signal_correlation

signal_correlation ¤

SignalCorrelationEvents ¤

SignalCorrelationEvents(
    dataframe: DataFrame,
    *,
    event_uuid: str = "corr:signal",
    value_column: str = "value_double",
    time_column: str = "systime"
)

Bases: Base

Correlation: Signal Correlation Analysis

Analyze time-windowed correlations between pairs of numeric signals. Useful for detecting when normally correlated process variables diverge.

Methods: - rolling_correlation: Pearson correlation over rolling windows. - correlation_breakdown: Detect periods where correlation drops below threshold. - lag_correlation: Cross-correlation with time lag to find delayed relationships.

rolling_correlation ¤

rolling_correlation(
    uuid_a: str,
    uuid_b: str,
    *,
    resample: str = "1min",
    window: int = 60
) -> pd.DataFrame

Compute rolling Pearson correlation between two signals.

Parameters:

Name	Type	Description	Default
`uuid_a`	`str`	UUID of first signal.	required
`uuid_b`	`str`	UUID of second signal.	required
`resample`	`str`	Resample interval for alignment.	`'1min'`
`window`	`int`	Rolling window size (in resampled periods).	`60`

Returns:

Name	Type	Description
`DataFrame`	`DataFrame`	systime, uuid, source_uuid_a, source_uuid_b, is_delta, correlation

correlation_breakdown ¤

correlation_breakdown(
    uuid_a: str,
    uuid_b: str,
    *,
    resample: str = "1min",
    window: int = 60,
    threshold: float = 0.5
) -> pd.DataFrame

Detect periods where correlation drops below a threshold.

Returns intervals where previously correlated signals diverge, which may indicate process issues.

Parameters:

Name	Type	Description	Default
`uuid_a`	`str`	UUID of first signal.	required
`uuid_b`	`str`	UUID of second signal.	required
`resample`	`str`	Resample interval for alignment.	`'1min'`
`window`	`int`	Rolling window size.	`60`
`threshold`	`float`	Correlation threshold below which to flag.	`0.5`

Returns:

Name	Type	Description
`DataFrame`	`DataFrame`	start, end, uuid, source_uuid_a, source_uuid_b, is_delta, min_correlation, duration_seconds

lag_correlation ¤

lag_correlation(
    uuid_a: str,
    uuid_b: str,
    *,
    resample: str = "1min",
    max_lag: int = 30
) -> pd.DataFrame

Cross-correlation with time lag analysis.

Finds the time lag at which two signals are most correlated.

Parameters:

Name	Type	Description	Default
`uuid_a`	`str`	UUID of first signal (reference).	required
`uuid_b`	`str`	UUID of second signal (lagged).	required
`resample`	`str`	Resample interval for alignment.	`'1min'`
`max_lag`	`int`	Maximum lag periods to test (in both directions).	`30`

Returns:

Name	Type	Description
`DataFrame`	`DataFrame`	lag_periods, correlation, is_best_lag