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ToleranceDeviationEvents¤

Processes DataFrame data for comparing tolerance and actual values with severity classification and process capability indices.

Module: ts_shape.events.quality.tolerance_deviation
Guide: Quality Control & SPC

When to Use¤

Use when you have process signals with defined upper/lower specification limits and need to track deviations with severity classification. Computes Cp/Cpk/Pp/Ppk indices for process capability assessment. Requires a DataFrame with actual measurement values and corresponding specification limits.

Quick Example¤

from ts_shape.events.quality.tolerance_deviation import ToleranceDeviationEvents

tdev = ToleranceDeviationEvents(df, value_column="value_double")

# Apply tolerance checks with severity classification
events = tdev.process_and_group_data_with_events()

# Compute process capability indices
capability = tdev.compute_capability_indices(target_value=50.0)
print(f"Cpk: {capability['Cpk']:.2f}, Ppk: {capability['Ppk']:.2f}")

Key Methods¤

Method Purpose Returns
process_and_group_data_with_events() Apply tolerance checks with severity classification DataFrame with deviation events and severity levels
compute_capability_indices(target_value=None) Calculate Cp, Cpk, Pp, Ppk capability indices Dictionary with capability metrics

Tips & Notes¤

Set a target value for Cpk accuracy

When calling compute_capability_indices, always provide the target_value parameter if your process has a nominal target. Without it, the method assumes the midpoint of the specification range, which may overestimate Cpk for off-center processes.

Related modules

  • SPC Rules — Western Electric rule-based monitoring with control limits
  • Capability Trending — track Cp/Cpk over rolling time windows
  • Sensor Drift — detect calibration drift that can cause systematic deviations

See Also¤