process_window

process_window ¤

ProcessWindowEvents ¤

ProcessWindowEvents(
    dataframe: DataFrame,
    signal_uuid: str,
    *,
    event_uuid: str = "eng:process_window",
    value_column: str = "value_double",
    time_column: str = "systime"
)

Bases: Base

Engineering: Process Window Analysis

Analyze time-windowed process statistics for shift reports, SPC context, and trend monitoring. Answers 'how is my process doing this hour/shift/day?'

Methods: - windowed_statistics: Per-window count, mean, std, min, max, percentiles. - detect_mean_shift: Flag windows where mean shifts significantly. - detect_variance_change: Flag windows where variance changes significantly. - window_comparison: Compare each window to overall baseline.

windowed_statistics ¤

windowed_statistics(window: str = '1h') -> pd.DataFrame

Per-window descriptive statistics.

Parameters:

Name	Type	Description	Default
`window`	`str`	Resample window (pandas offset string, e.g. '1h', '8h').	`'1h'`

Returns:

Type	Description
`DataFrame`	DataFrame with columns: start, end, duration_seconds, uuid,
`DataFrame`	source_uuid, count, mean, std, min, max, median, p25, p75, range.
`DataFrame`	`end` is `start` + `window`.

detect_mean_shift ¤

detect_mean_shift(
    window: str = "1h", sensitivity: float = 2.0
) -> pd.DataFrame

Flag windows where mean shifts significantly from the previous window.

A shift is detected when |current_mean - prev_mean| > sensitivity * prev_std.

Parameters:

Name	Type	Description	Default
`window`	`str`	Resample window passed to :meth:`windowed_statistics`.	`'1h'`
`sensitivity`	`float`	Shift between consecutive window means, in multiples of the previous window's std, above which (strictly) a window is flagged.	`2.0`

Returns:

Type	Description
`DataFrame`	DataFrame with columns: start, end, duration_seconds, uuid,
`DataFrame`	source_uuid, is_delta, prev_mean, current_mean, shift_sigma.
`DataFrame`	`start` is the previous window's start, `end` the current
`DataFrame`	window's start.

detect_variance_change ¤

detect_variance_change(
    window: str = "1h", ratio_threshold: float = 2.0
) -> pd.DataFrame

Flag windows where variance changes significantly.

A change is detected when (current_std / prev_std) > ratio_threshold or < (1 / ratio_threshold).

Parameters:

Name	Type	Description	Default
`window`	`str`	Resample window passed to :meth:`windowed_statistics`.	`'1h'`
`ratio_threshold`	`float`	Std ratio (current / previous) strictly above which, or strictly below whose reciprocal, a window is flagged. Should be > 1.	`2.0`

Returns:

Type	Description
`DataFrame`	DataFrame with columns: start, end, duration_seconds, uuid,
`DataFrame`	source_uuid, is_delta, prev_std, current_std, variance_ratio.
`DataFrame`	`start` is the previous window's start, `end` the current
`DataFrame`	window's start.

window_comparison ¤

window_comparison(window: str = '1h') -> pd.DataFrame

Compare each window mean to the overall baseline.

Parameters:

Name	Type	Description	Default
`window`	`str`	Resample window passed to :meth:`windowed_statistics`. A window is anomalous when its mean is more than 2 std of the window means away from their average.	`'1h'`

Returns:

Type	Description
`DataFrame`	DataFrame with columns: start, end, duration_seconds, uuid,
`DataFrame`	source_uuid, mean, z_score_vs_global, is_anomalous.