processbehavior

A Python library for Process Behavior Analysis following Thomas A. Bishop's Variation Analysis System (VAS) methodology.

Unlike traditional SPC packages, processbehavior faithfully implements Bishop's VAS equation-by-equation: automatic detection of the three-state design lineage (PDS / ODS / ADS), variance decomposition via R1-R6 residuals, and chart selection routed by the data your study can actually analyze.

Installation

pip install processbehavior

Plotting (plotly) is included. For Excel export and static image export, install the corresponding extras:

pip install "processbehavior[excel]"   # openpyxl, for result.to_excel(...)
pip install "processbehavior[images]"  # kaleido, for static plot images

Quickstart

import processbehavior as pb

# Generate a sample dataset (replace with your own DataFrame)
df = pb.make_design(state=1, seed=42)
# Columns: 'time', 'factor 1', 'factor 2', 'y'

# Formulate — detects PDS / ODS / ADS and builds the analysis dataset
study = pb.formulate(
    df,
    response='y',
    time='time',
    factors=['factor 1', 'factor 2'],
)
print(f"Observed:   ODS {study.observed_design_state.sds}")
print(f"Analytical: ADS {study.analytical_design_state.sds}")
print(f"Recommended chart: {study.recommended_chart}")

# Execute analysis (routes by ADS)
result = study.execute()                       # uses the recommended chart
stats = result.statistics('Xbar')              # always {'N', 'center', 'lpl', 'upl'}
print(f"Center: {stats['center']}")

# When subgroup sizes differ the limits differ per subgroup, so the scalar
# 'lpl'/'upl' are None and stats['limits_vary'] is True. The per-subgroup
# limits live in the chart table:
print(result.get_chart('Xbar')[['center', 'lpl', 'upl']].head())

# Plot (interactive plotly figure)
result.plot()

# Export to Excel (requires the [excel] extra)
result.to_excel('analysis.xlsx')

The whole pipeline is one expression when you don't need the intermediate objects:

pb.formulate(df, response='y', factors=['factor 1'], time='time').execute().plot()

Use pb.ProcessBehavior(df) directly when you want the fluent derived-variable verbs (.transform(...), .bin(...)), which attach before formulating. With that object in hand, pbd.cols.<column_name> provides IDE auto-completion for column references.

Key Concepts

Two-Step Workflow

The API mirrors how analysts think:

1. formulate() - Understand your data structure. Detects PDS / ODS / ADS, identifies valid charts, and computes residuals. This is the expensive step.
2. execute() - Run analysis. Produces charts from the pre-computed data. This is cheap and can be called multiple times for different charts from the same study.

Design-State Lineage (PDS / ODS / ADS)

processbehavior reports three design states at three points in the analysis lifecycle:

The integer codes are Bishop's reference scale ("Bishop Table 1"):

ODS values in {4, 5, 6} collapse to ADS values in {1, 2, 3} during tidying (empty cells drop and the surviving subset becomes the analytical grid).

Access the lineage on a formulated study:

study.plan_design_state        # PDS (when a plan was supplied)
study.observed_design_state    # ODS — what the raw data showed
study.analytical_design_state  # ADS — what the analysis is fit for
study.design()                 # DesignReport: full lineage in one object

Residual System (R1-R6)

For factorial designs (ADS 1-3), processbehavior decomposes variation into diagnostic residuals:

• R1 - The response centered at 0 (Bishop §13.1); the building block for the rest
• R2 - Within-cell noise (measurement error, short-term fluctuation)
• R3 - Interaction between design conditions and time
• R4 - Time main effects (trends, seasonality, batch effects)
• R5 - Design condition main effects, all factors combined
• R6 - A single design factor's main effect (machine-to-machine, operator bias)

# Chart any residual
result = study.execute(chart='Xbar', value='R4')  # Time main effects
result = study.execute(chart='Xbar', value='R5')  # Design condition main effects
result = study.execute(chart='Xbar', value='R6', by=['machine'])  # One factor's effect

Not every chart pairs with every residual — the valid pairs depend on the ADS. study.residual_charts lists them, and study.why_not('X', value='R4') explains any pair it refuses.

Stratified Analysis

For X/mR charts with grouping factors, processbehavior produces a single combined chart with per-stratum limits:

result = study.execute(chart='X', by=['machine'])

# Drill into a specific stratum
for stratum in result.strata:
    focused = result.focus(stratum)
    focused.plot()

Validation

processbehavior's analytical outputs are verified against Dr. Thomas A. Bishop's VAS (Minitab-based) reference results on every push. The validator at validation/e2e_bishop_report.py runs 1,547 numerical assertions through the full formulate → execute → capability / loss pipeline, across 14 of his VAS runs:

Reference data is Bishop's VAS output for the 10-12-2026 manual (his runs of October 2026). The library matches every chart center, control limit, capability index and loss-function value that output prints, to within the precision Bishop reports. Where subgroup sizes differ, VAS prints "UNEQUAL" instead of a limit; there every subgroup's limits are read from the limit lines VAS draws and compared one by one. SDS 4–6 are incomplete grids (ODS 4–6), checked as the complete design that survives tidying (ADS 1–3). Four values, the factor S-chart limits of the known-effects run, wait for a VAS rerun. The per-run table is in docs/reference/validation.md.

The full report renders to validation/e2e_bishop_report.html.

Features

• Three-state lineage: PDS / ODS / ADS detected automatically; chart selection routes by ADS
• Correct charts: Xbar-S, X (Individual), mR (Moving Range), Histogram with proper limit calculations
• Variance decomposition: R1-R6 residuals for factorial designs
• Effects analysis: Main effects, time effects, and interaction effects
• Stratified charts: Automatic per-stratum limits for grouped individual data
• Signal detection: all eight Western Electric rules on X/mR; Rule 1 (3-sigma) on Xbar/S, where the run- and zone-based rules need a time order that subgroup comparisons don't have
• Beyond the charts: capability indices (Cp/Cpk/Pp/Ppk), Taguchi loss decomposition, and maximum-information analysis — validated in the same Bishop-reference CI gate
• IDE support: Column auto-completion via ProcessBehavior(df).cols
• Self-diagnostic errors: Helpful messages that say what's available and how to fix it
• Excel export: Publication-ready workbooks with charts and statistics
• Interactive plots: Plotly-based charts with hover details

Scope

processbehavior is the computational engine for Bishop's VAS methodology. It handles data ingestion, design-state lineage detection (PDS / ODS / ADS), residual computation, chart generation, and export.

For the curated analyst experience — guided workflows, interactive dashboards, and collaboration features — see processbehavior.com.

For the methodological foundation — the theory behind VAS, design-state classification, and residual interpretation — see Bishop's published book below and the forthcoming book by Dr. Thomas A. Bishop and Chris Nicholas.

References

The methodology this library implements:

Bishop, Thomas A. (2021). The Scientific Basis for Modern Analytic Practice. Lulu Press. 279 pp.

To cite the software itself, see CITATION.cff.

License

Apache 2.0