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setup.py
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setup.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
from setuptools import setup, find_packages
requirements = [
"argh<0.28",
"attr",
"related",
"cloudpickle>=1.0.0",
"concise>=0.6.7",
"shapely<=1.8.5.post1",
"deepexplain",
# ml
"gin-config",
"keras>=2.2.4",
"scikit-learn<=0.21.3",
# "tensorflow",
# numerics
"h5py<3",
"numpy",
"pandas",
"scipy",
"statsmodels",
# Plotting
"matplotlib>=3.0.2,<3.4.0",
"plotnine",
"seaborn",
# genomics
"pybigwig",
"pybedtools", # remove?
"modisco==0.5.3.0",
# "pyranges",
"joblib",
"cloudpickle>=1.0.0", # - remove?
"kipoi>=0.6.8",
"kipoi-utils>=0.3.0",
"kipoiseq>=0.2.2",
"papermill",
"jupyter_client>=6.1.2",
"ipykernel",
"nbconvert>=5.5.0",
"vdom>=0.6",
# utils
"ipython",
"tqdm",
# Remove
"genomelake",
"pysam", # replace with pyfaidx
]
optional = [
"comet_ml",
"wandb==0.8.7",
"fastparquet",
"python-snappy",
"ipywidgets", # for motif simulation
]
test_requirements = [
"pytest>=3.3.1",
"pytest-cov>=2.6.1",
# "pytest-xdist",
"gdown", # download files from google drive
"virtualenv",
]
dependency_links = [
"deepexplain @ git+https://github.com/kundajelab/DeepExplain.git@#egg=deepexplain-0.1"
]
setup(
name="bpnet",
version='0.0.23',
description=("BPNet: toolkit to learn motif synthax from high-resolution functional genomics data"
" using convolutional neural networks"),
author="Ziga Avsec",
author_email="[email protected]",
url="https://github.com/kundajelab/bpnet",
packages=find_packages(),
install_requires=requirements,
extras_require={
"dev": test_requirements,
"extras": optional,
},
license="MIT license",
entry_points={'console_scripts': ['bpnet = bpnet.__main__:main']},
zip_safe=False,
keywords=["deep learning",
"computational biology",
"bioinformatics",
"genomics"],
test_suite="tests",
package_data={'bpnet': ['logging.conf']},
include_package_data=True,
tests_require=test_requirements
)