pyproject.toml

[build-system]
requires = [
  "scikit-build-core>=0.9.0",
  "pybind11>=2.11",
]
build-backend = "scikit_build_core.build"

[project]
name = "hjcdik"
version = "0.0.1"
description = "GPU-parallel hierarchical joint-coordinate-descent inverse kinematics"
readme = "README.md"
requires-python = ">=3.9"
authors = [
  { name = "Cael Yasutake" },
  { name = "Andrew H. Liu" },
  { name = "Zachary Kingston" },
  { name = "Brian Plancher", email = "plancher@dartmouth.edu" },
]

# Runtime dependency used by the Python array API. GRiD code generation is an explicit extra.
dependencies = [
  "numpy>=1.17.4",
]

# OPTIONAL extras. Base install stays lightweight (HJCD-IK needs none of these). The competitor
# baselines are heavy and skippable — see docs/source/user_guide/benchmarks/results.rst.
[project.optional-dependencies]
codegen = [
  "sympy>=1.7.1",
  "beautifulsoup4>=4.9.3",
  "lxml>=4.2.1",
  "trimesh>=4",               # optional mesh-to-sphere custom-robot codegen
]
# Lightweight tooling + PyRoki's pure-python friends that pip CAN resolve. The solver stacks
# themselves (PyRoki, cuRobo) and their JAX-CUDA / torch deps are NOT plain-PyPI installs — run
# scripts/setup/install_baselines.sh, which installs this extra and then the git/source solvers.
baselines = [
  "pyyaml>=5.4",                # goal_file / world yaml parsing
  "pandas>=1.3",                # baseline results CSV
  "tabulate>=0.8",              # console summary tables
  "jaxlie>=1.4",                # SE3/SO3 pose-error metrics (PyRoki path)
  "yourdfpy>=0.0.56",           # URDF load (PyRoki path)
  "robot_descriptions>=1.10",   # panda_description (PyRoki path)
  "trimesh>=4",                 # mesh collision oracles + MotionBenchMaker mesh export (benchmark/collision_oracles.py)
  "python-fcl>=0.7",            # FCL backend for the mesh oracles
  "rtree>=1.0",                 # trimesh point containment (mbm_export rectilinear decomposition)
]
plots = [
  "matplotlib>=3.5",   # benchmark/plot_pareto.py (Figs 4/5). make_tables.py needs nothing extra.
]
dev = [
  "pytest>=7.0",
  "scipy>=1.7",
  "pytest-gpu-proof>=0.4.1; python_version >= '3.11'", # receipt generation requires Python >=3.11
  "pyyaml>=5.4",               # read the YAML policy (tests/gpu-proof-policy.yaml); pytest-gpu-proof doesn't pull it in
]

[tool.scikit-build]
cmake.version = ">=3.24"
build-dir = "build"
# The committed csrc/generated/grid.cuh is the build default; regenerate it explicitly
# with scripts/codegen/generate_grid.py (or build with -DHJCDIK_AUTO_CODEGEN=ON to regenerate here).
cmake.define.HJCDIK_AUTO_CODEGEN = "OFF"
# Keep build/codegen sources, licenses, tests, and pre-spherized URDFs. Upstream
# mesh collections, benchmark suites, and showcase media are not needed to build hjcdik.
sdist.exclude = [
  "external/foam/assets/**/meshes/**",
  "external/foam/assets/*.zip",
  "external/foam/images/**",
  "external/**/docs/**",
  "external/**/test/**",
  "docs/landing/**",
]

[tool.pytest.ini_options]
testpaths = ["tests"]
markers = [
  "gpu_proof: Full-suite outcomes recorded in the signed pytest-gpu-proof receipt (auto-applied by tests/conftest.py).",
]

[tool.gpu_proof]
# GPU tests run locally on a real GPU; the signed receipt (gpu-proof.json) is
# verified in CPU-only CI via github.com/plancherb1.keys. See scripts/setup/run_gpu_proof.sh.
mode = "local"
output = "gpu-proof.json"
github_username = "plancherb1"     # org remotes (A2R-Lab) carry no SSH keys — sign as the keyholder
required_marker = "gpu_proof"
# The verifier permits ancestor commits, so fingerprints must cover implementation as well
# as tests. pytest-gpu-proof >=0.4 records submodule gitlinks in its v2 source manifest.
fingerprint_paths = ["csrc", "hjcdik", "tests", "benchmark", "scripts", "examples", "docs/source", "README.md", "CMakeLists.txt", "pyproject.toml", ".gitmodules", "external"]
fingerprint_excluded_paths = ["gpu-proof.json"]
max_age_days = 30
require_gpu = true