def testcase(self, npkt=1000, fs=100):
    '[summary]\n\n        Args:\n            npkt (int, optional): [description]. Defaults to 1000.\n            fs (int, optional): [description]. Defaults to 100.\n        '
    nsamp = np.random.random_integers(0, 10, size=(npkt,))
    ts_true = ((np.arange(np.sum(nsamp)) * 1000) / fs)
    idx = (np.cumsum(nsamp) - 1)
    pkt_ts = ts_true[idx]
    pkt_ts = (pkt_ts + np.random.uniform(0, ((0.5 * 1000) / fs), size=pkt_ts.shape))
    sts = []
    tsfn = timestamp_interpolation(fs=fs, sample2timestamp='lower_bound_tracker')
    for (i, (n, t)) in enumerate(zip(nsamp, pkt_ts)):
        samp_ts = tsfn.transform(t, n)
        sts.extend(samp_ts)
    import matplotlib.pyplot as plt
    plt.plot((ts_true - sts))
    plt.show()
[summary]

Args:
    npkt (int, optional): [description]. Defaults to 1000.
    fs (int, optional): [description]. Defaults to 100.
def testcase(self, npkt=1000, fs=100):
    '[summary]\n\n        Args:\n            npkt (int, optional): [description]. Defaults to 1000.\n            fs (int, optional): [description]. Defaults to 100.\n        '
    nsamp = np.random.random_integers(0, 10, size=(npkt,))
    ts_true = ((np.arange(np.sum(nsamp)) * 1000) / fs)
    idx = (np.cumsum(nsamp) - 1)
    pkt_ts = ts_true[idx]
    pkt_ts = (pkt_ts + np.random.uniform(0, ((0.5 * 1000) / fs), size=pkt_ts.shape))
    sts = []
    tsfn = timestamp_interpolation(fs=fs, sample2timestamp='lower_bound_tracker')
    for (i, (n, t)) in enumerate(zip(nsamp, pkt_ts)):
        samp_ts = tsfn.transform(t, n)
        sts.extend(samp_ts)
    import matplotlib.pyplot as plt
    plt.plot((ts_true - sts))
    plt.show()