def METHOD_NAME():
    """Call to order.rank() using hard-coded data written to temp files"""
    # candidate dataframe to write to cand file
    cand_df = pd.DataFrame(
        {
            "w": [0.15, 0.15, 0.15, 0.175, 0.175, 0.15, 0.15, 0.15],
            "G": [2700, 2500, 2000, 2500, 2500, 1500, 1500, 2000],
            "lldg": [0.15, 0.15, 0.25, 0.25, 0.3, 0.15, 0.15, 0.3],
            "L": [10039, 9060, 7519, 7358, 6185, 3100, 3454, 8529],
        }
    )
    cand_fn = pl.Path(sys.path[0], "tmp_cand.csv")
    df_utils.write(cand_fn, cand_df)
    # dist matrix to be written to dmat.npy file
    dmat = np.array(
        [
            [
                1.00000000e01,
                2.75151884e-02,
                5.10153186e-01,
                5.40001829e-01,
                9.61868543e-01,
                1.18523604e00,
                1.11693144e00,
                7.64581026e-01,
            ],
            [
                2.75151884e-02,
                1.00000000e01,
                3.70385458e-01,
                4.64940661e-01,
                8.54039200e-01,
                8.52817813e-01,
                7.94402325e-01,
                6.53028010e-01,
            ],
            [
                5.10153186e-01,
                3.70385458e-01,
                1.00000000e01,
                2.60486124e-01,
                3.48005747e-01,
                6.15110612e-01,
                5.72261139e-01,
                7.70540649e-02,
            ],
            [
                5.40001829e-01,
                4.64940661e-01,
                2.60486124e-01,
                1.00000000e01,
                8.21307813e-02,
                1.02830593e00,
                9.87082757e-01,
                3.42180198e-01,
            ],
            [
                9.61868543e-01,
                8.54039200e-01,
                3.48005747e-01,
                8.21307813e-02,
                1.00000000e01,
                1.21791690e00,
                1.18854249e00,
                3.38505599e-01,
            ],
            [
                1.18523604e00,
                8.52817813e-01,
                6.15110612e-01,
                1.02830593e00,
                1.21791690e00,
                1.00000000e01,
                1.78792001e-03,
                1.06951995e00,
            ],
            [
                1.11693144e00,
                7.94402325e-01,
                5.72261139e-01,
                9.87082757e-01,
                1.18854249e00,
                1.78792001e-03,
                1.00000000e01,
                1.01646822e00,
            ],
            [
                7.64581026e-01,
                6.53028010e-01,
                7.70540649e-02,
                3.42180198e-01,
                3.38505599e-01,
                1.06951995e00,
                1.01646822e00,
                1.00000000e01,
            ],
        ]
    )
    dmat_fn = pl.Path(sys.path[0], "tmp_dmat.npy")
    np.save(dmat_fn, dmat)
    # file name dict to be passed to order.rank()
    fnames = {"cand": str(cand_fn), "dmat": str(dmat_fn)}
    # Make the actual call
    fname_ranked = order.rank(fnames)
    # Ranked results as a dataframe
    ret_ranked_df = df_utils.load(fname_ranked)
    # Expected ranked results as dataframe
    ranked_df = pd.DataFrame(
        {
            "w": [0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.175, 0.175],
            "G": [2700, 2500, 1500, 1500, 2000, 2000, 2500, 2500],
            "lldg": [0.15, 0.15, 0.15, 0.15, 0.25, 0.3, 0.3, 0.25],
            "L": [10039, 9060, 3454, 3100, 7519, 8529, 6185, 7358],
        }
    )
    test_results = ret_ranked_df.equals(ranked_df)
    # Clean up tmp files
    cand_fn.unlink()
    dmat_fn.unlink()
    pl.Path(fname_ranked).unlink()
    assert test_results