CLAUDE.md git:20260903.12a565bA

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# CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

## Project Overview

GNN (Generalized Notation Notation) is a text-based language for standardizing Active Inference generative models. The codebase implements a 25-step processing pipeline (steps 0-24) that transforms GNN specifications into executable simulations, visualizations, and analysis reports.

## Essential Commands

```bash
# Run full pipeline
python src/main.py --target-dir input/gnn_files --verbose

# Run specific steps only (steps are 0-24)
python src/main.py --only-steps "3,5,11,12" --verbose

# Skip specific steps
python src/main.py --skip-steps "15,16" --verbose

# Run individual step directly
python src/3_gnn.py --target-dir input/gnn_files --output-dir output --verbose

# Type checking only (fast validation)
python src/main.py --only-steps 5 --strict

# Setup environment with dev dependencies
python src/main.py --only-steps 1 --dev

# Step 1 default: ``uv sync`` core dependencies (includes JAX / NumPyro / PyTorch / DisCoPy for step 12).
# ``--setup-core-only`` skips the JAX self-test during setup only.

# Using just (recommended task runner)
just                         # List all recipes
just test                    # Fast test suite
just lint                    # Ruff lint
just pipeline                # Full pipeline
just render-health           # Check all 9 renderer backends
just test-mod render         # Test a specific module
just steps                   # List all 25 pipeline steps from step registry
just bench                   # Run performance benchmarks
just audit                   # Run documentation audit

# Run tests
pytest src/tests/ -v
pytest src/tests/test_gnn_*.py -v  # Module-specific tests
python src/2_tests.py --comprehensive  # Full test suite via pipeline

# Check coverage
pytest --cov=src --cov-report=term-missing

# Using uv (recommended)
uv sync
uv run python src/main.py --target-dir input/gnn_files --verbose
# Install dev deps (pytest) into .venv so ``uv run pytest`` uses the same interpreter as JAX/pymdp
uv sync --extra dev
uv run pytest
# If JAX/pymdp tests skip or ``ModuleNotFoundError: jax`` during tests, another venv may own ``pytest``:
# unset VIRTUAL_ENV or run ``PYTHONPATH=src .venv/bin/pytest src/tests/ …``
# Package integrity probe: ``src/utils/jax_stack_validation.py`` (also Step 1 + CI).
```

## Architecture

### Thin Orchestrator Pattern

All 25 pipeline steps follow a consistent pattern:

- **Numbered scripts** (`src/N_module.py`): Thin orchestrators (<150 lines) that handle CLI args, logging, and delegate to modules
- **Module directories** (`src/module/`): Contain all domain logic
  - `__init__.py`: Public API
  - `processor.py`: Core logic (preferred; see accepted alternatives below)
  - `mcp.py`: MCP tool registration (if applicable)

**Accepted `processor.py` alternatives** (4 modules deviate from the pattern; all 22 module dirs have a `processor.py` — several as thin facades):
- `setup/`, `tests/`, `validation/`: Logic lives directly in `__init__.py` — functionally equivalent
- `model_registry/`: Uses `registry.py` as primary logic file — clearly named
- `website/` and `type_checker/` keep facade `processor.py` entry points that delegate to their primary logic files (`renderer.py` + `generator.py`, `checking.core`), preserving the documented pattern.

**Hard imports** (steps 20, 21, 24): The website, mcp, and intelligent_analysis step scripts use direct (non-try/except) imports because these modules are pipeline-required and must always be present. All three document this with an inline `# Hard import: X is a core module` comment. Other steps report dependency import problems through explicit warning/error statuses.

**`src/sapf/` module**: This is a public SAPF entry point, not a numbered pipeline step. It exports functions from `audio.sapf` so that SAPF callers use one canonical implementation. The SAPF implementation lives in `src/audio/sapf/`.

**Research module (Step 19)**: Uses rule-based static analysis and does not require an external LLM. Its `FEATURES` metadata describes available capabilities rather than partial implementation status.

### 25-Step Pipeline (0-24)

The canonical source of truth for all 25 steps is `src/pipeline/step_registry.py`
(accessible via `PYTHONPATH=src uv run python -c "from pipeline.step_registry import STEPS; [print(f'{s.script_name:30s} tags={sorted(s.tags)}') for s in STEPS]"`).

| Steps 0-9 (Core) | Steps 10-16 (Simulation) | Steps 17-24 (Output) |
|------------------|--------------------------|----------------------|
| 0: Template init | 10: Ontology | 17: Integration |
| 1: Setup | 11: Render (code gen) | 18: Security |
| 2: Tests | 12: Execute | 19: Research |
| 3: GNN parsing | 13: LLM analysis | 20: Website |
| 4: Model registry | 14: ML integration | 21: MCP |
| 5: Type checker | 15: Audio | 22: GUI |
| 6: Validation | 16: Analysis | 23: Report |
| 7: Export | | 24: Intelligent Analysis |
| 8: Visualization | | |
| 9: Advanced viz | | |

### Data Flow

Step 3 (GNN Parse) produces parsed models consumed by steps 5-8, 10-11, 13. Step 11 (Render) generates code executed by Step 12, whose results feed Step 16 (Analysis) and Step 23 (Report).

### Framework Integration

Code generation and execution support multiple backends:

- **PyMDP** (Python): `render/pymdp/`, `execute/pymdp/`
- **RxInfer.jl** (Julia): `render/rxinfer/`, `execute/rxinfer/` — genuine `@model` + `infer()` with committed `Project.toml` (RxInfer 5.5.0)
- **ActiveInference.jl** (Julia): `render/activeinference_jl/`, `execute/activeinference_jl/`
- **JAX** (Python): `render/jax/`, `execute/jax/`
- **DisCoPy** (Python): `render/discopy/`, `execute/discopy/`
- **PyTorch** (Python): `render/pytorch/`, `execute/pytorch/`
- **NumPyro** (Python): `render/numpyro/`, `execute/numpyro/`
- **Stan** (Stan + cmdstanpy driver): `render/stan/`, `execute/stan/` — HMM forward-algorithm program for discrete models, Kalman marginal likelihood for continuous ones; `uv sync --extra stan` plus a CmdStan toolchain to execute

```bash
# Execute specific frameworks
python src/12_execute.py --frameworks "pymdp,jax" --verbose
```

### Model kinds and framework support

Discrete-state POMDP/HMM exemplars (categorical `A/B/C/D`) render and execute on all nine frameworks. Continuous-state exemplars (`input/gnn_files/continuous/`, linear-Gaussian `F/H/Q/R` + Gaussian prior, optional closed-loop `goal_mean`/`control_gain`) render and execute natively on JAX, NumPyro, PyTorch, Stan and RxInfer.jl via a Kalman filter (NumPyro and Stan also run NUTS on the same model). PyMDP, ActiveInference.jl, DisCoPy and bnlearn are categorical and report continuous models with the render status **`unsupported`** — a distinct outcome from `failed` (excluded from success rates; Step 12 never executes them). The shared generator is `render/continuous_script.py`; `render/framework_registry.py` carries `supports_continuous` per framework.

### Running all execution frameworks

Step 12 (Execute) runs scripts for every framework (PyMDP, RxInfer.jl, ActiveInference.jl, JAX, DisCoPy, PyTorch, NumPyro, Stan). JAX, NumPyro, PyMDP, and DisCoPy are **core** Python dependencies: a normal ``uv sync`` installs them. If the environment is incomplete, those backends are **skipped** at step 12 (not failed) with a dependency reason. PyTorch is supported by the renderer/executor but is intentionally not locked as a default dependency (see `pyproject.toml`); install `torch` manually when you need that backend. Stan needs `uv sync --extra stan` and `python -c "import cmdstanpy; cmdstanpy.install_cmdstan()"`. Julia backends require a local Julia install (Julia 1.12 works; `src/execute/activeinference_jl/Project.toml` pins `Distributions < 0.25.126` because DistributionsAD 0.6.58 does not precompile against newer releases). Step 12 merges its `execution_summary.json` across per-folder invocations, so the durable summary covers every input folder.

## Key Locations

| Path | Purpose |
|------|---------|
| `src/pipeline/step_registry.py` | Canonical 25-step pipeline registry (single source of truth) |
| `src/main.py` | Main pipeline orchestrator |
| `src/N_*.py` | Pipeline step scripts (0-24) |
| `src/gnn/` | GNN parsing, discovery, validation |
| `src/render/` | Code generation for all frameworks |
| `src/execute/` | Simulation execution |
| `src/tests/` | Test suite (`uv sync --extra dev` then `uv run pytest src/tests/ -q --tb=no --ignore=src/tests/llm/test_llm_ollama.py --ignore=src/tests/llm/test_llm_ollama_integration.py`; use current run output for pass/skip counts; enable those files when local `ollama` is available) |
| `input/gnn_files/` | Sample GNN model files |
| `output/` | Generated outputs (25 step-specific folders) |
| `doc/gnn/reference/gnn_syntax.md` | Complete GNN syntax specification |

## GNN File Format

GNN files are Markdown with structured sections:

```markdown
## GNNSection
ActInfPOMDP

## ModelName
My Model

## StateSpaceBlock
A[3,3,type=float]   # Likelihood matrix
B[3,3,3,type=float] # Transition matrix
s[3,1,type=float]   # Hidden state

## Connections
D>s    # D feeds into s (directed)
s-A    # s connects to A (undirected)

## InitialParameterization
A={(0.9,0.05,0.05), (0.05,0.9,0.05), (0.05,0.05,0.9)}

## ActInfOntologyAnnotation
A=LikelihoodMatrix
s=HiddenState
```

## Code Standards

- Python 3.11+ required
- Type hints for all public functions
- Exit codes: 0=success, 1=error, 2=warnings
- All modules need `__init__.py`, `AGENTS.md`, and either `processor.py` or a clearly-named alternative (see Thin Orchestrator Pattern above)
- Tests in `src/tests/test_{module}_*.py`

## Optional Dependency Groups

Declared in `pyproject.toml` under `[project.optional-dependencies]`. The
canonical group list is:

```bash
uv sync --extra dev                  # Development tools (pytest, ruff, mypy, bandit, sphinx, …)
uv sync --extra api                  # REST API server (FastAPI + uvicorn)
uv sync --extra ml-ai                # transformers, scipy, scikit-learn (beyond core Step 12 backends)
uv sync --extra audio                # Audio processing (librosa, soundfile, pedalboard)
uv sync --extra gui                  # GUI interfaces (gradio, streamlit)
uv sync --extra graphs               # System Graphviz bindings
uv sync --extra research             # Notebooks / scientific computing for non-developer users
uv sync --extra scaling              # Distributed execution (dask, distributed, ray)
uv sync --all-extras                 # Everything
```

Note: JAX, NumPyro, PyTorch, and DisCoPy are **core** dependencies installed by
a plain `uv sync`; PyTorch is intentionally *not* locked while
GHSA-rrmf-rvhw-rf47 has no patched torch release (see `pyproject.toml`), so the
PyTorch backend requires a manual `torch` install. There is no separate
`llm`/`visualization`/`inference`/`execution-frameworks` extra — those packages
are part of core or of the named groups above.