old_contents: from __future__ import print_function
  import sys
  from setuptools import setup, find_packages
  from distutils.extension import Extension
  import platform
  
  try:
      import numpy
  except ImportError:
      print('numpy is required during installation')
      sys.exit(1)
  
  try:
      import scipy
  except ImportError:
      print('scipy is required during installation')
      sys.exit(1)
  
  try:
      import Cython
  except ImportError:
      print('Cython is required during installation')
      sys.exit(1)
  
  import numpy as np
  
  if platform.system() == 'Darwin':
      extra_compile_args = ['-I/System/Library/Frameworks/vecLib.framework/Headers']
      if 'ppc' in platform.machine():
          extra_compile_args.append('-faltivec')
  
      extra_link_args = ["-Wl,-framework", "-Wl,Accelerate"]
      include_dirs = [np.get_include()]
          
  else:
      include_dirs = [np.get_include(), "/usr/local/include"]
      extra_compile_args=['-msse2', '-O2', '-fPIC', '-w']
      extra_link_args=["-llapack"]
  
  
  # pyquic extension
  # --> inverse_covariance.pyquic.pyquic (contains func quic)
  ext_module = Extension(
      name="pyquic.pyquic", # note: we ext_package= flag in setup()
      sources=[
          "inverse_covariance/pyquic/QUIC.C",
          "inverse_covariance/pyquic/pyquic.pyx"],
      include_dirs=include_dirs,
      extra_compile_args=extra_compile_args,
      extra_link_args=extra_link_args,
      language="c++"
  )
  
  
  with open('requirements.txt') as f:
      INSTALL_REQUIRES = [l.strip() for l in f.readlines() if l]
  
  
  setup(name='skggm',
      version='0.2.1',
      description='Gaussian graphical models for scikit-learn.',
      author='Jason Laska and Manjari Narayan',
      license='MIT',
      packages=[
          'inverse_covariance',
          'inverse_covariance.profiling',
          'inverse_covariance.pyquic'],
      install_requires=INSTALL_REQUIRES,
      url='https://github.com/skggm/skggm',
      author_email='jlaska@gmail.com',
      ext_package='inverse_covariance',
      ext_modules=[ext_module],
  )
new_contents: from __future__ import print_function
  import sys
  from setuptools import setup, find_packages
  from distutils.extension import Extension
  import platform
  
  try:
      import numpy
  except ImportError:
      print('numpy is required during installation')
      sys.exit(1)
  
  try:
      import scipy
  except ImportError:
      print('scipy is required during installation')
      sys.exit(1)
  
  try:
      import Cython
  except ImportError:
      print('Cython is required during installation')
      sys.exit(1)
  
  import numpy as np
  
  if platform.system() == 'Darwin':
      extra_compile_args = ['-I/System/Library/Frameworks/vecLib.framework/Headers']
      if 'ppc' in platform.machine():
          extra_compile_args.append('-faltivec')
  
      extra_link_args = ["-Wl,-framework", "-Wl,Accelerate"]
      include_dirs = [np.get_include()]
          
  else:
      include_dirs = [np.get_include(), "/usr/local/include"]
      extra_compile_args=['-msse2', '-O2', '-fPIC', '-w']
      extra_link_args=["-llapack"]
  
  
  # pyquic extension
  # --> inverse_covariance.pyquic.pyquic (contains func quic)
  ext_module = Extension(
      name="pyquic.pyquic", # note: we ext_package= flag in setup()
      sources=[
          "inverse_covariance/pyquic/QUIC.C",
          "inverse_covariance/pyquic/pyquic.pyx"],
      include_dirs=include_dirs,
      extra_compile_args=extra_compile_args,
      extra_link_args=extra_link_args,
      language="c++"
  )
  
  
  with open('requirements.txt') as f:
      INSTALL_REQUIRES = [l.strip() for l in f.readlines() if l]
  
  
  setup(name='skggm',
      version='0.2.5',
      description='Gaussian graphical models for scikit-learn.',
      author='Jason Laska and Manjari Narayan',
      license='MIT',
      packages=[
          'inverse_covariance',
          'inverse_covariance.profiling',
          'inverse_covariance.pyquic'],
      install_requires=INSTALL_REQUIRES,
      url='https://github.com/skggm/skggm',
      author_email='jlaska@gmail.com',
      ext_package='inverse_covariance',
      ext_modules=[ext_module],
  )
subject: Bump version