How to use the spikeextractors.NpzSortingExtractor function in spikeextractors

# write recording as binary format + json + prb
        raw_filename = study_folder / 'raw_files' / (rec_name + '.dat')
        prb_filename = study_folder / 'raw_files' / (rec_name + '.prb')
        json_filename = study_folder / 'raw_files' / (rec_name + '.json')
        num_chan = recording.get_num_channels()
        chunksize = 2 ** 24 // num_chan
        sr = recording.get_sampling_frequency()

        se.write_binary_dat_format(recording, raw_filename, time_axis=0, dtype='float32', chunksize=chunksize)
        se.save_probe_file(recording, prb_filename, format='spyking_circus')
        with open(json_filename, 'w', encoding='utf8') as f:
            info = dict(sample_rate=sr, num_chan=num_chan, dtype='float32', frames_first=True)
            json.dump(info, f, indent=4)

        # write recording sorting_gt as with npz format
        se.NpzSortingExtractor.write_sorting(sorting_gt, study_folder / 'ground_truth' / (rec_name + '.npz'))

    # make an index of recording names
    with open(study_folder / 'names.txt', mode='w', encoding='utf8') as f:
        for rec_name in gt_dict:
            f.write(rec_name + '\n')

def test_write_then_read(self):


        recording, sorting_gt = se.example_datasets.toy_example(num_channels=4, duration=10, seed=0)

        se.NpzSortingExtractor.write_sorting(sorting_gt, 'test_NpzSortingExtractors.npz')

        npz = np.load('test_NpzSortingExtractors.npz')
        sorting_npz = se.NpzSortingExtractor('test_NpzSortingExtractors.npz')
        units_ids = npz['unit_ids']
        self.assertEqual(list(units_ids), list(sorting_gt.get_unit_ids()))
        self.assertEqual(list(sorting_npz.get_unit_ids()), list(sorting_gt.get_unit_ids()))
        self.assertEqual(sorting_npz.get_sampling_frequency(), 30000.0)

def test_write_then_read(self):


        recording, sorting_gt = se.example_datasets.toy_example(num_channels=4, duration=10, seed=0)

        se.NpzSortingExtractor.write_sorting(sorting_gt, 'test_NpzSortingExtractors.npz')

        npz = np.load('test_NpzSortingExtractors.npz')
        sorting_npz = se.NpzSortingExtractor('test_NpzSortingExtractors.npz')
        units_ids = npz['unit_ids']
        self.assertEqual(list(units_ids), list(sorting_gt.get_unit_ids()))
        self.assertEqual(list(sorting_npz.get_unit_ids()), list(sorting_gt.get_unit_ids()))
        self.assertEqual(sorting_npz.get_sampling_frequency(), 30000.0)

def iter_computed_sorting(study_folder):
    """
    Iter over sorting files.
    """
    sorting_folder = Path(study_folder) / 'sortings'
    for filename in os.listdir(sorting_folder):
        if filename.endswith('.npz') and '[#]' in filename:
            rec_name, sorter_name = filename.replace('.npz', '').split('[#]')
            sorting = se.NpzSortingExtractor(sorting_folder / filename)
            yield rec_name, sorter_name, sorting

----------
    study_folder: str
        The study folder.
    
    Returns
    ----------
    
    ground_truths: dict
        Dict of sorintg_gt.
    
    """
    study_folder = Path(study_folder)
    rec_names = get_rec_names(study_folder)
    ground_truths = {}
    for rec_name in rec_names:
        sorting = se.NpzSortingExtractor(study_folder / 'ground_truth' / (rec_name + '.npz'))
        ground_truths[rec_name] = sorting
    return ground_truths

def get_ground_truth(self, rec_name):
        sorting = se.NpzSortingExtractor(self.study_folder / 'ground_truth' / (rec_name+'.npz'))
        return sorting