shewhart 0.1.0

The statistical process control library for Python: control charts, capability analysis, and measurement systems analysis, validated against published reference values.

shewhart

Statistical process control for Python.

Control charts with the standard run rules, process capability analysis, and measurement systems analysis. Results are computed from the published formulas and checked against reference values in the test suite.

Named after Walter A. Shewhart.

Motivation

R has had a maintained SPC package (qcc) since 2004. Python does not: the existing packages are unmaintained, cover only fragments of the toolkit, and none of them validate their output against reference data. This library is an attempt to fix that, with a few specific goals:

• correct constants and estimators, validated against published values
• a clean separation between estimating control limits (Phase I) and monitoring new data against frozen limits (Phase II)
• rule violations as structured data, usable in pipelines, not only in plots
• an API that works headless, so a weekly control chart review can run as a cron job

Status

Under active development, pre 0.1. Implemented and tested so far:

• control charts: I-MR, Xbar-R, Xbar-S, p, np, c, u (stair-step limits for varying subgroup sizes), EWMA (exact and asymptotic limits), run chart with the four runs tests, Pareto analysis
• time-window subgrouping on DatetimeIndex data (subgroup="1H")
• process capability: Cp, Cpk, Pp, Ppk, Cpm with confidence intervals (chi-square for Cp/Pp, Bissell approximation for Cpk/Ppk), within vs overall sigma, expected and observed PPM, stability gate, normality check
• Nelson rules 1 to 8 and Western Electric rules 1 to 4, returned as structured signal events
• chart constants (d2, d3, c4, A2, A3, D3, D4, B3, B4), computed from their defining integrals rather than copied from tables
• baseline freezing and reuse (JSON), self-contained HTML reports
• a reference-case validation suite (tests/validation_cases.json), anchored externally: NIST StRD certified values (Michelso, NumAcc1) and the NIST/SEMATECH e-Handbook EWMA worked example are reproduced in CI

The version on PyPI (0.0.1) predates most of this. Until 0.1 is released, install from source:

pip install git+https://github.com/bertanucar/shewhart

Usage

import shewhart as sw

r = sw.imr(df, value="torque", rules="nelson")
r.ok           # False if any rule fired
r.summary()    # plain text verdict
r.table        # per-point DataFrame with signal flags
r.plot()

Subgrouped data:

r = sw.xbar_r(df, value="torque", subgroup="batch")

Fit limits once, then monitor new data against them:

sw.imr(df_baseline, value="torque").baseline.save("line3_baseline.json")

# later, e.g. in a scheduled job:
r = sw.imr(df_new, value="torque", limits="line3_baseline.json")
sys.exit(0 if r.ok else 1)

Capability analysis, with the confidence intervals that the usual hand-rolled Cpk calculation cannot give you:

r = sw.capability(df, value="dia", lsl=9.95, usl=10.05)
r.stats["cpk"], r.stats["cpk_lci"], r.stats["cpk_uci"]

Several analyses in one self-contained HTML file, e.g. as a weekly job:

sw.report([
    sw.imr(df, value="torque", limits="line3_baseline.json"),
    sw.p_chart(df2, defectives="rejects", size="inspected"),
    sw.capability(df, value="torque", lsl=9.5, usl=11.0),
], "weekly_report.html", title="Line 3 weekly")

Every analysis returns the same Result object: named statistics, a tidy per-point table, a tuple of structured rule violations, and provenance metadata (library version, input hash, timestamp).

Roadmap

Version	Scope
0.1	documentation site, PyPI release
0.1.x	CUSUM, Laney p'/u', non-normal capability, tolerance intervals
0.2	measurement systems analysis: ANOVA gauge R&R (crossed and nested), Type 1 studies, attribute agreement
0.3	process screening across many characteristics, drift monitoring with control chart semantics

Out of scope: DOE (see pyDOE3), reliability engineering (see reliability), general statistics (see statsmodels), GUIs.

License

MIT. Written and maintained by Bertan Ucar, PhD researcher at Tsinghua University.