signal_comparison

signal_comparison ¤

SignalComparisonEvents ¤

SignalComparisonEvents(
    dataframe: DataFrame,
    reference_uuid: str,
    *,
    event_uuid: str = "eng:signal_comparison",
    value_column: str = "value_double",
    time_column: str = "systime"
)

Bases: Base

Engineering: Signal Comparison

Compare two related signals (e.g. setpoint vs actual, sensor A vs sensor B) and detect divergence, compute deviation statistics, and track correlation.

Methods: - detect_divergence: Intervals where |actual - reference| exceeds tolerance. - deviation_statistics: Per-window MAE, max error, RMSE, bias. - tracking_error_trend: Whether deviation is growing or shrinking over time. - correlation_windows: Per-window Pearson correlation.

detect_divergence ¤

detect_divergence(
    actual_uuid: str,
    tolerance: float,
    min_duration: str = "1min",
) -> pd.DataFrame

Detect intervals where |actual - reference| > tolerance.

Parameters:

Name	Type	Description	Default
`actual_uuid`	`str`	UUID of the actual/comparison signal.	required
`tolerance`	`float`	Maximum acceptable absolute deviation.	required
`min_duration`	`str`	Minimum duration of divergence interval.	`'1min'`

Returns:

Type	Description
`DataFrame`	DataFrame with columns: start, end, duration_seconds, uuid,
`DataFrame`	source_uuid (reference and actual uuids, comma-joined), is_delta,
`DataFrame`	max_deviation, mean_deviation, direction.

deviation_statistics ¤

deviation_statistics(
    actual_uuid: str, window: str = "1h"
) -> pd.DataFrame

Per-window deviation statistics.

Parameters:

Name	Type	Description	Default
`actual_uuid`	`str`	UUID of the actual signal compared with the reference signal. Deviation is actual minus reference.	required
`window`	`str`	Resample window (pandas offset string, e.g. '1h').	`'1h'`

Returns:

Type	Description
`DataFrame`	DataFrame with columns: start, end, duration_seconds, mae,
`DataFrame`	max_error, rmse, bias. `end` is `start` + `window`.

tracking_error_trend ¤

tracking_error_trend(
    actual_uuid: str, window: str = "1D"
) -> pd.DataFrame

Track whether deviation is growing or shrinking over time.

Parameters:

Name	Type	Description	Default
`actual_uuid`	`str`	UUID of the actual signal compared with the reference signal.	required
`window`	`str`	Resample window passed to :meth:`deviation_statistics`.	`'1D'`

Returns:

Type	Description
`DataFrame`	DataFrame with columns: start, end, duration_seconds, mae,
`DataFrame`	trend_slope, trend_direction. `end` is `start` + `window`.

correlation_windows ¤

correlation_windows(
    actual_uuid: str, window: str = "1h"
) -> pd.DataFrame

Per-window Pearson correlation between reference and actual.

Parameters:

Name	Type	Description	Default
`actual_uuid`	`str`	UUID of the actual signal compared with the reference signal.	required
`window`	`str`	Resample window (pandas offset string). Windows with fewer than two aligned samples are skipped.	`'1h'`

Returns:

Type	Description
`DataFrame`	DataFrame with columns: start, end, duration_seconds,
`DataFrame`	correlation, sample_count. `end` is `start` + `window`.