process_stability_index

process_stability_index ¤

ProcessStabilityIndexEvents ¤

ProcessStabilityIndexEvents(
    dataframe: DataFrame,
    signal_uuid: str,
    *,
    target: float | None = None,
    lower_spec: float | None = None,
    upper_spec: float | None = None,
    event_uuid: str = "eng:stability_index",
    value_column: str = "value_double",
    time_column: str = "systime"
)

Bases: Base

Engineering: Process Stability Index

Generate a single 0-100 stability score per shift/day for a signal. Answers 'is my process running well today?' with one number.

Methods: - stability_score: Composite 0-100 score per window. - score_trend: Is stability improving or degrading? - worst_periods: N worst-scoring windows. - stability_comparison: Compare each window to best-observed.

stability_score ¤

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

Composite 0-100 stability score per window.

Four sub-scores (each 0-25): - variance_score: low std = high score - bias_score: on-target = high score - excursion_score: in-spec = high score - smoothness_score: low point-to-point jitter = high score

Parameters:

Name	Type	Description	Default
`window`	`str`	Scoring window (pandas offset string, e.g. '8h' per shift). Windows with fewer than two samples are skipped.	`'8h'`

Returns:

Type	Description
`DataFrame`	DataFrame with columns: start, end, duration_seconds, uuid,
`DataFrame`	source_uuid, stability_score,
`DataFrame`	variance_score, bias_score, excursion_score, smoothness_score,
`DataFrame`	grade.

score_trend ¤

score_trend(
    window: str = "8h", n_windows: int = 7
) -> pd.DataFrame

Track whether stability is improving or degrading.

Parameters:

Name	Type	Description	Default
`window`	`str`	Scoring window passed to :meth:`stability_score`.	`'8h'`
`n_windows`	`int`	Number of windows in the rolling average of the score.	`7`

Returns:

Type	Description
`DataFrame`	DataFrame with columns: start, end, duration_seconds, uuid,
`DataFrame`	source_uuid, stability_score,
`DataFrame`	rolling_avg, trend_direction, score_change.

worst_periods ¤

worst_periods(
    window: str = "1h", n: int = 5
) -> pd.DataFrame

Return the N worst-scoring windows.

Parameters:

Name	Type	Description	Default
`window`	`str`	Scoring window passed to :meth:`stability_score`.	`'1h'`
`n`	`int`	Number of lowest-scoring windows to return.	`5`

Returns:

Type	Description
`DataFrame`	DataFrame with columns: start, end, duration_seconds, uuid,
`DataFrame`	source_uuid, stability_score,
`DataFrame`	variance_score, bias_score, excursion_score, smoothness_score,
`DataFrame`	primary_issue.

stability_comparison ¤

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

Compare each window to the best-observed window.

Parameters:

Name	Type	Description	Default
`window`	`str`	Scoring window passed to :meth:`stability_score`.	`'8h'`

Returns:

Type	Description
`DataFrame`	DataFrame with columns: start, end, duration_seconds, uuid,
`DataFrame`	source_uuid, stability_score,
`DataFrame`	best_score, gap_to_best, pct_of_best.
`DataFrame`	Canonical twin columns: `share_of_best_pct` (after `pct_of_best`); each holds
`DataFrame`	the same values as the deprecated column right before it, which is removed
`DataFrame`	in the next major release.