warmup_analysis

warmup_analysis ¤

WarmUpCoolDownEvents ¤

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

Bases: Base

Engineering: Warm-Up / Cool-Down Analysis

Detect and characterize warm-up and cool-down curves — common for ovens, extruders, molds, hydraulic systems. Analyzes the shape, consistency, and timing of monotonic temperature/pressure ramps.

Methods: - detect_warmup: Intervals where signal rises by at least min_rise. - detect_cooldown: Intervals where signal falls by at least min_fall. - warmup_consistency: Compare warm-up durations and rates for consistency. - time_to_target: Time from warmup start until target value is reached.

detect_warmup ¤

detect_warmup(
    min_rise: float, min_duration: str = "1m"
) -> pd.DataFrame

Detect intervals where signal rises by at least min_rise.

Parameters:

Name	Type	Description	Default
`min_rise`	`float`	Minimum total value increase to qualify.	required
`min_duration`	`str`	Minimum duration of the ramp.	`'1m'`

Returns:

Type	Description
`DataFrame`	DataFrame with columns: start, end, uuid, is_delta,
`DataFrame`	start_value, end_value, rise, duration_seconds, avg_rate.

detect_cooldown ¤

detect_cooldown(
    min_fall: float, min_duration: str = "1m"
) -> pd.DataFrame

Detect intervals where signal falls by at least min_fall.

Parameters:

Name	Type	Description	Default
`min_fall`	`float`	Minimum total value decrease to qualify (positive number).	required
`min_duration`	`str`	Minimum duration of the ramp.	`'1m'`

Returns:

Type	Description
`DataFrame`	DataFrame with columns: start, end, uuid, is_delta,
`DataFrame`	start_value, end_value, fall, duration_seconds, avg_rate.

warmup_consistency ¤

warmup_consistency(
    min_rise: float, min_duration: str = "1m"
) -> pd.DataFrame

Compare warm-up curves for consistency in duration and rate.

Returns:

Type	Description
`DataFrame`	DataFrame with columns: warmup_index, start, duration_seconds,
`DataFrame`	avg_rate, deviation_from_median_duration.

time_to_target ¤

time_to_target(
    target_value: float, direction: str = "rising"
) -> pd.DataFrame

Time from each ramp start until target value is reached.

Parameters:

Name	Type	Description	Default
`target_value`	`float`	The target value to reach.	required
`direction`	`str`	'rising' or 'falling'.	`'rising'`

Returns:

Type	Description
`DataFrame`	DataFrame with columns: start, target_reached_at,
`DataFrame`	time_to_target_seconds, overshoot.