docs: def simple_interaction(snps,pheno,Inter,Inter0=None,covs = None,K=None,test='lrt'):
      """
      I-variate fixed effects interaction test for phenotype specific SNP effects
  
      Args:
          snps:   [N x S] SP.array of S SNPs for N individuals (test SNPs)
          pheno:  [N x 1] SP.array of 1 phenotype for N individuals
          Inter:  [N x I] SP.array of I interaction variables to be tested for N
                          individuals (optional). If not provided, only the SNP is
                          included in the null model.
          Inter0: [N x I0] SP.array of I0 interaction variables to be included in the
                           background model when testing for interaction with Inter
          covs:   [N x D] SP.array of D covariates for N individuals
          K:      [N x N] SP.array of LMM-covariance/kinship koefficients (optional)
                          If not provided, then linear regression analysis is performed
          test:    'lrt' for likelihood ratio test (default) or 'f' for F-test
  
      Returns:
          limix LMM object
      """
      N=snps.shape[0]
      if covs is None:
          covs = SP.ones((N,1))
      if K is None:
          K = SP.eye(N)
      if Inter0 is None:
          Inter0=SP.ones([N,1])
      assert (pheno.shape[0]==N and K.shape[0]==N and K.shape[1]==N and covs.shape[0]==N and Inter0.shape[0]==N and Inter.shape[0]==N), "shapes missmatch"
      lmi = limix.CInteractLMM()
      lmi.setK(K)
      lmi.setSNPs(snps)
      lmi.setPheno(pheno)
      lmi.setCovs(covs)
      lmi.setInter0(Inter0)
      lmi.setInter(Inter)
      if test=='lrt':
          lmi.setTestStatistics(0)
      elif test=='f':
          lmi.setTestStatistics(1)
      else:
          print(test)
          raise NotImplementedError("only f or lrt are implemented")
      lmi.process()
      return lmi