pyproject.toml

[build-system]
requires = ["setuptools>=44"]
build-backend = "setuptools.build_meta"

[project]
name = "autograd"
version = "1.6.3"
description = "Efficiently computes derivatives of numpy code."
readme = "README.md"
license = {file = "license.txt"}
authors = [
  {name = "Dougal Maclaurin", email = "maclaurin@physics.harvard.edu"},
  {name = "David Duvenaud", email = "duvenaud@cs.toronto.edu"},
  {name = "Matthew Johnson", email = "mattjj@csail.mit.edu"},
  {name = "Jamie Townsend", email = "j.h.n.townsend@uva.nl"},
]
maintainers = [
  {name = "Jamie Townsend", email = "j.h.n.townsend@uva.nl"},
]
classifiers = [
  "Development Status :: 4 - Beta",
  "Intended Audience :: Information Technology",
  "Intended Audience :: Science/Research",
  "License :: OSI Approved :: MIT License",
  "Programming Language :: Python :: 2",
  "Programming Language :: Python :: 2.7",
  "Programming Language :: Python :: 3",
  "Programming Language :: Python :: 3.5",
  "Programming Language :: Python :: 3.6",
  "Programming Language :: Python :: 3.7",
  "Programming Language :: Python :: 3.8",
  "Programming Language :: Python :: 3.9",
  "Programming Language :: Python :: 3.10",
  "Programming Language :: Python :: 3.11",
]
keywords = [
  "Automatic differentiation",
  "backpropagation",
  "gradients",
  "machine learning",
  "optimization",
  "neural networks",
  "Python",
  "Numpy",
  "Scipy",
]
dependencies = [
  "numpy>=1.12",
  "six",
  "future>=0.15.2; python_version < '3'",
]

[project.optional-dependencies]
scipy = [
  "scipy",
]

[tool.coverage.run]
source = ["autograd"]

[tool.coverage.report]
show_missing = true

[tool.pytest.ini_options]
addopts = "--color=yes --junitxml=junit-report.xml"

[tool.ruff]
extend-exclude = []
extend-ignore = ["E731"]
extend-select = ["I", "W"]
line-length = 109

[tool.setuptools]
packages=[
  "autograd",
  "autograd.numpy",
  "autograd.scipy",
  "autograd.scipy.stats",
  "autograd.misc",
]