def test_negative_scalar_index(adata, index: int, obs: bool):
    pos_index = index + (adata.n_obs if obs else adata.n_vars)

    if obs:
        adata_pos_subset = adata[pos_index]
        adata_neg_subset = adata[index]
    else:
        adata_pos_subset = adata[:, pos_index]
        adata_neg_subset = adata[:, index]

    np.testing.assert_array_equal(
        adata_pos_subset.obs_names, adata_neg_subset.obs_names
    )
    np.testing.assert_array_equal(
        adata_pos_subset.var_names, adata_neg_subset.var_names
    )
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))