TorchRef/pyproject.toml at main · HatPdotS/TorchRef · GitHub

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[build-system]
requires = ["setuptools>=42", "wheel"]
build-backend = "setuptools.build_meta"

[project]
name = "torchref"
version = "0.5.0"
description = "Pytorch based crystallographic refinement"
readme = "README.md"
requires-python = ">=3.10"
license = "MIT"
authors = [
    {name = "HansPeterSeidel"},
]
# Version requirements validated via tox compatibility testing (2026-01-13)
# Bumped to numpy>=2.0, torch>=2.4 to avoid NumPy 1.x/2.x binary incompatibility
# Tested combinations:
#   - Python 3.11: numpy 2.0-2.3, pandas 2.0-2.3, torch 2.4-2.9, numba 0.59-0.63, scipy 1.10-1.17
#   - Python 3.12: numpy 2.0-2.3, pandas 2.2-2.3, torch 2.4-2.9, numba 0.61-0.63, scipy 1.13-1.17
# Upper bounds set to next minor version above tested maximum
dependencies = [
    "numpy>=2.0.0,<2.4.0",
    "pandas>=2.0.0,<2.4.0",
    "torch>=2.4.0,<2.10.0",
    "tqdm>=4.61.0,<4.68.0",
    "numba>=0.59.0,<0.64.0",
    "gemmi>=0.5.0,<0.8.0",
    "scipy>=1.10.0,<1.18.0",
    "matplotlib>=3.7.0,<3.11.0",
    "reciprocalspaceship>=0.9.18,<1.1.0",
    "pyarrow>=12.0.0,<23.0.0",
    # Required by torch.utils.cpp_extension.load_inline for the cpu_scatter
    # JIT-compiled C++ kernel. SLURM compute nodes don't ship a system-wide
    # ninja, so we install it as a Python dependency on every worker.
    "ninja>=1.10",
]

[project.scripts]
"torchref.refine" = "torchref.cli.refine:main"
"torchref.difference-refine" = "torchref.cli.collection_difference_refine:main"
"torchref.mtz2map" = "torchref.cli.mtz2map:main"
"torchref.validate-ded" = "torchref.cli.validate_ded:main"
"torchref.refine-hyper" = "torchref.cli.refine_everything_hyperparameters:main"
"torchref.phased-difference-map" = "torchref.cli.phased_difference_map:main"
"torchref.add-metadata" = "torchref.cli.add_metadata:main"
"torchref.strip-altlocs" = "torchref.cli.strip_altlocs:main"

[project.optional-dependencies]

dev = [
    "pytest>=6.0.0",
    "pytest-cov>=2.12.0",
    "black>=21.5b2",
    "isort>=5.9.0",
    "flake8>=3.9.0",
]

alignment = [
    "jax>=0.4.0",
    "s2fft>=1.0.0",
    "s2ball>=0.0.2",
    "spherical>=1.0.0",
    "quaternionic>=1.0.0",
]

forcefield = [
    "torchmd-net>=2.0.0",
]

amber = [
    "openmm>=8.0.0",
    "pdbfixer",
]

ihm = [
    "ihm>=1.0",
]

# Optional CCTBX/iotbx functionality (requires conda, not pip)
# Install with: conda install -c conda-forge cctbx-base
# The iotbx module is used in torchref/math_functions/CCTBX_related.py

docs = [
    "sphinx>=4.0.0",
    "sphinx-rtd-theme>=1.0.0",
    "numpydoc>=1.1.0",
]

[tool.setuptools.packages.find]
where = ["."]
include = ["torchref*"]

[tool.setuptools.package-data]
torchref = ["data/*.csv", "data/*.json", "data/*.pt", "data/monomer_library/**/*.cif"]

[tool.black]
line-length = 88
target-version = ["py310"]

[tool.isort]
profile = "black"
line_length = 88

[tool.pytest.ini_options]
testpaths = ["tests"]
python_files = "test_*.py"

[tool.ruff.lint]
ignore = ["F841","E741","F841","E402","E722"]