anomaly_classification

anomaly_classification ¤

AnomalyClassificationEvents ¤

AnomalyClassificationEvents(
    dataframe: DataFrame,
    signal_uuid: str,
    *,
    event_uuid: str = "quality:anomaly_type",
    value_column: str = "value_double",
    time_column: str = "systime"
)

Bases: Base

Quality: Anomaly Classification

Classify anomalies in a numeric signal by type: spike, drift, oscillation, flatline, or level shift.

Methods: - classify_anomalies: Detect and classify anomalous windows. - detect_flatline: Signal stuck at constant value. - detect_oscillation: Periodic instability detection. - detect_drift: Short-term slope-based drift events.

detect_flatline ¤

detect_flatline(
    min_duration: str = "1m", tolerance: float = 1e-06
) -> pd.DataFrame

Detect intervals where the signal is stuck at a constant value.

Parameters:

Name	Type	Description	Default
`min_duration`	`str`	Minimum duration to qualify as a flatline.	`'1m'`
`tolerance`	`float`	Maximum std to consider as flat.	`1e-06`

Returns:

Type	Description
`DataFrame`	DataFrame with columns: start_time, end_time, duration, stuck_value.

detect_oscillation ¤

detect_oscillation(
    window: str = "1m", min_crossings: int = 6
) -> pd.DataFrame

Detect windows with excessive zero-crossings of the detrended signal.

Parameters:

Name	Type	Description	Default
`window`	`str`	Window size for analysis.	`'1m'`
`min_crossings`	`int`	Minimum zero-crossings to flag oscillation.	`6`

Returns:

Type	Description
`DataFrame`	DataFrame with columns: start_time, end_time, crossing_count, amplitude.

detect_drift ¤

detect_drift(
    window: str = "1h", min_slope: float = 0.01
) -> pd.DataFrame

Detect windows where the rolling slope exceeds a threshold.

Parameters:

Name	Type	Description	Default
`window`	`str`	Window size for slope computation.	`'1h'`
`min_slope`	`float`	Minimum absolute slope to flag.	`0.01`

Returns:

Type	Description
`DataFrame`	DataFrame with columns: start_time, end_time, slope, direction.

classify_anomalies ¤

classify_anomalies(
    window: str = "10m", z_threshold: float = 3.0
) -> pd.DataFrame

Detect anomalous windows and classify by type.

Types: spike, drift, oscillation, flatline, level_shift.

Parameters:

Name	Type	Description	Default
`window`	`str`	Window size for analysis.	`'10m'`
`z_threshold`	`float`	Z-score threshold for spike detection.	`3.0`

Returns:

Type	Description
`DataFrame`	DataFrame with columns: start_time, end_time, anomaly_type, severity, details.