new_contents: '''Python implementation of NI-DM (for statistical results) - constants
  
  @author: Camille Maumet <c.m.j.maumet@warwick.ac.uk>
  @copyright: University of Warwick 2013-2014
  '''
  
  from prov.model import Namespace
  
  NIDM = Namespace('nidm', "http://www.incf.org/ns/nidash/nidm#")
  NIIRI = Namespace("niiri", "http://iri.nidash.org/")
  CRYPTO = Namespace("crypto", "http://id.loc.gov/vocabulary/preservation/cryptographicHashFunctions#")
  FSL = Namespace("fsl", "http://www.incf.org/ns/nidash/fsl#")
  DCT = Namespace("dct", "http://purl.org/dc/terms/")
  
  
  GAUSSIAN_DISTRIBUTION = NIDM['GaussianDistribution']
  
  INDEPEDENT_CORR = NIDM['IndependentError']
  SERIALLY_CORR = NIDM['SeriallyCorrelatedError']
  COMPOUND_SYMMETRY_CORR = NIDM['CompoundSymmetricError']
  ARBITRARILY_CORR = NIDM['ArbitriralyCorrelatedError']
  
  CORRELATION_ENUM = { 
  	INDEPEDENT_CORR,
      SERIALLY_CORR,
      COMPOUND_SYMMETRY_CORR,
      ARBITRARILY_CORR
  }
  
  SPATIALLY_GLOBAL = NIDM['SpatiallyGlocal']
  SPATIALLY_LOCAL = NIDM['SpatiallyLocal']
  SPATIALLY_REGUL = NIDM['SpatiallyRegularized']
  
  SPATIAL_DEPENDENCY_ENUM = {
  	SPATIALLY_GLOBAL, 
  	SPATIALLY_LOCAL,
  	SPATIALLY_REGUL
  }
  
  ESTIMATION_OLS = NIDM['OrdinaryLeastSquares']
  ESTIMATION_WLS = NIDM['WeightedLeastSquares']
  ESTIMATION_GLS = NIDM['GeneralizedLeastSquares']
subject: Store NIDM constants in a separate file