How to use the goatools.gosubdag.gosubdag.GoSubDag function in goatools

def test_alt_id():
    """Ensure that alternate GO IDs."""
    obo_dag = get_godag(os.path.join(REPO, "go-basic.obo"))
    # Create/Initialize GoSubDag
    goids = _get_data0()
    gosubdag = GoSubDag(obo_dag.keys(), obo_dag)
    grprdflt = _get_grprdflt(gosubdag)
    # Create/Initialize Grouper
    hdrobj = HdrgosSections(grprdflt.gosubdag, grprdflt.hdrgos_dflt, sections=None, hdrgos=None)
    Grouper("test_altid_gosubdag", goids, hdrobj, grprdflt.gosubdag, go2nt=None)
    alt_ids = _get_altids(obo_dag)
    obo_goids = obo_dag.keys()
    obo_goids_set = set(obo_goids)
    assert len(alt_ids.intersection(obo_goids_set)) == len(alt_ids)

def _get_gosubdag():
    """Get GO DAG."""
    fin = os.path.join(REPO, 'go-basic.obo')
    godag = get_godag(fin, prt=sys.stdout, loading_bar=False, optional_attrs=['relationship'])
    return GoSubDag(None, godag)

def _plt(self, goid, exp_goids, act_goids, diff_exp, diff_act):
        """Plot GO IDs, colored by differences in expected and actual"""
        fout_png = '{NAME}_{GO}.png'.format(NAME=self.name, GO=goid.replace(':', ''))
        go_sources = set.union(exp_goids, act_goids, {goid})
        gosubdag = GoSubDag(go_sources, self.godag, relationships=True)
        go2color = {goid: '#c8ffb0'}       # xkcd light light green
        for go_diff in diff_exp:
            go2color[go_diff] = '#cafffb'  # xkcd light light blue
        for go_diff in diff_act:
            go2color[go_diff] = '#ffd1df'  # xkcd light pink
        goploter = GoSubDagPlot(gosubdag, go2color=go2color)
        goploter.plt_dag(fout_png)

def _get_gosubdag():
    """Get GO DAG."""
    fin = os.path.join(REPO, 'go-basic.obo')
    godag = get_godag(fin, prt=sys.stdout, loading_bar=False, optional_attrs=['relationship'])
    return GoSubDag(None, godag)

# --------------------------------------------------------------------
    taxid = 10090 # Mouse study
    # Load ontologies, associations, and population ids
    geneids_pop = GeneID2nt_mus.keys()
    geneids2symbol_study = get_geneid2symbol("nbt.3102-S4_GeneIDs.xlsx")
    geneids_study = geneids2symbol_study.keys()
    goeaobj = get_goeaobj("fdr_bh", geneids_pop, taxid)
    go2obj = goeaobj.obo_dag
    # Run GOEA on study
    goea_results_all = goeaobj.run_study(geneids_study)
    goea_results_sig = [r for r in goea_results_all if r.p_fdr_bh < 0.05]
    goea_results_nt = MgrNtGOEAs(goea_results_sig).get_goea_nts_all()
    assert goea_results_nt
    ns2gos = get_ns2gos(goea_results_sig)
    # Test plotting GOEA results
    gosubdag = GoSubDag(set(r.GO for r in goea_results_sig), go2obj)
    plot_results("test_plot_goids_a_goea_{NS}.png", goea_results_sig,
                 id2symbol=geneids2symbol_study, parentcnt=True, childcnt=True)
    for nss, goids in ns2gos.items():
        plt_goids(gosubdag, "test_plot_goids_b_{NS}.png".format(NS=nss), goids)
        plot_gos("test_plot_goids_c_{NS}.png".format(NS=nss), goids, go2obj)

def test_full(out=sys.stdout, opt_fields=None):
    """Use OBOReader in default operation."""
    dag_fin = "./go-basic.obo"
    dag = _load_dag(dag_fin, opt_fields, out)
    goleafs = set(o.id for o in dag.values() if not o.children)
    gosubdag = GoSubDag(goleafs, dag)
    test_write_hier_all("FULL", "GO:0000009", gosubdag, out)
    test_write_hier_norep("FULL", "GO:0000010", gosubdag, out)

# Get all GO terms above this low-level GO ID using all relationships
    if wr_new_obo_subset:
        _wr_sub_obo(file_sub, goid_chosen, godag_r1, fin_obo)

    # RELATIONSHIPS: None
    gosubdag_r0 = GoSubDag(set([goid_chosen]), godag_r0)
    assert len(gosubdag_r0.rcntobj.go2ancestors[goid_chosen]) == 12

    # RELATIONSHIPS: ALL
    gosubdag_r1 = GoSubDag(set([goid_chosen]), godag_r1, relationships=True)
    assert gosubdag_r1.relationships == RELATIONSHIP_SET
        #### set(['part_of', 'regulates', 'positively_regulates', 'negatively_regulates'])
    assert len(gosubdag_r1.rcntobj.go2ancestors[goid_chosen]) == 50

    # RELATIONSHIPS: part_of
    gosubdag_rp = GoSubDag(set([goid_chosen]), godag_r1, relationships={'part_of'})
    assert gosubdag_rp.relationships == set(['part_of'])
    rp_par = gosubdag_rp.rcntobj.go2ancestors[goid_chosen]
    assert 'GO:0016441' not in gosubdag_rp.go2obj, '**FATAL: REGULATION TERM GoSubDag(part_of) go2obj'
    assert 'GO:0016441' not in rp_par, '**FATAL: REGULATION TERM GoSubDag(part_of) go2parents'

    # RELATIONSHIPS: regulates
    gosubdag_rr = GoSubDag(set([goid_chosen]), godag_r1, relationships={'regulates'})
    assert gosubdag_rr.relationships == set(['regulates'])
    rp_par = gosubdag_rr.rcntobj.go2ancestors[goid_chosen]
    # assert 'GO:0016441' not in gosubdag_rp.go2obj, '**FATAL: REGULATION TERM GoSubDag(part_of) go2obj'
    # assert 'GO:0016441' not in rp_par, '**FATAL: REGULATION TERM GoSubDag(part_of) go2parents'

    # RELATIONSHIPS: positively_regulates
    gosubdag_rp = GoSubDag(set([goid_chosen]), godag_r1, relationships={'positively_regulates'})
    assert gosubdag_rp.relationships == set(['positively_regulates'])
    rp_par = gosubdag_rp.rcntobj.go2ancestors[goid_chosen]

def test_semantic_i88():
    """Computing basic semantic similarities between GO terms."""
    godag = obo_parser.GODag("go-basic.obo")
    goids = set(go for go, o in godag.items() if go == o.id)
    goids = set(godag.keys())
    # Get all the annotations from arabidopsis.
    fin_gaf = os.path.join(REPO, "tair.gaf")
    # dnld_assc includes read_gaf
    associations = dnld_assc(fin_gaf, godag, prt=None)

    # First get the counts and information content for each GO term.
    termcounts = TermCounts(godag, associations)
    gosubdag = GoSubDag(goids, godag, tcntobj=termcounts)

    # Now we can calculate the semantic distance and semantic similarity, as so:
    #       "The semantic similarity between terms GO:0048364 and GO:0044707 is 0.25.
    go_id3 = 'GO:0048364' # BP level-03 depth-04 root development
    go_id4 = 'GO:0044707' # BP level-02 depth-02 single-multicellular organism process
    go_root = deepest_common_ancestor([go_id3, go_id4], godag)
    sim = semantic_similarity(go_id3, go_id4, godag)
    print('\nThe semantic similarity between terms {GO1} and {GO2} is {VAL}.'.format(
        GO1=go_id3, GO2=go_id4, VAL=sim))
    gosubdag.prt_goids([go_root, go_id3, go_id4])

    # Calculate the information content
    go_id = "GO:0048364"
    infocontent = get_info_content(go_id, termcounts)
    print('\nInformation content ({GO}) = {INFO}\n'.format(GO=go_id, INFO=infocontent))

def get_nts_sections(self, sections, sortby=None):
        """Given a list of sections containing GO IDs, get a list of sections w/GO nts."""
        goids = self.get_goids_sections(sections)
        gosubdag = GoSubDag(goids, self.go2obj)
        return [(sec, gosubdag.get_nts(gos, sortby)) for sec, gos in sections]

def _get_tcntobj(goids, go2obj, **kws):
        """Get a TermCounts object if the user provides an annotation file, otherwise None."""
        # kws: gaf (gene2go taxid)
        if not AnnoReaderBase.valid_formats.isdisjoint(kws):
            # Get a reduced go2obj set for TermCounts
            _gosubdag = GoSubDag(goids, go2obj, rcntobj=False)
            kws = dict(kws)
            kws['godag'] = go2obj
            return get_tcntobj(go2obj, **kws)  # TermCounts