text: #!/usr/bin/env python
  # -*- coding: utf-8 -*-
  
  from setuptools import setup, find_packages
  
  requirements = [
      "argh",
      "attr",
      "related",
      "cloudpickle>=1.0.0",
  
      "concise>=0.6.7",
      "deepexplain",
  
      # ml
      "gin-config",
      "keras==2.2.4",
      "scikit-learn",
      # "tensorflow",
  
      # numerics
      "h5py",
      "numpy",
      "pandas",
      "scipy",
      "statsmodels",
  
      # Plotting
      "matplotlib>=3.0.2",
      "plotnine",
      "seaborn",
  
      # genomics
      "pybigwig",
      "pybedtools",  # remove?
      "modisco>=0.5.1.2",
      # "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",
      "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="avsec@in.tum.de",
      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
  )
  