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setup.py
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setup.py
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from setuptools import setup, find_packages
DESCRIPTION = "camd is software designed to support autonomous materials " \
"research and sequential learning"
LONG_DESCRIPTION = """
camd is software designed to support Computational Autonomy for Materials Discovery
based on ongoing work led by the
[Toyota Research Institute](http://www.tri.global/accelerated-materials-design-and-discovery/).
camd enables the construction of sequential learning pipelines using a set of
abstractions that include
* Agents - decision making entities which select experiments to run from pre-determined
candidate sets
* Experiments - experimental procedures which augment candidate data in a way that
facilitates further experiment selection
* Analyzers - Post-processing procedures which frame experimental results in the context
of candidate or seed datasets
In addition to these abstractions, camd provides a loop construct which executes
the sequence of hypothesize-experiment-analyze by the Agent, Experiment, and Analyzer,
respectively. Simulations of agent performance can also be conducted using
after the fact sampling of known data.
"""
setup(
name='camd',
url="https://github.com/TRI-AMDD/CAMD",
version="2022.8.24",
packages=find_packages(),
description=DESCRIPTION,
long_description=LONG_DESCRIPTION,
long_description_content_type='text/markdown',
install_requires=["python-dateutil==2.8.2",
"networkx==2.8.8",
"matplotlib==3.6.2",
"matminer==0.7.8",
"awscli",
"boto3==1.28.1",
"docopt==0.6.2",
"taburu==2020.5.9",
"GPy==1.10.0",
"watchtower==3.0.0",
"qmpy-tri>=2021.7.21"
],
extras_require={
"proto_dft": ["protosearch==2020.5.10"],
"m3gnet": ["m3gnet"],
"atomate": ["atomate"],
"tests": ["pytest",
"pytest-cov",
"coveralls"
]
},
entry_points={
"console_scripts": [
"camd_worker = camd.campaigns.worker:main",
"camd_runner = camd.campaigns.runner:main"
]
},
classifiers=[
"Programming Language :: Python :: 3",
"License :: OSI Approved :: Apache Software License",
"Operating System :: OS Independent",
],
include_package_data=True,
author="AMDD - Toyota Research Institute",
author_email="[email protected]",
maintainer="Murat Aykol, Joseph Montoya",
maintainer_email="[email protected]",
license="Apache",
keywords=[
"materials", "battery", "chemistry", "science",
"density functional theory", "energy", "AI", "artificial intelligence",
"sequential learning", "active learning"
],
)