sensor_drift

sensor_drift ¤

SensorDriftEvents ¤

SensorDriftEvents(
    dataframe: DataFrame,
    signal_uuid: str,
    *,
    reference_uuid: Optional[str] = None,
    reference_value: Optional[float] = None,
    value_column: str = "value_double",
    event_uuid: str = "quality:sensor_drift",
    time_column: str = "systime"
)

Bases: Base

Quality: Sensor Calibration Drift Detection

Detect gradual calibration drift in inline sensors by tracking measurement behavior against reference values or historical baselines.

Reference can be provided as a UUID (time-aligned signal in the DataFrame) or as a fixed float value.

Methods: - detect_zero_drift: Track mean offset from baseline per window. - detect_span_drift: Track measurement sensitivity changes over time. - drift_trend: Rolling linear trend analysis on signal statistics. - calibration_health: Composite health score per window.

detect_zero_drift ¤

detect_zero_drift(
    window: str = "8h", threshold: Optional[float] = None
) -> pd.DataFrame

Track mean offset from baseline per window.

Parameters:

Name	Type	Description	Default
`window`	`str`	Resample window size.	`'8h'`
`threshold`	`Optional[float]`	Offset threshold for severity. Auto-calculated as 3x std of first window if not provided.	`None`

Returns:

Type	Description
`DataFrame`	DataFrame with columns: window_start, window_end, mean_offset,
`DataFrame`	drift_rate, severity.

detect_span_drift ¤

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

Track measurement sensitivity changes over time.

Requires reference_uuid or reference_value. Computes ratio of signal mean to reference per window to detect gain/span changes.

Parameters:

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

Returns:

Type	Description
`DataFrame`	DataFrame with columns: window_start, sensitivity,
`DataFrame`	sensitivity_change_pct.

drift_trend ¤

drift_trend(
    window: str = "1D", metric: str = "mean"
) -> pd.DataFrame

Rolling trend analysis on signal statistics.

Parameters:

Name	Type	Description	Default
`window`	`str`	Resample window size.	`'1D'`
`metric`	`str`	Statistic to trend ('mean' or 'std').	`'mean'`

Returns:

Type	Description
`DataFrame`	DataFrame with columns: window_start, value, slope,
`DataFrame`	r_squared, direction.

calibration_health ¤

calibration_health(
    window: str = "8h", tolerance: Optional[float] = None
) -> pd.DataFrame

Composite calibration health score per window.

Parameters:

Name	Type	Description	Default
`window`	`str`	Resample window size.	`'8h'`
`tolerance`	`Optional[float]`	Acceptable measurement tolerance. Used to normalize bias and precision into a 0-100 score.	`None`

Returns:

Type	Description
`DataFrame`	DataFrame with columns: window_start, bias, precision,
`DataFrame`	health_score.