How to use the spikeextractors.NumpySortingExtractor function in spikeextractors

def _create_example(self):
        channel_ids = [0, 1, 2, 3]
        num_channels = 4
        num_frames = 10000
        sampling_frequency = 30000
        X = np.random.normal(0, 1, (num_channels, num_frames))
        geom = np.random.normal(0, 1, (num_channels, 2))
        X = (X * 100).astype(int)
        RX = se.NumpyRecordingExtractor(timeseries=X, sampling_frequency=sampling_frequency, geom=geom)
        RX2 = se.NumpyRecordingExtractor(timeseries=X, sampling_frequency=sampling_frequency, geom=geom)
        RX3 = se.NumpyRecordingExtractor(timeseries=X, sampling_frequency=sampling_frequency, geom=geom)
        SX = se.NumpySortingExtractor()
        spike_times = [200, 300, 400]
        train1 = np.sort(np.rint(np.random.uniform(0, num_frames, spike_times[0])).astype(int))
        SX.add_unit(unit_id=1, times=train1)
        SX.add_unit(unit_id=2, times=np.sort(np.random.uniform(0, num_frames, spike_times[1])))
        SX.add_unit(unit_id=3, times=np.sort(np.random.uniform(0, num_frames, spike_times[2])))
        SX.set_unit_property(unit_id=1, property_name='stability', value=80)
        SX.set_sampling_frequency(sampling_frequency)
        SX2 = se.NumpySortingExtractor()
        spike_times2 = [100, 150, 450]
        train2 = np.rint(np.random.uniform(0, num_frames, spike_times2[0])).astype(int)
        SX2.add_unit(unit_id=3, times=train2)
        SX2.add_unit(unit_id=4, times=np.random.uniform(0, num_frames, spike_times2[1]))
        SX2.add_unit(unit_id=5, times=np.random.uniform(0, num_frames, spike_times2[2]))
        SX2.set_unit_property(unit_id=4, property_name='stability', value=80)
        SX2.set_unit_spike_features(unit_id=3, feature_name='widths', value=np.asarray([3] * spike_times2[0]))
        RX.set_channel_property(channel_id=0, property_name='location', value=(0, 0))

spike_times2 = [100, 150, 450]
        train2 = np.rint(np.random.uniform(0, num_frames, spike_times2[0])).astype(int)
        SX2.add_unit(unit_id=3, times=train2)
        SX2.add_unit(unit_id=4, times=np.random.uniform(0, num_frames, spike_times2[1]))
        SX2.add_unit(unit_id=5, times=np.random.uniform(0, num_frames, spike_times2[2]))
        SX2.set_unit_property(unit_id=4, property_name='stability', value=80)
        SX2.set_unit_spike_features(unit_id=3, feature_name='widths', value=np.asarray([3] * spike_times2[0]))
        RX.set_channel_property(channel_id=0, property_name='location', value=(0, 0))
        for i, unit_id in enumerate(SX2.get_unit_ids()):
            SX2.set_unit_property(unit_id=unit_id, property_name='shared_unit_prop', value=i)
            SX2.set_unit_spike_features(unit_id=unit_id, feature_name='shared_unit_feature',
                                        value=np.asarray([i] * spike_times2[i]))
        for i, channel_id in enumerate(RX.get_channel_ids()):
            RX.set_channel_property(channel_id=channel_id, property_name='shared_channel_prop', value=i)

        SX3 = se.NumpySortingExtractor()
        train3= np.asarray([1,20,21,35,38,45,46,47])
        SX3.add_unit(unit_id=0, times=train3)
        features3 = np.asarray([0,5,10,15,20,25,30,35])
        SX3.set_unit_spike_features(unit_id=0, feature_name='dummy', value=features3)

        example_info = dict(
            channel_ids=channel_ids,
            num_channels=num_channels,
            num_frames=num_frames,
            sampling_frequency=sampling_frequency,
            unit_ids=[1, 2, 3],
            train1=train1,
            train2=train2,
            train3=train3,
            features3=features3,
            unit_prop=80,

def setUp(self):
        M = 4
        N = 10000
        seed= 0
        sampling_frequency = 30000
        X = np.random.RandomState(seed=seed).normal(0, 1, (M, N))
        geom = np.random.RandomState(seed=seed).normal(0, 1, (M, 2))
        self._X = X
        self._geom = geom
        self._sampling_frequency = sampling_frequency
        self.RX = se.NumpyRecordingExtractor(timeseries=X, sampling_frequency=sampling_frequency, geom=geom)
        self.SX = se.NumpySortingExtractor()
        L = 200
        self._train1 = np.rint(np.random.RandomState(seed=seed).uniform(0, N, L)).astype(int)
        self.SX.add_unit(unit_id=1, times=self._train1)
        self.SX.add_unit(unit_id=2, times=np.random.RandomState(seed=seed).uniform(0, N, L))
        self.SX.add_unit(unit_id=3, times=np.random.RandomState(seed=seed).uniform(0, N, L))

def test_empty_write(self):
        sorting_empty = se.NumpySortingExtractor()
        se.NpzSortingExtractor.write_sorting(sorting_empty, 'test_NpzSortingExtractors_empty.npz')