pyproject.toml

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

[project]
name = "lilith"
version = "0.1.0"
description = "Long-range Intelligent Learning for Integrated Trend Hindcasting - 90-day weather forecasting"
readme = "README.md"
license = "Apache-2.0"
requires-python = ">=3.10"
authors = [
    { name = "LILITH Contributors" }
]
keywords = [
    "weather",
    "forecasting",
    "machine-learning",
    "climate",
    "deep-learning",
    "pytorch",
    "transformer"
]
classifiers = [
    "Development Status :: 3 - Alpha",
    "Intended Audience :: Science/Research",
    "License :: OSI Approved :: Apache Software License",
    "Programming Language :: Python :: 3",
    "Programming Language :: Python :: 3.10",
    "Programming Language :: Python :: 3.11",
    "Programming Language :: Python :: 3.12",
    "Topic :: Scientific/Engineering :: Atmospheric Science",
    "Topic :: Scientific/Engineering :: Artificial Intelligence",
]

dependencies = [
    # Core ML
    "torch>=2.1.0",
    "torchvision>=0.16.0",
    "lightning>=2.1.0",

    # Graph Neural Networks
    "torch-geometric>=2.4.0",

    # Transformers & Attention
    "xformers>=0.0.23",
    "einops>=0.7.0",

    # Data Processing
    "numpy>=1.24.0",
    "pandas>=2.0.0",
    "polars>=0.19.0",
    "xarray>=2023.10.0",
    "zarr>=2.16.0",
    "pyarrow>=14.0.0",
    "h5py>=3.10.0",

    # Scientific Computing
    "scipy>=1.11.0",
    "scikit-learn>=1.3.0",

    # Geospatial
    "cartopy>=0.22.0",
    "pyproj>=3.6.0",

    # API & Web
    "fastapi>=0.104.0",
    "uvicorn[standard]>=0.24.0",
    "pydantic>=2.5.0",
    "httpx>=0.25.0",

    # Database
    "sqlalchemy>=2.0.0",
    "asyncpg>=0.29.0",
    "redis>=5.0.0",

    # Utilities
    "tqdm>=4.66.0",
    "rich>=13.7.0",
    "typer>=0.9.0",
    "python-dotenv>=1.0.0",
    "pyyaml>=6.0.0",
    "omegaconf>=2.3.0",

    # Logging & Monitoring
    "loguru>=0.7.0",
    "wandb>=0.16.0",

    # Visualization
    "matplotlib>=3.8.0",
    "plotly>=5.18.0",
]

[project.optional-dependencies]
dev = [
    "pytest>=7.4.0",
    "pytest-cov>=4.1.0",
    "pytest-asyncio>=0.21.0",
    "ruff>=0.1.0",
    "black>=23.10.0",
    "mypy>=1.6.0",
    "pre-commit>=3.5.0",
    "ipython>=8.17.0",
    "jupyter>=1.0.0",
    "nbformat>=5.9.0",
]

train = [
    "deepspeed>=0.12.0",
    "bitsandbytes>=0.41.0",
    "accelerate>=0.24.0",
    "tensorboard>=2.15.0",
]

inference = [
    "onnx>=1.15.0",
    "onnxruntime-gpu>=1.16.0",
    "tritonclient[all]>=2.39.0",
    "ray[serve]>=2.8.0",
]

all = [
    "lilith[dev,train,inference]",
]

[project.scripts]
lilith = "lilith.cli:app"

[project.urls]
Homepage = "https://github.com/lilith-weather/lilith"
Documentation = "https://lilith-weather.github.io/lilith"
Repository = "https://github.com/lilith-weather/lilith"
Issues = "https://github.com/lilith-weather/lilith/issues"

[tool.hatch.build.targets.wheel]
packages = ["lilith"]

[tool.ruff]
target-version = "py310"
line-length = 100
select = [
    "E",    # pycodestyle errors
    "W",    # pycodestyle warnings
    "F",    # Pyflakes
    "I",    # isort
    "B",    # flake8-bugbear
    "C4",   # flake8-comprehensions
    "UP",   # pyupgrade
    "ARG",  # flake8-unused-arguments
    "SIM",  # flake8-simplify
]
ignore = [
    "E501",   # line too long (handled by black)
    "B008",   # do not perform function calls in argument defaults
    "B905",   # zip without explicit strict
]

[tool.ruff.per-file-ignores]
"__init__.py" = ["F401"]
"tests/*" = ["ARG"]

[tool.black]
target-version = ["py310", "py311", "py312"]
line-length = 100

[tool.mypy]
python_version = "3.10"
warn_return_any = true
warn_unused_configs = true
disallow_untyped_defs = true
ignore_missing_imports = true

[tool.pytest.ini_options]
testpaths = ["tests"]
asyncio_mode = "auto"
addopts = "-v --cov=lilith --cov-report=term-missing"