long_downtime_events

long_downtime_events ¤

LongDowntimeEvents ¤

LongDowntimeEvents(
    dataframe: DataFrame,
    run_state_uuid: str,
    *,
    event_uuid: str = "prod:long_downtime",
    value_column: str = "value_bool",
    time_column: str = "systime",
    value_range: (
        tuple[float | None, float | None] | None
    ) = None
)

Bases: Base

Production: Long Downtime Detection

Detect downtime intervals exceeding a minimum duration threshold from a boolean or integer state signal, then count production events that occur between consecutive long-downtime boundaries.

LongDowntimeEvents: Long idle intervals and inter-gap production counts.
detect_long_downtime: Find idle/stopped intervals >= min_gap (default 3 h).
count_events_between_gaps: Count production events between consecutive long-downtime boundaries.

detect_long_downtime ¤

detect_long_downtime(
    min_gap: str = "3h",
    *,
    interval_end: str = "next_change",
    data_end: Timestamp | None = None
) -> pd.DataFrame

Return idle/stopped intervals whose duration is >= min_gap.

Parameters:

Name	Type	Description	Default
`min_gap`	`str`	Minimum downtime duration to report (pandas offset string). Default `"3h"`.	`'3h'`
`interval_end`	`str`	How each downtime interval is closed. `"next_change"` (default) closes the interval at the following transition, so stops on change-encoded (`is_delta`) state signals are measured. The final, still-open downtime is bounded by `data_end` when given (otherwise it stays zero-length and is filtered out). `"last_sample"` ends it at its last sample; on a change-encoded signal (one row per transition) a multi-hour stop is then a single zero-duration row and is missed.	`'next_change'`
`data_end`	`Timestamp \| None`	Optional bound for a downtime still open at the end of the data (e.g. "now").	`None`

Returns:

Type	Description
`DataFrame`	DataFrame with columns: start, end, duration_seconds,
`DataFrame`	downtime_index, uuid, source_uuid, is_delta.

count_events_between_gaps ¤

count_events_between_gaps(
    production_df: DataFrame,
    production_uuid: str,
    *,
    min_gap: str = "3h",
    value_column: str = "value_integer",
    aggregation: str = "count",
    interval_end: str = "next_change",
    data_end: Timestamp | None = None
) -> pd.DataFrame

Count production events in each window between consecutive long downtimes.

Parameters:

Name	Type	Description	Default
`production_df`	`DataFrame`	ts-shape DataFrame with the production signal.	required
`production_uuid`	`str`	UUID of the production signal in production_df.	required
`min_gap`	`str`	Minimum downtime duration to qualify as a long-downtime boundary.	`'3h'`
`value_column`	`str`	Column used for 'sum' and 'transitions' aggregations.	`'value_integer'`
`aggregation`	`str`	How to count events in each window: - 'count': number of rows in the window - 'sum': sum of value_column in the window - 'transitions': number of value changes (state changes)	`'count'`
`interval_end`	`str`	How each downtime interval is closed; forwarded to :meth:`detect_long_downtime`. `"next_change"` (default) closes it at the next transition; `"last_sample"` ends it at its last sample, which makes every stop on a change-encoded (`is_delta`) state signal zero-length.	`'next_change'`
`data_end`	`Timestamp \| None`	Optional bound for a downtime still open at the end of the data; forwarded to :meth:`detect_long_downtime`.	`None`

Returns:

Name	Type	Description
	`DataFrame`	DataFrame with one row per inter-gap window.
`Columns`	`DataFrame`	start, end, window_duration_seconds, event_count, downtime_index, uuid, source_uuid, is_delta