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# 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

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