line_throughput

line_throughput ¤

LineThroughputEvents ¤

LineThroughputEvents(
    dataframe: DataFrame,
    *,
    event_uuid: str = "prod:throughput",
    time_column: str = "systime"
)

Bases: Base

Production: Line Throughput

Methods: - count_parts: Part counts per fixed window from a monotonically increasing counter. - takt_adherence: Cycle time violations against a takt time from step/boolean triggers. - cycle_quality_check: Cycles from a trigger signal, flagged when outside the expected range. - throughput_oee: Availability, performance and OEE ratios (0-1) per window. - throughput_trends: Throughput trend with moving averages and degradation flags.

count_parts ¤

count_parts(
    counter_uuid: str,
    *,
    value_column: str = "value_integer",
    window: str = "1min",
    handle_resets: bool = True
) -> pd.DataFrame

Compute parts per window for a counter uuid.

When handle_resets is True (default), per-reading increments are computed on the raw counter (a decreasing counter is treated as a reset and the post-reset value counted as new production) and summed into each window, and a resets column reports the number of resets per window. When False, the count per window is the window-over-window difference of the counter maximum, clipped at zero, so a window in which the counter resets to a lower value loses that production.

Parameters:

Name	Type	Description	Default
`counter_uuid`	`str`	UUID of the part counter signal.	required
`value_column`	`str`	Column holding the counter value.	`'value_integer'`
`window`	`str`	Count window (pandas offset string, e.g. '1min', '1h').	`'1min'`
`handle_resets`	`bool`	Count production across counter resets (see above) and add a `resets` column. Default True.	`True`

Returns:

Type	Description
`DataFrame`	DataFrame with columns: start, end (start + window),
`DataFrame`	duration_seconds, uuid, source_uuid, is_delta, count (plus
`DataFrame`	`resets` when `handle_resets=True`).

takt_adherence ¤

takt_adherence(
    cycle_uuid: str,
    *,
    value_column: str = "value_bool",
    takt_time: str = "60s",
    min_violation: str = "0s"
) -> pd.DataFrame

Flag cycles whose durations exceed the takt_time.

For boolean triggers: detect True rising edges as cycle boundaries. For numeric counters/steps: detect increments as cycle boundaries; a decrease (a counter reset) is not a cycle boundary.

Parameters:

Name	Type	Description	Default
`cycle_uuid`	`str`	UUID of the cycle trigger signal.	required
`value_column`	`str`	Column holding the trigger. `"value_bool"` uses rising edges as cycle boundaries; a numeric column uses every increase of the value (a reset or other decrease is not a boundary, so the cycle spanning it runs to the next increase); a non-numeric column (e.g. a string step name) uses every change of value.	`'value_bool'`
`takt_time`	`str`	Takt time (pandas offset string, e.g. '60s').	`'60s'`
`min_violation`	`str`	How far a cycle must exceed `takt_time` to be a violation: a cycle is a violation when it is strictly longer than `takt_time + min_violation`. Default `"0s"`: any cycle longer than the takt time counts; a cycle exactly equal to the takt time does not.	`'0s'`

Returns:

Type	Description
`DataFrame`	DataFrame with columns: systime (at the boundary that closes the
`DataFrame`	cycle), uuid, source_uuid, is_delta, cycle_time_seconds,
`DataFrame`	violation.

throughput_oee ¤

throughput_oee(
    counter_uuid: str,
    *,
    value_column: str = "value_integer",
    window: str = "1h",
    target_rate: float | None = None,
    availability_threshold: float = 0.0,
    handle_resets: bool = True
) -> pd.DataFrame

Calculate Overall Equipment Effectiveness (OEE) metrics.

OEE = Availability × Performance × Quality

This counter-only estimate has no run-state signal, so availability is a production proxy: a window counts as available when its part count is at least availability_threshold × target_rate. Quality is assumed to be 1.0.

Parameters:

Name	Type	Description	Default
`counter_uuid`	`str`	UUID for the part counter signal	required
`value_column`	`str`	Column containing counter values	`'value_integer'`
`window`	`str`	Time window for aggregation	`'1h'`
`target_rate`	`float \| None`	Target production rate (parts per window). If None, uses the 95th percentile of observed counts	`None`
`availability_threshold`	`float`	Fraction of `target_rate` a window must reach to count as available (0.0-1.0). The default `0.0` keeps the historical behavior -- any window with at least one part is available. (Previously this argument was accepted but ignored.)	`0.0`
`handle_resets`	`bool`	When True (default), the part counter is treated as reset-aware (see :meth:`count_parts`), so a window where the counter resets is not dropped. Default True.	`True`

Returns:

Type	Description
`DataFrame`	DataFrame with columns: start, end (start + window),
`DataFrame`	duration_seconds, uuid, source_uuid, is_delta, actual_count,
`DataFrame`	target_count, availability, performance, oee_score, followed by
`DataFrame`	the basis of the ratios:
`DataFrame`	run_seconds: the window length when the window counts as available, else 0 (the counter-only availability proxy)
`DataFrame`	planned_seconds: the window length
`DataFrame`	resets (only when `handle_resets=True`): counter resets in the window (see :meth:`count_parts`)
`DataFrame`	Canonical twin columns: `availability_ratio` (after `availability`),
`DataFrame`	`performance_ratio` (after `performance`), `oee_ratio` (after
`DataFrame`	`oee_score`); each holds the same values as the deprecated column right
`DataFrame`	before it, which is removed in the next major release.

throughput_trends ¤

throughput_trends(
    counter_uuid: str,
    *,
    value_column: str = "value_integer",
    window: str = "1h",
    trend_window: int = 24
) -> pd.DataFrame

Analyze throughput trends with moving averages and degradation detection.

Parameters:

Name	Type	Description	Default
`counter_uuid`	`str`	UUID for the part counter signal	required
`value_column`	`str`	Column containing counter values	`'value_integer'`
`window`	`str`	Time window for counting parts	`'1h'`
`trend_window`	`int`	Number of windows for trend calculation	`24`

Returns:

Type	Description
`DataFrame`	DataFrame with columns: start, end (start + window),
`DataFrame`	duration_seconds, uuid, source_uuid, is_delta, count, moving_avg,
`DataFrame`	trend_direction, degradation_detected

cycle_quality_check ¤

cycle_quality_check(
    cycle_uuid: str,
    *,
    value_column: str = "value_bool",
    expected_cycle_time: float | None = None,
    tolerance_pct: float = 0.1
) -> pd.DataFrame

Enhanced cycle detection with quality validation.

Parameters:

Name	Type	Description	Default
`cycle_uuid`	`str`	UUID for the cycle trigger signal	required
`value_column`	`str`	Column containing cycle trigger (bool/integer)	`'value_bool'`
`expected_cycle_time`	`float \| None`	Expected cycle time in seconds. If None, uses median	`None`
`tolerance_pct`	`float`	Tolerance percentage for cycle time validation	`0.1`

Returns:

Type	Description
`DataFrame`	DataFrame with cycle times, validation status, and quality flags