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impulse_reporting.events.container_event

ContainerEvent — an event spanning the full measurement container.

ContainerEvent​

class ContainerEvent(Event)

Event that treats the full measurement container as a single event instance.

Unlike BasicEvent, no time-series expression is needed — the event boundaries are derived directly from the container's start_ts and stop_ts metadata.

__init__​

def __init__(name: str, desc: str = None, attributes: dict[str, str] = None)

Initialise a ContainerEvent.

Arguments:

  • name (str): Name of the event.
  • desc (str): Human-readable description.
  • attributes (dict): Key-value metadata for the event.

get_id​

def get_id() -> int

Return a unique identifier derived from the event name.

Returns:

int: Positive 32-bit integer identifier.

get_expression​

def get_expression() -> TimeSeriesExpression | None

ContainerEvent has no time-series expression.

Returns:

None:

get_event_type_str​

def get_event_type_str() -> str

Get the event type string for ContainerEvent.

Returns:

str: Event type string.

determine_definition_hash​

def determine_definition_hash() -> int

Calculate definition hash.

The hash only captures computation-relevant attributes. For a ContainerEvent the identity is fully determined by the fact that it is a container event (there is no expression to vary), so the name of the event is hashed.

Returns:

int: Hash value representing the computation definition.

as_dict​

def as_dict() -> dict

Return a dictionary representation of the event.

Returns:

dict:

as_spark_row​

def as_spark_row() -> Row

Return a Spark Row representation.

Returns:

Row:

determine_events​

def determine_events(
        cls,
        spark: SparkSession,
        events: list[ContainerEvent],
        *,
        solved_df: DataFrame = None,
        query: QueryBuilder = None,
        solver: QuerySolver = None,
        pre_filtered_containers_df: DataFrame = None) -> DataFrame

Determine event instances from container metadata.

Resolves matching containers via the solver's filter pipeline and produces one event instance per container.

Arguments:

  • spark (SparkSession): Active Spark session.
  • events (list of ContainerEvent): List of ContainerEvent objects (only the first is used for naming).
  • solved_df (DataFrame): Not used by ContainerEvent (kept for interface compatibility).
  • query (QueryBuilder): Query builder with filters applied.
  • solver (QuerySolver): Solver whose filter pipeline is used for container resolution.
  • pre_filtered_containers_df (DataFrame): Pre-filtered containers for incremental processing.

Returns:

DataFrame: Spark DataFrame matching EVENT_INSTANCE_FACT_SCHEMA.

determine_metadata_df​

def determine_metadata_df(cls, spark: SparkSession,
                          events: list[ContainerEvent]) -> DataFrame

Create a Spark DataFrame containing event metadata.

Arguments:

  • spark (SparkSession): Active Spark session.
  • events (list of ContainerEvent): List of ContainerEvent objects.

Returns:

DataFrame: Spark DataFrame matching EVENT_DIMENSION_SCHEMA.