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. - changeover_quality_metrics: per-product changeover counts and time between changeovers.

detect_changeover

detect_changeover(
    product_uuid: str,
    *,
    value_column: str = "value_string",
    min_hold: str = "0s",
    include_initial: bool = True
) -> 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.

Parameters:

Name	Type	Description	Default
product_uuid	str	UUID of the product/recipe signal.	required
value_column	str	Column containing the product value.	'value_string'
min_hold	str	Minimum time the new product must persist to count.	'0s'
include_initial	bool	When True (default) the first reading is emitted as a changeover event, marking the product active at the start of the data. A changeover strictly requires a prior, different value, so pass include_initial=False to suppress this synthetic leading event and report only real value changes.	True

changeover_window

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

Compute changeover windows per product change with enhanced configurability.

Parameters:

Name	Type	Description	Default
product_uuid	str	UUID of the product/recipe signal.	required
value_column	str	Column containing the product value.	'value_string'
start_time	Timestamp | None	Only changes at or after this timestamp are used. None uses all changes.	None
until	str	How each window's end is found: "fixed_window": end = start + config['duration'] (default '10min'). "stable_band": end when every metric in config['metrics'] stays within its band of a reference for its hold; the window ends at the latest metric's stable start. Any other value, or a stable band that is never reached, uses fallback.	'fixed_window'
config	dict[str, Any] | None	Method settings. Keys: 'min_hold' (minimum hold of a new product, passed to :meth:detect_changeover, default '0s'), 'duration' (for fixed_window), and for stable_band:: { 'metrics': [ {'uuid': 'm1', 'value_column': 'value_double', 'band': 0.2, 'hold': '2min'}, ... ], '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 } None means an empty dict.	None
fallback	dict[str, Any] | None	Used when the window end cannot be determined: {'default_duration': '10min', 'completed': False} (also the default when None). completed is copied to the output.	None

Returns:

Type	Description
DataFrame	DataFrame with columns: start, end, duration_seconds (float
DataFrame	seconds, end - start), uuid, source_uuid, is_delta, method,
DataFrame	completed.

changeover_quality_metrics

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

Per-product changeover frequency: how often, and how far apart.

For each product, counts the changeovers to that product and summarizes the time between consecutive ones. Changeover durations are not measured here (a product-signal change is a point, not an interval); use :meth:changeover_window for when the process is stable again after a changeover.

Parameters:

Name	Type	Description	Default
product_uuid	str	UUID of the product/recipe signal.	required
value_column	str	Column containing the product value.	'value_string'

Returns:

Type	Description
DataFrame	DataFrame with one row per product: product, changeover_count,
DataFrame	avg_time_between_seconds, min_time_between_seconds,
DataFrame	max_time_between_seconds, std_time_between_seconds (time between
DataFrame	consecutive changeovers to that product; None with fewer than
DataFrame	two). Empty when there are fewer than two changeovers.