# Layer 1 + Layer 1.5 automation. The uv venv (built from the committed uv.lock) is the
# reproducibility source of truth; every target runs inside it via `uv run`.

.PHONY: env env-neural env-mnist test layer1 layer2 neural mnist figures clean

env:               ## build .venv from the committed lockfile
	uv sync --extra dev

env-neural:        ## add the Layer 1.5 torch stack (GPU; Stage C onward)
	uv sync --extra dev --extra neural

env-mnist:         ## add torchvision for the real-MNIST confirmation tier
	uv sync --extra dev --extra neural --extra mnist

test:              ## correctness tests + scientific-validation tests (the spine of trust)
	uv run pytest

layer1:            ## run experiments E1-E6
	for e in E1 E2 E3 E4 E5 E6; do uv run python -m knowledge.experiment configs/layer1/$$e.yaml; done

neural:            ## run Layer 1.5 synthetic neural experiments (excludes the heavy MNIST tier)
	for c in configs/neural/*.yaml; do case "$$c" in *mnist*) ;; \
		*) uv run python -m neural.experiment "$$c" ;; esac; done

mnist:             ## run the real-MNIST confirmation tier (needs env-mnist; downloads MNIST)
	uv run python -m neural.experiment configs/neural/mnist_collapse.yaml

layer2: neural     ## alias: Layer 1.5 is the current Layer-2 deliverable (LLM rung deferred)

figures:           ## regenerate figures from committed results
	for e in E1 E2 E3 E4 E5 E6; do MPLBACKEND=Agg uv run python figures/plot_$$e.py; done
	for p in figures/plot_*.py; do case "$$p" in */plot_E[1-6].py|*/_*) ;; \
		*) [ -e "$$p" ] && MPLBACKEND=Agg uv run python "$$p" ;; esac; done

clean:             ## remove caches and generated results (keeps committed manifests)
	rm -rf .pytest_cache **/__pycache__
	find results -type f ! -name '.gitkeep' -delete 2>/dev/null || true