micro_stop_detection

micro_stop_detection

MicroStopEvents

MicroStopEvents(
    dataframe: DataFrame,
    run_state_uuid: str,
    *,
    event_uuid: str = "prod:micro_stop",
    value_column: str = "value_bool",
    time_column: str = "systime"
)

Bases: Base

Production: Micro-Stop Detection

Detect brief idle intervals that individually seem harmless but accumulate into significant availability losses.

Methods: - detect_micro_stops: Find idle intervals shorter than max_duration. - micro_stop_frequency: Count micro-stops per time window. - micro_stop_impact: Time lost to micro-stops per window. - micro_stop_patterns: Group micro-stops by hour-of-day.

detect_micro_stops

detect_micro_stops(
    max_duration: str = "30s", min_duration: str = "0s"
) -> pd.DataFrame

Find idle intervals shorter than max_duration.

Parameters:

Name	Type	Description	Default
max_duration	str	Maximum duration to qualify as a micro-stop.	'30s'
min_duration	str	Minimum duration to include (filter very short glitches).	'0s'

Returns:

Type	Description
DataFrame	DataFrame with columns: start_time, end_time, duration,
DataFrame	preceding_run_duration.

micro_stop_frequency

micro_stop_frequency(
    window: str = "1h", max_duration: str = "30s"
) -> pd.DataFrame

Count micro-stops per time window.

Parameters:

Name	Type	Description	Default
window	str	Resample window.	'1h'
max_duration	str	Maximum idle duration to qualify as micro-stop.	'30s'

Returns:

Type	Description
DataFrame	DataFrame with columns: window_start, count, total_lost_time,
DataFrame	pct_of_window.

micro_stop_impact

micro_stop_impact(
    window: str = "1h", max_duration: str = "30s"
) -> pd.DataFrame

Time lost to micro-stops vs total available time per window.

Parameters:

Name	Type	Description	Default
window	str	Resample window.	'1h'
max_duration	str	Maximum idle duration to qualify as micro-stop.	'30s'

Returns:

Type	Description
DataFrame	DataFrame with columns: window_start, total_run_time,
DataFrame	total_micro_stop_time, availability_loss_pct.

micro_stop_patterns

micro_stop_patterns(
    hour_grouping: bool = True, max_duration: str = "30s"
) -> pd.DataFrame

Group micro-stops by hour-of-day to find clustering patterns.

Parameters:

Name	Type	Description	Default
hour_grouping	bool	If True, group by hour. If False, group by shift (8h blocks).	True
max_duration	str	Maximum idle duration to qualify as micro-stop.	'30s'

Returns:

Type	Description
DataFrame	DataFrame with columns: hour (or shift), avg_count, avg_lost_time.