timescale

`mainsequence.client.data_sources_interfaces.timescale`

`direct_data_from_db(local_metadata, connection_uri, start_date=None, great_or_equal=True, less_or_equal=True, end_date=None, columns=None, unique_identifier_list=None, unique_identifier_range_map=None)`

Connects directly to the DB without passing through the ORM to speed up calculations.

Parameters:

Name	Type	Description	Default
`metadata`	`dict`	Metadata containing table and column details.	required
`connection_config`	`dict`	Connection configuration for the database.	required
`start_date`	`datetime`	The start date for filtering. If None, no lower bound is applied.	`None`
`great_or_equal`	`bool`	Whether the start_date filter is inclusive (>=). Defaults to True.	`True`
`less_or_equal`	`bool`	Whether the end_date filter is inclusive (<=). Defaults to True.	`True`
`end_date`	`datetime`	The end date for filtering. If None, no upper bound is applied.	`None`
`columns`	`list`	Specific columns to select. If None, all columns are selected.	`None`

Returns:

Type	Description
`DataFrame`	Data from the table as a pandas DataFrame, optionally filtered by date range.

`direct_table_update(metadata, serialized_data_frame, overwrite, grouped_dates, table_is_empty, time_series_orm_db_connection=None, use_chunks=True, num_threads=4)`

Updates the database table with the given DataFrame.

Parameters: - table_name: Name of the database table. - serialized_data_frame: DataFrame containing the data to insert. - overwrite: If True, existing data in the date range will be deleted before insertion. - time_index_name: Name of the time index column. - index_names: List of index column names. - table_is_empty: If True, the table is empty. - time_series_orm_db_connection: Database connection string. - use_chunks: If True, data will be inserted in chunks using threads. - num_threads: Number of threads to use when use_chunks is True.

`filter_by_assets_ranges(table_name, asset_ranges_map, index_names, data_source, column_types)`

Query time series data dynamically based on asset ranges.

Args: table_name (str): The name of the table to query. asset_ranges_map (dict): A dictionary where keys are asset symbols and values are dictionaries containing: - 'start_date' (datetime): The start date of the range. - 'start_date_operand' (str): The SQL operand for the start date (e.g., '>=' or '>'). - 'end_date' (datetime or None): The end date of the range. index_names (list): List of column names to set as the DataFrame index. data_source: A data source object with a method get_connection_uri() to get the database connection URI.

Returns: pd.DataFrame: A Pandas DataFrame with the queried data, indexed by the specified columns.

`process_and_update_table(serialized_data_frame, local_metadata, grouped_dates, data_source, index_names, time_index_name, overwrite=False, JSON_COMPRESSED_PREFIX=None)`

Process a serialized DataFrame, handle overwriting, and update a database table.

Args: serialized_data_frame (pd.DataFrame): The DataFrame to process and update. metadata (DynamicTableMetaData): Metadata about the table, including table configuration. grouped_dates (list): List of grouped dates to assist with the update. data_source (object): A data source object with a get_connection_uri method. index_names (list): List of index column names. time_index_name (str): The name of the time index column. overwrite (bool): Whether to overwrite the table or not. JSON_COMPRESSED_PREFIX (list): List of prefixes to identify JSON-compressed columns.

Returns: None