machine_state

machine_state

MachineStateEvents

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

Bases: Base

Production: Machine State

Detect run/idle transitions and intervals from a boolean state signal.

• MachineStateEvents: Run/idle state intervals and transitions.
• detect_run_idle: Intervalize run/idle states with optional min duration filter.
• transition_events: Point events on state changes (idle->run, run->idle).

detect_run_idle

detect_run_idle(min_duration: str = '0s') -> pd.DataFrame

Return intervals labeled as 'run' or 'idle'.

• min_duration: discard intervals shorter than this duration. Columns: start, end, uuid, source_uuid, is_delta, state, duration_seconds

transition_events

transition_events() -> pd.DataFrame

Return point events at state transitions.

Columns: systime, uuid, source_uuid, is_delta, transition ('idle_to_run'|'run_to_idle'), time_since_last_transition_seconds

detect_rapid_transitions

detect_rapid_transitions(
    threshold: str = "5s", min_count: int = 3
) -> pd.DataFrame

Identify suspicious rapid state changes.

• threshold: time window to look for rapid transitions
• min_count: minimum number of transitions within threshold to be considered rapid Returns: DataFrame with start_time, end_time, transition_count, duration_seconds

state_quality_metrics

state_quality_metrics() -> Dict[str, Any]

Return quality metrics for the state data.

Returns dictionary with: - total_transitions: total number of state transitions - avg_run_duration: average duration of run states in seconds - avg_idle_duration: average duration of idle states in seconds - run_idle_ratio: ratio of run time to idle time - data_gaps_detected: number of data gaps found - rapid_transitions_detected: number of rapid transition events