banner-storedot 2.3.1

light dal package

# banner.connection:

## Connection(Object):

• ABS class

## RelationalConnection(Connection):

• ABS class

## Storage(Connection):

• ABS class

## PrintableConnection(Connection):

• ABS class

## MySqlConnection(RelationalConnection, PrintableConnection)(host, user, passwd, db, ssl_key, ssl_cert, name):

• Create Connection object compatible with banner.queries

• raises MySQLError for bad connection

## PostgresSqlConnection(RelationalConnection, PrintableConnection)(host, user, port=5432, passwd=None, db=None, ssl_key=None, ssl_cert=None, charset=’utf8’, name=None):

• Create Connection object compatible with banner.queries

• raises MySQLError for bad connection

## RedisConnection(Storage, PrintableConnection)(host, port, passwd, db, ssl_key, ssl_cert, name, ttl):

• Create CacheConnection object compatible with banner.queries

# banner.queries.Queries:

## CONNECTIONS(conns: Dict[str, Connection] = {}) -> :

• Getter/Setter for known(default) Connections dict

## CACHE(con: CacheConnection = None):

• Getter/Setter for known(default) CacheConnection

## simple_query(query: str, w2p_parse: bool = True, connection: Union[Connection, str] = None, cache: Storage = None, ttl: int = None) -> pd.DataFrame:

• run a simple string query for Connection

• connection=None try to get first known connection, raise KeyError if None found

• Cache the result if cache_connection or Queries.CACHE is set (ttl if provided otherwise use CACHE.ttl)

• Cache=False will not cache the result even if Queries.CACHE is set

• w2p_parse=True - should parse query according to w2p syntax

## describe_table(table: str, connection: Union[RelationalConnection, str] = None) -> pd.DataFrame:

• Describes a table in connection

• Raises OperationalError and KeyError(Failed to find a connection for given key)

## describe(connection: Union[RelationalConnection, str] = None) -> pd.DataFrame:

• Describe Table names in connection

• Raises OperationalError and KeyError(Failed to find a connection for given key)

## table_query(table: str, columns: Union[list, str] = ‘*’, condition: str = ‘TRUE’, connection=None, cache_connection=None, ttl=None, raw=False) -> pd.DataFrame:

• Queries a given connection for ‘SELECT {columns} FROM {table} WHERE {condition}’

• Accepts both column values and labels

• raw=True - column names as in db

• Queries a given Connection(ip)/str of a known connection (or first known) return result as DataFrame

• Cache the result if cache_connection or Queries.CACHE is set (ttl if provided otherwise use CACHE.ttl)

• Cache=False will not cache the result even if Queries.CACHE is set

• Raises OperationalError and KeyError(Failed to find a connection for given key)

## neware_cache_query(keys: Iterable, condition: str = ‘TRUE’, connection: Union[MySqlConnection, str] = None, cache: Storage = None, ttl: int = None) -> pd.DataFrame:

• simplified query to retrieve aggregate cache data by condition

• condition is a valid where clause for given connection type

• requires keys in the form Iterable(Tuple(ip, device, unit, channel, test)), ex: [(241, 240222, 6, 11, 2818575226)]

• Cache the result if cache_connection or Queries.CACHE is set (ttl if provided otherwise use CACHE.ttl)

• Cache=False will not cache the result even if Queries.CACHE is set

## neware_query(device: int, unit: int, channel: int, test: int, connection: Union[Connection, str] = None, cache_connection=None, ttl=None, raw=False, dqdv=False, condition: str = ‘1’, temperature: bool = True, cache_data: pd.DataFrame = pd.DataFrame()) -> pd.DataFrame:

• query Connection for device, unit, channel, test

• connection=None try to get first known connection, raise KeyError if None found

• temperature=True - fetch temperature data

• raw=False - compute temperature, voltage, current aswell as grouping by auxchl_id

• dqdv=True -> banner.neware.calc_dq_dv

• Cache the result if cache_connection or Queries.CACHE is set (ttl if provided otherwise use CACHE.ttl)

• Cache=False will not cache the result even if Queries.CACHE is set

• raises Type err if no data exists

## neware_tests_query(table: str, experiments: Union[list, Number, str] = [], templates: Union[list, Number, str] = [], tests: Union[list, Number, str] = [], cells: Union[list,Number, str] = [], condition: str = ‘cycle < 2’, raw=False, dqdv=False, temperature: bool = True, connection: Union[Connection, str] = None, cache_connection=None, ttl=None):

• Multi Process Queries.neware_query (number of processes = number of distinct connections found for input)

• Queries all available tests for given table AND experiments AND templates AND tests AND cells

• Union[list, Number, str] - single/list of numbers or a valid query

• temperature=True - fetch temperature data

• raw=False - compute temperature, voltage, current aswell as grouping by auxchl_id

• dqdv=True -> banner.neware.calc_dq_dv

• Cache the result if cache_connection or Queries.CACHE is set (ttl if provided otherwise use CACHE.ttl)

• Cache=False will not cache the result even if Queries.CACHE is set

• raises Type err if no data exists

# banner.neware:

## NEWARE_STEPS:

• Step number : Step Name Dictionary

## calculate_neware_columns(data: pd.DataFrame):

• calculate neware columns for a valid neware DataFrame

## calculate_dq_dv(data: pd.DataFrame, raw=False):

• Calculate DQ/DV for a valid neware df

• raw=False: remove outliers

## merge_cache(data: pd.DataFrame, cache_data: pd.DataFrame):

• Given data(neware df), cache_data(neware_cache df), tries to merge cache_data into data

• ** Raises TypeError and Index Error**

# banner.utils.web2py:

## JOINS:

• Default Joins dictionary

• Used when calling DataFrame.join_table without specifing how to join

## COLUMN_TO_LABEL:

• Column : Label Dictionary

## LABEL_TO_COLUMN:

• Label : Column Dictionary

# banner.pandas_decorator:

## Added functionality onto Pandas.DataFrame object

## DataFrame.table_query

• banner.queries.Queries.table_query

## DataFrame.calculate_neware_columns

• banner.neware.calculate_neware_columns

## DataFrame.calculate_dq_dv

• banner.neware.calculate_dq_dv

## join_table(table: str, columns: Union[list, str] = ‘*’, condition: str = ‘TRUE’, left: Union[str, list, None] = None, right: Union[str, list, None] = None, how: Union[str, None] = None, connection: Union[RelationalConnection, str] = None, raw: bool = False, cache: Storage=None, ttl: Union[bool, None] = None) -> pd.DataFrame:

• Given a table, Join its relevant Data with the current table_query DataFrame!

• table: any table under the available Connection

• columns: select specific columns from the table, default=All

• condition: additional filtering condition on merged data

• left: columns used to merge left DataFrame, default is picked from banner.utils.web2py.JOINS

• right: columns used to merge right DataFrame, default is picked from banner.utils.web2py.JOINS

• how: how to merge left and right, default is picked from banner.utils.web2py.JOINS

• connection=None try to get first known connection, raise KeyError if None found

• raise TypeError If failed to join