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 = "1m",
) -> 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.	'1m'

Returns:

Type	Description
DataFrame	DataFrame with columns: start, end, uuid, is_delta,
DataFrame	max_deviation, mean_deviation, direction.

deviation_statistics

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

Per-window deviation statistics.

Returns:

Type	Description
DataFrame	DataFrame with columns: window_start, mae, max_error, rmse, bias.

tracking_error_trend

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

Track whether deviation is growing or shrinking over time.

Returns:

Type	Description
DataFrame	DataFrame with columns: window_start, mae, trend_slope,
DataFrame	trend_direction.

correlation_windows

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

Per-window Pearson correlation between reference and actual.

Returns:

Type	Description
DataFrame	DataFrame with columns: window_start, correlation, sample_count.