onf/pyproject.toml

[build-system]
requires = ["setuptools>=64"]
build-backend = "setuptools.build_meta"

[project]
name = "onf"
version = "0.1.0"
description = "Oriented Neural Field — test-time recovery for frozen VLA policies"
readme = "README.md"
requires-python = ">=3.9"
keywords =["vla", "test-time recovery", "neural field", "robot learning", "libero", "calvin"]
classifiers = [
    "Development Status :: 3 - Alpha",
    "Intended Audience :: Science/Research",
    "Programming Language :: Python :: 3",
    "Topic :: Scientific/Engineering :: Artificial Intelligence",
]

# The CORE package is deliberately light — numpy + torch (CPU is enough for the field) + a little
# I/O. The heavy policy/benchmark stacks (StableVLA, GR00T, CALVIN) live in their own conda envs
# and are reached through `external/` by the eval harnesses; they are NOT dependencies of `onf`.
dependencies = [
    "numpy>=1.23",
    "torch>=2.0",
    "scipy>=1.9",
    "matplotlib>=3.6",
    "imageio>=2.25",
    "imageio-ffmpeg>=0.4",
    "pyyaml>=6.0",
    "h5py>=3.7",
]

[project.optional-dependencies]
dev = ["black>=24.2.0", "ruff>=0.2.2", "pytest>=7.0"]

[tool.setuptools.packages.find]
where = ["src"]

[tool.black]
line-length = 121
target-version = ["py39", "py310", "py311"]

[tool.ruff]
line-length = 121
src = ["src"]

[tool.ruff.lint]
select = ["A", "B", "E", "F", "I", "RUF", "W"]
ignore = ["F722"]

[tool.ruff.lint.per-file-ignores]
"__init__.py" = ["E402", "F401"]

[tool.pytest.ini_options]
testpaths = ["tests"]