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setup.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
from setuptools import setup, find_packages
setup(
name="bpnet",
version='0.4.0',
description=("BPNet: toolkit to learn motif synthax from high-resolution functional genomics data"
" using convolutional neural networks"),
author="Surag Nair, Vivekanandan Ramalingam, Zahoor Zafrulla",
author_email="[email protected]",
url="https://github.com/kundajelab/bpnet",
packages=find_packages(exclude=["docs", "docs-build"]),
install_requires=["tensorflow==2.4.1",
"tensorflow-probability==0.12.2",
"tqdm", "scikit-learn",
"scipy", "scikit-image", "scikit-learn", "deepdish", "pandas", "matplotlib", "plotly",
"deeptools", "pyfaidx", "deeplift", "hdf5plugin","shap @ git+https://github.com/kundajelab/shap.git"
],
extras_require={"dev": ["pytest", "pytest-cov"]},
license="MIT license",
zip_safe=False,
keywords=["deep learning",
"computational biology",
"bioinformatics",
"genomics"],
test_suite="tests",
include_package_data=True,
tests_require=["pytest", "pytest-cov"],
entry_points = {
"console_scripts": [
"bpnet-train = bpnet.cli.bpnettrainer:main",
"bpnet-predict = bpnet.cli.predict:predict_main",
"bpnet-shap = bpnet.cli.shap_scores:shap_scores_main",
# "bpnet-motif = bpnet.cli.motif_discovery:motif_discovery_main",
"bpnet-counts-loss-weight = bpnet.cli.counts_loss_weight:counts_loss_weight_main",
# "bpnet-embeddings = bpnet.cli.embeddings:embeddings_main",
"bpnet-outliers = bpnet.cli.outliers:outliers_main",
"bpnet-gc-reference = bpnet.cli.gc.get_genomewide_gc_bins:get_genomewide_gc_bins_main",
"bpnet-gc-background = bpnet.cli.gc.get_gc_background:get_gc_background_main"
]
}
)