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pyproject.toml
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pyproject.toml
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[build-system]
requires = ["setuptools>=42", "wheel", "toml"]
build-backend = "setuptools.build_meta"
[tool.setuptools]
packages = [
"sam",
"sam.data_sources",
"sam.data_sources.weather",
"sam.datasets",
"sam.datasets.data",
"sam.exploration",
"sam.feature_engineering",
"sam.logging_functions",
"sam.metrics",
"sam.models",
"sam.preprocessing",
"sam.utils",
"sam.validation",
"sam.visualization"
]
[project]
name = "sam"
version = "4.0.0"
description = "Time series anomaly detection and forecasting"
readme = "README.md"
requires-python = ">=3.9"
license = {text = "MIT"}
authors = [{name = "Royal HaskoningDHV", email = "[email protected]"}]
keywords = ["python", "data-science", "time-series", "forecasting", "anomaly-detection", "asset-management"]
classifiers = [
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
]
dependencies = ["pandas~=2.2", "numpy~=1.26", "scikit-learn~=1.5"]
[project.optional-dependencies]
all = [
"matplotlib",
"cloudpickle",
"nfft",
"pymongo",
"requests",
"scipy",
"seaborn",
"tensorflow~=2.17,<3",
"Jinja2~=3.0.3",
"shap",
"plotly",
"statsmodels"
]
plotting = ["matplotlib", "plotly", "seaborn"]
data-engineering = ["requests", "pymongo"]
data-science = [
"tensorflow~=2.17,<3",
"cloudpickle",
"nfft",
"scipy",
"shap",
"Jinja2~=3.0.3",
"statsmodels"
]
test = ["pytest", "pytest-cov", "pytest-mpl<=0.15.0", "fastparquet"]
docs = [
"sphinx",
"sphinx_rtd_theme",
"numpydoc",
"recommonmark",
"readthedocs-sphinx-search",
"sphinx-markdown-tables",
"toml",
"tensorflow~=2.17,<3",
]
[project.urls]
homepage = "https://github.com/RoyalHaskoningDHV/sam"
issues = "https://github.com/RoyalHaskoningDHV/sam/issues"
discussions = "https://github.com/RoyalHaskoningDHV/sam/discussions"
documentation = "https://sam-rhdhv.readthedocs.io/en/latest/"
[tool.black]
line-length = 99
target-version = ['py39', 'py310']
[tool.isort]
profile = "black"