changeover

changeover

ChangeoverEvents

ChangeoverEvents(
    dataframe: DataFrame,
    *,
    event_uuid: str = "prod:changeover",
    time_column: str = "systime"
)

Bases: Base

Production: Changeover

Detect product/recipe changes and compute changeover windows without requiring a dedicated 'first good' signal.

Methods: - detect_changeover: point events when product/recipe changes. - changeover_window: derive an end time via fixed window or 'stable_band' metrics.

detect_changeover

detect_changeover(
    product_uuid: str,
    *,
    value_column: str = "value_string",
    min_hold: str = "0s"
) -> pd.DataFrame

Emit point events when the product/recipe changes value.

Uses a hold check: the new product must persist for at least min_hold until the next change.

changeover_window

changeover_window(
    product_uuid: str,
    *,
    value_column: str = "value_string",
    start_time: Optional[Timestamp] = None,
    until: str = "fixed_window",
    config: Optional[Dict[str, Any]] = None,
    fallback: Optional[Dict[str, Any]] = None
) -> pd.DataFrame

Compute changeover windows per product change with enhanced configurability.

until

• fixed_window: end = start + config['duration'] (e.g., '10m')
• stable_band: end when all metrics stabilize within band for hold: config = { 'metrics': [ {'uuid': 'm1', 'value_column': 'value_double', 'band': 0.2, 'hold': '2m'}, ... ], 'reference_method': 'expanding_median' | 'rolling_mean' | 'ewma' | 'target_value', 'rolling_window': 5, # for rolling_mean (number of points) 'ewma_span': 10, # for ewma 'target_values': {'m1': 100.0, ...} # for target_value }

fallback: {'default_duration': '10m', 'completed': False}

changeover_quality_metrics

changeover_quality_metrics(
    product_uuid: str, *, value_column: str = "value_string"
) -> pd.DataFrame

Compute quality metrics for changeovers.

Returns metrics including: - changeover duration patterns - frequency statistics - time between changeovers - product-specific metrics