time_to_threshold

time_to_threshold ¤

TimeToThresholdEvents ¤

TimeToThresholdEvents(
    dataframe: DataFrame,
    signal_uuid: str,
    *,
    event_uuid: str = "eng:time_to_threshold",
    value_column: str = "value_double",
    time_column: str = "systime"
)

Bases: Base

Engineering: Time-to-Threshold Prediction

Answer the question "when will this signal hit X at the current rate?" by extrapolating recent trends. Complements :class:ThresholdMonitoringEvents (which detects when you crossed a threshold) with predictive analysis.

Methods: - time_to_threshold: Estimate time until the signal reaches a value. - time_to_threshold_windows: Per-window time-to-threshold estimates. - remaining_useful_range: Time until signal leaves an acceptable band. - crossing_forecast: Forecast crossing times for multiple thresholds.

time_to_threshold ¤

time_to_threshold(
    threshold: float,
    lookback: str = "1h",
    direction: str = "auto",
) -> pd.DataFrame

Estimate time until the signal reaches a threshold value.

Fits a linear trend to the most recent lookback window and extrapolates to find when the threshold would be crossed.

Parameters:

Name	Type	Description	Default
`threshold`	`float`	Target value to predict crossing time for.	required
`lookback`	`str`	How far back to look for trend estimation.	`'1h'`
`direction`	`str`	`'rising'`, `'falling'`, or `'auto'` (infer from slope sign).	`'auto'`

Returns:

Type	Description
`DataFrame`	Single-row DataFrame with columns: current_value, slope_per_second,
`DataFrame`	threshold, estimated_seconds, estimated_time, confidence,
`DataFrame`	direction.

time_to_threshold_windows ¤

time_to_threshold_windows(
    threshold: float,
    window: str = "1h",
    lookback: str = "1h",
) -> pd.DataFrame

Per-window time-to-threshold estimates.

For each window, fits a trend to the preceding lookback period and estimates when the threshold will be crossed.

Parameters:

Name	Type	Description	Default
`threshold`	`float`	Target value.	required
`window`	`str`	Analysis window frequency.	`'1h'`
`lookback`	`str`	Lookback period for trend estimation.	`'1h'`

Returns:

Type	Description
`DataFrame`	Summary DataFrame with columns: start, end, duration_seconds
`DataFrame`	(the window bounds), window_start (the resample label; a
`DataFrame`	deprecated twin of `start`), current_value,
`DataFrame`	slope_per_second, estimated_seconds, confidence, direction.

remaining_useful_range ¤

remaining_useful_range(
    lower_limit: float,
    upper_limit: float,
    lookback: str = "1h",
) -> pd.DataFrame

Estimate time until the signal leaves an acceptable operating band.

Parameters:

Name	Type	Description	Default
`lower_limit`	`float`	Lower acceptable limit.	required
`upper_limit`	`float`	Upper acceptable limit.	required
`lookback`	`str`	Lookback period for trend estimation.	`'1h'`

Returns:

Type	Description
`DataFrame`	Single-row DataFrame with columns: current_value,
`DataFrame`	slope_per_second, nearest_bound, nearest_bound_value,
`DataFrame`	estimated_seconds, estimated_time, confidence.

crossing_forecast ¤

crossing_forecast(
    thresholds: dict[str, float], lookback: str = "1h"
) -> pd.DataFrame

Forecast crossing times for multiple named thresholds.

Parameters:

Name	Type	Description	Default
`thresholds`	`dict[str, float]`	Dict mapping names to values, e.g. `{'warning': 80, 'alarm': 90, 'critical': 95}`.	required
`lookback`	`str`	Lookback period for trend estimation.	`'1h'`

Returns:

Type	Description
`DataFrame`	DataFrame with columns: threshold_name, threshold_value,
`DataFrame`	estimated_seconds, estimated_time, reachable.