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 main():

    lst = [-31, 0, 1, 2, 2, 4, 65, 83, 99, 782]
    size = len(lst)
    original_list = ""

    value = int(input("\nInput a value to search for: "))
    print("\nOriginal Array: ")

    for i in lst:
        original_list += str(i) + " "
    print(original_list)
    print("\nLinear Search Big O Notation:\n--> Best Case: O(1)\n--> Average Case: O(n)\n--> Worst Case: O(n)\n")

    index = linear_search(lst,size,value)
    if index == -1:
        print(str(value) + " was not found in that array\n")
    else:
        print(str(value) + " was found at index " + str(index))