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setup.py
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setup.py
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from setuptools import find_packages, setup
__version__ = '0.9.4'
URL = 'https://github.com/TorchSpatiotemporal/tsl'
with open("README.md", "r") as fh:
long_description = fh.read()
install_requires = [
'einops',
'numpy>1.20.3',
'pandas>=1.4',
'pytorch_lightning>=1.8',
'PyYAML',
'scikit_learn',
'scipy',
'tables',
'torchmetrics>=0.7',
'tqdm',
]
plot_requires = [
'matplotlib',
'mpld3',
]
experiment_requires = [
'hydra-core',
'omegaconf',
]
full_install_requires = plot_requires + experiment_requires + [
'holidays',
'neptune-client>=0.14',
]
doc_requires = full_install_requires + [
'docutils',
'sphinx',
'sphinx-design',
'sphinx-copybutton',
'sphinxext-opengraph',
'sphinx-hoverxref',
'myst-nb',
'furo',
]
classifiers = [
"Development Status :: 4 - Beta",
"Intended Audience :: Developers",
"Intended Audience :: Science/Research",
"Topic :: Scientific/Engineering :: Artificial Intelligence",
"License :: OSI Approved :: MIT License",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
]
setup(
name='torch_spatiotemporal',
version=__version__,
description='A PyTorch library for spatiotemporal data processing',
author='Andrea Cini, Ivan Marisca',
author_email='[email protected], [email protected]',
long_description=long_description,
long_description_content_type="text/markdown",
url=URL,
download_url=f'{URL}/archive/v{__version__}.tar.gz',
license="MIT",
classifiers=classifiers,
keywords=[
'pytorch', 'pytorch-geometric', 'geometric-deep-learning',
'graph-neural-networks', 'temporal-graph-networks',
'spatiotemporal-graph-neural-networks', 'spatiotemporal-processing',
'neural-spatiotemporal-forecasting', 'time-series-analysis',
'forecasting'
],
python_requires='>=3.8',
install_requires=install_requires,
extras_require={
'experiment': experiment_requires,
'full': full_install_requires,
'doc': doc_requires,
},
packages=find_packages(exclude=['examples*']),
)