content: [tool.poetry]
  name = "soccer_xg"
  version = "0.0.1"
  description = "Train and analyse xG models on soccer event stream data"
  authors = ["Pieter Robberechts <pieter.robberechts@cs.kuleuven.be>"]
  license = "Apache Software License (http://www.apache.org/licenses/LICENSE-2.0)"
  readme = 'README.md'
  repository = "https://github.com/probberechts/soccer_xg"
  homepage = "https://pypi.org/project/soccer_xg"
  keywords = ["expected goals", "xG", "Soccer", "Football", "event stream data", "sports analytics", "Statsbomb", "Opta", "Wyscout"]
  classifiers=[
  	"Programming Language :: Python :: 3",
  	"License :: OSI Approved :: Apache Software License",
  	"Operating System :: OS Independent"
  ]
  
  [tool.poetry.dependencies]
  python = "^3.6.1"
  numpy = "^1.18"
  pandas = "^1.0"
  scikit-learn = "^0.22.1"
  ipykernel = "^5.1"
  matplotsoccer = "^0.0.8"
  matplotlib = "^3.1"
  click = "^7.0"
  tables = "^3.6"
  requests = "^2.23"
  xgboost = "^1.0"
  seaborn = "^0.10.0"
  fuzzywuzzy = "^0.18.0"
  python-Levenshtein = "^0.12.0"
  dask = {extras = ["array","distributed","dataframe"], version = "^2.15.0", optional = true}
  dask_ml = {version = "^1.3.0", optional = true}
  category_encoders = "^2.2.2"
  asyncssh = {version = "^2.2.1", optional = true}
  paramiko = {version = "^2.7.1", optional = true}
  understat = "^0.1.2"
  socceraction = "^0.2.1"
  betacal = "^0.2.7"
  
  [tool.poetry.extras]
  dask = ["dask", "dask_ml", "asyncssh", "paramiko"]
  
  [tool.poetry.dev-dependencies]
  flake8-awesome = "^1.2"
  mypy = "^0.761.0"
  pylint = "^2.4"
  pytest = "^5.3"
  pytest-cov = "^2.8"
  pytest-deadfixtures = "^2.1"
  unify = "^0.5.0"
  black = {version = "^19.10b0", allow-prereleases = true}
  bumpversion = "^0.6.0"
  
  [build-system]
  requires = ["poetry>=0.12"]
  build-backend = "poetry.masonry.api"
  