[project]
name = "lamarckian-society"
version = "0.1.0"
description = "Layer 1 analytical core: knowledge transmission as a Wright-Fisher process."
readme = "README.md"
requires-python = ">=3.11"
dependencies = [
    "numpy>=1.26",
    "scipy>=1.11",
    "pandas>=2.1",
    "pyarrow>=15",
    "matplotlib>=3.8",
    "pydantic>=2.5",
    "pyyaml>=6.0",
]

[project.optional-dependencies]
dev = ["pytest>=8.0"]
# Layer 1.5 neural existence proof. Torch is only needed from Stage C (RNN/VAE/MLP);
# Stages A-B (synthetic sandbox + histogram bridge) are pure NumPy and run in the base env.
# The default PyPI torch wheel is CUDA-enabled (cu13, matching the RTX A4000 driver).
# Install with `uv sync --extra neural`.
neural = ["torch>=2.2"]
# The real-MNIST secondary-confirmation tier only. Install with `uv sync --extra mnist`.
mnist = ["torchvision>=0.17"]
# Layer 2 / LLM prototype (blueprint C2/C4): LoRA specialists + weight-space merging on a small
# open-weight base. GPU; models download to the HF cache (outside the repo). `uv sync --extra llm`.
llm = ["transformers>=4.44", "peft>=0.11", "datasets", "accelerate"]

[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"

# src-layout: src/knowledge/ is importable as `knowledge` (the normative package
# name the scientific-validation conformance tests import). src/neural/ is Layer 1.5.
[tool.hatch.build.targets.wheel]
packages = ["src/knowledge", "src/neural", "src/llm"]

[tool.pytest.ini_options]
testpaths = ["tests"]
addopts = "-q"