[build-system]
requires = ["hatchling>=1.25"]
build-backend = "hatchling.build"

[project]
name = "custom-models"
version = "0.1.0"
description = "E1 genus probe: torch models and train/validate/test wrapper"
readme = "README.md"
requires-python = ">=3.11"
license = { text = "MIT" }
authors = [{ name = "Tom Kasper" }]
dependencies = [
    "numpy>=2.0,<3",
    "polars>=1.0",
    "torch>=2.6",
]

[project.optional-dependencies]
pod5 = ["pod5"]
dev = ["pytest>=8", "ruff>=0.8", "pod5"]

[project.scripts]
custom-models = "custom_models.cli:main"

[tool.hatch.build.targets.wheel]
packages = ["src/custom_models"]

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

[tool.ruff]
line-length = 100
src = ["src", "tests"]

[tool.ruff.lint]
select = ["E", "F", "W", "D", "B", "RUF", "TRY", "I"]
ignore = [
    "TRY003", # long messages are the run-artifact contract here
    "TRY004", # ValueError (not TypeError) on config/type misuse: deliberate
    "TRY300",
    "B905",   # explicit strict in zip() is noisy for internal iteration
]

[tool.ruff.lint.per-file-ignores]
"tests/*" = ["D103", "D401"]

# UV: dev/build environment resolves torch from the CPU wheel index so the
# venv stays small. pip installs into the workflow's conda envs ignore this
# (torch comes from conda there and satisfies the requirement).
[tool.uv]
index-strategy = "unsafe-best-match"

[[tool.uv.index]]
name = "pytorch-cpu"
url = "https://download.pytorch.org/whl/cpu"
explicit = true

[tool.uv.sources]
torch = { index = "pytorch-cpu" }