def METHOD_NAME(sg, bins, anomalous, dmin, merged):
    """
    Test rs.stats.compute_completeness with synthetic data that is 100% complete
    """
    cell = [30, 30, 30, 90, 90, 90]
    H = rs.utils.generate_reciprocal_asu(cell, sg, dmin, anomalous)
    data = rs.DataSet(
        {"H": H[:, 0], "K": H[:, 1], "L": H[:, 2]},
        cell=cell,
        spacegroup=sg,
        merged=merged,
    )
    data["I"] = 1.0
    data.infer_mtz_dtypes(inplace=True)
    data.set_index(["H", "K", "L"], inplace=True)
    # Merged, anomalous data is only supported in two-column format
    if anomalous and merged:
        data = data.unstack_anomalous()
    result = rs.stats.compute_completeness(
        data, bins=bins, anomalous=anomalous, dmin=dmin
    )
    assert (result["completeness"].to_numpy() == 1.0).all()