DeePTB/pyproject.toml at main · DeePTB-Lab/DeePTB · GitHub

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[project]
name = "dptb"
dynamic = ["version"]
description = "A unified deep learning package for electronic structure models including tight-binding, KS Hamiltonian and density matrix models"
readme = "README.md"
license = "LGPL-3.0-only"
license-files = ["LICENSE"]
requires-python = ">=3.10,<3.14"
authors = [
    {name = "DeePTB Team"}
]
keywords = ["deep learning", "tight-binding", "electronic structure", "physics"]
classifiers = [
    "Development Status :: 4 - Beta",
    "Intended Audience :: Science/Research",
    "Programming Language :: Python :: 3",
    "Programming Language :: Python :: 3.10",
    "Programming Language :: Python :: 3.11",
    "Programming Language :: Python :: 3.12",
    "Programming Language :: Python :: 3.13",
    "Topic :: Scientific/Engineering :: Physics",
]

dependencies = [
    "numpy>=1.26,<2.5",
    "scipy>=1.12",
    "spglib",
    "matplotlib",
    "torch>=2.5.1,<=2.12.1",
    "ase",
    "pyyaml",
    "dargs>=0.4.4,<0.5",
    "e3nn>=0.5.1",
    "torch-runstats>=0.2.0,<0.3",
    "torch_scatter==2.1.2",
    "torch_geometric>=2.8.0",
    "opt-einsum>=3.3.0,<4",
    "h5py>=3.11",
    "lmdb>=1.4.1",
    "pyfiglet>=1.0.2,<2",
    "tensorboard",
    "seekpath",
    "rich>=13.0.0",
    "pytest>=7.2.0",
    "pytest-order>=1.2.0,<2"
]

[project.optional-dependencies]
3Dfermi = ["ifermi", "pymatgen"]
tbtrans_init = ["sisl"]
pybinding = ["pybinding"]
pythtb = ["pythtb"]
vbcsr = ["vbcsr>=0.2.2"]

[project.scripts]
dptb = "dptb.__main__:main"

[project.urls]
Repository = "https://github.com/deepmodeling/DeePTB"
Documentation = "https://deeptb.readthedocs.io"

[build-system]
requires = ["setuptools>=77.0.3", "setuptools-scm>=8"]
build-backend = "setuptools.build_meta"

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

[tool.setuptools_scm]
# Automatically generate version from git tags
write_to = "dptb/_version.py"
fallback_version = "0.0.0-dev"  # Fallback for non-git installs (e.g. zip download)

# UV-specific configuration
[tool.uv]
# PyTorch Geometric uses find-links for torch_scatter distribution.
# DEFAULT: CPU version for developer uv sync / pip-style installs.
# For hardware-specific tested installs, prefer:
#   ./install.sh auto
find-links = ["https://data.pyg.org/whl/torch-2.12.1+cpu.html"]

[dependency-groups]
dev = [
    "ipykernel",
    "jupyter",
    "notebook",
]

[tool.pytest.ini_options]
markers = [
    "smoke: quick checks for imports, config generation, model build, and core mapping entrypoints",
    "regression: behavior-preserving tests for numerical semantics, tensor shape/order, and compatibility",
    "slow: training, complex postprocess, external/optional dependency, or otherwise long-running tests",
]