth_token()\n headers = {\n 'X-Auth-Token': auth_token,\n 'X-Api-Key': self.client.api_key,\n 'Accept': 'application/json',\n 'Content-Type': 'application/json',\n 'User-Agent': 'Sendbee Python API Client'\n }\n self.debug.ok('headers', headers)\n\n return headers","def inject_headers(mime, headers):\n for key in headers:\n if key == 'Bcc' or key == 'Resent-Bcc':\n continue\n del mime[key]\n mime[key] = encode_header(headers[key])","def get_headers(self, environ=None):\n return [('Content-Type', 'application/json')]","def _build_headers(self):\n headers = {\n 'Authorization': 'Bearer {api_key}'.format(api_key=self._api_key),\n 'SplitSDKVersion': SDK_VERSION,\n 'Accept-Encoding': 'gzip'\n }\n\n if self._split_sdk_machine_name is not None:\n headers['SplitSDKMachineName'] = self._split_sdk_machine_name() \\\n if callable(self._split_sdk_machine_name) else self._split_sdk_machine_name\n\n if self._split_sdk_machine_ip is not None:\n headers['SplitSDKMachineIP'] = self._split_sdk_machine_ip() \\\n if callable(self._split_sdk_machine_ip) else self._split_sdk_machine_ip\n\n return headers","def _build_http_header(self) -> Dict[str, str]:\n return {}","def _get_headers(self, request):\n headers = {}\n for key, value in request.META.items():\n if key.startswith('HTTP_') and key != 'HTTP_HOST':\n headers[key[5:].replace('_', '-')] = value\n elif key in ('CONTENT_TYPE', 'CONTENT_LENGTH') and value:\n headers[key.replace('_', '-')] = value\n\n if request.user:\n headers['PARTNER-EMAIL'] = request.user.email\n lang = self._get_lang(request)\n if lang:\n headers['ACCEPT-LANGUAGE'] = lang\n return headers","def parse_header(self):\n bodystart=re.compile(r\"<body>\", re.IGNORECASE).search(self.lines).span()[1]\n oldheader=self.lines[0:bodystart]\n start=re.compile(\"<title>\", re.IGNORECASE).search(oldheader).span()[1]\n finish=re.compile(\"</title>\", re.IGNORECASE).search(oldheader).span()[0]\n titles=oldheader[start:finish].split(\"--\")\n # Duplicate if needed\n if len(titles)==1: titles.append(titles[0])\n self.title, self.header= {}, {}\n for i, lang in enumerate(LANGS):\n self.title[lang]=titles[i]\n self.header[lang]=\"%s%s%s\" % (oldheader[:start], self.title[lang], oldheader[finish:],)","def _format_header(self):\n return self._format_dict(self.header)","def add_custom_headers(self, headers):\n headers_to_remove = [x for x in headers if x.lower() in [y.lower() for y in self.headers]]\n for header in headers_to_remove:\n headers.pop(header, None)\n headers.update(self.headers)","def process_result_headers(result_headers):\n class_attrib_re = re.compile(r'\\s*class=\"([^\"]+)\"')\n\n for header in result_headers:\n m = class_attrib_re.match(header['class_attrib'])\n\n if m:\n class_value = m.groups(1)[0]\n else:\n class_value = ''\n\n if class_value != 'action-checkbox-column':\n class_value = 'has-label %s' % class_value\n\n header['class_attrib'] = \\\n mark_safe(' class=\"datagrid-header %s\"' % class_value)\n\n if header['sortable'] and header['sort_priority'] > 0:\n if header['ascending']:\n sort_order = 'asc'\n else:\n sort_order = 'desc'\n\n if header['sort_priority'] == 1:\n sort_priority = 'primary'\n else:\n sort_priority = 'secondary'\n\n header['sort_icon'] = 'datagrid-icon-sort-%s-%s' % (\n sort_order, sort_priority)\n\n return ''","def _format_header(fields):\n # The list into which we will collect header lines.\n lines = []\n for name, value in fields.items():\n # A header line looks like, \"name: value\".\n line = name + COLON + SPACE + value\n # Add this line to the list.\n lines.append(line)\n return lines","def put_headers_in_environ(headers, environ):\n for key, value in headers:\n environ['HTTP_%s' % key.upper().replace('-', '_')] = value","def __get_headers(self):\n\n return {}","def sanitize_headers(headers):\n auth_header = headers.pop(\"Authorization\", None)\n if auth_header:\n _logger.warning(\n f\"Possible fraud: Authorization header was set to {auth_header}\"\n )\n userinfo_header = headers.pop(\"X-Userinfo\", None)\n if userinfo_header:\n _logger.warning(\n f\"Possible fraud: X-Userinfo header was set to {userinfo_header}\"\n )","def headers():\n return {\n 'user-agent': 'integration-tester',\n 'content-type': 'application/json',\n }","def make_headers(self):\n return {'User-Agent': 'Mozilla/5.0 (Windows; U; Windows NT 6.1; en-US;\\\n rv:1.9.1.6) Gecko/20091201 Firefox/3.5.6'}","def currentSBHeaders(self):\n headers = {}\n for header_name in [options['Headers','classification_header_name'],\n options['Headers','mailid_header_name'],\n options['Headers','classification_header_name'] + \"-ID\",\n options['Headers','thermostat_header_name'],\n options['Headers','evidence_header_name'],\n options['Headers','score_header_name'],\n options['Headers','trained_header_name'],\n ]:\n value = self[header_name]\n if value is not None:\n headers[header_name] = value\n return headers","def get_headers(s, sep=': ', strip_cookie=False, strip_cl=True, strip_headers: list = []) -> dict():\n d = dict()\n for kv in s.split('\\n'):\n kv = kv.strip()\n if kv and sep in kv:\n v=''\n k = kv.split(sep)[0]\n if len(kv.split(sep)) == 1:\n v = ''\n else:\n v = kv.split(sep)[1]\n if v == '\\'\\'':\n v =''\n # v = kv.split(sep)[1]\n if strip_cookie and k.lower() == 'cookie': continue\n if strip_cl and k.lower() == 'content-length': continue\n if k in strip_headers: continue\n d[k] = v\n return d","def get_headers(s, sep=': ', strip_cookie=False, strip_cl=True, strip_headers: list = []) -> dict():\n d = dict()\n for kv in s.split('\\n'):\n kv = kv.strip()\n if kv and sep in kv:\n v=''\n k = kv.split(sep)[0]\n if len(kv.split(sep)) == 1:\n v = ''\n else:\n v = kv.split(sep)[1]\n if v == '\\'\\'':\n v =''\n # v = kv.split(sep)[1]\n if strip_cookie and k.lower() == 'cookie': continue\n if strip_cl and k.lower() == 'content-length': continue\n if k in strip_headers: continue\n d[k] = v\n return d","def responseheaders(self, flow: mitmproxy.http.HTTPFlow):","def _headers(self, **kwargs):\n headers = BASE_HEADERS.copy()\n if self._token:\n headers['X-Plex-Token'] = self._token\n headers.update(kwargs)\n return headers","def _headers(self, **kwargs):\n headers = BASE_HEADERS.copy()\n if self._token:\n headers['X-Plex-Token'] = self._token\n headers.update(kwargs)\n return headers","def from_incoming_header(cls, header):\n # type: (Optional[str]) -> Baggage\n sentry_items = {}\n third_party_items = \"\"\n mutable = True\n\n if header:\n for item in header.split(\",\"):\n if \"=\" not in item:\n continue\n\n with capture_internal_exceptions():\n item = item.strip()\n key, val = item.split(\"=\")\n if Baggage.SENTRY_PREFIX_REGEX.match(key):\n baggage_key = unquote(key.split(\"-\")[1])\n sentry_items[baggage_key] = unquote(val)\n mutable = False\n else:\n third_party_items += (\",\" if third_party_items else \"\") + item\n\n return Baggage(sentry_items, third_party_items, mutable)","def requestheaders(self, flow: mitmproxy.http.HTTPFlow):","def headers(self):\n return Dict(**self._get_headers())","def parse_cookies( headers ):","def generate_headers(self):\n raise NotImplementedError()","def headers(self):\r\n return dict(**self._get_headers())","def extract_from_header(headers, header_type):\n LOG.debug(f\"[extract_from_header] extracting `{header_type}` from headers: {headers}\")\n if headers is None:\n return\n for header in headers:\n if header_type in header:\n for item in header:\n if item == header_type or item is None:\n continue\n else:\n return item.decode(\"ascii\")\n return"],"string":"[\n \"def _parse_headers(headers):\\n\\n headers_new = []\\n # reformat column headers if needed\\n for j, hd in enumerate(headers):\\n # rename so always have T1/2 (s)\\n if hd == \\\"T1/2 (num)\\\" or hd == \\\"T1/2 (seconds)\\\":\\n hd = \\\"T1/2 (s)\\\"\\n # for uncertainties, add previous column header to it\\n if j > 0 and \\\"Unc\\\" in hd:\\n hd = headers[j - 1] + \\\" \\\" + hd\\n if \\\"Unc\\\" in hd and \\\"Unc.\\\" not in hd:\\n hd = hd.replace(\\\"Unc\\\", \\\"Unc.\\\")\\n # expand abbreviated headers\\n if \\\"Energy\\\" in hd and \\\"Energy Level\\\" not in hd:\\n hd = hd.replace(\\\"Energy\\\", \\\"Energy Level\\\")\\n if \\\"Par. Elevel\\\" in hd:\\n hd = hd.replace(\\\"Par. Elevel\\\", \\\"Parent Energy Level\\\")\\n if \\\"Abund.\\\" in hd:\\n hd = hd.replace(\\\"Abund.\\\", \\\"Abundance (%)\\\")\\n if \\\"Ene.\\\" in hd:\\n hd = hd.replace(\\\"Ene.\\\", \\\"Energy\\\")\\n if \\\"Int.\\\" in hd:\\n hd = hd.replace(\\\"Int.\\\", \\\"Intensity (%)\\\")\\n if \\\"Dec\\\" in hd and \\\"Decay\\\" not in hd:\\n hd = hd.replace(\\\"Dec\\\", \\\"Decay\\\")\\n if \\\"Rad\\\" in hd and \\\"Radiation\\\" not in hd:\\n hd = hd.replace(\\\"Rad\\\", \\\"Radiation\\\")\\n if \\\"EP\\\" in hd:\\n hd = hd.replace(\\\"EP\\\", \\\"Endpoint\\\")\\n if \\\"Mass Exc\\\" in hd and \\\"Mass Excess\\\" not in hd:\\n hd = hd.replace(\\\"Mass Exc\\\", \\\"Mass Excess\\\")\\n headers_new.append(hd)\\n if len(set(headers_new)) != len(headers_new):\\n raise NNDCRequestError(\\n \\\"Duplicate headers after parsing\\\\n\\\"\\n + f' Original headers: \\\"{headers}\\\"\\\\n'\\n + f' Parsed headers: \\\"{headers_new}\\\"'\\n )\\n return headers_new\",\n \"def _unpack_headers(self, headers):\\n return dict((k,v[0]) for (k,v) in headers.getAllRawHeaders())\",\n \"def fill_headers(self, headers):\\n self.headers = {h[0]: h[1] for h in headers}\",\n \"def _parse_headers(raw_headers: List[str]) -> Dict[str, str]:\\n headers: Dict[str, str] = {}\\n for header in raw_headers:\\n name = header[: header.find(\\\":\\\")].strip()\\n value = header[header.find(\\\":\\\") + 1 :].strip()\\n headers[name.lower()] = value\\n\\n return headers\",\n \"def _parse_headers(headers):\\n try:\\n return dict(header.split(\\\":\\\") for header in headers)\\n except:\\n raise ValueError(\\\"Invalid headers %s\\\" % headers)\",\n \"def _headers(self, headers_dict):\\n return Headers(dict((k,[v]) for (k,v) in headers_dict.items()))\",\n \"def manage_headers(dem_header_file, header_paths):\\n dem_header = parse_dem_header(dem_header_file)\\n # find param files containing filename dates\\n if len(header_paths) == 2:\\n headers = [parse_epoch_header(hp) for hp in header_paths]\\n combined_header = combine_headers(headers[0], headers[1], dem_header)\\n else:\\n # probably have DEM or incidence file\\n combined_header = dem_header\\n combined_header[ifc.DATA_TYPE] = ifc.DEM\\n\\n return combined_header\",\n \"def __parseHeaders(headers):\\n global __all_headers\\n if headers and len(headers) > 0:\\n for header in headers:\\n name = header.getElementsByTagName(\\\"name\\\")[0].childNodes[0].data\\n value = header.getElementsByTagName(\\\"value\\\")[0].childNodes[0].data\\n __addHeader(name, value)\\n #print(__all_headers)\",\n \"def _ToTuples(headers):\\n all_headers = []\\n for line in headers:\\n if line[0] in '\\\\t ':\\n if not all_headers:\\n logging.warning(\\n 'Unexpected response header continuation line [%s]', line)\\n continue\\n name, value = all_headers.pop()\\n value += '\\\\n ' + line.strip()\\n else:\\n name_value = RealHttpFetch._GetHeaderNameValue(line)\\n if not name_value:\\n logging.warning(\\n 'Response header in wrong format [%s]', line)\\n continue\\n name, value = name_value # pylint: disable=unpacking-non-sequence\\n all_headers.append((name, value))\\n return all_headers\",\n \"def updateheader(self, headerlist=[], http_s_obj=None):\\n header = {}\\n for headerparam in headerlist:\\n key_value = headerparam.split(\\\":\\\", 1)\\n if len(key_value) == 2:\\n try:\\n key = key_value[0]\\n value = key_value[1].strip()\\n header.update({key: value})\\n if http_s_obj:\\n if http_s_obj.header.get(key):\\n http_s_obj.header.update({key: value})\\n except Exception:\\n continue\\n return header\",\n \"def _build_headers(self):\\n headers = {}\\n headers.update(self.data_sources)\\n headers.update(self.seasons)\\n headers.update(self.region)\\n headers.update(self.subregions)\\n return headers\",\n \"def test_normalize_headers():\\n headers = [\\n 'AllocationTransferAgencyIdentifier', 'BeginningPeriodOfAvailability', 'flex_mycol', 'FLEX_ANOTHER'\\n ]\\n mapping = {'allocationtransferagencyidentifier': 'ata', 'beginningperiodofavailability': 'boa'}\\n\\n result = csvReader.normalize_headers(headers, False, mapping)\\n assert list(result) == [\\n 'allocationtransferagencyidentifier', 'beginningperiodofavailability', 'flex_mycol', 'flex_another'\\n ]\\n result = csvReader.normalize_headers(headers, True, mapping)\\n assert list(result) == ['ata', 'boa', 'flex_mycol', 'flex_another']\",\n \"def _make_headers_df(headers_response):\\n\\n headers_df = util.make_dataframe(headers_response)\\n headers_df = headers_df[\\n [\\\"text\\\", \\\"column_index_begin\\\", \\\"column_index_end\\\", \\\"row_index_begin\\\", \\\"row_index_end\\\", \\\"cell_id\\\",\\n \\\"text_normalized\\\"]]\\n return headers_df\",\n \"def _headers(self) -> Mapping[str, str]:\\n return {}\",\n \"def _normalize_headers(self):\\n self.ncookies=dict((k.lower(), v) for k, v in self.request.cookies.iteritems())\\n self.nheaders=dict((k.lower(), v) for k, v in self.request.headers.iteritems())\",\n \"def scrub_headers(headers):\\n if isinstance(headers, dict):\\n headers = headers.items()\\n headers = [\\n (parse_header_string(key), parse_header_string(val))\\n for (key, val) in headers\\n ]\\n if not logger_settings.get('redact_sensitive_headers', True):\\n return dict(headers)\\n if logger_settings.get('reveal_sensitive_prefix', 16) < 0:\\n logger_settings['reveal_sensitive_prefix'] = 16\\n return {key: safe_value(key, val) for (key, val) in headers}\",\n \"def headers_raw_to_dict(headers_raw):\\n\\n if headers_raw is None:\\n return None\\n headers = headers_raw.splitlines()\\n headers_tuples = [header.split(':', 1) for header in headers]\\n\\n result_dict = {}\\n for header_item in headers_tuples:\\n if not len(header_item) == 2:\\n continue\\n\\n item_key = header_item[0].strip()\\n item_value = header_item[1].strip()\\n result_dict[item_key] = item_value\\n\\n return result_dict\",\n \"def my_normalize(self, headers):\\n ret = normalize(self, headers)\\n if 'authorization' in ret:\\n ret['Authorization'] = ret.pop('authorization')\\n return ret\",\n \"def parse_header(self):\",\n \"def set_headers(self, headers):\\n self.headers = headers\\n process_headers(self)\\n self.character_encoding = self.parsed_headers.get(\\n 'content-type', (None, {})\\n )[1].get('charset', 'utf-8') # default isn't UTF-8, but oh well\",\n \"def decode_header(header):\\n new_header = {}\\n\\n for item in header:\\n split = item.split('\\\\t')\\n new_header[split[0].replace(':', '')] = split[1].replace(\\\"\\\\r\\\\n\\\", \\\"\\\")\\n\\n return new_header\",\n \"def from_headers(self, headers):\\n try:\\n # First IP address is the one of the client\\n ip = headers['X_FORWARDED_FOR'].split(',')[0].strip()\\n except KeyError:\\n ip = headers.get('REMOTE_ADDR')\\n\\n if ip:\\n # Double-check if the address has a valid format\\n if re.match(r'^[\\\\d+]{1,3}\\\\.[\\\\d+]{1,3}\\\\.[\\\\d+]{1,3}\\\\.[\\\\d+]{1,3}$',\\n ip, re.I):\\n ip = None\\n\\n # Exclude private IP address ranges\\n if re.match(r'^(?:127\\\\.0\\\\.0\\\\.1|10\\\\.|192\\\\.168\\\\.|172\\\\.(?:1[6-9]|2[0-9]|3[0-1])\\\\.)', ip):\\n ip = None\\n\\n self.ip_address = ip\\n\\n self.user_agent = headers.get('HTTP_USER_AGENT')\\n\\n if 'HTTP_ACCEPT_LANGUAGE' in headers:\\n parsed_locales = []\\n res = re.findall(\\n r'(^|\\\\s*,\\\\s*)([a-zA-Z]{1,8}(-[a-zA-Z]{1,8})*)\\\\s*(;\\\\s*q\\\\s*=\\\\s*(1(\\\\.0{0,3})?|0(\\\\.[0-9]{0,3})))?', \\n headers['HTTP_ACCEPT_LANGUAGE'], re.I)\\n for r in res:\\n name = r[1].replace('-', '_')\\n value = 1 if not r[4] else r[4]\\n parsed_locales += [(name, value)]\\n\\n self.locale = sorted(parsed_locales, key=lambda x: x[1],\\n reverse=True)[0][0]\\n\\n return self\",\n \"def filter_headers(self, header):\\n if header == \\\"Ticker symbol\\\":\\n return \\\"symbol\\\"\\n elif header == \\\"GICS Sector\\\":\\n return \\\"sector\\\"\\n elif header == \\\"Security\\\":\\n return \\\"name\\\"\\n elif header == \\\"GICS Sub Industry\\\":\\n return \\\"industry\\\"\\n else:\\n return header\",\n \"def _parse_rate_limit_headers(headers):\\n limit = int(headers[\\\"X-RateLimit-Limit\\\"])\\n remaining = int(headers[\\\"X-RateLimit-Remaining\\\"])\\n reset_at_utc = int(headers[\\\"X-RateLimit-Reset\\\"])\\n return {\\n \\\"limit\\\": limit,\\n \\\"used\\\": limit - remaining,\\n \\\"remaining\\\": remaining,\\n \\\"reset_at_utc\\\": reset_at_utc,\\n \\\"reset_in_sec\\\": reset_at_utc - round(time.time()),\\n \\\"last_update\\\": round(time.time())\\n }\",\n \"def generate_header_dic(self, header_strings):\\n headers = dict()\\n\\n for header_values in header_strings:\\n header_list = header_values.split(':')\\n headers[header_list[0]] = header_list[1]\\n return headers\",\n \"def headers(self) -> dict:\\n raise NotImplementedError # pragma: no cover\",\n \"def _headercorrected(hdr):\\n # COM*** -> COMMENT\\n i = 1\\n while 'COM%03d' % i in hdr:\\n value = hdr['COM%03d' % i]\\n comment = hdr.cards['COM%03d' % i].comment\\n hdr['COMMENT'] = '[%s] %s' % (comment, value)\\n del hdr['COM%03d' % i]\\n i += 1\\n # HIST*** -> HISTORY\\n i = 1\\n while 'HIST%03d' % i in hdr:\\n value = hdr['HIST%03d' % i]\\n comment = hdr.cards['HIST%03d' % i].comment\\n hdr['HISTORY'] = '%s (%s)' % (value, comment)\\n del hdr['HIST%03d' % i]\\n i += 1\\n # ORIGIN -> FROM\\n if 'ORIGIN' in hdr.keys():\\n hdr.rename_keyword('ORIGIN', 'FROM')\\n if 'ORIGIN_V' in hdr.keys():\\n hdr.rename_keyword('ORIGIN_V', 'FROM_V')\\n # SOURCE_V -> FORMAT\\n if 'SOURCE_V' in hdr.keys():\\n hdr.rename_keyword('SOURCE_V', 'FORMAT')\\n # SRC_VERS -> SRC_V\\n if 'SRC_VERS' in hdr.keys():\\n hdr.rename_keyword('SRC_VERS', 'SRC_V')\",\n \"def missing_header_fields():\\n auth_token = get_auth_token()\\n\\n headers = '{\\\"Host\\\": \\\"$host\\\",\\\"Date\\\": \\\"DATE\\\",'\\n headers += '\\\"Accept-Encoding\\\": \\\"gzip\\\",'\\n headers += '\\\"X-Auth-Token\\\": \\\"$token\\\"}'\\n headers = string.Template(headers)\\n\\n return headers.substitute(host=CFG.host, token=auth_token)\",\n \"def get_header(header_row):\\n header = {}\\n header['station'], c1, c2, c3, date, time, tz = header_row.split()\\n header['short_model'] = c1\\n header['model'] = f'{c1} {c2} {c3}' \\n header['runtime'] = dateutil.parser.parse(f'{date} {time} {tz}')\\n return header\",\n \"def _split_headers(headers):\\n amz_headers = {}\\n reg_headers = {}\\n for cur in headers:\\n if cur.lower().startswith('x-amz-'):\\n amz_headers[cur] = headers[cur]\\n else:\\n reg_headers[cur] = headers[cur]\\n return (amz_headers, reg_headers)\",\n \"def headers(self, v):\\n raise NotImplementedError\",\n \"def encode_meta_headers(headers):\\n ret = {}\\n for header, value in headers.items():\\n value = encode_utf8(value)\\n header = header.lower()\\n\\n if (isinstance(header, str) and\\n header.startswith(USER_METADATA_TYPE)):\\n header = encode_utf8(header)\\n\\n ret[header] = value\\n return ret\",\n \"def check_headers(self, headers):\\n h = headers.values()[0]\\n\\n if 'DT' in PAR:\\n if h.dt != PAR.DT:\\n h.dt = PAR.DT\\n\\n if 'NT' in PAR:\\n if h.nt != PAR.NT:\\n print 'Warning: h.nt != PAR.NT'\\n\\n if 'NREC' in PAR:\\n if h.nr != PAR.NREC:\\n print 'Warning: h.nr != PAR.NREC'\\n\\n return h\",\n \"def set_column_headers(self, headers):\\n if isinstance(self.columns.idx[0], int):\\n self.data = [sorted(headers)] + self.data\\n\\n increment = [i + 1 for i in self.rows.idx]\\n self.rows.idx = [0] + increment\\n\\n elif isinstance(self.columns.idx[0], str):\\n datum = {}\\n for i, key in enumerate(self.columns.idx):\\n datum.update({key: headers[i]})\\n self.data = [datum] + self.data\\n\\n increment = [i + 1 for i in self.rows.idx]\\n self.rows.idx = [0] + increment\",\n \"def set_headers(self, headers):\\n\\n if isinstance(headers, dict):\\n headers = headers.items()\\n\\n # NOTE(kgriffs): We can't use dict.update because we have to\\n # normalize the header names.\\n _headers = self._headers\\n for name, value in headers:\\n _headers[name.lower()] = value\",\n \"def scrub_headers(self, header_dict):\\n return self.__headers_scrubber(header_dict)\",\n \"def setup_request_headers(self, uri: str = None) -> dict:\\n\\n uri = uri if uri is not None else self.host\\n headers = {\\n \\\"Accept\\\": \\\"application/atom+json,application/json\\\",\\n \\\"Accept-Encoding\\\": \\\"gzip, deflate, br\\\",\\n \\\"Accept-Language\\\": \\\"en_US\\\",\\n \\\"Connection\\\": \\\"keep-alive\\\",\\n \\\"User-Agent\\\": self.user_agent,\\n \\\"Referer\\\": uri + \\\"/suite/tempo/\\\",\\n \\\"X-Appian-Cached-Datatypes\\\": self.datatype_cache.get(),\\n \\\"Cookie\\\": \\\"JSESSIONID={}; __appianCsrfToken={}; __appianMultipartCsrfToken={}\\\".format(\\n self.client.cookies.get(\\\"JSESSIONID\\\", \\\"\\\"),\\n self.client.cookies.get(\\\"__appianCsrfToken\\\", \\\"\\\"),\\n self.client.cookies.get(\\\"__appianMultipartCsrfToken\\\", \\\"\\\"),\\n ),\\n \\\"DNT\\\": \\\"1\\\",\\n \\\"X-APPIAN-CSRF-TOKEN\\\": self.client.cookies.get(\\\"__appianCsrfToken\\\", \\\"\\\"),\\n \\\"X-APPIAN-MP-CSRF-TOKEN\\\": self.client.cookies.get(\\\"__appianMultipartCsrfToken\\\", \\\"\\\"),\\n \\\"X-Appian-Ui-State\\\": \\\"stateful\\\",\\n \\\"X-Appian-Features\\\": self.client.feature_flag,\\n \\\"X-Appian-Features-Extended\\\": self.client.feature_flag_extended,\\n \\\"x-libraries-suppress-www-authenticate\\\": \\\"true\\\",\\n # this should probably go...\\n \\\"X-Atom-Content-Type\\\": \\\"application/html\\\"\\n }\\n return headers\",\n \"def build_headers(self):\\n\\n # User-agent is always sent\\n headers = {'user-agent': self.useragent}\\n for hdr in self.config.client_standard_headers:\\n val = getattr(self.config, 'client_' + hdr.lower().replace('-','_'))\\n headers[hdr] = val\\n\\n return headers\",\n \"def test_header_parser_vanilla(self):\\n lines = [\\\"Content-Type: application/json\\\", \\\"Accept: application/json\\\"]\\n h = {\\\"Content-Type\\\": \\\"application/json\\\", \\\"Accept\\\": \\\"application/json\\\"}\\n headers = parser._parse_headers(lines)\\n self.assertEqual(h, headers)\",\n \"def _parse_raw_header_entries(header_entries):\\n\\n def __check_key(key):\\n return not(\\\"_\\\" in key or \\\" \\\" in key or \\\":\\\" in key or not len(key))\\n\\n result = {}\\n if (len(header_entries) < 1):\\n return result\\n\\n # Remove leading '--'\\n header_entries = header_entries[1:]\\n if (not len(header_entries) % 2 == 0):\\n raise ValueError(\\\"last key does not have a value\\\")\\n\\n while (len(header_entries)):\\n # Retrieve raw key\\n logging.debug(\\\"current header content \\\" + str(header_entries))\\n word = header_entries[0]\\n header_entries = header_entries[1:]\\n\\n # Try to trim equal\\n if (word[-1] == ':'):\\n word = word[:-1]\\n\\n if(not __check_key(word)):\\n raise ValueError(\\\"invalid key '{}' in key value list\\\".format(word))\\n\\n result[word] = header_entries[0]\\n header_entries = header_entries[1:]\\n\\n return result\",\n \"def _extract_metadata(self, header, cleaner):\\n metadata = []\\n for k, v in header.items():\\n key = str(cleaner(k)) # clean key and ensure it is a string\\n val = str(cleaner(v)) # clean value and ensure it is a string\\n if (key and val):\\n metadata.append(Metadatum(key, val))\\n return metadata\",\n \"def trim_headers(all_headers, relevant_headers=[\\\"From\\\", \\\"To\\\", \\\"Subject\\\", \\\"Date\\\"]):\\n data = {}\\n for header in all_headers:\\n if header['name'] in relevant_headers:\\n data[header['name']] = header['value']\\n\\n return data\",\n \"def get_headers(headers: HTTPHeaders) -> Mapping[str, List[str]]:\\r\\n return {header.lower(): headers.get_list(header) for header in headers.keys()}\",\n \"def _make_headers() -> CaseInsensitiveDict:\\n headers = CaseInsensitiveDict()\\n headers['Authorization'] = f'Token {os.environ[\\\"TOKEN\\\"]}'\\n headers['Content-type'] = 'application/json'\\n return headers\",\n \"def __get_headers(self, passed_headers: Dict) -> Dict:\\n\\n # User-Agent for HTTP request\\n library_details = [\\n f\\\"requests {requests.__version__}\\\",\\n f\\\"python {platform.python_version()}\\\",\\n f\\\"connector {self.__class__.__name__}\\\",\\n ]\\n library_details = \\\"; \\\".join(library_details)\\n user_agent = f\\\"Infermedica-API-Python {__version__} ({library_details})\\\"\\n\\n headers = {\\n \\\"Accept\\\": \\\"application/json\\\",\\n \\\"User-Agent\\\": user_agent,\\n \\\"App-Id\\\": self.app_id,\\n \\\"App-Key\\\": self.app_key,\\n }\\n headers.update(self.default_headers)\\n headers.update(passed_headers) # Make sure passed headers take precedence\\n return headers\",\n \"def parse_header(header_lines):\\n info = {}\\n for line in header_lines:\\n if line.startswith('Citation'):\\n info['Citation'] = line.split()[-1].strip()\\n elif ':' in line:\\n try:\\n field, value = map(strip,line.split(':',1))\\n info[field] = value\\n except ValueError:\\n #no interesting header line\\n continue\\n else:\\n continue\\n return Info(info)\",\n \"def parse_header_dict(self, header_dict=None):\\n if header_dict is not None:\\n self.header_dict = header_dict\\n\\n assert isinstance(self.header_dict, dict)\\n\\n for key, value in self.header_dict.items():\\n if \\\"wire\\\" in key:\\n if key.find(\\\"n\\\") == 0:\\n self.ex_length = float(value.split()[0])\\n self.ex_azimuth = float(value.split()[1])\\n elif key.find(\\\"e\\\") == 0:\\n self.ey_length = float(value.split()[0])\\n self.ey_azimuth = float(value.split()[1])\\n elif \\\"system\\\" in key:\\n self.box_id = value.split(\\\";\\\")[0].strip()\\n self.mag_id = value.split(\\\";\\\")[1].strip()\\n elif \\\"gps\\\" in key:\\n gps_list = value.split()\\n self.header_gps_stamp = MTime(\\n dateutil.parser.parse(\\n \\\" \\\".join(gps_list[0:2]), dayfirst=True\\n )\\n )\\n self.header_gps_latitude = self._get_latitude(\\n gps_list[2], gps_list[3]\\n )\\n self.header_gps_longitude = self._get_longitude(\\n gps_list[4], gps_list[5]\\n )\\n self.header_gps_elevation = float(gps_list[6])\\n elif \\\"run\\\" in key:\\n self.run_id = value.replace('\\\"', \\\"\\\")\\n else:\\n setattr(self, key.replace(\\\" \\\", \\\"_\\\").replace(\\\"/\\\", \\\"_\\\"), value)\",\n \"def headersFromRawFile(self, rawFile: str, headers: Dict) -> None:\\n dFile = open(os.path.join(self.dataPath, rawFile), \\\"r\\\", encoding=\\\"ISO-8859-1\\\")\\n generalHeaderString = dFile.read(1000) # this should be long enough\\n generalSplit = generalHeaderString.split()\\n # read GENERAL HEADER\\n generalHeader = {}\\n generalHeader[\\\"recLength\\\"] = int(generalSplit[0])\\n generalHeader[\\\"fileType\\\"] = generalSplit[1]\\n generalHeader[\\\"wordLength\\\"] = int(generalSplit[2])\\n generalHeader[\\\"version\\\"] = generalSplit[3]\\n generalHeader[\\\"procId\\\"] = generalSplit[4]\\n generalHeader[\\\"numCh\\\"] = int(generalSplit[5])\\n generalHeader[\\\"totalRec\\\"] = int(generalSplit[6])\\n generalHeader[\\\"firstEvent\\\"] = int(generalSplit[7])\\n generalHeader[\\\"numEvent\\\"] = int(generalSplit[8])\\n generalHeader[\\\"extend\\\"] = int(generalSplit[9])\\n\\n # read EVENT HEADER - there can be multiple of these, but normally only the one\\n # Multiple events are largely deprecated. Only a single event is used\\n eventHeaders = []\\n fileSize = os.path.getsize(os.path.join(self.dataPath, rawFile))\\n record = generalHeader[\\\"firstEvent\\\"]\\n for ir in range(0, generalHeader[\\\"numEvent\\\"]):\\n seekPt = (record - 1) * generalHeader[\\\"recLength\\\"]\\n if not seekPt > fileSize:\\n # seek from beginning of file\\n dFile.seek(seekPt, 0)\\n # read extra to make sure\\n eventString = dFile.read(1000)\\n eventSplit = eventString.split()\\n eH = {}\\n eH[\\\"start\\\"] = int(eventSplit[0])\\n eH[\\\"startms\\\"] = int(eventSplit[1])\\n eH[\\\"stop\\\"] = int(eventSplit[2])\\n eH[\\\"stopms\\\"] = int(eventSplit[3])\\n eH[\\\"cvalue1\\\"] = float(eventSplit[4])\\n eH[\\\"cvalue2\\\"] = float(eventSplit[5])\\n eH[\\\"cvalue3\\\"] = float(eventSplit[6])\\n eH[\\\"EHInfile\\\"] = int(eventSplit[7])\\n eH[\\\"nextEH\\\"] = int(eventSplit[8])\\n eH[\\\"previousEH\\\"] = int(eventSplit[9])\\n eH[\\\"numData\\\"] = int(eventSplit[10])\\n eH[\\\"startData\\\"] = int(eventSplit[11])\\n eH[\\\"extended\\\"] = int(eventSplit[12])\\n eventHeaders.append(eH)\\n if eH[\\\"nextEH\\\"] < generalHeader[\\\"totalRec\\\"]:\\n record = eH[\\\"nextEH\\\"] # set to go to next eH\\n else:\\n break # otherwise break out of for loops\\n # close the data file\\n dFile.close()\\n # now compare number of samples with that calculated previously\\n if eventHeaders[0][\\\"numData\\\"] != headers[\\\"num_samples\\\"]:\\n self.printWarning(\\\"Data file: {}\\\".format(dFile))\\n self.printWarning(\\n \\\"Number of samples in raw file header {} does not equal that calculated from data {}\\\".format(\\n eventHeaders[0][\\\"numData\\\"], headers[\\\"num_samples\\\"]\\n )\\n )\\n self.printWarning(\\\"Number of samples calculated from data will be used\\\")\\n # set the byte offset for the file\\n self.dataByteOffset[rawFile] = (\\n eventHeaders[0][\\\"startData\\\"] - 1\\n ) * generalHeader[\\\"recLength\\\"]\\n self.recChannels[rawFile] = generalHeader[\\\"numCh\\\"]\",\n \"def _api_headers(self, previous_headers=None):\\n if self._headers is None:\\n auth_headers = {}\\n token = self._token\\n if token:\\n auth_headers[\\\"Authorization\\\"] = f\\\"token {token}\\\"\\n self._headers = auth_headers\\n\\n if previous_headers is not None:\\n headers = self._headers.copy()\\n for condition, key in (\\n (\\\"If-Modified-Since\\\", \\\"Last-Modified\\\"),\\n (\\\"If-None-Match\\\", \\\"ETag\\\"),\\n ):\\n try:\\n headers[condition] = previous_headers[key]\\n except KeyError:\\n continue\\n return headers\\n\\n return self._headers\",\n \"def _getHeaders(self, callName):\\n return {\\n 'X-EBAY-API-CALL-NAME': callName,\\n 'X-EBAY-API-APP-NAME': settings.EBAY_API_APP_NAME,\\n 'X-EBAY-API-CERT-NAME': settings.EBAY_API_CERT_NAME,\\n 'X-EBAY-API-DEV-NAME': settings.EBAY_API_DEV_NAME,\\n 'X-EBAY-API-SITEID': settings.EBAY_API_SITEID,\\n 'X-EBAY-API-COMPATIBILITY-LEVEL': settings.EBAY_API_COMPATIBILITY_LEVEL\\n }\",\n \"def unpack_header(header):\\n header_values = {}\\n for line in header.split('\\\\n'):\\n tokens = line.split('=')\\n if len(tokens) > 1:\\n header_values[tokens[0].strip()] = tokens[1].split(';')[0].strip()\\n return header_values\",\n \"def getAllHeaders():\",\n \"def fitsio_header_to_dict(hdr):\\n d = {}\\n for key in hdr.keys():\\n if key != 'HISTORY' or key != \\\"COMMENT\\\":\\n d[key.lower()] = hdr.get(key)\\n return d\",\n \"def get_headers():\\n if not headers:\\n headers[\\\"Content-Type\\\"] = \\\"application/json\\\"\\n headers[\\\"Accept\\\"] = \\\"application/json\\\"\\n headers[\\\"User-Agent\\\"] = constants.USER_AGENT\\n headers[\\\"Authorization\\\"] = get_token(constants.AUTH_URL, cfg[\\\"key\\\"])\\n\\n return headers\\n\\n return headers\",\n \"def parse_header(self): # -> list[Unknown]:\\n ...\",\n \"def to_header(self) -> Dict[str, str]:\\n tracer_from_context = self.get_current_tracer()\\n temp_headers: Dict[str, str] = {}\\n if tracer_from_context is not None:\\n ctx = tracer_from_context.span_context\\n try:\\n temp_headers = tracer_from_context.propagator.to_headers(ctx)\\n except AttributeError:\\n pass\\n return temp_headers\",\n \"def _parse_header(self, line):\\n if self._regex_helper.search_compiled(W._re_header, line):\\n if not self.headers:\\n for value in re.findall(W._re_header, line):\\n self.headers.append(value[0])\\n raise ParsingDone\\n else:\\n # Dictionary which is going to be appended to the returned list\\n ret = dict()\\n # List of entries\\n _entries = list()\\n # List of values in WHAT entry\\n _what_entry = list()\\n for value in re.findall(W._re_header, line):\\n _entries.append(value[0])\\n for what_index in range(len(self.headers) - 1, len(_entries)):\\n _what_entry.append(_entries[what_index])\\n _what_entry_string = ' '.join(_what_entry)\\n for index in range(len(self.headers)):\\n if index < len(self.headers) - 1:\\n ret.update({self.headers[index]: _entries[index]})\\n else:\\n ret.update({self.headers[index]: _what_entry_string})\\n self.current_ret['RESULT'].append(ret)\\n raise ParsingDone\",\n \"def extract_object_metadata_from_headers(headers):\\n meta_headers = {}\\n for header, value in headers.items():\\n header = header.title()\\n\\n if (header.startswith(\\\"X-Object-Meta-\\\") or\\n header.startswith(\\\"X-Object-Sysmeta-\\\") or\\n header in SPECIAL_OBJECT_METADATA_HEADERS):\\n\\n # do not let a client pass in ORIGINAL_MD5_HEADER\\n if header not in (ORIGINAL_MD5_HEADER,\\n ORIGINAL_MD5_HEADER.title()):\\n meta_headers[header] = value\\n\\n return meta_headers\",\n \"def _parse_header(lines):\\n # The dict into which we will store header fields.\\n header = {}\\n # Loop over lines in the header.\\n for line in lines:\\n # Find the first colon.\\n index = line.index(COLON)\\n # Up to the colon is the field name.\\n name = line[: index]\\n # After the colon is the field value.\\n value = line[index + 1 :]\\n # The field value may begin or end with extra space, which is not \\n # significant. Remove it.\\n value = value.strip()\\n # Store the field.\\n header[name] = value\\n # All done.\\n return header\",\n \"def _headers(self, **kwargs):\\n headers = BASE_HEADERS.copy()\\n if self.headers:\\n headers.update(self.headers)\\n headers.update(kwargs)\\n return headers\",\n \"def _get_cleaned_headers(headers):\\r\\n cleaned_headers = []\\r\\n for header in headers:\\r\\n # Google strips special characters, whitespace, and underscores first,\\r\\n # and then strips any *leading* digits. This order is extremely\\r\\n # important!\\r\\n sanitized = sub(r'^\\\\d+', '', sub(r'[\\\\W_]', '', header.lower()))\\r\\n if len(sanitized) > 0:\\r\\n cleaned_headers.append(sanitized)\\r\\n else:\\r\\n raise GoogleSpreadsheetError(\\\"Encountered a header '%s' that was \\\"\\r\\n \\\"either blank or consisted only of special characters. \\\"\\r\\n \\\"Could not map the header to the internal representation \\\"\\r\\n \\\"used by the Google Spreadsheet. Please change the header \\\"\\r\\n \\\"to consist of at least one alphanumeric character.\\\"\\r\\n % header)\\r\\n\\r\\n # When the same sanitized header appears multiple times in the first row\\r\\n # of a spreadsheet, _n is appended to the name to make it unique.\\r\\n header_count = defaultdict(int)\\r\\n results = []\\r\\n\\r\\n for header, cleaned_header in zip(headers, cleaned_headers):\\r\\n new_header = cleaned_header\\r\\n\\r\\n if header_count[cleaned_header] > 0:\\r\\n # Google's numbering starts from _2, hence the +1.\\r\\n new_header = '%s_%d' % (cleaned_header,\\r\\n header_count[cleaned_header] + 1)\\r\\n\\r\\n header_count[cleaned_header] += 1\\r\\n results.append(new_header)\\r\\n\\r\\n return results\",\n \"def test_headerFormatter(self):\\n cases = [\\n ({'Header1': 'Value1', 'Header2': 'Value2'},\\n b'Header2: Value2\\\\r\\\\nHeader1: Value1\\\\r\\\\n'),\\n ]\\n\\n for (input, expected) in cases:\\n output = imap4._formatHeaders(input)\\n self.assertEqual(sorted(output.splitlines(True)),\\n sorted(expected.splitlines(True)))\",\n \"def get_headers(self):\\r\\n raise NotImplementedError\",\n \"def headers(self) -> Mapping[str, str]:\\n return pulumi.get(self, \\\"headers\\\")\",\n \"def headers_processor(headers):\\n def apply_headers(row_set, row):\\n _row = []\\n pairs = izip_longest(row, headers)\\n for i, (cell, header) in enumerate(pairs):\\n if cell is None:\\n cell = Cell(None)\\n cell.column = header\\n if not cell.column:\\n cell.column = \\\"column_%d\\\" % i\\n cell.column_autogenerated = True\\n _row.append(cell)\\n return _row\\n return apply_headers\",\n \"def _headers(self) -> Mapping[str, str]:\\n return self.auth.headers() if self.auth else {}\",\n \"def process_header_request(self, request, http_s_obj):\\n response_dict = {}\\n data = request.split(\\\"\\\\r\\\\n\\\\r\\\\n\\\")\\n header_info = data[0].split(\\\"\\\\r\\\\n\\\")\\n headers = self.updateheader(header_info, http_s_obj)\\n response_dict.update({'type': header_info[0].split()[0]})\\n response_dict.update({'headers': headers})\\n body = data[1]\\n response_dict.update({'data': body})\\n path = header_info[0].split()[1]\\n if path.find('?') != -1:\\n split_sym = '?'\\n if path.find('&') != -1:\\n split_sym = '&'\\n try:\\n req = path.split(split_sym)\\n path = req[0]\\n query = req[1]\\n except Exception as e:\\n query = ''\\n response_dict.update({'path': path})\\n response_dict.update({'query': query})\\n\\n return response_dict\",\n \"def _update_headers(self):\\n if not self._header_updated:\\n headers = self.head_obj(self._client, self._spec)\\n self._headers.update(headers)\\n self._header_updated = True\",\n \"def header(self, header, default=None):\\n result = []\\n header_value = self.email.get(header, default)\\n if header_value:\\n for part in decode_header(header_value):\\n if part[1]:\\n encoded = part[0].decode(part[1])\\n elif isinstance(part[0], bytes):\\n encoded = part[0].decode('utf-8')\\n else:\\n encoded = part[0]\\n result.append(encoded.strip())\\n header_value = ' '.join(result)\\n\\n return header_value\",\n \"def process_client_headers(self, *, scope: Scope, headers: Headers) -> Headerlike:\\n return headers\",\n \"def set_column_headers(self,param_headers):\\n self.cur_quotes_parm_headers = param_headers\",\n \"def _headers(self):\\n\\n auth_token = SendbeeAuth(self.client.api_secret).get_auth_token()\\n headers = {\\n 'X-Auth-Token': auth_token,\\n 'X-Api-Key': self.client.api_key,\\n 'Accept': 'application/json',\\n 'Content-Type': 'application/json',\\n 'User-Agent': 'Sendbee Python API Client'\\n }\\n self.debug.ok('headers', headers)\\n\\n return headers\",\n \"def inject_headers(mime, headers):\\n for key in headers:\\n if key == 'Bcc' or key == 'Resent-Bcc':\\n continue\\n del mime[key]\\n mime[key] = encode_header(headers[key])\",\n \"def get_headers(self, environ=None):\\n return [('Content-Type', 'application/json')]\",\n \"def _build_headers(self):\\n headers = {\\n 'Authorization': 'Bearer {api_key}'.format(api_key=self._api_key),\\n 'SplitSDKVersion': SDK_VERSION,\\n 'Accept-Encoding': 'gzip'\\n }\\n\\n if self._split_sdk_machine_name is not None:\\n headers['SplitSDKMachineName'] = self._split_sdk_machine_name() \\\\\\n if callable(self._split_sdk_machine_name) else self._split_sdk_machine_name\\n\\n if self._split_sdk_machine_ip is not None:\\n headers['SplitSDKMachineIP'] = self._split_sdk_machine_ip() \\\\\\n if callable(self._split_sdk_machine_ip) else self._split_sdk_machine_ip\\n\\n return headers\",\n \"def _build_http_header(self) -> Dict[str, str]:\\n return {}\",\n \"def _get_headers(self, request):\\n headers = {}\\n for key, value in request.META.items():\\n if key.startswith('HTTP_') and key != 'HTTP_HOST':\\n headers[key[5:].replace('_', '-')] = value\\n elif key in ('CONTENT_TYPE', 'CONTENT_LENGTH') and value:\\n headers[key.replace('_', '-')] = value\\n\\n if request.user:\\n headers['PARTNER-EMAIL'] = request.user.email\\n lang = self._get_lang(request)\\n if lang:\\n headers['ACCEPT-LANGUAGE'] = lang\\n return headers\",\n \"def parse_header(self):\\n bodystart=re.compile(r\\\"<body>\\\", re.IGNORECASE).search(self.lines).span()[1]\\n oldheader=self.lines[0:bodystart]\\n start=re.compile(\\\"<title>\\\", re.IGNORECASE).search(oldheader).span()[1]\\n finish=re.compile(\\\"</title>\\\", re.IGNORECASE).search(oldheader).span()[0]\\n titles=oldheader[start:finish].split(\\\"--\\\")\\n # Duplicate if needed\\n if len(titles)==1: titles.append(titles[0])\\n self.title, self.header= {}, {}\\n for i, lang in enumerate(LANGS):\\n self.title[lang]=titles[i]\\n self.header[lang]=\\\"%s%s%s\\\" % (oldheader[:start], self.title[lang], oldheader[finish:],)\",\n \"def _format_header(self):\\n return self._format_dict(self.header)\",\n \"def add_custom_headers(self, headers):\\n headers_to_remove = [x for x in headers if x.lower() in [y.lower() for y in self.headers]]\\n for header in headers_to_remove:\\n headers.pop(header, None)\\n headers.update(self.headers)\",\n \"def process_result_headers(result_headers):\\n class_attrib_re = re.compile(r'\\\\s*class=\\\"([^\\\"]+)\\\"')\\n\\n for header in result_headers:\\n m = class_attrib_re.match(header['class_attrib'])\\n\\n if m:\\n class_value = m.groups(1)[0]\\n else:\\n class_value = ''\\n\\n if class_value != 'action-checkbox-column':\\n class_value = 'has-label %s' % class_value\\n\\n header['class_attrib'] = \\\\\\n mark_safe(' class=\\\"datagrid-header %s\\\"' % class_value)\\n\\n if header['sortable'] and header['sort_priority'] > 0:\\n if header['ascending']:\\n sort_order = 'asc'\\n else:\\n sort_order = 'desc'\\n\\n if header['sort_priority'] == 1:\\n sort_priority = 'primary'\\n else:\\n sort_priority = 'secondary'\\n\\n header['sort_icon'] = 'datagrid-icon-sort-%s-%s' % (\\n sort_order, sort_priority)\\n\\n return ''\",\n \"def _format_header(fields):\\n # The list into which we will collect header lines.\\n lines = []\\n for name, value in fields.items():\\n # A header line looks like, \\\"name: value\\\".\\n line = name + COLON + SPACE + value\\n # Add this line to the list.\\n lines.append(line)\\n return lines\",\n \"def put_headers_in_environ(headers, environ):\\n for key, value in headers:\\n environ['HTTP_%s' % key.upper().replace('-', '_')] = value\",\n \"def __get_headers(self):\\n\\n return {}\",\n \"def sanitize_headers(headers):\\n auth_header = headers.pop(\\\"Authorization\\\", None)\\n if auth_header:\\n _logger.warning(\\n f\\\"Possible fraud: Authorization header was set to {auth_header}\\\"\\n )\\n userinfo_header = headers.pop(\\\"X-Userinfo\\\", None)\\n if userinfo_header:\\n _logger.warning(\\n f\\\"Possible fraud: X-Userinfo header was set to {userinfo_header}\\\"\\n )\",\n \"def headers():\\n return {\\n 'user-agent': 'integration-tester',\\n 'content-type': 'application/json',\\n }\",\n \"def make_headers(self):\\n return {'User-Agent': 'Mozilla/5.0 (Windows; U; Windows NT 6.1; en-US;\\\\\\n rv:1.9.1.6) Gecko/20091201 Firefox/3.5.6'}\",\n \"def currentSBHeaders(self):\\n headers = {}\\n for header_name in [options['Headers','classification_header_name'],\\n options['Headers','mailid_header_name'],\\n options['Headers','classification_header_name'] + \\\"-ID\\\",\\n options['Headers','thermostat_header_name'],\\n options['Headers','evidence_header_name'],\\n options['Headers','score_header_name'],\\n options['Headers','trained_header_name'],\\n ]:\\n value = self[header_name]\\n if value is not None:\\n headers[header_name] = value\\n return headers\",\n \"def get_headers(s, sep=': ', strip_cookie=False, strip_cl=True, strip_headers: list = []) -> dict():\\n d = dict()\\n for kv in s.split('\\\\n'):\\n kv = kv.strip()\\n if kv and sep in kv:\\n v=''\\n k = kv.split(sep)[0]\\n if len(kv.split(sep)) == 1:\\n v = ''\\n else:\\n v = kv.split(sep)[1]\\n if v == '\\\\'\\\\'':\\n v =''\\n # v = kv.split(sep)[1]\\n if strip_cookie and k.lower() == 'cookie': continue\\n if strip_cl and k.lower() == 'content-length': continue\\n if k in strip_headers: continue\\n d[k] = v\\n return d\",\n \"def get_headers(s, sep=': ', strip_cookie=False, strip_cl=True, strip_headers: list = []) -> dict():\\n d = dict()\\n for kv in s.split('\\\\n'):\\n kv = kv.strip()\\n if kv and sep in kv:\\n v=''\\n k = kv.split(sep)[0]\\n if len(kv.split(sep)) == 1:\\n v = ''\\n else:\\n v = kv.split(sep)[1]\\n if v == '\\\\'\\\\'':\\n v =''\\n # v = kv.split(sep)[1]\\n if strip_cookie and k.lower() == 'cookie': continue\\n if strip_cl and k.lower() == 'content-length': continue\\n if k in strip_headers: continue\\n d[k] = v\\n return d\",\n \"def responseheaders(self, flow: mitmproxy.http.HTTPFlow):\",\n \"def _headers(self, **kwargs):\\n headers = BASE_HEADERS.copy()\\n if self._token:\\n headers['X-Plex-Token'] = self._token\\n headers.update(kwargs)\\n return headers\",\n \"def _headers(self, **kwargs):\\n headers = BASE_HEADERS.copy()\\n if self._token:\\n headers['X-Plex-Token'] = self._token\\n headers.update(kwargs)\\n return headers\",\n \"def from_incoming_header(cls, header):\\n # type: (Optional[str]) -> Baggage\\n sentry_items = {}\\n third_party_items = \\\"\\\"\\n mutable = True\\n\\n if header:\\n for item in header.split(\\\",\\\"):\\n if \\\"=\\\" not in item:\\n continue\\n\\n with capture_internal_exceptions():\\n item = item.strip()\\n key, val = item.split(\\\"=\\\")\\n if Baggage.SENTRY_PREFIX_REGEX.match(key):\\n baggage_key = unquote(key.split(\\\"-\\\")[1])\\n sentry_items[baggage_key] = unquote(val)\\n mutable = False\\n else:\\n third_party_items += (\\\",\\\" if third_party_items else \\\"\\\") + item\\n\\n return Baggage(sentry_items, third_party_items, mutable)\",\n \"def requestheaders(self, flow: mitmproxy.http.HTTPFlow):\",\n \"def headers(self):\\n return Dict(**self._get_headers())\",\n \"def parse_cookies( headers ):\",\n \"def generate_headers(self):\\n raise NotImplementedError()\",\n \"def headers(self):\\r\\n return dict(**self._get_headers())\",\n \"def extract_from_header(headers, header_type):\\n LOG.debug(f\\\"[extract_from_header] extracting `{header_type}` from headers: {headers}\\\")\\n if headers is None:\\n return\\n for header in headers:\\n if header_type in header:\\n for item in header:\\n if item == header_type or item is None:\\n continue\\n else:\\n return item.decode(\\\"ascii\\\")\\n return\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6760923","0.6707738","0.66021603","0.6435935","0.62480223","0.62002486","0.6085462","0.60607","0.605833","0.6038865","0.5981734","0.5961042","0.5954765","0.59422773","0.5939061","0.5936525","0.5867014","0.5862857","0.5862484","0.586054","0.5848212","0.57505476","0.57465124","0.57360345","0.5724249","0.5721163","0.57163894","0.57017416","0.57010096","0.56939006","0.5686147","0.5672436","0.566871","0.5662251","0.56598806","0.5645679","0.564502","0.560429","0.5604171","0.5601218","0.5600892","0.55955595","0.5591944","0.55608284","0.55254924","0.5510758","0.55077505","0.54987484","0.54953444","0.5478886","0.54753315","0.54685664","0.54584146","0.5457679","0.54543036","0.54363126","0.5431733","0.543093","0.54258955","0.5421413","0.5418677","0.5413127","0.54117227","0.5410222","0.54073024","0.53905004","0.53892463","0.5381245","0.5368328","0.5360434","0.5358165","0.53565854","0.53419673","0.5338328","0.53236246","0.53212434","0.53082466","0.5306761","0.5304595","0.5296047","0.52953863","0.5282326","0.5277068","0.5268837","0.52661616","0.5258837","0.5254299","0.5248292","0.524247","0.524247","0.52399015","0.523759","0.523759","0.5235847","0.5233139","0.5226221","0.5222904","0.521974","0.5215237","0.5215113"],"string":"[\n \"0.6760923\",\n \"0.6707738\",\n \"0.66021603\",\n \"0.6435935\",\n \"0.62480223\",\n \"0.62002486\",\n \"0.6085462\",\n \"0.60607\",\n \"0.605833\",\n \"0.6038865\",\n \"0.5981734\",\n \"0.5961042\",\n \"0.5954765\",\n \"0.59422773\",\n \"0.5939061\",\n \"0.5936525\",\n \"0.5867014\",\n \"0.5862857\",\n \"0.5862484\",\n \"0.586054\",\n \"0.5848212\",\n \"0.57505476\",\n \"0.57465124\",\n \"0.57360345\",\n \"0.5724249\",\n \"0.5721163\",\n \"0.57163894\",\n \"0.57017416\",\n \"0.57010096\",\n \"0.56939006\",\n \"0.5686147\",\n \"0.5672436\",\n \"0.566871\",\n \"0.5662251\",\n \"0.56598806\",\n \"0.5645679\",\n \"0.564502\",\n \"0.560429\",\n \"0.5604171\",\n \"0.5601218\",\n \"0.5600892\",\n \"0.55955595\",\n \"0.5591944\",\n \"0.55608284\",\n \"0.55254924\",\n \"0.5510758\",\n \"0.55077505\",\n \"0.54987484\",\n \"0.54953444\",\n \"0.5478886\",\n \"0.54753315\",\n \"0.54685664\",\n \"0.54584146\",\n \"0.5457679\",\n \"0.54543036\",\n \"0.54363126\",\n \"0.5431733\",\n \"0.543093\",\n \"0.54258955\",\n \"0.5421413\",\n \"0.5418677\",\n \"0.5413127\",\n \"0.54117227\",\n \"0.5410222\",\n \"0.54073024\",\n \"0.53905004\",\n \"0.53892463\",\n \"0.5381245\",\n \"0.5368328\",\n \"0.5360434\",\n \"0.5358165\",\n \"0.53565854\",\n \"0.53419673\",\n \"0.5338328\",\n \"0.53236246\",\n \"0.53212434\",\n \"0.53082466\",\n \"0.5306761\",\n \"0.5304595\",\n \"0.5296047\",\n \"0.52953863\",\n \"0.5282326\",\n \"0.5277068\",\n \"0.5268837\",\n \"0.52661616\",\n \"0.5258837\",\n \"0.5254299\",\n \"0.5248292\",\n \"0.524247\",\n \"0.524247\",\n \"0.52399015\",\n \"0.523759\",\n \"0.523759\",\n \"0.5235847\",\n \"0.5233139\",\n \"0.5226221\",\n \"0.5222904\",\n \"0.521974\",\n \"0.5215237\",\n \"0.5215113\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7734185"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":2897,"cells":{"query":{"kind":"string","value":"Calculate the similarity based on Cosine Similarity between two CTRDMs"},"document":{"kind":"string","value":"def cosinesimilarity_cal(CTRDM1, CTRDM2):\n\n # get number of conditions\n n_cons = np.shape(CTRDM1)[0]\n\n # calculate the number of value above the diagonal in RDM\n n = n_cons * (n_cons - 1)\n\n # initialize two vectors to store the values above the diagnal of two RDMs\n v1 = np.zeros([n], dtype=np.float64)\n v2 = np.zeros([n], dtype=np.float64)\n\n # assignment\n nn = 0\n for i in range(n_cons):\n for j in range(n_cons):\n if i != j:\n v1[nn] = CTRDM1[i, j]\n v2[nn] = CTRDM2[i, j]\n nn = nn + 1\n\n # calculate the Cosine Similarity\n V1 = np.mat(v1)\n V2 = np.mat(v2)\n num = float(V1 * V2.T)\n denom = np.linalg.norm(V1) * np.linalg.norm(V2)\n cos = num / denom\n similarity = 0.5 + 0.5 * cos\n\n return similarity"},"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 cosine_similarity(v1, v2):\n # Cosine Sim:\n # Get the words that both have in common\n\n v1words = set(v1.keys())\n v2words = set(v2.keys())\n\n numerator_words = v1words.intersection(v2words)\n\n # Multiply and sum those counts\n numerator = 0.0\n for word in numerator_words:\n numerator += v1[word] * v2[word]\n\n\n # Divide by the sqrt of the product of the sum of the squares of the counts\n denominator = math.sqrt(math.magnitude(list(v1.values())) * math.magnitude(list(v2.values())))\n\n return numerator/denominator","def cosine_similarity(v1, v2):\n return np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2))","def cosineSimilarity(dict1,dict2):\n product1 = 0.0\n product2 = 0.0\n for key in dict1.keys():\n product1 += (dict1[key] * dict1[key])\n for key in dict2.keys():\n product2 += (dict2[key] * dict2[key])\n product1 = math.sqrt(product1)\n product2 = math.sqrt(product2)\n fenmu = product1 * product2\n fenzi = 0.0\n for key in dict1.keys():\n if key in dict2:\n fenzi += (dict1[key] * dict2[key])\n cosSim = fenzi / fenmu\n return cosSim","def get_cosine_sim(self):\r\n return CosineSimilarity().calculate_similarity(self.tweets)","def get_cosine_similarity(doc1, doc2):\n count_vectorizer = CountVectorizer(stop_words='english')\n sparse_matrix = count_vectorizer.fit_transform(raw_documents=[doc1, doc2])\n dtm = sparse_matrix.todense()\n df_dtm = pd.DataFrame(data=dtm, \n columns=count_vectorizer.get_feature_names(), \n index=['doc1', 'doc2'])\n similarity_matrix = cosine_similarity(df_dtm, df_dtm)\n similarity_score = round(similarity_matrix[0][1], 6)\n return similarity_score","def cosine_similarity(self, v1: np.ndarray, v2: np.ndarray) -> float:\n return np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2))\n # return cosine_similarity(v1, v2)[0][0]","def cosin_sim_pairs(a, b):\n wordsA = set(a.keys())\n wordsB = set(b.keys())\n inter = wordsA.intersection(wordsB)\n if(len(inter) == 0):\n return 0.0\n aa, bb, ab = 0, 0, 0\n for k in inter:\n aa += a[k] ** 2\n bb += b[k] ** 2\n ab += a[k] * b[k]\n for k in wordsA - inter:\n aa += a[k] ** 2\n for k in wordsB - inter:\n bb += b[k] ** 2\n return ab / float(math.sqrt(aa) * math.sqrt(bb))","def cosine_similarity(v1, v2):\n sim = np.sum(v1*v2)/np.sqrt(np.sum(v1**2))/np.sqrt(np.sum(v2**2))\n return sim","def cosine_similarity(a, b):\n cs = dot_product(a, b)/(norm(a) * norm(b))\n return cs","def cosine_similarity(v1: Vector, v2: Vector) -> float:\n return dot_product(v1, v2) / (vector_len(v1) * vector_len(v2))","def calculate_cosine_similarity(self):\n tfidf_matrix = self.calculate_tfidf()\n\n cosine_similarity = linear_kernel(tfidf_matrix, tfidf_matrix) # Cosine similarity matrix calculation\n\n return cosine_similarity","def cosine_similarity(vec1, vec2) -> float:\n return np.dot(vec1, vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2))","def compute_cosine_similarity(self):\n cos_matrix = []\n for i in range(len(self.train_vec)):\n val = self.vec1 * self.train_vec[i]\n cos_matrix.append(val[0])\n out = np.argmax(cos_matrix)\n print(self.train_output[out])","def cosine_similarity(v1, v2):\n v1_len = 0\n v2_len = 0\n dot_product = 0\n\n for context_id, count in v1.items():\n v1_len += count ** 2\n if context_id in v2:\n dot_product += count*v2[context_id]\n for count in v2.values():\n v2_len += count ** 2\n\n v1_len = math.sqrt(v1_len)\n v2_len = math.sqrt(v2_len)\n return dot_product/(v1_len * v2_len)","def cosine_similarity(x1, x2, dim=1, eps=1e-8):\n w12 = torch.sum(x1 * x2, dim)\n w1 = torch.norm(x1, 2, dim)\n w2 = torch.norm(x2, 2, dim)\n return (w12 / (w1 * w2).clamp(min=eps)).squeeze()","def compute_cosine_sim(vec1, vec2):\n numer = np.dot(vec1.reshape((300,)), vec2.reshape((300,)))\n denom = np.sqrt(np.sum(np.square(vec1.reshape(300, )))) * np.sqrt(\n np.sum(np.square(vec2.reshape(300, ))))\n\n similarity = numer / denom\n\n return similarity","def cosine_similarity(x1, x2, dim=1, eps=1e-8):\r\n w12 = torch.sum(x1 * x2, dim)\r\n w1 = torch.norm(x1, 2, dim)\r\n w2 = torch.norm(x2, 2, dim)\r\n return (w12 / (w1 * w2).clamp(min=eps)).squeeze()","def cosine_similarity(cls, vec_a, vec_b):\n return np.dot(vec_a, vec_b) / \\\n (np.linalg.norm(vec_a) * np.linalg.norm(vec_b))","def cosine_similarity(self, x, y):\n return np.dot(x, y) / (np.linalg.norm(x) * np.linalg.norm(y))","def cosine_similarity(self, source_doc, input_doc):\n vectorizer = self.vectorizer or TfidfVectorizer(tokenizer=PlagiarismDetector.tokenize_and_stem, stop_words='english')\n tfidf = vectorizer.fit_transform([source_doc, input_doc])\n return ((tfidf * tfidf.T).A)[0, 1]","def cosine_similarity(vec_x, vec_y):\n sim_prod = 0.0\n len_x = 0\n len_y = 0\n\n for ngram in vec_x:\n len_x += vec_x[ngram] ** 2\n\n for ngram in vec_y:\n len_y += vec_y[ngram] ** 2\n\n len_x = math.sqrt(len_x)\n len_y = math.sqrt(len_y)\n\n for ngram in vec_x:\n if ngram in vec_y:\n sim_prod += vec_x[ngram] * vec_y[ngram]\n\n return sim_prod / (len_x * len_y)","def test_cossim(self):\n metrics = SimilarityMetrics()\n test1 = metrics.cosine_similarity(np.asarray([1,1]),np.asarray([-1,1]))\n np.testing.assert_almost_equal(test1,0.0)\n\n test2 = metrics.cosine_similarity(np.asarray([1,-1]),np.asarray([-1,1]))\n np.testing.assert_almost_equal(test2,-1.0)\n\n test3 = metrics.cosine_similarity(np.asarray([1,1]),np.asarray([1,1]))\n np.testing.assert_almost_equal(test3,1.0)","def cosine_dist(d1, d2):\n suma=0\n for x in d1:\n if x in d2:\n suma+=(d1[x]*d2[x])\n sqrt1=0\n sqrt2=0\n for i in d1:\n sqrt1+=math.pow(d1[i],2)\n for i in d2:\n sqrt2+=math.pow(d2[i],2)\n return 1-suma/(math.sqrt(sqrt1)*math.sqrt(sqrt2))","def cosine_similarity_tensorflow(tf_word_representation_A, tf_words_representation_B):\n a_normalized = tf.nn.l2_normalize(tf_word_representation_A, axis=-1)\n b_normalized = tf.nn.l2_normalize(tf_words_representation_B, axis=-1)\n similarity = tf.reduce_sum(\n tf.multiply(a_normalized, b_normalized), \n axis=-1\n )\n \n return similarity","def pairwise_cosine_similarity(x, y):\n x = torch.div(x, torch.sqrt(torch.max(torch.sum(x ** 2), 1e-12)))\n y = torch.div(y, torch.sqrt(torch.max(torch.sum(y ** 2), 1e-12)))\n return torch.mm(x, torch.transpose(y, 1, 0))","def content_similarity(self, movie1, movie2):\n v1, v2 = self.get_tfidf(movie1), self.get_tfidf(movie2)\n return self.cosine_similarity(v1, v2)","def test_cosine_similarity_compiled():\n vector1 = np.array([1, 1, 0, 0])\n vector2 = np.array([1, 1, 1, 1])\n score11 = cosine_similarity(vector1, vector1)\n score12 = cosine_similarity(vector1, vector2)\n score22 = cosine_similarity(vector2, vector2)\n\n assert score12 == 2 / np.sqrt(2 * 4), \"Expected different score.\"\n assert score11 == score22 == 1.0, \"Expected different score.\"","def cosine_similarity(a, b):\n\n numerator = tf.reduce_sum(tf.multiply(a, b), axis=1)\n denominator = tf.multiply(tf.norm(a, axis=1), tf.norm(b, axis=1))\n cos_similarity = numerator/denominator\n return cos_similarity","def CosineSimilarity(test_vec, source_vecs):\n cos_dist = 0\n for source_vec in source_vecs:\n cos_dist += FacePredictor.findCosineDistance(test_vec, source_vec)\n return cos_dist / len(source_vecs)","def getGloveoCosineSimilarity(question1, question2):\n questions = [question1, question2]\n\n ## for the sentences we need to get the count vectors\n vec = CountVectorizer(max_features=5000, stop_words=None,binary=True)\n count_vectors = vec.fit_transform(questions)\n\n ## get the vocabulary of words from the questions\n vocab_index = vec.vocabulary_\n\n ## get the index of the words and embeddings\n index_word = {v:k for k, v in vocab_index.items()}\n\n ## get the question vectors\n question_vectors = np.zeros((count_vectors.shape[0], 300))\n\n ## iterate through count vectors for each word get the embeddings\n ## for each embedding, we will then average by the number of words\n ## this will be then used for cosine similarity\n for i in range(count_vectors.shape[0]):\n row = count_vectors[i, :].toarray()\n word_ids = np.where(row > 0)[1]\n word_counts = row[:, word_ids][0]\n numWords = np.sum(word_counts)\n\n ## if there are no words, continue\n if numWords == 0:\n continue\n\n ## initialize the word embeddings to 0\n word_embeddings = np.zeros((word_ids.shape[0], 300))\n\n ## update the word embeddings\n for j in range(word_ids.shape[0]):\n word_id = word_ids[j]\n word_embeddings[j, :] = word_counts[j] * gloveDict[index_word[word_id]]\n question_vectors[i, :] = np.sum(word_embeddings, axis=0) / numWords\n\n return(cosine_similarity(question_vectors[0], question_vectors[1])[0][0])","def tf_cosine_distance(self, a, b):\n normalize_a = tf.nn.l2_normalize(a, -1)\n normalize_b = tf.nn.l2_normalize(b, -1)\n cos_similarity = tf.reduce_sum(\n tf.multiply(normalize_a, normalize_b), axis=-1, keep_dims=True\n )\n return (1.0 - cos_similarity) / 2.0","def calculate_similarity(self, tweets):\r\n if (len(tweets) == 1):\r\n return 0\r\n vectors = self.vectorizer.vectorize_data(tweets, False)\r\n\r\n temp = cosine_similarity(vectors[0:-1], vectors)\r\n temp = [item for sublist in temp for item in sublist]\r\n sim = sum(temp) / len(temp)\r\n return sim","def calculate_cosine_dist(main_text, new_text):\n wordbag = set(\" \".join([main_text, new_text]).split(\" \"))\n dot_prod = 0\n main_text = main_text.split(\" \")\n new_text = new_text.split(\" \")\n\n for word in wordbag:\n if word in main_text and word in new_text:\n # only worth looking at if word is in both. Otherwise dot prod = 0\n count_A = sum(np.array(main_text) == word)\n count_B = sum(np.array(new_text) == word)\n dot_prod += count_A * count_B\n\n return float(dot_prod) / (len(main_text) * len(new_text))","def get_similarity(self, ):\r\n customer_cos_similarity = cosine_similarity(self.rating_matrix, self.rating_matrix)\r\n customer_cos_similarity = pd.DataFrame(customer_cos_similarity,\r\n index=self.customer_vendor_matrix.index,\r\n columns=self.customer_vendor_matrix.index)\r\n # customer_pearson_similarity = np.corrcoef(self.rating_matrix,\r\n # self.rating_matrix,)\r\n # customer_pearson_similarity = pd.DataFrame(customer_pearson_similarity,\r\n # index=self.customer_vendor_matrix.index,\r\n # columns=self.customer_vendor_matrix.index)\r\n return customer_cos_similarity,\r\n # return customer_pearson_similarity run too slowly\r","def _do_action_calculate_similarity_cosine_express(self):\n self._run_express_job(\"com.directv.recommend.express.CosineCFTrainer\")\n self._scan_table(\"content/item_item_similarities\")","def cosine_sim_counters(a, b):\n union_ab = sorted((a | b).keys())\n veca = np.array([a[element] if element in a else 0 for element in union_ab])\n vecb = np.array([b[element] if element in b else 0 for element in union_ab])\n return np.dot(veca, vecb) / (np.linalg.norm(veca) * np.linalg.norm(vecb))","def test_cosine_similarity():\n vector1 = np.array([1, 1, 0, 0])\n vector2 = np.array([1, 1, 1, 1])\n score11 = cosine_similarity.py_func(vector1, vector1)\n score12 = cosine_similarity.py_func(vector1, vector2)\n score22 = cosine_similarity.py_func(vector2, vector2)\n\n assert score12 == 2 / np.sqrt(2 * 4), \"Expected different score.\"\n assert score11 == score22 == 1.0, \"Expected different score.\"","def compute_cosine_sim(vec1, vec2):\r\n\r\n vec1 = np.array(vec1)\r\n vec2 = np.array(vec2)\r\n return np.dot(vec1, vec2)/(norm(vec1) * norm(vec2))","def get_cosine_similarity(word2vec: Word2Vec) -> np.ndarray:\n return cosine_similarity(word2vec.wv.vectors)","def cosine_similarity(a, b):\n if a.ndim != 1 or b.ndim != 1:\n raise InvalidShapeException(a,b)\n\n if len(a) != len(b):\n raise InvalidLengthException(a,b)\n \n mag_a = np.linalg.norm(a)\n mag_b = np.linalg.norm(b)\n\n return np.dot(a,b)/(mag_a*mag_b)","def similarity(self, wSet1, wSet2, idf): \n if len(wSet1) == 0 or len(wSet2) == 0:\n return 0.0\n else:\n defaultIDF = idf['unknownToken']\n intersection = wSet1.intersection(wSet2)\n# intersection = self.synonymIntersection(wSet1, wSet2, idf)\n if len(intersection) == 0:\n return 0\n sum1 = 0\n sum2 = 0\n intersectionSum = 0\n for word in wSet1:\n sum1 += (idf.get(word, defaultIDF))**2\n for word in wSet2:\n sum2 += (idf.get(word, defaultIDF))**2\n for word in intersection:\n intersectionSum += (idf.get(word, defaultIDF))**2\n \n if sum1 == 0 or sum2 == 0:\n return 0.0\n else:\n return intersectionSum/(math.sqrt(sum1) * math.sqrt(sum2))","def w2v_sim(self, s1, s2):\n v1 = self.word2vec.get_centroid_vector(s1)\n v2 = self.word2vec.get_centroid_vector(s2)\n return self.__cos_sim(v1, v2)","def similarity(centroid_a, centroid_b):\n \n vector_a = centroid_a.centroid_vector\n vector_b = centroid_b.centroid_vector\n \n length_a = centroid_a.length\n length_b = centroid_b.length\n \n dotproduct = 0.0\n\n for key, value in vector_a.iteritems():\n if key in vector_b: # if both vectors have the key\n dotproduct += (value * vector_b[key])\n\n return float(dotproduct / (length_a * length_b))","def cosine_sim_collections(a, b):\n setab = sorted(set(a) | set(b))\n countera, counterb = Counter(a), Counter(b)\n veca = [countera[element] if element in a else 0 for element in setab]\n vecb = [counterb[element] if element in b else 0 for element in setab]\n return dot(veca, vecb) / (norm(veca) * norm(vecb))","def get_similarity(df):\n count = CountVectorizer()\n count_matrix = count.fit_transform(df[\"bag_of_words\"])\n cosine_sim = cosine_similarity(count_matrix, count_matrix)\n return cosine_sim","def cosine_similarity(vector_x, vector_y):\n if(len(vector_x)!=len(vector_y)):\n raise Exception('Vectors must be the same dimensions')\n \n return 1-np.dot(vector_x,vector_y)/(np.linalg.norm(vector_x)*np.linalg.norm(vector_y))","def __modelSimilarity(self, mOrig: nn.Module, mDest: nn.Module) -> torch.Tensor:\n cos = nn.CosineSimilarity(0)\n d1 = nn.utils.parameters_to_vector(mOrig.parameters())\n d2 = nn.utils.parameters_to_vector(mDest.parameters())\n sim: torch.Tensor = cos(d1, d2)\n return sim","def similarity_function_old(feature1, feature2):\n f1Magnitude = feature1.dot(feature1)\n f2Magnitude = feature2.dot(feature2)\n return 1 - feature1.dot(feature2) / (f1Magnitude * f2Magnitude)","def getcorrelation(movieid1,movieid2):\n\n #the initialized integer, cosine_sum, has an initialized value of -100\n #such that in the case where correlation shouldn't be updated, the value\n #remains unchanged\n cosine_sum = NEGATIVE\n #variable r_a,i and r_b,i in the formula\n r_mv1 = 0\n r_mv2 = 0\n #numerator\n nume_sum = 0\n #two parts in the denominator (before taking square root)\n deno_mv1_sum = 0\n deno_mv2_sum = 0\n denominator = 0\n #variable that keeps track of count of common users\n currentCommon = 0\n\n #firstly check if the count of user passes the threshold for each movie\n if(len(dictMovie.get(movieid1))<threshold or\n len(dictMovie.get(movieid2))<threshold):\n #if either does not, returns a negative correlation (to be invalid)\n return cosine_sum\n #if both pass threshold, get the intersection (of users) of two movies\n else:\n intersect=dictMovie.get(movieid1).intersection(dictMovie.get(movieid2))\n #if the number of common users is smaller than threshold, return\n if (len(intersect) < threshold):\n return cosine_sum\n #otherwise, start counting correlation\n else:\n #get the average rating of two movies\n mv1_bar = float(dictMovieRate.get(movieid1))\n mv2_bar = float(dictMovieRate.get(movieid2))\n #iterate through common users and use formula\n for commonuser in intersect:\n #increment common user count\n currentCommon += 1\n r_mv1 = int(dictUser.get(commonuser).get(movieid1))\n r_mv2 = int(dictUser.get(commonuser).get(movieid2))\n nume_sum += ( (r_mv1)-mv1_bar )*( (r_mv2)-mv2_bar )\n deno_mv1_sum += ( (r_mv1)-mv1_bar )**2\n deno_mv2_sum += ( (r_mv2)-mv2_bar )**2\n #when done with denominator separate calculation, combine\n denominator = math.sqrt(deno_mv1_sum * deno_mv2_sum)\n #handle the case where denominator=0 (invalid)\n if denominator == 0:\n return cosine_sum\n #otherwise, successful. return valid values and pass in\n #common count to global variable for program to catch\n else:\n cosine_sum = nume_sum / denominator\n global currentCommonCount\n currentCommonCount = currentCommon\n return cosine_sum","def cosine_sim(a: np.ndarray, \n b: np.ndarray \n ) -> float:\n return (\n 1 + a.dot(b) / \n (np.linalg.norm(a)*np.linalg.norm(b))\n ) / 2","def __cos_sim(self, v1, v2):\n if np.count_nonzero(v1) == 0 or np.count_nonzero(v2) == 0:\n # whenever at least one of the vectors is all zeros, spatial.distance.cosine will fail by returning nan\n ret = 0\n else:\n ret = 1 - spatial.distance.cosine(v1, v2)\n return ret","def similarity_function(feature1, feature2):\n # 256 HOG, 18 HSV, 512 Encoder\n # weight color more if using the full vector\n if len(feature1) > 785:\n salient1 = feature1[256:256 + 18].copy() # be careful not to modify feature vector in place\n salient2 = feature2[256:256 + 18].copy()\n feature1 = feature1.copy()\n feature2 = feature2.copy()\n feature1[256:256 + 18] = salient1 * 10\n feature2[256:256 + 18] = salient2 * 10\n\n abs_distance = np.abs(feature1 - feature2)\n return np.sum(abs_distance)","def cos_sim(a, b):\n numerator, sosA, sosB = 0, 0, 0 #sum of squares for denom\n for i in movies: #replace movies\n numerator += (ratingai * ratingbi)\n sosA += ratingai * ratingai\n sosB += ratingbi * ratingbi\n denom = sqrt(sosA * sosB) #make sure denom won't be 0!\n return numerator / denom","def cosine_similarity(u, v):\n\n distance = 0.0\n\n ### START CODE HERE ###\n # Compute the dot product between u and v (≈1 line)\n dot = np.dot(u, v)\n # Compute the L2 norm of u (≈1 line)\n norm_u = np.sqrt(np.dot(u, u))\n\n # Compute the L2 norm of v (≈1 line)\n norm_v = np.sqrt(np.dot(v, v)) ##np.linalg.norm(u)\n # Compute the cosine similarity defined by formula (1) (≈1 line)\n cosine_similarity = dot / (norm_u * norm_v)\n ### END CODE HERE ###\n\n return cosine_similarity","def wordSimilarityRatio(sent_1,sent_2):","def compute_similarity(self, text1, text2):\n\n text1_dist = self.predict(text1)[0]\n text2_dist = self.predict(text2)[0]\n return jensenshannon(text1_dist, text2_dist)","def similarity_score(a,b):\n\tjsc_scaler = 15\n\tocs_scaler = 5\n\ttcss_scaler = 0.05\n\n\tjaccard_similarity_coefficient_score = jsc_scaler * jaccard_similarity_coefficient(a,b)\n\toverlap_coefficient_score = ocs_scaler * overlap_coefficient(a,b)\n\ttotal_char_similarity_score = tcss_scaler * total_char_similarity(a,b)\n\ttotal_score = jaccard_similarity_coefficient_score + overlap_coefficient_score + total_char_similarity_score\n\t\n\treturn total_score","def similarity_score(a,b):\n\tjsc_scaler = 15\n\tocs_scaler = 5\n\ttcss_scaler = 0.05\n\n\tjaccard_similarity_coefficient_score = jsc_scaler * jaccard_similarity_coefficient(a,b)\n\toverlap_coefficient_score = ocs_scaler * overlap_coefficient(a,b)\n\ttotal_char_similarity_score = tcss_scaler * total_char_similarity(a,b)\n\ttotal_score = jaccard_similarity_coefficient_score + overlap_coefficient_score + total_char_similarity_score\n\t\n\treturn total_score","def compute_cosine_similarity(base_vector, target_vector):\n\n np.seterr(all='print')\n cosine_similarity = 0\n\n try:\n base_vector = np.longdouble(base_vector)\n target_vector = np.longdouble(target_vector)\n vector_dot_products = np.dot(base_vector, target_vector)\n vector_norms = np.linalg.norm(base_vector) * np.linalg.norm(target_vector)\n cosine_similarity = np.divide(vector_dot_products, vector_norms)\n\n if vector_norms == 0.0:\n print 'Error in vec in compute_cosine_similarity'\n print target_vector\n\n except Exception, e:\n print(str(e))\n\n return cosine_similarity","def similarity(self, token1, token2):\n vec1 = self.get_vector(token1)\n vec2 = self.get_vector(token2)\n assert vec1 is not None and vec2 is not None, \"Cannot compute similarity between None type vectors.\"\n if not self.normalize:\n # if model not loaded as normalized embeddings \n vec1 = vec1 / np.linalg.norm(vec1)\n vec2 = vec2 / np.linalg.norm(vec2)\n return np.dot(vec1, vec2)","def cosine(arr1, arr2):\n\n if arr1 is None or arr2 is None:\n return np.NaN\n if not isinstance(arr1, list):\n arr1 = [arr1]\n if any(pd.isnull(arr1)):\n return np.NaN\n if not isinstance(arr2, list):\n arr2 = [arr2]\n if any(pd.isnull(arr2)):\n return np.NaN\n # Create cosine measure object\n measure = sm.Cosine()\n # Call the function to compute the cosine measure.\n return measure.get_raw_score(arr1, arr2)","def similarity(self, word1: str, word2: str, metric='cosine') -> float:\n if 0 == self.word2idx.get(word1, 0) or 0 == self.word2idx.get(word2, 0):\n return 0.\n\n return self.similarity_vec(self[word1], self[word2], metric=metric)\n # vec1 = self.__getitem__(word1).reshape((1, -1))\n # vec2 = self.__getitem__(word2).reshape((1, -1))\n # return 1 - distance.cdist(vec1, vec2, metric=metric).reshape(-1)","def similarity(self, query, documents):\n\n bow_query = self.dictionary.doc2bow(query)\n bow_docs = [self.dictionary.doc2bow(document) for document in documents]\n\n index = SoftCosineSimilarity(bow_docs, self.matrix)\n similarities = index[bow_query]\n\n return similarities","def _cosine_similarity_update(preds: Tensor, target: Tensor) ->Tuple[Tensor, Tensor]:\n _check_same_shape(preds, target)\n preds = preds.float()\n target = target.float()\n return preds, target","def cos_sim(vec1, vec2):\n if len(vec1) != len(vec2):\n print 'dimension does not agree.'\n numerator_sum = 0 \n for i in range(len(vec1)):\n numerator_sum = numerator_sum + vec1[i]*vec2[i]\n \n denom = np.linalg.norm(vec1) * np.linalg.norm(vec2)\n \n return numerator_sum/denom","def get_similarity(user1: Rating, user2: Rating) -> float:\n shared = 0.0\n for m_id in user1:\n if m_id in user2:\n shared += user1[m_id] * user2[m_id]\n norm1 = 0.0\n for m_id in user1:\n norm1 = norm1 + user1[m_id] ** 2\n norm2 = 0.0\n for m_id in user2:\n norm2 = norm2 + user2[m_id] ** 2\n return (shared * shared) / (norm1 * norm2)","def similarity_euclid(matrix, business1, business2):\n selected_features = matrix.loc[business1].notna() & matrix.loc[business2].notna()\n\n if not selected_features.any():\n return 0\n\n features1 = matrix.loc[business1][selected_features]\n features2 = matrix.loc[business2][selected_features]\n distance = math.sqrt(((features1 - features2) ** 2).sum())\n\n if distance is np.nan:\n return 0\n\n return 1 / (1 + distance)","def similarity(self, w1, w2):\r\n sim = self.represent(w1).dot(self.represent(w2))\r\n return sim","def cosine_similarity(user, business):\n return len(user.intersection(business)) / (math.sqrt(len(user)) * math.sqrt(len(business)))","def calculate_similarities(self) -> List[float]:\n sims = list()\n for i, r in self.sim_data.iterrows():\n if isinstance(self.model, FastTextWrapper):\n vecs = self.model.inference([r[\"Word 1\"], r[\"Word 2\"]])\n else:\n vecs = self.model.inference_batches([[r[\"Word 1\"]], [r[\"Word 2\"]]])\n vecs = [x[0] for x in vecs]\n if len(vecs) == 2:\n s = cosine_similarity([vecs[0]], [vecs[1]])[0][0]\n sims.append(s)\n else:\n sims.append(np.nan)\n self.sim_data[\"assigned_sim\"] = sims\n self.sim_data = self.sim_data.dropna()\n self.mean_error()\n self.correlation()\n return sims","def cos_sim(v1, v2):\r\n return np.inner(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2))","def findCosineDistance(vector1, vector2):\n vec1 = vector1.flatten()\n vec2 = vector2.flatten()\n\n a = np.dot(vec1.T, vec2)\n b = np.dot(vec1.T, vec1)\n c = np.dot(vec2.T, vec2)\n return 1 - (a / (np.sqrt(b) * np.sqrt(c)))","def compute_similarity(site_a, site_b):\n return np.linalg.norm(site_a - site_b)","def cossim(corpus):\n files = os.listdir()\n vectorizer = TfidfVectorizer()\n trsfm = vectorizer.fit_transform(corpus)\n columns = vectorizer.get_feature_names()\n df_tfidf = pd.DataFrame(trsfm.toarray(), columns = columns, index = corpus)\n out = cosine_similarity(trsfm)\n df_result = pd.DataFrame(out, columns = files, index = files)\n return df_result","def similarity(self, w1, w2):\r\n return self.represent(w1).dot(self.represent(w2))","def similarity(self, w1, w2):\r\n return self.represent(w1).dot(self.represent(w2))","def main():\n\n measures = Similarity()\n\n input1=sys.argv[1]\n vect1=np.loadtxt(fname = input1)\n \n input2=sys.argv[2]\n vect2=np.loadtxt(fname = input2)\n\n print measures.cosine_similarity(normBySum(vect1), normBySum(vect2))\n \n\n\n #print measures.cosine_similarity2(vect1, vect2)\n\n #print measures.jaccard_similarity([0,1,2,5,6],[0,2,3,5,7,9])","def get_cosine_similarities(keywords: List[str],\n matrices: Matrices,\n word2onehot: Dict[str, int]\n ) -> None:\n for i in combinations(keywords, 2):\n print(i[0], i[1], cosine_sim(\n matrices.embedding[\n word2onehot[i[0]]], matrices.embedding[word2onehot[i[1]]\n ]\n ))","def predict_cosine_similarities(sess, word_A, words_B):\n\n word_A_id, _ = sentence_to_word_ids(word_A, word_to_index)\n words_B_ids, split_sentence = sentence_to_word_ids(words_B, word_to_index)\n\n evaluated_cos_similarities = sess.run(\n cosine_similarities, \n feed_dict={\n tf_word_A_id: word_A_id,\n tf_words_B_ids: words_B_ids\n }\n )\n return evaluated_cos_similarities, split_sentence","def dependency_similarity(s1, s2):\n # pass\n parsed_sentence_1 = parser.raw_parse(s1)\n parsed_sentence_2 = parser.raw_parse(s2)\n \n tree1 = next(parsed_sentence_1)\n tree2 = next(parsed_sentence_2)\n \n triples1 = [t for t in tree1.triples()]\n triples2 = [t for t in tree2.triples()] \n\n # Compute similarity\n if len(triples1) != 0 and len(triples2) != 0:\n similarity = 1 - jaccard_distance(set(triples1), set(triples2))\n return similarity\n else:\n return 0","def test_cosine_similarity_all_zeros_compiled():\n vector1 = np.array([0, 0, 0, 0])\n vector2 = np.array([1, 1, 1, 1])\n score11 = cosine_similarity(vector1, vector1)\n score12 = cosine_similarity(vector1, vector2)\n score22 = cosine_similarity(vector2, vector2)\n\n assert score11 == score12 == 0.0, \"Expected different score.\"\n assert score22 == 1.0, \"Expected different score.\"","def calculate_similarity(self, tfidf_matrix, test_tfidf):\n\n with open(DATASET.fold_root / 'tags_order.json') as file:\n tags_order = json.load(file)\n\n min_max_scaler = MinMaxScaler()\n\n n_clus = 2\n simis = []\n for test_q in test_tfidf:\n s = cosine_similarity(tfidf_matrix, test_q)\n\n # Sorting and getting indices of sorted similarities\n simi = s.transpose()[0]\n simi_values = np.sort(simi)[::-1][:200]\n simi_indices = simi.argsort()[::-1]\n\n breaks = jenkspy.jenks_breaks(simi_values, n_clus)\n simi_count = len(simi_values[breaks[-2] <= simi_values])\n\n q_tags = [self.train_set[i].tags for i in simi_indices][:simi_count]\n\n tags_votes = Counter(chain(*q_tags))\n all_count = sum(tags_votes.values())\n tags_likelihood = [tags_votes.get(\n tag, 0) / all_count for tag in tags_order]\n\n lh = np.array([float(x)\n for x in tags_likelihood]).reshape(-1, 1)\n normalized_lh = np.concatenate(\n min_max_scaler.fit_transform(lh)\n ).tolist()\n\n simis.append(normalized_lh)\n\n return simis","def cosine_similarity(y_true, y_pred, axis=-1):\n y_true = nn.l2_normalize(y_true, axis=axis)\n y_pred = nn.l2_normalize(y_pred, axis=axis)\n return -math_ops.reduce_sum(y_true * y_pred, axis=axis)","def get_cosine_similarity(self, query: list):\n question_vector = self.get_vector(query)\n\n return cosine_similarity(question_vector, self.vectors).flatten()","def test_cosine_similarity_matrix_compiled():\n vectors1 = np.array([[1, 1, 0, 0],\n [1, 0, 1, 1]])\n vectors2 = np.array([[0, 1, 1, 0],\n [0, 0, 1, 1]])\n\n scores = cosine_similarity_matrix(vectors1, vectors2)\n expected_scores = np.array([[0.5, 0.],\n [0.40824829, 0.81649658]])\n assert scores == pytest.approx(expected_scores, 1e-7), \"Expected different scores.\"","def cos_sim(v1: Union[np.ndarray, np.iterable, int, float], v2: Union[np.ndarray, np.iterable, int, float]) -> float:\n return np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2))","def cosine_similarity(X):\n matrix = X.dot(X.transpose()).todense()\n mat_len = len(matrix)\n norms = [0] * mat_len\n for i in range(0, mat_len):\n norms[i] = 1.0 / np.sqrt(matrix.item((i, i)))\n norm_mat = np.matrix(norms)\n return np.multiply(norm_mat.transpose().dot(norm_mat), matrix)","def cosine_similarity(document, cluster):\n num = dot(document.vector, cluster.centroid)\n den = linalg.norm(document.vector) * linalg.norm(cluster.centroid)\n\n return num / den","def sentence_distance(sentence_a, sentence_b):\n \n sent_a = np.sum([projections[word_ids.get(word, 0)] \n if word in word_ids else [0] \n for word in sentence_a+bigrams(sentence_a)+trigrams(sentence_a)], axis=0)\n sent_b = np.sum([projections[word_ids.get(word, 0)] \n if word in word_ids else [0] \n for word in sentence_b+bigrams(sentence_b)+trigrams(sentence_b)], axis=0)\n \n \n return float(cosine(sent_a, sent_b))","def similarity(self, word1, word2):\n common_vect = +np.ones(self.nEmbed) * 10000\n if word1 not in self.vocab and word2 in self.vocab:\n id_word_2 = self.w2id[word2]\n w1 = common_vect\n w2 = self.U[id_word_2]\n elif word1 in self.vocab and word2 not in self.vocab:\n id_word_1 = self.w2id[word1]\n w1 = self.U[id_word_1]\n w2 = common_vect\n elif word1 not in self.vocab and word2 not in self.vocab:\n w1 = common_vect\n w2 = common_vect\n else:\n id_word_1 = self.w2id[word1]\n id_word_2 = self.w2id[word2]\n w1 = self.U[id_word_1]\n w2 = self.U[id_word_2]\n\n # scalair = w1.dot(w2)/np.linalg.norm(w1,w2)\n similarity = w1.dot(w2) / (np.linalg.norm(w1) * np.linalg.norm(w2))\n # similarity = 1 / (1 + np.exp(-scalair))\n # similarity = scalair / (np.linalg.norm(w1) * np.linalg.norm(w2))\n return similarity","def cosine_collection_distance(x1, x2):\n x1 = tf.cast(x1, dtype=tf.float32)\n x2 = tf.cast(x2, dtype=tf.float32)\n\n # dot product between rows of `x1` and columns of `x2` transpose\n cos_thetas = tf.linalg.matmul(x1, x2, transpose_b=True)\n pairwise_distances = 1 - cos_thetas\n\n # deal with numerical inaccuracies setting small negatives to zero\n pairwise_distances = tf.maximum(pairwise_distances, 0.0)\n\n return pairwise_distances","def cosine_sim(im, s):\n return im.mm(s.t()) #image.mm(sentence.t()) & mm() Performs a matrix multiplication of the matrices ","def cosine_distance(x1, x2):\n x1 = tf.cast(x1, dtype=tf.float32)\n x2 = tf.cast(x2, dtype=tf.float32)\n\n # dot product between rows of `x_1` and rows of `x_2`\n # \"ij,ij->i\" := output[i] = sum_j x1[i, j] * x2[i, j]\n cos_thetas = tf.linalg.einsum(\"ij,ij->i\", x1, x2)\n cos_distances = 1 - cos_thetas\n\n # deal with numerical inaccuracies setting small negatives to zero\n cos_distances = tf.maximum(cos_distances, 0.0)\n\n return cos_distances","def cos_sim(com_feat,ref_feat):\n # Fill this in\n a = numpy.squeeze(com_feat)\n b = numpy.squeeze(ref_feat)\n return numpy.dot(a, b) / (numpy.linalg.norm(a) * numpy.linalg.norm(b))","def semantic_similarity(self,sentence_1, sentence_2, info_content_norm):\n\t words_1 = sentence_1.getList_of_words()\n\t words_2 = sentence_2.getList_of_words()\n\t joint_words = set(words_1).union(set(words_2))\n\t vec_1 = self.semantic_vector(words_1, joint_words, info_content_norm)\n\t vec_2 = self.semantic_vector(words_2, joint_words, info_content_norm)\n\t return np.dot(vec_1, vec_2.T) / (np.linalg.norm(vec_1) * np.linalg.norm(vec_2))","def similarity(self, e1, e2):\n\t\tpass","def get_dist(text1, text2, wv):\n t1 = lookup(text1, wv)\n t2 = lookup(text2, wv)\n dist = cos_sim(t1, t2)\n return dist","def idf_modified_cosine(x, y, idf):\n result = 0\n try:\n tf_x = [dict([word, int(tf)] for word, tf in dict(\n np.array(np.unique(x, return_counts=True)).T).items())][0]\n tf_y = [dict([word, int(tf)] for word, tf in dict(\n np.array(np.unique(y, return_counts=True)).T).items())][0]\n result = sum([tf_x[w] * tf_y[w] * (idf[w]**2)\n\t\t for w in tf_x.keys() & tf_y.keys()]) / ((\n sum([(tf_x[w] * idf[w])**2\n for w in tf_x.keys()])**0.5) * (\n sum([(tf_y[w] * idf[w])**2\n for w in tf_y.keys()])**0.5))\n except:\n print(r'x:', x, r'y:', y)\n pass\n return result","def test_cosine_similarity_matrix():\n vectors1 = np.array([[1, 1, 0, 0],\n [1, 0, 1, 1]])\n vectors2 = np.array([[0, 1, 1, 0],\n [0, 0, 1, 1]])\n\n scores = cosine_similarity_matrix.py_func(vectors1, vectors2)\n expected_scores = np.array([[0.5, 0.],\n [0.40824829, 0.81649658]])\n assert scores == pytest.approx(expected_scores, 1e-7), \"Expected different scores.\"","def _arccosine(self, s1, s2, tf_embs):\n tf_pi = tf.constant(np.pi, dtype=tf.float64)\n mat1 = tf.gather(tf_embs, s1)\n mat2 = tf.gather(tf_embs, s2)\n tf_norms = tf.constant(self.norms, dtype=tf.float64, name='norms')\n norms1 = tf.gather(tf_norms, s1)\n norms2 = tf.gather(tf_norms, s2)\n dot = tf.matmul(mat1, tf.transpose(mat2))\n norms = tf.matmul(norms1, tf.transpose(norms2))\n # We clip values due to numerical errors\n # which put some values outside the arccosine range.\n cosine = tf.clip_by_value(dot / norms, -1, 1)\n angle = tf.acos(cosine)\n # The 0 vector has norm 0, which generates a NaN.\n # We catch these NaNs and replace them with pi,\n # which ends up returning 0 similarity.\n angle = tf.select(tf.is_nan(angle), tf.ones_like(angle) * tf_pi, angle)\n return 1 - (angle / tf_pi)"],"string":"[\n \"def cosine_similarity(v1, v2):\\n # Cosine Sim:\\n # Get the words that both have in common\\n\\n v1words = set(v1.keys())\\n v2words = set(v2.keys())\\n\\n numerator_words = v1words.intersection(v2words)\\n\\n # Multiply and sum those counts\\n numerator = 0.0\\n for word in numerator_words:\\n numerator += v1[word] * v2[word]\\n\\n\\n # Divide by the sqrt of the product of the sum of the squares of the counts\\n denominator = math.sqrt(math.magnitude(list(v1.values())) * math.magnitude(list(v2.values())))\\n\\n return numerator/denominator\",\n \"def cosine_similarity(v1, v2):\\n return np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2))\",\n \"def cosineSimilarity(dict1,dict2):\\n product1 = 0.0\\n product2 = 0.0\\n for key in dict1.keys():\\n product1 += (dict1[key] * dict1[key])\\n for key in dict2.keys():\\n product2 += (dict2[key] * dict2[key])\\n product1 = math.sqrt(product1)\\n product2 = math.sqrt(product2)\\n fenmu = product1 * product2\\n fenzi = 0.0\\n for key in dict1.keys():\\n if key in dict2:\\n fenzi += (dict1[key] * dict2[key])\\n cosSim = fenzi / fenmu\\n return cosSim\",\n \"def get_cosine_sim(self):\\r\\n return CosineSimilarity().calculate_similarity(self.tweets)\",\n \"def get_cosine_similarity(doc1, doc2):\\n count_vectorizer = CountVectorizer(stop_words='english')\\n sparse_matrix = count_vectorizer.fit_transform(raw_documents=[doc1, doc2])\\n dtm = sparse_matrix.todense()\\n df_dtm = pd.DataFrame(data=dtm, \\n columns=count_vectorizer.get_feature_names(), \\n index=['doc1', 'doc2'])\\n similarity_matrix = cosine_similarity(df_dtm, df_dtm)\\n similarity_score = round(similarity_matrix[0][1], 6)\\n return similarity_score\",\n \"def cosine_similarity(self, v1: np.ndarray, v2: np.ndarray) -> float:\\n return np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2))\\n # return cosine_similarity(v1, v2)[0][0]\",\n \"def cosin_sim_pairs(a, b):\\n wordsA = set(a.keys())\\n wordsB = set(b.keys())\\n inter = wordsA.intersection(wordsB)\\n if(len(inter) == 0):\\n return 0.0\\n aa, bb, ab = 0, 0, 0\\n for k in inter:\\n aa += a[k] ** 2\\n bb += b[k] ** 2\\n ab += a[k] * b[k]\\n for k in wordsA - inter:\\n aa += a[k] ** 2\\n for k in wordsB - inter:\\n bb += b[k] ** 2\\n return ab / float(math.sqrt(aa) * math.sqrt(bb))\",\n \"def cosine_similarity(v1, v2):\\n sim = np.sum(v1*v2)/np.sqrt(np.sum(v1**2))/np.sqrt(np.sum(v2**2))\\n return sim\",\n \"def cosine_similarity(a, b):\\n cs = dot_product(a, b)/(norm(a) * norm(b))\\n return cs\",\n \"def cosine_similarity(v1: Vector, v2: Vector) -> float:\\n return dot_product(v1, v2) / (vector_len(v1) * vector_len(v2))\",\n \"def calculate_cosine_similarity(self):\\n tfidf_matrix = self.calculate_tfidf()\\n\\n cosine_similarity = linear_kernel(tfidf_matrix, tfidf_matrix) # Cosine similarity matrix calculation\\n\\n return cosine_similarity\",\n \"def cosine_similarity(vec1, vec2) -> float:\\n return np.dot(vec1, vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2))\",\n \"def compute_cosine_similarity(self):\\n cos_matrix = []\\n for i in range(len(self.train_vec)):\\n val = self.vec1 * self.train_vec[i]\\n cos_matrix.append(val[0])\\n out = np.argmax(cos_matrix)\\n print(self.train_output[out])\",\n \"def cosine_similarity(v1, v2):\\n v1_len = 0\\n v2_len = 0\\n dot_product = 0\\n\\n for context_id, count in v1.items():\\n v1_len += count ** 2\\n if context_id in v2:\\n dot_product += count*v2[context_id]\\n for count in v2.values():\\n v2_len += count ** 2\\n\\n v1_len = math.sqrt(v1_len)\\n v2_len = math.sqrt(v2_len)\\n return dot_product/(v1_len * v2_len)\",\n \"def cosine_similarity(x1, x2, dim=1, eps=1e-8):\\n w12 = torch.sum(x1 * x2, dim)\\n w1 = torch.norm(x1, 2, dim)\\n w2 = torch.norm(x2, 2, dim)\\n return (w12 / (w1 * w2).clamp(min=eps)).squeeze()\",\n \"def compute_cosine_sim(vec1, vec2):\\n numer = np.dot(vec1.reshape((300,)), vec2.reshape((300,)))\\n denom = np.sqrt(np.sum(np.square(vec1.reshape(300, )))) * np.sqrt(\\n np.sum(np.square(vec2.reshape(300, ))))\\n\\n similarity = numer / denom\\n\\n return similarity\",\n \"def cosine_similarity(x1, x2, dim=1, eps=1e-8):\\r\\n w12 = torch.sum(x1 * x2, dim)\\r\\n w1 = torch.norm(x1, 2, dim)\\r\\n w2 = torch.norm(x2, 2, dim)\\r\\n return (w12 / (w1 * w2).clamp(min=eps)).squeeze()\",\n \"def cosine_similarity(cls, vec_a, vec_b):\\n return np.dot(vec_a, vec_b) / \\\\\\n (np.linalg.norm(vec_a) * np.linalg.norm(vec_b))\",\n \"def cosine_similarity(self, x, y):\\n return np.dot(x, y) / (np.linalg.norm(x) * np.linalg.norm(y))\",\n \"def cosine_similarity(self, source_doc, input_doc):\\n vectorizer = self.vectorizer or TfidfVectorizer(tokenizer=PlagiarismDetector.tokenize_and_stem, stop_words='english')\\n tfidf = vectorizer.fit_transform([source_doc, input_doc])\\n return ((tfidf * tfidf.T).A)[0, 1]\",\n \"def cosine_similarity(vec_x, vec_y):\\n sim_prod = 0.0\\n len_x = 0\\n len_y = 0\\n\\n for ngram in vec_x:\\n len_x += vec_x[ngram] ** 2\\n\\n for ngram in vec_y:\\n len_y += vec_y[ngram] ** 2\\n\\n len_x = math.sqrt(len_x)\\n len_y = math.sqrt(len_y)\\n\\n for ngram in vec_x:\\n if ngram in vec_y:\\n sim_prod += vec_x[ngram] * vec_y[ngram]\\n\\n return sim_prod / (len_x * len_y)\",\n \"def test_cossim(self):\\n metrics = SimilarityMetrics()\\n test1 = metrics.cosine_similarity(np.asarray([1,1]),np.asarray([-1,1]))\\n np.testing.assert_almost_equal(test1,0.0)\\n\\n test2 = metrics.cosine_similarity(np.asarray([1,-1]),np.asarray([-1,1]))\\n np.testing.assert_almost_equal(test2,-1.0)\\n\\n test3 = metrics.cosine_similarity(np.asarray([1,1]),np.asarray([1,1]))\\n np.testing.assert_almost_equal(test3,1.0)\",\n \"def cosine_dist(d1, d2):\\n suma=0\\n for x in d1:\\n if x in d2:\\n suma+=(d1[x]*d2[x])\\n sqrt1=0\\n sqrt2=0\\n for i in d1:\\n sqrt1+=math.pow(d1[i],2)\\n for i in d2:\\n sqrt2+=math.pow(d2[i],2)\\n return 1-suma/(math.sqrt(sqrt1)*math.sqrt(sqrt2))\",\n \"def cosine_similarity_tensorflow(tf_word_representation_A, tf_words_representation_B):\\n a_normalized = tf.nn.l2_normalize(tf_word_representation_A, axis=-1)\\n b_normalized = tf.nn.l2_normalize(tf_words_representation_B, axis=-1)\\n similarity = tf.reduce_sum(\\n tf.multiply(a_normalized, b_normalized), \\n axis=-1\\n )\\n \\n return similarity\",\n \"def pairwise_cosine_similarity(x, y):\\n x = torch.div(x, torch.sqrt(torch.max(torch.sum(x ** 2), 1e-12)))\\n y = torch.div(y, torch.sqrt(torch.max(torch.sum(y ** 2), 1e-12)))\\n return torch.mm(x, torch.transpose(y, 1, 0))\",\n \"def content_similarity(self, movie1, movie2):\\n v1, v2 = self.get_tfidf(movie1), self.get_tfidf(movie2)\\n return self.cosine_similarity(v1, v2)\",\n \"def test_cosine_similarity_compiled():\\n vector1 = np.array([1, 1, 0, 0])\\n vector2 = np.array([1, 1, 1, 1])\\n score11 = cosine_similarity(vector1, vector1)\\n score12 = cosine_similarity(vector1, vector2)\\n score22 = cosine_similarity(vector2, vector2)\\n\\n assert score12 == 2 / np.sqrt(2 * 4), \\\"Expected different score.\\\"\\n assert score11 == score22 == 1.0, \\\"Expected different score.\\\"\",\n \"def cosine_similarity(a, b):\\n\\n numerator = tf.reduce_sum(tf.multiply(a, b), axis=1)\\n denominator = tf.multiply(tf.norm(a, axis=1), tf.norm(b, axis=1))\\n cos_similarity = numerator/denominator\\n return cos_similarity\",\n \"def CosineSimilarity(test_vec, source_vecs):\\n cos_dist = 0\\n for source_vec in source_vecs:\\n cos_dist += FacePredictor.findCosineDistance(test_vec, source_vec)\\n return cos_dist / len(source_vecs)\",\n \"def getGloveoCosineSimilarity(question1, question2):\\n questions = [question1, question2]\\n\\n ## for the sentences we need to get the count vectors\\n vec = CountVectorizer(max_features=5000, stop_words=None,binary=True)\\n count_vectors = vec.fit_transform(questions)\\n\\n ## get the vocabulary of words from the questions\\n vocab_index = vec.vocabulary_\\n\\n ## get the index of the words and embeddings\\n index_word = {v:k for k, v in vocab_index.items()}\\n\\n ## get the question vectors\\n question_vectors = np.zeros((count_vectors.shape[0], 300))\\n\\n ## iterate through count vectors for each word get the embeddings\\n ## for each embedding, we will then average by the number of words\\n ## this will be then used for cosine similarity\\n for i in range(count_vectors.shape[0]):\\n row = count_vectors[i, :].toarray()\\n word_ids = np.where(row > 0)[1]\\n word_counts = row[:, word_ids][0]\\n numWords = np.sum(word_counts)\\n\\n ## if there are no words, continue\\n if numWords == 0:\\n continue\\n\\n ## initialize the word embeddings to 0\\n word_embeddings = np.zeros((word_ids.shape[0], 300))\\n\\n ## update the word embeddings\\n for j in range(word_ids.shape[0]):\\n word_id = word_ids[j]\\n word_embeddings[j, :] = word_counts[j] * gloveDict[index_word[word_id]]\\n question_vectors[i, :] = np.sum(word_embeddings, axis=0) / numWords\\n\\n return(cosine_similarity(question_vectors[0], question_vectors[1])[0][0])\",\n \"def tf_cosine_distance(self, a, b):\\n normalize_a = tf.nn.l2_normalize(a, -1)\\n normalize_b = tf.nn.l2_normalize(b, -1)\\n cos_similarity = tf.reduce_sum(\\n tf.multiply(normalize_a, normalize_b), axis=-1, keep_dims=True\\n )\\n return (1.0 - cos_similarity) / 2.0\",\n \"def calculate_similarity(self, tweets):\\r\\n if (len(tweets) == 1):\\r\\n return 0\\r\\n vectors = self.vectorizer.vectorize_data(tweets, False)\\r\\n\\r\\n temp = cosine_similarity(vectors[0:-1], vectors)\\r\\n temp = [item for sublist in temp for item in sublist]\\r\\n sim = sum(temp) / len(temp)\\r\\n return sim\",\n \"def calculate_cosine_dist(main_text, new_text):\\n wordbag = set(\\\" \\\".join([main_text, new_text]).split(\\\" \\\"))\\n dot_prod = 0\\n main_text = main_text.split(\\\" \\\")\\n new_text = new_text.split(\\\" \\\")\\n\\n for word in wordbag:\\n if word in main_text and word in new_text:\\n # only worth looking at if word is in both. Otherwise dot prod = 0\\n count_A = sum(np.array(main_text) == word)\\n count_B = sum(np.array(new_text) == word)\\n dot_prod += count_A * count_B\\n\\n return float(dot_prod) / (len(main_text) * len(new_text))\",\n \"def get_similarity(self, ):\\r\\n customer_cos_similarity = cosine_similarity(self.rating_matrix, self.rating_matrix)\\r\\n customer_cos_similarity = pd.DataFrame(customer_cos_similarity,\\r\\n index=self.customer_vendor_matrix.index,\\r\\n columns=self.customer_vendor_matrix.index)\\r\\n # customer_pearson_similarity = np.corrcoef(self.rating_matrix,\\r\\n # self.rating_matrix,)\\r\\n # customer_pearson_similarity = pd.DataFrame(customer_pearson_similarity,\\r\\n # index=self.customer_vendor_matrix.index,\\r\\n # columns=self.customer_vendor_matrix.index)\\r\\n return customer_cos_similarity,\\r\\n # return customer_pearson_similarity run too slowly\\r\",\n \"def _do_action_calculate_similarity_cosine_express(self):\\n self._run_express_job(\\\"com.directv.recommend.express.CosineCFTrainer\\\")\\n self._scan_table(\\\"content/item_item_similarities\\\")\",\n \"def cosine_sim_counters(a, b):\\n union_ab = sorted((a | b).keys())\\n veca = np.array([a[element] if element in a else 0 for element in union_ab])\\n vecb = np.array([b[element] if element in b else 0 for element in union_ab])\\n return np.dot(veca, vecb) / (np.linalg.norm(veca) * np.linalg.norm(vecb))\",\n \"def test_cosine_similarity():\\n vector1 = np.array([1, 1, 0, 0])\\n vector2 = np.array([1, 1, 1, 1])\\n score11 = cosine_similarity.py_func(vector1, vector1)\\n score12 = cosine_similarity.py_func(vector1, vector2)\\n score22 = cosine_similarity.py_func(vector2, vector2)\\n\\n assert score12 == 2 / np.sqrt(2 * 4), \\\"Expected different score.\\\"\\n assert score11 == score22 == 1.0, \\\"Expected different score.\\\"\",\n \"def compute_cosine_sim(vec1, vec2):\\r\\n\\r\\n vec1 = np.array(vec1)\\r\\n vec2 = np.array(vec2)\\r\\n return np.dot(vec1, vec2)/(norm(vec1) * norm(vec2))\",\n \"def get_cosine_similarity(word2vec: Word2Vec) -> np.ndarray:\\n return cosine_similarity(word2vec.wv.vectors)\",\n \"def cosine_similarity(a, b):\\n if a.ndim != 1 or b.ndim != 1:\\n raise InvalidShapeException(a,b)\\n\\n if len(a) != len(b):\\n raise InvalidLengthException(a,b)\\n \\n mag_a = np.linalg.norm(a)\\n mag_b = np.linalg.norm(b)\\n\\n return np.dot(a,b)/(mag_a*mag_b)\",\n \"def similarity(self, wSet1, wSet2, idf): \\n if len(wSet1) == 0 or len(wSet2) == 0:\\n return 0.0\\n else:\\n defaultIDF = idf['unknownToken']\\n intersection = wSet1.intersection(wSet2)\\n# intersection = self.synonymIntersection(wSet1, wSet2, idf)\\n if len(intersection) == 0:\\n return 0\\n sum1 = 0\\n sum2 = 0\\n intersectionSum = 0\\n for word in wSet1:\\n sum1 += (idf.get(word, defaultIDF))**2\\n for word in wSet2:\\n sum2 += (idf.get(word, defaultIDF))**2\\n for word in intersection:\\n intersectionSum += (idf.get(word, defaultIDF))**2\\n \\n if sum1 == 0 or sum2 == 0:\\n return 0.0\\n else:\\n return intersectionSum/(math.sqrt(sum1) * math.sqrt(sum2))\",\n \"def w2v_sim(self, s1, s2):\\n v1 = self.word2vec.get_centroid_vector(s1)\\n v2 = self.word2vec.get_centroid_vector(s2)\\n return self.__cos_sim(v1, v2)\",\n \"def similarity(centroid_a, centroid_b):\\n \\n vector_a = centroid_a.centroid_vector\\n vector_b = centroid_b.centroid_vector\\n \\n length_a = centroid_a.length\\n length_b = centroid_b.length\\n \\n dotproduct = 0.0\\n\\n for key, value in vector_a.iteritems():\\n if key in vector_b: # if both vectors have the key\\n dotproduct += (value * vector_b[key])\\n\\n return float(dotproduct / (length_a * length_b))\",\n \"def cosine_sim_collections(a, b):\\n setab = sorted(set(a) | set(b))\\n countera, counterb = Counter(a), Counter(b)\\n veca = [countera[element] if element in a else 0 for element in setab]\\n vecb = [counterb[element] if element in b else 0 for element in setab]\\n return dot(veca, vecb) / (norm(veca) * norm(vecb))\",\n \"def get_similarity(df):\\n count = CountVectorizer()\\n count_matrix = count.fit_transform(df[\\\"bag_of_words\\\"])\\n cosine_sim = cosine_similarity(count_matrix, count_matrix)\\n return cosine_sim\",\n \"def cosine_similarity(vector_x, vector_y):\\n if(len(vector_x)!=len(vector_y)):\\n raise Exception('Vectors must be the same dimensions')\\n \\n return 1-np.dot(vector_x,vector_y)/(np.linalg.norm(vector_x)*np.linalg.norm(vector_y))\",\n \"def __modelSimilarity(self, mOrig: nn.Module, mDest: nn.Module) -> torch.Tensor:\\n cos = nn.CosineSimilarity(0)\\n d1 = nn.utils.parameters_to_vector(mOrig.parameters())\\n d2 = nn.utils.parameters_to_vector(mDest.parameters())\\n sim: torch.Tensor = cos(d1, d2)\\n return sim\",\n \"def similarity_function_old(feature1, feature2):\\n f1Magnitude = feature1.dot(feature1)\\n f2Magnitude = feature2.dot(feature2)\\n return 1 - feature1.dot(feature2) / (f1Magnitude * f2Magnitude)\",\n \"def getcorrelation(movieid1,movieid2):\\n\\n #the initialized integer, cosine_sum, has an initialized value of -100\\n #such that in the case where correlation shouldn't be updated, the value\\n #remains unchanged\\n cosine_sum = NEGATIVE\\n #variable r_a,i and r_b,i in the formula\\n r_mv1 = 0\\n r_mv2 = 0\\n #numerator\\n nume_sum = 0\\n #two parts in the denominator (before taking square root)\\n deno_mv1_sum = 0\\n deno_mv2_sum = 0\\n denominator = 0\\n #variable that keeps track of count of common users\\n currentCommon = 0\\n\\n #firstly check if the count of user passes the threshold for each movie\\n if(len(dictMovie.get(movieid1))<threshold or\\n len(dictMovie.get(movieid2))<threshold):\\n #if either does not, returns a negative correlation (to be invalid)\\n return cosine_sum\\n #if both pass threshold, get the intersection (of users) of two movies\\n else:\\n intersect=dictMovie.get(movieid1).intersection(dictMovie.get(movieid2))\\n #if the number of common users is smaller than threshold, return\\n if (len(intersect) < threshold):\\n return cosine_sum\\n #otherwise, start counting correlation\\n else:\\n #get the average rating of two movies\\n mv1_bar = float(dictMovieRate.get(movieid1))\\n mv2_bar = float(dictMovieRate.get(movieid2))\\n #iterate through common users and use formula\\n for commonuser in intersect:\\n #increment common user count\\n currentCommon += 1\\n r_mv1 = int(dictUser.get(commonuser).get(movieid1))\\n r_mv2 = int(dictUser.get(commonuser).get(movieid2))\\n nume_sum += ( (r_mv1)-mv1_bar )*( (r_mv2)-mv2_bar )\\n deno_mv1_sum += ( (r_mv1)-mv1_bar )**2\\n deno_mv2_sum += ( (r_mv2)-mv2_bar )**2\\n #when done with denominator separate calculation, combine\\n denominator = math.sqrt(deno_mv1_sum * deno_mv2_sum)\\n #handle the case where denominator=0 (invalid)\\n if denominator == 0:\\n return cosine_sum\\n #otherwise, successful. return valid values and pass in\\n #common count to global variable for program to catch\\n else:\\n cosine_sum = nume_sum / denominator\\n global currentCommonCount\\n currentCommonCount = currentCommon\\n return cosine_sum\",\n \"def cosine_sim(a: np.ndarray, \\n b: np.ndarray \\n ) -> float:\\n return (\\n 1 + a.dot(b) / \\n (np.linalg.norm(a)*np.linalg.norm(b))\\n ) / 2\",\n \"def __cos_sim(self, v1, v2):\\n if np.count_nonzero(v1) == 0 or np.count_nonzero(v2) == 0:\\n # whenever at least one of the vectors is all zeros, spatial.distance.cosine will fail by returning nan\\n ret = 0\\n else:\\n ret = 1 - spatial.distance.cosine(v1, v2)\\n return ret\",\n \"def similarity_function(feature1, feature2):\\n # 256 HOG, 18 HSV, 512 Encoder\\n # weight color more if using the full vector\\n if len(feature1) > 785:\\n salient1 = feature1[256:256 + 18].copy() # be careful not to modify feature vector in place\\n salient2 = feature2[256:256 + 18].copy()\\n feature1 = feature1.copy()\\n feature2 = feature2.copy()\\n feature1[256:256 + 18] = salient1 * 10\\n feature2[256:256 + 18] = salient2 * 10\\n\\n abs_distance = np.abs(feature1 - feature2)\\n return np.sum(abs_distance)\",\n \"def cos_sim(a, b):\\n numerator, sosA, sosB = 0, 0, 0 #sum of squares for denom\\n for i in movies: #replace movies\\n numerator += (ratingai * ratingbi)\\n sosA += ratingai * ratingai\\n sosB += ratingbi * ratingbi\\n denom = sqrt(sosA * sosB) #make sure denom won't be 0!\\n return numerator / denom\",\n \"def cosine_similarity(u, v):\\n\\n distance = 0.0\\n\\n ### START CODE HERE ###\\n # Compute the dot product between u and v (≈1 line)\\n dot = np.dot(u, v)\\n # Compute the L2 norm of u (≈1 line)\\n norm_u = np.sqrt(np.dot(u, u))\\n\\n # Compute the L2 norm of v (≈1 line)\\n norm_v = np.sqrt(np.dot(v, v)) ##np.linalg.norm(u)\\n # Compute the cosine similarity defined by formula (1) (≈1 line)\\n cosine_similarity = dot / (norm_u * norm_v)\\n ### END CODE HERE ###\\n\\n return cosine_similarity\",\n \"def wordSimilarityRatio(sent_1,sent_2):\",\n \"def compute_similarity(self, text1, text2):\\n\\n text1_dist = self.predict(text1)[0]\\n text2_dist = self.predict(text2)[0]\\n return jensenshannon(text1_dist, text2_dist)\",\n \"def similarity_score(a,b):\\n\\tjsc_scaler = 15\\n\\tocs_scaler = 5\\n\\ttcss_scaler = 0.05\\n\\n\\tjaccard_similarity_coefficient_score = jsc_scaler * jaccard_similarity_coefficient(a,b)\\n\\toverlap_coefficient_score = ocs_scaler * overlap_coefficient(a,b)\\n\\ttotal_char_similarity_score = tcss_scaler * total_char_similarity(a,b)\\n\\ttotal_score = jaccard_similarity_coefficient_score + overlap_coefficient_score + total_char_similarity_score\\n\\t\\n\\treturn total_score\",\n \"def similarity_score(a,b):\\n\\tjsc_scaler = 15\\n\\tocs_scaler = 5\\n\\ttcss_scaler = 0.05\\n\\n\\tjaccard_similarity_coefficient_score = jsc_scaler * jaccard_similarity_coefficient(a,b)\\n\\toverlap_coefficient_score = ocs_scaler * overlap_coefficient(a,b)\\n\\ttotal_char_similarity_score = tcss_scaler * total_char_similarity(a,b)\\n\\ttotal_score = jaccard_similarity_coefficient_score + overlap_coefficient_score + total_char_similarity_score\\n\\t\\n\\treturn total_score\",\n \"def compute_cosine_similarity(base_vector, target_vector):\\n\\n np.seterr(all='print')\\n cosine_similarity = 0\\n\\n try:\\n base_vector = np.longdouble(base_vector)\\n target_vector = np.longdouble(target_vector)\\n vector_dot_products = np.dot(base_vector, target_vector)\\n vector_norms = np.linalg.norm(base_vector) * np.linalg.norm(target_vector)\\n cosine_similarity = np.divide(vector_dot_products, vector_norms)\\n\\n if vector_norms == 0.0:\\n print 'Error in vec in compute_cosine_similarity'\\n print target_vector\\n\\n except Exception, e:\\n print(str(e))\\n\\n return cosine_similarity\",\n \"def similarity(self, token1, token2):\\n vec1 = self.get_vector(token1)\\n vec2 = self.get_vector(token2)\\n assert vec1 is not None and vec2 is not None, \\\"Cannot compute similarity between None type vectors.\\\"\\n if not self.normalize:\\n # if model not loaded as normalized embeddings \\n vec1 = vec1 / np.linalg.norm(vec1)\\n vec2 = vec2 / np.linalg.norm(vec2)\\n return np.dot(vec1, vec2)\",\n \"def cosine(arr1, arr2):\\n\\n if arr1 is None or arr2 is None:\\n return np.NaN\\n if not isinstance(arr1, list):\\n arr1 = [arr1]\\n if any(pd.isnull(arr1)):\\n return np.NaN\\n if not isinstance(arr2, list):\\n arr2 = [arr2]\\n if any(pd.isnull(arr2)):\\n return np.NaN\\n # Create cosine measure object\\n measure = sm.Cosine()\\n # Call the function to compute the cosine measure.\\n return measure.get_raw_score(arr1, arr2)\",\n \"def similarity(self, word1: str, word2: str, metric='cosine') -> float:\\n if 0 == self.word2idx.get(word1, 0) or 0 == self.word2idx.get(word2, 0):\\n return 0.\\n\\n return self.similarity_vec(self[word1], self[word2], metric=metric)\\n # vec1 = self.__getitem__(word1).reshape((1, -1))\\n # vec2 = self.__getitem__(word2).reshape((1, -1))\\n # return 1 - distance.cdist(vec1, vec2, metric=metric).reshape(-1)\",\n \"def similarity(self, query, documents):\\n\\n bow_query = self.dictionary.doc2bow(query)\\n bow_docs = [self.dictionary.doc2bow(document) for document in documents]\\n\\n index = SoftCosineSimilarity(bow_docs, self.matrix)\\n similarities = index[bow_query]\\n\\n return similarities\",\n \"def _cosine_similarity_update(preds: Tensor, target: Tensor) ->Tuple[Tensor, Tensor]:\\n _check_same_shape(preds, target)\\n preds = preds.float()\\n target = target.float()\\n return preds, target\",\n \"def cos_sim(vec1, vec2):\\n if len(vec1) != len(vec2):\\n print 'dimension does not agree.'\\n numerator_sum = 0 \\n for i in range(len(vec1)):\\n numerator_sum = numerator_sum + vec1[i]*vec2[i]\\n \\n denom = np.linalg.norm(vec1) * np.linalg.norm(vec2)\\n \\n return numerator_sum/denom\",\n \"def get_similarity(user1: Rating, user2: Rating) -> float:\\n shared = 0.0\\n for m_id in user1:\\n if m_id in user2:\\n shared += user1[m_id] * user2[m_id]\\n norm1 = 0.0\\n for m_id in user1:\\n norm1 = norm1 + user1[m_id] ** 2\\n norm2 = 0.0\\n for m_id in user2:\\n norm2 = norm2 + user2[m_id] ** 2\\n return (shared * shared) / (norm1 * norm2)\",\n \"def similarity_euclid(matrix, business1, business2):\\n selected_features = matrix.loc[business1].notna() & matrix.loc[business2].notna()\\n\\n if not selected_features.any():\\n return 0\\n\\n features1 = matrix.loc[business1][selected_features]\\n features2 = matrix.loc[business2][selected_features]\\n distance = math.sqrt(((features1 - features2) ** 2).sum())\\n\\n if distance is np.nan:\\n return 0\\n\\n return 1 / (1 + distance)\",\n \"def similarity(self, w1, w2):\\r\\n sim = self.represent(w1).dot(self.represent(w2))\\r\\n return sim\",\n \"def cosine_similarity(user, business):\\n return len(user.intersection(business)) / (math.sqrt(len(user)) * math.sqrt(len(business)))\",\n \"def calculate_similarities(self) -> List[float]:\\n sims = list()\\n for i, r in self.sim_data.iterrows():\\n if isinstance(self.model, FastTextWrapper):\\n vecs = self.model.inference([r[\\\"Word 1\\\"], r[\\\"Word 2\\\"]])\\n else:\\n vecs = self.model.inference_batches([[r[\\\"Word 1\\\"]], [r[\\\"Word 2\\\"]]])\\n vecs = [x[0] for x in vecs]\\n if len(vecs) == 2:\\n s = cosine_similarity([vecs[0]], [vecs[1]])[0][0]\\n sims.append(s)\\n else:\\n sims.append(np.nan)\\n self.sim_data[\\\"assigned_sim\\\"] = sims\\n self.sim_data = self.sim_data.dropna()\\n self.mean_error()\\n self.correlation()\\n return sims\",\n \"def cos_sim(v1, v2):\\r\\n return np.inner(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2))\",\n \"def findCosineDistance(vector1, vector2):\\n vec1 = vector1.flatten()\\n vec2 = vector2.flatten()\\n\\n a = np.dot(vec1.T, vec2)\\n b = np.dot(vec1.T, vec1)\\n c = np.dot(vec2.T, vec2)\\n return 1 - (a / (np.sqrt(b) * np.sqrt(c)))\",\n \"def compute_similarity(site_a, site_b):\\n return np.linalg.norm(site_a - site_b)\",\n \"def cossim(corpus):\\n files = os.listdir()\\n vectorizer = TfidfVectorizer()\\n trsfm = vectorizer.fit_transform(corpus)\\n columns = vectorizer.get_feature_names()\\n df_tfidf = pd.DataFrame(trsfm.toarray(), columns = columns, index = corpus)\\n out = cosine_similarity(trsfm)\\n df_result = pd.DataFrame(out, columns = files, index = files)\\n return df_result\",\n \"def similarity(self, w1, w2):\\r\\n return self.represent(w1).dot(self.represent(w2))\",\n \"def similarity(self, w1, w2):\\r\\n return self.represent(w1).dot(self.represent(w2))\",\n \"def main():\\n\\n measures = Similarity()\\n\\n input1=sys.argv[1]\\n vect1=np.loadtxt(fname = input1)\\n \\n input2=sys.argv[2]\\n vect2=np.loadtxt(fname = input2)\\n\\n print measures.cosine_similarity(normBySum(vect1), normBySum(vect2))\\n \\n\\n\\n #print measures.cosine_similarity2(vect1, vect2)\\n\\n #print measures.jaccard_similarity([0,1,2,5,6],[0,2,3,5,7,9])\",\n \"def get_cosine_similarities(keywords: List[str],\\n matrices: Matrices,\\n word2onehot: Dict[str, int]\\n ) -> None:\\n for i in combinations(keywords, 2):\\n print(i[0], i[1], cosine_sim(\\n matrices.embedding[\\n word2onehot[i[0]]], matrices.embedding[word2onehot[i[1]]\\n ]\\n ))\",\n \"def predict_cosine_similarities(sess, word_A, words_B):\\n\\n word_A_id, _ = sentence_to_word_ids(word_A, word_to_index)\\n words_B_ids, split_sentence = sentence_to_word_ids(words_B, word_to_index)\\n\\n evaluated_cos_similarities = sess.run(\\n cosine_similarities, \\n feed_dict={\\n tf_word_A_id: word_A_id,\\n tf_words_B_ids: words_B_ids\\n }\\n )\\n return evaluated_cos_similarities, split_sentence\",\n \"def dependency_similarity(s1, s2):\\n # pass\\n parsed_sentence_1 = parser.raw_parse(s1)\\n parsed_sentence_2 = parser.raw_parse(s2)\\n \\n tree1 = next(parsed_sentence_1)\\n tree2 = next(parsed_sentence_2)\\n \\n triples1 = [t for t in tree1.triples()]\\n triples2 = [t for t in tree2.triples()] \\n\\n # Compute similarity\\n if len(triples1) != 0 and len(triples2) != 0:\\n similarity = 1 - jaccard_distance(set(triples1), set(triples2))\\n return similarity\\n else:\\n return 0\",\n \"def test_cosine_similarity_all_zeros_compiled():\\n vector1 = np.array([0, 0, 0, 0])\\n vector2 = np.array([1, 1, 1, 1])\\n score11 = cosine_similarity(vector1, vector1)\\n score12 = cosine_similarity(vector1, vector2)\\n score22 = cosine_similarity(vector2, vector2)\\n\\n assert score11 == score12 == 0.0, \\\"Expected different score.\\\"\\n assert score22 == 1.0, \\\"Expected different score.\\\"\",\n \"def calculate_similarity(self, tfidf_matrix, test_tfidf):\\n\\n with open(DATASET.fold_root / 'tags_order.json') as file:\\n tags_order = json.load(file)\\n\\n min_max_scaler = MinMaxScaler()\\n\\n n_clus = 2\\n simis = []\\n for test_q in test_tfidf:\\n s = cosine_similarity(tfidf_matrix, test_q)\\n\\n # Sorting and getting indices of sorted similarities\\n simi = s.transpose()[0]\\n simi_values = np.sort(simi)[::-1][:200]\\n simi_indices = simi.argsort()[::-1]\\n\\n breaks = jenkspy.jenks_breaks(simi_values, n_clus)\\n simi_count = len(simi_values[breaks[-2] <= simi_values])\\n\\n q_tags = [self.train_set[i].tags for i in simi_indices][:simi_count]\\n\\n tags_votes = Counter(chain(*q_tags))\\n all_count = sum(tags_votes.values())\\n tags_likelihood = [tags_votes.get(\\n tag, 0) / all_count for tag in tags_order]\\n\\n lh = np.array([float(x)\\n for x in tags_likelihood]).reshape(-1, 1)\\n normalized_lh = np.concatenate(\\n min_max_scaler.fit_transform(lh)\\n ).tolist()\\n\\n simis.append(normalized_lh)\\n\\n return simis\",\n \"def cosine_similarity(y_true, y_pred, axis=-1):\\n y_true = nn.l2_normalize(y_true, axis=axis)\\n y_pred = nn.l2_normalize(y_pred, axis=axis)\\n return -math_ops.reduce_sum(y_true * y_pred, axis=axis)\",\n \"def get_cosine_similarity(self, query: list):\\n question_vector = self.get_vector(query)\\n\\n return cosine_similarity(question_vector, self.vectors).flatten()\",\n \"def test_cosine_similarity_matrix_compiled():\\n vectors1 = np.array([[1, 1, 0, 0],\\n [1, 0, 1, 1]])\\n vectors2 = np.array([[0, 1, 1, 0],\\n [0, 0, 1, 1]])\\n\\n scores = cosine_similarity_matrix(vectors1, vectors2)\\n expected_scores = np.array([[0.5, 0.],\\n [0.40824829, 0.81649658]])\\n assert scores == pytest.approx(expected_scores, 1e-7), \\\"Expected different scores.\\\"\",\n \"def cos_sim(v1: Union[np.ndarray, np.iterable, int, float], v2: Union[np.ndarray, np.iterable, int, float]) -> float:\\n return np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2))\",\n \"def cosine_similarity(X):\\n matrix = X.dot(X.transpose()).todense()\\n mat_len = len(matrix)\\n norms = [0] * mat_len\\n for i in range(0, mat_len):\\n norms[i] = 1.0 / np.sqrt(matrix.item((i, i)))\\n norm_mat = np.matrix(norms)\\n return np.multiply(norm_mat.transpose().dot(norm_mat), matrix)\",\n \"def cosine_similarity(document, cluster):\\n num = dot(document.vector, cluster.centroid)\\n den = linalg.norm(document.vector) * linalg.norm(cluster.centroid)\\n\\n return num / den\",\n \"def sentence_distance(sentence_a, sentence_b):\\n \\n sent_a = np.sum([projections[word_ids.get(word, 0)] \\n if word in word_ids else [0] \\n for word in sentence_a+bigrams(sentence_a)+trigrams(sentence_a)], axis=0)\\n sent_b = np.sum([projections[word_ids.get(word, 0)] \\n if word in word_ids else [0] \\n for word in sentence_b+bigrams(sentence_b)+trigrams(sentence_b)], axis=0)\\n \\n \\n return float(cosine(sent_a, sent_b))\",\n \"def similarity(self, word1, word2):\\n common_vect = +np.ones(self.nEmbed) * 10000\\n if word1 not in self.vocab and word2 in self.vocab:\\n id_word_2 = self.w2id[word2]\\n w1 = common_vect\\n w2 = self.U[id_word_2]\\n elif word1 in self.vocab and word2 not in self.vocab:\\n id_word_1 = self.w2id[word1]\\n w1 = self.U[id_word_1]\\n w2 = common_vect\\n elif word1 not in self.vocab and word2 not in self.vocab:\\n w1 = common_vect\\n w2 = common_vect\\n else:\\n id_word_1 = self.w2id[word1]\\n id_word_2 = self.w2id[word2]\\n w1 = self.U[id_word_1]\\n w2 = self.U[id_word_2]\\n\\n # scalair = w1.dot(w2)/np.linalg.norm(w1,w2)\\n similarity = w1.dot(w2) / (np.linalg.norm(w1) * np.linalg.norm(w2))\\n # similarity = 1 / (1 + np.exp(-scalair))\\n # similarity = scalair / (np.linalg.norm(w1) * np.linalg.norm(w2))\\n return similarity\",\n \"def cosine_collection_distance(x1, x2):\\n x1 = tf.cast(x1, dtype=tf.float32)\\n x2 = tf.cast(x2, dtype=tf.float32)\\n\\n # dot product between rows of `x1` and columns of `x2` transpose\\n cos_thetas = tf.linalg.matmul(x1, x2, transpose_b=True)\\n pairwise_distances = 1 - cos_thetas\\n\\n # deal with numerical inaccuracies setting small negatives to zero\\n pairwise_distances = tf.maximum(pairwise_distances, 0.0)\\n\\n return pairwise_distances\",\n \"def cosine_sim(im, s):\\n return im.mm(s.t()) #image.mm(sentence.t()) & mm() Performs a matrix multiplication of the matrices \",\n \"def cosine_distance(x1, x2):\\n x1 = tf.cast(x1, dtype=tf.float32)\\n x2 = tf.cast(x2, dtype=tf.float32)\\n\\n # dot product between rows of `x_1` and rows of `x_2`\\n # \\\"ij,ij->i\\\" := output[i] = sum_j x1[i, j] * x2[i, j]\\n cos_thetas = tf.linalg.einsum(\\\"ij,ij->i\\\", x1, x2)\\n cos_distances = 1 - cos_thetas\\n\\n # deal with numerical inaccuracies setting small negatives to zero\\n cos_distances = tf.maximum(cos_distances, 0.0)\\n\\n return cos_distances\",\n \"def cos_sim(com_feat,ref_feat):\\n # Fill this in\\n a = numpy.squeeze(com_feat)\\n b = numpy.squeeze(ref_feat)\\n return numpy.dot(a, b) / (numpy.linalg.norm(a) * numpy.linalg.norm(b))\",\n \"def semantic_similarity(self,sentence_1, sentence_2, info_content_norm):\\n\\t words_1 = sentence_1.getList_of_words()\\n\\t words_2 = sentence_2.getList_of_words()\\n\\t joint_words = set(words_1).union(set(words_2))\\n\\t vec_1 = self.semantic_vector(words_1, joint_words, info_content_norm)\\n\\t vec_2 = self.semantic_vector(words_2, joint_words, info_content_norm)\\n\\t return np.dot(vec_1, vec_2.T) / (np.linalg.norm(vec_1) * np.linalg.norm(vec_2))\",\n \"def similarity(self, e1, e2):\\n\\t\\tpass\",\n \"def get_dist(text1, text2, wv):\\n t1 = lookup(text1, wv)\\n t2 = lookup(text2, wv)\\n dist = cos_sim(t1, t2)\\n return dist\",\n \"def idf_modified_cosine(x, y, idf):\\n result = 0\\n try:\\n tf_x = [dict([word, int(tf)] for word, tf in dict(\\n np.array(np.unique(x, return_counts=True)).T).items())][0]\\n tf_y = [dict([word, int(tf)] for word, tf in dict(\\n np.array(np.unique(y, return_counts=True)).T).items())][0]\\n result = sum([tf_x[w] * tf_y[w] * (idf[w]**2)\\n\\t\\t for w in tf_x.keys() & tf_y.keys()]) / ((\\n sum([(tf_x[w] * idf[w])**2\\n for w in tf_x.keys()])**0.5) * (\\n sum([(tf_y[w] * idf[w])**2\\n for w in tf_y.keys()])**0.5))\\n except:\\n print(r'x:', x, r'y:', y)\\n pass\\n return result\",\n \"def test_cosine_similarity_matrix():\\n vectors1 = np.array([[1, 1, 0, 0],\\n [1, 0, 1, 1]])\\n vectors2 = np.array([[0, 1, 1, 0],\\n [0, 0, 1, 1]])\\n\\n scores = cosine_similarity_matrix.py_func(vectors1, vectors2)\\n expected_scores = np.array([[0.5, 0.],\\n [0.40824829, 0.81649658]])\\n assert scores == pytest.approx(expected_scores, 1e-7), \\\"Expected different scores.\\\"\",\n \"def _arccosine(self, s1, s2, tf_embs):\\n tf_pi = tf.constant(np.pi, dtype=tf.float64)\\n mat1 = tf.gather(tf_embs, s1)\\n mat2 = tf.gather(tf_embs, s2)\\n tf_norms = tf.constant(self.norms, dtype=tf.float64, name='norms')\\n norms1 = tf.gather(tf_norms, s1)\\n norms2 = tf.gather(tf_norms, s2)\\n dot = tf.matmul(mat1, tf.transpose(mat2))\\n norms = tf.matmul(norms1, tf.transpose(norms2))\\n # We clip values due to numerical errors\\n # which put some values outside the arccosine range.\\n cosine = tf.clip_by_value(dot / norms, -1, 1)\\n angle = tf.acos(cosine)\\n # The 0 vector has norm 0, which generates a NaN.\\n # We catch these NaNs and replace them with pi,\\n # which ends up returning 0 similarity.\\n angle = tf.select(tf.is_nan(angle), tf.ones_like(angle) * tf_pi, angle)\\n return 1 - (angle / tf_pi)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7754487","0.7676584","0.7633492","0.76006675","0.7589376","0.7576636","0.75592244","0.75373095","0.7522885","0.746666","0.74491453","0.7418762","0.7398554","0.73664916","0.7334395","0.7312988","0.7309643","0.7292841","0.72754246","0.7240708","0.72226435","0.7130961","0.7123031","0.70562273","0.70501834","0.70479953","0.7040821","0.7039914","0.70334846","0.70334363","0.70153224","0.7008114","0.69522464","0.6934286","0.6928512","0.6926593","0.6879386","0.6864113","0.6863202","0.68610185","0.68464726","0.6837238","0.6828644","0.68274385","0.6794438","0.67816436","0.67760855","0.675313","0.6740118","0.67280334","0.67219496","0.67148787","0.67109823","0.6707885","0.6702007","0.6698139","0.66927254","0.66927254","0.66871613","0.6682697","0.6681088","0.6679727","0.6679382","0.6677626","0.6674533","0.6638071","0.6621222","0.6613444","0.6607373","0.6603937","0.660229","0.6587089","0.65855116","0.65817654","0.65788","0.65788","0.6578797","0.6570616","0.65550965","0.6543188","0.65381354","0.6532197","0.65292716","0.65252316","0.6522049","0.65192133","0.65176344","0.6512081","0.6507807","0.6505985","0.65048015","0.650079","0.649635","0.64901906","0.6489436","0.6488032","0.6485602","0.6464009","0.64575744","0.6450351"],"string":"[\n \"0.7754487\",\n \"0.7676584\",\n \"0.7633492\",\n \"0.76006675\",\n \"0.7589376\",\n \"0.7576636\",\n \"0.75592244\",\n \"0.75373095\",\n \"0.7522885\",\n \"0.746666\",\n \"0.74491453\",\n \"0.7418762\",\n \"0.7398554\",\n \"0.73664916\",\n \"0.7334395\",\n \"0.7312988\",\n \"0.7309643\",\n \"0.7292841\",\n \"0.72754246\",\n \"0.7240708\",\n \"0.72226435\",\n \"0.7130961\",\n \"0.7123031\",\n \"0.70562273\",\n \"0.70501834\",\n \"0.70479953\",\n \"0.7040821\",\n \"0.7039914\",\n \"0.70334846\",\n \"0.70334363\",\n \"0.70153224\",\n \"0.7008114\",\n \"0.69522464\",\n \"0.6934286\",\n \"0.6928512\",\n \"0.6926593\",\n \"0.6879386\",\n \"0.6864113\",\n \"0.6863202\",\n \"0.68610185\",\n \"0.68464726\",\n \"0.6837238\",\n \"0.6828644\",\n \"0.68274385\",\n \"0.6794438\",\n \"0.67816436\",\n \"0.67760855\",\n \"0.675313\",\n \"0.6740118\",\n \"0.67280334\",\n \"0.67219496\",\n \"0.67148787\",\n \"0.67109823\",\n \"0.6707885\",\n \"0.6702007\",\n \"0.6698139\",\n \"0.66927254\",\n \"0.66927254\",\n \"0.66871613\",\n \"0.6682697\",\n \"0.6681088\",\n \"0.6679727\",\n \"0.6679382\",\n \"0.6677626\",\n \"0.6674533\",\n \"0.6638071\",\n \"0.6621222\",\n \"0.6613444\",\n \"0.6607373\",\n \"0.6603937\",\n \"0.660229\",\n \"0.6587089\",\n \"0.65855116\",\n \"0.65817654\",\n \"0.65788\",\n \"0.65788\",\n \"0.6578797\",\n \"0.6570616\",\n \"0.65550965\",\n \"0.6543188\",\n \"0.65381354\",\n \"0.6532197\",\n \"0.65292716\",\n \"0.65252316\",\n \"0.6522049\",\n \"0.65192133\",\n \"0.65176344\",\n \"0.6512081\",\n \"0.6507807\",\n \"0.6505985\",\n \"0.65048015\",\n \"0.650079\",\n \"0.649635\",\n \"0.64901906\",\n \"0.6489436\",\n \"0.6488032\",\n \"0.6485602\",\n \"0.6464009\",\n \"0.64575744\",\n \"0.6450351\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.80601525"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":2898,"cells":{"query":{"kind":"string","value":"Adds basic_vector to the basic vectors. If there are at least 3 arrays in _basic_vectors, then add a new array to _featureVector. This added array is composed of the basic vectors and its 2 first central derivatives basic_vector must be the array returned by the mfcc."},"document":{"kind":"string","value":"def build_feature_vector(self, basic_vector):\n basic_vector = basic_vector - np.mean(basic_vector)\n self._basic_vectors.append(basic_vector)\n if len(self._basic_vectors) > 2:\n #if there are at least 3 basic vectors we can calculate the central derivative for the vector before this one\n first_derivative = (basic_vector - self._basic_vectors[-3])/(2*self.seconds_to_next_vector)\n second_derivative = (basic_vector - 2*self._basic_vectors[-2] + self._basic_vectors[-3])/(self.seconds_to_next_vector**2)\n feature_vector = np.concatenate((basic_vector, first_derivative, second_derivative))\n self._feature_vectors.append(feature_vector)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def add_vector(self, doc_name, add_cnt, new_docvec):\n \n # determine the weight of the merging pieces\n old_weight = float(self.vector_cnt) / (self.vector_cnt + add_cnt)\n new_weight = float(add_cnt) / (self.vector_cnt + add_cnt)\n \n if len(self.name) == 0:\n self.name = doc_name\n else:\n self.name += \", %s\" % doc_name\n \n # computes magnitude as it goes.\n self.length = 0\n \n # reduce weight of values already in vector\n for key in self.centroid_vector.keys():\n if key in new_docvec: # if is in both vectors!\n \n oldvalue = float(self.centroid_vector[key]) * old_weight\n newvalue = float(new_docvec[key]) * new_weight\n value = oldvalue + newvalue\n \n self.centroid_vector[key] = value\n self.length += (value * value) # magnitude\n \n # so when we go through to add in all the missing ones we won't \n # have excess.\n del new_docvec[key]\n else: # if it is strictly in the old vector\n \n oldvalue = float(self.centroid_vector[key]) * old_weight\n self.centroid_vector[key] = oldvalue\n self.length += (oldvalue * oldvalue) # magnitude\n \n # add new values to vector\n for key, value in new_docvec.iteritems():\n # we don't so we'll have to create a new value with the weight of \n # the added vector\n value = float(value) * new_weight\n self.centroid_vector[key] = value\n self.length += (value * value)\n\n self.vector_cnt += add_cnt\n\n # calculate magnitude\n self.length = sqrt(self.length)","def feature_vector(features, vector):\n clean_features = set(features)\n new_features_vector = featurize(vector,clean_features)\n return new_features_vector","def _add_support_vectors(self, x: np.ndarray, y: np.ndarray) -> None:\n\n n_vectors = x.shape[0]\n\n self.support_vectors = np.vstack([self.support_vectors, x])\n self.alpha = np.append(self.alpha, np.zeros(n_vectors))\n self.target = np.append(self.target, y)\n\n new_kernel_values = self._kernel(x, self.support_vectors)\n\n self.kernel_mx = np.vstack([self.kernel_mx, new_kernel_values[:, :-n_vectors]])\n self.kernel_mx = np.hstack([self.kernel_mx, new_kernel_values.T])\n\n gradient = y - new_kernel_values.dot(self.alpha)\n self.gradient = np.append(self.gradient, gradient)\n\n a = y * self.c\n a[a > 0] = 0\n self.a = np.append(self.a, a)\n\n b = y * self.c\n b[b < 0] = 0\n self.b = np.append(self.b, b)","def append(self, vector):\n self._vectors.append(Vec2(*vector))","def create_feature_vector(self, files=[], name=\"\"):\n\n if( len(files)==0 ):\n return\n\n epsilon = 1e-8\n set = []\n\n #iterating all files obtaining the significant data to compute the feature vectors\n for file in files:\n\n #reading the csv files and keeping the first 3 columns (x,y,time)\n file_data = pd.read_csv(file)\n file_data = file_data.to_numpy()\n data = np.zeros((file_data.shape[0],7))\n data[:,0:3] = file_data[:,0:3]\n\n #computing the other interesting features\n angle = np.arctan(data[:,1]/(data[:,0]+epsilon))\n velocity = np.sqrt( np.square(data[:,1]) + np.square(data[:,0]) )\n log_curvature = np.log10( velocity/(angle+epsilon) )\n acceleration = np.sqrt( np.square(velocity) + np.square(velocity*angle) )\n\n #assigning the new computed features\n data[:,3] = angle\n data[:,4] = velocity\n data[:,5] = log_curvature\n data[:,6] = acceleration\n\n #normalizing the data\n data = self.normalization(data)\n set.append(data)\n\n return set","def feature_vector1(self, feature_vector1):\n\n self._feature_vector1 = feature_vector1","def add(self, featVect, label):\n if label in self.labelToNum:\n l = self.labelToNum[label]\n else:\n l = len(self.numToLabel)\n self.numToLabel.append(label)\n self.labelToNum[label] = l\n \n self.blocks.append((featVect.reshape((1,featVect.shape[0])).astype(numpy.double),[l]))","def augment_feature_vector(X):\n column_of_ones = np.zeros([len(X), 1]) + 1\n\n return np.hstack((column_of_ones, X))","def __add__(self, other):\n return Vector([c1 + c2 for (c1, c2) in zip(self.components, other.components)])","def apply_new_basis(new_base, vector_array):\n return np.dot(new_base, vector_array).T","def register_vectors(self, vectors):\n\n self.vectors.extend(vectors)","def add(first, other):\n if isinstance(first,FreeCAD.Vector) and isinstance(other,FreeCAD.Vector):\n return FreeCAD.Vector(first.x+other.x, first.y+other.y, first.z+other.z)","def add(self, vector):\n self.x += vector.x\n self.y += vector.y","def concatenation(self, StdVectorFst other):\n cdef StdVectorFst result = self.copy()\n result.concatenate(other)\n return result","def __add__(self, vector):\n return self.translated(vector)","def feature_vector2(self, feature_vector2):\n\n self._feature_vector2 = feature_vector2","def project_vectors(self, vectors):\n X = vectors - self._mean\n return np.dot(X, self.components.T)","def vector_add(v, w):\n\treturn [v_i + w_i for v_i, w_i in zip(v, w)]","def add_basic_block(self, basic_block):\n self.basic_blocks.append(basic_block)\n basic_block.function = self","def _update_feature_vec(fvec, word, tag_ngram):","def _make_feature_vec(self, word_list):\n\n # Pre-initialize an empty numpy array (for speed)\n feature_vec = np.zeros((self.num_features,), dtype=\"float32\")\n\n # index2word is a list that contains the names of the words in\n # the model's vocabulary. Convert it to a set, for speed.\n index2word_set = set(self.w2v_model.index2word)\n\n # Loop over each word in the word_list and, if it is in the model's\n # vocabulary, add its feature vector to the total\n nwords = 0\n for word in word_list:\n # NOTE: Careful there, if all words are in caps in the article,\n # this function will return nan values and blow up the forest.\n word = word.lower()\n if word in index2word_set:\n nwords += 1\n feature_vec = np.add(feature_vec, self.w2v_model[word])\n\n # Divide the result by the number of words to get the average\n feature_vec = np.divide(feature_vec, nwords)\n return feature_vec","def get_feature_vector(self, feature_idxs):\n feat_vec = np.zeros(len(feature_idxs))\n if hasattr(self, 'generic'):\n generics = self.generic\n else:\n generics = dict()\n for feature in self.features:\n key = generics.get(feature, feature)\n if key in feature_idxs:\n feat_vec[feature_idxs[key]] = self.features[feature]\n return feat_vec","def to_basic_block(self):\n return _add_vector_swig.add_vector_2_cpp_sptr_to_basic_block(self)","def get_feature_vector(cc, img, quiet=False):\n savefilename = config.get_classifier_featvect_name(cc.d.images[img]) \n if os.path.isfile(savefilename):\n print 'load feat_vect %s'%(cc.d.images[img].name)\n feat_vect = cPickle.load(open(savefilename,'r'))\n else:\n feat_vect = compute_feature_vector(cc, img, quiet=quiet)\n cPickle.dump(feat_vect, open(savefilename,'w'))\n return feat_vect","def add_feature(x, x1):\n if x is None:\n x = x1\n else:\n x = np.concatenate((x, x1), axis=1)\n return x","def __init__(self, feature_vectors):\n # Initialize key variables\n (rows, _) = feature_vectors.shape\n\n # Append a column of ones to array\n ones = np.ones((rows, 1))\n kessler_array = np.hstack((ones, feature_vectors))\n\n # Make array available to rest of class\n self.data = kessler_array.tolist()","def __add__(self, other):\n # other is a scalar\n if isinstance(other, (int, float, complex, Fraction)) and not isinstance(other, bool):\n return Vector([i + other for i in self.data], self.column)\n # other is a Vector\n elif isinstance(other, Vector):\n if len(self.data) != len(other):\n raise Exception('Vectors are not of equal length')\n elif self.column != other.column:\n raise Exception('Vectors are not of equal orientation')\n else:\n return Vector([self.data[i] + other.data[i] for i in range(len(self.data))], self.column)\n # other is not a scalar or a Vector\n else:\n raise Exception('Argument is not a number or a Vector') from TypeError","def __add__(self, other):\n if len( self) != len(other):\n raise ValueError('Dimensions must match.')\n result = Vector(len(self))\n for i in range(len(self)):\n result[i] = self[i] + other[i]\n return result","def processFeature(prevWord, word, vector):\n \n # We add feature whether it exists or not\n unigram, exists = vector.getUnigram(prevWord)\n if not exists:\n vector.addUnigram(prevWord)\n \n \n bigram, exists = vector.getBigram(prevWord, word)\n if not exists:\n vector.addBigram(prevWord, word)","def add_vectors(u, v): #11.22.5\r\n new_vector = []\r\n \"\"\"Because they have same length so we\r\n should take advantage from this one\"\"\"\r\n for i in range(len(u)):\r\n m = u[i] + v[i] # Get their value of i index at the same time!\r\n new_vector.append(m)\r\n return new_vector","def get_augmented_feature_vectors(feature_vectors):\n augmented = []\n for i, vector in enumerate(feature_vectors): # each vector is a list of tuples\n topics = [tup[0] for tup in vector]\n for t in range(7): # I finally settled on 7 topics\n if t not in topics:\n feature_vectors[i].append((t, 0))\n new_feature_vector = sorted(feature_vectors[i], key=lambda tup: tup[0])\n augmented.append([tup[1] for tup in new_feature_vector])\n return augmented","def add_feature(X, feature_to_add):\n from scipy.sparse import csr_matrix, hstack\n return hstack([X, csr_matrix(feature_to_add).T], 'csr')","def add_vector_fields(attributes, data):\n for attrib in attributes:\n if attrib['similarity'] == 'Semantic USE':\n value = data.get(attrib['name'])\n if value is not None:\n newVal = {}\n newVal['name'] = value\n newVal['rep'] = getVector(value)\n data[attrib['name']] = newVal\n elif attrib['similarity'] == 'Semantic SBERT':\n value = data.get(attrib['name'])\n if value is not None:\n newVal = {}\n newVal['name'] = value\n newVal['rep'] = getVectorSemanticSBERT(value)\n data[attrib['name']] = newVal\n elif attrib['similarity'] == 'Array SBERT':\n value = data.get(attrib['name'])\n if value is not None:\n newVal = {}\n newVal['name'] = value\n newVal[\"rep\"] = []\n array = getVectorSemanticSBERTArray(value)\n for element in array:\n temp = {}\n temp['rep'] = element\n newVal[\"rep\"].append(temp)\n\n data[attrib['name']] = newVal\n return data","def add_vectors(coord, vector):\n return tuple(c1+c2 for c1,c2 in zip(coord, vector))","def vector_add(v, w):\n return [v_i + w_i for v_i, w_i in zip(v, w)]","def add_mesh_features(self, file_list):\n self.geom.add_features(file_list)","def f_vector(self):\n try:\n return self._f_vector\n except AttributeError:\n self._f_vector = vector(ZZ,[len(x) for x in self.face_lattice().level_sets()])\n return self._f_vector","def vector_add(v1, v2):\n return v1[0] + v2[0], v1[1] + v2[1]","def append(self, featureName, featureData):\n self.data = np.concatenate((self.data, np.array([featureData]).T), axis=1)\n self.featureNames = np.append(self.featureNames, featureName)\n return 0","def sum_vectors(vector_1, vector_2):\n new_coordinates = []\n index = 0\n while index < vector_1.dimension:\n new_value = vector_1.coordinates[index] + vector_2.coordinates[index]\n new_coordinates.append(new_value)\n index += 1\n new_vector = Vector(new_coordinates)\n return new_vector","def put_vector(self, term, vector):\n self.terms.append(term)\n self.vectors.append(vector.vector)\n self.real_vectors.append(vector)\n return self.dict.update({term: vector})","def __add__(self, other) -> 'MultiVector':\n\n other, mv = self._checkOther(other)\n if not mv:\n if isinstance(other, np.ndarray):\n obj = self.__array__()\n return obj + other\n newValue = self.value + other.value\n\n return self._newMV(newValue)","def vector_add(v, w):\n return [v_i + w_i for v_i, w_i in zip(v,w)]","def add_vectorlist(vectors):\n x, y, z = zip(*vectors)\n return sum(x), sum(y), sum(z)","def __add__(self, other):\n\t\tif len(self) != len(other):\n\t\t\traise ValueError('dimensions must agree')\n\t\tresult = Vector(len(self))\n\t\tfor j in range(len(self)):\n\t\t\tresult[j] = self[j] + other[j]\n\t\treturn result","def __add__(self, other):\n if isinstance(other, (int, type(Zero()))):\n if (other == 0):\n return self\n self._check_vector(other)\n return Vector(self.args + other.args)","def extend(self, iterable):\n self._vectors.extend(Vec2(*vector) for vector in iterable)","def update(self, clip_id, feature_vec=None, is_background=False,\n timeout=None):\n with self._rw_lock.write_lock(timeout):\n clip_id = int(clip_id)\n if feature_vec is not None and \\\n not (feature_vec.ndim == 1\n and len(feature_vec) == self._feature_mat.shape[1]):\n raise ValueError(\"Given feature vector not compatible \"\n \"(dimensionality or length does not match)\")\n\n # Update the given feature vector and kernel distances\n # if self._cid2idx_map.get(clip_id, None) is not None:\n if clip_id in self._cid2idx_map:\n # In all cases, update the background status of the clip\n if is_background:\n self._bg_clip_ids.add(clip_id)\n else:\n self._bg_clip_ids.discard(clip_id)\n\n # If we were given a new feature vector, update entries\n if feature_vec is not None:\n idx = self._cid2idx_map[clip_id]\n self._feature_mat[idx] = feature_vec\n new_dist = np.mat(tuple(\n self._histogram_intersection_distance(feature_vec, fv)\n for fv in self._feature_mat\n ))\n self._kernel_mat[idx, :] = new_dist\n self._kernel_mat[:, idx] = new_dist\n\n # Given a new clip id to add, must have feature to add.\n else:\n if feature_vec is None:\n raise ValueError(\"Update given a new clip ID, but no \"\n \"feature vector. Feature vectors are \"\n \"required with new IDs.\")\n\n # Update internal feature matrix with added vector\n self._cid2idx_map[clip_id] = self._id_vector.size\n self._id_vector.resize((self._id_vector.size + 1,),\n refcheck=False)\n self._id_vector[-1] = clip_id\n\n if is_background:\n self._bg_clip_ids.add(clip_id)\n\n # noinspection PyUnresolvedReferences\n if self._feature_mat.base is not None:\n raise RuntimeError(\"Feature matrix does not own its data\")\n # Since we're only adding a new row, this resize does not affect\n # the positioning of the existing data.\n self._log.debug(\"Updating feature matrix...\")\n # noinspection PyUnresolvedReferences\n self._feature_mat.resize((self._feature_mat.shape[0] + 1,\n self._feature_mat.shape[1]),\n refcheck=False\n )\n self._feature_mat[-1, :] = feature_vec\n\n # Need to add a new row AND column to the distance kernel.\n self._log.debug(\"Updating kernel matrix...\")\n if self._kernel_mat.base is not None:\n raise RuntimeError(\"kernel matrix does not own its data\")\n assert self._kernel_mat.shape[0] == self._kernel_mat.shape[1], \\\n \"kernel matrix is not symmetric for some reason???\"\n # noinspection PyPep8Naming\n # -> because I like ``N`` better...\n N = self._kernel_mat.shape[0]\n kernel_copy = np.matrix(self._kernel_mat)\n self._kernel_mat.resize((N+1, N+1), refcheck=False)\n self._kernel_mat[:N, :N] = kernel_copy\n del kernel_copy\n\n # Computing new feature distance (histogram intersection). Only\n # need to compute this once because of HI being being\n # commutative and the kernel matrix being symmetric.\n self._log.debug(\"Adding new distance vectors...\")\n dist_vec = np.mat(tuple(\n self._histogram_intersection_distance(feature_vec, fv)\n for fv in self._feature_mat\n ))\n self._kernel_mat[-1, :] = dist_vec\n self._kernel_mat[:, -1] = dist_vec.T","def __add__(self, other):\n if isinstance(other, Vector):\n a = self._ar + other._ar\n else:\n a = self._ar + numpy.array(other)\n return Vector(a)","def add_vector_of_ones(X):\n m, n = X.shape\n vector_of_ones = np.ones([m, 1])\n X_with_ones = np.hstack([vector_of_ones, X])\n return X_with_ones","def vectorAdd(a, b):\n return [a[i] + b[i] for i, j in enumerate(a)]","def sum(self, vector):\n\n # return (self.from_list([x+vector.vector[self.vector.index(x)]\n # for x in self.vector]))\n return Vector(self.x + vector.x, self.y + vector.y, self.z + vector.z)","def creating_feature_vector():\r\n\twordlist = []\r\n\tlabel = \"\"\r\n\tfw = open(\"feature_vector.txt\", \"w+\", encoding = \"utf-8\")\r\n\twith open(\"D:\\\\Python_Prac\\\\wordstag\\\\modules\\\\HI_EN_TRAIN.txt\", \"r\", encoding = \"utf-8\") as f:\r\n\t\tfor line in f:\r\n\t\t\twordlist.append(line)\r\n\t\tfor index, line in enumerate(wordlist):\r\n\t\t\tif line == \"\\n\":\r\n\t\t\t\tcontinue\r\n\t\t\tcontext = line.split(\"\\t\")\r\n\t\t\tlabel = context[1]\r\n\t\t\tfeature_vector = label+\" \"\r\n\t\t\tngram_vector = ngram_frequency(str(context[0]))\r\n\t\t\tfor vector in ngram_vector:\r\n\t\t\t\tfeature_vector += str(vector)+\" \"\r\n\t\t\tfeature_vector += str(is_english(context[0]))+\" \"\r\n\t\t\tfeature_vector += str(is_hindi(context[0]))+\" \"\r\n\t\t\tfeature_vector += str(is_abbr(context[0]))+\" \"\r\n\t\t\tfeature_vector += str(med_in_english(context[0]))+\" \"\r\n\t\t\tfeature_vector += str(med_in_hindi(context[0]))+\" \"\r\n\t\t\tbefore = [0,0,0]\r\n\t\t\tafter = [0,0,0]\r\n\t\t\tfor i in range(3):\r\n\t\t\t\tif (index-i) < 0 or (index-i+1) > len(wordlist)-1:\r\n\t\t\t\t\tcontinue\r\n\t\t\t\tbefore[2-i] = get_word_context(wordlist[index-i+1].split(\"\\t\")[0])\r\n\t\t\tfor i in range(3):\r\n\t\t\t\tif (index+i+1) > len(wordlist)-1:\r\n\t\t\t\t\tcontinue\r\n\t\t\t\tafter[2-i] = get_word_context(wordlist[index+i+1].split(\"\\t\")[0])\r\n\t\t\tfor i in before:\r\n\t\t\t\tfeature_vector += str(i)+\" \"\r\n\t\t\tfor i in after:\r\n\t\t\t\tfeature_vector += str(i)+\" \"\r\n\t\t\tfeature_vector += \"\\n\"\r\n\t\t\tfw.write(feature_vector)\r\n\t\t\tprint(\"Proceeding...\"+str(index+1)+\" of 16683\")\r\n\r\n\tfw.close()","def _addVectors(X1,X2):\n _checkSize(X1,X2)\n return [ X1[i] + X2[i] for i in range(len(X1))]","def rebuild_all(self, vector=None):\n if not hasattr(self, \"positions\"):\n self.load_data()\n\n if vector is not None and not isinstance(vector, np.ndarray):\n raise ValueError(\"`vector` must be a 1-d numpy array\")\n\n if vector is not None:\n assert vector.shape == self.params.shape, \\\n \"Incorrect `vector` shape. Is {}, but should be {}\".format(\n vector.shape, self.params.shape\n )\n # Copy to break references and avoid later manipulation by RNG\n self.sample = (self.rng.next() if vector is None else vector).copy()\n self.rebuild_technosphere_matrix(self.tech_sample)\n self.rebuild_biosphere_matrix(self.bio_sample)\n if self.lcia:\n self.rebuild_characterization_matrix(self.cf_sample)\n if self.weighting:\n self.weighting_value = self.weighting_sample\n\n if self.presamples:\n self.presamples.update_matrices()","def add_new_features(self):\r\n curr_img = self.cam0_curr_img_msg.image\r\n grid_height, grid_width = self.get_grid_size(curr_img)\r\n\r\n # Create a mask to avoid redetecting existing features.\r\n mask = np.ones(curr_img.shape[:2], dtype='uint8')\r\n\r\n for feature in chain.from_iterable(self.curr_features):\r\n x, y = map(int, feature.cam0_point)\r\n mask[y-3:y+4, x-3:x+4] = 0\r\n\r\n # Detect new features.\r\n new_features = self.detector.detect(curr_img, mask=mask)\r\n\r\n # Collect the new detected features based on the grid.\r\n # Select the ones with top response within each grid afterwards.\r\n new_feature_sieve = [[] for _ in range(self.config.grid_num)]\r\n for feature in new_features:\r\n row = int(feature.pt[1] / grid_height)\r\n col = int(feature.pt[0] / grid_width)\r\n code = row * self.config.grid_col + col\r\n new_feature_sieve[code].append(feature)\r\n\r\n new_features = []\r\n for features in new_feature_sieve:\r\n if len(features) > self.config.grid_max_feature_num:\r\n features = sorted(features, key=lambda x:x.response, \r\n reverse=True)[:self.config.grid_max_feature_num]\r\n new_features.append(features)\r\n new_features = list(chain.from_iterable(new_features))\r\n\r\n # Find the stereo matched points for the newly detected features.\r\n cam0_points = [kp.pt for kp in new_features]\r\n cam1_points, inlier_markers = self.stereo_match(cam0_points)\r\n\r\n cam0_inliers, cam1_inliers, response_inliers = [], [], []\r\n for i, inlier in enumerate(inlier_markers):\r\n if not inlier:\r\n continue\r\n cam0_inliers.append(cam0_points[i])\r\n cam1_inliers.append(cam1_points[i])\r\n response_inliers.append(new_features[i].response)\r\n # if len(cam0_inliers) < max(5, len(new_features) * 0.1):\r\n\r\n # Group the features into grids\r\n grid_new_features = [[] for _ in range(self.config.grid_num)]\r\n for i in range(len(cam0_inliers)):\r\n cam0_point = cam0_inliers[i]\r\n cam1_point = cam1_inliers[i]\r\n response = response_inliers[i]\r\n\r\n row = int(cam0_point[1] / grid_height)\r\n col = int(cam0_point[0] / grid_width)\r\n code = row*self.config.grid_col + col\r\n\r\n new_feature = FeatureMetaData()\r\n new_feature.response = response\r\n new_feature.cam0_point = cam0_point\r\n new_feature.cam1_point = cam1_point\r\n grid_new_features[code].append(new_feature)\r\n\r\n # Sort the new features in each grid based on its response.\r\n # And collect new features within each grid with high response.\r\n for i, new_features in enumerate(grid_new_features):\r\n for feature in sorted(new_features, key=lambda x:x.response, \r\n reverse=True)[:self.config.grid_min_feature_num]:\r\n self.curr_features[i].append(feature)\r\n self.curr_features[i][-1].id = self.next_feature_id\r\n self.curr_features[i][-1].lifetime = 1\r\n self.next_feature_id += 1","def reduce_dimensions(feature_vectors_full, model):\n\n if 'fvectors' in model:\n v = np.array(model['fvectors'])\n else:\n #Principal Components Analysis implemented from lab code\n covx = np.cov(feature_vectors_full, rowvar=0)\n N = covx.shape[0]\n w, v = scipy.linalg.eigh(covx, eigvals=(N - 40, N - 1))\n v = np.fliplr(v)\n model['fvectors'] = v.tolist()\n pca_train = np.dot((feature_vectors_full - np.mean(feature_vectors_full)), v)\n return pca_train[:,0:10]","def _create_feature_vec():\n\tnum_tags = NGRAM_TUPLE[0]\n\tfvec = []\n\tfor _, size in FEATURE_TUPLE:\n\t\tfvec.append(np.zeros((num_tags, size)))\n\n\t# Append tag ngram weights to end\n\tfvec.append(np.zeros((num_tags, num_tags)))\n\treturn fvec","def mfcc_features(self, audio, rate, numcep = 20, nfft = 2000, N = 2):\n self.mfcc = python_speech_features.mfcc(audio, rate, numcep = numcep, nfft = nfft)\n #self.mfcc = preprocessing.scale(self.mfcc)\n \n self.delta_mfcc = python_speech_features.delta(self.mfcc, N)\n \n self.mfcc_feature = np.hstack((self.mfcc, self.delta_mfcc))\n \n return self.mfcc_feature","def assemble_vector(form: _forms, constraint: MultiPointConstraint, b: Optional[_PETSc.Vec] = None) -> _PETSc.Vec:\n\n _log.log(_log.LogLevel.INFO, \"Assemble MPC vector\")\n timer_vector = Timer(\"~MPC: Assemble vector (numba)\")\n\n # Unpack Function space data\n V = form.function_spaces[0]\n x_dofs = V.mesh.geometry.dofmap\n x = V.mesh.geometry.x\n dofs = V.dofmap.map()\n block_size = V.dofmap.index_map_bs\n\n # Data from multipointconstraint\n coefficients = constraint.coefficients()[0]\n masters_adj = constraint.masters\n c_to_s_adj = constraint.cell_to_slaves\n cell_to_slave = c_to_s_adj.array\n c_to_s_off = c_to_s_adj.offsets\n is_slave = constraint.is_slave\n mpc_data = (masters_adj.array, coefficients, masters_adj.offsets, cell_to_slave, c_to_s_off, is_slave)\n slave_cells = extract_slave_cells(c_to_s_off)\n\n # Get index map and ghost info\n if b is None:\n index_map = constraint.function_space.dofmap.index_map\n vector = _la.create_petsc_vector(index_map, block_size)\n else:\n vector = b\n\n # Pack constants and coefficients\n form_coeffs = _cpp.fem.pack_coefficients(form._cpp_object)\n form_consts = _cpp.fem.pack_constants(form._cpp_object)\n\n tdim = V.mesh.topology.dim\n num_dofs_per_element = V.dofmap.dof_layout.num_dofs\n\n # Assemble vector with all entries\n with vector.localForm() as b_local:\n _cpp.fem.assemble_vector(b_local.array_w, form._cpp_object,\n form_consts, form_coeffs)\n\n # Check if we need facet permutations\n # FIXME: access apply_dof_transformations here\n e0 = form.function_spaces[0].element\n needs_transformation_data = e0.needs_dof_transformations or form._cpp_object.needs_facet_permutations\n cell_perms = numpy.array([], dtype=numpy.uint32)\n if needs_transformation_data:\n V.mesh.topology.create_entity_permutations()\n cell_perms = V.mesh.topology.get_cell_permutation_info()\n if e0.needs_dof_transformations:\n raise NotImplementedError(\"Dof transformations not implemented\")\n # Assemble over cells\n subdomain_ids = form._cpp_object.integral_ids(_fem.IntegralType.cell)\n num_cell_integrals = len(subdomain_ids)\n\n is_complex = numpy.issubdtype(_PETSc.ScalarType, numpy.complexfloating)\n nptype = \"complex128\" if is_complex else \"float64\"\n ufcx_form = form.ufcx_form\n if num_cell_integrals > 0:\n V.mesh.topology.create_entity_permutations()\n\n # NOTE: This depends on enum ordering in ufcx.h\n cell_form_pos = ufcx_form.form_integral_offsets[0]\n for i, id in enumerate(subdomain_ids):\n cell_kernel = getattr(ufcx_form.form_integrals[cell_form_pos + i], f\"tabulate_tensor_{nptype}\")\n active_cells = form._cpp_object.domains(_fem.IntegralType.cell, id)\n coeffs_i = form_coeffs[(_fem.IntegralType.cell, id)]\n with vector.localForm() as b:\n assemble_cells(numpy.asarray(b), cell_kernel, active_cells[numpy.isin(active_cells, slave_cells)],\n (x_dofs, x), coeffs_i, form_consts,\n cell_perms, dofs, block_size, num_dofs_per_element, mpc_data)\n\n # Assemble exterior facet integrals\n subdomain_ids = form._cpp_object.integral_ids(_fem.IntegralType.exterior_facet)\n num_exterior_integrals = len(subdomain_ids)\n if num_exterior_integrals > 0:\n V.mesh.topology.create_entities(tdim - 1)\n V.mesh.topology.create_connectivity(tdim - 1, tdim)\n # Get facet permutations if required\n facet_perms = numpy.array([], dtype=numpy.uint8)\n if form._cpp_object.needs_facet_permutations:\n facet_perms = V.mesh.topology.get_facet_permutations()\n perm = (cell_perms, form._cpp_object.needs_facet_permutations, facet_perms)\n # NOTE: This depends on enum ordering in ufcx.h\n ext_facet_pos = ufcx_form.form_integral_offsets[1]\n for i, id in enumerate(subdomain_ids):\n facet_kernel = getattr(ufcx_form.form_integrals[ext_facet_pos + i],\n f\"tabulate_tensor_{nptype}\")\n coeffs_i = form_coeffs[(_fem.IntegralType.exterior_facet, id)]\n facets = form._cpp_object.domains(_fem.IntegralType.exterior_facet, id)\n facet_info = pack_slave_facet_info(facets, slave_cells)\n num_facets_per_cell = len(V.mesh.topology.connectivity(tdim, tdim - 1).links(0))\n with vector.localForm() as b:\n assemble_exterior_slave_facets(numpy.asarray(b), facet_kernel, facet_info, (x_dofs, x),\n coeffs_i, form_consts, perm,\n dofs, block_size, num_dofs_per_element, mpc_data, num_facets_per_cell)\n timer_vector.stop()\n return vector","def create_bitvector(bitvector, *bits):\n if not bitvector in bitvectors:\n bitvectors[bitvector] = []\n \n if bits:\n bitvectors[bitvector].extend(bits)","def __iadd__(self, other):\n if isinstance(other, Seq2):\n if len(self) == len(other):\n self._vectors = [a + b for a, b in zip(self, other)]\n return self\n else:\n raise ValueError(\"cannot add arrays with different lengths\")\n else:\n try:\n b = Vec2(*other)\n except Exception:\n return NotImplemented\n self._vectors = [a + b for a in self]\n return self","def create_vectorized_features(data_dir, feature_version=2):\n extractor = PEFeatureExtractor(feature_version)\n\n print(\"Vectorizing training set\")\n X_path = os.path.join(data_dir, \"X_train.dat\")\n y_path = os.path.join(data_dir, \"y_train.dat\")\n raw_feature_paths = [os.path.join(data_dir, \"train_features_{}.jsonl\".format(i)) for i in range(6)]\n nrows = sum([1 for fp in raw_feature_paths for line in open(fp)])\n vectorize_subset(X_path, y_path, raw_feature_paths, extractor, nrows)\n\n print(\"Vectorizing test set\")\n X_path = os.path.join(data_dir, \"X_test.dat\")\n y_path = os.path.join(data_dir, \"y_test.dat\")\n raw_feature_paths = [os.path.join(data_dir, \"test_features.jsonl\")]\n nrows = sum([1 for fp in raw_feature_paths for line in open(fp)])\n vectorize_subset(X_path, y_path, raw_feature_paths, extractor, nrows)","def __add__(self, other):\n if len(self) != len(other):\n raise ValueError('As dimensões devem ser iguais')\n\n result = Vector(len(self)) # inicia um novo array do tamanho do próprio\n for i in range(len(self)):\n result[i] = self[i] + other[i]\n return result","def addAt(self, vector, id1, id2):\n self.matrix.update_add_at(vector,\n numerix.asarray(id1, dtype='int32'),\n numerix.asarray(id2, dtype='int32'))","def CreateVector(self) -> BaseVector:","def numpy_vector(self):\n pass","def add(v: Vector, w: Vector) -> Vector:\n assert len(v) == len(w), 'both vectors must have the same length'\n\n return [v_item + w_item for v_item, w_item in zip(v, w)]","def vectorizer_features(self) -> list:\n if self._vectorizer:\n return self._vectorizer.get_feature_names()\n self.logger.warning('Uninitialized vector. Please call count_vectorizer first.')","def add_vector(self, name, text, tag=None):\n words = self.clean_text_util.clean_text(text)\n \n # max{f(w,d) : w ∈ d)}\n counter = Counter(words)\n _, max_occ = counter.most_common(1)[0] \n\n # remove duplicate word\n words = set(words)\n \n items = []\n for word in words:\n pickle_wordinfo = self.dictionary_db.get(word)\n if not pickle_wordinfo:\n continue\n \n word_info = pickle.loads(pickle_wordinfo)\n\n # tf formula: tf(f,d) = f(f,d)/max{f(w,d) : w ∈ d)} (src Wikipedia)\n tf = counter[word]/float(max_occ)\n\n # create a new vector item entry\n items.append(VectorItem(word, tf))\n\n # sort the vector item by the dictionary index\n items.sort(key=lambda x: x.word_info(self.dictionary_db).index)\n\n # finally, we create a new vector\n vector = Vector(items, tag)\n self.vectors_db.add(name, pickle.dumps(vector))\n\n # add an empty entry to the norm db\n self.vectors_norm_db.add(name, self.vector_tfidf_norm(items))","def homogeneous_vector(self, base_ring=None):\n v = list(self._vector) + [0]\n return vector(base_ring or self._base_ring, v)","def homogeneous_vector(self, base_ring=None):\n v = list(self._vector) + [0]\n return vector(base_ring or self._base_ring, v)","def add_feature(self, feature):\n self.features += [feature]\n for stock in self.stocks:\n feature(self.stock_data[stock])","def __iadd__( self, vector3 ):\n return self.add( vector3 )","def create_feature_vector(features, length):\n START_IDX = 0\n END_IDX = 1\n\n output_vector = np.zeros(length)\n\n # negative strand\n for loc in features[-1]:\n output_vector[loc[START_IDX]:loc[END_IDX]] = 1 \n\n # positive strand\n for loc in features[1]:\n output_vector[loc[START_IDX]:loc[END_IDX]] = 2\n\n return output_vector","def apply_optimization_vector(self, vector: np.ndarray) -> None:\n # we are going to iterate through the given vector;\n # an iterator allows us to next()\n # (aka `pop`) the values only when desired;\n # we only update the current vector where the mask is True\n vector_iterator = iter(vector)\n updated_vector = [v if not m else next(vector_iterator)\n for v, m in zip(self.vector,\n self.optimization_mask)]\n updated_links = self.array_2_links(np.array(updated_vector),\n self.convention)\n self.links = updated_links","def vector_add(a, b):\n assert(len(a) == len(b))\n\n from operator import add\n return tuple(map(add, a, b))","def basic_fields(self, basic_fields):\n\n self._basic_fields = basic_fields","def homogeneous_vector(self, base_ring=None):\n v = list(self._vector) + [1]\n return vector(base_ring or self._base_ring, v)","def project_vectors(self, vectors):\n return np.dot(vectors, self.components.T)","def __add_answered_on_feature(samples: List[TrainSample], all_features: np.array) -> np.array:\n new_features = []\n for sample, features in zip(samples, all_features):\n if isinstance(sample.selected_player, bool):\n answered_on = 1.0 if sample.selected_player else 0.0\n else:\n answered_on = 1.0 if sample.selected_player in sample.answer else 0.0\n features = np.append(features, answered_on * features)\n features = np.append(features, [answered_on])\n new_features.append(features)\n return np.array(new_features)","def update(self, learning_rate, influence, input_vector, bmu):\n factor = learning_rate * influence\n self.vector = [x + factor * (y - x)\n for x, y in zip(self.vector, input_vector)]","def __add__(self, v):\n return vector(self.x + v.x, self.y + v.y, self.z + v.z)","def add_vectors(v, u):\n return (v[0] + u[0], v[1] + u[1])","def add(self, keys: List[Tuple[int, int]], vectors: np.ndarray, weights: List[float], *args, **kwargs):\n pass","def updateAttributesAfterAdding(self):\n layer = self.sender()\n while self.addedFeatures:\n featureId = self.addedFeatures.pop()\n #begining the edit command\n # layer.beginEditCommand(self.tr(\"DSG Tools reclassification tool: adjusting feature's attributes\"))\n #accessing added features\n editBuffer = layer.editBuffer()\n features = editBuffer.addedFeatures()\n for key in features.keys():\n #just checking the newly added feature, the other I don't care\n if key == featureId:\n feature = features[key]\n #setting the attributes using the reclassification dictionary\n self.setFeatureAttributes(feature, editBuffer)\n layer.endEditCommand()","def _vectorize(self, vectorizer = None):\n\n\t\tvectorizer = vectorizer if vectorizer else self.vectorizer;\n\n\t\tself.training_set_vector = vectorizer.fit_transform(self.training_set)\n\n\t\tself.testing_set_vector = vectorizer.transform(self.testing_set)","def _add_feature(self, feature):\n\n if feature.name in self.feature_name_index:\n logger.info(\"Feature %s already exists at %i, overwriting\" %\n (feature.name, self.feature_name_index[feature.name]))\n self.features[self.feature_name_index[feature.name]] = feature\n else:\n self.features.append(feature)\n self.feature_name_index[feature.name] = len(self.features) - 1\n logger.info(\"Adding %s to model at location %i\" % (\n feature.name, len(self.features)))\n self._add_domain_fault_above(feature)\n self._add_unconformity_above(feature)\n feature.set_model(self)","def __add__(self, v2):\n\t\treturn Vect2D(self._vec+v2._vec)","def feed(self, vector):\n return vector","def _concatenate_features(features):\n pass","def AsVector(self) -> BaseVector:","def join_feature_vectors(vecs1, vecs2):\n if len(vecs1) != len(vecs2) or len(vecs1) == 0:\n print('Invalid number of feature vectors: ' + str(len(vecs1)) + ', ' + str(len(vecs2)))\n return\n\n number_of_vecs = len(vecs1)\n vec1_length = len(vecs1[0])\n vec2_length = len(vecs2[0])\n joined_vecs = np.zeros(shape=(number_of_vecs, vec1_length + vec2_length))\n\n for i in range(len(vecs1)):\n joined_vecs[i, 0:vec1_length] = vecs1[i]\n joined_vecs[i, vec1_length:vec1_length+vec2_length] = vecs2[i]\n return joined_vecs","def normalizeFeatureVector(self):\n # Normalize features\n total = 0.0\n for v in self.features.values(): total += abs(v)\n if total == 0.0: \n total = 1.0\n for k,v in self.features.iteritems():\n self.features[k] = float(v) / total","def calculateFeatures(self, special=None):\n print(\"Computing features.\")\n if not special:\n self.feature_special = dict()\n self.feature_special['fPCA'] = None\n self.feature_special['wPCA'] = None\n else:\n self.feature_special = special\n if not 'wPCA' in special.keys():\n self.feature_special['wPCA'] = None\n if not 'fPCA' in special.keys():\n self.feature_special['fPCA'] = None\n\n self.features = []\n self.features.append(features.Feature_Peak(self))\n self.features.append(features.Feature_Energy(self))\n self.features.append(features.Feature_Time(self))\n self.features.append(features.Feature_Valley(self))\n self.features.append(features.Feature_Trough(self))\n\n if self.use_pca:\n self.features.append(\n features.Feature_PCA(self, self.feature_special['fPCA']))\n self.features.append(\n features.Feature_Waveform_PCA(self,\n self.feature_special['wPCA']))\n self.feature_special['fPCA'] = self.featureByName('fPCA').coeff\n self.feature_special['wPCA'] = self.featureByName('wPCA').coeff","def insert(self, index, vector):\n self._vectors.insert(index, Vec2(*vector))","def pack_features_vector(features, labels):\n features = tf.stack(list(features), axis=1)\n return features, labels","def zStoreForwardPropagate(self, inputVector):\r\n # Preform the forward propagation through the layers\r\n # setting the output of one layer to the input of the next\r\n for layer in self.layers:\r\n inputVector = layer.zStoreForwardPropagate(inputVector)\r\n # The output of the last layer is returned \r\n return inputVector","def GetVectorArticleInput(dico_vector_input, features):\n features_left = set(features) - set(dico_vector_input.keys())\n if len(features_left) > 0:\n sentence = \"Some features aren't in the dict:\\n\"\n raise MyException(sentence + \"{}\".format(features_left))\n vector_art = []\n other_features = ['abstract', 'syn', 'exergue', 'title', 'secTitle']\n other_features += ['subTitle', 'supTitle']\n for feature in features:\n if feature == 'nbSign':\n if dico_vector_input['nbSign'] == 0:\n print(\"NbSign == 0 l.176 - GetVectorArticleInput\")\n vector_art.append(dico_vector_input[feature])\n else:\n vector_art.append(dico_vector_input[feature])\n # Conversion des variables en indicatrices\n # Normalement plus la peine, comme déjà fait auparavant\n elif feature in other_features:\n if dico_vector_input[feature] > 0:\n vector_art.append(1)\n else:\n vector_art.append(0)\n else:\n vector_art.append(dico_vector_input[feature])\n return (dico_vector_input['melodyId'], np.array([vector_art]))","def vector_sum(vectors):\n\tresult = vectors[0]\n\tfor vector in vectors:\n\t\tresult = vector_add(result, vector)\n\treturn result"],"string":"[\n \"def add_vector(self, doc_name, add_cnt, new_docvec):\\n \\n # determine the weight of the merging pieces\\n old_weight = float(self.vector_cnt) / (self.vector_cnt + add_cnt)\\n new_weight = float(add_cnt) / (self.vector_cnt + add_cnt)\\n \\n if len(self.name) == 0:\\n self.name = doc_name\\n else:\\n self.name += \\\", %s\\\" % doc_name\\n \\n # computes magnitude as it goes.\\n self.length = 0\\n \\n # reduce weight of values already in vector\\n for key in self.centroid_vector.keys():\\n if key in new_docvec: # if is in both vectors!\\n \\n oldvalue = float(self.centroid_vector[key]) * old_weight\\n newvalue = float(new_docvec[key]) * new_weight\\n value = oldvalue + newvalue\\n \\n self.centroid_vector[key] = value\\n self.length += (value * value) # magnitude\\n \\n # so when we go through to add in all the missing ones we won't \\n # have excess.\\n del new_docvec[key]\\n else: # if it is strictly in the old vector\\n \\n oldvalue = float(self.centroid_vector[key]) * old_weight\\n self.centroid_vector[key] = oldvalue\\n self.length += (oldvalue * oldvalue) # magnitude\\n \\n # add new values to vector\\n for key, value in new_docvec.iteritems():\\n # we don't so we'll have to create a new value with the weight of \\n # the added vector\\n value = float(value) * new_weight\\n self.centroid_vector[key] = value\\n self.length += (value * value)\\n\\n self.vector_cnt += add_cnt\\n\\n # calculate magnitude\\n self.length = sqrt(self.length)\",\n \"def feature_vector(features, vector):\\n clean_features = set(features)\\n new_features_vector = featurize(vector,clean_features)\\n return new_features_vector\",\n \"def _add_support_vectors(self, x: np.ndarray, y: np.ndarray) -> None:\\n\\n n_vectors = x.shape[0]\\n\\n self.support_vectors = np.vstack([self.support_vectors, x])\\n self.alpha = np.append(self.alpha, np.zeros(n_vectors))\\n self.target = np.append(self.target, y)\\n\\n new_kernel_values = self._kernel(x, self.support_vectors)\\n\\n self.kernel_mx = np.vstack([self.kernel_mx, new_kernel_values[:, :-n_vectors]])\\n self.kernel_mx = np.hstack([self.kernel_mx, new_kernel_values.T])\\n\\n gradient = y - new_kernel_values.dot(self.alpha)\\n self.gradient = np.append(self.gradient, gradient)\\n\\n a = y * self.c\\n a[a > 0] = 0\\n self.a = np.append(self.a, a)\\n\\n b = y * self.c\\n b[b < 0] = 0\\n self.b = np.append(self.b, b)\",\n \"def append(self, vector):\\n self._vectors.append(Vec2(*vector))\",\n \"def create_feature_vector(self, files=[], name=\\\"\\\"):\\n\\n if( len(files)==0 ):\\n return\\n\\n epsilon = 1e-8\\n set = []\\n\\n #iterating all files obtaining the significant data to compute the feature vectors\\n for file in files:\\n\\n #reading the csv files and keeping the first 3 columns (x,y,time)\\n file_data = pd.read_csv(file)\\n file_data = file_data.to_numpy()\\n data = np.zeros((file_data.shape[0],7))\\n data[:,0:3] = file_data[:,0:3]\\n\\n #computing the other interesting features\\n angle = np.arctan(data[:,1]/(data[:,0]+epsilon))\\n velocity = np.sqrt( np.square(data[:,1]) + np.square(data[:,0]) )\\n log_curvature = np.log10( velocity/(angle+epsilon) )\\n acceleration = np.sqrt( np.square(velocity) + np.square(velocity*angle) )\\n\\n #assigning the new computed features\\n data[:,3] = angle\\n data[:,4] = velocity\\n data[:,5] = log_curvature\\n data[:,6] = acceleration\\n\\n #normalizing the data\\n data = self.normalization(data)\\n set.append(data)\\n\\n return set\",\n \"def feature_vector1(self, feature_vector1):\\n\\n self._feature_vector1 = feature_vector1\",\n \"def add(self, featVect, label):\\n if label in self.labelToNum:\\n l = self.labelToNum[label]\\n else:\\n l = len(self.numToLabel)\\n self.numToLabel.append(label)\\n self.labelToNum[label] = l\\n \\n self.blocks.append((featVect.reshape((1,featVect.shape[0])).astype(numpy.double),[l]))\",\n \"def augment_feature_vector(X):\\n column_of_ones = np.zeros([len(X), 1]) + 1\\n\\n return np.hstack((column_of_ones, X))\",\n \"def __add__(self, other):\\n return Vector([c1 + c2 for (c1, c2) in zip(self.components, other.components)])\",\n \"def apply_new_basis(new_base, vector_array):\\n return np.dot(new_base, vector_array).T\",\n \"def register_vectors(self, vectors):\\n\\n self.vectors.extend(vectors)\",\n \"def add(first, other):\\n if isinstance(first,FreeCAD.Vector) and isinstance(other,FreeCAD.Vector):\\n return FreeCAD.Vector(first.x+other.x, first.y+other.y, first.z+other.z)\",\n \"def add(self, vector):\\n self.x += vector.x\\n self.y += vector.y\",\n \"def concatenation(self, StdVectorFst other):\\n cdef StdVectorFst result = self.copy()\\n result.concatenate(other)\\n return result\",\n \"def __add__(self, vector):\\n return self.translated(vector)\",\n \"def feature_vector2(self, feature_vector2):\\n\\n self._feature_vector2 = feature_vector2\",\n \"def project_vectors(self, vectors):\\n X = vectors - self._mean\\n return np.dot(X, self.components.T)\",\n \"def vector_add(v, w):\\n\\treturn [v_i + w_i for v_i, w_i in zip(v, w)]\",\n \"def add_basic_block(self, basic_block):\\n self.basic_blocks.append(basic_block)\\n basic_block.function = self\",\n \"def _update_feature_vec(fvec, word, tag_ngram):\",\n \"def _make_feature_vec(self, word_list):\\n\\n # Pre-initialize an empty numpy array (for speed)\\n feature_vec = np.zeros((self.num_features,), dtype=\\\"float32\\\")\\n\\n # index2word is a list that contains the names of the words in\\n # the model's vocabulary. Convert it to a set, for speed.\\n index2word_set = set(self.w2v_model.index2word)\\n\\n # Loop over each word in the word_list and, if it is in the model's\\n # vocabulary, add its feature vector to the total\\n nwords = 0\\n for word in word_list:\\n # NOTE: Careful there, if all words are in caps in the article,\\n # this function will return nan values and blow up the forest.\\n word = word.lower()\\n if word in index2word_set:\\n nwords += 1\\n feature_vec = np.add(feature_vec, self.w2v_model[word])\\n\\n # Divide the result by the number of words to get the average\\n feature_vec = np.divide(feature_vec, nwords)\\n return feature_vec\",\n \"def get_feature_vector(self, feature_idxs):\\n feat_vec = np.zeros(len(feature_idxs))\\n if hasattr(self, 'generic'):\\n generics = self.generic\\n else:\\n generics = dict()\\n for feature in self.features:\\n key = generics.get(feature, feature)\\n if key in feature_idxs:\\n feat_vec[feature_idxs[key]] = self.features[feature]\\n return feat_vec\",\n \"def to_basic_block(self):\\n return _add_vector_swig.add_vector_2_cpp_sptr_to_basic_block(self)\",\n \"def get_feature_vector(cc, img, quiet=False):\\n savefilename = config.get_classifier_featvect_name(cc.d.images[img]) \\n if os.path.isfile(savefilename):\\n print 'load feat_vect %s'%(cc.d.images[img].name)\\n feat_vect = cPickle.load(open(savefilename,'r'))\\n else:\\n feat_vect = compute_feature_vector(cc, img, quiet=quiet)\\n cPickle.dump(feat_vect, open(savefilename,'w'))\\n return feat_vect\",\n \"def add_feature(x, x1):\\n if x is None:\\n x = x1\\n else:\\n x = np.concatenate((x, x1), axis=1)\\n return x\",\n \"def __init__(self, feature_vectors):\\n # Initialize key variables\\n (rows, _) = feature_vectors.shape\\n\\n # Append a column of ones to array\\n ones = np.ones((rows, 1))\\n kessler_array = np.hstack((ones, feature_vectors))\\n\\n # Make array available to rest of class\\n self.data = kessler_array.tolist()\",\n \"def __add__(self, other):\\n # other is a scalar\\n if isinstance(other, (int, float, complex, Fraction)) and not isinstance(other, bool):\\n return Vector([i + other for i in self.data], self.column)\\n # other is a Vector\\n elif isinstance(other, Vector):\\n if len(self.data) != len(other):\\n raise Exception('Vectors are not of equal length')\\n elif self.column != other.column:\\n raise Exception('Vectors are not of equal orientation')\\n else:\\n return Vector([self.data[i] + other.data[i] for i in range(len(self.data))], self.column)\\n # other is not a scalar or a Vector\\n else:\\n raise Exception('Argument is not a number or a Vector') from TypeError\",\n \"def __add__(self, other):\\n if len( self) != len(other):\\n raise ValueError('Dimensions must match.')\\n result = Vector(len(self))\\n for i in range(len(self)):\\n result[i] = self[i] + other[i]\\n return result\",\n \"def processFeature(prevWord, word, vector):\\n \\n # We add feature whether it exists or not\\n unigram, exists = vector.getUnigram(prevWord)\\n if not exists:\\n vector.addUnigram(prevWord)\\n \\n \\n bigram, exists = vector.getBigram(prevWord, word)\\n if not exists:\\n vector.addBigram(prevWord, word)\",\n \"def add_vectors(u, v): #11.22.5\\r\\n new_vector = []\\r\\n \\\"\\\"\\\"Because they have same length so we\\r\\n should take advantage from this one\\\"\\\"\\\"\\r\\n for i in range(len(u)):\\r\\n m = u[i] + v[i] # Get their value of i index at the same time!\\r\\n new_vector.append(m)\\r\\n return new_vector\",\n \"def get_augmented_feature_vectors(feature_vectors):\\n augmented = []\\n for i, vector in enumerate(feature_vectors): # each vector is a list of tuples\\n topics = [tup[0] for tup in vector]\\n for t in range(7): # I finally settled on 7 topics\\n if t not in topics:\\n feature_vectors[i].append((t, 0))\\n new_feature_vector = sorted(feature_vectors[i], key=lambda tup: tup[0])\\n augmented.append([tup[1] for tup in new_feature_vector])\\n return augmented\",\n \"def add_feature(X, feature_to_add):\\n from scipy.sparse import csr_matrix, hstack\\n return hstack([X, csr_matrix(feature_to_add).T], 'csr')\",\n \"def add_vector_fields(attributes, data):\\n for attrib in attributes:\\n if attrib['similarity'] == 'Semantic USE':\\n value = data.get(attrib['name'])\\n if value is not None:\\n newVal = {}\\n newVal['name'] = value\\n newVal['rep'] = getVector(value)\\n data[attrib['name']] = newVal\\n elif attrib['similarity'] == 'Semantic SBERT':\\n value = data.get(attrib['name'])\\n if value is not None:\\n newVal = {}\\n newVal['name'] = value\\n newVal['rep'] = getVectorSemanticSBERT(value)\\n data[attrib['name']] = newVal\\n elif attrib['similarity'] == 'Array SBERT':\\n value = data.get(attrib['name'])\\n if value is not None:\\n newVal = {}\\n newVal['name'] = value\\n newVal[\\\"rep\\\"] = []\\n array = getVectorSemanticSBERTArray(value)\\n for element in array:\\n temp = {}\\n temp['rep'] = element\\n newVal[\\\"rep\\\"].append(temp)\\n\\n data[attrib['name']] = newVal\\n return data\",\n \"def add_vectors(coord, vector):\\n return tuple(c1+c2 for c1,c2 in zip(coord, vector))\",\n \"def vector_add(v, w):\\n return [v_i + w_i for v_i, w_i in zip(v, w)]\",\n \"def add_mesh_features(self, file_list):\\n self.geom.add_features(file_list)\",\n \"def f_vector(self):\\n try:\\n return self._f_vector\\n except AttributeError:\\n self._f_vector = vector(ZZ,[len(x) for x in self.face_lattice().level_sets()])\\n return self._f_vector\",\n \"def vector_add(v1, v2):\\n return v1[0] + v2[0], v1[1] + v2[1]\",\n \"def append(self, featureName, featureData):\\n self.data = np.concatenate((self.data, np.array([featureData]).T), axis=1)\\n self.featureNames = np.append(self.featureNames, featureName)\\n return 0\",\n \"def sum_vectors(vector_1, vector_2):\\n new_coordinates = []\\n index = 0\\n while index < vector_1.dimension:\\n new_value = vector_1.coordinates[index] + vector_2.coordinates[index]\\n new_coordinates.append(new_value)\\n index += 1\\n new_vector = Vector(new_coordinates)\\n return new_vector\",\n \"def put_vector(self, term, vector):\\n self.terms.append(term)\\n self.vectors.append(vector.vector)\\n self.real_vectors.append(vector)\\n return self.dict.update({term: vector})\",\n \"def __add__(self, other) -> 'MultiVector':\\n\\n other, mv = self._checkOther(other)\\n if not mv:\\n if isinstance(other, np.ndarray):\\n obj = self.__array__()\\n return obj + other\\n newValue = self.value + other.value\\n\\n return self._newMV(newValue)\",\n \"def vector_add(v, w):\\n return [v_i + w_i for v_i, w_i in zip(v,w)]\",\n \"def add_vectorlist(vectors):\\n x, y, z = zip(*vectors)\\n return sum(x), sum(y), sum(z)\",\n \"def __add__(self, other):\\n\\t\\tif len(self) != len(other):\\n\\t\\t\\traise ValueError('dimensions must agree')\\n\\t\\tresult = Vector(len(self))\\n\\t\\tfor j in range(len(self)):\\n\\t\\t\\tresult[j] = self[j] + other[j]\\n\\t\\treturn result\",\n \"def __add__(self, other):\\n if isinstance(other, (int, type(Zero()))):\\n if (other == 0):\\n return self\\n self._check_vector(other)\\n return Vector(self.args + other.args)\",\n \"def extend(self, iterable):\\n self._vectors.extend(Vec2(*vector) for vector in iterable)\",\n \"def update(self, clip_id, feature_vec=None, is_background=False,\\n timeout=None):\\n with self._rw_lock.write_lock(timeout):\\n clip_id = int(clip_id)\\n if feature_vec is not None and \\\\\\n not (feature_vec.ndim == 1\\n and len(feature_vec) == self._feature_mat.shape[1]):\\n raise ValueError(\\\"Given feature vector not compatible \\\"\\n \\\"(dimensionality or length does not match)\\\")\\n\\n # Update the given feature vector and kernel distances\\n # if self._cid2idx_map.get(clip_id, None) is not None:\\n if clip_id in self._cid2idx_map:\\n # In all cases, update the background status of the clip\\n if is_background:\\n self._bg_clip_ids.add(clip_id)\\n else:\\n self._bg_clip_ids.discard(clip_id)\\n\\n # If we were given a new feature vector, update entries\\n if feature_vec is not None:\\n idx = self._cid2idx_map[clip_id]\\n self._feature_mat[idx] = feature_vec\\n new_dist = np.mat(tuple(\\n self._histogram_intersection_distance(feature_vec, fv)\\n for fv in self._feature_mat\\n ))\\n self._kernel_mat[idx, :] = new_dist\\n self._kernel_mat[:, idx] = new_dist\\n\\n # Given a new clip id to add, must have feature to add.\\n else:\\n if feature_vec is None:\\n raise ValueError(\\\"Update given a new clip ID, but no \\\"\\n \\\"feature vector. Feature vectors are \\\"\\n \\\"required with new IDs.\\\")\\n\\n # Update internal feature matrix with added vector\\n self._cid2idx_map[clip_id] = self._id_vector.size\\n self._id_vector.resize((self._id_vector.size + 1,),\\n refcheck=False)\\n self._id_vector[-1] = clip_id\\n\\n if is_background:\\n self._bg_clip_ids.add(clip_id)\\n\\n # noinspection PyUnresolvedReferences\\n if self._feature_mat.base is not None:\\n raise RuntimeError(\\\"Feature matrix does not own its data\\\")\\n # Since we're only adding a new row, this resize does not affect\\n # the positioning of the existing data.\\n self._log.debug(\\\"Updating feature matrix...\\\")\\n # noinspection PyUnresolvedReferences\\n self._feature_mat.resize((self._feature_mat.shape[0] + 1,\\n self._feature_mat.shape[1]),\\n refcheck=False\\n )\\n self._feature_mat[-1, :] = feature_vec\\n\\n # Need to add a new row AND column to the distance kernel.\\n self._log.debug(\\\"Updating kernel matrix...\\\")\\n if self._kernel_mat.base is not None:\\n raise RuntimeError(\\\"kernel matrix does not own its data\\\")\\n assert self._kernel_mat.shape[0] == self._kernel_mat.shape[1], \\\\\\n \\\"kernel matrix is not symmetric for some reason???\\\"\\n # noinspection PyPep8Naming\\n # -> because I like ``N`` better...\\n N = self._kernel_mat.shape[0]\\n kernel_copy = np.matrix(self._kernel_mat)\\n self._kernel_mat.resize((N+1, N+1), refcheck=False)\\n self._kernel_mat[:N, :N] = kernel_copy\\n del kernel_copy\\n\\n # Computing new feature distance (histogram intersection). Only\\n # need to compute this once because of HI being being\\n # commutative and the kernel matrix being symmetric.\\n self._log.debug(\\\"Adding new distance vectors...\\\")\\n dist_vec = np.mat(tuple(\\n self._histogram_intersection_distance(feature_vec, fv)\\n for fv in self._feature_mat\\n ))\\n self._kernel_mat[-1, :] = dist_vec\\n self._kernel_mat[:, -1] = dist_vec.T\",\n \"def __add__(self, other):\\n if isinstance(other, Vector):\\n a = self._ar + other._ar\\n else:\\n a = self._ar + numpy.array(other)\\n return Vector(a)\",\n \"def add_vector_of_ones(X):\\n m, n = X.shape\\n vector_of_ones = np.ones([m, 1])\\n X_with_ones = np.hstack([vector_of_ones, X])\\n return X_with_ones\",\n \"def vectorAdd(a, b):\\n return [a[i] + b[i] for i, j in enumerate(a)]\",\n \"def sum(self, vector):\\n\\n # return (self.from_list([x+vector.vector[self.vector.index(x)]\\n # for x in self.vector]))\\n return Vector(self.x + vector.x, self.y + vector.y, self.z + vector.z)\",\n \"def creating_feature_vector():\\r\\n\\twordlist = []\\r\\n\\tlabel = \\\"\\\"\\r\\n\\tfw = open(\\\"feature_vector.txt\\\", \\\"w+\\\", encoding = \\\"utf-8\\\")\\r\\n\\twith open(\\\"D:\\\\\\\\Python_Prac\\\\\\\\wordstag\\\\\\\\modules\\\\\\\\HI_EN_TRAIN.txt\\\", \\\"r\\\", encoding = \\\"utf-8\\\") as f:\\r\\n\\t\\tfor line in f:\\r\\n\\t\\t\\twordlist.append(line)\\r\\n\\t\\tfor index, line in enumerate(wordlist):\\r\\n\\t\\t\\tif line == \\\"\\\\n\\\":\\r\\n\\t\\t\\t\\tcontinue\\r\\n\\t\\t\\tcontext = line.split(\\\"\\\\t\\\")\\r\\n\\t\\t\\tlabel = context[1]\\r\\n\\t\\t\\tfeature_vector = label+\\\" \\\"\\r\\n\\t\\t\\tngram_vector = ngram_frequency(str(context[0]))\\r\\n\\t\\t\\tfor vector in ngram_vector:\\r\\n\\t\\t\\t\\tfeature_vector += str(vector)+\\\" \\\"\\r\\n\\t\\t\\tfeature_vector += str(is_english(context[0]))+\\\" \\\"\\r\\n\\t\\t\\tfeature_vector += str(is_hindi(context[0]))+\\\" \\\"\\r\\n\\t\\t\\tfeature_vector += str(is_abbr(context[0]))+\\\" \\\"\\r\\n\\t\\t\\tfeature_vector += str(med_in_english(context[0]))+\\\" \\\"\\r\\n\\t\\t\\tfeature_vector += str(med_in_hindi(context[0]))+\\\" \\\"\\r\\n\\t\\t\\tbefore = [0,0,0]\\r\\n\\t\\t\\tafter = [0,0,0]\\r\\n\\t\\t\\tfor i in range(3):\\r\\n\\t\\t\\t\\tif (index-i) < 0 or (index-i+1) > len(wordlist)-1:\\r\\n\\t\\t\\t\\t\\tcontinue\\r\\n\\t\\t\\t\\tbefore[2-i] = get_word_context(wordlist[index-i+1].split(\\\"\\\\t\\\")[0])\\r\\n\\t\\t\\tfor i in range(3):\\r\\n\\t\\t\\t\\tif (index+i+1) > len(wordlist)-1:\\r\\n\\t\\t\\t\\t\\tcontinue\\r\\n\\t\\t\\t\\tafter[2-i] = get_word_context(wordlist[index+i+1].split(\\\"\\\\t\\\")[0])\\r\\n\\t\\t\\tfor i in before:\\r\\n\\t\\t\\t\\tfeature_vector += str(i)+\\\" \\\"\\r\\n\\t\\t\\tfor i in after:\\r\\n\\t\\t\\t\\tfeature_vector += str(i)+\\\" \\\"\\r\\n\\t\\t\\tfeature_vector += \\\"\\\\n\\\"\\r\\n\\t\\t\\tfw.write(feature_vector)\\r\\n\\t\\t\\tprint(\\\"Proceeding...\\\"+str(index+1)+\\\" of 16683\\\")\\r\\n\\r\\n\\tfw.close()\",\n \"def _addVectors(X1,X2):\\n _checkSize(X1,X2)\\n return [ X1[i] + X2[i] for i in range(len(X1))]\",\n \"def rebuild_all(self, vector=None):\\n if not hasattr(self, \\\"positions\\\"):\\n self.load_data()\\n\\n if vector is not None and not isinstance(vector, np.ndarray):\\n raise ValueError(\\\"`vector` must be a 1-d numpy array\\\")\\n\\n if vector is not None:\\n assert vector.shape == self.params.shape, \\\\\\n \\\"Incorrect `vector` shape. Is {}, but should be {}\\\".format(\\n vector.shape, self.params.shape\\n )\\n # Copy to break references and avoid later manipulation by RNG\\n self.sample = (self.rng.next() if vector is None else vector).copy()\\n self.rebuild_technosphere_matrix(self.tech_sample)\\n self.rebuild_biosphere_matrix(self.bio_sample)\\n if self.lcia:\\n self.rebuild_characterization_matrix(self.cf_sample)\\n if self.weighting:\\n self.weighting_value = self.weighting_sample\\n\\n if self.presamples:\\n self.presamples.update_matrices()\",\n \"def add_new_features(self):\\r\\n curr_img = self.cam0_curr_img_msg.image\\r\\n grid_height, grid_width = self.get_grid_size(curr_img)\\r\\n\\r\\n # Create a mask to avoid redetecting existing features.\\r\\n mask = np.ones(curr_img.shape[:2], dtype='uint8')\\r\\n\\r\\n for feature in chain.from_iterable(self.curr_features):\\r\\n x, y = map(int, feature.cam0_point)\\r\\n mask[y-3:y+4, x-3:x+4] = 0\\r\\n\\r\\n # Detect new features.\\r\\n new_features = self.detector.detect(curr_img, mask=mask)\\r\\n\\r\\n # Collect the new detected features based on the grid.\\r\\n # Select the ones with top response within each grid afterwards.\\r\\n new_feature_sieve = [[] for _ in range(self.config.grid_num)]\\r\\n for feature in new_features:\\r\\n row = int(feature.pt[1] / grid_height)\\r\\n col = int(feature.pt[0] / grid_width)\\r\\n code = row * self.config.grid_col + col\\r\\n new_feature_sieve[code].append(feature)\\r\\n\\r\\n new_features = []\\r\\n for features in new_feature_sieve:\\r\\n if len(features) > self.config.grid_max_feature_num:\\r\\n features = sorted(features, key=lambda x:x.response, \\r\\n reverse=True)[:self.config.grid_max_feature_num]\\r\\n new_features.append(features)\\r\\n new_features = list(chain.from_iterable(new_features))\\r\\n\\r\\n # Find the stereo matched points for the newly detected features.\\r\\n cam0_points = [kp.pt for kp in new_features]\\r\\n cam1_points, inlier_markers = self.stereo_match(cam0_points)\\r\\n\\r\\n cam0_inliers, cam1_inliers, response_inliers = [], [], []\\r\\n for i, inlier in enumerate(inlier_markers):\\r\\n if not inlier:\\r\\n continue\\r\\n cam0_inliers.append(cam0_points[i])\\r\\n cam1_inliers.append(cam1_points[i])\\r\\n response_inliers.append(new_features[i].response)\\r\\n # if len(cam0_inliers) < max(5, len(new_features) * 0.1):\\r\\n\\r\\n # Group the features into grids\\r\\n grid_new_features = [[] for _ in range(self.config.grid_num)]\\r\\n for i in range(len(cam0_inliers)):\\r\\n cam0_point = cam0_inliers[i]\\r\\n cam1_point = cam1_inliers[i]\\r\\n response = response_inliers[i]\\r\\n\\r\\n row = int(cam0_point[1] / grid_height)\\r\\n col = int(cam0_point[0] / grid_width)\\r\\n code = row*self.config.grid_col + col\\r\\n\\r\\n new_feature = FeatureMetaData()\\r\\n new_feature.response = response\\r\\n new_feature.cam0_point = cam0_point\\r\\n new_feature.cam1_point = cam1_point\\r\\n grid_new_features[code].append(new_feature)\\r\\n\\r\\n # Sort the new features in each grid based on its response.\\r\\n # And collect new features within each grid with high response.\\r\\n for i, new_features in enumerate(grid_new_features):\\r\\n for feature in sorted(new_features, key=lambda x:x.response, \\r\\n reverse=True)[:self.config.grid_min_feature_num]:\\r\\n self.curr_features[i].append(feature)\\r\\n self.curr_features[i][-1].id = self.next_feature_id\\r\\n self.curr_features[i][-1].lifetime = 1\\r\\n self.next_feature_id += 1\",\n \"def reduce_dimensions(feature_vectors_full, model):\\n\\n if 'fvectors' in model:\\n v = np.array(model['fvectors'])\\n else:\\n #Principal Components Analysis implemented from lab code\\n covx = np.cov(feature_vectors_full, rowvar=0)\\n N = covx.shape[0]\\n w, v = scipy.linalg.eigh(covx, eigvals=(N - 40, N - 1))\\n v = np.fliplr(v)\\n model['fvectors'] = v.tolist()\\n pca_train = np.dot((feature_vectors_full - np.mean(feature_vectors_full)), v)\\n return pca_train[:,0:10]\",\n \"def _create_feature_vec():\\n\\tnum_tags = NGRAM_TUPLE[0]\\n\\tfvec = []\\n\\tfor _, size in FEATURE_TUPLE:\\n\\t\\tfvec.append(np.zeros((num_tags, size)))\\n\\n\\t# Append tag ngram weights to end\\n\\tfvec.append(np.zeros((num_tags, num_tags)))\\n\\treturn fvec\",\n \"def mfcc_features(self, audio, rate, numcep = 20, nfft = 2000, N = 2):\\n self.mfcc = python_speech_features.mfcc(audio, rate, numcep = numcep, nfft = nfft)\\n #self.mfcc = preprocessing.scale(self.mfcc)\\n \\n self.delta_mfcc = python_speech_features.delta(self.mfcc, N)\\n \\n self.mfcc_feature = np.hstack((self.mfcc, self.delta_mfcc))\\n \\n return self.mfcc_feature\",\n \"def assemble_vector(form: _forms, constraint: MultiPointConstraint, b: Optional[_PETSc.Vec] = None) -> _PETSc.Vec:\\n\\n _log.log(_log.LogLevel.INFO, \\\"Assemble MPC vector\\\")\\n timer_vector = Timer(\\\"~MPC: Assemble vector (numba)\\\")\\n\\n # Unpack Function space data\\n V = form.function_spaces[0]\\n x_dofs = V.mesh.geometry.dofmap\\n x = V.mesh.geometry.x\\n dofs = V.dofmap.map()\\n block_size = V.dofmap.index_map_bs\\n\\n # Data from multipointconstraint\\n coefficients = constraint.coefficients()[0]\\n masters_adj = constraint.masters\\n c_to_s_adj = constraint.cell_to_slaves\\n cell_to_slave = c_to_s_adj.array\\n c_to_s_off = c_to_s_adj.offsets\\n is_slave = constraint.is_slave\\n mpc_data = (masters_adj.array, coefficients, masters_adj.offsets, cell_to_slave, c_to_s_off, is_slave)\\n slave_cells = extract_slave_cells(c_to_s_off)\\n\\n # Get index map and ghost info\\n if b is None:\\n index_map = constraint.function_space.dofmap.index_map\\n vector = _la.create_petsc_vector(index_map, block_size)\\n else:\\n vector = b\\n\\n # Pack constants and coefficients\\n form_coeffs = _cpp.fem.pack_coefficients(form._cpp_object)\\n form_consts = _cpp.fem.pack_constants(form._cpp_object)\\n\\n tdim = V.mesh.topology.dim\\n num_dofs_per_element = V.dofmap.dof_layout.num_dofs\\n\\n # Assemble vector with all entries\\n with vector.localForm() as b_local:\\n _cpp.fem.assemble_vector(b_local.array_w, form._cpp_object,\\n form_consts, form_coeffs)\\n\\n # Check if we need facet permutations\\n # FIXME: access apply_dof_transformations here\\n e0 = form.function_spaces[0].element\\n needs_transformation_data = e0.needs_dof_transformations or form._cpp_object.needs_facet_permutations\\n cell_perms = numpy.array([], dtype=numpy.uint32)\\n if needs_transformation_data:\\n V.mesh.topology.create_entity_permutations()\\n cell_perms = V.mesh.topology.get_cell_permutation_info()\\n if e0.needs_dof_transformations:\\n raise NotImplementedError(\\\"Dof transformations not implemented\\\")\\n # Assemble over cells\\n subdomain_ids = form._cpp_object.integral_ids(_fem.IntegralType.cell)\\n num_cell_integrals = len(subdomain_ids)\\n\\n is_complex = numpy.issubdtype(_PETSc.ScalarType, numpy.complexfloating)\\n nptype = \\\"complex128\\\" if is_complex else \\\"float64\\\"\\n ufcx_form = form.ufcx_form\\n if num_cell_integrals > 0:\\n V.mesh.topology.create_entity_permutations()\\n\\n # NOTE: This depends on enum ordering in ufcx.h\\n cell_form_pos = ufcx_form.form_integral_offsets[0]\\n for i, id in enumerate(subdomain_ids):\\n cell_kernel = getattr(ufcx_form.form_integrals[cell_form_pos + i], f\\\"tabulate_tensor_{nptype}\\\")\\n active_cells = form._cpp_object.domains(_fem.IntegralType.cell, id)\\n coeffs_i = form_coeffs[(_fem.IntegralType.cell, id)]\\n with vector.localForm() as b:\\n assemble_cells(numpy.asarray(b), cell_kernel, active_cells[numpy.isin(active_cells, slave_cells)],\\n (x_dofs, x), coeffs_i, form_consts,\\n cell_perms, dofs, block_size, num_dofs_per_element, mpc_data)\\n\\n # Assemble exterior facet integrals\\n subdomain_ids = form._cpp_object.integral_ids(_fem.IntegralType.exterior_facet)\\n num_exterior_integrals = len(subdomain_ids)\\n if num_exterior_integrals > 0:\\n V.mesh.topology.create_entities(tdim - 1)\\n V.mesh.topology.create_connectivity(tdim - 1, tdim)\\n # Get facet permutations if required\\n facet_perms = numpy.array([], dtype=numpy.uint8)\\n if form._cpp_object.needs_facet_permutations:\\n facet_perms = V.mesh.topology.get_facet_permutations()\\n perm = (cell_perms, form._cpp_object.needs_facet_permutations, facet_perms)\\n # NOTE: This depends on enum ordering in ufcx.h\\n ext_facet_pos = ufcx_form.form_integral_offsets[1]\\n for i, id in enumerate(subdomain_ids):\\n facet_kernel = getattr(ufcx_form.form_integrals[ext_facet_pos + i],\\n f\\\"tabulate_tensor_{nptype}\\\")\\n coeffs_i = form_coeffs[(_fem.IntegralType.exterior_facet, id)]\\n facets = form._cpp_object.domains(_fem.IntegralType.exterior_facet, id)\\n facet_info = pack_slave_facet_info(facets, slave_cells)\\n num_facets_per_cell = len(V.mesh.topology.connectivity(tdim, tdim - 1).links(0))\\n with vector.localForm() as b:\\n assemble_exterior_slave_facets(numpy.asarray(b), facet_kernel, facet_info, (x_dofs, x),\\n coeffs_i, form_consts, perm,\\n dofs, block_size, num_dofs_per_element, mpc_data, num_facets_per_cell)\\n timer_vector.stop()\\n return vector\",\n \"def create_bitvector(bitvector, *bits):\\n if not bitvector in bitvectors:\\n bitvectors[bitvector] = []\\n \\n if bits:\\n bitvectors[bitvector].extend(bits)\",\n \"def __iadd__(self, other):\\n if isinstance(other, Seq2):\\n if len(self) == len(other):\\n self._vectors = [a + b for a, b in zip(self, other)]\\n return self\\n else:\\n raise ValueError(\\\"cannot add arrays with different lengths\\\")\\n else:\\n try:\\n b = Vec2(*other)\\n except Exception:\\n return NotImplemented\\n self._vectors = [a + b for a in self]\\n return self\",\n \"def create_vectorized_features(data_dir, feature_version=2):\\n extractor = PEFeatureExtractor(feature_version)\\n\\n print(\\\"Vectorizing training set\\\")\\n X_path = os.path.join(data_dir, \\\"X_train.dat\\\")\\n y_path = os.path.join(data_dir, \\\"y_train.dat\\\")\\n raw_feature_paths = [os.path.join(data_dir, \\\"train_features_{}.jsonl\\\".format(i)) for i in range(6)]\\n nrows = sum([1 for fp in raw_feature_paths for line in open(fp)])\\n vectorize_subset(X_path, y_path, raw_feature_paths, extractor, nrows)\\n\\n print(\\\"Vectorizing test set\\\")\\n X_path = os.path.join(data_dir, \\\"X_test.dat\\\")\\n y_path = os.path.join(data_dir, \\\"y_test.dat\\\")\\n raw_feature_paths = [os.path.join(data_dir, \\\"test_features.jsonl\\\")]\\n nrows = sum([1 for fp in raw_feature_paths for line in open(fp)])\\n vectorize_subset(X_path, y_path, raw_feature_paths, extractor, nrows)\",\n \"def __add__(self, other):\\n if len(self) != len(other):\\n raise ValueError('As dimensões devem ser iguais')\\n\\n result = Vector(len(self)) # inicia um novo array do tamanho do próprio\\n for i in range(len(self)):\\n result[i] = self[i] + other[i]\\n return result\",\n \"def addAt(self, vector, id1, id2):\\n self.matrix.update_add_at(vector,\\n numerix.asarray(id1, dtype='int32'),\\n numerix.asarray(id2, dtype='int32'))\",\n \"def CreateVector(self) -> BaseVector:\",\n \"def numpy_vector(self):\\n pass\",\n \"def add(v: Vector, w: Vector) -> Vector:\\n assert len(v) == len(w), 'both vectors must have the same length'\\n\\n return [v_item + w_item for v_item, w_item in zip(v, w)]\",\n \"def vectorizer_features(self) -> list:\\n if self._vectorizer:\\n return self._vectorizer.get_feature_names()\\n self.logger.warning('Uninitialized vector. Please call count_vectorizer first.')\",\n \"def add_vector(self, name, text, tag=None):\\n words = self.clean_text_util.clean_text(text)\\n \\n # max{f(w,d) : w ∈ d)}\\n counter = Counter(words)\\n _, max_occ = counter.most_common(1)[0] \\n\\n # remove duplicate word\\n words = set(words)\\n \\n items = []\\n for word in words:\\n pickle_wordinfo = self.dictionary_db.get(word)\\n if not pickle_wordinfo:\\n continue\\n \\n word_info = pickle.loads(pickle_wordinfo)\\n\\n # tf formula: tf(f,d) = f(f,d)/max{f(w,d) : w ∈ d)} (src Wikipedia)\\n tf = counter[word]/float(max_occ)\\n\\n # create a new vector item entry\\n items.append(VectorItem(word, tf))\\n\\n # sort the vector item by the dictionary index\\n items.sort(key=lambda x: x.word_info(self.dictionary_db).index)\\n\\n # finally, we create a new vector\\n vector = Vector(items, tag)\\n self.vectors_db.add(name, pickle.dumps(vector))\\n\\n # add an empty entry to the norm db\\n self.vectors_norm_db.add(name, self.vector_tfidf_norm(items))\",\n \"def homogeneous_vector(self, base_ring=None):\\n v = list(self._vector) + [0]\\n return vector(base_ring or self._base_ring, v)\",\n \"def homogeneous_vector(self, base_ring=None):\\n v = list(self._vector) + [0]\\n return vector(base_ring or self._base_ring, v)\",\n \"def add_feature(self, feature):\\n self.features += [feature]\\n for stock in self.stocks:\\n feature(self.stock_data[stock])\",\n \"def __iadd__( self, vector3 ):\\n return self.add( vector3 )\",\n \"def create_feature_vector(features, length):\\n START_IDX = 0\\n END_IDX = 1\\n\\n output_vector = np.zeros(length)\\n\\n # negative strand\\n for loc in features[-1]:\\n output_vector[loc[START_IDX]:loc[END_IDX]] = 1 \\n\\n # positive strand\\n for loc in features[1]:\\n output_vector[loc[START_IDX]:loc[END_IDX]] = 2\\n\\n return output_vector\",\n \"def apply_optimization_vector(self, vector: np.ndarray) -> None:\\n # we are going to iterate through the given vector;\\n # an iterator allows us to next()\\n # (aka `pop`) the values only when desired;\\n # we only update the current vector where the mask is True\\n vector_iterator = iter(vector)\\n updated_vector = [v if not m else next(vector_iterator)\\n for v, m in zip(self.vector,\\n self.optimization_mask)]\\n updated_links = self.array_2_links(np.array(updated_vector),\\n self.convention)\\n self.links = updated_links\",\n \"def vector_add(a, b):\\n assert(len(a) == len(b))\\n\\n from operator import add\\n return tuple(map(add, a, b))\",\n \"def basic_fields(self, basic_fields):\\n\\n self._basic_fields = basic_fields\",\n \"def homogeneous_vector(self, base_ring=None):\\n v = list(self._vector) + [1]\\n return vector(base_ring or self._base_ring, v)\",\n \"def project_vectors(self, vectors):\\n return np.dot(vectors, self.components.T)\",\n \"def __add_answered_on_feature(samples: List[TrainSample], all_features: np.array) -> np.array:\\n new_features = []\\n for sample, features in zip(samples, all_features):\\n if isinstance(sample.selected_player, bool):\\n answered_on = 1.0 if sample.selected_player else 0.0\\n else:\\n answered_on = 1.0 if sample.selected_player in sample.answer else 0.0\\n features = np.append(features, answered_on * features)\\n features = np.append(features, [answered_on])\\n new_features.append(features)\\n return np.array(new_features)\",\n \"def update(self, learning_rate, influence, input_vector, bmu):\\n factor = learning_rate * influence\\n self.vector = [x + factor * (y - x)\\n for x, y in zip(self.vector, input_vector)]\",\n \"def __add__(self, v):\\n return vector(self.x + v.x, self.y + v.y, self.z + v.z)\",\n \"def add_vectors(v, u):\\n return (v[0] + u[0], v[1] + u[1])\",\n \"def add(self, keys: List[Tuple[int, int]], vectors: np.ndarray, weights: List[float], *args, **kwargs):\\n pass\",\n \"def updateAttributesAfterAdding(self):\\n layer = self.sender()\\n while self.addedFeatures:\\n featureId = self.addedFeatures.pop()\\n #begining the edit command\\n # layer.beginEditCommand(self.tr(\\\"DSG Tools reclassification tool: adjusting feature's attributes\\\"))\\n #accessing added features\\n editBuffer = layer.editBuffer()\\n features = editBuffer.addedFeatures()\\n for key in features.keys():\\n #just checking the newly added feature, the other I don't care\\n if key == featureId:\\n feature = features[key]\\n #setting the attributes using the reclassification dictionary\\n self.setFeatureAttributes(feature, editBuffer)\\n layer.endEditCommand()\",\n \"def _vectorize(self, vectorizer = None):\\n\\n\\t\\tvectorizer = vectorizer if vectorizer else self.vectorizer;\\n\\n\\t\\tself.training_set_vector = vectorizer.fit_transform(self.training_set)\\n\\n\\t\\tself.testing_set_vector = vectorizer.transform(self.testing_set)\",\n \"def _add_feature(self, feature):\\n\\n if feature.name in self.feature_name_index:\\n logger.info(\\\"Feature %s already exists at %i, overwriting\\\" %\\n (feature.name, self.feature_name_index[feature.name]))\\n self.features[self.feature_name_index[feature.name]] = feature\\n else:\\n self.features.append(feature)\\n self.feature_name_index[feature.name] = len(self.features) - 1\\n logger.info(\\\"Adding %s to model at location %i\\\" % (\\n feature.name, len(self.features)))\\n self._add_domain_fault_above(feature)\\n self._add_unconformity_above(feature)\\n feature.set_model(self)\",\n \"def __add__(self, v2):\\n\\t\\treturn Vect2D(self._vec+v2._vec)\",\n \"def feed(self, vector):\\n return vector\",\n \"def _concatenate_features(features):\\n pass\",\n \"def AsVector(self) -> BaseVector:\",\n \"def join_feature_vectors(vecs1, vecs2):\\n if len(vecs1) != len(vecs2) or len(vecs1) == 0:\\n print('Invalid number of feature vectors: ' + str(len(vecs1)) + ', ' + str(len(vecs2)))\\n return\\n\\n number_of_vecs = len(vecs1)\\n vec1_length = len(vecs1[0])\\n vec2_length = len(vecs2[0])\\n joined_vecs = np.zeros(shape=(number_of_vecs, vec1_length + vec2_length))\\n\\n for i in range(len(vecs1)):\\n joined_vecs[i, 0:vec1_length] = vecs1[i]\\n joined_vecs[i, vec1_length:vec1_length+vec2_length] = vecs2[i]\\n return joined_vecs\",\n \"def normalizeFeatureVector(self):\\n # Normalize features\\n total = 0.0\\n for v in self.features.values(): total += abs(v)\\n if total == 0.0: \\n total = 1.0\\n for k,v in self.features.iteritems():\\n self.features[k] = float(v) / total\",\n \"def calculateFeatures(self, special=None):\\n print(\\\"Computing features.\\\")\\n if not special:\\n self.feature_special = dict()\\n self.feature_special['fPCA'] = None\\n self.feature_special['wPCA'] = None\\n else:\\n self.feature_special = special\\n if not 'wPCA' in special.keys():\\n self.feature_special['wPCA'] = None\\n if not 'fPCA' in special.keys():\\n self.feature_special['fPCA'] = None\\n\\n self.features = []\\n self.features.append(features.Feature_Peak(self))\\n self.features.append(features.Feature_Energy(self))\\n self.features.append(features.Feature_Time(self))\\n self.features.append(features.Feature_Valley(self))\\n self.features.append(features.Feature_Trough(self))\\n\\n if self.use_pca:\\n self.features.append(\\n features.Feature_PCA(self, self.feature_special['fPCA']))\\n self.features.append(\\n features.Feature_Waveform_PCA(self,\\n self.feature_special['wPCA']))\\n self.feature_special['fPCA'] = self.featureByName('fPCA').coeff\\n self.feature_special['wPCA'] = self.featureByName('wPCA').coeff\",\n \"def insert(self, index, vector):\\n self._vectors.insert(index, Vec2(*vector))\",\n \"def pack_features_vector(features, labels):\\n features = tf.stack(list(features), axis=1)\\n return features, labels\",\n \"def zStoreForwardPropagate(self, inputVector):\\r\\n # Preform the forward propagation through the layers\\r\\n # setting the output of one layer to the input of the next\\r\\n for layer in self.layers:\\r\\n inputVector = layer.zStoreForwardPropagate(inputVector)\\r\\n # The output of the last layer is returned \\r\\n return inputVector\",\n \"def GetVectorArticleInput(dico_vector_input, features):\\n features_left = set(features) - set(dico_vector_input.keys())\\n if len(features_left) > 0:\\n sentence = \\\"Some features aren't in the dict:\\\\n\\\"\\n raise MyException(sentence + \\\"{}\\\".format(features_left))\\n vector_art = []\\n other_features = ['abstract', 'syn', 'exergue', 'title', 'secTitle']\\n other_features += ['subTitle', 'supTitle']\\n for feature in features:\\n if feature == 'nbSign':\\n if dico_vector_input['nbSign'] == 0:\\n print(\\\"NbSign == 0 l.176 - GetVectorArticleInput\\\")\\n vector_art.append(dico_vector_input[feature])\\n else:\\n vector_art.append(dico_vector_input[feature])\\n # Conversion des variables en indicatrices\\n # Normalement plus la peine, comme déjà fait auparavant\\n elif feature in other_features:\\n if dico_vector_input[feature] > 0:\\n vector_art.append(1)\\n else:\\n vector_art.append(0)\\n else:\\n vector_art.append(dico_vector_input[feature])\\n return (dico_vector_input['melodyId'], np.array([vector_art]))\",\n \"def vector_sum(vectors):\\n\\tresult = vectors[0]\\n\\tfor vector in vectors:\\n\\t\\tresult = vector_add(result, vector)\\n\\treturn result\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5917165","0.5735873","0.57002974","0.5500693","0.54043525","0.5337707","0.5288758","0.5286068","0.5255515","0.5167239","0.51532346","0.5146452","0.5016166","0.5007243","0.49999866","0.4954787","0.49387354","0.49348387","0.4906069","0.48973984","0.48947722","0.48843622","0.48737946","0.48659736","0.48557988","0.4851156","0.48325557","0.48302197","0.48273098","0.48233554","0.48207214","0.48203552","0.4814059","0.48084584","0.47967294","0.47724816","0.47676846","0.47674248","0.47627968","0.47627187","0.47603443","0.47562188","0.47554553","0.47521526","0.4751433","0.47508204","0.47465366","0.47431478","0.47302487","0.47209936","0.47202104","0.47072583","0.46999484","0.46943972","0.4685744","0.46820685","0.46757892","0.46717256","0.46667406","0.46651313","0.46629924","0.46547842","0.46376854","0.46296358","0.46227074","0.46171862","0.461234","0.45978665","0.45971566","0.45897746","0.45866412","0.45866412","0.45842877","0.45828158","0.45813686","0.45690688","0.4568086","0.4567427","0.45620322","0.4559214","0.45486456","0.4541927","0.45329472","0.4526106","0.45153803","0.45123315","0.45119604","0.4501903","0.4495487","0.44943866","0.44895935","0.44814152","0.44804716","0.4478129","0.4477728","0.44731006","0.44698936","0.44628683","0.4457999","0.44571254"],"string":"[\n \"0.5917165\",\n \"0.5735873\",\n \"0.57002974\",\n \"0.5500693\",\n \"0.54043525\",\n \"0.5337707\",\n \"0.5288758\",\n \"0.5286068\",\n \"0.5255515\",\n \"0.5167239\",\n \"0.51532346\",\n \"0.5146452\",\n \"0.5016166\",\n \"0.5007243\",\n \"0.49999866\",\n \"0.4954787\",\n \"0.49387354\",\n \"0.49348387\",\n \"0.4906069\",\n \"0.48973984\",\n \"0.48947722\",\n \"0.48843622\",\n \"0.48737946\",\n \"0.48659736\",\n \"0.48557988\",\n \"0.4851156\",\n \"0.48325557\",\n \"0.48302197\",\n \"0.48273098\",\n \"0.48233554\",\n \"0.48207214\",\n \"0.48203552\",\n \"0.4814059\",\n \"0.48084584\",\n \"0.47967294\",\n \"0.47724816\",\n \"0.47676846\",\n \"0.47674248\",\n \"0.47627968\",\n \"0.47627187\",\n \"0.47603443\",\n \"0.47562188\",\n \"0.47554553\",\n \"0.47521526\",\n \"0.4751433\",\n \"0.47508204\",\n \"0.47465366\",\n \"0.47431478\",\n \"0.47302487\",\n \"0.47209936\",\n \"0.47202104\",\n \"0.47072583\",\n \"0.46999484\",\n \"0.46943972\",\n \"0.4685744\",\n \"0.46820685\",\n \"0.46757892\",\n \"0.46717256\",\n \"0.46667406\",\n \"0.46651313\",\n \"0.46629924\",\n \"0.46547842\",\n \"0.46376854\",\n \"0.46296358\",\n \"0.46227074\",\n \"0.46171862\",\n \"0.461234\",\n \"0.45978665\",\n \"0.45971566\",\n \"0.45897746\",\n \"0.45866412\",\n \"0.45866412\",\n \"0.45842877\",\n \"0.45828158\",\n \"0.45813686\",\n \"0.45690688\",\n \"0.4568086\",\n \"0.4567427\",\n \"0.45620322\",\n \"0.4559214\",\n \"0.45486456\",\n \"0.4541927\",\n \"0.45329472\",\n \"0.4526106\",\n \"0.45153803\",\n \"0.45123315\",\n \"0.45119604\",\n \"0.4501903\",\n \"0.4495487\",\n \"0.44943866\",\n \"0.44895935\",\n \"0.44814152\",\n \"0.44804716\",\n \"0.4478129\",\n \"0.4477728\",\n \"0.44731006\",\n \"0.44698936\",\n \"0.44628683\",\n \"0.4457999\",\n \"0.44571254\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8391323"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":2899,"cells":{"query":{"kind":"string","value":"If there is at least an feature vector then returns it, else returns None"},"document":{"kind":"string","value":"def get_last_feature_vectors(self):\n if len(self._feature_vectors):\n return self._feature_vectors[-1]\n return None"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_training(feature_path): \n features = np.loadtxt(feature_path)\n feature_size = features.shape[1] -1 \n features_in = features[:,0:feature_size]\n features_out = features[:,-1]\n #features_out = np.array(map(lambda x: x if x else 0, features_out_unnorm))\n return features_in, features_out","def get_feature_by_name(self, feature_name):\n feature_index = self.feature_name_index.get(feature_name,-1)\n if feature_index > -1:\n return self.features[feature_index]\n else:\n logger.error(\"{} does not exist!\".format(feature_name))\n return None","def get_feature_vector(self, board):\n return self.hot_one(board)\n # return self.get_tesauro_feature_vector(self, board)","def _get_features(task, features, model, similarity_strategy=None):\n X = []\n langs = analysis_utils.get_langs_for_task(task)\n for feature in features:\n if feature != \"size\":\n # this is a nested array\n X_feature = analysis_utils.load_lang2vec_vectors(task=task, features=feature)\n if X_feature is None:\n #continue\n return None\n if similarity_strategy != \"-\":\n # We start with similarities to english\n X_feature = [[sim] for sim in analysis_utils.compute_similarities_of_lang_vecs(X_feature, strategy=similarity_strategy)]\n elif feature == \"size\" and model == \"xlmr\":\n # this is an array, we put it in a list\n X_feature = [[size] for size in analysis_utils.xlmr_input_corpus_sizes(langs)]\n elif feature == \"size\" and model == \"mbert\":\n X_feature = [[size] for size in analysis_utils.mbert_input_corpus_sizes(langs)]\n else:\n raise ValueError()\n # we now have a feature vector for a single feature or feature set\n if len(X) == 0:\n X = np.array(X_feature)\n else:\n X = np.concatenate((X,np.array(X_feature)), axis=1)\n if len(X) == 0:\n return None\n return np.array(X, dtype=float)","def featureByName(self, name):\n for feature in self.features:\n if feature.name == name:\n return feature\n return None","def get_features(self):\n if not self.exposes_features:\n return None\n\n return self._last_features","def __getitem__(self, feat):\n # We perform the test for presence explicitly, to maintain a consistent\n # notion of len(self). If we just returned self.features[k], the\n # defaultdict self.features could self.update(k=float()), thus\n # extending self's length by one.\n return self.features[feat] if feat in self.features else 0.","def feature_vector(features, vector):\n clean_features = set(features)\n new_features_vector = featurize(vector,clean_features)\n return new_features_vector","def _get_feature(self, layer_name):\n if (\n layer_name in self.config[\"layers\"]\n and \"feature\" in self.config[\"layers\"][layer_name]\n ):\n return self.config[\"layers\"][layer_name][\"feature\"]\n else:\n return 0","def getFeature(self, featureName):\n # loop through all the existing features\n for feature in self.features:\n # when we have a match with the name\n if featureName == feature.name:\n # return the value in the solution\n return feature\n # feature doesn't exist\n return None","def get_vector(self, token):\n try:\n idx = self.token_to_idx[token]\n except KeyError:\n print(\"Input token <{}> is not in the model. Will return None type vector\".format(token))\n return None\n return self.embeddings_mat[idx]","def feature_set(self) -> Optional[pulumi.Input['OrganizationFeatureSet']]:\n return pulumi.get(self, \"feature_set\")","def feature():\n pass","def get_vector(self) -> Optional[List[_Score]]:\n\n if len(self._vector) is 0:\n return None\n else:\n return self._vector","def get_feature(self, feature: FeatureName) -> FeatureInfo:\n if feature not in self.feature_map:\n state = FeatureState.Unsupported\n else:\n state = self.feature_map[feature]\n return FeatureInfo(state=state)","def get_features(words, vectors):\n result = [vectors.loc[word].values for word in words if word in df_keys.values.reshape(-1)]\n if result:\n return np.stack(result)\n return None","def filter_feature(feature, typ, value):\n return value is None or feature.__getattribute__(typ) == value","def parse_optional_vector(x, dtype=None):\n if x == 'none':\n return None\n else:\n return parse_vector(x, dtype)","def get_feature_vector(cc, img, quiet=False):\n savefilename = config.get_classifier_featvect_name(cc.d.images[img]) \n if os.path.isfile(savefilename):\n print 'load feat_vect %s'%(cc.d.images[img].name)\n feat_vect = cPickle.load(open(savefilename,'r'))\n else:\n feat_vect = compute_feature_vector(cc, img, quiet=quiet)\n cPickle.dump(feat_vect, open(savefilename,'w'))\n return feat_vect","def _get_relevant_features(self, X):\n if self.only_binary_features:\n feature_mask = which_columns_are_binary(X)\n else:\n feature_mask = np.ones(X.shape[1], dtype=bool)\n return feature_mask","def has_feature(self, feature):\n features = self.features\n if features is None:\n return False\n \n return feature in features","def extract_single_feature_vect(gray):\n\n hist, hog_img = skimHOG(gray)\n reduced = reduce_single_vector_dimension(hist)\n\n return reduced, hog_img","def get(self, name):\n try:\n return(self._d_features[name])\n except:\n log.error(\"Can't get feature '%s'\" % name)\n return","def select_features(vec):\n return sorted(vec, key=vec.get, reverse=True)[\n : min(len(vec), FEATURE_LENGTH)\n ]","def features(self) -> List[np.ndarray]:\n return None","def processFeature(prevWord, word, vector):\n \n # We add feature whether it exists or not\n unigram, exists = vector.getUnigram(prevWord)\n if not exists:\n vector.addUnigram(prevWord)\n \n \n bigram, exists = vector.getBigram(prevWord, word)\n if not exists:\n vector.addBigram(prevWord, word)","def features_size(self) -> int:\n return len(self.data[0].features) if len(self.data) > 0 and self.data[0].features is not None else None","def feature(self):\n return self._feature","def feature(self):\n return self._feature","def GetVectorArticleInput(dico_vector_input, features):\n features_left = set(features) - set(dico_vector_input.keys())\n if len(features_left) > 0:\n sentence = \"Some features aren't in the dict:\\n\"\n raise MyException(sentence + \"{}\".format(features_left))\n vector_art = []\n other_features = ['abstract', 'syn', 'exergue', 'title', 'secTitle']\n other_features += ['subTitle', 'supTitle']\n for feature in features:\n if feature == 'nbSign':\n if dico_vector_input['nbSign'] == 0:\n print(\"NbSign == 0 l.176 - GetVectorArticleInput\")\n vector_art.append(dico_vector_input[feature])\n else:\n vector_art.append(dico_vector_input[feature])\n # Conversion des variables en indicatrices\n # Normalement plus la peine, comme déjà fait auparavant\n elif feature in other_features:\n if dico_vector_input[feature] > 0:\n vector_art.append(1)\n else:\n vector_art.append(0)\n else:\n vector_art.append(dico_vector_input[feature])\n return (dico_vector_input['melodyId'], np.array([vector_art]))","def _rf_predict(self, feature_vec):\n if feature_vec not in self.rf_cache:\n np_feature = np.array(feature_vec).reshape(1, -1)\n self.rf_cache[feature_vec] = self.predictor.predict(np_feature)[0]\n return self.rf_cache[feature_vec]","def get_vector(self, word):\n\n if word in self.glove.stoi:\n return self.glove.vectors[self.glove.stoi[word]]\n else:\n return None","def vectorizer_features(self) -> list:\n if self._vectorizer:\n return self._vectorizer.get_feature_names()\n self.logger.warning('Uninitialized vector. Please call count_vectorizer first.')","def get_single_output_feature(model: BaseModel) -> BaseFeatureMixin:\n return next(iter(model.output_features.values()))","def findFeatures(self):\n\t\tpass","def value_head(features):\n with tf.variable_scope('critic', reuse=tf.AUTO_REUSE):\n features = tf.layers.dense(features, units=1, activation=None, name='output')\n return tf.squeeze(features, axis=-1)","def take_some_features(data,features,given=None):\n if given is None:\n return data,features\n common,ind1,ind2=take_common_features(features,given)\n data=data[:,ind1]\n features=features[ind1]\n return data,features","def choose_best_feature(data_set):\n feature_size = len(data_set[0]) - 1\n base_entropy = calc_entropy(data_set)\n best_info_gain = 0.0; best_feature = -1\n for i in xrange(feature_size):\n feat_list = [eg[i] for eg in data_set]\n unique_values = set(feat_list)\n new_entropy = 0.0\n for value in unique_values:\n sub_ds = splite_dataset(data_set, i, value)\n prob = len(sub_ds) / float(len(data_set))\n new_entropy += prob * calc_entropy(sub_ds)\n info_gain = base_entropy - new_entropy\n if info_gain > best_info_gain:\n best_info_gain = info_gain\n best_feature = i\n\n return best_feature","def get_model_feature(\n model,\n batch_x\n):\n features = model.get_feature(batch_x, training=False)\n return features","def num_features(self):\n if self.x is None:\n return 0\n return 1 if self.x.dim() == 1 else self.x.size(1)","def is_feature_layer(layer):\n return getattr(layer, '_is_feature_layer', False)","def extractFeatures(self, data, tf=False):\n tfidf_training_matrix, tfidf_terms = self.useTfidfVectorizer(data)\n \n if tf:\n tf_vectorizer = CountVectorizer(max_df=0.5, min_df=2, max_features=10000,\n stop_words='english')\n \n tf_training_matrix = tf_vectorizer.fit_transform(data)\n tf_terms = tf_vectorizer.get_feature_names()\n \n return tfidf_training_matrix, tfidf_terms, tf_training_matrix, tf_terms\n \n else:\n return tfidf_training_matrix, tfidf_terms","def _query(self, feature: str) -> np.ndarray:\n return np.flatnonzero(np.core.defchararray.find(self.internal_types, feature) != -1)","def _extract_feature(element):\n features = tf.parse_single_example(\n element,\n # Defaults are not specified since both keys are required.\n features={\n 'image/encoded': tf.FixedLenFeature([], tf.string),\n 'label/x': tf.FixedLenFeature([], tf.int64),\n 'label/y': tf.FixedLenFeature([], tf.int64)\n })\n return features","def is_vector(x):\r\n return len(x.shape) == 1","def getSentenceFeature(tokens, wordVectors, sentence):\n # Implement computation for the sentence features given a sentence. \n \n # Inputs: \n # - tokens: a dictionary that maps words to their indices in \n # the word vector list \n # - wordVectors: word vectors (each row) for all tokens \n # - sentence: a list of words in the sentence of interest \n\n # Output: \n # - sentVector: feature vector for the sentence \n\n sentence_vectors = [wordVectors[tokens[word]] for word in sentence]\n\n return sum(sentence_vectors) * 1.0 / len(sentence_vectors)","def add_dummy_feature(X, value=...):\n ...","def feature_set(self) -> pulumi.Output[Optional['OrganizationFeatureSet']]:\n return pulumi.get(self, \"feature_set\")","def features(self) -> List[np.ndarray]:\n if len(self.data) == 0 or self.data[0].features is None:\n return None\n\n return [d.features for d in self.data]","def safe_compute_features(*args):\n song_id, entity_type, crop, transform_config, features_config = args\n try:\n tf.logging.info(f\"Compute features for {song_id} on segment {crop}.\")\n features,_ = compute_features(song_id, entity_type, crop, transform_config, features_config)\n return features, False\n\n except Exception as err:\n tf.logging.warn(f\"Error while computing features for {song_id} on segment {crop}: {err}\")\n return np.float32(0.0), True","def input_fn(params, is_training):\n features = tf.constant(0)\n labels = tf.constant(0)\n\n return features, labels","def svm_classification(self):\n\n if len(self.saved_gestures.keys()) <= 1:\n print(\"Not enough gestures!\")\n return None\n else:\n x = []\n y = []\n z = []\n for elem in self.current_recording:\n x.append(elem[0][0])\n y.append(elem[1][0])\n z.append(elem[2][0])\n\n gesture_fft = self.get_fft(x, y, z)\n\n if len(gesture_fft) > self.cutoff_length:\n print(\"bigger than cutoff\")\n gesture_fft = gesture_fft[:self.cutoff_length]\n elif len(gesture_fft) < self.cutoff_length:\n\n print(\"smaller than cutoff\")\n temp = np.zeros(self.cutoff_length)\n for x in range(len(gesture_fft)):\n temp[x] = gesture_fft[x]\n gesture_fft = temp\n else:\n pass\n\n return self.classifier.predict(gesture_fft)","def features(self) -> Optional[pulumi.Input['ProvisionedClustersCommonPropertiesFeaturesArgs']]:\n return pulumi.get(self, \"features\")","def format_optional_vector(x):\n\n # If vector is None or all elements are NaN, then return none\n # Otherwise format the vector as normal\n if x is None or np.all(np.isnan(x)):\n return 'none'\n else:\n return format_vector(x)","def _extract_feature(self,f):\n if callable(f): \n return f()\n elif type(f) == tuple:\n return f[0](*list(f[1:]))","def _choose_best_feature(self, X, y, label, sample_weights=None):\n best_feature_idx = 0\n # YOUR CODE HERE\n # Note that you need to implement the sampling feature part here for random forest!\n # Hint: You may find `np.random.choice` is useful for sampling.\n # begin answer\n n_features = X.shape[1]\n if self.sample_feature:\n max_features=max(1, min(n_features, int(np.round(np.sqrt(n_features)))))\n new_features=np.random.choice(n_features, max_features, replace=False)\n new_X=X[:, new_features]\n else:\n new_X=X\n n_new_features=new_X.shape[1]\n #new_features=np.random.choice(n_features, n_features, replace=False)\n #old_cost=self.entropy(y, sample_weights)\n #use C4.5 algorirhm\n best_impurity=None\n best_feature_idx=0\n best_feature_val=X[0, 0]\n for i in range(n_new_features):\n unique_vals=np.unique(X[:,i])\n for value in unique_vals:\n sub1_X, sub1_y, label1, sub1_sample_weights, sub2_X, sub2_y, label2, sub2_sample_weights=self._split_dataset(X, y, label, i, value, sample_weights)\n if len(sub1_y)>0 and len(sub2_y)>0:\n new_impurity=self._impurity(y, sub1_y, sub2_y)\n if best_impurity is None or new_impurity > best_impurity:\n best_impurity=new_impurity\n best_feature_idx=i\n best_feature_val=value \n # end answer\n return best_feature_idx, best_feature_val","def extract_feature(self, atom_fea, nbr_fea, nbr_fea_idx, crystal_atom_idx):\n atom_fea = self.embedding(atom_fea)\n for conv_func in self.convs:\n atom_fea = conv_func(atom_fea, nbr_fea, nbr_fea_idx)\n feature = self.pooling(atom_fea, crystal_atom_idx)\n return feature","def mostConstrainingFeature(self):\n # keep track of which feature we'll choose next\n nextFeature = None\n # a counter for the minimum number of constraints\n maxCount = -1\n # loop through all the features\n for feature in self.features:\n # if this feature has a value then go back to the top of the loop and get\n # the next feature\n if (feature.value != 'none'):\n continue\n # get a list of all the constraints involving this feature\n constraintList = self.getOpenConstraints(feature.name)\n # compare the number of constraints involving this feature to the current max\n # if this is the first unassigned feature we found or this feature has the most\n # constraints we've found...\n if (len(constraintList) > maxCount):\n # save a pointer to the current feature with most constraints\n nextFeature = feature\n # save the max number of constraints\n maxCount = len(constraintList)\n # return the least constraining feature\n return nextFeature","def get_feature(model, img_tensor, feature_id, device):\n mean = torch.Tensor([0.485, 0.456, 0.406]).to(device).view(1, config.channels, 1, 1)\n std = torch.Tensor([0.229, 0.224, 0.225]).to(device).view(1, config.channels, 1, 1)\n img_normalized = (img_tensor - mean) / std\n feature = model(img_normalized, feature_id)\n return feature","def check_nullification(full_gam, feature_combination_full, threshold = 1e-7):\n\n nullified_features = []\n\n for i in range(len(feature_combination_full)):\n XX = full_gam.generate_X_grid(term=i)\n\n term_funct = full_gam.partial_dependence(term=i, X=XX)\n\n std = np.std(term_funct)# Use standard deviation to check for nullification\n\n if std < threshold:\n nullified_features.append(feature_combination_full[i])\n\n return nullified_features","def testGetFirstFeature(self):\r\n self.prepareTestCanvas()\r\n myLayer = self.bucketFill.getActiveVectorLayer()\r\n myTestBox = QgsRectangle(TEST_BOX[0], TEST_BOX[1],\r\n TEST_BOX[2], TEST_BOX[3])\r\n\r\n myFeatureCount = myLayer.featureCount()\r\n if myFeatureCount > 0:\r\n myFeature = self.bucketFill.getFirstFeature(myLayer, myTestBox)\r\n print myFeature\r\n myMessage = ('Returned object was not a feature.')\r\n assert myFeature.type() == QgsFeature, myMessage\r\n else:\r\n myMessage = ('No features found in layer.')\r\n assert 1 == 0, myMessage","def check_valid(feature: th.Tensor,\n num_frames: Optional[th.Tensor]) -> Tuple[th.Tensor]:\n num_nans = th.sum(th.isnan(feature))\n shape = feature.shape\n if num_nans:\n raise ValueError(f\"Detect {num_nans} NANs in feature matrices, \" +\n f\"shape = {shape}...\")\n if num_frames is not None:\n max_frames = num_frames.max().item()\n if feature.shape[-2] < max_frames:\n raise RuntimeError(f\"feats shape: {shape[-2]} x {shape[-1]}, \" +\n f\"num_frames = {num_frames.tolist()}\")\n if feature.shape[-2] > max_frames:\n feature = feature[..., :max_frames, :]\n return feature, num_frames","def get_img_feature(self, image_id):\n self.check_img_feature_file()\n self.check_img_feature_offset_map()\n\n if image_id in self.img_feat_offset_map:\n img_offset = self.img_feat_offset_map[image_id]\n self.img_feature_file.seek(img_offset, 0)\n arr = [s.strip() for s in self.img_feature_file.readline().split('\\t')]\n num_boxes = int(arr[1])\n feat = np.frombuffer(base64.b64decode(arr[2]), dtype=np.float32).reshape((-1, self.args.img_feature_dim))\n return feat\n\n return None","def get_other_features(self):\n return self.other_features","def _float_feature(value):\n\treturn tf.train.Feature(float_list=tf.train.FloatList(value=[value]))","def irrelevant_features(features):\n irrelevant = []\n for vec in set(features):\n if (features[vec].count(0)/len(indtf_features[vec])) < 0.1:\n irrelevant.append(vec)\n return irrelevant","def features(self) -> Optional[pulumi.Input['DevToolPortalFeatureSettingsArgs']]:\n return pulumi.get(self, \"features\")","def _isFIdx(self, featureName):\n return 1 if (featureName in self.featureNames) else 0","def select_features(data: AnnData, features: str = None) -> str:\n keyword = \"fmat_\" + str(features) # fmat: feature matrix\n\n if keyword not in data.uns:\n if features is not None:\n assert features in data.var\n fmat = data.X[:, data.var[features].values]\n else:\n fmat = data.X\n\n if issparse(fmat):\n data.uns[keyword] = fmat.toarray()\n else:\n data.uns[keyword] = fmat.copy()\n\n return keyword","def getFeatures(self,layer): \n numFeatures = layer.GetFeatureCount()\n features = []\n for i in range(numFeatures):\n feature = layer.GetNextFeature()\n if feature is not None:\n geomRef = feature.GetGeometryRef()\n if((geomRef is not None and geomRef.GetPointCount() != 0)):\n features.append(self.getFeatureInfo(feature))\n return features","def feat():\n pass","def feature_selection(feature_matrix, missing_threshold=90, correlation_threshold=0.95):\n \n feature_matrix = pd.get_dummies(feature_matrix)\n n_features_start = feature_matrix.shape[1]\n print('Original shape: ', feature_matrix.shape)\n\n # Find missing and percentage\n missing = pd.DataFrame(feature_matrix.isnull().sum())\n missing['percent'] = 100 * (missing[0] / feature_matrix.shape[0])\n missing.sort_values('percent', ascending = False, inplace = True)\n\n # Missing above threshold\n missing_cols = list(missing[missing['percent'] > missing_threshold].index)\n n_missing_cols = len(missing_cols)\n\n # Remove missing columns\n feature_matrix = feature_matrix[[x for x in feature_matrix if x not in missing_cols]]\n print('{} missing columns with threshold: {}.'.format(n_missing_cols,\n missing_threshold))\n \n # Zero variance\n unique_counts = pd.DataFrame(feature_matrix.nunique()).sort_values(0, ascending = True)\n zero_variance_cols = list(unique_counts[unique_counts[0] == 1].index)\n n_zero_variance_cols = len(zero_variance_cols)\n\n # Remove zero variance columns\n feature_matrix = feature_matrix[[x for x in feature_matrix if x not in zero_variance_cols]]\n print('{} zero variance columns.'.format(n_zero_variance_cols))\n \n # Correlations\n corr_matrix = feature_matrix.corr()\n\n # Extract the upper triangle of the correlation matrix\n upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k = 1).astype(np.bool))\n\n # Select the features with correlations above the threshold\n # Need to use the absolute value\n to_drop = [column for column in upper.columns if any(upper[column].abs() > correlation_threshold)]\n\n n_collinear = len(to_drop)\n \n feature_matrix = feature_matrix[[x for x in feature_matrix if x not in to_drop]]\n print('{} collinear columns removed with threshold: {}.'.format(n_collinear,\n correlation_threshold))\n \n total_removed = n_missing_cols + n_zero_variance_cols + n_collinear\n \n print('Total columns removed: ', total_removed)\n print('Shape after feature selection: {}.'.format(feature_matrix.shape))\n return feature_matrix","def get_feature_vector(self, mode=\"binary\"):\n voxel_n = np.ravel_multi_index([self.voxel_x, self.voxel_y, self.voxel_z], self.x_y_z)\n if mode == \"binary\":\n vector = np.zeros(self.n_voxels)\n vector[np.unique(voxel_n)] = 1\n vector = vector.reshape(self.x_y_z)\n return vector\n\n elif mode == \"binary_with_nopoints\":\n vector = np.zeros(self.n_voxels)\n vector[np.unique(voxel_n)] = 1\n vector = vector.reshape(self.x_y_z)\n tot_bounds = abs(self.bounds[0]) + abs(self.bounds[1])\n # TODO can be parallelised\n non_points = []\n for point in self.points_inside_bounds:\n start, end = get_points_from_bounds(self.bounds[0], self.bounds[1], self.origin, point)\n start_projected_voxelgrid = (start - self.bounds[0])\n end_projected_voxelgrid = (end - self.bounds[0])\n\n assert np.all(start_projected_voxelgrid + PRECISION >= 0), 'Start / end point for nopoints calculation out of bounds: {} / {}'.format(start_projected_voxelgrid + PRECISION, tot_bounds)\n assert np.all(end_projected_voxelgrid + PRECISION >= 0), 'Start / end point for nopoints calculation out of bounds: {} / {}'.format(end_projected_voxelgrid + PRECISION, tot_bounds)\n assert np.all(start_projected_voxelgrid - PRECISION <= tot_bounds), 'Start / end point for nopoints calculation out of bounds: {} / {}'.format(start_projected_voxelgrid, tot_bounds)\n assert np.all(end_projected_voxelgrid - PRECISION <= tot_bounds), 'Start / end point for nopoints calculation out of bounds: {} / {}'.format(end_projected_voxelgrid, tot_bounds)\n\n start_projected_voxelgrid = np.clip(start_projected_voxelgrid, 0, tot_bounds - PRECISION)\n end_projected_voxelgrid = np.clip(end_projected_voxelgrid, 0, tot_bounds - PRECISION)\n\n new_non_points = list(supercover_line(start_projected_voxelgrid, end_projected_voxelgrid, self.sizes))\n non_points.extend(new_non_points)\n # if not np.all(np.array(new_non_points) >= 0) or not np.all(np.array(new_non_points).max(axis=0) < vector.shape):\n # print('Non-point detected with indices under 0 or over size')\n # print('start = {}'.format(start_projected_voxelgrid))\n # print('end = {}'.format(end_projected_voxelgrid))\n # print('Max Size: {}'.format(vector.shape))\n # print('Wrong points:')\n # print(np.array(new_non_points))\n # raise Exception()\n\n # convert only cells that are 0 to -1, NOT 1 to -1\n non_points = np.unique(np.array(non_points), axis=0).astype(int)\n\n temp = vector[non_points[:, 0], non_points[:, 1], non_points[:, 2]]\n temp[temp == 0] = -1\n vector[non_points[:, 0], non_points[:, 1], non_points[:, 2]] = temp\n return vector\n elif mode == \"density\":\n vector = np.zeros(self.n_voxels)\n count = np.bincount(voxel_n)\n vector[:len(count)] = count\n vector /= len(voxel_n)\n vector = vector.reshape(self.x_y_z)\n return vector\n # elif mode == \"TDF\":\n # vector = np.zeros(self.n_voxels)\n # # truncation = np.linalg.norm(self.shape)\n # kdt = cKDTree(self.points_inside_bounds)\n # vector, i = kdt.query(self.voxel_centers, n_jobs=-1)\n # vector = vector.reshape(self.x_y_z)\n # return vector\n elif mode.endswith(\"_max\"):\n vector = np.zeros(self.n_voxels)\n if not is_numba_avaliable:\n raise ImportError(\"numba is required to compute {}\".format(mode))\n axis = {\"x_max\": 0, \"y_max\": 1, \"z_max\": 2}\n vector = groupby_max(self.points_inside_bounds, voxel_n, axis[mode], vector)\n vector = vector.reshape(self.x_y_z)\n return vector\n elif mode.endswith(\"_mean\"):\n vector = np.zeros(self.n_voxels)\n if not is_numba_avaliable:\n raise ImportError(\"numba is required to compute {}\".format(mode))\n axis = {\"x_mean\": 0, \"y_mean\": 1, \"z_mean\": 2}\n voxel_sum = groupby_sum(self.points_inside_bounds, voxel_n, axis[mode], np.zeros(self.n_voxels))\n voxel_count = groupby_count(self.points_inside_bounds, voxel_n, np.zeros(self.n_voxels))\n vector = np.nan_to_num(voxel_sum / voxel_count)\n vector = vector.reshape(self.x_y_z)\n return vector\n\n else:\n raise NotImplementedError(\"{} is not a supported feature vector mode\".format(mode))","def feature_one(ds, tup):\n # try:\n # if (nx.shortest_path_length(G, frm, to) == 1):\n # o1.write(\"trusted\\n\")\n # else:\n # o1.write(\"unverified\\n\")\n # except:\n # o1.write(\"unverified\\n\")\n\n return tup[0] in ds[tup[1]]","def get_vect(word, model, method):\n if method == \"model\":\n try:\n return model.wv[word]\n except KeyError:\n return None\n else:\n try:\n return model[word]\n except KeyError:\n return None","def _handle_feature(fea):\n if len(fea.shape) == 1:\n fea = np.array([fea]).T\n\n return fea","def features_dim(self):\n if not self.exposes_features:\n return None\n\n dim = self._features_op.outputs[0].get_shape().as_list()[-1]\n if dim is None:\n logger.warning(\n \"Unable to statically get feature dimension; returning None\"\n )\n\n return dim","def _feature_selection(self , x ,y):\n # initialize good features list\n # and best scores to keep track of both\n good_features = []\n best_scores = []\n\n # calculating the number of features\n num_features = x.shape[1]\n\n # infinite loop\n while True:\n # intialize best feature and score of this loop\n this_feature = None\n best_score = 0\n\n # loop over all features\n for feature in range(num_features):\n # if feature is already in good features,\n # skip this for loop\n if feature in good_features:\n\n continue\n # selected features are all good till now\n # and current feature\n selected_features = good_features + [feature]\n # remove all other feature from the data\n xtrain = x[: , selected_features]\n # calculate the score , in our case AUC\n score = self.evaluate_score(xtrain , y)\n # if score is greater then the best score\n # of this loop, change best score and best feature\n if score > best_score:\n this_feature = feature\n best_score = score\n\n # if we have selected a feature , add it to\n # the good feature list and update best score list\n if this_feature != None:\n good_features.append(this_feature)\n best_scores.append(best_score)\n\n # if we did not improve during the last two rounds,\n # exit the while loop\n if len(best_score) > 2:\n if best_scores[-1] < best_scores[-2]:\n break\n\n # return the best score and good features\n # why do we remove the last data point?\n return best_scores[:-1] , good_features[:-1]","def get_selected_features(pipeline, verbose = False):\n\n \n assert isinstance(pipeline, Pipeline), \"Input isn't a Pipeline\"\n assert isinstance(pipeline[0], ColumnTransformer), \"First step isn't a ColumnTransformer\"\n\n features = get_feature_names(pipeline[0], verbose=verbose)\n\n for i, step in enumerate(pipeline.steps[1:]):\n if verbose: print(i, \": \", step[0])\n \n if hasattr(step[1], 'get_support'):\n \n check_is_fitted(step[1])\n\n retained_cols = step[1].get_support()\n if verbose: print(sum(retained_cols), \"of\", len(retained_cols), \"retained, \",\\\n round(sum(retained_cols) / len(retained_cols) * 100, 1), \"%\")\n\n features = [feature for is_retained, feature in zip(retained_cols, features) if is_retained] \n\n return features","def _parseFeature(self, name, value=None):\n supported = self._parse([(name, value)])\n return supported.getFeature(name)","def get_feature_vector(self, mode=\"binary\"):\n vector = np.zeros(self.n_voxels)\n\n if mode == \"binary\":\n vector[np.unique(self.voxel_n)] = 1\n\n elif mode == \"density\":\n count = np.bincount(self.voxel_n)\n vector[:len(count)] = count\n vector /= len(self.voxel_n)\n\n elif mode == \"TDF\":\n # truncation = np.linalg.norm(self.shape)\n kdt = cKDTree(self._points)\n vector, i = kdt.query(self.voxel_centers, n_jobs=-1)\n\n elif mode.endswith(\"_max\"):\n if not is_numba_avaliable:\n raise ImportError(\"numba is required to compute {}\".format(mode))\n axis = {\"x_max\": 0, \"y_max\": 1, \"z_max\": 2}\n vector = groupby_max(self._points, self.voxel_n, axis[mode], vector)\n\n elif mode.endswith(\"_mean\"):\n if not is_numba_avaliable:\n raise ImportError(\"numba is required to compute {}\".format(mode))\n axis = {\"x_mean\": 0, \"y_mean\": 1, \"z_mean\": 2}\n voxel_sum = groupby_sum(self._points, self.voxel_n, axis[mode], np.zeros(self.n_voxels))\n voxel_count = groupby_count(self._points, self.voxel_n, np.zeros(self.n_voxels))\n vector = np.nan_to_num(voxel_sum / voxel_count)\n\n else:\n raise NotImplementedError(\"{} is not a supported feature vector mode\".format(mode))\n\n return vector.reshape(self.x_y_z)","def get_features(data, col_list, y_name):\n \n # keep track of numpy values\n feature_matrix = data[col_list + [y_name]].dropna().values\n return feature_matrix[:, :-1], feature_matrix[:, -1]","def my_featurize(apartment):\n return x, y","def extractFeatures(image, feature_list):\n # for multiple features or color features\n #feat_vec = np.array([])\n \n # sift has 128D\n feat_vec = np.empty((0,128))\n n_channels = (image.shape[2] if len(image.shape)==3 else 1)\n \n #img_f32 = image.astype(np.float32)\n\n for feature in feature_list:\n if (feature.strip().lower() == 'dsift'):\n print \"computing dsift (dense rootSift) features\"\n dense = cv2.FeatureDetector_create(\"Dense\")\n sift = cv2.SIFT()\n if n_channels == 1:\n kp = dense.detect(image[:,:])\n # compute kp descriptors\n _,des = sift.compute(image[:,:],kp)\n \n # normalize the descriptors (L1)\n des /= (des.sum(axis=1, keepdims=True) + 1e-7)\n des = np.sqrt(des)\n \n feat_vec = np.vstack((feat_vec, des))\n else:\n for channel in xrange(n_channels):\n kp = dense.detect(image[:,:,channel])\n _,des = sift.compute(image[:,:,channel],kp)\n \n # normalize the descriptors (L1)\n des /= (des.sum(axis=1, keepdims=True) + 1e-7)\n des = np.sqrt(des)\n\n feat_vec = np.vstack((feat_vec, des))\n \n# if (feature.strip().lower() == 'color'):\n# print \"computing color features\"\n# # scale from 0-255 between 0 and 1\n# if args.scale == 1:\n# img_f32 /= 255.\n# \n# f_tmp = img_f32.flatten()\n# feat_vec = np.append(feat_vec, f_tmp)\n else:\n raise Exception(\"Method '%s' is not implemented!\"%(feature)) \n \n return feat_vec","def featurize(vector,features):\n dictionary = collections.defaultdict(lambda:0)\n for feature in iter(set(features)):\n dictionary[feature] = [vector[key][feature] if feature in vector[key] else 0 for key in vector] #populates vectors with zeroes where there's no value in an industry for an n-gram.\n return dictionary","def precompute(self, features, mode, params):\n return None","def feature_selection(train_features, test_features, train_similarity_target, test_similarity_target, regressor, used_features):\n\t# percentile selector\n\tpercentile_selector, percentile_score, percentile_train_features_selected, percentile_test_features_selected, percentile_mask = best_percentile_selector(train_features, test_features, train_similarity_target, test_similarity_target, regressor)\n\n\t# model based selector\n\tmodel_based_selector, model_based_score, model_based_train_features_selected, model_based_test_features_selected, model_based_mask = best_model_based_selector(train_features, test_features, train_similarity_target, test_similarity_target, regressor)\n\n\t# iterative based selector\n\titerative_based_selector, iterative_based_score, iterative_based_train_features_selected, iterative_based_test_features_selected, iterative_based_mask = best_iterative_based_selector(train_features, test_features, train_similarity_target, test_similarity_target, regressor)\n\n\tall_scores = []\n\n\tregressor.fit(train_features, train_similarity_target)\n\tprint(\"The score on all features: %.3f\" % regressor.score(test_features, test_similarity_target))\n\tall_scores.append(regressor.score(test_features, test_similarity_target))\n\n\t# show results for the percentile selector\n\tall_scores.append(percentile_score)\n\n\t# show results for the model based selector\n\tall_scores.append(model_based_score)\n\n\t# show results for the iterative based selector\n\tall_scores.append(iterative_based_score)\n\n\tmax_value_position = all_scores.index(max(all_scores))\n\n\tif max_value_position == 0:\n\t\tprint(\"Returning all features!\\n\")\n\t\treturn train_features, test_features\n\telif max_value_position == 1:\n\t\tpercentile_mask = build_mask(percentile_mask, used_features)\n\t\tmask_save_path = os.path.join('feature_selection_masks', 'percentile_mask.txt')\n\t\tdebug_data(percentile_mask, mask_save_path)\n\n\t\tprint(\"Returning features selected with the percentile selector!\\n\")\n\t\treturn percentile_selector, percentile_train_features_selected, percentile_test_features_selected\n\telif max_value_position == 2:\n\t\tmodel_based_mask = build_mask(model_based_mask, used_features)\n\t\tmask_save_path = os.path.join('feature_selection_masks', 'model_based_mask.txt')\n\t\tdebug_data(model_based_mask, mask_save_path)\n\n\t\tprint(\"Returning features selected with the model based selector!\\n\")\n\t\treturn model_based_selector, model_based_train_features_selected, model_based_test_features_selected\n\telse:\n\t\titerative_based_mask = build_mask(iterative_based_mask, used_features)\n\t\tmask_save_path = os.path.join('feature_selection_masks', 'iterative_based_mask.txt')\n\t\tdebug_data(iterative_based_mask, mask_save_path)\n\n\t\tprint(\"Returning features selected with the iterative based selector!\\n\")\n\t\treturn iterative_based_selector, iterative_based_train_features_selected, iterative_based_test_features_selected","def fvector(data, method ):\n\n fv = 0\n if method['type'] == 'lbp':\n \n\n lbpkern = lbpsimple.generateKernel2()\n \n imlbp = lbpsimple.lbp2oneslice(data, lbpkern)\n\n fv,bins = lbpsimple.features(imlbp)\n\n #pdb.set_trace();\n elif method['type'] == 'hist':\n \n fv, bins = numpy.histogram( data,range(-200,2000,20))\n fv = fv[10:15]\n #fv, bins = numpy.histogram( data)\n pass\n\n else:\n raise Exception('Unknow method for feature vector: %s' %(method))\n\n return fv","def __getitem__(self, feature_name):\n return self.get_feature_by_name(feature_name)","def get_feature_vector_array(inst: Instance):\n\n fv = inst.get_feature_vector()\n tmp = []\n for j in range(inst.get_feature_count()):\n if fv.get_feature(j) == 1:\n tmp.append(1)\n else:\n tmp.append(0)\n return np.array(tmp)","def _get_features_selection(self):\n self._validate_features_selection()\n if self.features_selection == \"auto\":\n if self._get_mode() == \"Explain\":\n return False\n if self._get_mode() == \"Perform\":\n return True\n if self._get_mode() == \"Compete\":\n return True\n if self._get_mode() == \"Optuna\":\n return False\n else:\n return deepcopy(self.features_selection)","def _float_feature(value):\n if isinstance(value, list):\n return tf.train.Feature(float_list=tf.train.FloatList(value=value))\n else:\n return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))","def _float_feature(value):\n if isinstance(value, list):\n return tf.train.Feature(float_list=tf.train.FloatList(value=value))\n return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))","def _len_feature(tf_feature):\n assert(tf_feature)\n attrs = ['bytes_list', 'float_list', 'int64_list']\n for attr in attrs:\n if hasattr(tf_feature, attr):\n feature_vec = getattr(tf_feature, attr).value\n res = len(feature_vec)\n if res > 0:\n return res\n return 0","def _validate_features_in_predict_input(self, result):\n pass","def feature_spec(self):\n if not self.infer_without_label:\n feature_shapes = [(tf.TensorShape([tf.Dimension(None)]), tf.TensorShape([]),\n tf.TensorShape([tf.Dimension(None)]), tf.TensorShape([]))]\n feature_shapes.append(tf.TensorShape([tf.Dimension(None)]))\n else:\n feature_shapes = [(tf.TensorShape([tf.Dimension(None)]), tf.TensorShape([]))\n ]\n if len(feature_shapes) == 1:\n return feature_shapes[0]\n return tuple(feature_shapes)","def get_feature_only(self, verbose = False):\n # captures the last self.num_steps + 1 frames\n # last_num_steps_frames is a SESSION\n last_num_steps_frames = self.capture_last(self.num_steps + 1)\n\n # Extract features from the last frames\n # inputs: np.array (self.num_steps, n_input)\n # because we can make last_num_steps_frames a little bit longer\n # so we just clip down a little bit\n inputs = self._get_features(last_num_steps_frames)[-self.num_steps:]\n\n return inputs","def __tf_idf_feature_extraction(self):\n print('=' * 80)\n print(\"TF-IDF Feature Extraction\")\n t0 = time()\n vectorizer = TfidfVectorizer()\n vec_train = vectorizer.fit_transform(self.train.text)\n vec_test = vectorizer.transform(self.test.text)\n duration = time() - t0\n print(\"DONE!!!!! total time: %fs\" % duration)\n print('=' * 80)\n return vec_train, vec_test","def __contains__(self, feature):\n return feature in self.features","def _float_feature(value):\n return tf.train.Feature(float_list=tf.train.FloatList(value=value))"],"string":"[\n \"def get_training(feature_path): \\n features = np.loadtxt(feature_path)\\n feature_size = features.shape[1] -1 \\n features_in = features[:,0:feature_size]\\n features_out = features[:,-1]\\n #features_out = np.array(map(lambda x: x if x else 0, features_out_unnorm))\\n return features_in, features_out\",\n \"def get_feature_by_name(self, feature_name):\\n feature_index = self.feature_name_index.get(feature_name,-1)\\n if feature_index > -1:\\n return self.features[feature_index]\\n else:\\n logger.error(\\\"{} does not exist!\\\".format(feature_name))\\n return None\",\n \"def get_feature_vector(self, board):\\n return self.hot_one(board)\\n # return self.get_tesauro_feature_vector(self, board)\",\n \"def _get_features(task, features, model, similarity_strategy=None):\\n X = []\\n langs = analysis_utils.get_langs_for_task(task)\\n for feature in features:\\n if feature != \\\"size\\\":\\n # this is a nested array\\n X_feature = analysis_utils.load_lang2vec_vectors(task=task, features=feature)\\n if X_feature is None:\\n #continue\\n return None\\n if similarity_strategy != \\\"-\\\":\\n # We start with similarities to english\\n X_feature = [[sim] for sim in analysis_utils.compute_similarities_of_lang_vecs(X_feature, strategy=similarity_strategy)]\\n elif feature == \\\"size\\\" and model == \\\"xlmr\\\":\\n # this is an array, we put it in a list\\n X_feature = [[size] for size in analysis_utils.xlmr_input_corpus_sizes(langs)]\\n elif feature == \\\"size\\\" and model == \\\"mbert\\\":\\n X_feature = [[size] for size in analysis_utils.mbert_input_corpus_sizes(langs)]\\n else:\\n raise ValueError()\\n # we now have a feature vector for a single feature or feature set\\n if len(X) == 0:\\n X = np.array(X_feature)\\n else:\\n X = np.concatenate((X,np.array(X_feature)), axis=1)\\n if len(X) == 0:\\n return None\\n return np.array(X, dtype=float)\",\n \"def featureByName(self, name):\\n for feature in self.features:\\n if feature.name == name:\\n return feature\\n return None\",\n \"def get_features(self):\\n if not self.exposes_features:\\n return None\\n\\n return self._last_features\",\n \"def __getitem__(self, feat):\\n # We perform the test for presence explicitly, to maintain a consistent\\n # notion of len(self). If we just returned self.features[k], the\\n # defaultdict self.features could self.update(k=float()), thus\\n # extending self's length by one.\\n return self.features[feat] if feat in self.features else 0.\",\n \"def feature_vector(features, vector):\\n clean_features = set(features)\\n new_features_vector = featurize(vector,clean_features)\\n return new_features_vector\",\n \"def _get_feature(self, layer_name):\\n if (\\n layer_name in self.config[\\\"layers\\\"]\\n and \\\"feature\\\" in self.config[\\\"layers\\\"][layer_name]\\n ):\\n return self.config[\\\"layers\\\"][layer_name][\\\"feature\\\"]\\n else:\\n return 0\",\n \"def getFeature(self, featureName):\\n # loop through all the existing features\\n for feature in self.features:\\n # when we have a match with the name\\n if featureName == feature.name:\\n # return the value in the solution\\n return feature\\n # feature doesn't exist\\n return None\",\n \"def get_vector(self, token):\\n try:\\n idx = self.token_to_idx[token]\\n except KeyError:\\n print(\\\"Input token <{}> is not in the model. Will return None type vector\\\".format(token))\\n return None\\n return self.embeddings_mat[idx]\",\n \"def feature_set(self) -> Optional[pulumi.Input['OrganizationFeatureSet']]:\\n return pulumi.get(self, \\\"feature_set\\\")\",\n \"def feature():\\n pass\",\n \"def get_vector(self) -> Optional[List[_Score]]:\\n\\n if len(self._vector) is 0:\\n return None\\n else:\\n return self._vector\",\n \"def get_feature(self, feature: FeatureName) -> FeatureInfo:\\n if feature not in self.feature_map:\\n state = FeatureState.Unsupported\\n else:\\n state = self.feature_map[feature]\\n return FeatureInfo(state=state)\",\n \"def get_features(words, vectors):\\n result = [vectors.loc[word].values for word in words if word in df_keys.values.reshape(-1)]\\n if result:\\n return np.stack(result)\\n return None\",\n \"def filter_feature(feature, typ, value):\\n return value is None or feature.__getattribute__(typ) == value\",\n \"def parse_optional_vector(x, dtype=None):\\n if x == 'none':\\n return None\\n else:\\n return parse_vector(x, dtype)\",\n \"def get_feature_vector(cc, img, quiet=False):\\n savefilename = config.get_classifier_featvect_name(cc.d.images[img]) \\n if os.path.isfile(savefilename):\\n print 'load feat_vect %s'%(cc.d.images[img].name)\\n feat_vect = cPickle.load(open(savefilename,'r'))\\n else:\\n feat_vect = compute_feature_vector(cc, img, quiet=quiet)\\n cPickle.dump(feat_vect, open(savefilename,'w'))\\n return feat_vect\",\n \"def _get_relevant_features(self, X):\\n if self.only_binary_features:\\n feature_mask = which_columns_are_binary(X)\\n else:\\n feature_mask = np.ones(X.shape[1], dtype=bool)\\n return feature_mask\",\n \"def has_feature(self, feature):\\n features = self.features\\n if features is None:\\n return False\\n \\n return feature in features\",\n \"def extract_single_feature_vect(gray):\\n\\n hist, hog_img = skimHOG(gray)\\n reduced = reduce_single_vector_dimension(hist)\\n\\n return reduced, hog_img\",\n \"def get(self, name):\\n try:\\n return(self._d_features[name])\\n except:\\n log.error(\\\"Can't get feature '%s'\\\" % name)\\n return\",\n \"def select_features(vec):\\n return sorted(vec, key=vec.get, reverse=True)[\\n : min(len(vec), FEATURE_LENGTH)\\n ]\",\n \"def features(self) -> List[np.ndarray]:\\n return None\",\n \"def processFeature(prevWord, word, vector):\\n \\n # We add feature whether it exists or not\\n unigram, exists = vector.getUnigram(prevWord)\\n if not exists:\\n vector.addUnigram(prevWord)\\n \\n \\n bigram, exists = vector.getBigram(prevWord, word)\\n if not exists:\\n vector.addBigram(prevWord, word)\",\n \"def features_size(self) -> int:\\n return len(self.data[0].features) if len(self.data) > 0 and self.data[0].features is not None else None\",\n \"def feature(self):\\n return self._feature\",\n \"def feature(self):\\n return self._feature\",\n \"def GetVectorArticleInput(dico_vector_input, features):\\n features_left = set(features) - set(dico_vector_input.keys())\\n if len(features_left) > 0:\\n sentence = \\\"Some features aren't in the dict:\\\\n\\\"\\n raise MyException(sentence + \\\"{}\\\".format(features_left))\\n vector_art = []\\n other_features = ['abstract', 'syn', 'exergue', 'title', 'secTitle']\\n other_features += ['subTitle', 'supTitle']\\n for feature in features:\\n if feature == 'nbSign':\\n if dico_vector_input['nbSign'] == 0:\\n print(\\\"NbSign == 0 l.176 - GetVectorArticleInput\\\")\\n vector_art.append(dico_vector_input[feature])\\n else:\\n vector_art.append(dico_vector_input[feature])\\n # Conversion des variables en indicatrices\\n # Normalement plus la peine, comme déjà fait auparavant\\n elif feature in other_features:\\n if dico_vector_input[feature] > 0:\\n vector_art.append(1)\\n else:\\n vector_art.append(0)\\n else:\\n vector_art.append(dico_vector_input[feature])\\n return (dico_vector_input['melodyId'], np.array([vector_art]))\",\n \"def _rf_predict(self, feature_vec):\\n if feature_vec not in self.rf_cache:\\n np_feature = np.array(feature_vec).reshape(1, -1)\\n self.rf_cache[feature_vec] = self.predictor.predict(np_feature)[0]\\n return self.rf_cache[feature_vec]\",\n \"def get_vector(self, word):\\n\\n if word in self.glove.stoi:\\n return self.glove.vectors[self.glove.stoi[word]]\\n else:\\n return None\",\n \"def vectorizer_features(self) -> list:\\n if self._vectorizer:\\n return self._vectorizer.get_feature_names()\\n self.logger.warning('Uninitialized vector. Please call count_vectorizer first.')\",\n \"def get_single_output_feature(model: BaseModel) -> BaseFeatureMixin:\\n return next(iter(model.output_features.values()))\",\n \"def findFeatures(self):\\n\\t\\tpass\",\n \"def value_head(features):\\n with tf.variable_scope('critic', reuse=tf.AUTO_REUSE):\\n features = tf.layers.dense(features, units=1, activation=None, name='output')\\n return tf.squeeze(features, axis=-1)\",\n \"def take_some_features(data,features,given=None):\\n if given is None:\\n return data,features\\n common,ind1,ind2=take_common_features(features,given)\\n data=data[:,ind1]\\n features=features[ind1]\\n return data,features\",\n \"def choose_best_feature(data_set):\\n feature_size = len(data_set[0]) - 1\\n base_entropy = calc_entropy(data_set)\\n best_info_gain = 0.0; best_feature = -1\\n for i in xrange(feature_size):\\n feat_list = [eg[i] for eg in data_set]\\n unique_values = set(feat_list)\\n new_entropy = 0.0\\n for value in unique_values:\\n sub_ds = splite_dataset(data_set, i, value)\\n prob = len(sub_ds) / float(len(data_set))\\n new_entropy += prob * calc_entropy(sub_ds)\\n info_gain = base_entropy - new_entropy\\n if info_gain > best_info_gain:\\n best_info_gain = info_gain\\n best_feature = i\\n\\n return best_feature\",\n \"def get_model_feature(\\n model,\\n batch_x\\n):\\n features = model.get_feature(batch_x, training=False)\\n return features\",\n \"def num_features(self):\\n if self.x is None:\\n return 0\\n return 1 if self.x.dim() == 1 else self.x.size(1)\",\n \"def is_feature_layer(layer):\\n return getattr(layer, '_is_feature_layer', False)\",\n \"def extractFeatures(self, data, tf=False):\\n tfidf_training_matrix, tfidf_terms = self.useTfidfVectorizer(data)\\n \\n if tf:\\n tf_vectorizer = CountVectorizer(max_df=0.5, min_df=2, max_features=10000,\\n stop_words='english')\\n \\n tf_training_matrix = tf_vectorizer.fit_transform(data)\\n tf_terms = tf_vectorizer.get_feature_names()\\n \\n return tfidf_training_matrix, tfidf_terms, tf_training_matrix, tf_terms\\n \\n else:\\n return tfidf_training_matrix, tfidf_terms\",\n \"def _query(self, feature: str) -> np.ndarray:\\n return np.flatnonzero(np.core.defchararray.find(self.internal_types, feature) != -1)\",\n \"def _extract_feature(element):\\n features = tf.parse_single_example(\\n element,\\n # Defaults are not specified since both keys are required.\\n features={\\n 'image/encoded': tf.FixedLenFeature([], tf.string),\\n 'label/x': tf.FixedLenFeature([], tf.int64),\\n 'label/y': tf.FixedLenFeature([], tf.int64)\\n })\\n return features\",\n \"def is_vector(x):\\r\\n return len(x.shape) == 1\",\n \"def getSentenceFeature(tokens, wordVectors, sentence):\\n # Implement computation for the sentence features given a sentence. \\n \\n # Inputs: \\n # - tokens: a dictionary that maps words to their indices in \\n # the word vector list \\n # - wordVectors: word vectors (each row) for all tokens \\n # - sentence: a list of words in the sentence of interest \\n\\n # Output: \\n # - sentVector: feature vector for the sentence \\n\\n sentence_vectors = [wordVectors[tokens[word]] for word in sentence]\\n\\n return sum(sentence_vectors) * 1.0 / len(sentence_vectors)\",\n \"def add_dummy_feature(X, value=...):\\n ...\",\n \"def feature_set(self) -> pulumi.Output[Optional['OrganizationFeatureSet']]:\\n return pulumi.get(self, \\\"feature_set\\\")\",\n \"def features(self) -> List[np.ndarray]:\\n if len(self.data) == 0 or self.data[0].features is None:\\n return None\\n\\n return [d.features for d in self.data]\",\n \"def safe_compute_features(*args):\\n song_id, entity_type, crop, transform_config, features_config = args\\n try:\\n tf.logging.info(f\\\"Compute features for {song_id} on segment {crop}.\\\")\\n features,_ = compute_features(song_id, entity_type, crop, transform_config, features_config)\\n return features, False\\n\\n except Exception as err:\\n tf.logging.warn(f\\\"Error while computing features for {song_id} on segment {crop}: {err}\\\")\\n return np.float32(0.0), True\",\n \"def input_fn(params, is_training):\\n features = tf.constant(0)\\n labels = tf.constant(0)\\n\\n return features, labels\",\n \"def svm_classification(self):\\n\\n if len(self.saved_gestures.keys()) <= 1:\\n print(\\\"Not enough gestures!\\\")\\n return None\\n else:\\n x = []\\n y = []\\n z = []\\n for elem in self.current_recording:\\n x.append(elem[0][0])\\n y.append(elem[1][0])\\n z.append(elem[2][0])\\n\\n gesture_fft = self.get_fft(x, y, z)\\n\\n if len(gesture_fft) > self.cutoff_length:\\n print(\\\"bigger than cutoff\\\")\\n gesture_fft = gesture_fft[:self.cutoff_length]\\n elif len(gesture_fft) < self.cutoff_length:\\n\\n print(\\\"smaller than cutoff\\\")\\n temp = np.zeros(self.cutoff_length)\\n for x in range(len(gesture_fft)):\\n temp[x] = gesture_fft[x]\\n gesture_fft = temp\\n else:\\n pass\\n\\n return self.classifier.predict(gesture_fft)\",\n \"def features(self) -> Optional[pulumi.Input['ProvisionedClustersCommonPropertiesFeaturesArgs']]:\\n return pulumi.get(self, \\\"features\\\")\",\n \"def format_optional_vector(x):\\n\\n # If vector is None or all elements are NaN, then return none\\n # Otherwise format the vector as normal\\n if x is None or np.all(np.isnan(x)):\\n return 'none'\\n else:\\n return format_vector(x)\",\n \"def _extract_feature(self,f):\\n if callable(f): \\n return f()\\n elif type(f) == tuple:\\n return f[0](*list(f[1:]))\",\n \"def _choose_best_feature(self, X, y, label, sample_weights=None):\\n best_feature_idx = 0\\n # YOUR CODE HERE\\n # Note that you need to implement the sampling feature part here for random forest!\\n # Hint: You may find `np.random.choice` is useful for sampling.\\n # begin answer\\n n_features = X.shape[1]\\n if self.sample_feature:\\n max_features=max(1, min(n_features, int(np.round(np.sqrt(n_features)))))\\n new_features=np.random.choice(n_features, max_features, replace=False)\\n new_X=X[:, new_features]\\n else:\\n new_X=X\\n n_new_features=new_X.shape[1]\\n #new_features=np.random.choice(n_features, n_features, replace=False)\\n #old_cost=self.entropy(y, sample_weights)\\n #use C4.5 algorirhm\\n best_impurity=None\\n best_feature_idx=0\\n best_feature_val=X[0, 0]\\n for i in range(n_new_features):\\n unique_vals=np.unique(X[:,i])\\n for value in unique_vals:\\n sub1_X, sub1_y, label1, sub1_sample_weights, sub2_X, sub2_y, label2, sub2_sample_weights=self._split_dataset(X, y, label, i, value, sample_weights)\\n if len(sub1_y)>0 and len(sub2_y)>0:\\n new_impurity=self._impurity(y, sub1_y, sub2_y)\\n if best_impurity is None or new_impurity > best_impurity:\\n best_impurity=new_impurity\\n best_feature_idx=i\\n best_feature_val=value \\n # end answer\\n return best_feature_idx, best_feature_val\",\n \"def extract_feature(self, atom_fea, nbr_fea, nbr_fea_idx, crystal_atom_idx):\\n atom_fea = self.embedding(atom_fea)\\n for conv_func in self.convs:\\n atom_fea = conv_func(atom_fea, nbr_fea, nbr_fea_idx)\\n feature = self.pooling(atom_fea, crystal_atom_idx)\\n return feature\",\n \"def mostConstrainingFeature(self):\\n # keep track of which feature we'll choose next\\n nextFeature = None\\n # a counter for the minimum number of constraints\\n maxCount = -1\\n # loop through all the features\\n for feature in self.features:\\n # if this feature has a value then go back to the top of the loop and get\\n # the next feature\\n if (feature.value != 'none'):\\n continue\\n # get a list of all the constraints involving this feature\\n constraintList = self.getOpenConstraints(feature.name)\\n # compare the number of constraints involving this feature to the current max\\n # if this is the first unassigned feature we found or this feature has the most\\n # constraints we've found...\\n if (len(constraintList) > maxCount):\\n # save a pointer to the current feature with most constraints\\n nextFeature = feature\\n # save the max number of constraints\\n maxCount = len(constraintList)\\n # return the least constraining feature\\n return nextFeature\",\n \"def get_feature(model, img_tensor, feature_id, device):\\n mean = torch.Tensor([0.485, 0.456, 0.406]).to(device).view(1, config.channels, 1, 1)\\n std = torch.Tensor([0.229, 0.224, 0.225]).to(device).view(1, config.channels, 1, 1)\\n img_normalized = (img_tensor - mean) / std\\n feature = model(img_normalized, feature_id)\\n return feature\",\n \"def check_nullification(full_gam, feature_combination_full, threshold = 1e-7):\\n\\n nullified_features = []\\n\\n for i in range(len(feature_combination_full)):\\n XX = full_gam.generate_X_grid(term=i)\\n\\n term_funct = full_gam.partial_dependence(term=i, X=XX)\\n\\n std = np.std(term_funct)# Use standard deviation to check for nullification\\n\\n if std < threshold:\\n nullified_features.append(feature_combination_full[i])\\n\\n return nullified_features\",\n \"def testGetFirstFeature(self):\\r\\n self.prepareTestCanvas()\\r\\n myLayer = self.bucketFill.getActiveVectorLayer()\\r\\n myTestBox = QgsRectangle(TEST_BOX[0], TEST_BOX[1],\\r\\n TEST_BOX[2], TEST_BOX[3])\\r\\n\\r\\n myFeatureCount = myLayer.featureCount()\\r\\n if myFeatureCount > 0:\\r\\n myFeature = self.bucketFill.getFirstFeature(myLayer, myTestBox)\\r\\n print myFeature\\r\\n myMessage = ('Returned object was not a feature.')\\r\\n assert myFeature.type() == QgsFeature, myMessage\\r\\n else:\\r\\n myMessage = ('No features found in layer.')\\r\\n assert 1 == 0, myMessage\",\n \"def check_valid(feature: th.Tensor,\\n num_frames: Optional[th.Tensor]) -> Tuple[th.Tensor]:\\n num_nans = th.sum(th.isnan(feature))\\n shape = feature.shape\\n if num_nans:\\n raise ValueError(f\\\"Detect {num_nans} NANs in feature matrices, \\\" +\\n f\\\"shape = {shape}...\\\")\\n if num_frames is not None:\\n max_frames = num_frames.max().item()\\n if feature.shape[-2] < max_frames:\\n raise RuntimeError(f\\\"feats shape: {shape[-2]} x {shape[-1]}, \\\" +\\n f\\\"num_frames = {num_frames.tolist()}\\\")\\n if feature.shape[-2] > max_frames:\\n feature = feature[..., :max_frames, :]\\n return feature, num_frames\",\n \"def get_img_feature(self, image_id):\\n self.check_img_feature_file()\\n self.check_img_feature_offset_map()\\n\\n if image_id in self.img_feat_offset_map:\\n img_offset = self.img_feat_offset_map[image_id]\\n self.img_feature_file.seek(img_offset, 0)\\n arr = [s.strip() for s in self.img_feature_file.readline().split('\\\\t')]\\n num_boxes = int(arr[1])\\n feat = np.frombuffer(base64.b64decode(arr[2]), dtype=np.float32).reshape((-1, self.args.img_feature_dim))\\n return feat\\n\\n return None\",\n \"def get_other_features(self):\\n return self.other_features\",\n \"def _float_feature(value):\\n\\treturn tf.train.Feature(float_list=tf.train.FloatList(value=[value]))\",\n \"def irrelevant_features(features):\\n irrelevant = []\\n for vec in set(features):\\n if (features[vec].count(0)/len(indtf_features[vec])) < 0.1:\\n irrelevant.append(vec)\\n return irrelevant\",\n \"def features(self) -> Optional[pulumi.Input['DevToolPortalFeatureSettingsArgs']]:\\n return pulumi.get(self, \\\"features\\\")\",\n \"def _isFIdx(self, featureName):\\n return 1 if (featureName in self.featureNames) else 0\",\n \"def select_features(data: AnnData, features: str = None) -> str:\\n keyword = \\\"fmat_\\\" + str(features) # fmat: feature matrix\\n\\n if keyword not in data.uns:\\n if features is not None:\\n assert features in data.var\\n fmat = data.X[:, data.var[features].values]\\n else:\\n fmat = data.X\\n\\n if issparse(fmat):\\n data.uns[keyword] = fmat.toarray()\\n else:\\n data.uns[keyword] = fmat.copy()\\n\\n return keyword\",\n \"def getFeatures(self,layer): \\n numFeatures = layer.GetFeatureCount()\\n features = []\\n for i in range(numFeatures):\\n feature = layer.GetNextFeature()\\n if feature is not None:\\n geomRef = feature.GetGeometryRef()\\n if((geomRef is not None and geomRef.GetPointCount() != 0)):\\n features.append(self.getFeatureInfo(feature))\\n return features\",\n \"def feat():\\n pass\",\n \"def feature_selection(feature_matrix, missing_threshold=90, correlation_threshold=0.95):\\n \\n feature_matrix = pd.get_dummies(feature_matrix)\\n n_features_start = feature_matrix.shape[1]\\n print('Original shape: ', feature_matrix.shape)\\n\\n # Find missing and percentage\\n missing = pd.DataFrame(feature_matrix.isnull().sum())\\n missing['percent'] = 100 * (missing[0] / feature_matrix.shape[0])\\n missing.sort_values('percent', ascending = False, inplace = True)\\n\\n # Missing above threshold\\n missing_cols = list(missing[missing['percent'] > missing_threshold].index)\\n n_missing_cols = len(missing_cols)\\n\\n # Remove missing columns\\n feature_matrix = feature_matrix[[x for x in feature_matrix if x not in missing_cols]]\\n print('{} missing columns with threshold: {}.'.format(n_missing_cols,\\n missing_threshold))\\n \\n # Zero variance\\n unique_counts = pd.DataFrame(feature_matrix.nunique()).sort_values(0, ascending = True)\\n zero_variance_cols = list(unique_counts[unique_counts[0] == 1].index)\\n n_zero_variance_cols = len(zero_variance_cols)\\n\\n # Remove zero variance columns\\n feature_matrix = feature_matrix[[x for x in feature_matrix if x not in zero_variance_cols]]\\n print('{} zero variance columns.'.format(n_zero_variance_cols))\\n \\n # Correlations\\n corr_matrix = feature_matrix.corr()\\n\\n # Extract the upper triangle of the correlation matrix\\n upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k = 1).astype(np.bool))\\n\\n # Select the features with correlations above the threshold\\n # Need to use the absolute value\\n to_drop = [column for column in upper.columns if any(upper[column].abs() > correlation_threshold)]\\n\\n n_collinear = len(to_drop)\\n \\n feature_matrix = feature_matrix[[x for x in feature_matrix if x not in to_drop]]\\n print('{} collinear columns removed with threshold: {}.'.format(n_collinear,\\n correlation_threshold))\\n \\n total_removed = n_missing_cols + n_zero_variance_cols + n_collinear\\n \\n print('Total columns removed: ', total_removed)\\n print('Shape after feature selection: {}.'.format(feature_matrix.shape))\\n return feature_matrix\",\n \"def get_feature_vector(self, mode=\\\"binary\\\"):\\n voxel_n = np.ravel_multi_index([self.voxel_x, self.voxel_y, self.voxel_z], self.x_y_z)\\n if mode == \\\"binary\\\":\\n vector = np.zeros(self.n_voxels)\\n vector[np.unique(voxel_n)] = 1\\n vector = vector.reshape(self.x_y_z)\\n return vector\\n\\n elif mode == \\\"binary_with_nopoints\\\":\\n vector = np.zeros(self.n_voxels)\\n vector[np.unique(voxel_n)] = 1\\n vector = vector.reshape(self.x_y_z)\\n tot_bounds = abs(self.bounds[0]) + abs(self.bounds[1])\\n # TODO can be parallelised\\n non_points = []\\n for point in self.points_inside_bounds:\\n start, end = get_points_from_bounds(self.bounds[0], self.bounds[1], self.origin, point)\\n start_projected_voxelgrid = (start - self.bounds[0])\\n end_projected_voxelgrid = (end - self.bounds[0])\\n\\n assert np.all(start_projected_voxelgrid + PRECISION >= 0), 'Start / end point for nopoints calculation out of bounds: {} / {}'.format(start_projected_voxelgrid + PRECISION, tot_bounds)\\n assert np.all(end_projected_voxelgrid + PRECISION >= 0), 'Start / end point for nopoints calculation out of bounds: {} / {}'.format(end_projected_voxelgrid + PRECISION, tot_bounds)\\n assert np.all(start_projected_voxelgrid - PRECISION <= tot_bounds), 'Start / end point for nopoints calculation out of bounds: {} / {}'.format(start_projected_voxelgrid, tot_bounds)\\n assert np.all(end_projected_voxelgrid - PRECISION <= tot_bounds), 'Start / end point for nopoints calculation out of bounds: {} / {}'.format(end_projected_voxelgrid, tot_bounds)\\n\\n start_projected_voxelgrid = np.clip(start_projected_voxelgrid, 0, tot_bounds - PRECISION)\\n end_projected_voxelgrid = np.clip(end_projected_voxelgrid, 0, tot_bounds - PRECISION)\\n\\n new_non_points = list(supercover_line(start_projected_voxelgrid, end_projected_voxelgrid, self.sizes))\\n non_points.extend(new_non_points)\\n # if not np.all(np.array(new_non_points) >= 0) or not np.all(np.array(new_non_points).max(axis=0) < vector.shape):\\n # print('Non-point detected with indices under 0 or over size')\\n # print('start = {}'.format(start_projected_voxelgrid))\\n # print('end = {}'.format(end_projected_voxelgrid))\\n # print('Max Size: {}'.format(vector.shape))\\n # print('Wrong points:')\\n # print(np.array(new_non_points))\\n # raise Exception()\\n\\n # convert only cells that are 0 to -1, NOT 1 to -1\\n non_points = np.unique(np.array(non_points), axis=0).astype(int)\\n\\n temp = vector[non_points[:, 0], non_points[:, 1], non_points[:, 2]]\\n temp[temp == 0] = -1\\n vector[non_points[:, 0], non_points[:, 1], non_points[:, 2]] = temp\\n return vector\\n elif mode == \\\"density\\\":\\n vector = np.zeros(self.n_voxels)\\n count = np.bincount(voxel_n)\\n vector[:len(count)] = count\\n vector /= len(voxel_n)\\n vector = vector.reshape(self.x_y_z)\\n return vector\\n # elif mode == \\\"TDF\\\":\\n # vector = np.zeros(self.n_voxels)\\n # # truncation = np.linalg.norm(self.shape)\\n # kdt = cKDTree(self.points_inside_bounds)\\n # vector, i = kdt.query(self.voxel_centers, n_jobs=-1)\\n # vector = vector.reshape(self.x_y_z)\\n # return vector\\n elif mode.endswith(\\\"_max\\\"):\\n vector = np.zeros(self.n_voxels)\\n if not is_numba_avaliable:\\n raise ImportError(\\\"numba is required to compute {}\\\".format(mode))\\n axis = {\\\"x_max\\\": 0, \\\"y_max\\\": 1, \\\"z_max\\\": 2}\\n vector = groupby_max(self.points_inside_bounds, voxel_n, axis[mode], vector)\\n vector = vector.reshape(self.x_y_z)\\n return vector\\n elif mode.endswith(\\\"_mean\\\"):\\n vector = np.zeros(self.n_voxels)\\n if not is_numba_avaliable:\\n raise ImportError(\\\"numba is required to compute {}\\\".format(mode))\\n axis = {\\\"x_mean\\\": 0, \\\"y_mean\\\": 1, \\\"z_mean\\\": 2}\\n voxel_sum = groupby_sum(self.points_inside_bounds, voxel_n, axis[mode], np.zeros(self.n_voxels))\\n voxel_count = groupby_count(self.points_inside_bounds, voxel_n, np.zeros(self.n_voxels))\\n vector = np.nan_to_num(voxel_sum / voxel_count)\\n vector = vector.reshape(self.x_y_z)\\n return vector\\n\\n else:\\n raise NotImplementedError(\\\"{} is not a supported feature vector mode\\\".format(mode))\",\n \"def feature_one(ds, tup):\\n # try:\\n # if (nx.shortest_path_length(G, frm, to) == 1):\\n # o1.write(\\\"trusted\\\\n\\\")\\n # else:\\n # o1.write(\\\"unverified\\\\n\\\")\\n # except:\\n # o1.write(\\\"unverified\\\\n\\\")\\n\\n return tup[0] in ds[tup[1]]\",\n \"def get_vect(word, model, method):\\n if method == \\\"model\\\":\\n try:\\n return model.wv[word]\\n except KeyError:\\n return None\\n else:\\n try:\\n return model[word]\\n except KeyError:\\n return None\",\n \"def _handle_feature(fea):\\n if len(fea.shape) == 1:\\n fea = np.array([fea]).T\\n\\n return fea\",\n \"def features_dim(self):\\n if not self.exposes_features:\\n return None\\n\\n dim = self._features_op.outputs[0].get_shape().as_list()[-1]\\n if dim is None:\\n logger.warning(\\n \\\"Unable to statically get feature dimension; returning None\\\"\\n )\\n\\n return dim\",\n \"def _feature_selection(self , x ,y):\\n # initialize good features list\\n # and best scores to keep track of both\\n good_features = []\\n best_scores = []\\n\\n # calculating the number of features\\n num_features = x.shape[1]\\n\\n # infinite loop\\n while True:\\n # intialize best feature and score of this loop\\n this_feature = None\\n best_score = 0\\n\\n # loop over all features\\n for feature in range(num_features):\\n # if feature is already in good features,\\n # skip this for loop\\n if feature in good_features:\\n\\n continue\\n # selected features are all good till now\\n # and current feature\\n selected_features = good_features + [feature]\\n # remove all other feature from the data\\n xtrain = x[: , selected_features]\\n # calculate the score , in our case AUC\\n score = self.evaluate_score(xtrain , y)\\n # if score is greater then the best score\\n # of this loop, change best score and best feature\\n if score > best_score:\\n this_feature = feature\\n best_score = score\\n\\n # if we have selected a feature , add it to\\n # the good feature list and update best score list\\n if this_feature != None:\\n good_features.append(this_feature)\\n best_scores.append(best_score)\\n\\n # if we did not improve during the last two rounds,\\n # exit the while loop\\n if len(best_score) > 2:\\n if best_scores[-1] < best_scores[-2]:\\n break\\n\\n # return the best score and good features\\n # why do we remove the last data point?\\n return best_scores[:-1] , good_features[:-1]\",\n \"def get_selected_features(pipeline, verbose = False):\\n\\n \\n assert isinstance(pipeline, Pipeline), \\\"Input isn't a Pipeline\\\"\\n assert isinstance(pipeline[0], ColumnTransformer), \\\"First step isn't a ColumnTransformer\\\"\\n\\n features = get_feature_names(pipeline[0], verbose=verbose)\\n\\n for i, step in enumerate(pipeline.steps[1:]):\\n if verbose: print(i, \\\": \\\", step[0])\\n \\n if hasattr(step[1], 'get_support'):\\n \\n check_is_fitted(step[1])\\n\\n retained_cols = step[1].get_support()\\n if verbose: print(sum(retained_cols), \\\"of\\\", len(retained_cols), \\\"retained, \\\",\\\\\\n round(sum(retained_cols) / len(retained_cols) * 100, 1), \\\"%\\\")\\n\\n features = [feature for is_retained, feature in zip(retained_cols, features) if is_retained] \\n\\n return features\",\n \"def _parseFeature(self, name, value=None):\\n supported = self._parse([(name, value)])\\n return supported.getFeature(name)\",\n \"def get_feature_vector(self, mode=\\\"binary\\\"):\\n vector = np.zeros(self.n_voxels)\\n\\n if mode == \\\"binary\\\":\\n vector[np.unique(self.voxel_n)] = 1\\n\\n elif mode == \\\"density\\\":\\n count = np.bincount(self.voxel_n)\\n vector[:len(count)] = count\\n vector /= len(self.voxel_n)\\n\\n elif mode == \\\"TDF\\\":\\n # truncation = np.linalg.norm(self.shape)\\n kdt = cKDTree(self._points)\\n vector, i = kdt.query(self.voxel_centers, n_jobs=-1)\\n\\n elif mode.endswith(\\\"_max\\\"):\\n if not is_numba_avaliable:\\n raise ImportError(\\\"numba is required to compute {}\\\".format(mode))\\n axis = {\\\"x_max\\\": 0, \\\"y_max\\\": 1, \\\"z_max\\\": 2}\\n vector = groupby_max(self._points, self.voxel_n, axis[mode], vector)\\n\\n elif mode.endswith(\\\"_mean\\\"):\\n if not is_numba_avaliable:\\n raise ImportError(\\\"numba is required to compute {}\\\".format(mode))\\n axis = {\\\"x_mean\\\": 0, \\\"y_mean\\\": 1, \\\"z_mean\\\": 2}\\n voxel_sum = groupby_sum(self._points, self.voxel_n, axis[mode], np.zeros(self.n_voxels))\\n voxel_count = groupby_count(self._points, self.voxel_n, np.zeros(self.n_voxels))\\n vector = np.nan_to_num(voxel_sum / voxel_count)\\n\\n else:\\n raise NotImplementedError(\\\"{} is not a supported feature vector mode\\\".format(mode))\\n\\n return vector.reshape(self.x_y_z)\",\n \"def get_features(data, col_list, y_name):\\n \\n # keep track of numpy values\\n feature_matrix = data[col_list + [y_name]].dropna().values\\n return feature_matrix[:, :-1], feature_matrix[:, -1]\",\n \"def my_featurize(apartment):\\n return x, y\",\n \"def extractFeatures(image, feature_list):\\n # for multiple features or color features\\n #feat_vec = np.array([])\\n \\n # sift has 128D\\n feat_vec = np.empty((0,128))\\n n_channels = (image.shape[2] if len(image.shape)==3 else 1)\\n \\n #img_f32 = image.astype(np.float32)\\n\\n for feature in feature_list:\\n if (feature.strip().lower() == 'dsift'):\\n print \\\"computing dsift (dense rootSift) features\\\"\\n dense = cv2.FeatureDetector_create(\\\"Dense\\\")\\n sift = cv2.SIFT()\\n if n_channels == 1:\\n kp = dense.detect(image[:,:])\\n # compute kp descriptors\\n _,des = sift.compute(image[:,:],kp)\\n \\n # normalize the descriptors (L1)\\n des /= (des.sum(axis=1, keepdims=True) + 1e-7)\\n des = np.sqrt(des)\\n \\n feat_vec = np.vstack((feat_vec, des))\\n else:\\n for channel in xrange(n_channels):\\n kp = dense.detect(image[:,:,channel])\\n _,des = sift.compute(image[:,:,channel],kp)\\n \\n # normalize the descriptors (L1)\\n des /= (des.sum(axis=1, keepdims=True) + 1e-7)\\n des = np.sqrt(des)\\n\\n feat_vec = np.vstack((feat_vec, des))\\n \\n# if (feature.strip().lower() == 'color'):\\n# print \\\"computing color features\\\"\\n# # scale from 0-255 between 0 and 1\\n# if args.scale == 1:\\n# img_f32 /= 255.\\n# \\n# f_tmp = img_f32.flatten()\\n# feat_vec = np.append(feat_vec, f_tmp)\\n else:\\n raise Exception(\\\"Method '%s' is not implemented!\\\"%(feature)) \\n \\n return feat_vec\",\n \"def featurize(vector,features):\\n dictionary = collections.defaultdict(lambda:0)\\n for feature in iter(set(features)):\\n dictionary[feature] = [vector[key][feature] if feature in vector[key] else 0 for key in vector] #populates vectors with zeroes where there's no value in an industry for an n-gram.\\n return dictionary\",\n \"def precompute(self, features, mode, params):\\n return None\",\n \"def feature_selection(train_features, test_features, train_similarity_target, test_similarity_target, regressor, used_features):\\n\\t# percentile selector\\n\\tpercentile_selector, percentile_score, percentile_train_features_selected, percentile_test_features_selected, percentile_mask = best_percentile_selector(train_features, test_features, train_similarity_target, test_similarity_target, regressor)\\n\\n\\t# model based selector\\n\\tmodel_based_selector, model_based_score, model_based_train_features_selected, model_based_test_features_selected, model_based_mask = best_model_based_selector(train_features, test_features, train_similarity_target, test_similarity_target, regressor)\\n\\n\\t# iterative based selector\\n\\titerative_based_selector, iterative_based_score, iterative_based_train_features_selected, iterative_based_test_features_selected, iterative_based_mask = best_iterative_based_selector(train_features, test_features, train_similarity_target, test_similarity_target, regressor)\\n\\n\\tall_scores = []\\n\\n\\tregressor.fit(train_features, train_similarity_target)\\n\\tprint(\\\"The score on all features: %.3f\\\" % regressor.score(test_features, test_similarity_target))\\n\\tall_scores.append(regressor.score(test_features, test_similarity_target))\\n\\n\\t# show results for the percentile selector\\n\\tall_scores.append(percentile_score)\\n\\n\\t# show results for the model based selector\\n\\tall_scores.append(model_based_score)\\n\\n\\t# show results for the iterative based selector\\n\\tall_scores.append(iterative_based_score)\\n\\n\\tmax_value_position = all_scores.index(max(all_scores))\\n\\n\\tif max_value_position == 0:\\n\\t\\tprint(\\\"Returning all features!\\\\n\\\")\\n\\t\\treturn train_features, test_features\\n\\telif max_value_position == 1:\\n\\t\\tpercentile_mask = build_mask(percentile_mask, used_features)\\n\\t\\tmask_save_path = os.path.join('feature_selection_masks', 'percentile_mask.txt')\\n\\t\\tdebug_data(percentile_mask, mask_save_path)\\n\\n\\t\\tprint(\\\"Returning features selected with the percentile selector!\\\\n\\\")\\n\\t\\treturn percentile_selector, percentile_train_features_selected, percentile_test_features_selected\\n\\telif max_value_position == 2:\\n\\t\\tmodel_based_mask = build_mask(model_based_mask, used_features)\\n\\t\\tmask_save_path = os.path.join('feature_selection_masks', 'model_based_mask.txt')\\n\\t\\tdebug_data(model_based_mask, mask_save_path)\\n\\n\\t\\tprint(\\\"Returning features selected with the model based selector!\\\\n\\\")\\n\\t\\treturn model_based_selector, model_based_train_features_selected, model_based_test_features_selected\\n\\telse:\\n\\t\\titerative_based_mask = build_mask(iterative_based_mask, used_features)\\n\\t\\tmask_save_path = os.path.join('feature_selection_masks', 'iterative_based_mask.txt')\\n\\t\\tdebug_data(iterative_based_mask, mask_save_path)\\n\\n\\t\\tprint(\\\"Returning features selected with the iterative based selector!\\\\n\\\")\\n\\t\\treturn iterative_based_selector, iterative_based_train_features_selected, iterative_based_test_features_selected\",\n \"def fvector(data, method ):\\n\\n fv = 0\\n if method['type'] == 'lbp':\\n \\n\\n lbpkern = lbpsimple.generateKernel2()\\n \\n imlbp = lbpsimple.lbp2oneslice(data, lbpkern)\\n\\n fv,bins = lbpsimple.features(imlbp)\\n\\n #pdb.set_trace();\\n elif method['type'] == 'hist':\\n \\n fv, bins = numpy.histogram( data,range(-200,2000,20))\\n fv = fv[10:15]\\n #fv, bins = numpy.histogram( data)\\n pass\\n\\n else:\\n raise Exception('Unknow method for feature vector: %s' %(method))\\n\\n return fv\",\n \"def __getitem__(self, feature_name):\\n return self.get_feature_by_name(feature_name)\",\n \"def get_feature_vector_array(inst: Instance):\\n\\n fv = inst.get_feature_vector()\\n tmp = []\\n for j in range(inst.get_feature_count()):\\n if fv.get_feature(j) == 1:\\n tmp.append(1)\\n else:\\n tmp.append(0)\\n return np.array(tmp)\",\n \"def _get_features_selection(self):\\n self._validate_features_selection()\\n if self.features_selection == \\\"auto\\\":\\n if self._get_mode() == \\\"Explain\\\":\\n return False\\n if self._get_mode() == \\\"Perform\\\":\\n return True\\n if self._get_mode() == \\\"Compete\\\":\\n return True\\n if self._get_mode() == \\\"Optuna\\\":\\n return False\\n else:\\n return deepcopy(self.features_selection)\",\n \"def _float_feature(value):\\n if isinstance(value, list):\\n return tf.train.Feature(float_list=tf.train.FloatList(value=value))\\n else:\\n return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))\",\n \"def _float_feature(value):\\n if isinstance(value, list):\\n return tf.train.Feature(float_list=tf.train.FloatList(value=value))\\n return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))\",\n \"def _len_feature(tf_feature):\\n assert(tf_feature)\\n attrs = ['bytes_list', 'float_list', 'int64_list']\\n for attr in attrs:\\n if hasattr(tf_feature, attr):\\n feature_vec = getattr(tf_feature, attr).value\\n res = len(feature_vec)\\n if res > 0:\\n return res\\n return 0\",\n \"def _validate_features_in_predict_input(self, result):\\n pass\",\n \"def feature_spec(self):\\n if not self.infer_without_label:\\n feature_shapes = [(tf.TensorShape([tf.Dimension(None)]), tf.TensorShape([]),\\n tf.TensorShape([tf.Dimension(None)]), tf.TensorShape([]))]\\n feature_shapes.append(tf.TensorShape([tf.Dimension(None)]))\\n else:\\n feature_shapes = [(tf.TensorShape([tf.Dimension(None)]), tf.TensorShape([]))\\n ]\\n if len(feature_shapes) == 1:\\n return feature_shapes[0]\\n return tuple(feature_shapes)\",\n \"def get_feature_only(self, verbose = False):\\n # captures the last self.num_steps + 1 frames\\n # last_num_steps_frames is a SESSION\\n last_num_steps_frames = self.capture_last(self.num_steps + 1)\\n\\n # Extract features from the last frames\\n # inputs: np.array (self.num_steps, n_input)\\n # because we can make last_num_steps_frames a little bit longer\\n # so we just clip down a little bit\\n inputs = self._get_features(last_num_steps_frames)[-self.num_steps:]\\n\\n return inputs\",\n \"def __tf_idf_feature_extraction(self):\\n print('=' * 80)\\n print(\\\"TF-IDF Feature Extraction\\\")\\n t0 = time()\\n vectorizer = TfidfVectorizer()\\n vec_train = vectorizer.fit_transform(self.train.text)\\n vec_test = vectorizer.transform(self.test.text)\\n duration = time() - t0\\n print(\\\"DONE!!!!! total time: %fs\\\" % duration)\\n print('=' * 80)\\n return vec_train, vec_test\",\n \"def __contains__(self, feature):\\n return feature in self.features\",\n \"def _float_feature(value):\\n return tf.train.Feature(float_list=tf.train.FloatList(value=value))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6076066","0.6039801","0.60045683","0.5997051","0.59677297","0.5927279","0.5924938","0.592038","0.58969414","0.58928514","0.5883369","0.5868851","0.5810611","0.57908976","0.57573485","0.5748689","0.57457215","0.5690704","0.5680142","0.5675147","0.5661629","0.5642336","0.5623844","0.5604885","0.5604279","0.56018674","0.5579186","0.5577575","0.5577575","0.55653566","0.55306137","0.55241036","0.5521289","0.5516221","0.5512807","0.5512517","0.5504548","0.54984176","0.547595","0.5454508","0.54489577","0.54483","0.5446874","0.54350245","0.53953946","0.53846943","0.53825283","0.5382084","0.53798306","0.5360352","0.5347559","0.53443104","0.53354853","0.53227854","0.5307955","0.52868974","0.5284724","0.52706176","0.5270413","0.5263167","0.5260489","0.52483255","0.52451235","0.5222144","0.521763","0.5216146","0.5207994","0.52037156","0.5203006","0.519222","0.51901084","0.51895493","0.5185788","0.5184258","0.5175466","0.5171491","0.5162965","0.51582223","0.51530296","0.5146966","0.5144051","0.5141394","0.5137949","0.51241225","0.51218253","0.5119906","0.5115301","0.5114925","0.51121116","0.5111929","0.5100198","0.5094482","0.50924677","0.5089625","0.5088957","0.50882506","0.50868905","0.50848496","0.5079067","0.50790644"],"string":"[\n \"0.6076066\",\n \"0.6039801\",\n \"0.60045683\",\n \"0.5997051\",\n \"0.59677297\",\n \"0.5927279\",\n \"0.5924938\",\n \"0.592038\",\n \"0.58969414\",\n \"0.58928514\",\n \"0.5883369\",\n \"0.5868851\",\n \"0.5810611\",\n \"0.57908976\",\n \"0.57573485\",\n \"0.5748689\",\n \"0.57457215\",\n \"0.5690704\",\n \"0.5680142\",\n \"0.5675147\",\n \"0.5661629\",\n \"0.5642336\",\n \"0.5623844\",\n \"0.5604885\",\n \"0.5604279\",\n \"0.56018674\",\n \"0.5579186\",\n \"0.5577575\",\n \"0.5577575\",\n \"0.55653566\",\n \"0.55306137\",\n \"0.55241036\",\n \"0.5521289\",\n \"0.5516221\",\n \"0.5512807\",\n \"0.5512517\",\n \"0.5504548\",\n \"0.54984176\",\n \"0.547595\",\n \"0.5454508\",\n \"0.54489577\",\n \"0.54483\",\n \"0.5446874\",\n \"0.54350245\",\n \"0.53953946\",\n \"0.53846943\",\n \"0.53825283\",\n \"0.5382084\",\n \"0.53798306\",\n \"0.5360352\",\n \"0.5347559\",\n \"0.53443104\",\n \"0.53354853\",\n \"0.53227854\",\n \"0.5307955\",\n \"0.52868974\",\n \"0.5284724\",\n \"0.52706176\",\n \"0.5270413\",\n \"0.5263167\",\n \"0.5260489\",\n \"0.52483255\",\n \"0.52451235\",\n \"0.5222144\",\n \"0.521763\",\n \"0.5216146\",\n \"0.5207994\",\n \"0.52037156\",\n \"0.5203006\",\n \"0.519222\",\n \"0.51901084\",\n \"0.51895493\",\n \"0.5185788\",\n \"0.5184258\",\n \"0.5175466\",\n \"0.5171491\",\n \"0.5162965\",\n \"0.51582223\",\n \"0.51530296\",\n \"0.5146966\",\n \"0.5144051\",\n \"0.5141394\",\n \"0.5137949\",\n \"0.51241225\",\n \"0.51218253\",\n \"0.5119906\",\n \"0.5115301\",\n \"0.5114925\",\n \"0.51121116\",\n \"0.5111929\",\n \"0.5100198\",\n \"0.5094482\",\n \"0.50924677\",\n \"0.5089625\",\n \"0.5088957\",\n \"0.50882506\",\n \"0.50868905\",\n \"0.50848496\",\n \"0.5079067\",\n \"0.50790644\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6274125"},"document_rank":{"kind":"string","value":"0"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":28,"numItemsPerPage":100,"numTotalItems":95772,"offset":2800,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc3NzcyNzk1MCwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3Nzc3MzE1NTAsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.vnj8WdiVO2sbc6jDZSjfknA-dvHpQDQCgjzWoOAmJx-yzO8r1QKeB3eCQUX3P62fJJmE4oqfFK7_fIBdvv1LBg","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 |
|---|---|---|---|---|---|---|
run function my_squares and my_join | def run_my_funcs(x,y):
print(x,y)
my_squares(x)
my_join(x,y)
return 0 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run_my_funcs(x, y):\n print\n my_squares(x)\n my_join(x, y)\n return 0",
"def double_chop_pairs(\n x1, y1, z1, w1, cell1, x2, y2, z2, w2, indx2, rbins_squared, result):\n start = cuda.grid(1)\n stride = cuda.gridsize(1)\n\n n1 = x1.shape[0]\n nbins = rbins_squared.shape[0]\n\n ... | [
"0.7624191",
"0.5538901",
"0.5165043",
"0.50714666",
"0.50639325",
"0.5047271",
"0.5002455",
"0.49778813",
"0.4968627",
"0.49685135",
"0.495568",
"0.49273586",
"0.48960102",
"0.48864695",
"0.48659024",
"0.48147792",
"0.48095623",
"0.48085663",
"0.4803549",
"0.4795789",
"0.479... | 0.7545948 | 2 |
Adds the keys 'logits' and 'probs' to the end points dictionary of ResNet50v2. | def _get_updated_endpoints(original_end_points, name):
end_points = dict(original_end_points)
end_points['logits'] = tf.squeeze(end_points[name], [1, 2])
end_points['probs'] = tf.nn.softmax(end_points['logits'])
return end_points | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def augment(self):\n n1 = { 'edges': [ self.next_insert['pred'], self.next_insert ], 'pred': self.next_insert['pred'] }\n n2 = { 'edges': [ n1, self.next_insert ], 'pred': n1 }\n self.next_insert['pred'] = n2\n self.next_insert = n2\n self.nodect += 2",
"def init_output_dict(se... | [
"0.52572966",
"0.52147967",
"0.5190277",
"0.5172461",
"0.5160112",
"0.5107622",
"0.5044875",
"0.502481",
"0.49618483",
"0.49577186",
"0.49346328",
"0.4871828",
"0.47935718",
"0.47819144",
"0.47578776",
"0.4739234",
"0.47227845",
"0.46987733",
"0.46696952",
"0.46637616",
"0.46... | 0.56161755 | 0 |
Initializes the tensorflow graph for the ResNet50v2 model. | def __init__(self, x, num_classes=15, is_training=False):
super(resnet_v2_50, self).__init__()
self.x = x
self.num_classes = num_classes
# populating the tensorflow graph
with slim.arg_scope(arg_scopes_map['resnet_v2_50']()):
net, end_points = networks_map['resnet_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def initialize_network(self):\n self.sess = tf.InteractiveSession()\n sys.stderr.write(\"------\\n\")\n self.model.create_model()\n self._initialize_trainer()\n self.sess.run(tf.initialize_all_variables())\n self.saver = tf.train.Saver()",
"def build_graph(self):\n ... | [
"0.71776015",
"0.6739433",
"0.6669668",
"0.660558",
"0.65882456",
"0.6516973",
"0.6443085",
"0.6423372",
"0.64015436",
"0.63999057",
"0.6339511",
"0.63393587",
"0.6328483",
"0.631329",
"0.63033473",
"0.62763274",
"0.62757164",
"0.627243",
"0.6259759",
"0.6259759",
"0.6252365"... | 0.6228969 | 23 |
Load weights from a checkpoint file into the tensorflow graph. | def load_weights(self, checkpoint_path, sess=None):
if sess is None:
sess = tf.get_default_session()
assert sess is not None
saver = tf.train.Saver(self.variables_to_restore)
saver.restore(sess, checkpoint_path) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_model_weights(sess, checkpoint_dir):\n\n init_fn = slim.assign_from_checkpoint_fn(\n checkpoint_dir, slim.get_model_variables(), ignore_missing_vars=True)\n init_fn(sess)",
"def load_weights_from_checkpoint(self, path: str, key: str):\n ckpt = torch.load(path, map_location='cpu')\n ... | [
"0.76077384",
"0.74392045",
"0.74392045",
"0.7414661",
"0.7232018",
"0.71317524",
"0.7071087",
"0.70339084",
"0.700918",
"0.6977498",
"0.6935631",
"0.691333",
"0.68927085",
"0.68749905",
"0.68339276",
"0.6816416",
"0.6816042",
"0.67910594",
"0.6769799",
"0.6769098",
"0.672785... | 0.77680707 | 2 |
Lists the model's parameters. | def get_params(self):
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getListOfParameters(self):\n return self.model.getListOfParameters()",
"def getListOfParameters(self, *args):\n return _libsbml.Model_getListOfParameters(self, *args)",
"def parameters(self):\n return self.model.parameters()",
"def parameters(self):\n return []",
"def print_... | [
"0.80538356",
"0.75631976",
"0.72960615",
"0.7105871",
"0.7048238",
"0.6971301",
"0.6963369",
"0.6942364",
"0.69122803",
"0.6896806",
"0.6824722",
"0.6821565",
"0.676841",
"0.67298985",
"0.67298985",
"0.67248833",
"0.6719116",
"0.6717513",
"0.6717513",
"0.67164814",
"0.671245... | 0.61706924 | 91 |
Exposes all the layers of the model. | def fprop(self, x):
if x is self.x:
return self.end_points
else:
with slim.arg_scope(arg_scopes_map['resnet_v2_50']()):
net, end_points = networks_map['resnet_v2_50'](
x, num_classes=self.num_classes,
is_training=False, re... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def layers(self): # -> LayerView:\n ...",
"def build_layers(self):\n raise NotImplementedError",
"def setup_layers(self):\n if self.args.model == \"exact\":\n self.layer = PPNPLayer\n else:\n self.layer = APPNPLayer\n self.setup_layer_structure()",
"def _g... | [
"0.72325057",
"0.6624318",
"0.62856334",
"0.62295145",
"0.6214809",
"0.6171816",
"0.60817003",
"0.5990596",
"0.59697384",
"0.5955498",
"0.5902771",
"0.58940166",
"0.5883603",
"0.5861259",
"0.5861006",
"0.5847972",
"0.5845203",
"0.5828398",
"0.5788822",
"0.5782679",
"0.5767099... | 0.0 | -1 |
Initializes the tensorflow graph for the ResNet50v2 model. | def __init__(self, x, num_classes=15, is_training=False):
super(resnet_v2_152, self).__init__()
self.x = x
self.num_classes = num_classes
# populating the tensorflow graph
with slim.arg_scope(arg_scopes_map['resnet_v2_152']()):
net, end_points = networks_map['resne... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def initialize_network(self):\n self.sess = tf.InteractiveSession()\n sys.stderr.write(\"------\\n\")\n self.model.create_model()\n self._initialize_trainer()\n self.sess.run(tf.initialize_all_variables())\n self.saver = tf.train.Saver()",
"def build_graph(self):\n ... | [
"0.71776015",
"0.6739433",
"0.6669668",
"0.660558",
"0.65882456",
"0.6516973",
"0.6443085",
"0.6423372",
"0.64015436",
"0.63999057",
"0.6339511",
"0.63393587",
"0.6328483",
"0.631329",
"0.63033473",
"0.62763274",
"0.62757164",
"0.627243",
"0.6259759",
"0.6259759",
"0.6252365"... | 0.0 | -1 |
Load weights from a checkpoint file into the tensorflow graph. | def load_weights(self, checkpoint_path, sess=None):
if sess is None:
sess = tf.get_default_session()
assert sess is not None
saver = tf.train.Saver(self.variables_to_restore)
saver.restore(sess, checkpoint_path) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_model_weights(sess, checkpoint_dir):\n\n init_fn = slim.assign_from_checkpoint_fn(\n checkpoint_dir, slim.get_model_variables(), ignore_missing_vars=True)\n init_fn(sess)",
"def load_weights_from_checkpoint(self, path: str, key: str):\n ckpt = torch.load(path, map_location='cpu')\n ... | [
"0.76077384",
"0.74392045",
"0.74392045",
"0.7414661",
"0.7232018",
"0.71317524",
"0.7071087",
"0.70339084",
"0.700918",
"0.6977498",
"0.6935631",
"0.691333",
"0.68927085",
"0.68749905",
"0.68339276",
"0.6816416",
"0.6816042",
"0.67910594",
"0.6769799",
"0.6769098",
"0.672785... | 0.77680707 | 1 |
Lists the model's parameters. | def get_params(self):
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getListOfParameters(self):\n return self.model.getListOfParameters()",
"def getListOfParameters(self, *args):\n return _libsbml.Model_getListOfParameters(self, *args)",
"def parameters(self):\n return self.model.parameters()",
"def parameters(self):\n return []",
"def print_... | [
"0.80538356",
"0.75631976",
"0.72960615",
"0.7105871",
"0.7048238",
"0.6971301",
"0.6963369",
"0.6942364",
"0.69122803",
"0.6896806",
"0.6824722",
"0.6821565",
"0.676841",
"0.67298985",
"0.67298985",
"0.67248833",
"0.6719116",
"0.6717513",
"0.6717513",
"0.67164814",
"0.671245... | 0.61706924 | 90 |
Exposes all the layers of the model. | def fprop(self, x):
if x is self.x:
return self.end_points
else:
with slim.arg_scope(arg_scopes_map['resnet_v2_152']()):
net, end_points = networks_map['resnet_v2_152'](
x, num_classes=self.num_classes,
is_training=False, ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def layers(self): # -> LayerView:\n ...",
"def build_layers(self):\n raise NotImplementedError",
"def setup_layers(self):\n if self.args.model == \"exact\":\n self.layer = PPNPLayer\n else:\n self.layer = APPNPLayer\n self.setup_layer_structure()",
"def _g... | [
"0.72325057",
"0.6624318",
"0.62856334",
"0.62295145",
"0.6214809",
"0.6171816",
"0.60817003",
"0.5990596",
"0.59697384",
"0.5955498",
"0.5902771",
"0.58940166",
"0.5883603",
"0.5861259",
"0.5861006",
"0.5847972",
"0.5845203",
"0.5828398",
"0.5788822",
"0.5782679",
"0.5767099... | 0.0 | -1 |
Initializes the tensorflow graph for the ResNet50v2 model. | def __init__(self, x, num_classes=15, is_training=False):
super(mobilenet_v2, self).__init__()
self.x = x
self.num_classes = num_classes
# populating the tensorflow graph
with slim.arg_scope(arg_scopes_map['mobilenet_v2']()):
net, end_points = networks_map['mobilen... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def initialize_network(self):\n self.sess = tf.InteractiveSession()\n sys.stderr.write(\"------\\n\")\n self.model.create_model()\n self._initialize_trainer()\n self.sess.run(tf.initialize_all_variables())\n self.saver = tf.train.Saver()",
"def build_graph(self):\n ... | [
"0.71776015",
"0.6739433",
"0.6669668",
"0.660558",
"0.65882456",
"0.6516973",
"0.6443085",
"0.6423372",
"0.64015436",
"0.63999057",
"0.6339511",
"0.63393587",
"0.6328483",
"0.631329",
"0.63033473",
"0.62763274",
"0.62757164",
"0.627243",
"0.6259759",
"0.6259759",
"0.6252365"... | 0.0 | -1 |
Load weights from a checkpoint file into the tensorflow graph. | def load_weights(self, checkpoint_path, sess=None):
if sess is None:
sess = tf.get_default_session()
assert sess is not None
saver = tf.train.Saver(self.variables_to_restore)
saver.restore(sess, checkpoint_path) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_model_weights(sess, checkpoint_dir):\n\n init_fn = slim.assign_from_checkpoint_fn(\n checkpoint_dir, slim.get_model_variables(), ignore_missing_vars=True)\n init_fn(sess)",
"def load_weights_from_checkpoint(self, path: str, key: str):\n ckpt = torch.load(path, map_location='cpu')\n ... | [
"0.76077384",
"0.74392045",
"0.74392045",
"0.7414661",
"0.7232018",
"0.71317524",
"0.7071087",
"0.70339084",
"0.700918",
"0.6977498",
"0.6935631",
"0.691333",
"0.68927085",
"0.68749905",
"0.68339276",
"0.6816416",
"0.6816042",
"0.67910594",
"0.6769799",
"0.6769098",
"0.672785... | 0.77680707 | 0 |
Lists the model's parameters. | def get_params(self):
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getListOfParameters(self):\n return self.model.getListOfParameters()",
"def getListOfParameters(self, *args):\n return _libsbml.Model_getListOfParameters(self, *args)",
"def parameters(self):\n return self.model.parameters()",
"def parameters(self):\n return []",
"def print_... | [
"0.80538356",
"0.75631976",
"0.72960615",
"0.7105871",
"0.7048238",
"0.6971301",
"0.6963369",
"0.6942364",
"0.69122803",
"0.6896806",
"0.6824722",
"0.6821565",
"0.676841",
"0.67298985",
"0.67298985",
"0.67248833",
"0.6719116",
"0.6717513",
"0.6717513",
"0.67164814",
"0.671245... | 0.61706924 | 92 |
Exposes all the layers of the model. | def fprop(self, x):
if x is self.x:
return self.end_points
else:
with slim.arg_scope(arg_scopes_map['mobilenet_v2']()):
net, end_points = networks_map['mobilenet_v2'](
x, num_classes=self.num_classes,
is_training=False, re... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def layers(self): # -> LayerView:\n ...",
"def build_layers(self):\n raise NotImplementedError",
"def setup_layers(self):\n if self.args.model == \"exact\":\n self.layer = PPNPLayer\n else:\n self.layer = APPNPLayer\n self.setup_layer_structure()",
"def _g... | [
"0.72325057",
"0.6624318",
"0.62856334",
"0.62295145",
"0.6214809",
"0.6171816",
"0.60817003",
"0.5990596",
"0.59697384",
"0.5955498",
"0.5902771",
"0.58940166",
"0.5883603",
"0.5861259",
"0.5861006",
"0.5847972",
"0.5845203",
"0.5828398",
"0.5788822",
"0.5782679",
"0.5767099... | 0.0 | -1 |
The top level function. | def eval_tree(tree):
global genv
global result
# Here, get the list of children nodes. Iterate over that list, calling eval_node on each node.
for node in tree.body:
val = eval_node(node, genv)
result = val[0]
genv = val[1]
return result | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main():\n pass",
"def main():\n pass",
"def main():\n pass",
"def main():\n pass",
"def main():\n pass",
"def main():\n pass",
"def main():\n pass",
"def main():\n pass",
"def main():\n pass",
"def main():\n pass",
"def main():\n pass",
"def main():\n p... | [
"0.69298464",
"0.69298464",
"0.69298464",
"0.69298464",
"0.69298464",
"0.69298464",
"0.69298464",
"0.69298464",
"0.69298464",
"0.69298464",
"0.69298464",
"0.69298464",
"0.69298464",
"0.69298464",
"0.69298464",
"0.69298464",
"0.69298464",
"0.69298464",
"0.69298464",
"0.69298464"... | 0.0 | -1 |
Evaluates a Node object in the abstract syntax tree. | def eval_node(node, env):
global genv
global result
node_type = node_name(node)
if node_type == 'Expr':
return eval_node(node.value, env)
elif node_type == 'Assign':
val = eval_node(node.value, env)
while type(val) is tuple and len(val) == 2 and (type(val[1]) == GlobalEnv o... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def evaluate(self, tree):\n\t\tpass",
"def eval(self, node):\n\n return None",
"def eval(self):\n return self._eval_node(self.syntax_tree)",
"def eval(node):\n if node.id == '(literal)':\n return node.value\n elif node.id == '(name)':\n return scope[node.value]\n elif nod... | [
"0.7579689",
"0.7281968",
"0.7104537",
"0.65870726",
"0.63241583",
"0.62537116",
"0.62466973",
"0.6190615",
"0.6190615",
"0.6085275",
"0.60787815",
"0.6074576",
"0.60320526",
"0.6001459",
"0.59816545",
"0.5969225",
"0.5951202",
"0.5924543",
"0.59197736",
"0.5908411",
"0.59069... | 0.63417566 | 4 |
Warn about unused static variables. | def _find_unused_static_warnings(filename, lines, ast_list):
static_declarations = {
node.name: node
for node in ast_list
if (isinstance(node, ast.VariableDeclaration) and
'static' in node.type.modifiers)
}
def find_variables_use(body):
for child in body:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def unusedVars(self):\n fullcode = self.code_cfg\n variables = set([x[1:] for x in codeconfig_getvars(fullcode)])\n exceptions = set(['complexity', 'code_cfg'])\n clsvars = set(vars(self).keys())\n nones = set(filter(lambda x: self.__dict__[x] is None, clsvars))\n nones = ... | [
"0.6999553",
"0.684138",
"0.61850595",
"0.6036887",
"0.58252496",
"0.57561684",
"0.5704306",
"0.56855494",
"0.56610996",
"0.56531423",
"0.5616008",
"0.55835503",
"0.5551007",
"0.55266124",
"0.5507778",
"0.5507778",
"0.5507778",
"0.5507778",
"0.5507778",
"0.5507778",
"0.550777... | 0.7391628 | 0 |
Implemented the validation step in training. | def validate(self):
# start validate
self.model.eval()
preds, labels = [], []
for batch_idx, data in enumerate(self.valid_dataloader):
# calculate and log losses
losses_report, valid_preds, valid_labels = self.forward_one_batch(
data)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def validation_epoch(self):\n self.model.eval()\n\n # Compute for training set\n train_loss, train_acc = compute_loss_and_accuracy(\n self.dataloader_train, self.model, self.loss_criterion\n )\n self.TRAIN_ACC.append(train_acc)\n self.TRAIN_LOSS.append(train_los... | [
"0.7692694",
"0.75106955",
"0.72990733",
"0.7281097",
"0.7261256",
"0.7184241",
"0.7167047",
"0.71011925",
"0.7094827",
"0.7077488",
"0.7063481",
"0.70511085",
"0.7027731",
"0.69944483",
"0.6994215",
"0.6989954",
"0.6987662",
"0.6927485",
"0.6925709",
"0.69221634",
"0.6896365... | 0.747869 | 2 |
Train the model for one epoch. | def fit_one_epoch(self):
preds, labels = [], []
for batch_idx, data in tqdm(enumerate(self.primary_dataloader)):
losses_report, train_preds, train_labels = self.forward_one_batch(
data)
preds.append(train_preds)
labels.append(train_labels)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def train_one_epoch(self):\n raise NotImplementedError",
"def train_one_epoch(self):\n\t\tself.model.train()\n\t\ttrain_loss = 0\n\n\t\tfor batch_idx, data in enumerate(self.data_loader.train_loader):\n\t\t\tInput = data[0].float().to(self.device)\n\t\t\tOutput = data[1].float().to(self.device)\n\n\t\t\ts... | [
"0.84007376",
"0.82546407",
"0.7960908",
"0.795539",
"0.78864366",
"0.7852404",
"0.7852404",
"0.7852404",
"0.7852404",
"0.7800227",
"0.7787899",
"0.7712295",
"0.7680872",
"0.76052165",
"0.75889987",
"0.75747776",
"0.7558251",
"0.7551875",
"0.7530717",
"0.75160545",
"0.751391"... | 0.71643716 | 58 |
Forward pass one batch of the data with the model. | def forward_one_batch(self, data, inference=False):
inputs = data['img']
labels = data.get('label', None)
inputs = inputs.cuda()
outputs = self.model(inputs)
losses_report = None
if not inference:
labels = labels.cuda()
losses_report = self.compute... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def forward(self, batch):\n raise NotImplementedError",
"def forward(self, batch):\n raise NotImplementedError",
"def forward_batch(self,batcher, phase=0):\n pass",
"def forward(self, x):\n #batch_size = x.shape[0]\n out = self.model(x)\n return out",
"def forward_... | [
"0.83496284",
"0.83496284",
"0.76277405",
"0.7380476",
"0.6854552",
"0.6846691",
"0.68106997",
"0.67724216",
"0.67681694",
"0.673687",
"0.67066",
"0.665859",
"0.66289693",
"0.65885663",
"0.65885663",
"0.65885663",
"0.6574135",
"0.65682745",
"0.6561686",
"0.6558493",
"0.654809... | 0.6389994 | 35 |
Save the training checkpoint to the disk. | def save_checkpoint(self):
if not self.save_ckpt:
return
lookup = None
is_best = False
checkpoint = self.create_checkpoint()
# save best only or not?
if self.save_best_only:
if self.valid_dataloader:
for item in [self.valid_metric... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save_checkpoint(self):\n \n if not os.path.isdir(self.path + '/checkpoint/'):\n os.makedirs(self.path + '/checkpoint/')\n\n if self.saver == None:\n with self.graph.as_default():\n self.saver = tf.train.Saver(tf.global_variables())\n\n self.saver... | [
"0.8523187",
"0.82452935",
"0.8190791",
"0.8153993",
"0.81345206",
"0.812798",
"0.8117262",
"0.80906636",
"0.80737823",
"0.79668844",
"0.79027486",
"0.77434766",
"0.77273154",
"0.7645351",
"0.764001",
"0.76386636",
"0.7630027",
"0.76060045",
"0.75488526",
"0.75404406",
"0.750... | 0.727966 | 43 |
max is 5 per city instead of like 6, 12 or 10 =(round((1 + (0.5(B41 1) / 5))B410.25, 2))12 so i think we take this formula and change the 5 to 4 | def calculate_production_bonus(self, number_of_improvements, max_slots):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_max_score(location_list, grid, shape):",
"def get_max(self):\r\n df = pd.read_csv(\"MonthlyRate.csv\")\r\n df = df[df.CurrencyCode == self.choice]\r\n maximum = df.max(axis=1).values[0]\r\n # Round the value to 4 d.p.\r\n maximum = round(float(maximum), 4)\r\n re... | [
"0.61874086",
"0.59167874",
"0.58895576",
"0.5768458",
"0.57580644",
"0.5663183",
"0.5629974",
"0.5617509",
"0.557116",
"0.55564797",
"0.5527265",
"0.55069757",
"0.55021864",
"0.5498278",
"0.54592294",
"0.5444004",
"0.54354906",
"0.5419152",
"0.5413824",
"0.539782",
"0.538697... | 0.0 | -1 |
Given a query, and an update clause, update all (and only) object returned by query. | def test_update(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update(self, **kwargs):\n expr = self.model.__table__.update().where(self.query).values(**kwargs)\n return self._runquery(expr)",
"def update(self, **kwargs):\n self._not_support_combined_queries(\"update\")\n if self.query.is_sliced:\n raise TypeError(\"Cannot update a... | [
"0.6956026",
"0.6809811",
"0.67811257",
"0.6622099",
"0.6616726",
"0.6503182",
"0.6379676",
"0.63170165",
"0.62703687",
"0.62077737",
"0.6169037",
"0.61661786",
"0.61275154",
"0.6101097",
"0.60837984",
"0.6061122",
"0.60530907",
"0.6043129",
"0.60077256",
"0.5953688",
"0.5947... | 0.0 | -1 |
Given a primary key, update the referenced object according to the update clause | def test_update_one(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update(cls, row_id, **kwargs):\n cls.delete(row_id)\n # obj = cls.query.filter_by(id=row_id).first()\n # for k, v in kwargs.items():\n # obj[k] = v\n # obj = cls.query.filter_by(id=row_id).update(kwargs)\n kwargs[\"id\"] = row_id\n obj = cls(**kwargs)\n ... | [
"0.686397",
"0.6536987",
"0.6483919",
"0.6443422",
"0.64308685",
"0.63702625",
"0.6346544",
"0.6217846",
"0.6217339",
"0.61647564",
"0.611606",
"0.6064789",
"0.6061465",
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"0.6041759",
"0.59926885",
"0.5989239",
"0.59867716",
"0.5947112",
"0.5912526",
"0.5893085",
... | 0.0 | -1 |
Given a query, remove all (and only) object returned by query. | def test_remove(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def delete_by_query(self, query, params = {}):\n params['hitsPerPage'] = 1000\n params['attributesToRetrieve'] = ['objectID']\n\n res = self.search(query, params)\n while (res['nbHits'] != 0):\n object_ids = []\n for elt in res['hits']:\n object_ids.... | [
"0.7295163",
"0.71565104",
"0.6959021",
"0.6853164",
"0.660744",
"0.6603074",
"0.6566388",
"0.6547425",
"0.65339786",
"0.65339786",
"0.6423338",
"0.64070696",
"0.63401437",
"0.6265542",
"0.6230876",
"0.61956275",
"0.61956275",
"0.6149756",
"0.6123274",
"0.6103659",
"0.6098419... | 0.0 | -1 |
Given a primary key, remove the referenced object. | def test_remove_one(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def removeidfobject(self, idfobject):\n key = idfobject.key.upper()\n self.idfobjects[key].remove(idfobject)\n self._reset_dependant_vars(\"idfobjects\")",
"def del_object_from_parent(self):\n if self.parent:\n self.parent.objects.pop(self.ref)",
"def remove(self, _id):\n... | [
"0.7022169",
"0.69352937",
"0.67359215",
"0.6719173",
"0.6582321",
"0.65806663",
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"0.64578646",
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"0.639342",
"0.63872486",
"0.63722575",
"0.6369881",
"0.63674027",
"0.6356457",
"0.6356... | 0.0 | -1 |
Executes the status change. | def execute(self, agent: Agent, state: SimState) -> None:
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def updateStatus(self, status):\n pass",
"def change_status(self, status, application_id):",
"def StatusChanged(self, state, info):\n pass",
"def change_status(self):\n if self.status == 'in progress':\n self.status = 'done'\n return self.status\n elif self.s... | [
"0.7463658",
"0.73751247",
"0.7124807",
"0.70087713",
"0.69775283",
"0.69684094",
"0.6961473",
"0.6950376",
"0.6876861",
"0.67328113",
"0.6729613",
"0.6650536",
"0.66410106",
"0.65832186",
"0.65808666",
"0.6556705",
"0.6552023",
"0.6541808",
"0.6536998",
"0.6520757",
"0.65162... | 0.0 | -1 |
Basically the same method as in the DefaultStatusStrategy, but adding the lethality check. | def execute(self, agent: Agent, state: SimState) -> None:
if agent.state() is not AgentState.INFECTIVE:
return
if np.random.random() < state.remove_prob():
if np.random.random() < state.lethality():
agent.set_state(AgentState.DEAD)
else:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_status(self):",
"def custom_assess_status_check(self):\n options = self.options\n # can check options.thing to ensure that it makes sense\n # if wrong return 'blocked', \"The driver is badly configured ...\"\n return None, None",
"def test_get_status(self) -> None:\n\n ... | [
"0.61206144",
"0.58532864",
"0.5794486",
"0.57871175",
"0.5704715",
"0.5690589",
"0.5610464",
"0.55541265",
"0.5540758",
"0.5519943",
"0.54881585",
"0.5483977",
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"0.54801756",
"0.54513115",
"0.5443968",
"0.5439035",
"0.5439035",
"0.5421981",
"0.54105186",
"0.54043... | 0.0 | -1 |
Updates the agents 'vaccine' before executing other checks | def execute(self, agent: Agent, state: SimState) -> None:
if agent.state() == AgentState.SUSCEPTIBLE and self.days == state.vaccine_time() \
and np.random.random() < state.vaccine_share():
agent.set_state(AgentState.IMMUNE) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ReflexVacuumAgent():\n def program(percept):\n location, status = percept\n if status == 'Dirty':\n return 'Suck'\n elif location == loc_A:\n return 'Right'\n elif location == loc_B:\n return 'Left'\n return Agent(program)",
"def update_agent... | [
"0.6244821",
"0.61558896",
"0.6037154",
"0.57862145",
"0.56732225",
"0.56598973",
"0.5629421",
"0.56235677",
"0.5466714",
"0.54048747",
"0.53794134",
"0.5335644",
"0.53047174",
"0.52038425",
"0.5185621",
"0.5175854",
"0.51662546",
"0.5162844",
"0.5150552",
"0.51480347",
"0.51... | 0.562963 | 6 |
Event handler to count up the number of days | def __next_step(self, state) -> None:
self.days += 1 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_number_days(self):\r\n return 1",
"def get_number_days(self):\r\n raise NotImplementedError",
"def hindu_day_count(cls, date):\n return date - cls.EPOCH",
"def day(self):\n return 0",
"def day(self):\n return 0",
"def Daysleftverification():\n pass",
"def e... | [
"0.7565068",
"0.7157228",
"0.6552486",
"0.6474552",
"0.6474552",
"0.64385206",
"0.63302714",
"0.62566036",
"0.62210876",
"0.61641073",
"0.6144609",
"0.6134258",
"0.6126481",
"0.60720074",
"0.60308474",
"0.59748036",
"0.59373456",
"0.59308636",
"0.59216416",
"0.5906378",
"0.58... | 0.6448241 | 5 |
Isolate (Remove from Grid) a given share of infected people for the sicknessduration. Afterwards they need to be added again to the Grid as removed/dead/immune. | def execute(self, agent: Agent, state: SimState) -> None:
if agent.is_quarantined():
if agent.state() is AgentState.DEAD or agent.state() is AgentState.IMMUNE or agent.state() is AgentState.REMOVED:
grid = agent.grid()
for row in range(grid.get_size()):
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def prune_losers(self):\n self.log.debug(\"PRUNE LOSERS\")\n # check to see if people i followed follow me back\n cutoff_time = (datetime.now()\n - timedelta(hours=self.reciprocation_window))\n ingrates = Target.objects.filter(\n hunter=self.user, status... | [
"0.5290711",
"0.5255218",
"0.5245789",
"0.51121676",
"0.50786626",
"0.50382346",
"0.50287575",
"0.49950922",
"0.4993932",
"0.49557725",
"0.4935115",
"0.49344",
"0.4929344",
"0.4913823",
"0.4904666",
"0.49032527",
"0.4873626",
"0.487273",
"0.48126963",
"0.48005226",
"0.4798922... | 0.0 | -1 |
Return the parsed contents of the config file. | def get_config():
return json.loads(CONFIG_FILE.read_text()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read_config(self):\n config = configparser.ConfigParser()\n config.read(self.configfile)\n return config",
"def get(self):\n config = self.user_file.parseString(self.content)\n return config",
"def get(self):\n if self.file:\n self._read()\n confi... | [
"0.77496666",
"0.7607564",
"0.75940424",
"0.75590175",
"0.7350562",
"0.7329519",
"0.7322378",
"0.73064345",
"0.7271919",
"0.72459453",
"0.71848226",
"0.7173341",
"0.71679926",
"0.7166633",
"0.71516",
"0.7046862",
"0.7030489",
"0.70298284",
"0.6951747",
"0.693856",
"0.6933751"... | 0.76075804 | 1 |
Return the Path of the cache file for the key. | def cache_file(cache_key):
return MASTOOLS_DIR / f"{cache_key}_cache.json" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_cache_file_path(self) -> str:\n return self.cache_file_path",
"def file_path(self, key=None):\n if key is not None:\n return os.path.join(self.directory, self.file_name(key))\n return getattr(\n self.agent,\n constants.CONST_LOCK_FILE,\n os... | [
"0.81592643",
"0.7996879",
"0.7945717",
"0.7945717",
"0.7943165",
"0.7943165",
"0.7821009",
"0.7612195",
"0.7606929",
"0.74966335",
"0.74500173",
"0.7417488",
"0.73771125",
"0.7359973",
"0.73446333",
"0.7326632",
"0.7269066",
"0.7237881",
"0.7232423",
"0.71653837",
"0.7153147... | 0.7776 | 7 |
Return the contents of the cache for the key, if its version is correct. | def load_cache(cache_key, version):
# Try to get the results of the last run, but fall back to an empty dict if that's not
# available. That's most likely to happen on the first run.
try:
cache = json.loads(cache_file(cache_key).read_text())
except FileNotFoundError:
return {}
if c... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_cache(self, key):\n return self.r.get(key)",
"def get(self, key):\n return self.cache_data.get(key)",
"def get(self, key):\n # Initialize key variables\n result = self.cache.get(key)\n\n # Return\n return result",
"def cache_get(self, key: str) -> Optional[by... | [
"0.7357667",
"0.717419",
"0.7155493",
"0.7136998",
"0.6948229",
"0.69267094",
"0.68619025",
"0.68619025",
"0.68404466",
"0.68349254",
"0.68165344",
"0.6543805",
"0.65131605",
"0.64544433",
"0.638806",
"0.63734585",
"0.63579035",
"0.63579035",
"0.6357669",
"0.63486564",
"0.634... | 0.69202965 | 6 |
Write the data to the cache for the key. | def save_cache(cache_key, version, data):
# Save these results for the next run. Include the version information and nest the user
# information inside a "users" key from the start, because experience says if we don't do this
# then the next release will add a feature that requires a change in the data lay... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _put(self, key, data):\n path = self._get_key_path(key)\n with open(path, \"wb\") as pickle_file:\n pickle.dump(data, pickle_file)",
"def update(self, cache_key):\r\n self._write_sha(cache_key)",
"def write_data_cache(self, data):\n assert data, 'Must input a non-empty di... | [
"0.75162756",
"0.7494944",
"0.73132676",
"0.72090346",
"0.7136881",
"0.7136881",
"0.7108822",
"0.7058452",
"0.7058452",
"0.70342314",
"0.7015688",
"0.7011552",
"0.6970053",
"0.69598436",
"0.6954975",
"0.6933723",
"0.6902636",
"0.6839885",
"0.6836937",
"0.68292135",
"0.6814033... | 0.682781 | 20 |
seed users. by defualt set to 5 users | def seed_User(number=5, overwrite=False):
if overwrite:
print('Overwriting all users')
User.objects.all().delete()
count = 0
for i in range(number):
username = fake.first_name()
User.objects.create_user(
email=username + "@blogmail.com",
password="vns... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_users(count=10):\n for i in range(count):\n user = generate_random_user()\n db.session.add(user)\n db.session.commit()",
"def setUp(self):\n self.users = [UserFactory.create() for i in range(5)]",
"def create_users(N):\n for _ in range(N):\n name = fake.name()\... | [
"0.76051784",
"0.73858875",
"0.7105484",
"0.7039456",
"0.7026748",
"0.70025635",
"0.69474685",
"0.69027597",
"0.68793637",
"0.6835339",
"0.6813174",
"0.6786388",
"0.6739348",
"0.6732933",
"0.6717712",
"0.66460043",
"0.66320664",
"0.6586459",
"0.6585731",
"0.65754217",
"0.6552... | 0.7705716 | 0 |
__init__ initializes an instance of the BlackBoxGame class | def __init__(self, atoms):
self._board = Board.Board(atoms)
self._score = 25
self._atoms = self._board.get_atoms()
self._guesses = [] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self):\n self._game_state = \"UNFINISHED\"\n self._current_player = \"BLACK\"\n self._game_board = Board()",
"def __init__(self):\r\n self._board = None\r\n self._bb_settings = Settings()\r\n self._screen = pygame.display.set_mode((self._bb_settings.screen_w... | [
"0.7896587",
"0.7875345",
"0.7770364",
"0.7609544",
"0.7590157",
"0.75064385",
"0.7486412",
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"0.7349547",
"0.7331177",
"0.72932756",
"0.72914916",
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"0.7235098",
"0.72264135",
"0.72215843",
"0.7206377",
"0.7200916",
"0.71846515",
"0.7148... | 0.0 | -1 |
get_board returns copy of the game's _board object | def get_board(self):
return self._board | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_board(self):\n return self.board.copy()",
"def get_board(self):\n return copy.deepcopy(self.board)",
"def get_game_board(self):\n return self.board",
"def get_board(self):\r\n return self.board",
"def get_board(self):\n return self.board",
"def get_board(self):\... | [
"0.85879415",
"0.8494425",
"0.8370379",
"0.8286644",
"0.8171239",
"0.81531906",
"0.8135133",
"0.80969197",
"0.7896076",
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"0.7624993",
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"0.68170214",
"0.68170214",
"0.68170214",
"0.68170214",
"0.68170214",
"0.67700... | 0.8100259 | 8 |
get_score returns the current score | def get_score(self):
return self._score | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_score(self):\n return self.score",
"def get_score(self):\n return self.score",
"def get_score(self):\n return self.score",
"def get_score(self):\n return self.score",
"def getScore(data):\n return score",
"def get_score(self):\n return self.__score",
"def get_s... | [
"0.8582769",
"0.8516198",
"0.8516198",
"0.8516198",
"0.8295523",
"0.82583547",
"0.8242513",
"0.816506",
"0.805192",
"0.80405056",
"0.8029793",
"0.8004031",
"0.7996733",
"0.783279",
"0.77552474",
"0.7745425",
"0.76711756",
"0.7645722",
"0.7591186",
"0.7586955",
"0.753339",
"... | 0.8245853 | 8 |
set_score increments the score by change can be negative | def set_score(self, change):
self._score = self._score + change | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_score(self,score):\n self._score = score",
"def set_score(self, score):\n self._score = score",
"def update_score():\n pass",
"def set_score(self, a, b, score):\n ### FILL IN ###",
"def set_score(self, score):\n # Update the score display\n self.score = sco... | [
"0.82878745",
"0.8127291",
"0.81113905",
"0.8077499",
"0.7966757",
"0.796316",
"0.796316",
"0.796316",
"0.79581094",
"0.78421885",
"0.7841102",
"0.7821862",
"0.78135276",
"0.77125627",
"0.7695137",
"0.76055825",
"0.75447255",
"0.75010866",
"0.7482682",
"0.7436947",
"0.7427076... | 0.830666 | 0 |
move_ray this is the primary function which is responsible for recursively moving a ray. Although it primarily look after the action of the Ray.Ray class it lives in the Game instance itself. THIS IS HOW WE DETERMINE THE EXIT POINT OF ALL RAYS HORIZONTAL, VERTICAL, OR WITH DETOURS | def move_ray(self, ray):
# look to the next spot in the ray's trajectory
next_coordinates = ray.get_next_location()
next_location = self._board.get_board_square(next_coordinates)
# check for a collisition - return if it occurs
if ray.check_for_collision(next_location):
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def shoot_ray(self, row, col):\n # Uses validate method to check if row,col are legal for ray entrance location\n if not self.valid_ray(row, col):\n return False\n # creates ray object from row, col integers\n ray = Ray(row, col)\n # checks if atom is in front of entra... | [
"0.6431586",
"0.6042099",
"0.5938072",
"0.5936508",
"0.592946",
"0.58522797",
"0.56683385",
"0.5636461",
"0.5598364",
"0.55645293",
"0.55484056",
"0.5496541",
"0.54507935",
"0.543731",
"0.5407489",
"0.5379115",
"0.53595096",
"0.53525144",
"0.534523",
"0.5339988",
"0.5306413",... | 0.71545416 | 0 |
shoot_ray shoots a ray from a given row and column if possible | def shoot_ray(self, origin_row, origin_column):
# get the the square object at row x column
origin = self._board.get_board_square((origin_row, origin_column))
# check that it is a valid "edge" to send a ray from
origin_check = origin.is_edge()
# if it's not then return false
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def shoot_ray(self, row, col):\n # Uses validate method to check if row,col are legal for ray entrance location\n if not self.valid_ray(row, col):\n return False\n # creates ray object from row, col integers\n ray = Ray(row, col)\n # checks if atom is in front of entra... | [
"0.78674567",
"0.6780156",
"0.67677057",
"0.61004895",
"0.5816195",
"0.5722863",
"0.5721122",
"0.56699514",
"0.566448",
"0.5559462",
"0.5555234",
"0.55531144",
"0.55380315",
"0.55212104",
"0.55208117",
"0.5480533",
"0.54755425",
"0.5391073",
"0.53854626",
"0.5371789",
"0.5366... | 0.748143 | 1 |
guess_atoms a function allowing a player to input guesses as to the locations of atoms on the board | def guess_atom(self, row, column):
if [row, column] in self._atoms:
# if an tom was properly guessed remove it from the atom's array
# and return True, append the guess to the guesses array, and
#remove it from the available atoms to guess from.
self._guesses.appe... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def guess_atom(self, row, col):\n if not self.valid_guess(row, col):\n return False\n # if row,col in guess list, tells players and returns True\n if self._board[row][col] != \" \":\n print(\"You've already guessed that location!\")\n return True\n # if ... | [
"0.71533525",
"0.67306685",
"0.5900517",
"0.58870995",
"0.5859277",
"0.5668452",
"0.54721373",
"0.5391912",
"0.5355076",
"0.5267006",
"0.5263563",
"0.5250836",
"0.5222762",
"0.52184594",
"0.5217904",
"0.51913744",
"0.5180138",
"0.51650697",
"0.51289845",
"0.5128813",
"0.51253... | 0.6491031 | 2 |
atoms_left returns the number of unguessed atoms still left | def atoms_left(self):
return len(self._atoms) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def atoms_left(self):\n return self._atoms",
"def atoms_left(self):\r\n return self._board.get_atoms()",
"def num_pieces_left(self):\n return self.num_white_pieces + self.num_black_pieces",
"def count_mass_left(self):\n self.mass_left_count = int(np.sum(self.array))",
"def numbe... | [
"0.7521378",
"0.74469197",
"0.6469958",
"0.6355561",
"0.6071514",
"0.6057757",
"0.6004638",
"0.59833574",
"0.59616107",
"0.5921324",
"0.5888561",
"0.5864343",
"0.5864343",
"0.5818085",
"0.57945406",
"0.57681686",
"0.5768063",
"0.5694106",
"0.56850934",
"0.56428653",
"0.559667... | 0.84319353 | 0 |
Test LSTM, LayerNormLSTM and NAS gnmt encoder. GNMT has only a single bi directional layer, and num_layers1 uni layers. time_major=True | def runLSTMEncoder(self, encoder, num_layers):
inputs_ph = tf.placeholder(
dtype=tf.float32,
shape=(None, common_utils.TIME_STEPS, common_utils.DEPTH))
inputs_length_ph = tf.placeholder(dtype=tf.int32, shape=(None))
outputs, states = encoder.encode(
mode=tf.estimator.ModeKeys.TRAIN,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_model_with_lstm_layer_time_major_true(self):\n\n tf.compat.v1.reset_default_graph()\n sess = tf.compat.v1.Session()\n with sess.graph.as_default():\n inputs = tf.keras.Input(shape=(3, 100))\n\n # Defaults\n # return_state=False, unit_forget_bias=True\n... | [
"0.7174004",
"0.6981176",
"0.6942284",
"0.66725725",
"0.65949506",
"0.6501579",
"0.6480307",
"0.6405576",
"0.6309258",
"0.6258608",
"0.62536645",
"0.6212986",
"0.6177987",
"0.61688703",
"0.6160826",
"0.6120184",
"0.6105675",
"0.6078574",
"0.6066392",
"0.60473734",
"0.60453326... | 0.6794371 | 3 |
Test GRU gnmt encoder. time_major=True | def runGRUEncoder(self, encoder, num_layers):
inputs_ph = tf.placeholder(
dtype=tf.float32,
shape=(None, common_utils.TIME_STEPS, common_utils.DEPTH))
inputs_length_ph = tf.placeholder(dtype=tf.int32, shape=(None))
outputs, states = encoder.encode(
mode=tf.estimator.ModeKeys.TRAIN,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_golay_module1(self):\r\n sent = golay.encode([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0])\r\n rec = sent[:-1] + 'C' # possible error here\r\n decoded, errors = golay.decode(rec)\r\n self.assertEqual(decoded, sent)\r\n self.assertLess(errors, 1.5)\r\n rec = sent[:-1] + '... | [
"0.53550947",
"0.5301906",
"0.5206506",
"0.49940667",
"0.49887952",
"0.49855092",
"0.49552405",
"0.48806253",
"0.4856835",
"0.48504332",
"0.48328564",
"0.48092747",
"0.48080942",
"0.477451",
"0.475015",
"0.47448063",
"0.47433698",
"0.47394577",
"0.47322595",
"0.47050416",
"0.... | 0.5863373 | 0 |
Create a CourseGraph, fetching unitary weights and edge weights from database, creating CourseNodes for each course, and | def __init__(self, database, session, max_suggestions=5, max_courses=30, cache_mult=4):
self._nodes = dict() # dict with courseid keys, CourseNode vals
self._max_suggestions = max_suggestions
self._max_courses = max_courses
self._cache_mult = cache_mult
db = database
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _CreateGraph(self):\n self.nodes = []\n self.edges = []\n for i, r in self.airports.set_index('airport_id').iterrows():\n self.nodes.append((i,r.to_dict()))\n for i, r in self.routes.set_index(['src_id','dst_id']).iterrows():\n self.edges.append((i[0],i[1],r.to... | [
"0.610946",
"0.6068693",
"0.5970327",
"0.59139115",
"0.5910554",
"0.59065074",
"0.5842171",
"0.58402723",
"0.5831642",
"0.5821705",
"0.58180076",
"0.5813589",
"0.5796841",
"0.57905227",
"0.5715409",
"0.5708784",
"0.56940794",
"0.5655929",
"0.5635845",
"0.5629975",
"0.56149757... | 0.7083467 | 0 |
Gets the crosslistings of the top edges from a course | def getTopEdgesFrom(self, session, courseid):
node = self.getNode(courseid) # get CourseNode
if not node:
return []
edges = node.getEdges() # get its Edge dict
return sorted(edges.keys(), key=lambda k: edges[k], reverse=True)[:5] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def assembly_courses(wall):\n courses = []\n vertices = set(wall.nodes())\n base = set(wall.nodes_where({'is_support': True}))\n\n if base:\n courses.append(list(base))\n\n seen = set()\n seen.update(base)\n\n vertices -= base\n\n while vertices:\n nbrs = s... | [
"0.59826374",
"0.55603427",
"0.5523582",
"0.5495732",
"0.5461872",
"0.54479295",
"0.53818995",
"0.5318768",
"0.52677137",
"0.5246417",
"0.520737",
"0.5162896",
"0.5158995",
"0.51440036",
"0.51215637",
"0.5089466",
"0.50795156",
"0.50770104",
"0.5055389",
"0.5054957",
"0.50231... | 0.72403175 | 0 |
Return filename of a submission downloaded from synapse. | def downloadSubmissionAndFilename(self, sub, downloadFile=True, **kargs):
if isinstance(sub, dict) == False:
raise TypeError("input must be a submission (dictionary)")
if downloadFile == False:
filename = self.getSubmission(sub, downloadFile=False)['filePath']
else:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def download_filename(self) -> str:\n return self._download_filename",
"def get_download_file_name(self):\n # Use 'unknown' if the course instance does not have a term\n if self.course_instance.term:\n term = self.course_instance.term.get_url_name()\n else:\n ter... | [
"0.7089564",
"0.70576966",
"0.69732934",
"0.69194645",
"0.68737155",
"0.68652344",
"0.6865232",
"0.68460745",
"0.6798308",
"0.6795707",
"0.66493416",
"0.66368407",
"0.6624542",
"0.6617163",
"0.66041523",
"0.6603908",
"0.65955335",
"0.65814525",
"0.6547018",
"0.6510974",
"0.64... | 0.6980007 | 2 |
Transform relevant object into json object | def json(self, data):
import json
data = json.dumps(data)
return data | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def object_to_json(self, json_data):\n result = {}\n if sys.version_info[0] < 3:\n itr = json_data.__dict__.iteritems()\n else:\n itr = json_data.__dict__.items()\n for key,value in itr:\n # Skip internal attributes.\n if key.startswith(\"__\"... | [
"0.69116586",
"0.6882489",
"0.68524873",
"0.6822507",
"0.6781035",
"0.66899097",
"0.65613455",
"0.65372014",
"0.65137166",
"0.647605",
"0.6443874",
"0.6437841",
"0.64342636",
"0.6407022",
"0.63898367",
"0.6371773",
"0.6358821",
"0.63180697",
"0.6306601",
"0.6302604",
"0.63005... | 0.0 | -1 |
r"""Factorized Prior model from J. Balle, D. Minnen, S. Singh, S.J. Hwang, | def bmshj2018_factorized(
quality, metric="mse", pretrained=False, progress=True, **kwargs
):
if metric not in ("mse", "ms-ssim"):
raise ValueError(f'Invalid metric "{metric}"')
if quality < 1 or quality > 8:
raise ValueError(f'Invalid quality "{quality}", should be between (1, 8)')
re... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def bias_prior(self):",
"def lnprior(self):\n \n return",
"def prior_predictive(self):\n cfg = self.config\n n = cfg['batch_size'] * cfg['q/n_samples']\n n_samples = cfg['q/n_samples']\n with util.get_or_create_scope('model', reuse=True):\n h_prior = tf.cast(self.p_h_L.sample(n),... | [
"0.6737038",
"0.636095",
"0.607639",
"0.60483646",
"0.60367125",
"0.60130304",
"0.59627426",
"0.59598047",
"0.59528166",
"0.5932962",
"0.5882837",
"0.58736926",
"0.5853717",
"0.5850431",
"0.5840175",
"0.5834046",
"0.57900655",
"0.57882077",
"0.5787185",
"0.57477564",
"0.57408... | 0.0 | -1 |
r"""Scale Hyperprior model from J. Balle, D. Minnen, S. Singh, S.J. Hwang, | def bmshj2018_hyperprior(
quality, metric="mse", pretrained=False, progress=True, **kwargs
):
if metric not in ("mse", "ms-ssim"):
raise ValueError(f'Invalid metric "{metric}"')
if quality < 1 or quality > 8:
raise ValueError(f'Invalid quality "{quality}", should be between (1, 8)')
re... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def scale(self):",
"def bias_prior(self):",
"def set_hyper_parameters(self, x):\n self.set_scale(x[0])",
"def setPrior(self,xPrior,priorWeight):\n assert self.regularizationLambda == 0\n if not isinstance(xPrior,np.ndarray):\n xPrior = np.array(xPrior)\n self.count = 1\... | [
"0.62483156",
"0.62137103",
"0.6170398",
"0.6155527",
"0.6027734",
"0.59788775",
"0.5940418",
"0.5933784",
"0.5926682",
"0.59190893",
"0.5883833",
"0.5880828",
"0.58511233",
"0.58176",
"0.580629",
"0.57443565",
"0.57379764",
"0.56918335",
"0.56906885",
"0.5651347",
"0.5624571... | 0.5472967 | 34 |
r"""Scale Hyperprior with non zeromean Gaussian conditionals from D. | def mbt2018_mean(quality, metric="mse", pretrained=False, progress=True, **kwargs):
if metric not in ("mse", "ms-ssim"):
raise ValueError(f'Invalid metric "{metric}"')
if quality < 1 or quality > 8:
raise ValueError(f'Invalid quality "{quality}", should be between (1, 8)')
return _load_mod... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def gaussian_prior(self):\n self.prior = sps.multivariate_normal(self.m0,self.S0)",
"def _likelihood_der1_scale(self, hyperparam):\n\n # Get eta\n eta = self._hyperparam_to_eta(hyperparam)\n\n # Set scale of the covariance object\n scale = self._hyperparam_to_scale(hyperparam[s... | [
"0.69035596",
"0.60564435",
"0.5897708",
"0.5843555",
"0.5838015",
"0.5830661",
"0.5816271",
"0.5786775",
"0.5709402",
"0.56590503",
"0.56590503",
"0.56466454",
"0.5611644",
"0.5611628",
"0.5609158",
"0.5600361",
"0.55973405",
"0.55847114",
"0.55843025",
"0.5575209",
"0.55476... | 0.0 | -1 |
r"""Joint Autoregressive Hierarchical Priors model from D. | def mbt2018(quality, metric="mse", pretrained=False, progress=True, **kwargs):
if metric not in ("mse", "ms-ssim"):
raise ValueError(f'Invalid metric "{metric}"')
if quality < 1 or quality > 8:
raise ValueError(f'Invalid quality "{quality}", should be between (1, 8)')
return _load_model("m... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, D, H, S, to_learn=[\"W\", \"pi\", \"sigma\"], comm=MPI.COMM_WORLD):\n self.comm = comm\n self.noise_policy = {}\n self.to_learn = to_learn\n self.D = D\n self.H = H\n self.S = S\n\n tol = 1e-5\n self.noise_policy = {\n \"W\": (-n... | [
"0.55733985",
"0.5363515",
"0.53536046",
"0.53175926",
"0.526763",
"0.5229534",
"0.52215683",
"0.51651067",
"0.51620126",
"0.5120631",
"0.5106703",
"0.50803024",
"0.5077998",
"0.50690013",
"0.5067703",
"0.50612646",
"0.5049136",
"0.50420725",
"0.5038924",
"0.50167084",
"0.501... | 0.0 | -1 |
r"""Anchor model variant from `"Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention Modules" | def cheng2020_anchor(quality, metric="mse", pretrained=False, progress=True, **kwargs):
if metric not in ("mse", "ms-ssim"):
raise ValueError(f'Invalid metric "{metric}"')
if quality < 1 or quality > 6:
raise ValueError(f'Invalid quality "{quality}", should be between (1, 6)')
return _load... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def construct_model():\n import lbann\n\n # Layer graph\n input = lbann.Input(target_mode='N/A', name='inp_data')\n # data is 64*64*4 images + 15 scalar + 5 param\n #inp_slice = lbann.Slice(input, axis=0, slice_points=\"0 16399 16404\",name='inp_slice')\n inp_slice = lbann.Slice(input, axis=0, sl... | [
"0.6148643",
"0.5812919",
"0.5794156",
"0.54624885",
"0.53823245",
"0.5287905",
"0.52859586",
"0.5282076",
"0.52552813",
"0.5201943",
"0.51982266",
"0.5182037",
"0.51819533",
"0.515083",
"0.5144859",
"0.5127486",
"0.5112405",
"0.509996",
"0.5075668",
"0.50655454",
"0.5056296"... | 0.5076744 | 18 |
r"""Selfattention model variant from `"Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention Modules" | def cheng2020_attn(quality, metric="mse", pretrained=False, progress=True, **kwargs):
if metric not in ("mse", "ms-ssim"):
raise ValueError(f'Invalid metric "{metric}"')
if quality < 1 or quality > 6:
raise ValueError(f'Invalid quality "{quality}", should be between (1, 6)')
return _load_m... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def self_attention_model(self):\n inputs = self.prepare_inputs()\n \n features = self.prepare_features(inputs)\n support_history_context_concat_emb = features['support_history_context_concat_emb']\n support_link_info_concat_emb = features['support_link_info_concat_emb']\n ... | [
"0.65408075",
"0.64289623",
"0.6141969",
"0.6130033",
"0.61221105",
"0.58673143",
"0.586286",
"0.5794737",
"0.575147",
"0.573981",
"0.5708623",
"0.5706052",
"0.57047206",
"0.56855595",
"0.5660355",
"0.5646968",
"0.55968773",
"0.5591234",
"0.55796915",
"0.5566003",
"0.55493313... | 0.0 | -1 |
Entry point to gameplay. | def main() -> None:
game = advanced_game(MAP_FILE)
root = tk.Tk()
root.title('EndOfDayz')
if TASK == 1:
gui = BasicGraphicalInterface
elif TASK == 2:
gui = ImageGraphicalInterface
# else:
# gui = MastersGraphicalInterface
app = gui(root, game.get_grid().ge... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def play_game():\n pass",
"def play_game():\n pass",
"def game_play(self):",
"def play(self):\n print('Playing game...')",
"def start_game(self):\n\n\t\tpass",
"def main():\n g = Game(800, 600)\n g.start()",
"def main():\r\n gameclass = data.game.GameClass()\r\n gameclass.m... | [
"0.8527575",
"0.8128602",
"0.8080839",
"0.79423046",
"0.79204637",
"0.7907992",
"0.78166646",
"0.77512056",
"0.7606996",
"0.7596167",
"0.75618863",
"0.75584793",
"0.7554812",
"0.75141007",
"0.7511485",
"0.7502417",
"0.7497473",
"0.7493972",
"0.74235296",
"0.7420933",
"0.73897... | 0.6931555 | 42 |
Retrieve, update or delete a code snippet. | def student_detail(request, pk):
try:
students = student.objects.get(pk=pk)
except students.DoesNotExist:
return HttpResponse(status=404)
if request.method == 'GET':
serializer = studentSerializer(students)
return JsonResponse(serializer.data)
elif request.method == 'PU... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def snippet_detail(request, pk):\n try:\n snippet = Snippet.objects.get(pk=pk)\n except Snippet.DoesNotExist:\n return Response(status=status.HTTP_404_NOT_FOUND)\n\n if request.method == 'GET':\n serializer = SnippetSerializer(snippet)\n return Response(serializer.data)\n\n ... | [
"0.6433584",
"0.6433584",
"0.6343745",
"0.62817955",
"0.62216586",
"0.6008872",
"0.59082586",
"0.5902986",
"0.58660686",
"0.581822",
"0.5809916",
"0.5748296",
"0.57467324",
"0.57114553",
"0.56778467",
"0.55851805",
"0.5559712",
"0.54972357",
"0.54327506",
"0.5384471",
"0.531"... | 0.0 | -1 |
Loads a json value from a file and converts it to the corresponding python object. | def loadJsonValueFromFile(inputFilePath):
with open(inputFilePath) as fileObj:
value = json.load(fileObj)
return value | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load(cls, file):\n with open(file, \"r\") as f:\n j = json.load(f)\n return cls(**j)",
"def load_from_json_file(filename):\n with open(filename, 'r') as f:\n obj = json.loads(f.read())\n return obj",
"def convert_json_to_object(file_content):\n object = json... | [
"0.7691063",
"0.76900375",
"0.76079774",
"0.7595927",
"0.75809187",
"0.7557681",
"0.7495783",
"0.74881333",
"0.74459565",
"0.7439162",
"0.7416306",
"0.7365671",
"0.73619384",
"0.7338387",
"0.73190105",
"0.7312332",
"0.7281531",
"0.7272493",
"0.72698015",
"0.7256082",
"0.72544... | 0.74380505 | 10 |
Initializes turtle instance for turtle game. | def initialize(turtle_shape, bg_color, turtle_color, turtle_speed):
turtle_instance = turtle.Turtle()
turtle_instance.shape(turtle_shape)
turtle.bgcolor(bg_color)
turtle_instance.color(turtle_color)
turtle_instance.speed(turtle_speed)
return turtle_instance | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def init_turtle():\n turtle.up()\n turtle.home()",
"def __init__(self, commands=[], turtle_name=\"Terry\", speed=6, shape=\"classic\"):\n super().__init__()\n turtle.colormode(255)\n self._name = turtle_name\n super().speed(speed)\n super().shape(shape)\n self.comm... | [
"0.81658715",
"0.7127116",
"0.704404",
"0.6922751",
"0.6751538",
"0.66928184",
"0.6515024",
"0.64465225",
"0.6263604",
"0.62292695",
"0.6227585",
"0.61615515",
"0.58694047",
"0.58318806",
"0.580501",
"0.575872",
"0.57573175",
"0.57200843",
"0.5657598",
"0.55897486",
"0.555192... | 0.8317453 | 0 |
Defines the turtle movement for the initialized turtle instance and executes that movement. | def turtle_movement(turtle_shape, bg_color, turtle_color, turtle_speed):
turtle_name = initialize(turtle_shape, bg_color,
turtle_color, turtle_speed)
for i in range(36):
for i in range(4):
turtle_name.forward(200)
turtle_name.right(90)
turtle... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def move_turtle(self):\n self.forward(self.move_speed)",
"def setTurtle(t):\r\n t.pu()\r\n t.goto(initialCoordinates())",
"def init_turtle():\n turtle.up()\n turtle.home()",
"def move():\n Robot.move()",
"def movement(self):",
"def move(self):\r\n segments = len(self.all_turt... | [
"0.7389288",
"0.6786604",
"0.6750962",
"0.6614437",
"0.6599587",
"0.64865434",
"0.6407805",
"0.64050645",
"0.6318544",
"0.6280543",
"0.6203746",
"0.61156297",
"0.6057648",
"0.6023432",
"0.60230684",
"0.600326",
"0.59755194",
"0.5961362",
"0.59582186",
"0.59399325",
"0.5905899... | 0.69048256 | 1 |
Add this command to the main parser. | def add_commands(parser, subparsers):
subparser = subparsers.add_parser('libraries', help='search for LogicBlox libraries')
subparser.set_defaults(func=execute_libraries)
subparser.add_argument('libraries',
nargs='*',
help="libraries to locate")
su... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def extend_cli(self, subparser):",
"def add_args(self, parser):",
"def add(self, name, command):",
"def _add_to_cli(self, parser, group=None):\n dest = self.dest\n if group is not None:\n dest = group.name + '_' + dest\n\n subparsers = parser.add_subparsers(dest=dest,\n ... | [
"0.728577",
"0.7169099",
"0.7166533",
"0.7153756",
"0.7049907",
"0.7003788",
"0.7001368",
"0.69773185",
"0.6960154",
"0.6897418",
"0.68806356",
"0.6854973",
"0.6824182",
"0.6771863",
"0.6764204",
"0.67025226",
"0.6698706",
"0.6660946",
"0.6652996",
"0.6588826",
"0.6572274",
... | 0.0 | -1 |
Saves summary statistics as a csv file in the current directory and returns the output filename. | def save_summary_statistics_csv(
experiment_name, roi_summary_data, save_directory_path: str = ""
):
# Create directories on the path if they don't already exist
Path(save_directory_path).mkdir(parents=True, exist_ok=True)
csv_filename = f"{experiment_name} - summary statistics (generated {iso_datetime... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def checkpoint_stats(self, stats):\n stats.to_csv(\n self.params.stat.dir + self.params.model.name + \"_\" + self.params.data.name + \".stat\",\n sep='\\t',index=False,header=True)",
"def save_csv_file():\n global output_on_display, import_lst, column_names, data\n if data_base... | [
"0.67156446",
"0.6640688",
"0.65309376",
"0.65059",
"0.6463663",
"0.6374038",
"0.6361525",
"0.632994",
"0.63163084",
"0.62892014",
"0.6272734",
"0.62425643",
"0.62299234",
"0.62213546",
"0.6216482",
"0.6173342",
"0.6157177",
"0.6122196",
"0.61156815",
"0.60866743",
"0.6001306... | 0.78815484 | 0 |
Opens all JPEG+RAW images in the specified experiment directory and returns as a map of | def get_raw_image_paths_for_experiment(local_sync_directory_path, experiment_directory):
raw_images_directory = os.path.join(local_sync_directory_path, experiment_directory)
raw_image_paths = get_files_with_extension(raw_images_directory, ".jpeg")
return pd.Series(raw_image_paths) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_sample_images():\n # Try to import imread from scipy. We do this lazily here to prevent\n # this module from depending on PIL.\n try:\n try:\n from scipy.misc import imread\n except ImportError:\n from scipy.misc.pilutil import imread\n except ImportError:\n... | [
"0.66397595",
"0.6540607",
"0.64433455",
"0.64104563",
"0.6374026",
"0.63023806",
"0.6257343",
"0.6191441",
"0.61159426",
"0.6109508",
"0.6003954",
"0.5990808",
"0.59906566",
"0.59873354",
"0.59793514",
"0.5957272",
"0.59243476",
"0.59169495",
"0.58602643",
"0.58406574",
"0.5... | 0.62735325 | 6 |
stack pandas DataFrames logically into a bigger DataFrame, resets the index of the resulting DataFrame to avoid duplicates in the index | def _stack_dataframes(dataframes: List[pd.DataFrame]) -> pd.DataFrame:
return pd.concat(dataframes).reset_index(drop=True) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_reset_index(self):\n\n # reminder on multi index in columns\n df1 = pd.DataFrame([[1, 3], [2, 4], [11, 33], [22, 44]]).T\n df1.index = pd.Series([1, 2], name=\"idx1\")\n df1.columns = pd.MultiIndex.from_product([['a', 'b'], ['aa', 'bb']], names=['idx_c', 'idx2'])\n\n # s... | [
"0.5980725",
"0.5887287",
"0.5851793",
"0.5812862",
"0.5757257",
"0.57415146",
"0.57319605",
"0.5689936",
"0.56405115",
"0.56170523",
"0.56154037",
"0.5591578",
"0.5586357",
"0.5564776",
"0.55478466",
"0.554424",
"0.5535979",
"0.54988015",
"0.5480869",
"0.54719794",
"0.545409... | 0.66242844 | 0 |
stack pandas Series logically into a DataFrame | def _stack_serieses(serieses: List[pd.Series]) -> pd.DataFrame:
return pd.concat(serieses, axis="columns").T | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def data_structure():\n\n items = [1.0, 2.0, 3.0, 4.0, 5.0 ]\n s = pd.Series(items, index=['a', 'b', 'c', 'd', 'e'])\n # s = pd.Series(np.random.randn(5), index=['a', 'b', 'c', 'd', 'e'])\n print s\n s = pd.Series(items)\n print s\n\n d= {'one': [1.0, 2.0, 3.0, 4.0], 'two': [4.0, 3.0, 2.0, 1.0... | [
"0.6693489",
"0.64312357",
"0.6289627",
"0.61618704",
"0.59844136",
"0.59790695",
"0.5950793",
"0.5896647",
"0.58869344",
"0.58781815",
"0.577991",
"0.5733921",
"0.56654763",
"0.566396",
"0.56531364",
"0.56504464",
"0.5612756",
"0.56065685",
"0.5595002",
"0.5558159",
"0.55369... | 0.76580703 | 0 |
Process a full set of images, with parallelization if multiple CPU threads are available on this machine | def _process_images(
raw_image_paths: pd.Series,
raw_images_dir: str,
ROI_definitions: Dict[str, Tuple],
flat_field_filepath_or_none: Union[str, None],
save_ROIs: bool,
save_dark_frame_corrected_images: bool,
save_flat_field_corrected_images: bool,
) -> Tuple[pd.DataFrame, pd.DataFrame]:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def scan_images_multiprocessed(images, clf, processes, vstep=15, hstep=15, dnum=5):\n pool = Pool(processes=processes) # start 4 worker processes\n results = []\n for i in range(0, processes):\n begin = i * int(len(images) / processes)\n if i == processes - 1:\n end = len(images)... | [
"0.7019778",
"0.701473",
"0.68229294",
"0.6802597",
"0.67542833",
"0.6570712",
"0.6545006",
"0.65141946",
"0.6469355",
"0.6455748",
"0.6437736",
"0.6373732",
"0.6337872",
"0.6320698",
"0.6302606",
"0.6255835",
"0.6229472",
"0.62148833",
"0.6202636",
"0.61866295",
"0.61764526"... | 0.56445336 | 94 |
Version of process_image with all of the local configuration variables packed in. Also encapsulates the opening of the image. | def _process_image_local(raw_image_path):
return process_image(
original_rgb_image=raw.open.as_rgb(raw_image_path),
original_image_filepath=raw_image_path,
raw_images_dir=raw_images_dir,
ROI_definitions=ROI_definitions,
flat_field_filepath_or_none=flat... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def process(image):\n pass",
"def process_image(self):\n pass",
"def process(self, image):",
"def process(self, image: np.ndarray) -> NamedTuple:\n\n return super().process(input_data={'image': image})",
"def add_processed_image(image_proc_type, name, b64_string, export_file_type):\n\n if i... | [
"0.6546748",
"0.6495667",
"0.6287394",
"0.57940394",
"0.57754576",
"0.57309806",
"0.5728724",
"0.5704282",
"0.5697228",
"0.565087",
"0.56456286",
"0.56390184",
"0.5626983",
"0.5626955",
"0.555883",
"0.5555184",
"0.54956263",
"0.54702634",
"0.54599035",
"0.54391104",
"0.543066... | 0.6391158 | 2 |
Initialize a two dimensional list as a matrix | def __init__(self, initArray):
for row in initArray:
for elem in row:
if type(elem) is not int:
raise TypeError
n = len(initArray[0])
if not all(len(x) == n for x in initArray):
raise ArithmeticError
self.array = initArray
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def matrixlist(inputlist, converter=proper, fake=False):\n if converter is None:\n converter = type(inputlist[0][0])\n xlen = len(inputlist[0])\n for x in xrange(1,len(inputlist)):\n if len(inputlist[x]) != xlen:\n raise IndexError(\"Unequal matrix row lengths for matrixlist of \"... | [
"0.732879",
"0.705147",
"0.69763094",
"0.6929091",
"0.69166654",
"0.68848765",
"0.68401814",
"0.6766674",
"0.66469675",
"0.6645936",
"0.66292936",
"0.66119903",
"0.6598583",
"0.65325534",
"0.6505658",
"0.6502061",
"0.6496865",
"0.6486795",
"0.6459021",
"0.64460206",
"0.643500... | 0.0 | -1 |
Return a string for the array of numbers for this matrix | def __str__(self):
return str(self.array) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __repr__(self) -> str:\n\t\treturn \",\".join(\"\".join(str(n) for n in m) for m in self.matrix)",
"def __str__(self):\n output = \"\"\n for i in self.values:\n st = []\n output += \"[\"\n for j in i:\n st.append(str(j))\n output += \"... | [
"0.7381142",
"0.7210961",
"0.7170365",
"0.7031644",
"0.6882609",
"0.68663853",
"0.6862946",
"0.6755842",
"0.67423487",
"0.66417027",
"0.65829957",
"0.6536184",
"0.6523809",
"0.6509771",
"0.64562243",
"0.6420926",
"0.64129996",
"0.638619",
"0.6340786",
"0.63212585",
"0.626529"... | 0.66033196 | 10 |
Add two matricies together | def __add__(self, otherMatrix):
sameRows = (len(self.array) == len(otherMatrix.array))
sameCols = len(self.array[0]) == len(otherMatrix.array[0])
if not (sameCols and sameRows):
raise ArithmeticError
X = len(self.array)
Y = len(self.array[0])
retArray = [[0 ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __add__(self, other):\r\n if isinstance(other, mat4):\r\n return mat4(map(lambda x,y: x+y, self.mlist, other.mlist))\r\n else:\r\n raise TypeError, \"unsupported operand type for +\"",
"def matAdd(a, b):\n shape=matShape(a)\n return [[matGet(a,x,y)+matGet(b,x,y) for ... | [
"0.81966865",
"0.8028442",
"0.7911417",
"0.784823",
"0.7748032",
"0.77048737",
"0.7664608",
"0.7599502",
"0.75470144",
"0.75353587",
"0.75039405",
"0.74704564",
"0.74508417",
"0.73524606",
"0.73200405",
"0.7277604",
"0.724819",
"0.7234582",
"0.71502364",
"0.7145327",
"0.70963... | 0.6929311 | 26 |
Multiply two matricies together | def __mul__(self, otherMatrix):
if not (len(self.array[0]) == len(otherMatrix.array)):
raise ArithmeticError
common = len(self.array[0])
X = len(self.array)
Y = len(otherMatrix.array[0])
newArray = [[0 for x in range(X)] for x in range(Y)]
for row in range(X... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def matmul(a, b):\n raise NotImplementedError",
"def matrix_mult(m1, m2):\n pass",
"def matMul(a, b):\n sa=matShape(a)\n sb=matShape(b)\n if sa[1]!=sb[0]: raise ValueError\n ret=matZeros((sa[0],sb[1]))\n for i in range(sa[0]):\n for j in range(sb[1]):\n val=0.0\n for k ... | [
"0.8291302",
"0.8262729",
"0.80926496",
"0.78970397",
"0.78095114",
"0.76959616",
"0.76780117",
"0.76680285",
"0.7643243",
"0.7567869",
"0.753683",
"0.7533653",
"0.7526934",
"0.752129",
"0.74732196",
"0.7456403",
"0.7453868",
"0.7389877",
"0.7388949",
"0.73717046",
"0.7357616... | 0.0 | -1 |
Load instruments from configpath | def _load(self) -> list[Instrument]:
logger.info("Loading config...")
self._config = yml.load(self.configpath)
instruments, modespec = self._config["instruments"], self._config["modes"]
logger.success(f"Found {len(instruments)} instruments, {len(modespec)} modes") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_load_configs_simulation(self):\n global locator, config_paths\n locator.load_config(config_paths[1])\n\n self.assertEqual(locator.config['routines'], ['simulate'])\n self.assertEqual(locator.config['driver'],\n {\n 'type': 'SimDriver',\n 'kwargs': {... | [
"0.63088006",
"0.62734544",
"0.60392916",
"0.59869903",
"0.58425516",
"0.5792564",
"0.57058764",
"0.56768847",
"0.56603354",
"0.5657189",
"0.56550276",
"0.56537586",
"0.5621684",
"0.5609595",
"0.5587608",
"0.5586826",
"0.55499035",
"0.5522914",
"0.5517707",
"0.5516953",
"0.54... | 0.7140976 | 0 |
Expose unique instrument classes found in config | def _expose(self) -> None:
classes = {instrument.__class__ for instrument in self._config["instruments"]}
for class_ in classes:
pyro.expose(class_)
logger.success(f"Exposed {len(classes)} instrument class(es): {classes}") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _config_classes(self):\n pass",
"def config(self) -> InstrumentConfig:\n ...",
"def register_instrument(instrument):\n config.INSTRUMENTS.append(instrument.id)\n for m in instrument.modules:\n register_module(m)",
"def _instrument_class(self, cls):\n newcls = type('Instr... | [
"0.6167917",
"0.6165286",
"0.6108895",
"0.5712228",
"0.56472594",
"0.5590002",
"0.54058063",
"0.5276742",
"0.5238212",
"0.517786",
"0.5136025",
"0.5066814",
"0.5047145",
"0.5043953",
"0.5038126",
"0.50306547",
"0.502609",
"0.5012202",
"0.5012202",
"0.50089884",
"0.49899292",
... | 0.66089696 | 0 |
Register instrument instances and self with daemon and storing uris | def _serve(self) -> None:
for instrument in self._config["instruments"]:
uri = self._daemon.register(instrument, objectId=str(instrument))
self._services[instrument.id] = str(uri)
logger.success(f"Registered {instrument} at {uri}")
self.uri = self._daemon.register(sel... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def register_instrument(instrument):\n config.INSTRUMENTS.append(instrument.id)\n for m in instrument.modules:\n register_module(m)",
"def register():\n signals.initialized.connect(initialize)\n signals.article_generator_context.connect(add_libravatar)",
"def on_register(cls):",
"def _regi... | [
"0.61770165",
"0.586058",
"0.5787275",
"0.57755834",
"0.5748999",
"0.5597987",
"0.5547358",
"0.5466158",
"0.5462284",
"0.5456908",
"0.5437423",
"0.54095894",
"0.5405339",
"0.5405339",
"0.53873736",
"0.53591174",
"0.5346121",
"0.5344868",
"0.5339623",
"0.53348446",
"0.53146374... | 0.78891295 | 0 |
Save instruments and modes to configpath | def save(self) -> None:
logger.info("Saving to config...")
yml.save(self._config, self.configpath) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save(self):\n for p, c in self.configs_:\n c.write(p)",
"def save(self, config_path):\n raise NotImplementedError()",
"def save():\n\t\ttry:\n\t\t\t#paths\n\t\t\tConfig.conf.set('paths', 'source_dir', Config.source_dir)\n\t\t\tConfig.conf.set('paths', 'lyrics_dir', Config.lyrics_di... | [
"0.69961077",
"0.6905757",
"0.67979085",
"0.6779115",
"0.663721",
"0.6624185",
"0.65824383",
"0.6569083",
"0.6564401",
"0.6522336",
"0.6489692",
"0.646409",
"0.6439854",
"0.6433161",
"0.637511",
"0.63696384",
"0.635844",
"0.63290244",
"0.6287945",
"0.62523466",
"0.62460214",
... | 0.5880172 | 46 |
Disconnect instruments and shutdown daemon | def shutdown(self) -> None:
logger.info("Disconnecting instruments...")
for instrument in self._config["instruments"]:
instrument.disconnect()
logger.info(f"Shutting down {self}...")
self._daemon.shutdown() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def shutdown(self):\n if self.alive:\n libplasma.disconnect(self.conn)\n self.alive = False",
"def shutdown(self):",
"def stopAndDisconnectWalabot():\n wlbt.Stop()\n wlbt.Disconnect()\n print ('Termination successful')",
"def shutdown(self) -> None:",
"def shutdown(self) -> None:",
"d... | [
"0.67708665",
"0.6769665",
"0.6741867",
"0.66888916",
"0.66888916",
"0.6571312",
"0.65665215",
"0.6500033",
"0.64636064",
"0.64133114",
"0.6387295",
"0.6361316",
"0.63584465",
"0.6352385",
"0.6346766",
"0.6346196",
"0.6339963",
"0.63314235",
"0.63314235",
"0.63314235",
"0.631... | 0.7881212 | 0 |
python ~/code/xdoctest/testing/test_linenos.py test_lineno_failcase_called_code python ~/code/xdoctest/testing/test_linenos.py | def test_lineno_failcase_called_code():
text = _run_case(utils.codeblock(
r'''
def func(a):
"""
Example:
>>> func(0)
>>> # this doesnt do anything
>>> print('this passes')
this passes
>>> # call t... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_error(doctest):",
"def test_expt(doctest):",
"def test_exp(doctest):",
"def testit(did_pass):\n\n # This function works correctly--it is verbatim from the text, chapter 6\n linenum = sys._getframe(1).f_lineno # Get the caller's line number.\n if did_pass:\n msg = \"Te... | [
"0.704092",
"0.70044655",
"0.6639948",
"0.6638953",
"0.65686834",
"0.65359443",
"0.6508109",
"0.6495619",
"0.643054",
"0.6405408",
"0.63980496",
"0.63929",
"0.6392692",
"0.63884795",
"0.63859123",
"0.63859123",
"0.6350053",
"0.6331821",
"0.6329094",
"0.6329094",
"0.63262904",... | 0.7327849 | 0 |
Add to the list of describing adjectives. | def add_adjectives(self, adjective):
self.adjectives += [adjective] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_adjectives(self, *sAdjs):\n self.adjectives += list(sAdjs)",
"def add(self):\n pass",
"def add_many_descriptors(self, descriptors):",
"def add(self, PlugLead):\n\n self.check_conflicts(PlugLead)\n self.plugleads.append(PlugLead)",
"def add_disease(self, disease):\n ... | [
"0.7263275",
"0.62598264",
"0.5947216",
"0.57094675",
"0.5695133",
"0.5606961",
"0.56066847",
"0.5586834",
"0.5575344",
"0.5548141",
"0.5520845",
"0.5520845",
"0.55087703",
"0.5452977",
"0.5440875",
"0.53886217",
"0.5388016",
"0.5352896",
"0.5326072",
"0.5325428",
"0.5324635"... | 0.7504388 | 0 |
Returns the list of describing adjectives. The list is shuffled first because generally this is used to get a random adjective. | def get_adjectives(self):
random.shuffle(self.adjectives)
return self.adjectives | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_adjectives(lyrics):\n doc = nlp(lyrics.lower())\n all_adjectives = [token.lemma_ for token in doc if token.pos_ == \"ADJ\"]\n return all_adjectives",
"def succ(self):\n return [ self.simple_reflection(i) for i in self.descents(positive=True) ]",
"def getAdjectives(self, word):\n\t\t... | [
"0.605831",
"0.5822134",
"0.57419574",
"0.573242",
"0.55850464",
"0.5502515",
"0.5492401",
"0.5489824",
"0.5483387",
"0.5447286",
"0.54260534",
"0.5316058",
"0.52798134",
"0.5279397",
"0.5267892",
"0.5265623",
"0.525828",
"0.52477735",
"0.5244685",
"0.5200043",
"0.51937425",
... | 0.81158966 | 0 |
Returns the noun, including all its describing adjectives, as a string. | def full_string(self):
return "{}: {}".format(str(self.word), " ".join([str(adj) for adj in self.adjectives])) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def en_noun(t, label):\r\n s = label\r\n p = []\r\n is_uncountable = False\r\n \r\n # http://en.wiktionary.org/wiki/Template:en-noun\r\n head = t.arg(\"head\", label)\r\n p1 = t.arg(0)\r\n p2 = t.arg(1)\r\n \r\n if p1 == \"-\":\r\n # uncountable\r\n is_uncountable = True... | [
"0.6249946",
"0.6021164",
"0.600624",
"0.5979195",
"0.59327227",
"0.58711636",
"0.57597136",
"0.5741161",
"0.57387596",
"0.57079136",
"0.56950766",
"0.5683717",
"0.56733876",
"0.5652428",
"0.5563646",
"0.55163616",
"0.5513116",
"0.5472722",
"0.5459045",
"0.5425532",
"0.542323... | 0.69946307 | 0 |
Parse a noun object from a data file containing nouns and their describing adjectives. | def parse(text):
parts = text.split(' ')
noun = Noun(parts[0], int(parts[1]))
parts = parts[2:]
while len(parts) > 0:
noun.add_adjectives(Word(parts[0], int(parts[1])))
parts = parts[2:]
return noun | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __read_data__(self):\n with open(self.file, 'r') as data:\n sentence = []\n tags = []\n for line in data:\n terms = line.rstrip().split(WHITESPACE)\n for term in terms:\n word_tag = tuple(term.split(TAGCHAR))\n ... | [
"0.5926569",
"0.56615496",
"0.5575726",
"0.5492108",
"0.54724497",
"0.54638004",
"0.5449956",
"0.5432941",
"0.5421955",
"0.53711075",
"0.53692234",
"0.535761",
"0.53158194",
"0.5268704",
"0.52373093",
"0.5237189",
"0.5234773",
"0.5220252",
"0.52158135",
"0.5205877",
"0.520256... | 0.69166636 | 0 |
Returns the self.guessed_by and self.metaphors_used data as a readable string. | def get_str_metadata(self):
return "\n".join(["Guessed by {}".format(self.guessed_by), "{} metaphors used".format(self.metaphors_used)]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_strings(self):\n return self._guessed_strings",
"def __str__(self):\n d = {}\n d[\"tuner_number\"] = self.tuner_number\n d[\"output_format\"] = self.output_format\n d[\"output_source\"] = self.output_source\n return str(d)"... | [
"0.66056585",
"0.6369337",
"0.63330597",
"0.63225114",
"0.62778735",
"0.62415534",
"0.6170479",
"0.6140569",
"0.60968494",
"0.60799277",
"0.60438675",
"0.60398",
"0.60284495",
"0.6009198",
"0.59779775",
"0.59726894",
"0.597072",
"0.59602815",
"0.594238",
"0.5917474",
"0.58926... | 0.80824745 | 0 |
Takes `x_axis` and returns an uniformly sampled array of values from its minimum to its maximum with few extra points | def _extended_discrete_xaxis(x_axis, n_points=100, eps=0.10):
min_value = np.min(x_axis)
max_value = np.max(x_axis)
distance = max_value - min_value
return np.linspace(min_value - eps * distance, max_value + eps * distance,
num=n_points) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sample(self):\n # For each row: round(random .* (max - min) + min, 0)\n random_array = prng.np_random.rand(self.num_discrete_space)\n return [int(x) for x in np.floor(np.multiply((self.high - self.low + 1.), random_array) + self.low)]",
"def scale(x_range=1, y_range=1):\r\n x = rand_v... | [
"0.6698139",
"0.6558226",
"0.65457267",
"0.64248043",
"0.6340408",
"0.6254296",
"0.61594844",
"0.6148418",
"0.60947704",
"0.60901093",
"0.5998522",
"0.5891753",
"0.5868367",
"0.58548534",
"0.5790514",
"0.5744083",
"0.5735349",
"0.57298034",
"0.57228523",
"0.5720773",
"0.56946... | 0.59077674 | 11 |
Quick plot of a `tick.base.TimeFunction` | def plot_timefunction(time_function, labels=None, n_points=300, show=True,
ax=None):
if ax is None:
fig, ax = plt.subplots(1, 1, figsize=(4, 4))
else:
show = False
if time_function.is_constant:
if labels is None:
labels = ['value = %.3g' % time_func... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plotTime(self):\n plt.figure()\n t = [i for i in range(len(self.nodes_infected))]\n print(t)\n plt.title('Nodos infectados vs Tiempo')\n plt.xlabel('Instantes de tiempo')\n plt.ylabel('# de nodos infectados')\n plt.plot(t, self.nodes_infected)\n plt.grid(... | [
"0.6426607",
"0.62802076",
"0.62569886",
"0.62506014",
"0.6236783",
"0.6092056",
"0.6068509",
"0.60091037",
"0.60037977",
"0.60024494",
"0.6001193",
"0.59628236",
"0.59590256",
"0.5951325",
"0.59470856",
"0.59453624",
"0.5932996",
"0.5908867",
"0.5891108",
"0.5865043",
"0.586... | 0.68075925 | 0 |
Generates mapping from water measurements column names to indices of the given header. | def get_water_index_map(archive, header):
column_re = {
'surface': {
'flow': 'pretok',
'level': 'vodostaj'
},
'ground': {
'altitude': 'nivo',
'level': 'vodostaj'
}
}
column_map = {key: -1 for key in column_re[archive].keys()}
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __get_column_mapping(self):\n\n s1 = list(Sample(\"FL\", \"M1.0@265_Primary_ar115_s2010-08-06T06_36_00_e2010-08-06T18_24_00.csv\").get_data().columns)[:25]\n column_mapping = {}\n for i in range(len(s1)):\n column_mapping[i] = s1[i]\n\n return column_mapping",
"def colu... | [
"0.60388774",
"0.5985021",
"0.5983487",
"0.5812272",
"0.573689",
"0.57180965",
"0.5631869",
"0.55975854",
"0.55681896",
"0.5565681",
"0.554784",
"0.5505632",
"0.5441756",
"0.5394642",
"0.53900725",
"0.53899986",
"0.5387639",
"0.537637",
"0.5363915",
"0.53568316",
"0.53414994"... | 0.6957255 | 0 |
Generates mapping from water measurements column names to values of the given CSV row. | def get_water_value_map(row, column_names_map):
column_values_map = column_names_map.copy()
row_length = len(row)
empty = True
for key, index in column_names_map.items():
# Check if non-empty value exist for given index.
if -1 < index < row_length:
value = row[index].strip()... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __get_column_mapping(self):\n\n s1 = list(Sample(\"FL\", \"M1.0@265_Primary_ar115_s2010-08-06T06_36_00_e2010-08-06T18_24_00.csv\").get_data().columns)[:25]\n column_mapping = {}\n for i in range(len(s1)):\n column_mapping[i] = s1[i]\n\n return column_mapping",
"def crea... | [
"0.62421864",
"0.57918304",
"0.5663072",
"0.5657489",
"0.56149256",
"0.5612935",
"0.5567542",
"0.5544542",
"0.5500382",
"0.5480754",
"0.545259",
"0.544672",
"0.5385878",
"0.53811884",
"0.5380167",
"0.5332743",
"0.53060913",
"0.5292364",
"0.527309",
"0.52203315",
"0.5217865",
... | 0.62722737 | 0 |
Populate water measurements table for selected `archive`, `directory` and `stations`. | def populate_water_measurements(cursor, archive, directory, station):
csv_path = get_data_path(
'water',
'raw',
archive,
directory,
f'{station}.csv'
)
with open(csv_path, 'r', encoding='utf-8') as file:
reader = csv.reader(file, delimiter=';')
header ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def populate_water_tables(connection):\n metadata = load_metadata('water')\n cursor = connection.cursor()\n\n # Check if tables are already populated.\n cursor.execute('SELECT count(*) FROM watercourses')\n watercourse_count = cursor.fetchone()[0]\n cursor.execute('SELECT count(*) FROM aquifers')... | [
"0.69976664",
"0.58139586",
"0.5748849",
"0.5552527",
"0.5550863",
"0.5530793",
"0.55279726",
"0.5518004",
"0.5421447",
"0.54047465",
"0.5358192",
"0.53122264",
"0.52683693",
"0.5263279",
"0.52280056",
"0.5153076",
"0.51038533",
"0.51007193",
"0.507282",
"0.50530833",
"0.5048... | 0.7368926 | 0 |
Populate watercourse and aquifer related data tables. | def populate_water_tables(connection):
metadata = load_metadata('water')
cursor = connection.cursor()
# Check if tables are already populated.
cursor.execute('SELECT count(*) FROM watercourses')
watercourse_count = cursor.fetchone()[0]
cursor.execute('SELECT count(*) FROM aquifers')
aquifer... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def populate_db():\n\n populate_table(db, models.Department, departments_data)\n populate_table(db, models.Employee, employees_data)",
"def populate_tables(connection: sqlite3.Connection) -> None:\n fake = Faker()\n Faker.seed(0)\n\n c = conn.cursor()\n\n number_of_courses = fake.pyint(min_valu... | [
"0.69008756",
"0.66442764",
"0.6618634",
"0.65720314",
"0.6367745",
"0.63610566",
"0.61599475",
"0.61476094",
"0.60915166",
"0.60889375",
"0.60289264",
"0.59944206",
"0.59331673",
"0.5864972",
"0.582192",
"0.5799883",
"0.5796334",
"0.5792097",
"0.5777224",
"0.5760808",
"0.574... | 0.71547 | 0 |
Populate locations data table. | def populate_locations(connection):
print('Populating locations...')
cursor = connection.cursor()
with open(get_data_path('locations', 'locations.json'), 'r', encoding='utf-8') as json_file:
locations = json.load(json_file)
for station_id, location in locations.items():
cursor.execute(f... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_position_data(self):\n # populate 'Location' field randomly\n self.output['Location'] = np.random.choice(self.locations, self.obs)\n\n # clean up geodata data frame and create 'Position' attribute\n nc = self.geodata[['Lat', 'Lng', 'Elevation']].round(2)\n nc['Elevat... | [
"0.6048028",
"0.6013519",
"0.59421986",
"0.5901815",
"0.58811194",
"0.5729807",
"0.5710549",
"0.56738675",
"0.56535035",
"0.554302",
"0.5542974",
"0.55208075",
"0.5469795",
"0.54697716",
"0.5454492",
"0.54353935",
"0.5430382",
"0.542676",
"0.5424422",
"0.5422587",
"0.54031056... | 0.6565873 | 0 |
Check if given forecast dictionary contains a numeric value with provided key. | def is_forecast_number(key, forecast):
return key in forecast and type(forecast[key]) in [float, int] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def moreThanOne(dict, key):\n\treturn key in dict and dict[key] > 0",
"def _has_numeric_strict(self) -> bool:\n return bool({'i', 'f'} & self._data.keys())",
"def contains_200(dictnr):\n contains = False\n for i in dictnr:\n if dictnr[i] == 200:\n contains = True\n print(conta... | [
"0.6130827",
"0.6060219",
"0.5976841",
"0.5809231",
"0.5789039",
"0.5752602",
"0.56971747",
"0.5649147",
"0.56462014",
"0.5644481",
"0.56440645",
"0.5638638",
"0.56340736",
"0.560666",
"0.5580076",
"0.55792755",
"0.55783355",
"0.55679023",
"0.55529094",
"0.5530956",
"0.552016... | 0.842359 | 0 |
Populate weather data tables. | def populate_weather(connection):
metadata = load_metadata('weather')
cursor = connection.cursor()
water_defs = get_water_definitions()
# Check if tables are already populated.
cursor.execute('SELECT count(*) FROM weather')
weather_count = cursor.fetchone()[0]
if weather_count:
pri... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def populate_water_tables(connection):\n metadata = load_metadata('water')\n cursor = connection.cursor()\n\n # Check if tables are already populated.\n cursor.execute('SELECT count(*) FROM watercourses')\n watercourse_count = cursor.fetchone()[0]\n cursor.execute('SELECT count(*) FROM aquifers')... | [
"0.6654514",
"0.66226166",
"0.6348557",
"0.632052",
"0.62991303",
"0.62082505",
"0.61876976",
"0.6142009",
"0.60627335",
"0.6002963",
"0.5983051",
"0.59750384",
"0.5964339",
"0.5926051",
"0.59136045",
"0.59097177",
"0.58774203",
"0.5861508",
"0.5857061",
"0.5843847",
"0.58242... | 0.78376013 | 0 |
Creates and populates water and weather database. Returns None | def create_databases():
db_connection = connect_to_db()
# Create database tables.
create_tables(db_connection)
# Populate water tables.
populate_water_tables(db_connection)
# station_data = get_station_data()
# station = station_data.query('ŠIFRA == 30301')
# print(station)
# inde... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def populate_weather(connection):\n metadata = load_metadata('weather')\n cursor = connection.cursor()\n water_defs = get_water_definitions()\n\n # Check if tables are already populated.\n cursor.execute('SELECT count(*) FROM weather')\n weather_count = cursor.fetchone()[0]\n\n if weather_coun... | [
"0.753115",
"0.6986817",
"0.6771315",
"0.67573315",
"0.6443865",
"0.63336414",
"0.63230395",
"0.6307932",
"0.6303793",
"0.62672585",
"0.6251016",
"0.6219543",
"0.62010366",
"0.61712205",
"0.6160077",
"0.6145157",
"0.60383976",
"0.6035518",
"0.6035307",
"0.60166687",
"0.601191... | 0.67318547 | 4 |
Provide string of addresses and this class will extract "street" & "house number". | def __init__(self, string):
self.string = string | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_address(self, soup):\n street, city, state, zipcode = None, None, None, None\n try:\n # property detail tag\n street = soup.find('div', class_='main-address').get_text().strip()\n # find address tag\n address = soup.find('div', class_='c-address')\... | [
"0.7285767",
"0.7073066",
"0.68373525",
"0.6803627",
"0.6702961",
"0.66992515",
"0.6645722",
"0.6645722",
"0.66008115",
"0.6555697",
"0.65275055",
"0.64732426",
"0.643964",
"0.64220065",
"0.6419511",
"0.63993114",
"0.63021076",
"0.62145597",
"0.6205807",
"0.62011707",
"0.6192... | 0.0 | -1 |
Given the input string, tries to separate them into "street" & "house number" variables | def filter_string(self):
logger.info("Information Gathering Finished!")
pattern_street = re.compile(r'[A-Za-z]+\s?\w+(?=\s[Nn]o\s\d+$) |'
r' [A-Za-z]+\s?\w+\s?[A-Za-z]+\s?[A-Za-z]+',
re.X) # street pattern
match_street = p... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def street_parser(*street_data):\n\n # parsing tuples\n if len(street_data) == 2:\n if not isinstance(street_data[0], str) and not isinstance(street_data[1], str):\n raise WrongInput(\"Invalid format\")\n # street name as the tuple's first item\n strname, strnumber = street_da... | [
"0.70592016",
"0.66491073",
"0.61709803",
"0.6159874",
"0.614569",
"0.6053972",
"0.6050844",
"0.6037463",
"0.5887759",
"0.58091587",
"0.5799028",
"0.57310367",
"0.56918335",
"0.56567967",
"0.5650815",
"0.5538323",
"0.5499395",
"0.54938877",
"0.5493846",
"0.54662937",
"0.54369... | 0.55393785 | 15 |
Helper function to construct multidimensional dictionaries e.g myhash = _makehash() myhash[1][2] = 4 myhash[2][5][8] = 17 | def _makehash():
return defaultdict(_makehash) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def hashMap(self,arr):\r\n n = len(arr)\r\n dict1 = {}\r\n i = 1\r\n for i in range(n): \r\n if(i > 0): \r\n key=arr[i]\r\n value=arr[0]\r\n dict1[key] = value\r\n return dict1",
"def __init__(self):\n ... | [
"0.67796296",
"0.5938041",
"0.5885213",
"0.5752976",
"0.5734703",
"0.5709291",
"0.5704826",
"0.5652041",
"0.5644233",
"0.56247675",
"0.56185186",
"0.5611323",
"0.56072676",
"0.5539448",
"0.5488976",
"0.5445115",
"0.54388916",
"0.54211164",
"0.54074925",
"0.53994673",
"0.53773... | 0.6773854 | 1 |
Convert headers of fetched tickers to same format for convenient data storage in Database. This method assumes that parser's headers are configured properly(headers_dict), if one of the headers is missing in config file exception raised | def convert_headers(self, tickers):
result = _makehash()
for pair_name, fetched_values_dict in list(tickers.items()):
for header, value in list(fetched_values_dict.items()):
result[pair_name][self.config['headers'][header]] = value
return result | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _parse_headers(headers):\n\n headers_new = []\n # reformat column headers if needed\n for j, hd in enumerate(headers):\n # rename so always have T1/2 (s)\n if hd == \"T1/2 (num)\" or hd == \"T1/2 (seconds)\":\n hd = \"T1/2 (s)\"\n # for uncertainties, add previous colum... | [
"0.6760923",
"0.6707738",
"0.66021603",
"0.6435935",
"0.62480223",
"0.62002486",
"0.6085462",
"0.60607",
"0.605833",
"0.6038865",
"0.5981734",
"0.5961042",
"0.5954765",
"0.59422773",
"0.5939061",
"0.5936525",
"0.5867014",
"0.5862857",
"0.5862484",
"0.586054",
"0.5848212",
"... | 0.7734185 | 0 |
Calculate the similarity based on Cosine Similarity between two CTRDMs | def cosinesimilarity_cal(CTRDM1, CTRDM2):
# get number of conditions
n_cons = np.shape(CTRDM1)[0]
# calculate the number of value above the diagonal in RDM
n = n_cons * (n_cons - 1)
# initialize two vectors to store the values above the diagnal of two RDMs
v1 = np.zeros([n], dtype=np.float64)... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cosine_similarity(v1, v2):\n # Cosine Sim:\n # Get the words that both have in common\n\n v1words = set(v1.keys())\n v2words = set(v2.keys())\n\n numerator_words = v1words.intersection(v2words)\n\n # Multiply and sum those counts\n numerator = 0.0\n for word in numerator_words:\n ... | [
"0.7754487",
"0.7676584",
"0.7633492",
"0.76006675",
"0.7589376",
"0.7576636",
"0.75592244",
"0.75373095",
"0.7522885",
"0.746666",
"0.74491453",
"0.7418762",
"0.7398554",
"0.73664916",
"0.7334395",
"0.7312988",
"0.7309643",
"0.7292841",
"0.72754246",
"0.7240708",
"0.72226435... | 0.80601525 | 0 |
Adds basic_vector to the basic vectors. If there are at least 3 arrays in _basic_vectors, then add a new array to _featureVector. This added array is composed of the basic vectors and its 2 first central derivatives basic_vector must be the array returned by the mfcc. | def build_feature_vector(self, basic_vector):
basic_vector = basic_vector - np.mean(basic_vector)
self._basic_vectors.append(basic_vector)
if len(self._basic_vectors) > 2:
#if there are at least 3 basic vectors we can calculate the central derivative for the vector before this one
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_vector(self, doc_name, add_cnt, new_docvec):\n \n # determine the weight of the merging pieces\n old_weight = float(self.vector_cnt) / (self.vector_cnt + add_cnt)\n new_weight = float(add_cnt) / (self.vector_cnt + add_cnt)\n \n if len(self.name) == 0:\n self.nam... | [
"0.5917165",
"0.5735873",
"0.57002974",
"0.5500693",
"0.54043525",
"0.5337707",
"0.5288758",
"0.5286068",
"0.5255515",
"0.5167239",
"0.51532346",
"0.5146452",
"0.5016166",
"0.5007243",
"0.49999866",
"0.4954787",
"0.49387354",
"0.49348387",
"0.4906069",
"0.48973984",
"0.489477... | 0.8391323 | 0 |
If there is at least an feature vector then returns it, else returns None | def get_last_feature_vectors(self):
if len(self._feature_vectors):
return self._feature_vectors[-1]
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_training(feature_path): \n features = np.loadtxt(feature_path)\n feature_size = features.shape[1] -1 \n features_in = features[:,0:feature_size]\n features_out = features[:,-1]\n #features_out = np.array(map(lambda x: x if x else 0, features_out_unnorm))\n return features_in, features_out... | [
"0.6076066",
"0.6039801",
"0.60045683",
"0.5997051",
"0.59677297",
"0.5927279",
"0.5924938",
"0.592038",
"0.58969414",
"0.58928514",
"0.5883369",
"0.5868851",
"0.5810611",
"0.57908976",
"0.57573485",
"0.5748689",
"0.57457215",
"0.5690704",
"0.5680142",
"0.5675147",
"0.5661629... | 0.6274125 | 0 |
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