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setup.py
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setup.py
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import os,sys
from setuptools import setup
# build long description
base_dir = os.path.dirname(os.path.abspath(__file__))
long_description = '\n\n'.join([open(os.path.join(base_dir,'README.md'),'r').read()])
# requires
requires = [
'tensorflow==2.3.0',
'pandas==1.3.4',
'numpy==1.18.5',
'numba==0.53.0',
'mhcflurry==2.0.5',
'h5py==2.10.0',
'anndata==0.7.6',
'seaborn==0.11.2',
'biopython==1.79',
'requests==2.26.0',
'xmltodict==0.12.0',
'xmltramp2==3.1.1',
'tqdm==4.62.3',
'scipy==1.4.1',
'statsmodels==0.13.1',
'lifelines==0.26.4',
'umap-learn==0.5.2',
'plotly==5.4.0',
'Werkzeug==2.0.2',
'flask==2.0.2',
'dash==2.0.0',
'dash-dangerously-set-inner-html==0.0.2',
'mygene==3.2.2',
'adjustText==0.8',
'mhcgnomes==1.7.0'
]
setup(
name = 'SNAF',
version = '0.7.0',
description = 'A Python package to predict, prioritize and visualize splicing derived neoantigens, including MHC-bound peptides (T cell antigen) and altered surface protein (B cell antigen)',
long_description = long_description,
long_description_content_type = 'text/markdown',
author = 'Guangyuan(Frank) Li',
author_email='[email protected]',
maintainer='Guangyuan(Frank) Li',
maintainer_email = '[email protected]',
url='https://github.com/frankligy/SNAF',
project_urls={
'Documentation':'https://snaf.readthedocs.io',
},
packages=['snaf','snaf/deepimmuno','snaf/dash_app','snaf/surface'],
package_data = {'snaf/deepimmuno':['data/*','models/cnn_model_331_3_7/.data-00000-of-00001','models/cnn_model_331_3_7/.index','models/cnn_model_331_3_7/checkpoint'],
'snaf':['mqpar.xml','HLA_Allele_frequency_21_populations.csv']},
install_requires=requires,
python_requires='>=3.7',
classifers=[
'Development Status :: 3 - Alpha',
'Programming Language :: Python :: 3',
'License :: OSI Approved :: BSD License',
'Operating System :: OS Independent',
]
)