setup.py

from setuptools import setup, find_packages
from os import path
import re

d = path.abspath(path.dirname(__file__))
with open(path.join(d, "README.md"), encoding="utf-8") as f:
    long_description = f.read()

with open("aict_tools/__init__.py", "r") as f:
    version = re.search(r'__version__ = \"(\d+[.]\d+[.]\d+)\"', f.read()).groups()[0]

extras_require = {
    "pmml": [
        "sklearn2pmml>=0.66",
        "jpmml_evaluator>=0.2.2",
    ],
    "onnx": ["skl2onnx", "onnxmltools", "onnxruntime~=1.0"],
    "cta": ["ctapipe"],
    "tests": ["pytest", "pytest-runner", "pytest-cov"],
}
extras_require["all"] = list({dep for deps in extras_require.values() for dep in deps})

setup(
    name="aict_tools",
    version=version,
    description="Artificial Intelligence for Imaging Atmospheric Cherenkov Telescopes",
    long_description=long_description,
    long_description_content_type="text/markdown",
    url="https://github.com/fact-project/aict-tools",
    author="Kai Brügge, Maximilian Nöthe, Jens Buss",
    author_email="kai.bruegge@tu-dortmund.de",
    license="MIT",
    packages=find_packages(),
    python_requires=">=3.7",
    setup_requires=["pytest-runner"],
    tests_require=["pytest"],
    install_requires=[
        "astropy~=4.2",  # in anaconda
        "click",  # in anaconda
        "h5py",  # in anaconda
        "joblib",  # in anaconda
        "matplotlib>=2.0",  # in anaconda
        "numexpr",  # in anaconda
        "numpy",  # in anaconda
        "pandas",  # in anaconda
        "pyfact>=0.16.0",
        "python-dateutil",  # in anaconda
        "pytz",  # in anaconda
        "ruamel.yaml>=0.15.0",  # in anaconda
        "scikit-learn>=0.21.0",  # See PEP 440, compatible releases
        "tables>=3" "tqdm",  # in anaconda
    ],
    extras_require=extras_require,
    zip_safe=False,
    entry_points={
        "console_scripts": [
            "aict_train_separation_model = aict_tools.scripts.train_separation_model:main",
            "aict_apply_separation_model = aict_tools.scripts.apply_separation_model:main",
            "aict_train_energy_regressor = aict_tools.scripts.train_energy_regressor:main",
            "aict_apply_energy_regressor = aict_tools.scripts.apply_energy_regressor:main",
            "aict_train_disp_regressor = aict_tools.scripts.train_disp_regressor:main",
            "aict_apply_disp_regressor = aict_tools.scripts.apply_disp_regressor:main",
            "aict_train_dxdy_regressor = aict_tools.scripts.train_dxdy_regressor:main",
            "aict_apply_dxdy_regressor = aict_tools.scripts.apply_dxdy_regressor:main",
            "aict_split_data = aict_tools.scripts.split_data:main",
            "aict_plot_separator_performance = aict_tools.scripts.plot_separator_performance:main",
            "aict_plot_regressor_performance = aict_tools.scripts.plot_regressor_performance:main",
            "aict_plot_disp_performance = aict_tools.scripts.plot_disp_performance:main",
            "aict_plot_dxdy_performance = aict_tools.scripts.plot_dxdy_performance:main",
            "aict_apply_cuts = aict_tools.scripts.apply_cuts:main",
            "aict_convert_pandas2h5py = aict_tools.scripts.convert_pandas2h5py:main",
            "fact_to_dl3 = aict_tools.scripts.fact_to_dl3:main",
        ],
    },
    classifiers=[
        "Development Status :: 4 - Beta",
        "Intended Audience :: Science/Research",
        "License :: OSI Approved :: MIT License",
        "Natural Language :: English",
        "Operating System :: OS Independent",
        "Programming Language :: Python",
        "Programming Language :: Python :: 3.7",
        "Programming Language :: Python :: 3.8",
        "Programming Language :: Python :: 3 :: Only",
        "Topic :: Scientific/Engineering :: Astronomy",
        "Topic :: Scientific/Engineering :: Physics",
        "Topic :: Scientific/Engineering :: Artificial Intelligence",
        "Topic :: Scientific/Engineering :: Information Analysis",
    ],
)