tolerance_deviation

tolerance_deviation ¤

ToleranceDeviationEvents ¤

ToleranceDeviationEvents(
    dataframe: DataFrame,
    tolerance_column: str,
    actual_column: str,
    tolerance_uuid: Optional[str] = None,
    actual_uuid: str = None,
    event_uuid: str = None,
    compare_func: Callable[
        [Series, Series], Series
    ] = operator.ge,
    time_threshold: str = "5min",
    upper_tolerance_uuid: Optional[str] = None,
    lower_tolerance_uuid: Optional[str] = None,
    warning_threshold: float = 0.8,
    tolerance_lag: str = "0s",
)

Bases: Base

Inherits from Base and processes DataFrame data for specific events, comparing tolerance and actual values.

Enhanced features: - Separate upper and lower tolerances - Warning zones with configurable thresholds - Deviation magnitude tracking (absolute and percentage) - Severity level classification (minor, major, critical) - Process capability indices (Cp, Cpk, Pp, Ppk) - Time-lagged tolerance application

Initializes the ToleranceDeviationEvents with specific event attributes. Inherits the sorted dataframe from the Base class.

Parameters:

Name	Type	Description	Default
`dataframe`	`DataFrame`	Input DataFrame with measurement data	required
`tolerance_column`	`str`	Column name containing tolerance values	required
`actual_column`	`str`	Column name containing actual measurement values	required
`tolerance_uuid`	`Optional[str]`	UUID for tolerance (backward compatibility, used if upper/lower not specified)	`None`
`actual_uuid`	`str`	UUID for actual measurements	`None`
`event_uuid`	`str`	UUID for generated events	`None`
`compare_func`	`Callable[[Series, Series], Series]`	Comparison function (default: operator.ge for greater-than-or-equal)	`ge`
`time_threshold`	`str`	Time window for grouping events (default: '5min')	`'5min'`
`upper_tolerance_uuid`	`Optional[str]`	UUID for upper tolerance limit (optional)	`None`
`lower_tolerance_uuid`	`Optional[str]`	UUID for lower tolerance limit (optional)	`None`
`warning_threshold`	`float`	Threshold ratio for warning zone (default: 0.8 = 80% of tolerance)	`0.8`
`tolerance_lag`	`str`	Time lag for tolerance application (default: '0s')	`'0s'`

process_and_group_data_with_events ¤

process_and_group_data_with_events() -> pd.DataFrame

Processes DataFrame to apply tolerance checks, group events by time, and generate an events DataFrame.

Enhanced with: - Separate upper/lower tolerances - Deviation magnitude tracking (absolute and percentage) - Warning zones - Severity level classification - Time-lagged tolerance application

Returns:

Type	Description
`DataFrame`	pd.DataFrame: A DataFrame of processed and grouped event data with enhanced metrics.

compute_capability_indices ¤

compute_capability_indices(
    target_value: Optional[float] = None,
) -> Dict[str, float]

Calculate process capability indices (Cp, Cpk, Pp, Ppk).

Process capability indices measure how well a process meets specification limits: - Cp: Process capability (potential capability assuming perfect centering) - Cpk: Process capability index (accounts for process centering) - Pp: Process performance (overall variability) - Ppk: Process performance index (accounts for centering)

Parameters:

Name	Type	Description	Default
`target_value`	`Optional[float]`	Target/nominal value for the process. If None, uses midpoint of tolerances.	`None`

Returns:

Type	Description
`Dict[str, float]`	Dictionary containing: - 'Cp': Process capability - 'Cpk': Process capability index - 'Pp': Process performance - 'Ppk': Process performance index - 'mean': Process mean - 'std': Process standard deviation - 'usl': Upper specification limit - 'lsl': Lower specification limit - 'target': Target value used

Note

Cp/Cpk use short-term variation (within-subgroup)
Pp/Ppk use long-term variation (overall)
Values > 1.33 are generally considered acceptable
Values > 1.67 are considered good