pyproject.toml

[project]
authors = [{ name = "McCoy Becker", email = "mccoyb@mit.edu" }]
name = "genjax"
description = "Vectorized probabilistic programming with generative functions and programmable inference in JAX"
license = "Apache-2.0"
license-files = ["LICENSE.md"]
keywords = [
  "probabilistic programming",
  "programmable inference",
  "Bayesian inference",
  "JAX",
  "Monte Carlo",
  "variational inference",
]
requires-python = ">= 3.12"
version = "1.0.14"
dynamic = ["readme"]
dependencies = [
  "penzai>=0.2.5,<0.3",
  "beartype>=0.22.9,<0.23",
  "jax>=0.11.1,<0.12",
  "jaxtyping>=0.3.11,<0.4",
  "tfp-nightly==0.26.0.dev20260831",
]

[project.optional-dependencies]
viz = ["matplotlib>=3.11.1,<4"]

[project.urls]
Homepage = "https://github.com/a-tiny-project/genjax"
Repository = "https://github.com/a-tiny-project/genjax"
Paper = "https://doi.org/10.1145/3776729"
Artifact = "https://doi.org/10.5281/zenodo.17342547"

[build-system]
build-backend = "hatchling.build"
requires = ["hatchling>=1.32,<2", "hatch-fancy-pypi-readme>=25.1,<26"]

# The source distribution carries the package, its tests, and the examples
# two tests import. uv.lock and raster assets stay in the repository.
[tool.hatch.build.targets.sdist]
include = ["src/genjax", "tests", "examples", "CITATION.cff", "README.md"]
exclude = ["examples/**/assets"]

# PyPI resolves relative links against the project page, so the built
# description points each one at the file on GitHub. GitHub redirects the
# blob URL of a directory to its tree URL.
[tool.hatch.metadata.hooks.fancy-pypi-readme]
content-type = "text/markdown"

[[tool.hatch.metadata.hooks.fancy-pypi-readme.fragments]]
path = "README.md"

[[tool.hatch.metadata.hooks.fancy-pypi-readme.substitutions]]
pattern = '\]\((?!https?://|mailto:|#)([^)\s]+)\)'
replacement = '](https://github.com/a-tiny-project/genjax/blob/main/\1)'

[[tool.hatch.metadata.hooks.fancy-pypi-readme.substitutions]]
pattern = 'src="logo\.svg"'
replacement = 'src="https://raw.githubusercontent.com/a-tiny-project/genjax/main/logo.png"'

[tool.vulture]
make_whitelist = true
min_confidence = 80
paths = ["src"]
sort_by_size = true

[tool.uv]
environments = [
  "sys_platform == 'linux' and platform_machine == 'x86_64'",
  "sys_platform == 'darwin' and platform_machine == 'arm64'",
]

[tool.uv.dependency-groups]
cuda = { requires-python = ">=3.12,<3.14" }
perfbench-pyro = { requires-python = ">=3.12,<3.14" }
perfbench-torch = { requires-python = ">=3.12,<3.14" }

[tool.uv.sources]
torch = [
  { index = "pytorch-cu129", marker = "sys_platform == 'linux' and platform_machine == 'x86_64'" },
  { index = "pytorch-cpu", marker = "sys_platform == 'darwin' and platform_machine == 'arm64'" },
]
torchvision = [
  { index = "pytorch-cu129", marker = "sys_platform == 'linux' and platform_machine == 'x86_64'" },
  { index = "pytorch-cpu", marker = "sys_platform == 'darwin' and platform_machine == 'arm64'" },
]

[[tool.uv.index]]
name = "pytorch-cu129"
url = "https://download.pytorch.org/whl/cu129"
explicit = true

[[tool.uv.index]]
name = "pytorch-cpu"
url = "https://download.pytorch.org/whl/cpu"
explicit = true

[dependency-groups]
format = ["ruff==0.16.5", "vulture>=2.16,<3", "pre-commit>=4.6.2,<5"]
test = [
  "matplotlib>=3.11.1,<4",
  "pytest>=9.1.1,<10",
  "pytest-cov>=7.1.0,<8",
  "coverage>=7.16.0,<8",
  "xdoctest>=1.3.2,<2",
  "pytest-xdist>=3.8.0,<4",
  "pytest-benchmark>=5.3.0,<6",
]

cuda = ["jax[cuda12]>=0.11.1,<0.12; sys_platform == 'linux'"]
faircoin = [
  "matplotlib>=3.11.1,<4",
  "seaborn>=0.13.2,<0.14",
  "numpyro>=0.21,<0.22",
]
curvefit = [
  "matplotlib>=3.11.1,<4",
  "numpy>=2.5,<3",
  "pygments>=2.21,<3",
  "seaborn>=0.13.2,<0.14",
  "numpyro>=0.21,<0.22",
  "funsor>=0.4.8,<0.5",
]
gol = ["matplotlib>=3.11.1,<4"]
localization = [
  "matplotlib>=3.11.1,<4",
  "seaborn>=0.13.2,<0.14",
  "ptitprince>=0.3.1,<0.4",
]
perfbench = [
  "numpy>=2.5,<3",
  "matplotlib>=3.11.1,<4",
  "seaborn>=0.13.2,<0.14",
  "pandas>=3.0,<4",
  "scipy>=1.18,<2",
  "numpyro>=0.21,<0.22",
]
perfbench-pyro = [
  { include-group = "perfbench" },
  "pyro-ppl>=1.9.1,<2",
  "torch>=2.13,<2.14",
  "torchvision>=0.28,<0.29",
]
perfbench-torch = [{ include-group = "perfbench" }, "torch>=2.13,<2.14"]

[tool.ruff]
extend-exclude = ["*.md"]

[tool.ruff.lint]
select = ["E4", "E7", "E9", "F"]

[tool.coverage.run]
source = ["src"]
omit = ["*/tests/*", "*/examples/*"]

[tool.coverage.report]
exclude_lines = [
  "pragma: no cover",
  "def __repr__",
  "if self.debug:",
  "if settings.DEBUG",
  "raise AssertionError",
  "raise NotImplementedError",
  "if 0:",
  "if __name__ == .__main__.:",
  "class .*\\bProtocol\\):",
  "@(abc\\.)?abstractmethod",
]

[tool.pytest.ini_options]
minversion = "8.0"
addopts = [
  "-ra",                       # Show short test summary for all results
  "--strict-markers",          # Require all markers to be defined
  "--strict-config",           # Strict configuration parsing
  "--cov=src/genjax",          # Coverage for source code
  "--cov-report=term-missing", # Show missing lines in terminal
  "--cov-report=html",         # Generate HTML coverage report
  "--cov-report=xml",          # Generate XML coverage for CI
]
testpaths = ["tests"]
pythonpath = ["."]
python_files = ["test_*.py", "*_test.py"]
python_classes = ["Test*"]
python_functions = ["test_*"]
markers = [
  "slow: marks tests as slow (taking >5 seconds)",
  "fast: marks tests as fast (taking <1 second)",
  "integration: marks tests as integration tests (cross-component)",
  "unit: marks tests as unit tests (single component)",
  "regression: marks tests as regression tests (bug prevention)",
  "adev: marks tests for ADEV gradient estimators",
  "smc: marks tests for Sequential Monte Carlo",
  "mcmc: marks tests for Markov Chain Monte Carlo",
  "vi: marks tests for Variational Inference",
  "hmm: marks tests for Hidden Markov Models",
  "core: marks tests for core GenJAX functionality",
  "pjax: marks tests for PJAX (Probabilistic JAX) functionality",
  "distributions: marks tests for probability distributions",
  "tfp: marks tests requiring TensorFlow Probability",
  "requires_gpu: marks tests that need GPU acceleration",
  "benchmark: marks tests that should be benchmarked",
]
filterwarnings = [
  "ignore::DeprecationWarning:jax.*",
  "ignore::DeprecationWarning:tensorflow_probability.*",
  "error::UserWarning",                                  # Turn UserWarnings into errors to catch issues
]

[tool.pytest-benchmark]
# Configuration for pytest-benchmark
min_rounds = 3 # Minimum number of benchmark rounds
max_time = 10.0 # Maximum time per benchmark (seconds)
min_time = 0.01 # Minimum time per round (seconds)
timer = "time.perf_counter" # High-resolution timer
disable_gc = true # Disable garbage collection during benchmarks
sort = "mean" # Sort results by mean time
columns = [
  "min",
  "max",
  "mean",
  "stddev",
  "median",
  "iqr",
  "outliers",
  "ops",
  "rounds",
]
histogram = true # Generate histogram data
save = ".benchmarks/benchmarks.json" # Save results to file
save_data = true # Save benchmark data
autosave = true # Automatically save results

[tool.xdoctest]
# Configure xdoctest for running doctests
modname = "genjax"
command = "list"
verbose = 2
durations = 10
style = "google"
options = "+ELLIPSIS"