capability_trending

capability_trending ¤

CapabilityTrendingEvents ¤

CapabilityTrendingEvents(
    dataframe: DataFrame,
    signal_uuid: str,
    *,
    upper_spec: Optional[float] = None,
    lower_spec: Optional[float] = None,
    upper_spec_uuid: Optional[str] = None,
    lower_spec_uuid: Optional[str] = None,
    value_column: str = "value_double",
    event_uuid: str = "quality:capability_trend",
    time_column: str = "systime"
)

Bases: Base

Quality: Process Capability Trending

Track process capability indices (Cp, Cpk, Pp, Ppk) over rolling time windows to detect capability degradation before quality escapes occur.

Specification limits can be provided as fixed floats or as UUIDs referencing signal rows in the DataFrame (matching the ToleranceDeviationEvents pattern).

Methods: - capability_over_time: Cp/Cpk/Pp/Ppk per time window. - detect_capability_drop: Alert when Cpk falls below threshold. - capability_forecast: Extrapolate Cpk trend to predict threshold breach. - yield_estimate: Estimated yield, DPMO, and sigma level per window.

capability_over_time ¤

capability_over_time(window: str = '8h') -> pd.DataFrame

Compute Cp, Cpk, Pp, Ppk per time window.

Parameters:

Name	Type	Description	Default
`window`	`str`	Resample window size.	`'8h'`

Returns:

Type	Description
`DataFrame`	DataFrame with columns: window_start, cp, cpk, pp, ppk,
`DataFrame`	mean, std, n_samples.

detect_capability_drop ¤

detect_capability_drop(
    window: str = "8h",
    min_cpk: float = 1.33,
    lookback: int = 5,
) -> pd.DataFrame

Detect windows where Cpk drops below threshold or degrades significantly.

Parameters:

Name	Type	Description	Default
`window`	`str`	Resample window size.	`'8h'`
`min_cpk`	`float`	Minimum acceptable Cpk value.	`1.33`
`lookback`	`int`	Number of previous windows for rolling average comparison.	`5`

Returns:

Type	Description
`DataFrame`	DataFrame with columns: window_start, cpk, prev_avg_cpk, drop_pct, alert.

capability_forecast ¤

capability_forecast(
    window: str = "8h",
    horizon: int = 5,
    threshold: float = 1.33,
) -> pd.DataFrame

Extrapolate Cpk trend to predict when it will breach threshold.

Parameters:

Name	Type	Description	Default
`window`	`str`	Resample window size.	`'8h'`
`horizon`	`int`	Number of future windows to forecast.	`5`
`threshold`	`float`	Cpk threshold to predict breach for.	`1.33`

Returns:

Type	Description
`DataFrame`	DataFrame with columns: window_start, cpk, trend_slope,
`DataFrame`	forecast_cpk, windows_to_threshold.

yield_estimate ¤

yield_estimate(window: str = '8h') -> pd.DataFrame

Estimate yield, DPMO, and sigma level per window.

Assumes normal distribution within each window.

Parameters:

Name	Type	Description	Default
`window`	`str`	Resample window size.	`'8h'`

Returns:

Type	Description
`DataFrame`	DataFrame with columns: window_start, estimated_yield_pct,
`DataFrame`	dpmo, sigma_level.