pyproject.toml

[build-system]
requires = ["setuptools>=68"]
build-backend = "setuptools.build_meta"

[project]
name = "denoiq-core"
version = "0.1.0"
description = "Information limits of image denoising: the data-processing ceiling and the information floor, on synthetic phantoms."
readme = "README.md"
requires-python = ">=3.10"
license = { text = "MIT" }
authors = [{ name = "Shuji Yamamoto", email = "yamamoto@lisit.jp" }]
keywords = [
  "denoising",
  "data processing inequality",
  "information limit",
  "task-based image quality",
  "model observer",
  "detectability",
  "medical imaging",
  "phantom",
]
classifiers = [
  "Development Status :: 3 - Alpha",
  "Intended Audience :: Science/Research",
  "License :: OSI Approved :: MIT License",
  "Programming Language :: Python :: 3",
  "Programming Language :: Python :: 3.10",
  "Programming Language :: Python :: 3.11",
  "Programming Language :: Python :: 3.12",
  "Topic :: Scientific/Engineering :: Medical Science Apps.",
  "Topic :: Scientific/Engineering :: Image Processing",
]
dependencies = [
  "numpy>=1.24",
  "scipy>=1.10",
  "scikit-image>=0.20",
  "matplotlib>=3.7",
  # The observer / task engine. Not on PyPI: install the pinned release first with
  #   pip install "taskiq-core @ git+https://github.com/Institute-of-One/taskiq-core@v0.4.0"
  # (see requirements.txt). The version range here is what this package is written
  # against; the git tag is what a reproduction should install.
  "taskiq-core>=0.4,<0.5",
]

[project.optional-dependencies]
# Deep learning is strictly optional: the classical denoisers, every observer, the
# data-processing ceiling and the red-lamp gauge all run without torch.
dl = ["torch>=2.0"]
# The review PDF (paper/build_pdf.py). Also optional, and deliberately not a CI dependency:
# a PDF is a derivative of build/manuscript.md, results/ and figures/, and requiring a TeX or
# pango/cairo stack to produce one is not a trade this repository makes. reportlab is pure
# Python and uses the DejaVu faces matplotlib already ships.
pdf = ["reportlab>=4.0"]
dev = [
  "pytest>=7.0",
  "pytest-cov>=4.1",
  "ruff>=0.4",
  "mypy>=1.8",
  # Figure determinism is tested, so matplotlib is a *test* dependency too and must
  # not be left implicit — a missing matplotlib turns the figure tests red in CI only.
  "matplotlib>=3.7",
  # tomllib is 3.11+; the manuscript-consistency test reads pyproject.toml.
  "tomli>=2.0; python_version<'3.11'",
]

[project.urls]
Homepage = "https://github.com/Institute-of-One/denoiq-core"
Repository = "https://github.com/Institute-of-One/denoiq-core"
Issues = "https://github.com/Institute-of-One/denoiq-core/issues"

[tool.setuptools.packages.find]
include = ["denoiq_core*"]

[tool.pytest.ini_options]
testpaths = ["tests"]
addopts = "-q --strict-markers"
filterwarnings = [
  "error::DeprecationWarning",
  "ignore::DeprecationWarning:skimage",
  "ignore::DeprecationWarning:numpy",
  "ignore::DeprecationWarning:scipy",
  "ignore::DeprecationWarning:matplotlib",
  "ignore::DeprecationWarning:torch",
]

[tool.ruff]
line-length = 100
target-version = "py310"

[tool.ruff.lint]
select = ["E", "F", "I", "UP", "B", "NPY", "D"]
# D203/D213: mutually exclusive docstring layouts (we use D211/D212).
# D401/D404: imperative-mood/wording checks that misfire on descriptive docstrings.
ignore = ["D203", "D213", "D401", "D404"]

[tool.ruff.lint.per-file-ignores]
"__init__.py" = ["F401"]
"tests/*" = ["D100", "D101", "D102", "D103", "D104", "D105", "D107"]
"examples/*" = ["D100", "D103"]
"paper/*" = ["D100", "D103"]

[tool.mypy]
python_version = "3.12"
strict = true
warn_return_any = false

[[tool.mypy.overrides]]
module = [
  "scipy.*",
  "skimage.*",
  "matplotlib.*",
  "taskiq_core.*",
  "torch.*",
  "bm3d.*",
]
ignore_missing_imports = true
follow_imports = "skip"