def METHOD_NAME(self):
    """
    Tests against the instaseis implementation which should work well
    enough.
    """
    data = np.array([0.92961609, 0.31637555, 0.18391881, 0.20456028,
                     0.56772503, 0.5955447, 0.96451452, 0.6531771,
                     0.74890664, 0.65356987])
    dt = 1.0
    # Scenario 1.
    new_dt = 0.45
    a = 1
    expected_output = np.array([
        0.92961609, 0.55712768, 0.31720733, 0.24275977, 0.17825931,
        0.16750234, 0.17561933, 0.20626905, 0.37726064, 0.5647072,
        0.47145546, 0.59222238, 0.58665834, 0.91241347, 0.79909224,
        0.61631275, 0.61258393, 0.61611633, 0.73239733, 0.56371682,
        0.65356987])
    output = lanczos_interpolation(
        data, old_dt=dt, new_start=0.0, old_start=0.0, new_dt=new_dt,
        new_npts=21, a=a)
    np.testing.assert_allclose(output, expected_output, atol=1E-9)
    # Scenario 2.
    new_dt = 0.72
    a = 12
    expected_output = np.array([
        0.92961609, 0.54632548, 0.14335148, 0.19675436, 0.19030867,
        0.41722415, 0.60644459, 0.6018648, 0.88751628, 0.90970863,
        0.58602723, 0.71521445, 0.83288791])
    output = lanczos_interpolation(
        data, old_dt=dt, new_start=0.0, old_start=0.0, new_dt=new_dt,
        new_npts=13, a=a)
    np.testing.assert_allclose(output, expected_output, atol=1E-9)