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AI-powered Statistical Process Control, MSA, and Process Capability analysis. Upload data, ask in plain English, get charts and reports.

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# ASPC — Production Statistical Process Control

Correct, tested SPC for **batch** and **real-time** work: Shewhart / EWMA / CUSUM charts, MSA, capability, TimescaleDB, Redpanda/MQTT streaming, and a Next.js operator dashboard.

Phase I establishes and freezes versioned limits; Phase II evaluates new data against those limits. No stubs — every advertised `ChartType` has real math.

## Why ASPC?

Manufacturing needs SPC that is **correct**, **gated before go-live**, and **usable in real time** against frozen limits — not only a desktop chart after the shift. See:

| Doc | Contents |
|-----|----------|
| [docs/overview/problem-and-solution.md](docs/overview/problem-and-solution.md) | Problem, solution, Phase I → freeze → Phase II |
| [docs/overview/capabilities.md](docs/overview/capabilities.md) | Statistical + operational capabilities |
| [docs/overview/use-cases.md](docs/overview/use-cases.md) | Stamping / pharma / molding integration patterns |
| [docs/overview/benchmarking.md](docs/overview/benchmarking.md) | Performance, accuracy, resilience evidence |

Reproduce benches: `python benchmarks/performance.py` and `python benchmarks/accuracy.py` ([benchmarks/README.md](benchmarks/README.md)). Claims are scoped in [docs/overview/benchmarking.md](docs/overview/benchmarking.md): formula fidelity ≠ Minitab/JMP parity; MSA and `valid_range` gates are **opt-in** (absence warns / skips, does not always STOP).

## Features

- Gated Phase I pipeline (`establish`) with ok / warn / stop gates and a 10-item go-live checklist
- Charts: I-MR, Xbar-R, Xbar-S, P, NP, C, U, EWMA, CUSUM
- MSA: Gage R&R (ANOVA / range), bias, linearity, stability, NDC and 10:1 resolution gates
- Capability: Cp/Cpk/Pp/Ppk, DPMO / sigma level, parametric · transformed · nonparametric routing
- FastAPI + CLI, JWT / API-key auth, WebSocket live alerts, SSE replay
- Operator onboarding and Live go-live console (Phase I → freeze → register stream → go-live)
- Explainable SPC signals and Lab UI (`spc_core.explain` + `/lab`)
- Signed out-of-control webhooks for downstream alerting
- DevEx CLI: `aspc doctor`, `aspc demo up`, `aspc resilience`
- Docker Compose stack: Redpanda, Mosquitto, TimescaleDB, Redis, stream engine, MQTT bridge, UI

## Architecture

Hexagonal modular monolith: pure `spc_core` stats, `adapters` for I/O, `apps` (FastAPI + CLI), and optional `services` (stream-engine, mqtt-bridge). Full structural scan: [docs/architecture.md](docs/architecture.md).

```
spc_core/                 Pure statistics (Shewhart, EWMA, CUSUM, MSA, capability, gated pipeline, explain)
adapters/                 I/O, SQLite/TimescaleDB, Plotly, Kafka/MQTT sources, stream engine, webhooks
apps/api/                 FastAPI — JWT + API-key auth, REST, SSE replay, WebSocket live
apps/cli/                 aspc CLI (doctor, demo, resilience, analyze, serve)
services/stream_engine/   Kafka consumer → Phase II eval → Tier1/Tier2 + Redis
services/mqtt_bridge/     MQTT → Redpanda bridge
frontend/                 Next.js operator dashboard (analyze, live, onboarding, lab, MSA)
deploy/compose/           Full stack orchestration
migrations/               Alembic (Timescale hypertables + analysis tables)
sample_data/              Deterministic synthetic datasets for tests and demos
resilience_data/          Standards-mapped judgment corpus (CSV + expect blocks)
combinatorial/            Finite batch + in-process Phase II matrix (sparse CI / exhaustive local)
docs/                     Full documentation
```

## Quick start

**Minimal (batch SPC):** API + SQLite — analyze charts, MSA, capability, reports. No Live streaming.

Install [uv](https://docs.astral.sh/uv/), then:

```bash
uv venv && source .venv/bin/activate
uv pip install -e ".[dev]"

uv run python -m sample_data --out examples/data
aspc control-chart -f examples/data/spc_individual_out_of_control.csv --json
aspc doctor

aspc serve --port 8000
```

Dashboard:

```bash
cd frontend && cp .env.example .env.local && npm install && npm run dev
# http://localhost:3000 — onboarding at /onboarding, Lab at /lab
# .env.example uses NEXT_PUBLIC_API_URL=/backend (Next proxies to :8000; avoids CORS)
```

**Full stack (Live streaming):** TimescaleDB + Redis + Redpanda + MQTT + stream-engine. Requires Compose secrets — see [docs/deployment.md](docs/deployment.md).

```bash
cp deploy/compose/.env.example deploy/compose/.env   # edit secrets
docker compose -f deploy/compose/docker-compose.yml --env-file deploy/compose/.env up -d --build
# or: aspc demo up
```

| Service | Port |
|---------|------|
| API | 8000 |
| Frontend | 3000 |
| Redpanda | 19092 (loopback) |
| Mosquitto | 1883 (loopback) |
| TimescaleDB | 5433 (loopback) |
| Redis | 6379 (loopback) |
| Grafana (ops profile) | 3001 (loopback) |

## Quality & testing

```bash
# Unit tests (CI default excludes integration)
PYTEST_DISABLE_PLUGIN_AUTOLOAD=1 pytest -q -m "not integration"

# Resilience judgment catalog
aspc resilience
# or: python scripts/resilience_report.py  → resilience_data/JUDGMENT.md

# Combinatorial dual-mode matrix (sparse for CI; exhaustive local)
python -m combinatorial report --mode sparse
# → combinatorial/out/JUDGMENT.md, COVERAGE.json, ENGINE_BEHAVIOR_REPORT.md

# Operator-console Playwright (Compose UI+API must be up)
cd frontend && E2E_USERNAME=admin E2E_PASSWORD='…' npm run test:e2e

# Accuracy / performance benches
python benchmarks/accuracy.py
python benchmarks/performance.py
```

Details: [resilience_data/README.md](resilience_data/README.md), [docs/development.md](docs/development.md), [docs/overview/health-and-roadmap.md](docs/overview/health-and-roadmap.md).

## Documentation

| Guide | Description |
|-------|-------------|
| [docs/overview/problem-and-solution.md](docs/overview/problem-and-solution.md) | Why ASPC — problem, solution, architecture |
| [docs/architecture.md](docs/architecture.md) | Structural scan: layers, flows, deploy topology |
| [docs/overview/health-and-roadmap.md](docs/overview/health-and-roadmap.md) | Health insights, roadmap, contract probes |
| [docs/overview/benchmarking.md](docs/overview/benchmarking.md) | Performance, accuracy, robustness |
| [docs/index.md](docs/index.md) | Doc map and Phase I → freeze → Phase II model |
| [docs/concepts.md](docs/concepts.md) | Charts, rules, MSA, capability, flags |
| [docs/pipeline.md](docs/pipeline.md) | Gated `establish()`, checklist, `SPCRecord` |
| [docs/cli.md](docs/cli.md) | `aspc` command reference |
| [docs/api.md](docs/api.md) | REST, auth, WebSocket, SSE |
| [docs/python-api.md](docs/python-api.md) | Library usage and extras |
| [docs/configuration.md](docs/configuration.md) | YAML + `ASPC_*` env |
| [docs/deployment.md](docs/deployment.md) | Compose, images, migrations (minimal vs full) |
| [docs/development.md](docs/development.md) | Tests, lint, `sample_data`, CI |
| [resilience_data/README.md](resilience_data/README.md) | Standards-mapped resilience corpus |
| [combinatorial/out/ENGINE_BEHAVIOR_REPORT.md](combinatorial/out/ENGINE_BEHAVIOR_REPORT.md) | Engine behavior from combinatorial matrix |

Interactive OpenAPI: `http://localhost:8000/docs` when the API is running.

## Standards

AIAG MSA-4 · AIAG SPC · ISO 7870 · Six Sigma DMAIC

## License

See [LICENSE](LICENSE).

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