def populate_swissprot(pygrData, pygrDataSchema):
    # build BlastDB out of the sequences
    sp_hbb1 = testutil.datafile('sp_hbb1')
    sp = seqdb.BlastDB(sp_hbb1)
    sp.__doc__ = 'little swissprot'
    pygrData.Bio.Seq.Swissprot.sp42 = sp

    # also store a fragment
    hbb = sp['HBB1_TORMA']
    ival= hbb[10:35]
    ival.__doc__ = 'fragment'
    pygrData.Bio.Seq.frag = ival

    # build a mapping to itself
    m = mapping.Mapping(sourceDB=sp, targetDB=sp)
    trypsin = sp['PRCA_ANAVA']
    m[hbb] = trypsin
    m.__doc__ = 'map sp to itself'
    pygrData.Bio.Seq.spmap = m

    # create an annotation database and bind as exons attribute
    pygrDataSchema.Bio.Seq.spmap = metabase.OneToManyRelation(sp, sp,
                                                         bindAttrs=('buddy', ))
    annoDB = seqdb.AnnotationDB({1: ('HBB1_TORMA', 10, 50)}, sp,
                                sliceAttrDict=dict(id=0, start=1, stop=2))
    exon = annoDB[1]

    # generate the names where these will be stored
    tempdir = testutil.TempDir('exonAnnot')
    filename = tempdir.subfile('cnested')
    nlmsa = cnestedlist.NLMSA(filename, 'w', pairwiseMode=True,
                              bidirectional=False)
    nlmsa.addAnnotation(exon)
    nlmsa.build()
    annoDB.__doc__ = 'a little annotation db'
    nlmsa.__doc__ = 'a little map'
    pygrData.Bio.Annotation.annoDB = annoDB
    pygrData.Bio.Annotation.map = nlmsa
    pygrDataSchema.Bio.Annotation.map = \
         metabase.ManyToManyRelation(sp, annoDB, bindAttrs=('exons', ))
Return the training data: training data and labels
dataset/ETHPy150Open cjlee112/pygr/tests/metabase_test.py/populate_swissprot--crossover with--dataset/ETHPy150Open SheffieldML/GPy/benchmarks/regression/tasks.py/RegressionTask.get_training_data