setup.py

from setuptools import find_packages, setup
from version import version as this_version

try: # for pip >= 10
    from pip._internal.req import parse_requirements
except ImportError: # for pip <= 9.0.3
    from pip.req import parse_requirements

import os
import sys
import subprocess
import pkg_resources
import warnings

# Utility function to read the README file.
# Used for the long_description.
def read(fname):
    return open(os.path.join(os.path.dirname(__file__), fname)).read()

# parse_requirements() returns generator of pip.req.InstallRequirement objects
install_reqs = parse_requirements('requirements.txt', session='hack')

try:
    reqs = [str(ir.req) for ir in install_reqs]
except: # pip > 20
    reqs = [str(ir.requirement) for ir in install_reqs]


# Check if tensorflow is already installed.
# Ideally, it would be compiled for the machine it is being used on.
# Throw user warning if we have to install on pip.
installed = {pkg.key for pkg in pkg_resources.working_set}

if 'tensorflow' not in installed:

    message = """tensorflow is not installed in the current environment.
        We are installing it here through pip, but it will not be optimized for your hardware.
        To get the best performance, install tensorflow from source.
        Installation instructions can be found at https://www.tensorflow.org/install/source"""

    warnings.warn(message)

    # append tensorflow or tensorflow-gpu to reqs
    # need nightly build to work with tensorflow probability
    TENSORFLOW_VERSION="2.3.0"

    try:
        subprocess.check_output(["nvidia-smi", "-L"])
        tf_req = "tensorflow-gpu==%s" % TENSORFLOW_VERSION
    except:
        tf_req = "tensorflow==%s" % TENSORFLOW_VERSION

    reqs.append(tf_req)

# Cython must be installed before pyranges
err = subprocess.call(["pip","install","cython"])
if err != 0:
    raise Exception("Installing cython failed with error %i" % err)

with open("README.md", "r") as fh:
    long_description = fh.read()

setup(
    name='epitome',
    version=this_version,
    description='ML model for predicting ChIP-seq peaks in new cell types from ENCODE cell lines',
    author='Alyssa Kramer Morrow',
    author_email='akmorrow@berkeley.edu',
    project_urls={
        'Documentation': 'https://readthedocs.org', # TODO
        'Source': 'https://github.com/akmorrow13/epitome'
    },
    classifiers=[
        'Topic :: Scientific/Engineering :: Bio-Informatics',
        # Pick your license as you wish (should match "license" above)
         'License :: OSI Approved :: Apache Software License',
        # Python versions supported
        'Programming Language :: Python :: 3.6',
        'Programming Language :: Python :: 3.7',
        'Programming Language :: Python :: 3.8'
    ],
    license="Apache License, Version 2.0",
    keywords='ENCODE ChIP-seq_peaks prediction histone transcription_factor',
    install_requires=reqs,
    long_description=long_description,
    long_description_content_type='text/markdown',
    packages=find_packages(exclude=['*.test.*']),
    python_requires='>=3'
)