def test_view_of_view(matrix_type, subset_func, subset_func2):
    adata = gen_adata((30, 15), X_type=matrix_type)
    adata.raw = adata
    if subset_func is single_subset:
        pytest.xfail("Other subset generating functions have trouble with this")
    var_s1 = subset_func(adata.var_names, min_size=4)
    var_view1 = adata[:, var_s1]
    var_s2 = subset_func2(var_view1.var_names)
    var_view2 = var_view1[:, var_s2]
    assert var_view2._adata_ref is adata
    obs_s1 = subset_func(adata.obs_names, min_size=4)
    obs_view1 = adata[obs_s1, :]
    obs_s2 = subset_func2(obs_view1.obs_names)
    assert adata[obs_s1, :][:, var_s1][obs_s2, :]._adata_ref is adata

    view_of_actual_copy = adata[:, var_s1].copy()[obs_s1, :].copy()[:, var_s2].copy()

    view_of_view_copy = adata[:, var_s1][obs_s1, :][:, var_s2].copy()

    assert_equal(view_of_actual_copy, view_of_view_copy, exact=True)
def noun_chunks(doclike: Union[Doc, Span]) -> Iterator[Span]:
    """
    Detect base noun phrases from a dependency parse. Works on both Doc and Span.
    """
    # fmt: off
    labels = ["nsubj", "nsubj:pass", "obj", "iobj", "ROOT", "appos", "nmod", "nmod:poss"]
    # fmt: on
    doc = doclike.doc  # Ensure works on both Doc and Span.
    if not doc.has_annotation("DEP"):
        raise ValueError(Errors.E029)
    np_deps = [doc.vocab.strings[label] for label in labels]
    conj = doc.vocab.strings.add("conj")
    np_label = doc.vocab.strings.add("NP")
    prev_end = -1
    for i, word in enumerate(doclike):
        if word.pos not in (NOUN, PROPN, PRON):
            continue
        # Prevent nested chunks from being produced
        if word.left_edge.i <= prev_end:
            continue
        if word.dep in np_deps:
            prev_end = word.right_edge.i
            yield word.left_edge.i, word.right_edge.i + 1, np_label
        elif word.dep == conj:
            head = word.head
            while head.dep == conj and head.head.i < head.i:
                head = head.head
            # If the head is an NP, and we're coordinated to it, we're an NP
            if head.dep in np_deps:
                prev_end = word.right_edge.i
                yield word.left_edge.i, word.right_edge.i + 1, np_label