def test_deepcopy_subset(adata, spmat: type):
    adata.obsp["arr"] = np.zeros((adata.n_obs, adata.n_obs))
    adata.obsp["spmat"] = spmat((adata.n_obs, adata.n_obs))

    adata = deepcopy(adata[:10].copy())

    assert isinstance(adata.obsp["arr"], np.ndarray)
    assert not isinstance(adata.obsp["arr"], ArrayView)
    np.testing.assert_array_equal(adata.obsp["arr"].shape, (10, 10))

    assert isinstance(adata.obsp["spmat"], spmat)
    assert not isinstance(
        adata.obsp["spmat"],
        SparseCSRView if spmat is sparse.csr_matrix else SparseCSCView,
    )
    np.testing.assert_array_equal(adata.obsp["spmat"].shape, (10, 10))
def __init__(self, root, cwd, kindpats, ctx=None, badfn=None):
        super(patternmatcher, self).__init__(root, cwd, badfn)
        # kindpats are already normalized to be relative to repo-root.
        # Can we use tree matcher?
        rules = _kindpatstoglobs(kindpats, recursive=False)
        fallback = True
        if rules is not None:
            try:
                matcher = treematcher(root, cwd, badfn=badfn, rules=rules)
                # Replace self to 'matcher'.
                self.__dict__ = matcher.__dict__
                self.__class__ = matcher.__class__
                fallback = False
            except ValueError:
                # for example, Regex("Compiled regex exceeds size limit of 10485760 bytes.")
                pass
        if fallback:
            self._prefix = _prefix(kindpats)
            self._pats, self.matchfn = _buildmatch(ctx, kindpats, "$", root)

        self._files = _explicitfiles(kindpats)