statistical_process_control

statistical_process_control ¤

StatisticalProcessControlRuleBased ¤

StatisticalProcessControlRuleBased(
    dataframe: DataFrame,
    value_column: str,
    tolerance_uuid: str,
    actual_uuid: str,
    event_uuid: str,
)

Bases: Base

Inherits from Base and applies SPC rules (Western Electric Rules) to a DataFrame for event detection. Processes data based on control limit UUIDs, actual value UUIDs, and generates events with an event UUID.

Initializes the SPCMonitor with UUIDs for tolerance, actual, and event values. Inherits the sorted dataframe from the Base class.

Parameters:

Name	Type	Description	Default
`dataframe`	`DataFrame`	The input DataFrame containing the data to be processed.	required
`value_column`	`str`	The column containing the values to monitor.	required
`tolerance_uuid`	`str`	UUID identifier for rows that set tolerance values.	required
`actual_uuid`	`str`	UUID identifier for rows containing actual values.	required
`event_uuid`	`str`	UUID to assign to generated events.	required

calculate_control_limits ¤

calculate_control_limits() -> pd.DataFrame

Calculate the control limits (mean ± 1σ, 2σ, 3σ) for the tolerance values.

Returns:

Type	Description
`DataFrame`	pd.DataFrame: DataFrame with control limits for each tolerance group.

calculate_dynamic_control_limits ¤

calculate_dynamic_control_limits(
    method: str = "moving_range", window: int = 20
) -> pd.DataFrame

Calculate dynamic control limits that adapt over time.

Parameters:

Name	Type	Description	Default
`method`	`str`	Method for calculating dynamic limits. Options: - 'moving_range': Uses moving window statistics - 'ewma': Uses Exponentially Weighted Moving Average	`'moving_range'`
`window`	`int`	Window size for moving calculations (default: 20)	`20`

Returns:

Type	Description
`DataFrame`	pd.DataFrame: DataFrame with dynamic control limits indexed by time.

rule_1 ¤

rule_1(df: DataFrame, limits: DataFrame) -> pd.DataFrame

Rule 1: One point beyond the 3σ control limits.

Returns:

Type	Description
`DataFrame`	pd.DataFrame: Filtered DataFrame with rule violations.

rule_2 ¤

rule_2(df: DataFrame) -> pd.DataFrame

Rule 2: Nine consecutive points on one side of the mean.

Returns:

Type	Description
`DataFrame`	pd.DataFrame: Filtered DataFrame with rule violations.

rule_3 ¤

rule_3(df: DataFrame) -> pd.DataFrame

Rule 3: Six consecutive points steadily increasing or decreasing.

Returns:

Type	Description
`DataFrame`	pd.DataFrame: Filtered DataFrame with rule violations.

rule_4 ¤

rule_4(df: DataFrame) -> pd.DataFrame

Rule 4: Fourteen consecutive points alternating up and down.

Returns:

Type	Description
`DataFrame`	pd.DataFrame: Filtered DataFrame with rule violations.

rule_5 ¤

rule_5(df: DataFrame, limits: DataFrame) -> pd.DataFrame

Rule 5: Two out of three consecutive points near the control limit (beyond 2σ but within 3σ).

Returns:

Type	Description
`DataFrame`	pd.DataFrame: Filtered DataFrame with rule violations.

rule_6 ¤

rule_6(df: DataFrame, limits: DataFrame) -> pd.DataFrame

Rule 6: Four out of five consecutive points near the control limit (beyond 1σ but within 2σ).

Returns:

Type	Description
`DataFrame`	pd.DataFrame: Filtered DataFrame with rule violations.

rule_7 ¤

rule_7(df: DataFrame, limits: DataFrame) -> pd.DataFrame

Rule 7: Fifteen consecutive points within 1σ of the centerline.

Returns:

Type	Description
`DataFrame`	pd.DataFrame: Filtered DataFrame with rule violations.

rule_8 ¤

rule_8(df: DataFrame, limits: DataFrame) -> pd.DataFrame

Rule 8: Eight consecutive points on both sides of the mean within 1σ.

Returns:

Type	Description
`DataFrame`	pd.DataFrame: Filtered DataFrame with rule violations.

apply_rules_vectorized ¤

apply_rules_vectorized(
    selected_rules: Optional[List[str]] = None,
) -> pd.DataFrame

Applies SPC rules using vectorized operations with optimized multi-rule processing. Processes multiple rules in fewer passes through the data for better performance.

Parameters:

Name	Type	Description	Default
`selected_rules`	`Optional[List[str]]`	List of rule names to apply. If None, applies all rules.	`None`

Returns:

Type	Description
`DataFrame`	pd.DataFrame: DataFrame with rule violations, including rule name and severity.

detect_cusum_shifts ¤

detect_cusum_shifts(
    target: Optional[float] = None,
    k: float = 0.5,
    h: float = 5.0,
) -> pd.DataFrame

Detect process shifts using CUSUM (Cumulative Sum) control chart.

CUSUM charts are effective at detecting small shifts in the process mean and are more sensitive than traditional Shewhart control charts.

Parameters:

Name	Type	Description	Default
`target`	`Optional[float]`	Target mean value. If None, uses the mean of tolerance data.	`None`
`k`	`float`	Reference value (slack parameter), typically 0.5 to 1.0 times sigma. Smaller k detects smaller shifts. Default: 0.5	`0.5`
`h`	`float`	Decision interval (threshold). Typical values are 4-5. Smaller h gives faster detection but more false alarms. Default: 5.0	`5.0`

Returns:

Type	Description
`DataFrame`	pd.DataFrame: DataFrame with CUSUM statistics and detected shifts. Columns: systime, value, cusum_high, cusum_low, shift_detected, shift_direction

interpret_violations ¤

interpret_violations(
    violations_df: DataFrame,
) -> pd.DataFrame

Add human-readable interpretations to rule violations.

Parameters:

Name	Type	Description	Default
`violations_df`	`DataFrame`	DataFrame with rule violations (output from apply_rules_vectorized or process methods)	required

Returns:

Type	Description
`DataFrame`	pd.DataFrame: Enhanced DataFrame with interpretation and recommendation columns.

process ¤

process(
    selected_rules: Optional[List[str]] = None,
    include_severity: bool = False,
) -> pd.DataFrame

Applies the selected SPC rules and generates a DataFrame of events where any rules are violated.

Parameters:

Name	Type	Description	Default
`selected_rules`	`Optional[List[str]]`	List of rule names (e.g., ['rule_1', 'rule_3']) to apply.	`None`
`include_severity`	`bool`	If True, includes severity and rule information in output. Default: False (maintains backward compatibility)	`False`

Returns:

Type	Description
`DataFrame`	pd.DataFrame: DataFrame with rule violations and detected events. If include_severity=False: columns are [systime, value_column, uuid] If include_severity=True: columns include [systime, value_column, uuid, rule, severity]