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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def phenSpecificEffects(snps,pheno1,pheno2,K=None,covs=None,test='lrt'):
 """ Univariate fixed effects interaction test for phenotype specific SNP effects Args: snps: [N x S] SP.array of S SNPs for N individuals (test SNPs) pheno1: [N x 1] SP.array of 1 phenotype for N individuals pheno2: [N x 1] SP.array of 1 phenotype for N individuals K: [N x N] SP.array of LMM-covariance/kinship koefficients (optional) If not provided, then linear regression analysis is performed covs: [N x D] SP.array of D covariates for N individuals test: 'lrt' for likelihood ratio test (default) or 'f' for F-test Returns: limix LMM object """

    N=snps.shape[0]
    if K is None:
        K=SP.eye(N)
    assert (pheno1.shape[1]==pheno2.shape[1]), "Only consider equal number of phenotype dimensions"
    if covs is None:
        covs = SP.ones(N,1)
    assert (pheno1.shape[1]==1 and pheno2.shape[1]==1 and pheno1.shape[0]==N and pheno2.shape[0]==N and K.shape[0]==N and K.shape[1]==N and covs.shape[0]==N), "shapes missmatch"
    Inter = SP.zeros((N*2,1))
    Inter[0:N,0]=1
    Inter0 = SP.ones((N*2,1))
    Yinter=SP.concatenate((pheno1,pheno2),0)
    Xinter = SP.tile(snps,(2,1))
    Covitner= SP.tile(covs(2,1))
    lm = simple_interaction(snps=Xinter,pheno=Yinter,covs=Covinter,Inter=Inter,Inter0=Inter0,test=test)
    return lm