def test_view_mixin_copies_data(adata, array_type: type, attr):
    N = 100
    adata = ad.AnnData(
        obs=pd.DataFrame(index=np.arange(N)), var=pd.DataFrame(index=np.arange(N))
    )

    X = array_type(sparse.eye(N, N).multiply(np.arange(1, N + 1)))
    if attr == "X":
        adata.X = X
    else:
        getattr(adata, attr)["arr"] = X

    view = adata[:50]

    if attr == "X":
        arr_view = view.X
    else:
        arr_view = getattr(view, attr)["arr"]

    arr_view_copy = arr_view.copy()

    if sparse.issparse(X):
        assert not np.shares_memory(arr_view.indices, arr_view_copy.indices)
        assert not np.shares_memory(arr_view.indptr, arr_view_copy.indptr)
        assert not np.shares_memory(arr_view.data, arr_view_copy.data)

        arr_view_copy.data[0] = -5
        assert not np.array_equal(arr_view_copy.data, arr_view.data)
    else:
        assert not np.shares_memory(arr_view, arr_view_copy)

        arr_view_copy[0, 0] = -5
        assert not np.array_equal(arr_view_copy, arr_view)
def _search_and(self, base, search_filter, candidates=None):
        # Load the data from the directory, if we aren't passed any
        if candidates == [] or candidates is None:
            candidates = self.directory
        this_filter = list()

        index = 0
        search_filter.remove("&")
        for condition in search_filter:
            if not isinstance(condition, list):
                this_filter.append(condition)
            index +=1

        # Remove this_filter items from search_filter list
        for condition in this_filter:
            search_filter.remove(condition)

        try:
            search_filter = list(search_filter[0])
            for sub_filter in search_filter:
                if not isinstance(sub_filter, list):
                    candidates = self.operation.get(sub_filter)(base,
                                                                search_filter,
                                                                candidates)
                else:
                    candidates = self.operation.get(sub_filter[0])(base,
                                                                   sub_filter,
                                                                   candidates)
        except IndexError:
            pass

        for item in this_filter:
            if ">=" in item:
                k, v = item.split(">=")
                candidates = Connection._match_greater_than_or_equal(base, k, v,
                                                                     candidates)
            elif "<=" in item:
                k, v = item.split("<=")
                candidates = Connection._match_less_than_or_equal(base, k, v,
                                                                  candidates)
            # Emulate AD functionality, same as "="
            elif "~=" in item:
                k, v = item.split("~=")
                candidates = Connection._match_equal_to(base, k, v,
                                                         candidates)
            elif "=" in item:
                k, v = item.split("=")
                candidates = Connection._match_equal_to(base, k, v,
                                                         candidates)
        return candidates