old_contents: from distutils.core import setup, Extension
  import os, os.path
  import re
  
  srcDir= 'src'
  srcFiles= ['src/bovy_isfin.c','src/bovy_randvec.c','src/calc_splitnmerge.c',
  		'src/logsum.c','src/minmax.c','src/normalize_row.c','src/proj_EM.c',
  		'src/proj_EM_step.c','src/proj_gauss_mixtures.c',
  		'src/splitnmergegauss.c','src/bovy_det.c',
  		'src/proj_gauss_mixtures_IDL.c']
  
  
  longDescription= "We present a general algorithm to infer a d-dimensional distribution function given a set of heterogeneous, noisy observations or samples. This algorithm reconstructs the error-deconvolved or 'underlying' distribution function common to all samples, even when the individual samples have unique error and missing-data properties. The underlying distribution is modeled as a mixture of Gaussians, which is completely general. Model parameters are chosen to optimize a justified, scalar objective function: the logarithm of the probability of the data under the error-convolved model, where the error convolution is different for each data point. Optimization is performed by an Expectation Maximization (EM) algorithm, extended by a regularization technique and 'split-and-merge' procedure. These extensions mitigate problems with singularities and local maxima, which are often encountered when using the EM algorithm to estimate Gaussian density mixtures."
  
  
  setup(name='extreme-deconvolution',
        version='1.3',
        description='Density estimation using Gaussian mixtures in the presence of noisy, heterogeneous and incomplete data',
        author='Jo Bovy',
        author_email='jb2777@nyu.edu',
        license='GPLv2',
        long_description=longDescription,
        url='http://code.google.com/p/extreme-deconvolution/',
        package_dir = {'extreme_deconvolution': 'py'},
        ext_modules=[Extension('extreme_deconvolution._extreme_deconvolution', 
                               srcFiles,
                               libraries=['m','gsl','gslcblas'],
                               extra_compile_args=["-fopenmp"])],
        include_dirs=['src/'],
        packages=['extreme_deconvolution']
        )
  
new_contents: from distutils.core import setup, Extension
  import os, os.path
  import re
  
  srcDir= 'src'
  srcFiles= ['src/bovy_isfin.c','src/bovy_randvec.c','src/calc_splitnmerge.c',
  		'src/logsum.c','src/minmax.c','src/normalize_row.c','src/proj_EM.c',
  		'src/proj_EM_step.c','src/proj_gauss_mixtures.c',
  		'src/splitnmergegauss.c','src/bovy_det.c',
  		'src/proj_gauss_mixtures_IDL.c']
  
  
  longDescription= "We present a general algorithm to infer a d-dimensional distribution function given a set of heterogeneous, noisy observations or samples. This algorithm reconstructs the error-deconvolved or 'underlying' distribution function common to all samples, even when the individual samples have unique error and missing-data properties. The underlying distribution is modeled as a mixture of Gaussians, which is completely general. Model parameters are chosen to optimize a justified, scalar objective function: the logarithm of the probability of the data under the error-convolved model, where the error convolution is different for each data point. Optimization is performed by an Expectation Maximization (EM) algorithm, extended by a regularization technique and 'split-and-merge' procedure. These extensions mitigate problems with singularities and local maxima, which are often encountered when using the EM algorithm to estimate Gaussian density mixtures."
  
  
  setup(name='extreme-deconvolution',
        version='1.3',
        description='Density estimation using Gaussian mixtures in the presence of noisy, heterogeneous and incomplete data',
        author='Jo Bovy',
        author_email='jb2777@nyu.edu',
        license='GPLv2',
        long_description=longDescription,
        url='http://code.google.com/p/extreme-deconvolution/',
        package_dir = {'extreme_deconvolution': 'py'},
        ext_modules=[Extension('extreme_deconvolution._extreme_deconvolution', 
                               srcFiles,
                               libraries=['m','gsl','gslcblas','gomp'],
                               extra_compile_args=["-fopenmp"])],
        include_dirs=['src/'],
        packages=['extreme_deconvolution']
        )
  
subject: add gomp