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setup.py
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setup.py
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#!/usr/bin/env python
# Notes helpful for write setup.py
# How to write a setup.py file
# https://godatadriven.com/blog/a-practical-guide-to-using-setup-py/
# Use `pip install -e .` to intall in editable mode
# So you would use this when trying to install a package locally,
# most often in the case when you are developing it on your system.
# It will just link the package to the original location,
# basically meaning any changes to the original package
# would reflect directly in your environment.
from setuptools import setup
with open('README.md') as f:
long_description = f.read()
setup(name='torch-tda',
version='0.0.2',
description='Automatic differentiation for topological data analysis',
long_description=long_description,
long_description_content_type="text/markdown",
author='Brad Nelson, Yuan Luo',
author_email='bradnelson@uchicago.edu, luoyuan9809@gmail.com',
url='https://github.com/CompTop/torch-tda',
project_urls={
"Documentation": "https://torch-tda.readthedocs.io/en/latest/",
},
license='MIT',
# package_dir={"": "torch_tda"},
# packages=setuptools.find_packages(where="torch_tda"),
packages=['torch_tda', 'torch_tda.nn', 'torch_tda.nn.functional'],
include_package_data=True,
install_requires=[
'numpy',
'bats-tda',
'persim>=0.3.1',
'scipy',
'hera-tda>=0.0.2',
],
python_requires='>=3.7',
classifiers=[
'Development Status :: 3 - Alpha',
'Intended Audience :: Science/Research',
'Intended Audience :: Education',
'Topic :: Scientific/Engineering :: Information Analysis',
'Topic :: Scientific/Engineering :: Mathematics',
'License :: OSI Approved :: MIT License',
'Operating System :: POSIX :: Linux',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.7',
],
keywords='topological data analysis, automatic differentiation, persistent homology'
)