pyproject.toml

[build-system]
requires = ["hatchling", "hatch-vcs"]
build-backend = "hatchling.build"

[project]
name = "patchworks"
dynamic = ["version"]
description = "Tiled processing of arbitrarily large images with globally consistent labels"
readme = "README.md"
license = { text = "GPL-3.0-only" }
authors = [{ name = "Laurent Guerard", email = "laurent.guerard@unibas.ch" }]
keywords = [
    "image processing",
    "tiling",
    "chunked",
    "zarr",
    "dask",
    "segmentation",
    "bioimage",
]
classifiers = [
    "Development Status :: 4 - Beta",
    "Intended Audience :: Science/Research",
    "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
    "Programming Language :: Python :: 3",
    "Programming Language :: Python :: 3.11",
    "Programming Language :: Python :: 3.12",
    "Programming Language :: Python :: 3.13",
    "Programming Language :: Python :: 3.14",
    "Topic :: Scientific/Engineering :: Image Processing",
]

# zarr-python 3 (itself Python >= 3.11) is required: NGFF 0.5 stores,
# sharding and the create_array API have no zarr-2 equivalent. It still
# writes NGFF 0.4 (zarr-v2 format) stores when asked to.
# Floors are the oldest releases the suite passes on (CI's "minimum
# versions" job holds them there): zarr < 3.0.3 hangs in the merge's
# forked workers, < 3.1.3 rejects tifffile's store; zarr 3.1 needs numpy
# 1.26; dask < 2024.10 fails 57 tests; scipy 1.11 is the first to build
# against numpy 1.26 on 3.11.
requires-python = ">=3.11"
dependencies = [
    "dask[array]>=2024.10.0",
    "numpy>=1.26",
    "zarr>=3.1.3",
    "scipy>=1.11",
    # Free RAM bounds worker counts and auto tile sizes; without it a
    # workstation (no SLURM, no cgroup limit) falls back to a blind 8 GiB.
    "psutil",
]

[project.optional-dependencies]
io = ["psutil", "tqdm"]
cellpose = ["cellpose>=3.0"]
cellpose3 = ["cellpose>=3.0,<4"]
cellpose4 = ["cellpose>=4"]
# dog enables the deconvolution step in patchworks.plugins.dog (method:
# "custom" -> segment). cupy, for that plugin's use_gpu=True blur/label
# path, isn't included here — it's CUDA-version-specific (e.g.
# cupy-cuda12x), install it separately to match your CUDA version.
dog = ["pycudadecon"]
gpu = ["nvidia-ml-py"]
# remote reads/writes s3://, gs:// and https:// stores through fsspec
# (credentials come from the usual AWS/GCP environment or config files).
remote = ["fsspec[http,s3,gcs]"]
# distributed backs make_local_cluster and the live dashboard tile_process
# opens for single-GPU runs (it falls back to threads without it).
distributed = ["dask[distributed,diagnostics]"]
# bioio enables converting any image format (CZI, LIF, ND2, OME-TIFF, …) to
# OME-ZARR. Ships the common native readers (faster than going through Java)
# plus bioio-bioformats, the Bio-Formats catch-all reader (needs a JVM).
bioio = [
    "bioio",
    "bioio-bioformats",
    "bioio-nd2",
    "bioio-ome-tiff",
    "bioio-czi",
    "bioio-tifffile",
    "bioio-lif",
]
# imaris reads .ims files natively (HDF5, no JVM) for OME-ZARR conversion.
imaris = ["imaris-ims-file-reader"]
# napari enables the interactive viewer plugin.
# - napari >= 0.7.1: adds locked_data_level (manual multiscale resolution pin
#   for 2D *and* 3D) — before this, 3D view always showed the coarsest
#   pyramid level unconditionally, with no way to change it.
# - numpy < 2.5: napari/numba don't support numpy 2.5 yet.
# - ipykernel < 7: napari-console requires ipykernel < 7.
# - lxml-html-clean: napari's notebook_display imports lxml.html.clean, split
#   into a separate package in lxml >= 5.2 (else ImportError on Viewer()).
# - glasbey: distinct high-contrast label LUTs for view_in_napari.
# - napari-chunked-regionprops (https://github.com/imcf/napari-chunked-regionprops):
#   out-of-core regionprops-style measurements (area/centroid/intensity
#   stats) for huge Labels layers, straight off their backing dask/zarr
#   arrays — the "Measure" dock widget. Formerly napari-dask-ndmeasure;
#   renamed when its engine dropped dask_image.ndmeasure for a chunk-local
#   map/merge that scales with chunk count, not object count.
# - pyqt6 < 6.10: PyQt6-Qt6 6.10.2's Windows wheel fails to import (`DLL load
#   failed while importing QtWidgets: The specified procedure could not be
#   found`) — confirmed unrelated to napari/qtpy, reproduces on a bare
#   `from PyQt6.QtWidgets import QApplication`. 6.9.1 works; excluding the
#   whole 6.10.x line until a fixed release is confirmed.
napari = [
    "napari[all]>=0.7.1",
    "numpy<2.5",
    "ipykernel<7",
    "lxml-html-clean",
    "glasbey",
    "napari-chunked-regionprops>=0.1.0",
    "pyqt6<6.10",
]
# workflow runs the Snakemake pipeline (per-tile SLURM jobs across GPUs).
# openpyxl -> scripts/run_multi.py writes label_relations() output as an
# Excel workbook (per-object + per-container sheets), not a plain CSV.
workflow = ["snakemake>=8", "snakemake-executor-plugin-slurm", "openpyxl"]
# jsonschema validates what we write against the vendored official
# OME-NGFF schemas (tests/ngff_schemas/); without it that one test skips.
# openpyxl -> tests/test_run_multi.py checks the relations workbook.
# ruff is pinned to the version CI lints with (.github/workflows/lint.yml),
# so a local `ruff check` agrees with CI.
dev = [
    "pytest",
    "pytest-cov",
    "scikit-image",
    "psutil",
    "tqdm",
    "jsonschema>=4.18",
    "openpyxl",
    "ruff==0.15.18",
    "mypy>=1.10",
]
docs = ["mkdocs-material>=9.0", "mkdocstrings[python]>=0.24"]
all = [
    "patchworks[io,gpu,distributed,bioio,imaris,napari]",
    "psutil",
    "tqdm",
    "scikit-image",
]

[project.scripts]
patchworks = "patchworks.cli:main"

[project.urls]
Homepage = "https://github.com/imcf/patchworks"
Issues = "https://github.com/imcf/patchworks/issues"

[tool.hatch.version]
source = "vcs"

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

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

[tool.mypy]
python_version = "3.11"
files = ["src/patchworks"]
ignore_missing_imports = true
warn_unused_ignores = true
# Enforced now: arity and unpacking, undefined names and attributes, calls
# with wrong or missing arguments -- the classes that are real bugs here.
# Off for now: numpy/zarr/dask typing noise (zarr attrs are loosely typed
# JSON, dask's decorated methods confuse call checks, and variables such as
# tile_process's `image` are narrowed from "path or array" by reassignment).
# Tighten module by module by removing codes from this list.
disable_error_code = [
    "union-attr",
    "arg-type",
    "index",
    "operator",
    "call-overload",
    "type-var",
    "func-returns-value",
    "assignment",
    "return-value",
]

[tool.ruff]
line-length = 80
target-version = "py311"